Nine C programming labs to turn students into filtering and signal analysis experts

Author(s):  
Jacob Gunther ◽  
Todd Moon
2020 ◽  
pp. 24-33
Author(s):  
K. V. Rozov

The article presents the structure, content and results of approbation of the C++ programming course developed for the 10th grade students of physics and mathematics profile and implemented as part of the academic subject “Informatics”. The aim of the course is to develop in the student not only knowledge and skills in programming, but also his algorithmic culture and programming culture as important qualities of a potential IT-specialist. This is facilitated by special control of educational process by the teacher, which consists in monitoring the activities of students in writing programs and timely correction of this activity. The assessment of the level of development of student algorithmic culture and programming culture relative to the basic level of their formation (when mastering the basics of algorithmization and programming in the 9th grade) was carried out on the basis of a number of criteria presented in the article. The results of approbation showed that the specially organized teacher activity makes it possible to increase the level of algorithmic culture and programming culture of high school students when studying the basics of programming in C++.


Author(s):  
A. A. Nedbaylov

The calculations required in project activities for engineering students are commonly performed in electronic spreadsheets. Practice has shown that utilizing those calculations could prove to be quite difficult for students of other fields. One of the causes for such situation (as well as partly for problems observed during Java and C programming languages courses) lies in the lack of a streamlined distribution structure for both the source data and the end results. A solution could be found in utilizing a shared approach for information structuring in spreadsheet and software environment, called “the Book Method”, which takes into account the engineering psychology issues regarding the user friendliness of working with electronic information. This method can be applied at different levels in academic institutions and at teacher training courses.


Author(s):  
Weihai Sun ◽  
Lemei Han

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.


2012 ◽  
Vol 17 (4) ◽  
pp. 319-326 ◽  
Author(s):  
Zbigniew Chaniecki ◽  
Krzysztof Grudzień ◽  
Tomasz Jaworski ◽  
Grzegorz Rybak ◽  
Andrzej Romanowski ◽  
...  

Abstract The paper presents results of the scale-up silo flow investigation in based on accelerometer signal analysis and Wi-Fi transmission, performed in distributed laboratory environment. Prepared, by the authors, a set of 8 accelerometers allows to measure a three-dimensional acceleration vector. The accelerometers were located outside silo, on its perimeter. The accelerometers signal changes allowed to analyze dynamic behavior of solid (vibrations/pulsations) at silo wall during discharging process. These dynamic effects are caused by stick-slip friction between the wall and the granular material. Information about the material pulsations and vibrations is crucial for monitoring the interaction between silo construction and particle during flow. Additionally such spatial position of accelerometers sensor allowed to collect information about nonsymmetrical flow inside silo.


Author(s):  
Sebastian Brand ◽  
Matthias Petzold ◽  
Peter Czurratis ◽  
Peter Hoffrogge

Abstract In industrial manufacturing of microelectronic components, non-destructive failure analysis methods are required for either quality control or for providing a rapid fault isolation and defect localization prior to detailed investigations requiring target preparation. Scanning acoustic microscopy (SAM) is a powerful tool enabling the inspection of internal structures in optically opaque materials non-destructively. In addition, depth specific information can be employed for two- and three-dimensional internal imaging without the need of time consuming tomographic scan procedures. The resolution achievable by acoustic microscopy is depending on parameters of both the test equipment and the sample under investigation. However, if applying acoustic microscopy for pure intensity imaging most of its potential remains unused. The aim of the current work was the development of a comprehensive analysis toolbox for extending the application of SAM by employing its full potential. Thus, typical case examples representing different fields of application were considered ranging from high density interconnect flip-chip devices over wafer-bonded components to solder tape connectors of a photovoltaic (PV) solar panel. The progress achieved during this work can be split into three categories: Signal Analysis and Parametric Imaging (SA-PI), Signal Analysis and Defect Evaluation (SA-DE) and Image Processing and Resolution Enhancement (IP-RE). Data acquisition was performed using a commercially available scanning acoustic microscope equipped with several ultrasonic transducers covering the frequency range from 15 MHz to 175 MHz. The acoustic data recorded were subjected to sophisticated algorithms operating in time-, frequency- and spatial domain for performing signal- and image analysis. In all three of the presented applications acoustic microscopy combined with signal- and image processing algorithms proved to be a powerful tool for non-destructive inspection.


2017 ◽  
Vol 34 (1-2) ◽  
pp. 33-44
Author(s):  
Kun YANG ◽  
Linyan XUE ◽  
Kang YIN ◽  
Shuang LIU ◽  
Jie MENG

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