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Database to store, search and retrieve DNA physical properties profiles has been developed and its use for analysis of E. coli promoters has been demonstrated. Unique feature of the database is in its ability to handle whole profile as single internal object type in a way similar to integers or character strings. To demonstrate utility of such database it was populated with data of 1227 known promoters, their nucleotide sequence, profile of electrostatic potential, transcription factor binding sites. Each promoter is also connected to all genes, whose transcription is controlled by that promoter. Content of the database is available for search via web interface. Source code of profile datatype and library to work with it from R/Bioconductor are available from the internet, dump of the database is available from authors by request.

The paper develops a new mathematical method of the joint signal and noise calculation at the Rice statistical distribution based on combing the maximum likelihood method and the method of moments. The calculation of the sough-for values of signal and noise is implemented by processing the sampled measurements of the analyzed Rician signal’s amplitude. The explicit equations’ system has been obtained for required signal and noise parameters and the results of its numerical solution are provided confirming the efficiency of the proposed technique. It has been shown that solving the two-parameter task by means of the proposed technique does not lead to the increase of the volume of demanded calculative resources if compared with solving the task in one-parameter approximation. An analytical solution of the task has been obtained for the particular case of small value of the signal-to-noise ratio. The paper presents the investigation of the dependence of the sought for parameters estimation accuracy and dispersion on the quantity of measurements in experimental sample. According to the results of numerical experiments, the dispersion values of the estimated sought-for signal and noise parameters calculated by means of the proposed technique change in inverse proportion to the quantity of measurements in a sample. There has been implemented a comparison of the accuracy of the soughtfor Rician parameters’ estimation by means of the proposed technique and by earlier developed version of the method of moments. The problem having been considered in the paper is meaningful for the purposes of Rician data processing, in particular, at the systems of magnetic-resonance visualization, in devices of ultrasonic visualization, at optical signals’ analysis in range-measuring systems, at radar signals’ analysis, as well as at solving many other scientific and applied tasks that are adequately described by the Rice statistical model.

Gesture recognition is an urgent challenge in developing systems of human-machine interfaces. We analyzed machine learning methods for gesture classification based on electromyographic muscle signals to identify the most effective one. Methods such as the naive Bayesian classifier (NBC), logistic regression, decision tree, random forest, gradient boosting, support vector machine (SVM), $k$-nearest neighbor algorithm, and ensembles (NBC and decision tree, NBC and gradient boosting, gradient boosting and decision tree) were considered. Electromyography (EMG) was chosen as a method of obtaining information about gestures. This solution does not require the location of the hand in the field of view of the camera and can be used to recognize finger movements. To test the effectiveness of the selected methods of gesture recognition, a device was developed for recording the EMG signal, which includes three electrodes and an EMG sensor connected to the microcontroller and the power supply. The following gestures were chosen: clenched fist, “thumb up”, “Victory”, squeezing an index finger and waving a hand from right to left. Accuracy, precision, recall and execution time were used to evaluate the effectiveness of classifiers. These parameters were calculated for three options for the location of EMG electrodes on the forearm. According to the test results, the most effective methods are $k$-nearest neighbors’ algorithm, random forest and the ensemble of NBC and gradient boosting, the average accuracy of ensemble for three electrode positions was 81.55%. The position of the electrodes was also determined at which machine learning methods achieve the maximum accuracy. In this position, one of the differential electrodes is located at the intersection of the flexor digitorum profundus and flexor pollicis longus, the second — above the flexor digitorum superficialis.