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The Burrows–Wheeler Transform (BWT) is a string transformation technique widely used in areas such as bioinformatics and file compression. Many applications combine a run-length encoding (RLE) with the BWT in a way which preserves the ability to query the compressed data efficiently. However, these methods may not take full advantage of the compressibility of the BWT as they do not modify the alphabet ordering for the sorting step embedded in computing the BWT. Indeed, any such alteration of the alphabet ordering can have a considerable impact on the output of the BWT, in particular on the number of runs. For an alphabet Σ containing σ characters, the space of all alphabet orderings is of size σ ! . While for small alphabets an exhaustive investigation is possible, finding the optimal ordering for larger alphabets is not feasible. Therefore, there is a need for a more informed search strategy than brute-force sampling the entire space, which motivates a new heuristic approach. In this paper, we explore the non-trivial cases for the problem of minimizing the size of a run-length encoded BWT (RLBWT) via selecting a new ordering for the alphabet. We show that random sampling of the space of alphabet orderings usually gives sub-optimal orderings for compression and that a local search strategy can provide a large improvement in relatively few steps. We also inspect a selection of initial alphabet orderings, including ASCII, letter appearance, and letter frequency. While this alphabet ordering problem is computationally hard we demonstrate gain in compressibility. [ABSTRACT FROM AUTHOR]

Multimedia applications, such as image processing including image and video transfer, heavily rely on reduction. The traditional device methods to picture reduction use more space, energy, and processing time. The majority of current efforts use the Golomb‐Rice encoding, due to its larger memory requirement and higher computing difficulty. So, this research concentrated on hardware design‐oriented probability run length (PRL) coding technique based on lossless colour image compression. The block truncation coding (BTC) features of the compression process are used by the suggested PRL method. The proposed image compression hardware consists of various modules such as a Parameter calculator, fuzzy table, bitmap generator, BTC parameters training, prediction, and error control, and PRL‐based finite state machine (PRL‐FSM). The proposed image compressor utilizes the parameter calculator block, which estimates the block type based on the image pixel intensities for each sub‐block. Thus, each block of the image is compressed by using a new block type and generates a variable block size. The proposed method utilizes the PRL‐BTC encoding method, which also calculates the probability of error between the compressed image to the test image. The process is iterated until the performance trade‐off between hardware cost and compression ratio (CR) is achieved. Hence, both smooth regions and non‐smooth regions of images are perfectly compressed by the probability‐based block selection. The simulation results show that the proposed method resulted in a better area, power, delay metrics, peak signal‐to‐noise ratio (PSNR), and CR compared to the state of art approaches. [ABSTRACT FROM AUTHOR]

BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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. [ABSTRACT FROM AUTHOR]

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.

In this paper, we propose a data-driven procedure to segment a binary sequence as an alternative to the popular hidden Markov model (HMM) based procedure. Unlike the HMM, our procedure does not make any distributional or model assumption to the data. To segment the sequence, we suggest to minimize the least square distance from the observations under total variation regularization to the solution, and develop a polynomial time algorithm for it. Finally, we illustrate the algorithm using a toy example and apply it to the Gemini boat race data between Oxford and Cambridge University. Further, we numerically compare the performance of our procedure to the HMM based segmentation through these examples. [ABSTRACT FROM AUTHOR]

In telemedicine-based healthcare system, such as cardiac health monitoring system, large amount of data needs to be stored and transferred. This requires stupendous bandwidth and affects the channel efficiency. The main objective is to develop an efficient compression technique for solving such problems in healthcare systems. In this work, the coalition of empirical mode decomposition (EMD) and tunable quality wavelet transform (TQWT) scheme has been proposed for ECG signal compression with a suitable decomposition level. Thus, the maximum energy is packed for fewer coefficients which have a significant contribution to the original signal. The dynamic thresholding and dead-zone quantization are evaluated, to discard the wavelet coefficients with a small value near zero. Subsequently, a run-length encoding (RLE) lossless compression scheme is employed to encode the wavelet coefficients. The presented technique was evaluated on the Massachusetts Institute of Technology-Beth Israel Hospital (MITDB) arrhythmias dataset which contain regular and irregular heart rhythm. The compression ratio (CR%), percent root-mean-square error (PRD%), normalized PRD (NPRD%), quality score (QS), and signal-to-noise ratio (SNR) of 33.11, 4.35, 8.21, 7.59, and 51.09 have been achieved, after implementing on 48 ECG records with 30-min duration. The presented method was also implemented for normal and abnormal heartbeat classification for validation. The random forest algorithm (RFA) is employed for the classification of cardiac rhythm. The results show minimal distortion with an improved reconstruction of a signal after the compression and show a better performance than the state-of-art technique. [ABSTRACT FROM AUTHOR]

Multi-robot Simultaneous Localization and Mapping (SLAM) systems employing 2D lidar scans are effective for exploration and navigation within GNSS-limited environments. However, scalability concerns arise with larger environments and increased robot numbers, as 2D mapping necessitates substantial processor memory and inter-robot communication bandwidth. Thus, data compression prior to transmission becomes imperative. This study investigates the problem of communication-efficient multi-robot SLAM based on 2D maps and introduces an architecture that enables compressed communication, facilitating the transmission of full maps with significantly reduced bandwidth. We propose a framework employing a lightweight feature extraction Convolutional Neural Network (CNN) for a full map, followed by an encoder combining Huffman and Run-Length Encoding (RLE) algorithms to further compress a full map. Subsequently, a lightweight recovery CNN was designed to restore map features. Experimental validation involves applying our compressed communication framework to a two-robot SLAM system. The results demonstrate that our approach reduces communication overhead by 99% while maintaining map quality. This compressed communication strategy effectively addresses bandwidth constraints in multi-robot SLAM scenarios, offering a practical solution for collaborative SLAM applications. [ABSTRACT FROM AUTHOR]

