Length Encoding Research Articles

A data compression algorithm with the improved SRLE for high-throughput neural signal acquisition device.

Multi-channel acquisition systems of brain neural signals can provide a powerful tool with a wide range of information for the clinical application of brain computer interfaces. High-throughput implantable systems are limited by size and power consumption, posing challenges to system design. To acquire more comprehensive neural signals and wirelessly transmit high-throughput brain neural signals, a FPGA-based acquisition system for multi-channel brain nerve signals has been developed. And the Bluetooth transmission with low-power technology are utilized. To wirelessly transmit large amount of data with limited Bluetooth bandwidth and improve the accuracy of neural signal decoding, an improved sharing run length encoding (SRLE) is proposed to compress the spike data of brain neural signal to improve the transmission efficiency of the system. The functional prototype has been developed, which consists of multi-channel data acquisition chips, FPGA main control module with the improved SRLE, a wireless data transmitter, a wireless data receiver and an upper computer. And the developed functional prototype was tested for spike detection of brain neural signal by animal experiments. From the animal experiments, it shows that the system can successfully collect and transmit brain nerve signals. And the improved SRLE algorithm has an excellent compression effect with the average compression rate of 5.94%, compared to the double run-length encoding, the FDR encoding, and the traditional run-length encoding. The developed system, incorporating the improved SRLE algorithm, is capable of wirelessly capturing spike signals with 1024 channels, thereby realizing the implantable systems of High-throughput brain neural signals.

Some Aspects of Improvement of the Run Length Encoding Compression Method

Some Aspects of Improvement of the Run Length Encoding Compression Method

Utilizing IoT-Enhanced Multilayer Perceptron and Run Length Encoding for Classifying Plant Suitability Based on pH and Soil Humidity Parameters

This research proposes an IoT-based system for classifying plant suitability using pH data and soil humidity parameters. The system utilizes Run-Length Encoding (RLE) to compress sensor data, which is transmitted to a database server via the Esp8266 module. A Multilayer Perceptron (MLP) algorithm is employed to classify the data, achieving an accuracy of 0.82 with only two parameters. The classification results are displayed on a website, providing real-time recommendations for farmers. The system's performance is evaluated using a dataset from Kaggle. The Kaggle dataset contains 2200 instances for 22 different plants and the results show that the proposed system can effectively classify plant suitability based on environmental factors. This research contributes to the development of IoT-based recommendation systems for precision agriculture, and future studies can build upon this work to improve accuracy and quality.

Run length encoding based weld seam detection from point clouds of ship stiffened panel

A vision-based weld seam detector is crucial for intelligent welding in ship stiffened panel as it influences the working efficiency of welding robots. This paper introduces an innovative Run Length Encoding (RLE) based Line Segment Detector (RSD) that is capable of extracting line segments from point clouds. The RLE is firstly employed to compress point cloud data by encoding them into runs. Then, the directional feature of significant line segments is determined according to the analysis of level line angles, facilitating a global line segment detection mechanism. This enables RSD to capture more complete line segments compared to region-based detection methods. Notably, this method obviates the need for manual parameter adjustments and does not require any prior information. The effectiveness and superiority of RSD are confirmed by testing various workpieces in a laboratory setting and comparing its performance with other detectors.

An Authentic-based Privacy Preservation System for Smart E-Healthcare Systems Based on Zero Run Length Encoding and DNA Cryptography

An Authentic-based Privacy Preservation System for Smart E-Healthcare Systems Based on Zero Run Length Encoding and DNA Cryptography

CADC-FPRLE: Content-aware deduplication clustering analysis using file partitioning and a running length encoder for cloud storage optimization

To address this storage issue, we propose a Content-Aware Deduplication Clustering Analysis for Cloud Storage Optimization (CADC-FPRLE) based on a file partitioning running length encoder. At first, preprocessing was done by indexing, counting terms, cleansing, and tokenizing. Further multi-objective clustering points are analysed based on the bisecting divisible partition block, which divides a set of documents. The count terms are filtered from the divisible blocks and make up the count terms content block. Using Content-Aware Multi-Hash Ensemble Clustering (CAMH-EC) to group the similar blocks into clusters. This creates a high-dimensional Euclidean interval to create the number of clusters, and points are performed randomly to set the initial collection. Then, the Magnitude Vector Space Rate (MVSR) estimates the similarity distance between the groups to select the highest scatter value content for indexing. Finally, the Running Block Parity Encoder (RBPE) generates similarity parity in order to reduce the content to a redundant, singularized file in order to optimise storage. This implementation proves a higher level of storage optimization compared to the previous system than other methods.

