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Low Routing Complexity Multiframe Pipelined LDPC Decoder Based on a Novel Pseudo Marginalized Min-Sum Algorithm for High Throughput Applications

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IEEE Transactions on Very Large Scale Integration (VLSI) Systems 2023年第1期31卷 29-42页

作者： Davila, HenryLopez Wu, Tsung-Han Jou, Shyh-Jye Jerry Chen, Sau-Gee Tsai, Pei-Yun National Chiao Tung University Eecs International Graduate Program Hsinchu30010 Taiwan National Yang Ming Chiao Tung University Institute of Electronics Hsinchu30010 Taiwan National Central University Electrical Engineering Department Taoyuan32001 Taiwan

This article presents a high throughput and low routing complexity multiframe pipelined low-density parity check (LDPC) decoder design based on a novel pseudo marginalized min-sum (PMMS) message passing approach. The proposed PMMS approach reduces the required number of interconnections in the routing network allowing the design to be implemented with reduced hardware complexity, low power consumption and high throughput capability while supporting multiple coding rates with short and long codewords as defined in many application standards. Implementation results for IEEE802.11ad/ay standards show that the proposed design satisfies a target bit error rate (BER) requirement of 3× 10-7 with 64 quadrature amp mod (QAM) targeting high throughput applications. Furthermore, the proposed design is able to achieve a throughput of 62 and 101.8 Gb/s with two pipelining stages under 28-nm CMOS and 16-nm FinFET CMOS process, respectively. As compared to the existing NMS algorithm, the proposed design based on the PMMS approach reduces the number of wires in the routing network by 45.5%, and the wirelength of the overall decoder by 17%. The area and power consumption are also reduced by 9.4% and 12%, respectively, as compared to the conventional normalized min-sum (NMS) algorithm. © 1993-2012 IEEE.

关键词： Message passing

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On Reliability Assessment of a Battery Energy Storage Systems Supporting PV Plants 8

On Reliability Assessment of a Battery Energy Storage System...

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8th Southern Power Electronics Conference and the 17th Brazilian Power Electronics Conference, SPEC / COBEP 2023

作者： Amorim, William Caires Silva Cupertino, Allan Fagner Mendes, Victor Flores De Sousa, Renata Oliveira De Barros, Rodrigo Cássio Pereira, Heverton Augusto Federal University of Minas Gerais Graduate Program in Electrical Engineering Belo Horizonte Brazil Federal University of Juz de Fora Department of Electrical Energy Juiz de Fora Brazil Federal University of Minas Gerais Department of Electrical Engineering Belo Horizonte Brazil Federal University of Tech. - Paraná Department of Electronics Curitiba Brazil Federal University of Recôncavo da Bahia Department of Electrical Engineering Cruz das Almas Brazil Federal University of Viçosa Department of Electrical Engineering Viçosa Brazil

ISBN: (纸本)9798350321128

Battery energy storage system (BESS) has been highlighted for its possibilities of performing ancillary services to the power system, such as voltage and frequency regulation, power quality, power smoothing, and peak shaving, among others. The BESS is very promising especially associated with renewable energies, such as photovoltaic (PV) and wind. Among some of these ancillary services, this work evaluates the battery lifetime and reliability. The proposed methodology includes the combined analysis of rainflow counting algorithm, Monte Carlo simulation, and nonlinear damage accumulation rule for battery reliability assessment. Monte Carlo simulation is used to approximate the battery reliability estimation of practical applications, by considering stochastic variations in the battery lifetime model. The case study is based on a 100 kW PV system, assessed using a one-year mission profile of solar irradiance and ambient temperature from Goiânla-Brazll associated with a mission profile of 100 kW industrial load. The PV plant is connected to a 440 V grid associated with a BESS that mitigates oscillations of 10% of PV nominal power per minute. The peak shaving service presented a decrease in the battery lifetime by approximately 11 % in relation to the deterministic battery lifetime analysis. On the other hand, the peak shaving presented a decrease in the battery lifetime by approximately 17.4 %. In addition, the pack-level analysis revealed a maximum reduction in the battery lifetime by approximately 30%, when compared to the conventional deterministic battery reliability estimation procedure. © 2023 IEEE.

关键词： Battery energy storage system mission profile Monte Carlo peak shaving power smoothing reliability

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A Performance Study of Selected Machine Learning Techniques for Predicting Heart Diseases 2nd

A Performance Study of Selected Machine Learning Techniques ...

