Multistage classifier-based approach for Alzheimer's disease prediction and retrieval

The most prevalent and common type of dementia is Alzheimer's disease (AD). However, it is notable that very few people who are suffering from AD are diagnosed correctly and in a timely manner. The definite cause and cure of the disease are still unavailable. The symptoms might be more manageable and its treatment can be more effective, when the impairment is still at an earlier stage or at MCI (mild cognitive impairment). AD can be clinically diagnosed by physical and neurological examination, so there is an need for developing better and efficient diagnostic tools for AD. In recent years, content-based image retrieval (CBIR) systems have been widely researched and applied in many medical applications. Combining an automated image classification system and the radiologist's professional knowledge, to increase the accuracy of prediction and diagnosis, were the main motives. In this paper, a multistage classifier using machine learning, including Naive Bayes classifier, support vector machine (SVM), and K-nearest neighbor (KNN), was used to classify Alzheimer's disease more acceptably and efficiently. For this, MRI (Magnetic resonance imaging) scans were processed by FreeSurfer, a powerful software tool suitable for processing and normalizing brain MRI images. We also applied a feature selection technique - PSO (particle swarm optimization) to many feature vectors in order to obtain the best features that represent the salient characteristics of AD. The results of the proposed method outperform individual techniques in a benchmark database provided by the Alzheimer's Disease Neuroimaging Institute (ADNI). Keywords: Alzheimer's disease, Machine learning, Content-based image retrieval, Multistage classifier, PSO, Structural MRI, SVM, K-N

A facility for electrical contact resistance measurement

A test facility was developed for directly measuring the electrical contact resistance of switch gear contacts of real sizes under different environmental conditions and contact pressures. It can measure contact resistance to an order of one-tenth of a mu Omega. It sources a constant current in the order of mA and hence avoids Initial arcing across contacts, Experiments were conducted on brass-brass samples with different contact pressures and the results are in agreement with published theoretical calculations

An improved maximum power point tracker using a step-up converter with current locked loop

lt is well known that for a given solar radiation intensity and solar cell temperature there exists a Maximum Power Point at which the power generated from the PV panel is at its maximum. A system designer is interested in optimal matching of the load to the PV generator so that the maximum power can be obtained during operating period. A Maximum Power Point Tracker (MPPT) using a step up converter with a current locked loop is developed. Its performance is compared with the literature (the step down power converter using PWM technique), under different solar irradiance and ambient temperatures. It showed an improvement in the output power by 22.5% (average) over a wide range of solar irradiation in a day