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检索条件"任意字段=IEEE International Conference on Signal Image Technology and Internet Based Systems"

共 20484 条记录，以下是421-430 订阅

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Traffic Localization in Smart Cities Using internet of Crowd Sourcing 1

Traffic Localization in Smart Cities Using Internet of Crowd...

引用

1st international conference on Emerging Technologies for Dependable internet of Things, ICETI 2024

作者： Elnour, Razan A.M. Saeed, Mamoon M. Yousif, Ibtihal Mokhtar, Rania A. Khartoum Sudan Yemen Dept. of Computer Engineering Taif University Taif Saudi Arabia

ISBN: (纸本)9798331533557

This paper aims to examine how IoT localization technologies have impacted on the smart city traffic application including internet Vehicles (IoV), internet of Crowed Sourcing (IoCS), internet of Mobility (IoM), and the internet of Artificial Intelligence (IoAI). IoV improves car to car and car to infrastructure interfaces by increasing traffic flow and reducing or eliminating risks. IoAI employs big data and machine learning technology to accurately capture traffic information and to timely signal appropriate traffic lights for better and safe navigation of self-driven cars. IoCS extending it to Crowdsourcing infrastructure to real-time data systems to improve traffic control and safety of pedestrians. IoM develops a single unified network of transportation opportunities incorporating both mass transit and personal vehicle modes, as well as micro-mobility solutions. Potential issues like data protection, integration, and system extensibility are also discussed throughout the paper, together with potential advancements in autonomous vehicles and artificial intelligence traffic management. Lastly, the research discusses the prospects of IoT localization for Smart City transportation developments. These systems integrating services like sensors, GPS, RFID and AI based models provide real time navigation, thus resulting in efficient traffic control and enhancing the prospects of developing sustainable urban environments. © 2024 ieee.

关键词： Traffic congestion

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Hierarchical DNN-based image Segmentation Algorithm Using Texton, Superpixels, and Layer-Adaptive Loss Functions 16

Hierarchical DNN-Based Image Segmentation Algorithm Using Te...

引用

16th international conference on signal-image technology and internet-based systems (SITIS)

作者： Yu, Cheng-Hsuan Ding, Jian-Jiun Natl Taiwan Univ Grad Inst Commun Engn Taipei Taiwan

ISBN: (纸本)9781665464956

image segmentation is critical to object-oriented image processing. Many conventional segmentation algorithms are based on the superpixel, since it integrates the pixels with similar colors and locations in prior and is beneficial for segmentation. Recently, several segmentation algorithms based on deep learning were developed. However, due to the irregular shape and size of superpixels, it is hard to apply the superpixel directly in a leaning-based segmentation algorithm. In this paper, we propose a novel segmentation method that well integrates the techniques of the deep neural network (DNN), the superpixel, adaptive loss functions, and multi-layer feature extraction. First, different from other learning-based algorithm, which applies an image or its bounding boxes as the input, we adopt the mean and the histogram differences of the features of two superpixels as the input of the DNN to determine whether they should be merged. Moreover, to well consider both largescaled and small-scaled features, a hierarchical architecture is adopted. For different layers, the DNN models with different loss functions are applied. A larger penalty for over-merging is applied in the first layer and a larger penalty for oversegmentation is applied in the following layer. Moreover, according to human perception, the features of colors, areas, the gradient at the boundary, and the texton, which is highly related to the texture, are applied. Experiments show that the proposed method outperforms other state-of-the-art image segmentation methods and produces highly accurate segmentation results.

关键词： image segmentation superpixel merging deep learning

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Quantum image encryption algorithm based on generalized Arnold transform and Lorenz chaotic systems

Quantum image encryption algorithm based on generalized Arno...

引用

2024 international conference Optoelectronic Information and Optical Engineering, OIOE 2024

作者： Zhang, Ruqing School of Information Engineering Shanghai Maritime University Shanghai201306 China

ISBN: (数字)9781510688209

ISBN: (纸本)9781510688193

With the rapid development of the internet and information technology, the traditional encryption methods suffer from drawbacks such as poor processing capacity and low security, making them vulnerable to attacks. This paper presents a hybrid encryption algorithm that combines the Lorenz chaotic system and the generalized Arnold transform to enhance information security and confidentiality in digital images. The proposed quantum encryption scheme involves two stages: interfering with pixel coordinate information and grayscale values using the generalized Arnold transform, followed by spatial domain scrambling of pixel positions using the Lorenz chaotic system. MATLAB simulations demonstrate the effectiveness of this algorithm in achieving the desired confusion and diffusion of pixel values, making it a superior encryption method for cryptography. © 2025 SPIE.

关键词： Encryption algorithms

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Joint Identification of Space-Time Block Code and Modulation Methods based on Trajectory image 3

Joint Identification of Space-Time Block Code and Modulation...

