The detection of insulation failures in buildings could potentially conserve energy supplies and improve future designs. Improvements to thermal insulation in buildings include the development of models to assess fabric gain -heat flux through exterior walls in the building- and heating processes. Thermal insulation standards are now contractual obligations in new buildings, and the energy efficiency of buildings constructed prior to these regulations has yet to be determined. The main assumption is that it will be based on heat flux and conductivity measurement. Diagnostic systems to detect thermal insulation failures should recognize anomalous situations in a building that relate to insulation, heating and ventilation. This highly relevant issue in the construction sector today is approached through a novel intelligent procedure that can be programmed according to local building and heating system regulations and the specific features of a given climate zone. It is based on the following phases. Firstly, the dynamic thermal performance of dif\-ferent variables is specifically modeled. Secondly, an exploratory projection pursuit method called Cooperative Maximum-Likelihood Hebbian Learning extracts the relevant features. Finally, a supervised neural model and identification techniques constitute the model for the diagnosis of thermal insulation failures in building due to the heat flux through exterior walls, using relevant features of the data set. The reliability of the proposed method is validated with real datasets from several Spanish cities in winter time.
This paper presents results from the exam ination of flood embankments by means of three geophysical methods: GPR, mutual impedance of loop antennas measurements and D.C. resistivity method. In order to increase measuring accuracy, the mutual impedance measuring system works at a high frequency. Parameters of mutual impedance measuring system were presented. A method of mutual im pedance measurement results was described. Flood embankments examination results showed that the simultaneous use of few geophysical methods increases accuracy of inhomogeneities detection in near-surface structure of the ground., Remigiusz Mydlikowski, Grzegorz Beziuk and Adam Szynkiewicz., and Obsahuje bibliografické odkazy
SPRi (Surface Plasmon Resonance Imaging) biosensors are used for detection in real time with advantage of processing large number parallel measurements. Our goal is fast detection of low concentrations of ovalbumin and similar proteins. A low concentration testing of proteins in urea or blood serum is needed for clinical tests. The current SPRi method is not capable of measuring ovalbumin concentrations lower than 1 mg/l directly. This article shows measurements of biochemical substance concentrations and a possibility of identification of very low concentrations on several experiments. Strengthening of the signal was achieved by coupling of a secondary biotinylized antibody (anti-ovalbumin), to which streptavidin is further attached. and Bionsenzory SPRi (Surface Plasmon Resonance Imaging) jsou používány pro detekci v reálném čase s výhodou zpracování velkého počtu paralelních měření. Naším cílem je detekce nízkých koncentrací ovalbuminu a podobných proteinů. Testování nízké koncentrace proteinů v moči a krevním séru jsou nutné pro klinické zkoušky. Současná metoda SPRi nedokáže přímo měřit koncentrace ovalbuminu nižší než 1 mg/l. V tomto příspěvku je na několika experimentech předvedeno měření koncentrací biochemické substance a možnost určení velmi nízkých koncentrací. Zesílení signálu bylo dosaženo přidáním biotinu a streptavidinu.
This paper presents an algorithm for the design of a computer aided diagnosis system to detect, quantify and classify the lesions of non-proliferative diabetic retinopathy as well as dry age related macular degeneration from the fundus retina images. Symptoms of non-proliferative diabetic retinopathy in images consist of bright lesions like hard exudates, cotton wool spots and dark lesions like microaneurysms, hemorrhages. Dry age related macular degeneration is manifested as a bright lesion called drusen. The proposed system consists of two parts: image processing, where preprocessed gray scale images are segmented to extract candidate lesions using a combination of Gaussian filtering and multilevel thresholding followed by classification of the different lesions in non-proliferative diabetic retinopathy and age related macular degeneration using perceptron, support vector machine and naive Bayes classifier. From the comparative performance analysis of the classification techniques, it is observed that comparable results are obtained from single layer perceptron and support vector machine and they both outperform naive Bayes classifier. The classification accuracy of support vector machine classifier for dark lesion class is 97.13% and the classification accuracy of single layer perceptron for bright lesion class is 95.13% with optimal feature set.
The retinopathy diseases occur when the neurons do not transmit signals from retina to the brain. These disorders are: Diabetic retinopathy, hypertensive retinopathy, macular degeneration, vein branch occlusion, vitreous hemorrhage, and normal retina. This work presents a novel detection algorithm about retinopathy disorders from retina images. For this purpose, the retina images were pre-processed and resized at first. Then the discrete cosine transform was used as feature extraction before applying a neural network classifier. The performance of recognition rates of the novel detection algorithm were found as 50%, 70%, 85%, 90%, and 95% for testing five retinopathy cases respectively.
