Normalized Cross Correlation Research Articles

A Platform Errors Estimation Method Based on Radar Echo for Spaceborne Doppler Scatterometer

The Doppler scatterometer is a type of rotating radar platform used for the estimation and monitoring of ocean current to achieve fast mapping of global vector current with a monostatic system. Accurate measurement of the ocean surface current imposes strong requirements on the accuracy of the platform errors. If the ocean surface current speed measurement accuracy can be less than 5 cm/s, the slant range error should be smaller than 20 m and the attitude measurement error should reach 1×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> °. No available attitude determination technique is capable of this level of accuracy. In this study, we propose a method of platform errors estimation based on echo signals. A model describing the relation of the Doppler centroid to platform errors is established. Then, a Doppler centroid frequency estimation method based on deramping is proposed to improve the estimation accuracy, which showed a 26.15% improvement compared with the classical average cross-correlation coefficient (ACCC) method. Finally, using the weighted least square technique, the platform errors estimation method based on Doppler centroid estimation of echo signals from the stationary land area is constructed. Simulation experiments were performed to analyze the accuracy, feasibility, and robustness of the proposed method. Through simulation of one circle echo data, the root-mean-square errors of slant range error, roll, pitch, and yaw were shown to reach 1.12×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> m, 1.69×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-6</sup> °, 2.44×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-6</sup> °, and 8.52×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-7</sup> °, respectively. These findings represent a much smaller error level than the requirement for the slant range error and attitude angles.

Optimization of Cross Correlation Algorithm for Annual Mapping of Alpine Glacier Flow Velocities; Application to Sentinel-2

Nowadays, satellite observations cover most of the Earth’s surface in a repetitive manner. This information is crucial for documenting variability and environmental changes such as glacier surface velocity. With this in mind, digital image processing has been developed and improved over the past decades. The processing challenges are now related to optimizing parameters that account for the high variability of natural processes, as well as filtering and aggregating the results to provide useful products to end-users. Based on the normalized cross-correlation (NCC) method applied to Sentinel-2 optical satellite observations up to 400 days apart, we present a series of tests to derive optimal parameter values for the quantification of alpine glacier ice velocity that we have applied to the Mont-Blanc massif where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measurements are available. We found that a search distance adapted to the temporal baseline, a 16×16 pixel window size and a 5×5 pixels sampling provide an appropriate combination of parameters to process Sentinel-2 with the NCC method when applied to small alpine glaciers. Combining several spatial and temporal filters applied to a large set of more than 18,000 displacement maps obtained between 2015 and 2020, then aggregating these filtered maps using statistical or linear regressions into annual maps, yields near-complete maps of the test region with a RMSE reduced to about 12 m.yr <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> compared to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measurements.

Performing Dynamic Weight-Shifting Balance Exercises With a Smartphone-Based Wearable Telerehabilitation System for Home Use by Individuals With Parkinson's Disease: A Proof-of-Concept Study.

Telerehabilitation technology often helps individuals with Parkinson's disease (PD) to control their balance and improve postural stability. This proof-of-concept study describes the redesign of a smartphone-based wearable balance rehabilitation system, or Smarter Balance System (SBS) intended for in-home use, and determines the number of exercise sessions required to achieve steady-state balance exercise performance by people with PD who performed 6 weeks of in-home dynamic weight-shifting balance exercises. The redesigned SBS supplied real-time multimodal (visual and vibrotactile) biofeedback during dynamic weight-shifting balance exercises (WSBEs). A Technology Acceptance Model (TAM) questionnaire completed by participants validated its acceptability and use. The results of regression analyses of participants' balance exercise performance, based on the average cross-correlations and absolute position errors between the target motion and the exerciser's motion, showed exponential trends, a performance plateau after 3 weeks, and a quasi-steady state performance by the end of 6 consecutive weeks.

