Sharif Digital Repository

A dense monitoring network is vital for the reliable assessment of PM10 in different parts of an urban area. In this study, a new idea is employed for the re-construction of the 20 shut-down PM10 monitoring stations of Berlin. It endeavours to find the non-linear relationship between the hourly PM10 concentration of both the still operating and the shut-down PM10 monitoring stations by using a fuzzy modelling technique, called modified active learning method (MALM). In addition, the simulations were performed by using not only raw PM10 databases but also log-transformed PM10 databases for skewness reduction. According to the results of hourly PM10 simulation (root mean square error about... 

Background: Nutrigenomic has revolutionized our understanding of nutrition. As plants make up a noticeable part of our diet, in the present study we chose microRNAs of edible plants and investigated if they can perfectly match human genes, indicating potential regulatory functionalities. Methods: miRNAs were obtained using the PNRD database. Edible plants were separated and microRNAs in common in at least four of them entered our analysis. Using vmatchPattern, these 64 miRNAs went through four steps of refinement to improve target prediction: Alignment with the whole genome (2581 results), filtered for those in gene regions (1371 results), filtered for exon regions (66 results) and finally... 

In this paper, we present new adaptive linear discriminant analysis (LDA) algorithm and apply them for adaptive facial feature extraction. Adaptive nature of the proposed algorithm is advantageous for real world applications in which one confronts with a sequence of data such as online face recognition and mobile robotics. Application of the new algorithm on feature extraction from facial image sequences is given in three steps: i) adaptive image preprocessing, ii) adaptive dimension reduction and iii) adaptive LDA feature estimation. Steps 1 and 2 are done simultaneously and outputs of stage 2 are used as a sequence of inputs for stage3. The proposed system was tested on Yale and PIE face... 

The aim of the present study is to provide an Atlas of IRAN Offshore Renewable Energy Resources (hereafter called 'the Atlas') to map out wave and tidal resources at a national scale, extending over the area of the Persian Gulf and Sea of Oman. Such an Atlas can provide necessary tools to identify the areas with greatest resource potential and within reach of present technology development. To estimate available tidal energy resources at the site, a two-dimensional tidally driven hydrodynamic numerical model of Persian Gulf was developed using the hydrodynamic model in the MIKE 21 Flow Model (MIKE 21HD), with validation using tidal elevation measurements and tidal stream diamonds from... 

In this paper, we propose a novel method for extracting fiducial points (FPs) of the beats in electrocardiogram (ECG) signals using switching Kalman filter (SKF). In this method, according to McSharry's model, ECG waveforms (P-wave, QRS complex and T-wave) are modeled with Gaussian functions and ECG baselines are modeled with first order auto regressive models. In the proposed method, a discrete state variable called “switch” is considered that affects only the observation equations. We denote a mode as a specific observation equation and switch changes between 7 modes and corresponds to different segments of an ECG beat. At each time instant, the probability of each mode is calculated and... 

The joint analysis of multiple data sets to extract their interdependency information has wide applications in biomedical and health informatics. In this paper, we propose an algorithm to extract joint and individual sources of multisubject data sets by using a deflation-based procedure, which is referred to as joint/individual thin independent component analysis (JI-ThICA). The proposed algorithm is based on two cost functions utilizing higher order cumulants to extract joint and individual sources. Joint sources are discriminated by fusing signals of all subjects, whereas individual sources are extracted separately for each subject. Furthermore, JI-ThICA algorithm estimates the number of... 

Background: It has been previously shown that palindromic sequences are frequently observed in proteins. However, our knowledge about their evolutionary origin and their possible importance is incomplete. Results: In this work, we tried to revisit this relatively neglected phenomenon. Several questions are addressed in this work. (1) It is known that there is a large chance of finding a palindrome in low complexity sequences (i.e. sequences with extreme amino acid usage bias). What is the role of sequence complexity in the evolution of palindromic sequences in proteins? (2) Do palindromes coincide with conserved protein sequences? If yes, what are the functions of these conserved segments?... 

