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Optical interconnects as appropriate alternatives for electrical interconnects in the computer chips and boards can be realized by CMOS-based integrated silicon photonics. Dielectric slot waveguide, as one of the newest optical waveguide structures, can form the infrastructure of the passive and active components in these integrated circuits. The passive components have the linear behavior. In order to realize the all-optical active components such as laser, amplifier, and modulator we can use the nonlinear effects in the silicon photonics waveguides. On the other hand, Si-nc:SiO2 as a new material, has a stronger nonlinear property than Si. The results of the full-wave analyzes of the slot waveguide in the linear and nonlinear regimes show that the slot region of this waveguide can be filled with the Si-nc:SiO2 and also realize a high optical intensity. Therefore, this waveguide intensifies the nonlinear behaviors by two factors.

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Blind source separation is the technique that anyone can separate the original signals from their mixtures without any knowledge about the mixing process, but using some statistical properties of original source signals. Independent component analysis is a statistical method expressed as a set of multidimensional observations that are combinations of unknown variables which are assumed to be statistically independent with respect to each other. In this paper we will use the nonlinear autcorrelation function as an object function to separate the source signals from the noisy mixing signals. Also we apply the wavelet transform in our proposed algorithm. Maximization of the object function in wavelet domain using the LMS algorithm will be obtained the coefficients of a linear filter which separate the source signals with high SNR. To calculate the performance of the proposed algorithm, two parameters of Performance Index and Signal to Noise and Interference Ratio will be used. To test the proposed algorithm, we will use Inovation Gaussian signals, Speech signals and ECG signals. Finally level of wavelet decomposition effects will be consider on the obtained results. It will be shown that the proposed algorithm gives better results than the other methods such as NoisyNA method that has been proposed by Shi.

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The dynamic equations of an autonomous underwater vehicle (AUV) are described as a nonlinear system with multiple hydrodynamic coefficients which strongly affect the performance, maneuverability and controllability of AUV. On the other hand, the values of these coefficients depend on the vehicle speed and the geometric properties. In this paper, the nonlinear model identification problem of NPS AUV II, as a six degree-of-freedom (DOF) autonomous underwater vehicle, is addressed by using the nonlinear continuous-time extended Kalman filter (EKF) observer with guaranteed convergence. To this end, the hydrodynamic coefficients of AUV are considered as the augmented state variables of a six DOF nonlinear model. Based on the input-output data at the presence of the measurement noise of sensors, the state variables and the hydrodynamic coefficients of the nonlinear model in a (path) helical maneuver, are suitably estimated by using the EKF observer. In order to analyze the numerical performance of the proposed method, the dynamic equations of the vehicle are introduced, and a comparison is made between the identified model outputs and those of the real model.

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In this paper, a new nonlinear model for magnetorheological (MR) damper based on the bouc-wen model is identified. This model including the hysteresis loop is linear with respect to the input current. This advantage makes it easy to use the proposed model in semi-active suspension systems as the inverse model. For better comparison, a polynomial model developed in the prevalent papers is also designed. The performance evaluation of two models shows that the proposed bouc-wen model has higher accuracy in the smaller velocity area. At the rest of the paper, an LQR controller designed for calculation of the active suspension force is integrated with the two identified inverse models. The simulation studies are carried out for active and semi-active suspension systems on a standard road. The results show that the simplified nonlinear bouc-wen model has better performance in controlling the semi active suspension system compared with the other models. In high amplitude velocities of the damper, the performance of two models is close to each other.

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The development of renewable energy sources, distributed generation, energy storage and nonlinear controllable loads in modern distribution networks has led to the consideration of the state estimation in intelligent and active distribution networks. The performance of the Energy Management Central's distribution network depends on the results of the state estimation. In this paper, two-stage estimation with the network reduction process is proposed. The number of distribution network meter is low, so obtaining accurate initial information from network conditions improves performance state estimation. The initial state estimation with the network reduction process to obtain accurate initial data is performed. The initial data are used as a measure to improve the performance of secondary estimation. This method solves the problem of scarcity of accurate measurements and improves the accuracy of the state estimation in distribution network. The results of the proposed methodology are demonstrated on the 69 nodes of IEEE  standard distribution network.

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Human facial generation of example image is used as a requirement for biometric applications for the purpose of identifying individuals. In this paper, face generation consists of three main steps. In the first step, detection of significant lines and edges of the example image are carried out using nonlinear grayscale morphology. Then, hair areas are identified from the face of sample. The final step combines images from previous steps. Similarity and matching between synthesized face sketch and artistic sketch are compared with two methods of extracting features, Principle Component Analysis and Linear Discriminant Analysis, and time of the process is calculated. The experiments on the pair of CUHK database images show that the proposed method compared with state of the art methods such as: Eigen transformation, LLE, and MRF, has no computational complexity and creates a person's face with good quality and much less time. Matching of synthesized face sketch of the proposed method is achieved with a maximum value of 90% when Linear Discriminant Analysis is used to extract feature. The proposed method is also resistant to background effects and brightness of example images.

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In this paper, a fluidic biosensor with possibility to fabricate by Micro-Electro-Mechanical Systems (MEMS) technology is proposed for biomedical mass detection and lab-on-chip applications. This is designed by electromechanical coupling of harmonic micromechanical resonators with harmonic springers as a mechanical resonator array. It can disperse mechanical wave along the array by electrostatic method using interdigitate capacitors as actuators and sensors. It has some vital advantages like: low cost fabrication method, low fluidic interference damping effect, and high sensitivity with large absorbent area. In order to estimate the sensitivity of the proposed biosensor against the mass perturbation, the measurability of capacitance changes and fluidic interference damping effect, the stimulated analysis is conducted by COMSOL. It results, a suitable sensitivity and possibility to measure the biosensor outputs by available electronic instrumentations.

