Electromagnetic Detection System with Magnetic Dipole Source for Near-Surface Detection.

This paper proposes a nondestructive, separate transmitter-receiver (TX-RX) electromagnetic measurement system for near-surface detection. Different from the traditional dual-coil integrated design, the proposed transient electromagnetic (TEM) system performs shallow subsurface detection using independent TX coil and movable RX coils. This configuration requires a large primary field so that the far-away secondary field is able to generate reliably induced voltages. To achieve this goal, a bipolar current-pulsed power supply (BCPPS) with a late resonant charging strategy is designed to produce a sufficiently large magnetic moment for the exciting coil with low source interference. The magnetic dipole source (MDS) with a large proportion of weight is separated from the field observation device and does not need to be dragged or transported during the detection process. This setup lowers the weight of the scanning device to 3 kg and greatly improves the measurement efficiency. The results of the laboratory test verify the effectiveness of the separate MDS and RX module system. Field experimental detection further demonstrates that the proposed system can realize highly efficient and shallow surface detection within a 200 m range of the MDS device.

Adaptive single-pixel imaging based on guided coefficients.

The existing adaptive single-pixel imaging methods suffer from a waste of sampling resources. The sampling resources are not used adequately for superior localization of significant coefficients and reconstruction. In this paper, an adaptive single-pixel imaging method via the guided coefficients in the Haar wavelet tree is proposed. The goal is to achieve high quality imaging with less sampling resources. The guided coefficients are selected from the unsampled coefficients by a proposed same-scale prediction method based on the sampled coefficients. These guided coefficients are used to localize the significant coefficients with higher resolution belonging to the sampled coefficients and the significant coefficients belonging to the guided coefficients by a proposed guided prediction method. The significant guided coefficients are then used in the composite reconstruction method to reconstruct the image. Performance analysis shows that the proposed method reduces waste of the sampling resources and localizes more significant coefficients. Simulation results demonstrate that the proposed method improves the imaging quality in terms of peak signal-to-noise ratio up to 29.7 dB for the images containing regular and chaotic textures in the noise-free environment. The sampling rate for the same imaging quality can be reduced up to 56%. Under the noisy condition, the proposed method also achieves better imaging quality at a lower sampling rate.

Color-direction patch-sparsity-based image inpainting using multidirection features.

This paper proposes a color-direction patch sparsity-based image in painting method to better maintain structure coherence, texture clarity, and neighborhood consistence of the in painted region of an image. The method uses super-wavelet transform to estimate the multi-direction features of a degraded image, and combines with color information to construct the weighted color-direction distance (WCDD) to measure the difference between two patches. Based on the WCDD, the color-direction structure sparsity is defined to obtain a more robust filling order and more suitable multiple candidate patches are searched. Then, the target patches are sparsely represented by the multiple candidate patches under neighborhood consistency constraints in both the color and the multi-direction spaces. Experimental results are presented to demonstrate the effectiveness of the proposed approach on tasks such as scratch removal, text removal, block removal, and object removal. The effects of super-wavelet transforms and direction features are also investigated.

Nonlinear analysis in cognition process.

Cognition process is directly related to the brain functionality and is a dynamically changing system. Nonlinear analysis of EEG signals has been used as a means for studying the dynamical changes in cortical networks. In the course of cognition process, the activity complexity of the neuronal units is continually shifting. This phenomenon can be viewed with the topographic map and nonlinear EEG measures. The surrogate data method was used to show that EEG signals during cognitive activity are nonlinear. Additionally, we employ the compressed spectral array method to show that the gammaband EEG is closely related to cognitive processes.

Correlation dimensions of EEG changes during mental tasks.

Nonlinear analysis of electroencephalogram (EEG) signals provides a means for studying the dynamical changes in cortical networks related to brain electrical activity. In this study, the correlation dimension (D2) and point correlation dimension (PD2) were used to investigate the quantitative complexity of EEG during cognitive processes. EEGs were recorded in 30 normal subjects under seven conditions: two resting states and five mental activities. Results show that D2 and PD2 are significantly correlated. Compared to D2, PD2 is more sensitive to EEG data and less computationally intensive.