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A Decode-and-Forward Scheme for LDPC Coded Three-Way Relay Fading Channels

China Communications 2015年第8期12卷 46-54页

作者： YE Xia GAO Feifei Department of Computer Science & Information Management Wenzhou Medical University Department of Automation Tsinghua University State Key Lab of Intelligent Technologies and Systems Tsinghua National Laboratory for Information Science and Technology (TNList)

In this paper a low-density pairwise check(LDPC) coded three-way relay system is considered, where three user nodes desire to exchange messages with the help of one relay node. Since physical-layer network coding is applied, two time slots are sufficient for one round information exchange. In this paper, we present a decode-and-forward(DF) scheme based on joint LDPC decoding for three-way relay channels, where relay decoder partially decodes the network code rather than fully decodes all the user messages. Simulation results show that the new DF scheme considerably outperforms other common schemes in three-way relay fading channels.

关键词： three-way relay channel physicallayer network coding LDPC codes decode-and forward

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Wireless Hallucination in Generative AI-enabled Communications: Concepts, Issues, and Solutions

arXiv

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arXiv 2025年

作者： Wang, Xudong Wang, Jiacheng Feng, Lei Niyato, Dusit Zhang, Ruichen Kang, Jiawen Xiong, Zehui Du, Hongyang Mao, Shiwen State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China College of Computing and Data Science Nanyang Technological University Singapore School of Automation Guangdong University of Technology Guangzhou510006 China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong Department of Electrical and Computer Engineering Auburn University Auburn United States

Generative AI (GenAI) is driving the intelligence of wireless communications. Due to data limitations, random generation, and dynamic environments, GenAI may generate channel information or optimization strategies that violate physical laws or deviate from actual real-world requirements. We refer to this phenomenon as wireless hallucination, which results in invalid channel information, spectrum wastage, and low communication reliability but remains underexplored. To address this gap, this article provides a comprehensive concept of wireless hallucinations in GenAI-driven communications, focusing on hallucination mitigation. Specifically, we first introduce the fundamental, analyze its causes based on the GenAI workflow, and propose mitigation solutions at the data, model, and post-generation levels. Then, we systematically examines representative hallucination scenarios in GenAI-enabled communications and their corresponding solutions. Finally, we propose a novel integrated mitigation solution for GenAI-based channel estimation. At the data level, we establish a channel estimation hallucination dataset and employ generative adversarial networks (GANs)-based data augmentation. Additionally, we incorporate attention mechanisms and large language models (LLMs) to enhance both training and inference performance. Experimental results demonstrate that the proposed hybrid solutions reduce the normalized mean square error (NMSE) by 0.19, effectively reducing wireless hallucinations. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Cloud-Fog automation: The New Paradigm towards Autonomous Industrial Cyber-Physical systems

arXiv

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arXiv 2025年

作者： Jin, Jiong Pang, Zhibo Kua, Jonathan Zhu, Quanyan Johansson, Karl H. Marchenko, Nikolaj Cavalcanti, Dave School of Engineering Swinburne University of Technology HawthornVIC3122 Australia Department of Intelligent Systems KTH Royal Institute of Technology Stockholm10044 Sweden Department of Automation Technology ABB Corporate Research Sweden Västerås72226 Sweden School of Information Technology Deakin University GeelongVIC3220 Australia Department of Electrical and Computer Engineering Tandon School of Engineering New York University BrooklynNY11201 United States Division of Decision and Control Systems School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Stockholm10044 Sweden Corporate Research Robert Bosch GmbH Renningen71272 Germany Intel Corporation Santa ClaraCA95054 United States

Autonomous Industrial Cyber-Physical systems (ICPS) represent a future vision where industrial systems achieve full autonomy, integrating physical processes seamlessly with communication, computing and control technologies while holistically embedding intelligence. Cloud-Fog automation is a new digitalized industrial automation reference architecture that has been recently proposed. This architecture is a fundamental paradigm shift from the traditional International Society of automation (ISA)-95 model to accelerate the convergence and synergy of communication, computing, and control towards a fully autonomous ICPS. With the deployment of new wireless technologies to enable almost-deterministic ultra-reliable low-latency communications, a joint design of optimal control and computing has become increasingly important in modern ICPS. It is also imperative that system-wide cyber-physical security are critically enforced. Despite recent advancements in the field, there are still significant research gaps and open technical challenges. Therefore, a deliberate rethink in co-designing and synergizing communications, computing, and control (which we term "3C co-design") is required. In this paper, we position Cloud-Fog automation with 3C co-design as the new paradigm to realize the vision of autonomous ICPS. We articulate the state-of-the-art and future directions in the field, and specifically discuss how goal-oriented communication, virtualization-empowered computing, and Quality of Service (QoS)-aware control can drive Cloud-Fog automation towards a fully autonomous ICPS, while accounting for system-wide cyber-physical security. © 2025, CC BY-SA.

