Abstract: A system includes a first server for transmitting data, a second server for receiving the data from the first server; a network for connecting the first server and the second server to each other; and a clock for measuring passage of time, the data having such a structure that at least amount among value and amount thereof is reduced with passage of time, the clock transmitting a time-passage signal to the second server every time a predetermined period of time has passed from a point of time at which the data was transmitted to the second server from the first server through the network, the second server, on receipt of the time-passage signal, transmitting a reduced amount of the data to the first server.

Abstract: A meshed virtual private network system includes L junction nodes each including a device being capable of making a radio VPN communication and each being registered in advance, wherein L is an integer equal to or greater than 2, and a database, the meshed virtual private network system making communication to a receiver node from a sender node through the junction nodes, each of the junction nodes being capable of acting as a VPN server by carrying out predetermined steps.

Abstract: A meshed virtual private network system includes L junction nodes each including a device being capable of making a radio VPN communication and each being registered in advance, wherein L is an integer equal to or greater than 2, and a database, the meshed virtual private network system making communication to a receiver node from a sender node through the junction nodes, each of the junction nodes being capable of acting as a VPN server by carrying out predetermined steps.

Abstract: A neuron device network is provided with a speech input layer, a context layer, a hidden layer, a speech output layer and a hypothesis layer. A phoneme to be learned is spectral-analyzed by an FFT unit and a vector row at a time point t is input to a speech input layer. Also, a vector state of the hidden layer at a time t-1 is input to the context layer, the vector row at a time t+1 is input to the speech output layer as an instructor signal, and a code row for hypothesizing the phoneme, or the code row, is input to the hypothesis layer. The time series relation of the vector rows and the phoneme are hypothetically learned. Alternatively, a spectrum, a cepstrum or a speech vector row based on outputs from the hidden layer of an auto-associative neural network is input to the speech input layer, and the code row is output from the hypothesis layer, taking into account the time series relation.