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Research on Image Denoising Algorithm Based on Dictionary Learning 24

Research on Image Denoising Algorithm Based on Dictionary Le...

引用

3rd international conference on computer, Artificial Intelligence and control Engineering, CAICE 2024

作者： Lin, Deshu Cheng, Caifeng School of Computer and Information Engineering Guangdong Songshan Polytechnic Guangdong Shaoguan China

ISBN: (纸本)9798400716942

This paper proposes an image denoising algorithm based on dictionary learning, aiming to improve the effectiveness and performance of image denoising. Firstly, we construct a dictionary learning model by sparsely representing the image signal to better represent the noise and detail information in the image signal. Secondly, we use the dictionary learning algorithm to denoise the image by optimizing the atoms and their corresponding coefficients in the dictionary. Experimental results demonstrate that this algorithm has good effectiveness and performance in image denoising. © 2024 ACM.

关键词： Dictionary learning Image denoising Optimization algorithm Sparse representation

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An improved longitude correction algorithm for fisheye image edge distortion 3

An improved longitude correction algorithm for fisheye image...

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3rd international conference on Artificial Intelligence and computer Engineering, ICAICE 2022

作者： Ji, Xiu Yang, Huamin Han, Cheng Wang, Hexin Lang, Chaoyue School of Computer Science and Technology Changchun University of Science and Technology Jilin Changchun130022 China School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin132022 China

ISBN: (纸本)9781510663473

The fisheye camera is widely used in computer vision because of its large field of view. However, in optical theory, the large field of view is at the cost of distortion. It is impossible to obtain effective information directly from the fisheye image, so the original image needs to be distorted first to become a linear image without distortion. Aiming at the existence of traditional longitude correction in the image center, edge correction effect is different and edge distortion, introducing the repositioning center algorithm and stretch factor. Firstly, the effective area of fisheye image is obtained by row-by-row column scanning method. And then uses the repositioning center algorithm to obtain the new center and radius, according to the distortion principle to calculate the distortion principle. The mapping relationship between the target image and the original image is obtained, and finally modified by the bilinear interpolation method. Compared with the traditional longitude correction, the proposed algorithm can correct the fisheye image accurately and effectively, and improve the quality of correction. © 2023 SPIE.

关键词： Image enhancement

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Deep Learning with Histogram of Oriented Gradients- based computer-Aided Diagnosis for Breast Cancer Detection and Classification 3

Deep Learning with Histogram of Oriented Gradients- based Co...

引用

3rd international conference on Smart Data Intelligence, ICSMDI 2023

作者： Ponraj, Anitha Canessane, R.Aroul Sathyabama Institute of Science and Technology Sathyabama University Chennai India

ISBN: (纸本)9781665464871

In the modern era, cancer is a major public health concern. Breast cancer is one of the leading causes of death among women. Breast cancer is becoming the top cause of death in women worldwide. Early identification of breast cancer allows patients to receive proper treatment, improving their chances of survival. The proposed Generative Adversarial Networks (GAN) approach is designed to aid in the detection and diagnosis of breast cancer. GANs are deep learning algorithms that generate new data instances that mimic the training data. GAN is made up of two parts: a generator that learns to generate false data and a discriminator that learns from this false data. Furthermore, the histogram of oriented gradients (HOG) is utilized as a feature descriptor in image processing and other computer vision techniques. Gradient orientation in the detection window and region of interest is determined by the histogram of oriented gradients descriptor approach. Using an image dataset and deep learning techniques, the proposed research (GAN-HOG) aims to improve the efficiency and performance of breast cancer diagnosis. The deep learning method is used here to analyze image data by segmenting and classifying the input photographs from the dataset. Unlike many existing nonlinear classification models, the proposed method employs a conditional distribution for the outputs. The proposed model GAN-HOG had an accuracy of 98.435%, a ResNet50 accuracy of 87.826%, a DCNN accuracy of 92.547%, a VGG16 accuracy of 89.453%, and an SVM accuracy of 95.546%. © 2023 IEEE.

关键词： Classification (of information)

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A control-Based Model of IPMSM with Hysteresis Current control PWM 3

A Control-Based Model of IPMSM with Hysteresis Current Contr...

