Run-length Encoding Method Research Articles

Adaptive Speech Coding Method Based on Singular Value Decomposition and Grey Wolf Optimization for Arabic Language

Speech coding plays a crucial role in maintaining speech quality while optimizing network resources and expediting transmission, as well as facilitating the storage of speech data. In this paper, an adaptive method for speech coding using singular value decomposition (SVD), grey wolf optimization (GWO), and run-length encoding (RLE) was proposed. The proposed method streamlines the speech matrix through preprocessing, converting it into short intervals. Subsequently, each interval undergoes decomposition using SVD, followed by optimization of compression quality using GWO. Finally, RLE is employed as the last step to increase space-saving. The developed method was conducted on two datasets: Quran and LibriSpeech using PSNR, PSEQ, and MOS tests. The results demonstrate promising outcomes, achieving space-saving up to 89.80, 84.04, 74.76, 67.24, and 59.52, respectively, for different values of quality (10, 20, 30, 40, and 50). GWO was used to optimize the quality factor which varies in each block, further increasing the space-saving up to 85.77. The average value of PSNR was equal to 21.3, MOS = 4.71, and PSEQ was equal to 3.95. Lastly, the RLE method effectively reduced the size of speech matrices, yielding a highly satisfactory space saving of up to 90.77, while maintaining excellent speech quality.

Pengaruh Peningkatan Kualitas Citra Menggunakan Modifikasi Kontras Pada Kompresi Data RLE

Data compression is needed so that the need for storage media and data transfer time becomes more efficient. This study compressed image data using the Run-Length Encoding (RLE) method. The test data is the original image (gray scale) and the image results of improving image quality (image enhancement) using contrast modification. Modification of contrast using contrast stretching methods. Through experiments wanting to know the extent to which the RLE method works less effectively for images with complex color intensity. The image of contrast modification results has a more complex color intensity or more varied pixel value. Obtained the number of pairs (p, q) RLE in the image of contrast modification results is less than the original image, with the pair ratio (p, q) RLE ranges from 0.64% to 1.59%. Although this image has a more varied pixel value than its original image, it can produce a compression ratio of the number of pairs (p, q) RLE.

Data reduction based on machine learning algorithms for fog computing in IoT smart agriculture

Smart agriculture applications that analyse and manage agricultural yield using IoT systems may suffer from intermittent operation due to cloud disconnections commonly occurring in rural areas. A fog computing solution enables the IoT system to process data faster and deal with intermittent connectivity. However, the fog needs to send a high volume of data to the cloud and this can cause link congestion with unusable data traffic. Here we propose an approach to collect and store data in a fog-based smart agriculture environment and different data reduction methods. Sixteen techniques for data reduction are investigated; eight machine learning (ML) methods combined with run-length encoding, and eight combined with Huffman encoding. Our experiment uses two real data sets, where the first contains air temperature and humidity values, and the second has soil moisture and temperature conditions. The fog filters cluster the unlabelled data using unsupervised machine learning algorithms that group data into categories according to their value ranges in all experiments. Supervised learning classification methods are also used to predict the class of data samples from these categories. After that, the fog filter compresses the identified categories using two data compression techniques, run-length encoding (RLE) and the Huffman encoding, preserving the data time series nature. Our results reveal that a k-means combined with RLE method achieved the highest reduction, where the fog needed to store and transmit only 3%–6% of the original data generated by sensors. • It is a challenge to manage a massive amount of data generated by sensors in IoT. • Combining machine learning with data compression results in a larger data reduction. • Depending on the context, the fog needs to decide which classifier should use.

KOMPRESI DATA CITRA EFISIENSI PENYIMPANAN DAN PENGIRIMAN PADA MEDIA KOMUNIKASI MOBILE

In data communication between work networks, especially image data or image files, messages sent are often of very large size so that sending time is long, as a result the network becomes busier, it can even result in corrupted image data, as well as in image data storage or large image files take up large storage space. Both of these problems can be overcome by encoding the message or the contents of the archive as short as possible, so the message delivery time is also relatively fast, and the storage space required is also small. This method of coding is called data compression or compression. In this study the Run Length Encoding Method is used for compression or compression of image or image data. The study was conducted using the literature study method, testing the type of sample data and making comparison tables. The results obtained provide input regarding the implementation of Image Data Compression (Image) applications and contribute to computer network, internet, intranet or extranet users in sending image data, both for personal and organizational needs. The final results of this study, the size of the image file or image can be reduced to 95.23% of the actual size (can be seen in table 1). Keywords : compression, decompression, image data, Â Run Length Encoding

