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—In this paper, a de-noising approach in conjunction with channel estimation (CE) algorithm for OFDM systems using singular spectrum analysis (SSA) is presented. In the proposed algorithm, the initial CE is computed with the aid of traditional linear minimum mean square error (LMMSE) algorithm, and then further channel is evaluated by considering the low rank eigenvalue approximation of channel correlation matrix related to channel using SSA. Simulation results on bit error rate (BER) revealed that the method attains an improvement of 7 dB, 5 dB and 3 dB compared to common LSE, MMSE and SVD based methods respectively. With the help of statistical correlation coefficient (C) and kurtosis (k), the SSA method utilized to de-noise the received OFDM signal in addition to CE. In the process of denoising, the received OFDM signal will be decomposed into different empirical orthogonal functions (EOFs) based on the singular values. It was established that the correlation coefficients worked well in identifying useful EOFs only up to moderate SNR  12dB. For low SNR

SVD (Singular Value Decomposition) algorithm is used in recommendation systems, machine learning, image processing, and in various algorithms for working with matrices which can be very large and Big Data, so, given the peculiarities of this algorithm, it can be performed on a large number of computing threads that have only video cards.CUDA is a parallel computing platform and application programming interface model created by Nvidia. It allows software developers and software engineers to use a CUDA-enabled graphics processing unit for general purpose processing – an approach termed GPGPU (general-purpose computing on graphics processing units). The GPU provides much higher instruction throughput and memory bandwidth than the CPU within a similar price and power envelope. Many applications leverage these higher capabilities to run faster on the GPU than on the CPU. Other computing devices, like FPGAs, are also very energy efficient, but they offer much less programming flexibility than GPUs.The developed modification uses the CUDA architecture, which is intended for a large number of simultaneous calculations, which allows to quickly process matrices of very large sizes. The algorithm of parallel SVD for a three-diagonal matrix based on the Givents rotation provides a high accuracy of calculations. Also the algorithm has a number of optimizations to work with memory and multiplication algorithms that can significantly reduce the computation time discarding empty iterations.This article proposes an approach that will reduce the computation time and, consequently, resources and costs. The developed algorithm can be used with the help of a simple and convenient API in C ++ and Java, as well as will be improved by using dynamic parallelism or parallelization of multiplication operations. Also the obtained results can be used by other developers for comparison, as all conditions of the research are described in detail, and the code is in free access., Ця робота пропонує реалізацію паралельного алгоритму SVD для тридіагональної матриці на відеокарті з використанням архітектури Nvidia CUDA для роботи з великими матрицями. Для цього було досліджено роботу послідовного алгоритму, розроблено модель паралельного алгоритму на Java, який враховує особливості роботи відеокарти, і реалізовано та протестовано алгоритми для відеокарти з використанням різних типів пам’яті відеокарти, які можна використовувати у програмах на Java та С/C++.

With the increase in point of care services, communication of digital patient records through open network has multi-folded. This digital data is used to obtain the remote medical assistance from the smart healthcare centres. Protecting this data during transmission is a very big challenge. One of the most important medical data is electrocardiogram (ECG) signal which detects the cardiovascular diseases and any alteration in the signal may affect the diagnosis. In this work, an ECG watermarking based on redundant discrete wavelet transform (RDWT) and singular value decomposition (SVD) is developed. First, the ECG signal is converted into 2-D matrix using pan-tompkins algorithm. Then, we use the hybrid of RDWT and SVD to conceal the patient data and logo image into the 2-D ECG image. We also use hybrid of optimization scheme to improve the robustness of the watermark. Preliminary experimental results indicate the optimal invisibility and robustness result is more effective up to 97.89% than the traditional schemes respectively, which makes it suitable for ownership authentication of ECG signal.

_ Data by CATA (Check-All-That-Apply) method was analyzed by SVD (Singular Value Decomposition). SVD extracts row and column informations of the cross table of CATA data. An example of SVD of a cross table, which is made of kinds of rice, column variables, and of questions, row variables, produced interesting results. SVD of a cross table can be considered to produce a representation by inner products of vectors. This interpretation was pointed out to me by Akinori Okada, Professor Emeritus of Rikkyo University (2022.11, personal communication). When interpreted in Inner Product Model, configuration can be orthogonally rotated. Cluster analysis also can be performed for coordinate vectors in Inner Product Model. For details, see the file ReadMe.pdf in the Files Box below. The script file and a sample data file were archived into the file samplefiles.zip in the Files Box below. These files can be used freely. _

Among the most widespread systems in industrial plants are automated drive systems, the key and most common element of which is the induction motor. In view of challenging operating conditions of equipment, the task of fault detection based on the analysis of electrical parameters is relevant. The authors propose the identification of patterns characterizing the occurrence and development of the bearing defect by the singular analysis method as applied to the stator current signature. As a result of the decomposition, the time series of the three-phase current are represented by singular triples ordered by decreasing contribution, which are reconstructed into the form of time series for subsequent analysis using a Hankelization of matrices. Experimental studies with bearing damage imitation made it possible to establish the relationship between the changes in the contribution of the reconstructed time series and the presence of different levels of bearing defects. By using the contribution level and tracking the movement of the specific time series, it became possible to observe both the appearance of new components in the current signal and the changes in the contribution of the components corresponding to the defect to the overall structure. The authors verified the clustering results based on a visual assessment of the component matrices’ structure similarity using scattergrams and hierarchical clustering. The reconstruction of the time series from the results of the component grouping allows the use of these components for the subsequent prediction of faults development in electric motors.

With high performances of image capturing tools, image information can be easily obtained by screenshots that make image copyright protection a challenging task. The existing screen-shooting watermarking algorithms suffer from a huge running time, in addition to their low robustness against different screenshot attacks, such as different distances and capturing angles of the screenshots. In this paper, a fast and robust high-capacity flexible watermarking algorithm for screenshot images is proposed. Firstly, Oriented FAST and Rotated BRIEF (ORB) feature points are extracted from the input image. Secondly, the feature points are then sorted in a descending order according to their response values. Then, the first five non-overlapping feature points are selected for the embedding by using Hamming window-based filtering method. Furthermore, we exploit the multi-resolution property of Discrete Wavelet Transform (DWT) and energy compaction property of Singular Value Decomposition (SVD) to embed the watermark. Therefore, the classical DWT combined with Singular Value Decomposition (SVD) are adopted to improve the robustness and capacity of the proposed watermarking algorithm. At the extraction side, the sum of the response values for the three RGB channels of the color-ripped image is calculated to improve the feature point localization accuracy. Experimental results show that the proposed screen-shooting watermarking algorithm improves running speed while ensuring the robustness. Furthermore, it has less time complexity and high robustness compared with the state-of-the-art watermarking algorithms against different screenshot attacks.

