M. Pourmahyabadi, Sh. Mohammad Nejad,
Volume 5, Issue 3 (9-2009)
Abstract
In this article, perfectly matched layer (PML) for the boundary treatment and an efficient compact two dimensional finite-difference frequency-domain (2-D FDFD) method were combined to model photonic crystal fibers (PCF). For photonic crystal fibers, if we assume that the propagation constant along the propagation direction is fixed, three-dimensional hybrid guided modes can be calculated by using only a two-dimensional mesh. An index-guiding PCF with an array of air-holes surrounding the silica core region has special characteristics compared with conventional single-mode fibers (SMFs). Using this model, the fundamental characteristics of single mode photonic crystal fibers (SMPCFs) such as confinement loss, bending loss, effective mode area and chromatic dispersion are numerically investigated. The results revealed that low confinement loss and zero-flattened chromatic dispersion can be obtained by varying the air-holes diameter of each ring along the PCF radius. In this work, an especial PCF with nearly zero-flattened dispersion (1.3 ps/nm/km) over a wide wavelength range which covers O, E, S, C, L and U telecommunication wavelength bands and low confinement loss (0.06 dB/km at 1.55μm) is designed. Macro-bending loss performance of the designed PCF is also studied and it is found that the fiber shows low bending losses for the smallest feasible bending radius of 5 mm. Also, it is revealed that the temperature sensitivity of PCFs is very low in compared with the conventional fibers.
M. Masoumi,
Volume 8, Issue 1 (3-2012)
Abstract
Differential Power Analysis (DPA) implies measuring the supply current of a cipher-circuit in an attempt to uncover part of a cipher key. Cryptographic security gets compromised if the current waveforms obtained correlate with those from a hypothetical power model of the circuit. As FPGAs are becoming integral parts of embedded systems and increasingly popular for cryptographic applications and rapid prototyping, it is imperative to consider security on FPGAs as a whole. During last years, there has been a large amount of work done dealing with the algorithmic and architectural aspects of cryptographic schemes implemented on FPGAs, however, there are only a few articles that assess their vulnerability to such attacks which, in practice, pose far a greater danger than algorithmic attacks. This paper first demonstrates the vulnerability of the Advanced Encryption Standard Algorithm (AES) implemented on a FPGA and then presents a novel approach for implementation of the AES algorithm which provides a significantly improved strength against differential power analysis with a minimal additional hardware overhead. The efficiency of the proposed technique was verified by practical results obtained from real implementation on a Xilinx Spartan-II FPGA.
M. Shams Esfand Abadi, M.s. Shafiee,
Volume 9, Issue 1 (3-2013)
Abstract
This paper presents a new variable step-size normalized subband adaptive filter (VSS-NSAF) algorithm. The proposed algorithm uses the prior knowledge of the system impulse
response statistics and the optimal step-size vector is obtained by minimizing the mean-square deviation(MSD). In comparison with NSAF, the VSS-NSAF algorithm has faster convergence speed and lower MSD. To reduce the computational complexity of VSSNSAF, the VSS selective partial update NSAF (VSS-SPU-NSAF) is proposed where the filter coefficients are partially updated in each subband at every iteration. We demonstrated the good performance of the proposed algorithms in convergence speed and steady-state MSD for a system identification set-up.
M. H. Savoji, S. Chehrehsa,
Volume 10, Issue 3 (9-2014)
Abstract
Gaussian Mixture Models (GMMs) of power spectral densities of speech and noise are used with explicit Bayesian estimations in Wiener filtering of noisy speech. No assumption is made on the nature or stationarity of the noise. No voice activity detection (VAD) or any other means is employed to estimate the input SNR. The GMM mean vectors are used to form sets of over-determined system of equations whose solutions lead to the first estimates of speech and noise power spectra. The noise source is also identified and the input SNR estimated in this first step. These first estimates are then refined using approximate but explicit MMSE and MAP estimation formulations. The refined estimates are then used in a Wiener filter to reduce noise and enhance the noisy speech. The proposed schemes show good results. Nevertheless, it is shown that the MAP explicit solution, introduced here for the first time, reduces the computation time to less than one third with a slight higher improvement in SNR and PESQ score and also less distortion in comparison to the MMSE solution.
