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D-STAR: Dual Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces for Joint Uplink/Downlink Transmission

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IEEE Transactions on Communications 2024年第6期72卷 3305-3322页

作者： Shen, Li-Hsiang Wu, Po-Chen Ku, Chia-Jou Li, Yu-Ting Feng, Kai-Ten Liu, Yuanwei Hanzo, Lajos National Central University Department of Communication Engineering Taoyuan320317 Taiwan Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom University of Southampton Next Generation Wireless Research Group SouthamptonSO17 1BJ United Kingdom

The joint uplink/downlink (JUD) design of simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) is conceived in support of both uplink (UL) and downlink (DL) users. Furthermore, the dual STAR-RISs (D-STAR) concept is conceived as a promising architecture for 360-degree full-plane service coverage, including UL/DL users located between the base station (BS) and the D-STAR as well as beyond. The corresponding regions are termed as primary (P) and secondary (S) regions. Both BS/users exist in the P-region, but only users are located in the S-region. The primary STAR-RIS (STAR-P) plays an important role in terms of tackling the P-region inter-user interference, the self-interference (SI) from the BS and from the reflective as well as refractive UL users imposed on the DL receiver. By contrast, the secondary STAR-RIS (STAR-S) aims for mitigating the S-region interferences. The non-linear and non-convex rate-maximization problem formulated is solved by alternating optimization amongst the decomposed convex sub-problems of the BS beamformer, and the D-STAR amplitude as well as phase shift configurations. We also propose a D-STAR based active beamforming and passive STAR-RIS amplitude/phase (DBAP) optimization scheme to solve the respective sub-problems by Lagrange dual with Dinkelbach's transformation, alternating direction method of multipliers (ADMM) with successive convex approximation (SCA), and penalty convex-concave procedure (PCCP). Our simulation results reveal that the proposed D-STAR architecture outperforms the conventional single RIS, single STAR-RIS, and half-duplex networks. The proposed DBAP of D-STAR outperforms the state-of-the-art solutions found in the open literature for different numbers of quantization levels, geographic deployment, transmit power and for diverse numbers of transmit antennas, patch partitions as well as D-STAR elements. © 1972-2012 IEEE.

关键词： Beamforming

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A Post-Processing Algorithm for Boosting Contrast of MRI Images

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computers, Materials & Continua 2022年第8期72卷 2749-2763页

作者： B.Priestly Shan O.Jeba Shiney Sharzeel Saleem V.Rajinikanth Atef Zaguia Dilbag Singh Department of Electronics&Communication Engineering Chandigarh UniversityMohali140413India Department of Electrical Electronics&Communication EngineeringGalgotias UniversityGreater Noida201310India Department of Electronics and Instrumentation Engineering St.Joseph’s College of EngineeringChennai600119Tamil NaduIndia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia School of Electrical Engineering and Computer Science Gwangju Institute of Science and TechnologyGwangju61005Korea

Low contrast of Magnetic Resonance(MR)images limits the visibility of subtle structures and adversely affects the outcome of both subjective and automated ***-of-the-art contrast boosting techniques intolerably alter inherent features of MR *** changes in brightness features,induced by post-processing are not appreciated in medical imaging as the grey level values have certain diagnostic *** overcome these issues this paper proposes an algorithm that enhance the contrast of MR images while preserving the underlying features as *** method termed as Power-law and Logarithmic Modification-based Histogram Equalization(PLMHE)partitions the histogram of the image into two sub histograms after a power-law transformation and a log *** a modification intended for improving the dispersion of the sub-histograms and subsequent normalization,cumulative histograms are *** grey level values are computed from the resultant cumulative *** performance of the PLMHE algorithm is comparedwith traditional histogram equalization based algorithms and it has been observed from the results that PLMHE can boost the image contrast without causing dynamic range compression,a significant change in mean brightness,and contrast-overshoot.

