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An overview on IRS-enabled sensing and communications for 6G: architectures, fundamental limits, and joint beamforming designs

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science China(Information sciences) 2025年第5期68卷 170-193页

作者： Xianxin SONG Yuan FANG Feng WANG Zixiang REN Xianghao YU Ye ZHANG Fan LIU Jie XU Derrick Wing Kwan NG Rui ZHANG Shuguang CUI School of Science and Engineering (SSE) Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Department of Electrical Engineering City University of Hong Kong School of Information Engineering Guangdong University of Technology Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Computer School Beijing Information Science and Technology University National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University School of Electrical Engineering and Telecommunications University of New South Wales School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.

关键词： integrated sensing and communication (ISAC) intelligent reflecting surface (IRS) non-line-of-sight (NLoS) sensing sensing and communication tradeoff

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Supply Chain Management Using Internet of Things and Blockchain for Smart Meat Packaging 5

Supply Chain Management Using Internet of Things and Blockch...

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5th International Conference on data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Kour, Kanwal Preet Singh, S. Kanwal Deep Bali, Malvinder Singh Ghantasala, G. S. Pradeep Rajesh Sharma, R. Ananthanagu, U. Manipal University School of Computer Science and Engineering Jaipur India Federation University Department of Computer Science Australia Manipal University Department of Data Science and Engineering Rajasthan Jaipur India Alliance College of Engineering and Design Alliance University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350389609

In recent times, blockchain has evolved the security of traditional supply chain systems. Different issues of supply chain management like flexibility and reliability can be easily addressed using blockchain. Meat Packing Industry involves the complete process of processing, packaging and supplying the meat and its products throughout the world for human consumption. The animals used for the meat supply chain include pigs, sheep, cattle etc. Due to the enormous increase in the number of meat-eaters worldwide, its packing and timely supply are of utmost importance. The meat packing industry has revolutionized a lot by using the latest supply chain methods based on new technologies like blockchain. These include adulteration, long-term delays leading to spoilage of the product, mixing of other similar products which are lower in price etc. All these issues can be easily handled by using blockchain and IoT for meat supply chain management. Using these technologies, the challenges ranging from timely transportation of good quality meat to the best quality meat delivered to the customer can be easily addressed. In this paper, an effective blockchain and IoT-based model has been proposed which will counter the above challenges and improve the reliability and security factor. © 2024 IEEE.

关键词： Supply chain management

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Comparative Analysis of Optimization Algorithms for Feature Selection in Heart Disease Classification 2nd

Comparative Analysis of Optimization Algorithms for Feature ...

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2nd International Conference on Intelligent Computing Systems and Applications, ICICSA 2023

作者： Kapila, Ramdas Saleti, Sumalatha Telangana Maisammaguda Secunderabad India Data Science Laboratory Computer Science and Engineering SRM University-AP Amaravathi India

ISBN: (纸本)9789819754113

In order to classify the Cleveland Heart Disease dataset, this study evaluates the performance of three optimization methods, namely Fruit Fly Optimization (FFO), Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO). The top 10 features are identified using FFO, with remarkable results for accuracy (88.52%), precision (87.88%), recall (90.63%), and f1-score (89.23%). After using PSO, the accuracy, precision, recall, specificity, and f1-score are all 85.25%, 87.10%, 84.38%, and 85.71% respectively. Finally, GWO is used, which results in precision, accuracy, recall, and f1-score values of 93.33%, 90.16%, 90.11%, 87.5%, and 90.32% respectively, highlighting its consistent superior performance. FFO shows competitive outcomes with notable accuracy and recall through a comparative examination. PSO displays comparable precision and recall while displaying a somewhat poorer accuracy. In contrast, GWO performs better than both FFO and PSO, displaying great accuracy and precision along with remarkable recall and specificity. These results provide important information on the effectiveness of feature selection methods utilized in optimization algorithms for heart disease classification. The study also highlights the need for more investigation into the potential of these optimization algorithms in many fields, broadening their use beyond the classification of disease. This kind of work might improve the progress of the field of feature selection and aid in the creation of better classification models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Particle swarm optimization (PSO)

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A New Concordance Correlation Coefficient based Fitness Function for Genetic Programming for Symbolic Regression 13

A New Concordance Correlation Coefficient based Fitness Func...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Xu, Jizhong Chen, Qi Xue, Bing Zhang, Mengjie Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

ISBN: (纸本)9798350308365

Coefficients learning has long been challenging in genetic programming based symbolic regression (GPSR). Recent GPSR methods employ Pearson correlation coefficient for fitness assessment with post-hoc linear scaling for coefficient learning. However, this approach often leads to sub-optimal coefficient learning and inadequate consideration of nonlinear relationships between input variables and outputs. To solve those issues, this study introduces an innovative approach to integrating the Concordance Correlation Coefficient (CCC) into GPSR. Unlike Pearson correlation, CCC can effectively assess both linear and non-linear agreements between two sets of variables. Experimental results on eight regression datasets highlight the potential of CCC as a promising fitness function for GPSR without the need of a more advanced coefficient optimisation method for linear scaling. © 2024 IEEE.

