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ACSim: A Novel Acoustic Camera Simulator with Recursive Ray Tracing, Artifact Modeling and Ground Truthing

IEEE Transactions on Robotics 2025年 41卷 2970-2989页

作者： Wang, Yusheng Ji, Yonghoon Tsuchiya, Hiroshi Ota, Jun Asama, Hajime Yamashita, Atsushi Research into Artifacts Center for Engineering University of Tokyo Japan JAIST Graduate School of Advanced Science and Technology Japan Wakachiku Construction Co. Ltd. Research Institute Japan Tokyo College University of Tokyo Japan Department of Human and Engineered Environmental Studies Graduate School of Frontier Sciences University of Tokyo Japan

We present a novel acoustic camera simulator (ACSim) that generates realistic sonar images by incorporating recursive ray tracing and sonar artifact modeling and provides various ground truth labels, enabling benchmarking and learning purposes. The 2D forward-looking sonar (FLS), also known as the acoustic camera, produces high-quality 2D images. Conducting real-world underwater experiments is challenging, making realistic sonar image simulation a necessary alternative. However, existing simulators often lack sufficient realism or are limited to specific scenes and phenomena. As a result, training on simulations and testing on real sonar images (i.e., sim-to-real) remains an open problem for deep learning-based applications. Our work introduces a novel sonar simulator with a customized rendering engine. We use recursive ray tracing to model multipath reflections in arbitrary scenes and propose physics-based shading for intensity computation. We propose a resampling method for anti-aliasing and model significant artifacts such as rolling shutter distortions and cross-talk noise. The simulator provides various ground truths for benchmarking and deep learning applications. We tested several tasks by training on synthetic images and demonstrated that the models also work on real images. We developed a Blender add-on for an enhanced user interface and will make the simulator open-source to advance future research. © 2004-2012 IEEE.

关键词： Crosstalk

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Distance-based Relative Orbital Transition for Palm-Sized Satellite Swarm with Guaranteed Escape-Avoidance

Distance-based Relative Orbital Transition for Palm-Sized Sa...

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AIAA Science and technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Takahashi, Yuta Shim, Seang Sakai, Shin-Ichiro Department of Mechanical Engineering Tokyo Institute of Technology Tokyo152-8550 Japan Department of Space and Astronautical Science The Graduate University for Advanced Studies Kanagawa252-5210 Japan Department of Spacecraft Engineering Institute of Space and Astronautical Science Japan Aerospace Exploration Agency Kanagawa252-5210 Japan

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper presents autonomous guidance and control systems for palm-sized satellites that realize large, distributed space structures for innovative communication and observation. We propose a distance-based orbital controller with escape avoidance constraints to guarantee autonomous deployment into a monolithic formation. The continuous formation control is achieved using magnetorquers (MTQs), satellite on board electromagnetic coils, for long-termformation stability without thruster usage. To address the computational limitations, each satellite incorporates deep neural networks that learn its optimal control behavior, i.e., the power-optimal current allocation of MTQs to realize desired magnetic-field interactions. Along with the optimal system design to maximize the mission efficiency, we demonstrate promising results showing that 500 palm-sized satellites (0.5 kg, (70 mm)3) are realizing tens of meters by tens of meters distributed structures in a low-earth orbit. These results lower the technical barriers of large-scale space systems and improve system performance in terms of resolution, communication speed, and effective isotropic radiated power. © 2025, American institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Orbits

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Computer-Aided Diagnostic System for Alzheimer’s Disease Using 3D MRI Images 4th

Computer-Aided Diagnostic System for Alzheimer’s Disease Us...

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4th International Conference on advanced Network Technologies and Intelligent Computing, ANTIC 2024

作者： Thamizhvani, T.R. Hemalatha, R.J. Department of Biomedical Engineering Vel’s Institute of Science Technology and Advanced Studies Chennai600 117 India

ISBN: (纸本)9783031837890

Alzheimer’s disease (AD) is a brain ailment that gradually impairs thinking and memory abilities as well as the capacity to do even the most basic tasks. A proper diagnosis of Alzheimer’s disease (AD) is crucial for patient treatment, particularly in the early stages of the illness when patients can take precautions before suffering irreparable brain damage. Many machine detection techniques are constrained by congenital observations, even though several recent research have employed computers to diagnose stages of AD. In the proposed approach, the hippocampus area is identified as a biomarker by segmenting the region using 3D deep learning algorithms and classification of stages of AD performed using 3D network-based transfer learning techniques. Thus, an efficient segmentation and classification technique for the detection of the hippocampus region and identification of the different stages of AD is determined to design a computer aided diagnostic system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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Convolutional and Graph Neural Networks for the prediction of Papulosquamous Diseases (PSDs)

