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914 results for “filtered”
Bulk RNA sequencing data of intact and digested rice root for Single-cell RNA-seq protoplasting induced gene filtering
GEO Series GSE283509. Oryza sativa. 8 samples. Type: Expression profiling by high throughput sequencing.
Differential messenger RNA expression in Granulosa Cells from polycystic ovary syndrome with Normoandrogen and Hyperandrogen: Identification of gene sets through bioinformatic Filtering analysis
GEO Series GSE137684. Homo sapiens. 12 samples. Type: Expression profiling by array.
Lookup Tables and Code for "Impact of Scene-Specific Enhancement Spectra on Matched Filter Greenhouse Gas Retrievals from Imaging Spectroscopy"
<p>Lookup Tables and Code for <a href="https://doi.org/10.1016/j.rse.2021.112574">"Impact of Scene-Specific Enhancement Spectra on Matched Filter Greenhouse Gas Retrievals from Imaging Spectroscopy"</a></p><p>The <a href="https://hive.utah.edu/concern/datasets/9w0323039?locale=en">original dataset link</a> is broken, so I uploaded it here for personal use.</p>
A workflow and digital filters for correcting speed and equalisation errors on digitised audio open-reel magnetic tapes - Audio Samples
<p>This repository makes available the audio samples related to the paper:</p> <p>Niccolò Pretto, Nadir Dalla Pozza, Alberto Padoan, Anthony Chmiel, Kurt James Werner, Alessandra Micalizzi, Emery Schubert, Antonio Rodà, Simone Milani and Sergio Canazza, <em>A workflow and digital filters for compensating speed and equalisation errors on digitised audio open-reel magnetic tapes</em>, Journal of the Audio Engineering Society, Special Issue on Audio Filter Design, 2022.</p> <p>The experiment and the three case studies are described in the publication above.</p> <p>This repository contains two main directories (<strong>bold</strong> indicates directory names):</p> <ul> <li> <p><strong>Experiment Samples</strong>: the 10 seconds long samples used in the experiment;</p> </li> <li> <p><strong>Long Samples</strong>: the original 6 minutes long samples, one for each of the identified cases.</p> </li> </ul> <p>Here is the notation of the file naming:</p> <ul> <li> <p>W: recording (writing);</p> </li> <li> <p>R: reproducing;</p> </li> <li> <p>3: 3.75 NAB;</p> </li> <li> <p>7N: 7.5 NAB;</p> </li> <li> <p>7C: 7.5 CCIR;</p> </li> <li> <p>15C: 15 CCIR.</p> </li> </ul> <p>For what concerns the <strong>Experiment Samples</strong> directory, here is the summary of the recording/reproducing standards of the adopted samples and their notation:</p> <ul> <li> <p>SET A: Recording 3.75 NAB (W3) - Reproducing 7.5 CCIR (R7C);</p> </li> <li> <p>SET B: Recording 3.75 NAB (W3) - Reproducing 15 CCIR (R15C);</p> </li> <li> <p>SET C: Recording 7.5 NAB (W7N) - Reproducing 15 CCIR (R15C).</p> </li> </ul> <p>Here are the variants of the samples:</p> <ul> <li> <p>REFERENCE: produced by using the correct equalization standard;</p> </li> <li> <p>ANCHOR: the "Reference" altered with a low-pass filter, with pass band set at 7 kHz for music and 3.5 kHz for speech;</p> </li> <li> <p>INCORRECT: produced by using an intentionally incorrect equalization, created by mismatching the recording and reading curves and resampled to the correct speed;</p> </li> <li> <p>MATLAB: the “Incorrect” variant corrected by means of a Matlab script;</p> </li> <li> <p>API: the “Incorrect” variant corrected by means of an <em>ad hoc</em> web interface adopting Web Audio API, for simulating real-time correction in web applications.</p> </li> </ul> <p>Here is the samples list:</p> <ul> <li> <p>SET A:</p> <ul> <li> <p>Training:</p> <ul> <li> <p>sample4: Richard Wagner - <em>Ride of the Valkyries</em>;</p> </li> </ul> </li> <li> <p>Test:</p> <ul> <li> <p>sample1: Taylor Swift - <em>Shake It Off</em>;</p> </li> <li> <p>sample5: Queen - <em>We Will Rock You</em>;</p> </li> <li> <p>sample8: Bruno Maderna - <em>Continuo</em>;</p> </li> <li> <p>sample9: Luciano Berio - <em>Différences</em>.