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3rd International Conference on Micro and Nanoelectronics Devices, Circuits and Systems, MNDCS 2023

作者： Singh, Rajan Purnachandra Rao, G. Lenka, Trupti Ranjan Prasad, S.V.S. Dasari, Kiran Singh, Pulkit Nguyen, Hieu Pham Trung Department of Electronics and Communication Engineering MLR Institute of Technology Telangana Hyderabad500043 India Department of Electronics and Communication Engineering National Institute of Technology Assam Silchar788010 India Department of Electrical and Computer Engineering New Jersey Institute of Tech NewarkNJ07102 United States

ISBN: (纸本)9789819944941

In this paper, we present a normally-off beta-gallium oxide (β-Ga2O3) metal–oxide–semiconductor field-effect transistor (MOSFET) with ferroelectric gate (Fe-G). The enhancement-mode (E-Mode) operations are achieved by depleting the charge carriers from the channel using the gate charge trapped in the ferroelectric material at the ferroelectric–dielectric (Fe-De) interface. The enhancement-mode (E-mode) operation has a negligible degradation in saturation current as compared to depletion-mode (D-Mode) operation. Ohmic-contacts access region resistances are minimized in the E-mode using highly-doped ohmic-contacts access regions. Furthermore, device achieves a significant low specific on-resistance (RON,sp) of 48 mΩ-cm2 in E-mode compared to 103 mΩ-cm2 in the D-mode. Furthermore, a high breakdown voltage (VBR) of 2250 V in E-mode combined with RON,sp brings in a figure of merit (VBR2/RON,sp) of 49 MW/cm2, showing its potential for futuristic wide bandgap (WBG) power devices. In addition, the E-mode device shows a hysteresis-free subthreshold swing (SS) of 65 mV/dec, which indicates its suitability for fast switching applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ohmic contacts

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Multiuser Capacity Enhancement in B5G Networks using Hybrid CDMA-NOMA Scheme

Multiuser Capacity Enhancement in B5G Networks using Hybrid ...

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2024 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2024

作者： Sherien, Sabahat Sadia, Haleema Iqbal, Hafsa Ahmed Hablulmateen, Ridha Alaa Ceme Nust Department of Electrical Engineering Islamabad Pakistan School of Electrical Engineering and Computer Science Nust Islamabad Pakistan University of Kufa Department of Electronics and Communication Engineering Najaf Iraq

ISBN: (纸本)9798350357905

The rapid advancement of beyond fifth-generation (B5G) networks has opened a new era of high throughput demands, necessitating novel access schemes to overcome the associated challenges. Non-orthogonal multiple access (NOMA) has emerged as a promising technique, outperforming conventional orthogonal multiple access (OMA) in heterogeneous wireless networks. This paper introduces a new hybrid code division multiple access (CDMA)-NOMA scheme to improve multi-user capacity and power allocation (PA) efficiency in wireless networks. By combining the strengths of CDMA and NOMA, our proposed scheme forms NOMA clusters, each accommodating multiple users with varying channel characteristics. Intra-cluster interference is mitigated through successive interference cancellation (SIC), while inter-cluster interference is eliminated using CDMA spreading codes. We address the challenge of PA, a critical aspect of NOMA networks, by assigning distinct codes to all users within a cell. Through rigorous analysis, we evaluate the bit error rate performance of our hybrid scheme in diverse channel conditions, including the NYUSIM, AWGN, Rician, and Rayleigh channel models. The results presents an innovative solution to enhance B5G network capacity, demonstrating the potential of our hybrid CDMA-NOMA scheme to support more users efficiently while reducing SIC complexity. © 2024 IEEE.

关键词： Code division multiple access

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Multi-Feature Audio Fusion for Nonverbal Vocalization Classification

Multi-Feature Audio Fusion for Nonverbal Vocalization Classi...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Shah, Siddhant Bikram Johnson, Kristina T. Department of Electrical and Computer Engineering Northeastern University Boston United States Department of Electrical and Computer Engineering Department of Communication Sciences and Disorders Northeastern University Boston United States

