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small Target Detection on Sea Surface based on Feature-Weighted Support Vector Machine

Small Target Detection on Sea Surface based on Feature-Weigh...

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IEEE International conference on signal and Image processing (ICSIP)

作者： Yaqing Chen Shijian Shen Zhiyong Lei Nanjing Research Institute of Electronics Technology Nanjing China

Because the sea clutter is greatly affected by the complex and changeable environment, it is difficult to describe its characteristics through a single amplitude statistical model. In addition, the great variety and the small radar cross section of the target on the sea surface increase the detection difficulty. this paper proposes a feature-weighted SVM algorithm, which regards the detection of small targets on the sea surface as a binary classification problem. the algorithm extracts six features from different domains of the sea clutter, calculates the weight of the features according to the contribution of the features and constructs the feature space for detection with weighted feature vectors. Experiments on IPIX data sets show that the detection method improves the detection accuracy compared with the traditional algorithm and it is helpful for sea surface monitoring.

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A 2.8-to-7.2GT/s DDR5 Registering Clock Driver IC with Parallel-data Timing and Pin-to-Pin Skew Calibration for a Dual In-Line Memory Module

A 2.8-to-7.2GT/s DDR5 Registering Clock Driver IC with Paral...

引用

2024 IEEE Asian Solid-State Circuits conference, A-SSCC 2024

作者： Kim, Joomyoung Jung, Jaehong Lim, Kwangmin Sung, Barosaim Kim, Jaekwon Lim, Baekmin Noh, Tae-Gun Lee, Jaehoon Seok, Hyun-Gi Cho, Youngsea Kim, Gyusik Nomiyama, Takahiro Kang, Sunhye Jeong, Yeongcheol Cho, Sanguk Kim, Gyumin Oh, Do-Hyun Kim, Jaenam Lim, Yong Kim, Seungjin Oh, Seunghyun Lee, Jongwoo Samsung Electronics Hwaseong Korea Republic of CambridgeMA United States

ISBN: (纸本)9798350376326

the big-data platforms such as data centers and servers require high-speed, high-density, and low-energy dual in-line memory module (DIMM) including an array of DRAMs mounted on a small board in Fig. 1. Each DRAM is working based on the double data rate (DDR) memory interface to accomplish 2x speed compared to its counterpart, single data rate (SDR). However, the rapidly growing CPU speed has needed more the number of DRAMs on the DIMM board, leading to a necessity of a registering clock driver (RCD) IC [1]. It plays a critical role in connecting the CPU and 40EA DRAMs through a time-interleaved DRAM operation by segmenting the the DIMM board to 2-channels (A, B) and 2-groups (A, B). In other words, the RCD IC should enhance the timing synchronization among memory modules by refining the command-address (DCA) data and the chip-selection (DCS) data along with the differential clock (DCK) from the CPU. Additionally, it significantly contributes to the system stability improvement by ensuring the consistent signal transmissions, but the bandwidth evolution to 4th-generation (7.2GT/s) causes formidable challenges in processing 7-bit DCA, 2-bit DCS and 1-differential DCK and transmitting refined 14bit QCA, 2-bit QCS and 2-differential QCK with the minimal pin-to-pin skew (CA and DCS. the PI-imbedded PLL mitigates the timing slack through jitter-filtering of Dck input (4.72 to 0.20psrms at 3.6 GHz). Additionally, the auxiliary-DAC (A-DAC)-based current-mode PI (CMPI) compensates for phase and amplitude distortions of the conventional CMPI, yielding INLp-p of © 2024 IEEE.

关键词： Clocks

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Fault Diagnosis of Rolling Bearing based on Improved Twin Neural Network with small Samples

Fault Diagnosis of Rolling Bearing based on Improved Twin Ne...

