Abstract: An authentication system determines if a counterfeit ineligible unit is installed in a main device. When connected with a battery pack, a notebook PC generates and combines a random number and a function determination signal using a signal combining unit and transmits same to the battery pack. A first function calculation unit calculates the function of the random number. The battery pack has a signal separation unit to separate the combined signal into the random number and function determination signal, and a second function calculation unit to calculate the function of the random number for transmitting back to the notebook PC. A comparison unit compares the calculation results by the first and second function calculation units to determine whether the connected battery pack is an authorized one and denies connection if the pack is an unauthorized one.

Abstract: An authentication system determines if a counterfeit ineligible unit is installed in a main device. When connected with a battery pack, a notebook PC generates and combines a random number and a function determination signal using a signal combining unit and transmits same to the battery pack. A first function calculation unit calculates the function of the random number. The battery pack has a signal separation unit to separate the combined signal into the random number and function determination signal, and a second function calculation unit to calculate the function of the random number for transmitting back to the notebook PC. A comparison unit compares the calculation results by the first and second function calculation units to determine whether the connected battery pack is an authorized one and denies connection if the pack is an unauthorized one.

Abstract: An authentication system determines if a counterfeit ineligible unit is installed in a main device. When connected with a battery pack, a notebook PC generates and combines a random number and a function determination signal using a signal combining unit and transmits same to the battery pack. A first function calculation unit calculates the function of the random number. The battery pack has a signal separation unit to separate the combined signal into the random number and function determination signal, and a second function calculation unit to calculate the function of the random number for transmitting back to the notebook PC. A comparison unit compares the calculation results by the first and second function calculation units to determine whether the connected battery pack is an authorized one and denies connection if the pack is an unauthorized one.

Abstract: An authentication system determines if a counterfeit ineligible unit is installed in a main device. When connected with a battery pack, a notebook PC generates and combines a random number and a function determination signal using a signal combining unit and transmits same to the battery pack. A first function calculation unit calculates the function of the random number. The battery pack has a signal separation unit to separate the combined signal into the random number and function determination signal, and a second function calculation unit to calculate the function of the random number for transmitting back to the notebook PC. A comparison unit compares the calculation results by the first and second function calculation units to determine whether the connected battery pack is an authorized one and denies connection if the pack is an unauthorized one.

Abstract: An authentication system determines if a counterfeit ineligible unit is installed in a main device. When connected with a battery pack, a notebook PC generates and combines a random number and a function determination signal using a signal combining unit and transmits same to the battery pack. A first function calculation unit calculates the function of the random number. The battery pack has a signal separation unit to separate the combined signal into the random number and function determination signal, and a second function calculation unit to calculate the function of the random number for transmitting back to the notebook PC. A comparison unit compares the calculation results by the first and second function calculation units to determine whether the connected battery pack is an authorized one and denies connection if the pack is an unauthorized one.

Abstract: An authentication system determines if a counterfeit ineligible unit is installed in a main device. When connected with a battery pack, a notebook PC generates and combines a random number and a function determination signal using a signal combining unit and transmits same to the battery pack. A first function calculation unit calculates the function of the random number. The battery pack has a signal separation unit to separate the combined signal into the random number and function determination signal, and a second function calculation unit to calculate the function of the random number for transmitting back to the notebook PC. A comparison unit compares the calculation results by the first and second function calculation units to determine whether the connected battery pack is an authorized one and denies connection if the pack is an unauthorized one.

Abstract: An authentication system determines if a counterfeit ineligible unit is installed in a main device. When connected with a battery pack, a notebook PC generates and combines a random number and a function determination signal using a signal combining unit and transmits same to the battery pack. A first function calculation unit calculates the function of the random number. The battery pack has a signal separation unit to separate the combined signal into the random number and function determination signal, and a second function calculation unit to calculate the function of the random number for transmitting back to the notebook PC. A comparison unit compares the calculation results by the first and second function calculation units to determine whether the connected battery pack is an authorized one and denies connection if the pack is an unauthorized one.