S. K. Agrawal, O. P. Sahu,
Volume 10, Issue 4 (12-2014)
Abstract
In this paper, a novel technique for the design of two-channel Quadrature Mirror Filter (QMF) banks with linear phase in frequency domain is presented. To satisfy the exact reconstruction condition of the filter bank, low-pass prototype filter response in pass-band, transition band and stop band is optimized using unconstrained indirect update optimization method. The objective function is formulated as a weighted sum of pass-band error and stop-band residual energy of low-pass prototype filter, and the square error of the distortion transfer function of the QMF bank at the quadrature frequency. The performance of the proposed algorithm is evaluated in terms of Peak Reconstruction Error (PRE), mean square error in pass-band and stop-band regions and stop-band edge attenuation. Design examples are included to illustrate the performance of the proposed algorithm and the quality of the filter banks that can be designed.
Sh. Shirvani Moghaddam, Z. Ebadi, V. Tabataba Vakili,
Volume 11, Issue 1 (3-2015)
Abstract
In this paper, a new combination of Minimum Description Length (MDL) or Eigenvalue Gradient Method (EGM), Joint Approximate Diagonalization of Eigenmatrices (JADE) and Modified Forward-Backward Linear Prediction (MFBLP) algorithms is proposed which determines the number of non-coherent source groups and estimates the Direction Of Arrivals (DOAs) of coherent signals in each group. First, the MDL/EGM algorithm determines the number of non-coherent signal groups, and then the JADE algorithm estimates the generalized steering vectors considering white/colored Gaussian noise. Finally, the MFBLP algorithm is applied to estimate DOAs of coherent signals in each group. A new Normalized Root Mean Square Error (NRMSE) is also proposed that introduces a more realistic metric to compare the performance of DOA estimation methods. Simulation results show that the proposed algorithm can resolve sources regardless of QAM modulation size. In addition, simulations in white/colored Gaussian noises show that the proposed algorithm outperforms the JADE-MUSIC algorithm in a wide range of Signal to Noise Ratios (SNRs).
M. Pashaian, M. R. Mosavi, M. S. Moghaddasi, M. J. Rezaei,
Volume 12, Issue 1 (3-2016)
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. R. Mosavi, M. Khishe, Y. Hatam Khani, M. Shabani,
Volume 13, Issue 1 (3-2017)
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 (6-2017)
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. Shams Esfand Abadi, H. Mesgarani, S. M. Khademiyan,
Volume 13, Issue 3 (9-2017)
Abstract
The wavelet transform-domain least-mean square (WTDLMS) algorithm uses the self-orthogonalizing technique to improve the convergence performance of LMS. In WTDLMS algorithm, the trade-off between the steady-state error and the convergence rate is obtained by the fixed step-size. In this paper, the WTDLMS adaptive algorithm with variable step-size (VSS) is established. The step-size in each subfilter changes according to the largest decrease in mean square deviation. The simulation results show that the proposed VSS-WTDLMS has faster convergence rate and lower misadjustment than ordinary WTDLMS.
G. Alipoor,
Volume 13, Issue 4 (12-2017)
Abstract
Performance of the linear models, widely used within the framework of adaptive line enhancement (ALE), deteriorates dramatically in the presence of non-Gaussian noises. On the other hand, adaptive implementation of nonlinear models, e.g. the Volterra filters, suffers from the severe problems of large number of parameters and slow convergence. Nonetheless, kernel methods are emerging solutions that can tackle these problems by nonlinearly mapping the original input space to the reproducing kernel Hilbert spaces. The aim of the current paper is to exploit kernel adaptive filters within the ALE structure for speech signal enhancement. Performance of these nonlinear algorithms is compared with that of their linear as well as nonlinear Volterra counterparts, in the presence of various types of noises. Simulation results show that the kernel LMS algorithm, as compared to its counterparts, leads to a higher improvement in the quality of the enhanced speech. This improvement is more significant for non-Gaussian noises.
M. Evazi, M. Shahsavan, M. Heidari, A. Razminia,
Volume 14, Issue 4 (12-2018)
Abstract
This paper addresses a new method for decreasing error in secure chaotic communication which utilizes an adaptive law in demodulator part. The basic tools in this process are the Total Least Square as the fundamental technique in demodulating section and a chaotic signal as the carrier one which impose some complexities on the overall system. This algorithm may be used in digital filter for estimating parameters with lower error. Using this approach an improvement can be achieved in estimating the desired signal in comparison with two famous methods, namely, ordinary Least Mean Square (LMS) and Constrained-Stability LMS (CS-LMS). An illustrative example has been used to verify the presented technique through numerical simulation.
