Search results for: wavelet threshold denoising

Authors: Georgi I. Petkov, Ivan I. Vankov, Yolina A. Petrova

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A structure-based model of category learning and categorization at different levels of abstraction is presented. The model compares different structures and expresses their similarity implicitly in the forms of mappings. Based on this similarity, the model can categorize different targets either as members of categories that it already has or creates new categories. The model is novel using two threshold parameters to evaluate the structural correspondence. If the similarity between two structures exceeds the higher threshold, a new sub-ordinate category is created. Vice versa, if the similarity does not exceed the higher threshold but does the lower one, the model creates a new category on higher level of abstraction.

Keywords: analogy-making, categorization, learning of categories, abstraction, hierarchical structure

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Authors: Rajkumar Kolangarakandy

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Feature extraction and selection is the primary part of any mammogram classification algorithms. The choice of feature, attribute or measurements have an important influence in any classification system. Discrete Wavelet Transformation (DWT) coefficients are one of the prominent features for representing images in frequency domain. The features obtained after the decomposition of the mammogram images using wavelet transformations have higher dimension. Even though the features are higher in dimension, they were highly correlated and redundant in nature. The dimensionality reduction techniques play an important role in selecting the optimum number of features from the higher dimension data, which are highly correlated. PCA is a mathematical tool that reduces the dimensionality of the data while retaining most of the variation in the dataset. In this paper, a multilevel classification of mammogram images using reduced discrete wavelet transformation coefficients and lazy classifiers is proposed. The classification is accomplished in two different levels. In the first level, mammogram ROIs extracted from the dataset is classified as normal and abnormal types. In the second level, all the abnormal mammogram ROIs is classified into benign and malignant too. A further classification is also accomplished based on the variation in structure and intensity distribution of the images in the dataset. The Lazy classifiers called Kstar, IBL and LWL are used for classification. The classification results obtained with the reduced feature set is highly promising and the result is also compared with the performance obtained without dimension reduction.

Keywords: PCA, wavelet transformation, lazy classifiers, Kstar, IBL, LWL

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Authors: Ji-Yuan Lin, Yu-Ming Liou, Yi-Ting Chen, Chen-Syuan Lin

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Soil water index obtained by tank model is now commonly used in soil and sand disaster alarm system in Japan. Comparing with the rainfall trigging index in Taiwan, the tank model is easy to predict the slope water content on large-scale landslide. Therefore, this study aims to estimate the threshold value of large-scale landslide using the soil water index Sixteen typhoons and heavy rainfall events, were selected to establish the, to relationship between landslide event and soil water index. Finally, the proposed threshold values for landslides on country road No.89 are suggested in this study. The study results show that 95% landslide cases occurred in soil water index more than 125mm, and 30% of the more serious slope failure occurred in the soil water index is greater than 250mm. Beside, this study speculates when soil water index more than 250mm and the difference value between second tank and third tank less than -25mm, it leads to large-scale landslide more probably.

Keywords: soil water index, tank model, landslide, threshold values

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Authors: Seyed Sadegh Naseralavi, Sohrab Nemati, Ehsan Khojastehfar, Sadegh Balaghi

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In the present research, various formulations of wavelet transform are applied on acceleration time history of earthquake. The mentioned transforms decompose the strong ground motion into low and high frequency parts. Since the high frequency portion of strong ground motion has a minor effect on dynamic response of structures, the structure is excited by low frequency part. Consequently, the seismic response of structure is predicted consuming one half of computational time, comparing with conventional time history analysis. Towards reducing the computational effort needed in seismic optimization of structure, seismic optimization of a shear frame structure is conducted by applying various forms of mentioned transformation through genetic algorithm.

