Shared feature extraction for nearest neighbor face recognition

In this paper, we propose a new supervised linearfeature extraction technique for multiclass classification problemsthat is specially suited to the nearest neighbor classifier (NN).The problem of finding the optimal linear projection matrix isdefined as a classification problem and the Adaboost algorithmis used to compute it in an iterative way. This strategy allowsthe introduction of a multitask learning (MTL) criterion in themethod and results in a solution that makes no assumptions aboutthe data distribution and that is specially appropriated to solvethe small sample size problem. The performance of the methodis illustrated by an application to the face recognition problem.The experiments show that the representation obtained followingthe multitask approach improves the classic feature extractionalgorithms when using the NN classifier, especially when we havea few examples from each class

A Sparse Bayesian Approach for Joint Feature Selection and Classifier Learning

Peer-reviewed

Feature-based matching of underwater images

This work investigates performance of recent feature-based matching techniques when applied to registration of underwater images. Matching methods are tested versus different contrast enhancing pre-processing of images. As a result of the performed experiments for various dominating in images underwater artifacts and present deformation, the outperforming preprocessing, detection and description methods are proposed

Feature selection for automatic analysis of emotional response based on nonlinear speech modeling suitable for diagnosis of Alzheimer׳s disease

Alzheimer׳s disease (AD) is the most common type of dementia among the elderly. This work is part of a larger study that aims to identify novel technologies and biomarkers or features for the early detection of AD and its degree of severity. The diagnosis is made by analyzing several biomarkers and conducting a variety of tests (although only a post-mortem examination of the patients’ brain tissue is considered to provide definitive confirmation). Non-invasive intelligent diagnosis techniques would be a very valuable diagnostic aid. This paper concerns the Automatic Analysis of Emotional Response (AAER) in spontaneous speech based on classical and new emotional speech features: Emotional Temperature (ET) and fractal dimension (FD). This is a pre-clinical study aiming to validate tests and biomarkers for future diagnostic use. The method has the great advantage of being non-invasive, low cost, and without any side effects. The AAER shows very promising results for the definition of features useful in the early diagnosis of AD.

A hybrid feature selection approach for the early diagnosis of Alzheimer's disease

Objective. Recently, significant advances have been made in the early diagnosis of Alzheimer’s disease from EEG. However, choosing suitable measures is a challenging task. Among other measures, frequency Relative Power and loss of complexity have been used with promising results. In the present study we investigate the early diagnosis of AD using synchrony measures and frequency Relative Power on EEG signals, examining the changes found in different frequency ranges. Approach. We first explore the use of a single feature for computing the classification rate, looking for the best frequency range. Then, we present a multiple feature classification system that outperforms all previous results using a feature selection strategy. These two approaches are tested in two different databases, one containing MCI and healthy subjects (patients age: 71.9 ± 10.2, healthy subjects age: 71.7 ± 8.3), and the other containing Mild AD and healthy subjects (patients age: 77.6 ± 10.0; healthy subjects age: 69.4± 11.5). Main Results. Using a single feature to compute classification rates we achieve a performance of 78.33% for the MCI data set and of 97.56 % for Mild AD. Results are clearly improved using the multiple feature classification, where a classification rate of 95% is found for the MCI data set using 11 features, and 100% for the Mild AD data set using 4 features. Significance. The new features selection method described in this work may be a reliable tool that could help to design a realistic system that does not require prior knowledge of a patient's status. With that aim, we explore the standardization of features for MCI and Mild AD data sets with promising results.

Simulations of 3D Stripixel Detectors

Large Hadron Collider (LHC) is the main particle accelerator at CERN. LHC is created with main goal to search elementary particles and help science investigate our universe. Radiation in LHC is caused by charged particles circular acceleration, therefore detectors tracing particles in existed severe conditions during the experiments must be radiation tolerant. Moreover, further upgrade of luminosity (up to 1035 cm-2s-1) requires development of particle detector’s structure. This work is dedicated to show the new type 3D stripixel detector with serious structural improvement. The new type of radiation-hard detector has a three-dimensional (3D) array of the p+ and n+ electrodes that penetrate into the detector bulk. The electrons and holes are then collected at oppositely biased electrodes. Proposed 3D stripixel detector demonstrates that full depletion voltage is lower that that for planar detectors. Low depletion voltage is one of the main advantages because only depleted part of the device is active are. Because of small spacing between electrodes, charge collection distances are smaller which results in high speed of the detector’s response. In this work is also briefly discussed dual-column type detectors, meaning consisting both n+ and p+ type columnar electrodes in its structure, and was declared that dual-column detectors show better electric filed distribution then single sided radiation detectors. The dead space or in other words low electric field region in significantly suppressed. Simulations were carried out by using Atlas device simulation software. As a simulation results in this work are represented the electric field distribution under different bias voltages.

