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49th International Symposium on Mathematical Foundations of computer science, MFCS 2024

作者： Antony, Dhanyamol Cao, Yixin Pal, Sagartanu Sandeep, R.B. Department of Computer Science and Automation Indian Institute of Science Bengaluru India Department of Computing Hong Kong Polytechnic University Hong Kong Hong Kong Department of Computer Science & Engineering Indian Institute of Technology Dharwad India

ISBN: (纸本)9783959773355

In a graph, the switching operation reverses adjacencies between a subset of vertices and the others. For a hereditary graph class G, we are concerned with the maximum subclass and the minimum superclass of G that are closed under switching. We characterize the maximum subclass for many important classes G, and prove that it is finite when G is minor-closed and omits at least one graph. For several graph classes, we develop polynomial-time algorithms to recognize the minimum superclass. We also show that the recognition of the superclass is NP-hard for H-free graphs when H is a sufficiently long path or cycle, and it cannot be solved in subexponential time assuming the Exponential Time Hypothesis. © Dhanyamol Antony, Yixin Cao, Sagartanu Pal, and R. B. Sandeep.

关键词： Graph algorithms

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System Design and Validation of a Decentralized Automated Vehicle Compute System for Energy Efficiency

System Design and Validation of a Decentralized Automated Ve...

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2023 IEEE International Automated Vehicle Validation Conference, IAVVC 2023

作者： Kothari, Aadi Gerdes, Ryan Billington, Thomas Jaggannagari, Rohan Virginia Polytechnic Institute and State University Department of Electrical and Computer Engineering Blacksburg United States Virginia Polytechnic Institute and State University Department of Electrical and Computer Engineering Arlington United States Virginia Polytechnic Institute and State University Department of Computer Science Blacksburg United States

ISBN: (纸本)9798350322538

With the rise of Artificial Intelligence (AI), Connected and Automated Vehicles (CAVs) have made significant strides towards improving road safety. These advancements have also made driving easier and more energy efficient. With the advanced perception capabilities provided by Vehicle-To-Everything (V2X) radios, vision, LiDAR, and radar sensors, CA V s can detect potential hazards earlier than traditional vehicles. However, advanced sensing requires greater computational resources, increasing power consumption and straining the vehicle's compute device(s). As the demand for power increases, electric vehicles (EV s) lose their valuable range, reducing their appeal to customers. As the High Voltage (HV) system powers the compute, the inclusion of powerful Graphical Processing Units (GPUs) can take away from the valuable range and may not fully warrant their utilization for most automated vehicle systems. We propose a technique to identify microcontroller boards that are powered by the system's Low Voltage (LV) battery, supporting efficient hardware acceleration on the edge. This would enable EV s to support advanced computing capabilities without compromising the range. © 2023 IEEE.

关键词： Energy efficiency

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A CMT Device for Electrical Energy Meter Detection

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Recent Advances in computer science and Communications 2022年第5期15卷 765-772页

作者： Zeng, Zhi Zhou, Yongfu School of Computer Science and Engineering Huizhou University Huizhou China Institute of Electronic and Information Engineering Heyuan Polytechnic Heyuan China

Background: Detection technology is a product development technique that serves as a basis for quality assurance. As electric energy meters (EEMs) are measurement instruments whose use is mandatory in several nations, their accuracy, which directly depends on their reliability and proper functioning, is paramount. In this study, to eliminate electromagnetic interference, a device is developed for testing a set of EEMs under a constant magnetic field interference. The detection device can simultaneously test 6 electric meters;moreover, in the future, it will be able to measure the influence of magnetic field strength on the measurement accuracy of EEMs, thereby improving the production efficiency of electric meter manufacturers. Methods: In this study, we first design a 3D model of the detection device for a single meter component;then, we establish a network, which includes a control system and performs the planning of the path of a block that generates a constant magnetic field. Finally, we control the three-axis motion and rotation of the block using a PLC to implement detection for the five sides of the EEM. Results and Discussion: The designed device can accurately determine whether an EEM can adequately function within the error range prescribed by Chinese national standard, under electromagnetic interference;this can enable reliable, automatic testing and fault detection for EEMs. Experiments show that our device can decrease the labor cost for EEM manufacturers and improve the production efficiency. © 2022 Bentham science Publishers.

