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检索条件"主题词=Signal Processing Algorithms"

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Progressive Sub-Graph Clustering Algorithm for Semi-Supervised Domain Adaptation Speaker Verification

Progressive Sub-Graph Clustering Algorithm for Semi-Supervis...

引用

IEEE International Conference on signal processing, Communications and Computing (ICSPCC)

作者： Zhuo Li Jingze Lu Zhenduo Zhao Wenchao Wang Mou Wang Ziteng Wang Xin Liu OPPO Beijing China Key Laboratory of Noise and Vibration Research Chinese Academy of Sciences Institute of Acoustics Beijing China Key Laboratory of Speech Acoustics and Content Understanding Chinese Academy of Sciences Institute of Acoustics Beijing China

ISBN: (数字)9798350366556

ISBN: (纸本)9798350366563

Utilizing the large-scale unlabeled data from the target domain via pseudo-label clustering algorithms is an important approach for addressing domain adaptation problems in speaker verification tasks. In this paper, we propose a novel progressive subgraph clustering algorithm based on multi-model voting and double-Gaussian based assessment (MVGPG clustering). To fully exploit the relationships among utterances and the complementarity among multiple models, our method constructs multiple k-nearest neighbors graphs based on diverse models and generates high-confidence edges using a voting mechanism. Further, to maximize the intra-class diversity, the connected sub graph is utilized to obtain the initial pseudo-labels. Finally, to prevent disastrous clustering results, we adopt an iterative approach that progressively increases k and employs a double-Gaussian based assessment algorithm to decide whether merging sub-classes.

关键词： Adaptation models Merging signal processing algorithms Clustering algorithms signal processing Nearest neighbor methods Iterative algorithms Calibration Optimization

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Agile Time Quantum Round Robin Algorithm

Agile Time Quantum Round Robin Algorithm

引用

IEEE International Conference on signal processing, Informatics, Communication and Energy Systems (SPICES)

作者： Sulalah Qais Mirkar Amiksha Bobal Sanjana Gattani Dept. of Information Technology Mukesh Patel School of Technology Management & Engineering Mumbai India CSE-Cyber Security Mukesh Patel School of Technology Management & Engineering Mumbai India

ISBN: (数字)9798350376135

ISBN: (纸本)9798350376142

In multitasking operating systems, CPU scheduling algorithms are accountable for the queuing of tasks for execution on the CPU. The effectiveness of a CPU scheduling algorithm is measured through CPU utilization, throughput, and process waiting times. One such scheduling algorithm is Round Robin (RR), a preemptive methodology that allocates time in quanta (parts) to execute the processes in a loop. The problems with a static quantum, such as too many context switches and process starvation are addressed by the proposed algorithm named Agile Time Quantum Round Robin (ATQRR). The algorithm dynamically modifies the remaining time based on real-time system factors. To ensure a thorough validation, a comprehensive analysis of the existing literature has been undertaken to compare ATQRR with other similar approaches. This review has focused on many criteria, like average turnaround time, waiting times, throughput, context switch overhead, and the fairness of process scheduling.

关键词： Reviews Heuristic algorithms Operating systems signal processing algorithms Switches signal processing Throughput SPICE Real-time systems Round robin

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Empowering Secure Transactions: Blockchain-Enabled Payments with AI Sentiment Analysis

Empowering Secure Transactions: Blockchain-Enabled Payments ...

引用

International Conference on Communications and signal processing

作者： M Karthik Kirtik Soni C Lokchandar V Radhamani Department of ECE Rajalakshmi Engineering College Chennai India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

In the modern era, transactional security is paramount, yet user experience often remains detached from the robustness of secure systems. This project addresses this gap by proposing a fusion of blockchain security and AI sentiment analysis, aiming to create a transactional framework that seamlessly integrates cryptographic assurance with user emotional intelligence. The inherent problem lies in the disconnection between traditional secure systems and user-centric experiences, leading to potential vulnerabilities in transactions. To bridge this gap, this project leverages the power of blockchain technology, specifically utilizing the Ganache personal blockchain, coupled with smart contracts to establish immutable, transparent, and tamper-resistant transactional processes. Simultaneously, sophisticated AI sentiment analysis algorithms assess user-provided text data, adding an emotional layer to the decision-making process. The envisioned solution envisions a dynamic response to user emotions, influencing adaptive actions within the blockchain. The proposed system will be encapsulated within an intuitive web-based application for widespread accessibility and ease of use.

