1990 — 1993 |
Andreou, Andreas |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Ria: Fault Tolerance in Analog Vlsi Focal Plane Processors @ Johns Hopkins University
Andreou Analog VLSI is a promising approach to the design of systems for machine vision since it leads to distributed, data driven architectures, that map the light stimuli directly to the crux of computing hardware, the silicon substrate. This emerging class of signal processing systems is based on relatively simple computational primitives such as logarithmic photodetectors, aggregating resistive networks and coupled non-linear resistive networks. Furthermore, there are power and area efficient features that make them attractive for wafer scale integration. However, manufacturing of such systems at the wafer scale will strongly depend on establishing design methodologies that make them robust to defects in the fabrication process. Equally important, these must be augmented with yield estimation models as well as testing and failure analysis procedures. The above issues are addressed in this research in a comprehensive program that involves both analytical calculations and experimental studies.
|
1 |
1993 — 1996 |
Andreou, Andreas Pineda, Fernando |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Analog Computation and Vlsi Architectures For Contraction Mappings @ Johns Hopkins University
9313934 Andreou This project will attempt to develop a new class of recurrent networks. The architecture of the networks is inspired by recent work on image encoding based on iterated transformation theory and it's associated inverse problems. The PIs purpose to restrict our investigation to networks that can be physically implemented in subthreshold analog VLSI. With their approach analog components that implement high quality arithmetic operations are unnecessary. Indeed, significant departures from ideal linear behavior can be tolerated, provided that these departures are reproducible across chips. As a concrete application they will consider that task of data compression and decompression. Compression is accomplished by the relaxation of an electronic circuit to a steady state while compression is preformed either off-line or with an adaptive analog VLSI neural network architecture. For hardware compression they propose to use a learning algorithm that learns both the weight and the connection topology. Hence, the ability to gate and switch electrical current is central to the operation of these networks. The main thrust of this investigation is to design and characterize the circuits that implement the required transformations in one dimension. A successful outcome to this investigation has the potential for making compression and decompression technology available for all low-power applications. ***
|
1 |
2001 — 2005 |
Cauwenberghs, Gert [⬀] Andreou, Andreas Vorontsov, Mikhail Etienne-Cummings, Ralph (co-PI) [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Microscale Adaptive Optical Wavefront Correction @ Johns Hopkins University
Phase distortions due to inhomogeneities in the optical path severely limit the perforinancc of a large class of optical systems for ground-to-ground and space communications, imaging through the atmosphere, medical laser beam focusing, among others. Demands on increased spatial resolutions and larger bandwidths call for an integrated approach to adaptive optics that modulates the wavefront in parallel at microscopic scale.
This collaborative effort combines expertise in adaptive optics, analog parallel very-large scale integrated (VLSI) niicrosys-tems, microfabrication and liquid-crystal molecular systems to create a new generation of adaptive micro-optical systems for high-resolution wavefront correction, with over 10,000 fully autonomous control elements integrated on a single, hybrid opti-cal/electronic chip. Autonomy is essential for high-bandwidth operation, and is obtained by integrating all adaptive functions directly on-chip.
At the architectural level, model-free adaptive control is implemented using parallel perturbation stochastic gradient descent optimization of an arbitrary, externally provided metric of system performance. At the physical level, high-speed wavefront control at micro-scale resolution is obtained by integrating a new type of fast nematic liquid-crystal (LC), operating at kilohertz- range bandwidths, onto the adaptive control chip. Silicon-on-sapphire (SoS) technology with ultra-thin silicon (UTSi) transis-tors provides a high-quality, low-noise, transparent active medium for high-density optical and electronic integration. We will investigate microscale structures of LC material sandwiched in between two transparent SoS wafers, implementing arrays of phase modulators with active electrodes implementing the adaptive algorithms in parallel. directly interfacing with the wave- front. The architectural and technological innovations combine to yield a projected system performance in excess of 108 control updates/sec. at least a factor 1,000 better than presently existing adaptive optics systems in speed, density and cost.