关键词： Contrast enhancement histogram equalisation image quality magnetic resonance imaging medical image analysis post-processing

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Cognitive Congestion Alleviation Framework in IoT-Enabled WSN for Next-Gen Intelligent Transport Systems via Optimized Capsule Attention Network 3

Cognitive Congestion Alleviation Framework in IoT-Enabled WS...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Mohandas, R. Sudha, Y. Narmatha, P. Venkata Prasad, P. Manikandan, N. Department of Electronics & Communication Engineering Chennai Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Presidency University Karnataka Bangalore India Department of Computer Science and Engineering Sri Sairam College of Engineering Karnataka Bangalore India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Telangana Hyderabad India Department of Business Administration Kalasalingam Business School Kalasalingam Academy of Research and Education Viruthunagar Krishnankoil India

ISBN: (纸本)9798331527549

The rapid advancement of Intelligent Transportation Systems (ITS) has heightened the importance of reliable, real-time data transmission in Wireless Sensor Networks (WSNs). However, congestion in data-heavy Internet of Things (IoT)-enabled networks remains a critical challenge, impacting communication efficiency and decision-making in ITS. In high-density IoT-ITS applications, traditional congestion control methods are insufficient to address the complexity and dynamics of data flows. Existing solutions often fail to adapt in real time to varying traffic loads, leading to delays and data loss. There is a strong need for a congestion alleviation framework capable of intelligently prioritizing data while dynamically managing network resources to ensure seamless information exchange in large-scale deployments. This study presents a Cognitive Congestion Alleviation Framework in IoT-Enabled WSN for Next-Gen Intelligent Transport Systems via Optimized Capsule Attention Network (V-CapMiAN-Parr), which combines the Vectorized Adaptive Capsule Neural Network (V-AdCapNet) and Multi-instance Attention Network (MAN), fine-tuned by the Parrot Optimizer (ParrOpt). This integration aims to effectively detect, mitigate, and prevent congestion through an advanced capsule-based attention mechanism with adaptive optimization. The suggested V-CapMiAN-Parr framework demonstrated significant improvements in congestion control, achieving data throughput efficiencies above 99.7%, packet delivery rates above 99.5%, and network reliability reaching 99.2% under high traffic loads. The model’s adaptive weighting mechanism ensures real-time responsiveness and reliability, crucial for next-gen ITS applications. The V-CapMiAN-Parr framework effectively addresses congestion issues in IoT-enabled WSNs, providing a robust, scalable solution for ITS applications. This cognitive framework’s advanced data prioritization and adaptive optimization capabilities are well-suited for enhancing the performanc

关键词： Traffic congestion

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Deploying Faster R-CNN for Real-Time Tiger Monitoring in Wildlife Conservation 3

Deploying Faster R-CNN for Real-Time Tiger Monitoring in Wil...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Saravanan, S. Raj, J. Relin Francis Rajkumar, G. Vinoth Kumar, C. Antony Vasantha Soundiraraj, N. Bharathi, K. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering J.P. College of Engineering Tenkasi India Department of Mechanical Engineering Rohini College of Engineering and Technology Kanyakumari India Department of Electrical and Electronics Engineering PSNA College of Engineering and Technology Dindigul India Department of Computer Science and Engineering SCAD College of Engineering and Technology Cheranmahadevi India

