An Auditory Localization and Coordinate Transform Chip

The localization and orientation to various novel or interesting events in the environment is a critical sensorimotor ability in all animals, predator or prey. In mammals, the superior colliculus (SC) plays a major role in this behavior, the deeper layers exhibiting topographically mapped responses to visual, auditory, and somatosensory stimuli. Sensory information arriving from different modalities should then be represented in the same coordinate frame. Auditory cues, in particular, are thought to be computed in head-based coordinates which must then be transformed to retinal coordinates. In this paper, an analog VLSI implementation for auditory localization in the azimuthal plane is described which extends the architecture proposed for the barn owl to a primate eye movement system where further transformation is required. This transformation is intended to model the projection in primates from auditory cortical areas to the deeper layers of the primate superior colliculus. This system is interfaced with an analog VLSI-based saccadic eye movement system also being constructed in our laboratory

Analog VLSI-Based Modeling of the Primate Oculomotor System

One way to understand a neurobiological system is by building a simulacrum that replicates its behavior in real time using similar constraints. Analog very large-scale integrated (VLSI) electronic circuit technology provides such an enabling technology. We here describe a neuromorphic system that is part of a long-term effort to understand the primate oculomotor system. It requires both fast sensory processing and fast motor control to interact with the world. A one-dimensional hardware model of the primate eye has been built that simulates the physical dynamics of the biological system. It is driven by two different analog VLSI chips, one mimicking cortical visual processing for target selection and tracking and another modeling brain stem circuits that drive the eye muscles. Our oculomotor plant demonstrates both smooth pursuit movements, driven by a retinal velocity error signal, and saccadic eye movements, controlled by retinal position error, and can reproduce several behavioral, stimulation, lesion, and adaptation experiments performed on primates

KAVUAKA: a low-power application-specific processor architecture for digital hearing aids

The power consumption of digital hearing aids is very restricted due to their small physical size and the available hardware resources for signal processing are limited. However, there is a demand for more processing performance to make future hearing aids more useful and smarter. Future hearing aids should be able to detect, localize, and recognize target speakers in complex acoustic environments to further improve the speech intelligibility of the individual hearing aid user. Computationally intensive algorithms are required for this task. To maintain acceptable battery life, the hearing aid processing architecture must be highly optimized for extremely low-power consumption and high processing performance.The integration of application-specific instruction-set processors (ASIPs) into hearing aids enables a wide range of architectural customizations to meet the stringent power consumption and performance requirements. In this thesis, the application-specific hearing aid processor KAVUAKA is presented, which is customized and optimized with state-of-the-art hearing aid algorithms such as speaker localization, noise reduction, beamforming algorithms, and speech recognition. Specialized and application-specific instructions are designed and added to the baseline instruction set architecture (ISA). Among the major contributions are a multiply-accumulate (MAC) unit for real- and complex-valued numbers, architectures for power reduction during register accesses, co-processors and a low-latency audio interface. With the proposed MAC architecture, the KAVUAKA processor requires 16 % less cycles for the computation of a 128-point fast Fourier transform (FFT) compared to related programmable digital signal processors. The power consumption during register file accesses is decreased by 6 %to 17 % with isolation and by-pass techniques. The hardware-induced audio latency is 34 %lower compared to related audio interfaces for frame size of 64 samples.The final hearing aid system-on-chip (SoC) with four KAVUAKA processor cores and ten co-processors is integrated as an application-specific integrated circuit (ASIC) using a 40 nm low-power technology. The die size is 3.6 mm2. Each of the processors and co-processors contains individual customizations and hardware features with a varying datapath width between 24-bit to 64-bit. The core area of the 64-bit processor configuration is 0.134 mm2. The processors are organized in two clusters that share memory, an audio interface, co-processors and serial interfaces. The average power consumption at a clock speed of 10 MHz is 2.4 mW for SoC and 0.6 mW for the 64-bit processor.Case studies with four reference hearing aid algorithms are used to present and evaluate the proposed hardware architectures and optimizations. The program code for each processor and co-processor is generated and optimized with evolutionary algorithms for operation merging,instruction scheduling and register allocation. The KAVUAKA processor architecture is com-pared to related processor architectures in terms of processing performance, average power consumption, and silicon area requirements

An auditory classifier employing a wavelet neural network implemented in a digital design

