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Evaluation of the Energy Efficiency of Software Calculations in the Design of Microcontroller Devices

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Evaluation of the Energy Efficiency of Software Calculations...

International Conference on Modern Problems of Radio engineering, Telecommunications and computer science

作者： Andrii Topalov Taras Styslo Vitalii Tkach Volodymyr Peschanenko Oksana Styslo Rostyslav Skrypyuk Department of computerized control systems Admiral Makarov National University of Shipbuilding Mykolaiv Ukraine Department of IT King Danylo University Ivano-Frankivsk Ukraine Department of computer science and software engineering Kherson State University Ivano-Frankivsk Ukraine Department of automation and computer-integrated technologies Ivano-Frankivsk National Technical University of Oil and Gas Ivano-Frankivsk Ukraine

ISBN: (数字)9798331520564

ISBN: (纸本)9798331520571

The paper presents a study of the effectiveness of software from the point of view of minimizing the energy consumption of microprocessor devices. In this case, the programming of the microcontroller in various programming languages for arithmetic operations, as well as data output through the port, is considered. In the course of the tests, the peculiarities of the application of this or that program and their operation, taking into account the energy saving of the microcontroller, are noted.

关键词： Energy consumption computer languages Microcontrollers Energy conservation Programming Market research Software Energy efficiency Telecommunications Arithmetic

Adaptive Point Transformer

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arXiv 2024年

作者： Baiocchi, Alessandro Spinelli, Indro Nicolosi, Alessandro Scardapane, Simone Sapienza University of Rome Department of Computer Control and Management Engineering Via Ariosto 25 Rome00185 Italy Sapienza University of Rome Department of Computer Science via Salaria 113 Rome00198 Italy Leonardo Labs Piazza Monte Grappa 4 Rome00195 Italy Sapienza University of Rome Department of Electronics and Telecommunication Engineering via Eudossiana 18 Rome00184 Italy

The recent surge in 3D data acquisition has spurred the development of geometric deep learning models for point cloud processing, boosted by the remarkable success of transformers in natural language processing. While point cloud transformers (PTs) have achieved impressive results recently, their quadratic scaling with respect to the point cloud size poses a significant scalability challenge for real-world applications. To address this issue, we propose the Adaptive Point Cloud Transformer (AdaPT), a standard PT model augmented by an adaptive token selection mechanism. AdaPT dynamically reduces the number of tokens during inference, enabling efficient processing of large point clouds. Furthermore, we introduce a budget mechanism to flexibly adjust the computational cost of the model at inference time without the need for retraining or fine-tuning separate models. Our extensive experimental evaluation on point cloud classification tasks demonstrates that AdaPT significantly reduces computational complexity while maintaining competitive accuracy compared to standard PTs. The code for AdaPT is made publicly available. © 2024, CC BY.

关键词： Deep learning

A network-based bioinformatic analysis for identifying potential repurposable active molecules in different types of human cancers

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A network-based bioinformatic analysis for identifying poten...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Martina Brunetti Paola Paci Giulia Fiscon Department of Computer Control and Management Engineering "A. Ruberti" Sapienza University of Rome Rome Italy Karolinska Institutet Stockholm Sweden Institute for Systems Analysis and Computer Science "A. Ruberti" National Research Council of Rome Rome Italy

Drug repurposing, also known as drug repositioning, is the process of identifying novel therapeutic indications for existing drugs, offering a cost-effective and time-efficient strategy to drug discovery. In this context, we developed a network-based algorithm, named SAveRUNNER (Searching off-lAbel dRUg aNd NEtwoRk), which predicts drug-disease associations by accounting for the interaction between the drug targets and disease-associated genes in the human interactome, implementing a novel network-based similarity measure that prioritizes associations between drugs and diseases locating in the same network neighborhoods. Following its successful applications to different disorders (such as viral infections and neurological diseases), in this study, we applied SAveRUNNER on a panel of 13 types of cancers using both disease-associated genes downloaded from widely-used databases and from gene expression data.

