Coding Algorithm Research Articles

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.

A Fast Algorithm for VVC Intra Coding Based on the Most Probable Partition Pattern List

Compared with High-Efficiency Video Coding (HEVC), Versatile Video Coding (VVC) has more flexible division and higher compression efficiency, but it also has higher computational complexity. In order to reduce the coding complexity, a fast algorithm based on the most probable partition pattern list (MPPPL)and pixel content similarity is proposed. Firstly, the MPPPL is constructed by using the average texture complexity difference of the sub-coding unit under different partition modes. Then, the sub-block pixel mean difference is used to decide the best partition mode or shorten the MPPPL. Finally, the selection rules of the reference lines in the intra prediction process are counted and the unnecessary reference lines are skipped by using the pixel content similarity. The experimental results show that compared with VTM-13.0, the proposed algorithm can save 52.26% of the encoding time, and the BDBR (Bjontegarrd delta bit rate) only increases by 1.23%.

Stegano-Purge: An integer wavelet transformation based adaptive universal image sterilizer for steganography removal

Sterilization is the method of annihilating steganographic data hidden in a cover media. This paper proposes a visual quality preserving image sterilization method that effectively modifies stego-data embedded by various steganography algorithms on random greyscale images. The method applies one-level Integer Wavelet Transform (IWT) on a suspected stego image to produce HH, HL, LH and LL sub-bands. The value of a sub-band coefficient is modified according to its agitation quotient. The coefficient agitation is computed using the proposed metric Unnormalized Hellinger Distance (UHD) between a specific sub-band coefficient and its neighbours. To mitigate the issue of variable noise tolerance of IWT sub-bands, the proposed method performs parameter-controlled adaptive modification. Purification of up to 3 bits is done in the most noise-resilient HH channel and up to 2 bits in moderately noise-sensitive HL and LH channels. The highly noise-susceptible LL channel is sterilized by adaptive bit-flipping. A parameter control algorithm balances the amount of channel-wise bit modification to optimize the visual quality. The stego images for evaluation have been prepared by applying eleven state-of-the-art steganography methods including UNIWARD and HUGO on benchmark datasets of USC-SIPI and BossBase 1.01. The proposed method achieves improved performance in comparison with the state-of-the-art sterilization techniques in terms of the visual quality of sterilized image, embedding scheme-wise percentage of sterilization and the sterilization time.

Cerebrovascular disease case identification in inpatient electronic medical record data using natural language processing

Backgrounding cerebrovascular disease (CeVD) from inpatient electronic medical records (EMRs) through natural language processing (NLP) is pivotal for automated disease surveillance and improving patient outcomes. Existing methods rely on coders’ abstraction, which has time delays and under-coding issues. This study sought to develop an NLP-based method to detect CeVD using EMR clinical notes.MethodsCeVD status was confirmed through a chart review on randomly selected hospitalized patients who were 18 years or older and discharged from 3 hospitals in Calgary, Alberta, Canada, between January 1 and June 30, 2015. These patients’ chart data were linked to administrative discharge abstract database (DAD) and Sunrise™ Clinical Manager (SCM) EMR database records by Personal Health Number (a unique lifetime identifier) and admission date. We trained multiple natural language processing (NLP) predictive models by combining two clinical concept extraction methods and two supervised machine learning (ML) methods: random forest and XGBoost. Using chart review as the reference standard, we compared the model performances with those of the commonly applied International Classification of Diseases (ICD-10-CA) codes, on the metrics of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).ResultOf the study sample (n = 3036), the prevalence of CeVD was 11.8% (n = 360); the median patient age was 63; and females accounted for 50.3% (n = 1528) based on chart data. Among 49 extracted clinical documents from the EMR, four document types were identified as the most influential text sources for identifying CeVD disease (“nursing transfer report,” “discharge summary,” “nursing notes,” and “inpatient consultation.”). The best performing NLP model was XGBoost, combining the Unified Medical Language System concepts extracted by cTAKES (e.g., top-ranked concepts, “Cerebrovascular accident” and “Transient ischemic attack”), and the term frequency-inverse document frequency vectorizer. Compared with ICD codes, the model achieved higher validity overall, such as sensitivity (25.0% vs 70.0%), specificity (99.3% vs 99.1%), PPV (82.6 vs. 87.8%), and NPV (90.8% vs 97.1%).ConclusionThe NLP algorithm developed in this study performed better than the ICD code algorithm in detecting CeVD. The NLP models could result in an automated EMR tool for identifying CeVD cases and be applied for future studies such as surveillance, and longitudinal studies.

