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Novel Lightweight Encryption Scheme based on Transformation Matrix and ChaCha20 Algorithm for Secure Snap Transmission

Novel Lightweight Encryption Scheme Based on Transformation ...

引用

2024 IEEE international conference on Intelligent systems and Advanced Applications, ICISAA 2024

作者： Sellam, V. Kumar, M. Ranjith Sudeesh Kumar, V. Mahendira Kumar, S. Tharun Kaarthik, G.K. Vellore Institute of Technology School of Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engg. Chennai India

ISBN: (纸本)9798331539948

A revolution for ultra-security in image transmission, due to the innovation and upgrade of cryptographic algorithms occurred during recent years. Proposed image encryption algorithm with ChaCha20 Our matrix-based shuffling not only improves image security but also adds to the complexity of data stealth. The scheme is made up of four main operations: Rearranging pixels in the image with a transform matrix, encryption using ChaCha20, decryption to get back the original image and finally descrambling it by applying inverse transformation changes. In this way, integrity is maintained and image privacy becomes secure. The provided transformation matrix makes scrambling with reversible operations and speed possible against all of the known standard attacks to which ChaCha20 encryption is subjected. The suggested method, led to a Peak signal Noise Ratio of 361.20 and zero Mean Squared Error indicating a top-quality reconstructed image concerning the original image. The study is raising the security alerts provided by ChaCha20, a relatively new but efficient and high-fidelity encryption solution that makes it an attractive option in today's increasingly digitalized environment where data security has become everyone's top priority. © 2024 IEEE.

关键词： signal to noise ratio

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Transformer based image Dehazing

Transformer based Image Dehazing

引用

international IEEE conference on signal-image Technologies and internet-based System

作者： Patricia L. Suárez Dario Carpio Angel D. Sappa Henry O. Velesaca ESPOL Polytechnic University Guayaquil Ecuador Computer Vision Center Barcelona Spain

This paper presents a novel approach to remove non homogeneous haze from real images. The proposed method consists mainly of image feature extraction, haze removal, and image reconstruction. To accomplish this challenging task, we propose an architecture based on transformers, which have been recently introduced and have shown great potential in different computer vision tasks. Our model is based on the SwinIR an image restoration architecture based on a transformer, but by modifying the deep feature extraction module, the depth level of the model, and by applying a combined loss function that improves styling and adapts the model for the non-homogeneous haze removal present in images. The obtained results prove to be superior to those obtained by state-of-the-art models.

关键词： image quality Adaptation models Computational modeling Computer architecture Transformers Feature extraction image restoration

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Design of a Gate Driver based-on E-mode p-GaN HEMTs Handling 650V/10A GaN Power Device 22

Design of a Gate Driver Based-on E-mode p-GaN HEMTs Handling...

引用

22nd IEEE Interregional NEWCAS conference (NEWCAS)

作者： Pozo, Nataly Procel, Luis-Miguel Trojman, Lionel Univ San Francisco Quito Inst Micro & Nanoelect IMNE Quito Ecuador Isep Lab Informat Signal & Image Elect & Telecommun L Paris France

ISBN: (纸本)9798350361766;9798350361759

This paper presents the design of a monolithically integrated Gallium Nitride (GaN)-IC gate driver based on Enhancement-mode p-GaN HEMT technology. The design uses 650V GaN-on-SOI Process Designing Kit (PDK). The design complexity of this technology lies in the absence of p-type devices. Consequently, Resistor-Transistor Logic (RTL) inverters and Push-Pull Buffers are introduced instead of a complementary logic. To effectively handle the power device (650V/10A) switching, the gate driver is sized to achieve 7V in the output. To validate the performance of the proposed gate driver, transistor-level and post-layout simulations are carried out considering an input signal of 2MHz. Turn-on/turn-off time (3.77ns/2.32ns), overall circuit efficiency (99.3%) and temperature variation effects are analysed.

