HF spectrum activity prediction model based on HMM for cognitive radio applications

Although most of the research on Cognitive Radio is focused on communication bands above the HF upper limit (30 MHz), Cognitive Radio principles can also be applied to HF communications to make use of the extremely scarce spectrum more efficiently. In this work we consider legacy users as primary users since these users transmit without resorting to any smart procedure, and our stations using the HFDVL (HF Data+Voice Link) architecture as secondary users. Our goal is to enhance an efficient use of the HF band by detecting the presence of uncoordinated primary users and avoiding collisions with them while transmitting in different HF channels using our broad-band HF transceiver. A model of the primary user activity dynamics in the HF band is developed in this work to make short-term predictions of the sojourn time of a primary user in the band and avoid collisions. It is based on Hidden Markov Models (HMM) which are a powerful tool for modelling stochastic random processes and are trained with real measurements of the 14 MHz band. By using the proposed HMM based model, the prediction model achieves an average 10.3% prediction error rate with one minute-long channel knowledge but it can be reduced when this knowledge is extended: with the previous 8 min knowledge, an average 5.8% prediction error rate is achieved. These results suggest that the resulting activity model for the HF band could actually be used to predict primary users activity and included in a future HF cognitive radio based station.

Efficiency improvement of HF communications using cognitive radio principles

Cognitive Radio principles can be applied to HF communications to make a more efficient use of the extremely scarce spectrum. In this contribution we focus on analyzing the usage of the available channels done by the legacy users, which are regarded as primary users since they are allowed to transmit without resorting any smart procedure, and consider the possibilities for our stations -over the HFDVL (HF Data+Voice Link) architecture- to participate as secondary users. Our goal is to enhance an efficient use of the HF band by detecting the presence of uncoordinated primary users and avoiding collisions with them while transmitting in different HF channels using our broad-band HF transceiver. A model of the primary user activity dynamics in the HF band is developed in this work. It is based on Hidden Markov Models (HMM) which are a powerful tool for modelling stochastic random processes, and is trained with real measurements from the 14 MHz band.

Propuesta, implementación y evaluación de la biometría de firma en el aire como sistema de verificación en teléfonos móviles

En esta tesis doctoral se propone una técnica biométrica de verificación en teléfonos móviles consistente en realizar una firma en el aire con la mano que sujeta el teléfono móvil. Los acelerómetros integrados en el dispositivo muestrean las aceleraciones del movimiento de la firma en el aire, generando tres señales temporales que pueden utilizarse para la verificación del usuario. Se proponen varios enfoques para la implementación del sistema de verificación, a partir de los enfoques más utilizados en biometría de firma manuscrita: correspondencia de patrones, con variantes de los algoritmos de Needleman-Wusch (NW) y Dynamic Time Warping (DTW), modelos ocultos de Markov (HMM) y clasificador estadístico basado en Máquinas de Vector Soporte (SVM). Al no existir bases de datos públicas de firmas en el aire y con el fin de evaluar los métodos propuestos en esta tesis doctoral, se han capturado dos con distintas características; una con falsificaciones reales a partir del estudio de las grabaciones de usuarios auténticos y otra con muestras de usuarios obtenidas en diferentes sesiones a lo largo del tiempo. Utilizando estas bases de datos se han evaluado una gran cantidad de algoritmos para implementar un sistema de verificación basado en firma en el aire. Esta evaluación se ha realizado de acuerdo con el estándar ISO/IEC 19795, añadiendo el caso de verificación en mundo abierto no incluido en la norma. Además, se han analizado las características que hacen que una firma sea suficientemente segura. Por otro lado, se ha estudiado la permanencia de las firmas en el aire a lo largo del tiempo, proponiendo distintos métodos de actualización, basados en una adaptación dinámica del patrón, para mejorar su rendimiento. Finalmente, se ha implementado un prototipo de la técnica de firma en el aire para teléfonos Android e iOS. Los resultados de esta tesis doctoral han tenido un gran impacto, generando varias publicaciones en revistas internacionales, congresos y libros. La firma en el aire ha sido nombrada también en varias revistas de divulgación, portales de noticias Web y televisión. Además, se han obtenido varios premios en competiciones de ideas innovadoras y se ha firmado un acuerdo de explotación de la tecnología con una empresa extranjera. ABSTRACT This thesis proposes a biometric verification technique on mobile phones consisting on making a signature in the air with the hand holding a mobile phone. The accelerometers integrated in the device capture the movement accelerations, generating three temporal signals that can be used for verification. This thesis suggests several approaches for implementing the verification system, based on the most widely used approaches in handwritten signature biometrics: template matching, with a lot of variations of the Needleman- Wusch (NW) and Dynamic Time Warping (DTW) algorithms, Hidden Markov Models (HMM) and Supported Vector Machines (SVM). As there are no public databases of in-air signatures and with the aim of assessing the proposed methods, there have been captured two databases; one. with real falsification attempts from the study of recordings captured when genuine users made their signatures in front of a camera, and other, with samples obtained in different sessions over a long period of time. These databases have been used to evaluate a lot of algorithms in order to implement a verification system based on in-air signatures. This evaluation has been conducted according to the standard ISO/IEC 19795, adding the open-set verification scenario not included in the norm. In addition, the characteristics of a secure signature are also investigated, as well as the permanence of in-air signatures over time, proposing several updating strategies to improve its performance. Finally, a prototype of in-air signature has been developed for iOS and Android phones. The results of this thesis have achieved a high impact, publishing several articles in SCI journals, conferences and books. The in-air signature deployed in this thesis has been also referred in numerous media. Additionally, this technique has won several awards in the entrepreneurship field and also an exploitation agreement has been signed with a foreign company.

