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It is known that the following five counting problems lead to the same integer sequence f t (n) : the number of nonequivalent compact Huffman codes of length n over an alphabet of t letters, the number of "nonequivalent" complete rooted t-ary trees (level-greedy trees) with n leaves, the number of "proper" words (in the sense of Even and Lempel), the number of bounded degree sequences (in the sense of Komlós, Moser, and Nemetz), and the number of ways of writing 1 = 1 t x 1 + ⋯ + 1 t x n with integers 0 ≤ x 1 ≤ x 2 ≤ ⋯ ≤ x n . In this work, we show that one can compute this sequence for all n < N with essentially one power series division. In total we need at most N 1 + ε additions and multiplications of integers of cN bits (for a positive constant c < 1 depending on t only) or N 2 + ε bit operations, respectively, for any ε > 0 . This improves an earlier bound by Even and Lempel who needed O (N 3) operations in the integer ring or O (N 4) bit operations, respectively. [ABSTRACT FROM AUTHOR]

The compressed code of Absolute Moment Block Truncation Coding (AMBTC) consists of quantized values (QVs) and bitmaps. The QVs exhibit greater predictability, and the bitmaps themselves carry more randomness. While existing joint coding and data hiding based methods have focused on compressing the QVs, none of them have addressed the coding of bitmaps. Furthermore, evidence also reveals that the sub-divided bitmaps exhibit a highly uneven pattern distribution. Therefore, we propose an efficient method to compress the bitmaps by representing sub-divided bitmaps as decimal digits. This exploits the varying frequency of certain digits, allowing Huffman encoding for shorter codewords to represent frequently occurring digits. Moreover, we have observed that less frequent digits, which require longer codewords, tend to appear among blocks with smaller difference of QVs. As a solution, we employ an adaptive approach to directly record the original bitmap when the difference is below a specific threshold. Otherwise, we employ Huffman encoding to reduce the code length. The experimental results demonstrate the effectiveness of our approach in reducing the storage space required for Lena's bitmaps by 7.36%. Moreover, the reduction in bitrate is more pronounced when the test image exhibits a smooth texture. [ABSTRACT FROM AUTHOR]

Dynamic compression methods continuously update the model of the underlying text file to be compressed according to the already processed part, assuming that such models accurately predict the distribution in the remaining part. Since this premise is not necessarily true, we suggest to update the model only selectively. We give empirical evidence that this hardly affects the compression efficiency, while it obviously may save processing time and facilitate the use of the compression scheme for cryptographic applications. [ABSTRACT FROM AUTHOR]

The rapid evolution of computer technology and social networks has led to massive data generation through interpersonal communications, necessitating improved methods for information mining and relational analysis in areas such as criminal activity. This paper introduces a Social Network Forensic Analysis model that employs network representation learning to identify and analyze key figures within criminal networks, including leadership structures. The model incorporates traditional web forensics and community algorithms, utilizing concepts such as centrality and similarity measures and integrating the Deepwalk, Line, and Node2vec algorithms to map criminal networks into vector spaces. This maintains node features and structural information that are crucial for the relational analysis. The model refines node relationships through modified random walk sampling, using BFS and DFS, and employs a Continuous Bag-of-Words with Hierarchical Softmax for node vectorization, optimizing the value distribution via the Huffman tree. Hierarchical clustering and distance measures (cosine and Euclidean) were used to identify the key nodes and establish a hierarchy of influence. The findings demonstrate the effectiveness of the model in accurately vectorizing nodes, enhancing inter-node relationship precision, and optimizing clustering, thereby advancing the tools for combating complex criminal networks. [ABSTRACT FROM AUTHOR]

Randomness extraction is a key problem in cryptography and theoretical computer science. With the recent rapid development of quantum cryptography, quantum‐proof randomness extraction has also been widely studied, addressing the security issues in the presence of a quantum adversary. In contrast with conventional quantum‐proof randomness extractors characterizing the input raw data as min‐entropy sources, it is found that the input raw data generated by a large class of trusted‐device quantum random number generators can be characterized as the so‐called reverse block source. This fact enables us to design improved extractors. Two novel quantum‐proof randomness extractors for reverse block sources that realize real‐time block‐wise extraction are proposed specifically. In comparison with the general min‐entropy randomness extractors, the designs achieve a significantly higher extraction speed and a longer output data length with the same seed length. In addition, they enjoy the property of online algorithms, which process the raw data on the fly without waiting for the entire input raw data to be available. These features make the design an adequate choice for the real‐time post‐processing of practical quantum random number generators. Applying the extractors to the raw data generated by a widely used quantum random number generator, a simulated extraction speed as high as 300 Gbps is achieved. [ABSTRACT FROM AUTHOR]

The rapid growth on the amount of generated 3D data, particularly in the form of Light Detection And Ranging (LiDAR) point clouds (PCs), poses very significant challenges in terms of data storage, transmission, and processing. Point cloud (PC) representation of 3D visual information has shown to be a very flexible format with many applications ranging from multimedia immersive communication to machine vision tasks in the robotics and autonomous driving domains. In this paper, we investigate the performance of four reference 3D object detection techniques, when the input PCs are compressed with varying levels of degradation. Compression is performed using two MPEG standard coders based on 2D projections and octree decomposition, as well as two coding methods based on Deep Learning (DL). For the DL coding methods, we used a Joint Photographic Experts Group (JPEG) reference PC coder, that we adapted to accept LiDAR PCs in both Cartesian and cylindrical coordinate systems. The detection performance of the four reference 3D object detection methods was evaluated using both pre-trained models and models specifically trained using degraded PCs reconstructed from compressed representations. It is shown that LiDAR PCs can be compressed down to 6 bits per point with no significant degradation on the object detection precision. Furthermore, employing specifically trained detection models improves the detection capabilities even at compression rates as low as 2 bits per point. These results show that LiDAR PCs can be coded to enable efficient storage and transmission, without significant object detection performance loss. [ABSTRACT FROM AUTHOR]

The compression method for wellbore trajectory data is crucial for monitoring wellbore stability. However, classical methods like methods based on Huffman coding, compressed sensing, and Differential Pulse Code Modulation (DPCM) suffer from low real-time performance, low compression ratios, and large errors between the reconstructed data and the source data. To address these issues, a new compression method is proposed, leveraging a deep autoencoder for the first time to significantly improve the compression ratio. Additionally, the method reduces error by compressing and transmitting residual data from the feature extraction process using quantization coding and Huffman coding. Furthermore, a mean filter based on the optimal standard deviation threshold is applied to further minimize error. Experimental results show that the proposed method achieves an average compression ratio of 4.05 for inclination and azimuth data; compared to the DPCM method, it is improved by 118.54%. Meanwhile, the average mean square error of the proposed method is 76.88, which is decreased by 82.46% when compared to the DPCM method. Ablation studies confirm the effectiveness of the proposed improvements. These findings highlight the efficacy of the proposed method in enhancing wellbore stability monitoring performance. [ABSTRACT FROM AUTHOR]