关键词： computer control

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Data-driven modeling of hysteresis in ReRAM devices: A multifactorial approach for enhanced memory performance 35

Data-driven modeling of hysteresis in ReRAM devices: A multi...

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35th GI-Workshop on Foundations of Database systems, GvDB 2024

作者： Zamankhani, Saba TU Ilmenau Department of Computer Science and Automation Databases and Information Systems Group Germany

Many physical systems in the real world exhibit complex behavior, making it difficult to identify their dynamics. To overcome this problem, prototypes are created to provide a better insight into the behavior of such systems. However, building these prototypes using traditional methods is often slow due to the computational intensity required to accurately capture the detailed complexities within these systems and the large parameter space that needs to be explored. Data-driven approaches, such as machine learning and deep learning frameworks, have the potential to significantly speed up this process by generalizing the model. In this study, we explore the use of such an approach to understand the complex and non-linear behavior of dynamical systems, using resistive random access memory (ReRAM) devices as a case study. It is important to emphasize that modeling ReRAM is only one specific example in our wider investigation, which aims to design predictive models for a range of non-linear dynamical systems. Our work attempts to overcome the limitations of traditional research and development by using neural networks to reproduce the complex behavior of ReRAM cells accurately. We introduce a hybrid dual-input neural network architecture (HDiNN), equipped with a custom loss function, to capture both spatial and temporal patterns, improving the predictability of cell behavior under different conditions. This involves integrating important factors such as material properties, device geometry, and electrical interactions into our model to explain the complexities of ReRAM technology. However, our ambitions extend far beyond ReRAM to develop methods to help create innovative, durable solutions in various fields. This study highlights the impact of predictive modeling in advancing materials science and demonstrates the transformative potential of neural networks in improving the design and optimization of future technologies. © 2024 Copyright for this paper by its authors.

关键词： Neural networks

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Proactive computing in process monitoring: information agents for operator support

Proactive computing in process monitoring: Information agent...

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11th IEEE International Conference on Emerging Technologies and Factory automation

作者： Pakonen, Antti Pirttioja, Teppo Seilonen, Ilkka Tommila, Teemu VTT Technical Research Centre of Finland Automation Technology Laboratory Helsinki University of Technology Finland Information and Computer Systems in Automation Helsinki University of Technology

ISBN: (纸本)9780780397583

While automation systems can track thousands of measurements it is still up to human process operators to determine the operational situation of the controlled process, particularly in abnormal situations. To fully exploit the computing power of embedded processors and to release humans from simple data harvesting activities, the concept of proactive computing tries to exploit the strengths of both man and machine. Proactive features can be implemented using intelligent agent technology, enabling humans to move from simple interaction with computers into supervisory tasks. Autonomous information agents can handle massive amounts of heterogeneous data. They perform tedious tasks of information retrieving, combining and monitoring on the behalf of their users. This paper presents a multi-agent-based architecture for process automation, which aims to support process operators in their monitoring activities. The approach is tested with a scenario inspired by a real-world industrial challenge.

关键词： Process control

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DCFNet:Discriminant Correlation Filters Network for Visual Tracking

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Journal of computer Science & Technology 2024年第3期39卷 691-714页

作者：胡卫明王强高晋李兵 Stephen Maybank State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of Sciences Beijing 100190China Department of Computer Science and Information Systems Birkbeck CollegeLondon WC1E 7HXU.K.

CNN(convolutional neural network)based real time trackers usually do not carry out online network update in order to maintain rapid tracking *** inevitably influences the adaptability to changes in object *** filter based trackers can update the model parameters online in real *** this paper,we present an end-to-end lightweight network architecture,namely Discriminant Correlation Filter Network(DCFNet).A differentiable DCF(discriminant correlation filter)layer is incorporated into a Siamese network architecture in order to learn the convolutional features and the correlation filter *** correlation filter can be efficiently updated *** previous work,we introduced a joint scale-position space to the DCFNet,forming a scale DCFNet which carries out the predictions of object scale and position *** combine the scale DCFNet with the convolutional-deconvolutional network,learning both the high-level embedding space representations and the low-level fine-grained representations for *** adaptability of the fine-grained correlation analysis and the generalization capability of the semantic embedding are complementary for visual *** back-propagation is derived in the Fourier frequency domain throughout the entire work,preserving the efficiency of the *** evaluations on the OTB(Object Tracking Benchmark)and VOT(Visual Object Tracking Challenge)datasets demonstrate that the proposed trackers have fast speeds,while maintaining tracking accuracy.

关键词： correlation filter convolutional neural network(CNN) visual tracking

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Algorithmic realization of system for short-term weather forecasting 07

Algorithmic realization of system for short-term weather for...