关键词： Signal interference

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AI-Based Vehicle Detection and Its Emission Impact on AQI 1

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19th Control Instrumentation System Conference, CISCON 2022

作者： Bhat, Suhas Sudhir Pai, M. D. Varun Holla, B. Ashutosh Pai, M. M. Manohar Department of Computer Science and Engineering Manipal Institute of Technology Manipal576104 India Department of Computer Science and Engineering Government Polytechnic Karkala Karkala574104 India Department of Information and Communication Technology Manipal Institute of Technology Manipal576104 India

ISBN: (数字)9789819995547

ISBN: (纸本)9789819995530

Vehicle traffic has a significant impact on urban quality of life. One major impact is on the air quality of the surrounding area. With increasing vehicle traffic, air quality gets drastically affected in urban cities. A dense accumulation of vehicles is commonly observed across a network of roads during peak hours, drastically affecting the air quality index of the surrounding environment. Adverse measures and prevention are required to control air quality to maintain a healthier environment. Hence, determining vehicle emission details is necessary to address the overall impact in the surrounding areas. For any organization/gated campus, it is required to minimize the air pollution caused by vehicles that regularly visit the campus. By identifying the vehicles appearing on the campus premises, their emission impact on the surrounding can be determined using the PUC certificate of the vehicle. This study performs a vehicle emission impact on a gated campus using a deep learning approach. Real-time surveillance footage is processed with a deep learning model to detect vehicle and its license plate. Furthermore, air quality sensors deployed at strategical locations provide real-time data on pollutant concentration. This combined information is further utilized by a Web application to provide statistics of AQI in real time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Air quality

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Robust 3D point clouds classification based on declarative defenders

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Neural Computing and Applications 2025年第3期37卷 1209-1221页

作者： Li, Kaidong Zhang, Tianxiao Zhong, Cuncong Zhang, Ziming Wang, Guanghui Department of Electrical Engineering & Computer Science University of Kansas 1520 West 15th St. LawrenceKS66045 United States Department of Electrical & Computer Engineering Worcester Polytechnic Institute 100 Institute Rd. WorcesterMA01609 United States Department of Computer Science Toronto Metropolitan University 350 Victoria Street TorontoONM5B 2K3 Canada

3D point cloud classification requires distinct models from 2D image classification due to the divergent characteristics of the respective input data. While 3D point clouds are unstructured and sparse, 2D images are structured and dense. Bridging the domain gap between these two data types is a non-trivial challenge to enable model interchangeability. Recent research using lattice point classifier (LPC) highlights the feasibility of cross-domain applicability. However, the lattice projection operation in LPC generates 2D images with disconnected projected pixels. In this paper, we explore three distinct algorithms for mapping 3D point clouds into 2D images. Through extensive experiments, we thoroughly examine and analyze their performance and defense mechanisms. Leveraging current large foundation models, we scrutinize the feature disparities between regular 2D images and projected 2D images. The proposed approaches demonstrate superior accuracy and robustness against adversarial attacks. The generative model-based mapping algorithms yield regular 2D images, further minimizing the domain gap from regular 2D classification tasks. The source code is available at https://***/KaidongLi/***. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Generative adversarial networks

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Machine Learning Yield Forecast in the IoT System for Cultivating Accuracy 2nd

Machine Learning Yield Forecast in the IoT System for Cultiv...

