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International Conference on Intelligent Transportation

作者： Florian Alexander Schiegg Anna–Lisa Rueeck Jörg Gamerdinger Hugues Tchouankem Edmir Xhoxhi Georg Volk Distributed Intelligence and Systems Corporate Research Robert Bosch GmbH Hildesheim Germany Function Development 3D Surround View Engineering Parking Functions Robert Bosch GmbH Feuerbach Germany Department of Computer Science Embedded Systems Group University of Tübingen Tübingen Germany Institute of Communication Technology Leibniz University Hanover Hanover Germany

The number of traffic participants has increased exponentially over the past century, making a traffic accident the most likely cause of death for children and young adults today. Vehicle-to-Everything (V2X) communication is regarded as one of the most promising emerging technologies to enhance road safety. It allows traffic participants to exchange relevant information to increase their environmental perception. Cooperative Awareness, a V2X service that is already being deployed in Europe, enables road users to share their dynamic states. Collective Perception (CP), currently in the final stage of standardization in the different regions of the world, enables the exchange of sensor-detected objects. The latter is thus not only relevant for connected vehicles but can further be used by sensor-equipped infrastructure to support vehicular perception, opening a whole new range of possibilities. The objective of this work is to investigate mechanisms to account for the special role of Infrastructure-assisted Collective Perception (ICP). Packet duplication is introduced for ICP to enhance the communication reliability. Additionally, different message generation rules prioritizing ICP are proposed and compared based on the enabled environmental perception. Simulations show significant improvements of the average precision, reaching an average increase of about 60% for ICP in comparison to V2V -based CP in scenarios with low connected vehicle densities.

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Enhancing Drone Surveillance with NeRF: Real-World Applications and Simulated Environments

Enhancing Drone Surveillance with NeRF: Real-World Applicati...

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Conference on Digital Avionics systems (DASC)

作者： Joakim Lindén Giovanni Burresi Håkan Forsberg Masoud Daneshtalab Ingemar Söderquis Division of Intelligent Future Technologies School of Innovation Design and Engineering Malardalen University Vasteras Sweden Saab Aeronautics Stockholm Sweden Division of Electronics and Embedded Systems School of Electrical Engineering and Computer Science Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9798350349610

ISBN: (纸本)9798350349627

Machine Learning (ML) systems require representative and diverse datasets to accurately learn the objective task. In supervised learning data needs to be accurately annotated, which is an expensive and error-prone process. We present a method for generating synthetic data tailored to the use-case achieving excellent performance in a real-world usecase. We provide a method for producing automatically annotated synthetic visual data of multi rotor unmanned aerial vehicles (UAV) and other airborne objects in a simulated environment with a high degree of scene diversity, from collection of 3D models to generation of annotated synthetic datasets (synthsets). In our data generation framework SynRender we introduce a novel method of using Neural Radiance Field (NeRF) methods to capture photo-realistic high-fidelity 3D-models of multirotor UAVs in order to automate data generation for an object detection task in diverse environments. By producing data tailored to the real-world setting, our NeRF-derived results show an advantage over generic 3D asset collection-based methods where the domain gap between the simulated and real-world is unacceptably large. In the spirit of keeping research open and accessible to the research community we release our dataset VISER DroneDiversity used in this project, where visual images, annotated boxes, instance segmentation and depth maps are all generated for each image sample.

关键词： Training Visualization Three-dimensional displays Object detection Machine learning Data collection Neural radiance field Autonomous aerial vehicles Data models Synthetic data

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A New Intelligent Scheduler to Improve Reactive OpenFlow Communication in SDN-Based IoT Data Streams

SSRN

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SSRN 2023年

作者： Batista, Ernando Alencar, Brenno M. Silva, Eliabe Canário, João Paulo Rios, Ricardo A. Dustdar, Schahram Figueiredo, Gustavo Bittencourt Prazeres, Cássio Federal Institute of Bahia Brazil Federal University of Bahia Brazil TU Wien University Distributed Systems Group Vienna Austria Stone Co. Bahia Salvador Brazil Department of Computer Science Brazil

