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International conference on communications and Signal processing

作者： V.B. Gadicha A.S. Alvi Department of Computer Sci. & Engg P.R.Patil College of Engg & Tech Amravati (MH) Department of Computer Sci. & Engg PRMIT&R Badnera Amravati (MH).

ISBN: (纸本)9781479980826

Passwords are used for user authentication, validation, and verification in various software applications (may be Commercial, or Research based). All the existing schemes which are used to generate the password will always provide enough scope for the Intruder (Cracker) to crack or guess the password. All the existing schemes of password generation works on a basic assumption that the resultant password should be easy to remember for a user. This ultimately brings the downfall of the scheme. Therefore, a Password Generator Scheme is proposed here which will restrict the Intruders to crack the password or a scheme should exist which will cause sufficient delay in cracking of a password so that the Generator of the password will change the previous password. Here a strong password generator scheme is proposed by using image Fusion & visual key Cryptography.

关键词： visual Crypto Algorithm image Fusion Two-Share Cryptography

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An Industrial Instrument Panel Identification System Design Based on an Improved YOLOv7 and UAV

An Industrial Instrument Panel Identification System Design ...

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International conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC

作者： Miao Ye Yuejin Huang Qiuxiang Jiang Peng Wen Hongwen Hu Jin Cheng School of Information and Communications Guilin University of Electronic Technology China

Industrial production often requires collecting and reading visual information on instrument panels. Traditional manual inspection and identification methods require considerable work, resulting in inefficient and inaccurate inspections. It is important to design an industrial instrument panel inspection and recognition system that can be applied in real time. In this paper, an industrial instrument panel recognition system is designed by combining an unmanned aerial vehicle (UAV) platform and deep learning techniques. For mobile UAVs, an ultrawideband (UWB) positioning module is used to implement the real-time UAV positioning and its fixed-point cruising function at each instrument panel location point. As the UAV moves around the instrument panel, a modified YOLOv7 model, compressed through a combination of pruning and quantization, is loaded to perform initial detection and recognition of the instrument panel image to ensure real-time transmission of the captured raw image to the ground side. On the ground-side server, the original YOLOv7 model is deployed to perform instrument panel detection on the initially captured images, filter out redundant background information, and then extract the tick marks and pointer information on the instrument panel through the U-Net semantic segmentation model, after which the tick marks and pointer information are processed using traditional image-processing methods. Finally, the instrument panel is identified through the angle method. This method can alleviate the accuracy loss caused by a lightweight model on the mobile side, thus ensuring the quality of accuracy. Finally, a web client is designed using Flask technology to display the uploaded instrument panel images and recognition results via a web terminal. A series of test experiments and their results show that the designed industrial instrument panel detection and recognition system is reliable and efficient in reading industrial instrument panel information in real tim

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Low SNR Sonar image Restoration Based on Mixed Probability Statistical Model in Wavelet Domain

Low SNR Sonar Image Restoration Based on Mixed Probability S...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Ping Xia Qiang Ren Dong-xia Shi Bang-jun Lei Yao-bin Zou Guang-zhu Xv Hubei Key Laboratory of Intelligent Vision based Monitoring for Hydroelectric Engineering College of Computer and Information Technology Three Gorges University YiChang China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Since it is difficult to get texture details for sonar image denoising with strong noise and weak feature information. A sonar image enhancement algorithm in wavelet domain based on Gaussian mixture model and Gaussian mixture model is proposed to preserve the weak feature information of the sonar image. Under strong noise interference, the traditional enhancement method has certain difficulties in measuring the similarity of the details of the sonar image. For this reason, wavelet multi-scale analysis is performed on the sonar image to extract the weak feature information of each resolution. Secondly, a directed probability map model between adjacent scales in the wavelet domain is constructed to realize the similar weak feature information association. According to the parent node state probability and the transition probability matrix, the state values of the corresponding child nodes are determined, and the relationship between the state information of parent and child node is constructed. Thirdly, the Gaussian mixture model is used to fit the wavelet coefficient state distribution, and the neighborhood coefficient correlation is used to describe the relationship between the weak feature information in the scale. Finally, the wavelet coefficient estimation of the restored image signal is calculated by the state probability obtained in the expectation maximization (EM) algorithm, and the sonar image is reconstructed. The results of the contrast experiment are verified by visual effects and objective evaluation. The proposed algorithm can preserve the weak edge and contour information while suppressing the noise of the sonar image. It has better peak signal-to-noise ratio, signal-to-noise ratio and Structural similarity.