ISBN: (纸本)9798331532420

This study introduces a real-time tiger monitoring system built on the Faster R-CNN deep learning framework, designed to support wildlife conservation through accurate tiger detection and tracking. The primary aim is to effectively identify and monitor tigers across various environments, using an extensive dataset drawn from Google’s Open Images Dataset and further enriched with synthetic images created by Generative Adversarial Networks (GANs). These synthetic images simulate challenging conditions, such as low lighting and uncommon camera angles, to improve the model’s robustness and adaptability in field settings. The model is initialized with a pre-trained ResNet50 backbone, utilizing transfer learning to accelerate training while ensuring precise detection. Advanced techniques such as dynamic data augmentation and hard negative mining have been integrated to minimize false positives and increase model resilience. Hyperparameter tuning, including cyclical learning rate scheduling and anchor box optimization, is applied to maximize both accuracy and recall, refining the model's ability to capture tiger-specific features under diverse conditions. To facilitate real-time monitoring, the model is deployed on NVIDIA Jetson devices, supporting edge processing without relying on cloud-based infrastructure. This deployment strategy ensures efficient data processing in remote areas, where stable network connectivity may be limited. Non-Maximum Suppression is used to reduce duplicate detections, while a Kalman Filter maintains tracking stability across video frames, ensuring seamless observation of tigers in motion. This optimized Faster R-CNN-based approach enables high-accuracy, low-latency tiger detection, equipping conservationists with reliable data on tiger presence and movement patterns. By enhancing detection and tracking capabilities, this system aims to support informed decision-making in wildlife conservation and habitat management. © 2024 IEEE.

关键词： Decision making

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A Novel Intelligent Fractional Order Cascade Control to Enhance Wind Energy Conversion in Wind Farms: A Practical Case Study

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IEEE Transactions on Energy Conversion 2025年

作者： Peykarporsan, Rasool Oshnoei, Soroush Fathollahi, Arman Lie, Tek Tjing the School of Engineering Computer and Mathematical Sciences Auckland University of Technology New Zealand the Department of Electrical and Computer Engineering Aarhus University Aarhus8200 Denmark

—As the world’s demand for electricity is rising with a growing emphasis on environmental sustainability, the need for efficient renewable energy solutions becomes increasingly critical. Wind power, which comprises 26% of renewable resources, is essential in this transition. Nevertheless, the performance of wind farms (WFs) can be adversely affected by uncertainties in wind speed. In response to this challenge, we introduce a novel four-degree-of-freedom (4DoF)-based fractional-order cascade control approach for WFs based on doubly fed induction generators (DFIGs) to enhance the efficiency and robustness of wind energy conversion systems (WECSs). The presented control method leverages the flexibility and disturbance-reduction capabilities of fractional-order proportional-integral-derivative and tilt-integral-derivative controllers in a 4DoF framework called 4DoF-IHYB. Then, the 4DoF-IHYB controller is cascaded with a fractional-order tilt-derivative controller to mitigate the impact of input noises and disturbances. Furthermore, a deep deterministic policy gradient (DDPG) method is utilized to optimize the controller’s parameters and improve the control system’s efficiency in the face of uncertainties stemming from volatile environmental conditions. DDPG is an algorithm based on deep reinforcement learning that integrates the advantages of both deep learning and policy gradient methods. The proposed control technique’s effectiveness is assessed in a case study of a prominent wind energy facility in New Zealand subject to various operating conditions. Moreover, the presented control method’s efficiency is compared with control methods available in the literature. The simulation results disclose that the proposed control method provides much better dynamic stability for the practical case study than the other methods. © 2025 IEEE. All rights reserved,

关键词： Asynchronous generators

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KDLPCCA-Based Projection for Feature Extraction in SSVEP-Based Brain-computer Interfaces

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Journal of Shanghai Jiaotong university(Science) 2022年第2期27卷 168-175页

作者： Huang Jiayang Yang Pengfei Wan Bo Zhang Zhiqiang School of Computer Science and Technology Xidian UniversityXi’an710071China School of Electronic and Electrical Engineering University of LeedsLeedsLS29JTUK

An electroencephalogram(EEG)signal projection using kernel discriminative locality preserving canonical correlation analysis(KDLPCCA)-based correlation with steady-state visual evoked potential(SSVEP)templates for frequency recognition is presented in this *** KDLPCCA,not only a non-linear correlation but also local properties and discriminative information of each class sample are considered to extract temporal and frequency features of SSVEP *** new projected EEG features are classified with classical machine learning algorithms,namely,K-nearest neighbors(KNNs),naive Bayes,and random forest *** demonstrate the effectiveness of the proposed method,16-channel SSVEP data corresponding to 4 frequencies collected from 5 subjects were used to evaluate the *** with the state of the art canonical correlation analysis(CCA),experimental results show significant improvements in classification accuracy and information transfer rate(ITR),achieving 100%and 240 bits/min with 0.5 s sample *** superior performance demonstrates that this method holds the promising potential to achieve satisfactory performance for high-accuracy SSVEP-based brain-computer interfaces.

