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Adaptive Frequency-Domain Normalized Implementations of Widely-Linear Complex-Valued Filter

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2021年 69卷 5801-5814页

作者： Zhang, Sheng Zhang, Jiashu Xia, Yili So, Hing Cheung Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Sichuan Peoples R China Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 611756 Sichuan Peoples R China Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong 852 Peoples R China

The widely-linear complex-valued least-mean-square (WL-CLMS) algorithm exhibits slow convergence in the presence of non-circular and highly correlated filter input signals. To tackle such an issue with reduced computational complexity, this paper introduces adaptive frequency-domain normalized implementations of widely-linear complex-valued filter. Two normalized algorithms are firstly devised based on the circulant matrices of weight coefficients and the regression vector, respectively. In the design, the normalization matrix using the second-order complementary statistical information of the input signal helps increase the algorithm convergence speed. Then, mean-square and complementary mean-square performance of periodic update frequency-domain widely-linear normalized LMS (P-FDWL-NLMS) algorithm for non-circular complex signals is analyzed. In addition, by introducing a variable-periodic (VP) mechanism, we propose the VP-FDWL-NLMS method that provides faster convergence than the P-FDWL-NLMS scheme. Computer simulation results show the superiority of the proposed approach over the fullband widely-linear complex-valued least-mean-square, augmented affine projection algorithm and its variable step-size version, in terms of both complexity and convergence rate.

关键词： Frequency-domain analysis Signal processing algorithms Adaptive filters Convergence filtering algorithms Adaptation models Information filters Adaptive filter frequency domain widely-linear model mean-square analysis variable-periodic method

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Barrier Forming: Separating Polygonal Sets with Minimum Number of Lines 39

Barrier Forming: Separating Polygonal Sets with Minimum Numb...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Peng, Si Wei Yu, Jingjin Rutgers State Univ Dept Comp Sci Piscataway NJ 08854 USA

ISBN: (纸本)9781728196817

In this work, we carry out structural and algorithmic studies of a problem of barrier forming: selecting the minimum number of straight line segments (barriers) that separate several sets of mutually disjoint objects in the plane. The problem models the optimal placement of line sensors (e.g., infrared laser beams) for isolating many types of regions in a pair-wise manner for practical purposes (e.g., guarding against intrusions). The problem is NP-hard even if we want to find the minimum number of lines to separate two sets of points in the plane. Under the umbrella problem of barrier forming with minimum number of line segments, three settings are examined: barrier forming for point sets, point sets with polygonal obstacles, polygonal sets with polygonal obstacles. We describe methods for computing the optimal solution for the first two settings with the assistance of mathematical programming, and provide a 2-OPT solution for the third. We demonstrate the effectiveness of our methods through extensive simulations.

关键词： NP-hard problem Particle separators Heuristic algorithms filtering algorithms Laser modes Mathematical models Sensors

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VLSI Implementation of a Real-time Modified Decision-based Algorithm for Impulse Noise Removal

VLSI Implementation of a Real-time Modified Decision-based A...

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IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS)

作者： Sanki, Pradyut Kumar Bevara, Vasudeva Supriya, Medarametla Deepthi Vignesh, Devireddy Harshath, Peram Bhanu Sai Kuchina, Siavya SRM Univ AP UGC Elect & Commun Engn Guntur Andhra Pradesh India

ISBN: (纸本)9781665486842

In this paper, a Real-Time Impulse Noise Removal (RTINR) algorithm and its hardware architecture are proposed for denoising images corrupted with fixed valued impulse noise. A decision-based algorithm is modified in the proposed RTINR algorithm where the corrupted pixel is first detected & is restored with median or previous pixel value depending on the number of corrupted pixels in the image. The proposed RTINR architecture has been designed to reduce the hardware complexity as it requires 21 comparators, 4 adders, and 2 line buffers which in turn improve the execution time. The proposed architecture results better in qualitative and quantitative performance in comparison to different denoising schemes while evaluated based on PSNR, IEF, MSE, EKI, SSIM,& FOM. The proposed architecture has been simulated using the VIRTEX7 FPGA device and the reported maximum post place& route frequency is 360.88 MHz. The proposed RTINR architecture is capable of denoising images of size 512 x 512 at a frame rate of 686. The architecture has also been synthesized using UMC 90 nm technology where 103 mW power is consumed at a clock frequency of 100 MHz with a gate count of 23K (NAND2) including two memory buffers.

