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International Electrical engineering Congress (iEECON)

作者： Chalermkiat Sutacha Teerapon Yodrot Teerapong Orachon Khwanjit Orkweha Department of Computer Network Engineering Faculty of Industrial Technology Ubon Ratchathani Rajabhat University Ubon Ratchathani Thailand Department of Electrical Engineering Technology Faculty of Industry and Technology Rajamangala University of Technology Rattanakosin Prachuap Khiri Khan Thailand Department of Computer Engineering KOSEN of King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand School of Telecommunication Engineering Suranaree University of Technology Nakhon Ratchasima Thailand

ISBN: (数字)9798350383591

ISBN: (纸本)9798350383607

Agriculture is a foundational element of global food security and economic stability, yet it faces the perennial challenge of crop diseases, which can significantly impact yield and quality. Traditional disease detection methods, reliant on manual inspection, are labor-intensive and can lack precision. Addressing this, we propose a hybrid model that combines the strengths of Convolutional Neural networks (CNNs) and machine learning classifiers for the automated detection of diseases in cucumber plants. Specifically, we employ the VGG16 network, renowned for its feature extraction capabilities, coupled with Support Vector Machine (SVM), Random Forest (RF), Decision Trees (DT), and K-Nearest Neighbors (KNN) classifiers. Our model is trained on a dataset consisting of high-resolution cucumber images, with 80% used for training and 20% for validation. Performance metrics such as accuracy, precision, recall, and F1-score are utilized for evaluation, with a particular emphasis on the F1-score for its balance between precision and recall. The SVM classifier emerges as the most effective, followed by RF, KNN, and DT. The results, analyzed through confusion matrices, validate the SVM's superior performance in accurately classifying the health status of cucumber plants. This study not only enhances disease detection accuracy but also suggests a direction for future agricultural practices, highlighting the potential integration of such models into real-time crop management systems.

关键词： Support vector machines Training Productivity Crops Stability analysis Real-time systems Convolutional neural networks

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Towards a Quantum-Resilient Future: Strategies for Transitioning to Post-Quantum Cryptography

Towards a Quantum-Resilient Future: Strategies for Transitio...

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International Conference on the network of the Future (NOF)

作者： Abdullah Aydeger Engin Zeydan Awaneesh Kumar Yadav Kasun T. Hemachandra Madhusanka Liyanage Dept. of Electrical Engineering and Computer Science Florida Institute of Technology Melbourne FL USA Centre Tecnològic de Telecomunicacions de Catalunya (CTTC) Spain Network Softwarization and Security Lab (NetsLab) School of Computer Science University College Dublin Ireland Department of Electronic and Telecommunication Engineering University of Moratuwa Sri Lanka

ISBN: (数字)9798350377767

ISBN: (纸本)9798350377774

The imminent emergence of quantum computers poses unprecedented challenges to the current cryptographic standards that secure vital digital communications and data worldwide. This paper addresses the vulnerabilities of existing cryptographic systems susceptible to quantum-based attacks and emphasizes a dedicated transition plan to post-quantum cryptography (PQC). We first examine the current state of cryptographic practices, highlight the quantum threat, and present the framework and need for PQC. In addition, a comprehensive strategy is presented that encompasses the identification, protection, detection, response, and recovery phases and integrates PQC into existing security infrastructures, specifically the Cybersecurity Framework Version 2.0 of the National Institute of Standards and Technology (NIST). Methods and technologies discussed include the development of robust PQC algorithms, tools for their large-scale implementation, and hybrid cryptographic systems that combine traditional and quantum-resistant techniques. Practical implementations on various hardware and software platforms are also presented in case studies that provide real-world insight into the challenges and innovations in using PQC. We conclude with a discussion of hybrid approaches, regulatory considerations, and recommendations derived from early adopters to guide future efforts to create a quantum-resilient digital landscape.

关键词： Technological innovation Quantum computing Software algorithms Public key NIST Software Hardware Cryptography Protection Standards

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Video smoke removal based on low-rank tensor completion via spatial-temporal continuity constraint

Video smoke removal based on low-rank tensor completion via ...