Model-Based Deep Learning Algorithm for Pulse Shape Discrimination in High Event Rates

Pulse shape discrimination (PSD) is at the core of radioactive particles monitoring. Conventional PSD methods are geared towards low event rates, and struggle in the presence of pileups resulting from high rate. In this work we develop a PSD algorithm that combines classic approaches with deep learning techniques, that is highly suitable for coping with the dramatic challenges associated with classifying pulses in high event rates. Common PSD algorithms for high event rates limit their research to two piled-up pulses. Our algorithm is designed and tested under severe pileup conditions, where three or more pulses were piled-up. We tested the algorithm on simulated data based on Cs2LiYCl6:Ce (CLYC) based detector pulse shapes and compare its performance to both traditional PSD algorithms and data-driven deep neural network (DNN) based algorithms. In high event rates, ranging up to 10 Mcps, the algorithm demonstrates up to 8 times fewer miss-classifications than the traditional normalized cross-correlation (NCC) approach, and up to 1.7 times fewer miss-classifications than a purely data-driven DNN-aided method.

3D Feature-Based Sampled-Data Visual Tracking*

3D feature-based Visual Servoing (VS) on the one hand shows attractive peculiarities, on the other hand it suffers from drawbacks related to the existence of local minima, which may affect the convergence character of the VS control loop. Furthermore, the performance of the visual tracking module may constitute a bottleneck enforcing severe constraints on the workspace and visual task execution speed. In this paper we introduce a novel sampled-data model of the 3D feature-based VS, and, in order to avoid drawbacks due to local minima, we plan the target reference trajectory in the feature space with the aim to constraint the feature error dynamics to remain close to the desired equilibrium point. Then, we propose a novel feature generation based on the homography provided by a template matching algorithm based on the Zero mean Normalized Cross Correlation (ZNCC) and the design of a visual tracking scheme by resorting to the Extended Kalman Filter (EKF) and Lyapunov direct method, which explicitly takes into account the camera velocity limits, while ensuring stability.

An unsupervised deep learning-based image translation method for retrospective motion correction of high resolution kidney MRI

A primary challenge for in vivo kidney magnetic resonance imaging (MRI) is the presence of different types of involuntary physiological motion, affecting the diagnostic utility of acquired images due to severe motion artifacts. Existing prospective and retrospective motion correction methods remain ineffective when dealing with complex large amplitude nonrigid motion artifacts. Here, we introduce an unsupervised deep learning-based image to image translation method between motion-affected and motion-free image domains, for correction of rigid-body, respiratory and nonrigid motion artifacts in vivo kidney MRI.High resolution (i.e., 156 × 156 × 370 μm) ex vivo 3 Tesla MRI scans of 13 porcine kidneys (because of their anatomical homology to human kidney) were conducted using a 3D T2-weighted turbo spin echo sequence. Rigid-body, respiratory and nonrigid motion-affected images were then simulated using the magnitude-only ex vivo motion-free image set. Each 2D coronal slice of motion-affected and motion-free image volume was then divided into patches of 128 × 128 for training the model. We proposed to add normalised cross-correlation loss to cycle consistency generative adversarial network structure (NCC-CycleGAN), to enforce edge alignment between motion-corrected and motion-free image domains.Our NCC-CycleGAN motion correction model demonstrated high performance with an in-tissue structural similarity index measure of 0.77 ± 0.08, peak signal-to-noise ratio of 26.67 ± 3.44 and learned perceptual image patch similarity of 0.173 ± 0.05 between the reconstructed motion-corrected and ground truth motion-free images. This corresponds to a significant respective average improvement of 34%, 23% and 39% (p < 0.05; paired t-test) for the three metrics to correct the three different types of simulated motion artifacts.We demonstrated the feasibility of developing an unsupervised deep learning-based method for efficient automated retrospective kidney MRI motion correction, while preserving microscopic tissue structures in high resolution imaging.

A Multitask Learning Model for the Prediction of NOx Emissions in Municipal Solid Waste Incineration Processes

