Massive multiple-input multiple-output system can enable multi-stream transmission for high spectrum efficiency, which is a key technology in future 5G cellular networks. In this paper, we degenerate a direction of arrival (DOA) related 2-D weighted subspace fitting function into two independent parameterized 1-D versions. Based on this, we develop a novel 2-D DOA estimation algorithm, which can be utilized to assist in performing downlink precoding. Furthermore, we also make an analysis on the computational complexity and the theoretical Cramér-Rao lower bound. The direct merits are as follows: the proposed algorithm includes only once polynomial rooting and also does not require angle paring, hence it is of computational efficiency; in addition, compared with some existing algorithms, it can achieve higher 2-D angle estimating accuracy. A series of Monte Carlo simulations are subsequently carried out, which demonstrate the effectiveness of the proposed algorithm.

Immediate and accurate detection of foreign metal objects (FMOs) in clothing products is important for guaranteeing human safety. This article proposes an online detection approach based on deep learning, which is suitable for detecting small FMOs from X-ray images of clothing packages. A conveyor belt X-ray scanning system is developed for image collection. The X-ray images are preprocessed by using the morphological erosion operation to improve the accuracy of FMOs detection. These images are then down-sampled to reduce the computation cost. Feature pyramid network (FPN) is adopted for aggregating feature maps with different resolutions, which proved to be effective for small FMOs detection. The stochastic gradient descent (SGD) is used to optimize a multitask loss. The trained model was tested offline on 200 X-ray images, which achieved precision = 0.999, recall = 0.988, F1-score = 0.993, and AP = 0.946. Compared to original Faster region-based convolutional neural networks (R-CNN), the proposed method significantly improved the performance for small FMOs detection in terms of precision and recall rate.

In this paper, we consider the problem of determining the direction of arrival (DOA) of two sources for a coprime array. The novelty of this work is that we present an efficient algorithm to estimate two DOAs from their wrapped phases with errors. The proposed robust generalized Chinese remainder theorem (CRT) is considerably less computational complex than the searching method, while maintaining comparable estimation precision. Moreover, the largest range of DOA that leads to an unambiguous determination is obtained for a given coprime array. Numerical simulations are presented to verify the efficiency and the robustness of the proposed algorithm. The simulation results show that the proposed algorithm is much faster than the searching method while achieving a similar root-mean-square error.

In spatial spectrum estimation, difference co-array can provide extra degrees-of-freedom (DOFs) for promoting parameter identifiability and parameter estimation accuracy. For the sake of acquiring as more DOFs as possible with a given number of physical sensors, we herein design a novel sensor array geometry named cascade array. This structure is generated by systematically connecting a uniform linear array (ULA) and a non-uniform linear array, and can provide more DOFs than some exist array structures but less than the upper-bound indicated by minimum redundant array (MRA). We further apply this cascade array into multiple input multiple output (MIMO) array systems, and propose a novel joint direction of departure (DOD) and direction of arrival (DOA) estimation algorithm, which is based on a reduced-dimensional weighted subspace fitting technique. The algorithm is angle auto-paired and computationally efficient. Theoretical analysis and numerical simulations prove the advantages and effectiveness of the proposed array structure and the related algorithm.

We present herein a novel method of bipolar field-effect control on DC electroosmosis (DCEO) from a physical point of view, in the context of an intelligent and robust operation tool for stratified laminar streams in microscale systems. In this unique design of the DC flow field-effect-transistor (DC-FFET), a pair of face-to-face external gate terminals are imposed with opposite gate-voltage polarities. Diffuse-charge dynamics induces heteropolar Debye screening charge within the diffuse double layer adjacent to the face-to-face oppositely-polarized gates, respectively. A background electric field is applied across the source-drain terminal and forces the face-to-face counterionic charge of reversed polarities into induced-charge electroosmotic (ICEO) vortex flow in the lateral direction. The chaotic turbulence of the transverse ICEO whirlpool interacts actively with the conventional plug flow of DCEO, giving rise to twisted streamlines for simultaneous DCEO pumping and ICEO mixing of fluid samples along the channel length direction. A mathematical model in thin-layer approximation and the low-voltage limit is subsequently established to test the feasibility of the bipolar DC-FFET configuration in electrokinetic manipulation of fluids at the micrometer dimension. According to our simulation analysis, an integrated device design with two sets of side-by-side, but upside-down gate electrode pair exhibits outstanding performance in electroconvective pumping and mixing even without any externally-applied pressure difference. Moreover, a paradigm of a microdevice for fully electrokinetics-driven analyte treatment is established with an array of reversed bipolar gate-terminal pairs arranged on top of the dielectric membrane along the channel length direction, from which we can obtain almost a perfect liquid mixture by using a smaller magnitude of gate voltages for causing less detrimental effects at a small Dukhin number. Sustained by theoretical analysis, our physical demonstration on bipolar field-effect flow control for the microfluidic device of dual functionalities in simultaneous electroconvective pumping and mixing holds great potential in the development of fully-automated liquid-phase actuators in modern microfluidic systems.

In target detection, tracking, and recognition tasks, high-quality images can achieve better results. However, in actual scenarios, the visual effects and data quality of images are greatly reduced due to the influence of environmental factors, which affect subsequent detection, recognition, and other tasks. Therefore, this paper proposes an image rain removal algorithm based on multi-scale features, which can effectively remove rain streaks. First of all, this paper proposes a deraining algorithm that combines spatial information to improve the network’s generalization ability on real images, aiming at the problem of synthetic datasets used by previous deraining algorithms. Then, by proposing a multi-scale rain removal algorithm, it improves the feature extraction capabilities of existing deraining algorithms. Before extracting deep rain features, a preliminary fusion of multi-scale shallow features can be performed, which can show better performance in images of different sizes. In addition, a spatial attention module and channel are introduced. The attention module increases the ability to extract rain information at each scale; the resulting multi-scale feature image rain removal algorithm is called MFD. Finally, the rain removal algorithm is validated on the rain removal dataset, and the proposed method can effectively remove rain patterns, provide strong performance improvement on several datasets in the image rain removal task, and provide high-quality images for subsequent detection and recognition tasks.