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International Symposium on Advanced Computing and Communication (ISACC)

作者： Brindha Subburaj Aditya Sai Shantanu Shantanu Daehan Won School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Systems Science and Industrial Engineering Binghamton University USA

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

This paper investigates the growing role of steganography in cybersecurity and presents a hybrid implementation called Multi-level Discrete Cosine Convolution (MDCC) that applies a Multi-level Discrete Cosine Transform (MDCT) and a Convolutional Neural Network (CNN). Steganography provides some advantages compared to cryptography, mainly its invisibility, but there are weaknesses in information capacity and resistance to attacks in traditional techniques. The paper suggests solving these difficulties with a new steganography method using deep learning. The proposed MDCC method provides an opportunity to encrypt large amounts of data in realistic images that are beyond human visual perception. The system is trained on the ImageNet dataset by using a loss function to balance the quality of the cover image and the information it conceals within it. The MDCC model proposed yields accurate results for all metrics since PSNR, SSIM, and bits per pixel come out to be 80.13dB, 0.9918, and 12.5 bpp, respectively.

关键词： Deep learning Measurement Steganography Convolution Color Discrete cosine transforms Convolutional neural networks Intelligent systems Visual perception Immune system

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Emerging Trends in AI-Powered IoT: Innovations, Key Challenges, and Future Directions

Emerging Trends in AI-Powered IoT: Innovations, Key Challeng...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Abdelraouf, Hussien Abouyoussef, Mahmoud Fouda, Mostafa M. Fadlullah, Zubair Md Ibrahem, Mohamed I. School of Computer and Cyber Sciences Augusta University AugustaGA30912 United States North Carlina A&T State University Department of Computer Systems Technology GreensboroNC27411 United States Idaho State University Department of Electrical and Computer Engineering PocatelloID83209 United States Western University Department of Computer Science LondonON Canada

ISBN: (纸本)9798331504847

This survey provides a comprehensive overview of recent emerging technologies in artificial intelligence (AI) applied to the Internet of Things (IoT), highlighting their significance and applications across various domains. Our study covers key advancements, including Concept Drift, Transformers, TinyML, Explainable AI (XAI), and Federated Learning (FL). Concept Drift addresses changes in data patterns to maintain the accuracy of machine learning models in dynamic environments. Transformers revolutionize Natural language processing (NLP) by efficiently capturing long-range dependencies. TinyML enables smart IoT applications on low-power devices, while XAI promotes transparency in AI decisions. FL facilitates decentralized model training while preserving data privacy. Our discussion also covers challenges, limitations, and future research directions to improve AI and IoT integration. © 2025 IEEE.

关键词： Federated learning

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Fault-tolerant Q-learning for discrete-time linear systems with actuator and sensor faults using input-output measured data

Franklin Open

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Franklin Open 2025年 11卷

作者： Kankashvar, Mohammadrasoul Rafiee, Sajad Bolandi, Hossein Department of Control Systems Engineering Iran University of Science and Technology Tehran *** Iran

This study presents a novel output feedback Q-learning algorithm specifically designed for fault-tolerant control in real-time applications, circumventing the necessity for explicit system models or detailed actuator and sensor fault information. A significant benefit of this algorithm is its capability to simultaneously achieve optimality and stabilize systems with both actuator and sensor faults. Unlike traditional methods, it learns online using input-output data from the faulty system, bypassing the need for full-state measurements. We develop a unique expression of the Fault-Tolerant Q-function (FTQF) in the input-output format and derive a model-free optimal output feedback fault-tolerant control (FTC) policy. Furthermore, the algorithm's real-time implementation process is detailed, showing its adaptability in acquiring optimal output feedback FTC policies without prior knowledge of system dynamics or faults. The proposed method remains unaffected by excitation noise bias, even without a discount factor, and guarantees closed-loop stability and convergence to optimal solutions. Validation through numerical simulations on an F-16 autopilot aircraft underscores its effectiveness. © 2025

关键词： Actuator faults Fault-tolerant control Output feedback Q-learning Reinforcement learning Sensor faults

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Real-Time Testing of Microgrid control Algorithms: Development of Experimental Setup 29

Real-Time Testing of Microgrid Control Algorithms: Developme...