关键词： Impulse noise image denoising filtering algorithms image quality PSNR field programmable gate arrays

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Heart Sound Classification based on Residual Shrinkage Networks 44

Heart Sound Classification based on Residual Shrinkage Netwo...

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44th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Zhu, Lixian Qian, Kun Wang, Zhihua Hu, Bin Yamamoto, Yoshiharu Schuller, Bjoern W. Beijing Inst Technol Sch Med Technol Lab Brain Hlth Engn BHE 5 Zhongguancun South St Beijing 100081 Peoples R China Univ Tokyo Grad Sch Educ Educ Physiol Lab 7-3-1 HongoBunkyo Ku Tokyo 1130033 Japan China Univ Min & Technol Sch Mechatron Engn Xuzhou Jiangsu Peoples R China Imperial Coll London GLAM Grp Language Audio & Mus 180 Queens GateHuxley Bldg London SW7 2AZ England Univ Augsburg Chair Embedded Intelligence Hlth Care & Wellbeing Eichleitnerstr 30 D-86159 Augsburg Germany

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

Heart sound classification is one of the noninvasive methods for early detection of the cardiovascular diseases (CVDs), the leading cause for deaths. In recent years, Computer Audition (CA) technology has become increasingly sophisticated, auxiliary diagnosis technology of heart disease based on CA has become a popular research area. This paper proposes a deep Convolutional Neural Network (CNN) model for heart sound classification. To improve the classification accuracy of heart sound, we design a classification algorithm combining classical Residual Network (ResNet) and Long ShortTerm Memory (LSTM). The model performance is evaluated in the PhysioNet / CinC Challenges 2016 datasets using a 2D time-frequency feature. We extract the four features from different filter-bank coefficients, including Filterbank (Fbank), Mel-Frequency Spectral Coefficients (MFSCs), and Mel-Frequency Cepstral Coefficients (MFCCs). The experimental results show the MFSCs feature outperforms the other features in the proposed CNN model. The proposed model performs well on the test set, particularly the F1 score of 84.3% - the accuracy of 84.4 %, the sensitivity of 84.3 %, and the specificity of 85.6 %. Compared with the classical ResNet model, an accuracy of 4.9% improvement is observed in the proposed model.

关键词： Heart Time-frequency analysis Sensitivity Biological system modeling Filter banks Medical services filtering algorithms

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Recognition of Normalization Grounded Adaptive filtering Methods For Noise Cancellation In EEG Signal Recordings

Recognition of Normalization Grounded Adaptive Filtering Met...

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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： Rishabh Bhardwaj Centre for Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Electroencephalograms (EEGs) are promising tools for the diagnosis and treatment of neurological and mental disorders, and they are gaining ground as a measurement of brain activity. The electrical activity of the brain can be measured and recorded with an electroencephalogram (EEG). Electrodes are usually placed along the scalp and wired to a recorder to convey the data. The brain and spinal cord together make up the central nervous system, which controls every function in the human body. It receives data from the sensory organs, analyses and organizes it, and then decides what actions to direct the rest of the body to carry out based on that data. When a human brain is damaged, it can have catastrophic effects on every area of that person's existence. Accidents, assaults, concussions, lack of oxygen (near drowning), Alzheimer's disease and other degenerative diseases (dementia, Parkinson's disease), alcohol and other drugs, brain tumors, epilepsy, seizures, infections, and diseases (meningitis, encephalitis) are all examples of what are considered brain disorders. The goal is to design a variety of innovative ANC's for EEG signal denoising that address the challenges of computational complexity, Signal-to-Noise Ratio, Mis-regulation, and convergence. This is done by taking into account three types of adaptive algorithms: Adaptive algorithms with normalization.