</p> </li> </ul> </li> </ul> </li> <li> <p>SET B (the track title reflects the name of the file from which the track itself was extracted, from the CLIPS project of the University of Napoli: <a href="http://www.clips.unina.it/en/index.jsp">http://www.clips.unina.it/en/index.jsp</a>):</p> <ul> <li> <p>Training:</p> <ul> <li> <p>sample22: CLIPS project - <em>LP4m18bZ</em>;</p> </li> </ul> </li> <li> <p>Test:</p> <ul> <li> <p>sample15: CLIPS project - <em>LP1f20bZ</em>;</p> </li> <li> <p>sample16: CLIPS project - <em>LP4m20bZ</em>;</p> </li> <li> <p>sample17: CLIPS project - <em>LP1f19bZ</em>;</p> </li> <li> <p>sample18: CLIPS project - <em>LP4m19bZ</em>.</p> </li> </ul> </li> </ul> </li> <li> <p>SET C:</p> <ul> <li> <p>Training:</p> <ul> <li> <p>sample3: Carl Orff - <em>Carmina Burana - O Fortuna</em>;</p> </li> </ul> </li> <li> <p>Test:</p> <ul> <li> <p>sample2: The Weeknd - <em>Save Your Tears</em>;</p> </li> <li> <p>sample6: Eagles - <em>Hotel California</em>;</p> </li> <li> <p>sample10: Bruno Maderna - <em>Musica su Due Dimensioni</em>;</p> </li> <li> <p>sample12: Bruno Maderna - <em>Syntaxis</em>.</p> </li> </ul> </li> </ul> </li> </ul>
Switched capacitor filter transient responses for fault detection
<h2>Dataset Description</h2> <p>This dataset comprises both measured and simulated responses of second-order switched capacitor filters designed for failure detection. The study encompasses two types of filters:</p> <ul> <li> Overdamped</li> <li> Underdamped</li> </ul> <p>The measured data is segregated into two subsets:</p> <h3>Training Data</h3> <p>The training data exclusively contains non-failure responses. It serves as the foundation for model training and validation.</p> <h3>Test Data</h3> <p>The test data includes both failure and non-failure responses. Each response is labeled to indicate whether it is a non-failure or failure response, specifying which capacitor has been modified and its new value.</p> <p>For the simulated data, it is exclusively utilized for testing purposes. For each simulated case (% deviation of the nominal value), a metadata file named "*_metadata.csv" is provided. Each row in this file meticulously outlines the characteristics of the response corresponding to the elements in the accompanying .npy file.</p>
Molecular Dynamics simulations (filtered small system)
<p><span>Molecular Dynamics of E. coli ribosomes stalled by the nascent peptide SecM(Ms).</span></p> <p><span>Four systems were simulated: Control, K, N and WT.<span> </span></span></p> <p><span>To facilitate analysis, each system was filtered to include all the residues within 15 Angstroms of the </span><span>nascent chain, including the nascent chain, and the complete tRNA. The resulting structure is shared as a </span><span>pdb file with suffix "within15_tunnel.pdb"</span></p> <p><span>For each system, 5x50 ns trajectories were simulated. </span><span>And the concatenated trajectory is shared (suffix: "within15_concatenated.xtc")</span></p>
A workflow and novel digital filters for compensating speed and equalization errors on digitized audio open-reel tapes: audio samples
<p>This repository makes available the audio samples related to the experiments described in the paper: </p> <p><em>Niccolò Pretto, Nadir Dalla Pozza, Alberto Padoan, Anthony Chmiel, Kurt James Werner, Alessandra Micalizzi, Emery Schubert, Antonio Rodà, Simone Milani and Sergio Canazza. 2021. A workflow and novel digital filters for compensating speed and equalization errors on digitized audio open-reel tapes. In Proceedings of the 16th International Conference on Audio Mostly (AM '21). Association for Computing Machinery, New York, NY, USA</em></p> <p>The experiment and the three case studies are described in the publication below. Here is the summary of their recording/reproducing standards and their notation.</p> <ul> <li>SET A: Recording 3.75 NAB (W3) - Reproducing 7.5 CCIR (R7C);</li> <li>SET B: Recording 3.75 NAB (W3) - Reproducing 15 CCIR (R15C);</li> <li>SET C: Recording 7.5 NAB (W7N) - Reproducing 15 CCIR (R15C).