This program integrates research and education in a sequence of project-intensive courses, where teams of graduate and undergraduate students learn to design. prototype and test adaptive optics co-processors, implemented in analog VLSI and fabricated through MOSIS. The adaptive co-processors will be configured to externally control a variety of fast LC and other spatial light phase modulators, available for experimentation at the Army Research Laboratory (ARL). In addition, we will make use of full-size UTSi SoS wafers provided by Peregrine Semiconductor, custom-fabricated in a special arrangement with Hopkins, to prototype a fully integrated version of consistent optical quality. The already polished SoS wafers will be post-processed at the JHU Microfabrication Laboratory and at Boulder Nonlinear Systems. Inc.. to pattern and deposit fast nematic LC in contact with SoS for fast spatial light phase modulation. The prototyped adaptive micro-optical systems will be experimentally demonstrated on various adaptive optics and imaging tasks including laser beam focusing and stabilization for optical communications.
|
1 |
2002 — 2004 |
Andreou, Andreas |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
A Comparative Study of Information Processing in Biological and Bio-Inspired Systems: Performance Criteria, Resources Tradeoffs and Fundamental Limits @ Johns Hopkins University
EIA-0130812 Andreas G. Andreou Johns Hopkins University A Comparative Study of Information Processing in Biological and Bio-inspired systems: Performance Criteria, Resources Tradeoffs and Fundamental Limits
We propose a research program towards a fundamental and quantitative understanding of the tradeoffs between system performance and resources such as size, reliability and energy requirements for biological and bio-inspired microsystems. We employ the mathematical tools of communication theory and model natural or synthetic physical structures as micro-scale communication networks, studying them under physical constraints at two different levels of abstraction. At the functional level we examine the operational and task specification, while at the implementation level we examine the physical specification and realization. Both levels of abstraction are characterized by Shannon's channel capacity, as determined by the channel bandwidth, the signal power; and the noise power. The link between the functional level and the physical level of abstraction is established through first principles and phenomenological otherwise, models for transformations on the signal, physical constraints on the system, and noise that degrades the signal.
|
1 |
2002 — 2005 |
Andreou, Andreas Smela, Elisabeth [⬀] Smela, Elisabeth [⬀] Abshire, Pamela (co-PI) [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Integrated Sensing: Cell Clinics On a Chip @ University of Maryland College Park
0225489 Smela
The PIs will develop integrated microstructures and circuitry for single cell capture and characterization. Each of these "clinics" will consist of a cell-sized cavity, or microvial, and a lid. The lid can be opened and closed by hinges constructed from polypyrrole microactuators which link rigid plates to the substrate. These clinics will be fabricated on conventional bulk silicon as well as on ultra-thin silicon-on-sapphire (SOS) substrates. Integration of the microstructures with CMOS electronics allows integrating sensors, detection, and stimulation circuitry together, while SOS provides a transparent substrate which allows optical measurements of cell activity within the chamber. Sensing modalities will be tailored for specific applications and include: impedance measurements, extracellular recording and stimulation of electrically active cells, optical measurements, and current or voltage clamp measurements using microfabricated needles within the chamber. The potential applications are numerous, spanning a spectrum from detailed physiological studies of specific mechanisms to whole cell studies of ecology or developmental biology to collecting concentrated cell secretions to statistical studies of assayed cell properties.
|
0.939 |
2004 — 2006 |
Cauwenberghs, Gert [⬀] West, James Andreou, Andreas Diehl, Christopher |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Acoustic Target Identification and Localization @ Johns Hopkins University
This project investigates signal processing and machine learning algorithms for identifying and localizing one or multiple targets in the acoustic scene. The algorithms will be mapped onto a parallel architecture suitable for integration with micro-power mixed-signal hardware. A biologically inspired gradient flow signal representation blindly separates and localizes targets using a miniature array of sub-wavelength aperture. A support vector machine identifies the time-frequency signatures of the localized targets. The goal of the one-year project is to determine the achievable energy efficiency and integration density of the autonomous sensor and the feasibility of its deployment in a large-scale network, and to evaluate the concept using hardware prototypes. Advanced power management using wake-up detection will be pursued to reduce standby power. The effort will also investigate efficient means of embedding acoustic sensors onto CMOS circuits towards a highly integrated directional and intelligent acoustic sensor. Miniature integration and micro-power operation are essential to provide an autonomous sensing and processing node for distributed intelligence in a sensor network. The outcomes of this project will advance the state of the art in acoustic sensing technology for surveillance, homeland security, and as an aid to the soldier's awareness in the digital battlefield. The results will also impact new developments in intelligent hearing aids and other assistive listening technologies and human-computer interfaces.
