Compressive sampling (or compressive sensing) is an important theory in signal processing. It allows efficient acquisition and reconstruction of a signal and may also be the basis for user identification. The works cited here were published or presented between January and August of 2014.

• Wei Wang; Xiao-Yi Pan; Yong-Cai Liu; De-Jun Feng; Qi-Xiang Fu, "Sub-Nyquist Sampling Jamming Against ISAR With Compressive Sensing," Sensors Journal, IEEE, vol.14, no.9, pp.3131,3136, Sept. 2014. doi: 10.1109/JSEN.2014.2323978 Shannon-Nyquist theorem indicates that under-sampling at low rates will lead to aliasing in the frequency domain of signal and can be utilized in electronic warfare. However, the question is whether it still works when the compressive sensing (CS) algorithm is applied into reconstruction of target. This paper concerns sub-Nyquist sampled jamming signals and its corresponding influence on inverse synthetic aperture radar (ISAR) imaging via CS. Results show that multiple deceptive false-target images with finer resolution will be induced after the sub-Nyquist sampled jamming signals dealed with CS-based reconstruction algorithm; hence, the sub-Nyquist sampling can be adopted in the generation of decoys against ISAR with CS. Experimental results of the scattering model of the Yak-42 plane and real data are used to verify the correctness of the analyses.
  Keywords: compressed sensing; image reconstruction; image resolution; image sampling; jamming; radar imaging; synthetic aperture radar; CS-based reconstruction algorithm; ISAR imaging; Shannon-Nyquist theorem;Yak-42 plane; compressive sensing algorithm; decoy generation; electronic warfare; frequency domain analysis; inverse synthetic aperture radar imaging; multiple deceptive false-target image resolution; scattering model; subNyquist sampled jamming signal; Compressed sensing; mage resolution; Imaging; Jamming; Radar imaging; Scattering; Sub-Nyquist sampling; compressive sensing (CS); deception jamming; inverse syntheticaperture radar (ISAR) (ID#:14-2713)
  URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6815640&isnumber=6862121

• Lagunas, E.; Najar, M., "Robust Primary User Identification Using Compressive Sampling For Cognitive Radios," Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on, pp.2347,2351, 4-9 May 2014. doi: 10.1109/ICASSP.2014.6854019 In cognitive radio (CR), the problem of limited spectral resources is solved by enabling unlicensed systems to opportunistically utilize the unused licensed bands. Compressive Sensing (CS) has been successfully applied to alleviate the sampling bottleneck in wideband spectrum sensing leveraging the sparseness of the signal spectrum in open-access networks. This has inspired the design of a number of techniques that identify spectrum holes from sub-Nyquist samples. However, the existence of interference emanating from low-regulated transmissions, which cannot be taken into account in the CS model because of their non-regulated nature, greatly degrades the identification of licensed activity. Capitalizing on the sparsity described by licensed users, this paper introduces a feature-based technique for primary user's spectrum identification with interference immunity which works with a reduced amount of data. The proposed method detects which channels are occupied by primary users' and also identify the primary users transmission powers without ever reconstructing the signals involved. Simulation results show the effectiveness of the proposed technique for interference suppression and primary user detection.
  Keywords: cognitive radio; compressed sensing; interference suppression; radio spectrum management; cognitive radio; compressive sensing; feature-based technique; interference immunity; interference suppression; licensed users; limited spectral resources; low-regulated transmissions; open-access networks; primary user detection; sampling bottleneck; signal spectrum; spectrum holes; spectrum identification; sub-Nyquist samples; unlicensed systems; unused licensed bands; wideband spectrum sensing; Correlation; Feature extraction; Interference; Noise; Sensors; Spectral shape; Vectors (ID#:14-2714)
  URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6854019&isnumber=6853544

