UEELC: An Ultra Energy Efficient Lossless Compression Method for Wireless Body Area Networks

The exponentially increasing high throughput of sequencing data calls for efficient specific compression methods to address the challenges posed by the storage and transmission of such data. In this work, we develop a GPU version of lossless reference-based compression method namely G-FQZip by introducing a GPU-based arithmetic coding, a template matching approach, and a parallel light-weight mapping model. The comparison experiments demonstrate that G-FQZip can improve the (de) compression speed while maintaining comparable compression ratios. Besides, the follow-up evaluation demonstrated the efficiency of the GPU-based arithmetic coding and the template matching approach.

An efficient texture compression method is proposed based on a block matching process between the current block and the previously encoded blocks. Texture mapping is widely used to improve the quality of rendering results in real-time applications. For fast texture mapping, it is important to find an optimal trade-off between compression efficiency and computational complexity. Low-complexity methods (e.g., ETC1 and DXT1) have often been adopted in real-time rendering applications because conventional compression methods (e.g., JPEG) achieve a high compression ratio at the cost of high complexity. We propose a block matching-based compression method that can achieve a higher compression ratio than ETC1 and DXT1 while maintaining computational complexity lower than that of JPEG. Through a comparison between the proposed method and existing compression methods, we confirm our expectations on the performance of the proposed method.

In the field of health care, the Wireless Body Area Networks(WBANs), through placed miniaturization, low power consumption of sensor nodes in patient's body or on the surface to measure a variety of physiological parameters, to monitor or diagnose the health of human body. However, in this process, WBANs nodes usually use batteries, especially for implantable flexible node it is difficult to accomplish the replacement of batteries, the energy that the node can carry is very limited, so the efficient use of energy is the most important problem to consider when designing WBANs routing algorithm. By considering the factors such as residual energy of node, the importance level of node, path cost and path energy difference ratio, this paper gives the definition of Optimal Path of Energy Consumption(OPEC) in WBANs, and The Optimal Energy Consumption Artificial Bee Colony(ABC) Based WBANs(OEABC) is designed. The performance simulation is carried out to verify the effectiveness of the OEABC, compared with Genetic Algorithm and Ant Colony Algorithm, it shows that t the OEABC has a fast convergence rate.

With the development of the pavement management system (PMS), the amount of the acquired and transmitted date is getting much larger [1][2]. Regard this, a compression method is needed for data analyzing. As there are lots of differences between the image of PMS and general image in the luminance distribution and the useful information region, general compression methods can not fit the special compression requirement of the PMS image. Although the PMS develops rapidly, the relevant image compression has little significant study. Based on Huffman coding and edge detection, this paper provides an efficient compression method, which can reserve all the useful information of the original image. The preliminary results show that the compression ration is higher than IS, which means it can work for the PMS.

Ion mobility spectrometry is a rapid scanning measurement method for which compression methods that facilitate the handling of large collections of data are beneficial. Peak distortion in reconstructed ion mobility spectra from linear wavelet compression is problematic in that artifact peaks may cause false positive alarms. Peak shifting also may cause false alarms if target peaks shift out of or interfering peaks shift into detection windows. Nonlinear wavelet compression (NLWC) preserves peak shape and can lessen the degree of distortion, shifting, and artifact peaks in the reconstructed spectra. NLWC was applied to achieve high compression and fidelity in the reconstructed spectra. Another benefit is that NLWC improves signal-to-noise ratios and thus the models built from compressed data are improved. By compressing both the drift time order and the spectrum acquisition order, greater compressions maybe achieved. A two-way nonlinear wavelet compression method that incorporates alternating least squares (2W-NLWC-ALS) algorithm was devised by applying ALS to partially reconstructed wavelet coefficients generated from two-way NLWC. The number of components in a data set can be determined automatically using ASIMPLISMA. The smaller ALS models are saved as the final compressed data and can be used to reconstruct the entire data set efficiently without maintaining the compressed wavelet coefficient matrix of the original data set. The 2W-NLWC-ALS algorithm provides greater compression ratios compared to regular wavelet compression and interpretable models. Using this method, large volumes of data can be acquired and easily evaluated through a simple compressed model. A compression ratio of 510 ppm, root-mean-square error (E(RMS)) of 6.3 mV (full-scale signal is usually 1 V or larger), and relative root-mean-square error (RE(RMS)) of 1.62% were achieved for data sets collected by CAM. A compression ratio of 46 ppm, E(RMS) of 9.2 mV, and RE(RMS) of 0.42% were achieved for data sets collected with an ITEMISER instrument. The 2W-NLWC-ALS algorithm is an efficient compression method that provides the benefits of a simple model.

With the progress of technology in the field of wireless communication, wireless body area networks (WBANs) have been introduced. WBANs consist of a number of intelligent biomedical sensor nodes and a special node called sink. Sensors are placed on the patient's body or under the skin, and their task is to check physiological parameters and report to the sink. The resources of these nodes are limited, and communication is subject to various issues. Maintaining the quality of communication due to the high importance of the transmitted data and severe limitations is one of the important issues in WBANs. The important challenge in this area is designing a framework for next‐hop node selection with covering the quality needs of the transmitted data. This paper proposes a Priority‐Based QoS Aware Routing Protocol for WBANs (PQARP). PQARP is a three‐step method. In the first step, the sensors are configured. In the second step, data is separated and marked. In the third step, the proposed PQARP routing is done and the most appropriate intermediate node is selected to send data. The simulation results using NS‐2 showed the remarkable capabilities of PQARP in covering quality requirements of different data sent and improving the metrics of end‐to‐end delay, successful delivery ratio, throughput and energy consumption compared to other similar methods.

