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Torrential floods had placed a record in the history of Kerala, the state on the south-western coast of India, in 2018. This study focusses primarily on the assessment of flood inundation mapping using the Sentinel-1 SAR imagery applying the Otsu's thresholding algorithm for the Alappuzha region. Though the pre-processing of SAR imagery is difficult, the availability of software and enhanced hardware facilitates efficacy in image processing. Normalized Difference Water Index (NDWI) was computed to aid in differentiating the permanent waterbody from the flooded region. The results implicate the spread of flood-inundated zones has considerably increased in August, thereby indicating the devastating effect of the flood in August. Based on the results obtained, 13.86% area of the region has been covered by flood in August, excluding the permanent water bodies, which is comparatively high compared to the previous months. It is necessary to note that, extreme rainfall accompanied by the full reservoir level of the dams in the region served to be the key to the disaster in August. The resultant flood inundation map could be utilized quite effectively for relief measures and damage assessment. [ABSTRACT FROM AUTHOR]

Abstract Background The association of the severity of clinical symptoms and level of functional performance with the degree of magnetic resonance imaging abnormalities in patients with lateral epicondylitis has not been fully elucidated. This study aimed to investigate the association between the degree of anatomical abnormalities by evaluating three-dimensional magnetic resonance imaging models of the common extensor tendon and clinical parameters in patients with lateral epicondylitis. Materials and methods A total of 61 patients (24 men and 37 women) with lateral epicondylitis were included in this study. 3-Tesla magnetic resonance imaging was performed for all patients, and clinical parameters, including pain visual analog scale score, Quick Disabilities of Arm, Shoulder and Hand questionnaire score, elbow range of motion, and demographic factors, were evaluated. The proportion of lesion volume of common extensor tendon was adopted for three-dimensional model analysis. To determine the factors associated with clinical parameters, univariate, and multivariate linear regression analyses were performed. Results The proportion of lesion volume of common extensor tendon was not associated with clinical parameters. Gender and muscle edema were independently associated with pain visual analog scale scores. However, demographic factors and magnetic resonance imaging abnormalities were not associated with the Quick Disabilities of Arm, Shoulder, and Hand questionnaire score or elbow range of motion. Conclusions The three-dimensional volumetric lesion size of common extensor tendon was not associated with clinical symptoms and functional performance in patients with lateral epicondylitis. The clinical parameters of lateral epicondylitis may be influenced by several factors.

A camera vision system is a fast and effective approach to monitoring leaves. It can be used to monitor plant growth, detect diseases, and conduct plant phenotyping. However, due to the outdoor environment of plants, it becomes challenging to use. This paper addresses the problems of Vetiveria zizanioides leaf segmentation by comparing different camera types and segmentation techniques. Visible, no infrared filter (NoIR), and thermal cameras interfaced in an embedded device were used to capture plants during the day and at night. Several popular thresholding techniques and the K-Means algorithm were employed for leaf segmentation, and their performance was measured using Recall, Precision, and F1 score. The comparison results show that the visible camera achieved the best performance on daytime images, with the highest Recall of 0.934 using Triangle thresholding, the highest Precision of 0.751 using K-Means (K = 3), and the highest F1 score of 0.794 using Multi-Otsu thresholding. For nighttime images, the highest Recall of 0.990 was achieved by the thermal camera using Isodata and Otsu thresholding, the highest Precision of 0.572 was achieved by the NoIR camera using K-Means (K = 3), and the highest F1 score of 0.636 was achieved by the NoIR camera using K-Means (K = 3). To compare the leaf segmentation performance of the thresholding techniques and the K-Means algorithm between our image dataset and the well-known plant image dataset, we also evaluated the methods using the Ara2012 image dataset. The results showed that K-Means (K-3) achieved the best performance. The execution time of K-Means was about 3 s, which was longer than the thresholding techniques. However, it is still acceptable for the real-time plant monitoring system.

Preservation of image features caused by binary conversion is a difficult task under variation of illumination conditions. Several binary conversion-based methods have used an adaptive thresholding technique to improve their performance under illumination variation conditions because of its robustness. However, the performances of existing methods were still limited under high differences illumination conditions especially for uncontrolled lighting sources. In addition, various length of face-to-camera distance gives significant problem affect for the performance of face recognition method. It happens when various images are available for the same person with different length face-to-camera distances due to the appearance of varying facial features of the same person. Therefore, this study proposed to combine the strength normalization and feature-based method to build an illumination distribution model to overcome this problem. With the proposed method the illumination model will fit with variation of illumination conditions in a whole image to generate an adaptive threshold for a novel columnar binary conversion method. The proposed method consists of five main stages, starting with eye area detection using the developed Viola-Jones algorithm. Next, the iris is detected using the Circular Hough Transform (CHT) method and will convert it into binary using the proposed Columnar Binary Conversion (CBC) method to preserve the appearance of the facial features under the illumination variation. Then, the proposed Facial Feature Region Normalization (FFRN) method is performed to improve the effects of different optical zooms for the classification step. The classification is conducted based on the similarity measurement between the extracted normalised binary face region and the dataset that must be converted into their equivalent normalised binary images. The proposed method is evaluated on two different smartphone databases, namely as Smartphone Face Video (SFV) and MOBIO. The performance results showed the outperformance of the proposed method.

