Entropy feature extraction of EEG signals for automatic person identification

The use of human brain electroencephalography (EEG) signals for automatic person identi cation has been investigated for a decade. It has been found that the performance of an EEG-based person identication system highly depends on what feature to be extracted from multi-channel EEG signals. Linear methods such as Power Spectral Density and Autoregressive Model have been used to extract EEG features. However these methods assumed that EEG signals are stationary. In fact, EEG signals are complex, non-linear, non-stationary, and random in nature. In addition, other factors such as brain condition or human characteristics may have impacts on the performance, however these factors have not been investigated and evaluated in previous studies. It has been found in the literature that entropy is used to measure the randomness of non-linear time series data. Entropy is also used to measure the level of chaos of braincomputer interface systems. Therefore, this thesis proposes to study the role of entropy in non-linear analysis of EEG signals to discover new features for EEG-based person identi- cation. Five dierent entropy methods including Shannon Entropy, Approximate Entropy, Sample Entropy, Spectral Entropy, and Conditional Entropy have been proposed to extract entropy features that are used to evaluate the performance of EEG-based person identication systems and the impacts of epilepsy, alcohol, age and gender characteristics on these systems. Experiments were performed on the Australian EEG and Alcoholism datasets. Experimental results have shown that, in most cases, the proposed entropy features yield very fast person identication, yet with compatible accuracy because the feature dimension is low. In real life security operation, timely response is critical. The experimental results have also shown that epilepsy, alcohol, age and gender characteristics have impacts on the EEG-based person identication systems.

Automatic Incident Detection and Driver Warning in PORTICO.

This paper focuses on the low-volume incident detection and subsequent driver warning objectives of the PORTICO project. It proposes Automatic Incident Detection (AID) which uses a multimodel approach comprised of a number of different algorithms. A set of thresholds and conditions are defined which determine which algorithm(s) need to indicate an incident under different traffic conditions in order to trigger an alarm

Page Composition using PPML as a Link-editing Script

The advantages of a COG (Component Object Graphic) approach to the composition of PDF pages have been set out in a previous paper [1]. However, if pages are to be composed in this way then the individual graphic objects must have known bounding boxes and must be correctly placed on the page in a process that resembles the link editing of a multi-module computer program. Ideally the linker should be able to utilize all declared resource information attached to each COG. We have investigated the use of an XML application called Personalized Print Markup Language (PPML) to control the link editing process for PDF COGs. Our experiments, though successful, have shown up the shortcomings of PPML's resource handling capabilities which are currently active at the document and page levels but which cannot be elegantly applied to individual graphic objects at a sub-page level. Proposals are put forward for modifications to PPML that would make easier any COG-based approach to page composition.

Data-driven Automatic Edge Sharpening

Sharpening is a powerful image transformation because sharp edges can bring out image details. Sharpness is achieved by increasing local contrast and reducing edge widths. We present a method that enhances sharpness of images and thereby their perceptual quality. Most existing enhancement techniques require user input to improve the perception of the scene in a manner most pleasing to the particular user. Our goal of image enhancement is to improve the perception of sharpness in digital images for human viewers. We consider two parameters in order to exaggerate the differences between local intensities. The two parameters exploit local contrast and widths of edges. We start from the assumption that color, texture, or objects of focus such as faces affect the human perception of photographs. When human raters are presented with a collection of images with different sharpness and asked to rank them according to perceived sharpness, the results have shown that there is a statistical consensus among the raters. We introduce a ramp enhancement technique by modifying the optimal overshoot in the ramp for different region contrasts as well as the new ramp width. Optimal parameter values are searched to be applied to regions under the criteria mentioned above. In this way, we aim to enhance digital images automatically to create pleasing image output for common users.

Engineering the zinc finger recombinase for use in targeted genomic editing

No abstract available.

