جستجوی مقالات مرتبط با کلیدواژه « حسگری فشرده » در نشریات گروه « برق »

To overcome the problem of channel estimation in massive multiple-input multiple-output (M-MIMO) systems, in this paper we propose a downlink link channel estimation scheme in frequency-division duplex (FDD) based on structured compressive sensing to reduce the pilot required by which Intrinsic spatial sparsity of M-MIMO delay channels are amplified. For this purpose, first, after discussing the different methods of channel estimation and examining the existing challenges, we define our roadmap and propose our algorithm, in which we estimate the channel based on the greedy orthogonal matching pursuit (OMP) algorithm. In this algorithm, spatial correlation between the channel impulse response of different transmitter antennas is used for accurate channel estimation. This algorithm obtains the channel sparsity in an adaptive way, which negates the ideal assumption of the previous works that the channel sparsity is in hand. In this case, this algorithm estimates the channel with good accuracy in cases when the exact amount of channel sparsity is not known. Finally, we present simulations that demonstrate the ability of this method to reduce the required pilot. The simulations show that the proposed channel estimation reliably obtains the channel sparsity level and the support set compared to similar methods.

Special conditions of wireless sensor networks, such as energy limitation, make it essential to accelerate the convergence of algorithms in this field, especially in the distributed compressive sensing (DCS) scenarios, which have a complex reconstruction phase. This paper presents a DCS reconstruction algorithm that provides a higher convergence rate. The proposed algorithm is a distributed primal-dual algorithm in a bidirectional incremental cooperation mode where the parameters change with time. The parameters are changed systematically in the convex optimization problems in which the constraint and cooperation functions are strongly convex. The proposed method is supported by simulations, which show the higher performance of the proposed algorithm in terms of convergence rate, even in stricter conditions such as the small number of measurements or the lower degree of sparsity.

Nowadays, wireless sensor networks (WSNs) have found many applications in a variety of topics. The main purpose of these networks is to measure environmental phenomena and to send read data in multi-hop paths to the sink to be exploited by users. The most important challenge in WSNs is to minimize energy consumption in sensor batteries and increase network lifetime. One of the most important techniques for reducing energy consumption in WSNs is the compressive sensing (CS) technique. CS reduces network energy consumption by reducing data transmission in the network and increasing the network lifetime. The use of CS technique in a WSN results in the production of different models of CS signals. These models are based on spatial, temporal and spatio-temporal sensors readings. On the other hand, in order to overcome the challenge of energy consumption, the exact recognition of energy resources in the network is essential.  Energy consumption in a sensor node can be divided into two parts: (a) the energy used for computing; and (b) the energy consumed by the communication. The energy used for the computing consists of three components: 1. sensor energy consumption (data reading), 2. background energy consumption, and 3. energy consumption for processing. The power consumption of the communication includes the following: 1. energy consumption for data transmission; 2. energy consumption for data receiving; 3. energy consumption for sending messages; and 4. energy consumption for receiving messages. Hence, the existence of a model for analyzing energy consumption in a CS-based WSN is necessary. Several models have been developed to analyze energy consumption in a WSN, but there is not a complete model for analyzing energy consumption in a CS-based WSN.  In this paper, we study all energy consumption components mentioned above in a CS-based WSN and present a complete model for energy consumption analysis. This model can optimize the design of CS-based WSNs energy efficiency improvement approach. To evaluate the proposed model, we use this model to analyze energy consumption in the compressive data gathering technique which is a CS-based data aggregation method. Using this model can optimize the design of CS-based WSNs.

Using of compressive sensing in radar systems caused to eliminate the matched filter from receiver, and to reduce the required receiver analog-to-digital conversion bandwidth in radar systems. One of compressive sensing parameters is measurement matrix. Measurement matrix for radar systems is usually random matrix. Although exact recovery of signal using random matrices is possible with high probability and this matrix is incoherent with every basis matrix but implementation of that is impossible in practice. So it is useful to use deterministic matrices as measurement matrix. One of these matrices is Alltop matrix that obtained from Alltop sequence. There are limitations in use of this matrix for compressive sensing. We not only will resolve These limitations in this article but also will present a suitable alternative for matched filter block based on compressive sensing that has better performance in comparison to matched filter and can reconstruct radar targets with lower error than matched filter.

In this paper, the compressive sensing (CS) method is used in the through the wall radar imaging (TWRI) to reduce the measurement points and data acquisition time, consequently. In fact, the large required amount of measurement points is considered as one of the main challenges in TWRI which can be mitigated by this proposed method. The diffraction tomography (DT) method is the most effeicient conventional method used in TWRI process. By exploiting the advantages of the CS and non uniform fast Fourier transform (NUFFT), the effectiveness and speediness of the DT method is significantly increased. Simulation and experiment results have verified the validity of the proposed imaging method.

