A measurement method of batch solution concentration based on normalized compressed sensing

Measuring large batches of solution concentrations is a cumbersome task that is time consuming and involves many reagents. Determining how to improve the measurement efficiency of batch concentrations is an urgent problem to be solved. This paper introduces an efficient method for the measurement of batch solution concentrations based on normalized compressed sensing. The method is based on the sparsity of natural signals and can reconstruct the original batch concentration signals with a high level of accuracy while taking fewer measurements. The proposed method extracts subsamples from the original samples according to a sampling matrix; the number of subsamples can be much smaller than the original number of samples. Then the solution concentration of the original samples can be reconstructed by measuring the subsamples. The specific process includes sparse signal representation, non-related observation, and nonlinear optimization reconstruction. Compared with the traditional measurement method, the proposed method is demonstrably superior for the measurement of batch solution concentrations; satisfactory batch solution concentration distribution results can be obtained with a number of measurements that is much smaller than the number of samples. The proposed method will greatly reduce the time and cost of measurement.