Compressed Sampling Data Research Articles

Wavelet Transforms Significantly Sparsify and Compress Tactile Interactions.

As higher spatiotemporal resolution tactile sensing systems are being developed for prosthetics, wearables, and other biomedical applications, they demand faster sampling rates and generate larger data streams. Sparsifying transformations can alleviate these requirements by enabling compressive sampling and efficient data storage through compression. However, research on the best sparsifying transforms for tactile interactions is lagging. In this work we construct a library of orthogonal and biorthogonal wavelet transforms as sparsifying transforms for tactile interactions and compare their tradeoffs in compression and sparsity. We tested the sparsifying transforms on a publicly available high-density tactile object grasping dataset (548 sensor tactile glove, grasping 26 objects). In addition, we investigated which dimension wavelet transform-1D, 2D, or 3D-would best compress these tactile interactions. Our results show that wavelet transforms are highly efficient at compressing tactile data and can lead to very sparse and compact tactile representations. Additionally, our results show that 1D transforms achieve the sparsest representations, followed by 3D, and lastly 2D. Overall, the best wavelet for coarse approximation is Symlets 4 evaluated temporally which can sparsify to 0.5% sparsity and compress 10-bit tactile data to an average of 0.04 bits per pixel. Future studies can leverage the results of this paper to assist in the compressive sampling of large tactile arrays and free up computational resources for real-time processing on computationally constrained mobile platforms like neuroprosthetics.

Estimation of LFM signal parameters using RD compressed sampling and the DFRFT dictionary

In this paper, a method combining random demodulator (RD) and discrete fractional Fourier transform (DFRFT) dictionary is suggested to directly estimate the parameters of linear frequency modulation (LFM) signals from compressed sampling data. First, the RD system parameters are modified in accordance with the characteristics of the LFM signal to produce effective compressed sampling data. Next, a DFRFT dictionary is built using the fractional Fourier transform theory, and sparse representation coefficients are obtained by reconstructing the compressed sampling data using the recovery algorithm and DFRFT dictionary. The signal exhibits characteristics that make it pulse under the best fractional transform order, so the problem of signal parameter estimation can be reduced to searching for the location of the maximum value of sparse representation coefficients. The location is determined by a parameter optimization algorithm, and from there, the initial frequency and Chirp rate of the LFM signal can be estimated. Lastly, simulation and real data tests are performed to confirm that the suggested method can directly be utilized to estimate the parameter of LFM signals using compressed sampling data in addition to having high sparse representation ability for LFM signals.

A method for LFM signal parameter estimation based on MWC system and STFT-DPT

A method of estimating linear frequency modulation (LFM) signal parameters using compressed sampling (CS) data of a Modulated Wideband Converter (MWC) system is proposed. The short-time Fourier transform algorithm is used to analyze the CS signal of the MWC system, and the obtained CS signal frequency will present a form of multi-segment distribution. By intercepting the CS signal corresponding to the first segment frequency and analyzing the intercepted segment signal with discrete polynomial transform and Rife algorithm, the chirp rate and initial frequency estimation of the segment CS signal can be preliminarily estimated. According to the random sequence with cyclic displacement, the spectrum shift relationship between the input signal and the output signal can be calculated. Therefore, the parameter of the original signal can be estimated by using single channel CS data. Then, all channels are analyzed with the same method, and a more stable parameter estimation result can be obtained than the single channel output. Finally, combined with the simulation experiment, the proposed method can be used to estimate the initial frequency and chirp rate of the LFM signal using CS data effectively.

