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arXiv 2023年

作者： Siddique, Abubakar Browne, Will N. Grimshaw, Gina M. School of Engineering and Computer Science Victoria University of Wellington PO Box 600 Wellington6140 New Zealand Faculty of Engineering School of Electrical Engineering & Robotics Queensland University of Technology Brisbane4000 Australia Cognitive and Affective Neuroscience lab School of Psychology Victoria University of Wellington PO Box 600 Wellington6140 New Zealand

Lateralization is ubiquitous in vertebrate brains which, as well as its role in locomotion, is considered an important factor in biological intelligence. Lateralization has been associated with both poor and good performance. It has been hypothesized that lateralization has benefits that may counterbalance its costs. Given that lateralization is ubiquitous, it likely has advantages that can benefit artificial intelligence. In turn, lateralized artificial intelligent systems can be used as tools to advance the understanding of lateralization in biological intelligence. Recently lateralization has been incorporated into artificially intelligent systems to solve complex problems in computer vision and navigation domains. Here we describe and test two novel lateralized artificial intelligent systems that simultaneously represent and address given problems at constituent and holistic levels. The experimental results demonstrate that the lateralized systems outperformed state-of-the-art non-lateralized systems in resolving complex problems. The advantages arise from the abilities, (i) to represent an input signal at both the constituent level and holistic level simultaneously, such that the most appropriate viewpoint controls the system;(ii) to avoid extraneous computations by generating excite and inhibit signals. The computational costs associated with the lateralized AI systems are either less than the conventional AI systems or countered by providing better solutions. Copyright © 2023, The Authors. All rights reserved.

关键词： Intelligent systems

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Reachability-Based Confidence-Aware Probabilistic Collision Detection in Highway Driving

arXiv

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arXiv 2023年

作者： Wang, Xinwei Li, Zirui Alonso-Mora, Javier Wang, Meng School of Engineering and Materials Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Cognitive Robotics Delft University of Technology Delft2628 CD Netherlands School of Mechanical Engineering Beijing Institute of Technology Beijing100081 China Traffic Process Automation "Friedrich List" Faculty of Transport and Traffic Sciences TU Dresden Dresden01069 Germany

Risk assessment is a crucial component of collision warning and avoidance systems in intelligent vehicles. To accurately detect potential vehicle collisions, reachability-based formal approaches have been developed to ensure driving safety, but suffer from over-conservatism, potentially leading to false-positive risk events in complicated real-world applications. In this work, we combine two reachability analysis techniques, i.e., backward reachable set (BRS) and stochastic forward reachable set (FRS), and propose an integrated probabilistic collision detection framework in highway driving. Within the framework, we can firstly use a BRS to formally check whether a two-vehicle interaction is safe;otherwise, a prediction-based stochastic FRS is employed to estimate a collision probability at each future time step. In doing so, the framework can not only identify non-risky events with guaranteed safety, but also provide accurate collision risk estimation in safety-critical events. To construct the stochastic FRS, we develop a neural network-based acceleration model for surrounding vehicles, and further incorporate confidence-aware dynamic belief to improve the prediction accuracy. Extensive experiments are conducted to validate the performance of the acceleration prediction model based on naturalistic highway driving data, and the efficiency and effectiveness of the framework with the infused confidence belief are tested both in naturalistic and simulated highway scenarios. The proposed risk assessment framework is promising in real-world applications. © 2023, CC BY.

