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arXiv 2023年

作者： Kästner, Linh Carstens, Reyk Nahrwold, Lena Liebig, Christopher Shcherbyna, Volodymyr Lee, Subhin Lambrecht, Jens The Chair Industry Grade Networks and Clouds Faculty of Electrical Engineering and Computer Science Berlin Institute of Technology Berlin Germany

In recent years, mobile robot navigation approaches have become increasingly important due to various application areas ranging from healthcare to warehouse logistics. In particular, Deep Reinforcement Learning approaches have gained popularity for robot navigation but are not easily accessible to non-experts and complex to develop. In recent years, efforts have been made to make these sophisticated approaches accessible to a wider audience. In this paper, we present Arena-Web, a web-based development and evaluation suite for developing, training, and testing DRL-based navigation planners for various robotic platforms and scenarios. The interface is designed to be intuitive and engaging to appeal to non-experts and make the technology accessible to a wider audience. With Arena-Web and its interface, training and developing Deep Reinforcement Learning agents is simplified and made easy without a single line of code. The web-app is free to use and openly available under the link stated in the supplementary materials. © 2023, CC BY.

关键词： Websites

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Holistic Deep-Reinforcement-Learning-based Training of Autonomous Navigation Systems

arXiv

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arXiv 2023年

作者： Kästner, Linh Meusel, Marvin Bhuiyan, Teham Lambrecht, Jens The Chair Industry Grade Networks and Clouds Faculty of Electrical Engineering and Computer Science Berlin Institute of Technology Berlin Germany

In recent years, Deep Reinforcement Learning emerged as a promising approach for autonomous navigation of ground vehicles and has been utilized in various areas of navigation such as cruise control, lane changing, or obstacle avoidance. However, most research works either focus on providing an end-to-end solution training the whole system using Deep Reinforcement Learning or focus on one specific aspect such as local motion planning. This however, comes along with a number of problems such as catastrophic forgetfulness, inefficient navigation behavior, and non-optimal synchronization between different entities of the navigation stack. In this paper, we propose a holistic Deep Reinforcement Learning training approach in which the training procedure is involving all entities of the navigation stack. This should enhance the synchronization between- and understanding of all entities of the navigation stack and as a result, improve navigational performance. We trained several agents with a number of different observation spaces to study the impact of different input on the navigation behavior of the agent. In profound evaluations against multiple learning-based and classic model-based navigation approaches, our proposed agent could outperform the baselines in terms of efficiency and safety attaining shorter path lengths, less roundabout paths, and less collisions. © 2023, CC BY.

关键词： Reinforcement learning

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FMC-CEE: A Collaborative Execution Environment with Flash Memory Controllers in IoT

FMC-CEE: A Collaborative Execution Environment with Flash Me...

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IEEE Annual Conference on Pervasive Computing and Communications Workshops (PerCom)

作者： Zheng Zhang Jingfeng Xue Thar Baker Tian Chen Yuhang Zhao Weizhi Meng School of Computer Science and Technology Beijing Institute of Technology Beijing China School of Architecture Technology and Engineering University of Brighton Brighton UK School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China Department of Applied Mathematics and Computer Science Technical University of Denmark Copenhagen Denmark

Applications running on an Internet of Things (IoT) device are usually deployed in an untrusted environment. This introduces risks of vulnerability to malware, and loss of intellectual property associated with security sensitive code. Trusted execution environments (TEEs) and TEE-based applications have been widely adopted to run security sensitive workloads and protect the security of applications. However, existing approaches require specialized CPU support or hardware peripherals equipped with co-processors, precluding widely deployment on low-cost IoT devices. In this paper, we propose a flash memory controller-based collaborative execution environment (FMC-CEE), a lightweight security solution constructed on the target flash device to provide code confidentiality and basic security primitives for low-cost IoT devices and embedded devices. FMC-CEE leverages the microprocessor of the target flash device as a co-processor that executes security-sensitive workloads collaboratively with the target system. We implemented a prototype of FMC-CEE on a Trans-Flash (TF) card and executed security-sensitive tasks of the target host. The experimental results show that FMC-CEE takes $590.748 \mu \mathrm{s}$ to execute the remote code (512 bytes), thus incurring very little overhead on the host system.

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VNet: a versatile network to train real-time semantic segmentation models on a single GPU

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science China(Information sciences) 2022年第3期65卷 272-273页

作者： Wenxing LI Ning LIN Mingzhe ZHANG Hang LU Xiaoming CHEN Xiaowei LI College of Computer Science and Technology Guizhou University State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences

Dear editor,Modern semantic segmentation, which has important applications such as medical image analysis, image editing, and video surveillance, has made remarkable progress using deep convolution neural network models. Recently, an efficient real-time semantic segmentation method has received considerable attention, as intelligent edge devices not only have faster inference speed requirements for semantic segmentation models but also cannot rely on the cloud services of data centers. There are two feasible approaches to develop an efficient semantic segmentation model.