A procedure for testing occurrance of a transient change in mean of a sequence is suggested where inside an epidemic interval the mean is a linear function of time points. Asymptotic behavior of considered trimmed maximum-type test statistics is presented. Approximate critical values are obtained using an approximation of exceedance probabilities over a high level by Gaussian fields with a locally stationary structure.
This paper presents advanced methodology for the analysis of the electroencephalographic activity (EEG) of the brain aimed to monitor the cognitive states of an operator. The methodology of EEG analysis is based on two main approaches: linear methods based on Fourier transform, Linear Stochastic Models, Multi-covariance analysis, and nonlinear methods based on estimation of state space attractor, state space dimension, D2 dimension and the Largest Lyapunov Exponent (LLE). The correct application of these methods is supported by the study of stability, dynamics and space distribution of EEG signal. The uncertainty of adopting a new methodology, such as presented chaos theory, for EEG signal analysis is minimized by the adequate setup of experiments and by evaluation of results against well adopted power spectral estimates calculated by Fourier transform. For better understanding of the underlying processes behind EEG, the basic mental states such as relaxation, single and complex number count, and Raven test are analyzed and compared with the vigilance states. The averaged behavior of the computed markers of the EEG signal is studied with respect to a reaction time scale by the evaluation of a set of experiments. Because of this complex approach, the presented methodology is able to track the ongoing changes in EEG activity during the process of falling asleep. The automatic detection of vigilance changes is a consequent step to this work. Usability of such device in various fields of everyday life is of the high importance.
Application of Surface Plasmon Resonance (SPR) is one from the most developed optical detection techniques in the frame of biosensors. Surface Plasmon Resonance imaging (SPRi) can monitor biomolecular interactions and detect materials without previous marking of biochemical. We can on line monitor the biochip surface during the biomolecules interactions. It is possible to measure simultaneously several tens of various interactions. The motivation consists in the determination of ovalbumine level in the patient’s urine. and Aplikace rezonanční excitace povrchových plasmonů (SPR, Surface Plasmon Resonance) patří mezi jednu z nejrozvinutějších optických detekčních technik v oblasti biosenzorů. SPRi (SPRi, Surface Plasmon Resonance imaging) umožňuje monitorovat interakce biomolekul a detekovat látky bez nutnosti předchozího značení jedné z biochemikálií. V reálném čase lze sledovat přímo zobrazení povrchu biočipu při interakcích biomolekul. Lze paralelně měřit několik desítek různých interakcí. Motivací je možnost specifikovat hladinu ovalbuminu v moči pacientů.
Účinná detekce elektronů ve všech typech elektronových mikroskopů je základním předpokladem pro získávání kvalitní informace o povaze zkoumaného vzorku a dosažení vyššího rozlišení detailů na povrchu vzorku. Tento přehledový článek shrnuje výsledky, které byly dosaženy v oblasti detekce signálních elektronů, zejména sekundárních a zpětně odražených elektronů, v rastrovacích elektronových mikroskopech. Podává charakteristiku obrazu tvořeného sekundárními a zpětně odraženými elektrony a soustřeďuje se především na scintilačně-fotonásobičové systémy. Uvádí přehled detekčních metod používaných v mikroskopech střední a vyšší třídy a v mikroskopech s nízkou energií primárního elektronového svazku při jejich dopadu na vzorek., Rudolf Autrata, Bohumila Lencová, Vilém Neděla., and Obsahuje seznam literatury
V rámci studia předpokládaných chemických pochodů v mezihvězdném prostoru je zmíněn mechanismus základní interstellární chemie, demonstrovaný na procesech probíhajících za přítomnosti mezihvězdného vodíku a uhlíku. Hlavní pozornost je věnována možnosti laboratorního měření rotačních spekter kationtů a aniontů a jejich následné identifikaci v mezihvězdném prostoru. Podrobněji jsou diskutovány současné znalosti o chování a o detekci astronomicky nejzajímavějších a nejnadějnějších kandidátů aniontů: SH-, C7-, C2- a CN-., Svatopluk Civiš, Tereza Šedivcová., and Obsahuje seznam literatury