Communications, Networks, Security

Communication networks, hardware, information theory and security

Communication systems research is one of ISR's foundational areas of inquiry. ISR’s best-known research breakthrough may be the algorithms and protocols by which internet services can be delivered over satellite, using a high-speed, two-way connection through low-earth-orbit satellites. In addition, high quality joint source-channel coding of images and related modem technologies were developed for voice and data. Modeling, analytic and formal models for cross-layer design of wireless network and mobile ad-hoc network protocols have been developed using a component-based, model-based systems engineering approach. A systems perspective inspired by control and communications theory and methods has brought security to wireless networks beyond traditional cryptographic methods. Current work spans integrated security from the physical layer—hardware and signal processing—to protocols, applications and human users. ISR is active in the Electronics Resurgence Initiative and is highly active in cybersecurity, cyberprivace and cyberdeception research.

Recent news

Recent publications

2022

A Fast and Scalable Resource Allocation Scheme for End-to-End Network Slices

Panagiotis I. Nikolaidis, John Baras

Proposes an online resource allocation scheme for end-to-end network slices. The scheme is based on an optimization problem, where bandwidth allocation is jointly performed in the radio access network, and service function chain is embedded in the core network. The scheme has polynomial time complexity and is fast and highly scalable with respect to the number of users.

2021 IEEE Global Communications Conference (GLOBECOM)

Collaborative Beamforming for Agents with Localization Errors

Erfaun Noorani, Yagiz Savas, Alec Koppel, John Baras, Ufuk Topcu, Brian M. Sadler

Considers a group of agents that estimate their locations in an environment through sensor measurements and aim to transmit a message signal to a client via collaborative beamforming.

2021 55th Asilomar Conference on Signals, Systems, and Computers

Multi-User Beam Alignment in Presence of Multi-Path

Nariman Torkzaban, Mohammad Amir Khojastepour, John Baras

To overcome high path loss and the intense shadowing in millimeter-wave (mmWave) communications, effective beamforming schemes are required which incorporate narrow beams with high beamforming gains. The mmWave channel consists of a few spatial clusters each associated with an angle of departure (AoD). The narrow beams must be aligned with the channel AoDs to increase the beamforming gain. This is achieved through a procedure called beam alignment (BA). The authors propose efficient BA schemes in presence of multipath.

arXiv.org

2021

Sensor Scheduling for Linear Systems: A Covariance Tracking Approach

Dipankar Maity, David Hartman, John Baras

The authors consider the classical sensor scheduling problem for linear systems where only one sensor is activated at each time. They show that the sensor scheduling problem has a close relation to the sensor design problem and the solution of a sensor schedule problem can be extracted from an equivalent sensor design problem. They also propose a convex relaxation to the sensor design problem and a reference covariance trajectory is obtained from solving the relaxed sensor design problem.

arXiv.org

Controller Placement in SDN-enabled 5G Satellite-Terrestrial Networks

Nariman Torkzaban, John Baras

SDN-enabled Integrated satellite-terrestrial networks (ISTNs), can provide several advantages including global seamless coverage, high reliability, low latency, etc. and can be a key enabler towards next generation networks. To deal with the complexity of the control and management of the integrated network, leveraging the concept of software-defined networking (SDN) will be helpful. In this regard, the SDN controller placement problem in SDN-enabled ISTNs becomes of paramount importance. The authors formulate an optimization problem for the SDN controller placement with the objective of minimizing the average failure probability of SDN control paths to ensure the SDN switches receive the instructions in the most reliable fashion.

arXiv.org

On the Importance of Trust in Next-Generation Networked CPS Systems: An AI Perspective

Anousheh Gholami, Nariman Torkzaban, John Baras

The paper proposes trust as a measure to evaluate the status of network agents and improve the decision making process. The authors interpret trust as a relation among entities that participate in various protocols.

arXiv.org

Value of Information in Feedback Control: Global Optimality

Touraj Soleymani, John Baras, Sandra Hirche, Karl Johansson

The rate-regulation trade-off defined between two objective functions, one penalizing the packet rate and the other, the state deviation and control effort, can express the performance bound of a networked control system. However, the characterization of the set of globally optimal solutions in this trade-off for multi-dimensional controlled Gauss-Markov processes has been an open problem. In the present article, we characterize a policy profile that belongs to this set. We prove that such a policy profile consists of a symmetric threshold triggering policy, which can be expressed in terms of the value of information, and a certainty-equivalent control policy, which uses a conditional expectation with linear dynamics.

arXiv.org

Joint Satellite Gateway Deployment & Controller Placement in Software-Defined 5G-Satellite Integrated Networks

Nariman Torkzaban, John Baras

Several challenging optimization problems arise while considering the deployment of the space-air-ground integrated networks (SAGINs), among which the optimal satellite gateway deployment problem is of significant importance. Moreover, with the increasing interest in the software-defined integration of 5G networks and satellites, the existence of an effective scheme for optimal placement of SDN controllers, is essential. The authors discuss the interrelation between the two problems above and propose suitable methods to solve them under various network design criteria.

arXiv.org

Value of Information in Networked Control Systems Subject to Delay

Siyi Wang, Qingchen Liu, Precious Ugo Abara, John Baras, Sandra Hirche

The authors address the trade-off between control performance and communication cost for a multi-loop NCS. They analytically characterize the relationship between quality of control and VoI function. The derived VoI functions properly reflect the relevance of information including temporal aspects for the control task and are parameterized by the coupling variables such as delay induced by the network. The data packet is transmitted through the network whenever the value of information is positive to preserve the control tasks. Finally, the numerical simulation is provided to verify the effectiveness of the VoI-based scheduling policy.

arXiv.org

2020

Detection of Dynamically Changing Leaders in Complex Swarms from Observed Dynamic Data

Christos Mavridis, Nilesh Suriyarachchi, John Baras

Considers the problem of defending against adversarial attacks from UAV swarms performing complex maneuvers,driven by multiple, dynamically changing, leaders.

GameSec 2020 Conference

Trust-Aware Service Function Chain Embedding: A Path-Based Approach

Nariman Torkzaban, John Baras

Introduces a framework for the path-based trust-aware service chain embedding problem. The paper extends a previous work on trust-aware service chain embedding with generalizing the role of trust by incorporating the trustworthiness of the service network links and substrate network paths into the SFC embedding decision process.

arXiv.org

Joint Satellite Gateway Placement and Routing for Integrated Satellite-Terrestrial Networks

Nariman Torkzaban, Anousheh Gholami, Chrysa Papagianni, John Baras

Introduces the joint satellite gateway placement and routing problem over an ISTN, for facilitating terrestrial-satellite communications while adhering to propagation latency requirements, in a cost-optimal manner. The corresponding load between selected gateways is also balanced.

arXiv.org

A Cross-layer Optimal Co-design of Control and Networking in Time-sensitive Cyber-Physical Systems

Mohammad Mamduhi, Dipankar Maity, John Baras, Karl Johansson

In the design of cyber-physical systems (CPS) where multiple heterogeneous physical systems are coupled via a communication network, a key aspect is to study how network services are distributed among the users. The authors derive the joint optimal time-sensitive control and service allocation policies for each physical system.

KTH Royal Institute of Technology, Stockholm

Distributed Beamforming for Agents with Localization Errors

Erfaun Noorani, Yagiz Savas, Alec Koppel, John Baras, Ufuk Topcu, Brian M. Sadler

In this wireless networks paper, the authors formulate a subset selection problem that aims to find a subset of agents, each of which is equipped with an idealisotropic antenna, that forms a reliable communication linkwith a client through beamforming. They present three algorithms for solving the subset selection problem, and  discussed their computational complexity and optimality. All the proposed algorithms can be thought of as attempts towards approximate trade-off analysis and attempts towards finding desirable Pareto points.

arXiv.org

2022

On the Size of Maximal Binary Codes with 2, 3, and 4 Distances

Alexander Barg, Alexey Glazyrin, Wei-Jiun Kao, Ching-Yi Lai, Pin-Chieh Tseng, Wei-Hsuan Yu

The paper addresses the maximum size of binary codes and binary constant weight codes with few distances. Previous works established a number of bounds for these quantities as well as the exact values for a range of small code lengths. The researchers determine the exact size of maximal binary codes with two distances for all lengths n ≥ 6 as well as the exact size of maximal binary constant weight codes with 2, 3, and 4 distances for several values of the weight and for all but small lengths.

arXiv.org

Interior-Point Regenerating Codes on Graphs

Adway Patra, Alexander Barg

The authors consider the use of regenerating codes in distributed storage systems where connections between the nodes are constrained by a graph.

2022 IEEE International Symposium on Information Theory (ISIT)

Recoverable Systems on Lines and Grids

Alexander Barg, Ohad Elishco, Ryan Gabrys, Eitan Yaakobi

A storage code is an assignment of symbols to the vertices of a connected graph G(V, E) with the property that the value of each vertex is a function of the values of its neighbors, or more generally, of a certain neighborhood of the vertex in G. Under the name of recoverable systems, a class of storage codes on Z was recently studied relying on methods from constrained systems and ergodic theory. In this work, the authors address the question of the maximum capacity of recoverable systems on Z and Z2 from a combinatorial perspective. They establish a closed form formula for the capacity of several one- and two-dimensional systems, depending on their recovery set, using connections between storage codes, graphs, anticodes, and difference-avoiding sets.

2022 IEEE International Symposium on Information Theory (ISIT)

Semidefinite Programming Bounds for Few-Distance Sets in the Hamming and Johnson Spaces

Alexander Barg, Ching-Yi Lai, Pin-Chieh Tseng, Wei-Hsuan Yu

A study of the maximum cardinality problem of a set of few distances in the Hamming and Johnson spaces. The authors formulate semidefinite programs for this problem and extend the 2011 works by Barg-Musin and Musin-Nozaki. They find new parameters for which the maximum size of two- and three-distance sets is known exactly.

arXiv.org

2021

High-Rate Storage Codes on Triangle-Free Graphs

Alexander Barg, Gilles Zémor

This paper considers a class of codes on graphs known as storage codes. The authors construct infinite families of linear storage codes with high rate relying on coset graphs of binary linear codes. They also derive necessary conditions for such codes to have high rate, and even rate potentially close to one.

arXiv.org

Capacity and Construction of Recoverable Systems

Ohad Elishco, Alexander Barg

Motivated by the established notion of storage codes, we consider sets of infinite sequences over a finite alphabet such that every k -tuple of consecutive entries is uniquely recoverable from its l -neighborhood in the sequence.

2021 IEEE International Symposium on Information Theory (ISIT)

Node Repair on Connected Graphs

Adway Patra, Alexander Barg

A study of the problem of erasure correction (node repair) for regenerating codes defined on graphs wherein the cost of transmitting the information to the failed node depends on the graphical distance from this node to the helper vertices of the graph.

arXiv.org

A Construction of Maximally Recoverable Codes

Alexander Barg, Zitan Chen, Itzhak Tamo

The authors construct a family of linear maximally recoverable codes with locality r and dimension r + 1. For codes of length n with r ≈ nα, 0 ≤ α ≤ 1 the code alphabet is of the order n1+3α, which improves upon the previously known constructions of maximally recoverable codes.

arXiv.org

Guest Editorial Special Issue: “From Deletion-Correction to Graph Reconstruction: In Memory of Vladimir I. Levenshtein”

Alexander Barg, Lara Dolecek, Ryan Gabrys, Gyula Katona, Janos Korner, Andrew McGregor, Olgica Milenkovic, Sihem Mesnager, Gilles Zemor

In this guest editorial for a special issue dedicated to Vladimir Iosifovich Levenshtein, Barg and the other guest editors express their admiration of Levenshtein's contributions to combinatorics, coding, and information theory, his elegant problem formulations, ingenious algorithmic solutions, and highly original proof techniques.

IEEE Transactions on Information Theory

Bounds for the sum of distances of spherical sets of small size

Alexander Barg, Peter Boyvalenkov, Maya Stoyanova

The authors derive upper and lower bounds on the sum of distances of a spherical code of size N in n dimensions when N „ nα, 0 ă α ď 2. The bounds are derived by specializing recent general, universal bounds on energy of spherical sets.

arXiv.org

2020

Repair of RS codes with optimal access and error correction

Zitan Chen, Min Ye, Alexander Barg

Addresses two aspects of the repair problem of Reed-Solomon codes.

2020 IEEE International Symposium on Information Theory (ISIT)

Cyclic LRC codes with hierarchy and availability

Zitan Chen, Alexander Barg

Locally recoverable (LRC) codes form a family of erasure codes, motivated by applications in distributed storage, that support repair of a failed storage node by contacting a small number of other nodes in the cluster. This paper presents two results regarding codes with hierarchical locality and codes with availability.

