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arXiv 2022年

作者： Ryu, J. Jon Kim, Young-Han Department of Electrical Engineering and Computer Science Massachussetts Institute of Technology CambridgeMA02139 United States Department of Electrical and Computer Engineering University of California La Jolla San DiegoCA92093 United States

This paper presents how to perform minimax optimal classification, regression, and density estimation based on fixed-k nearest neighbor (NN) searches. We consider a distributed learning scenario, in which a massive dataset is split into smaller groups, where the kNNs are found for a query point with respect to each subset of data. We propose optimal rules to aggregate the fixed-k-NN information for classification, regression, and density estimation that achieve minimax optimal rates for the respective problems. We show that the distributed algorithm with a fixed k over a sufficiently large number of groups attains a minimax optimal error rate up to a multiplicative logarithmic factor under some regularity conditions. Roughly speaking, distributed k-NN rules with M groups has a performance comparable to the standard Θ(kM)-NN rules even for fixed k. © 2022, CC BY.

关键词： k-nearest neighbors

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Circulogy: An AI-Enabled Blockchain-Based e-Waste Management Framework Using Non-Fungible Tokens (NFT) to Achieve Net Zero and Imply the Circular Economy

Circulogy: An AI-Enabled Blockchain-Based e-Waste Management...

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IEEE International Conference on Blockchain and Cryptocurrency (ICBC)

作者： Md Mahmudul Hasan Rajesh Kumar Plamthottathil Jabed Morshed Dipto Sarkar Nazia Hameed Silvia Cirstea Faculty of Science and Engineering School of Computing and Information Science Anglia Ruskin University Cambridge UK TrackGenesis ONE Tech Hub Schoolhill Aberdeen Scotland UK Lecturer in Cyber Security Program La Trobe University Australia Research and Development Team intelliCreate University of Nottingham School of Computer Science Computing and AI Group School of Computing and Information Science (CIS)

Overconsumption of resources is a global issue. To deal with resource depletion and mitigate impending crises, the circular economy (CE) solution provides an ecosystem by reducing waste via the reuse, repair, refurbishment, and recycling the existing materials and products. However, as the complexity of supply chains is increasing an effective CE management is very crucial. We want to address this issue by performing a feasibility study with AI-enabled blockchain technology using our developed customised NFT platform, TrackGenesis NFT, along with the *** architecture for CE management to decrease transaction costs, enhance performance and communication along the supply chain, and reduce carbon footprints. Circulogy is an e-waste management system that can respond to supply chain challenges using blockchain technologies. A supply chain can get complicated very quickly, considering that each product component has its supply chain. In our proposed solution, blockchain provides a solution to this by establishing transparency in every node of the product's lifecycle and users can exchange or sell/buy NFTs. There are multiple copies of the audit trail for every transaction using blockchain, which will provide the ability to track and reuse/recycle Waste Electric and Electronic Equipment (WEEE).

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Adaptive Velocity and Acceleration Control of Autonomous Vehicle Systems

TechRxiv

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TechRxiv 2023年

作者： Ren, Tianqun Wang, Baorui Gu, Guoxiang Chen, Xiang The School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China The School of Electrical Engineering and Computer Science Louisiana State University Baton RougeLA70803-5901 United States The Department of Electrical and Computer Engineering University of Windsor 401 Sunset Avenue WindsorONN9B 3P4 Canada

This paper proposes a Combined Adaptive Velocity and Acceleration Control (CAVAC) law for autonomous vehicles in the presence of unknown and uncertain parameters. In particular, the global asymptotic stability of the nonlinear vehicle system under the proposed adaptive velocity control is shown to be held without real-time estimation of the vehicle parameters. Moreover, the CAVAC law is applied to ensure the required inter-vehicle spacing, crucial to avoiding collisions in vehicle platoons. The simulation studies illustrate the advantages of the CAVAC law for the autonomous vehicle platoon in dealing with the speed limit changes, in mitigating issues induced by merging-exiting, and in ensuring collision avoidance. The experiments conducted on an electric vehicle also validate the effectiveness of the proposed control laws. © 2023, CC BY.

