Analysis of Heart-Sound Characteristics during Motion Based on a Graphic Representation

In this paper, the graphic representation method is used to study the multiple characteristics of heart sounds from a resting state to a state of motion based on single- and four-channel heart-sound signals. Based on the concept of integration, we explore the representation method of heart sound and blood pressure during motion. To develop a single- and four-channel heart-sound collector, we propose new concepts such as a sound-direction vector of heart sound, a motion–response curve of heart sound, the difference value, and a state-change-trend diagram. Based on the acoustic principle, the reasons for the differences between multiple-channel heart-sound signals are analyzed. Through a comparative analysis of four-channel motion and resting-heart sounds, from a resting state to a state of motion, the maximum and minimum similarity distances in the corresponding state-change-trend graphs were found to be 0.0038 and 0.0006, respectively. In addition, we provide several characteristic parameters that are both sensitive (such as heart sound amplitude, blood pressure, systolic duration, and diastolic duration) and insensitive (such as sound-direction vector, state-change-trend diagram, and difference value) to motion, thus providing a new technique for the diverse analysis of heart sounds in motion.

Geometric models of the run method

This article discusses models of the run method in the pursuit problem. The considered models are based on the correction of the direction vector. Let’s assume that the intended direction on a plane is the line of sight between the pursuer and the target. The direction correction consists in the rotation of the velocity vector until it coincides with the line of sight. When constructing trajectories on the surface, a line of sight is built on the horizontal projection plane. After calculating horizontal projections, all points are projected back onto the surface. On the basis of the research carried out proposed a mathematical model, proposed mathematical models of the method of pursuit on a plane and on a surface given in an explicit form. Mathematical models are the development of chase and parallel approach methods. A modification of these methods is that the speed of the pursuer and the target are directed at random. These models can be in demand by developers of autonomous unmanned vehicles equipped with artificial intelligence systems.

Elongated Small Object Detection from Remote Sensing Images Using Hierarchical Scale-Sensitive Networks

The detection of elongated objects, such as ships, from satellite images has very important application prospects in marine transportation, shipping management, and many other scenarios. At present, the research of general object detection using neural networks has made significant progress. However, in the context of ship detection from remote sensing images, due to the elongated shape of ship structure and the wide variety of ship size, the detection accuracy is often unsatisfactory. In particular, the detection accuracy of small-scale ships is much lower than that of the large-scale ones. To this end, in this paper, we propose a hierarchical scale sensitive CenterNet (HSSCenterNet) for ship detection from remote sensing images. HSSCenterNet adopts a multi-task learning strategy. First, it presents a dual-direction vector to represent the posture or direction of the tilted bounding box, and employs a two-layer network to predict the dual direction vector, which improves the detection block of CenterNet, and cultivates the ability of detecting targets with tilted posture. Second, it divides the full-scale detection task into three parallel sub-tasks for large-scale, medium-scale, and small-scale ship detection, respectively, and obtains the final results with non-maximum suppression. Experimental results show that, HSSCenterNet achieves a significant improved performance in detecting small-scale ship targets while maintaining a high performance at medium and large scales.

A Robust and Dynamic Fire Detection Algorithm using Convolutional Neural Network

The major part of the underlying idea is going to detect the fire from upcoming smoke and the shade color of the smoke using convolutional neural network. The fire detection followed by the smoke detection is going to depend on the shade and the direction vector analysis in this paper. Image processing from the available set of data is very vague ideation so in order to strengthen the idea we are incorporating two main features that is the smoke shade and direction vector. For this major process we will involve data preprocessing through bi-variate hypothesis to select two variables as the color of smoke and the direction of the smoke and hence do the further analysis on other features that how are they going to help in the upcoming detection neurons for the robust algorithm of fire detection.

A Robust and Dynamic Fire Detection Algorithm using Convolutional Neural Network

The major part of the underlying idea is going to detect the fire from upcoming smoke and the shade color of the smoke using convolutional neural network. The fire detection followed by the smoke detection is going to depend on the shade and the direction vector analysis in this paper. Image processing from the available set of data is very vague ideation so in order to strengthen the idea we are incorporating two main features that is the smoke shade and direction vector. For this major process we will involve data preprocessing through bi-variate hypothesis to select two variables as the color of smoke and the direction of the smoke and hence do the further analysis on other features that how are they going to help in the upcoming detection neurons for the robust algorithm of fire detection

ASSESSMENT OF THE ACTIVATOR OF SOCIO-ECONOMIC DEVELOPMENT OF THE REGION

The article identifies the key factors influencing the activity of the socio-economic development of the region. The dynamics of this factor is analyzed. The indicator is investigated for the sufficiency and validity of the application, as well as for the effectiveness and efficiency with the designation of the direction vector, forecast, assessment for the future use of this leverage, to improve the economic situation at the regional level.

Using the satellite images for the territory of Ukraine

The purpose of this article is to identify the wind direction and speed using the images from geostationary satellites and through application of two-dimensional wind vectors, the magnitude and direction of which corresponds to the speed and direction of cloud masses, on a satellite image. The results may be used for making a short-term forecast of dangerous weather events within the territory of Ukraine. To make the technique work, it is necessary to select cloud areas on a satellite image using the threshold method. Then, based on the brightness temperature distribution between two tracking modules (parts of an image based on which two consecutive satellite images are compared), the maximum correlation coefficient for infrared brightness temperature is to be determined. The coefficient corresponds to the movement of cloud masses and sets the beginning and end of the wind direction vector. To determine the optimum application of the technique for the territory of Ukraine, the analysis of accuracy of tracking modules of different sizes was also performed. The analysis revealed that the accuracy of determining the wind vector direction depends on the tracking module size: the larger it is, the more accurate is the direction vector found, but given that the time interval between images is 15 minutes, the optimum algorithm to be used in Ukraine is the one with 5x5 pixel tracking module. The technique performance was also compared with the data of ICON and GFS forecast models. The results of the applied algorithm showed that the direction of air masses was more reliable than the data retrieved from the above-mentioned forecast models, because the algorithm analyzes the real-time movement of air masses while the forecast models assess the formation and movement of air masses in advance (with an interval of several hours up to dozens of hours). Numerical wind speed forecast of ICON and GFS models is more accurate, because the algorithm determines the wind speed based on the movement of cloud masses on satellite images whereas the forecast models consider several factors (pressure fields, development and subsequent evolution of cyclones, anticyclones, geographical characteristics etc.) which makes them more realistic.

Reversible Data Hiding for Encrypted 3D Model Based on Prediction Error Expansion

Since 3D models can intuitively display real-world information, there are potential scenarios in many application fields, such as architectural models and medical organ models. However, a 3D model shared through the internet can be easily obtained by an unauthorized user. In order to solve the security problem of 3D model in the cloud, a reversible data hiding method for encrypted 3D model based on prediction error expansion is proposed. In this method, the original 3D model is preprocessed, and the vertex of 3D model is encrypted by using the Paillier cryptosystem. In the cloud, in order to improve accuracy of data extraction, the dyeing method is designed to classify all vertices into the embedded set and the referenced set. After that, secret data is embedded by expanding direction of prediction error with direction vector. The prediction error of the vertex in the embedded set is computed by using the referenced set, and the direction vector is obtained according to the mapping table, which is designed to map several bits to a direction vector. Secret data can be extracted by comparing the angle between the direction of prediction error and direction vector, and the original model can be restored using the referenced set. Experiment results show that compared with the existing data hiding method for encrypted 3D model, the proposed method has higher data hiding capacity, and the accuracy of data extraction have improved. Moreover, the directly decrypted model has less distortion.