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  • Open Access

    ARTICLE

    A New Speed Limit Recognition Methodology Based on Ensemble Learning: Hardware Validation

    Mohamed Karray1,*, Nesrine Triki2,*, Mohamed Ksantini2

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 119-138, 2024, DOI:10.32604/cmc.2024.051562

    Abstract Advanced Driver Assistance Systems (ADAS) technologies can assist drivers or be part of automatic driving systems to support the driving process and improve the level of safety and comfort on the road. Traffic Sign Recognition System (TSRS) is one of the most important components of ADAS. Among the challenges with TSRS is being able to recognize road signs with the highest accuracy and the shortest processing time. Accordingly, this paper introduces a new real time methodology recognizing Speed Limit Signs based on a trio of developed modules. Firstly, the Speed Limit Detection (SLD) module uses… More >

  • Open Access

    ARTICLE

    A Novel Anti-Collision Algorithm for Large Scale of UHF RFID Tags Access Systems

    Xu Zhang1, Yi He1, Haiwen Yi1, Yulu Zhang2, Yuan Li2, Shuai Ma2, Gui Li3, Zhiyuan Zhao4, Yue Liu1, Junyang Liu1, Guangjun Wen1, Jian Li1,*

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 897-912, 2024, DOI:10.32604/cmc.2024.050000

    Abstract When the radio frequency identification (RFID) system inventories multiple tags, the recognition rate will be seriously affected due to collisions. Based on the existing dynamic frame slotted Aloha (DFSA) algorithm, a sub-frame observation and cyclic redundancy check (CRC) grouping combined dynamic framed slotted Aloha (SUBF-CGDFSA) algorithm is proposed. The algorithm combines the precise estimation method of the quantity of large-scale tags, the large-scale tags grouping mechanism based on CRC pseudo-random characteristics, and the Aloha anti-collision optimization mechanism based on sub-frame observation. By grouping tags and sequentially identifying them within subframes, it accurately estimates the number More >

  • Open Access

    REVIEW

    Maximum Power Point Tracking Technology for PV Systems: Current Status and Perspectives

    Bo Yang1,2, Rui Xie1, Zhengxun Guo3,4,*

    Energy Engineering, Vol.121, No.8, pp. 2009-2022, 2024, DOI:10.32604/ee.2024.049423

    Abstract Maximum power point tracking (MPPT) technology plays a key role in improving the energy conversion efficiency of photovoltaic (PV) systems, especially when multiple local maximum power points (LMPPs) occur under partial shading conditions (PSC). It is necessary to modify the operating point efficiently and accurately with the help of MPPT technology to maximize the collected power. Even though a lot of research has been carried out and impressive progress achieved for MPPT technology, it still faces some challenges and dilemmas. Firstly, the mathematical model established for PV cells is not precise enough. Second, the existing… More > Graphic Abstract

    Maximum Power Point Tracking Technology for PV Systems: Current Status and Perspectives

  • Open Access

    REVIEW

    IoMT-Based Healthcare Systems: A Review

    Tahir Abbas1,*, Ali Haider Khan2, Khadija Kanwal3, Ali Daud4,*, Muhammad Irfan5, Amal Bukhari6, Riad Alharbey6

    Computer Systems Science and Engineering, Vol.48, No.4, pp. 871-895, 2024, DOI:10.32604/csse.2024.049026

    Abstract The integration of the Internet of Medical Things (IoMT) and the Internet of Things (IoT), which has revolutionized patient care through features like remote critical care and real-time therapy, is examined in this study in response to the changing healthcare landscape. Even with these improvements, security threats are associated with the increased connectivity of medical equipment, which calls for a thorough assessment. With a primary focus on addressing security and performance enhancement challenges, the research classifies current IoT communication devices, examines their applications in IoMT, and investigates important aspects of IoMT devices in healthcare. The More >

  • Open Access

    ARTICLE

    Finite Difference-Peridynamic Differential Operator for Solving Transient Heat Conduction Problems

    Chunlei Ruan1,2,*, Cengceng Dong1, Zeyue Zhang1, Boyu Chen1, Zhijun Liu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2707-2728, 2024, DOI:10.32604/cmes.2024.050003

    Abstract Transient heat conduction problems widely exist in engineering. In previous work on the peridynamic differential operator (PDDO) method for solving such problems, both time and spatial derivatives were discretized using the PDDO method, resulting in increased complexity and programming difficulty. In this work, the forward difference formula, the backward difference formula, and the centered difference formula are used to discretize the time derivative, while the PDDO method is used to discretize the spatial derivative. Three new schemes for solving transient heat conduction equations have been developed, namely, the forward-in-time and PDDO in space (FT-PDDO) scheme,… More >