引用

3rd IEEE international Maghreb Meeting of the conference on Sciences and Techniques of Automatic control and computer Engineering, MI-STA 2023

作者： Alrimali, Abdulsalam Ali Aljehaimi, Akrem Mohamed Misurata University Electrical and Electronic Engineering Department Misurata Libya

ISBN: (纸本)9798350319897

This paper presents an electric circuit model of an IPMSM which is based on the 90-degree torque angle vector control method. The electrical and mechanical characteristic equations of an IPMSM are modeled with one electric circuit. The model is simple yet accurate. It is tested for speed control with the hysteresis current control pulse width modulation (HCC PWM) technique. This method of controlling the current is one of the most popular methods due to its simplicity and ease of implementation. The HCC PWM is well explained in this paper using op-amps and SR flip flops. The motor model and its control are simulated in Power Simulation environment and show good and fast performance in tracking the commanded speed and load torque. © 2023 IEEE.

关键词： Pulse width modulation

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Mask Removal Algorithm Using GAN Based Models 3

Mask Removal Algorithm Using GAN Based Models

引用

3rd international conference on Electronic Communication and Artificial Intelligence, IWECAI 2022

作者： Ye, Liqin Information And Computer Science University of California Irvine United States

ISBN: (纸本)9781665479974

Recent deep learning network models have been enhanced dramatically to generating images, removing objects, and improving resolution of images. From a pragmatic purpose, we must utilize it to solve some occurrent real-world problems, for example, facial mask removal. People's frequency of wearing masks has increased rapidly due to the pandemic. The objective of this research is to remove the facial mask on face images using generative adversarial network, or GAN, model. We set up three different training process to explore how to remove a facial mask effectively. By comparing among these training processes, we find out that adding an additional binary mask segmentation is conducive to both the generation quality of face region other than mask and inpainting quality of missing mask region. Moreover, our customized GAN model outperforms the Pix2Pix GAN model in both region inpainting. © 2022 IEEE.

关键词： computer vision

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Research on Cloud Computing Security Technology under computer Big Data Network 3

Research on Cloud Computing Security Technology under Comput...

引用

3rd IEEE international conference on Electronic Technology, Communication and Information, ICETCI 2023

作者： Zhou, Qiang Ou, Wenjisi Zheng, Wei Jiangxi Vocational and Technical College of Information Application Department of Computer Technology Nanchang330043 China

ISBN: (纸本)9798350398410

In this paper, a parameter estimation-based security access integration method for computer network smart data is designed. The scale of data clustering is determined according to the distribution and evolution characteristics in the clustering process. Finally, this paper proposes a new security access control scheme. The system uses local abnormal factors to judge non-safe data and eliminates abnormal data according to the judgment results. The system sorts and outputs the factors that may have abnormal data points in the current time window. The Java object-oriented programming language is used. The network attack will be divided into fuzzy measures of DoS attack and non-DoS attack, and finally, the results of various attacks will be displayed. Simulation results show that this paper's entire access control scheme is highly feasible. The proposed algorithm has a high recognition rate. © 2023 IEEE.

关键词： Access control

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Tracking control of Adaptive MIMO Nonlinear Systems Using Fuzzy Logic control and Extreme Learning Machine 3

Tracking Control of Adaptive MIMO Nonlinear Systems Using Fu...

引用

3rd IEEE international Maghreb Meeting of the conference on Sciences and Techniques of Automatic control and computer Engineering, MI-STA 2023

作者： Abdusamad, Abdunaser Aburakhis, Mohamed Azzaytuna University Tarhuna Libya The High Institute for Engineering Techniques Tripoli Libya

ISBN: (纸本)9798350319897

This paper presents two different control techniques for tracking an output reference signal to any unknown parameter vector. The two techniques are implemented to perform adaptive control approach for MIMO nonlinear systems. The first technique is fuzzy logic control (FLC). In FLC the appropriate approximation can be established based on the number of membership functions. The designed FLC-based controller will make the system outputs follow the reference input signals. The second technique is called an extreme learning machine (ELM). The ELM is a newly developed learning algorithm for generalized single-hidden-layer neural networks, where the output weights are calculated computationally and the hidden node parameters are produced at random. This work focuses on decreasing the control energy in conjunction with reducing the overall MSE, which makes the output tracks the reference input smoothly. © 2023 IEEE.

关键词： Membership functions

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Markov Process Based Reliability Study of Private Network System 3

Markov Process Based Reliability Study of Private Network Sy...

引用

3rd IEEE international conference on Power, Electronics and computer Applications, ICPECA 2023

作者： Li, Qingyu Liu, Jianming Huang, Yao Liu, Zhen He, Wanbing Zhou, Longxiang China Satellite Maritime Tracking and Control Department Wuxi China

ISBN: (纸本)9781665472784

A private network refers to a network dedicated to some services with high confidentiality requirements, such as an internal private network of an enterprise or a private network of a confidential state department, whose security and reliability is an issue that cannot be ignored. In this paper, we analyze the general private network composition and study the evaluation method of network system reliability based on Markov process from the current situation of the network, and consider the influence on the reliability of the elements that generally have in private networks, such as routers, firewalls, encryption equipment, switches and transmission links, and give the evaluation model to derive the network system reliability. © 2023 IEEE.