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作者： Zhu, Hu Xu, Guoxia Liu, Lu Deng, Lizhen Jiangsu Province Key Lab on Image Processing and Image Communication Nanjing University of Posts and Telecommunication Nanjing China Department of Computer Science Norwegian University of Science and Technology Gjovik Norway National Engineering Research Center of Communication and Network Technology Nanjing University of Posts and Telecommunication Nanjing China

Smoke has a very bad effect on the outdoor vision system. Not only are the videos with poor visual effects obtained, but also the quality and structure of the videos are reduced. In this paper, we propose a video smoke removal method based on low-rank tensor completion via spatial-temporal continuity constraint. The proposed method is based on the smoke mixing model and consider the sparseness of smoke and the global and local consistency of clean video. Then, the optimal solution of the smoke removal algorithm model is quickly realized by the Alternating Direction Method of Multiplier. Finally, we evaluate the experiment results of real-world data and simulated data from the visual effects and objective indicators. And the experiment results show that our proposed algorithm can achieve better smoke removal results. © 2021 John Wiley & Sons, Ltd.

关键词： Smoke

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Empowering Software engineering Students: The Role of Gamification in Promoting Learning Independence

Empowering Software Engineering Students: The Role of Gamifi...

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Vocational Education and Electrical engineering, International Conference on

作者： Wahyu Nur Hidayat Achmad Hamdan Muhammad Aris Ichwanto Muslihati Gulsun Kurubacak Cakir Rahajeng Kartika Sari Nur Afni Mochammad Raffi Fadhil Hisyam Department of Electrical Engineering and Informatics Faculty of Engineering Universitas Negeri Malang Malang Indonesia Department of Electronic Systems Engineering Technology Faculty of Vocational Universitas Negeri Malang Malang Indonesia Department of Civil Engineering and Planning Faculty of Engineering Universitas Negeri Malang Malang Indonesia Department of Guidance and Counseling Faculty of Education Science Universitas Negeri Malang Malang Indonesia Department of Journalism College of Communication Ankara Haci Bayram Veli University Ankara Turkey Department of Computer Network and Telecommunication Engineering SMK Negeri 10 Malang Malang Indonesia

ISBN: (数字)9798331505103

ISBN: (纸本)9798331505110

The role of learning media greatly supports the learning process, increasing motivation and interest in the process. Learning media can also be a place to improve competence optimally through bootcamp training menus and the like with more specific training objectives. The main objective of this study is to develop a gamification-based website to enhance students’ preparedness for their Final Competency Test by tackling challenges in comprehending programming absent practical experience. The learning method implemented in this website is case-based learning that integrates dynamic coding activities. The research method uses the Plomp approach with 3 main stages, namely preliminary research, prototyping, and assessment stage. The trial of the gamification website began with media and content validation followed by small group testing and large group testing. Validation by media and material experts led to the execution of small group trials (95.7%) and large group trials (91.7%), producing exceptionally positive outcomes. The results of the small group test with 12 respondents produced a very suitable value with a score of 95.7 and the large group test with 27 respondents produced a score of $\mathbf{9 1. 7 \%}$ with very suitable criteria. This indicates that the web-based platform is highly viable for assisting vocational students in acquiring software development skills, enhancing their preparedness for practical IT positions such as software developers and system analysts. Subsequent study involved the incorporation of gamification, such as computer-supported collaborative learning.

