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Physics-Based 2D Direction Finding of an Amplitude Modulated Signal Using a Uniform Triangular Array

Progress in Electromagnetics Research C 2025年 155卷 121-126页

作者： Ren, Kai Department of Electrical Engineering and Computer Science South Dakota School of Mines and Technology Rapid CitySD57701 United States

A uniform triangular array (UTA) is proposed for physics-based 2D direction-of-arrival (DOA) estimations of unknown incoming signals. Three capacitively loaded top-hat antennas are used as array elements. Unlike conventional array-based direction finding (DF) systems, complex antenna radiation patterns are used in array manifold calculations and DOA predictions, where coupling among array elements is naturally resolved. Both continuous wave (CW) signals and single tone signals with amplitude modulation (AM) are considered in DF simulations. Cramer-Rao bound (CRB) values are calculated to provide theoretical lower bounds of DF accuracies. Multiple signal classification (MUSIC) algorithm is used to further demonstrate the DF performance of the triangular antenna array without any angle ambiguities within the field of view from 0◦ to 360◦ in the azimuth direction and from 1.25◦ to 178.75◦ in the elevation direction. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Amplitude modulation

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Efficient identification of photovoltaic cell parameters via Bayesian neural network-artificial ecosystem optimization algorithm

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Global Energy Interconnection 2025年第2期8卷 316-337页

作者： Bo Yang Ruyi Zheng Yucun Qian Boxiao Liang Jingbo Wang Faculty of Electric Power Engineering Kunming University of Science and TechnologyKunming 650500PR China Department of Electrical Engineering and Electronics University of Liverpool L693GJ LiverpoolUK

Accurate identification of unknown internal parameters in photovoltaic(PV)cells is crucial and significantly affects the subsequent system-performance analysis and ***,noise,insufficient data acquisition,and loss of recorded data can deteriorate the extraction accuracy of unknown ***,this study proposes an intelligent parameter-identification strategy that integrates artificial ecosystem optimization(AEO)and a Bayesian neural network(BNN)for PV cell parameter extraction.A BNN is used for data preprocessing,including data denoising and ***,the AEO algorithm is utilized to identify unknown parameters in the single-diode model(SDM),double-diode model(DDM),and three-diode model(TDM).Nine other metaheuristic algorithms(MhAs)are adopted for an unbiased and comprehensive *** results show that BNN-based data preprocessing com-bined with effective MhAs significantly improve the parameter-extraction accuracy and stability compared with methods without data *** instance,under denoised data,the accuracies of the SDM,DDM,and TDM increase by 99.69%,99.70%,and 99.69%,respectively,whereas their accuracy improvements increase by 66.71%,59.65%,and 70.36%,respectively.

关键词： Photovoltaic cell Bayesian neural network Artificial ecosystem optimization Parameter identification

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Design and Analysis of a 4-element Wideband MIMO Antenna for THz Applications 2

Design and Analysis of a 4-element Wideband MIMO Antenna for...

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2nd International Conference on Power, electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Jafor, Md. Shakiul Paul, Liton Chandra Mimi, Afsana Hossain, Md. Sadik Al Mamun, Md. Shajol Rahman, Md. Hasibur Department of Electrical and Electronic Engineering Pabna University of Science & Technology Electronic and Communication Engineering Department of Electrical Pabna Bangladesh Pundra University of Science & Technology Department of Computer Science & Engineering Bogura Bangladesh

ISBN: (纸本)9798331517984

In this work, initially a rectangular microstrip patch antenna measuring (94.8 × 110 × 10) μm3 with a polyamide substrate has been analyzed and developed. The antenna has a bandwidth of 170 GHz (1.98 - 2.15 THz) and is tuned to resonate at a frequency of 2.08 THz. Its return loss is -26.733 dB, radiation efficiency is 92%, and maximum gain is 6.35 dB. In addition, the MIMO technology has been implemented to improve performance. Its bandwidth is 210 GHz (1.98-2.19 THz), and it offers a reflection coefficient of -37.75 dB, a radiation efficiency of 95%, and a peak gain of 6.01 dB. The substrate of the four-element MIMO antenna measures (154.8x170x10) μm3. The HFSS platform has been used to perform the modelling and simulation. © 2024 IEEE.

