Jorge Munilla

Bio: Jorge Munilla is an academic researcher from University of Málaga. The author has contributed to research in topics: Radio-frequency identification & Authentication. The author has an hindex of 16, co-authored 63 publications receiving 1079 citations.

Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer’s Disease

Abstract: Computer Aided Diagnosis (CAD) constitutes an important tool for the early diagnosis of Alzheimer's Disease (AD), which, in turn, allows the application of treatments that can be simpler and more likely to be effective. This paper explores the construction of classification methods based on deep learning architectures applied on brain regions defined by the Automated Anatomical Labeling (AAL). Gray Matter (GM) images from each brain area have been split into 3D patches according to the regions defined by the AAL atlas and these patches are used to train different deep belief networks. An ensemble of deep belief networks is then composed where the final prediction is determined by a voting scheme. Two deep learning based structures and four different voting schemes are implemented and compared, giving as a result a potent classification architecture where discriminative features are computed in an unsupervised fashion. The resulting method has been evaluated using a large dataset from the Alzheimer's disease Neuroimaging Initiative (ADNI). Classification results assessed by cross-validation prove that the proposed method is not only valid for differentiate between controls (NC) and AD images, but it also provides good performances when tested for the more challenging case of classifying Mild Cognitive Impairment (MCI) Subjects. In particular, the classification architecture provides accuracy values up to 0.90 and AUC of 0.95 for NC/AD classification, 0.84 and AUC of 0.91 for stable MCI/AD classification and 0.83 and AUC of 0.95 for NC/MCI converters classification.

Distance bounding protocols for RFID enhanced by using void-challenges and analysis in noisy channels

Abstract: RFID systems are vulnerable to different attacks related to the location; distance fraud attack, relay attack and terrorist attack. The main countermeasure against these attacks is the use of protocols capable of measuring the round trip time of single challenge-response bit. In this paper, we consider a modification of these protocols applying a new feature; the ‘void challenges’. This way, the success probability for an adversary to access to the system decreases. We use as reference-point the most popular of this kind of protocols, the Hancke and Kuhn's protocol, to show the improvements achieved when different cases are analysed. Copyright © 2008 John Wiley & Sons, Ltd.

Exploratory graphical models of functional and structural connectivity patterns for Alzheimer's Disease diagnosis.

Abstract: Alzheimer's Disease (AD) is the most common neurodegenerative disease in elderly people. Its development has been shown to be closely related to changes in the brain connectivity network and in the brain activation patterns along with structural changes caused by the neurodegenerative process. Methods to infer dependence between brain regions are usually derived from the analysis of covariance between activation levels in the different areas. However, these covariance-based methods are not able to estimate conditional independence between variables to factor out the influence of other regions. Conversely, models based on the inverse covariance, or precision matrix, such as Sparse Gaussian Graphical Models allow revealing conditional independence between regions by estimating the covariance between two variables given the rest as constant. This paper uses Sparse Inverse Covariance Estimation (SICE) methods to learn undirected graphs in order to derive functional and structural connectivity patterns from Fludeoxyglucose (18F-FDG) Position Emission Tomography (PET) data and segmented Magnetic Resonance images (MRI), drawn from the ADNI database, for Control, MCI (Mild Cognitive Impairment Subjects), and AD subjects. Sparse computation fits perfectly here as brain regions usually only interact with a few other areas. The models clearly show different metabolic covariation patters between subject groups, revealing the loss of strong connections in AD and MCI subjects when compared to Controls. Similarly, the variance between GM (Gray Matter) densities of different regions reveals different structural covariation patterns between the different groups. Thus, the different connectivity patterns for controls and AD are used in this paper to select regions of interest in PET and GM images with discriminative power for early AD diagnosis. Finally, functional an structural models are combined to leverage the classification accuracy. The results obtained in this work show the usefulness of the Sparse Gaussian Graphical models to reveal functional and structural connectivity patterns. This information provided by the sparse inverse covariance matrices is not only used in an exploratory way but we also propose a method to use it in a discriminative way. Regression coefficients are used to compute reconstruction errors for the different classes that are then introduced in a SVM for classification. Classification experiments performed using 68 Controls, 70 AD, and 111 MCI images and assessed by cross-validation show the effectiveness of the proposed method.

Lightweight RFID authentication with forward and backward security

Abstract: We propose a lightweight RFID authentication protocol that supports forward and backward security. The only cryptographic mechanism that this protocol uses is a pseudorandom number generator (PRNG) that is shared with the backend Server. Authentication is achieved by exchanging a few numbers (3 or 5) drawn from the PRNG. The lookup time is constant, and the protocol can be easily adapted to prevent online man-in-the-middle relay attacks. Security is proven in the UC security framework.