|
1 |
2012 — 2015 |
Shamma, Shihab (co-PI) [⬀] Andreou, Andreas Fermuller, Cornelia [⬀] Horiuchi, Timothy (co-PI) [⬀] Etienne-Cummings, Ralph (co-PI) [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Inspire: Signals to Symbols: From Bio-Inspired Hardware to Cognitive Systems @ University of Maryland College Park
This INSPIRE award is partially funded by the Science of Learning Centers Program in the Division of Behavioral, Cognitive and Social Sciences in the Directorate for Social, Behavioral and Economic Sciences; the Perception, Action, and Cognition Program in the Division of Behavioral, Cognitive and Social Sciences in the Directorate for Social, Behavioral and Economic Sciences; the Energy, Power, and Adaptive Systems Program in the Division of Electrical Communication and Cyber Systems in the Directorate of Engineering; and the Applied Mathematics and Mathematical Biology Program in the Division of Mathematical Sciences in the Directorate for Mathematical and Physical Sciences. This research project draws on knowledge from many disciplines (neuroscience, cognitive science, computational science, mathematics and engineering) to create cognitive systems capable of interpreting observed, complex human movements and actions. New design methodologies will be developed for the integration of sensory modalities (vision, audition, touch) and their support of higher cognitive function (language, reasoning). In contrast to existing approaches which tend to be assemblies of modular components each solving its task in isolation, this team takes a novel approach called Active Cognition which has the following features: 1) Instead of modeling the different perceptual processes (vision, audition, and haptics), cognition, and motor control in isolation, the modules are integrated and capabilities co-developed in the tradition of dynamical systems theory to obtain a reasoning system where "the whole is greater than the sum of its parts"; 2) instead of segregating the low level processing of signals from the processing of higher level symbolic information, they will interact in a continuous dialogue, such that high level knowledge will leverage perception; and 3) instead of separating physical embodiment from algorithmic considerations, biologically inspired real-time hardware will be developed that implements complex functions by integrating signals and symbols. The project is organized in two working groups. The first group will develop a cognitive robot that can recognize complex human activities using visual and auditory signals captured by biological-inspired hardware. The second group will study attention in humans by measuring human response to audition and vision through EEG and MEG, and subsequently implementing the findings in robots. A yearly three-week, hands-on workshop will educate students, serve as testing ground for the team's ideas, and stimulate new collaborations. This workshop will also engage the involvement of the interdisciplinary research community that has formed around the goal of building biologically inspired cognitive systems.
Success in integrating different components of a cognitive system (hardware, sensors, and software) has the potential to catalyze a new industry of biologically-inspired cognitive systems, including household and service robots, and systems for intelligent transportation and smart manufacturing. In addition, this interdisciplinary project will play a significant role in building capacity for a new emphasis area in engineering and training of cognitive systems engineers who need combined expertise in computer science, electrical engineering and cognitive neuroscience.
|
0.939 |
2013 — 2017 |
Thompson, William Mittal, Rajat Abraham, Theodore Andreou, Andreas |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Sch: Int: Mapping the Cardiac Acousteome: Biosensing and Computational Modeling Applied to Smart Diagnosis and Monitoring of Heart Conditions @ Johns Hopkins University
The goal of the project is to develop fundamental science, knowledge, tools, and technologies for smart diagnosis and monitoring of heart conditions based on automated cardiac auscultation. An innovative wearable multimodal acoustic array (the StethoVest) is proposed. This sensory array localizes and separates acoustic broadband sources in space by measuring spatial and temporal derivatives of the acoustic field. Using this StethoVest, first-of-their-kind maps of the cardiac acousteome are generated. These maps include not only 4D (3D space and time) measurements of heart sounds; they are accompanied by high-fidelity hemoacoustic simulations that delineate cause-and-effect, as well as simulation-guided source-identification algorithms that provide unprecedented diagnostic sensitivity and specificity. The simulations take cardiac imaging data as input, and simulate cardiac blood flow as well as the associated heart sounds. The latter part of this four-year project focuses on investigating the physics of aortic valve murmurs as well as StethoVest based screening for hypertrophic obstructive cardiomyopathy.
Annual national expenditure on heart disease exceeds half a trillion dollars with over half a million deaths attributed to this disease each year. The proposed research leverages emerging capabilities in biosensing, computational modeling, imaging and signal processing, to produce a diagnostic technology that moves us away from management of heart disease that is mostly reactive, expensive and hospital-centric, towards an approach that is smart, proactive, patient-centric and cost-effective. The sound inventory generated from continuous, automated monitoring and interpretation of heart sounds has the potential to generate unprecedented understanding of human physiology. The project promotes interdisciplinary education and workforce development through involvement of undergraduates, graduate students, and postdocs in the research, development of courses and clinical training tools, and local and international outreach activities.
