检索结果-内蒙古大学图书馆

An enhanced AMBTC for color image compression using color palette

Multimedia Tools and Applications 2024年第11期83卷 31783-31803页

作者： Xiong, Lizhi Zhang, Mengtao Yang, Ching-Nung Kim, Cheonshik School of Computer Science Nanjing University of Information Science and Technology Nanjing China Department of Computer Science and Information Engineering National Dong Hwa University Hualien Taiwan Department of Computer Engineering Sejong University Seoul Korea Republic of

Digital image has been used in various fields as an essential carrier. Many color images have been constantly produced since their more realistic description, which takes up much storage space and network bandwidth. Thus, color image compression has become an essential key technology. Absolute Moment Block Truncation Coding (AMBTC) has been widely studied as one of the classical image compression methods. However, in the existing methods, the visual quality of the reconstructed images and the compression rate are all relatively low. Therefore, this paper proposes an enhanced AMBTC for color image compression using a color palette. In the proposed method, the K-means clustering algorithm is utilized for training the image's palette pattern. The color palette obtained by K-mean will be more suitable for reconstructing this image than the standard color palette, and the visual quality will be higher. The six clustered central pixels are matched with the palette through a color difference formula, and the obtained index values are used as the quantization levels. Huffman coding is used to build a bitmap to achieve a higher compression rate, that is, a lower bit rate. At last, a block of a color image can be represented by six index values and a bitmap. Experimental results and theoretical analysis demonstrate that the proposed method has better visual quality and bit rate than similar schemes. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Color

来源：评论

学校读者我要写书评

暂无评论

Contemporary Approaches to Analyze Non-stationary Time-series: Some Solutions and Challenges

引用

Recent Advances in computer science and Communications 2023年第2期16卷 61-80页

作者： Dixit, Ankit Jain, Shikha Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Enhancement of technology yields more complex time-dependent outcomes for better understanding and analysis. These outcomes generate more complex, unstable, and high-dimensional data from non-stationary environments. Hence, more challenges are arising day by day to fulfill the increasing demand for future estimation. Thus, in this paper, an extensive study has been presented to comprehend the statistical complexity and randomness of Non-Stationary Time Series (NS-TS) data at the atomic level. This survey briefly explains the basic principles and terms related to Non-Stationary Time Series (NS-TS). After understanding the fundamentals of NS-TS, this survey categorized non-stationary time series into groups and subgroups based on a change in statistical behavior. It also provides a comprehensive discussion on contemporary approaches proposed by researchers in each category of non-stationarity. These algorithms include clustering, classification, and regression techniques to deal with different types of domains. Every category of non-stationarity consists of a separate table to draw some advantages and disadvantages of existing approaches. At the end of each non-stationarity type, a short discussion and critical analysis have been done. In the conclusion section, it is observed that this research sphere still has many open challenges that need to be addressed and demand more exploration. Furthermore, it discusses the possible solution of improvisation in future research. © 2023 Bentham science Publishers.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

A Post-Stroke Rehabilitation System With Compensatory Movement Detection Using Virtual Reality and Electroencephalogram Technologies

引用

IEEE Access 2024年 12卷 61418-61432页

作者： Shih, Chi-Huang Lin, Pei-Jung Chen, Yen-Lin Chen, Shu-Ling National Chin-Yi University of Technology Department of Computer Science and Information Engineering Taichung411030 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Hungkuang University Department of Nursing Taichung43302 Taiwan

