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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： C. Shanmugasundaram C. Umamaheswari A. Vijayalakshmi Prabha Elizabeth Varghese Department of Electrical and Electronics Engineering Manakula Vinayagar Institute of Technology Puducherry Department of Computer Science with Data Science St. Thomas College of Arts and Science Tamil Nadu Department of Biomedical Engineering Sri Manakula Vinayagar Engineering College Puducherry

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

The agriculture industry significantly contributes to the nation's economy by employing a large segment of the population and enhancing the GDP. It is a primary source of livelihood for many people, particularly in rural areas. India's agricultural sector is known for its diversity, producing a wide range of crops including rice, wheat pulses, sugarcane, cotton, fruits, and vegetables. This industry faces a significant challenge in predicting crop yield production and crop demand accurately. Efforts have been made to address these challenges through various government initiatives such as subsidies and continued investment in agricultural research and development. The traditional methods of predicting crop yield and market demand is cumbersome and time consuming and is susceptible to errors, resulting in inefficient resource utilization and revenue losses. There is a need for predicting the most efficient crop based on various influential environmental markers and forecasting the profitability. This project aims to develop an intelligent platform that uses machine learning algorithms to estimate the crop for cultivation in each area based on environmental factors. The project also aims to forecast market demand and pricing trends for the identified crop with the aim of increasing the profitability for the farmers and other stakeholders in the agriculture industry.

关键词： Profitability Rural areas Estimation Production Pricing Stakeholders Resource management Forecasting Crop yield Research and development

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A peristaltic soft, wearable robot for compression and massage therapy

arXiv

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arXiv 2022年

作者： Zhu, Mengjia Ferstera, Adrian Dinulescu, Stejara Kastor, Nikolas Linnander, Max Hawkes, Elliot W. Visell, Yon Media Arts and Technology Program Department of Electrical and Computer Engineering Department of Mechanical Engineering California NanoSystems Institute University of California Santa Barbara United States Mechanical Engineering Program Department of Mechanical Engineering ETH Zurich Switzerland

Soft robotics is attractive for wearable applications that require conformal interactions with the human body. Soft wearable robotic garments hold promise for supplying dynamic compression or massage therapies, such as are applied for disorders affecting lymphatic and blood circulation. In this paper, we present a wearable robot capable of supplying dynamic compression and massage therapy via peristaltic motion of finger-sized soft, fluidic actuators. We show that this peristaltic wearable robot can supply dynamic compression pressures exceeding 22 kPa at frequencies of 14 Hz or more, meeting requirements for compression and massage therapy. A large variety of software-programmable compression wave patterns can be generated by varying frequency, amplitude, phase delay, and duration parameters. We first demonstrate the utility of this peristaltic wearable robot for compression therapy, showing fluid transport in a laboratory model of the upper limb. We theoretically and empirically identify driving regimes that optimize fluid transport. We second demonstrate the utility of this garment for dynamic massage therapy. These findings show the potential of such a wearable robot for the treatment of several health disorders associated with lymphatic and blood circulation, such as lymphedema and blood clots. © 2022, CC BY.

关键词： Blood

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STAR-RIS Enhanced Covert Communication with Delay Constraint

STAR-RIS Enhanced Covert Communication with Delay Constraint

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Cheng, Beixi Hu, Jinsong Chen, Youjia Shu, Feng Chen, Zhizhang David Fuzhou University College of Physics and Information Engineering Fujian Fuzhou350108 China Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information Fuzhou350108 China Nanjing University of Science and Technology School of Electronic and Optical Engineering Nanjing210094 China Hainan University School of Information and Communication Engineering Haikou570228 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9798350345384

This work confirms the performance gain achieved by deploying a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) in covert communication. To maximize the system covert rate, we jointly optimize the power allocation coefficients at the transmitter and the transmission and reflection coefficients at the STAR-RIS, subject to the delay-constrained and power-constrained. Furthermore, we propose a two-stage alternating optimization algorithm based on the semidefinite relaxation (SDR) method to derive the optimal solutions to the above non-convex optimization problem. The effectiveness and superiority of the proposed algorithm and STAR-RIS-assisted covert scheme are demonstrated by the numerical results. © 2023 IEEE.

