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arXiv 2023年

作者： Pai, Cheng-Yu Liu, Zilong Li, Chunlei Chen, Chao-Yu Department of Engineering Science National Cheng Kung University Taiwan School of Computer Science and Electronic Engineering University of Essex United Kingdom Department of Informatics University of Bergen Norway Department of Electrical Engineering Institute of Computer and Communication Engineering National Cheng Kung University Taiwan

This paper presents a novel class of complex-valued sparse complementary pairs (SCPs), each consisting of a number of zero values and with additional zero-correlation zone (ZCZ) property for the aperiodic autocorrelations and crosscorrelations of the two constituent sequences. Direct constructions of SCPs and their mutually-orthogonal mates based on restricted generalized Boolean functions are proposed. It is shown that such SCPs exist with arbitrary lengths and controllable sparsity levels, making them a disruptive sequence candidate for modern low-complexity, low-latency, and low-storage signal processing applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal processing

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Examining Rooftop Solar Power with Integrated Storage Systems under Current Vietnam Pricing Mechanism

Examining Rooftop Solar Power with Integrated Storage System...

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International Conference on Green Energy and Applications (ICGEA)

作者： Phuong Trinh-Minh Tuyen Nguyen-Duc Hai Tran-Quoc Dan Nguyen-Linh Hirotaka Takano Department of Electrical Engineering School of Electrical and Electronic Engineering HUST Hanoi Vietnam Department of Industrial Economics School of Economics and Management HUST Hanoi Vietnam Department. of Electrical Electronic and Computer Engineering Gifu University Gifu Japan

ISBN: (数字)9798331530778

ISBN: (纸本)9798331530785

With growing concerns about climate change, energy security, and rising electricity costs, a rooftop solar system with storage offers a compelling solution for both residential and commercial consumers. Over the past decade, Vietnam has consistently implemented a series of mechanisms aimed at determining renewable energy pricing and supporting developers due to the high costs of self-investment and the imperative for profitable subsidies. The study examines a rooftop solar system with battery energy storage for a banking office building. By employing the self-consumption method, the system's installed capacity is optimized for economic efficiency. This smart investment solution not only reduces electricity costs for the building but also minimizes reliance on the grid, leading to reduced losses for utilities and a decreased carbon footprint. Furthermore, it encourages sustainable practices and demonstrates social responsibility by reducing the environmental impact of electricity generation.

关键词： Climate change Costs Electricity Green products Pricing Power system stability Air pollution Solar system Carbon footprint Investment

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Lecture Hall Monitoring System using Access Control Mechanism at the University of Ghana

Lecture Hall Monitoring System using Access Control Mechanis...

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International Conference on Adaptive Science & Technology (ICAST)

作者： Aboagye Isaac Adjaye Napari Abdul-Kadir Norman Kelly Amui Michael Sowah Nii Longdon Okae Percy Abubakari Alidu Kwabena Kan-Dapaah Department of Computer Engineering University of Ghana Ghana Department of Electrical and Electronic Engineering Academic City University College Ghana Department of Biomedical Engineering University of Ghana Ghana

ISBN: (数字)9798350385403

ISBN: (纸本)9798350385410

In an era where technology is evolving rapidly, there is a need to efficiently secure access control systems within educational institutions and office environments. Access control systems are crucial in ensuring the security and legal access to physical and digital resources. In this research, a system was designed and implemented to monitor the lecture hall and office environment within the School of engineering Sciences, University of Ghana. The system integrated multiple layers of access controls, including biometric fingerprint recognition, Radio Frequency Identification (RFID) card authentication, and personal identification number (PIN). A database was generated to identify people who use the facilities. This provided a robust and user-friendly hybrid system. The system leverages modern database technologies to record and manage access, enabling real-time monitoring of lecture halls and office spaces. Moreover, it offers a user-friendly interface for administrators, lecturers, lab attendants, and other users. The database generated could also be used to check class attendance. The results demonstrate significant improvements in security, attendance tracking, and overall access management efficiency in the School of engineering Sciences.

关键词： Access control Databases Law Authentication Fingerprint recognition Real-time systems Pins Biomedical monitoring Monitoring Radiofrequency identification

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LISAC: Learned Coded Waveform Design for ISAC with OFDM

LISAC: Learned Coded Waveform Design for ISAC with OFDM

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IEEE Conference on Wireless Communications and Networking

作者： Chenghong Bian Yumeng Zhang Deniz Gündüz Department of Electrical and Electronic Engineering Imperial College London Department of Electrical and Computer Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

We propose a novel deep learning based method to design a coded waveform for integrated sensing and communication (ISAC) system based on orthogonal frequency-division multiplexing (OFDM). Our ultimate goal is to design a coded waveform, which is capable of providing satisfactory sensing performance of the target while maintaining high communication quality measured in terms of the bit error rate (BER). The proposed LISAC provides an improved waveform design with the assistance of deep neural networks for the encoding and decoding of the information bits. In particular, the transmitter, parameterized by a recurrent neural network (RNN), encodes the input bit sequence into the transmitted waveform for both sensing and communications. The receiver employs a RNN-based decoder to decode the information bits while the transmitter senses the target via maximum likelihood detection. We optimize the system considering both the communication and sensing performance. Simulation results show that the proposed LISAC waveform achieves a better tradeoff curve compared to existing alternatives.

