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information technology (ACIT)

作者： Mohammad Rasmi Al-Mousa Mohammad Abdulqawi Hazem Jihad Badarneh Ala’a Al-Shaikh Mahmoud Asassfeh Ala'a Saeb Al-Sherideh Ghassan Samara Ali Elrashidi Raed Alazaidah Department of Cybersecurity Zarqa University Zarqa Jordan Department of Computer Science Zarqa University Zarqa Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia Faculty of Information Technology Zarqa University Zarqa Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

The Internet of Things (IoT) is considered the base factor, that helps in developing many sectors. Additionally, IoT provides novel applications and services, i.e. smart homes, connected cars, and industrial automation. However, this development aggravates the security and privacy issues. This is because the connected devices may be vulnerable to cyber attacks in addition to generated data. Accordingly, it is of utmost importance to deploy appropriate security measures to alleviate these attacks. To overcome the mentioned problem, this work employs lightweight cryptography on limited processing capabilities of IoT devices i.e. limited memory, computing power, and battery life. These limited capabilities obstruct employing cryptography methods that require nontrivial computational resources. This work presents a multiround lightweight cryptography approach for IoT devices, which defines the security and protection of generated and transferred data. Multi-round cryptography mitigates the required device resources while ensuring robust protection and maintaining the stability of IoT devices.

关键词： Privacy Connected vehicles Smart homes Battery charge measurement Encryption Batteries Internet of Things Protection information technology Cyberattack

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A Door Detection System for Convenience Stores in Taiwan

A Door Detection System for Convenience Stores in Taiwan

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2021 International Conference on computer System, information technology, and Electrical engineering, COSITE 2021

作者： Thaipisutikul, Tipajin Prompol, Kanatip Lin, Chih-Yang Chang, Wen-Thong Muchtar, Kahlil Mahidol University Faculty of Information and Communication Technology Thailand Yuan Ze Universit College of Electrical and Communication Engineering Department of Electrical Engineering Taiwan Asia University Dept. of Computer Science and Information Engineering Taichung Taiwan Universitas Syiah Kuala Department of Electrical and Computer Engineering Aceh Indonesia

ISBN: (纸本)9781665425094

A door is a very substantial element since it enables a person to enter a target place. Though detecting a doorway is an easy task for a regular person, it is challenging for robots or visually impaired people. Although most existing deep learning object detection models have shown promising results, they have a limitation in distinguishing between glass doors and glass walls in a convenience store. To address this issue, we propose an effective door detection system for convenience stores in Taiwan. Our system consists of two main models: 1) the object detection and 2) the door bounding box models. The former model uses the re-train YOLOv4 as the main building box. The latter model uses a fully connected neural network as the main building box. In particular, we utilize the surrounding objects in the scene to improve the performance and robustness of convenient glass door entrance detection. The experimental results demonstrate that our proposed method not only achieves the quantitative accuracy result up to 93% but also provides decent qualitative results. © 2021 IEEE.

关键词： Object detection

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Heterogeneous Hadoop Cluster-Based Image Processing Workload Distribution Framework between CPU and GPU

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Scientific Programming 2023年第1期2023卷

作者： Naz, Najia Zada, Islam Malik, Abdul Haseeb Nadeem, Muhammad Ali, Sikandar Department of Computer Science Bacha Khan University Charsadda Khyber Pakhtunkhwa Peshawar24420 Pakistan Department of Computer Science and Software Engineering Faculty of Computing and Information Technology International Islamic University Islamabad44000 Pakistan Department of Computer Science University of Peshawar Khyber Pakhtunkhwa Peshawar25120 Pakistan Department of Information Technology The University of Haripur Khyber Pakhtunkhwa Haripur22620 Pakistan

