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The micropulse multibeam photon-counting laser altimeter significantly improves the sampling density along the orbit while introducing abundant noise. Therefore, noise removal is crucial for the subsequent applications of the photon-counting laser altimeter. This study proposes a new multidimensional statistics algorithm based on the Gaussian mixture model for photon noise removal. The method can simultaneously utilize multiple point cloud statistics and avoid manually selecting denoising thresholds, dividing signals and noise adaptively. To evaluate the performance of the algorithm, it is applied to both simulated ICESat-2 data and real ICESat-2 data, and evaluation indicators including recall, precision, and the harmonic means of recall and precision are used for analysis. Experimental results indicate that compared to denoising using a single statistic, denoising with multidimensional statistics (specifically, the sum of the distance of k-nearest neighbors, the photon density, and the standard deviation of the height difference within an elliptic neighborhood of the target photon) generally yields better results. Multidimensional statistics can influence and constrain each other, leading to improved decision-making outcomes. After comparing the algorithm with mainstream DBSCAN and KNN methods, the results indicate that the GMM method achieves the best denoising results, with average F-values 5.22% and 7.26% higher than those of DBSCAN and KNN methods, respectively, across all datasets. In conclusion, this algorithm can comprehensively assess and make decisions based on different statistical measures, demonstrating robustness and adaptability in extracting signal photons, and is worth promoting in the photon denoising applications.

Constant false alarm rate (CFAR) detector is a common method for ship detection in polarimetric synthetic aperture radar (PolSAR) images. CFAR detectors greatly depend on the clutter modeling that can be easily affected by the contamination caused by both lower- and higher-intensity outliers, such as spilled oil and intensive targets. Traditional CFAR detectors perform detection in a pixel-by-pixel manner, which ignores the spatial information. Both the bias in clutter modeling and the absence of spatial information can degrade the ship target detection performance. In this study, a superpixel-level polarimetric bilateral truncated statistics CFAR detector is proposed to promote the ship target detection performance in complex ocean scenarios. As the preprocessing of the PolSAR image, the superpixel segmentation is conducted based on the multilook polarimetric whitening filter result to select candidate ship target superpixels for bilateral truncation and background clutter modeling. The elliptical truncation is expanded to a complex situation and the relationship between the second moments before and after truncation is derived. The maximum-likelihood estimation estimator of the equivalent number of looks based on the bilateral truncation distribution is derived and compared with other parameter estimators. The influence of the truncation depth on estimator performance is analyzed, according to which the adaptive bilateral truncation method is determined. The Gaussian mixture model and the Parzen window kernel method are compared with the model-based method and utilized for data fitting. The proposed method performs bilateral truncation based on the superpixel segmentation result to provide pure clutter samples for accurate parameter estimation and clutter distribution modeling, reducing time consumption and false alarms. The method is validated efficient on both simulated and measured data from RADARSAT-2.

We study the normalized field statistics inside nested reverberation chambers (NRCs), through the analysis of the shielding effectiveness of enclosures and material samples. The accurate knowledge of field statistics allows to improve the estimation of probability distributions for high field values inside electrically large enclosures. Furthermore, the study allows for better selection of the measurement model for both shielding effectiveness of enclosures and materials and gaskets. We find the correct procedures to achieve such distributions. We study basic cases of single and electrically small coupling apertures filtering single or double field components and we show the theoretical statistics of the field components inside NRCs. The change of inner field distributions is driven by the electrical size of coupling apertures and has been verified experimentally. The theory developed to obtain such statistics is based upon physics and the results are supported by measurements. The procedures and results shown in this paper are applicable to predict the statistics in NRCs as well as in adjacent RCs.

Circular statistics have been developed and used in a wide range of branches of knowledge related to the study and observation of the Earth, such as physical, geographical, ecological, and biological sciences. Both to facilitate its use by a less specialised community and to extend its analysis within the research community, this work presents the application of circular statistics in QGIS, an open-source Geographic Information Systems, through the design and implementation of a novel and complete plugin, named QCircularStats. This free and open-source plugin provides a comprehensive, understandable, and visual way to conduct circular analysis over remote sensing data. The behaviour of the proposed QCircularStats is evaluated over real satellite data. Particularly, data on wind speed and direction over the oceans collected by the microwave sensor allocated on the QuikSCAT satellite have been considered. Results obtained demonstrate the performance, functionality, and versatility of the implemented plugin.

