The present disclosure is directed to a device with enhanced human activity recognition. The device detects a human activity using one more motion sensors, and enhances the detected human activity depending on whether the device is in an indoor environment or an outdoor environment. The device utilizes one or more electrostatic charge sensors to determine whether the device is in an indoor environment or an outdoor environment.

Various examples related to real time detection with recurrent networks are presented. These can be utilized in automatic insect recognition to provide accurate and rapid in situ identification. In one example, among others, a method includes training parameters of a kernel adaptive autoregressive-moving average (KAARMA) using a signal of an input space. The signal can include source information in its time varying structure. A surrogate embodiment of the trained KAARMA can be determined based upon clustering or digitizing of the input space, binarization of the trained KAARMA state and a transition table using the outputs of the trained KAARMA for each input in the training set. A recurrent network detector can then be implemented in processing circuitry (e.g., flip-flops, FPGA, ASIC, or dedicated VLSI) based upon the surrogate embodiment of the KAARMA The recurrent network detector can be configured to identify a signal class.

An imaging device includes one or more processors; and a computer readable medium storing instructions that, when executed by the one or more processors, cause the imaging device to perform functions including: capturing a first image and thereafter a second image; making a determination of whether or not a difference between the first image and the second image is greater than a threshold value; generating a third image by processing the second image using an image processing algorithm that corresponds to the determination; and displaying the third image.

A method for measuring the performance of a classifier for radar, ultrasound or audio spectra. The classifier is configured to map a radar, ultrasound or audio spectrum to a set of classification scores with respect to classes of a given classification. The method includes: providing a set of test radar, ultrasound or audio spectra that form part of, and/or define, a common distribution or manifold; obtaining at least one evaluation spectrum that is a modification of at least one test spectrum with substantially the same semantic content as this at least one test spectrum, and/or does not form part of the common distribution or manifold; mapping, using the classifier, the at least one evaluation spectrum to a set of evaluation classification scores; and determining the performance based on the set of evaluation classification scores, and/or on a further outcome produced by the classifier during the processing of the evaluation spectrum.

According to one embodiment, an information processing method includes: calculating a first feature amount of query data in a first field; calculating first similarity degrees between the first feature amount and second feature amounts in the first field; obtaining, based on the first similarity degrees, third feature amounts in a second field that are associated with feature amounts selected from the second feature amounts, the second field being different from the first field; calculating fourth feature amounts in the second field, for choices concerning the query data; calculating second similarity degrees between the third feature amounts and the fourth feature amounts; and selecting, based on the second similarity degrees, an answer to the query data among answer candidates corresponding to the third feature amounts.

A preprocessing method and a preprocessing system for improving image recognition are provided. The preprocessing method includes the following steps: disposing light-emitting diodes to surround an image sensor, in which the image sensor corresponds to an image capture region; turning on the light-emitting diodes for emitting a white light source having a color temperature of 3200K, in which the white light source has a fixed illumination area range, and the illumination area range covers the image capture region, such that a color temperature of the image capture region is approximately or equal to 3200K; and turning off the light-emitting diodes for a time interval, such that the image sensor captures images to generate preprocessing frames under a low illuminance condition that is between 0.0004 lux and 1 lux.

Systems and methods for detecting, decoupling and quantifying unresolved signals in trace signal data in the presence of noise with no prior knowledge of the signal characteristics (e.g., signal peak location, intensity and width) of the unresolved signals. The systems and methods are useful for analyzing any trace data signals having one or multiple overlapping constituent signals and particularly useful for analyzing data signals which often contain an unknown number of constituent signals with varying signal characteristics, such as peak location, peak intensity and peak width, and varying resolutions. A general signal model function is assumed for each unknown, constituent signal in the trace signal data. In a first phase, the number of constituent signals and signal characteristics are determined automatically in a parallel fashion by executing multiple simultaneous evaluations iteratively starting with an initial set of trial signals. Making simultaneous evaluations and systematically reducing the number of trial signals allows for convergence to an optimal, final set of signals in a very fast and efficient manner.

Depth data processing systems and methods are disclosed. A mapping system receives, from one or more depth sensors, depth sensor data that includes a plurality of points corresponding to an environment. The mapping system uses one or more trained machine learning models to perform semantic segmentation of the plurality of points, to classify a first subset of the points into a first category and to classify a second subset of the points into a second category. The mapping system clusters the plurality of points into a plurality of clusters based on the semantic segmentation. At least some of the first subset of the points are clustered into a first cluster and at least some of the second subset of the points are clustered into a second cluster. The mapping system generates a map of at least a portion of the environment based on the plurality of clusters.

When conducting imaging mass analysis for a region to be measured on a sample, an individual reference value calculating part obtains a maximum value in P.sub.i/I.sub.i of respective measuring points, and stores the value together with measured data as an individual reference value. When performing comparison analysis for a plurality of the data obtained from different samples, a common reference value determining part reads out corresponding a plurality of the individual reference values and determines a minimum value as a common reference value Fmin. A normalization calculation processing part normalizes the respective intensity values by multiplying the intensity values read out from an external memory device by a normalization coefficient long_Max×(Fmin/P.sub.i) obtained from the common reference value Fmin, TIC values Pi at the respective measuring points, and a maximum allowable value long_Max of a variable storing the intensity values at the time of operation.

An information processing method for system identification includes: generating a fitting curve represented by a sum of exponential functions for each of a set of digital inputs and a set of digital outputs for a physical system that is represented by one or plural equations including m-order differential operators (m is an integer equal to or greater than 1); and calculating coefficients of the differential operators, which are included in first coefficients, so that a first coefficient of each exponential function included in an expression obtained by a product of the differential operators and the fitting curve for the set of the digital inputs is equal to a second coefficient of the same exponential function, which is included in the fitting curve for the set of the digital outputs.