Alzheimer's Disease (AD) is an incurable disease that causes dementia. Rehabilitation efforts of this disease focus on slowing down the rate of progression and improving the quality of life of the patients by enhancing their ability to engage with the environment and society surrounding them. In this paper, we present an integrated application (ADcope) that utilizes mobility and advanced communication features of smartphones to rehabilitate AD patients. ADcope integrates quality of life enhancing modules such as the memory wallet, calendaring,and NFC enclosure content tagging, and dementia exercising modules that incorporate audio assisted memory training and spaced retrieval exercises. Initial trials of the ADcope application with AD patients confirm that the benefits of previously proposed AD tools and exercises can also be achieved using a smartphone application. The simplicity of using the ADcope application can increase the rate of adoption of AD tools in dealing with AD patients.978-1-936968-80-0

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Neural networksand patternrecognition techniques have been effectively used in characterizingand classifying patterns.In this paper, we focus on the performanceof both techniques in classifying ultrasonic transducers.The work was aimed at comparing the performanceof both techniques for : (1) preclustering of transducers into classes;(2)~ognizing transducers.'l'hecharacterizationalgorithms used are based on neural network and pattern recognition techniques. The competitive learning technique provides poor results as compared to the K-Means for prtxlustering. While for recognition, it is found that artificial neural network techniques provide tkr better classifkation nsults as compared to the pattern remgnition techniques. A multilayerbackpmpagation neural network is developed for characterizing the transducers which provides a misclassifwation rate of 6%. Two other multilayer neural networks, sum-of-products and a newly devised neural network called hybrid sum-of-products have a misclassification rateof 10%and 7%, respectively. The best patternrecognition technique for this application is found to be the perception which provides a misclassification rate of 23%. The worst patternrecognition technique is found to be the Bayes' theorem method which provides a misclassification rate of 54%.

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