The study of diagnostic accuracy of chest nodules by using different compression methods

SummaryThe technological progression in the area of Wireless Body Area Network (WBAN) has made it possible to design various sensing modules operating for detecting the multitudinous physical attributes of a patient's body. However, the limited battery of these sensing devices has restricted the scope for WBAN. Hence, it is imperative to design an energy efficient algorithm to combat the gigantic energy consumption of these sensor nodes. Therefore, in this paper, we propose a Novel Energy Efficient hybrid Meta‐Heuristic Approach (NEEMA) for WBAN. We adopt this hybrid approach by using Tunicate Swarm Algorithm (TSA) and Genetic Algorithm (GA); we name it as T‐GA, to deliver the high convergence and large exploitation and exploration capabilities. We follow the clustering approach by selecting the Cluster Head (CH) with the help of the fitness function of T‐GA. We consider the novel parameters for selecting the CH that helps in the overall energy preservation of sensor nodes. Our proposed work focuses on multi‐hop communication among the patients on whose body, the body sensor nodes are installed, so that the data through the multi‐hop reach to the healthcare. The Relay Head (RH) node is used for forwarding the data to next RH and hence to sink. RH selection follows the same method of selection as that of CH. The experimental outcomes of NEEMA outperform the state‐of‐the‐art algorithms and prove to be highly beneficial for various WBAN applications.

A close inspection of Fiala and Green's implementation of Ziv-Lempel'77 dictionary compression method reveals a surprising inefficiency. The match they are searching for from the root down the suffix tree can be obtained for free from the suffix tree construction algorithm! This observation suggests that the output of this compression method is in a way just a description of the suffix tree construction for the input string. If taken one step further this leads to the idea that there exists a whole family of compression methods replacing the input string with a description of the suffix tree construction for this string. This family contains some (multi-)dictionary methods not dissimilar to those of Bloom or Hoang et.al., as well as some prediction by partial matching variants. We give the general description of this family along with details about some of its members, discuss implementation issues and show some early experimental results. A possible application to other suffix structures like the compact directed acyclic word graph is also outlined.

With advancement of technology, the image processing techniques play an important role. Image processing is required to improve the pictorial information for human perception and for autonomous machine applications. It finds application in various fields such as quality control, remote sensing, imaging science, etc. It also finds application in areas where efficient storage and transmission of images is necessary, Say for example; we want to store an image in the computer with less disk space. Also, in cases where we want to signal an image or video on a transmission medium with less bandwidth communication channel. Images are usually in the form of matrices and an uncompressed image uses huge number of bytes for storage. Image compression is used to reduce the size of the bitmap without compromising on the quality of an image. This enables to store large number of images in the same given amount of storage space. Image compression will also reduce the time taken to transmit or to download images from the internet. Many methods are available to compress an image file. The image compression methods which have gained popularity are the transform based coding methods like Discrete Cosine Transform (DCT), discrete wavelet Transform (DWT) and fractals. However these methods have drawbacks like low compression ratio and high encoding time. In this paper a new hybrid methodology is proposed by combining lossy and lossless compression methods. The proposed hybrid technique combines DCT and fractal quadtree decomposition with Huffman encoding of threshold value 0.2. The results obtained from the proposed method are then compared with state-of-the-art compression methods.

EASC: An exception-aware semantic compression framework for real-world knowledge graphs

A new near-lossless EEG compression method using ANN-based reconstruction technique

Modern image and video compression technologies includes many compression methods both lossless and lossy. Entropy encoding has a special place among lossless compression methods - it performs final data compression before its conversion to output bitstream.

This paper conducts a comparative analysis of the impact of unsparsing by data scaling on lossless data compression methods. The most commonly used data compression algorithms such as gzip, zlib, bzip2, and lzma are tested by their compression efficiency in unsparsed datasets by the performance metrics such as compression time, decompression time, and compression ratio for both the original and scaled datasets. Five different data scaling techniques are investigated to transform the original data into unsparsed data such as min-max, robust, absolute max, standardization, and normalization. Our research reveals relationships between data scaling methodologies and compression performance, with disparities in compression efficiency and computational complexity. Fur-thermore, we investigate the effects of scaling on compression ratio and provide results for improving the understanding of the factors that influence lossless data compression methods for non-sparse tabular data.

This paper presents a new method of lossless image compression. An image is characterized by homogeneous parts. The bit planes, which are of high weight are characterized by sequences of 0 and 1 are successive encoded with RLE, whereas the other bit planes are encoded by the arithmetic coding (AC) (static or adaptive model). By combining an AC (adaptive or static) with the RLE, a high degree of adaptation and compression efficiency is achieved. The proposed method is compared to both static and adaptive model. Experimental results, based on a set of 12 gray-level images, demonstrate that the proposed scheme gives mean compression ratio that are higher those compared to the conventional arithmetic encoders.