614 results about "Independent component analysis" patented technology

A method and apparatus for recognizing and searching for a face using 2nd-order independent component analysis (ICA) are provided. The method for describing feature points uses 2nd-order ICA d to describe a facial image space and improve recognition performance in various illumination conditions. According to the method and apparatus, use of pose or illumination invariant face descriptor enables retrieval of human faces and authentication of a specific individual.

A method and system for blind estimation of time and frequency parameters of an incoming signal. A spectrogram of the signal is processed using singular value decomposition (SVD) and ICA (independent component analysis) techniques. Time and frequency parameters are estimated from time and frequency eigenvectors, respectively.

EKG sensors ((150) are placed on a patient (140) to receive electrocardiogram (EKG) recording signals, which are typically combinations of original signals from different sources, such as pacemaker signals, QRS complex signals, and irregular oscillatory signals that suggest an arrhythmia condition. A computing module (120) uses independent component analysis to separate the recorded EKG signals. The separated signals are displayed to help physicians to analyze heart conditions and to identify probably locations of abnormal heart conditions. At least a portion of the separated signals can be further displayed in a chaos phase space portrait to help detect abnormality in heart conditions.

A method for coding and decoding seismic data acquired, based on the concept of multishooting, is disclosed. In this concept, waves generated simultaneously from several locations at the surface of the earth, near the sea surface, at the sea floor, or inside a borehole propagate in the subsurface before being recorded at sensor locations as mixtures of various signals. The coding and decoding method for seismic data described here works with both instantaneous mixtures and convolutive mixtures. Furthermore, the mixtures can be underdetemined [i.e., the number of mixtures (K) is smaller than the number of seismic sources (I) associated with a multishot] or determined [i.e., the number of mixtures is equal to or greater than the number of sources). When mixtures are determined, we can reorganize our seismic data as zero-mean random variables and use the independent component analysis (ICA) or, alternatively, the principal component analysis (PCA) to decode. We can also alternatively take advantage of the sparsity of seismic data in our decoding process. When mixtures are underdetermined and the number of mixtures is at least two, we utilize higher-order statistics to overcome the underdeterminacy. Alternatively, we can use the constraint that seismic data are sparse to overcome the underdeterminacy. When mixtures are underdetermined and limited to single mixtures, we use a priori knowledge about seismic acquisition to computationally generate additional mixtures from the actual recorded mixtures. Then we organize our data as zero-mean random variables and use ICA or PCA to decode the data. The a priori knowledge includes source encoding, seismic acquisition geometries, and reference data collected for the purpose of aiding the decoding processing.The coding and decoding processes described can be used to acquire and process real seismic data in the field or in laboratories, and to model and process synthetic data.

A method and apparatus for the application of Blind Source Separation (BSS), specifically independent Component Analysis (ICA) to mixture signals obtained by a pulse oximeter sensor. In pulse oximetry, the signals measured at different wavelengths represent the mixture signals, while the plethysmographic signal, motion artifact, respiratory artifact and instrumental noise represent the source components. The BSS is carried out by a two-step method including an ICA. In the first step, the method uses Principal Component Analysis (PCA) as a preprocessing step, and the Principal Components are then used to derive sat and the Independent Components, where the Independent Components are determined in a second step. In one embodiment, the independent components are obtained by high-order decorrelation of the principal components, achieved by maximizing the sum of the squares of the higher-order cumulants of the plurality of mixture signals.

A cardiac monitoring and / or stimulation method and systems provide monitoring, defibrillation and / or pacing therapies, including systems detecting and / or treating cardiac arrhythmia. A system includes a housing coupled to a plurality of electrodes configured for subcutaneous non-intrathoracic sensing. A signal processor receives a plurality of composite signals associated with a plurality of sources, separates a signal from the plurality of composite signals using blind source separation, and identifies a cardiac signal. The signal processor may iteratively separate signals from the plurality of composite signals until the cardiac signal is identified. A method of signal separation includes detecting a plurality of composite signals at a plurality of locations, separating a signal using blind source separation, and identifying a cardiac signal. The separation may include a principal component analysis and / or an independent component analysis. The composite signals may be filtered before separation using band-pass filtering, adaptive, or other filters.