To assist the clinical diagnosis and treatment of speech dysarthria, automatic dysarthric speech detection techniques providing reliable and cost-effective assessment are indispensable. Based on clinical evidence on spectro-temporal distortions associated with dysarthric speech, we propose to automatically discriminate between healthy and dysarthric speakers exploiting spectro-temporal subspaces of speech. Spectro-temporal subspaces are extracted using singular value decomposition, and dysarthric speech detection is achieved by applying a subspace-based discriminant analysis. Experimental results on databases of healthy and dysarthric speakers for different languages and pathologies show that the proposed subspace-based approach using temporal subspaces is more advantageous than using spectral subspaces, also outperforming several state-of-the-art automatic dysarthric speech detection techniques.

In-network data compression plays an important role in the elimination of redundant time-series data in a wireless sensor network (WSN). Inconsistency of data and high computational process in cluster formation remain to be challenging issues of in-network data compression particularly for energy-constraint WSNs. This paper develops a new data clustering technique for in-network data preprocessing and compression called Error-Control Truncated Singular Value Decomposition (ETSVD) to achieve online outlier detection and adaptive data compression. The ETSVD is divided into two modules which are Adaptive Recursive Outlier Detection and Smoothing (ARODS) and Adaptive Data Compression (DC). Firstly, the ARODS pre-processes the collected data for outlier detection and smoothing in order to improve the data quality. Secondly, the DC decomposes the pre-processed data into vector space to compress the spatio-temporal correlated data based on the predefined error threshold at the sending end. After the compression of correlated data, the distinct decomposed data are reconstructed at the receiver end which is performed offline. The simulation results show that the proposed technique is able to compress 91.49% of spatio-temporal environmental temperature data with reconstruction error having a minimum tolerance of $\pm 1.0^\circ $ C. The performance improvement of ETSVD in terms of error and accuracy compared to the performance of conventional SVD are 85.26% and 33.49%, respectively. Moreover, the ETSVD provides efficient error-control data preprocessing and compression solutions within the networks with minimum space and time complexities.

The electromagnetic acoustic transducer (EMAT) is a powerful and useful non-destructive testing technology for structural health monitoring. However, EMAT has an issue of low efficiency in conversion and its signal is easily affected by noise, which make it difficult to accurately identify and evaluate structural defects. Thereby, signal de-noising preprocessing is essential for the evaluation of defects. In this paper, we proposed an improved singular value decomposition (SVD) de-noising method based on the fitting threshold for EMAT signal. For SVD de-noising method, the key point is to determine the singular value threshold for reconstructing the signal. We applied a segmented regression model to find the appropriate threshold in this approach. To investigate the efficacy of the proposed method, simulation signals and experimental signals are used for verification respectively. A comparative analysis has been under-taken to confirm that the proposed signal de-noising has advantages over other methods in EMAT signal noise reduction, and it is useful for more accurate evaluation of defects.

In this paper a new technique of dual image watermarking is proposed for protection of ownership rights which utilizes salient properties of homomorphic transform (HT), discrete wavelet transform (DWT), singular value decomposition (SVD) and Arnold transform (AT). In embedding algorithm host image is splitted into reflectance and illumination components using HT, DWT is further applied to the reflectance component resulting in frequency subbands (HL and LH) which are transformed by SVD. Two image watermarks are selected for embedding process whereas security of proposed algorithm is strengthen by performing scrambling of second watermark through AT. Both watermarks are transformed with DWT and SVD. Singular values (SVs) of both transformed watermark are embedded into SVs of host image. Simulation results clearly signifies for high robustness and imperceptibility of proposed algorithm as it is examined under various attacks. Superiority of proposed technique is illustrated by comparing it with other reported methods.

Oil field research in regional Banyuasin “HUF” , South Sumatra have been done by the gravity data with objective of knowing fault structure based on analysis of hydrocarbon SVD and knows hollow structure based on the 3D model of the Bouguer Anomaly and Residual Anomaly. Study areas had Bouguer Anomaly between 13 mgal up to 33 mgal to the interval 1 mgal, where the value of Bouguer Anomaly high have a range value 26 mgal up to 33 mgal which is in the direction of west. While the low value of Bouguer Anomaly have a range value 13 mgal to 20 mgal that is in the east. To knew the existence of structure fault, filtering Second Vertical Derivative (SVD) on a Bouguer Anomaly, Regional and Residua mapl.Pattern of structure fault indicated the contours of a zero value and between the high and low contours. From the results of the analysis SVD Complete Bouguer Anomaly and SVD Residual Anomaly there were 4 (four) fault, while from SVD Regional Anomaly there are 3 (three) fault. 3D modeling the Residual Anomaly were conducted to prove the existence of the fault SVD analyzed based on the results of the analysis and to know the hydrocarbon basin. Based on the results of the inversion of 3D the Residual Anomaly, basin was found in the depth of 1500 m – 3000 m with a value of the density ranges from 2.24 gram/cc until 2.32 gram/cc which identified as sandstone basin.

Gravity method generally can read the difference of rock density to identify subsurface structures. This research was conducted in Riau area with the aim of knowing fault and subsurface structure, and determining sub-basin boundary. Data processing was done by using spectral analysis, SHD and SVD analysis, and 2D modeling then later correlated with seismic section to find out the subsurface structure of research area. The results showed that the bouguer anomaly value had a range of values between 5.6 mGal to 33.2 mGal with a surface density of 1.95 g / cc. High anomaly were in the eastern region and low anomaly were in the NW - SE trending region. 6 low anomalies indicated as a sub-basin pattern were separated by a relatively high altitude area of North West Southeast. The average residual anomaly depth was about 2.3 km. Based on the 2D subsurface modeling results, there was layer structure from the youngest to eldest that were alluvial deposits, Petani Formation, Sihapas group and granitic rock as the base rock. The projected subsurface section to surface showed 5 sub-basins with basin width 4-12 km located in the sub-basin and fault indication area.

Image watermarking schemes based on singular value decomposition (SVD) have become popular due to a good trade-off between robustness and imperceptibility. However, the false positive problem (FPP) is the main drawback of SVD-based watermarking schemes. The singular value is the main cause of FPP issues because it a fixed value that does not hold structural information of an image. In this paper, a new SVD-based image watermarking scheme that uses a chaotic map is proposed to overcome this issue. The secret key is first extracted from both the host and watermark image. This key is used to generate a new chaotic matrix and chaotic multiple scaling factors (CMSF) to increase the sensitivity of the proposed scheme. The watermark image is then transformed based on the chaotic matrix before being directly embedded into the singular value of the host image by using the CMSF. The extracted secret key is unique to the host and the watermark images, which improves security and overcomes FPP issues. Experimental results show that the proposed scheme fulfils all watermarking requirements in terms of robustness, imperceptibility, security, and payload. Furthermore, it achieves high robustness with different scaling factors, and outperforms several existing schemes.

