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Wavelet Analysis: Part II (Applications in EViews)
This is the second of two entries devoted to wavelets. Part I was devoted to theoretical underpinnings. Here, we demonstrate the use and application of these principles to empirical exercises using the wavelet engine released with EViews 12.
Wavelet Transforms
Example 1: Wavelet Transforms as Informal Tests for (Non-)Stationarity
Example 2: MRA as Seasonal Adjustment
Example 3: DWT vs. MODWT
Variance Decomposition
Example: MODWT Unbiased Variance Decomposition
Wavelet Thresholding
Example: Thresholding as Signal Extraction
Outlier Detection
Example: Bilen and Huzurbazar (2002) Outlier Detection
Introduction to Wavelets
The new EViews 12 release has introduced several new statistical and econometric procedures. Among them is an engine for wavelet analysis. This is a complement to the existing battery of techniques in EViews used to analyze and isolate features which characterize a time series. While there are undoubtedly numerous applications to wavelets such as regression, unit root testing, fractional integration order estimation, and bootstrapping (wavestrapping), here we highlight the new EViews wavelet engine. In particular, we focuses on four popular and most often used areas of wavelet analysis:
Thresholding
The first step in wavelet analysis is usually a wavelet transform of a time series of interest. This is similar in spirit to a Fourier transform. The time series is decomposed into its constituent spectral (frequency) features on a scale-by-scale basis. Recall that the idea of scale in wavelet analysis is akin to frequency in Fourier analysis. This is nothing more than a re-expression of time series observations in time, to their behaviour in the frequency domain. This allows us to see which scales (frequencies) dominate in terms of activity.
Many important and routine tasks in time series analysis require classifying data as stationary or non-stationary. Any of the unit root tests available in EViews are designed to formally address such classifications. Nevertheless, wavelet transforms such as the discrete wavelet transform (DWT) or the maximum overlap discrete wavelet transform (MODWT) can also be used for a similar purpose. While formal wavelet-based unit root tests are available in the literature, here we focus on demonstrating how wavelets can be used as an exploratory tool for stationarity determination in lieu of a formal test.
Recall from the theoretical discussion of Mallat's algorithm in Part I that discrete wavelet transforms partition the frequency range into finer and finer blocks. For instance, at the first scale, the frequency range is split into two equal parts. The first, lower frequency part, is captured by the scaling coefficients and corresponds to the traditional (Fourier) frequency range $ \sbrace{0,\, \pi} $. The second, higher frequency part, is captured by the wavelet coefficients and corresponds to the traditional frequency range $ \sbrace{\pi,\, 2\pi} $. At the second stage, the lower frequency from the previous scale, namely the frequency region roughly corresponding to $ \sbrace{0,\, \pi} $ in the traditional Fourier context, is again split into two equal portions. Accordingly, the wavelet coefficients at scale 2 would roughly correspond to the traditional frequency region $ \sbrace{\frac{\pi}{2},\, \pi} $, whereas the scaling coefficients would roughly correspond to the traditional frequency region $ \sbrace{0,\, \frac{\pi}{2}} $, and so on.
This decomposition affords the ability to identify which features of the original time series data are dominant at which scale. In particular, if the spectra (read wavelet/scaling coefficient magnitudes) at a given scale are high, this would indicate that those coefficients are registering behaviours in the underlying data which dominate at said scale and frequency region. For instance, in the traditional Fourier context, if a series has very pronounced spectra near the frequency zero, this indicates that observations of that time series are very persistent (die off slowly). Naturally, one would classify such a series as non-stationary, possibly exhibiting a unit root. Alternatively, if a series has very pronounced spectra at higher frequencies, this indicates that the time series is driven by dynamics that frequently appear and disappear. In other words, the time series is driven by transient features and one would classify the time series as stationary. The analogue of this analysis in the context of wavelet analysis would proceed as follows.
At the first scale, if wavelet spectra dominate scaling spectra, the underlying series is dominated by higher frequency (transitory) forces and the series is most likely stationary. At scale two, if the scaling spectra dominate the wavelet spectra from the first and second scales, this indicates that lower frequency forces dominate higher frequency dynamics, providing evidence of non-stationarity. Naturally, this scale-based analysis carries on until the final decomposition scale.
To demonstrate the dynamics outlined above, we'll consider Canadian real exchange rate data extracted from the dataset in Pesaran (2007). This is a quarterly time series running from 1973Q1 to 1998Q4. The data can be found in WAVELETS.WF1. The series we're interested in is CANADA_RER. We'll demonstrate with a discrete wavelet transform (DWT) and the Haar wavelet filter. To facilitate the discussion to follow, we will consider the transformation only up to the first scale.
To perform the transform, proceed in the following steps:
Double click on CANADA_RER to open the series window.
Click on View/Wavelet Analysis/Transforms...
From the Max scale dropdown, select 1.
Click on OK.
Figure 2a: Canadian RER: Discrete Wavelet Transform Part 1 Figure 2b: Canadian RER: Discrete Wavelet Transform Part 2
The output is a spool object with the spool tree listing the summary, original series, as well as wavelet and scaling coefficients for each scale (in this case just 1). The first of these is a summary of the wavelet transformation performed. Note here that since the number of available observations is 104 a dyadic adjustment using the series mean was applied to achieve dyadic length.
The first plot in the output is a plot of the original series, in addition to the padded values in case a dyadic adjustment was applied. The last two plots are respectively the wavelet and scaling coefficients. Recall that at the first scale, the wavelet decomposition effectively splits the frequency spectrum into two equal portions: the low and high frequency portions, respectively. Recall further that the low frequency portion is associated with the scaling coefficients $ \mathbf{V} $ whereas the high frequency portion is associated with the wavelet coefficients $ \mathbf{W} $.
Evidently, the spectra characterizing the wavelet coefficients are significantly less pronounced than those characterizing the scaling coefficients. This is an indication that the Canadian real exchange series is possibly non-stationary. Furthermore, observe that the wavelet plot has two dashed red lines. These represent the $ \pm 1 $ standard deviation of the coefficients at that scale. This is particularly useful in visualizing which wavelet coefficients should be shrunk to zero (are insignificant) in wavelet shrinkage applications. (We will return to this later when we discuss wavelet thresholding outright.) Recall that coefficients exceeding some threshold bound (in this case the standard deviation) ought to be retained, while the remaining coefficients are shrunk to zero. From this we see that the majority of wavelet coefficients at scale 1 can be discarded. This is further evidence that high frequency forces in the CANADA_RER series are not very pronounced.
To justify the intuition, we can perform a quick ADF unit root test on CANADA_RER. To do so, from the open CANADA_RER series window, proceed as follows:
Click on View/Unit Root Tests/Standard Unit Root Test...
Figure 3: Canadian RER Unit Root Test
Our intuition is indeed correct. From the unit root test output it is clear that the p-value associated with the ADF unit root test is 0.7643 -- too high to reject the null hypothesis of a unit root at any meaningful significance level.
While the wavelet decomposition is not a formal test, it is certainly a great way of identifying which scales (read frequencies) dominate the underlying series behaviour. Naturally, this analysis is not limited to the first scale. To see this, we will repeat the exercise above using the maximum overlap discrete wavelet transform (MODWT) with the Daubechies (daublet) filter of length 6. We will also perform the transform upto the maximum scale possible, and also indicate which and how many wavelet coefficient are affected by the boundary. (See Part I for a discussion of boundary conditions.)
From the open CANADA_RER series window, we proceed in the following steps:
Change the Decomposition dropdown to Overlap transform - MODWT.
Change the Class dropdown to Daubechies.
From the Length dropdown select 6.
Figure 4a: Canadian RER: MODWT Part 1 Figure 4b: Canadian RER: MODWT Part 2 Figure 4c: Canadian RER: MODWT Part 3
As before, the output is a spool object with wavelet and scaling coefficients as individual spool elements. Since the MODWT is not an orthonormal transform and since it uses all of the available observations, wavelet and scaling coefficients are of input series length and do not require length adjustments. Notice the significantly more pronounced ''wave'' behvaviour across wavelet coefficients and scales. This is a consequence of the fact that the MODWT is not an orthonormal transform and is significantly more redundant than the DWT counterpart. In other words, patterns retain their momentum as they evolve.
Analogous to the DWT, the MODWT partitions the frequency range into finer and finer blocks. At the first scale, we see that only a few wavelet coefficients exhibit significant spikes (ie. exceed the threshold bounds). At scales two and three, it is evident that transient features persist, but after that, don't seem to contribute much. Alternatively, the scaling coefficients at the final scale (scale 6) are roughly twice as large (0.20) as the largest wavelet spectrum (0.10) which manifests at scales 1 and 2. These are all indications that lower frequency forces dominate those at higher frequencies and that the underlying series is most likely non-stationary.
Finally, notice that for each scale, those coefficients affected by the boundary are displayed in red, and their count reported in the legends. A vertical dashed black line shows the region upto which the boundary conditions persist. Boundary coefficients are an important consequence of longer filters and higher scales. Evidently, as the scale is increased, boundary coefficients consume the entire set of coefficients. Moreover, since the MODWT is a redundant transform, the number of boundary coefficients will always be greater than those in the orthonormal DWT. As before the $ \pm 1 $ standard deviation bounds are available for reference.
It's worth noting that multiresolution analysis (MRA) is often used as an intermediate step toward some final inferential procedure. For instance, if the objective is to run a unit root test on some series, we may we wish to do so on the true signal, having discarded the noise, in order to get a more reliable test. Similarly, we may wish to run regressions on series which have been smoothed. Discarding noise from regressors may prevent clouding of inferential conclusions. This is the idea behind most existing smoothing techniques in the literature.
