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Low-complexity signal detection for massive MIMO systems via trace iterative method

Journal of Systems engineering and Electronics 2024年第3期35卷 549-557页

作者： IMRAN A.Khoso ZHANG Xiaofei ABDUL Hayee Shaikh IHSAN A.Khoso ZAHEER Ahmed Dayo College of Electronic and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China School of Electrical Engineering Korea UniversitySeoul 02841Korea School of Mathematics South China University of TechnologyGuangzhou 510641China Department of Computer Science Huanggang Normal UniversityHuanggang 438000China

Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which entails high *** avoid the exact matrix inversion,a considerable number of implicit and explicit approximate matrix inversion based detection methods is *** combining the advantages of both the explicit and the implicit matrix inversion,this paper introduces a new low-complexity signal detection ***,the relationship between implicit and explicit techniques is ***,an enhanced Newton iteration method is introduced to realize an approximate MMSE detection for massive MIMO uplink *** proposed improved Newton iteration significantly reduces the complexity of conventional Newton ***,its complexity is still high for higher ***,it is applied only for first two *** subsequent iterations,we propose a novel trace iterative method(TIM)based low-complexity algorithm,which has significantly lower complexity than higher Newton *** guarantees of the proposed detector are also *** simulations verify that the proposed detector exhibits significant performance enhancement over recently reported iterative detectors and achieves close-to-MMSE performance while retaining the low-complexity advantage for systems with hundreds of antennas.

关键词： signal detection low-complexity linear minimum mean square error(MMSE) massive multiple-input multiple-output(MIMO) trace iterative method(TIM)

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A Survey on Digital Twins: Enabling Technologies, Use Cases, Application, Open Issues, and More

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2025年 2卷 84-107页

作者： Hassija, Vikas Chamola, Vinay De, Rajdipta Das, Soham Chakrabarti, Arjab Sangwan, Kuldip Singh Pandey, Amit Deemed to be University School of Computer Engineering Bhubaneswar751024 India BITS-Pilani Department of Electrical and Electronics Engineering Pilani Campus Pilani333031 India BITS Pilani Department of Mechanical Engineering Pilani Campus Pilani333031 India Bennett University Computer Science Engineering and Technology Department Greater Noida201310 India

Digital Twins, sophisticated digital replicas of physical entities, have been gaining significant attention, especially after NASA's endorsement, and are poised to revolutionize numerous fields, such as medicine and construction. These advanced models offer dynamic, real-time simulations, leveraging enabling technologies, such as artificial intelligence, machine learning, IoT, cloud computing, and Big Data analytics to enhance their functionality and applicability. In the medical field, Digital Twins facilitate personalized treatment plans and predictive maintenance of medical equipment by simulating human organs with precision. In construction, they enable efficient building design and urban planning, optimizing resource use, and reducing costs through predictive maintenance. Startups are innovatively employing Digital Twins in various sectors, from smart cities - where they optimize traffic flow, energy consumption, and waste management - to industrial machinery, ensuring predictive maintenance and minimizing downtime. This survey delves into the diverse use cases, market potential, and challenges of Digital Twins, such as data security and interoperability, while emphasizing their transformative impact on industries. The future prospects are promising, with continuous advancements in AI, ML, IoT, and cloud computing driving further expansion and application of Digital Twin technologies. © 2024 IEEE.

关键词： Predictive maintenance

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DLMNN Based Heart Disease Prediction with PD-SS Optimization Algorithm

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Intelligent Automation & Soft computing 2023年第2期35卷 1353-1368页

作者： S.Raghavendra Vasudev Parvati R.Manjula Ashok Kumar Nanda Ruby Singh D.Lakshmi S.Velmurugan Department of Information and Communication Technology Manipal Institute of TechnologyManipal Academy of Higher EducationManipal576104India Department of Information Science and Engineering SDM College of Engineering and TechnologyDharwad580002India Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyKattankulathurChennai603203India Department of Computer Science and Engineering B.V Raju Institute of TechnologyTelangana502313India Faculty of Engineering and Technology SRM Institute of Science and TechnologyNCR CampusUttar Prasesh201204India School of Computing Science and Engineering VIT Bhopal UniversityMadhya Pradesh466114India Department of Computer Science and Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India