Background and Objective: Voice disorders are pathological conditions that directly affect voice production. Computer based diagnosis may play a major role in the early detection and in tracking and even development of efficient pathological speech diagnosis, based on a computerized acoustic evaluation. The health of the Voice is assessed by several acoustic parameters. The exactness of these parameters is often linked to algorithms used to estimate them for speech noise identification. That is why main effort of the scientists is to study acoustic parameters and to apply classification methods that achieve a high precision in discrimination. The primary aim of this paper is for a meta-analysis on voice disorder databases i.e. SVD, MEEI and AVPD and machine learning techniques applied on it. Materials and Methods: This field of study was systematically reviewed in compliance with PRISMA guidelines. A search was performed with a set of formulated keywords on three databases i.e. Science Direct, PubMed, and IEEE Xplore. A proper screening and analysis of articles were performed after which several articles were also excluded. Results: Forty-five studies that fulfills the eligibility criteria were included in this meta-analysis. After applying eligibility criteria on the peer reviewed and research article and studies that were published in authentic journals and conferences proceedings till June 2020 were chosen for further full-text screening. In general, only those articles that used voice recordings from SVD, MEEI and AVPD databases as a dataset is included in this meta-analysis. Conclusion: We discussed the strengths and weaknesses of SVD, MEEI and AVPD. After detailed analysis of the studies including the techniques used and outcome measurements, it was also concluded that Support Vector Machine (SVM) is the most common used algorithm for the detection of voice disorders. Other than was also noticed that researchers focus on supervised techniques for the clinical diagnosis of voice disorder rather than using unsupervised techniques. It was also concluded that more work needs to be on voice pathology detection using AVPD database.

The study assesses the covariability of Sea Surface Temperature (SST) and March to May (MAM) rainfall variability on East Africa (EA) from 1981 to 2018. Singular Value Decomposition (SVD) analysis reveals the significant influence of SST anomalies on MAM rainfall, with covariability of 91%, 88.61%, and 82.9% for Indian, Atlantic, and the Pacific Ocean, respectively. The Indian Ocean explains the variability of rainfall to the large extent followed by the Atlantic Ocean and the Pacific Ocean. The rainfall patterns over the EA correspond to SST variability over the western, central, and Eastern Indian Ocean. Likewise, the variability of SST anomalies was observed over the central, south, and North of the Atlantic Ocean while the Pacific Ocean captured the El Nino Modoki (ENSO) like pattern in the SVD1 (SVD2). The heterogeneous correlation of Indian SST anomalies and rainfall over EA of the first (second) principal component (PC) shows a positive correlation over much of the domain (central region). The SST anomalies over the Pacific Ocean show higher correlation values with the rainfall over much of the study domain except over the southwestern highland and southern region of Tanzania. Over the Atlantic Ocean, the correlation result shows the patterns of positive (negative) values over the northern (southern) part for PC1, while PC2 depicts negative correlation values over much of the Ocean. SST anomalies over the Indian (Atlantic) Ocean are highly correlated with MAM rainfall when SST leads by 1(7) month(s). The Pacific Ocean shows a weak (strong) correlation across all (zero) lead seasons.

With the development of radar technology, the automotive millimeter wave radar is widely applied in the fields including internet of vehicles, Artificial Intelligence (AI)-based autonomous driving, health monitoring, etc. Eye blink, as one of the most common human activities, can effectively reflect the person’s consciousness and fatigue. The contacted eye blink detection often leads to uncomfortable experience and the camera-based eye blink detection has privacy issues. As an alternative, the non-contacted eye blink detection based on automotive millimeter wave radar resolves the aforementioned issues and has been received much attention. This paper proposes an eye blink detection method using the frequency modulated continuous wave radar. Firstly, the position of the person’s head is estimated by carrying out fast Fourier transform on the intermediate frequency signal, and the signals of the range bins at the head are extracted. Then, the complete ensemble empirical mode decomposition with adaptive noise algorithm is applied to decompose the eye signals into a series of intrinsic mode functions (IMFs), and the singular value decomposition is adopted to constrain the selection and reconstruction of the useful IMFs related to the eye blink signal. Finally, the short-time Fourier transformation and cell average constant false alarm rate are applied to detect the eye blink behavior. Experiments are carried out to validate the effectiveness of the proposed eye blink detection method.

Potential field data have long been used in geophysical exploration for archeological, mineral, and reservoir targets. For all these targets, the increased search of highly detailed three-dimensional subsurface volumes has also promoted the recollection of high-density contrast data sets. While there are several approaches to handle these large-scale inverse problems, most of them rely on either the extensive use of high-performance computing architectures or data-model compression strategies that may sacrifice some level of model resolution. We posit that the superposition and convolutional properties of the potential fields can be easily used to compress the information needed for data inversion and also to reduce significantly redundant mathematical computations. For this, we developed a convolution-based conjugate gradient 3D inversion algorithm for the most common types of potential field data. We demonstrate the performance of the algorithm using a resolution test and a synthetic experiment. We then apply our algorithm to gravity and magnetic data for a geothermal prospect in the Acoculco caldera in Mexico. The resulting three-dimensional model meaningfully determined the distribution of the existent volcanic infill in the caldera as well as the interrelation of various intrusions in the basement of the area. We propose that these intrusive bodies play an important role either as a low-permeability host of the heated fluid or as the heat source for the potential development of an enhanced geothermal system.

Le Contrôle Non Destructif (CND, consistant en la caractérisation thermique de matériaux et de défauts) fait face à de nombreuses difficultés dues à la grande quantité de données à traiter et au caractère mal posé du problème inverse, impliquant un mauvais conditionnement numérique (faibles sensibilités aux paramètres, nombres de paramètres, …), qui associé à des problèmes de faibles rapports signal sur bruit rend complexe le diagnostic (détection et caractérisation) quantitatif des défauts. Ainsi, de nombreuses méthodes ont été développées en vue de réaliser et améliorer la détectivité et/ou la caractérisation thermique quantitative des défauts, dont les plus fréquentes sont la « Thermal Signal Reconstruction » (TSR), la « Pulsed Phase Transformation » (PPT), ou encore la « Principal Component Thermography » « PCT », appartenant aux trois grandes classes de méthodes que sont la thermographie pulsée, non pulsée, ou modulée. Cependant, les performances de ces dernières sont souvent dépendantes du contexte expérimental (matériaux, type de défauts, profondeurs, …) dans lequel elles sont appliquées, rendant délicat d'en extirper une qui soit globalement plus performante que les autres. Par ailleurs, la complexité et la durée de mise en œuvre sont également grandement variables d'une méthode à l'autre. C'est donc dans ce contexte, qu'une méthode de CND quantitatif est proposée, dont les objectifs sont de permettre en une expérience de caractériser les propriétés thermiques des zones saines et des défauts, en étant à la fois rapide de mise en œuvre, précise et robuste. Expérimentalement, la méthode est basée sur une excitation optique (pouvant être impulsionnelle ou non) et une mesure par thermographie infrarouge permettant une mesure simultanée de températures en face avant et arrière des échantillons. Dans le cadre de ce papier, on se restreindra à une caractérisation thermique 1D des défauts (supposés être modélisables par des résistances thermiques) et des zones saines, en supposant que le problème est résolu en se plaçant au centre des défauts et des zones saines. L'originalité du problème inverse proposé repose l'utilisation de la construction d'une fonctionnelle duale (des mesures de températures) basée sur l'usage de températures en face avant et/ou arrière dans les zones saines et/ou avec défauts. Les performances statistiques de la méthode développée seront analysées sur des données bruitées et non bruitées à l'aide d'une méthode de Monte Carlo, et dont les résultats associés aux différentes configurations (avant et/ou arrière) offertes par la méthode seront comparées entre elles et par rapport à ceux obtenus par une méthode classique basée sur l'estimation des paramètres du défaut à partir de la courbe de contraste réalisée en face avant ou arrière.

