Alzheimer's disease is a degenerative brain disorder that affects millions of people around the world and still without cure. A very common application of Hopfield neural networks is to simulate a human memory as well as to evaluate problems of degeneration and memory loss. On the other hand, from the control area, one has Lurie's problem, which emerged in the 1940s and which still does not have a general solution. However many works and results came in an attempt to solve it. In this paper, the Hopfield's network is shown as a particular case of Lurie's problem, then one of the consequences of Alzheimer's disease, memory failure, is modeled using Hopfield's networks and nally a recent result of Lurie's problem is applied to the computationally modeled disease to correct the problem of memory loss. The correction is made using a controller via DK-iteration. Simulations are performed to validate the computational model of the disease and to demonstrate the effectiveness of the application of the recent Lurie's problem theorem. Therefore, in addition to the results presented, this work aims at encouraging the researches in the area, so that in the future, better diagnostic and treatment conditions will be achieved.

Head pose estimation (HPE) has been widely studied in the last years due to its many applications in face analysis systems. The use of such systems ranges from the analysis of focus of attention, social interactions or the use in mobile applications in the realization of the currently popular facial animations and / or in face recognition process, where the frontal faces are especially important. Many approaches were proposed focusing mainly in Random Forests and Convolutional Neural Networks (CNN). In this paper, a framework for estimation of the head pose was proposed computing the degrees of freedom (DOF) of the human head using 2D images data only. The framework implements some computer vision algorithms available in publicly machine learning libraries such as OpenCV and Dlib, which allows easy application and re-implementation. In addition, a Support Vector Machine (SVM) model with Radial Basis Function (RBF) kernel was developed for frontal face classification. Experiments conducted on 2D image datasets in constrained environment show that the approach is capable of real-time performance. Were designed three protocols of experiments with two databases for testing the SVM model. Values of 100% and 98% for precision and recall, respectively, were achieved classifying frontal faces. Significant results were obtained measuring yaw rotation with 4.24 of mean absolute error for frontal face.