Abstract: One aspect provides operating a mobile pipe inspection platform to obtain two or more types of sensor data for the interior of a pipe; analyzing, using a processor, the two or more types of sensor data using a trained model, where the trained model is trained using a dataset including training sensor data of pipe interiors; the analyzing including performing: identifying, using a processor, a pipe feature location using a first type of the two or more types of sensor data; and classifying, using a processor, an identified pipe feature using a second type of the two or more types of sensor data; and thereafter producing, using a processor, an output including an indication of the classified pipe feature. Other aspects are described and claimed.

Abstract: One embodiment provides a system, including: an inspection platform configured to move through underground infrastructure; an imaging device coupled to the inspection platform; the imaging device comprising a camera housing that arranges an array of four or more cameras in a predetermined configuration; the camera housing comprising a plurality of apertures, wherein each aperture houses a respective camera therein with a viewing axis offset about 30 degrees to about 120 degrees from a viewing axis of an adjacent camera within the array; and circuitry that operates the imaging device to capture a plurality of images using the four or more cameras; where the circuitry captures the plurality of images for a composite image of an interior region of the underground infrastructure, and where the region is larger than a single viewing field of any of the four or more cameras. Other embodiments are described and claimed.

Abstract: One aspect provides a method, including: obtaining sensor data from a ground penetrating radar (GPR) unit; analyzing, using a processor, the sensor data to detect a first object and a second object, the second object being associated with the first object based on location; identifying, with the processor, an underground pipe feature based on the analyzing; associating a position of the underground pipe feature with a location in a pipe network; selecting a subset of the pipe network including a pipe segment associated with the position of the underground pipe feature; and providing the subset of the pipe network as displayable data to a display device. Other aspects are described and claimed.

Abstract: One aspect provides a method, including: obtaining sensor data from a ground penetrating radar (GPR) unit; analyzing, using a processor, the sensor data to detect a first object and a second object, the second object being associated with the first object based on location; identifying, with the processor, an underground pipe feature based on the analyzing; associating a position of the underground pipe feature with a location in a pipe network; selecting a subset of the pipe network including a pipe segment associated with the position of the underground pipe feature; and providing the subset of the pipe network as displayable data to a display device. Other aspects are described and claimed.

Abstract: An embodiment provides a method of controlling a pipe inspection robot, including: detecting, from a headset, brain activity of a user wearing the headset; binning the brain activity of the user into one of a plurality of different bins; the binning comprising accumulating brain activity values for a period of time to establish a brain activity value for the period of time; determining, using the brain activity value, if a threshold level of brain activity has accumulated over the period of time; after the threshold level has been accumulated, identifying a control action to be sent to the pipe inspection robot based on the brain activity value; and controlling the movement of the pipe inspection robot using a control signal associated with the control action. Other embodiments are described and claimed.

Abstract: One aspect provides a method, including: obtaining sensor data from an unmanned aerial vehicle (UAV); the sensor data comprising data obtained by one or more sensors of the UAV; analyzing, using a processor, the sensor data to detect underground water associated with a pipe; and identifying, with the processor, an underground feature based on the analyzing. Other aspects are described and claimed.

Abstract: One aspect provides operating a mobile pipe inspection platform to obtain two or more types of sensor data for the interior of a pipe; analyzing, using a processor, the two or more types of sensor data using a trained model, where the trained model is trained using a dataset including training sensor data of pipe interiors; the analyzing including performing: identifying, using a processor, a pipe feature location using a first type of the two or more types of sensor data; and classifying, using a processor, an identified pipe feature using a second type of the two or more types of sensor data; and thereafter producing, using a processor, an output including an indication of the classified pipe feature. Other aspects are described and claimed.

Abstract: One aspect provides a method, including: displaying, at a display screen, an image of an interior of a pipe, the image being obtained using a pipe inspection robot; accessing, using a processor, calibration data associated with the image; receiving, via an input device, user input marking at least a portion of the image; determining, using a processor, quantitative pipe feature data for at least one feature of the pipe using the marking and the calibration data; and providing, based on the determining, data associated with the at least one feature. Other aspects are described and claimed.

Abstract: One aspect provides a method, including: operating a mobile pipe inspection platform to obtain sensor data for the interior of a pipe; analyzing, using a processor, the sensor data using a trained model, where the trained model is trained using a dataset including sensor data of pipe interiors and one or more of: metadata identifying pipe feature locations contained within the sensor data of the dataset and metadata classifying pipe features contained within the sensor data of the dataset; performing one or more of: identifying, using a processor, a pipe feature location within the sensor data; and classifying, using a processor, a pipe feature of the sensor data; and thereafter producing, using a processor, an output including one or more of an indication of the identifying and an indication of the classifying. Other aspects are described and claimed.

