Second-order Statistical Analysis Research Articles

RIS-Assisted Multi-Aperture FSO Communication Network for High-Speed Train: Second-Order Statistical Analysis

RIS-Assisted Multi-Aperture FSO Communication Network for High-Speed Train: Second-Order Statistical Analysis

Automatic Detection of Benign Brain Tumors Using Statistical Analysis

Tumor is an uncontrolled growth of harmful cells within the skull that raises intracranial pressure. In the field of medical research, medical picture classification is critical. Imaging plays a crucial role in the diagnosis of brain tumors. Magnetic resonance imaging is a noninvasive, 3-dimensional imaging technique that produces high-quality images. The interpretation of an image might not be completely precise and require the assistance of a second opinion, variability in diagnosis made by different doctors and even by the same doctor under different circumstances due to job load, observation accuracy for the physician, picture clarity, noise, or the physician's vision or mood. Based on the previously mentioned reasons, we have developed a computer-aided diagnosis system to aid in the identification or detection of benign tumor in brain magnetic resonance imaging scans. In the first stage of this study, image enhancement and correct segmentation processes have been conducted into the images in order to facilitate the system to give an accurate classification of brain tumor type. In the second stage, we investigated several statistical features using a technique called gray-level co-occurrence matrix. The implementation of this technique was done with software called MATLAB (Matrix Laboratory) to determine the best features for diagnosing benign tumors of the brain. Gray-level co-occurrence matrix is second-order statistical analysis that describes spatial relationships and the information about the pixel position that has a similar gray-level value; we have found that some features have a big cutoff range between normal and abnormal tissues. Classification was done using back-propagation artificial neural network. The detection findings and quantitative data analysis show that our suggested system is effective, with a detection accuracy of 99.8%.

TextureWGAN: texture preserving WGAN with multitask regularizer for computed tomography inverse problems.

This paper presents a deep learning (DL) based method called TextureWGAN. It is designed to preserve image texture while maintaining high pixel fidelity for computed tomography (CT) inverse problems. Over-smoothed images by postprocessing algorithms have been a well-known problem in the medical imaging industry. Therefore, our method tries to solve the over-smoothing problem without compromising pixel fidelity. The TextureWGAN extends from Wasserstein GAN (WGAN). The WGAN can create an image that looks like a genuine image. This aspect of the WGAN helps preserve image texture. However, an output image from the WGAN is not correlated to the corresponding ground truth image. To solve this problem, we introduce the multitask regularizer (MTR) to the WGAN framework to make a generated image highly correlated to the corresponding ground truth image so that the TextureWGAN can achieve high-level pixel fidelity. The MTR is capable of using multiple objective functions. In this research, we adopt a mean squared error (MSE) loss to maintain pixel fidelity. We also use a perception loss to improve the look and feel of result images. Furthermore, the regularization parameters in the MTR are trained along with generator network weights to maximize the performance of the TextureWGAN generator. The proposed method was evaluated in CT image reconstruction applications in addition to super-resolution and image-denoising applications. We conducted extensive qualitative and quantitative evaluations. We used PSNR and SSIM for pixel fidelity analysis and the first-order and the second-order statistical texture analysis for image texture. The results show that the TextureWGAN is more effective in preserving image texture compared with other well-known methods such as the conventional CNN and nonlocal mean filter (NLM). In addition, we demonstrate that TextureWGAN can achieve competitive pixel fidelity performance compared with CNN and NLM. The CNN with MSE loss can attain high-level pixel fidelity, but it often damages image texture. TextureWGAN can preserve image texture while maintaining pixel fidelity. The MTR is not only helpful to stabilize the TextureWGAN's generator training but also maximizes the generator performance.

Characterization of Colorectal Cancer Gross Target Volume with 18F-FDG PET/CT Image using the Second-Order Statistical Texture Analysis

Characterization of Colorectal Cancer Gross Target Volume with 18F-FDG PET/CT Image using the Second-Order Statistical Texture Analysis

Unmanned Aerial Vehicle Propagation Channel over Vegetation and Lake Areas: First- and Second-Order Statistical Analysis.

The use of unmanned aerial vehicles (UAV) to provide services such as the Internet, goods delivery, and air taxis has become a reality in recent years. The use of these aircraft requires a secure communication between the control station and the UAV, which demands the characterization of the communication channel. This paper aims to present a measurement setup using an unmanned aircraft to acquire data for the characterization of the radio frequency channel in a propagation environment with particular vegetation (Caatinga) and a lake. This paper presents the following contributions: identification of the communication channel model that best describes the characteristics of communication; characterization of the effects of large-scale fading, such as path loss and log-normal shadowing; characterization of small-scale fading (multipath and Doppler); and estimation of the aircraft speed from the identified Doppler frequency.