Due to the rapid development of image data and the necessity to analyze it to extract meaningful information, heterogeneous systems have gained prominence. One of the most critical aspects of distributed systems is load balancing. When it comes to the distribution of workload in a balanced manner in a cluster, some heterogeneous systems are used for image processing. When workloads are allocated in these systems, the computational power of the processors is not considered. As a result, in these heterogeneous systems for image processing applications, an uneven workload distribution issue is found. A workload distribution programming framework is presented and discussed in this paper. The proposed strategy consists of two parts. As a first step, image data is split into optimal split sizes and distributed across nodes, then the image data is distributed across CPU and GPU in a second step for processing. A heterogeneous environment is created by combining the CPU and GPU. The OpenCL Java bindings are used to set up both the CPU and GPU to run the program. To assess the performance of the suggested technique, certain tests are carried out and compared to current platforms. For image processing applications in heterogeneous clusters, the proposed workload distribution approach distributed image data efficiently. The results of the proposed solution (Hadoop + GPU) show that using an effective workload allocation mechanism in heterogeneous systems reduces average execution time while improving overall application performance. © 2023 Najia Naz et al.

关键词： Graphics processing unit

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A New Model Based on Graph Neural Network Feedback for Crystal Property Prediction

A New Model Based on Graph Neural Network Feedback for Cryst...

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Big Data & Artificial Intelligence & Software engineering (ICBASE), International Conference on

作者： Jian Zhu Yongshen Ren Huaijuan Zang Shu Zhan School of Computer and Information Engineering Hefei University of Technology Hefei China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei China Faculty of Metallurgical and Energy Engineering Kunming University of Science and Technology Kunming China National Engineering Research Center of Vacuum Metallurgy Kunming University of Science and Technology Kunming China

ISBN: (数字)9798331506612

ISBN: (纸本)9798331506629

The prediction of crystal properties is crucial in crystal design. Currently, most methods employ graph neural networks to model crystal structures and have achieved satisfactory prediction accuracy. However, accurately simulating the complex relationships between atoms and molecules in crystal structures remains challenging. The prediction performance of crystal graph networks also depends on the effective extraction of crystal features, typically requiring a close correlation between the properties to be predicted and the extracted features. In this study, a model retraining method is utilized, where the loss function in the retraining phase is constructed using feedback from a pre-trained crystal graph network model to adjust model parameters and enhance the accuracy of predicting crystal properties. This method enhances the predictive performance of multiple crystal graph neural networks and accelerates the development of high-performance crystals without the need to add various features or incorporate additional neural network layers. The research findings demonstrate the significant effectiveness of the proposed method in enhancing the prediction performance of four models: CGCNN, OGCNN, GATGNN, and ALIGNN. Optimization metrics support this claim.

关键词： Measurement Accuracy Correlation Crystals Predictive models Feature extraction Graph neural networks Optimization Material properties Software engineering

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A Comparison between Deep Belief Network and LSTM in Chaotic Time Series Forecasting 21

A Comparison between Deep Belief Network and LSTM in Chaotic...

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4th International Conference on Machine Learning and Machine Intelligence, MLMI 2021

作者： Ngoc Phien, Nguyen Tuan Anh, Duong Platos, Jan Center for Applied Information Technology Ton Duc Thang University Vietnam and Department of Computer Science Faculty of Electrical Engineering and Computer Science VSB Technical University of Ostrava Czech Republic

ISBN: (纸本)9781450384247

The application of deep neural networks in forecasting time series data is increasingly popular, aiming to improve prediction accuracy in this problem. However, as for chaotic time series, a special kind of time series data generated from the deterministic dynamics of a nonlinear system, there are very few research works applying deep neural networks to forecast this kind of time series data. So far, Deep Belief Network (DBN) and Long Short Term Memory (LSTM) are two kinds of deep neural networks used to extract meaningful features from the chaotic time series before forecasting. This article aims to compare the prediction performance of the LSTM model with that of the DBN model on chaotic time series data. Experimental results on six synthetic and real-world datasets in this study show that LSTM brings out better prediction accuracy than DBN in terms of three evaluation criteria. © 2021 ACM.

关键词： Forecasting

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A Machine-learning and Discrete Multi-verse-optimizer-based Hybrid Method for Feature Selection

A Machine-learning and Discrete Multi-verse-optimizer-based ...