关键词： Efficiency Machine learning Real-time systems Segmentation Light-weight

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Artificial Neural Network and Implementation of Robotic Arm for Mineral Extraction in Mines with Permanent Magnet Synchronous Motor 6

Artificial Neural Network and Implementation of Robotic Arm ...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Yadagir, G. Sudha, M. Vidya Sagar Reddy, G. Shobha Rani, D. Sophia, S. Femila Roseline, J. Collegiate Education Jayashankar Vidya Bhavan Telangana Hyderabad India Tamil Nadu Namakkal India School of Architecture Koneru Lakshmaiah Educational Foundation Andhra Pradesh Guntur India Department of Computer Science and Engineering Chadalawada Ramanamma Engineering College Tamil Nadu Tirupathi India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India Tamil Nadu Chennai India

ISBN: (纸本)9798350398496

The implementation of a robotic arm for mineral extraction in mines is a challenging task that requires advanced technology and innovation. This paper proposes a solution using artificial intelligence (AI) and a permanent magnet synchronous motor (PMSM) to control the robotic arm's movement. A robotic arm with several degrees of freedom, a PMSM to move the arm, and AI algorithms to manage its motion make up the proposed system. The PMSM gives the arm's movements excellent control and enhances the precision and effectiveness of the mineral extraction procedure. The AI algorithms are in charge of directing the robotic arm's motion by examining sensor data and making choices following the situation at hand. The system is capable of adapting to its surroundings to improve the efficiency of mineral extraction. Compared to conventional mining methods, the suggested system has several benefits, including greater safety, increased efficiency, and lower costs. The system can completely transform the mining sector and can be applied in a variety of mining applications. Using a robotic arm and AI technologies, the suggested method offers a novel approach to mineral extraction in mines. The technology has several advantages over conventional mining methods and has the potential to greatly increase the productivity and security of the mining sector. © 2023 IEEE.

关键词： Synchronous motors

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Using Eye-Tracking to Evaluate Novel Augmented Reality Applications for Maritime Operations 68th

Using Eye-Tracking to Evaluate Novel Augmented Reality Appli...

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68th International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2024

作者： Mallam, Steven C. Attarzadeh, Amin Samanta, Akash Bhattacharjee, Ankan Nordby, Kjetil Fisheries and Marine Institute Memorial University of Newfoundland St. John’sNLA1C 5R3 Canada Electrical and Computer Engineering Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John’s Canada Institute of Design The Oslo School of Architecture and Design Akershus Norway

This paper presents the developments across a multi-year collaborative industry-academia R&D project designing and testing novel Augmented Reality (AR) solutions for differing maritime operations and work tasks. We describe the step-wise approach taken in our Human Factors testing program exploring the effects of AR on maritime operators. This paper focuses specifically on our empirical simulator laboratory experiments using differing data collection designs, tools and operational scenarios to investigate operator Situation Awareness, cognitive workload, performance and usability. Moving from comparatively rudimentary measures using static scenarios, desktop data collections and post hoc video analysis to implementing eye-tracking and automated object identification in dynamic scenarios and full-mission simulated environments, we present an overview of how our testing program has evolved and lessons learned. Further, we discuss ongoing research and future plans to better understand the effects of AR on maritime end-users to implement human-centred solutions more effectively in maritime settings. © 2024 Human Factors and Ergonomics Society.

关键词： Head-up displays

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Modeling the failures of power-aware data centers by leveraging heat recirculation

Modeling the failures of power-aware data centers by leverag...

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作者： Feng, Hao Deng, Yuhui Yu, Liang Department of Computer Science Jinan University Guangzhou China Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

With the explosive growth of data, hundreds of thousands of servers may be contained in a single data center. Hence, node failures are unavoidable and generally negatively effects the performance of the whole data center. Additionally, data centers with a large number of nodes will cause plenty of energy consumption. Many existing task scheduling techniques can effectively reduce the power consumption in data centers by considering heat recirculation. However, the traditional techniques do not take the situation of node failures into account. This paper proposes an airflow-based failure model for data centers by leveraging heat recirculation. In this model, the spatial distribution and time distribution of failures are considered. Furthermore, a genetic algorithm (GA) and a simulated annealing algorithm (SA) are implemented to evaluate the proposed failure model. Because the positions of node failures have a significant impact on the heat recirculation and the energy consumption of data centers, failures with different positions are analyzed and evaluated. The experimental results demonstrate that the energy consumption of data centers can be significantly reduced by using the GA and SA algorithms for task scheduling based on the proposed failure model. © 2019 John Wiley & Sons, Ltd.