2020 IEEE International Symposium on Information Theory (ISIT)

Recoverable Systems

Ohad Elishco, Alexander Barg

Motivated by the established notion of storage codes, the authors consider sets of infinite sequences over a finite alphabet such that every k-tuple of consecutive entries is uniquely recoverable from its l-neighborhood in the sequence.

arXiv.org

Bounds for Discrepancies in the Hamming Space

Alexander Barg, Maxim Skriganov

Shows that the behavior of discrepancies in the Hamming space differs fundamentally because the volume of the ball in this space depends on its radius exponentially while such a dependence for the Riemannian manifolds is polynomial.

arXiv.org

Stolarsky's Invariance Principle for Finite Metric Spaces

Alexander Barg

Considers the case of finite metric spaces, relating the quadratic discrepancy of a subset to a certain function of the distribution of distances in it. Using linear programming, the author finds several bounds on the minimal discrepancy and give examples of minimizing configurations. In particular, we show that all binary perfect codes have the smallest possible discrepancy.

arXiv.org

Cyclic and Convolutional Codes with Locality

Zitan Chen, Alexander Barg

The work focuses on cyclic constructions of LRC codes and derives conditions on the zeros of the code that support the property of hierarchical locality. The authors obtain a general family of hierarchical LRC codes for a new range of code parameters.

arXiv.org

Enabling optimal access and error correction for the repair of Reed-Solomon codes

Zitan Chen, Min Ye, Alexander Barg

Reed-Solomon codes possess a repair scheme that supports repair of failed nodes with optimal repair bandwidth. This paper extends this result in two directions.

arXiv.org

2022

Breaking RSA Generically is Equivalent to Factoring, with Preprocessing

Dana Dachman-Soled, Julian Loss, Adam O’Neill, Nikki Sigurdson

The authors investigate the relationship between the classical RSA and factoring problems when preprocessing is considered. Specifically, they investigate whether there is a superpolynomial gap between the runtime of the best attack on RSA with preprocessing and on factoring with preprocessing. Their main result rules this out with respect to algorithms in a natural adaptation of the generic ring model to the preprocessing setting. In particular, in this setting the authors show the existence of a factoring algorithm (albeit in the random oracle model) with polynomially related parameters, for any setting of RSA parameters.

Cryptology ePrint Archive

When Frodo Flips: End-to-End Key Recovery on FrodoKEM via Rowhammer

Michael Fahr Jr., Hunter Kippen (UMD), Andrew Kwong, Thinh Dang, Jacob Lichtinger, Dana Dachman-Soled (UMD),Daniel Genkin, Alexander Nelson, Ray Perlner, Arkady Yerukhimovich, Daniel Apon

The authors recover the private key material of the FrodoKEM key exchange mechanism as submitted to the NIST Post Quantum Cryptography (PQC) standardization process. The new mechanism that allows for this is a Rowhammer-assisted \emph{poisoning} of the FrodoKEM Key Generation (KeyGen) process. The Rowhammer side-channel is a hardware-based security exploit that allows flipping bits in DRAM by “hammering” rows of memory adjacent to some target-victim memory location by repeated memory accesses. Using Rowhammer, the FrodoKEM software is induced.

2022 ACM Conference on Computer and Communications Security

2022

Analysis of an Age-Dependent Stochastic Hybrid System

Ali Maatouk, Mohamad Assaad, Anthony Ephremides

The paper provides an analysis of a status update system modeled through the Stochastic Hybrid Systems (SHSs) tool. The authors provide an approach, dubbed as the moment closure technique, to compute the m-th moment of the age process for any m≥1. Interestingly, this technique allows the approximation of the average age of various systems by solving a simple set of linear equations.

2022 IEEE International Symposium on Information Theory (ISIT)

Preempting to Minimize Age of Incorrect Information under Random Delay

Yutao Chen, Anthony Ephremides

This paper considers optimizing the performance of a transmitter-receiver system measured by the Age of Incorrect Information (AoII). It aims to optimize the transmitter decision in each time slot to minimize the AoII of the system.

arXiv.org

Minimizing Age of Incorrect Information in the Presence of Timeouts

Yutao Chen, Anthony Ephremides

This paper considers the problem of minimizing the Age of Incorrect Information in a slotted-time system with a transmitter-receiver pair. The authors adopt the Age of Incorrect Information (AoII) as the performance metric and investigate the problem of optimizing the transmitter’s action in each time slot to minimize AoII.

arXiv.org

Age of Incorrect Information under Delay

Yutao Chen, Anthony Ephremides

This paper investigates the problem of minimizing the Age of Incorrect Information (AoII) when the communication channel has a random delay. The authors consider a slotted-time system where a transmitter observes a dynamic source and decides when to send updates to a remote receiver through a channel with random delay. The receiver maintains estimates of the state of the dynamic source based on the received updates. AoII is adopted as the performance metric and the authors investigate the problem of optimizing the transmitter’s action in each time slot to minimize AoII.

arXiv.org

Achieving Ultra High Freshness in Real-Time Monitoring and Decision Making with Incremental Decoding

Shaoling Hu, Junjie Wu, Wei Chen, Anthony Ephremides

Real-time monitoring and remote control of stochastic systems have attracted considerable attention due to their potential in task-oriented communications and industrial Internet of Things (IIoT). How to achieve ultra high-freshness in real-time monitoring and remote control becomes a challenging problem. This paper examines freshness-oriented source coding with incremental decoding in contrast to conventional source encoding/decoding.

2021 IEEE Global Communications Conference (GLOBECOM)

2021

The Role of AoI in a Cognitive Radio Network: Lyapunov Optimization and Tradeoffs

Clement Kam, Sastry Kompella, Anthony Ephremides

A study of the problem of a two-user, single-channel cognitive radio network, in which the objective is to maximize the secondary user throughput subject to a constraint on the probability of collision experienced by the primary user. The authors apply a Lyapunov framework to identify the tradeoff between three fundamental information qualities: AoI, accuracy, and completeness. Characterizing these types of tradeoffs can be a useful intermediate step towards optimizing a variety of objectives.

IEEE Military Communications Conference (MILCOM) 2021

Scheduling to Minimize Age of Incorrect Information with Imperfect Channel State Information

Yuotao Chen, Anthony Ephremides

The authors study a slotted-time system where a base station needs to update multiple users at the same time. Due to the limited resources, only part of the users can be updated in each time slot. They consider the problem of minimizing the Age of Incorrect Information (AoII) when imperfect Channel State Information (CSI) is available.

Entropy Special Issue on Age of Information: Concept, Metric and Tool for Network Control

Age of Sensed Information in a Cognitive Radio Network

Clement Kam, Sastry Kompella, Anthony Ephremides

Age of information is often studied as a primary objective to be optimized, but for problems where age is not the primary objective, it can still have a major role that can be utilized. This work studies a two-user, single-channel cognitive radio network, where the primary user’s transmit/idle dynamics are modeled as a binary Markov chain, and the secondary user decides to either sense or transmit. The authors transform the problem by converting the randomized policy to its induced age distribution function. As a result, the age distribution-based formulation results in a linear program, which can be solved efficiently.

19th International Symposium on Modeling and Optimization in Mobile, Ad hoc, and Wireless Networks (WiOpt 2021)

Guest Editorial: Special Issue on Age of Information and Data Semantics for Sensing, Communication, and Control Co-Design in IoT

Sheng Zhou, Zhiyuan Jiang, Nikolaos Pappas, Anthony Ephremides, Luis DaSilva

This special issue is focused on the AoI-inspired sensing, communication, and control co-design in IoT systems.

IEEE Internet of Things Journal

A Dynamic Scheduling Policy for a Network with Heterogeneous Time-Sensitive Traffic

Emmanouil Fountoulakis, Themistoklis Charalambous, Anthony Ephremides, Nikolaos Pappas

In 5G and beyond systems, the notion of latency is being considered in wireless connectivity as a metric for serving real-time communications requirements. However, research indicates that latency could be inefficient to handle applications with data freshness requirements. Recently, the notion of Age of Information (AoI) that can capture the freshness of the data has attracted attention. The authors consider mixed traffic with time-sensitive users; a deadline-constrained user, and an AoI-oriented user. To develop an efficient scheduling policy, they cast a novel optimization problem formulation for minimizing average AoI while satisfying timely throughput constraints.

arXiv.org

Age-Aware Stochastic Hybrid Systems: Stability, Solutions and Applications

Ali Maatouk, Mohamad Assaad, Anthony Ephremides

The authors analyze status update systems modeled through the Stochastic Hybrid Systems (SHSs) tool.

arXiv.org

Resource Allocation for Heterogeneous Traffic with Power Consumption Constraints

Emmanouil Fountoulakis; Nikolaos Pappas; Anthony Ephremides

Future wireless networks will be characterized by users with heterogeneous requirements. Such users can require low-latency or minimum-throughput requirements. In addition, due to the limited-power budget of the mobile devices, a power-efficient scheduling scheme is required by the network. In this work, the authors cast a stochastic network optimization problem for minimizing the packet drop rate while guaranteeing a minimum throughput and taking into account the limited-power capabilities of the users.

IEEE Conference on Computer Communications Workshops (Infocom 2021)

Semantic Communications in Networked Systems

Elif Uysal, Onur Kaya, Anthony Ephremides, James Gross, Marian Codreanu, Petar Popovski, Mohamad Assaad, Gianluigi Liva, Andrea Munari, Touraj Soleymani, Beatriz Soret, Karl Henrik Johansson

A vision for a departure from the established way of architecting and assessing communication networks, by incorporating the semantics of information for communications and control in networked systems.

arXiv.org

Age of Information: An Indirect Way To Improve Control System Performance

Onur Ayan, Anthony Ephremides, Wolfgang Kellerer

The authors consider N heterogeneous control sub-systems sharing a wireless communication channel. Network resources are limited and are allocated by a centralized scheduler. Each transmission is lost with a probability that is higher or lower depending on the portion each sub-system receives from the pool of network resources. Furthermore, state measurements go through a first come first serve (FCFS) Geo/Geo/1 transmission queue after they are generated by each sensor. In such a setting, the information at each remote controller that is observing the state measurements through the wireless channel gets outdated. Age of Information (AoI)captures this effect and measures the information freshness a teach controller. By definition, AoI is control unaware thus not a standalone metric to capture the heterogeneous requirements of control sub-systems. However, we show how the stationary distribution of Age of information (AoI) can be employed as an intermediate metric to obtain the expected control performance in the network. As a result, we solve the resource allocation problem optimally and show by simulations that we are able to improve the control performance indirectly through AoI.

IEEE INFOCOM: Age of Information Workshop 2021

Joint Sampling and Transmission Policies for Minimizing Cost under AoI Constraints

Emmanouil Fountoulakis, Marian Codreanu, Anthony Ephremides, Nikolaos Pappas

This work considers the problem of jointly minimizing the average cost of sampling and transmitting status updates by users over a wireless channel subject to average Age of Information (AoI) constraints.

arXiv.org

Minimizing Age of Incorrect Information for Unreliable Channel with Power Constraint

Yuotao Chen, Anthony Ephremides

Age of Incorrect Information (AoII) is a newly introduced performance metric that is adaptable to a variety of communication goals. The fundamental nature of AoII has been elusive so far. In this work, the authors consider the AoII in a system where a transmitter sends updates about a multi-state Markovian source to a remote receiver through an unreliable channel. The communication goal is to minimize AoII subject to a power constraint. The problem is cast into a Constrained Markov Decision Process. The research proves that the optimal policy is a mixture of two deterministic threshold policies.

arXiv.org

2020

Timely Updates with Priorities: Lexicographic Age Optimality

Ali Maatouk, Yin Sun, Anthony Ephremides, and Mohamad Assaad

The authors consider a scheduling problem in which several streams of status update packets with different priority levels are sent through a shared channel to their destinations. They introduce a notion of lexicographic age optimality, or simply lex-age-optimality, to evaluate the performance of multi-class status update policies.