Stroke is a leading cause of global population mortality and disability, imposing burdens on patients and caregivers, and significantly affecting the quality of life of patients. Therefore, in this study, we aimed to explore the application of virtual reality technology in physical therapy by using immersive interactive training and designing rehabilitation modes for individual and group settings. We also aimed to provide patients with stroke with a comprehensive home-based rehabilitation and treatment plan, ultimately enhancing training effectiveness. Patients can engage in home-based rehabilitation through this system and undergo functional, mirror, and constraint-induced therapies tailored to different task contents. Simultaneously, using brain-computer interface technology, an emotion analysis mechanism was designed to map the patients' brainwave signal data onto a two-dimensional space of positive-negative valence arousal;this approach can enable remote physical therapists to discern the patients' emotional states during the rehabilitation process through virtual spaces, facilitating timely adjustments to rehabilitation tasks. Moreover, to prevent compromised effectiveness owing to improper training postures leading to compensation, the system offers real-time identification and recording, promptly issuing alerts when compensation occurs. The system provides a multiuser virtual rehabilitation space, enabling timely corrections and data observations, offering patients with stroke a home-based rehabilitation program, thereby realizing a localized aging care model. © 2013 IEEE.

关键词： Virtual reality

来源：评论

学校读者我要写书评

暂无评论

Biometric-Based Secure Encryption Key Generation Using Convolutional Neural Networks and Particle Swarm Optimization

Informatica (Slovenia)

引用

Informatica (Slovenia) 2025年第16期49卷 213-234页

作者： Almola, Sahera A.S. Khudeyer, Raidah S. Younis, Hameed Abdulkareem Department of Computer Information Systems College of Computer Science and Information Technology University of Basrah Basrah Iraq

With the rapid expansion of computer networks and information technology, ensuring secure data transmission is increasingly vital—especially for image data, which often contains sensitive information. This research presents a biometric-based encryption system that uses fingerprint recognition and deep learning to generate strong, random encryption keys. Two convolutional neural networks (CNNs) are employed: one to verify identity based on a user’s ID and another to extract fingerprint features for key generation. These keys are optimized using Particle Swarm Optimization (PSO), enhancing their randomness and resistance to brute-force attacks. The system generates keys in real-time, eliminating the need for storage and minimizing the risk of theft or leakage. To further improve security, encryption keys are automatically updated after every ten messages, with different keys generated from multiple fingerprints of the same individual. Testing with the SOCOFing dataset (6,000 original and 49,270 synthetic images) achieved 99.75% identity verification and 99.83% classification accuracy. Performance metrics—entropy of 7.89, correlation factor of 0.00628, and zero repetition—demonstrate high robustness. This approach offers a secure, adaptive, and personalized encryption method ideal for sensitive domains like finance and healthcare. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Hybrid IT? Algorithm for Large-Scale Colored Traveling Salesman Problem

引用

Chinese Journal of Electronics 2024年第6期33卷 1337-1345页

作者： Xueshi DONG College of Computer Science and Technology Qingdao University Beijing Key Laboratory of Urban Spatial Information Engineering School of Computer Science Wuhan University

In the fields of intelligent transportation and multi-task cooperation, many practical problems can be modeled by colored traveling salesman problem(CTSP). When solving large-scale CTSP with a scale of more than 1000dimensions, their convergence speed and the quality of their solutions are limited. This paper proposes a new hybrid IT?(HIT?) algorithm, which integrates two new strategies, crossover operator and mutation strategy, into the standard IT?. In the iteration process of HIT?, the feasible solution of CTSP is represented by the double chromosome coding, and the random drift and wave operators are used to explore and develop new unknown regions. In this process, the drift operator is executed by the improved crossover operator, and the wave operator is performed by the optimized mutation strategy. Experiments show that HIT? is superior to the known comparison algorithms in terms of the quality solution.

