Image Reconstruction of Multiphase Flow Metering: A Compressed Sensing Approach

The traditional iris recognition algorithm exists some shortcomings, such as the higher iris feature dimension, and which caused low efficiency of algorithm execution. A quick iris recognition method based on improved Compressive Sensing (CS) algorithm is proposed in this paper. The conventional construction process of redundant atom library is just based on image gray information. It converts two-dimensional image into a one-dimensional column vector, ignoring the image texture informations. There is a high dimension of the shortcomings of one-dimensional vector. In this paper, a redundant atom library construction algorithm is proposed. It is based on Gabor + PCA (GPCA) to retain the image texture features, greatly reduces the dimension of the image at the same time. Orthogonal Matching Pursuit (OMP) algorithm is used to achieve the sparse coefficient, and then identify the image. The experimental results show that: the iris recognition algorithms based on GPCA and CS can reduce the dimension of the image features greatly, and execution speed and recognition accuracy is high.

In this paper, channel overhead is reduced by exploiting channel sparsity for multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) system. Where, compressive sensing (CS) based dictionary design algorithms has been adopted as a channel estimation technique in high mobile systems with minimal number of pilots, such as high-speed train (HST) systems. A novel framework design of the dictionary-based CS was proposed considering both delay and Doppler effects in order to correctly recover the channel response. The channel under consideration is a 2 by 2 space-time block code (STBC) MIMO channel. Simulation tests according to the international telecommunication union (ITU) channel model demonstrated the suitability of the proposed dictionary for estimating the channel impulse response (CIR) of a liner time varying (LTV) channel with a mobility approaches 675 Km/h related to a Doppler frequency of 1500 Hz and 2.4 GHz carrier frequency. Two CS recovery algorithms were applied; orthogonal matching pursuit (OMP) and basis pursuit (BP), where by about 7 dB gain in signal to noise ratio (SNR) was achieved with mobility of 675 Km/h using OMP as compared to BP at a bit error rate (BER) of with 128 OFDM subcarriers.

The availability of the radio spectrum is limited. To compromise the increasing demand for high data rate devices, this fixed spectrum need to be used efficiently. The existing challenge is that the larger portion of the licensed spectrum is underutilized. Cognitive Radio is a technology introduced to help to detect the radio spectrum is occupied or not. Spectrum sensing helps to detect the spectrum holes and provides high spectral resolution capability. This is done using sparse techniques such as Compressive sensing and Sparse Bayesian Learning techniques. Compressive Sensing algorithms such as Basis Pursuit and Orthogonal Matching Pursuit are analyzed. Based on the concept of Sparse Bayesian learning, an expectation maximization algorithm is introduced for spectrum sensing and recovery of the original transmitted signal in cognitive radio systems. Performance comparison is done between proposed algorithms and is validated using Register Transfer Level- Software Defined Radio.

The orthogonal matching pursuit (OMP) algorithm is widely used because of its simple algorithmic structure, the linear magnitude of its computational complexity and other advantages. Algorithms of improved and extended OMP types are continually emerging. To make the OMP algorithm more suitable for the new transmission requirements of the fifth-generation (5G) network, the orthogonal matching pursuit with multiple users (OMP_mu) algorithm is proposed for cell-free massive multi-input multi-output (MIMO) networks with multiuser spatial index modulation. The OMP_mu algorithm considers the constraint condition for the sparsity of each user on the basis of each iterative search in the OMP algorithm until all users meet the constraint. The simulation results show that compared with the original OMP algorithm, the OMP_mu algorithm has a greatly improved demodulation performance. Compared with the orthogonal matching pursuit step-by-step (OMP_step) algorithm, which takes the user sparsity constraint condition into account in two steps, the OMP_mu algorithm greatly reduces the operation time and number of iterations while achieving a slightly better performance. The OMP_mu algorithm provides an effective reference for the application of spatial index modulation in cell-free massive MIMO networks.

The recovery algorithm is a crucial issue of the compressed sensing (CS). This paper presents a greedy algorithm called optimized orthogonal matching pursuit (OOMP) for sparse signal recovery. The OOMP algorithm improves the orthogonal matching pursuit (OMP) algorithm via providing the projection onto the subspace generated by the selected measurements and minimizing the corresponding residual error at each iteration. Compared with the OMP algorithm, the simulation results show that the proposed algorithm provides a better approximation of a given signal and reduces measurements needed to recover the signal accurately.

