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检索条件"主题词=detection algorithms"

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Research on Real-time Lesion detection Algorithm for Robotic Ultrasound-Guided Images for Percutaneous Transluminal Surgery

Research on Real-time Lesion Detection Algorithm for Robotic...

引用

International Conference on Vision, Image and Signal Processing (ICVISP)

作者： Shijie Li Xinjie Ao Yonghang Tai Junzhen Du Yunnan Key Laboratory of Optoelectronic Information Technology School of Physics and Electronic Information Yunnan Normal University Kunming China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

With the advancement of robotic technology and ultrasound imaging, robot-assisted percutaneous puncture under ultrasound guidance has become a hot research topic in recent years. Currently, due to issues such as the real-time performance and accuracy of autonomous ultrasound image recognition, robot-assisted percutaneous puncture surgeries largely rely on the experience and professional judgment of physicians. To enhance the autonomy of surgical robots and reduce the operational burden on surgeons as well as training costs for surgical interns, this paper proposes a real-time lesion detection algorithm for robot-assisted percutaneous puncture under ultrasound guidance based on YOLOv8. Firstly, the SimAM (Simultaneous Attention Module) attention mechanism is introduced into the network model to enhance the extraction of lesion features in ultrasound images and the differentiation from the background. Secondly, depthwise separable convolutions replace some traditional convolutions in the feature extraction network to reduce the model's parameter count, further enhancing the detection speed, and the CIoU (Complete IoU loss) is used instead of the original loss function to improve detection accuracy. Finally, a robot-assisted percutaneous puncture system under ultrasound guidance is set up, incorporating the entire real-time lesion detection algorithm. Experimental results show that with the guidance of this algorithm, physicians can more easily complete puncture procedures, achieving a puncture accuracy of 94.43% and a time of 47.27s in multiple puncture experiments, with a real-time frame rate improvement of 11.9 FPS compared to the traditional YOLOv8 algorithm, proving the effectiveness of this real-time lesion detection algorithm for ultrasound-guided percutaneous puncture surgical robots.

关键词： Training Ultrasonic imaging Medical robotics Accuracy Surgery Signal processing algorithms Feature extraction Real-time systems Lesions detection algorithms

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Deep Unfolding-Assisted Fully Decentralized Projected Gradient MIMO detection Algorithm

Deep Unfolding-Assisted Fully Decentralized Projected Gradie...

引用

IEEE International Conference on Communications (ICC)

作者： Masaya Kumagai Tadashi Wadayama Ayano Nakai-Kasai Nagoya Institute of Technology

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

In this paper, we introduce the LWCoopPG (Learned Weighting Cooperative Projected Gradient Descent) algorithm for fully distributed MIMO signal detection. The proposed algorithm is a fully distributed algorithm, implementing the projected gradient method over a network, and incorporating a trainable step size and trainable weight parameters, which are tuned by deep unfolding. The computations of the proposed algorithm are fully distributed, eliminating the need for a central processor. Numerical experiments affirmed the superiority of the LWCoopPG algorithm over the conventional MMSE algorithm in terms of detection performance.

关键词： Gradient methods Distributed algorithms detection algorithms Signal detection

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A State Partition Particle Filter based Approach for detection of Switching Attack

A State Partition Particle Filter based Approach for Detecti...

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IEEE International Conference on Smart Grid Communications (SmartGridComm)

作者： Seema Yadav Nand Kishor Shubhi Purwar Vetrivel Subramaniam Rajkumar Alexandru Ştefanov Department of Electrical Engineering Motilal Nehru National Institute of Technology Prayagraj India Department of Engineering Østfold University College Fredrikstad Norway Department of Electrical Sustainable Energy Delft University of Technology Delft The Netherlands

ISBN: (数字)9798350318555

ISBN: (纸本)9798350318562

The increasing risk of cyber-physical attacks (CPAs) on power infrastructure has led to need for reliable detection technologies. As the landscape of cyber threats evolves, it becomes imperative to continually update and enhance attack detection techniques. This research investigates the formulation of detection algorithm, via combination of State Partition Particle Filter (SP-PF) theories for power system security. The proposed approach applies intelligent partitioning of the state space so as to be accurately represented with fewer particles. This reduction in computational demand enhances the algorithm’s efficiency, making it more practical for real-time applications. The detection algorithm based on SP-PF is tested against switching attacks (SAs) launched on the governor and excitation systems associated with the generator. The RTDS platform is utilized for conducting real-time simulations of IEEE 9-bus power network in order to demonstrate the efficacy of proposed SP-PF based detection SA in real-time.