The significant adoption of the Internet of Things (IoT) has increased the challenges in providing adequate IoT infrastructures meeting essential requirements, such as dynamicity networks and low latency. In this context, the Software-Defined Networking (SDN) paradigm and the OpenFlow protocol provide new possibilities for IoT networks. Based on the global view of network elements enabled by the Controller, SDN allows the programmability and control of the infrastructure according to the actual demands of applications. The OpenFlow protocol defines the exchange of messages between controllers and switches, enabling communication and network control. OpenFlow implements three operation modes: proactive, reactive, and hybrid. Due to the dynamic characteristic of the IoT data stream, the reactive mode is mainly used and indicated for IoT environments. As the OpenFlow controller installs rules dynamically, there is no need to know the network's sources, destinations, and paths in advance. Although reactive mode introduces dynamicity, it can generate additional delay due to switch-controller communication. This delay increases the response time of the IoT data stream. We propose an SDN-IoT scheduler based on Deep Neural Networks (DNN) to predict the time between data stream changes from IoT devices and install rules in advance, suppressing the existing delay in reactive mode. The proposal automatically uses previous data from the IoT data stream to calculate the time of the following communication from IoT devices. Our results indicate that predicting IoT data stream changes and installing OpenFlow rules in advance reduced about 51% of communications response time. © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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3D printing of hierarchical structures made of inorganic silicon-rich glass featuring self-forming nanogratings

arXiv

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arXiv 2024年

作者： Huang, Po-Han Chen, Shiqian Hartwig, Oliver Marschner, David E. Duesberg, Georg S. Stemme, Göran Li, Jiantong Gylfason, Kristinn B. Niklaus, Frank Division of Micro and Nanosystems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden Division of Electronics and Embedded Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Kista16440 Sweden Institute of Physics EIT 2 Faculty of Electrical Engineering and Information Technology University of the Bundeswehr Munich & SENS Research Center Neubiberg85577 Germany

Hierarchical structures are abundant in nature, such as in the superhydrophobic surfaces of lotus leaves and the structural coloration of butterfly wings. They consist of ordered features across multiple size scales, and their unique properties have attracted enormous interest in wide-ranging fields, including energy storage, nanofluidics, and nanophotonics. Femtosecond lasers, capable of inducing various material modifications, have shown promise for manufacturing tailored hierarchical structures. However, existing methods such as multiphoton lithography and 3D printing using nanoparticle-filled inks typically involve polymers and suffer from high process complexity. Here, we demonstrate 3D printing of hierarchical structures in inorganic silicon-rich glass featuring self-forming nanogratings. This approach takes advantage of our finding that femtosecond laser pulses can induce simultaneous multiphoton crosslinking and self-formation of nanogratings in hydrogen silsesquioxane (HSQ). The 3D printing process combines the 3D patterning capability of multiphoton lithography and the efficient generation of periodic structures by the self-formation of nanogratings. We 3D-printed micro-supercapacitors with large surface areas and a remarkable areal capacitance of 1 mF/cm2 at an ultrahigh scan rate of 50 V/s, thereby demonstrating the utility of our 3D printing approach for device applications in emerging fields such as energy storage. Copyright © 2024, The Authors. All rights reserved.

关键词： 3D printing

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Adaptively Aggregating Pyramid Spatial-temporal Feature for Video Person Re-Identification 25

Adaptively Aggregating Pyramid Spatial-temporal Feature for ...

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Proceedings of the 4th International Conference on computer, Artificial Intelligence and Control Engineering

作者： Zhong Han Qianyue Duan Jianfeng An Huanjie Tao Yangzhou Collaborative Innovation Research Institute of Shenyang Aircraft Design and Research Institute Co. Ltd Yangzhou Jiangsu China School of Computer Science Northwestern Polytechnical University Xi' an Shaanxi China School of Computer Science Northwestern Polytechnical University Xi' an Shaanxi China Engineering and Research Center of Embedded Systems Integration (Northwestern Polytechnical University) Ministry of Education Xi' an Shaanxi China

ISBN: (纸本)9798400712647

Video person re-identification (ReID) aims to identify a particular person from the non-overlapping camera views by matching video sequences. It is challenging due to many complex factors, such as similar appearance, occlusion, pose, and viewpoint variation. Pyramid spatial-temporal aggregation (PSTA) is an important method that addresses these challenges. However, PSTA adopts a simple feature addition operation to fuse the hierarchical feature vectors into the video-level representations for video person ReID, which may cause the discriminative information in the feature vectors to be diluted. To solve the above issue, this paper presents an adaptive pyramid spatial-temporal aggregation network (APSNet) to learn more discriminative features. In APSNet, an adaptive feature fusion module (AFFM) is designed to adaptively recalibrate the channel feature responses by explicitly modeling the interdependence between channels. The designed AFFM can enhance the useful features and suppress the interference features during the feature fusion stage, resulting in more discriminative video-level feature representations. Experimental results demonstrate that APSNet has a better performance than PSTA on the MARS dataset, which reaches 86.2% mAP accuracy and 91.2% Rank-1 accuracy. Our APSNet improves the mAP accuracy by 0.4% compared with PSTA.

