Variable Window Size Research Articles

Non-stationary dynamics in the bouncing ball: a wavelet perspective.

The non-stationary dynamics of a bouncing ball, comprising both periodic as well as chaotic behavior, is studied through wavelet transform. The multi-scale characterization of the time series displays clear signatures of self-similarity, complex scaling behavior, and periodicity. Self-similar behavior is quantified by the generalized Hurst exponent, obtained through both wavelet based multi-fractal detrended fluctuation analysis and Fourier methods. The scale dependent variable window size of the wavelets aptly captures both the transients and non-stationary periodic behavior, including the phase synchronization of different modes. The optimal time-frequency localization of the continuous Morlet wavelet is found to delineate the scales corresponding to neutral turbulence, viscous dissipation regions, and different time varying periodic modulations.

An alternative approach to multiple testing for methylation QTL mapping reduces the proportion of falsely identified CpGs.

An increasing number of studies investigates the influence of local genetic variation on DNA methylation levels, so-called in cis methylation quantitative trait loci (meQTLs). A common multiple testing approach in genome-wide cis meQTL studies limits the false discovery rate (FDR) among all CpG-SNP pairs to 0.05 and reports on CpGs from the significant CpG-SNP pairs. However, a statistical test for each CpG is not performed, potentially increasing the proportion of CpGs falsely reported on. Here, we presented an alternative approach that properly control for multiple testing at the CpG level. We performed cis meQTL mapping for varying window sizes using publicly available single-nucleotide polymorphism (SNP) and 450 kb data, extracting the CpGs from the significant CpG-SNP pairs ([Formula: see text]). Using a new bait-and-switch simulation approach, we show that up to 50% of the CpGs found in the simulated data may be false-positive results. We present an alternative two-step multiple testing approach using the Simes and Benjamini-Hochberg procedures that does control the FDR among the CpGs, as confirmed by the bait-and-switch simulation. This approach indicates the use of window sizes in cis meQTL mapping studies that are significantly smaller than commonly adopted. Our approach to cis meQTL mapping properly controls the FDR at the CpG level, is computationally fast and can also be applied to cis eQTL studies. An examplary R script for performing the Simes procedure is available as supplementary material. e.w.van_zwet@lumc.nl or b.t.heijmans@lumc.nl Supplementary data are available at Bioinformatics online.

Human shoulder motion extraction using EMG signals

This paper suggests a joint angle extraction method for shoulder flexion movement in a sagittal body plane. Surface electromyogram (EMG) signals measured at trapezius muscle are utilized for joint angle extraction in real-time. The relationship between the shoulder motion and the measured EMG signal can be modeled using a spring-damper pendulum model. In the suggested model, the EMG signal is described as the function of the shoulder flexion joint angle and its derivative with dynamic model parameters. In preprocessing procedures, the raw EMG signals are processed by taking root mean square (RMS) and filtering out noises with low-pass filter (LPF). Also, the model parameters are determined through an optimization for the measured EMG signals and their corresponding real joint angles measured from vision tracker system. A part of the model parameters are modified with two different slopes when the shoulder joint angle exceeds 90 degrees. For the main procedures, the moving average filter-based model dynamics is implemented to extract the shoulder angle, here, the moving average filtering is performed with the varying window size to reduce the oscillations of the EMG signals caused by the muscle fatigue. Finally, we show the effectiveness the suggested method through several experiments.

Multi-criteria decision analysis to select the optimum position and proper field of view of a photosensor