Keywords: time history analysis, wavelet transform, optimization, earthquake

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Authors: Peiyuan Guan, Tao Wan, Dewei Chu

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With the flash memory approaching its physical limit, the emerging resistive random-access memory (RRAM) has been considered as one of the most promising candidates for the next-generation non-volatile memory. One selector-one resistor configuration has shown the most promising way to resolve the crosstalk issue without affecting the scalability and high-density integration of the RRAM array. By comparison with other candidates of selectors (such as diodes and nonlinear devices), threshold switching selectors dominated by formation/spontaneous rupture of fragile conductive filaments have been proved to possess low voltages, high selectivity, and ultra-low current leakage. However, the flexibility and transparency of selectors are barely mentioned. Therefore, it is a matter of urgency to develop a selector with highly flexible and transparent properties to assist the application of RRAM for a diversity of memory devices. In this work, threshold switching selectors were designed using a facilely solution-processed fabrication on AgNWs@PDMS composite films, which show high flexibility, transparency and scratch resistance. As-fabricated threshold switching selectors also have revealed relatively high selectivity (~107), low operating voltages (Vth < 1 V) and good switching performance.

Keywords: flexible and transparent, resistive random-access memory, silver nanowires, threshold switching selector

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Authors: Toni Maristela C. Estabillo, Michaela V. Matienzo, Mikaela L. Sabangan, Rosette M. Tienzo, Justine L. Bahinting

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This study is about blind watermarking on images with different categories and properties using two algorithms namely, Discrete Wavelet Transform and Patchwork Algorithm. A program is created to perform watermark embedding, extraction and evaluation. The evaluation is based on three watermarking criteria namely: image quality degradation, perceptual transparency and security. Image quality is measured by comparing the original properties with the processed one. Perceptual transparency is measured by a visual inspection on a survey. Security is measured by implementing geometrical and non-geometrical attacks through a pass or fail testing. Values used to measure the following criteria are mostly based on Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR). The results are based on statistical methods used to interpret and collect data such as averaging, z Test and survey. The study concluded that the combined DWT and Patchwork algorithms were less efficient and less capable of watermarking than DWT algorithm only.

Keywords: blind watermarking, discrete wavelet transform algorithm, patchwork algorithm, digital watermark

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Authors: Bruno S. Soria, Mauricio R. Policena, Andre J. Souza

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The state of the cutting tool is an important factor to consider during machining to achieve a good surface quality. The vibration generated during material cutting can also directly affect the surface quality and life of the cutting tool. In this work, the effect of mechanical broken failure (MBF) on carbide insert tools during face milling of AISI 304 stainless steel was evaluated using three levels of feed rate and two spindle speeds for each tool condition: three carbide inserts have perfect geometry, and three other carbide inserts have MBF. The axial and radial depths remained constant. The cutting forces were determined through a sensory system that consists of a piezoelectric dynamometer and data acquisition system. Discrete Wavelet Transform was used to separate the static part of the signals of force and vibration. The roughness of the machined surface was analyzed for each machining condition. The MBF of the tool increased the intensity and force of vibration and worsened the roughness factors.

Keywords: face milling, stainless steel, tool condition monitoring, wavelet discrete transform

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Authors: Shahriar Farzam, Maryam Rastgarpour

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Image denoising plays extremely important role in digital image processing. Enhancement of clinical image research based on Curvelet has been developed rapidly in recent years. In this paper, we present a method for image contrast enhancement for cone beam CT (CBCT) images based on fast discrete curvelet transforms (FDCT) that work through Unequally Spaced Fast Fourier Transform (USFFT). These transforms return a table of Curvelet transform coefficients indexed by a scale parameter, an orientation and a spatial location. Accordingly, the coefficients obtained from FDCT-USFFT can be modified in order to enhance contrast in an image. Our proposed method first uses a two-dimensional mathematical transform, namely the FDCT through unequal-space fast Fourier transform on input image and then applies thresholding on coefficients of Curvelet to enhance the CBCT images. Consequently, applying unequal-space fast Fourier Transform leads to an accurate reconstruction of the image with high resolution. The experimental results indicate the performance of the proposed method is superior to the existing ones in terms of Peak Signal to Noise Ratio (PSNR) and Effective Measure of Enhancement (EME).

