High-frequency flow field reconstruction method and application based on time-domain uniform undersampling compressed sensing

Wideband spectrum sensing is regarded as one of the key features in cognitive radio systems. Compressive sensing (CS) has recently become one of the promising techniques to deal with the Nyquist sampling rate bottleneck of wideband spectrum sensing. Theoretical analyses and simulation have shown that CS could achieve high detection probability and low false alarm for wideband spectrum sensing. However, implementation of CS on the real-time signals and real-time processing poses significant challenges due to the iterative nature of the CS algorithms. In this paper, we propose a novel adaptively regularized iterative reweighted least squares (AR-IRLS) algorithm to implement the real-time signal recovery on the CS based wideband spectrum sensing. The proposed algorithm moves estimated solutions along an exponential-linear path by regularizing weights with a series of non- increasing penalty terms, which significantly speeds up the convergence of reconstruction and provides high fidelity guarantee to cope with the varying bandwidths and power levels of occupied channels. The proposed algorithm presents robustness against different sparsity levels at low compressive ratio without degradation on the reconstruction performance, and is tested on the real-time signals over TV white space spectrum after having been validated on the simulated signals. Both the simulation and real-time experiments show that the proposed algorithm outperforms the conventional iterative reweighted least squares (IRLS) algorithms in terms of convergence speed, reconstruction accuracy, and compressive ratio requirement.

In this paper, we investigate the performance of compressive sampling (CS) for ECG compression in telecardiology, when the signal acquisition is noisy and unavoidable body movements lead to varying heartbeat rate and sparsity of the signal. We show analytically that CS recovery noise does not scale linearly with the input noise. Hence, it is not easy to reduce the adverse impact of noise in CS. Additionally, any variation in the heartbeat rate changes the sparsity and can adversely affect compression. We compare the performance of CS with thresholding discrete wavelet transform (TH-DWT), which is the best technique for real-time ECG compression. We show that CS is quite sensitive to sparsity and compression ratio, while the reconstruction quality of TH-DWT is quite stable. Our results suggest that while CS is an attractive option for telecardiology due to its encoder simplicity, caution should be exercised in applying it for ECG signal compression.

Quantitative image analysis and measurement of flow fields in convective heat transfer has great importance for the optimum energy consumption problems. In natural and forced convection phenomena of fluids, the complexity of flow field prevents us from detailed three dimensional (3D) experimental analyses of steady/unsteady dynamics in fluids. These flow fields have locally different density and temperature values and yet to be observed quantitatively. Recent development of the Particle Image Velocimetry (PIV) and Particle Tracking Velocimetry (PTV) techniques lead us to the quantitative investigation of flow fields in experimental researches. On the other hand, in image measurements density and temperature distributions have been grasped only in two-dimensions (2D). These qualitative image analyses of flow fields were obtained by using classical flow visualizing techniques, such as shadowgraph and color schlieren method. This paper describes the quantitative measurement of convective flow field using our originally proposed color striped background oriented schlieren (CSBOS) method. The obtained measured image data is used for CT reconstruction and 3D temperature gradient distributions.

Compressed sensing (CS) is a technique to sample compressible signals below the Nyquist rate, whilst still allowing near optimal reconstruction of the signal. In this paper, we apply the iterative hard thresholding (IHT) algorithm for compressed sensing on the speech signal. The interested speech signal is transformed to the frequency domain using Discrete Fourier Transform (DCT) and then compressed sensing is applied to that signal. The compressed signal can be reconstructed using the recently introduced Iterative Hard Thresholding (IHT) algorithm and also by the tradditional \( \ell_{1} \) minimization (basic pursuit) for comparison. It is shown that the compressed sensing can provide better root mean square error (RMSE) than the tradition DCT compression method, given the same compression ratio.