关键词： Forecasting

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GVPS: Global Visual Position System for Drones

GVPS: Global Visual Position System for Drones

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Hamid Didari Hamid D. Taghirad Parnia Shokri Fatemeh Ghofrani Advanced Robotics & Automated Systems (ARAS) Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran University of Calgary Calgary Canada Faculty of Electrical Engineering University of Science and Technology Tehran Iran

ISBN: (纸本)9781665420952

Drones are contributing significantly to transportation, rescue, commercial, and safety purposes through their rapid technology development. Operating in an unknown outdoor environment is a stringent requirement in these applications, while usually Global Positioning System (GPS) is exploited to determine the global position of the drone. However, relying on an external input source is not a secure solution due to the hijacking possibilities. To tackle this problem, a positioning system that is independent of any external signals is proposed in this paper. This is accomplished in two steps of relative position estimation and global position derivation. First, the relative position of a UAV is estimated by a monocular camera. Since the estimated relative position has an unknown scale, an extended Kalman filter (EKF) is employed to fuse IMU data to that of the relative position. Next, the global position of UAV is derived by using satellite images in addition to the estimated relative position. The proposed algorithm is implemented on a custom made drone equipped with a Jetson TX2 GPU. Experimental results verify the effectiveness of the online implementation of the proposed algorithm in practice.

关键词： Visualization Satellites Rural areas Estimation Transportation Cameras Real-time systems

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Wireless Sensor Networks and Green Networking for 6G communication- An Overview

Wireless Sensor Networks and Green Networking for 6G communi...

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Computational Intelligence, Communication technology and Networking (CICTN), International Conference on

作者： Ramiz Salama Fadi Al-Turjman Dibyahash Bordoloi Satya Prakash Yadav Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia Turkey Artificial Intelligence Engineering Dept. AI and Robotics Institute Near East University Nicosia Turkey Faculty of Engineering Research Center for AI and IoT University of Kyrenia Kyrenia Turkey Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun RS CSE Graphic Era University Dehradun Department of Computer Science and Engineering G.L. Bajaj Institute of technology and Management (GLBITM) Greater Noida India

Wireless sensor networks and green networking have been major research areas in the field of communication technology for the past few decades. In particular, the development of 6G communication systems has brought renewed focus on these areas, as the demands for higher data rates, more diverse applications, and lower energy consumption continue to increase. The use of wireless sensor networks in 6G communication systems has been widely explored in the literature, and has shown great potential for a wide range of applications, including environmental monitoring, industrial process control, and healthcare. However, a key challenge in the deployment of these networks is ensuring their energy efficiency, as the demands on the network will be much higher than in previous generations. To address this challenge, researchers have proposed various green networking techniques, such as energy harvesting and low-power communication protocols, which have been shown to significantly reduce the energy consumption of the sensors. In addition to improving the energy efficiency of wireless sensor networks, researchers have also studied their reliability, scalability, and adaptability. These properties are important in 6G communication systems, as the demands on the network are expected to be much higher than in previous generations. Researchers have proposed various methods to improve the reliability, scalability, and adaptability of wireless sensor networks, including the use of error-correction codes, redundant communication channels, and machine learning algorithms.

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Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology 2022年第4期60卷 589-590页

作者： F van den Noort C Manzini C H van der Vaart M A J van Limbeek C H Slump A T M Grob Robotics and Mechatronics Faculty of Electrical Engineering Mathematics and Computer Science Technical Medical Centre University of Twente Enschede The Netherlands. Department of Obstetrics and Gynecology University Medical Centre Utrecht Utrecht The Netherlands. Dynamics of Complex Fluids Max Planck Institute for Dynamics and Self-Organization Göttingen Germany. Multi-Modality Medical Imaging Faculty of Science and Technology Technical Medical Centre University of Twente Enschede The Netherlands.

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Correction to: Development of a dedicated 3D printed myocardial perfusion phantom: proof-of-concept in dynamic SPECT

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Medical & biological engineering & computing 2022年第6期60卷 1551-1553页