关键词： steady-state visual evoked potential(SSVEP) brain-computer interface feature extraction kernel discriminative locality preserving canonical correlation analysis(KDLPCCA)

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Spatio-temporal Attention Graph Convolutions for Skeleton-based Action Recognition 23rd

Spatio-temporal Attention Graph Convolutions for Skeleton-b...

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22nd Scandinavian Conference on Image Analysis, SCIA 2023

作者： Le, Cuong Liu, Xin Computer Vision and Pattern Recognition Laboratory School of Engineering Science Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland Computer Vision Laboratory Department of Electrical Engineering Linköping University Linköping Sweden

ISBN: (纸本)9783031314346

In skeleton-based action recognition, graph convolutional networks (GCN) have been applied to extract features based on the dynamic of the human body and the method has achieved excellent results recently. However, GCN-based techniques only focus on the spatial correlations between human joints and often overlook the temporal relationships. In an action sequence, the consecutive frames in a neighborhood contain similar poses and using only temporal convolutions for extracting local features limits the flow of useful information into the calculations. In many cases, the discriminative features can present in long-range time steps and it is important to also consider them in the calculations to create stronger representations. We propose an attentional graph convolutional network, which adapts self-attention mechanisms to respectively model the correlations between human joints and between every time steps for skeleton-based action recognition. On two common datasets, the NTU-RGB+D60 and the NTU-RGB+D120, the proposed method achieved competitive classification results compared to state-of-the-art methods. The project’s GitHub page: STA-GCN. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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Enhancing Kriging with Inductive Spatio-Temporal GraphODE

Enhancing Kriging with Inductive Spatio-Temporal GraphODE

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Iranian Conference on electrical engineering (ICEE)

作者： Amin Sheykhzadeh Behzad Moshiri Ebrahim Ghafar-Zadeh School of Electrical and Computer Engineering University College of Engineering University of Tehran Tehran Iran Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada Department of Electrical Engineering and Computer Science York University Toronto Canada

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

Sensory networks in environmental monitoring provide real-time data on critical parameters, but the costs of installation and maintenance limit high-resolution data acquisition. Researchers aim to estimate values at specific locations without prior data samples, considering two approaches: virtual sensors and kriging. While virtual sensors face challenges in dynamic sensor networks where for every sensor added or disconnected the whole network should be retrained, kriging, especially spatio-temporal kriging using Graph Neural Networks, overcomes traditional kriging drawbacks and allows adaptability in dynamic sensor networks without frequent retraining. Despite their success, existing spatio-temporal kriging methods face challenges, notably the over-smoothing problem, restricting their ability to utilize deeper graph structures for a more comprehensive latent representation. In this paper, we propose a two-part method based on neural differential equations. The first part estimates values using spatial adjacency, while the second part refines these estimates considering temporal dependencies. Our approach explicitly addresses the over-smoothing problem, leading to a 2-8% improvement over state-of-the-art baseline methods. The results hold promise for enhancing the accuracy and effectiveness of environmental monitoring applications.

关键词： Backpropagation Adaptation models Soft sensors Face recognition Data integration Predictive models Mathematical models

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Land-Cover Classification and its Impact on Peshawar’s Land Surface Temperature Using Remote Sensing

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computers, Materials & Continua 2022年第2期70卷 4123-4145页