A Method Based on Random Demodulator and Waveform Matching Dictionary to Estimate LFM Signal Parameter

The compressed sampling theory provides a good idea for sampling broadband linear frequency‐modulated (LFM) signal, but the signal reconstruction after sampling should be considered when using the compressed sampling system to sample the signal. For the problem of estimating the parameters of the LFM signal under the condition of nonreconstruction, a method combining random demodulation (RD) and waveform matching (WM) dictionary is proposed in this paper. Firstly, the RD system is used to collect the compressed sampling data of LFM signal. In order to ensure the validity of the compressed sampling data, the value of the compressed sampling rate R in the RD system is discussed. Then, the feasibility of using compressed sampling data to estimate LFM signal parameters is theoretically demonstrated. According to the construction principle of waveform matching dictionary, the corresponding relationship between sparse representation coefficients, dictionary atoms, and signal parameters can be determined. Using this theory, the LFM signal parameters can be estimated. Finally, simulation and real signal experiments verify that the proposed method can realize the estimation of initial frequency and chirp rate of LFM signal when some prior information is known.

A lightweight solution of industrial computed tomography with convolutional neural network

As an advanced non-destructive testing and quality control technique, industrial computed tomography (ICT) has found many applications in smart manufacturing. The existing ICT devices are usually bulky and involve mass data processing and transmission. It results in a low efficiency and cannot keep pace with smart manufacturing. In this paper, with the support from Internet of things (IoT) and convolutional neural network (CNN), we proposed a lightweight solution of ICT devices for smart manufacturing. It consists of efforts from two aspects: distributed hardware allocation and data reduction. At the first aspect, ICT devices are separated into four functional units: data acquisition, cloud storage, computing center and control terminals. They are distributed and interconnected by IoT. Only the data acquisition unit still remains in the production lines. This distribution not only slims the ICT device, but also permits the share of the same functional units. At the second aspect, in the data acquisition unit, sparse sampling strategy is adopted to reduce the raw data and singular value decomposition (SVD) is used to compress these data. They are then transmitted to the cloud storage. At the computing center, an ICT image reconstruction algorithm and a CNN are applied to these compressed sparse sampling data to obtain high quality CT images. The experiments with practical ICT data have been executed to demonstrate the validity of the proposed solution. The results indicate that this solution can achieve a drastic data reduction, a storage space save and an efficiency improvement without significant image degradation. The presented work has been helpful to push the applications of ICT in smart manufacturing.

Single-pixel foreground imaging without a priori background sensing.

The single-pixel imaging technique, which is significantly different from conventional multi-pixel imaging, utilizes the signal recorded by a single-pixel detector and a stream of structured illumination patterns to reconstruct an image. We design and experimentally demonstrate a real-time single-pixel foreground imaging system with fewer samples and without a priori sensing of the background by performing incremental principal component analysis on online compressed sampling data. A fast ℓ1 compressed sensing algorithm is adopted to realize real-time foreground imaging of 10 frames per second with an image size of 127 × 127 pixels and a compression ratio of 3%. When applied to a surveillance system that requires long-distance video transmission, this scheme can greatly reduce the compression ratio and allow the system to work with smaller communication bandwidths.

For hyperspectral compressed sensing method on linear mixed spectrum model

Hyperspectral Compressed Sensing(HCS) is crucial for data storage and the real-time transmission of airborne-or spacebornebased imaging platforms. The Linear Mixing Model(LMM) has been successfully applied to HCS reconstruction. However, the obtained spectrum may be disturbed, thereby limiting the improvement of reconstruction quality due to the influence of illumination conditions,topographic changes, and atmospheric effects. Spectral disturbance is corrected on the basis of LMM by introducing the spectral correction term, and a linear mixing model for spectral perturbation correction is proposed. Moreover, an improved HCS method based on modified LMM is proposed. This proposed model only performs spectral compressed sampling on the original hyperspectral images at the sampling end. The proposed method uses the proposed spectral perturbation correction model to reconstruct the original hyperspectral images based on the compressed sampling data. The alternating direction multiplier method is used to estimate the optimal values of each component in the modified LMM to obtain the optimal reconstruction quality. Experimental results show that the proposed method can achieve better reconstructed performance compared with other classical HCS methods.

