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共 64335 条记录，以下是941-950 订阅

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Next-Generation Satellite IoT networks: A HAPS-Enabled Solution to Enhance Optical Data Transfer 35

Next-Generation Satellite IoT Networks: A HAPS-Enabled Solut...

引用

35th IEEE international symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Fettes, Ethan Madoery, Pablo G. Yanikomeroglu, Halim Kurt, Gunes Karabulut Bellinger, Colin Martel, Stephane Ahmed, Khaled Siddiqui, Sameera Lab Department of Systems and Computer Engineering Canada Polytechnique Montréal Poly-Grames Research Center Department of Electrical Engineering Montréal Canada National Research Council Canada Canada Satellite Systems Mda Canada Defence Research and Development Canada Canada

ISBN: (纸本)9798350362244

For decades, satellites have facilitated remote internet of things (IoT) services. However, the recent proliferation of increasingly capable sensors and a surge in the number deployed, has led to a substantial growth in the volume of data that needs to be transmitted via satellites. In response to this growing demand, free space optical communication systems have been proposed, as they allow for the use of large bandwidths of unlicensed spectrum, enabling high data rates. However, optical communications are highly vulnerable to weather-induced disruptions, thereby limiting their high potential. This paper proposes the use of high altitude platform station (HAPS) systems in conjunction with delay-tolerant networking techniques to increase the amount of data that can be transmitted to the ground from satellites when compared to the use of traditional ground station network architectures. The architectural proposal is evaluated in terms of delivery ratio and buffer occupancy, and the subsequent discussion analyzes the advantages, challenges and potential areas for future research. © 2024 IEEE.

关键词： Delay tolerant networks

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Survey and Enhancements on Deploying LSTM Recurrent Neural networks on Embedded systems

Survey and Enhancements on Deploying LSTM Recurrent Neural N...

引用

IEEE international Conference on Communications (IEEE ICC)

作者： Abib, Ghalid Castel, Florian Satouri, Nissrine Afifi, Hossam Said, Adel Mounir Inst Polytech Paris SAMOVAR Telecom SudParis Palaiseau France Inst Polytech Paris Telecom SudParis Palaiseau France Natl Telecommun Inst NTI Switching Dept Cairo Egypt

ISBN: (纸本)9781538674628

The real implementation of a recurrent neural network (RNN) in a low complexity IoT device is evaluated in order to predict the time series of power consumption in tertiary buildings. The RNN type long short-term memory (LSTM) algorithm is adapted for a 32-bit microcontroller unit (MCU) and the backpropagation (BP) algorithm is implemented in-house. We therefore demonstrate that Intelligent IoT (IIoT) devices, such as the Espressif ESP32 MCU, not only implement neural networks (NNs), but also learn on their own. The resulting IIoT architecture has been proven to operate efficiently and compared to the traditional computer-based learning platform. The selected results confirm that stand-alone IoT devices are a truly efficient solution that adds flexibility to the architecture, reduces storage and computation costs, and is more energy-friendly. As a conclusion, it is practically more efficient to exploit low-power and processing-time IIoT for our prediction use case rather than relying on server based distributed systems.

关键词： Intelligent IoT Machine Learning Recurrent Neural networks Long Short-Term Memory Edge AI Embedded systems

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Design of an Efficient distributed Delivery Service for Group Key Agreement Protocols 16th

Design of an Efficient Distributed Delivery Service for Grou...