关键词： Markov processes

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Improved Adaptive Fairness-Indexed Technique for Congestion control in Underwater Networks 3

Improved Adaptive Fairness-Indexed Technique for Congestion ...

引用

3rd international conference on Communication, control, and Intelligent Systems, CCIS 2024

作者： Balaji Vijayan, V. Manojee, K.S. Mohammed Ibrahim, M. Nivethitha, R. Thamaraimanalan, T. Ranjithkumar, M. Hkbk College of Engineering Department of Information Science and Engineering Karnataka Bengaluru India Kongunadu College of Engineering and Technology Department of Information Technology Tamilnadu Trichy India Sona College of Technology Department of Computer Science and Engineering Tamilnadu Salem India K.S.R. College of Engineering Department of Computer Science and Engineering Tamilnadu Thiruchengode India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamilnadu Coimbatore India Knowledge Institute of Technology Department of Computer Science and Business Systems Tamilnadu Salem India

ISBN: (纸本)9798331528201

In underwater acoustic sensor networks (UASNs), effective congestion control is essential for ensuring reliable data transmission and resource efficiency, especially in the face of challenging environmental factors such as water velocity, pressure, temperature, and salinity. This paper introduces a novel Adaptive Fairness-Indexed Congestion control (AFICC) mechanism that employs Q-learning to dynamically adjust transmission rates among nodes, ensuring equitable resource distribution and minimizing congestion. Our simulations, conducted with 100 nodes at depths of up to 1000 meters, reveal that AFICC significantly outperforms two existing algorithms: Rate-Based Congestion control (RBCC) and Energy-Aware Congestion control (EACC). Specifically, AFICC achieved a Packet Delivery Ratio (PDR) of 93%, compared to 85% for RBCC and 80% for EACC. Additionally, the End-to-End Delay was reduced to an average of 150 milliseconds with AFICC, while RBCC and EACC recorded delays of 220 milliseconds and 210 milliseconds, respectively. AFICC also demonstrated a 25% improvement in Energy Efficiency over EACC and maintained a Jain's Fairness Index of 0.92, surpassing the 0.82 and 0.78 indices achieved by RBCC and EACC, respectively. These results confirm that AFICC offers a robust framework for congestion control in UASNs, effectively adapting to fluctuating underwater conditions while ensuring reliable communication and equitable resource allocation. © 2024 IEEE.

关键词： Energy efficiency

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Potato Plant Leaf Disease Detection and Recognition Using R-CNN Model 3

Potato Plant Leaf Disease Detection and Recognition Using R-...

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3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Balakrishnan, Minu Raja, M. Kumar, P.Vinoth Subramaniam, Suvitha Latha, R. Sri Eshwar College of Engineering Department of Information Technology Coimbatore India Cmr Institute of Technology Department of Computer Science and Engineering Bengaluru India Chennai Institute of Technology Depatment of Mechatronics Chennai India Kpr Institute of Engineering and Technology Department of English Coimbatore India Chennai India

ISBN: (纸本)9798350322842

A smart farming system that makes use of the appropriate infrastructure is an example of a new technology that aids in the improvement of the quality and quantity of the country's agricultural goods, such as potatoes. Diseases cannot be prevented since developing potato plants requires consideration of factors such as the environment, soil, and quantity of sunlight. Deep learning has resulted in recent advances in computer vision. These advancements have enabled Potato to utilize a camera to diagnose disease. This study developed an effective new method for detecting infections in potato plants. Yukon Gold, a kind of potato, served as the trial subject. Specifically, Alternaria, Blackleg, and Target Spot were targeted for detection using this method. Using 5,615 images of diseased and healthy Potato plant leaves taken in controlled environments, deep CNN is trained to identify between the presence and absence of three illnesses. To identify the Potato illnesses affecting the tracked plants, the system used a CNN. Anomaly detection using the F-RCNN-trained model achieved a confidence score of 80%, while illness identification using the Transfer Learning model achieved an accuracy of 97.85%. The effectiveness of the automated image-capturing system for diagnosing diseases in Potato plant leaves was determined to be 93.33%. © 2023 IEEE.

关键词： Convolutional neural networks

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