Currently, an approach involving a coder with a combined structure for compressing images combining several different coders, the system for connecting them to various bit planes, and the control system for these connections have not been studied. Thus, there is a need to develop a structure and study the effectiveness of a combined codec for compressing images of various types without loss in the spatial domain based on arithmetic and (Run-Length Encoding) RLE-coding algorithms. The essence of separate effective coding is to use independent coders of the same type or one coder connected to the planes alternately in order to compress the higher and lower bit planes of the image or their combinations. In this paper, the results of studying the effectiveness of using a combination of arithmetic and RLE coding for several types of images are presented. As a result of developing this structure, the effectiveness of combined coding for compressing the differences in the channels of hyperspectral images (HSI) has been established, as hyperspectral images consist of multi-spectral bands, instead of just the typical three bands (RGB) or (YCbCr) found in regular images. Where, each pixel in a hyperspectral image represents the entire spectrum of light reflected by the object or scene at that particular location. [ABSTRACT FROM AUTHOR]

Goal: Purpose of this paper is Study of the efficiency of using RLE and Hoffman compression methods for image compression, taking into account the limited resources of microcontrollers and, accordingly, the need to reduce image transmission time and reduce energy consumption in the wireless sensor networks. Method: An experimental study of the degree of image compression was conducted using the Hoffman and RLE methods. The algorithms are implemented in software for the ESP32-CAM microcontroller module with a built-in camera. The degree of image compression performed using the Hoffman and RLE algorithms was evaluated and compared. Results: A software module was created to implement the Huffman and RLE compression algorithms on the ESP32-CAM microcontroller. The effectiveness of using Huffman and RLE algorithms for image compression with limited microcontroller resources is investigated, compression efficiency and compressed file sizes are compared. As a result of the research, it was found that the RLE method showed a low compression efficiency (less than 1%) compared to the Huffman method, which provided a compression ratio of 11% for the image under test. The previous use of the Haar wavelet transform has a positive effect on the compression results for both investigated methods: for RLE, the degree of compression increased to 16%; for Huffman to 31% without loss of image quality. The JPEG method was used for efficiency comparison, which provides a degree of compression of the same image up to 70%, but with a loss of compressed data quality. Scientific innovation: An analysis of the data compression efficiency using the Huffman and RLE methods, implemented taking into account the limited computing capabilities of 32-bit microcontrollers with the use of additional external memory, was performed. The influence of the choice of method on the time of image processing and the amount of memory used was studied. Practical significance: Software module has been created that implements Huffman and RLE data compression algorithms. The expediency of using the above methods in systems on 32-bit microcontrollers to reduce energy consumption during image transformations and transmission, which makes it possible to extend the battery life of the sensor node, has been confirmed. At the same time, the investigated algorithms for image compression can ensure the quality of the original image with the least possible data loss. [ABSTRACT FROM AUTHOR]

The electrocardiogram (ECG) signals are commonly used to identify heart complications. These recordings generate large data that needed to be stored or transferred in telemedicine applications, which require more storage space and bandwidth. Therefore, a strong motivation is present to develop efficient compression algorithms for ECG signals. In the above context, this work proposes a novel compression algorithm using adaptive tunable-Q wavelet transform (TQWT) and modified dead-zone quantizer (DZQ). The parameters of TQWT and threshold values of DZQ are selected using the proposed Sparse-grey wolf optimization (Sparse-GWO) algorithm. The Sparse-GWO is proposed in this work to reduce the computation time of the original GWO. Moreover, it is also compared with some popular algorithms such as original GWO, particle swarm optimization (PSO), Hybrid PSOGWO, and Sparse-PSO. The DZQ has been utilized to perform thresholding and quantization. Then, run-length encoding (RLE) has been used to encode the quantized coefficients. The proposed work has been performed on the MIT-BIH arrhythmia database. Quality assessment performed on reconstructed signals ensure the minimal impact of compression on the morphology of reconstructed ECG signals. The compression performance of proposed algorithm is measured in terms of the following evaluation matrices: percent root-mean-square difference (PRD 1), compression ratio (CR), signal-to-noise ratio (SNR), and quality score (QS 1). The obtained average values are 3.21%, 20.56, 30.62 dB, and 7.79, respectively. • Proposed an ECG compression techniques using adaptive TQWT and modified DZQ. • DZQ and run-length encoding are used for compression. • Sparse-GWO algorithm is proposed by modifying standard GWO. • Sparse-GWO is used for parameter optimization along with other algorithms. • Results are compared with existing ECG signal compression techniques. [ABSTRACT FROM AUTHOR]

