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Figs 93–100. Live specimens. 93–96 in New leaf- and litter-dwelling species of the genus Pholcus from Southeast Asia (Araneae, Pholcidae)

Figs 93–100. Live specimens. 93–96. Pholcus tambunan Huber sp. nov., Crocker Range, ♂, penultimate ♂, and ♀ with egg-sac. 97–100. P. bario Huber sp. nov., Bario, ♂ and ♀ with egg-sac.

Figs 59–66. Live specimens. 59–62 in New leaf- and litter-dwelling species of the genus Pholcus from Southeast Asia (Araneae, Pholcidae)

Figs 59–66. Live specimens. 59–62. Pholcus bukittimah Huber sp. nov., Dairy Farm, ♂, penultimate ♂, and ♀ with partly parasitized egg-sac. 63–66. P. barisan Huber sp. nov., Bukit Barisan, ♂ and ♀♀ with variably expanded abdomens.

Figs 32–38. Live specimens. 32–35. Pholcus gombak Huber, 2011 in New leaf- and litter-dwelling species of the genus Pholcus from Southeast Asia (Araneae, Pholcidae)

Figs 32–38. Live specimens. 32–35. Pholcus gombak Huber, 2011, Kemensah (32) and Gunung Liang (33–35), ♂, ♀ with parasitized egg-sac seven days before eclosion of wasps (33), one day before eclosion (34), and at eclosion (35). 36–38. P. ledang Huber, 2011, Gunung Ledang, ♂ and ♀ with egg-sac.

Figs 2–9. Live specimens. 2–3. Pholcus phui Huber, 2011 in New leaf- and litter-dwelling species of the genus Pholcus from Southeast Asia (Araneae, Pholcidae)

Figs 2–9. Live specimens. 2–3. Pholcus phui Huber, 2011, Hala Bala, ♂ and ♀ with egg-sac. 4–7. P. tanahrata Huber sp. nov., Cameron Highlands, ♂, penultimate ♂, and ♀. 8–9. P. uludong Huber sp. nov., Ulu Dong, ♂ and ♀.

opencc-by-4.0May 2017View details →

Multisite and multispecies live fuel moisture content (LFMC) series in the French Mediterranean since 1996

