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A Neuro-Fuzzy Approach to Road Traffic Congestion Prediction

computers, Materials & Continua 2022年第10期73卷 295-310页

作者： Mohammed Gollapalli Atta-ur-Rahman Dhiaa Musleh Nehad Ibrahim Muhammad Adnan Khan Sagheer Abbas Ayesha Atta Muhammad Aftab Khan Mehwash Farooqui Tahir Iqbal Mohammed Salih Ahmed Mohammed Imran BAhmed Dakheel Almoqbil Majd Nabeel Abdullah Omer Department of Computer Information System(CIS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Science(CS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Pattern Recognition and Machine Learning Lab Department of Software EngineeringGachon UniversityInchonKorea Department of Computer Science National College of Business Administration and EconomicsLahorePunjab54000Pakistan Department of Computer Science Government College University(GCU)LahorePunjab54000Pakistan Department of Computer Engineering(CE) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia College of Business Administration Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Networks and Communications(NC) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia

The fast-paced growth of artificial intelligence applications provides unparalleled opportunities to improve the efficiency of various *** as the transportation sector faces many obstacles following the implementation and integration of different vehicular and environmental aspects *** congestion is among the major issues in this regard which demands serious attention due to the rapid growth in the number of vehicles on the *** address this overwhelming problem,in this article,a cloudbased intelligent road traffic congestion prediction model is proposed that is empowered with a hybrid Neuro-Fuzzy *** aim of the study is to reduce the delay in the queues,the vehicles experience at different road junctions across the *** proposed model also intended to help the automated traffic control systems by minimizing the congestion particularly in a smart city environment where observational data is obtained from various implanted Internet of Things(IoT)sensors across the *** due preprocessing over the cloud server,the proposed approach makes use of this data by incorporating the neuro-fuzzy ***,it possesses a high level of accuracy by means of intelligent decision making with minimum error *** results reveal the accuracy of the proposed model as 98.72%during the validation phase in contrast to the highest accuracies achieved by state-of-the-art techniques in the literature such as 90.6%,95.84%,97.56%and 98.03%,*** far as the training phase analysis is concerned,the proposed scheme exhibits 99.214% accuracy. The proposed prediction modelis a potential contribution towards smart cities environment.

关键词： Neuro-fuzzy machine learning congestion prediction AI cloud computing smart cities

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Wavelength-division-multiplexed Transmission and Dispersion Compensation Enabled by Soliton Microcombs and Cascaded Microrings

Wavelength-division-multiplexed Transmission and Dispersion ...

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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Liu, Yuanbin Zhang, Hongyi Liu, Jiacheng Lu, Liangjun Du, Jiangbing Li, Yu He, Zuyuan Chen, Jianping Zhou, Linjie Poon, Andrew W. State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China Photonic Device Laboratory Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong

We demonstrate wavelength-division-multiplexed data transmission and dispersion compensation of 25 Gb/s × 9 on-off-keying signals over a 20-km singlemode fiber using an integrated single-soliton microcomb and a cascaded microring resonator-based reconfigurable dispersion compensator. © Optica Publishing Group 2024

关键词： Dispersion compensation

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W-band Wireless Transmission based on 98 GHz Packaged Silicon Photonics Optical Clock Generator

W-band Wireless Transmission based on 98 GHz Packaged Silico...

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Optical Fiber Communication (OFC) Conference

作者： Antonio Malacarne Alberto Montanaro Fawad Ahmad Gaurav Pandey Antonio D’Errico Marco Romagnoli Antonella Bogoni Claudio Porzi Photonic Networks & Technologies National Lab — CNIT Pisa Italy Telecommunications Computer Engineering and Photonics Institute (TeCIP) Scuola Superiore Sant’Anna Pisa Italy Ericsson Research Pisa Italy

A fully packaged CMOS-compatible photonic integrated frequency-tunable optical clock synthesizer is used for 93 GHz wireless transmission of complex modulation formats up to 4 Gb/s data rate, with noise performance su... 详细信息

