A sub-Nyquist radar receiver based on photonics-assisted compressed sensing is proposed. Cascaded dictionaries are applied to extract the delay and the Doppler frequency of the echo signals, which do not need to accumulate multiple echo periods and can achieve better Doppler accuracy. An experiment is performed. Radar echoes with different delays and Doppler frequencies are undersampled and successfully reconstructed to obtain the delay and Doppler information of the targets. Experimental results show that the average reconstruction error of the Doppler frequency is 5.33 kHz using an 8-μs radar signal under the compression ratio of 5. The proposed method provides a promising solution for the sub-Nyquist radar receiver.

We propose a time-gated-single-pixel-camera as a promising sensor for image-free object detection for automotive application in adverse weather conditions. By combining the well-known principles of time-gating and single-pixel detection with neural networks, we aim to ultimately detect objects within the scene rapidly and robustly with a low-cost sensor. Here, we evaluate the possible data reduction such a system can provide compared to a conventional time-gated camera.

Hadamard single-pixel imaging is an appealing imaging technique due to its features of low hardware complexity and industrial cost. To improve imaging efficiency, many studies have focused on sorting Hadamard patterns to obtain reliable reconstructed images with very few samples. In this study, we propose an efficient Hadamard basis sampling strategy that employs an exponential probability function to sample Hadamard patterns in a direction with high energy concentration of the Hadamard spectrum. We used the compressed-sensing algorithm for image reconstruction. The simulation and experimental results show that this sampling strategy can reconstruct object reliably and preserves the edge and details of images.

多输入多输出（Multiple-input-multiple-output，MIMO）可见光通信（Visible Light Communications，VLC）系统接收端需精确的信道状态信息用以解调信号，而常用的最小二乘算法对噪声敏感，估计误差较大，难以保证可靠性。基于信道稀疏特性，利用压缩感知方法进行MIMO-VLC信道估计，提出一种基于离散傅里叶变换（Discrete Fourier Transform，DFT）的稀疏度预测自适应匹配追踪（DFT Based Prediction-sparsity Adaptive Matching Pursuit，DFT-SAMP）算法。首先，通过DFT的稀疏度预测方法对信道冲激响应的稀疏度进行预估计，将估计的稀疏度作为算法初始步长，以快速逼近真实稀疏度，提高算法效率；其次，采用SAMP算法重构信道冲激响应，提高信道估计准确性，保证通信可靠性。基于2×2的MIMO-VLC系统信道估计实验结果表明，导频数为32时，本文算法相较于最小二乘算法在误码率满足前向纠错的误码率阈值（3.8×10^-3）时所需的信噪比降低4.5 dB；利用DFT-SAMP算法进行信道估计，在保证可靠性的同时，运行效率相比SAMP算法提升约69%，为MIMO-VLC系统信道估计提供了更为有效的方式。

Presented is a novel way to combine snapshot compressive imaging and lateral shearing interferometry in order to capture the spatio-spectral phase of an ultrashort laser pulse in a single shot. A deep unrolling algorithm is utilized for snapshot compressive imaging reconstruction due to its parameter efficiency and superior speed relative to other methods, potentially allowing for online reconstruction. The algorithm’s regularization term is represented using a neural network with 3D convolutional layers to exploit the spatio-spectral correlations that exist in laser wavefronts. Compressed sensing is not typically applied to modulated signals, but we demonstrate its success here. Furthermore, we train a neural network to predict the wavefronts from a lateral shearing interferogram in terms of Zernike polynomials, which again increases the speed of our technique without sacrificing fidelity. This method is supported with simulation-based results. While applied to the example of lateral shearing interferometry, the methods presented here are generally applicable to a wide range of signals, including Shack–Hartmann-type sensors. The results may be of interest beyond the context of laser wavefront characterization, including within quantitative phase imaging.

集成成像技术作为一种重要的裸眼三维显示技术，在完整记录三维场景信息的同时，庞大的数据量给传输和存储带来了压力。为了实现图像的有效压缩和重构，根据光子计数集成成像的特点，基于分布式压缩感知理论，提出用于图像压缩与重构的方案。该方案将图像分为参考图像和非参考图像两类，对其设置不同的测量率并分别进行重构。为保证非参考图像的重构质量，提出一种联合重构算法。该算法首先对非参考图像进行分块测量，依据与参考图像之间的相关性进行图像块分类，然后结合参考图像测量值信息构建新的测量矢量，利用新的测量矢量完成初次图像重构。为了进一步提升图像重构质量，对初次重构结果进行二次残差补偿重构，获得最终重构结果。最后通过设置不同的测量率进行了大量实验，实验结果表明，所提算法在测量率为0.25时，图像重构质量可以达到30 dB，测量率为0.4时，图像质量可以达到35 dB，算法性能具有一定的优越性。