Convolutional and Graph Neural Networks for the prediction o...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： G. Nagarajan Arun Raaza Department of Electronics and Communication Engineering VELS Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The precise identification of papulosquamous diseases (PSDs), including psoriasis and seborrheic dermatitis, is a challenge for dermatologists due to their complicated and heterogeneous clinical presentations. In the discipline of dermatology, artificial intelligence (AI) has recently made major advancements in image classification and malignancy prediction. Deep learning is becoming increasingly popular for treating a variety of diseases. This study proposes a CNN (Convolutional Neural Networks)+GNN (Graph Neural Networks) architecture and evaluates and compares the performance of multiple deep learning models, including LSTM (Long Short-Term Memory), Autoencoders (AEs), and Recurrent Neural Networks (RNNs), for the automatic categorization of Papulosquamous Diseases(PSDs). . A hybrid strategy combining CNN and GNN was selected as the most efficient model, achieving the maximum performance across all measures with 93% accuracy, 92% precision, 94% recall, and 93% F1-Score.

关键词： Deep learning Renewable energy sources Accuracy Graph neural networks Convolutional neural networks Lesions Artificial intelligence Long short term memory Standards Diseases

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Implementing and Managing Green IT Strategies to Enhance Corporate Sustainability

Implementing and Managing Green IT Strategies to Enhance Cor...

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2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Sudharson, K. Vijayakumar, N. Anitha, L. Subhashini, V. Madhumita, G. Sureshkumar, A. R.M.D. Engineering College Department of Aiml Chennai India B.S.Abdur Rahman Crescent Institute of Science & Technology Department of Commerce Chennai India Jnn Institute of Engineering Department of Mba Chennai India Dwaraka Doss Goverdhan DossVaishnav College Department of Accounting and Finance Chennai India Vels Institute of Science Technology and Advanced Studies VISTAS School of Management Studies Chennai India VelTech HighTech Dr. Rangarajan Dr. Sankunthala Engineering College Department of Mba India

ISBN: (纸本)9798331529833

Importance of Green IT practices is that it improves corporate sustainability through conserving the natural environment and minimizing the use of resource. This paper focuses on next generation IT management for power-optimized hardware, virtualization, cloud computing, and intelligent system through SITOF. Some of the key technologies are: ARM-based low power servers, vSphere-server visualization, Docker- Containerization, Solar/wind hybrid power. It shows that the main benefits of the IT of smart buildings are a 35% saving in energy and 40% reduction in carbons, as well as a quarter decrease in operation costs when compared to traditional IT practices. Moreover, data analytic improve performance measurement and management and hence contributing to an improvement in sustainability issues. These results also show that SITOF is feasible to assist firms in attaining corporate sustainability objectives while embracing the best practice of technology management. © 2025 IEEE.

关键词： Green development

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Investigation of Turning Parameters on AL7075 Alloy Reinforcement with Silicon Carbide for a Surface Roughness of Composite Material 5th

Investigation of Turning Parameters on AL7075 Alloy Reinforc...

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5th International Conference on Innovative Product Design and Intelligent Manufacturing Systems, ICIPDIMS 2023

作者： Padmanabhan, S. Siva Ramakrishna, Ch. Lalitha Saravanan, A. Bokde, Ramesh Venugopal, S. Manikkavasakan, V. Pandey, Krishna Kant School of Mechanical and Construction Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai600062 India Department of Mechanical Engineering Vignan’s Institute of Information Technology Andhra Pradesh Visakhapatnam530049 India Deptartment of Mechanical Engineering SRM Institute of Science & Technology Vadapalani Campus Tamil Nadu Chennai600026 India Department of Mechanical Engineering IES Institute of Technology & Management IES University Madhya Pradesh Bhopal462044 India Department of Automobile Engineering Vels Institute of Science Technology and Advanced Studies Tamil Nadu Chennai600117 India Department of Mechatronics Engineering Manipal University Jaipur Rajasthan Jaipur303007 India

