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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Fernandez, Marcel Kabatiansky, Grigory Kamel, Ibrahim Miao, Ying Rabie, Tamer Farouk The Technical University of Catalonia Department of Network Engineering Spain Center for Next Generation Wireless and IoT Skolkovo Institute of Science and Technology Russia University of Sharjah Electrical and Computer Engineering United Arab Emirates Institute of Engineering Information and Systems University of Tsukuba Japan

ISBN: (纸本)9798350335590

It has recently been shown that there are no multimedia fingerprinting codes that can find all malicious users when they use arbitrary linear attacks plus adversarial noise. It is shown that such codes exist if the complete recovery property is limited to the IPP property, i.e., the property to find at least one malicious user. Moreover, we extend this property to a property that allows us to detect all users whose contribution to the forgery is large enough. Efficient decoding (tracing traitors) algorithms are developed for these codes. © 2023 IEEE.

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A Comprehensive Methodology for Assessing the Economic Viability of EV Charging Infrastructure in Brazil

A Comprehensive Methodology for Assessing the Economic Viabi...

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Smart Power & Internet Energy systems (SPIES), International Conference on

作者： Daywes Pinheiro Neto António Paulo Coimbra Aníbal T. De Almeida Tony R. Almeida Pedro Moura Letícia C. Ucker Raphael A. Gomes Alana S. Magalhães Center for Experimental and Technological Research and Studies Federal Institute of Goiás Brazil Dpt. of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Portugal

ISBN: (数字)9798350368864

ISBN: (纸本)9798350368871

This study proposes a comprehensive methodology to assess the economic viability of electric vehicle (EV) charging infrastructure projects in Brazil, specifically targeting connections with a capacity of up to 112.5 kW, which are subject to flat electricity tariffs. The methodology employs a novel EV energy consumption model, integrates the regulatory framework of net metering and relevant tax considerations, explores the potential cost-saving benefits of photovoltaic (PV) system integration, and utilizes key economic indicators like Net Present Value and Discounted Payback Time to quantify project profitability. By accounting for a set of EV demand scenarios (low, moderate, and high), the methodology provides a multifaceted assessment of project viability. A case study demonstrates the methodology's application, highlighting the potential economic benefits of such investments. The results suggest that the minimum charging service fee required for economic viability ranges from 0.17 to 0.29 USD/kWh, with a clear cost advantage associated with PV integration observed across all scenarios.

关键词： Economics Photovoltaic systems Costs Finance Transportation Tariffs System integration Charging stations Electric vehicle charging Sustainable development

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CKTGNN: CIRCUIT GRAPH NEURAL NETWORK FOR ELECTRONIC DESIGN AUTOMATION 11

CKTGNN: CIRCUIT GRAPH NEURAL NETWORK FOR ELECTRONIC DESIGN A...

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11th International Conference on Learning Representations, ICLR 2023

作者： Dong, Zehao Cao, Weidong Zhang, Muhan Tao, Dacheng Chen, Yixin Zhang, Xuan Department of Computer Science & Engineering Washington University St.Louis United States Department of Electrical & Systems Engineering Washington University St.Louis United States Institute for Artificial Intelligence Peking University China School of Computer Science The University of Sydney Australia

The electronic design automation of analog circuits has been a longstanding challenge in the integrated circuit field due to the huge design space and complex design trade-offs among circuit *** the past decades, intensive research efforts have mostly been paid to automate the transistor sizing with a given circuit *** recognizing the graph nature of circuits, this paper presents a Circuit Graph Neural Network (CktGNN) that simultaneously automates the circuit topology generation and device sizing based on the encoder-dependent optimization ***, CktGNN encodes circuit graphs using a two-level GNN framework (of nested GNN) where circuits are represented as combinations of subgraphs in a known subgraph *** this way, it significantly improves design efficiency by reducing the number of subgraphs to perform message ***, another critical roadblock to advancing learning-assisted circuit design automation is a lack of public benchmarks to perform canonical assessment and reproducible *** tackle the challenge, we introduce Open Circuit Benchmark (OCB), an open-sourced dataset that contains 10K distinct operational amplifiers with carefully-extracted circuit *** is also equipped with communicative circuit generation and evaluation capabilities such that it can help to generalize CktGNN to design various analog circuits by producing corresponding *** on OCB show the extraordinary advantages of CktGNN through representation-based optimization frameworks over other recent powerful GNN baselines and human experts' manual *** work paves the way toward a learning-based open-sourced design automation for analog *** source code is available at https://***/zehao-dong/CktGNN. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Timing circuits

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Benchmarking Convolutional Neural Network Inference on Low-Power Edge Devices

