Spatial Region Of Interest Research Articles

Recent Technological Advances in Spatial Active Noise Control Systems

This article provides a broad overview of the recent advances in the field of active noise control techniques to reduce unwanted noise over a certain spatial region of interest. By both commercial and technological advances in local active noise control systems extending the size of the quiet zone seems to be a crucial step to develop the next generation of active control systems for a more personalized and quieter audio products. In this review article, the advances over the past decade in design and development of spatial active noise control techniques to enlarge the controlled sound zone is reviewed. The focus is specifically on the adaptive control techniques and the methods proposed in the frequency domain to control the sound field. The study has paid a specific attention into the most important performance measures in designing a spatial active noise control system such as convergence rate, stability and robustness of the algorithm, and the size of quite zone and how it can be enlarged by configuring the loudspeaker and microphone array geometries. Finally, the authors will discuss the current and future challenges that should be overcome to improve the effectiveness of the recently proposed methods to expand the silence zone.

Integrated Waveform Design for MIMO Radar and Communication via Spatio-Spectral Modulation

This paper considers the integrated waveform design to simultaneously achieve a desired radar beampattern and multi-users communication for a dual-function Multiple-Input Multiple-Output (MIMO) system. To this end, a spatio-spectral modulation strategy via shaping the spatial waveform Energy Spectral Density (ESD) in directions of communication is proposed for the communication function, while beampattern Integrated Sidelobe Level (ISL) is minimized to enhance radar detectability. Meanwhile, Peak-to-Average Ratio (PAR) and power restrictions to comply with the current hardware technique and the mainlobe width constraint to cohere the beampattern main energy on the spatial region of interest are forced, respectively. Exploiting an equivalent reformulation of the original non-convex optimization problem, a Sequential Block Enhancement (SBE) framework that alternately updates each waveform in each emitting antenna is developed to monotonically decrease ISL. Each block involves the Dinkelbach’s procedure, sequential convex approximation and Alternating Direction Method of Multipliers (ADMM) to obtain single waveform, while the analytic proof with the converged block being a Karush-Kuhn-Tucker (KKT) point is provided. Finally, numerical results highlight the effectiveness of both the proposed dual function scheme and the waveform synthesis technique in comparison with some counterparts.

Does spatial region of interest (ROI) matter in multispectral and hyperspectral imaging of segmented wheat kernels?

Advances in optics technology and computational processing have brought multispectral and hyperspectral imaging to commercial sorting of fruits and vegetables, yet the application of imaging to single cereal seeds has lagged due to the enormity in numbers of seeds and challenges posed by lighting, shadowing, and seed curvature that are less problematic with larger objects. This study examined the effect of region of interest (ROI) size on the seed surface with respect to the ability to sort seed into accept and reject categories. Regions of interest (ROI) size ranged from 5 centrally located pixels arranged in a cross to all pixels (typically 100) contained in the viewed surface of a kernel. Two modeling structures were used; the first involving all 87 samples, with approximately 220 kernels per sample, in which mixture level of sound and fusarium-damaged kernels is known, but individual kernel class is unknown; and the second involving 5 samples, of which an equal number of 287 known sound and known fusarium-damaged kernels were used. Accordingly, the larger set model characterised the dispersion (by standard deviation) of kernel-to-kernel reflectance at a single representative wavelength, while the smaller set was used to develop linear discriminant analysis classification models using one to three wavelengths. With either case, it was found that the smaller ROIs should be sufficient for a two-class (accepts, rejects) structure. • Hyperspectral imaging of cereal seeds for sorting is gaining popularity. • Seed curvature poses challenges to reflection measurements. • For accept/reject sorting, region of interest may be smaller than the viewed seed.

