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IEEE Transactions on Field Robotics 2024年 1卷 476-526页

作者： Benjamin Morrell Kyohei Otsu Ali Agha David D. Fan Sung-Kyun Kim Muhammad Fadhil Ginting Xianmei Lei Jeffrey Edlund Seyed Fakoorian Amanda Bouman Fernando Chavez Taeyeon Kim Gustavo J. Correa Maira Saboia Angel Santamaria-Navarro Brett Lopez Boseong Kim Chanyoung Jung Mamoru Sobue Oriana Claudia Peltzer Joshua Ott Robert Trybula Thomas Touma Marcel Kaufmann Tiago Stegun Vaquero Torkom Pailevanian Matteo Palieri Yun Chang Andrzej Reinke Matthew Anderson Frederik E. T. Schöller Patrick Spieler Lillian M. Clark Avak Archanian Kenny Chen Hovhannes Melikyan Anushri Dixit Harrison Delecki Daniel Pastor Barry Ridge Nicolas Marchal Jose Uribe Sharmita Dey Kamak Ebadi Kyle Coble Alexander Nikitas Dimopoulos Vivek Thangavelu Vivek S. Varadharajan Nicholas Palomo Antoni Rosinol Arghya Chatterjee Christoforos Kanellakis Bjorn Lindqvist Micah Corah Kyle Strickland Ryan Stonebraker Michael Milano Christopher E. Denniston Sami Sahnoune Thomas Claudet Seungwook Lee Gautam Salhotra Edward Terry Rithvik Musuku Robin Schmid Tony Tran Ara Kourchians Justin Schachter Hector Azpurua Levi Resende Arash Kalantari Jeremy Nash Josh Lee Christopher Patterson Jennifer G. Blank Kartik Patath Yuki Kubo Ryan Alimo Yasin Almalioglu Aaron Curtis Jacqueline Sly Tesla Wells Nhut T. Ho Mykel Kochenderfer Giovanni Beltrame George Nikolakopoulos David Shim Luca Carlone And Joel Burdick NASA Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA Department of Mechanical and Civil Engineering California Institute of Technology Pasadena CA USA Institut de Robòtica i Informàtica Industrial CSIC-UPC Barcelona Spain Department of Aerospace Engineering Korea Advanced Institute of Science and Technology Daejeon South Korea Department of Aeronautics and Astronautics Stanford University Stanford CA USA Department of Electrical And Information Engineering Polytechnic University of Bari Bari Italy Department of Aeronautics and Astronautics Massachusetts Institute of Technology Cambridge MA USA Department of Electrical and Computer Engineering Cornell University Ithaca NY USA Department of Computer Engineering and Software Engineering École Polytechnique de Montréal Montreal QC Canada Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden Department of Mechanical Engineering California State University at Northridge Northridge CA USA Department of Earth and Planetary Sciences McGill University Montreal QC Canada Blue Marble Space Institute of Science Seattle WA USA

This article presents an appendix to the original NeBula autonomy solution developed by the Team Collaborative SubTerranean Autonomous Robots (CoSTAR), participating in the DARPA Subterranean Challenge. Specifically, this article presents extensions to NeBula’s hardware, software, and algorithmic components that focus on increasing the range and scale of the exploration environment. From the algorithmic perspective, we discuss the following extensions to the original NeBula framework: 1) large-scale geometric and semantic environment mapping; 2) an adaptive positioning system; 3) probabilistic traversability analysis and local planning; 4) large-scale partially observable Markov decision process (POMDP)-based global motion planning and exploration behavior; 5) large-scale networking and decentralized reasoning; 6) communicationaware mission planning; and 7) multimodal ground–aerial exploration *** demonstrate the application and deployment of the presented systems and solutions in various large-scale underground environments, including limestone mine exploration scenarios as well as deployment in the DARPA Subterranean challenge.