Human activity recognition by inertial signals obtained from a smartphone

Human Activity Recognition (HAR) is an emerging research field with the aim to identify the actions carried out by a person given a set of observations and the surrounding environment. The wide growth in this research field inside the scientific community is mainly explained by the high number of applications that are arising in the last years. A great part of the most promising applications are related to the healthcare field, where it is possible to track the mobility of patients with motor dysfunction as also the physical activity in patients with cardiovascular risk. Until a few years ago, by using distinct kind of sensors, a patient follow-up was possible. However, far from being a long-term solution and with the smartphone irruption, that monitoring can be achieved in a non-invasive way by using the embedded smartphone’s sensors. For these reasons this Final Degree Project arises with the main target to evaluate new feature extraction techniques in order to carry out an activity and user recognition, and also an activity segmentation. The recognition is done thanks to the inertial signals integration obtained by two widespread sensors in the greater part of smartphones: accelerometer and gyroscope. In particular, six different activities are evaluated walking, walking-upstairs, walking-downstairs, sitting, standing and lying. Furthermore, a segmentation task is carried out taking into account the activities performed by thirty users. This can be done by using Hidden Markov Models and also a set of tools tested satisfactory in speech recognition: HTK (Hidden Markov Model Toolkit).

Identification of a human nuclear receptor defines a new signaling pathway for CYP3A induction

Nuclear receptors regulate metabolic pathways in response to changes in the environment by appropriate alterations in gene expression of key metabolic enzymes. Here, a computational search approach based on iteratively built hidden Markov models of nuclear receptors was used to identify a human nuclear receptor, termed hPAR, that is expressed in liver and intestines. hPAR was found to be efficiently activated by pregnanes and by clinically used drugs including rifampicin, an antibiotic known to selectively induce human but not murine CYP3A expression. The CYP3A drug-metabolizing enzymes are expressed in gut and liver in response to environmental chemicals and clinically used drugs. Interestingly, hPAR is not activated by pregnenolone 16α-carbonitrile, which is a potent inducer of murine CYP3A genes and an activator of the mouse receptor PXR.1. Furthermore, hPAR was found to bind to and trans-activate through a conserved regulatory sequence present in human but not murine CYP3A genes. These results provide evidence that hPAR and PXR.1 may represent orthologous genes from different species that have evolved to regulate overlapping target genes in response to pharmacologically distinct CYP3A activators, and have potential implications for the in vitro identification of drug interactions important to humans.

The InterPro database, an integrated documentation resource for protein families, domains and functional sites

Signature databases are vital tools for identifying distant relationships in novel sequences and hence for inferring protein function. InterPro is an integrated documentation resource for protein families, domains and functional sites, which amalgamates the efforts of the PROSITE, PRINTS, Pfam and ProDom database projects. Each InterPro entry includes a functional description, annotation, literature references and links back to the relevant member database(s). Release 2.0 of InterPro (October 2000) contains over 3000 entries, representing families, domains, repeats and sites of post-translational modification encoded by a total of 6804 different regular expressions, profiles, fingerprints and Hidden Markov Models. Each InterPro entry lists all the matches against SWISS-PROT and TrEMBL (more than 1 000 000 hits from 462 500 proteins in SWISS-PROT and TrEMBL). The database is accessible for text- and sequence-based searches at http://www.ebi.ac.uk/interpro/. Questions can be emailed to interhelp@ebi.ac.uk.

