Arduino and RaspberryPi for signal acquisition and automation
From 25 £ /h
Arduino (microcontroller) and raspberryPi (mini linux PC) are commonly used in industry for robotics, data acquisition, real-time analysis and automation.
In this class, we will discuss how to use these for designing custom instruments for automating tasks.
In this class, we will discuss how to use these for designing custom instruments for automating tasks.
Extra information
Please bring your laptop with Arduino IDE installed.
Location
At student's location :
- Around Tokyo, Japan
At teacher's location :
- Shimo-Ochiai, Shinjuku City, Tokyo, Japan
About Me
Hello,
I am a scientist by profession and interested in solving complex problems to understand nature. About my background, I am a doctorate in Science (Physical chemistry) with extensive knowledge in physics, chemistry, mathematics and computer programming.
Framing a technical problem into computer program is my expertise.
I have worked with several institutes offering high-performance compute facilities for scientific purposes, and can program in python, igorpro, labview, C++, bash shell and FORTRAN. I follow standard software design principles, detailed documentation, automated tests and git for version control.
Apart from scientific exploration, I use python with bash for image analysis and editing, automation, web-programming and associated technologies.
I am a scientist by profession and interested in solving complex problems to understand nature. About my background, I am a doctorate in Science (Physical chemistry) with extensive knowledge in physics, chemistry, mathematics and computer programming.
Framing a technical problem into computer program is my expertise.
I have worked with several institutes offering high-performance compute facilities for scientific purposes, and can program in python, igorpro, labview, C++, bash shell and FORTRAN. I follow standard software design principles, detailed documentation, automated tests and git for version control.
Apart from scientific exploration, I use python with bash for image analysis and editing, automation, web-programming and associated technologies.
Education
Doctor of Philosophy (Physical Chemistry), NYCU Taiwan - Spectroscopy, Quantum chemistry
Masters in Science (Molecular Science), NYCU Taiwan - Spectroscopy
Bachelors in Science (Chemistry Honors), BHU India
Masters in Science (Molecular Science), NYCU Taiwan - Spectroscopy
Bachelors in Science (Chemistry Honors), BHU India
Experience / Qualifications
Training for two master and one doctorate students over a period of 2 years in advanced data analysis and processing, via python programming and data visualization.
Maintaining software repositories for analysis of spectroscopic data
Maintaining software repositories for analysis of spectroscopic data
Age
Children (7-12 years old)
Teenagers (13-17 years old)
Adults (18-64 years old)
Seniors (65+ years old)
Student level
Beginner
Intermediate
Advanced
Duration
60 minutes
The class is taught in
English
Hindi
Reviews
Availability of a typical week
(GMT -04:00)
New York
At teacher's location
At student's home
Mon
Tue
Wed
Thu
Fri
Sat
Sun
00-04
04-08
08-12
12-16
16-20
20-24
This class is designed for both beginners and advanced levels, depending on the student.
The target is to cover a balance of common use cases : daily use, terminal, automating tasks, bash and bash programming.
Course content is subject to adjustment depending on the students target.
The target is to cover a balance of common use cases : daily use, terminal, automating tasks, bash and bash programming.
Course content is subject to adjustment depending on the students target.
This course focuses on developing practical and conceptual skills for advanced data analysis in scientific research. Students will work primarily in Python, though the use of other programming environments is welcome.
The emphasis will be on understanding data at a deeper level: moving beyond routine processing to extracting meaningful physical and scientific insights. Core numerical techniques will be introduced and applied, including differentiation, integration, curve fitting, and frequency-domain analysis.
Structured test cases will be provided, and students will learn to approach them systematically, from data preprocessing to interpretation of results. In addition, open-source datasets will be used to explore real-world trends and to assess the effectiveness of different analytical approaches.
The course encourages active discussion, critical thinking, and comparison of methods, with an open forum for exploring numerical techniques and their appropriate application across scientific problems.
The emphasis will be on understanding data at a deeper level: moving beyond routine processing to extracting meaningful physical and scientific insights. Core numerical techniques will be introduced and applied, including differentiation, integration, curve fitting, and frequency-domain analysis.
Structured test cases will be provided, and students will learn to approach them systematically, from data preprocessing to interpretation of results. In addition, open-source datasets will be used to explore real-world trends and to assess the effectiveness of different analytical approaches.
The course encourages active discussion, critical thinking, and comparison of methods, with an open forum for exploring numerical techniques and their appropriate application across scientific problems.
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