Keywords: curvelet transform, CBCT, image enhancement, image denoising

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Authors: Mohsen Khaledian, Razali Ismail, Mehdi Saeidmanesh, Mahdiar Hosseinghadiry

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A semi-analytical model for impact ionization coefficient of graphene nanoribbon (GNR) is presented. The model is derived by calculating probability of electrons reaching ionization threshold energy Et and the distance traveled by electron gaining Et. In addition, ionization threshold energy is semi-analytically modeled for GNR. We justify our assumptions using analytic modeling and comparison with simulation results. Gaussian simulator together with analytical modeling is used in order to calculate ionization threshold energy and Kinetic Monte Carlo is employed to calculate ionization coefficient and verify the analytical results. Finally, the profile of ionization is presented using the proposed models and simulation and the results are compared with that of silicon.

Keywords: nanostructures, electronic transport, semiconductor modeling, systems engineering

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Authors: Rong Yuan, Zhao Tao

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Only a handful of literature have used to non-linear model to investigate the influencing factors of greenhouse gas (GHG) emissions in China’s manufacturing sectors. And there is a limit in investigating quantitatively and systematically the mechanism of correlation between economic development and GHG emissions considering inherent differences among manufacturing sub-sectors. Considering the sectorial characteristics, the manufacturing sub-sectors with various impacts of output on GHG emissions may be explained by different development modes in each manufacturing sub-sector, such as investment scale, technology level and the level of international competition. In order to assess the environmental impact associated with any specific level of economic development and explore the factors that affect GHG emissions in China’s manufacturing industry during the process of economic growth, using the threshold Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model, this paper investigated the influence impacts of GHG emissions for China’s manufacturing sectors of different stages of economic development. A data set from 28 manufacturing sectors covering an 18-year period was used. Results demonstrate that output per capita and investment scale contribute to increasing GHG emissions while energy efficiency, R&D intensity and FDI mitigate GHG emissions. Results also verify the nonlinear effect of output per capita on emissions as: (1) the Environmental Kuznets Curve (EKC) hypothesis is supported when threshold point RMB 31.19 million is surpassed; (2) the driving strength of output per capita on GHG emissions becomes stronger as increasing investment scale; (3) the threshold exists for energy efficiency with the positive coefficient first and negative coefficient later; (4) the coefficient of output per capita on GHG emissions decreases as R&D intensity increases. (5) FDI shows a reduction in elasticity when the threshold is compassed.

Keywords: China, GHG emissions, manufacturing industry, threshold STIRPAT model

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Authors: V. Monisha, D. Sindhuja, M. Sowmiya

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Over the decade, the applications about multimedia have been developed rapidly. The advancement in the communication field at the faster pace, it is necessary to protect the data during transmission. Thus, security of multimedia contents becomes a vital issue, and it is a need for protecting the digital content against malfunctions. Digital watermarking becomes the solution for the copyright protection and authentication of data in the network. In multimedia applications, embedded watermarks should be robust, and imperceptible. For improving robustness, the discrete wavelet transform is used. Both encoding and extraction algorithm can be done using MATLAB R2012a. In this Discrete wavelet transform (DWT) domain of digital image, watermarking algorithm is used, and hardware implementation can be done on Xilinx based FPGA.

Keywords: digital watermarking, DWT, robustness, FPGA

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Authors: Madhu Jain, Rakesh Kumar Meena

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This paper investigates a finite M/G/1 fault tolerant multi-component machining system. The system incorporates the features such as standby support, threshold recovery and imperfect coverage make the study closer to real time systems. The performance prediction of M/G/1 fault tolerant system is carried out using recursive approach by treating remaining service time as a supplementary variable. The numerical results are presented to illustrate the computational tractability of analytical results by taking three different service time distributions viz. exponential, 3-stage Erlang and deterministic. Moreover, the cost function is constructed to determine the optimal choice of system descriptors to upgrading the system.