In fact, wavelets are very well adapted to isolating many different kinds of trends and patterns, whether seasonal, non-stationary, non-linear, etc. Here we demonstrate their potential using an artificial dataset with a quarterly seasonality. In particular, we generate 128 random normal variates and excite every first quarter with a shock. These modified normal variates are then fed as innovations into a stationary autoregressive (AR) process. This is achieved with a few commands in the command window or an EViews program as follows:
rndseed 128 'set the random seed
wfcreate q 1989 2020 'make quarterly workfile with 128 quarter
series eps = 8*(@quarter=1) + @rnorm 'create random normal innovations with each first quarter having mean 8
series x 'create a series x
x(1) = @rnorm 'set the first observation to a random normal value
smpl 1989q2 @last 'start the sample at the 2nd quarter
x =  0.75*x(-1) + eps 'generate an AR process using eps as innovations
smpl @all 'reset the sample to the full workfile range
To truly appreciate the idea behind MRA, one ought to set the maximum decomposition level to a lower value. This is because the smooth series extracts the ''signal'' from the original series for all scales beyond the maximum decomposition level, whereas the ''noise'' portion of the original series is decomposed on a scale-by-scale basis for all scales upto the maximum decomposition level. We now perform a MODWT MRA on the X series using a Daubechies filter of length 4 and maximum decomposition level 2, as follows:
Double click on X to open the series.
Change the Decomposition dropdown to Overlap multires. - MODWT MRA.
Set the Max scale textbox to 2.
Figure 6a: Quarterly Seasonality: MODWT MRA Part 1 Figure 6b: Quarterly Seasonality: MODWT MRA Part 2
The output is again a spool object with smooth and detail series as individual spool elements. The first plot is that of the smooth series at the maximum decomposition level overlaying the original series for context. Any observations affected by boundary coefficients will be reported in red and their number reported in the legend. Furthermore, since observations affected by the boundary will be split between the beginning and end of original series observations, two dashed vertical lines are provided at each decomposition scale. These isolate the areas which partition the total set of observations into those affected by the boundary, and those which are not.
It is clear from the smooth series that seasonal patterns have been dropped from the underlying trend approximation of the original data. This is precisely what we want and the idea behind other well known seasonal adjustments techniques such as TRAMO/SEATS, X-12, X-13, STL Decompositions, etc., all of which can also be performed in EViews for comparison. In fact, the figure below plots our MRA smooth series against the STL decomposition trend series performed on the same data.
Figure 7: MODWT MRA Smooth vs. STL Trend
The two series are undoubtedly very similar, as they should be!
This figure above also suggests that the STL seasonal series should be very similar to the details from our MODWT MRA decomposition. Before demonstrating this, we remind readers that whereas the STL decomposition produces a single series estimate of the seasonal pattern, wavelet MRA procedures decompose noise (in this case seasonal patterns) on a scale by scale basis. Accordingly, at scale 1, the the MRA detail series captures all movements on a scale of 0 to 2 quarters. At scale 2, the MRA detail series captures movements on a scale of 2 to 4 quarters, and so on. In general, for each scale $ j $, the detail series capture patterns on a scale $ 2^{j-1} $ to $ 2^{j} $ units, whereas the smooth series captures patterns on a scale of $ 2^{j} $ units.
Finally, turning to the comparison of seasonal variation estimates between the MRA and STL, we need to sum all detail series to compound their effect and produce a single series estimate of noise. We can then compare this with single series estimate of seasonality from the STL decomposition.
Figure 8: MODWT MRA Details vs. STL Seasonality
As expected, the series are nearly identical.
To demonstrate this in the context of non-artificial data, we'll run a MODWT MRA on the Canadian real exchange rate data using a Least Asymmetric filter of length 12 and a maximum decomposition scale 3.
Figure 5a: Canadian RER: MODWT Multiresolution Analysis Part 1 Figure 5b: Canadian RER: MODWT Multiresolution Analysis Part 2
Recall that the main use for MRA is the separation of the true ''signal'' of the underlying series from its noise, at a given decomposition level. Here, the ''Smooths 3'' series is the signal approximation and from the plot seems to follow the contours of the original data. The remaining three series - ''Details 3'', ''Details 2'', and ''Details 1'' - approximate the noise at their scales. Clearly at the first scale, noise is rather negligible. This is an indication that the majority of the signal is in the lower frequency range. As we move to the second scale, the noise becomes more prominent, but still relatively negligible. Again, this confirms that the true signal is in a frequency range lower still, and so on. More importantly, this is indicative that the dynamics driving the noise are not particularly transitory. Accordingly, this would rule out traditional seasonality as a force driving the noise, but would not necessarily preclude the existence of non-stationary seasonality such as seasonal unit roots.
We have already mentioned that the primary difference between the DWT and MODWT is redundancy. The DWT is an orthonormal decomposition whereas the MODWT is not. This is certainly an advantage of the DWT over its MODWT counterpart since it guarantees that at each scale, the decomposition captures only those features which characterize that scale, and that scale alone. Nevertheless, the DWT requires input series to be of dyadic length, whereas the MODWT does not. This is an advantage of the MODWT since information is never dropped or added to derive the transform. Nevertheless, the MODWT has an additional advantage over the DWT and it has to do with spectral-time alignment - any pronounced observations in the time domain register as spikes in the wavelet domain at the same time spot. This is unlike the DWT where this alignment fails to hold. Formally, it is said that the MODWT is associated with a zero-phase filter, whereas the DWT does not. In practice, this means that outlying characteristics (spikes) in the DWT MRA will not align with outlying features of the original time series, whereas they will in the case of the MODWT MRA.
To demonstrate this difference we will generate a time series of length 128 and fill it with random normal observations. We will then introduce a large outlying observation at observation 64. We will then perform a DWT MRA and a MODWT MRA decomposition of the same data using a Daubechies filter of length 4 and study the differences. We will also only consider the first scale since the remaining scales do little to further the intuition.
We can begin by creating our artificial data by typing in the following set of commands in the command window:
wfcreate u 128
series x = @rnorm
x(64) = 40
These commands create a workfile of length 128, and a series X filled with random normal variates. The 64th observation is then set to 40 - roughly 10 times as large as observations in the top 1\% of the Gaussian distribution.
We then generate a DWT MRA and a MODWT MRA transform of the same series. The output is summarized in the plots below.
Figure 9a: Outlying Observation: DWT MRA Figure 9b: Outlying Observation: MODWT MRA
Evidently the peak of the ''shark fin'' pattern in the DWT MRA smooth series does not align with the outlying observation that generated it in the original data. In other words, whereas the outlying observation is at time $ t = 64 $, the peak of the smooth series occurs at time $ t = 63 $. This in contrast to the MODWT MRA smooth series which clearly aligns its peak with the outlying observation in the original data.
Another traditional application of wavelets is to variance decomposition. Just as wavelet transforms can decompose a series signal across scales, they can also decompose a series variance across scales. In particular, this is a decomposition of the amount of original variation attributed to a given scale. Naturally, the conclusions derived above on transience would hold here as well. For instance, if the contribution to overall variation is largest at scale 1, this would indicate that it is transitory forces which contribute most to overall variation. The opposite is true if higher scales are associated with larger contributions to overall variation.
To demonstrate the procedure, we will use Japanese real exchange rate data from 1973Q1 to 1988Q4, again extracted from the Pesaran (2007) dataset. The series of interest is called JAPAN_RER. We will produce a scale-by-scale decomposition of variance contributions using the MODWT with a Daubechies filter of length 4. Furthermore, we'll produce a 95% confidence intervals using the asymptotic Chi-squared distribution with a band-pass estimate for the EDOF. The band-pass EDOF is preferred here since the sample size is less than 128 and the asymptotic approximation to the EDOF requires a sample size of at least 128 observations for decent results.
From the open series window, proceed in the following steps:
Click on View/Wavelet Analysis/Variance Decomposition...
Change the CI type dropdown to Asymp. Band-Limited.
From the Decomposition dropdown select Overlap transform - MODWT.
Set the Class dropdown to Daubechies.
Figure 11a: Japanese RER: MODWT Variance Decomp. Part 1 Figure 11b: Japanese RER: MODWT Variance Decomp. Part 2
The output is a spool object with the spool tree listing the summary, spectrum table, variance distribution across-scales, confidence intervals (CIs) across scales, and the cumulative variance and CIs. The spectrum table lists the contribution to overall variance by wavelet coefficients at each scale. In particular, the column titled Variance shows the variance contributed to the total at a given scale. Columns titled Rel. Proport. and Cum. Proport. display, respectivel, the proportion of overall variance contributing to the total at a given scale and its cumulative total. Lastly, in case CIs are produced, the last two columns display, respectively, the lower and upper confidence interval values at a given scale.
The first plot is a histogram of variances at each given scale. It is clear that the majority of variation in the JAPAN_RER series comes from higher scales, or lower frequencies. This is indicative of persistent behaviour in the original data, and possibly evidence of a unit root. A quick unit root test on the series will confirm this intuition. The plot below summarizes the output of a unit root test on JAPAN_RER.
Figure 12: Japanese RER Unit Root Test
Returning to the wavelet variance decomposition output, following the distribution plot is a plot of the variance values along with their 95% confidence intervals at each scale. At last, the final plot displays variances and CIs accumulated across scales.
