Coding Algorithm Research Articles

A Rehabilitation of Pixel-Based Spectral Reconstruction from RGB Images.

Recently, many deep neural networks (DNN) have been proposed to solve the spectral reconstruction (SR) problem: recovering spectra from RGB measurements. Most DNNs seek to learn the relationship between an RGB viewed in a given spatial context and its corresponding spectra. Significantly, it is argued that the same RGB can map to different spectra depending on the context with respect to which it is seen and, more generally, that accounting for spatial context leads to improved SR. However, as it stands, DNN performance is only slightly better than the much simpler pixel-based methods where spatial context is not used. In this paper, we present a new pixel-based algorithm called A++ (an extension of the A+ sparse coding algorithm). In A+, RGBs are clustered, and within each cluster, a designated linear SR map is trained to recover spectra. In A++, we cluster the spectra instead in an attempt to ensure neighboring spectra (i.e., spectra in the same cluster) are recovered by the same SR map. A polynomial regression framework is developed to estimate the spectral neighborhoods given only the RGB values in testing, which in turn determines which mapping should be used to map each testing RGB to its reconstructed spectrum. Compared to the leading DNNs, not only does A++ deliver the best results, it is parameterized by orders of magnitude fewer parameters and has a significantly faster implementation. Moreover, in contradistinction to some DNN methods, A++ uses pixel-based processing, which is robust to image manipulations that alter the spatial context (e.g., blurring and rotations). Our demonstration on the scene relighting application also shows that, while SR methods, in general, provide more accurate relighting results compared to the traditional diagonal matrix correction, A++ provides superior color accuracy and robustness compared to the top DNN methods.

A fast parallel SC-Fano decoding algorithm for PAC codes

A fast parallel SC-Fano decoding algorithm for PAC codes

The syndromes decoding algorithm in group codes

Our aim is the design of an efficient decoding algorithm in group codes. The algorithm is inspired by the well known syndrome decoding algorithm for linear codes and uses the decomposition of a semisimple group algebra KG as a direct sum of two-sided ideals, each of them generated by a central idempotent of KG. When G is an abelian group the algorithm can be modified to make it very simple and efficient. Some illustrative examples are presented.

Human activity recognition from UAV videos using a novel DMLC-CNN model

Human Activity Recognition (HAR) remains a challenging issue that requires to be resolved. Utilizing images, smart phones, or sensors, HAR could be done. Recent studies have explored HAR utilizing Unmanned Aerial Vehicle (UAV) videos. But, owing to numerous restrictions correlated to the platform, HAR from videos captured by drones remains a challenge. To resolve this issue, a novel mechanism for HAR from UAV videos utilizing the Diminutive Multi-Dimensional Locality Coding based Convolutional Neural Network (DMLC-CNN) model is proposed. Primarily, the input video is converted into frames; also, Bounding Boxes (BBs) are produced for humans. For attaining the number of clusters grounded on human presence, the BB is given to the Robust Lucrative Sensitive Amalgam K-Nearest Neighbor (RoLSA-KNN) clustering. After that, utilizing the LR-fit-centric Superior Photogrammetric Silhouette (LR-SPS) segmentation, the humans' silhouettes are segmented. The attained output is divided into patches; then, it is converted into stacked layer frames. Afterward, these frames are converted into a 3D kernel. The Diminutive Multi-Dimensional Hyper Graph (DMHG) extracted the features; then, the Locality-constrained Orthonormal Feature Space Coding (LOFSC) Algorithm encoded the extracted features. Lastly, for recognizing Human Activity (HA), DMLC-CNN is wielded. The proposed framework is contrasted with the conventional techniques. After experimental evaluation, the proposed mechanism was found to be more efficient in HAR.

