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In this work, a new efficient cryptographic scheme based on the concept of chaotic map and optimized watermarking scheme is proposed. In the optimized watermarking scheme, a combination of discrete wave transformation (DWT), hessenberg decomposition (HD), and singular value decomposition (SVD) are used. In this, the host image is first broken down into several sub-bands using multi-level DWT, and the resulting coefficients are then fed into HD during the embedding phase. Simultaneous watermark operation is performed on SVD. Finally, the scale factor embeds the watermark into the host image. The Differential evolution method is used to find the best scaling factor for the optimized watermarking scheme. The resulting watermarked image is then encrypted by the session key based scheme. In this scheme for each image encryption, a new random session key will be produced. The presented approach uses 64-bit plaintext and a variable size key that will be decided at the time of encryption for encrypting an image. Since session keys change with each transmission, this approach does not involve extracting and remembering session keys in order to produce subsequent session keys. IoT devices are used to test the developed method for security. The experiment's findings shows that the suggested method works better than the current scheme in several aspects. [ABSTRACT FROM AUTHOR]

To address the issues of high cost and low accuracy in the manual detection method, an improved singular value decomposition (SVD)-based fabric defect detection method was proposed in this study. The method first performed noise reduction by wavelet transform; then the image was segmented. Finally, SVD was applied to remove background texture information and improve detection accuracy. The results for the detection of different types of fabric defects showed that the improved SVD method for stiffness detection of fabrics was highly efficient and accurate. The computational complexity, data redundancy and detection results of different sub-image sizes of pixels were all significant. The area under the curve (AUC) value of the star and check fabric was inferior to the defect fabric. The method is highly accurate for different fabric types and can be subsequently applied to the detection of stiffness in apparel fabrics, providing a reference for textile manufacturing production. [ABSTRACT FROM AUTHOR]

DNA methylation is an important epigenetic mark that modulates gene expression through the inhibition of transcriptional proteins binding to DNA. As in many other omics experiments, the issue of missing values is an important one, and appropriate imputation techniques are important in avoiding an unnecessary sample size reduction as well as to optimally leverage the information collected. We consider the case where relatively few samples are processed via an expensive high-density whole genome bisulfite sequencing (WGBS) strategy and a larger number of samples is processed using more affordable low-density, array-based technologies. In such cases, one can impute the low-coverage (array-based) methylation data using the high-density information provided by the WGBS samples. In this paper, we propose an efficient Linear Model of Coregionalisation with informative Covariates (LMCC) to predict missing values based on observed values and covariates. Our model assumes that at each site, the methylation vector of all samples is linked to the set of fixed factors (covariates) and a set of latent factors. Furthermore, we exploit the functional nature of the data and the spatial correlation across sites by assuming some Gaussian processes on the fixed and latent coefficient vectors, respectively. Our simulations show that the use of covariates can significantly improve the accuracy of imputed values, especially in cases where missing data contain some relevant information about the explanatory variable. We also showed that our proposed model is particularly efficient when the number of columns is much greater than the number of rows—which is usually the case in methylation data analysis. Finally, we apply and compare our proposed method with alternative approaches on two real methylation datasets, showing how covariates such as cell type, tissue type or age can enhance the accuracy of imputed values. [ABSTRACT FROM AUTHOR]

Tensor wheel (TW) decomposition combines the popular tensor ring and fully connected tensor network decompositions and has achieved excellent performance in tensor completion problem. A standard method to compute this decomposition is the alternating least squares (ALS). However, it usually suffers from slow convergence and numerical instability. In this work, the fast and robust SVD-based algorithms are investigated. Based on a result on TW-ranks, we first propose a deterministic algorithm that can estimate the TW decomposition of the target tensor under a controllable accuracy. Then, the randomized versions of this algorithm are presented, which can be divided into two categories and allow various types of sketching. Numerical results on synthetic and real data show that our algorithms have much better performance than the ALS-based method and are also quite robust. In addition, with one SVD-based algorithm, we also numerically explore the variability of TW decomposition with respect to TW-ranks and the comparisons between TW decomposition and other famous formats in terms of the performance on approximation and compression. [ABSTRACT FROM AUTHOR]

Research with gravity data in the area of Semeru volcano has been undertaken. The study aims to determine the subsurface fault structure of the Semeru volcano by utilizing derivative analysis of gravity data anomalies. A comprehensive dataset of 1,929 measurement points, spaced 500 m apart, was analyzed to identify variations in the Bouguer anomaly. Complete Bouguer anomaly values ranged from 37 to 111 mGal. The contrast of Bouguer anomaly variations effectively delineates the boundaries of different rock formations: Mandalika, Wuni, Quarter Volcanic Semeru and Quarter Volcanic Jembangan. These formations are instrumental in causing significant variations in gravity anomalies, indicating an underlying geological structure. The derivative analysis, encompassing horizontal (FHD) and vertical (SVD) anomalies, unveiled a pronounced fault structure southeast of the Semeru crater, characterized by a NESW orientation. Advanced modeling, informed by residual anomaly incision lines and depth estimates derived from the radial spectrum, revealed a complex subsurface stratigraphy consisting of 5 types: Volcanic clastics, tuff, breccia, basaltic lava and andesitic lava. This research advances our understanding of the Semeru volcano's subsurface architecture. It introduces an enhanced methodology for fault detection and characterization in volcanic areas, showcasing the potential of gravity data in geological investigations. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) are now often used in deep learning and computer vision applications. Its convolutional layer accounts for most calculations and should be computed fast in a local edge device. Field‐programmable gate arrays (FPGAs) have been adequately explored as promising hardware accelerators for CNNs due to their high performance, energy efficiency, and reconfigurability. This paper developed an efficient FPGA‐based 16‐bit fixed‐point hardware accelerator unit for deep learning applications on the 32‐bit low‐memory edge device (PYNQ‐Z2 board). Additionally, singular value decomposition is applied to the fully connected layer for dimensionality reduction of weight parameters. The accelerator unit was designed for all five layers and employed eight processing elements in convolution layers 1 and 2 for parallel computations. In addition, array partitioning, loop unrolling, and pipelining are the techniques used to increase the speed of calculations. The AXI‐Lite interface was also used to communicate between IP and other blocks. Moreover, the design is tested with grayscale image classification on MNIST handwritten digit dataset and color image classification on the Tumor dataset. The experimental results show that the proposed accelerator unit implementation performs faster than the software‐based implementation. Its inference speed is 89.03% more than INTEL 3‐core CPU, 86.12% higher than Haswell 2‐core CPU, and 82.45% more than NVIDIA Tesla K80 GPU. Furthermore, the throughput of the proposed design is 4.33GOP/s, which is better than the conventional CNN accelerator architectures. [ABSTRACT FROM AUTHOR]

