Practical classification of different moving targets using automotive radar and deep neural networks

In this work, the authors present results for classification of different classes of targets (car, single and multiple people, bicycle) using automotive radar data and different neural networks. A fast implementation of radar algorithms for detection, tracking, and micro-Doppler extraction is proposed in conjunction with the automotive radar transceiver TEF810X and microcontroller unit SR32R274 manufactured by NXP Semiconductors. Three different types of neural networks are considered, namely a classic convolutional network, a residual network, and a combination of convolutional and recurrent network, for different classification problems across the four classes of targets recorded. Considerable accuracy (close to 100% in some cases) and low latency of the radar pre-processing prior to classification (∼0.55 s to produce a 0.5 s long spectrogram) are demonstrated in this study, and possible shortcomings and outstanding issues are discussed

Custom Integrated Circuits

Contains reports on twelve research projects.Analog Devices, Inc.International Business Machines, Inc.Joint Services Electronics Program (Contract DAAL03-86-K-0002)Joint Services Electronics Program (Contract DAAL03-89-C-0001)U.S. Air Force - Office of Scientific Research (Grant AFOSR 86-0164)Rockwell International CorporationOKI Semiconductor, Inc.U.S. Navy - Office of Naval Research (Contract N00014-81-K-0742)Charles Stark Draper LaboratoryNational Science Foundation (Grant MIP 84-07285)National Science Foundation (Grant MIP 87-14969)Battelle LaboratoriesNational Science Foundation (Grant MIP 88-14612)DuPont CorporationDefense Advanced Research Projects Agency/U.S. Navy - Office of Naval Research (Contract N00014-87-K-0825)American Telephone and TelegraphDigital Equipment CorporationNational Science Foundation (Grant MIP-88-58764

An experimental synthetic aperture SONAR

Aperture synthesis is a mature technique that has been used with success in a number of remote sensing fields. Sonars can also potentially benefit from the technique, though to date the limitations of slow acoustic propagation and difficulty in maintaining a stable platform has hindered investigation. This thesis investigates aperture synthesis for high resolution underwater imaging. A prototype sonar is designed and fabricated for the study. The performance of the sonar is assessed in both tank and sea trials and the results presented in this thesis

Design techniques for low noise and high speed A/D converters

Analog-to-digital (A/D) conversion is a process that bridges the real analog world to digital signal processing. It takes a continuous-time, continuous amplitude signal as its input and outputs a discrete-time, discrete-amplitude signal. The resolution and sampling rate of an A/D converter vary depending on the application. Recently, there has been a growing demand for broadband (>1 MHz), high-resolution (>14bits) A/D converters. Applications that demand such converters include asymmetric digital subscriber line (ADSL) modems, cellular systems, high accuracy instrumentation, and medical imaging systems. This thesis suggests some design techniques for such high resolution and high sampling rate A/D converters. As the A/D converter performance keeps on increasing it becomes increasingly difficult for the input driver to settle to required accuracy within the sampling time. This is because of the use of larger sampling capacitor (increased resolution) and a decrease in sampling time (higher speed). So there is an increasing trend to have a driver integrated onchip along with A/D converter. The first contribution of this thesis is to present a new precharge scheme which enables integrating the input buffer with A/D converter in standard CMOS process. The buffer also uses a novel multi-path common mode feedback scheme to stabilize the common mode loop at high speeds. Another major problem in achieving very high Signal to Noise and Distortion Ratio (SNDR) is the capacitor mismatch in Digital to Analog Converters (DAC) inherent in the A/D converters. The mismatch between the capacitor causes harmonic distortion, which may not be acceptable. The analysis of Dynamic Element Matching (DEM) technique as applicable to broadband data-converters is presented and a novel second order notch-DEM is introduced. In this thesis we present a method to calibrate the DAC. We also show that a combination of digital error correction and dynamic element matching is optimal in terms of test time or calibration time. Even if we are using dynamic element matching techniques, it is still critical to get the best matching of unit elements possible in a given technology. The matching obtained may be limited either by random variations in the unit capacitor or by gradient effects. In this thesis we present layout techniques for capacitor arrays, and the matching results obtained in measurement from a test-chip are presented. Thus we present various design techniques for high speed and low noise A/D converters in this thesis. The techniques described are quite general and can be applied to most of the types of A/D converters

