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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Aniruddh Sikdar Qiranul Saadiyean Prahlad Anand Suresh Sundaram Robert Bosch Centre for Cyber-Physical Systems Indian Institute of Science Bangalore Department of Aerospace Engineering Indian Institute of Science Bangalore School of Computer Science and Engineering Vellore Institute of Technology Vellore

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

The scarcity of annotated infrared (IR) image datasets limits deep learning networks from achieving performances comparable to those achieved with RGB data. To address this, we introduce a novel semi-supervised RGB-to-IR Image-to-Image Translation model (SSL-RGB2IR) that generates synthetic IR data from RGB images. Our model effectively preserves the IR characteristics in the generated images from both synthetic and real-world data. Compared to existing image-to-image translation techniques, training models on this generated IR data significantly improves performance in downstream tasks like segmentation and detection. Notably, in sim-to-real transfer, the segmentation model trained on SSL-RGB2IR generated IR images outperforms baselines and other Image-to-Image (I2I) models. Furthermore, for real-world applications utilizing EO/IR fusion images, this approach solves the well-known challenge of co-registering EO and IR images, which often have inherent misalignment’s due to differing sensor characteristics. Our code is available at https://***/prahlad-anand/ssl-rgb2ir https://***/prahlad-anand/ssl-rgb2ir.

关键词： Training Deep learning YOLO Visualization Translation Supervised learning Superresolution Semisupervised learning Data models Unsupervised learning

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An Efficient and Scalable Byzantine Fault Tolerant Consensus for Vehicular Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Deshmukh, Ameya Baiju, Dheeraj Atish, Atish Alladi, Tejasvi Yu, F. Richard BITS-Pilani Department of Computer Science and Information Systems Pilani333031 India BITS-Pilani Department of Electrical and Electronics Engineering Pilani333031 India Carleton University Department of Information Technology OttawaON Canada

Vehicular networks represent a new distributed system paradigm that requires robust fault tolerance to ensure reliable operation. As a burgeoning area of research, the scalability and optimization of consensus mechanisms for these networks are critical. Traditional Byzantine Fault Tolerant (BFT) algorithms like PBFT are not inherently optimized for the localized needs of vehicular networks, suffering from scalability issues due to their global nature and high messaging complexity. In response, we introduce a two-tiered consensus framework that refines PBFT for the specific context of vehicular networks. By organizing nodes into clusters based on geographic proximity, our approach reduces messaging complexity from O(n2) to O(n1.5), significantly improving scalability. The framework distinguishes between local and global state transitions, adding two phases to the PBFT protocol to manage these efficiently. This tailored consensus process aligns with the localized communication patterns of vehicular networks, enhancing both efficiency and scalability. The framework addresses the critical challenges of traditional BFT algorithms in vehicular networks, offering a solution that is both scalable and resilient. It is a step toward enabling vehicular networks to fulfil their potential as a reliable component of modern distributed systems. © 1967-2012 IEEE.

关键词： Blockchain Byzantine Fault Tolerance Distributed consensus Vehicular networks

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Investigating Role of Renewable Resources Including Wind Turbines in Energy Hub by Considering Simultaneous Demand for Electricity, Heating and Cooling

Investigating Role of Renewable Resources Including Wind Tur...

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Iranian Conference on Renewable Energy and Distributed Generation (ICREDG)

作者： Daryoush Tavangar Rizi Mohammad Hassan Nazari Ali Maleki Seyed Hossein Hosseinian Maryam Fani Gevork B. Gharehpetian Department of Physics and Energy Engineering Amirkabir University of Technology Tehran Iran Smart Control Systems Department Niroo Research Institute (NRI) Tehran Iran School of Electrical Engineering & Computer Science Washington State University Pullman Washington Department of Electrical Engineering Amirkabir University of Technology Tehran Iran

ISBN: (数字)9798350361322

ISBN: (纸本)9798350361339

As a crucial component of multi-energy systems (MES), the energy hub significantly enhances their performance and reliability. In the energy system, the utilization of renewable energy sources (RES) can presents a significant optimization problem, capable of substantially reducing environmental pollution and lowering energy costs for users. This paper presents an optimal load dispatch model incorporating a collection of wind turbines, aimed at reducing the overall cost of operating an energy hub. It is also proposes a hub structure based on wind, natural gas, and electricity as a combined heat and power system, utilizing converters and energy storage mechanisms to obtain electricity, thermal, and cooling energy. To accomplish this, two situations are executed in the network, aiming to minimize costs by applying a problem-solving approach to the installation of wind turbines at an energy hub. After implementing energy hub management in the optimal mode, the system’s operating costs are reduced by $16 \%$, demonstrating its advantage.

关键词： Renewable energy sources Costs Electricity Wind turbines Problem-solving Trigeneration Optimization

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Public Perception of UAM: Are we ready for the new mobility that we have dreamed of? 66th

Public Perception of UAM: Are we ready for the new mobility ...