Metodologia di editing immagini per analisi dati morfometrici dei primi stadi di sviluppo di Engraulis encrasicolus (Linnaeus, 1758)

Tra le più importanti risorse alieutiche di molte regioni del Mar Mediterraneo vi sonole acciughe (Engraulisencrasicolus, Linnaeus, 1758), piccoli pesci pelagici appartenenti alle famiglie degli Engraulidae. Dati IREPA del 2009, hanno di fatto reso noto che in Italia, la pesca di E. encrasicolusha rappresentano in media il 26% circa del pescato totale. Questa specie viene continuamente monitorata e grazie a tali programmi è stato evidenziato che vi sono delle fluttuazioni inter-annuali molto pronunciate (Cergoleet al., 2002; Cingolani, 2004), le cui cause possono essere molteplici, da fattori antropicicome l’elevato sforzo di pesca a fattori naturali (Borjia et al., 1996). Va però posta molta attenzione sulle dinamiche biologiche ed ambientali che influiscono sulla sopravvivenza dei primi stadi di vita di questa specie, che ricadendo sul successivo reclutamento, possono essere una delle cause fondamentali delle contrazioni e degli incrementi annuali dello stock adulto (Thikonova et al., 2000; James et al., 2003; Cuttitta et al., 2003, 2006).Lo studio delle fasi ittioplanctoniche e delle sue relazioni con l’ambiente e gli altri organismi, risulta quindi di primaria importanza nell’ambito delle conoscenze necessarie per il corretto sfruttamento delle risorse alieutiche.

Automatic Glaucoma Detection Based on Optic Disc Segmentation and Texture Feature Extraction

The use of digital image processing techniques is prominent in medical settings for the automatic diagnosis of diseases. Glaucoma is the second leading cause of blindness in the world and it has no cure. Currently, there are treatments to prevent vision loss, but the disease must be detected in the early stages. Thus, the objective of this work is to develop an automatic detection method of Glaucoma in retinal images. The methodology used in the study were: acquisition of image database, Optic Disc segmentation, texture feature extraction in different color models and classification of images in glaucomatous or not. We obtained results of 93% accuracy

Automatic Target Recognition in Synthetic Aperture Radar Imagery: A State-of-the-Art Review

The purpose of this paper is to survey and assess the state-of-the-art in automatic target recognition for synthetic aperture radar imagery (SAR-ATR). The aim is not to develop an exhaustive survey of the voluminous literature, but rather to capture in one place the various approaches for implementing the SAR-ATR system. This paper is meant to be as self-contained as possible, and it approaches the SAR-ATR problem from a holistic end-to-end perspective. A brief overview for the breadth of the SAR-ATR challenges is conducted. This is couched in terms of a single-channel SAR, and it is extendable to multi-channel SAR systems. Stages pertinent to the basic SAR-ATR system structure are defined, and the motivations of the requirements and constraints on the system constituents are addressed. For each stage in the SAR-ATR processing chain, a taxonomization methodology for surveying the numerous methods published in the open literature is proposed. Carefully selected works from the literature are presented under the taxa proposed. Novel comparisons, discussions, and comments are pinpointed throughout this paper. A two-fold benchmarking scheme for evaluating existing SAR-ATR systems and motivating new system designs is proposed. The scheme is applied to the works surveyed in this paper. Finally, a discussion is presented in which various interrelated issues, such as standard operating conditions, extended operating conditions, and target-model design, are addressed. This paper is a contribution toward fulfilling an objective of end-to-end SAR-ATR system design.

Automatic dynamic mask extraction for PIV images containing an unsteady interface, bubbles, and a moving structure

When performing Particle Image Velocimetry (PIV) measurements in complex fluid flows with moving interfaces and a two-phase flow, it is necessary to develop a mask to remove non-physical measurements. This is the case when studying, for example, the complex bubble sweep-down phenomenon observed in oceanographic research vessels. Indeed, in such a configuration, the presence of an unsteady free surface, of a solid–liquid interface and of bubbles in the PIV frame, leads to generate numerous laser reflections and therefore spurious velocity vectors. In this note, an image masking process is developed to successively identify the boundaries of the ship and the free surface interface. As the presence of the solid hull surface induces laser reflections, the hull edge contours are simply detected in the first PIV frame and dynamically estimated for consecutive ones. As for the unsteady surface determination, a specific process is implemented like the following: i) the edge detection of the gradient magnitude in the PIV frame, ii) the extraction of the particles by filtering high-intensity large areas related to the bubbles and/or hull reflections, iii) the extraction of the rough region containing these particles and their reflections, iv) the removal of these reflections. The unsteady surface is finally obtained with a fifth-order polynomial interpolation. The resulted free surface is successfully validated from the Fourier analysis and by visualizing selected PIV images containing numerous spurious high intensity areas. This paper demonstrates how this data analysis process leads to PIV images database without reflections and an automatic detection of both the free surface and the rigid body. An application of this new mask is finally detailed, allowing a preliminary analysis of the hydrodynamic flow.