Purpose: Balanced steady‐state free precession (bSSFP) imaging is susceptible to outflow effects where excited spins leaving the slice as part of the blood stream are misprojected back onto the imaging plane. Previous work proposed using slice‐encoding steps to localize these outflow effects from corrupting the target slice, at the expense of prolonged scan time. This present study extends this idea by proposing a means of significantly reducing most of the outflowing signal from the imaged slice using a coil localization method that acquires a slice‐encoded calibration scan in addition to the 2D data, without being nearly as time‐demanding as our previous method. This coil localization method is titled UNfolding Coil Localized Errors from an imperfect slice profile using a Structured Autocalibration Matrix (UNCLE SAM). Methods: Retrospective and prospective evaluations were carried out. Both featured a 2D acquisition and a separate slice‐encoded calibration of the center in‐plane k‐space lines across all desired slice‐encoding steps. Results: Retrospective results featured a slice‐by‐slice comparison of the slice‐encoded images with UNCLE SAM. UNCLE SAM's subtraction from the slice‐encoded image was compared with a subtraction from the flow‐corrupted 2D image, to demonstrate UNCLE SAM's capability to unfold outflowing spins. UNCLE SAM's comparison with slice encoding showed that UNCLE SAM was able to unfold up to 74% of what slice encoding achieved. Prospective results showed significant reduction in outflow effects with only a marginal increase in scan time from the 2D acquisition. Conclusions: We developed a method that effectively unfolds most outflowing spins from corrupting the target slice and does not require the explicit use of slice‐encoding gradients. This development offers a method to reduce most outflow effects from the target slice within a clinically feasible scan duration compared with the fully sampled slice‐encoding technique. [ABSTRACT FROM AUTHOR]

In this research, a GAmified Mobile Leaning Framework (GAMOLEAF) developed as a new intelligent application designed for mobile devices to ensure learning, assessing, and advancing learners' knowledge in programming complex data structures in Java programming language. GAMOLEAF adopted motivational strategies to solve motivational problems during the COVID-19 pandemic by employing a gamification module, that integrates levels, scores, badges, leaderboard, and feedback. Furthermore, in order to assist learners to find useful and relevant lessons and best solutions for each data structure, GAMOLEAF incorporated personalized recommendations through two intelligent modules: a Lessons Recommendation Module (LRecM) and a problem-solving Solutions Recommender Module (PSSORecM). LrecM aims to provide learners with personalized lessons depending on the ratings collected explicitly from them. Whereas, PSSORecM bases on learners' behaviors and directs them to consult other solutions. Both modules were based on the collaborative filtering method and used Matrix Factorization (MF) applying Singular Value Decomposition (SVD) and Negative Matrix Factorization (NMF) algorithms, respectively. To explore how the integration of personalized recommendations and gamification impact on students motivation and learning achievements in higher education to learning programming complex data structures course using mobile technologies, especially in difficult times like COVID-19, an experiment was carried out to compare the learning achievement and motivation of 90 students divided into three groups (control group, first experimental group, and second experimental group) using three versions of GAMOLEAF respectively: GAMOLEAF-V1 without gamification and without recommendation, GAMOLEAF-V2 integrating gamification only and GAMOLEAF-V3 integrating both gamification and recommendation. The One-way ANOVA (analysis of variance) test and Post hoc Tukey test were employed to analyze the performances of the three groups before and after the learning activity. The results suggested that the students who learned with GAMOLEAF-V3 using gamification and recommendation achieved significantly better learning achievement than those who learned with GAMOLEAF-V2 and GAMOLEAF-V1. From the experimental results, it was found that the gamification applied in GAMOLEAF-V2 and GAMOLEAF-V3 had significantly better effectiveness in improving only students' motivation without improving their learning achievement. Moreover, the analysis result of the learning achievement indicated that the students in the second experimental group showed significantly higher learning achievement using GAMOLEAF-V3 in comparison with those in both the control group and the first experimental group who used GAMOLEAF-V1 and GAMOLEAF-V2 respectively. Such findings indicate that the personalized recommendations offered by the Lessons Recommendation Module (LRecM) and the problem-solving Solutions Recommender Module (PSSORecM) in GAMOLEAF-V3 may be one of the reasons why the learning achievement of students was increased. [ABSTRACT FROM AUTHOR]

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Objectives: SARS-CoV-2 infection is associated with a decline in functional outcomes; many patients experience persistent symptoms, while the underlying pathophysiology remains unclear. This study investigated white matter (WM) integrity on brain MRI in hospitalized COVID-19 patients and its associations with clinical outcomes, including long COVID. Materials and methods: We included hospitalized COVID-19 patients and controls from CORONavirus and Ischemic Stroke (CORONIS), an observational cohort study, who underwent MRI-DWI imaging at baseline shortly after discharge (<3 months after positive PCR) and 3 months after baseline scanning. We assessed WM integrity using diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) and performed comparisons between groups and within patients. Clinical assessment was conducted at 3 and 12 months with functional outcomes such as modified Rankin Scale (mRS), Post-COVID-19 Functional Status scale (PCFS), Visual Analogue Scale (VAS), and long COVID, cognitive assessment was conducted by the Modified Telephone Interview for Cognitive Status (TICS-M), and the Hospital Anxiety and Depression Scale (HADS) was used to assess mood disorder. Associations between WM integrity and clinical outcomes were evaluated using logistic regression and linear regression. Results: A total of 49 patients (mean age 59.5 years) showed higher overall peak width of skeletonized mean diffusivity (PSMD) (p = 0.030) and lower neurite density index (NDI) in several WM regions compared with 25 controls at the baseline (p < 0.05; FWE-corrected) but did not remain statistically significant after adjusting for WM hyperintensities. Orientation dispersion index (ODI) increased after 3-month follow-up in several WM regions within patients (p < 0.05), which remained significant after correction for changes in WMH volume. Patients exhibited worse clinical outcomes compared with controls. Low NDI at baseline was associated with worse performance on the Post-COVID-19 Functional Status scale after 12 months (p = 0.018). Conclusion: After adjusting for WMH, hospitalized COVID-19 patients no longer exhibited lower WM integrity compared with controls. WM integrity was generally not associated with clinical assessments as measured shortly after discharge, suggesting that factors other than underlying WM integrity play a role in worse clinical outcomes or long COVID. [ABSTRACT FROM AUTHOR]

Telemedicine is the field that uses medical images for diagnosing various diseases. The transmitting and storing of medical images via the cloud-based network must meet several stringent criteria, including confidentiality, validity, and security, and are viewed as very sensitive, irrespective of the image processing context. Medical image copyright protection has been essential since little alterations can even put the lives of patients in danger. Hence numerous significant watermarking methods need to be developed. Watermarking conceals sensitive data by embedding it in a harmless medium, such as a cover. The challenging issue of quick and extremely safe image encryption can be solved more effectively with chaos-based cryptography. Thus the image is encrypted by using enhanced chaos with the Fruit Fly Optimization Algorithm (FFOA). The work proposes an efficient medical image watermarking approach using the Two-level Stationary Wavelet Transform (SWT) and Singular Value Decomposition (SVD) technique on chaotic encrypted medical images. Finally, the proposed method is compared with existing methods to demonstrate higher performance. To check robustness and imperceptibility results, the work is examined under various attacks and produces good results in Peak Signal-to-Noise Ratio (PSNR) and Normalized Cross Correlation (NCC) measures. For different kinds of medical images, the PSNR of the suggested methodology is greater than 40 dB, and NCC values are close to 1 illustrating the technique's superior efficacy. [ABSTRACT FROM AUTHOR]