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29th International Conference on Information Technology, IT 2025

作者： Forcan, Miodrag Dobrić, Goran Abed, Amir Žarković, Mileta Ikić, Marko Forcan, Jovana Barragán-Villarejo, Manuel Maza-Ortega, José María De Paula Garcia-López, Francisco University of East Sarajevo Faculty of Electrical Engineering East Sarajevo71123 Bosnia and Herzegovina University of Belgrade School of Electrical Engineering Belgrade11120 Serbia University of East Sarajevo Faculty of Philosophy Department of Computer Science and Systems East Sarajevo71420 Bosnia and Herzegovina University of Sevilla Department of Electrical Engineering Seville41004 Spain

ISBN: (纸本)9798331517649

This paper provides an overview of microgrid control strategies, examining differences between centralized and decentralized approaches, and focusing on classical hierarchical control schemes as well as the structure of three-layer microgrid control systems. It also explores Hardware-in-the-Loop modeling, a crucial approach for microgrid simulation, with particular emphasis on devices supporting microgrid modeling. The paper primarily focuses on the design and implementation requirements of an experimental setup for real-time testing of microgrid control algorithms. The setup incorporates Hardware-in-the-Loop device, a power amplifier, a power converter, a battery energy storage system, and a digital controller. It is specifically designed to handle both power and information (control) signals, facilitating a comprehensive approach to evaluating and validating microgrid control strategies in real-world conditions. A detailed analysis of the setup elements and their interactions is also provided. © 2025 IEEE.

关键词： Hardware-in-the-loop simulation

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Worst-Case Integrity Attacks and Resilient State Estimation With Partially Secured Measurements

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IEEE Transactions on control of Network systems 2025年

作者： Zhou, Jing Shang, Jun Chen, Tongwen University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China

This paper examines the problem of optimal deception attacks against state estimation with partially secured measurements, where smart sensors transmit innovation sequences to the remote end for information fusion. Due to resource limitations or defensive countermeasures, the adversary can only modify data packets transmitted through unreliable channels. Meanwhile, the attack should be synthesized with sophistication to deceive an anomaly detector. To investigate the vulnerabilities of such estimation systems without feedback links and enhance security performance, the optimal attack policy is derived by formulating and explicitly solving a convex optimization problem, with the goal of maximizing the sum of estimation errors. Subsequently, a novel attack detection and resilient state estimation algorithm is proposed to ensure an acceptable level of estimation accuracy. The theoretical performance metrics, including false alarm rates for the proposed detector, are provided. Finally, the effectiveness of the results is confirmed through numerical examples. © 2014 IEEE.

关键词： Optimal detection

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Community Membership Hiding via Gradient-based Optimization

arXiv

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arXiv 2025年

作者： Silvestri, Matteo Gabrielli, Edoardo Silvestri, Fabrizio Tolomei, Gabriele Department of Computer Science Sapienza University of Rome Rome Italy Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

We tackle the problem of community membership hiding, which involves strategically altering a network’s structure to obscure a target node’s membership in a specific community identified by a detection algorithm. We reformulate the original discrete counterfactual graph objective as a differentiable constrained optimization task. To solve this, we propose ∇- CMH, a gradient-based method that modifies the network’s structure within the feasible bounds for an individual target node, effectively concealing its membership. Experimental results across multiple datasets and community detection algorithms show that our approach surpasses existing baselines, offering a better balance between accuracy and computational efficiency. © 2025, CC BY.

