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Simplified Beam Steering and Fast Error Compensation of Optical Phased Arrays by Using a Novel Weighted-Grouping Algorithm

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IEEE Access 2024年 12卷 63701-63712页

作者： Gayatri, N.S.R. Lin, Yi-Hsuan Donati, Silvano Lee, San-Liang National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei10607 Taiwan Taipei10607 Taiwan

Optical phased arrays (OPAs) serve as pivotal components for non-mechanical beam steering and find relevance in various applications. However, they necessitate correction of optical phase errors within the antenna array, arising from fabrication imperfections in waveguides. We introduce a novel weighted group phase compensation (w-GPC) algorithm to simplify phase compensation and beam steering for OPAs but also enhances sidelobe suppression. A key advantage lies in its reduced hardware requirements, enabling phase compensation with only half or a quarter of the original number of electrodes. Simulation-based verification confirms that beam steering remains achievable even with the reduced number of phase shifts. Consequently, this minimizes the necessary electrodes on the OPA chip and significantly streamlines control circuitry. This feature is especially beneficial for large-scale OPAs comprising hundreds or even thousands of phase shifters. The w-GPC's phase compensation significantly outpaces traditional Genetic and PSO algorithms in computation speed. Applying the proposed algorithm with half controlled phases to optimize 64- and 1024-element OPAs yields impressive peak-to-sidelobe level (PSLL) enhancements, reaching up to 15 dB and 22 dB, respectively. Remarkably, PSLL remains over 19 dB even when only one sixteen of the phases are utilized to correct phase errors and steer the beam for a 1024-element OPA. These outcomes affirm that exceptional PSLL values can be achieved by great reduction in the number of control electrodes. © 2013 IEEE.

关键词： Error compensation

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Numerical Analysis of Pareto Set Modeling 1

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13th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2025

作者： Shu, Tianye Ishibuchi, Hisao Nan, Yang Pang, Lie Meng Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (数字)9789819635382

ISBN: (纸本)9789819635375

Recently, neural network-based inverse models have been used for multi-objective optimization. The basic idea is to approximate the mapping from the Pareto front to the Pareto set. In general, inverse modeling from a low-dimensional objective space to a high-dimensional decision space is difficult. However, good results have been reported in the literature. In this paper, we numerically examine the performance of inverse modeling. For visual examination, we use 2- and 3-objective distance minimization problems with 2 to 1,000 decision variables. Our experimental results show that inverse modeling improves the quality of the final population, which is used as the training data. One interesting observation is that improving fitting accuracy by increasing the neural network complexity does not improve the quality of the model outputs. We also demonstrate that inverse modeling works well even for large-scale multi-objective problems with 1,000 decision variables when high-quality training data are available. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Neural network models

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Automated Learning Engines and their Potential to Enhance information Retrieval Accuracy 3

Automated Learning Engines and their Potential to Enhance In...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Sakhare, Nitin N. Thangarasu, N. Rajarajeswari, S. Dedgaonkar, Suruchi G. Rengarajan, A. Pareek, Ravi Kant Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Department of Computer Science and Information Technology Karnataka Bangalore India Vivekananda Global University Department of Civil Engineering Jaipur India

ISBN: (纸本)9798350319125

Latest advances in artificial intelligence (AI) have given upward push to automated mastering engines which can be capable of leveraging big quantities of records to become aware of and extract applicable information with top notch accuracy. This has enabled more green records retrieval strategies, which has capability to revolutionize how human beings use and get admission to facts. computerized studying engines are capable of acting complex duties consisting of natural language processing, sentiment analysis, and object recognition, all of which require extensive computation strength. because of this possibilities for more suitable statistics retrieval systems are getting an increasing number of available to *** automatic mastering engine is particularly beneficial in situations wherein there's an abundance of statistics. The engine is able to filter and narrow down the facts to identify and extract simplest the most beneficial data. This allows to keep away from clutter and false positives from the hunt consequences, accordingly main to greater accurate and reliable results. moreover, their capacity to examine from the records they process way that they can be tailor-made to particular use instances, thereby making searches greater green and powerful. furthermore, automated mastering engines can perform at a miles quicker fee than traditional seek techniques. through automating the technique of statistics collection, evaluation, and usage, time is stored and the efficiency of the whole information retrieval system is substantially improved. this may have a ways-reaching implications. © 2023 IEEE.