Keywords: fault tolerant, machine repair, threshold recovery policy, imperfect coverage, supplementary variable technique

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Authors: Ana M. Korol, Bibiana Riquelme, Osvaldo A. Rosso

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Since erythrocyte deformability analysis is mostly qualitative, the development of quantitative nonlinear methods is crucial for restricting subjectivity in the study of cell behaviour. An electro-optic mechanic system called erythrodeformeter has been developed and constructed in our laboratory in order to evaluate the erythrocytes' viscoelasticity. A numerical method formulated on the basis of fractal approximation for ordinary (OBM) and fractionary Brownian motion (FBM), as well as wavelet transform analysis, are proposed to distinguish chaos from noise based on the assumption that diffractometric data involves both deterministic and stochastic components, so it could be modelled as a system of bounded correlated random walk. Here we report studies on 25 donors: 4 alpha thalassaemic patients, 11 beta thalassaemic patients, and 10 healthy controls non-alcoholic and non-smoker individuals. The Correlation Coefficient, a nonlinear parameter, showed evidence of the changes in the erythrocyte deformability; the Wavelet Entropy could quantify those differences which are detected by the light diffraction patterns. Such quantifiers allow a good deal of promise and the possibility of a better understanding of the rheological erythrocytes aspects and also could help in clinical diagnosis.

Keywords: red blood cells, deformability, nonlinear dynamics, chaos theory, wavelet trannsform

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Authors: Borzou Faramarzi, Farideh Azimi, Azam Gohardoust, Abbas Ghasemi Ghasemvand, Maryam Mirzaei, Mandana Amani

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In this study, attempts were made to examine and analyze the trend for rainfall changes in Kangan City, Booshehr Province, during the time span 1964 to 2003, using seven rainfall threshold indices based on 50 climate extremes indices approved by WMO–CCL/CLIVAR. These indices include days with heavy precipitations, days with rainfalls, frequency of rainfall threshold values, intensity of rainfall threshold values, percentage of rainfall threshold values, successive days of rainfall, and successive days with no precipitation. Results are indicative of the fact that Kangan City climatic conditions have become more dried than before. Indices days with heavy precipitations and days with rainfalls do not show a certain trend in Kangan City. Frequency, intensity, and percentage of rainfall threshold values in the station under investigation do not indicate a certain trend. In analysis of time series of rainfall extreme indices, generally, it was revealed that Kangan City is influenced by general factors of global warming. Calculation of values for the next 10 years based on ARIMA models demonstrates a continuation of warming trends in Kangan City. On the whole, rainfall conditions in Kangan City have experienced more dry periods compared to the past, the trend which is also observable for next 10 years.

Keywords: climatic indices, climate change, extreme temperature and precipitation, time series

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Authors: Vinod Ponnagandla, Brenton McLaury, Siamack Shirazi

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Sand production can lead to deposition of particles or erosion. Low production rates resulting in deposition can partially clog systems and cause under deposit corrosion. Commercially available nonintrusive acoustic sand detectors are attractive as they claim to detect sand production. Acoustic sand detectors are used during oil and gas production; however, operators often do not know the threshold detection limits of these devices. It is imperative to know the detection limits to appropriately plan for cleaning of separation equipment or examine risk of erosion. These monitors are based on detecting the acoustic signature of sand as the particles impact the pipe walls. The objective of this work is to determine threshold detection limits for acoustic sand monitors that are commercially available. The minimum threshold sand concentration that can be detected in a pipe are determined as a function of flowing gas and liquid velocities. A large scale flow loop with a 4-inch test section is utilized. Commercially available sand monitors (ClampOn and Roxar) are evaluated for different flow regimes, sand sizes and pipe orientation (vertical and horizontal). The manufacturers’ recommend that the monitors be placed on a bend to maximize the number of particle impacts, so results are shown for monitors placed at 45 and 90 degree positions in a bend. Acoustic sand monitors that clamp to the outside of pipe are passive and listen for solid particle impact noise. The threshold sand rate is calculated by eliminating the background noise created by the flow of gas and liquid in the pipe for various flow regimes that are generated in horizontal and vertical test sections. The average sand sizes examined are 150 and 300 microns. For stratified and bubbly flows the threshold sand rates are much higher than other flow regimes such as slug and annular flow regimes that are investigated. However, the background noise generated by slug flow regime is very high and cause a high uncertainty in detection limits. The threshold sand rates for annular flow and dry gas conditions are the lowest because of high gas velocities. The effects of monitor placement around elbows that are in vertical and horizontal pipes are also examined for 150 micron. The results show that the threshold sand rates that are detected in vertical orientation are generally lower for all various flow regimes that are investigated.