High order statistics are useful for automatic modulation recognition and parameter estimations. In this paper, we cast the problem of recovering high order statistics of PSK signals taken from nonuniform compressive samples as a one of recovering a low-rank matrix from missing or corrupted observations. This is a new model to describe the high order statistics of PSK signals. Unlike traditional uniformly Nyquist samples, our method uses the advanced optimization technique, which is guaranteed to find the low-rank matrix by simultaneously fixing the missing entries. Simulation results demonstrate that our method achieves accurate estimates of the major portion of the high order statistics. The new technique can be used to fulfil automatic modulation recognition (AMR) and rough estimations of parameters. More specifically, low-rank structure of PSK signals is studied. In contrast to the existing $l_{1}$ optimization criteria, our method proposed here is computationally more efficient and provides high accuracy.

Image dehazing is a crucial task in the early stages of real-time image processing pipelines used in applications such as surveillance and advance driver assistance systems. Dehazing algorithms mitigate the spatially selective degradation of image details caused by natural phenomenon such as fog and sandstorms. The problem is exacerbated in the presence of color cast which can affect the color-sensitive processing in the downstream tasks. In order to correct these two aberrations, we have proposed a light-weight algorithm which is not only quantitively more effective than the state-of-the-art works but also uses minimal computational resources. Specifically, it has been proposed to tackle both aforementioned problems by processing luminance and chrominance channels separately through a custom resource-efficient colorspace transform. Moreover, it has been proposed to employ online calculation of the relevant video stream statistics to estimate the degradation model over several temporally adjacent frames. This approach not only reduces the hardware resource utilization when implemented inside the video processing pipeline but also reduces the flicker effect observed when frames are processed individually. It has been demonstrated through quantitative analysis on standard datasets that the proposed approach either works at par or better than the reference works in terms of image quality metrics. Furthermore, the proposed framework has been developed as a real-time video processing system on an FPGA platform. The synthesis results of this implementation suggest that the proposed framework achieves this performance using minimal logic resources.

A critical task in forensics investigation is the recovery of the manipulation history of the image under analysis. To this aim, considering a typical life-cycle of a digital image, the estimation of the misalignment occurred between consecutive JPEG compressions can be considered an useful starting point to localize forgeries and retrieve information about the camera that took the picture through first quantization matrix estimation. In this work, starting from statistics computed from the AC histograms obtained applying a third JPEG compression, an effective and robust deep learning based approach devoted to estimate the aforementioned misalignment has been designed. Finally, to assess the performance of the proposed solution a series of tests has been conducted at varying of patch sizes, quantization matrices, employed datasets, and comparisons with state-of-the-art solutions.

In the contemporary cybersecurity landscape, robust attack detection mechanisms are important for organizations. However, the current state of research in Software-Defined Networking (SDN) suffers from a notable lack of recent SDN-OpenFlow-based datasets. This study seeks to bridge this gap by introducing a novel dataset for intrusion detection in Software-Defined Networking named SDNFlow. The dataset, derived from OpenFlow statistics gathered from real traffic, integrates a comprehensive range of network activities. An empirical evaluation leveraging diverse Machine and deep Learning algorithms was performed. Namely, Logistic regression, decision tree, random forest, K-nearest neighbors, Support Vector Machines, and Multilayer Perceptron were tested getting pretty good results with a precision average of 98% to 99% in binary classification and from 97% to 99% in multiclass classification depending of the attack, we highlight the efficacy of K-Nearest Neighbors (KNN) for traffic classification, particularly in detecting DDoS attacks and port scanning. The dataset is valuable for evaluating intrusion detection systems within SDN environments and deepening the understanding of traffic patterns in Software Defined Networks.