关键词： Velocity control

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Uncertainty-Autoencoder-Based Privacy and Utility Preserving Data Type Conscious Transformation

arXiv

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arXiv 2022年

作者： Mandal, Bishwas Amariucai, George Wei, Shuangqing Department of Computer Science Kansas State University ManhattanKS66506 United States Division of Electrical & Computer Engineering Louisiana State University Baton RougeLA70803 United States

We propose an adversarial learning framework that deals with the privacy-utility tradeoff problem under two types of conditions: data-type ignorant, and data-type aware. Under data-type aware conditions, the privacy mechanism provides a one-hot encoding of categorical features, representing exactly one class, while under data-type ignorant conditions the categorical variables are represented by a collection of scores, one for each class. We use a neural network architecture consisting of a generator and a discriminator, where the generator consists of an encoder-decoder pair, and the discriminator consists of an adversary and a utility provider. Unlike previous research considering this kind of architecture, which leverages autoencoders (AEs) without introducing any randomness, or variational autoencoders (VAEs) based on learning latent representations which are then forced into a Gaussian assumption, our proposed technique introduces randomness and removes the Gaussian assumption restriction on the latent variables, only focusing on the end-to-end stochastic mapping of the input to privatized data. We test our framework on different datasets: MNIST, FashionMNIST, UCI Adult, and US Census Demographic Data, providing a wide range of possible private and utility attributes. We use multiple adversaries simultaneously to test our privacy mechanism – some trained from the ground truth data and some trained from the perturbed data generated by our privacy mechanism. Through comparative analysis, our results demonstrate better privacy and utility guarantees than the existing works under similar, data-type ignorant conditions, even when the latter are considered under their original restrictive single-adversary model. © 2022, CC BY.

关键词： Learning systems

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Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation

arXiv

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arXiv 2025年

作者： Liu, Jian Sun, Wei Yang, Hui Deng, Pengchao Liu, Chongpei Sebe, Nicu Rahmani, Hossein Mian, Ajmal National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering School of Robotics State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China Department of Information Engineering and Computer Science University of Trento Trento38123 Italy School of Computing and Communications Lancaster University LA1 4YW United Kingdom Department of Computer Science The University of Western Australia WA6009 Australia

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance. Our code will be made public at https://***/CNJianLiu/Diff9D. Copyright © 2025, The Authors. All rights reserved.

关键词： Augmented reality

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Electroencephalogram and Event-Related Potential in Mild Cognitive Impairment: Recent Developments in Signal Processing, Machine Learning, and Deep Learning

IEEE Journal of Selected Areas in Sensors

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IEEE Journal of Selected Areas in Sensors 2025年 2卷 162-184页

作者： Azami, Hamed Mirjalili, Mina Rajji, Tarek K. Wu, Chien-Te Humeau-Heurtier, Anne Jung, Tzyy-Ping Wei, Chun-Shu Trinh, Thanh-Tung Liu, Yi-Hung University of Toronto Centre for Addiction and Mental Health TorontoONM5S 1A1 Canada University of Texas Southwestern Medical Center DallasTX75390 United States University of Florida Department of Occupational Therapy GainesvilleFL32611 United States University of Florida Center for Cognitive Aging and Memory McKnight Brain Institute GainesvilleFL32611 United States University of Angers LARIS SFR MATHSTIC AngersF-49000 France University of California Institute for Neural Computation Institute of Engineering in Medicine La Jolla CA92093 United States National Yang Ming Chiao Tung University Department of Computer Science Hsinchu City30010 Taiwan National Taipei University of Technology Graduate Institute of Manufacturing Technology College of Mechanical and Electrical Engineering Taipei10608 Taiwan Vietnam National University Department of Information and Communication Technology Faculty of Nontraditional Security HSB Hanoi720325 Viet Nam National Yang Ming Chiao Tung University Institute of Electrical and Control Engineering Hsinchu City30010 Taiwan