ISBN: (纸本)9798350377583

关键词： Optical fibers Integrated optics 6G mobile communication Frequency modulation Optical design Optical device fabrication Silicon photonics

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Wavelength-division-multiplexed Transmission and Dispersion Compensation Enabled by Soliton Microcombs and Cascaded Microrings

Wavelength-division-multiplexed Transmission and Dispersion ...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

ISBN: (纸本)9781957171395

关键词： Optical fibers

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Addressing the Load Estimation Problem: Cell Switching in HAPS-Assisted Sustainable 6G networks

arXiv

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arXiv 2024年

作者： Salamatmoghadasi, Maryam Ozturk, Metin Yanikomeroglu, Halim Non-Terrestrial Networks Lab Department of Systems and Computer Engineering Carleton University OttawaONK1S5B6 Canada Electrical and Electronics Engineering Ankara Yildirim Beyazit University Ankara06010 Turkey

This study aims to introduce and address the problem of traffic load estimation in the cell switching concept within the evolving landscape of vertical heterogeneous networks (vHetNets). The problem is that the practice of cell switching faces a significant challenge due to the lack of accurate data on the traffic load of sleeping small base stations (SBSs). This problem makes the majority of the studies in the literature, particularly those employing load-dependent approaches, impractical due to their basic assumption of perfect knowledge of the traffic loads of sleeping SBSs for the next time slot. Rather than developing another advanced cell switching algorithm, this study investigates the impacts of estimation errors and explores possible solutions through established methodologies in a novel vHetNet environment that includes the integration of a high altitude platform (HAPS) as a super macro base station (SMBS) into the terrestrial network. In other words, this study adopts a more foundational perspective, focusing on eliminating a significant obstacle for the application of advanced cell switching algorithms. To this end, we explore the potential of three distinct spatial interpolation-based estimation schemes: random neighboring selection, distance-based selection, and clustering-based selection. Utilizing a real dataset for empirical validations, we evaluate the efficacy of our proposed traffic load estimation schemes. Our results demonstrate that the multi-level clustering (MLC) algorithm performs exceptionally well, with an insignificant difference (i.e., 0.8%) observed between its estimated and actual network power consumption, highlighting its potential to significantly improve energy efficiency in vHetNets. Copyright © 2024, The Authors. All rights reserved.

关键词： Base stations

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BotScan: An Unsupervised Bot Detection Based on Adversarial Learning and Social Perception

BotScan: An Unsupervised Bot Detection Based on Adversarial ...

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Asian Control Conference

作者： Hong Lin Nuo Chen Yang Chen Xiang Li Cong Li Department of Electronic Engineering Adaptive Networks and Control Lab School of Information Science and Technology Fudan University China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

Within online social networks, the rapid expansion of automated accounts, known as social bots, poses a significant challenge. This dynamic has sparked a strategic contest between bots and detectors. Both sides continually learn and adjust their strategies to gain an edge in this silent war. To effectively manage risks from bot evolution, proactive detection methods have garnered widespread attention. In this work, we propose a graph-based and structure-aware adversarial learning framework for multi-category social bot detection. In detail, we adopt a categorical generative adversarial network to create synthetic evolved bot samples for adversarial learning, aiming to boost the detection performance. Besides, we design a user representation learning module, including local and remote feature extractors to mine neighborhood information and achieve multiple network structure representations. Results demonstrate that our model outperforms state-of-the-art approaches and exhibits strong robustness.

关键词： Representation learning Training Technological innovation Social networking (online) Image edge detection Detectors Feature extraction

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Hemispherical Massive MIMO Architecture for High-Altitude Platform Station (HAPS)

Hemispherical Massive MIMO Architecture for High-Altitude Pl...