关键词： information positives quicker system traditional

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Strategies for Effective Malware Analysis and Detection Using Machine Learning Algorithms

Strategies for Effective Malware Analysis and Detection Usin...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Roy, Abin Hossain, Sadia Dipti, Fahmida Afroze Ahmmed, Bahauddin Islam, Md. Safayet Moheuddin, Md. Sultan Reza, Ahmed Wasif Arefin, Mohammad Shamsul East West University Department of Computer Science and Engineering Dhaka Bangladesh Chittagong University of Engineering and Technology Department of Computer Science and Engineering Chittagong Bangladesh

ISBN: (纸本)9798331529765

This research explores the issues that modern digital environments face due to advanced and widespread malware. The study focuses on issues related to feature selection, model efficiency, and the effectiveness of machine learning algorithms in categorizing Android malware because it acknowledges the drawbacks of conventional detection techniques in keeping up with the rapid evolution of threats. The main goals are identifying malware features, and creating optimized machine-learning models like Lightgbm, Xgboost, Decision-Tree, Random-Forest, etc. for real-world deployment. This study has achieved early successes in precise model development, significant feature identification, and optimization for a balanced accuracy-efficiency by utilizing the dataset, feature extraction, and cross-validated machine learning algorithms. Notably, the introduction of the HybridVRKD machine learning approach in this research lays the foundation for future developments in the field of cybersecurity making a significant contribution to threat identification. In every category, the Hybrid-VKRD model demonstrated 98% improved accuracy, making it the top performer. © 2024 IEEE.

关键词： Cybersecurity Decision Tree Enesmble method Hybrid-VKRD KNN LightBgm Machine Learning Malware Xgboost

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Comprehensive Earth System Analysis for Advancing Community Resilience: Integrating Remote Sensing with Hybrid GAN-RNN Techniques

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 788-803页

作者： Sundar, Ganapathy Patchaiammal, Pitchaimuthu Lakkshmanan, Ajanthaa Ramya, Saminathan Pallavi, Lanke Thiagarajan, Ranganathan Department of Computer Applications Sindhi College Chennai India Department of Computing Technologies SRM Institute of Science and Technology Kattankaluthur Chengalpattu District India Department of Computer Science and Engineering Kongu Engineering College Perundurai Tamil Nadu Erode India Department of CSE B V Raju Institute Of Technology Narsapur India Department of Information Technology Prathyusha Engineering College Thiruvallur India

Effective disaster prediction is essential for climate resilience, especially in areas prone to extreme weather events. Conventional models often face challenges in predicting complex or uncommon events, largely due to data scarcity and the difficulty of modeling temporal dependencies in sequential data. This paper introduces an innovative hybrid GAN-RNN model that leverages remote sensing and IoT sensor data to improve disaster prediction accuracy. By combining generative adversarial networks (GANs) for generating synthetic data and recurrent neural networks (RNNs) for recognizing temporal patterns, this model forms a potent predictive tool. Achieving a remarkable testing accuracy of 98.16%, the hybrid GAN-RNN model significantly surpasses traditional predictive methods. Comprehensive evaluations demonstrate its enhanced capability to predict complex and rare events effectively, establishing it as a crucial resource for disaster risk management and environmental monitoring. This research advances AI-driven methodologies in climate resilience, offering a solid framework for future innovations in the field. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： AI-driven solutions Climate resilience Data augmentation Disaster prediction Environmental monitoring Hybrid GAN-RNN IoT sensors Remote sensing Synthetic data Temporal pattern recognition

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Robust Dwell Time Allocation for Multiple Ballistic Reentry Target Tracking in Phased Array Radar

Robust Dwell Time Allocation for Multiple Ballistic Reentry ...

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

作者： Zhang, Zishi Yuan, Ye Yi, Wei Varshney, Pramod K. School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Department of Electrical Engineering and Computer Science Syracuse University SyracuseNY United States

ISBN: (纸本)9798350368741

Phased array radar (PAR) is shown to provide an enhanced performance for target tracking due to its beam agility and ability for time resource allocation. Existing algorithms for PAR resource allocation often consider standard and simplified dynamic models for target motion, which are not suitable for complex scenarios such as for ballistic target tracking. To expand the applications of resource allocation algorithms in this context, this paper proposes a robust dwell time allocation algorithm for multiple ballistic reentry target (BRT) tracking in PAR. We aim to achieve an online dwell time allocation by establishing and solving optimization problems at each tracking interval. The motion of BRT is characterized by using the flat earth model, and the predicted Cramer-Rao lower bound (PCRLB) for BRT is derived to quantify the performance of tracking. By minimizing the weighted sum of time consumption of the PAR and cost of attaining the desired performance, a robust dwell time allocation approach is established. Finally, the superiority of the proposed algorithm is demonstrated through simulation. © 2025 IEEE.

关键词： ballistic reentry targets dwell time allocation multi-target tracking Phased array radar resource allocation

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Privacy Preservation of Clusters with Distance as Sensitivity Measure 16

Privacy Preservation of Clusters with Distance as Sensitivit...

