scholarly journals Interference Signal Identification of Sensor Array Based on Convolutional Neural Network and FPGA Implementation

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2867
Author(s):  
Lin Huang ◽  
Xingguang Geng ◽  
Hao Xu ◽  
Yitao Zhang ◽  
Zhiqiang Li ◽  
...  

The pulse carries important physiological and pathological information about the human body. The piezoresistive sensor used to capture vascular pulsation information has transitioned from a single-point to a sensor array. However, the interference signal between channels has become a key bottleneck restricting the development of the sensor array pulse diagnosis equipment. The sensor in contact with vascular pulsation obtains the pulse signal. When some sensors are displaced due to vascular pulsation, other sensors will be driven to move, which will produce interference signals. Signal interference is a common problem for sensor arrays, but few people have analyzed this problem from the perspective of the algorithm. In this paper, an interference signal recognition algorithm of the sensor array based on a convolutional neural network (CNN) is proposed. Firstly, a simple mechanical structure model was established to analyze the generation mechanism of interference signals in one MEMS sensor array acquisition system. Then, a CNN model with fewer parameters was designed for identifying interference signals. Finally, the CNN model was implemented on a field-programmable gate array (FPGA). The results show that the CNN algorithm could identify interference signals well, and the accuracy of the algorithm was 99.3%. The power consumption of the CNN accelerator was 0.673 W at a working frequency of 100 MHz. The interference signal identification algorithm is proposed to ensure the accurate analysis of array signals. FPGA implementation lays the foundation for the miniaturization and portability of the equipment.

2020 ◽  
Vol 10 (24) ◽  
pp. 9005
Author(s):  
Chien-Cheng Lee ◽  
Zhongjian Gao

Sign language is an important way for deaf people to understand and communicate with others. Many researchers use Wi-Fi signals to recognize hand and finger gestures in a non-invasive manner. However, Wi-Fi signals usually contain signal interference, background noise, and mixed multipath noise. In this study, Wi-Fi Channel State Information (CSI) is preprocessed by singular value decomposition (SVD) to obtain the essential signals. Sign language includes the positional relationship of gestures in space and the changes of actions over time. We propose a novel dual-output two-stream convolutional neural network. It not only combines the spatial-stream network and the motion-stream network, but also effectively alleviates the backpropagation problem of the two-stream convolutional neural network (CNN) and improves its recognition accuracy. After the two stream networks are fused, an attention mechanism is applied to select the important features learned by the two-stream networks. Our method has been validated by the public dataset SignFi and adopted five-fold cross-validation. Experimental results show that SVD preprocessing can improve the performance of our dual-output two-stream network. For home, lab, and lab + home environment, the average recognition accuracy rates are 99.13%, 96.79%, and 97.08%, respectively. Compared with other methods, our method has good performance and better generalization capability.


2020 ◽  
Vol 9 (2) ◽  
pp. 74
Author(s):  
Eric Hsueh-Chan Lu ◽  
Jing-Mei Ciou

With the rapid development of surveying and spatial information technologies, more and more attention has been given to positioning. In outdoor environments, people can easily obtain positioning services through global navigation satellite systems (GNSS). In indoor environments, the GNSS signal is often lost, while other positioning problems, such as dead reckoning and wireless signals, will face accumulated errors and signal interference. Therefore, this research uses images to realize a positioning service. The main concept of this work is to establish a model for an indoor field image and its coordinate information and to judge its position by image eigenvalue matching. Based on the architecture of PoseNet, the image is input into a 23-layer convolutional neural network according to various sizes to train end-to-end location identification tasks, and the three-dimensional position vector of the camera is regressed. The experimental data are taken from the underground parking lot and the Palace Museum. The preliminary experimental results show that this new method designed by us can effectively improve the accuracy of indoor positioning by about 20% to 30%. In addition, this paper also discusses other architectures, field sizes, camera parameters, and error corrections for this neural network system. The preliminary experimental results show that the angle error correction method designed by us can effectively improve positioning by about 20%.


2020 ◽  
Vol 28 (5) ◽  
pp. 428-439
Author(s):  
Min Beom Kim ◽  
Sanglyn Lee ◽  
Ilho Kim ◽  
Hee Jung Hong ◽  
Chang Gone Kim ◽  
...  

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