Abstract: An adipose or fat tissue transplantation device is disclosed. The device employs a cylindrical vessel for housing a conventional piston operated syringe. A handle is pivotally attached to the cylindrical vessel with a proximal end secured to a puller positioned to engage steps attached to the piston. A distal end of the handle provides a thumb operated lever for advancing the piston a predetermined distance upon depressing the handle to move the piston. The handle includes a link arm of a length sized to move the puller upon engaging a step a predetermined distance. A spring is coupled to the link arm to maintain the distal end of the handle in a normally raised position. In a preferred embodiment, the link arm and steps are constructed and arranged to advance the piston to cause a predetermine dispensing of tissue for each handle depression.

Abstract: One aspect provides a method, including: displaying, at a display screen, an image of an interior of a pipe, the image being obtained using a pipe inspection robot; accessing, using a processor, calibration data associated with the image; receiving, via an input device, user input marking at least a portion of the image; determining, using a processor, quantitative pipe feature data for at least one feature of the pipe using the marking and the calibration data; and providing, based on the determining, data associated with the at least one feature. Other aspects are described and claimed.

Abstract: One aspect provides a method, including: operating a mobile pipe inspection platform to obtain sensor data for the interior of a pipe; analyzing, using a processor, the sensor data using a trained model, where the trained model is trained using a dataset including sensor data of pipe interiors and one or more of: metadata identifying pipe feature locations contained within the sensor data of the dataset and metadata classifying pipe features contained within the sensor data of the dataset; performing one or more of: identifying, using a processor, a pipe feature location within the sensor data; and classifying, using a processor, a pipe feature of the sensor data; and thereafter producing, using a processor, an output including one or more of an indication of the identifying and an indication of the classifying. Other aspects are described and claimed.

Abstract: One aspect provides a method, including: obtaining sensor data from an unmanned aerial vehicle (UAV); the sensor data comprising data obtained by one or more sensors of the UAV; analyzing, using a processor, the sensor data to detect underground water associated with a pipe; and identifying, with the processor, an underground feature based on the analyzing. Other aspects are described and claimed.

Abstract: An embodiment provides a method of controlling a pipe inspection robot, including: detecting, from a headset, brain activity of a user wearing the headset; binning the brain activity of the user into one of a plurality of different bins; the binning comprising accumulating brain activity values for a period of time to establish a brain activity value for the period of time; determining, using the brain activity value, if a threshold level of brain activity has accumulated over the period of time; after the threshold level has been accumulated, identifying a control action to be sent to the pipe inspection robot based on the brain activity value; and controlling the movement of the pipe inspection robot using a control signal associated with the control action. Other embodiments are described and claimed.

Abstract: One embodiment provides a system, including: an inspection platform configured to move through underground infrastructure; an imaging device coupled to the inspection platform; the imaging device comprising a camera housing that arranges an array of four or more cameras in a predetermined configuration; the camera housing comprising a plurality of apertures, wherein each aperture houses a respective camera therein with a viewing axis offset about 30 degrees to about 120 degrees from a viewing axis of an adjacent camera within the array; and circuitry that operates the imaging device to capture a plurality of images using the four or more cameras; where the circuitry captures the plurality of images for a composite image of an interior region of the underground infrastructure, and where the region is larger than a single viewing field of any of the four or more cameras. Other embodiments are described and claimed.

Abstract: A light assembly, comprising: a base; at least one electromagnetic radiation source mounted on the base; a cover located over the base, the cover being transparent to the electromagnetic radiation produced by the electromagnetic radiation source; and at least one photo detector, optically Isolated from the at least one electromagnetic radiation source, the at least one photo detector being configured to receive electromagnetic radiation conveyed by the cover.

Abstract: A light assembly, comprising: a base; at least one electromagnetic radiation source mounted on the base; a cover located over the base, the cover being transparent to the electromagnetic radiation produced by the electromagnetic radiation source; and at least one photo detector, optically Isolated from the at least one electromagnetic radiation source, the at least one photo detector being configured to receive electromagnetic radiation conveyed by the cover.

Abstract: A technique for providing end-to-end error detection coding between a requesting module and a memory module have been disclosed. A method includes translating a first logical address of a memory request to a physical address. The method includes translating an error control code and data associated with the memory request between a first format and a second format. The error control code and data having the first format is generated based on the first logical address. The error control code and data having the second format is generated based on a second address. The method includes generating an error indicator based on the error control code, the data, and one of the first logical address and the second address.

Abstract: A technique for providing end-to-end error detection coding between a requesting module and a memory module have been disclosed. A method includes translating a first logical address of a memory request to a physical address. The method includes translating an error control code and data associated with the memory request between a first format and a second format. The error control code and data having the first format is generated based on the first logical address. The error control code and data having the second format is generated based on a second address. The method includes generating an error indicator based on the error control code, the data, and one of the first logical address and the second address.