Bearing failures are the most frequent causes of breakdowns in rotating machinery. Different levels of severity in these failures exhibit distinct fault characteristics in the vibration signal. This paper presents a bearing fault diagnosis method that considers different severity levels, involving the selection of statistics detection index symptom parameter and the application of canonical discriminant analysis (CDA). Initially, kurtosis is employed to detect abnormalities in the bearing. Subsequently, statistical analysis theory is utilized to extract efficient symptom parameters from the time domain and frequency domain vibration signals. As a statistical analysis method, CDA can discriminate between different signals by maximizing the between-group difference and minimizing the inter-group difference. By analyzing the distribution of CDA canonical scores, bearing faults can be intuitively diagnosed. The proposed method is validated using vibration signals obtained from an experimental bench with three different bearing conditions (normal, inner race fault, outer race fault) exhibiting varying severity levels. The results demonstrate the effectiveness and feasibility of diagnosing faults under different severity levels.

This paper develops an efficient channel estimation algorithm based on second-order statistics of time division duplex (TDD) multiuser massive multiple-input multiple-output (MIMO) systems. The algorithm uses the received signal correlation to determine the most significant lags (MSLs) of the received signal. We first employ these MSLs to propose a novel set containing the channel’s four most significant taps (MSTs). Then, by using them, we propose an efficient semi-blind iterative algorithm called enhanced modified-subspace pursuit (EM-SP). It uses the set mentioned above and two theoretical results (Lemma 1 and Theorem 1) to estimate an arbitrary number of MSTs efficiently. Simulation results show that the normalized mean square error (NMSE) of the proposed EM-SP algorithm is much smaller than that of the subspace pursuit (SP) and orthogonal matching pursuit (OMP) algorithms at the cost of 0.3 % and 2 % more computational complexity for the channels with three and six nonzero paths, respectively. Moreover, the NMSE of it is very close to that of the optimal genie-aided least square algorithm.

It is common for older people to live alone, which can have tragic consequences if they have an accident and can’t call for help in time. This is particularly acute in an aging society where falling is one of the most common accidents. According to the CDC, 1/4 of people over the age of 65 in the United States fall each year. The development of IoT and MEMS has made it possible to detect falls in time and automatically call for help. The presented fall detection system focuses on the walk-fall-still pattern, collects accelerations through the wrist-worn M5StickC-Plus watch, analyses the data locally in the watch, detects falls using an algorithm based on observations in the statistics of acceleration in one second, and then transmits the alarm signal to a remote healthcare system in real-time via WIFI. The lightweight algorithm has been proven to be 90% accurate in detecting falls, and the system can notify service staff of accidents within 1 second. The features of comfort, lightness, and timeliness make the device more practical than similar products. The low-cost, non-intrusive device can be used in care homes and is also suitable for elderly people living alone.

The United States (U.S.) is a global leader in the production and exportation of soybeans and corn. Accurate monitoring and estimation of soybean and corn yields in the U.S. is essential for improving global food security. However, there is currently a lack of publicly available spatial distribution datasets with high temporal and spatial resolution for U.S. corn and soybean yields, which hampers related research and policy-making. Therefore, in this study, we proposed a statistical downscaling framework to produce spatially explicit crop yield estimates by utilizing multisource environmental covariates and ensemble machine learning methods. We produced distribution maps of soybean and corn yields in the U.S. from 2006 to 2021 at a 1-km resolution through the optimal Cubist model, resulting in the USASoy&CornYield1km dataset. The results demonstrated stable accuracy, with R2 values for corn ranging from 0.70 to 0.89 (average of 0.80) and for soybeans ranging from 0.74 to 0.90 (average of 0.81) during the period 2006–2021. Comparison with the spatial production allocation model (SPAM) dataset further confirmed the reliability of this dataset, with correlations of 0.84 for soybeans and 0.78 for corn when compared to SPAM2010. Spatial uncertainty analysis showed that the yield estimation uncertainty was 14.04% for soybeans and 20.49% for corn, indicating a generally low level of uncertainty. Overall, the USASoy&CornYield1km dataset offers higher spatial and temporal resolution, captures yield variations within counties, and covers a long time span. This study provides significant insights for analyzing U.S. soybean and corn yields and improving agricultural production.

