In this work, anin situmonitoring installation for tea extract oxidation including an oxidation system and a spectroscopy system was developed.

In millimeter-wave (mmWave)-based massive multiple-input-multiple-output (MIMO) systems, hybrid precoding is considered one of the indispensable techniques in the next generation wireless communication systems (5G) to reduce the number of radio-frequency (RF) chains. However, the existing hybrid precoding techniques often cause performance loss. To solve this problem, the switch and inverter (SI)-based hybrid precoding architecture has been proposed recently as an energy-efficient solution for these challenges. In this paper, a detailed performance analysis on sum-rate as well as energy-efficiency is provided through simulation on the two-stage hybrid precoding, antenna selection (AS)-based hybrid precoding, and adaptive cross-entropy (ACE)-based hybrid precoding. It is aimed to prove that the performance of the ACEbased scheme is much superior to that of the others with the limited ranges of values of all parameters. At last, the suitable parameters are determined and we prove that they can lead to the optimal performance.

Channel state information (CSI) is required for both precoding at the transmitter and detection at the receiver in millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. Accurate channel estimation poses significant technique challenges for designing the mmWave MIMO systems. Considering the channel sparsity in mmWave massive MIMO systems with hybrid precoding, this paper proposes an 1/2 -regularization-based sparse channel estimation method. The basic idea of the proposed method is to formulate the sparse channel estimation problem as a compressed sensing problem. Specifically, the method firstly constructs an objective function, which is a weighted sum of the 1/2 -regularization and error constraint term. It is then optimized via the gradient descent method iteratively and the weight parameter in the function is also updated in each iteration. In contrast to conventional algorithms, our proposed method can avoid the quantization error and finally realize super-resolution performance. The simulation experiments verified that the proposed method can achieve better performance than traditional ones.INDEX TERMS Sparse channel estimation, 1/2 -regularization, iterative reweighted methods, massive MIMO, millimeter-wave (mmWave).

Deep learning is one of the notable solutions when developing intelligent making systems (InMASs) for students' test papers and assignments to replace the workload of the teachers and educators. This paper recommends a design method of InMAS based on the You Only Look Once (YOLOv3) algorithm. Such a method can be used in carrying out experiments on algorithm problems and creating two dedicated datasets. The first is for localization and the second is for recognition. The YOLOv3 network is employed to identify the location and extraction of each mathematical problem in every image. In the recognition part, because of the low recognition rate of traditional optical character recognition (OCR) on the handwritten characters, the YOLOv3 network is widely used to identify the characters in each arithmetic problem. In the final step, the numerical operation is held on the output characters. The template matching approach is used to evaluate the arithmetic problems with the wrong operation in the original pictures. The experimental results show that the accuracy of localization is near to 1. The proposed method carries out favorably against Baidu OCR based on recognition accuracy, showing a high accuracy of 97.15%.

Recently, the L 1/2 regularization has shown its great potential to eliminate the bias problems caused by the convex L 1 regularization in many compressive sensing (CS) tasks. CS-based magnetic resonance imaging (CS-MRI) aims at reconstructing a high-resolution image from under-sampled k-space data, which can shorten the imaging time efficiently. Theoretically, the L 1/2 regularization-based CS-MRI will reconstruct the MR images with higher quality to investigate and study the potential and feasibility of the L 1/2 regularization for the CS-MRI problem. In this paper, we employ the nonconvex L 1/2 -norm to exploit the sparsity of the MR images under the tight frame. Then, two novel iterative half thresholding algorithms (IHTAs) for the analysis of the L 1/2 regularization are introduced to solve the nonconvex optimization problem, namely, smoothing-IHTA and projected-IHTA. To evaluate the performance of the L 1/2 regularization, we conduct our experiments on the real-world MR data using three different popular sampling masks. All experimental results demonstrate that the L 1/2 regularization can improve the L 1 regularization significantly and show the potential and feasibility for future practical applications.INDEX TERMS L 1/2 regularization, compressive sensing, analysis model, iterative half thresholding algorithm, tight frame, smoothing, magnetic resonance imaging.