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The Treasure Beneath Multiple Annotations: An Uncertainty-aware Edge Detector

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arXiv 2023年

作者： Zhou, Caixia Huang, Yaping Pu, Mengyang Guan, Qingji Huang, Li Ling, Haibin Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing Jiaotong University China School of Control and Computer Engineering North China Electric Power University China Department of Computer Science Stony Brook University United States

Deep learning-based edge detectors heavily rely on pixel-wise labels which are often provided by multiple annotators. Existing methods fuse multiple annotations using a simple voting process, ignoring the inherent ambiguity of edges and labeling bias of annotators. In this paper, we propose a novel uncertainty-aware edge detector (UAED), which employs uncertainty to investigate the subjectivity and ambiguity of diverse annotations. Specifically, we first convert the deterministic label space into a learnable Gaussian distribution, whose variance measures the degree of ambiguity among different annotations. Then we regard the learned variance as the estimated uncertainty of the predicted edge maps, and pixels with higher uncertainty are likely to be hard samples for edge detection. Therefore we design an adaptive weighting loss to emphasize the learning from those pixels with high uncertainty, which helps the network to gradually concentrate on the important pixels. UAED can be combined with various encoder-decoder backbones, and the extensive experiments demonstrate that UAED achieves superior performance consistently across multiple edge detection benchmarks. The source code is available at https://***/ZhouCX117/ UAED. Copyright © 2023, The Authors. All rights reserved.

关键词： Pixels

Do We Run Large-scale Multi-Robot Systems on the Edge? More Evidence for Two-Phase Performance in System Size Scaling

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arXiv 2023年

作者： Kuckling, Jonas Luckey, Robin Avrutin, Viktor Vardy, Andrew Reina, Andreagiovanni Hamann, Heiko The Department of Computer and Information Science University of Konstanz Konstanz78457 Germany The Centre for the Advanced Study of Collective Behaviour University of Konstanz Germany The Institute of Computer Engineering University of Lübeck Lübeck Germany The Institute for Systems Theory and Automatic Control University of Stuttgart Stuttgart Germany The Department of Computer Science The Department of Electrical and Computer Engineering Memorial University of Newfoundland St. John's Canada IRIDIA Université Libre de Bruxelles Brussels Belgium

With increasing numbers of mobile robots arriving in real-world applications, more robots coexist in the same space, interact, and possibly collaborate. Methods to provide such systems with system size scalability are known, for example, from swarm robotics. Example strategies are self-organizing behavior, a strict decentralized approach, and limiting the robot-robot communication. Despite applying such strategies, any multi-robot system breaks above a certain critical system size (i.e., number of robots) as too many robots share a resource (e.g., space, communication channel). We provide additional evidence based on simulations, that at these critical system sizes, the system performance separates into two phases: nearly optimal and minimal performance. We speculate that in real-world applications that are configured for optimal system size, the supposedly high-performing system may actually live on borrowed time as it is on a transient to breakdown. We provide two modeling options (based on queueing theory and a population model) that may help to support this reasoning. © 2023, CC BY.

关键词： Multipurpose robots

Cluster Forming of Multiagent Systems in Rolling Horizon Games with Non-uniform Horizons

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arXiv 2023年

作者： Nugraha, Yurid Cetinkaya, Ahmet Hayakawa, Tomohisa Ishii, Hideaki Zhu, Quanyan The Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo152-8552 Japan The Shibaura Institute of Technology Tokyo135-8548 Japan The Department of Computer Science Tokyo Insitute of Technology Yokohama226-8502 Japan The Department of Electrical and Computer Engineering New York University BrooklynNY11201 United States

Consensus and cluster forming of multiagent systems in the face of jamming attacks along with reactive recovery actions by a defender are discussed. The attacker is capable to disable some of the edges of the network with the objective to divide the agents into a smaller size of clusters while, in response, the defender recovers some of the edges by increasing the transmission power. We consider repeated games where the resulting optimal strategies for the two players are derived in a rolling horizon fashion. The attacker and the defender possess different computational abilities to calculate their strategies. This aspect is represented by the non-uniform values of the horizon lengths and the game periods. Theoretical and simulation based results demonstrate the effects of the horizon lengths and the game periods on the agents’ states. © 2023, CC BY.