关键词： Sonar Wavelet coefficients Wavelet domain Hidden Markov models image enhancement Feature extraction Wavelet analysis

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Multi-Sensor Fusion SLAM Based on Fast Euclidean Clustering to Filter Dynamic Targets

Multi-Sensor Fusion SLAM Based on Fast Euclidean Clustering ...

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IEEE International conference on Signal and image processing (ICSIP)

作者： Ming Geng Yongxing Jia Xiaojian Qian Ziang Zhao College of Communications Engineering Army Engineering University of PLA Nanjing China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

R3LIVE is a new multi-sensor fusion SLAM framework, which mainly uses the three sensors of lidar, camera and IMU to realize the effective fusion of heterogeneous data through the error state iterative Kalman filtering method, so as to achieve the function of efficient and accurate simultaneous positioning and mapping. Specifically, this system uses lidar and IMU to construct laser inertial odometer (LIO) to construct the geometry of the external environment, and also it builds a visual inertial odometer (VIO) with a camera and IMU to reconstruct a dense RGB map of the environment. However, in the face of the environment where dynamic objects appear, the positioning accuracy of the system will be affected due to the point cloud of the sensor on the dynamic objects. Moreover, the continuous point cloud of the dynamic objects will cause continuous “ghost” in the dense RGB map constructed. In order to solve the above problems, a fast Euclidean clustering algorithm is proposed to filter out dynamic targets, that is, before the LIO system laser point cloud registration, the input point cloud is initially classified by using a fast Euclidean clustering algorithm, and the original point cloud is divided into ground points, background points and foreground points, among which the ground points and background points are usually considered static points. The foreground points may be either dynamic or static points. Then, the viewpoint visibility method is used for the foreground points to further verify the existence of dynamic points, and the dynamic points in the foreground points are filtered out. Finally, a widely distributed sparse extraction method is designed to solve the problem of excessive extraction of surface features in scenes with rich structure and texture. This enhances the real-time performance and robustness of the system in dynamic scenarios.

关键词： Point cloud compression visualization Accuracy Simultaneous localization and mapping Heuristic algorithms Clustering algorithms Filtering algorithms

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Malware Classification Based on GAF visualization of Dynamic API Call Sequences

Malware Classification Based on GAF Visualization of Dynamic...

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International conference on Communication Technology (ICCT)

作者： Hongmei Zhang Xiaoqian Yun Xiaofang Deng Xiaoxiong Zhong School of Information and Communication Guilin University of Electronic Technology Guilin China Peng Cheng Laboratory Shenzhen China

Due to the constant updates of malware and its variants and the continuous development of malware obfuscation techniques. Malware intrusions targeting Windows hosts are also on the rise. Traditional static analysis methods such as signature matching mechanisms have been difficult to adapt to the detection of new malware. Therefore, a novel visual detection method of malware is proposed for first-time to convert the Windows API call sequence with sequential nature into feature images based on the Gramian Angular Field (GAF) idea, and train a neural network to identify malware. The experimental results demonstrate the effectiveness of our proposed method. For the binary classification of malware, the GAF visualization image of the API call sequence is compared with its original sequence. After GAF visualization, the classification accuracy of the classic machine learning model MLP is improved by 9.64%, and the classification accuracy of the deep learning model CNN is improved by 4.82%. Furthermore, our experiments show that the proposed method is also feasible and effective for the multi-class classification of malware.