关键词： Attention Mechanism

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Evaluating Quantum Optimization for Dynamic Self-Reliant Community Detection

arXiv

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arXiv 2024年

作者： Bucher, David Porawski, Daniel Wimmer, Benedikt Nüßlein, Jonas O’Meara, Corey Mohseni, Naeimeh Cortiana, Giorgio Linnhoff-Popien, Claudia Aqarios GmbH Prinzregentenstraße 120 Munich81677 Germany Ludwig-Maximilians University Munich Institute for Computer Science Mobile and Distributed Systems Chair Germany E.ON Digital Technology GmbH Laatzener Str. 1 Hannover30539 Germany

Power grid partitioning is an important requirement for resilient distribution grids. Since electricity production is progressively shifted to the distribution side, dynamic identification of self-reliant grid subsets becomes crucial for operation. This problem can be represented as a modification to the well-known NP-hard Community Detection (CD) problem. We formulate it as a Quadratic Unconstrained Binary Optimization (QUBO) problem suitable for solving using quantum computation, which is expected to find better-quality partitions faster. The formulation aims to find communities with maximal self-sufficiency and minimal power flowing between them. To assess quantum optimization for sizeable problems, we apply a hierarchical divisive method that solves sub-problem QUBOs to perform grid bisections. Furthermore, we propose a customization of the Louvain heuristic that includes self-reliance. In the evaluation, we first demonstrate that this problem examines exponential runtime scaling classically. Then, using different IEEE power system test cases, we benchmark the solution quality for multiple approaches: D-Wave’s hybrid quantum-classical solvers, classical heuristics, and a branch-and-bound solver. As a result, we observe that the hybrid solvers provide very promising results, both with and without the divisive algorithm, regarding solution quality achieved within a given time frame. Directly utilizing D-Wave’s Quantum Annealing (QA) hardware shows inferior partitioning. © 2024, CC BY.

关键词： Hierarchical clustering

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Fuzzy Querying in the Cloud-based Environment for Data Stream-driven Predictive Maintenance in AGV-enabled Smart Factories

Fuzzy Querying in the Cloud-based Environment for Data Strea...

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IEEE International Conference on Big Data

作者： Bożena Małysiak-Mrozek Dominik Romanów Piotr Grzesik Paweł Benecki Alexandre Niyomugaba Theodore Habimana Daniel Kostrzewa Krzysztof Tokarz Che-Lun Hung Dariusz Mrozek Department of Distributed Systems and Informatics Devices Silesian University of Technology Gliwice Poland Department of Applied Informatics Silesian University of Technology Gliwice Poland Department of Computer Science INES Ruhengeri - Institute of Applied Sciences Rwanda Department of Graphics Computer Vision and Digital Systems Silesian University of Technology Gliwice Poland Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei Taiwan

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Fuzzy data processing enables data enrichment and increases data interpretation in industrial environments. In the cloud-based IoT data ingestion pipelines, fuzzy data processing can be implemented in several locations, closer to the IoT events gateways, stream processors, or the persistence layer before the data is visualized. Since Automated Guided Vehicles (AGV)-enabled manufacturing can produce vast amounts of data, the decision on the placement of the fuzzy data processing can be important for secondary processes performed on the enriched data, like the predictive maintenance inferencing. In this paper, we analyze two locations of fuzzy data processing in the cloud-based environment built for monitoring AGVs in smart factories - by formulating fuzzy queries against data streams on stream processing units and data at rest in a database. The querying scenarios cover fuzzy filtering with simple and complex criteria, fuzzy filtering through assignment to a linguistic variable, and joining data streams by representing joining attributes as fuzzy numbers. The experimental results show that querying the data stream can be more efficient and profitable in the scalable environment of many AGVs. However, the enrichment provided for the data at rest is also beneficial when gathering data for building future predictive maintenance models.