ISBN: (纸本)9789819767311

Presently, manufacturing sector largely depends on the utilization of alloys and composite materials in the fabrication of a diverse array of components. Both the automotive and aerospace sectors are actively seeking materials and solutions with exact surface characteristics that enable them to effectively showcase their respective goods. The investigation of the surface finish of novel alloy materials is of utmost importance in order to effectively cater to the requirements of various industries. The significance of the output reactions offered by these materials is essential for industrial applications. In the current study, the alloy Al7075 is exposed to reinforcement with silicon carbide at three distinct percentages. In order to determine the influence of several constraints on the surface roughness of composite materials, variations will be made to the machine feed rate, cutting spindle speed, depth of cut, and the reinforcement of silicon carbide. The results revealed that spindle speed of 600 rpm, machine feed of 0.175 mm/rev, depth of cut of 0.4 mm, and SiC addition of 5% produce the better surface finish on the Al7075 material. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Reinforcement

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Fe single atom trigger asymmetric In-In polarized site pairs boosting near-infrared N2 photoreduction

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Materials Today 2025年 86卷 96-103页

作者： Liang, Huimin Ye, Caichao Wu, Yao Li, Yezi Long, Ran Xiong, Jun Jiang, Wei Di, Jun School of Chemistry and Chemical Engineering National Special Superfine Powder Engineering Research Center Nanjing University of Science and Technology Nanjing210094 China Academy for Advanced Interdisciplinary Studies & Department of Materials Science and Engineering Guangdong Provincial Key Laboratory of Computational Science and Material Design Southern University of Science and Technology Shenzhen518055 China School of Materials Science & Engineering Nanyang Technological University Singapore639798 Singapore National Synchrotron Radiation Laboratory CAS Center for Excellence in Nanoscience School of Chemistry and Materials Science University of Science and Technology of China Hefei230026 China Institute for Energy Research Jiangsu University Zhenjiang212013 China

Building differentiated charge distributed polarized atomic pairs may be an alternative for N2 activation. In this work, CuInS2 atomic layers with asymmetric In-In polarized site pairs are prepared by doping Fe single atoms, as proved by X-ray absorption fine structure spectroscopy, quasi in situ X-ray photoelectron spectra and calculated atomic charge distribution. The engineered Fe single atoms tuned the surface atomic charge distribution, building local polarization center to boost directional surface charge separation. The formed In-In polarized site pairs with asymmetric charge distribution creates distinct interaction with two N atoms in N2 molecule, contributing to the enhanced activation of N2. The alternating pathway is regarded as hydrogenation pathway over Fe-CuInS2 with *NNH formation as rate-determining step. The charge-redistributed surface atomic structure after engineered Fe single atoms strengthens intermediate interaction and lower the rate-determining step energy barrier. Benefiting from these features, Fe-CuInS2 exhibits excellent photocatalytic activity under near-infrared (NIR) irradiation with an ammonia synthesis rate of 94.1 μmol g−1 h−1. In comparison, the N2-NH3 conversion rate of Fe-CuInS2 under ultraviolet–visible (UV–Vis) irradiation could reach 137.4 μmol g−1 h−1, which is about 6.8 times higher than that of CuInS2. The apparent quantum efficiencies of Fe-CuInS2-2 at 380, 400, 450, 500, 550, 650, 700, 800, 850 and 1064 nm were 3.0 %, 3.3 %, 2.7 %, 2.1 %, 2.0 %, 1.5 %, 1.3 %, 0.4 %, 0.7 % and 0.6 %, respectively. © 2025 Elsevier Ltd

关键词： X ray absorption fine structure spectroscopy

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A Robust Simultaneous Likelihood Ratio Test Detector for Wideband Spectrum Sensing under Non-Gaussian Noise

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Bhavana, Bandaru Sabat, Samrat L. Panigrahi, Trilochan University of Hyderabad Center for Advanced Studies in Electronics Science and Technology School of Physics India National Institute of Technology Dept. of Electronics and Communication Engineering Goa India