</li> </ul> <p>Here is the notation of the file naming: </p> <ul> <li>W: recording (writing);</li> <li>R: reproducing;</li> <li>3: 3.75 NAB;</li> <li>7N: 7.5 NAB;</li> <li>7C: 7.5 CCIR;</li> <li>15C: 15 CCIR.</li> </ul> <p>Here are the variants of the samples:</p> <ul> <li>REFERENCE: produced by using the correct equalization standard;</li> <li>ANCHOR: the "Reference" altered with a low-pass filter, with pass band set at 7 kHz for music and 3.5 kHz for speech;</li> <li>INCORRECT: produced by using an intentionally incorrect equalization, created by mismatching the recording and reading curves and resampled to the correct speed;</li> <li>MATLAB: the “Incorrect” variant corrected by means of a Matlab script;</li> <li>API: the “Incorrect” variant corrected by means of an <em>ad hoc</em> web interface adopting Web Audio API, for simulating real-time correction in web applications.</li> </ul> <p>Here is the samples list:</p> <p>SET A:</p> <ul> <li>Training: <ul> <li>sample4: Richard Wagner - <em>Ride of the Valkyries</em>;</li> </ul> </li> <li>Test: <ul> <li>sample1: Taylor Swift - <em>Shake It Off</em>;</li> <li>sample5: Queen - <em>We Will Rock You</em>;</li> <li>sample8: Bruno Maderna - <em>Continuo</em>;</li> <li>sample9: Luciano Berio - <em>Différences</em>.</li> </ul> </li> </ul> <p>SET B (the track title reflects the name of the file from which the track itself was extracted, from the CLIPS project of the University of Napoli: <a href="http://www.clips.unina.it/en/index.jsp">http://www.clips.unina.it/en/index.jsp</a>):</p> <ul> <li>Training: <ul> <li>sample22: CLIPS project - <em>LP4m18bZ</em>;</li> </ul> </li> <li>Test: <ul> <li>sample15: CLIPS project - <em>LP1f20bZ</em>;</li> <li>sample16: CLIPS project - <em>LP4m20bZ</em>;</li> <li>sample17: CLIPS project - <em>LP1f19bZ</em>;</li> <li>sample18: CLIPS project - <em>LP4m19bZ</em>.</li> </ul> </li> </ul> <p>SET C:</p> <ul> <li>Training: <ul> <li>sample3: Carl Orff - <em>Carmina Burana - O Fortuna</em>;</li> </ul> </li> <li>Test: <ul> <li>sample2: The Weeknd - <em>Save Your Tears</em>;</li> <li>sample6: Eagles - <em>Hotel California</em>;</li> <li>sample10: Bruno Maderna - <em>Musica su Due Dimensioni</em>;</li> <li>sample12: Bruno Maderna - <em>Syntaxis</em>.</li> </ul> </li> </ul> <p>Supplementary material can be found at the following DOI: 10.5281/zenodo.5118708</p> <p> </p>
Crux Biomedical IVC Filter - Evaluation of the Crux Inferior Vena Cava Filter System (Retrieve)
ClinicalTrials.gov study NCT00605332. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Single cell transcriptomics reveal disrupted kidney filter cell-cell interactions after early and selective podocyte injury
GEO Series GSE174102. Mus musculus. 14 samples. Type: Expression profiling by high throughput sequencing.
A Comparison of Filter-based Approaches for Model-based Prognostics
Model-based prognostics approaches use domain knowledge about a system and its failure modes through the use of physics-based models. Model-based prognosis is generally divided into two sequential problems: a joint state-parameter estimation problem, in which, using the model, the health of a system or component is determined based on the observations; and a prediction problem, in which, using the model, the state-parameter distribution is simulated forward in time to compute end of life and remaining useful life. The first problem is typically solved through the use of a state observer, or filter. The choice of filter depends on the assumptions that may be made about the system, and on the desired algorithm performance. In this paper, we review three separate filters for the solution to the first problem: the Daum filter, an exact nonlinear filter; the unscented Kalman filter, which approximates nonlinearities through the use of a deterministic sampling method known as the unscented transform; and the particle filter, which approximates the state distribution using a finite set of discrete, weighted samples, called particles. Using a centrifugal pump as a case study, we conduct a number of simulation-based experiments investigating the performance of the different algorithms as applied to prognostics.
