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timestamp Peripherals for Precise real-time Programming 23

Timestamp Peripherals for Precise Real-Time Programming

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21st ACM/ieee international Symposium on Formal Methods and Models for System Design (MEMOCODE)

作者： Hui, John Edwards, Kyle J. Edwards, Stephen A. Columbia Univ New York NY 10027 USA

ISBN: (纸本)9798400703188

On microcontrollers, timer devices provide high-precision timing, but that precision is lost when using high-level languages without suitable abstractions for temporal behavior. So, for timing-sensitive applications, programmers resort to low-level languages like C which lack expressiveness and safety guarantees. Other programmers use specialized precision-timing hardware which is expensive and difficult to obtain. In this work, we achieve sub-microsecond precision from a high-level real-time programming language on the RP2040, a cheap, widely available microcontroller. Our work takes advantage of the RP2040's Programmable I/O (PIO) devices, which are cycle-accurate coprocessors designed for implementing hardware protocols over the RP2040's GPIO pins. We use the PIO devices to implement timestamp peripherals, which are input capture and output compare devices. We use timestamp peripherals to mediate I/O from programs written in Sslang, a real-time programming language with deterministic concurrency. We show that timestamp peripherals help Sslang programs achieve the precise timing behavior prescribed by Sslang's Sparse Synchronous Programming model.

关键词： real time systems concurrency control computer languages timing

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Surveillance-Bsed Road Accident Detection Method Using Improved YOLOv8 for IIoT applications

Surveillance-Bsed Road Accident Detection Method Using Impro...

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ieee/CIC international conference on Communications in China (ICCC)

作者： Chen, Jiahui Jin, Yuncheng Chen, Xuan Sun, Bin Jin, Xinyu Zhejiang Univ Polytech Inst Hangzhou 310027 Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China

ISBN: (纸本)9798350378412

real-time detection of traffic accidents holds crucial significance for emergency management in Industrial Internet of Things (IIoT) systems. Deep learning-based object detection techniques offer effective means to reduce manual monitoring costs and have shown promising results in accident detection research. However, existing methods often fail to consider learning the overall characteristics of road accidents and exhibit weak feature extraction capabilities, particularly in detecting distant small targets, thereby making it challenging to accurately identify traffic accidents from the perspective of road surveillance. To address these challenges, in this paper, we designed a data collection and preprocessing system based on road surveillance and established a dataset with abundant samples of road accidents for training and validation. Additionally, we propose a realtime, accurate road accident detection method named YOLOSED, which is an improvement over YOLOv8. We introduce the SPD-Conv and SE attention block to enhance the model's ability to retain more details in feature extraction and improve inter-channel modeling capability. YOLO-SED compensates for the lack of feature extraction for small targets at long distances and enhances the ability to distinguish the overall features of accidents from road scenes in the surveillance view. Experiments on real datasets demonstrate the effectiveness of our approach, with YOLO-SED yielding significant improvements compared to the baseline model.

关键词： Industrial Internet of Things Surveillance-based accident detection SPD-Conv SE attention block

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t-path Based Reliable and real-time Routing in Battery-Free Wireless Sensor Network 23

t-path Based Reliable and Real-time Routing in Battery-Free ...

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23rd ieee international conference on High Performance computing and Communications, 7th ieee international conference on Data Science and systems, 19th ieee international conference on Smart City and 7th ieee international conference on Dependability in Sensor, Cloud and Big Data systems and applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Chen, Kunyi Gao, Hong Cheng, Siyao Chen, Quan Li, Jianzhong Harbin Institute of Technology China Guangdong University of Technology China

