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GPU Acceleration of Multi-Vehicle Detection and Classification for Unmanned Aerial Vehicle Surveillance Application using YOLOv3 3

GPU Acceleration of Multi-Vehicle Detection and Classificati...

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3rd IEEE international conference on Recent Advances in Systems Science and Engineering, RASSE 2023

作者： Kumar, Devshree Khand, Atharva Satyanarayana Murthy, P.V. Unmanned Aerial Vehicle Design and Integration Division CSIR-NAL Bengaluru India Manipal Institute of Technology Bengaluru India

ISBN: (纸本)9798350341676

This paper describes faster computation technique for Object Classification in Unmanned Aerial Vehicle (UAV), real-Time flight video data by the use of Compute Unified Device Architecture (CUDA) Graphics processing Unit (GPU) Acceleration, an approach called general-purpose computing on GPUs (GPGPU). The implementation was done to proof the concept that frame per second (FPS) doesn't drop much in case of GPU for multi object detection and classification in video captured by Quadrotor from a height of 50 m above Ground Level as compared to Central processing Unit (CPU) usage. Here, in-house designed NAL Quadrotor was used for collection of vehicle dataset which were labelled and classified using You Only Look Once (YOLOv3) Object detector using Darknet53 as feature extractor. This detector was specially selected for its accuracy in detecting small objects as aerially viewed from UAV gimballed camera. The approach was successfully implemented on vehicle dataset and proper identification in different categories like car, truck etc., was done. A novel object counter algorithm was designed to count the objects and assign class identification and object counter number. Each objects were accurately detected and assigned proper IDs. The processing of intelligent object classification increased to 10 times when GPU acceleration was used on tensors in PyTorch. The results are attached for understanding and prove that this detector works best for recognition of objects in aerial view. © 2023 IEEE.

关键词： Graphics processing unit

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Faster R-CNN and YOLOv3: A general analysis between popular object detection networks 3

Faster R-CNN and YOLOv3: A general analysis between popular ...

引用

3rd international conference on signal processing and Machine Learning, CONF-SPML 2023

作者： Dong, Wenbo Computer Science University of Science and Technology of China Anhui Hefei230026 China

Computer vision is developing really fast in recent years, and object detection is now one of the hottest topics. faster region-based convolutional neural network (faster R-CNN) and You Only Look Once (YOLOv3) are two popular modules to solve object detection problems, but they are implemented in different ways and thus have different performance in practice. This article is going to introduce these two modern modules and make a few experiments to compare the performance of each module with various datasets. This analysis will focus on speed, accuracy and the performances in different situations. Two specific studies will be mentioned as two typical examples of object detection application: face mask detection and greenhouse detection from satellite images. At last, the article will draw out a conclusion, make some suggestions for the choice of faster R-CNN and YOLOv3 and make a prospect for the future. It turns out that generally faster R-CNN fits tasks that require high accuracy better and YOLOv3 can realize real-time detection tasks. © Published under licence by IOP Publishing Ltd.

关键词： Object detection

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Language Identification Based on Residual Network and Attention Mechanism 3

Language Identification Based on Residual Network and Attent...

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3rd international conference on Pattern Recognition and Machine Learning, PRML 2022

作者： Zeng, Ailing Ablimit, Mijit Hamdulla, Askar School of Information Science and Engineering Xinjiang University of China China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China Xinjiang Key Laboratory of Multilingual Information Technology China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

Language identification process is necessary for the downstream tasks of cross lingual and multi-lingual application. This paper proposes a language recognition method based on ResNet-CA-BiGRU-Attention model. Firstly, the speech signal is converted into the corresponding spectrogram, and then the residual network is used for feature extraction according to the distribution of speech energy on the spectrogram. Coordinate Attention and Attention are used to highlight corelated features and suppress irrelevant features. In this paper, five languages are selected from the Common Voice data set. Experimental results show that the proposed language recognition method based on ResNet-CA-BiGRU-Attention model can effectively improve the performance of language recognition. © 2022 IEEE.

关键词： Natural language processing systems

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Transfer Learning for Hand Arthritis Prediction from X-Ray Images 3

Transfer Learning for Hand Arthritis Prediction from X-Ray I...