  • Open Access

    ARTICLE

    A Novel Method for Determining the Void Fraction in Gas-Liquid Multi-Phase Systems Using a Dynamic Conductivity Probe

    Xiaochu Luo1, Xiaobing Qi2, Zhao Luo3, Zhonghao Li4, Ruiquan Liao1, Xingkai Zhang1,*

    FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.6, pp. 1233-1249, 2024, DOI:10.32604/fdmp.2023.045737

    Abstract Conventional conductivity methods for measuring the void fraction in gas-liquid multiphase systems are typically affected by accuracy problems due to the presence of fluid flow and salinity. This study presents a novel approach for determining the void fraction based on a reciprocating dynamic conductivity probe used to measure the liquid film thickness under forced annular-flow conditions. The measurement system comprises a cyclone, a conductivity probe, a probe reciprocating device, and a data acquisition and processing system. This method ensures that the flow pattern is adjusted to a forced annular flow, thereby minimizing the influence of More >

  • Open Access

    ARTICLE

    Enhancing Secure Development in Globally Distributed Software Product Lines: A Machine Learning-Powered Framework for Cyber-Resilient Ecosystems

    Marya Iqbal1, Yaser Hafeez1, Nabil Almashfi2, Amjad Alsirhani3, Faeiz Alserhani4, Sadia Ali1, Mamoona Humayun5,*, Muhammad Jamal6

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5031-5049, 2024, DOI:10.32604/cmc.2024.051371

    Abstract Embracing software product lines (SPLs) is pivotal in the dynamic landscape of contemporary software development. However, the flexibility and global distribution inherent in modern systems pose significant challenges to managing SPL variability, underscoring the critical importance of robust cybersecurity measures. This paper advocates for leveraging machine learning (ML) to address variability management issues and fortify the security of SPL. In the context of the broader special issue theme on innovative cybersecurity approaches, our proposed ML-based framework offers an interdisciplinary perspective, blending insights from computing, social sciences, and business. Specifically, it employs ML for demand analysis, More >

  • Open Access

    ARTICLE

    Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria

    Djeldjli Halima1,*, Benatiallah Djelloul1, Ghasri Mehdi2, Tanougast Camel3, Benatiallah Ali4, Benabdelkrim Bouchra1

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4725-4740, 2024, DOI:10.32604/cmc.2024.051002

    Abstract When designing solar systems and assessing the effectiveness of their many uses, estimating sun irradiance is a crucial first step. This study examined three approaches (ANN, GA-ANN, and ANFIS) for estimating daily global solar radiation (GSR) in the south of Algeria: Adrar, Ouargla, and Bechar. The proposed hybrid GA-ANN model, based on genetic algorithm-based optimization, was developed to improve the ANN model. The GA-ANN and ANFIS models performed better than the standalone ANN-based model, with GA-ANN being better suited for forecasting in all sites, and it performed the best with the best values in the… More > Graphic Abstract

    Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria

  • Open Access

    ARTICLE

    Transparent and Accountable Training Data Sharing in Decentralized Machine Learning Systems

    Siwan Noh1, Kyung-Hyune Rhee2,*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3805-3826, 2024, DOI:10.32604/cmc.2024.050949

    Abstract In Decentralized Machine Learning (DML) systems, system participants contribute their resources to assist others in developing machine learning solutions. Identifying malicious contributions in DML systems is challenging, which has led to the exploration of blockchain technology. Blockchain leverages its transparency and immutability to record the provenance and reliability of training data. However, storing massive datasets or implementing model evaluation processes on smart contracts incurs high computational costs. Additionally, current research on preventing malicious contributions in DML systems primarily focuses on protecting models from being exploited by workers who contribute incorrect or misleading data. However, less… More >

  • Open Access

    ARTICLE

    Exploring Multi-Task Learning for Forecasting Energy-Cost Resource Allocation in IoT-Cloud Systems

    Mohammad Aldossary1,*, Hatem A. Alharbi2, Nasir Ayub3

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4603-4620, 2024, DOI:10.32604/cmc.2024.050862

    Abstract Cloud computing has become increasingly popular due to its capacity to perform computations without relying on physical infrastructure, thereby revolutionizing computer processes. However, the rising energy consumption in cloud centers poses a significant challenge, especially with the escalating energy costs. This paper tackles this issue by introducing efficient solutions for data placement and node management, with a clear emphasis on the crucial role of the Internet of Things (IoT) throughout the research process. The IoT assumes a pivotal role in this study by actively collecting real-time data from various sensors strategically positioned in and around… More >

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