Feature Elimination through Data Complexity for Error-Correcting Output Codes based micro-expression recognition

Micro-Expression (ME) is a kind of short-lived and uncontrollable facial expressions. The MEs recognition task poses a great challenge to both the psychological and computer vision research communities. In this study, a Feature Elimination through Data Complexity-based Error-Correcting Output Codes (FEDC-ECOC) algorithm is proposed. In the generation of the coding matrix, a set of data complexity measures are utilized as the division criteria to form a coding matrix. Meanwhile, the sliding window and the greedy search algorithm are applied to improve the discriminative ability of the coding matrix for various emotion types. On the other hand, this study proposes a feature selection algorithm to identify essential features to enhance the performance of classifiers. Comprehensive experiments are conducted, and the results confirm the robustness and effectiveness of our FEDC-ECOC. Detailed analysis is given to further provide insights of the proposed method.

A New Algorithm to Determine Adomian Polynomials for Nonlinear Polynomial Functions

We present a new algorithm by which the Adomian polynomials can be determined for scalar-valued nonlinear polynomial functional in a Hilbert space. This algorithm calculates the Adomian polynomials without the complicated operations such as parametrization, expansion, regrouping, differentiation, etc. The algorithm involves only some matrix operations. Because of the simplicity in the mathematical operations, the new algorithm is faster and more efficient than the other algorithms previously reported in the literature. We also implement the algorithm in the MATHEMATICA code. The computing speed and efficiency of the new algorithm are compared with some other algorithms in the one-dimensional case.

Analysis and Implementation of Steganography Using Playfair Techniques and DNA Substitution To Improve Message Security

Along with the development of the internet, the need for data security is increasing. Ensuring the security of data requires special ways. Among these are cryptography and steganography. Because of the need for this method, many new data security methods have emerged. One of them is using DNA media. DNA cryptography and steganography utilize the properties of DNA to secure and hide secret messages. This paper proposes a combined method between the Playfair cipher cryptographic technique and the DNA substitution steganography algorithm for data security. Comparative analysis was conducted on techniques that are being tested by using similar methods. Four tests are conducted to measure the performance of the proposed method and another method was tested for comparative purposes. Four tests that were conducted are hiding capacity and key length test, speed test, endurance test, and steganalysis test. The result shows that the hiding capacity and key length test is better than other techniques. Steganalysis test result is also better than other tested techniques. Furthermore, in the speed test and endurance test, the results are quite satisfactory, not far from the others technique that is tested. The proposed method had a better performance than any other method that get tested, especially in speed performance and endurance performance.

Development and Validation of the Veterans Affairs Eosinophilic Esophagitis Cohort

Gaps remain in understanding the epidemiology of eosinophilic esophagitis (EoE). Our aim was to identify and validate a national cohort of individuals with EoE using Veterans Health Administration (VHA) data. We used 2 validation strategies to develop algorithms that identified adults with EoE between 1999 and 2020. The first validation strategy applied International Classification of Diseases (ICD) code algorithms to a base cohort of individuals who underwent esophagogastroduodenoscopy with esophageal biopsy specimens. The second applied ICD code algorithms to a base cohort of all individuals in the VHA. For each ICD algorithm applied, a random sample of candidate EoE cases and non-EoE controls were selected and the charts were reviewed manually by a blinded reviewer. Each algorithm was modified iteratively until the prespecified diagnostic accuracy end point (95% confidence lower bound for a positive predictive value [PPV], >88%) was achieved. We compiled individuals from each strategy's maximum performance algorithm to construct the Veterans Affairs Eosinophilic Esophagitis Cohort. The maximum performance algorithm from the first validation strategy included 2 or more ICD code encounters for EoE separated by more than 30 days and achieved a 93.3% PPV (lower bound, 88.1%) for identifying true EoE cases. The maximum performance algorithm from the second validation strategy included 4 or more ICD code encounters for EoE in which 2 codes were separated by at least 30 days, and similarly achieved a 93.3% PPV (lower bound, 88.1%). Combining both strategies yielded 6637 individuals, which comprised the Veterans Affairs Eosinophilic Esophagitis Cohort. We developed and validated 2 highly accurate coding algorithms for EoE and established a nationwide VHA cohort of adults with EoE for future studies.