Most wearable medical devices are designed to continuously acquire photoplethysmography (PPG) for measuring vital signs and transmitting acquired PPG data wirelessly to edge-computing device or cloud-computing server. These devices are constrained with limited battery power and data-rate capacity. Therefore, in this paper, we present a lightweight effective data-reduction method by investigating the performance of compressed sensing (CS)-based and and partial discrete cosine transform (DCT)-based compression methods with major objectives of achieving higher compression ratio (CR) with minimal waveform distortion with low reconstruction time. By using both normal and abnormal PPG signals, the performance of the CS-based and DCT-based compression methods is evaluated in terms of CR, global and local distortion measures and processing time. Evaluation results showed that CR values of the partial-DCT based method are 3 times higher (CR ranging from 7.50 to 9.38) without distorting fiducial points and shapes of the PPG signal (percentage root-mean-square difference (PRD) ranging from 1% to 2%) as compared to the CS-based data method (CR from 2.50 to 3.13 for PRD from 2% of 4%). The higher data reduction with acceptable level of reconstruction quality demonstrates that the partial DCT-based method can lead to provide better overall energy consumption reduction solution for resource-constrained wearable devices.

Due to proper use of the image sparsity, CS (compressed sensing) image compression has made great achievements in the field of image compression without the constraint of Nyquist sampling law. A great deal of researches indicate that there exist obvious statistical and structural priors regularity for image information distribution, while traditional CS image compression algorithms only use the sparse characteristic of the image information. In this paper, we propose a CS image reconstruction algorithm based on the joint statistical and structural priors which can achieve efficient image reconstruction via a small amount of measurements. With the full use of inter-scale and intra-scale relations of the sparse coefficients, we optimize the iterative hard thresholding CS algorithm specifically by building a GSM model to constrain the local coefficients distribution statistically, and a tree model to constrain the global coefficients distribution structurally. Extensive simulations have been conducted and the results show that the proposed method has achieved a considerable promotion both on the speed and the PSNR gain of image reconstruction, compared with the traditional recovery algorithms under the same compression ratio.

Compressive sensing (CS) is one of the effective data compression methods where a small number of measurements of a sparse signal is required to have an exact recovery. In Compressive sensing technique, the data is acquired and compressed at the same time. CS concept allows capturing only fewer sparse and compressible signal which contains only useful information that helps to recover a proper signal. In this paper, image compression is performed with compressive sensing concept. The different basis matrices and sensing matrices are considered which satisfy the Restricted Isometric Property (RIP) and Independent and Identically Distributed (IID). The hardware implementation of CS is covered to have real-time compression for various image-based applications. The performance is observed regarding SNR, compression ratio, and correlation. The reconstruction algorithms are implemented on MATLAB platform. The obtained results show satisfactory performance for CS based image compression.

Compressive Sensing (CS) is an emerging technique in Internet of Medical Things (IoMT) application especially for smart wearable devices to prolong the sensor lifetime, and enable a continuous healthcare monitoring system. This paper describes the performance of CS on our wrist-based cuff-less biosensor for estimating blood pressure (BP) continuously. The proposed biosensor offers a novel BP estimation method by only using the biosignal from subject wrist. A CS technique is implemented to encrypt and reduce the data transmission load of the dual biosignal, which include impedance plethysmography (IPG) and photo plethysmography (PPG). Therefore, multiple compression ratio (CR) were tested to the original signal. We further compare the CS-based extracted features called pulse transit times (PTTs). Based on our experiments, CS-based PTT value that calculated from the IPG peak point to the PPG max2 point (F2), achieved the best correlation coefficient (R) of -0.85 and -0.43 to the systolic BP and diastolic BP, respectively. These results suggest that the implementation of CS on our proposed wrist biosensor is suitable for non-intrusive, yet long-term continuous BP monitoring.

Compressed sensing (CS) sampling is a sampling method which is based on the signal sparse. Much information can be extracted from as little as possible of the data by applying CS and this method is the idea of great theoretical and applied prospects. In the framework of compressed sensing theory, the sampling rate is no longer decided in the bandwidth of the signal, but it depends on the structure and content of the information in the signal. In this paper, the signal is the sparse in the Fourier transform and random sparse sampling is advanced by programing random observation matrix for peak random base. The signal is successfully restored by the use of Bregman algorithm. The signal is described in the transform space, and a theoretical framework is established with a new signal descriptions and processing. By making the case to ensure that the information loss, signal is sampled at much lower than the Nyquist sampling theorem requiring rate,but also the signal is completely restored in high probability. The random sampling has following advantages: alias-free sampling frequency need not obey the Nyquist limit and higher frequency resolution.