关键词： Sentiment analysis Heuristic algorithms Smart contracts Decision making signal processing algorithms signal processing User experience

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A Deep Dive into Relevant Feature Identification for Unveiling PII Leakage in Smartphones

A Deep Dive into Relevant Feature Identification for Unveili...

引用

International Conference on signal processing and Communications, SPCOM

作者： Rishika Kohli Shaifu Gupta Manoj Singh Gaur Computer Science and Engineering Indian Institute of Technology Jammu Jammu and Kashmir India

ISBN: (数字)9798350350456

ISBN: (纸本)9798350350463

Advancement in networking technologies has led to an increase in mobile phone usage exponentially, simplifying people's lives. However, this surge has created pathways for unauthorized access to personal information. These devices store large amounts of sensitive data, raising concerns about data leakage or exfiltration. Therefore, detecting unauthorized data leakage from these devices becomes critical. To address this, we propose enhanced machine learning frameworks aimed at accurately detecting personal information exposure. Our framework integrates the XAI algorithm, LIME, to enhance inter-pretability and comprehension of the detection process. Through experiments, we demonstrate that incorporating LIME leads to improved explanations and responsiveness, offering a more user-friendly and transparent solution.

关键词： Performance evaluation Data privacy Privacy Neural networks signal processing algorithms Telecommunication traffic signal processing

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Human Motion Tracking: A Comparative Analysis of Recent Advancements

Human Motion Tracking: A Comparative Analysis of Recent Adva...

引用

International Conference on Advanced Robotics and Intelligent Systems (ARIS)

作者： Yan-Ru Lee Jin-Siang Shaw Fu-Shin Lee Department of Computer Science & Information Engineering National Taipei University of Technology Taipei Taiwan Department of Mechanical Engineering National Taipei University of Technology Taipei Taiwan Department of Smart Living Huafan University New Taipei City Taiwan

ISBN: (数字)9798350362572

ISBN: (纸本)9798350362589

This paper explores the advancements in human motion-tracking technology within the past three years, analyzing research that presents novel methods for capturing and analyzing human movement. We examine the methodologies employed by each approach, including sensor-based systems, radar technology, Wi-Fi signal utilization, lightweight networks for real-time action recognition, and a system for joint camera localization and human motion tracking. This paper compares each surveyed research's advantages, disadvantages, and limitations, highlighting their practical implications and potential applications. Finally, this review discusses the future directions and challenges within human motion tracking.

关键词： Simultaneous localization and mapping Tracking signal processing algorithms Radar Radar tracking Cameras Real-time systems

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Super-Resolving SAR Images with Diffusion Models: A Dual Evaluation of Metrics and Applications

Super-Resolving SAR Images with Diffusion Models: A Dual Eva...

引用

International Conference on signal processing Proceedings (ICSP)

作者： Zilong Chen Siqian Zhang Boli Xiong College of Electronic Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

SAR, recognized for its capability to capture images under various weather conditions and at any time, is served as a key technology in remote sensing domains. Nonetheless, the inherent limitations of imaging systems often result in SAR images that do not achieve high resolution, thereby constraining their specific applications. Drawing inspiration from the efficacy of diffusion models in generative tasks involving natural images, we have devised a model that converts Gaussian noise into a super-resolution image derived from low-resolution input, utilizing a Markov chain framework and integrating residual prediction to facilitate faster convergence. The performance of this model is assessed in comparison to leading methodologies, including Generative Adversarial Networks and Transformers, employing both traditional evaluation metrics and semantic segmentation tasks. Experimental findings derived from the MSTAR and SARBake datasets reveal that the super-resolution algorithm utilizing a diffusion model exhibits superior performance relative to alternative models, as evidenced by conventional metrics. This algorithm not only demonstrates exceptional visual quality but also proves effective in downstream applications, underscoring its considerable potential for tasks involving super-resolution of SAR images.

关键词： Measurement Visualization Semantic segmentation Superresolution signal processing algorithms Diffusion models Transformers Radar polarimetry signal resolution Remote sensing

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Impact of Fragmentation on Temporal Event Localization for Speech EEG

Impact of Fragmentation on Temporal Event Localization for S...

引用

International Conference on signal processing and Communications, SPCOM

作者： Arun B Debasis Samanta Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India

ISBN: (数字)9798350350456

ISBN: (纸本)9798350350463

This study investigates the effect of temporal event localization of speech-imagery in electroencephalogram (EEG) signals. Temporal event localization is required for speech-imagery EEG signals as they are often recorded over long segments with the actual event being a fraction of the recorded duration. It is achieved by fragmenting the EEG epochs into smaller components and identifying those fragments from different signals that are common to the same class. After extracting these fragments and merging them into a new set of signals, they are validated using a set of classifiers. The results show a significant improvement in the classifier performance when compared to using signals without fragmentation. Extracting these fragments would also allow for extracting features of syllables (and eventually, phonemes), as the same algorithm used to fragment the signals may be used to extract features of those word components.

