An imaging system connected to an occupant monitoring system includes communications with an apparatus for measuring gas or airborne compound concentrations in a vehicle cabin. The apparatus includes a housing configured as a flow tube in fluid communication with ambient air in the vehicle cabin. A spectrometer is mounted within the housing and subject to ambient air flow through the housing, and the spectrometer is connected to a light source and receives reflected light from the air flow to detect by spectrum analysis the concentration of target gases and/or airborne compounds. The spectrometer identifies spectral changes in the light and reflected light within the ambient air flow. The spectrometer communicates with computerized vehicle control systems, and runs software stored to calculate the concentration of target gases and/or airborne compounds from the spectral changes.

In example implementations, an apparatus is provided. The apparatus includes a channel, a camera, and a processor. The channel contains a fluid and an object. The fluid is to move the object through the channel. The camera system is to capture video images of the object in the channel. The processor is to track movement of the object in the channel via the video images based on known flow dynamics of the channel.

There is provided a cell analysis apparatus that comprises image capture circuitry for capturing a brightfield image of a cell using brightfield imaging. The cell has been dyed by a functional dye that indicates, during fluorescence imaging and during brightfield imaging, whether the cell has a given characteristic. A model derived by machine learning is stored and used in combination with the brightfield image to determine whether the cell has the given characteristic. There is also provided a method for creating a cell categorisation model, comprising applying a functional dye to one or more samples comprising a plurality of cells. The functional dye indicates during fluorescence imaging and during brightfield imaging whether each of the cells has a given characteristic. A brightfield image and a corresponding fluorescence image for each of the plurality of cells to which the dye has been applied are captured and a machine learning process is used to generate a model that predicts whether a cell has the given characteristic from a brightfield image. The model is generated by using the brightfield image and the corresponding fluorescence image of each of the plurality of cells as training data.

In a bubble measurement device for measuring bubbles moving in a liquid, the bubble measurement device includes a measurement chamber in which the bubbles in the liquid containing solid materials are introduced into the measurement chamber from below the measurement chamber, and providing a transparent slope facing diagonally downward at a position where the introduced bubbles rise, an image capturing device to capture an image of the bubbles passing the transparent slope, an introduction pipe provided below the measurement chamber to introduce the bubbles into the measurement chamber, and a bubble introduction valve that is immersed in the liquid to be measured and performs the introduction and blocking of the bubbles into the introduction pipe.

A method and a system for determining an indicator of processing quality of an agricultural harvested material using a mobile device is disclosed. A computing unit analyzes image data of a prepared sample of harvested material containing grain components and non-grain components in an analytical routine to determine the indicator of the processing quality of the agricultural harvested material. Further, the computing unit uses a trained machine learning model in the analytical routine to perform at least one step of determining the indicator of the processing quality of the agricultural harvested material and that the computing unit adjusts at least one machine parameter of the forage harvester based on the indicator of processing quality.

The present disclosure provides methods, systems, and kits for detecting molecules in a sample with a pre-equilibrium digital immunoassay. The methods and systems provide means for quantifying molecules in a biological sample of minimal volume in short amounts of time.

For analyzing a sample containing particles of at least two categories, such as a sample containing blood cells, a particle counter subject to a detection limit is coupled with an analyzer capable of discerning particle number ratios, such as a visual analyzer, and a processor. A first category of particles can be present beyond detection range limits while a second category of particles is present within respective detection range limits. The concentration of the second category of particles is determined by the particle counter. A ratio of counts of the first category to the second category is determined on the analyzer. The concentration of particles in the first category is calculated on the processor based on the ratio and the count or concentration of particles in the second category.

To optimize an imaging range in a depth direction in terms of a relationship with a resolution, an OCT apparatus includes a signal processor that determines a reflected light intensity distribution of an imaging object on the basis of a spectrum of a detected interference light. The signal processor performs spectrum conversion, having a conversion characteristic with which a light source spectrum is converted to a Gaussian distribution curve, on the spectrum of the interference light, and determines the reflected light intensity distribution by Fourier-transforming a spectrum resulting from the spectrum conversion. In the conversion characteristic, the light source spectrum and the Gaussian distribution curve have center wavelengths differing from each other.

Disclosed are methods, systems, and articles of manufacture for performing a process on biological samples. An analysis of biological samples in multiple regions of interest in a microfluidic device and a timeline correlated with the analysis may be identified. One or more region-of-interest types for the multiple regions of interest may be determined; and multiple characteristics may be determined for the biological samples based at least in part upon the one or more region-of-interest types. Associated data that respectively correspond to the multiple regions of interest in a user interface for at least a portion of the biological samples in the user interface based at least in part upon the multiple identifiers and the timeline. A count of the biological samples in a region of interest may be determined based at least in part upon a class or type of data using a convolutional neural network (CNN).

An electronically-controlled digital ferrofluidic device is disclosed which employs a network of individually addressable coils in conjunction with one or more movable permanent magnets, where each moveable permanent magnet delivers the designated fluid manipulation-based tasks. The underlying mechanism facilitating fluidic operations is realized by addressable electromagnetic actuation of miniaturized mobile magnets that exert localized magnetic body forces on droplets filled with magnetic nanoparticles. The reconfigurable, contactless, and non-interfering magnetic-field operation properties of the underlying actuation mechanism allow for the integration of passive and active components to implement advanced and diverse operations with high efficiency (e.g., droplet sorting, dispensing, generation, merging, mixing, filtering, and analysis).