Keywords: acoustic monitor, sand, multiphase flow, threshold

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Authors: Neziha Jaouedi, Noureddine Boujnah, Mohamed Salim Bouhlel

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In the framework of human machine interaction systems enhancement, we focus throw this paper on human behavior analysis and action recognition. Human behavior is characterized by actions and reactions duality (movements, psychological modification, verbal and emotional expression). It’s worth noting that many information is hidden behind gesture, sudden motion points trajectories and speeds, many research works reconstructed an information retrieval issues. In our work we will focus on motion extraction, tracking and action recognition using wavelet network approaches. Our contribution uses an analysis of human subtraction by Gaussian Mixture Model (GMM) and body movement through trajectory models of motion constructed from kalman filter. These models allow to remove the noise using the extraction of the main motion features and constitute a stable base to identify the evolutions of human activity. Each modality is used to recognize a human action using wavelets of derived beta distributions approach. The proposed approach has been validated successfully on a subset of KTH and UCF sports database.

Keywords: feautures extraction, human action classifier, wavelet neural network, beta wavelet

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Authors: Varun Agarwal

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Introduction: With the advent of personalized medicine, histopathological review of whole slide images (WSIs) for cancer diagnosis presents an exceedingly time-consuming, complex task. Specifically, detecting metastatic regions in WSIs of sentinel lymph node biopsies necessitates a full-scanned, holistic evaluation of the image. Thus, digital pathology, low-level image manipulation algorithms, and machine learning provide significant advancements in improving the efficiency and accuracy of WSI analysis. Using Camelyon16 data, this paper proposes a deep learning pipeline to automate and ameliorate breast cancer metastasis localization and WSI classification. Methodology: The model broadly follows five stages -region of interest detection, WSI partitioning into image tiles, convolutional neural network (CNN) image-segment classifications, probabilistic mapping of tumor localizations, and further processing for whole WSI classification. Transfer learning is applied to the task, with the implementation of Inception-ResNetV2 - an effective CNN classifier that uses residual connections to enhance feature representation, adding convolved outputs in the inception unit to the proceeding input data. Moreover, in order to augment the performance of the transfer learning CNN, a stack of convolutional denoising autoencoders (CDAE) is applied to produce embeddings that enrich image representation. Through a saliency-detection algorithm, visual training segments are generated, which are then processed through a denoising autoencoder -primarily consisting of convolutional, leaky rectified linear unit, and batch normalization layers- and subsequently a contrast-normalization function. A spatial pyramid pooling algorithm extracts the key features from the processed image, creating a viable feature map for the CNN that minimizes spatial resolution and noise. Results and Conclusion: The simplified and effective architecture of the fine-tuned transfer learning Inception-ResNetV2 network enhanced with the CDAE stack yields state of the art performance in WSI classification and tumor localization, achieving AUC scores of 0.947 and 0.753, respectively. The convolutional feature retention and compilation with the residual connections to inception units synergized with the input denoising algorithm enable the pipeline to serve as an effective, efficient tool in the histopathological review of WSIs.

