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A paper currency recognition system has a wide range of applications such as self receiver machines for automated teller machines and automatic good-selling machines. In this paper a new paper currency recognition system based on Fourier-Mellin transform, Markovian characteristics and Support Vector Machine (SVM) is presented. In the first, a pre-processing algorithm by Fourier-Mellin transform is performed. The key feature of Fourier-Mellin transform is that it is invariant in rotation, translation and scale of the input image. Then, obtained image is segmented and markovian characteristics of each segment have been utilized to construct a feature vectors. These vectors are then fed into SVM classifier for paper currency recognition. In order to evaluate the effectiveness of the system several experiments are carried out. Experimental result indicates that the proposed method achieved high accuracy rate in paper currency recognition.

In this paper the dynamic behavior of the Narmab earth dam (Iran), considering dam-foundation interaction, under normalized Manjil earthquake as input motion has been studied. In order to assess the effect of the dam heights and the foundation widths, in the finite difference model on the earthquake response, various dam-foundation coupled models are analyzed by FLAC as finite difference program for solving mining and geotechnical engineering problems. In this research, a fitness function has been defined based on NonLinear Energy Operator (NLEO). The dam heights and the foundation widths has been chosen optimally using particle swarm optimization (PSO) method. The simulation results indicate considerable differences in the seismic responses.

traffic for anomaly detection. The accurate and rapid detection of network traffic anomaly is crucial to enhance the effective operation of a network. It is often difficult to detect the time when the faults occur in a network. In this paper, a new algorithm is presented to monitor the aggregate network traffic to rapidly detect the time anomaly occurs in a network. This is accomplished by monitoring the statistical characteristics of the time series representing the network behavior. The technique analyzes the network behavior using fractal dimension and discrete stationary wavelet transform. In the proposed method, after applying discrete stationary wavelet transform on the signal representing the network traffic, the fractal dimension of the decomposed signal is calculated in a sliding window. Then, variations of signal fractal dimension are considered for anomaly detection. Performance of the proposed method is compared with that of three other existing methods using both synthetic signal and real data. The results indicate superiority of the proposed technique in terms of accuracy compared to existing methods.

are short-time broadband events, which can last 20ms- 70ms and amplitude is among 100μV-200μV. In this research, a novel spike detection method based on stationary wavelet transform (SWT) and time-varying autoregressive model is proposed. In the proposed method, the discrete stationary wavelet transform is initially applied on the signal under analysis to show the important underlying unadulterated form of the data. The time-varying AR (TVAR) is used as an effective tool for analyzing non-stationary signals such as spikes. The performance of the proposed method is compared with other existing methods using both synthetic signals and real newborn Electroencephalogram (EEG). The simulation results indicate the absolute advantages of the proposed method.