This work contributes to the comprehension of Bayes’ theorem inclusive Bayesian probabilities and Bayesian inferencing within the framework of STEM (Science, Technology, Engineering, Arts, and Mathematics) and cognitive learning w.r.t Bloom’s taxonomy (BT). Bayes’ theorem is taken as a crucial statistical instrument employed in the development of intelligent systems and the management of risks, commonly utilized by engineers for tasks in machine learning and managerial decision-making. The fundamental concept behind Bayes’ theorem revolves around comprehending the degree of truth within the confines of an explicit perspective. This involves partitioning the entire sample space of possible evidence and utilizing the subset containing the relevant perspective to estimate the uncertainty of an event or the reliability of a model. However, it is often found difficult for students to understand Bayes’ theorem to the level of applying it to real-world problems. Considering this, the proposed learning method in this paper elucidated the acquisition of Bayes’ mathematical formulation by leveraging computational thinking, leading to the development of a computational model. The proposed model is named the Bayesian Computational Learning Model (BCLM). Subsequently, we have probed the utility of BCLM in the design and plan of learning activities, coherent to the STEM paradigm and BT cognitive learning hierarchy.

Specialized compute blocks have been developed for efficient nn execution. However, due to the vast amount of data and parameter movements, the interconnects and on-chip memories form another bottleneck, impairing power and performance. This work addresses this bottleneck by contributing a low-power technique for edge-AI inference engines that combines overhead-free coding with a statistical analysis of the data and parameters of neural networks. Our approach reduces the power consumption of the logic, interconnect, and memory blocks used for data storage and movements by up to 80% for state-of-the-art benchmarks while providing additional power savings for the compute blocks by up to 39 %. These power improvements are achieved with no loss of accuracy and negligible hardware cost.

The performance evaluation (PE) of the Photovoltaic(PV) system is an index representing the efficiency and reliability of the system. Most PE indicators evaluate the ratio of theoretically calculated power generation to actually measured power generation. The closer the ratio is to 1, the more ideal the PV system is. PV system varies depending on weather conditions and regional characteristics, especially on the types of sensors and measuring variables. Floating Photovoltaics (FPVs) and Marine photovoltaics (MPVs) vary with the environmental variables more as it is installed on the water and sea. In this paper, on the contrary to the existing PE methods, the most accurate regression model considering ambient temperature, relative humidity, and wind speed was used to predict the power in order to improve the accuracy. The optimal PE method for the PV system to easily and accurately detect failures of the PV system is proposed. Data from three FPVs in the same environment were analyzed for 1 year. The PV power prediction model including the wind speed and relative humidity was used to improve accuracy. The quality diagnosis was performed with an improved PE and the impact of various events can be represented through this. In this paper, the distribution of the corresponding Power Performance Index (PPI) values was analyzed using a descriptive statistic method, and indicators in terms of quality control were presented. The PV power generation state can be determined by the location of the average and median values of the boxplot. The fluctuation of PV power generation was identified using the size change of Inter Quartile Range (IQR), which represents the degree of data scattering. As a result, it was confirmed that failure occurred in the system when the IQR value is 2 times bigger than the normal IQR value.

The existing approach for evaluating the statistical characteristics of speckle intensity distribution is applicable only to completely decorrelated laser beams. In the current study, a theoretical method based on a coherent matrix is developed for evaluating the first-order speckle statistics of partially correlated beams. A rigorous analytical formula for the calculation of speckle contrast is obtained based on the proposed coherent matrix method. Subsequently, an approximate analysis model of the speckle intensity distribution in a laser spot illuminated by partially correlated beams is proposed. The simulation results show that the accuracy of the approximate analysis model increases with the number of beams and is acceptable for practical application, even for a small number of beams. Applications of the proposed method are presented for analyzing two different optical schemes for speckle suppression. It is shown that the developed theoretical method is straightforward and effective for the analysis of light intensity statistics of laser spots illuminated by partially correlated beams. Therefore, the developed method can be easily generalized to imaging systems with partially correlated laser beams for evaluating the speckle intensity statistics.

The high penetration of distributed generation (DG) has set up a challenge for energy management and consequently for the monitoring and assessment of power quality (PQ). Besides, there are new types of disturbances owing to the uncontrolled connections of non-linear loads. The stochastic behaviour triggers the need for new holistic indicators which also deal with big data of PQ in terms of compression and scalability so as to extract the useful information regarding different network states and the prevailing PQ disturbances for future risk assessment and energy management systems. Permanent and continuous monitoring would guarantee the report to claim for damages and to assess the risk of PQ distortions. In this context, we propose a measurement method that postulates the use of two-dimensional (2D) diagrams based on higher-order statistics (HOSs) and a previous voltage quality index that assesses the voltage supply waveform in a continous monitoring campaign. Being suitable for both PQ and reliability applications, the results conclude that the inclusion of HOS measurements in the industrial metrological reports helps characterize the deviations of the voltage supply waveform, extracting the individual customers' pattern fingerprint, and compressing the data from both time and spatial aspects. The method allows a continuous and robust performance needed in the SG framework. Consequently, the method can be used by an average consumer as a probabilistic method to assess the risk of PQ deviations in site characterization.

