Coding Algorithm Research Articles

Cyclic redundancy check-aided successive cancellation-based polar decoders

<span>Research on channel coding for network transmission using polar codes has produced excellent results. By removing error redundancy from the decoding process, cyclic redundancy check (CRC) is frequently used by researchers to increase a system’s performance. In prior research, the application of decoder algorithms for polar codes was examined but not thoroughly compared. For the general capabilities of the previously proposed algorithms to be ascertained, it is crucial to analyze the employment of polar decoders especially successive cancellation (SC)-based polar decoders and the use of CRC in additive white gaussian noise (AWGN). Hence, this paper analyzes the performance of CRC with SC-based polar decoders in AWGN. In the simulation setup, (256,128) polar codes and CRCs with three-bit sizes (6, 8 and 11) were utilized. SC-based polar decoders, such as SC, soft-output cancellation, SC list (SCL) and simplified SC, were applied at the decoder part. The outcomes show that CRC-aided SC-based polar decoders reduced redundancy error. Among all the decoders, the SCL decoder with 11-bits CRC performed well when the normalized signal-to-noise ratio was high. Based on the analysis, removing errors at the highest level is possible using a suitable CRC size for SC-based polar decoders.</span>

Robust image steganography against lossy JPEG compression based on embedding domain selection and adaptive error correction

Transmitting images for communication on social networks has become routine, which is helpful for covert communication. The traditional steganography algorithm is unable to successfully convey secret information since the social network channel will perform lossy operations on images, such as JPEG compression. Previous studies tried to solve this problem by enhancing the robustness or making the cover adapt to the channel processing. In this study, we proposed a robust image steganography method against lossy JPEG compression based on embedding domain selection and adaptive error correction. To improve anti-steganalysis performance, the embedding domain is selected adaptively. To increase robustness and lessen the impact on anti-steganalysis performance, the error correction capacity of the error correction code is adaptively adjusted to eliminate redundancy. The experimental results show that the proposed method achieves better anti-steganalysis and robustness.

Cubic-Permutation-Polynomial-Based Sliding Window Network Coding Algorithm in IoT

Network coding (NC) is an information transmission technology that encodes and transmits data packets on a network. NC can be applied to the Internet of Things (IoT) and can effectively improve the data transmission quality and throughput of the IoT. The complexity of data packets encoding and decoding is an important problem in NC applications. The cubic permutation polynomial (CPP) can better optimize the data packets participating in NC, reduce the computational complexity of data packet encoding/decoding, and improve the data packet transmission bandwidth and data throughput of the IoT. This paper proposes a Cubic Permutation Polynomial-based Sliding Window NC algorithm in IoT (CPP-SWNC). In the paper, firstly, the fusion theory of CPP and sliding window (SW) was discussed. Secondly, the theory that optimize the size of sliding windows was demonstrated. Thirdly, an analysis was conducted from the perspective of mathematical theory. Finally, simulation experiments were conducted to evaluate the performance of the CPP-SWNC algorithm using Network Simulation (NS) software, mainly by comparing and analyzing the network throughput, average packet encoding overhead, and packet decoding delay. The simulation results show that CPP-SWNC algorithm can improve the performance and data packet transmission quality of the IoT, and can further improve the application field of the IoT.

Optimal location using genetic algorithms for chaotic image steganography technique based on discrete framelet transform

The development of network technology and its use have led society into the internet world and communication through digital data. The ease of access to digital resources and the possibility of unauthorized copies and distribution of them have gave rise to the significant challenge of protecting the ownership of these data. In this context, steganography is cited as a possible solution to some of these problems. Steganography can be understood as the practice of hiding secret information within another object, as for instance a text, image or video, such that the presence of this hidden information is not evident and it can be extracted if necessary. In this paper we provide an image steganography algorithm in order to insert a grayscale secret image into a RGB color cover image. This algorithm is based on a general hybrid chaos map and a 3D shift function applied to the pixels of a color image. In order to select suitable hiding places in the host image, we develop a genetic algorithm with permutation encoding aiming to optimize this task. We demonstrate empirically the effectiveness of the proposed method from various perspectives, including transparency, security, and resistance.

