Sharif Digital Repository

In the last few years there has been growing interest in the use of functional Magnetic Resonance Imaging (fMRI) for brain mapping. To decode brain patterns in fMRI data, we need reliable and accurate classifiers. Towards this goal, we compared performance of eleven popular pattern recognition methods. Before performing pattern recognition, applying the dimensionality reduction methods can improve the classification performance; therefore, seven methods in region of interest (RDI) have been compared to answer the following question: which dimensionality reduction procedure performs best? In both tasks, in addition to measuring prediction accuracy, we estimated standard deviation of... 

Traffic density estimation plays an integral role in intelligent transportation systems (ITS), using which provides important information for signal control and effective traffic management. In this paper, we present a new framework for traffic density estimation based on topic model, which is an unsupervised model. This framework uses a set of visual features without any need to individual vehicle detection and tracking, and discovers the motion patterns automatically in traffic scenes by using topic model. Then, likelihood value allocated to each video clip enables us to estimate its traffic density. Results on a standard dataset show high classification performance of our proposed... 

Transcranial Doppler ultrasound can be used to detect circulating cerebral eraboli. Embolie signals have characteristic transient chirps suitable for wavelet analysis. We have implemented and evaluated the first online selective selective wavelet transient enhancement filter to amplify embolic signals in a preprocessing system. Our approach is similar to wavelet de-noising for signal enhancement, but, in order to retain blood flow information, we do not use traditional threshold methods. The selective wavelet amplifier uses the matched filter properties of wavelets to enhance embolic signals significantly and improve classification performance using a novel noise tolerant approach. Even the... 

In the present work, a new approach based on external parameter orthogonalization combined with support vector machine (EPO-SVM) is proposed for processing of attenuated total reflectance-Fourier transform mid-infrared (ATR-FT-MIR) spectra with the goal of solving authentication problem in saffron, the most expensive spice in the world. First, one-hundred authentic saffron samples are clustered by principal component analysis (PCA) with EPO as the best preprocessing strategy. Then, EPO-SVM is used for the detection of four commonly used plant-derived adulterants (i.e. safflower, calendula, rubia, and style) in binary mixtures (saffron and each of plant adulterants) and its performance is... 

In this paper we study the effectiveness of using multiple classifier combination for EEG signal classification aiming to obtain more accurate results than it possible from each of the constituent classifiers. The developed system employs two linear classifiers (SVM,LDA) fused at the abstract and measurement levels for integrating information to reach a collective decision. For making decision, the majority voting scheme has been used. While at the measurement level, two types of combination methods have been investigated: one used fixed combination rules that don't require prior training and a trainable combination method. For the second type, the fuzzy integral method was used. The...