<span>ARM processors are widely used in embedded systems. They are often implemented as microcontrollers, field-programmable gate arrays (FPGAs) or systems-on-chip. In this paper, a variety of ARM processor platform implementations are reviewed, such as implementation into a microcontroller, a system-on-chip and a hybrid ARM-FPGA platform. Furthermore, the implementation of a specific ARM processor, the Cortex-A9 processor, into a system-on-chip (SoC) on an FPGA is discussed using Xilinx’s Vivado and SDK software system and execution on a Xilinx Zynq Board.</span>

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Normalization is a process of removing systematic variation that affects measured gene expression levels in the microarray experiment. The purpose is to get more accurate DNA microarray result by deleting the systematic errors that may have occurred during the making of DNA microarray Image. In this paper, five normalization methods of Global, Lowess, House-keeping, Quantile and Print-tip are discussed. The Print Tip normalization was chosen for its high accuracy (32.89 dB and its final MA graph shape was well normalized. Print tip normalization with PSNR value of 33.15dB has been chosen as a new normalization method. The results were validated using four images from the formal database for DNA microarray data. The new proposed method showed more accurate results than the existing methods in term of four parameters: MSE, PSNR, RMSE and MAE.

Most microarray image scanning approaches provide an estimation of the intensity of the foreground and background for each spot. Background intensity must be corrected in order to remove the effect of non-specific binding or spatial heterogeneity across the array, but when such corrections are applied many problems appear, such as negative intensity for the spot or high variability of low-intensity log ratios. In this paper, many alternative methods for calculating background intensity are discussed and many approaches for background correction are tested and compared. GenePix, ScanAlyze and QuantArry are the strategies that were reviewed for background locations to extract their intensity. Similarly, to GenePix, a new approach for background calculation was proposed and tested. It shows more accurate results and the occurrences of error become lesser.