• Yuxin Chen; Goldsmith, AJ.; Eldar, Y.C., "Channel Capacity Under Sub-Nyquist Nonuniform Sampling," Information Theory, IEEE Transactions on, vol.60, no.8, pp.4739,4756, Aug. 2014. doi: 10.1109/TIT.2014.2323406 This paper investigates the effect of sub-Nyquist sampling upon the capacity of an analog channel. The channel is assumed to be a linear time-invariant Gaussian channel, where perfect channel knowledge is available at both the transmitter and the receiver. We consider a general class of right-invertible time-preserving sampling methods which includes irregular nonuniform sampling, and characterize in closed form the channel capacity achievable by this class of sampling methods, under a sampling rate and power constraint. Our results indicate that the optimal sampling structures extract out the set of frequencies that exhibits the highest signal-to-noise ratio among all spectral sets of measure equal to the sampling rate. This can be attained through filterbank sampling with uniform sampling grid employed at each branch with possibly different rates, or through a single branch of modulation and filtering followed by uniform sampling. These results reveal that for a large class of channels, employing irregular nonuniform sampling sets, while are typically complicated to realize in practice, does not provide capacity gain over uniform sampling sets with appropriate preprocessing. Our findings demonstrate that aliasing or scrambling of spectral components does not provide capacity gain in this scenario, which is in contrast to the benefits obtained from random mixing in spectrum-blind compressive sampling schemes.
  Keywords: Gaussian channels; channel bank filters; channel capacity; sampling methods; transceivers; analog channel;capacity gain; channel capacity; filterbank sampling; filtering single branch ;frequency set; irregular nonuniform sampling; irregular nonuniform sampling sets ;linear time-invariant Gaussian channel; modulation single branch; optimal sampling structures; power constraint; random mixing; receiver; right-invertible time-preserving sampling methods; sampling rate; signal-to-noise ratio; spectral components aliasing; spectral components scrambling; spectral sets; spectrum-blind compressive sampling schemes;s ubNyquist nonuniform sampling effect; transmitter; uniform sampling grid; Channel capacity; Data preprocessing; Measurement; Modulation; Nonuniform sampling; Upper bound; Be
  URLing density; Nonuniform sampling; channel capacity; irregular sampling; sampled analog channels; sub-Nyquist sampling; time-preserving sampling systems(ID#:14-2715)
  URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6814945&isnumber=6851961

• Feng Xi; Shengyao Chen; Zhong Liu, "Quadrature Compressive Sampling for Radar Signals," Signal Processing, IEEE Transactions on, vol.62, no.11, pp.2787,2802, June1, 2014. doi: 10.1109/TSP.2014.2315168 Quadrature sampling has been widely applied in coherent radar systems to extract in-phase and quadrature ( I and Q) components in the received radar signal. However, the sampling is inefficient because the received signal contains only a small number of significant target signals. This paper incorporates the compressive sampling (CS) theory into the design of the quadrature sampling system, and develops a quadrature compressive sampling (QuadCS) system to acquire the I and Q components with low sampling rate. The QuadCS system first randomly projects the received signal into a compressive bandpass signal and then utilizes the quadrature sampling to output compressive I and Q components. The compressive outputs are used to reconstruct the I and Q components. To understand the system performance, we establish the frequency domain representation of the QuadCS system. With the waveform-matched dictionary, we prove that the QuadCS system satisfies the restricted isometry property with overwhelming probability. For K target signals in the observation interval T, simulations show that the QuadCS requires just O(Klog(BT/K)) samples to stably reconstruct the signal, where B is the signal bandwidth. The reconstructed signal-to-noise ratio decreases by 3 dB for every octave increase in the target number K and increases by 3 dB for every octave increase in the compressive bandwidth. Theoretical analyses and simulations verify that the proposed QuadCS is a valid system to acquire the I and Q components in the received radar signals.
  Keywords: compressed sensing; frequency-domain analysis; probability; radar receivers; radar signal processing; signal reconstruction; signal sampling; QuadCS system; compressive bandpass signal; frequency domain representation; noise figure 3 dB; probability; quadrature compressive sampling theory; received radar signal sampling ;signal reconstruction; signal-to-noise ratio;waveform-matching;Bandwidth;Baseband;Demodulation;Dictionaries;Frequency-domain analysis; Radar; Vectors; Analog-to-digital conversion; compressive sampling; quadrature sampling; restricted isometry property; sparse signal reconstruction (ID#:14-2716)
  URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6781614&isnumber=6809867