引用

16th international symposium on Foundations and Practice of Security (FPS)

作者： Paillat, Ludovic Ignat, Claudia-Lavinia Freya, Davide Turuani, Mathieu Ismail, Amine Hive Comp Serv Cannes France Univ Lorraine CNRS Inria LORIA Nancy France Univ Rennes CNRS IRISA INRIA Rennes France

ISBN: (纸本)9783031575365;9783031575372

End-to-end encrypted messaging applications such as Signal became widely popular thanks to their capability to ensure the confidentiality and integrity of online communication. While the highest security guarantees were long reserved to two-party communication, solutions for n-party communication remained either inefficient or less secure until the standardization of the MLS Protocol (Messaging Layer Security). This new protocol offers an efficient way to provide end-to-end secure communication with the same guarantees originally offered by the Signal Protocol for two-party communication. However, both solutions still rely on a centralized component for message delivery, called the Delivery Service in the MLS Protocol. The centralization of the Delivery Service makes it an ideal target for attackers and threatens the availability of any protocol relying on MLS. In order to overcome this issue, we propose the design of a fully distributed Delivery Service that allows clients to exchange protocol messages efficiently and without any intermediary. It uses a Probabilistic Reliable-Broadcast mechanism to efficiently deliver messages and the Cascade Consensus Protocol to handle messages requiring an agreement. Our solution strengthens the availability of the MLS Protocol without compromising its security.

关键词： distributed systems Group key agreement Consensus protocols Reliable broadcast

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Efficient Energy Management by Using SJF Scheduling in Wireless Sensor Network 1

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5th international Conference on Advances in distributed Computing and Machine Learning, ICADCML 2024

作者： Mohapatra, Hitesh Kolhar, Manjur Dalai, Asish Kumar School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar751024 India Department of Computer Science College of Arts and Science Prince Sattam bin Abdulaziz University Al-Kharj16278 Saudi Arabia VIT AP University AP Amaravati India

ISBN: (数字)9789819718412

ISBN: (纸本)9789819718405

A wireless sensor network is a collection of small, low-power devices called sensors that are deployed in an area to monitor and gather data from the surrounding environment. Sensors can be deployed in two type either in a static way or in mobile way. This work have presented a optimized routing strategy by using shortest job first techniques. In this model the computation time has been used to schedule the sleep and wake up phase of sensor nodes. The simulation result shows the better energy management with this technique. The result shows that the energy management through shortest job first technique outperforms by 46.52% then first come, first served, and Round Robin techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Energy management

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UNLOCKING THE POTENTIAL OF VISION-LANGUAGE MODELS FOR MAMMOGRAPHY ANALYSIS 21

UNLOCKING THE POTENTIAL OF VISION-LANGUAGE MODELS FOR MAMMOG...

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21st IEEE international symposium on Biomedical Imaging (ISBI)

作者： de Moura, Luis Vinicius Ravazio, Rafaela Mattjie, Christian Kupssinsku, Lucas Silveira Dal Sasso Freitas, Carla Maria Barros, Rodrigo C. Pontificia Univ Catolica Rio Grande do Sul Machine Learning Theory & Applicat Lab MALTA Porto Alegre RS Brazil Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil

ISBN: (纸本)9798350313345;9798350313338

Mammography is the most important diagnostic tool for early detection of breast cancer, which accounts for a significant proportion of cancer-related mortality in women. computer-aided diagnosis systems, especially those empowered by deep neural networks, offer promising advancements in mammographic analysis. This paper evaluates four state-of-the-art Vision-Language Models (VLMs), namely CLIP, BiomedCLIP, PubMedCLIP, and ALIGN, on two essential tasks: Breast Density and BI-RADS Assessment. Leveraging VinDr-Mammo and EMBED datasets, our experiments investigate both zero-shot and fine-tuning approaches, as well as the impact of dataset distribution, data efficiency, and undersampling techniques. Our findings indicate that fine-tuning pre-trained models on mammography-specific data significantly enhanced model performance with varying degrees of improvement across different tasks and models.

关键词： Vision-language Models Mammography BI-RADS Deep Learning computer-aided Diagnosis

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Analysis of the Performance Impact in Industrial Control Process of Wastewater systems when using distributed Ledger Technology Sensor Authentication 8

Analysis of the Performance Impact in Industrial Control Pro...