Variational Mode Decomposition (VMD) is widely used for inspection purposes. The initial parameters are usually set manually, which is a limitation of this technique. In this paper, a method to automatically select these parameters through a combination of Singular Value Decomposition (SVD) and Improved-VMD (IVMD) is proposed. VMD is applied multiple times with a varying K-value parameter. The original signal and its sub-signals arising from VMD decomposition are all subjected to SVD. An index representing the relevance between sub-signals and the original signal is obtained by comparing eigenvalues, which are calculated by SVD. The result shows the effectiveness of VMD with different initial K-value parameters. SVD is then further applied to the VMD result for the selected K-value parameter to obtain Shannon entropy, which can be used in the detection and classification of corrosion on the underside of the rail. Comparing with current energy-based methods, the Shannon entropy obtained by IVMD–SVD has the advantage of reducing environmental interference to obtain more uniform energy results. The proposed method can improve the effectiveness of VMD for the impact response signal. The classification of underside corrosion of rails can be realised according to the results obtained from the proposed method.

Nowadays, recommendation systems are used successfully to provide items (example: movies, music, books, news, images) tailored to user preferences. Amongst the approaches existing to recommend adequate content, we use the collaborative filtering approach of finding the information that satisfies the user by using the reviews of other users. These reviews are stored in matrices that their sizes increase exponentially to predict whether an item is relevant or not. The evaluation shows that these systems provide unsatisfactory recommendations because of what we call the cold start factor. Our objective is to apply a hybrid approach to improve the quality of our recommendation system. The benefit of this approach is the fact that it does not require a new algorithm for calculating the predictions. We are going to apply two algorithms: k-nearest neighbours (KNN) and the matrix factorization algorithm of collaborative filtering which are based on the method of (singular-value-decomposition). Our combined model has a very high precision and the experiments show that our method can achieve better results.

e-Learning is a sought-after option for learners during pandemic situations. In e-Learning platforms, there are many courses available, and the user needs to select the best option for them. Thus, recommender systems play an important role to provide better automation services to users in making course choices. It makes recommendations for users in selecting the desired option based on their preferences. This system can use machine intelligence (MI)-based techniques to carry out the recommendation mechanism. Based on the preferences and history, this system is able to know what the users like most. In this work, a recommender system is proposed using the collaborative filtering mechanism for e-Learning course recommendation. This work is focused on MI-based models such as K-nearest neighbor (KNN), Singular Value Decomposition (SVD) and neural network–based collaborative filtering (NCF) models. Here, one lakh of Coursera’s course review dataset is taken from Kaggle for analysis. The proposed work can help learners to select the e-Learning courses as per their preferences. This work is implemented using Python language. The performance of these models is evaluated using performance metrics such as hit rate (HR), average reciprocal hit ranking (ARHR) and mean absolute error (MAE). From the results, it is observed that KNN is able to perform better in terms of higher HR and ARHR and lower MAE values as compared to other models.

When reconstructing seismic data, the traditional singular value decomposition (SVD) denoising method has the challenge of difficult rank selection. Therefore, we propose a seismic data denoising method that combines SVD and deep learning. In this method, seismic data with different signal-to-noise ratios (SNRs) are processed by SVD. Data sets are created from the decomposed right singular vectors and data sets divided into two categories: effective signal and noise. The lightweight MobileNetV2 network was chosen for training because of its quick response speed and great accuracy. We forecasted and categorized the right singular vectors by SVD using the trained MobileNetV2 network. The right singular vector (RSV) corresponding to the noise in the seismic data was removed during reconstruction, but the effective signal was kept. The effective signal was projected to smooth the RSV. Finally, the goal of low SNR denoising of two-dimensional seismic data was accomplished. This approach addresses issues with deep learning in seismic data processing, including the challenge of gathering sample data and the weak generalizability of the training model. Compared with the traditional denoising method, the improved denoising method performs well at removing Gaussian and irregular noise with strong amplitudes.

Singular value decomposition (SVD) is a key step in many algorithms in statistics, machine learning and numerical linear algebra. While classical singular value decomposition has been made efficient in terms of computational complexity, classical algorithms are not able to fully utilise modern computing environments. The goal of this work is to survey various implementations and applications of randomized algorithms for SVD. Algorithms are compared in terms of accuracy and time of execution. On example of robust principal component analysis (RPCA), it is shown that using randomized algorithms can yield a significant speedup for image processing and similar applications.

Changing climate and disturbance regimes are increasingly challenging the resilience of forest ecosystems around the globe. A powerful indicator for the loss of resilience is regeneration failure, that is, the inability of the prevailing tree species to regenerate after disturbance. Regeneration failure can result from the interplay among disturbance changes (e.g., larger and more frequent fires), altered climate conditions (e.g., increased drought), and functional traits (e.g., method of seed dispersal). This complexity makes projections of regeneration failure challenging. Here we applied a novel simulation approach assimilating data-driven fire projections with vegetation responses from process modeling by means of deep neural networks. We (i) quantified the future probability of regeneration failure; (ii) identified spatial hotspots of regeneration failure; and (iii) assessed how current forest types differ in their ability to regenerate under future climate and fire. We focused on the Greater Yellowstone Ecosystem (2.9×106 ha of forest) in the Rocky Mountains of the USA, which has experienced large wildfires in the past and is expected to undergo drastic changes in climate and fire in the future. We simulated four climate scenarios until 2100 at a fine spatial grain (100m). Both wildfire activity and unstocked forest area increased substantially throughout the 21st century in all simulated scenarios. By 2100, between 28% and 59% of the forested area failed to regenerate, indicating considerable loss of resilience. Areas disproportionally at risk occurred where fires are not constrained by topography and in valleys aligned with predominant winds. High-elevation forest types not adapted to fire (i.e., Picea engelmannii-Abies lasiocarpa as well as non-serotinous Pinus contorta var. latifolia forests) were especially vulnerable to regeneration failure. We conclude that changing climate and fire could exceed the resilience of forests in a substantial portion of Greater Yellowstone, with profound implications for carbon, biodiversity, and recreation.

