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Limited research is available to guide the intelligent selection of test parameters such as load level, grid size, grid spacing, and sample surface roughness to design nanoindentation experiments on cementitious materials. A cohort of nanoindentation experiments on four different cement pastes utilizing varying indentation depth, load level, grid spacing, grid size, and surface roughness were performed. Results from this study indicate that a “critical indentation depth” exists for each sample such that individual indentations shallower than the critical depth demonstrate greater variability for all measured properties than deeper indentations. Most commercial nanoindentation equipment is load controlled, so careful load selection is important to ensure that the critical indentation depth is achieved. Elastic indentation properties appear to show dependence on grid spacing and grid size. Surface roughness and the polishing method profoundly influence measured properties. The study demonstrates that the choice of test parameters is influenced by the microstructural details (phases) under consideration, as well as the porosity of the sample and therefore cannot be generalized.

The proposed work aims at developing a solution for the detection of sleep apnea disorder using ECG signal analysis, which is an established diagnostic modality. Under this work, the standard research resource, ECG-Apnea database from MIT’s Physionet.org., having ECG signal night time recordings, is used. The sequential procedure of Preprocessing, Peak or QRS complex detection, Feature extraction, Feature reduction, and Classification is used. Preprocessing of the ECG signal is performed to free it from noise resulted from baseline wander, power-line interference, and muscle artifacts. Thus, the improved signal quality is estimated in terms of its Signal to Noise Ratio (SNR) and entropy value. QRS detection is implemented using the popular Pan-Tompkins algorithm that provides the reference for the feature extraction process. The performance of the detection algorithm is measured in terms of the average values of accuracy and specificity as 98% and 96%, respectively. Feature extraction algorithm involves the collection of selected 30 feature values related to the time domain and the frequency domain gathered from each of the test recordings of the ECG database, minute-wise for 7 hours. Feature reduction technique is followed to reduce the data size to a set of 20 ECG signal features using Principal Component Analysis (PCA) and avoid redundancy. Hence the trained Adaptive Neuro-Fuzzy Classifier is used on the output feature set derived from PCA to detect the presence or absence of Sleep apnea disorder with an estimated accuracy and specificity as 95% and 96%, respectively.

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