Pre-IdentifyNet: An Improved Neural Network for Image Recognition

With the rise and development of artificial intelligence, image recognition and classification technology has received more and more attention as an important branch of its research field. Among them, the introduction of deep learning networks and the construction of neural network structures not only avoid a lot of the tedious work of manual extraction, but also improve the accuracy of image recognition. Convolutional neural networks have many advantages that conventional neural networks do not have. Therefore, image classification systems based on convolutional neural networks emerge in endlessly, but there is still much room for improvement in terms of recognition accuracy and recognition speed. Based on this, this paper proposes an improved deep convolutional neural network to improve the accuracy of the network by changing a series of parameters such as the number of channels of the convolution layer, the size of the convolution kernel, the learning rate, the number of iterations, and the size of the small batch with speed. In this paper, three data sets were selected, namely sewage, animals and the Simpson Family. Comparing the improved convolutional neural network network with the existing SqueezeNet and GoogleNet. It is found that the accuracy of the network is maintained while maintaining a similar speed. Both F1-score and F1-score have been improved with a higher recognition rate and better recognition effect in image recognition classification

Joint Satellite-Transmitter and Ground-Receiver Digital Pre-Distortion for Active Phased Arrays in LEO Satellite Communications

A novel joint satellite-transmitter and ground-receiver (JSG) digital pre-distortion (DPD) (JSG-DPD) technique is proposed to improve the linearity and power efficiency of the space-borne active phased arrays (APAs) in low Earth orbit (LEO) satellite communications. Different from the conventional DPD technique that requires a complex RF feedback loop, the DPD coefficients based on a generalized memory polynomial (GMP) model are extracted at the ground-receiver and then transmitted to the digital baseband front-end of the LEO satellite-transmitter via a satellite–ground bi-directional transmission link. The issue of the additive white Gaussian noise (AWGN) of the satellite–ground channel affecting the extraction of DPD coefficients is tackled using a superimposing training sequences (STS) method. The proposed technique has been experimentally verified using a 28 GHz phased array. The performance improvements in terms of error vector amplitude (EVM) and adjacent channel power ratio (ACPR) are 7.5% and 3.6 dB, respectively. Requiring limited space-borne resources, this technique offers a promising solution to achieve APA DPD for LEO satellite communications

Robust Digital Signal Recovery for LEO Satellite Communications Subject to High SNR Variation and Transmitter Memory Effects

This paper proposes a robust digital signal recovery (DSR) technique to tackle the high signal-to-noise ratio (SNR) variation and transmitter memory effects for broadband power efficient down-link in next-generation low Earth orbit (LEO) satellite constellations. The robustness against low SNR is achieved by concurrently integrating magnitude normalization and noise feature filtering using a filtering block built with one batch normalization (BN) layer and two bidirectional long short-term memory (BiLSTM) layers. Moreover, unlike existing deep neural network-based DSR techniques (DNN-DSR), which failed to effectively take into account the memory effects of radio-frequency power amplifiers (RF-PAs) in the model design, the proposed BiLSTM-DSR technique can extracts the sequential characteristics of the adjacent in-phase (I) and quadrature (Q) samples, and hence can obtain superior memory effects compensation compared with the DNN-DSR technique. Experimental validation results of the proposed BiLSTM-DSR with a 100 MHz bandwidth OFDM signal demonstrate an excellent performance of 11.83 dB and 9.4% improvement for adjacent channel power ratio (ACPR) and error vector magnitude (EVM), respectively. BiLSTM-DSR also outperforms the existing DNN-DSR technique in terms of the ACPR and EVM by 2.4 dB and 0.9%, which provides a promising solution for developing deep learning-assisted receivers for high-throughput LEO satellite networks

Mobility of primary health care workers in China

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