A particularly important aspect of empirical work is discerning useful data from noise. In other words, if an observed time series is obscured by the presence of unwanted noise, it is critical to obtain an estimate of this noise and filter it from the observed data in order to retain the useful information, or the signal. Traditionally, this filtration and signal extraction was achieved using Fourier transforms or a number of previously mentioned routines such as the STL decomposition. While the former is typically better suited to stationary data, the latter can accommodate non-stationarities, non-linearities, and seasonalities of arbitrary type. This makes STL an attractive tool in this space and similar (but ultimately different) in function to wavelet thresholding. The following examples explores these nuances.
Given a series of observed data, recall that STL decomposition produces three curves:
The last of these is obtained by subtracting from the original data the first two curves. As an additional byproduct, STL also produces a seasonally adjusted version of the original data which derives by subtracting from the original data the seasonality curve.
In contrast, recall from the theoretical discussion in Part I of this series that the principle governing wavelet-based signal extraction, otherwise known as wavelet thresholding or wavelet shrinkage, is to shrink any wavelet coefficients not exceeding some threshold to zero and then exploit the MRA to synthesize the signal of interest using the modified wavelet coefficients. This produces two curves:
where the latter is just the original data minus the signal estimate.
Because wavelet thresholding treats any insignificant transient features as noise, it is very likely that any reticent cylclicality would be treated as noise and driven to zero. In this regard, the extracted signal, while perhaps free of cyclical dynamics, would really be so only by technicality, and not by intention. This is in contrast to STL which derives an explicit estimate of seasonal features, and then removes those from the original data to derive the seasonally adjusted curve. Nevertheless, in many instances, the STL seasonally adjusted curve may behave quite similarly to the signal extracted via wavelet thresholding. To demonstrate this, we'll use French real exchange rate data from 1973Q1 to 1988Q4 extracted from the Pesaran (2007) dataset. The series of interest is called FRANCE_RER. We'll also start with performing a MODWT threshold using a Least Asymmetric filter of length 12, and maximum decomposition level 1.
Double click on the FRANCE_RER series to open its window and proceed as follows:
Click on View/Wavelet Analysis/Thresholding (Denoising)...
Set the Max scale to 1.
Change the Class dropdown to Least Asymmetric.
Set the Length dropdown to 12.
Figure 14: French RER: MODWT Thresholding
The output is a spool object with the spool tree listing the summary, denoised function, and noise. The table is a summary of the thresholding procedure performed. The first plot is the de-noised function (signal) superimposed over the original series for context. The second plot is the noise process extracted from the original series.
Next, let's derive the STL decomposition of the same data. The plots below superimpose the wavelet signal estimate on top of the STL seasonally adjusted curve, as well as the wavelet thresholded noise on top of the STL remainder series.
Figure 15a: Japanese RER: STL Seas. Adj. vs. Wavelet Tresh. Signal Figure 15b: Japanese RER: STL Remainder vs. Wavelet Tresh. Noise
Clearly the STL seasonally adjusted series is very similar to the wavelet signal curve. However, this is really only because the cyclical components in the underlying data are negligible. This can be confirmed by looking at the magnitude of the STL seasonality curve. Nevertheless, a close inspection of the STL remainder and wavelet threshold noise series reveals noticeable differences. It is these differences that drive any differences in the STL seasonal adjustment and wavelet threshold signal curves.
A particularly important and useful application of wavelets is outlier detection. While the subject matter has received some attention over the years starting with Greenblatt (1996), we focus here on a rather simple and appealing contribution by Bilen and Huzurbazar (2002). The appeal of their approach is that it doesn't require model estimation, is not restricted to processes generated via ARIMA, and works in the presence of both additive and innovational outliers. The approach does assume that wavelet coefficients are approximately independent and identically normal variates. This is a rather weak assumption since the independence assumption (the more difficult to satisfy) is typically guaranteed using the DWT. While EVIews offers the ability to perform this procedure using a MODWT, it's generally better suited to the orthonormal transform.
Bilen and Huzurbazar (2002) also suggest that Haar is the preferred filter here. This is because the latter yields coefficients large in magnitude in the presence of jumps or outliers. They also suggest that the transformation be carried out only at the first scale. Nevertheless, EViews does offer the ability to stray away from these suggestions.
The overall procedure works on the principle of thresholding and the authors suggest the use of the universal threshold. The idea here is that extreme (outlying) values will register as noticeable spikes in the spectrum. As such, those values would be candidates for outlying observations. In particular, if $ m_{j} $ denotes the number of wavelet coefficients at scale $ \lambda_{j} $, the entire algorithm is summarized (and generalized) as follows:
Apply a wavelet transform to the original data up to some scale $ J \leq M $.
Specify a threshold value $ \eta $.
For each $ j = 1, \ldots, J $:
Find the set of indices $ S = \cbrace{s_{1}, \ldots, s_{m_{j}}} $ such that $ |W_{i, j}| > \eta $ for $ i = 1, \ldots, m_{j} $.
Find the exact location of the outlier among original observations. For instance, if $ s_{i} $ is an index associated with an outlier:
If the wavelet transform is the DWT, the original observation associated with that outlier is either $ 2^{j}s_{i} $ or $ (2^{j}s_{i} - 1) $. To discern between the two, let $ \tilde{\mu} $ denote the mean of the original observations with observations located at $ 2s_{i} $ and $ (2s_{i} - 1) $. That is: $$ \tilde{\mu} = \frac{1}{T-2}\sum_{t \neq 2^{j}s_{i}\, ,\, (2^{j}s_{i} - 1)}{y_{t}} $$ If $ |y_{2^{j}s_{i}} - \tilde{\mu}| > |y_{2^{j}s_{i} - 1} - \tilde{\mu}| $, the location of the outlier is $ 2^{j}s_{i} $, otherwise, the location of the outlier is $ (2^{j}s_{i} - 1) $.
If the wavelet transform is the MODWT, the outlier is associated with observation $ i $.
To demonstrate outlier detection, data is obtained from the US Geological Survey website https://www.usgs.gov/. As discussed in Bilen and Huzurbazar (2002), data collected in this database comes from many different sources and is generally notorious for input errors. Here we focus on a monthly dataset, collected at irregular intervals from May 19876 to June 2020, measuring water conductance at the Green River near Greendale, UT. The dataset is identified by site number 09234500.
A quick summary of the series indicates that there is a large drop from typical values (500 to 800 units) in September 1999. The value recorded at this date is roughly 7.4 units. This is an unusually large drop and is almost certainly an outlying observation.
In an attempt to identify the aforementioned outlier, and perhaps uncover others, we use aforementioned wavelet outlier detection method. We stick with the defaults suggested in the paper and use a DWT transform with a Haar filter, universal threshold, a mean median absolute deviation estimator for wavelet coefficient variance, and a maximum decomposition scale set to unity.
To proceed, either download the data from the source, or open the tab Outliers in the workfile provided. The series we're interested in is WATER_CONDUCTANCE. Next, open the series window and proceed as follows:
Click on View/Wavelet Analysis/Outlier Detection...
Set the Max scale dropdown to 1.
Under the Threshold group, set the Method dropdown to Hard.
Under the Wavelet coefficient variance group, set the Method dropdown to Mean Med. Abs. Dev..
Figure 16: Water Conductance: Outlier Detection
The output is a spool object with the spool tree listing the summary, outlier table, and outlier graphs for each scale (in this case just one). The first of these is a summary of the outlier detection procedure performed. Next is a table listing the exact location of a detected outlier along with its value and absolute deviation from the series mean and median, respectively. The plot that follows is that of the original series with red dots identifying outlying observations along with a dotted vertical line at said locations for easier identification.
Evidently, the large outlying observation in September 1999 is accurately identified. In addition there are three other possible outlying observations identified in September 1988, January 1992, and June 2020.
In this first entry of our series on wavelets, we provided a theoretical overview of the most important aspects in wavelet analysis. Here we demonstrated how these principles are applied to real and artificial data using the new EViews 12 wavelet engine.
WAVELETS.WF1
WAVELETS.PRG
Bilen C and Huzurbazar S (2002), "Wavelet-based detection of outliers in time series", Journal of Computational and Graphical Statistics. Vol. 11(2), pp. 311-327. Taylor & Francis.
Greenblatt SA (1996), "Wavelets in econometrics", In Computational Economic Systems. , pp. 139-160. Springer.
Pesaran MH (2007), "A simple panel unit root test in the presence of cross-section dependence", Journal of applied econometrics. Vol. 22(2), pp. 265-312. Wiley Online Library.
Posted by IHSEViews at 8:06 AM
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Rescue and Retriever
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"It just grew and grew," Nygaard said Saturday. "When the Internet came, we just blew up."
Last year, the group rescued 516 golden retrievers and placed them in homes across Minnesota, Wisconsin, Iowa and the Dakotas. So far this year, it has saved 487 dogs.
In 22 years, Nygaard estimates the organization has found homes for 5,000 dogs. A majority are golden retrievers, but golden retriever mixes are not denied help.
About 40 Duluth families have adopted golden retrievers from RAGOM in the past seven years, Johnson said.
"We get them for all reasons -- divorces, they're moving, the owner goes into a nursing home," said Kathy Beyer of Blaine, who has picked up dogs from donors and provided foster care for about six years. "In some of the sad cases, the people just don't want them. But we don't judge the people who turn them in."
Some dogs have come from puppy mills, others from families that didn't realize cute puppies quickly become fully grown, energetic dogs. Nygaard's dog, Ford, was found in a trash can as a puppy; Mileski's dog, Charlie, was surrendered by a breeder after suffering a bad bout of allergies that made him lose the hair on his chest and tail
On Saturday, Charlie looked healthy and seemed happy to be around the other dogs. Mileski said he didn't get involved with the group until his first golden retriever began to decline in health three years ago. Now, both his dogs are from RAGOM.
"They captivate me," he said.

