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DUAL SIGN ALGORITHM FOR ADAPTIVE filtering

IEEE TRANSACTIONS ON COMMUNICATIONS 1986年第12期34卷 1272-1274页

作者： KWONG, CP Department of Electronics Chinese University of Hong Kong New Territories Hong Kong China

A new algorithm, which is a variant of the sign algorithm, is proposed for the adaptive adjustment of an FIR digital filter with an aim of improving the original convergence characteristics, yet retaining the advantage of hardware simplicity. Based on a recently proposed theory for the sign algorithm, a practical design method is derived for the new algorithm, and it is shown by computer simulation that the new algorithm in fact performs significantly better than the original algorithm.

关键词： filtering algorithms Adaptive filters Convergence Quantization Finite impulse response filter Digital filters Hardware Communications Society Design methodology Computer simulation

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A Parallel Algorithm of Image Mean filtering Based on OpenCL

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IEEE ACCESS 2021年 9卷 65001-65016页

作者： Xiao, Han Guo, Baoyun Zhang, Hongyan Li, Cailin Zhengzhou Normal Univ Sch Informat Sci & Technol Zhengzhou 450044 Peoples R China Shandong Univ Technol Sch Civil & Architectural Engn Zibo 255000 Peoples R China

The image will be contaminated by noise during the imaging process, which severely degrades the image quality. It is necessary to filter the collected image. With the increasing amount of image data, the traditional single-processor or multiprocessor computing equipment has been unable to meet the requirements of real-time data processing. In this paper, the computational model of weighted mean filtering and the characteristics of high performance computer architecture are studied. An efficient hierarchical image weighted mean filtering parallel algorithm for Open Computing Language (OpenCL) is designed and implemented, which can fully express the parallelism of the computing model. The parallel algorithm takes full account of the characteristics of image discrete convolution computing and the multi-layer logic architecture of high performance computer, deeply excavates the parallelism of the computing platform and computing model, and realizes the efficient task mapping from computing model to computing resources. The model is implemented in parallel with the two levels of work-group and work-item. The experimental results show that compared with the serial algorithm based on CPU, the parallel algorithm based on Open Multi-Processing (OpenMP) and the parallel algorithm based on Compute Unified Device Architecture (CUDA), the parallel algorithm of weighted mean filtering achieves 20.88 times, 18.52 times and 1.26 times acceleration ratio on the NVIDIA GPU computing platform based on OpenCL architecture, respectively. It realizes better computing performance and runs on different Graphic Processing Unit (GPU) computing platforms, and has good portability and scalability.

关键词： Graphics processing units filtering Parallel algorithms filtering algorithms Computational modeling Acceleration Signal processing algorithms Weighted mean filtering Gaussian noise Graphic Processing Unit (GPU) Open Computing Language (OpenCL) parallel algorithm

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A CONSTRAINED ANTI-HEBBIAN LEARNING ALGORITHM FOR TOTAL LEAST-SQUARES ESTIMATION WITH APPLICATIONS TO ADAPTIVE FIR AND IIR filtering

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-ANALOG AND DIGITAL SIGNAL PROCESSING 1994年第11期41卷 718-729页

作者： GAO, KQ AHMAD, MO SWAMY, MNS CONCORDIA UNIV CTR SIGNAL PROC & COMMUNDEPT ELECT & COMP ENGNMONTREALPQ H3G 1M8CANADA

In this paper, a new Hebbian-type learning algorithm for the total least-squares parameter estimation is presented. The algorithm is derived from the classical Hebbian rule. An asymptotic analysis is carried out to show that the algorithm allows the weight vector of a linear neuron unit to converge to the eigenvector associated with the smallest eigenvalue of the correlation matrix of the input signal. When the algorithm is applied to solve parameter estimation problems, the converged weights directly yield the total least-squares solution. Since the process of obtaining the estimate is optimal in the total least-squares sense, its noise rejection capability is superior to those of the least-squares-based algorithms. It is shown that the implementations of the proposed algorithm have the simplicity of those of the LMS algorithm. The applicability and performance of the algorithm are demonstrated through computer simulations of adaptive FIR and IIR parameter estimation problems.