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IEEE International Conference on Networking, Sensing and Control

作者： Shuo Zhang YingJun Ji XiWang Guo ShuJin Qin Qi Kang Moitrayee Chatterjee Information and Control Engineering Liaoning Petrochemical University Fushun China Faculty of Information Liaoning University Shenyang China Economics and Management Shangqiu Normal University Shangqiu China Electrical and Computer Engineering New Jersey Institute of Technology Newark New Jersey USA Department of Computer Science New Jersey City University New Jersey USA

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

Accurately identifying key quality features can significantly streamline the manufacturing process by reducing the number of controlled variables and enhancing product quality prediction. To maximize prediction accuracy while minimizing the number of features, this study proposes a Machine-learning and Discrete Multi-verse-optimizer-based Hybrid method called MDMH. MDMH combines machine learning and the discrete Multi-verse optimizer, utilizing hierarchical clustering for effective feature grouping and employing a wrapper technique to find the optimal solution. After comparing its results with those of the exact solver and other intelligent optimization methods, the proposed method demonstrates superiority in terms of accuracy.

关键词： Accuracy Manufacturing processes Process control Optimization methods Machine learning Feature extraction Product design Quality assessment Sensors Manufacturing

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A Continuous MPPT Technique Independent of Inversion Factor for Piezoelectric Energy Harvesting

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Chen, Zhidong Xia, Yinshui Shi, Ge Wang, Xiudeng Xia, Huakang Qian, Libo Zhejiang Wanli University College of Information and Intelligence Engineering Ningbo315100 China Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China China Jiliang University College of Mechanical and Electrical Engineering Hangzhou310018 China Xidian University School of Microelectronics Hangzhou Institute of Technology Hangzhou311231 China

The paper proposes a continuous maximum power point tracking (MPPT) technique for piezoelectric energy harvesting that is independent of the inversion factor. The MPPT solution is obtained using the proposed fractional differential voltage (FDV) method, which is implemented by sampling the peak voltage and inversion voltage of the working piezoelectric transducer (PZT) during continuous energy harvesting. Compared to existing solutions, the proposed method no longer disconnects the PZT from the interface circuit to achieve open circuit voltage sampling or pre-measures the interface circuit’s inversion factor. In addition, the interface is implemented by the self-powered rectifier-less synchronized switch harvesting on inductor (ReL-SSHI) circuit. The experiment results show that the proposed MPPT method can assist the ReL-SSHI circuit to maintain the maximum power output regardless of the changing types of interfaced PZTs and inductors, and a peak MPPT efficiency of 98% is obtained. © 2013 IEEE.

关键词： Piezoelectric transducers

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Segment Together: A Versatile Paradigm for Semi-Supervised Medical Image Segmentation

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Zeng, Qingjie Xie, Yutong Lu, Zilin Lu, Mengkang Wu, Yicheng Xia, Yong Northwestern Polytechnical University National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Xi’an710072 China The University of Adelaide Australian Institute for Machine Learning AdelaideSA5000 Australia Monash University Faculty of Information Technology Department of Data Science and AI Australia

The scarcity of annotations has become a significant obstacle in training powerful deep-learning models for medical image segmentation, limiting their clinical application. To overcome this, semi-supervised learning that leverages abundant unlabeled data is highly desirable to enhance model training. However, most existing works still focus on specific medical tasks and underestimate the potential of learning across diverse tasks and datasets. In this paper, we propose a Versatile Semi-supervised framework (VerSemi) to present a new perspective that integrates various SSL tasks into a unified model with an extensive label space, exploiting more unlabeled data for semi-supervised medical image segmentation. Specifically, we introduce a dynamic task-prompted design to segment various targets from different datasets. Next, this unified model is used to identify the foreground regions from all labeled data, capturing cross-dataset semantics. Particularly, we create a synthetic task with a CutMix strategy to augment foreground targets within the expanded label space. To effectively utilize unlabeled data, we introduce a consistency constraint that aligns aggregated predictions from various tasks with those from the synthetic task, further guiding the model to accurately segment foreground regions during training. We evaluated our VerSemi framework against seven established SSL methods on four public benchmarking datasets. Our results suggest that VerSemi consistently outperforms all competing methods, beating the second-best method with a 2.69% average Dice gain on four datasets and setting a new state of the art for semi-supervised medical image segmentation. Code is available at https://***/maxwell0027/VerSemi. © 1982-2012 IEEE.

