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2nd International Conference on Multidisciplinary engineering and Applied Science, ICMEAS 2023

作者： Ifijeh, Ayodele H. Adetiba, Emmanuel Adewale, Adeyinka Thakur, Surrendra Moyo, Sibusiso Emmanuel, Dare O. Ukpong, Udeme C. Dept. of Electrical and Information Engineering Nigeria HRA Institute for Systems Science Durban University of Technology Durban South Africa Covenant University Dept. of Electrical and Information Engineering Ota Canaanland Nigeria Durban University of Technology KZN E-skills CoLab Durban4001 South Africa School for Data Science and Computational Thinking Stellenbosch University Department of Mathematical Sciences Stellenbosch7602 South Africa

ISBN: (纸本)9798350358834

Advancement in wireless transmission has enabled the communication among multiple devices all over the world, as well as expanding the frontiers of wireless Internet access. As a result of these advancement, numerous devices are questing for wireless connectivity thereby congesting the existing spectrum. If new measures are not taken to reduce depletion of the reserved radio resource, new allocations of wireless Internet access will be impossible, thereby risking the development of the entire wireless ecosystem. The demands for resourceful spectrum consumption have continually driven researchers and stakeholders in the wireless industry to search for spare radio spectrum to offer affordable and reliable wireless connectivity. The TV broadcasting is not fully utilizing the spectrum in some geographical zone. These TV unused spaces called TV White Spaces (TVWS), are well-thought-out to be the probable future resolutions for unavailability of spectrum. The low frequency band (470 to 694 MHz) of the unutilized TV channel has exceptional broadcast features over extensive distances and enables transmissions through high-rise buildings, trees, and mountains. These features allow the applications of TVWS in Internet of Things (IoT), wireless sensor network, wireless connectivity, wireless switching, smart grid and cities, automated transportation, and logistics as well as connectivity in cloud infrastructure. This paper presents a study on the advantages and benefits of television white space as an alternative to wireless broadband connectivity. © 2023 IEEE.

关键词： Tall buildings

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Semantic Wireless Communications: Unveiling the Next Frontier in Intelligent Connectivity

Semantic Wireless Communications: Unveiling the Next Frontie...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Baduge, Dinith Primal P. Rathu Ponnimbaduge Perera, Tharindu D. Jayakody, Dushantha Nalin K. Sri Lanka Institute of Information Technology Faculty of Computing Dept. of CSE Malabe10115 Sri Lanka CIET Sri Lanka Institute of Information Technology Faculty of Engineering Malabe10115 Sri Lanka COPELABS Lusofona University Lisbon1749-024 Portugal The University of Melbourne Faculty of Engineering & IT Australia

ISBN: (纸本)9798331540685

Semantic communication, a multidisciplinary field, transforms communication technology by integrating semantic understanding and improving information flow efficiency. This study surveys various aspects of semantic communication to explore its multiple facets. This paper delves into the fundamental concepts of semantics in communication, emphasizing the importance of meaning extraction and interpretation. In this approach, The semantic problem focuses on the precision of conveying meaning, ensuring accurate information transmission while maintaining its original semantic relevance. This work presents an overview, and advancement to date, as well as identifies research issues and challenges in semantic communication. The paper, in principle, addresses the overview, technical issues, systems, and innovative technologies associated with semantic communication. The paper finally presents recommendations and future trends associated with this emerging future communication system. This provides a valuable reference and new avenues for future research in this direction. © 2024 IEEE.

关键词： Latent semantic analysis

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InfiniWolf: Energy Efficient Smart Bracelet for Edge Computing with Dual Source Energy Harvesting

arXiv

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arXiv 2020年

作者： Magno, Michele Wang, Xiaying Eggimann, Manuel Cavigelli, Lukas Benini, Luca Dept. of Information Technology and Electrical Engineering Eth Zürich Switzerland Dept. of Electrical Electronic and Information Engineering University of Bologna Italy

