Secure Distributed System inspired by Ant Colonies for Road Traffic Management in Emergency Situations

We have proposed an algorithm, based on ant colonies, for road traffic management. The implementation of the algorithm does not rely on fixed infrastructures in order to operate in emergency situations. It only uses the VANET V2V communications and location systems that do not require contact with a fixed infrastructure. The algorithm uses signature aggregation and reputation lists to ensure system security. Furthermore, the algorithm has an implicit security that minimizes the risks in case of attacks. A scale prototype has been designed and implemented to validate the algorithm using RFID location system.In this work, we present a distributed system designed for road traffic management. The system is inspired by the behavior of the ant colonies. The distributed design responds to the particular limitations of an emergency situation; mainly, the fixed infrastructures are out of service because no energy supply is available. The implementation is based on the VANET facilities complemented with passive RFID tags or GPS localization. The vehicles can use the information of previous vehicles to dynamically decide the best path. A scale prototype has been developed to validate the system. It consists of several small size robotic vehicles, a test road circuit and a visual monitorization system. The security of the system is provided by a combination of data aggregation and reputation lists.Proyecto TIN 2011-25452 (TUERI: Technologies for secUre and Efficient wiReless networks within the Internet of things with applications in transport and logistic). Y Universidad de Málaga-Campus de Excelencia Internacional Andalucia Tech

Configuración Software de la directividad de arrays lineales

A line array can be defined as a column of loudspeakers that is designed so that these work together to achieve a higher directivity. This paper presents an application that enables a user to rotate the wavefront of uniform line arrays. Theoretical background and details of the implementation are provided. The validity of the application is tested with measurements of the directivity that are also compared with simulations.Este trabajo ha sido financiado Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech y el grupo de investigación Aplicación de las Tecnologías de la Información y Comunicaciones (PAI TIC-208)

RFID ownership transfer with positive secrecy capacity channels

RFID ownership transfer protocols (OTPs) transfer tag ownership rights. Recently, there has been considerable interest in such protocols, however, guaranteeing privacy for symmetric-key settings without trusted third parties (TTPs) is a challenge still unresolved. In this paper, we address this issue and show that it can be solved by using channels with positive secrecy capacity. We implement these channels with noisy tags and provide practical values, thus proving that perfect secrecy is theoretically possible. We then define a communication model that captures spatiotemporal events and describe a first example of symmetric-key based OTP that: (i) is formally secure in the proposed communication model and (ii) achieves privacy with a noisy tag wiretap channel without TTPs

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

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.This work was partly supported by the MICINN un der the projects TEC2012-34306 and PSI2015-65848- R, and the Consejer´ıa de Innovaci´on, Ciencia y Em presa (Junta de Andaluc´ıa, Spain) under the Ex cellence Projects P09-TIC-4530, P11-TIC-7103 and the Universidad de M´alaga. Programa de fortalec imiento de las capacidades de I+D+I en las Uni versidades 2014-2015, de la Consejer´ıa de Econom´ıa, Innovaci´on, Ciencia y Empleo, cofinanciado por el fondo europeo de desarrollo regional (FEDER) un der the project FC14-SAF30. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Ini tiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bio engineering, and through generous contributions from the following: AbbVie, Alzheimer’s Associa tion; Alzheimer’s Drug Discovery Foundation; Ara clon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; Eu roImmun; F. Hoffmann-La Roche Ltd and its affili ated company Genentech, Inc.; Fujirebio; GE Health care; IXICO Ltd.; Janssen Alzheimer Immunother apy Research & Development, LLC.;Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity ; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Re search is providing funds to support ADNI clinical sites in Canada. Private sector contributions are fa cilitated by the Foundation for the National Insti tutes of Health (www.fnih.org). The grantee organi zation is the Northern California Institute for Re search and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California

