Reconstrucción Mejorada de Datos de Resonancia Magnética Mediante Aproximación por Descomposición por Valores Singulares

The reconstruction of magnetic resonance imaging (MRI) data can be a computationally demanding task. Signal-to-noise ratio is also a concern, especially in high-resolution imaging. Data compression may be useful not only for reducing reconstruction complexity and memory requirements, but also for reducing noise, as it is capable of eliminating spurious components.This work proposes the use of a singular value decomposition low-rank approximation for reconstruction and denoising of MRI data. The Akaike Information Criterion is used to estimate the appropriate model order, which is used to remove noise components and to reduce the amount of data to be stored and processed. The proposed method is evaluated using in vivo MRI data. We present images reconstructed using less than 20% of the original data size, and with a similar quality in terms of visual inspection. A quantitative evaluation is also presentedLa reconstrucción de datos de resonancia magnética (RM) puede ser una tarea computacionalmente ardua. La razón señal-ruido también puede presentar complicaciones, especialmente en imágenes de alta resolución. En este sentido, la compresión de datos puede ser útil no sólo para reducir la complejidad y los requerimientos de memoria, sino también para reducir el ruido, hasta inclusive permitir eliminar componentes espurios.El presente trabajo propone el uso de un sistema basado en la descomposición por valores singulares de bajo orden para reconstrucción y reducción de ruido en imágenes de RM. El criterio de información de Akaike se utiliza para estimar el orden del modelo, que es usado para remover los componentes ruidosos y reducir la cantidad de datos procesados y almacenados. El método propuesto es evaluado usando datos de RM in vivo. Se presentan imágenes reconstruidas con menos de 20% de los datos originales y con calidad similar en cuanto a su inspección visual. Igualmente se presenta una evaluación cuantitativa del método

A parallel approach to pca based malicious activitydetection in distributed honeypot data

Model order selection (MOS) schemes, which are frequently employed inseveral signal processing applications, are shown to be effective tools for the detectionof malicious activities in honeypot data. In this paper, we extend previous results byproposing an efficient and parallel MOS method for blind automatic malicious activitydetection in distributed honeypots. Our proposed scheme does not require any previousinformation on attacks or human intervention. We model network traffic data as signalsand noise and then apply modified signal processing methods. However, differently fromthe previous centralized solutions, we propose that the data colected by each honeypotnode be processed by nodes in a cluster (that may consist of the collection nodesthemselves) and then grouped to obtain the final results. This is achieved by having eachnode locally compute the Eigenvalue Decomposition (EVD) to its own sample correlationmatrix (obtained from the honeypot data) and transmit the resulting eigenvalues to acentral node, where the global eigenvalues and final model order are computed. Themodel order computed from the global eigenvalues through RADOI represents the numberof malicious activities detected in the analysed data. The feasibility of the proposedapproach is demonstrated through simulation experiments

From feature engineering and topics models to enhanced prediction rates in phishing detection

Phishing is a type of fraud attempt in which the attacker, usually by e-mail, pretends to be a trusted person or entity in order to obtain sensitive information from a target. Most recent phishing detection researches have focused on obtaining highly distinctive features from the metadata and text of these e-mails. The obtained attributes are then used to feed classification algorithms in order to determine whether they are phishing or legitimate messages. In this paper, it is proposed an approach based on machine learning to detect phishing e-mail attacks. The methods that compose this approach are performed through a feature engineering process based on natural language processing, lemmatization, topics modeling, improved learning techniques for resampling and cross-validation, and hyperparameters configuration. The first proposed method uses all the features obtained from the Document-Term Matrix (DTM) in the classification algorithms. The second one uses Latent Dirichlet Allocation (LDA) as a operation to deal with the problems of the “curse of dimensionality”, the sparsity, and the text context portion included in the obtained representation. The proposed approach reached marks with an F1-measure of 99.95% success rate using the XGBoost algorithm. It outperforms state-of-the-art phishing detection researches for an accredited data set, in applications based only on the body of the e-mails, without using other e-mail features such as its header, IP information or number of links in the text

Data security and trading framework for smart grids in neighborhood area networks

Due to the drastic increase of electricity prosumers, i.e., energy consumers that are also producers, smart grids have become a key solution for electricity infrastructure. In smart grids, one of the most crucial requirements is the privacy of the final users. The vast majority of the literature addresses the privacy issue by providing ways of hiding user’s electricity consumption. However, open issues in the literature related to the privacy of the electricity producers still remain. In this paper, we propose a framework that preserves the secrecy of prosumers’ identities and provides protection against the traffic analysis attack in a competitive market for energy trade in a Neighborhood Area Network (NAN). In addition, the amount of bidders and of successful bids are hidden from malicious attackers by our framework. Due to the need for small data throughput for the bidders, the communication links of our framework are based on a proprietary communication system. Still, in terms of data security, we adopt the Advanced Encryption Standard (AES) 128bit with Exclusive-OR (XOR) keys due to their reduced computational complexity, allowing fast processing. Our framework outperforms the state-of-the-art solutions in terms of privacy protection and trading flexibility in a prosumer-to-prosumer design

