Small Rectangular Region Research Articles

Forecasting tectonic tremor activity using a renewal process model

In many tectonically active regions of the world, a variety of slow deformation phenomena have been discovered and collectively termed slow earthquakes. Tectonic tremor is the high-frequency component of slow earthquakes and can be analyzed to monitor the overall slow deformation process, both spatially and temporally. Although tectonic tremor activity is complex, it does possess some characteristic patterns, such as spatial segmentation, a quasi-periodic recurrence, migration, and tidal modulation. These features are helpful for forecasting future activity if they are properly modeled in a quantitative manner. Here, we propose a stochastic renewal process to standardize and forecast tectonic tremor activity in the Nankai subduction zone, southwest Japan, using a 12.5-year tremor catalog that is divided into a 10-year estimation period and 2.5-year forecasting period. We group the tremor events into small rectangular 10-km regions and observe that the distribution of inter-event times is nearly bimodal, with the short and long inter-event times representing the characteristic times of nearby tremor interactions and long-term stress accumulation processes, respectively. Therefore, as the probabilistic distribution for the renewal process, we adopt a mixture distribution of log-normal and Brownian passage time distributions for the short and long inter-event times, respectively. The model parameters are successfully estimated for 72% of the entire tremor zone using a maximum likelihood method. This standard model can be used to extract anomalous tremor activity, such as that associated with long-term slow-slip events. We derive a scaling relationship between two characteristic times, the relative plate motion, episodicity of tremor activity, and tremor duration by characterizing the spatial differences in tremor activity. We confirm that the model can forecast the occurrence of the next tremor event at a given reference time for a certain prediction interval. This study can serve as a first step for implementing more complex models to improve the space–time forecasting of slow earthquakes.

Low-profile dual-band pixelated defected ground antenna for multistandard IoT devices

A low-profile dual-band pixelated defected ground antenna has been proposed at 3.5 GHz and 5.8 GHz bands. This work presents a flexible design guide for achieving single-band and dual-band antenna using pixelated defected ground (PDG). The unique pixelated defected ground has been designed using the binary particle swarm optimization (BPSO) algorithm. Computer Simulation Technology Microwave Studio incorporated with Matlab has been utilized in the antenna design process. The PDG configuration provides freedom of exploration to achieve the desired antenna performance. Compact antenna design can be achieved by making the best use of designated design space on the defected ground (DG) plane. Further, a V-shaped transfer function based on BPSO with fast convergence allows us to efficiently implement the PDG technique. In the design procedure, pixelization is applied to a small rectangular region of the ground plane. The square pixels on the designated defected ground area of the antenna have been formed using a binary bit string, consisting of 512 bits taken during each iteration of the algorithm. The PDG method is concerned with the shape of the DG and does not rely on the geometrical dimension analysis used in traditional defected ground antennas. Initially, three single band antennas have been designed at 3.5 GHz, 5.2 GHz and 5.8 GHz using PDG technique. Finally, same PDG area has been used to design a dual-band antenna at 3.5 GHz and 5.8 GHz. The proposed antenna exhibits almost omnidirectional radiation performance with nearly 90% efficiency. It also shows dual radiation pattern property with similar patterns having different polarizations at each operational band. The antenna is fabricated on a ROGERS RO4003 substrate with 1.52 mm thickness. Reflection coefficient and radiation patterns are measured to validate its performance. The simulated and measured results of the antenna are closely correlated. The proposed antenna is suitable for different applications in Internet of Things.

Radiographic healing patterns after tooth-borne distraction in canine model.

BackgroundThe osteogenesis distraction technique applied to the craniofacial skeleton is an alternative treatment for dentofacial deformities. Despite the advantages of tooth-borne distractors, few studies have evaluated their clinical implementation in sagittal dentoskeletal deformities. This study aimed provide a radiographic assessment of the effect of two different activations of tooth-borne distraction in the lengthening of the mandible in canines. Material and MethodsTen male beagle dogs, approximately one year old, were used for this experimental study. Three remained as a control group and seven underwent a mandibular tooth-borne distraction protocol with single daily activation in one hemimandible and two daily activations in the other, during ten days. The consolidation period took 12 weeks. Occlusal radiographs were performed immediately pre- and postoperatively. ResultsAfter the distraction period, the host bone margins presented very well-defined outlines with regular contours. Concerning the consolidation period, between the second and fourth weeks, all hemimandibles showed small rectangular radiopaque regions with parallel orientation to the distraction axis. At the twelfth week, all hemimandibles presented an entire mineralization of the distraction gap with no axial deviations of the anterior and posterior host bone, nine of which with both margins showing corticalization. ConclusionsRadiographic analysis showed bone regeneration in order to achieve the original bone architecture, especially in the group of multiple distraction. Tooth-borne distraction allowed successful sagittal lengthening of the mandible in a canine model. Key words:Orthodontics, osteogenesis, distraction, mandibular advancement, orthodontic appliance design.