关键词： Contrastive Learning

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Multimodal contrastive learning for radiology report generation

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Journal of Ambient Intelligence and Humanized Computing 2023年第8期14卷 11185-11194页

作者： Wu, Xing Li, Jingwen Wang, Jianjia Qian, Quan School of Computer Engineering and Science Shanghai University Shanghai200444 China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China

Automated radiology report generation can not only lighten the workload of clinicians but also improve the efficiency of disease diagnosis. However, it is a challenging task to generate semantically coherent radiology reports that are also highly consistent with medical images. To meet the challenge, we propose a Multimodal Recursive model with Contrastive Learning (MRCL). The proposed MRCL method incorporates both visual and semantic features to generate "Impression" and "Findings" of radiology reports through a recursive network, in which a contrastive pre-training method is proposed to improve the expressiveness of both visual and textual representations. Extensive experiments and analyses prove the efficacy of the proposed MRCL, which can not only generate semantically coherent radiology reports but also outperform state-of-the-art methods. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Semantics

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Learning in sinusoidal spaces with physics-informed neural networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 985-1000页

作者： Wong, Jian Cheng Ooi, Chin Chun Gupta, Abhishek Ong, Yew Soon Institute of High Performance Computing Agency for Science Technology and Research Singapore138632 Singapore School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Singapore Institute of Manufacturing Technology Agency for Science Technology and Research Singapore138632 Singapore Agency for Science Technology and Research Singapore138632 Singapore Data Science and Artificial Intelligence Research Centre School of Computer Science and Engineering Nanyang Technological University Singapore138632 Singapore

A physics-informed neural network (PINN) uses physics-Augmented loss functions, e.g., incorporating the residual term from governing partial differential equations (PDEs), to ensure its output is consistent with fundamental physics laws. However, it turns out to be difficult to train an accurate PINN model for many problems in practice. In this article, we present a novel perspective of the merits of learning in sinusoidal spaces with PINNs. By analyzing behavior at model initialization, we first show that a PINN of increasing expressiveness induces an initial bias around flat output functions. Notably, this initial solution can be very close to satisfying many physics PDEs, i.e., falling into a localminimum of the PINN loss that onlyminimizes PDE residuals, while still being far from the true solution that jointly minimizes PDE residuals and the initial and/or boundary conditions. It is difficult for gradient descent optimization to escape from such a local minimum trap, often causing the training to stall. We then prove that the sinusoidalmapping of inputs-in an architecture we label as sf-PINN-is effective to increase input gradient variability, thus avoiding being trapped in such deceptive local minimum. The level of variability can be effectively modulated to match high-frequency patterns in the problem at hand. A key facet of this article is the comprehensive empirical study that demonstrates the efficacy of learning in sinusoidal spaces with PINNs for a wide range of forward and inversemodeling problems spanning multiple physics domains. Impact Statement-Falling under the emerging field of physicsinformed machine learning, PINN models have tremendous potential as a unifying AI framework for assimilating physics theory and measurement data. However, they remain infeasible for broad science and engineering applications due to computational cost and training challenges, especially for more complex problems. Instead of focusing on empirical demonstration of appli

关键词： Gradient methods

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A Cooperative Coevolution Neural Architecture Search Approach for Evolving Convolutional Neural Networks

A Cooperative Coevolution Neural Architecture Search Approac...

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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Londt, Trevor Gao, Xiaoying Mei, Yi Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

ISBN: (纸本)9798400704956

Evolutionary Neural Architecture Search (ENAS) automates convolutional neural network (CNNs) construction. Cooperative Coevolution (COCE) divides an optimisation problem into sub-problems. Our COCE ENAS algorithm decomposes a CNN into sub-architectures, evolving each sub-architecture separately but in cooperation. Tested on Fashion-MNIST, CIFAR-10, AG's News, and Yelp Reviews Full datasets, our proposed algorithm surpasses several state-of-the-art NAS methods in performance. Our algorithm is the first COCE-based ENAS algorithm that focuses on cooperatively evolving the four CNN sub-architectures and is effective in both image and text classification tasks. © 2024 held by the owner/author(s).