18th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt 2020)

Dynamic Power Control for Time-Critical Networking with Heterogeneous Traffic

Emmanouil Fountoulakis, Nikolaos Pappas, Anthony Ephremides

Future wireless networks will be characterized by heterogeneous traffic requirements. Such requirements can below-latency or minimum-throughput, so the network must adjust to different needs. Usually, users with low-latency requirements have to deliver their demand within a specific time frame, i.e., before a deadline, and they co-exist with throughput-oriented users. In addition, the users are mobile and they share the same wireless channel. Therefore, they have to adjust their power transmission to achieve reliable communication. However, due to the limited-power budget of wireless mobile devices, a power-efficient scheduling scheme is required by the network. The authors cast a stochastic network optimization problem for minimizing the packet drop rate while guaranteeing a minimum-throughput and taking into account the limited-power capabilities of the users.

arXiv.org

Asymptotically Optimal Scheduling Policy For Minimizing The Age of Information

Ali Maatouk, Saad Kriouile, Mohamad Assaad, Anthony Ephremides

This paper considers the average age minimization problem where a central entity schedules M users among the N available users for transmission over unreliable channels.

2020 IEEE International Symposium on Information Theory (ISIT)

Age of Incorrect Information for Remote Estimation of a Binary Markov Source

Clement Kam, Sastry Kompella, Anthony Ephremides

For monitoring applications, the Age of Information (AoI) metric has been the primary focus of recent research, but closely related to monitoring is the problem of real-time or remote estimation. Age of Information has been shown to be insufficient for minimizing remote estimation error, but recently a metric known as Age of Incorrect Information (AoII) was proposed that characterizes the cost of a monitor being in an erroneous state over time. This work studies the AoII metric in the simple context of monitoring a symmetric binary information source over a delay system with feedback. It compares three different performance metrics: real-time error, AoI, and AoII. For each metric, the optimal sampling problem as a Markov decision process is formulated. A dynamic programming algorithm to compute the optimal performance and policy is applied.

2020 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

Real-Time Reconstruction of a Counting Process through First-Come-First-Serve Queue Systems

Meng Wang, Wei Chen, Anthony Ephremides

One of the most critical problems for the emerging Internet of Things is the real-time remote reconstruction of ongoing signals (or their functions) from a set of measurements that are under-sampled and delayed in the network. The authors address this problem under three special sampling policies.

IEEE Transactions on Information Theory

Optimal Sampling Cost in Wireless Networks with Age of Information Constraints

Emmanouil Fountoulakis, Nikolaos Pappas, Marian Codreanu, Anthony Ephremides

Considers the problem of minimizing the time average cost of sampling and transmitting status updates by users over a wireless channel subject to average Age of Information constraints. A stochastic optimization problem is formulated and solved with a dynamic algorithm that takes decisions in a slot-by-slot basis.

arXiv.org

Non-Linear Age of Information in a Discrete Time Queue: Stationary Distribution and Average Performance Analysis

Antzela Kosta, Nikolaos Pappas, Anthony Ephremides, Vangelis Angelakis

Investigates a sample path of the age of information (AoI) stochastic process and provides a general framework that establishes a relation among the AoI, the system delay, and the peak AoI. The aim is to be able to analyze any non-linear function of AoI and provide a wide range of potential uses of information ageing depending on the application.

arXiv.org

Status Updates with Priorities: Lexicographic Optimality

Ali Maatouk, Yin Sun, Anthony Ephremides, Mohamad Assaad

Introduces the idea of lex-age-optimality that captures both age-optimality and the order of time-cruciality betweeen streams in a general multi-class priority-based scheduling scenario.

Laboratoire des Signaux & Systemes, L2S Central Supelec, France

On the Optimality of the Whittle’s Index Policy for Minimizing the Age of Information

Ali Maatouk, Saad Kriouile, Mohamad Assaad, Tony Ephremides

The authors prove that the Whittle’s index policy is age-optimal for the general asymmetrical case in the burgeoning many-users regime of Internet of Things interconnected devices.

arXiv.org

2019

Bandwidth Partition and Allocation for Efficient Spectrum Utilization in Cognitive Communications

Song Huang, Di Yuan, Anthony Ephremides

This article related to spectrum scarcity studies bandwidth partition and allocation to optimize spectrum utilitzation in cognitive communications under the interweave paradigm.

IEEE Journal of Communications & Networks

2022

Masked Autoencoders Are Articulatory Learners

Ahmed Adel Attia, Carol Espy-Wilson

A deep learning-based approach using Masked Autoencoders to accurately reconstruct the mistracked articulatory recordings for 41 out of 47 speakers of the XRMB dataset. (The University of Wisconsin X-Ray Microbeam (XRMB) dataset is one of various datasets that provide articulatory recordings synced with audio recordings.) The authors' model is able to reconstruct articulatory trajectories that closely match ground truth, even when three out of eight articulators are mistracked, and retrieve 3.28 out of 3.4 hours of previously unusable recordings.

arXiv.org

An Empirical Analysis on the Vulnerabilities of End-to-End Speech Segregation Models

Rahil Parikh, Gaspar Rochette, Carol Espy-Wilson, Shihab Shamma

The authors perform a thorough investigation on ConvTasnet and DPT-Net to analyze how they perform a harmonic analysis of the input mixture.

arXiv.org

Harmonicity plays a critical role in DNN-based versus in biologically inspired monaural speech segregation systems

Rahil Parikh, Ilya Kavalerov, Carol Espy-Wilson, Shihab Shamma

Recent advancements in deep learning have led to drastic improvements in speech segregation models. Despite their success and growing applicability, few efforts have been made to analyze the underlying principles that these networks learn to perform segregation. The authors analyze the role of harmonicity on two state-of-the-art Deep Neural Networks (DNN)-based models- Conv-TasNet and DPT-Net.

arXiv.org

2021

Emotion recognition with speech articulatory coordination features

Yashish Siriwardena, Nadee Seneviratne, Carol Espy-Wilson

Mental health illnesses like Major Depressive Disorder and Schizophrenia affect the coordination between articulatory gestures in speech production. Coordination features derived from Vocal tract variables (TVs) predicted by a speech inversion system can quantify the changes in articulatory gestures and have proven to be effective in the classification of mental health disorders. In this study we use data from the IEMOCAP (acted emotions) and MSP Podcast (natural emotions) datasets to understand how coordination features extracted from TVs can be used to capture changes between different emotions for the first time. We compared the eigenspectra extracted from channel delay correlation matrices for Angry, Sad and Happy emotions with respect to the “Neutral” emotion. Across both the datasets, it was observed that the “Sad” emotion follows a pattern suggesting simpler articulatory coordination while the “Angry” emotion follows the opposite showing signs of complex articulatory coordination. For the majority of subjects, the ‘Happy’ emotion follows a complex articulatory coordination pattern, but has significant confusion with “Neutral” emotion. We trained a Convolutional Neural Network with the coordination features as inputs to perform emotion classification. A detailed interpretation of the differences in eigenspectra and the results of the classification experiments will be discussed.

This is a meeting abstract published in the Journal of the Acoustical Society of America

Speech acoustics and mental health assessment

Carol Espy-Wilson

Dr. Espy-Wilson discusses a speech inversion system her group has developed that maps the acoustic signal to vocal tract variables (TVs). The trajectories of the TVs show the timing and spatial movement of speech gestures. She explains how her group uses machine learning techniques to compute articulatory coordination features (ACFs) from the TVs. The ACFs serve as an input into a deep learning model for mental health classification. Espy-Wilson also illustrates the key acoustic differences between speech produced by subjects when they are mentally ill relative to when they are in remission and relative to healthy controls. The ultimate goal of this research is the development of a technology (perhaps an app) for patients that can help them, their therapists and caregivers monitor their mental health status between therapy sessions.

Keynote speech at the 2021 Acoustical Society of America Annual Meeting, June 8, 2021
View a press release from the Acoustical Society of America about this speech

Speech-based Depression Severity Level Classification Using a Multi-Stage Dilated CNN-LSTM Model

Nadee Seneviratne, Carol Espy-Wilson

The paper proposes a new multi-stage architecture trained on vocal tract variable (TV)-based articulatory coordination features (ACFs) for depression severity classification which clearly outperforms the baseline models. The authors establish that the robustness of ACFs based on TVs holds beyond mere detection of depression and even in severity level classification. This work can be extended to develop a multi-modal system that can take advantage of textual information obtained through Automatic Speech Recognition tools. Linguistic features can reveal important information regarding the verbal content of a depressed patient relating to their mental health condition.

arXiv.org; accepted for Interspeech2021, Aug. 30-Sept. 3, 2021

Inverted Vocal Tract Variables and Facial Action Units to Quantify Neuromotor Coordination in Schizophrenia

Yashish Maduwantha, Chris Kitchen, Deanna L. Kelly, Carol Espy-Wilson

This study, conducted with AIM-HI funding, investigates speech articulatory coordination in schizophrenia subjects exhibiting strong positive symptoms (e.g.hallucinations and delusions), using a time delay embedded correlation analysis. It finds a distinction between healthy and schizophrenia subjects in neuromotor coordination in speech.

ResearchGate.net

2020

Deep Learning Based Generalized Models for Depression Classification

Nadee Seneviratne, Carol Espy-Wilson

The paper develops a generalized classifier for depression detection using a dilated convolutional neural network which is trained on articulatory coordination features (ACFs) extracted from two depression databases.

arXiv.org; accepted for Interspeech2021, Aug. 30-Sept. 3, 2021

Extended Study on the Use of Vocal Tract Variables to Quantify Neuromotor Coordination in Depression

Nadee Seneviratne, James Williamson, Adam Lammert, Thomas Quatieri, Carol Espy-Wilson

Changes in speech production that occur as a result of psychomotor slowing, a key feature of Major Depressive Disorder(MDD), are used to non-invasively diagnose MDD. In previous work using data from seven subjects, the authors showed that using speech-inverted vocal tract variables (TVs) as a direct measure of articulation to quantify changes in the way speech is produced when depressed relative to being not depressed out-performs formant information as a proxy for articulatory information. In this paper, the authors make significant extensions by using more subjects, taking into account more eigenvalue features and incorporating TVs related to place of articulation and the glottal source, resulting in a significant improvement in accuracy.

Interspeech 2020

2019

Multi-Corpus Acoustic-to-Articulatory Speech Inversion

Nadee Seneviratne, Ganesh Sivaraman, Carol Espy-Wilson

This paper proposes a multi-corpus speech inversion system for automatic speech recognition, pronounciation training and speech therapy.

Interspeech 2019

Assessing Neuromotor Coordination in Depression Using Inverted Vocal Tract Variables

Carol Espy-Wilson, Adam Lammert, Nadee Seneviratne, Thomas Quatieri

A new articulary inversion process provides a potentially powerful way of detecting depression based on speech patterns.

Interspeech 2019

Multi-modal learning for speech emotion recognition: An analysis and comparison of ASR outputs with ground truth transcription

Saurabh Sahu, Vikramjit Mitra, Nadee Seneviratne, Carol Espy-Wilson

The paper leverages multi-modal learning and automated speech recognition (ASR) systems toward building a speech-only emotion recognition model.

Interspeech 2019

2021

Stabilizing a Queue Subject to Activity-Dependent Server Performance

Michael Lin, Nuno Martins, Richard La

The researchers investigate the problem of designing a task scheduler policy when the efficiency of the server is allowed to depend on the past utilization, which is modeled using an internal state of the server. They propose a new framework for studying the stability of the queue length of the system. They then characterize the set of task arrival rates for which there exists a stabilizing stationary scheduler policy and identify an optimal threshold policy that stabilizes the system whenever the task arrival rate lies in the interior of the aforementioned set for which there is a stabilizing policy.

IEEE Transactions on Control of Network Systems

2020

Miniature Robot Path Planning for Bridge Inspection: Min-Max Cycle Cover-Based Approach

Michael Lin, Richard La

Develops a new algorithm to position depots where bridge inspection robots would be stored and recharged, and determines a set of sites for each robot to inspect on the bridge.

arXiv.org

Queing Subject to Action-Dependent Server Performance: Utilization Rate Reduction

Michael Lin, Nuno Martins, Richard La

This work expands on stabilizability results recently obtained for a framework to establish methods to design scheduling policies that not only stabilize the queue but also reduce the utilization rate—understood as the infinite-horizon time-averaged expected portion of time the server is working.

arXiv.org

2021

Stabilizing a Queue Subject to Activity-Dependent Server Performance

Michael Lin, Nuno Martins, Richard La

The researchers investigate the problem of designing a task scheduler policy when the efficiency of the server is allowed to depend on the past utilization, which is modeled using an internal state of the server. They propose a new framework for studying the stability of the queue length of the system. They then characterize the set of task arrival rates for which there exists a stabilizing stationary scheduler policy and identify an optimal threshold policy that stabilizes the system whenever the task arrival rate lies in the interior of the aforementioned set for which there is a stabilizing policy.