A full second-order statistical analysis of strictly linear and widely linear estimators with MSE and Gaussian entropy criteria

Novel physical insights are provided into second-order weight error bounds for both strictly linear (SL) and widely linear (WL) estimators for noncircular Gaussian data, under both mean square error (MSE) and Gaussian entropy criteria. This is achieved by first defining complementary weight error variances of these estimators and by further exploiting the so obtained additional degrees of freedom, related to complex noncircularity. Next, the strong uncorrelating transform (SUT) is employed for a joint diagonalization of both the input covariance matrix and the complementary covariance matrix, to allow for the boundedness of complementary weight error variances for different estimators to be addressed. Furthermore, a joint consideration of both the standard and complementary weight error variance analyses is shown to make it possible to measure weight error power distribution of these estimators over both real and imaginary data channels, an important finding which is not possible to obtain through the standard variance analysis only. Simulations in both system identification and channel estimation settings support the analysis.

Texture Based Classification of Malaria Parasites from Giemsa-Stained Thin Blood Films

Quantification of parasitemia is an important part of a microscopic malaria diagnosis. Giemsa-stained thin blood smear is the gold standard method for detecting malaria parasite enumeration. However, manual counting reveals the limitations of human inconsistency and fatigue, as well as the unreliability of accuracy and non-reproducibility. Inaccurate parasitemia affects clinical diagnosis and therapeutic procedure. Automated quantification is therefore useful to improve the performance of quantifying parasite density. In this paper, the texture-based classification approach is investigated. The methods consist of the following processes: pre-processing, segmentation, feature extraction and the classification of erythrocytes. The pre-processing is applied for image conversion and enhancement. The segmentation combines local adaptive thresholding, morphological process and watershed transform to extract red blood cells, separate touching and overlapping cells. Texture analysis is performed to establish parameters obtained from first-order, second-order and higher-order statistical analysis and wavelet transform. Two feature selection approaches, the sequential forward selection method and sequential backward elimination method, integrated with a support vector machine classifier are examined to obtain the optimal feature set for identifying the Plasmodium falciparum stages. We found that gray-level co-occurrence matrices based textural features were highly selected. The optimal feature set selected by the sequential forward selection yields lesser number of features and tends to give a higher degree of accuracy than the feature set selected by sequential backward elimination. The proposed method produces 98.87% accuracy for binary classification, 99.56% accuracy for ring stage classification, and 99.48% accuracy for tropozoite stage classification. Grey-level co-occurrence matrices based texture analysis is the dominant method compared to first-order and higher-order statistical texture analysis as well as wavelet transform.

Digital image forensic approach based on the second-order statistical analysis of CFA artifacts

The truthfulness of digital images can be evaluated by investigating the CFA artifacts introduced due to the interpolation process of the image acquisition phase. In this paper, an image tampering detection technique is proposed by exposing the CFA artifacts in difference domain through the higher-order statistical analysis based on the Markov transition probability matrix (MTPM). Firstly, the given image is re-interpolated with most commonly used four Bayer CFA patterns. The re-interpolation process is performed by using bilinear interpolation scheme for simplicity purpose. Then, the difference between the given image and its re-interpolated versions is evaluated to analyze the CFA inconsistencies. The target difference image is selected corresponding to the maximum sum which is further processed to evaluate the MTPM based second-order statistical feature. The recommended approach is assessed on different images from UCID dataset and various social networking websites based on scalar-based and SVM/machine learning based forensic detectors. The experiment results confirm that the projected method offers improved efficiency in comparison to the existing techniques based on different forgery scenarios.

Seismic stability reliability assessment of nuclear power plant’s bank revetment considering unreinforced and reinforced situations

Bank revetments are built near nuclear power plant sites and are used to protect nuclear power plants from potential damage due to incoming water, such as from flooding or large waves. However, the uncertainty of earthquake behavior can make it difficult to adequately design the bank revetments against seismic activity. In earthquake prone areas, such a situation is dangerous, especially when near bodies of water. Thus, when designing the safety of a nuclear power plant near large bodies of water, the seismic behavior of the bank revetments should be taken into consideration. This paper discusses the development and implementation of a probability density evolution method (PDEM) that can be used to predict the effect of stochastic seismic ground motion on the dynamic stability and reliability of nuclear power plant bank revetments. The probability density equation of a bank revetment is derived through performing a set of deterministic, dynamic finite element analyses and stability analyses. The analyses are supported by second-order statistical analysis of the safety factor of two levels of seismic activity: SL-1 (operational safety earthquake) and SL-2 (ultimate safety earthquake). Then, the probability information is obtained at the two levels by solving the probability density evolution equation. The results demonstrate that the required safety factor can depend on the nature of the seismic excitation. Through the above procedures, the reliability assessment of bank revetments under SL-1 and SL-2 loading can be accomplished. In a comparative case study, the seismic responses of unreinforced and reinforced bank revetments to stochastically generated seismic excitation under SL-2 levels correctly indicates that reinforcement can significantly increase the reliability of the bank revetment.