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IEEE Conference on Wireless Communications and Networking

作者： Omid Abbasi Halim Yanikomeroglu Georges Kaddoum Department of Systems and Computer Engineering Non-Terrestrial Networks (NTN) Lab Carleton University Ottawa ON Canada Department of Electrical Engineering École de Technologie Supérieure (ETS) Université du Québec Montréal QC Canada

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

In this paper, we present a novel hemispherical antenna array (HAA) designed for High-Altitude Platform Stations (HAPS). Traditional rectangular antenna arrays for HAPS suffer from a significant limitation - their antenna elements are perpetually oriented downward, resulting in low gain for distant users. Meanwhile, cylindrical antenna arrays were introduced to mitigate this drawback, but they, in turn, exhibit a distinct problem: their antenna elements continually face the horizon, leading to suboptimal gain for users located beneath the HAPS. To address these challenges, we introduce the HAA. In the HAA configuration, antenna elements are strategically distributed across the surface of a hemisphere, ensuring that each user receives direct alignment with specific antenna elements, thereby maximizing the gain for all users. We derive the achievable data rates for users within this proposed scheme, employing an analog beamforming technique that leverages the steering vectors of the selected antenna elements for each user. We also formulate an op-timization problem focused on maximizing the minimum Signal-to-Interference-plus-Noise Ratio (SINR) for users. Additionally, we introduce an antenna selection algorithm based on the gains of the antenna elements. To further enhance system performance, we employ the Bisection method to determine the optimal power allocation for each user. Our simulation results substantiate the superior rate performance of the proposed HAA when compared to the conventional rectangular and cylindrical baseline arrays. The proposed approach demonstrates to reach sum data rates of up to 14 Gigabit/s. Furthermore, in contrast to the baseline schemes, the proposed scheme achieves more consistent spectral efficiencies across the entire coverage area.

关键词： Spectral efficiency Urban areas Receiving antennas Interference Bandwidth Vectors Resource management

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JAX-CNV:A Whole-genome Sequencing-based Algorithm for Copy Number Detection at Clinical Grade Level

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Genomics, Proteomics & Bioinformatics 2022年第6期20卷 1197-1206页

作者： Wan-Ping Lee Qihui Zhu Xiaofei Yang Silvia Liu Eliza Cerveira Mallory Ryan Adam Mil-Homens Lauren Bellfy Kai Ye Charles Lee Chengsheng Zhang Precision Medicine Center The First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China The Jackson Laboratory for Genomic Medicine FarmingtonCT 06032USA School of Cyber Science and Engineering Xi’an Jiaotong UniversityXi’an 710049China Department of Pathology and Laboratory Medicine Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPA 19104USA School of Computer Science and Technology Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China MOE Key Lab for Intelligent Networks&Networks Security Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China Department of Life Sciences Ewha Womans UniversitySeoul 03760South Korea

We aimed to develop a whole-genome sequencing(WGS)-based copy number variant(CNV)calling algorithm with the potential of replacing chromosomal microarray assay(CMA)for clinical ***-CNV is thus developed for CNV detection from WGS *** performance of this CNV calling algorithm was evaluated in a blinded manner on 31 samples and compared to the 112 CNVs reported by clinically validated CMAs for these 31 *** result showed that JAX-CNV recalled 100%of these ***,JAX-CNV identified an average of 30 CNVs per individual,representing an approximately seven-fold increase compared to calls of clinically validated *** validation of 24 randomly selected CNVs showed one false positive,i.e.,a false discovery rate(FDR)of 4.17%.A robustness test on lowercoverage data revealed a 100%sensitivity for CNVs larger than 300 kb(the current threshold for College of American Pathologists)down to 10×*** CNVs larger than 50 kb,sensitivities were 100%for coverages deeper than 20×,97%for 15×,and 95%for 10×.We developed a WGS-based CNV pipeline,including this newly developed CNV caller JAX-CNV,and found it capable of detecting CMA-reported CNVs at a sensitivity of 100%with about a FDR of 4%.We propose that JAX-CNV could be further examined in a multi-institutional study to justify the transition of first-tier genetic testing from CMAs to ***-CNV is available at https://***/TheJacksonlaboratory/JAX-CNV.