Role of Compressed Medical Images In Healthcare

This paper will focus on techniques used in patients monitoring in healthcare based on medical images. Lossless coding algorithm, watermark compression (add safety to data), GLCM, Huffman coding, LZW, DCT and soft compression techniques are used for efficient image compression. Lossless coding algorithms helps to reduce image size without losing the data. An image watermarking secret key (ROI) is used in this research to create a watermark that is unique to each picture. By comparing LZW compression with traditional approaches, it was observed that the LZW methodology has better performance than others.

Implementasi Algoritma Yamamoto’s Recursive Code Dalam Mengkompresi File Gambar

The size of the data stored whether it is stored on a computer or in cloud storage is one of the problems in the computer world. One of the best solutions to overcome this problem is to use compression techniques. In this study, the author uses Yamamoto's Recursive Code Algorithm to compress image files. Image files with bitmap (bmp) extension have a smaller size than those in jpeg format, this causes problems if the recipient's download speed is slow or the internet connection is unstable, information, information cannot be processed immediately because the data transfer time is too long. For that we need a technique to change the size of the data to be smaller . This technique is known as data compression. Data compression is a process of converting a set of data into a form of code to save the need for data storage. On this basis, the application of Yamamoto's Recursive Code Algorithm in Compressing Image Files is made in order to help computer users compress image files that are initially large into their smallest size.

An Improved Bit-Flipping Algorithm of Successive Cancellation List Decoding for Polar Codes

Polar codes, as the coding scheme for the control channel in fifth-generation mobile communication technology (5G), have attracted widespread attention since their proposal. As a mainstream decoding algorithm for polar codes, the successive cancellation list (SCL) decoder usually improves the error correction performance by increasing the list size, but this method suffers from the problems of high decoding complexity. To address this problem, this paper proposes a layered-search bit-flipping (LS-SCLF) decoding algorithm based on SCL decoding. Firstly, a new flip-bit metric is proposed, which derives a formula to approximate the probability of an error occurring in an information bit. This formula introduces a perturbation parameter to improve the calculation accuracy. Secondly, a compromise scheme for determining the perturbation parameter is proposed. The scheme uses Monte Carlo simulation to determine an optimized parameter for the precise positioning of the first erroneous decoded bit under different decoding conditions. Finally, a layered search strategy is adopted to sequentially search the erroneous decoded bits from the low order to high order, which can correct up to multiple bits at the same time. Simulation results show that the proposed algorithm achieves improved error correction performance with a slight increase in decoding complexity compared to the generalized SCL-Flip (GSCLF) decoding algorithm. This algorithm also achieves a good balance between the error correction performance and decoding complexity.

Image Steganography Using LSB and Hybrid Encryption Algorithms

In today’s era of widespread web technology and cloud computing, ensuring data security has become a crucial concern across various industries. Instances of data breaches and vulnerabilities in cloud storage have emphasized the need for robust data protection and communication protocols, particularly in sectors like social media, military, and research. This research proposes a Multi-Level Steganography (MLS) algorithm that employs two encryption algorithms, AES and Blow-Fish, to secure the cover image and embed encryption keys as key images within the stego image. The proposed MLS algorithm incorporates a robust pixel randomization function to enhance the security of the encrypted data. Experimental results demonstrate that the proposed algorithm effectively protects data with high Peak Signal-to-Noise Ratio (PSNR) and low Mean Square Error (MSE) values, ensuring superior image quality, reliable encryption, and decryption of secret messages. The utilization of hybrid encryption with AES and BlowFish algorithms further strengthens the algorithm’s security by augmenting the complexity of the encryption process.

A review of coverless steganography

With the enhancement of people’s security awareness, transmitting secret information securely has gradually become a demand for the public. Steganography is a technology of representing secret information within another carrier, aiming at transmitting secret information without causing suspicion. Most of the traditional steganographic algorithms hide secret information by modifying the statistical characteristics, which will leave traces to the carriers. Although these modifications are too tiny to be distinguished by human eyes, they can be detected by steganalysis algorithms. Differently, coverless steganography, also written as steganography without embedding, accomplish the process of information hiding by constructing the relationship between secret information and carriers. Due to no modification to the carriers, all of the steganalysis algorithms are expired. In this paper, more than 90 papers are included to provide a review in coverless steganographic algorithms, covering the major development process of coverless image and video steganographic algorithms. The main contribution of the existing methods is summarized. Besides, the current general issues of capacity, robustness, and security are discussed adequately for both image and video algorithms. Especially, the security of coverless steganography is discussed for the first time from theoretical analysis to actual investigation in this review.

