The computer programs implement the adaptive algorithms for real-time ECG signal filtering and numerical simulation for evaluation of filter effectiveness. The adaptive algorithms with complex use Hampel identifiers and Z-parameter are an author’s development. To launch a program, enter the name of the program and the ECG model signal in the command line. For example: ahzmthp.exe clean.txt. The test signal parameters (its length) and parameters of the filtering algorithms are read from the text file named as “filters.txt”. The program requests the additive and multiplicative noise variance, the probability and the amplitude of the spikes, and the number of realizations for statistical averaging of the calculated filter performance indicators. For example, via the “space” key enter: 0.0001 0 0 0 200, then press “Enter”. To apply filtering to a test signal which is read from a text file, select the menu item "Load from file" by pressing the key "6". The filter results are put in the “RESULT” subfolder. The filter efficiency estimates are written to the "MSE.res" and "SNR.res" output text files. The input signal has an extension “.x” (no noise), “.xn” (with simulated noise), “.xns” (with noise and spikes). The signals from filter algorithms outputs have the extension “.yf”. Also, files with functions of identifiers used to adapt the algorithm parameters to the local signal behavior and to the changes in the noise level and with adaptable filter parameters, and other intermediate signals are put to the “RESULT” subfolder. The program was compiled by Free Pascal.

The algorithms in this data release implement a State-Space Model (SSM) of vertical infiltration through the unsaturated zone and recharge to the water table. These algorithms build on previous investigations available at https://doi.org/10.1029/2020WR029110 and https://doi.org/10.1111/gwat.13206. The SSM is defined by observed states (i.e., the water-table altitude) and unobserved states (i.e., fluxes through the unsaturated zone and recharge to the water table)and interprets time-series data for observations of water-table altitude and meteorological inputs (i.e., the liquid precipitation rate and the Potential Evapotranspiration (PET) rate). The algorithms first perform the estimation of the SSM parameters from the time-series data over a Parameter-Estimation Window (PEW). The estimated model parameters are then used in a subsequent State-Estimation Window (SEW) to estimate the observed and unobserved systems states of the SSM using the Kalman Filter (KF). The application of th ...

These MATLAB files accompany the following publication:

Kulikova M.V., Tsyganova J.V. (2015) "Constructing numerically stable Kalman filter-based algorithms for gradient-based adaptive filtering", International Journal of Adaptive Control and Signal Processing, 29(11):1411-1426. DOI http://dx.doi.org/10.1002/acs.2552

The paper addresses the numerical aspects of adaptive filtering (AF) techniques for simultaneous state and parameters estimation (e.g. by the method of maximum likelihood). Here, we show that various square-root AF schemes can be derived from only two main theoretical results. These elegant and simple computational techniques replace the standard methodology based on direct differentiation of the conventional KF equations (with their inherent numerical instability) by advanced square-root filters (and its derivatives as well).

The codes have been presented here for their instructional value only. They have been tested with care but are not guaranteed to be free of error and, hence, they should not be relied on as the sole basis to solve problems.

If you use these codes in your research, please, cite to the corresponding article.

In this version of the computer programs, the data type has been changed from “integer” to “longint” for processing longer signals. Multilevel noise estimation and, respectively, adaptive switching of a larger number of filter sets are used. The filter parameters are adjusted by numerical simulation for a very wide range of variance of the additive Gaussian noise from its absence and very high input SNRs to their negative values. An increase in the number of possible parameter values that can be adaptively switched during processing improves the filters dynamic and statistical properties, and does not significantly decrease the processing speed. The algorithms parameters are given in the “filters.txt” file. Optionally, the number of parameters can be reduced by setting the same filter parameters for the next sets. For the parameters adjustment an optimization algorithm was not used. Therefore, the parameters only close to optimal have been selected. A typical ECG cycle is used as a model signal for numerical simulation and evaluation of the filter efficiency. As examples, the parameters of the proposed filtering algorithms are adjusted for the signals from the NSTB and PTB Physionet databases at the sampling rates of 360 Hz and 1000 Hz. The advantages of the proposed algorithms for non-stationary noise suppression in ECG are their high efficiency and low processing delay, allowing high-speed performances in real time mode.

In recent years, various real-time processing methods have been developed for Satellite Laser Ranging (SLR) data. However, the recognition rate of the single-stage Graz filtering algorithm for high-orbit satellites is less than 1%, and traditional two-stage filtering algorithms, such as polynomial fitting and iterative filtering techniques, exhibit high false and missed detection rates. These issues compromise the accuracy of laser positioning and real-time adjustments during observations. To address these problems, we propose a new, efficient real-time SLR data processing method. This algorithm combines single-stage filtering with a histogram-based approach and incorporates polynomial fitting to establish a predictive model. This offers the advantage of fast and efficient real-tim e signal recognition. The experimental results demonstrate that the proposed algorithm compensates for the limitations of single-stage filtering algorithms and performs better than traditional two-stage filtering algorithms in identifying medium- and high-orbit satellite signals. The false detection rate was reduced to below 15%, while achieving faster computation speeds. This method is convenience for researchers in their observations and offers new insights and directions for further research and application in the real-time identification of satellite laser ranging echo signals.

