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Listless Block Cube Tree Coding for Low Resource Hyperspectral Image Compression Sensors

Listless Block Cube Tree Coding for Low Resource Hyperspectr...

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International conference on Multimedia, Signal Processing and Communication Technologies, (IMPACT)

作者： Harshit Chandra Shrish Bajpai CE Department Faculty of Engineering and Information Technology Integral University Lucknow Uttar Pradesh India

ISBN: (纸本)9781665476485

Hyperspectral (HS) image has rich spectral information content, which facilitates multiple applications including remote sensing. Due to the big data size of the HS image, compression is a required process for the efficiency of image storage and transmission. However, the complexity of the compression algorithms turns real-time compression into a very challenging task. A novel listless set partitioned hyperspectral image compression algorithm is proposed. the proposed compression algorithm uses zero block cube tree structure to exploit the inter and intra sub-band correlation to achieve the compression. From the result, it has been clear that the proposed compression algorithm has low coding complexity with at-par coding efficiency. thus, it can be a suitable contender for low-resource hyperspectral image sensors.

关键词： Image sensors Image coding Correlation Signal processing algorithms Transforms Signal processing Computational complexity

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Enhancing Scene Text Detection with Parameter-Free Attention Mechanism and an Improved FPN

Enhancing Scene Text Detection with Parameter-Free Attention...

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Machine Learning and Computer Application (ICMLCA), International conference on

作者： Yuan Li Yong Yan Aba Tibetan and Qiang autonomous prefecture School of Physics and Electronic-Electrical Engineering ABA Teachers University Sichuan China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

In scene text detection, models like DBNet excel in handling texts of arbitrary shapes, but there is still potential for improvement in detecting small texts. this study introduces two key enhancements to the DBNet framework: first, a Simple and Parameter-Free Attention Module that sharpens the network's focus on important features while diminishing the impact of complex backgrounds, thus improving detection accuracy for small text areas. Second, a Cross-Level Enhanced Feature Fusion Module is proposed to integrate features across different scales, effectively utilizing local and global contextual information. this multi-scale fusion significantly boosts the model's capability to extract features of small texts, ensuring precise localization. Comprehensive experimental results demonstrate that these improvements notably enhance the model's performance across various challenging datasets, especially in detecting small text objects, showcasing superior accuracy and robustness.

关键词： Location awareness Training Accuracy Shape Heuristic algorithms Text detection Computer applications Feature extraction Robustness complexity theory

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Optimization Study of BP Neural Network Based on Sparrow Search Algorithm

Optimization Study of BP Neural Network Based on Sparrow Sea...

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Artificial Intelligence and Electromechanical Automation (AIEA), International conference on

作者： Jinpeng Guo Jiaxuan Ding Rongxin Zou Mingjun Liang School of Computer Science Zhuhai College of Science and Technology Zhuhai China School of Finance and Trade Zhuhai College of Science and Technology Zhuhai China School of Aeronautical Engineering Zhuhai College of Science and Technology Zhuhai China

ISBN: (数字)9798350366174

ISBN: (纸本)9798350366181

In the rapidly developing field of information technology today, the optimization of neural network algorithms has become a common focus of attention in both academia and industry. Especially when dealing with complex data and solving practical problems, optimizing the performance of neural networks is crucial. therefore, seeking effective optimization algorithms has become the focus of current research. this study is based on the sparrow search algorithm and optimizes the traditional BP neural network to improve its performance in data classification and pattern recognition tasks. through experimental results analysis, we found that the optimized BP neural network has achieved significant improvements in performance indicators such as accuracy, mean absolute error (MAE), mean square error (MSE), and coefficient of determination ($\mathrm{R}^algorithms$), which is significantly better than traditional BP neural networks. Specifically, the optimized model achieved an accuracy of 91.6667%, with a MAE of only 0.012, while the traditional BP neural network was 0.039%; the MSE is 0.014, significantly lower than the 0.042 of traditional BP neural networks; the determination coefficient is 0.93, while the traditional BP neural network is 0.77. these results indicate that the sparrow search algorithm has a significant effect on optimizing BP neural networks and provides important empirical support for improving the performance of neural networks in practical applications.

