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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第2期35卷 1977-1990页

作者： Xarchakos, Ioannis Koudas, Nick Univ Toronto Toronto ON M5S 1A1 Canada

Deep Learning and Computer Vision advances enabled sophisticated information extraction out of images and videos. Recent research aims to make objects, their types and relative locations, first class citizens for query processing purposes. We initiate research to explore declarative queries for real time video streams involving objects and their interactions. We seek to efficiently identify frames in which an object is interacting with another in a specific way. We propose progressive filters (PF) algorithm which deploys a sequence of inexpensive and less accurate filters to detect the presence of query specified objects on frames. We demonstrate that PF derives a least cost sequence of filters given the query objects' current selectivities. Since selectivities may vary as the video evolves, we present a statistical test to determine when to trigger filters' re-optimization. Finally, we present Interaction Sheave, a filtering approach that uses learned spatial information about objects and interactions to prune frames that are unlikely to involve the query specified action between them, thus improving the frame processing rate. We present the results of a thorough experimental evaluation involving real datasets. We experimentally demonstrate that our techniques can improve query performance (up to an order of magnitude) while maintaining competitive F1-score.

关键词： Videos Query processing Heuristic algorithms Surveillance Object recognition filtering algorithms Proposals video stream filter optimization human-object interaction

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Variational Bayesian-Based Maximum Correntropy Cubature Kalman Filter Method for State-of-Charge Estimation of Li-Ion Battery Cells

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第3期72卷 3090-3104页

作者： Hafez, Ishaq Wadi, Ali Abdel-Hafez, Mamoun F. Hussein, Ala A. Amer Univ Sharjah Dept Mech Engn Sharjah 26666 U Arab Emirates Univ Cent Florida Prince Mohammad Bin Fahd Univ Saudi Arabia & Florida Solar Energy Ctr Khobar Orlando FL 31952 USA

State-of-charge estimation is an essential part of a battery management system. Charging and discharging batteries involve complex chemical processes that could lead to undesired consequences, such as premature end of life or fire hazards if the battery's state-of-charge is not closely monitored. This work proposes a state-of-charge estimation method that compensates for noisy measurements and parametric uncertainties in a battery cell to improve the estimation accuracy. In this work, a robust and adaptive estimation scheme based on a Cubature Kalman filter is proposed. The algorithm utilizes the Variational Bayesian method to identify the measurements noise covariance magnitude online. Additionally, the algorithm is augmented with the ability to suppress outliers based on the Maximum Correntropy Criterion. The proposed filter is experimentally verified on standard tests including aggressive electric vehicle drive cycles, and it is compared against the extended Kalman filter, the Cubature Kalman filter, and the Variational Bayesian Cubature Kalman filter.

关键词： filtering algorithms State of charge Estimation Batteries Kalman filters Adaptive filters Noise measurement Battery cell estimation extended kalman filter state-of-charge

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Digital Filter Architecture Based on Modified Winograd Method F(2x 2, 5x 5) and Residue Number System

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IEEE ACCESS 2023年 11卷 26807-26819页

作者： Valuev, Georgii Valueva, Maria Babenko, Mikhail Tchernykh, Andrei North Caucasus Fed Univ North Caucasus Ctr Math Res Stavropol 355017 Russia Ivannikov Inst Syst Programming Control Management & Appl Math Moscow 109004 Russia CICESE Res Ctr Comp Sci Dept Ensenada 22800 Mexico

Improving the characteristics of digital signal processing devices is an important task in many practical problems. The paper proposes the architecture of a two-dimensional digital filter with a $5\times 5$ mask, in which calculations are performed according to the Winograd method in the Residue Number System (RNS) with moduli of a special type. Theoretical analysis and hardware Field-Programmable Gate Array simulation are presented. The results show that the fragment throughput fr/s (number of fragments per second) of the device is 29.6%- 724.7% higher than state-of-the-art solutions. This is achieved by the combination of the Winograd method, which reduces the number of multiplications, with the RNS arithmetic, which performs addition and multiplication under smaller operands in parallel. However, our experiments showed that the proposed method requires up to 2.54%- 11.01% more Look-Up Tables and 3.58%- 19.83% higher power consumption compared to known analogues.

