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JDart-Hea: Hybrid Evolutionary Algorithm in Dynamic Symbolic Execution 8

JDart-Hea: Hybrid Evolutionary Algorithm in Dynamic Symbolic...

引用

8th international conference on signal and image Processing, ICSIP 2023

作者： Wu, Zihao Liu, Yuxuan Qin, Zhongyuan Sun, Xin Wang, Wen Southeast University School of Cyber Science and Engineering Nanjing211189 China State Grid Zhejiang Electric Power Co. Ltd. Research Institute Internet Technology Center Hangzhou310063 China State Grid Zhejiang Electric Power Co. Ltd. Internet Department Hangzhou310063 China

ISBN: (纸本)9798350397932

Symbolic execution is a widely-used program analysis technique. It collects and solves path conditions to guide the program traversing. However, due to the limitation of the constraint solvers, it is difficult to apply symbolic execution on programs with complex path conditions, like nonlinear constraints. In this paper, we propose a dynamic symbolic execution method based on the assistance of hybrid evolutionary algorithm, JDartHea, to solve this problem. We firstly generate an initial population according to the collected path constraints, and then perform crossover and mutation operations to generate new offspring. Secondly, we improve the quality of offspring using simulated annealing algorithm. Population iterations are continuously performed to produce solution that satisfy the path constraints. The experimental results show that JDart-Hea can achieve better coverage on complex programs. © 2023 ieee.

关键词： Simulated annealing

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Enhancing Retinal image Analysis through Deep Learning-based Blood Vessel Segmentation 10

Enhancing Retinal Image Analysis through Deep Learning-Based...

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10th international conference on Communication and signal Processing, ICCSP 2024

作者： Anandha Prakash, S. Denisha, M. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350353068

Blood vessel segmentation is the process of identifying and describing blood vessels from retinal pictures, which is required for quantitative analysis and anomaly detection. However, the accuracy and efficiency of the current blood vessel segmentation approach are frequently inadequate. By using deep learning techniques for blood vessel segmentation, it aims to increase retinal analysis's effectiveness and precision. based on our findings, UNet outperforms FCN (94.02%) and SegNet (95.89%) in terms of accuracy, reaching a whopping 98.89%. UNet is well-suited for biomedical image segmentation tasks because to its symmetric encoder-decoder structure and skip connections, which allow for good feature extraction and spatial preservation. A user interface is used to provide the segmented image when a retinal picture is injected. It helps in the creation of computer-aided diagnostic tools for retinal diseases and showing in the application by providing a trustworthy method for correct blood vessel segmentation, which is essential for early detection and monitoring of various ocular pathologies. © 2024 ieee.

关键词： User interfaces

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Superpixels-based Segmentation and Classification using Modified Fuzzy C-Means Clustering and Color Features 10

Superpixels-based Segmentation and Classification using Modi...

引用

10th international conference on Communication and signal Processing, ICCSP 2024

作者： Akilandeswari, A. Alexander, T.Jerry Ganesan, P. Janakiraman, V. Akila, D. Sajiv, G. Simats Saveetha School of Engineering Tamilnadu Chennai India Swarnandhra College of Engineering and Technology Dept. of ECE AP Narsapur India Vidya Jyothi Institute of Technology Dept. of Electronics and Communication Engineering Hyderabad India Simtas Saveetha College of Liberal Arts and Sciences Dept of Computer Applications Chennai India

ISBN: (纸本)9798350353068

This work provides a novel method of image analysis that combines color characteristics with Modified Fuzzy C-Means (MFCM) clustering by means of superpixels based segmentation and classification. The suggested technique improves the effectiveness and precision of image segmentation by utilizing the advantages of Superpixel representation. A hierarchical framework that minimizes computing complexity and maintains spatial coherence is used to construct superpixels. After that, the MFCM clustering technique is used to split superpixels more precisely, improving border adherence and adaptability while managing intricate image structures. Furthermore, color features are included to capture fine details in every cluster. The use of color information improves the suggested method's ability to discriminate between objects that have similar textures but differing chromatic properties. The experimental findings demonstrate how well the suggested framework can handle problems pertaining to segmentation and classification tasks. © 2024 ieee.