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  Common Gate (CG) topologies are commonly used as the first stage in Transimpedance Amplifiers (TIA), due to their low input resistance. But, this structure is not solely used as a TIA and comes with other topologies such as differential amplifiers or negative resistances and capacitances. This paper deals with analyzing the effect of adding an active feedback network to a common gate topology. Generally, the feedback network is used to reduce the input resistance of the CGs topology, but in this paper it is shown that an active feedback network, which occupies a small area, not only reduces further the input resistance of CG topologies, but also forms an active inductive behavior, which can be used to resonate with the large parasitic capacitance of the photodiode and hence obtain a wide bandwidth. Mathematical analysis is done in this paper to prove the existence of this active inductor, which is also proved in the simulations. Finally, it is shown that this stage alongside its active feedback can be used as a high-speed and low-power transimpedance amplifier for optical communication applications.

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In this paper, a low power and wideband Regulated Cascode (RGC)-based Transimpedance Amplifier (TIA) is presented to be used for the short range optical receiver systems. In this structure, input dominant parasitic capacitance is isolated by adding a cascoded inverter amplifier as a fully active feedback network in the booster of an RGC amplifier. As a result, a 6.4 GHz bandwidth is obtained at a lower power consumption. In addition, for eliminating the effect of output parasitic capacitance by resonating with an inductor and widening the bandwidth, an active inductive load is implemented at the output node of the proposed TIA circuit. Therefore, considering two main points of isolation of input parasitic capacitance effect and reduction of load parasitic capacitance effect, bandwidth is increased without using a high amount of power consumption. Based on the results simulated in HSPICE using 90 nm CMOS technology, the proposed TIA can reach the data bit rate of 10Gb/s. In addition, the proposed TIA consumes only 1.6mW of power, and has the gain of 40dBΩ across the 6.4 GHz of bandwidth.

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Nowadays, various kind of smart phones and 3D software are produced, which require large memory space. However, large number of vertices and faces in 3D models not only decrease the speed of sending and receiving of data but also can make problem in systems with low memory space. In this paper, an anisotropic re-meshing of 3D models is proposed. In this algorithm, the Nyquist theorem is employed for sampling from each selected segment of the mesh, locally. Then, the re-triangulation algorithm is applied to the selecte samples to construct the simplified mesh. In order to construct a high quality mesh from the remeshed model, a non linear subdivision is employed. The achieved results show that the algorithm can reduce the number of vertices and faces beside preserving details of model. The proposed method is also compared to the state-of-the-art algorithms are used in simplification studies, the outcomes illustrate the ability of the proposed method in producing high quality models.
 

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In this paper, a new adaptive model predictive control based on Laguerre functions is proposed for the load-frequency control problem of a multi-area power system, in which the estimation of the internal model of the power system is updated online using the recursive least squares method. The use of the adaptive reduced-order internal model in the structure of model predictive control is the innovation of this research. In the studied system, the controller of each area is designed independently so that the stability of the overall closed-loop system is guaranteed. Numerical simulations for a three-area power system are carried out to validate the effectiveness of the proposed scheme and the results were compared with those of conventional model predictive control (MPC) and proportional-integral-derivative control (PID). The simulation results show that the proposed scheme performs better than PID and MPC in rejecting step load disturbance (with respect to nominal and uncertain parameters) and nevertheless, thanks to the use of the reduced-order model and Laguerre functions, reduces the computational burden significantly compared to conventional MPC.         
 

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Nowadays, sleep deprivation is a pervasive problem that affects human physical and mental health. In this research, the effects of sleep deprivation on brain function and its diagnosis have been studied using electroencephalogram (EEG) signals recorded from 30 subjects after complete sleep and one day of sleep deprivation with open and closed eyes. Linear features like signal power and nonlinear features consisting of Shannon, Renyi, sample, and permutation entropies were extracted from signals. We used the PCA algorithm and Wilcoxon feature ranking method to extract the superior features and employed SVM, KNN, and a Decision tree to detect sleep-deprived cases. Brain maps of extracted features were plotted using the sLORETA algorithm to investigate the effects of sleep deprivation. Based on the results, the decision tree classifier with 100 superior selected features of Wilcoxon achieved the best performance with accuracy and precision of 99.0% and 99.8%, respectively. Also, comparing the results of linear and nonlinear features reveals the impressive role of the nonlinear features in the classification problem of this work. The maps of the features revealed noticeable changes in the level of attention, concentration, decision-making, and visual and movement activities.

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Insufficient training data is one of the main challenges of utilizing deep Convolutional Neural Networks (CNNs) for Environmental Sound Classification (ESC). As a promising solution, Transfer Learning (TL) has addressed this issue by adapting a network pre-trained on a large-scale dataset to the target task. In this paper, we demonstrate that not all neurons/kernels of every layer in CNN networks are equally utilized to process the inputs of different classes, but there is a specific subgroups of neurons/kernels in every layer that play the key role in classification of every output class. Based on this observation and due to similarities that exist between feature spaces of some source and target classes, we propose to concentrate the fine-tuning process only on those neurons/kernels that do need changes and have the greatest impact on misclassifying target data. To identify these neurons/kernels, we pose a nested optimization problem for which we propose an effective evolutionary approach as solution.  Compared to the conventional fine-tuning approach, our proposed method achieves absolute improvements of about 1.9% and 2.3% in accuracy on ESC-50 and DCASE-17, respectively; remarkable improvements produced not by adding augmented data but with a more efficient utilization of knowledge stored in the pre-trained network. It is noteworthy that the computation time overhead of the proposed evolutionary method is rather small (about one third of the time required to train the model from scratch.