With high temporal resolution demand under noise contamination, the travel time estimation for the pulse-echo is noticed in the acoustic logging instrument design. To this end, an intelligent ultrasonic logging system is built to collect borehole information, and a framework with domain adaptation theory is proposed. The modified maximum mean discrepancy minimization combined with spatial pyramid pooling is constructed on different deep neural networks, where the transfer from the microseismic P-wave picking to the estimation of echo travel time is achieved. Versus the conventional travel time extraction algorithms, the proposed scheme improves the picking accuracy to 83.55% of the 10-dB signal-to-noise ratio. Experiments over the ultrasonic logging tool demonstrate the feasibility of the confusion domain, the effectiveness of travel time estimation, and the versatility of algorithm application.

In this paper, we investigate the channel estimation and decoding methods exploiting the channel sparsity in pilot-assisted Multiple-Input Multiple-Output (MIMO) Vector Orthogonal Frequency Division Multiplexing (V-OFDM) systems. Based on the sparse multipath channels, we utilize orthogonal and non-orthogonal pilot schemes to design the compressed sensing (CS) measurement process. For the optimization of the sensing matrix, we discuss the influence of pilot search algorithms and evaluation criteria and propose a particle swarm optimization (PSO) based pilot search algorithm with the simplified evaluation criterion to improve the pilot design procedure. Meanwhile, the effect of pilot insertion on the Peak-to-Average Power Ratio (PAPR) is reduced by a particular precoding matrix method without affecting the decoding complexity. Simulation data are used to evaluate the classical sparsity adaptive matching (SAMP) algorithms and the proposed Variable Threshold SAMP (VTSAMP) algorithm, and the results show that the improved method has higher channel estimation accuracy with unknown sparsity. On the other hand, to overcome the complexity of CS-based decoding, we design the fully connected Deep Neural Network (FC-DNN) decoders, which combine the results of channel estimation results with the prevalent neural network technology. We observe that when the sparse channels are estimated accurately by CS methods, the proposed FC-DNN can achieve the same performance as the high-precision linear decoder by using the time-domain pilots and channel estimation results.

In the field of ultrasonic nondestructive testing (NDT), robust and accurate detection of defects is a challenging task because of the attenuation and noising of the ultrasonic wave from the structure. For determining the reflection characteristics representing the position and amplitude of ultrasonic detection signals, sparse blind deconvolution methods have been implemented to separate overlapping echoes when the ultrasonic transducer impulse response is unknown. This letter introduces the ${\ell }_1/{\ell }_2$ ratio regularization function to model the deconvolution as a nonconvex optimization problem. The initialization influences the accuracy of estimation and, for this purpose, the alternating direction method of multipliers (ADMM) combined with blind gain calibration is used to find the initial approximation to the real solution, given multiple observations in a joint sparsity case. The proximal alternating linearized minimization (PALM) algorithm is embedded in the iterate solution, in which the majorize-minimize (MM) approach accelerates convergence. Compared with conventional blind deconvolution algorithms, the proposed methods demonstrate the robustness and capability of separating overlapping echoes in the context of synthetic experiments.

The time-difference method is a common one for measuring wind speed ultrasonically, and its core is the precise arrival-time determination of the ultrasonic echo signal. However, because of background noise and different types of ultrasonic sensors, it is difficult to measure the arrival time of the echo signal accurately in practice. In this paper, a method based on the wavelet transform (WT) and Bayesian information criteria (BIC) is proposed for determining the arrival time of the echo signal. First, the time-frequency distribution of the echo signal is obtained by using the determined WT and rough arrival time. After setting up a time window around the rough arrival time point, the BIC function is calculated in the time window, and the arrival time is determined by using the BIC function. The proposed method is tested in a wind tunnel with an ultrasonic anemometer. The experimental results show that, even in the low-signal-to-noise-ratio area, the deviation between mostly measured values and preset standard values is mostly within 5 μs, and the standard deviation of measured wind speed is within 0.2 m/s.

Multiple-input multiple-output (MIMO) with Orthogonal Frequency Division Multiplexing (OFDM) technology has both the advantages of MIMO and OFDM. Vector Orthogonal Frequency Division Multiplexing (V-OFDM) is an extension of OFDM, which makes data transmission flexible. In MIMO systems using V-OFDM technology, different novel schemes are proposed to improve channel estimation performance for different channel sparsity. The 2-D Kriging interpolation scheme is proposed for the non-sparse channels, which can significantly improve the performance of conventional Least Square (LS) and Minimum Mean Square Error (MMSE) algorithms. When the channel is sparse, the estimation process can be modeled as a sparse recovery problem using compressed sensing (CS) theory. In this paper, the measurement matrix is determined by pilot locations, and a pilot search algorithm based on random genetic algorithm (RGA) is proposed to minimize the cross-correlation value of the measurement matrix. Besides, a variable threshold sparsity adaptive matching pursuit (VTSAMP) algorithm is designed to obtain more accurate estimates, which achieves better Normalized Mean Square Error (NMSE) performance, higher calculation speed, and lower implementation complexity.

The fast compressed sensing (CS) reconstruction for natural image processing aims to infer the original pixel vector from randomized measurements as correctly as possible. Current first-order proximal mainstream schemes, including alternating direction methods of multipliers (ADMM) and the iterative shrinkage thresholding algorithm (ISTA), have advantages in convergence and stability at the expense of computing speed and complex tuning strategy. This paper proposes the deep iterative shrinkage network for real-time applications, where network layers replace the iterative optimization steps. The hyperparameters associated with shrinkage thresholds and step sizes are adjusted by standard deep learning updates. Numerical results suggest that the proposed method has flexibility and effectiveness in image reconstruction with different compression ratios while preserving the computational simplicity of end-to-end methods.