Data Reconstructing Algorithm in Unreliable Links Based on Matrix Completion for Heterogeneous Wireless Sensor Networks

In heterogeneous wireless sensor networks, the data collection method based on compressed sensing technology is susceptible to packet loss and noise, which leads to a decrease in data reconstruction accuracy in unreliable links. Combining compressed sensing and matrix completion, we propose a clustering optimization algorithm based on structured noise matrix completion, in which the cluster head transmits the compressed sampling data and compression strategy to the base station. The algorithm we proposed can reduce the energy consumption of the node in the process of data collection, redundant data and transmission delay. The rank-1 matrix completion algorithm constructs an extremely sparse observation matrix, which is adopted by the sink node to complete the reconstruction of the whole network data. Simulation experiments show that the proposed algorithm reduces network transmission data, balances node energy consumption, improves data transmission efficiency and reconstruction accuracy, and extends the network life cycle.

High-Efficiency Observations: Compressive Sensing and Recovery of Seismic Waveform Data

We present a new sampling scheme for seismic network observations and seismic exploration data acquisition based on compressive sensing theory. According to this theory, seismic data can be recovered with a compressive sampling scheme, using fewer samples than in traditional methods, provided that two prerequisites are met. The first prerequisite is sparse representation of the data in a transform domain. We use a one-dimensional wavelet transform to sparsely express the waveform data of the seismic network. For seismic exploration data, we use a curvelet transform as the sparse transform. The second prerequisite is incoherence between the sampling method and sparse transform. To enhance the incoherence, we propose a random sampling scheme for network and exploration observations, as random sampling is incoherent to most data transforms. In particular, we propose temporal random sampling for seismic network data observation and a full random sampling scheme in time and space for seismic exploration data. Compared with random sampling in spatial dimensions only, full random sampling further enhances incoherence because it adds the temporal dimension for randomization. Finally, seismic data are recovered from the compressive sampling data by calculating a sparsity-promoting algorithm in the sparse transform domain. We perform a real data test and synthetic data tests to illustrate that the proposed method can be used stably to achieve compressive sampling and successful recovery of high-resolution seismic waveform data. The results show that good sparse representation of the data and high incoherence between the sampling scheme and the data are important for successful recovery.

Multiple-image encryption scheme via compressive sensing and orthogonal encoding based on double random phase encoding

ABSTRACTWe propose an optical multiple-image encryption scheme based on compressive sensing and double random phase encoding. The orthogonal encoding method is used for integrating and extracting multiple-image compressed sampling data. In the encryption process, each plain image is sampled by compressive sensing and the sampled data of all the images are integrated into a synthesized ciphertext by orthogonal encoding method. The synthesized ciphertext is re-encrypted through the double random phase encoding technique to form final ciphertext. In order to reduce the data of keys, chaotic matrix, of which only the initial value should be memorized, is employed in the compressive sampling process and double random phase encoding process. Numerical simulation and the analysis of attacks on encrypted image are implemented to demonstrate the security and validity of the proposed approach.

Joint carrier frequency and DOA estimation using a modified ULA based MWC discrete compressed sampling receiver

The uniform linear array (ULA) based modulated wideband converter discrete compressed sampling (CS) receiver is a recently introduced sub-Nyquist sampling scheme for effective acquisition of the carrier frequency (CF) and direction-of-arrival (DOA) of the received radar signal. However, the ULA based system needs to correct the phase differences of the CS data in order to estimate DOA, which would cause a low DOA estimation performance. In this study, the authors propose a modified ULA based system to improve the DOA estimation performance, where another similar branch is added in each antenna to construct the proposed symmetry system. First, the received signal is mixed to basebands. Then, the mixed signals are low-pass filtered and down-sampled to get the CS data. Second, the two branches of the CS data received in one antenna can be utilised to estimate CF. The CS data received in the added branches can be used to estimate DOA without correcting the phase differences. Finally, simulations illustrate the validity of the proposed modified ULA based system and show the proposed system outperforms the ULA based system in DOA estimation when signal-to-noise ratio is more than −10 dB and the number of snapshots is more than 20.