关键词： filtering Finite impulse response filters Performance evaluation IIR filters Hardware filtering algorithms Computer architecture Residue number system winograd method field-programmable gate array digital filter digital signal processing

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A Distributed Integrated Feature Selection Scheme for Column Subset Selection

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第3期35卷 2193-2205页

作者： Xiao, Zheng Wei, PengCheng Chronopoulos, Anthony Theodore Elster, Anne C. C. Hunan Univ Coll Informat Sci & Engn Changsha 410082 Hunan Peoples R China Univ Texas San Antonio Dept Comp Sci San Antonio TX 78249 USA Univ Patras Dept Comp Engn & Informat Rion 26500 Greece Norwegian Univ Sci & Technol Dept Comp Sci N-7491 Trondheim Norway

The emergence of computer applications often encounter huge volumes of data which need to be stored and processed in a distributed way. Most of the existing distributed feature selection schemes neglect how good the subsets are that are mapped to the computational nodes, which causes a waste of time and hardware resources. In this paper, we propose a distributed integrated feature selection scheme (DIFS) with Subset Quality Evaluation (SQE). SQE studies the relevance between the quality of a subset and the number of selected features from this subset, which helps shorten the feature selection time efficiently. Feature selection algorithms used in our method and the evaluation metric used in SQE are integrable. Then, we have given the implementation of our scheme for the Column Subset Selection (CSS) problem. More specifically, we integrate a CSS algorithm in DIFS and information entropy as the SQE metric. Theoretically, we prove that the speedup of DIFS can reach m3 compared to the centralized algorithm in ideal situations where $m$m is the number of computational nodes, and give a well bounded approximation guarantee of the solution generated by scheme for CSS problem. Extensive experiments on eight data sets are used to verify the performance of scheme. Experiments results demonstrate the effectiveness of SQE and the impressive speedup DIFS can achieve. Although there is a slight increase of the reconstruction error value in some situations. Additional experiments of classification tasks reveal that the performance of DIFS is better than existing state-of-the-art distributed algorithms.

关键词： Feature extraction Distributed databases Measurement Approximation algorithms Optimized production technology Information filters filtering algorithms Feature selection distributed integrated scheme subset quality evaluation column subset selection

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Target-Aware Transformer Tracking

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2023年第9期33卷 4542-4551页

作者： Zheng, Yuhui Zhang, Yan Xiao, Bin Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Chongqing Univ Posts & Telecommun Sch Comp Sci & Technol Chongqing 400065 Peoples R China

Object tracking is aimed at locating a specific object in the image sequence, such as pedestrians, vehicles, and so on. The existing algorithms based on siamese neural network predict the target through similarity matching. Although these algorithms have achieved satisfactory performance, in the process of similarity calculation between template image and search image, only local information is often concerned, which makes the algorithms difficult to obtain the optimal solution. To deal with the abovementioned problems, we propose a model based on Transformer, named TaTrack. Specifically, we first use the encoders to enhance the features. Then, the dependency between template features and search features is established through the target-aware module. Finally, we utilize the classification regression network to locate the target, and use the classification score to adapt to update the template image. Experiments show that our model can achieve great performance on GOT-10k, LaSOT, and TrackingNet datasets.

