The present disclosure relates to systems and methods for detecting and identifying anomalies within a discrete wavelet database. In one implementation, the system may include one or more memories storing instructions and one or more processors configured to execute the instructions. The instructions may include instructions to receive a new wavelet, convert the net transaction to a wavelet, convert the wavelet to a tensor using an exponential smoothing average, calculate a difference field between the tensor and a field having one or more previous transactions represented as tensors, perform a weighted summation of the difference field to produce a difference vector, apply one or more models to the difference vector to determine a likelihood of the new wavelet representing an anomaly, and add the new wavelet to the field when the likelihood is below a threshold.

A transformation on raw data is applied to produce transformed data, where the transformation includes at least one selected from among a summary of the raw data or a transform of the raw data between different domains. In response to a query to access data, the query is processed using the transformed data.

The present invention discloses a method for noninvasive imaging of cardiac electrophysiological based on low rank and sparse constraints. This method decomposes the spatio-temporal distribution of endocardial and epicardial potentials into a low-rank matrix representing smooth potential components and a sparse matrix representing the details of potential salience according to the prior condition of spatio-temporal correlation of the endocardial and epicardial potential distribution of the heart. By introducing low rank and sparse constraints, the solution of the ill-conditioned inverse problem of ECG is constrained to the unique optimal solution. The invention combines the individualized three-dimensional heart model of the subject to obtain a three-dimensional dynamic distribution image of the cardiac endocardial and epicardial potential of the subject, which has important practical application value.

An artificial intelligence (AI) system is disclosed that employs windowed cellular automata to create plausible alternatives. A cellular automata-based technique may be utilized to perform pattern recognition and assess the best path available (i.e., instant improv). Alternative sequences (i.e., pattern improv) may also be used to determine alternative paths. This instant improv and pattern improv may then be used to create completely new, plausible alternative nodes. The subsequent evaluation of the sentiment further creates new, dynamic capabilities. Through the use of windowed memory learning, recall, and interpolation, new plausible structures are generated that predict dynamic systems.

The present disclosure relates to systems and methods for detecting and identifying anomalies within a discrete wavelet database. In one implementation, the system may include one or more memories storing instructions and one or more processors configured to execute the instructions. The instructions may include instructions to receive a new wavelet, convert the net transaction to a wavelet, convert the wavelet to a tensor using an exponential smoothing average, calculate a difference field between the tensor and a field having one or more previous transactions represented as tensors, perform a weighted summation of the difference field to produce a difference vector, apply one or more models to the difference vector to determine a likelihood of the new wavelet representing an anomaly, and add the new wavelet to the field when the likelihood is below a threshold.

In one example, an apparatus includes: a wavelet transform engine to receive a first signal stream and perform a wavelet transform on a first time domain sample of the first signal stream, the first wavelet transform engine to output at least one first coefficient for a first frequency range; an energy calculation circuit to compute a first energy signature for the at least one first coefficient; and a correlation circuit to generate a correlation value using the first energy signature, a second energy signature and a plurality of previous energy signatures.

The present disclosure relates to systems and methods for detecting and identifying anomalies within a discrete wavelet database. In one implementation, the system may include one or more memories storing instructions and one or more processors configured to execute the instructions. The instructions may include instructions to receive a new wavelet, convert the net transaction to a wavelet, convert the wavelet to a tensor using an exponential smoothing average, calculate a difference field between the tensor and a field having one or more previous transactions represented as tensors, perform a weighted summation of the difference field to produce a difference vector, apply one or more models to the difference vector to determine a likelihood of the new wavelet representing an anomaly, and add the new wavelet to the field when the likelihood is below a threshold.

Signal analysis is applied in various industries and medical field. In signal analysis, wavelet analysis plays an important role. The wavelet analysis needs to identify a mother wavelet associated with an input signal. However, identifying the mother wavelet associated with the input signal in an automatic way is challenging. Systems and methods of the present disclosure provides signal analysis with automatic selection of wavelets associated with the input signal. The method provided in the present disclosure receives the input signal and a set of parameters associated with the signal. Further, the input signal is analyzed converted into waveform. The waveforms are analyzed to provide image units. Further, the image units are processed by a plurality of deep architectures. The deep architectures provides a set of comparison scores and a matching wavelet family is determined by utilizing the set of comparison scores.

A method for producing a thinned representation of vibration waveform data. The waveform vibration data is received and divided into sequential blocks. For each sequential block, each serially designated in turn as a current block, the following steps are performed. When the current block is also a first block, the current block is passed as a reference block. A representative value for the current block is computed and compared to the representative value for the reference block to determine a difference. The representative value for the current block is compared to a minimum representative value. The current block is transformed into a spectrum and compared to the spectrum for the reference block to determine a correlation value. When the representative value for the current block is above the minimum representative value, the current block is passed as the reference block whenever at least one of the following is true, (a) the first difference is greater than a given difference, (b) the correlation value is less than a given correlation value, and (c) a numerical count of blocks between the current block and a most recently passed reference block is greater than a given maximum.

An iterative Fourier transform unit of a modulation pattern calculation apparatus performs a Fourier transform on a waveform function including an intensity spectrum function and a phase spectrum function, performs a replacement of a temporal intensity waveform function based on a desired waveform after the Fourier transform, and then performs an inverse Fourier transform. The iterative Fourier transform unit performs the replacement using a result of multiplying a function representing the desired waveform by a coefficient. The coefficient has a value with which a difference between the function after the multiplication of the coefficient and the temporal intensity waveform function after the Fourier transform is smaller than a difference before the multiplication, and a ratio of the difference is smaller when an intensity is higher at each time of the function before the multiplication.