Research and Implementation of High-Efficiency and Low-Complexity LDPC Coding Algorithm

In this work, we proposed a high-efficiency and low-complexity encoding algorithm and its corresponding implementation structure during the design and implementation process of an LDPC encoder and decoder. This proposal was derived from extensive research on and analysis of standard encoding algorithms and recursive iterative encoding algorithms, specifically targeting the problem of high computational complexity in encoding algorithms. Subsequently, we combined binary phase-shift keying modulation mode and additive white Gaussian noise channel transmission with the min-sum decoding algorithm to realize the (1536, 1024) LDPC codec. This codec was uniformly quantized with a (6, 2) configuration, executed eight iterations, and achieved a 2/3 code rate in the IEEE802.16e standard. At the bit error rate (BER) of 10−5, the codec’s BER obtained by the proposed coding algorithm was about 0.25 dB lower than the recursive-iterative coding algorithm and was about 1.25 dB lower than the standard coding algorithm, which confirms the correctness, effectiveness, and feasibility of the proposed algorithm.

A High-Performance Rate Control Algorithm in Versatile Video Coding Based on Spatial and Temporal Feature Complexity

This paper presents a high-performance rate control (HPRC) algorithm for the Versatile Video Coding (VVC) standard that aims to achieve higher coding efficiency by considering spatial and temporal feature complexity. The HPRC algorithm differs from conventional rate control (RC) algorithms in that it uses an adaptive Canny operator (ACO) with a novel double-threshold algorithm to obtain the feature complexity of video content accurately. Moreover, an advanced bit allocation strategy at the frame-level and coding tree unit (CTU)-level is constructed to realize rational RC by thoroughly exploring the relationship between coding bits and complexity. To improve the precision of RC, an appropriate parameter updating based on the quasi-Newton algorithm is also proposed. Experimental results demonstrate that the proposed HPRC algorithm outperforms the default RC algorithm in VVC Test Model (VTM) 18.0, with Bjontegaard Delta Rate (BD-Rate) savings of 8.77 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\%$</tex-math> </inline-formula> and 10.34 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\%$</tex-math> </inline-formula> under Low Delay_P and Random Access configurations, respectively. Furthermore, the proposed algorithm also shows performance enhancements compared to other advanced algorithms.

Reed-Solomon Coding Algorithms Based on Reed-Muller Transform for Any Number of Parities

Based on the Reed-Muller (RM) transform, this paper proposes a Reed-Solomon (RS) encoding/erasure decoding algorithm for any number of parities. Specifically, we first generalize the previous RM-based syndrome calculation, which allows only up to seven parities, to support any number of parities. Then we propose a general encoding/erasure decoding algorithm. The proposed encoding algorithm eliminates the operations in solving linear equations, and this improves the computational efficiency of existing RM-based RS algorithms. In terms of erasure decoding, this paper employs the generalized RM-based syndrome calculation and lower-upper (LU) decomposition to accelerate the computational efficiency. Analysis shows that the proposed encoding/erasure decoding algorithm approaches the complexity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\lfloor \mathrm{lg} T \rfloor + 1$</tex-math></inline-formula> XORs per data bit with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> increasing, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$T$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> denote the number of parities and codeword length respectively. To highlight the advantage of the proposed RM-based algorithms, the implementations with Single Instruction Multiple Data (SIMD) technology are provided. Simulation results show that the proposed algorithms are competitive, as compared with other cutting-edge implementations.

Examining the Burden of Potentially Avoidable Heart Failure Hospitalizations.

Two-thirds of the 1 million annual US CHF hospitalizations are for diuresis only; some may be avoidable. We describe a population of low-severity short-stay (</= 4 days) patients admitted for CHF. We conducted a retrospective cohort study within the Premier Healthcare Database, 2016-2021. CHF was defined via an administrative code algorithm. High severity (CHF-H) was marked by cardiogenic shock, the need for respiratory or circulatory support, and/or a Charlson comorbidity index >2. We compared baseline characteristics, processes of care, and outcomes in low-severity (CHF-L) to CHF-H. Among 301,672 short-stay CHF patients, 135,304 (44.8%) were CHF-L. Compared to CHF-H, CHF-L was younger (70.5 ± 14.1 vs 72.1 ± 13.6 years, p < 0.001), more commonly female (48.6% vs 45.8%, p < 0.001), and more likely to receive IV ACE-I/ARB agents (0.5% vs 0.4%, p = 0.003). Most other IV medications were more common in CHF-H, and anticoagulation was the most prevalent non-diuretic IV therapy in both groups (23.8% vs 33.3%, p < 0.001). Hospital mortality (0.2% vs 1.5%, p < 0.001) and CHF-related 30-day readmissions (8.1% vs 10.5%, p < 0.001) were lower in CHF-L than CHF-H. Among short-stay CHF patients, nearly ½ meet criteria for CHF-L, and are mainly admitted for fluid management. Avoiding these admissions could result in substantial savings.

