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MILCOM, Military Communications Conference

作者： Hisham A. Kholidy Andrew Karam James Sidoran Mohammad A. Rahman Mohammed Mahmoud Mahmoud Badr Maqsood Mahmud Ahmed F. Sayed Dept. of Networks and Computer Security College of Engineering SUNY Polytechnic Institute Utica NY USA The Air Force Research Laboratory (AFRL RIGB) Rome NY USA Dept of Electrical and Computer Engineering Florida International University USA Dept. of Electrical and Comp. Engineering Tennessee Technological Univ. TN USA Dept. of Management College of Business Administration University. of Bahrain Bahrain Transmission department Telecom Egypt Fayoum Egypt

Developing trust in 5G network slicing is an important issue since as mobile networks evolve the number of internal components, network functions, and the use of virtualized elements increase. The current security practices that are based on a perimeter/defense-in-dept. cybersecurity approach are proven to be ineffective to address the current and future cybersecurity challenges because they assume that everything on the inside of a network is trustworthy. One promising approach of growing significance in the telecom security sphere is the Zero-Trust (ZT) security model. This paper opens a new research direction for implementing a real-time ZT framework that enables trustworthy deployment and secure management of network slices in 5G open architecture.

关键词： Military communication 5G mobile communication Network slicing Real-time systems Zero Trust Telecommunications computer security

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Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder

arXiv

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arXiv 2022年

作者： van Haren, Max Poot, Maurice Kostić, Dragan van Es, Robin Portegies, Jim Oomen, Tom Control Systems Technology Dept. of Mechanical Engineering Eindhoven University of Technology Eindhoven Netherlands ASM Pacific Technology Centre of Competency Beuningen Netherlands CASA Dept. of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands Delft Center for Systems and Control Delft University of Technology Delft Netherlands

Mechatronic systems have increasingly stringent performance requirements for motion control, leading to a situation where many factors, such as position-dependency, cannot be neglected in feedforward control. The aim of this paper is to compensate for position-dependent effects by modeling feedforward parameters as a function of position. A framework to model and identify feedforward parameters as a continuous function of position is developed by combining Gaussian processes and feedforward parameter learning techniques. The framework results in a fully data-driven approach, which can be readily implemented for industrial control applications. The framework is experimentally validated and shows a significant performance increase on a commercial wire bonder. © 2022, CC BY.

关键词： Gaussian distribution

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Deep learning-based multi-brain capsule network for Next-Gen Clinical Emotion recognition using EEG signals

Neuroscience Informatics

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Neuroscience Informatics 2025年第2期5卷 100203-100203页

作者： Dahiya, Ritu G, Mamatha Jawale, Shila Sumol Das, Santanu Choudhary, Sagar Rathod, Vinod Motiram Rajput, Bhawna Janghel Dept. of Chemistry Chhotu Ram Arya College Haryana Sonepat India Department of Management Studies Sri Siddhartha Institute of Business Management Karnataka Tumkur India Department of Computer Engineering Somaiya School of Engineering (formerly K J Somaiya College of Engineering) Somaiya Vidyavihar University Vidyavihar Maharashtra Mumbai India Department of Biotechnology Seshadripuram First Grade College Yelahanka New Town Bangalore India Department of Mathematics Bioinformatics and Computer Application MANIT Madhya Pradesh Bhopal India Bharati Vidyapeeth Deemed University Department of Engineering and Technology Maharashtra Navi Mumbai India Department of Computer Science and Engineering Rungta College of Engineering and Technology Chhattisgarh Bhilai India