In contemporary medicine,cardiovascular disease is a major public health *** diseases are one of the leading causes of death *** are classiﬁed as vascular,ischemic,or *** information contained in patients’Electronic Health Records(EHR)enables clin-icians to identify and monitor heart *** failure rates have risen drama-tically in recent years as a result of changes in modern *** diseases are becoming more prevalent in today’s medical *** year,a substantial number of people die as a result of cardiac *** primary cause of these deaths is the improper use of pharmaceuticals without the supervision of a physician and the late detection of *** improve the efﬁciency of the classiﬁcation algo-rithms,we construct a data pre-processing stage using feature ***-ments using unidirectional and bidirectional neural network models found that a Deep Learning Modiﬁed Neural Network(DLMNN)model combined with the Pet Dog-Smell Sensing(PD-SS)algorithm predicted the highest classiﬁcation performance on the UCI Machine Learning Heart Disease *** DLMNN-based PDSS achieved an accuracy of 94.21%,an F-score of 92.38%,a recall of 94.62%,and a precision of 93.86%.These results are competitive and promising for a heart disease *** demonstrated that a DLMNN framework based on deep models may be used to solve the categorization problem for an unbalanced heart disease *** proposed approach can result in exceptionally accurate models that can be utilized to analyze and diagnose clinical real-world data.

关键词： Machine learning random forest coronary heart disease cardiovascular optimization algorithm

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Elite-guided equilibrium optimiser based on information enhancement:Algorithm and mobile edge computing applications

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CAAI Transactions on Intelligence Technology 2024年第5期9卷 1126-1171页

作者： Zong-Shan Wang Shi-Jin Li Hong-Wei Ding Gaurav Dhiman Peng Hou Ai-Shan Li Peng Hu Zhi-Jun Yang Jie Wang School of Information Science and Engineering Yunnan UniversityKunmingChina Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhenChina Academic Affairs Office Yunnan University of Finance and EconomicsKunmingChina Faculty of Management and Economics Kunming University of TechnologyKunmingChina Department of Electrical and Computer Engineering Lebanese American UniversityBeirutLebanon Department of Computer Science and Engineering University Centre for Research and DevelopmentChandigarh UniversityChennaiIndia Department of Computer Science and Engineering Graphic Era Deemed to Be UniversityDehradunIndia School of Computer Science Fudan UniversityShanghaiChina Rackham Graduate School University of MichiganAnn ArborMIUSA Research and Development Department Youbei Technology Co.LTDKunmingChina School of Mechanical and Power Engineering Zhengzhou UniversityZhengzhouChina

The Equilibrium Optimiser(EO)has been demonstrated to be one of the metaheuristic algorithms that can effectively solve global optimisation *** the paradox between exploration and exploitation operations while enhancing the ability to jump out of the local optimum are two key points to be addressed in EO *** alleviate these limitations,an EO variant named adaptive elite-guided Equilibrium Optimiser(AEEO)is ***,the adaptive elite-guided search mechanism enhances the balance between exploration and *** modified mutualism phase reinforces the information interaction among particles and local optima *** cooperation of these two mechanisms boosts the overall performance of the basic *** AEEO is subjected to competitive experiments with state-of-the-art algorithms and modified algorithms on 23 classical benchmark functions and IEE CEC 2017 function test *** results demonstrate that AEEO outperforms several well-performing EO variants,DE variants,PSO variants,SSA variants,and GWO variants in terms of convergence speed and *** addition,the AEEO algorithm is used for the edge server(ES)placement problem in mobile edge computing(MEC)*** experimental results show that the author’s approach outperforms the representative approaches compared in terms of access latency and deployment cost.

关键词： ANT COLONY optimization CLOUD computing GENETIC ALGORITHMS SWARM intelligence

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Enabling Precise Diabetic Retinopathy Severity Prediction through Convolutional Neural Networks 10

Enabling Precise Diabetic Retinopathy Severity Prediction th...