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66th International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2022

作者： Park, Geebeum Park, Hyunjae Park, Hyunjoo Chun, Nathan Kim, Sang-Hwan Lee, Kyungwon Department of Industrial and Manufacturing Systems Engineering College of Engineering and Computer Science DearbornMI48128 United States Department of Management Studies College of Business University of Michigan - Dearborn DearbornMI48128 United States

Urban Air Mobility (UAM) has lately emerged as a time-saving mode of air transportation in congested urban areas. However, several challenges to the commercialization and adoption of UAM vehicles exist, such as licensing and restrictions, security, and construction infrastructure. Besides these, public perception is one of the significant aspects of easing psychological stress and representing users’ needs. A survey study was carried out to understand the public perception of UAM and to comprehend how the public perceives and expects UAM aircraft in terms of user requirement analysis as part of human-centered design. To achieve this, a total of 2,847 valid data sets were analyzed. A preliminary data analysis revealed the general level of awareness of UAM machines, expected costs and values for specific scenarios, perceived advantages of UAM vehicles, and overall opinion, along with distinctions based on demographic information such as age groups, residential areas, and income levels. Future research topics might include in-depth data analysis and subsequent user surveys to discover underlying requirements. © 2022 by Human Factors and Ergonomics Society. All rights reserved.

关键词： Ergonomics

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A Design and Implementation of Water Heater Model Plant with Temperature Estimation Model for IOT Studies 7

A Design and Implementation of Water Heater Model Plant with...

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7th International Conference on Vocational Education and Electrical engineering, ICVEE 2024

作者： Pradhana, Anak Agung Surya Funabiki, Nobuo Kotama, I Nyoman Darma Desnanjaya, I Gusti Made Ngurah Radhitya, Made Leo Mahajaya, Nyoman Sarasuartha Okayama University Graduate School of Environmental Life Natural Science and Technology Okayama Japan Okayama University Department of Electrical and Communication Engineering Okayama Japan Institute of Business and Technology Indonesia Department of Computer System Engineering Denpasar Indonesia Institute of Business and Technology Indonesia Department of Informatic Technology Denpasar Indonesia Institut Teknologi Dan Bisnis Stikom Departement of Magister Information Systems Denpasar Indonesia

ISBN: (纸本)9798331505103

This study presents the development of an Internet of Things (IoT) system for a water heater model, focusing on enhancing reliability during sensor malfunctions that could disrupt operations. Using the SEMAR IoT platform, the system efficiently collects and analyzes data, while the integration of the Holt-Winter algorithm ensures accurate temperature estimates, even with unreliable or missing sensor data. The algorithm successfully auto-calibrates sensors, predicts temperature at specific time intervals based on real sensor behavior, and fills in missing data during sensor failures. Tests show strong correlations between estimated and actual temperatures, allowing uninterrupted operation. This solution offers a flexible, cost-effective alternative to traditional redundancy, ensuring system reliability during sensor failures. Future research will explore alternative predictive models and conduct real-world testing to validate the system's resilience under complex conditions. © 2024 IEEE.

关键词： Temperature measurement

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One-bit Compressed Sensing using Generative Models

arXiv

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arXiv 2025年

作者： Kafle, Swatantra Joseph, Geethu Varshney, Pramod K. ANDRO Computational Solutions NY United States Electrical Engineering and Computer Science Department Syracuse University NY United States Signal Processing Systems group Electrical Engineering Mathematics and Computer Science faculty the Delft University of Technology Netherlands

This paper addresses the classical problem of one-bit compressed sensing using a deep learning-based reconstruction algorithm that leverages a trained generative model to enhance the signal reconstruction performance. The generator, a pretrained neural network, learns to map from a low-dimensional latent space to a higher-dimensional set of sparse vectors. This generator is then used to reconstruct sparse vectors from their one-bit measurements by searching over its range. The presented algorithm provides an excellent reconstruction performance because the generative model can learn additional structural information about the signal beyond sparsity. Furthermore, we provide theoretical guarantees on the reconstruction accuracy and sample complexity of the algorithm. Through numerical experiments using three publicly available image datasets, MNIST, Fashion-MNIST, and Omniglot, we demonstrate the superior performance of the algorithm compared to other existing algorithms and show that our algorithm can recover both the amplitude and the direction of the signal from one-bit measurements. © 2025, CC BY.

关键词： Compressed sensing

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Human Age Prediction Based on Brain MRI Using Density-Based Regression

Human Age Prediction Based on Brain MRI Using Density-Based ...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Ornela Bregu Nuha Zamzami Nizar Bouguila Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Computer Science and AI College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia

In this paper, we have applied a density-based regression technique to predict the age of a human brain based on Magnetic Resonance Images. We propose four different multivariate discrete distributions to accommodate different mean-covariance and correlation structures, given the challenging nature of medical datasets. Then, we use a novel Minorization-Maximization framework to address the issue of high dimensional optimization. Finally, to handle the time and memory requirements of processing large amounts of high-resolution images, we have adopted a real-time framework, where the learning behavior is gradually improved as new data become available.

