16 In Figure 3B, the bar chart shows the strength of the correlation between envelope amplitude and connectivity, averaged over regions within individual frequency bands. Note that the relationship peaks in the theta to low gamma range although significance compared to surrogate data (denoted by *) extends to the delta band. The inset brain images show the cortical AAL regions that are driving this relationship for each frequency band. The slopes are plotted for regions that show a significant correlation. Note that correlations are found to be widespread across the entire cortex, but where slopes are strongest in the alpha band in occipital regions and in beta in the sensorimotor regions. As a post-hoc analysis we selected time windows of low and high amplitude oscillations (threshold based on the mean amplitude envelopes (over regions) plus/minus 2 standard deviations respectively), and computed phase connectivity between all possible AAL region pairs, represented as a 78 x 78 weighted adjacency matrix, within these windows. Results are shown in Figure 3C and D. Note that across all frequency bands, high oscillatory amplitude coexists with strong network connectivity structure whereas low amplitude oscillations coincide with weak (unstructured) network connectivity. Note that results obtained with surrogate data based upon a uniform phase randomization process show a similar effect (Figure S8).
An original high resolution range profile (HRRP), a complex vector, is composed of amplitude and phase information of coherent summations of the complex returns from target scatterers in each range cell, which represents the projection of the complex returned echoes from the target scattering centers onto the radar line-of-sight (LOS) [1, 2]. Among several kinds of the windband radar target signatures, such as 2-D and 3-D radar target images , HRRP is a promising signature and more easy to be acquired in actual application, but it is highly sensitive to target-aspect, time-shift and amplitude-scale variations [4–6], so how to extract robust and effective feature from the raw signal becomes a key problem in HRRP-based radar automatic target recognition (RATR). During the past decade, many measured and simulated experimental results also confirmed that some physical structure information naturally contained in complex HRRPs, such as target size , scatterer distribution [8, 9], amplitude fluctuation [10–16], is very beneficial to HRRP-based RATR, and accordingly, a number of statistical methods have been proposed for feature extraction and dimension reduction.
Both the YBT and FMS are purported to assess dynamic postural control, stability, mobility, movement patterns, functional symmetry, and identify individuals that are at elevated risk of injury [2, 5, 12]. Accordingly, we would expect that YBT reach distances and FMS scores should be correlated. This relationship has been indirectly investigated in several studies. For example, the FMS scores and YBT reach distances have been compared between student-athletes and general college students . There was no significant difference between these groups in the aggregate FMS score; however female athletes reached further than general college students in all directions in the YBT . Another study administered the FMS and the YBT in 200 NCAA Division I athletes and found that individuals with a self-reported history of injury or surgery had significantly lower aggregate FMS scores . They also reported that female athletes had lower scores on some of the individual tests within the FMS (TSPU and RS) and higher scores on other tests (ILL, SM, and ASLR ). However, they did not observe statistically significant differences in the YBT reach distances between individuals with and without a self-reported history of injury or surgery, nor between male and female participants . The YBT reach distances and FMS scores have also been combined in the Move2Perform algorithm . This proprietary algorithm uses demographic information, injury history, and the FMS scores and YBT reach distances to assess injury risk by placing participants into four risk categories (normal, slight, moderate, and substantial). The efficacy of this tool was investigated in a group of NCAA athletes during one competitive season; they found a significant difference in lower extremity injury risk when the ‘moderate’ and ‘substantial’, and ‘slight’ and ‘normal’ were grouped together (reducing the number of risk categories to ‘high risk’ and ‘low risk’ ). Normative FMS and YBT data in a population of military personnel has also been reported . That study found increased FMS, power, mobility, and balance scores in individuals younger than 30 years of age compared to those older than 30. They also reported that men had higher balance, power, and stability scores than women . One recent study determined there was no correlation between individuals’ scores on the FMS and YBT anterior right-left difference and composite reach directions in a military population . Since these earlier studies have investigated the relationship between FMS scores and the YBT in college-aged students and military personnel, the purpose of the current study was to directly assess this relationship in a healthy, general population, in order to determine whether dynamic postural control is a
An observation to make is that, unlike the amplitude comparator output of the AD8302 module, the phase output of the detection module is not linear. This means that for each output of the phase detector that’s read, it has 2 interpretations, one positive value and one negative value. For example, a phase detector output of 1.2V should both represent a -60° phase difference and a 60° phase difference. This means that for each value received from the phase detector output, both possible phase differences have to go through the equation above and 4 possible directions that the RF source can be coming from are calculated. This also means that both antenna pairs, relative to the 0° reference, reveal the same 4 possible directions, one per quadrant.
illustrated the quadrature oscillator's phase accuracy describing the generalized Adler's equation. In the work of Ghonoodi and Miar Naimi  a new approach was used to find the amplitude and phase error of a quadrature oscillator using different coupling factors. As it can be seen the mentioned works mostly focused on the quadrature phase accuracy except for  which considered the same coupling factors for all stages. So, in this work we derive closed form equations for phase error and output amplitude of an oscillator with arbitrary stage numbers considering different coupling factors for each stage while trying to improve the achieved results reported elsewhere .
