Enhancing Text Compression Method Using Information Source Indexing

Text compression methods where the original texts are directly mapped into binary domain are attractive to compress English text files. This paper proposes an intermediate mapping scheme in which the original English text is transformed firstly to decimal domain and then to binary domain. Each two-decimal-digit value in the resulting intermediate decimal file represents the index to the location of each alphabet found in the original text. If the already indexed alphabet is seen again, it will be replaced by the previously given decimal-index number. The decimal file is converted into binary domain by assigning each decimal digit a 4-bit weighted code in according to its frequency of occurrence that is akin to BCD code. The assigned codes aim at generating an equivalent binary file with entropy as close as much to that of the original one. Thereafter, any conventional compression algorithm such as Lempel-Ziv algorithms can be applied to the generated binary file. The obtained compression ratios outperform those ones obtained when applying the same compression algorithm to the binary files generated either via direct mapping of the original text or via mapping the decimal file using Binary Coded Decimal (BCD) codes. Keywords: Lossless data compression; Source encoding, LZW coding, Hamming weights, Compression ratio

A Panoramic Study of Fall Detection Technologies

Falls are a major risk of injury for elderly aged 65 or over, blind people, people with balance disorder and leg weakness. In this regard, assistive technology which aims to identify fall events at real time can reduce the rate of impairments and mortality. This study offer a literature research reference value for bioengineers for further research. Much of the past and the current fall detection research, the vital signals features and the way features are extracted and fed to a classifier are introduced. The study concludes with an assessment of the current technologies highlighting their critical limitations along with suggestions for future research direction in this rapidly developing field of study.http://dx.doi.org/10.20943/01201603.626

Fuzzy-GRA trust model for cloud risk management

Cloud computing is not adequately secure due to the currently used traditional trust methods such as global trust model and local trust model. These are prone to security vulnerabilities. This paper introduces a trust model based on the fuzzy mathematics and gray relational theory. Fuzzy mathematics and gray relational analysis (Fuzzy-GRA) aims to improve the poor dynamic adaptability of cloud computing. Fuzzy-GRA platform is used to test and validate the behavior of the model. Furthermore, our proposed model is compared to other known models. Based on the experimental results, we prove that our model has the edge over other existing models

Android CompCache Based on Graphics Processing Unit

Android systems have been successfully developed to meet the demands of users. The following four methods are used in Android systems for memory management: backing swap, CompCache, traditional Linux swap, and low memory killer. These memory management methods are fully functioning. However, Android phones cannot swap memory into solid-state drives, thus slowing the processor and reducing storage lifetime. In addition, the compression and decompression processes consume additional energy and latency. Therefore, the CompCache requires an extension. An extended Android CompCache using a graphics processing unit to compress and decompress memory pages on demand and reduce the latency is introduced in this paper. This paper characterizes each data compression and decompression utility by measuring compression ratio, compression and decompression throughput, and energy efficiency to validate the process. Experimental results prove that data compression and decompression utilities can be beneficial to reduce the latency and perform faster compression and decompression compared with existing approache

The Optimal Application of Lagrangian Mathematical Equations in Computer Data Analysis

Because the current computer sensor data positioning analysis has positioning difficulties and false positioning problems, we use the Lagrangian multiplier method of the interactive direction to disassemble the computer sensor sound source. Through this algorithm, the information fusion of computer sensor nodes is realized. After using Lagrangian mathematical equations, these error correction measurements have achieved better target positioning results. Theoretical analysis and experimental results show that the algorithm improves the speed of computer sensor data association. To a certain extent, the correlation accuracy is improved

Research on Dynamics of Flexible Multibody System with Deployable Antenna Based on Static Lagrangian Function

