In this paper, the problem of distributed state estimation of a nonlinear dynamical system in a decentralized Wireless Sensor Network (WSN) in the presence of state-dependent observation Noise is considered. Some bearings or ranging devices, such as ultrasonic Sensors, have distance-dependent measurement error and their measurement Noise variance grows as their relative distance to the target increases. This state-dependent measurement error leads to poor performance of estimation algorithm. To solve this problem, a consensus-based distributed state estimation methodology is presented in this paper by reaching a consensus on likelihood functions in the presence of state-dependent observation Noise of bearings Sensors. To reduce energy consumption in WSN, a distributed Sensor selection algorithm is proposed. Unlike centralized networks, no fusion center is deployed in decentralized networks to gather and process the collected data, globally. Moreover, there is no global knowledge of the network topology in decentralized networks. Therefore, the Posterior Cramér-Rao Lower Bound (PCRLB) is derived in a distributed fashion in the presence of state-dependent Noise of bearings Sensors, to perform an adaptive Sensor selection algorithm. Simulation results demonstrate the effectiveness of the proposed state estimation and Sensor selection algorithms for a target tracking problem.

In this paper, we consider the problem of Sensor placement for passive source localization under conditions where there are multiple sources and Noise measurement is distance dependent. To this end, first we derive the Cramer-Rao bound for the angle of arrival (AOA) based source localization for distance-dependent Noise model and existence of multiple sources. Then, the optimal arrangement of Sensors to achieve this bound is obtained.

Introduction: Cochlear dead zones are defined as areas where the inner hair cells have been destroyed.Thresholds on the audiograms show the integrity of those parts of the ear that are tested. Care must be taken in interpretating audiograms. Thanks to the advances in understanding of cochlear functions, it is now possible to spot false responses that come from dead zones of the cochlea. Recently, cochlear dead regions have been detected via TEN (Threshold Equalizing Noise) test in which ipsilateral broadband Noise and threshold shifting are used.Materials and Methods: A review of the literature on the subject of dead zones published from 1993 to 2003 was performed using Pubmed, Ebsco, Science Direct, Google Scholar Thieme ProQuest databases and library sources. key word: were "cochlear dead zone", "traveling wave", "ten (threshold equalizing Noise) test", "ipsilateral Noise" and "real-ear measurement for hearing aids prescription".Conclusion: Hearing aids fitting process for patients with severe and sloping Sensory neural hearing loss must be noted specially by amplifying active zone and avoiding amplification for dead region i.e., offering amplification to the transition frequencies that have better hearing than others, those among the fine regions and the dead zones. Dead zone detection may help in hearing aids fitting and fine tuning.

In this paper, a new method for extending and relaxing the Noise-coupling (NC) technique is proposed to enhance the Noise-shaping order without adding the number of integrators. The Noise-shaping order of the introduced ∑∆ modulator whit applying a second-order Noise-coupling technique is enhanced and its performance with optimizing the Noise transfer function (NTF) zeros is improved. Also, by removing the analog adder at feedforward path and transferring it to a new feedback branch before the last integrator and adding second-order NC path can be decreased the input voltage swing to the quantizer. Thus, by improving the modulator resolution, power consumption can be reduced. Mathematical analyses and behavioural simulation results confirm the effectiveness of the new NC method. To examine its performance, a 2nd-order single loop ΣΔ modulator was designed. The new Noise-coupling method is used to achieve the three-order Noise shaping to increase the resolution with low complexity and low-power. The results show an outstanding improvement in signal-to-Noise and distortion ratio (SNDR) compared to the conventional structure.

Improving the performance of satellite attitude control with reaction wheel actuator and considering the Sensor Noise in this article, robust optimization method has been used to reduce the effect of Sensor Noise on the performance of the satellite attitude control with reaction wheel actuator. In this regard, the mean absolute pointing error has been chosen as the main performance index. The optimization based on the genetic algorithm and the Monte Carlo method of successive iterations have been used to include the effect of Noise and obtain the control coefficients. First-order dynamics have been used to model the reaction wheel, taking into account the practical limit of the maximum production torque. Also, a modified proportional-integral-derivative (PI-D) controller with a observer method has been used to control the satellite. Gaussian white Noise is added to angular velocity and angular feedback signals after passing through a first order filter. To compare the results, the control coefficients for the same simulation conditions have been obtained for two approaches: robust optimization and deterministic optimization. The performance in terms of the Noise power spectral density function has been investigated for two approaches. The comparative results show that the control system adjusted by the robust optimization method, its performance criterion is more resistant in the face of Noise and has less changes, while the performance criterion of the deterministic optimization method has more changes in Exposure to Noise. The results indicate the preference of using the robust optimization approach for the control system exposed to Noise.

In this paper, a robust and reliable optimization approach has been used to reduce the effect of Sensor Noise on the performance of the spacecraft attitude control system with reaction wheel actuators. The absolute mean value of the pointing error on the satellite attitude has been chosen as the main control performance criterion. The optimizer algorithm is based on the genetic method and based on Monte Carlo successive iterations to include the effect of Noise and obtain control coefficients. The first-order dynamics was used to model the reaction wheel as the main control operator, taking into account the practical limits of the maximum production torque, and a proportional-integral-derivative (PI-D) controller modified by the method, the observer is used to control the satellite. Gaussian white Noise signal after passing through a first-order filter is added to the angle and angular velocity feedback path signals and applied to the control circuit. In order to compare the results fairly, the control coefficients for the same simulation conditions were obtained for the two approaches of deterministic optimization and robust and reliable optimization, and the performance criteria in terms of the Noise power spectral density function were investigated for the two optimization approaches. The comparative results show that the control system adjusted by robust and reliability optimization method, its performance criterion is more robust in the face of Noise and its performance reliability number is higher than the results of deterministic optimization. The results indicate the preference of using a robust and reliable optimization approach

Identifying the source camera of an image is one of the most important issues of digital court and is useful in many applications, such as images that are presented in court as evidence. In many methods, the image Noise characteristics, extraction of Sensor Pattern Noise and its correlation with non-uniformity of the light response (PNU) are used. In this paper we have presented a method based on photo response non uniformity (PRNU) that provides some features for classification by support vector machine (SVM). Because the Noise model is affected by the complexity of the image, we used the wavelet transform to de-Noise and reduce edge effects in PRNU Noise pattern and also raise the detection accuracy. We also used the Precision processing theory to reduce the image size, then we simplified and summarized the data using the Single Value Decomposition (SVD) Or principal component analysis (PCA). The results show that using two-level wavelet transform and summarized data is more suitable using PCA.

Automatic, efficient, accurate, and stable image matching is one of the most critical issues in remote sensing, photogrammetry, and machine vision. In recent decades, various algorithms have been proposed based on the feature-based framework, which concentrate on detecting and describing local features. Understanding the characteristics of different matching algorithms in various applications increases the potential of successful matching in a given application. Numerous studies have evaluated and analyzed many of these algorithms in various applications. However, performance evaluation of image matching methods in multi-Sensor images, especially optical-radar and noisy images, is really limited. This research will evaluate the performance of the state-of-theart-detectors, including SURF, KAZE, SIFT, PC, FAST, and Harris detectors for multi-Sensor image matching. Moreover, we integrated the employed detectors with the uniform competency algorithm to identify the most reliable features with uniform distribution. Next, we employed a scaleinvariant version of the HOSS descriptor to describe the extracted features. The results show the superiority of the KAZE detector in the presence of Noise and various geometric and radiometric distortions.