The proposed work presents efficient objection detection and tracking algorithm for autonomous vehicles, which is developed in MATLAB with Image Processing Computer Version Toolbox and Automated Driving Toolbox. The developed algorithm track and detects moving and stationary objects such as other vehicles, pedestrians, and traffic lanes. Accurate and efficient tracking are important to analyze object behavior. For this work various build in detectors from MATLAB tool box were tested and compared. The evaluation of algorithm was carried on for 19 short videos from 8 seconds to 23 seconds, and then it applied to K-dataset and full road experiments by the Automated Driving Lab. The full road tests are between one to five minutes, including CAR to CAR WEST, CAR WEST to CAR, and campus marked roads.

A Deoxyribonucleic Acid (DNA) microarray is a collection of microscopic DNA spots attached to a solid surface, such as glass, plastic or silicon chip forming an array. The analysis of DNA microarray images allows the identification of gene expressions to draw biological conclusions for applications ranging from genetic profiling to diagnosis of cancer. The DNA microarray image analysis includes three tasks: gridding, segmentation and intensity extraction. In this paper, a method for noise removal in microarray image using Bidimensional Empirical Mode Decomposition (BEMD) is presented. The BEMD decomposes the image into IMFs and residual components. Then, the selected high frequency IMFs are de-noised with wavelet model and finally the BEMD reconstruction gives the de-noised image. The experimental results show the proposed algorithm can perform significantly better in terms of noise suppression and detail preservation in microarray images.

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