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Delay lock loop (DLL) is a key element in circuits such as clock synchronization, clock and data clock recovery. In this paper, new structures for phase frequency detector (PFD), charge pump (CP) and delay cell for low power applications are presented. A dynamic PFD based on CMOS inverter is proposed which has low power consumption and its operating frequency range is wide. The proposed CP is based on gate-driven and positive feedback techniques which has a good current matching. The delay cell uses the bulk-driven technique and has less power consumption than conventional structure. To assess the performance of the proposed structures, some simulations are performed in a 0.18 µm CMOS process with the supply voltage of 1.8 V. The simulation results show higher efficiency of the proposed structures than the existing structures in terms of accuracy and power consumption. The simulation results show that the maximum operating frequency of the PFD is 2 GHz. The mismatch between up and down currents of the CP is less than 0.3%. The power consumption of the proposed delay cell is 25% less than the conventional structure.

Operational trans-conductance amplifiers (OTAs) are an essential building block in analogue and mixed-signal circuits. In this study, a new two-stage class-AB OTA is proposed. The class-AB operation is obtained without extra power dissipation, and without unity-gain bandwidth or noise degradation. The circuit was fabricated using 1P6M 180-nm CMOS technology under 1.8 V supply voltage. The measurement results indicate 13 dB enhancement in DC-gain and 1.8 times improvement in slew rate compared to its conventional counterpart. Comparison results indicate that the proposed OTA demonstrates better performance than the state-of-the-art designs.

In this paper, a new two-stage class-AB operational trans-conductance amplifier (OTA) is proposed. Using active loads in the first stage and improved recycling structure, the effective trans-conductance of the first stage is increased. The nonlinear current mirrors boost the current of the second stage in the slewing mode, leading to the increase of the slew rate (SR). Exploiting the multi-path scheme and increasing the effective trans-conductance cause the OTA DC-gain to be enhanced. The proposed OTA has been fabricated using 1P6M 180-nm CMOS technology under 1.8 V supply voltage. The OTA exhibits 98 dB DC-gain and 21 MHz unity-gain bandwidth with a 100 pF capacitive load, consuming 3 mW from 1.8 V supply voltage.

This article proposes a new two-stage class-AB operational trans-conductance amplifier (OTA). In this OTA, the characteristics of class-AB are applied in both of the stages. By increasing the effective trans-conductance of the first stage, DC gain is improved. Non-linear current mirror boosts the current of the second stage; therefore, slew rate (SR) is increased. In order to verify the performance of the proposed OTA, some simulations are carried out in a 0.18-μm CMOS process in which the supply voltage is 1.8 V. The simulation results indicate 21-dB enhancement in DC gain and 2.7 times improvement in SR compared to its conventional counterpart. The post-layout and Monte Carlo simulations show the proposed OTA has better performance than the state-of-the art designs.

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.

Signal analysis tools such as Fourier transform are often applicable on data with a limited length. Segmentation is an important pre-processing step in many signal processing applications. Statistical characteristics of the signal in a segment are often preferred to be similar. This characteristic, stationarity, improves performance of signal analysis technique. This article develops an adaptive segmentation method based on wavelet transform and fractal dimension from two aspects. One is to use discrete stationary wavelet transform in pre-processing step instead of using classical wavelet transform. The other is to choose the optimal parameters. Two parameters are needed to calculate the fractal dimension of a decomposed signal, window length and percentage overlapping of the successive windows, which affect the performance of the proposed approach. These parameters are optimally set using the particle swarm optimisation algorithm. Performance of the proposed method is compared with three other existing s...

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.

In many signal processing applications, it is often needed to segment signals into small epochs with similar characteristics such as amplitude and/or frequency that are particularly meaningful to clinicians and for assessment by neurophysiologists. This paper presents a novel adaptive segmentation method based on the time-varying autoregressive (TVAR) model, integral, and basic generalized likelihood ratio (GLR). Since autoregressive (AR) model for the GLR method is valid for only stationary signals, TVAR is employed as a valuable and powerful tool for non-stationary signals. Moreover, to improve the performance of the basic GLR and increase its speed, we propose to use moving steps more than one sample for successive windows in the basic GLR method. Performance of the proposed method is compared with existing GLR and wavelet GLR (WGLR) methods using both the synthetic signal and real EEG data. The simulation results indicate the high accuracy of the proposed method.

The effect of geometry on the relationship between measurement error and position determination error is described by geometric dilution of precision (GDOP). It is illustrated that a subset with lowest GDOP will result in lowest error. Since the global positioning system (GPS) GDOP computation based on complicated transformation and inversion of measurement matrices is a time consuming procedure, the neural network (NN) is used as an approximator or classifier for GDOP data. The back propagation (BP) is a most common method to train a feed-forward NN. However, in many applications including the GPS GDOP classification, it cannot train an NN with an acceptable speed and accuracy. Therefore, in this paper, a new approach to classify the GPS GDOP by using scaled conjugate gradient algorithm (CGA) to train a feed-forward NN and principal component analysis (PCA) is proposed. Scaled CGA is a powerful tool to train an NN, which is widely used in many applications that need to a high speed. PCA is a well-known method to reduce and optimize the dimensions of the data. PCA is applied on entire dataset in order to have some few uncorrelated and informative features. The results show that the scaled CGA with PCA has better performance than the scaled CGA without PCA and also, scaled CGA without PCA has better performance than the basic BP.