In cognitive radio, wideband spectrum sensing is challenging in detecting low signal-to-noise ratio signals dominated by non-Gaussian noises. Traditional methods outlined in current literature often fail to provide satisfactory detection performance in the aforesaid scenario. To address this issue, we propose the use of a simultaneous likelihood ratio test (SLRT) as a statistical method to detect the primary signal in a non-Gaussian noise environment. The SLRT-based detector (SLRTD) detects the changes in the received signal statistics with respect to the additive noise. In the SLRT detector, the test statistics of the received signal are estimated using the robust standard deviation to counteract the impact of non-Gaussian noise. Furthermore, detection performance is enhanced through cooperative sensing in a multi-node environment. Our simulations, conducted in both Gaussian and non-Gaussian noise environments using a Universal-Filtered Multi-Carrier (UFMC) signal, demonstrate the superior accuracy of our robust detectors in the presence of non-Gaussian such as Bernoulli, Laplacian, symmetric α-stable distributions noise. This improved accuracy provides reassurance of the effectiveness of our proposed method. © 2015 IEEE.

关键词： Gaussian noise (electronic)

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A Music Recommender System to Identify the Emotional State of a Person Using Machine Learning Algorithms 4

A Music Recommender System to Identify the Emotional State o...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Muruganantham, A. Malarvizhi, C. Divya Vahini, S. Saranya, D. Uthiramoorthy, A. Karnataka Bengaluru India Psg Institute of Advanced Studies Department of Computer Science Coimbatore India Dr.Mahalingam College of Engineering and Technology Department of Computer Applications Tamil Nadu Pollachi India Sri Eshwar College of Engineering Department of Information Technology Coimbatore India Rathinam College of Arts and Science Department of Computer Science India

ISBN: (纸本)9798331523923

In today's digital world, customized recommendation algorithms can improve user experiences in many different areas. Within the sphere of intelligent human-technology interaction, this paper specifically examines a music recommender system driven by machine learning algorithms. It figures out what mood users are in and can provide an appropriate response in real time. The system being propounded in this essay uses advanced technology in facial expression recognition. With the help of a webcam, it aims to accurately detect user emotions, including joy, sadness, and anger. Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Support Vector Machines (SVM) have been employed to effectively classify people's mood and recommend an appropriate song. Our extensive tests and examinations show that CNN, RNN, and SVM models are all very good at guessing users' moods - with high precision, recall, F1-score, and accuracy percentages. Furthermore, in-depth analyses like confusion matrixes illuminate how well each model serves as an emotion recognizer. This study's findings are representative of future prospects in affective computing around the globe with major implications for intelligent recommendation systems to which it's important that the models are sensitive. In conclusion, the suggested Music Recommender System is something new for taking a more personal view: We can be as deeply entertained as the web allows us to be - make tailored entertainment paths in the digital era. © 2025 IEEE.

关键词： Convolutional neural networks

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Achieving 5G Network Energy Efficiency by Hybrid Machine Learning Models for Channel and Power Allocation

Achieving 5G Network Energy Efficiency by Hybrid Machine Lea...

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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： Ramya H Jaya T Rajendran V Department of Electronic and Communication Engineering Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai Tamil Nadu India

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

High data speeds, low latency, and huge connection are becoming increasingly important in 5G networks, making energy efficiency a significant concern. In order to achieve the twin objectives of lowering energy consumption and keeping network performance intact, our research centers on creating hybrid machine learning models to improve channel and power allocation. 5G networks are complex and dynamic, with varying user requirements, fluctuating traffic loads, and different environmental circumstances. Traditional resource allocation algorithms have a hard time keeping up. The authors of this work offer a hybrid strategy that is both adaptable and scalable by integrating supervised and unsupervised learning with reinforcement learning (RL). To begin, the hybrid model uses supervised learning to allocate channels and power using past network data. Next, reinforcement learning takes into account network circumstances, user mobility, and traffic variations to make real-time adjustments to the allocation. To improve the distribution of resources in dense networks, unsupervised learning is used to find clusters and patterns in the data. The system is able to optimize energy consumption and anticipate and respond to changes in the network environment thanks to the integration of these models. With throughput and quality of service (QoS) unaffected, the simulation results show that the hybrid machine learning model is 30% more energy efficient than traditional techniques. In light of increasing data demands and strict energy requirements, the suggested technology provides a viable option for 5G networks that are both environmentally friendly and highly efficient.

关键词： Adaptation models 5G mobile communication Computational modeling Supervised learning Quality of service Reinforcement learning Hybrid power systems Energy efficiency Resource management Unsupervised learning

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