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Wireless Sensors and wireless sensor networks have been used broadly in academic communities. That is owing to increasingly development of small scale equipment in engineering that prepares enormous applications. Numerous researches have been done in order to using of these sensors as well as establishing them as a network and large variety of solutions have been suggested. One heuristic method to modeling wireless sensor networks is distributed adaptive modeling. Processing is done in fully distributed manner at this way. An adaptive network contains a number of nodes those are capable to learn and adapt. Every node exchanges its data with neighbor nodes so as to the network could finally solve an estimation or interference problem. In this paper, at first, the position of distributed adaptive processing will be explained, then we review some proposed strategies for adaptive modeling and after that simulation results will be compared.

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The particle swarm optimizer (PSO) is a population-based metaheuristic optimization method that can be applied to a wide range of problems but it has the drawbacks like it easily falls into local optima and suffers from slow convergence in the later stages. In order to solve these problems, improved PSO (IPSO) variants, have been proposed. To bring about a balance between the exploration and exploitation characteristics of PSO, this paper introduces computationally fast and efficient IPSO algorithms based on a novel class of exponential learning factors (ELF-PSO). This class contains time-varying exponential learning factors (TELF), random exponential learning factors (RELF), self-adjusting exponential learning factors (SELF) and linear-exponential learning factors (LELF) strategies. Experiment is performed and compared with a set of well-known constant, random, time-varying and adaptive learning factors strategies on a suite of nonlinear benchmark functions. The experimental results and statistical analysis prove that ELF-PSO algorithms are able to solve a wide range of difficult nonlinear optimization problems efficiently. Also these results show that the proposed methods outperform other algorithms in most cases.

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In this paper, a flexible Continuous time (CT) feedforward (FF) quadrature delta sigma modulator (QDSM) with variable bandwidth (BW) is proposed. The modulator BW and center frequency (fc) are variable which can be adjusted with two separate parameters. The modulator signal-to-noise ratio (SNR) will be optimized by these parameters at every center frequency and bandwidth. The modulator BW is changed by varies in the complex coefficients, and the structure of the modulator is no changed.
 The proposed modulator is usable in multi standard receivers as well as in receivers with variable frequency input signal or frequency hopping. The center frequency of this modulator is from 0 to 1/4 fs and its bandwidth can be changed from zero to 1/12 fs.An example of quadrature modulator is designed with the proposed design method at the circuit level. The modulator is simulated for 2MHz and 5MHz bandwidths with a sampling frequency of 64MHz. The SNR for these bandwidths are 76.42dB and 56.59dB, respectively. The FOM has been calculated to be 0.375 and 1.46 (pj / conv), respectively.

 

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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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In this paper, a real-time interactive high resolution soft tissue modeling is implemented that enriches a coarse model in a data-driven approach to produce a fine model. As a preprocess step, a set of corresponding coarse and fine models are simulated for the database. In the test step, by using a regressor, the coarse model in the test set is compared to the coarse models in the training set and the blending weights are assigned to the training coarse models. These weights are used for approximating the fine model as a linear combination of the corresponding fine models in the train set. To decrease the computational complexity, assuming that applying a force on the tissue results in a local deformation, a feature extraction algorithm is proposed that considers the displacements of the contact node and its neighbor nodes and ignores the rest. This results in a low dimensional feature vector and decreases the computational complexity. In order to compute the blending weights, a nonlinear regressor with Gaussian kernel is leveraged. To eliminate the artefacts resulting from negative weights, a nonnegative least square algorithm is used for regression. Simulation results of applying the proposed method on two soft tissue models are investigated regarding the reconstruction accuracy, computational complexity and running time.