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arXiv 2022年

作者： Al Makdah, Abed AlRahman Krishnan, Vishaal Katewa, Vaibhav Pasqualetti, Fabio Department of Electrical and Computer Engineering The Department of Mechanical Engineering The University of California Riverside United States School of Engineering and Applied Sciences Harvard University United States Department of Electrical Communication Engineering The Indian Institute of Science Bangalore India

In this work, we revisit the Linear Quadratic Gaussian (LQG) optimal control problem from a behavioral perspective. Motivated by the suitability of behavioral models for data-driven control, we begin with a reformulation of the LQG problem in the space of input-output behaviors and obtain a complete characterization of the optimal solutions. In particular, we show that the optimal LQG controller can be expressed as a static behavioral-feedback gain, thereby eliminating the need for dynamic state estimation characteristic of state space methods. The static form of the optimal LQG gain also makes it amenable to its computation by gradient descent, which we investigate via numerical experiments. Furthermore, we highlight the advantage of this approach in the data-driven control setting of learning the optimal LQG controller from expert demonstrations. Copyright © 2022, The Authors. All rights reserved.

关键词： State space methods

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Hierarchical Evolutionary Optimization with Predictive Modeling for Stable Delay-Constrained Routing in Vehicular Networks

Hierarchical Evolutionary Optimization with Predictive Model...

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Consumer Life Tech (ICLT), IEEE

作者： Zhiou Zhang Weian Guo Qin Zhang Meibin Lin Dongyang Li School of Computer Science and Engineering University of New South Wales Sydney Australia Sino-German College of Applied Sciences Tongji University Shanghai China Department of Information Engineering Fuzhou Polytechnic College Fuzhou China School of Traffic Engineering Fuzhou Polytechnic College Fuzhou China

ISBN: (数字)9798331519339

ISBN: (纸本)9798331519346

Vehicular Ad Hoc Networks (VANETs) are a cornerstone of intelligent transportation systems, facilitating real-time communication between vehicles and infrastructure. However, the dynamic nature of VANETs introduces significant challenges in routing, especially in minimizing communication delay while ensuring route stability. This paper proposes a hierarchical evolutionary optimization framework for delay-constrained routing in vehicular networks. Leveraging multi-objective optimization, the framework balances delay and stability objectives and incorporates adaptive mechanisms like incremental route adjustments and LSTM-based predictive modeling. Simulation results confirm that the proposed framework maintains low delay and high stability, adapting effectively to frequent topology changes in dynamic vehicular environments.

关键词： Adaptation models Network topology Simulation Predictive models Routing Stability analysis Real-time systems Delays Vehicle dynamics Optimization

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Synthetic frequency dimension state coupling in modulated LNOI ring cavity devices

Synthetic frequency dimension state coupling in modulated LN...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Balčytis, Armandas Dinh, Xuan Hiep Ozawa, Tomoki Ota, Yasutomo Baba, Toshihiko Iwamoto, Satoshi Mitchell, Arnan Nguyen, Thach G. Integrated Photonics and Applications Centre RMIT University MelbourneVIC3000 Australia Advanced Institute for Materials Research Tohoku University Sendai9808577 Japan Department of Applied Physics and PhysicoInformatics Keio University Yokohama2238522 Japan Department of Electrical and Computer Engineering Yokohama National University 795 Tokiwadai Hodogayaku Yokohama2408501 Japan Research Center for Advanced Science and Technology The University of Tokyo 461 Komaba Meguroku Tokyo1538904 Japan

ISBN: (纸本)9781957171258

We created a modulatorequipped thinfilm lithium niobate integrated ring resonator simulating a synthetic frequency dimension tightbinding model. It produces frequency states spaced over a 600 GHz bandwidth. Reconfigurable coupling enables simulation of disparate twodimensional interactions. © Optica Publishing Group 2023 © 2023 The Author(s)

关键词： Niobium compounds

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Temperature Sensor Technologies for Enhanced Insulation Durability in Military Electrical Machines

