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Structural health monitoring (SHM) is the technique to identify the damage in the building or physical structures. Several structural health monitoring has been proposed in recent years. Increase in number of sensors and its wiring mechanism causes issue in signal processing demand, efficiency, and unreliability. In current state, already present sensors cannot be used for Structural Health Monitoring because of its high cost and low battery capacity, although none of these techniques are capable of monitoring the condition of complex physical structures. The measurement of strain can be done by using patch antenna as strain sensors. In currently available sensors only the wireless range. The main objective of this paper is to investigate the feasibility of using circular and rectangular slotted microstrip patch antenna as a strain sensor for structural health monitoring without these afore mentioned problems. These antenna sensors’ (slotted rectangular and circular microstrip patch antennas) simulation results are confirmed. From the simulation results, the sensor provides information about crack orientation. [ABSTRACT FROM AUTHOR]

The problem of mammographic image classification has been handled using various measures and features. The methods consider only small set of features to perform classification, but still the methods suffer to produce efficient classification accuracy. To overcome the problem of accuracy in mammographic image classification, a region-specific multi-attribute white mass estimation technique is proposed. The method uses the white mass value, density measure, and binding to identify the microcalcification. First, the peak white mass value is identified by visiting throughout the mammogram region. Second, the method splits the mammographic image into a number of small scale integral images. Third, for each integral image, the method computes multi-attribute white mass value, and based on computed white mass value, the method identifies the region being affected by the calcification. The method produces efficient result in mammogram image classification. [ABSTRACT FROM AUTHOR]