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International conference on signal processing Systems, ICSPS

作者： Yifei Yang Jiacheng Fang Ocean College Jiangsu University of Science and Technology Zhenjiang China School of Automation Jiangsu University of Science and Technology Zhenjiang China

Traditional deep learning methods need big monitoring data as support, however, the real-time fault data that can be obtained is often small samples in most cases. therefore, an improved twin neural network model based on small samples is proposed. Taking the traditional twin neural network as the backbone, in the feature extraction stage, the deep belief neural network (DBN) and convolution neural network (CNN) are used to extract the temporal and spatial features of fault signals, which can fully mine the feature information on data samples. Secondly, the fault diagnosis classification module is added, and the convolutional neural network with the first layer of wide convolution (WDCNN) faults diagnosis algorithm model is introduced to fully classify the fault data. the experimental results show that the fault diagnostic accuracy of the improved twin neural network model on the training set and the test set is more than 98%. the visualization of t-SNE classification shows that it has a good effect on the classification of all kinds of tagged fault data. the comparison of the confusion matrix shows that the improved twin neural network model can effectively improve the problem of various fault misclassification compared with the original model, greatly reduced the diagnostic error, and meet the accuracy requirements of fault diagnosis in case of small samples.

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An Intelligent Particle Filter Based Joint Probabilistic data Association Algorithm for Long-Duration Multi-Target Tracking in Clutter Environment

An Intelligent Particle Filter Based Joint Probabilistic Dat...

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IEEE International conference on Information Communication and signal processing (ICICSP)

作者： Xihao Wang Die Chen Yanru Zhao School of Mathematical Sciences University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

the Joint Probabilistic data Association Algorithm based on Particle Filter (JPDA-PF) is used in the tracking problem of nonlinear multi-target systems in dense clutter environments. However, during long time tracking, the tracking accuracy decreases with the increase of time. Meanwhile JPDA produces a combinatorial explosion in the number of associated events as the number of targets and clutter increases, greatly increasing the computational task. In this paper, a Joint Probabilistic data Association Algorithm based on dolphin swarm particle filter (JPDA-DSPF) is proposed. Using the dolphin swarm optimization algorithm as a resampling method for particle filter can optimize the particle distribution to be closer to the region of high likelihood, and enhance the particle diversity to weaken the particle degradation phenomenon. We use an adaptive sequential approach to reduce computational costs by discarding correlated events with small probabilities. Finally, it is proved by simulation experiments that the method can effectively reduce the tracking error, inhibit the increasing trend of the error with time, effectively reduce the computation task and enhance the timeliness during multi-target encounters.

关键词： Target tracking signal processing algorithms signal processing Probabilistic logic Market research Information filters Particle filters Clutter Particle swarm optimization Dolphins

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Cyber Security Implementation Issues in small to Medium-sized Enterprises (SMEs) and their Potential Solutions: A Comprehensive Analysis

Cyber Security Implementation Issues in Small to Medium-size...

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International conference on Computing and Networking Technology (ICCNT)

作者： Saral Kandpal Sachin Bhatt Lalit Mohan Amit Patwal Pushpendra Kumar Department of CSE Graphic Era Hill University Bhimtal India Department of CSE Uka Tarsadia University Gujarat India Department of Computer Science Uka Tarsadia University Gujarat India

As the tech industry is advancing day by day, the digital data they store, their dependency on technology and other resources is also enlarging. Although large businesses account for returning high returns and turnover, small and Medium-sized Enterprises (SMEs) also have a large market share in this evolving era of technology. As these SMEs are becoming dependent on technology and moving towards the cyber space, the cyber security risks for these companies are also increasing. In current scenario, providing cyber security is more inclined towards large businesses, but what about the SMEs? Today, SMEs are not well prepared to defend themselves from cyber threats and attacks, and some of them can't even detect data breaches and manage cyber security risks. In addition, the mindset of the people within these SMEs is like "We SMEs are too small for any cyber-attack or threat". But this is just a myth and a mistake. Since they are small, lack resources and capital, SMEs become one of the prime targets of hackers. therefore, this review paper gives a literature review and insights about the research conducted so far around the world regarding the cyber security problems faced by SMEs and how they can cope with cyber security threats and defend themselves against malicious cyber-attacks.

