The system receives exemplary time-series sensor signals comprising ground truth versions of signals generated by a monitored system associated with a target use case and a synchronization objective, which specifies a desired tradeoff between synchronization compute cost and synchronization accuracy for the target use case. The system performance-tests multiple synchronization techniques by introducing randomized lag times into the exemplary time-series sensor signals to produce time-shifted time-series sensor signals, and then uses each of the multiple synchronization techniques to synchronize the time-shifted time-series sensor signals across a range of different numbers of time-series sensor signals, and a range of different numbers of observations for each time-series sensor signal. The system uses the synchronization objective to evaluate results of the performance-testing in terms of compute cost and synchronization accuracy. Finally, the system selects one of the multiple synchronization techniques for the target use case based on the evaluation.

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.

Disclosed are systems and methods for an industry-wide and predictive approach to maintenance of commercial equipment. In one embodiment, multiple instances of a frontend infrastructure can be deployed to various sites where one or more physical parameters of industrial equipment are monitored with wireless sensors and routed to a backend infrastructure. The backend infrastructure can process the sensor data received from the multiple sites and generate predictive maintenance notifications.

Direct targeted sequencing (DTS) methods and a hidden Markov model (HMV) 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.

A data processing method includes a step of obtaining scores of time-series data by comparing the time-series data with reference data in order to process time-series data acquired in a substrate processing apparatus having one or more processing units, a step of classifying the scores into a plurality of levels, and a step of displaying an evaluation result screen including a display area including a graph showing an occurrence rate of each level of the scores, the number of occurrences of each level, and a graph showing temporal change in the number of occurrences of a worst level of the scores when substrates have been processed through a predetermined method with respect to each of the two or more processing units. Accordingly, a data processing method through which a state of the substrate processing apparatus can be easily ascertained is provided.

Techniques allow a computer to responsively search for graph shapes similar to a user-selected graph shape much faster. Data can be pre-processed and stored as vectors, along with an index. The index can be used to find similar vectors that represent graph shapes similar to a user-selected shape in a computationally efficient manner. Vectors of multiple resolutions can be used to anticipate different sizes of a graph that a user can select, and comparisons can be repeated and refined. When a satisfactorily small number of candidate vectors are determined, more computationally intensive distance calculations can be performed on data reconstructed from the vectors.

A method and system for detecting a commercial transaction through physical interactions with products, the system comprising a frame that is placed within a product display unit. The frame includes a shelf, at least one camera, a weight sensor, and display screens. The system further comprises a computing device communicatively connected to the frame. The computing device is configured to detect physical interactions with items that are placed on the shelf based on data that is received from the at least one camera and the weight sensor. The computing device is further configured to determine a commercial transaction associated with physical interactions and transmit item and price data to the display screens based on machine learning of the physical interactions with items.

An automated point of sale system uses one or more cameras to capture images of food products selected by a user for purchase at a food establishment. The point of sale system then compares the images captured to previously captured reference images of such food products being offered at the establishment to find a match for each product, determines the price of each food product the user has selected based on the comparison and match, identifies an account of the user based on a biometric identification of the user and/or reading information from a machine readable token of the user, and automatically causes the account of the user to be charged or debited for the food products identified, thus reducing the check-out time and increasing efficiency of food establishment point of sale systems.

A method includes receiving, with a computing device, a first client request from a first client that identifies a machine learning model and a sensor. The method includes sending, with the computing device, a call to a server to apply the identified machine learning model to a set of data from the identified sensor, in response to the first client request. The method includes receiving, with the computing device, a second client request from a second client that identifies a same machine learning model and sensor as the first client request. The method includes sending, with the computing device, response data from the identified machine learning model to both the first client and the second client without sending an additional call to the server in response to the second client request.

A state estimation device calculates a state transition table indicating a state transition assumed in an object every time a connection pattern between partial waveforms is changed, selects a connection pattern from the state transition table on the basis of entropy that is a statistical index of the state transition of the object, and estimates a state of the object at each time and a state transition of the object on the basis of the selected connection pattern.