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检索条件"任意字段=International Conference on Pervasive Computing, Signal Processing and Applications"

共 5177 条记录，以下是1-10 订阅

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Proceedings - 2010 1st international conference on pervasive computing, signal processing and applications, PCSPA 2010

Proceedings - 2010 1st International Conference on Pervasive...

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1st international conference on pervasive computing, signal processing and applications, PCSPA 2010

ISBN: (纸本)9780769541808

The proceedings contain 315 papers. The topics discussed include: research on optical air chamber of infrared gas sensor;the methods of smart structural dynamic response analysis of SINS;robust joint space tracking method based on particle filter;provably secure certificateless authenticated key agreement protocol in the standard model;a comprehensive approach of SA based software deployment reliability estimation in neural networks;a new spam traffic characterizatione mechanism;the implement of UBL in the E-business;simulation of pulsed current wire behavior in magnetic field using FEA;an efficient routing protocol in a bus-based vehicular ad hoc network;fast pedestrian detection using slice-based motion analysis;on-line discriminative appearance modeling for robust object tracking;and private small-cloud computing in connection with Linux thin client.

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Proceedings - 2010 1st international conference on pervasive computing, signal processing and applications, PCSPA 2010: Preface

Proceedings - 2010 1st International Conference on Pervasive...

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Proceedings - 2010 1st international conference on pervasive computing, signal processing and applications, PCSPA 2010 2010年 xxviii页

作者： Sun, Sheng-He Pan, Jeng-Shyang

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Advanced VLSI-Based Design of Multiple Path OTAs with Dual Flipped Voltage Follower for signal processing & Communication applications 3

Advanced VLSI-Based Design of Multiple Path OTAs with Dual F...

引用

3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Balaji, G.H. Pavithra, G. Manjunath, T.C. Ltd. Bangalore India Dayananda Sagar College of Engineering Department of Electronics & Communication Engineering Karnataka Bangalore560111 India

ISBN: (纸本)9798331515911

This research paper presents the Advanced VLSI-Based Design of Multiple Path OTAs with Dual Flipped Voltage Follower for signal processing & Communication applications, targeted for VLSI-based signal processing cases. Leveraging 50nm CMOS technology and Cadence tools, the proposed OTA architecture offers enhanced performance and versatility in signal processing tasks. The design incorporates multiple paths for signal processing, enabling improved bandwidth, gain, and linearity while minimizing common-mode noise. The inclusion of Dual Flipped Voltage Follower enhances the OTA's stability and dynamic range, ensuring reliable operation in high-speed VLSI systems. Through extensive simulation studies, the paper demonstrates the effectiveness of the proposed OTA design in meeting the stringent requirements of modern signal processing applications. This research contributes to the advancement of VLSI design methodologies, offering a promising solution for high-performance signal processing tasks in nanoscale CMOS technologies. The simulation results from the cadence tool shows the effectiveness of the methodology that is being presented in this research article. © 2025 IEEE.

关键词： Operational amplifiers

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Analysis and design of pervasive computing intelligent surveillance system integrating optical sensors and behavioral analysis for industrial internet 2

Analysis and design of pervasive computing intelligent surve...

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2nd international conference on Big Data, Computational Intelligence, and applications, BDCIA 2024

作者： Yao, Di Luo, Chenyao Cheng, Weiwei Song, Huaju Beijing100083 China Portland Institute Nanjing University of Posts and Telecommunications Nanjing210003 China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China School of Environmental Science NanJing XiaoZhuang University Nanjing210017 China

ISBN: (纸本)9781510689053

Nowadays, the surveillance system performing as an indispensable part for modern industrial Internet has attracted much attention. With the continuous progress of microprocessor technology, sensor technology, particularly optical sensors, and network technology, it has entered the era of pervasive computing, which leads to the breakthrough of the surveillance system and the emergence of intelligent environment safety monitoring systems. However, the intelligence of surveillance systems is still a significant challenge. In this paper, we propose a surveillance system for detecting and recognizing people based on their behaviors from images or video sequences using computer vision techniques and optical sensor data, which is implemented by designing the system architecture through a hierarchical structure, applying key techniques such as background subtraction, modeling, and optical sensing analysis, and experimentally processing and testing a variety of behavioral samples. The alarm information generated by the proposed surveillance system aided by pervasive computing system can be delivered to remote terminals. Experimental results reveal that the environmental monitoring and alarm information can be processed in real time with devices, such as cell phones, telephones, and PCs. Therefore, the proposed approach can improve the performance of the surveillance systems. © 2025 SPIE.

