A Python-based laboratory course for image and video signal processing on embedded systems

Jaskolka, Karina; Seiler, Jurgen; Beyer, Florian; Kaup, André

doi:10.1016/j.heliyon.2019.e02560

Cited by 11 publications

(6 citation statements)

References 18 publications

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“…Based on teaching tools and learning approaches, different scholars have proposed a variety of teaching methods, such as computer-aided programming teaching [13], multimedia teaching [14], MATLAB (matrix laboratory) assisted teaching [15][16][17], etc. In addition, in the aspect of experimental teaching, many scholars have designed a variety of experimental systems based on hardware, software, and a variety of combinations to improve the experimental teaching methods [18][19][20][21][22]. To sum up, the above documents are all aimed at offline teaching methods or some aspects of experimental teaching, such as case teaching, multimedia teaching, MATLAB simulation experiment, hardware experiment, etc.…”

Section: Current Situation Of Ssc Teachingmentioning

confidence: 99%

Blended Teaching for Signal and System Course Based on Internet-Engineering Education

Dou

Wang²,

Li³

et al. 2023

Research Reports on Computer Science

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Aiming at the teaching difficulties of signal and system course, adapting to the new situation of the information age and the engineering education of outcome-based education (OBE), adhering to the traditional classroom teaching and making full use of the network teaching platform and internet technology, a blended teaching mode of internet-engineering education (BTM-IEE) is proposed. In this mode, we adhered to the educational concept of OBE, formulated teaching objectives, optimized teaching content, and built matching teaching resources. In three teaching stages: before class, in class and after class, online-offline blended teaching was effectively organized and implemented by using internet technology, network teaching platform, independently developed a comprehensive experimental system, remote virtual experiment platform, QQ group, etc. In order to ensure a good cycle of teaching quality, a multi-dimensional evaluation system is constructed. A variety of application examples and questionnaire data showed that BTM-IEE can achieve a deep integration of in class-out of class and online-offline, improve students’ engineering application ability, autonomous learning ability, cooperation, communication ability, and cultivate innovative thinking. The horizontal and vertical comparison between the traditional and the improved teaching mode shows that the BTM-IEE can improve the teaching effect, the excellent rate is significantly increased, and the failure rate is significantly reduced. This mode provides a reference for the improvement of the teaching quality of professional courses and has important practical significance.

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Section: Current Situation Of Ssc Teachingmentioning

confidence: 99%

Blended Teaching for Signal and System Course Based on Internet-Engineering Education

Dou

Wang²,

Li³

et al. 2023

Research Reports on Computer Science

View full text Add to dashboard Cite

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“…Image and signal processing by its object-oriented algorithms of Python are pervasive in geosciences and provide solutions to a wide range of problems. Examples of using Python have been reported in a variety of domains, including geodetic studies [62], topographic analysis [63], hydrogeological modelling [64], modelling air pollution and land use [65], batch spatial data processing [66], creating panorama images [67] and tunneling microscopy [68], to mention a few. R is a fundamental programming tool for statistical and spatial data processing [69].…”

Section: Related Workmentioning

confidence: 99%

Satellite Image Processing by Python and R Using Landsat 9 OLI/TIRS and SRTM DEM Data on Côte d’Ivoire, West Africa

Lemenkova

Debeir

2022

J. Imaging

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In this paper, we propose an advanced scripting approach using Python and R for satellite image processing and modelling terrain in Côte d’Ivoire, West Africa. Data include Landsat 9 OLI/TIRS C2 L1 and the SRTM digital elevation model (DEM). The EarthPy library of Python and `raster’ and `terra’ packages of R are used as tools for data processing. The methodology includes computing vegetation indices to derive information on vegetation coverage and terrain modelling. Four vegetation indices were computed and visualised using R: the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index 2 (EVI2), Soil-Adjusted Vegetation Index (SAVI) and Atmospherically Resistant Vegetation Index 2 (ARVI2). The SAVI index is demonstrated to be more suitable and better adjusted to the vegetation analysis, which is beneficial for agricultural monitoring in Côte d’Ivoire. The terrain analysis is performed using Python and includes slope, aspect, hillshade and relief modelling with changed parameters for the sun azimuth and angle. The vegetation pattern in Côte d’Ivoire is heterogeneous, which reflects the complexity of the terrain structure. Therefore, the terrain and vegetation data modelling is aimed at the analysis of the relationship between the regional topography and environmental setting in the study area. The upscaled mapping is performed as regional environmental analysis of the Yamoussoukro surroundings and local topographic modelling of the Kossou Lake. The algorithms of the data processing include image resampling, band composition, statistical analysis and map algebra used for calculation of the vegetation indices in Côte d’Ivoire. This study demonstrates the effective application of the advanced programming algorithms in Python and R for satellite image processing.

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“…Python allowed the development of a friendly application with a graphical user interface (GUI), with execution libraries such as bootstrap, data tables, jquery moment [32,43]. Furthermore, python is a language compatible with the Raspberry Pi 4 computer; it allows internet connections through the ethernet port, Wi-Fi or Bluetooth [44,45].…”

Section: Sam Web Application Designmentioning

confidence: 99%

Fluid Level Measurement System in Oil Storage. Python, Lab-Based Scale

Escandón-Panchana¹,

Morante-Carballo²,

Herrera-Franco³

et al. 2022

MMEP

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Refineries, fuel depots, airports and storage terminals commonly use fluid level measurement in storage tanks. Different fluid level measurement techniques and devices differ in the inaccuracy of measurement results, costs, and company needs. In addition, these measurements seek reliability of measurement data, immediate response times, control in operations, oil movement, custody transfer and inventory control. The objective is to develop a computer system for measuring fluid levels in oil storage tanks, using ultrasonic and temperature sensors, creating a web application for an automated measurement system (SAM) for managing volumes of Petroleum. The study methodology is i) Analysis of measurement reports. ii) Selection of physical components of the computer system. iii) SAM algorithm design and web application, and iv) Validate the system. The SAM application developed in open source proposes functional modules for administration, control, security, management and monitoring of storage tanks, the status of physical components and generation of dynamic reports in real-time. The results show the control characteristics of storage tanks such as maximum and minimum volume, temperature, time, precise data records in less time than certain current computer structures.

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A Python-based laboratory course for image and video signal processing on embedded systems

Cited by 11 publications

References 18 publications

Blended Teaching for Signal and System Course Based on Internet-Engineering Education

Blended Teaching for Signal and System Course Based on Internet-Engineering Education

Satellite Image Processing by Python and R Using Landsat 9 OLI/TIRS and SRTM DEM Data on Côte d’Ivoire, West Africa

Fluid Level Measurement System in Oil Storage. Python, Lab-Based Scale

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