Temperature Sensor Technologies for Enhanced Insulation Dura...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： B. Annapurna Raj Kumar Pawan Kumar V.J. Vijayalakshmi G. Premananthan Ashwini Ranjit Nawadkar Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Department of Applied Sciences Invertis University Bareilly Uttar Pradesh CCSIT TMU Department of Electrical And Electronics Engineering Karpagam Academy of Higher Education Coimbatore Department of Electronics and Communication Engineering Karpagam College of Engineering Coimbatore AI & Ds Vishwakarma Institute of Information Technology Pune

Manufacturer of aircraft and engines have proposed the More Modern Airplane and More Modern Engine idea to lessen the dependency lowering fuel costs. Unfortunately, these ideas have created a need for higher grid current, which puts good dielectric systems under pressure. Under more demanding circumstances, found in the engines, the insulating system must tolerate this operating voltage. In more electric aero planes, power supplies play a crucial role, and because lightweight and space are limited, their dependability must be great. Materials will be employed as), since conventional hydrocarbons cannot sustain such a body heat. in various high-voltage situations for a considerable amount of time, their electrical behavior is still not entirely known, particularly when it comes to the insulators for aircraft electrical machines. The preliminary testing findings of three potential inorganic wire candidates' fundamental electrical performance will be covered in depth in this study. In order to assess important ageing processes utilized in an aeronautical environment, the findings will serve as a standard for further experiments. Every potential cable's electrical insulation performance is shown and compared. To investigate variations in the insulating layer architectures of the materials, SEM scans has been done. Afterwards, analysis was performed using both the sensor readings and indeed the scan findings. A small number of temperature cycles and the cold air circumstances where the real system is anticipated to function have been used in all of the experiments.

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Enhancing Chewing Detection with a CNN-BiLSTM Network Utilizing IMU and Photoplethysmography Data from an Earable Device

Enhancing Chewing Detection with a CNN-BiLSTM Network Utiliz...

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IEEE International Conference on Software engineering and Service sciences (ICSESS)

作者： Sakorn Mekruksavanich Wikanda Phaphan Phaisarn Jeefoo Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand Geographic Information System School of Information and Communication Technology University of Phayao Phayao Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350389166

ISBN: (纸本)9798350389173

Detecting chewing is vital to tracking eating habits, which is necessary to preserve good well-being and health. Advancements in wearable sensors have recently enabled cap-turing a wide range of sensor data, including signals from inertial measurement units (IMUs) and photoplethysmography (PPG). This research presents a new deep-learning design that efficiently detects chewing occurrences employing IMU and PPG sensor data acquired from an early instrument. The design includes convolutional neural networks (CNN) and bidirectional long short-term memory (LSTM). The CNN-BiLSTM network presented in this study combines the advantages of both CNN and BiLSTM to capture spatial and temporal characteristics from the sensor data effectively. The CNN layers extract localized patterns and characteristics from the IMU and PPG data, whereas the BiLSTM layers represent the extended relationships and time-based changes in chewing behavior. By integrating these two formidable designs, our network attains a high level of resilience and precision in detecting chewing. We assess the efficacy of the CNN-BiLSTM network using a standardized dataset obtained from several individuals wearing wearable devices while engaging in eating events. The dataset comprises IMU and PPG sensor data that have been annotated with associated chewing sessions. The experimental findings clearly show that the suggested network outperforms the current leading approaches, obtaining an accu-racy of 98.18 % and an F1-score of 98.29 %, the highest recorded. The CNN-BiLSTM network performs better than baseline models such as CNN, LSTM, BiLSTM, GRU, and BiGRU, emphasizing the efficacy of integrating spatial and temporal modeling for chewing identification.

关键词： Performance evaluation Recurrent neural networks Measurement units Photoplethysmography Convolutional neural networks Wearable devices Long short term memory Wearable sensors Software engineering Resilience

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Gym Exercise Recognition Using Deep Convolutional and LSTM Neural Network Based on IMU Sensor Data

Gym Exercise Recognition Using Deep Convolutional and LSTM N...