关键词： Convex optimization

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Predicting Electricity Consumption in Commercial and Residential Buildings Using A-CNN-LSTM Model

Predicting Electricity Consumption in Commercial and Residen...

引用

Intelligent Algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： Uppara Raghu Babu Neeraj Kumar Kantimahanti Padmaja V. Bhoopathy Mohammad Shabbir Alam S. Rukmani Devi BK Samudram Mandal Srinivasa Ramanujan Institute of Technology Anantapur India Department of Electrical Engineering Doon Institute of Engineering and Technology Rishikesh India Geetanjali College of Engineering Hyderabad India Department of Computer Science and Engineering Sree Rama Engineering College Tirupathi India Department of Computer Science Jazan University Jizan Kingdom of Saudi Arabia Department of Computer Science Saveetha College of Liberal Arts and Sciences Chennai India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

In this proposed approach an A-CNN-LSTM model that, over the medium to long term, can forecast, down to the hourly level, the patterns of electricity use in commercial and residential buildings. Institutional and residential buildings account for the vast bulk of America's overall energy usage. Thanks to developments in smart technologies and sensors, there is a need to forecast the medium- to long-term hourly electricity consumption of home and business buildings. This is to help with decision-making for operations, demand response strategies, and distributed generation system installations. To achieve the best possible results during model training, feature selection, and preprocessing, it is essential to adhere strictly to this strategy. Since some data on daily precipitation was lost during pre-processing, the Lagrange interpolation method was used to fill in the gaps. Feature selection makes use of PSO-K-means clustering technique. An A-CNN-LSTM was employed for the purpose of training the model. Traditional methods, such as LSTM and CNN, are surpassed by the suggested approach. Applying the strategy resulted in a 93.15 % improvement in accuracy.

关键词： Training Energy consumption Interpolation Precipitation Electricity Computational modeling Buildings Predictive models Feature extraction Long short term memory

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Application of IoT to Monitor the Shipping of Goods in Maritime Transportation System

Application of IoT to Monitor the Shipping of Goods in Marit...

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2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022

作者： Shaikh, Naim Kasat, Kishori Moyal, Vishal Kumar, N. Dilip Ponnusamy, Sivakumar Pavithra, K. Pune India Symbiosis School for Liberal Arts Symbiosis International Deemed University Pune India SVKM's Institute of Technology Electrical Engineering Dhule India Annamacharya Institute of Technology and Sciences Department of Electronics and Communication Engineering Tirupati India SRM Institute of Science and Technology Department of Computer Science and Engineering Delhi India Panimalar Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9781665453424

Surprising occurrences like temperature variations, humidity shifts, and vibrations might devalue the products being carried. ICT must be used to stop unwelcome occurrences from happening and fix some problems with the delivery of products. Making it possible to monitor the conditions of storage and transportation of products is one of the main issues. The IoT offers opportunities for tracking and locating goods from point of origin to point of destination and for monitoring traffic conditions. IoT-based solutions make it possible to identify the circumstances around certain delivery procedure abnormalities, including their timing, location, and cause. Additionally, these systems offer the ability to inform the client, transportation providers, or other participants to request urgent help. Businesses may enhance their product portfolio, improve the competitiveness of their business, and enhance route travel times and transportation circumstances. As a result, IoT technology can be advantageous to all parties involved in the logistics chain. The potential of IoT applications for supply chain and logistics management applications, such as vehicle fleet surveillance, commodities control, monitoring location-based assistance, etc., are presented in this article. © 2022 IEEE.

关键词： Travel time

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Navigating cloud computing security: strategies, risks, and best practices

Navigating cloud computing security: strategies, risks, and ...