Fracture modeling plays a valuable role to understand the fluid flow in carbonate reservoirs. For this, the fracture characterization to generate Discrete Fracture Networks (DFNs) can take advantage of analogue outcrops through Virtual Outcrop Models (VOMs), acquired by Unmanned Aerial Vehicles (UAV) and digital photogrammetry. The stochastic DFN generation is an important step in reservoir modeling as it brings more representative data to the process and has long been studied. However, optimizations concerning automatizing some of the steps necessary to its generation like data clustering are still open to advancements. In this sense, this work aims the fracture data clustering and the definition of the number of clusters when gathering data for the stochastic process, developing an Elbow method for spherical data and a balanced K-means, both based on Fisher statistics. For this, we interpreted fracture planes in a VOM that recreates a carbonate reservoir analogue from the Jandaíra Formation, in the Northeast, Brazil. As result, we show a workflow for immersive fracture interpretation alongside a 3D stochastic DFN model with fracture intensity of 22.57m−1 for cell sizes of 1m3. Regarding the clustering balance, our method achieved a lower standard deviation between sets while maintaining the Fisher values greater to obtain fracture sets with lower dispersion. Additionally, the Elbow method implementation proved a beneficial step to the workflow as it reduced the interpretation bias of family clusters. These results alongside the proposed workflow bring a better understanding of the outcrop geometry while offering data scalability for reservoir modeling.

An advantage of using the composite fading models (CFMs) is their ability to concurrently address the impact of multi-path and shadowing phenomena on the system performance in wireless communications. A Fisher-Snedecor (FS) $\mathcal{F}$ CFM has been recently proposed as an experimentally verified and tractable fading model that can be efficiently applied for 5G and beyond 5G wireless communication systems. This paper provides second-order (s-order) performance analysis of the product of $N$ independent but not identically distributed (i.n.i.d) FS $\mathcal{F}$ random variables (RVs). In particular, accurate and closedform approximations for level crossing rate (LCR) and average fade duration (AFD) of the product of $N$ i.n.i.d FS $\mathcal{F}(N-{\mathrm {FS}}~ \mathcal{F})$ RVs are successfully derived by exploiting a general property of a Laplace approximation method for evaluation of the $N$ -folded integral-form LCR expression. Based on the obtained s-order statistical results, the burst error rate and maximum symbol rate of the $N$ -FS $\mathcal{F}$ distribution are addressed and thoroughly examined. The numerical results of the considered performance measures are discussed in relation to the $N-\mathrm{FS}~ \mathcal{F}$ multi-path and shadowing severity parameters. Moreover, the impact of the number of hops $(N)$ of the $N$ -FS $\mathcal{F}$ CFM on the s-order metrics, the burst error rate and maximum symbol rate are numerically evaluated and investigated. The derived s-order statistical results can be used to address the cooperative relay-assisted (RA) communications for vehicular systems. Monte-Carlo $(\mathrm{M}-\mathrm{C})$ simulations for the addressed statistical measures are developed in order to confirm the provided theoretical results.

Towards customer-centric automotive systems engineering, it is essential to incorporate physical models and vehicle usage behavior into decision support systems (DSSs). Such DSSs tend to apply digital twin concepts, where simulations are parameterized with fine-grained time-series data acquired from customer fleets. However, logging vast amounts of data from customer fleets is costly and raises privacy concerns. Alternatively, these time-series data can be aggregated into vehicle usage statistics. The feasibility of creating digital twins from these vehicle usage statistics and the corresponding DSSs for systems engineering is yet to be established. This paper aims to demonstrate this feasibility by proposing a DSS framework that integrates four key elements of digital twinning: aggregate usage statistics from customer fleets, logging data from testing fleets, physical models for vehicle simulation, and evaluation models to derive decision support metrics. The digital twinning involves a four-step process: pre-processing, profiling, simulation, and post-processing. Based on a real-world fleet of 57110 vehicles and four evaluation metrics, a proof of concept is conducted. Results show that the digital twin covers the evaluation metrics of 99% of the vehicles and reaches an average fleet twinning accuracy of 91.09%, which indicates the feasibility and plausibility of the proposed DSS framework.