关键词： Signal processing algorithms Finite impulse response filter Parameter estimation Signal processing Vectors Equations filtering algorithms Adaptive filters IIR filters Signal analysis

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Investigation of filtering Algorithm for Noise Reduction in Displacement Sensing Signal

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IEEE SENSORS JOURNAL 2021年第6期21卷 7808-7812页

作者： Li, Changping Huang, Ruining Yi, Ying Bermak, Amine Chongqing Univ Posts & Telecommun Coll Commun & Informat Engn Chongqing 400065 Peoples R China Harbin Inst Technol Sch Mech Engn & Automat Shenzhen 518055 Peoples R China Hamad Bin Khalifa Univ Qatar Fdn Coll Sci & Engn Div Informat & Comp Technol Doha 34100 Qatar

With the emergence of new sensing technologies, soft substrate-based displacement sensing has gained popularity in the last decade. Nevertheless, performance degradation due to noise from unexpected vibrations of the substrate can affect the sensors accuracy and lead to poor measurements. Few studies have focused on noise reduction to improve the accuracy of the sensing data. Based on mathematical modeling, this paper compares the two most representative filtering algorithms based on minimum mean square error (MMSE), namely recursive least squares (RLS) and Kalman algorithms for their predictive ability, accuracy, and reliability. Based on the modeling and comparison, we propose a Kalman filtering algorithm to suppress the vibration noise in the displacement-based dosage sensor. The filter-processed data from the sensors measurement mainly contains the actual resistance change upon mechanical displacement of the sensor substrate and the undesired noise from its vibration. With the filtering algorithm, the noise can be reduced from the sensors measurement, and an accurate correlation between resistance change and displacement of the sensor can be established. This would provide a straightforward sensing signal that reflects the displacement, and in turn, achieves precise dosage monitoring in drug delivery devices, which can be used as a test-bed application for the sensor.

关键词： Sensors filtering algorithms Kalman filters Noise measurement Resistance Noise reduction Estimation Displacement sensor Kalman filtering algorithm noise reduction

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REAL-TIME SYSTOLIC ARRAY PROCESSOR FOR 2-D SPATIAL-filtering

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS 1988年第4期35卷 451-455页

作者： ABOULNASR, T STEENAART, W Department of Electrical Engineering University of Ottawa ONT Canada

The use of systolic arrays (SA) to implement two-D local state-space (LSS) digital filters for real-time image processing is presented. The simple modular structure of the SA is ideal for VLSI implementation. The array used is composed of ROM/adder (or multiplier/adder) cells and is shown to be 100% efficient. The size of the array is equal to the overall size of the system matrix and is much smaller than arrays where the number of cells equals the number of image pixels. Still, this array is capable of processing a 512*512 image in less than 1/30 seconds allowed in real-time applications for second-order filters. The use of ROMs along with the inherent good performance of LSS filters under the effect of finite-length registers, makes this implementation desirable. The array is also far less expensive than other proposed real-time two-D digital filter implementations.< >

关键词： Systolic arrays Digital filters Finite impulse response filter Very large scale integration Signal processing algorithms Clocks Adders filtering algorithms IIR filters Delay

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Vehicle Position Prediction Using Particle filtering Based on 3D CNN-LSTM Model

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第4期23卷 2992-3004页

作者： Wang, Jiaqin Liu, Kai Gong, Yi Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Beihang Univ Hangzhou Innovat Inst Hangzhou 310051 Peoples R China Beijing Informat Sci & Technol Univ Sch Informat & Commun Engn Minist Informat Ind Beijing 100876 Peoples R China Beijing Informat Sci & Technol Univ Key Lab Informat & Commun Syst Minist Informat Ind Beijing 100876 Peoples R China Beijing Informat Sci & Technol Univ Key Lab Modern Measurement & Control Technol Minist Informat Ind Beijing 100876 Peoples R China

Vehicle position prediction (VPP) is of great significance for navigation planning and traffic safety of intelligent vehicles. In general, particle filtering (PF) uses global navigation satellite system (GNSS) to implement VPP. However, it does not consider geographic layer information (GLI) and its particle weight is not combined with the real-world geographic position information, which leads to insufficient prediction preparation. To resolve this problem, we propose a novel PF-based VPP method by using three-dimensional convolutional neural network and long short-term memory (3D CNN-LSTM) network model. First, for data preprocessing, we extract kinematic information features from GNSS, and evenly divide the area around each GNSS point into multiple grids and calculate the probability of grids center belonging to each GLI type. In addition, in order to better reflect the relationship between two consecutive positions due to the factors such as the conversion angle, we construct tilted cells to represent possible positions of each vehicle at any time. Second, a novel 3D CNN-LSTM model is designed to calculate the vehicle occurrence probability (VOP) in each tilted cell by processing the GLI and GNSS data, which can optimize the PF weight of each particle, and then improve PF to make more precise position prediction. Finally, the experimental results demonstrate that the proposed VPP method can improve the cell prediction accuracy, and then significantly improve the position prediction precision.