ISBN: (纸本)9781665494571

Wireless Sensor Networks are deployed to sense data from the physical world and transmit these data to the server(s). To reduce the maintenance cost of replacing batteries, Energy-Harvesting nodes are developed which can get recharged from Solar Power, Radio Frequency, and other environmental energy resources. We term the network consist of energy-harvesting nodes as Battery-Free WSNs (BF-WSNs). Environmental energy is inherently time-varying and exhibits a high degree of heterogeneity. These features bring challenges to routing design. In this paper, we focus on the reliability and real-time issues and propose an opportunistic strategy as time synchronization is hard and expensive to reach in BF-WSN. We first model BF-WSNs as a directed weighted multigraph and propose a novel definition called t-path to describe the time-varying availability feature of BF-nodes. Further, we show that the problem is NP-Hard. We present a centralized approximate algorithm and prove that the ratio bound is 2 under two conditions. This paper also presents a distributed algorithm. Comprehensive simulations have been performed to evaluate the performance of the proposed algorithms and the results show that the efficiency of our algorithms. © 2021 ieee.

关键词： Wireless sensor networks Simulation Reliability theory Approximation algorithms Routing Reliability engineering real-time systems

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15.8 A 22nm 10.8Mb embedded STT-MRAM Macro Achieving over 200MHz Random-Read Access and a 10.4MB/s Write Throughput with an In-Field Programmable 0.3Mb MTJ-OTP for High-End MCUs

15.8 A 22nm 10.8Mb Embedded STT-MRAM Macro Achieving over 20...

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2024 ieee international Solid-State Circuits conference, ISSCC 2024

作者： Ogawa, Tomoya Matsubara, Ken Taito, Yasuhiko Saito, Tomoya Izuna, Masayuki Takeda, Koichi Kaneda, Yoshinobu Shimoi, Takahiro Mitani, Hidenori Ito, Takashi Kono, Takashi Renesas Electronics Tokyo Japan

ISBN: (纸本)9798350306200

As the range of applications for industrial and ioT devices expands, such as with the integration of Al, higher-speed information processing will be desired for endpoint devices to reduce network latency and power consumption, due to network communication with the cloud. For sophisticated control and real-time processing, high-processing performance with GHz-class CPU clock frequencies and robust security is required in the crossover area located on the performance boundary between high-end microcontroller units (MCUs) and low-end microprocessor units (MPUs) [1]. One possible solution for such MCUs is to apply DVFS, which dynamically controls the supply voltage and the clock frequency depending on the processing load. In terms of process scaling, finer logic processes, 22 nm and beyond are being applied, but the development and manufacturing of embedded flash memories (eFlash) become more difficult and expensive [1]. For this reason, various types of emerging memories have been developed, and an embedded STT-MRAM (eMRAM) has begun to be used for mass production, since eMRAM has advantages such as high reliability and fast write speed compared to other emerging memories. © 2024 ieee.

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Live Demonstration: A fully embedded adaptive real-time hand gesture classifier leveraging HD-sEMG and deep learning

Live Demonstration: A fully embedded adaptive real-time hand...

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2023 ieee Biomedical Circuits and systems conference, BioCAS 2023

作者： Isabel, Xavier Labbe, Thomas Chamberland, Felix Buteau, Etienne Campbell, Evan Cote-Allard, Ulysse Tam, Simon Fortier, Paul Campeau-Lecours, Alexandre Boukadoum, Mounir Scheme, Erik Gosselin, Benoit Laval University Electrical Engineering and Computer Engineering Department Canada University of New-Brunswick Institute of Biomedical Engineering Canada University of Oslo Department off Technology Systems Norway Laval University Mechanical Engineering Department Canada University of Quebec at Montreal Computer Science Department Canada

ISBN: (纸本)9798350300260

In a fully immersive virtual reality setting, visitors will actively participate in our demonstration, engaging in an interactive videogame utilizing our cutting-edge HD-sEMG Bracelet. Prior to this, they will undergo state-of-the-art training to personalize their machine learning gesture recognition model. The gameplay will be broadcast on a computer monitor, allowing onlookers to observe while the demonstrator explains the core concept of our work. By wearing the bracelet, the player gains control over their limb in a game inspired by Simon Says. Throughout the demonstration, visitors will gain firsthand experience and knowledge about the forthcoming advantages of HD-sEMG for control applications, such as prosthesis control. They will also discover how the EMaGer HD-sEMG Bracelet capitalizes on its uniform and high electrode density to enhance the reliability of hand gesture recognition models utilized in myoelectric prosthesis control. © 2023 ieee.