IEEE Transactions on Control of Network Systems

2020

Queing Subject to Action-Dependent Server Performance: Utilization Rate Reduction

Michael Lin, Nuno Martins, Richard La

This work expands on stabilizability results recently obtained for a framework to establish methods to design scheduling policies that not only stabilize the queue but also reduce the utilization rate—understood as the infinite-horizon time-averaged expected portion of time the server is working.

arXiv.org

2022

Shared Information for a Markhov Chain on a Tree

Sagnik Bhattacharya; Prakash Narayan

Shared information is a measure of mutual dependence among m ≥ 2 jointly distributed discrete random variables. For a Markov chain on a tree with a given joint distribution, the authors give a new proof of an explicit characterization of shared information. When the joint distribution is not known, they exploit the special form of this characterization to provide a multiarmed bandit algorithm for estimating shared information, and analyze its error performance.

2022 IEEE International Symposium on Information Theory (ISIT)

2021

Universal Single-Shot Sampling Rate Distortion

Sagnik Bhattacharya; Prakash Narayan

Consider a finite set of multiple sources, described by a random variable with m components. Only k≤m source components are sampled and jointly compressed in order to reconstruct all the m components under an excess distortion criterion. Sampling can be that of a fixed subset A with |A|=k or randomized over all subsets of size k . In the case of random sampling, the sampler may or may not be aware of the m source components. The compression code consists of an encoder whose input is the realization of the sampler and the sampled source components; the decoder input is solely the encoder output. The combined sampling mechanism and rate distortion code are universal in that they must be devised without exact knowledge of the prevailing source probability distribution. In a Bayesian setting, considering coordinated single-shot sampling and compression, our contributions involve achievability results for the cases of fixed-set, source-independent and source-dependent random sampling.

2021 IEEE International Symposium on Information Theory (ISIT)

Distribution Privacy Under Function p-Recoverability

Ajaykrishnan Nageswaran; Prakash Narayan

A user generates n independent and identically distributed data rvs with a pmf that must be guarded from a querier. The querier must recover, with a prescribed accuracy, a given function of the data from each of n independent and identically distributed user-devised query responses. The user chooses the data pmf and the random query responses to maximize distribution privacy as gauged by the divergence between the pmf and the querier's best estimate of it based on the n query responses. Considering an arbitrary function, a basic achievable lower bound, that does not depend on n, is provided for distribution privacy. Next, upper (converse) and lower (achievable) bounds, dependent on n, are developed that converge to said basic bound as n grows. Explicit strategies for the user and the querier are identified.

2021 IEEE International Symposium on Information Theory (ISIT)

2020

Distribution Privacy Under Function p-Recoverability

Ajaykrishnan Nageswaran; Prakash Narayan

In this paper, a user generates n independent and identically distributed data random variables with a probability mass function that must be guarded from a querier. The querier must recover, with a prescribed accuracy, a given function of the data from each of n independent and identically distributed user-devised query responses. The user chooses the data pmf and the random query responses to maximize distribution privacy as gauged by the divergence between the pmf and the querier's best estimate of it based on the n query responses. A general lower bound is provided for distribution privacy; and, for the case of binary valued functions, upper and lower bounds that converge to said bound as n grows. Explicit strategies for the user and querier are identified.

2020 IEEE International Symposium on Information Theory (ISIT)

2022

Using Online Planning and Acting to Recover from Cyberattacks on Software-defined Networks

Vincent Hsiao, Dana Nau, Rina Dechter

Bayesian Networks are useful for analyzing the properties of systems with large populations of interacting agents (e.g., in social modeling applications and distributed service applications). These networks typically have large functions (CPTs), making exact inference intractable. However, often these models have additive symmetry. In this paper the authors show how summation-based CPTs, especially in the presence of symmetry, can be computed efficiently through the usage of the Fast Fourier Transform (FFT).

Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS) 2022

2021

Using Online Planning and Acting to Recover from Cyberattacks on Software-defined Networks

Sunandita Patra, Alexander Velazquez, Myong Kang, Dana Nau

The authors describe ACR-SDN, a system to monitor, diagnose, and quickly respond to attacks or failures that may occur in software-defined networks (SDNs). An integral part of ACR-SDN is its use of RAE+UPOM, an automated acting and planning engine that uses hierarchical refinement. To advise ACR-SDN on how to recover a target system from faults and attacks, RAE+UPOM uses attack recovery procedures writ-ten as hierarchical operational models. Our experimental results show that the use of refinement planning in ACR-SDN is successful in recovering SDNs from attacks with respect to three performance metrics: estimated time for recovery, efficiency, and retry ratio.

Association for the Advancement of Artificial Intelligence

2022

MES-Attacks: Software-Controlled Covert Channels based on Mutual Exclusion and Synchronization

Chaoqun Shen, Jiliang Zhang, Gang Qu

Multi-process concurrency is effective in improving program efficiency and maximizing CPU utilization. The correct execution of concurrency is ensured by the mutual exclusion and synchronization mechanism (MESM) that manages the shared hardware and software resources. MES-Attacks is a new set of software-controlled covert channel attacks based on MESM to transmit confidential information.

arXiv.org

Building Hardware Security Primitives Using Scan-based Design-for-Testability

Omid Aramoon, Gang Qu, Aijiao Cui

Scan chain is typically used to provide test engineers with complete controllability and observability to the circuit under test to reduce the complexity of VLSI testing. However, it should not be dismissed as just a one-hit-wonder that merely facilitates the test of digital circuits. This study presents a comprehensive review of the recent proposals on how scan chain design can present its versatility as security primitives in different areas of hardware security. More specifically, the authors elaborate its usage in hardware intellectual property watermarking, fingerprinting, and metering, as well as in the design of physical unclonable functions and counterfeit detection. They analyze the challenges and opportunities in building hardware security primitives using modern scan-based design-for-testability (DfT).

IEEE 65th International Midwest Symposium on Circuits and Systems (2022)

PMUSpill: The Counters in Performance Monitor Unit that Leak SGX-Protected Secrets

Pengfei Qiu, Yongqiang Lyu, Haixia Wang, Dongsheng Wang, Chang Liu, Qiang Gao, Chunlu Wang, Rihui Sun, Gang Qu

Performance Monitor Unit (PMU) is a significant hardware module on current processors, which counts the events launched by processor into a set of PMU counters. Ideally, the events triggered by instructions that are executed but not successfully committed (transient execution) should not be recorded. However, in this study, Gang Qu and eight colleagues from Tsinghua University, Harbin Institute of Technology and the Beijing University of Posts and Telecommunications in China, discover that some PMU events triggered by the transient execution instructions will actually be recorded by PMU. Based on this, they propose the PMUSpill attack, which enables attackers to maliciously leak the secret data that are loaded during transient executions.

arXiv.org

DA PUF: dual-state analog PUF

Jiliang Zhang, Lin Ding, Zhuojun Chen, Wenshang Li, Gang Qu

Gang Qu and four colleagues from Hunan University in Changsha, China, propose a novel dual-state analog PUF (DA PUF) which has been successfully fabricated in 55nm process. Physical unclonable function (PUF) is a promising lightweight hardware security primitive that exploits process variations during chip fabrication for applications such as key generation and device authentication. Reliability of the PUF information plays a vital role and poses a major challenge for PUF design.

Proceedings of the 59th ACM/IEEE Design Automation Conference (DAC '22)

An Approximate Memory-based Defense against Model Inversion Attacks to Neural Networks

Qian Xu, Md Tanvir Arafin, Gang Qu

Diverse and comprehensive training data is critical in building robust machine learning (ML) models. However, model inversion attacks (MIA) have demonstrated that an ML model can leak important information about its training dataset. This work examines the existing MIAs and proposes a hardware-oriented solution to protect the training data from such attacks. The proposed solution — MIDAS: Model Inversion Defenses with an Approximate memory System — intentionally introduces memory faults to thwart MIA without compromising the original ML model.

IEEE Transactions on Emerging Topics in Computing

An Effective Test Method for Block RAMs in Heterogeneous FPGAs Based on a Novel Partial Bitstream Relocation Technique

Wei Xiong, Yanze Li, Changpeng Sun, Hualin Luo, Jiafeng Liu, Jian Wang, Jinmei Lai, Gang Qu

Block RAMs (BRAMs) play an important role in modern heterogenous FPGAs, hence how to test them comprehensively and effectively becomes a major concern. On-chip Partial Bitstream Relocation (PBR) technique based on FPGA Dynamic Partial Reconfiguration (DPR) can decrease the time spent on configuring modules in FPGA while reducing the memory resources overhead for storing partial bitstreams of the reconfigurable modules. The previous PBR technique is difficult to be combined with BRAM test directly, because they are somehow tedious, unsuitable for large-scale design or limited to specific devices. Besides, the problem exists for BRAM testing is that fault model is still incomplete and testing algorithms need to be improved to achieve higher fault coverage. In this paper, Gang Qu and colleagues from Fudan University in Shanghai, China, propose sn effective BRAM test method based on a novel PBR technique. The test method establishes a complete fault model for BRAM and improves the testing algorithms for faults in BRAM ECC circuits and intra-word coupling faults in SRAM cells.

Proceedings of the Great Lakes Symposium on VLSI 2022

A Memristor-based Secure Scan Design against the Scan-based Side-Channel Attacks

Mengqiang Lu, Aijiao Cui, Yan Shao, Gang Qu

Scan chain design can improve the testability of a circuit while it can be used as a side-channel to access the sensitive information inside a cryptographic chip for the crack of cipher key. Gang Qu and colleagues at the Harbin Institute of Technology and the Chinese Academy of Sciences in Guangdong, China, present a memristor-based secure scan design that can secure the scan design while maintaining its testability. A lock and key scheme is introduced.

Proceedings of the Great Lakes Symposium on VLSI 2022

AutoTEA: An Automated Transistor-level Efficient and Accurate design tool for FPGA design

Yanze Li, Yufan Zhang, Jiafeng Liu, Jun Gong, Jian Wang, Jinmei Lai, Xinxuan Tao, Gang Qu

Gang Qu and colleagues at Fudan University in Shanghai, China, present AutoTEA. For FPGA circuit design, exploring the FPGA design space for the optimal performance becomes important and also challenging. The popular tool COFFE was built on an academic architecture and cannot be applied to modern commercial FPGA chips with the general routing matrix (GRM) architecture. The authors report the design, implementation, and evaluation of their Automated Transistor-level Efficient and Accurate tool, AutoTEA, which extracts the key sub-circuits, uses the initial transistor sizes to construct hspice netlists, and finds the optimal circuit transistor sizes.

Integration,an Elsevier journal

DVFSspy: Using Dynamic Voltage and Frequency Scaling as a Covert Channel for Multiple Procedures

Pengfei Qiu, Dongsheng Wang, Yongqiang Lyu, Gang Qu

Gang Qu and colleagues at Tsinghua University in China present CacheGuard, a behavior model checker for cache timing side-channel security. Compared to current state-of-the-art prose rule-based security analysis methods, CacheGuard covers the whole state space for a given cache design to discover unknown side-channel attacks. Checking results on standard cache and state-of-the-art secure cache designs discovers 5 new attack strategies, and potentially makes it possible to develop a timing side channel-safe cache with the aid of CacheGuard.

Invited paper at the 2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC)

CacheGuard: A Behavior Model Checker for Cache Timing Side-Channel Security

Zihan Xu, Lingfeng Yin, Yongqiang Lyu, Haixia Wang, Gang Qu, Dongsheng Wang

Gang Qu and colleagues at Tsinghua University have discovered a vulnerability in the implementation of the DVFS technology that allowed them to measure the processor's frequency in the userspace. By exploiting this vulnerability, they successfully implement a covert channel on the commercial Intel platform and demonstrate that the covert channel can reach a throughput of 28.41bps with an error rate of 0.53%. This work indicates that the processor's hardware information that is unintentionally leaked to the userspace by the privileged kernel modules may cause security risks.

2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC)

AID: Attesting the Integrity of Deep Neural Networks

Omid Aramoon, Pin-Yu Chen, Gang Qu

Due to their crucial role in many decision-making tasks, Deep Neural Networks (DNNs) are common targets for a large array of integrity breaches. The authors propose AID, a novel methodology to Attest the Integrity of DNNs. AID generates a set of test cases called edge-points that can reveal whether a model has been compromised. AID does not require access to parameters of the DNN and can work with a restricted black-box access to the model, which makes it applicable to most real life scenarios.