关键词： Copy number variant Chromosomal microarray assay Whole-genome sequencing JAX-CNV Genetic testing

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Handoff-Aware Distributed Computing in High Altitude Platform Station (HAPS)–Assisted Vehicular networks

arXiv

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arXiv 2023年

作者： Ren, Qiqi Abbasi, Omid Kurt, Gunes Karabulut Yanikomeroglu, Halim Chen, Jian State Key Laboratory of Integrated Service Networks Xidian University Shaanxi Xi’an710071 China Lab Department of Systems and Computer Engineering Carleton University OttawaON Canada Lab Department of of Systems and Computer Engineering Carleton University OttawaON Canada Department of Electrical Engineering Polytechnique Montréal Montréal Canada

Distributed computing enables Internet of vehicle (IoV) services by collaboratively utilizing the computing resources from the network edge and the vehicles. However, the computing interruption issue caused by frequent edge network handoffs, and a severe shortage of computing resources are two problems in providing IoV services. High altitude platform station (HAPS) computing can be a promising addition to existing distributed computing frameworks because of its wide coverage and strong computational capabilities. In this regard, this paper proposes an adaptive scheme in a new distributed computing framework that involves HAPS computing to deal with the two problems of the IoV. Based on the diverse demands of vehicles, network dynamics, and the time-sensitivity of handoffs, the proposed scheme flexibly divides each task into three parts and assigns them to the vehicle, roadside units (RSUs), and a HAPS to perform synchronous computing. The scheme also constrains the computing of tasks at RSUs such that they are completed before handoffs to avoid the risk of computing interruptions. On this basis, we formulate a delay minimization problem that considers task-splitting ratio, transmit power, bandwidth allocation, and computing resource allocation. To solve the problem, variable replacement and successive convex approximation–based method are proposed. The simulation results show that this scheme not only avoids the negative effects caused by handoffs in a flexible manner, it also takes delay performance into account and maintains the delay stability. Copyright © 2023, The Authors. All rights reserved.

关键词： Vehicles

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Correcting Noisy Multilabel Predictions: Modeling label Noise through Latent Space Shifts

arXiv

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arXiv 2025年

作者： Huang, Weipeng Li, Qin Xiao, Yang Qiao, Cheng Cai, Tie Liang, Junwei Hurley, Neil J. Piao, Guangyuan School of Computer Science and Software Engineering Shenzhen Institute of Information Technology Guangdong China State Key Lab of Integrated Service Networks School of Cyber Engineering Xidian University Shaanxi China Cyberspace Institute of Advanced Technology Guangzhou University Guangdong China School of Computer Science University College Dublin Dublin Ireland

Noise in data appears to be inevitable in most realworld machine learning applications and would cause severe overfitting problems. Not only can data features contain noise, but labels are also prone to be noisy due to human input. In this paper, rather than noisy label learning in multiclass classifications, we instead focus on the less explored area of noisy label learning for multilabel classifications. Specifically, we investigate the post-correction of predictions generated from classifiers learned with noisy labels. The reasons are two-fold. Firstly, this approach can directly work with the trained models to save computational resources. Secondly, it could be applied on top of other noisy label correction techniques to achieve further improvements. To handle this problem, we appeal to deep generative approaches that are possible for uncertainty estimation. Our model posits that label noise arises from a stochastic shift in the latent variable, providing a more robust and beneficial means for noisy learning. We develop both unsupervised and semi-supervised learning methods for our model. The extensive empirical study presents solid evidence to that our approach is able to consistently improve the independent models and performs better than a number of existing methods across various noisy label settings. Moreover, a comprehensive empirical analysis of the proposed method is carried out to validate its robustness, including sensitivity analysis and an ablation study, among other elements. © 2025, CC BY.

关键词： Contrastive Learning

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