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ABSTRACT Interval Time Series are present in everyday life. An example is the opening and closing value of some stock in a market for certain time intervals. The forecasting plays an essential role in many financial organisations. The development of new mathematical tools or improving the existing ones will lead to more accurate forecasting techniques. Interval Arithmetic is a mathematical field that uses intervals by nature and algorithms that use it are involved in the solution of Interval Time Series. Another classical algorithm is VAR models. In this paper, a method that comes from the insurance sector is used to forecast Brent Oil monthly values. The innovation of what we propose is that it converts the Interval Time Series system into a single time series and can propagate the results back to each time series of the system. This way the researcher works with only one time series instead of two (or more). The forecasting algorithm is a choice of the researcher, expediting the development of forecasting (even ARIMA can be applied). We demonstrate our methodology in forecasting the Brent Oil monthly prices by applying the ANFIS algorithm. [ABSTRACT FROM AUTHOR]

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]

ABSTRACT Most existing remote sensing image watermarking algorithms concentrate on excavation of particular embedding templates, image features, or geometric invariant domains, which present challenges in terms of resistance to desynchronization attacks, embedding domain repetition, and insufficient algorithm versatility. To address these issues, this study proposes a watermarking algorithm that is robust to desynchronization attacks and can adapt to different types of remote sensing images using the geometric invariant domain and hybrid frequency domain. The algorithm uses the multi‐scale SIFT to identify feature points in remote sensing images, then creates a Delaunay triangulation network (DTN) based on these feature points, extracts the tangent circles of triangles, and normalizes these tangent circles using image moment and affine transformation, and the feature domains with geometric invariance are constructed. On this basis, the discrete wavelet transform (DWT) transforms the feature domain to the frequency decomposition state, and the singular value decomposition (SVD) further mines the watermark embedding domain, ensuring the stability of the watermark transforming back and forth in the embedding domain and improving the overall invisibility of the watermarking algorithm. The experimental results indicate that, compared to related algorithms, the proposed watermarking algorithm not only adapts better to remote sensing images with different bands and bit depths but also provides superior invisibility and demonstrates strong robustness against various desynchronization attacks such as splicing, panning, rotating, as well as image processing like noise addition, filtering, and compression. [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]

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]

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]

Digital watermarking encrypts private information with the original data to protect the ownership rights of the digital asset. This paper suggests a brand-new nested watermarking technique (two-level watermarking technique) to protect copyrights of digital images. It based on a hybrid discrete wave transformation-Hessenberg decomposition-Singular value decomposition (DWT-HD-SVD). The gray-watermark image is first split into n parts, and each portion goes through two steps of watermarking, using two host images, using hybrid DWT-HD-SVD method and one final level of encryption (based on the Lightweight algorithm, the high-speed cryptographic method). The results of the simulation reveal that the suggested system offers a high level of imperceptibility, reliability and robustness. Also it can be used for both the colored and the grayed images.

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]

Swiftly and accurately analyze non-stationary disturbance signals within the power grid， a location and detection method for transient power quality disturbance that combines singular value decomposition （SVD） and improved local mean decomposition （ILMD） is proposed. First， noise information is processed by using ILMD and a fuzzy membership function threshold to mitigate noise interference. Then， a difference signal is formulated， and a sliding window SVD is employed to amplify the disturbance features while further suppressing noise interference. In conclusion， an adaptive threshold truncation-based approach for localizing and detecting transient power quality disturbances is proposed， utilizing the feature-enhanced signal. Simulation analysis and algorithm comparisons confirm that the proposed method exhibits precise location， robust resistance to noise， and low computational complexity. Moreover， it demonstrates excellent performance in detecting zero-crossing and minor disturbances.

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]

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]

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]

This paper discusses how to use SVD (Singular Value Decomposition) to reduce the data size as a preprocessing method before applying machine learning algorithms. Data reduction can lead to more efficient, and possibly better-performing machine learning models, especially when datasets are large, noisy, or high-dimensional. Specifically, we demonstrate two methods: PCA (Primary Component Analysis) and the data compression technique using SVD. For each method, we explain how it works and show the execution time, memory usage, and data reduction ratio using the random forest classification algorithm. All demonstrations of these methods are implemented in Python and the Python code is provided. [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]

Recently the applications of remotely sensed Solar-Induced chlorophyll Fluorescence (SIF) in the study of photosynthesis, stress conditions, and gross primary production have increased significantly. The full SIF spectrum spans over a spectral region of 650 ∼ 850 nm with two characteristic peaks around 685 nm and 740 nm. Over recent decades, many retrieval algorithms have been developed to estimate SIF at Top-Of-Canopy (TOC) using in-situ measurements of solar irradiance and canopy radiance spectra. Although the majority of retrieval methods retrieve SIF at a narrow spectral window, there exists a potential for retrieval of SIF in the full emission spectrum. Moreover, solar irradiance and canopy radiance spectra should ideally be measured at the same time but are usually measured sequentially with a certain time lag, raising potential errors in SIF retrieval. In this study, an enhanced retrieval algorithm of the full SIF spectrum at TOC is proposed. The proposed algorithm attempts to minimize the errors owing to time mismatch in measurements of solar irradiance and canopy radiance spectra. As an improvement to the previous algorithm (advanced Fluorescence Spectrum Reconstruction, aFSR), this proposed algorithm (aFSR-SVE) models the SIF-free contribution with principal components using the singular value decomposition technique. The optimal parameter settings in the forward model were determined for the experimental data collected by spectrometers used in the study. Firstly, the proposed algorithm was used to reconstruct full SIF spectrum for simulated data. The results were compared with known reference SIF values. After achieving satisfying results from simulated data, the proposed algorithm was compared with retrievals from established algorithms using experimental data. The results show improved SIF retrieval accuracy, without the need to simultaneously measure solar irradiance and canopy radiance spectra. The retrieval values comply with the results of previous algorithms in terms of spectral shape, diurnal trend, and temporal variations. The induced errors in SIF retrievals due to non-simultaneous measurements of solar irradiance and canopy radiance spectra were also investigated and the proposed algorithm was found to be less prone to such errors. Hence, the proposed algorithm is an improvement in reconstructing the full SIF spectrum with near-ground measurements. With the help of the proposed algorithm, field measurements using sequential systems and automated measurements of multiple targets can be performed effectively as it relaxes the requirement of concurrent measurement of solar irradiance and canopy radiance spectra. For future work, the applicability of this method can be investigated under more variable illumination conditions, like high cirrus clouds, passing clouds or persistent thin clouds.

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]