关键词： Multi agent systems

Vulnerability of Machine Learning Approaches Applied in IoT-based Smart Grid: A Review

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arXiv 2023年

作者： Zhang, Zhenyong Liu, Mengxiang Sun, Mingyang Deng, Ruilong Cheng, Peng Niyato, Dusit Chow, Mo-Yuen Chen, Jiming The State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550000 China The Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS10 2TN United Kingdom The State Key Laboratory of Industrial Control Technology the College of Control Science and Engineering Zhejiang University Hangzhou310027 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The UM SJTU Joint Institute Shanghai Jiao Tong University Shanghai200240 China

Machine learning (ML) sees an increasing prevalence of being used in the internet-of-things (IoT)-based smart grid. However, the trustworthiness of ML is a severe issue that must be addressed to accommodate the trend of ML-based smart grid applications (MLsgAPPs). The adversarial distortion injected into the power signal will greatly affect the system’s normal control and operation. Therefore, it is imperative to conduct vulnerability assessment for MLsgAPPs applied in the context of safety-critical power systems. In this paper, we provide a comprehensive review of the recent progress in designing attack and defense methods for MLsgAPPs. Unlike the traditional survey about ML security, this is the first review work about the security of MLsgAPPs that focuses on the characteristics of power systems. We first highlight the specifics for constructing the adversarial attacks on MLsgAPPs. Then, the vulnerability of MLsgAPP is analyzed from both the aspects of the power system and ML model. Afterward, a comprehensive survey is conducted to review and compare existing studies about the adversarial attacks on MLsgAPPs in scenarios of generation, transmission, distribution, and consumption, and the countermeasures are reviewed according to the attacks that they defend against. Finally, the future research directions are discussed on the attacker’s and defender’s side, respectively. We also analyze the potential vulnerability of large language model-based (e.g., ChatGPT) power system applications. Overall, we encourage more researchers to contribute to investigating the adversarial issues of MLsgAPPs. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

Real Time Sound Source Localization Using von-Mises ResNet

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Real Time Sound Source Localization Using von-Mises ResNet

IEEE/SICE International Symposium on System Integration

作者： Mert Bozkurtlar Benjamin Yen Katsutoshi Itoyama Kazuhiro Nakadai Department of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Tokyo Japan Department of Industrial Engineering Faculty of Management Istanbul Technical University Harbiye Maçka/Beşiktaş/İstanbul Türkiye Department of Computer Engineering Faculty of Computer and Informatics Engineering Istanbul Technical University Ayazaga Sariyer/İstanbul Türkiye Japan Society for the Promotion of Science (JSPS) at the Nakadai Lab Tokyo Institute of Technology Honda Research Institute Japan Co. Ltd Saitama Japan

This paper addresses the task of learning periodic information using deep neural networks to achieve real-time, environment-independent sound source localization. Previous papers showed phase data is the most significant cue in sound source localization tasks and the proposed vM-B DNN was validated to be able to handle such periodic information using on synthesized data. However, they haven't shown its effectiveness and robustness in realistic use cases. This paper introduces a more complex model based on residual networks and adapts vM-B activation function for convolutional layers for use cases that require real-time predictions in dynamically changing environments.

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Development of a Converter-based Supercapacitor System Emulator for PV Applications

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Development of a Converter-based Supercapacitor System Emula...

IEEE Energy Conversion Congress and Exposition, ECCE

作者： Paychuda Kritprajun Elizabeth Sutton Leon M. Tolbert Yunting Liu Jingxin Wang Nattapat Praisuwanna Maximiliano Ferrari Department of Electrical Engineering and Computer Science The University of Tennessee Knoxville TN USA Department of Electrical Engineering The Pennsylvania State University University Park PA USA Grid Components and Control Oak Ridge National Laboratory Oak Ridge TN USA

As the utilization of supercapacitors in power system applications continues to increase, it is important to observe their behavior under transient and long-term operations in order to understand their impact on power grids. A real-time reconfigurable hardware testbed (HTB) is a power network emulator that provides flexibility to study various power system scenarios. This paper presents an emulation of a supercapacitor for a photovoltaic (PV) system on the HTB platform such that its dynamic behavior during power system scenarios can be observed. The developed emulator on the HTB is verified by comparing the emulation results with the model developed in MATLAB/Simulink. The experimental results of the emulator are consistent with the simulation results under the grid support scenarios. This supercapacitor emulator can be potentially used for various power system scenarios in addition to the PV applications presented in this paper.

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