Deployable antennas, as the core content of national defense technology research at this stage, have the advantages of large flexibility and light weight in practical applications, so this type of antenna can also be called a rigid-flexible hybrid structure. According to the theoretical analysis of the dynamics of flexible multi-body systems, it can be seen that effective control of unfolding antennas is a basic requirement for practical applications. Due to the instability of the specific numerical solution of the static Lagrangian function, it is difficult to completely meet the constraint equation, which is also the main factor affecting the steady development of flexible multibody dynamics. Therefore, in-depth discussion of the numerical calculation method of the static Lagrangian function, obtaining efficient and stable numerical methods from it, and providing effective information for the process control of the deployment of the antenna, is the focus of scientific research scholars at this stage. Based on the understanding of the flexible multi-body dynamics modeling method, this paper systematically analyzes the calculation method of the differential equations, and combines the direct modification method of the constraint violation of the augmentation method to propose the simulation calculation of the model. The final results prove that the dynamics of flexible multi-body systems have a positive effect on the application analysis of deployable antennas

Abnormal Behavior of Fractional Differential Equations in Processing Computer Big Data

We use the Legendre wavelet method to study nonlinear fractional differential equations. Based on the in-depth study of the characteristics of various fractional-order dynamic system models, this paper designs a system for solving fractional-order differential equations, and we apply them to the anomaly analysis of big computer data. This method can improve the efficiency of big data classification. The results of computer numerical simulation show that the designed algorithm for solving fractional differential equations has high accuracy. At the same time, the algorithm can avoid misclassification and omission in big data analysis

Application of Nonlinear Fractional Differential Equations in Computer Artificial Intelligence Algorithms

In order to study the application of nonlinear fractional differential equations in computer artificial intelligence algorithms. First, the concept, properties and commonly used neural network models of artificial neural network are introduced, the domestic and foreign status quo of the application of fractional calculus theory to neural network technology is described. Then, the definition, properties and numerical calculation methods of fractional calculus theory are introduced in detail. Then, based on the analysis of artificial intelligence neural network algorithm, the theory of fractional differentiation is introduced, construct BP neural network based on fractional order theory. The Sigmoid function is used as the node function of the neural network, and the actual data is used as the sample set, train a fractional-order network. Finally, by training the network, summarize the change of the two parameters a and p in the function, the impact on the training of the entire network, and make a simple comparison with the fractional order neural network based on the sigmoid function. Experiments show that a variable-order iterative learning algorithm is proposed and applied to the training of neural networks, the results show the feasibility of this algorithm and its advantages in convergence speed and convergence accuracy

A Grayscale Semi-Lossless Image Compression Technique Using RLE

This paper presents a new compression technique based on Run-Length Encoding scheme (RLE). The technique is semi-lossless and utilizes pixel value rather than bit value. The encoding process starts by mapping the colors of an image to a vector where each value of the vector is decimal ranging from 0 to 255. To maximize the efficiency of the decimal RLE, the 4 LSB of each of the values will be reset and utilized for compression purposes. Then, the RLE is applied on the result vector to obtain a new vector of pairs on the form , where each item consists of 8 bits. The frequency of occurrences is stored in the 4 LSB of the color value so as to reduce the total size of the image. The decoding process reverses the encoding process steps to obtain the original image. The experimental results showed that the technique has achieved high compression ratio using different images with multi-features

Context-Aware Latency Reduction Protocol for Secure Encryption and Decryption

© 2018 Inderscience Enterprises Ltd. Security is one of the biggest challenges for data communication. As the use of mobile phones has increased, people started using several mobile applications to handle the internet-ofthings. On other hand, the frequent use of mobile phones in the business caught the attention of researchers to protect the manufacturers and customers. Handling these issues, several paradigms for encrypting and decrypting the data-outsourcing are proposed. However, there are still new threats that challenge researchers due to new attacking methods and malicious actions of adversaries. Though, the manufacturers have implemented an industry standard to protect the customers\u27 privacy, but existing standards do not provide comfortable zone for the customers, particularly reducing the latency of the emerging applications being used in internet-of-things. In this paper, we introduce context-aware security (ConSec) protocol to support the internet-ofthings applications to reduce the latency while encrypting and decrypting the applications. Furthermore, elliptic curve cryptography is used to fully secure the encryption and decryption processes. The proposed method is implemented using Java platform and results are verified and compared with full disk method from the latency perspective