Beampattern Synthesis via First-Order Iterative Convex Approximation

This letter deals with a shaped beampattern synthesis problem through controlling the mainlobe ripple and the output filter energy based on the minimization of the peak sidelobe level in the spatial region of interest. To tackle the challenging optimization problem with nonconvex constraints, a novel first-order iterative convex approximation (FOICA) procedure that handles the nonconvex constraints with their convex upper bound functions resorting to the first-order Taylor expansion is developed. Hence, in each iteration, the original problem is approximated through a convex problem globally solved via a polynomial time method. Additionally, a nonnegative slack variable to ensure a decreasing trend in the objective function during the iteration is introduced. Finally, the numerical simulations are provided to verify that the proposed algorithm outperforms the state-of-the-art algorithms in terms of achieved beampattern performance.

Dot-Shaped 3D Range-Angle Dependent Beamforming With Discular Frequency Diverse Array

A frequency diverse array (FDA) concept has drawn substantial attention in recent years because of its particular range-angle-dependent beam pattern. This interesting feature promises new opportunities and capabilities for antenna array applications, especially in beamforming. The dot-shaped beam pattern is also a new and exceptional phenomenon that can focus the transmit energy in a 3-D spatial region of interest. However, almost all of the works and studies in the literature with this title is related to the linear array geometry and involves range and one angle only. Therefore, the beam defined as dot-shaped is actually only 2-D and extends in one angle, thus breaking the dot shape. Moreover, to obtain such a beam shape, complex mathematical techniques and algorithms are used. In this study, a real 3-D dot-shaped beamforming is realized with discular FDA (DFDA) and outward radial frequency offsetting scheme. This study extends the research geometries of frequency diversity concept further by investigating the discular geometry in-depth. The theory, analysis, and the basic beam-steering properties of the DFDA is presented and compared with other geometries.

The Concept of AI-Based Algorithm: Analysis of CEUS Images and HSPs for Identification of Early Parenchymal Changes in Severe Acute Pancreatitis

(1) Background: Identifying early pancreas parenchymal changes remains a challenging radiologic diagnostic task. In this study, we hypothesized that applying artificial intelligence (AI) to contrast-enhanced ultrasound (CEUS) along with measurement of Heat Shock Protein (HSP)-70 levels could improve detection of early pancreatic necrosis in acute pancreatitis. (2) Methods: Acute pancreatitis $(n=146)$ and age- and sex matched healthy controls $(n=50)$ were enrolled in the study. The severity of acute pancreatitis was determined according to the revised Atlanta classification. The selected severe acute pancreatitis (AP) patient and an age/sex-matched healthy control were analysed for the algorithm initiation. Peripheral blood samples from the pancreatitis patient were collected on admission and HSP-70 levels were measured using ELISA. A CEUS device acquired multiple mechanical index contrast-specific mode images. Manual contour selection of the two-dimensional (2D) spatial region of interest (ROI) followed by calculations of the set of quantitative parameters. Image processing calculations and extraction of quantitative parameters from the CEUS diagnostic images were performed using algorithms implemented in the MATLAB software. (3) Results: Serum HSP-70 levels were 100.246 ng/ml (mean 76.4 ng/ml) at the time of the acute pancreatitis diagnosis. The CEUS Peek value was higher (155.5) and the mean transit time was longer (40.1 s) for healthy pancreas than in parenchyma affected by necrosis (46.5 and 34.6 s, respectively). (4) Conclusions: The extracted quantitative parameters and HSP-70 biochemical changes are suitable to be used further for AI-based classification of pancreas pathology cases and automatic estimation of pancreatic necrosis in AP.