• Xianbiao Shu; Jianchao Yang; Ahuja, N., "Non-local Compressive Sampling Recovery," Computational Photography (ICCP), 2014 IEEE International Conference on, pp.1,8, 2-4 May 2014. doi: 10.1109/ICCPHOT.2014.6831806 Compressive sampling (CS) aims at acquiring a signal at a sampling rate below the Nyquist rate by exploiting prior knowledge that a signal is sparse or correlated in some domain. Despite the remarkable progress in the theory of CS, the sampling rate on a single image required by CS is still very high in practice. In this paper, a non-local compressive sampling (NLCS) recovery method is proposed to further reduce the sampling rate by exploiting non-local patch correlation and local piecewise smoothness present in natural images. Two non-local sparsity measures, i.e., non-local wavelet sparsity and non-local joint sparsity, are proposed to exploit the patch correlation in NLCS. An efficient iterative algorithm is developed to solve the NLCS recovery problem, which is shown to have stable convergence behavior in experiments. The experimental results show that our NLCS significantly improves the state-of-the-art of image compressive sampling.
  Keywords: compressed sensing; correlation theory ;image sampling; iterative methods; natural scenes; wavelet transforms; NLCS recovery method; Nyquist rate; image compressive sampling; iterative algorithm; local piecewise smoothness; natural images; nonlocal compressive sampling recovery; nonlocal joint sparsity; nonlocal patch correlation; nonlocal sparsity measure; nonlocal wavelet sparsity; sampling rate reduction; signal acquisition; sparse signal; Correlation; Image coding; Imaging; Joints; Three-dimensional displays; Videos; Wavelet transforms (ID#:14-2717)
  URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6831806&isnumber=6831796

• Banitalebi-Dehkordi, M.; Abouei, J.; Plataniotis, K.N., "Compressive-Sampling-Based Positioning in Wireless Body Area Networks," Biomedical and Health Informatics, IEEE Journal of, vol.18, no.1, pp.335, 344, Jan. 2014.doi: 10.1109/JBHI.2013.2261997 Recent achievements in wireless technologies have opened up enormous opportunities for the implementation of ubiquitous health care systems in providing rich contextual information and warning mechanisms against abnormal conditions. This helps with the automatic and remote monitoring/tracking of patients in hospitals and facilitates and with the supervision of fragile, elderly people in their own domestic environment through automatic systems to handle the remote drug delivery. This paper presents a new modeling and analysis framework for the multipatient positioning in a wireless body area network (WBAN) which exploits the spatial sparsity of patients and a sparse fast Fourier transform (FFT)-based feature extraction mechanism for monitoring of patients and for reporting the movement tracking to a central database server containing patient vital information. The main goal of this paper is to achieve a high degree of accuracy and resolution in the patient localization with less computational complexity in the implementation using the compressive sensing theory. We represent the patients' positions as a sparse vector obtained by the discrete segmentation of the patient movement space in a circular grid. To estimate this vector, a compressive-sampling-based two-level FFT (CS-2FFT) feature vector is synthesized for each received signal from the biosensors embedded on the patient's body at each grid point. This feature extraction process benefits in the combination of both short-time and long-time properties of the received signals. The robustness of the proposed CS-2FFT-based algorithm in terms of the average positioning error is numerically evaluated using the realistic parameters in the IEEE 802.15.6-WBAN standard in the presence of additive white Gaussian noise. Due to the circular grid pattern and the CS-2FFT feature extraction method, the proposed scheme represents a significant reduction in the computational complexity, while improving the level of the resolut- on and the localization accuracy when compared to some classical CS-based positioning algorithms.
  Keywords: AWGN; body sensor networks; compressed sensing; drug delivery systems; fast Fourier transforms; feature extraction; geriatrics; health care; hospitals; medical signal processing; patient monitoring; personal area networks; telemedicine; tracking; ubiquitous computing; CS-2FFT feature extraction method; CS-2FFT feature vector synthesis; CS-2FFT-based algorithm robustness; FFT-based feature extraction mechanism; IEEE 802.15.6-WBAN standard; abnormal condition contextual information; abnormal condition warning mechanism; additive white Gaussian noise; automatic drug delivery system; automatic patient monitoring; automatic patient tracking; average positioning error; biosensor signal; central database server; circular grid pattern; classical CS-based positioning algorithm; compressive sensing theory; compressive-sampling-based positioning; compressive-sampling-based two-level FFT feature vector; computational complexity reduction; feature extraction process; fragile elderly people supervision; hospital; movement tracking reporting; multipatient positioning analysis; multipatient positioning modeling; numerical evaluation; patient localization accuracy; patient localization resolution; patient movement space discrete segmentation; patient spatial sparsity; patient vital information; remote drug delivery; remote patient monitoring; remote patient tracking; signal long-time properties; signal short-time properties; sparse fast Fourier transform;sparse vector estimation; ubiquitous health care system; wireless body area network; wireless technology; Compressive sampling (CS);patient localization; spatial sparsity; wireless body area networks (WBANs) (ID#:14-2718)
  URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6514596&isnumber=6701130

