Various embodiments are directed to a device for detecting fluid particle characteristics comprising: a collection fluid dispense assembly configured to selectively dispense a volume of collection fluid onto an absorbent media disposed within an internal sensor portion of a fluid composition sensor, producing a collection media based on interaction between the volume of collection fluid and the absorbent media; and a controller configured to determine, based on a particle image captured by an imaging device, a particle characteristic associated with a particle captured at the collection media. In various embodiments a device is configured to receive therein a collection media comprising a biologically nutritive substance; and may comprise an imaging device and a controller configured to determine a biological particle characteristic based on a comparison of first particle data and second particle data generated by the imaging device, the second particle data being associated with an incubated particle configuration.

A fluid sampling device, the device having a fluid composition sensor configured to receive a fluid sample and capture a plurality of particles from the fluid sample at a collection media, wherein the fluid composition sensor is further configured to generate particle data associated with the plurality of particles using a particle imaging operation, and a controller, the controller being configured to: determine an optimal sample volume associated with a sample collection operation based at least in part on a particle load condition defined by the plurality of particles captured at the collection media during the sample collection operation, and update one or more operational characteristics of the fluid composition sensor such that the sample collection operation is defined at least in part by the optimal sample volume.

A cell area extraction unit (241) extracts a cell area in a phase image that is created based on a hologram obtained by in-line holographic microscope (IHM). A background value acquisition unit (242) obtains a background value from phase values at a plurality of positions outside the cell area. An intracellular phase value acquisition unit (243) averages a plurality of phase values on a sampling line set at a position close to the periphery of a cell, while avoiding a central portion in which the phase value may be lowered in the cell area, to obtain an intracellular phase value. A phase change amount calculation unit (244) obtains the difference between the intracellular phase value and the background value. A phase change amount determination unit (245) compares the value of the difference with thresholds in two levels to determine whether the cell is in an undifferentiated state or an undifferentiation deviant state. It is thereby possible to automatically make a correct determination while removing the influence of a theoretical measurement error by IHM.

A device for detecting particles in air; said device comprising: a flow channel configured to allow a flow of air comprising particles through the flow channel; a light source configured to illuminate the particles, such that an interference pattern is formed by interference between light being scattered by the particles and non-scattered light from the light source; an image sensor configured to detect incident light, detect the interference pattern, and to acquire a time-sequence of image frames, each image frame comprising a plurality of pixels, each pixel representing a detected intensity of light; and a frame processor configured to filter information in the time-sequence of image frames, wherein said filtering comprises:
identifying pixels of interest in the time-sequence of image frames, said pixels of interest picturing an interference pattern potentially representing a particle in the flow of air, and outputting said identified pixels of interest for performing digital holographic reconstruction.

A particle monitoring device includes a camera sensor for imaging particles, a set of light sources, and an optical component. A first light source provides light of a first color component. A second light source provides light of a second color component. The optical component receives light of the first color component in a first direction from the first light source, and redirects the light of the first color component in an output direction towards the particles to illuminate the particles using light of the first color component. The optical component receives light of a second color component in a second direction, different from the first direction, from the second light source, and redirects the light of the second color component in the output direction towards the particles to illuminate the particles using light of the second color component.

A method having the steps of obtaining prepared image data captured by an image sensor receiving light propagated across a sample volume, containing a fluid possibly comprising microscopic objects of foreign origin, while illuminating the sample volume by coherent light. The prepared image data comprising, for a microscopic object, a prepared hologram pattern with prepared spatially alternating intensity formed by the interference fringes; providing filtered image data, comprising automatically filtering the prepared image data by an edge enhancing filter. the filtered image data comprising, for a prepared hologram pattern, a filtered hologram pattern. The presence of the microscopic object associated with the filtered hologram pattern in the sample volume of the fluid is automatically detected on the basis of the filtered hologram pattern.

A method comprises obtaining input data comprising a hologram of a 3-dimensional (3D) particle field, a depth map of the 3D particle field, and a maximum phase projection of the 3D particle field. The method also comprises applying a U-net convolutional neural network (CNN) to the input data to generate output data. Encoder blocks have residual connections between a first layer and a second layer that skips over a convolution layer of the encoder block. Decoder blocks have residual connections between a first layer and a second layer that skips over a convolution layer of the decoder block. The output data includes a channel in which pixel intensity corresponds to relative depth of particles in the 3D particle field and an output image indicating locations of centroids of the particles in the 3D particle field.

The inventive concept relates to a device for detecting particles in air, said device comprising a receiver for receiving a flow of air comprising particles, a sample carrier, and a particle capturing arrangement. The particle capturing arrangement is configured to separate the particles from the flow of air for and to collect a set of particles on a surface of the sample carrier. The device further comprises a light source configured to illuminate the particles on the sample carrier, such that an interference pattern is formed by interference between light being scattered by the particles and non-scattered light from the light source. The device further comprises an image sensor configured to detect the interference pattern. The device further comprises a cleaner configured for cleaning the surface of the sample carrier for enabling re-use of the surface for collection of a subsequent set of particles.

Apparatuses and methods for analyzing fluid samples are provided. For example, an example apparatus may include a fluid imaging chamber, at least one illumination source component, and an image sensor component. In some examples, the fluid imaging chamber comprises a flow channel for receiving a fluid sample. In some examples, the at least one illumination source component is configured to emit at least one light beam, and the at least one light beam is directed through the fluid sample in the flow channel from a top surface of the fluid imaging chamber. In some embodiments, the image sensor component is positioned under a bottom surface of the fluid imaging chamber and configured to generate digital holography image data of the fluid sample.

A label-free bio-aerosol sensing platform and method uses a field-portable and cost-effective device based on holographic microscopy and deep-learning, which screens bio-aerosols at a high throughput level. Two different deep neural networks are utilized to rapidly reconstruct the amplitude and phase images of the captured bio-aerosols, and to output particle information for each bio-aerosol that is imaged. This includes, a classification of the type or species of the particle, particle size, particle shape, particle thickness, or spatial feature(s) of the particle. The platform was validated using the label-free sensing of common bio-aerosol types, e.g., Bermuda grass pollen, oak tree pollen, ragweed pollen, Aspergillus spore, and Alternaria spore and achieved >94% classification accuracy. The label-free bio-aerosol platform, with its mobility and cost-effectiveness, will find several applications in indoor and outdoor air quality monitoring.