ISBN: (纸本)9798350368741

Nonverbal vocalizations serve an essential role in general communication and expression, and they are particularly integral for non- and minimally-speaking individuals with autism and other neurodevelopmental disorders. Accurate analysis and classification of these vocalizations are crucial for enhancing communication and deepening our understanding of this underserved population. However, most audio-processing machine-learning techniques rely on linguistic cues like speech fluency and word pronunciation, and they do not generalize well to nonverbal vocalizations. We propose a multi-level fusion network that combines three types of audio features - Wav2Vec2 representations, mel-spectrograms, and low-level descriptors - using 6, 551 samples across 7 vocalization classes from the open-access ReCANVo dataset. The model achieves an accuracy of 64.09% for the 7-class classification task, outperforming 8 traditional audio classification methods and 3 feature-fusion approaches. To address heterogeneity and noise in the real-world audio data, we tested four sample augmentation techniques, resulting in a 14% relative increase in accuracy. We further examined a single individual (1, 595 samples), obtaining a 6.29% relative increase in accuracy, highlighting the effects of speaker and environment variability in population-level models. We also achieved a binary classification accuracy of 85.80% for vocalizations associated with positive/negative affective states, suggesting a potentially robust and highly separable latent structure underlying the valence of these sounds. Finally, we quantified the inter-class relationships between vocalizations using cosine similarity to offer additional insights into the acoustic patterns between classes. Our exploratory study not only uncovers key challenges in processing nonverbal vocalizations but also provides a foundational framework for future machine learning research with rare, real-world audio data, including multi-feature fusion and

关键词： Affective Computing Audio Autism Machine Learning Nonverbal Vocalizations Speech

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Sustainable augmented framework using smart sensors with ATM inspired 5G technologies for enterprise networks

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1805-1812页

作者： Kagi, Shivakumar Venugopal, Anita Ramaiah, V. Subba Panda, Sunita Ambikapathy, A. MB, Abhishek Department of Computer Science and Engineering Sharnbasva University Karnataka Kalaburagi India IT Unit Dhofar University Salalah Oman Department of CSE Mahatma Gandhi Institute of Technology Telangana Hyderabad India Department of Electrical Electronics and Communication Engineering GITAM School of Technology Bengaluru Campus Karnataka Bengaluru India Department of Electronics and Communication Engineering ITS Engineering College Uttar Pradesh Greater Noida India Department of Electronics and Communication Engineering Dayananda Sagar College of Engineering Bengaluru India

The primary driving force behind the development of fifth-generation (5G) applications is the necessity of Concurrent Multipath Transfer (CMT). Nevertheless, CMT systems that rely on Transport Control Protocol/Internet Protocol (TCP/IP) will continue to be the key point in 5G as multipath transport protocols are not often utilized. In today’s worldwide networks, integrating TCP/IP with asynchronous transfer mode (ATM) is a difficult structural problem since the TCP/IP protocol has to accommodate the special features of ATM. An effective mechanism for the effective use of both approaches will be a detailed characterization of TCP/IP performance via ATM. ATM network performance may be negatively impacted by improper use of TCP error and flow control methods, which were created for best-effort networks. In order to facilitate effective communication, this article offers a comprehensive and standardized examination and analysis of TCP/IP over ATM in the context of the 5G network architecture. It also proposes a tuned energy efficient transmission control protocol (TEETCP) to enhance Quality of Service (QoS) in TCP/IP applications. The performance of the approach is validated against existing recent works based on evaluation metrics such as packet delivery ratio (PDR), average throughput (AP), end-to-end delay (e2e), utilization of energy and computation overhead. The results indicate the better performance of our proposed work in all aspects. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： 5G ATM network CMT Network performance TEETCP

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Ontology-Based Semantic Reasoning for Multi-Source Heterogeneous Industrial Devices Using OPC UA

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IEEE Internet of Things Journal 2025年第13期12卷 25020-25032页

作者： Bi, Jing Wu, Rina Yuan, Haitao Wang, Ziqi Zhang, Jia Zhou, MengChu Beijing University of Technology College of Computer Science Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing China Southern Methodist University Department of Computer Science Lyle School of Engineering DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

The advent of smart manufacturing in Industry 4.0 signifies the era of connections. As a communication protocol, Object linking and embedding for Process Control Unified Architecture (OPC UA) can address most semantic heterogeneity issues. However, its semantics are not formally defined at the application layer. To address the information silo problem caused by semantic heterogeneity, an integration framework named Querying of Ontology Mapping-based OPC UA (QOMOU) is proposed. QOMOU extracts information models of OPC UA servers into resource description framework triples and utilizes web ontology language for semantic enrichment and inference. Then, an Event Class Semantic Similarity Calculation (ECSSC) method is proposed for device type identification, enabling the classification of semantically heterogeneous OPC UA devices. The effectiveness of ECSSC is validated through queries with the RDF query language (SPARQL) protocol in Apache Jena. Experimental results demonstrate that ECSSC improves the accuracy of device identification by approximately 7% compared to benchmark device identification models. Specifically, compared with graph embedding-based methods, QOMOU’s query performance is approximately 13% higher, and its query efficiency is 5% higher on average compared to both structured query and extensible markup languages. Moreover, by employing a keyword-matching algorithm, the query accuracy of the existing heterogeneous data integration scheme is improved by 4% on average. This enhancement can boost the operational efficiency of Internet of Things systems based on the OPC UA architecture. © 2014 IEEE.