Counter JPEG Anti-Forensic Approach Based on the Second-Order Statistical Analysis

The forensic investigation of JPEG compression generally relies on the analysis of first-order statistics based on image histogram. The JPEG compression detection methods based on such methodology can be effortlessly circumvented by adopting some anti-forensic attacks. This paper presents a counter JPEG anti-forensic method by considering the second-order statistical analysis based on the co-occurrence matrices (CMs). The proposed framework comprises three stages: selection of the target difference image, evaluation of CMs, and generation of second-order statistical feature based on CMs. In the first stage, we explore the effects of dithering operation of JPEG anti-forensics by analyzing the variance inconsistencies along the diagonals. Afterward, CMs are evaluated in the second stage to highlight the effects of grainy noise introduced during the dithering operation. The third stage is devoted to generate an optimal second-order statistical feature which is fed to the SVM classifier. The experimental results based on the uncompressed color image database and BOSSBase dataset images demonstrated that the proposed forensic detector based on CM is very efficient even in the presence of anti-forensic attacks. Moreover, the experimental results also confirm the competency of the proposed method in counter median filtering and contrast enhancement anti-forensics. The proposed scheme also provides satisfactory results in detecting other image processing operations such as mean filtering, Gaussian filtering, Weiner filtering, scaling, and rotation, thereby revealing its multi-purpose nature.

A Statistical Approach for Studying the Spatio-Temporal Distribution of Geolocated Tweets in Urban Environments

An in-depth descriptive approach to the dynamics of the urban population is fundamental as a first step towards promoting effective planning and designing processes in cities. Understanding the behavioral aspects of human activities can contribute to their effective management and control. We present a framework, based on statistical methods, for studying the spatio-temporal distribution of geolocated tweets as a proxy for where and when people carry out their activities. We have evaluated our proposal by analyzing the distribution of collected geolocated tweets over a two-week period in the summer of 2017 in Lisbon, London, and Manhattan. Our proposal considers a negative binomial regression analysis for the time series of counts of tweets as a first step. We further estimate a functional principal component analysis of second-order summary statistics of the hourly spatial point patterns formed by the locations of the tweets. Finally, we find groups of hours with a similar spatial arrangement of places where humans develop their activities through hierarchical clustering over the principal scores. Social media events are found to show strong temporal trends such as seasonal variation due to the hour of the day and the day of the week in addition to autoregressive schemas. We have also identified spatio-temporal patterns of clustering, i.e., groups of hours of the day that present a similar spatial distribution of human activities.

Efficient Code for Second Order Analysis of Events on a Linear Network

We describe efficient algorithms and open-source code for the second-order statistical analysis of point events on a linear network. Typical summary statistics are adaptations of Ripley's K-function and the pair correlation function to the case of a linear network, with distance measured by the shortest path in the network. Simple implementations consume substantial time and memory. For an efficient implementation, the data structure representing the network must be economical in its use of memory, but must also enable rapid searches to be made. We have developed such an efficient implementation in C with an R interface written as an extension to the R package spatstat. The algorithms handle realistic large networks, as we demonstrate using a database of all road accidents recorded in Western Australia.

Hybrid independent component analysis (H-ICA) with simultaneous analysis of high-order and second-order statistics for industrial process monitoring

Both the Gaussian and non-Gaussian information exist simultaneously in many real industrial processes. However, the standard independent component analysis (ICA) cannot effectively explore process information when there is more than one independent component (IC) following Gaussian distribution. Besides, process may present an obvious temporal structure due to wide applications of controllers, which has not been investigated by standard ICA. To solve the above problem, the paper proposed a hybrid ICA (H-ICA) algorithm for process monitoring by concurrent analysis of both high-order and second-order statistics. The time-delayed correlation matrices are first diagonalized for robust whitening to remove the effect of the additive white noise. Then, non-Gaussian information is explored by FastICA with the utilization of higher-order statistics, and Gaussian information is extracted by analyzing the time structure information contained in second-order nonzero-delayed covariance matrices. By the hybrid analysis of high-order and second-order statistics, both non-Gaussian-distributed and Gaussian-distributed ICs are extracted. In this way, more comprehensive process information is fully investigated and analyzed for the development of process monitoring strategy. The proposed H-ICA process monitoring algorithm is verified by both a numerical example and a real thermal power plant process which illustrates its feasibility and efficacy.