Modal Analysis of Acoustic Directivity in Turbulent Jets

The directivity of noise from three large-eddy simulations of turbulent jets at Mach 0.7, 0.9, and 1.5 is investigated using spectral proper orthogonal decomposition (SPOD). The most energetic patterns of acoustic radiation are extracted using the far-field pressure 2-norm. Specialization of the norm to the far field is accomplished through localized spatial weighting. Radiation patterns to specific jet inlet angles are isolated by further restricting the spatial weighting to small rectangular regions in the far field. The most energetic radiation pattern for all cases and relevant frequencies is a single superdirective acoustic beam in the downstream direction. The source region of these beams is traced back to the end of the potential core for low frequencies and the shear-layer region for higher frequencies. In the sideline direction, to low angles, the acoustic patterns consist of beams that propagate upstream or perpendicular to the jet axis. The sideline radiation patterns are found to originate from the same source locations as the dominant superdirective beams. Inspection of the SPOD modes reveals that sideline radiation is directly linked to directive downstream radiation. Within the restricted radial extent of the computational domain, these results indicate that the sources of sideline and downstream radiation are intimately linked.

Mobile Robot Path Planning in Dynamic Environment Using Voronoi Diagram and Computation Geometry Technique

This paper presents a novel path planning method for mobile robots in complex and dynamic environments using Voronoi diagram (VD) and computation geometry technique (CGT) termed, VD-CGT. An algorithm to categorize moving obstacles based on their positions, velocities, distances, and directions to ascertain their collision threat level and possible replanning decision is introduced. The initial path computation is done using morphological dilation, VD, A-star, and cubic spline algorithms. Instead of considering the entire map of the environment, CGT is used to compute a small rectangular region estimated to enclose a detected collision-threat obstacle and the current position of the robot. The roadmap is computed in the geometrical shape using VD and nodes are added to the initial roadmap nodes to compute a new path for replanning. To avoid increasing time and space requirements, these nodes are discarded before subsequent replanning is done. The results indicate better path replanning performance in complex and dynamic environments in terms of success path computation rate, path cost, time, and the number of replanning computations compared with other five popular related path planning approaches. The proposed method is efficient, and it computes safe and shortest replan path to goal with low computation time requirement. Unnecessary replanning computations are avoided which aid in reducing time and distance to get to the goal. With the performance results, the proposed method is a promising method for achieving safe, less path cost, and time in path replanning computations in complex and dynamic environments.

A robust method for segmenting pectoral muscle in mediolateral oblique (MLO) mammograms.

Accurately detecting and removing pectoral muscle areas depicting on mediolateral oblique (MLO) view mammograms are an important step to develop a computer-aided detection scheme to assess global mammographic density or tissue patterns. This study aims to develop and test a new fully automated, accurate and robust method for segmenting pectoral muscle in MLO mammograms. The new method includes the following steps. First, a small rectangular region in the top-left corner of the MLO mammogram which may contain pectoral muscle is captured and enhanced by the fractional differential method. Next, an improved iterative threshold method is applied to segment a rough binary boundary of the pectoral muscle in the small region. Then, a rough contour is fitted with the least squares method on the basis of points of the rough boundary. Last, the fitting contour is subjected to local active contour evolution to obtain the final pectoral muscle segmentation line. The method has been tested on 720 MLO mammograms. The segmentation results generated using the new scheme were evaluated by two expert mammographic radiologists using a 5-scale rating system. More than 65% were rated above scale 3. When assessing the segmentation results generated using Hough transform, morphologic thresholding methods and Unet-based model, less than 20%, 35% and 47% of segmentation results were rated above scale 3 by two radiologists, respectively. Quantitative data analysis results show that the Dice coefficient of 0.986 ± 0.005 is obtained. In addition, the mean rate of errors and Hausdorff distance between the contours detected by automated and manual segmentation are FP = 1.71 ± 3.82%, FN = 5.20 ± 3.94% and 2.75 ± 1.39mm separately. The proposed method can be used to segment the pectoral muscle in MLO mammograms with higher accuracy and robustness.