关键词： Armband Control Convolution Deep Learning Edge computing Electromyography Gesture HD-EMG Machine Learning Myoelectric Neural Network

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Forest Fire Prevention and Detection System 5

Forest Fire Prevention and Detection System

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5th ieee international conference on Power, Electronics and Computer applications, ICPECA 2025

作者： Jayakody, Lasitha Edirisinghe, Chamathka Senasinghe, Tharush Dissanayake, Shaminda Gamage, Narmada Pandithage, Dinithi Wijayaraja, Jaliya L. Sri Lanka Institute of Information Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331533694

Forest fires pose significant threats to both the environment and human life, necessitating the development of advanced detection and prevention systems. In this study, we propose an integrated IoT (Internet of Things)-based system for early detection and management of forest fires. The system incorporates a range of sensors including heat, gas, and thermal imaging sensors, along with IoT technology for real-time monitoring and communication. Utilizing image processing techniques and (ML) Machine Learning algorithms, the system can accurately detect fires, predict their spreading patterns, and analyze environmental conditions. Key features include the deployment of disposable heat sensors for temperature monitoring, IoT sensors for gas detection and weather monitoring, and a centralized processing node for data analysis. Alerts are sent to nearby authorities and residents, while automated firefighting devices are activated to suppress fires until backup arrives. Experimental results demonstrate the effectiveness of the proposed system in early detection and prevention of forest fires. © 2025 ieee.

关键词： Temperature sensors

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Recommendation systems in Healthcare: Challenges, Limitations, and Its applications

Recommendation Systems in Healthcare: Challenges, Limitation...

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2024 ieee international conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Nguyen, Luong Vuong FPT University Department of Artificial Intelligence Danang550000 Viet Nam

ISBN: (纸本)9798331530839

The rapid advancement of healthcare technologies has led to the increasing integration of recommendation systems in various aspects of medical practice. This survey aims to provide a comprehensive overview of the current state-of-the-art recommendation systems in healthcare, examining their applications, underlying techniques, and the challenges they face. This paper highlights their role in personalized treatment plans, drug recommendations, diagnostic support, and lifestyle man-agement by categorizing and analyzing diverse recommendation methods, including content-based filtering, collaborative filtering, and hybrid approaches. Furthermore, we explore the types of data utilized, ranging from electronic health records to wearable device outputs, and discuss the significance of machine learning, deep learning, and hybrid models in enhancing recommendation accuracy and efficacy. Through detailed case studies and com-parative analyses, we illustrate successful implementations and extract lessons from less effective attempts. Finally, we outline emerging trends and future directions, emphasizing the potential of technologies like blockchain, federated learning, and real-time data analysis from IoT devices. © 2024 ieee.

关键词： Hybrid Models Recommendation systems Recommender systems in Healthcare

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Discrete Geometric Coded Data Layout for Large-scale Object Storage systems 21

Discrete Geometric Coded Data Layout for Large-scale Object ...

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21st ieee international Symposium on Parallel and Distributed Processing with applications, 13th ieee international conference on Big Data and Cloud computing, 16th ieee international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Tian, Yi Xu, Guangping Yang, Hongzhang Ni, Yue Cao, JiaXin Yang, Lei Tianjin University of Technology School of Computer Science and Engineering Tianjin China Novel Software Technology Tianjin Key Laboratory of Intelligence Computing Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China Information Technology Co. Ltd. China