Mild cognitive impairment (MCI) is an early stage of non-age-related cognitive decline with an increased risk of progressing to dementia. Early detection of MCI is essential for implementing preventative strategies that can delay or prevent the onset of dementia, ultimately improving patient outcomes and reducing healthcare costs. Electroencephalograms (EEGs) and event-related potentials (ERPs) have shown significant promise in detecting MCI due to their affordability, real-time monitoring capabilities, and noninvasiveness. EEG provides continuous brain activity data, while ERPs offer insights into specific cognitive processes by analyzing brain responses to stimuli. These methods can complement each other in MCI diagnosis by providing a comprehensive view of overall brain function and detailed information on specific cognitive processes. However, EEG and ERP are susceptible to noise and interindividual variability, which can hinder their reliability. In addition, applying machine learning models on EEG or ERP for MCI detection presents challenges such as the risk of overfitting and difficulties in interpreting the underlying decision-making process. This review emphasizes recent advancements in signal processing and feature extraction methods applied to EEG and ERP data and explores the use of machine learning and deep learning techniques to enhance diagnostic accuracy and interpretative depth. By integrating these methodologies, the review highlights how EEG and ERP can contribute to a more effective understanding and monitoring of cognitive changes associated with MCI, underscoring the importance of early diagnosis for timely intervention and improved patient care. Finally, the review focuses on future research directions, including the development of advanced analytical techniques and multimodal integration approaches involving EEG and ERP to further improve diagnostic accuracy and clinical application. © 2024 IEEE.

关键词： Deep learning

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Transforming the public transport sector in the Philippines through service contracts: A program evaluation study

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Transportation Research Procedia 2025年 82卷 890-907页

作者： Varsolo Sunio Wilhansen Li Joemier Pontawe Albert Dizon Alexis Fillone Science Engineering and Management Research Institute University of Asia and the Pacific Pasig City Metro Manila 1600 Philippines Toulan School of Urban Studies and Planning Portland State University Oregon 97201 United States Sakay Mobility Philippines Corporation Quezon City Metro Manila 1110 Philippines Department of Information Systems and Computer Science School of Science and Engineering Ateneo de Manila University Quezon City Metro Manila 1108 Philippines School of Architecture and Planning The University of Auckland New Zealand Civil Engineering Department De La Salle University Manila Metro Manila 1004 Philippines

We evaluate two schemes of service contracting (SC) program implemented for the first time in the Philippines: gross-cost and net-cost. Using an automatic vehicle location (AVL) dataset generated using a mobile application, we examine the supply and performance of jeepney, a form of paratransit, on 20 routes during the pandemic. Ten of these routes are under gross-cost, and the other ten are under net-cost. Our evaluation suggests that gross-cost SC has resulted in more kilometer-run travelled, more arrivals at the stop, shorter headway duration, which are indicative of increased supply, compared to net-cost. However, we obtain mixed results for metrics associated with performance (headway regularity, dwell time and route compliance) for gross- and net- cost. We recommend that for SC to maximize its impact and raise performance, the government should set standards on metrics associated with fleet management and enforce strict compliance. Since SC is typically applied for the management of formal public transport, our work represents a novel contribution on the application of SC on the management of paratransit in the Global South.

关键词： service contract public transport reform Global South paratransit

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CovidXNet: An Ensemble of Deep Neural Networks for Non-invasive Binary Identification of COVID-19 through Chest Radiographs

CovidXNet: An Ensemble of Deep Neural Networks for Non-invas...

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International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Jonathan Daniel S. Ong Aron Jake P. Vidad Ira C. Valenzuela Ronnie Concepcion Nilo T. Bugtai Raouf N.G. Naguib Department of Manufacturing Engineering and Management De La Salle University Manila Philippines School of Mathematics Computer Science and Engineering Liverpool Hope University Liverpool UK

This study compared five different image classification algorithms, namely VGG16, VGG19, AlexNet, DenseNet, and ConVNext, based on their ability to detect and classify COVID-19-related cases given chest X-ray images. Using performance metrics like accuracy, F1 score, precision, recall, and MCC compared these intelligent classification algorithms. Upon testing these algorithms, the accuracy for each model was quite unsatisfactory, ranging from 80.00% to 92.50%, provided it is for medical application. As such, an ensemble learning-based image classification model, made up of AlexNet and VGG19 called CovidXNet, was proposed to detect COVID-19 through chest X-ray images discriminating between health and pneumonic lung images. CovidXNet achieved an accuracy of 97.00%, which was significantly better considering past results. Further studies may be conducted to increase the accuracy, particularly for identifying and classifying chest radiographs for COVID-19-related cases, since the current model may still provide false negatives, which may be detrimental to the prevention of the spread of the virus.