A novel cold spray process flow technique - a numerical investigation

The particle deposition rates on the coating surface for three different geometries during the high-pressure cold spray processes were studied and verified in the paper. Analyzes were performed for three different techniques, based on the same de Laval nozzle geometry, but each with a different method of supplying working gas to the system. One case of geometry is called the based case and described the classical flow commonly used in industry. The second case called, the vortex case employs the six attached tubes located at the very beginning of the converging section. The last one, called the combined case, uses classical and vortex cases and is defined as the use of a direct nozzle inlet along with additional tubes for the working gas. For all cases, the spraying process took approximately 0.05 seconds, where the value of the deposition rate was calculated using an internal UDF code algorithm written and implemented in the Ansys FLUENT module. The working fluid was the air combined with the spherical copper particles with a diameter of 20μm. The combined variant achieved the highest value of the accretion rate compared to the other two methods and was 33.6 kgm2. On the contrary, the lowest rate was achieved by the vortex method with a rate of 10.7 kgm2. The distribution of the particles sprayed in this combined method forms a cone; in the basic method, the distribution is uniform and in the vortex method, no repeating structure is formed. It was proven that the combined method also gives the highest accretion rate values.

AC-PLT: An algorithm for computer-assisted coding of semantic property listing data.

In this paper, we present a novel algorithm that uses machine learning and natural language processing techniques to facilitate the coding of feature listing data. Feature listing is a method in which participants are asked to provide a list of features that are typically true of a given concept or word. This method is commonly used in research studies to gain insights into people's understanding of various concepts. The standard procedure for extracting meaning from feature listings is to manually code the data, which can be time-consuming and prone to errors, leading to reliability concerns. Our algorithm aims at addressing these challenges by automatically assigning human-created codes to feature listing data that achieve a quantitatively good agreement with human coders. Our preliminary results suggest that our algorithm has the potential to improve the efficiency and accuracy of content analysis of feature listing data. Additionally, this tool is an important step toward developing a fully automated coding algorithm, which we are currently preliminarily devising.

List decoding of maximal order codes over number fields

In this paper, we provide two distinct list decoding algorithms tailored for specific classes of maximal order number field codes. The first algorithm focuses on the unit codes, while the second algorithm is dedicated to the Arakelov divisor-based number field codes. These codes were originally introduced by Maire and Oggier. To achieve our decoding goals, we utilize the list decoding algorithm for general number field codes proposed by Biasse and Quintin. Our approach involves carefully calculating the required parameter B from Biasse and Quintin's algorithm, and adjusting the returned list to satisfy the desired conditions. For unit codes, we consider two cases: when the underlying number field is totally real and when it has an arbitrary signature. We separate these cases because the parameter B can be improved in the totally real scenario. Additionally, we present our list decoding algorithm for Arakelov divisor-based number field codes, focusing on number fields with arbitrary signatures.

Developing a novel DNA-based steganography algorithm using random table generation with segmentation

Developing a novel DNA-based steganography algorithm using random table generation with segmentation

Neural Network Based Rate Control for Versatile Video Coding

In this work, we propose a neural network based rate control algorithm for Versatile Video Coding (VVC). The proposed method relies on the modeling of the Rate-Quantization (R-Q) and Distortion-Quantization (D-Q) relationships in a data driven manner based upon the characteristics of prediction residuals. In particular, a pre-analysis framework is adopted, in an effort to obtain the prediction residuals which govern the Rate-Distortion (R-D) behaviors. By inferring from the prediction residuals with deep neural networks, the Coding Tree Unit (CTU) level R-Q and D-Q model parameters are derived, which could efficiently guide the optimal bit allocation. Subsequently, the coding parameters, including Quantization Parameter (QP) and λ, at both frame and CTU levels, are obtained according to allocated bit-rates. We implement the proposed rate control algorithm on VVC Test Model (VTM-13.0). Experimental results exhibit that the proposed rate control algorithm achieves 0.77% BD-Rate savings under Low Delay B (LDB) configurations when compared to the default rate control algorithm used in VTM-13.0. For Random Access (RA) configurations, 1.77% BD-Rate savings can be observed. Furthermore, with better bit-rate estimation, more stable buffer status can be observed, further demonstrating the advantages of the proposed rate control method.