Here is a dataset of live fuel moisture content (LFMC, computed as the water mass over dry mass of living shoots) time series, collected in the French Mediterranean area by the French National Forest Organization (“Office National des Forêts”) for operational fire prevention. A network of 53 sites (called the "Reseau Hydrique" network) were sampled, among which 35 are geolocalized for a maximum period extending from 1996 to 2016. For each site and year available, LFMC is measured during the summer season on shrub species (between one and three species per site) at a weekly to biweekly frequency depending on site and year. The dataset can be used to validate or calibrate fire danger model, assess remote sensing drought indices and understand the physiological and climatic determinants of LFMC. There are 584 site*year data (a total of   22787 individual data) for several shrub species of the French Mediterranean area. From the raw dataset, researchers from the Ecology of Mediterranean Forest Unit at INRA (French National Institute for Research in Agronomy) of Avignon (France) have produced an improved dataset that includes corrections, outlier identifications and error estimations. Preliminary validation assessments of the data quality were also produced. A data paper describing in detail the method and all the modifications, error estimations and evaluations of the raw dataset that were performed is under review in Annals of Forest Science. Both the raw and improved datasets are made available on Zenodo (Cabane et al 2017, DOI 10.5281/zenodo.162978). The attached dataset consists of four tables: <ol> <li>The first table (LFMC_final_Table.csv) contains the live fuel moisture content (LFMC) on a dry weight basis (see Supplementary S1 for details). These are the robust estimates of LFMC and their associated standard errors which were both estimated from raw data with the method fully described above described in a data paper under revision (Martin-StPaul et al., under review in Annals of Forest Science). Each row in the table describe the LFMC at a given date, for a given species and a given site. The table has eleven columns. The first eight columns indicate the site identifier (SiteCode and SiteName), the species (Species), a unique identifier for a given species at a given site (SitexSpecies), the date (Date, Year, Month, Day of Year). The last three columns are respectively the robust LFMC (labelled RobustLFMC), the standard error SE (labelled RobustStandErrLFMC) and the number of valid measurements that were not identified as outliers (labelled RobustNval). RobustStandErrLFMC, and that can be used to estimate confidence limits depending on the desired confidence rate.</li> </ol>   <ol> <li>The second table (RainTable.csv) contains rainfall measurements. The site identifiers are given (SiteCode and SiteName) and the rainfall amount (rainfall) corresponding to rainfall occurring between the day of year of the previous measurement (PreviousDoy) and the day of year when the measurement was performed (Doy). The last column enables to identify the doubtful measurements (RainFlag = 1), when the discharge of the gauge during the previous measurement was uncertain.</li> </ol>     <ol> <li>The fourth table (InfoSite_ReseauHydrique.csv) contains a basic description of each site. It includes the identifier of the site (SiteCode and SiteName), the coordinates of the site in WGS84 (Longitude and Latitude), a flag indicating whether the site is still active (1= active, 0= inactive), the names of measured species (SpeciesName1 and up to SpeciesName3), the first and last year of measurement, as well as the number of measurement year available, for each species (StartYear, EndYear, NbYears).</li> </ol>   <ol> <li>The third table contains raw data as produced by the French National Forest Organization (LFMC_raw_Table.csv). The first twelve columns indicate site name, species name, and date, as in the first table. The six following columns indicate individual LFMC values (LFMC1 to LFMC5), and the mean LFMC value (FFSLFMC) released by the French Forest Service. The last six columns correspond to flags identifying outliers (LFMC1Flag to LFMC5Flag). Flags were attributed either manually or automatically (see Martin-StPaul et al., under review in Annals of Forest Science). Missing values (e.g. following an unforcasted rain event, see Methods) were represented by the symbol “NA“ (Not Available).</li> </ol> Note that the initiative was funded by a French organization dedicated the protection of the Mediterranean forest (The "Délégation à la Protection de la Forêt Méditerranéenne") and the raw dataset is available on a French website (http://www.reseau-hydrique.org/). However the raw dataset is not fully adapted to scientific purposes for several reasons. The dataset is not referenced (i.e. does not have a DOI) and its description is in French. In addition, the labels of sampling sites have evolved over time and some species were given a vernacular name. Finally, raw data are expressed on fresh mass basis (instead of dry mass as generally done in scientific studies) and present some outliers, duplications and inconsistencies. Additionally, uncertainties were not provided in the raw datasets. This is why INRA researchers recommend the usage of the improved dataset. In the forthcoming month, additional data regarding the environmental description, the ecology and history of the sites will be provided.   Martin-StPaul, N; Pimont, F; Dupuy JL; Rigolot E; Ruffault J; Fargeon H; Cabane E; Duché Y; Savazzi R; Toutchkov M. Multisite and multispecies live fuel moisture content (LFMC) series in the French Mediterranean area since 1996. Under revision in Annals of Forest Science.

Figure 10. Conceptual diagram illustrating vector quantization codeword formation-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives

After the conversion process converts the analogue speech signal into a series words, then converting recognized word to facial animation based on VRML.

Figure 9. Mel Cepstral Coefficients in time domain-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives

In this section, Feature matching Algorithm has been discussed. The goal of feature matching is to classify objects into one of a number of categories or classes. In this project, Vector Quantization approach will be used and the best matching result will be the desired voice.

Figure 6. Change the frequency at Hertz scale to Mel Scale.-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives

Following computing the spectrum power and applying the above equation on frequency axis, some mediating filters equal to identical overlapping are applied on the scaled spectrum and each filters energy is computed as particularity. This is because conception of a particular frequency by the auditory system is affected by a critical band of frequencies surrounding it. Number of filters is usually between 20 and 30. Logarithmic non linear change operations on obtained particulates for adjusting amplified of particularities and their important though coordinating them with the structure of the auditory system following computing energy of each filter is done as follows: in the following equation F1 is filter in I th, and e (i) is logarithm of energy at ith band.

Figure 4. Hamming Window applied to each frame-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives

Use of speech spectrum for modifying work domain on signals from time to frequency is made possible using Fourier coefficients. At such applications the rapid and practical way of estimating the spectrum is use of rapid Fourier changes.