Abstract: An authentication system is provided, which prevents any glitches or accidents caused by a counterfeit ineligible unit being installed in a main device. When connected with a battery pack (an authenticated unit), a notebook PC (an authenticating device) allows a random number and a function determination signal to be generated and then combined by a signal combining unit to be transmitted to the battery pack. Meanwhile, a first function calculation unit calculates the function of the random number. The battery pack allows a signal separation unit to separate the combined signal into the random number and function determination signal, and a second function calculation unit to calculate the function of the random number for transmitting back to the notebook PC. A comparison unit compares the calculation results by the first and second function calculation units to determine whether the connected battery pack is an authorized one.

Abstract: A device capable of achieving recognition at a high accuracy and with fewer calculations and which utilizes an HMM. The present device has a vector quantizing circuit generating a model by quantizing vectors of a training pattern having a vector series, and converting the vectors into a label series of clusters to which they belong, a continuous distribution probability density HMM generating circuit for generating a continuous distribution probability density HMM from a quantized vector series corresponding to each label of the label series, and a label incidence calculating circuit for calculating the incidence of the labels in each state from the training vectors classified in the same clusters and the continuous distribution probability density HMM.

Abstract: A HMM device, and a DP matching device, capable of performing word spotting accurately with a small amount of calculation is provided. For that purpose, a code book is provided in which representative vectors of respective clusters are stored in a form searchable by their labels, wherein in HMM, similarity degrees based on Kullbach-Leibler Divergence of distributions of occurrence probabilities of the clusters under respective states and distribution of degrees of input feature vectors to be recognized to the respective clusters are rendered occurrence degrees of the feature vectors from the states and in DP matching, similarity degrees based on Kullbach-Leibler Divergence of distributions of membership degrees of feature vectors forming reference patterns to the respective clusters and distribution of degrees of input feature vectors to the respective clusters are rendered inter-frame similarity degrees of frames of the input patterns and frames of corresponding reference patterns.

Abstract: A codebook for vector quantization is speaker-adapted or a speaker is normalized so that it complies with the codebook using a small number of samples for learning. A deviation vector is set for the centroid of each cluster or an input vector. The deviation vector is set so that a separately defined objective function is maximized if it is defined to be maximized, or minimized if it is defined to be minimized. The maximization or minimization is performed using samples for learning obtained from a speaker who uses the system when the centroid or input vector is moved by an amount corresponding to the deviation vector. By moving the centroid or input vector using the deviation vector, speaker adaptation is performed if the former is moved and speaker normalization is performed if the latter is moved.

Abstract: A method and apparatus for estimating parameters of a new hidden Markov model (HMM) applicable to pattern recognition such as recognition of speech signals which are time series signals, and a method and apparatus for pattern recognition employing this HMM are provided. It is easily applicable to speech and other time series signals. In particular, the pattern recognition degree of a time series observation vector signaly, received from an information source, is calculated by using function values u(y,1), u(y,2), . . . , u(y,M) and occurrence probabilities of signals C.sub.1, C.sub.2, . . . , C.sub.M which are composed of set C, where u(y,m) is the image into which the pair (C.sub.m,Y) is mapped, u(y,m).di-elect cons.U(U=[a,b], a,b.di-elect cons.R.sup.1 and 0.ltoreq.a.ltoreq.b) and y.di-elect cons.R.sup.n (R.sup.n : n-dimensional Euclidean space). C is a set of signals against which the observation vector signal y is compared.

Abstract: A method and apparatus for estimating parameters of a new hidden Markov model (HMM) applicable to pattern recognition such as recognition of speech signals which are time series signals, and a method and apparatus for pattern recognition employing this HMM. It is easily applicable to speech and other time series signals. In particular, the pattern recognition degree of a time series observation vector signal y, received from an information source, is calculated by using function values u(y,1), u(y,2), . . . , u(y,M) and occurrence probabilities of signals C.sub.1, C.sub.2, . . . , C.sub.M which are composed of set C, where u(y,m) is the image into which the pair (C.sub.m,Y) is mapped, u(y,m).epsilon.U (U=[a,b], a,b.epsilon.R.sup.1 and 0.ltoreq.a.ltoreq.b) and y.epsilon.R.sup.n (R.sup.n :n-dimensional Euclidean space). C is a set of signals against which the observation vector signal y is compared.