Receding horizon observer for linear time-varying systems

In this paper, we present a receding horizon observer for linear time-varying systems. Our main contribution is that known deterministic input has been fully dealt with. This poses considerable challenges for recursive formulation of the filter. The suggested observer can be used in closed-loop feedback control systems. The existing finite memory filters lack this ability. First- and second-order statistical convergence analysis carried out in this paper provide an insight into the stochastic behaviour of the observer. Some examples demonstrate the utility of the proposed filter.

Analysis and Simulation of Second-Order Statistics with Modified Characteristic Function Parameters in a Multipath Fading Environment

In this paper, we analyse and implement a modified low pass filtering based characteristic function for a multipath Rayleigh fading channel. A new class of this modified filtering sequence based statistical simulation model is proposed for a Rayleigh fading environment. Comparing with the sum-of-sinusoids (SOS) deterministic model and its modifications as a new SOS model, the proposed model reintroduces the randomness to Doppler frequency and initial phase of the sinusoids to have non-deterministic simulators with desired statistical properties. The expressions and simulation results of level crossing rate (LCR) and average fade duration are derived and shown in this paper. Comparison of power spectra reveals that the spectrum spread is much less for our proposed method. Therefore, we would expect reduction in the rapidity of fading which is observed in LCR calculations.

Volcanic ash cloud detection from MODIS image based on CPIWS method

Volcanic ash cloud detection has been a difficult problem in moderate-resolution imaging spectroradiometer (MODIS) multispectral remote sensing application. Principal component analysis (PCA) and independent component analysis (ICA) are effective feature extraction methods based on second-order and higher order statistical analysis, and the support vector machine (SVM) can realize the nonlinear classification in low-dimensional space. Based on the characteristics of MODIS multispectral remote sensing image, via presenting a new volcanic ash cloud detection method, named combined PCA-ICA-weighted and SVM (CPIWS), the current study tested the real volcanic ash cloud detection cases, i.e., Sangeang Api volcanic ash cloud of 30 May 2014. Our experiments suggest that the overall accuracy and Kappa coefficient of the proposed CPIWS method reach 87.20 and 0.7958%, respectively, under certain conditions with the suitable weighted values; this has certain feasibility and practical significance.

Semivariogram Analysis of Bone Images Implemented on FPGA Architectures.

Osteoporotic fractures are a major concern for the healthcare of elderly and female populations. Early diagnosis of patients with a high risk of osteoporotic fractures can be enhanced by introducing second-order statistical analysis of bone image data using techniques such as variogram analysis. Such analysis is computationally intensive thereby creating an impediment for introduction into imaging machines found in common clinical settings. This paper investigates the fast implementation of the semivariogram algorithm, which has been proven to be effective in modeling bone strength, and should be of interest to readers in the areas of computer-aided diagnosis and quantitative image analysis. The semivariogram is a statistical measure of the spatial distribution of data, and is based on Markov Random Fields (MRFs). Semivariogram analysis is a computationally intensive algorithm that has typically seen applications in the geosciences and remote sensing areas. Recently, applications in the area of medical imaging have been investigated, resulting in the need for efficient real time implementation of the algorithm. A semi-variance, γ(h), is defined as the half of the expected squared differences of pixel values between any two data locations with a lag distance of h. Due to the need to examine each pair of pixels in the image or sub-image being processed, the base algorithm complexity for an image window with n pixels is O (n2) Field Programmable Gate Arrays (FPGAs) are an attractive solution for such demanding applications due to their parallel processing capability. FPGAs also tend to operate at relatively modest clock rates measured in a few hundreds of megahertz. This paper presents a technique for the fast computation of the semivariogram using two custom FPGA architectures. A modular architecture approach is chosen to allow for replication of processing units. This allows for high throughput due to concurrent processing of pixel pairs. The current implementation is focused on isotropic semivariogram computations only. The algorithm is benchmarked using VHDL on a Xilinx XUPV5-LX110T development Kit, which utilizes the Virtex5 FPGA. Medical image data from DXA scans are utilized for the experiments. Implementation results show that a significant advantage in computational speed is attained by the architectures with respect to implementation on a personal computer with an Intel i7 multi-core processor.