Keywords: breast cancer, convolutional neural networks, metastasis mapping, whole slide images

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Authors: Sadam Alwadi

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Outlier values become a problem that frequently occurs in the data observation or recording process. Thus, the need for data imputation has become an essential matter. In this work, it will make use of the methods described in the prior work to detect the outlier values based on a collection of stock market data. In order to implement the detection and find some solutions that maybe helpful for investors, real closed price data were obtained from the Amman Stock Exchange (ASE). Tukey and Maximum Overlapping Discrete Wavelet Transform (MODWT) methods will be used to impute the detect the outlier values.

Keywords: outlier values, imputation, stock market data, detecting, estimation

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Authors: Mohmmad A. Obeidat, Amjed Al Fahoum, Ayman M. Mansour

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The recognition of an abnormal activity of the brain functionality is a vital issue. To determine the type of the abnormal activity either a brain image or brain signal are usually considered. Imaging localizes the defect within the brain area and relates this area with somebody functionalities. However, some functions may be disturbed without affecting the brain as in epilepsy. In this case, imaging may not provide the symptoms of the problem. A cheaper yet efficient approach that can be utilized to detect abnormal activity is the measurement and analysis of the electroencephalogram (EEG) signals. The main goal of this work is to come up with a new method to facilitate the classification of the abnormal and disorder activities within the brain directly using EEG signal processing, which makes it possible to be applied in an on-line monitoring system.

Keywords: EEG, wavelet, epilepsy, detection

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Authors: Jiahe Liu, Hongyang Yu, Miao Luo, Feng Qian

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The widespread application of 3D human body reconstruction spans various fields. Smplify-X, an algorithm reliant on single-image input, employs three distinct body parameter templates, necessitating gender classification of individuals within the input image. Researchers employed a ResNet18 network to train a gender classifier within the Smplify-X framework, setting the threshold at 0.9, designating images falling below this threshold as having neutral gender. This model achieved 62.38% accurate predictions and 7.54% incorrect predictions. Our improvement involved refining the MobileNet network, resulting in a raised threshold of 0.97. Consequently, we attained 78.89% accurate predictions and a mere 0.2% incorrect predictions, markedly enhancing prediction precision and enabling more precise 3D human body reconstruction.

Keywords: SMPLX, mobileNet, gender classification, 3D human reconstruction

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Authors: B. Elshafei, X. Mao

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The demand for renewable energy is significantly increasing, major investments are being supplied to the wind power generation industry as a leading source of clean energy. The wind energy sector is entirely dependable and driven by the prediction of wind speed, which by the nature of wind is very stochastic and widely random. This s0tudy employs deep multi-fidelity Gaussian process regression, used to predict wind speeds for medium term time horizons. Data of the RUNE experiment in the west coast of Denmark were provided by the Technical University of Denmark, which represent the wind speed across the study area from the period between December 2015 and March 2016. The study aims to investigate the effect of pre-processing the data by denoising the signal using empirical wavelet transform (EWT) and engaging the vector components of wind speed to increase the number of input data layers for data fusion using deep multi-fidelity Gaussian process regression (GPR). The outcomes were compared using root mean square error (RMSE) and the results demonstrated a significant increase in the accuracy of predictions which demonstrated that using vector components of the wind speed as additional predictors exhibits more accurate predictions than strategies that ignore them, reflecting the importance of the inclusion of all sub data and pre-processing signals for wind speed forecasting models.

Keywords: data fusion, Gaussian process regression, signal denoise, temporal extrapolation

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Authors: Tran Thanh Le, Pham Trong Duc

