List of Articles Compressive Sensing

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  1 - Cooperative Game Approach for Mobile Primary User Localization Based on Compressive Sensing in Multi-antenna Cognitive Sensor Networks
  Maryam Najimi
  10.7508/jist.2019.02.005
  In this paper, the problem of joint energy efficient spectrum sensing and determining the mobile primary user location is proposed based on compressive sensing in cognitive sensor networks. By utilizing compressive sensing, the ratio of measurements for the sensing node More

  In this paper, the problem of joint energy efficient spectrum sensing and determining the mobile primary user location is proposed based on compressive sensing in cognitive sensor networks. By utilizing compressive sensing, the ratio of measurements for the sensing nodes are considerably reduced. Therefore, energy consumption is improved significantly in spectrum sensing. The multi-antenna sensors is also considered to save more energy. On the other hand, multi-antenna sensor utilization is a proper solution instead of applying more sensors. The problem is formulated to maximize the network lifetime and find the mobile primary user position by sensors selection under the detection performance and accuracy of localization constraints. For this purpose, a cooperative game is proposed to study this problem. It is shown that with the proposed game, the network lifetime is maximized while the proper sensors which participate in spectrum sensing and primary user localization are determined. Simulation results show that the network lifetime is improved while the detection performance constraint is satisfied and the location of the primary user is determined with high accuracy. Manuscript profile
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  2 - Wavelet-based Bayesian Algorithm for Distributed Compressed Sensing
  Razieh Torkamani Ramezan Ali Sadeghzadeh
  10.7508/jist.2019.02.001
  The emerging field of compressive sensing (CS) enables the reconstruction of the signal from a small set of linear projections. Traditional CS deals with a single signal; while one can jointly reconstruct multiple signals via distributed CS (DCS) algorithm. DCS inversio More

  The emerging field of compressive sensing (CS) enables the reconstruction of the signal from a small set of linear projections. Traditional CS deals with a single signal; while one can jointly reconstruct multiple signals via distributed CS (DCS) algorithm. DCS inversion method exploits both the inter- and intra-signal correlations via joint sparsity models (JSM). Since the wavelet coefficients of many signals is sparse, in this paper, the wavelet transform is used as sparsifying transform, and a new wavelet-based Bayesian DCS algorithm (WB-DCS) is proposed, which takes into account the inter-scale dependencies among the wavelet coefficients via hidden Markov tree model (HMT), as well as the inter-signal correlations. This paper uses the Bayesian procedure to statistically model this correlations via the prior distributions. Also, in this work, a type-1 JSM (JSM-1) signal model is used for jointly sparse signals, in which every sparse coefficient vector is considered as the sum of a common component and an innovation component. In order to jointly reconstruct multiple sparse signals, the centralized approach is used in DCS, in which all the data is processed in the fusion center (FC). Also, variational Bayes (VB) procedure is used to infer the posterior distributions of unknown variables. Simulation results demonstrate that the structure exploited within the wavelet coefficients provides superior performance in terms of average reconstruction error and structural similarity index. Manuscript profile
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  3 - The Extraction of Fetal ECG from Abdominal Recordings Using Sparse Representation of ECG Signals
  Parya Tavoosi قاسم عازمی پگاه زرجام
  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 e More

  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. Manuscript profile
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  4 - A Novel Detector based on Compressive Sensing for Uplink Massive MIMO Systems
  Mojtaba Amiri Amir Akhavan
  10.52547/jist.34192.10.40.249
  Massive multiple-input multiple-output is a promising technology in future communication networks where a large number of antennas are used. It provides huge advantages to the future communication systems in data rate, the quality of services, energy efficiency, and spe More

  Massive multiple-input multiple-output is a promising technology in future communication networks where a large number of antennas are used. It provides huge advantages to the future communication systems in data rate, the quality of services, energy efficiency, and spectral efficiency. Linear detection algorithms can achieve a near-optimal performance in large-scale MIMO systems, due to the asymptotic orthogonal channel property. But, the performance of linear MIMO detectors degrades when the number of transmit antennas is close to the number of receive antennas (loaded scenario). Therefore, this paper proposes a series of detectors for large MIMO systems, which is capable of achieving promising performance in loaded scenarios. The main idea is to improve the performance of the detector by finding the hidden sparsity in the residual error of the received signal. At the first step, the conventional MIMO model is converted into the sparse model via the symbol error vector obtained from a linear detector. With the aid of the compressive sensing methods, the incorrectly detected symbols are recovered and performance improvement in the detector output is obtained. Different sparse recovery algorithms have been considered to reconstruct the sparse error signal. This study reveals that error recovery by imposing sparse constraint would decrease the bit error rate of the MIMO detector. Simulation results show that the iteratively reweighted least squares method achieves the best performance among other sparse recovery methods. Manuscript profile
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  5 - Community Detection in Complex Dynamic Networks Based on Graph Embedding and Clustering Ensemble
  Majid Mohammadpour Seyedakbar Mostafavi وحید رنجبر
  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 pap More

  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. Manuscript profile
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  6 - Distributed Primal-Dual Algorithm with Variable Parameters and Bidirectional Incremental Cooperation
  Ghanbar  Azarnia
  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 pap More

  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. Manuscript profile