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Fast and Accurate Detection of UAV Objects Based on Mobile-Yolo Network

Fast and Accurate Detection of UAV Objects Based on Mobile-Y...

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International conference on Wireless Communications and signal processing (WCSP)

作者： Jun Wang Wang Hongjun Jian Liu Rui Zhou Chunhao Chen Chuang Liu College of Electronic Engineering National University of Defense Technology Hefei China Unit 32032 of PLA Beijing China

ISBN: (纸本)9781665450867

With the development and popularization of unmanned aerial vehicle (UAV) technology, the UAV devices have been widely used in practice. Aiming at the problems of low accuracy and slow speed in detecting UAV objects, this paper constructs a UAV object dataset and proposes an efficient UAV object detection method based on Mobile-YOLO Network (MYN). Firstly, a UAV data set was constructed, including 3,698 UAV images, in which the proportion of large, medium and small-scale objects was about 3:1:1, providing a data basis for algorithm research and experimental verification. Secondly, we construct a Mobile-YOLO network model for UAV object detection based on YOLOv4, enhancing the detection speed to 51FPS under the premise of high precision. the results show that the Mobile-YOLO network has fewer parameters, faster operation speed and better comprehensive performance.

关键词： Wireless communication signal processing algorithms Object detection signal processing Autonomous aerial vehicles Solids Feature extraction

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14th Asian conference on Intelligent Information and database Systems, ACIIDS 2022

14th Asian Conference on Intelligent Information and Databas...

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14th Asian conference on Intelligent Information and database Systems, ACIIDS 2022

ISBN: (纸本)9789811982330

the proceedings contain 58 papers. the special focus in this conference is on Intelligent Information and database Systems. the topics include: Machine Learning-Based Recommender System for Tweeting Factory in Industry 5.0 Paradigm;automated Late Fusion of Low Level Descriptors for Feature Extraction and Texture Classification Using data Augmentation;door-to-Door Sampling Service with Drone;Error Investigation of Pre-trained BERTology Models on Vietnamese Natural Language Inference;supervised Learning Use to Acquire Knowledge from 2D Analytic Geometry Problems;forecasting Cryptocurrency Price Fluctuations with Granger Causality Analysis;Using Brain-Computer Interface (BCI) and Artificial Intelligence for EEG signal Analysis;predicting Metastasis-Free Survival Using Clinical data in Non-small Cell Lung Cancer;Impact of Design Decisions on Performance of Embarrassingly *** database Application;LDA+: An Extended LDA Model for Topic Hierarchy and Discovery;machine Learning to Diagnose Neurodegenerative Multiple Sclerosis Disease;a Novel Integrating Approach Between Graph Neural Network and Complex Representation for Link Prediction in Knowledge Graph;expandable-Convolutional-Block Neural Inference with Spatial-Fused Multi-resolution Features and Terse Convolution for IoT-Based Intelligent Automated Systems;traffic Management in Smart City;identifying Non-intuitive Relationships Within Returns data of a Furniture Online-Shop Using Temporal data Mining;DACE: Did I Catch You at a Good Time?;analysis of Dynamics of Emergence and Decline of Scientific Ideas Based on Optimistic and Pessimistic Fuzzy Aggregation Norms;Fusing Deep Learning with Support Vector Machines to Detect COVID-19 in X-Ray Images;image-Based Contextual Pill Recognition with Medical Knowledge Graph Assistance;a Legal Information Retrieval System for Statute Law;schema Formalism for Semantic Summary Based on Labeled Graph from Heterogeneous data;exploring Retriever-Reader Approaches in Question-Ans

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CWA-YOLO: Rethinking the YOLOv8 Architecture at the Level of Feature Information and Applying it to Substation Equipment Detection

CWA-YOLO: Rethinking the YOLOv8 Architecture at the Level of...