Abstract --- Fractional Fourier Transform is a generalization of the classical Fourier Transform, which has found its most useful applications for the transient analysis of the signals. This paper studies the span of the Fractional Fourier Transform in relation with the amplitude and phase functions of the signal and provides a mathematical derivation for the generalized case. The derived expression is shown to be useful for calculating the optimal transform order to achieve minimal possible span for various signals. Based on the derived expression, we further establish the fact that the Fractional Fourier Transform (other than the transform order of π 2 ) is most effective for the analysis of chirp signals. We provide a comparison of our method for finding optimal transform order with previously given methods and show that it provides better results.
surface snow temperature is observed about 10 days later, close to 30 December (continuous measurements since 2010, Lefebvre et al. (2012), J.-R. Petit, personal communication, 2014). At Dome C, 3 years continuous measurements of sur- face snow temperature between 2006 and 2009 have shown that the maximum of temperature occurs 15–20 days after the summer solstice (Landais et al., 2012, confirmed by the continuous measurements since then). These regional differ- ences highlight the fact that, today, surface snow tempera- ture does not reach its summer maximum in phase with local summer solstice insolation. As a consequence, different inso- lation target curves for δO 2 /N 2 should be considered for the
Renal biopsies in 45 patients with insulin-dependent diabetes mellitus (IDDM) were examined by semiquantitative light microscopy and quantitative electron microscopic stereologic morphometry. In these 14 males and 31 females, aged 13-52 yr, who had had IDDM for 2.5-29 yr there was no strong relationship between either glomerular basement membrane (GBM) thickness or mesangial expansion and duration of IDDM. There was only a weak relationship between the thickness of the GBM and expansion of the mesangium. Thus, GBM thickening and mesangial expansion in IDDM occur at rates that often differ from one another and that vary greatly among patients. The clinical manifestations of diabetic nephropathy, albuminuria, hypertension, and decreased glomerular filtration rate related poorly or not at all to GBM thickening. In contrast, all light and electron microscopic measures of mesangial expansion were strongly related to the clinical manifestations of diabetic nephropathy, although in the absence of these clinical findings, it was not possible to predict the severity of any of the diabetic glomerular lesions. Mesangial expansion had strong inverse correlations with capillary filtering surface area density. It is hypothesized that mesangial expansion could lead to glomerular functional deterioration in IDDM by
If one is able to accomplish the required level of photometric precision, the greatest impediment to studying the planetary phasevariations will be the intrinsic stellar variability. An analysis of Kepler data by Ciardi et al. (2011) found that most dwarf stars are stable down to the precision of the Kepler spacecraft, with G dwarfs being the most stable of the studied spectral types. The main cause of photometric variability in F–G–K stars is starspots and rotation. The rotation period for 55 Cnc has been measured on numerous occasions through photometric variations. Simpson et al. (2010) calculate a rotation period of 44.1 days and Fischer et al. (2008) measure a rotation period of 44.7 days. Winn et al. (2011) also observed variation of the order 10 − 4 , which is assumed to be the result of both stellar activity and rotation. For the c planet, where the orbital period is close to the rotation period of the star, the variation due to phase and rotation may be difficult to disentangle. The peaks in the power spectrum from a Fourier analysis of the photometry may separate to a degree where the starspot variability can be isolated from the phase signature. The known phase of the planet from the RV analysis will be the greatest aid in discriminating these two signals. It should also be noted that there is an M dwarf binary companion, 55 Cnc B, with an angular separation of 84. 7 (1150 AU) (Mugrauer et al. 2006), so it is unlikely to be inside a photometric aperture.
