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Comparative Evaluation of Various Wavelet Techniques for image Compression

Comparative Evaluation of Various Wavelet Techniques for Ima...

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international conference on Applied Electromagnetics, signal Processing and Communication (AESPC)

作者： Hidayat Un Nisa Samrah Mehraj Shabir A. Parah dept. of Electronics and Instrumentation Technology University of Kashmir Srinagar India

ISBN: (数字)9798331528171

ISBN: (纸本)9798331528188

image compression is a critical component of digital systems, as it reduces the file size of images while preserving quality, thereby facilitating minimal storage requirements and seamless transmission. Wavelet transforms have emerged as powerful tools in image compression for providing both time and frequency information, making them particularly effective in capturing essential image features. Despite numerous advancements, there is still significant demand for image compression techniques that achieve higher compression ratios with minimal loss of quality. This paper presents a comprehensive analysis of various lossy image compression techniques, focusing on the strengths of wavelet-based methods. Techniques such as Discrete Wavelet Transform, Lifting Wavelet Transform, and Integer Wavelet Transform are examined in detail, with key metrics including Peak signal-to-Noise Ratio (PSNR), compression ratio, and time efficiency analyzed to provide a thorough understanding of each technique's effectiveness. The timing analysis is particularly crucial for assessing the practical applicability of these methods in real-world scenarios. This study aims to offer valuable insights for developing more advanced image compression methods that meet the growing demands of modern data management systems.

关键词： image quality Measurement Time-frequency analysis image coding PSNR Wavelet domain Wavelet analysis Real-time systems Discrete wavelet transforms Timing

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Uncertainty Quantification based on Gaussian Processes for image Segmentation Tasks

Uncertainty Quantification Based on Gaussian Processes for I...

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Mobile internet, Cloud Computing and Information Security (MICCIS), international conference on

作者： Bing Gao Rui Chen Tingting Yu Beijing Sunwise Information Technology Ltd Beijing Institute of Control Engineering Beijing China

ISBN: (数字)9798350389906

ISBN: (纸本)9798350389913

Over the past several years, deep neural networks have permeated many fields of science research and have become an essential part of real-world applications. However, when the model encounters a test sample with uncertainty, compared with the distribution of the training set data, it is crucial for users to determine which of the model’s predicted outputs for those test data are trustworthy and which are not, rather than being forced to give an untrustworthy prediction output. To this end, it is necessary to evaluate the model uncertainty, which enables to improve utilization of the model prediction results, especially some safety-critical systems. For image segmentation tasks of safety-critical systems, this paper proposes an uncertainty quantification method based on Gaussian processes to evaluate the trustworthiness of the output predicted by neural network models for the given input, so as to facilitate the selection and optimization of segmentation models and provide the model interpretability. The simulation results indicate that for the pixel error rate of each image, this method can give the confidence interval of the predicted output and the stability of the predicted output, achieving the objective of quantifying and understanding the confidence level of deep learning models.

关键词： Training image segmentation Uncertainty Computational modeling Simulation Gaussian processes Predictive models

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image Cryptography Scheme based on Improvement Chaos Logistic Function, Chaos Arnold Cat Map and Gingerbread Man Process

Image Cryptography Scheme Based on Improvement Chaos Logisti...

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Artificial Intelligence and Mechatronics systems (AIMS), international conference on

作者： Mohammed Mahdi Hashim Abdul Razzaq Jabr Almajidi Raed Khalid Ibrahim Bashar I. Jasem Mohammed Ayad Saad Abdullah A. Nahi Ministry of Higher Education and Scientific Research Baghdad Iraq AL-Esraa University Baghdad Iraq Department of Medical instruments engineering Al-Farahidi University Baghdad Iraq Department of Computer Technology Engineering Al-Hadba University College Mosul Iraq Department of Medical Instrumentations Technique Engineering Jannat Iraq College Ramadi Iraq Department of Information Technologies Management Technical College Southern Technical University Basrah Iraq

Recently, information security has emerged as a crucial aspect of information technology and communication considering the internet's proliferation. The development of image cryptography emerged as a method to enhance the confidentiality of communication. In order to achieve these aims, a framework comprising three primary phases has been developed for the purpose of image cryptography. During the first phase, a fresh key generator was developed, incorporating a substantial degree of randomness by utilizing several chaotic maps and the suggested Improvement Chaos Logistic function (ICLF). In the second phase, the proposed method involves the introduction of a confusion process. This phase entails the sorting of the key generator in ascending order, followed by the permutation of the image pixels depending on the sorting key. Finally, the diffusion phase is described, which involves the utilization of the Gingerbread Man Method (GGM), Arnold Cat Map (ACM) transform, and XOR operation. based on the findings, it has been confirmed that the suggested image cryptography method has a better result in the field of cryptography when compared with existing methods.