关键词： Alarm systems

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SPT: Sequence Prompt Transformer for Interactive Image Segmentation

SPT: Sequence Prompt Transformer for Interactive Image Segme...

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Cheng, Senlin Sun, Haopeng Xie, Tao Zhao, Hangyue Chen, Yiqiang Xu, Bolei Li, XiaoBo Ant Group Hangzhou China BAIDU Beijing China Beiiing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350368741

Interactive segmentation aims to extract objects of interest from an image based on user-provided clicks. In real-world applications, there is often a need to segment a series of images featuring the same target object. However, existing methods typically process one image at a time, failing to consider the sequential nature of the images. To overcome this limitation, we propose a novel method called Sequence Prompt Transformer (SPT), the first to utilize sequential image information for interactive segmentation. Our model comprises two key components: (1) Sequence Prompt Transformer (SPT) for acquiring information from sequence of images, clicks and masks to improve accurate. (2) Top-k Prompt Selection (TPS) selects precise prompts for SPT to further enhance the segmentation effect. Additionally, we create the ADE20KSeq benchmark to better evaluate model performance. We evaluate our approach on multiple benchmark datasets and show that our model surpasses state-of-the-art methods across all datasets. © 2025 IEEE.

关键词： computer vision interactive image segmentation

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Using Convolutional Neural Networks for Image Recognition in IoT applications

Using Convolutional Neural Networks for Image Recognition in...

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2025 international conference on pervasive Computational Technologies, ICPCT 2025

作者： Rajababu, Durgam Veerraju, Sampenga Kumar, K. Suresh Gupta, Sonali Thurai Raaj, V.B. Joshi, Rahul Sr University School of Engineering Department of Eee Telangana Warangal India Dadi Institute of Engineering and Technology Department of Electronics and Communications Engineering Andhra Pradesh Anakapalli Visakhapatnam531002 India Panimalar Engineering College Mba Department Varadarajapuram Poonamallee Chennai600123 India J C Bose University of Science and Technology Ymca Faridabad India Madanapalle Institute of Technology & Science Electrical and Electronics Engineering Andhra Pradesh Madanapalle India Manav Rachna International Institute of Research and Studies Department of Journalism & Mass Communication SMeH Haryana India

ISBN: (纸本)9798331508685

Convolutional Neural Networks (CNNs) have grown into a powerful picture identification tool. They are an important part of making Internet of Things (IoT) apps. In this regard, CNNs make it possible to analyse visual data gathered from networked devices, like cameras and sensors, effectively and accurately. With an emphasis on its potential for real-time applications for example smart surveillance, healthcare monitoring, autonomous cars, and industrial automation, this study investigates the incorporation of CNN-based image identification into IoT contexts. We tackle important issues such as the requirement for low-latency processing, energy economy, and computing limitations on edge devices. In order to maximise CNN performance in the resource-constrained IoT ecosystem, strategies like model compression, edge computing, and distributed architectures are covered. The ability to interpret large volumes of visual data is improved by the integration of CNNs with IoT, providing creative solutions for automation and intelligent decision-making across numerous industries. © 2025 IEEE.

关键词： Convolution

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Deploying AI-Based Environmental Monitoring applications at the Edge: Two Case Studies 15th

Deploying AI-Based Environmental Monitoring Applications at...

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15th international conference on Parallel processing and Applied Mathematics, PPAM 2024

作者： de Lucia, Gianluca Laccetti, Giuliano Lapegna, Marco Montella, Raffaele Romano, Diego Rome Italy Department of Mathematics and Applications Univ. of Naples Federico II Naples Italy Department of Science and Technolgy University of Naples Parthenope Naples Italy Naples Italy

ISBN: (纸本)9783031856990

The Edge computing environments enable the development of pervasive applications distributed across extensive geographical areas, overcoming specific issues associated with centralized information processing, such as network bandwidth saturation and the need for large computing infrastructures. This work presents two case studies of deploying applications for environmental monitoring on low-energy and high-performance edge computing devices, employing accelerated artificial intelligence techniques based on GPUs. The first problem entails the classification of various materials within hyperspectral images, while the second problem focuses on identifying floating plastic debris. The applications were validated on a Nvidia Jetson Nano sensor board, demonstrating good accuracy, effectiveness, and energy consumption results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Environmental monitoring

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Personalized User Interaction in Web applications using Adaptive LLM Model

Personalized User Interaction in Web Applications using Adap...