|
1 |
2015 — 2018 |
Shamma, Shihab (co-PI) [⬀] Andreou, Andreas Fermuller, Cornelia [⬀] Horiuchi, Timothy (co-PI) [⬀] Etienne-Cummings, Ralph (co-PI) [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Sl-Cn: Cortical Architectures For Robust Adaptive Perception and Action @ University of Maryland College Park
The motivation for this biologically-inspired approach is to design systems that perceive and act in cluttered and noisy scenes that they have never experienced. This stands in contrast with the state of the art in computational engineering systems that need to be re-trained each time they confront an unanticipated environment. The main reason is that current approaches to perception address specific problems in isolation and do not consider that the primary role of perception is to support systems with bodies in action. As a result, they are constrained to the situations for which they were trained and cannot react to changing tasks and scenes. By focusing on cognition primitives rather than specific applications, the work is expected to greatly advance the state of the art of machine perception and lead to the development of systems that can robustly and on-line adapt to new environments, react to novel situations and learn new contexts. To do so, novel theoretical formulations of perception and action and high-speed, low-power, hardware implementations with on-line learning capabilities will be studied while assimilating new insights from the neurosciences. Consequently, this work will network neuroscience, cognitive science, applied mathematics, computer science and engineering so as to lower one of the few remaining barriers that keeps interactive robots in the realm of science fiction. Beyond the scholarly contribution, the work is expected to provide know-how for the design of systems with adaptive perception in a modular fashion with reusable components. Such systems have applications in computational vision and auditory perception problems and can advance the industry of cognitive biologically-inspired robotics and assistive devices.
This proposal sets forward novel ideas in the design of intelligent perceptual systems and the development of synthetic intelligence. Just about any task which an intelligent system solves involves the interplay of four basic processes that are devoted to: (a) context, (b) attention, (c) segmentation and (d) categorization. The members of the proposed network will study these canonical cognitive primitives by combining neural modeling with neural and behavioral experiments, theoretical and computational modeling and implementation in robotics. The findings of theoretical insights will then be adapted to satisfy the demands of realistic behavior, and to develop technological solutions for applications of robust and invariant perception and action. The proposed collaborative network will consist of a small science and engineering research team to directly address the questions in robust adaptive perception and action. It will then direct personnel, and inject results and pedagogical content to a Summer Workshop that aims to include a global network of researchers.
|
0.939 |
2021 — 2025 |
Shamma, Shihab (co-PI) [⬀] Andreou, Andreas Fermuller, Cornelia [⬀] Etienne-Cummings, Ralph (co-PI) [⬀] Babadi, Behtash (co-PI) [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Accelnet: Accelerating Research On Neuromorphic Perception, Action, and Cognition @ University of Maryland College Park
Artificial intelligence is becoming ubiquitous in modern life. To build systems under the current paradigm, large amounts of energy are required for computing and sensing. This causes environmental problems, pollution, and challenges for small-sized systems, as well as privacy issues. The field of neuromorphic science and technology offers an alternative by seeking to understand principles of biological brains and build on their basis artificial systems using low-power hardware and software solutions. While its advantages have been demonstrated, further advances are necessary and will require common computational tools and principled experimental approaches. This AccelNet project, NeuroPacNet, links international experts in neuromorphic engineering with computational neuroscientists, roboticists, control theorists, and researchers of perception from seven global networks to set the foundations for building systems that can robustly process real-world signals in time and adapt to changes. This network of networks will facilitate the development of new methods and approaches for intelligent system design and prepare the next generation of leaders in neuromorphic science and technology. As different industries adopt neuromorphic hardware, society will have access to new applications, such as in computing on cell phones, neuroprostheses, intelligent hearing aids, and smart sensory systems with predictive capabilities.
NeuroPacNet will advance computational research on modeling the integration of perception, action, and cognition. The network of network will coordinate across those research thrusts and develop new approaches grounded in theoretical neuroscience for sensorimotor control, motor learning, event-based computations, and learning in spiking neural networks. NeuroPacNet will also include robotics research in the areas of drone navigation and human activity understanding for humanoids and will address social and ethical issues in humanoid robotics. The network of networks will use innovative hardware design and mixed signals computational systems to address computation for emerging and unconventional technologies. International collaboration and knowledge exchange will include an immersive research exchange program providing scholarships to students and postdoctoral researchers, an annual workshop to discuss common issues and concerns in a stimulating environment and to engage in hands-on projects, meetings to define challenges, opportunities, and actions to accelerate progress, and competitions with two challenges to be solved by teams of researchers and students. An interactive project website will become a portal for archived webinar talks, tools, and data.
The Accelerating Research through International Network-to-Network Collaborations (AccelNet) program is designed to accelerate the process of scientific discovery and prepare the next generation of U.S. researchers for multiteam international collaborations. The AccelNet program supports strategic linkages among U.S. research networks and complementary networks abroad that will leverage research and educational resources to tackle grand scientific challenges that require significant coordinated international efforts.
Co-funding for this project is provided by the Directorate for Social, Behavioral, and Economic Sciences.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
|
0.939 |