A cloud server platform end is used to construct a mass spectrum species classification model, extract a mass spectrum data feature, and construct a training model of the convolutional neural network; a user platform end is used to upload the mass spectrum, experiment condition and device data, directly screen and identify the type of the mass spectrum based on the mass spectrum species classification model or the mass spectrum information base, automatically compare and identify the species and name of the pesticides based on the neural network model trained by the cloud server platform end, and feedback the comparison result to the user. The disclosure solves the restriction on the purchase of standards for user, the use of the system is not limited by the location, and the pesticide residues could be detected automatically, quickly and accurately.

Systems and methods for detecting and aligning land property boundaries on aerial imagery are provided. The system receives an aerial imagery having land properties. The system applies a feature encoder having a plurality of levels to the aerial imagery. A first level of the plurality of levels includes a convolution block and a discrete wavelet transform layer. The discrete wavelet transform layer decomposes an input feature tensor to the first level into a low-frequency band and a high-frequency band. The high-frequency band is cached and processed with side-convolutional blocks before the high-frequency band are passed to a feature decoder. The system applies the feature decoder to an output of the feature encoder based at least in part on one of inverse discrete wavelet transform layers. The system determines boundaries of the one or more land properties based at least in part on a boundary cross-entropy loss function.

Provided in a fingerprint image preprocessing method including receiving an input fingerprint image, performing a short-time Fourier transform (STFT) on the input fingerprint image to obtain a transformed fingerprint image, comparing the input fingerprint image and the transformed fingerprint image, and generating a combined image by combining the input fingerprint image and the transformed fingerprint image based on a result of the comparing.

A CMOS image sensor for a code reader in an optical code recognition system incorporates a digital processing circuit that applies a calculation process to the capture image data as said data acquired by the sequential readout circuit of the sensor, in order to calculate a macro-image from the capture image data, which corresponds to location information of code(s) in the capture image, and transmit this macro-image in the image frame following the capture image data, in the footer of the frame.

Disclosed are various examples and embodiments of systems, devices, components and methods configured to calculate the information content of data and information, which in some embodiments are based on new metrics integrating the real space and Fourier space properties of the data or information collected. Among other things, these systems, devices, components and methods provide an assessment of a full information collection chain; the information content of data in a harvesting experiment; global and local resolution; and the information content within objects of interest. Information and data metrics are measured in “bits”. The disclosed systems, devices, components and methods fall within the fields of information processing, information theory, digital signal processing, image processing, image analysis, channel capacity, signal transducers, and analogous fields, and include within their scope computing devices exploiting the new signal processing techniques and algorithms.

A computer-implemented method and a system for object identification and/or classification. The computer-implemented method includes receiving digital hologram data of a digital hologram of an object. The digital hologram data comprises phase information and magnitude information. The computer-implemented method further includes processing the digital hologram data based on a neural-network-based ensemble model to identify and/or classify the object.

This analytical data analysis method uses machine learning of analysis result data (31) measured by an analyzer (1), and includes generating simulated data (32) in which a data variation has been added to the analysis result data (31) within a range that does not affect identification, performing the machine learning using the generated simulated data (32), and performing discrimination using a discrimination criterion (23b) obtained through the machine learning.

The present disclosure provides a computer-implemented method, a device, and a computer program product for deep lesion tracker. The method includes inputting a search image into a first three-dimensional DenseFPN (feature pyramid network) of an image encoder and inputting a template image into a second three-dimensional DenseFPN of the image encoder to extract image features; encoding anatomy signals of the search image and the template image as Gaussian heatmaps, and inputting the Gaussian heatmap of the template image into a first anatomy signal encoders (ASE) and inputting the Gaussian heatmap of the search image into a second ASE to extract anatomy features; inputting the image features and the anatomy features into a fast cross-correlation layer to generate correspondence maps, and computing a probability map according to the correspondence maps; and performing supervised learning or self-supervised learning to predict a lesion center in the search image.

A system and method of combining different sensor data for higher reliable diagnosis information on a portable mobile device includes using a camera for imaging a region of interest of a subject to obtain an image signal, a microphone for capturing acoustic information from the subject, and one or more processors. The one or more processors can be configured to spectrally analyze the image signal, estimate a first vital-sign of the subject corresponding to a diagnosis using the image signal, analyze the acoustic information, estimate a second vital-sign of the subject corresponding to the diagnosis using the acoustic information, and combine the first vital sign with the second vital-sign to provide a higher confidence level diagnostic of the diagnosis.

Apparatuses, systems, and techniques to scale textured images using a Fourier transform in conjunction with one or more neural networks. In at least one embodiment, a neural network generates an expanded image from an input image by applying a Fourier transform to one or more feature maps generated by said neural network and up-scaling one or more resulting frequency domain feature maps before generating an expanded output image based on up-scaled feature maps.