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IEEE Applied signal processing Conference (ASPCON)

作者： Tanmoy Kundu Salil Kumar Sanyal Iti Saha Misra Department of Electronics and Telecommunication Engineering Jadavpur University Kolkata India

ISBN: (纸本)9781538666876

Three dimensional beamforming (3DBF) is a famed technology that enhances spectral efficiency, capacity and coverage area of the 5G networks compared to conventional two dimensional (2D) beamforming by implementing both user specific horizontal and vertical beamforming. In this paper, three LMS based adaptive spatial filtering algorithms, namely, Ang's, Mathew's and Fixed step-size LMS have been implemented and evaluated on FPGA for 3DBF scenarios. The associated signal processing issues, specifically for FPGA implementation have been critically investigated. A prototype design of a 3DBF system deploying these adaptive algorithms has been demonstrated using Xilinx Zynq®-7000 family based FPGA platform. Coexistence of Xilinx System Generator ™ and MATLAB Simulink® environment has been suitably utilized for the purpose. The performance metrics in terms of convergence properties like Mean Square Error (MSE) and Step-size along with beam pattern of all algorithms have been evaluated. Finally, hardware resource utilization of these algorithms has also been analyzed. Investigations reveal the superiority of Mathew's algorithm over the other two for 3DBF.

关键词： signal processing algorithms Field programmable gate arrays Convergence Array signal processing Hardware Prototypes

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Novel signal processing architectures for knowledge-based STAP algorithms [radar SIGPRO]

Novel signal processing architectures for knowledge-based ST...

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IEEE National Conference on Radar

作者： M.C. French Jinwoo Suh J. Damoulakis S.P. Crago Information Sciences Institute University of Southern California Arlington VA USA

New algorithms are being developed in the radar community that blend a priori knowledge source processing with traditional digital signal processing concepts. This operational blend necessitates a system-level architecture capable of delivering both high processing throughput and memory bandwidth. This paper derives these system parameters from the knowledge aided pre-whitening algorithm and evaluates the performance of two high performance embedded computing architectures, the Imagine and Raw processors, on these kernels. The implementation results are compared with the measured performance of a conventional system based on the PowerPC with Altivec. The results show these processors exhibit significant improvements over conventional systems and that each architecture has its own strengths and weaknesses.

关键词： signal processing algorithms Radar signal processing Computer architecture Spaceborne radar Synthetic aperture radar Digital signal processing Throughput Radar imaging Hardware Delay

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New digital signal processing approach to the design of the algorithms for frequency deviation measurement

New digital signal processing approach to the design of the ...

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Annual Conference of Industrial Electronics Society

作者： B. Perunicic M. Kezunovic P. Spasojevic Elektrotechnički Fakultet Sarajevo Yugoslavia Department of Electrical Engineering Texas A and M University College Station TX USA

A novel approach to the design of frequency deviation measurement algorithms is defined. This approach is based on a signal processing scheme that uses quadratic forms of signal samples. The approach is general, and methods to use it as a design tool for developing new algorithms are presented. The performance of an algorithm developed to illustrate the design approach is evaluated using computer simulation tests.< >

关键词： Digital signal processing signal design Algorithm design and analysis signal processing algorithms Frequency measurement Power system protection Electric variables measurement Monitoring Control systems Power system measurements

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Subspace-based algorithms without eigendecomposition for array signal processing

Subspace-based algorithms without eigendecomposition for arr...

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

作者： P. Stoica A. Eriksson T. Soderstrom Systems and Control Group University of Uppsala Uppsala Sweden Department of Automatic Control Polytechnic Institute of Bucharest Romania

A class of subspace-based methods for estimating the direction-of-arrival (DOA) of plane waves impinging on an array of sensors is proposed. The methods estimate the DOA using only linear transformations of the data. This is of special interest for cases when the number of sensors is large and the computational advantages of these methods are significant. These methods use a less restrictive noise model and, e.g., can accommodate cases where the noise variance is different for different sensors. Large sample variance expressions for the estimates of the DOAs are derived, and the statistical properties of the proposed method are compared against the properties of multiple signal classification (MUSIC).< >

关键词： signal processing algorithms Array signal processing Sensor arrays Direction of arrival estimation Covariance matrix Multiple signal classification Cost function White noise Additive noise Control systems

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Medical signal processing by means of immune algorithms

Medical signal processing by means of immune algorithms

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E-Health and Bioengineering Conference (EHB)

作者： Hariton Costin Silviu Ioan Bejinariu Faculty of Medical Bioengineering “Grigore T Popa” Romanian Academy Iaşi Branch Iaşi Romania Institute of Computer Science Romanian Academy Iaşi Branch Iaşi Romania

Medical signals are increasingly being used within healthcare for diagnosis, planning treatment, guiding treatment and monitoring disease evolution. Medical imaging mainly treats and processes missing, ambiguous, complementary, redundant, contradictory, distorted data, and information has a strong structural character. This paper reports two “immune algorithms” based approaches for medical signal processing. Immune algorithms belong to the Artificial Immune Systems field. The first proposed approach uses the Clonal Selection Algorithm (CSA) for geometric transform estimation in image registration (IR). The second approach, the Dendritic Cell Algorithm (DCA) is used for automatic driver stress detection using biomedical signals.

