Multiscale wavelet decomposition based functional autoregression for monthly anchovy catches forecasting

In this paper, we use a functional autoregressive (FAR) model combined with multi-scale stationary wavelet decomposition technique for one-month-ahead monthly sardine catches forecasting in northern area of Chile (18 o 21***S *** 24 o S ).The monthly sardine catches data were collected from the database of the National Marine Fisheries Service for the period between 1 January 1973 and 30 December 2007. The proposed forecasting strategy is to decompose the raw sardine catches data set into trend component and residual component by using multi-scale stationary wavelet transform. In wavelet domain, the trend component and residual component are predicted by use a linear autoregressive model and FAR model; respectively. Hence, proposed forecaster is the co-addition of two predicted components. We find that the proposed forecasting method achieves a 99% of the explained variance with a reduced parsimonious and high accuracy. Besides, is showed that the wavelet-autoregressive forecaster is more accurate and performs better than both multilayer perceptron neural network model and FAR model.

Streamflow forecasting is crucial in hydrology and hydraulic engineering since it is capable of optimizing water resource systems or planning future expansion. This study investigated the performances of three different soft computing methods, multilayer perceptron neural network (MLPNN), optimally pruned extreme learning machine (OP-ELM), and evolutionary polynomial regression (EPR) in forecasting daily streamflow. Data from three different stations, Soleyman Tange, Perorich Abad, and Ali Abad located on the Tajan River of Iran were used to estimate the daily streamflow. MLPNN model was employed to determine the optimal input combinations of each station implementing evaluation criteria. In both training and testing stages in the three stations, the results of comparison indicated that the EPR technique would generally perform more efficiently than MLPNN and OP-ELM models. EPR model represented the best performance to simulate the peak flow compared to MLPNN and OP-ELM models while the MLPNN provided significantly under/overestimations. EPR models which include explicit mathematical formulations are recommended for daily streamflow forecasting which is necessary in watershed hydrology management.

Relation Between Depressed Cardiac Response to Exercise and Autonomic Nervous Activity in Mildly Symptomatic Patients With Idiopathic Dilated Cardiomyopathy

Music is known to reduce pain, anxiety and fear in several stressful conditions in both males and females. Further, listening to preferred music enhances the endurance during running performance of women rather than listening to non-preferred music. In recent years Heart Rate Variability (HRV) has been used as an indicator of autonomic nervous activity. This study was aimed to assess the effectiveness of preferential music on HRV after moderate exercise. This was an experimental study done in 30 healthy students aged between 20-25 years, of either sex. HRV was measured at rest, 15 minutes of exercise only and 15 minutes of exercise with listening preferential music in same participants. Data was analysed by One-Way ANOVA and Tukey HSD Post-hoc Test. Statistical significance was taken to be a p-value of less than 0.05. Low frequency and high frequency component was significantly increased followed by only exercise. Music minimized increase in both high and low frequency component followed by exercise. However, only high frequency change was statistically significant. LF/HF ratio was significantly increased followed by only exercise. Music significantly minimized increase in LF/HF ratio. This study provides the preliminary evidence that listening to preferential music could be an effective method of relaxation, as indicated by a shift of the autonomic balance towards the parasympathetic activity among medical students.

