| Class / Patent application number | Description | Number of patent applications / Date published |
|---|
| 706022000 | Signal processing (e.g., filter) | 14 |
| 20090164398 | SIGNAL PROCESSING APPARATUS, SIGNAL PROCESSING METHOD, SIGNAL PROCESSING PROGRAM AND LEARNING APPARATUS - Disclosed herein is a signal processing apparatus for carrying out signal processing to convert input data into output data with a quality higher than the quality of the input data, the data processing apparatus including: a first data extraction section; a nonlinear feature quantity computation section; a processing-coefficient generation section; a second data extraction section; and a data prediction section. | 06-25-2009 |
| 20090259609 | METHOD AND SYSTEM FOR PROVIDING A LINEAR SIGNAL FROM A MAGNETORESISTIVE POSITION SENSOR - A method and system for providing a linear signal from a non-contact magnetoresistive position sensors utilizing a multilayer perception neural network. The neural network multiplies a number of non-linear inputs from the magnetoresistive position sensor by a number of first layer interconnection weights, which are summed by a number of first layer summing nodes and processed by a number of nonlinear activation function. The processed data can then be multiplied by a number of second layer interconnection weights and summed by an output layer-summing node. The output from the output layer-summing node can further be processed by an output activation function in order to produce a linear output signal. | 10-15-2009 |
| 20100057653 | DEVICE AND METHOD RESPONSIVE TO INFLUENCES OF MIND - V An anomalous effect detector ( | 03-04-2010 |
| 20130036078 | DEVICE AND METHOD RESPONSIVE TO INFLUENCES OF MIND - V An anomalous effect detector ( | 02-07-2013 |
| 20140046885 | METHOD AND APPARATUS FOR OPTIMIZED REPRESENTATION OF VARIABLES IN NEURAL SYSTEMS - Certain aspects of the present disclosure support a technique for optimized representation of variables in neural systems. Bit-allocation for neural signals and parameters in a neural network described in the present disclosure may comprise allocating quantization levels to the neural signals based on at least one measure of sensitivity of a pre-determined performance metric to quantization errors in the neural signals, and allocating bits to the parameters based on the at least one measure of sensitivity of the pre-determined performance metric to quantization errors in the parameters. | 02-13-2014 |
| 20140129497 | METHODS AND APPARATUS FOR PERFORMING ONSET DETECTION IN A NEURONAL SPIKING REPRESENTATION OF A SIGNAL - Certain aspects of the present disclosure provide methods and apparatus for performing onset detection in a neuronal spiking representation of a signal, such as an auditory signal. One example method generally includes receiving a signal; filtering the signal into a plurality of channels using a plurality of filters having different frequency passbands; sending the filtered signal in each of the channels to a first type of spiking neuron model; sending the filtered signal in each of the channels to a second type of spiking neuron model; and detecting one or more onsets of the signal based on a first output of the first type of spiking neuron model and a second output of the second type of spiking neuron model for each of the channels. | 05-08-2014 |
| 20140279777 | SIGNAL PROCESSING SYSTEMS - We describe a signal processor, the signal processor comprising: a probability vector generation system, wherein said probability vector generation system has an input to receive a category vector for a category of output example and an output to provide a probability vector for said category of output example, wherein said output example comprises a set of data points, and wherein said probability vector defines a probability of each of said set of data points for said category of output example; a memory storing a plurality of said category vectors, one for each of a plurality of said categories of output example; and a stochastic selector to select a said stored category of output example for presentation of the corresponding category vector to said probability vector generation system; wherein said signal processor is configured to output data for an output example corresponding to said selected stored category. | 09-18-2014 |
| 20140279778 | Systems and Methods for Time Encoding and Decoding Machines - Systems and methods for system identification, encoding and decoding signals in a non-linear system are disclosed. An exemplary method can include receiving the one or more input signals and performing dendritic processing on the input signals. The method can also encode the output of the dendritic processing of the input signals, at a neuron, to provide encoded signals. | 09-18-2014 |