Process-based upscaling of reactive flow in geological formations

Recently, there is an increased interest in reactive flow in porous media, in groundwater, agricultural and fuel recovery applications. Reactive flow modeling involves vastly different reaction rates, i.e., differing by many orders of magnitude. Solving the ensuing model equations can be computationally intensive. Categorizing reactions according to their speeds makes it possible to greatly simplify the relevant model equations. Indeed some reactions proceed so slow that they can be disregarded. Other reactions occur so fast that they are well described by thermodynamic equilibrium in the time and spatial region of interest. At intermediate rates kinetics needs to be taken into account. In this paper, we categorize selected reactions as slow, fast or intermediate. We model 2D radially symmetric reactive flow with a reaction-convection-diffusion equation. We show that we can subdivide the PeDaII phasespace in three regions. Region I (slow reaction); reaction can be ignored, region II (intermediate reaction); initially kinetics need to be taken into account, region III (fast reaction); all reaction takes places in a very narrow region around the injection point. We investigate these aspects for a few specific examples. We compute the location in phase space of a few selected minerals depending on salinity and temperature. We note that the conditions, e.g., salinity and temperature may be essential for assigning the reaction to the correct region in phase space. The methodology described can be applied to any mineral precipitation/decomposition problem and consequently greatly simplifies reactive flow modeling in porous media.

Motion prediction enables simulated MR-imaging of freely moving model organisms.

Magnetic resonance tomography typically applies the Fourier transform to k-space signals repeatedly acquired from a frequency encoded spatial region of interest, therefore requiring a stationary object during scanning. Any movement of the object results in phase errors in the recorded signal, leading to deformed images, phantoms, and artifacts, since the encoded information does not originate from the intended region of the object. However, if the type and magnitude of movement is known instantaneously, the scanner or the reconstruction algorithm could be adjusted to compensate for the movement, directly allowing high quality imaging with non-stationary objects. This would be an enormous boon to studies that tie cell metabolomics to spontaneous organism behaviour, eliminating the stress otherwise necessitated by restraining measures such as anesthesia or clamping. In the present theoretical study, we use a phantom of the animal model C. elegans to examine the feasibility to automatically predict its movement and position, and to evaluate the impact of movement prediction, within a sufficiently long time horizon, on image reconstruction. For this purpose, we use automated image processing to annotate body parts in freely moving C. elegans, and predict their path of movement. We further introduce an MRI simulation platform based on bright field videos of the moving worm, combined with a stack of high resolution transmission electron microscope (TEM) slice images as virtual high resolution phantoms. A phantom provides an indication of the spatial distribution of signal-generating nuclei on a particular imaging slice. We show that adjustment of the scanning to the predicted movements strongly reduces distortions in the resulting image, opening the door for implementation in a high-resolution NMR scanner.

Analysis of Long Molecular Dynamics Simulations Using Interactive Focus+Context Visualization

AbstractAnalyzing molecular dynamics (MD) simulations is a key aspect to understand protein dynamics and function. With increasing computational power, it is now possible to generate very long and complex simulations, which are cumbersome to explore using traditional 3D animations of protein movements. Guided by requirements derived from multiple focus groups with protein engineering experts, we designed and developed a novel interactive visual analysis approach for long and crowded MD simulations. In this approach, we link a dynamic 3D focus+context visualization with a 2D chart of time series data to guide the detection and navigation towards important spatio‐temporal events. The 3D visualization renders elements of interest in more detail and increases the temporal resolution dependent on the time series data or the spatial region of interest. In case studies with different MD simulation data sets and research questions, we found that the proposed visual analysis approach facilitates exploratory analysis to generate, confirm, or reject hypotheses about causalities. Finally, we derived design guidelines for interactive visual analysis of complex MD simulation data.

Fusion Based on Geostatistics to Improve the Altimetry Accuracies of Digital Elevation Models

Fusions based on geostatistical methods are used in this article to improve the accuracy of the altimetry attribute of Digital Elevation Models (DEMs). Ordinary kriging, kriging with external drift, regression kriging and cokriging procedures, are applied to assess uncertainty representations from which it is possible to get altimetry predictions and other information. The fusion data modeling is performed from existing DEMs, mainly available for free in the internet, and additional high accurate set of 3D sample points. Although the freeware DEMs are dense and generally have good spatial distributions, the accuracy of their altimetry informationmight not be suitable for many applications. A way of mitigating this problem is to combine, in the data modeling processes, the available DEM data along with additional information coming from various other sources and having better quality. Usually, high accurate altimetry data are collected in field works, with higher cost,at specific point locations inside the spatial region of interest. In short, this work aims to integrate, through geostatistical methods, spatial elevation information of different sources, data structures and elevation accuracies to obtain better accurate DEMs. The methodology addressed in this research was applied to a casestudy in a Brazilian Southeast geographical region. Quantitative and qualitative validations were performed using an independent high accurate data set and comparisons based on DEM differences and drainage network automatic extraction. For the considered study area, the kriging with external drift and the regression kriging have led to similar quantitative and qualitative improvements, better than the co-kriging approach.

