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signal processing Methods to Interpret Polychlorinated Biphenyls in Airborne Samples

IEEE ACCESS 2020年 8卷 147738-147755页

作者： McCarthy, Ryan A. Gupta, Ananya Sen Kubicek, Bernice Awad, Andrew M. Martinez, Andres Marek, Rachel F. Hornbuckle, Keri C. Univ Iowa Dept Elect & Comp Engn Iowa City IA 52242 USA Univ Iowa Iowa Technol Inst ITI Iowa City IA 52242 USA Univ Iowa Dept Civil & Environm Engn Iowa City IA 52242 USA Univ Iowa IIHR Hydrosci & Engn Iowa City IA 52242 USA

The main contribution of this interdisciplinary work is a robust computational framework to autonomously discover and quantify previously unknown associations between well-known (target) and potentially unknown (non-target) toxic industrial air pollutants. In this work, the variability of polychlorinated biphenyl (PCB) data is evaluated using a combination of statistical, signal processing, and graph-based informatics techniques to interpret the raw instrument signal from gas chromatography-mass spectrometry (GC/MS/MS) data sets. Specifically, minimum mean-squared techniques from the adaptive signal processing literature are extended to detect and separate coeluted (overlapped) peaks in the raw instrument signal. A graph-based visualization is provided which bridges two complementary approaches to quantitative pollution studies: (i) peak-cognizant target analysis (limits data analysis to few well-known compounds) and (ii) chemometric analysis (statistical large-scale data analysis) that is agnostic of specific compounds. Further, peak fitting techniques based on L2 error minimization are employed to autonomously calculate the amount of each PCB present with a normalized mean square error of -18.4851 dB. Graph-based visualization of associations between known and unknown compounds are developed through principal component analysis and both fuzzy c-means (FCM) and k-means clustering techniques are implemented and compared. The efficiency of these methods are compared using 150 air samples analyzed for individual PCBs with GC/MS/MS against traditional target-only techniques that perform analysis across only the known (target) PCBs. Parameter optimization techniques are employed to evaluate the relative contribution of PCB signals against ten potential source signals representing legacy signatures from historical manufacture of Aroclors and modern sources of PCBs produced as byproducts of pigment and polymer manufacturing. Aroclors 1232, 1254, 1016, and 1221 as well as

关键词： Instruments Compounds signal processing Principal component analysis Chemicals Load modeling signal processing algorithms Identifying sources interpreting GC MS MS PCBs signal processing

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VLSI SYSTEM COMPILER FOR DIGITAL signal-processing - MODULARIZATION AND SYNCHRONIZATION

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS 1991年第4期38卷 423-433页

作者： ITO, K KUNIEDA, H Department of Electrical and Electronic Engineering Tokyo Institute of Technology Meguro Tokyo Japan

An overview of a VLSI system compiler that generates a highly parallel and fast processor array on a VLSI chip for general digital signal processing algorithms is described here. In line with this overview, this paper describes the modularization and the synchronization of general digital signal processing algorithms that convert them into suitable forms for the implementation by a processor array on a VLSI chip. First, signal processing algorithms are modularized into the minimum number of inner-product modules by the proposed modularization procedure. Modularizing digital signal processing algorithms with the low coefficient sensitivity parameters is also proposed. Then these modules are assigned to inner-product processors. After processors are placed and communication paths between them are routed on a VLSI chip, the synchronization procedure derives a schedule for this VLSI system. When data transfer conflict occurs on interprocessor communications links and operation execution conflict occurs on processors, how they are resolved is discussed in this paper.

关键词： Very large scale integration Digital signal processing signal processing algorithms Processor scheduling Digital signal processing chips Parallel processing Systolic arrays Indium tin oxide signal generators signal resolution

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The development of a new signal processing program at the Queensland University of Technology

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IEEE TRANSACTIONS ON EDUCATION 1996年第2期39卷 186-191页

作者： Boashash, B Sridharan, S Chandran, V Signal Processing Research Center School of Electronic Systems Engineering Queensland University of Technology Brisbane Australia

The School of Electrical and Electronic Systems Engineering at Queensland University of Technology, Brisbane, Australia (QUT), offers three bachelor degree courses in electrical and computer engineering. In all its courses there is a strong emphasis on signal processing. A newly established signal processing Research Center (SPRC) has played an important role in the development of the signal processing units in these courses. This paper describes the unique design of the undergraduate program in signal professing at QUT, the laboratories developed to support it, and the criteria that influenced the design.