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2025 international conference on pervasive Computational Technologies, ICPCT 2025

作者： Mahto, Manoj Kumar Srivastava, Durgesh Kumar, Ranjit Sah, Basant Singh, Hare Ram Maakar, Sunil Kr. Vignan Institute of Technology and Science Department of Cse Telangana Hyderabad India Chitkara University Institute of Engineering and Technology Chitkara University Department of Cse Punjab Rajpura India Maharaja Agrasen University Mait Department of Cse Himachal Pradesh India Koneru Lakshmaiah Education Foundation Department of Cse AP Guntur India Iimt College of Engineering Department of Cse Greator Noida India Galgotias University School of Computing Science & Engineering Greater Noida India

ISBN: (纸本)9798331508685

The rapid evolution of web applications has made personalization and intuitive user experiences increasingly essential. The aim of this work is to provide a tailored large language model (LLM) architecture allowing context-aware interactions within online contexts. Using real-world datasets like as Microsoft COCO and Amazon Customer Reviews enhanced with contextual metadata and behavioural analytics, we taught LLMs to grasp customer wants via explicit patterns and contextual signals. To allow the models to dynamically modify their replies, improve user engagement, and expedite processes, sophisticated natural language processing methods and contextual embedding strategies were used. In comparison to conventional static models, extensive experimentation and user testing exposed notable increases in user happiness and interaction efficiency. Significant problems like data privacy, model scalability, and real-time processing were resolved to guarantee the feasibility of these bespoke LLMs throughout several web application environments. © 2025 IEEE.

关键词： Modeling languages

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FedTG: Text-guided Federated Domain Generalization

FedTG: Text-guided Federated Domain Generalization

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Chen, Yiming He, Nan Sun, Lifeng Department of Computer Science and Technology Tsinghua University China Key Laboratory of Pervasive Computing Ministry of Education China

ISBN: (纸本)9798350368741

Federated Domain Generalization is aimed at learning a global model that generalizes well to new clients with domain shift in a privacy-preserving manner. Inspired by the powerful generation capability of Large language models (LLMs), we propose a novel method for this problem, named Text-guided Federated Domain Generalization (FedTG), which utilizes text generated by LLMs to guide the training of visual models. In the client, we align the image embeddings to the text embeddings, which provides strong generalization knowledge. In the server, we generate abundant textual descriptions about different classes, subsequently converting them into embeddings for fine-tuning the global classifier, which helps the global classifier not overfit to a particular domain. Moreover, we utilize the powerful image generation capabilities of LLMs to generate a cross-domain validation dataset to help select the most generalized model. We conduct experiments on several datasets, and the results prove that FedTG outperforms relevant baselines. © 2025 IEEE.

关键词： Data Heterogeneity Domain Shift Federated Domain Generalization Federated Learning

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Contrast Memory for Unsupervised Anomaly Detection

Contrast Memory for Unsupervised Anomaly Detection

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2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Li, Jiahao Chen, Yiqiang Xing, Yunbing Gu, Yang Lan, Xiangyuan Institute of Computing Technology Chinese Academy of Sciences Beijing China Pengcheng Laboratory Shenzhen China University of Chinese Academy of Sciences Beijing China Beijing Key Laboratory of Mobile Computing and Pervasive Device Beijing China Guangzhou China

ISBN: (纸本)9798350368741

Real-world multivariate time series unsupervised anomaly detection is a challenging problem due to intricate temporal correlations. Recently, impressive progress have been made in tackling this issue through the design of large-scale models, facilitated by the growing model parameters. However, in resource-constrained scenarios such as ubiquitous computing and edge computing, the large-scale models suffer from issues like high parameter complexity and expensive training overheads. Existing methods can only strive for a direct tradeoff between model size and performance. To address this challenge, we propose DiMER (Diminutive Memory-Enhanced Reconstruction), a model with parameters of the order of 0.1M. In DiMER, we introduce a novel contrast memory mechanism to learn normal patterns with diminutive network and propose a temporal reconstruction loss nto add the autocorrelation information. In addition, we introduce a multi-space composite detection criterion, an anomaly score calculation that takes into account both memory space and data space. Extensive experiments on real-world datasets across various domains demonstrate that the proposed model achieves comparable or even superior performance to large-scale models while maintaining lightweight. © 2025 IEEE.

关键词： Anomaly Timeseries Unsupervised Learning

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