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April 2016

A novel all-elastomer MEMS tactile sensor for high dynamic range shear and normal force sensing

Alexi Charalambides (ME Ph.D. student) and Sarah Bergbreiter (ME/ISR)

Test setup of the Thorlabs and ATI equipment. The AD7745/46 capacitive board, not shown, has pen-style probes which may be pressed against the fabricated sensor’s electrical leads to acquire data.

The paper describes a novel all-elastomer MEMS tactile sensor with high dynamic force range. Conductive elastomeric capacitors are formed from electrodes of varying heights, which enables robust sensing in both shear and normal directions without the need for multi-layered assembly. The sensor geometry is tailored to maximize shear force sensitivity using multiphysics finite element simulations, and a simple molding microfabrication process rapidly creates the sensing skins.

This research appeared in the Sept. 2015 Journal of Micromechanics and Microengineering. The journal later named it a “JMM Highlight of 2015.” A PDF of the article can be downloaded here.


A novel mathematical model that can be used instead of computer simulation

Jeffrey Herrmann (ME/ISR)

Design teams may separate complex design problems into subproblems. This research addresses when the subproblem approach is the superior approach and how subproblems should be assigned to team members. Mathematical models of searches were created to represent bounded rational decision-makers (“agents”) solving a design problem. These discrete-time Markov chains were used to calculate the probability distribution of the value of the solution found and the expected number of steps required.

The research found using a separation increases the likelihood of finding a high-value solution when high-value solutions are less likely. The optimal assignment of team members to subproblems depended upon the distribution of values in the solution space. The results suggest that more effort should be spent developing better concepts when high-quality concepts are rare.  When concepts have similar performance, more effort should be spent searching for better designs that implement the selected concept.

The research appeared in the Nov. 2015 IEEE Transactions on Engineering Management. A PDF of the article can be downloaded here.


A multi-input bridgeless resonant AC-DC converter for electromagnetic energy harvesting

Y. Tang (ECE Ph.D. 2015) and A. Khaligh (ECE/ISR)

Multichannel EMR generators and PEI system: (a) conventional PEI and (b) proposed multiinput PEI.

Flapping electromagnetic-reed generators are investigated to harvest wind energy, even at low cutoff wind speeds. Power electronic interfaces are intended to address ac–dc conversion and power conditioning for single- or multiple-channel systems. However, the generated voltage of each generator reed at low wind speed is usually below the threshold voltage of power electronic semiconductor devices, increasing the difficulty and inefficiency of rectification, particularly at relatively low output powers. This paper proposes a multi-input bridgeless resonant ac–dc converter to achieve ac–dc conversion, step-up voltage and match optimal impedance for a multichannel electromagnetic energy harvesting system. Alternating voltage of each generator is stepped up through the switching LC network and then rectified by a free-wheeling diode. Its resonant operation enhances efficiency and enables miniaturization through high frequency switching. The optimal electrical impedance can be adjusted through resonance impedance matching and pulse-frequency-modulation control. A 5cm × 3 cm, six-input standalone prototype is fabricated to address power conditioning for a six-channel wind panel.

The research appeared in the vol. 31, no. 3, pp. 2254-2263, Mar. 2016 IEEE Transactions on Power Electronics. A PDF of the article can be downloaded here.


Robust Decoding of Selective Auditory Attention from MEG in a Competing-Speaker Environment via State-Space Modeling

S. Akram (ECE Ph.D. 2015), A. Presacco (NACS Ph.D. student), J. Z. Simon (ECE/ISR), S. A. Shamma (ECE/ISR) and B. Babadi (ECE; ISR affiliate)

A step-wise illustration of the EM convergence. A) The output of the state-space decoder is plotted after each EM iteration for sample trials of attending to speaker 1 (green curves), and attending to speaker 2 (orange curves), in the Constant-Attention experiment. B) EM iterations are plotted for sample trials of the Attention-Switch experiment and for attention switches from speaker 1 to speaker 2 (green curves), and from speaker 2 to speaker 1 (orange curves).