Deep learning has achieved satisfactory results in polarimetric synthetic aperture radar (PolSAR) image classification, which requires a large number of labeled samples for training. However, in practice, labeling work is time-consuming and laborious. As a result, an insufficient number of labeled samples will lead to a limited ability of the network to recognize different terrains. To alleviate this problem, we take advantage of labeled and unlabeled samples simultaneously to train the deep learning model and, thus, propose a semisupervised complex network with spatial statistics fusion (SCN-SSF) for PolSAR image classification. First, the semisupervised complex network (SCN) continuously updates the pseudolabels of unlabeled samples during the training of complex-valued CNN, and their errors constitute the regularization term of the objective function, which improves the generalization of the network. As a result, SCN can recognize different terrains more accurately, and the classification has a higher belief. Then, a parameter-free spatial statistics module is built to model neighborhood label interactions based on the product of experts (POEs), thus enhancing contextual smoothness and correcting some misclassifications. Finally, based on the Dempster–Shafer theory, the contextual label information of POE and pixel-level information obtained by SCN are integrated to preserve image structure. Overall, with only a small number of labeled samples, SCN-SSF can accurately identify each terrain and obtain smooth classification while preserving edge information. The effectiveness of SCN-SSF is demonstrated by classifying PolSAR images with a small number of labeled samples.

Superdirective arrays have been extensively studied because of their considerable potential accompanied, unfortunately, by a high sensitivity to random errors that affect the responses and positions of array elements. However, the statistics of their actual beam pattern (BP) has never been systematically investigated. This paper shows that the Rician probability density function (PDF), sometimes adopted to study the impact of errors in conventional arrays, is a valid approximation for superdirective BP statistics only where some mathematical terms are negligible. The paper also shows that this is the case for all linear end-fire arrays considered. A similar study is proposed concerning the correlation between BP lobes, showing that for the superdirective arrays considered the lobes, especially non-adjacent ones, are almost independent. Furthermore, knowledge of the PDF of the actual BP allows one to define quantile BP functions, whose probability of being exceeded, at any point, is fixed. Combining the lobes’ independence with quantile BP functions, an empirical equation for the probability that the entire actual BP will not exceed a quantile function over an interval larger than a given size is obtained. This new knowledge and these tools make it possible to devise new methods to design robust superdirective arrays via optimization goals with clearer and more relevant statistical meaning. [ABSTRACT FROM AUTHOR]

This article addresses the measurement of the power spectrum of red noise processes at the lowest frequencies, where the minimum acquisition time is so long that it is impossible to average on a sequence of data record. Therefore, averaging is possible only on simultaneous observation of multiple instruments. This is the case of radio astronomy, which we take as the paradigm, but examples may be found in other fields such as climatology and geodesy. We compare the Bayesian confidence interval of the red noise parameter using two estimators, the spectrum average and the cross-spectrum. While the spectrum average is widely used, the cross-spectrum using multiple instruments is rather uncommon. With two instruments, the cross-spectrum estimator leads to the Variance–Gamma distribution. A generalization to ${q}$ devices based on the Fourier transform of characteristic functions is provided, with the example of the observation of millisecond pulsars with five radio telescopes (RTs). The simulations show that the spectrum average is by a small amount more efficient than the cross-spectrum, chiefly when the background exceeds the signal. However, some notable differences between their upper limit indicate that it should be wiser to compute both estimators. [ABSTRACT FROM AUTHOR]

