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P4toNFV: Offloading from P4 switches to NFV in programmable data planes

International Journal of Communication Systems 2024年第5期37卷

作者： Pekar, Adrian Makara, Laszlo A. Lai, Yuan-Cheng Lin, Ying-Dar Seah, Winston Department of Networked Systems and Services Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary HUN-REN-BME Information Systems Research Group Budapest University of Technology and Economics Budapest Hungary Department of Information Management National Taiwan University of Science and Technology Taipei Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

P4 combines the benefits of hardware-based networking with the adaptability of software-based network operations. However, when faced with intricate network functions, P4 switches reveal constraints in memory and processing primitives. To address these, we advocate offloading traffic demanding intricate processing from the programmable data plane to network function virtualization (NFV). By leveraging this approach, P4 switches handle the core data plane, ensuring maximum performance, whereas virtualized network functions (VNF) cater to the intricate processing. Central to our research is the optimization of this offloading process, specifically considering delay constraints. We developed an analytical model that examines a P4 switch overseen by an SDN controller, integrating an offloading capability to NFV. The principal objective was to determine an offloading rate that minimizes packet processing delay. To this end, we employed a Bounded method, an advancement from Brent's method, to determine this optimal rate. The findings indicate that offloading approximately 66% of packets to the VNF achieves the lowest total delay, registering at 0.1505 (Formula presented.) s. This strategy of optimal offloading can notably reduce the system's average delay as the demand for network functions increases. The optimization technique we adopted exhibited rapid convergence in our experiments, reflecting the method's efficacy. Furthermore, a rigorous parametric sensitivity analysis spanning no offloading, full offloading, and optimal offloading strategies underscores that optimal offloading to NFV consistently augments system performance, particularly in terms of delay reduction. Conclusively, our study furnishes valuable insights into offloading strategies, augmenting the narrative on resource allocation in both PNFs and VNFs. © 2023 The Authors. International Journal of Communication Systems published by John Wiley & Sons Ltd.

关键词： Network function virtualization

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Analyzing and Unifying Robustness Measures for Excitation Transfer Control in Spin Networks

arXiv

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arXiv 2023年

作者： O’Neil, S.P. Khalid, I. Rompokos, A.A. Weidner, C.A. Langbein, F.C. Schirmer, S. Jonckheere, E.A. Dept of Electrical Engineering University of Southern California CA United States School of Computer Science and Informatics Cardiff University United Kingdom Quantum Engineering Technologies Laboratories University of Bristol United Kingdom Faculty of Science & Engineering Physics Swansea University United Kingdom

Recent achievements in quantum control have resulted in advanced techniques for designing controllers for applications in quantum communication, computing, and sensing. However, the susceptibility of such systems to noise and uncertainties necessitates robust controllers that perform effectively under these conditions to realize the full potential of quantum devices. The time-domain log-sensitivity and a recently introduced robustness infidelity measure (RIM) are two means to quantify controller robustness in quantum systems. The former can be found analytically, while the latter requires Monte-Carlo sampling. In this work, the correlation between the log-sensitivity and the RIM for evaluating the robustness of single excitation transfer fidelity in spin chains and rings in the presence of dephasing is investigated. We show that the expected differential sensitivity of the error agrees with the differential sensitivity of the RIM, where the expectation is over the error probability distribution. Statistical analysis also demonstrates that the log-sensitivity and the RIM are linked via the differential sensitivity, and that the differential sensitivity and RIM are highly concordant. This unification of two means (one analytic and one via sampling) to assess controller robustness in a variety of realistic scenarios provides a first step in unifying various tools to model and assess robustness of quantum controllers. Copyright © 2023, The Authors. All rights reserved.

关键词： Robust control

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Advancements in computer Vision for Automated Fruit Quality Inspection: A Focus on Apple Detection and Grading

Advancements in Computer Vision for Automated Fruit Quality ...

