Sharif Digital Repository

Segmentation of a scene to the part made is a challenging work. In this paper a graphical model is used for this task. The methods based on geometrical derivatives such as curvature and normal often haven't good result in segmentation of geometrically-complex architecture and lead to over-segmentation and even failure. Proposed method for segmentation contains two steps. At first region growing based on curvature, normal and color is used for growing region. This segmented cloud is used for unary potential in graphical model. Fully connected graph for Conditional Random Field with Gaussian kernel for pair wise potentials is used for correcting this segmentation. Gaussian kernels are based on... 

We present generalizations of the previously published artificial models for generating abnormal cardiac rhythms to provide simulations of coupled cardiovascular (CV) signals with different beat morphologies. Using a joint dynamical formulation, we generate the normal morphologies of the cardiac cycle using a sum of Gaussian kernels, fitted to real CV recordings. The joint inter-dependencies of CV signals are introduced by assuming the same angular frequency and a phase coupling. Abnormal beats are then specified as new dynamical trajectories. An ergadic first-order Markov chain is also used for switching between normal and abnormal beat types. Probability transitions can be learned from... 

In this communication, a hybrid model based on Least Square Support Vector Machine (LSSVM) was constructed to predict CO2 corrosion rate. The input parameters of the model are temperature, CO2 partial pressure, flow velocity and pH. The data used for training and testing of the developed model are 612 and 109 data, respectively. In order to benefit LSSVM from Kernel learning, we compared three kernel functions to select the most efficient one. Furthermore, Coupled Simulated Annealing (CSA) optimization technique was adapted to choose the best optimal values of the model parameters. The results elucidate that Gaussian Kernel functions is the desired function which can afford high accuracy for... 

In this paper, a novel idea is developed to construct energy spectrum of inorganic scintillator detector (e.g. NaI(Tl)) using energy spectrum of organic scintillator detector (e.g. NE102) by means of a model-free method. For this purpose, support vector machine (SVM) accompanied with different kernel functions (i.e. linear, polynomial, and Gaussian) is applied. The spectra of NE102 and NaI(Tl) detectors of the single radioisotopes (i.e. Co60, Cs137, Na22, and Am241) are utilized for training of SVM. In other words, data of NE102 detector are input spectrums of training patterns and data of NaI(Tl) detector are target spectrums of training patterns. To construct an appropriate mapping... 

We present generalizations of our previously published artificial models for generating multi-channel ECG so that the simulation of abnormal rhythms is possible. Using a three-dimensional vectorcardiogram (VCG) formulation, we generate the normal cardiac dipole for a patient using a sum of Gaussian kernels, fitted to real VCG recordings. Abnormal beats are then specified either as new dipoles, or as perturbations of the existing dipole. Switching between normal and abnormal beat types is achieved using a hidden Markov model (HMM). Probability transitions can be learned from real data or modeled by coupling to heart rate and sympathovagal balance. Natural morphology changes form beat-to-beat... 

In this paper cross-correlation of measurable/unmeasurable parameters of nuclear power plants (NPPs) are detected. Correlation techniques including Pearson's, Spearman's, and Kendall-tau give appropriate input parameters for training/prediction of the target unmeasurable parameters. Fuel and clad maximum temperatures of uncontrolled withdrawal of control rods (UWCR) transient of Bushehr nuclear power plant (BNPP) are used as the case study target parameters. Different model-free methods including decision tree (DT), feed-forward back propagation neural network (FFBPNN) accompany with different learning algorithms (i.e. gradient descent with momentum (GDM), scaled conjugate gradient (SCG),... 

This paper presents efficient denoising and lossy compression schemes for electrocardiogram (ECG) signals based on a modified extended Kalman filter (EKF) structure. We have used a previously introduced two-dimensional EKF structure and modified its governing equations to be extended to a 17-dimensional case. The new EKF structure is used not only for denoising, but also for compression, since it provides estimation for each of the new 15 model parameters. Using these specific parameters, the signal is reconstructed with regard to the dynamical equations of the model. The performances of the proposed method are evaluated using standard denoising and compression efficiency measures. For...