Module 3-4: Measurement and Estimation

We  studied fundamentals of sensor measurement: measure magnitude of physical quantity and convert it to a signal that can be read and processed by a processor.

We also learnt how to estimate parameters based on collected sensory data through experiments. Approximation is mostly based on probabilistic model. Basic methods of circuit fitting techniques to find a mathematical model and standard deviation used to examine sensory data are briefed.

A case study of infrared ranging sensor is presented to illustrate the concepts of measurement and estimation with sensory data. 

Lecture 3-3: Perception

The lecture about sensing vs. perception was given. 

We emphasized on how a machine can interpret a situation based on raw sensory data and context. The recipe is a combination of raw data + probabilistic model + context.

All the concepts and processes were explained through examples and video demonstrations of our research works (www.morelab.org).

Lecture 3-2: Advanced Sensors and their work principles

We studied the functionalities of advanced sensors consisting of infrared ranging sensors, heading sensors (gyroscope, accelerometer, compass), GPS, ultrasound ranging sensors, laser ranging scanner, 3D camera, and omni-directional camera.

More importantly, we discussed about the working principles of such sensors as well as how they can be used in the real-world applications.