关键词： Immune system signal processing algorithms Sociology Statistics Transforms Image registration Stress

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From algorithms to devices: Enabling machine learning through ultra-low-power VLSI mixed-signal array processing

From algorithms to devices: Enabling machine learning throug...

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Custom Integrated Circuits Conference (CICC)

作者： Siddharth Joshi Chul Kim Sohmyung Ha Gert Cauwenberghs University of California San Diego CA New York University Abu Dhabi Abu Dhabi UAE

Machine learning and related statistical signal processing are expected to endow sensor networks with adaptive machine intelligence and greatly facilitate the Internet of Things. As such, architectures embedding adaptive and learning algorithms on-chip are oft-ignored by the design community and present a new set of design trade-offs. This review focuses on efficient implementation of mixed-signal matrix-vector multiplication as a central computational primitive enabling machine learning and statistical signal processing, with specific examples in spatial filtering for adaptive beamforming. We describe adaptive algorithms amenable for efficient implementation with such primitives in the presence of noise and analog variability. We also briefly highlight current trends in high-density integration in emerging memory device technologies and their use in highdimensional adaptive computing.

关键词： Gain Dynamic range signal processing algorithms Parallel processing Switches Digital signal processing

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Adaptive algorithms for digital signal processing

Adaptive algorithms for digital signal processing

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European Conference on Electrotechnics, Conference Proceedings on Area Communication, EuroCON

作者： N. Rozic S. Rajilic D. Begusic University of Split Split Yugoslavia University of Zagreb Zagreb Croatia

A comparison is made of three characteristic algorithms for adaptive signal processing in ISDN (integrated-services digital network) user interfaces: a fast RLS (recursive least squares) algorithm with transversal FIR (finite impulse response) structure, an LMS (least mean squares) algorithm with transversal FIR structure, and a sign algorithm with memory filter structure. The algorithms are compared by using a simulated system for data transmission, which is applied to the study of the characteristics of adaptive algorithms and for the education of the students in the area of telecommunications.< >

关键词： Adaptive algorithm Digital signal processing signal processing algorithms Finite impulse response filter Adaptive signal processing ISDN User interfaces Resonance light scattering Least squares methods Least squares approximation

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Generalized Levinson algorithms and Ladder filters for nonstationary signal processing

Generalized Levinson algorithms and Ladder filters for nonst...

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

作者： T. Kailath Information Systems Laboratory University of Stanford Stanford CA USA

The Levinson algorithm, implemented via a ladder filter, has been widely used in seismic signal processing and more recently in speech analysis. This algorithm was originally developed (in 1947) for prediction of stationary stochastic processes. It has been adapted to applications where only a single sample function (time-series) is available by the use of appropriate "window functions" to "simulate stationarity". Such windowing is often somewhat artificial and can introduce undesirable artefacts in situations where only a limited amount of data is available. However, more realistic "windowing" will destroy the analogy to the stationary stochastic process case and thus apparently prohibit use of the Levinson-ladder- filter implementations. In this talk, we shall first show how by using a concept of how close a given process is to being stationary, we can develop generalized Levinson algorithms and generalized ladder filters applicable to any stochastic process, stationary or not. The complexity of the algorithm and the filter is proportional to the distance from stationarity of the given process. We shall then show that this structure can include analyses of several windowing strategies for applications where only a single observed record is available, thus including results obtained earlier for such problems by Morf, Vieira and Lee. The present results are based on work with D. Lee and H. Lev-Ari.

关键词： signal processing algorithms Stochastic processes Contracts Information systems Laboratories Information filtering Information filters signal processing Speech analysis

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Fast Convergent Jacobi algorithms Using High Dimensional Rotations For Vlsi signal processing

Fast Convergent Jacobi Algorithms Using High Dimensional Rot...

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Workshop on VLSI signal processing

作者： J. Angell Kung Yao UCLA Los Angeles CA USA

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Heart sound classification using signal processing and machine learning algorithms

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MACHINE LEARNING WITH APPLICATIONS 2022年 7卷

作者： Zeinali, Yasser Niaki, Seyed Taghi Akhavan Sharif Univ Technol Dept Ind Engn Tehran Iran Sharif Univ Technol Dept Ind Engn POB 11155-9414 Azadi Ave Tehran *** Iran

According to global statistics and the world health organization (WHO), about 17.5 million people die each year from cardiovascular disease. In this paper, the heart sounds gathered by a stethoscope are analyzed to diagnose several diseases caused by heart failure. This research's primary process is to identify and classify the data related to the heart sounds categorized in four general groups of S 1 to S 4 . The sounds S 1 and S 2 are considered as the heart's normal sounds, and the sounds S 3 and S 4 are the abnormal sounds of the heart (heart murmurs), each expressing a specific type of heart disease. In this regard, the desired features are first extracted after retrieving the data by signal processing algorithms. In the next step, feature selection algorithms are used to select the compelling features to reduce the problem's dimensions and obtain the optimal answer faster. While the existing algorithms in the literature classify the sound into two groups of normal and abnormal, in the final section, some of the most popular classification algorithms are utilized to classify the type of sound into three classes of normal, S 3 and S 4 categories. The proposed methodology obtained an accuracy rate of 87.5% and 95% for multiclass data (3 classes) and 98% for binary classification (normal vs. abnormal) problems.

关键词： Heart sound signal processing algorithms Machine learning algorithms Dimensional reduction algorithms Feature selection Gradient boosting classifier

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