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International Technical Conference o Circuits/Systems, computers and Communications (ITC-CSCC)

作者： Sakorn Mekruksavanich Datchakorn Tancharoen Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Faculty of Engineering and Technology Panyapiwat Institute of Management Nonthaburi Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350379051

ISBN: (纸本)9798350379068

This study delves into the utilization of wearable devices equipped with inertial measurement units (IMUs) to gather data for human activity recognition. It explores the application of deep neural networks for automatically identifying gym workouts using IMU sensors. Our methodology involves developing a framework that integrates convolutional neural networks (CNNs) to extract features from raw sensor data, followed by using long short-term memory (LSTM) recurrent neural networks to classify sequences. IMU data from accelerometers and gyroscopes are collected from 10 individuals performing 30 standard gym routines. The CNN-LSTM pipeline is supervised on a comprehensive dataset comprising multiple sensors and subjects to distinguish between different workouts accurately. During the evaluation, the CNN-LSTM model achieved an accuracy of 93.81 % in categorizing 30 workout categories based solely on accelerometer data. Through augmentation, this accuracy is further improved to 95.75 %. This solution outperforms independently utilized CNN and LSTM models and traditional machine learning approaches. Detailed assessments offer valuable insights into the benefits of combining diverse sensor types and model architectures for robust exercise classification. This research raises a precise and dependable wearable system for identifying gym exercises using deep neural networks. The findings suggest promising avenues for future exploration in human activity recognition, mainly focusing on utilizing on-body sensors and deep learning to analyze more complex human movements.

关键词： Accelerometers Accuracy Sensor phenomena and characterization Feature extraction Sensors Convolutional neural networks Human activity recognition

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On the Value of Project Productivity for Early Effort Estimation

arXiv

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arXiv 2022年

作者： Azzeh, Mohammad Nassif, Ali Bou Elsheikh, Yousef Angelis, Lefteris Department of Data Science Princess Sumaya University for Technology Amman Jordan Department of Computer Engineering University of Sharjah Sharjah United Arab Emirates Department of Computer Science Applied Science Private University Jordan School of Informatics Aristotle University of Thessaloniki Thessaloniki Greece

In general, estimating software effort using a Use Case Point (UCP) size requires the use of productivity as a second prediction factor. However, there are three drawbacks to this approach: (1) there is no clear procedure for predicting productivity in the early stages, (2) the use of fixed or limited productivity ratios does not allow research to reflect the realities of the software industry, and (3) productivity from historical data is often challenging. The new UCP datasets now available allow us to perform further empirical investigations of the productivity variable in order to estimate the UCP effort. Accordingly, four different prediction models based on productivity were used. The results showed that learning productivity from historical data is more efficient than using classical approaches that rely on default or limited productivity values. In addition, predicting productivity from historical environmental factors is not often accurate. From here we conclude that productivity is an effective factor for estimating the software effort based on the UCP in the presence and absence of previous historical data. Moreover, productivity measurement should be flexible and adjustable when historical data is available. Copyright © 2022, The Authors. All rights reserved.

关键词： Productivity

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Performance Evaluation of Fully Generalized Spatial Modulation with Hybrid Beamforming in Vehicular Networks: mmWave vs. IEEE 802.11ad

Performance Evaluation of Fully Generalized Spatial Modulati...

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IEEE International Symposium on Telecommunication Technologies (ISTT)

作者： Sheemaa Ali Mohamed Hashim Rudzidatul Akmam Dziyauddin Mohd Azri Bin Mohd Izhar Norhudah Seman Sharul Kamal Abd. Rahim Periklis Chatzimisios Faculty of Artificial Intelligence Universiti Teknologi Malaysia Kuala Lumpur Malaysia Computer Science Department Applied College in Khamis Mushait King Khalid University Abha Saudi Arabia Faculty of Electrical Engineering Universiti Teknologi Malaysia Johor Malaysia Department of Information and Electronic Engineering International Hellenic University Thermi Greece