Medium sized song list

This dataset consists of a list of songs arranged as follow:

• ID_user1,ID_song,rating

• ID_user1,ID_song,rating

...

• ID_user2,ID_song,rating

• ID_user2,ID_song,rating

...

• ID_usern,ID_song,rating

• ID_usern,ID_song,rating

The main idea for this dataset is to implement recommendation algorithms based on collaborative filters. In addition to grouping data, reduce and compress lists. It is distributed under the CC 4.0 license. It's educational purpose.

In fact, as the music is coded in an ID, the dataset could be for anything else like, movies, places, etc. Use it for training your collaborative filters. (The data truly represent songs)

In recent years, various real-time processing methods have been developed for Satellite Laser Ranging (SLR) data. However, the recognition rate of the single-stage Graz filtering algorithm for high-orbit satellites is less than 1%, and traditional two-stage filtering algorithms, such as polynomial fitting and iterative filtering techniques, exhibit high false and missed detection rates. These issues compromise the accuracy of laser positioning and real-time adjustments during observations. To address these problems, we propose a new, efficient real-time SLR data processing method. This algorithm combines single-stage filtering with a histogram-based approach and incorporates polynomial fitting to establish a predictive model. This offers the advantage of fast and efficient real-tim e signal recognition. The experimental results demonstrate that the proposed algorithm compensates for the limitations of single-stage filtering algorithms and performs better than traditional two-stage filtering algorithms in identifying medium- and high-orbit satellite signals. The false detection rate was reduced to below 15%, while achieving faster computation speeds. This method is convenience for researchers in their observations and offers new insights and directions for further research and application in the real-time identification of satellite laser ranging echo signals.

The spam filter dataset is a collection of data used for training a spam filter algorithm. It typically contains a large number of emails, some of which are labeled as spam and others that are labeled as legitimate. The dataset is used to teach the algorithm to recognize the characteristics of spam emails so that it can accurately classify new emails as spam or not.

One common method for analyzing the spam filter dataset is the Naive Bayes algorithm. This algorithm uses probabilities to determine the likelihood of an email being spam based on its characteristics, such as the presence of certain keywords or the length of the email.

The Naive Bayes algorithm assumes that the presence or absence of each characteristic is independent of the others, and calculates the probability of an email being spam or not based on the joint probability of all the characteristics. This makes it a fast and efficient method for analyzing large datasets, such as the spam filter dataset.

Overall, the spam filter dataset and the Naive Bayes algorithm are powerful tools for combating the proliferation of spam emails in modern communication.

This data set contains the Lightning Cluster-Filter Algorithm (LCFA) data from the GOES-16 satellite Geostationary Lightning Mapper (GLM) during the period of ICICLE operations. The data are in NetCDF files packed into daily tar files.

The lightning detection data from the GOES-16 satellite Geostationary Lightning Mapper (GLM) during the period of PERiLS_2022 operations. The data are in NetCDF files packed into daily tar files.

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.

Comparison of proposed algorithm with random, K-Means and Collaborative Filtering in terms of coverage, RMSE, and ROC.

Complex microbial communities can be characterized by metagenomics and metaproteomics. However, metagenome assemblies often generate enormous, and yet incomplete, protein databases, which undermines the identification of peptides and proteins in metaproteomics. This challenge calls for increased discrimination of target identifications from decoy identifications by database searching and filtering algorithms in metaproteomics. Sipros Ensemble was developed here for metaproteomics using an ensemble approach to addressing this challenge.

