Abstract: An object of the present invention is to make it possible for smart devices such as smartphones to provide new operability to users. A gesture detecting device includes a motion detecting unit and a processor. The processor calculates deviation value outputted by the motion detecting unit. At timing when the deviation value exceeds a threshold value at the time of shaking out, the processor obtains a direction determination value at the time of shaking out, whereas at timing when the deviation value falls below a threshold value at the time of shaking back the smartphone, the processor obtains a direction determination value at the time of shaking back. Further, on the basis of a combination of the direction determination value, the processor detects a gesture of the gesture detecting device.

Abstract: An object of the present invention is to make it possible for smart devices such as smartphones to provide new operability to users. A gesture detecting device includes a motion detecting unit and a processor. The processor calculates deviation value outputted by the motion detecting unit. At timing when the deviation value exceeds a threshold value at the time of shaking out, the processor obtains a direction determination value at the time of shaking out, whereas at timing when the deviation value falls below a threshold value at the time of shaking back the smartphone, the processor obtains a direction determination value at the time of shaking back. Further, on the basis of a combination of the direction determination value, the processor detects a gesture of the gesture detecting device.

Abstract: Mathematical calculation processing is executed at a high speed for an input signal of image information or the like. Weight coefficients corresponding to a DCT (discrete cosine transform) are set in a neural network NN of an n-layer structure including an input layer I1, (an intermediate layer H1) and an output layer O1. The input layer I1 is constituted from a number of input interfaces equal to the number of picture elements included in a block 12, and the output layer O1 is constituted from a number of neurons equal to the number of DCT coefficients to be outputted. When input information of the block 12 is inputted to the neural network NN, DCT coefficients a(0) to a(63) which are results of DCT processing of the image information are outputted immediately. The weight coefficient is determined by learning using, as teacher data, DCT coefficients obtained by actual mathematical calculation of the same image information as that inputted to the neural network NN by a DCT transform section.

Abstract: A system is disclosed for multiplexing, transmitting, and receiving picture data and sound data, wherein the picture data and sound data are balanced in a limited transmission capacity so as to obtain a better overall quality to effectively use the transmission ability of a medium. The content of at least one of the picture data and sound data are detected and used for control of the content of the transmission. The content of the transmission is controlled by changing data and sound to correct a difference between the processing speed of the picture data and the sound data, thereby improving the quality of a video conference system or the like where the transmission capacity is insufficient.