引用

8th IET Smart Cities Symposium (SCS 2024)

作者： Ammar Odeh Qasem S. Abu Al-Haija Tareq Alhajahjeh Raed Mesleh Computer Science Princess Sumaya University For Technology Amman Jordan Department of Cybersecurity Jordan University of Science and Technology Irbid Jordan Tareq Alhajahjeh Faculty of Computing Engineering and Media De Montfort University Leicester UK Electrical Engineering Department German Jordanian University Amman Jordan

ISBN: (数字)9781837243105

This research analyzes cloud computing environments' inherent security and privacy risks. It examines crucial aspects of cloud security, including data encryption, access control mechanisms, identity management, and the implementation of firewalls and intrusion detection systems. The study emphasizes the significance of regular security audits, compliance checks, and the development of robust data backup and disaster recovery plans. Furthermore, it highlights the importance of employee training and awareness in mitigating security threats. The paper delves into the complexities of secure APIs, segmentation, isolation strategies, and the necessity of regular system updates and patching. Additionally, it examines the evolving landscape of cloud security through the lenses of Cloud Security Posture Management (CSPM), the Shared Responsibility Model, and Zero Trust Architecture. This comprehensive approach provides valuable insights into enhancing security measures in cloud computing, thereby safeguarding sensitive data and maintaining user trust in an era where data security is paramount.

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Manipulating quantum impurity spins via dynamical modes of nanomagnets

arXiv

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arXiv 2022年

作者： Rustagi, Avinash Kajale, Shivam Upadhyaya, Pramey Elmore Family School of Electrical And Computer Engineering Purdue University West LafayetteIN47907 United States Department of Electrical Engineering Indian Institute of Technology Maharashtra Bombay400076 India Media Arts And Sciences Massachusetts Institute of Technology CambridgeMA02142 United States

Quantum impurity (QI) spins offer promising information processing and sensing applications by harnessing up to room-temperature quantum coherence. Challenged by the requirement of designing local coherent drives and improving sensitivity to various signals for such applications, the search of hybrid systems coupling QI spins with matter excitations have garnered significant recent interest. We propose and theoretically study a hybrid system that couples spin-1 QI with the dynamical excitations of nanomagnets, which are controlled by mechanisms uncovered in classical spintronics. We show that in such systems the QI-spin decoherence, due to coupling to thermally excited modes of the nanomagnet, can be designed across a wide range by exploiting the control over nanomagnet's mode ellipticity and the chiral nature of the coupling between the QI spin and nanomagnet. On the other hand, when activated electrically via voltage-induced torques, we demonstrate that QI spins can be driven coherently with large quality factors at room temperature by leveraging inherent nonlinear precessional modes of the nanomagnet. Our results provide theoretical guidance for enabling unique quantum spintronic functionalities, such as local coherent driving of QI spins up to ambient conditions, and the design of nanomagnet-enhanced QI-based hybrid sensors. Copyright © 2022, The Authors. All rights reserved.

关键词： Nanomagnetics

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Speech Enhancement Generative Adversarial Network Architecture with Gated Linear Units and Dual-Path Transformers

Speech Enhancement Generative Adversarial Network Architectu...

引用

2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Zhang, Dehui Dong, Anming Yu, Jiguo Cao, Yi Zhang, Chuanting Zhou, You Qilu University of Technology Shandong Academy of Sciences School of Computer Science and Technology Jinan China Jinan China University of Jinan School of Information Science and Engineering Jinan China University of Bristol Department of Electrical and Electronic Engineering Bristol United Kingdom Shandong HiCon New Media Institute Co. Ltd. Jinan China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Generative Adversarial Networks (GANs) have been used in the field of speech enhancement due to their huge potentials in reducing the noise mixed in the signals. Most of existing GAN-based speech enhancement approaches either operate on time domain or exploit the magnitude spectra in time-frequency domain, but lack consideration of direct optimization of the phase. In this paper, we propose a GAN architecture for speech enhancement based on gated linear units (GLUs) and Dual-Path Transformers (DPTs), which simultaneously deals with the amplitude and phase information on the time-frequency domain. The generator of the proposed GAN architecture is designed following an autoencoder structure fed by the real and imaginary parts of the time-frequency frames. The encoder of the generator is constructed by multiple cascaded convolutional GLUs (ConvGLUs), while the decoder consists of two groups of cascaded deconvolutional GLUs (DeconvGLUs), one for the real part of the spectrogram and the other for the imaginary part. The GLUs are adopted since they are potential in avoiding the gradient vanishing issue dwelling in deep architectures by providing a linear path for the gradients while retaining non-linear capabilities. Aiming at capturing the long-range dependent features in speech, we place DPTs between the encoder and the decoder of the generator, which contains multi-head attention modules and Bi-directional Gated Recurrent Units (BiGRUs). Moreover, the DPT structure is also merged with multiple one-dimensional convolutional layers in the discriminator of the GAN. Such a design not only improves the speech enhancement performance of GAN by focusing on multiple features of speech, but also reducing the volume of model parameters of GAN. Experimental results suggest that the proposed GAN architecture outperforms the existing benchmark GANs in terms of both objective speech intelligibility and quality with less computational complexity. © 2022 IEEE.

