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As high-throughput sequencing technologies are generating vast amounts of data, there is urgent need to develop efficient algorithms for sequencing data compression. Existing methods usually dispatch the similar sequences into the same bucket based on their same minimizer, that is the lexicographical smallest k-mer within the sequence, for data compression. However, when the sequencing error existed...
In the last few years, data compression has become essential for both storage and transmission especially in the biomedical domain. In this paper, we propose a new technique for the electrocardiogram data compression. It is based on the singular value decomposition and a new adaptive vector quantization. In the proposed method, the codebook is generated adaptively at each quantization stage. The result...
Nowadays, compression of binary image is vital due to enormous amount of images that are being processed and stored in a wide range of applications such as text, maps, fingerprint etc. In this study, we proposed a new method for lossless compression of binary images. The general principle of image compression algorithms is transforming binary digits into a new one that specifies the same information...
Currently, Blackbox records all important information that happens during the accident that is Flight Data Recorder (FDR) and Cockpit Voice Recorder (CVR). The data is for by applying a compression method to make both of them get into transmission with on demand. This way is probably to present the data without has to find the black box. It will make the investigator easier on the evacuation process...
The black box records all the important information that takes place on a flying plane. The black box is a tool used on airplanes to store all activities during flight. The black box has a Flight Data Recorder (FDR) and a Cockpit Voice Recorder. FDR and CVR function to record and record the existing in the aircraft. The black box has recordings of information that occurs during the flight. Data generated...
In recent years, the high resolution, high precision and high sensitivity telescopes have been widely developed for astronomical observation in the world, such as SKA, Arecibo, ALMA and MingantU SpEctral Radioheliograpy (Muser). Along with these newly developed modern telescopes, a “big data” challenge was raised for data storage and processing. For example, Muser produces about 4.3TB raw data for...
Data deduplication is the latest data compression method which will succours to reduce the data storage space by eliminating redundant data stored and keeps exactly one copy of the data. The amount of digital information is growing drastically due to increase in the use of internet and IoT [Internet of Things] devices. Since multiple users are dealing with same data, miscellaneous copies of same data...
Global remote sensing and large-scale environment modeling have generated vast amounts of raster geospatial data. Performing spatial queries over such data has applications in many domains, such as climate impact studies, water and wildlife management, and urban planning. Processing those queries is greatly facilitated by the existence of spatial indices. However, I/O transfer is still a major bottleneck...
This article offers a method of structural representation of telemetry data, based on displaying the original data onto plane or torus. Such method allows potentially detect the implicit correlation dependences both in single data frame and in sequence of such frames. It offers algorithms of compression based on displaying the original data, represented as bit-form, onto the surface of plane or torus...
Hierarchical approaches for representation learning have the ability to encode relevant features at multiple scales or levels of abstraction. However, most hierarchical approaches exploit only the last level in the hierarchy, or provide a multiscale representation that holds a significant amount of redundancy. We argue that removing redundancy across the multiple levels of abstraction is important...
Wireless sensor networks (WSNs) have significant limitations in available bandwidth and energy. The limited bandwidth in sensor networks can cause higher message delivery latency. In this demo, we demonstrate working of novel Zorder based data compression schemes (Z- compression) to reduce energy and save bandwidth without increasing the message delivery latency using a TelosB motes based sensor network...
Computers with their growing demands have their applicability in every field, one of the applications is the computer vision, where the function of an human eye has been replaced by using various sensors to capture the environment in the similar way a human eye does. We know that capturing the environment through sensors lead to origin of various kinds of images and videos of different shapes and...
Recently, the wide deployment of surveillance systems such as driving recorders and sensor networks has highlighted the significance of time-series data, that are, the data indexed by timestamps. This trend has thus driven a rapidly increasing demand of high-performance, cost-effective storage and management systems for time-series data. In a typical use case, the storage system is accessed by range...
Bitmap indexing is a promising approach for indexing. However the huge space consumption hinders the wide adoption of bitmap indexing, especially in memory-critical area such as packet classification. To this end, a variety of compression scheme are proposed to reduce the space consumption and simultaneously maintain the fast calculation which is a focused feature of bitmap indexing. In this paper,...
Image compression is gaining good deal of attention and development nowadays. There are many uses and benefits incorporated with this application. Compressing an image will reduce its size to some amount that will enhance its storage or transmission over a network. In this paper, we proposed and compared two (Shift Coding) based techniques that we developed to gain the best compression ratios while...
High Efficiency Video Coding (HEVC), the latest video standard, provides new algorithmic features that are substantial advances over those in previous standards in order to optimize the coding efficiency. HEVC performs with a high data compression ratio at the same level of video quality when compared to its predecessors, or substantially improved video quality at the same bitrate and it supports...
In this paper, an efficient motion estimation method for quadtree plus binary tree (QTBT) structure is presented for JVET future video coding (FVC). To exploit the possibility to design a new coding standard better than HEVC, an activity called future video coding has been active in MPEG and VCEG. One of the most influential new technologies proposed for FVC is QTBT structure, which can give more...
This paper presents two sets of modifications to band offset type of the Sample Adaptive Offset technique in HEVC. First, some constraints on the SAO semantics are added to solve sub-optimal syntax issue and to exploit the actual range information of reconstructed samples. Next, the classification process is adapted to the particular samples statistics.
Widespread of virtual reality technologies across entertainment formats has created a diverse infrastructure of related technologies as head-mount displays, dome screens and virtual reality multi-camera platforms. As omnidirectional content is processing pipeline is completely different form conventional planar video and involves multiple conversion steps which affect quality in a different way. As...
With the rapid development of Unmanned Aerial Vehicle (UAV), the compression of video data captured by UAV has become a growing critical issue. However, most advanced coding schemes, like H.264 and HEVC, are oriented for common videos and thus cannot afford ideal coding efficiency when applied to UAV platform. Considering the characteristics of UAV video, much more improvement could be imposed onto...
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