Disclosed is a method of generating a functional singularity at a point or collection of points. The method may include determining a relationship between one or more parameters associated with a physical structure and a spatial gradient of field values of at least one of electromagnetic energy, sound energy, particle beam, or water waves manipulated by the physical structure, configuring, according to the relationship, the spatial gradient of field values to represent a functional singularity at a point, performing backpropagation using the spatial gradient of field values to obtain design parameters corresponding to values for the one or more parameters that achieve the functional singularity at the point, and producing a physical structure having the design parameters.

A quantum circuit for Daubechies-6 wavelet transform includes: a B quantum circuit configured to receive a first part of n-dimensional data and generate a first intermediate result; a Q.sub.2.sub.n.Math.Q.sub.2.sub.n quantum circuit configured to receive a second part of the n-dimensional data, and the Q.sub.2.sub.n.Math.Q.sub.2.sub.n quantum circuit coupled to the B quantum circuit to receive the first intermediate result, and the Q.sub.2.sub.n.Math.Q.sub.2.sub.n quantum circuit generating a second intermediate result corresponding to the first intermediate result and a first result corresponding to the second part; and an A quantum circuit coupled to the Q.sub.2.sub.n.Math.Q.sub.2.sub.n quantum circuit to receive the second intermediate result and to generate a second result according to the second intermediate result. The present disclosure further discloses a manufacturing method of a quantum circuit for Daubechies-6 wavelet transform and a quantum circuit for Daubechies-6 wavelet inverse transform corresponding to the aforementioned quantum circuit for Daubechies-6 wavelet transform.

Waveforms in power grids typically reveal a certain pattern with specific features and peculiarities driven by the system operating conditions, internal and external uncertainties, etc. This prompts an observation of different types of waveforms at the measurement points (substations). An innovative next-generation smart sensor technology includes a measurement unit embedded with sophisticated analytics for power grid online surveillance and situational awareness. The smart sensor brings additional levels of smartness into the existing phasor measurement units (PMUs) and intelligent electronic devices (IEDs). It unlocks the full potential of advanced signal processing and machine learning for online power grid monitoring in a distributed paradigm. Within the smart sensor are several interconnected units for signal acquisition, feature extraction, machine learning-based event detection, and a suite of multiple measurement algorithms where the best-fit algorithm is selected in real-time based on the detected operating condition. Embedding such analytics within the sensors and closer to where the data is generated, the distributed intelligence mechanism mitigates the potential risks to communication failures and latencies, as well as malicious cyber threats, which would otherwise compromise the trustworthiness of the end-use applications in distant control centers. The smart sensor achieves a promising classification accuracy on multiple classes of prevailing conditions in the power grid and accordingly improves the measurement quality across the power grid.

A method of similarity-based deduplication comprising the steps of: receiving an input data block; computing discrete wavelet transform (DWT) coefficients; extracting feature-related DWT data from the computed DWT coefficients; applying quantization to the extracted feature-related DWT data to obtain keys as results of the quantization; constructing a locality-sensitive fingerprint of the input data block; computing a similarity degree between the locality-sensitive fingerprint of the input data block and a locality-sensitive fingerprint of each data block in the plurality of the data blocks in a cache memory; selecting an optimal reference data block as the data block; determining a differential compression is required to be applied based on the similarity degree between the input data block and the optimal reference data block; applying the differential compression to the input data block and the optimal reference data block.

Techniques in wavelet filtering for advanced deep learning provide improvements in one or more of accuracy, performance, and energy efficiency. An array of processing elements comprising a portion of a neural network accelerator performs flow-based computations on wavelets of data. Each processing element comprises a compute element to execute programmed instructions using the data and a router to route the wavelets in accordance with virtual channel specifiers. Each processing element is enabled to perform local filtering of wavelets received at the processing element, selectively, conditionally, and/or optionally discarding zero or more of the received wavelets, thereby preventing further processing of the discarded wavelets. The wavelet filtering is performed by one or more configurable wavelet filters operable in various modes, such as counter, sparse, and range modes.

Systems and methods for obtaining optimal mother wavelets for facilitating machine learning tasks. The traditional systems and methods provide for selecting a mother wavelet and signal classification using some traditional techniques and methods but none them provide for selecting an optimal mother wavelet to facilitate machine learning tasks. Embodiments of the present disclosure provide for obtaining an optimal mother wavelet to facilitate machine learning tasks by computing values of energy and entropy based upon labelled datasets and a probable set of mother wavelets, computing values of centroids and standard deviations based upon the values of energy and entropy, computing a set of distance values and normalizing the set of distance values and obtaining the optimal mother wavelet based upon the set of distance values for performing a wavelet transform and further facilitating machine learning tasks by classifying or regressing, a new set of signal classes, corresponding to a new set of signals.

Techniques in wavelet filtering for advanced deep learning provide improvements in one or more of accuracy, performance, and energy efficiency. An array of processing elements comprising a portion of a neural network accelerator performs flow-based computations on wavelets of data. Each processing element comprises a compute element to execute programmed instructions using the data and a router to route the wavelets in accordance with virtual channel specifiers. Each processing element is enabled to perform local filtering of wavelets received at the processing element, selectively, conditionally, and/or optionally discarding zero or more of the received wavelets, thereby preventing further processing of the discarded wavelets. The wavelet filtering is performed by one or more configurable wavelet filters operable in various modes, such as counter, sparse, and range modes.

A computer implemented method of decoding a signal. The method includes receiving a signal (which may be an electromagnetic signal), sampling the received signal to generate an input waveform having magnitude and phase components, applying a transform operation to the input waveform to generate a first decoded signal, and outputting the first decoded signal. The transform operation includes pre-processing the input waveform to generate a mirrored inverted waveform and applying a continuous wavelet transform to the mirrored inverted waveform to generate the first decoded signal. This allows inversion of the frequency and temporal resolution of the continuous wavelet transform, thereby enabling improved temporal and frequency decoding of a signal. The method is particularly suitable for signal filters and filtering units.

The present invention relates to improved systems and methods of image processing and more particularly to improved systems and method of image processing using modified image data to produce enhanced data and images using fewer processing cycles and lower system power.

This disclosure provides wearable-device based user-interested information determination methods, apparatuses and wearable devices. The method includes: receiving, by an electrocardiography (ECG) sensor associated with the wearable device, an ECG signal of a user, determining a feature set for the ECG signal, in which the feature set includes time-domain feature data of the ECG signal and frequency-domain feature data of the ECG signal, and determining the user-interested information based on similarity between the feature set and reference feature sets indicative of the user-interested information, in which the user-interested information includes health information associated with a disease. The wearable device includes an ECG sensor configured to receive an ECG signal and an FPGA system. The FPGA system includes modules for determine user-interested information based on the ECG signal. The apparatus includes a processor and a memory coupled to the processor. The memory is configured to store instructions to implement the method.