关键词： Deep learning visualization Neural networks Static analysis Feature extraction Malware communications technology

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USTC-TD: A Test Dataset and Benchmark for image and Video Coding in 2020s

arXiv

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arXiv 2024年

作者： Li, Zhuoyuan Liao, Junqi Tang, Chuanbo Zhang, Haotian Li, Yuqi Bian, Yifan Sheng, Xihua Feng, Xinmin Li, Yao Gao, Changsheng Li, Li Liu, Dong Wu, Feng MOE Key Laboratory of Brain-Inspired Intelligent Perception and Cognition University of Science and Technology of China Hefei230027 China

image/video coding has been a remarkable research area for both academia and industry for many years. Testing datasets, especially high-quality image/video datasets are desirable for the justified evaluation of coding-related research, practical applications, and standardization activities. We put forward a test dataset namely USTC-TD, which has been successfully adopted in the practical end-to-end image/video coding challenge of the IEEE International conference on visual communications and lmage processing (VCIP) in 2022 and 2023. USTC-TD contains 40 images at 4K spatial resolution and 10 video sequences at 1080p spatial resolution, featuring various content due to the diverse environmental factors (e.g. scene type, texture, motion, view) and the designed imaging factors (e.g. illumination, lens, shadow). We quantitatively evaluate USTC-TD on different image/video features (spatial, temporal, color, lightness), and compare it with the previous image/video test datasets, which verifies its excellent compensation for the shortcomings of existing datasets. We also evaluate both classic standardized and recently learned image/video coding schemes on USTC-TD using objective quality metrics (PSNR, MS-SSIM, VMAF) and subjective quality metric (MOS), providing an extensive benchmark for these evaluated schemes. Based on the characteristics and specific design of the proposed test dataset, we analyze the benchmark performance and shed light on the future research and development of image/video coding. All the data are released online: https://***/USTC-TD . Copyright © 2024, The Authors. All rights reserved.

关键词： image coding

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A stochastic modelling framework for the reconstruction of cardiovascular signals

A stochastic modelling framework for the reconstruction of c...

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Annual International conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Diego Martín-Martínez Pablo Casaseca-de-la-Higuera Marcos Martín-Fernández Abbes Amira Chunbo Luo Christos Grecos Carlos Alberola-López Laboratory of Image Processing University of Valladolid Spain Audio Visual Communications and Networking Research Centre University of the West of Scotland United Kingdom

This paper presents a common stochastic modelling framework for physiological signals which allows patient simulation following a synthesis-by-analysis approach. Within this framework, we propose a general model-based methodology able to reconstruct missing or artifacted signal intervals in cardiovascular monitoring applications. The proposed model consists of independent stages which provide high flexibility to incorporate signals of different nature in terms of shape, cross-correlation and variability. The reconstruction methodology is based on model sampling and selection based on a wide range of boundary conditions, which include prior information. Results on real data show how the proposed methodology fits the particular approaches presented so far for electrocardiogram (ECG) reconstruction and how a simple extension within the framework can significantly improve their performance.

关键词： Electrocardiography Joints Autoregressive processes Vectors Stochastic processes Principal component analysis Shape

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image-Based Indoor Localization Using Smartphone Cameras In Textureless Environment

Image-Based Indoor Localization Using Smartphone Cameras In ...