The invention discloses a method for recognizing a road traffic sign for an unmanned vehicle, comprising the following steps of: (1) changing the RGB (Red, Green and Blue) pixel value of an image to strengthen a traffic sign feature color region, and cutting the image by using a threshold; (2) carrying out edge detection and connection on a gray level image to reconstruct an interested region; (3) extracting a labeled graph of the interested region as a shape feature of the interested region, classifying the shape of the region by using a nearest neighbor classification method, and removing a non-traffic sign region; and (4) graying and normalizing the image of the interested region of the traffic sign, carrying out dual-tree complex wavelet transform on the image to form a feature vector of the image, reducing the dimension of the feature vector by using a two-dimension independent component analysis method, and sending the feature vector into a support vector machine of a radial basis function to judge the type of the traffic sign of the interested region. By using the method, various types of traffic signs in a running environment of the unmanned vehicle can be stably and efficiently detected and recognized.

The system and method for spectral analysis uses a set of spectral data. The spectral data is arranged according to a second dimension, such as time, temperature, position, or other condition. The arranged spectral data is used in a signal separation process, such as an independent component analysis (ICA), which generates independent signals. The independent signals are then used for identifying or quantifying a target component.

The invention relates to a dynamic visual attention area extraction method based on features in the technical field of machine vision. The method comprises the following steps: first, an independent component analysis method is adopted for carrying out sparse decomposition to mass natural images so as to obtain a group of filtering base functions and a group of corresponding reconfigurable base functions and the input images are divided into small RGB blocks of m multiplied by m and projected on a group of the base to obtain the features of the images; second, effective encoding principle is utilized to measure an incremental encoding length index for each feature; third, according to the incremental encoding length index, the remarkable degree of each small block is processed through the energy reallocation of each feature and finally a remarkable map is obtained. The method can reduce a 'time slice', realize continuous sampling, therefore, data of different frames can direct the processing of remarkable degrees together, and the problem that the remarkable degrees of different frames require independent process is solved so as to realize the dynamic performance.

A system and method for separating a mixture of audio signal into desired audio signals (430) (e.g., speech) and a noise sign (440) is disclosed. Microphones (310, 320) are positioned to receive the mixed audio signals, and an independent component analysis (ICA) processes (212) the sound mixture using stability constraints. The ICA process (508) uses predefined characteristics of the desired speech signal to identify and isolate a target sound signal (430). Filter coefficients are adapted with a learning rule and filter weight update dynamics are stabilized to assist convergence to a stable separated ICA signal result. The separated signals may be peripherally-processed to further reduce noise effects using post-processing (214) and pre-processing (220, 230) techniques and information. The proposed system is designed and easily adaptable for implementation on DSP units or CPUs in audio communication hardware environments.

The invention provides a video flame detecting method based on a multi-feature fusion technology. The video flame detecting method includes firstly using a cumulative geometrical independent component analysis (C-GICA) method to capture a moving target in combination with a flame color decision model, tracking moving targets in current and historical frames in combination with a multi-target tracking technology based on moving target areas, extracting color features, edge features, circularity degrees and textural features of the targets, inputting the features into a back propagation (BP) neural network, and further detecting flames after the decision of the BP neural network. According to the video flame detecting method, spatial-temporal features of the moving features, color features, textural features and the like are comprehensively applied, the defects of algorithms of existing video flame detecting technologies are overcome, and reliability and applicability of the video flame detecting method are effectively improved.