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Real-Time Object Detection in Overexposed images based on YOLOv5

Real-Time Object Detection in Overexposed Images Based on YO...

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Electronic technology, Communication and Information (ICETCI), IEEE international conference on

作者： Leyang Wang Jiaqi He Junhao Chen Wuhan University of Technology Wuhan China

ISBN: (数字)9798350361643

ISBN: (纸本)9798350361650

In the context of rapid advancements in the fields of the internet of Things (IoT) and artificial intelligence (AI), edge computing technology and its devices have undergone significant development and wide application, leading to the increasingly common deployment of lightweight neural networks on these devices. Despite this, research on real-time object detection tasks on edge computing devices under conditions of poor image quality remains relatively scarce. This is primarily because real-time object detection requires consideration of the computational power limitations of edge computing devices, making it challenging to satisfy both detection quality and speed simultaneously. In response to this challenge, this study proposes a YOLOv5 object detection method based on the Jetson Orin Nano edge computing device, aimed at addressing overexposure issues to ensure that detection accuracy is maintained while effectively solving overexposure correction problems.

关键词： YOLO Performance evaluation Accuracy image edge detection Computational modeling Lighting Real-time systems

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A Novel CNN-based IoT System Architecture for Real-Time Detection and Prevention of Animal Intrusion in Farmland 4

A Novel CNN-Based IoT System Architecture for Real-Time Dete...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Ashokkumar, C. Kumar, M. Arun Krishnan, R. Santhana Priya, S. Mohanap Narayanan, K. Lakshmi Julie, E. Golden School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Chennai India PSNA College of Engineering and Technology Department of Information Technology Dindigul India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Scad Polytechnic College Department of Computer Science and Engineering Cheranmahadevi India Francis Xavier Engineering College Department of Electronics and Communication Engineering Tirunelveli India Anna University Regional Campus Department of Computer Science and Engineering Tirunelveli India

ISBN: (纸本)9798350300888

Animal intrusion is a significant challenge for farmers, causing extensive crop damage, human injuries, and substantial financial losses. Traditional animal movement monitoring and surveillance methods alone are insufficient to provide a permanent solution. To solve this problem, a novel system architecture that combines the latest convolutional neural network (CNN) algorithm and internet of Things (IoT) technology is proposed. Our proposed system architecture integrates various components, including a Raspberry Pi as a central processing unit, cloud storage for efficient data management, and a GSM module for instant alert generation. To train the CNN algorithm, a comprehensive and diverse Animal Dataset consisting of various animal species commonly found in farmland areas is curated. The dataset encompasses a wide range of annotated images, enabling the CNN algorithm to accurately identify and classify animals. The Raspberry Pi serves as the core of the system, responsible for real-time image processing and analysis. Utilizing the power of the CNN algorithm, the Raspberry Pi processes the captured images from strategically placed surveillance cameras. When an animal intrusion is detected, the system promptly generates an alert via the integrated GSM module, providing immediate notifications to farmers and relevant authorities. Furthermore, the system leverages cloud storage to store and manage the collected data, facilitating easy access and retrieval for analysis and system improvement. This cloud-based approach enables scalability, allowing the system to handle large amounts of data efficiently. By integrating the CNN algorithm, IoT, Raspberry Pi, cloud storage, and GSM module, a comprehensive and robust framework for real-time detection and prevention of animal intrusion is provided. The system's ability to swiftly identify and alert farmers and authorities about potential threats minimizes crop damage, ensures human safety, and significantly reduces financial l

关键词： signal detection

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Photography Style Analysis using Convolutional Neural Networks

Photography Style Analysis using Convolutional Neural Networ...

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

作者： Michael Zouros Theodoros Giannakopoulos University of Piraeus Piraeus Greece Multimedia Analysis Group (MagCIL) Computational Intelligence Lab NCSR Demokritos Athens Greece

In this paper, we present a novel dataset of photographs annotated in terms of the respective image aesthetics. We also examine the ability of Convolutional Neural Networks (CNNs) to distinguish between the adopted photography style classes. In particular, we have defined five photography style classification tasks, related to the following aesthetic attributes: Color, Depth of Field (DoF), Palette, Composition and Type. We then followed an annotation procedure using on a set of 1832 photos selected from the Unsplash dataset. Multiple annotators have also been used, in order to measure inter-annotator agreement. As soon as the dataset was compiled, we trained and evaluated a Residual Neural Network (ResNet50). The experimental results prove that, despite the imbalanced dataset, our model was able to achieve acceptable classification results. The dataset is openly provided, along with the trained models and Python code to use them.

关键词： Photography Codes image color analysis Annotations Convolutional neural networks Task analysis Residual neural networks

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Single image Deraining based on Denoising Diffusion Implicit Models

Single Image Deraining Based on Denoising Diffusion Implicit...