Enhanced Security in Medical Imaging: A Novel Watermarking and Compression Approach

Medical images, including magnetic resonance imaging (MRI), ultrasound (US), computerized tomography (CT), X-rays, and electrocardiography (ECG), each have distinct benefits and drawbacks. Accurate identification of these images is crucial for maintaining patient-specific data integrity. This study proposes a novel watermarking technique that employs Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Singular Value Decomposition (SVD) to enhance the security, confidentiality, and integrity of medical images. Previous research by Badshah et al. underlines that digital watermarking significantly bolsters the protection of medical images. Additionally, we incorporate Run Length Encoding (RLE) as a compression method to efficiently reduce data memory requirements. The implementation of these techniques demonstrated a marked improvement in the Peak Signal-to-Noise Ratio (PSNR), increasing by up to 5 dB in watermarked images compared to non-watermarked ones, indicating enhanced imperceptibility. Moreover, the file size reduction achieved through our compression approach ranged from 15% to 30%, ensuring that high-quality images consume less storage space. These advancements facilitate the secure and efficient handling of medical image data, supporting their use in clinical environments.

Load Flow Calculation For Electrical Power System Based On Run Length Encoding Algorithm

This paper presents Data Compression Simulated algorithm for load flow calculation in electrical power systems. Real time monitor of grids required less computation time in calculation of power system analysis. Load flow problem is heart of this analysis and it basically required calculate active and reactive power flow in lines connected between buses in networks. Many topology and structures for Transmission and Distribution Systems has been proposed to reduce CPU times and memory. The proposed algorithm used Data compression technique tested different systems and results shows it is efficiency. More accuracy for large systems will need more iterations calculation which mean increasing time consumption, while Run Length Encoding (RLE) algorithm is fitness to optimized calculation numbers to exact number cause it has no zero values included. Network structure was represented as one dimension vector instead of 2D Matrix and it is effectiveness results was valid, by avoid exponential increased, by utilized this algorithm. Matlab results obtained by applied this algorithm match theoretical results.

Image compression and reconstruction based on GUI and Huffman coding

Huffman coding is an important part of image compression technology, the image compression platform is based on GUI, and Huffman is also widely used. This paper introduces the basic principle of the Huffman algorithm, compares it with arithmetic coding and run length encoding, and expounds on the application of these three algorithms in JPEG compression. The AC algorithm combined block-based, fine texture models and adaptive arithmetic coding in the given an example. The RLE algorithm used automatic threshold, direction judgment, and selective value counts to improve its compression efficiency. JPEG algorithm adopted an adaptive quantization table to reduce the distortion rate of image compression. This paper proves the possibility of a better compression rate and distortion rate of the hybrid compression algorithm by demonstrating the improved example of the basic image compression algorithm. In the future, the improved basic algorithms can be combined based on the original JPEG algorithm, and different algorithms can be integrated on the GUI to face different use environments.

Data Hiding using Slantlet Fusion Method and T-Codes

This paper presents the study of steganography algorithm based on Slantlet fusion method using a self-synchronizing variable length encoder, viz., T-codes. The Slantlet fusion method is the high bit rate data hiding scheme based on Slantlet Transorm. In this method, the fusion process takes place between the SLT of the secret data and the SLT of the cover image. The secret data is obtained by the application of SSVLC, T-codes to the original data. The proposed algorithm is compared with the Wavelet fusion method using the metrics PSNR, PSNRE, PSNRY, CQM, SSIM and KLdiv respectively. It is found that the proposed scheme is highly secure and efficient in comparison to the state of the art works.

Compression Run Length Encoding On Watermarking Using a Combination of DCT, DWT and SVD

This study focus on identifying medical images by proposing the Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD) transformation watermarking technique and prove an increase the quality of watermarked images is good in terms of imperceptibility. Due to reduce the need for data memory compression is applied to the host image, where the compression technique chosen is lossless so that the compressed host image experiences a decrease in file size while maintaining data integrity, to maintain image degradation perceptions and diagnostic quality standards during the watermarking process. Here, we use DWT-DCT-SVD and Run Length Encoding (RLE). A good Peak Signal to Noise Ratio (PSNR) more than 30 dB using over than 200 compression size. The extracted watermark image is quite good with a fairly high PSNR value. The highest compression result size is in 32.2511.