An ECG signal compression scheme using both Discrete Anamorphic Stretch Transform (DAST) and Discrete wavelet transform (DWT) is reported in the literature. In this scheme, a linear filter is used for the recovery of the phase of the DAST for the computation of inverse DAST. However, at a higher compression ratio, the phase recovered and the reconstructed signal became increasingly inaccurate. To overcome this problem, a phase recovery technique that uses only an adder with fixed offset is proposed in this paper. A scheme for optimizing the DAST kernels to increase the compression ratio is also proposed. To evaluate their effectiveness, the ECG signals of the MIT-BIH Arrhythmia database are compressed using 6 level 1D DWT and run-length encoding with and without DAST pre-compressor and their performances are compared. The phase of DAST of the ECG signals recovered using both linear filter and adder are computed and compared for different kernels. The proposed phase recovery scheme with the optimized sublinear and linear kernels provides a 12.2% and 10.91% increase in data compression ratio and 24.63% and 28.73% decrease in percentage root mean square difference (PRD) with 99.9% of energy packing efficiency (EPE) compared to the scheme using the filter. The compression scheme using DAST provides a 2.81 times higher compression ratio compared to that without DAST. The proposed compression scheme yields a higher Compression Ratio and lower PRD than those reported in the literature. It can be used as pre-compressor for the real time transmission of ECG signal with lower bandwidth. [ABSTRACT FROM AUTHOR]

Motivation The Positional Burrows–Wheeler Transform (⁠ PBWT ⁠) is a data structure that indexes haplotype sequences in a manner that enables finding maximal haplotype matches in h sequences containing w variation sites in O (h w) time. This represents a significant improvement over classical quadratic-time approaches. However, the original PBWT data structure does not allow for queries over Biobank panels that consist of several millions of haplotypes, if an index of the haplotypes must be kept entirely in memory. Results In this article, we leverage the notion of r-index proposed for the BWT to present a memory-efficient method for constructing and storing the run-length encoded PBWT, and computing set maximal matches (SMEMs) queries in haplotype sequences. We implement our method, which we refer to as μ - PBWT ⁠ , and evaluate it on datasets of 1000 Genome Project and UK Biobank data. Our experiments demonstrate that the μ - PBWT reduces the memory usage up to a factor of 20% compared to the best current PBWT-based indexing. In particular, μ - PBWT produces an index that stores high-coverage whole genome sequencing data of chromosome 20 in about a third of the space of its BCF file. μ - PBWT is an adaptation of techniques for the run-length compressed BWT for the PBWT (RLPBWT) and it is based on keeping in memory only a succinct representation of the RLPBWT that still allows the efficient computation of set maximal matches (SMEMs) over the original panel. Availability and implementation Our implementation is open source and available at https://github.com/dlcgold/muPBWT. The binary is available at https://bioconda.github.io/recipes/mupbwt/README.html. [ABSTRACT FROM AUTHOR]

In this paper, we have presented a comprehensive study optimizing the performance of the N-th degree truncated polynomial ring units (NTRU) post-quantum cryptography (PQC) on advanced RISC machines (ARM)- based internet of things (IoT) devices, with specific focus on Raspberry Pi boards, for integration within edge layer and fog-based IoT architecture. Our objective was to expedite encryption and decryption processing times for five security levels. To achieve this, we adopted Shamir's Secret Sharing (SSS) to facilitate secure NTRU public key exchanges and utilized run-length encoding (RLE) to compress ciphertext effectively. Our results confirmed a compression ratio of approximately 69.25%, suggesting a strong potential for efficient data transfer. The empirical outcomes of our research evidenced considerable improvements in NTRU performance on ARM-based devices, with encryption and decryption times improvement by 80% and 33%, respectively, for 128-bit payloads. [ABSTRACT FROM AUTHOR]

The block reversal of a word w , denoted by B R (w) , is a generalization of the concept of the reversal of a word, obtained by concatenating the blocks of the word in the reverse order. We characterize non-binary and binary words whose block reversal contains only rich words. We prove that for a binary word w , richness of all elements of B R (w) depends on l (w) , the length of the run sequence of w. We show that if all elements of B R (w) are rich, then 2 ≤ l (w) ≤ 8. We also provide the structure of such words. [ABSTRACT FROM AUTHOR]

With its wider acceptability, cloud can host a diverse set of data and applications ranging from entertainment to personal to industry. The foundation of cloud computing is based on virtual machines where boundaries among the application data are very thin, and the potential of data leakage exists all the time. For instance, a virtual machine covert timing channel is an aggressive mechanism to leak confidential information through shared components or networks by violating isolation and security policies in practice. The performance of a covert timing channel (covert channel) is crucial to adversaries and attempts have been made to improve the performance of covert timing channels by advancing the encoding mechanism and covert information carriers. Though promising, the redundancy of the covert message is mainly overlooked. This paper applies three encoding schemes namely run-length, Huffman, and arithmetic encoding schemes for data compression of a virtual machine covert timing channel by exploiting redundancy. Accordingly, the paper studies the performance of such channels according to their capacity. Unfortunately, we show that these encoding schemes still contain redundancy in a covert channel scenario, and thereby a new encoding scheme namely optimized Runlength encoding (OptRLE) is presented that greatly enhances the performance of a covert timing channel. Several optimizations schemes adopted by OptRLE are also discussed, and a mathematical model of the behavior of an OptRLE-based covert timing channel is proposed. The theoretical capacity of a channel can be obtained using the proposed model. Our analysis reveals that OptRLE further improves the performance of a covert timing channel, in addition to the effects of the optimizations. Experimental result shows how OptRLE affects the size of covert data and the capacity of covert timing channels, and why the performance of the covert timing channel is improved. [ABSTRACT FROM AUTHOR]