The lack of knowledge concerning the commissioning of a daylight responsive system constitutes a serious impediment to their widespread use. Furthermore, installation details and tools regarding their photosensor position and field of view (FOV) are insufficient. This paper presents a decision making method capable to estimate the best position of a photosensor on the ceiling and its proper FOV based on multiple criteria analysis. The criteria used are (a) the correlation of the lighting levels between the working plane and the ceiling, (b) the corresponding energy savings and (c) the lighting adequacy which is defined as the percentage for occupied time with total illuminance exceeding design illuminance (i.e 500lux EN 12464-1, 2011) and is strongly affected by the control algorithm.A number of simulations with variable FOV and position of photosensors were performed in order to clarify the calculation procedure of the proposed methodology. Three different typical room geometries have been used with variable window sizes and orientation. Furthermore a prototype photosensor with variable FOV through the use of a telescopic cylinder was constructed and placed in a scale room (1:10) in order to verify the results of simulations. The verification was based on a set of experimental procedures concerning measurements of the spatial response of the prototype photosensor (for various FOVs) and measurements of ceiling/workplane illuminance inside the scaled room for various combinations of position and FOV of the photosensor. The proposed methodology can be used as a tool for the determination of the optimum operation of a daylight responsive system with photosensors.

P‐55: Adaptive Noise Reduction Method using Variable Window Size Based on Region Analysis

AbstractThis paper proposes a novel denoising method which uses a variable window size for noise filter depending on the results of local region analysis. The proposed method classifies local regions of a given noisy image as detail or smooth regions using a new parameter which is calculated by the ratio of estimated noise variance to local variance. The new parameter, a detail descriptor, represents the amount of image details in a local region. Then, the proposed method adaptively applies a noise reduction filter to remove image noise while preserving image details by regulating the filtering window size depending on the detail descriptor.

Improvement of spatially continuous forest LAI retrieval by integration of discrete airborne LiDAR and remote sensing multi-angle optical data

Forest leaf area index (LAI) is a critical variable in modeling climates and ecosystems, and is required on regional and global scales for models. However, forest LAI has proven to be difficult to obtain. In this study, we sought to improve forest LAI retrieval in a large study area in the Dayekou forest, Gansu province, by combining airborne discrete LiDAR, MODIS, and MISR data. In our retrieval scheme, canopy height is the key parameter, and the canopy height precision is of great importance when estimating LAI. To address this issue, we introduced LiDAR data and combined it with the MODIS and MISR products. First, the canopy height for the LiDAR data coverage was calculated using a local maximum filtering algorithm with a variable window size. Then, a multivariate linear regression model was developed to extrapolate the LiDAR-derived canopy height to the whole study area using the MODIS BRDF/Albedo product. In addition, the bi-directional reflectances from MODIS and MISR were used to invert the geometric-optical mutual-shadowing (GOMS) model structural parameters (nR2, b/R, h/b, Δh/b) of the forest. These structural parameters were then combined with the forest canopy height and field measurements to retrieve the LAI of the continuous forest area at a 500-m resolution. After comparison with the true LAI measured by LAI-2000 combined with TRAC, and by TRAC alone, the highest R2 values of the estimated LAI were 0.73 and 0.69, respectively. The results indicate that the LiDAR canopy height derived from the optical multi-angle remote sensing data can be used to retrieve the large-scale forest LAI when combined with the canopy structure information derived from GOMS model.

영상 복원을 위한 MRF 기반 적응적 노이즈 탐지 알고리즘

In this paper, we presents a spatially adaptive noise detection and removal algorithm. Under the assumption that an observed image and the additive noise have Gaussian distribution, the noise parameters are estimated with local statistics, and the parameters are used to define the constraints on the noise detection process, where the first order Markov Random Field (MRF) is used. In addition, an adaptive low-pass filter having a variable window sizes defined by the constraints on noise detection is used to control the degree of smoothness of the reconstructed image. Experimental results demonstrate the capability of the proposed algorithm.

An efficient new method for the detection of QRS in electrocardiogram

A simple and efficient new method for QRS detection in Electrocardiogram (ECG) is proposed in this paper. Initially data is preprocessed using two stage median filter for removing baseline drift. The second stage enhances the peaks of ECG wave components by using sixth power of signal. The next stage identifies the QRS complex by taking a variable window size. The detection sensitivity (Se) and positive predictivity (+P) of CSE (Common Standards for Quantitative Electrocardiography) measurement database, MIT/BIH (Massachusetts Institute of Technology/Beth Israel Hospital) Arrhythmia database, European ST-T database and QT database are Se 99.51 & +P 99.69%, Se 99.21 & +P 99.34%, Se 99.53 & +P 99.72% and Se 99.87 & +P 99.95% respectively. These four standard databases used to perform QRS detection considered 368 cases, tested 1,006,168 beats and achieved overall average sensitivity 99.52% and positive predictivity 99.69%. The MIT/BIH Noise Stress Test Database also tested by proposed method.