Quantification of Wellbore-Collision Probability by Novel Analytic Methods

Summary Unintentional collision between two wellbores may have serious economic and health, safety, and environmental consequences. It is therefore important to evaluate the probability of such an event in the well-planning phase and at critical stages during the drilling phase. A commonly used approach is to analyze the collision probability between two points, one in each wellbore, which are determined from geometric criteria only. This procedure may ignore point pairs with higher collision probabilities, and thereby lead to overoptimistic conclusions. Typically, the results from such methods will be accurate only for simple wellbore geometries, such as straight sections, and for position uncertainties that are highly symmetrical with respect to the wellbores. More advanced methods that overcome such limitations are impractical for general application because of high conceptual or computational complexity. This paper proposes novel analytic methods that may potentially overcome these problems. Formulae are derived for two important situations: the direct-hit (DH) and the unintentional-crossing (UC) scenarios. In both cases, the spatial region of interest is divided into carefully designed segments, such that the collision probability can be accurately evaluated for each segment. The total collision probability is then found by summing the results over all segments. The main advantage of this approach is that it gives accurate results for arbitrary well geometries and uncertainty-ellipsoid orientations. The algorithms can easily be integrated in existing software for wellbore-anticollision analysis. The paper shows examples of results that are all in good agreement with control calculations. Compared with existing methods, the proposed methods are therefore believed to represent an improvement to quantitative collision-probability analysis for both the wellbore-planning and the drilling phases.

Human Visual System-based Unequal Error Protection for Robust Video Coding

To increase the overall visual quality of the video services without increasing data rate, a human visual system-based video coding, founded on a hierarchy of the video stream in different levels of importance, is developed. Determining these importance levels takes in count three classification criteria: the position of current image in the group of images (image level), the importance of the motion vectors of macroblocks in the current image (macroblock level) and belonging or not of a pixel in a spatial region of interest (pixel level). At the end of this classification process, an interpolation between the results of the three-level selection allows to establish an index of importance for each macroblock of the image to be encoded. This index determines the type of channel coding to be applied to the corresponding macroblock. Tests have shown that the technique presented in this paper achieves better results in PSNR and SSIM (structural similarity) than an equal error protection technique.

Non-normal distribution of the top-of-atmosphere satellite optical measurements over calibration sites

ABSTRACTThis paper studies the distribution associated with the measurement of the satellite-derived top-of-atmosphere (TOA) reflectance on a pixel-to-pixel level, within a defined spatial region of interest (ROI) within a vicarious calibration target site. The study analyses the effects of the atmosphere and surface reflectance distribution spatial shape. The analysis shows that some of the contributing effects are inherently nonlinear, so produce non-normal distributions. For these non-normal distributions, the use of the mean and standard deviation alone does not allow sufficient parameterization of the distribution to capture all the information associated with the ROI reflectance measurement. Therefore, additional information concerning the distribution is required to provide a full site reflectance characterization. This additional information can be useful in establishing the sources of change in the distribution and ultimately improve the site radiometric characterization, particularly for long-term monitoring. In this study, Landsat-8 (L8) Operational Land Imager (OLI) measurements over the CEOS Libya-4 Pseudo Invariant Calibration Site (PICS) are used as a demonstration.