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The fresh groundwater lens of the Pleistocene aquifer in Nam Dinh was formed since 12,900 years ago. Currently, the Pleistocene aquifer has been continuously exploited on average of 154,163m3/day, distributed mainly in the districts of Nghia Hung, Hai Hau, a part of Truc Ninh, Y Yen, Nam Truc and Giao Thuy. The groundwater level is still on a declining trend, saltwater intrusion in this freshwater lens can occur if the growth rate in exploitation is maintained. This study focused on groundwater sustainable use by means of 4 groups of criteria including: Groundwater quality and pollution; Aquifers’ productivity and capacity; Environment impacts due to exploitation (groundwater level decline, land subsidence due to water exploitation); Social and economic impacts. Using a combination of methods including field surveys, geophysics, hydrogeochemistry, isotope and numerical models to determine safe groundwater exploitation thresholds for the whole study area has been determined to be 544,314m3/day and the actual exploitation amount is currently about 30% compared to the safe exploitation threshold. However, it should also be noted that the current groundwater exploitation threshold and level of its exploitation compared to the safe exploitation threshold of each locality are not the same. From this result, the groundwater exploitation threshold map of the study area was established to serve the management, licensing and orientation of groundwater exploitation.

Keywords: criteria, groundwater, fresh groundwater lens, pleistocene, Nam Dinh

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Authors: Somveer Singh, Vineet Kumar Singh

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This work contributes a numerical method based on Legendre wavelet approximation for the treatment of partial integro-differential equation (PIDE). Operational matrices of Legendre wavelets reduce the solution of PIDE into the system of algebraic equations. Some useful results concerning the computational order of convergence and error estimates associated to the suggested scheme are presented. Illustrative examples are provided to show the effectiveness and accuracy of proposed numerical method.

Keywords: legendre wavelets, operational matrices, partial integro-differential equation, viscoelasticity

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Authors: Ramesh Chandra Patra, Ajay P. Gautam, Patitapaban Mohanty

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Headache disorders are one of the 10 most disabling conditions for men and women. Headache that originated from upper cervical spine and refereed to the one side of the head and/or face is known as cervicogenic headache (CH) which constitute15% to 20% among all the headaches. In our best knowledge manual therapy is often advocated for managing CH, but very little focus given on muscle system although it is a musculoskeletal disorder. In this study, 75 patients with CH were selected and divided into two groups Group A: Manual therapy and Group B: dry needling along with manual therapy group. Assessment was done using NPRS (0-10) for pain, wide spread pressure pain threshold using an algometer at the beginning and end of the study. There is a consistent reduction in pain and tenderness in both the group but significant improvement was shown in combined group. Outcome of the study has explored that the effectiveness of dry needling along with Mulligan is more beneficial in patients with cervicogenic headaches.

Keywords: cervicogenic headaches, dry needling, NPRS, pressure point threshold

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Authors: N. Dahraoui, M. Boulakroune, D. Benatia

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In this paper, the improvement by deconvolution of the depth resolution in Secondary Ion Mass Spectrometry (SIMS) analysis is considered. Indeed, we have developed a new Tikhonov-Miller deconvolution algorithm where a priori model of the solution is included. This is a denoisy and pre-deconvoluted signal obtained from: firstly, by the application of wavelet shrinkage algorithm, secondly by the introduction of the obtained denoisy signal in an iterative deconvolution algorithm. In particular, we have focused the light on the effect of the iterations number on the evolution of the deconvoluted signals. The SIMS profiles are multilayers of Boron in Silicon matrix.

Keywords: DRF, in-depth resolution, multiresolution deconvolution, SIMS, wavelet shrinkage

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Authors: Obe Olumide Olayinka, Victor Balanica, Eugen Neagoe

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The effects of hypertension are often lethal thus its early detection and prevention is very important for everybody. In this paper, a neural network (NN) model was developed and trained based on a dataset of hypertension causative parameters in order to forecast the likelihood of occurrence of hypertension in patients. Our research goal was to analyze the potential of the presented NN to predict, for a period of time, the risk of hypertension or the risk of developing this disease for patients that are or not currently hypertensive. The results of the analysis for a given patient can support doctors in taking pro-active measures for averting the occurrence of hypertension such as recommendations regarding the patient behavior in order to lower his hypertension risk. Moreover, the paper envisages a set of three example scenarios in order to determine the age when the patient becomes hypertensive, i.e. determine the threshold for hypertensive age, to analyze what happens if the threshold hypertensive age is set to a certain age and the weight of the patient if being varied, and, to set the ideal weight for the patient and analyze what happens with the threshold of hypertensive age.