The recent rise in interest in point clouds as an imaging modality has motivated standardization groups such as JPEG and MPEG to launch activities aiming at developing compression standards for point clouds. Lossy compression usually introduces visual artifacts that negatively impact the perceived quality of media, which can only be reliably measured through subjective visual quality assessment experiments. While MPEG standards have been subjectively evaluated in previous studies on multiple occasions, no work has yet assessed the performance of the recent JPEG Pleno standard in comparison to them. In this study, a comprehensive performance evaluation of JPEG and MPEG standards for point cloud compression is conducted. The impact of different configuration parameters on the performance of the codecs is first analyzed with the help of objective quality metrics. The results from this analysis are used to define three rate allocation strategies for each codec, which are employed to compress a set of point clouds at four target rates. The set of distorted point clouds is then subjectively evaluated following two subjective quality assessment protocols. Finally, the obtained results are used to compare the performance of these compression standards and draw insights about best coding practices. [ABSTRACT FROM AUTHOR]

The integration of Internet of Things (IoT) technology into agriculture has revolutionized farming practices by using connected devices and sensors to optimize processes and facilitate sustainable execution. Because most IoT devices have limited resources, the vital requirement to efficiently manage data traffic while ensuring data security in agricultural IoT solutions creates several challenges. Therefore, it is important to study the data amount that IoT protocols generate for resource-constrained devices, as it has a direct impact on the device performance and overall usability of the IoT solution. In this paper, we present a comprehensive study that focuses on optimizing data transmission in agricultural IoT solutions with the use of compression algorithms and secure technologies. Through experimentation and analysis, we evaluate different approaches to minimize data traffic while protecting sensitive agricultural data. Our results highlight the effectiveness of compression algorithms, especially Huffman coding, in reducing data size and optimizing resource usage. In addition, the integration of encryption techniques, such as AES, provides the security of the transmitted data without incurring significant overhead. By assessing different communication scenarios, we identify the most efficient approach, a combination of Huffman encoding and AES encryption, to strike a balance between data security and transmission efficiency. [ABSTRACT FROM AUTHOR]

Micro-electro-mechanical systems inertial measurement units (MEMS-IMUs) are increasingly being employed for measuring the attitude of bottom hole assemblies (BHAs). However, the reliability and measurement precision of a single MEMS-IMU may not meet drilling's stringent needs. Redundant MEMS-IMU systems can effectively enhance the reliability and precision. This paper proposes a redundant configuration method for MEMS sensors tailored to BHA attitude measurement. Firstly, based on reliability theory and a cost-benefit analysis, considering factors such as cost, size, and reliability, the optimal number of sensors in the redundant system was determined to be six. Considering the structural characteristics of the BHA, a hollow hexagonal prism-shaped redundant configuration scheme was proposed, ensuring the circulation of drilling fluid within the drill pipe. Next, by employing Kalman filtering to integrate the output data from the six sensors, a virtual IMU (VIMU) was formed. Finally, experimental verification was carried out. The results confirmed that, after redundancy implementation, the velocity random walk of the accelerometer decreased by an average of 58% compared to a single MEMS-IMU, and bias instability was reduced by an average of 54%. The angular random walk of the gyroscope decreased by an average of 58%, and bias instability was reduced by an average of 37%. This research provides a theoretical foundation for enhancing the precision and reliability of BHA attitude measurements. [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]

In reversible data-hiding techniques, the quality of the steganographic image and its embedding capacity are the most crucial characteristics. The main objective of this study is to enhance the Biswapati et al. approach, which embeds secret data directly in interpolated pixels without taking context pixel properties into account. Additionally, they recorded an extremely large position value of the weighted matrix, which led to a significant amount of visual distortion. To deal with this issue, a novel reversible data-hiding method based on a multilayer center folding technique (MCFT) is developed. The suggested approach divides the interpolated cover image into 5 × 5 non-overlapping blocks and then sorts them in descending order of standard deviations. As a result of this sorting, edges and textures are more effectively preserved while also reducing the appearance of frequent interpolation defects. Unlike previous weighted matrix approaches, the position value of the weighted matrix is not embedded directly to generate the stego-image. MCFT is used before embedding to reduce the difference between an image pixel and a stego pixel so that image quality is not destroyed. The Huffman encoding technique is used to preprocess the acquired secret data to increase the embedding capacity as much as feasible. Additionally, to increase embedding rates, dual images are used for exploiting the characteristics of exploiting modification direction. The findings achieved significantly outperform state-of-the-art algorithms, which provide visual quality of more than 50.8 dB peak signal-to-noise ratio PSNR. When compared to modern techniques, the approach also offers the highest level of security and can retrieve the original image without losing any data. [ABSTRACT FROM AUTHOR]

To detect software vulnerabilities with better performance, deep neural networks (DNNs) have received extensive attention recently. However, these vulnerability detection DNN models trained with code representations are vulnerable to specific perturbations on code representations. This motivates us to rethink the bane of software vulnerability detection and find function-agnostic features during code representation which we name as semantic redundant features. This paper first identifies a tight correlation between function-agnostic triggers and semantic redundant feature space (where the redundant features reside) in these DNN models. For correlation identification, we propose a novel Backdoor-based Semantic Redundancy Exploration (BSemRE) framework. In BSemRE, the sensitivity of the trained models to function-agnostic triggers is observed to verify the existence of semantic redundancy in various code representations. Specifically, acting as the typical manifestations of semantic redundancy, naming conventions, ternary operators and identically-true conditions are exploited to generate function-agnostic triggers. Extensive comparative experiments on 1,613,823 samples of eight representative vulnerability datasets and state-of-the-art code representation techniques and vulnerability detection models demonstrate that the existence of semantic redundancy determines the upper trustworthiness limit of DNN-based software vulnerability detection. To the best of our knowledge, this is the first work exploring the bane of software vulnerability detection using backdoor triggers. [ABSTRACT FROM AUTHOR]