redpajama_set_name: RedPajamaCommonCrawl


Definition (NCI) One of two small paired endocrine glands, superior and inferior, usually found embedded in the connective tissue capsule on the posterior surface of the thyroid gland; these glands secrete parathyroid hormone that regulates the metabolism of calcium and phosphorus. The parenchyma is composed of chief and oxyphilic cells arranged in anastomosing cords.
Definition (CSP) small paired endocrine glands in the region of the thyroid gland which secrete parathyroid hormone and are concerned with the metabolism of calcium and phosphorus.
Definition (MSH) Two pairs of small oval-shaped glands located in the front and the base of the NECK and adjacent to the two lobes of THYROID GLAND. They secrete PARATHYROID HORMONE that regulates the balance of CALCIUM; PHOSPHORUS; and MAGNESIUM in the body.


Browse through a list of found cats on the Pet Harbor website.
Browse through a list of found dogs on the Pet Harbor website.
Browse through a list of things you can do if you find a lost animal.
View a list of things you can do to proactively locate a lost pet.
Use the online interactive search tool to help you locate a lost pet.
Here you can find instructions on how to use our interactive search tool to find a lost pet.
Keep your dog safe with Finding Rover, the revolutionary facial recognition app that scans the unique features of your dog's face and keeps it on file in case your dog ever gets lost. The app is free to register on your iPhone, Android devices, and on the Web. When your dog is lost, you can report your lost dog in the app and Finding Rover can alert a network of partners, such as dog organizations, nearby members, and social media outlets. And if you see a lost dog, you can report it by snapping a photo of the dog and uploading it into the app. The app is able to do a facial recognition search in its database. If there's a match, the owner's contact info pops up.


Travis County is "surrounded by red counties."
By W. Gardner Selby on Tuesday, January 21st, 2014 at 6:00 a.m.
In this November 2013 video we spotted in January 2014, Lloyd Doggett says Travis County is surrounded by red counties.
In a video endorsement of Andy Brown for Travis County judge, U.S. Rep. Lloyd Doggett stressed the Austin lawyer's past chairmanship of the county's Democratic Party, adding: "I know he shares our values. He will stand up to the Republicans. We are surrounded by red counties."
Six counties enwrap the capital's county: Bastrop, Caldwell, Hays, Blanco, Burnet and Williamson.
Unlike Democratic-blue Travis, are they all Republican-red?
A standard way of gauging whether a state is red or blue, so to speak, is whether the Republican or Democratic nominee won the state in the latest presidential election.
In 2012, Republican presidential nominee Mitt Romney carried each of Travis County's contiguous counties, drawing 54 percent to 77 percent of the vote, according to election results posted online by the state. Republican U.S. Senate nominee Ted Cruz similarly prevailed across the board, drawing 51 percent to 73 percent of each county's vote. That November, Democrat Barack Obama won 60 percent of Travis County's vote; Democratic Senate nominee Paul Sadler got 59 percent.
Next, we scrolled presidential election results for the surrounding counties from 1992 through 2008. We largely saw red: The Republican nominee won Blanco, Burnet and Williamson counties all five elections. The nominee carried Hays County in all but 1992 and Bastrop and Caldwell counties from 2000 on.
Separately, we asked the state parties how they would gauge the red or blue qualities of counties. By email, the chairman of the Republican Party of Texas, Steve Munisteri, said the party draws on a proprietary index for each county, its Optimal Republican Voting Strength or ORVS, which he described as based on a mathematical formula originated years ago. Munisteri said the ORVS draws on particular election results to indicate the percentage of the vote a party nominee would likely get in a general election in the best of circumstances.
Munisteri sent us a chart indicating the ORVS for each of the six counties: Bastrop (56.2 percent); Blanco (72.5 percent); Burnet (74.3 percent); Caldwell (55 percent); Hays (56.2 percent); and Williamson (62.2 percent).
By phone, Munisteri said the fact that Democrats have been elected to offices in some of the counties means the congressman's claim isn't entirely so.
Election results for 2012 show local Democrats winning countywide races in two of the Travis-bordering counties, sometimes after they didn't draw a Republican opponent. In 2010, Democrats won countywide offices in Bastrop, Caldwell and Hays counties.
In Bastrop County, according to 2012 results, a Republican won for county judge and the Democrats did not have a sheriff nominee. On the flip side, there was no Republican choice for county tax assessor-collector; Democrat Linda Harmon won without opposition. Two years earlier, Republicans won two contested county races, though a Democrat beat a Republican for county clerk, the results indicate.
In Hays County, Republican victors in 2012 for sheriff and county tax assessor-collector ran unopposed, according to results emailed to us by Joyce Cowan, a county elections official. In 2010, Democrat Sherri Tibbe ran unopposed for criminal district attorney as Republican Bert Cobb defeated Democrat Jeff Barton for county judge, according to results.
By telephone, Jon Leonard of the Hays County Democratic Party agreed that the county is red, though he expects Democratic candidates for statewide office to fare well there in November 2014; Obama, he pointed out, lost the county in 2008 by two percentage points.
In Caldwell County, Democrats Daniel Law and Darla Law defeated Republicans to win 2012 races for sheriff and county tax assessor-collector, respectively, according to results emailed to us by a county official, Joy Morris Pardo. Two years before, victorious Democrats ran unopposed for the offices of criminal district attorney, county clerk, district clerk and county treasurer, though Republican Tom Bonn defeated Democrat Morris Alexander for county judge.
In Burnet County, Democrats had no 2012 nominees for the several county offices, according to county-posted results. Two years earlier, several Republicans nominated for county offices (including county judge) lacked Democratic opponents. In the only red-blue 2010 showdown for county office, Republican Casie Wills handily defeated Democrat Cynthia Chisolm for district clerk.
In Williamson County, according to county-posted results, Democrats had no 2012 nominee for sheriff as the losing Democratic nominee for district attorney garnered 40 percent against Republican Jana Duty. In 2010, Republicans cruised past Democrats for two contested county court-at-law judgeships.
We did not obtain official records of Blanco County election results. But by telephone, Barbara Hudson, who chairs the county's Democrats, told us that while the party seeks candidates to run countywide, for now, "we don't even have Democrats who run for local offices."
Doggett said Travis County is "surrounded by red counties."
That's supported in each of the six counties by the outcome of presidential elections since 2000, though there has been some purpling in that Democrats won a few countywide offices in Bastrop, Caldwell and Hays counties in 2012 and 2010.
Color this statement, with nothing significant askew, as True.
TRUE – The statement is accurate and there's nothing significant missing.
Published: Tuesday, January 21st, 2014 at 6:00 a.m.