关键词： Optimization algorithms

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Drosophila Larvae-Inspired Soft Crawling Robot with Multimodal Locomotion and Versatile Applications

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Research 2025年第1期7卷 35-46页

作者： Qin Fang Jingyu Zhang Yinhui He Nenggan Zheng Yue Wang Rong Xiong Zhefeng Gong Haojian Lu State Key Laboratory of Industrial Control and Technology Zhejiang UniversityHangzhou 310027China Institute of Cyber-Systems and Control Zhejiang UniversityHangzhou 310027China Medical School of Zhejiang University Hangzhou 310027China College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China Stomatology Hospital School of StomatologyZhejiang University School of MedicineZhejiang Provincial Clinical Research Center for Oral DiseasesKey Laboratory of Oral Biomedical Research of Zhejiang ProvinceCancer Center of Zhejiang UniversityEngineering Research Center of Oral Biomaterials and Devices of Zhejiang ProvinceHangzhou 310000China

Soft crawling robots have been widely studied and applied because of their excellent environmental adaptability and flexible ***,most existing soft crawling robots typically exhibit a single-motion mode and lack diverse *** by Drosophila larvae,this paper proposes a compact soft crawling robot(weight,13 g;length,165 mm;diameter,35 mm)with multimodal locomotion(forward,turning,rolling,and twisting).Each robot module uses 4 sets of high-power-density shape memory alloy actuators,endowing it with 4 degrees of motion *** analyze the mechanical characteristics of the robot modules through experiments and simulation *** plug-and-play modules can be quickly assembled to meet different motion and task *** soft crawling robot can be remotely operated with an external controller,showcasing multimodal motion on various material *** a narrow maze,the robot demonstrates agile movement and effective maneuvering around *** addition,leveraging the inherent bistable characteristics of the robot modules,we used the robot modules as anchoring units and installed a microcamera on the robot's head for pipeline *** robot completed the inspection in horizontal,vertical,curved,and branched pipelines,adjusted the camera view,and twisted a valve in the pipeline for the first *** research highlights the robot's superior locomotion and application capabilities,providing an innovative strategy for the development of lightweight,compact,and multifunctional soft crawling robots.

关键词： multimodal locomotion versatile applications Drosophila larvae soft crawling robot weight soft crawling robots shape memory alloy actuators

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Explainable Application Intent for Zero-Touch Networking: An Incorporation of Hypergraph and Transformer

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IEEE Transactions on Communications 2025年

作者： Wu, Bing Zou, Sai Liwang, Minghui Ni, Wei Wang, Xianbin Guizhou University College of Big Data and Information Engineering China National Key Laboratory of Autonomous Intelligent Unmanned Systems Department of Control Science and Engineering China Tongji University Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai China Data61 CSIRO Australia Department of Electrical and Computer Engineering Western University Canada

The autonomous interpretation of application intent (APPI) represents the primary step towards achieving closed-loop autonomy in zero-touch networking (ZTN) and also a prerequisite for intent-based networking (IBN). However, understanding APPIs and invoking the corresponding network resources require network professionals with extensive technical expertise to customize network service requests (NSRs), which presents significant challenges for the large-scale deployment of ZTN. This paper investigates an interesting problem of autonomous interpretation of APPIs for ZTN, where a novel mechanism integrating hypergraph and transformer with completeness assurance (HyperTrans-CA) is proposed. In particular, we first involve the Bayesian theory to model APPIs interpretability as maximizing the correct transition probability, where hypergraph is used to describe the complex relationship between application characteristics (e.g., scenario function, and performance) and NSRs, including network devices, virtual network functions (VNFs), and resources. Then, the hypergraph is integrated into the encoder, decoder, and attention mechanisms of Transformer, and a completeness assurance mechanism is designed to improve the prediction accuracy. The convergence of HyperTrans-CA and the corresponding convergence speed of the hypergraph-boosted Transformer in the graph search process are also analyzed. Comprehensive simulations and empirical measurements regarding industrial internet demonstrate that HyperTrans-CA can effectively explain/understand APPIs. Compared to the state-of-the-art Transformer and ChatGPT3.5 models, HyperTrans-CA improves the prediction accuracy of APPIs mapped to VNFs by 23% and 46%, respectively, while raising the prediction accuracy of VNF locations by 8.6 and 17.3 times. © 2025 IEEE.