This article studies deep unsupervised hashing, which has attracted increasing attention in large-scale image retrieval. The majority of recent approaches usually reconstruct semantic similarity information, which then guides the hash code learning. However, they still fail to achieve satisfactory performance in reality for two reasons. On the one hand, without accurate supervised information, these methods usually fail to produce independent and robust hash codes with semantics information well preserved, which may hinder effective image retrieval. On the other hand, due to discrete constraints, how to effectively optimize the hashing network in an end-to-end manner with small quantization errors remains a problem. To address these difficulties, we propose a novel unsupervised hashing method called HARR to learn discriminative and high-quality hash codes. To comprehensively explore semantic similarity structure, HARR adopts the Winner-Take-All hash to model the similarity structure. Then similarity-preserving hash codes are learned under the reliable guidance of the reconstructed similarity structure. Additionally, we improve the quality of hash codes by a bit correlation reduction module, which forces the cross-correlation matrix between a batch of hash codes under different augmentations to approach the identity matrix. In this way, the generated hash bits are expected to be invariant to disturbances with minimal redundancy, which can be further interpreted as an instantiation of the information bottleneck principle. Finally, for effective hashing network training, we minimize the cosine distances between real-value network outputs and their binary codes for small quantization errors. Extensive experiments demonstrate the effectiveness of our proposed HARR. [ABSTRACT FROM AUTHOR]

We consider the problem of constructing prefix-free codes in which a designated symbol, a space, can only appear at the end of codewords. We provide a linear-time algorithm to construct almost-optimal codes with this property, meaning that their average length differs from the minimum possible by at most one. We obtain our results by uncovering a relation between our class of codes and the class of one-to-one codes. Additionally, we derive upper and lower bounds to the average length of optimal prefix-free codes with a space in terms of the source entropy. [ABSTRACT FROM AUTHOR]

This paper presents prefix data augmentation (Prd) as an innovative method for enhancing sentence embedding learning through unsupervised contrastive learning. The framework, dubbed PrdSimCSE, uses Prd to create both positive and negative sample pairs. By appending positive and negative prefixes to a sentence, the basis for contrastive learning is formed, outperforming the baseline unsupervised SimCSE. PrdSimCSE is positioned within a probabilistic framework that expands the semantic similarity event space and generates superior negative samples, contributing to more accurate semantic similarity estimations. The model's efficacy is validated on standard semantic similarity tasks, showing a notable improvement over that of existing unsupervised models, specifically a 1.08% enhancement in performance on BERTbase. Through detailed experiments, the effectiveness of positive and negative prefixes in data augmentation and their impact on the learning model are explored, and the broader implications of prefix data augmentation are discussed for unsupervised sentence embedding learning. [ABSTRACT FROM AUTHOR]

In network security, safeguarding digital cyberspace infrastructure against malicious intrusions remains a paramount concern. This study addresses this imperative by proposing a novel approach that integrates digital watermarking, Huffman coding, and sophisticated attack classification techniques within the Controller Area Network (CAN) protocol framework. The proliferation of interconnected systems and the increasing sophistication of cyber threats necessitate novel strategies to fortify network defenses and ensure resilience in the face of adversarial activities. The research problem revolves around the need to develop an effective method for detecting and categorizing reconnaissance and violent attacks within cyberspace networks, particularly those utilizing the CAN protocol. Despite advancements in security protocols and intrusion detection systems, existing methodologies often fall short in accurately distinguishing between benign and malicious network activities, leaving critical infrastructure vulnerable to exploitation. This research uses digital watermarking to embed metadata within network packets, Huffman coding for efficient data compression, and advanced attack classification algorithms for real-time threat identification. The experimental results demonstrate the efficacy of the proposed approach in accurately differentiating between reconnaissance probes and violent attacks, thereby enabling timely and targeted responses to mitigate security threats. [ABSTRACT FROM AUTHOR]

With the aggravation of environmental conflicts, exploring the green development path is an inevitable choice to alleviate land tension, guarantee the harmony of human–land relations, and promote coordinated regional development. The Yellow River Basin (YRB) is an important national ecological barrier area, but the impact of its environmental problems can no longer be ignored. Ecological security patterns (ESPs) provide remarkable means for the identification, configuration, and optimization of ecological spaces, which can help people identify the most central zone that needs to be protected. In this paper, we proposed environmental optimization strategies applicable to YRB as a case area. Firstly, the basic framework of ESPs was constructed based on the "three‐step" method, where ecological sources were delineated by importance/sensitivity, resistance surfaces were created by entropy weight method, and the distribution ranges of ecological corridors, nodes, and barriers were confirmed by circuit theory. Meanwhile, stepping stones were introduced to optimize the ESPs, which were obtained through hotspot analysis and buffer analysis. The results displayed that (1) ecological sources covered an area of 166,699.32 km2, occupying 21% of the basin area. They were widely distributed in the upper and middle junctions, southeast and east of the basin, and of the land types that make up them, cropland, woodland, and grassland account for 97.6%. (2) The spatial distribution of resistance values varied considerably with low values predominantly found along water systems and high values predominantly found in densely populated urban areas. (3) Through the Linkage Mapper tool, 344 ecological corridors were acquired, with dense and short corridors in the southeast and sparse and long corridors in the northwest; 146 ecological nodes were acquired, which were the priority areas for ecological protection; and 154 barriers were acquired, the improvement or removal of which can enhance the connectivity between sources. In addition, the application of stepping stones further enhanced the connectivity between landscapes. The ESPs assessment framework constructed in this paper can provide strong support for relevant departments to carry out ecological monitoring and formulate environmental protection strategies. [ABSTRACT FROM AUTHOR]

This paper presents an image encryption scheme with data and appearance security, by adopting inter-intra-block scrambling and weighted diffusion. The 2D robust hyper-chaotic map with flexible geometric distribution and rich hyper-chaotic parameter space is employed to generate the key stream for encryption, by considering the characteristics of plaintext. The plain image is first preprocessed by Huffman coding for getting compressed image. Then, the compressed image is divided into four sub-blocks and is further permuted by the designed inter-intra-block scrambling scheme, which can improve the scrambling effect by making the pixel far away from the original adjacent pixels. After that, a weighted diffusion method strongly related to plaintext and key stream is introduced to diffuse the shuffled image to obtain the noise-like cipher image. And in pursuit of higher security, the meaningless noise-like image is embedded into host image to create the visually meaningful cipher image. A series of experiment tests and analyses are carried out to further demonstrate the excellent performances of the encryption scheme. [ABSTRACT FROM AUTHOR]