Parallelization Strategies for Graph-Code-Based Similarity Search

The volume of multimedia assets in collections is growing exponentially, and the retrieval of information is becoming more complex. The indexing and retrieval of multimedia content is generally implemented by employing feature graphs. Feature graphs contain semantic information on multimedia assets. Machine learning can produce detailed semantic information on multimedia assets, reflected in a high volume of nodes and edges in the feature graphs. While increasing the effectiveness of the information retrieval results, the high level of detail and also the growing collections increase the processing time. Addressing this problem, Multimedia Feature Graphs (MMFGs) and Graph Codes (GCs) have been proven to be fast and effective structures for information retrieval. However, the huge volume of data requires more processing time. As Graph Code algorithms were designed to be parallelizable, different paths of parallelization can be employed to prove or evaluate the scalability options of Graph Code processing. These include horizontal and vertical scaling with the use of Graphic Processing Units (GPUs), Multicore Central Processing Units (CPUs), and distributed computing. In this paper, we show how different parallelization strategies based on Graph Codes can be combined to provide a significant improvement in efficiency. Our modeling work shows excellent scalability with a theoretical speedup of 16,711 on a top-of-the-line Nvidia H100 GPU with 16,896 cores. Our experiments with a mediocre GPU show that a speedup of 225 can be achieved and give credence to the theoretical speedup. Thus, Graph Codes provide fast and effective multimedia indexing and retrieval, even in billion-scale use cases.

Detection of Low Frequency Pulse Signal in LSI Based on Deep Self Coding Algorithm

In order to detect the pulse of LSI with high accuracy and efficiency, a new detection method of low frequency pulse signal of LSI based on deep self coding algorithm is proposed in this paper. Firstly, Radon transform is used to reduce the dimension of the time-frequency diagram of the pulse signal of the integrated circuit, and the short-time Fourier transform of the pulse signal is completed according to the Hamming window. Secondly, the principle of the depth self encoder is analyzed, and the pulse signal after the short time Fourier transform processing is input into the depth self encoder. Finally, according to the distance function, the gradient descent algorithm is used to update the parameters. Through iterative calculation, the detection of low frequency pulse signal of LSI is completed. The experimental results show that, compared with the traditional low-frequency pulse signal detection method, the detection accuracy of this method is greatly improved, up to 99.79%, and can shorten the overall detection time.

Efficient Decoding Algorithm for Binary Quadratic Residue Codes Using Reduced Permutation Sets

The Quadratic Residue (QR) codes have a rich mathematic structure. Unfortunately, their Algebraic Decoding (AD) is not generalizable for all QR codes. In this study, an efficient hard decoding algorithm is proposed to generalize the decoding of the binary systematic Quadratic Residue (QR) codes. The proposed decoder corrects t erroneous bits or less, in the received word, based on a reduced set of permutations derived from the large automorphism group of QR codes. This set of permutations is applied to the received word to move the error positions and trap all of them in redundancy. Then, to evaluate the proposed method, we applied it to many binary QR codes of moderate code length starting with 17 until 113 with reducible and irreducible generator polynomials. The proposed decoder was validated by inserting all possible error patterns, that have t or less erroneous positions, as input of the proposed decoder and the output is always a correct codeword. The complexity study, in terms of the number of operations used, reveals that the light permutation decoding LPD algorithm significantly decreases decoding complexity without performance loss. So, it is qualified to be a good competitor to decode QR codes with lower lengths but is the best for QR codes with higher lengths.

Resonance Graphs and a Binary Coding of Perfect Matchings of Outerplane Bipartite Graphs

The aim of this paper is to investigate resonance graphs of 2- connected outerplane bipartite graphs, which include various families of molecular graphs. Firstly, we present an algorithm for a binary coding of perfect matchings of these graphs. Further, 2- connected outerplane bipartite graphs with isomorphic resonance graphs are considered. In particular, it is shown that if two 2- connected outerplane bipartite graphs are evenly homeomorphic, then its resonance graphs are isomorphic. Moreover, we prove that for any 2-connected outerplane bipartite graph G there exists a catacondensed even ring systems H such that the resonance graphs of G and H are isomorphic. We conclude with the characterization of 2-connected outerplane bipartite graphs whose resonance graphs are daisy cubes.