关键词： Biomedical signal processing signal processing algorithms signal processing Australia Digital signal processing signal design Biomedical engineering Geophysical signal processing Radar signal processing Systems engineering and theory

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Well-Log and Seismic Data Integration for Reservoir Characterization A signal processing and machine-learning perspective

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IEEE signal processing MAGAZINE 2018年第2期35卷 72-81页

作者： Chaki, Soumi Routray, Aurobinda Mohanty, William K. Indian Inst Technol Kharagpur Elect Engn Kharagpur W Bengal India Indian Inst Technol Kharagpur Dept Elect Engn Kharagpur W Bengal India Indian Inst Technol Kharagpur Dept Geol & Geophys Kharagpur W Bengal India

Reservoir characterization (RC) is a process of finding petrophysical properties of the subsurface mainly from the seismic and well-log data. The nonlinear and heterogeneous nature of the subsurface is the major bottleneck in estimating the reservoir properties. In the past two decades, the RC has eventually turned out to be an interdisciplinary field of research involving computational science, signal processing, geostatistics, and geophysics. This article provides the interdisciplinary perspective in RC focusing on the applications of signal processing and machine learning (ML). We provide an account of various state-of-the-art algorithms while categorizing them into three stages in an RC framework: preprocessing, prediction, and postprocessing. RC has been known to be a highly data-driven problem. Huge volumes of seismic and well-log data are cleverly integrated by experts to decipher the subsurface properties. Some of the anomalies may lead to the existence of a potential reservoir. The signal processing tools are primarily required for information matching, preprocessing for noise and artifacts, and postprocessing for removing irregularities in the prediction, whereas the ML tools are required to map the seismic data to well logs. This article provides a comprehensive study on the recent advances in RC involving seismic volumes and well logs.

关键词： Prediction algorithms signal processing algorithms Feature extraction Artificial neural networks signal resolution Reservoirs

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Hallway Gait Monitoring Using Novel Radar signal processing and Unsupervised Learning

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IEEE SENSORS JOURNAL 2022年第15期22卷 15133-15145页

作者： Abedi, Hajar Boger, Jennifer Morita, Plinio P. Wong, Alexander Shaker, George Univ Waterloo Syst Design Engn Dept Waterloo ON N2L 3G1 Canada Univ Waterloo Sch Publ Hlth Sci Waterloo ON N2L 3G1 Canada Univ Waterloo Elect Engn Dept Waterloo ON N2L 3G1 Canada

We propose a novel corridor or hallway gait monitoring system based on radar signal processing, unsupervised learning, and a subject detection, association and tracking method. This paper proposes an algorithm that could be paired with any type of MIMO FMCW radar to capture human gait in a highly cluttered environment without needing radar antenna alteration. We validate algorithm functionality by capturing spatiotemporal gait values (e.g., speed, step points, step time, step length, and step count) of people walking in a long hallway. We show that our proposed algorithm yields an average absolute error for speed estimation between 0.0040 m/s to 0.0435 m/s. These preliminary results demonstrate the promising potential of our algorithm to accurately monitor gait in hallways, which increases opportunities for its applications in institutional and home environments.

关键词： Radar Clutter signal processing algorithms Legged locomotion Sensors Radar antennas Monitoring Contactless monitoring doppler frequency-modulated continuous-wave multipath multiple-input and multiple-output radar gait analysis

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Secure signal processing using fully homomorphic encryption

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IET INFORMATION SECURITY 2020年第1期14卷 51-59页

作者： Shortell, Thomas Shokoufandeh, Ali Drexel Univ Comp Sci Dept 3141 Chestnut St Philadelphia PA 19104 USA

In a world becoming dependent on cloud computing, multiple and many techniques are needed to meet security and privacy requirements. This study investigates using Fully Homomorphic Encryption (FHE) in the cloud for a set of signal processing algorithms. Using FHE provides a level of security and privacy for cloud computing. This research implements a framework with a set of real number processing formats that allows FHE to be used in the cloud. Developing formats for real numbers is required because FHE operates over integers. Both a fixed point binary format and a rational number format are implemented to allow for real number processing. Experimentally, the framework is implemented and verified with three algorithms: fast Fourier transform, speeded up robust features, and histogram of oriented gradients. Each implementation is bounded with error caused by the formats and examined against images (two-dimensional signals).

关键词： fast Fourier transforms cryptography cloud computing data privacy image coding computer network security fully homomorphic encryption cloud computing FHE signal processing algorithms fixed point binary format rational number format 2D signals real number processing formats fast Fourier transform speeded up robust features histogram of oriented gradients

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Hybrid digital signal processing neural network CDMA multiuser detection scheme

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-ANALOG AND DIGITAL signal processing 1996年第2期43卷 96-104页

作者： Kechriotis, G Manolakos, ES NORTHEASTERN UNIV COMMUN & DIGITAL SIGNAL PROC CTR RES & GRAD STUDIDEPT ELECT & COMP ENGNBOSTONMA 02115 AWARE INC CAMBRIDGEMA

We present a new hybrid digital signal processing-neural network two step multiuser detection scheme whose small computational complexity makes it attractive for real-time CDMA multiuser detection, An investigation on the nature of the local minima of the Optimal Multiuser Detector's (OMD) objective function leads to the development of an efficient algorithmic stage that can reduce significantly the size of the OMD optimization problem. This stage may then be followed by a Hopfield Neural Network employed to solve a smaller size residual problem of the same form. The performance of the hybrid detector is evaluated via simulations and it is shown to exceed that of other suboptimal receivers at a much lower computational cost in both synchronous and asynchronous CDMA transmission cases.