An Information Theoretic Approach to Automatic Query Expansion

International audience

Design of a variable width pulse generator feasible for manual or automatic control

A variable width pulse generator featuring more than 4-V peak amplitude and less than 10-ns FWHM is described. In this design the width of the pulses is controlled by means of the control signal slope. Thus, a variable transition time control circuit (TTCC) is also developed, based on the charge and discharge of a capacitor by means of two tunable current sources. Additionally, it is possible to activate/deactivate the pulses when required, therefore allowing the creation of any desired pulse pattern. Furthermore, the implementation presented here can be electronically controlled. In conclusion, due to its versatility, compactness and low cost it can be used in a wide variety of applications.

Automatic identification of whole-part relations in Portuguese

Dissertação de Mestrado, Ciências da Linguagem, Faculdade de Ciências Humanas e Sociais, Universidade do Algarve, 2014

Automatic learning of pre-miRNAs from different species.

Discovery of microRNAs (miRNAs) relies on predictive models for characteristic features from miRNA precursors (pre-miRNAs). The short length of miRNA genes and the lack of pronounced sequence features complicate this task. To accommodate the peculiarities of plant and animal miRNAs systems, tools for both systems have evolved differently. However, these tools are biased towards the species for which they were primarily developed and, consequently, their predictive performance on data sets from other species of the same kingdom might be lower. While these biases are intrinsic to the species, their characterization can lead to computational approaches capable of diminishing their negative effect on the accuracy of pre-miRNAs predictive models. We investigate in this study how 45 predictive models induced for data sets from 45 species, distributed in eight subphyla/classes, perform when applied to a species different from the species used in its induction. Results: Our computational experiments show that the separability of pre-miRNAs and pseudo pre-miRNAs instances is species-dependent and no feature set performs well for all species, even within the same subphylum/class. Mitigating this species dependency, we show that an ensemble of classifiers reduced the classification errors for all 45 species. As the ensemble members were obtained using meaningful, and yet computationally viable feature sets, the ensembles also have a lower computational cost than individual classifiers that rely on energy stability parameters, which are of prohibitive computational cost in large scale applications. Conclusion: In this study, the combination of multiple pre-miRNAs feature sets and multiple learning biases enhanced the predictive accuracy of pre-miRNAs classifiers of 45 species. This is certainly a promising approach to be incorporated in miRNA discovery tools towards more accurate and less species-dependent tools.

En/titled: The space where writing, editing and teaching intersect

Editing a literary magazine offers us a cultural space where our ideas and aesthetics can be expressed collectively and therefore be heard more effectively. This informs and frames our own writing by increasing our confidence in our own unusual voices. The sense of belonging Brand creates further breaks down the isolation of the writing life. The internationalism of Brand reinforces our own cultural identities as non-English writers. However, acting as a facilitator of others’ creativity can sometimes dissipate or even deplete creative energy. Editing and teaching can take over your writing to the point of annihilation. Further, in terms of external perceptions, you run the risk of disappearing as a writer. We shall look at how this can happen and explore ways that we can prevent it e.g. keeping the boundaries firm and clear.

AUTOMATIC LABELING OF RSS ARTICLES USING ONLINE LATENT DIRICHLET ALLOCATION

The amount of information contained within the Internet has exploded in recent decades. As more and more news, blogs, and many other kinds of articles that are published on the Internet, categorization of articles and documents are increasingly desired. Among the approaches to categorize articles, labeling is one of the most common method; it provides a relatively intuitive and effective way to separate articles into different categories. However, manual labeling is limited by its efficiency, even thought the labels selected manually have relatively high quality. This report explores the topic modeling approach of Online Latent Dirichlet Allocation (Online-LDA). Additionally, a method to automatically label articles with their latent topics by combining the Online-LDA posterior with a probabilistic automatic labeling algorithm is implemented. The goal of this report is to examine the accuracy of the labels generated automatically by a topic model and probabilistic relevance algorithm for a set of real-world, dynamically updated articles from an online Rich Site Summary (RSS) service.