CRCNS: Imaging and Modeling of Cortical Microvascular Dynamics
High-Density, Wearable Dry-Electrode EEG Recording System Incorporating Online Artifact Rejection and Data Reduction
High-Density Integrated Adaptive Wavefront Control
SST: Minimally-Attended Integrated Visual Surveillance Network
Integrated Multi-Signal Adaptive Microphone
Trainable Visual Aids for Object Detection and
Identification
MEMS Acoustic Sensors and Adaptive VLSI Signal
Processing
Neuromorphic Autoadaptive Systems and Independent Component Analysis
Low-Power Biosonar Signal Processor for Buried Target
Recognition
Engineering Research and Education in Analog VLSI
Parallel Computational Systems
Micropower Analog VLSI Continuous Speech Recognition
Algorithms and Architectures for VLSI Neuromorphic
Systems
VLSI High-Performance A/D Conversion
Smart Silicon Controller for Optical Phase Distortion
Suppression
Smart Focal-Plane Arrays
Neuromorphic VLSI Modelling of Attention-Based Visual
Search
Development and application of high-resolution functional imaging techniques to study the interaction between bloodflow and neural activity in cortex at micrometer and millisecond resolution.
Design and implementation of wireless instrumentation for high-resolution EEG functional brain imaging, using dry electrode sensing technology and on-line independent component analysis.
Investigation into high-resolution microscale adaptive optics and stochastic adaptive control for aberration correction, implemented in parallel analog VLSI.
Design, analysis and implementation of vision algorithms onto focal-plane image processors, for surveillance of the visual scene.
Design and implementation of an integrated MEMS/VLSI optical microphone array and signal processor to extract multiple sound sources from the acoustic environment.
Projects towards the development of a custom-trainable, versatile, self-contained and mobile system for visually impaired users. The system will aid the user in interacting freely with other people and the environment, by rapidly detecting and localizing key visual environmental cues, and rapidly recognizing and identifying familiar people and objects. At the core of the system is the Kerneltron, a massively parallel Support Vector "Machine" (SVM) in silicon.
In this collaborative project with the University of Maryland, University of Syndey, and Signal Systems Corporation, our goals are to design and demonstrate novel MEMS acoustic sensor arrays, and to integrate these MEMS sensors with adaptive VLSI systems for real-time applications of signal tracking, source identification and active noise control where micropower and miniature operation are a strict requirement.
An investigation in the problem of separating mixtures of signals when nothing or little is known about the sources of the signals or the way they are mixed. Independent Component Analysis provides a mathematical basis for some of this work. The objectives are to retrieve the sources from the mixture(s) and to identify their spatial origin. The goal is to develop novel algorithms, and prototype integrated processors that implement the algorithms in real time for portable applications in speech, acoustics and sonar signal processing.
A collaborative project with Orincon to develop an integrated signal processor for real-time classification of underwater buried targets from active sonar inspired by dolphin and bat echolocation. The goal is a miniaturized low-power system suitable for use in an autonomous underwater vehicle.
An interdisciplinary program of research and education in custom integrated computational systems, concentrating on analog VLSI systems for multimedia sensory integration, covering applications of speech processing, visual motion estimation, and communications.
Grant supporting Ph.D. research on interdisciplinary research involving circuit design. Low-power mixed analog-digital VLSI implementation of a hybrid ANN/HMM (artificial neural network/hidden markov model) system for continuous speech recognition.
A multidisciplinary research initiative (MURI) center for automated vision and sensing systems, with applications to synthetic aperture radar (SAR) image processing and pattern recognition, and automated active vision systems with sensory-motor integration. The Johns Hopkins component of the project contributes dedicated analog VLSI sensory information processing systems as key components of active vision systems, and as dedicated co-processors interfacing with digital computers.
Design and VLSI prototyping of high-speed oversampling and pipelined analog-to-digital converters in CMOS, BiCMOS and SiGe technology. With the Systems Development and Technology division of Northrop Grumman.
A seed project to interface analog VLSI electronics with optical diffraction systems for on-line correction of abberation of optical propagation in atmospheric media. In collaboration with the Intelligent Optics Laboratory at ARL.
Development of hybridized focal-plane array IR sensors, interfacing HgCdTe arrays and other IR-responsive materials with silicon circuits through flip bonding. VLSI algorithms for on-line offset correction. Joint work with Army CECOM Fort Belvoir.
An interdisciplinary project combining theoretical and experimental neuroscience with analog VLSI emulation of mechanisms of attention in biological neural systems. In collaboration with and coordinated by the Mind-Brain Institute.