Motorola Inc.

Curriculum development methodology based on the work process,analysis and application of high-frequency electronic circuits curriculum reform practice to good effect.Determine the course positioning,determine the tasks and learning content,course design,a new evaluation of teaching effectiveness,introduced some of the practices of the reform,reform of teaching students the ability to improve the effectiveness of teaching better results.

High-frequency Electronic Circuit Analysis and Applications Teaching Reform Based on the Work Process

This paper presents a design single stage low noise amplifier(LNA) using L-matching technique for WiMAX applications. The amplifier use FHX76LP Low Noise SuperHEMT FET. The LNA designed used L-matching network consisting of lump reactive element at the input and the output terminal. The LNA produced gain of 18.34dB and noise Figure (NF) of 1.34dB. Furthermore, the input reflection (S11) and output return loss(S22) are -16.25dB and -7.52dB respectively. The bandwidth of the amplifier recorded is 1.24GHz. The input sensitivity is compliant with the IEEE 802.16 standards.

Design single stage lna using l-matching network for wimax applications

This paper proposes a computational method for the reliability of 2-separable networks. Based on graph theory and probability theory, this method simplifies the calculation process by constructing a network equivalent model and designing corresponding algorithms to achieve the efficient evaluation of reliability. Considering independent random failures of edges with equal probability q, this method can accurately calculate the reliability of 2-separable networks, and its effectiveness and accuracy are verified through examples. In addition, to demonstrate the generality of our method, we have also applied it to other 2-separable networks with fractal structures and proposed linear algorithms for calculating their all-terminal reliability. 

A Method for Calculating the Reliability of 2-Separable Networks and Its Applications

MAPFUNet: Multi-attention Perception-Fusion U-Net for Liver Tumor Segmentation

Har monic rejection ability and reflection coefficient are the most important factors in the design of impedance matching network. However, dynamic stability of impedance matching should be taken into account in applications existing working frequency drift, load impedance variation and components deviation due to tolerances and process variation. In order to study the dynamic performance of T network impedance matching, theoretical analysis is conducted in accordance with the Q (quality)-based design method. The relationships between the matching result (reflection coefficient) and components deviation, load impedance variation and operating frequency drift are obtained. The analysis results suggest that higher Q value produces worse dynamic stability and that matching result is more sensitive to load impedance variation when the load reflection coefficient of static state is larger. In designing a T network, it is need to make the tradeoff between dynamic stability and harmonic rejection ability. The analysis results provide theoretical basis for the design and characteristics analysis of T network impedance matching.

/pdf/dynamic-performance-analysis-of-t-network-impedance-matching-5c3ebaq0uh.pdf

Dynamic performance analysis of T network impedance matching

Sleep is an essential process in everyone's life, and sleep quality is closely related to human physical and mental health. For checking the quality of human's sleep, going through the sleep staging process would be the first step to take. In this paper, an end-to-end network is introduced to extract the living sign features from the optical fiber signal. Experiments show that fiber optic signal can replace PSG(Polysomnography) for sleep staging to some extent, and the deep learning model-based non-contact device is as accurate as the traditional EEG device. Finally, the detected sleep staging is used to calculate the sleep score and efficiency to evaluate the quality of the whole night sleep.

End to End Sleep Staging using Fiber Optic Signal

Heart rate and respiratory value are important indicators that doctors care about. In this paper, we propose an end-to-end measurement method of human respiration and heart rate based on convolutional neural network (CNN). Also, we transplant this AI model into STM32 chips to extract the feature of optical fiber signal. We make a data set of human signals collected in fiber optics to train the AI model. Experiments show that our AI model and embedded devices can predict people's respiration and heart rate effectively.

Embedded Vital Signs Monitoring Device Based on Convolutional Neural Network

Considering the heterogeneity, diffusive shape, and complex background of tumors, automatic segmentation of hepatic lesions in computed tomography (CT) images has been considered a challenging task. The performance of existing methods remains subject to segmentation uncertainties, especially in tumor boundary regions. The pixel information in these regions will be affected by both sides, thereby exposing the segmentation results to missing marks. To this end, a new network architecture named Two Direction Segmentation U-Net (TDS-U-Net) is hereby designed based on the classic Attention U-Net to tackle this problem. As the most important blocks of the Attention U-Net network, attention gates (AGs) focus on the target structures of different shapes and sizes. In the last layer of TDS-U-Net, two dichotomous convolutional networks are applied to obtain the segmentation maps of the liver and the tumor respectively. Superimposing two segmented maps to obtain the final image addresses the above problems. The entire structure has been verified on two widely accepted public CT datasets, LiTS17 and KiTS19. Compared with the state of the art, this method exhibits superior performance and excellent shape extractions with high detection sensitivity, perfectly demonstrating its effectiveness in medical image segmentation.

TDS-U-Net: Automatic liver and tumor separate segmentation of CT volumes using attention gates1

Enumeration of subtrees of planar two-tree networks

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