| 20160019456 | DECOMPOSING CONVOLUTION OPERATION IN NEURAL NETWORKS - A method of training a neural network includes encouraging one or more filters in the neural network to have a low rank. | 01-21-2016 |
| 20160019459 | NOISE-ENHANCED CONVOLUTIONAL NEURAL NETWORKS - A learning computer system may include a data processing system and a hardware processor and may estimate parameters and states of a stochastic or uncertain system. The system may receive data from a user or other source; process the received data through layers of processing units, thereby generating processed data; apply masks or filters to the processed data using convolutional processing; process the masked or filtered data to produce one or more intermediate and output signals; compare the output signals with reference signals to generate error signals; send and process the error signals back through the layers of processing units; generate random, chaotic, fuzzy, or other numerical perturbations of the received data, the processed data, or the output signals; estimate the parameters and states of the stochastic or uncertain system using the received data, the numerical perturbations, and previous parameters and states of the stochastic or uncertain system; determine whether the generated numerical perturbations satisfy a condition; and, if the numerical perturbations satisfy the condition, inject the numerical perturbations into the estimated parameters or states, the received data, the processed data, the masked or filtered data, or the processing units. | 01-21-2016 |
| 20160034810 | Neural Networks for Transforming Signals - A method for transforms input signals, by first defining a model for transforming the input signals, wherein the model is specified by constraints and a set of model parameters. An iterative inference procedure is derived from the model and the set of model parameters and unfolded into a set of layers, wherein there is one layer for each iteration of the procedure, and wherein a same set of network parameters is used by all layers. A neural network is formed by untying the set of network parameters such that there is one set of network parameters for each layer and each set of network parameters is separately maintainable and separately applicable to the corresponding layer. The neural network is trined to obtain a trained neural network, and then input signals are transformed using the trained neural network to obtain output signals. | 02-04-2016 |
| 20160071003 | Multilayer Perceptron for Dual SIM Dual Active Interference Cancellation - The various embodiments include methods and apparatuses for cancelling nonlinear interference during concurrent communication of dual-technology wireless communication devices. Nonlinear interference may be estimated using a multilayer perceptron neural network by augmenting aggressor signal(s) by weight factors, executing a linear combination of the augmented aggressor signals, and executing a nonlinear sigmoid function for the combined aggressor signals at a hidden layer of multilayer perceptron neural network to produce a hidden layer output signal. Multiple hidden layers may repeat the process for the hidden layer output signals. At an output layer, hidden layer output signals may be augmented by weight factors, and the augmented hidden layer output signals may be linearly combined to produce an estimated nonlinear interference used to cancel the nonlinear interference of a victim signal. The weight factors may be trained based on a determination of an error of the estimated nonlinear interference. | 03-10-2016 |
| 20160110643 | SYSTEMS AND METHODS FOR CLASSIFYING ELECTRICAL SIGNALS - An analog implementation is proposed of an adaptive signal processing model of a kind requiring a plurality of randomly-set variables. In particular, following a digital to analog conversion of a digital input signal, analog processing is used to transform the data input to the model into data which is subsequently processed by an adaptively-created layer of the model. In the analog processing, multiplication operations involving the randomly-set variables are performed by analog circuitry in which the randomly-set variables are the consequence of inherent tolerances in electrical components. This eliminates the need for the randomly-set variables to be implemented in some other way, for example as random variables stored in memory. | 04-21-2016 |
| 20160132768 | SYSTEMS AND METHODS FOR TRAINING MULTIPATH FILTERING SYSTEMS - A method for training a neural network to be configured to filter a multipath corrupted signal is provided. The method includes receiving, at the neural network, real or simulated multipath corrupted signal data, and training the neural network on the multipath corrupted signal data using a complex iterated least square thresholding algorithm (CILST) capable of processing both real and complex signals. | 05-12-2016 |