An Adaptive Chase-Pyndiah Algorithm for Turbo Product Codes

The Chase-Pyndiah algorithm is a soft decoding algorithm for turbo product codes (TPCs) with promising error correction performance. In this letter, we propose an adaptive Chase-Pyndiah algorithm to improve the error correction performance for TPCs with negligible complexity increase. The proposed algorithm adaptively adjusts the weighting factor and reliability factor according to the analog weight of the decision codeword for a component code. Simulation results show that the proposed algorithm can achieve a signal-to-noise ratio (SNR) gain of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.16\sim 0.18$ </tex-math></inline-formula> dB at the bit-error-rate (BER) of 10−6 over additive white Gaussian noise (AWGN) channels.

An investigation of low-density parity-check codes and polar codes for future communication systems

In the fifth-generation (5G) era and future, mobile internet and internet of things (IoT) are the driving forces for mobile communications’ development. The three main 5G usage scenarios: enhanced mobile broadband (eMBB), massive machine-type communication (mMTC), and ultra-reliable and low-latency communications (URLLC), require improvement in throughput, reliability, and latency as compared with the previous fourth generation (4G) system. In this paper, an investigation is done on the coding part of the wireless communication systems. Two channel coding types; low-density parity-check (LDPC) code which is used as the coding scheme for data transmission, and Polar code which is utilized for control in 5G are discussed. Moreover, simulations are performed to assess their performance. The simulation results revealed the superiority of polar code for transmitting short information messages and LDPC for transmitting long data messages. The use of LDPC and polar codes in 5G communication systems is justified by their ability to accommodate a wide range of data lengths and code rates, as well as their good bit error rate (BER) performance. Furthermore, the effect of the number of iterations on the BER performance of LDPC code and different decoding algorithms of polar code are considered.

PENGAMANAN DATA DIGITAL MENGGUNAKAN TEKNIK INTEGER WAVELET TRANSFORM DAN LEAST SIGNIFICANT BIT

AbstractData is an asset for all parties. So that it must always be guarded or secured so that it is not misused by irresponsible parties. One of the methods used for data security is steganography. In this paper, efforts to secure data are discussed through the development of steganographic algorithms using the Haar integer wavelet transform (IWT-Haar) and least significant bit (LSB) techniques to hide secret messages in the form of digital text into digital images (cover images) to produce stego images. The results show that the extracting process is much faster than the embedding process. The PSNR values obtained ranged from 55-71,5 dB (far greater than 40 dB). This shows that the resulting stego image is of very good quality. In plain view, the difference between the cover image and the stego image has not changed significantly.Key words: digital image, embedding, extraction, Haar integer wavelet transform, steganographyÂ

A Master Mind Game Code Algorithm Approach to Help Surgical Decision-Making between Retropharyngeal Fat Grafting and Pharyngoplasty for the Treatment of Velopharyngeal Incompetence.

Introduction Velopharyngeal insufficiency (VPI) is a controversial pathology with many surgical options. Objective To compare pharyngoplasty and retropharyngeal fat grafting and to build a prognostic tool to achieve perfect speech. Methods Retrospective observational cohort study of 114 patients operated for VPI from 1982 to 2019 in a single tertiary center. The instrumental assessment was made using an aerophonoscope and nasofibroscopy. The variables sex, age, genetic syndromes, and type of diagnosis were analyzed with logistic regression model adjusted with propensity score. To generalize results and to build a surgical predictive tool, a marginal analysis concludes the study. Results Among the patients (median [range] age 7 [4-48]), 63 (55.26%) underwent pharyngoplasty and 51 (44.74%) graft. The graft group had no complication, but it had a failure rate of 7.84%. The pharyngoplasty group had no failure, but one patient had postoperative obstructive sleep apnea. The marginal analysis demonstrated that age lower than 7 years, cleft lip and palate, absence of syndrome, and intermittent VPI were important predictive factors of good result regardless of surgical technique. Conclusions Without a statistical demonstration of the superiority of pharyngoplasty over graft, and in the uncertainty of literature background, our perfect-speech patient profile represents an important tool for a postoperative forecast of results in which, like in the Master Mind game, every feature has to be considered not individually but as a pattern of characteristics whose association contributes to the outcome.