关键词： Slot antennas

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Kolmogorov Arnold Network for Hyperspectral Image Classification: A Detailed Explanation 2

Kolmogorov Arnold Network for Hyperspectral Image Classifica...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Suman, Saurabh Pacharaney, Utkarsha Choudhary, Vishal Kumar Raushan, Rahul Kumar, Abhishek Kumar, Gulshan Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha India

ISBN: (纸本)9798331528713

Hyperspectral imaging (HSI) has been proved to be useful in numerous fields because of its ability to acquire the spectral information across the hundreds of contiguous bands. Nevertheless, the vast dimensionality of the hyperspectral data presents a great problem especially during data processing as well as during classification. Recently, one of the techniques that have attracted most interest for the classification of hyperspectral images is Kolmogorov Arnold Network (KAN), which is efficient with regard to those high-dimensional non- linear data sets found in hyperspectral imagery. This study focuses on how KAN can be utilized for classifying hyperspectral images, how B-spline curves can be used for augmenting the performance of the model, and what experimentation was done with the state-of-the-art hyperspectral data set. From these results, it was established that KAN plus B-spline curves was a good strategy for hyperspectral images classification compared to existing systems in both accuracy and speed. © 2024 IEEE.

关键词： Splines

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Passively Variable Flux PM Motor with Patterned Multi-Layer PCB Coil Technique 50

Passively Variable Flux PM Motor with Patterned Multi-Layer ...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Aoyama, Masahiro Department of Electrical and Electronic Engineering Faculty of Science and Technology Shizuoka Institute of Science and Technology Shizuoka Japan

ISBN: (纸本)9781665464543

This paper proposes a motor with a passive variable Flux function that uses a self-excited wound-field and permanent magnets. The feature of this motor is that the stator and rotor coils are not wound, but are composed of printed circuit board coils that apply multi-layer pattern coil technique. First, the principle and structure of the proposed motor will be explained. Next, performance will be predicted using electromagnetic field simulation, and then the prototype will be revealed. Finally, the preliminary experimental test results will be demonstrated using the actual machine. © 2024 IEEE.

关键词： Permanent magnets

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Optimizing White Blood Cell Nuclei Segmentation: A Proficient Method for Improved Diagnostic Accuracy

Optimizing White Blood Cell Nuclei Segmentation: A Proficien...

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2024 IEEE International Conference on Data and Software engineering, ICoDSE 2024

作者： Adam, Abdisalam Abdulkadir Farah, Ibrahim Abdullahi Yusuf, Mohamed Abdirahman Hussien, Bashir Abdirahman Siyad, Abdulaziz Ahmed Department of Electrical Engineering Faculty of Engineering Jamhuriya University of Science and Technology Mogadishu Somalia

ISBN: (纸本)9798331506407

Clinical evaluations involving blood testing are the most crucial and often requested tests. If a blood sample seems odd, it must be examined manually under a microscope. This is a tedious, random, and time-consuming manual process. Automating the categorization of microscopic blood samples might be advantageous in order to speed up and improve the accuracy of pathologists' job. Segmentation is the first and most important step in the automated classification of blood cells. In this work, we present a practical technique for automatically dividing blood cell nuclei. Grayscale contrast enhancement and filtering are the foundation of the technique. There is now a minimum section size in place to get rid of fake items. 365 images of blood are used to evaluate the procedure. A quantitative assessment of the test set revealed a 79.7% segmentation performance. This performance is outstanding when compared to other published methods that have been applied to the same dataset. We compare the individual functions of the five typical kinds of white blood cells. Eosinophils have the lowest segmentation accuracy (69.3%), whereas monocytes have the best (86.3%). For comparison and replication, the blood image dataset and MATLAB source code are accessible. © 2024 IEEE.

关键词： Image segmentation

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Abnormal Activity Detection and Classification of Bus Passengers with In-Vehicle Image Sensing

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IEEE Access 2024年 12卷 23057-23065页

作者： Lin, Huei-Yung Tseng, Chun-Han National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106 Taiwan National Chung Cheng University Department of Electrical Engineering Chiayi621 Taiwan

As the self-driving technology is getting mature for public transportation applications, the safety concern of onboard passengers has become an important issue. It is essential to identify inappropriate or hazardous behaviors of passengers for the vehicles without human operators. In this work, we propose a technique to detect and classify the abnormal activities of passengers in a bus environment. Different from the existing human activity classification algorithms, our approach reduces the occlusion and increases the recognition rate by acquiring images from an overhead vision system. To overcome the increased complexity on feature extraction and classification, an action recognition network for top-view images are proposed by incorporating both spatial and temporal information. An image dataset, BUS-HAR, is generated for practical application scenarios with bus passengers. Experiments using real-world scene images have demonstrated the feasibility of our technique compared to existing approaches. The codes and image dataset are made available publicly at https://***/richardkuo1999/passenger-action-recognition. © 2013 IEEE.

