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Modified Turnstile Antenna Design for Multi-Band and Broadband applications

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Modified Turnstile Antenna Design for Multi-Band and Broadba...

USNC-URSI Radio science Meeting (Joint with AP-S Symposium)

作者： I-Fong Chen Chia-Mei Fei-Lung Wu Jian-An Xie Dept. of Computer and Communication School of Electronics and Electrical Engineering Dongguan Polytechnic Jinwen University of Science and Technology New Taipei City Taiwan Department of Communications Engineering Feng Chia University Taichung City Taiwan Ph.D. Program of Electrical and Communications Engineering Feng Chia University Taichung City Taiwan

ISBN: (数字)9789463968119

ISBN: (纸本)9798350359497

The proposed antenna with a size of 90 mm x 90 mm, and the ground portion is 64 mm x 64 mm, which material is FR4 glass epoxy substrate with the thickness of 1.6 mm, relative permittivity of 4.3 and loss tangent of 0.023, consists of four modified dipole elements and four power dividers, as shown in Figure. 1. For the multi-band and broadband operation, three dipole antennas were shunted to be a dipole element. The electrical-length of the dipole element can be determined from the one quarter-wave length at the 2.45 GHz, 5.5 GHz and 6.525 GHz, for covering 2400 MHz-2500 MHz, 5150 MHz-5850 MHz and 5925 MHz-7125 MHz. In order to operates in various modes such as normal mode and axial mode, the modified four port triple-band microstrip series power divider was designed. In normal mode, it radiates horizontally polarized waves where as in axial mode, it radiates circularly polarized waves. The radiated patterns of the proposed antenna as shown in Figure. 2. The features of the proposed antenna are shown in the Table I. Impressive radiated gains and efficiencies are obtained.

关键词： Power dividers Dipole antennas Conferences Glass Broadband communication Broadband antennas Microstrip

Training of Spiking Neural Network joint Curriculum Learning Strategy

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arXiv 2023年

作者： Tang, Lingling Chu, Jielei Gong, Zhiguo Li, Tianrui The School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China The State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China

Starting with small and simple concepts, and gradually introducing complex and difficult concepts is the natural process of human learning. Spiking Neural Networks (SNNs) aim to mimic the way humans process information, but current SNNs models treat all samples equally, which does not align with the principles of human learning and overlooks the biological plausibility of SNNs. To address this, we propose a CL-SNN model that introduces Curriculum Learning(CL) into SNNs, making SNNs learn more like humans and providing higher biological interpretability. CL is a training strategy that advocates presenting easier data to models before gradually introducing more challenging data, mimicking the human learning process. We use a confidence-aware loss to measure and process the samples with different difficulty levels. By learning the confidence of different samples, the model reduces the contribution of difficult samples to parameter optimization automatically. We conducted experiments on static image datasets MNIST, Fashion-MNIST, CIFAR10, and neuromorphic datasets N-MNIST, CIFAR10-DVS, DVS-Gesture. The results are promising. To our best knowledge, this is the first proposal to enhance the biologically plausibility of SNNs by introducing CL. Copyright © 2023, The Authors. All rights reserved.

关键词： Curricula

Intelligent Reflective Surface Assisted Integrated Sensing and Wireless Power Transfer

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arXiv 2023年

作者： Li, Zheng Zhu, Zhengyu Chu, Zheng Guan, Yingying Mi, De Liu, Fan Yang, Lie-Liang School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China National Mobile Communications Research Laboratory Southeast University Nanjing210019 China University of Surrey GuildfordGU2 7XH United Kingdom School of Computer Science and Engineering Northeastern University Shenyang110819 China School of Computing and Digital Technology Birmingham City University BirminghamB4 7XG United Kingdom Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Wireless sensing and wireless energy are enablers to pave the way for smart transportation and a greener future. In this paper, an intelligent reflecting surface (IRS) assisted integrated sensing and wireless power transfer (ISWPT) system is investigated, where the transmitter in transportation infrastructure networks sends signals to sense multiple targets and simultaneously to multiple energy harvesting devices (EHDs) to power them. In light of the performance tradeoff between energy harvesting and sensing, we propose to jointly optimize the system performance via optimizing the beamforming and IRS phase shift. However, the coupling of optimization variables makes the formulated problem non-convex. Thus, an alternative optimization approach is introduced and based on which two algorithms are proposed to solve the problem. Specifically, the first one involves a semi-definite program technique, while the second one features a low-complexity optimization algorithm based on successive convex approximation and majorization minimization. Our simulation results validate the proposed algorithms and demonstrate the advantages of using IRS to assist wireless power transfer in ISWPT systems. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy transfer

NMR Spectra Denoising with Vandermonde Constraints

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arXiv 2023年

作者： Guo, Di Xu, Runmin Wu, Jinyu Lin, Meijin Du, Xiaofeng Qu, Xiaobo School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361102 China Department of Applied Marine Physics and Engineering Xiamen University Xiamen361102 China