With the COVID-19 pandemic having caused unprecedented numbers of infections and deaths, large research efforts have been undertaken to increase our understanding of the disease and the factors which determine diverse clinical evolutions. Here we focused on a fully data-driven exploration regarding which factors (clinical or otherwise) were most informative for SARS-CoV-2 pneumonia severity prediction via machine learning (ML). In particular, feature selection techniques (FS), designed to reduce the dimensionality of data, allowed us to characterize which of our variables were the most useful for ML prognosis. We conducted a multi-centre clinical study, enrolling n = 1548 patients hospitalized due to SARS-CoV-2 pneumonia: where 792, 238, and 598 patients experienced low, medium and high-severity evolutions, respectively. Up to 106 patient-specific clinical variables were collected at admission, although 14 of them had to be discarded for containing ⩾60% missing values. Alongside 7 socioeconomic attributes and 32 exposures to air pollution (chronic and acute), these became d = 148 features after variable encoding. We addressed this ordinal classification problem both as a ML classification and regression task. Two imputation techniques for missing data were explored, along with a total of 166 unique FS algorithm configurations: 46 filters, 100 wrappers and 20 embeddeds. Of these, 21 setups achieved satisfactory bootstrap stability (⩾0.70) with reasonable computation times: 16 filters, 2 wrappers, and 3 embeddeds. The subsets of features selected by each technique showed modest Jaccard similarities across them. However, they consistently pointed out the importance of certain explanatory variables. Namely: patient’s C-reactive protein (CRP), pneumonia severity index (PSI), respiratory rate (RR) and oxygen levels –saturation Sp O2, quotients Sp O2/RR and arterial Sat O2/Fi O2–, the neutrophil-to-lymphocyte ratio (NLR) –to certain extent, also neutrophil and lymphocyte counts separately–, lactate dehydrogenase (LDH), and procalcitonin (PCT) levels in blood. A remarkable agreement has been found a posteriori between our strategy and independent clinical research works investigating risk factors for COVID-19 severity. Hence, these findings stress the suitability of this type of fully data-driven approaches for knowledge extraction, as a complementary to clinical perspectives.

This collection gives details of a number of packages of source code and data concerned with low-level feature extraction using the edge detection method. Images and results generated can be viewed as pgm images using software such as Gimp (available for Windows, Linux and Mac platforms). This collection contains code based on Gaussian filtering for Canny and Marr-Hildreth edge detection for 2D images based on a fast recursive one dimensional filtering algorithm. The collection includes code written in C and has been used on Linux and OSX platforms. The main image format used is the open standard pbm format.

Comparison of the simple t-test and paired t-test values of the proposed algorithm with k-means, Collaborative Filtering and random algorithm.

This data set contains the lightning detection data from the GOES-16 satellite Geostationary Lightning Mapper (GLM) during the period of VORTEX-SE Meso18-19 operations. The data are in NetCDF files packed into daily tar files.

Mobile robots require basic information to navigate through an environment: they need to know where they are (localization) and they need to know where they are going. For the latter, robots need a map of the environment. Using sensors of a variety of forms, robots gather information as they move through an environment in order to build a map. In this paper we present a novel sampling algorithm to solving the simultaneous mapping and localization (SLAM) problem in indoor environments. We approach the problem from a Bayesian statistics perspective. The data correspond to a set of range finder and odometer measurements, obtained at discrete time instants. We focus on the estimation of the posterior distribution over the space of possible maps given the data. By exploiting different factorizations of this distribution, we derive three sampling algorithms based on importance sampling. We illustrate the results of our approach by testing the algorithms with two real data sets obtained through robot navigation inside office buildings at Carnegie Mellon University and the Pontificia Universidad Católica de Chile.

Phylogenetic inference is generally performed on the basis of multiple sequence alignments (MSA). Because errors in an alignment can lead to errors in tree estimation, there is a strong interest in identifying and removing unreliable parts of the alignment. In recent years several automated filtering approaches have been proposed, but despite their popularity, a systematic and comprehensive comparison of different alignment filtering methods on real data has been lacking. Here, we extend and apply recently introduced phylogenetic tests of alignment accuracy on a large number of gene families and contrast the performance of unfiltered versus filtered alignments in the context of single-gene phylogeny reconstruction. Based on multiple genome-wide empirical and simulated data sets, we show that the trees obtained from filtered MSAs are on average worse than those obtained from unfiltered MSAs. Furthermore, alignment filtering often leads to an increase in the proportion of well-supported branches that are actually wrong. We confirm that our findings hold for a wide range of parameters and methods. Although our results suggest that light filtering (up to 20% of alignment positions) has little impact on tree accuracy and may save some computation time, contrary to widespread practice, we do not generally recommend the use of current alignment filtering methods for phylogenetic inference. By providing a way to rigorously and systematically measure the impact of filtering on alignments, the methodology set forth here will guide the development of better filtering algorithms.

Particle filters (PF) have been established as the de facto state of the art in failure prognosis. They combine advantages of the rigors of Bayesian estimation to nonlinear prediction while also providing uncertainty estimates with a given solution. Within the context of particle filters, this paper introduces several novel methods for uncertainty representations and uncertainty management. The prediction uncertainty is modeled via a rescaled Epanechnikov kernel and is assisted with resampling techniques and regularization algorithms. Uncertainty management is accomplished through parametric adjustments in a feedback correction loop of the state model and its noise distributions. The correction loop provides the mechanism to incorporate information that can improve solution accuracy and reduce uncertainty bounds. In addition, this approach results in reduction in computational burden. The scheme is illustrated with real vibration feature data from a fatigue-driven fault in a critical aircraft component.