A frame storage stores an image obtained by imaging a region of at least part of the body of a user. A vital sign signal detector detects a signal sequence of a vital sign that cyclically varies from plural imaged regions of the body of the user by using captured images of a predetermined number of frames stored in the frame storage. A correlation calculator obtains the correlation between the signal sequences of the vital sign detected from the respective imaged regions of the body. An identity determining section determines whether or not the respective imaged regions of the body belong to the same user based on the correlation between the signal sequences of the vital sign detected from the respective imaged regions of the body.

An image processing apparatus is provided. The image processing apparatus includes a data receiver, an image specifier, a replacement specifier, and a data generator. The data receiver receives data. The image specifier specifies a first image contained in the data. The replacement specifier specifies replacement data for the first image on the basis of a characteristic of the data. The data generator generates data in which the first image has been replaced with an image represented by the replacement data.

Biometric identification using electroencephalogram (EEG) signals is provided. Embodiments are targeted for biometric applications, where an individual can be identified with a precision of over 99%, using sensed brain signals. In particular, a method is described which can extract unique biomarkers from EEG response signals to classify individuals, also referred to as simple visual reaction task-based EEG biometry (SVRTEB). A subject experiences a simple stimulus or task, and a multi-channel EEG response is recorded. Unique biomarkers are extracted from the recorded EEG response (e.g., as periodogram data points corresponding to different frequencies observed in the brain waves, which can be used to identify a person). A novel signal processing approach uses neural network-based architecture to analyze the EEG response and identify the subject. This signal processing architecture can be readily implemented on hardware and provides high accuracy, precision, and recall.

A system and method for monitoring sensors via surveillance cameras is disclosed. In the system, surveillance cameras generating image data of scenes, where the sensors are included in the scenes. The sensors detect information concerning the scenes and encode the information so that it can be derived from the image data. Preferably, the sensors encode the information concerning the scenes by modulating a visible light source, the signals of which are captured within the image data by the cameras. An analytics system then analyzes the image data and decodes the information from the sensors encoded within the image data. Preferably, an integrated analytics system of the cameras executes the analysis. Exemplary sensors include sensors for detecting temperature, moisture, audio/voice, and product selection in a retail premises.

A method, computer program and system to identify peaks generated by different physical ions in a solution including substances by analyzing mass and intensity coordinates of all peaks in a set of mass spectra measured with errors for a certain concentration c of the solution is here disclosed. The peaks in different mass spectra are associated to a same ion if they are sufficiently ‘close’ according to specific discrimination criteria that go beyond the proximity of mass values. A two stage process is applied, each stage consisting in applying the method to identify peaks in mass spectra. In stage 1, the method to identify peaks is applied on each set of mass spectra for each concentration. Resulting sequences of peaks, one peak in each spectrum, are associated to different ions. This output of stage 1 is converted into a set of virtual mass spectra having as virtual peaks, average peak coordinate values calculated on each sequence. The method to identify peaks is applied once on the virtual mass spectra and the resulting ion identification table refers to peak coordinates values associated to one ion for each concentration of the solution.

Embodiments disclose a device for testing biological specimen. The device includes a sample carrier and a detachable cover. The sample carrier includes a specimen holding area. The detachable cover is placed on top of the specimen holding area. The detachable cover includes a magnifying component configured to align with the specimen holding area. The focal length of the magnifying component is from 0.1 mm to 8.5 mm. The magnifying component has a linear magnification ratio of at least 1. Some embodiments further include a multi-camera configuration. These embodiments include a first camera module and a second camera module arranged to capture one or more images of the first holding area and the second holding area, respectively. The processor may perform different analytic processes on the captured images of different holding areas to determine an outcome with regard to the biological specimen.

A processor-implemented method of generating feature data includes: receiving an input image; generating, based on a pixel value of the input image, at least one low-bit image having a number of bits per pixel lower than a number of bits per pixel of the input image; and generating, using at least one neural network, feature data corresponding to the input image from the at least one low-bit image.

The present invention generally relates to novel preparations of mesenchymal stromal cells (MSCs) derived from hemangioblasts, methods for obtaining such MSCs, and method sof treating a pathology using such MSCs. The methods of the present invention produce substantial numbers of MSCs having a potency-retaining youthful phenotype, which are useful in the treatment of pathologies.

Systems and methods are provided for receiving a two-dimensional (2D) image comprising a 2D object; identifying a contour of the 2D object; generating a three-dimensional (3D) mesh based on the contour of the 2D object; and applying a texture of the 2D object to the 3D mesh to output a 3D object representing the 2D object.

A patient diabetes monitoring system with an efficient unsupervised daily monitoring profile clustering algorithm, a method, and a computer product thereof are disclosed. The system may include a physiological data input device or sensor which receives a plurality of physiological measurements to generate a dataset, a memory which stores a clustering algorithm, and a processor. The clustering algorithm when executed by the processor, causes the processor to automatically pre-process the dataset to control an amount of bias/aggressiveness from the collected unsupervised daily monitoring profiles, thereby generating a pre-processed dataset, build a similarity matrix from the pre-processed dataset, and output an optimum number of similarity clusters found by the processor from the similarity matrix.