关键词： Structured Query Language

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Analysis of wind turbine dataset and machine learning based forecasting in SCADA-system

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 8035-8044页

作者： Singh, Upma Rizwan, M. Department of Electrical Engineering Delhi Technological University Delhi110042 India Department of Electronics and Communication Engineering Maharaja Surajmal Institute of Technology Delhi110058 India

In this paper, Machine Learning (ML) based techniques known as Support Vector Regression (SVR) and Gradient Boosting Regression Trees (GBRT) are utilized for improving the forecast accuracy of short-term wind power production. In addition, data analysis framework is also prepared to visualize the data acquired from the Supervisory Control and Data Acquisition (SCADA) system. Dataset analysis has been carried out in Polar and Cartesian coordinates to identify the produced power and wind relationship. Based upon different statistical indices, the performance of the developed model has been evaluated. In addition, the comparative analysis reveals that GBRT model performance is superior than that of the SVR model with Mean-Square Error (MSE) value of 0.026, Root Mean-Square Error (RMSE) 0.301, Mean Absolute Percentage Error (MAPE) 0.063, Mean Absolute Error (MAE) 0.004 and Coefficient of determination (R2) 0.969. The results obtained illustrate that the proposed model would be applicable in real-life scenarios and help the management team to handle the produced power from wind turbines. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： SCADA systems

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Transforming Sustainable Aquaculture: Synergizing Fuzzy Systems and Deep Learning Innovations

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International Journal of Fuzzy Systems 2024年第8期26卷 2536-2552页

作者： Haobijam, Basanta Huang, Yo-Ping Chang, Yue-Shan Chang, Tsun-Wei Department of Electrical Engineering National Taipei University New Taipei City23741 Taiwan Department of Electrical Engineering National Penghu University of Science and Technology Penghu88046 Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei10608 Taiwan Department of Computer Science and Information Engineering National Taipei University New Taipei City23741 Taiwan Department of Information and Communication Engineering Chaoyang University of Technology Taichung41349 Taiwan Department of Computer Science and Information Engineering HungKuo Delin University of Technology New Taipei City236302 Taiwan

Pisciculture encounters an array of intricate challenges that span disease management, preservation of water quality, prevention of genetic hybridization, ensuring the integrity of net systems, sourcing sustainable aquatic feed, and comprehending fish growth and reproductive dynamics. Addressing these multifaceted challenges necessitates a comprehensive research approach. This study employs an innovative synergy of fuzzy logic and deep learning techniques, resulting in a robust strategy to tackle these obstacles effectively. Fuzzy logic excels in assessing stressed fish conditions by handling inherent uncertainties. Simultaneously, YOLOv7 with fuzzy color enhancement (YOLOv7FCE) is used to detect damaged fish nets, thereby mitigating losses and upholding the integrity of the net infrastructure. This approach also leverages YOLOv7FCE for identifying Cobia fish within shoals, streamlining the identification process. Subsequently, DeepLabv3 is implemented to meticulously segment the recognized Cobia fish, facilitating precise measurements of their physical attributes. This comprehensive methodology yields profound insights into growth patterns and feeding tendencies within the confined aquatic environment. By embracing this approach, the research presents a versatile and adaptive framework that not only enhances our comprehension of piscine dynamics but also holds the potential to revolutionize the aquaculture industry. © The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2024.

关键词： Fish

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Fluid Antenna Systems for Terahertz Wireless Power Transfer

Fluid Antenna Systems for Terahertz Wireless Power Transfer

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2024 IEEE Global communications Conference, GLOBECOM 2024

作者： Mavrovoltsos, Triantafyllos Demarchou, Eleni Psomas, Constantinos Krikidis, Ioannis University of Cyprus Irida Research Centre for Communication Technologies Department of Electrical and Computer Engineering Cyprus

ISBN: (纸本)9798350351255

Sixth generation networks will establish the terahertz (THz) era, introducing numerous potential applications for wireless power transfer (WPT). In this work, we focus on a fluid-antenna (FA)-aided WPT system which operates over the THz bands, featuring a non-monotonic diode-based rectifier. By taking into account the circuit's unconventional behavior, we first exploit a linear piece-wise function to approximate rectifier's input-output power relationship. Based on this, we provide an analytical framework in terms of the energy outage probability for three FA port selection (PS) schemes, namely (i) the input-based selection, (ii) the harvesting-based selection, and (iii) the random selection, each corresponding to different complexity and performance. Numerical results which validate our analysis, reveal a novel utilization of FAs, stemming from the alignment of the PS process with the non-monotonic harvesting characteristics. © 2024 IEEE.