Enhancing Data Security: A Cutting-Edge Approach Utilizing Protein Chains in Cryptography and Steganography

Nowadays, with the increase in cyber-attacks, hacking, and data theft, maintaining data security and confidentiality is of paramount importance. Several techniques are used in cryptography and steganography to ensure their safety during the transfer of information between the two parties without interference from an unauthorized third party. This paper proposes a modern approach to cryptography and steganography based on exploiting a new environment: bases and protein chains used to encrypt and hide sensitive data. The protein bases are used to form a cipher key whose length is twice the length of the data to be encrypted. During the encryption process, the plain data and the cipher key are represented in several forms, including hexadecimal and binary representation, and several arithmetic operations are performed on them, in addition to the use of logic gates in the encryption process to increase encrypted data randomness. As for the protein chains, they are used as a cover to hide the encrypted data. The process of hiding inside the protein bases will be performed in a sophisticated manner that is undetectable by statistical analysis methods, where each byte will be fragmented into three groups of bits in a special order, and each group will be included in one specific protein base that will be allocated to this group only, depending on the classifications of bits that have been previously stored in special databases. Each byte of the encrypted data will be hidden in three protein bases, and these protein bases will be distributed randomly over the protein chain, depending on an equation designed for this purpose. The advantages of these proposed algorithms are that they are fast in encrypting and hiding data, scalable, i.e., insensitive to the size of plain data, and lossless algorithms. The experiments showed that the proposed cryptography algorithm outperforms the most recent algorithms in terms of entropy and correlation values that reach −0.6778 and 7.99941, and the proposed steganography algorithm has the highest payload of 2.666 among five well-known hiding algorithms that used DNA sequences as the cover of the data.

BOTDA Sensing System Based on Differential Golay Coding and Deconvolution Algorithm

BOTDA Sensing System Based on Differential Golay Coding and Deconvolution Algorithm

Data Hiding using Slantlet Fusion Method and T-Codes

This paper presents the study of steganography algorithm based on Slantlet fusion method using a self-synchronizing variable length encoder, viz., T-codes. The Slantlet fusion method is the high bit rate data hiding scheme based on Slantlet Transorm. In this method, the fusion process takes place between the SLT of the secret data and the SLT of the cover image. The secret data is obtained by the application of SSVLC, T-codes to the original data. The proposed algorithm is compared with the Wavelet fusion method using the metrics PSNR, PSNRE, PSNRY, CQM, SSIM and KLdiv respectively. It is found that the proposed scheme is highly secure and efficient in comparison to the state of the art works.

A Blind Image Steganography Algorithm Based on Knight Tour Algorithm and QR Codes

Internet proliferation and technological progress have made multimedia information quickly accessible, but they have also posed a threat to privacy and security. Researchers have been interested in digital images due to their capacity to store large amounts of data due to the possibility of protecting sensitive information through digital steganography. Despite their visual imperceptibility, robustness, and ability to embed information, existing image steganography techniques face several challenges. To overcome these challenges, a novel image steganography approach based on a blind model strategy has been proposed for hiding covert messages. The model consists of two stages: embedding and extracting. In the embedding stage, a suitable cover image is selected using the FAST feature point detector. A text message is then converted to a QR code and embedded in the feature points' neighbors using knight tour steps in a chess game. The result is a stego image that appears identical to the original cover image but contains the secret message in its feature points' neighbors. The extracting stage involves finding the feature points and extracting the QR code to obtain the original text message. Since the feature points were not altered during the embedding process, the proposed model is known as a blind model. This approach eliminates the need for the original cover image during the extracting stage. The proposed model was evaluated using several metrics, including the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). The results demonstrate that the proposed algorithm can effectively embed and extract secret messages with high accuracy while maintaining the visual quality of the cover image with 100% of (SSIM), and 73.48 as an average of (PSNR).

Application of target-optimized image steganography algorithm for fine art sculpture design

Application of target-optimized image steganography algorithm for fine art sculpture design