关键词： Switches Reliability theory Real-time systems Particle filters Generators Smart grids Power system reliability Resource management Power system security detection algorithms

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Non-Local Means Filtering based Landslides Coherence Change detection for Multitemporal SAR Images

Non-Local Means Filtering based Landslides Coherence Change ...

引用

Signal, Information and Data Processing (ICSIDP), IEEE International Conference on

作者： Zhanze Wang Peifeng Ma Institute of Space and Earth Information Science The Chinese University of Hong Kong Hong Kong China Department of Geography and Resource Management The Chinese University of Hong Kong Hong Kong China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Landslides often occur in mountains, where the low coherence characteristics result in decreased accuracy of traditional SAR-based change detection algorithms. In this paper, a non-local means filtering based coherence change detection algorithm is proposed for multitemporal SAR images. First, Persistent Scatterers (PSs) and Distributed Scatterers (DSs) are both detected to improve the monitoring density in low coherence area. Then, coherence change detection is adopted based on the multitemporal SAR images to replace traditional amplitude based change detection algorithm. Finally, non-local means filtering is used to improve the change detection accuracy of the whole area. The raw data of landslides occurred in Hong Kong is used to indicate the effectiveness of the proposed algorithm.

关键词： Landslides Accuracy Filtering Change detection algorithms Urban areas Coherence Radar polarimetry Robustness detection algorithms Monitoring

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Large-Scale Graphs Community detection using Spark GraphFrames

Large-Scale Graphs Community Detection using Spark GraphFram...

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International Symposium on Parallel and Distributed Computing

作者： Elena-Simona Apostol Adrian-Cosmin Cojocaru Ciprian-Octavian Truică National University of Science and Technology Politehnica Bucharest 313 Independentei Bucharest Romania Academy of Romanian Scientists 3 Ilfov Bucharest Romania

ISBN: (数字)9798350369199

ISBN: (纸本)9798350369205

With the emergence of social networks, online platforms dedicated to different use cases, and sensor networks, the emergence of large-scale graph community detection has become a steady field of research with real-world applications. Community detection algorithms have numerous practical applications, particularly due to their scalability with data size. Nonetheless, a notable drawback of community detection algorithms is their computational intensity [2], resulting in decreasing performance as data size increases. For this purpose, new frameworks that employ distributed systems such as Apache Hadoop and Apache Spark which can seamlessly handle large-scale graphs must be developed. In this paper, we propose a novel framework for community detection algorithms, i.e., K-Cliques, Louvain, and Fast Greedy, developed using Apache Spark GraphFrames. We test their performance and scalability on two real-world datasets. The experimental results prove the feasibility of developing graph mining algorithms using Apache Spark GraphFrames.

关键词： Distributed processing Social networking (online) Scalability Distributed databases Cluster computing Sparks detection algorithms

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Enhanced Student-graph Representation for At-risk Student detection

Enhanced Student-graph Representation for At-risk Student De...

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Wei Qiu Andy W. H. Khong Fun Siong Lim Applications of Teaching & Learning Analytics for Students (ATLAS) Nanyang Technological University (NTU) Singapore School of Electrical and Electronic Engineering (EEE) Nanyang Technological University (NTU) Singapore

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Predicting examination grades is essential to facilitate early interventions and to enhance student retention rates in an academic institution. We propose a predictive model based solely on historical academic performance made available before the beginning of each semester. The proposed model employs singular value decomposition to distill the underlying student-course graph structure, resulting in a student representation vector that holistically captures a student’s academic ability across courses in relation to their cohort. This representation vector is then fused with the student’s historical academic records for grade prediction. Data for training the proposed prediction model was sourced from approximately five thousand Electrical and Electronic Engineering students across seventeen core courses, including Circuit Analysis and Analog Electronics taught in the sophomore year. Students identified as at risk of failing a course at the beginning of each semester may be offered targeted (academic) support such as peer tutoring programs.

关键词： Training Predictive models Vectors Data models Circuit analysis Engineering students detection algorithms

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Joint Application of Signals With Controlled ISI and Iterative Decision-Feedback detection Algorithm in DVB-S2

Joint Application of Signals With Controlled ISI and Iterati...

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IEEE International Conference on Electrical Engineering and Photonics (EExPolytech)

作者： Sophia Belkova Nguyen Dac Cu Ilya Lavrenyuk Peter the Great St. Petersburg Polytechnic University St. Petersburg Russia Faculty of Electrical and Electronic Engineering Phenikaa University Hanoi Vietnam

ISBN: (数字)9798350388879

ISBN: (纸本)9798350388886

the paper considers the application of signals with controlled ISI in the DVB-S2 standard. An iterative processing algorithm with decisions feedback used together with the error-correcting coding defined in the standard. The possibility of increase in transmission rate shown with permissibly small losses in energy efficiency. In addition, for some parameters of the symbol transmission rate and code rate, it is possible to achieve an increase in energy efficiency with a fixed bandwidth efficiency. The results given for two extreme cases of possible signal constellations defined by the standard - QPSK and 32APSK.