Multispectral images contain more spectral information about real-world scenes and are easily affected by gaussian noise when captured by sensors. Hence, denoising multispectral images is a crucial preprocessing step for various subsequent image-processing tasks, including classification, segmentation, compression, recognition, and object extraction. This article presents a novel channel-by-channel approach to denoise MSIs corrupted by Gaussian noise where each channel is subjected to a 2-level discrete wavelet transform (DWT), followed by the implementation of diverse denoising algorithms on each sub-band. However, noise does not affect each channel equally; hence, an accurate noise estimation technique is required to adaptively denoise the data. As a result, a noise estimation approach that combines DWT and singular vector decomposition is used, with the estimated variance used to determine which channels require denoising. The proposed algorithm for noise estimation and denoising is initially assessed on a LIVE dataset and then evaluated and analyzed on Sentinel-2 images. The experimental results on the multispectral data set illustrate the effectiveness of the proposed denoising technique. Experimental results on the Sentinel-2 dataset demonstrate that the proposed method outperforms the state-of-the-art methods in terms of both qualitative and quantitative analyses. [ABSTRACT FROM AUTHOR]

One of the challenges that Singular Value Decomposition (SVD)-based algorithms in watermarking face is the false positive problem while maintaining important parameters in watermarking, especially in different attacks. False positives occur when the watermark is placed on the S component of the SVD transformation of the host image. The proposed solutions have problems such as the low-quality watermarked image and its lack of stability against various attacks. To solve such problems, low-frequency bands of the host image, which are obtained by transforming the Contourlet obtained by taking the SVD transform and separating the S component are embedded by multiplying a parameter called delta obtained from the PSO (Particle swarm optimization) algorithm in the watermark image, and the embedding stage ends at this point. The result of this method is a relatively improved NC value, especially for the salt and pepper attack, which is compared with different articles in the results section. In the extraction section, the contourlet transform is taken from both the host image and the watermark, and the results are expressed with and without the denoising convolutional neural network algorithm (DnCNN). The values obtained for the false positive problem in the results section show that this scheme, while not having such a problem, is also resistant to various attacks. [ABSTRACT FROM AUTHOR]

Symmetries and symmetry-breaking play significant roles in data security. While remote sensing images, being extremely sensitive geospatial data, require protection against tampering or destruction, as well as assurance of the reliability of the data source during application. In view of the increasing complexity of data security of remote sensing images, a single watermark algorithm is no longer adequate to meet the demand of sophisticated applications. Therefore, this study proposes a dual watermarking algorithm that considers both integrity authentication and copyright protection of remote sensing images. The algorithm utilizes Discrete Wavelet Transform (DWT) to decompose remote sensing images, then constructs integrity watermark information by applying Chebyshev mapping to the mean of horizontal and vertical components. This semi-fragile watermark information is embedded into the high-frequency region of DWT using Quantization Index Modulation (QIM). On the other hand, the robust watermarking uses entropy to determine the embedding position within the DWT domain. It combines the stability of Singular Value Decomposition (SVD) and embeds the watermark according to the relationship between the singular values of horizontal, vertical, and high-frequency components. The experiment showed that the proposed watermarking successfully maintains a high level of invisibility even if embedded with dual watermarks. The semi-fragile watermark can accurately identify tampered regions in remote sensing images under conventional image processing. Moreover, the robust watermark exhibits excellent resistance to various attacks such as noise, filtering, compression, panning, rotating, and scaling. [ABSTRACT FROM AUTHOR]

This article introduces FiGaRo, an algorithm for computing the upper-triangular matrix in the QR decomposition of the matrix defined by the natural join over relational data. FiGaRo 's main novelty is that it pushes the QR decomposition past the join. This leads to several desirable properties. For acyclic joins, it takes time linear in the database size and independent of the join size. Its execution is equivalent to the application of a sequence of Givens rotations proportional to the join size. Its number of rounding errors relative to the classical QR decomposition algorithms is on par with the database size relative to the join output size. The QR decomposition lies at the core of many linear algebra computations including the singular value decomposition (SVD) and the principal component analysis (PCA). We show how FiGaRo can be used to compute the orthogonal matrix in the QR decomposition, the SVD and the PCA of the join output without the need to materialize the join output. A suite of experiments validate that FiGaRo can outperform both in runtime performance and numerical accuracy the LAPACK library Intel MKL by a factor proportional to the gap between the sizes of the join output and input. [ABSTRACT FROM AUTHOR]

In this paper, we present a natural implementation of singular value decomposition (SVD) and polar decomposition of an arbitrary multivector in nondegenerate real and complexified Clifford geometric algebras of arbitrary dimension and signature. The new theorems involve only operations in geometric algebras and do not involve matrix operations. We naturally define these and other related structures such as Hermitian conjugation, Euclidean space, and Lie groups in geometric algebras. The results can be used in various applications of geometric algebras in computer science, engineering, and physics. [ABSTRACT FROM AUTHOR]

Informed watermarking relates to a particular strategy in digital watermarking, a method utilized to embed data in multimedia content. There are potential shortcomings of existing approaches that could be addressed or improved. These limitations are limited Robustness, insufficient security measures, Complexity and Efficiency Trade-offs and limited imperceptibility. In the proposed approach, I be able to verify the reliability of the original image based on the extracted watermark information. In case of any modification, I can retrieve and regain the host image from the obtained extracted watermark information. The focal contribution of the paper is to obtain a secure approach with high imperceptibility of the watermark, high robustness of the extracted watermark and less complexity approach. I apply OTP encryption to the watermarked image to increase the security concept of the approach. My proposed watermarking method introduces several notable advancements over existing algorithms. Firstly, it combines an informed and recovery watermarking approach, allowing for the embedding of imperceptible data in multimedia content while enabling the retrieval of the original. Additionally, my method incorporates OTP encryption to bolster security measures, ensuring the integrity and confidentiality of the embedded watermark. Furthermore, by leveraging Singular Value Decomposition (SVD) and coefficient insertion techniques, I achieve high imperceptibility and robustness of the watermark while minimizing computational complexity. Tested against various attacks, my approach demonstrates superior performance with a PSNR of approximately 42 dB and a BER of 0.0012. Overall, the method fills a critical gap in watermarking technology by providing a secure, robust, and imperceptible solution for multimedia content protection. [ABSTRACT FROM AUTHOR]