Early diagnosis of any disease at a lower cost is preferable. Automatic medical diagnosis classification tools reduce financial burden on health care systems. In medical diagnosis, patterns consist of observable symptoms and the results of diagnostic tests, which have various associated costs and risks. In this paper, we have experimented and suggested an automated pattern classification method for classifying four diseases into two classes. In the literature on machine learning or data mining, regression and classification problems are typically viewed as two distinct problems differentiated by continuous or categorical dependent variables. There are endeavors to use regression methods to... 

A total of 21 833 inhibitors of the central nervous system (CNS) were collected from Binding-database and analyzed using discriminant analysis (DA) techniques. A combination of genetic algorithm and quadratic discriminant analysis (GA-QDA) was proposed as a tool for the classification of molecules based on their therapeutic targets and activities. The results indicated that the one-against-one (OAO) QDA classifiers correctly separate the molecules based on their therapeutic targets and are comparable with support vector machines. These classifiers help in charting the chemical space of the CNS inhibitors and finding specific subspaces occupied by particular classes of molecules. As a next... 

Objective: Epileptic seizure detection is a key step for epilepsy assessment. In this work, using the pentylenetetrazole (PTZ) model, seizures were induced in rats, and ECoG signals in interictal, preictal, ictal, and postictal periods were recorded. The recorded ECoG signals were then analyzed to detect epileptic seizures in the epileptic rats. Methods: Two different approaches were considered in this work: thresholding and classification. In the thresholding approach, a feature is calculated in consecutive windows, and the resulted index is tracked over time and compared with a threshold. The moment the index crosses the threshold is considered as the moment of seizure onset. In the... 

This paper presents an innovative hybrid approach for the estimation of the solar global radiation. New prediction equations were developed for the global radiation using an integrated search method of genetic programming (GP) and simulated annealing (SA), called GP/SA. The solar radiation was formulated in terms of several climatological and meteorological parameters. Comprehensive databases containing monthly data collected for 6 years in two cities of Iran were used to develop GP/SA-based models. Separate models were established for each city. The generalization of the models was verified using a separate testing database. A sensitivity analysis was conducted to investigate the... 

In the present study, a prediction model was derived for the effective angle of shearing resistance (φ′) of soils using a novel hybrid method coupling genetic programming (GP) and orthogonal least squares algorithm (OLS). The proposed nonlinear model relates φ′ to the basic soil physical properties. A comprehensive experimental database of consolidated-drained triaxial tests was used to develop the model. Traditional GP and least square regression analyses were performed to benchmark the GP/OLS model against classical approaches. Validity of the model was verified using a part of laboratory data that were not involved in the calibration process. The statistical measures of correlation... 

Electrocardiogram is one of the most prominent cardiac signals being capable to be utilized for medical uses such as arrhythmia detection. Over the years, the feasibility of using this signal for human identification issue has been investigated, and some methods have been proposed. In this Paper a novel approach is proposed for electrocardiogram (ECG) based human identification using Extended Kalman Filter (EKF). The innovation signal of EKF has been considered as feature which is used to classify different subjects. In this paper a general issue, human identification, is summarized to a classification problem in which the proposed features of each subject is calculated, and the... 

Transportation has significant and long lasting economical, social and environmental impacts, and so is an important dimension of urban sustainability. Some attempts have been made to develop sustainable transport indicators, STI. A few studies actually apply STI to compare sustainability among various world cities. In this paper various world cities ranked in terms of urban sustainable transport composite index. The study database is created from UITP databank: "Millennium cities database for sustainable mobility" or MCDST. Firstly sustainable transportation indicators were selected by reviewing past researches. Some indicators are edited or redefined. Consequently 9 STI were developed, 3... 

A new face recognition method is proposed in this paper. The proposed method is based on fuzzy regularized linear discriminant analysis (FR-LDA) and combines the regularized linear discriminant analysis (R-LDA) and the fuzzy set theory. R-LDA is based on a new regularized Fisher's discriminant criterion, which is particularly robust against the small sample size problem compared to the traditional one used in LDA. In the proposed method, we calculate the membership degree matrix by Fuzzy K-nearest neighbor (FKNN) and then incorporate the membership degree into the definition of the between-class and within-class scatter matrices and get the fuzzy between-class and within-class scatter...