TIGRFAMs: a protein family resource for the functional identification of proteins

TIGRFAMs is a collection of protein families featuring curated multiple sequence alignments, hidden Markov models and associated information designed to support the automated functional identification of proteins by sequence homology. We introduce the term ‘equivalog’ to describe members of a set of homologous proteins that are conserved with respect to function since their last common ancestor. Related proteins are grouped into equivalog families where possible, and otherwise into protein families with other hierarchically defined homology types. TIGRFAMs currently contains over 800 protein families, available for searching or downloading at www.tigr.org/TIGRFAMs. Classification by equivalog family, where achievable, complements classification by orthology, superfamily, domain or motif. It provides the information best suited for automatic assignment of specific functions to proteins from large-scale genome sequencing projects.

Training and search methods for speech recognition.

Speech recognition involves three processes: extraction of acoustic indices from the speech signal, estimation of the probability that the observed index string was caused by a hypothesized utterance segment, and determination of the recognized utterance via a search among hypothesized alternatives. This paper is not concerned with the first process. Estimation of the probability of an index string involves a model of index production by any given utterance segment (e.g., a word). Hidden Markov models (HMMs) are used for this purpose [Makhoul, J. & Schwartz, R. (1995) Proc. Natl. Acad. Sci. USA 92, 9956-9963]. Their parameters are state transition probabilities and output probability distributions associated with the transitions. The Baum algorithm that obtains the values of these parameters from speech data via their successive reestimation will be described in this paper. The recognizer wishes to find the most probable utterance that could have caused the observed acoustic index string. That probability is the product of two factors: the probability that the utterance will produce the string and the probability that the speaker will wish to produce the utterance (the language model probability). Even if the vocabulary size is moderate, it is impossible to search for the utterance exhaustively. One practical algorithm is described [Viterbi, A. J. (1967) IEEE Trans. Inf. Theory IT-13, 260-267] that, given the index string, has a high likelihood of finding the most probable utterance.

Contexto e modularização em redes neurais recorrentes para aprendizagem de seqüências temporais

Este trabalho apresenta um sistema neural modular, que processa separadamente informações de contexto espacial e temporal, para a tarefa de reprodução de sequências temporais. Para o desenvolvimento do sistema neural foram considerados redes neurais recorrentes, modelos estocásticos, sistemas neurais modulares e processamento de informações de contexto. Em seguida, foram estudados três modelos com abordagens distintas para aprendizagem de seqüências temporais: uma rede neural parcialmente recorrente, um exemplo de sistema neural modular e um modelo estocástico utilizando a teoria de modelos markovianos escondidos. Com base nos estudos e modelos apresentados, esta pesquisa propõe um sistema formado por dois módulos sucessivos distintos. Uma rede de propagação direta (módulo estimador de contexto espacial) realiza o processamento de contexto espacial identificando a seqüência a ser reproduzida e fornecendo um protótipo do contexto para o segundo módulo. Este é formado por uma rede parcialmente recorrente (módulo de reprodução de sequências temporais) para aprender as informações de contexto temporal e reproduzir em suas saídas a seqüência identificada pelo módulo anterior. Para a finalidade mencionada, este mestrado utiliza a distribuição de Gibbs na saída do módulo para contexto espacial de forma que este forneça probabilidades de contexto espacial, indicando o grau de certeza do módulo e possibilitando a utilização de procedimentos especiais para os casos de dúvida. O sistema neural foi testado em conjuntos contendo trajetórias abertas, fechadas, e com diferentes situações de ambigüidade e complexidade. Duas situações distintas foram avaliadas: (a) capacidade do sistema em reproduzir trajetórias a partir de pontos iniciais treinados; e (b) capacidade de generalização do sistema reproduzindo trajetórias considerando pontos iniciais ou finais em situações não treinadas. A situação (b) é um problema de difícil ) solução em redes neurais devido à falta de contexto temporal, essencial na reprodução de seqüências. Foram realizados experimentos comparando o desempenho do sistema modular proposto com o de uma rede parcialmente recorrente operando sozinha e um sistema modular neural (TOTEM). Os resultados sugerem que o sistema proposto apresentou uma capacidade de generalização significamente melhor, sem que houvesse uma deterioração na capacidade de reproduzir seqüências treinadas. Esses resultados foram obtidos em sistema mais simples que o TOTEM.

Human Motion Analysis Based on Sequential Modeling of Radar Signal and Stereo Image Features