关键词： Convolutional neural networks

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Adaptive Treatment Planning via Multi-class Segmentation of GI Tract Tumours 6th

Adaptive Treatment Planning via Multi-class Segmentation of ...

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6th International conference on communication and computational technologies, ICCCT 2024

作者： Jain, Samyak Pal, Aarush Andrew, J. Maheshwari, Saatvik Department of Computer Science and Engineering and Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

ISBN: (纸本)9789819774258

This study addresses gastrointestinal cancer radiation therapy challenges by implementing advanced deep learning techniques. We focus on automating manual segmentation tasks during treatment planning to enhance efficiency and patient tolerance. Our methodology uses a diverse 3D MRI scan dataset to employ image analysis, Convolutional Neural Networks (CNNs), and Image Segmentation. The primary architectural choices involve the U-Net and InceptionResNetv2 models, contributing to developing a predictive segmentation model. This substantial result underscores the effectiveness of our approach in achieving accurate segmentations in the clinical setting. The overarching objective of our project is to automate the segmentation of the stomach and intestines in MRI scans, thereby streamlining radiation therapy planning for improved efficiency and patient care. In our evaluations, the implemented model demonstrates a commendable dice coefficient of 0.7, as indicated by metrics such as dice loss. Anticipated outcomes encompass reduced treatment times, enhanced patient comfort, and optimised tumour radiation doses while minimising healthy tissue exposure. The successful implementation of this advanced model holds the potential to revolutionise current radiation therapy practices, significantly elevating cancer treatment outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Red deer algorithm to detect the secret key of the monoalphabetic cryptosystem

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Soft Computing 2024年第17-18期28卷 10569-10582页

作者： Jain, Ashish Bansal, Sulabh Das, Nripendra Narayan Gupta, Shyam Sunder Department of Information Technology School of Information Security and Data Science Manipal University Jaipur Jaipur303007 India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Madhya Pradesh Gwalior474005 India

Encryption of a plaintext involves a secret key. The secret key of classical cryptosystems can be successfully determined by utilizing metaheuristic techniques. Monoalphabetic cryptosystem is one of the famous classical cryptosystems. In this paper, we determine the secret key of the monoalphabetic cryptosystem using a recently proposed metaheuristic technique, namely, red deer algorithm. The existing red deer algorithm framework has been tailored to solve the above considered problem. Performance of the developed red deer algorithm is compared with the following metaheuristic techniques: tabu search, genetic algorithm, and cuckoo search using three criteria, namely, effectiveness, efficiency, and accuracy. The results obtained show that the proposed red deer algorithm can compete with all the above three algorithms with respect to all the criteria. This signifies the importance of the proposed red deer algorithm is that it can be utilized to solve the similar problems effectively, efficiently, and with more accuracy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Tabu search

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A Study on Object Detection using Faster R-CNN, Retina net, RPN_R_50_FPN, RPN_R_50_C4

A Study on Object Detection using Faster R-CNN, Retina net, ...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Kadiyam, Aswin Tirumala Someswar Padhan, Chinmaya Ranjan Panigrahi, Sibarama National Institute of Technology Computer Science and Engineering Andhra Pradesh India National Institute of Technology Intelligent Data Analytics Laboratory Computer Science and Engineering Rourkela India

ISBN: (纸本)9798350369175

The computer vision concept that involves the detection and characterization of objects in images is known as object detection. This research mainly performs this function using four popular models faster R-CNN, RetinaNet, RPN_R_50_FPN, and RPN_R_50_C4 across multiple datasets namely udacity self-driving car dataset, tomatOD dataset, and chess piece detection dataset. Faster R-CNN is famous for its outstanding accuracy although RetinaNet employs a unique focal loss function to balance between speed and accuracy. The purpose of the models RPN_R_50_FPN and RPN_R_50_C4 is to generate region proposals with high quality using different stages in the ResNet-50 backbone. For normalization techniques, the datasets are preprocessed in this research. Instead of training the models from scratch, transfer learning is used whereby starting with weights that were pre-trained on the COCO dataset and after that fine-tuning the required datasets. Accuracy was quantified by evaluating two efficiency measures which are minimum loss and total training duration. Across the different datasets, various performances were demonstrated by different models. © 2024 IEEE.

关键词： Eye protection

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