关键词： Gate Driver Gallium Nitride Integrated Circuit IC Enhancement-mode p-GaN HEMT GaN-IC

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Distributed Honey pot based on Block chain 10

Distributed Honey pot Based on Block chain

引用

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

作者： Vinod, S. Pandi, C. Sheik Dhanveer Hussain, S. Pavithran, C. Arjun Sathish, M. Tejas, R.S. Vel Tech Multi Tech Dr. Rangarajan Dr.Sakunthala Engineering College Department of Cse Chennai Avadi India

ISBN: (纸本)9798350353068

Proactive protection policies must be activated in order to protect our networks, systems, and data assets. On the internet, millions of individuals as well as bots are waiting to attack vulnerable systems. Honeypot technology is a useful tool for attracting attackers and draining their resources. In contrast, the traditional static honeypot can be easily recognized thanks to anti-honeypot technology. By instance, most dynamic honeypots can imitate the real system in real time and let users engage with a surreptitious invader. The project's goal is to use the Blockchain technology to store port access data on a private chain and decentralize our system. With information that needs to be protected, many organizations and institutions have real-time cyber concerns that can be addressed by the system that is being built. Numerous uses of the blockchain already exist in other cyberspaces, such as cryptocurrency, etc. Numerous researchers are utilizing it to address a range of other data security-related issues. © 2024 IEEE.

关键词： Blockchain

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5th international conference on 3D Imaging Technologies—Multidimensional signal Processing and Deep Learning, 3DIT-MSP and DL 2023

5th International Conference on 3D Imaging Technologies—Mul...

引用

5th international conference on 3D Imaging Technologies—Multidimensional signal Processing and Deep Learning, 3DIT-MSP and DL 2023

ISBN: (纸本)9789819751808

The proceedings contain 59 papers. The special focus in this conference is on 3D Imaging Technologies—Multidimensional signal Processing and Deep Learning. The topics include: Digital Twin technology Approach based on the Hierarchical IDP Tensor Decomposition;modeling technology for Complex Dynamic Operating Environment of Power Grid based on Digital Twins;design of a Digital Twin Platform based on Distributed Computing and Resource Optimization Algorithms;deep Learning Network Optimization Combining 3D Imaging and Multidimensional signal Processing;time Series Prediction Application of Deep Learning in Multidimensional signal Processing;improving Abstractive Summarization with Graph Sequence Model;advancing Semantic Segmentation and Interpretation of 3D images Through Integrated Deep Learning and Natural Language Processing Techniques;children’s Toy Product Design based on Augmented Reality technology;based on the Neural Network Classification of Human Behavior Research;development of an Independent Adversarial Sample Detection Model, based on image Features;Visualization and Analysis of CNN Adversarial Training;video Violence Detection Method based on Multi-Feature and Graph Convolutional Network;Enhancing Realized Volatility Prediction: An Exploration into LightGBM Baseline Models;terminal Anomaly Discovery technology based on Service Behavior Deviation;An Efficient CRNN Model with the Multi-scale Feature Fusion for Text Recognition from Chinese Medical Reports;advancing Precision Drug Screening: Integrating Imaging technology and Artificial Intelligence for Novel Models;a Short Video Retrieval Method based on Spatio-Temporal Feature Fusion;spatial Data Transformation and Fusion Expression based on Geometric Algebra;EM2-YOLO: Lightweight Remote Sensing image Detection;Tower-Type Detection of UAV Aerial image based on YOLOV5 Network Model.

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A Neural Network-based Method for Eliminating Stripe Noise 4

A Neural Network-Based Method for Eliminating Stripe Noise

引用

4th IEEE international conference on Electronic Communications, internet of Things and Big Data, ICEIB 2024

作者： Qiu, Shuaihui Wang, Haitao Li, Junhua Chi, Mingwei Harbin Xinguang Optic-electronics Technology Co. Ltd. Harbin150028 China Geely University of China Chengdu641423 China

ISBN: (纸本)9798350360721

The uneven photoelectric response of the infrared imaging detector system due to the level of craftsmanship results in a fixed deviation in the response of each pixel to the incident light signal. The deviation appears as a stripe noise on the output image, seriously reducing image quality. Infrared images with stripe noise seriously affect application results, therefore the original details of the image need to be effectively preserved by removing all stripe noise artifacts and balancing real-time performance. We proposed a new method for removing stripe noise using the correlation between adjacent column signals to remove independent column stripe noise. Deep neural networks were used to implement this method, using the estimated noise output from the previous network iteration as the input to the next iteration of the network. This approach effectively reduced the search space for function approximation and allowed for effective training on larger datasets. This method can be used to estimate the stripe noise of infrared images and preserve scene details to a large extent. A large number of experimental results showed that the proposed research method has significant effects. © 2024 IEEE.

关键词： Iterative methods

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A Novel Three-Stage PolSAR Ship Detection Framework based on Deep Learning 2

A Novel Three-Stage PolSAR Ship Detection Framework based on...