关键词： Training Federated learning Media Programming Encoding Software Research and development Testing Software engineering Software development management

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Breast cancer detection and classification: A study on the specification and implementation of multilayer perceptron analog artificial neural networks

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computers in Biology and Medicine 2025年 190卷 110060-110060页

作者： Silas, Koagne Longpa T. Djimeli-Tsajio Alain, B. Thierry, Noulamo Jean-Pierre, Lienou T. Ejuh, Geh Wilson Research Unit of Automation and Applied Computer Science URAIA IUT-FV University of Dschang P.O. Box 134 Bandjoun Cameroon Department of Telecommunication and Network Engineering IUT-FV University of Dschang P.O. Box 134 Bandjoun Cameroon Department of Computer Engineering IUT-FV University of Dschang P.O. Box 134 Bandjoun Cameroon Department of General and Scientific Studies IUT-FV University of Dschang P.O. Box 134 Bandjoun Cameroon Department of Electrical and Electronic Engineering National Higher Polytechnic Institute University of Bamenda P. O. Box 39 Bambili Cameroon

Breast cancer is a leading cause of mortality worldwide. Screening therefore remains the best defense against this disease, highlighting the need for accurate and efficient diagnostic methods. Previous authors addressed this issue by implementing digital hardware with low processing speed and high power consumption constraints. Moreover, a similar approach was developed focusing on implementing an off-chip learning on-chip inference methodology resulting in a high complexity of the proposed systems and complex communication between modules regarding the operations performed by each circuit. This research presents a fully integrated multilayer perceptron analog artificial neural network, classifying breast tumor cells as benign or malignant. A novel analog network architecture and fundamental analog circuit blocks realized by dint of the 90 nm complementary metal-oxide semiconductor technology are developed in this work for breast cancer analysis. The model's reliability findings are confirmed through comprehensive evaluation metrics, achieving sensitivity, specificity, accuracy, F1-Scoree, Intersection-over-Union, and Matthew's correlation coefficient of 0.9668, 0.9869, 0.9800, 0.9708, 0.9433, and 0.9556, respectively. Monte Carlo analysis further validates a 98.00 % accuracy on the Wisconsin Breast Cancer Dataset, with a power consumption of 23.06 mW, using 122 MB of memory while operating at a power supply rail of ±900 mV per analog circuit component and 27 °C computational unit temperature. These findings suggest a significant opportunity for integrating the proposed model into clinical practice, thereby improving patient care in medical diagnostics. © 2025 Elsevier Ltd

关键词： Oxide semiconductors

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A CPK-Based Identity Authentication Scheme for IoT

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computer Systems Science & engineering 2022年第3期40卷 1217-1231页

作者： Mingming Zhang Jiaming Mao Yuanyuan Ma Liangjie Xu Chuanjun Wang Ran Zhao Zhihao Li Lu Chen Wenbing Zhao State Grid Jiangsu Electric Power Co. Ltd.Information&Telecommunication BranchNanjing210008China State Grid Key Laboratory of Information&Network Security Institute of Information and CommunicationGlobal Energy Interconnection Research InstituteNanjing210003China Anhui Jiyuan Software Co.Ltd SGITGHefei230088China Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Research and Development Center of Post Industry Technology of the State Posts Bureau(Internet of Things Technology)Engineering Research Center of Broadband Wireless Communication Technology of the Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing210003China Department of Electrical Engineering and Computer Science Cleveland State UniversityCleveland44115USA

As the power Internet of Things(IoT)enters the security construction stage,the massive use of perception layer devices urgently requires an identity authentication scheme that considers both security and *** existing public key infrastructure(PKI)-based security authentication scheme is currently difficult to apply in many terminals in *** key distribution and management costs are high,which hinders the development of power IoT security *** Public Key(CPK)technology uses a small number of seeds to generate unlimited public *** is very suitable for identity authentication in the power Internet of *** this paper,we propose a novel identity authentication scheme for power *** scheme combines the physical unclonable function(PUF)with improved CPK technology to achieve mutual identity authentication between power IoT terminals and *** proposed scheme does not require third-party authentication and improves the security of identity authentication for power ***,the scheme reduces the resource consumption of power IoT *** improved CPK algorithm solves the key collision problem,and the third party only needs to save the private key and the public key *** results show that the amount of storage resources occupied in our scheme is *** proposed scheme is more suitable for the power IoT.