The invention provides a magnetic tile surface defect feature extraction and defect classification method based on machine vision. A concrete algorithm comprises a first step of building a 5-scale and 8-direction Gabor filter bank suitable for magnetic tile surface defect feature extraction, conducting filtering to an original image and obtaining a 40-width component plot, a second step of respectively extracting a gray average and a variance feature of the component plot and forming a 80-dimension feature vector, a third step of conducting dimensionality reduction to the original 80-dimension feature vector through a principal component analysis (PCA) method and an independent component analysis (ICA) method, removing relevance and redundancy and obtaining a 20-dimension feature vector, a fourth step of conducting normalization pretreatment to feature vector data, wherein the original data are normalized between zero and one, and a fifth step of adopting a grid method and a K-CV method to achieve SVM parameter optimization at first and training an SVM model using training sample data offline, wherein pretreated testing sample data are input into a support vector machine during online testing, and automatic classification and identification of defects can be achieved. The feature extraction method can effectively filter interference and prominent defects of magnetic tile surface texture, extracted features can reflect defect information accurately, data values are small, and a classifier used for classifying the defects can achieve defect identification fast and accurately online.

Electrocardiogram (ECG) recorded signals are processed by a computer-implemented method to substantially remove extraneous signals to produce intermediary signals, and to separate the intermediary signals using a non-orthogonal transformation method such as independent component analysis to produce independent components of signals. The separated signals are displayed to help physicians to analyze medical conditions and to identify locations of abnormal heart conditions.

A method and system decomposes a cardiac signal, such as an electrocardiogram (ECG) signal, into components. The components are then usable to assist in the detection of an abnormal heart condition. More particularly, a single lead sensor is used to generate a single lead cardiac signal. The cardiac signal is segmented into a set of cycle segments according to detected heart waveforms. The cycle segments are aligned and used to generate a set of cross-sectional signals. The cross-sectional signals are aligned and presented as inputs to a signal separation process, which separates the cardiac signal into a set of components. The components may be grouped according to predefined criteria. The components or groups may be analyzed or displayed to assist in the detection of an abnormal cardiac signal, which may be indicative of an abnormal heart condition. In one example, the signal separation process is a non-orthogonal transformation method such as independent component analysis (ICA).

A system and method of signal denoising using Independent Component Analysis (ICA) and fractal dimension analysis of the signal components in the ICA domain is described. The signal components with fractal dimensions higher than a pre-determined threshold are automatically attenuated or canceled in order to alleviate the noise in the signal. The denoised signal is reconstructed using inverse ICA transform of the signal components.

The invention discloses a method for extracting fault characteristic frequencies of train rolling bearings with variable rotational speeds, and belongs to the field of technologies for diagnosing faults and processing signals. The method includes steps of analyzing vibration signals of the bearings with the variable rotational speeds in time and frequency domains, searching local peak values of the vibration signals and extracting instantaneous frequency values corresponding to the rotational speeds at different moments; fitting instantaneous frequencies by the aid of neural networks, acquiring rotational speed curves of reference spindles, re-sampling original signals at uniform angles on the basis of the rotational speed curves and analyzing order ratios of the original signals on the basis of the rotational speed curves; separating signals with mixed order ratio signals by the aid of fixed-point independent component analysis and spectrum peak search technologies to acquire order ratio component characteristics of fault components of the bearings. The method has the advantages that the method is used for estimating the rotational speeds of the train bearings without tachometers in real time, the instable fault bearing signals can be converted into the stable signals in uniform-angle domains, independent order ratio components can be effectively separated from the signals, and the method is favorable for extracting the fault characteristic frequencies of the train bearings and detecting the fault characteristic frequencies of the train bearings in an online manner.

A propylene polymerization production process optimal soft-measurement meter based on genetic algorithm optimized BP neural network comprises a propylene polymerization production process, a site intelligent meter, a control station, a DCS databank used for storing data, an optimal soft measurement model based on genetic algorithm optimized BP neural network, and a melting index soft-measurement value indicator. The site intelligent meter and the control station are connected with the propylene polymerization production process and the DCS databank; the optimal soft-measurement model is connected with the DCS databank and the soft-measurement value indicator. The optimal soft measurement model based on genetic algorithm optimized BP neural network comprises a data pre-processing module, an ICA dependent-component analysis module, a BP neural network modeling module and a genetic algorithm optimized BP neural network module. The invention also provides a soft measurement method adopting the soft measurement meter. The invention can realize on-line measurement and on-line automatic parameter optimization, with quick calculation, automatic model updating, strong anti-interference capability and high accuracy.