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international conference on Fuzzy systems and Knowledge Discovery (FSKD)

作者： Luyao Chang Zhi Zhang College of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

To address the challenges of prolonged sampling time and the limitation to process specific image sizes in current image deraining research, we propose a novel single image deraining approach based on denoising diffusion implicit models. The method adopts a variant of the U-Net fully convolutional network architecture based on SR3, concatenating rainy images of arbi-trary sizes and noise images along the channel dimension to iteratively obtain high-resolution rain-free images. Specifically, we in-corporate residual blocks from BigGAN-deep to replace traditional residual blocks, thereby deepening the network to enhance model generalization. Additionally, we employ smooth noise esti-mation via image overlapping block processing to improve image fidelity. Introducing deterministic acceleration sampling, we expe-dite the sampling process by uniformly selecting time steps. Comprehensive testing on synthetic and real datasets demonstrates that the proposed method achieves deraining for images of any size, yielding significant improvements in peak signal-to-noise ra-tio and structural similarity. The generated images preserve more complete details, showing superior deraining efficacy.

关键词： Rain Computational modeling Noise reduction Noise Estimation Computer architecture Knowledge discovery image restoration Convolutional neural networks Fuzzy systems

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FPGA Implementation of QC-LDPC Decoder with Rapid Cyclic Shifter for CV-QKD systems

FPGA Implementation of QC-LDPC Decoder with Rapid Cyclic Shi...

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Communication, image and signal Processing (CCISP), international conference on

作者： Zeyuan Jin Yinghua Xie Xue-Qin Jiang Genlong Chen School of Information Science and Technology Donghua University Shanghai China

ISBN: (数字)9798350356656

ISBN: (纸本)9798350356663

In continuous variable quantum key distribution (CV-QKD) systems, low-density parity check (LDPC) decoders have gained widespread adoption due to their robust error correction capabilities and good adaptability to parallel processing hardware designs. We introduce a syndrome-based normalized min-sum algorithm (S-NMSA) and implement the decoder on FPGAs for high throughput and minimal resource consumption. The decoder utilizes a semi-parallel architecture. The proposed rapid cyclic shifter (RCS) can complete the shift of any dimension matrix in one clock cycle and realizes a significant reduction in hardware resource consumption. The semi-parallel decoding structure attains a 68.5 Mbps processing throughput on the condition that the system clock of the quasi-cyclic (QC) LDPC decoder is 250 Mhz.

关键词： Phase change materials signal processing algorithms Throughput Parity check codes Hardware Decoding Quantum key distribution Resource management Field programmable gate arrays Clocks

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Text-to-image Retrieval based on Zero-shot Transfer Learning with CLIP Model and Vector Database

Text-to-image Retrieval Based on Zero-shot Transfer Learning...

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international conference on Behavior, Economic and Social Computing (BESC)

作者： Junfeng Xie Junying Chen School of Software Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798331531904

ISBN: (纸本)9798331531911

With the continuous increase in the number of internet users, the explosion of multimodal data on the web has led to a growing demand for image retrieval. Current text-to-image retrieval systems often rely on keyword matching, which frequently fails to accurately capture users' retrieval needs. This work proposes a text-to-image retrieval method based on zero-shot transfer learning with the CLIP model and vector database, designed to be applied in text-to-image retrieval systems. By leveraging the semantic alignment capability of the CLIP model for text and images, this study effectively addresses the semantic gap problem in text-to-image retrieval. Additionally, the method utilizes the Milvus database for efficient storage and retrieval of image feature vectors, thereby enhancing system stability. Experimental results on the Flickr30k dataset demonstrate that this method improves the accuracy of text-to-image retrieval, verifying its effectiveness. Furthermore, this work extends the proposed method by incorporating the iFLYTEK Spark Cognitive Model to support image retrieval using Chinese text descriptions, further broadening the system's applicability and reliability.

关键词： Accuracy Databases Transfer learning Semantics image retrieval Text to image Vectors Stability analysis Sparks Usability

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Automatic Music Classification Algorithm based on AI internet and image Processing technology

Automatic Music Classification Algorithm based on AI Interne...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE international conference on

作者： Chen Wang School of Children’s Welfare Changchun Humanities and Sciences College Changchun China

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

Automatic music classification algorithms have become a research hotspot in the field of music information retrieval. This study uses deep learning to denoise and normalize audio signals, and then uses Mel-frequency cepstral coefficients (MFCC) and image texture, color and other feature extraction techniques to establish rich feature representations for music works. On this basis, a music classification model is constructed using a long short-term memory network (LSTM) and the audio and image features are effectively fused through a late fusion strategy. The proposed algorithm shows excellent performance on multiple independent test sets, and the evaluation indicators such as classification accuracy, precision, recall rate, and F1 score all reach a high level, demonstrating the model's generalization ability for different music types.

关键词： Training Accuracy Computational modeling Scalability Feature extraction Classification algorithms Multiple signal classification Integrated circuit modeling Artificial intelligence Long short term memory

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