The space-time block code (STBC) classification has recently been recognized as a prominent fundamental tool for the development of smart communication systems, which are applied in both the defense and private sectors. The use of multiple antennas at the transmitter introduces a new dimension of difficulty to the signal classification task since it is essential to make judgments about the number and arrangement of broadcast antennas. The reported research has only addressed STBC classification over non-cooperative environments, with no solutions offered for cooperative transmissions. A comprehensive STBC classification algorithm for amplify-and-forward cooperative transmissions is proposed in this investigation. The classification procedure is predicated on analytical formulas established for the time-domain features of the STBC signals. There are two phases involved in implementing the offered algorithm into practice. The first stage includes estimating the correlation functions of the incoming signals across two different time periods using second-order statistics, and a false alarm oriented assessment is utilized in the second stage to produce a judgment. The proposed algorithm’s many benefits involve not necessitating any previous information about the modulation form, channel characteristics, signal-to-noise ratio (SNR), or the start of STBC blocks. Simulation experiments demonstrate that the proposed algorithm can give adequate classification performance in the presence of transmission imperfections even at relatively low SNR levels.

Batch Normalization (BatchNorm) is an effective architectural component in deep learning models that helps to improve model performance and speed up training. However, it has also been found to increase the vulnerability of models to adversarial attacks. In this study, we investigate the mechanism behind this vulnerability and took first steps towards a solution called RobustNorm. We observed that adversarial inputs tend to shift the distributions of the output of the BatchNorm layer, leading to inaccurate train-time statistics and increased vulnerability. Through a series of experiments on various architectures and datasets, we confirm our hypothesis. We also demonstrate the effectiveness of RobustNorm in improving the robustness of models under adversarial perturbation while maintaining the benefits of BatchNorm.

In polarimetric synthetic aperture radar (SAR) images, speckle is removed by multilooking and the local covariance matrix is the main parameter of interest. In the covariance matrix from a backscatter with reflection symmetry, the terms $\bm{S_{hh}} \bm{S}_{\bm{hv}}^{*}$, $\bm{S_{vv}} \bm{S}_{\bm{hv}}^{*}$, and their complex conjugates are 0. The backscatter from natural covers, such as fields and forested areas, is typically reflection-symmetric, as these four elements have near-zero values. The backscatter from urban areas and man-made structures is substantially different, and the backscatter from buildings not aligned with the radar line of sight usually does not have reflection symmetry. A novel block-diagonality test statistic for reflection symmetry with a constant false alarm rate property is proposed. It is compared with an approximate test built on a change detection test statistic for Wishart-distributed covariance matrices. Their use on quad-polarimetric data in different situations shows their high potential for man-made structure detection. Applied after an orientation correction of the covariance matrices, these test statistics highlight with high-contrast buildings and urban areas. We also apply this test for ship detection at sea, and show that while the results are unconvincing at X-band, it can also be applied at longer wavelengths such as L-band.

The newest video coding standard, Versatile Video Coding (VVC), adopts a QTMT, quad-tree plus multi-type tree (MTT), block partition structure and improves the compression performance by about 30% ${\sim }50$ %, compared with the previous High-Efficiency Video Coding (HEVC) one, at the cost of higher time complexity. To make practical video communication applications feasible, we have to reduce the high time complexity resulting from an exhaustive rate-distortion optimization (RDO) search procedure. We proposed to predict Coding Unit (CU) split modes by a learned model whose input comprises neighboring line pixels and quantization parameters (QP). In addition, we set thresholds based on statistical image features and coding behaviors to eliminate unnecessary coding operations in critical coding control modules. Experiments showed that, compared with the default VVC intra-coding, the proposed method saves 46.73% of encoding time, with Bjøntegaard Delta Bit Rate (BDBR) increment of 1.16%. After retraining the learned model with a specified QP, the time reduction rate can achieve 51.79%, and the BDBR slightly increases to 2.07%. The proposed speedup coding scheme effectively reduced the VVC time complexity to a large extent.

An intelligent reflecting surface (IRS) enhances the performance of a wireless channel by controlling the phase of signals reflected by them. This paper investigates an IRS-aided single input single output wireless communication system. Owing to the closely spaced placement of the IRS elements, the channels between the IRS and the transmitter, as well as the channels between the IRS and the receiver, are considered to be spatially correlated. Assuming optimal phase configuration at the IRS, correlated Nakagami- $m$ random vectors are used to model the magnitude of the channel coefficient vectors between the IRS and the transmitter as well as between the IRS and the receiver. A statistical approximation of the distribution of composite channel is obtained as a Gamma distribution. The parameters of the approximated Gamma distribution are derived in terms of the scale parameter and spread parameter of Nakagami- $m$ random vectors. The approximated Gamma distribution is validated using Monte-Carlo simulations and found to be closely following the actual distribution of the composite channel coefficient. Using the approximation, closed form expressions of the performance metrics such as symbol error probability (SEP), outage probability and average channel capacity are derived and validated.