• Gishkori, S.; Lottici, V.; Leus, G., "Compressive Sampling-Based Multiple Symbol Differential Detection for UWB Communications," Wireless Communications, IEEE Transactions on, vol.13, no.7, pp.3778,3 790, July 2014. doi: 10.1109/TWC.2014.2317175 Compressive sampling (CS) based multiple symbol differential detectors are proposed for impulse-radio ultra-wideband signaling, using the principles of generalized likelihood ratio tests. The CS based detectors correspond to two communication scenarios. One, where the signaling is fully synchronized at the receiver and the other, where there exists a symbol level synchronization only. With the help of CS, the sampling rates are reduced much below the Nyquist rate to save on the high power consumed by the analog-to-digital converters. In stark contrast to the usual compressive sampling practices, the proposed detectors work on the compressed samples directly, thereby avoiding a complicated reconstruction step and resulting in a reduction of the implementation complexity. To resolve the detection of multiple symbols, compressed sphere decoders are proposed as well, for both communication scenarios, which can further help to reduce the system complexity. Differential detection directly on the compressed symbols is generally marred by the requirement of an identical measurement process for every received symbol. Our proposed detectors are valid for scenarios where the measurement process is the same as well as where it is different for each received symbol.
  Keywords: compressed sensing; signal detection; signal reconstruction; signal sampling; statistical testing; synchronisation; ultra wideband communication; CS based detectors; Nyquist rate; UWB communications; analog-to-digital converters; complicated reconstruction step; c ompressed sphere decoders; compressive sampling-based multiple symbol differential detection; generalized likelihood ratio tests; identical measurement process ;impulse-radio ultra-wideband signaling; symbol level synchronization; system complexity reduction; Complexity theory;Detectors;Joints;Receivers;Synchronization;Vectors;Compressive sampling (CS);multiple symbol differential detection (MSDD);sphere decoding (SD);ultra-wideband impulse radio (UWB-IR) (ID#:14-2719)
  URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6797969&isnumber=6850111

• Shuyuan Yang; HongHong Jin; Min Wang; Yu Ren; Licheng Jiao, "Data-Driven Compressive Sampling and Learning Sparse Coding for Hyperspectral Image Classification," Geoscience and Remote Sensing Letters, IEEE, vol.11, no.2, pp.479, 483, Feb. 2014. doi: 10.1109/LGRS.2013.2268847 Exploring the sparsity in classifying hyperspectral vectors proves to lead to state-of-the-art performance. To learn a compact and discriminative dictionary for accurate and fast classification of hyperspectral images, a data-driven Compressive Sampling (CS) and learning sparse coding scheme are use to reduce the dimensionality and size of the dictionary respectively. First, a sparse radial basis function (RBF) kernel learning network (S-RBFKLN) is constructed to learn a compact dictionary for sparsely representing hyperspectral vectors. Then a data-driven compressive sampling scheme is designed to reduce the dimensionality of the dictionary, and labels of new samples are derived from coding coefficients. Some experiments are taken on NASA EO-1 Hyperion data and AVIRIS Indian Pines data to investigate the performance of the proposed method, and the results show its superiority to its counterparts.
  Keywords: geophysical image processing; hyperspectral imaging; image classification; AVIRIS Indian Pines data; NASA EO-1 Hyperion data; coding coefficients; data-driven compressive sampling; hyperspectral image classification; hyperspectral vectors ;learning sparse coding scheme; Dictionaries; Hyperspectral imaging; Image coding; Kernel; Training; Vectors; Compressive sampling (CS); data-driven; hyperspectral image classification ;sparse radial basis function kernel learning network (S-RBFKLN) (ID#:14-2720)
  URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6578556&isnumber=6675034