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IEEE International conference on Information Communication and signal processing (ICICSP)

作者： Li Zhuyun Xu Chong Yang Yingyi Lai Jiayang Xian Qu Mai Xiaoming Ieiri Yuya Yoshie Osamu China Southern Power Grid Technology Co. Ltd. Guangzhou China Graduate School of Information Production and Systems Waseda University Fukuoka Japan BIWIN Storage Technology Company Limited Shenzhen China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

Substations act as bridges connecting power gener-ation with consumption and play crucial roles in electrical grids such as power energy conversion, operational dispatching, and information monitoring. With the rapid development of electrical grids in recent years, the increasing contradiction between the shortage of production staff and the growing workload of inspections has urgently necessitated intelligent recognition algorithms for the condition of indoor substation equipment. this paper proposes a novel indoor substation equipment target detection framework based on YOLOv8, aimed at addressing issues like scale variation, occlusion handling, and real-time performance optimization, thereby enhancing detection accuracy and speed in complex environments. By incorporating the ConvNeXt feature extraction network and BiFPN, combined with the Wise-IoU loss function and adding detection heads for small targets, this approach has improved the detection capabilities for multi-scale targets, particularly in recognizing minute objects. Experimental results with actual field data indicate that the proposed CWA-YOLO model outperforms existing methods in both detection accuracy and speed, with the model mAP0.5 and mAP0.5:0.95 being 0.918 and 0.623 respectively, marking improvements of 6.50% and 11.05% over the YOLOv81 model, 7.49% and 11.85% over YOLOv71, and 9.55% and 15.80% over YOLOv51.

关键词： Substations Accuracy Target recognition signal processing algorithms Object detection Production signal processing Feature extraction data models Real-time systems

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Improved quantized iterative learning control in the presence of data dropouts

Improved quantized iterative learning control in the presenc...

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Asian Control conference

作者： Zhen Shao Yu Chen Fanrong Kong College of Artificial Intelligence and Automation Hohai University Nanjing P. R. China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

this paper focuses on solving the simultaneous data quantization and data dropouts problems in multi-input multi-output unknown nonlinear discrete systems, with an emphasis on their full tracking control. First, an innovative quantization model-free adaptive iterative learning control scheme is proposed based on the dynamic linearization model of the system. Using a uniform quantizer with an improved encoding-decoding mechanism to quantise the system output, modelling the data dropouts problem as a Bernoulli sequence, and employing intermittent updating to deal with the effects of the random data dropouts problem in the system output, the inherent errors due to data quantization and data dropouts are successfully eliminated, and it is verified that only a small amount of input/output data is required to achieve the tracking and controlling error convergence. Finally, the effectiveness of the method can be demonstrated through theoretical derivation and simulation experiments.

关键词： Adaptation models Quantization (signal) Adaptive systems data models Nonlinear dynamical systems Iterative learning control Convergence

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small Target Detection Method in Sea Clutter Based on Interframe Multi-feature Iteration 6

Small Target Detection Method in Sea Clutter Based on Interf...

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6th International conference on signal and Image processing, ICSIP 2021

作者： Xu, Shuwen Ma, Yitu Bai, Xiaohui Xidian University National Laboratory of Radar Signal Processing Xi'an China

ISBN: (纸本)9780738133737

For marine radar, since sea clutter is complex and dynamic, and small targets are difficult to model, the detection of floating small targets in sea clutter is always the focus and difficulty of radar target detection. the radar cross section of the floating small targets is small and the velocity is slow, so traditional detection methods have some bottlenecks, which belong to the problem of "over-clutter detection". the multi-feature-based detection method is an effective way to solve the detection of small targets on the sea surface, but traditional feature-based detection generally requires long accumulated time. Traditional scanning radar needs to take into account the surveillance space, which makes the number of accumulated pulses at each beam position restricted. It limits the application of feature detection methods to scanning radars. In this paper, aiming at the problem that the radar cannot stare at an angle for a long time in scanning mode, three features of relative average amplitude, relative Doppler peak height, and relative Doppler vector entropy are firstly extract. After that, the idea of multi-feature iteration between frames is used to update the feature values in the feature space, and then the fast convex hull algorithm is used to construct the decision region for detection. the measured data verifies that the proposed method can be well applied to scanning radar and obtains better detection performance than original methods. © 2021 IEEE.

关键词： Radar cross section

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