Methods, systems, and computer-readable storage media for accurate time delay estimation using coherent averaging. A plurality of out-of-bracket acoustical impulses are generated in a pipe segment of a fluid distribution system. Signal data representing the acoustical impulses sensed at two locations along the pipe segment are recorded. Precise timings for the generation of the acoustical impulses are obtained, and the acoustical impulses in the signal data recorded from the first location are averaged based on the precise timings to produce a near-sensor average impulse. Similarly, the acoustical impulses in the signal data recorded from the second location are averaged based on the same precise timings to produce a far-sensor average impulse. A time delay between arrival of the plurality of out-of-bracket acoustical impulses at the first and second locations is estimated from the timing of the near-sensor average impulse and the far-sensor average impulse.

A method is implemented by a computer, and includes: (a) acquiring at least one set of waveform data having a time duration from physiological information waveform data; (b) classifying a waveform included in the waveform data into a predetermined type of waveform; (c) determining validity of a classification result of the waveform; and (d) correcting the classification result in accordance with the validity of the classification result.

A recognition method includes retrieving an input including data of a first window size. The method further includes classifying the input based on comparison of warping distance of the input with a pruning threshold.

Methods, systems, and computer-readable storage media for accurate time delay estimation using coherent averaging. A plurality of out-of-bracket acoustical impulses are generated in a pipe segment of a fluid distribution system. Signal data representing the acoustical impulses sensed at two locations along the pipe segment are recorded. Precise timings for the generation of the acoustical impulses are obtained, and the acoustical impulses in the signal data recorded from the first location are averaged based on the precise timings to produce a near-sensor average impulse. Similarly, the acoustical impulses in the signal data recorded from the second location are averaged based on the same precise timings to produce a far-sensor average impulse. A time delay between arrival of the plurality of out-of-bracket acoustical impulses at the first and second locations is estimated from the timing of the near-sensor average impulse and the far-sensor average impulse.

Direct targeted sequencing (DTS) methods and a hidden Markov model (HMM) can be used to call the copy number of a segment of interest within a region of interest. Described herein are methods for calling a copy number variant or a copy number variant abnormality using an HMM, and methods for determining a copy number based on a copy number likelihood model, in a test sequencing library that has be sequenced using DTS methods. Also described herein are methods for determining a copy number of a segment, including accounting for spurious capture probes that may arise from the DTS methods.

Systems and methods are described for identifying a behavior of a machine. A computer system receives a signal indicative of operation of a field machine and applies a deep learning algorithm to identify a pattern in a collection of signals stored on a computer-readable memory. The collection of signals includes the received signal indicative of operation of the field machine and other signals. A series of targeted tests are performed using a test machine while monitoring a signal indicative of operation of the test machine. A behavior is identified during the series of targeted tests that produces a signal that matches the pattern identified by the deep learning algorithm. An occurrence of the behavior is then automatically identified in the field machine in response to detecting the pattern in the received signal indicative of operation of the field machine.

A method of authenticating physical activity in a monitored space, comprising: obtaining monitored space activity data from at least one activity detector deployed in the monitored space to capture simultaneously physical activity of a plurality of moving objects in said monitored space; analyzing said monitored space activity data to identify a plurality of spatiotemporal movement patterns in said monitored space; obtaining a plurality of inertial data patterns from each one of a plurality of applications executed in a plurality of mobile devices, each one of said inertial data patterns is recorded using an inertial sensing device installed in one of said plurality of mobile devices, each one of said plurality of mobile devices is associated with one of a plurality of persons; identifying at least one correlation between at least one of said plurality of inertial data patterns and at least one of said plurality of spatiotemporal movement patterns; authenticating said physical activity according to said at least one correlation.

Aspects of the present disclosure provide techniques for document classification through signal processing. Embodiments include receiving a document for classification. Embodiments include generating an image of the document. Embodiments include producing a signal representation of the document based on numbers of non-white pixels in each horizontal scan line or vertical scan line of the image of the document. Embodiments include comparing the signal representation of the document to signal representations of previously-classified documents. Embodiments include determining, based on the comparing, a classification for the document. Embodiments include performing additional processing with respect to the document based on the classification for the document.

Method and system including displaying a first representation of a medication treatment parameter profile, displaying a first representation of a physiological profile associated with the medication treatment parameter profile, detecting a modification to a segment of the medication treatment parameter profile, displaying a modified representation of the medication treatment parameter profile and the physiological profile based on the detected modification to the segment of the medication treatment parameter profile, modifying an attribute of the first representation of the medication treatment parameter profile, and modifying an attribute of the first representation of the physiological profile are provided.

A system for classifying the usage of a handheld consumer device having a sensor and an analyzer. The sensor determines usage data at successive time instants and provides a temporally successive sequence of usage data during a usage session. The analyzer classifies the usage of the device with respect to at least one set usage classes and assembles a temporally successive sequence of input tuples of usage data relating to a predetermined time period of the usage session, each of the input tuples having at least one element representing the usage data at the respective time instant and for inputting the sequence of input tuples into at least one artificial neural network arranged to output at least one output tuple comprising a number of elements in accordance with the number of usage classes, each element of the output tuple representing a prediction value that the usage of the consumer device at the given time instant relates to a respective usage class.