The underlying mechanism of how the human brain solves the cocktail party problem is largely unknown. Recent neuroimaging studies suggest salient temporal correlations between the auditory neural response and the attended auditory object. Using magnetoencephalography (MEG) recordings of the neural responses of human subjects, the researchers proposed a decoding approach for tracking the attentional state while subjects selectively listened to one of the two speech streams embedded in a competing-speaker environment. They developed a biophysically-inspired state-space model to account for the modulation of the neural response with respect to the attentional state of the listener. The constructed decoder is based on a maximum a posteriori (MAP) estimate of the state parameters via the Expectation Maximization (EM) algorithm. Using only the envelope of the two speech streams as covariates, the decoder enabled the researchers to track the attentional state of the listener with a temporal resolution of the order of seconds, together with statistical confidence intervals. They evaluated the performance of their model using numerical simulations and experimentally measured evoked MEG responses from the human brain. Analysis reveals considerable performance gains provided by the state-space model in terms of temporal resolution, computational complexity and decoding accuracy.

ISR faculty and their students are the authors; the paper appeared in NeuroImage, 2016, 124, 906–917. A PDF of the article can be downloaded here.


Physics-inspired motion planning for information-theoretic target detection using multiple aerial robots

N. Sydney (AE Ph.D. 2015), D. A. Paley (AE/ISR), and D. Sofge (Naval Research Laboratory)

Diagram depicting the three emergent behaviors of the proposed algorithm. Vehicles in cold areas near a target form a crystalline formation with nearby agents. Vehicles flow like a liquid from warm areas to cold areas. Since speed is proportional to temperature, vehicles in hot areas travel quickly like gas molecules.

This paper presents a motion-planning strategy for multiple, mobile sensor platforms using visual sensors with a finite field of view. Visual sensors are used to collect position measurements of potential targets within the search domain. Measurements are assimilated into a multi-target Bayesian likelihood ratio tracker that recursively produces a probability density function over the possible target positions. Vehicles are dynamically routed using a controller based on a concept from artificial physics, where vehicle motion depends on the target probability at their location as well as the distance to nearby agents. In this paradigm, the inverse log-likelihood ratio represents temperature, i.e., high likelihood corresponds to cold temperature and low likelihood corresponds to high temperature. Vehicles move at a temperature-dependent speed along the negative gradient of the temperature surface while interacting locally with other agents via a Lennard-Jones potential in order to emergently transition between the three states of matter—solid, liquid, and gas. We show that the gradient-following behavior corresponds to locally maximizing the mutual information between the measurements and the target state. The performance of the algorithm is experimentally demonstrated for visual measurements in a motion capture facility using quadrotor sensor platforms equipped with downward facing cameras.

The paper appeared in Autonomous Robots, pages 1–11, 2015.  DOI: 10.1007/s10514-015-9542-0. A PDF of the article can be downloaded here.

Real-time Monitoring of Macromolecular Biosensing Probe Self-assembly and On-chip ELISA using Impedimetric Microsensors

Faheng Zang (ECE Ph.D. student), Konstantinos Gerasopoulos (ECE Ph.D. 2011), Xiao Zhu Fan (ECE Ph.D. 2013), Adam Brown (BioE Ph.D. student), James Culver (Plant Science & Landscape Architecture/IBBR), Reza Ghodssi (ECE/ISR)


Schematic of a three dimensional segment of VLP with helical arrangement of genetically modified cysteine residues and FLAG-tag sequences on coat proteins.

This paper presents a comprehensive study of the self-assembly dynamics and the biosensing efficacy of genetically modified Tobacco mosaic virus-like particles (TMV VLPs) sensing probes using an impedimetric microsensor platform. The dynamics of VLP self-assembly on the sensor were studied by the continuous monitoring of impedance changes on the microsensors, revealing saturation of sensor surface coverage at 68% through VLP self-assembly within 8 hours. The VLP functionalized impedance sensors responded to 12 ng/ml - 1.2 μg/ml of target antibodies in the enzyme-linked immunosorbent assays (ELISA), and yielded 18% - 35% total impedance increases, respectively. These results combined highlight the significant potential of genetically modified VLPs as selective nanostructured probes for autonomous sensor functionalization and enhanced biosensing.

The paper appeared in Biosensors and Bioelectronics, 81 (2016) 401–407. A PDF of the article can be downloaded here.