ABSTRACT: The phenotypic relationshipsbetween type traits and functional traits were analyzed in 47 786 Czech Fleckvieh cows first calved from 1994 to 2003. Functional survival was defined as the number of days from first calving to culling. All the cows were scored for conformation during the first lactation. Type information consisted of phenotypic type scores for 17 objectively scored linear type traits (with 9 classes each) and of the measurements for 6 body traits (measured in cm). The impact of the chosen conformation traits on functional longevity was estimated using the Cox proportional hazards model. The statistical model included the combined time-dependent effects of lactation and stage of lactation, age of the first calving, effects of the herd of culling, effects of year-season of culling, effects of the first lactation milk yield calculated within herd-year deviation. Analysis was performed separately for each of 23 type traits. The relative cull- ing risk was calculated for animals in each class after taking into account the previously mentioned effects. All the traits analysed showed a relationship with the functional survival. Among linear type traits, rump angle, body depth, rear legs side view and pastern, fore udder length, rear udder attachment, and teats traits exhibited an intermediate optimum. Height at the sacrum, muscularity, rump length, and rump width, hock, and hoof angle, central ligament, and udder depth tended toward a linear relationship to functional survival. Body measurement traits showed an almost linear relationship concerning longevity except for chest girth, which exhibited an intermediate optimum. Body measurement traits and body conformation traits had an impact on functional survival especially in extreme classes, e.g. extremely ascending rump. Larger, broader, muscular cows had a higher risk of being culled compared with smaller and narrower cows and, hence, a shorter length of productive live. Foot and leg traits had an important influence on functional longevity, especially rear legs side view and pastern. Among udder traits, fore udder length, rear udder attachment, and front teat placement showed a higher impact on cows’ longevity than the other analyzed udder traits.
Our study presented some limitations that should be noted while interpreting the results. First, participants living in a small area of Italy were involved, making it impossible to generalize the results to the entire Italian aged population. Second, the cross-sectional design of the research did not allow to establish the predictive value of frailty on disability, limiting the possibility to study the trajectories of frailty over time and their impact on the outcomes. Third, the sample size within the groups used in the ANOVA was, in some cases, very limited. This analysis should be intended as a first step toward understanding whether different frailty measures capture as frail individuals with different functional status in each of the domains analyzed. More data will be necessary to confirm the assumptions made in this paper. Finally, there was an overlapping between the indicators used as dependent variables in the ANOVA (the IPAQ and the CES-D) and the CHS index. In fact, the IPAQ and the two items of the CES-D were used to categorize frail individuals for physical activity level, and poor endurance and energy component, respec- tively. This overlapping may contribute to the differences found in the ANOVA, and further analysis with the use of a larger number of functional status indicators (eg, walking time, memory, social network size) will be needed.
First the targets and source shape are chosen. The aberration separable basis (ASB) can be pre-computed via computation of the TCC if aerial image data is used. Otherwise, the basis will need to be computed via PCA. Next, a simulated full factorial experiment is run. Using aerial image data this experiment results in a set of intensity volumes, and results in a set of ΔCD functions if using CD data. Each simulated function (intensity volume or ΔCD) is projected onto the ASB eigenfunctions and a model is constructed between the treatment combinations and projection coefficients. Each model consists of a system of polynomial equations fit to the projection coefficients via non-linear least squares. If the relationship is linear the model can be formalized as
Increasing in the oil prices can effect differently and directly or indirectly macroeconomic variables. There are many studies related to this issue in the literature and some of these studies indicate that oil prices effect considerably country economies and another group of these studies demonstrate that oil prices effect slightly or do not effect country economies. Furthermore, some of these studies show that there are non-linear or asymmetric relationshipsbetween oil prices and macroeconomic variables. The asymmetric relationshipsbetween oil prices and macroeconomic variables express that oil prices in the event that whether excessing a threshold value or not can effect the macroeconomic variables differently. While the econometric methodology is very important in case of these non-linear relationships, implementing policies are quite important in terms of economic. In case of non-linear relationships, models assuming linear relationships can cause to be biased and inconsistent estimation results. On the other hand, obtaining information about that how oil prices effect macroeconomic variables before and after the threshold level is important in terms of policies to be implemented on the national economy. Although oil prices uncontrollable because of speculations in the oil prices, getting information about that oil prices effect mostly or less macroeconomic variables at which level can provide important policy implications in the sense of non-oil producing countries.