Benchmarking Convolutional Neural Network Inference on Low-P...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Oscar Ferraz Helder Araujo Vitor Silva Gabriel Falcao Instituto de Telecomunicações Dept. of Electrical and Computer Engineering University of Coimbra Portugal Institute of Systems and Robotics

The massive adoption of IoT devices, the recent developments in the efficiency of AI systems, and the increase of edge computational power, accelerated the deployment of edge AI systems. The implementation of these systems through the use of low-power embedded devices scattered across the edges of a network allows for reduced latency and cost, compared to traditional cloud-based AI computing systems. As a result of the low-complexity AI models and the available low-power embedded systems on the market, this paper provides a comparative study on the inference performance of convolutional neural networks for different edge devices, by exploiting low-power GPUs and dedicated AI hardware. The benchmark results were able to achieve 864 inferences/s for the Jetson AGX Xavier board on a pre-trained SqueezeNet, while reaching a high power efficiency of 52.6 inferences/s per Watt. For the dedicated Movidius neural stick, the system requires only 1.5 W for processing 24.2 inferences/s.

关键词： Performance evaluation Benchmark testing Signal processing Throughput Hardware Convolutional neural networks Internet of Things

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An Automatic Extraction of Features from Water Bodies by using Contrast Vision Transformer 2

An Automatic Extraction of Features from Water Bodies by usi...

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2nd IEEE International Conference on Integrated Intelligence and Communication systems, ICIICS 2024

作者： Madhumitha, A. Al-Fatlawi, Muhammed Sai Srinivas, T. Aditya Ramachandra, A.C. Venkatasubramanian, R. Department of Computer Science and Business Systems Bannari Amman Institute of Technology Sathyamangalam Erode India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Artificial Intelligence and Machine Learning Jayaprakash Naryan College of Engineering Mahabubnagar India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (纸本)9798331504960

In recent days, for strengthening the climate change adaption water features are utilizing as the most crucial environmental element. The integration of Remote Sensing (RS) with Artificial Intelligence (AI) is becoming the major sought-after approach for extracting the features of water information. In existing, the Faster Regional Convolution Neural Network (FR_CNN) is utilized for the water bodies’ extraction. However, the existing FR_CNN model struggled with computational complexity. To overcome this issue, Contrast Vision Transformer (CoVT) is proposed to extract features from water bodies. The transformer with self-attention mechanism reduces the computational complexity by eliminating regional proposal networks. Initially, the satellite images are taken from sentinel-2 dataset and further processed into preprocessing. In preprocessing Gaussian Median Filter (GMF) and Atmospheric Correction (AC) are utilized to reduce the noise and atmospheric effects from images. After that, proposed CoVT extract features from preprocessed images and fed into projection head to generate projected features. Finally, the projected features are further processed with prediction head to predict the satellite image features of water bodies. From the results, the proposed CoVT provided better results when compared to existing FR_CNN by providing results such as accuracy (99.4%), precision (99.1%), recall (98.5%) and F1-score (98.7%) respectively. © 2024 IEEE.

关键词： Median filters

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A Compliant Robot-Assisted Mold Polishing Application Using the Franka Emika Robot

A Compliant Robot-Assisted Mold Polishing Application Using ...

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Annual Conference of Industrial Electronics Society

作者： Michael Christopher Xenya Rui Cortesão Electrical and Computer Engineering Department Institute of Systems and Robotics University of Coimbra Coimbra Portugal FOE GCTU PMB100 Accra Ghana

Metal mold casting for many application areas remains imperfect since this involves the control of numerous factors that affect workpiece quality. Surface roughness which remains one of the many issues related to metal mold cast is often mitigated by means of grinding and polishing. As many attempts are being made to address this challenge through robot-assisted techniques, we focus on rapid mold polishing using the Franka Emika (FE) robot and its programming resources, including a built-in impedance control architecture. To accomplish the objective, mold surface segmentation is necessary. For each segment, the polishing approach is based on surface normals, relative motion, and torque sensor data. Results were obtained with open platform communication-unified architecture (opc-ua) client and server that record torque and force data. Results show that tuning internal impedance control parameters for polishing tasks is feasible.

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Towards Proactive Safe Human-Robot Collaborations via Data-Efficient Conditional Behavior Prediction

Towards Proactive Safe Human-Robot Collaborations via Data-E...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Ravi Pandya Zhuoyuan Wang Yorie Nakahira Changliu Liu Robotics Institute Carnegie Mellon University Pittsburgh Pennsylvania Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh Pennsylvania

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

We focus on the problem of how we can enable a robot to collaborate seamlessly with a human partner, specifically in scenarios where preexisting data is sparse. Much prior work in human-robot collaboration uses observational models of humans (i.e. models that treat the robot purely as an observer) to choose the robot’s behavior, but such models do not account for the influence the robot has on the human’s actions, which may lead to inefficient interactions. We instead formulate the problem of optimally choosing a collaborative robot’s behavior based on a conditional model of the human that depends on the robot’s future behavior. First, we propose a novel model-based formulation of conditional behavior prediction that allows the robot to infer the human’s intentions based on its future plan in data-sparse environments. We then show how to utilize a conditional model for proactive goal selection and safe trajectory generation around human collaborators. Finally, we use our proposed proactive controller in a collaborative task with real users to show that it can improve users’ interactions with a robot collaborator quantitatively and qualitatively.