A method to calculate gravitational accelerations within discrete localized regions in the Milky Way

We present a method to calculate gravitational potential gradients within regions containing few 10s of thousands stars with known phase space coordinates. The central idea of the method is to calculate orbital arcs for each star within a given region for a certain parametrized potential (gravitational acceleration) and to assume that position of each star on its orbital arc is a random variable with a uniform probability density in time. Thereafter, by combining individual probability densities of stars it is possible to calculate the overall probability density distribution and likelihood for a given region as a function of gravitational acceleration parameters. The likelihood has a maximum if the calculated probability distribution and the observed distribution of stars in phase space are consistent. This allows us to constrain gravitational accelerations and potential gradient values. The method assumes that phases of stars are mixed within the regions where stellar orbits are calculated. We tested the method for 12 small rectangular regions within simulated disc galaxy from Gaia Wiki. Tests show that even with a rather simple acceleration form the calculated accelerations in galactic plane coincide with their true values from simulation about 5 per cent, misalignment between the calculated and true acceleration vector directions is less than 1 deg (median values). The model can be used with the Milky Way Gaia complete solution data.

HPV guided object tracking: Theoretical advances on fast pattern matching technique

Summary Pattern matching is a fundamental machine vision problem that deals with searching an object in a comparatively large scene. It can use to solve many vision problems ranging from typical human detection to searching defective parts in industrial automation. This paper reports a fast pattern matching technique which makes use of cumulative subtraction and cumulative division operations based on Image Integral model. The idea is to use both the cumulative subtraction and division operations to evaluate the image values on a very small rectangular region of the image scene as well as on the input pattern to be searched for. Image values are transformed to Haar Projection Values (HPVs) using Haar transform in order to achieve pattern matching on sliding window of the image scene. Computation of HPV needs seven arithmetic operations, including two addition and five subtraction operations, which are found to be same as that of Image Integral technique. Besides, the proposed pattern matching technique is identified as computationally effective in terms of both time and memory.

Box-Particle Implementation and Comparison of Cardinalized Probability Hypothesis Density Filter

This paper develops a box-particle implementa- tion of cardinalized probability hypothesis density filter to track multiple targets and estimate the unknown number of targets. A box particle is a random sample that occupies a small and controllable rectangular region of nonzero volume in the target state space. In box-particle filter the huge number of traditional point observations is instead by a remarkably reduced number of interval measurements. It decreases the number of particles significantly and reduces the runtime considerably. The proposed algorithm based on box-particle is able to reach a similar accuracy to a Sequential Monte Carlo cardinalized probability hy- pothesis density (SMC-CPHD) filter with much less computational costs. Not only does it propagates the PHD, but also propagates the cardinality distribution of target number. Therefore, it generates more accurate and stable instantaneous estimates of target number as well as target state than the box-particle probability hypothesis density (BP-PHD) filter does especially in dense clutter environ- ment. Comparison and analysis based on the simulations in different probability of detection and different clutter rate have been done. The effectiveness and reliability of the proposed algorithm are verified by the simulation results.

Box-particle probability hypothesis density filtering

This paper develops a novel approach for multitarget tracking, called box-particle probability hypothesis density filter (box-PHD filter). The approach is able to track multiple targets and estimates the unknown number of targets. Furthermore, it is capable of dealing with three sources of uncertainty: stochastic, set-theoretic, and data association uncertainty. The box-PHD filter reduces the number of particles significantly, which improves the runtime considerably. The small number of box-particles makes this approach attractive for distributed inference, especially when particles have to be shared over networks. A box-particle is a random sample that occupies a small and controllable rectangular region of non-zero volume. Manipulation of boxes utilizes methods from the field of interval analysis. The theoretical derivation of the box-PHD filter is presented followed by a comparative analysis with a standard sequential Monte Carlo (SMC) version of the PHD filter. To measure the performance objectively three measures are used: inclusion, volume, and the optimum subpattern assignment (OSPA) metric. Our studies suggest that the box-PHD filter reaches similar accuracy results, like an SMC-PHD filter but with considerably less computational costs. Furthermore, we can show that in the presence of strongly biased measurement the box-PHD filter even outperforms the classical SMC-PHD filter.