A method for echo cancellation in multichannel audio signals includes receiving a plurality of time-domain signals, including multichannel audio signals and at least one reference signal, transforming the time-domain signals to K under-sampled complex-valued subband signals using an analysis filter bank, and performing, for each of the K under-sampled complex-value subband signals, linear echo cancellation of the reference signal from each channel using an acoustic echo canceller. A probability of acoustic echo dominance is produced using a single-double talk estimator, and a semi-blind multichannel source separation is performed based on the probability and independent component analysis (“ICA”) to decompose the audio signals into a near-end source signal and a residual echoes using subband semi-blind source separation. The residual echo components are removed from the near-end source signal using a spectral filter bank, and the subband audio signals are reconstructed to a multichannel time-domain audio signal using a subband synthesis filter.

An apparatus and method for human activity and facial expression modeling and recognition are based on feature extraction techniques from time sequential images. The human activity modeling includes determining principal components of depth and / or binary shape images of human activities extracted from video clips. Independent Component Analysis (ICA) representations are determined based on the principal components. Features are determined through Linear Discriminant Analysis (LDA) based on the ICA representations. A codebook is determined using vector quantization. Observation symbol sequences in the video clips are determined. And human activities are learned using the Hidden Markov Model (HMM) based on status transition and an observation matrix.

The invention discloses a single channel blind source separation method, and belongs to the technical field of electronic information. The single channel blind source separation method is characterized by adopting an extreme point symmetric prolongation method, carrying out processing of removing an end effect to ensemble empirical mode decomposition, transforming one-way mixed signals to intrinsic mode functions (IMFs) by using the ensemble empirical mode decomposition, restraining noise, carrying out dimension reduction processing to multi-channel IMFs by utilizing principal component analysis, removing invalid components in the IMFs, and carrying out independent component analysis to multi-channel signals after dimensionality reduction to achieve blind source separation. Implementation steps comprise carrying out linear adding to the multi-channel signals and mixing the multi-channel signals to single-channel signals to transmit, recovering source signals simply, fast and effectively under the condition of not influencing later stage pattern recognition effect, and achieving the outputting of multi-channel outputs. The single channel blind source separation method has the advantages of being capable of separating the multi-channel frequency-spectrum-overlapped signals mixed to one channel under the condition of not influencing the later stage recognition effect.

A clinically valuable method is provided for evaluating the onset or progression of Alzheimer's disease using a non-invasive biomarker obtained from an independent component analysis (ICA) of an individual's resting state functional MRI. The method is relatively more automated and objective than previous methods and exploits dysfunctional connectivity across an entire network of brain regions in Alzheimer's disease. It eliminates the need for investigator's intervention as much as possible and is more robust than structural and functional methods targeting the hippocampus.

The invention discloses a heart rate measuring method based on a low-end imaging device. The method is mainly applied to measuring a non-contact heart rate under daily ambient light illumination conditions. The method comprises the following steps that the video including the human face area is read according to RGB channels, the grey average of the human face area in each frame is calculated, and three curves are drawn with the number of frames as variables. A threshold value is set based on the information entropy principle, and the cross-color situation of the imaging device is judged according to the entropy of the three curves. When the cross-color situation is serious, the waveform of a representative pulse wave is extracted by utilizing the independent component analysis method. When the cross-color situation is not serious, a simple division algorithm is carried out on a red channel curve and a green channel curve, the waveform of the representative pulse is obtained, the Fourier transform is carried out on the waveform, and the heart rate is obtained by extracting the frequency corresponding to the maximum frequency value within the range between 0.5Hz and 3Hz.