Local Ionospheric Modeling Using the Localized Global Ionospheric Map and Terrestrial GPS

global ionosphere maps are generated on a daily basis at CODE using data from about 200 GPS/GLONASS sites of the IGS and other institutions. The vertical total electron content is modeled in a solargeomagnetic reference frame using a spherical harmonics expansion up to degree and order 15. The spherical Slepian basis is a set of bandlimited functions which have the majority of their energy concentrated by optimization inside an arbitrarily defined region, yet remain orthogonal within the spatial region of interest. Hence, they are suitable for decomposing the spherical harmonic models into the portions that have significant strength only in the selected areas. In this study, the converted spherical harmonics to the Slepian bases were updated by the terrestrial GPS observations by use of the least-squares estimation with weighted parameters for local ionospheric modeling. Validations show that the approach adopted in this study is highly capable of yielding reliable results.

Feasibility of long-distance heart rate monitoring using transmittance photoplethysmographic imaging (PPGI).

Photoplethysmography (PPG) devices are widely used for monitoring cardiovascular function. However, these devices require skin contact, which restricts their use to at-rest short-term monitoring. Photoplethysmographic imaging (PPGI) has been recently proposed as a non-contact monitoring alternative by measuring blood pulse signals across a spatial region of interest. Existing systems operate in reflectance mode, many of which are limited to short-distance monitoring and are prone to temporal changes in ambient illumination. This paper is the first study to investigate the feasibility of long-distance non-contact cardiovascular monitoring at the supermeter level using transmittance PPGI. For this purpose, a novel PPGI system was designed at the hardware and software level. Temporally coded illumination (TCI) is proposed for ambient correction, and a signal processing pipeline is proposed for PPGI signal extraction. Experimental results show that the processing steps yielded a substantially more pulsatile PPGI signal than the raw acquired signal, resulting in statistically significant increases in correlation to ground-truth PPG in both short- and long-distance monitoring. The results support the hypothesis that long-distance heart rate monitoring is feasible using transmittance PPGI, allowing for new possibilities of monitoring cardiovascular function in a non-contact manner.

A Bayesian spatio-temporal geostatistical model with an auxiliary lattice for large datasets

When spatio-temporal datasets are large, the computational burden can lead to failures in the implementation of traditional geostatistical tools. In this pa- per, we propose a computationally efficient Bayesian hierarchical spatio-temporal model in which the spatial dependence is approximated by a Gaussian Markov random field (GMRF) while the temporal correlation is described using a vector autoregressive model. By introducing an auxiliary lattice on the spatial region of interest, the proposed method is not only able to handle irregularly spaced observa- tions in the spatial domain, but it is also able to bypass the missing data problem in a spatio-temporal process. Because the computational complexity of the proposed Markov chain Monte Carlo algorithm is of the order O(n) with n the total number of observations in space and time, our method can be used to handle very large spatio-temporal datasets with reasonable CPU times. The performance of the pro- posed model is illustrated using simulation studies and a dataset of precipitation data from the coterminous United States.

Low-Cost Eye Gaze Prediction System for Interactive Networked Video Streaming

Eye gaze is now used as a content adaptation trigger in interactive media applications, such as customized advertisement in video, and bit allocation in streaming video based on region-of-interest (ROI). The reaction time of a gaze-based networked system, however, is lower-bounded by the network round trip time (RTT). Furthermore, only low-sampling-rate gaze data is available when commonly available webcam is employed for gaze tracking. To realize responsive adaptation of media content even under non-negligible RTT and using common low-cost webcams, we propose a Hidden Markov Model (HMM) based gaze-prediction system that utilizes the visual saliency of the content being viewed. Specifically, our HMM has two states corresponding to two of human's intrinsic gaze behavioral movements, and its model parameters are derived offline via analysis of each video's visual saliency maps. Due to the strong prior of likely gaze locations offered by saliency information, accurate runtime gaze prediction is possible even under large RTT and using common webcam. We demonstrate the applicability of our low-cost gaze prediction system by focusing on ROI-based bit allocation for networked video streaming. To reduce transmission rate of a video stream without degrading viewer's perceived visual quality, we allocate more bits to encode the viewer's current spatial ROI, while devoting fewer bits in other spatial regions. The challenge lies in overcoming the delay between the time a viewer's ROI is detected by gaze tracking, to the time the effected video is encoded, delivered and displayed at the viewer's terminal. To this end, we use our proposed low-cost gaze prediction system to predict future eye gaze locations, so that optimized bit allocation can be performed for future frames. Through extensive subjective testing, we show that bit-rate can be reduced by up to 29% without noticeable visual quality degradation when RTT is as high as 200 ms.