关键词： Deep learning Recurrent neural networks Transmitters OFDM Simulation Bit error rate Receivers Integrated sensing and communication Particle measurements Numerical models

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Performance and Implementation Comparison of Knuth-Morris-Pratt and Boyer-Moore String Search Algorithms 2

Performance and Implementation Comparison of Knuth-Morris-Pr...

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2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Saleh, Taj Ergin, Fatma Corut Malkawi, Malek Alhajj, Reda Department of Computer Engineering Marmara University Istanbul Turkey Department of Computer Engineering Istanbul Medipol University Istanbul Turkey Department of Computer Science University of Calgary Alberta Canada Department of Heath Informatics University of Southern Denmark Odense Denmark

ISBN: (纸本)9798331506995

String search algorithms play an important role in many research areas such as data mining and bioinformatics. While there exist a number of algorithms that handles the topic, we are exploring the the Knuth-Morris-Pratt (KMP) and Boyer-Moore algorithms due to their efficiency and versatility. In this work, we compared the algorithms in terms of characteristics, performance and implementation details. We also tested both the algorithms with various patterns and texts that differs in size. We also analyzed the performance of the algorithms on 4 different processors to understand the technological advancements effects on their performance. Our findings suggest that the BM algorithm perform better with large texts and patterns, while the KMP algorithm is better suited for smaller ones. Also, while newer processor generally exhibit improved performance, the significance of these enhancements may vary. Thus, we should rather be looking specific architectural advancements within generations rather than focusing solely on the generational gap. © 2025 IEEE.

关键词： String searching algorithms

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Multimodal Medical Image Registration and Fusion for Quality Enhancement

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computers, Materials & Continua 2021年第7期68卷 821-840页

作者： Muhammad Adeel Azam Khan Bahadar Khan Muhammad Ahmad Manuel Mazzara Department of Electronic Engineering The Islamia University of Bahawalpur63100Pakistan Department of Telecommunication Engineering The Islamia University of Bahawalpur63100Pakistan Department of Computer Science National University of Computer and Emerging SciencesChiniot35400Pakistan Institute of Software Development and Engineering Innopolis UniversityInnopolis420500Russia

For the last two decades,physicians and clinical experts have used a single imaging modality to identify the normal and abnormal structure of the human ***,most of the time,medical experts are unable to accurately analyze and examine the information from a single imaging modality due to the limited *** overcome this problem,a multimodal approach is adopted to increase the qualitative and quantitative medical information which helps the doctors to easily diagnose diseases in their early *** the proposed method,a Multi-resolution Rigid Registration(MRR)technique is used for multimodal image registration while Discrete Wavelet Transform(DWT)along with Principal Component Averaging(PCAv)is utilized for image *** proposed MRR method provides more accurate results as compared with Single Rigid Registration(SRR),while the proposed DWT-PCAv fusion process adds-on more constructive information with less computational *** proposed method is tested on CT and MRI brain imaging modalities of the HARVARD *** fusion results of the proposed method are compared with the existing fusion *** quality assessment metrics such as Mutual Information(MI),Normalize Crosscorrelation(NCC)and Feature Mutual Information(FMI)are computed for statistical comparison of the proposed *** proposed methodology provides more accurate results,better image quality and valuable information for medical diagnoses.

关键词： Multimodal registration fusion multi-resolution rigid registration discrete wavelet transform principle component averaging

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Edge Impulse Based ML-Tensor Flow Method for Precise Prediction of Remaining Useful Life (RUL) of EV Batteries

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Journal of Power and Energy engineering 2024年第6期12卷 1-15页

作者： Tarik Hawsawi Mohamed Zohdy Department of Electrical and Computer Engineering Oakland University Rochester USA Department of Electronic Technology Technical Vocational Training Corporation Dammam KSA

Electric Vehicle (EV) adoption is rapidly increasing, necessitating efficient and precise methods for predicting EV charging requirements. The early and precise prediction of the battery discharging status is helpful to avoid the complete discharging of the battery. The complete discharge of the battery degrades its lifetime and requires a longer charging duration. In the present work, a novel approach leverages the Edge Impulse platform for live prediction of the battery status and early alert signal to avoid complete discharging. The proposed method predicts the actual remaining useful life of batteries. A powerful edge computing platform utilizes Tensor Flow-based machine learning models to predict EV charging needs accurately. The proposed method improves the overall lifetime of the battery by the efficient utilization and precise prediction of the battery status. The EON-Tuner and DSP processing blocks are used for efficient results. The performance of the proposed method is analyzed in terms of accuracy, mean square error and other performance parameters.