关键词： Remotely guided vehicles Program processors Filtering Pipelines Predictive models Logic gates Data processing Monitoring Predictive maintenance Smart manufacturing

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SatCooper: Enhancing Cooperative Inference Analytics for Satellite Service via Multi-exit DNNs

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IEEE Transactions on Mobile Computing 2025年

作者： Zhang, Qiyang Wang, Shangguang Guan, Jinglong Donta, Praveen Kumar Ma, Xiao Prasad, RangaRao Venkatesha Dustdar, Schahram Liu, Xuanzhe Peking University School of Computer Science Beijing100871 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China Stockholm University Department of Computer and Systems Sciences Kista16455 Sweden Beiyou Shenzhen Institute Shenzhen China Netherlands TU Wien Distributed System Group Wien1040 Austria UPF Barcelona Spain

As a key technology of intelligent satellite-enabled services in B5G or 6G networks, deploying Deep Neural Networks (DNN) models on satellites has been a notable trend, catering to the daily demand for extensive computing-intensive and latency-sensitive tasks. The computing resources are strategically deployed on satellites where sensor data is generated or collected, facilitating the fine-grained computational inference of DNN-based tasks. However, no prior study has comprehensively explored the crucial inference challenges-e.g., the trade-off between the number of tasks completed and accuracy and partitioning models in multi-exit models-in the resource-constrained space environment. Effective scheduling frameworks cater to various streams of inference tasks are scarce because inference performance may deviate from the ideal situation due to changes in task system status, such as task profiles and network state. To this end, we first formulate a gain-aware in-orbit computing inference problem to strike a proper trade-off between inference latency and the number of tasks completed by dynamically selecting optimal early exit points and model partitioning points. We propose an offline dynamic programming-based algorithm that provides an effective solution when comprehensive system details are to be predicted. We have developed an online learning-based method to schedule inference tasks with uncertain and dynamic system statuses in real-world situations. Our evaluation shows that, compared to baseline methods, the online learning-based algorithm can improve task gain by an average of 87.3% across various tasks. © 2002-2012 IEEE.

关键词： Dynamic programming

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Information Processing Factory 2.0 - Self-awareness for Autonomous Collaborative systems

Information Processing Factory 2.0 - Self-awareness for Auto...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Nora Sperling Alex Bendrick Dominik Stöhrmann Rolf Ernst Bryan Donyanavard Florian Maurer Oliver Lenke Anmol Surhonne Andreas Herkersdorf Walaa Amer Caio Batista de Melo Ping-Xiang Chen Quang Anh Hoang Rachid Karami Biswadip Maity Paul Nikolian Mariam Rakka Dongjoo Seo Saehanseul Yi Minjun Seo Nikil Dutt Fadi Kurdahi Institute of Computer and Network Engineering TU Braunschweig Germany Department of Computer Science San Diego State University School of Computation Information & Technology Technical University of Munich Germany Center for Embedded and Cyber-physical Systems (CECS) University of California Irvine

This paper summarizes the talks of a special session on the IPF 2.0 project, a collaborative German-US research project that leverages self-awareness principles for the self-management of distributed systems of autono...

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EtherClue:Digital investigation of attacks on Ethereum smart contracts

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Blockchain(Research and Applications) 2021年第4期2卷 12-29页

作者： Simon Joseph Aquilina Fran Casino Mark Vella Joshua Ellul Constantinos Patsakis Department of Computer Science University of MaltaMsidaMSD 2080Malta Department of Informatics University of PiraeusPiraeus18534Greece Information Management Systems Institute Athena Research CenterMarousi15125Greece Centre for Distributed Ledger Technologies University of MaltaMsidaMSD 2080Malta

Programming errors in Ethereum smart contracts can result in catastrophic financial losses from stolen *** vulnerability detectors can prevent vulnerable contracts from being deployed,this does not mean that such contracts will not be *** a vulnerable contract is instantiated on the blockchain and becomes the target of attacks,the identification of exploit transactions becomes indispensable in assessing whether it has been actually exploited and identifying which malicious or subverted accounts were *** this work,we study the problem of post-factum investigation of Ethereum attacks using Indicators of Compromise(IoC)specially crafted for use in the *** definitions need to capture the side-effects of successful exploitation in the context of the Ethereum ***,we define a model for smart contract execution,comprising multiple abstraction levels that mirror the multiple views of code execution on a ***,we compare IoCs defined across the different levels in terms of their effectiveness and practicality through EtherClue,a prototype tool for investigating Ethereum security *** results illustrate that coarse-grained IoCs defined over blocks of transactions can detect exploit transactions with less ***,they are contract-specific and suffer from false *** the other hand,fine-grained IoCs defined over virtual machine instructions can avoid these pitfalls at the expense of increased computation,which is nevertheless applicable for practical use.

关键词： Blockchain forensics Ethereum attacks Indicators of compromise Ethereum operational semantics

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