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A Novel JFG Detection-Decoding Approach of a Vertical Shuffle Scheduling for Iterative MIMO Receivers with NB-LDPC Codes

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SIGNAL PROCESSING 2022年 190卷 108287-108287页

作者： Haroun, Ali Al-Ghouwayel, Ali Chamas Hijazi, Hussein Jego, Christophe [a]Computer and Communications Engineering Department International University of Beirut BIU Beirut Lebanon [b]Computer and Communications Engineering department Lebanese International University LIU Bekaa Lebanon [c]BU SYSTEMS EXPLEO GROUP Toulouse France [d]Embedded Systems Division Ecole FRançaise d’Electronique et d’Informatique EFREI Paris France [e]Bordeaux INP CNRS IMS UMR 5218 Université de BordeauxTalence France

MIMO systems exploit multi-path propagation in a rich broadcast environment to improve throughput and performance of digital communication systems. The correspondent demappers require additional computational complexity. In this paper, a new detection-decoding approach based on vertical shuffle scheduling is proposed. Space exploration for the maximum likelihood detection is reduced to a dynamic region that surrounds the received symbol. Thus, a dynamic approach is proposed to detect the closest point of the channel observation without calculating all the candidate symbols in a constellation. In addition, a novel method based on EXIT chart study is proposed to find the sufficient number of neighboring points to search around a local minimum and then compute by recursion their Euclidean distances. Compared to the conventional Maximum Likelihood detector, the proposed algorithm allows a reduction in the computational complexity, in terms of number of multiplication, reaching 98 %. The simulation results show that the proposed algorithm outperforms the state-of-the-art algorithms like MMSE, sphere decoding, DBTC and DaVinci codes. Keywords: MIMO systems, Iterative Belief Propagation, Joint factor Graph, Vertical Shuffle Schedule, Dynamic Closest Point, EXIT Chart.

关键词： MIMO systems Iterative belief propagation Joint factor graph Vertical shuffle schedule Dynamic closest point EXIT Chart

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Multi-Objective Optimization of 3D Cell Deployment in Sustainable B5G/6G Networks: Balancing Performance and Sustainability

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IEEE Transactions on Network and Service Management 2025年

作者： Chien, Wei-Che Jeon, Gwanggil Cho, Hsin-Hung National Dong Hwa University Department of Computer Science and Information Engineering Hualien Taiwan Department of Embedded Systems Engineering Incheon National University Incheon Korea Republic of National Ilan University Department of Computer Science and Information Engineering YiLan26047 Taiwan

In recent years, the exponential increase in mobile and Internet of Things (IoT) data traffic has placed substantial demands on infrastructure for Internet Service Providers (ISPs). To meet these demands sustainably, it is critical to enhance energy efficiency, resource utilization, and cost-effectiveness while reducing the carbon footprint. Simply adding hardware is not a viable solution. This study introduces an innovative approach to 3D cellular deployment in sustainable B5G/6G networks, designed to optimize Quality of Service (QoS) for users and IoT devices. Although 5G/B5G utilizes millimeter waves for high data rate transmission, their limited coverage and susceptibility to interference from buildings pose unique deployment challenges. To address these, we formulate the 3D cellular deployment problem as a Multi-Objective Optimization (MOO) problem and propose an advanced deployment strategy using VA-NSGA-II, a metaheuristic-based algorithm. By factoring in building interference, Received Signal Strength Indicator (RSSI), coverage, deployment cost, and a balance between performance and sustainability, VA-NSGA-II provides an optimal deployment solution. Simulation results demonstrate that VA-NSGA-II achieves effective deployment performance across various building materials, highlighting its adaptability and effectiveness in different environmental scenarios. © 2004-2012 IEEE.

关键词： Internet of things

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Catalyzing EEG signal analysis: unveiling the potential of machine learning-enabled smart K nearest neighbor outlier detection

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International Journal of Information Technology (Singapore) 2024年 1-12页

作者： Aymen, Abid Khediri, Salim El Thaljaoui, Adel Miladi, Moahmed Kachouri, Abdennaceur Computer & amp Embedded Systems Lab National Engineering School of Sfax University of Sfax Sfax Tunisia Department of Information Technology College of Computer Qassim University Buraydah Saudi Arabia Department of Computer Science Faculty of Science of Gafsa University of Gafsa Gafsa Tunisia Preparatory Institute for Engineering Studies of Gafsa Gafsa Tunisia Department of Computer Science and Information College of Science at Zulfi Majmaah University Al-Majmaah 11952 Saudi Arabia Higher Institute of Computer Science and Multimedia of Sfax University of Sfax Sfax Tunisia Advanced Fluid Dynamics Energetics and Environment Lab National Engineering School of Sfax University of Sfax Sfax Tunisia