Broadband low-voltage power line communication (PLC) has many advantages including less investment cost, construction speed, and convenient access. Since the orthogonal frequency division multiplexing (OFDM) technology has strong anti-jamming and anti-frequency selective fading characteristics naturally it becomes a better low voltage power line communication solution. We proposed an OFDM channel estimation method based on compressed sensing (CS) technique according to the channel characteristics of low-voltage power lines. CS algorithm in OFDM system was discussed and an orthogonal matching pursuit (OMP) algorithm was applied to reconstruct the PLC channel information. The simulation results showed that the communication channel estimation method based on CS technique was feasible in PLC system, and the validity of information transmission in OFDM systems can be enhanced.

Due to the broad bandwidth nature of the direct sequence spread spectrum (DSSS) signal, the application of narrowband interference (NBI) cancelation algorithms based on the Nyquist‐Shannon sampling theory are limited by the high sampling rate. To solve the sampling and processing difficulty in the traditional DSSS communication interference cancelation method, we used the compressive sensing (CS) to reduce the sampling rate of the DSSS communication system, and further proposed a compressed domain NBI elimination method in this study. We proved that NBI with a certain bandwidth and DSSS signal are both sparse in specific dictionaries and proposed the corresponding sparse dictionary construction method. Detailed analysis of the compressed domain features of NBI and the DSSS signal demonstrated that it is possible to extract the strong NBI components from the compressed measurements. Two compressed domain NBI cancelation algorithms were proposed based on this idea. The extracted NBI components were filtered out from the compressed measurements directly in the first algorithm, and then we achieved the DSSS signal demodulation in compressed domain using the separated DSSS signal components with the orthogonal matching pursuit (OMP) algorithm. A proposed extraction filter block sparse Bayesian learning (EFBSBL) framework algorithm was utilized to estimate the NBI from the compressed measurements in the second algorithm. The estimated NBI was eliminated from the received signal, and then we recompressed the DSSS signal after NBI cancelation and realized the DSSS signal demodulation in compressed domain with the OMP algorithm. Simulation results support our theoretical analysis results and reveal that the two proposed algorithms are both effective for NBI cancelation and significant interference cancelation performance are achieved.

In this paper, we present a novel architecture based on field-programmable gate arrays (FPGAs) for the reconstruction of compressively sensed signal using the orthogonal matching pursuit (OMP) algorithm. We have analyzed the computational complexities and data dependence between different stages of OMP algorithm to design its architecture that provides higher throughput with less area consumption. Since the solution of least square problem involves a large part of the overall computation time, we have suggested a parallel low-complexity architecture for the solution of the linear system. We have further modeled the proposed design using Simulink and carried out the implementation on FPGA using Xilinx system generator tool. We have presented here a methodology to optimize both area and execution time in Simulink environment. The execution time of the proposed design is reduced by maximizing parallelism by appropriate level of unfolding, while the FPGA resources are reduced by sharing the hardware for matrix-vector multiplication across the data-dependent sections of the algorithm. The hardware implementation on the Virtex6 FPGA provides significantly superior performance in terms of resource utilization measured in the number of occupied slices, and maximum usable frequency compared with the existing implementations. Compared with the existing similar design, the proposed structure involves 328 more DSP48s, but it involves 25,802 less slices and 1.85 times less computation time for signal reconstruction with N = 1024, K = 256, and m = 36, where N is the number of samples, K is the size of the measurement vector, and m is the sparsity. It also provides a higher peak signal-to-noise ratio value of 38.9 dB with a reconstruction time of 0.34 s, which is twice faster than the existing design. In addition, we have presented a performance metric to implement the OMP algorithm in resource constrained FPGA for the better quality of signal reconstruction.

For the bogie traction motor bearings of rail vehicles under road transportation conditions, its vibration signal full-time domain data acquisition data volume, storage pressure, subsequent data analysis difficulties, and other problems. This study proposes a data compression acquisition and data reconstruction method for bearing vibration signals based on compressed sensing (CS), which can achieve compressed sampling, transmission, and storage of the original signal with fewer observations, and restore the original signal by combining reconstruction algorithms to achieve low error reconstruction. We study the effects of different sparse transform methods on signal sparsity, as well as the signal sparse performance based on different sparse transforms and the signal reconstruction effect of different reconstruction algorithms, and propose a sparse representation method based on the discrete Fourier transform (DFT) and the generalized orthogonal matching pursuit (gOMP) algorithm for CS. Through simulation and experimental comparison, it is concluded that the method can restore and reconstruct the original high-dimensional signal with small reconstruction error, and solve the problems of massive data, inconvenient transmission, and difficult data analysis in engineering.