Deep learning techniques are crucial for next-generation clinical applications, particularly in Next-Gen Clinical Emotion recognition. To enhance classification accuracy, we propose an Attention mechanism based Capsule Network Model (At-CapNet) for Multi-Brain Region. EEG-tNIRS signals were collected using Next-Gen Clinical Emotion-inducing visual stimuli to construct the TYUT3.0 dataset, from which EEG and tNIRS features were extracted and mapped into matrices. A multi-brain region attention mechanism was applied to integrate EEG and tNIRS features, assigning different weights to features from distinct brain regions to obtain high-quality primary capsules. Additionally, a capsule network module was introduced to optimize the number of capsules entering the dynamic routing mechanism, improving computational efficiency. Experimental validation on the TYUT3.0 Next-Gen Clinical Emotion dataset demonstrates that integrating EEG and tNIRS improves recognition accuracy by 1.53% and 14.35% compared to single-modality signals. Moreover, the At-CapNet model achieves an average accuracy improvement of 4.98% over the original CapsNet model and outperforms existing CapsNet-based Next-Gen Clinical Emotion recognition models by 1% to 5%. This research contributes to the advancement of non-invasive neurotechnology for precise Next-Gen Clinical Emotion recognition, with potential implications for next-generation clinical diagnostics and interventions. © 2025 The Author(s)

关键词： Capsule network Clinical emotion recognition Deep learning EEG Transactional NIRS

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The Future of Business with Generative AI Models and Insights 3

The Future of Business with Generative AI Models and Insight...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Sharma, Manish Selvi, V. Chauhan, Rahul Khan, Shamim Ahmad Siddiqua, Ayasha Balakumar, A. Maharaja Agrasen Institute of Management Studies Department of Business Administration Delhi India Mother Teresa Women's University Department of Computer Science Kodaikanal India Graphic Era Hill University Dehradun Department of CSE Uttarakhand Dehradun India Graphic Era Deemed to be University Uttarakhand Dehradun India Glocal University Glocal School of Science & Technology Department of ECE U.P India Jazan University Dept. of Information Technology and Security Jazan Saudi Arabia K. Ramakrishnan College of Engineering Department of ECE Trichy India

ISBN: (纸本)9798331523893

In today's world, fashion is vital because it shapes and reflects consumer behavior and societal trends while also impacting economic activity, cultural expression, and sense of self. Using generative AI to advance business insights and provide a holistic solution for streamlining e-commerce operations and improving the shopping experience for customers in the fashion industry. The suggested framework presents a revolutionary method for applying generative AI to e-commerce, particularly in the fashion sector. Through advanced data analysis and predictive modeling, this framework intends to improve consumer experiences and company strategies by integrating GPT-4 with the Fashion E-commerce User Data dataset. The framework advances fashion e-commerce by utilizing GPT-4. Customer Insight Generation analyses user behavior to provide trend and recommendation insights;Sales Prediction Modeling forecasts trends to optimize inventory and marketing;Intelligent Chatbot Interactions provides individualized, human-like customer support;and Trend Forecasting anticipates future fashion trends to inform strategic planning. This framework provides an effective approach for applying generative AI in business by bridging the gap between state-of-the-art AI models and conventional data analysis methodologies. Prominent results encompass the creation of models for forecasting fashion trends, enhancing consumer segmentation, and refining sales tactics, all propelled by the many insights extracted from the information. This study offers a guide for utilizing AI models such as GPT-4 to improve business outcomes, emphasizing the extensive consequences of generative AI in modeling the future of corporate judgment and client interaction. © 2025 IEEE.

关键词： Marketplaces

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Uncertain AoI in Stochastic Optimal control of Networked and Constrained LTI Systems

arXiv

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arXiv 2021年

作者： Hahn, Jannik Stursberg, Olaf Control and System Theory Dept. of Electrical Engineering and Computer Science University of Kassel Kassel34121 Germany

— This paper addresses finite-time horizon optimal control of single-loop networked control systems with stochastically modeled communication channel and disturbances. To cope with the uncertainties, an optimization-based control scheme is proposed which uses a disturbance feedback and the age of information as central aspects. The disturbance feedback is an extension of the control law used for balanced stochastic optimal control previously proposed for control systems without network. Balanced optimality is understood as a compromise between minimizing of expected deviations from the reference and minimization of the uncertainty of future states. Time-varying state constraints as well as time-invariant input constraints are considered, and the controllers are synthesized by semi-definite programs. © 2021, CC BY.