Coding shape by means of frequency analysis ot the boundaryA. Grattarola ,S. Gaglio, L. Massone, P. MorassoDepartment of Communication, Computer and Systems Science University of Genova - Via Opera Pia 11A, 1-16145 Genoa, ItalyAbstractIn this paper an approach to the analysis of boundary in planar shapes is presented. It is possible to distinguish, in the of an object, between a structure and the overlying details or textures; at this purpose,- a complementarity principle for the description of shape is proposed. It is then suggested that the two entities can be separated in terms of frequency analysis, the low frequency component being associated with the main structure, and high frequency components with details/textures. A representation for high-frequency components is proposed, based on the zero-crossing of details/textures which extends to the contours of shapes the theorem by Logan.IntroductionCoding the shape contours can be considered from two different points of wiew; the first is concerned with the data compression aspects only (the link between the resulting data and the basic features of the original shape is not taken into account), while in the second the main purpose is to generate a data suitable for further processing tasks. In the second approach, which is the topic of this work, it is then necessary to define a model for representing shape. Defining shape models is of primary importance in all those areas in which applications require to C°|P^ with real world objects. This is the case of pattern, recognition and image analysis ' , computer graph­ ics , solid modelling , robot vision ' .Different object modelling schemes have been proposed, but all of them are peculiar of the particular application domain and capture only a few aspects of the richness of the phisical world as, in contrast, is perceived by humans.An important intuitive notion, useful to understand the concept of shape, is the dis­ tinction, in the of objects, between a structure and the overlying details or textures. For example, different door keys might be characterized by a similar (an oblongated protuberance linked to a rounded body) but by different (the teeth of the key). A complementarity principle for the description of shape is then introduced:Shape? demands description at different levels of detail: global descriptions require low detail whereas local descriptions require higher detail. The relative emphasis dependson the application.The complementarity principle implies that the shape of natural objects cannot be described uniquely: it is possible indeed to go from the molecular to the macroscopic passing through a large number of significant levels of description.The suggestion that is put forward in this work is that the two entities can be separated in terms of frequency analysis, the low frequency component being associated with the main structure, and the high frequency component with -details/textures. A representation for the low frequency component has been proposed in ' ; this paper shows how the high frequency component can be extracted and coded.Filtering the boundary of shapesLet's consider planar shapes without holes, that is shapes with connected contours (holes can, in fact, be treated separately and their representation added to the shape description). The contour of a planar shape is the hodograph of a periodical 2-D vector

A systematic study has been made of the infrared absorption band near 725 cm—1 which arises from the rocking vibration of methylene groups in n-paraffins and in polyethylene. In unoriented crystals of n-paraffins, this band exhibits two components of equal intensity below the transition point; above the transition temperature and in the liquid state only the higher frequency component is found. In solid cold-drawn polyethylene the two components are of unequal intensity, the low frequency component being the stronger; in liquid polyethylene only the lower frequency component is found. Studies were made of polyethylene in various states of crystallinity and orientation, using both polarized and unpolarized radiation. The results obtained can be consistently interpreted, if the higher frequency component is attributed to crystallites in the polyethylene and the lower frequency component is assumed to be due partly to the crystalline and partly to the amorphous form of the polyethylene. It is concluded that the doubling of this frequency arises from some interaction between methylene groups which is peculiar to the crystalline state of long chain n-paraffins below their transition points.

Accurate forecasts of how animals respond to climate‐driven environmental change are needed to prepare for future redistributions, however, it is unclear which temporal scales of environmental variability give rise to predictability of species distributions. We examined the temporal scales of environmental variability that best predicted spatial abundance of a marine predator, swordfish Xiphias gladius , in the California Current. To understand which temporal scales of environmental variability provide biological predictability, we decomposed physical variables into three components: a monthly climatology (long‐term average), a low frequency component representing interannual variability, and a high frequency (sub‐annual) component that captures ephemeral features. We then assessed each component's contribution to predictive skill for spatially‐explicit swordfish catch. The monthly climatology was the primary source of predictability in swordfish spatial catch, reflecting the spatial distribution associated with seasonal movements in this region. Importantly, we found that the low frequency component (capturing interannual variability) provided significant skill in predicting anomalous swordfish distribution and catch, which the monthly climatology cannot. The addition of the high frequency component added only minor improvement in predictability. By examining models' ability to predict species distribution anomalies, we assess the models in a way that is consistent with the goal of distribution forecasts – to predict deviations of species distributions from their average historical locations. The critical importance of low frequency climate variability in describing anomalous swordfish distributions and catch matches the target timescales of physical climate forecasts, suggesting potential for skillful ecological forecasts of swordfish distributions across short (seasonal) and long (climate) timescales. Understanding sources of prediction skill for species environmental responses gives confidence in our ability to accurately predict species distributions and abundance, and to know which responses are likely less predictable, under future climate change. This is important as climate change continues to cause an unprecedented redistribution of life on Earth.

A novel non-ferrous metal price hybrid forecasting model based on data preprocessing and error correction

Model inversion of BCVL based on DWD of MISR image

In this paper, the aim is to apply a functional autoregressive (FAR) model combined with wavelet smoothing analysis for monthly bigeye tuna catches forecasting in the equatorial Indian ocean. The raw monthly tuna catches data is smoothed by using discrete stationary wavelet transform and then appropriate is used as inputs to the FAR model. We find that the proposed forecasting strategy achieves $97\%$ of the explained variance with reduced parsimony and high accuracy.