关键词： signal processing Multiaccess communication Multiuser detection Detectors Neural networks Hopfield neural networks Computational efficiency Digital signal processing signal processing algorithms Spread spectrum communication

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Community-Aware Graph signal processing: Modularity Defines New Ways of processing Graph signals

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IEEE signal processing MAGAZINE 2020年第6期37卷 150-159页

作者： Miljan, Petrovic Liegeois, Raphael Bolton, Thomas A. W. van de Ville, Dimitri Med Image Proc Lab MIPLab Lausanne Switzerland Ecole Polytech Fed Lausanne Med Image Proc Lab Lausanne Switzerland Ecole Polytech Fed Lausanne Lausanne Switzerland Ecole Polytech Fed Lausanne Bioengn Lausanne Switzerland Univ Geneva Geneva Switzerland

The emerging field of graph signal processing (GSP) allows one to transpose classical signal processing operations (e.g., filtering) to signals on graphs. The GSP framework is generally built upon the graph Laplacian, which plays a crucial role in studying graph properties and measuring graph signal smoothness. Here, instead, we propose the graph modularity matrix as the centerpiece of GSP to incorporate knowledge about graph community structure when processing signals on the graph but without the need for community detection. We study this approach in several generic settings, such as filtering, optimal sampling and reconstruction, surrogate data generation, and denoising. Feasibility is illustrated by a small-scale example and a transportation network data set as well as one application in human neuroimaging where community-aware GSP reveals relationships between behavior and brain features that are not shown by Laplacian-based GSP. This work demonstrates how concepts from network science can lead to new, meaningful operations on graph signals.

关键词： Communities signal processing algorithms Laplace equations Neuroimaging Graphical models

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UAV-Mounted SIM: A Hybrid Optical-Electronic Neural Network for DoA Estimation

UAV-Mounted SIM: A Hybrid Optical-Electronic Neural Network ...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Shining Lin Jiancheng An Lu Gan Mérouane Debbah University of Electronic Science and Technology of China Nanyang Technological University Yibin Institute of UESTC Khalifa University of Science and Technology

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Unmanned aerial vehicle (UAV) communication plays a pivotal role in achieving ubiquitous connectivity for the sixth-generation (6G) networks. Accurate and real-time direction of arrival (DOA) estimation is crucial for beamforming in UAV communication systems. However, the existing high-precision DOA estimation algorithms encounter high computational complexity when being implemented on a UAV with the on-board signal processing constraints. To tackle this issue, a hybrid optical-electric neural network (HOENN) is utilized for DOA estimation, which is capable of generating angular spectrum based solely on amplitude observation. The proposed HOENN consists of two components: a stacked intelligent metasurfaces (SIM)-enabled diffractive neural network, which is mounted on UAV and can process signals in the wave domain at the speed of light with low energy consumption, and a fully connected layer for processing the received amplitude signal. Finally, the simulation results validate that the HOENN achieves significant performance gain compared to the conventional beamforming (CBF) method, albeit with its lower cost and RF-related power consumption.

关键词： Integrated optics Direction-of-arrival estimation Optical diffraction Neural networks Estimation signal processing algorithms Optical computing Optical fiber networks Autonomous aerial vehicles Optical signal processing

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Rate-Constrained Quantization for Communication-Efficient Federated Learning

Rate-Constrained Quantization for Communication-Efficient Fe...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Shayan Mohajer Hamidi Ali Bereyhi ECE Department University of Waterloo ECE Department University of Toronto

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Quantization is a common approach to mitigate the communication cost of federated learning (FL). In practice, the quantized local parameters are further encoded via an entropy coding technique, such as Huffman coding, for efficient data compression. In this case, the exact communication overhead is determined by the bit rate of the encoded gradients. Recognizing this fact, this work deviates from the existing approaches in the literature and develops a novel quantized FL framework, called rate-constrained federated learning (RC-FED), in which we deploy the conventional entropy-constrained scalar quantization technique to quantize the gradients subject to both fidelity and data rate constraints. Particularly, we formulate this scheme, as a joint optimization in which the quantization distortion is minimized while the rate of encoded gradients is kept below a target threshold. This enables for a tunable trade-off between quantization distortion and communication cost. We analyze the convergence behavior of RC-FED, and show its superior performance against baseline quantized FL schemes on several datasets.

关键词： Quantization (signal) Acoustic distortion Costs Accuracy Federated learning signal processing algorithms Speech processing Huffman coding Optimization Convergence

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