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Quality Evaluation Algorithm of Human Video Motion image Segmentation based on Visual Perception

Quality Evaluation Algorithm of Human Video Motion Image Seg...

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international conference on Business Intelligence and Information technology, BIIT 2022

作者： Wang, Qingwei Wang, Xinyu Lv, Zhifeng Tan, Dahai Harbin Huade University Harbin150025 China

ISBN: (纸本)9789819934157

In view of the problems of the existing human video motion image segmentation quality (SQ) evaluation method with low evaluation accuracy and poor anti-interference during the evaluation process, a human video motion image SQ evaluation algorithm based on visual perception was proposed. Firstly, the model of edge contour feature detection and pixel reconstruction is constructed to realize multi-level feature decomposition and edge pixel features. Secondly, the visual feature reconstruction model is established, and the image is segmented by the method of spatial region reconstruction. The spatial region reconstruction in the segmentation process is realized through similarity information fusion, and the number of fuzzy features of the image is extracted. Finally, pixel space fusion matching technology is adopted to realize the visual perception of the image, obtain its sparse segmentation model, and realize the evaluation of the SQ of human body motion video images based on template matching. The results show that this method is more accurate in evaluating the SQ of human video motion images, and the output evaluation results have a higher signal-to-noise ratio and good image SQ detection performance. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image segmentation

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Secure Trust-based Attribute Access Control Mechanism Using FK-MFCMC and MOEHO-XGBOOST Techniques 6th

Secure Trust-Based Attribute Access Control Mechanism Using ...

引用

6th international conference on Soft Computing and signal Processing, ICSCSP 2023

作者： Vanitha, Padala Srikanth, Banda Department of ECE Malla Reddy College of Engineering and Technology Telangana State Hyderabad500100 India University College of Engineering Kakatiya University Telangana State Kothagudem507118 India

ISBN: (纸本)9789819984503

The internet of things (IoT) possesses various devices, which are switching data continuously and are acquired throughout via lossy networks. This promotes the necessity for a lightweight, flexible along with adaptive access control (AC) method to handle the insidious behavior of such global ecological units and also guarantees the reliability betwixt the trusted devices. To provide users with adaptive authentication centered on dynamic trust estimation, a secure trust along with attribute-based AC (ABAC) approach is invented in this research. First, Trust Evaluation (TE) is executed utilizing Fisher score Kernel-based Minkowski fuzzy C-Means Clustering (FK-MFCMC) centered on trusted data balance by means of instructive features being extricated. A successful association is kept up by TE betwixt the nodes installed in the network. And, it guarantees that the entire nodes work in a reliable form. Subsequently, to ingress the request, the TE makes a decision utilizing the multi-objective elephant herding optimization based on an extreme gradient boosting (MOEHO-XGBoost) methodology. In this methodology, the ABAC request is converted into a permission decision vector. Then, it is converted into a binary classification problem that checks whether to permit or reject access. On the whole, the proposed methodology attains higher trust accuracy and TE for several nodes in IoT together with obtains a higher security level in correlation with the prevailing novel methodologies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： internet of things

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SPACEBORNE SAR image SIMULATION based ON MULTI-POLARIZED SAR AIRBORNE DATA

SPACEBORNE SAR IMAGE SIMULATION BASED ON MULTI-POLARIZED SAR...

引用

IET international Radar conference 2023, IRC 2023

作者： Chen, Xi Chen, Renyuan Earth Observation Research and Development Center No. 38 Research Institute of China Electronic Technology Group Corporation No. 199 Xiangzhang Avenue High tech Development Zone Anhui Hefei China

ISBN: (纸本)9781839539954

SAR image simulation is a technology that simulates SAR image features or the working process of SAR systems on a computer platform to obtain simulated SAR images. It overcomes the high cost and long cycle of obtaining SAR images with different parameters in real SAR systems, and plays an important role in SAR system development and algorithm *** paper aims to solve the problems in existing technologies by providing a method for predicting multi-spaceborne polarimetric SAR images using airborne high-resolution SAR data. The correlation model between polarimetric distortion parameters, target polarimetric complex data information (including amplitude and phase), and the echoes of various polarimetric channels on board the satellite was reconstructed to generate and imaged the spaceborne SAR echoes. This simulation method was validated and evaluated through airborne and spaceborne SAR data. The results showed that the accuracy of typical scene similarity and polarimetric feature similarity estimation was better than 80%. © The Institution of Engineering & technology 2023.

关键词： Polarimeters

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PLC signal Level based Traffic Light System Using RPi: An Experimental Setup

PLC Signal Level Based Traffic Light System Using RPi: An Ex...