关键词： Finite impulse response filters Adaptive filters Adaptive algorithms IIR filters filtering algorithms Noise cancellation Electroencephalography

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Simulation of Wireless Video Multipath Signal Processing Model Based on Adaptive filtering Algorithm

Simulation of Wireless Video Multipath Signal Processing Mod...

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Telecommunications, Electronics and Informatics (ICTEI), International Conference on

作者： Junyi Zheng The College of IOT Engineering Hohai University Changzhou Jiangsu China

ISBN: (数字)9798350342963

ISBN: (纸本)9798350342970

The research of adaptive filtering algorithm has always been a hot spot in the field of signal and information processing, and has been widely used in Speech processing, echo cancellation, channel equalization, system identification, spectral line enhancement, adaptive array processing, biomedical signal processing and many other fields. The amplitude control effect of multipath wireless video transmission is unstable, resulting in low bit rates for multipath single view transmission. In order to denoise and filter wireless video multipath signals, this paper proposes a variable step size LMS adaptive filtering algorithm for wireless video multipath signal processing to address the problem of wireless video signals being covered by noise. The algorithm is based on the feedback control function and combines the arctangent function and Exponential function together. The filtering performance of the algorithm is analyzed through MATLAB, and is applied to wireless video multi-path signal processing. The simulation results show that the algorithm has improved in Rate of convergence and steady-state error, and the error suppression ability has also improved in the filtering processing effect of wireless video multi-path signals, with good convergence performance and steady-state characteristics. The filtering effect has achieved satisfactory results.

关键词： Wireless communication Adaptive systems filtering Noise Signal processing algorithms Signal processing filtering algorithms

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A Study on Product Recommendation System based on Deep Learning and Collaborative filtering

A Study on Product Recommendation System based on Deep Learn...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Mosami D. Bhagat Prashant N. Chatur Department of Computer Science & Engineering Govt. College of Engineering Amravati India

This essay analyses and contrasts the literature that has already been written about various product recommendation systems and the mechanisms that underlie them. Although there are many research contributions in the literature, in this article I have critically and thoroughly examined recent research and review papers that are relevant to AI-based product recommendation systems. The present techniques are divided into groups based on the fundamental ideas included into their processes. The idea put out by the concerned writers, the experimental technique, and the performance assessment criteria are highlighted. Also noted are the researchers' assertions. The flaws that have been found are highlighted together with our results from the thorough literature study. This work is crucial for the comparison of different AI-based product recommendation systems, which is a necessary step before dealing with related problems. Following a review of the literature, I have put up a system for recommending products to users, in which the tool suggests products that a particular user would want to buy. I have gathered information about items and users by using ML algorithms. The suggested system establishes a link between the people and the products.

关键词： Deep learning Collaborative filtering Collaboration filtering algorithms Recommender systems

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Recommendation System Of Product Sales Ideas For MSMEs Using Content-based filtering and Collaborative filtering Methods

Recommendation System Of Product Sales Ideas For MSMEs Using...

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Computer Science, Information Technology and Engineering (ICCoSITE), International Conference on

作者： Refa Septiansyah Mulyana Asep Id Hadiana Edvin Ramadhan Department of Informatics Universitas Jenderal Achmad Yani Cimahi Indonesia

Looking for an idea to differentiate a product from other sellers is not easy. Sometimes sellers of MSME products need sales recommendations on what is trending among the public. A product recommendation can help users recommend a product that is interesting and needed by that user. Recommendation systems can help users come up with previously unknown or unthinkable information, which can directly aid user knowledge in their search results. In this research, a recommendation system will be built to search for product ideas. This study uses content-based filtering and collaborative filtering methods as well as the TF-IDF algorithm to assist users in recommending the products they are looking for to assist users in finding product-selling ideas they expect. Previous research has examined the recommendation system for Modern Musical Instrument Sales using the Simple Additive Weighing method but has the drawback that the weighting calculation must use fuzzy numbers. Therefore, the content-based and collaborative filtering methods are assisted by the TF-IDF algorithm used in this study to answer these problems. After implementation, we test accuracy by dividing the test data and training data differently. System testing is done by using a confusion matrix. The results that have been tested get an accuracy of 78%. Subsequent research suggests adding MSME product data in recommending product sales ideas to MSMEs so that recommendations are more optimal.