关键词： Accuracy Sensitivity Neural networks Mean square error methods Search problems Stability analysis Classification algorithms Reliability Computational complexity Optimization

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Enhanced RSA Cryptosystem: A Secure and Nimble Approach

Enhanced RSA Cryptosystem: A Secure and Nimble Approach

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Advances in Science and Technology (ICAST), International conference on

作者： Tanay Gandhi Meith Navlakha Rahul Raheja Varun Mehta Yash Jhaveri Narendra Shekokar Computer Engineering Dwarkadas J. Sanghvi College of Engineering Mumbai India

ISBN: (纸本)9781665492645

With the rise in Internet and Network applications in the last decade, there is a need for making communication over the internet to be more secure and strong in order to avoid cyber-attacks. the use of digital signatures has been widely used and there is a need to strengthen the existing public key algorithms. RSA is among the most common public key algorithms but is prone to security risks and attacks due to the advancing computing technology. this paper suggests a new algorithm based on RSA which increases the randomness and diffusion of the RSA algorithm making it more secure from cyber-attacks than RSA. the suggested approach employs four prime integers to generate two sets of public and private keys and involves double encryption and decryption. this modified form of the RSA method adds to the complexity of the encryption process while simultaneously decreasing the decryption time by leveraging the Chinese Remainder theorem.

关键词： Computers Force Public key Robustness Hardware Encryption complexity theory

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A New Dunhuang Murals Inpainting Method with Adaptive Selection Module

A New Dunhuang Murals Inpainting Method with Adaptive Select...

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Information Science and Education (ICISE-IE), International conference on

作者： Huan Wang Jiaxuan Cheng Lingyu Liang Industrial Technology Research Center Guangdong Institute of Scientific & Technical Information Guangzhou China School of Electronics and Information Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798331506766

ISBN: (纸本)9798331506773

the Dunhuang murals represent the world's largest and best-preserved collection of grotto art. However, prolonged exposure to environmental factors may lead to fading, deformation, and damage, and the preservation and restoration of these murals is a critical task. Existing restoration methods often fail to achieve optimal results due to inadequate consideration of the distinct properties of structural and textural elements in the murals. this paper proposes a novel inpainting method for Dunhuang murals, featuring an adaptive selection module designed to utilize both structural and textural information effectively. Our approach involves dividing the restoration area into texture and structure regions and employing a restoration strategy that prioritizes texture restoration followed by structural restoration. For each region, we introduce a patch texture similarity function and a patch structure complexity function. these functions derive a priority function, enabling the adaptive selection of blocks. Additionally, we have developed new quality evaluation functions that facilitate the adaptive selection of parameters, further enhancing the adaptive selection module. the experimental results demonstrate that our method outperforms several leading algorithms, offering superior restoration quality for the complex structural and textural information characteristic of Dunhuang murals.

关键词： Measurement Fading channels Information science Art Deformation Education Environmental factors Image restoration complexity theory

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Low complexity Massive MIMO detection algorithm based on improved LAS 5

Low complexity Massive MIMO detection algorithm based on imp...

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2021 5th International Workshop on Advanced algorithms and Control Engineering, IWAACE 2021

作者： Yuan, Quan Zhang, Juan School of Communication and Information Engineering Chongqing University of Posts and Telecommunications China Research Center of New Telecommunication Technology Applications Chongqing University of Posts and Telecommunications China Chongqing Information Technology Designing Co. Ltd Chongqing China

Massive multiple-input multiple-output (M-MIMO) technology is a key technology for 5G communications and future mobile wireless networks. Although the likelihood ascent search (LAS) algorithm in the existing detection algorithms is relatively low in complexity, the algorithm is easy to fall into the local optimum, resulting in poor global performance. therefore, based on this algorithm, this paper proposes an improved detection scheme based on the LAS algorithm in the reduced neighborhood. this algorithm combines the idea of a reduced neighborhood and iteratively improves the LAS algorithm. the algorithm is designed by reducing the size of the neighborhood and increasing the number of iterations. the algorithm compares the BER performance of different neighborhood parameters, and obtains a set of parameters to significantly reduce BER through simulation comparison. © Published under licence by IOP Publishing Ltd.

关键词： MIMO systems

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IPSO-BP Power Quality Disturbance Identification Method Based on Feature Screening

IPSO-BP Power Quality Disturbance Identification Method Base...

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IEEE International conference on Electronics and Communication Engineering (ICECE)

作者： Chaojun Li Yinfei Xu Xiaodian Deng Junxia Zhang Zhaocheng Han Qin Xu Hangzhou Power Equipment Manufacturing Co. LTD Hangzhou China

ISBN: (纸本)9781665487900

Among the current methods for power quality disturbance analysis, the time domain analysis method requires multiple calculations so that the characteristics of the power quality disturbance signal cannot be quickly extracted. How to effectively utilize the time domain characteristics of the original signal within a specific range needs to be solved. Key issues, and when extracting time-domain features, multiple features need to be collected. Multiple features lead to high feature dimensions of the dataset, which increases the structural complexity of the classifier and makes it difficult to optimize model parameters. therefore, this paper adopts a feature score, and the genetic algorithm is a two-stage feature selection algorithm. After screening a large number of features, the features are screened before the evolutionary calculation starts, and the screened features are fully utilized in the evolutionary calculation process to improve the classification accuracy, and an IPSO-BP algorithm is used, reducing the time spent on classification.