Analysis of Time Optimization for Watermark Image Quality Using Run Length Encoding Compression

Internet technology continues to have a significant impact on digital media such as text, images, audio and video. One effect is the ease of exchange, distribution, and duplication of digital data; on the other hand, this ease also raises the problem of digital data being protected by copyright or digital data is confidential. Watermarking is a solution to the problem of digital data copyright protection. Extensive research on watermarking was conducted before one of them was a hybrid DWT-DCT-SVD. Several studies have found weaknesses in the process of message insertion; for example, the time to insertion of a watermark image is relatively long, particularly for large images. The problem of long message insertion time is that it is necessary to continue research on watermarking to apply the compression process to the original image before the watermark image insertion process is performed. The original image to be inserted into the watermark image is compressed using the run-length encoding algorithm. The result of the RLE compression shows that the image file size becomes large because the image dataset used has various intensity values. The results from watermarking obtained by watermarked images with RLE compression preprocessing have better imperceptibility than watermarked images without preprocessing RLE compression

A new lossless electroencephalogram compression technique for fog computing‐based IoHT networks

SummaryThe ability to design smarter, more predictive healthcare solutions for use in the community (at work and at home) and in healthcare facilities has been greatly enhanced by recent developments in the Internet of Health Things (IoHT) and cyber physical systems (CPS). These data collected by such medical sensors will be sent in a large quantity to the fog gateway of IoHT networks. These data should be forwarded to far cloud for further analysis and processing. Therefore, sending all of these data over the IoHT network to the data center will impose a significant burden on the IoHT network. This paper proposed a new lossless electroencephalogram (EEG) compression technique (NeLECoT) for fog computing‐based IoHT networks. It encodes the data of the patient at the fog gateway prior to sending it to the data center, thereby reducing the data's size. In the fog node, the NeLECoT combines three efficient techniques: clustering based on density‐based spatial clustering of applications with noise (DBSCAN), RLE (run length encoding), and Huffman encoding (HE). The clustering based on DBSCAN separates a massive volume of captured data into small groups of closely related (or similar) data. RLE encodes clustered EEG data, and the resulting file is encoded with HE. The fog gateway then transmits the encoded file to the cloud. Numerous simulation experiments were carried out, and the findings demonstrated that the suggested NeLECoT achieved better results than the competing techniques in terms of transmitted data size, compression ratio, compression power, compression time, decompression time, and average compression power.

Run Length Encoding Compresion on Virtual Tour Campus to Enhance Load Access Performance

Virtual tour is one of the rapidly growing applications of multimedia technology which is used for various purposes, including the dissemination of information in an interesting way. The education sector is also not spared from using virtual tour media for promotional purposes, and campuses are no exception to this rule. Large virtual tour content causes high access speed, ultimately reducing the level of comfort experienced by users. This study aims to compress panoramic images displayed on a campus virtual tour using a lossless compression method and the Run Length Encoding (RLE) algorithm. First, panoramic images are combined into one, then individual images are compressed. When recreating a virtual campus tour, compressed images are used so that the amount of data transferred is smaller. The load access speed index increases from 7,233 seconds to 3,789 seconds when images are compressed from 64 bits to 8 bits, with a compression percentage of 27%. The findings from this research are that the RLE algorithm has not been able to compress large files effectively even though it is quite successful in increasing the load access of the virtual tour website.

Comparative Analysis of Multimedia Compression Algorithms

Data compression reduces the amount of bits needed to encode information, conserving expensive resources like disc space and transmission bandwidth. By using data compression, an information block's real size is kept as little as possible. The compression process consists of two steps: encoding, which uses fewer bits to compress a message, and decoding, which uses the compressed representation to roughly resemble or replicate the original message. The two main types of data compression are lossless compression and lossy approaches. The Huffman, Lempel-Ziv, and Run Length encoding techniques were all examined in this research, along with three other commonly used compression techniques. According to the research, Run Length Encoding was the best method for reducing the size of text, web pages, images, and sound.

MAS-Net OCT: a deep-learning-based speckle-free multiple aperture synthetic optical coherence tomography.

High-resolution spectral domain optical coherence tomography (SD-OCT) is a vital clinical technique that suffers from the inherent compromise between transverse resolution and depth of focus (DOF). Meanwhile, speckle noise worsens OCT imaging resolving power and restricts potential resolution-enhancement techniques. Multiple aperture synthetic (MAS) OCT transmits light signals and records sample echoes along a synthetic aperture to extend DOF, acquired by time-encoding or optical path length encoding. In this work, a deep-learning-based multiple aperture synthetic OCT termed MAS-Net OCT, which integrated a speckle-free model based on self-supervised learning, was proposed. MAS-Net was trained on datasets generated by the MAS OCT system. Here we performed experiments on homemade microparticle samples and various biological tissues. Results demonstrated that the proposed MAS-Net OCT could effectively improve the transverse resolution in a large imaging depth as well as reduced most speckle noise.