Estimating the speed of aerial user equipment (UE) is critically important to provide reliable mobility management for cellular-connected unmanned aerial vehicles (UAVs) since this can enhance the quality of service. The 3GPP standard uses the number of handovers made by a UE during a predefined time period to estimate the speed and the mobility state efficiently. In this article, we introduce an approximation to the probability mass function of handover count (HOC) as a function of a cellular-connected UAV's height and velocity, HOC measurement time window, and different ground base station (GBS) densities. Afterward, we derive the Cramer-Rao lower bound (CRLB) for the speed estimate of a UAV and also provide a simple biased estimator for its speed based on the GBS density and HOC measurement period. We show that for a low handover parameter, the biased estimator turns into a minimum variance unbiased estimator (MVUE). Using this estimator, we study the problem of detecting the mobility state of a UAV as low, medium, or high mobility as per the 3GPP specifications. Using our proposed MVUE, we also characterize the accuracy improvement in speed estimation and mobility state detection as the GBS density and the HOC measurement window increase.

Money laundering is a profound global problem. Nonetheless, there is little scientific literature on statistical and machine learning methods for anti-money laundering. In this paper, we focus on anti-money laundering in banks and provide an introduction and review of the literature. We propose a unifying terminology with two central elements: (i) client risk profiling and (ii) suspicious behavior flagging. We find that client risk profiling is characterized by diagnostics, i.e., efforts to find and explain risk factors. On the other hand, suspicious behavior flagging is characterized by non-disclosed features and hand-crafted risk indices. Finally, we discuss directions for future research. One major challenge is the need for more public data sets. This may potentially be addressed by synthetic data generation. Other possible research directions include semi-supervised and deep learning, interpretability, and fairness of the results.

In this paper, two widely used spectrum sensing algorithms, sphericity test and energy detection are considered. The statistics of sphericity test and energy detector with estimated noise power are weighted and fused to propose a mixture detector. The proposed mixture detector based on the fusion test statistics can simultaneously utilize the power information and correlation feature of the received signals. Specifically, the theoretical optimal weight is derived by maximizing the deflection coefficient of the mixture detector. Besides, the analytical expressions for the decision threshold, false alarm probability and detection probability of this algorithm are derived by moment-matching Beta approximation. Numerical examples are provided to illustrate the validity of theoretical analysis. And simulations show that the proposed mixture detector with optimal weight can improve the spectrum sensing performance compared with existing algorithms. [ABSTRACT FROM AUTHOR]

Deep sleep staging networks have reached top performance on large-scale datasets. However, these models perform poorer when training and testing on small sleep cohorts due to data inefficiency. Transferring well-trained models from large-scale datasets (source domain) to small sleep cohorts (target domain) is a promising solution but still remains challenging due to the domain-shift issue. In this work, an unsupervised domain adaptation approach, domain statistics alignment (DSA), is developed to bridge the gap between the data distribution of source and target domains. DSA adapts the source models on the target domain by modulating the domain-specific statistics of deep features stored in the Batch Normalization (BN) layers. Furthermore, we have extended DSA by introducing cross-domain statistics in each BN layer to perform DSA adaptively (AdaDSA). The proposed methods merely need the well-trained source model without access to the source data, which may be proprietary and inaccessible. DSA and AdaDSA are universally applicable to various deep sleep staging networks that have BN layers. We have validated the proposed methods by extensive experiments on two state-of-the-art deep sleep staging networks, DeepSleepNet+ and U-time. The performance was evaluated by conducting various transfer tasks on six sleep databases, including two large-scale databases, MASS and SHHS, as the source domain, four small sleep databases as the target domain. Thereinto, clinical sleep records acquired in Huashan Hospital, Shanghai, were used. The results show that both DSA and AdaDSA could significantly improve the performance of source models on target domains, providing novel insights into the domain generalization problem in sleep staging tasks.