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International conference on Information and Computer Technologies (ICICT)

作者： Ping Zheng Danyang Qin Jianan Bai Lin Ma Department of Electronics and Communication Engineering Heilongjiang University Harbin China Heilongjiang University Harbin China National Mobile Communications Research Laboratory Southeast University Nanjing China Department of Information and Communication Engineering Heilongjiang University Harbin China Department of Electronics and Information Engineering Harbin Institute of Technology Harbin China

In recent years, Location Based Services (LBS) have developed rapidly. There is an urgent need to establish a low-cost and convenient method to get precise positions indoors. Therefore, we choose an easier way of using monocular camera mounted on a mobile terminal to achieve indoor visual positioning. In this paper, two main problems are studied under this condition. Firstly, the feature extraction method is designed to minimize the loss of positioning information while processing quickly in textureless environment. Secondly, the relative direction vector obtained from the epipolar geometry cannot converge at the point to be located due to errors, so a position determination method is proposed to transform the problem of positioning into distance calculation. A WeChat mini program was built for users to obtain location information. Experimental results show that 90% positioning error was less than 0. 5m, and the average error was less than 0.32m.

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Light-weight Super-Resolution Network based on Classified Measurement-domain Features 23

Light-weight Super-Resolution Network based on Classified Me...

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Proceedings of the 2023 6th International conference on Artificial Intelligence and Pattern Recognition

作者： Shuo Li Yinghua Li Dan Xu Ying Zhang Jinglu He School of Communications and Information Engineering Xi'an University of Posts and Telecommunications China Sichuan Jiuzhou Electric Group Co. Ltd. China

ISBN: (纸本)9798400707674

In contemporary research, natural images are frequently reconstructed using methods that are computationally intensive but highly effective. To solve this issue, we propose a light-weight super-resolution(SR) network based on classified measurement-domain features, abbreviated as SRCMF. images of nature can be divided into multiple smaller images for processing purposes. On the basis of this line of reasoning, it is possible to conclude that various image regions present unique recovery challenges and can be processed by models with differing degrees of complexity. Textured regions make supersegmentation more difficult than flat regions. To take advantage of this property, we can manage the various subgraphs of the split using an appropriate SR model, i.e. a simple small model for smooth regions and a complex large model for textured regions. With this as our starting point, we propose the SRCMF, a classification and supersegmentation integrating framework. Specifically, it employs Class Modules to categorize subgraphs based on their recovery difficulty, followed by numerous SRModules to super-score the subgraphs in each category. The computational cost can be drastically reduced because the preponderance of sub-images will travel through fewer networks. ClassSR is founded on a compression-aware image block processing method on the measurement domain, while SRModule is comprised of SR models of varying proportions. When reconstructing natural images, experimental data indicate that our SRCMF can save FLOPs and reduce the computational complexity of reconstruction. This general structure is applicable to additional simple visual duties.

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Multiresolution Point Cloud Compression for Real-Time visualization and Streaming of Large 3D Datasets

Multiresolution Point Cloud Compression for Real-Time Visual...

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Asilomar conference on Signals, Systems & Computers

作者： Dat Thanh Nguyen Daniel Zieger Markus Gambietz Anne D. Koelewijn Marc Stamminger André Kaup Chair of Multimedia Communications and Signal Processing Erlangen Germany Chair of Visual Computing Erlangen Germany Chair of Autonomous Systems and Mechatronics Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

In recent years, the integration of point cloud data has been observed across diverse domains such as immersive media, autonomous driving, and healthcare. However, processing and visualizing 3D point cloud data requires more computational power and processing time than typical 2D images or videos. In this paper, we present a multiresolution point cloud visualization approach that enables the encoding and reconstruction of human body point clouds at multiple quality/bitrate levels for streaming or real-time visualization applications. Specifically, we partition the 3D model into cells and build an octree structure to store the point cloud at multiple levels of detail. The hierarchical structure is encoded efficiently for fast loading times. We optimize the resolution selection for each visible node based on the camera viewpoint and the geometric error perceived by the user. We validate the proposed method using a widely known point cloud dataset from the Moving Picture Experts Group. The experiments demonstrate that the method can dynamically adapt the resolution of point clouds and provide faster rendering times compared to well-known point cloud visualization software.

关键词： Point cloud compression Three-dimensional displays image resolution Data visualization Rendering (computer graphics) Cameras Real-time systems Signal resolution Iterative decoding Videos

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