• Yan Jing; Naizhang Feng; Yi Shen, "Bearing Estimation Of Coherent Signals Using Compressive Sampling Array," Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, 2014 IEEE International, pp.1221,1225, 12-15 May 2014. doi: 10.1109/I2MTC.2014.6860938 Compressive sampling (CS) is an attractive theory which can achieve sparse signals acquisition and compression simultaneously. Exploiting the sparse property in the spatial domain, the direction of arrival (DOA) of narrowband signals is studied by using compressive sampling measurements in the form of random projections of sensor arrays. The proposed approach, CS array DOA estimation based on eigen space (CSA-ES-DOA) uses a very small number of measurements to resolve the DOA estimation of the coherent signals and two closely adjacent signals. Theoretical analysis and simulation results demonstrate that the proposed approaches can maintain high angular resolution, low hardware complexity and low computational cost.
  Keywords: {compressed sensing; direction-of-arrival estimation; eigenvalues and eigenfunctions; signal detection; signal sampling; DOA estimation; bearing estimation; coherent signals; compressive sampling array; direction of arrival; narrowband signals; sparse signals acquisition; Arrays; Compressed sensing; Direction-of-arrival estimation; Estimation; Multiple signal classification; Signal resolution; Vectors; coherent signals; compressive sampling array; direction of arrival; eigen space (ID#:14-2721)
  URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6860938&isnumber=6860504

• Angrisani, L.; Bonavolonta, F.; Lo Moriello, R.S.; Andreone, A; Casini, R.; Papari, G.; Accardo, D., "First Steps Towards An Innovative Compressive Sampling Based-Thz Imaging System For Early Crack Detection On Aereospace Plates," Metrology for Aerospace (MetroAeroSpace), 2014 IEEE, pp.488,493, 29-30 May 2014. doi: 10.1109/MetroAeroSpace.2014.6865974 The paper deals with the problem of early detecting cracks in composite materials for avionic applications. In particular, the authors present a THz imaging system that exploits compressive sampling (CS) to detect submillimeter cracks with a reduced measurement burden. Traditional methods for THz imaging usually involve raster scan of the issue of interest by means of highly collimated radiations and the corresponding image is achieved by measuring the received THz power in different positions (pixels) of the desired image. As it can be expected, the higher the required resolution, the longer the measurement time. On the contrary, two different approaches for THz imaging (namely, continuous wave and time domain spectroscopy) combined with a proper CS solution are used to assure as good results as those granted by traditional raster scan; a proper set of masks (each of which characterized by a specific random pattern) are defined to the purpose. A number of tests conducted on simulated data highlighted the promising performance of the proposed method thus suggesting its implementation in an actual measurement setup.
  Keywords: aerospace materials; avionics; composite materials; compressed sensing; condition monitoring; crack detection; plates (structures);terahertz wave imaging; CS; THz imaging system; aerospace plates; avionic applications; composite materials; compressive sampling; continuous wave; early crack detection; submillimeter cracks; time domain spectroscopy; Detectors; Image reconstruction; Image resolution ;Imaging; Laser excitation; Quantum cascade lasers; Skin; compressive sampling THz imaging ;cracks detection; nondestructive evaluation (ID#:14-2722)
  URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6865974&isnumber=6865882