Q_Filters_Essential_tremor_4_classes
<p>Dataset preview for reviewers.</p>
Single channel approach for filtering electroencephalographic signals strongly contaminated with facial electromyography
<p><strong>Data: EEG contaminated by facial EMG</strong></p> <p> </p> <p>The dataset contains the EMG and EEG signals collected for the study of contamination by facial EMG on EEG.</p> <p> </p> <p>For each volunteer (S1 to S10), there are five “.mat” files, referring to the five muscles activated to generate contamination of facial EMG on EEG.</p> <p>For each muscle there are some variables:</p> <p>1. <strong>amplifier_data</strong>:</p> <p>It is a matrix with the EMG (card A) and EEG (card B) signals collected. The</p> <p>amplifier_channels structure and the t_amplifier vector refere to <strong>amplifier_data</strong></p> <p>matrix.</p> <p>2. <strong>board_dig_in_data</strong>:</p> <p>As expected in the experimental protocol, the collection is performed with the</p> <p>eyes in two states: only open and only closed. The <strong>board_dig_in_data </strong>variable</p> <p>contains digital values (0 or 1). The first moment containing 1s marks the</p> <p>beginning of the eye-open-only state and the second moment indicates the eye-closed-only state. The board_dig_in_channels structure and the t_dig vector refere</p> <p>to <strong>board_dig_in_data </strong>variable.</p> <p>3. <strong>supply_voltage_data</strong>:</p> <p>Data collection is performed by two acquisition cards, A(EMG) and B(EEG). The</p> <p>supply voltage of each card is monitored, and the measured values are stored in</p> <p>this variable. The supply_voltage_channels structure and the t_supply_voltage</p> <p>vector refere to <strong>supply_voltage_data </strong>variable.</p> <p> </p> <p><strong>EMG and EEG data</strong></p> <p>The data of interest for the collection are in the <strong>amplifier_data </strong>variable, a matrix</p> <p>with n rows and m columns, where n is the number of channels (EMG and EEG) and m is the number of data acquired at the sampling frequency for the entire collection time interval. EMG channels are collected by “Port A”, and EEG channels are collected by “Port B”, but all EMG and EEG data are in the same <strong>amplifier_data </strong>variable. The name “Port A” or “Port B” comes from the choice of cable used to connect the acquisition cards to the RHD2000 USB interface system. Thus, based on the choice adopted in connecting the EMG and EEG cards in this research, the <strong>amplifier_data </strong>variable has the first lines belonging to “Port A” (EMG) and the other lines of the matrix being from “Port B” (EEG). The amplifier_channels structure indicates which card was used with the name “Port A” and “Port B” in the port_name field.</p> <p> </p> <p><strong>EMG data</strong></p> <p>The muscle signal data does not have a specific and fixed channel between collections. Thus, the channel number for a muscle must be verified at each data collection. The information for each channel must be consulted in the amplifier_channels structure at the custom_channel_name field.</p> <p> </p> <p><strong>EEG data</strong></p> <p>A bipolar 16-channel acquisition card acquires EEG signals. The channels of each</p> <p>EEG sensor are the same for all collections, thanks to the use of the adapted EEG cap, and the connection of the EEG signals card is always carried out on the same cable B. The EEG cap has bipolar transverse mounting configuration.</p> <p> </p> <p>EEG sensors channel list, channel x EEG sensor:</p> <p>Ch00: F3-Fz; Ch01: F4-F8; Ch02: F7-F3; Ch03: Fp1-Fp2;</p> <p>Ch04: T3-C3; Ch05: C4-T4; Ch06: T5-P3; Ch07: P4-T6;</p> <p>Ch08: Fp2-F8; Ch09: F7-Fp1; Ch10: Fz-F4; Ch11: Cz-C4;</p> <p>Ch12: O1-O2; Ch13: P3-Pz; Ch14: Pz-P4; Ch15: C3-CZ.</p>
An Edge-Coupled Magnetostatic Bandpass Filter
<p>Corresponding dataset to "An Edge-Coupled Magnetostatic Bandpass Filter" paper.</p>
Filtered seurat object with metadata and analysis for Figure 4-5
<p>Please see metadata clustering resolution of 0.4 for clusters in figure, and annotations in "NK2". Please use ADT_denoised_iso_quant for adt (protein) normalized data.</p>
THEMIS-E: Probe Electric Field Instrument and Search Coil Magnetometer Instrument, Digital Fields Board - digitally computed Filter Bank spectra and E12 peak and average in HF band (FBK).