Spectral and spatial decomposition of lithospheric magnetic field models using spherical Slepian functions

Global magnetic field models are typically expressed as spherical-harmonic expansion coefficients. Slepian functions are linear combinations of spherical harmonics that produce new basis functions, which vanish approximately outside chosen geographical boundaries but also remain orthogonal within the spatial region of interest. Hence, they are suitable for decomposing spherical-harmonic models into portions that have significant magnetic field strength only in selected areas. Slepian functions are spatio-spectrally concentrated, balancing spatial bias and spectral leakage. Here, we employ them as a basis to decompose the global lithospheric magnetic field model MF7 up to degree and order 72, into two distinct regions. One of the resultant fields is concentrated within the ensemble of continental domains, and the other is localized over its complement, the oceans. Our procedure neatly divides the spectral power at each harmonic degree into two parts. The field over the continents dominates the overall crustal magnetic field, and each region has a distinct power-spectral signature. The oceanic power spectrum is approximately flat, while that of the continental region shows increasing power as the spherical-harmonic degree increases.We provide a further breakdown of the field into smaller, non-overlapping continental and oceanic regions, and speculate on the source of the variability in their spectral signatures.

Feedback flow control employing local dynamical modelling with wavelets

In this paper, we utilize wavelet transform to obtain dynamical models describing the behaviour of fluid flow in a local spatial region of interest. First, snapshots of the flow are obtained from experiments or from computational fluid dynamics (CFD) simulations of the governing equations. A wavelet family and decomposition level is selected by assessing the reconstruction success under the resulting inverse transform. The flow is then expanded onto a set of basis vectors that are constructed from the wavelet function. The wavelet coefficients associated with the basis vectors capture the time variation of the flow within the spatial region covered by the support of the basis vectors. A dynamical model is established for these coefficients by using subspace identification methods. The approach developed is applied to a sample flow configuration on a square domain where the input affects the system through the boundary conditions. It is observed that there is good agreement between CFD simulation results and the predictions of the dynamical model. A controller is designed based on the dynamical model and is seen to be successful in regulating the velocity of a given point within the region of interest.

Domain Decomposition Methodology with Robin Interface Matching Conditions for Solving Strongly Coupled Fluid-Structure Problems

The inverse energy deposition problem represents a particular subclass of the more general inverse heat conduction problem, where certain features that are associated with upstreamto- downstream spatial weighting of the temperature field diffusion pattern dominate. The present paper focuses on the case of rapid energy deposition processes, where it is shown that the influence of windage can be correlated with the extremely strong filtering of spatial and temporal structure within the associated diffusion pattern. This strong filtering tends to establish conditions where system identification, or in particular, reconstruction of detailed features of the energy source, based on data-driven inverse analysis is not well posed. Similarly, the strong filtering conditions associated with very rapid energy deposition imply consequences with respect to qualitative analysis using numerical simulations based on basic principles or the direct problem approach. That is to say, any experiment or basic theoretical information that is available concerning the coupling of energy into a spatial region of interest from a surface or interface, that is, the site of deposition, will be difficult to correlate with experimental observations of the associated energy diffusion pattern. Finally, it has been established that the primary implication of the analysis and simulations is that rapid energy deposition processes should be characterized by two distinctly separate scales for both spatial and temporal structures. The results of the analysis presented here indicate that the inverse rapid energy deposition problem requires a formulation with respect to system identification and parameterization that should be cast in terms of two separate sets of parameters. One should represent energy source characteristics on spatial and temporal scales commensurate with that of thermal diffusivity within the material. The other parameter set should represent energy source characteristics on spatial and temporal scales commensurate with those of surface phenomena.