关键词： Time-frequency analysis Heuristic algorithms Power quality Feature extraction Classification algorithms complexity theory Compounds

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Design of an Efficient Encryption and Decryption Modular Exponentiation for Single Word Size

Design of an Efficient Encryption and Decryption Modular Exp...

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Global conference for Advancement in Technology (GCAT)

作者： Ragi Abhinav Sreekar S. G. Krishna Sindhoor Sarda Sharma Sandeep Singh Chauhan Department of Electronics and Communication Engineering Amrita School of Engineering Bangalore India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

Modular exponentiation (ME) is a mathematical operation that is used for several public-key cryptosystems (PKCs) and other applications. It is a complex task that is an essential computational tool that enables efficient handling of large numbers, especially in cryptography and computer algorithms. Its efficiency is obtained from advanced mathematical techniques like exponentiation by squaring, which is known to reduce computational complexity significantly. In this method, an efficient ME operation i.e. the modular exponentiation method is presented which improves raising any integer x to a power, namely k, and reducing the result to the modulo some integer n. this ME operation is easy to implement on paper but is expensive for resource-constrained devices in terms of the time of computation, energy consumption, etc. To tackle this problem, several different methods of computation have been proposed its implementation efficiently. In this paper, we present the implementation of an efficient ME method for single word size and then implement the method in hardware using Verilog HDL. then, after implementation, we compare the results of the implementation of our results with two of the most frequently used conventional methods of ME i.e. Montgomery method and the Barret method. the performance parameters like clock cycles, power, and utilization are compared and presented. the low power and faster exponentiation can have potential application in emerging internet of things (IoT) and low power sensor nodes in future.

关键词： Energy consumption Public key cryptography Computational efficiency Encryption Internet of things Hardware design languages Computational complexity Hardware acceleration Clocks

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complexity Dynamic Analysis of Fractional-order Permanent Magnet Synchronous Motor in CNC Machine Tool 5

Complexity Dynamic Analysis of Fractional-order Permanent Ma...

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2021 5th International Workshop on Advanced algorithms and Control Engineering, IWAACE 2021

作者： Hai-Yan, F.U. Tengfei, Lei Min, Su Tingyang, Yan School of Control Mechanical and Electrical Engineering Qilu Institute of Technology Jinan Shangdong250200 China

In this paper, permanent magnet synchronous motor system for fractional-order machine tools is studied. the nonlinear term of the system is decomposed by Adomian decomposition method, and the attractor and poincare cross-section of the system are drawn by Matalb. Meanwhile, the system is analyzed by numerical simulation including bifurcation diagram, SE complexity, C0 complexity and attractor phase diagram under parameter change, which further reveals the realizable dynamic characteristics of fractional-order chaotic system. the related research results lay a good foundation for the control of CNC ( Computer Numerical Control ) machine tools. © Published under licence by IOP Publishing Ltd.

关键词： Machine tools

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Strategies for Broadcast Information Finding Anomalies Based on Deep Learning and the Past: A review analysis

Strategies for Broadcast Information Finding Anomalies Based...

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Innovative Trends in Information Technology (ICITIIT), International conference on

作者： R. Mariswari V. Narayani Department of Computer Science St. Xavier’s College (Autonomous) Palayamkottai Tirunelveli

ISBN: (数字)9798350386813

ISBN: (纸本)9798350386820

the demand for effective as a consequence of the Internet of things (IoT), the identification of anomalies in streaming data has risen. Devices increasingly permeate daily life, many dozens based on clusters to operate, kernels, and concentrations, and additionally, there have been distance-based algorithms suggested for the identification of anomalies. the resilience of deep neural networks (DNN) has recently led to the development of a variety of strategies for spotting irregularities based on deep learning. Despite all these quick advancements, there aren’t many investigations contrasting distinct anomaly discovering tools. Notwithstanding those studies, the streaming data isn’t taken into account; the comparison is solely made using standard anomaly detection settings. It is crucial to examine how frequently used anomaly detection algorithms behave on streaming data because crucial time-series traits including trend, seasonality, and change point were evident. Additionally, the comparison juxtaposing conventional tactics with deep learningbased approaches exposes intriguing insights about the data that traditional methods usually neglect. In this study, we investigate 13 techniques for identifying anomalies on two commonly used streaming data sets. To assess the procedures from various angles, we applied four distinct assessment measures. Our research shows that deep learning-based identification of anomalies strategies are superior to traditional anomaly detection techniques in terms of effectiveness.

关键词： Deep learning Reviews Artificial neural networks Market research Internet of things Object recognition Time complexity

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