作者： Shahab Ul Islam Saifullah Jan Abdul Waheed Gulzar Mehmood Mahdi Zareei Faisal Alanazi Department of Computer Science IQRA National UniversityPeshawar25124Pakistan Department of Computer Science City University of Science&Information TechnologyPeshawar25124Pakistan Department of Information Technology Hazara UniversityMansehra21120Pakistan School of Electrical and Computer Engineering Seoul National UniversitySeoul08826Korea Department of Computer Science and Information Technology University of Malakand23021Pakistan Tecnologico de Monterrey School of Engineering and SciencesZapopan45201Mexico Department of Electrical Engineering Prince Sattam Bin Abdulaziz UniversityCollege of EngineeringAl Kharj16278Saudi Arabia

Spatial and temporal informationon urban infrastructure is essential and requires various land-cover/land-use planning and management ***,a change in infrastructure has a direct impact on other land-cover and climatic *** study assessed changes in the rate and spatial distribution of Peshawar district’s infrastructure and its effects on Land Surface Temperature(LST)during the years 1996 and *** this purpose,firstly,satellite images of bands7 and 8 ETM+(Enhanced Thematic Mapper)plus and OLI(Operational Land Imager)of 30 m resolution were ***,for classification and image processing,remote sensing(RS)applications ENVI(Environment for Visualising Images)and GIS(Geographic Information System)were ***,for better visualization and more in-depth analysis of land sat images,pre-processing techniques were *** Land use and Land cover(LU/LC)four types of land cover areas were identified-vegetation area,water cover,urbanized area,and infertile land for the years under *** composition of red,green,and near infra-red bands was used for supervised *** images were extracted for analyzing the relative infrastructure change.A comparative analysis for the classification of images is performed for SVM(Support Vector Machine)and ANN(Artificial Neural Network).Based on analyzing these images,the result shows the rise in the average temperature from 30.04℃ to 45.25℃.This only possible reason is the increase in the built-up area from 78.73 to 332.78 Area km^(2) from 1996 to *** has also been witnessed that the city’s sides are hotter than the city’s center due to the barren land on the borders.

关键词： Remote sensing temperature extraction urbanization satellite image classification artificial neural network support vector machine LU/LC land surface temperature

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Machine Learning on Spectral Data from Miniature Devices for Food Quality Analysis - A Case Study 23

Machine Learning on Spectral Data from Miniature Devices for...

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7th International Conference on Machine Learning and Soft Computing, ICMLSC 2023

作者： Asharindavida, Fayas Nibouche, Omar Uhomoibhi, James Liu, Jun Wang, Hui School of Computing Ulster University Northern Ireland United Kingdom School of Engineering Ulster University United Kingdom School of Electronics Electrical Engineering and Computer Science Queen's University of Belfast Northern Ireland United Kingdom

ISBN: (纸本)9781450398633

Food quality analysis can be carried out by spectral data acquired from spectrometers with its advantage of non-destructive way of testing. Portable and miniature spectroscopy can be a suitable solution when it meets the specifications such as portability, cost, and short processing time requirements, to enable ordinary citizens to use such a device in the fight against food fraud. Compared to more expensive, bulky, and non-portable devices, the data collected using miniature and portable spectrometers is of a lower quality and thus adversely affect the quality of the analysis. Research have been carried out to use machine learning (ML) classifiers on spectral data analysis for food quality assessment. The present work focuses on two aspects: firstly, preliminary exploratory statistical analysis is conducted on the real spectral data on different food products including oils, fruits and spices acquired from such miniature devices, which aims to evaluate and illustrate the distinctive characteristics of such spectral data, data distribution and difference in the spectra across multiple data acquisitions etc. along with a summary of the key challenges to face and explore. Secondly, a case study for the differentiation of extra virgin olive from adulterated with vegetable oil is provided to analyze and evaluate how some commonly used ML classifiers can be used for classification, while the impact of different preprocessing methods to improve the accuracy and efficiency is also provided. The case study demonstrates the good potential of using data analytics for spectral data from miniature device, although the overall performance of those ML classifiers is not exceptional (the classification rates of up to 83.32%) which is partially due to the quality of data, and partially due to limiting to only some classifiers. More elaborate data pre-processing and cleaning methods can be used to address the key challenges of the spectral data from miniature device, and other types

关键词： Data Analytics

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