The Python language: Adopt a snake!

2020 ◽  
pp. 9-48
Author(s):  
Lamy Jean-Baptiste
Keyword(s):  
Python Language

scholarly journals UAVs in rail damage image diagnostics supported by deep-learning networks

2021 ◽  
Vol 11 (1) ◽  
pp. 339-348
Author(s):  
Piotr Bojarczak ◽  
Piotr Lesiak
Keyword(s):  
Deep Learning ◽  
Head Width ◽  
Learning Networks ◽  
Image Brightness ◽  
Learning Network ◽  
Rail Head ◽  
Image Recording ◽  
Python Language ◽  
Deep Learning Network ◽  
Efficiency Rate

Abstract The article uses images from Unmanned Aerial Vehicles (UAVs) for rail diagnostics. The main advantage of such a solution compared to traditional surveys performed with measuring vehicles is the elimination of decreased train traffic. The authors, in the study, limited themselves to the diagnosis of hazardous split defects in rails. An algorithm has been proposed to detect them with an efficiency rate of about 81% for defects not less than 6.9% of the rail head width. It uses the FCN-8 deep-learning network, implemented in the Tensorflow environment, to extract the rail head by image segmentation. Using this type of network for segmentation increases the resistance of the algorithm to changes in the recorded rail image brightness. This is of fundamental importance in the case of variable conditions for image recording by UAVs. The detection of these defects in the rail head is performed using an algorithm in the Python language and the OpenCV library. To locate the defect, it uses the contour of a separate rail head together with a rectangle circumscribed around it. The use of UAVs together with artificial intelligence to detect split defects is an important element of novelty presented in this work.

Implementation of Selection Sort Algorithm in Various Programming Languages

2021 ◽  
Vol 10 (4) ◽  
pp. 2249-2255
Keyword(s):  
Programming Languages ◽  
Data Science ◽  
Efficient Algorithms ◽  
Huge Number ◽  
Running Time ◽  
Fast Running ◽  
Python Language ◽  
Sort Algorithm

Sorting algorithmdeals with the arrangement of alphanumeric data in static order.It plays an important roleinthe field of data science. Selection sort is one ofthe simplest and efficient algorithms which can be applied for the huge number of elements it works likeby giving list of unsorted information, the calculation which breaksintotwo partitions. One section has all the sorted information and another sectionhas all thestaying unsorted information. The calculation rehashes itself, by finding the smallestcomponentinside the rundown of unsorted information and swappingitwith the furthest left component, in the end setting everything straight information.This researchpresents the implementationof selection sort usingC/C++, Python, and Rust and measuredthetime complexity. After experiment,we have collectedtheresults in terms of running time, andanalyzed the outcomes.It was observed that python language hasvery smallamount of line of code, and it also consumesless storage and fast running time then other two languages.

Unambiguity of Python Language Elements for Static Analysis

2021 ◽  
Author(s):  
Bence Nagy ◽  
Tibor Brunner ◽  
Zoltan Porkolab
Keyword(s):  
Static Analysis ◽  
Python Language

Automation Means for Controlling the Correctness of Typical Sections in a University Teacher's Document

2021 ◽  
Vol 12 (5) ◽  
pp. 274-280
Author(s):  
D. N. Kobzarenko ◽  
◽  
S. E. Savzikhanova ◽  
B. I. Shikhsaidov ◽  
◽  
...  
Keyword(s):  
Human Resources ◽  
Web Application ◽  
Specific Task ◽  
Software Environment ◽  
University Teachers ◽  
Autonomous Operation ◽  
Python Language ◽  
Python Programming Language ◽  
Python Programming ◽  
Automation Tools

The paper discusses approach to automating the processes of monitoring and editing standard sections in a document of a university teacher in streaming mode. As a toolkit for solving the problem, it is proposed to use the Python programming language with the connection of the python-docx and pandas libraries. Despite the fact that the projected automation tools are focused on a specific task to control documents of a university teacher, the development of the concept of their construction is carried out with an abstraction from the type of documentation. The set of functions may vary depending on the problem being solved. As a result of work on the creation of tools for automating the control of the correctness of typical sections of a university teachers document, a framework of an open software environment for streaming verification of typical sections of documents in the Python language was designed, capable of adapting to the required verification tasks. The project is currently implemented for autonomous operation in Windows OS, it is also expected to be implemented as a web application. For active use in practice for specific tasks, the development of appropriate script packages is required. The main purpose of the development is to release human resources from the routine process of document control and direct it to the implementation of the employees creative ideas.

The Reform and Exploration of Intelligent PYTHON Language Teaching

2021 ◽  
Author(s):  
Yutian Huang ◽  
Juanjuan Zhao ◽  
Yan Qiang ◽  
Tengxuan Hou ◽  
Xiaoling Ren ◽  
...  
Keyword(s):  
Language Teaching ◽  
Python Language

scholarly journals Pico satélite educativo CanSat desarrollado en las plataformas ARM-CortexM4 y FPGA

2019 ◽  
pp. 9-17
Author(s):  
Fernando Sánchez-Texis ◽  
Mariana Natalia Ibarra-Bonilla ◽  
Ivan Reyes-Castillo
Keyword(s):  
Data Storage ◽  
Inertial Sensors ◽  
Video Capture ◽  
Python Language ◽  
Fourth Level ◽  
Sensor Module ◽  
Four Levels ◽  
Gps System ◽  
Protective Case ◽  
Vision Module

This paper presents the design and implementation of a CanSat pico-satellite developed on the ARM-CortexM4 and FPGA Spartan6 platforms. The CanSat structure consists of four modules distributed in four levels of construction. The first level is the sensor module, consisting of: the IC BME280 (barometer, humidity and temperature), the L70 GPS system and an inertial sensors system MPU6050 and AK8975. The second level is the mission control module and incorporates an ARM-STM32F407 microcontroller integrated with the OS-Micropython that allows programming in Python language. This module has the capacity of data storage using an EEPROM-M24C32 memory and a micro-SD. The third level is the LoRa technology wireless communications module with a 10Km range. The fourth level is the vision module, consisting of an OV7670 camera interconnected with the FPGA XC6SLX16, which functions as a photo and video capture system. The design of the modules was made with SMD technology in PCB of up to four layers. The design of the CanSat protective case in PLA material, manufactured by 3D printing of FDM technology, is presented. Finally, the results of preliminary performance tests are presented.

scholarly journals Visual Assistant for Blind People using Raspberry Pi

2021 ◽  
pp. 671-675
Author(s):  
Tejal Adep ◽  
Rutuja Nikam ◽  
Sayali Wanewe ◽  
Dr. Ketaki B. Naik
Keyword(s):  
Visually Impaired ◽  
Daily Life ◽  
Artificial Vision ◽  
Raspberry Pi ◽  
Blind People ◽  
Python Language ◽  
Visually Impaired People ◽  
Impaired People ◽  
Visual Aid ◽  
Impaired Person

Blind people face the problem in daily life. They can't even walk without any aid. Many times they rely on others for help. Several technologies for the assistance of visually impaired people have been developed. Among the various technologies being utilized to assist the blind, Computer Vision-based solutions are emerging as one of the most promising options due to their affordability and accessibility. This paper proposes a system for visually impaired people. The proposed system aims to create a wearable visual aid for visually impaired people in which speech commands are accepted by the user. Its functionality addresses the identification of objects and signboards. This will help the visually impaired person to manage day-to-day activities and navigate through his/her surroundings. Raspberry Pi is used to implement artificial vision using python language on the Open CV platform.

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