关键词： Resonant tunneling diodes

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SecureDec: A Decentralized Scheduling Pipeline with Federated Learning and Efficient Encryption for Electricity-Gas Coupled Systems

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IEEE Transactions on Smart Grid 2025年第4期16卷 2858-2870页

作者： Liu, Haizhou Liu, Haitian Hu, Qinran Zhang, Xuan Sun, Hongbin Shahidehpour, Mohammad The School of Electrical Engineering Southeast University Nanjing210096 China The Shenzhen International Graduate School Tsinghua University Shenzhen518055 China The Department of Electrical Engineering Tsinghua University Beijing100084 China The College of Electrical and Power Engineering Taiyuan University of Technology Taiyuan030024 China The Electrical and Computer Engineering Department Illinois Institute of Technology ChicagoIL60616 United States

This paper proposes SecureDec, a decentralized and secure computational pipeline for the day-ahead scheduling of electricity-gas coupled systems. At the lower level, we propose SecureADMM for the decentralized optimization of the coupled subsystems, where all exchanged variables in decentralized ADMM are properly secured with ex-ante Diffie-Hellman (D-H) keys. The generation of D-H keys, being the most time-consuming step in encryption, can be independently performed ahead of optimization, allowing subsystems to collaboratively update variables without compromising optimization efficiency. As the convergence of ADMM is contingent on the quality of starting points, at the upper level, we introduce multi-entity SecureBoost, a decentralized federated learning framework, to predict the warm starts of SecureADMM in a similarly decentralized and privacy-preserving manner. A two-stage statistical packing algorithm is incorporated in the preceding SecureBoost training process to improve the efficiency in data protection, which reduces the frequency of time-consuming encryption/decryption by 2–6 times. Case studies demonstrate that the convergence of SecureDec is up by 50.0% higher than the conventional ADMM due to hyper-accurate warm starts provided by SecureBoost. Further, 63.9% and 96.7% efficiency boosts are observed in the federated learning and day-ahead optimization of an electricity-gas coupled system, respectively, with no compromises in information privacy and scheduling quality. The pipeline is further scalable to larger systems, and demonstrates potentials in addressing non-convergence in the conventional ADMM. © 2010-2012 IEEE.

关键词： Associated gas

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FisPro Software System based Earthquake Forecasting

FisPro Software System based Earthquake Forecasting

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2024 International Conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Kumar, A Naresh Chitte, Anil Gururaj, Bharathi Ramesha, M. Kumar, K Naveen Allamraju, K Viswanath Institute of Aeromautical Engineering Department of EEE Hyderabad India Hyderabad India ACS College of Engineering Department of Electronics and Communication Engineering Bengaluru India Department of Electrical Electronics and Communication Engineering Bengaluru India Marri Laxman Reddy Institute of Technology and Management Department of Electronics and Communication Engineering Hyderabad India Institute of Aeromautical Engineering Department of Mechanical Engineering Hyderabad India

ISBN: (纸本)9798350381887

Forecasting of earthquake gives an initial warning for building stakeholders and lead to required decisions for structures retrofitting before earthquake occurs, the legislating urban provision of building execution specially in earthquake prone areas then critical situations management after that rescue and relief managing. Designing the earthquake predictor needs estimating event location, magnitude, depth and future time. This article intended to design a system for forecasting of earthquake magnitude and depth using fuzzy rule-based inference system (FRIS). The system having four stages: data acquisition stage, pre-processing stage, feature extracting stage and fuzzy inference stage. The significant affects in the specified area are input to the FRIS. It forecasts the earthquake magnitude, future time and depth at particular location with 98% accuracy. This benefit helps minimize damages and affect the human lives. The system is helpful in strengthening and supporting the preparedness and the emergency relief activities owing to earthquakes. Thus, based on FRIS study results, it could be noted that the earthquake magnitude is forecasted by suggested system within the specified region of given location will happen for the next month. Furthermore, discussion covers the reginal and the global seismic data employed, tools used, to forecast earthquake for various geographical area. © 2024 IEEE.

关键词： Data acquisition

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