The security of images has gained great interest in modern communication systems. This is due to the massive critical applications that are based on images. Medical imaging is at the top of these applications. However, the rising number of heterogenous attacks push toward the development of securing algorithms and methods for imaging systems. To this end, this work considers developing a novel authentication, intellectual property protection, ownership, and security technique for imaging systems, mainly for medical imaging. The developed algorithm includes two security modules for safeguarding various picture kinds. The first unit is accomplished by applying watermarking authentication in the frequency domain. The singular value decomposition (SVD) is performed for the host image’s discrete cosine transform (DCT) coefficients. The singular values (S) are divided into 64 × 64 non-overlapping blocks, followed by embedding the watermark in each block to be robust to any attack. The second unit is made up of two encryption layers to provide double-layer security to the watermarked image. The double random phase encryption (DRPE) and chaotic encryption have been tested and examined in the encryption unit. The suggested approach is resistant to common image processing attacks, including rotation, cropping, and adding Gaussian noise, according to the findings of the experiments. The encryption of watermarked images in the spatial and DCT domains and fused watermarked images in the DCT domain are all discussed. The transparency and security of the method are assessed using various measurements. The proposed approach achieves high-quality reconstructed watermarks and high security by using encryption to images and achieves robustness against any obstructive attacks. The developed hybrid algorithm recovers the watermark even in the presence of an attack with a correlation near 0.8.

The digital watermarking technique is a quite promising technique for both image copyright protection and secure transmission. However, many existing techniques are not as one might have expected for robustness and capacity simultaneously. In this paper, we propose a robust semi-blind image watermarking scheme with a high capacity. Firstly, we perform a discrete wavelet transformation (DWT) transformation on the carrier image. Then, the watermark images are compressed via a compressive sampling technique for saving storage space. Thirdly, a Combination of One and Two-Dimensional Chaotic Map based on the Tent and Logistic map (TL-COTDCM) is used to scramble the compressed watermark image with high security and dramatically reduce the false positive problem (FPP). Finally, a singular value decomposition (SVD) component is used to embed into the decomposed carrier image to finish the embedding process. With this scheme, eight 256×256 grayscale watermark images are perfectly embedded into a 512×512 carrier image, the capacity of which is eight times over that of the existing watermark techniques on average. The scheme has been tested through several common attacks on high strength, and the experiment results show the superiority of our method via the two most used evaluation indicators, normalized correlation coefficient (NCC) values and the peak signal-to-noise ratio (PSNR). Our method outperforms the state-of-the-art in the aspects of robustness, security, and capacity of digital watermarking, which exhibits great potential in multimedia application in the immediate future.

This paper presents a novel architecture for the Singular Value Decomposition (SVD) algorithm. The architecture embraces the reductions offered by the use of Approximate Computing (AxC) as a trade-off between complexity and accuracy. A shallow Neural Network (NN) consisting of three layers is used to compute the SVD of an input matrix, offering a comparable Mean Squared Error (MSE) with exact computations. The NN is implemented using High-Level Synthesis (HLS) on a Virtex7 FPGA device. When compared to an exact implementation of the SVD algorithm, the proposed architecture achieves a computational speedup between $5\times $ and $19\times $ with an average reduced hardware area of up to 80% with a noticeable $6\times $ reduction in the DSP usage.

The present study computes B-W extension model (extended Bursa-Wolf model) coordinate transformation parameters from World Geodetic System 1984 (WGS-84) to the Everest datum namely Everest (1830) and Everest (1956) using records of coordinate measurements from Global Positioning System (GPS) observable across Nepal region. Synthetic or modeled coordinates were determined by using the Artificial Neural Network (ANN) and Singular Value Decomposition (SVD) methods. We studied 9-transformation parameters with the help of the ANN technique and validated the outcomes with the SVD method. The comparative analysis of the ANN, as well as SVD methods, was done with the observed output following one way ANOVA test. The analysis showed that the null hypothesis for both datums were acceptable and suggesting all models statistically significantly equivalent to each other. The outcomes from this study would complement a relatively better understanding of the techniques for coordinate transformation and precise coordinate assignment while assimilating data sets from different resources.

In the case of simultaneous localization and mapping, route planning and navigation are based on data captured by multiple sensors, including built-in cameras. Nowadays, mobile devices frequently have more than one camera with overlapping fields of view, leading to solutions where depth information can also be gathered along with ordinary RGB color data. Using these RGB-D sensors, two- and three-dimensional point clouds can be recorded from the mobile devices, which provide additional information for localization and mapping. The method of matching point clouds during the movement of the device is essential: reducing noise while having an acceptable processing time is crucial for a real-life application. In this paper, we present a novel ISVD-based method for displacement estimation, using key points detected by SURF and ORB feature detectors. The ISVD algorithm is a fitting procedure based on SVD resolution, which removes outliers from the point clouds to be fitted in several steps. The developed method removes these outlying points in several steps, in each iteration examining the relative error of the point pairs and then progressively reducing the maximum error for the next matching step. An advantage over relevant methods is that this method always gives the same result, as no random steps are included.

Related metabolites can be grouped into sets in many ways, e.g., by their participation in series of chemical reactions (forming metabolic pathways), or based on fragmentation spectral similarities or shared chemical substructures. Understanding how such metabolite sets change in relation to experimental factors can be incredibly useful in the interpretation and understanding of complex metabolomics data sets. However, many of the available tools that are used to perform this analysis are not entirely suitable for the analysis of untargeted metabolomics measurements. Here, we present PALS (Pathway Activity Level Scoring), a Python library, command line tool, and Web application that performs the ranking of significantly changing metabolite sets over different experimental conditions. The main algorithm in PALS is based on the pathway level analysis of gene expression (PLAGE) factorisation method and is denoted as mPLAGE (PLAGE for metabolomics). As an example of an application, PALS is used to analyse metabolites grouped as metabolic pathways and by shared tandem mass spectrometry fragmentation patterns. A comparison of mPLAGE with two other commonly used methods (overrepresentation analysis (ORA) and gene set enrichment analysis (GSEA)) is also given and reveals that mPLAGE is more robust to missing features and noisy data than the alternatives. As further examples, PALS is also applied to human African trypanosomiasis, Rhamnaceae, and American Gut Project data. In addition, normalisation can have a significant impact on pathway analysis results, and PALS offers a framework to further investigate this. PALS is freely available from our project Web site.