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2nd International Conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

作者： Olorunfemi, Blessing Oluwatobi Ogunde, Adewale Opeoluwa Adeniyi, Abidemi Emmanuel Awotunde, Joseph Bamidele Imoize, Agbotiname Lucky Li, Chun-Ta Faculty of Natural Sciences Department of Computer Science Redeemer’s University Osun Nigeria Department of Computer Science Bowen University Iwo Nigeria Faculty of Information and Communication Sciences Department of Computer Science University of Ilorin Ilorin240003 Nigeria Faculty of Engineering Department of Electrical and Electronics Engineering University of Lagos Lagos Akoka100213 Nigeria Bachelor’s Program of Artificial Intelligence and Information Security Fu Jen Catholic University New Taipei City24206 Taiwan

ISBN: (纸本)9789819777853

Heart Disease remains a leading cause of mortality worldwide. It alarmingly rises at a quick rate, making early heart disease prediction crucial for effective prevention and timely intervention. Heart disease diagnosis is a difficult process that requires technical skills and accuracy to complete. With improvements in technology, computing has lent its voice to simplify the diagnosis of various health problems. Machine learning uses past or existing history to predict future results. Various machine learning techniques have been developed over the years and used in predicting heart diseases with various levels of performance. Identifying the best-suited machine learning technique to use for prediction purposes can be a challenging task. This research work analyses the performance of seven (7) machine learning techniques, comprising AdaBoost Algorithm, KNN, Logistic Regression, Naïve Bayes Classifier, Random Forest, SVM, and XGBoost. The heart disease dataset was downloaded from the UCI repository and analysed using Python programming language in the Jupyter Notebook environment. A comparative analysis of the seven (7) techniques was performed based on Accuracy, Precision, and Recall. From the results obtained, KNN, Random Forest, and XGBoost showed superior performance over the others with an accuracy of 100%, AdaBoost Algorithm followed with an accuracy of 92.2%, SVM followed with an accuracy of 91.71%, Naïve Bayes Classifier followed with an accuracy of 88.29% while Logistic Regression has the least accuracy of 86.34%. KNN, RF, and XGBoost outperformed AdaBoost, SVN, and LR. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Hyperspectral Image Classification Using Custom Spectral Convolutional Neural Networks (CSCNNs) 14

Hyperspectral Image Classification Using Custom Spectral Con...

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14th International Conference on Information Science and Technology, ICIST 2024

作者： Rouamba, Stephanie Zhang, Nian Mahmoud, Wagdy Thompson, Lara Denis, Max Deksissa, Tolessa University of the District of Columbia Department of Electrical and Computer Engineering WashingtonDC United States University of the District of Columbia Department of Mechanical Engineering Biomedical Engineering Program WashingtonDC United States University of the District of Columbia Department of Mechanical Engineering WashingtonDC United States Water Resources Research Institute University of the District of Columbia WashingtonDC United States

ISBN: (纸本)9798350353334

Custom spectral convolutional neural networks (CSCNNs) combine the strengths of convolutional neural networks with specialized spectral processing, resulting in improved classification accuracy by effectively capturing subtle variations in hyperspectral data. This paper proposes a CSCNNs based approach to classify hyperspectral images. The proposed method leverages the high-dimensional spectral data inherent in hyperspectral images, employing convolutional layers specifically designed to capture spectral-spatial features. By reducing dimensionality through principal component analysis and creating image patches, the model is trained to recognize complex patterns across different spectral bands. In addition, a comprehensive analysis of CSCNN performance is carried out, focusing on its architecture, key features, and benefits in computational efficiency and spectral representation. Experimental results on datasets such as Salinas-A, Pavia University (Pavia-U), and Indian Pines demonstrate that the CSCNN model surpasses traditional methods, achieving higher classification accuracy and more robust performance metrics like overall accuracy (OA), average accuracy (AA), and Kappa coefficient. The proposed model achieved an overall accuracy (OA) of 98.2%, 96.7%, and 92.5% for the Salinas-A, Pavia-U, and Indian Pines datasets, respectively. The model's average accuracy (AA) was 97.5%, 95.4%, and 89.8% for the same datasets. Furthermore, the model attained Kappa coefficients of 97.7%, 95.2%, and 89.1% for Salinas-A, Pavia-U, and Indian Pines, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Exponentially Enhanced Scheme for the Heralded Qudit Greenberger-Horne-Zeilinger State in Linear Optics