This work presents InfiniWolf, a novel multi-sensor smartwatch that can achieve self-sustainability exploiting thermal and solar energy harvesting, performing computationally high demanding tasks. The smartwatch embeds both a System-on-Chip (SoC) with an ARM Cortex-M processor and Bluetooth Low Energy (BLE) and Mr. Wolf, an open-hardware RISC-V based parallel ultra-low-power processor that boosts the processing capabilities on board by more than one order of magnitude, while also increasing energy efficiency. We demonstrate its functionality based on a sample application scenario performing stress detection with multi-layer artificial neural networks on a wearable multi-sensor bracelet. Experimental results show the benefits in terms of energy efficiency and latency of Mr. Wolf over an ARM Cortex-M4F micro-controllers and the possibility, under specific assumptions, to be self-sustainable using thermal and solar energy harvesting while performing up to 24 stress classifications per minute in indoor conditions. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy harvesting

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An OpenPLC-based Active Real-time Anomaly Detection Framework for Industrial control Systems

An OpenPLC-based Active Real-time Anomaly Detection Framewor...

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2022 Chinese Automation Congress, CAC 2022

作者： Zheng, Chengxu Wang, Xiaopeng Luo, Xiaoyu Fang, Chongrong He, Jianping Shanghai Jiao Tong University The Key Laboratory of System Control and Information Processing Ministry of Education of China And Shanghai Engineering Research Center of Intelligent Control and Management The Dept. of Automation Shanghai China NSFOCUS Technologies Group Co. Ltd Beijing China

ISBN: (纸本)9781665465335

In recent years, the design of anomaly detectors has attracted a tremendous surge of interest due to security issues in industrial control systems (ICS). Restricted by hardware resources, most anomaly detectors can only be deployed at the remote monitoring ends, far away from the control sites, which brings potential threats to anomaly detection. In this paper, we propose an active real-time anomaly detection framework deployed in the controller of OpenPLC, which is a standardized open-source PLC and has high scalability. Specifically, we add adaptive active noises to control signals, and then identify a linear dynamic system model of the plant offline and implement it in the controller. Finally, we design two filters to process the estimated residuals based on the obtained model and use χ2 detector for anomaly detection. Extensive experiments are conducted on an industrial control virtual platform to show the effectiveness of the proposed detection framework. © 2022 IEEE.

关键词： Anomaly detection

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State estimation and tracking problems: A comparison between Kalman filter and recurrent neural networks

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11th International Conference on Neural information Processing, ICONIP 2004

作者： Kumar Chenna, S. Jain, Yogesh Kr. Kapoor, Himanshu Bapi, Raju S. Yadaiah, N. Negi, Atul Seshagiri Rao, V. Deekshatulu, B.L. Dept. Of Computer and Information Sciences University of Hyderabad India Honeywell Technology Solutions Bangalore India Dept. Of Electrical Engineering JNTU Hyderabad India SHAR Computer Facility ISRO SriharikotaAP India

ISBN: (纸本)3540239316

The aim of this paper is to demonstrate the suitability of recurrent neural networks (RNN) for state estimation and tracking problems that are traditionally solved using Kalman Filters (KF). This paper details a simulation study in which the performance of a basic discrete time KF is compared with that of an equivalent neural filter built using an RNN. Real time recurrent learning (RTRL) algorithm is used to train the RNN. The neural network is found to provide comparable performance to that of the KF in both the state estimation and tracking problems. The relative merits and demerits of KF vs RNN are discussed with respect to computational complexity, ease of training and real time issues. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Kalman filters

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Ultra-Efficient On-Device Object Detection on AI-Integrated Smart Glasses with TinyissimoYOLO

arXiv

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arXiv 2023年

作者： Moosmann, Julian Bonazzi, Pietro Li, Yawei Bian, Sizhen Mayer, Philipp Benini, Luca Magno, Michele Dept. of Information Technology and Electrical Engineering ETH Zürich Switzerland Dept. of Electrical Electronic and Information Engineering University of Bologna Italy