关键词： Phase shift keying Spectral efficiency Symbols Prototypes Iterative algorithms Energy efficiency Transceivers detection algorithms Standards Gain

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YOLO-DC: Enhancing object detection with deformable convolutions and contextual mechanism

YOLO-DC: Enhancing object detection with deformable convolut...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Dengyong Zhang Chuanzhen Xu Jiaxin Chen Bin Deng Xin Liao School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China School of Hydraulic and Environmental Engineering Changsha University of Science and Technology Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

Existing object detection methods often optimize model structure, loss functions, and data preprocessing, but enhancements via convolutional techniques are often overlooked. Additionally, increasing network depth leads to the loss of key features. This paper introduces YOLO-DC, which enhances object detection with deformable convolution and contextual mechanisms. YOLO-DC includes a Deformable Convolutional Module (DCM) and a Contextual Fusion Downsampling Module (CFD). The DCM module uses a modified deformable convolution of Multiscale Spatial Channel Attention (MSCA) to extend the receptive field and improve feature extraction. The CFD integrates contextual and local features post-downsampling, reducing information loss. Compared to YOLOv8-N, YOLO-DC-N improves average precision (AP) by 3.5%, reaching 40.8% on the Microsoft COCO 2017 dataset, with similar FPS and inference time. The model demonstrates superior performance compared to other state-of-the-art detection algorithms. The source code is available at: https://***/Object-detection-01/***.

关键词： Convolutional codes Deformable models Convolution Source coding Data preprocessing Object detection Information processing Feature extraction Data models detection algorithms

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Community detection Algorithm Based On High-Order Variational Graph Autoencoder

Community Detection Algorithm Based On High-Order Variationa...

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Image Processing (ASIP), Asia Symposium on

作者： Yiwen Shen Xiaoyan Zheng Ran Yang School of Information Technology and Engineering Tianjin University of Technology and Education Tianjin China

ISBN: (数字)9798350376159

ISBN: (纸本)9798350376166

The topic of community discovery algorithm has important research significance in complex network scientific research, and the acquisition of network topology structure information is one of its key research areas. With the exponential growth of community network size, there is not only the problem of dimensionality disaster, but also the difficulty of extracting sufficient community structure information. Obviously, the experimental performance of traditional community discovery algorithms is difficult to meet our needs in such an environment. At the same time, most existing models only use traditional adjacency matrices to obtain topological structure information. In response to the above problems, this paper proposes a community discovery algorithm (Aggregation-VGAE) based on variational graph autoencoders to obtain high-order topological structure information, extracting more accurate high-order topological structure information to find community structures more accurately. First, an aggregation matrix is generated by extracting the first-order and second-order structural information of the topological graph; secondly, a variational graph autoencoder is used to extract and reduce the dimensionality of the feature matrix containing network attributes to obtain a low-dimensional embedding matrix; finally, K- means clustering algorithm to obtain community structure information. We compare Aggregation-VGAE with the baseline model VGAE and its 7 other algorithms using the NMI evaluation metric on the citation network dataset. By evaluating the algorithm on a large real data set, the experimental results and visualization of the clustering results show that the Aggregation-VGAE algorithm has better community segmentation results in the citation network than other various algorithms.

关键词： Measurement Dimensionality reduction Disasters Clustering algorithms Data visualization Complex networks Feature extraction Robustness Data mining detection algorithms

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Enabling Grid Voltage and Frequency Support with Islanding detection in V2G Capable Electric Vehicle Charging Stations

Enabling Grid Voltage and Frequency Support with Islanding D...

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IEEE Annual Southern Power Electronics Conference (SPEC)

作者： Victor Cordeiro De Arruda Eliabe Duarte Queiroz Joel Filipe Guerreiro Department of Hardware Design Eldorado Research Institute Campinas Brazil School of Electrical and Computer Engineering Universidade Estadual de Campinas Campinas Brazil

ISBN: (数字)9798350351156

ISBN: (纸本)9798350351163

Electric vehicle charging stations are able to support the voltage and frequency of the grid by offering ancillary services, leading to a more reliable grid. This work presents the benefits of using VoltVAr, FreqWatt, and frequency and voltage ride-through for grid stability. While offering ancillary services, V2G capable charging stations may falsely detect islanding. A methodology is proposed to solve this emerging issue.

关键词： Vehicle-to-grid Islanding Voltage Power system stability Charging stations Electric vehicle charging Power electronics Reliability detection algorithms System analysis and design

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