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Optimization method for 3D visualization of mobile medical images based on user interaction prediction

Optimization method for 3D visualization of mobile medical i...

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2024 international conference on image, signal Processing, and Pattern Recognition, ISPP 2024

作者： Zhai, Tongtong Cui, Yong Lin, Yusong School of Cyber Science and Engineering Zhengzhou University Zhengzhou China School of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou China Collaborative Innovation Center for Internet Healthcare Zhengzhou University Zhengzhou China Hanwei IoT Institute Zhengzhou University Zhengzhou China

ISBN: (纸本)9781510680425

With the advancement in mobile device performance and the growing demand for 3D visualization of medical images, achieving efficient 3D visualization on mobile devices has become paramount. In this study, we dynamically select rendering methods and quality based on different interaction methods and process dynamics during the user exploring volume data, aiming to swiftly address user interaction requests. Additionally, we employ a trajectory prediction model by leverage of LSTM+MDN to discern inherent patterns in virtual camera parameter sequences from users' historical interaction statistics, predicting virtual camera motion trajectories for pre-rendering and further reducing waiting time for remote rendering. Through comprehensive experimental evaluations encompassing various network conditions, medical image data, and devices, we validate the efficacy of the proposed rendering optimization strategy and prediction model. The experimental outcomes demonstrate a 10% reduction in overall network transmission volume while preserving image quality and response time, thus enhancing user satisfaction with high-quality visualization and interactive exploration of mobile medical images. © 2024 SPIE.

关键词： Rendering (computer graphics)

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Evaluation of Art Works based on Expert System and Improved image Algorithm 5th

Evaluation of Art Works Based on Expert System and Improved ...

引用

5th international conference on 3D Imaging Technologies-Multidimensional signal Processing and Deep Learning, 3DIT-MSP and DL 2023

作者： Li, Liangci School of Art & Design Hubei University of Technology Hubei Wuhan430000 China

ISBN: (纸本)9789819751839

In order to improve the quality of digital evaluation of works of art, this paper combines an expert system and an improved image algorithm to build an evaluation model of works of art, realizes rapid analysis of works of art through the intelligent model, puts forward some image features for classification of works of art, and uses multi-core learning to classify images of works of art. The purpose of this paper is to provide some objective criteria for experts by using image classification technology and to assist experts in art works to identify the text information such as the origin, theme, and age of art works more accurately. In order to extract coloring style information of works of art, we measure the number of colors in an image by color saturation and then extract style information by analyzing the color distribution of a colored area. The research results of this paper show that the evaluation system of art works based on expert system and improved image algorithm can effectively improve the digital processing effect of art works and enhance the intelligent evaluation efficiency of art works. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Expert systems

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A Distributed Radar Multi-Jamming Recognition Method based on Transformer Network 2

A Distributed Radar Multi-Jamming Recognition Method Based o...

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2nd IEEE international conference on signal, Information and Data Processing, ICSIDP 2024

作者： Li, Renjie Hu, Zijun Tian, Dezhi He, Pufeng Liang, Zhennan Liu, Quanhua Beijing Institute of Technology Key Laboratory of Electronic and Information Technology in Satellite Navigation Beijing China Intelligent Science and Technology Academy Casic Beijing China Chongqing Innovation Center Beijing Institute of Technology Chongqing China

ISBN: (纸本)9798331515669

With the advancement of deception jamming and multi-radar cooperative anti-jamming technologies, multi-radar systems designed to counter multiple jammers have become a research hotspot, with jamming recognition being a critical step. In recent years, deep learning has been successfully applied to radar jamming recognition, where the jamming signal is transformed into a time-frequency map and subsequently treated as an image recognition problem. However, the performance of jamming recognition algorithms deteriorates significantly when multiple jamming signals overlap in the time-frequency domain. To address this issue, this paper proposes a distributed radar multi-jamming recognition method based on a Transformer network. This method employs adaptive beamforming to separate jamming echoes in the spatial domain, and then converts the jamming echoes from each radar node into time-frequency spectrograms using the short-time Fourier transform. These spectrograms are then input into the Transformer network to detect the type of jamming and estimate jamming parameters based on their relative positions in the time-frequency spectrograms. Finally, the multi-node processing results are fused to output the jamming type and the corresponding fused jamming parameters. Simulation experiments verify the effectiveness of the proposed algorithm. © 2024 IEEE.

关键词： Frequency domain analysis

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Case study of digital innovation process improvement, using BPMN, case-based reasoning, and text mining

Case study of digital innovation process improvement, using ...