关键词： Feature extraction Global navigation satellite system Prediction algorithms Three-dimensional displays Convolutional neural networks Predictive models filtering algorithms 3D CNN geographic layer information LSTM particle filtering vehicle position prediction

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Efficient Memory Layout for Pre-Alignment filtering of Long DNA Reads Using Racetrack Memory

IEEE COMPUTER ARCHITECTURE LETTERS

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IEEE COMPUTER ARCHITECTURE LETTERS 2024年第1期23卷 129-132页

作者： Khan, Asif Ali Hameed, Fazal Shahroodi, Taha Jones, Alex K. Castrillon, Jeronimo Tech Univ Dresden D-01069 Dresden Germany Amer Univ Sharjah AUS Sharjah U Arab Emirates Univ Pittsburgh Pittsburgh PA 15261 USA Delft Univ Technol NL-2628 Delft Netherlands

DNA sequence alignment is a fundamental and computationally expensive operation in bioinformatics. Researchers have developed pre-alignment filters that effectively reduce the amount of data consumed by the alignment process by discarding locations that result in a poor match. However, the filtering operation itself is memory-intensive for which the conventional Von-Neumann architectures perform poorly. Therefore, recent designs advocate compute near memory (CNM) accelerators based on stacked DRAM and more exotic memory technologies such as racetrack memories (RTM). However, these designs only support small DNA reads of circa 100 nucleotides, referred to as short reads. This letter proposes a CNM system for handling both long and short reads. It introduces a novel data-placement solution that significantly increases parallelism and reduces overhead. Evaluation results show substantial reductions in execution time ($1.32\times$1.32x) and energy consumption (50%), compared to the state-of-the-art.

关键词： Bioinformatics Genomics filtering filtering algorithms Layout DNA Sequential analysis Domain wall memory near memory computing racetrack memory sequence alignment

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A 16-BIT MICROCOMPUTER FOR AUDIO BANDWIDTH DIGITAL filtering

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IEEE TRANSACTIONS ON EDUCATION 1985年第2期28卷 76-78页

作者： STEADMAN, JW JACQUOT, RG REED, KA Department of Electrical Engineering University of Wurzburg Laramie WY USA Hewlett Packard Corporation Palo Alto CA USA

Small microcomputers using 8-bit machines have been used in teaching digital filtering and discrete-time control. These systems have two major limitations: 1) bandwidth limited to approximately 125 Hz for fourth-order algorithms, and 2) limited precision for representing the coefficients in the algorithm. This paper describes the development and application of a high-speed 16-bit machine which largely overcomes the limitations of previous systems. A special-purpose digital filtering and control computer was built using the MC68000 microprocessor operating at 12.5 MHz. This machine uses 16-bit data transfers to memory and 32-bit arithmetic. The multiply and divide instructions are microcoded in the instruction set, and therefore execute much faster than subroutines used in 8-bit machines. The system has a general-purpose monitor routine and a digital filtering algorithm in firmware, user RAM, two channels of A/D, two channels of D/A, a programmable timer, and an RS232 interface for a CRT. The firmware implements any filtering or compensation algorithm through eighth order in the form of a linear difference equation. The greater precision of the 16-bit machine allows the coefficients in the difference equation to be represented accurately without extensive rationalization procedures. The firmware is written for student use in digital filtering and control laboratories. The computer prompts the user for coefficients and the sampling interval, informing him of any input errors. It then continuously runs the selected algorithm until interrupted. This system has been found to be of much wider bandwidth than previous machines. Signals of 4.

关键词： Random access memory Analog-digital conversion filtering Digital-analog conversion Microprogramming Read only memory filtering algorithms

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Fast converging and low complexity adaptive filtering using an averaged Kalman filter

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1998年第2期46卷 515-518页

作者： Wigren, T Ericsson Radio Syst AB Voice Signal Proc Grp R&D Div Stockholm Sweden

Kalman filtering is applied to obtain a fast converging, low complexity adaptive filter that is of the matrix stepsize normalized least mean square (NLMS) type. By replacing certain variables with averages, the solution of an averaged diagonal Riccati equation allows optimal time varying adaptation gains to be precomputed or computed online with a small number of scalar Riccati equations. The adaptation gains are computed from prior assumptions on impulse response power and shape. This fact results in a systematic procedure for adaptation gain tuning in the time-varying matrix stepsize case. Simulations using music as input, show significant performance improvements as compared with the NLMS algorithm.

关键词： Adaptive filters Signal processing algorithms Riccati equations Shape Gaussian noise Kalman filters Convergence Steady-state filtering algorithms Noise measurement

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A FAST ALGORITHM OF NONLINEAR VOLTERRA filtering

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1991年第10期39卷 2353-2356页

作者： MORHAC, M Institute of Physics Electrophysical Research Center Slovak Academy of Sciences Bratislava Slovakia

This correspondence describes a new fast algorithm to calculate the output of a discrete nonlinear Volterra system. The derived algorithm has a parallel structure where the calculation process is converted to a set of linear convolution filters. The usage of the multiprocessor system with parallel structure implementating the algorithm should enable us to reduce the execution time to the level comparable with the calculation time of linear filters.

关键词： filtering algorithms Signal processing algorithms Nonlinear filters Matrix decomposition Kernel Speech processing Signal processing Convolution Difference equations Symmetric matrices

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