Falls are one of the greatest threats to elderly health in their daily living routines and activities. Therefore, it is very important to detect falls of an elderly in a timely and accurate manner, so that immediate response and proper care can be provided, by sending fall alarms to caregivers. Radar is an effective non-intrusive sensing modality which is well suited for this purpose, which can detect human motions in all types of environments, penetrate walls and fabrics, preserve privacy, and is insensitive to lighting conditions. Micro-Doppler features are utilized in radar signal corresponding to human body motions and gait to detect falls using a narrowband pulse-Doppler radar. Human motions cause time-varying Doppler signatures, which are analyzed using time-frequency representations and matching pursuit decomposition (MPD) for feature extraction and fall detection. The extracted features include MPD features and the principal components of the time-frequency signal representations. To analyze the sequential characteristics of typical falls, the extracted features are used for training and testing hidden Markov models (HMM) in different falling scenarios. Experimental results demonstrate that the proposed algorithm and method achieve fast and accurate fall detections. The risk of falls increases sharply when the elderly or patients try to exit beds. Thus, if a bed exit can be detected at an early stage of this motion, the related injuries can be prevented with a high probability. To detect bed exit for fall prevention, the trajectory of head movements is used for recognize such human motion. A head detector is trained using the histogram of oriented gradient (HOG) features of the head and shoulder areas from recorded bed exit images. A data association algorithm is applied on the head detection results to eliminate head detection false alarms. Then the three dimensional (3D) head trajectories are constructed by matching scale-invariant feature transform (SIFT) keypoints in the detected head areas from both the left and right stereo images. The extracted 3D head trajectories are used for training and testing an HMM based classifier for recognizing bed exit activities. The results of the classifier are presented and discussed in the thesis, which demonstrates the effectiveness of the proposed stereo vision based bed exit detection approach.

Search of Method for Analyzing "Viability" of Innovative Projects

Questions of "viability" evaluation of innovation projects are considered in this article. As a method of evaluation Hidden Markov Models are used. Problem of determining model parameters, which reproduce test data with highest accuracy are solving. For training the model statistical data on the implementation of innovative projects are used. Baum-Welch algorithm is used as a training algorithm.

Identification and analysis of chromodomain-containing proteins encoded in the mouse transcriptome

The chromodomain is 40-50 amino acids in length and is conserved in a wide range of chromatic and regulatory proteins involved in chromatin remodeling. Chromodomain-containing proteins can be classified into families based on their broader characteristics, in particular the presence of other types of domains, and which correlate with different subclasses of the chromodomains themselves. Hidden Markov model (HMM)-generated profiles of different subclasses of chromodomains were used here to identify sequences encoding chromodomain-containing proteins in the mouse transcriptome and genome. A total of 36 different loci encoding proteins containing chromodomains, including 17 novel loci, were identified. Six of these loci (including three apparent pseudogenes, a novel HP1 ortholog, and two novel Msl-3 transcription factor-like proteins) are not present in the human genome, whereas the human genome contains four loci (two CDY orthologs and two apparent CDY pseuclogenes) that are not present in mouse. A number of these loci exhibit alternative splicing to produce different isoforms, including 43 novel variants, some of which lack the chromodomain. The likely functions of these proteins are discussed in relation to the known functions of other chromodomain-containing proteins within the same family.

Wurst: a protein threading server with a structural scoring function, sequence profiles and optimized substitution matrices

Wurst is a protein threading program with an emphasis on high quality sequence to structure alignments (http://www.zbh.uni-hamburg.de/wurst). Submitted sequences are aligned to each of about 3000 templates with a conventional dynamic programming algorithm, but using a score function with sophisticated structure and sequence terms. The structure terms are a log-odds probability of sequence to structure fragment compatibility, obtained from a Bayesian classification procedure. A simplex optimization was used to optimize the sequence-based terms for the goal of alignment and model quality and to balance the sequence and structural contributions against each other. Both sequence and structural terms operate with sequence profiles.

Predictive vaccinology: Optimisation of predictions using support vector machine classifiers

Promiscuous human leukocyte antigen (HLA) binding peptides are ideal targets for vaccine development. Existing computational models for prediction of promiscuous peptides used hidden Markov models and artificial neural networks as prediction algorithms. We report a system based on support vector machines that outperforms previously published methods. Preliminary testing showed that it can predict peptides binding to HLA-A2 and -A3 super-type molecules with excellent accuracy, even for molecules where no binding data are currently available.

MULTIPRED: A computational system for prediction of promiscuous HLA binding peptides

MULTIPRED is a web-based computational system for the prediction of peptide binding to multiple molecules ( proteins) belonging to human leukocyte antigens (HLA) class I A2, A3 and class II DR supertypes. It uses hidden Markov models and artificial neural network methods as predictive engines. A novel data representation method enables MULTIPRED to predict peptides that promiscuously bind multiple HLA alleles within one HLA supertype. Extensive testing was performed for validation of the prediction models. Testing results show that MULTIPRED is both sensitive and specific and it has good predictive ability ( area under the receiver operating characteristic curve A(ROC) > 0.80). MULTIPRED can be used for the mapping of promiscuous T-cell epitopes as well as the regions of high concentration of these targets termed T-cell epitope hotspots. MULTIPRED is available at http:// antigen.i2r.a-star.edu.sg/ multipred/.