Electroencephalography (EEG) data comes with a large size due to the data's high sampling rate. Therefore, compressing EEG data is very important for storing the EEG files efficiently with less space and bandwidth capacity requirement. This research develops an efficient system for EEG data compression. The recorded EEG data are preprocessed and scaled using certain Resolution Factor and truncated to integer numbers, then the scaled EEG samples are classified into small and large vectors using a proposed adaptive thresholding which is based on using three computed factors: Standard deviation, Average of samples (Mean), and the multiplier factor (α). Then, each sample is passed through one of three procedures, then saved into the output file using multi-shift coding algorithm The best values are chosen as the tradeoff between the compression ratio and the processing time. The results indicated that the value of α parameter is significantly affects the threshold calculation, where the best-proven value for α is 1.30; the system achieves a compression gain of 65% while managing a reasonable processing time of 4.007 Second. The resolution factor affected the Mean Squared Error (MSE) and Mean Absolute Error (MEA) significantly, but it had a slight effect on the Compression Ratio (Cr). The α parameter has a great effect on Cr and a slight on MSE. The findings show a consistent trend whereby, as the resolution factor gradually decreases from 2 to 0.1, a concurrent decrease is observed in the MAE, MSE, Bitrate, Cr, and the overall processing time.

To address the power constraints of the emerging Internet of Things (IoT) era, we propose a compression-efficient feature extraction method for a CMOS image sensor that can extract binary feature data. This sensor outputs six-channel binary feature data, comprising three channels of binarized luminance signals and three channels of horizontal edge signals, compressed via a run length encoding (RLE) method. This approach significantly reduces data transmission volume while maintaining image recognition accuracy. The simulation results obtained using a YOLOv7-based model designed for edge GPUs demonstrate that our approach achieves a large object recognition accuracy () of 60.7% on the COCO dataset while reducing the data size by 99.2% relative to conventional 8-bit RGB color images. Furthermore, the image classification results using MobileNetV3 tailored for mobile devices on the Visual Wake Words (VWW) dataset show that our approach reduces data size by 99.0% relative to conventional 8-bit RGB color images and achieves an image classification accuracy of 89.4%. These results are superior to the conventional trade-off between recognition accuracy and data size, thereby enabling the realization of low-power image recognition systems.

ABSTRACT Home service robots have the capability to monitor and perform statistical analyses of daily activities through action recognition techniques. For autonomous mobile home service robots with limited computing power, it is crucial to develop lightweight and highly accurate action recognition networks. The paper presents a native length encoding approach alongside a human skeleton‐based action recognition model (named CLS‐Transformer). The model incorporates a novel cue coding strategy that captures richer feature information by accounting for variations among different skeletal joints within the same frame as well as changes in identical joints across consecutive frames. To minimise redundant dense skeleton computations and eliminate processing of irrelevant padded video frames, the CLS‐Transformer's spatiotemporal encoder integrates both spatial and linear multi‐head attention mechanisms. Due to poor generalisation and robustness of existing models, a contextual attention mechanism is employed to enhance feature aggregation. CLS‐Transformer is evaluated on three datasets: NTU RGB+D 60, NTU RGB+D 120 and HS‐AR (a self‐constructed home service action recognition dataset). Related ablations are also done to demonstrate the effectiveness of each key component in CLS‐Transformer. The experimental results demonstrate that CLS‐Transformer achieves superior recognition accuracy and inference speed compared to most current methods, while maintaining lower computational complexity.

This paper presents a dataset-level evaluation of six lossless compression and data transformation techniques applied to visual-cryptographic (VC) shares derived from QR codes. We processed 40,000 QR samples, comprising 10,000 QR images (Versions 1-4, 2,500 per version), 10,000 QR images (Versions 1-10, 1,000 per version across ten application domains), and 20,000 augmented QR images (with noise, rotation, shear, cropping, and brightness variations). Each QR image is converted to VC share (share1), flattened to a bitstream, and evaluated under traditional compression techniques such as Run Length Encoding (RLE), Huffman Coding, Lempel Ziv-Welch (LZW), and data transformation techniques such as Binary-to-Integer, Base64 Encoding, and (BWT + MTF + Huffman Coding) Burrows Wheeler Transform (BWT), Move-To-Front (MTF), and Huffman Coding as a combined pipeline. Our experiments report Shannon entropy, compressed character count, compressed character count percentage, compression time, decompression time, memory usage, peak memory, lossless fidelity, metadata, payload size, storage size, and compression ratio. Empirical results show near-maximal entropy in QR-derived VC data (∼0.99), providing constraints on compression performance for traditional algorithms. Base64 consistently yields the best compression performance across both clean and augmented datasets, with an average compression rate of 499%. This work contributes a reproducible pipeline, a generalized dataset, and a benchmark reference for compression research on a highly randomized binary dataset.