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2nd IEEE international conference on signal, Information and Data Processing, ICSIDP 2024

作者： Xue, Junfan Yin, Junjun Yang, Jian University of Science and Technology Beijing School of Computer & Communication Engineering Beijing China Tsinghua University Department of Electronic Engineering Beijing China

ISBN: (纸本)9798331515669

Polarimetric Synthetic Aperture Radar (PolSAR) image ship detection is one of the crucial applications of PolSAR systems and holds significant research value. However, current deep learning-based polarimetric SAR image ship detection methods face challenges such as difficult post-processing and slow detection speed. To solve the above problems, we propose a novel Three-Stage Polarimetric SAR Ship Detection Framework based on deep learning. The main concept is to use a sliding window to extract image slice samples, then employ the Ship Coarse Location Module (SCLM) to identify slice samples containing ships. Next, the Polarization Feature Enhancement Module (PFEM) enhances the features of these slice samples to reduce the feature distance of pixels of the same category and increase the feature distance of pixels of different categories. Afterward, the enhanced slice samples are fed into the KAN Vision Transformer Detection Module (KVITDM) for patch-level detection. Finally, decision fusion is applied to combine the detection results generated from different initial sliding window positions. Experimental results show that the proposed method has good performance in different scenes and outperforms traditional and existing deep learning-based polarimetric SAR ship detection methods. © 2024 IEEE.

关键词： Deep learning

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Supervision system on internet advertising based on artificial intelligence technology 8

Supervision system on Internet advertising based on artifici...

引用

8th international conference on Information systems Engineering, ICISE 2023

作者： Qin, Dongyang Li, Zuyang Yan, Yu Wuhan Donghu University School of Literature and Law Wuhan China

ISBN: (纸本)9798350307009

With the rapid development of internet advertising, regulatory issues have become increasingly acute, and traditional regulatory methods have been failed to meet the changing needs of internet advertising. Therefore, this paper, based on artificial intelligence technologies such as natural language processing, image recognition, machine learning and data tracking, designs an internet advertising supervision system for strengthening supervision, improving recognition rates, establishing a unified database and data prediction system. This study is conducive to the optimization and upgrading of advertising supervision and the sound development of the internet advertising industry. © 2023 IEEE.

关键词： Marketing

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A YOLOv9-based Night-Time signal Light Detection Method in Carla Environment

A YOLOv9-Based Night-Time Signal Light Detection Method in C...

引用

2024 IEEE international conference on Unmanned systems, ICUS 2024

作者： Wan, He Ruan, Jiageng An, Weichuan Hao, Ruohan Beijing University of Technology Beijing China

ISBN: (纸本)9798350384185

With the continuous progress of machine learning technology, automatic driving perception technology is gradually becoming mature. As a guarantee of normal traffic operation, signal lights can effectively guide vehicles to pass correctly. The recognition of signal lights mainly relies on pure visual target detection technology. And the correct recognition of traffic signals can reduce the occurrence of traffic accidents and provide safety for unmanned driving. However, the recognition effect of small targets at night is poor, and is prone to misdetection and omission, etc. Therefore, the pure visual target detection technology for traffic signals still faces great challenges to accurately identify targets in special scenes. In this paper, the latest YOLOv9 image target recognition technology is used to improve the target detection ability in harsh environments. The advantages and disadvantages of different algorithms in low illumination environments such as night time are evaluated by using YOLOv8 and YOLOv9 target detection algorithms. Carla simulation environment was conducted to validate the recognition effectiveness of YOLOv9. The results show that the YOLOv9 algorithm is better at recognizing signal lights at night and the detection accuracy exceeds that of the previous versions of the series, ensuring the recognition accuracy of signal lights in different environments. © 2024 IEEE.

关键词： Traffic signals

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Towards reliable medical image analysis based on deep learning with XAI

Towards reliable medical image analysis based on deep learni...

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2023 international conference on image, signal Processing, and Pattern Recognition, ISPP 2023

作者： Hao, Jingbo School of Information and Artificial Intelligence Nanchang Institute of Science & Technology 998 Gezaoshan Ave. Honggutan District Nanchang China

ISBN: (纸本)9781510666351

Deep learning-based medical image analysis (DLB-MIA) has achieved great success hitherto, but is in urgent need of model interpretability. Explainable artificial intelligence (XAI) can be used for improving model interpretability and is expected to help in providing crucial trustworthiness in DLB-MIA. To expedite the utilization of XAI technology for reliable DLB-MIA, the current status of XAI methods and toolkits is introduced firstly. The application of XAI technology in DLB-MIA tasks is then discussed with close attention in respect of method taxonomies and adversarial robustness respectively. © 2023 SPIE.

关键词： Deep learning

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