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Physical Review Letters 2024年第25期133卷 253601-253601页

作者： Seungbeom Chin Junghee Ryu Yong-Su Kim Okinawa Institute of Science and Technology Graduate University Okinawa 904-0495 Japan and Department of Electrical and Computer Engineering Sungkyunkwan University Suwon 16419 Korea

High-dimensional multipartite entanglement plays a crucial role in quantum information science. However, existing schemes for generating such entanglement become complex and costly as the dimension of quantum units increases. In this Letter, we overcome the limitation by proposing a significantly enhanced linear optical heralded scheme that generates the d-level N-partite Greenberger-Horne-Zeilinger (GHZ) state with single-photon sources and linear operations. Our scheme requires dN photons, which is the minimal required photon number, with substantially improved success probability from previous schemes. It employs linear optical logic gates compatible with any qudit encoding system and can generate generalized GHZ states with installments of beam splitters. With efficient generations of high-dimensional resource states, our work opens avenues for further exploration in high-dimensional quantum information processing.

关键词： Photons

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System Identification Study to Model a Digital Twin for a Heavy-Duty Vehicle's Oil System

System Identification Study to Model a Digital Twin for a He...

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IEEE/IAS International Conference on Industry Applications (INDUSCON)

作者： Mayron Vinicius Lopes de Camargo Elder Oroski Marco Wehrmeister Graduate Program in Electrical and Computer Engineering (CPGEI) Federal University of Technology - Parana Curitiba PR Brazil Graduate Program in Electrical Engineering (PPGSE) Federal University of Technology - Parana Curitiba PR Brazil

This paper explores the application of system identification to a lubrication system found in heavy-duty diesel engines. These engines are equipped with a variable oil pump and a cooling piston jet. The objective is to establish a dynamic model that accurately captures the relationship between the duty cycle of the valves and the resulting pressure values under normal road operating conditions to be used as a digital twin of the system. Additionally, the study aims to determine whether a simple recursive model can sufficiently describe the system with enough precision. Different linear and nonlinear models were evaluated and validated to identify the best fit for the system. Ultimately, the system was described using a Hammerstein-Wiener model, resulting in an 83.86% Normalized Root Mean Squared Error (NRMSE) for main gallery pressure and 82.69% for piston cooling jet gallery pressure.

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A∗ Algorithm-Based Indoor Navigation System for Multi-Story Building 13

A∗ Algorithm-Based Indoor Navigation System for Multi-Story...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Sariman, Muhammad Shazmin Othman, Maisara Saon, Sharifah Mahamad, Abd Kadir Kadir, Herdawatie Abdul Ahmadon, Mohd Anuaruddin Yamaguchi, Shingo Universiti Tun Hussein Onn Malaysia Faculty of Electrical and Electronic Engineering Johor Malaysia Universiti Teknologi Petronas Department of Computer and Information Sciences Perak Malaysia Yamaguchi University Graduate School of Sciences and Technology for Innovation Japan

ISBN: (纸本)9798350355079

Augmented reality (AR)-based indoor guidance is gaining popularity in various industries. With AR's capability to connect to other devices and perform innovative tasks, technological firms are investing heavily in its development. At the beginning of each semester, university students often struggle to navigate the campus, relying on maps or seeking help from others. This project addresses the need for an AR-based indoor navigation app specifically for Block QB, Faculty of electrical and Electronic engineering (FKEE), Universiti Tun Hussein Onn Malaysia (UTHM). The proposed method addresses a shortest path planning problem in a multi-story building in a computationally efficient way by adopting an A∗ algorithm. The app incorporates the A∗ algorithm to ensure users can find the fastest and most accurate routes. The indoor navigation app was successfully developed for Android users utilizing the 3D Unity software. It gives users with real-time, precise guidance within the three-story Block QB FKEE building. © 2024 IEEE.

关键词： Motion planning

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A Multi Soft-sensor Approach for the Development of Emergencies Detection Units on the Edge 6

A Multi Soft-sensor Approach for the Development of Emergenc...