Background The rate of cesarean section (CS) births has been rapidly increasing in Saudi Arabia during the last two decades. Using the Robson Ten Group Classification System (TGCS) to classify and analyze the causes of the high CS rate. Objective To assess the increasing rates of CS by the implementation of the Robson TGCS on all CS births in our chosen population. Study design An observational, cross-sectional study conducted among all deliveries at the King Abdul-Aziz Medical City (KAMC), Jeddah, Saudi Arabia during most of 2018. Over the study period, 3168 births were enrolled in the study. Results The analysis of 3168 births, where 870 women gave birth through CS, resulted in a CS rate of 27.5%. The three major TGCS which contributed to the CS rate were group 5, 2 (divided into 2A and 2B), and 3. Class 5 (Previous CS, single cephalic, ≥37 weeks) contributed the most to the CS rate by 36.5%. Followed by Class 2 (divided into 2A; nulliparous, singleton, cephalic, ≥37 weeks, induced labor and 2B; nulliparous, singleton, ≥37, pre-labor CS) which contributed by 12.9%. Class 3 (multiparous (no previous CS), singleton, ≥37 weeks, spontaneous labor) was the third-highest contributing group by 9.2%. Women who gave birth spontaneously and vaginally were 1403 (44.3%) where women whose labor was induced were 1286 (40.6%). Conclusion The CS rate in KAMC was 27.5%. After classifying these patients according to the TGCS, Class 5 had the largest percentage of patients going for CS (36.2%). While they are individually low together, Robson classes from Class 1 to 4 (which are considered as low-risk classes) were responsible for 37.8% of the patients going for CS. Since the previously mentioned groups are considered low-risk they should be targeted by health institutions to reduce the CS rate. Improved education of nulliparous and multiparous women who never underwent a CS to prevent nonmedically indicated CS is in order, to preclude repeated CS births in the future and further increase the CS rate.

Traffic management in a smart city mainly relies on video feeds from various sources such as street cameras, car dash cams, traffic signal cameras, and so on. Ensuring the confidentiality of these video feeds during transmission is necessary. However, due to these devices’ poor processing power and memory capacity, the applicability of traditional encryption algorithms is not feasible. Therefore, a selective encryption system based on singular value decomposition (SVD) and chaotic maps is presented in this study. The proposed cryptosystem can be used in smart traffic management. We apply SVD to identify the most significant parts of each frame of the video feed for encryption. Chaotic systems were deployed to achieve high diffusion and confusion properties in the resulted cipher. Our results suggest that the computational overhead is significantly less than that of the traditional approaches with no compromise on the strength of the encryption.

In multichannel active noise control systems, when reference signals are correlated, the disturbance enhancement phenomenon is likely to occur, i.e., the resulting sound is enhanced instead of being reduced in some frequency bands, if the filter is designed to minimize the total energy for all frequencies. In previous works, a truncated singular value decomposition method was applied to the system autocorrelation matrix to mitigate the disturbance enhancement due to the correlation of reference signals. Some small singular values and the associated singular vectors are removed, if they are responsible for unwanted disturbance enhancement in some frequency bands. However, some of these removed singular vectors may still contribute to noise control performance in other frequency bands, thus a direct truncation will degrade the noise control performance. In the present work, through an additional filtering process, the set of singular vectors that cause the disturbance enhancement are replaced by a set of new singular vectors whose frequency responses are attenuated in the frequency band where disturbance enhancement occurs, while the frequency responses in other frequency bands are unchanged. Compared with truncation, the proposed method can maintain the performance in the noise reduction bands, while mitigating the influence in disturbance enhancement bands.

Background: Pregnancy also known as gestation is the time during which one or more offspring develop inside a female ,it represents a time of rapid change -changes in physical proportions and physiology.Vitamin D deficiency is observed worldwide in pregnant women ,it is important during pregnancy because it reduces the risk of preeclampsia, LBW,SGA,gestational diabetes,neonatal hypocalcemia and neonatal asthma . AIMS And Objectives: The research aims to find out outcomes of low vitamin D level in pregnant females . METHODOLOGY: Study design and Sampling:It was a longitudinal study.The sampling technique used was non probability purposive sampling. Study Setting and Duration:The study was conducted in Basic Health Unit Kotli said Amir Tehsil and District Sialkot from Dec 2018-Dec2019. Inclusion and Exclusion Criteria: All pregnant females were included except multigravida with previous history of C-section,preeclampsia,gestational diabetes and any known previous pregnancy related complication. Data collection and analysis:The sample was selected using non probability purposive sampling technique.Pregnant females PA ,USG and baselines were checked on antenatal visits.Serum vitamin D levels were checked at the time of delivery.The data was analyzed using SPSS 20. RESULT:The study included 300 patients of BHU kotli Said Amir Sialkot .The age of sample ranged from 18years to 40 years with mean age of 28 years.Sixty three percent (189 females) were multigravida ,37%(111 females) were primigravida. Sixty one percent (183 females) had vitamin level below 30ng/ml measured at the time of delivery, among them 70% (128 females) pregnant females were multigravida and 30% (55 females) were primigravida . Among vitamin D deficient pregnant females fifty eight percent (106 females) underwent C Section, 73 pregnant females (40% females) presented with preterm labour Conclusion:It is concluded from the research that there is a high prevalence of vitamin D deficiency among pregnant females and there is significant role of vitamin D on pregnancy as well as growth and development of fetus.This research points out the importance of regular vitamin D supplementation during pregnancy. &nbsp

In this article, an improved game theory-based co-localization algorithm is proposed to precisely and cooperatively locate the multi-robot system in the wireless sensor network and efficiently eliminate the information conflict caused by multi-sensor. Specifically, the extended Kalman filter in the original algorithm is replaced by the unscented Kalman filter in the optimized algorithm, which contributes to lower linearization errors and higher localization precision. Then, the computational complexity is analyzed, and the derivative method is introduced to reduce the extra computation burden brought by the unscented Kalman filter. Subsequently, the stability issue resulting from the derivative method is addressed by introducing the singular value decomposition (SVD). In this context, the optimized algorithm is capable of precisely locating the multi-robot system, while maintaining the stability and not increasing the computational burden. Moreover, as demonstrated by the simulation results, the optimized algorithm has greater localization precision than the original algorithm, while they have similar computational burdens.

