Abstract: Disclosed is an air microorganism enrichment device in farms, and relates to the technical field of air microorganism collection.

Abstract: The present disclosure discloses an advanced mental fatigue early warning system, electronic device and storage medium, including a data acquisition module, a preprocessing module, an adjustment ability calculation module, a fatigue early warning module, a communication module and a storage module; the data acquisition module is configured for obtaining pupil diameter data; the preprocessing module is configured for preprocessing the pupil diameter data, removing invalid data, and performing linear interpolation; the adjustment ability calculation module is configured for performing calculation and correcting the preprocessed pupil diameter data; the fatigue early warning module is configured for performing mathematical modeling, establishing an advanced prediction model, and calculating a remaining time for an upcoming mental fatigue occurs and issuing an alarm; the communication module is configured for uploading mental fatigue adjustment abilities and alarm methods; the storage module is configured for arc

Abstract: The present disclosure provides a blending method of high-quality and dual-purpose flour for bread and noodles, belonging to the technical field of flour processing. The method includes: selecting flour of a high-quality and dual-purpose wheat variety for bread and noodles as a high-quality basic flour for blending; according to a large gradient experimental design, selecting a gradient range ratio with a sedimentation value ?46.0 mL and a dough development time ?9.6 min, followed by subdividing for small gradient experiments; selecting a ratio with flour sedimentation value and dough development time that reach an ideal value to blend a large amount of flour; and making bread and noodles for scoring, followed by determining a blending ratio if a scoring result reaches an ideal value.

Abstract: In various examples, sensor data—such as masked sensor data—may be used as input to a machine learning model to determine a confidence for object to person associations. The masked sensor data may focus the machine learning model on particular regions of the image that correspond to persons, objects, or some combination thereof. In some embodiments, coordinates corresponding to persons, objects, or combinations thereof, in addition to area ratios between various regions of the image corresponding to the persons, objects, or combinations thereof, may be used to further aid the machine learning model in focusing on important regions of the image for determining the object to person associations.

Abstract: A method of path MTU determination in Generic Routing Encapsulation (GRE) tunnel is presented. A source network device (ND) transmits, to a destination ND that is a second endpoint of the GRE tunnel, a first outer packet including a first inner packet, where the first inner packet includes a first inner header that is used to deliver the first inner packet to the source network device, a first inner GRE header, and a first payload. The source ND receives the first inner packet. The source ND transmits a second outer packet including a second inner packet that includes a second payload that has a size greater than a size of the first payload. The source ND determines that the second inner packet is not received and determines a path MTU between the source ND and the destination ND based on a size of the first and the second outer packets.

Abstract: In various examples, sensor data—such as masked sensor data—may be used as input to a machine learning model to determine a confidence for object to person associations. The masked sensor data may focus the machine learning model on particular regions of the image that correspond to persons, objects, or some combination thereof. In some embodiments, coordinates corresponding to persons, objects, or combinations thereof, in addition to area ratios between various regions of the image corresponding to the persons, objects, or combinations thereof, may be used to further aid the machine learning model in focusing on important regions of the image for determining the object to person associations.

Abstract: Path MTU determination in Generic Routing Encapsulation (GRE) tunnel is presented. A source network device transmits, to a destination network device that is a second endpoint of the GRE tunnel, multiple GRE encapsulated packets that include multiple inner packets respectively, where each inner packet has an inner header used to deliver that inner packet to the source network device and a different payload, and where each of these GRE encapsulated packets has a different size. The source network device receives a first portion of the inner packets from the destination network device and does not receive a second portion of the inner packets. The source network device determines a path MTU to the destination network device based on the size of the GRE encapsulated packet with a largest size for which a corresponding inner packet is received at the source network device from the destination network device.

Abstract: In various examples, sensor data—such as masked sensor data—may be used as input to a machine learning model to determine a confidence for object to person associations. The masked sensor data may focus the machine learning model on particular regions of the image that correspond to persons, objects, or some combination thereof. In some embodiments, coordinates corresponding to persons, objects, or combinations thereof, in addition to area ratios between various regions of the image corresponding to the persons, objects, or combinations thereof, may be used to further aid the machine learning model in focusing on important regions of the image for determining the object to person associations.

Abstract: A method of path MTU determination in Generic Routing Encapsulation (GRE) tunnel is presented. A source network device (ND) transmits, to a destination ND that is a second endpoint of the GRE tunnel, a first outer packet including a first inner packet, where the first inner packet includes a first inner header that is used to deliver the first inner packet to the source network device, a first inner GRE header, and a first payload. The source ND receives the first inner packet. The source ND transmits a second outer packet including a second inner packet that includes a second payload that has a size greater than a size of the first payload. The source ND determines that the second inner packet is not received and determines a path MTU between the source ND and the destination ND based on a size of the first and the second outer packets.