The ECG (electrocardiogram) signals are an indicator of the electrical activity of the heart. Given its noninvasive nature ECG are an extremely popular medium for heart checkups. With the advent of modern technology, the world is moving toward a connected environment, and with the availability of wearable devices, there is an exponential increase in the transmission and storage of ECG and other physiological signals. It becomes necessary to compress the ECG signals for storage and transmission. Therefore, this paper presents an ECG compression algorithm based on discrete wavelet transform (DWT) and several nature-inspired optimization techniques. The ECG compression method uses optimization techniques to find the optimal values of wavelet design parameters and optimal threshold levels. In the proposed work, DWT is used to decompose the signal into sub-bands, and coefficients are obtained. Then, threshold values for each sub-band are selected using the optimization algorithms. After thresholding, the coefficients are further compressed using the modified run-length encoding (MRLE). The proposed work shows promising results and the original signal features are well preserved after reconstruction. The performance of this algorithm is tested by calculating different parameters such as percentage root-mean-square difference (PRD), quality score (QS), signal-to-noise ratio (SNR), and compression ratio (CR). This method is capable of providing a higher compression ratio with minimum distortion in ECG signal. [ABSTRACT FROM AUTHOR]

The exponential growth of the Internet of Things (IoT) has resulted in an unprecedented surge in data, requiring the development of innovative methods to effectively handle and analyze information. This study investigates the 1management of data in a smart manner within the IoT framework. It specifically examines the utilization of Wireless Sensor Networks (WSN) to accomplish effective information processing. The study examines two main elements: the utilization of Run-Length Encoding (RLE) for data compression and the incorporation of energy-aware extensions into the Ad Hoc On-Demand Distance Vector (AODV-EA) routing protocol. The purpose of employing Run-Length Encoding (RLE) is to enhance the efficiency of transmitting and storing data by effectively representing recurring sequences in sensor data. This compression technique is especially applicable for resource-constrained WSN where the conservation of bandwidth and energy is of utmost importance. The study investigates how communication protocols can be improved by integrating energy-conscious extensions into AODV. The objective of this approach is to enhance the energy efficiency of communication in WSN by taking into account the energy levels of each node in real-time when establishing routes. The NS-3 simulation framework is used to assess the proposed methodologies. NS-3 offers a flexible and expandable framework for simulating communication protocols and network scenarios. The study evaluates the performance of the integrated system by using simulation and analyzing important metrics such as accuracy, precision, Latency, data compression and energy efficiency. The research findings provide valuable insights into the field of Smart Data Management in IoT, demonstrating how the combination of data compression and energy-aware routing protocols can improve the efficiency of Wireless Sensor Networks for information processing. [ABSTRACT FROM AUTHOR]

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To further improve the performance of reversible data hiding in encrypted image, an algorithm for lossless compression of the prediction error bit-plane using joint encoding was proposed, which could make full use of image redundancy and reserve more embedding room.Firstly, the image owner calculated the prediction error of the image and divided the prediction error bit-plane into non-overlapping blocks of the same size.Then, the prediction error bit-plane was rearranged according to blocks and the rearranged bitstream was compressed by run-length encoding and Huffman encoding to reserve room.The data hider embedded information in the reserved room of the encrypted image.At the receiving end, the legitimate receiver extracted information and recovered images losslessly and separately.Experimental results show that the proposed algorithm makes full use of the bit-plane distribution characteristics and achieves higher embedding performance.The average embedding rates in BOSSbase and BOWS-2 datasets reach 3.763 bpp and 3.642 bpp, which are at least 0.081 bpp and 0.058 bpp higher than the state-of-the-art algorithms.

A non-linear encryption approach for compressed grayscale and color images is proposed in Double Random Phase Encoding (DRPE) setup using two compression algorithms: Set Partitioning in Hierarchical Trees (SPIHT) and Run-length encoding (RLE). The compressed image is then segmented and embedded in multiple Quick Response (QR) codes, which are combined again into a single image. Further, this single image is scrambled by two-coupled chaos logistic maps, at which the scrambled image is now ready for DRPE encryption. The use of two-coupled chaos logistic increases the security level of this proposed DRPE encryption from various attacks. The proposed DRPE encryption is extended to handed color images, which are segregated into three color-channels (Red, Green and Blue), where each channel is individually encrypted. For grayscale images, the proposed system will have three security key codes while for color images it has nine security key codes. The robustness and invulnerability of the proposed scheme against various attacks is demonstrated through cryptoanalysis and computer simulation results. [ABSTRACT FROM AUTHOR]

This paper describes a lossy compression scheme for high dynamic range graylevel and color imagery for data transmission purposes in real-time mapping scenarios. The five stages of the implemented non-standard transform coder are written in portable C++ code and do not require specialized hardware to run. Storage space occupied by the bitmaps is reduced via a color space change, 2D integer discrete cosine transform (DCT) approximation, coefficient quantization, two-size run-length encoding and dictionary matching hinged on the LZ4 algorithm. Quantization matrices to eliminate insignificant DCT coefficients are derived from a representative image set through genetic optimization. The underlying fitness function incorporates the obtained output size, classic image quality metrics and the unique color count. Together with a zone-based adaptation mechanism, this allows to specify target bitrates instead of percentage values or abstract quality factors for the reduction rate to be directly matched to the available communication channel capacities. Results on a camera control unit of a fixed-wing unmanned aircraft system built around entry-level PC hardware revealed single-thread compression and decompression throughputs of several hundred mebibytes per second for full-swing 16 and 32 bit RGB imagery at medium compression ratios. A degradation in image quality compared to popular compression libraries could be identified, however, at acceptable levels statistically and visually. [ABSTRACT FROM AUTHOR]