Selective non-catalytic reduction (SNCR) is a commercially available technology that can effectively reduce the nitrogen oxides ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm {NO}}_{x}$ </tex-math></inline-formula> ) emissions in municipal solid waste incineration (MSWI) processes. Real-time measurement of NOx emissions is important to improve the denitration efficiency of SNCR. However, there are commonly time delays to obtain the measurement results from the traditional continuous emission monitoring system (CEMS). In this study, a prediction model based on multitask learning (MTL) is proposed for real-time measurement of NOx emissions. First, time delays are analyzed by using the maximum average cross-correlation function (MACCF) method, and sequences are adjusted according to the time delays, thereby recovering the original data pattern. Second, input variables are selected using a maximal information coefficient (MIC) method to reduce redundant information. Then, an MTL model is constructed for the prediction of NOx emissions. By using the MTL mechanism to share information of related prediction tasks, the potential correlations of industrial time-series data are mined, therefore improving the generalization performance of the model. Also, a self-organizing radial basis function neural network is designed as the module of the MTL model to further improve the prediction accuracy of the model. A strategy for incrementally constructing the MTL model is developed to guarantee the computational efficiency of the model. Finally, the established model is tested using a benchmark dataset and the real industrial data from an MSWI plant. The experimental results show that the proposed MTL model outperforms other state-of-the-art models on computational efficiency and prediction accuracy, demonstrating the potentiality of the MTL model in real-time measurement of NOx emissions in MSWI processes.

Reflective tactile sensor assisted by multimode fiber-based optical coupler and fiber specklegram

A tactile sensor based on the detection of specklegram was proposed using a multimode fiber (MMF)-based optical coupler (OC) with a reflection configuration. Human-like sensations of multiple tactile information are successfully demonstrated. The laser beam was guided by one arm of the MMF-based OC with a coupling ratio of 50:50 and reflected by a soft mirror embedded in a 3D-printed finger-shaped sensor probe. The specklegram interrogated by a camera was obtained at the reflection end of the MMF-based OC. Contact force sensing was firstly realized by six kinds of processing algorithms and the zero-mean normalized cross-correlation coefficient was further used to make an in-depth investigation. Maximal linear measurement range of 30 N was obtained. To evaluate the texture detection ability, the proposed sensor was tested by scanning four 3D-printed texture plates with different periods, and the period obtained by processing the experimental results was highly consistent with that of the texture plates. In addition, the specklegrams from 55 cases when 11 different contact forces were applied at 5 different positions of the sensor probe can be classified with an accuracy of 98 %, which shows the sensor is capable of recognizing the contact position. Furthermore, three kinds of sensor probes with different shore hardness and three different MMF-based OCs were used for comparative analysis.

A novel tissue mechanics-based method for improved motion tracking in quasi-static ultrasound elastography.

Most cancers are associated with biological and structural changes that lead to tissue stiffening. Therefore, imaging tissue stiffness using quasi-static ultrasound elastography (USE) can potentially be effective in cancer diagnosis. USE techniques developed for stiffness image reconstruction use noisy displacement data to obtain the stiffness images. In this study, we propose a technique to substantially improve the accuracy of the displacement data computed through ultrasound tissue motion tracking techniques, especially in the lateral direction. The proposed technique uses mathematical constraints derived from fundamental tissue mechanics principles to regularize displacement and strain fields obtained using Global Ultrasound Elastography (GLUE) and Second-Order Ultrasound Elastography (SOUL) methods. The principles include a novel technique to enforce (1) tissue incompressibility using 3D Boussinesq model and (2) deformation compatibility using the compatibility differential equation. The technique was validated thoroughly using metrics pertaining to Signal-to-Noise-Ratio (SNR), Contrast-to-Noise-Ratio (CNR) and Normalized Cross Correlation (NCC) for four tissue-mimicking phantom models and two clinical breast ultrasound elastography cases. The results show substantial improvement in the displacement and strain images generated using the proposed technique. The tissue-mimicking phantom study results indicate that the proposed method is superior in improving image quality compared to the GLUE and SOUL techniques as it shows an average axial strain SNR and CNR improvement of 44% and 63%, and lateral strain SNR and CNR improvement of 130% and 435%, respectively. The results of the phantom study also indicate higher accuracy of displacement images obtained using the proposed technique, including improvement ranges of 7-84% and 26-140% for axial and lateral displacement images, respectively. For the clinical cases, the results indicate average improvement of 48% and 64% in SNR and CNR, respectively, in the axial strain images, and average improvement of 40% and 41% in SNR and CNR, respectively, in the lateral strain images. The proposed method is very effective in producing improved estimate of tissue displacement and strain images, especially with the lateral displacement and strain where the improvement is highly remarkable. While the method shows promise for clinical applications, further investigation is necessary for rigorous assessment of the method's performance in the clinic.