关键词： Transformers Feature extraction Correlation Target tracking Prediction algorithms filtering algorithms Neural networks Object tracking transformer target-aware module classification regression network

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Event-Triggered Consensus LMB Filter for Distributed Multitarget Tracking

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第1期59卷 712-719页

作者： Li, Gaiyou Battistelli, Giorgio Chisci, Luigi Gao, Lin Wei, Ping Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Univ Firenze Dipartimento Ingn Informaz DINFO I-50139 Florence Italy

This correspondence investigates the problem of reducing energy and bandwidth consumption of a sensor network for distributed multitarget tracking by the labeled multi-Bernoulli filter. To this end, an event-triggered method is adopted together with a consensus strategy for each sensor node to transmit only Bernoulli components (BCs) that achieve enough information gain. Two message transmission strategies, i.e., joint and independent transmission, are devised. The possibility to separately trigger BCs allows the proposed method to better tradeoff tracking performance versus communication load with respect to existing distributed multitarget tracking approaches.

关键词： Information filters Peer-to-peer computing Radio frequency Energy consumption Bandwidth filtering algorithms Density measurement Distributed multitarget tracking event-triggered generalized covariance intersection labeled Bernoulli densities random finite set (RFS)

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Data-Compatibility Analysis Based on Expectation-Maximization Algorithm With Unknown Noise Statistics

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第5期59卷 5503-5515页

作者： Zuo, Junyi Qiu, Shi Wang, Wenzheng Northwestern Polytech Univ Sch Aeronaut Xian 710072 Peoples R China Univ Elect Sci & Technol China Sch Aeronaut & Astronaut Chengdu 611731 Peoples R China

In this article, data-compatibility analysis (DCA) problem is investigated with both the measurement noise and input noise considered. The expectation-maximization (EM) algorithm is exploited to deal with the unknown noise statistics. Motivated by the fact that although there exists an excellent EM based estimator for dynamic systems, it cannot be used in the context of DCA due to the presence of nonadditive input noises, a generalized EM based estimator is developed to reconstruct the flight path, as well as to estimate the sensor model parameters. Making full use of the special dependencies among the state components and the fact that the associated dynamic system has an affine nonlinear structure, the generalized estimator retains many advantages of the existing EM based one, such as the simple optimization process for the unknowns. Experimental results show that it can give more accurate results than the classical output error method and the existing EM based one in the case of high input noise.

关键词： Mathematical models Noise measurement Aerodynamics Dynamical systems Atmospheric modeling filtering algorithms Measurement uncertainty

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MultiFDF: Multi-Community Clustering for Fairness-Aware Recommendation

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2023年第6期10卷 2959-2970页

作者： Wang, Bang Song, Shipeng Liu, Shenghao Deng, Xianjun Huazhong Univ Sci & Technol HUST Sch Elect Informat & Commun Hubei Key Lab Smart Internet Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol HUST Sch Elect Informat & Commun Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol HUST Hubei Engn Res Ctr Big Data Secur Sch Cyber Sci & Engn Hubei Key Lab Distributed Syst Secur Wuhan 430074 Peoples R China

The fairness consideration has received increasing attention in artificial intelligence (AI), especially in the significant application, recommender system. One kind of fairness issue is to balance the exposure of popular items and less popular items. Existing works mainly compensate the less popular items when predicting their ratings. However, the compensation for the items may result in a loss of recommendation accuracy, as the compensated items may not match user's preference. For this problem, we propose a multi-community clustering recommendation with fair decision fusion (MultiFDF) framework to compensate less popular items locally, which can reduce the negative impact on user's preferred popular item. We first design an experiment and deliver a causal graph-based mathematical proof to demonstrate the feasibility of local compensation. It proves that there is a discrete tendency of ratings predicted by a recommendation algorithm and a recommendation algorithm gives a higher rating to the popular item than the less popular item. The MultiFDF consists of three parts, community exploration module, local recommendation module, and fair decision fusion module. The community exploration module outputs several communities for local recommendation module to generate local recommendation lists, respectively. The fair decision fusion module then computes the discrete ratings of items based on local recommendation lists and designs an edge reranking strategy based on their discrete ratings to obtain the final fair top- $N$ recommendation list. To verify the superiority of our proposed MultiFDF, we conduct experiments on three real world datasets and the results demonstrate that MultiFDF can improve fairness at the cost of lower accuracy than the state-of the-art algorithms.