2021 58th ACM/IEEE Design Automation Conference

Accelerating Graph Connected Component Computation with Emerging Processing-In-Memory Architecture

Xuhang Chen, Xueyan Wang, Xiaotao Jia, Jianlei Yang, Gang Qu, Weisheng Zhao

Gang Qu and colleagues from Beihang University in China propose to accelerate connected component computation with the emerging processing-in-Memory (PIM) architecture through an algorithm-architecture co-design manner. The innovation lies in computing connected component with bitwise logical operations (such as AND and OR), and the customized data flow management methods to accelerate computation and reduce energy consumption.

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

A Novel Circuit Authentication Scheme Based on Partial Polymorphic Gates

Timothy Dunlap, Omid Aramoon, Gang Qu, Tian Wang, Xiaoxin Cui, Dunshan Yu

The paper introduces the concept of partial polymorphic gates, which deliver multiple incomplete functions with non-deterministic outputs at certain input combinations. The non-deterministic output is a result of process variations, which are generally believed to be random, unclonable, and different from chip to chip. The authors utilize this uncertainty as a new mechanism for implementing chip IDs and propose a circuit authentication scheme based on such IDs.

2021 Asian Hardware Oriented Security and Trust Symposium (AsianHOST)

SATAM: A SAT Attack Resistant Active Metering Against IC Overbuilding

Aijiao Cui, Zhen Weng, Hui Zhang, Gang Qu, Huawei Li

SATAM is a new active metering scheme in which a new cell of switchable scannable flip-flop (WFF) is introduced to be inserted in the non-critical paths or replace some original scan cells. Without a correct key on these WFFs, the synchronization status of the original design is violated and hence the circuit logic is locked (obfuscated).

IEEE Transactions on Emerging Topics in Computing

Fooling the Eyes of Autonomous Vehicles: Robust Physical Adversarial Examples Against Traffic Sign Recognition Systems

Wei Jia, Zhaojun Lu, Haichun Zhang, and Zhenglin Liu, Jie Wang, Gang Qu

The presence of deliberately deceptive traffic signs could interfere with the real-world object detectors autonomous vehicles rely upon, resulting in life-threatening situations for the vehicles’ occupants. The researchers developed a systematic pipeline that could generate robust physical AEs to use against real-world object detectors, and concluded such physical AEs and associated attacks could result in vehicular havoc.

arXiv.org

2021

Lightning: Striking the Secure Isolation on GPU Clouds with Transient Hardware Faults

Pengfei Qiu, Rihui Sun, Jian Dong, Yongqiang Lyu, Haixia Wang, Ningxuan Feng, Peichen Guo, Gang Qu, Dongsheng Wang

A study of the impact of GPU chips hardware faults on the security of cloud "trusted" execution environment using Deep Neural Network (DNN) as the underlying application. The authors show that transient hardware faults of GPUs can be generated by exploiting the Dynamic Voltage and Frequency Scaling (DVFS) technology, and these faults may cause computation errors, but they have limited impact on the inference accuracy of DNN due to the robustness and fault-tolerant nature of well-developed DNN models. They propose the Lightning attack to locate the fault injection targets of DNNs and to control the fault injection precision in terms of timing and position. They demonstrate that the secure isolation on GPU clouds is vulnerable against transient hardware faults and the computation results may not be trusted.

The 28th ACM Symposium on Operating Systems Principles (2021)

Triangle Counting Accelerations: From Algorithm to In-Memory Computing Architecture

Xueyan Wang, Jianlei Yang, Yinglin Zhao, Xiaotao Jia, Rong Yin, Xuhang Chen, Gang Qu, Weisheng Zhao

Triangles are the basic substructure of networks and triangle counting (TC) has been a fundamental graph computing problem in numerous fields such as social network analysis. Nevertheless, like other graph computing problems, due to the high memory-computation ratio and random memory access pattern, TC involves a large amount of data transfers thus suffers from the bandwidth bottleneck in the traditional Von-Neumann architecture. To overcome this challenge, the authors accelerate TC with emerging processing-in-memory (PIM) architecture through an algorithm-architecture co-optimization. They enable efficient in-memory implementations by reformulating TC with bitwise logic operations (such as AND), and develop customized graph compression and mapping techniques for efficient data flow management. With the emerging computational Spin-Transfer Torque Magnetic RAM (STT-MRAM) array, which is one of the most promising PIM enabling techniques, the device-to-architecture co-simulation results demonstrate that the proposed TC in-memory accelerator outperforms the state-of-the-art GPU and FPGA accelerations by 12.2 and 31.8, respectively, and achieves a 34 energy efficiency improvement over the FPGA accelerator.

IEEE Transactions on Computing

Double-Shift: A Low-Power DNN Weights Storage and Access Framework based on Approximate Decomposition and Quantization

Ming Han, Ye Wang, Jian Dong, Gang Qu

One major challenge in deploying Deep Neural Network (DNN) in resource-constrained applications, such as edge nodes, mobile embedded systems, and IoT devices, is its high energy cost. The emerging approximate computing methodology can effectively reduce the energy consumption during the computing process in DNN. However, a recent study shows that the weight storage and access operations can dominate DNN's energy consumption due to the fact that the huge size of DNN weights must be stored in the high-energy-cost DRAM. The authors propose Double-Shift, a low-power DNN weight storage and access framework, to solve this problem. Enabled by approximate decomposition and quantization, Double-Shift can reduce the data size of the weights effectively. By designing a novel weight storage allocation strategy, Double-Shift can boost the energy efficiency by trading the energy consuming weight storage and access operations for low-energy-cost computations.

ACM Transactions on Design Automation of Electronic Systems

EarArray: Defending against DolphinAttack via Acoustic Attenuation

Guoming Zhang, Xiaoyu Ji, Xinfeng Li, Gang Qu, Wenyuan Xu

DolphinAttacks (i.e., inaudible voice commands) modulate audible voices over ultrasounds to inject malicious commands silently into voice assistants and manipulate controlled systems (e.g., doors or smart speakers). Eliminating DolphinAttacks is challenging if ever possible since it requires to modify the microphone hardware. In this paper, we design EarArray, a lightweight method that can not only detect such attacks but also identify the direction of attackers without requiring any extra hardware or hardware modification.

web.archive.org

Provably Accurate Memory Fault Detection Method for Deep Neural Networks

Omid Aramoon, Gang Qu

A novel methodology to diagnose the presence of faults in the memory of DNN accelerators. The authors' method queries the protected DNN with a set of specially crafted test cases that can accurately reveal if model parameters stored in the hardware are faulty.

GLSVLSI '21: Proceedings of the 2021 on Great Lakes Symposium on VLSI

Novel Memristor-based Nonvolatile D Latch and Flip-flop Designs

Zhenxing Chang, Aijiao Cui, Ziming Wang, Gang Qu

Sequential devices are the fundamental building blocks for almost all digital electronic systems with memory. Due to the importance of instant data recovery after unexpected data loss such as unplanned power down, sequential devices need to have the nonvolatile property, which motivates the recent research and practice in integrating the nonvolatile memristor into CMOS devices. In this paper, the authors study how to apply this approach to improve the quality of nonvolatile D latch.

22nd IEEE International Symposium on Quality Electronic Design, 2021

Don't forget to sign the gradients!

Omid Aramoon, Pin-Yu Chen, Gang Qu

Engineering a top-notch deep learning model is an expensive procedure that involves collecting data, hiring human resources with expertise in machine learning, and providing high computational resources. For that reason, deep learning models are considered as valuable Intellectual Properties(IPs) of the model vendors. To ensure reliable commercialization of deep learning models, it is crucial to develop techniques to protect model vendors against IP infringements. One of such techniques that recently has shown great promise is digital watermarking. The authors present GradSigns, a novel watermarking framework for deep neural networks (DNNs). GradSigns is robust against all known counter-watermark attacks and can embed a large amount of information into DNNs.

Proceedings of the 4th MLSys Conference, 2021

AutoTEA: Automated Transistor-level Efficient and Accurate Optimization for GRM FPGA Design

Yanze Li, Yufan Zhang, Jiafeng Liu, Jian Wang, Jinmei Lai, Gang Qu

With emerging applications such as AI/ML, exploring the FPGA design space for optimal performance becomes important and challenging. The popular tool COFFE was built on an academic architecture and cannot be applied directly to modern FPGA chips with GRM (general routing matrix) architecture. This paper presents a recently developed fully Automated Transistor-level Efficient and Accurate tool, AutoTEA, which features accurate area and delay models, and a fast solution space exploration method for GRM FPGA circuit optimization. The results show that AutoTEA is able to improve a previously manually optimized design (on the tape-out FPGA chip) by 11%.

IEEE 29th Annual International Symposium on Field-Programmable Custom Computing Machines

Voltage Over-scaling-based Lightweight Authentication for IoT Security

Jiliang Zhang, Chaoqun Shen, Haihan Su, Md Tanvir Arafin, Gang Qu

The paper presents machine learning-based modeling attacks to break authentication.

IEEE Transactions on Computers

RIME: A Scalable and Energy-Efficient Processing-In-Memory Architecture for Floating-Point Operations

Zhaojun Lu, Md Tanvir Arafin, Gang Qu

Explores the analog properties of the resistive random access memory (RRAM) crossbar and propose a scalable RRAM-based in-memory floating-point computation architeture (RIME).

ASPDAC '21: Proceedings of the 26th Asia and South Pacific Design Automation Conference

Security of Neural Networks from Hardware Perspective: A Survey and Beyond

Qian Xu, Md Tanvir Arafin, Gang Qu

A survey of the security challenges and opportunities in computing hardware used in implementing deep neural networks.

ASPDAC '21: Proceedings of the 26th Asia and South Pacific Design Automation Conference

Integrated Circuit Digital Fingerprinting–Based Authentication

Xi Chen, Gang Qu

Digital fingerprinting was first proposed in 1999 for the protection of very large scale integration (VLSI) design intellectual properties (IP). Various techniques have been developed to make each copy of the IP unique in order to track the usage of the IP and trace any traitors who have misused the IP. The authors review the general requirements and the available schemes to create digital fingerprints for IP protection. They discuss the challenges of applying these methods for device authentication in IoT applications and how to overcome these difficulties.

Chapter 1 in Authentication of Embedded Devices (Springer book)

Hardware-Based Authentication Applications

Md Tanvir Arafin, Gang Qu

The authors discuss hardware-oriented security applications for the authentication of users, devices, and data. These applications illustrate the use of physical properties of computing hardware such as main memory, computing units, and clocks for authentication applications in low power on the IoT devices and systems.

Chapter 6 in Authentication of Embedded Devices (Springer book)

2020

VoltJockey: Abusing the Processor Voltage to Break Arm TrustZone

Pengfei Qui, Dongsheng Wang, Yongqiang Lyu, Gang Qu

Based on the concept of hardware separation, ARM introduced TrustZone to build a trusted execution environment for applications. It has been quite successful in defending against various software attacks and forcing attackers to explore vulnerabilities in interface designs and side channels. In this article, we propose an innovative software-controlled hardware fault-based attack, VoltJockey, on multi-core processors that adopt dynamic voltage and frequency scaling (DVFS) techniques for energy efficiency. We deliberately manipulate the processor voltage via DVFS to induce hardware faults into the victim cores, and therefore breaking TrustZone. The entire attack process is based on software without any involvement of hardware, which makes VoltJockey stealthy and hard to prevent.

ACM GetMobile: Mobile Computing and Communications

VoltJockey: Abusing the Processor Voltage to Break Arm TrustZone

Pengfei Qui, Dongsheng Wang, Yongqiang Lyu, Gang Qu

Based on the concept of hardware separation, ARM introduced TrustZone to build a trusted execution environment for applications. It has been quite successful in defending against various software attacks and forcing attackers to explore vulnerabilities in interface designs and side channels. In this article, we propose an innovative software-controlled hardware fault-based attack, VoltJockey, on multi-core processors that adopt dynamic voltage and frequency scaling (DVFS) techniques for energy efficiency. We deliberately manipulate the processor voltage via DVFS to induce hardware faults into the victim cores, and therefore breaking TrustZone. The entire attack process is based on software without any involvement of hardware, which makes VoltJockey stealthy and hard to prevent.

ACM GetMobile: Mobile Computing and Communications

MagView: A Distributed Magnetic Covert Channel via Video Encoding and Decoding

Juchuan Zhang, Xiaoyu Ji, Wenyuan Xu, Yi-Chao Chen, Yuting Tang, Gang Qu

MagView is a distributed magnetic cover channel, where sensitive information is embedded in other data such as video and can be transmitted over an air-gapped internal network.

