Advanced telemedicine requires the gathering of big data through wireless body area network or internet of things based applications. These networks perform several tasks that consume maximum energy while transmitting...
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Advanced telemedicine requires the gathering of big data through wireless body area network or internet of things based applications. These networks perform several tasks that consume maximum energy while transmitting the big data, which in turn discharges the battery and would require a battery replacement frequently. Also, the big data to be transferred and stored would require a significant amount of storage space. The above problem is rectified by compressing the big data acquired from the sensors before transmission that would reduce the consumption of power as well as use the storage space efficiently. In this paper, a hybrid compression algorithm (HCA) based on Rice golomb coding is proposed. The efficiency of the proposed compression algorithm is tested on ECG data from the physionet ATM database and real-time data acquired from the ECG sensor. The proposed HCA comprises of both lossy and lossless compression. The real-time implementation of the proposed compression algorithm is carried out using NI myRIO hardware and LabVIEW graphical tool. The compressed data then stored in the Google cloud, and the analysis of storage space using the HCA shows a reduction of 70% storage space for 10 minutes of ECG data.
Past access to files of integers is crucial for the efficient resolution of queries to databases. Integers are the basis of indexes used to resolve queries, for example, in large internet search systems, and numeric d...
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Past access to files of integers is crucial for the efficient resolution of queries to databases. Integers are the basis of indexes used to resolve queries, for example, in large internet search systems, and numeric data forms a large part of most databases. Disk access costs can be reduced by compression, if the cost of retrieving a compressed representation from disk and the CPU cost of decoding such a representation is less than that of retrieving uncompressed data. In this paper we show experimentally that, for large or small collections, storing integers in a compressed format reduces the time required for either sequential stream access or random access. We compare different approaches to compressing integers, including the Elias gamma and delta codes, golomb coding, and a variable-byte integer scheme. As a conclusion, we recommend that, for fast access to integers, files be stored compressed.
The main challenge in video capsule endoscopic system is to reduce the area and power consumption while maintaining acceptable video reconstruction. In this paper, a subsample-based data compressor for video endoscopy...
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The main challenge in video capsule endoscopic system is to reduce the area and power consumption while maintaining acceptable video reconstruction. In this paper, a subsample-based data compressor for video endoscopy application is presented. The algorithm is developed around the special features of endoscopic images that consists of a differential pulse-coded modulation (DPCM) followed by golomb-Rice coding. Based on the nature of endoscopic images, several subsampling schemes on the chrominance components are applied. This video compressor is designed in a way to work with any commercial low-power image sensors that outputs image pixels in a raster scan fashion, eliminating the need of memory buffer and temporary storage (as needed in transform coding schemes). An image corner clipping algorithm is also introduced. The reconstructed images have been verified by five medical doctors for acceptability. The proposed low-complexity design is implemented in a 0.18 mu m CMOS technology and consumes 592 standard cells, 0.16 mm x 0.16 mm silicon area, and 42 mu W of power. Compared to other algorithms targeted to video capsule endoscopy, the proposed raster-scan-based scheme performs strongly with a compression ratio of 80% and a very high reconstruction peak signal-to-noise ratio (over 48 dB).
A new method for encoding a sequence of integers, named Binary Adaptive Sequential coding with Return to Bias, is proposed in this paper. It extends the compressing pipeline for chain codes? compression consisting of ...
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A new method for encoding a sequence of integers, named Binary Adaptive Sequential coding with Return to Bias, is proposed in this paper. It extends the compressing pipeline for chain codes? compression consisting of Burrows Wheeler Transform, Move-To-Front Transform, and Adaptive Arithmetic coding. We also explain when to include the Zero-Run Transform into the above-mentioned pipeline. The Zero-Run Transform generates a sequence of integers corresponding to the number of zero-runs. This sequence is encoded by golomb coding, Binary Adaptive Sequential coding, and the new Binary Adaptive Sequential coding with Return to Bias. Finally, a comparison is performed with the two state-of-the-art methods. The proposed method achieved similar compression efficiency for the Freeman chain code in eight directions. However, for the chain codes with shorter alphabets (Freeman chain code in four directions, Vertex Chain Code, and Three-OrThogonal chain code), the introduced method outperforms the referenced ones.
A new lossless compression scheme of compressing the initially-acquired continuous-intensity images with a lossy compression algorithm to obtain higher compression efficiency is proposed. Even if a lossy algorithm is ...
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A new lossless compression scheme of compressing the initially-acquired continuous-intensity images with a lossy compression algorithm to obtain higher compression efficiency is proposed. Even if a lossy algorithm is employed, for decoded original images, there is no loss of data in the same sense as the conventional lossless scheme. To realize the new idea, the compression efficiency of the existing lossy subband compression algorithm is improved at high bitrates. For the entropy coding part, a run-length based, symbol-grouping entropy coding method is introduced. For the quantization part, the entropy-constrained scalar quantization is implemented using a novel and simple thresholding method. coding results show that bit savings of the proposed lossless scheme, which employs a lossy algorithm, over the conventional lossless scheme achieve a maximum of 27.2% and an average of 11.4% in our test.
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