关键词： Planning State estimation Semantics NASA Hardware Three-dimensional displays Systems architecture

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Top-Down Target Object Detection Through Context

Top-Down Target Object Detection Through Context

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International Conference on Image and Vision Computing New Zealand, IVCNZ

作者： Ibrahim M. H. Rahman Christopher Hollitt Mengjie Zhang Osama Rehman Aisha Ajmal Simon Jigwan Park Information Technology The Open Polytechnic of New Zealand Wellington New Zealand School of Engg. and Computer Science Victoria University of Wellington Wellington New Zealand Department of Software Engineering Bahria University Karachi Pakistan

ISBN: (数字)9798331518776

ISBN: (纸本)9798331518783

Visual attention is crucial for identifying the most salient regions in an image. However, when the objective involves higher-level visual tasks, such as target object detection, it becomes necessary to incorporate high-level information to guide the search for the target. This process, known as top-down saliency detection, leverages guidance sources like contextual information and target features to identify regions of interest that are more likely to contain the target object. In this paper, we propose a model that generates top-down saliency maps by adjusting the feature map weights of a universal visual attention model based on contextual information. While contextual information has traditionally been used to understand the gist of an image, it has not been integrated into the creation of saliency maps for target object detection. We demonstrate that incorporating contextual information into a visual attention model enhances target object detection performance. The proposed model, tested on six datasets, shows significant improvements in detecting target objects compared to models that do not utilize contextual information.

关键词： Weight measurement Visualization Accuracy Object detection Feature extraction Search problems Tuning Optimization Context modeling Saliency detection

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A Phase Estimation Algorithm for Quantum Speed-Up Multi-Party Computing

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computers, Materials & Continua 2021年第4期67卷 241-252页

作者： Wenbin Yu Hao Feng Yinsong Xu Na Yin Yadang Chen Zhi-Xin Yang Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Jiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Department of Computer Science and Engineering Michigan State UniversityEast Lansing48824MIUSA State Key Laboratory of Internet of Things for Smart City and Department of Electromechanical Engineering University of Macao999078Macao

Security and privacy issues have attracted the attention of researchers in the field of IoT as the information processing scale grows in sensor *** computing,theoretically known as an absolutely secure way to store and transmit information as well as a speed-up way to accelerate local or distributed classical algorithms that are hard to solve with polynomial complexity in computation or *** this paper,we focus on the phase estimation method that is crucial to the realization of a general multi-party computing model,which is able to be accelerated by quantum algorithms.A novel multi-party phase estimation algorithm and the related quantum circuit are proposed by using a distributed Oracle operator with *** proved theoretical communication complexity of this algorithm shows it can give the phase estimation before applying multi-party computing efficiently without increasing any additional ***,a practical problem of multi-party dating investigated shows it can make a successful estimation of the number of solution in advance with zero communication complexity by utilizing its special statistic *** simulations present the correctness,validity and efficiency of the proposed estimation method.

关键词： Edge computing security multi-party computing quantum algorithm phase estimation communication complexity

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A Survey of Graph Neural Networks and Their Industrial Applications

SSRN

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SSRN 2024年

作者： Lu, Haoran Wang, Lei Ma, Xiaoliang Cheng, Jun Zhou, Mengchu Department of Statistics University of Michigan Michigan MI48105 United States Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China New Jersey Institute of Technology NJ United States

Graph Neural Networks (GNNs) have emerged as a powerful tool for analyzing and modeling graph-structured data. In recent years, GNNs have gained significant attention in various domains. This review paper aims to provide an overview of the state-of-the-art graph neural network techniques and their industrial ***, we introduce the fundamental concepts and architectures of GNNs, highlighting their ability to capture complex relationships and dependencies in graph data. We then delve into the variants and evolution of graphs, including directed graphs, heterogeneous graphs, dynamic graphs, and hypergraphs. Next, we discuss the interpretability of GNN, and GNN theory including graph augmentation, expressivity, and ***, we introduce the specific use cases of GNNs in industrial settings, including finance, biology, knowledge graphs, recommendation systems, Internet of Things (IoT), and knowledge distillation. This review paper highlights the immense potential of GNNs in solving real-world problems, while also addressing the challenges and opportunities for further advancement in this field. © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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Enhancing IoT-Based Smart Farming Security with a Hybrid GRU Intrusion Detection System

Enhancing IoT-Based Smart Farming Security with a Hybrid GRU...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Sivakumar Nagarajan S. Vijay S. Karthi RVS Praveen Ramesh Vijaya Babu K Burri Naresh Technical Architect I & I Software Inc Bethlehem USA Department of Data Science Sacred Heart College (Autonomous) Tamilnadu India Department of Computer Science and Engineering V.S.B. Engineering College Karur Tamilnadu India Director Digital Engineering & Assurance LTIMindtree Limited Hyderabad Telangana India SRKR Engineering College Bhimavaram Andhra Pradesh India Department of CSE - Cyber Security CVR College of Engineering Hyderabad Telangana India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