关键词： Networked control systems

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LPV Modeling of the Atmospheric Flight Dynamics of a Generic Parafoil Return Vehicle

arXiv

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arXiv 2022年

作者： de Lange, Matthis H. Verhoek, Chris Preda, Valentin Tóth, Roland Control Systems Group Dept. of Electrical Engineering Eindhoven University of Technology Eindhoven5600MB Netherlands ESTEC European Space Agency Noordwijk2200AG Netherlands Systems and Control Lab Institute for Computer Science and Control Budapest1111 Hungary

Obtaining models that can be used for control is of utmost importance to ensure the guidance and navigation of spacecraft, like a Generic Parafoil Return Vehicle (GPRV). In this paper, we convert a nonlinear model of the atmospheric flight dynamics of an GPRV to a Linear Parameter-Varying (LPV) description, such that the LPV model is suitable for navigation control design. Automated conversion methods for nonlinear models can result in complex LPV representation, which are not suitable for controller synthesis. We apply several state-of-the-art techniques, including learning based approaches, to optimize the complexity and conservatism of the LPV embedding for an GPRV. The results show that we can obtain an LPV embedding that approximates the complex nonlinear dynamics sufficiently well, where the balance between complexity, conservatism and model performance is optimal. © 2022, CC BY-NC-SA.

关键词： Principal component analysis

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Intelligent Heart Rate Classification with Adaptive Neuro-Fuzzy Inference System Approach

Intelligent Heart Rate Classification with Adaptive Neuro-Fu...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Banothu Seva Bhupchand Kumhar Jyoti Gupta Sharda Patel Shaik Khaja Mohiddin Basha Archi Jain Department of CSE MLR Institute of Technology Hyderabad Department of Computer Science & Engineering IES Institute of Technology and Management IES University Bhopal M.P. India Dept. of Mathematics Indore Institute of Science and Technology Indore Madhya Pradesh India Dept of CSE Gyan Ganga Institute of Technology & Sciences Jabalpur Dept.of CSE Narasaraopeta Engineering College GGITS Jabalpur MP India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

The human heart is considered among the most crucial internal organs found in the body. An ECG signal is defined as the widespread name given to any electrical signal that is generated by the circulatory system*. By using the ECG signal, one can estimate anomalies present in a heart. This study is aimed at grouping the electrocardiogram (ECG) signals, and thereby using an adaptive neuro fuzzy inference system (ANFIS) algorithm. In this study, the ANFIS which is associated with the back propagation approach is used in order to perform the categorising task. ANFIS can be developed from the integration of fuzzy logic which is a qualitative technique with the neural network aspect in allowing adaptability. Attribute selection is done before the classification exercise is conducted. Four different types of ECG beats can be retrieved from the datasets available at PhysioBank. Classification of the cardiac signals is done employing four ANFIS classifiers. The fifth ANFIS decoder is used to get a higher discrimination ratio in order to classify the electrocardiograms accurately.

关键词： Fuzzy logic Wavelet transforms Adaptive systems Accuracy Sensitivity Electrocardiography Wavelet analysis Real-time systems Signal detection Testing

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The Impact and Potential of Block chain Applications Based on IoT

The Impact and Potential of Block chain Applications Based o...

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2021 International Conference on Simulation, Automation and Smart Manufacturing, SASM 2021

作者： Srivastava, Sandeep Aeron, Anurag Bikku, Thulasi Kaur, Manisha Varsha Billewar, Satish Rupraoji Gl Bajaj of Institute of Technology and Management Greater Noida201306 India Dept. of Computer Science and Engineering Dit University Dehradun248009 India Dept. of Computer Science and Engineering Vignan's Nirula Institute of Technology and Science for Women Guntur Andhra Pradesh Pin 522009 India Shahpur Jalandhar144020 India I.K.Gujral Punjab Technical University Jalandhar144603 India Maharashtra Mumbai400074 India

ISBN: (纸本)9781665403573

The Block chain is a course of action of recording information to such an extent that makes it irritating or hard to alter, slash, or swindle the system. In blockchain decentralized amend the proceedings that could be used to indicate, join up also send wide scope of agreement to various social affairs in association. The scope of block chain applications stretches out to open and public administrations including wellbeing, financial, automotive, Internet of Things (IoT) and risk management. This paper presents a thorough outline on blockchain innovation. Lately, block chain innovation has become extremely stylish and infiltrated various areas, generally because of the crypto currencies determination of rotator for the particular sort of suspension needs some fundamental information. © 2021 IEEE.