Active thermography uses IR camera to capture the thermal response from a test sample to detect defects in the materials. Depending on the material geometry, externally Quadrature Frequency Modulated (QFM) optical stimulation consisting of very low frequency components usually less than 1 HZ is projected on the test sample. IR camera captures the thermal response from test material at a frequency much higher than the required nyquist rate. This is wastage of resources such as sensors, memory and power. In this paper Compressive Sensing (CS) is used to capture the thermal response from aluminum and bone test samples using IR camera at a reduced frame rate and then complete response is recovered using CS recovery algorithms Basis Pursuit (BP) and Orthogonal Matching Pursuit (OMP). Recovered thermal response is processed using correlation based pulse compression approach to perform subsurface analysis to detect the defects. To verify the effectiveness of the recovery algorithms comparison is done between original and recovered data and the results are promising.

We investigate ultra-wideband (UWB) channel estimation based on compressive sampling (CS), where the orthogonal matching pursuit (OMP) algorithm is employed to recover the channel waveform from noisy measurements. In order to boost the robustness of OMP in the presence of additive Gaussian noise (AWGN), we propose a weighted OMP (WOMP) algorithm. For a given sparse dictionary, weighting factors are assigned to the atoms and a weighted matching process is performed by WOMP. Simulation results show that the proposed WOMP is more robust than the original OMP and can be used to gain better channel estimation precision.

In this paper, the estimation of doubly spread underwater acoustic (UWA) channels is investigated. The UWA channels are characterized by severe delay spread and significant Doppler effects, and can be well modeled as a multi-scale multi-lag (MSML) channel. Furthermore, exploiting the sparsity of UWA channels, MSML channel estimation can be transformed into the estimation of parameter sets (amplitude, Doppler scale factor, time delay). Based on this, the orthogonal matching pursuit (OMP) algorithm has been widely used. However, the estimation accuracy of OMP depends on the size of the dictionary, which is related with both delay spread and Doppler spread. Thus it requires high computational complexity. This paper proposes a new method, called wavelet transform (WT) based algorithm, for the UWA channel estimation. Different from OMP algorithm which needs to search in both time domain and Doppler domain, WT-based algorithm only needs to search in time domain by using the Doppler invariant characteristic of hyperbolic frequency modulation (HFM) signal. The performance of the proposed algorithm is evaluated by computer simulations based on BELLHOP. The simulation results show that WT-based algorithm performs slightly better than OMP algorithm in low signal to noise ratio (SNR) while can greatly reduce computational complexity.

In this paper, a fast orthogonal matching pursuit (OMP) algorithm based on optimized iterative process is proposed for sparse time-varying underwater acoustic (UWA) channel estimation. The channel estimation consists of calculating amplitude, delay and Doppler scaling factor of each path using the received multi-path signal. This algorithm, called as OIP-FOMP, can reduce the computationally complexity of the traditional OMP algorithm and maintain accuracy in the presence of severe inter-carrier interference that exists in the time-varying UWA channels. In this algorithm, repeated inner product operations used in the OMP algorithm are removed by calculating the candidate path signature Hermitian inner product matrix in advance. Efficient QR decomposition is used to estimate the path amplitude, and the problem of reconstruction failure caused by inaccurate delay selection is avoided by optimizing the Hermitian inner product matrix. Theoretical analysis and simulation results show that the computational complexity of the OIP-FOMP algorithm is reduced by about 1/4 compared with the OMP algorithm, without any loss of accuracy.

Complementary metal-oxide-semiconductor (CMOS) radar has recently gained much research attraction because small and low-power CMOS devices are very suitable for deploying sensing nodes in a low-power wireless sensing system. This study focuses on the signal processing of a wireless CMOS impulse radar system that can detect humans and objects in the home-care internet-of-things sensing system. The challenges of low-power CMOS radar systems are the weakness of human signals and the high computational complexity of the target detection algorithm. The compressive sensing-based detection algorithm can relax the computational costs by avoiding the utilization of matched filters and reducing the analog-to-digital converter bandwidth requirement. The orthogonal matching pursuit (OMP) is one of the popular signal reconstruction algorithms for compressive sensing radar; however, the complexity is still very high because the high resolution of human respiration leads to high-dimension signal reconstruction. Thus, this paper proposes a two-stage reconstruction algorithm for compressive sensing radar. The proposed algorithm not only has lower complexity than the OMP algorithm by 75% but also achieves better positioning performance than the OMP algorithm especially in noisy environments. This study also designed and implemented the algorithm by using Vertex-7 FPGA chip (Xilinx, San Jose, CA, USA). The proposed reconstruction processor can support the real-time radar image display with a throughput of 28.2 frames per second.

FOMP algorithm for Direction of Arrival estimation