Readout electronics for low dark count pixel detectors based on geiger mode avalanche photodiodes fabricated in conventional CMOS technologies for future linear colliders

The high sensitivity and excellent timing accuracy of Geiger mode avalanche photodiodes makes them ideal sensors as pixel detectors for particle tracking in high energy physics experiments to be performed in future linear colliders. Nevertheless, it is well known that these sensors suffer from dark counts and afterpulsing noise, which induce false hits (indistinguishable from event detection) as well as an increase of the necessary area of the readout system. In this work, we present a comparison between APDs fabricated in a high voltage 0.35 µm and a high integration 0.13 µm commercially available CMOS technologies that has been performed to determine which of them best fits the particle collider requirements. In addition, a readout circuit that allows low noise operation is introduced. Experimental characterization of the proposed pixel is also presented in this work.

3D Stripixel Detectors Simulation

Solid-state silicon detectors have replaced conventional ones in almost all recent high-energy physics experiments. Pixel silicon sensors don't have any alternative in the area near the interaction point because of their high resolution and fast operation speed. However, present detectors hardly withstand high radiation doses. Forthcoming upgrade of the LHC in 2014 requires development of a new generation of pixel detectors which will be able to operate under ten times increased luminosity. A planar fabrication technique has some physical limitations; an improvement of the radiation hardness will reduce sensitivity of a detector. In that case a 3D pixel detector seems to be the most promising device which can overcome these difficulties. The objective of this work was to model a structure of the 3D stripixel detector and to simulate electrical characteristics of the device. Silvaco Atlas software has been used for these purposes. The structures of single and double sided dual column detectors with active edges were described using special command language. Simulations of these detectors have shown that electric field inside an active area has more uniform distribution in comparison to the planar structure. A smaller interelectrode space leads to a stronger field and also decreases the collection time. This makes the new type of detectors more radiation resistant. Other discovered advantages are the lower full depletion voltage and increased charge collection efficiency. So the 3D stripixel detectors have demonstrated improved characteristics and will be a suitable replacement for the planar ones.

Evaluation of gas diffusion electrodes as detectors in amperometric hydrogen sensors

This work is directed to the study and evaluation of gas diffusion electrodes as detectors in hydrogen sensors. Electrochemical experiments were carried out with rotating disk electrodes with a thin porous coating of the catalyst as a previous step to select useful parameters for the sensor. An experimental arrangement made in the laboratory that simulates the sensor was found appropriate to detect volumetric hydrogen percentages above 0.25% in mixtures H2:N2. The system shows a linear response for volumetric percentages of hydrogen between 0.25 and 2 %.

Feature selection using Fuzzy Entropy measures with Yu's Similarity measure

In this study, feature selection in classification based problems is highlighted. The role of feature selection methods is to select important features by discarding redundant and irrelevant features in the data set, we investigated this case by using fuzzy entropy measures. We developed fuzzy entropy based feature selection method using Yu's similarity and test this using similarity classifier. As the similarity classifier we used Yu's similarity, we tested our similarity on the real world data set which is dermatological data set. By performing feature selection based on fuzzy entropy measures before classification on our data set the empirical results were very promising, the highest classification accuracy of 98.83% was achieved when testing our similarity measure to the data set. The achieved results were then compared with some other results previously obtained using different similarity classifiers, the obtained results show better accuracy than the one achieved before. The used methods helped to reduce the dimensionality of the used data set, to speed up the computation time of a learning algorithm and therefore have simplified the classification task

The Design and Implementation of a Power Management Feature to a Configuration Management System

Green IT is a term that covers various tasks and concepts that are related to reducing the environmental impact of IT. At enterprise level, Green IT has significant potential to generate sustainable cost savings: the total amount of devices is growing and electricity prices are rising. The lifecycle of a computer can be made more environmentally sustainable using Green IT, e.g. by using energy efficient components and by implementing device power management. The challenge using power management at enterprise level is how to measure and follow-up the impact of power management policies? During the thesis a power management feature was developed to a configuration management system. The feature can be used to automatically power down and power on PCs using a pre-defined schedule and to estimate the total power usage of devices. Measurements indicate that using the feature the device power consumption can be monitored quite precisely and the power consumption can be reduced, which generates electricity cost savings and reduces the environmental impact of IT.