关键词： Location awareness Merging signal processing algorithms signal processing Feature extraction Electroencephalography Classification algorithms

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Comparative Analysis of Sparse and Conventional Channel Estimation algorithms in Wireless MIMO Channel Models

Comparative Analysis of Sparse and Conventional Channel Esti...

引用

Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Vaibhav N Patil Vishal R Sanjay M Belgaonkar Shrehari BM Vipula Singh Department of ECE RNS Institute of Technology Bengaluru India Department of AI & ML BMS Institute of Technology and Management Bengaluru India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

In the present scenario, Multiple Input Multiple Output (MIMO) systems are in use because of the high data capacity and efficiency of the system. There is a special importance on correct channel estimation to in getting the highest performance out of these systems. Seeking efficient solutions to this problem, various practitioners have resorted to the application of sparse signal processing that makes use of the sparsity character of wireless channels. This paper focuses on the scope of sparse signal processing in the field of wireless communications with special focus on this study on the BP and OMP algorithms while comparing them to LS and ZF estimation. The simulations are conducted in several surroundings including urban and rural as well as in free space. A clear impact of sparse signal processing in alleviating the training requirements and the computing cost has been achieved with BP considered to be the most effective tool for compressed sensing. In this regard, the effectiveness of sparse approaches in MIMO channel estimation is anticipated.

关键词： Wireless sensor networks Accuracy signal processing algorithms Channel estimation Matching pursuit algorithms Estimation Approximation algorithms Real-time systems Computational complexity Tuning

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Development of Intelligence Software for GPR Investigation of Buried Object

Development of Intelligence Software for GPR Investigation o...

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Space, Aerospace and Defence Conference (SPACE), IEEE

作者： Sangeeta Tarai Swarna Laxmi Panda Buddepu Santosh Kumar Ajit Kumar Sahoo Subrata Maiti Dept. of ECE NIT Rourkela Rourkela India

ISBN: (数字)9798350367386

ISBN: (纸本)9798350367393

Ground Penetrating Radar (GPR) is crucial for non-destructive subsurface investigation. However, it encounters many challenges like signal interference, clutter, complex media characteristics, etc. Advanced signal processing algorithms are essential to overcome these challenges, ensuring accurate identification and differentiation of subsurface targets. In addition to addressing these needs, this paper presents the development of robust GPR software using the MATLAB App Designer tool. The software incorporates data handling, clutter reduction, velocity estimation, and imaging. It also includes classification methodologies for target discernment. This comprehensive approach ensures accurate and reliable results, enhancing the utility of GPR technology for diverse applications.

关键词： Accuracy Ground penetrating radar signal processing algorithms Media Feature extraction Data processing Software

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Classifying Static and Dynamic Tracks for LiDAR-Based Navigation of Autonomous Vehicle Systems

Classifying Static and Dynamic Tracks for LiDAR-Based Naviga...

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International Conference on Frontiers of signal processing (ICFSP)

作者： Muhammad Adnan Pamela Zontone Lucio Marcenaro David Martín Gómez Carlo Regazzoni University of Genoa Genova Italy University Charles III of Madrid Avda de la Universidad 30 Leganés Madrid Spain

ISBN: (数字)9798350353235

ISBN: (纸本)9798350353242

In recent years we have seen a significant increase in research on autonomous vehicles, which is attracting considerable attention because of its potential to revolutionize transportation. In this paper, a method is presented for classifying static and dynamic tracks in autonomous vehicles using odometry and LiDAR data. Using the multi-target tracking algorithm, we are able to detect and track objects with high precision. We used an unsupervised clustering algorithm to create innovative interaction models, which will be employed for vehicle localization in the future. Because clustering is based upon time and spatial proximity, we are able to create interaction models using dynamic bayesian networks (DBNs) that allow us to classify and differentiate static from dynamic tracks. Based on our method, we achieve an accuracy of 87% in distinguishing between static and dynamic obstacles, thereby enhancing navigation by identifying and differentiating different types of obstacles efficiently.

关键词： Location awareness Laser radar Navigation Heuristic algorithms signal processing algorithms Clustering algorithms Classification algorithms Odometry Vehicle dynamics Autonomous vehicles

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