SePaS: Word sense disambiguation bysequentialpatterns insentences

AbstractAn open problem in natural language processing is word sense disambiguation (WSD). A word may have several meanings, but WSD is the task of selecting the correct sense of a polysemous word based on its context. Proposed solutions are based on supervised and unsupervised learning methods. The majority of researchers in the area focused on choosing proper size of ‘n’ in n-gram that is used for WSD problem. In this research, the concept has been taken to a new level by using variable ‘n’ and variable size window. The concept is based on the iterative patterns extracted from the text. We show that this type of sequential pattern is more effective than many other solutions for WSD. Using regular data mining algorithms on the extracted features, we significantly outperformed most monolingual WSD solutions. The state-of-the-art results were obtained using external knowledge like various translations of the same sentence. Our method improved the accuracy of the multilingual system more than 4 percent, although we were using monolingual features.

Detection and quantification of intracerebral and intraventricular hemorrhage from computed tomography images with adaptive thresholding and case-based reasoning

Hemorrhage within the brain space (HWBS) involves the brain parenchyma and ventricle systems, and is associated with high morbidity and mortality. Computed tomography (CT) head scans are the recommended modality for diagnosis and treatment for HWBS. However, HWBS detection may be difficult when the hemorrhage is inconspicuous, while quantification is hard as hemorrhage can have very variable intensity that overlaps with normal brain tissue. An algorithm is proposed to detect and quantify HWBS. Adaptive thresholding and case-based reasoning (CBR) were applied to HWBS in four steps: preprocessing to extract the brain, adaptive thresholding based on local contrast with varied window sizes to derive candidate HWBS regions, case representation to represent each candidate HWBS region by parameters on context as well as intensity and geometrical characteristics, and classification of HWBS by taking each candidate HWBS region as a case and applying CBR. Additionally, case base indexing and weights optimization were used to increase retrieval speed and improve performances. Refinement of each recognized HWBS was performed for quantifying HWBS. Validation on 426 clinical CT data indicates that the proposed algorithm achieved a detection rate of 94.4% and recall of 79.2% for detecting HWBS regions. Visually, the HWBS regions calculated from adaptive thresholding plus refinement agreed well with expert delineation. For 10 representative data with small to large hemorrhage, the algorithm quantitatively yielded a segmentation accuracy of [Formula: see text]. Case base indexing increased the retrieval speed by 41.1 times at the expense of decreasing detection rate of 0.5% and recall of 2.6%. Genetic algorithm optimization enhanced the detection rate and recall to, respectively, 94.9 and 83.5%. We developed and tested an algorithm that combined adaptive thresholding and CBR for detecting and quantifying HWBS. Experiments showed that adaptive thresholding could provide suitable candidates, while CBR was able to identify HWBS regions. The proposed method has potential as a new tool for accurately detecting and quantifying HWBS.

Time Varying Herding and Anti-Herding Behavior and Dynamic Conditional Correlations in European Market Indices

We examine the existence of herding and anti-herding (positive herding) behavior in major European benchmarking stock market indices. Following the recent events that unfolded in the Eurozone sovereign debt crisis our analysis is further expanded on two subsamples namely north and south European countries. In order to identify any time-dependent properties reflected in time-varying parameters, we employ an overlapping rolling window regression of varying window sizes. The empirical evidence confirms a herding behavior during the global financial crisis for the South countries, and anti-herding behavior for both North and South subsets before and after the shock, independent of the window size. The herding and anti-herding behavior, are explained with the use of dynamic conditional correlation via DCC-GARCH family multivariate modeling. The results indicate that the correlations have recorded a significant decrease before and after the crisis for both North and South subsamples, accounting for the anti-herding behavior, whereas correlations for the South countries during the crisis have increased, thus justifying the herding behavior. Our results entail significant implications for international portfolio diversification strategies.