ORIENTATION OF IMAGES WITH LOW CONTRAST TEXTURES AND TRANSPARENT OBJECTS

Abstract. Objects with non-collaborative or transparent surfaces pose challenges to image orientation procedures and are an open research task in photogrammetry and computer vision. In this paper, we analyse the critical issues that cause image orientation failures and propose two approaches that leverage the low-contrast textures present on object surfaces to accurately orient an image block. Both approaches privilege tie point detection on low-contrast textures, discarding specular reflections and static tie points. In the first approach local descriptors are extracted in those regions where roughness and micro-structures are better highlighted, applying the normalized cross-correlation (NCC) on the gradient map of the images to fully exploit the geometrical content of the patches. The second approach builds on the first method modifying the classic RootSIFT pipeline and obtaining a faster and more reliable approach. Different transparent objects with different surface characteristics are tested to evaluate the efficiency and reliability of the proposed pipelines for image orientation and successive dense 3D reconstruction.

Deep learning-based motion compensation for four-dimensional cone-beam computed tomography (4D-CBCT) reconstruction.

Motion-compensated (MoCo) reconstruction shows great promise in improving four-dimensional cone-beam computed tomography (4D-CBCT) image quality. MoCo reconstruction for a 4D-CBCT could be more accurate using motion information at the CBCT imaging time than that obtained from previous 4D-CT scans. However, such data-driven approaches are hampered by the quality of initial 4D-CBCT images used for motion modeling. This study aims to develop a deep-learning method to generate high-quality motion models for MoCo reconstruction to improve the quality of final 4D-CBCT images. A 3D artifact-reduction convolutional neural network (CNN) was proposed to improve conventional phase-correlated Feldkamp-Davis-Kress (PCF) reconstructions by reducing undersampling-induced streaking artifacts while maintaining motion information. The CNN-generated artifact-mitigated 4D-CBCT images (CNN enhanced) were then used to build a motion model which was used by MoCo reconstruction (CNN+MoCo). The proposed procedure was evaluated using in-vivo patient datasets, an extended cardiac-torso (XCAT) phantom, and the public SPARE challenge datasets. The quality of reconstructed images for XCAT phantom and SPARE datasets was quantitatively assessed using root-mean-square-error (RMSE) and normalized cross-correlation (NCC). The trained CNN effectively reduced the streaking artifacts of PCF CBCT images for all datasets. More detailed structures can be recovered using the proposed CNN+MoCo reconstruction procedure. XCAT phantom experiments showed that the accuracy of estimated motion model using CNN enhanced images was greatly improved over PCF. CNN+MoCo showed lower RMSE and higher NCC compared to PCF, CNN enhanced and conventional MoCo. For the SPARE datasets, the average(±standard deviation) RMSE in mm-1 for body region of PCF, CNN enhanced, conventional MoCo and CNN+MoCo were 0.0040±0.0009, 0.0029±0.0002, 0.0024±0.0003 and 0.0021±0.0003. Corresponding NCC were 0.84±0.05, 0.91±0.05, 0.91±0.05 and 0.93±0.04. CNN-based artifact reduction can substantially reduce the artifacts in the initial 4D-CBCT images. The improved images could be used to enhance the motion modeling and ultimately improve the quality of the final 4D-CBCT images reconstructed using MoCo.

An efficient registration-based approach for retinal blood vessel segmentation using generalized Pareto and fatigue pdf.