Zach and Nathan talk about Christmas shows and Christmas sermons that have been good and bad. This was a fun episode, but really the first part of a two parter. (Note: this episode is being released a week later than it was supposed to be). Don't forget to check out Missional Wear for all of your great reformed needs.


text:
Obituaries » Carmel Lorraine Pleva
Carmel Lorraine Pleva
February 21, 1922 - October 19, 2021
Carmel Lorraine Pleva peacefully passed away at home surrounded by the love of her family at 9:27p.m. on October 19, 2021.
Born February 21, 1922 in Alameda, CA to Frederick and Lucretia (Beck) Chenoweth, Carmel was raised at age 13 by her aunt and uncle, Earl J. and Rowena B. Gaines, who became her adoptive parents after moving to Pittsburgh, PA.
Carmel was preceded in death by her parents, her husband August P. (Gus) Pleva, Jr., her daughters Lorraine (Lori) Pleva McKiernan, and Annette Pleva, and her grandsons Brian P. Burke and baby boy Schuyler.
After graduating college Carmel became a member of the American Women's Voluntary Service (AWVS) providing support services to help the nation during WWII. She worked at Warner Brothers Studios in Pittsburgh where she met Augie Pleva and her new story began.
Carmel's greatest joy was being a wife and mother after marrying on August 9, 1947. She supported her husband in his work, military career and in his mission of becoming a Permanent Deacon. She spent her time as a homemaker and an active room mother when her daughters were in primary school after moving to Lima, OH. Carmel was a parish volunteer and enjoyed assisting the church and had been active in the St. Charles Parish choir and the Altar Rosary Society.
In later years Carmel loved to travel with her husband and family to Bermuda, the coastal beaches and to various destinations on many cruises. She was an avid Cincinnati Reds fan, regularly listening to the radio broadcasts of their games.
Carmel is survived by her daughters Louise Pleva and Christine Pleva of Lima, OH and Elaine (Ronnie) Snyder of Clemmons, NC; her grandchildren Elliot (Holly) Burke, Alexa (Scott) Gross, Betsy (Michael) Montgomery, Brendan (Donna) Nolte, Kevin (Kim) Nolte, and Sarah (Dereck) Lee; and 13 great-grandchildren. A special thank you to family friends Tom Gibson and Jim Schrock, and to Amber Siefker who was Carmel's caring healthcare aid.
It was Carmel's wish that a private family service be observed, and there will be no visitation. Burial will be in Gethsemani Cemetery. Memorial contributions may be made in Carmel's honor to the August Pleva Memorial Scholarship at St. Charles Catholic School in Lima, OH. Condolences may be expressed at chiles-lamanfh.com.


text:
Servers & Clusters
Global Datacenters
San Diego Datacenters
HomeMarketingSocial Media
Your company started a blog. That's great! But how are you driving traffic and how are you turning readers into sales or leads? Your company is on Twitter. Way to go! But are you building a meaningful following of evangelists that will promote your company? How are you engaging an audience on Twitter and starting a conversation with them? Solasus answers all of these questions with our time-tested solutions. Our services have proven to produce results and drive traffic, sales, and revenue for our clients:
Optimized Blog Development and Design
A blog is the best way for a company to reach out and build a community with customer, clients, and users. A blog allows the company to have a transparent platform wherein the customer is able to interact with the company, offer feedback, and learn more about the company culture. Solasus will design and develop the blog and optimize it for search engines and for social media. We know the exact Viral DNA necessary for a popular blog and understand how to optimize usability and readability.
Virally Enabled, Emotionally Charged Content Writing
A well-written, value-driven blog post is essential in driving traffic, establishing authority, and adding value to the community. The blog is not a press release page; instead, it should target a highly relevant audience, teaching them new things about the Client's industry, reviewing products, or just displaying the Client's fun company culture. Solasus will teach a client how to write value-driven articles that transcend the mundane PR releases that every company develops. By using our validated methods, a company will capture its social media audience and create evangelists and fans that will promote the company.
Optimized Twitter Campaign
Twitter is an essential tool for a company to engage its audience and to build a conversation with the people that matter. Twitter is not a place to blast advertisements; instead, the benefits of using Twitter are to create a community and following around the company brand. Solasus will not only create the Twitter profile, design the background, but we will teach the Client how to use Twitter internally for a dynamic company culture, at events to interact with participants and the community, and to target relevant users that will buy the Client products or services.
Explosive and Sustainable Traffic Generation
When used effectively, Social Bookmarking sites and Social Networks are great tools to use to spread a message and articles. The first step is to write a high-quality post; the next step is to use Stumble, Digg, and Facebook to spread the message. Solasus will develop and execute on a strategy to produce high-quality, entertaining content that social media users love and value.
No content or message can be guaranteed to go "viral." However, by using our validated methods, a company is able to drive traffic to a site and position itself to gain fans and evangelists by becoming an authority figure in its industry.
Robust Brand Monitoring
Social tools now provide companies with a great way to monitor their brand, and most importantly, to respond to anyone who is talking about them. Solasus will develop systems, tools, and processes so that a company is able to quickly find relevant users on Twitter and monitor the space for mentions of the company.
Remember, the conversation is already happening online. We will make it possible for you to join the conversation in the most effective way possible that allows you to talk to your audience and build a powerfully relevant following.
Analytics and Metrics
No campaign is complete without the proper analytics and metrics to measure the success of every facet of the campaign. Here is just a sample of the questions that our analytic tools answer:
How many leads or sales is your blog producing
How many evangelists and fans have Twitter and Facbeook created
Who are your evangelists and fans and how can you reward them
How much traffic have bloggers and Twitter users produced for your site
What is the conversion rate for each of your social tools
By analyzing these metrics, Solasus is able to adjust and revise the campaign to maximize ROI.
X-Factor strategies go beyond social networks, blogs, and micro-blog development. X-Factor strategies are the methods in which a company develops and explosive buzz by creating unique content that compels social media users to share with their friends.
X-Factor campaigns can include competitions, polls, scavenger hunts, videos, podcasts, celebrity endorsements, guest blogs posts, and many other unique social media strategies that will take the Client to the next level of marketing and PR.
Thank you very much for getting to know how our services and solutions can help you build a powerful social media presence. Give us a call or shoot us an email right now to take the first steps in dominating the social media landscape. We will be here every step of the way and will help you love the social space.
give it a nice home…
Dedicated & Managed Servers
feed it well…
and care for it…
Website Assessments
It WILL grow....
and grow.... and GROW!!!
About Solasus
info@solasus.com
© 2020 Solasus Web Solutions / BugRoom Services, Inc. All Rights Reserved.


text:
Q: angular2 call remote rest api I am a beginner of Anuglar2. I don't understand how to call remote rest api using http or jsonp in Angular2.
The URI I'm trying to call is
https://securedomain:8081/api/Authentication/AuthenticationQuery?strUserName={username}&strPassword={password}&dtLocal={timestamp}&bIsImpersonating=false&strTargetServerUri={TargetURI}&strDefaultCustomerDatabase={dbname}&strVersion=Angular2%201.0.0.0&strOriginatingPlatform=Angular2&strDeviceID={deviceID}

It can open in browser to check the result, it shows in XML format
<AuthenticationResult xmlns:i="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://schemas.datacontract.org/2004/07/Test.WebAPI.Models">
<CustomerServerIPAddr/>
<ErrorMessage>null</ErrorMessage>
<ForceChangePassword>true</ForceChangePassword>
<PasswordRaw/>
<SessionID>00000000-0000-0000-0000-000000000000</SessionID>
</AuthenticationResult>

I am trying to use http to call the API
    let header = new Headers()
    header = new Headers();
    header.append('Content-Type', 'application/json');
    header.append('Access-Control-Allow-Credentials', 'true');
    header.append('Access-Control-Allow-Origin', '*');
this.http.get(fullURI, {header: header})
      .map(res => res.json() )
      .catch(this.handleError)
      .subscribe(function (res) {
          console.log(res)
        }
      );

But I got error:

No 'Access-Control-Allow-Origin' header is present on the requested
  resource. Origin 'http://localhost:3000' is therefore not allowed
  access.

