Showing 21 results for Mosavi
M. R. Mosavi,
Volume 5, Issue 4 (December 2009)
Abstract
This paper presents design and implementation of three new Infrared Counter-Countermeasure (IRCCM) efficient methods using Neural Network (NN), Fuzzy System (FS), and Kalman Filter (KF). The proposed algorithms estimate tracking error or correction signal when jamming occurs. An experimental test setup is designed and implemented for performance evaluation of the proposed methods. The methods validity is verified with experiments on IR seeker reticle based on a Digital Signal Processing (DSP) processor. The practical results emphasize that the proposed algorithms are highly effective and can reduce the jamming effects. The experimental results obtained strongly support the potential of the method using FS to eliminate the IRCM effect 83%.
M. Rafei, M. R. Mosavi,
Volume 8, Issue 2 (June 2012)
Abstract
One of the most important features of the Active Inductors (AIs) is their input
equivalent resistance, namely series-loss resistance, which should be low enough to have a
high Quality Factor (QF). Most of the previous methods by this goal did not yield a high
enough QF. This paper presents a new method, namely applying an RC feedback, to cancel
series-loss resistance entirely. As the RC feedback cancels series-loss resistance, it
enhances the Self-Resonant Frequency (SRF) as well. The SRF of the AI has a range as
high as 0.25-12.5 GHz. Compared to the previous reports, the QF has been improved by
applying the RC feedback. The structure is such that the QF can be adjusted independent of
the SRF. For example, a very high quality factor of 13159 at the frequency of 6.6 GHz with
a 2.2 nH inductance is obtained, while noise voltage and power dissipation are less than 4.6
nV Hz and 4 mW, respectively. The AI is designed and simulated using 90 nm CMOS
process and 1.2 V power supply. To the best of authors’ knowledge, this is the first time an
RC feedback has been implemented to cancel series-loss resistance.
M. R. Mosavi, A. Akhyani,
Volume 9, Issue 2 (June 2013)
Abstract
In this paper, optimal placement of Phasor Measurement Unit (PMU) using Global Positioning System (GPS) is discussed. Ant Colony Optimization (ACO), Simulated Annealing (SA), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are used for this problem. Pheromone evaporation coefficient and the probability of moving from state x to state y by ant are introduced into the ACO. The modified algorithm overcomes the ACO in obtaining global optimal solution and convergence speed, when applied to optimizing the PMU placement problem. We also compare this simulink with SA, PSO and GA that to find capability of ACO in the search of optimal solution. The fitness function includes observability, redundancy and number of PMU. Logarithmic Least Square Method (LLSM) is used to calculate the weights of fitness function. The suggested optimization method is applied in 30-bus IEEE system and the simulation results show modified ACO find results better than PSO and SA, but same result with GA.
M. R. Mosavi, S. Azarshahi, I. Emamgholipour , A. A. Abedi,
Volume 10, Issue 1 (March 2014)
Abstract
In present study, using Least Squares (LS) method, we determine the position smoothing in GPS single-frequency receiver by means of pseudo-range and carrier phase measurements. The application of pseudo-range or carrier phase measurements in GPS receiver positioning separately can lead to defects. By means of pseudo-range data, we have position with less precision and more distortion. By use of carrier phase data, we do not have absolute position and just dislocation is available, but the accuracy is high. In present research, we have combined pseudo-range and carrier phase data using LS method in order to determine GPS receiver's position smoothing. The results of comparison by LS method show less RMS error, less calculation volume and more smoother in using carrier phase-pseudo-range data together relative to pseudo-range data in isolation.