关键词： Collaboration Predictive models Observers Behavioral sciences Trajectory Task analysis Robots

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Role of RPA to Seize Opportunities in 5G Edge Computing in Telecommunications

Role of RPA to Seize Opportunities in 5G Edge Computing in T...

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2024 International Conference on Advances in Computing Research on Science engineering and Technology, ACROSET 2024

作者： Yendluri, Dileep Kumar Tatikonda, Reshmi Tatikonda, Ramya Ponnala, Jainath Kempanna, M. Bhuvanesh, A. University of the Cumberlands Department of Computer Science Williamsburg United States University of the Central Missouri Department of Computer Information Systems Warrensburg United States University of the Cumberlands Department of Information Technology Williamsburg United States Jawaharlal Technological University Department of Electronics and Communication Engineering Hyderabad India Bangalore Institute of Technology Department of Ai and Ml Bangalore India Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tirunelveli India

ISBN: (纸本)9798350388800

This paper explores the role of Robotic Process Automation (RPA) in leveraging opportunities presented by 5G edge computing in the telecommunications industry. Integrating RPA and 5G edge computing offers numerous benefits, including enhanced operational efficiency, real-time decision-making, improved service delivery, and cost savings. However, it also poses challenges such as complex integration, security concerns, and the need for effective change management. The paper comprehensively analyzes the potential benefits, challenges, and recommendations for implementing RPA in 5G edge computing. It explores use cases, future advancements, emerging technologies, and anticipated opportunities and challenges. The findings highlight the need for clear objectives, process optimization, collaboration between IT and business teams, and continuous monitoring to leverage RPA in 5G edge computing effectively. © 2024 IEEE.

关键词： Telecommunication industry

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Real-Time Whole-Body Control of Legged Robots with Model-Predictive Path Integral Control

arXiv

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arXiv 2024年

作者： Alvarez-Padilla, Juan Zhang, John Z. Kwok, Sofia Dolan, John M. Manchester, Zachary The Department of Electrical and Computer Engineering Carnegie Mellon University United States The Robotics Institute Carnegie Mellon University United States

This paper presents a system for enabling real-time synthesis of whole-body locomotion and manipulation policies for real-world legged robots. Motivated by recent advancements in robot simulation, we leverage the efficient parallelization capabilities of the MuJoCo simulator to achieve fast sampling over the robot state and action trajectories. Our results show surprisingly effective real-world locomotion and manipulation capabilities with a very simple control strategy. We demonstrate our approach on several hardware and simulation experiments: robust locomotion over flat and uneven terrains, climbing over a box whose height is comparable to the robot, and pushing a box to a goal position. To our knowledge, this is the first successful deployment of whole-body sampling-based MPC on real-world legged robot hardware. Experiment videos and code can be found at: ***. Copyright © 2024, The Authors. All rights reserved.

关键词： Biped locomotion

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Learnable Data Augmentation for One-Shot Unsupervised Domain Adaptation

arXiv

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arXiv 2023年

作者： Carrazco, Julio Ivan Davila Morerio, Pietro Del Bue, Alessio Murino, Vittorio Italian Institute of Technology Genoa Italy Department of Computer Science University of Verona Verona Italy Department of Computer Science and Technology Bioengineering Robotics and Systems Engineering University of Genova Genoa Italy Department of Marine Electrical Electronic and Telecommunications Engineering Robotics and Systems Engineering University of Genova Genoa Italy

This paper presents a classification framework based on learnable data augmentation to tackle the One-Shot Unsupervised Domain Adaptation (OS-UDA) problem. OS-UDA is the most challenging setting in Domain Adaptation, as only one single unlabeled target sample is assumed to be available for model adaptation. Driven by such single sample, our method LearnAug-UDA learns how to augment source data, making it perceptually similar to the target. As a result, a classifier trained on such augmented data will generalize well for the target domain. To achieve this, we designed an encoder-decoder architecture that exploits a perceptual loss and style transfer strategies to augment the source data. Our method achieves state-of-the-art performance on two well-known Domain Adaptation benchmarks, DomainNet and VisDA. The project code is available at https://***/IIT-PAVIS/LearnAug-UDA. © 2023, CC BY.

关键词： Benchmarking

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