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Path Tracking Using Echoes in an Unknown Environment: The Issue of Symmetries and How to Break Them

International Journal of Computational Geometry and Applications 2024年第3-4期34卷 89-111页

作者： Boutin, Mireille Kemper, Gregor Department of Mathematics and Computer Science Technische Universiteit Eindhoven P.O. Box 513 Eindhoven 5600 MB Netherlands Department of Mathematics Purdue University 150 N. University St. West Lafayette 47907 IN United States Technical University of Munich Germany TUM School of Computation Information and Technology Department of Mathematics Boltzmannstr. 3 Garching bei München 85748 Germany

This paper deals with the problem of reconstructing the path of a vehicle in an unknown environment consisting of planar structures using sound. Many systems in the literature do this by using a loudspeaker and microphones mounted on a vehicle. Symmetries in the environment lead to solution ambiguities for such systems. We propose to resolve this issue by placing the loudspeaker at a fixed location in the environment rather than on the vehicle. The question of whether this will remove ambiguities regardless of the environment geometry leads to a question about breaking symmetries that can be phrased in purely mathematical terms. We solve this question in the affirmative if the geometry is in dimension three or bigger, and give counterexamples in dimension two. Excluding the rare situations where the counterexamples arise, we also give an affirmative answer in dimension two. Our results lead to a simple path reconstruction algorithm for a vehicle carrying four microphones navigating within an environment in which a loudspeaker at a fixed position emits short bursts of sounds. This algorithm could be combined with other methods from the literature to construct a path tracking system for vehicles navigating within a potentially symmetric environment. © 2024 World Scientific Publishing Company.

关键词： acoustic SLAM Cayley-Menger matrix Euclidean symmetries hyperplane arrangements localization path reconstruction SLAM

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IoT-based Automated Theft Detection in Vehicles: A Combined Approach of YOLO Detection and MediaPipe Pose Analysis

IoT-based Automated Theft Detection in Vehicles: A Combined ...

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International Conference on Control, Automation and Systems ( ICCAS)

作者： Seungheon Lee Sol Kim Inkyoung Byun Jiyun An Colten Glover Department of Computer Science Hoseo University Seoul Republic of Korea Seoul Korea Division of Computer Convergence Chungnam National University Daejeon Republic of Korea Daejeon Korea Department of Computer and Information Technology Purdue University West Lafayette IN USA

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

Vehicle theft has emerged as a notable societal concern within the United States. Despite advancements in detection technology, rates of vehicle theft persist in an upward trend. In response to this exigent issue, an Internet of Things (IoT) system has been devised to confront the challenges encountered by car owners, particularly those whose vehicles lack inherent theft prevention mechanisms. The objective of this research is to introduce a comprehensive system for preventing vehicle theft. The proposed system integrates two fundamental technologies: computer vision for identifying suspicious activity, and vibration and pressure sensors to detect potential physical breaches into the vehicle. Upon detection of suspicious activity, the proposed system quickly dispatches a notification to the user along with a 20-second video capturing the behavior. This IoT system surmounts the constraints of conventional anti-theft systems, thereby enhancing the security of vehicle owners in the United States and curbing the proliferation of vehicle theft.

关键词： Vibrations Pressure sensors YOLO Prevention and mitigation Cameras Sensor systems Object recognition Internet of Things Security Reliability

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Eco-Friendly Laser Deterrent System for Woodpeckers Utilizing YOLOv8

Eco-Friendly Laser Deterrent System for Woodpeckers Utilizin...

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IEEE International Conference on Robotic Computing (IRC)

作者： Wonah Kim Seung-Hun Han Hyojin Kim Jaden Soroka Justin Allange Anthony Smith Artificial Intelligence Dong-A University Busan Republic of Korea Software and Computer Engineering Ajou University Suwon Gyeonggi-do Republic of Korea Computer and Information Technology Purdue University West Lafayette Indiana United States

ISBN: (数字)9798331521554

ISBN: (纸本)9798331521561

This research introduces a novel approach for deterring woodpeckers to safeguard wooden structures in an environmentally-friendly way. Woodpecker drilling and drumming poses a significant threat to wooden structures, causing extensive physical and financial damage. These issues range from property damage to noise, underscoring the urgent need for an effective deterrent method. Our study proposes a laser-based deterrent mechanism, designed to be eco-friendly by minimizing effects on other wildlife and humans. This environmentally conscious approach offers a humane solution to woodpecker control, ensuring compliance with the Migratory Bird Treaty Act (MBTA). Additionally, our approach utilizes deep learning technology to chase away specific targets. Our system integrates YOLOv8 for accurate and low-power identification, creating an efficient operating solution with independent internet connectivity. This enhancement enables the system to adapt across various environments. Testing in a controlled environment has verified the system's effectiveness in deterring woodpeckers in an eco-friendly way. This approach ensures minimal impact on other wildlife and humans, adhering to legal and environmental constraints.