关键词： Transportation Traveling salesman problems Multitasking Encoding Standards Biological cells Convergence

来源：评论

学校读者我要写书评

暂无评论

Visual quality-enhanced reversible data hiding scheme based on interpolation and variant Sudoku matrics

引用

Multimedia Tools and Applications 2024年 1-29页

作者： Yuan, Longfei Lin, Chia-Chen Chang, Chin-Chen Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung Taiwan Department of Information Engineering and Computer Science Feng Chia University Taichung Taiwan

Utilizing interpolation techniques (IT) within reversible data hiding (RDH) algorithms presents the advantage of a substantial embedding capacity. Nevertheless, prevalent algorithms often straightforwardly embed confidential data into non-reference pixels without accounting for the relationships between neighboring pixels. This practice results in suboptimal visual quality in the stego images. To rectify this issue, we have introduced the augmentation and modification interpolation (AMI). Additionally, we have devised two variant Sudoku matrices to facilitate distinct data-hiding strategies tailored for different types of reference pixels: Single type and Paired type. By incorporating these innovative designs, the interpolation operation now preserves the pattern-level information of the block in the original image, rather than focusing solely on the pixel level. In assessing the effectiveness of our proposed scheme, we performed experiments utilizing two representative image datasets. The outcomes of these experiments validate that our scheme attains an average PSNR of 44.11 dB and an average hiding capacity of 1.63 bpp. Consequently, it is affirmed that the advantage offered by our proposed scheme is well-suited for scenarios demanding high-quality stego images. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Interpolation

来源：评论

学校读者我要写书评

暂无评论

A Novel Energy and Communication Aware Scheduling on Green Cloud Computing

引用

computers, Materials & Continua 2023年第12期77卷 2791-2811页

作者： Laila Almutairi Shabnam Mohamed Aslam Department of Computer Engineering Computer Science and Information Technology CollegeMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Information Technology Computer Science and Information Technology CollegeMajmaah UniversityAl Majmaah11952Saudi Arabia

The rapid growth of service-oriented and cloud computing has created large-scale data centres *** data centres’operating costs mostly come from back-end cloud infrastructure and energy *** cloud computing,extensive communication resources are ***,cloud applications require more bandwidth to transfer large amounts of data to satisfy end-user *** is also essential that no communication source can cause congestion or bag loss owing to unnecessary switching *** paper proposes a novel Energy and Communication(EC)aware scheduling(EC-scheduler)algorithm for green cloud computing,which optimizes data centre energy consumption and traffic *** primary goal of the proposed EC-scheduler is to assign user applications to cloud data centre resources with minimal utilization of data *** first introduce a Multi-Objective Leader Salp Swarm(MLSS)algorithm for task sorting,which ensures traffic load balancing,and then an Emotional Artificial Neural Network(EANN)for efficient resource ***-scheduler schedules cloud user requirements to the cloud server by optimizing both energy and communication delay,which supports the lower emission of carbon dioxide by the cloud server system,enabling a green,unalloyed *** tested the proposed plan and existing cloud scheduling methods using the GreenCloud simulator to analyze the efficiency of optimizing data centre energy and other scheduler *** EC-scheduler parameters Power Usage Effectiveness(PUE),Data Centre Energy Productivity(DCEP),Throughput,Average Execution Time(AET),Energy Consumption,and Makespan showed up to 26.738%,37.59%,50%,4.34%,34.2%,and 33.54%higher efficiency,respectively,than existing state of the art schedulers concerning number of user applications and number of user requests.

关键词： EC-scheduler green cloud energy efficiency task scheduling task sorting resource allocation

来源：评论

学校读者我要写书评

暂无评论

Metarelation2vec:A Metapath-Free Scalable Representation Learning Model for Heterogeneous Networks

引用

Tsinghua science and technology 2024年第2期29卷 553-575页

作者： Lei Chen Yuan Li Yong Lei Xingye Deng School of Information and Electrical Engineering Hunan University of Science and TechnologyXiangtan 411201China School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan 411201China

