Diagnostic features in mammograms vary widely in size and shape. Classical image enhancement techniques cannot adapt to the varying characteristics of such features. An adaptive method for enhancing the contrast of mammographic features of varying size and shape is presented. The method uses each pixel in the image as a seed to grow a region. The extent and shape of the region adapt to local image gray-level variations, corresponding to an image feature. The contrast of each region is calculated with respect to its individual background. Contrast is then enhanced by applying an empirical transformation based on each region's seed pixel value, its contrast, and its background. A quantitative measure of image contrast improvement is also defined based on a histogram of region contrast and used for comparison of results. Using mammogram images digitized at high resolution (less than 0.1 mm pixel size), it is shown that the validity of microcalcification clusters and anatomic details is considerably improved in the processed images.

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Demand response (DR) is a recent effort to improve the efficiency of the electricity market and the stability of the power system. A successful implementation relies on both appropriate policy design and enabling technology. This paper presents a multiagent system to evaluate optimal residential DR implementation in a distribution network, in which the main stakeholders are modeled by heterogeneous home agents (HAs) and a retailer agent (RA). The HA is able to predict and control electricity load demand. A real-time price prediction model is developed for the HA and the RA. The optimal control of electricity consumption is formulated into a convex programming problem to minimize electricity payment and waiting time under real-time pricing. Simulation results show that the peak-to-average power ratio and electricity payments are significantly reduced using the proposed algorithms. The HA, with the proposed optimal control algorithms, can be embedded into a home energy management system to make intelligent decisions on behalf of homeowners responding to DR policies. The proposed agent system can be utilized to evaluate various strategies and emerging technologies that enable the implementation of DR.

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