Design and characterization of large area position sensitive radiation detectors

Planar, large area, position sensitive silicon detectors are widely utilized in high energy physics research and in medical, computed tomography (CT). This thesis describes author's research work relating to development of such detector components. The key motivation and objective for the research work has been the development of novel, position sensitive detectors improving the performance of the instruments they are intended for. Silicon strip detectors are the key components of barrel-shaped tracking instruments which are typically the innermost structures of high energy physics experimental stations. Particle colliders such as the former LEP collider or present LHC produce particle collisions and the silicon strip detector based trackers locate the trajectories of particles emanating from such collisions. Medical CT has become a regular part of everyday medical care in all developed countries. CT scanning enables x-ray imaging of all parts of the human body with an outstanding structural resolution and contrast. Brain, chest and abdomen slice images with a resolution of 0.5 mm are possible and latest CT machines are able to image whole human heart between heart beats. The two application areas are presented shortly and the radiation detection properties of planar silicon detectors are discussed. Fabrication methods and preamplifier electronics of the planar detectors are presented. Designs of the developed, large area silicon detectors are presented and measurement results of the key operating parameters are discussed. Static and dynamic performance of the developed silicon strip detectors are shown to be very satisfactory for experimental physics applications. Results relating to the developed, novel CT detector chips are found to be very promising for further development and all key performance goals are met.

Scaling up stepwise feature introduction to construction of large software systems

Developing software is a difficult and error-prone activity. Furthermore, the complexity of modern computer applications is significant. Hence,an organised approach to software construction is crucial. Stepwise Feature Introduction – created by R.-J. Back – is a development paradigm, in which software is constructed by adding functionality in small increments. The resulting code has an organised, layered structure and can be easily reused. Moreover, the interaction with the users of the software and the correctness concerns are essential elements of the development process, contributing to high quality and functionality of the final product. The paradigm of Stepwise Feature Introduction has been successfully applied in an academic environment, to a number of small-scale developments. The thesis examines the paradigm and its suitability to construction of large and complex software systems by focusing on the development of two software systems of significant complexity. Throughout the thesis we propose a number of improvements and modifications that should be applied to the paradigm when developing or reengineering large and complex software systems. The discussion in the thesis covers various aspects of software development that relate to Stepwise Feature Introduction. More specifically, we evaluate the paradigm based on the common practices of object-oriented programming and design and agile development methodologies. We also outline the strategy to testing systems built with the paradigm of Stepwise Feature Introduction.

Readout Electronics for Gas Electron Multiplier Detectors

The European Organization for Nuclear Research (CERN) operates the largest particle collider in the world. This particle collider is called the Large Hadron Collider (LHC) and it will undergo a maintenance break sometime in 2017 or 2018. During the break, the particle detectors, which operate around the particle collider, will be serviced and upgraded. Following the improvement in performance of the particle collider, the requirements for the detector electronics will be more demanding. In particular, the high amount of radiation during the operation of the particle collider sets requirements for the electronics that are uncommon in commercial electronics. Electronics that are built to function in the challenging environment of the collider have been designed at CERN. In order to meet the future challenges of data transmission, a GigaBit Transceiver data transmission module and an E-Link data bus have been developed. The next generation of readout electronics is designed to benefit from these technologies. However, the current readout electronics chips are not compatible with these technologies. As a result, in addition to new Gas Electron Multiplier (GEM) detectors and other technology, a new compatible chip is developed to function within the GEMs for the Compact Muon Solenoid (CMS) project. In this thesis, the objective was to study a data transmission interface that will be located on the readout chip between the E-Link bus and the control logic of the chip. The function of the module is to handle data transmission between the chip and the E-Link. In the study, a model of the interface was implemented with the Verilog hardware description language. This process was simulated by using chip design software by Cadence. State machines and operating principles with alternative possibilities for implementation are introduced in the E-Link interface design procedure. The functionality of the designed logic is demonstrated in simulation results, in which the implemented model is proven to be suitable for its task. Finally, suggestions that should be considered for improving the design have been presented.