Electroencephalogram (EEG) data are susceptible to artifacts, such as lapses in concentration or poor imagination, which can significantly impact the accuracy of disease diagnosis in e-health applications. To mitigate this issue, the use of machine learning (ML) and potentially artificial intelligence (AI) solutions to accurately identify outliers becomes crucial. Unlike many AI methods that incorporate unnecessary or redundant input variables, our study focuses on detecting anomalous values in EEG data through the K nearest neighbor (KNN) process and Euclidean distance metric. Our proposed unsupervised non-parametric algorithm, known as the smart KNN outlier detector (SKOD), eliminates the need for initial parameter configurations such as the number of neighbors (K), while achieving high performance. Evaluation of SKOD using real EEG data from 140 trials demonstrated sensitivity and specificity exceeding 60%, with nearly perfect accuracy in detecting outliers reaching close to 100%. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Data analysis EEG signal Healthcare Knowledge-based systems Machine learning Outlier detection

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Development of a Wireless CPR Assistance Glove to Monitor and Guide Rescuers in Performing Cardiopulmonary Resuscitation 16

Development of a Wireless CPR Assistance Glove to Monitor an...

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16th IEEE Malaysia International Conference on Communication, MICC 2023

作者： Chang, Kai Han Chong, Kim Soon Tiang, Sew Sun Lim, Wei Hong Mokayef, Mastaneh Tan, Bee Sian Technology & Built Environment UCSI University Faculty of Engineering Kuala Lumpur Malaysia UCSI University Department Electrical & Electronic Engineering Kuala Lumpur Malaysia Tunku Abdul Rahman University of Management and Technology Department of Computer Science and Embedded Systems Kuala Lumpur Malaysia

ISBN: (纸本)9798350304343

Cardiopulmonary resuscitation (CPR) assistance glove represents a valuable tool for monitoring chest compressions during CPR, which is crucial for effective cardiac arrest response. By incorporating an MPU6050 accelerometer sensor and a force sensing resistor, the glove enables accurate measurement of the chest compression rate and force exerted. Preliminary testing demonstrated the glove's effectiveness in simulating chest compressions, with data collected and transmitted wirelessly to MATLAB software. Manual counting and MATLAB analysis validated the accuracy of compression rate calculations, while force sensing resistor voltage readings provided insights into the force exerted during compressions. These results highlight the glove's potential to significantly enhance the precision and quality of CPR administration, improving outcomes for cardiac arrest patients. © 2023 IEEE.

关键词： Resuscitation

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Asynchronous, Spatiotemporal Filtering using an Analog Cellular Neural Network Processor

Asynchronous, Spatiotemporal Filtering using an Analog Cellu...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Jonah P. Sengupta Michael A. Tomlinson Daniel R. Mendat Martin Villemur Andreas G. Andreou Department of Electrical & Computer Engineering Johns Hopkins University Embedded Systems Division Silicon Austria Labs

Neuromorphic processing architectures seek to emulate the functionality of the brain by realizing parallel, efficient, event-based processing which can be directly applied to solve many of the pressing problems within artificial intelligence and big data. However, implementation of these systems leads to slow response times, high power dissipation, or incoherent output. In this paper, an analog cellular neural network processing element is demonstrated to perform asynchronous spatiotemporal filtering operations in an area and power efficient manner. It utilizes a pair of analog memories to encode spike timings and perform event-based bandpass temporal processing. Information from the local clique of temporal filters is leveraged by a parallel, spatial processor which maps CNN arithmetic to the current-domain for compact computation. Preliminary circuit verification demonstrated the ability of the element to perform spatiotemporal filtering operations with latencies less than $1.8\mu\mathrm{s}$ while only consuming 1.6pJ/spike.

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RUL Estimation for Power Electronic Devices Using RNNs

RUL Estimation for Power Electronic Devices Using RNNs

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Prognostics and System Health Management Conference (PHM)

作者： Congrui Cai Zhonghai Lu Embedded Systems Master Program School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Department of Electrical Engineering School of Electrical Engineering and Computer Science KTH Royal Institute of Technology

ISBN: (数字)9798350360585

ISBN: (纸本)9798350360592

Recurrent Neural Networks (RNNs) are commonly used in data-driven approaches to estimate the Remaining Useful Lifetime (RUL) of power electronic devices. RNNs are preferred because their intrinsic feedback mechanisms are better suited to model time-series data. However, the impact of RNN complexity on estimation accuracy is rarely discussed in the literature. This issue is important because choosing a lower-complexity model that delivers the same or similar performance as a higher-complexity model can increase implementation efficiency. In the paper, we use three RNN models, namely, the vanilla version, LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) to conduct RUL estimation for power electronic devices. We use two accelerated aging datasets, one dataset targeting the package failure of MOSFETs, and the other dataset targeting package failure of power diodes. Our study shows that a lower-complexity RNN does not necessarily deliver a lower performance. Similarly, a higher-complexity model does not assure a higher performance. As such, our work highlights the importance of selecting a proper neural network for RUL estimation not biased towards complex models. This is especially useful and important for implementing such RUL estimation techniques in embedded resource-constrained and speed-limited computins platforms.

关键词： Performance evaluation Semiconductor device modeling Recurrent neural networks Computational modeling Estimation Power electronics Data models

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Intelligent Safety Early Warning System for High-Risk Areas Based On BeiDou and STM32 4

Intelligent Safety Early Warning System for High-Risk Areas ...