引用

3rd ieee international conference on Information technology, Big Data and Artificial Intelligence, ICIBA 2023

作者： Hu, Qing Huo, Dan Wang, Yulong Tong, Junhao Wang, Jiawei Unit 61191 of the Chinese People's Liberation Army Hangzhou China

ISBN: (纸本)9781665490788

A trajectory image-based identification algorithm is proposed for the joint identification of Space-Time Block Code (STBC) and modulation method used in multiple input multiple-output (MIMO) systems. First, the correspondence between the combination of STBC, modulation method, and trajectory image of the received signal was analyzed, and the feasibility of joint identification of STBC and modulation methods based on trajectory image was demonstrated. Two improvement schemes for trajectory images were given to increase the differentiation of different STBCs on trajectory images and the detailed information contained in trajectory images under BPSK. Finally, using the modified LeNet network to classify the trajectory image. The simulation verifies the ability of the algorithm to jointly discriminate between 24 combinations of 4 modulation methods and 6 STBCs, showing the algorithm's feasibility to together to identify modulation methods and STBC jointly. © 2023 ieee.

关键词： Trajectories

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image Processing and Visualization technology Application of an Improved Deep Learning Algorithm 2

Image Processing and Visualization Technology Application of...

引用

2nd ieee international conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Deng, Yunsheng Huang, Yinghui Chen, Zhangbao Chen, Yan Bengbu University Bengbu Anhui Huainan City China

ISBN: (纸本)9798350372892

In today's world, technology is changing our way of life and work at an alarming rate. This paper studies the performance of an improved deep learning algorithm in image processing tasks, introduces the implementation principle of algorithm design, and puts forward an improved deep learning algorithm. In the experimental method part, a group of experiments are designed to evaluate the image quality performance of the improved deep learning algorithm, which is based on three key performance indicators: Peak signal-to-Noise Ratio (PSNR), Interactive Response Time (IRT) and Structural Similarity Index Measure (SSIM). The research conclusion shows that the peak signal-to-noise ratio (PSNR) of the improved image quality-preserving deep learning algorithm is as high as 58 dB. The maximum IRT measurement of the improved algorithm is only 95 ms, which provides users with faster response speed and enables users to experience a smoother interactive experience in real-time image processing applications. © 2024 ieee.

关键词： Contrastive Learning

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Overview of Research on Named Data Network Architecture 2

Overview of Research on Named Data Network Architecture

引用

2nd international conference on signal Processing and Intelligent Computing, SPIC 2024

作者： Liu, Ying Tan, Xiaobo Liu, Jingyu Shenyang Ligong University Shenyang China Liaoning Co. Ltd China Mobile Communications Group Shenyang China

ISBN: (纸本)9798350368888

Due to the revolutionary development of computer network technology, the current internet architecture has also faced varying degrees of challenges. Therefore, Named Data Networking (NDN) emerged as a well-known and efficient ICN project funded by the Future internet Architecture Program in the United States. It is a new type of internet architecture based on named data, not a service. The core idea of NDN is to shift the focus of network communication to the name of the data itself, where the data is given a unique name, rather than simply addressing and locating through the host address. By using data names, NDN has achieved a more flexible, secure, and efficient information transmission method, providing a more scalable, secure, and inherently routing capable internet architecture, laying the groundwork for future research and learning on NDN. © 2024 ieee.

关键词： Naming data network architecture NDN New internet Architecture

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Design of Programmable Logic Controller Remote Monitoring System based on Industrial internet

Design of Programmable Logic Controller Remote Monitoring Sy...

引用

2024 international conference on Artificial Intelligence and Power systems, AIPS 2024

作者： Liu, Liping Qi, Hongfeng Qingdao Institute of Technology Shandong Province Qingdao266300 China

ISBN: (纸本)9798350392210

With the rapid rise of Industry 4.0 and intelligent manufacturing., production enterprises have higher and higher requirements for the use and management of various types of automation equipment. However., under the traditional equipment management mode., there are problems such as low data transmission efficiency., unstable signal and difficult remote control between field equipment and local server., which further reduces the overall production management efficiency and increases the production cost of enterprises. In this regard., based on the actual needs of current production enterprises., this paper will integrate PLC., industrial internet gateway., cloud data server and other components to design a set of PLC remote monitoring system based on industrial internet., so as to realize remote control., real-time data monitoring and online maintenance of various types of production equipment. Practice has proved that the system adopts WG793 industrial intelligent gateway to adapt to different types of PLC to complete data acquisition tasks. At the same time., the data will be transmitted to the cloud server by 4G/WIFI/ Ethernet and other networking methods to complete the real-time detection., analysis and processing of data., which is not only beneficial to promote the reform of enterprise equipment management mode., but also contributes to the intelligent transformation and upgrading of enterprises. © 2024 ieee.