Segmentation of Retinal Blood Vessel (RBV) extraction in the retina images and Registration of segmented RBV structure is implemented to identify changes in vessel structure by ophthalmologists in diagnosis of various illnesses like Glaucoma, Diabetes, and Hypertension's. The Retinal Blood Vessel provides blood to the inner retinal neurons, RBV are located mainly in internal retina but it may partly in the ganglion cell layer, following network failure haven't been identified with past methods. Classifications of accurate RBV and Registration of segmented blood vessels are challenging tasks in the low intensity background of Retinal Image. So, we projected a novel approach of segmentation of RBV extraction used matched filter of Generalized Pareto Probability Distribution Function (pdf) and Registration approach on feature-based segmented retinal blood vessel of Binary Robust Invariant Scalable Key point (BRISK). The BRISK provides the predefined sampling pattern as compared to Pdf. The BRISK feature is implemented for attention point recognition & matching approach for change in vessel structure. The proposed approaches contain 3 levels: pre-processing, matched filter-based Generalized Pareto pdf as a source along with the novel approach of fatigue pdf as a target, and BRISK framework is used for Registration on segmented retinal images of supply & intention images. This implemented system's performance is estimated in experimental analysis by the Average accuracy, Normalized Cross-Correlation (NCC), and computation time process of the segmented retinal source and target image. The NCC is main element to give more statistical information about retinal image segmentation. The proposed approach of Generalized Pareto value pdf has Average Accuracy of 95.21%, NCC of both image pairs is 93%, and Average accuracy of Registration of segmented source images and the target image is 98.51% respectively. The proposed approach of average computational time taken is around 1.4s, which has been identified on boundary condition of Pdf function.

CArdiac and REspiratory adaptive Computed Tomography (CARE-CT): a proof-of-concept digital phantom study

Current respiratory 4DCT imaging for high-dose rate thoracic radiotherapy treatments are negatively affected by the complex interaction of cardiac and respiratory motion. We propose an imaging method to reduce artifacts caused by thoracic motion, CArdiac and REspiratory adaptive CT (CARE-CT), that monitors respiratory motion and ECG signals in real-time, triggering CT acquisition during combined cardiac and respiratory bins. Using a digital phantom, conventional 4DCT and CARE-CT acquisitions for nineteen patient-measured physiological traces were simulated. Ten respiratory bins were acquired for conventional 4DCT scans and ten respiratory bins during cardiac diastole were acquired for CARE-CT scans. Image artifacts were quantified for 10 common thoracic organs at risk (OAR) substructures using the differential normalized cross correlation between axial slices (ΔNCC), mean squared error (MSE) and sensitivity. For all images, on average, CARE-CT improved the ΔNCC for 18/19 and the MSE and sensitivity for all patient traces. The ΔNCC was reduced for all cardiac OARs (mean reduction 21%). The MSE was reduced for all OARs (mean reduction 36%). In the digital phantom study, the average scan time was increased from 1.8 ± 0.4 min to 7.5 ± 2.2 min with a reduction in average beam on time from 98 ± 28 s to 45 s using CARE-CT compared to conventional 4DCT. The proof-of-concept study indicates the potential for CARE-CT to image the thorax in real-time during the cardiac and respiratory cycle simultaneously, to reduce image artifacts for common thoracic OARs.

Motion-robust, blood-suppressed, reduced-distortion diffusion MRI of the liver.

To evaluate feasibility and reproducibility of liver diffusion-weighted (DW) MRI using cardiac-motion-robust, blood-suppressed, reduced-distortion techniques. DW-MRI data were acquired at 3T in an anatomically accurate liver phantom including controlled pulsatile motion, in eight healthy volunteers and four patients with known or suspected liver metastases. Standard monopolar and motion-robust (M1-nulled, and M1-optimized) DW gradient waveforms were each acquired with single-shot echo-planar imaging (ssEPI) and multishot EPI (msEPI). In the motion phantom, apparent diffusion coefficient (ADC) was measured in the motion-affected volume. In healthy volunteers, ADC was measured in the left and right liver lobes separately to evaluate ADC reproducibility between the two lobes. Image distortions were quantified using the normalized cross-correlation coefficient, with an undistorted T2-weighted reference. In the motion phantom, ADC mean and SD in motion-affected volumes substantially increased with increasing motion for monopolar waveforms. ADC remained stable in the presence of increasing motion when using motion-robust waveforms. M1-optimized waveforms suppressed slow flow signal present with M1-nulled waveforms. In healthy volunteers, monopolar waveforms generated significantly different ADC measurements between left and right liver lobes ( , reproducibility coefficients (RPC)= mm /s for monopolar-msEPI), while M1-optimized waveforms showed more reproducible ADC values ( , mm /s for M1-optimized-msEPI). In phantom and healthy volunteer studies, motion-robust acquisitions with msEPI showed significantly reduced image distortion ( ) compared to ssEPI. Patient scans showed reduction of wormhole artifacts when combining M1-optimized waveforms with msEPI. Synergistic effects of combined M1-optimized diffusion waveforms and msEPI acquisitions enable reproducible liver DWI with motion robustness, blood signal suppression, and reduced distortion.