The analysis of gene sequences is fundamentally important for exploring biological functions. Recently, the cost of gene sequencing has dropped sharply, thereby resulting in the production of considerable genomic data. However, the costs of saving, processing, and transferring these data are rising. At present, processing this massive volume of information is done by character based method which is highly time - consuming. Alternative methods challenge these problems in the realm of signal processing. Accordingly, the signal outlook to the genome, signal processing of the genome and compression of the genome are presently hot issues which are practically in demand. Compression reduces the cost, memory space, bandwidth for exchange, and the time required for analysis. In this study, the character genes were firstly represented as signals. Then, these genomic signals were compressed by compressed sensing. Consequently, they were reconstructed by bayesian learning method. Adopted criteria for reconstruction were PRD and NMSE, respectively. Then, signals were selected with a compression rate of 75% for comparison. Meanwhile, the same cluster analysis was run with character based method. The results indicated that the time needed for signal based method was considerably lower than the character based method.

The use of multi-input multi-output (MIMO) systems in addition to increasing capacity, reducing the destructive effects of multi-path phenomena, reducing interference with other users, will lead to higher data rates. On the other hand, the use of millimeter-wave technology and work in high-frequency bands can prevent issues such as traffic and interference, and can significantly increase the data rates, spectral efficiency and the bandwidth. The millimeter-wave massive MIMO with the lens antenna array can significantly reduce the number of radio-frequency chains. In this paper, two novel algorithms are proposed for channel estimation in millimeter-wave massive MIMO. In this regard, a new algorithm using the compressive sensing based on the convex optimization is presented for channel estimation with high accuracy and low complexity. Then, the second new algorithm based on the greedy methods is provided. One of the benefits of this algorithm is its reduced computational complexity, and its high recovery speed. Finally, both proposed algorithms are compared with other existing algorithms. The simulation results confirm that the proposed algorithms outperform the existing algorithms.

one of the most prevalent causes for mortality of infants is cardiac failure. Recordings of heart electrical activities by Electrocardiogram (ECG) are a safe method to detect abnormal arrhythmia in time and reduce cardiac failure in newborns. However, the non-invasive extraction of fetal ECG (fECG) from the maternal abdominal is quite challenging, since the fECG signals are often corrupted by some electrical noises from other sources such as: maternal heart activity, uterine contractions, and respiration, in addition to instrumental noises. Among such signals, the maternal heart signal (due to high amplitude) has the most disruptive effect and the fetal brain signal (due to low amplitude) has the least effect on distortion of the fetal heart signal. In this paper, a new method for extracting fECG signals from multichannel abdominal recordings is proposed. The proposed method uses Compressive Sensing (CS)to reduce the computational complexity and fast Independent Component Analysis (fICA) algorithm to estimate the sources. Also, for finding sparse representations of the acquired ECG signals, two dictionaries namely: discrete cosine transformation and discrete wavelet transform are deployed here. The proposed method is then implemented and its performance is tested using the well-known and publicly available database used in 2013 Physionet Challenge. The performance results are compared with that of the best performing existing methods. The results show that the proposed method based on CS and ICA outperforms the existing detection methods with a Mean Minimum Square Error (MMSE) of 171.65, and therefore can be used for non-invasive and reliable extraction fECG from abdominal recordings.

In this paper, sparse representation of EEG signal is used to automatically classify sleep stages. In this regard, two general sparse representation trends are proposed to classify 4-class sleep stages. The first proposed method is based on sparse principal component analysis (SPCA) which uses different features including time, frequency, and time-frequency features applied to support vector machine (SVM) classifier. The second proposed method is based on sparse representation-based classifier (SRC) which uses orthogonal matching pursuit (OMP) algorithm to obtain sparse coding of the EEG signal. In order to evaluate the effectiveness of the proposed algorithms, their performance is compared with the conventional SVM classification based on PCA method using time, frequency, and time-frequency features. The study is carried out on EEG signal from Physionet international database. Simulation results show on the average 8.36% and 8.26% improvement of the first proposed method in terms of classification accuracy compared to the PCA and deep learning methods, respectively, while the second proposed method has achieved the running time of 118% and 72% faster than the existing PCA and deep learning methods, respectively.

A new category of radar systems that have been introduced in the last decade are MIMO radars. These systems have many advantages in terms of detecting and estimating target parameters compared to the previous systems. Due to the limitations of conventional positioning methods, to utilize all the advantages of MIMO radars, it is necessary to use new methods for signal processing. In this paper, the DOA estimation for MIMO radar is investigated using compact sensor methods. Given that in practical DOA estimation applications, there exists a prior information about the location of targets, in the proposed method(P1,2,w) by applying appropriate weighting, retrieving direction, amplitude and resolution of targets is done with smaller number of measurements and has better results. In this case, a 23% improvement over conventional methods is observed. Also, the recovery problem has been investigated for two measuring matrices, and according to the simulation results, the signal can be recovered with 8% less measurements by selecting the Gaussian measurement matrix instead of the partial identity measurement matrix.

