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For most microwave power transmission (MPT) applications, the microwave power is received within the near field of a large-scale transmitting antenna from a limited distance. This letter explores the design of a near-field focused transmitting antenna to concentrate the microwave power on the receiving antenna's aperture, and thereby to enhance the transmission efficiency of the MPT. A near-field planar array with a size of 1 m × 1 m working at 5.8 GHz is manufactured. To focus the microwave power at a distance of 10 m, its 8 × 8 radiation elements are fed with an equal amplitude but different phase using coaxial cables of different lengths. An MPT experiment is conducted. The measurement shows that, compared with a traditional equal-phase array, the near-field focused array obtains a much more concentrated microwave beam. The transmission efficiency is significantly increased from 32.98% to 41.85%.

As convolutional neural networks (CNN) and recurrent neural networks (RNN) have achieved excellent results in the field of Chinese text sentiment analysis. More and more researchers are extracting features of text based on the advantages of CNN and RNN in extracting features. However, the current scholars fail to make full use of sentiment language resources such as sentiment words, negatives and degree adverbs when they adopt deep learning methods. For implicit texts without explicit sentiment words, they cannot fully identify the differences between words and sentiment tendency. At the same time, it fails to consider the grammatical structure of the text, which leads to poor classification effect for some turning sentences or summary sentences. In addition, most of the models are mainly input in the form of word vector. For English, it is very convenient to segment words through spaces between words, but for Chinese, there may be inaccurate word segmentation, which will reduce the accuracy of classification. To solve these problems, a dual channel sentiment classification model based on grammar rules and multi attention (DCGA) is proposed. Firstly, the text with clear sentiment tendency is obtained according to the grammar rules, and the local features of the text are extracted by CNN channel. Considering that the grammar rules may ignore the context information, bi-directional long short term memory network (Bi-LSTM) channel is used to extract the global features containing the context information, and attention pooling is used to improve the sentiment information extracted by CNN channel. Then, the part of speech attention mechanism is used to mine the implicit sentiment features in the sentiment text to solve the poor classification effect caused by the existence of implicit text. Finally, the local features obtained by CNN channel and the global features obtained by RNN channel are fused for classification. Experimental results on four Chinese comment text datasets show that the proposed model outperforms most existing methods in accuracy.

The existing video sentiment analysis methods only obtain features from the spatial and temporal signals of the video for sentiment classification, and cannot solve the difficulty of not knowing which emotion contributes the most to the entire video sentiment analysis in the video sentiment analysis. To solve this problem, a neural network with video frame weight vector is proposed. First, the video frame feature is obtained through the reel neural network, and then the weight vector layer is used to calculate the weight of the feature, and finally the frame feature with weight is put into the LSTM Training to obtain a video sentiment analysis model. We verified on the BAUM-1s data set. The results show that this method is better than existing methods in accuracy.

For microwave power transmission (MPT), the transmitting (Tx) array with a tapered amplitude distribution can achieve a maximum beam collection efficiency, but it needs a feeding network with an arbitrary power division ratio; thus, it is complicated and costly in practical applications. This work explores realizing high transmission efficiency by a Tx array with the equal-ratio steps amplitude distribution (ERSAD). By using the ERSAD, the amplitude value of the central units of the Tx array is uniform, and the amplitude feeding of the edge units only needs equal power divisions. Thus, the amplitude feeding is greatly simplified. Besides, a compensated phase distribution is used to focus the radiating beam on the receiving aperture. To validate the proposed design, a Tx antenna consisting of 8 × 8 units with a total aperture size of 1 × 1 m2 is designed. The operating frequency and the focused distance are 5.8 GHz and 10 m, respectively. A 64-way microwave power source is used to adjust the amplitude and the phase of the Tx array, and an MPT experiment is conducted. The measurement shows that, compared with the 10 dB Gaussian amplitude array, the proposed array nearly has the same transmission efficiency against different receiving apertures. Published version