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4th International Symposium on Smart and Healthy Cities, ISHC 2022

作者： Mei, Yongbiao Zhang, Zhourong Yu, Fang Jiang, Liang Liu, Zicong Chen, Junjie Xiangnan University School of Computer and Artificial Intelligence Xiangnan University Hunan Engineering Research Center of Advanced Embedded Computing Intelligent Medical Systems Hunan Chenzhou China

ISBN: (纸本)9781665491808

Aiming at the requirements of safety production, this paper designs an Intelligent Safety Early Warning System for High-Risk Areas based on BeiDou and STM32. The system utilizes the high-precision positioning of the BeiDou Navigation Satellite System, combined with an electronic fence and a sensing technology, to limit the safety grade of different areas for all personnel in high-risk areas, and meanwhile, the surrounding environment, the posture of workers and the like are intelligently detected by using an Internet of Things technology such as a sensor, achieving the role of timely supervision and safety warning, effectively improving the safety of operations in high-risk areas, and enhancing management efficiency. © 2022 IEEE.

关键词： Human resource management

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Digital Twin Ancient Architecture: The Exploration of the Digital Protection of Ancient Architecture

Digital Twin Ancient Architecture: The Exploration of the Di...

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作者： Li, Shanglin Lin, Anping School of Computer Science and Artificial Intelligence Xiangnan University Hunan Chenzhou423000 China Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems Hunan Chenzhou China School of Physics and Electronic Electrical Engineering Xiangnan University Hunan Chenzhou China

The digital protection of ancient architectures has achieved important achievements. However, technology development has encountered a bottleneck. The main problem is that the digitization of ancient buildings is limited to building static and isolated three-dimensional models, which cannot meet the increasing professional requirements of digital protection. To solve the above problems, the concept of digital twin ancient architecture (DTAA) is proposed, focusing on the high-dimensional data integration method of the DTAA, as well as the construction method and technical route. Finally, the development and application of digital twin ancient architecture are discussed. The proposed method can bring new ideas to the field of digital protection of ancient architectures. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： 3D modeling

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Optimization Mechanism of Virtualized Database Log System Under the Background of Intelligent Technology

Optimization Mechanism of Virtualized Database Log System Un...

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作者： Xie, Hui Wei, Li Kumar, Aditya School of Computer and Artificial Intelligence Xiangnan University Hunan Chenzhou China Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems Xiangnan University Hunan Chenzhou China Gachon University Seongnam Incheon Korea Republic of

With the development of industrial science and technology, all walks of life are developing in the direction of intelligence, integration and datamation. Under the background of intelligent technology development, big data will become one of the main directions of the development of industrial data information integration in the future. While the log system as the data behavior recording terminal in the database, it makes node records on the behavior of the huge data stream terminal users. But, the existing database log system cannot accurately reflect the "activity" of the data in the real world, and there may be the problem of inconsistency in granularity between the users’ actual operation behavior and the log node in the database. Therefore, this study will propose specific optimization measures based on the Oracle database log system so as to make the nodes of the database log system more accurate and improve the efficiency and security of the system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Database systems

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Optimal Deep Dense Convolutional Neural Network Based Classification Model for COVID-19 Disease

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computers, Materials & Continua 2022年第1期70卷 2031-2047页

作者： A.Sheryl Oliver P.Suresh A.Mohanarathinam Seifedine Kadry Orawit Thinnukool Department of Computer Science and Engineering St.Joseph’s College of Engineering600119ChennaiIndia Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Faculty of Engineering Karpagam Academy of Higher EducationCoimbatore641021India Faculty of Applied Computing and Technology Noroff University CollegeKristiansand4608Norway Research Group of Embedded Systems and Mobile Application in Health Science College of ArtsMedia and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Early diagnosis and detection are important tasks in controlling the spread of COVID-19.A number of Deep Learning techniques has been established by researchers to detect the presence of COVID-19 using CT scan images and ***,these methods suffer from biased results and inaccurate detection of the ***,the current research article developed Oppositional-based Chimp Optimization Algorithm and Deep Dense Convolutional Neural Network(OCOA-DDCNN)for COVID-19 prediction using CT images in IoT *** proposed methodology works on the basis of two stages such as pre-processing and ***,CT scan images generated from prospective COVID-19 are collected from open-source system using IoT *** collected images are then preprocessed using Gaussian *** filter can be utilized in the removal of unwanted noise from the collected CT scan ***,the preprocessed images are sent to prediction *** this phase,Deep Dense Convolutional Neural Network(DDCNN)is applied upon the pre-processed *** proposed classifier is optimally designed with the consideration of Oppositional-basedChimp Optimization Algorithm(OCOA).This algorithm is utilized in the selection of optimal parameters for the proposed ***,the proposed technique is used in the prediction of COVID-19 and classify the results as either COVID-19 or *** projected method was implemented in MATLAB and the performances were evaluated through statistical *** proposed method was contrasted with conventional techniques such as Convolutional Neural Network-Firefly Algorithm(CNN-FA),Emperor Penguin Optimization(CNN-EPO)*** results established the supremacy of the proposed model.

关键词： Deep learning deep dense convolutional neural network covid-19 CT images chimp optimization algorithm

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