A. Khoshsaadat , M. R. Mosavi, J. S. Moghani,
Volume 10, Issue 3 (September 2014)
Abstract
Static Synchronous Series Compensator (SSSC) is a series compensating Flexible AC Transmission System (FACTS) controller for maintaining to the power flow control on a transmission line by injecting a voltage in quadrature with the line current and in series mode with the line. In this work, an Adaptive Network-based Fuzzy Inference System controller (ANFISC) has been proposed for controlling of the SSSC-based damping system and applied to a Single Machine Infinite Bus (SMIB) power system. For implementation of the learning process in this controller, we use of the one approach of the learning ability that named as Forward Signal and Backward Error Back-Propagation (FSBEBP) method for improving of the system efficiency. This artificial intelligence-based control model leads to a controller with adaptive structure, improved correctness, high damping ability and dynamic performance. System implementation is easy and it requires 49 fuzzy rules for inference engine of the system. As compared with the other complex neuro-fuzzy systems, this controller has medium number of the fuzzy rules and low number of layers, but it has high accuracy. In order to demonstrate of the proposed controller ability, it is simulated and its output compared with that of classic Lead-Lag-based Controller (LLC) and PI controller.
F. Farabi, M. R. Mosavi, S. Karami,
Volume 11, Issue 2 (June 2015)
Abstract
Impressive development of computer networks has been required precise evaluation of efficiency of these networks for users and especially internet service providers. Considering the extent of these networks, there has been numerous factors affecting their performance and thoroughly investigation of these networks needs evaluation of the effective parameters by using suitable tools. There are several tools to measure network's performance which evaluate and analyze the parameters affecting the performance of the network. D-ITG traffic generator and measuring tool is one of the efficient tools in this field with significant advantages over other tools. One of D-ITG drawbacks is the need to determine input parameters by user in which the procedure of determining the input variables would have an important role on the results. So, introducing an automatic method to determine the input parameters considering the characteristics of the network to be tested would be a great improvement in the application of this tool. In this paper, an efficient method has been proposed to determine optimal input variables applying evolutionary algorithms. Then, automatic D-ITG tool operation would be studied. The results indicate that these algorithms effectively determine the optimal input variables which significantly improve the D-ITG application.
M. R. Mosavi, Z. Shokhmzan,
Volume 11, Issue 3 (September 2015)
Abstract
The Global Positioning System (GPS) signals are very weak signal over wireless channels, so they are vulnerable to in-band interferences. Therefore, even a low-power interference can easily spoof GPS receivers. Among the variety of GPS signal interference, spoofing is considered as the most dangerous intentional interference. The spoofing effects can mitigate with an appropriate strategy in the receiver. In this paper, we use methods of adaptive filter based on Least Mean Squares (LMS) and Normalized Least Mean Squares (NLMS) algorithms in-order to defense against spoofing. The proposed techniques are applied in the acquisition stage of the receiver. The proposed methods have been implemented on real dataset. The results explain that the suggested algorithms significantly decrease spoofing. Also, they improve Position Dilution of Precision (PDOP) parameter. Based on the results, NLMS algorithm has better performance than LMS algorithm.
M. Mousavi Moaiied, M. R. Mosavi,
Volume 12, Issue 1 (March 2016)
Abstract
In this paper, combined GPS and GLONASS positioning systems are discussed and some solutions have been proposed to improve the accuracy of navigation. Global Satellite Navigation System (GNSS) is able to provide position, velocity and time with respect to coordinated universal time. GNSS positioning is based on received satellite signals, so its performance is highly dependent on the quality of these received signals. The effect of noise and multi-path can often be large enough to produce significant errors in positioning. Satellite navigation is difficult in this situation. In such circumstances, GPS or GLONASS alone are often not able to ensure consistency and accuracy in positioning due to the absence (or low quality) of signals. The combination of these two systems is an appropriate solution to improve the situation. In positioning a receiver, one of the ways that is often used to reduce the error due to observation noise and calculation errors is Kalman Filter (KF) estimation. In this paper, some changes in the structure of the KF is applied to improve the accuracy of positioning. Process of updating KF's gain, is done in fuzzy form based on the parameters available in RINEX files, including the P code pseudo-range used as an input of the proposed fuzzy system. Simulation results show that applying a fuzzy KF based on P code pseudo-range on the available data sets, in terms of noise and blocking condition, reduces the positioning error respectively from 24 to 14 meters and 90 to 25 meters.