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Analyzing the Impact of External Interventions on Water and Energy Demand: Cybersecurity’s Role in Sustainable Development

Analyzing the Impact of External Interventions on Water and ...

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Iranian Conference on Renewable Energy and Distributed Generation (ICREDG)

作者： Daryoush Tavangar Rizi Mohammad Hassan Nazari Ali Maleki Seyed Hossein Hosseinian Maryam Fani G. B. Gharehpetian Department of Physics & Energy Engineering Amirkabir University of Technology Tehran Iran Smart Control Systems Department Niroo Research Institute (NRI) Tehran Iran School of Electrical Engineering & Computer Science Washington State University Pullman Washington Department of Electrical Engineering Amirkabir University of Technology Tehran Iran

ISBN: (数字)9798350361322

ISBN: (纸本)9798350361339

The nexus of water, food, and energy constitutes a fundamental aspect of sustainable development. Furthermore, the demand for water and energy is becoming more pronounced. Moreover, there is a growing importance placed on studying cyber security within the water-energy nexus. This research investigates the interdependence of these two systems, conceptualizing them as a water-energy nexus and effort has been conducted on optimizing the economic efficiency of electricity and water systems in order to minimize operating expenses. Subsequently, external interventions, such as the simultaneous injection of false data on both water and energy demand, were addressed through an optimization process in GAMS software. The results suggest that the value of the cost objective function remains constant when inaccurate data is introduced, which hinders the accurate estimation of the system by the system operator. in this system, both water and power loads are directly impacted by these interventions. In particular, the power and water load of the system have exhibited changes ranging from 1.5% to 17.99% for electric load and 3.87% to 17.1% for water load, in comparison to the previous state.

关键词： Meters Renewable energy sources Electricity Distance measurement Software computer crime Sustainable development

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Motion Planning at Intersections with Safe Differential Games based on control Barrier Function

Motion Planning at Intersections with Safe Differential Game...

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IEEE Symposium on Intelligent Vehicle

作者： Peng Yi Wenyuan Wang Qingwen Liu Yiguang Hong Shanghai Research Institute for Intelligent Autonomous Systems Tongji University National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Department of Control Science and Engineering Tongji University

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Motion planning at intersections is a challenging problem in autonomous driving due to the complicated interactions. The existing pipeline of "planning after predicting" is too conservative, can reduce traffic efficiency. Using game theory to model the non-cooperative coupling relationships between multiple vehicles can resolve the above problems, but such methods cannot guarantee safety without collision. This paper presents motion planning for autonomous driving with safe differential games based on control Barrier Function (CBF), and also provides a safety-critical generalized Nash equilibrium seeking algorithm. We handle the hard CBF constraints through augmented Lagrangian multiplier method. Motivated by iterative Linear-Quadratic Game (iLQG) algorithm, we use the Taylor expansion method to approximate the model into an Linear-Quadratic (LQ) structure, and then incrementally solve this problem with an iterative feedback LQ game algorithm. Through Carla simulation and hardware testing, our results indicate that the algorithm can find a balance between safety and efficiency while maintaining real-time implementation performance.

关键词： Differential games Games Approximation algorithms Real-time systems Iterative algorithms Hardware Planning

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Pediatric Bone Age Prediction Using Deep Learning

Pediatric Bone Age Prediction Using Deep Learning

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International Conference on computer and Information Technology (ICCIT)

作者： Al Zadid Sultan Bin Habib Md. Ekramul Islam Md Asif Bin Syed Md Younus Ahamed Tanpia Tasnim Lane Department of Computer Science and Electrical Engineering West Virginia University Morgantown USA Department of Computer Science & Engineering Stamford University Bangladesh Dhaka Bangladesh Department of Industrial and Management Systems Engineering West Virginia University Morgantown USA Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh

Pediatric bone age prediction is a crucial task in clinical practice that can help diagnose endocrine disorders and provide insight into a child’s growth and development. However, conventional bone age prediction methods are often labor-intensive and require specialized radiological expertise. This paper presents a Deep Learning (DL)-based approach to pediatric bone age prediction using EfficientNet with Additive Attention, a state-of-the-art neural network architecture for image classification and regression tasks. The method utilizes over 12,000 X-ray images from the RSNA bone age dataset. It involves image preprocessing, transforming them into three-channel images, and training a Convolutional Neural Network (CNN) to automatically learn the features of hand bone images. This approach provides a more effective and accurate solution for predicting bone age, which is critical in diagnosing pediatric endocrine diseases. This work uses two variations of the EfficientNet model (B0 and B4), where EfficientNetB4 is also finetuned with the Additive Attention mechanism. These three models predict the age for the original age, and their comparison is shown in curves. The predicted ages depict that in most cases, EfficientNetB4 and EfficientNetB4 with Additive Attention (EN-AA) successfully predicted the bone ages more accurately regarding the original age, and their performance was better than the EfficientNetB0. Specific performance metrics are provided to underscore this improvement. Learning curves for training and validation loss confirm effective learning without overfitting or underfitting, further validating our approach’s efficacy in pediatric endocrine disease diagnosis.

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