IEEE INFOCOM 2020: IEEE Conference on Computer Communications

BWOLF: Bit-Width Optimization for Statistical Divergence with -Logarithmic Functions

Qian Xu, Guowei Sun, Gang Qu

Approximate computing is a promising technique in improving the energy efficiency for error-resilient applications such as multimedia, signal processing and neural network. The paper shows how to apply the truncation method to the floating-point logarithmic operation. It analyzes the tradeoff between the precision of computation and the energy it requires, and derives a formula on the most energy-efficient implementation of the logarithm unit for a given error variance range. Based on this theoretical result, the paper proposes BWOLF (Bit-Width optimization for Logarithmic Function), which uses a sequential quadratic programming algorithm to determine the way to truncate data (i.e., bit-width optimization) in a program with logarithm and other arithmetic operations such that the energy consumption is minimized under a fixed error budget.

2020 IEEE 31st International Conference on Application-Specific Systems, Architectures and Processors

AoI-Minimal Trajectory Planning and Data Collection in UAV-Assisted Wireless Powered IoT Networks

Huimin Hu, Ke Xiong, Gang Qu, Qiang Ni, Pingyi Fan, Khaled Ben Letaief

UAVs equipped with  communication transceivers can be used as aerial relays or mobile base stations to help improve the performance of terrestrial wireless communication systems. This paper investigates a UAV-assisted wireless powered IoT system, where a UAV takes off from a data center, flies to each of the ground sensor nodes (SNs) in order to transfer energy and collect data form the SNs, and then returns to the data center.

IEEE Internet of Things Journal

TCIM: Triangle Counting Acceleration with Processing-in-MRAM Architecture

Xueyan Wang, Jianlei Yang, Yinglin Zhao, Yingjie Qi, Meichen Liu, Xingzhou Cheng, Xiaotao Jia, Xiaoming Chen, Gang Qu and Weisheng Zhao

Triangle counting (TC) is a fundamental problem in graph analysis and has found numerous applications, which motivates many TC acceleration solutions in the traditional computing platforms like GPU and FPGA. However, these approaches suffer from the bandwidth bottleneck because TC calculation involves a large amount of data transfers. This paper proposes to overcome the challenge by designing a TC accelerator utilizing the emerging processing-in-MRAM (PIM) architecture.

arXiv.org

New Secure Scan Design with PUF-based Key for Authentication

Gang Qu, Qidong Wang, Aijiao Cui, Huawei Li

A new secure scan design scheme for integrated circuit manufacturing with a unique PUF-based key for each design, to provide authentication and alleviate security concerns.

2020 IEEE 38th VLSI Test Symposium

Hardware security in spin-based computing-in-memory: Analysis, exploits and mitigation techniques

Xueyan Wang, Jianlei Yang, Yinglin Zhao, Xiaotao Jia, Gang Qu, Weisheng Zhao

Computing-in-memory (CIM) could alleviate the processor-memory data transfer bottleneck in traditional Von-Neumann architectures, and spintronics-based magnetic memory has demonstrated many facilitations in implementing CIM paradigm. Hardware security has become one of the major concerns in circuit designs. This paper, for the first time, investigates spin-based CIM from a security perspective.

ACM Journal on Emerging Technologies in Computing Systems

A Guaranteed Secure Scan Design based on Test Data Obfuscation by Cryptographic Hash

Aikiao Cui, Mengyang Li, Gang Qu, Huawei Li

A proposal to use 'cryptographic hash' to thwart attackers seeking the cipher keys of sensitive integrated circuits during manufacturing and testing.

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

VoltJockey: Breaking SGX by Software-Controlled Voltage-Induced Hardware Faults

Pengfei Qiu, Dongsheng Wang, Yongqiang Lyu, Gang Qu

Intel software-guard extensions (SGX) allows applications to run in a trusted space (enclave), which provides a highly secure primitive for the running codes and data. The authors propose the first fault injection attack to break SGX by using voltage-induced hardware faults.

voltjockey.com

2019

VoltJockey: Breaching TrustZone by Software-ControlledVoltage Manipulation over Multi-Core Frequencies

Pengfei Qiu, Dongsheng Wang, Yongqiang Lyu, Gang Qu

VoltJockey is an innovative software-controlled, hardware fault-based attack on multi-core processors that adopt dynamic voltage and frequency scaling (DVFS) techniques for energy efficiency.

Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security

Physical Unclonable Function-based Key Sharing via Machine Leaning for IoT Security

Jiliang Zhang, Gang Qu

In many Industry Internet of Things (IIoT) applications, resources like CPU, memory, and battery power are limited and cannot afford the classic cryptographic security solutions. Silicon Physical Unclonable Function (PUF) is a lightweight security primitive that exploits manufacturing variations during the chip fabrication process for key generation and/or device authentication. The paper proposes a PUF-based key sharing method for the first time.

IEEE Transactions on Industrial Electronics

LEAP: A Lightweight Encryption and Authentication Protocol for In-Vehicle Communications

Zhaojun Lu, Qian Wang, Xi Chen, Gang Qu, Yongqiang Lyu, Zhenglin Liu

Controller Area Network is standard for in-vehicle communications, but attackers can compromise to remotely control vehicles. The researchers have developed a low-cost, high-efficiency encryption and authentication protocol to improve security.

arXiv.org

Pass and Run: A Privacy Preserving Delay Tolerant Network Communication Protocol for CyberVehicles

Zhaojun Lu, Zhenglin Liu, Carson Dunbar, Mingze Gao, Gang Qu

This paper on intelligent transportation systems proposes pass and run protocol for vehicular delay tolerant networks to address vehicle location privacy in communicating with roadside units.

IEEE Design & Test

 

2020

Trace Logic Locking: Improving the Parametric Space of Logic Locking

Michael Zuzak, Yuntao Liu, Ankur Srivastava

The paper proposes trace logic locking (TLL), a provably secure and scalable enhancement to existing logic locking techniques which locks a sequence of primary inputs, known as a trace. Through architectural simulations, the paper shows that TLL achieved both error severity and SAT resilience simultaneously.

IEEE Transactions on Computer-Aided Design of Integrated Circuity and Systems

Evaluating the Security of Delay-Locked Circuits

Abhishek Chakraborty, Yuntao Liu, Ankur Srivastava

A novel SAT formulation-based attack approach called TimingSAT to deobfuscate the functionalities of such delay locked designs.

IEEE Transactions on Computer-Aided Design of Integrated Circuity and Systems

Benchmarking at the Frontier of Hardware Security: Lessons from Logic Locking

Michael Zuzak, Ankur Srivastava and 33 others

The authors prepared, ran, and reflected on the first benchmarking effort in logic locking for ICs, demonstrating the value of coordinated evaluation of hardware security techniques. With industry, government, and academic support, logic locking and other hardware security techniques can benefit from formal and ongoing evaluation. By making these processes regular and structured, researchers could submit new techniques on an ongoing basis for rigorous assessment. Such a process would increase confidence in hardware security technologies.

arXiv.org

Spintronics-based Reconfigurable Ising Model Architecture

Ankit Mondal, Ankur Srivastava

The Ising model has been explored as a framework for modeling NP-hard problems, with several diverse systems proposed to solve it. The Magnetic Tunnel Junction (MTJ)-based Magnetic RAM is capable of replacing CMOS in memory chips. The authors propose the use of MTJs for representing the units of an Ising model and leveraging its intrinsic physics for finding the ground state of the system through annealing.

...

DRAMsim3: a Cycle-Accurate, Thermal-Capable DRAM Simulator

Ankur Srivastava , Zhiyuan Yang, Bruce Jacob, Shang Li, Dhiraj Reddy

The paper develops DRAMsim3, a successor to the earlier simulator DRAMSim2 developed by Jacob and his two former students Paul Rosenfeld (ECE Ph.D. 2014), and Elliott Cooper-Balis (ECE Ph.D. 2012). It is is a fast, cycle-accurate, validated, thermal-capable DRAM simulator that can simulate and model almost all modern DRAM protocols along with many of their unique features.

IEEE Computer Architecture Letters

2019

Hardware-Software Co-Design Based Obfuscation of Hardware Accelerators

Abhishek Chakraborty and Ankur Srivastava

Paper on hardware-software co-design based obfuscation of hardware accelerators proposes hardware-software co-design based obfuscation approach to render unactivated accelerator chip functionally useless.

IEEE Annual Symposium on VLSI 2019

Energy-efficient Design of MTJ-based Neural Networks with Stochastic Computing

Ankit Mondal, Ankur Srivastava

The research proposes the use of magnetic tunnel junctions as stochastic number generators in an SC-based hardware implementation of neural networks. The proposed algorithm brings about a 40% reduction in energy consumption with less than 1% accuracy loss on the 2-layer MNIST network.

ACM Journal on Emerging Technologies in Computing Systems

Keynote: A Disquisition on Logic Locking

Abhishek Chakraborty, Nithyashankari Gummidipoondi Jayasankaran, Yuntao Liu, Jeyavijayan Rajendran, Ozgur Sinanoglu, Ankur Srivastava, Yang Xie, Muhammad Yasin, Michael Zuzak

A survey of the evolution of logic locking and a primer for researchers interested in developing novel techniques in new domains. The authors introduce various “cat and mouse” games involved in logic locking along with its novel applications—including, processor pipelines, graphics-processing units, and analog circuits.

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

 

 

2022

Personalized Federated Multi-Task Learning over Wireless Fading Channels

Matin Mortaheb, Cemil Vahapoglu, Sennur Ulukus

The authors develop FedGradNorm, a distributed dynamic weighting algorithm that balances learning speeds across tasks by normalizing the corresponding gradient norms in PF-MTL, and HOTA-FedGradNorm, which uses over-the-air aggregation (OTA) with FedGradNorm in a hierarchical FL (HFL) setting. HOTA-FedGradNorm is designed to have efficient communication between a parameter server (PS) and clients in the power- and bandwidth-limited regime. Both frameworks are capable of achieving a faster training performance compared to equal-weighting strategies, and compensate for imbalanced datasets across clients and adverse channel effects.

Algorithms

Private Read Update Write (PRUW) in Federated Submodel Learning (FSL): Communication Efficient Schemes With and Without Sparsification

Sajani Vithana, Sennur Ulukus

An investigation of the problem of private read update write (PRUW) in relation to private federated submodel learning (FSL), where a machine learning model is divided into multiple submodels based on the different types of data used to train the model.

arXiv.org

Gradient Coding with Clustering and Multi-message Communication

Emre Ozfatura, Deniz Gündüz, Sennur Ulukus

Gradient descent methods are commonly employed in machine learning problems to optimize the parameters of the model in an iterative fashion. The authors propose a novel gradient coding scheme which allows multiple coded computations to be conveyed from each worker to the master per iteration. They numerically show that the proposed scheme with multi-message communication, together with clustering, provides significant improvements in the average completion time (of each iteration), with minimal or no increase in communication load.

arXiv.org

Private Federated Submodel Learning with Sparsification

Sajani Vithana, Sennur Ulukus

The authors investigate the problem of private read update write (PRUW) in federated submodel learning (FSL) with sparsification.

arXiv.org

Dynamic SAFFRON: Disease Control over Time Via Group Testing

Batuhan Arasli, Sennur Ulukus

Considers a dynamic infection spread model based on the discrete SIR model which assumes infections to be spread over time via infected and non-isolated individuals. Introduces and studies a novel performance metric that can be used to measure how fast a given algorithm can control the spread of a disease. The authors introduce and characterize the performance of a novel dynamic SAFFRON based group testing algorithm.

arXiv.org

Digital Blind Box: Random Symmetric Private Information Retrieval

Zhusheng Wang, Sennur Ulukus

Following the concepts of gachapon as well as blind box, the authors introduce a digital blind box between a user and a server in a communication network. This is a new concept called random SPIR (RSPIR). In reference to the conventional SPIR, the only difference is that, in RSPIR there is no input at the user side. That is, the user does not send any queries to the databases, and ultimately receives a random message from the databases. This requirement is referred to as random reliability. Interestingly, the three requirements of RSPIR, namely, random reliability, database privacy and user privacy, strictly correspond to ththree characteristics of the digital blind box, making it equivalent to the RSPIR.

arXiv.org

Susceptibility of Age of Gossip to Timestomping

Priyanka Kaswan, Sennur Ulukus

A study of the effects of timestomping attacks on the age of gossip in a large fully connected network.