Dynamic Time Warping (DTW) is a well-known similarity measure for time series. The standard dynamic programming approach to compute the DTW distance of two length-n time series, however, requires O (n 2) time, which is often too slow for real-world applications. Therefore, many heuristics have been proposed to speed up the DTW computation. These are often based on lower bounding techniques, approximating the DTW distance, or considering special input data such as binary or piecewise constant time series. In this paper, we present a first exact algorithm to compute the DTW distance of two run-length encoded time series whose running time only depends on the encoding lengths of the inputs. The worst-case running time is cubic in the encoding length. In experiments we show that our algorithm is indeed fast for time series with short encoding lengths. [ABSTRACT FROM AUTHOR]

The Extended Exponentially Weighted Moving Average (extended EWMA) control chart is one of the control charts and can be used to quickly detect a small shift. The performance of control charts can be evaluated with the average run length (ARL). Due to the deriving explicit formulas for the ARL on a two-sided extended EWMA control chart for trend autoregressive or trend AR(p) model has not been reported previously. The aim of this study is to derive the explicit formulas for the ARL on a two-sided extended EWMA control chart for the trend AR(p) model as well as the trend AR(1) and trend AR(2) models with exponential white noise. The analytical solution accuracy was obtained with the extended EWMA control chart and was compared to the numerical integral equation (NIE) method. The results show that the ARL obtained by the explicit formula and the NIE method is hardly different, but the explicit formula can help decrease the computational (CPU) time. Furthermore, this is also expanded to comparative performance with the Exponentially Weighted Moving Average (EWMA) control chart. The performance of the extended EWMA control chart is better than the EWMA control chart for all situations, both the trend AR (1) and trend AR(2) models. Finally, the analytical solution of ARL is applied to real-world data in the health field, such as COVID-19 data in the United Kingdom and Sweden, to demonstrate the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

This article presents a polynomial-based image compression scheme, which consists of using the color model (YUV) to represent color contents and using two-dimensional polynomial coding (first-order) with variable block size according to correlation between neighbor pixels. The residual part of the polynomial for all bands is analyzed into two parts, most important (big) part, and least important (small) parts. Due to the significant subjective importance of the big group; lossless compression (based on Run-Length spatial coding) is used to represent it. Furthermore, a lossy compression system scheme is utilized to approximately represent the small group; it is based on an error-limited adaptive coding system and using the transform coding scheme (discrete cosine transform or bi-orthogonal transform). Experimentally, the developed system has achieved high compression ratios with acceptable quality for color images. The performance results are comparable to those introduced in recent studies; the accomplishment of the introduced image compression system was analyzed and compared with the performance of the JPEG standard. The results of the developed system show better performance than that of the JPEG standard. [ABSTRACT FROM AUTHOR]

Test data volume reduction and power consumption during testing time outlines are two main problems for Very Large Scale Integration (VLSI) gadgets. Most the code-based arrangements have been utilized to diminish test data volume, although the most notable way that test data volume is high. The switching action that happens between the test carriers leads would expand power consumption. This work presents a compression/decompression methodology for limiting the amount of test data that should be kept on a tester and conveyed to each center in a System on a Chip (SOC) during a test utilizing the Adaptive Multiscale Transformation Run Length Code (AMT-RLC). The data transmission capacity between the tester and the SOC is a block when testing modern SOCs with a few centers to check how long the test can run. The proposed work keeps the tester in the SOC and the test vectors in a compacted memory structure. A modest quantity of On-chip equipment is needed to release different test vectors. Due to its variouscharacteristics that decrease the test data volume, the Adaptive Multiscale Transformation Run Length Code is picked for a center test vector framework. These attributes ensure that the code words overhead is decoded (and applied to the center's output exhibit grouping commitment) utilizing an exact cell cluster that occupies less space. The suggested strategy may provide test data compression with much-reduced Area overhead for the decoder and low power consumption, according to the results. The abstract shall be running continuously (not structured) and shall not include reference citations. Abbreviations should be defined in full the first time they appear. Abbreviations could be then used, quoted in-between parentheses. [ABSTRACT FROM AUTHOR]

Automatic extraction of the skeleton lines of river systems from high-resolution remote-sensing images has great significance for surveying and managing water resources. A large number of existing methods for the automatic extraction of skeleton lines from raster images are primarily used for simple graphs and images (e.g., fingerprint, text, and character recognition). These methods generally are memory intensive and have low computational efficiency. These shortcomings preclude their direct use in the extraction of skeleton lines from large volumes of high-resolution remote-sensing images. In this study, we developed a method to extract river skeleton lines based entirely on run-length encoding. This method attempts to replace direct raster encoding with run-length encoding for storing river data, which can considerably compress raster data. A run-length boundary tracing strategy is used instead of complete raster matrix traversal to quickly determine redundant pixels, thereby significantly improving the computational efficiency. An experiment was performed using a 0.5 m-resolution remote-sensing image of Yiwu city in the Chinese province of Zhejiang. Raster data for the rivers in Yiwu were obtained using both the DeepLabv3+ deep learning model and the conventional visual interpretation method. Subsequently, the proposed method was used to extract the skeleton lines of the rivers in Yiwu. To compare the proposed method with the classical raster-based skeleton line extraction algorithm developed by Zhang and Suen in terms of memory consumption and computational efficiency, the visually interpreted river data were used to generate skeleton lines at different raster resolutions. The results showed that the proposed method consumed less than 1% of the memory consumed by the classical method and was over 10 times more computationally efficient. This finding suggests that the proposed method has the potential for river skeleton line extraction from terabyte-scale remote-sensing image data on personal computers. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• The proposed compression technique is based on modified run-length encoding of wavelet coefficients. • Wavelet decomposition packs maximum energy of the signal to few transform coefficients. • Dead-zone quantization discards small valued transform coefficients. • Modified run-length encoding offers higher compression ratio without any information loss. In cardiac patient-care, compression of long-term ECG data is essential to minimize the data storage requirement and transmission cost. Hence, this paper presents a novel electrocardiogram data compression technique which utilizes modified run-length encoding of wavelet coefficients. First, wavelet transform is applied to the ECG data which decomposes it and packs maximum energy to less number of transform coefficients. The wavelet transform coefficients are quantized using dead-zone quantization. It discards small valued coefficients lying in the dead-zone interval while other coefficients are kept at the formulated quantized output interval. Among all the quantized coefficients, an average value is assigned to those coefficients for which energy packing efficiency is less than 99.99%. The obtained coefficients are encoded using modified run-length coding. It offers higher compression ratio than conventional run-length coding without any loss of information. Compression performance of the proposed technique is evaluated using different ECG records taken from the MIT-BIH arrhythmia database. The average compression performance in terms of compression ratio, percent root mean square difference, normalized percent mean square difference, and signal to noise ratio are 17.18, 3.92, 6.36, and 28.27 dB respectively for 48 ECG records. The compression results obtained by the proposed technique is better than techniques recently introduced by others. The proposed technique can be utilized for compression of ECG records of Holter monitoring. [ABSTRACT FROM AUTHOR]