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International Conference on Smart Structures and Systems (ICSSS)

作者： V. Raji E. Rajendran V Balaji Dinesh Kumar Bhayal N. Rishikesh D. Karthikeyan Department of Computer Science and Engineering SKP Engineering College Tiruvannamalai Tamil Nadu India Department of Electrical and Electronics Engineering SKP Engineering College Tiruvannamalai Tamil Nadu India Faculty of Electrical and Electronics Engineering MAI-NEFHI College of Engineering and Technology Asmara Eritera Department of Information Technology Medi-Caps University Indore India Department of Electrical and Electronics Engineering Bannari Amman Institute of Technology Erode India Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Chennai

The paper discusses the most current advancements in image analysis and computer vision systems and how they are being used to assess the grade of food products. computer vision is a quick, reliable, and objective examination method that has spread throughout many different sectors. Its efficiency and precision enable the creation of fully automated systems by meeting the growing needs for quality and output. With a focus on monochrome image processing, colour images, and multispectral imaging for contemporary sorting and grading systems, the needs and most current advancements in hardware and software for machine vision systems are reviewed. The suggested method utilises the SVM algorithm to learn and identify fruit quality while using processing of images to extract features. Research experiments show how accurate the system is. Also included are demonstrations of uses for spotting faults, pollution, and illness on vegetables and fruits.

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Capacity Maximization for V2I-VLC System with Angular Diversity Receiver

Capacity Maximization for V2I-VLC System with Angular Divers...

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Communications and Networking (MeditCom), IEEE International Mediterranean Conference on

作者： Selma Yahia Yassine Meraihi Amar Ramdane- Cherif Hossien B. Eldeeb Sami Muhaidat Tu Dac Ho LIST Laboratory University of M’Hamed Bougara Boumerdes Boumerdes Algeria LISV Laboratory University of Versailles St-Quentin-en-Yvelines Velizy France Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi UAE Department of Electrical Engineering Faculty of Engineering Science and Technology UiT Arctic University of Norway

This paper investigates the effectiveness of angle diversity technology-based vehicle-to-infrastructure (V2I) visible light communication (VLC) system designed for a multi-lane road scenario. Cars use their headlights to transmit signals to a traffic light pole, which is equipped with an angle diversity receiver (ADR) comprising three photodetectors (PD) arranged in different directions to improve signal reception from various directions. We utilize an advanced ray tracing channel modeling approach and investigate the impact of the number of PDs and the elevation angles on the received power. Additionally, we conduct a comprehensive performance analysis in terms of capacity, considering different car positions along the road. The results demonstrate that the V2I-VLC system with ADR achieves a channel capacity of over 2.5 Mb/s at a transmission distance of 50 meters, highlighting its potential to enhance V2I-VLC connectivity.

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Deep Learning-Powered Edge Analytics for IoT-based Sensor Networks 2

Deep Learning-Powered Edge Analytics for IoT-based Sensor Ne...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Karthikeyan, M. Saravana Subramani, Ponnusamy Selvam, M. Panneer Sworna Kokila, M.L. Vijayalakshmi, R. Sundaravadivel, P. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Physics Chennai India Sona College of Technology Department of Electrical and Electronics Engineering Salem India School of Computing SRM Institute of Science and Technology Kattankulathur Faculty of Engineering and Technology Department of Computing Technologies Chennai India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331505745

The rapid expansion of loT-based sensor networks has necessitated the development of efficient edge analytics frameworks to process vast amounts of data in real time while minimizing computational overhead. Deep learning-powered edge computing has emerged as a transformative approach, enhancing data processing capabilities at the network edge while addressing constraints related to latency, bandwidth, and energy consumption. This study introduces a novel algorithm, Lightweight AI Model Compression for Energy-Efficient IoT Edge Devices (LiteEdgeAI), designed to optimize deep learning models for resource-constrained edge environments. The proposed method employs quantization, pruning, and knowledge distillation techniques to achieve significant reductions in model size and computational complexity while maintaining high inference accuracy. To validate the efficiency of LiteEdgeAI, a comparative simulation analysis was conducted against existing state-of-the-art algorithms using key performance metrics such prediction accuracy, energy efficiency and computational complexity. Experimental results demonstrated that LiteEdgeAI outperformed baseline models. The findings highlight the potential of LiteEdgeAI in enabling scalable, low-power, and real-time edge intelligence for IoT-based sensor networks. © 2025 IEEE.