[Resumo] A importancia dos métodos de prevención secundaria para os trastornos mentais é crucial xa que disto depende a calidade de vida futura das persoas que a sofren no presente. Os medios sociais poden favorecer un gran avance no desenvolvemento destes métodos debido a que son canles de comunicación nos que os usuarios e usuarias da Internet participan de forma activa e prevese que isto vaia aumentando co paso dos anos. O obxectivo principal deste proxecto é deseñar e implementar unha plataforma software que sirva de soporte aos especialistas da saúde mental, como psicólogos ou psiquiatras, para etiquetar aos suxeitos de maneira máis áxil, rápida e sinxela. A aplicación posibilitará a persoa usuaria procesar coleccións de documentos para ver as súas estadísticas, buscar documentos e realizar consultas a través delas. Tamén permitirá ao usuario ou usuaria analizar os resultados dos agrupamentos por similitude dos documentos ou termos que as conforman. Para poder alcanzar os obxectivos marcados eficientemente, decidiuse usar unha metodoloxía iterativa e incremental debido á flexibilidade e adaptación que aporta ás distintas situacións polas que un proxecto atravesa. Finalmente, conseguiuse unha plataforma software que cumpre cos requisitos especificados. [Abstract] Methods of secondary prevention for mental disorders are of crucial relevance because these depend the quality of future life of the people who are suffering mental diseases at the moment. Social media can improve develop these methods for the reason that it is a channel of communication where a lot of Internet users are involved and it is expected to increase over the years. The main goal of this project is to design and implement a software plataform to give support to mental health specialists, as psychologists or psychiatrists, to tag subjects agilely, quickly and easily. The application will allow the user process document collections to check their statistics, search documents and search queries in them. It will also allow the user to analyze the clustering results by similarity of the documents or terms that make them up. In order to achieve the goals set efficiently, it has been decided to use an iterative and incremental methodology for its flexibility and adaptation to the different situations that a project goes through. Eventually, it has been achieved an application that meets with the mentioned requirements. Traballo fin de grao (UDC.FIC). Enxeñaría informática. Curso 2019/2020

Recommendation System is a major area which is very popular and useful for people to take proper decision. It is a method that helps user to find out the information which is beneficial for the user from variety of data available. When it comes to Movie Recommendation System, recommendation is done based on similarity between users (Collaborative Filtering) or by considering particular user’s activity (Content Based Filtering) which he wants to engage with. So to overcome the limitations of collaborative and content based filtering generally, combination of collaborative and content based filtering is used so that a better recommendation system can be developed. Also various similarity measures are used to find out similarity between users for recommendation. In this paper, we have reviewed different similarity measures. Various companies like face book which recommends friends, LinkedIn which recommends job, Pandora recommends music, Netflix recommends movies, Amazon recommends products etc. use recommendation system to increase their profit and also benefit their customers. This paper mainly concentrates on the brief review of the different techniques and its methods for movie recommendation, so that research in recommendation system can be explored.

As the noise power increases, the target signal features become less obvious, which leads to the failure of weak signal detection methods. To address this problem, a quantum weak signal detection method, Local Semi-Classical Signal Analysis-Singular Value Decomposition (LSCSA-SVD), for strengthening target signal features under strong white Gaussian noise is proposed. Firstly, the time domain weak signal is quantized by the Schrodinger operator and its discrete spectrum formula. Then, in the quantum domain, the later eigenvalues are used to reconstruct the time domain signal, which can protect and enhance the target signal features. Finally, the difference between signal and noise in the singular value vector is used to further extract the reconstruction signal features. In simulation, the LSCSA-SVD can accurately extract target signals from white Gaussian noise signals with a signal-to-noise ratio (SNR) of −30 dB, which is better than the comparison methods. In the experiment, the weak acceleration sensor signal and the weak signal of the test circuit are successfully extracted. The results show that the LSCSA-SVD can suppress strong noise and improve the SNR.

Autoregressive models (AR) are fundamental for analysis, representation, and prediction of signals. AR modelling uses the premise that past signal values influence current ones. This influence is causal and is modelled as a linear superposition, because a weighted addition of past values is used. The calculation of the required linear superposition parameters or weights can be done by the classical Yule-Walker approach or by least squares procedures. Here we show how to use singular value decomposition (SVD) for generalized linear autoregression (GLAR), i.e. using SVD to compute the weights of a linear combination of functions of given signal values and to check or optimize the GLAR model. The GLAR approach opens the possibility to take directly into account non-linear influences from past to current signal values. T he potential of this approach for analysis and representation is presented and demonstrated for simulated signals, i.e. pure and noisy sequences of non-linear recursions, and biomedical signals.

With the rapid growth of financial markets, analyzers are paying more attention on predictions. Stock data are time series data, with huge amounts. Feasible solution for handling the increasing amount of data is to use a cluster for parallel processing, and Hadoop parallel computing platform is a typical representative. There are various statistical models for forecasting time series data, but accurate clusters are a pre-requirement. Clustering analysis for time series data is one of the main methods for mining time series data for many other analysis processes. However, general clustering algorithms cannot perform clustering for time series data because series data has a special structure and a high dimensionality has highly co-related values due to high noise level. A novel model for time series clustering is presented using BIRCH, based on piecewise SVD, leading to a novel dimension reduction approach. Highly co-related features are handled using SVD with a novel approach for dimensionality reduction in order to keep co-related behavior optimal and then use BIRCH for clustering. The algorithm is a novel model that can handle massive time series data. Finally, this new model is successfully applied to real stock time series data of Yahoo finance with satisfactory results.

This paper presents a blind separation algorithm based on singular value decomposition (SVD) of reduced dimension spectral matrix. Furthermore, a mathematical matrix model of the multicomponent seismic wavefield is developed as a framework for implementing the proposed algorithm. The proposed blind separation algorithm organizes the frequency transformed multicomponent seismic wavefield into one long data vector. The blind separation of the desired seismic wavefield is accomplished by projecting the long data vector onto the eigenvectors of the dimensionally reduced spectral matrix according to the energy of the eigenvalues. The proposed algorithm is tested on both synthetic and real multicomponent seismic wavefields. Results show outstanding performance compared to the MC-WBSMF algorithm. Therefore, the computational complexity is reduced by a factor greater than 14,400 and there is an improvement in accuracy of 17.5%.

Objective The objective of the present study was to develop sustained release tablets of stavudine (SVD), an anti-retroviral drug and effect of retardants on drug release. Methods The sustained release tablets were prepared by wet granulation method. The various batches (F1 to F10) were prepared by altering the process and formulation parameters in order to design ideal formulae for the treatment of infection with better patient compliance. The release retardants were used in the formulation development namely tamarind gum (TG), sodium alginate (SA) and sodium carboxy methyl cellulose (SCMC). Results Compatibility of the drug with various excipients was studied by FTIR peak matching technique and the ingredients were found compatible with each other. The compressed tablets were evaluated and showed compliance with standard limits. Based on the experimental results, the F9 was considered as an ideal formulation and the release mechanism was monitored with application of mathematical models. Conclusion From all the evaluation studies, the release of SVD from its dosage from (F9) was sustained and thereby expected to provide patient compliance with reduced frequency of administration and side effects by avoiding the sudden burst release.