Reversible data hiding (RDH) is a recently emerged research domain in the field of information security domain with broad applications in medical images and meta-data handling in the cloud. The amount of data required to handle the healthcare sector has exponentially increased due to the increase in the population. Medical images and various reports such as discharge summaries and diagnosis reports are the most common data in the healthcare sector. The RDH schemes are widely explored to embed the medical reports in the medical image instead of sending them as separate files. The receiver can extract the clinical reports and recover the original medical image for further diagnosis. This manuscript proposes an approach that uses a new lossless compression-based RDH scheme that creates vacant room for data hiding. The proposed scheme uses run-length encoding and a modified Elias gamma encoding scheme on higher-order bit planes for lossless compression. The conventional Elias gamma encoding process is modified in the proposed method to embed some additional data bits during the encoding process itself. The revised approach ensures a high embedding rate and lossless recovery of medical images at the receiver side. The experimental study is conducted on both natural images and medical images. The average embedding rate from the proposed scheme for the medical images is 0.75 bits per pixel. The scheme achieved a 0 bit error rate during image recovery and data extraction. The experimental study shows that the newly introduced scheme performs better when compared with the existing RDH schemes. [ABSTRACT FROM AUTHOR]

This paper proposes a lossless encryption–compression algorithm for large-scale remote sensing images. Firstly, the red, green and blue components of color image are compressed by a lossless predictive encoding. Then, the lifting wavelet transform (LWT) is used to decompose the encoding results. And a new Rubik's cube transformation is introduced to scramble the decomposed coefficients, which uses the chaotic sequence generated by 2D Cubic–Chebyshev map (2D-CCM). The initial values of 2D-CCM are obtained from the chi-square test values of the three components, which leads to the algorithm related to the plaintext image. After that, the scrambling coefficients are thresholding, and the position sequences generated in the process are encrypted and compressed by the proposed encrypted run-length encoding (E-RLE). The processed coefficients are further compressed by Huffman encoding. At the end, the final results are obtained by a novel helix diffusion which is related to the chaotic sequence. Experimental results show that, this algorithm achieves higher lossless compression ratio with lower time complexity, and the encryption scheme has higher security. [ABSTRACT FROM AUTHOR]

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The increase in popularity of point-cloud-oriented applications has triggered the development of specialized compression algorithms. In this paper, a novel algorithm is developed for the lossless geometry compression of voxelized point clouds following an intra-frame design. The encoded voxels are arranged into runs and are encoded through a single-pass application directly on the voxel domain. This is done without representing the point cloud via an octree nor rendering the voxel space through an occupancy matrix, therefore decreasing the memory requirements of the method. Each run is compressed using a context-adaptive arithmetic encoder yielding state-of-the-art compression results, with gains of up to 15% over TMC13, MPEG’s standard for point cloud geometry compression. Several proposed contributions accelerate the calculations of each run’s probability limits prior to arithmetic encoding. As a result, the encoder attains a low computational complexity described by a linear relation to the number of occupied voxels leading to an average speedup of 1.8 over TMC13 in encoding speeds. Various experiments are conducted assessing the proposed algorithm’s state-of-the-art performance in terms of compression ratio and encoding speeds. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) equipped with visible light communication (VLC) technology can simultaneously offer flexible communications and illumination to service ground users. Since a poor UAV working environment increases interference sent to the VLC link, there is a pressing need to further ensure reliable data communications. Run-length limited (RLL) codes are commonly utilized to ensure reliable data transmission and flicker-free perception in VLC technology. Conventional RLL decoding methods depend upon look-up tables, which can be prone to erroneous transmissions. This paper proposes a novel recurrent neural network (RNN)-based decoder for RLL codes that uses sequence to sequence (seq2seq) models. With a well-trained model, the decoder has a significant performance advantage over the look-up table method, and it can approach the bit error rate of maximum a posteriori (MAP) criterion-based decoding. Moreover, the decoder is use to deal with multiple frames simultaneously, such that the totality of RLL-coded frames can be decoded by only one-shot decoding within one time slot, which is able to enhance the system throughput. This shows our decoder's great potential for practical UAV applications with VLC technology. [ABSTRACT FROM AUTHOR]