Background: The association of the severity of clinical symptoms and level of functional performance with the degree of magnetic resonance imaging abnormalities in patients with lateral epicondylitis has not been fully elucidated. This study aimed to investigate the association between the degree of anatomical abnormalities by evaluating three-dimensional magnetic resonance imaging models of the common extensor tendon and clinical parameters in patients with lateral epicondylitis. Materials and methods: A total of 61 patients (24 men and 37 women) with lateral epicondylitis were included in this study. 3-Tesla magnetic resonance imaging was performed for all patients, and clinical parameters, including pain visual analog scale score, Quick Disabilities of Arm, Shoulder and Hand questionnaire score, elbow range of motion, and demographic factors, were evaluated. The proportion of lesion volume of common extensor tendon was adopted for three-dimensional model analysis. To determine the factors associated with clinical parameters, univariate, and multivariate linear regression analyses were performed. Results: The proportion of lesion volume of common extensor tendon was not associated with clinical parameters. Gender and muscle edema were independently associated with pain visual analog scale scores. However, demographic factors and magnetic resonance imaging abnormalities were not associated with the Quick Disabilities of Arm, Shoulder, and Hand questionnaire score or elbow range of motion. Conclusions: The three-dimensional volumetric lesion size of common extensor tendon was not associated with clinical symptoms and functional performance in patients with lateral epicondylitis. The clinical parameters of lateral epicondylitis may be influenced by several factors. [ABSTRACT FROM AUTHOR]

In this paper, we present a unique modulation classification method that is based on determining an attractive relation between higher-order cumulants (HOCs) using a decision tree-classifier to improve the extracted features employed for the recognition of modulation schemes, such as phase shift keying (PSK) and quadrature amplitude modulation (QAM). A threshold algorithm is applied to the proposed classifier, which consists of sub-classifiers, each comprising a single feature, and each being capable of distinguishing the modulation types individually. In this work, a high-accuracy classifier system is utilized to recognize modulation schemes, such as QAM (16, 32, 64, 128, and 256) and (2, 4, and 8) PSK at a low signal-to-noise ratio (SNR). In this study, 1000 signals are studied for each SNR of –5 dB to 30 dB. The most prominent results of the classifier decisions range from 88% to 100% with regard to distinguishing the same types of PSK and QAM. In the long run, the proposed classifier module will be advantageous in terms of accuracy and computational complexity relative to the other classifiers in the literature. The results demonstrate that the proposed algorithm has a significantly better classification accuracy in comparison with the previously proposed ones. • A new method for automatic modulation classification is presented. • Optimize the extracted features distribution using logarithmic properties. • Multiple logarithmic hierarchical classifier outperforms over conventional classifiers. • Enhanced classification performance through multiple tree algorithm. • Simulation results confirmed the efficiency of the proposed algorithm under different noise scenarios. [ABSTRACT FROM AUTHOR]

The research paper explores an optimum water extraction technique using synthetic aperture radar (SAR) data to give the flood inundation map for irregular flood scenario, in which the planned algorithm depends on thresholding technique using MRS amplitude SAR data. For classification, supervised maximum likelihood method is used, and gamma filtering technique was used for speckle removal. Since water layer in SAR data has low amplitude value, thresholding technique for amplitude SAR data could efficiently take out water layer. The mean backscatter basics of water layers in SAR data is not to be compared with inundated regions caused by sharp release and is not considered as a terrain value. The speckle filtering method was used for the power generated noise subtraction using a window size 5x5. The threshold values were calculated from backscatter values and their average was taken as threshold, which gives clear difference of backscatter values between the land and the water in the river and the flood plain areas. The maximum likelihood classifier provides 99% overall accuracy. The Landsat 5 TM data was used in this research for delineating water feature and flood accurately. The results obtained from SAR data were validated with Landsat TM data. Finally, the flood inundation map using optical data and SAR data was generated. [ABSTRACT FROM AUTHOR]