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Jayakarthik, R. Kumar, Santosh Goswami, Sohan Mahaveerakannan, R. Hafeez, Shaik Abdul Tamilnadu Chennai India Department of Computer Science ERA University Uttar Pradesh Lucknow India Department of Computer Science and Technology Technique Polytechnic Institute West Bengal Panchrakhi India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (纸本)9789819776023

Crop production is a very complicated attribute that depends on a number of variables, including genotype, environment, and their interactions. The functional relationship between yield and these interaction elements must be fundamentally understood for accurate yield prediction to be possible, and both large datasets and potent algorithms are needed to show this relationship. In order to accurately estimate crops, we applied cutting-edge machine learning techniques. Our ability to gather data for huge agricultural areas spread throughout far-flung regions of the globe is made possible by the Internet of Things’ architectural principles. So that we may make forecasts about crops, our machine learning system can use this data. Nitrogen, phosphorus, potassium, temperature, humidity, and rainfall all have a role in determining the best crop to grow. Recommendations are made in light of these factors. There are 2200 total occurrences in the data collection, and there are 8 associated attributes for each one. For each of the 8 possible characteristic combinations, around twenty-two distinct plant species are available as potential options. The most effective model can be produced by utilizing the supervised learning approach and few of the machine learning methods available in WEKA. As prospective possibilities for the methods of machine learning that would be used in the classification process, the decision table classifier and the multilayer perceptron rules-based classifier JRip were selected. The system’s design took into account both the growing Internet of Things and the essential measurements required for farming. The average weighted value of the Receiving Operator Attributes has been found to be 1, the performance measured by the classifiers that were chosen has a value of 98.2272%, and the maximum time needed to construct the model is 8.03 s. Employing machine learning in agriculture aims to increase the production and nutritional value of the plants generated

关键词： Adversarial machine learning

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Robot-Assisted Wide-Area Photoacoustic System

Robot-Assisted Wide-Area Photoacoustic System

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Gao, Shang Ma, Xihan Zhang, Haichong K. Worcester Polytechnic Institute Department of Robotics Engineering Worcester United States Worcester Polytechnic Institute Department of Biomedical Engineering Worcester United States Worcester Polytechnic Institute Department of Computer Science Worcester United States

ISBN: (纸本)9798350346459

Liver ischemia and reperfusion injury (IRI) is a vascular complication that can lead to hepatic functional impairment. IRI is relevant in liver transplantation and during liver surgery performed with intermittent vascular inflow occlusion. Detecting intraoperative liver oxygenation impairment and ischemia could help prevent IRI, which is challenging partially due to the multiple hepatic vascular inflows. We propose a robot-assisted wide-area photoacoustic (PA) imaging system to monitor the liver oxygenation level intraoperatively. PA imaging has been widely used for oxygenation detection in preclinical research. We used a robotic arm to actuate the 2D PA probe and scan across the liver surface. An accurate volumetric tomography can be reconstructed via registering 2D images to the recorded robot trajectory. An ex vivo porcine liver sample was scanned using the proposed system. The result suggested that the robot-assisted wide-area PA tomography has the potential to be incorporated into clinical practice to evaluate liver SO2 intraoperatively. © 2023 IEEE.

关键词： Photoacoustic effect

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An Efficient ODR for computer Night Vision Using Multi-Scale Retinex Network-Aided Enhanced Images 4

An Efficient ODR for Computer Night Vision Using Multi-Scale...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Charles Prabu, V. Pandiaraja, P. Sathiyamoorthi, V. Durgadevi, P. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Government Polytechnic College Department of Computer Engineering Baisuhalli Tamilnadu Dharmapuri India Srm Institute of Science and Technology Department of Computer Science and Engineering Vadapalani Campus Tamilnadu Chennai India

ISBN: (纸本)9798331508845

In the current scenario, recognizing various objects and tracking their movements in the real-time surveillance footage is the most difficult task. To detect objects, a combination of image processing and computer vision algorithms is utilized. computer-vision based automatic human activity recognition from surveillance video can be utilized for applications such as the identification of violent acts and the study of human behavior. Consequently, this work implements a new "Object Detection and Recognition (ODR)"model for computer night vision utilizing a deep learning technique. In order to improve the supplied input image, the combined images are first transmitted to the "Multi-scale Retinex (MSR)"model. The You Only Look Once Version 7 (YoloV7) model uses the improved image from MSR as input to identify and recognize things. After conducting experiments, the implemented ODR model on the ExDark Dataset obtained an efficiency of 94.8%. © 2024 IEEE.