The growing use of IoT devices in smart agriculture makes the industry vulnerable to several cyber risks. This study introduces an innovative hybrid Intrusion Detection System (IDS) utilising Gated Recurrent Units (GRUs) to improve the security of IoT-enabled smart agriculture. compared to current LSTM and CNN-based intrusion detection systems, the proposed model effectively incorporates sequential data dependencies, hence minimising false positives and enhancing detection accuracy. Experimental findings indicate that the suggested model surpasses conventional techniques, with an accuracy of 97.5%, an F1 score of 0.96, and a recall of 0.97. These developments create a strong, efficient, and scalable solution for safeguarding IoT-based agricultural systems from cyber-attacks.

关键词： Smart agriculture Technological innovation Accuracy Computational modeling Scalability Intrusion detection Internet of Things Object recognition Protection Long short term memory

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Leveraging Blockchain-as-a-Certificate Authority for Authentication in 6G-Enabled Spatial Crowdsourcing Drone Services

Leveraging Blockchain-as-a-Certificate Authority for Authent...

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IEEE Conference on Vehicular Technology (VTC)

作者： Junaid Akram Ali Anaissi Sagar Sidana Deepak Kumar Rutvij H. Jhaveri School of Computer Science The University of Sydney NSW Australia TD School University of Technology Sydney Ultimo NSW Australia Principal Software Engineer McKinsey & Company Dallas USA Department of Computer Science and Engineering National Institute of Technology Meghalaya India School of Technology Pandit Deendayal Energy University India

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

The integration of the Internet of Drone Things (IoDT) with spatial crowdsourcing, enhanced by 6G technology, has revolutionized environmental monitoring, particularly in managing Australian bushfires. This approach leverages drones’ mobility, multidimensional motion, and ease of deployment to gather real-time data from hazardous or inaccessible areas. However, the unsecured wireless communication channels and limited computational resources of drones in typical IoDT scenarios make them susceptible to cyber-attacks, including spoofing, GPS manipulation, impersonation, man-in-the-middle, and hijacking. To counter these threats, we propose a robust security protocol that utilizes blockchain technology augmented by Hyperelliptic Curve Cryptography (HECC). By employing blockchain as a Certificate Authority (CA) and treating transactions as certifications, our framework, DronCert, eliminates the need for traditional CAs or Trusted Third Parties (TTP). This decentralized approach, combined with the high-speed, low-latency capabilities of 6G, significantly enhances data transmission security within the IoDT network. A comprehensive security analysis demonstrates DronCert’s resilience against various attacks, such as Denial-of-Service (DoS), man-in-the-middle, replay, and unauthorized device representation.

关键词： Crowdsourcing 6G mobile communication Wireless communication Vehicular and wireless technologies Authentication Resists Blockchains Security Drones Resilience

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Unlocking DtS-IoT Medium Access Through the Massively Scalable Distributed Queuing Protocol

Unlocking DtS-IoT Medium Access Through the Massively Scalab...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Jason Gerard Juan A. Fraire Sandra Céspedes Department of Computer Science & Software Engineering Concordia University Montreal Canada Inria INSA Lyon CITI UR3720 Villeurbanne France CONICET - Universidad Nacional de Córdoba Córdoba Argentina

ISBN: (数字)9798350373011

ISBN: (纸本)9798350373028

Direct-to-satellite Internet of Things networks (DtSIoT) offer a cost-effective way to establish global communication between IoT devices and low Earth orbit satellites. However, budget-conscious DtS-IoT systems face resource constraints in user, space, and ground segments. An increasing limitation in the performance of satellite IoT networks is observed in the link layer. This stems from the prevalent use of ALOHA-based MAC protocols, leading to constraints in managing larger networks. The requirements of DtS-IoT deployments with extensive network sizes amplify the growing demands for energy-efficient and scalable MAC protocols. In this paper, we formulate a new energy-efficient MAC protocol, Massively Scalable Distributed Queuing (MSDQ), inspired by components of the Distributed Queuing (DQ) protocol and the RESS-IoT protocol. Utilizing FLoRaSat, a realistic LoRa-based DtS-IoT network simulator, we showcase the performance of MSDQ, DQ, and RESS-IoT. Results indicate that MSDQ outperforms DQ by 2.2 times the throughput and 19 times the node energy efficiency of RESSIoT. These findings highlight the performance gap of existing MAC protocols for large and geographically distributed DtSIoT systems.