ISBN: (纸本)9798350329223

Regenerating codes are new network codes proposed to reduce the data required for fault repair, which can improve the recovery efficiency of faulty nodes in data storage systems. However, unlike Reed-Solomon code, which repairs at the granularity of bytes, regenerating codes require data stored in large chunks but leads to severe read amplification, which reads out excess data when degraded read objects and increases degraded read time. That reflects a mutual constraint between improving recovery efficiency and degraded read performance, as manifested in the amplification of data reads, a vital issue considered in this *** solve this problem, we propose a new type of data layout - discrete geometric, which splits the object into a series of geometric sequences of data blocks. They are placed discretely into corresponding containers on the disks at different nodes, with containers of the same size made into a strip for encoding. The discrete characteristic ensures lower repair costs for degraded reads. The geometric characteristic ensures the repair performance of regenerating codes by large blocks, and read amplification can be mitigated through small blocks. To reduce IOPS for discrete geometric, we propose Discrete Geometric-Locally Regenerating Codes (DG-LRCs), guaranteeing lower degraded read latency while improving recovery *** results show that the degraded read time of DG-LRCs compared to regenerating codes combined with geometric partitioning is 22.56% lower at 2Gbpsand 60.56% lower at 4Gbps, and the recovery performance is 7.04 times better than that of RS code. © 2023 ieee.

关键词： Efficiency

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AI/ML Deployment at the Edge for Run-by-Run and real-time Analysis 30

AI/ML Deployment at the Edge for Run-by-Run and Real-Time An...

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30th international Symposium on Semiconductor Manufacturing, ISSM 2024

作者： Vasquez, Anthony J Drake, Kyle Bramante, James Inficon Research Data Scientist Applications and Research Syracuse United States Inficon Senior Data Scientist Smart Manufacturing Syracuse United States

ISBN: (纸本)9798331510589

As semiconductor manufacturing advances to smaller technology nodes, achieving high yield demands precise, stable processes supported by advanced sensors and responsive control systems. Conventional AI/ML model deployment on a central server limits time-sensitive applications critical for real-time and run-by-run (RbR) fault detection and control (FDC). This paper introduces a system for deploying AI/ML models at the edge, integrating the low-latency Libtorch library with INFICON's FabGuard. This design facilitates real-time analysis, enhances FDC capabilities, and enables seamless integration of user-developed models without requiring additional MLOps infrastructure. We demonstrate the system's utility through two applications. First, a deep learning-based endpoint detection model processes complex multivariate sensor signals for low open area etch processes, enabling real-time detection where traditional univariate methods fail. Second, a multivariate anomaly detection model deployed at the edge interprets continuous signals from a quartz crystal microbalance (QCM) for RbR interdiction. These examples highlight the system's ability to extract actionable insights from complex sensor data, enhancing yield and process reliability in semiconductor manufacturing. © 2024 ieee.

关键词： Quartz crystal microbalances

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AI-Driven HR Optimization Strategies in Finance and Marketing: Methodological Framework and applications

AI-Driven HR Optimization Strategies in Finance and Marketin...

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2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Lekshmi, R.S. Mary, V. Sheela Arasuraja, G. Krishnamoorthy, V. Kaliappan, S. Selvameena, R. St.Joseph's College of Engineering Department of Management Studies Tamil Nadu Chennai India Aarupadai Veedu Institute of Technology Vinayaka Mission's Research Foundation Department of Management Studies Tamil Nadu Chennai India Kongu Engineering College Department of Management Studies Perundurai Tamil Nadu Erode India Lovely Professional University Division of Research and Development Punjab Phagwara India Dr.M.G.R Educational and Research Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331543617

The proposed systems that include AI and the IoT present innovative financial and marketing opportunities. AI analytics capabilities and networked devices with the capability to provide real-time data mean the possibility to significantly improve recruitment, employee engagement, performance management, and retention. It combines natural language processing and machine learning algorithms, offering personalized recommendations, and in the vision insights, and in the case of environmental sensors, smart devices, and wearable health monitors, it facilitates accumulation of significant data. This framework will integrate IoT with AI to enhance HR procedures: more engaging, efficient, and enjoyable work environment. Apart from better decision-making and automated occupations, it also advocates for a data-driven approach to HRM. Further, it can result in the formation of intelligent and responsive HR systems that have the potential of improving organizational effectiveness and work productivity. © 2024 ieee.

关键词： Marketing

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