关键词： Generative adversarial networks

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Measuring the Interaction of Conflict-Minimizing and Goal-Seeking Motor Imperatives in Autism Spectrum Disorder 1

引用

53rd International Simulation and Gaming Association Conference, ISAGA 2022

作者： Rengarajan, Sundararaman Cannon, Jonathan Baron, Brendan Mohan, Naren Chukoskie, Leanne Department of Physical Therapy Movement and Rehabilitation Sciences Northeastern University Boston United States Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge United States Khoury College of Computer Sciences Northeastern University Boston United States Department of Mechanical and Industrial Engineering College of Engineering Northeastern University Boston United States Art + Design College of Arts Media and Design Northeastern University Boston United States

ISBN: (数字)9783031371714

ISBN: (纸本)9783031371707

When subjected to sensory conflict, our bodies respond spontaneously and unconsciously to reduce or resolve it. We developed a Virtual Reality (VR) phase-matching task to establish a framework for explaining behaviors under conflict using hand-tracking, eye-tracking and pupillometry. We also describe a computational model of this behavior that was designed to help us understand differences in observed behavior from autistic individuals and non-autistic peers. We expect that known differences in prediction and attention mechanisms in autism will be important for accounting for behavioral differences observed in the task, highlighting these mechanisms as targets for future intervention. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Eye tracking

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IRFD: A Feature engineering based Ensemble Classification for Detecting Electricity Fraud in Traditional Meters 24

IRFD: A Feature Engineering based Ensemble Classification fo...

引用

24th International Conference on computer and Information technology, ICCIT 2021

作者： Mia, Md. Zesun Ahmed Islam, Md. Moinul Haque, Monjurul Islam, Saiful Rahman, S.M.A Mohaiminur Bangladesh University of Engineering and Technology University of Liberal Arts Bangladesh Electrical and Electronic Engineering Dhaka Bangladesh Chittagong University of Engineering and Technology Computer Science and Engineering Chittagong Bangladesh Rajshahi University of Engineering and Technology Electronics and Telecommunication Engineering Rajshahi Bangladesh Ahsanullah University of Science and Technology Computer Science and Engineering Dhaka Bangladesh Chittagong University of Engineering and Technology Mechanical Engineering Chittagong Bangladesh

ISBN: (纸本)9781665494359

Nations have suffered significant economic losses as a result of non-technical electric losses resulting from power fraud. It is a criminal act of stealing electricity by applying various mechanisms that incorporate unauthorized tapping to the power line, bypassing the smart meter, etc. Electricity theft is a significant concern for not only developing countries but also developed countries as well. However, for most developing countries, the implications are catastrophic, given that their usage is always less than their demands. Electricity theft must be detected precisely and quickly in order to be mitigated. In our study, we have proposed a method of predictive ensemble machine learning techniques (IRFD) with a novel combination of feature distinction methods to detect electricity theft. In our proposed model, we have combined feature selection technique, Recursive Feature Elimination with Stratified 10-Fold cross-validation (RFECV) and Isolation Forest (IF), to identify and remove outliers along with several machine learning classifiers to forecast the theft of electricity. This study additionally enhances the management of highly imbalanced fraudulent data with Borderline-SMOTE with SVM (SVMSMOTE) and feature scaling with StandardScaler. Following the study, the Random Forest classifier observed a higher degree of accuracy (97.06%) with higher precision, recall, and F1-Score. To evaluate the efficacy of our proposed model, comparative analysis of the classification metrics is also assessed with several machine learning classifiers like Logistic Regression, Gradient Boosting, XGBoost, AdaBoost, KNN, ANN, along with Random Forest before and after fitting our proposed feature engineering techniques. © 2021 IEEE.

关键词： Anomaly detection

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