A Quantitative Approach for the Accurate CFD Simulation of Hover in Turbulent Flow

Time-dependent Navier–Stokes simulations have been carried out for a V22 rotor in hover using an improved, lowdissipation, HLLE ++ upwind algorithm in the OVERFLOW code. Emphasis is placed on lessons learned over the past decade regarding the effects of high-order spatial accuracy, grid resolution, and the use of detached eddy simulation in predicting the figure-of-merit, the rotor's chief performance parameter. A general quick-start procedure is described together with a statistical measure of FM convergence that reduces hover computations by fourfold, similar to computational work for forward flight. Cartesian adaptive mesh refinement is used to resolve the tip–vortex to its correct physical size. This adaptive mesh refinement in the rotor wake also revealed a complex turbulent flow with worm-like structures of various scales. These structures were numerically found a decade ago and recently observed in experiment.

Validation of Electronic Health Record-Based Algorithms to Identify Specialist Palliative Care Within the Department of Veterans Affairs.

The measurement of specialist palliative care (SPC) across Department of Veterans Affairs (VA) facilities relies on algorithms applied to administrative databases. However, the validity of these algorithms has not been systematically assessed. In a cohort of people with heart failure identified by ICD 9/10 codes, we validated the performance of algorithms to identify SPC consultation in administrative data and differentiate outpatient from inpatient encounters. We derived separate samples of people by receipt of SPC using combinations of stop codes signifying specific clinics, current procedural terminology (CPT), a variable representing encounter location, and ICD-9/ICD-10 codes for SPC. We calculated sensitivity, specificity, and positive and negative predictive values (PPV, NPV) for each algorithm using chart review as the reference standard. Among 200 people who did and did not receive SPC (mean age=73.9 years (standard deviation [SD]=11.5), 98% male, 73% White), the validity of the stop code plus CPT algorithm to identify any SPC consultation was: Sensitivity=0.89 (95% Confidence Interval [CI] 0.82-0.94), Specificity=1.0 [0.96-1.0], PPV=1.0 [0.96-1.0], NPV=0.93 [0.86-0.97]. The addition of ICD codes increased sensitivity but decreased specificity. Among 200 people who received SPC (mean age=74.2 years [SD=11.8], 99% male, 71% White), algorithm performance in differentiating outpatient from inpatient encounters was: Sensitivity=0.95 (0.88-0.99), Specificity=0.81 (0.72-0.87), PPV=0.38 (0.29-0.49), and NPV=0.99 (0.95-1.0). Adding encounter location improved the sensitivity and specificity of this algorithm. VA algorithms are highly sensitive and specific in identifying SPC and in differentiating outpatient from inpatient encounters. These algorithms can be used with confidence to measure SPC in quality improvement and research across the VA.

A method for extracting the semantic features of speech signal recognition based on empirical wavelet transform