A hybrid model for multimedia data compression using generative adversarial networks and chaotic encryption

This paper introduced a novel storage format for covering arrays, designed to optimize file size through efficient compression techniques. The proposed format employed Asymmetric Numeral System (ANS) encoding for array data, as well as Run-Length Encoding (RLE) and Variable Length Encoding (VLE) for metadata storage. The goal was to provide a compact, standardized format that facilitates easier sharing and utilization of covering arrays across different applications. Experimental evaluations on a dataset of 21964 covering arrays from the National Institute of Standards and Technology (NIST) demonstrated that the new format outperforms general-purpose compression algorithms such as ZIP, BZIP2, and XZ in most cases, particularly for larger covering arrays with high parameter counts. While previous work on covering array storage focused on archival and retrieval efficiency, the proposed method significantly reduces storage requirements without loss of structural integrity. The proposed method preserved the combinatorial properties of covering arrays while reducing redundancy, making it a practical alternative for large-scale combinatorial testing applications.

ABSTRACTContinuous photoacoustic microscopy (PAM) imaging generates large volumes of data, resulting in significant storage demands. Here, we propose a high‐fidelity real‐time compression algorithm for PAM data in LabVIEW by combining Discrete Cosine Transform (DCT) with adaptive thresholding and Run Length Encoding (RLE), which we term Adaptive Run Length Encoded DCT (AR‐DCT) compression. This algorithm reduces data storage requirements while preserving all the details of the images. AR‐DCT ensures real‐time compression, achieving superior compression ratios (CRs) compared to traditional DCT compression. We evaluated the performance of AR‐DCT using in vivo mouse brain imaging data, demonstrating a CR of ~50, with a structural similarity index of 0.980 and minimal degradation in signal quality (percentage‐root‐mean‐square‐difference of 1.345%). The results show that AR‐DCT outperforms traditional DCT, offering higher compression efficiency without significantly sacrificing image quality. These findings suggest that AR‐DCT provides an effective solution for applications requiring continuous experiments, such as cerebral hemodynamics studies.

Due to the maximal transistor count, Multi-Processor System-on-Chip (MPSoC) delivers more performance than uniprocessor systems. Network on Chip (NoC) in MPSoC provides scalable connectivity compared to traditional bus-based interconnects. Still, NoC designs significantly impact MPSoC design as it increases power consumption and network latency. A solution to this problem is packet compression which minimizes the data redundancy within NoC packets and reduces the overall power consumption of the whole network by minimizing a data packet size. Latency and overhead of compressor and decompressor require more memory access time, even though the packet compression is good for the improved performance of NoC. So, this problem demands a simple and lightweight compression method like delta compression. Consequently, this research proposes a new delta-difference Hybrid Tree coding (∆DHT-Zip) to de/compress the data packet in the NoC framework. In this compression approach, the Delta encoding, Huffman encoding and DNA tree (deoxyribonucleic acid) coding are hybridized to perform the data packet de/compression approach. Moreover, a time series approach named Run Length Encoding (RLE) is used to compress the metadata obtained from both the encoding and decoding processes. This research produces decreased packet loss and significant power savings by using the proposed ∆DHT-Zip method. The simulation results show that the proposed ∆DHT-Zip algorithm minimizes packet latency and outperforms existing data compression approaches with a mean Compression Ratio (CR) of 1.2%, which is 79.06% greater than the existing Flitzip algorithm.

A web-based cloud computing application is basically used to save data with a view of accessing it from anywhere at any time. After analyzing the literature review, it is known that the work for cloud data security is either maintaining the security level or increasing the transmission speed of plain text of cloud, but failed to prove both security level as well as data transmission speed of cloud from one end to another end. Hence, to strengthen the data security of cloud and also to improve the data transmission speed, an integration of encoding, compression and cryptographic algorithms is important. An encoding technique of Prime Factorization (PF) for changing the original plain text into an intermediate plain text as encoded plain text followed by compression technique of Run Length Encoding (RLE) to reduce the file size so that the transmission speed of encoded message will be increased as well as the compression ratio will be higher and finally the Dynamic RSA algorithm is pertained to intensify the security by converting the compressed message into cipher text wherein Integrated Compressed Cryptosystem (ICC) and hence Prime Factorization Encoded Compressed Cryptosystems (PFECCRS) is proposed. The comparative analysis proved that the proposed methodology has increased the security level to 99.25%.