关键词： Deep learning Accuracy Wildlife Lasers Transfer learning Noise Real-time systems Robots Testing Overfitting

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Cost-Effective Solution for Fallen Tree Recognition Using YOLOX Object Detection

Cost-Effective Solution for Fallen Tree Recognition Using YO...

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IEEE International Conference on Robotic Computing (IRC)

作者： Hearim Moon Eunsik Park Junghyun Moon Juyeong Lee Minji Lee Doyoon Kim Minsun Lee Eric T. Matson Department of Computer Science and Engineering Chungnam National University Department of Computer and Information Technology Purdue University

ISBN: (纸本)9781665472616

Tropical cyclones are the world’s most deadly natural disasters, especially causing tree death by pulling out or breaking the roots of trees, which has a great impact on the forest ecosystem and forest owners. To minimize additional damage, an efficient approach is required to identify the location and distribution information of fallen trees. Several past studies have attempted to detect fallen trees, but most studies are expensive and difficult to utilize. Therefore, the purpose of this study is to solve these problems. Using a cost-effective, high-resolution secondary camera-equipped unmanned aerial vehicle (UAV) to collect data and use this data to train a YOLOX model, an object detection algorithm that can perform accurate detection in a very short time. The solution led in this study can be utilized in all scenarios that require low-cost and high-reliability object detection results. The experimental results show that our solution detected 88% of fallen trees in the image using YOLOX. The proposed model also implemented a visualization application that displays the detection results computed by the trained model in a client-friendly way. Our solution recognizes fallen trees as images or videos and presents the analysis results as a web-based visualization.

关键词： Biological system modeling Computational modeling Data visualization Vegetation Forestry Object detection Autonomous aerial vehicles

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Optimizing Cybersecurity Budgets with AttackSimulation

Optimizing Cybersecurity Budgets with AttackSimulation

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IEEE Conference on Technologies for Homeland Security

作者： Alexander Master George Hamilton J. Eric Dietz Department of Computer and Information Technology Purdue Homeland Security Institute Purdue University West Lafayette USA

ISBN: (纸本)9781665494052

Modern organizations need effective ways to assess cybersecurity risk. Successful cyber attacks can result in data breaches, which may inflict significant loss of money, time, and public trust. Small businesses and non-profit organizations have limited resources to invest in cybersecurity controls and often do not have the in-house expertise to assess their risk. Cyber threat actors also vary in sophistication, motivation, and effectiveness. This paper builds on the previous work of Lerums et al., who presented an AnyLogic model for simulating aspects of a cyber attack and the efficacy of controls in a generic enterprise network. This paper argues that their model is an effective quantitative means of measuring the probability of success of a threat actor and implements two primary changes to increase the model's accuracy. First, the authors modified the model's inputs, allowing users to select threat actors based on the organization's specific threat model. Threat actor effectiveness is evaluated based on publicly available breach data (in addition to security control efficacy), resulting in further refined attack success probabilities. Second, all three elements - threat effectiveness, control efficacy, and model variance - are computed and evaluated at each node to increase the estimation fidelity in place of pooled variance calculations. Visualization graphs, multiple simulation runs (up to 1 million), attack path customization, and code efficiency changes are also implemented. The result is a simulation tool that provides valuable insight to decision-makers and practitioners about where to most efficiently invest resources in their computing environment to increase cybersecurity posture. AttackSimulation and its source code are freely available on GitHub.

关键词： Threat modeling Computational modeling Source coding Estimation Data visualization Organizations US Department of Homeland Security

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Cognitive Load-based Affective Workload Allocation for Multi-human Multi-robot Teams

arXiv

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arXiv 2023年

作者： Jo, Wonse Wang, Ruiqi Yang, Baijian Foti, Dan Rastgaar, Mo Min, Byung-Cheol Department of Computer and Information Technology Purdue University West LafayetteIN47906 United States Department of Psychological Sciences Purdue University West LafayetteIN47906 United States School of Engineering Technology Purdue University West LafayetteIN47906 United States

The interaction and collaboration between humans and multiple robots represent a novel field of research known as human multi-robot systems. Adequately designed systems within this field allow teams composed of both humans and robots to work together effectively on tasks such as monitoring, exploration, and search and rescue operations. This paper presents a deep reinforcement learning-based affective workload allocation controller specifically for multi-human multi-robot teams. The proposed controller can dynamically reallocate workloads based on the performance of the operators during collaborative missions with multi-robot systems. The operators’ performances are evaluated through the scores of a self-reported questionnaire (i.e., subjective measurement) and the results of a deep learning-based cognitive workload prediction algorithm that uses physiological and behavioral data (i.e., objective measurement). To evaluate the effectiveness of the proposed controller, we conduct an exploratory user experiment with various allocation strategies. The user experiment uses a multi-human multi-robot CCTV monitoring task as an example and carry out comprehensive real-world experiments with 32 human subjects for both quantitative measurement and qualitative analysis. Our results demonstrate the performance and effectiveness of the proposed controller and highlight the importance of incorporating both subjective and objective measurements of the operators’ cognitive workload as well as seeking consent for workload transitions, to enhance the performance of multi-human multi-robot teams. © 2023, CC BY.