The Filter Bank is part of the Digital fields board and provides band-pass filtering for EFI and SCM spectra as well as E12HF peak and average value calculations. The Filter Bank provides band-pass filtering for less computationally and power intensive spectra than the FFT would provide. The process is as follows: Signals are fed to the Filter Bank via a low-pass FIR filter with a cut-off frequency half that of the original signal maximum. The output is passed to the band-pass filters, is differenced from the original signal, then absolute value of the data is taken and averaged. The output from the low-pass filter is also sent to a second FIR filter with 2:1 decimation. This output is then fed back through the system. The process runs through 12 cascades for input at 8,192 samples/s and 13 for input at 16,384 samples/sec (EAC input only), reducing the signal and computing power by a factor 2 at each cascade. At each cascade a set of data is produced at a sampling frequency of 2^n from 2 Hz to the initial sampling frequency (frequency characteristics for each step are shown below in Table 1). The average from the Filter Bank is compressed to 8 bits with a pseudo-logarithmic encoder. The data is stored in sets of six frequency bins at 2.689 kHz, 572 Hz, 144.2 Hz, 36.2 Hz, 9.05 Hz, and 2.26 Hz. The average of the coupled E12HF signal and it's peak value are recorded over 62.5 ms windows (i.e. a 16 Hz sampling rate). Accumulation of values from signal 31.25 ms windows is performed externally. The analog signals fed into the FBK are E12DC and SCM1. Sensor and electronics design provided by UCB (J. W. Bonnell, F. S. Mozer), Digital Fields Board provided by LASP (R. Ergun), Search coil data provided by CETP (A. Roux). Table 1: Frequency Properties. Cascade Frequency content of Input Signal Low-pass Filter Cutoff Frequency Freuency Content of Low-pass Output Signal Filter Bank Frequency Band 0* 0 - 8 kHz 4 kHz 0 - 4 kHz 4 - 8 kHz 1 0 - 4 kHz 2 kHz 0 - 2 kHz 2 - 4 kHz 2 0 - 2 kHz 1 kHz 0 - 1 kHz 1 - 2 kHz 3 0 - 1 kHz 512 Hz 0 - 512 Hz 512 Hz - 1 kHz 4 0 - 512 Hz 256 Hz 0 - 256 Hz 256 - 512 Hz 5 0 - 256 Hz 128 Hz 0 - 128 Hz 128 - 256 Hz 6 0 - 128 Hz 64 Hz 0 - 64 Hz 64 - 128 Hz 7 0 - 64 Hz 32 Hz 0 - 32 Hz 32 - 64 Hz 8 0 - 32 Hz 16 Hz 0 - 16 Hz 16 - 32 Hz 9 0 - 16 Hz 8 Hz 0 - 8 Hz 8 - 16 Hz 10 0 - 8 Hz 4 Hz 0 - 4 Hz 4 - 8 Hz 11 0 - 4 Hz 2 Hz 0 - 2 Hz 2 - 4 Hz 12 0 - 2 Hz 1 Hz 0 - 1 Hz 1 - 2 Hz *Only available for 16,384 Hz sampling.
THEMIS-D: Probe Electric Field Instrument and Search Coil Magnetometer Instrument, Digital Fields Board - digitally computed Filter Bank spectra and E12 peak and average in HF band (FBK).