Keywords: neural network, hypertension, data set, training set, supervised learning

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Authors: Ajish Sreedharan

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Visual cryptography encodes a secret image into shares of random binary patterns. If the shares are exerted onto transparencies, the secret image can be visually decoded by superimposing a qualified subset of transparencies, but no secret information can be obtained from the superposition of a forbidden subset. The binary patterns of the shares, however, have no visual meaning and hinder the objectives of visual cryptography. In the Secret Message Transmission through Meaningful Shares a secret message to be transmitted is converted to grey scale image. Then (2,2) visual cryptographic shares are generated from this converted gray scale image. The shares are encrypted using A Chaos-Based Image Encryption Algorithm Using Wavelet Transform. Two separate color images which are of the same size of the shares, taken as cover image of the respective shares to hide the shares into them. The encrypted shares which are covered by meaningful images so that a potential eavesdropper wont know there is a message to be read. The meaningful shares are transmitted through two different transmission medium. During decoding shares are fetched from received meaningful images and decrypted using A Chaos-Based Image Encryption Algorithm Using Wavelet Transform. The shares are combined to regenerate the grey scale image from where the secret message is obtained.

Keywords: visual cryptography, wavelet transform, meaningful shares, grey scale image

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Authors: Enqing Chen, Jianbo Wang

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It is well known that the wavelet transform provides a very effective framework for multiscale edges analysis. However, wavelets are not very effective in representing images containing distributed discontinuities such as edges. In this paper, we propose a novel multiscale edge detection method in nonsubsampled contourlet transform (NSCT) domain, which is based on the dominant multiscale, multidirection edge expression and outstanding edge location of NSCT. Through real images experiments, simulation results demonstrate that the proposed method is better than other edge detection methods based on Canny operator, wavelet and contourlet. Additionally, the proposed method also works well for noisy images.

Keywords: edge detection, NSCT, shift invariant, modulus maxima

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Authors: Hadjadj Abdechafik, Kious Mecheri, Ameur Aissa

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The objective of this study is to develop a process of treatment of the vibratory signals generated during a horizontal high speed milling process without applying any coolant in order to establish a monitoring system able to improve the machining performance. Thus, many tests were carried out on the horizontal high speed centre (PCI Météor 10), in given cutting conditions, by using a milling cutter with only one insert and measured its frontal wear from its new state that is considered as a reference state until a worn state that is considered as unsuitable for the tool to be used. The results obtained show that the first harmonic follow well the evolution of frontal wear, on another hand a wavelet transform is used for signal processing and is found to be useful for observing the evolution of the wavelet approximations through the cutting tool life. The power and the Root Mean Square (RMS) values of the wavelet transformed signal gave the best results and can be used for tool wear estimation. All this features can constitute the suitable indicators for an effective detection of tool wear and then used for the input parameters of an online monitoring system. Although we noted the remarkable influence of the machining cycle on the quality of measurements by the introduction of a bias on the signal, this phenomenon appears in particular in horizontal milling and in the majority of studies is ignored.

Keywords: flank wear, vibration, milling, signal processing, monitoring

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Authors: Mohammad H. Fattahi

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Chaotic analysis has been performed on the river flow time series before and after applying the wavelet based de-noising techniques in order to investigate the noise content effects on chaotic nature of flow series. In this study, 38 years of monthly runoff data of three gauging stations were used. Gauging stations were located in Ghar-e-Aghaj river basin, Fars province, Iran. The noise level of time series was estimated with the aid of Gaussian kernel algorithm. This step was found to be crucial in preventing removal of the vital data such as memory, correlation and trend from the time series in addition to the noise during de-noising process.

Keywords: chaotic behavior, wavelet, noise reduction, river flow

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