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8th international symposium on Electrical and Electronics Engineering, ISEEE 2023

作者： Bogdan-Daniel, Portasa Larisa, Diaconu 'Dunarea de Jos' University of Galati Faculty of Automation Computer Sciences Electronics and Electrical Engineering Galai Romania

ISBN: (纸本)9798350301670

In recent years, the integration of advanced technologies in industrial control processes has gained significant attention, particularly in the domain of wastewater systems. One emerging technology with promising potential is distributed Ledger Technology (DLT), which offers secure and transparent data management through blockchain-based solutions. This paper presents an in-depth analysis of the performance impact that arises when incorporating DLT-based sensor authentication in industrial control processes of wastewater systems. The study aims to evaluate the benefits and challenges associated with this integration, providing insights into the effectiveness and efficiency of DLT-based sensor authentication in ensuring data integrity and enhancing the overall control process performance. © 2023 IEEE.

关键词： Authentication

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A Model for Vulnerability Classification based on Res-CNN-BiTLSTM 22

A Model for Vulnerability Classification based on Res-CNN-Bi...

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22nd IEEE international symposium on Parallel and distributed Processing with Applications, ISPA 2024

作者： Li, Jiazheng Ren, Jiadong Yang, Shuailin Zhi, Chenghao Bao, Chunjiao Yanshan University School of Information Science and Engineering Qinhuangdao China

ISBN: (纸本)9798331509712

Vulnerabilities in software and distributed systems are increasing, and system security becomes a challenge for developers. Giving a quick vulnerability classification for newly discovered vulnerabilities is helpful for developers to quickly analyze the vulnerabilities and complete the vulnerability fix. Therefore, Res-CNN-BiTLSTM is proposed for vulnerability classification in this paper. The model consists of Text Convolutional Neural Network (TextCNN), Bidirectional improved gating mechanism (BiTLSTM) and residual blocks. Firstly, TextCNN is employed to capture local features from vulnerability description information, according to different convolutional kernel sizes. Secondly, an improved gating mechanism (TLSTM) is constructed to enhance the expressive power of Long Short-Term Memory (LSTM). BiTLSTM is employed to extract long-text features of vulnerability descriptions through both forward and backward directions. Thirdly, the residual block is used for enhancing and fusing the local features with the long text features to retain important text features. Finally, a fully connected layer is employed to classify vulnerabilities. The results show that Res-CNN-BiTLSTM has the highest macro precision (MacroP), macro recall (MacroR), and macro F1 (MacroF1) on National Vulnerability Database (NVD) dataset. It has a high prediction precision on CWE-352 and CWE-416. The performance of Res-CNN-BiTLSTM is better than the other comparative models. It is effective for vulnerability automatic classification. Meanwhile, the effectiveness of each part of the model is proved by ablation experiments. © 2024 IEEE.

关键词： Convolutional neural networks

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Evolutionary Patterns in Modern-Era Cloud-Based Healthcare Technologies 1

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8th international Conference on Information and Communication Technology for Competitive Strategies, ICTCS 2023

作者： Shrivastava, Vishal Pathak, Vibhakar Mishra, Saumya Buri, Ram Babu Sharma, Sangeeta Mishra, Chandrabhan Computer Science and Engineering Arya College of Engineering & I.T. Jaipur India