关键词： image analysis

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Subband Fusion based W-GAN for Biomedical image Super-Resolution 4

Subband Fusion Based W-GAN for Biomedical Image Super-Resolu...

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4th ieee international conference on Applied Electromagnetics, signal Processing, and Communication, AESPC 2024

作者： Sahoo, Dharitri Tanaya Patnaik, Suprava Sabut, Sukant Kumar School of Electronics Engineering Kalinga Institute of Industrial Technology Odisha Bhubaneswar India

ISBN: (纸本)9798331528171

Finding tiny abnormality, textual deviation and other details are crucial for diagnosis in medical images. imageprocessing resulting into super-resolution high quality medical imaging helps clinicians for accurate analysis. Medical images are acquired using different modalities and developing a common method for biomedical images of different modalities is immensely challenging. Popular biomedical imaging modalities, are CT, PETCT, MRI, and ultrasound etc. Super-resolution achieved through iinterpolation-based techniques are often reported for its quality to computational complexity trade-off. Learning based upsampling using transposed convolution and sub-pixel layers are anticipated overcome the shortcomings of interpolation method. SRGAN and ESRGAN models have recently become popular generative models for their performance in superresolution. This paper proposes a novel Wavelet GAN (W-GAN). Proposed method hasbeen tested on medical images from different modalities. Results are compared for the conventional interpolation method, ESRGAN, and the proposed MS-SRGAN. Though Deep Super resolution itself is a multiscale approach, the innovation of the proposed work is decomposing the input low resolution image into subbands by using discrete wavelet transform. Therefore, deep representation along with separated complementary textual and edge features has been proven efficient. Training of GAN models gets streamlined and efficient for images of different resolution and having similar high frequency information. Reconstructed SR image obtained from inverse discrete wavelet transform has better clarity than ground truth image. The parameters like Peak signal to noise ratio (PSNR) and structural index similarity (SSIM) are used to quantify the improvement in super resolute images. In our proposed method we observed that the quantitative result is better in terms of PSNR and SSIM as compared to state of art methods. © 2024 ieee.

关键词： signal to noise ratio

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A New Feature Information Enhancement Method based on Matrix Transformation and Cubic Spline Interpolation for Train Positioning 11

A New Feature Information Enhancement Method Based on Matrix...

引用

11th Joint international Information technology and Artificial Intelligence conference, ITAIC 2023

作者： Meng, Yang Feng, Jingxiao Luoyang Normal College Department of Physics & Electronic Information Luoyang China

ISBN: (纸本)9798350333664

The output accuracy of train video odometry depends on high-quality images. When preprocessing the original image, the common methods tend to focus on the estimation of the interpolation data, and the feature information of the source image cannot be well preserved. Aiming at the loss of feature information after source image preprocessing, an image feature information enhancement method based on matrix transformation and Cubic Spline Interpolation is proposed. The method firstly transforms the original image matrix through singular value decomposition, and uses the cubic spline method to interpolate the transformed image matrix, and reconstructs a high-resolution enhanced image from the interpolated matrix, The feature information of the source image is preserved in the process of image preprocessing. The simulation results show that compared with other standard interpolation methods, the enhanced image obtained by the proposed algorithm can better retain the edge structure and texture information, and obtain better image enhancement effect. based on the quantification criteria for objective evaluation of image quality, the enhanced image can improve the signal-to-noise ratio and peak signal-to-noise ratio, and at the same time, the root mean square error is also lower than other standard interpolation algorithms. © 2023 ieee.

关键词： cubic spline Interpolation image feature enhancement matrix transformation train video odometer

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Sensing the Future: Challenges and Trends in IoT Sensor technology 4

Sensing the Future: Challenges and Trends in IoT Sensor Tech...