In statistical process control, measurement error plays a key role that is usually ignored. Measurement error can yield incorrect conclusions about the performance of the process. This study examined the effect of measurement error on the shift detection ability of the mixed Exponentially Weighted Moving Average-Cumulative Sum (EWMA-CUSUM) control chart. Then, it investigated the performance of the mixed EWMA-CUSUM chart in case of mean shift through (i) covariate method, (ii) multiple measurement method, and (iii) linearly increasing variance method. The performance measurement tools such as Average Run Length (ARL) and Standard Deviation of Run Length (SDRL) were estimated using the Monte-Carlo simulation method. It was concluded that the performance of the mixed EWMA-CUSUM control chart was adversely affected by considering the measurement error. It was revealed from the comparative study that the mixed EWMA-CUSUM control chart outperformed the EWMA and CUSUM control charts in the presence of measurement error. An illustrative example was presented to demonstrate the performance of control charts in case of measurement error. [ABSTRACT FROM AUTHOR]

The problem of increasing the efficiency of coding of halftone images in the space of bit planes of differences in pixel values obtained using differential coding (DPCM – Differential pulse-code modulation) is considered. For a compact representation of DPCM pixel values, it is proposed to use a combined compression encoder that implements arithmetic coding and run-length coding. An arithmetic encoder provides high compression ratios, but has high computational complexity and significant encoding overhead. This makes it effective primarily for compressing the mean-value bit-planes of DPCM pixel values. Run-length coding is extremely simple and outperforms arithmetic coding in compressing long sequences of repetitive symbols that often occur in the upper bit planes of DPCM pixel values. For DPCM bit planes of pixel values of any image, a combination of simple run length coders and complex arithmetic coders can be selected that provides the maximum compression ratio for each bit plane and all planes in general with the least computational complexity. As a result, each image has its own effective combined encoder structure, which depends on the distribution of bits in the bit planes of the DPCM pixel values. To adapt the structure of the combined encoder to the distribution of bits in the bit planes of DPCM pixel values, the article proposes to use prediction of the volume of arithmetic code based on entropy and comparison of the obtained predicted value with the volume of run length code. The entropy is calculated based on the values of the number of repetitions of ones and zero symbols, which are obtained as intermediate results of the run length encoding. This does not require additional computational costs. It was found that in comparison with the adaptation of the combined encoder structure using direct determination of the arithmetic code volume of each bit plane of DPCM pixel values, the proposed encoder structure provides a significant reduction in computational complexity while maintaining high image compression ratios.

Lossless data compression algorithms can use statistical redundancy to represent data using a fewer number of bits in comparison to the original uncompressed data. Run-Length Encoding (RLE) is one of the simplest lossless compression algorithms in terms of understanding its principles and software implementation, as well as in terms of temporal and spatial complexity. If this principle is applied to individual bits of original uncompressed data without respecting the byte boundaries, this approach is referred to as bit-level Run-Length Encoding. Lightweight algorithm for lossless data compression proposed in this paper optimizes bit-level RLE data compression, uses special encoding of repeating data blocks, and, if necessary, combines it with delta data transformation or representation of data in its original form intending to increase compression efficiency compared to a conventional bit-level RLE approach. The advantage of the algorithm proposed in this paper is in its low time and memory consumption which are basic features of RLE, along with the simultaneous increase of compression ratio, compared to the classical bit-level RLE approach.

Objective : Globally, cardiovascular diseases (CVDs) are one of the most leading causes of death. In medical screening and diagnostic procedures of CVDs, electrocardiogram (ECG) signals are widely used. Early detection of CVDs requires acquisition of longer ECG signals. It has triggered the development of personal healthcare systems which can be used by cardio-patients to manage the disease. These healthcare systems continuously record, store, and transmit the ECG data via wired/wireless communication channels. There are many issues with these systems such as data storage limitation, bandwidth limitation and limited battery life. Involvement of ECG data compression techniques can resolve all these issues. Method : In the past, numerous ECG data compression techniques have been proposed. This paper presents a methodological review of different ECG data compression techniques based on their experimental performance on ECG records of the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Results : It is observed that experimental performance of different compression techniques depends on several parameters. The existing compression techniques are validated using different distortion measures. Conclusion : This study elaborates advantages and disadvantages of different ECG data compression techniques. It also includes different validation methods of ECG compression techniques. Although compression techniques have been developed very widely but the validation of compression methods is still a prospective research area to accomplish an efficient and reliable performance. [ABSTRACT FROM AUTHOR]

The field of image compression became a mandatory tool to face the increasing and advancing production of medical images, besides the inevitable need for smaller size of medical images in telemedicine systems. In spite of its simplicity, run-length encoding (RLE) technique is a considerably effective and practical tool in the field of lossless image compression. Such that, it is widely recommended for 2D space that utilizes common searching techniques like linear and zigzag. This paper adopts a new algorithm taking advantage of the potential simplicity of the run-length algorithm to contribute a volumetric RLE approach for binary medical data in the 3D form. The proposed volumetric-RLE (VRLE) algorithm differs from the 2D RLE approach utilizing correlations of intra-slice only, which is used for compressing binary medical data utilizing voxel-correlations of inter-slice. Furthermore, several forms of scanning are used to extending proposed technique like Hilbert and Perimeter, which determines the best possible procedure of scanning suitable for data morphology considering the segmented organ. This work employs proposed algorithm on four image datasets to get as sufficient as possible evaluation. Experimental results and benchmarking illustrate that the performance of the proposed technique surpasses other state-of-the-art techniques with 1:30 enhancement on average. [ABSTRACT FROM AUTHOR]