A novel image steganography algorithm based on hybrid machine leaning and its application in cyberspace security

As an important technology in the field of cyberspace security, image steganography is of great strategic significance to the research of image steganography. With the wider application of image steganography technology, there are fewer and fewer cases of image steganography using a single algorithm, and more adaptive steganography of images is used instead. According to the different sensitivities of human eyes to the foreground area and background area of the image, the key to solving this problem is to segment the foreground and background of the carrier image. Aiming at the problem of low maximum embedding capacity of traditional steganography algorithm and poor perception quality of carrier image after steganography, this paper firstly uses adaptive threshold segmentation to process carrier removal image, and divides the image into foreground and background regions. Secondly, in order to improve the embedding capacity, high-capacity steganography is used for pixels in the foreground area of the image, and low-capacity steganography is used for the background area of the image. At the same time, in order to improve the security of the secret information, hybrid machine learning is used to encrypt before the secret information is embedded, and finally BFS (Breadth First Search) is used to complete the steganography of the carrier image. The experimental results show that the PSNR (Peak Signal to Noise Ratio) value of the pictures after steganography by this algorithm is above 52.71 dB, which has high image perception quality. Better protection of confidential information.

Color image quantum steganography scheme and circuit design based on DWT+DCT+SVD

This paper presents a new steganographic algorithm for quantum color images, which utilizes DWT+DCT+SVD embedding technology. Firstly, in order to improve the security of secret information, a novel three-dimensional hyperchaotic system that combines the Chebyshev map and ICMIC map using a closed-loop coupling method (3D-CICC) is proposed. In addition, this paper proposes a new steganography algorithm based on DWT+DCT+SVD, which can effectively hide the secret image in the carrier image without being detected. At the same time, an efficient quantum circuit for quantum steganography is designed. The simulation and numerical analysis results show that the algorithm has good performance in invisibility analysis, security analysis, and robustness analysis.

Puncturing scheme for polar codes based on channel reliability estimation

The compiled code algorithms for polar codes, which were proposed by Arikan, are of low complexity, with excellent performance for a given code length when using optimized decoding algorithms. They have attracted widespread attention and gained popularity since its inception. The construction of punctured polar codes allows for more flexibility in coding, making them suitable for a greater variety of scenarios. This paper proposes an improved puncturing scheme for polar codes. With the constraints imposed by conventional punctured polar codes, the error probability of each polarized sub-channel is then calculated on the basis of the channel reliability estimation methodology, so as to puncture the less reliable polarized sub-channels. Furthermore, to achieve better decoding performance, this scheme sets the initial log-likelihood rate (LLR) of the punctured bits to infinity (or negative infinity). The simulation outcomes indicate that the improved punctured polar codes as proposed in this paper perform better than the conventional punctured polar codes.

Evolving Generative Adversarial Networks to improve image steganography

Images have been repeatedly used as the perfect environment to hide information through the use of steganography techniques. Whether messages, documents or even other images, the bitmap of an digital picture provides a place where hidden data can be embedded without human notice. So far, a plethora of steganography methods can be found in the state-of-the-art literature, together with steganalysis techniques, devoted to detect the presence of hidden information in files. Recent steganography techniques rely on Convolutional Neural Networks, trying to embed as information as possible while minimising visual changes in the image. Following this trend, this article tries to demonstrate that a Generative Adversarial Network (GAN) can be used to improve the ability of a spatial domain steganalysis method and to insert secret information with minimal image alteration. Through a training process, the GAN learns how to adapt an image to later introduce a message using the Least Significant Bit steganography algorithm. The results evidence that the approach is successful at avoiding detection by a state-of-the-art Deep Learning steganalysis architecture.