作者： Marije E Kamphuis Gijs J de Vries Henny Kuipers Marloes Saaltink Jacqueline Verschoor Marcel J W Greuter Riemer H J A Slart Cornelis H Slump Multi-Modality Medical Imaging (M3i) Group Faculty of Science and Technology Technical Medical Centre 2386 University of Twente P.O. Box 217 7500 AE Enschede The Netherlands. kamphuis@utwente.nl. Robotics and Mechatronics (RaM) Group Faculty of Electrical Engineering Mathematics and Computer Science Technical Medical Centre University of Twente Enschede The Netherlands. kamphuis@utwente.nl. Robotics and Mechatronics (RaM) Group Faculty of Electrical Engineering Mathematics and Computer Science Technical Medical Centre University of Twente Enschede The Netherlands. Department of Nuclear Medicine Ziekenhuis Groep Twente Hengelo The Netherlands. Medical Imaging Centre Department of Nuclear Medicine and Molecular Imaging University Medical Center Groningen University of Groningen Groningen The Netherlands. Biomedical Photonic Imaging Group Faculty of Science and Technology Technical Medical Centre University of Twente Enschede The Netherlands.

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Exploiting the Intrinsic Neighborhood Semantic Structure for Domain Adaptation in EEG-based Emotion Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Yi Wang, Ze Song, Yu Jia, Ziyu Wang, Boyu Jung, Tzyy-Ping Wan, Feng Macau University of Science and Technology Macao Centre for Mathematical Sciences Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovation Engineering 999078 China Tianjin University of Technology School of Electrical Engineering and Automation Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin300384 China Chinese Academy of Sciences Beijing Key Laboratory of Brainnetome and Brain-Computer Interface and Brainnetome Center Institute of Automation Beijing100045 China Western University Department of Computer Science Brain Mind Institute LondonONN6A 3K7 Canada University of California at San Diego Swartz Center for Computational Neuroscience Institute for Neural Computation La Jolla CA92093 United States University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology China University of Macau Centre for Cognitive and Brain Sciences Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation 999078 China

Due to the inherent non-stationarity and individual differences present in electroencephalogram (EEG) signals, developing a generalizable model that performs well on new subjects is challenging in EEG-based emotion recognition. Most existing domain adaptation (DA) methods typically mitigate these discrepancies by aligning the marginal distributions of domain feature representations. However, when there is a significant difference in the class-conditional distribution between domain features and labels, the domain-invariant features learned by aligning marginal distributions may have limited discriminative ability for unlabeled target instances or even prove counterproductive. To address this issue, we propose a Neighborhood Semantic Aware Learning-based Dynamic Graph Attention Convolution (NSAL-DGAT) approach that learns target semantic information by considering the inter-domain semantic topological structure, thereby improving classifier adaptation for target instances. Specifically, the proposed NSAL framework is designed to capitalize on the insight that after domain feature alignment, some target samples and their neighboring source samples exhibit similar semantics. By leveraging the neighborhood topological structure, we extract and incorporate semantic target features to train a more transferable classifier. Besides, we implement an entropy weighting mechanism to emphasize representative target semantic information, encouraging target instances to prioritize high-confidence individuals within the source neighborhood. We have conducted extensive experiments on the public SEED dataset and our collected the Hearing-Impaired EEG Dataset (HIED). The experimental results underscore the efficacy of our proposed NSAL-DGAT approach, showcasing state-of-the-art accuracy in subject-dependent as well as subject-independent scenarios. The source code is available at https://***/YYingDL/NSAL-DGAT. © 2010-2012 IEEE.

关键词： Speech analysis

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Heterogeneous Oblique Double Random Forest

arXiv

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arXiv 2023年

作者： Ganaie, M.A. Tanveer, M. Beheshti, I. Ahmad, N. Suganthan, P.N. Department of Robotics University of Michigan Ann ArborMI United States Department of Mathematics Indian Institute of Technology Indore Simrol Indore453552 India Department of Human Anatomy and Cell Science Rady Faculty of Health Sciences Max Rady College of Medicine University of Manitoba WinnipegMB Canada Department of International Program of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan KINDI Center for Computing Research College of Engineering Qatar University Doha Qatar