Cloud computing is a technology that holds great promise and has potential to revolutionize the healthcare sector. Many security and privacy issues are brought up by the cloud's centralization of data for both patients and healthcare professionals. There is a need for maintaining secrecy in communication in exchanging medical data between the sender and the receiver, which can be done by cryptography. This article presents a cryptographic algorithm (encryption and decryption) to have a secure communication of digital health care confidential data using DNA cryptography and Huffman coding. The interesting property is the cipher size obtained from our algorithm is equal to the size of the cipher obtained from the character set of given data. Security analysis is provided to show the security of data when stored and transmitted to the cloud. The cryptographic requirements, key space analysis, key and plain text sensitivity, sensitive score analysis, sensitivity and specificity, optimal threshold, randomness analysis, uniqueness of implementation, entropies of binary bits, DNA bases, DNA bases with Huffman code, Huffman encoded binary bits and cloud service provider's risk are analyzed. The method proposed is compared with other cryptographic methods and results that it is more secure and stronger than other methods. [ABSTRACT FROM AUTHOR]

The deaf society supports Sign Language Recognition (SLR) since it is used to educate individuals in communication, education, and socialization. In this study, the results of using the modified Convolutional Neural Network (CNN) technique to develop a model for real-time Kurdish sign recognition are presented. Recognizing the Kurdish alphabet is the primary focus of this investigation. Using a variety of activation functions over several iterations, the model was trained and then used to make predictions on the KuSL2023 dataset. There are a total of 71,400 pictures in the dataset, drawn from two separate sources, representing the 34 sign languages and alphabets used by the Kurds. A large collection of real user images is used to evaluate the accuracy of the suggested strategy. A novel Kurdish Sign Language (KuSL) model for classification is presented in this research. Furthermore, the hand region must be identified in a picture with a complex backdrop, including lighting, ambience, and image color changes of varying intensities. Using a genuine public dataset, real-time classification, and personal independence while maintaining high classification accuracy, the proposed technique is an improvement over previous research on KuSL detection. The collected findings demonstrate that the performance of the proposed system offers improvements, with an average training accuracy of 99.05% for both classification and prediction models. Compared to earlier research on KuSL, these outcomes indicate very strong performance. [ABSTRACT FROM AUTHOR]

With the rapid development of multimedia technology and the massive accumulation of user data, a huge amount of data is rapidly generated and shared over the network, while the problems of inappropriate data access and abuse persist. Reversible data hiding in encrypted images (RDHEI) is a privacy-preserving method that embeds protected data in an encrypted domain and accurately extracts the embedded data without affecting the original content. However, the amount of embedded data has been one of the major limitations in the performance and application of RDHEI. Currently, the main approaches to improve the capacity of RDHEI are either to increase the overall capacity or to reduce the length of the auxiliary information. In this paper, we propose a novel RDHEI scheme based on multi-prediction and adaptive Huffman encoding. To increase the overall capacity, we propose a multi-prediction, called MED+GAP predictor, to generate the label map data of non-reference pixels prior to image encryption. Then, an adaptive Huffman coding is designed to compress the generated labels in order to reduce the embedding length of the auxiliary information used for the extraction and recovery. Experiments show that the proposed method with MED+GAP predictor and adaptive Huffman coding improves 0.052 bpp, 0.023 bpp, and 0.047 bpp on average over the other state-of-the-art methods on the BOSSBase, BOWS-2, and UCID datasets, respectively, while maintaining security and reversibility. [ABSTRACT FROM AUTHOR]

In the distributional Twenty Questions game, Bob chooses a number x from 1 to n according to a distribution μ, and Alice (who knows\mu) attempts to identify x using yes/no questions, which Bob answers truthfully. Her goal is to minimize the expected number of questions. The optimal strategy for the Twenty Questions game corresponds to a Huffman code for μ, yet this strategy could potentially uses all 2n possible questions. Dagan et al. constructed a set of 1.25n+o(n) questions which suffice to construct an optimal strategy for all \mu, and showed that this number is optimal (up to subexponential factors) for infinitely many n. We determine the optimal size of such a set of questions for all n (up to subexponential factors), answering an open question of Dagan et al. In addition, we generalize the results of Dagan et al. to the d-ary setting, obtaining similar results with 1.25 replaced by 1+ (d-1)/dd/(d-1). [ABSTRACT FROM AUTHOR]

Biological data analyses involve researchers from several fields. To store and manipulate the huge volume of biological data obtained from different aspects is difficult. Compression algorithms considerably increase the storage medium's capacity while reducing the number of bits required representing the sequence. The core concept behind EIBDNASCA involves creating an optimized index file, which stores the non-repetitive bases (run length less than 8). This index file plays a crucial role in swiftly retrieving and reconstructing specific segments of the DNA sequence during the decompression process. In addition to the index file, EIBDNASCA incorporates a work file, which stores the repetitive bases (run length above 8) and represented in binary form. This work file allows the algorithm to perform various pre-processing and transformation tasks on the DNA sequence before generating the final compressed output. Finally, an enhanced Huffman coding technique is applied to the symbols present in the index file, optimizing the encoding process for more efficient compression. The proposed algorithm is examined using a variety of different GenBank database sources. Compression ratio, compression gain, and time required to compress and decompress the sequences are the metrics used to assess the performance. The experimental findings indicate that EIBDNASCA attains an average compression ratio of 1.23 bpb (bits per base) with an average compression gain of 84.52%. The average compression time is recorded at 0.590 seconds, and decompression is completed in 0.625 seconds. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) offer a multitude of advantages and find applications across various domains, garnering substantial research interest. However, a notable drawback in these networks is the energy consumption, which can be mitigated through compression techniques. Additionally, the limited lifespan of sensor batteries remains a concern. Even when incorporating renewable energy sources, ensuring energy efficiency in WSNs is imperative. One prevailing issue is the disregard for spatial data correlation in existing data clustering methods within WSNs. Addressing these challenges necessitates effective modeling and the acquisition of event source locations in the proposed scheme. In this work, we propose an energy-efficient lifetime-aware cluster based routing (EELCR) for WSN. In EELCR technique, modified giant trevally optimization (MGTO) algorithm is introduced for efficient balanced clustering which minimizes energy consumption. An optimal squirrel search (OSS) algorithm is used to selects the best optimal node, named as cluster head (CH) for prolonging the lifetime in the sensor networks. Each CH nodes compress clustering data using optimal selective Huffman compression to achieve maximum compression ratio which overcomes inefficiency of area overhead problem in existing Huffman compression. Furthermore, we develop a hybrid deep learning technique which combines deep neural network (DNN) with Granular neural network (GNN) (named as DGNN) to find optimal way for data broadcast from CH to base station (BS). Finally, we assess the efficacy of the proposed EELCR approach through various simulation scenarios, demonstrating its effectiveness concerning Quality of Service (QoS) parameters. The outcomes reveal a notable enhancement in our coding scheme, with an average compression rate improvement of 9.346% when compared to state-of-the-art coding techniques. Furthermore, our proposed EELCR technique significantly outperforms existing routing methods, exhibiting an average network lifetime improvement of 51.88% in node density considerations and 52.625% in simulation rounds, respectively. [ABSTRACT FROM AUTHOR]