arXiv.org

Age of Gossip in Ring Networks in the Presence of Jamming Attacks

Priyanka Kaswan, Sennur Ulukus

The authors consider a system with a source that maintains the most current version of a file, and a ring network of n user nodes that wish to acquire the latest version of the file. The source gets updated with newer file versions as a point process, and forwards them to the user nodes, which further forward them to their neighbors using a memoryless gossip protocol. They then construct an alternate system model of mini-rings and prove that the version age of the original model can be sandwiched between constant multiples of the version age of the alternate model.

arXiv.org

Editorial for the Issue on “Information Theoretic Foundations of Future Communication Systems”

Elza Erkip, Giuseppe Durisi, Robert Heath, Thomas Marzetta, Petar Popovski, Meixia Tao, Sennur Ulukus

The introductory editorial for a special issue exploring how new advances in information theory can impact future communication systems. Papers address issues at the heart of next generation wireless and wired networks<./p>

IEEE Journal on Selected Areas in Information Theory

FedGradNorm: Personalized Federated Gradient-Normalized Multi-Task Learning

Matin Mortheb, Cemil Vahapoglu, Sennur Ulukus

FedGradNorm uses a dynamic-weighting method to normalize gradient norms to balance learning speeds among different tasks. It improves the overall learning performance in a personalized federated learning setting.

arXiv.org

Using Timeliness in Tracking Infections

Mehlih Bastopcu, Sennur Ulukus

The researchers consider timely tracking of infection status of individuals in a population. For exponential infection and healing processes with given rates, they determined the rates of exponential testing processes. They considered errors on the test measurements and observed that in order to combat the test errors, a limited portion of the population may be tested with higher test rates. They observed in numerical results that the test rates depend on the individuals’ infection and healing rates, the individuals’ last known state of healthy or infected, as well as the health care provider’s priorities of detecting infected people versus detecting recovered people more quickly.

arXiv.org

Efficient Private Federated Submodel Learning

Sajani Vithana, Sennur Ulukus

Investigates the problem of private federated submodel learning, where a machine learning model is divided into M submodels and stored in N databases, from which a given user privately reads, updates and writes back an arbitrary submodel.

IEEE International Conference on Communications 2022

State Amplification and Masking while Timely Updating

Omur Ozel, Aylin Yener, Sennur Ulukus

In status update systems, multiple features carried by the status updating process require pursuit of objectives beyond timeliness measured by the age of information of updates. This paper consider such a problem where the transmitter sends status update messages through a noiseless binary energy harvesting channel that is equivalent to a timing channel. The transmitter aims to amplify or mask the energy state information that is carried in the updating process. The receiver extracts encoded information, infers the energy state sequence while maintaining timeliness of status updates. Consequently, the timings of the updates must be designed to control the message rate, the energy state uncertainty, and the age of information. The authors investigate this three-way trade-off between the achievable rate, the reduction in energy arrival state uncertainty, and the age of information, for zero and infinite battery cases.

arXiv.org

Game Theoretic Analysis of an Adversarial Status Updating System

Subhankar Banerjee, Sennur Ulukus

The authors investigate the game theoretic equilibrium points of a status updating system with an adversary that jams the updates in the downlink. They consider the system models both with and without diversity.

arXiv.org

Age of Information in the Presence of an Adversary

Subhankar Banerjee, Sennur Ulukus

Considers a communication system where a base station serves N users, one user at a time, over a wireless channel. The authors consider the timeliness of the communication of each user via the age of information metric. A constrained adversary can block at most a given fraction, α, of the time slots over a horizon of T slots, i.e., it can block at most αT slots. They show that an optimum adversary blocks αT consecutive time slots of a randomly selected user. The interesting consecutive property of the blocked time slots is due to the cumulative nature of the age metric.

arXiv.org

Private Read Update Write (PRUW) with Storage Constrained Databases

Sajani Vithana, Sennur Ulukus

Investigates the problem of private read update write (PRUW) in relation to federated submodel learning (FSL) with storage constrained databases.

arXiv.org

Communication Cost of Two-Database Symmetric Private Information Retrieval: A Conditional Disclosure of Multiple Secrets Perspective

Zhusheng Wang, Sennur Ulukus

This work considers the total (upload plus download) communication cost of two-database symmetric private information retrieval (SPIR) through its relationship to conditional disclosure of secrets (CDS).

arXiv.org

Timely Gossiping with File Slicing and Network Coding

Priyanka Kaswan, Sennur Ulukus

Presents a class of gossip protocols that achieve O(1) age at a typical node in a single-file system and O(n) age at a typical node for a given file in an n-file system. Shows that file slicing and network coding based protocols fall under the presented class of protocols.

arXiv.org

Dynamic Infection Spread Model Based Group Testing

Batuhan Arasli, Sennur Ulukus

A study of a dynamic infection spread model, inspired by the discrete time SIR model, where infections are spread via non-isolated infected individuals.

arXiv.org

Dynamical Dorfman Testing with Quarantine

Mustafa Doger, Sennur Ulukus

The authors consider dynamical group testing problem with a community structure. With a discrete-time SIR (susceptible, infectious, recovered) model, we use Dorfman’s two-step group testing approach to identify infections, and step in whenever necessary to inhibit infection spread via quarantines.

arXiv.org

2021

Covert Communications via Adversarial Machine Learning and Reconfigurable Intelligent Surfaces

Brian Kim, Tugba Erpek, Yalin E. Sagduyu, Sennur Ulukus

By moving from massive antennas to antenna surfaces for software-defined wireless systems, the reconfigurable intelligent surfaces (RISs) rely on arrays of unit cells to control the scattering and reflection profiles of signals, mitigating the propagation loss and multipath attenuation, and thereby improving the coverage and spectral efficiency. In this paper, covert communication is considered in the presence of the RIS.

arXiv.org

Guest Editoral: Signal Processing Advances in Wireless Transmission of Information and Power

Bruno Clerck, Sennur Ulukus, Stark Draper, Salman Avestimehr, Osvaldo Simeone

Wireless power transfer (WPT) and wireless information and power transfer (WIPT) have received growing attention in the research community in the past few years. In this special issue, a total of fourteen papers present state-of-the-art results in the broad area of wireless transmission of information and power with a special emphasis on signal processing advances. The special issue starts with a guest editor-authored tutorial overview paper that reviews the signal processing, machine learning, sensing, and computing techniques, challenges and opportunities in future networks based on WPT and WIPT. The tutorial paper is then followed by thirteen technical papers.

IEEE Journal on Selected Topics in Signal Processing, special issue, Vol. 15, No. 5

Guest Editorial for Special Issue on Coded Computing

Pulkit Grover, Viveck Cadambe, Sennur Ulukus, Stark Draper, Salman Avestimehr, Osvaldo Simeone

Computing is the next frontier for information theory. Intellectually, the goal of coded computing has been of interest from the days of von Neumann and Shannon. von Neumann examined this issue in his 1956 paper, “Probabilistic Logics and the Synthesis of Reliable Organisms from Unreliable Components,” which was in turn motivated intellectually by Shannon’s 1948 paper, and by the application of understanding reliability of seemingly noisy biological systems. While the original biological application remains ill-understood, the recent increasing use of decentralized and distributed computing architectures, as well as increasingly noisy technologies at a device level, have motivated a resurgence of interest in the problem. This special issue covers several areas within this problem space.

IEEE Journal on Selected Areas in Information Theory, special issue, Vol. 2, No. 3

Adversarial Attacks against Deep Learning Based Power Control in Wireless Communications

Brian Kim, Yi Shi, Yalin E. Sagduyu, Tugba Erpek, Sennur Ulukus

A consideration of adversarial machine learning-based attacks on power allocation where the base station (BS) allocates its transmit power to multiple orthogonal subcarriers by using a deep neural network (DNN) to serve multiple user equipments (UEs).

arXiv.org

Group Testing with Non-identical Infection Probabilities

Mustafa Doger, Sennur Ulukus

The authors consider a zero-error probabilistic group testing problem where individuals are defective independently but not with identical probabilities, and propose a greedy set formation method to build sets of individuals to be tested together.

arXiv.org

Gossiping with Binary Freshness Metric

Ahmed Arafa, Jing Yang, Sennur Ulukus, H. Vincent Poor

A status updating system is considered in which data from multiple sources are sampled by an energy harvesting sensor and transmitted to a remote destination through an erasure channel. The goal is to deliver status updates of all sources in a timely manner, such that the cumulative long-term average age-of-information (AoI) is minimized.

IEEE Transactions on Green Communications and Networking

Gossiping with Binary Freshness Metric

Melih Bastopcu, Baturalp Buyukates, Sennur Ulukus

The authors consider the binary freshness metric for gossip networks that consist of a single source and n end-nodes, where the end-nodes are allowed to share their stored versions of the source information with the other nodes.

arXiv.org

Graph and Cluster Formation Based Group Testing

Batuhan Arasli, Sennur Ulukus

A novel infection spread model based on a random connection graph which represents connections between n individuals is proposed. Infection spreads via connections between individuals and this results in a probabilistic cluster formation structure as well as a non-i.i.d. (correlated) infection status for individuals.

2021 IEEE International Symposium on Information Theory

An Information-Theoretic Scheme for Multi-Party Private Set Intersection

Zhusheng Wang, Karim Banawan, Sennur Ulukus

Here, the authors consider the problem of multi-party private set intersection (MP-PSI).

2021 IEEE International Symposium on Information Theory

Semantic Private Information Retrieval from MDS-Coded Databases

Sajani Vithana, Karim Banawan, Sennur Ulukus

Investigates the problem of semantic private information retrieval (PIR) from coded databases, where a user requires to download a message out of M independent messages, without revealing its identity to the databases.

2021 IEEE International Symposium on Information Theory

Timely Private Information Retrieval

Karim Banawan, Ahmed Arafa, Sennur Ulukus

The authors introduce the problem of timely private information retrieval (PIR) from N non-colluding and replicated servers.

arXiv.org

Guest Editorial: Age of Information

Roy Yates, Yin Sun, D. Richard Brown III, Sanjit K. Kaul, Eytan Modiano, Sennur Ulukus

The authors are guest editors for a special issue on Age of Information of the IEEE Journal on Selected Areas in Communications. These editors have contributed a survey that introduces research in data freshness and provides a broad summary of recent work. The survey is followed by 20 contributed papers that reflect the state of the art in AoI research.

IEEE Journal on Selected Areas in Communications, special issue on Age of Information in real-time cyberphysical systems

Adversarial Attacks on Deep Learning Based mmWave Beam Prediction in 5G and Beyond

Brian Kim, Yalin E. Sagduyu, Tugba Erpek, Sennur Ulukus

The authors present an adversarial attack by generating adversarial perturbations to manipulate over-the-air captured RSSs as input to the DNN. This attack reduces the IA performance significantly and fools the DNN into choosing beams with small RSSs compared to jamming attacks with Gaussian or uniform noise.

arXiv.org

Age of Gossip in Networks with Community Structure

Baturalp Buyukates, Melih Bastopcu, Sennur Ulukus

The authors use the version age metric to quantify information timeliness at receiver nodes. They consider disconnected, ring, and fully connected network topologies for each cluster.

arXiv.org

Symmetric Private Information Retrieval with User-Side Common Randomness

Zhusheng Wang, Sennur Ulukus

A look at the problem of symmetric private information retrieval (SPIR) with user-side common randomness.

arXiv.org

Freshness Based Cache Updating in Parallel Relay Networks

Priyanka Kaswan, Melih Bastopcu, Sennur Ulukus

The paper observes that freshness for a file increases with increase in consolidation of rates across caches. To solve the multi-cache problem, the authors first solve the auxiliary problem of a single-cache system. They then rework this auxiliary solution to a parallel-cache network by consolidating rates to single routes as much as possible. This yields an approximate (sub-optimal) solution for the original problem.

arXiv.org

Cache Freshness in Information Updating Systems

Melih Bastopcu, Sennur Ulukus

The authors consider a cache updating system with a source, m caches and a user. They note that for a given set of update rates for the user (resp. for the caches), the optimal rate allocation policy for the caches (resp. for the user) is a threshold policy, where the optimal update rates for rapidly changing files at the source may be equal to zero.

Sennur Ulukus website

2020

Timely Communication in Federated Learning

Baturalp Buyukates, Sennur Ulukus

Considers a federated learning framework in which a parameter server trains a global model by using n clients without actually storing the client data centrally at a cloud server. Focusing on a setting where the client datasets are highly changing and temporal in nature, the authors investigate the timeliness of model updates and propose a novel timely communication scheme.

arXiv.org

Timely Updates in Distributed Computation Systems with Stragglers

Baturalp Buyukates, Sennur Ulukus

A study of the age performance of uncoded and coded (repetition coded, MDS coded, and multi-message MDS (MM-MDS) coded) schemes in the presence of stragglers under i.i.d. exponential transmission delays and i.i.d. shifted exponential computation times.