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16th International Conference on Developments in eSystems engineering, DeSE 2023

作者： Sowmya, S.R. Manjunath, S.S. Dayananda Sagar Academy of Technology and Management Department of Information Science and Engineering Bangalore India Academy of Technology and Management Department of Computer Science and Engineering Mysore India

ISBN: (纸本)9798350381344

Preserving privacy in data mining is critical to protecting sensitive information while gaining valuable insights. Clustering algorithms using Euclidean distance measures often face privacy challenges due to the potential disclosure of sensitive information. This paper explores the application of micro aggregation as a protection method to improve privacy within clusters. The study focus on distance measure as Euclidian measure to identify sensitive data. The study evaluates the effectiveness of micro aggregation in mitigating privacy risks while maintaining clustering accuracy. © 2023 IEEE.

关键词： Data mining

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An attention-based channel estimation network with residual mechanism for 5G OFDM systems 4

An attention-based channel estimation network with residual ...

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4th International Conference on Electronic Communication, computer science and technology, ECCST 2024

作者： Jiang, Jinjing Huang, Weipeng Zhang, Xiao Yang, Kun Peng, Tong School of Information Engineering Zhejiang Ocean University China School of Computer Science and Software Engineering Shenzhen Institute of Information Technology China

Due to the significant increase in data transmission speed and gradual increase in Doppler frequency shift, channel estimation accuracy has become one of the most prioritized considerations in many cases. Specifically, we propose an attention-based channel estimation network with a residual mechanism, named TRCE-Net. In our design, the use of the multi-head attention mechanism at the input of the encoder is to obtain richer feature details. A linear series decoder module is implemented by leveraging multiple residual networks with skip connections. This novel design is capable of improving the channel estimation accuracy after deepening the network while effectively preventing degradation. To lessen the number of parameters that the network needs to learn, we introduce the bi-linear interpolation mechanism to replace the traditional up-sampling layer to mitigate both computational overhead and model complexity. According to the simulation results, our proposed TRCE-Net is superior to other state-of-the-art methods with regard to estimation accuracy and robustness to different maximum Doppler shifts, such as ResNet and InterpolateNet. © Published under licence by IOP Publishing Ltd.

关键词： Orthogonal frequency division multiplexing

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Small Population Size is Enough in Many Cases with External Archives 1

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13th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2025

作者： Nan, Yang Ishibuchi, Hisao Pang, Lie Meng Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (数字)9789819635382

ISBN: (纸本)9789819635375

In the evolutionary multi-objective optimization (EMO) field, evolutionary algorithms have a population to store a well-converged and well-distributed solution set. In the literature, the population size is usually specified in the range of 100–300 to approximate the entire Pareto front. Recently, a new framework with an external archive has been actively studied where a solution set selected from the archive is used to approximate the Pareto front. In this framework, a small population size can be better than a standard population size since a smaller population size means a larger number of generations under the termination condition specified by the same number of evaluated solutions. In this paper, we numerically examine whether better results are obtained from a smaller population size specification than a standard specification when the algorithm performance is evaluated using a solution set selected from an external archive. In our experiments, we examine the performance of four representative EMO algorithms (NSGA-II, NSGA-III, MOEA/D, and SMS-EMOA) with four specifications of the population size (36, 66, 120, and 210) on two frequently-used artificial test suites (DTLZ and WFG) and three real-world test suites (RWA, DDMOP, and RE). Our experimental results show that better results are obtained from a smaller population size specification in many cases when we cannot evaluate many solutions (i.e., when solution evaluation is expensive). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Evolutionary multi-objective optimization Performance comparison Small population size

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Design of Chest Visual Based Image Reclamation Method Using Dual Tree Complex Wavelet Transform and Edge Preservation Smoothing Algorithm

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SN computer science 2024年第4期5卷 352页

作者： Kumar, B. P. Pradeep Darshan, S. L. Shiva Naresh, E. Srinidhi, N.N. Shreyas, J. Department of ECE HKBKCE Karnataka Bangalore 560045 India Department of Computer Science and Engineering National Institute of Technology Warangal Warangal 506004 India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

The proposed article put forward a new scheme for image reclamation using second phase discrete symlet transform for medical images. The current medical image reclamation approaches have limitations in providing accurate reclamation fallouts with high visual insight and low computational complexity. To address these issues, this model presents a methodology for creating a medical image database using Image Reclamation using DT-CWT and EPS filter suited for the Resolution Enhancement of query chest image sample well utilized to get better retrieval rate where DWT algorithm is utilized for feature extraction of query input images. Flat and perpendicular prognoses of summation of pixels are analyzed to extract BC quantities, which are then used to compute the matching score of similarity for the images present in the database. The system selects the samples that are most pertinent to the given query sample image based on the matching score. The system’s untrained database is used to obtain the photographs with the highest BC value. The projected method aims to improve the enhancement of sampled image by DT-CWT EPS algorithm to leads to increase the accuracy and efficiency of medical image reclamation for various research applications. © The Author(s) 2024.

关键词： B quantities DWT Healthcare systems Medical image Visual BIR

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