The decision tree ensembles use a single data feature at each node for splitting the data. However, splitting in this manner may fail to capture the geometric properties of the data. Thus, oblique decision trees generate the oblique hyperplane for splitting the data at each non-leaf node. Oblique decision trees capture the geometric properties of the data and hence, show better generalization. The performance of the oblique decision trees depends on the way oblique hyperplanes are generate and the data used for the generation of those hyperplanes. Recently, multiple classifiers have been used in a heterogeneous random forest (RaF) classifier, however, it fails to generate the trees of proper depth. Moreover, double RaF studies highlighted that larger trees can be generated via bootstrapping the data at each non-leaf node and splitting the original data instead of the bootstrapped data recently. The study of heterogeneous RaF lacks the generation of larger trees while as the double RaF based model fails to take over the geometric characteristics of the data. To address these shortcomings, we propose heterogeneous oblique double RaF. The proposed model employs several linear classifiers at each non-leaf node on the bootstrapped data and splits the original data based on the optimal linear classifier. The optimal hyperplane corresponds to the models based on the optimized impurity criterion. The experimental analysis indicates that the performance of the introduced heterogeneous double random forest is comparatively better than the baseline models. To demonstrate the effectiveness of the proposed heterogeneous double random forest, we used it for the diagnosis of Schizophrenia disease. The proposed model predicted the disease more accurately compared to the baseline models. © 2023, CC BY-NC-ND.

关键词： Decision trees

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An Empirical Study of Security Practices for Microservices Systems

SSRN

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SSRN 2022年

作者： Nasab, Ali Rezaei Shahin, Mojtaba Raviz, Seyed Ali Hoseyni Liang, Peng Mashmool, Amir Lenarduzzi, Valentina School of Computer Science Wuhan University Wuhan430072 China School of Computing Technologies RMIT University Melbourne3000 Australia Department of Computer Science Bioengineering Robotics and System Engineering University of Genova Genova16126 Italy Faculty of Information Technology and Electrical Engineering University of Oulu OuluFI-90014 Finland

Despite the numerous benefits of microservices systems, security has been a critical issue in such systems. Several factors explain this difficulty, including a knowledge gap among microservices practitioners on properly securing a microservices system. To (partially) bridge this gap, we conducted an empirical study to manually analyze 861 security points collected from 10 GitHub open-source microservices systems and Stack Overflow posts concerning security in microservices systems, leading to a catalog of 28 microservices security practices. We then ran a survey with 63 microservices practitioners to evaluate the usefulness of these 28 practices. Our findings demonstrate that the survey respondents affirmed the usefulness of the 28 practices. We believe that the catalog of microservices security practices can serve as a valuable resource for microservices practitioners to more effectively address security issues in microservices systems. It can also inform the research community of the required or less explored areas to develop microservices-specific security practices and tools. © 2022, The Authors. All rights reserved.

关键词： Surveys

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Interaction between Nanoplastics and Pectin, a Water-Soluble Polysaccharide, in the Presence of Fe(Iii) Ion

SSRN

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SSRN 2022年

作者： Moriwaki, Hiroshi Komori, Naoya Akiyama, Yoshitake Shinshu University Department of Applied Biology Faculty of Textile Science and Technology 3-15-1 Tokida Ueda386-8567 Japan Shinshu University Research Initiative for Supra-Materials Interdisciplinary Cluster for Cutting Edge Research 3-15-1 Tokida Ueda386-8567 Japan Shinshu University Department of Mechanical Engineering and Robotics Faculty of Textile Science and Technology 3-15-1 Tokida Ueda386-8567 Japan

Recently, the potential adverse effects of nanoplastics on aquatic organisms have been frequently reported. It is important to understand the behavior of nanoplastics in the water environment. In this report, the interaction between nanoplastics and pectin, a water-soluble polysaccharide, has been studied. The liquid suspension of a polystyrene nanoplastic (10 mg L-1) was mixed with pectin (15 mg L-1) and Fe(III) (0.10 mM). The aggregation and sedimentation of the nanoplastics were then observed. The removal in percent of the nanoplastics in the supernatant after storage for 2 days was 97.3%. These results indicate that nanoplastics in water can be separated by the environmentally friendly biopolymer and provide information about the behavior of nanoplastics in the water environment. © 2022, The Authors. All rights reserved.

关键词： Iron compounds

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