Metapaths with specific complex semantics are critical to learning diverse semantic and structural information of heterogeneous networks(HNs)for most of the existing representation learning ***,any metapaths consisting of multiple,simple metarelations must be driven by domain *** sensitive,expensive,and limited metapaths severely reduce the flexibility and scalability of the existing models.A metapath-free,scalable representation learning model,called Metarelation2vec,is proposed for HNs with biased joint learning of all metarelations in a bid to address this ***,a metarelation-aware,biased walk strategy is first designed to obtain better training samples by using autogenerating cooperation probabilities for all metarelations rather than using expert-given ***,grouped nodes by the type,a common and shallow skip-gram model is used to separately learn structural proximity for each node ***,grouped links by the type,a novel and shallow model is used to separately learn the semantic proximity for each link ***,supervised by the cooperation probabilities of all meta-words,the biased training samples are thrown into the shallow models to jointly learn the structural and semantic information in the HNs,ensuring the accuracy and scalability of the *** experimental results on three tasks and four open datasets demonstrate the advantages of our proposed model.

关键词： metarelation random walk heterogeneous network metapath representation learning

来源：评论

学校读者我要写书评

暂无评论

An Experimental Study on Dynamic Lifelong Learning with GPT for Mitigating Catastrophic Forgetting in Aspect-Based Sentiment Analysis

引用

IEEE Access 2025年 13卷 90316-90332页

作者： Huang, Huang Asemi, Adeleh Universiti Malaya Faculty of Computer Science Information Technology Department of Software Engineering Kuala Lumpur Malaysia

GPT is widely recognized as one of the most versatile and powerful large language models, excelling across diverse domains. However, its significant computational demands often render it economically unfeasible for individuals and small businesses, underscoring the need for efficient, domainspecific alternatives. In Aspect-Based Sentiment Analysis (ABSA), existing models are typically optimized for single domains, facing challenges in performing effectively across multiple domains. A key issue, known as "catastrophic forgetting," arises when models trained on one domain lose previously learned knowledge upon exposure to new domain data. This leads to two significant problems: limited cross-domain generalization and difficulty in retaining prior knowledge while learning domain-specific information. To address these challenges, we introduce the Dynamic Lifelong Learning Aspect-Based Sentiment Analysis GPT (DllaGPT), a model designed to handle multiple domains while mitigating catastrophic forgetting. Leveraging datasets from four ABSA domains—Laptops, Restaurants, Tweets, and Finance—this study fine-tunes a pretrained GPT model from HuggingFace sequentially across domains. DllaGPT employs a mechanism to retain real data from earlier domains during new domain training, effectively preserving prior knowledge. Experimental results highlight that DllaGPT achieves an average accuracy of 0.85 and a Backward Transfer (BWT) score of -0.09 across the four domains, showcasing its high accuracy and robust lifelong learning capabilities. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Powder spreading process monitoring of selective laser melting manufacturing by using a convolutional Takagi–Sugeno–Kang fuzzy neural network

引用

International Journal of Advanced Manufacturing technology 2024年第9-10期132卷 4989-5004页

作者： Lin, Chun-Hui Lin, Cheng-Jian Wang, Shyh-Hau Department of Computer Science and Information Engineering National Cheng Kung University Tainan701 Taiwan Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung411 Taiwan

Process monitoring plays a pivotal role in elucidating the intricate interplay among process, structure, and property in additive manufacturing production. The control of powder spreading affects not only particle adhesion but also rearrangement, both of which are indispensable constituents in the selective laser melting (SLM) process. To develop a novel monitoring process, this study proposed a convolutional Takagi–Sugeno–Kang fuzzy neural network (C-TSKFNN) to assess images with uneven powder distribution. The performance of the model was compared with that of conventional image analysis and state-of-the-art deep learning models. With its fusion layer and TSKFNN design, the C-TSKFNN model retains the distinctive characteristics of input images and minimizes expenditures through trainable parameters. The experimental results demonstrated that the average F1 score of the C-TSKFNN model achieved 97.07% and that it had the lowest standard deviation compared to state-of-the-art deep learning models. The trainable parameters were only 103,506, which is lower than those of models. Defect information was also provided locally through a model interpretation technique, facilitating the monitoring of SLM manufacturing. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Process monitoring

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：