Purpose: The working environment of the mine is complex, and there are many interference noises. Microseismic signals and noises are mixed together, which interferes with the analysis of microseismic signals and affects the subsequent work of phase arrival picking and source location. Therefore, it is necessary to study the noise reduction of microseismic signals. Method: In view of the randomness and non-stationarity of microseismic signals, a signal denoising method based on the multi-layer joint is proposed in this paper. SVD (singular value decomposition) is used as the first layer denoising method to remove the white noise in the signal. EMD( Empirical Mode Decomposition) is used as the second layer noise reduction method to deal with random and non-stationary microseismic signals. As the third layer noise reduction method, the wavelet threshold eliminates the modal aliasing phenomenon generated by EMD. Result: The experimental analysis shows that the proposed method has a good noise reduction effect on the simulation signal and the actual microseismic signal with different signal-to-noise ratios. Conclusion: It overcomes the disadvantage of the poor noise reduction effect of a single noise reduction method, can greatly improve the signal-to-noise ratio of the signal, and has a high similarity with the original signal, which provides a new idea for microseismic signal noise reduction. [ABSTRACT FROM AUTHOR]

This paper is mainly concerned with the embedding of optical images in auxiliary media. Optical images may contain sensitive information. They are embedded in cover media such as speech signals. This process is regarded as a type of watermarking. The Singular Value Decomposition (SVD) of 2D matrices generated from the cover media is used for watermark embedding. It is well-known that the Singular Values (SVs) of 2D matrices have low sensitivity to variations in the cover signal represented as noise or enhancement through processing algorithms. Noise affects the watermarked speech signal and affects the extraction of the watermark. Different enhancement algorithms are considered and compared for testing of the proposed scheme. It is clear from the obtained results that the proposed scheme is highly efficient for optical image hiding, even with signal processing techniques applied to cover signals. Simulation experiments indicate the effect of the presence of noise on the watermark extraction and also the effect of applying speech enhancement on the watermark extraction. The correlation coefficient (Cr) between the embedded and extracted watermarks is used to indicate the performance of different enhancement methods. The adaptive Wiener filter leads to the highest Cr, which equals 0.7491. Signal-to-Noise Ratio (SNR) is used to evaluate the speech enhancement performance. The SNR reaches the highest value equal to 12.0481 dB with adaptive Wiener filter. [ABSTRACT FROM AUTHOR]

The popular fully-connected tensor network (FCTN) decomposition has achieved successful applications in many fields. A standard method to this decomposition is the alternating least squares. However, it often converges slowly and suffers from issues of numerical stability. In this work, we investigate the SVD-based algorithms for FCTN decomposition to tackle the aforementioned deficiencies. On the basis of a result about FCTN-ranks, a deterministic algorithm, namely FCTN-SVD, is first proposed, which can approximate the FCTN decomposition under a fixed accuracy. Then, we present the randomized version of the algorithm. Both synthetic and real data are used to test our algorithms. Numerical results show that they perform much better than the existing methods, and the randomized algorithm can indeed yield acceleration on FCTN-SVD. Moreover, we also apply our algorithms to tensor-on-vector regression and achieve quite decent performance. [ABSTRACT FROM AUTHOR]

The copy-move forgery technique is the most commonly exercised among different image tampering techniques. In this approach, some part of an image is copied and pasted into the different locations of the same image. In this paper authors suggested a block-based copy move forgery detection technique utilizing SWT-SVD features. First, the shift-invariant approximation band has been obtained by using the SWT technique. In the next step, the obtained approximate band has been decomposed by block-based SVD. In the proposed scheme, the obtained SVD plane is represented as a multiresolution plane, and further statistical features are derived from those planes. Thus, an elegant copy-move forgery detection technique is introduced by considering significantly small feature vectors. This method has been tested on CoMoFoD dataset images and exhibits desired results compared with other state-of-the-art copy-move forgery detection methods in spite of having a lesser feature vector size. This method works effectively even under various post-processing attacks showing its applicability in a real-life scenario. [ABSTRACT FROM AUTHOR]

Background:Identification of significant coronary artery disease (CAD)in NSTEMI/UA/CSA patients without invasive methodsremains a clinical challenge. Severe CAD is known to cause LV dysfunction. However, the LV ejection fraction is usually normal at a relatively early stage. Therefore, establishing a more sensitive index for the detection of early-stage LV dysfunction is of utmost importance. Methods: This was a cross sectional observational study conducted on 136 patients with NSTEMI/UA/CSAto evaluate the value of global longitudinal strain (GLS) at rest to predict the presence of significant CAD and to determine the severity of CAD. The patients underwent transthoracic echocardiography (TTE) to measure 2DSTE to measure GLS. Results: Overall, Left Anterior Descending artery(43.38%) was the commonest vessel involved. Study revealed that mean GLS value was significantly lower in significant CAD (>70% stenosis)i.e. -15.95+ 2.78 than those with non-significant CAD (<70% stenosis) i.e. -20 + 1.73 with p < 0.001. The sensitivity and specificity for SVD, DVD and TVD were67.53% andTVD were67.53% and 89.83%, 88.90% and 82.60%, 85.30% and 70.40% respectively. Conclusion:Global longitudinal strain (GLS) assessed by 2D speckle tracking echocardiography at rest has good sensitivity and specificity to predict the presence, extent and severity of CAD in patients. [ABSTRACT FROM AUTHOR]

Video surveillance systems are now commonly deployed in both public and private settings globally. Use of watermarking in the surveillance systems is emerging as one of the alternative to cryptography for ensuring video authenticity and integrity. In this paper, we propose a novel video watermarking approach designed to protect the ownership of surveillance system videos. The proposed scheme utilizes various algorithms to achieve effective watermark embedding and enhances security levels also. The process begins by detecting video shots using the Modified Color Layout Descriptor (M-CLD) approach, which reduces false detection rates caused by noise, illumination changes, object motions, and camera operations. The entropy value of each frame within a shot is calculated, and the frame with the maximum entropy is selected as the key-frame. Entropy serves as a statistical measure to assess randomness and classify frames. To increase security, the proposed scheme employs a multi-watermark embedding approach. Three binary watermarks are generated from a grayscale image using the Two Threshold Binary Decomposition (TTBB) algorithm. A Watermark Embedding Code (WEC) is generated based on the entropy values of each key-frame, and a secure watermark selection algorithm is introduced to choose the appropriate watermark for each key-frame. The selected watermark is then embedded into the key-frame using Singular Value Decomposition (SVD) and Discrete Wavelet Transform (DWT) techniques, ensuring good perceptual quality. Experimental results demonstrate that the proposed scheme achieves high visual imperceptibility, improved performance in terms of normalized correlation, and low bit error rate. A comparative analysis with existing schemes reveals the superior robustness, enhanced imperceptibility, and reduced computational time of the proposed approach. [ABSTRACT FROM AUTHOR]