I check the network header:
request:
Request Method:GET
Status Code:200 OK
Accept:*/*
Accept-Encoding:gzip, deflate, sdch, br
Accept-Language:en-US,en;q=0.8
Connection:keep-alive
Host:securedomain:8081
Origin:http://localhost:3000
Referer:http://localhost:3000/
User-Agent:Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36

But no data in response
When I use jsonp to call the API
this.jsonp.get(fullURI)
      .map(res => res.json())
      .catch(this.handleError)
      .subscribe(function (res) {
          console.log(res)
        }
      );

I got error:

Uncaught SyntaxError: Unexpected token :
  AuthenticationQuery?strUserName=test&strPassword=123&dtLocal=2016-05-30
  09:45:36&bIsImpersonating=false&st…:1

I check the network header:
request:
Request Method:GET
Status Code:200 OK
Accept:*/*
Accept-Encoding:gzip, deflate, sdch, br
Accept-Language:en-US,en;q=0.8
Connection:keep-alive
Host:Host:securedomain:8081
Referer:http://localhost:3000/
User-Agent:Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36

But the different is there are some data in resopnse
{"CustomerServerIPAddr":"","PasswordRaw":"","SessionID":"00000000-0000-0000-0000-000000000000","ErrorMessage":"null","ForceChangePassword":true,"CustomerDatabase":""}

I guess I should use JSONP to call the API, but I don't know why it shows error when I subscribe the data.
I found some problems about "jsonp callback", they said the response format shoud be like call_back({data:data}) in jsonp. But I don't have permission to change the API to warp the data like that.
Please help me to understand how I get data from remote API in Angular2?

redpajama_set_name: RedPajamaStackExchange


text:
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text:
Edan Rotenberg
Edan@superlawgroup.com
Edan Rotenberg is a graduate of the Yale Law School and holds a master's degree in environmental management from the Yale School of Forestry & Environmental Studies. He has published research on topics including energy efficiency, climate change, and grandfathered pollution controls in environmental law. Edan has also served as an Advisor to the United Nations Development Program on climate change, was an Associate Attorney with Earthjustice, and assisted the chambers of Judge Shira A. Scheindlin (Southern District of New York) with the trial of one of the largest groundwater contamination cases in U.S. history.
PODCAST- Listen to Edan Rotenberg in conversation with Joya Sonnenfeldt, a dual degree student at Yale Law School and the Yale School of Forestry and Environmental Studies, about protecting the environment through private environmental law practice. Edan tells Joya about Super Law Group's work, what drives him, and how lawyers in private practice can protect the public interest (15 minutes).
180 Maiden Lane, Suite 603, New York, New York 10038 · tel: 212-242-2355
© 2019 by Super Law Group, LLC. All Rights Reserved. Disclaimer


* Read alouds - My favorite is The Hallo-weiner by Dav Pilkey.
* Art projects - Tomorrow we are making Fact Family Frankensteins. Keep on eye an my Instagram for pics.
* Review Work - This group needs work that they know they can master. For this, I have given review work on expanded form, rounding (this group really gets this skill), and greater/less than. They get so excited when the work is "easy."
These are the sheets we used.
* Task Cards - I have three sets of task cards for different levels of students. My lowest two students were told they had to work on the cards at one table. These were one digit cards. The rest of my students were given the chance to work on the cards they thought would be a good fit. Almost half decided on the three digit cards. I love how the kids want to challenge themselves!
These task cards helped me group students by ability.
* When in doubt, a single Hershey's Kiss makes great motivation for students. A new incentive we tried was when you finished the regular work (correctly), students had the chance to work on a challenge sheet (the same work at a more difficult level). If students mastered that work, they got one Hershey Kiss. Bonus, the teacher (aka me) got one too!
And now a little bonus for you, for the next 24 hours, I have marked the task cards below as Free on Teachers Pay Teachers. Just click the pic to take you to the resource.
Good luck on Halloween...and the rest of the week!


As mentioned in the post on our future ADS-B setup we have conducted a series of measurements on amateur radio low noise amplifiers (LNAs). I had the opportunity to spend some time at NTNUs microwave laboratory while working on my masters thesis. Some of these results and figures are also presented in the thesis ("Ground station considerations for the AMOS satellite programme", Øyvind Karlsen, NTNU, 2017). This post shows how we measured the Noise Figure (NF), Gain, Third-order intercept (TOI/IP3), spurious free dynamic range (DR_f) and insertion loss of the LNAs in the figure below.
A simple way to check if LNAs are working, is to turn on the power supply with a multimeter inline. The multimeter should then be able to read that the LNA is drawing the amount of current specified in the datasheet.
By toggling on and off the power to the LNA there should also be an observable increase of the noise floor at the receiver compared to when the LNA is off. To get a better idea of how they contribute to receive performance more sophisticated measurements must be made.
For all measurements an Aim-TTi EL302Tv precision power supply is used to power the LNAs. All devices are operated at 12 V, except the LNA4ALL which is operated at 5 V.
The noise figure of an active component describes how much noise it adds to an incoming signal. The noise and gain performance of the first stage in a receiver is particularly important as it contributes the most to the system noise figure. The background for this lies in Friis formula for noise, for a more thorough explanation see this link. Using the FSV-K30 option on a R&S FSQ signal analyser together with a HP 346B noise source, the gain and noise figure of the LNAs can be measured. The measurement setup is seen the figure below.
The measurement works by having a wideband noise source, which is a device with a very precise noise contribution over a large frequency range, connected and calibrated to a spectrum analyzer (in this case called a signal analyzer). The noise contribution of this noise source is listed in a table at the back of the device, as seen below.
Signal analyzer calibration procedure. Noise source table shown on the back of the noise source to the right.
Once the spectrum analyzer is calibrated to the noise source, the noise source is connected to the input of the device under test (DUT). The output of the DUT is then connected to the spectrum analyzer. The resulting signal measured at the spectrum analyzer will now be the amplified noise from the noise source. Since the power level and noise contribution of the source is precisely known it is possible to calculate both noise and gain of the DUT.
This is where option FSV-K30 makes the measurement very simple. The signal analyzer sweeps and calculates automatically, the output is a plot of both noise figure and gain vs frequency.
These measurements are very temperature sensitive. They are done at room temperature, and for thermal stability the equipment is powered on for an hour before starting measurements. Furthermore, the signal analyser is regularly re-calibrated to the noise source.
The SSB-Electronic LNAs feature adjustable gain by tuning a potentiometer. Each LNA is tested for three cases: maximum position, middle position and minimum position.
The linearity of a LNA will limit what signals it can reliably amplify. Any active device will generate spurious emissions, for a LNA the close-by intermodulation products that occur between two or more tones in close proximity are the most detrimental. We will show two linearity measures that originate from the third order intermodulation product (IM3) of a two tone test, third order intercept point (TOI or IP3) and Spurious Free Dynamic Range (SFDR or DR_f).
The n-th order intercept point (IPn) is where the n-th order intermodulation-product (IMn) crosses the linear growth curve. This is commonly used as a measure of linearity for non-linear devices, and a higher IPn value is better. Since our devices are highly linear, the measurement is noise floor limited beyond third order intercept (IP3). The relation between the two tone test and IP3 is shown in the figure below.
The two tone test is conducted by setting up two equally strong carriers with a given spacing. By setting up a two-tone test, intermodulation products will be generated in any non-linear device. We are interested in the third order intermodulation products, these can be found at F_high + F_spacing and F_low – F_spacing.
The measured level of these intermodulation products may be used to calculacte output referred IP3 (OIP3) and input referred IP3 (IIP3), as illustrated in the figure above.
There is no guarantee that the low and high intermodulation power is equal, so both low and high side powers must be recorded.
Two-tone measurement is performed with 100 kHz spacing generated from a R&S SMU 200A signal generator that is connected to the device under test. The resulting distortion is measured by a R&S FSQ 40 signal analyzer. Inherent test setup distortion is measured for all frequency ranges, but intermodulation products were buried in the noise floor for generator output power of -37 dBm per carrier.
For the SSB-Electronic devices the IP3 measurement for mid-level of potentiometer is done right after NF and gain measurement to ensure that these occur at the same level.
In presence of a strong transmitter the LNA might saturate. If this happens it may be necessary to add filtering to attenuate the saturation source.
The measure of how much input power an amplifier can take before spurious emissions are generated is the Spurious Free Dynamic Range (DR_f). DR_f is given as the magnitude relation between the fundamental power and the third order intermodulation product.
If the goal is to measure signals that are at the noise floor, the signal that will induce detrimental spurious behaviour is located at DR_f dB over the noise floor.
Finally the insertion loss of the LNAs with internal bypass relays are measured. This is measured by a R&S ZNB 8 Vector Network Analyser (VNA) . Calibration is done with a HP 85052B calibration kit.
In the following figures the gain of the devices is shown with a black trace with values in dB on the right hand axis. Similarly the noise figure is shown with a blue trace with values in dB on the left hand axis.
Click each text section to expand the results for the different LNAs.
The IP3 is found to be on average 34.75 dB by measurements done by DG1TRF, these can be found on the LNA4ALL product page. Below is the noise and gain measurements from 30 MHz to 2 GHz measured with 5 V supply.
Please repeat IP3 measurements for higher output tone. Your IP3 results are too optimistic, even for Kuhne LNA 131AH device with 15mA drain current.
Seems like this could be a one-size-fits-all problem. Since the measurement was repeated with the same settings across many LNAs, I might've gotten sloppy with checking the results. Unfortunately we don't have these amplifiers around anymore (they were loaned from various places), so I won't be able to re-validate.
Indeed, the input signal should not be pushed up to the compression point.
0…7dm output tone should not be a problem if the LNA really is capable of such a good IMD figure.
It is also a good practice to repeat the measurement by changing the input level +/- several dB. This can confirm that the errors are small.
For example "Kuhne LNA 131AH" no has IP3 specification because it is a regular LNA (only 15mA current can say many things about IP3…). The high IP3 version "Kuhne LNA 132 AH" has 80mA and OIP3=+27dBm according Kuhne datasheet. OIP3 around of 38dB, IIP3 around of 14dBm are too beautiful to be true.