The zero capacity region for hexagonal $(d,k)$ run-length constraints is known for many, but not all, $d$ and $k$. The pairs $(d,k)$ for which it has been unproven whether the capacity is zero or positive consist of: (i) $k=d+2$ when $d\ge 2$ ; (ii) $k=d+3$ when $d \ge 1$ ; (iii) $k=d+4$ when either $d=4$ or $d$ is odd and $d \ge 3$ ; and (iv) $k=d+5$ when $d=4$. Here, we prove the capacity is zero in case (i) when $2 \le d \le 9$ , in case (ii) when $3 \le d \le 11$ , and in case (iii) when $d \in \{ 4,5,7,9 \}$. We also prove the capacity is positive in case (ii) when $d \in \{1,2\}$ , in case (iii) when $d = 3$ , and in case (iv). The zero capacities for $k=d+4$ are the first and only known cases equal to zero when $k-d > 3$. All of our results are obtained by developing three algorithms that automatically and rigorously assist in proving either the zero or positive capacity results by efficiently searching large numbers of configurations. The proofs involve either upper bounding the number of paths through certain large directed graphs, finding forbidden strings, or building distinct tileable square labelings. Some of the proofs examine over 20 billion cases using a supercomputer. In Part I of this two-part series, it is proven that the capacity is zero for all of case (i), and for case (ii) whenever $d \ge 7$. Thus, the only remaining unknown cases are now when $k=d+4$ , for any odd $d \ge 11$. [ABSTRACT FROM AUTHOR]

Wireless sensor nodes play an important role for Internet of Things (IoT) applications. However, these devices often come with limited memory sizes and battery life. Thus, to overcome these problems, this work focuses on studying the data compression algorithm suitable for wireless sensor nodes. In this work, run-length encoding (RLE) compression algorithm performance is studied, especially when compressing various climate datasets. This dataset includes temperature, sea-level pressure, air pollution index, and water level. In our experiment, the RLE algorithm gives the best compression ratio for temperature and sea-level pressure, with 0.62 and 0.63 compression ratios, respectively. These are equivalent to around 40% data saving. For air pollution index and water level dataset, our experiment gives 0.96 and 0.93 compression ratios, respectively. Since this data has a low number of repetitive values, the RLE achieves around 10% saving for this kind of data. [ABSTRACT FROM AUTHOR]

The aim of this work is to reduce the computational complexity of lossless compression in the spatial domain due to the combined coding (arithmetic and Run-Length Encoding) of a series of bits of bit planes. Known effective compression encoders separately encode the bit planes of the image or transform coefficients, which leads to an increase in computational complexity due to multiple processing of each pixel. The paper proposes the rules for combined coding and combined encoders for bit planes of pixel differences of images with a tunable and constant structure, which have lower computational complexity and the same compression ratio as compared to an arithmetic encoder of bit planes.

The modified algorithms length coding to compress grayscale images without losses that are different from the basic algorithm taking into account the likelihood of the occurrence of pixel values and images that reduce the size of the encoded data.

• Compression performance comparative analysis. • Compression ratio. • Data reconstruction without diagnostic information. Compression of an electrocardiogram (ECG) signal has given much consideration to the researchers since the computer-aided analysis of ECG has come into being. In some critical cases, viz. , astronauts, a person under cardiac surveillance, ambulatory patients and in Holter monitoring system, continuous ECG data recording and transmitting from one location to other location is required. However, the size of the recorded data becomes so voluminous, that its transmission of data becomes practically impossible. In this paper, ECG data compression using wavelet-based techniques are presented, such that: a) wavelet packet transform with run-length encoding (RLE), b) wavelet transform with Huffman encoding, c) wavelet transform with RLE and d) wavelet transform and Lempel ZivWelch (LZW). The results have been tested using MIT-BIH (Massachusetts Institute of Technology/Beth Israel Hospital) arrhythmia databases. The performances of these methodologies are examined in the quantitative and qualitative manner. From Tabular results, it can be observed that the methodology based on WT and LZW provides efficient results in terms of compression ratio (CR ∼ = 20 to 30) and peak root mean square difference (PRD ∼ = 0.01 to 1.8) both, hence the overall QS value is improved. [ABSTRACT FROM AUTHOR]

In the last two decades, incredible progress in various medical imaging modalities and sensing techniques have been made, leading to the proliferation of three‐dimensional (3D) imagery. Byproduct of such great progress is the production of huge volume of medical images and this big data place a burden on automatic image processing methods for diagnostic assistance processes. Moreover, large amount of medical imaging data needs to be transmitted with no loss of information for the purpose of telemedicine, remote diagnosis etc. In this work, we consider a hybrid lossless compression technique with object‐based features for three‐dimensional (3D) medical images. Our approach utilizes two phases as follows: first we determine the volume of interest (VOI) for a given 3D medical imagery using selective bounding volume (SBV) method, and second the obtained VOI is encoded using a hybrid lossless algorithm using Lembel‐Ziv‐Welch Coding (LZW) followed by arithmetic coding (L to A). Experimental results show that our proposed 3D medical image compression method is comparable with other existing standard lossless encoding methods such as Huffman Coding, Run Length Coding, LZW, and Arithmetic Coding and obtains superior results overall. [ABSTRACT FROM AUTHOR]