关键词： Prediction algorithms Artificial intelligence Training Recommender systems Neural networks filtering algorithms Costs Fairness machine learning random walk recommender system

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Robust Filter Method for SINS/DVL/USBL Tight Integrated Navigation System

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IEEE SENSORS JOURNAL 2023年第10期23卷 10912-10923页

作者： Wang, Di Wang, Bing Huang, Haoqian Yao, Yiqing Xu, Xiang Hohai Univ Coll Energy & Elect Engn Nanjing 211100 Peoples R China Southeast Univ Sch Instrument Sci & Engn Key Lab Microinertial Instrument & Adv Nav Technol Minist Educ Nanjing 210096 Peoples R China Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China

In order to solve the problem of strap-down inertial navigation system (SINS)/Doppler velocity log (DVL)/ultrashort baseline (USBL) system interference in complex underwater environment, an asynchronous sequential robust filter method is proposed in this article. The USBL original information of azimuth, slant range and altitude are introduced as the measurement information. The problem of large amount of computation caused by the high measurement dimension of multisensor information fusion is solved by introducing asynchronous sequential filter technology. Meanwhile, a robust Kalman filter (KF) algorithm is proposed, which is based on the Mahalanobis distance. The outlier detection is designed based on statistical characteristics. Finally, the effectiveness of the method proposed in this article is verified by simulation and River experiments. The experimental results show that the method proposed in this article can effectively eliminate outliers and improve the navigation accuracy while reducing the amount of computation.

关键词： Navigation Mathematical models filtering algorithms Sea measurements Sensors Kalman filters Measurement uncertainty Asynchronous sequential navigation robust filter strap-down inertial navigation system (SINS) Doppler velocity log (DVL) ultrashort baseline (USBL)

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A Frequency-Domain I/Q Imbalance Calibration Algorithm for Wideband Direct Conversion Receivers Using Low-Cost Compensator

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IEEE ACCESS 2023年 11卷 48739-48748页

作者： Peng, Xiaoye Yu, Faxin Wang, Zhiyu Liu, Jiarui Wang, Chenge Wang, Jing Zhejiang Univ Inst Astronaut Elect Engn Sch Aeronaut & Astronaut Hangzhou 310007 Peoples R China Hangzhou Core Device Technol Co Ltd Hangzhou 310007 Peoples R China

The amplitude and phase imbalances of the in-phase (I) and quadrature (Q) branches inherent in the direct conversion receiver structure cause the generation of image frequency interference signals. In this paper, a frequency-domain I/Q imbalance calibration algorithm is proposed for wideband direct conversion receivers. The I/Q imbalance model is rebuilt by applying the infinitesimal method and FFT algorithm. The mathematical expressions for the exact computation of the I/Q imbalance parameters are derived based on the frequency-domain statistical properties of the baseband signal. The two phase parameters, frequency-dependent I/Q imbalance (FD-IQI) and frequency-independent I/Q imbalance (FI-IQI), are separated according to the parity properties of the imbalance parameter. To avoid the interference from the transmitter, the receiver impairment estimation is performed using a frequency offset (FO) DC training signal, and a low-cost real-valued compensation (RVC) filter is introduced to correct the impairments of the received signal. The performances of the proposed calibration model are evaluated through simulations and experiments. The simulation results show that the image rejection ratio (IRR) is improved to 80-120 dBc and can also exceed 40 dBc at high noise levels. The experimental results based on the CX9261A evaluation board show that the average IRR of the multi-tone signal is increased by 24.99 dB, and the IRR of the wideband signal is increased by 19.08 dB.

关键词： Receivers Baseband Calibration Wideband Training filtering algorithms Mathematical models Frequency domain analysis Direct conversion receiver frequency-dependent I Q imbalance frequency-independent I frequency-domain statistical properties infinitesimal method real-valued compensation filter

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