The Filter Bank is part of the Digital fields board and provides band-pass filtering for EFI and SCM spectra as well as E12HF peak and average value calculations. The Filter Bank provides band-pass filtering for less computationally and power intensive spectra than the FFT would provide. The process is as follows: Signals are fed to the Filter Bank via a low-pass FIR filter with a cut-off frequency half that of the original signal maximum. The output is passed to the band-pass filters, is differenced from the original signal, then absolute value of the data is taken and averaged. The output from the low-pass filter is also sent to a second FIR filter with 2:1 decimation. This output is then fed back through the system. The process runs through 12 cascades for input at 8,192 samples/s and 13 for input at 16,384 samples/sec (EAC input only), reducing the signal and computing power by a factor 2 at each cascade. At each cascade a set of data is produced at a sampling frequency of 2^n from 2 Hz to the initial sampling frequency (frequency characteristics for each step are shown below in Table 1). The average from the Filter Bank is compressed to 8 bits with a pseudo-logarithmic encoder. The data is stored in sets of six frequency bins at 2.689 kHz, 572 Hz, 144.2 Hz, 36.2 Hz, 9.05 Hz, and 2.26 Hz. The average of the coupled E12HF signal and it's peak value are recorded over 62.5 ms windows (i.e. a 16 Hz sampling rate). Accumulation of values from signal 31.25 ms windows is performed externally. The analog signals fed into the FBK are E12DC and SCM1. Sensor and electronics design provided by UCB (J. W. Bonnell, F. S. Mozer), Digital Fields Board provided by LASP (R. Ergun), Search coil data provided by CETP (A. Roux). Table 1: Frequency Properties. Cascade Frequency content of Input Signal Low-pass Filter Cutoff Frequency Freuency Content of Low-pass Output Signal Filter Bank Frequency Band 0* 0 - 8 kHz 4 kHz 0 - 4 kHz 4 - 8 kHz 1 0 - 4 kHz 2 kHz 0 - 2 kHz 2 - 4 kHz 2 0 - 2 kHz 1 kHz 0 - 1 kHz 1 - 2 kHz 3 0 - 1 kHz 512 Hz 0 - 512 Hz 512 Hz - 1 kHz 4 0 - 512 Hz 256 Hz 0 - 256 Hz 256 - 512 Hz 5 0 - 256 Hz 128 Hz 0 - 128 Hz 128 - 256 Hz 6 0 - 128 Hz 64 Hz 0 - 64 Hz 64 - 128 Hz 7 0 - 64 Hz 32 Hz 0 - 32 Hz 32 - 64 Hz 8 0 - 32 Hz 16 Hz 0 - 16 Hz 16 - 32 Hz 9 0 - 16 Hz 8 Hz 0 - 8 Hz 8 - 16 Hz 10 0 - 8 Hz 4 Hz 0 - 4 Hz 4 - 8 Hz 11 0 - 4 Hz 2 Hz 0 - 2 Hz 2 - 4 Hz 12 0 - 2 Hz 1 Hz 0 - 1 Hz 1 - 2 Hz *Only available for 16,384 Hz sampling.
IHW COMET HALLEY INFRARED FILTER MEASUREMENTS, V2.0
This data set contains filter curves for all IR filters referenced in any of the data sets archived by the International Halley Watch (IHW) Infrared Studies Network (IRSN).
THEMIS-C: Probe Electric Field Instrument and Search Coil Magnetometer Instrument, Digital Fields Board - digitally computed Filter Bank spectra and E12 peak and average in HF band (FBK).