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Advancement in technology (ICONAT), international conference for

作者： Ashok Kumar Kumawat Dept. of Mechatronics Engineering Manipal University Jaipur Jaipur India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

It is necessary to regularly enhance the traffic light system’s control using the newest technology. The PLC-based systems that are now in use make it difficult to adopt modern technologies like artificial intelligence, machine learning, and the internet of things etc. This work aims to convert the logic level signal based on PLC to a logic level signal based on Raspberry Pi. A raspberry pi is used as the control unit in the development of an experimental four direction-based traffic light prototype. Two different types of logic level converters are used in this system to create signals that are compatible with both 24 V traffic light system and Raspberry Pi. Python programming on the Raspberry Pi is used to control the entire system.

关键词： Prototypes Machine learning Programming Vision sensors Control systems Sensor systems Real-time systems Logic internet of Things Python

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Deep Joint Source-Channel Coding for Person Re-Identification With Related image Sources 23

Deep Joint Source-Channel Coding for Person Re-Identificatio...

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23rd IEEE international conference on Communication technology, ICCT 2023

作者： Chu, Zhihang Chen, Wei Wang, Ning Ai, Bo State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Frontiers Science Center for Smart High-speed Railway System China Key Laboratory of Railway Industry of Broadband Mobile Information Communications China School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (纸本)9798350325959

In this paper, we propose a deep learning based joint source-channel coding (JSCC) scheme for wireless transmission of multiple related image sources in person re-identification tasks. Our network utilizes parameter sharing for the encoder at the transmitter and adopts a joint decoding architecture for the decoder at the receiver, reducing storage space and computational resource consumption. By leveraging object-related information from multiple perspectives, the decoder fuses richer features to perform corresponding re-identification tasks. Experimental results demonstrate that the performance of our proposed method degrades gracefully as available bandwidth resources decrease, and the proposed multi-transmitter JSCC scheme achieves significant gains under low bandwidth and low signal-to-noise ratio conditions, compared with the existing approach. This work shows the potential of the JSCC-based approach for efficient and robust wireless transmission of related image sources in person re-identification scenarios. © 2023 IEEE.

关键词： deep joint source and channel coding image retrieval person re-identification

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Light weight encoder-decoder for underwater images in internet of underwater things (IoUT) 2

Light weight encoder-decoder for underwater images in intern...

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2nd international conference on Artificial Intelligence and signal Processing, AISP 2022

作者： Nair, Rashmi S. Agrawal, Rohit Domnic, S. Department of Computer Applications National Institute of Technology Tamil Nadu Tiruchirappalli India Department of Production Engineering National Institute of Technology Tamil Nadu Tiruchirappalli India

ISBN: (纸本)9781665442909

internet of Underwater Things (IoUT) explores the applications of internet of Things (IoT) to monitor the sea animal habitat, observe atmosphere, and predict defense and predict defense and disaster. Raw underwater images are affected by absorption and dispersal of light due to underwater environment. Low power computational devices are preferred to cut down the cost of IoUT devices. Because of underwater environment nature, transmission of underwater images captured by underwater devices is considered as a big challenge. There is a need to provide solutions to amplify color, contrast and brightness aspects of captured underwater images to provide good visual understanding. Conventional compression techniques used for terrestrial environment, causes ringing artefacts due to the variable characteristics of underwater images. Deep image compression techniques consume more computational power and time, making them least efficient for low power computational devices. In this study, a low computational power and less time-consuming image compression technique is proposed to achieve high encoding efficiency and good reconstruction quality of underwater images. The proposed technique suggests using Convolutional Neural Network (CNN) at encoder side, which compresses and retains the structural data of the underwater image. And relative global histogram stretching based technique has been used at the decoder side to enhance the reconstructed underwater image. The proposed methodology is compared with conventional methods like Joint Pictures Experts Group (JPEG), Better Portable Graphics (BPG), Contrast Limited Adaptive Histogram Equalization (CLAHE) and deep learning techniques like Super Resolution Convolutional Neural Network (SRCNN) and Residual encoder-decoder methods to evaluate the reconstructed image quality. The presented work provides high quality image in comparison with both conventional and SRCNN method. © 2022 IEEE.

关键词： Convolutional neural networks

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TERAHERTZ image RESTORATION BENCHMARKING DATASET 47

TERAHERTZ IMAGE RESTORATION BENCHMARKING DATASET

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47th IEEE international conference on Acoustics, Speech and signal Processing (ICASSP)

作者： Zhang, Yixiong Su, Zhipeng Qi, Feng Zhou, Jianyang Zhang, Xiao-Ping Xiamen Univ Sch Informat Xiamen 361005 Peoples R China Chinese Acad Sci Shenyang Inst Automat Shenyang 110016 Peoples R China Xiamen Univ Sch Elect Sci & Engn Xiamen Peoples R China Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON Canada

ISBN: (纸本)9781665405409

The paper introduces a new terahertz (THz) image bench-marking dataset for THz imaging. The degradation of THz image quality is one of the main problems caused by system noise, intrinsic long-wavelength, and diffraction phenomena. In this paper, the point spread function of the THz imaging process is reconstructed firstly. The THz datasets with ground-truth and degraded images are then synthesized using the point spread function (PSF). We propose a Dense Instantiation Normalization Block (DIN Block) to reconstruct clean THz images. based on the DIN block, a powerful multistage network is designed, named as DINet. DINet achieves the state-of-the-art (SOTA) restoration performance on image rain removal datasets and the proposed THz datasets. To the best of our knowledge, the THz image benchmarking dataset is the first public dataset, which is available at https://***/hellogry/THzDatasets.

