Engineering Commons^™

Energy-Efficient Neuromorphic Architectures For Nuclear Radiation Detection Applications, Jorge I. Canales-Verdial, Jamison R. Wagner, Landon A. Schmucker, Mark Wetzel, Nathan J. Withers, Philippe Erol Proctor, Christof Teuscher, Multiple Additional Authors

Electrical and Computer Engineering Faculty Publications and Presentations

A comprehensive analysis and simulation of two memristor-based neuromorphic architectures for nuclear radiation detection is presented. Both scalable architectures retrofit a locally competitive algorithm to solve overcomplete sparse approximation problems by harnessing memristor crossbar execution of vector–matrix multiplications. The proposed systems demonstrate excellent accuracy and throughput while consuming minimal energy for radionuclide detection. To ensure that the simulation results of our proposed hardware are realistic, the memristor parameters are chosen from our own fabricated memristor devices. Based on these results, we conclude that memristor-based computing is the preeminent technology for a radiation detection platform.

Vessel Trajectory Prediction Using Historical Ais Data, Jagir Laxmichand Charla

Dissertations and Theses

Maritime vessel position coordinates are important information for maritime situational planning and organization. A better estimate of future locations of the maritime vessels, from their current locations, can help maritime authorities to make planned decisions, which can be helpful to avoid traffic congestion and longer waiting times. This thesis develops a method for estimating future locations of the vessels using their current and previous locations and other data.

The motivating scenario for this work is that of determining the future locations of the vessels based on their current location and previous locations for accurate modelling of underwater acoustic noise. As …

Flight Simulator Modeling Using Recurrent Neural Networks, Nickolas Sabatini, Andreas Natsis

Undergraduate Research & Mentoring Program

Recurrent neural networks (RNNs) are a form of machine learning used to predict future values. This project uses RNNs tor predict future values for a flight simulator. Coded in Python using the Keras library, the model demonstrates training loss and validation loss, referring to the error when training the model.

An Application Of Deep Learning Models To Automate Food Waste Classification, Alejandro Zachary Espinoza

Dissertations and Theses

Food wastage is a problem that affects all demographics and regions of the world. Each year, approximately one-third of food produced for human consumption is thrown away. In an effort to track and reduce food waste in the commercial sector, some companies utilize third party devices which collect data to analyze individual contributions to the global problem. These devices track the type of food wasted (such as vegetables, fruit, boneless chicken, pasta) along with the weight. Some devices also allow the user to leave the food in a kitchen container while it is weighed, so the container weight must also …

Radiation Source Localization By Using Backpropagation Neural Network, Jian Meng, Christof Teuscher, Walt Woods

Student Research Symposium

The most difficult part of the radiation localization is that we cannot use the traditional acoustic localization method to determine where the radiation source is. It’s mainly because the electromagnetic waves are totally different with the sound wave. From the expression of the radioactive intensity, we can tell that the intensity of radiation not only depend on the distance from the radiation but also related to the type of the nuclide. In general, the relationship between the intensity and the distance satisfy the inverse-square law, which is a non-linear relationship. In other words, if we can use the measurement and …

Using Reservoir Computing To Build A Robust Interface With Dna Circuits In Determining Genetic Similarities Between Pathogens, Christopher Neighbor, Christof Teuscher

Student Research Symposium

As computational power increases, the field of neural networks has advanced exponentially. In particular recurrent neural networks (RNNs) are being utilized to simulate dynamic systems and to learn to predict time series data. Reservoir computing is an architecture which has the potential to increase training speed while reducing computational costs. Reservoir computing consists of a RNN with a fixed connections “reservoir” while only the output layer is trained. The purpose of this research is to explore the effective use of reservoir computing networks with the eventual application towards use in a DNA based molecular computing reservoir for use in pathogen …

Fast And Accurate Sparse Coding Of Visual Stimuli With A Simple, Ultra-Low-Energy Spiking Architecture, Walt Woods, Christof Teuscher