Visual Question Answering (VQA) is one of the attractive topics in the field of multimedia, affective, and empathic computing to garner user interest. Unlike existing models which aim at addressing challenges of VQA for the scene images, this work aims at developing a new model for Personality Trait Question Answering (PQA). It uses Twitter account information, which includes shared images, profile pictures, banners, text in the images, and descriptions of the images. Motivated by the accomplishments of the transformer, for encoding visual features of the images, a new InfoGain Multi-Axial Wavelet Vision Transformer (IgMaWaViT) is explored here. For encoding textual features in the images and descriptions, a new Information Gain BERT (InfoBert) method is introduced, which can handle the variable length encoding of text by choosing the optimal discriminator. Furthermore, the model fuses encodings of images and text according to the questions on different personality traits for question answering. The model is called InfoGain Multi-Axial Wavelet Vision Transformer for Personality Traits Question Answering (IgMaWaViT-PQA). To validate the efficacy of the proposed model, a dataset has been constructed, and it is used along with standard datasets for experimentation. Comprehensive experiments show that the proposed model is better than the state-of-the-art models. The code is available at the link: https://github.com/biswaskunal29/InfoGain_MultiAxial_PQA .

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.

The object of research presented in this article is the RLE method and its application to the compression of bitmap images. The purpose of this research is to invent more advanced codeword formats for packing chains of repeated pixels compatible with coding single pixels of an image to increase the degree of compression by the RLE method. To achieve this goal, a generalization of the known formats of code words in the form of a corresponding structural model was performed. This made it possible to find some directions for improvement of RLE coding. Several new ways of packing chains of pixels together with single pixels are proposed to increase the degree of image compression compared to the already-known versions of RLE. These latest methods are included in the set of packaging methods called RLE_BP. The RLE_BP encoder automatically searches for the optimal parameters of the codewords to achieve the minimum possible amount of binary code for a particular image. Experimental studies of raster compression based on synthetic tests were performed to compare the proposed coding methods with known implementations of the RLE method. The proposed coding methods allow to achievement of greater compression of certain categories of high-resolution bitmap images compared to known ones. The results of the performed research can be used to build a wide class of hardware and software tools.

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.

Technology is developing very quickly and will continue to increase, so it plays an important role in the process of sending information or data from one device to another. The speed of transmission depends on the size of the data to be sent. Data with a larger size requires a longer delivery time. The amount of storage space required increases as more files are stored. This has led to the development of file shrinking techniques, also known as data compression techniques, with the aim of minimizing the loss of data quality after transmission and reducing the amount of storage space required. Compression techniques have several algorithms that can be used to reduce file size. As in this research, the compression process is done with the run length encoding algorithm and the fixed length binary encoding algorithm. Both algorithms have different compression results, so it is necessary to make a comparison. To make the comparison, 6 grayscale image files with *.jpg extension are used with different resolutions and compare their performance according to predetermined parameters. The compression comparison results of one image data resolution of 300 x 300 in the Run Length Encoding algorithm has a Ratio of Compression (RC) 1.038792, Compression Ratio (CR) 96.266%, Redundancy (Rd) 3.734%, Compression time 399ms, and Decompression time 297ms. While the Fixed Length Binary Encoding algorithm has a Ratio of Compression (RC) of 1.37, Compression Ratio (CR) of 73.248%, Redundancy (Rd) of 26.752%, Compression time of 3258ms, and Decompression time of 1047ms. So from these results it can be said that the better performance in compressing images is the Fixed Length Binary Encoding algorithm compared to Run Length Encoding.

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

Text data compression is done to make the file size smaller. Algorithm is a sequence of steps that aims to solve a problem. In this text data compress research using the Run Length Encoding (RLE) data compress method, it can be used to trim data to minimize the use of storage space so that it can be utilized better. As the function of the data compress itself to trim the file, its use is very beneficial for future technology. The programming applied is a python application, applying the concept of structured programming. Structured programming is a programming concept or paradigm that solves problems structurally, without looking at objects or divisions but must be structured, The result of this research is that the phyton application is able to trim text data so as to minimize the use of storage space so that it can be utilized better.

Nowadays, Information security involves protecting such a piece of sensitive information from unauthorized access which includes either inspection, modification, recording, or any disruption or destruction. That's why important strategic resources and large corporations ensure the safety and the privacy of critical data such as customer account details, financial data, or intellectual property. To make sure that the information reaches the intended persons (usually the sender and the receiver), all the weaknesses of security systems must be supported by creating novel algorithms that are based on recent secure technologies like DNA cryptography. This study aimed to propose a crypto-compression system that is based on a hybridization of data compression using zero-Run-Length Encoding (zRLE) and data encryption using DNA cryptography. Such a proposed system reconstructed the secret compressed data with similarity percent 100% (Lossless compression) and zero mean square error (accurate data reconstruction) which resulted in increasing transmission speed for confidential data.

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.

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.

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.