关键词： terahertz (THz) image benchmarking dataset point spread function (PSF) terahertz imaging systems Dense Instantiation Normalization Block (DIN Block) THz image restoration

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Vision-based Distributed Multi-UAV Collision Avoidance via Deep Reinforcement Learning for Navigation

Vision-based Distributed Multi-UAV Collision Avoidance via D...

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IEEE/RSJ international conference on Intelligent Robots and systems (IROS)

作者： Huang, Huaxing Zhu, Guijie Fan, Zhun Zhai, Hao Cai, Yuwei Shi, Ze Dong, Zhaohui Hao, Zhifeng Shantou Univ Coll Engn Key Lab Digital Signal & Image Proc Guangdong Pro Shantou Peoples R China

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

Online path planning for multiple unmanned aerial vehicle (multi-UAV) systems is considered a challenging task. It needs to ensure collision-free path planning in real-time, especially when the multi-UAV systems can become very crowded on certain occasions. In this paper, we presented a vision-based decentralized collision-avoidance policy learning method for multi-UAV systems. The policy takes depth images and inertial measurements as sensory inputs and outputs UAV's steering commands, and it is trained together with the latent representation of depth images using a policy gradient-based reinforcement learning algorithm and autoencoder in the multi-UAV three-dimensional workspaces. Each UAV follows the same trained policy and acts independently to reach the goal without colliding or communicating with other UAVs. We validate our method in various simulated scenarios. The experimental results show that our learned policy can guarantee fully autonomous collision-free navigation for multi-UAV in three-dimensional workspaces, and its navigation performance will not be greatly affected by the increase in the number of UAVs.

关键词： Learning systems Training Navigation Reinforcement learning Autonomous aerial vehicles Path planning Robustness

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IoT Network Attack Types by Application Domains

IoT Network Attack Types by Application Domains

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international conference on Artificial Intelligence and Smart Environment, ICAISE 2022

作者： Fadli, Ouijdane Balboul, Younes Fattah, Mohammed Mazer, Said Elbekkali, Moulhime Artificial Intelligence and Data Science and Emerging Systems Laboratory Sidi Mohamed Ben Abdellah University of Fez Fez Morocco Image Laboratory University of Moulay Ismail of Meknes Meknes Morocco

ISBN: (纸本)9783031262531

In a world dominated by digital technology, the internet of Things (IoT) has occupied our daily lives beyond our knowledge. It has established an ecosystem that connects multiple systems to provide intelligent performance in each task of our lifestyles and it has made everything and everyone connected to the internet. From wearables to smart cities and smart industries, our hobbies, our movements are facilitated by this technology that greatly improves our quality of life and increases our comfort. based on low-cost sensors, the IoT provides the possibility to render the several devices and objects around us addressable, identifiable, and locatable. Though the IoT brought infinite benefits, it creates several challenges, especially in security and privacy. These security challenges are complicated due to the variety of IoT protocols that are heterogeneous, serve various requirements, and are utilized in diverse application domains. However, because IoT is commonly deployed in hostile or remote environments and operate without monitoring, preventing attacks and protecting the privacy of sensitive collected data is critical for many IoT applications. This paper represents a detailed study of the general concepts on the security of IoT networks by application domains by describing their vulnerabilities, their security needs. The classification of the types of attacks on IoT networks by application domains is also discussed. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： internet of things

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An image retrieval approach based on feature extraction and self-supervised learning 2

An image retrieval approach based on feature extraction and ...

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2nd international conference on Distributed Computing and High Performance Computing (DCHPC)

作者： Kolahkaj, Maral Islamic Azad Univ Sousangerd Branch Dept Comp Engn Sousangerd Iran

ISBN: (纸本)9781665496728

Today, due to the development of technology and the advent of web 2.0 applications, different users prefer to do many of their personal tasks over the internet. Due to the huge amount of information on the web, retrieving the appropriate information for each user has become a challenging task. Content-based image retrieval is one of the most important research fields in digital image processing domain, which searches the similar images to the target image by extracting visual content from the query image. In this regard, many studies have been conducted to increase the accuracy of image retrieval systems. However, due to the explosive growth of storage resources and the lack of a responsible system for image retrieval, it is still considered as one of the most attractive fields of research. In this paper, a method is proposed that extracts the appropriate features using a hybrid method, and then searches the images that are similar to the target image. In this way, self-supervised learning approach is utilized to provide the most similar images. Experimental results based on the Corel dataset show that the accuracy of the proposed method has increased compared to the other methods.

关键词： image Retrieval feature extraction self-supervised learning frequent pattern mining

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