关键词： Efficiency

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Malware Detection Techniques and Research based on image Analysis 5

Malware Detection Techniques and Research Based on Image Ana...

引用

5th international conference on Computer Engineering and Application (ICCEA)

作者： Li, Chenyu Wang, Yanan Quan, Wen Liang, Xuanhao Air Force Engn Univ Coll Air Traff Control & Nav Xian Peoples R China Air Force Engn Univ Coll Air & Missile Def Xian Peoples R China

ISBN: (纸本)9798350386783;9798350386776

With the development of internet, the speed of malware iteration is accelerating. To cope with the new scale and rapid variation, further optimize the model structure of malware detection, and improve the detection efficiency and accuracy, we establish a malware detection model based on image analysis. The steps of the model are as follows: (1) image the malware into gray-scale image through B2M algorithm, and organize the malware gray-scale map dataset;(2) Establish the Keras-based CNN model and fill the model for training and testing;(3) Save the model parameters to establish malware Detection model. We introduce the image analysis technology and convolutional neural network correlation theory, as well as the construction steps of the detection model, and then evaluates and analyzes the detection results, compared with traditional image vector-based PCA methods and LDA methods, singular value decomposition methods for image feature extraction, and other algorithms. The model has efficient structure, which realizes lightweight with low time and spatial complexity, improves the efficiency of detection while maintaining high detection accuracy, and also has good detection capability for variant malware. Currently, it can better cope with the new characteristics of large-scale and rapid transformation of malware, which has a meaningful future for development.

关键词： malware detection B2M algorithms convolutional neural networks network security

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Research on the Application of Computer image Vision technology in the Curriculum System of Preschool Education 6

Research on the Application of Computer Image Vision Technol...

引用

2023 ieee 6th international conference on Information systems and Computer Aided Education, ICISCAE 2023

作者： Niu, Jun Meng, Yang Changchun Humanities and Sciences College Changchun China

ISBN: (纸本)9798350313444

An interactive learning system based on intelligent image recognition for young children is proposed. The platform consists of cloud server and learning platform. The learning machine is connected to the cloud server through the internet or WIFI, and the cloud server includes a cloud service module, a cloud data module, an information comparison module and an information feedback module. The learning machine includes an image scanning module, an image acquisition module, a central processing unit, an image display module, a 3D virtual model modeling module, a 3D scene design module and an interactive roaming experience module. A voice-based interactive system for young children is established. It can promote children's development of language goals. Children's interest in language can be increased through timbre, language input, language conversion, phonetic word conversion and other methods. It can build a "cognition"model in children's brains, enabling children to distinguish the differences between similar things, thereby improving children's cognition level. © 2023 ieee.

关键词： image vision preschool education situational interaction system interaction

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Research on internet of Things signals Acquisition and Processing based on Edge Computation 5

Research on Internet of Things Signals Acquisition and Proce...

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5th international conference on Computer Engineering and Intelligent Control, ICCEIC 2024

作者： Zhang, Xiaolong Qiu, Wenheng Yang, Huaping Hou, Aohua College of Electrical Engineering and New Energy China Three Gorges University Yichang Hubei443002 China Hubei University of Arts and Sciences Faculty of Automotive and Traffic Engineering Xiangyang Hubei441053 China

ISBN: (纸本)9798331507992

The proliferation of internet of Things (IoT) devices has led to the generation of vast quantities of data, placing considerable strain on conventional cloud computing infrastructure. Edge computing has emerged as a viable solution to address this challenge. In this study, we propose an internet of Things (IoT) signal processing framework that is combined with distributed edge nodes. The objective is to improve data transmission efficiency and real-time performance. Firstly, in consideration of the constrained resources of edge devices, we have devised lightweight preprocessing algorithms, including frequency domain filtering based on Fourier transform and a denoising algorithm based on wavelet transform, with the objective of reducing the noise and redundant information present in the original signal. Subsequently, the adaptive compressed sensing technology was employed to compress the preprocessed signal, which resulted in a notable reduction in the bandwidth requirement for data transmission. Subsequently, in order to fully utilise the computing capabilities of edge nodes, we introduce a multi-task learning collaborative computing model. The model permits the sharing of computing tasks between multiple edge nodes, facilitates data collaboration between edge nodes through federated learning, and reduces computing redundancy and communication overhead. The experimental phase employs the Google Edge TPU Dataset to evaluate the dataset pertinent to the internet of Things. The results indicate that the proposed methodology exhibits a notable enhancement in data processing latency and transmission efficacy in comparison to the conventional approach. © 2024 ieee.

关键词： Collaborative Computing Edge Computation internet of Things signal Processing

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