The analysis of gene sequences is fundamentally important for exploring biological functions. Recently, the cost of gene sequencing has dropped sharply, thereby resulting in the production of considerable genomic data. However, the costs of saving, processing, and transferring these data are rising. At present, processing this massive volume of information is done by character based method which is highly time - consuming. Alternative methods challenge these problems in the realm of signal processing. Accordingly, the signal outlook to the genome, signal processing of the genome and compression of the genome are presently hot issues which are practically in demand. Compression reduces the cost, memory space, bandwidth for exchange, and the time required for analysis. In this study, the character genes were firstly represented as signals. Then, these genomic signals were compressed by compressed sensing. Consequently, they were reconstructed by bayesian learning method. Adopted criteria for reconstruction were PRD and NMSE, respectively. Then, signals were selected with a compression rate of 75% for comparison. Meanwhile, the same cluster analysis was run with character based method. The results indicated that the time needed for signal based method was considerably lower than the character based method.

In this paper, a new method is proposed to estimate the direction of arrival (DOA) using non-uniform linear array structure and modeling the measurement matrix as a DFT matrix. In order to estimate the DOA using compressive sensing (CS), continuous angle space should be divided into a discrete set using small steps. This division, leads to the increment of mutual coherence between columns of the measurement matrix and performance of the sparse recovery algorithms is degraded. To solve this problem, we propose a new method in which DFT matrix with mutual coherence of zero is used as the measurement matrix. In order to increase the accuracy of estimation, the size of DFT matrix or the number of antennas should be increased. Implementation of an array with large number of antennas is complex and expensive. A solution to decrease the number of antennas is using a non-uniform linear array and constructing a virtual uniform linear array. A virtual uniform linear array can be constructed by vectorizing the correlation matrix of the received signal of a non-uniform linear array. Increasing the number of antennas in the virtual array will increase the size of DFT matrix. Therefore, the accuracy of DOA estimation will be increased. Simulation results show that DOA estimation using compressive sensing, based on DFT measurement matrix, has a good performance in terms of mean square error of estimation.

The purpose of this paper is to exploit the compressed sensing theory in order to compress multi-lead ECG channels with a high compression ratio and minimum reconstruction error. In order to obtain the sparse representation of the ECG signals a basis matrix with Gaussian kernels which have the maximum resemblance with ECG signals, is constructed. Then using Orthogonal matching pursuit, algorithm which is a greedy/iterative optimization technique, the sparse representation is acquired. Finally, utilizing the compressed sensing theory is possible. In order to prove the accuracy of the algorithm the same optimization technique is used to reconstruct the compressed signal. Using a wavelet basis is also common to obtain the sparse representation. The compressed sensing theory is also applied to the ECG signals for which their sparse representations have been obtained using a wavelet basis. The results show the superiority of the proposed method over the wavelet basis.

Based on the compressed sensing theory, if a signal is sparse in a suitable space, by using the optimization methods, signal could be accurately reconstructed from measurements that are significantly less than the theoretical Shannon requirements. The sparse representation may exist for the signal and it is not available for the noise; this could be used to distinguish these two. On the other hand, in compressed sensing, finding the answer hinges on finding the most sparse solution; thus this technique can separate clean signal from the noise. In FMCW radar, the distance of a target could be obtained from the frequency of the receiver output signal. Since this signal has a sparse representation in the frequency domain, based on compressed sensing theory, it could be reconstructed from a few number of data. In this paper, a new method for signal processing of FMCW radar is presented based on compressed sensing. Moreover, by considering noise removal feature that is in the nature of this technique, it is shown that the effect of noise on the receiver output signal can be reduced and the system performance of the radar can be improved.

Stepped frequency radars attract many attentions due to their high range resolution. Conventional processing technique in these radars is based on the IDFT. In detecting moving targets, the IDFT has the problems of range shifting and range spreading, which result in degrading target amplitude, loss of range accuracy and range resolution. To overcome these problems, the velocity compensation method is used. Since the target velocity is unknown, one should compensate the received signal with all possible velocities and choose the one with the highest and sharpest IDFT output. In this paper, by using the compressive sensing based algorithms, a new method for determining the range and the velocity of moving targets in the stepped frequency radar is proposed. The results show that the proposed method does not have any problem with range shifting and range spreading, and has a better performance compared with other methods in literature. Coherent processing concept is also investigated. Simulations show that using the coherent processing, the performance of compressive sensing based radar detectors is considerably improved.

Speckle noise seriously degrades the quality of SAR images and complicates the image exploitation using automated image analysis techniques. Recently, the application of compressed sensing (CS) is explored in the SAR signal processing. In this paper, first, a linear model is derived for speckled SAR data. Then, using this model and the compressed sensing theory, a speckle reduction method is proposed. In the proposed method, the image backgrounds as well as the bright point targets are also reconstructed together with noise reduction. The important feature of the proposed method is the joint noise reduction simultaneous with the SAR image formation. Moreover, using simulated and real SAR images, the performance of the proposed method in noise reduction and preserving image features is evaluated and compared to the performance of some de-noising approaches.