Improved MRI Reconstruction Using a Singular Value Decomposition Approximation

La reconstrucción de datos de resonancia magnética (RM) puede ser una tarea computacionalmente ardua. La razón señal-ruido también puede presentar complicaciones, especialmente en imágenes de alta resolución. En este sentido, la compresión de datos puede ser útil no sólo para reducir la complejidad y los requerimientos de memoria, sino también para reducir el ruido, hasta inclusive permitir eliminar componentes espurios.El presente trabajo propone el uso de un sistema basado en la descomposición por valores singulares de bajo orden para reconstrucción y reducción de ruido en imágenes de RM. El criterio de información de Akaike se utiliza para estimar el orden del modelo, que es usado para remover los componentes ruidosos y reducir la cantidad de datos procesados y almacenados. El método propuesto es evaluado usando datos de RM in vivo. Se presentan imágenes reconstruidas con menos de 20% de los datos originales y con calidad similar en cuanto a su inspección visual. Igualmente se presenta una evaluación cuantitativa del método.The reconstruction of magnetic resonance imaging (MRI) data can be a computationally demanding task. Signal-to-noise ratio is also a concern, especially in high-resolution imaging. Data compression may be useful not only for reducing reconstruction complexity and memory requirements, but also for reducing noise, as it is capable of eliminating spurious components.This work proposes the use of a singular value decomposition low-rank approximation for reconstruction and denoising of MRI data. The Akaike Information Criterion is used to estimate the appropriate model order, which is used to remove noise components and to reduce the amount of data to be stored and processed. The proposed method is evaluated using in vivo MRI data. We present images reconstructed using less than 20% of the original data size, and with a similar quality in terms of visual inspection. A quantitative evaluation is also presente

Improved MRI Reconstruction Using a Singular Value Decomposition Approximation.

The reconstruction of magnetic resonance imaging (MRI) data can be a computationally demanding task. Signal-to-noise ratio is also a concern, especially in high-resolution imaging. Data compression may be useful not only for reducing reconstruction complexity and memory requirements, but also for reducing noise, as it is capable of eliminating spurious components. This work proposes the use of a singular value decomposition low-rank approximation for reconstruction and denoising of MRI data. The Akaike Information Criterion is used to estimate the appropriate model order, which is used to remove noise components and to reduce the amount of data to be stored and processed. The proposed method is evaluated using in vivo MRI data. We present images reconstructed using less than 20% of the original data size, and with a similar quality in terms of visual inspection. A quantitative evaluation is also presented.La reconstrucción de datos de resonancia magnética (RM) puede ser una tarea computacionalmente ardua. La razón señal-ruido también puede presentar complicaciones, especialmente en imágenes de alta resolución. En este sentido, la compresión de datos puede ser útil no sólo para reducir la complejidad y los requerimientos de memoria, sino también para reducir el ruido, hasta inclusive permitir eliminar componentes espurios. El presente trabajo propone el uso de un sistema basado en la descomposición por valores singulares de bajo orden para reconstrucción y reducción de ruido en imágenes de RM. El criterio de información de Akaike se utiliza para estimar el orden del modelo, que es usado para remover los componentes ruidosos y reducir la cantidad de datos procesados y almacenados. El método propuesto es evaluado usando datos de RM in vivo. Se presentan imágenes reconstruidas con menos de 20% de los datos originales y con calidad similar en cuanto a su inspección visual. Igualmente se presenta una evaluación cuantitativa del método

Conception, optimisation et intégration d amplificateurs de puissance Doherty pour des communications 3G/4G

Les signaux des nouveaux standard de communications (LTE) ont une grande différence entre la puissance maximale et moyenne (PAPR), cela n'est pas favorable pour l'utilisation dans les amplificateurs conventionnels vu qu'ils présentent un rendement maximale seulement quand ils travaillent au niveau de puissance maximale. Des amplificateurs de puissance Doherty pour présenter une efficacité constante pour une large gamme de puissance constituent une solution favorable à ce problème. Ce travail présente la méthodologie de conception et des résultats de mesure d'un amplificateur de puissance Doherty entièrement intégré dans la technologie 65 nm CMOS avec une constante PAE sur un 7 dB de plage de puissance. Mesures de 2,4 GHz à 2,6 GHz montrent des performances constantes PAE à partir du niveau de 20% jusqu'à 24% avec une puissance de sortie maximale de 23,4 dBm. Le circuit a été conçu avec une attention particulière pour le faible coût.The signals of the new communication standards (LTE) show a great difference between the peak and its average power (PAPR) being unsuitable for use with conventional power amplifiers because they present maximum efficiency only when working with maximum power. Doherty power amplifiers for presenting a constant efficiency for a wide power range represent a favorable solution to this problem. This work presents the design methodology and measurements results of a fully integrated Doherty Power Amplifier in 65 nm CMOS technology with constant PAE over a 7 dB backoff. Measurements from 2.4 GHz to 2.6 GHz show constant PAE performance starting in 20% level up to 24% with a maximum output power of 23.4 dBm.The circuit was designed with special attention to low cost.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF