Analysis of laughter events in real science classes by using multiple environment sensor data

2014 ◽  
Author(s):  
Carlos Ishi ◽  
Hiroaki Hatano ◽  
Norihiro Hagita
Keyword(s):  
Sensor Data ◽  
Real Science ◽  
Science Classes

scholarly journals The Data Sensor Hub (DaSH): A Physical Computing System to Support Middle School Inquiry Science Instruction

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6243
Author(s):  
Alexandra Gendreau Chakarov ◽  
Quentin Biddy ◽  
Colin Hennessy Elliott ◽  
Mimi Recker
Keyword(s):  
Data Streams ◽  
Teacher Practices ◽  
Computing System ◽  
Sensor Data ◽  
Programming Environment ◽  
Physical Computing ◽  
Science Practices ◽  
Instructional Units ◽  
Scientific Phenomena ◽  
Science Classes

This article describes a sensor-based physical computing system, called the Data Sensor Hub (DaSH), which enables students to process, analyze, and display data streams collected using a variety of sensors. The system is built around the portable and affordable BBC micro:bit microcontroller (expanded with the gator:bit), which students program using a visual, cloud-based programming environment intended for novices. Students connect a variety of sensors (measuring temperature, humidity, carbon dioxide, sound, acceleration, magnetism, etc.) and write programs to analyze and visualize the collected sensor data streams. The article also describes two instructional units intended for middle grade science classes that use this sensor-based system. These inquiry-oriented units engage students in designing the system to collect data from the world around them to investigate scientific phenomena of interest. The units are designed to help students develop the ability to meaningfully integrate computing as they engage in place-based learning activities while using tools that more closely approximate the practices of contemporary scientists as well as other STEM workers. Finally, the article articulates how the DaSH and units have elicited different kinds of teacher practices using student drawn modeling activities, facilitating debugging practices, and developing place-based science practices.

Inquiry Learning

2009 ◽  
Vol 23 (2) ◽  
pp. 117-127 ◽  
Author(s):  
Astrid Wichmann ◽  
Detlev Leutner
Keyword(s):  
Scientific Reasoning ◽  
Inquiry Learning ◽  
Instructional Support ◽  
Test Results ◽  
Knowledge Test ◽  
Simulation Based ◽  
Scientific Experiments ◽  
Specific Support ◽  
Science Classes

Seventy-nine students from three science classes conducted simulation-based scientific experiments. They received one of three kinds of instructional support in order to encourage scientific reasoning during inquiry learning: (1) basic inquiry support, (2) advanced inquiry support including explanation prompts, or (3) advanced inquiry support including explanation prompts and regulation prompts. Knowledge test as well as application test results show that students with regulation prompts significantly outperformed students with explanation prompts (knowledge: d = 0.65; application: d = 0.80) and students with basic inquiry support only (knowledge: d = 0.57; application: d = 0.83). The results are in line with a theoretical focus on inquiry learning according to which students need specific support with respect to the regulation of scientific reasoning when developing explanations during experimentation activities.

Auditory visualization of landmine detector sensor data

2009 ◽  
Author(s):  
Bradley M. Davis ◽  
Woodrow W. Winchester ◽  
Jason D. Zedlitz
Keyword(s):  
Sensor Data

Anomaly Detection in Sensor Data

2018 ◽  
Vol 18 (1) ◽  
pp. 20-32 ◽  
Author(s):  
Jong-Min Kim ◽  
Jaiwook Baik
Keyword(s):  
Anomaly Detection ◽  
Sensor Data

Study on Fault Diagnosis of Planetary Gearbox in Unmanned Aerial Vehicle Using Multi sensor Data

2020 ◽  
Vol 20 (4) ◽  
pp. 332-342
Author(s):  
Hyung Jun Park ◽  
Seong Hee Cho ◽  
Kyung-Hwan Jang ◽  
Jin-Woon Seol ◽  
Byung-Gi Kwon ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Unmanned Aerial Vehicle ◽  
Sensor Data ◽  
Planetary Gearbox ◽  
Aerial Vehicle

A practical approach on non-regular sampling and universal demosaicing of raw image sensor data

2020 ◽  
Vol 2020 (1) ◽  
pp. 91-95
Author(s):  
Philipp Backes ◽  
Jan Fröhlich
Keyword(s):  
Image Quality ◽  
Source Code ◽  
Image Sensor ◽  
Sensor Data ◽  
Color Filter ◽  
Lowpass Filter ◽  
Sampling Errors ◽  
Regular Sampling ◽  
Single Sensor ◽  
Similar Image Quality

Non-regular sampling is a well-known method to avoid aliasing in digital images. However, the vast majority of single sensor cameras use regular organized color filter arrays (CFAs), that require an optical-lowpass filter (OLPF) and sophisticated demosaicing algorithms to suppress sampling errors. In this paper a variety of non-regular sampling patterns are evaluated, and a new universal demosaicing algorithm based on the frequency selective reconstruction is presented. By simulating such sensors it is shown that images acquired with non-regular CFAs and no OLPF can lead to a similar image quality compared to their filtered and regular sampled counterparts. The MATLAB source code and results are available at: http://github. com/PhilippBackes/dFSR

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