S. Mavaddati,
Volume 15, Issue 2 (6-2019)
Abstract
A new single channel singing voice separation algorithm is presented in this paper. This field of signal processing provides important capability in various areas dealing with singer identification, voice recognition, data retrieval. This separation procedure is done using a decomposition model based on the spectrogram of singing voice signals. The novelty of the proposed separation algorithm is related to different issues listed in the following: 1) The decomposition scheme employs the vocal and music models learned using sparse non-negative matrix factorization algorithm. The vocal signal and music accompaniment can be considered as sparse and low-rank components of a singing voice segment, respectively. 2) An alternating factorization algorithm is used to decompose input data based on the modeled structures of the vocal and musical components. 3) A voice activity detection algorithm is introduced based on the energy of coding coefficients matrix in the training step to learn the basis vectors that are related to instrumental parts. 4) In the separation phase, these non-vocal atoms are updated to the new test conditions using the domain transfer approach to result in a proper separation procedure with low reconstruction error. The performance evaluation of the proposed algorithm is done using different measures and leads to significantly better results in comparison with the earlier methods in this context and the traditional procedures. The average improvement values of the proposed separation algorithm for PESQ, fwSegSNR, SDI, and GNSDR measures in comparison with previous separation methods in two defined test scenario and three mentioned SMR levels are 0.53, 0.84, 0.39, and 2.19, respectively.
S. Mavaddati,
Volume 15, Issue 3 (9-2019)
Abstract
Blind voice separation refers to retrieve a set of independent sources combined by an unknown destructive system. The proposed separation procedure is based on processing of the observed sources without having any information about the combinational model or statistics of the source signals. Also, the number of combined sources is usually predefined and it is difficult to estimate based on the combined sources. In this paper, a new algorithm is introduced to resolve these issues using empirical mode decomposition technique as a pre-processing step. The proposed method can determine precisely the number of mixed voice signals based on the energy and kurtosis criteria of the captured intrinsic mode functions. Also, the separation procedure employs a grey wolf optimization algorithm with a new cost function in the optimization procedure. The experimental results show that the proposed separation algorithm performs prominently better than the earlier methods in this context. Moreover, the simulation results in the presence of white noise emphasize the proper performance of the presented method and the prominent role of the presented cost function especially when the number of sources is high.
C. S. Vinitha, R. K. Sharma,
Volume 15, Issue 4 (12-2019)
Abstract
An efficient Lookup Table (LUT) design for memory-based multiplier is proposed. This multiplier can be preferred in DSP computation where one of the inputs, which is filter coefficient to the multiplier, is fixed. In this design, all possible product terms of input multiplicand with the fixed coefficient are stored directly in memory. In contrast to an earlier proposition Odd Multiple Storage (OMS), we have proposed utilizing Even Multiple Storage (EMS) scheme for memory-based multiplication and by doing so we are able to achieve a less complex and high-speed design. Because of the very simpler control circuit used in our design, to extract the odd multiples of the product term, we are also able to achieve a significant reduction in path delay and area complexity. For validation, the proposed design of the multiplier is coded in VHDL, simulated and synthesized using Xilinx tool and then implemented in Virtex 7 XC7vx330tffg1157 FPGA. Various key performance metrics like number of slices, number of slice LUT’s and maximum combinational path delay is estimated for different input word length. Also, the performance metrics are compared with the existing OMS design. It is found that the proposed EMS design occupies nearly 62% less area in terms of number of slices as compared to the OMS design and the maximum path delay is decreased by 77% for a 64-bit input. Further, the proposed multipliers are used in Transposed FIR filter and its performance is compared with the OMS multiplier based filter for various filter orders and various input lengths.
