Biblio

With the continuous improvement of people's safety awareness, infrared products as human motion detection technology have been widely used in the field of security. In order to better apply infrared products to life, improving the performance of infrared products and reducing the cost of products has become the main goal. According to the signal collected by Pyroelectric infrared (PIR) sensor, this paper establishes a database model. According to the data collected, Kalman filter is used to preprocess the data. The validity of the data after preprocessing is judged by the algorithm. The experimental results show that the accuracy of the model can reach 97% by using a support vector machine (SVM) algorithm incorporated with Fast Fourier Transform (FFT). According to the above algorithm flow, a real-time intellectual property (IP) core is designed by using hardware description language, after establishing the data processing algorithm. The interface design, timing design and function design of the IP core are designed. The IP core can be connected to the microcontroller unit (MCU) as an independent peripheral to form a PIR special processor, which can detect the distance of 15 m in real time.

Support vector machines (SVMs) have been widely used for classification in machine learning and data mining. However, SVM faces a huge challenge in large scale classification tasks. Recent progresses have enabled additive kernel version of SVM efficiently solves such large scale problems nearly as fast as a linear classifier. This paper proposes a new accelerated mini-batch stochastic gradient descent algorithm for SVM classification with additive kernel (AK-ASGD). On the one hand, the gradient is approximated by the sum of a scalar polynomial function for each feature dimension; on the other hand, Nesterov's acceleration strategy is used. The experimental results on benchmark large scale classification data sets show that our proposed algorithm can achieve higher testing accuracies and has faster convergence rate.