Ministry of Training, Tradition, Sports, Science and Expertise Internet Web site. The actions of universities and universities of applied sciences promote Finnish competitiveness, effectively-being, training and learning in addition to sustainable growth. In every case, you might also flip your skills to broader science schooling contexts comparable to science communication, museums and publishing.
Field programs are a key characteristic of the examine of ecology at UQ. College students gain first-hand practical experience in solving ecological problems within the rainforest at Lamington National Park, Queensland outback, on the Great Barrier Reef at Heron Island, and on Stradbroke Island in Moreton Bay.
The Science Schooling Program gives preparation for college kids considering jobs instructing science in faculties or in informal learning settings, reminiscent of park naturalists or museum educators. Greater education and science policy work that takes an extended-term view strengthens Finland's higher schooling institutions and analysis and innovation techniques. Increased schooling establishments are developed as an internationally aggressive entity that also flexibly meets regional needs.
In case you are contemplating a career in teaching and have a robust curiosity in science, this can be the course for you. This four-yr programme attracts students who have wonderful communication skills, and are strongly motivated and captivated with science instructing.


text:
Q: angular ng-include does not parse ui-view I learn AngularJS
and I stuck on this moment: Plunker
I try toggle layout (ng-include) when scope model changed, and ui-view inside does not load  initially.
But if I toggle twice - all ok.
I think, that I doing something completely wrong, but can't to understand what.


Why are we afraid of death? Should we tell someone they are dying? Is reincarnation true? With depth, clarity, compassion, and even humor, Osho answers the questions we all have about this most sacred of mysteries and offers practical guidance for meditation and support. He reveals not only that our fear of death is based on a misunderstanding, but that dying is an opportunity for inner growth. When life is lived consciously and totally, death is not a catastrophe but a joyous climax.


Paranormal investigator Lorraine Warren dead at 92 KIRO SeattleLorraine Warren, who investigated haunted houses and other paranormal activity, died Thursday in Monroe, Conn., The New York Times reported. She was 92.
Lorraine Warren, paranormal investigator who inspired 'The Conjuring,' dies at 92 NBC NewsLorraine Warren, paranormal investigator and demonologist whose life inspired franchises like "The Conjuring" and "The Amityville Horror," has died.
Paranormal convention looks at 'other side' | Local News The Chronicle JournalWhen Dee McCullay was around eight years old, he says he began experiencing paranormal activity in his home in Red Rock.


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text:
The Progressive Pope's Regressive Stance on Birth Control
By John Seager | 25 September 2015
Population Connection
In a commentary in the journal Nature Climate Change, Paul Ehrlich, a senior fellow at the Stanford Woods Institute for the Environment, argues that Pope Francis is simply wrong in trying to fight climate change without also addressing the additional strain on global resources from population rise. "That's raving nonsense," Ehrlich told the Guardian. "He is right on some things but he is just dead wrong on that."
I get why people like Pope Francis—he is charismatic, humble, and has an awfully contagious smile. He's also radical by papal standards; being the first pope to dominate the social media realm and the first to be named "Person of the Year' by an LGBT magazine. I'd be lying if I said I didn't admire his cool tenacity.
But as crowds gather to catch a glimpse of Pope Francis—the religious rock star—on his East Coast tour, I can't help but wonder about the fans who praise him for his enlightened stance on poverty, inequality, and climate change. Because, unless Francis ends his backwards proscription of birth control, it will be impossible for him to make any long-lasting improvements in those arenas.
From championing efforts to reduce greenhouse gas emissions to speaking out against economic disparity, the pope's attempts to make the world a better place are futile alongside his reluctance to endorse access to the full range of contraceptives as a necessary and moral good.
Contraception, quite literally, saves lives—especially those of women and girls in the developing world who are deprived of antenatal services. Mothers and babies perish when women are prevented from delaying, spacing, or avoiding pregnancies. And young children whose mothers die during pregnancy and childbirth are cruelly left to fend for themselves.
In fact, if every single woman living in the developing world used modern contraception, the number of unintended pregnancies would be reduced by 70 percent and unsafe abortions would drop by 74 percent. Imagine how much that would enable women and girls to obtain an education and career; not to mention boost entire economies.
Increasing universal access to contraceptives is also central to addressing the challenge of climate change—a cause that the pope considers so important that he revolved his entire encyclical around it. While it's a welcome step in the right direction, Francis remains opposed to breaking the doctrinal chains that prevent the Vatican from recognizing the environmental impacts of unintended pregnancy and, in turn, unsustainable population growth.
According to a recent study, slowing population growth could significantly reduce carbon emissions and diminish the onslaught of dangerous climate change. It truly is a matter of life and death for millions of, what Francis would term, God's creatures.
Having the ability to choose when and whether to have children gives people—especially women and girls living in poor nations—a greater shot at enduring and recovering from extreme weather events. Families that can prevent unintended pregnancies are usually much better equipped, financially and otherwise, to counter the effects of climate change.
But, for Francis, science and facts take a backseat to doctrine. It's a fact that eliminating the barriers that prevent women from accessing birth control reduces the rate of abortion—one of the greatest "sins" in the eyes of the Catholic Church. It's also a fact that increased use of birth control is crucial for attaining many of the proposed UN Sustainable Development Goals (SDGs), such as empowering women and girls. Yet, even those basic truths are not enough to sway the pope's edict on modern contraception.
While reformism may have its limits in the Vatican, the championing of human rights shouldn't. Planning and preventing pregnancy not only saves lives; it helps to fight poverty, close the inequality gap, and reduce the amount of greenhouse gas emissions that get pumped into the atmosphere.
It's time for His Holiness to cease his condemnation of modern contraception and live up to his reputation as a progressive, egalitarian pope. Otherwise, Francis, the rock star, may become Francis, the one-hit wonder.
John Seager is the President and CEO of Population Connection. Before joining Population Connection, he was with the U.S. Environmental Protection Agency during the Clinton Administration. A veteran of more than 50 political campaigns, Seager has published op/eds and articles on population growth for news outlets like Huffington Post, GlobalPost, and RH Reality Check.
John Seager: World Population, the Environment and Social Equity (AHA Conference 2015)
Professor Paul Ehrlich: Can a collapse of global civilization be avoided?
What Melinda Gates would tell the Pope
People and Planet – Sir David Attenborough's take on overpopulation
Noam Chomsky: Climate Change & Nuclear Proliferation Pose the Worst Threat Ever Faced by Humans (VIDEO)
RECOMMENDED ON CLIMATE CHANGE
'God intended it as a disposable planet': meet the US pastor preaching climate change denial
Half of the world's population will be living in water-stressed areas by 2025
Coronavirus: 'Nature is sending us a message', says UN environment chief
In times of climate crisis, how many children should you have?
E. O. Wilson: Runaway population growth at epicenter of environmental problems


*Price is inclusive of delivery to anywhere in Australia using Australia Post receipted delivery. All mail orders will be dispatched twice a week, every Monday and Thursday. If orders are received late and miss the Thursday dispatch date, it will be dispatched the following Monday.
This delicately balanced oil is perfect for serving at the table with fresh bread as well as for making dips, pesto and sauces. Also wonderful for light summer salads of leafy greens and for drizzling over tomatoes.