LPI Radar Waveform Recognition Based on Multi-Branch MWC Compressed Sampling Receiver

In this paper, a new wideband digital receiver based on the modulated wideband converter compressed sampling (CS) system is proposed to replace a conventional wideband digital receiver, solve the cross-channel signal problem, and achieve intra-pulse modulation recognition for the low probability of intercept (LPI) radar signals. The proposed receiver uses cyclic-shifted pseudo-random sequences to mix the received signals to baseband. The mixed signals are low-pass filtered and down-sampled to obtain compressed sampling data containing the full information of the received signals. Since the phases of the multi-branch CS data are designed to change regularly, we propose a phase correction factor to correct the phases of the multi-branch CS data, which can be superposed to increase the output signal-to-noise ratio (SNR). Then, a recognition method based on the short-time Fourier transform (STFT) and spectrum energy focusing rate tests are proposed. First, the spectrum modulation bandwidth based on the STFT spectrum of the superposed CS data is tested to distinguish phase modulation signals and frequency modulation signals approximately. Then, the spectrum energy focusing rate of the superposed CS data is tested to determine the intra-pulse modulation type specifically. Finally, the superiority of the proposed recognition system for LPI radar signals and cross-channel signals is demonstrated by simulations and analysis. Simulation results show that the overall ratio of the successful recognition (RSR) is 100% when the SNR is greater than 0 dB.

A ULA-Based MWC Discrete Compressed Sampling Structure for Carrier Frequency and AOA Estimation

In this paper, we propose a new wideband digital receiver based on the modulated wideband converter (MWC) discrete compressed sampling (CS) structure, and we further propose a uniform linear array (ULA)-based MWC discrete CS structure to estimate the carrier frequency and angle-of-arrival (AOA). The proposed receiver and ULA-based system use a bank of pseudorandom sequences to mix signals to baseband and other sub-bands. The product is then low-pass filtered and down-sampled at a low rate to obtain the baseband CS data. Meanwhile, the cross-channel signal problem can be solved flexibly, since the frequency spectrum of the cross-channel signal is all mixed to the baseband. And the sensitivity can be increased a lot, because the bandwidth of the baseband is very small. However, the CS data lose the true carrier frequency and phase differences information, because of the mixing operation.Therefore, we propose to utilize the cyclic-shifted pseudorandom sequences in the ULA-based system in order to design a special phase relationship for the CS data, and on this basis, we can get the carrier frequency estimation. Then, we correct the phase differences of the CS data to estimate AOA. Finally, simulation experiments show that the proposed systems are effective and demonstrate the superior estimation performance in the case of small numbers of snapshots and low signal-to-noise ratios.

Compressive Sampling Photoacoustic Microscope System based on Low Rank Matrix Completion

Abstract Photoacoustic Microscopy (PAM) has developed into a powerful tool for deep tissue imaging with a better spatial resolution. But the data acquisition time in PAM is so long that it is a great challenge for real time imaging. In this paper, a new PAM data acquisition and image recovery method, called Compressive Sampling PAM System based on Low Rank Matrix Completion (CSLRM-PAM) is proposed to obtain a high-resolution PAM image with relatively low sampling rates. In order to successfully set up a CSLRM-PAM system, the two key problems which we need to keep focus on are design of the compressive sampling scheme and the corresponding image recovery algorithm. In this paper, two compressive sampling schemes based on expander graphs are proposed to replace the conventional point-by-point scanning scheme to implement fast data acquisition. Then, the low rank matrix completion is utilized to obtain high-resolution PAM image directly from the compressive sampling data. The effectiveness of the proposed scheme is validated using both numerical analysis and PAM experiments. In contrast with the conventional system, the proposed CSLRM-PAM system is able to dramatically decrease the total sampling points for a relatively high-resolution PAM image and to implement accelerated data acquisition.