Smart glasses are rapidly gaining advanced functionality thanks to cutting-edge computing technologies, accelerated hardware architectures, and tiny Artificial Intelligence (AI) algorithms. Integrating AI into smart glasses featuring a small form factor and limited battery capacity is still challenging when targeting full-day usage for a satisfactory user experience. This paper illustrates the design and implementation of tiny machine-learning algorithms exploiting novel low-power processors to enable prolonged continuous operation in smart glasses. We explore the energy- and latency-efficient of smart glasses in the case of real-time object detection. To this goal, we designed a smart glasses prototype as a research platform featuring two microcontrollers, including a novel milliwatt-power RISC-V parallel processor with a hardware accelerator for visual AI, and a Bluetooth low-power module for communication. The smart glasses integrate power cycling mechanisms, including image and audio sensing interfaces. Furthermore, we developed a family of novel tiny deep-learning models based on YOLO with sub-million parameters customized for microcontroller-based inference dubbed TinyissimoYOLO v1.3, v5, and v8, aiming at benchmarking object detection with smart glasses for energy and latency. Evaluations on the prototype of the smart glasses demonstrate TinyissimoYOLO’s 17ms inference latency and 1.59mJ energy consumption per inference while ensuring acceptable detection accuracy. Further evaluation reveals an end-to-end latency from image capturing to the algorithm’s prediction of 56ms or equivalently 18 frames per seconds (FPS), with a total power consumption of 62.9mW, equivalent to a 9.3 hours of continuous run time on a 154mAh battery. These results outperform MCUNet (TinyNAS+TinyEngine), which runs a simpler task (image classification) at just 7.3 FPS per second. © 2023, CC BY.

关键词： Object detection

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A Passive and Asynchronous Wake-up Receiver for Acoustic Underwater Communication

A Passive and Asynchronous Wake-up Receiver for Acoustic Und...

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International Symposium on Power Electronics, Electrical Drives, Automation and Motion, SPEEDAM

作者： Lukas Schulthess Philipp Mayer Luca Benini Michele Magno Dept. of Information Technology and Electrical Engineering ETH Zürich Switzerland Dept. of Electrical Electronic and Information Engineering University of Bologna Italy

ISBN: (数字)9798350387599

ISBN: (纸本)9798350387605

Establishing reliable data exchange in an underwater domain using energy and power-efficient communication methods is crucial and challenging. Radio frequencies are absorbed by the salty and mineral-rich water and optical signals are obstructed and scattered after short distances. In contrast, acoustic communication benefits from low absorption and enables communication over long distances. Underwater communication must match low power and energy requirements as underwater sensor systems must have a long battery lifetime and need to work reliably due to their deployment and maintenance cost. For long-term deployments, the sensors' overall power consumption is determined by the power consumption during idle state. It can be reduced by integrating asynchronous always-on wake-up circuits with nano-watt power consumption. However, this approach does reduce but not eliminate idle power consumption, leaving a margin for improvement. This paper presents a passive and asynchronous wake-up receiver for acoustic underwater communication enabling zero-power always-on listening. Zero-power listening is achieved by combining energy and information transmission using a low-power wake-up receiver that extracts energy out of the acoustic signal and eliminates radio frontend idle consumption. In-field evaluations demonstrate that the wake-up circuit requires only 63µW to detect and compare an 8-bit UUID at a data rate of 200 bps up to a distance of 5m and that the needed energy can directly be extracted from the acoustic signal.

关键词： Underwater communication Power demand Acoustics Sensor systems Power electronics Optical receivers Maintenance

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Investigation of the significance of the plaque morphology evolution in plaque rupture events using computational biomechanics 46

Investigation of the significance of the plaque morphology e...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Pleouras, Dimitrios S. Siogkas, Panagiotis K. Potsika, Vassiliki T. Tsakanikas, Vasilis D. Dimos, Sokratis S. Mantzaris, Michalis D. Koncar, Igor Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering IoanninaGR45110 Greece University of Belgrade Faculty of Medicine Department of Vascular and Endovascular Surgery Belgrade Serbia University of Ioannina Unit of Medical Technology and Intelligent Information Systems Biomedical Research Institute Forth Dept. of Materials Science and Engineering IoanninaGR45110 Greece