Coal fires are common and serious phenomena in most coal-producing countries in the world. Coal fires not only burn valuable non-renewable coal reserves but also severely affect the local and global environment. The Rujigou coalfield in Shizuishan City, Ningxia, NW China, is well known for being a storehouse of anthracite coal. This coalfield is also known for having more coal fires than most other coalfields in China. In this study, an attempt was made to study the dynamics of coal fires in the Rujigou coalfield, from 2001 to 2007, using multi-temporal nighttime Landsat data. The multi-temporal nighttime short wave infrared (SWIR) data sets based on a fixed thresholding technique were used to detect and monitor the surface coal fires and the nighttime enhanced thematic mapper (ETM+) thermal infrared (TIR) data sets, based on a dynamic thresholding technique, were used to identify the thermal anomalies related to subsurface coal fires. By validating the coal fires identified in the nighttime satellite data and the coal fires extracted from daytime satellite data with the coal fire map (CFM) manufactured by field survey, we found that the results from the daytime satellite data had higher omission and commission errors than the results from the nighttime satellite data. Then, two aspects of coal fire dynamics were analyzed: first, a quantitative analysis of the spatial changes in the extent of coal fires was conducted and the results showed that, from 2001 to 2007, the spatial extent of coal fires increased greatly to an annual average area of 0.167 km2; second, the spreading direction and propagation of coal fires was analyzed and predicted from 2001 to 2007, and these results showed that the coal fires generally spread towards the north or northeast, but also spread in some places toward the east.

Mammogram is an easy and affordable means of diagnosis of breast cancer. Like other medical data, it is also affected by noise during acquisition. Therefore, it is a challenge for the researchers to denoise the mammograms for clear data extraction. This paper aims at denoising the mammogram by the wavelet and the curvelet transform with a motive to investigate the role of the “embedded” thresholding algorithm. As the thresholding technique is a key factor for the noise reduction, a comprehensive study on the employment of the different types of the thresholding techniques with the transforms have been presented methodically. A standard mammogram from the Mammographic Image Analysis Society database is supplemented with different types of noise and then denoised by the wavelet and the curvelet transforms using the three commonly used thresholding techniques to compare the denoising performance of the thresholding algorithms along with the transforms. This investigation renders a clear insight to the selection of the thresholding technique while denoising the mammogram with the transform. [ABSTRACT FROM PUBLISHER]

Skin detection is used in many applications, such as face recognition, hand tracking, and human-computer interaction. There are many skin color detection algorithms that are used to extract human skin color regions that are based on the thresholding technique since it is simple and fast for computation. The efficiency of each color space depends on Its robustness to the change in lighting and the ability to distinguish skin color pixels in images that have a complex background. For more accurate skin detection, we are proposing a new threshold based on RGB and YUV color spaces. The proposed approach starts by converting the RGB color space to the YUV color model. Then it separates the Y channel, which represents the intensity of the color model from the U and V channels to eliminate the effects of luminance. After that the threshold values are selected based on the testing of the boundary of skin colors with the help of the color histogram. Finally, the threshold was applied to the input image to extract skin parts. The detected skin regions were quantitatively compared to the actual skin parts in the input images to measure the accuracy and to compare the results of our threshold to the results of other's thresholds to prove the efficiency of our approach. The results of the experiment show that the proposed threshold is more robust in terms of dealing with the complex background and light conditions than others. [ABSTRACT FROM AUTHOR]

The size of the test suite and the duration of time determines the time taken by the regression testing. Conversely, the testers can prioritise the test cases by the use of a competent prioritisation technique to obtain an increased rate of fault detection in the system, allowing for earlier corrections, and getting higher overall confidence that the software has been tested suitably. A prioritised test suite is more likely to be more effective during that time period than would have been achieved via a random ordering if execution needs to be suspended after some time. An enhanced test case ordering may be probable if the desired implementation time to run the test cases is proven earlier. This research work's main intention is to prioritise the regression‐testing test cases. In order to prioritise the test cases some factors are considered here. These factors are employed in the prioritisation algorithm. The trace events are one of the important factors, used to find the most significant test cases in the projects. The requirement factor value is calculated and subsequently a weightage is calculated and assigned to each test case in the software based on these factors by using a thresholding technique. Later, the test cases are prioritised according to the weightage allocated to them. Executing the test cases based on the prioritisation will greatly decreases the computation cost and time. The proposed technique is efficient in prioritising the regression test cases. The new prioritised subsequences of the given unit test suites are executed on Java programs after the completion of prioritisation. Average of the percentage of faults detected is an evaluation metric used for evaluating the 'superiority' of these orderings. [ABSTRACT FROM AUTHOR]

This correspondence presents an individual-activation-factor proportionate normalized least-mean-square (IAF-PNLMS) algorithm that (during the adaptive process) uses a new gain distribution strategy for updating the filter coefficients. This strategy consists of increasing the gain assigned to the inactive coefficients as the active ones approach convergence. For such, whenever a predefined threshold is crossed during the learning process, a new gain distribution is carried out, rather than to assign gains proportional to coefficient magnitudes as the IAF-PNLMS algorithm does. This new version of the IAF-PNLMS algorithm leads to a better distribution of the adaptation energy over the whole learning process. As a consequence, for impulse responses exhibiting high sparseness, the proposed algorithm achieves faster convergence, outperforming the IAF-PNLMS and other well-known PNLMS-type algorithms. [ABSTRACT FROM AUTHOR]