With recent advanced science and technology, it is much easier to transmit, search and find information on the Internet platform. However, sharing secret data on the Internet is challenging because of the increased number of unauthorized users. Thus, the proposed work introduces a new data-hiding model to enhance the payload capacity with higher security. Initially, the secret image is transformed into binary form, and then four consecutive binary bits are considered to obtain a sequence of decimal values. This work proposes a new adaptive keyed Huffman multi-layer midpoint folding strategy (aKH-MMF) method to minimize the embedded data size. This method uses the Huffman encoding scheme to encode the secret data with a secret key. Additionally, a multi-layer midpoint folding approach is introduced to minimize distortion during the embedding process. The proposed aKH-MMF method finds out an index for each encoded bit. These encoded bits are embedded in the cover image using an Equilibrium-optimized 2D mapping scheme. This scheme utilizes a DeepWave-Hist model (deep wavelet neural network) to generate multiple histograms for the cover images. After the generation of histograms, an optimal mapping scheme is selected for all histograms using the Equilibrium optimization (EO) approach. Finally, the encoded index is embedded into the cover image with the help of a Least Significant Method (LSB). The experimental setup is done using MATLAB software, and the proposed data-hiding model is processed through the MISC dataset. The simulation results show that the proposed model obtains better results in improved payload capacity as 2262.1155 bpp for the images like Lena, Tiffany and Airplane. Also, the attained PSNR of Lena is 57.27 dB, Tiffany is 58 dB, and Airplane is 56.48 dB. Compared with other existing data-hiding models, the results achieved for the proposed study are highly superior. [ABSTRACT FROM AUTHOR]

The attacker in white-box model has full access to software implementation of a cryptographic algorithm and full control over its execution environment. In order to solve the issues of high storage cost and inadequate security about most current white-box cryptographic schemes, WAS, an improved white-box cryptographic algorithm over AS iteration is proposed. This scheme utilizes the AS iterative structure to construct a lookup table with a five-layer ASASA structure, and the maximum distance separable matrix is used as a linear layer to achieve complete diffusion in a small number of rounds. Attackers can be prevented from recovering the key under black-box model. The length of nonlinear layer S and affine layer A in lookup table is 16 bits, which effectively avoids decomposition attack against the ASASA structure and makes the algorithm possess anti-key extraction security under the white-box model, while WAS possesses weak white-box (32 KB, 112)-space hardness to satisfy anti-code lifting security. WAS has provable security and better storage cost than existing schemes, with the same anti-key extraction security and anti-code lifting security, only 128 KB of memory space is required in WAS, which is only 14% of SPACE-16 algorithm and 33% of Yoroi-16 algorithm. [ABSTRACT FROM AUTHOR]

Summary: Developing a reliable and robust underwater acoustic communication system is a difficult task due to the complicated nature of the underwater channel, non‐stationary noise, and several other factors. Indeed, channel estimation or equalization presents numerous challenges in this non‐stationary, highly Doppler, multipath environment; as a result, traditional equalizers and PLL‐based methods have limited performance. Generally, communication over such time‐varying channels is accomplished via packets that contain a prefix/preamble signal for training, a payload containing the actual data, and a silent period for proper alignment. The prefix signal must be designed properly because it is used to estimate the channel and determine the start of the packet. In this paper, we propose a new prefix signal based on the hyperbolic chirp signal, which has Doppler invariance properties. These properties enable the extraction of the entire packet even under severe multipath and Doppler. Our new proposed prefix signal can accurately and efficiently characterize an underwater channel by estimating the multipath delay, amplitude, and Doppler scales. Our new method has been validated through extensive simulations using various channel models for its robustness and effectiveness under various conditions. It has also been tested on a real‐world channel. [ABSTRACT FROM AUTHOR]

In order to realize the real-time progressive compression of massive data and ensure the quality of compressed data, a real-time progressive compression method of massive data based on improved clustering algorithm is proposed in this paper. Through the micro clustering stage of birch method based on K-Medoids clustering, Clustering Feature Tree hierarchy is constructed and numerical clustering features are extracted; Taking this feature as the input of macro clustering order, the Clustering Feature Tree leaf nodes are clustered based on the improved K-Medoids clustering method, and the clustering data cluster set is output; The set is used as the original data of real-time progressive compression, and the data is denoised and compressed by lifting format wavelet transform. On this basis, Huffman coding is used to compress the data losslessly. The test results show that this method has good clustering effect under the optimal number of clustering centers, can complete the real-time progressive compression of a large number of data, and the availability of compressed data is more than 92%. [ABSTRACT FROM AUTHOR]

Recently, with the rapid development of cloud storage, secure cloud computing and privacy protection have attracted widespread attention. Reversible data hiding in encrypted images (RDHEI) plays an important role in it and has been paid more and more attention. In this paper, an improved RHDEI is proposed. By combining adaptive Arithmetic coding and static Huffman coding, the image bit-plane is effectively compressed and a lot of space is made for data embedding. The security of image and embedded data is guaranteed by XOR-encryption and scrambling encryption. The data extractor can embed and extract data without decrypting the carrier image to protect the privacy of the image owner. Experimental results show that the scheme can achieve an average embedding capacity (EC) of up to 3 bpp while ensuring lossless recovery of carrier images and correct extraction of embedded data. Compared with state-of-the-art RDHEI methods, our scheme achieves higher EC and better security. [ABSTRACT FROM AUTHOR]

Protecting information from manipulation and theft is a top priority as a result of progress in technology and the infrastructure of the multimedia network in addition to the development of illegal methods of obtaining information. One of the means of protecting and preserving information is to hide it in a digital medium. The motivation for introducing such a system is to enhance the security of confidential data by providing ways to protect the data and reduce attempts to attack it. The proposed system contains several steps summarized as follows: The sender side includes firstly the stage of generating hiding locations randomly depending on the genetic algorithm (GA) to generate rows and seed to generate columns. Secondly, the stage of including data after compressing it by the Huffman method. Data embedding depends on the pixel index as an indicator to choose one of the three bands to hide using LSB. The recipient side extracts the important information hiding in the two last rows which helps to extract the data and convert it into the original text. The proposed system gained efficiency and robustness with the help of genetic and Huffman where genetic chooses the best way for hidding among a set of suggested solution in addition to the randomness it possesses. The role of Huffman reduce data size and thus increase the cover capacity. System efficiency has been measured by PSNR through conducting a number of experiments that included using set of texts with different sizes and two types of standard colored cover images. [ABSTRACT FROM AUTHOR]