关键词： Energy efficiency

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Crop Yield Identification Using CNN

Crop Yield Identification Using CNN

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Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF), International Conference on

作者： G. Yogapriya R. Shalini Kumari S. Suganthi K. Agalya T. Elangovan D. Selva Pandian School of Environment Architecture and Design SRM Institute of Science and Technology Ramapuram Chennai Department of Architecture Periyar Maniammai Institute of science and Technology Thanjavuri Tamilnadu Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Department of Computer Science and Engineering Karpagam Academy of Higher Education Eachanari Coimbatore

The number of people living in the world is continuously rising, which means that there must be an increase in crop production. The estimation of agricultural yields as well as the monitoring of the growth of crops are very significant for the overall economic development of a nation. The prediction of crop production is highly difficult since it is dependent on a wide variety of factors, including the genotype of the crop, environmental factors, management strategies, and the relationships between these elements. Deep learning is gaining relevance in environmental monitoring, crop type segmentation, and crop yield estimation applications as a result of recent advancements in image classification achieved by the utilization of deep Convolutional Neural Networks. Convolutional neural networks, or CNNs, are a type of deep learning approach that has shown remarkable performance in picture classification tasks. In this study, CNNs are utilized to construct a model for crop production prediction.

关键词： Deep learning Training Neural networks Crops Production Predictive models Prediction algorithms

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Maximizing Energy Efficiency in IRS-Assisted Phase Cooperative PS-SWIPT based Self-Sustainable IoT Network

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年 6卷 4311-4327页

作者： Sadia, Haleema Hassan, Ahmad Kamal Abbas, Ziaul Haq Abbas, Ghulam Baker, Thar Saeed, Nasir United Arab Emirates University College of Engineering Department of Electrical and Communication Engineering Al Ain United Arab Emirates Ghulam Ishaq Khan Institute of Engineering and Technology Faculty of Electrical Engineering Pakistan Ghulam Ishaq Khan Institute of Engineering and Technology Faculty of Computer Science and Engineering Pakistan University of Brighton School of Architecture Technology and Engineering BrightonBN2 4GJ United Kingdom

Power splitting based simultaneous wireless information and power transfer (PS-SWIPT) appears to be a promising solution to support future self-sustainable Internet of Things (SS-IoT) networks. However, the performance of these networks is constrained by radio frequency signal strength and channel impairments. To overcome this challenge, intelligent reflecting surfaces (IRSs) have recently been introduced in IoT networks to improve network efficiency by controlling signal reflections. In this article, we propose a novel IRS-enabled phase cooperative framework that aims to maximize the energy efficiency (EE) of the PS-SWIPT based SS-IoT network. Our framework leverages the idea of phase cooperation between two distinct networks without requiring additional hardware resources. We employed transmit beamforming (BF) at access points (APs) and phase shift optimization at the IRS with effective phase cooperation between APs. The EE maximization problem turns out to be NP-hard, so first, an alternating optimization (AO) is solved for the user’s network using heuristic BF approaches and phase optimization via semidefinite relaxation (SDR) approaches. To combat the computational complexity of AO, we also propose a low-complexity alternative solution by exploiting heuristic BF schemes and an iterative algorithm, i.e., the element-wise block-coordinate descent method for phase shifts optimization. The proposed solutions’ analysis shows low computational complexity and fast convergence, achieving near-optimal EE performance for different network settings. We conducted extensive simulations to evaluate our proposed framework’s performance and unveiled significant EE performance achieved by the IoT network consistent with the numerical findings. This work demonstrates the potential of IRS phase cooperation for enhancing the EE of different networks without constraining hardware resources. © 2020 IEEE.

关键词： Energy transfer

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Secrets Leaking Through Quicksand: Covert Channels in Approximate Computing

Secrets Leaking Through Quicksand: Covert Channels in Approx...

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IEEE European Test Symposium (ETS)

作者： Lorenzo Masciullo Roberto Passerone Francesco Regazzoni Ilia Polian Department of Information Engineering and Computer Science University of Trento Italy University of Amsterdam Amsterdam The Netherlands Universita Della Svizzera Italiana Lugano Switzerland Institute of Computer Architecture and Computer Engineering University of Stuttgart Germany

Approximate computing (AxC) has emerged as an attractive architectural paradigm especially for artificial-intelligence applications, yet its security implications are being neglected. We demonstrate a novel covert channel where the malicious sender modulates transmission by switching between regular and AxC realizations of the same computational task. The malicious receiver identifies the transmitted information by either reading out the workload statistics or by creating controlled congestion. We demonstrate the channel on both an Android simulator and an actual smartphone and systematically study measures to increase its robustness. The achievable transmission rates are comparable with earlier covert channels based on power consumption, but the malicious behavior of our channel is more stealthy and less detectable.

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