The subject of this study is methods for improving the efficiency of semantic coding of speech signals. The purpose of this study is to develop a method for improving the efficiency of semantic coding of speech signals. Coding efficiency refers to the reduction of the information transmission rate with a given probability of error-free recognition of semantic features of speech signals, which will significantly reduce the required source bandwidth, thereby increasing the communication channel bandwidth. To achieve this goal, it is necessary to solve the following scientific tasks: (1) to investigate a known method for improving the efficiency of semantic coding of speech signals based on mel-frequency cepstral coefficients; (2) to substantiate the effectiveness of using the adaptive empirical wavelet transform in the tasks of multiple-scale analysis and semantic coding of speech signals; (3) to develop a method of semantic coding of speech signals based on adaptive empirical wavelet transform with further application of Hilbert spectral analysis and optimal thresholding; and (4) to perform an objective quantitative assessment of the increase in the efficiency of the developed method of semantic coding of speech signals in contrast to the existing method. The following scientific results were obtained during the study: a method of semantic coding of speech signals based on empirical wavelet transform is developed for the first time, which differs from existing methods by constructing a set of adaptive bandpass Meyer wavelet filters with further application of Hilbert spectral analysis to find the instantaneous amplitudes and frequencies of the functions of internal empirical modes, which will allow the identification of semantic features of speech signals and increase the efficiency of their coding; for the first time, it is proposed to use the method of adaptive empirical wavelet transform in the tasks of multiple-scale analysis and semantic coding of speech signals, which will increase the efficiency of spectral analysis by decomposing the high-frequency speech oscillation into its low-frequency components, namely internal empirical modes; the method of semantic coding of speech signals based on mel-frequency cepstral coefficients was further developed, but using the basic principles of adaptive spectral analysis with the help of empirical wavelet transform, which increases the efficiency of this method. Conclusions: We developed a method for semantic coding of speech signals based on empirical wavelet transform, which reduces the encoding rate from 320 to 192 bps and the required bandwidth from 40 to 24 Hz with a probability of error-free recognition of approximately 0.96 (96%) and a signal-to-noise ratio of 48 dB, according to which its efficiency is increased by 1.6 times as compared to the existing method. We developed an algorithm for semantic coding of speech signals based on empirical wavelet transform and its software implementation in the MATLAB R2022b programing language.

Performance of polar codes with low complexity cyclic redundancy check aided bit‐flipping for successive cancellation list decoding over power line channels

SummaryPower lines have complex transmission characteristics and noise interference when used as information transmission channel, thus limiting their data‐carrying rate and reliability. Impulsive noise is the most dominant factor degrading the performance of communication systems over power lines. To combat the impulsive noise, polar codes are introduced into the orthogonal frequency division multiplexing (OFDM)‐based power line communication (PLC) system. Because the successive cancelation list bit‐flip (SCLF) decoding algorithm of polar codes has high decoding complexity, a low complexity segmented cyclic redundancy check (CRC)‐aided SCLF decoding is proposed to improve the decoding performance. The decoding process can be terminated early with segmented CRC, which can reduce the bit‐flip decoding complexity significantly with short and medium block lengths. Experimental results demonstrate that for various configurations, the proposed decoder outperforms the traditional CRC‐aided SCL decoder in terms of decoding performance and complexity, and the suggested polar codes‐based OFDM‐based PLC system can reduce the bit error rate of the system in the presence of impulsive noise.

Prediction of deck grillages lifting deformation using a data-driven Def-GAN network

The thin-walled deck grillages are prone to deformation in lifting process of ship blocks construction due to huge rope tension loads. Optimizing hoisting scheme with the aid of CAE analysis techniques before implementing is conductive to controlling structure deformation. A deformation prediction method based on generative adversarial network (Def-GAN) is developed in this paper to perform quick-response and precise deformation predictions under a given hoisting scheme of deck grillages. This artificial intelligence network model has an adversarial architecture and comprises a pair of submodels, namely generator and discriminator. The datasets including lifting conditions and deformation distributions of deck grillages are set up to train and test the Def-GAN model. The deformation distributions in datasets are obtained from a series of FEM analyses. An image coding and mapping algorithm is employed to deal with input data of lifting conditions and output data of deformation distributions respectively. The results show that the predictive model achieved an R-squared accuracy of 0.997 and a mean squared error of 0.005 compared to numerical results for the test dataset. In addition to predicting deformation of grillages lifting, the Def-GAN model is a promising candidate tool for hoisting scheme optimization.

Efficient indoor localization with wireless sensor networks via learnable sparse coding

In this study, the indoor localization problem for human movement is built as a sparse representation, and a learnable sparse coding algorithm is proposed by introducing a nonconvex penalty function as the sparse constraints. The good properties of the penalty are able to help reduce the iteration cost to find a sparse solution indicating the target locations. The sparse code can achieve its optimal value from the trained neural network model with parametrization. It is instead of a piece of algorithmic processing in the conventional methods requiring a large iteration. A real-world experiment using a Bluetooth low energy wireless sensor network validates the proposed method and shows the improvement of the error reduction at the first five iteration points on average compared to iterative shrinkage and thresholding algorithm (ISTA), and achieves competitive performance in the localization tasks for learned-ISTA-based algorithms.