This thesis addresses the problem of classifying audio as either voice or music. The goal was to solve this problem by means of digital logic circuit, capable of performing the classification in real time. Since digital audio is essentially a discrete non-periodic timeseries, it was necessary to extract features from the audio which are suitable for classification. The discrete wavelet transform combined with a feature extraction method was found to produce such features. The task of classifying these features was found to be best performed by an artificial neural network. Collectively known as a wavelet neural network, the digital logic design implementation of this architecture was effective in correctly identifying the test data sets. The wavelet neural network was first implemented as a software model, to develop the network architecture and parameters, and to determine ideal results. The unconstrained software simulation was capable of correctly classifying test data sets with greater than 90% accuracy. This model was not feasible as a digital logic design however, as the size of the implementation would have been prohibitive. The size of the resulting hardware model was constrained by reducing the widths of the data paths and storage registers. The hardware implementation of the wavelet processor consisted of a novel pipelined design with a novel data-flow control structure. The neural network training was performed entirely in software by way of a novel training algorithm, and the resulting weights were made to be available to be uploaded to the hardware model. The digital design of the wavelet neural network was modeled in VHDL and was synthesized with Synplicity Synplify, using Actel ProASICPlus APA600 synthesized library cells with a target clock frequency of 11.025 KHz, to match the sampling rate of the digital audio. The results of the synthesis indicated that the design could operate at 15.6 MHz, and required 96,265 logic cells. The resulting constrained wavelet neural network processor was capable of correctly classifying test data sets with greater than 70% accuracy. Additional modeling showed that with a reasonable increase in hardware size, greater than 86% accuracy is attainable. This thesis focused on classifying audio as either voice or music, and future research could readily extend this work to the problem of speaker recognition and multimedia indexing

On-scalp MEG using high-Tc SQUIDs: Measuring brain activity with superconducting magnetometers

This thesis describes work done towards realizing on-scalp magnetoencephalography (MEG) based on high critical temperature (high-Tc) superconducting quantum interference device (SQUID) sensors. MEG is a non-invasive neuroimaging modality that records the magnetic fields produced by neural currents with good spatial and high temporal resolution. However, state-of-the-art MEG is limited by the use of liquid helium-cooled sensors (T ~ 4 K). The amount of thermal insulation between the sensors and the subject\u27s head that is required to achieve the extreme temperature difference (~300 K), typically realized in the form of superinsulation foil and ~2 centimeters of vacuum, limits measurable signals. Replacing the sensors with high-Tc SQUIDs can mitigate this problem. High-Tc SQUIDs operate at much higher temperatures (90 K) allowing significant reduction of the stand-off distance (to ~1 mm). They can furthermore be cooled with liquid nitrogen (77 K), a cheaper, more sustainable alternative to the liquid helium used for cooling in conventional MEG systems.The work described in this thesis can be divided into three main areas: (I) simulation work for practical implementations of on-scalp systems, (II) development of a 7-channel high-Tc SQUID-based on-scalp MEG system, and (III) on-scalp MEG recordings.In the first part, spatial information density (SID), a metric to evaluate the performance of simulated MEG sensor arrays, is introduced and - along with total information capacity - used to compare the performance of various simulated full-head on-scalp MEG sensor arrays.\ua0Simulations demonstrate the potential of on-scalp MEG, with all on-scalp systems exhibiting higher information capacity than the state-of-the-art. SID further reveals more homogeneous sampling of the brain with flexible systems. A method for localizing magnetometers in on-scalp MEG systems is introduced and tested in simulations. The method uses small, magnetic dipole-like coils to determine the location and orientation of individual sensors, enabling straightforward co-registration in flexible on-scalp MEG systems. The effects of different uncertainties and errors on the accuracy of the method were quantified.In the second part, design, construction, and performance of a 7-channel on-scalp MEG system is described. The system houses seven densely-packed (2 mm edge-to-edge), head-aligned high-Tc SQUID magnetometers (9.2 mm x 8.6 mm) inside a single, liquid nitrogen-cooled cryostat. With a single filling, the system can be utilized for MEG recordings for >16 h with low noise levels (~0-130 fT). Using synchronized clocks and a direct injection feedback scheme, the system achieves low sensor crosstalk (<0.6%).\ua0In the third part, on-scalp MEG recordings with the 7-channel system as well as its predecessor, a single-channel system, are presented. The recordings are divided into proof-of-principle and benchmarking experiments. The former consist of well-studied, simple paradigms such as auditory evoked activity and visual alpha. Expected signal components were clearly seen in the on-scalp recordings. The benchmarking studies were done to compare and contrast on-scalp with state-of-the-art MEG. To this end, a number of experimental stimulus paradigms were recorded on human subjects with the high-Tc SQUID-based on-scalp systems as well as a state-of-the-art, commercial full-head MEG system. Results include the expected signal gains that are associated with recording on-scalp as well as new details of the neurophysiological signals. Using the previously described on-scalp MEG co-registration method enabled source localization with high agreement to the full-head recording (the distance between dipoles localized with the two systems was 4.2 mm)