An Introduction to Box Particle Filtering [Lecture Notes

Resulting from the synergy between the sequential Monte Carlo (SMC) method [1] and interval analysis [2], box particle filtering is an approach that has recently emerged [3] and is aimed at solving a general class of nonlinear filtering problems. This approach is particularly appealing in practical situations involving imprecise stochastic measurements that result in very broad posterior densities. It relies on the concept of a box particle that occupies a small and controllable rectangular region having a nonzero volume in the state space. Key advantages of the box particle filter (box-PF) against the standard particle filter (PF) are its reduced computational complexity and its suitability for distributed filtering. Indeed, in some applications where the sampling importance resampling (SIR) PF may require thousands of particles to achieve accurate and reliable performance, the box-PF can reach the same level of accuracy with just a few dozen box particles. Recent developments [4] also show that a box-PF can be interpreted as a Bayes? filter approximation allowing the application of box-PF to challenging target tracking problems [5].

Bernoulli Particle/Box-Particle Filters for Detection and Tracking in the Presence of Triple Measurement Uncertainty

This work presents sequential Bayesian detection and estimation methods for nonlinear dynamic stochastic systems using measurements affected by three sources of uncertainty: stochastic, set-theoretic and data association uncertainty. Following Mahler's framework for information fusion, the paper develops the optimal Bayes filter for this problem in the form of the Bernoulli filter for interval measurements. Two numerical implementations of the optimal filter are developed. The first is the Bernoulli particle filter (PF), which turns out to require a large number of particles in order to achieve a satisfactory performance. For the sake of reduction in the number of particles, the paper also develops an implementation based on box particles, referred to as the Bernoulli Box-PF. A box particle is a random sample that occupies a small and controllable rectangular region of nonzero volume in the target state space. Manipulation of boxes utilizes the methods of interval analysis. The two implementations are compared numerically and found to perform remarkably well: the target is reliably detected and the posterior probability density function of the target state is estimated accurately. The Bernoulli Box-PF, however, when designed carefully, is computationally more efficient.

Method of Passive Image Based Crater Autonomous Detection

Impact craters are commonly found on the surface of planets, satellites, asteroids, and other solar system bodies. The application field of crater detection algorithm ranges from estimation of planetary surface age to autonomous landing on planets and advanced statistical analyses. this article introduced a method of passive image based crater autonomous detection. Candidate area, is defined as a small rectangular region including craters. The criterion to select a candidate area is there being one or a few craters in it. Then after a brief discussion of pre-processing of crater candidate area to obtain edge information of craters, comes a description of the theory of chord midpoint Hough transform and the process of crater detection. The article analyzes the reason behind the production of false center points of circularity as well. Experiment evidences the viability of discovering relatively large craters with clear edges.

Structural Basis for Pattern Recognition by the Receptor for Advanced Glycation End Products (RAGE)

The receptor for advanced glycated end products (RAGE) is a multiligand receptor that is implicated in the pathogenesis of various diseases, including diabetic complications, neurodegenerative disorders, and inflammatory responses. The ability of RAGE to recognize advanced glycated end products (AGEs) formed by nonenzymatic glycoxidation of cellular proteins places RAGE in the category of pattern recognition receptors. The structural mechanism of AGE recognition was an enigma due to the diversity of chemical structures found in AGE-modified proteins. Here, using NMR spectroscopy we showed that the immunoglobulin V-type domain of RAGE is responsible for recognizing various classes of AGEs. Three distinct surfaces of the V domain were identified to mediate AGE-V domain interactions. They are located in the positively charged areas of the V domain. The first interaction surface consists of strand C and loop CC ', the second interaction surface consists of strand C ', strand F, and loop FG, and the third interaction surface consists of strand A ' and loop EF. The secondary structure elements of the interaction surfaces exhibit significant flexibility on the ms-micros time scale. Despite highly specific AGE-V domain interactions, the binding affinity of AGEs for an isolated V domain is low, approximately 10 microm. Using in-cell fluorescence resonance energy transfer we show that RAGE is a constitutive oligomer on the plasma membrane. We propose that constitutive oligomerization of RAGE is responsible for recognizing patterns of AGE-modified proteins with affinities less than 100 nm.