Hybrid genetic algorithm for clustering IC topographies of EEGs

Clustering of independent component (IC) topographies of Electroencephalograms (EEG) is an effective way to find brain-generated IC processes associated with a population of interest, particularly for those cases where event-related potential features are not available. This paper proposes a novel algorithm for the clustering of these IC topographies and compares its results with the most currently used clustering algorithms. In this study, 32-electrode EEG signals were recorded at a sampling rate of 500 Hz for 48 participants. EEG signals were pre-processed and IC topographies computed using the AMICA algorithm. The algorithm implements a hybrid approach where genetic algorithms are used to compute more accurate versions of the centroids and the final clusters after a pre-clustering phase based on spectral clustering. The algorithm automatically selects the optimum number of clusters by using a fitness function that involves local-density along with compactness and separation criteria. Specific internal validation metrics adapted to the use of the absolute correlation coefficient as the similarity measure are defined for the benchmarking process. Assessed results across different ICA decompositions and groups of subjects show that the proposed clustering algorithm significantly outperforms the (baseline) clustering algorithms provided by the software EEGLAB, including CORRMAP.Funding for open access charge: Universidad de Málaga / CBUA Funding for open access publishing: Universidad Málaga/CBUA. This work was supported by projects PGC2018-098,813-B C32 (Spanish “Ministerio de Ciencia, Innovación y Universidades”), UMA20-FEDERJA-086 (Consejería de economía y conocimiento, Junta de Andalucía), Project P18-rt-1624, and by European Regional Development Funds (ERDF). We also thank the Leeduca research group and Junta de Andalucía for the data supplied and the support

Empirical Functional PCA for 3D Image Feature Extraction Through Fractal Sampling.

Medical image classification is currently a challenging task that can be used to aid the diagnosis of different brain diseases. Thus, exploratory and discriminative analysis techniques aiming to obtain representative features from the images play a decisive role in the design of effective Computer Aided Diagnosis (CAD) systems, which is especially important in the early diagnosis of dementia. In this work, we present a technique that allows using specific time series analysis techniques with 3D images. This is achieved by sampling the image using a fractal-based method which preserves the spatial relationship among voxels. In addition, a method called Empirical functional PCA (EfPCA) is presented, which combines Empirical Mode Decomposition (EMD) with functional PCA to express an image in the space spanned by a basis of empirical functions, instead of using components computed by a predefined basis as in Fourier or Wavelet analysis. The devised technique has been used to classify images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Parkinson Progression Markers Initiative (PPMI), achieving accuracies up to 93% and 92% differential diagnosis tasks (AD versus controls and PD versus Controls, respectively). The results obtained validate the method, proving that the information retrieved by our methodology is significantly linked to the diseases.This work was partly supported by the MINECO/ FEDER under TEC2015-64718-R and PSI2015- 65848-R projects and the Consejer´ıa de Innovaci´on, Ciencia y Empresa (Junta de Andaluc´ıa, Spain) under the Excellence Project P11-TIC-7103 as well as the Salvador deMadariaga Mobility Grants 2017. Data collection and sharing for this project was funded by the ADNI (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Depart ment of Defense award number W81XWH-12-2- 0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contribu tions from the following: AbbVie, Alzheimer’s Asso ciation; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol Myer Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Ho mann-La Roche Ltd and its ali ated company Genentech, Inc.; Fujirebio; GE Health care; IXICO Ltd.; Janssen Alzheimer Immunother apy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; P zer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clin ical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coor dinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern Cali fornia. PPMI a public-private partnership is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including [list the full names of all of the PPMI funding partners found at www.ppmi-info.org/fundingpartners]

Label Aided Deep Ranking for the Automatic Diagnosis of Parkinsonian Syndromes.