Crack Classification and Interpolation of Old Digital Paintings

Paintings which was handled roughly or made from low quality paint or base usually suffers from crack in a long run, which causes them to lose some of the information. This paper discuss about automatic approach for classification and interpolation of cracks. For classification supervised and unsupervised methods were implemented and for interpolation different order statistics filter were applied. Experimental result shows that unsupervised classification works better than supervised classification. And variable size window filter works best for interpolation of cracks.

A Fusion Approach for Tree Crown Delineation from Lidar Data

This study proposes a multi-step method (the COTH method) to delineate individual tree crowns in dense forest conditions using lidar data, with the intent for the final delineation results to be used in a biomass estimation study. The study was conducted for an even-aged Norway Spruce (Picea abies) plantation containing 188 trees located in Tully, New York, and owned by the State University of New York College of Environmental Science and Forestry (SUNY ESF). Lidar data with a point density of 12.7 points/m 2 was collected in August 2010, and field data were collected to measure tree height and species in August 2010 as well. Field data containing tree height and crown width, an important component of treetop detection, were collected in summer 2006. By combining heuristically (genetic algorithm) optimized object recognition to detect tree crown objects, local maxima filtering with variable window size to detect treetops, and a modified hill climbing algorithm to segment crown objects; treetops were identified with 86.2 percent accuracy and 23.9 percent commission error. The overall areal accuracy of the delineation was 72.5 percent. The automated COTH method represents an improvement in crown delineation accuracy for lidar data.

Enhancing the QOS of A Voip call Using an Adaptive Jitter Buffer Playout Algorithm with Variable Window Size

Enhancing the QOS of A Voip call Using an Adaptive Jitter Buffer Playout Algorithm with Variable Window Size

High-resolution DEMs in the study of rainfall- and earthquake-induced landslides: Use of a variable window size method in digital terrain analysis

We undertake digital terrain analyses of rainfall- and earthquake-induced landslides in Japan, using high-resolution orthoimagery and Light Detection and Ranging (LiDAR) DEMs. Our aims are twofold: to demonstrate an effective method for dealing with high-resolution DEMs, which are often too detailed for landslide assessments, and to evaluate the topographic differences between rainfall- and earthquake-induced landslides. The study areas include the Izumozaki (1961 and 2004 heavy rainfalls), Niihama (2004 heavy rainfalls), Houfu (2009 heavy rainfalls), and Hanokidachi/Kurikoma-dam regions (the 2008M 7.2 Iwate–Miyagi Nairiku earthquake). The study areas include 7,106 landslides in these five regions. We use two topographic attributes (the slope gradient and the Laplacian) calculated from DEMs in varying window sizes. The hit rates for statistical prediction of landslide cells through discriminant analyses are calculated using the two topographic attributes as explanatory variables, and the landslide inventory data as the dependent variable. In cases of surface failure, the hit rates are found to diminish when the window size of the topographic attributes is too large or too small, indicating that an optimal scale factor is key in assessing shallow landslides. The representative window sizes are approximately 30m for shallow landslides; the optimal window size may be directly related to the average size of landslides in each region. We also find a stark contrast between rainfall- and earthquake-induced landslides. Rainfall-induced landslides are always most common at a slope gradient of 30°, but the frequency of earthquake-induced landslides increases exponentially with slope gradient. We find that the Laplacian, i.e., the attributes of surface convexity and concavity, and the slope gradient are both important factors for rainfall-induced landslides, whereas earthquake-induced landslides are influenced mainly by slope steepness.