Electrical and Computer Engineering Faculty Publications and Presentations

Memristive crossbars have become a popular means for realizing unsupervised and supervised learning techniques. Often, to preserve mathematical rigor, the crossbar itself is separated from the neuron capacitors. In this work, we sought to simplify the design, removing extraneous components to consume significantly lower power at a minimal cost of accuracy. This work provides derivations for the design of such a network, named the Simple Spiking Locally Competitive Algorithm, or SSLCA, as well as CMOS designs and results on the CIFAR and MNIST datasets. Compared to a non-spiking model which scored 33% on CIFAR-10 with a single-layer classifier, this hardware …

Architectures And Algorithms For Intrinsic Computation With Memristive Devices, Jens Bürger

Dissertations and Theses

Neuromorphic engineering is the research field dedicated to the study and design of brain-inspired hardware and software tools. Recent advances in emerging nanoelectronics promote the implementation of synaptic connections based on memristive devices. Their non-volatile modifiable conductance was shown to exhibit the synaptic properties often used in connecting and training neural layers. With their nanoscale size and non-volatile memory property, they promise a next step in designing more area and energy efficient neuromorphic hardware.

My research deals with the challenges of harnessing memristive device properties that go beyond the behaviors utilized for synaptic weight storage. Based on devices that exhibit …

Memory And Information Processing In Recurrent Neural Networks, Alireza Goudarzi, Sarah Marzen, Peter Banda, Guy Feldman, Matthew R. Lakin, Christof Teuscher, Darko Stefanovic

Electrical and Computer Engineering Faculty Publications and Presentations

Recurrent neural networks (RNN) are simple dynamical systems whose computational power has been attributed to their short-term memory. Short-term memory of RNNs has been previously studied analytically only for the case of orthogonal networks, and only under annealed approximation, and uncorrelated input. Here for the first time, we present an exact solution to the memory capacity and the task-solving performance as a function of the structure of a given network instance, enabling direct determination of the function-structure relation in RNNs. We calculate the memory capacity for arbitrary networks with exponentially correlated input and further related it to the performance of …

Sparse Adaptive Local Machine Learning Algorithms For Sensing And Analytics, Jack Cannon

Undergraduate Research & Mentoring Program

The goal of digital image processing is to capture, transmit, and display images as efficiently as possible. Such tasks are computationally intensive because an image is digitally represented by large amounts of data. It is possible to render an image by reconstructing it with a subset of the most relevant data. One such procedure used to accomplish this task is commonly referred to as sparse coding. For our purpose, we use images of handwritten digits that are presented to an artificial neural network. The network implements Rozell's locally competitive algorithm (LCA) to generate a sparse code. This sparse code is …

A Brief Review Of Speaker Recognition Technology, Clark D. Shaver, John M. Acken

Electrical and Computer Engineering Faculty Publications and Presentations

This paper reviews the development of speaker recognition systems from pre-computing days to current trends. Advances in various sciences which have allowed autonomous speaker recognition systems to become a practical means of identity authentication are also reviewed.

Modeling And Experimental Demonstration Of A Hopfield Network Analog-To-Digital Converter With Hybrid Cmos/Memristor Circuits, Xinjie Guo, Farnood Merrikh-Bayat, Ligang Gao, Brian D. Hoskins, Fabien Alibart, Bernabe Linares-Barranco, Luke Theogarajan, Christof Teuscher, Dmitri B. Strukov

Electrical and Computer Engineering Faculty Publications and Presentations

The purpose of this work was to demonstrate the feasibility of building recurrent artificial neural networks with hybrid complementary metal oxide semiconductor (CMOS)/memristor circuits. To do so, we modeled a Hopfield network implementing an analog-to-digital converter (ADC) with up to 8 bits of precision. Major shortcomings affecting the ADC's precision, such as the non-ideal behavior of CMOS circuitry and the specific limitations of memristors, were investigated and an effective solution was proposed, capitalizing on the in-field programmability of memristors. The theoretical work was validated experimentally by demonstrating the successful operation of a 4-bit ADC circuit implemented with discrete Pt/TiO_{2− …}

Blackfingers A Sophisticated Hand Prothesis, Michele Folgheraiter, Giuseppina Gini, Marek Perkowski, Mikhail Pivtoraiko