Nuclear magnetic resonance (NMR) spectroscopy serves as an important tool to analyze chemicals and proteins in bioengineering. However, NMR signals are easily contaminated by noise during the data acquisition, which can affect subsequent quantitative analysis. Therefore, denoising NMR signals has been a long-time concern. In this work, we propose an optimization model-based iterative denoising method, CHORD-V, by treating the time-domain NMR signal as damped exponentials and maintaining the exponential signal form with a Vandermonde factorization. Results on both synthetic and realistic NMR data show that CHORD-V has a superior denoising performance over typical Cadzow and rQRd methods, and the state-of-the-art CHORD method. CHORD-V restores low-intensity spectral peaks more accurately, especially when the noise is relatively high. © 2023, CC BY.

关键词： Nuclear magnetic resonance spectroscopy

Multi-stage image denoising with the wavelet transform

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arXiv 2022年

作者： Tian, Chunwei Zheng, Menghua Zuo, Wangmeng Zhang, Bob Zhang, Yanning Zhang, David School of Software Northwestern Polytechnical University Shaanxi Xi’an710129 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710129 China School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150001 China Peng Cheng Laboratory Guangdong Shenzhen518055 China Department of Computer and Information Science University of Macau 999078 China School of Computer Science Northwestern Polytechnical University Shaanxi Xi’an710129 China Guangdong Shenzhen518172 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

Deep convolutional neural networks (CNNs) are used for image denoising via automatically mining accurate structure information. However, most of existing CNNs depend on enlarging depth of designed networks to obtain better denoising performance, which may cause training difficulty. In this paper, we propose a multi-stage image denoising CNN with the wavelet transform (MWDCNN) via three stages, i.e., a dynamic convolutional block (DCB), two cascaded wavelet transform and enhancement blocks (WEBs) and a residual block (RB). DCB uses a dynamic convolution to dynamically adjust parameters of several convolutions for making a tradeoff between denoising performance and computational costs. WEB uses a combination of signal processing technique (i.e., wavelet transformation) and discriminative learning to suppress noise for recovering more detailed information in image denoising. To further remove redundant features, RB is used to refine obtained features for improving denoising effects and reconstruct clean images via improved residual dense architectures. Experimental results show that the proposed MWDCNN outperforms some popular denoising methods in terms of quantitative and qualitative analysis. Codes are available at https://***/hellloxiaotian/MWDCNN. © 2022, CC BY-NC-ND.

关键词： Wavelet transforms

ACSeg: Adaptive Conceptualization for Unsupervised Semantic Segmentation

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ACSeg: Adaptive Conceptualization for Unsupervised Semantic ...

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Kehan Li Zhennan Wang Zesen Cheng Runyi Yu Yian Zhao Guoli Song Chang Liu Li Yuan Jie Chen School of Electronic and Computer Engineering Peking University Shenzhen China AI for Science (AI4S)-Preferred Program Peking University Shenzhen Graduate School Shenzhen China Peng Cheng Laboratory Shenzhen China Dalian University of Technology Department of Automation and BNRist Tsinghua University Beijing China

Recently, self-supervised large-scale visual pre-training models have shown great promise in representing pixel-level semantic relationships, significantly promoting the development of unsupervised dense prediction tasks, e.g., unsupervised semantic segmentation (USS). The extracted relationship among pixel-level representations typically contains rich class-aware information that semantically identical pixel embeddings in the representation space gather together to form sophisticated concepts. However, leveraging the learned models to ascertain semantically consistent pixel groups or regions in the image is non-trivial since over/ under-clustering overwhelms the conceptualization procedure under various semantic distributions of different images. In this work, we investigate the pixel-level semantic aggregation in self-supervised ViT pre-trained models as image Segmentation and propose the Adaptive Conceptualization approach for USS, termed ACSeg. Concretely, we explicitly encode concepts into learnable prototypes and design the Adaptive Concept Generator (ACG), which adaptively maps these prototypes to informative concepts for each image. Meanwhile, considering the scene complexity of different images, we propose the modularity loss to optimize ACG independent of the concept number based on estimating the intensity of pixel pairs belonging to the same concept. Finally, we turn the USS task into classifying the discovered concepts in an unsupervised manner. Extensive experiments with state-of-the-art results demonstrate the effectiveness of the proposed ACSeg.

