This paper proposes a Micro Electro Mechanical Systems piezoresistive accelerometer based on a whole SiC substrate. Compared with Si-based sensors, SiC-based sensors have stronger mechanical advantages and unique advantages for applications in ultra-high temperature environments. The characteristics of the accelerometer are designed and numerically simulated, and the accelerometer is evaluated in terms of stress load and working frequency band. An innovative design is carried out to eliminate the stress concentration phenomenon in the corner area of the sensor, which guarantees the working safety of the fragile structure of SiC. After fabrication, packaging and vibration experiment, it is found that the sensor’s working sensitivity can reach 0.21mv/g, and its linearity can reach 98%.
Road accidents cause a lot of financial and human losses every year. One of the causes of these accidents is human error, and the driver ignores traffic signs. Therefore, accurate detection of these signs will help to increase the safety of drivers and pedestrians and reduce accidents. In recent years, much research has been done to increase the accuracy of panel recognition, most of which are problems that affect the diagnosis, such as adverse weather conditions, light reflection, and complex backgrounds. In the present study, considering the diversity of traffic signs' geometric shapes, the ngis detection part has been done using a torsional neural network. Then, in the feature extraction section, we used LBP and HOG techniques, and at the end, the section was identified and classified using the ELM algorithm. The results obtained on 12569 images, 75% of which were used for training and 25% for experimentation, show that the accuracy of this research has improved by 95% compared to the essential work by 93%.
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