Electrical and Computer Engineering Faculty Publications and Presentations

Our goal is to develop the low level control system for an artificial hand ”Blackfingers”. Blackfingers was thought to have two main applications: as humanoid robot’s hand or as a human hand prothesis. In this last application our intent is to realize a device more sophisticated respect the actual commerce prothesis. Also in our intention is to use some biological paradigms to create a human like reflex control easy to be interfaced also with the human nervous system. In this paper we illustrate the properties and the morphology of a human like neural reflex controller, used to set the stiffness …

Le Bio Wall : Un Tissue Informatique Pour Le Prototypage De Systèmes Bio-Inspirés, Andre Stauffer, Daniel Mange, Gianluca Tempesti, Christof Teuscher

Electrical and Computer Engineering Faculty Publications and Presentations

Dans cet article, nous décrivons le BioWall, un tissu informatique reconfigurable géant développé dans le but d’y implémenter des machines mettant en oeuvre les principes de notre projet Embryonique. Bien que ses dimensions et ses caractéristiques en font d’abord un objet de démonstration publique, le BioWall constitue également un outil de recherche précieux, du fait que sa faculté de reprogrammation et sa structure cellulaire s’adaptent parfaitement à l’implémentation de toutes sortes de systèmes bioinspirés. Pour illustrer ces capacités, nous décrivons un ensemble d’applications qui reflètent différentes sources d’inspiration biologique allant des systèmes biologiques ontogénétiques aux dispositifs évolutifs phylogénétiques, en passant …

Training Strategies For Critic And Action Neural Networks In Dual Heuristic Programming Method, Christian Peter Paintz

Dissertations and Theses

This thesis discusses strategies for and details of training procedures for the Dual Heuristic Programming (DHP) methodology. This and other approximate dynamic programming approaches (HDP, DHP, GDHP) have been discussed in some detail in the literature, all being members of the Adaptive Critic Design (ACD) family. The example applications used here are the inverted pendulum problem and a fully nonlinear constant velocity bicycle steering model. The inverted pendulum has been successfully controlled using DHP, as reported in the literature. This thesis suggests and investigates several alternative D HP training procedures and compares their performance with respect to convergence speed and …

Automatic Tuning Of Integrated Filters Using Neural Networks, Lutz Henning Lenz

Dissertations and Theses

Component values of integrated filters vary considerably due to· manufacturing tolerances and environmental changes. Thus it is of major importance that the components of an integrated filter be electronically tunable. The method explored in this thesis is the transconductance-C-method.

A method of realizing higher-order filters is to use a cascade structure of second-order filters. In this context, a method of tuning second-order filters becomes important.

The research objective of this thesis is to determine if the Neural Network methodology can be used to facilitate the filter tuning process for a second-order filter (realized via the transconductance-C-method). Since this thesis is, …

A Gaze-Addressing Communication System Using Artificial Neural Networks, Gabriel Baud-Bovy

Dissertations and Theses

Severe motor disabilities can render a person almost completely incapable of communication. Nevertheless, in many cases, the sensory systems are intact and the eye movements are still under good control. In these cases, one can use a device such as the Brain Response Interface (BRI) to command a remote control (e.g. room temperature, bed position), a word-processor, a speech synthesizer, and so on.

A New Design Approach For Numeric-To-Symbolic Conversion Using Neural Networks, Zibin Tang

Dissertations and Theses

A new approach is proposed which uses a combination of a Backprop paradigm neural network along with some perceptron processing elements performing logic operations to construct a numeric-to-symbolic converter. The design approach proposed herein is capable of implementing a decision region defined by a multi-dimensional, non-linear boundary surface. By defining a "two-valued" subspace of the boundary surface, a Backprop paradigm neural network is used to model the boundary surf ace. An input vector is tested by the neural network boundary model (along with perceptron logic gates) to determine whether the incoming vector point is within the decision region or not. …

Neural Network Character Recognition With A 2-D Fourier Transform Preprocessor, Daqiao Du

Dissertations and Theses

In pattern recognition applications, it is usually important that the same identification be given for a pattern, independent of a variety of positions, rotations and /or distortions of the pattern within the recognition device's field of view. This research relates to development of a preprocessor for a neural network character recognition system, where the role of the preprocessor is to assist in minimizing the difficulties related to variations of position and rotations of a character within the field of view. The preprocessor explored here was suggested in 1970' (Lendaris & Stanly, 1970), and is implemented here with more recent advances …