关键词： System testing Collaborative filtering Instruments Urban areas Training data Music filtering algorithms

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Accurately Identify Time-decaying Heavy Hitters by Decay-aware Cuckoo Filter along Kicking Path 30

Accurately Identify Time-decaying Heavy Hitters by Decay-awa...

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30th IEEE/ACM International Symposium on Quality of Service (IWQoS)

作者： Xiao, Qingjun Wang, Haotian Pan, Guannan Southeast Univ Sch Cyber Sci & Engn Nanjing Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China

ISBN: (纸本)9781665468244

In high-speed networks, flow-level traffic measurement is an essential tool to understand how network bandwidth is being utilized. It can be used to detect anomalous traffic behaviors due to operational or security issues. Perhaps the most important measurement task is to track the heavy hitters (HHs), i.e., the flows occupying the greatest shares of bandwidth. But most existing solutions have no concept of time window: Whenever a measurement period ends, the data sketch, which is deployed in the data plane for monitoring HHs, must be transferred to the control plane and then reset to zeros. It is better to capture network conditions of the continuous recent past by designing a HHs measurement solution that can support time-decaying window. As a result, recently several related works are devoted to tracking the time-decaying heavy hitters, including time-decaying Count-Min and time-decaying Space-Saving. However, their memory-accuracy tradeoff is still suboptimal. In this paper, we attain higher performance by proposing a new algorithm named Decay-Aware Cuckoo Filter along Kicking Path (DAKP-CF). It can be regarded as a variant of cuckoo filter (an improved version of hash table with better memory efficiency), which transforms each bucket into a bucket-level min-heap. Its key advantage is that, when we update the table as a packet arrive, it can discover and replace the most time-decayed flow along the kicking path of a cuckoo filter. We deliberately avoid scanning the entire table to keep the high time efficiency. The experiment results show that our DAKP-CF can reach the same identification accuracy as existing methods with roughly 25% memory cost. In addition, we build a prototype of our DAKP-CF by P4-programmable BMv2 software switch.

关键词： Costs Prototypes Switches Bandwidth Transforms filtering algorithms Approximation algorithms

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UPC-Faster-RCNN: A Dynamic Self-Labeling Algorithm for Open-Set Object Detection Based on Unknown Proposal Clustering

UPC-Faster-RCNN: A Dynamic Self-Labeling Algorithm for Open-...

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IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

作者： Liao, Yujun Wu, Yan Mo, Yujian Liu, Feilin He, Yufei Zhao, Junqiao Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China Tongji Univ Inst Intelligent Vehicle Shanghai Peoples R China

ISBN: (数字)9781665460262

ISBN: (纸本)9781665460262

To promote the development of object detection in a more realistic world, efforts have been made to a new task named open-set object detection. This task aims to increase the model's ability to recognize unknown classes. In this work, we propose a novel dynamic self-labeling algorithm, named UPC-Faster-RCNN. The wisdom of DBSCAN is applied to build our unknown proposal clustering algorithm, which aims to filter and cluster the unknown objects proposals. An effective dynamic self-labeling algorithm is proposed to generate high-quality pseudo labels from clustered proposals. We evaluate UPC-Faster-RCNN on a composite dataset of PASCAL VOC and COCO. The extensive experiments show that UPC-Faster-RCNN effectively increases the ability upon Faster-RCNN baseline to detect unknown target, while keeping the ability to detect known targets. Specifically, UPC-Faster-RCNN decreases the WI by 23.8%, decreases the A-OSE by 6542, and slightly increase the mAP in known classes by 0.3%.

关键词： Target recognition Heuristic algorithms Clustering algorithms Object detection filtering algorithms Proposals Task analysis

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