ISBN: (纸本)9798350371499

Forwarded by the technological urge of this era, several computational methods are implemented to give further insights into possible outcomes of diseases. In this context, atherosclerosis, which is one of the most fatal diseases nowadays, is treated alike, where several computational models are proposed annually allowing for the evaluation of several outcomes for patient specific cases. Among them, one of the most significant models is able to predict the atherosclerotic evolution over time. In this proof-of-concept study, we aim to investigate the effect of plaque morphology on plaque rupture in a two-case scenario - a longitudinal and a bulk plaque evolution in 3D-reconstructed patient-specific carotids arteries. Our approach is based on a three-step process: i) the implementation of a state-of-the-art plaque growth model that predicts evolving and new plaques in real patient specific carotid arteries, ii) the selection of 2 patient cases, one with longitudinal plaque evolution and one with bulk plaque evolution and, finally, iii) the evaluation the maximum principal stress over the plaques and the endothelium layers to assess the plaque rupture risk. The results indicate that the evolving plaques towards the lumen, not only cause stenoses but also are more prone to rupture. Clinical relevance- This proof-of-concept work establishes that the plaques that grow towards the luminal border present with a higher risk of potential rupture compared to plaques that grow longitudinally, thus giving valuable insights to clinicians for important decision making regarding potential endarterectomy procedures. © 2024 IEEE.

关键词： 3D reconstruction

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Induction Furnace Virtual Temperature Sensor Based on Robust Adaptive Estimation 21

Induction Furnace Virtual Temperature Sensor Based on Robust...

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21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Sa'Id, Waladin K. Jassim, Marwa Fadhel Raafat, Safanah M. Jasim, Omar F. Al-Mustaqbal University College BME Dept. Babylon Iraq University of Technology-Iraq Control and Systems Engineering Dept Baghdad Iraq Grid Solutions Ltd GE Vernova Stafford United Kingdom

ISBN: (纸本)9798350374131

This paper focuses on the development of a virtual thermal sensor for estimating the metal charge temperature in an induction metal melting furnace. In foundry workshops, it is crucial to accurately measure the existing temperature of the metal charge. However, continuous temperature measuring instruments are typically unavailable for such harsh working operating conditions. The proposed method is used consistently to estimate the charge melting temperature using a single temperature sensor inserted in an easily accessible location in the furnace lining. The virtual sensor is created as a model-based dynamical observer, which means it employs a mathematical model to estimate the charge temperature based on the furnace's lining temperature measurement. The authors developed a Multiple Model Adaptive Estimator (MMAE) to assure the reliability of using the virtual sensor. This estimator is designed to account for uncertainties such as variations in initial conditions, power disruptions during the melting process, and variations in average charge temperature resulting from density differences. Therefore, this paper presents an efficient method for estimating the temperature of metal charges in an induction furnace using a virtual sensor, which can be a valuable tool in foundry workshops where continuous temperature measurements are not readily available. © 2024 IEEE.

关键词： Induction heating

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Rough Set based features ensemble learning

Rough Set based features ensemble learning

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WCICA 2004 - Fifth World Congress on Intelligent control and Automation, Conference Proceedings

作者： Wang, Quande Wang, Xianjia Huang, Hao Lu, Yuchang School of Electronics Information Wuhan University WuHan 430072 China Institute of System Engineering Wuhan University WuHan 430072 China Dept. of Comp. Sci. and Technology Beijing Institute of Technology BeiJing 100084 China Dept. of Comp. Sci. and Technology Tsinghua University BeiJing 100084 China

The minimal reduct of features in Rough Set theory is a criterion to select the best feature subset based it's ability to discrimination objects. In this paper, a multi-classes features ensemble learning algorithm based on feature reduct is presented. The algorithm maintains a weight distribution in train set, which is used to compute minimal approximate reduct of features in each iteration of algorithm. Weak classifiers are constructed from the minimal approximate reduct and the weight distribution is updated according to examples in the train set which have been misclassified. The ensemble classifier are constructed by weighted votes of all weak classifiers. The result sof testing in several dataset show that the algorithm has high accuracy of prediction and strong ability of generalization.

关键词： Rough set theory

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