A method of detecting a biometric event in an input signal comprises: performing principal component analysis PCA on samples of a plurality of model signals to generate a transformation matrix having more informative components and less informative components, each model signal comprising a known signal which includes the biometric event to be detected; reducing a dimensionality of the transformation matrix by discarding one or more of the more informative components; transforming a plurality of samples of the input signal using the reduced dimensionality transformation matrix; determining a probability that the biometric event is present in the plurality of samples of the input signal, by calculating a predefined probability function for the transformed samples; and determining that the input signal includes the biometric event if the probability is higher than a threshold.

Disclosed is a system for face recognition based on dynamic updating of facial features, comprising an image acquisition unit, a face image standardization unit, a facial feature comparison unit, and a facial feature update unit. The image acquisition unit acquires an original image which is processed by the face image standardization unit, and then the facial feature comparison unit completes extraction and comparison of a facial feature vector to determine whether the original image belongs to a user ID or a stranger, or to complete entry of the facial feature vector. Each user ID corresponds to one or more facial feature vectors. The facial feature update unit automatically updates the facial feature vector in a normal workflow to improve reliability and accuracy of face recognition. Also disclosed is a method for face recognition using the system. The disclosure has the advantages of simple deployment and simple to use, improving the accuracy of face recognition without increasing the size of a face recognition network, and may quickly and effectively adapt to changes in environment or user's appearance.

The present disclosure relates to system(s) and method(s) for continuous business optimization of an organization based on a cognitive decision making process. In one embodiment, the method comprises generating an opportunity instance package associated with a business opportunity from a set of business opportunities associated with an organization based on analysis of a stream of raw data. Further, the method comprises generating a strategy using the opportunity instance package and one or more of a predictive technique, prescriptive technique and optimization technique. Furthermore, the method comprises generating a set of instruction associated with one or more actors associated with the organization based on the strategy, thereby enabling continuous business optimization of the organization based on a cognitive decision-making process.

A method for eliminating pump noise by empirical mode decomposition and particle swarm optimization is provided. In the method, based on a hypothesis of the pump noise being a linear combination of a group of bases, an extracted pump noise sample is decomposed into a group of signals as bases by the empirical mode decomposition. Coefficients of the optimized linear combination of the group of bases is determined by the particle swarm optimization, thus updating the pump noise sample and improving a noise elimination effect. During a limited number of noise elimination periods, a current pump noise sample is modified by weighting, such that in a limited number of iterations, the current pump noise sample gradually converges to the pump noise waveform in the unit of a varied period, so as to be applicable to a slow variation of the pump noise during a long-time operation of the system.

An apparatus for correcting an input signal is configured for receiving the input signal, the received input signal comprising a series of input values. The apparatus is configured for matching a series of template values to the series of input values by warping the series of template values and the series of input values relatively to each other so as to assign one or more template values to one or more input values, wherein the series of template values represents an approximation of a noise signal that is expected to be comprised in the input signal. The apparatus is configured for obtaining a series of corrected input values based on a mismatch between the input values and their respective assigned template values. The apparatus is configured for providing a corrected signal based on the series of corrected input values.

A method of operating an activity recognition system includes capturing ambient radio frequency (RF) data by an RF sniffer. The ambient RF data is then received by a processor. The processor reduces noise content of the ambient RF data. Background is then subtracted from the ambient RF data by the processor. The processed ambient RF data is then converted into an image by the processor. The system generates successive images for each one of a plurality of time intervals. An image processing algorithm, stored in a storage medium and executed by the processor, is applied to the plurality of successive images to determine activity recognition.

A system and method for detecting, amplifying, and sorting non-transitory signals stemming from cellular activity of tissue in an extracellular medium is presented herein. Weak signals are difficult to detect, especially when they originate far from the measuring electrode. The invention takes advantage of stochastic resonance, i.e. adding noise to signals to amplify them and make them more detectable, to improve signal detection from a single electrode.

Embodiments of the present invention are directed to a noise-model based sensor converter configured to map a sensor measurement output to discrete, nonlinear steps of constant uncertainty. In a non-limiting embodiment of the invention, the sensor converter receives an output signal from a sensor. The output signal can include a measurement. The sensor converter can also receive a noise model. The output signal is mapped to a discrete set of steps based on the noise model. The discrete set of steps are nonlinearly spaced to provide constant uncertainty between adjacent steps. The sensor converter generates an output based on the discrete set of steps.

An image processing apparatus includes a reading unit configured to read an image of a document and generate image data, a first binarization unit configured to generate binary image data by performing halftone processing on the image data generated by the reading unit, a smoothing unit configured to perform smoothing processing on a character region of the binary image data, a second binarization unit configured to perform binarization processing on the character region having been subjected to the smoothing processing by the smoothing unit, and a character recognition unit configured to perform character recognition processing on the character region having been subjected to the binarization processing by the second binarization unit.

An identity recognition device, and an substrate and an identity recognition circuit are disclosed. In the substrate, a plurality of second recognition output lines are added to be arranged in pairs with first recognition output lines. A control signal loaded to the control signal line will be coupled to the first and second recognition output lines by the parasitic capacitance between the control signal line and the recognition output lines to cause similar signal interference to the first and second recognition output lines. During the identity recognition time period, the signal interference in the electric signal outputted by the first recognition output line can be removed by utilizing the similar noise signal received by the second recognition output line, in such a way to improve the signal-to-noise ratio and detection precision of the identity recognition signal.