Abstract:
A wireless sensor actuator network (WSAN) is provided, which includes: at least one sensor, for sensing situations of surrounding environments to generate a transferring condition, the transferring condition corresponding to a transferring condition serial number; at least one actuator, driven by a driving signal; and a gateway, for receiving the transferring condition of the sensor corresponding to the transferring condition serial number, transferring an active state serial number to a transferring state serial number which meets the transferring condition according to an encoding table of the gateway, and executing functions called by the transferring state serial number to generate the driving signal for driving the actuator.
Abstract:
The present invention discloses an optical input device, which comprises a display panel having a displaying area functioning as an interface for detecting a position of an object; a backlight module providing light sources for the display panel; and at least one image sensor arranged behind the backlight module and capturing a positional image, which is formed on the displaying area by an object reflecting the light emitted by the light sources. The present invention not only can decrease the thickness of the optical input device but also can reduce the complexity of the optical system.
Abstract:
A method uses a hybrid neural network including a self organizing mapping neural network (SOM NN) and a, back-propagation neural network (BP NN) for color identification. In the method the red, green and blue (RGB) of color samples are input as features of training samples and are automatically classified by way of SOM NN. Afterwards, the outcomes of SOM NN are respectively delivered to various BP NN for further learning; and the map relationship of the input and the output defines the X,Y, Z corresponding the x, y and z values of a coordinate system of the standard color samples of RGB and IT8. By way of the above learning structure, a non-linear model of color identification can be set up. After color samples are self organized and classified by SOM NN network, data can be categorized in clusters as a result of characteristic difference thereof. Then the data are respectively sent to BP NN for learning whereby-the learning system not only can be quickly converged but also lower error discrepancy in operation effectively.