Parkinsonism is the second most common neurodegenerative disease in the world. Its diagnosis usually relies on visual analysis of Emission Computed Tomography (SPECT) images acquired using 123I − io f lupane radiotracer. This aims to detect a deficit of dopamine transporters at the striatum. The use of Computer Aided tools for diagnosis based on statistical data processing and machine learning methods have significantly improved the diagnosis accuracy. In this paper we propose a classification method based on Deep Ranking which learns an embedding function that projects the source images into a new space in which samples belonging to the same class are closer to each other, while samples from different classes are moved apart. Moreover, the proposed approach introduces a new cost-sensitive loss function to avoid overfitting due to class imbalance (an usual issue in practical biomedical applications), along with label information to produce sparser embedding spaces. The experiments carried out in this work demonstrate the superiority of the proposed method, improving the diagnosis accuracy achieved by previous methodologies and validate our approach as an efficient way to construct linear classifiers.This work was partly supported by the MINECO/FEDER under TEC2015-64718- R and PSI2015-65848-R projects. We gratefully acknowledge the support of NVIDIA Corporation with the donation of one of the GPUs used for this research. PPMI - a pub435 lic - private partnership - is funded by The Michael J. Fox Foundation for Parkinson’s Research and funding partners, including Abbott, Biogen Idec, F. Hoffman-La Roche Ltd., GE Healthcare, Genentech and Pfizer Inc

Evaluación de la latencia de NB-IoT con medidas reales

In the 3GPP LTE Release 13, NB-IoT was standardized to provide wide-area connectivity for IoT. To optimize network signaling and power consumption, control plane (CP) optimization was introduced. In Release 15, to support infrequent small data transmissions, Early Data Transmission (EDT) was also included, in which the data are sent during the random access procedure. Thus, this paper analyses the latency performance of the different NB-IoT optimizations for the CP. The study, carried out in a real equipment, has been performed for different packet sizes and coverage levels. Evaluation results show lower latencies for EDT, particularly with small packets, where a reduced transport block is used, being more efficient from a network point of view. Additionally, we verify that EDT, unlike Release 13 optimization, fulfills 3GPP latency requirement for extreme coverage.Este trabajo ha sido parcialmente financiado por el Ministerio de Asuntos Económicos y Transformación Digital y la Unión Europea – NextGenerationEU, en el marco del Plan de Recuperación, Transformación y Resiliencia y el Mecanismo de Recuperación y Resiliencia bajo el proyecto MAORI y, por la Junta de Andalucía mediante el proyecto EDEL4.0 (UMA-18-FEDERJA-172). Se agradece también la financiación parcial de la Universidad de Málaga con el II Plan Propio de Investigación y Transferencia. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Evaluación de los modos de conexión para NB-IoT

In the 3GPP LTE Release 13, NB-IoT was standardized to provide wide-area connectivity for IoT. To optimize network signalling and power consumption, control plane (CP) and user plane (UP) optimizations were introduced. Also, to support infrequent small data transmissions, in Release 15 Early Data Transmissions (EDT) was introduced, where the data is sent during the random access procedure. Therefore, this paper analyses the latency performance of the different NB-IoT optimizations. The study, which has been carried out in NS-3, has been performed for different packet sizes. Evaluation results show that with low packet size, EDT with CP provides lower latency. However, with higher packet sizes, user plane solutions provide better latency.Este trabajo ha sido parcialmente financiado por la Junta de Andalucía mediante los proyectos AECMA-5G (UMA-CEIATECH-14) y EDEL4.0 (UMA-18-FEDERJA-172). Se agradece también la financiación parcial de la Universidad de Málaga con el Plan Propio de Investigación y Transferencia. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Desafío tecnológico: herramienta para trabajar y evaluar las competencias básicas y generales en los estudios de grado de la E.T.S.I. de Telecomunicación

This work presents the evolution of the five editions of the educational activity named “Technological Challenge” specially focused on the students at “Escuela Técnica Superior de Ingeniería de Telecomunicación” (ETSIT), but open to all students of the “Universidad de Málaga” (UMA). This initiative has been developed in the context of the educational innovation project PIE17-021 funded by UMA. The “Technological Challenge” consists on the formulation of specific real problems, which students must face in a competitive regime. This activity allows the reinforcement and evaluation of basic and general competences reached by the graduate students in the ETSIT. After nearly five years, this paper describes the evaluation of the results, regarding interest and participation of the students in the “Technology Challenge” along with the basic and general competences reached by the students.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. PIE17-02