M. Pashaian, M. R. Mosavi, M. S. Moghaddasi, M. J. Rezaei,
Volume 12, Issue 1 (March 2016)
Abstract
This paper proposes a new method for rejecting the Continuous Wave Interferences (CWI) in the Global Positioning System (GPS) receivers. The proposed filter is made by cascading an adaptive Finite Impulse Response (FIR) filter and a Wavelet Packet Transform (WPT) based filter. Although adaptive FIR filters are easy to implement and have a linear phase, they create self-noise in the rejection of strong interferences. Moreover, the WPT which provides detailed signal decomposition can be used for the excision of single-tone and multi-tone CWI and also for de-noising the retrieved GPS signal. By cascading these two filters, the self-noise imposed by FIR filter and the remaining jamming effects on GPS signal can be eliminated by the WPT based filter. The performance analysis of the proposed cascade filter is presented in this paper and it is compared with the FIR and the WPT based filters. Experimental results illustrate that the proposed method offers a better performance under the interference environments of interest in terms of the signal-to-noise ratio gain and mean square error factors compared to previous methods.
M. Safari, M. Eghtesadi, M. R. Mosavi,
Volume 12, Issue 2 (June 2016)
Abstract
In this paper, a new design of concurrent dual-band Low Noise Amplifier (LNA) for multi-band single-channel Global Navigation Satellite System (GNSS) receivers is proposed. This new structure is able to operate concurrently at frequency of 1.2 and 1.57 GHz. Parallel and series resonance parts are employed in the input matching in order to achieve concurrent performance. With respect to used pseudo-differential structure, LNA is basically a single-ended-to-differential conversion and it consequently has no need to balun. In addition, an inductively degenerated cascode approach is employed to have better simultaneous matching and Noise Figure (NF). Simulations are performed with TSMC 0.18 μm technology in ADS software. Results analysis present that LNA achieves input matchings of -11.024 and -13.131 dB, NFs of 2.315 and 2.333 dB, gains of 26.926 and 27.576 dB, P-1dB of -15.3 and -13 dBm, IIP3 of -0.9 and 2.2 dBm at 1.2 and 1.57 GHz, respectively. Besides, LNA consumes 8.32 mA DC current from a 1.8 V supply voltage.
A. A. Abedi, M. R. Mosavi, K. Mohammadi, M. R. Daliri,
Volume 12, Issue 3 (September 2016)
Abstract
One of the instruments for determination of position used in several applications is the Global Positioning System (GPS). With a cheap GPS receiver, we can easily find the approximate position of an object. Accuracy estimation depends on some parameters such as dilution of precision, atmospheric error, receiver noise, and multipath. In this study, position accuracy with GPS receiver is classified in three classes. Nine classification methods are utilized and compared. Finally, a new method is selected for classification. Results are verified with experimental data. Success rate for classificationis approximately 84%.
M. R. Mosavi, M. Khishe, Y. Hatam Khani, M. Shabani,
Volume 13, Issue 1 (March 2017)
Abstract
Radial Basis Function Neural Networks (RBF NNs) are one of the most applicable NNs in the classification of real targets. Despite the use of recursive methods and gradient descent for training RBF NNs, classification improper accuracy, failing to local minimum and low-convergence speed are defects of this type of network. In order to overcome these defects, heuristic and meta-heuristic algorithms have been conventional to training RBF network in the recent years. This study uses Stochastic Fractal Search Algorithm (SFSA) for training RBF NNs. The particles in the new algorithm explore the search space more efficiently by using the diffusion property, which is observed regularly in arbitrary fractals. To assess the performance of the proposed classifier, this network will be evaluated with the two benchmark datasets and a high-dimensional practical dataset (i.e., sonar). Results indicate that new classifier classifies sonar dataset six percent better than the best algorithm and its convergence speed is better than the other algorithms. Also has better performance than classic benchmark algorithms about all datasets.