关键词： Extraction

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Deep learning-based human activity recognition using CNN, ConvLSTM, and LRCN

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 259-268页

作者： Uddin, Md. Ashraf Talukder, Md. Alamin Uzzaman, Muhammad Sajib Debnath, Chandan Chanda, Moumita Paul, Souvik Islam, Md. Manowarul Khraisat, Ansam Alazab, Ammar Aryal, Sunil School of Information Technology Deakin University Waurn Ponds Campus Geelong Australia Department of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh Department of Computer Science and Engineering Jagannath University Dhaka Bangladesh Centre for Artificial Intelligence and Optimization Torrens University Australia Department of Electrical and Computer Engineering University of Ottawa Canada

Human activity recognition (HAR) plays a crucial role in assisting the elderly and individuals with vascular dementia by providing support and monitoring for their daily activities. This paper presents a deep learning (DL)-based approach to HAR, leveraging convolutional neural network (CNN), convolutional long short-term memory (ConvLSTM), and long-term recurrent convolutional network (LRCN) architectures. These models are designed to extract spatial features and capture temporal dependencies in video data, enhancing the accuracy of activity classification. We conducted experiments on the UCF50 and HMDB51 video datasets, encompassing diverse human activities. Our evaluation demonstrates that the ConvLSTM model achieves an accuracy of 82% on UCF50 and 68% on HMDB51, while the LRCN model gives accuracies of 93.44% and 71.55%, respectively. Finally, the CNN model outperforms with an accuracy rate of 99.58% for the UCF50 and 92.70% for the HMDB51 datasets. These significant improvements showcase the effectiveness of integrating convolutional and recurrent neural networks for HAR tasks. Our research contributes to advancing HAR systems with potential applications in healthcare, assisted living, and surveillance. By accurately recognizing human activities, our models can assist in remote patient monitoring, fall detection, and public safety initiatives. These findings underscore the importance of DL in enhancing the quality of life and safety for individuals in various contexts. © 2024 The Authors

关键词： Long short-term memory

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Minimum Energy Price to Make Net Zero Energy Buildings Cost-Effective - A Study in Various Geographical Climates of Iran 9

Minimum Energy Price to Make Net Zero Energy Buildings Cost-...

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9th International Conference on technology and Energy Management, ICTEM 2024

作者： Babou-Kuhestani, Alireza Ahmadi, Iraj Gholami, Mohammad University of Science and Technology of Mazandaran Department of Electrical and Computer Engineering Behshahr Iran

ISBN: (纸本)9798350329797

The energy price has a vital role in encouraging people to make their buildings net zero energy buildings (NZEB). The minimum energy price to make NZEB cost-effective depends on the efficiency of renewable energy generators (REGs) of the building, and the building's energy consumption, which are functions of geographical climate (GC). In this paper, a 10-unit apartment, each containing several appliances, is selected as a sample building. Regarding the usage time of appliances, the load curves of apartments and buildings are derived for all load seasons in four GC zones of Iran. A computer program has been developed and the minimum amount of REGs for sample building to be NZEB in four GC zones is determined. afterward, the annual energy cost of the building and the minimum energy price of the grid to make the NZEB cost-effective are calculated. © 2024 IEEE.

关键词： Green buildings

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Synthesis of Wideband Bandpass Filter With In-Band Complex Transmission Zeros for Frequency- and Attenuation-Reconfigurable In-Band Notch

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IEEE Transactions on Microwave Theory and Techniques 2024年第2期72卷 1162-1175页

作者： Liu, Bin Li, Kun Chi, Pei-Ling Yang, Tao University of Electronic Science and Technology of China School of Electronic Science and Engineering Chengdu610054 China National Chiao Tung University Department of Electrical and Computer Engineering Hsinchu300 Taiwan

A novel synthesis method for wideband bandpass filter (BPF) with two in-band conjugate complex transmission zeros is proposed for realizing frequency- and attenuation-reconfigurable in-band notch. A new characteristic function with wideband bandpass response is constructed by merging two BPFs with relatively narrow bandwidths. With the newly constructed characteristic function, complex transmission zeros will be introduced in the filter passband, facilitating the generation and control of the in-band notch. Moreover, quasi-Chebyshev filtering response (with quasi-equal ripple level) is also guaranteed in the whole passband. Based on the new characteristic function, a series of filter topologies can be derived using matrix similarity transformations. By only tuning the resonant frequencies of particular resonators, these topologies can exhibit wideband bandpass responses with frequency- and attenuation-reconfigurable in-band notch. To demonstrate the proposed techniques, one prototype of the wideband BPF using waveguide resonators is designed, simulated, and fabricated. The fabricated wideband BPF exhibits a passband frequency from 8.12 to 11.76 GHz with one transmission zero at 13.36 GHz and a reconfigurable in-band notch with frequency tuning from 9.3 to 10.4 GHz and attenuation tuning from 2 to 60 dB. The simulated and measured results are in good agreement, validating the proposed technique. © 1963-2012 IEEE.

关键词： Topology

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