关键词： Bayesian networks

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Bridging research gaps in breast cancer detection: An ensemble approach informed by bibliometric analysis

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Biomedical Signal Processing and control 2025年 109卷

作者： ZainEldin, Hanaa Bamaqa, Amna Farsi, Mohammed Badawy, Mahmoud Elhosseini, Mostafa A. Computers and Control Systems Engineering Department Mansoura University Faculty of Engineering Mansoura35516 Egypt Department of Computer Science and Information Applied College Taibah University Madinah42353 Saudi Arabia Department of Information Systems College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia

One of the leading causes of cancer-related death for women is still breast cancer, which highlights the importance of early and precise diagnosis techniques. Despite medical imaging and deep learning advances, current models fail to combine innovative object identification and classification methods, limiting their diagnostic performance. A bibliometric study of 1145 research articles identified key gaps, including the lack of integration between advanced object detection models like YOLOv9 and YOLOv10 with high-performance classification architectures such as EfficientNet B7, as well as the limited utilization of diverse imaging modalities. This study proposes a novel ensemble model for Breast Cancer Classification and Detection by combining YOLOv9, YOLOv10, and EfficientNet B7 (BC2D-Y/EN) to address these gaps. The proposed model combines the advanced object detection capabilities of YOLOv9 and YOLOv10 with the high-performance classification power of EfficientNet B7. This method aims to improve detection accuracy and computational efficiency in breast cancer identification. The proposed approach is validated using four publicly available datasets: CBIS-DDSM, CBIS-DDSM RGB breast cancer dataset, The Breast1 MRI dataset, and the Breast Ultrasound Tumor Object Detection dataset, all containing expert-annotated cases of benign and malignant tumors. The experimental findings indicate that the suggested model surpasses other models, achieving precision rates of 98.56% with EfficientNet B7, 98.3% with YOLOv9, and 100% with YOLOv9. These findings highlight the advantages of advanced models and diverse imaging methodologies to enhance diagnostic accuracy and effectiveness in breast cancer detection. © 2025 Elsevier Ltd

关键词： Deep learning

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Toward Understanding Federated Learning over Unreliable Networks

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2025年 3卷 80-97页

作者： Feng, Chenyuan Arafa, Ahmed Chen, Zihan Zhao, Mingxiong Quek, Tony Q. S. Yang, Howard H. EURECOM Department of Communication Biot06410 France University of North Carolina at Charlotte Department of Electrical and Computer Engineering CharlotteNC28223 United States Singapore University of Technology and Design Information Systems Technology and Design Pillar Tampines487372 Singapore Yunnan University Engineering Research Center of Cyberspace National Pilot School of Software Kunming650106 China Zhejiang University ZJU-UIUC Institute Haining314400 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310007 China University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering ChampaignIL61820 United States

This paper studies the efficiency of training a statistical model among an edge server and multiple clients via Federated Learning (FL) - a machine learning method that preserves data privacy in the training process - over wireless networks. Due to unreliable wireless channels and constrained communication resources, the server can only choose a handful of clients for parameter updates during each communication round. To address this issue, analytical expressions are derived to characterize the FL convergence rate, accounting for key features from both communication and algorithmic aspects, including transmission reliability, scheduling policies, and momentum method. First, the analysis reveals that either delicately designed user scheduling policies or expanding higher bandwidth to accommodate more clients in each communication round can expedite model training in networks with reliable connections. However, these methods become ineffective when the connection is erratic. Second, it has been verified that incorporating the momentum method into the model training algorithm accelerates the rate of convergence and provides greater resilience against transmission failures. Last, extensive empirical simulations are provided to verify these theoretical discoveries and enhancements in performance. © 2023 CCBY.

关键词： Federated learning

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