Small vessel disease (SVD) refers to conditions where damage to arterioles and capillaries is predominant, leading to reduced, or interrupted perfusion of the affected organ. Data suggest that when this condition is evident in any organ, it is already systemic in its occurrence and consequences. SVD affects primarily organs that receive significant portions of cardiac output such as the brain, the kidney, and the retina. Thus, SVD is a major etiologic cause in debilitating conditions such as renal failure, blindness, lacunar infarcts, and dementia. The factors that lead to this devastating condition include all the known vascular risk factors when they are not strictly controlled, but lifestyles that include sedentary existence, obesity, and poor sleep patterns are also recognized drivers of SVD. In addition, depression is now recognized as a vascular risk factor. Inflammation is a mediator of SVD, but it is not known which factor(s) predominate in its etiology. This article emphasizes the need for more investigations to define this link further and suggests clinical and societal responses that might reduce the major impacts of this condition on populations.

The Wireless sensor networks have limitations regarding data redundancy, power and require high bandwidth when used for multimedia data. Image compression methods overcome these problems. Non-negative Matrix Factorization (NMF) method is useful in approximating high dimensional data where the data has non-negative components. Another method of the NMF called (PNMF) Projective Nonnegative Matrix Factorization is used for learning spatially localized visual patterns. Simulation results show the comparison between SVD, NMF, PNMF compression schemes. Compressed images are transmitted from base station to cluster head node and received from ordinary nodes. The station takes on the image restoration. Image quality, compression ratio, signal to noise ratio and energy consumption are the essential metrics measured for compression performance. In this paper, the compression methods are designed using Matlab.The parameters like PSNR, the total node energy consumption are calculated. RTL schematic of NMF SVD, PNMF methods is generated by using Verilog HDL.

In order to help undergraduate students towards successfully completing their degrees, developing tools that can assist students during the course selection process is a significant task in the education domain. The optimal set of courses for each student should include courses that help him/her graduate in a timely fashion and for which he/she is well-prepared for so as to get a good grade in. To this end, we propose two different grade-aware course recommendation approaches to recommend to each student his/her optimal set of courses. The first approach ranks the courses by using an objective function that differentiates between courses that are expected to increase or decrease a student's GPA. The second approach combines the grades predicted by grade prediction methods with the rankings produced by course recommendation methods to improve the final course rankings. To obtain the course rankings in the first approach, we adapt two widely-used representation learning techniques to learn the optimal temporal ordering between courses. Our experiments on a large dataset obtained from the University of Minnesota that includes students from 23 different majors show that the grade-aware course recommendation methods can do better on recommending more courses in which the students are expected to perform well and recommending fewer courses in which they are expected not to perform well in than grade-unaware course recommendation methods., Comment: Under revision for Journal of Educational Data Mining (JEDM)

Face recognition and identification are very important applications in machine learning. Due to the increasing amount of available data, traditional approaches based on matricization and matrix PCA methods can be difficult to implement. Moreover, the tensorial approaches are a natural choice, due to the mere structure of the databases, for example in the case of color images. Nevertheless, even though various authors proposed factorization strategies for tensors, the size of the considered tensors can pose some serious issues. Indeed, the most demanding part of the computational effort in recognition or identification problems resides in the training process. When only a few features are needed to construct the projection space, there is no need to compute a SVD on the whole data. Two versions of the tensor Golub–Kahan algorithm are considered in this manuscript, as an alternative to the classical use of the tensor SVD which is based on truncated strategies. In this paper, we consider the Tensor Tubal Golub–Kahan Principal Component Analysis method which purpose it to extract the main features of images using the tensor singular value decomposition (SVD) based on the tensor cosine product that uses the discrete cosine transform. This approach is applied for classification and face recognition and numerical tests show its effectiveness.

Image watermarking technique is one of effective solutions to protect copyright, and it is applied to a variety of information security application domains. It needs to meet four requirements of imperceptibility, robustness, capacity and security. A multi-scale and secure image watermarking method is proposed in this work, which is based on the Integer Wavelet Transform (IWT) and Singular Value Decomposition (SVD). Four IWT sub-bands are firstly obtained after 1-level IWT on the host image, and the corresponding singular diagonal matrices of four sub-bands can be obtained using SVD. Then, each singular diagonal matrix is divided into four non-overlapping sections in terms of the size of embedding watermark. Particularly, the size of upper left part is same as the size of watermark. The watermark can be directly embedded into four upper left parts afterwards by multiplying different scaling factors to complete the final watermarking operation. Especially, a novel optimized authentication mechanism is designed to resolve the false positive problem, which exists in the SVD-based watermarking algorithms. In addition, three-dimensional optimal mapping algorithm is proposed to search the optimal scaling factors through a novel objective evaluation function, and it can significantly improve the imperceptibility and robustness. The experimental test and comparison analysis illustrate that the proposed watermark scheme demonstrates a high imperceptibility with peak signal to noise ratio values of 45 dB and strong robustness with average normalized correlation values of 0.92.

Pinwise nuclide number density (ND) data from the lattice physics code CASMO5 is compressed using a preconditioned truncated singular value decomposition (TSVD) to reduce storage requirements. Previously only assembly-average or single-pin NDs were optionally saved in the CMS5 few-group cross section library. However, backend analysis has prompted the desire to have pin-by-pin NDs available in the library for use by the SNF code. Adding this data set significantly increases the size of the library, particularly for lattices modeled in full assembly geometry (that is, not in half or octant symmetry). To reduce the required storage, the SVD is used to approximate the entire data with a reduced basis. In the four test cases, compression ratios of 2.7 to 8.5 were achieved for the PWR cases, with maximum errors less than 0.1%. However, the rodded BWR segment proved more difficult, with an average compression ratio of 1.6. One advantage of this technique is that the compression ratio is higher for full-symmetry cases, where the need for the compression is also highest.

Diabetes is one of the emerging threats to public health all over the world. According to projections by the World Health Organization, diabetes will be the seventh foremost cause of death in 2030 (WHO, Diabetes, 2020. https://www.afro.who.int/health-topics/diabetes). Diabetic retinopathy (DR) results from long-lasting diabetes and is the fifth leading cause of visual impairment, worldwide. Early diagnosis and treatment processes are critical to overcoming this disease. The diagnostic procedure is challenging, especially in low-resource settings, or time-consuming, depending on the ophthalmologist's experience. Recently, automated systems now address DR classification tasks. This study proposes an automated DR classification system based on preprocessing, feature extraction, and classification steps using deep convolutional neural network (CNN) and machine learning methods. Features are extracted from a pretrained model by the transfer learning approach. DR images are classified by several machine learning methods. XGBoost outperforms other methods. Dimensionality reduction algorithms are applied to obtain a lower-dimensional representation of extracted features. The proposed model is trained and evaluated on a publicly available dataset. Grid search and calibration are used in the analysis. This study provides researchers with performance comparisons of different machine learning methods. The proposed model offers a robust solution for detecting DR with a small number of images. We used a transfer learning approach, which differs from other studies in the literature, during the feature extraction. It provides a data-driven, cost-effective solution, which includes comprehensive preprocessing and fine-tuning processes.