The paper studies the multi-user precoding problem as a non-convex optimization problem for wireless multiple inputs and multiple outputs (MIMO) systems. In our work, we approximate the target Spectral Efficiency function with a novel computationally simpler function. Then, we reduce the precoding problem to an unconstrained optimization task using a special differential projection method and solve it by the Quasi-Newton L-BFGS iterative procedure to achieve gains in capacity. We are testing the proposed approach in several scenarios generated using Quadriga-open-source software for generating realistic radio channel impulse response. Our method shows monotonic improvement over heuristic methods with reasonable computation time. The proposed L-BFGS optimization scheme is novel in this area and shows a significant advantage over the standard approaches. The proposed method has simple implementation and can be a good reference for other heuristic algorithms in this field. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel and robust watermarking method that combines the Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Set Partitioning in Hierarchical Trees (SPIHT) algorithm. The proposed method is designed to embed two different types of watermarks, Watermark 1 and Watermark 2, in different sub-bands of the decomposed host image. Initially cover image and watermark1 is optimized using hybrid optimization techniques (combination of whale and Cookoo optimization techniques). To ensure the security and robustness of the embedded watermarks, Watermark 1 is encrypted using the Arnold transform, while Watermark 2 is encoded using a Hamming encoder. Both encrypted watermarks are then embedded in their respective sub-bands. Finally, to compress the watermarked image without losing the embedded watermarks, we apply the SPIHT compression algorithm. Further, performance of the proposed method is evaluated against various watermarking attacks, including JPEG compression, Gaussian noise, and salt and pepper noise. Our experimental results demonstrate the high robustness of the proposed method against various watermarking attacks, including JPEG compression, Gaussian noise, and salt and pepper noise. The peak signal-to-noise ratio (PSNR), normalized correlation (NC), bit error rate (BER), and structured similarity index (SSIM) values were used as performance metrics. The proposed method achieves a maximum PSNR value of 42.52 dB and a maximum NC value of 0.9999, indicating the high fidelity and similarity between the original and watermarked images. The BER values were found to be consistently low, indicating the accuracy of watermark retrieval. Additionally, the proposed method maintains the quality of the cover image with minimal distortion. Overall, the proposed method offers a secure and robust solution for digital image watermarking, which can be applied in various applications, such as copyright protection and authentication. [ABSTRACT FROM AUTHOR]

Introduction: Posterior reversible encephalopathy syndrome (PRES) is a rare and complex disorder with variable clinical presentation and a typical magnetic resonance imaging (MRI) pattern of vasogenic edema with typical and atypical locations. It is often triggered by other diseases and drugs and the most prototypical association is with persistently elevated arterial pressure values. Among the potential cerebrovascular complications, intracranial bleeding has been described, but ischemic stroke is uncommonly reported. Methods: We are presenting a case of a male patient with prolonged and sustained arterial hypertension acutely presenting with lacunar ischemic stroke involving the right corona radiata and composite MRI findings with the association of chronic small vessel disease (SVD) markers, acute symptomatic lacunar stroke, and atypical, central variant, posterior fossa dominant PRES. In the MRI follow-up, the white matter hyperintensities in T2-fluid attenuated inversion recovery (FLAIR sequences) due to PRES. Discussion: The pathophysiology of PRES is not yet fully known, but the association with markedly increased values of arterial pressure is typical. In this context, ischemic stroke has not been considered in the clinical and neuroradiological manifestations of PRES and it has been only occasionally reported in the literature. In this case, the main hypothesis is that sustained hypertension may have triggered both manifestations, PRES, and ischemic stroke and the last one allowed to diagnose the first one. Conclusions: Atypical variants of PRES are not so rare and it may also occur in typical triggering situations. The association with ischemic stroke is even rarer and it may add some clues to the pathomechanisms of PRES. [ABSTRACT FROM AUTHOR]

Collaborative filtering has emerged as one of the most prevalent techniques for various commercial recommendations. Utilizing a similarity measure to identify similar neighbors is essential to collaborative filtering. Behavior scores and user ratings have recently become increasingly important factors in determining similarity. However, the added users' behavior scores generate a more complex computation. This research proposes a new similarity technique incorporating the matrix factorization and users' behavior score-based similarity to minimize computation time. The matrix factorization technique uses singular value decomposition (SVD), and the users' behavior score-based similarity employs normalized cumulative genre (NCG). Compared to the previous algorithm (i.e., users' scores probability-based collaborative filtering), the experimental findings with the MovieLens 1M and 100k datasets demonstrated a faster computing time. In addition, with these datasets, our similarity reduces the root mean square error (RMSE) by 8.14% and 11.99% and the mean absolute error (MAE) by 13.52% and 15.81%. [ABSTRACT FROM AUTHOR]

With increasing connectivity, unprotected digital content is vulnerable to attacks and thus watermarking them is essential for copyright protection. This paper presents an effective adaptive reversible image watermarking. The approach utilizes the selection of optimal location for embedding according to entropy, where appropriate threshold for entropy selection is taken care by particle swarm optimization (PSO) algorithm. A technique in integer wavelet transform (IWT) domain is proposed, further discrete cosine transform (DCT) and singular value decomposition (SVD) is hybridized for embedding process in all the chosen blocks, and the Fractal encrypted watermark bits are integrated into coefficients of image using the average proximity coefficient. The embedding process is governed by optimal scale, which is adaptively generated by moth-flame optimization (MFO) according to specially designed fitness function. The proposed technique when evaluated using several standard indicators exhibited improved reliability, robustness, and imperceptibility in comparison to the existing schemes. [ABSTRACT FROM AUTHOR]

Digital watermarking can be used to ensure the authenticity and copyright protection of images. In watermarking balancing the trade-offs between its features is an important issue. To address this issue, in this work an adaptive hybrid domain color image watermarking based on Discrete Wavelet Transform (DWT), Walsh Hadamard Transform (WHT), and Singular Value Decomposition (SVD) is proposed. Here, watermarking is carried on Y C b C r color space. In this work, the embedding factor is calculated adaptively using the visual entropy and edge entropy. For better robustness the watermark is inserted into the Y component of Y C b C r color Space. Further, Arnold Transform (AT) is used to secure the watermark. The average PSNR and SSIM of the proposed hybrid domain adaptive watermarking scheme is 40.0876 dB and 0.9883 respectively. The experimental results compared to the recent hybrid domain color watermarking, illustrate the superiority of the suggested approach. [ABSTRACT FROM AUTHOR]

Primary Angiitis of the Central Nervous System (PACNS) is a rare cerebrovascular disease involving the arteries of the leptomeninges, brain and spinal cord. Its diagnosis can be challenging, and the current diagnostic criteria show several limitations. Among the clinical and neuroimaging manifestations of PACNS, intracranial bleeding, particularly intracerebral hemorrhage (ICH), is poorly described in the available literature, and it is considered infrequent. This review aims to summarize the available data addressing this issue with a dedicated focus on the clinical, neuroradiological and neuropathological perspectives. Moreover, the limitations of the actual data and the unanswered questions about hemorrhagic PACNS are addressed from a double point of view (PACNS subtyping and ICH etiology). Fewer than 20% of patients diagnosed as PACNS had an ICH during the course of the disease, and in cases where ICH was reported, it usually did not occur at presentation. As trigger factors, both sympathomimetic drugs and illicit drugs have been proposed, under the hypothesis of an inflammatory response due to vasoconstriction in the distal cerebral arteries. Most neuroradiological descriptions documented a lobar location, and both the large-vessel PACNS (LV-PACNS) and small-vessel PACNS (SV-PACNS) subtypes might be the underlying associated phenotypes. Surprisingly, amyloid beta deposition was not associated with ICH when histopathology was available. Moreover, PACNS is not explicitly included in the etiological classification of spontaneous ICH. This issue has received little attention in the past, and it could be addressed in future prospective studies. [ABSTRACT FROM AUTHOR]