关键词： Deep learning

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A Surveillance System Utilizing mmWave-FMCW Radar for Restricted Control Area

A Surveillance System Utilizing mmWave-FMCW Radar for Restri...

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International Conference on electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON

作者： Nattakarn Shutimarrungson Seksan Eiadkaew Akkarat Boonpoonga Krit Athikulwongse Suramate Chalermwisutkul Department of Business Computer Mahasarakham Business School Mahasarakham University Maha Sarakham Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering Research Center of Innovation Digital and Electromagnetic Technology (iDEMT) King Mongkut's University of Technology North Bangkok Bangkok Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Pathumthani Thailand Communication and Smart System Engineering The Sirindhorn International Thai- German Graduate School of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

In this paper, we introduce a surveillance system utilizing mmWave-FMCW radar designed for restricted control areas. The experiment, conducted in our laboratory, defines the restricted control area parameters. Employing the TI-IWR6843AOPEVM model, operating at 60–64 GHz, and providing point cloud and Doppler information, we assumed walking paths for testing, partitioning scenarios into four distinct cases to simulate real-world scenarios. System performance evaluation, assessed through confusion matrices, demonstrates an achieved accuracy of up to 76.9%. Notably, errors are mainly caused by multipath effects due to static objects within the testing environment.

关键词： Point cloud compression Measurement Accuracy Surveillance System performance Radar tracking Doppler radar

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A-LIOn - Alignment Learning through Inconsistency negatives of the aligned Ontologies 17

A-LIOn - Alignment Learning through Inconsistency negatives ...

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17th International Workshop on Ontology Matching, OM 2022

作者： Alghamdi, Sarah M. Zhapa-Camach, Fernando Hoehndorf, Robert Computational Bioscience Research Center Computer Electrical & Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology 4700 KAUST Thuwal23955 Saudi Arabia King Abdul-Aziz University Faculty of Computing and Information Technology Rabigh25732 Saudi Arabia

Ontologies play an important role in sharing and reusing knowledge. Several ontologies have been developed to describe a particular domain but from different perspectives from communities of developers and users. This has led to the existence of multiple ontologies covering the same or a different domain with varying degrees of variability. Ontology Alignment is typically used to identify correspondences between semantically related elements of two or more ontologies in order to address this problem. We propose A-LIOn a system that learns alignments by combining lexical and semantic approaches as well as machine learning. The system utilizes OWL EL reasoning for negative sampling which is iteratively used to inform the correction of the learning of the alignments. We demonstrate that A-LIOn produces alignments that are coherent with respect to OWL EL. © 2022 Copyright for this paper by its authors.

关键词： Ontology

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Novel Size-Reduced Coaxial Cavity Filters For 5G Networks

Novel Size-Reduced Coaxial Cavity Filters For 5G Networks

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Tran Thi Thu Huong An Tri Bao Trong-Hieu Le Vu Van Yem Faculty of Electronics and Computer Engineering University of Economics - Technology for Industries Hanoi Vietnam 11 Physics 2 Hanoi - Amsterdam High School for The Gifted Hanoi Vietnam Faculty of Electronics and Telecommunications Electric Power University Hanoi Vietnam School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi Vietnam

ISBN: (数字)9798350351019

ISBN: (纸本)9798350351026

This paper presents a novel coaxial cavity filter using in 5G NR Base Stations. The resonant cylinder utilizes stepped impedance technology combined with folding edge bending and rectangular zigzag arm structure on the top to improve the electrical coupling mainline. Therefore, dimensions of cavity filter are reduced while maintaining the filter performance parameters. This proposed resonator is verified in the design of one filter integrated in C-Band 5G station with ten orders and four zero points. The dimensions of the filter are only 56.81 mm x 15.56 mm x 9.5 mm (length x width x height). The simulation results show the passband of 304MHz, the return loss of 15.6dB, the insertion loss of 0.1dB. These specifications are suitable for applying in Band C 5G network.