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3rd international conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Raman, Ramakrishnan Inbamalar, T.M. Pushpalatha, N. Meenakshi, S. Kumar, Ashok Razia, Shaik Gopinath, N. Symbiosis Institute of Business Management Maharashtra Pune India Symbiosis International Maharashtra Pune India R.M.K. College of Engineering Department of Electronics &communication Tamil Nadu Thiruvallur India Marri Laxman Reddy Institute of Technology Department of Computer Science Telangana Hyderabad India R.M.K. Engineering College Department of Science and Humanities Tamil Nadu Kavarapettai India Banasthali Vidyapith Department of Computer Science Rajasthan 304022 India Koneru Lakshmaiah Education Foundation Department of Computer Science Andhra Pradesh Vaddeswaram India Sri Sairam Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350336238

Arthritis is a bone disorder that includes swelling and pain in one or more joints. Everyone can develop osteoarthritis, but it grows more common as individuals get older. When arthritis deteriorates over time, it can lead to persistent pain, making it challenging to do daily tasks, and making activities like walking and climbing stairs painful and difficult. If arthritis is correctly identified and treated in its early stages, these consequences can be avoided. The goal of this project is to create two transfer learning models that, by spotting arthritis in its earliest stages, can lower the likelihood of acquiring chronic arthritis. For this purpose, Google served as the source of the images used in this study. After being purchased from Google, the data collection is preprocessed using three different methods. Image scaling, noise reduction, and image enhancement are a few of the pre-processing approaches. The transfer learning models are trained and assessed using this preprocessed dataset. In this work, two distinct transfer learning models are established. The models include SegNet and ENet. On a graph, the outcomes for the performances of both models are displayed. The training data from the first few epochs of the ENet model and SegNet model are also used in the analysis. The models' final accuracy and loss values are then assessed. In the end, it was discovered that the SegNet model had a lower loss value and more accuracy than the other. The model created in this study can be utilised as a preliminary test for arthritis when a person exhibits moderate arthritis symptoms because the final accuracy of the model is higher than or equal to 95%. © 2023 IEEE.

关键词： Image enhancement

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2022 3rd international conference on signal processing and Computer Science, SPCS 2022

2022 3rd International Conference on Signal Processing and C...

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2022 3rd international conference on signal processing and Computer Science, SPCS 2022

The proceedings contain 21 papers. The topics discussed include: Crucian carp position and pose recognition based on an improved photo-model method;parameter measurement method of PD radar based on non-coherent accumulation;speech recognition and hardware accelerator design based on hummingbird core;ISAR imaging method for high-speed maneuvering target based on MFT-UNET3+ network;main lobe interference suppression by FDA-MIMO radar based on a novel log function frequency offset;a high dynamic weak spread spectrum signal acquisition strategy based on the transfer model;nonuniform ISRJ suppression based on time shift alignment and morphological processing;data augmentation based on contrastive learning in audio anomaly detection;main lobe interference suppression method of skywave OTHR based on fast-time STAP;design of occupational accident prevention system for transportation vehicles based on TOF;and programmable logic controller memory management vulnerability analysis.

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AI Voice-Assisted Fitness Coach with Body Pose Recognition 3rd

AI Voice-Assisted Fitness Coach with Body Pose Recognition

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3rd international conference on data Science, Machine Learning and Applications, ICDSMLA 2021

作者： Moulya, S. Pragathi, T.R. Kambali, Pandurang S. ISE RVCE Mysore Rd RV Vidyaniketan Post Karnataka Bengaluru560059 India ECE RVCE Mysore Rd RV Vidyaniketan Post Karnataka Bengaluru560059 India

ISBN: (纸本)9789811959356

The aim of this work was to develop an AI-controlled fitness trainer to tend to each user’s needs. It includes an AI-based voice assistant that acts as a virtual fitness trainer to guide the user in performing a certain routine of exercises, which was implemented through the use of NLP to recognize the user’s voice for commands to activate the trainer and body pose recognition to monitor the user’s postures for the workouts in real-time. This work combined two different features and collectively helped the user to workout in a more efficient way. The trainer can currently perform these operations over a set of 10 workouts—7 for muscle workouts and 3 for cardio. Once the workout session is complete, a bar plot consisting of all the exercises performed during that session is constructed and stored on the user’s device. The study below goes into further detail on the major insinuations for future fitness coach design and assessment. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Natural language processing systems

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Research and implementation of image enhancement method based on improved retinex algorithm 3

Research and implementation of image enhancement method base...

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3rd international conference on Optics and Image processing, ICOIP 2023

作者： Zhang, Diya Wang, Yumeng Wang, Yanrui Pu, Yile Bi, Zhenbo Rao, Wei School of Information Engineering Zhejiang Ocean University Zhoushan316022 China Department of Physics Key Laboratory of Oceanographic Big Data Mining Application of Zhejiang Province Zhejiang Ocean University Zhoushan316000 China

ISBN: (纸本)9781510667426

In view of the phenomenon of detail loss and edge blurring in color images collected underwater, noise reduction is mostly ignored in underwater image enhancement research. In this paper, an improved image enhancement method based on Retinex algorithm is proposed. First,the original underwater image is transferred from RGB channels to HSI color space, and the saturation component is adaptively linearly stretched by gamma correction, The improved Retinex decomposition network based on homomorphic filtering is used to decompose and enhance the Lightness component separately. Eventually, the fused image is transferred from HSI to RGB color space, and the processed image is output. According to the experimental results, this algorithm can not only eliminate the halo artifacts, but also improve the brightness and contrast of the image, restore the original details of the image, and effectively remove the image noise. It greatly improves the visual effect and objective evaluation results of images. © 2023 SPIE. All rights reserved.