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10th International Conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Shruti, P. Reddy, Tanuja Konda Abhiram, S. Singh, Rimjhim Padam Kanchan, Sneha Amrita School of Computing Amrita Vishwa Vidyapeetam Department of Computer Science & Engineering Bengaluru India Universiti Tunku Abdul Rahman Dept. of Internet Engineering and Computer science Malaysia

ISBN: (纸本)9798350384369

Diabetes retinopathy (DR) continues to be a major public health concern, needing efficient and reliable approaches for early identification and severity categorization. In this work, we used deep learning to improve the diagnosis procedure of diabetic retinopathy, which is blindness caused by diabetes. Deep learning algorithms, notably neural networks, have shown extraordinary performance in image analysis tasks, demonstrating their potential to transform medical picture classification. The dataset used was the APTOS-2019 dataset with 3930 retinal images and corresponding classes of severity of the disease. Using a custom-designed CNN architecture, we focused on diabetic retinopathy severity classification, a multiclass classification consisting of 5 classes. The suggested custom model outperformed existing pretrained models in terms of accuracy with training accuracy at a high 98% and testing accuracy of 93%, showing its efficacy in diabetic retinopathy severity categorization. Our discoveries revealed the potential of customized deep learning models to improve diagnostic speed and accuracy, providing a major advancement toward individualized and successful patient treatment in the field of managing diabetic retinopathy. © 2024 IEEE.

关键词： Convolutional neural networks

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Next-Generation Consumer Electronics Data Auditing Scheme Toward Cloud-Edge Distributed and Resilient Machine Learning

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 2244-2256页

作者： Li, Yi Shen, Jian Vijayakumar, Pandi Lai, Chin-Feng Sivaraman, Audithan Sharma, Pradip Kumar Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Zhejiang Sci-Tech University School of Information Science and Engineering Hangzhou310018 China University College of Engineering Tindivanam Anna University Chennai Department of Computer Science and Engineering Tindivanam604001 India National Cheng Kung University Department of Engineering Science Tainan701 Taiwan Netaji Subhas University of Technology Department of Information Technology New Delhi110078 India University of Aberdeen Department of Computing Science AberdeenAB24 3UE United Kingdom

Distributed and resilient machine learning (DRML) endues next-generation consumer electronics with AI function. Intuitively, AI provides innovative, humanized, convenient applications based on the data extended by next-generation consumer electronics. Cloud-edge computing is an ideal undertaken architecture of DRML due to its distributed property. However, as one of the core elements driving AI applications, data could be lost or corrupted owing to damaged electronics, unstable communication, and even cloud providers' malicious behavior. It is essential to ensure the data integrity of next-generation electronics before AI applications. To this end, we proposed a privacy-protection distributed data auditing scheme for cloud-edge DRML. An efficient data integrity verification method that only uses algebraic operation is constructed. Then, Function Secret Share (FSS) extends the data integrity verification method to protect consumer privacy. Besides, a consensus for data auditing results is designed among the edge servers. Finally, we present an abundance of theoretical analyses and experimental findings to substantiate and validate the efficiency and effectiveness of our proposed scheme. © 1975-2011 IEEE.

关键词： Edge computing

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Accurate and Optimized Labelling of Fashion Products Through Attention Based Spiking Neural Networks 10

Accurate and Optimized Labelling of Fashion Products Through...

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10th International Conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Vithyatharshana, N. Thrinayani, Yedhoti Kulkarni, Yogeshwar Nitin Singh, Rimjhim Padam Kanchan, Sneha Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Universiti Tunku Abdul Rahman Department of Internet Engineering and Computer science Malaysia

ISBN: (纸本)9798350384369

Automation of garment classification has become the backbone of e-commerce, influencing product search, personalization, and customer experience. This paper uses the Fashion MNIST dataset, a rich collection of clothing images to investigate the potential of Spiking Neural Networks (SNNs) for accurate and efficient classification. SNNs are an excellent choice for widespread usage and real-world integration due to their low power consumption and biomimicry. With the intention of revolutionizing product search, personalization, and customer experience, SNNs are made to address style differences, perspectives, and illumination. In order to fully utilize Fashion MNIST, this paper suggests strategic improvements to the SNN architecture. By addressing key challenges and introducing novel optimizations, our research contributes to a more robust and intelligent future for automated fashion product labelling. An SNN model with 93.6% accuracy has been developed as the base model for all the applications for the same. This data-driven approach leads the way to flawless product searches, highly customized recommendations, and a more intelligent fashion technology in future. © 2024 IEEE.

关键词： Deep learning

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Exploring Video Event Classification: Leveraging Two-Stage Neural Networks and Customized CNN Models with UCF-101 and CCV Datasets 18

Exploring Video Event Classification: Leveraging Two-Stage N...

