A scheduler includes circuitry configured to, based on similarity between execution time and power consumption information of jobs executed in a system, classifies jobs into groups, construct respective time series prediction models for the groups using a power waveform included in each of the groups as teacher data, predict a power waveform at an interval including a first time from each of the constructed time series prediction models, compare a power waveform at an interval including a first time of a job in execution for which power is to be predicted with the predicted power waveform of each of the groups to identify a similar time series prediction model, based on the identified time series prediction model, predict power consumption at a predetermined interval including a second time for the job for which power is to be predicted, and control job execution based on the predicted power consumption.

A component of an Autonomous Vehicle (AV) system, the component having at least one processor; and a non-transitory computer-readable storage medium including instructions that, when executed by the at least one processor, cause the at least one processor to decode data encoded in a signal, wherein the data identifies a pattern of protrusions embedded in a driving surface, the signal being received from at least one vehicle sensor resulting from a vehicle driving over the pattern of protrusions in the driving surface.

An anomaly detection method includes collecting and preprocessing time series data every preset detection cycle; detecting an anomaly in time series data preprocessed for a current detection cycle using a deep learning model trained with an unsupervised learning scheme using features of time series data of a previous detection cycle; retraining the deep learning model by further using the time series data preprocessed for at least one detection cycle included in the current learning cycle; and detecting an anomaly in time series data collected and preprocessed for a detection cycle after the current learning cycle using the retrained deep learning model.

A system may include a stimulator, sensing circuitry and a controller. The stimulator may be operably connected to at least one stimulation electrode, and configured to deliver an electrical waveform for an electrical therapy using the at least one stimulation electrode. The sensing circuitry may be operably connected to at least one sensing electrode, and configured to sense electrical potentials that are evoked by the electrical waveform to provide sensed evoked signals. The controller may be operably connected to the stimulator and the sensing circuitry. The controller may be configured to automatically define a sampling window, sample the sensed evoked potentials during the sampling window to provide sampled values, detect at least one feature from the sampled values, and automatically provide feedback for closed-loop control of the electrical therapy based on the at least one feature.

Systems and methods for automatically injecting effects in cyber-physical systems and their simulations are provided herein. In one example, the cyber-physical system under test can include one or more watch-point monitors that can analyze messages between components of the system to determine the presence of one or more particular patterns present in the messages being passed between components of the system during operation. In one or more examples, upon detection of one or more conditions matching a watch point, the systems and methods presented herein can activate an effect and inject it into the cyber-physical system under test based on the detected watch point. In one or more examples, the systems and methods can provide a domain-specific “effects language” (EL) that can allow a user to specify a watch point and an effect corresponding to the watch point.

An analysis and observation device includes: a component analysis section that performs component analysis of an analyte; an output section that outputs one component analysis result to an analysis history holding section; the analysis history holding section that holds a plurality of component analysis results as an analysis history; and an identifying section that identifies a component analysis result similar to the component analysis result obtained by the component analysis section from among the plurality of component analysis results held in the analysis history holding section. The analysis history holding section holds the analysis history to which the component analysis result has been newly added according to the output of the component analysis result by the output section, and the identifying section identifies a component analysis result similar to the one component analysis result from among results of the component analysis performed by the component analysis section.

An information processing apparatus includes an extraction unit, a determination unit, a display control unit, and a display unit. The extraction unit is configured to extract, by predetermined pattern matching, candidate peaks in a certain arbitrary period of time from among at least one or more pieces of waveform data. The determination unit is configured to determine, from among the candidate peaks of the waveform data, a single peak based on a score related to the pattern matching. The display control unit is configured to output display information for displaying a position of the peak. The display unit is configured to display the display information.

A system for detecting steps of a user includes processing circuitry and a sensor configured to detect a variation of electrostatic charge of the user during a step of the user and generate a charge-variation signal. An accelerometer is configured to detect an acceleration as a consequence of the step and generate an acceleration signal. The processing circuitry is configured to: acquire the charge-variation signal; acquire the acceleration signal; detect, in the charge-variation signal, a first characteristic identifying the step; detect, in the acceleration signal, a second characteristic identifying the step. If both of the first and second characteristics have been detected, the presence of the step can be validated.

Stack fault inspection apparatus and method are disclosed. The apparatus includes a sample stage fixing the silicon carbide substrate and allow the incident light to scan the substrate surface; an incident light source configured to irradiate a vertical illumination light of a wavelength corresponding to an energy greater than a band gap energy of the substrate to at least a portion of a surface of the substrate in a direction substantially perpendicular to the surface of the substrate; a photomultiplier tube (PMT) configured to obtain a photoluminescence mapping image having a wavelength corresponding to the band gap energy of the substrate from the surface of the substrate; and a controller configured to process the mapping image and identify stacking faults.

Methods and apparatus for identifying media content using temporal signal characteristics are disclosed. An example apparatus includes at least one memory, computer readable instructions, and at least one processor to execute the instructions to identify intervals in a media signal; generate interval sums for respective ones of the intervals, a first interval sum of the interval sums based on a sum of magnitudes of first peaks of the media signal that occur between zero crossings of a first interval of the intervals of the media signal; identify second peaks based on the interval sums; and generate a signature representative of the media signal based on the second peaks.