Abstract: An image object classification optimizing method and system are disclosed. The method is executed by a processor coupled to a memory. The method includes steps: providing an image file including at least one image object; performing a process of characteristics enhancement on the image object; performing a process of characteristics classification on the enhanced image object by an odd number of two-dimensional masks whose sizes are sequentially doubled, based on a plurality of characteristic parameters of a preferred classification model, to generate a plurality of classification results; and estimating variabilities of the plurality of classification results, sorting the variabilities, and selecting at least one of the classification results whose variability is lower than a variation tolerance as at least one optimization result, according to the sorting result.

Abstract: A character recognition method includes inputting an input image of a document, with the input image including a plurality of characters; selecting the plurality of characters through an object detection module to form at least one character region; separating the plurality of characters in the at least one character region to form a plurality of character boxes; performing calculation to determine a format of a character in each of the plurality of character boxes; recognizing the characters in the at least one character region through an object recognition module to determine a symbol content of the character in each of the plurality of character boxes; and converting the plurality of characters according to the format and symbol content of the character in each of the plurality of character boxes, and outputting corresponding editable characters.

Abstract: An electronic component packaging type classification system using artificial neural network to execute classification; the electronic component packaging system includes a service database, an external database, a feature selection module, a data-integration module and a classification processing module. The service database receives electronic component patterns externally inputted. The external database stores the packaging type data of electronic components. The feature selection module records the packaging type features of the electronic components. The data-integration module performs the data-processing and the normalization for the selected features to obtain the data to be processed. The classification processing module receives the data to be processed and shows the classification result on the service database.

Abstract: An automated electronic component footprint setup system and a method thereof are provided in the present disclosure. The system is available to not only an external first user for configuring characteristic parameters of an electronic component for the database but also an external second user for configuring setup parameters of an electronic component footprint to be created. Then, the system is to create an electronic component footprint of a specific electronic layout system according to the characteristic parameters of the electronic component, component setup regulations and the setup parameters, all of which correspond to the electronic component footprint.

Abstract: An automated electronic component footprint setup system and a method thereof are provided in the present disclosure. The system is available to not only an external first user for configuring characteristic parameters of an electronic component for the database but also an external second user for configuring setup parameters of an electronic component footprint to be created. Then, the system is to create an electronic component footprint of a specific electronic layout system according to the characteristic parameters of the electronic component, component setup regulations and the setup parameters, all of which correspond to the electronic component footprint.

Abstract: A method of reducing energy consumption is to be performed by a network node, and includes configuring the network node to perform the consecutive steps of: b1) setting an initial value for each of first and second parameters; b2) skipping to step b5) if the second parameter is smaller than a random value; b3) estimating an amount of energy required for, and computing a probability of, data transmission therefrom; b4) selecting one of other network nodes corresponding to a greatest value of the first parameter for serving as a transmission route; b5) determining whether data transmitted thereby has reached a destination network node, and skipping to step b7) if affirmative; b6) updating the first parameters of the other network nodes, and returning to step b2); and b7) updating the first parameters of the network nodes belonging to an optimal route.

Abstract: A method for estimating an operational parameter of a motor is to be implemented by an estimating device. In the method, the estimating device is configured to: receive an acoustic signal attributed to operation of the motor; process the acoustic signal to obtain a plurality of sample points in the frequency domain, each of which has a frequency and a corresponding amplitude; compute an estimated peak frequency using a centroid method based upon the frequencies and the amplitudes; from a plurality of peak frequencies and a plurality of known values of the operational parameter corresponding respectively to the peak frequencies, select a part of the peak frequencies approximate to the estimated peak frequency and a corresponding part of the known values of the operational parameter; and compute an estimated value of the operational parameter based upon the peak frequencies and the known values of the operational parameter.