M. H. Refan, A. Dameshghi,
Volume 16, Issue 2 (6-2020)
Abstract
Geometric Dilution of Precision (GDOP) is a coefficient for constellations of Global Positioning System (GPS) satellites. These satellites are organized geometrically. Traditionally, GPS GDOP computation is based on the inversion matrix with complicated measurement equations. A new strategy for calculation of GPS GDOP is construction of time series problem; it employs machine learning and artificial intelligence methods for problem-solving. In this paper, the Time Delay Neural Network (TDNN) is introduced to the GPS satellite DOP classification. The TDNN has a memory for archiving past event that is critical in GDOP approximation. The TDNN approach is evaluated all subsets of satellites with the less computational burden. Therefore, the use of the inverse matrix method is not required. The proposed approach is conducted for approximation or classification of the GDOP. The experiments show that the approximate total RMS error of TDNN is less than 0.00022 and total performance of satellite classification is 99.48%.
P. Ramezanpour, M. Aghababaie, M. R. Mosavi, D. M. de Andrés,
Volume 18, Issue 2 (6-2022)
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.
S. Tidjani, Z. Hammoudi,
Volume 19, Issue 2 (6-2023)
Abstract
This paper describes a spectrum observatory (SO) performed outdoor in two locations in Algeria and highlights the importance of the SO in the improvement
of spectrum management in cognitive radio networks. These measurements were achieved in conjunction with the ANF (Agence Nationale des Fréquences), between January and February 2020. It surveys second, third, and fourth-generation mobile networks and DVB-T frequency bands. A comparative study of two measurement campaigns (in urban & rural) that were carried out via identical setup and equipment is presented. Some major short-duration measurement campaigns are cited and summarized for the state-of-the-art. Additionally, Different statistics are imputed and 3D graphics of the spectrum occupancy are plotted to highlight the spectrum opportunities in this region. This work aims to analyze the radio environment in Algeria and identify frequency bands that could be invested for the integration of new wireless systems and Cognitive Radio opportunistic networks. The evaluation of measurement results reveals low resource occupation, lower than 30.27%, for Constantine and 8.43% for Ouargla. The final part of the study inspects the effect of specific SO features upon the management strategy parameters’ selection. Via a meaningful SO, an efficient spectrum management strategy can achieve the safest users access to the idlest channels with the minimum costs and risks.
M. J. Jahantab, S. Tohidi, Mohammad Reza Mosavi, Ahmad Ayatollahi,
Volume 20, Issue 4 (11-2024)
Abstract
Global Positioning System (GPS)-based positioning has become an indispensable part of our daily lives. A GPS receiver calculates its distance from a satellite by measuring the signal reception delay. Then, after determining its position relative to at least four satellites, the receiver obtains its precise location in three dimensions. There is a fundamental flaw in this positioning system, namely that satellite signals at ground level are very weak and susceptible to interference in the bandwidth; therefore, even a slight interference can disrupt the GPS receiver. In this paper, spoofing detection based on the Cross Ambiguity Function (CAF) is used. Furthermore, a dimension reduction algorithm is proposed to improve the speed and performance of the detection process. The reduced-dimensional images are trained by a Convolutional Neural Network (CNN). Additionally, a modified CNN model as Transformed-CNN (TCNN) is presented to enhance accuracy in this paper. The simulation results show a 98.67% improvement in network training speed compared to images with original dimensions, a 1.16% improvement in detection accuracy compared to the baseline model with reduced dimensions, and a 9.83% improvement compared to the original dimensions in detecting spoofing, demonstrating the effectiveness of the proposed algorithm and model.
Nerjes Rahemi, Kurosh Zarrinnegar, Mohammad Reza Mosavi,
Volume 21, Issue 3 (8-2025)
Abstract
In determining position using GPS, due to local effects, pseudo-range errors cannot be mitigated by methods such as the use of reference stations or mathematical models; however, by using precise carrier phase observations and deploying a statistically optimal filter such as Phase-Adjusted Pseudo-range (PAPR) algorithm, the error can be significantly reduced. Additionally, the correlation between observations is a factor affecting positioning accuracy. In this paper, by using both pseudo-range and carrier phase observations and taking into account the effect of spatial correlation between observations to determine the variance-covariance matrix, the accuracy of position determination using the recursive Least Squares method is increased. For this purpose, the PAPR algorithm was implemented to reduce error. Next, a non-diagonal variance-covariance matrix was introduced to estimate the variance of the observations based on their spatial correlations. Experimental results on real data show that the proposed method improves positioning accuracy by at least 10% compared to previous methods. To evaluate the complexity of the proposed models, we employed an ARM STM32H743 processor. The findings indicate a modest increase in the proposed model complexity compared to earlier models, along with a substantial improvement in positioning accuracy.