Adaptive map alignment in the superior colliculus of the barn owl: a neuromorphic implementation

Adaptation is one of the basic phenomena of biology, while adaptability is an important feature for neural network. Young barn owl can well adapt its visual and auditory integration to the environmental change, such as prism wearing. At first, a mathematical model is introduced by the related study in biological experiment. The model well explained the mechanism of the sensory map realignment through axongenesis and synaptogenesis. Simulation results of this model are consistent with the biological data. Thereafter, to test the model’s application in hardware, the model is implemented into a robot. Visual and auditory signals are acquired by the sensors of the robot and transferred back to PC through bluetooth. Results of the robot experiment are presented, which shows the SC model allowing the robot to adjust visual and auditory integration to counteract the effects of a prism. Finally, based on the model, a silicon Superior Colliculus is designed in VLSI circuit and fabricated. Performance of the fabricated chip has shown the synaptogenesis and axogenesis can be emulated in VLSI circuit. The circuit of neural model provides a new method to update signals and reconfigure the switch network (the chip has an automatic reconfigurable network which is used to correct the disparity between signals). The chip is also the first Superior Colliculus VLSI circuit to emulate the sensory map realignment

Virtual Audio - Three-Dimensional Audio in Virtual Environments

Three-dimensional interactive audio has a variety ofpotential uses in human-machine interfaces. After lagging seriously behind the visual components, the importance of sound is now becoming increas-ingly accepted. This paper mainly discusses background and techniques to implement three-dimensional audio in computer interfaces. A case study of a system for three-dimensional audio, implemented by the author, is described in great detail. The audio system was moreover integrated with a virtual reality system and conclusions on user tests and use of the audio system is presented along with proposals for future work at the end of the paper. The thesis begins with a definition of three-dimensional audio and a survey on the human auditory system to give the reader the needed knowledge of what three-dimensional audio is and how human auditory perception works

Design, modeling and analysis of object localization through acoustical signals for cognitive electronic travel aid for blind people

El objetivo de la tesis consiste en el estudio y análisis de la localización de objetos en el entorno real mediante sonidos, así como la posterior integración y ensayo de un dispositivo real basado en tal técnica y destinado a personas con discapacidad visual. Con el propósito de poder comprender y analizar la localización de objetos se ha realizado un profundo estado de arte sobre los Sistemas de Navegación desarrollados durante las últimas décadas y orientados a personas con distintos grados de discapacidad visual. En el citado estado del arte, se han analizado y estructurado los dispositivos de navegación existentes, clasificándolos de acuerdo con los componentes de adquisición de datos del entorno utilizados. A este respecto, hay que señalar que, hasta el momento, se conocen tres clases de dispositivos de navegación: 'detectores de obstáculos', que se basan en dispositivos de ultrasonidos y sensores instalados en los dispositivos electrónicos de navegación con el objetivo de detectar los objetos que aparecen en el área de trabajo del sistema; 'sensores del entorno' - que tienen como objetivo la detección del objeto y del usuario. Esta clase de dispositivos se instalan en las estaciones de autobús, metro, tren, pasos de peatones etc., de forma que cuando el sensor del usuario penetra en el área de alcance de los sensores instalados en la estación, éstos informan al usuario sobre la presencia de la misma. Asimismo, el sensor del usuario detecta también los medios de transporte que tienen instalado el correspondiente dispositivo basado en láser o ultrasonidos, ofreciendo al usuario información relativa a número de autobús, ruta etc La tercera clase de sistemas electrónicos de navegación son los 'dispositivos de navegación'. Estos elementos se basan en dispositivos GPS, indicando al usuario tanto su locación, como la ruta que debe seguir para llegar a su punto de destino. Tras la primera etapa de elaboración del estaDunai ., L. (2010). Design, modeling and analysis of object localization through acoustical signals for cognitive electronic travel aid for blind people [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8441Palanci