关键词： Internet of things

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Machine Learning Segmentation and Classification Algorithm to Support Simulated Point Cloud As-Built Model Applications

Machine Learning Segmentation and Classification Algorithm t...

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2021 International Conference on Computing in Civil engineering, I3CE 2021

作者： Shen, Junzhe Ren, Ran Nicholas Dib, H. McGraw, Tim Habib, Ayman Dept. of Computer Graphics Technology West LafayetteIN United States School of Construction Management Technology West LafayetteIN United States School of Construction Management Technology Knoy Hall of Technology West LafayetteIN United States Lyles School of Civil Engineering West LafayetteIN United States

ISBN: (纸本)9780784483893

management and maintenance of existing buildings remains a major problem due to the lack of existing three-dimensional (3D) models and accurate as-built representation. In this paper the authors propose to use 3D scanner technology to capture the as-built and existing conditions of the buildings combined with building information modeling (BIM) as the underlying technology, which is a 3D semantic representation of all the life cycle phases of a building. This paper presents the results from creating as-built BIM models of existing buildings, using point cloud (a set of points in 3D space) and machine learning as an intermediate medium. Machine learning methodologies are used to speed up the computation of segmentation and classification of point clouds from a 3D virtual indoor environment using procedural modeling, which focused on two attributes, point density and the level of random errors. In this paper we will present findings on the evaluation of the performance of machine learning segmentation and classification algorithm based on the comparison of ten different point cloud data sets. Different sets of segmentation and classification models with comparison between models and within themselves were provided, which included the mean loss and accuracy between models with different point density. © 2021 Computing in Civil engineering 2021 - Selected Papers from the ASCE International Conference on Computing in Civil engineering 2021. All rights reserved.

关键词： Life cycle

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Spintronic Device Potential in Neuromorphic Computing from a Device-Circuit Modeling Perspective

Spintronic Device Potential in Neuromorphic Computing from a...

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International Conference on Power Energy, Environment and Intelligent control (PEEIC)

作者： Bashetty Suman N M Deepika B Rajalakshm Jay Singh Ginni Nijhawan Mazin Riyadh Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Institute of Aeronautical Engineering Dundigal Hyderabad Department of Computer Science New Horizon College of Engineering Bangalore Dept. of EEE GL Bajaj Institute of Technology & Management Greater Noida U.P. India Lovely Professional University Phagwara AL-Hameed College of Engineering Technology National University of Science and Technology Dhi Qar Iraq

Recent years have seen tremendous progress in the area of neuromorphic computing, which seeks to mimic the parallel processing power and energy efficiency of the human brain. From the standpoint of device-circuit modelling, this work explores how spintronic devices might improve the scalability and performance of neuromorphic systems. Utilizing the inherent spin of the electron and the magnetic moment that goes along with it, spintronics has special benefits in terms of non-volatility, low power consumption, and high integration density. We provide a thorough model that accurately depicts the complex behaviour of spintronic components and how they are incorporated into neuromorphic circuits. Our results suggest that spintronic devices may improve the overall efficiency of neuromorphic systems by reducing the power-delay product substantially. Furthermore, it is possible to realize synaptic capabilities and plasticity processes with better accuracy by using the intrinsic features of spintronic devices, such as magnetoresistance and spin-transfer torque. Additionally, the research identifies problems with device variability and suggests possible fixes. In summary, spintronic devices have great potential to transform the field of neuromorphic computing if they are properly modelled and integrated.

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