The recovery scheme of computer-generated holography encryption–hiding images based on deep learning

In order to improve the security of ciphertext images in transmission, a scheme of using neural network is proposed to restore the encryption–hiding images based on chaotic iris phase mask and computer-generated holography (CGH), which solve the great difficulty of illegal attacks in the symmetric–asymmetric hybrid encryption system. Firstly, the ciphertext image based on CGH and chaotic iris phase mask is generated, and the ciphertext image is hidden into a carrier image. A large number of hidden image and plaintext image pairs are produced as a dataset. Next, the neural network is built by continuous training and testing. Finally, the established neural network can fit the mapping relationship between the hidden image and the plaintext image. It is not necessary to extract the ciphertext image from the hidden image before decrypting. We compare the image recovered by the neural network with the plaintext image. The experimental results show that the average cross-correlation coefficient (CC) is 0.994, the average peak signal-to-noise ratio (PSNR) is 70.2 dB, and the average structural similarity (SSIM) is 0.929. This scheme can directly realize the decryption of the hidden image. The scheme (encryption–decryption–hiding) are elaborated in detail. The simulation experiments show that the scheme is feasible and has good robustness.

Enhancing detection of low-coherence interferometry signals acquired with a spatial heterodyne detector

The use of low-coherence interferometry for industrial applications is constantly growing due to its non-destructive nature and high resolution. Fourier domain systems are usually preferred as they provide a scanning-free method. Recently, a spatial heterodyne detector has proved to be useful in extending the measuring range without losing the micrometric resolution. This system Fourier transforms the interferometric signal, giving the information of interest as fringes in the output image. However, for low light intensities, the images might need to be processed to facilitate the visualization and detection of these fringes. In this work we analyze two main approaches to achieve this: one that uses a cross correlation method and the other one that uses a specially designed two-dimensional adaptive filtering technique. Both methods showed an improved performance when compared with conventional image processing techniques, enhancing the fringes contrast. Additionally, we showed that combining normalized cross correlation and the image gradient is an effective method to locate low intensity signals.

Performance boosting of image matching-based iris recognition systems using deformable circular hollow kernels and uniform histogram fusion images

Identification of people using different biometric data is becoming more important in network society. Biometrics include voice, ears, palms, fingerprints, faces, iris, retina, and hand shapes. Among these features, iris detection gets more attention because each iris type is unique and does not change throughout life. In this study, an iris recognition framework is proposed using deformable circular hollow kernels and uniform histogram fusion images (UHFIs). This system introduces two different approaches for the iris recognition: one with image matching without machine learning and the other one with machine learning. In the first approach, image matching through Gabor features is employed. After getting the circularly cropped UHFI, Gabor features are extracted and employed in the image matching-based recognition system. In addition, a normalized cross-correlation coefficient is used as a similarity metric to compare the Gabor feature vectors. In the second approach, Gabor feature images are extracted and employed in deep learning (DL). According to the experimental results, the proposed system reaches around 89% accuracy on MMU1, 86% accuracy on MMU2, and about 50% accuracy on the CASIAV3 and CASIAV4 datasets when no machine learning is employed. Extensive benchmarking results also indicate that the proposed system boosts the performance of conventional systems by at least around 40% in terms of accuracy in the absence of machine learning. However, in the presence of machine learning, experimental results show that the proposed method with DL achieves an accuracy of up to 100% for MMU1, MMU2, CASIAV3, and CASIAV4 datasets.