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Enhancing drug-target interaction predictions in context of neurodegenerative diseases using bidirectional long short-term memory in male Swiss albino mice pharmaco-EEG analysis

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Heliyon 2024年第21期10卷 e39279页

作者： Qureshi, Shahnawaz Iqbal, Syed Muhammad Zeeshan Ameer, Asif Karrila, Seppo Ghadi, Yazeed Yasin Shah, Syed Aziz Intelligent Biomedical Application Lab Sino-Pak center for Artificial Intelligence School of Computing Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology Mang Haripur 22620 Pakistan Research and Development BrightWare LLC Riyadh 13323 Saudi Arabia Department of Computer Science National University of Computing and Emerging Sciences Faisalabad 38000 Pakistan Faculty of Science and Industrial Technology Prince of Songkla University Surat Thani Campus Muang Surat Thani 84000 Thailand Department of Computer Science Al Ain University Abu Dhab Al Ain United Arab Emirates Healthcare Sensing Technology Faculty Research Centre for Intelligent Healthcare Coventry University Coventry United Kingdom

Background and Objective: Emerging diseases like Parkinson or Alzheimer's, which are not curable, endanger human mental health and are challenging to research. Drug target interactions (DTI) are pivotal in the screening of candidate drugs and focus on a small pool of drug targets. Electroencephalogram shows the responses to psychotropic medicines in the brain bioelectric activity. Synaptic activity can be analyzed by using Local Field Potential recordings obtained from micro-electrodes implanted in the brain. The aim is to evaluate the effects of drug on brain bioelectric activity and increase the drug classification accuracy. The ultimate goal is to advance our understanding of how drugs affect synaptic activity and open the door to more focused treatment for neurodegenerative diseases. Methods: In this study, Pharmaco-EEG recordings are processed using Advanced neural network models, particularly Convolutional Neural Networks, to assess the effects of medications. The five different medicines used in this study are Ephedrine, Fluoxetine, Kratom, Morphine, and Saline. The signals observed are local field potential signals. To overcome some limits of DTI prediction, we propose Bidirectional Long Short-Term Memory (LSTM) for the categorization of intracranial EEG (i-EEG) data, departing from standard approaches. Similar EEG patterns are presumably caused by drugs that work by homologous pharmacological pathways, producing similar psychotropic effects. To improve accuracy and reduce training loss, our study introduces a bidirectional LSTM model for classification along with Bayesian optimization Results: High recall, precision, and F1-Scores, particularly a 95% F1-Score for morphine, ephedrine, fluoxetine, and saline, suggest good performance in predicting these drug classes. Kratom produces a somewhat lower recall of 94%, but a high F1-Score of 97% and perfect precision of 1.00. The weighted average F1-Score, macro average, and overall accuracy are all consistently h

关键词： Convolutional neural network Drug target interaction Intracranial EEG Local field potential Long short-term memory

An IoT and Information Mining Algorithm-Based Self-Monitoring and Analysis System for Solar Power Plants

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An IoT and Information Mining Algorithm-Based Self-Monitorin...

Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： Ali Hadi Abdulwahid Kruti Jani Navdeep Dhaliwal Rajesh Singh A. Bhasha Jayapal Lande Control and Automation Engineering Department Engineering Technical College Southern Technical University Basra Iraq Shri Chimanbhai Patel Post Graduate Institute of Computer Application Ahmedabad Lovely Professional University Phagwara Uttaranchal Institute of Technology Uttaranchal University India Dept of IT St. Martin's Engineering College Secunderabad Telangana India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

Because of their affordability and sustainability, renewable sources of energy are receiving a lot of study interest. Particularly, solar power plants are regarded as a type of renewable sources of energy that may be employed in many areas since, while requiring less space than traditional systems, they demand less money for installation and maintenance. The majority of small generating units control space occupancy by putting the machinery on an exposed terrace. However, acres of land is needed for the building of huge power producing stations. It is difficult for human workers to maintain such a huge region of a power plant. The suggested algorithm would assist human workers in identifying the consistency of energy production and failure or faulty areas in solar energy systems using IoT and database mining (DM) techniques. This enables immediate fault repair action to be taken, which increases producing station effectiveness.

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The Numerical Results of Binary Coherent-State Signal’s Quantum Detection in the Presence of Noise 6th

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The Numerical Results of Binary Coherent-State Signal’s Qua...

6th International Conference on Artificial Intelligence and Security, ICAIS 2020

作者： Yu, Wenbin Xiong, Zijia Dong, Zangqiang Xu, Yinsong Liu, Wenjie Qu, Zhiguo Liu, Alex X. Jiangsu Engineering Center of Network Monitoring School of Computer and Software Nanjing University of Information Science and Technology NanjingJiangsu210044 China The Department of Computer Science and Application Zhengzhou Institute of Aeronautical Industry Management ZhengzhouHenan450015 China Department of Computer Science and Engineering Michigan State University East LansingMI48824-1226 United States

ISBN: (纸本)9783030578831

In the optical frequency level communication, quantum effect becomes the source of significant communication errors. One alternative to classical detection is orthogonal projection measurement. In this paper, based on the coherent state’s quantum measurement and discrimination, the quantum optimal detection of binary coherent state signal in the presence of thermal noise was studied. Our numerical results showed the error probabilities of quantum detection under the OOK and BPSK modulation, and verified the performance of these two modulation methods in the thermal noise field background. © 2020, Springer Nature Switzerland AG.

关键词： Optical communication