关键词： Deep learning

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Marine Life Analysis Based on Ocean Water Level Rise and Climate Change Using Underwater Imaging Techniques

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 657-669页

作者： Mate, Anuja Dinesh Arulananth, T.S. Sathiya, T. Jyothi, B. Swarna Raja, A. Wasim Yalawar, Mrutyunjaya S. Computer Technology Sou.Venutai Chavan Polytechnic Pune India Department of Electronics and Communication Engineering MLR Institute of Technology Telangana Hyderabad 500043 India Department of Computer Science and Engineering Sona College of Technology Tamil Nadu Salem India Computer Science & amp Engineering Madanapalle Institute of Technology & amp Science Andhra Pradesh Madanapalle India Artificial Intelligence and Data Science Sri Krishna College of Engineering and Technology Coimbatore India Department of Computer Science and Engineering CMR Engineering College Hyderabad India

Forecasting sea levels is crucial for harbour operations and coastal structure design. The oceans make up two-thirds of Earth’s surface;therefore, historically, the marine economy has been extremely diversified as well as abundant. In order to explore as well as harness this difficult environment, prominent businesses and ecological groups are developing and deploying novel gadgets backed by state-of-the-art marine mechatronics solutions. This research proposes a novel method of marine life monitoring with ocean water level rise analysis based on climate changes by underwater imaging using machine learning techniques. Here, the input has been collected as underwater images as well as processed for noise removal and normalisation. Then, these images were segmented for monitoring marine life using U-Net graph cut fuzzy clustering with a spatial neural network. Then this segmented image has been classified using multilayer region-based Q-belief vector neural networks for the analysis of sea level rise analysis based on climate change training. Experimental analysis has been carried out for various marine life monitoring datasets and climate change datasets in terms of training accuracy, specificity, recall, AUC, and Kappa-coefficient. Proposed techniques achieved specificity 93%, training accuracy 91%, AUC 95%, recall 96%, and Kappa-coefficient 90%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate changes Machine learning techniques Marine life monitoring Q-belief vector Water level rise analysis

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Closed-loop contrast source regulation through real-time spectroscopic photoacoustic imaging: phantom evaluation

Closed-loop contrast source regulation through real-time spe...

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Photons Plus Ultrasound: Imaging and Sensing 2023

作者： Murakami, Ryo Gao, Shang Zhang, Haichong K. Robotics Engineering Worcester Polytechnic Institute WorcesterMA United States Biomedical Engineering Worcester Polytechnic Institute WorcesterMA United States Computer Science Worcester Polytechnic Institute WorcesterMA United States

ISBN: (纸本)9781510658639

Photoacoutisc (PA) imaging can not only depict the geometrical features of a target like ultrasound but also allow to decompose of the contribution from different material sources based on their own specific optical absorption characteristics. With the potential of PA imaging, how to utilize the extracted information beyond diagnosis and monitoring is an open research question. Despite this interest in the field, the PA-based automation of regulating conditions of contrast sources such as position and concentration has not been actively studied. This regulation has the potential to be used for several applications including precise/long-term dose control of exogenous contrast agents in the context of drug delivery. In addition, material decomposition allows for the selective regulation of specific contrast agents. This would be advantageous in clinical situations where it is necessary to use multiple types of contrast agents simultaneously and independently. In this report, we propose a system architecture to realize closed-loop source regulation through real-time spectroscopic PA imaging and develop an experimental setup to demonstrate the feasibility of this approach. The system autonomously terminated the ICG flow within the target performance range which is defined considering the specifications of the system. © 2023 SPIE.

关键词： Inverse problems

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