Perception of length and orientation in dry immersion.

How does gravity (or lack thereof) affect sensory-motor processing? We analyze sensorimotor estimation dynamics for line segments with varying direction (orientation) in a 7-day dry immersion (DI), a ground-based model of gravitational unloading. The measurements were carried out before the start of the DI, on the first, third, fifth and seventh days of the DI, and after its completion. At the memorization stage, the volunteers led the leading hand along the visible segment on a touchscreen display, and at the reproduction stage they repeated this movement on an empty screen. A control group followed the same procedure without DI. Both in the DI and control groups, when memorizing, the overall error in estimating the lengths and directions of the segments was small and did not have pronounced dynamics; when reproducing, an oblique effect (higher variability of responses to oblique orientations compared to cardinal ones) was obtained. We then separated biases (systematic error) and uncertainty (random error) in subjects' responses. At the same time, two opposite trends were more pronounced in the DI group during the DI. On the one hand the cardinal bias (a repulsion of orientation estimates away from cardinal axes) and, to a small extent, the variability of direction estimates decreased. On the other hand, the overestimation bias in length estimates increased. Such error pattern strongly supports the hypotheses of the vector encoding, in which the direction and length of the planned movement are encoded independently of each other when the DI disrupts primarily the movement length encoding.

Non-Decimated Wavelet Transform and Vector Quantization for Lossy Medical Images Compression

This study presents a new approach for lossy medical image compression using vector quantization. Recently, the digital image has been a reliable replacement for a hard copy of medical images, therefore, an effort has been made to ensure maintaining high-quality images to use for archiving, classification, or automated diagnostics support. Although the medical application contains all sorts of the images like microscopic, X-rays, tomography, and fiber optics imaging by angioplasty, all of this comes at the cost of using digital storage that needs to be regularly backed up and maintained and to help minimize the need for larger storage media, this study is focusing on applying Non-Decimated Wavelet Transform (NDWT) and combined lossy and lossless compression techniques that will allow the images to take much smaller storage space while maintaining the high level of quality for these images. This study is focusing on chest X-ray images compression using a combination of lossy compression techniques using two Vector Quantization (VQ) algorithms such as k-means clustering and Linde, Buzo, and Gray (LBG) algorithm, and three lossless compression techniques such as Arithmetic Coding (AC), Run Length Encoding (RLE) and Huffman Coding (HC) and choose the optimum combination of them. Then, the performance is measured using Compression Ratio (CR), processing time, or called run time, Peak Signal to Noise Ratio (PSNR), and Bit Rate.

Three States QRLE (Quantized Run Length Encoding) Based JPEG Image Compression Method

Three States QRLE (Quantized Run Length Encoding) Based JPEG Image Compression Method

Investigation of Energy Cost of Data Compression Algorithms in WSN for IoT Applications.

The exponential growth in remote sensing, coupled with advancements in integrated circuits (IC) design and fabrication technology for communication, has prompted the progress of Wireless Sensor Networks (WSN). WSN comprises of sensor nodes and hubs fit for detecting, processing, and communicating remotely. Sensor nodes have limited resources such as memory, energy and computation capabilities restricting their ability to process large volume of data that is generated. Compressing the data before transmission will help alleviate the problem. Many data compression methods have been proposed but mainly for image processing and a vast majority of them are not pertinent on sensor nodes because of memory impediment, energy utilization and handling speed. To overcome this issue, authors in this research have chosen Run Length Encoding (RLE) and Adaptive Huffman Encoding (AHE) data compression techniques as they can be executed on sensor nodes. Both RLE and AHE are capable of balancing compression ratio and energy utilization. In this paper, a hybrid method comprising RLE and AHE, named as H-RLEAHE, is proposed and further investigated for sensor nodes. In order to verify the efficacy of the data compression algorithms, simulations were run, and the results compared with the compression techniques employing RLE, AHE, H-RLEAHE, and without the use of any compression approach for five distinct scenarios. The results demonstrate the RLE’s efficiency, as it surpasses alternative data compression methods in terms of energy efficiency, network speed, packet delivery rate, and residual energy throughout all iterations.