The rotor vibration fault identification of hydro-generator is a difficult problem in hydropower station operation and maintenance. Therefore, a fault identification method based on rotor vibration signal is proposed. Firstly, for the non-stationary and non-linear vibration signals of generator rotor, singular value decomposition (SVD) is used for denoising preprocessing combined with energy difference spectrum theory; the preprocessed data is transformed into time-frequency graph by continuous wavelet transform (CWT) and formed into image data set. Then the image data set is used as convolutional neural network (CNN) input, and distributed fault feature expression is formed by CNN multi-layer pooling and convolution. Finally hydro-generator rotor fault mode recognition and classification are realized. The experimental results show that the accuracy of this method is above 99.5%, which can effectively identify the fault types of hydro-generator rotor. [ABSTRACT FROM AUTHOR]

We propose the pass-efficient truncated UTV algorithm, a faster variant of the TUXV algorithm for low-rank approximations. Compared with the TUXV algorithm, data transfer and the complexity of the proposed algorithm are reduced. Therefore, our algorithm is suitable for large matrices stored out of memory or generated by streaming data. We also develop residual error upper bounds and singular value approximation error bounds for the pass-efficient truncated UTV algorithm. Numerical experiments are reported to demonstrate the efficiency and effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

Neurovascular alterations are increasingly recognized as a key feature of many brain diseases. They can manifest as a reduction in resting cerebral blood flow or cerebrovascular reactivity (CVR) in the whole brain or in specific regions, depending on the underlying condition. Neurovascular impairment is observed in hypertension, Alzheimer's disease, stroke, multiple sclerosis and cerebral small vessel disease. Magnetic resonance imaging (MRI)-derived CVR mapping is a reliable marker of vascular dysfunction and has been performed mainly at standard functional MRI (fMRI) resolutions of 2-3 mm using the blood oxygen level dependent (BOLD) contrast. However, vascular alterations may occur at a finer scale (i.e., in the capillary bed) which would be better characterized with smaller voxel sizes. Capillaries in gray matter deliver oxygen and glucose to neural tissue and are arranged in a mesh structure, with variable density across the cortical depth. Given that the human cortex is, on average, 2.5 mm thick, submillimetric voxel sizes are effective in increasing the spatial specificity of measurements of hemodynamic and metabolic changes. Novel MRI sequences offer the possibility to map physiological parameters at high resolution with relatively simple experimental setups. In particular, pairing the BOLD acquisition with a contrast sensitive to blood volume changes, while administering a mild hypercapnic challenge, allows for simultaneous mapping of CVR, cerebral metabolic rate of oxygen consumption and other relevant parameters at a high resolution and can be performed at the clinical field strength of 3 T. We propose that this approach will help provide crucial insights into vascular impairment. [ABSTRACT FROM AUTHOR]

Turntables play an essential role in various industrial areas due to their ability to improve the degree of freedom of workpieces or tools. In applications where positional accuracy of the objects is required, the accuracy of the system parameters of the turntable is critical for the applications to work properly. This paper proposes a new approach to calibrating the turntable system. First, we propose an optimized RANSAC (random sample consensus) algorithm to obtain high-accuracy reference planes from multiple frames of point clouds grabbed from the reference object placed on a turntable. Second, an singular value decomposition-based two-step method is proposed to calculate the rotation axis vector and the center position of the turntable based on the reference planes extracted by the modified RANSAC algorithm. Finally, several experiments are performed to verify the proposed methods. Compared with the existing methods, our approach gets the analytical results and tries to correct the rotation angles. The experimental results show that the proposed method considerably improves calibration accuracy and efficiency without complex devices. [ABSTRACT FROM AUTHOR]

Digital multimedia elements such as text, image, audio, and video can be easily manipulated because of the rapid rise of multimedia technology, making data protection a prime concern. Hence, copyright protection, content authentication, and integrity verification are today's new challenging issues. To address these issues, digital image watermarking techniques have been proposed by several researchers. Image watermarking can be conducted through several transformations, such as discrete wavelet transform (DWT), singular value decomposition (SVD), orthogonal matrix Q and upper triangular matrix R (QR) decomposition, and non-subsampled contourlet transform (NSCT). However, a single transformation cannot simultaneously satisfy all the design requirements of image watermarking, which makes a platform to design a hybrid invisible image watermarking technique in this work. The proposed work combines four-level (4L) DWT and two-level (2L) SVD. The Arnold map initially encrypts the watermark image, and 2L SVD is applied to it to extract the s components of the watermark image. A 4L DWT is applied to the host image to extract the LL sub-band, and then 2L SVD is applied to extract s components that are embedded into the host image to generate the watermarked image. The dynamic-sized watermark maintains a balanced visual impact and non-blind watermarking preserves the quality and integrity of the host image. We have evaluated the performance after applying several intentional and unintentional attacks and found high imperceptibility and improved robustness with enhanced security to the system than existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Background: There exist several fMRI quality assurance measures to assess scanner stability. Because they have practical and/or theoretical limitations, a different and more practical measure for instability would be desirable. Purpose: To develop and test a sensitive, reliable and widely applicable temporal instability measure (TIM) for fMRI quality assurance. Study Type: Technical development. Phantom: Spherical gel phantom. Population: A total of 120 datasets from a local Philips scanner with two different receive‐only head coils (32ch and 8ch, 60 datasets per coil) were collected as well as 29 additional datasets with three different receive‐only head coils (20ch, 32ch, and 64ch) from two additional sites with GE (seven runs with 32ch) and Siemens scanners (seven runs with 32ch and Multiband imaging, five runs with 20ch, 32ch, and 64ch) were borrowed. Field Strength/Sequence: 2D Echo‐planar‐imaging (EPI). Assessment: A new TIM was proposed that is based on the eigenratio of the correlation coefficient matrix, where each entry of the matrix is a correlation coefficient between two time‐points of the time‐series. Statistical Tests: Nonparametric bootstrap resampling was used twice to estimate confidence intervals (CI) of the TIM values and to assess the improved sensitivity of this measure. Differences in coil performance were assessed via a nonparametric bootstrap two‐sample t‐test. P‐values <0.05 were considered significant. Results: The TIM values ranged between 60 parts‐per‐million and 10,780 parts‐per‐million across all 149 experiments. The mean CI was 2.96% and 2.16% for the 120 and 29 fMRI datasets, respectively (the repeated bootstrap analysis gave 2.9% and 2.19%, respectively). The 32ch coils of the local Philips data provided more stable measurements than the 8ch coil (observed two‐sample t‐values = 26.36, −0.2 and −6.2 for TIM, tSNR, and RDC, respectively. PtSNR = 0.58). Data Conclusion: The proposed TIM is particularly useful for multichannel coils with spatially nonuniform receive sensitivity and overcomes several limitations of other measures. As such, it provides a reliable test for ascertaining scanner stability for fMRI experiments. Evidence Level: 5. Technical Efficacy: Stage 1. [ABSTRACT FROM AUTHOR]

