Abstract: A data acquisition circuit and method for a battery module are provided. The circuit includes a signal processing board and a signal acquisition board. The signal processing board includes an acquisition circuit module. The signal acquisition board is provided with n voltage acquisition chips each provided with a temperature sensor, and n?1 cells of the battery module. The signal acquisition board is connected to the signal processing board through a connector. The first end of the (m?1)th voltage acquisition chip is connected to the negative electrode of the (m?1)th cell. The first end of the mth voltage acquisition chip is connected to the positive electrode of the (m?1)th cell. The second end of the voltage acquisition chip is connected to the signal processing board through the connector. The first end of the temperature sensor is connected to the acquisition circuit module in the signal processing board.

Abstract: A road region detection method is provided. The method includes: obtaining a first image captured by a camera at a first time point and a second image captured by the camera at a second time point (S101), converting the first and second images into a first top view and a second top view, respectively (S103), obtaining a movement vector matrix which substantially represents movement of a road region relative to the camera between the first and second time points (S105), and determining whether a candidate point belongs to the road region by determining whether a position change of the candidate point between the first and second top views conforms to the movement vector matrix. The accuracy and efficiency may be improved.

Abstract: A method for moving object detection is provided. The method includes: obtaining a first image captured by a monocular camera at a first time point and a second image captured by the monocular camera at a second time point (S101); calculating dense optical flows based on the first and second images (S105); and identifying a moving object based on the calculated dense optical flows (S107 and S109). Since the moving object detection method is based on dense optical flows and the monocular camera, both high detection accuracy and low cost can be achieved.

Abstract: A road region detection method is provided. The method includes: obtaining a first image captured by a camera at a first time point and a second image captured by the camera at a second time point (S101), converting the first and second images into a first top view and a second top view, respectively (S103), obtaining a movement vector matrix which substantially represents movement of a road region relative to the camera between the first and second time points (S105), and determining whether a candidate point belongs to the road region by determining whether a position change of the candidate point between the first and second top views conforms to the movement vector matrix. The accuracy and efficiency may be improved.

Abstract: A multi-class discriminant subspace analysis technique is described that improves the discriminant power of Linear Discriminant Analysis (LDA). In one embodiment of the multi-class discriminant subspace analysis technique, multi-class feature selection occurs as follows. A data set containing multiple classes of features is input. Discriminative information for the data set is determined from the differences of class means and the differences in class scatter matrices by computing an optimal orthogonal matrix that approximately simultaneously diagonalizes autocorrelation matrices for all classes in the data set. The discriminative information is used to extract features for different classes of features from the data set.