Object-Based High-Rise Building Detection Using Morphological Building Index and Digital Map

High-rise buildings (HRBs) as modern and visually unique land use continue to increase due to urbanization. Therefore, large-scale monitoring of HRB is very important for urban planning and environmental protection. This paper performed object-based HRB detection using high-resolution satellite image and digital map. Three study areas were acquired from KOMPSAT-3A, KOMPSAT-3, and WorldView-3, and object-based HRB detection was performed using the direction according to relief displacement by satellite image. Object-based multiresolution segmentation images were generated, focusing on HRB in each satellite image, and then combined with pixel-based building detection results obtained from MBI through majority voting to derive object-based building detection results. After that, to remove objects misdetected by HRB, the direction between HRB in the polygon layer of the digital map HRB and the HRB in the object-based building detection result was calculated. It was confirmed that the direction between the two calculated using the centroid coordinates of each building object converged with the azimuth angle of the satellite image, and results outside the error range were removed from the object-based HRB results. The HRBs in satellite images were defined as reference data, and the performance of the results obtained through the proposed method was analyzed. In addition, to evaluate the efficiency of the proposed technique, it was confirmed that the proposed method provides relatively good performance compared to the results of object-based HRB detection using shadows.

Contribution to the "Atlas of the Russian Flora": Twelve local floras of European Russia

The purpose of this dataset is to deliver to a wider audience in the form of GBIF-mediated data vast floristic materials collected by the author across various localities of European Russia from 2001-2019 (Arkhangelsk, Tver, Vladimir, Tula, Lipetsk, Voronezh Oblasts, Krasnodar Krai, City of Moscow and Komi Republic). Taxonomic data on vascular plants for ten locations were mobilised from the papers and technical reports published in Russian and standardised. Floristic treatments for two locations (Yasnaya Polyana and Tsaritsyno) have never been published before. The newly-prepared dataset includes 5,309 species records, i.e. one species record per each local flora. These are either native or alien (fully naturalised and casual) species. All records within one local flora have the same centroid coordinates and coordinate uncertainty in metres. Floristic inventories from the following locations were mobilised: 01. Ustya, Arkhangelsk Oblast (543 species, 1,500 km2); 02. Zaseki, Tula Oblast (593 species, 60 km2); 03. Polibino, Lipetsk Oblast (553 species, 70 km2); 04. Khrenovoye, Voronezh Oblast (665 species, 200 km2); 05. Troyeruchitsa, Tver Oblast (501 species, 10 km2); 06. Man-Pupu-Ner, Komi Republic (182 species, ca. 300 km2); 07. Middle Lyaga, Komi Republic (143 species, ca. 300 km2); 08. Utrish, Krasnodar Krai (933 species, 195 km2); 09. Yasnaya Polyana, Tula Oblast (236 species, 2.2 km2); 10. Bogolyubovsky Lug, Vladimir Oblast (289 species, 1.7 km2); 11. Tsaritsyno, City of Moscow (359 species, 5.3 km2); 12. Patakino, Vladimir Oblast (312 species, 1.1 km2). According to the GBIF taxonomic backbone, the dataset covers 1,806 species, 669 genera and 127 families of tracheophytes.

Character Recognition Based on Euclidean Distance Calculation Over Predictive and Centroid Coordinates

Now a day people are living with internet technology but those technologies brings many problems to the people through many hacking techniques. Image spam is the one among them. In the earlier stages, hackers used to annoy targeted victims with their fabricated text called spam text. Hackers are passing their bogus information on many ways such as advertising, spam emails, buttons, query distributions etc. From which spam emails are very specific to attack and they are filtered by text based filter. Then attackers nurtured their attacks on new way i.e., spreading spam mails by images. Those images are non related content to the concerned users on their corresponding mails or any web pages. Because of those spam images, text based filter couldn’t identify spam texts. On the basis of an image’s features, Attackers used to embed their spam text or mischief coded links into some of the attracted images. To identify spam contents from an image, security functions of a system must be able to recognize the characters imbedding on any images. This research paper is going to present views on image spam, Data mining approaches for dataset analysis, proposed optical character recognizer model and implementation of character recognition from images using Euclidean distance values.

Restoration Method of a Blurred Star Image for a Star Sensor Under Dynamic Conditions

Under the dynamic working conditions of a star sensor, motion blur of the star will appear due to its energy dispersion during imaging, leading to the degradation of the star centroid accuracy and attitude accuracy of the star sensor. To address this, a restoration method of a blurred star image for a star sensor under dynamic conditions is presented in this paper. First, a kinematic model of the star centroid and the degradation function of blurred star image under different conditions are analyzed. Then, an improved curvature filtering method based on energy function is proposed to remove the noise and improve the signal-to-noise ratio of the star image. Finally, the Richardson Lucy algorithm is used and the termination condition of the iterative equation is established by using the star centroid coordinates in three consecutive frames of restored images to ensure the restoration effect of the blurred star image and the accuracy of the star centroid coordinates. Under the dynamic condition of 0~4°/s, the proposed algorithm can effectively improve the signal-to-noise ratio of a blurred star image and maintain an error of the star centroid coordinates that is less than 0.1 pixels, which meets the requirement for high centroid accuracy.

Technological Approaches to Evaluating Training Effects of Power Load in Primary Schoolers

Research objective. To experimentally ground the technological approaches to evaluating training effects of primary schoolers’ power loads.Materials and methods. To achieve the objectives outlined, the research used the following methods: analysis of scientific and methodological literature, pedagogical testing, modeling, pedagogical observation and experiment, methods of mathematical experiment planning (complete factorial experiment, 2k type), discriminant analysis. The participants in the study were 32 second grade boys.Research results. The training pattern affects the dynamics in the test results for the second grade boys. The test results can improve provided that the power load brings about significant changes after the training at each spot, after the training (immediate training effect), and twenty four hours after the power load (delayed training effect). The higher the dynamics is for the immediate and delayed training effects, the more significant are the improved power tests results observed after as few as three lessons.Conclusions. The discriminant function can be used to classify the training effects of the second grade boys’ power loads. The first canonical function explains the results variation by 85.0%, the second one — by 14.0%. This suggests that they are highly informative. The centroid coordinates for five groups allow to interpret the canonical functions according to the role differentiating the grades by the training effects of the primary schoolers’ power loads. The positive pole of the first function has the condition centroids at the beginning of the power loads, after three, six, nine and twelve lessons; the negative pole has the centroids of the training effects after the power loads, after the lesson, after 24 hours.Thus, the reaction to the power load has immediate, delayed and cumulative training effects.

Analysis on Obstacle-Surmounting of Coal Mine Detection Robot with Mechanical Arm

This paper took the coal mine detection robot model TUT-CMDR for research. First of all, the three degrees freedom mechanical arm was installed on the robots body. By the same time, the theory of Euler angle and D-H method were established to analysis the posture and position of robot body and mechanical arm. After the integration, the centroid coordinates on the robot in any location was calculated. In the next place, mechanical arm angle change and the size of the load effect on both forward obstacle-surmounting and the barrier height were discussed. In the end, the three dimensional relationship diagrams of barrier height, barycenter, pitch angle, arms' swing angle and mechanical arms swing angle were drawn and the results of mechanical arm gesture planning were compared with the initial state. The theory will provide basis for robot obstacle-navigation control.