The Filter Bank is part of the Digital fields board and provides band-pass filtering for EFI and SCM spectra as well as E12HF peak and average value calculations. The Filter Bank provides band-pass filtering for less computationally and power intensive spectra than the FFT would provide. The process is as follows: Signals are fed to the Filter Bank via a low-pass FIR filter with a cut-off frequency half that of the original signal maximum. The output is passed to the band-pass filters, is differenced from the original signal, then absolute value of the data is taken and averaged. The output from the low-pass filter is also sent to a second FIR filter with 2:1 decimation. This output is then fed back through the system. The process runs through 12 cascades for input at 8,192 samples/s and 13 for input at 16,384 samples/sec (EAC input only), reducing the signal and computing power by a factor 2 at each cascade. At each cascade a set of data is produced at a sampling frequency of 2^n from 2 Hz to the initial sampling frequency (frequency characteristics for each step are shown below in Table 1). The average from the Filter Bank is compressed to 8 bits with a pseudo-logarithmic encoder. The data is stored in sets of six frequency bins at 2.689 kHz, 572 Hz, 144.2 Hz, 36.2 Hz, 9.05 Hz, and 2.26 Hz. The average of the coupled E12HF signal and it's peak value are recorded over 62.5 ms windows (i.e. a 16 Hz sampling rate). Accumulation of values from signal 31.25 ms windows is performed externally. The analog signals fed into the FBK are E12DC and SCM1. Sensor and electronics design provided by UCB (J. W. Bonnell, F. S. Mozer), Digital Fields Board provided by LASP (R. Ergun), Search coil data provided by CETP (A. Roux). Table 1: Frequency Properties. Cascade Frequency content of Input Signal Low-pass Filter Cutoff Frequency Freuency Content of Low-pass Output Signal Filter Bank Frequency Band 0* 0 - 8 kHz 4 kHz 0 - 4 kHz 4 - 8 kHz 1 0 - 4 kHz 2 kHz 0 - 2 kHz 2 - 4 kHz 2 0 - 2 kHz 1 kHz 0 - 1 kHz 1 - 2 kHz 3 0 - 1 kHz 512 Hz 0 - 512 Hz 512 Hz - 1 kHz 4 0 - 512 Hz 256 Hz 0 - 256 Hz 256 - 512 Hz 5 0 - 256 Hz 128 Hz 0 - 128 Hz 128 - 256 Hz 6 0 - 128 Hz 64 Hz 0 - 64 Hz 64 - 128 Hz 7 0 - 64 Hz 32 Hz 0 - 32 Hz 32 - 64 Hz 8 0 - 32 Hz 16 Hz 0 - 16 Hz 16 - 32 Hz 9 0 - 16 Hz 8 Hz 0 - 8 Hz 8 - 16 Hz 10 0 - 8 Hz 4 Hz 0 - 4 Hz 4 - 8 Hz 11 0 - 4 Hz 2 Hz 0 - 2 Hz 2 - 4 Hz 12 0 - 2 Hz 1 Hz 0 - 1 Hz 1 - 2 Hz *Only available for 16,384 Hz sampling.
THEMIS-B: Probe Electric Field Instrument and Search Coil Magnetometer Instrument, Digital Fields Board - digitally computed Filter Bank spectra and E12 peak and average in HF band (FBK).
The Filter Bank is part of the Digital fields board and provides band-pass filtering for EFI and SCM spectra as well as E12HF peak and average value calculations. The Filter Bank provides band-pass filtering for less computationally and power intensive spectra than the FFT would provide. The process is as follows: Signals are fed to the Filter Bank via a low-pass FIR filter with a cut-off frequency half that of the original signal maximum. The output is passed to the band-pass filters, is differenced from the original signal, then absolute value of the data is taken and averaged. The output from the low-pass filter is also sent to a second FIR filter with 2:1 decimation. This output is then fed back through the system. The process runs through 12 cascades for input at 8,192 samples/s and 13 for input at 16,384 samples/sec (EAC input only), reducing the signal and computing power by a factor 2 at each cascade. At each cascade a set of data is produced at a sampling frequency of 2^n from 2 Hz to the initial sampling frequency (frequency characteristics for each step are shown below in Table 1). The average from the Filter Bank is compressed to 8 bits with a pseudo-logarithmic encoder. The data is stored in sets of six frequency bins at 2.689 kHz, 572 Hz, 144.2 Hz, 36.2 Hz, 9.05 Hz, and 2.26 Hz. The average of the coupled E12HF signal and it's peak value are recorded over 62.5 ms windows (i.e. a 16 Hz sampling rate). Accumulation of values from signal 31.25 ms windows is performed externally. The analog signals fed into the FBK are E12DC and SCM1. Sensor and electronics design provided by UCB (J. W. Bonnell, F. S. Mozer), Digital Fields Board provided by LASP (R. Ergun), Search coil data provided by CETP (A. Roux). Table 1: Frequency Properties. Cascade Frequency content of Input Signal Low-pass Filter Cutoff Frequency Freuency Content of Low-pass Output Signal Filter Bank Frequency Band 0* 0 - 8 kHz 4 kHz 0 - 4 kHz 4 - 8 kHz 1 0 - 4 kHz 2 kHz 0 - 2 kHz 2 - 4 kHz 2 0 - 2 kHz 1 kHz 0 - 1 kHz 1 - 2 kHz 3 0 - 1 kHz 512 Hz 0 - 512 Hz 512 Hz - 1 kHz 4 0 - 512 Hz 256 Hz 0 - 256 Hz 256 - 512 Hz 5 0 - 256 Hz 128 Hz 0 - 128 Hz 128 - 256 Hz 6 0 - 128 Hz 64 Hz 0 - 64 Hz 64 - 128 Hz 7 0 - 64 Hz 32 Hz 0 - 32 Hz 32 - 64 Hz 8 0 - 32 Hz 16 Hz 0 - 16 Hz 16 - 32 Hz 9 0 - 16 Hz 8 Hz 0 - 8 Hz 8 - 16 Hz 10 0 - 8 Hz 4 Hz 0 - 4 Hz 4 - 8 Hz 11 0 - 4 Hz 2 Hz 0 - 2 Hz 2 - 4 Hz 12 0 - 2 Hz 1 Hz 0 - 1 Hz 1 - 2 Hz *Only available for 16,384 Hz sampling.