Go on admit it! You suffer from shopping envy don't you?
If it's not advertising it's magazine editors or bloggers telling us about their latest purchase, what we should or shouldn't be buying. It's everywhere, on our social media feeds, on our televisions, in the magazines we read. The most covetable items, what's hot for summer, 'lusting lately'. All phrases to get our sartorial juices flowing and whet our fashionable appetites.
Our eyes widen and stomachs flutter at the alluring thought of 'the experience'. Bloggers tease us in a peepshow like manner, slowly revealing their latest 'haul's through a seductive collection of instagrams, tweets and pins. The desire of the shopping experience in everyone of us piqued.
I just got back from a trip to Nottigham to see some of my fabulous, fellow stylists; Cleo Lacey and Keeley Stone. That's the reason this post is a little behind my usual Saturday posting schedule. Don't blame me, blame them, ha ha, joking ladies. Today I wanted to talk about myths surround personal stylists and image consultants. When I explain to people what I do it's often met with intrigue followed by a stream of questions. Everyone seems to be familiar with the terms, but for most there is an air of ambiguity around what we do. You see, their only reference point is what they see on TV or read about in magazines. This is usually a far cry from the reality of what we do with our clients. So what I wanted to do was shed a bit of light on the world of a personal stylist and dispel some common myths about us.
I don't know where this concept comes from, but whenever I do a search for the term 'personal stylist' on twitter the results are full of the above. Some people think we act as their maids and dress them every day before work or school, usually school. I'm very confident that no one really needs to be shown how to physically put on their clothes.
I want you to do a little experiment for me. Open your wardrobe and go through anything you haven't worn in the last year, anything that you bought, took home then realised it didn't look 'right' but kept because you may change your mind or lose that extra 10lbs. Have a look for clothes that you've fallen out of love with since buying. Any clothes that have been damaged but you've not had the heart to throw away. This includes accessories and shoes as well. Once you've got all your bits together lay them on the bed and add up how much this little stash originally cost you. You've got a figure right? Well, whatever that figure is I can assure it's much more than what we cost and with us you can kiss goodbye to expensive mistakes.
I often get asked "but won't I need a lot of money for new clothes?" The answer is no, not really. While I can't comment for most personal stylists, the philosophy I stand behind emphasises working with what you already have first. Many people think the answer lies in more, more clothes, more money, instead of assessing what they already have. If there is a need to buy new clothes a good personal stylist should be able to work with whatever budget you give them.
I think this partly links in with #2 but even after dispelling that myth, yes some celebrities have personal stylists. But if you look at your regular average joe or jane they wouldn't try and fix the plumbing in their house themselves, they would get a specialist to come and do it. The same is true of someone's style. As in most cases there are exceptions, some people just have an almost innate sense of style and would probably never require our services. But for many, certain aspects of shopping or putting outfits together is an absolute minefield for them. That's why most of the people I work with are normal just like you and I, no celebrity status, no excessive bank balance.
Yes we need to know what is happening on the catwalk and what is currently hot on the high street. But that doesn't mean we'll be forcing you to look like you've just touched down from Paris fashion week. It's all about working with the individual and making sure their new style is reflective of their personality and life style. Not kitting you out, head to toe in the latest trends, after all… if it was that easy you'd just copy what you saw in the glossies.
I really wanted to put this out there and hopefully lay to bed some common misconceptions around personal styling. I hope I've done that in this short space of time and maybe it's answered some questions that you may have had but never really sought to ask or find out.
I go through stages where I love designer clothing and loathe the high street then it swings back the other way. I was recently checking out quite a few high end designer pieces and comparing them to mid price point high street and low end high street pieces. I tend to do this when doing a preselection for a personal shop to get a feel for the different pieces that are out there. I have to say that in some instances the high street trumps designers in terms of quality and price. Often you associate buying designer goods with automatic superior quality, but depending on what you're buying it's not always the case. There are still pieces where spending more does guarantee you quality, that tends to still be true when it comes to tailoring. But keeping your mind open can often lead to getting both great quality from the high street without paying designer prices. So if you want to make sure you're getting the best quality for your money check out my tips below.
I wrote a post about fabrics last year which I really recommend you read. You see, one of the key ways you to compare quality is looking at the fabric. Certain fabrics usually come with a higher price, most notably knitwear. If you're comparing cotton to cashmere then you know the latter is going to be expensive because it's a more luxurious fabric. But if you look around you can easily get comparable lambs wool pieces that would normally cost £100+ with a designer name for around the £35-£60 price mark.
Another quality usually associate with designer pieces is the overall cut and fit of the garment. You pay more money because the fit is going to be better. But that's not always the case, getting any piece of clothing in the correct size is always going to do wonders. So regardless of whether it's from the high street or designer make sure you try on a few different sizes. Again, paying more does not guarantee a better fit. Take the results of my recent shopping trip for example; I was looking at white shirts comparing one from TM Lewin for about £35, Reiss for £80 and D&G for £135. In terms of quality there was no difference, in fact I actually thought the TM Lewin one was slightly better. In terms of fit the D&G shirt was slightly better than the others, but not enough to warrant £100.
Which brings me onto my next tip, don't forget about alterations. If the level of quality is the same as the equivalent designer piece then paying for an alteration will still work out cheaper. Take the shirt example above, I could easily have paid an extra £15 for the body to be adjusted if I had felt like it and I'd have still paid less than the same item from Reiss and D&G.
Learn to thrift. I'm no expert myself but by simply following a few fashion blogs it's taught me that if you put the time in you can easily find some amazingly great quality pieces from thrifting. I've never had the time to really get into thrifting, but I've heard that once you've had a few great finds it can become really addictive.
She knows good quality when she sees it, do you?
The final way to is consider all of the above and wait for sale time, and take advantage of the money that is slashed off the recommended retail prices. If you have a clear idea of what you want and again are happy to take your time hunting for it during the sales, then it's a great way to get great quality at cheaper prices.
Well I hope you've found this useful, if you have any questions or care to share some of your own tips on how you ensure you get the best quality pieces, leave a comment after the break.


text:
describe 'Projects export', :download_file => true do

  before :each do
    Capybara.current_session.current_window.resize_to(1280, 800)

    DownloadHelpers.clear_downloads

    @lab  = FactoryBot.create(:lab)
    @user = FactoryBot.create(:user, :labs => [@lab])

    project1 = FactoryBot.create(:project, :lab => @lab, :name => 'Projet A', :start_date => Date.yesterday)
    project2 = FactoryBot.create(:project, :lab => @lab, :name => 'Projet B', :start_date => Date.today)

    contact1 = FactoryBot.create(:contact, :lab => @lab, :first_name => 'Albert',  :last_name => 'Dupont')
    contact2 = FactoryBot.create(:contact, :lab => @lab, :first_name => 'Josiane', :last_name => 'Dubois')

    organization1 = FactoryBot.create(:organization, :lab => @lab, :name => 'Shell')
    organization2 = FactoryBot.create(:organization, :lab => @lab, :name => 'Q8')

    event1 = FactoryBot.create(:event, :lab => @lab, :name => 'Event A')
    event2 = FactoryBot.create(:event, :lab => @lab, :name => 'Event B')

    OrganizationProjectLink.create(:project_id => project2.id, :organization_id => organization1.id)
    OrganizationProjectLink.create(:project_id => project2.id, :organization_id => organization2.id)

    ContactProjectLink.create(:contact_id => contact1.id, :project_id => project2.id)
    ContactProjectLink.create(:contact_id => contact2.id, :project_id => project2.id)

    EventProjectLink.create(:event_id => event1.id, :project_id => project2.id)
    EventProjectLink.create(:event_id => event2.id, :project_id => project2.id)

    FactoryBot.create(:note, :notable => project1, :privacy => :public, :text => 'bla bla')

    Project.__elasticsearch__.refresh_index!

    login_as @user
  end

  it 'exports an XLSX file', js: true do
    visit lab_projects_path(@lab)

    within '.quick-search' do
      new_window = window_opened_by do
        find('.fa-file-excel-o').click
      end

      within_window new_window do
        expect(File.basename(DownloadHelpers.download_path)).to eq("projects.xlsx")

        xlsx_data = File.read(DownloadHelpers.download_path)
        workbook  = RubyXL::Parser.parse_buffer(xlsx_data)
        worksheet = workbook.worksheets[0]

        expect(worksheet[1][0].value).to eq("Projet B")
        expect(worksheet[1][4].value).to eq("Albert Dupont, Josiane Dubois")
        expect(worksheet[1][5].value).to eq("Shell, Q8")
        expect(worksheet[1][6].value).to eq("Event A, Event B")

        expect(worksheet[2][0].value).to eq("Projet A")
        expect(worksheet[2][7].value).to eq(" — bla bla")
      end
    end
  end

end

redpajama_set_name: RedPajamaGithub


The palate tastes it and is astonished. The fine scent of the Arabica still breathes the mountain air and care it needs. Areas at 600 m to 1800 m above sea level are best suited for growing.
From 1000 to 200: En route to raw coffee, only one-fifth is left over – regardless of which bean or refining method is involved.
Unripe beans in raw coffee can ruin the flavour of an entire package. That is why sorting machines, with a throughput of 400 beans per second ensure purity for the Arabica.
The prevalence as far as Central America – at the right height, Arabica grows in Vietnam and Mexico, from 22 degrees latitude north to 26 degrees latitude south.
Brazilian Arabica is harvested using the stripping method. The berries are stripped from the branch, spread out and dried until the beans can be removed without any residues.
The no.1 in terms of quality among the Coffea Arabicas are the "Colombian" / "Other Milds". The berries are picked from the branch by hand (picking method) and wet processed, i.e. directly washed, pre-sorted with their skin and fruit flesh removed immediately. After the beans are washed again they are dried.


text:
Q: Изменить N-е совпадение символа в строке у меня есть строка:
"sample sample sample"

И мне нужно изменить, например, второе совпадение, чтобы на выходе получилось:
"sample hello sample"

Как заменить все три совпадения я знаю:
"sample sample sample".replace(/sample/g, "hello");

Но как ограничиться именно вторым?

A: Вторым параметром у replace() может быть функция:

var count = 0;
var num = 2;
var str = 'sample sample sample';
str = str.replace(/sample/g, function(a) {
  ++count;
  if (count == num) {
    return 'hello';
  } else {
    return a;
  }
});
console.log(str);

A:

var str = "sample sample sample";
var arr = str.split(' ');
var count = 0;
var i = 0;
for (; i < arr.length; i++) {
  if (arr[i] == "sample") {
    count++
  }
  if (count == 2) { // в count записываем какое совпадение по счету нужно изменить
    arr[i] = "hello";
    break;
  }
}
str = arr.join(' ');
alert(str);

A: При крайне малом разнообразии структуры, можно использовать что-то вроде этого:

console.log('sample sample sample'.replace(/((sample).*?)(\2)/, '$1hello'));