Lithofacies prediction in non-cored wells from the Sif Fatima oil field (Berkine basin, southern Algeria): A comparative study of multilayer perceptron neural network and cluster analysis-based approaches

We focus on the discussion of Wavelet transform profilometry based on dual-frequency fringe projection in this work. Employing a deformed fringe pattern with dual-frequency components captured by a CCD camera, we can calculate two wavelet ridge-lines from its continuous wavelet transform coefficients. Then, two sets of wrapped phase maps with difference resolution can be extracted. The longer the equivalent wave length is, the lower the measurement accuracy is. However, the direct shape reconstruction is not accuracy from low frequency component even though the fringe order from the low frequency component is easier to obtain correctly than from the high frequency component. Therefore the fringe order from low frequency component is used to guide the calculation of the fringe order of the high frequency component in dual-frequency fringe projection technique. Employing the relationship between the two carriers f2 and f1 (supposing f1<f2). we discuss the structure condition and sampling condition of wavelet transform profilometry based on dual-frequency fringe projection from the aspect of frequency analysis, which guarantee the correct shape reconstruction from the dual-frequency fringe. We finished the theoretical derivation and computer simulations to verify the correction of the theory.

The aim of this study is classification of correlative data through comparison between ECG-Derived Respiration (EDR) and High Frequency (HF) component obtained frequency analysis of heart rate variability (HRV) from ECG using two of conductive textiles. Generally, individual frequency range of the HF component has been determined only by means of distinctive parameters of respiration such as respiratory rate, range and median value of respiratory rate, etc. However, in many cases Low Frequency (LF) components and High Frequency (HF) components may be superimposed on each other totally or particularly and can not be diagnostically estimated because respiratory rhythms are individually much remarkably differentiated. This study, in consequence, analyze frequency component of EDR derived from ECG and then compare with high frequency components of HRV and finally find out valuable data for further analysis. Hardware constitution is used to bed type ECG measurement equipment based on U-Healthcare, can identify user using Radio Frequency (RF) module as well as manage data each of user. We used Frequency Modulation (FM) method based on Respiration Sinus Arrhythmia (RSA) in order to extract EDR from ECG signal. ECG data and EDR data were processed with Lab-VIEW, then classified through each of frequency analysis.

Crispness is regarded as a significant quality index for apples. Currently, destructive sensory evaluation is the accepted method used to detect apple crispness, making it essential to develop a method that can detect apple crispness in a nondestructive manner. In this study, spectroscopy was proposed as the nondestructive technique for detecting apples' crispness, ultimately obtaining a spectral reflectance curve between 450 nm and 1,000 nm. In order to simplify and improve modeling efficiency, successive projections algorithm (SPA) and x‐loading weights (x‐LW) methods were used to select the most effective wavelengths. Partial least squares (PLS) algorithm, radial basis neural networks (RBNN), and multilayer perceptron neural networks (MLPNN) methods were used to establish the models and to predict the crispness of “Fuji” and “Qinguan” apple varieties. Based on the full wavelength (FW), the prediction accuracy of the PLS model for “Fuji” and “Qinguan” apple varieties was 92.05% and 95.87%, respectively. The effective wavelengths selected via SPA for the “Fuji” apple variety were 450.41 nm, 476.80 nm, 677.75 nm, and 750.72 nm, and the effective wavelengths selected via x‐LW for the “Qinguan” apple variety were 542.51 nm, 544.79 nm, 676.96 nm, and 718.29 nm. The prediction accuracy of the PLS model based on effective wavelengths for “Fuji” and “Qinguan” apple varieties reached 91.31% and 96.41%, respectively. Compared with the RBNN model, the MLPNN model achieved better prediction results for both “Fuji” and “Qinguan” apples, with the prediction accuracy reaching 97.8% and 99.9%, respectively. Based on the above findings, effective wavelength selection and MLPNN modeling were able to detect apple crispness with the highest accuracy. Overall, it can be concluded that the less effective wavelengths are conducive to developing an instrument for crispness detection.

Human eyes have different sensitivity to different frequency components of image signals, typically, low frequency components are relatively more crucial to the perceptual quality of images than high frequency components. Based on this observation, we propose a novel sampling scheme for compressive sensing framework by designing a weighting scheme for the sampling matrix. By adjusting the weighting coefficients, we can tune the structure of the sampling matrix to favor the frequency components that are important to human perception, so that those components could be more precisely recovered in the reconstruction procedure. Experimental results reveal that our proposed scheme can greatly enhance the performance of compressive sensing framework in both PSNR and visual quality without increasing the complexity of the framework structure or computational procedure.