ISBN: (数字)9789819994892

ISBN: (纸本)9789819994885

distributed computing plays a pivotal role in revolutionizing the management of clinical data within the healthcare sector. This technology facilitates the global exchange of healthcare documents through electronic commerce, contributing significantly to promoting overall well-being and advancing the field of medicine. Thanks to this groundbreaking innovation, seamless communication with healthcare networks worldwide becomes a reality. In the realm of healthcare, distributed computing has existed for some time and has continued to evolve in tandem with business development. This invention introduces standardized specifications for a wide range of applications within the medical field, enabling open access to various devices. The security of data is ensured through distributed computing and efficient cloud server management, safeguarding critical information. Medical professionals can now offer guidance to their patients on maintaining their health, discussing routine medical care, and ensuring overall physical and mental well-being. Video conferencing has become an invaluable tool for doctors and therapists, fostering a sense of comfort and connection with their patients. This white paper delves into the objectives of employing distributed computing in medical services, emphasizing the awareness of its benefits, limitations, and challenges within the healthcare domain. Additionally, we explore vital recent trends and developments in health and medicinal concern. The concerned can readily acquire equipments that are IoT devised from vast list of manufacturers providing health-concern equipments. Consequently, the convergence of rapidly evolving technologies such as Big Data analytics, computational thinking, and the IoT of health concern, in conjunction with distributed computing, presents numerous means to enhance efficiency and streamline health-service. Thus, we enhance interoperability, expands resource sharing with cost reduction. © The Author(s), under exclusiv

关键词： Internet of things

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Solving Sparse Triangular Linear systems: A Review of Parallel and distributed Solutions 23th

Solving Sparse Triangular Linear Systems: A Review of Paral...

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23rd international Conference on Intelligent systems Design and Applications, ISDA 2023

作者： Marrakchi, Sirine Kaaniche, Heni Department of Computer Science and Communications Faculty of Sciences University of Sfax Sfax Tunisia

ISBN: (纸本)9783031648496

Solving sparse triangular linear systems (spTRSV) represents a basic core in numerous scientific and computational applications such as numerical linear algebra routines including Gaussian Elimination, LU and Cholesky decompositions, etc. Finding a parallel implementation for SpTRSV is a challenging task owing to the sequential structure of the steps concerned. In this study, we review various existing approaches for spTRSV. These methods broadly split into two major categories based on sets (level or color) and methods without set creation. Another classification consists of fine-grained parallelism (small tasks) and coarse-grained parallelism (large tasks). This paper discusses and provides a performance comparison of several recent approaches for spTRSV carried out on different types of parallel and distributed architectures. It investigates the literature in terms of the structure of sparse matrices, task granularity, scheduling, data dependencies, and architecture of the target parallel machine. Ultimately, the relevant findings and future research challenges are also addressed. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Linear systems

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CiMLoop: A Flexible, Accurate, and Fast Compute-In-Memory Modeling Tool

CiMLoop: A Flexible, Accurate, and Fast Compute-In-Memory Mo...

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2024 IEEE international symposium on Performance Analysis of systems and Software, ISPASS 2024

作者： Andrulis, Tanner Emer, Joel S. Sze, Vivienne MIT Cambridge United States MIT Nvidia Cambridge United States

ISBN: (纸本)9798350376388

Compute-In-Memory (CiM) is a promising solution to accelerate Deep Neural networks (DNNs) as it can avoid energy-intensive DNN weight movement and use memory arrays to perform low-energy, high-density computations. These benefits have inspired research across the CiM stack, but CiM research often focuses on only one level of the stack (i.e., devices, circuits, architecture, workload, or mapping) or only one design point (e.g., one fabricated chip). There is a need for a full-stack modeling tool to evaluate design decisions in the context of full systems (e.g., see how a circuit impacts system energy) and to perform rapid early -stage exploration of the CiM co-design space. To address this need, we propose CiMLoop: an open-source tool to model diverse CiM systems and explore decisions across the CiM stack. CiMLoop introduces (1) a flexible specification that lets users describe, model, and map workloads to both circuits and architecture, (2) an accurate energy model that captures the interaction between DNN operand values, hardware data representations, and analog/digital values propagated by circuits, and (3) a fast statistical model that can explore the design space orders-of-magnitude more quickly than other high-accuracy models. Using CiMLoop, researchers can evaluate design choices at different levels of the CiM stack, co-design across all levels, fairly compare different implementations, and rapidly explore the design space. © 2024 IEEE.

关键词： Deep neural networks

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