关键词： Electric Vehicle EV Charging Machine Learning Lifetime Edge Computing

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Dipole Fitting in Unit-Level Spacecraft Equipment with Deep Neural Networks

Dipole Fitting in Unit-Level Spacecraft Equipment with Deep ...

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2022 ESA Workshop on Aerospace EMC, Aerospace EMC 2022

作者： Giannopoulos, Anastasios E. Spantideas, Sotirios T. Nikolopoulos, Christos D. Baklezos, Anargyros T. Capsalis, Christos N. National Technical University of Athens School of Electrical and Computer Engineering 15780 Greece Hellenic Mediterranean University Department of Electronic Engineering Crete73133 Greece

ISBN: (纸本)9789082684742

Magnetic Dipole Modeling (MDM) is a well- established method for fitting the spatial magnetic signature generated by magnetic sources into a model comprised of magnetic dipoles. In this paper, we study the dipole fitting problem using Machine Learning (ML) techniques. Simulated data were used to estimate the dipole parameters that can accurately reconstruct the measured magnetic field. We applied our methodology in two widely used measurement facilities, namely the Magnetic Coil Facility (MCF) and the Multi Magnetometer Facility (MMF). To solve this regression problem, Artificial Neural Networks (ANNs) were considered. Simulation results showed that ANNs significantly outperform other benchmark ML methods in terms of the achieved accuracy of the estimated dipole parameters. Finally, we observed that MMF model shows enhanced field reconstruction accuracy with reduced position estimation loss (70% lower position error), magnetic moment estimation loss (55% lower moment error) and model complexity (33.3% lower complexity) compared to the MCF model. © 2022 ESA ESTEC.

关键词： Deep neural networks

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AgileVision Academy: Indoor Drone Navigation and Obstacle Avoidance 14

AgileVision Academy: Indoor Drone Navigation and Obstacle Av...

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14th International Conference on Information Technology in Medicine and Education, ITME 2024

作者： Jiang, Haolong Gu, Lingdan Wang, Jiayu Lan, Xiao Liu, Qinyao Wang, Fanxin Xi'an Jiaotong Liverpool University University of Liverpool School of Advanced Technology Department of Electrical and Electronic Engineering Suzhou China Xi'an Jiaotong Liverpool University School of Robotics Department of Intelligence Robotics Engineering With Contemporary Entrepreneurialism Suzhou China Xi'an Jiaotong Liverpool University School of Advanced Technology Department of Information and Computer Science Suzhou China Xi'an Jiaotong Liverpool University School of Advanced Technology Department of Electrical and Electronic Engineering Suzhou China Xi'an Jiaotong Liverpool University School of Advanced Technology Department of Mechatronics and Robotics Suzhou China

ISBN: (纸本)9798350356670

Navigating cluttered indoor environments presents a significant challenge for aerial robots, requiring agility, speed, and a high level of reliability to avoid collisions. This project aims to address this challenge by developing advanced algorithms for lightweight drones, enabling them to reach target location and carrying out delivery efficiently and safely. Utilizing camera data to map and avoid obstacles, we create a system that not only maps and perceives the environment but also navigates its path to the destination. Included in the educational content, this project demonstrates the effectiveness of the technology through practical applications and real-world testing, helping the next generation of engineers accumulate technical experience and broaden their understanding of drone navigation algorithms and applications in system integration. It has a milestone impact on education and development of the drone field. © 2024 IEEE.

关键词： Drones

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Exploring Effective Distillation of Self-Supervised Speech Models for Automatic Speech Recognition

Exploring Effective Distillation of Self-Supervised Speech M...

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2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023

作者： Wang, Yujin Tang, Changli Ma, Ziyang Zheng, Zhisheng Chen, Xie Zhang, Wei-Qiang Tsinghua University Department of Electronic Engineering China Ai Institute X-LANCE Lab Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Department of Computer Science and Engineering Shanghai China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9798350306897

Self-supervised learning (SSL) has achieved great success in speech processing, but always with a large model size to increase the modeling capacity. This may limit its potential applications due to the expensive computation and memory costs introduced by the oversize model. Compression for SSL models has become an important research direction of practical value. To this end, we explore the effective distillation of HuBERT-based SSL models for automatic speech recognition. First, a comprehensive study of different student model structures is conducted. On top of this, as a supplement to the regression loss widely adopted in previous works, a discriminative loss is introduced for HuBERT to enhance the distillation performance, especially in low-resource scenarios. In addition, we design a simple and effective algorithm to distill the front-end input from waveform to Fbank feature, resulting in 17% parameter reduction and doubling inference speed, at marginal performance degradation. © 2023 IEEE.

关键词： Distillation

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