Cross-project defect prediction (CPDP) utilizes other finished projects (i.e., source project) data to predict defects of the current working project. Transfer learning (TL) has been mainly applied at CPDP to improve prediction performance by alleviating the data distribution discrepancy between different projects. However, existing TL-based CPDP techniques are not applicable at the unit testing phase since they require the entire historical target project data. As a result, they lose the chance to increase the product's reliability in the early phase by applying the prediction results. The objective of the present study is to increase the product's reliability in the early phase by proposing a novel TL-based CPDP technique applicable at the unit testing phase (i.e., eCPDP). We utilize singular value decomposition (SVD), which only requires source project data for TL. eCPDP performs similarly or better than the 8 state-of-the-art TL-based CPDP techniques on 9 different performance metrics over 24 projects. In conclusion, (1) we show that eCPDP is an applicable CPDP model at the unit testing phase. (2) It can help practitioners find and fix defects in an earlier phase than other TL-based CPDP techniques. [ABSTRACT FROM AUTHOR]

The following study introduces a digital watermarking technique that is both imperceptible and secure. It uses Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD), as well as Human Visual System (HVS) principles. This approach ensures two-way security by first inserting a randomized binary sequence, followed by embedding a logo that relates to copyright ownership. A color image is initially transformed into Hue, Saturation, and Value (HSV) color space, and then the 2-level DWT is applied on its V channel components. From there, the decomposed components LH, HL, and HH are divided into 4 × 4 blocks and transformed into singular values using SVD. LH singular values are utilized to incorporate a random binary sequence, while the watermark logo is embedded in the singular values of HL and HH. The results of the exploratory analysis demonstrate that the method presented is effective against various types of attacks, including Salt and Pepper, Scaling, Gaussian noise, and JPEG compression when applied to standard image samples. After undergoing multiple attacks, the proposed approach demonstrates superior performance in terms of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Normalized Cross-Correlation (NCC), Mean Square Error (MSE), and Perception-based Image Quality Evaluation (PIQE) when compared to traditional watermarking techniques. [ABSTRACT FROM AUTHOR]

Sentiment Analysis (SA) is a prominent field of study concerned with the classification of sentences within a document as either positive or negative. The extraction of features from a document plays a crucial role in SA for achieving precise text classification. This study employs the Latent Semantic Indexing (LSI) algorithm for feature extraction, designed to address the limitations inherent in the Term Frequency-Inverse Document Frequency (TFIDF) technique. The features extracted are then utilized in the Convolutional Neural Network (CNN) classification algorithm, which encompasses two convolutional layers, a single polling layer, a fully-connected layer, and two output nodes. This is done to evaluate the efficacy of the proposed model. Experimental results indicate that the combination of LSI and CNN significantly improves text classification. Customer reviews exert considerable influence on individuals' travel plans, with a preference typically shown towards hotels with a preponderance of positive reviews. Consequently, these reviews serve as crucial resources for managers seeking to enhance their services. In this study, a dataset of hotel reviews is employed, and the resulting data is evaluated using standard metrics such as precision, recall, f-score, and accuracy, yielding results of 89%, 77%, 80.5%, and 87% respectively. [ABSTRACT FROM AUTHOR]

This paper explores the intricate relationship between music and emotions through a neurological perspective. We present a methodology to enhance music recommendation systems by integrating Facial Emotion Recognition (FER) through the YOLOv8 algorithm. Our aim is to develop a robust recommendation framework that seamlessly integrates Content-Based Filtering and Collaborative Filtering techniques with FER to facilitate real-time adaptability. Using natural language processing and machine learning concepts, we utilize TF-IDF for genre analysis and cosine similarity for quantifying song similarity. Singular Value Decomposition is used to facilitate User-Based Collaborative Filtering, while k-Nearest Neighbors is employed for Item-Based Collaborative Filtering. By extracting a dataset from Spotify, our recommendation system harnesses a rich source of music-related information. Our YOLOv8 model achieved 86.20% accuracy rate with the FER+ dataset. We also discuss future research directions aimed at addressing emotional recognition limitations, enhancing dataset diversity, and implementing comprehensive evaluation metrics for robust performance assessment. [ABSTRACT FROM AUTHOR]

In the Eastern Ghats Mobile Belt of Odisha, India, the interpretation of sub-surface resistivity using the direct current (DC) resistivity method can be enhanced by considering the inherent anisotropy of the Precambrian gneissic basement. To investigate this, two-dimensional resistivity and far-field controlled-source radiomagnetotelluric (CSRMT) data were collected in the area. A newly developed 2D code was used to perform anisotropic inversion of the 2D resistivity data, which was validated by the satisfactory model parameter reconstruction for synthetic data before inverting the field data. To ascertain the accuracy of the newly developed 2D code, the joint 1D inversion of a galvanic DC resistivity sounding and an inductive CSRMT method was carried out, a standard approach for estimating the subsurface anisotropy. The results of the joint DC-CSRMT inversion were compared with the 2D anisotropic DC resistivity sections. It was observed that the anisotropy obtained by the joint inversion was higher than that reported by 2D anisotropic DC resistivity, particularly in the layers corresponding to the fractured zone and below in the borehole. To verify the authenticity of the 1D inversion, the importance, damping parameters, and V-matrix of parameters obtained from singular value decomposition analysis were also analyzed. [ABSTRACT FROM AUTHOR]

Nowadays, multimedia security is a major issue. Images, video, audio, and text files lose credibility in most times, because they face several attacks related to illegal distribution, duplication, and manipulation of the information conveyed by them. Digital watermarking is an important tool for protecting digital content. This paper introduces an efficient hybrid digital image watermarking scheme. It consists of two stages of Singular Value Decomposition (SVD). The first one is the embedding stage, which is performed by using SVD followed by block-based SVD (B-SVD). The second stage is the extraction stage, which depends on the SVD on the whole image and then B-SVD to get the original watermarks. In this scheme, the watermarking requirements are satisfied, while capacity is increased. Furthermore, watermark detection, robustness, and security are improved. The performance of the proposed scheme is evaluated by the correlation coefficient Cr between the original and extracted watermarks, and the Peak Signal-to-Noise Ratio (PSNR) between the original and watermarked images. The experimental results demonstrate that the proposed scheme has a good performance, where Cr reaches 0.9975 and PSNR reaches 45.8605. It is more secure than the previous schemes, when subjected to attacks. In addition, the proposed scheme is compared to the most recent schemes, revealing its superiority. [ABSTRACT FROM AUTHOR]