M. Moazedi, M. R. Mosavi, A. Sadr,
Volume 13, Issue 2 (June 2017)
Abstract
Global Positioning System (GPS) spoofing could pose a major threat for GPS navigation systems, so the GPS users have to gain a better understanding of the broader implications of GPS. In this paper, a plenary anti-spoofing approach based on correlation is proposed to distinguish spoofing effects. The suggested method can be easily implemented in tracking loop of GPS receiver. We will study a real-time spoof recognition with a clear certainty by introducing a reliable novel metric. As a primary step, the proposed technique is implemented in software receiver to prove the concept of idea in a multipath-free scenario. Three rooftop data sets, collected in our GPS laboratory, are used in the performance assessment of the proposed method. The results indicate that investigated algorithm is able to perform a real-time detection in all date sets.
M. R. Mosavi, A. Rashidinia,
Volume 13, Issue 3 (September 2017)
Abstract
Differential Global Positioning System (DGPS) provides differential corrections for a GPS receiver in order to improve the navigation solution accuracy. DGPS position signals are accurate, but very slow updates. Improving DGPS corrections prediction accuracy has received considerable attention in past decades. In this research work, the Neural Network (NN) based on the Gaussian Radial Basis Function (RBF) has been developed. In many previous works all parameter of RBF NN are optimizing by evolutionary algorithm such as Particle Swarm Optimization (PSO), but in our approach shape parameter and centers of RBF NN are calculated in better way, in addition, search space for PSO algorithm will be reduced which cause more accurate and faster approach. The obtained results show that RMS has been reduced about 0.13 meter. Moreover, results are tabulated in the tables which verify the accuracy and faster convergence nature of our approach in both on-line and off-line training methods.
N. Okati, M. R. Mosavi, H. Behroozi,
Volume 13, Issue 4 (December 2017)
Abstract
Node cooperation can protect wireless networks from eavesdropping by using the physical characteristics of wireless channels rather than cryptographic methods. Allocating the proper amount of power to cooperative nodes is a challenging task. In this paper, we use three cooperative nodes, one as relay to increase throughput at the destination and two friendly jammers to degrade eavesdropper’s link. For this scenario, the secrecy rate function is a non-linear non-convex problem. So, in this case, exact optimization methods can only achieve suboptimal solution. In this paper, we applied different meta-heuristic optimization techniques, like Genetic Algorithm (GA), Partial Swarm Optimization (PSO), Bee Algorithm (BA), Tabu Search (TS), Simulated Annealing (SA) and Teaching-Learning-Based Optimization (TLBO). They are compared with each other to obtain solution for power allocation in a wiretap wireless network. Although all these techniques find suboptimal solutions, but they appear superlative to exact optimization methods. Finally, we define a Figure of Merit (FOM) as a rule of thumb to determine the best meta-heuristic algorithm. This FOM considers quality of solution, number of required iterations to converge, and CPU time.
Z. Shokhmzan, M. R. Mosavi, M. Moazedi,
Volume 13, Issue 4 (December 2017)
Abstract
The vulnerability of civil GPS receiver to interference may be intentional or unintentional. Among all types of interference, replay attack intended as the most dangerous intentional one. The signal structure of replay attack is almost the same with the satellite signal. The interference effects can be reduce with the design of an appropriate filter in the receiver. This paper presents two methods based on Finite Impulse Response (FIR) filter in frequency and time domain to mitigate the interference effect on GPS signals. Designed FIR filter protects GPS against the replay attack. The suggested filter is applied in the acquisition of the receiver. The proposed method has been implemented on collected dataset. The results show that the proposed algorithms significantly reduce interference. Also, they improve Position Dilution of Precision (PDOP) parameter. Based on the results, the FIR filter technique in time domain has better performance than the frequency domain.