关键词： Self-supervised learning

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Imaging artefacts in scattering-type scanning near field optical microscopy arising from optical diffraction effects and contrast-active sub-surface features

arXiv

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arXiv 2025年

作者： Tranca, Denis E. Stanciu, Stefan G. Hristu, Radu Schatzberg, Yotam Zalevsky, Zeev Kusnetz, Binyamin Karsenty, Avi Banica, Cosmin K. Stanciu, George A. Center for Microscopy-Microanalysis and Information Processing National University of Science and Technology Politehnica Bucharest Romania Ramat Gan5290002 Israel Faculty of Engineering Jerusalem College of Technology Jerusalem9116001 Israel SC Wing Computer Group SRL Bucharest Romania

The scattering-type Scanning Near-Field Optical Microscope (s-SNOM) is acknowledged as an excellent tool to investigate the optical properties of different materials and biological samples at the nanoscale. In this study we show that s-SNOM data are susceptible to being affected by specific artefacts related to the light diffraction phenomena and to stray contributions from shallow buried, contrast-active, structures. We focus on discussing the diffraction contributions from sample edges, next to those corresponding to one- or two-dimensional periodic structures, and undesired contributions from shallow buried periodic features. Each scenario was examined individually through both experimental methods and simulations. Our experimental findings reveal that such artefacts affect not only s-SNOM images demodulated at the direct-current (DC) component and the fundamental frequency, but also images demodulated at higher harmonic frequencies. We show that image artefacts caused by diffraction resemble the undesirable effects caused by illumination with a laser beam of unstable intensity, and that buried features can yield s-SNOM signals that cannot be distinguished from those originating from the sample surface, in absence of prior knowledge of the sample structure. Performed simulations confirm these experimental findings. This work enhances the understanding of s-SNOM data and paves the way for new data acquisition and postprocessing methods that can enable next-generation s-SNOM imaging and spectroscopy with significantly enhanced signal-to-noise ratio and resolution. © 2025, CC BY.

关键词： Laser beams

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Reconfigurable and Multiband Graphene THz Antenna Design Optimized Using Machine Learning

Reconfigurable and Multiband Graphene THz Antenna Design Opt...

引用

International Conference on Advanced Technologies for Communications, ATC

作者： Shobhit K. Patel Dhruvik Agravat Tien Dung Nguyen Thi Thanh Kieu Nguyen Truong Khang Nguyen Department of Computer Engineering Marwadi University Rajkot Gujrat India Department of Physics Marwadi University Rajkot Gujrat India Faculty of Control and Automation Electric Power University Ha Noi Vietnam Faculty of Engineering Binh Thuan College Binh Thuan VietNam Faculty of Information Technology School of Technology Van Lang University Ho Chi Minh City Vietnam

ISBN: (数字)9798350353983

ISBN: (纸本)9798350353990

This study investigates the concept of multiband graphene THz antennas. We investigate the advantages of graphene in THz antenna applications with standard materials. Using a graphene strip, we built a multiband GSSPlp-MIMO antenna in this study that achieves triple operating frequencies of 7.64 THz, 9.57 THz, and 10.90 THz. The reflection response (S 11 ) of -29.2135 dB, which shows excellent signal confinement within the antenna, was greatly improved upon the inclusion of graphene. Superior impedance matching was the trade-off for this, even if the gain decreased slightly (10.41 dB) in comparison to the antenna without graphene (12.30 dB). The parameters are optimized using machine learning, yielding a maximum accuracy (R2) of 0.996622 and a mean square error of 5.3934 ×10 -5 . This indicates that high-performance terahertz antennas that balance gain and effective signal transmission can be designed using graphene. The antenna's capacity to handle multiple bands makes it ideal for future ultra-broadband wireless communication systems. This results in higher data transfer rates and supports emerging technologies such as 6G and beyond.

关键词： Design methodology Graphene Terahertz radiation Machine learning Mean square error methods Data transfer Reflection Antennas Gain Terahertz communications

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