Figure 3 Frame blocking of the speech signal-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives

The next frame will begin M samples (i.e. 156 samples) after the first frame, and it will overlap the first frame by N-M samples (256 – 156 = 100 samples). Then the third frame will start at 2M samples after the first frame and it will overlap first frame by N-2M. The fourth frame will start at 3M samples after the first, and it will overlap it by N-3M. The process will continue until all input signal is accounted for. The result of this step plotted using MATLAB plot command and displayed in Figure 3. Figure 3 Frame blocking of the speech signal The next step in the processing is to window each individual frame so as to minimize the signal discontinuities at the beginning and end of each frame. The concept here is to minimize the spectral distortion by using the window to taper the signal to zero at the beginning and end of each frame. If we define the window as w(n), 0 ≤ n ≤ N −1, where N is the number of samples in each frame, then the result of windowing is the signal y (n) = x (n)w(n), 0 ≤ n ≤ N −1 l l

opencc-by-4.0Dec 2010View details →

Figure 1. The Structure of the Automatic Translate Voice to Sign Language Animation System-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives

All technologies of voice recognition, speaker identification and verification, each has its own advantages and disadvantages and may requires different treatments and techniques. The choice of which technology to use is application-specific. At the highest level, all voice recognition systems contain two main modules: feature extraction and feature matching. Feature extraction is the process that extracts a small amount of data from the voice signal that can later be used to represent each word. Feature matching involves the actual procedure to identify the unknown word by comparing extracted features from his/her voice input with the ones from a set of known words. A wide range of possibilities exist for parametrically representing the speech signal for the voice recognition task, such as Linear Prediction Coding (LPC), RASTA-PLP and Mel-Frequency Cepstrum Coefficients (MFCC).

Figure 8. Speech signal after frequency wrapping-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives

Use of speech spectrum for changing the work domain on the signal from time to frequency is used via Fourier conventions. The rapid and practical way of estimating Spectrum in such applications is employment of rapid Fourier transform. The final stage is extracting particularities is use of discrete cosine to return particularities to the time domain and converse FFT approximation. Major advantage of this method is decrease of number of particularities of number of filter from f N to c N in which c f N ≤ N . In addition, doing so, includes making independent of the obtained particularity and rendering them non dependant which leads matrix covariance features to become axial. The following equation shows this point.

Figure 7. Speech signal after FFT-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives

Figure 2. MFCC Block Diagram Step 1-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives

Mel Frequency Cepstral Coefficients (MFCC) are coefficients that represent audio, based on perception. It is derived from the Fourier Transform (FFT) or the Discrete Cosine Transform (DCT) of the audio clip. The basic difference between the FFT/DCT and the MFCC is that in the MFCC, the frequency bands are positioned logarithmically (on the Mel scale) which approximates the human auditory system's response more closely than the linearly spaced frequency bands of FFT or DCT. This allows for better processing of data. The main purpose of the MFCC processor is to mimic the behavior of the human ears. Overall the MFCC process has 5 steps that show in figure 2.

Figure 11. A sample of speech to facial animation system-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives

To increase the autonomy of deaf and hard of hearing people in their day-to-day professional and social lives, in this paper design and initial implementation of a new approach based on MFCC and Vector Quantization Method is described. This approach includes analyses of speech to animate the talking head. Our future work will include the conception of new test types and performance patterns. We are particularly interested in extending this approach to testing to include implementation of applications under real-time constraints

Figure 5. Mel-spaced filter bank-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives

Physiologic changes show that human comprehension of frequency content of sound does not obey a linear space. Therefore for each individual it is computed and measure with a real frequency of sound peak at Mel seal. Using the below equation one can change the frequency at Hertz scale to Mel Scale.

opencc-by-4.0May 2024View details →

DEATH IN "LIVE BROADCAST"

Supplementary Table S1 Valid ethological categories of trace fossils (based on Vallon et al. 2016, related references inside).   Supplementary Table S2 Overview of published specimens of trace fossils with preserved tracemaker (related references inside).

opencc-by-4.0Jan 2024View details →

Figure 3 in Grazing of free-living Pylaiella littoralis by the amphipod Gammarus tigrinus

Figure 3: Fecal pellets produced by Gammarus tigrinus in culture with unialgal free-living Pylaiella littoralis (top). Scale bar = 1 mm.

opencc-by-4.0Jan 2024View details →

Figure 2 in Grazing of free-living Pylaiella littoralis by the amphipod Gammarus tigrinus

Figure 2: Gut contents of Gammarus tigrinus collected among free-living Pylaiella littoralis, with intact filament resembling P.littoralis. Scale bar = 25 μm.