Sound source separation, without permutation, using convolutional mixing independent component analysis based on a priori knowledge of the target sound source is disclosed. The target sound source can be a human speaker. The reconstruction filters used in the sound source separation take into account the a priori knowledge of the target sound source, such as an estimate the spectra of the target sound source. The filters may be generally constructed based on a speech recognition system. Matching the words of the dictionary of the speech recognition system to a reconstructed signal indicates whether proper separation has occurred. More specifically, the filters may be constructed based on a vector quantization codebook of vectors representing typical sound source patterns. Matching the vectors of the codebook to a reconstructed signal indicates whether proper separation has occurred. The vectors may be linear prediction vectors, among others.

The invention provides a method of automatically removing ocular artifacts from an electroencephalogram signal without setting a threshold value, belongs to the field of biological information technology, and is mainly applied to the preprocessing process of the electroencephalogram signal. The method particularly comprises the following steps: performing an independent component decomposition to a captured electroencephalogram signal containing the ocular artifacts; gaining the kurtosis, the sequence renyi entropy and the sample entropy of each independent component as feature vectors, so as to automatically recognize an independent component containing the ocular artifacts by k-means cluster analysis, and setting the independent component to be zero and other components to be constant, reconstructing the signal, and obtaining a pure electroencephalogram signal. The method provided by the invention solves the problems that the artifacts are identified by means of manual work during the traditional process for removing the ocular artifacts, so that time and labors are wasted and the workload is heavy. In addition, the method provided by the invention can realize the purposes of automatically identifying and removing the ocular artifacts without setting the threshold value by manual work, so that the shortcoming in the existing method that a researcher is required to have definite future knowledges and strong subjectivity during the setting of the the threshold value is overcame.

A novel application of independent component analysis (ICA) to data acquired by a single sensor. The technique exploits the unique relationship between multiple physiologic (source) and electronic (artifact) components in surface recorded sensory nerve action potential (SNAP) waveforms that are evoked by different activating magnitudes. A forward model of the SNAP is developed and used to test the approach on a simplified data simulation. The method is applied to experimental data and shown to be effective at separating artifact and source components and reconstructing artifact-free traces. A method of automated reconstruction for use within an expert system is also disclosed.

Methods and apparatus for signal processing are disclosed. Source separation can be performed to extract source signals from mixtures of source signals and perform acoustic echo cancellation. Independent component analysis may be used to perform the source separation in conjunction with acoustic echo cancellation on the time-frequency domain mixed signals to generate at least one estimated source signal corresponding to at least one of the original source signals. It is emphasized that this abstract is provided to comply with the rules requiring an abstract that will allow a searcher or other reader to quickly ascertain the subject matter of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims.

A principal component analysis or an independent component analysis is conducted on the living body signals, e.g., local cerebral blood amount change signals obtained by the living body optical measurement to extract and display multiple component signals thereof. Signals other than the component signals containing noise are selected from these component signals automatically or manually, and the local cerebral blood amount change signals are reconstructed by using selected signals. The reconstructed signals is displayed and further subjected to the component analysis or the reconstruction as appropriate, and used for the profiling of information necessary for the diagnosis. Consequently, high-precision target signals can be obtained by completely removing external noise included in the living body signals, particularly the noise which cannot be adequately removed by the moving average and the filtering processings.

A novel application of independent component analysis (ICA) to data acquired by a single sensor. The technique exploits the unique relationship between multiple physiologic (source) and electronic (artifact) components in surface recorded sensory nerve action potential (SNAP) waveforms that are evoked by different activating magnitudes. A forward model of the SNAP is developed and used to test the approach on a simplified data simulation. The method is applied to experimental data and shown to be effective at separating artifact and source components and reconstructing artifact-free traces. A method of automated reconstruction for use within an expert system is also disclosed.

Data is analysed by an independent component analysis to derive a transform matrix W encoding properties of the data. The data is encoded using this transform matrix W, and a watermark is embedded into it. Then that the inverse of the transform matrix is applied to obtain watermarked data. The presence of the watermark is found by applying the transform matrix again, and examining the result for the presence of the watermark. The invention is particularly, but not exclusively suitable for data which is image data 10.