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18th INDIAcom;11th International Conference on computing for Sustainable Global Development, INDIACom 2024

作者： Sachdeva, Kumud Sandhu, Jasminder Kaur Sahu, Rakesh Chandigarh University Department of Computer Science and Engineering Punjab Gharuan India Galgotias University School of Computing Science & Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9789380544519

Rapid technological advances have significantly improved our ability to analyse video data. This comprehensive review examines machine learning (ML) models applied to video event detection and classification, including CNNs, deep neural networks (DNNs), and RNNs. When evaluated on benchmark datasets for accuracy, these approaches demonstrate their relative strengths and weaknesses. Researchers have encountered numerous challenges in video event detection, which are addressed throughout the review. However, achieving high detection precision remains challenging due to diverse event types, video quality issues, model over fitting risks, and lack of large labeled training datasets. Background scenes, lighting, and object occlusion further complicate accurate identification. As datasets and computational power grow, video event detection stands to gain significantly. This review assessed action recognition models trained on the UCF-101 and CCV databases. On CCV, a 2-stage neural network achieved 75% accuracy;while a multi-stream deep learning (DL) system obtained 77.5%. For the larger UCF101, 2-stream and RNN architectures realized 92% and 89% accuracy using video-level prediction. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： CCV datasets Convolutional Neural Network (CNN) Deep Neural Network (DNN) Recurrent Neural Network (RNN) UCF-101 Video Event Detection (VED)

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Revisiting LARS for Large Batch Training Generalization of Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1321-1333页

作者： Do, Khoi Nguyen, Minh-Duong Hoa, Nguyen Tien Tran-Thanh, Long Tran, Nguyen H. Pham, Quoc-Viet Trinity College Dublin School of Computer Science and Statistics Dublin 2 D02PN40 Ireland Pusan National University Department of Information Convergence Engineering Busan46241 Korea Republic of Hanoi University of Science and Technology School of Electrical and Electronic Engineering Hanoi100000 Viet Nam University of Warwick School of Computer Science CoventryCV4 7AL United Kingdom The University of Sydney School of Computer Science DarlingtonNSW2006 Australia Kyung Hee University Department of Computer Science and Engineering Yongin446-701 Korea Republic of

This article investigates large batch training techniques using layer-wise adaptive scaling ratio (LARS) across diverse settings. In particular, we first show that a state-of-the-art technique, called LARS with the warm-up, tends to be trapped in sharp minimizers early on due to redundant ratio scaling. Additionally, a fixed steep decline in the latter phase restricts deep neural networks from effectively navigating early-phase sharp minimizers. To address these issues, we propose time varying LARS (TVLARS), a novel algorithm that replaces warm-up with a configurable sigmoid-like function for robust training in the initial phase. TVLARS promotes gradient exploration early on, surpassing sharp optimizers and gradually transitioning to LARS for robustness in later stages. Extensive experiments demonstrate that TVLARS consistently outperforms LARS and LAMB in most cases, with up to 2% improvement in classification scenarios. In all self-supervised learning cases, TVLARS achieves up to 10% performance improvement. © 2020 IEEE.

关键词： Self-supervised learning

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Lungcanary- Pioneering Early Lung Cancer Detection using Machine Learning Algorithms 8

Lungcanary- Pioneering Early Lung Cancer Detection using Mac...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Govindarajan, Priya Abhishek, M. Niharika, K. Tesfahun, Abebe Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science Mysuru Campus India School of Electrical & Computer Engineering Debre Markos Institute of Technology Debre Markos University Ethiopia Ethiopia

ISBN: (纸本)9798331505462

Lung cancer, a malignant tumour originating in the lung cells, is notoriously challenging to detect early through conventional clinical procedures, which often require invasive techniques. This study introduces LungCanary, an innovative approach leveraging machine learning algorithms to address the global health challenge of early lung cancer detection. By employing advanced computational methods and utilizing transfer learning principles, LungCanary effectively extracts meaningful features from medical imaging data. The model incorporates multiple decision-making components to capture diverse data patterns, significantly enhancing diagnostic precision. Through rigorous experimentation, LungCanary demonstrated superior performance with an accuracy of 98.36%, a precision of 96%, and an error rate of just 1.64%. These findings highlight LungCanary's potential to outperform existing models, marking a breakthrough in the accuracy and reliability of lung cancer diagnostics. The significance of this research lies in its potential to revolutionize early detection methodologies, ultimately improving patient outcomes. © 2024 IEEE.

关键词： Lung cancer

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