Bed Topography and Sand Transport Responses to a Step Change in Discharge and Water Depth

AbstractAt base-flow conditions, ephemeral streams with mobile beds may inherit bed topography caused by previous higher flow events, resulting in bed topography that is not in equilibrium with flow conditions. Major flow events can generate large bedforms that result in hydrodynamics and sediment transport that are not in equilibrium with subsequent lower flow conditions. To simulate a large storm runoff–driven stream flow followed by lower base-flow conditions, changes in sand bed topography and sand transport caused by a simultaneous reduction in discharge and flow depth in a laboratory flume with an equilibrium sand bed and steady, approximately uniform, flow were investigated. Bed topography was evaluated using second-order structure functions, semivariograms, and statistical analyses. Sediment transport responded rapidly to a near-instantaneous negative step in mean flow depth and discharge. The relative contributions of reduced bedform height and celerity to the decreased transport rate are quantif...

Large eddy simulation of two-way coupling sediment transport

In the present paper numerical simulations are used to investigate suspended sediment transport and its effect on the dynamics of the turbulent boundary layer. We use an Euler–Euler methodology based on single-phase approach. Large eddy simulation is employed to resolve the large scales of motion, whereas the contribution of the small scales is parametrized by the use of a dynamic Smagorinsky model. In order to account for sediment-induced buoyancy on momentum, a buoyancy term is considered in the three-dimensional Navier–Stokes equations through the use of the Boussinesq approximation. We consider four sediment sizes and the simulations are performed for both one-way and two-way coupling approaches to gain a better description of sediment–turbulence interaction. The level of stratification for each particle size is qualified by the bulk Richardson number which increases by decreasing the grain size. The analysis reveals that the reduction of sediment size produces a larger suspension and sediment concentration in the flow field, due to the concurrence of increased available concentration at the wall and reduced deposition velocity. Comparison of concentration profiles between one-way and two-way coupling clearly shows the remarkable effect of stratification on the velocity and concentration mean profiles. This is particularly true for small sediments which are more likely suspended in the fluid column. In agreement with experimental literature results, our study shows that suspended sediment concentration reduces the von Kàrmàn constant of the velocity profile. The analysis of second order statistics and energy power spectra show turbulent suppression due to stratification effects, in agreement with previous studies. The gradient Richardson number distribution along the channel height demonstrates the increased level of stratification along the fluid column by decreasing the grain size. Momentum and concentration diffusivity are also discussed. The non-dimensional concentration diffusivity compares very well with the Coleman’s experimental data in the range of parameters shared by the two studies. Overall, the results of our study confirm that a single-phase mathematical model is a good candidate to simulate suspended sediment transport. Our study also shows that differences between one-way and two-way coupling approaches are negligible for relatively large sediments, that, on the other hand, are more likely transported according to the bed-load mode. For smaller particles, transported according to the suspension-load mode, the two-way coupling approach reproduces the reduction of turbulence activity already observed in physical experiments.

Numerical simulation of water mixing and renewals in the Barcelona harbour area: the winter season

In the present paper, we use numerical simulation to investigate currents, mixing and water renewal in Barcelona harbour under typical conditions of wind forcing for the winter season. This site is of particular importance due to the interplay between touristic and commercial activities, requiring detailed and high-definition studies of water quality within the harbour. We use Large Eddy Simulation (LES) which directly resolves the anisotropic and energetic large scales of motion and parametrizes the small, dissipative, ones. Small-scale turbulence is modelled by the anisotropic Smagorinsky model (ASM) to be employed in presence of large cell anisotropy. The complexity of the harbour is modelled using a combination of curvilinear, structured, non-staggered grid and the immersed boundary method. Boundary conditions for wind and currents at the inlets of the port are obtained from in-situ measurements. Analysis of the numerical results is carried out based on both instantaneous and time-averaged velocity fields. First- and second-order statistics, such as turbulent kinetic energy and horizontal and vertical eddy viscosities, are calculated and their spatial distribution is discussed. The study shows the presence of intense current in the narrow and elongated part of the harbour together with sub-surface along-shore elongated rolling structures (with a time scale of a few hours), and they contribute to the vertical water mixing. Time-averaged velocity field reveals intense upwelling and downwelling zones along the walls of the harbour. The analysis of second-order statistics shows strong inhomogeneity of turbulent kinetic energy and horizontal and vertical eddy viscosities in the horizontal plane, with larger values in the regions characterized by stronger currents. The water renewal within the port is quantified for particular sub-domain regions, showing that the complexity of the harbour is such that certain in-harbour basins have a water renewal of over five days, including the yacht marina area. The LES solution compares favourably with available current-meter data. The LES solution is also compared with a RANS solution obtained in literature for the same site under the same forcing conditions, the comparison demonstrating a large sensitivity of properties to model resolution and frictional parametrization.