关键词： COVID-19 Radiography Pulmonary diseases Neural networks Classification algorithms X-ray imaging Nanotechnology

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Enhanced electroencephalography effective connectivity in frontal low-gamma band correlates of emotional regulation after mindfulness training

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Journal of Neuroscience Research 2023年第6期101卷 901-915页

作者： Ng, Hei-Yin Hydra Wu, Changwei W. Huang, Feng-Ying Huang, Chih-Mao Hsu, Chia-Fen Chao, Yi-Ping Jung, Tzyy-Ping Chuang, Chun-Hsiang Research Center for Education and Mind Sciences College of Education National Tsing Hua University Hsinchu Taiwan Institute of Information Systems and Applications College of Electrical Engineering and Computer Science National Tsing Hua University Hsinchu Taiwan Department of Educational Psychology and Counseling College of Education National Tsing Hua University Hsinchu Taiwan Graduate Institute of Mind Brain and Consciousness Taipei Medical University Taipei Taiwan Brain and Consciousness Research Center Taipei Medical University-Shuang Ho Hospital New Taipei Taiwan Department of Education National Taipei University of Education Taipei Taiwan Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B) National Yang Ming Chiao Tung University Hsinchu Taiwan Graduate Institute of Behavioral Sciences Chang Gung University Taoyuan Taiwan Department of Child Psychiatry Chang Gung Memorial Hospital at Linkou Taoyuan Taiwan Department of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan Department of Otolaryngology-Head and Neck Surgery Chang Gung Memorial Hospital at Linkou Taoyuan Taiwan Institute for Neural Computation and Institute of Engineering in Medicine University of California San Diego La Jolla CA United States

Practicing mindfulness, focusing attention on the internal and external experiences occurring in the present moment with open and nonjudgement stance, can lead to the development of emotional regulation skills. Yet, the effective connectivity of brain regions during mindfulness has been largely unexplored. Studies have shown that mindfulness practice promotes functional connectivity in practitioners, potentially due to improved emotional regulation abilities and increased connectivity in the lateral prefrontal areas. To examine the changes in effective connectivity due to mindfulness training, we analyzed electroencephalogram (EEG) signals taken before and after mindfulness training, focusing on training-related effective connectivity changes in the frontal area. The mindfulness training group participated in an 8-week mindfulness-based stress reduction (MBSR) program. The control group did not take part. Regardless of the specific mindfulness practice used, low-gamma band effective connectivity increased globally after the mindfulness training. High-beta band effective connectivity increased globally only during Breathing. Moreover, relatively higher outgoing effective connectivity strength was seen during Resting and Breathing and Body-scan. By analyzing the changes in outgoing and incoming connectivity edges, both F7 and F8 exhibited strong parietal connectivity during Resting and Breathing. Multiple regression analysis revealed that the changes in effective connectivity of the right lateral prefrontal area predicted mindfulness and emotional regulation abilities. These results partially support the theory that the lateral prefrontal areas have top-down modulatory control, as these areas have high outflow effective connectivity, implying that mindfulness training cultivates better emotional regulation. © 2023 Wiley Periodicals LLC.

关键词： direct directed transfer function effective connectivity emotional regulation MBSR mindfulness

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Comparative Assessment between PD and Fuzzy Control for a Novel Unmanned Aerial Vehicle Inspired by Owls 21

Comparative Assessment between PD and Fuzzy Control for a No...

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21st IEEE International Conference on sciences and Techniques of Automatic Control and computer engineering, STA 2022

作者： Boucetta, Rahma Romaniuk, Pawel Saeed, Khalid National Engineering School of Sfax University of Sfax Sfax Lab-STA LR11ES50 Faculty of Sciences of Sfax Department of Physics Soukra Road Sfax Tunisia Bialystok University of Technology Faculty of Computer Science Bialystok Poland Universidad de la Costa Barranquilla Colombia

ISBN: (纸本)9781665482615

This paper proposes a comparative assessment between PD and fuzzy controls applied to an autonomous aerial vehicle inspired by owls to accomplish a silent and stable flight. Firstly, the owls' flight is analysed to determine different components of a particular trirotor drone. Afterward a detailed study, the mathematical model is developed using kinematics and aerodynamics laws. Next, the theoretical model is employed to synthesize a control strategy based on PD and then on fuzzy concepts to stabilize the drone in a hovering situation with the adequate altitude and attitude. The fuzzy controller is compared to the PD to display its performances in terms of precision and trajectory tracking. © 2022 IEEE.

关键词： Aerodynamics

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