The Boston University Photonics Center annual report 2013-2014

This repository item contains an annual report that summarizes activities of the Boston University Photonics Center in the 2013-2014 academic year. The report provides quantitative and descriptive information regarding photonics programs in education, interdisciplinary research, business innovation, and technology development. The Boston University Photonics Center (BUPC) is an interdisciplinary hub for education, research, scholarship, innovation, and technology development associated with practical uses of light.This annual report summarizes activities of the Boston University Photonics Center in the 2013–2014 academic year.This has been a good year for the Photonics Center. In the following pages, you will see that the center’s faculty received prodigious honors and awards, generated more than 100 notable scholarly publications in the leading journals in our field, and attracted $14.5M in new research grants and contracts this year. Faculty and staff also expanded their efforts in education and training, through National Science Foundation–sponsored sites for Research Experiences for Undergraduates and for Teachers. As a community, we hosted a compelling series of distinguished invited speakers, and emphasized the theme of Innovations at the Intersections of Micro/Nanofabrication Technology, Biology, and Biomedicine at our annual Future of Light Symposium. We took a leadership role in running national workshops on emerging photonic fields, including an OSA Incubator on Controlled Light Propagation through Complex Media, and an NSF Workshop on Noninvasive Imaging of Brain Function. Highlights of our research achievements for the year include a distinctive Presidential Early Career Award for Scientists and Engineers (PECASE) for Assistant Professor Xue Han, an ambitious new DoD-sponsored grant for Multi-Scale Multi-Disciplinary Modeling of Electronic Materials led by Professor Enrico Bellotti, launch of our NIH-sponsored Center for Innovation in Point of Care Technologies for the Future of Cancer Care led by Professor Cathy Klapperich, and successful completion of the ambitious IARPA-funded contract for Next Generation Solid Immersion Microscopy for Fault Isolation in Back-Side Circuit Analysis led by Professor Bennett Goldberg. These three programs, which represent more than$20M in research funding for the University, are indicative of the breadth of Photonics Center research interests: from fundamental modeling of optoelectronic materials to practical development of cancer diagnostics, from exciting new discoveries in optogenetics for understanding brain function to the achievement of world-record resolution in semiconductor circuit microscopy. Our community welcomed an auspicious cohort of new faculty members, including a newly hired assistant professor and a newly hired professor (and Chair of the Mechanical Engineering Department). The Industry/University Cooperative Research Center—the centerpiece of our translational biophotonics program—continues to focus on advancing the health care and medical device industries, and has entered its fourth year of operation with a strong record of achievement and with the support of an enthusiastic industrial membership base

The Boston University Photonics Center annual report 2013-2014

This repository item contains an annual report that summarizes activities of the Boston University Photonics Center in the 2013-2014 academic year. The report provides quantitative and descriptive information regarding photonics programs in education, interdisciplinary research, business innovation, and technology development. The Boston University Photonics Center (BUPC) is an interdisciplinary hub for education, research, scholarship, innovation, and technology development associated with practical uses of light.This annual report summarizes activities of the Boston University Photonics Center in the 2013–2014 academic year.This has been a good year for the Photonics Center. In the following pages, you will see that the center’s faculty received prodigious honors and awards, generated more than 100 notable scholarly publications in the leading journals in our field, and attracted $14.5M in new research grants and contracts this year. Faculty and staff also expanded their efforts in education and training, through National Science Foundation–sponsored sites for Research Experiences for Undergraduates and for Teachers. As a community, we hosted a compelling series of distinguished invited speakers, and emphasized the theme of Innovations at the Intersections of Micro/Nanofabrication Technology, Biology, and Biomedicine at our annual Future of Light Symposium. We took a leadership role in running national workshops on emerging photonic fields, including an OSA Incubator on Controlled Light Propagation through Complex Media, and an NSF Workshop on Noninvasive Imaging of Brain Function. Highlights of our research achievements for the year include a distinctive Presidential Early Career Award for Scientists and Engineers (PECASE) for Assistant Professor Xue Han, an ambitious new DoD-sponsored grant for Multi-Scale Multi-Disciplinary Modeling of Electronic Materials led by Professor Enrico Bellotti, launch of our NIH-sponsored Center for Innovation in Point of Care Technologies for the Future of Cancer Care led by Professor Cathy Klapperich, and successful completion of the ambitious IARPA-funded contract for Next Generation Solid Immersion Microscopy for Fault Isolation in Back-Side Circuit Analysis led by Professor Bennett Goldberg. These three programs, which represent more than$20M in research funding for the University, are indicative of the breadth of Photonics Center research interests: from fundamental modeling of optoelectronic materials to practical development of cancer diagnostics, from exciting new discoveries in optogenetics for understanding brain function to the achievement of world-record resolution in semiconductor circuit microscopy. Our community welcomed an auspicious cohort of new faculty members, including a newly hired assistant professor and a newly hired professor (and Chair of the Mechanical Engineering Department). The Industry/University Cooperative Research Center—the centerpiece of our translational biophotonics program—continues to focus on advancing the health care and medical device industries, and has entered its fourth year of operation with a strong record of achievement and with the support of an enthusiastic industrial membership base