关键词： Power Internet of Things terminal equipment identity authentication CPK PUF

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Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial network

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computer Systems Science & engineering 2021年第12期39卷 373-390页

作者： Jiaming Mao Mingming Zhang Mu Chen Lu Chen Fei Xia Lei Fan ZiXuan Wang Wenbing Zhao State Grid Jiangsu Electric Power Co. Ltd.Information and Telecommunication BranchNanJing210003China Institute of Information and Communication Global Energy Interconnection Research InstituteState Grid Key Laboratory of Information and Network SecurityNanjing210003China Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Research and Development Center of Post Industry Technology of the State Posts Bureau(Internet of Things Technology)Broadband Wireless Communication Technology Engineering Research Center of the Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing210003China Department of Electrical Engineering and Computer Science Cleveland State UniversityCleveland44115USA

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased net-work traffic *** the past few decades,network traffic identification has been a research hotspot in the field of network management and security ***,as more network services use encryption technology,network traffic identification faces many *** classic machine learning methods can solve many problems that cannot be solved by port-and payload-based methods,manually extract features that are frequently updated is time-consuming and *** learning has good automatic feature learning capabilities and is an ideal method for network traffic identification,particularly encrypted traffic identification;Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled ***,in real scenarios,labeled samples are often difficult to *** paper adjusts the structure of the auxiliary classification generation adversarial network(ACGAN)so that it can use unlabeled samples for training,and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised *** results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network(CNN)based classifier.

关键词： Encrypted traffic recognition deep learning generative adversarial network traffic classification semisupervised learning

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SCENIC: An Area and Energy-Efficient CNN-based Hardware Accelerator for Discernable Classification of Brain Pathologies using MRI

SCENIC: An Area and Energy-Efficient CNN-based Hardware Acce...

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International Conference on VLSI Design

作者： Bodepu Sai Tirumala Naidu Shreya Biswas Rounak Chatterjee Sayak Mandal Srijan Pratihar Ayan Chatterjee Arnab Raha Amitava Mukherjee Janet Paluh SandLogic Technologies Bangalore India Department of Electronics and Telecommunication Engineering Jadavpur University Kolkata India Network Science Institute Northeastern University Boston USA Advanced Architecture Research Intel Edge.AI Intel Corporation Santa Clara USA Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri India College of Nanoscale Science and Engineering Nanobioscience SUNY Polytechnic Institute Albany New York USA

ISBN: (数字)9781665485050

ISBN: (纸本)9781665496612

Biomedical brain imaging lies at the interface of visual, and spatial neuropathology and neurosurgical intervention. Future treatments are expected to require greater feature detail in imaging as well as underlying mechanisms that will be synergistically advanced. This will be made more accessible via low power embedded devices and cloud platforms through applied deep learning software and hardware analysis. Towards that end, we develop a hardware-software co-design technique referred to as SCENIC (or, Separable Convolution Enabled Non-Invasive Classification) for the identification and classification of glioma brain tumors, using physical tissue features reflected in parameter weighted MRI scan types such as-T1-w, T1-ce, T2-w and FLAIR. The high performance hardware exceeds current accuracy, resource efficiency and time consumption parameters. The proposed SCENIC-CNN Accelerator is synthesized on 45 nm process technology andit can operate at a minimum frequency of 1GHz while maintaining low-power consumption of only 0.36 W and a low chip-area size of 0.431mm 2 . Our classification accuracy achieves 98.3% in detection of the presence of a tumor pathology and 99.62% within classification of imaging modalities that relate to tissue parameters such as fat content, blood or CSF flow and tissue density. Compared to prominent state-of the-art Convolutional Neural network (CN) models being designed for biomedical imaging, SCENIC is competitive versus XceptionNet, InceptionV3, ResNet-50, and VGG-16. With model compression techniques, SCENIC requires a memory space of less than 0.265MB. We discuss design methodology as applied to future goals to meet challenging needs to distinguish tumor origins, such as glioma and metastasized tumors, along with other neuropathologies that may be TBI, vascular or developmental. SCENIC will aid impactful, cost-effective, rapid and accurate neurosurgical intervention and treatments.