关键词： Multipurpose robots

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Music Emotion Recognition Using Hierarchical Contrastive Learning

Music Emotion Recognition Using Hierarchical Contrastive Lea...

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Artificial Intelligence x Humanities, Education, and Art (AIxHEART)

作者： Siyeol Jung Yubin Choi E. Cho Smith Mia Y. Wang Artificial Intelligence Graduate School UNIST Ulsan Korea Computer Science and Engineering Soongsil University Seoul Korea Computer and Information Technology Purdue University West Lafayette USA Department of Computer Science College of Charleston Charleston USA

ISBN: (数字)9798350391015

ISBN: (纸本)9798350391022

The Music Emotion Recognition (MER) task has garnered significant attention from both academic and industrial fields due to its versatility in various fields, such as music recommendation systems and psychotherapy. Due to advancements in language model performance, there has been a rise in approaches that address the MER tasks using only lyric data. Despite the empirical evidence suggesting that valence is a more reliable indicator of emotion disambiguation than arousal, previous studies have often overlooked this relationship. To address this research gap in the literature and enhance performance, we propose considering valence and arousal within a hierarchical framework. To preserve this hierarchical structure in the embedding space, we employed Hierarchical Contrastive Learning to enhance the MER performance while preserving the hierarchical organization. The experimental results show that this approach improves the classification performance and leads to a more balanced model. Furthermore, it is noteworthy that the model’s performance improvement rate is faster when compared to the application of cross-entropy loss.

关键词： Measurement Emotion recognition Standards organizations Contrastive learning Organizations Data models Reliability Proposals Recommender systems Convergence

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Answer Comments As Reviews: Predicting Acceptance By Measuring Valence On Stack Exchange

Answer Comments As Reviews: Predicting Acceptance By Measuri...

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IEEE International Conference on Big Data

作者： William Ledbetter John Springer Department of Computer and Information Technology Purdue University West Lafayette Indiana USA

ISBN: (纸本)9781665480468

Online communication has increased the need to interpret complex emotions rapidly; due to the volatility of the data involved, machine learning tasks that process text aim to address the related challenges. Exploring text in comments that supports ideas through computational methods is a logical next step considering similar research for these question-and-answer sites. A lack of current algorithms that can accurately predict accepted answers with equal votes suggests a gap in this knowledge. Measuring collaborative signals in comments finds common keywords to move a problem toward a *** a dataset from questions posted to Stack Exchange on the subject area of machine learning, the researchers constructed a model using the comments of posts made on accepted answers. Intending to discover whether predictions of marked solutions are accurate by treating the comments as reviews, the researchers find a reduction in error by incorporating reviews of answers as a feature in the predictive algorithm.

关键词： Knowledge engineering Machine learning algorithms Natural languages Collaboration Machine learning Manuals Big Data

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A Corpus of Encoded Malware Byte information as Images for Efficient Classification

A Corpus of Encoded Malware Byte Information as Images for E...

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International IEEE Conference on Signal-Image Technologies and Internet-Based System

作者： Ryan Frederick Joseph Shapiro Ricardo A. Calix Computer Information Technology Purdue University Northwest Hammond United States of America

Malware has become an increasing problem in today’s world. In an attempt to combat this ever-changing problem, this research presents a machine-learning approach for analyzing malware using image classification. This paper discusses the idea of using image-based machine learning (ML) in malware analysis. Ways of applying this technique into the real-world are also presented and discussed. The highest accuracy achieved with the dataset used for training was 98.21%. Other datasets were also tested and achieved up to 93.22%. This shows that not only is this an effective method, but also that it can be used on other datasets and that a pre-trained model is more effective. The pre-trained model, dataset, and application are made available here to the wider research community.

关键词： Training Machine learning Predictive models Malware Anti-virus software Task analysis computer crime

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A face recognition algorithm based on feature fusion

A face recognition algorithm based on feature fusion

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作者： Zhang, Jiwei Yan, Xiaodan Cheng, Zelei Shen, Xueqi School of Software Engineering Beijing University of Posts and Telecommunications Beijing China School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China Department of Computer and Information Technology Purdue University West LafayetteIN United States

In the process of building a smart city, face recognition can be applied to the transformation of enterprises, communities, and parks. The combination of building security system and face recognition technology can improve the security experience of enterprises and citizens through the solution of hardware and software integration. Face recognition is still facing the challenges of illumination, occlusion, and attitude change in the actual application process. In addition, the end-to-end convolutional neural networks (CNN) seldom make use of the hierarchical feature of the network. So, we propose a hierarchy feature fusion method for face recognition, which uses supervisory information to learn shallow and deep facial features. The features are fused to enhance the recognition accuracy of face recognition against illumination and occlusion. The method is applied to the transformation of the visual geometry group network and Lightened CNN. The face recognition experiments are carried out using the hierarchy network. Our method has achieved good recognition results in the labeled faces in the wild (LFW) and AR face databases. © 2020 John Wiley & Sons, Ltd.

关键词： Face recognition

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