Improved Deduplication Method based on Variable-Size Sliding Window

Improved Deduplication Method based on Variable-Size Sliding Window

Individual tree detection based on variable and fixed window size local maxima filtering applied to IKONOS imagery for even-aged Eucalyptus plantation forests

Detection of individual trees remains a challenge for forest inventory efforts especially in homogeneous, even-aged plantation scenarios. Airborne imagery has mainly been used for detection of individual trees using local maxima filtering, as point spread function and signal-to-noise ratio are smaller than with satellite-borne imagery. This led to the development of a novel approach to local maxima filtering for tree detection in plantation forests in KwaZulu-Natal, South Africa, using satellite remote sensing imagery. Our approach is based on Gaussian smoothing for noise elimination and image classification, that is, natural break classification to determine the threshold for removing pixels of extremely bright and dark areas in the imagery. These pixels are assumed to belong to the background and hinder the search for tree peaks. A semivariogram technique was applied to determine variable window sizes for local maxima filtering within a plantation stand. A fixed window size for local maxima filtering was also applied using pre-determined tree spacing. Evaluation of the various approaches was based on aggregated assessment methods. The overall accuracy using a variable window size was 85%, root mean square error (RMSE) = 189 trees, whereas a fixed window size resulted in an accuracy of 80%, RMSE = 258 trees. The approach worked remarkably well in mature forest stands as compared to young forest stands. These results are encouraging for temperate–warm climate plantation forest companies, who deal with even-aged, broadleaf plantations and forest inventory practices that require assessment 1 year before harvesting.

GPU를 이용한 스테레오 정합 알고리즘의 구현

본 논문에서는 최종 변이영상의 정확도를 높이기 위해 영상의 특징점을 이용한 적응적 가변 정합창 방법과 교차 일치성 검사의 신뢰도를 높이는 방법을 제안한다. 제안한 적응적 가변 정합창 방법은 색상정보를 이용하여 영상을 분할하고 분할된 각 영상의 특징점을 찾아 그 특징점들의 유무에 따라 정합창의 크기를 적응적으로 가변시키는 방법이다. 또한 제안한 알고리즘을 GPU를 기반으로 구현하여 연산속도가 평균 128배 빨라졌다. GPU는 NVIDIA의 GeForce GTX296를 사용하였고, CUDA를 기반으로 프로그래밍 하였다. In this paper, we propose an adaptive variable-sized matching window method using the characteristic points of the image and a method to increase the reliability of the cross-consistency check to raise the correctness of the final disparity image. The proposed adaptive variable-sized window method segments the image with the color information, finds the characteristic points inside the window. Also the proposed algorithm implement using a graphic processing unit(GPU). The GPU, we used in this paper is GeForce GTX296 (NVIDIA) and we can use programming based on CUDA. The calculation speed realizes a speed approximately 128 times faster than that of a CPU.

Variable window adaptive Kernel Principal Component Analysis for nonlinear nonstationary process monitoring

On-line control of nonlinear nonstationary processes using multivariate statistical methods has recently prompt a lot of interest due to its industrial practical importance. Indeed basic process control methods do not allow monitoring of such processes. For this purpose this study proposes a variable window real-time monitoring system based on a fast block adaptive Kernel Principal Component Analysis scheme. While previous adaptive KPCA models allow only handling of one observation at a time, in this study we propose a way to fast update or downdate the KPCA model when a block of data is provided and not only one observation. Using a variable window size procedure to determine the model size and adaptive chart parameters, this model is applied to monitor two simulated benchmark processes. A comparison of performances of the adopted control strategy with various Principal Component Analysis (PCA) control models shows that the derived strategy is robust and yields better detection abilities of disturbances.

Modified ALV for selecting the optimal spatial resolution and its scale effect on image classification accuracy

Faced with the prevalence of multi-spatial resolution satellite data sets, selecting data with an appropriate resolution has become a new problem. This paper analyses the significance of scale selection of remote sensing images and discusses a geo-statistics based method for selecting the optimal spatial resolution of a remote sensing image. Breaking through the limitation of traditional average local variance (ALV), the modified ALV method based on variable window size and variable resolution is proposed to quantitatively select the optimal spatial resolution of a remote sensing image. In order to verify the validity of this method and interpret the relationship among spatial resolution, local variance and classification accuracy, this paper gives further image classification experiments with different spatial resolution. The experimental results show that the trend of classification accuracy along spatial resolution is basically accordant with that of modified ALV, which means that the image classification accuracy of the optimal resolution image is basically higher than those of others. Therefore, the modified ALV method for quantitively selecting the optimal spatial resolution of remote sensing image has theoretical and instructional meaning to a certain extent.