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Analysis, circuit realization and controls of an autonomous Morse jerk oscillator

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SeMA Journal 2021年第3期78卷 415-433页

作者： Ainamon, Cyrille Tamba, Victor Kamdoum Pone, Justin Roger Mboupda Kingni, Sifeu Takougang Malwe, Hubert Boudoue Orou, Jean Bio Chabi Institut de Mathématiques et de Sciences Physiques Université d’Abomey-Calavi B.P. 613 Porto Novo Benin Department of Telecommunication and Network Engineering IUT-Fotso Victor of Bandjoun University of Dschang PO Box 134 Bandjoun Cameroon Electrical Engineering Department of IUT-Fotso Victor of Bandjoun Research Unit of Automation and Applied Computer (RU-AIA) University of Dschang PO Box 134 Bandjoun Cameroon Department of Mechanical Petroleum and Gas Engineering Faculty of Mines and Petroleum Industries University of Maroua PO Box 46 Maroua Cameroon Department of Physics Faculty of Science University of Maroua PO Box 814 Maroua Cameroon

In this paper, an autonomous Morse jerk oscillator which is designed by converting an autonomous two-dimensional Morse oscillator to a jerk oscillator, is analysed. The stability of its unique equilibrium point reveals the existence of Hopf bifurcation. Periodic and chaotic oscillations, antimonotonicity, chaotic bubbles and coexisting attractors are generated in the proposed jerk oscillator. Then, this proposed jerk oscillator is implemented in PSIM software and realized in a printed circuit board to verify the numerical results. The experimental/PSIM results agree well with the numerical simulations. Moreover, it is possible to control partially or totally the amplitude of its signals by introducing two additional parameters in the rate-equations describing the proposed jerk oscillator. Furthermore based on the Routh–Hurwitz conditions and using a single linear feedback controller, the proposed jerk oscillator is controlled to its unique equilibrium point. Finally, the coexistence between periodic and chaotic attractors is destroyed and controlled to a desired trajectory thank to the linear augmentation method. © 2021, The Author(s), under exclusive licence to Sociedad Española de Matemática Aplicada.

关键词： Antimonotonicity Chaotic and coexisting attractors Control Electronic circuit realization Hopf bifurcation Morse jerk oscillator

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MedSteGAN: Medical Embedding-Optimized Steganography with Improved GAN

SSRN

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SSRN 2023年

作者： Peng, Yuxiang Fu, Chong Zheng, Yu Tian, Yunjia Cao, Guixing Chen, Junxin School of Computer Science and Engineering Northeastern University Liaoning Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Liaoning Shenyang110819 China Department of Information Engineering The Chinese University of Hong Kong Ma Liu Shui Shatin New Territories Hong Kong Institute of Telecommunication Satellite China Academy of Space Technology Beijing100094 China School of Software Dalian University of Technology Liaoning Dalian116621 China

Medical diagnosing systems contain sensitive information, especially the resulting certificate of diagnosis that involves the patient’s privacy, required to be protected carefully. An effective way is to secretly embed privacy-sensitive information into medical-generated images, eventually being viewed as arbitrary images of nobody/nothing in public. The current research line of medical steganography targets simple data-hiding methods, yet remains barren in optimal embedding and stronger robustness. To fill this gap, we propose an end-to-end steganographic framework for medical images. Our idea is to integrate medical-image adaptive steganography with a generative adversarial network (GAN), named MedSteGAN. To improve both robustness, we design an optimized U-shape gen- erator that extracts features more efficiently utilizing larger kernel convolutions and the ReLU6 function. Also, deconvolutions are replaced by upsampling and convolution layers to avoid the checkerboard effect. To enhance security and image quality, we employ visual quality loss and mean squared error (MSE) loss based on the multi-scale structural similarity index metric (MS- SSIM) and the high-pass filters, respectively. Extensive experiments demonstrate that MedSteGAN outperforms state-of-the-art steganographic schemes in terms of robustness and image quality. © 2023, The Authors. All rights reserved.

关键词： Image quality

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