2020 Asilomar Conference on Signals, Systems and Computers

Gradient Coding with Dynamic Clustering for Straggler Mitigation

Baturalp Buyukates, Emre Ozfatura, Sennur Ulukus, and Deniz Gündüz

Proposes a novel gradient coding (GC) scheme that utilizes dynamic clustering, denoted by GC-DC, to speed up the gradient calculation.

arXiv.org

Multi-Party Private Set Intersection: An Information-Theoretic Approach

Zhusheng Wang, Karim Banawan, Sennur Ulukus

Proposes a novel achievable scheme for the MP-PSI problem. The scheme hinges on a careful design and sharing of randomness between client parties prior to commencing the MP-PSI operation.

arXiv.org

Maximizing Information Freshness in Caching Systems with Limited Caching Storage Capacity

Melih Bastopcu, Sennur Ulukus

Considers a cache updating system with a source, a cache with limited storage capacity, and a user. Studies the tradeoff between storing files at the cahce and directly opbtining files from the source at the expense of additional transmission times.

arXiv.org

Semantic Private Information Retrieval: Effects of Heterogeneous Message Sizes and Popularities

Sajani Vithana, Karim Banawan, Sennur Ulukus

The authors derive conditions for the semantic PIR capacity to exceed the classical PIR capacity with equal priors and sizes. Our results show that the semantic PIR capacity can be larger than the classical PIR capacity when longer messages have higher popularities.

ece.umd.edu

Age of Information: An Introduction and Survey

Roy D. Yates, Yin Sun, D. Richard Brown III, Sanjit K. Kaul, Eytan Modiano, Sennur Ulukus

A summary of recent contributions in the broad area of age of information (AoI).

arXiv.org

Adversarial Attacks with Multiple Antennas against Deep Learning-Based Modulation Classifiers

Brian Kim, Yalin E. Sagduyu, Tugba Erpek, Kemal Davaslioglu, Sennur Ulukus

From an adversarial machine learning perspective, the work shows how to use multiple antennas at the adversary to improve the adversarial (evasion) attack performance.

arXiv.org

How to make 5G communications "invisible": Adversarial machine learning for wireless privacy

Brian Kim, Yalin Sagduyu, Kemal Davaslioglu, Tugba Erpek, Sennur Ulukus

Demonstrates the feasibility of covert communications in a wireless communication system when a cooperative jammer designs its perturbation signal to fool an eavesdropper's DL-based classfier into classifying ongoing transmissions as noise.

arXiv.org

Private Set Intersection using Multi-Message Symmetric Private Information Retrieval

Zhusheng Wang, Karim Banawan, Sennur Ulukus

A study of the problem of private set intersection. Presents a novel capacity-achieving scheme that builds seamlessly over the multi-message private information retrieval scheme.

conference paper

Information Freshness in Cache Updating Systems

Melih Bastopcu, Sennur Ulukus

For a cache updating system with a source, a cache, and a user, the authors provide an alternating maximization-based method to find update rates for the cache(s) and the user to maximize the freshness of files at the user.

arXiv.org

Channel-Aware Adversarial Attacks against Deep Learning-based Wireless Signal Classifiers

Brian Kim, Yalin E. Sagduyu, Kemal Davaslioglu, Tugba Erpek, Sennur Ulukus

Presents over-the-air adversarial attacks against deep learning-based modulation classifiers, accounting for realistic channel and broadcast transmission effects. A certified defense method using randomized smoothing is also included.

arXiv.org

Selective Encoding Policies for Maximizing Information Freshness

Melih Bastopcu, Baturalp Buyukates, Sennur Ulukus

Proposes a selective encoding scheme for a status updating system in which an information source generates independent and identically distributed update packets based on an observed random variable X which takes n values based on a known pmf.

arXiv.org

Semantic Private Information Retrieval

Sajani Vithana, Karim Banawan, Sennur Ulukus

The paper proposes two achievable schemes for achieving semantic private information retrieval capacity.

arXiv.org

Age of Information with Gilbert-Elliot Servers and Samplers

Baturalp Buyukates, Sennur Ulukus

This work looks at an information update system in which status update packets are generated by a sampler and sent to a monitor node through a server node. Two scenarios are considered: Gilbert-Elliot service times and i.i.d. interarrival times; and Gilbert-Elliot interarrival times and i.i.d. service times. The authors determined the average age at the monitor node for both scenarios and characterized the age-optimal state transition matrix for the underlying Markov chain with and without an average cost constraint on the operation of the system.

arXiv.org

Partial Updates: Losing Information for Freshness

Melih Bastopcu, Sennur Ulukus

This work considers an information updating system where a source produces updates as requested by a transmitter, and observes a tradeoff between the attained average age and the mutual information between the original and partial updates.

arXiv.org

Who Should Google Scholar Update More Often?

Melih Bastopcu, Sennur Ulukus

This work addresses the problem of optimal operation of a resource-constrained sampler that wishes to track multiple independent counting processes in a way that is as up to date as possible.

arXiv.org

Optimal Selective Encoding for Timely Updates

Melih Bastopcu, Baturalp Buyukates, Sennur Ulukus

The researchers consider a status updating system in which an information source generates independent and identically distributed update packets based on an observed random variable X which takes n values based on a known probability mass function (pmf). The proposed selective policy achieves a lower average age than encoding all the realizations and determine the age-optimal k values for arbitrary pmfs.

arXiv.org

Optimal Selective Encoding for Timely Updates with Empty Symbol

Baturalp Buyukates, Melih Bastopcu, Sennur Ulukus

The authors consider two scenarios: when the empty symbol does not reset the age, and when the empty symbol resets the age. They find the time average age of information and the age-optimal real codeword lengths, including the codeword length for the empty symbol, for both of these scenarios. Through numerical evaluations for arbitrary pmfs, the authors show that this selective encoding policy yields a lower age at the receiver than encoding every realization, and find the corresponding age-optimal k values.

conference paper

Scaling Laws for Age of Information in Wireless Networks

Baturalp Buyukates, Alkan Soysal, Sennur Ulukus

A study of age of information in a multiple source-multiple destination setting with a focus on its scaling in large wireless networks.

ece.umd.edu

Private Set Intersection: A Multi-Message Symmetric Private Information Retrieval Perspective

Zhusheng Wang, Karim Banawan, Sennur Ulukus

An information theoretic approach to the Private Set Intersection (PSI) problem shows that it can be successfully recast as a multi-message symmetric private information retrieval (MM-SPIR) problem with message size 1.

arXiv.org

2019

Age of Information for Updates with Distortion: Constant and Age-Dependent Distortion Constraints

Melih Bastopcu, Sennur Ulukus

The authors design an information update system that strikes a desired balance between information quality and freshness by solving for the optimum update scheme subject to a desired distortion level.

arXiv.org

Timely Distributed Computation with Stragglers

Baturalp Buyukates, Sennur Ulukus

Investigates the age performance of uncoded and coded computation distribution algorithms and shows that a minimum data set-coded task distribution scheme asymptotically outperforms uncoded and repetition coded schemes.

arXiv.org

Secure Degrees of Freedom in Networks with User Misbehavior

Karim Banawan, Sennur Ulukus

Explores the secure degrees of freedom of three new channel models: broadcast channel with combating helper, interference channel with selfish users, and multiple-access wiretap channel with deviating users. The paper investigates various malicious interactions that arise in networks, including active adversaries, and proves that a deviating user can drive the secure degrees of freedom to zero. However, the remaining users can exploit the intentional jamming signals as cooperative jamming signals against the eavesdropper and achieve an optimum secure degrees of freedom.

Entropy

2022

Beyond Microphone: mmWave-Based Interference-Resilient Voice Activity Detection

Muhammed Zahid Ozturk, Chenshu Wu, Beibei Wang, Min Wu, K. J. Ray Liu

Microphone-based voice activity detection systems usually require hotword detection and cannot perform well under the presence of interference and noise. Users attending online meetings in noisy environments usually mute and unmute their microphones manually due to the limited performance of interference-resilient VAD. To automate voice detection in challenging environments without dictionary limitations, the authors explore beyond microphones and propose to use mmWave-based sensing, which is already available in many smart phones and IoT devices. Their preliminary experiments in multiple places with several users indicate that mmWave-based VAD can match and surpass the performance of an audio-based VAD in noisy conditions, while being robust against interference.

IASA '22: Proceedings of the 1st ACM International Workshop on Intelligent Acoustic Systems and Applications, July 2022

Detecting Essential Landmarks Directly in Thermal Images for Remote Body Temperature and Respiratory Rate Measurement With a Two-Phase System

Min Wu, Zachary McBride Lazri, Qiang Zhu, Mingliang Chen, Quanzeng Wang

Infrared thermographs (IRTs, also called thermal cameras) have been used to remotely measure elevated body temperature (BT) and respiratory rate (RR) during infectious disease outbreaks, such as COVID-19. To facilitate the fast measurement of BT and RR using IRTs in densely populated venues, it is desirable to have IRT algorithms that can automatically identify the best facial locations in thermal images to extract these vital signs. This paper introduces a unique system that can detect inner canthi and outer nostril edges directly in thermal images in two phases.

IEEE Access

2021

A Multi-Channel Ratio-of-Ratios Method for Noncontact Hand Video Based SpO2 Monitoring Using Smartphone Cameras

Joshua Mathew, Xin Tian, Chau-Wai Wong, Sushant M. Ranadive, Min Wu

It is recommended to regularly monitor the blood oxygen level for precaution. This paper proposes a noncontact method for SpO2 monitoring using hand videos acquired by smartphones.

arXiv.org

Remote Blood Oxygen Estimation From Videos Using Neural Networks

Joshua Mathew, Xin Tian, Min Wu, Chau-Wai Wong

Blood oxygen saturation (SpO2) is an essential indicator of respiratory functionality. This paper proposes the first convolutional neural network-based noncontact SpO2 estimation scheme using smartphone cameras.

arXiv.org

Exploiting Micro-Signals for Physiological Forensics

Ravi Garg, Adi Hajj-Ahmad, Min Wu

Electric Network Frequency (ENF) is a signature of power distribution networks that can be captured by multimedia recordings made in areas where there is electrical activity. This work explores the unchartered application of ENF signal analysis for intra-grid location estimation of multimedia data. This first study conducts experiments on power ENF signals and provides encouraging results in that direction. ENF signals offer a strong potential to be used as a location-stamp for recordings.

techrXiv.org

2020

Exploiting Micro-Signals for Physiological Forensics

Min Wu

A variety of nearly invisible “micro-signals” have played important roles in media security and forensics. These noise-like micro-signals are ubiquitous and typically an order of magnitude lower in strength or scale than the dominant ones. They are traditionally removed or ignored as nuances outside the forensic domain. This talk discusses the recent research harnessing micro-signals to infer a person’s physiological conditions. One type of such signals is the subtle changes in facial skin color in accordance with the heartbeat. Video analysis of this repeating change provides a contact-free way to capture photo-plethysmogram (PPG). While heart rate can be tracked from videos of resting cases, it is challenging to do so for cases involving substantial motion, such as when a person is walking around, running on a treadmill, or driving on a bumpy road. It will be shown in this talk how the expertise with micro-signals from media forensics has enabled the exploration of new opportunities in physiological forensics and a broad range of applications.

Keynote Invited Talk at 8th ACM Workshop on Information Hiding and Multimedia Security June 22–24, 2020 (virtual conference)

Towards Threshold Invariant Fair Classification

Mingliang Chen, Min Wu

Introduces the notion of threshold invariant fairness, which enforces equitable performances across different groups independent of the decision threshold. The paper proposes to equalize the risk distributions among the groups via two approximation methods.

arXiv.org

Time Reversal Based Robust Gesture Recognition Using Wifi

Sai Deepika Regani, Beibei Wang, Min Wu, K. J. Ray Liu

Gesture recognition using wireless sensing opened a plethora of applications in the field of human-computer interaction. However, most existing works are not robust without requiring wearables or tedious training/calibration. WiGRep is a time reversal-based gesture recognition approach using Wi-Fi, which can recognize different gestures by counting the number of repeating gesture segments. Built upon the time reversal phenomenon in RF transmission, the Time Reversal Resonating Strength is used to detect repeating patterns in a gesture. A robust low-complexity algorithm is proposed to accommodate possible variations of gestures and indoor environments. WiGRep is calibration-free and location and environment independent.

2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)


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