Three-dimensional video (3DV) communication through wireless channels suffers from bit stream losses. Therefore, the efficient performance of 3DV transmission techniques over wireless networks is considerably a hot research topic. A high compression ratio must be introduced to meet future bandwidth restriction for optimized 3DV transmission. Unfortunately, the compressed 3DV bit streams are more sensitive and vulnerable to packet losses. In this paper, we suggest the application of chaotic Baker map interleaving with equalization and convolution coding for efficient discrete wavelet transform (DWT)-based singular value decomposition watermarked 3DV transmission over a multi-carrier code division multiple access wireless channels. Firstly, the compressed 3DV frames are watermarked using the DWT + SVD watermarking process. After that, chaotic interleaving is applied to minimize the channel effects on the transported bit streams, and it also adds an encryption level to the transported 3DV frames. Several experiments on different (DWT + SVD) watermarked 3DV frames have been tested to test the performance of the proposed hybrid techniques. The experimental results confirm that the received watermarked 3DV frames still have high peak signal-to-noise ratios, and efficient watermark extraction is possible. Thus, the main contribution of this paper is to present efficient hybrid techniques for secure 3DV communication. These proposed hybrid techniques efficiently protect the copyright of the 3DV streams to preserve both robustness and imperceptibility. Therefore, they have high quality, acceptable bit rates, low computational complexity, and adequate immunity to different types of multimedia attacks compared to the traditional techniques. [ABSTRACT FROM AUTHOR]

In this article, a method is proposed to effectively extract weak fault features and accurately diagnose faults in ball screws, even in the presence of strong background noise. This method combines singular value decomposition (SVD), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the 1.5-dimensional spectrum (1.5D) to process and analyze fault vibration signals. The first step involves decomposing the fault signal using the SVD algorithm. The singular values are then screened, and the part of the screen containing more noise information is extracted to complete the first denoising step. The second step involves decomposing the signal after the initial denoising process using CEEMDAN and removing some of the false components from the intrinsic mode function (IMF) components, based on the kurtosis correlation function index. The signal is then reconstructed to complete the second denoising step. Finally, the denoised signal is analyzed using Teager energy operator demodulation and 1.5D spectral analysis to extract the fault frequency and determine the location of the fault in the ball screw. This method has been compared with other denoising methods, such as wavelet packet decomposition combined with CEEMDAN or SVD combined with variational mode decomposition (VMD), and the results show that under the condition of strong background noise, the proposed method can better extract the fault frequency of ball screw. [ABSTRACT FROM AUTHOR]

Digital watermarking technology is adequate for copyright protection and content authentication. There needs to be more research on the watermarking algorithm after printing and scanning. Aiming at the problem that existing anti-print scanning text image watermarking algorithms cannot take into account the invisibility and robustness of the watermark, an anti-print scanning watermarking algorithm suitable for text images is proposed. This algorithm first performs a series of image enhancement preprocessing operations on the printed scanned image to eliminate the interference of incorrect bit information on watermark embedding and then uses a combination of Discrete Wavelet Transform (DWT)-Singular Value Decomposition (SVD) to embed the watermark. Experiments show that the average Normalized Correlation (NC) of the watermark extracted by this algorithm against attacks such as Joint Photographic Experts Group (JPEG) compression, JPEG2000 compression, and print scanning is above 0.93. Especially, the average NC of the watermark extracted after print scanning attacks is greater than 0.964, and the average Bit Error Ratio (BER) is 5.15%. This indicates that this algorithm has strong resistance to various attacks and print scanning attacks and can better take into account the invisibility of the watermark. [ABSTRACT FROM AUTHOR]

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In this article, a novel Euclidean distance mapping (EDM) technique for space vector pulse width modulated (SVPWM) voltage source inverter (VSI) is presented. The EDM technique processes space vector diagram (SVD) as an image, which simplifies execution of SVPWM. In the proposed technique, Euclidean distance calculation between all eight switching vectors and the tip of the revolving reference voltage vector for every subcycle is carried out. The identification of nearest three voltage vectors (NTV) and switching sequence designs are done on the basis of these Euclidean distances. The EDM-SVPWM technique achieves improvement in the inverter performance in the context of the factors like stator flux ripple over a subcycle, distortion factor (DF), total harmonics distortion (THD) of line voltage, and line current ripple of the inverter. The performance of EDM-SVPWM technique is verified analytically and confirmed experimentally by hardware implementation over the wide range of fundamental frequencies and modulation indices. [ABSTRACT FROM AUTHOR]

Cerebral microhemorrhages (CMHs, microbleeds), a manifestation of age-related cerebral small vessel disease, contribute to the pathogenesis of cognitive decline and dementia in older adults. Histological studies have revealed that CMHs exhibit distinct morphologies, which may be attributed to differences in intravascular pressure and the size of the vessels of origin. Our study aimed to establish a direct relationship between the size/morphology of CMHs and the size/anatomy of the microvessel of origin. To achieve this goal, we adapted and optimized intravital two-photon microscopy-based imaging methods to monitor the development of CMHs in mice equipped with a chronic cranial window upon high-energy laser light-induced photodisruption of a targeted cortical arteriole, capillary, or venule. We assessed the time course of extravasation of fluorescently labeled blood and determined the morphology and size/volume of the induced CMHs. Our findings reveal striking similarities between the bleed morphologies observed in hypertension-induced CMHs in models of aging and those originating from different targeted vessels via multiphoton laser ablation. Arteriolar bleeds, which are larger (> 100 μm) and more widely dispersed, are distinguished from venular bleeds, which are smaller and exhibit a distinct diffuse morphology. Capillary bleeds are circular and smaller (< 10 μm) in size. Our study supports the concept that CMHs can occur at any location in the vascular tree, and that each type of vessel produces microbleeds with a distinct morphology. Development of CMHs resulted in immediate constriction of capillaries, likely due to pericyte activation and constriction of precapillary arterioles. Additionally, tissue displacement observed in association with arteriolar CMHs suggests that they can affect an area with a radius of ~ 50 μm to ~ 100 μm, creating an area at risk for ischemia. Longitudinal imaging of CMHs allowed us to visualize reactive astrocytosis and bleed resolution during a 30-day period. Our study provides new insights into the development and morphology of CMHs, highlighting the potential clinical implications of differentiating between the types of vessels involved in the pathogenesis of CMHs. This information may help in the development of targeted interventions aimed at reducing the risk of cerebral small vessel disease-related cognitive decline and dementia in older adults. [ABSTRACT FROM AUTHOR]

Blind receiver technologies for paired carrier multiple access (PCMA) signals have always been a challenging task with many technical difficulties, among which the estimation of modulation parameters is a fundamental but important element. Despite some achievements in previous studies, more systematic and sophisticated estimation methods have not been adequately investigated. In this paper, schemes for the blind estimation of the symbol timing phase, amplitude attenuation, frequency offset, and carrier phase for PCMA signals in satellite communications are proposed. The data flow transmitted in satellite communication often has a certain frame structure, the most important of which is the synchronization data, namely the so-called cycle features. The proposed schemes assume that the modulated signals have fixed frame length and frame sync code and that the symbol rate has been estimated when the signals are encoded asynchronously. Distinct from the previous methods, our schemes exploit the sync waveform and the overlapping waveform, which are estimated via singular value decomposition (SVD) (using the frame cyclic features) and interference cancelation, together with their demodulation results as aid data, for the estimation of the desired parameters. The simulation results demonstrate that the schemes are effective in the parameters estimation of PCMA signals and outperform the comparison algorithms. [ABSTRACT FROM AUTHOR]