Gyroscope and Accelerometer Calibration with Raspberry Pi

This is the second entry into the series entitled "Calibration of an Inertial Measurement Unit (IMU) with Raspberry Pi" where the gyroscope and accelerometer are calibrated using our Calibration Block. Python is used as the coding language on the Raspberry Pi to find the calibration coefficients for the two sensors. Validation methods are also used to integrate the IMU variables to test the calibration of each sensor. The gyroscope shows a fairly accurate response when calibrated and integrated, and found to be within a degree of the actual rotation test. The accelerometer was slightly less accurate, likely due to the double integration required to approximate displacement and the unbalanced table upon which the IMU was calibrated. Filtering methods are also introduced to smooth the accelerometer data for integration. The final sensor, the magnetometer (AK8963), will be calibration in the next iteration of this series.

Loudspeaker Analysis and Experiments: Part I

In this tutorial, a loudspeaker will be analyzed by calculating the Thiele-Small parameters from impedance measurements using an inexpensive USB data acquisition system (minimum sampling rate of 44.1 kHz). The methods used in this project will educate the user on multiple engineering topics ranging from: data acquisition, electronics, acoustics, signal processing, and computer programming.

Audio Processing in Python Part II: Exploring Windowing, Sound Pressure Levels, and A-Weighting Using an iPhone X

Raspberry Pi 3B+ acoustic analysis using Python. Audio recording and signal processing with Python, beginning with a discussion of windowing and sampling, which will outline the limitations of the Fourier space representation of a signal. Discussion of the frequency spectrum, and weighting phenomenon in relation to the human auditory system will also be explored. Lastly, the significance of microphone pressure units and conversion to the decibel will be briefly introduced and explained.

Audio Processing in Python Part I: Sampling, Nyquist, and the Fast Fourier Transform

Fourier Series has been widespread in applications of engineering ranging from heat transfer, vibration analysis, fluid mechanics, noise control, and much more. After evolutions in computation and algorithm development, the use of the Fast Fourier Transform (FFT) has also become ubiquitous in applications in acoustic analysis and even turbulence research. In this tutorial, I describe the basic process for emulating a sampled signal and then processing that signal using the FFT algorithm in Python. This will allow the user to get started with analysis of acoustic-like signals and understand the fundamentals of the Fast Fourier Transform.

Arduino Sound Level Meter