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4th international conference on Innovative Practices in technology and Management, ICIPTM 2024

作者： Rajora, Ricky Rajora, Aarju Sharma, Bhanu Aggarwal, Priyanshi Thapliyal, Siddhant Chitkara University Institute of Engineering and Technology Chitkara University Punjab 140401 India Chandigarh Engineering College Department of Computer Science & Engineering Mohali India Computer Science & Engineering Graphic Era Hill University Uttarakhand Dehradun248002 India Computer Science & Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350307757

In the ever-evolving world of the internet of Things (IoT), sensors play a crucial role as essential parts, linking and merging different elements to create a smart worldwide network. This study examines the crucial function of sensors in the internet of Things (IoT) ecosystem, where they serve as sensory organs that translate the intricacies of the physical world into a uniform language for networked systems. The main emphasis is on their ability to gather and evaluate factual data from the surroundings, which impacts the performance and effectiveness of IoT applications in many industries. This research explores the uses and relevance of many sensor types, including temperature, humidity, proximity, motion, gas, biometric, vibration, image, infrared (IR), occupancy, water quality, pressure, light, and sound sensors. Although sensors contribute to the improvement of automation, control systems, and predictive analysis, they also give rise to issues in terms of security, interoperability, and power efficiency. The convergence of edge computing, artificial intelligence (AI), quantum sensors, 5G connection, and improved sensor integration has the potential to bring about significant changes in the internet of Things (IoT) environment. The study provides a thorough survey of sensor applications in several domain including healthcare, agriculture, smart cities, and automotive sectors. Lastly, it examines the difficulties and potential opportunities, highlighting the vital significance of sensors in influencing the future of interconnection and data-driven decision-making. © 2024 ieee.

关键词： internet of things

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Inpainting of Remote Sensing Sea Surface Temperature image with Multi-scale Physical Constraints

Inpainting of Remote Sensing Sea Surface Temperature image w...

引用

ieee international conference on Multimedia and Expo (ICME)

作者： Wei, Qichen Zuo, Zijie Nie, Jie Du, Jiahao Diao, Yaning Ye, Min Liang, Xinyue Ocean Univ China Coll Informat Sci & Engn Qingdao Peoples R China

ISBN: (纸本)9781665468916

Sea Surface Temperature (SST) is a significant environmental factor indicating marine revolutions, which is popularly applied in the meteorological forecasting and fishing industry. Due to the limited sensing ability and occlusion caused by clouds or ice, it is difficult to obtain complete SST data. Compared to traditional interpolation-based methods which refill missed data only referred to current SST data, inpainting-based methods have been carried out with the advantage of using historical SST images to train Generative adversarial Networks (GAN) by terms of considering SST data reconstruction task as an image inpainting task. However, different from common inpainting tasks constrained by semantics, the SST image is a scientific data visualization image without semantics but physical constraints. To address this problem, this paper proposes a multi-scale inpainting GAN-based neural networks to guarantee the physical constraint and realize reasonable SST image reconstruction. The proposed framework mainly contains two modules including the Average Estimation Module (AEM) to realize a global constraint so as not to generate excessive deviation, and the Multi-scale Anomaly Decouple Module (MSADM) to preserve data specificity of current SST image from well-designed multi-scale and decoupled perspectives. Finally, a post-fusion module concatenates the "average" and "specificity" features together to accomplish our multi-scale physical constraints SST image inpainting task. Sufficient experiments have been carried out to verify the effectiveness and physical consistency compared with prior SOTA methods applied to the public AVHRR Pathfinder SST dataset.

关键词： sea surface temperature deep learning generative adversarial network physical constraint multi-scale

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Fundus image Enhancement Using Hybrid Deep Learning Approaches 1

Fundus Image Enhancement Using Hybrid Deep Learning Approach...