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international IEEE conference on signal-image Technologies and internet-based System

作者： Maksym Proskurin Davy Monticolo Vincent Boly Brunelle Marche Université de Lorraine ERPI EA 3767 Nancy France

Managers of digital products frequently receive feature requests from customers, end users, C-suite executives, and other company officials. They need to collect, process, and implement such requests to implement the most valuable features. Moreover, it is often difficult to decide which requests should be applied. The product backlog prioritization strategy is a well-known method widely utilized to enhance this process. However, it is time-consuming because of the large amount of data and is also subject to the human factor. Moreover, the current literature lacks models allowing automated recommendations to help speed up feature request processing, especially for digital startups with a lack of resources. In this paper, we used BPMN to illustrate a case study of redesigning an innovation process in a digital startup. Furthermore, based on the To-Be Process model, we created an approach to recommend new features using case-based reasoning and text mining.

关键词： Text mining Technological innovation Systematics Companies Human factors Modems Cognition

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Real-time Monitoring and Early Warning of Cotton Diseases and Pests based on Agricultural internet of Things 4

Real-time Monitoring and Early Warning of Cotton Diseases an...

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4th international conference on Machine Learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Cao, Bingyu Zhou, Peng Chen, Wei Wang, Huan Liu, Shengzhi School of Information Science and Technology Xinjiang College of Science & Technology Xinjiang Korla841000 China

During the entire process of cotton growth and development, diseases and pests occur frequently. Therefore, effective prevention and control of them has always been a difficult problem for agricultural scientists and cotton farmers. This article designed a real-time monitoring and early warning system for cotton pests and diseases. The data processing and analysis module can process and analyze the collected data, and uses technologies such as image recognition and data mining to achieve real-time recognition of cotton diseases and pests. Real-time monitoring of its operation was achieved through the mobile display and management module, enabling users to timely grasp the situation of cotton fields and take corresponding measures. This article compared the monitoring effectiveness and efficiency of traditional manual monitoring methods and agricultural internet of Things (IoT) based systems in the same farmland. The accuracy of traditional manual monitoring methods was 85%, while agricultural IoT systems showed higher accuracy, reaching 95%. Real-time monitoring and early warning of cotton diseases and pests based on the agricultural internet of Things is helpful for the prevention and control of flower diseases and pests and cotton field management. © 2024 The Authors.

关键词： Cotton

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EEG-based Emotion Recognition Using Deep Learning Models 2

EEG-Based Emotion Recognition Using Deep Learning Models

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2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications systems, ICMACC 2024

作者： Meghana, Alokam BilakantiVanshika, V. VedaSamhitha, Karnam Murali, Keerthana Singh, Rimjhim Padam Palaniswamy, Suja Amrita School of Computing Department of Computer Science & Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350366570

Emotion recognition is an emerging field of research and modalities like images, videos, speech, text, physiological signals, and EEG are used for the same. In this work, EEG based emotion recognition has been proposed using deep learning models. The recognition of emotion from EEG has attracted a lot of interest and has been applied in numerous fields, particularly in health, technology, and computing. In this work, two methods are proposed for emotion recognition from EEG signals, viz., a signal-based approach and an image conversion approach. the accuracy achieved by the models CNN+GRU, 2D- CNN and LSTM are 96.54%, 90.58%, and 87%, respectively. This work also addresses some of the issues related to model deployment, how to deal with logical and causal relations, and how to implement the outcomes in a real-time environment. © 2024 IEEE.

关键词： Emotion Recognition

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Strong real-time transmission technology of lossless video based on Fibre Channel

Strong real-time transmission technology of lossless video b...

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2023 international conference on image, signal Processing, and Pattern Recognition, ISPP 2023

作者： Fu, Nian Tong, Wentao Zhao, Yupu Wuhan Digital Engineering Research Institute Hubei Wuhan430074 China

ISBN: (纸本)9781510666351

In order to meet the demand of strong real-time display in large integrated video network, this paper proposes a lossless video strong real-time transmission display model based on fibre channel protocol. By accessing the original video data of multi-standard sensors, the normalized video format is packaged into fibre channel protocol data for long-distance lossless transmission and real-time display. Finally, the multi-format original video transmission display based on fibre channel FC-AE-ASM protocol is realized by FPGA accelerating platform, this technology can support the real-time display of multi-format and multi-resolution raw data, the maximum bandwidth is no less than 130MB/S, the maximum resolution is no less than the 1920 times 1080@60HZ, and the total system delay is no more than 0.79ms. © 2023 SPIE.

