Identifying activity in an area even during periods of poor visibility using a video camera and an audio sensor are disclosed. The video camera is used to identify visible events of interest and the audio sensor is used to capture audio occurring temporally with the identified visible events of interest. A sound profile is determined for each of the identified visible events of interest based on sounds captured by the audio sensor during the corresponding identified visible event of interest. Then, during a time of poor visibility, a subsequent sound event is identified in a subsequent audio stream captured by the audio sensor. One or more sound characteristics of the subsequent sound event are compared with the sound profiles associated with each of the identified visible events of interest, and if there is a match, one or more matching sound profiles are filtered out from the subsequent audio stream.

A method of performing antimicrobial susceptibility testing (AST) on a sample uses a reader device that mounts on a mobile phone having a camera. A microtiter plate containing wells preloaded with the bacteria-containing sample, growth medium, and drugs of differing concentrations is loaded into the reader device. The wells are illuminated using an array of illumination sources contained in the reader device. Images of the wells are acquired with the camera of the mobile phone. In one embodiment, the images are transmitted to a separate computing device for processing to classify each well as turbid or not turbid and generating MIC values and a susceptibility characterization for each drug in the panel based on the turbidity classification of the array of wells. The MIC values and the susceptibility characterizations for each drug are transmitted or returned to the mobile phone for display thereon.

A method of performing antimicrobial susceptibility testing (AST) on a sample uses a reader device that mounts on a mobile phone having a camera. A microtiter plate containing wells preloaded with the bacteria-containing sample, growth medium, and drugs of differing concentrations is loaded into the reader device. The wells are illuminated using an array of illumination sources contained in the reader device. Images of the wells are acquired with the camera of the mobile phone. In one embodiment, the images are transmitted to a separate computing device for processing to classify each well as turbid or not turbid and generating MIC values and a susceptibility characterization for each drug in the panel based on the turbidity classification of the array of wells. The MIC values and the susceptibility characterizations for each drug are transmitted or returned to the mobile phone for display thereon.

An image test system includes a test assembly and an image capture card. The test assembly is provided for obtaining a test signal from a test object, and includes an interface conversion circuit for converting signal transmission form of the test signal. The image capture card is provided for obtaining the test signal from the test assembly, and obtaining an image data from the test signal. The image test system further includes a test signal clock generation circuit for obtaining a test signal clock from the test signal, or the image capture card further includes a pair of clock input pins for obtaining the test signal clock directly from the test object.

An input sensing device including: a power line; driving lines; a first signal line including sub-lines; a second signal line connected to the sub-lines; and sensor pixels connected to the power line, the driving lines, and the first signal line, wherein at least one sensor pixel of the sensor pixels includes: an optical sensor that transfers a photoelectrically converted charge from the power line to a first node in response to a driving signal provided through a first driving line of the driving lines; a first transistor connected between the first node and a first sub-line among the sub-lines, wherein the first transistor includes a gate electrode connected to the first driving line; and a second transistor connected between the first node and a second sub-line among the sub-lines, wherein the second transistor includes a gate electrode connected to the first driving line.

Disclosed is a quaternion multi-degree-of-freedom neuron-based multispectral welding image recognition method, comprising: using three cameras having different wavebands to obtain multispectral weld pool images, and respectively performing pre-processing and edge extraction on the weld pool images having the different wavebands obtained at a same moment by the three cameras; establishing a quaternion-based multispectral weld pool image edge model; extracting low-frequency features after a quaternion discrete cosine transform; using a quaternion-based multi-degree-of-freedom neuron network to perform classification, training and recognition on edge features of the multispectral weld pool images. Compared to traditional means, the present invention has multiple recognition information sources, strong anti-interference capabilities and high recognition accuracy.

An object detection device is configured to execute a first process, a second process, and an object detection process (third and fourth processes). The first process estimates a shape of a road surface in a real space on the basis of a first disparity map. The first disparity map is generated on the basis of an output of a stereo camera that captures an image including the road surface, and is a map in which a disparity obtained from the output of the stereo camera is associated with two-dimensional coordinates formed by a first direction corresponding to a horizontal direction of the image captured by the stereo camera and a second direction intersecting the first direction. The second process removes from the first disparity map a disparity for which a height from the road surface in the real space corresponds to a predetermined range on the basis of the estimated shape of the road surface to generate a second disparity map. The object detection process (third and fourth processes) detects an object on the basis of the second disparity map.

An object detection device is configured to execute a first process, a second process, and an object detection process (third and fourth processes). The first process estimates a shape of a road surface in a real space on the basis of a first disparity map. The first disparity map is generated on the basis of an output of a stereo camera that captures an image including the road surface, and is a map in which a disparity obtained from the output of the stereo camera is associated with two-dimensional coordinates formed by a first direction corresponding to a horizontal direction of the image captured by the stereo camera and a second direction intersecting the first direction. The second process removes from the first disparity map a disparity for which a height from the road surface in the real space corresponds to a predetermined range on the basis of the estimated shape of the road surface to generate a second disparity map. The object detection process (third and fourth processes) detects an object on the basis of the second disparity map.

An object detection device is configured to execute a road surface detection process, an object disparity determination process, and an object detection process. The road surface detection process estimates a position of a road surface on the basis of a first disparity map. The first disparity map is generated on the basis of an output of a stereo camera and is a map in which a disparity is associated with two-dimensional coordinates formed by a first direction corresponding to a horizontal direction of an image captured by the stereo camera and a second direction intersecting the first direction. The object disparity determination process determines disparities as object disparities when the number of occurrences of each of the disparities for respective coordinate ranges in the first direction of the first disparity map exceeds a predetermined threshold corresponding to the disparity. The object detection process detects an object by converting information on the object disparities into points on an x-z coordinate space and by extracting a group of points.

An object detection device is configured to execute a road surface detection process, an object disparity determination process, and an object detection process. The road surface detection process estimates a position of a road surface on the basis of a first disparity map. The first disparity map is generated on the basis of an output of a stereo camera and is a map in which a disparity is associated with two-dimensional coordinates formed by a first direction corresponding to a horizontal direction of an image captured by the stereo camera and a second direction intersecting the first direction. The object disparity determination process determines disparities as object disparities when the number of occurrences of each of the disparities for respective coordinate ranges in the first direction of the first disparity map exceeds a predetermined threshold corresponding to the disparity. The object detection process detects an object by converting information on the object disparities into points on an x-z coordinate space and by extracting a group of points.