Advanced Dipper-Throated Meta-Heuristic Optimization Algorithm for Digital Image Watermarking

Recently, piracy and copyright violations of digital content have become major concerns as computer science has advanced. In order to prevent unauthorized usage of content, digital watermarking is usually employed. This work proposes a new approach to digital image watermarking that makes use of the discrete cosine transform (DCT), discrete wavelet transform (DWT), dipper-throated optimization (DTO), and stochastic fractal search (SFS) algorithms. The proposed approach involves computing the discrete wavelet transform (DWT) on the cover image to extract its sub-components, followed by the performance of a discrete cosine transform (DCT) to convert these sub-components into the frequency domain. Finding the best scale factor for watermarking is a significant challenge in most watermarking methods. The authors used an advanced optimization algorithm, which is referred to as DTOSFS, to determine the best two parameters—namely, the scaling factor and embedding coefficient—to be used while inserting a watermark into a cover image. Using the optimal values of these parameters, a watermark image can be inserted into a cover image more efficiently. The suggested approach is evaluated in comparison with the current gold standard. The normalized cross-correlation (NCC), peak-signal-to-noise ratio (PSNR), and image fidelity (IF) are used to measure the success of the proposed approach. In addition, a statistical analysis is performed to evaluate the significance and superiority of the proposed approach. The experimental results confirm the effectiveness of the proposed approach in improving upon standard watermarking methods based on the DWT and DCT. Moreover, a set of attacks is considered to study the robustness of the proposed approach, and the results confirm the expected outcomes. It is shown by the achieved results that the proposed approach can be utilized for practical digital image watermarking, and that it significantly outperforms other digital image watermarking methods.

CONNECTEDNESS BETWEEN CRUDE OIL AND US EQUITIES: THE IMPACT OF THE COVID-19 PANDEMIC

This paper contributes to the literature by employing a multifractal cross-correlation analysis (MFCCA) to study the effect of the global COVID-19 pandemic on cross-correlations between oil and US equity markets. First, we examine the detrended moving average cross-correlation coefficient between oil and S&amp;P 500 returns before and during COVID-19 and find that US stock markets became more correlated with oil during the pandemic in the long term. Second, we find that the pandemic has caused an increase in the long-range cross-correlations over the small fluctuations. Third, the MF-DCCA method shows that the pandemic caused an increase in cross-correlations between the two markets. In sum, the pandemic caused a closer correlation between oil and US equities in the long range and a deeper dynamic connection between oil and US equity markets, as indicated by the multifractality tests. We also investigate the connectedness between oil and the S&amp;P 500 using a dynamic procedure based on time-varying parameter vector autoregression. We find that oil is a net transmitter of shocks to the forecast error variance of the S&amp;P 500 during March, April and May 2020, whereas the S&amp;P 500 is a net transmitter of shocks to oil variance early in the pandemic (January and February 2020).

System requirements to improve adaptive 4-dimensional computed tomography (4D CT) imaging

Four-Dimensional Computed Tomography (4D CT) is of increasing importance in stereotactic body radiotherapy (SBRT) treatments affected by respiratory motion. However, 4D CT images are commonly impacted by irregular breathing, causing image artifacts that can propagate through to treatment, negatively effecting local control. REspiratory Adaptive CT (REACT) is a real-time gating method demonstrated to reduce motion artifacts by avoiding imaging during irregular respiration. Despite artifact reduction seen through in silico and clinical phantom-based studies, REACT has not been able to remove all artifacts. Here, we explore several hardware and software latencies (gantry rotation time, couch shifts, acquisition delays and phase calculation method) inherently linked to REACT and 4D CT in general and investigate their contribution to artifacts beyond those caused by irregular breathing. Imaging was simulated using the digital extended cardiac-torso (XCAT) phantom for fifty patient-measured respiratory traces. Imaging protocols included conventional cine 4D CT and five REACT scans with systematically varied parameters to test the effect of different latencies on artifacts. Artifacts were quantified by comparing the image normalized cross correlation across couch transition points and determining the volume error compared to a static phantom ground truth both as a total error and individually across pixel rows in the main plane of motion. Artifacts were determined for each lung, the whole heart and lung tumour and were compared back to conventional 4D CT and REACT with standard clinical scanning parameters. The gantry rotation time and acquisition delay were found to have the largest impact on reducing image artifacts and should be the focus of future development. The phase calculation method was also found to influence motion artifacts and should potentially be assessed on a patient-to-patient basis. Finally, the correlation between an increase in artifacts and baseline drift suggests that longer scan times allowing drift to occur may impact image quality.