• Das, S.; Singh Sidhu, T., "Application of Compressive Sampling in Synchrophasor Data Communication in WAMS," Industrial Informatics, IEEE Transactions on, vol.10, no.1, pp.450, 460, Feb. 2014. doi: 10.1109/TII.2013.2272088 In this paper, areas of power system synchrophasor data communication which can be improved by compressive sampling (CS) theory are identified. CS reduces the network bandwidth requirements of Wide Area Measurement Systems (WAMS). It is shown that CS can reconstruct synchrophasors at higher rates while satisfying the accuracy requirements of IEEE standard C37.118.1-2011. Different steady state and dynamic power system scenarios are considered here using mathematical models of C37.118.1-2011. Synchrophasors of lower reporting rates are exempted from satisfying the accuracy requirements of C37.118.1-2011 during system dynamics. In this work, synchrophasors are accurately reconstructed from above and below Nyquist rates. Missing data often pose challenges to the WAMS applications. It is shown that missing and bad data can be reconstructed satisfactorily using CS. Performance of CS is found to be better than the existing interpolation techniques for WAMS communication.
  Keywords: IEEE standards; compressed sensing; interpolation;phasor measurement; CS theory; IEEE standard C37.118.1-2011; Nyquist rates; WAMS communication; compressive sampling; dynamic power system scenario; interpolation technique; mathematical model; network bandwidth requirements; power system synchrophasor data communication; steady state power system scenario; wide area measurement systems; Compressive sampling; phasor measurement unit; smart grid; synchrophasor; wide area measurement system (WAMS) (ID#:14-2723)
  URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6553079&isnumber=6683081

• Xi, Feng; Chen, Shengyao; Liu, Zhong, "Quadrature Compressive Sampling For Radar Signals: Output Noise And Robust Reconstruction," Signal and Information Processing (ChinaSIP), 2014 IEEE China Summit & International Conference on, pp.790,794, 9-13 July 2014. doi: 10.1109/ChinaSIP.2014.6889353 The quadrature compressive sampling (QuadCS) system is a recently developed low-rate sampling system for acquiring inphase and quadrature (I and Q) components of radar signals. This paper investigates the output noise and robust reconstruction of the QuadCS system with the practical non-ideal bandpass filter. For independently and identically distributed Gaussian input noise, we find that the output noise is a correlated Gaussian one in the non-ideal case. Then we exploit the correlation property and develop a robust reconstruction formulation. Simulations show that the reconstructed signal-to-noise ratio is enhanced 3–4dB with the robust formulation.
  Keywords: Compressive sampling; Gaussian noise; quadrature demodulation; radar signals (ID#:14-2724)
  URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6889353&isnumber=6889177

• Budillon, A; Ferraioli, G.; Schirinzi, G., "Localization Performance of Multiple Scatterers in Compressive Sampling SAR Tomography: Results on COSMO-SkyMed Data," Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of, vol.7, no.7, pp.2902, 2910, July 2014. doi: 10.1109/JSTARS.2014.2344916 The 3-D SAR tomographic technique based on compressive sampling (CS) has been proven very performing in recovering the 3-D reflectivity function and hence in estimating multiple scatterers lying in the same range-azimuth resolution cell, but at different elevations. In this paper, a detection method for multiple scatterers, assuming the number of scatterers to be known or preliminarily estimated, has been investigated. The performance of CS processing for identifying and locating multiple scatterers has been analyzed for different number of measurements and different reciprocal distances between the scatterers, in presence of the off-grid effect, and in the case of super-resolution imaging. The proposed method has been tested on simulated and real COSMO-SkyMed data.
  Keywords: Detectors ;Image resolution; Signal resolution; Signal to noise ratio; Synthetic aperture radar; Tomography; Compressive sampling (CS); detection; synthetic aperture radar (SAR); tomography (ID#:14-2725)
  URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6881815&isnumber=6881766