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6th IEEE International Workshop on Metrology for Industry 4.0 and IoT, MetroInd4.0 and IoT 2023

作者： Oliveira, Franklin Costa, Daniel G. Assis, Flavio Silva, Ivanovitch Federal University of Bahia Graduate Program in Mechatronics Salvador Brazil University of Porto INEGI Faculty of Engineering Porto Portugal Federal University of Rio Grande Do Norte Department of Computer Engineering and Automation Natal Brazil

ISBN: (纸本)9798350396577

The adoption of multi-sensor hardware units to detect different types of emergencies in complex scenarios such as Industry 4.0 and Smart Cities is a recent development trend, which have demanded research efforts to optimize the way these units are assembled, configured, and deployed. However, although their expected advantages, usual multi-sensors emergency-oriented approaches may be constrained by the tight association between physical sensor devices and the emergency detection process. Therefore, this paper proposes an innovative approach to concurrently detect multiple emergencies exploiting the concept of soft-sensor modules, which are implemented on Emergency Detection Units (EDUs) as concurrent tasks along with a high-priority emergency alerting task. The soft-sensor concept defines physical sensors as ordinary input data streams, which will be processed according to the defined algorithms by each particular soft-sensor, allowing detection decisions to be performed on the edge. In order to support the practical exploitation of this concept, a formal specification based on Petri Nets is defined, modeling the concurrent operation of the tasks within an EDU. A proof of concept is also designed based on the affordable Raspberry Pi Pico microcontroller, demonstrating how the proposed approach would behave when two soft-sensors are implemented in a typical detection unit. © 2023 IEEE.

关键词： Industry 4.0

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SODIUM-ION BATTERY CYCLE LIFE PREDICTION USING MACHINE LEARNING

SODIUM-ION BATTERY CYCLE LIFE PREDICTION USING MACHINE LEARN...

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International Conference on Green Energy, Computing and Intelligent Technology 2024, GEn-CITy 2024

作者： Liu, Linfeng Juwono, Filbert H. Wong, W.K. Purwanto, Erick Jin, Tingyan Zhen, Yuyang Sustainable Energy Technology Program Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Electronic Engineering Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Computer Engineering Curtin University Malaysia Miri Malaysia Department of Computing Xi'an Jiaotong-Liverpool University Suzhou China School of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing China School of Instrument Science and Technology Beijing University of Technology Beijing China

ISBN: (纸本)9781837242689

In this study, we explore the classification and prediction capabilities of three models-Genetic programming (GP), Logistic Regression (LR), and the Kolmogorov-Arnold Network (KAN)-on the task of sodium-ion battery life prediction. By leveraging a dataset composed of multiple battery characteristics, we aim to determine the remaining power of sodium-ion batteries using these machine learning models. The KAN model, being a novel approach, demonstrates superior performance across various metrics, including accuracy, precision, recall, and F1 score, when compared to the other two models. This highlights the potential of KAN as a robust model for complex classification tasks in the field of battery life prediction. © The Institution of engineering & Technology 2024.

关键词： Genetic programming

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Wildlife Intrusion Detection System for Agriculture Protection Based on Image Recognition 13

Wildlife Intrusion Detection System for Agriculture Protecti...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Kiat, Yong Kit Som Mahamad, Abd Kadir Saon, Sharifah Othman, Maisara Kadir, Herdawatie Binti Abdul Ahmadon, Mohd Anuaruddin Yamaguchi, Shingo Universiti Tun Hussein Onn Malaysia Faculty of Electrical and Electronic Engineering Johor Malaysia Universiti Teknologi Petronas Department of Computer and Information Sciences Perak Malaysia Yamaguchi University Graduate School of Sciences and Technology for Innovation Japan

ISBN: (纸本)9798350355079

The proposed project uses a Raspberry Pi microcontroller to prevent crop losses caused by animals like dog, wild pigs, and monkeys. These animals pose a significant threat to farmers, leading to financial losses. This project offers an innovative solution, as it is impractical to physically secure entire fields or have farmers present at all times. Raspberry Pi 4 and the Raspberry Pi Camera Module 2 NoIR havebeen used in this project, to captures images of intruding animals, sends notifications to users, and triggers an alarm. The model obtained an accuracy of 95% for intruder detection. The goal is to provide timely alerts, allowing farmers to respond quickly and minimize crop damage, effectively addressing wildlife-induced agricultural losses. © 2024 IEEE.

关键词： Livestock

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