Compensating for camera translation in video eye-movement recordings by tracking a representative landmark selected automatically by a genetic algorithm

It is common in oculomotor and vestibular research to use video or still cameras to acquire data on eye movements. Unfortunately, such data are often contaminated by unwanted motion of the face relative to the camera, especially during experiments in dynamic motion environments. We develop a method for estimating the motion of a camera relative to a highly deformable surface, specifically the movement of a camera relative to the face and eyes. A small rectangular region of interest (ROI) on the face is automatically selected and tracked throughout a set of video frames as a measure of vertical camera translation. The specific goal is to present a process based on a genetic algorithm that selects a suitable ROI for tracking: one whose translation within the camera image accurately matches the actual relative motion of the camera. We find that co-correlation, a statistic describing the time series of a large group of ROIs, predicts the accuracy of the ROIs, and can be used to select the best ROI from a group. After the genetic algorithm finds the best ROIs from a group, it uses recombination to form a new generation of ROIs that inherit properties of the ROIs from the previous generation. We show that the algorithm can select an ROI that will estimate camera translation and determine the direction that the eye is looking with an average accuracy of 0.75°, even with camera translations of 2.5 mm at a viewing distance of 120 mm, which would cause an error of 11° without correction.

Evolutionary Segmentation of Texture Image Using Perturbation Optimization

Texture segmentation methods using split and merge, pyramid node linking, or active contour model, et al. has been proposed. However, they need to tune parameters for the optimum segmentation of every image querying. It is difficult to fix a parameter set for all the query images. To solve this problem, a powerful method using simple genetic algorithm (SGA) is proposed. Segmentation results do not vary according to a tuned parameter set for the evolutionary algorithm. But, since it takes the global and parallel optimization approach on an image plane, it cannot search the optimum solution efficiently. In this paper, for efficient optimization, we propose a new evolutionary segmentation method of texture image using perturbation optimization. After dividing the original image into small rectangular regions, we extract feature vectors from small regions. We search the candidate of cluster numbers for each small region by perturbating the cluster number into another one and by inspecting the change of the fitness for each perturbed number. The combination of cluster numbers is optimized locally and successively on an image plane by the perturbation optimization. This brings us the enhancement of segmentation accuracy and the curtailment of processing time compared with the method using SGA.

Calibration Free Virtual Display System Using Video Projector onto Real Object Surface

In this paper, we propose a novel virtual display system for a real object surface by using a video projector, so that the viewer can feel as if digital images are printed on the real surface with arbitrary shape. This system consists of an uncalibrated camera and video projector connected to a same PC and creates a virtual object by rendering 2D contents preserved beforehand onto a white object in a real world via a projector. For geometry registration between the rendered image and the object surface correctly, we regard the object surface as a set of a number of small rectangular regions and perform geometry registration by calculating homographies between the projector image plane and the each divided regions. By using such a homography-based method, we can avoid calibration of a camera and a projector that is necessary in a conventional method. In this system, we perform following two processes. First of all, we acquire the status of the object surface from images which capture the scene that color-coded checker patterns are projected on it and generate image rendered on it without distortion by calculating homographies. After once the projection image is generated, the rendered image can be updated if the object surface moves, or refined when it is stationary by observing the object surface. By this second process, the system always offers more accurate display. In implementation, we demonstrate our system in various conditions. This system enables it to project them as if it is printed on a real paper surface of a book. By using this system, we expect the realization of a virtual museum or other industrial application.

A New and Interesting Class of Limit Cycles in Recursive Digital Filters

Limit cycles oscillations often occur in recursive digital filters due to the quantization of products in the feedback section, A new and interesting class of limit cycles has been discovered and categorized for second-order sections that round either sign-magnitude or twoscomplement products. These limit cycles are named rolling-pin limit cycles. They are completely defined by three integers and a simple construction rule and exist for B <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> − B <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> pairs lying within small rectangular regions in the B <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> − B <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> (coefficient) plane. Each set of integers completely defines the peak amplitude, the length, and the region of existence. The amplitude of these limit cycles can be made close to but does not exceed three times the Jackson peak estimate. Rolling-pin limit cycles often occur in filters with high Q poles located near dc or half the sampling frequency. When these large amplitude limit cycles occur, the idle channel performance of a filter is often unacceptable. Specialized techniques, requiring extra circuitry, can be used to suppress them. Alternatively, it may be less costly and more efficient to avoid the small rectangular regions within which the rolling-pin limit cycles exist in the B <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> − B <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> coefficient plane.