This paper presents a novel hardware-oriented image compression algorithm and its very large-scale integration (VLSI) implementation for wireless sensor networks. The proposed novel image compression algorithm consists of a fuzzy decision, block partition, digital halftoning, and block truncation coding (BTC) techniques. A novel variable-size block partition technique was used in the proposed algorithm to improve image quality and compression performance. In addition, eight different types of blocks were encoded by Huffman coding according to probability to increase the compression ratio further. In order to achieve the low-cost and low-power characteristics, a novel iteration-based BTC training module was created to get representative levels and meet the requirement of wireless sensor networks. A prediction and a modified Golomb-Rice coding modules were designed to encode the information of representative levels to achieve higher compression performance. [ABSTRACT FROM AUTHOR]

A reversible data hiding (RDH) in encrypted JPEG images (JPEG-RDH-EI) with an adaptive encryption key generated by minimum coded units histogram (MCUH) is proposed. However, the marked encrypted images are vulnerable to chosen-plaintext attack (CPA) since the MCUH is unchanged during the encryption and RDH process, which allows the encryption key can be reproduced by the marked encrypted image. To improve the security of marked encrypted images, this paper changes the MCUH in the RDH process by 0-1 conversion (refers to changing the value of coefficients from 0 to 1). To reduce the file growth, this paper explores the condition that the 0-1 conversion without file growth first. Then, a histogram shift scheme with embeddable zero coefficients is designed. The experimental results show that the proposed scheme can enhance the security of marked encrypted images by changing MCUH. In addition, the proposed algorithm has a smaller file growth and higher maximum embedding capacity than the existing histogram shift schemes since it embeds additional data in zero coefficients. [ABSTRACT FROM AUTHOR]

The exponential growth of the Internet of Things (IoT) has led to an increased demand for secure and efficient data transmission methods. However, there is a tradeoff in the image quality and hiding capacity in the data hiding methods. Therefore, the maximum amount of data that could be stored in the image media is a difficult challenge while maintaining the image quality. Thus, to make the balance between the quality of the images and the embedding capacity, a novel interpolation-based revisable data hiding (RDH) approach is developed for IoT applications. The proposed interpolation technique takes the average of the root value for the product of two neighboring original pixel values and the third original pixel value. And for the central pixel, it takes an average of two interpolated pixels. By doing so, most of the original pixels are considered and the calculated interpolated pixel is much enhanced as its average value. Furthermore, the data hiding is performed in two stages. In the first stage, RSA is performed on the secret message, and then embedding is done based on which intensity range group. The experimental results indicate that the proposed technique enhanced the embedding capacity by 17.58% and produced 7.80% higher PSNR values for the test images as compared to the baseline methods. [ABSTRACT FROM AUTHOR]

Today, telemedicine has become very essential, because it provides the possibility for the health centers, hospitals, and research centers to exchange medical and diagnostic data, via IoT. Since the volume of the generated medical information in IoT is very high, transmitting this data via channels of limited bandwidth is time-consuming; thus, the information should be compressed before transmission. Todays, some of the methods compress data significantly, but quality of the restored data in these methods is very low. Considering the importance of images and information for diagnostic and medical applications, desired quality is of great importance. Thus, this study tries to present a hybrid medical information compression technique such that quality of the restored information is desired and compression is efficient. This compression technique combines two lossless compression methods, including Huffman encoding and Lempel-Ziv-Welch (LZW) that rearranges information. In this combination, a binary information arrangement is used between Hoffman and LZW techniques so that the integration of binary information reaches an information mapping and, while simplifying the mapping, is completely different for each piece of information and makes this article stand out. The simulation results of the proposed method show that medical information including signal, text, and image is reduced by an average of 37.85%. Remarkably, quality of the restored image is not degraded. [ABSTRACT FROM AUTHOR]

Reversible Data Hiding(RDH) has been extensively investigated, recently, due to its numerous applications in the field of defence, medical, law enforcement and image authentication. However, most of RDH techniques suffer from low secret data hiding capacity and communication overhead. For this, multistage high-capacity reversible data hiding technique without overhead is proposed in this manuscript. Proposed reversible data hiding approach exploits histogram peaks for embedding the secret data along with overhead bits both in plain and encrypted domain. First, marked image is obtained by embedding secret data in the plain domain which is further processed using affine cipher maintaining correlation among the pixels. In second stage, overhead bits are embedded in the encrypted marked image. High embedding capacity is achieved through exploiting histogram peak for embedding multiple bits of secret data. Proposed approach is experimentally validated on different datasets and results are compared with the state-of-the-art techniques over different images. [ABSTRACT FROM AUTHOR]

In deep neural network (DNN) accelerators, it is expensive to transfer model parameters from the main memory to the processing elements. Data movement accounts for a large number of the inference latency and energy consumption. In this paper, we present three position-based techniques to compress the DNN model parameters. The techniques could lead to significant energy and performance improvement. The three presented compression techniques are lossless. The first technique takes into consideration the regularly repeat property of the DNN weights to compress them. The second technique saves the relative distance between weights instead of the weights to compress the model. The third technique applies Huffman coding on the relative distance based on the second technique. The proposed techniques are assessed on several DNNs. The results show that, the first technique could decrease 38% of latency and 36% energy, respectively. The second technique could decrease 41% of latency and 39% energy, respectively. The third technique could decrease 45% of latency and 43% energy, respectively. Applying Huffman code could achieve additional 7% reduction in both latency and energy based on the second technique. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the existing spatial keyword group query problem did not consider the query requirements with exclusion keywords and time attributes, a time-aware group query problem with exclusion keywords (TEGSKQ) is proposed for the first time. To solve this problem effectively, this paper proposes a query method based on the EKTIR-Tree index and dominating group (EKTDG). This method first proposes the EKTIR-tree index, which incorporates Huffman coding and integrates Bloom filters to deal with excluded keywords in order to improve the hit rate of keyword queries, significantly improving the query efficiency and reducing the storage occupancy. Then, the Candidate algorithm is proposed based on the EKTIR-tree index to filter out the spatial–textual objects that meet the query's keywords and time requirements, narrowing the search space for subsequent queries on a large scale. To address the problem of the low efficiency of existing algorithms based on a spatial distance query, a distance-dominating group is defined and a pruning algorithm based on a spatial distance-dominating group is proposed, which is a refining process of query results and greatly improves the search efficiency of the query. Theoretical and experimental studies show that the proposed method can better handle group queries with exclusion keywords based on time awareness. [ABSTRACT FROM AUTHOR]

