Perceptual compressive sensing for image signals

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

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

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.

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.

Intradialytic hypotension, a major source of morbidity during hemodialysis and ultrafiltration, is often accompanied by paradoxical bradycardia. Relatively little is known about the sequential changes in autonomic nervous system activity up to and during the hypotensive episode. Continuous, beat-to-beat measurements of BP and heart rate were made during hemodialysis in patients prone (n = 8) and not prone (n = 11) to develop intradialytic hypotension. Off-line spectral analysis of heart rate variability (HRV) was performed to assess changes in autonomic nervous system activity during dialysis sessions both with and without hypotension. The low frequency (LF) component of HRV is thought to correlate with sympathetic nervous system activity, the high frequency (HF) component with that of the parasympathetic nervous system. In the sessions not complicated by symptomatic hypotension (n = 26), mean arterial BP (MAP) hardly fell, whereas heart rate increased from 77 +/- 2 to 89 +/- 5 bpm (P < 0.05). The LF component of HRV increased from 45.2 +/- 5.0 normalized units (nu) to 59.9 +/- 4.9 nu (P < 0.05), whereas the HF component fell from 54.8 +/- 5.0 to 40.2 +/- 4.4 nu (P < 0.05). These changes agree with compensatory baroreflex-mediated activation of the sympathetic nervous system (and suppressed parasympathetic activity) during ultrafiltration-induced intravascular volume depletion. In the sessions complicated by severe symptomatic hypotension (n = 22), the changes in heart rate and the results of spectral analysis of HRV were similar to those reported above up to the moment of sudden symptomatic (nausea, vomiting, dizziness, cramps) hypotension, whereas MAP had already fallen gradually from 94 +/- 3 to 85 +/- 3 mmHg (P < 0.05). The sudden further reduction in MAP (to 55 +/- 2 mmHg, P < 0.02) was invariably accompanied by bradycardia (heart rate directly before hypotension 90 +/- 2 bpm, during hypotension 69 +/- 3 bpm, P < 0.002). The LF component of HRV fell from 62.8 +/- 4.6 nu directly before to 40.0 +/- 3.7 nu (P < 0.05) during hypotension, whereas the HF component increased from 37.9 +/- 4.7 to 60.3 +/- 3.7 nu (P < 0.05). These findings agree with activation of the cardiodepressor reflex, involving decreased sympathetic and increased parasympathetic nervous system activity, respectively. These findings indicate that activation of the sympatho-inhibitory cardiodepressor reflex (Bezold-Jarisch reflex), which is a physiologic response to a critical reduction in intravascular volume and cardiac filling, is the cause of sudden intradialytic hypotension.

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.

Mammalian skeletal muscles are composed of a mixture of motor unit types, which contribute a range of mechanical and physiological properties to the muscle. For a muscle to effectively contribute to smooth, co-ordinated movement it must activate an appropriate number and combination of motor units to generate the required force over a suitable time period. Much evidence exists indicating that motor units are activated in an orderly fashion, from the slowest through to the fastest. A growing body of evidence, however, indicates that such a recruitment strategy does not always hold true. Here we investigate how motor unit recruitment patterns were influenced by changes in locomotor velocity and incline. Kinematics data and myoelectric signals were collected from three rat ankle extensor muscles during running on a treadmill at nine velocity and incline combinations. Wavelet and principal component analysis were used to simultaneously decompose the signals into time and frequency space. The relative frequency components of the signals were quantified during 20 time windows of a stride from each locomotor condition. Differences in signal frequency components existed between muscles and locomotor conditions. Faster locomotor velocities led to a relative increase in high frequency components, whereas greater inclines led to a relative increase in the low frequency components. These data were interpreted as representing changes in motor unit recruitment patterns in response to changes in the locomotor demand. Motor units were not always recruited in an orderly manner, indicating that recruitment is a multi-factorial phenomenon that is not yet fully understood.

Each resident-type (R-type) killer whale pod has a set of stereotyped calls that are culturally transmitted from mother to offspring. The functions of particular call types are not yet clearly understood, but it is believed that calls with two independently modulated frequency components (biphonic calls) play an important role in pod communication and cohesion at long ranges. In this study we examined the possible functions of biphonic calls in R-type killer whales. First, we tested the hypothesis that the additional component enhances the potential of a call to identify the family affiliation. We found that the similarity patterns of the lower- and higher frequency components across the families were largely unrelated. Calls were classified more accurately to their respective family when both lower- and higher-frequency components were considered. Second, we tested the long-range detectability of the lower- and higher-frequency components. After adjusting the received levels by the killer whale hearing sensitivity to different frequency ranges, the sensation level of the higher-frequency component was higher than the amplitude of the lower-frequency component. Our results suggest that the higher-frequency component of killer whale biphonic calls varies independently of the lower-frequency component, which enhances the efficiency of these calls as family markers. The acoustic variation of the higher-frequency component allows the recognition of family identity of a caller even if the shape of the lower-frequency component accidentally becomes similar in unrelated families. The higher-frequency component can also facilitate family recognition when the lower-frequency component is masked by low-frequency noise.

Pixel-level image fusion, which is widely used in remote sensing, medical imaging, surveillance and etc., directly combines the original information in the source images. As a pixel-level method, multi-focus image fusion is designed to combine the partially focused images into one fully fused single image, which is expected to be more informative for human or machine perception. To achieve this purpose, an algorithm using spatial frequency (SF) measure and discrete wavelet transform (DWT) for multi-focus image fusion is proposed. In this work, the source images are decomposed into low frequency components and high frequency components by using DWT. Then the spatial frequency of the low frequency components is calculated. The spatial frequency is used to judge the focused regions, followed by the morphological filter and median filter. The fused low frequency can be obtained. And the high frequency components are fused using traditional method. Finally, the fused image is obtained by doing inverse discrete wavelet transform. To do the comparison, the proposed algorithm is compared with several existing fusion algorithms in qualitative and quantitative ways. Experimental results demonstrate that our method can be competitive or even outperforms the methods in comparison.

In this paper, a multi-scale stationary wavelet decomposition technique combined with functional auto-regression is used to improve the prediction accuracy and parsimony of anchovy monthly catches forecasting in area north of Chile (18 21'S-24 S). The general idea behind this approach is to decompose the observed anchovy catches data into low frequency (LF) component and high frequency (HF) component by using stationary wavelet transform and to separately forecast each frequency component. The forecasting strategy was evaluated for a period of 42 years, starting from 1-Jun-1963 to 31-Dec-2007 and we find that the proposed forecasting method achieves a 98% of the explained variance with a reduced parsimony 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 functional autoregressive model.

Physiology and clinical implications of variability of cardiovascular parameters with focus on heart rate and blood pressure

This paper discusses underdetermined blind source separation (BSS) using a compressed sensing (CS) approach, which contains two stages. In the first stage we exploit a modified K-means method to estimate the unknown mixing matrix. The second stage is to separate the sources from the mixed signals using the estimated mixing matrix from the first stage. In the second stage a two-layer sparsity model is used. The two-layer sparsity model assumes that the low frequency components of speech signals are sparse on K-SVD dictionary and the high frequency components are sparse on discrete cosine transformation (DCT) dictionary. This model, taking advantage of two dictionaries, can produce effective separation performance even if the sources are not sparse in time-frequency (TF) domain.

Association of estrogen receptor-α gene polymorphisms with cardiac autonomic nervous activity in healthy young Japanese males