关键词： image communication systems

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Acoustic Scene Classification Using Deep C-RNN based on Log Mel Spectrogram and Gammatone Frequency Cepstral Coefficients Features 3

Acoustic Scene Classification Using Deep C-RNN Based on Log ...

引用

3rd international conference on Artificial Intelligence For internet of Things, AIIoT 2024

作者： Khine, Kyawt Kay Su, Chaw Department of Information Technology Taunggyi Myanmar Faculty of Informationand Communication Technology Pyin Oo Lwin Myanmar

ISBN: (纸本)9798350372120

Acoustic scene classification, or ASC, has gained much interest recently due to its numerous applications. However, ASC's performance is significantly poorer than other audio processing domains, such as speech recognition and music classification. Many machine learning and acoustic signal processing techniques have been proposed for high-performing ASC systems. To investigate the acoustics scenes from various audio signal versions, this work assesses ASC based on the characteristics extracted from Mel Frequency Cepstral Coefficients (MFCC) and Gammatone Frequency Cepstral Coefficients (GFCC). During the feature extraction step, each frequency call is extracted and parametrized using MFCC, GFCC, and combination features (M-GFCC) to segment the signals. This paper proposes a Deep Convolutional-Recurrent Neural Network (Deep C-RNN) strategy to categorize the efficacy of learning acoustics variations in the classification stage, making use of recent developments in Deep Learning techniques. Firstly, a combination of Mel-Gammatone filters is used in convolutional layers to extract high-level spectrum information. After that, the recurrent layers are used to extract the high-level characteristics' long-term temporal context. The method proposed in this paper is applied to a large dataset called Detection and Classification of Acoustic Scenes and Events (DCASE 2017). The system is implemented on the Matlab2023b platform and achieved an overall average accuracy score of 81.27%. © 2024 IEEE.

关键词： Recurrent neural networks

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Research on Remote Farm Management System based on Computer internet of Things Data Set Information Photoelectric Communication

Research on Remote Farm Management System Based on Computer ...

引用

2023 IEEE international conference on image Processing and Computer Applications, ICIPCA 2023

作者： Ma, Chuantao Lu, Jing School of Physics and Electronic Engineering Taishan University Tai'an271000 China

ISBN: (纸本)9798350314670

Firstly, the interference characteristics of photodetector for optical communication are analyzed. The interference characteristics of photodetector are analyzed and simulated by means of bit interference and clock extraction interference. According to the design index of the Angle measuring system required by the satellite-borne laser communication terminal, the code disk, the indicator grating and the extraction method of photoelectric signal of the photoelectric Angle encoder are designed and selected respectively. An experimental system is set up in the laboratory and the photoelectric detector interference is tested experimentally. The results show that the effect of jamming the photodetector is related to the frequency and power of the interference source. © 2023 IEEE.

关键词： internet of things

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Split Computing based Quantized PoseNet Model for Distributed AI Architecture in IoT-Edge 15

Split Computing based Quantized PoseNet Model for Distribute...

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15th international conference on Communication systems and Networks (COMSNETS)

作者： Karjee, Jyotirmoy Naik, Praveen S. Anand, Kartik Byadgi, Chandrashekhar S. Dabbiru, Ramesh Babu Venkat Srinidhi, N. Samsung R&D Inst India Bangalore Karnataka India

ISBN: (纸本)9781665477062

In recent times, internet of Things (IoT) devices is gaining popularity in advanced wireless technology (i.e., 5G). However, in 5G applications (say in edge platform), the IoT devices have limited computation & processing capabilities which makes it challenging to execute Deep Neural Network (DNN) models on them. To address this, we introduce Split Computing technology, to partition DNN inference layers based on the computational capabilities (such as bandwidth, battery level and processing power, etc.) of IoT and edge (computationally powerful) devices, respectively. To validate split computing, we propose a framework called Distributed Artificial Intelligence (DAI) architecture. We use the architecture for a fitness application (use-case) where we detect the pose of a person for our proposed Quantized Split PoseNet DNN (QSP-DNN) algorithm which partitions the DNN layers among IoT device and edge based on Wi-Fi bandwidth. We perform experiments to validate the QSP-DNN algorithm using DAI architecture. The QSP-DNN with DAI compares split execution (computed among IoT device & edge) for partial offload and full-offload executed on edge device. The result shows that using QSP-DNN in DAI architecture provides split execution performing 20.76 % improvement compared to the full offload case.

关键词： Performance evaluation Computational modeling image edge detection Computer architecture Bandwidth Inference algorithms Partitioning algorithms

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