Real-Time Passive Acoustic Tracking of Underwater Vehicles

Com o crescente interesse na exploração oceânica, sistemas de localização subaquática têm sido largamente usados pela industria e comunidade cientifica. Neste trabalho foi desenvolvido um sistema de localização acústica passiva em tempo real, com uma topologia idêntica ao do ultra-short baseline. Este sistema calcula a posição a duas dimensões de uma fonte acústica submersa conhecida, com base na integração de medições da direção do som ao longo do tempo. O ângulo de chegada da onda sonora é estimado pelo atraso de fase entre os sinais adquiridos por dois hidrofones colocados perto um do outro. Esta configuração permite atenuar as diferenças nos sinais recebidos devidas a perturbações do canal acústico subaquático. Este algoritmo foi implementado em tempo real numa plataforma SoC reconfigurável (CPU ARM + FPGA), e validado com ensaios de campo realizados no mar

Software and FPGA-Based Hardware to Accelerate Machine Learning Classifiers

This thesis improves the accuracy and run-time of two selected machine learning algorithms, the first in software and the second on a field-programmable gate array (FPGA) device. We first implement triplet loss and triplet mining methods on large margin metric learning, inspired by Siamese networks, and we analyze the proposed methods. In addition, we propose a new hierarchical approach to accelerate the optimization, where triplets are selected by stratified sampling in hierarchical hyperspheres. The method results in faster optimization time and in almost all cases, and shows improved accuracy. This method is further studied for high-dimensional feature spaces with the goal of finding a projection subspace to increase and decrease the inter- and intra class variances, respectively. We also studied hardware acceleration of random forests (RFs) to improve the classification run-time for large datasets. RFs are a widely used classification and regression algorithm, typically implemented in software. Hardware implementations can be used to accelerate RF especially on FPGA platforms due to concurrent memory access and parallel computational abilities. This thesis proposes a method to decrease the training time by expanding on memory usage on an Intel Arria 10 (10AX115N 3F 45I2SG) FPGA, while keeping high accuracy comparable with CPU implementations

A scalable real-time processing chain for radar exploiting illuminators of opportunity

Includes bibliographical references.This thesis details the design of a processing chain and system software for a commensal radar system, that is, a radar that makes use of illuminators of opportunity to provide the transmitted waveform. The stages of data acquisition from receiver back-end, direct path interference and clutter suppression, range/Doppler processing and target detection are described and targeted to general purpose commercial off-the-shelf computing hardware. A detailed low level design of such a processing chain for commensal radar which includes both processing stages and processing stage interactions has, to date, not been presented in the Literature. Furthermore, a novel deployment configuration for a networked multi-site FM broadcast band commensal radar system is presented in which the reference and surveillance channels are record at separate locations

Applications in Electronics Pervading Industry, Environment and Society

This book features the manuscripts accepted for the Special Issue “Applications in Electronics Pervading Industry, Environment and Society—Sensing Systems and Pervasive Intelligence” of the MDPI journal Sensors. Most of the papers come from a selection of the best papers of the 2019 edition of the “Applications in Electronics Pervading Industry, Environment and Society” (APPLEPIES) Conference, which was held in November 2019. All these papers have been significantly enhanced with novel experimental results. The papers give an overview of the trends in research and development activities concerning the pervasive application of electronics in industry, the environment, and society. The focus of these papers is on cyber physical systems (CPS), with research proposals for new sensor acquisition and ADC (analog to digital converter) methods, high-speed communication systems, cybersecurity, big data management, and data processing including emerging machine learning techniques. Physical implementation aspects are discussed as well as the trade-off found between functional performance and hardware/system costs