Abstract: In various examples, sensor data—such as masked sensor data—may be used as input to a machine learning model to determine a confidence for object to person associations. The masked sensor data may focus the machine learning model on particular regions of the image that correspond to persons, objects, or some combination thereof. In some embodiments, coordinates corresponding to persons, objects, or combinations thereof, in addition to area ratios between various regions of the image corresponding to the persons, objects, or combinations thereof, may be used to further aid the machine learning model in focusing on important regions of the image for determining the object to person associations.

Abstract: A method of path MTU determination in Generic Routing Encapsulation (GRE) tunnel is presented. A source network device (ND) transmits, to a destination ND that is a second endpoint of the GRE tunnel, a first outer packet including a first inner packet, where the first inner packet includes a first inner header that is used to deliver the first inner packet to the source network device, a first inner GRE header, and a first payload. The source ND receives the first inner packet. The source ND transmits a second outer packet including a second inner packet that includes a second payload that has a size greater than a size of the first payload. The source ND determines that the second inner packet is not received and determines a path MTU between the source ND and the destination ND based on a size of the first and the second outer packets.

Abstract: A method of path MTU determination in Generic Routing Encapsulation (GRE) tunnel is presented. A source network device (ND) transmits, to a destination ND that is a second endpoint of the GRE tunnel, a first outer packet including a first inner packet, where the first inner packet includes a first inner header that is used to deliver the first inner packet to the source network device, a first inner GRE header, and a first payload. The source ND receives the first inner packet. The source ND transmits a second outer packet including a second inner packet that includes a second payload that has a size greater than a size of the first payload. The source ND determines that the second inner packet is not received and determines a path MTU between the source ND and the destination ND based on a size of the first and the second outer packets.

Abstract: A method of performing lane detection without the use of a frame buffer may include capturing image frames with an image sensor in a vehicular imaging system. Feature extraction circuitry in the vehicular imaging system may analyze a frame to detect features corresponding to possible lane markers. The features may be extracted and stored in memory for further processing, while the rest of the frame may not be stored in memory and may not undergo further processing. Processing circuitry may perform a first estimation of lane marker location based on a continuous feature that is present in multiple different image frames and may perform a second estimation of lane marker location based on multiple features in a single image frame that form a connected feature. The processing circuitry may determine the presence of a lane marker based on the continuous feature and the connected feature.

Abstract: In various examples, sensor data—such as masked sensor data—may be used as input to a machine learning model to determine a confidence for object to person associations. The masked sensor data may focus the machine learning model on particular regions of the image that correspond to persons, objects, or some combination thereof. In some embodiments, coordinates corresponding to persons, objects, or combinations thereof, in addition to area ratios between various regions of the image corresponding to the persons, objects, or combinations thereof, may be used to further aid the machine learning model in focusing on important regions of the image for determining the object to person associations.

Abstract: A method of performing lane detection without the use of a frame buffer may include capturing image frames with an image sensor in a vehicular imaging system. Feature extraction circuitry in the vehicular imaging system may analyze a frame to detect features corresponding to possible lane markers. The features may be extracted and stored in memory for further processing, while the rest of the frame may not be stored in memory and may not undergo further processing. Processing circuitry may perform a first estimation of lane marker location based on a continuous feature that is present in multiple different image frames and may perform a second estimation of lane marker location based on multiple features in a single image frame that form a connected feature. The processing circuitry may determine the presence of a lane marker based on the continuous feature and the connected feature.

Abstract: Disclosed herein is a method comprising: obtaining one or more responses of a security sensor to events from each of a plurality of sources; clustering each of the sources into one or more clusters, based on an amount of responses of the security sensor to the events from that source; training a classifier with the sources and the clusters they belong; and reconfiguring the security sensor based on the classifier.

Abstract: The present invention relates to the filed of fabrication and application of CCD devices, and provides a CCD pixel element with a geometry capable of improving a resolution of a CCD device having such pixel element. According to the present invention, a conventional CCD pixel element is divided into four sub-regions having same areas by two intersecting straight lines or cures with an intersection point positioned within the pixel element, and one of the sub-regions is removed so as to form the CCD pixel element geometry. It is possible to improve a resolution of a CCD device having pixel elements with such geometry.