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International Conference on computer systems and Technologies and Workshop for PhD Students in Computing, CompSysTech '07

作者： Simeonov, Ivan Kilifarev, Hristo Ilarionov, Rajcho Department Computer Systems and Technologies Technical University of Gabrovo Bulgaria Department of Automation Information and Control Systems Technical University of Gabrovo Bulgaria

ISBN: (纸本)9789549641509

In this paper is presented algorithmic realization of system for short-term weather forecasting, which makes acquisition, processing and visualization of information, related to the parameters temperature, atmospheric pressure, humidity, wind speed and direction. The proposed algorithm is defined from the juxtaposition of the collected statistical data with the currently measured data, on the chosen methods for weather forecasting and the determined correlation between the processed meteorological quantities. The choice of the methods for weather prediction is made according the needed input data and the resources for their realization. © 2007 ACM.

关键词： Weather forecasting

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Expectation maximization for average reward decentralized POMDPs

Expectation maximization for average reward decentralized PO...

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European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2013

作者： Pajarinen, Joni Peltonen, Jaakko Department of Automation and Systems Technology Aalto University Finland Department of Information and Computer Science Aalto University Finland

ISBN: (纸本)9783642409875

Planning for multiple agents under uncertainty is often based on decentralized partially observable Markov decision processes (Dec-POMDPs), but current methods must de-emphasize long-term effects of actions by a discount factor. In tasks like wireless networking, agents are evaluated by average performance over time, both short and long-term effects of actions are crucial, and discounting based solutions can perform poorly. We show that under a common set of conditions expectation maximization (EM) for average reward Dec-POMDPs is stuck in a local optimum. We introduce a new average reward EM method;it outperforms a state of the art discounted-reward Dec-POMDP method in experiments. © 2013 Springer-Verlag.

关键词： Multi agent systems

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Modeling and Measurement of 3D Solenoid Inductor Based on Through-Silicon Vias

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Chinese Journal of Electronics 2023年第2期32卷 365-374页

作者： YIN Xiangkun WANG Fengjuan ZHU Zhangming Vasilis F.Pavlidis LIU Xiaoxian LU Qijun LIU Yang YANG Yintang Shaanxi Key Lab.of Integrated Circuits and Systems School of Microelectronics Xidian University School of Automation and Information Engineering Xi'an University of Technique Department of Computer Science University of Manchester

Through-silicon via(TSV) provides vertical interconnectivity among the stacked dies in three-dimensional integrated circuits(3D ICs) and is a promising option to minimize 3D solenoid inductors for on-chip radio-frequency applications. In this paper, a rigorous analytical inductance model of 3D solenoid inductor is proposed based on the concept of loop and partial inductance. And a series of 3D samples are fabricated on 12-in high-resistivity silicon wafer using low-cost standard CMOS-compatible process. The results of the proposed model match very well with those obtained by simulation and measurement. With this model, the inductance can be estimated accurately and efficiently over a wide range of inductor windings, TSV height, space, and pitch.

关键词： Semiconductor device modeling Inductance Analytical models Solid modeling Three-dimensional displays Integrated circuit modeling Inductors

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Language Guided Concept Bottleneck Models for Interpretable Continual Learning

arXiv

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arXiv 2025年

作者： Yu, Lu Han, Haoyu Tao, Zhe Yao, Hantao Xu, Changsheng School of Computer Science and Engineering Tianjin University of Technology China School of Information Science and Technology University of Science and Technology of China China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation University of Chinese Academy of Sciences China

Continual learning (CL) aims to enable learning systems to acquire new knowledge constantly without forgetting previously learned information. CL faces the challenge of mitigating catastrophic forgetting while maintaining interpretability across tasks. Most existing CL methods focus primarily on preserving learned knowledge to improve model performance. However, as new information is introduced, the interpretability of the learning process becomes crucial for understanding the evolving decision-making process, yet it is rarely explored. In this paper, we introduce a novel framework that integrates language-guided Concept Bottleneck Models (CBMs) to address both challenges. Our approach leverages the Concept Bottleneck Layer, aligning semantic consistency with CLIP models to learn human-understandable concepts that can generalize across tasks. By focusing on interpretable concepts, our method not only enhances the model’s ability to retain knowledge over time but also provides transparent decision-making insights. We demonstrate the effectiveness of our approach by achieving superior performance on several datasets, outperforming state-of-the-art methods with an improvement of up to 3.06% in final average accuracy on ImageNet-subset. Additionally, we offer concept visualizations for model predictions, further advancing the understanding of interpretable continual learning. Code is available at https://***/FisherCats/CLG-CBM. © 2025, CC BY-NC-ND.

关键词： Decision making

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