structed the equation of motion of a rotating aspherical Earth following Woodhouse (1980) and sought its eigen- functions and eigenfrequencies. In this method, both spher- ically symmetric and aspherical free oscillation problems are included into a large-scale generalized non-Hermitian eigenvalue problem (Watada et al., 1993; Deuss and Wood- house, 2001) without assuming a ﬁducial frequency. As a result, eigenfunctions and eigenfrequencies within a wide frequency range are obtained simultaneously. A feature of this method is that when the non-sphericity of the Earth ap- proaches zero, the eigenfunctions and eigenfrequencies nat- urally come close to those of a spherically symmetric Earth model. In our computation, the Harvard CMT solution (http://www.seismology.harvard.edu/CMTsearch.html) has been adopted for the 2004 Sumatra-Andaman earthquake. Note that the absolute amplitude of 0 S 0 is different for var-
Amplitude: a t ( ) = x t ( ) + jH x t ( ( ) ) , (3) Phase: φ ( ) t = Im ln ( ( x t ( ) + jH x t ( ( ) ) ) ) , (4) where “ Im() ” denotes the imaginary part operator and “ ln() ” is the natural logarithm operator. After Hilbert transform, x t ( ) ( ) = a t cos φ ( ) t turns to a complex signal x t h ( ) ( ) = a t e j φ ( ) t , which is typical AM-FM signal with the AM component a t ( ) and the FM component e j φ ( ) t . In fact, the FM component e j φ ( ) t is a special AM-FM signal with the amplitude being 1. This process is called demodulation.
For our experiments we used the Berkeley Segmentation Dataset (BSDS300) , which is a com- monly used benchmark for density modeling of image patches and the tiny images dataset . For BSDS300 we follow the same setup of Uria et al. , which is best practice for this dataset. 8 by 8 grayscale patches are drawn from images of the dataset. The train and test sets consists of 200 and 100 images respectively. Because each pixel is quantized, it can only contain integer values between 0 and 255. To make the integer pixel values continuous, uniform noise (between 0 and 1) is added. Afterwards, the images are divided by 256 so that the pixel values lie in the range [0, 1]. Next, the patches are preprocessed by removing the mean pixel value of every image patch. Because this reduces the implicit dimensionality of the data, the last pixel value is removed. This results in the data points having 63 dimensions. For the tiny images dataset we rescale the images to 8 by 8 and then follow the same setup. This way we also have low resolution image data to evaluate on. In all the experiments described in this section, we used the following setup for training Deep GMMs. We used the hard-EM variant, with the aforementioned heuristic in the E-step. For each M-step we used LBFGS-B for 1000 iterations by using equations (13) and (14) for the objective and gradient. The total number of iterations we used for EM was fixed to 100, although fewer iterations were usually sufficient. The only hyperparameters were the number of components for each layer, which were optimized on a validation set.
The first 2D MS pulse sequence was proposed by Pfändler et al. for a Fourier transform ion cyclotron resonance mass spectrometer (FT-ICR MS) in 1987 [3 – 7]. For two decades, the demands of 2D MS in terms of data storage and processing exceeded commonly available computational capacities. The theory of 2D MS, as well as alternative pulse sequences for ion radius modulation such as stored waveform ion radius modu- lation and Hadamard transformation, was proposed and studied [8–11]. In 2010, 2D MS was revived and successfully applied to a commercial FT-ICR mass spectrometer with infrared mul- tiphoton dissociation (IRMPD), electron capture dissociation (ECD), and infrared activated electron capture dissociation (IR- ECD) as fragmentation methods [12–14]. Denoising algo- rithms have been proposed in order to decrease the amount of scintillation noise in 2D mass spectra and the pulse sequence was optimized [15–17]. 2D MS has been applied to small molecules and bottom-up and top-down proteomics, as well as polymer analysis [18 – 25]. Automated peak-assignment al- gorithms are being developed for routine analysis . A pulse sequence for 2D MS in a linear ion trap has been proposed, which expands the potential of 2D MS to mass analyzers beyond FT-ICR MS [27–29]. Nevertheless, the question of how to improve the signal-to-noise ratio and the resolving power of 2D mass spectra remains. One significant improve- ment has been proposed with the use of a non-uniform sam- pling technique . Another answer can be phase correction for absorption mode 2D mass spectra.
In Figs. 2(a) and 2(b) the amplitude of the radar reflectivity map and the height profile (measured in meters) used to simulate SAR images are shown. Fig. 2(c), instead, shows the inaccurate a priori DEM exploited in the estimation, exhibiting an height error with maximum values of ± 20 m respect to the true DEM, and with a standard deviation of 3.5 m. Note that the height error values producing a phase of π/4 in correspondence of the minimum and maximum baselines are 3.5 m and 1.2 m, respectively. Then, height error error values between 1.2 m and 3.5 m produce a not negligible unknown phase factor at least in one of the images considered, while height error values larger than 3.5 m produce an unknown phase factor in all images of the considered set.
Method 4. In terms of clustering quality . In the fi rst proposed method, for each alter- native class is the matrix of pair connectivity (for example, the absolute value of the Pear- son correlation coeffi cient) between variables, whose elements equal zero, if the calculated value is less than a certain threshold level. Classes for characteristics that are candidates for inclusion in the informative tuple linguis- tic variables are determined by the number of links – and and calculated differences