The rapid development of the Internet of Things has opened up commercial and economic opportunities, but there are also risks and threats to cybersecurity: a significant amount of private data is exposed in an untrusted cyberspace. In view of this issue, the research on reversible data hiding in encrypted image (RDHEI) aims to establish a secure and private communication channel between a sender and a recipient by embedding data into encrypted images. Therefore, this paper proposes a novel RDHEI scheme that employs a Huffman-based lossless image coding technique to achieve a high embedding capacity. Specifically, we compress the original image into a bitstream through the JPEG-like compression and then further encode the difference values between the reconstructed JPEG image and the original image via the Huffman coding. Due to the high compression rate and coding efficiency of the combined strategy, a large embedding space can be created. Our proposed scheme not only ensures a lossless recovery of the original image but also an error-free extraction of the secret messages. Several experiments are conducted to verify that our proposed scheme achieves a state-of-the-art performance compared with the prior art in terms of embedding capacity. [ABSTRACT FROM AUTHOR]

Of late, image compression has become crucial due to the rising need for faster encoding and decoding. To achieve this objective, the present study proposes the use of canonical Huffman coding (CHC) as an entropy coder, which entails a lower decoding time compared to binary Huffman codes. For image compression, discrete wavelet transform (DWT) and CHC with principal component analysis (PCA) were combined. The lossy method was introduced by using PCA, followed by DWT and CHC to enhance compression efficiency. By using DWT and CHC instead of PCA alone, the reconstructed images have a better peak signal-to-noise ratio (PSNR). In this study, we also developed a hybrid compression model combining the advantages of DWT, CHC and PCA. With the increasing use of image data, better image compression techniques are necessary for the efficient use of storage space. The proposed technique achieved up to 60% compression while maintaining high visual quality. This method also outperformed the currently available techniques in terms of both PSNR (in dB) and bit-per-pixel (bpp) scores. This approach was tested on various color images, including Peppers 512 × 512 × 3 and Couple 256 × 256 × 3, showing improvements by 17 dB and 22 dB, respectively, while reducing the bpp by 0.56 and 0.10, respectively. For grayscale images as well, i.e., Lena 512 × 512 and Boat 256 × 256, the proposed method showed improvements by 5 dB and 8 dB, respectively, with a decrease of 0.02 bpp in both cases. [ABSTRACT FROM AUTHOR]

The compression of data is fundamental to alleviating the costs of transmitting and storing massive datasets employed in myriad fields of our society. Most compression systems employ an entropy coder in their coding pipeline to remove the redundancy of coded symbols. The entropy-coding stage needs to be efficient, to yield high compression ratios, and fast, to process large amounts of data rapidly. Despite their widespread use, entropy coders are commonly assessed for some particular scenario or coding system. This work provides a general framework to assess and optimize different entropy coders. First, the paper describes three main families of entropy coders, namely those based on variable-to-variable length codes (V2VLC), arithmetic coding (AC), and tabled asymmetric numeral systems (tANS). Then, a low-complexity architecture for the most representative coder(s) of each family is presented—more precisely, a general version of V2VLC, the MQ, M, and a fixed-length version of AC and two different implementations of tANS. These coders are evaluated under different coding conditions in terms of compression efficiency and computational throughput. The results obtained suggest that V2VLC and tANS achieve the highest compression ratios for most coding rates and that the AC coder that uses fixed-length codewords attains the highest throughput. The experimental evaluation discloses the advantages and shortcomings of each entropy-coding scheme, providing insights that may help to select this stage in forthcoming compression systems. [ABSTRACT FROM AUTHOR]

In wireless sensor network (WSN), the high volume of observe and transmitted data among sensor nodes make it requires to maintain the security. Even though numerous secure data transmission approaches designed over a network, an inadequate resource and the complex environment cause not able to used in WSNs. Moreover, secure data communication is a big challenging problem in WSNs especially for the military application. This paper proposes a dingo algorithm-based forwarder selection and huffman coding (DAHC) to improve authentication in internet of things (IoT) WSN. Initially, it detects the anomalous nodes by applying support vector machine (SVM) algorithm based on sensor node energy, node selfishness, and signal to noise ratio (SNR). Next, we using the dingo algorithm to select the forwarder node. This dingo algorithm computes the fitness function based on node degreee, node distance and node energy. Finally, the huffman coding to provide end to end authentication established on node energy from sender to receiver. During data transmission, the huffman coding to build the binary hop count value, it improves the authentication in the WSN. Performance results specify that this approach enhances the detection ratio and throughput. [ABSTRACT FROM AUTHOR]

The work aims to study the application of Deoxyribonucleic Acid (DNA) multi-source data storage in Digital Twins (DT). Through the investigation of the research status of DT and DNA computing, the work puts forward the concept of DNA multi-source data storage for DT. Raptor code is improved from the design direction of degree distribution function, and six degree function distribution schemes are proposed in turn in the process of describing the research method. Additionally, a quaternary dynamic Huffman coding method is applied in DNA data storage, combined with the improved concatenated code as the error correction code. Considering the content of cytosine deoxynucleotide (C) and guanine deoxynucleotide Guanine (G) and the distribution of homopolymer in DNA storage, the work proposes and verifies an improved concatenated code algorithm Deoxyribonucleic Acid-Improved Concatenated code (DNA-ICC). The results show that while the Signal-to-Noise Ratio (SNR) increases, the Bit Error Rate (BER) decreases gradually and the trend is similar. But the anti-interference ability of the degree distribution function optimized by the probability transfer method is better. The BER of DNA-ICC scheme decreases with the decrease of error probability, which is stronger than other error correction codes. Compared with the original concatenated code, it saves at least 1.65 s, and has a good control effect on homopolymer. When the size of homopolymer exceeds 4 nt, the probability of homopolymer is only 0.44%. The proposed Quaternary dynamic Huffman code and concatenated error correction code have excellent performance. [ABSTRACT FROM AUTHOR]