P. Teymouri, M. R. Mosavi, M. Moazedi,
Volume 14, Issue 3 (September 2018)
Abstract
Due to widespread use of Global Positioning System (GPS) in different applications, the issue of GPS signal interference cancelation is becoming an increasing concern. One of the most important intentional interferences is spoofing signals. An effective interference (delay spoof) reduction method based on adaptive filtering is developed in this paper. The principle of method is using adaptive filters to eliminate interference, obtain an estimate of interfering signal and subtract that from the corrupted signal. So, what remains in the output is the desired signal. Here, for updating the filter coefficients adaptive algorithms in both time (statistical and deterministic) and transform domain will be studied. The proposed adaptive filter is applied to a batch of spoofing GPS data in pseudo-range level. The results indicate that all investigated algorithms are able to reduce positioning steady-state miss-adjustment up to 70 percent. In this context, the variable step-size least mean square algorithm performs better than others do.
A. Nobahari, M. R. Mosavi, A. Vahedi,
Volume 16, Issue 1 (March 2020)
Abstract
A methodology is proposed for optimal shaping of permanent magnets with non-conventional and complex geometries, used in synchronous motors. The algorithm includes artificial neural network-based surrogate model and multi-objective search based optimization method that will lead to Pareto front solutions. An interior permanent magnet topology with crescent-shaped magnets is also introduced as the case study, on which the proposed optimal shaping methodology is applied. Produced torque per magnets mass and percentage torque ripple are considered as the objectives, in order to take both performance and cost into account. Multi-layer perceptron architecture used to create the approximated model is trained to fit the samples collected via time-stepping finite element simulations. The methodology can be easily generalized to offer a fast and accurate method to optimally define arbitrary permanent magnet shape parameters in various synchronous motors.
P. Ramezanpour, M. Aghababaie, M. R. Mosavi, D. M. de Andrés,
Volume 18, Issue 2 (June 2022)
Abstract
Through beamforming, the desired signal is estimated by calculating the weighted sum of the input signals of an array of antenna elements. In the classical beamforming methods, computing the optimal weight vector requires prior knowledge on the direction of arrival (DoA) of the desired signal sources. However, in practice, the DoA of the signal of interest is unknown. In this paper, we introduce two different deep-neural-network-based beamformers which can estimate the signal of interest while suppressing noise and interferences in two/three stages when the DoAs are unknown. Employing deep neural networks (DNNs) such as convolutional neural networks (CNNs) and bidirectional long short-term memory (bi-LSTM) networks enables the proposed method to have better performance than existing methods. In most cases, the output signal to interference and noise ratio (SINR) of the proposed beamformer is more than 10dB higher than the output SINR of the classical beamformers.
K. Zarrinnegar, S. Tohidi, M. R. Mosavi, A. Sadr, D. M. de Andrés,
Volume 19, Issue 1 (March 2023)
Abstract
The Global Positioning System (GPS) is vulnerable to various deliberate and unintentional interferences. Therefore, identifying and coping with various interferences in this system is essential. This paper analyzes a method of reducing the dimensions of Cross Ambiguity Function (CAF) images in improving the identification of spoofing interference at the GPS using Multi-Layer Perceptron Neural Network (MLP NN) and Convolutional Neural Network (CNN). Using the proposed method reduces data complexity, which can reduce the number of learning data requirements. The simulation results indicate that, by applying the proposed image processing algorithm for different dimensions of CAF images, the CNN performs better than MLP NN in terms of training accuracy; the MLP NN is superior to CNN in terms of convergence speed of training. In addition, the results demonstrate that the operation of the proposed method is appropriate in the case of small-delay spoofed signals. Therefore, for the intervals above 0.25 code chip, the proposed method detects spoofing attacks with a correct detection probability close to one.