引用

1st ieee international conference on Innovations in High Speed Communication and signal Processing, IHCSP 2023

作者： Vijai Shree, S. Harine, M.S. Arun Kumar, S. Gopinath, P. Sasikala, S. Kumaraguru College of Technology Department of Electronics and Communication Engineering Coimbatore India School of Electronics Department of Communication Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350345957

Imaging of the retinal fundus plays a main role in retinal disease diagnosis. Intricate information in retinal fundus images, such as small vessels, microaneurysms, and effusions, may appear unclear due to border artifacts, abrupt changes in colour levels, and uneven illumination which will result in poor diagnosis of retinal diseases. The existing retinal image enhancement methods mostly address either the vessel contrast or uneven illumination. Further preserving the edge features along with enhancing the contrast as well as adjusting the illumination is addressed in the proposed method. First, Haar Wavelet transform is applied to the retinal images followed by gamma adjustment to achieve a uniform and an improved illumination. Then the images are processed using a CofE-Net architecture with RSA and LQA modules to obtain an image along with basic background information. The image containing the background basic information is then merged with the original image to obtain a contrast improved fundus image. The proposed method achieves better results based on visual assessment as well quality parameters like PSNR, structure similarity measure, Enhancement measurement error and Edge based Contrast measurement. © 2023 ieee.

关键词： Wavelet transforms

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Design of LEO-MIMO Precoding Update Algorithm based on Time Sliding Window 5

Design of LEO-MIMO Precoding Update Algorithm Based on Time ...

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5th international conference on Electronic Communication and Artificial Intelligence, ICECAI 2024

作者： Hu, Zewei Zheng, Lin Yang, Chao School of Information and Communication Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin541004 China

ISBN: (纸本)9798350386943

In the Low Earth Orbit (LEO) satellite internet of Things (IoT), there is a significant imbalance in the distribution of ground terminal locations. Under a fixed beam regime, this leads to scenarios where satellite network capacity is simultaneously overloaded and underutilized, resulting in low capacity utilization rates for LEO satellite-to-earth communications. Distributed LEO-MIMO (Low Earth Orbit-Multiple Input Multiple Output) can alleviate overloads and distribute capacity by extending the satellite beam coverage area. However, unlike terrestrial distributed MIMO, which faces co-channel interference issues, distributed satellite networking in LEO systems also deals with the heterogeneity of Doppler shifts across different satellite links. To address these issues, a distributed LEO-MIMO linear precoding update algorithm is proposed. This paper, utilizing spectral graph theory, reduces the problem of constructing a single-frame precoding matrix to a graph signal correlation calculation problem. We introduce a precoding solving algorithm based on a time sliding window, which enhance data processing efficiency and reduce communication latency. © 2024 ieee.

关键词： Satellite links

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A Smart NIDS Design for SDN-based Cloud IoT Network using Optimal Path Selection and RNN 9

A Smart NIDS Design for SDN-based Cloud IoT Network using Op...

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9th international conference on signal Processing and Communication, ICSC 2023

作者： Kumar, Narendra Aeron, Anurag Dawar, Ishaan Kumar, Santosh Department of Computer Science and Engineering Amity University Jharkhand Ranchi India School of Computing DIT University Dehradun Dehradun India Computer science and Information Technology Siksha 'O' Anusandhan University Bhubaneshwar India

ISBN: (纸本)9798350383201

The internet of Things (IoT) has greatly increased the possibility for developing intelligent connections and applications in many facets of daily life. Traditional security solutions frequently fail to solve security issues in cloud-based IoT systems, proving ineffective and insufficient. Software Defined Networking (SDN), which effectively detects and monitors network security vulnerabilities thanks to its programmable characteristics, presents a viable solution to these problems. In order to strengthen computer systems and fend off threats to network security, machine learning (ML) techniques have recently been included into SDN-Network Intrusion Detection systems (NIDS). Deep learning (DL), one of these cutting-edge ML techniques, has become more popular in the SDN environment. For SDN-based cloud IoT networks, this paper design an efficient path selection system in this study utilizing DL approaches. The strategy comprises employing a Recurrent Neural Network (RNN) to identify threats and a selection mechanism to pick the best route for data delivery. This study performed tests on two datasets, KDD and UNSWNB15, and the results show that the suggested RNN outperformed existing DL methods by achieving an amazing accuracy ranging from 91% to 95% on both datasets. © 2023 ieee.

关键词： internet of things

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