ISBN: (数字)9798350392074

ISBN: (纸本)9798350392081

The increasing need for high data rates in vehicular networks necessitates the development of innovative technologies to improve spectral efficiency (SE) and guarantee the reliability of cellular connections. An innovative approach that uses an integration of Fully Generalized Spatial Modulation (FGSM) with hybrid beamforming (H-FGSM) to improve vehicular communication, exhibiting notable enhancements in SE. This paper explores the utilization of FGSM in conjunction with hybrid beamforming within vehicular communication systems, specifically emphasizing two predominant frameworks: millimeter-wave (mmWave) and IEEE 802.11ad. It employs a complete performance analysis to investigate the effects of H-FGSM on the aforementioned communication frameworks. It is worth mentioning that the mmWave framework has a superior SE of 7bps/Hz in comparison to IEEE 802.11ad, in addition to extended coherence time by 3 times and decreased frequency of adjustments to channel state information (CSI). These characteristics render mmWave very suitable for dynamic vehicular environments that require reliable communication routes. Consequently, the integration of FGSM with hybrid beamforming presents a potentially viable approach for addressing the requirements of advanced intelligent transportation systems in future vehicle networks, which leads to the foundation for future investigations aim at enhancing the efficiency of mmWave communication systems in dynamic vehicular situations.

关键词： Time-frequency analysis Array signal processing Spectral efficiency Simulation Millimeter wave technology Modulation Telecommunication network reliability Millimeter wave communication Vehicle dynamics Vehicle-to-everything

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Use of digital platforms for enhancing academic performance. Systematic Review

Use of digital platforms for enhancing academic performance....

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International IEEE Conference and Workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： Cristina Anamaria Costescu Mălina Șogor Sabina Pălimariu Jozsef Katona Ioana Tufar Carla Maria Avram Special Education Department Babeș-Bolyai University Cluj-Napoca Romania Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

The purpose of this study was to determine the effect of technology-based interventions on academic performance. To achieve this purpose a systematic review was conducted. Within the scope of this study EBSCO, Springer, ProQuest, science Direct, Wiley Online Library, Taylor and Francis Online, databases were scanned, and 18 studies were included in the review. Our results show that the iPad is the most preferred digital device for enhancing math and reading skills and significant results can be seen when used. Similar positive effects were observed with other technological devices, reinforcing the effectiveness of technology as a tool for supporting academic performance. This evidence underscores the value of incorporating digital devices in educational strategies, particularly for improving key academic skills.

关键词： Performance evaluation Power engineering Databases Personal digital devices Tablet computers Libraries

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Morphological Investigation and Ph Sensing Properties of β-Ga2O3 Egfet-Ph Sensor

SSRN

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SSRN 2023年

作者： Wuu, Dong-Sing Yadlapalli, Bharath Kumar Chou, Hsin-Yu Chiang, Jung-Lung Department of Materials Science and Engineering National Chung Hsing University Taichung40227 Taiwan Ph.D. Program Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung411030 Taiwan Department of Applied Materials and Optoelectronic Engineering National Chi Nan University Nantou54561 Taiwan

In our study, β-Ga2O3 particles of various morphologies deposited on FTO glass substrates through hydrothermal method under different pH conditions and pre-stirring temperatures. The particles examined using XRD, FESEM, FTIR and Raman spectroscopy instruments. The results showed that the β-Ga2O3 microrods prepared with pH=3.15 solution at different pre-stirring temperatures followed by hydrothermal reaction had several crystalline orientations ([−401], [002], [403]). The FTIR and Raman spectra measurements confirmed the existence of Ga−O−Ga and Ga−OH bending modes. In the range of pH=3-11, the EGFET-pH sensor prepared with β-Ga2O3 microrods shown a high current-pH sensitivity of 0.541 (mA)1/2 /pH with a linearity of 97.83 % and a high voltage-pH sensitivity of 73.99 mV/pH with linearity of 96.85 %. These results suggest that β-Ga2O3 microrods have pH sensitivity higher than the ideal Nernstian response. This confirms the use of β-Ga2O3 microrods as a sensing membrane in high-performance pH sensors or biosensors. © 2023, The Authors. All rights reserved.

关键词： Gallium compounds

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