English

In this paper we present a new lossless audio coding algorithm using Burrows-Wheeler Transform (BWT) and Run Length Encoding (RLE).Audio signals used are assumed to be of floating point values. The BWT is applied to the audio signals to get the transformed coefficients and then these resulting coefficients are better compressed using Run Length Encoding. Two entropy coding are used which are Run Length Encoding and Huffman coding. Proposed compression algorithm is experimented and analyzed for two different stereo type audio signals. Compression ratio and Bit rate for audio coding has been used as a comparison parameter for proposed audio coding algorithm. Experimental result shows that the lossless audio coding algorithm outperforms other lossless audio coding methods; using combined Burrows Wheeler Transform & Move to front coding method ,using combined Burrows Wheeler Transform and Huffman coding method, and using Burrows Wheeler Transform ,Move to front coding method & Run Length Encoding method.

Implementation of Health Monitoring System using Mixed Environment

Background: Research shows that the health care cost per capita have grown 2.4 percent faster than the gross domestic product (GDP) since 1970. The aim of this paper is to present a useful model of wireless system that can combine both hardware and software environments and it can also integrate with other technologies or infrastructure at a low cost, which can be used for the patient monitoring system. Methods: The biomedical sensor which is attached to the patient, will read analog data. The recorded data will be converted digitally by using Analog to Digital Converter (ADC) and a FPGA transmitter will be used to send this data to Phase Shift Keying (PSK) transmitter. The modulated data will be received by PSK receiver and another FPGA receiver will be used to get the data back on the system. For behavioural modelling Verilog hardware descriptive language is used to provide a high level abstraction and language constructions. And the Simulink software is used to provide a high-level mathematical modelling condition for digital communication system which can be used for the verification and algorithm development.The important modules implemented for the transmitter and receiver FPGA are bus interfacing, compression and the data framing. Findings: A short range wireless health monitoring wireless system is modelled by using a mixed software and hardware simulation environment.At all the stages of the hardware and software designs, different types of languages like Verilog HDL codes and MATLAB used to verify the operation of the modules. The behavioural HDL designed of the FPGA transmitter and receiver will be interfaced with the RF Simulink models of PSK transmitter and receiver by using System Generator (Sysgen) tool that acts as the converter simulator. A realistic design model of health monitoring system is implemented which can be used for future hardware design. Compression and framing are the two main operations used in the transmitter side. Data compression is implemented by Run-Length Encoding method and for data framing high data link control protocol has been used. The unique features of this models simulation are low cost, less complexity, low power dissipation and efficient data transmission. Finally the FPGA based healthcare system allows us to change the design configurations or up gradation of the system based on the requirements. Conclusion: In this paper distributed simulation approach is used for designing the health monitoring system which allows checking the specification of design at all the stages. Simulation by HDL and Simulink mixed models in not the objective of this work, but to prove that realistic design is possible for future health care system design.

An Adaptive Run Length Encoding method for the compression of electrocardiograms

A compression method, based on the choice of a wavelet that minimizes the distortion of compression for each electrocardiogram considered, is proposed in this paper. The scaling filter used on the determination of the wavelet function is obtained from the resolution of an optimization problem, which is unconstrained since the scaling filter is parametrized in a way that the constraints applied to the scaling filter are embedded on the parameters. The coefficients of projection of the signal over the wavelet subspaces are calculated and only the most significant ones are retained, being the significant coefficients determined in order to satisfy a pre-specified distortion measure. The bitmap that informs the positions of the retained coefficients is encoded along with the values of the coefficients by using an improved version of the Run Length Encoding technique. Experiments that compare the proposed approach with other techniques illustrate the efficiency of the method.

Fast computation of hologram patterns of a 3D object using run-length encoding and novel look-up table methods

In this paper we propose a new approach for fast generation of computer-generated holograms (CGHs) of a 3D object by using the run-length encoding (RLE) and the novel look-up table (N-LUT) methods. With the RLE method, spatially redundant data of a 3D object are extracted and regrouped into the N-point redundancy map according to the number of the adjacent object points having the same 3D value. Based on this redundancy map, N-point principle fringe patterns (PFPs) are newly calculated by using the 1-point PFP of the N-LUT, and the CGH pattern for the 3D object is generated with these N-point PFPs. In this approach, object points to be involved in calculation of the CGH pattern can be dramatically reduced and, as a result, an increase of computational speed can be obtained. Some experiments with a test 3D object are carried out and the results are compared to those of the conventional methods.