Orthogonal Frequency Division Multiplexing (OFDM) is a modern and suitable modulation strategy for commercial high-speed broadband wireless communication systems. This is because wireless services and demands have grown so quickly in recent years. While creating wireless communication, the channel environment is crucial. A square grid with identical vertical and horizontal spacing is used for the constellation points in quadrature amplitude modulation (QAM). More bits can be transferred per symbol to higher-order modulation forms. This research has looked at how Orthogonal Frequency Division Multiplexing OFDM systems perform using different modulation techniques on channels with additive white Gaussian noise (AWGN). Using MATLAB software to do an experiment with different modulation methods and parameters such as FFT length and cyclic prefix findings showed that 64-QAM modulation sends the same amount of data faster and with fewer symbols than QPSK 16-QAM. Here, the 64- QAM modulation technique performs better. An extensive literature survey has been carried out. The work done here involved working on many parameters to improve the performance of OFDM, i.e., by reducing BER, changing modulation techniques, and analyzing the trade-off between cyclic prefix length and Bit Error Rate. Compared to the existing work, the proposed system provides better performance. [ABSTRACT FROM AUTHOR]

Modern, commonly used cryptosystems based on encryption keys require that the length of the stream of encrypted data is approximately the length of the key or longer. In practice, this approach unnecessarily complicates strong encryption of very short messages commonly used for example in ultra-low-power and resource-constrained wireless network sensor nodes based on microcontrollers (MCUs). In such cases, the data payload can be as short as a few bits of data while the typical length of the key is several hundred bits or more. The article proposes an idea of employing a complex of two algorithms, initially applied for data compression, acting as a standard-length encryption key algorithm to increase the transmission security of very short data sequences, even as short as one or a few bytes. In this article, we present and evaluate an approach that uses LZW and Huffman coding to achieve data transmission obfuscation and a basic level of security. [ABSTRACT FROM AUTHOR]

The complexes of historic timber-structure buildings are extremely complicated and have various fire threats. A targeted approach to fire risk assessment of historic timber structures is conducive to focused fire plans. Due to the sparsity of the historical fire event dataset, it is quite challenging to predict the fire risk of heritage buildings based on historical fire events data. In this study, a fire risk index-driven solution is proposed based on machine learning to predict the fire risk level of timber heritage buildings. The suggested fire risk index system emphasizes the specificities of timber and historic structures, and the indicators are processed on temporal, spatial, and attribute scales. Considering the uncertainty of fire risk, this approach objectively rates the historical fire risk of heritage buildings using entropy-weighted TOPSIS. The typical machine learning model, XGBoost, is enhanced by applying the spatially unique values to predict the monthly fire risk level of a single heritage building. It solves the issue that general models only take into account the attribute characteristics of heritage buildings. Through the proposed solution, the monthly fire risk level of the heritage buildings of the Palace Museum of China in 2019 was assessed by the XGBoost model using the fire risk index data of the historical years (2016–2018). The accuracy evaluation showed that XGBoost performed better than other models (GBDT, CatBoost, Adaboost, and SVM), which is expected to provide information support for the safety inspection work of the fire department in the Palace Museum. [ABSTRACT FROM AUTHOR]

The widespread availability of digital image-processing software has given rise to various forms of image manipulation and forgery, which can pose a significant challenge in different fields, such as law enforcement, journalism, etc. It can also lead to privacy concerns. We are proposing that a privacy-preserving framework to encrypt images before processing them is vital to maintain the privacy and confidentiality of sensitive images, especially those used for the purpose of investigation. To address these challenges, we propose a novel solution that detects image forgeries while preserving the privacy of the images. Our method proposes a privacy-preserving framework that encrypts the images before processing them, making it difficult for unauthorized individuals to access them. The proposed method utilizes a compression quality analysis in the encrypted domain to detect the presence of forgeries in images by determining if the forged portion (dummy image) has a compression quality different from that of the original image (featured image) in the encrypted domain. This approach effectively localizes the tampered portions of the image, even for small pixel blocks of size 10 × 10 in the encrypted domain. Furthermore, the method identifies the featured image's JPEG quality using the first minima in the energy graph. [ABSTRACT FROM AUTHOR]

In the recent couple of years, due to the accelerated popularity of the internet, various organizations such as government offices, military, private companies, etc. use different transferring methods for exchanging their information. The Internet has various benefits and some demerits, but the primary bad mark is security of information transmission over an unreliable network, and widely uses of images. So, Steganography is the state of the art of implanting a message in the cover objects, that nobody can suspect or identify it. Therefore, in the field of cover steganography, it is very critical to track down a mechanism for concealing data by utilizing different blends of compression strategies. Amplifying the payload limit, and robustness, and working on the visual quality are the vital factors of this research to make a reliable mechanism. Different cover steganography research strategies have been recommended, and each adores its benefits and impediments but there is a need to foster some better cover steganography implements to accomplish dependability between the essential model of cover steganography. To handle these issues, in this paper we proposed a method in view of Huffman code, Least Significant Bits (LSB) based cover steganography utilizing Multi-Level Encryption (MLE) and colorless part (HC-LSBIS-MLE-AC) of the picture. It also used different substitution and flicking concepts, MLE, Magic matrix, and achromatic concepts for proving the proficiency, and significance of the method. The algorithm was also statistically investigated based on some Statistical Assessment Metrics (SAM) such as Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR), Normalized Cross Correlation (NCC), Structural Similarity Index Metric (SSIM), etc. and different perspectives. The observational outcomes show the likelihood of the proposed algorithm and the capacity to give unwavering quality between security, payload, perception, computation, and temper protection. [ABSTRACT FROM AUTHOR]

Summary: Deflate coding is a very popular lossless data compression method used in zlib, gzip (GNU zip), and zip, which performs the LZSS compression algorithm with Huffman coding. Deflate encoding and decoding involve sequential operations and their parallel acceleration using a GPU is quite hard. The main purpose of this paper is to present GPU implementations for encoding and decoding of Deflate coding. For efficient GPU implementations of Deflate coding, we have used multiple small hash tables for finding matching subsequences in the dictionary by multiple threads in parallel and applied the Single Kernel Soft Synchronization (SKSS) technique to fully utilize GPU computing resources. We have also adopted Huffman coding with gap arrays to accelerate parallel Huffman decoding. We have evaluated the performance of our GPU implementations using an NVIDIA A100 GPU and compared them with parallel/sequential Deflate encoding and decoding on the Intel X86 multicore CPUs using multiple threads/a single thread. Our GPU implementation of Deflate decoding is 1.66x–8.33x faster than the multiple thread implementation and 4.13x–36.56x faster than the single thread implementation. [ABSTRACT FROM AUTHOR]