Abstract: A method and apparatus are disclosed for estimating parameters of a new hidden Markov model (HMM) applicable to pattern recognition such as recognition of speech signals which are time series signals, and a method and apparatus for pattern recognition employing this HMM. It is easily applicable to speech and other time series signals. In particular, the pattern recognition degree of a time series observation vector signal y, received from an information source, is calculated by using function values u(y,1), u(y,2), . . . , u(y,M) and occurrence probabilities of signals C.sub.1, C.sub.2, . . . , C.sub.M which are composed of set C, where u(y,m) is the image into which the pair (C.sub.m, Y) is mapped, u(y,m).di-elect cons.U (U=[a,b], a,b.di-elect cons.R.sup.1 and 0.ltoreq.a.ltoreq.b) and y.di-elect cons.R.sup.n (R.sup.n : n-dimensional Euclidean space). C is a set of signals against which the observation vector signal y is compared.

Abstract: A neural network with a high recognition rate when applied to static patterns is made applicable to dynamic time series patterns such as voice signals. Plural units with one or more inputs and outputs are interconnected, and a unique load coefficient is assigned to each connection to weight the signals flowing through that connection. The neural network includes an input unit group to which are input the components of plural vectors included in the input feature vector series {y(t)}; an output unit which outputs the converted vectors, which are produced by passing the input vectors through each unit and the associated connections; and J paths from input unit group to the output unit group. The units are connected to form a Hidden Markov Model wherein each signal path identified as j=1, 2, . . . , J corresponds to the same state.

Abstract: An analyzing system analyzes object signals, particularly voice signals, by estimating a generation likelihood of an observation vector sequence being a time series of feature vectors with use of a Markov model having a plurality of states and given transition probabilities from state to state. A state designation section determines a state i at a time t stochastically using the Markov model. Plural predictors, each of which is composed of a neural network and is provided per each state of the Markov model, are provided for generating a predictional vector of the feature vector x.sub.t in the state i at the time t based on values of the feature vectors other than the feature vector x.sub.t. A first calculation section calculates an error vector of the predictional vector to the feature vector x.sub.T. A second calculation section calculates a generation likelihood of the error vector using a predetermined probability distribution of the error vector according to which the error vector is generated.

Abstract: A pattern recognition apparatus using the hidden Markov model technique in which parameters for defining a mean vector representing the probability density function in each one of plural states for composing the hidden Markov model vary with time. Accordingly, the recognition precision may be enhanced when this apparatus is used in, for example, voice recognition.

Abstract: Devices for performing fast Hadamard transforms based upon a predetermined algorithm are provided. A buffer memory (random-access memory) with the capacity equal to the number of components of an input vector to be transformed is provided and is addressed in such a way computation of Hadamard transforms can be performed at faster speeds with a higher degree of efficiency.

Abstract: The present invention is a random accessible signal filing system containing magnetic discs, at least two buffer memories and a circuit for controlling the buffer memories in such a way that the write-in and read-out of at least two of the buffer memories may be alternately performed, whereby through random access, the required signals are selected and read out so as to be outputted as a series of signals.In this system, during the outputting of the signals, the signals coming from the magnetic discs are written into one of the buffer memories sector by sector at the read-out speed of the magnetic discs, and thereafter, are read out at the demodulating speed of the demodulator. The two buffer memories are so controlled that while the signals are written into one of them; the signals are read out from the other.

Abstract: An image display apparatus has an: image signal receiver for simultaneously receiving two different image signals, a first image signal and a second image signal; a memory for writing therein the second image signal; controlling means for controlling the writing position, writing frequency, writing sequence, reading-out sequence, reading-out frequency and start-timing of reading-out of the second image signal; a display for displaying full picture information or partial picture information of the second image signal a part of a displayed picture of the first image signal by selectively reading-out the second image signal under the control of the controller; and zoom-up device for stepwisely or continuously varying the writing position and reading-out position of the second image signal controlled by the controller so as to zoomwisely display the full or partial picture of the second image signal on the picture of the first image signal.