关键词： Image enhancement

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3rd EAI international conference on IoT and Big data Technologies for Health Care, IotCARE 2022

3rd EAI International Conference on IoT and Big Data Technol...

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3rd EAI international conference on IoT and Big data Technologies for Health Care, IotCARE 2022

ISBN: (纸本)9783031335440

The proceedings contain 23 papers. The special focus in this conference is on IoT and Big data Technologies for Health Care. The topics include: Personalized Recommendation Method of Maternal and Child Health Education Resources Based on Association Rule Mining Algorithm;methods of Integrating Ideological and Political Education into Health Management in Colleges and Universities Based on Internet of Things Technology;data Mining of Psychological Tendency and Health of Ideological and Political Students in Higher Vocational Tourism English Courses;intelligent Imaging Method of Nuclear Magnetic Resonance Medical Devices Based on Compression Sensing;evaluation of Post Fitness of Employees in Health Care Enterprises Based on Big data;design of Telemedicine and Health Care System Based on Embedded Technology;anomaly Detection Method of Healthcare Internet of Things Gateway Supporting Edge Computing;research on Fast Encryption of Electronic Health Record data Based on Privacy Protection;research on Secure Storage of Healthcare data in the Environment of Internet of Things;research on Cloud Health Privacy Information Protection Algorithm Based on data Mining;architecture of Wide Area Health Monitoring System;internet of Things Technologies in Healthcare for People with Hearing Impairments;artificial Intelligence-Based Early Warning Method for Abnormal Operation and Maintenance data of Medical and Health Equipment;abnormal signal Recognition Method of Wearable Sensor Based on Machine Learning;data Security Mining Method for Social Media Users’ Mental Health Status Test Based on Machine Learning Algorithm;early Warning Method of College Students Mental Subhealth Based on Internet of Things.

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The configuration optimization of the plants in vertical farming by 3D modeling 3

The configuration optimization of the plants in vertical far...

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3rd international conference on signal processing and Machine Learning, CONF-SPML 2023

作者： Tan, Kege Zhang, Ying Department of Architecture School of Architecture and Design Southwest Jiaotong University Chengdu611756 China Department Geography Planning and Environment Faculty of Arts and Science Concordia University Montreal Canada

Vertical farming, a terminology in landscape architecture, is designed to cater for the demand of vegetation under the impact of excessive exploid of land, especially in densely-populated areas. The study takes vertical farming in Singapore as an example, applies 3D modeling to investigate the effect of different arrangement patterns on plant light absorption in vertical farms, and proposes some measures. Through the 3D simulation process, each square of the soil for planting can be quantified in sunlight-exposure hour. The experiment result shows that configuration with an array piling up is judicious and feasible for greenhouse growth, especially for commercial usages. Different configurations of plants influence the efficiency of utilizing sunlight, and there is a better layout for a particular site in different usages. For some cities far away from tropical areas, this configuration may alleviate the impact of lacking sunlight exposure. The main advantage of vertical farms is efficiency, as they require less land and water with more yield per acre compared to conventional farms. © Published under licence by IOP Publishing Ltd.

关键词： Tropics

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Software Engineering Classification Model and Algorithm Based on Big data Technology 3

Software Engineering Classification Model and Algorithm Base...

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3rd international conference on Machine Learning and Big data Analytics for IoT Security and Privacy, SPIoT2023

作者： Hu, Tao Department of Intelligent Science and Engineering Yunnan Technology and Business University Yunnan Kunming China

With the development of internet technology, cloud computing is becoming increasingly popular, and it has a wide range of applications in various fields, such as mobile payments and the Internet of Things. The big data model is a very important and valuable set of useful and unique information. This article mainly introduces the establishment of an object-oriented architecture-based machine learning system classification model using big data analysis methods, as well as the use of neural network algorithms to construct machine learning system classification patterns. Through examples, a comparative experiment is conducted to verify the effectiveness of traditional manual annotation modeling methods combined with parallel processing. Its experimental results show that the model has high accuracy, with an accuracy rate above 92% and a recall rate above 94%, and its F1 value is infinitely close to 1, indicating that the average accuracy and precision of the model is very high. © 2023 The Authors. Published by Elsevier B.V.

关键词： Software engineering

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