University of Illinois

Southern Illinois University

Haibo Wang

Ajay Mahajan

Spyros Tragoudas

Manoj K Mohanty

Baojie Zhang

Stefan Leitner

Systems and methods for compressive image sensor techniques based on sparse measurement matrices are disclosed. A method to perform compressive sensing (CS) measurement operations for image sensors limits pixel summation to be within neighboring pixels and hence dramatically simplifies CS image sensor circuits and reduces their power consumption while providing better image quality compared to conventional random measurement matrix based methods. A sparse measurement matrix is applied to pixel data to generate a desired number of summation groups, each summation group consisting of outputs from an equal number of pixel cells. Each pair of summation groups contains the same number of shared outputs from pixel cells. From the summation groups, an image captured by the pixel cells is recovered.

Systems and methods for compressive image sensor techniques utilizing sparse measurement matrices

An apparatus and method for sensor and control system, which automatically adjusts a product splitter position of a full-scale spiral. An electrical conductivity-based automation system is described and claimed herein and has been successfully developed and demonstrated as illustrated herein. The system includes a sensor and a microprocessor based and controlled servo or gear motor that is utilized to adjust the splitter of an operating coal/mineral spiral based on the readings of the sensor. The device as described and claimed herein converts a traditional coal spiral to an automated system for controlling the splitter thereby giving the spiral unit the ability to automatically adjust a key process variable, i.e., its splitter position, in real time as and when the feed coal or other mineral property changes to maintain the performance of the spiral at the optimum level.

Automated system for coal spiral

Growing community of inventors

Carbondale, IL, United States of America

Haibo Wang