• Ningfei Dong; Jianxin Wang, "Channel Gain Mismatch And Time Delay Calibration For Modulated Wideband Converter-Based Compressive Sampling," Signal Processing, IET, vol.8, no.2, pp.211, 219, April 2014. doi: 10.1049/iet-spr.2013.0137 The modulated wideband converter (MWC) is a recently proposed compressive sampling system for acquiring sparse multiband signals. For the MWC with digital sub-channel separation block, channel gain mismatch and time delay will lead to a potential performance loss in reconstruction. These gains and delays are represented as an unknown multiplicative diagonal matrix here. The authors formulate the estimation problem as a convex optimisation problem, which can be efficiently solved by utilising least squares estimation. Then the calibrated system model is obtained and the estimates of the gains and time delays of physical channels from the estimate of this matrix are calculated. Numerical simulations verify the effectiveness of the proposed approach.
  Keywords: {channel estimation; compressed sensing; delay estimation ;least mean squares methods; matrix multiplication; modulation; signal detection; signal reconstruction; MWC; channel gain mismatch; compressive sampling system; convex optimisation problem; digital subchannel separation block; gain estimation; least square estimation; modulated wideband converter; numerical simulation; potential performance loss; signal reconstruction; sparse multiband signal acquisition; time delay calibration; time delay estimation; unknown multiplicative diagonal matrix (ID#:14-2726)
  URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6786869&isnumber=6786851

• Sejdić, E.; Rothfuss, M.A; Gimbel, M.L.; Mickle, M.H., "Comparative Analysis Of Compressive Sensing Approaches For Recovery Of Missing Samples In Implantable Wireless Doppler Device," Signal Processing, IET, vol.8, no.3, pp.230, 238, May 2014. doi: 10.1049/iet-spr.2013.0402 An implantable wireless Doppler device used in microsurgical free flap surgeries can suffer from lost data points. To recover the lost samples, the authors considered the approaches based on a recently proposed compressive sensing. In this paper, they performed a comparative analysis of several different approaches by using synthetic and real signals obtained during blood flow monitoring in four pigs. They considered three different bases functions: Fourier bases, discrete prolate spheroidal sequences and modulated discrete prolate spheroidal sequences, respectively. To avoid the computational burden, they considered the approaches based on the l1 minimisation for all the three bases. To understand the trade-off between the computational complexity and the accuracy, they also used a recovery process based on a matching pursuit and modulated discrete prolate spheroidal sequences bases. For both the synthetic and the real signals, the matching approach with modulated discrete prolate spheroidal sequences provided the most accurate results. Future studies should focus on the optimisation of the modulated discrete prolate spheroidal sequences in order to further decrease the computational complexity and increase the accuracy.
  Keywords: blood flow measurement; compressed sensing; computational complexity; medical signal processing; minimisation; prosthetics; signal sampling; blood flow monitoring; compressive sensing; implantable wireless Doppler device; matching pursuit; microsurgical free flap surgery; missing sample recovery; modulated discrete prolate spheroidal sequences base; recovery process (ID#:14-2727)
  URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6817399&isnumber=6816971

• Mihajlovic, Radomir; Scekic, Marijana; Draganic, Andjela; Stankovic, Srdjan, "An Analysis Of CS Algorithms Efficiency For Sparse Communication Signals Reconstruction," Embedded Computing (MECO), 2014 3rd Mediterranean Conference on, pp.221,224, 15-19 June 2014. doi: 10.1109/MECO.2014.6862700 As need for increasing the speed and accuracy of the real applications is constantly growing, the new algorithms and methods for signal processing are intensively developing. Traditional sampling approach based on Sampling theorem is, in many applications, inefficient because of production a large number of signal samples. Generally, small number of significant information is presented within the signal compared to its length. Therefore, the Compressive Sensing method is developed as an alternative sampling strategy. This method provides efficient signal processing and reconstruction, without need for collecting all of the signal samples. Signal is sampled in a random way, with number of acquired samples significantly smaller than the signal length. In this paper, the comparison of the several algorithms for Compressive Sensing reconstruction is presented. The one dimensional band-limited signals that appear in wireless communications are observed and the performance of the algorithms in non-noisy and noisy environments is tested. Reconstruction errors and execution times are compared between different algorithms, as well.
  Keywords: Compressed sensing; Image reconstruction; Matching pursuit algorithms; Optimization; Reconstruction algorithms; Signal processing; Signal processing algorithms; Compressive Sensing; basis pursuit; iterative hard thresholding; orthogonal matching pursuit; wireless signals (ID#:14-2728)
  URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6862700&isnumber=6862649 isnumber=6862649

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