THEMIS-A: Probe Electric Field Instrument and Search Coil Magnetometer Instrument, Digital Fields Board - digitally computed Filter Bank spectra and E12 peak and average in HF band (FBK).
The Filter Bank is part of the Digital fields board and provides band-pass filtering for EFI and SCM spectra as well as E12HF peak and average value calculations. The Filter Bank provides band-pass filtering for less computationally and power intensive spectra than the FFT would provide. The process is as follows: Signals are fed to the Filter Bank via a low-pass FIR filter with a cut-off frequency half that of the original signal maximum. The output is passed to the band-pass filters, is differenced from the original signal, then absolute value of the data is taken and averaged. The output from the low-pass filter is also sent to a second FIR filter with 2:1 decimation. This output is then fed back through the system. The process runs through 12 cascades for input at 8,192 samples/s and 13 for input at 16,384 samples/sec (EAC input only), reducing the signal and computing power by a factor 2 at each cascade. At each cascade a set of data is produced at a sampling frequency of 2^n from 2 Hz to the initial sampling frequency (frequency characteristics for each step are shown below in Table 1). The average from the Filter Bank is compressed to 8 bits with a pseudo-logarithmic encoder. The data is stored in sets of six frequency bins at 2.689 kHz, 572 Hz, 144.2 Hz, 36.2 Hz, 9.05 Hz, and 2.26 Hz. The average of the coupled E12HF signal and it's peak value are recorded over 62.5 ms windows (i.e. a 16 Hz sampling rate). Accumulation of values from signal 31.25 ms windows is performed externally. The analog signals fed into the FBK are E12DC and SCM1. Sensor and electronics design provided by UCB (J. W. Bonnell, F. S. Mozer), Digital Fields Board provided by LASP (R. Ergun), Search coil data provided by CETP (A. Roux). Table 1: Frequency Properties. Cascade Frequency content of Input Signal Low-pass Filter Cutoff Frequency Freuency Content of Low-pass Output Signal Filter Bank Frequency Band 0* 0 - 8 kHz 4 kHz 0 - 4 kHz 4 - 8 kHz 1 0 - 4 kHz 2 kHz 0 - 2 kHz 2 - 4 kHz 2 0 - 2 kHz 1 kHz 0 - 1 kHz 1 - 2 kHz 3 0 - 1 kHz 512 Hz 0 - 512 Hz 512 Hz - 1 kHz 4 0 - 512 Hz 256 Hz 0 - 256 Hz 256 - 512 Hz 5 0 - 256 Hz 128 Hz 0 - 128 Hz 128 - 256 Hz 6 0 - 128 Hz 64 Hz 0 - 64 Hz 64 - 128 Hz 7 0 - 64 Hz 32 Hz 0 - 32 Hz 32 - 64 Hz 8 0 - 32 Hz 16 Hz 0 - 16 Hz 16 - 32 Hz 9 0 - 16 Hz 8 Hz 0 - 8 Hz 8 - 16 Hz 10 0 - 8 Hz 4 Hz 0 - 4 Hz 4 - 8 Hz 11 0 - 4 Hz 2 Hz 0 - 2 Hz 2 - 4 Hz 12 0 - 2 Hz 1 Hz 0 - 1 Hz 1 - 2 Hz *Only available for 16,384 Hz sampling.
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