关键词： Couplings 5G mobile communication C-band Simulation Resonator filters Resonant frequency Insertion loss Cavity resonators Passband Impedance

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Enhanced obesity classification with wavelet packet decomposition and ANN–PSO: a biomedical signal processing approach

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The European Physical Journal Special Topics 2025年 1-12页

作者： Arslan, Rukiye Uzun Erkaymaz, Okan Yapici, İrem Senyer Perc, Matjaž Department of Electrical and Electronics Engineering Zonguldak Bulent Ecevit University Zonguldak Turkey Department of Computer Engineering Turkish Naval Academy National Defence University Istanbul Turkey Department of Computer Engineering Zonguldak Bulent Ecevit University Zonguldak Turkey Faculty of Natural Sciences and Mathematics University of Maribor Maribor Slovenia Community Healthcare Center Dr. Adolf Drolc Maribor Vošnjakova ulica 2 Maribor Slovenia Complexity Science Hub Vienna Vienna Austria

Obesity diagnosis using biomedical signals has received increasing attention in recent years and requires advanced signal processing techniques in order to accurately classify obesity. In this context, this study proposes an intelligent diagnostic system for obesity classification using flash electroretinogram (fERG) signals, with a specific focus on cone responses. A novel feature extraction method based on Wavelet Packet Decomposition (WPD) is employed to decompose the cone responses into high- and low-frequency components, enabling detailed time–frequency analysis with high resolution. Subsequently, statistical features, such as mean, standard deviation, skewness, and kurtosis, are extracted from the decomposed signals and refined to enhance the training of artificial neural networks (ANNs). To optimize model performance, Particle Swarm Optimization (PSO) is integrated with ANN, resulting in an ANN–PSO hybrid model. The experimental dataset, comprising fERG signals from 47 subjects across diverse obesity categories, was utilized to evaluate the proposed hybrid model. The ANN–PSO model demonstrated high classification performance, achieving average accuracies of $$95.74\%$$ and $$96.60\%$$ for right and left eye signals, respectively, outperforming traditional ANN models. These findings highlight the effectiveness of WPD in capturing intricate signal characteristics relevant to obesity levels and confirm the potential of the ANN–PSO model as a robust, efficient, and reliable diagnostic tool for clinical applications beyond conventional BMI assessments.

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Detecting Characteristic Points for the Analysis of Bioimpedance Signal Through a Synergy of Fuzzy Rule-based models and Granular Neural Networks

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IEEE Transactions on Fuzzy Systems 2024年

作者： Wang, Dan Richter, Monika Zhu, Xiubin Pedrycz, Witold Gacek, Adam Sobotnicki, Aleksander Li, Zhiwu Xi'an University of Science and Technology College of Computer Science and Technology Xi'an710054 China Center for Biomedical Engineering Lukasiewicz Research Network-Krakow Institute of Technology Zabrze41-800 Poland Xidian University School of Mechano-Electronic Engineering Xi'an710071 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey Macau University of Science and Technology Institute of Systems Engineering Taipa China

In this study, we propose a novel methodology for determining accurate positions of characteristic points encountered in the analysis of bioimpedance signals. The proposed approach fully utilizes two fundamental modeling pursuits based on fuzzy rule-based models and neural networks. We take advantages of the unique capabilities of fuzzy rule-based models to characterize the nonlinear relationship between the acquired bioimpedance signals and its temporal coordinate. The fuzzy modeling approach is used to approximate the process that generates the bioimpedance signals through a collection of rules (if-then statements). In the sequel, the parameters of the models are used as the inputs of neural network models to determine the position of characteristic points. We further augment the numeric neural network to its granular counterpart to accommodate the uncertainty in the available experimental evidence through allocating a certain level of information granularity across the parameter space. The resulting granular outputs (intervals) become reflective of the quality and a level of confidence associated with the prediction results. The quality of the prediction results is quantified in terms of the coverage and specificity criteria. The performance index is also enhanced to deal with the situation when the positions provided by experts are also information granules (intervals). The performance of the proposed approach is justified through a collection of experiments carried out on the collected real-world bioimpedance signals. Experimental results show that the proposed approach achieved higher accuracy in determining the position of characteristic points in comparison with other existing methods. © 1993-2012 IEEE.

关键词： Fuzzy models

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