On the basis of the compressed sensing theory, this study proposed an improved wavelet sparse represented compressed sensing based image fusion algorithm. This algorithm firstly got the wavelet sparse domain linear measurement values of the original images by the dual radial sampling mode. Then a simple maximum absolute value fusion rule was adopted on the compressed sensing domain. Finally, the minimum total variation method was used to reconstruct the fused image. The experiment result shows that this algorithm has good fusion effect.

Compressive Sensing (CS) is a stable and robust technique that allows for the sub-sampling of data at a given data rate: ‘compressive sampling’ or ‘compressive sensing’ at rates smaller than the Nyquist sampling rate. It makes it possible to create standalone and net-centric applications with fewer resources required in Internet of Things (IoT). CS-based signal and information acquisition/compression paradigm combines the nonlinear reconstruction algorithm and random sampling on a sparse basis that provides a promising approach to compress signal and data in information systems. In this paper, we investigates how CS can provide new insights into coexisting heterogeneous IoT environments. First, we briefly introduce the CS theory with respect to the sampling through providing a compressed sampling process with low computation costs. Then, a CS-based framework is proposed for IoT, in which the hub nodes measure, transmit, and store the sampled data into the fusion center. Then, an efficient cluster-sparse reconstruction algorithm is proposed for in-network compression aiming at more accurate data reconstruction and lower energy efficiency. Therefore, compression should be performed locally at each Access Point (AP) and reconstruction is executed jointly to consider dependencies in the acquired data by the final fusion center.

We demonstrate a method to build signal dependent sparse representation dictionary for neural action potentials using K-SVD algorithm and Discrete Wavelets Transform. We also show a method to utilize this dictionary to recover the neural signal in the Compressive Sensing (CS) framework. Comparing against the non-signal dependent CS recovery algorithms, this new recovery algorithm can achieve same reconstruction quality with 2.5 times less compressed sensing measurements. For the same compression ratio, the purposed approach can increase recovery signal's signal to noise and distortion ratio (SNDR) by around 6 dB compare to non-signal dependent recovery method. We also evaluated the recovered signal using spike sorting techniques. The results have shown that the spikes clusters still maintain clear separation even when the compression ratio is at 15-20% of the Nyquist rate. This work also implies that any hardware implementation of compressed sensing could be scaled down in term of power and chip area by the same order if this signal dependent framework is used to recover the signal.

Compressive Sensing (CS) can alleviate the sensing effort involved in the acquisition of three dimensional image (3D) data. The most common CS sampling schemes employ uniformly random sampling because it is universal, thus it is applicable to almost any signals. However, by considering general properties of images and properties of the acquisition mechanism, it is possible to design random sampling schemes with variable density that have improved CS performance. We have introduced the concept of non-uniform CS random sampling a decade ago for holography. In this paper we overview the non-uniform CS random concept evolution and application for coherent holography, incoherent holography and for 3D LiDAR imaging.

Compressive sampling (CS) has emerged as significant signal processing framework to acquire and reconstruct sparse signals at rates significantly below the Nyquist rate. However, most of the CS development to-date has focused on finite-length signals and representations. In this paper we discuss a streaming CS framework and greedy reconstruction algorithm, the Streaming Greedy Pursuit (SGP), to reconstruct signals with sparse frequency content. Our proposed sampling framework and the SGP are explicitly intended for streaming applications and signals of unknown length. The measurement framework we propose is designed to be causal and implementable using existing hardware architectures. Furthermore, our reconstruction algorithm provides specific computational guarantees, which makes it appropriate for real-time system implementations. Our experimental results on very long signals demonstrate the good performance of the SGP and validate our approach.

The principal features of primary zone determine the performance parameters of the whole combustion chamber, such as the pollutant emissions and combustion efficiency. In this work, flow field and major species concentration measurements are conducted in the primary zone of an aero-engine combustion chamber. The operating conditions such as air inlet temperature, chamber pressure, and air-to-fuel ratio are chosen to replicate the realistic operating conditions. The velocity field and streamlines are obtained by particle imaging velocimetry technology. The concentrations of major species are acquired by a spontaneous Raman scattering system. This article validates the feasibility of two laser diagnostic measurement techniques and presents the initial results under realistic aero-engine conditions.