关键词： Space vehicles Performance evaluation Satellites Media Access Control Low earth orbit satellites Quality of service Traffic control Throughput Energy efficiency Internet of Things

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Panel: Faculty Leadership

Panel: Faculty Leadership

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Frontiers in Education (FIE) Conference

作者： Lina Karam Georges Zissis Jason Yao Jill Nelson Minho Jo Rafal Sliz Michele Nogueira Steve Watkins Huihui Wang School of Electrical Computer and Energy Engineering Arizona State University TEMPE AZ USA Science and Engineering Univestité Toulouse III - Paul Sabatier Toulouse France Department of Engineering East Carolina University Greenville NC USA Department of Electrical and Computer Engineering George Mason University Fairfax VA USA Department of Computer Convergence Software Korea University Sejong City S. Korea Optoelectronics and Measurement Techniques Unit University of Oulu Oulu Finland Department of Computer Science Federal University of Minas Gerais Belo Horizonte MG Brazil Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA Department of Computer Science and Cybersecurity St. Bonaventure University St. Bonaventure USA

This faculty leadership panel will be held jointly by the IEEE Education Society (IEEE EdSoc) and IEEE Educational Activities Board (EAB) Faculty Resources Committee (FRC). The panel intends to provide participants introductory knowledge and skills. Two moderators and four panelists will focus on discussing their various academic leadership experiences and practices. The panel will be held in an interactive way so that the participants will have enough time to ask questions or communicate with the panelists. It is expected that this joint panel may potentially help to build up networks and mentorships.

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Uniqueness Proof of Optimal Power Management Strategies for Energy Storage Dynamic Models

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SSRN 2024年

作者： Goldstein-Tweg, Tom Ginzburg-Ganz, Elinor Belikov, Juri Levron, Yoash The Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering Technion—Israel Institute of Technology Haifa3200003 Israel Department of Software Science Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia

The current paper belongs to a class of research works that aim to find analytic and semi-analytic solutions to optimal power flow problems that involve storage systems. The main contribution of this work is a rigorous proof for the uniqueness of the ``shortest-path" optimal solution, which is a cornerstone in this class of algorithms, and relies upon a graphical design procedure presented in an earlier work. The proof builds upon five consequential lemmas, each provides a different characteristic of the optimal solution. These characteristics are then merged to prove the uniqueness of the optimal solution, which is given by the shortest path of the generated energy within bounds. The presented proof provides not only a sound theoretical foundation for this class of algorithms, but may also help develop additional analytic solutions for more complex optimal control problems involving storage systems. © 2024, The Authors. All rights reserved.

关键词： Power management

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An Intelligent Method for Detecting Jute Pests Using Deep Convolutional Neural Networks

An Intelligent Method for Detecting Jute Pests Using Deep Co...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： S. Vignesh S. Prabhu R. Sampathkumar M. Thillai Rani J. Rejina Parvin G. Uma Maheswari R. Saravanakumar Department of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Mechatronics Engineering KPR Institute of Engineering and Technology Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Sri Krishna College of Technology Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Pest attacks pose a serious threat to the production of jute and other significant crops. Jute farmers often utilize their visual sense and hands-on expertise to distinguish between multiple diseases that seem to be identical. The intelligent model we built for the identification of jute pests was based on a deep convolutional neural network (DCNN). This practical difficulty led to the development of this concept. The proposed DCNN model can automatically identify jute pest concerns with high speed and accuracy based on image recognition. In addition, images of the four most common jute bugs are included in an organized image graphics collection. Our model yields a DCNN accuracy is 96.72% for the four most significant jute pest kinds. Further proof of the model's effectiveness is provided by the accuracy, recall, F1-score, and confusion matrix metrics.

关键词： Performance evaluation Visualization Accuracy Operating systems Production Pest control Feature extraction software Convolutional neural networks Overfitting

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