Abstract: A sensor system comprising multiple sensor units non-collocated at a site; processing circuitry operatively coupled to memory is configured to perform operations comprising: producing sensor unit tracks, each sensor unit track comprising one or more object attributes including relative object location attributes and non-location attributes; for each sensor unit track, translating the one or more relative object location attributes of the sensor unit track, to one or more universal object location attributes; fusing sets of sensor unit tracks, based at least in part upon corresponding object attributes of the sets of sensor unit tracks, to produce unified site tracks that include the corresponding object attributes; and saving the unified site tracks in a non-transitory storage device.

Abstract: A sensor system comprising multiple sensor units non-collocated at a site; processing circuitry operatively coupled to memory is configured to perform operations comprising: producing sensor unit tracks, each sensor unit track comprising one or more object attributes including relative object location attributes and non-location attributes; for each sensor unit track, translating the one or more relative object location attributes of the sensor unit track, to one or more universal object location attributes; fusing sets of sensor unit tracks, based at least in part upon corresponding object attributes of the sets of sensor unit tracks, to produce unified site tracks that include the corresponding object attributes; and saving the unified site tracks in a non-transitory storage device.

Abstract: Systems and methods for smart device control using radar are disclosed. According to some aspects, a machine receives, using a millimeter-wave multiple antenna array, a radar signal. The machine preprocesses the radar signal to generate radar metadata. The machine determines, using a trained machine learning engine and based on at least the radar metadata, a moving entity and a movement type. The machine identifies, based on at least the determined moving entity and the determined movement type, a smart device and an action for the smart device to take in response to the movement type by the moving entity. The machine transmits, to the smart device, a control signal for the identified action.

Abstract: The current disclosure is directed to the field of pathology, and the automated handling of biological specimens from containers containing clear solutions wherein the biological specimens reside. A computer-implemented method is disclosed for extracting specimens from such containers via an extraction device attached to a robotic arm. The robotic arm is controlled by a robotic system controller. The three-dimensional location of all specimens are estimated using image analysis techniques using images obtained from a plurality of imaging systems. Image analysis is used to simultaneously guide the extraction device and track the location of specimens inside the container.

Abstract: The current disclosure is directed to the field of pathology, and the automated handling of biological specimens from containers containing clear solutions wherein the biological specimens reside. A computer-implemented method is disclosed for extracting specimens from such containers via an extraction device attached to a robotic arm. The robotic arm is controlled by a robotic system controller. The three-dimensional location of all specimens are estimated using image analysis techniques using images obtained from a plurality of imaging systems. Image analysis is used to simultaneously guide the extraction device and track the location of specimens inside the container.

Abstract: The present techniques provide systems and methods for decoding an optical data signal in an optical system to retrieve source information while decreasing errors resulting from optical and electronic noise in the optical system. The techniques involve using decoding algorithms to estimate the a posteriori state probabilities and the a posteriori transition probabilities of the data encoding, and estimating bit state probabilities. The probability density function used to estimate bit states is parameterized by the expected optical and electronic noise in the optical system. Different optical and electronic noise variances, or different probability densities, may be stored in registers or look-up tables to be accessed by a decoder while decoding the optical data signal.

Abstract: The present techniques provide systems and methods for decoding an optical data signal returned from an optical disc to retrieve source information. The decoding method is based on a 16 state trellis diagram, and may decode an optical data signal encoded through a modulation code where the input-to-output relationship is not convolutional, such as the 17 Parity Preserve/Prohibit (17pp) modulation code. A trellis diagram may enable non-convolutional trellis-modulated data to be more efficiently decoded. Further, the 16 state trellis diagram of the present techniques provides a unique path for each input-to-output bit pair, such that no information about input bits may be lost on parallel paths in a trellis diagram.

Abstract: The present techniques provide systems and methods for decoding a data signal with a control bit to improve bit estimation. The techniques in one embodiment involve using decoding algorithms to estimate the a posteriori state probabilities and the a posteriori transition probabilities of the data encoding, and estimating bit state probabilities. The techniques further involve using a control bit in the bit stream and comparing the estimation of the control bit state in the segment of the bit stream with a test control bit determined based on an average of bit states from the encoded segment of the bit stream. If the estimation of the control bit and the test control bit are not equal, the state of the bit estimate with the lowest confidence probability will be changed.

Abstract: The present techniques provide systems and methods for decoding a data signal with a control bit to improve bit estimation. The techniques in one embodiment involve using decoding algorithms to estimate the a posteriori state probabilities and the a posteriori transition probabilities of the data encoding, and estimating bit state probabilities. The techniques further involve using a control bit in the bit stream and comparing the estimation of the control bit state in the segment of the bit stream with a test control bit determined based on an average of bit states from the encoded segment of the bit stream. If the estimation of the control bit and the test control bit are not equal, the state of the bit estimate with the lowest confidence probability will be changed.

Abstract: The present techniques provide systems and methods for decoding an optical data signal returned from an optical disc to retrieve source information. The decoding method is based on a 16 state trellis diagram, and may decode an optical data signal encoded through a modulation code where the input-to-output relationship is not convolutional, such as the 17 Parity Preserve/Prohibit (17pp) modulation code. A trellis diagram may enable non-convolutional trellis-modulated data to be more efficiently decoded. Further, the 16 state trellis diagram of the present techniques provides a unique path for each input-to-output bit pair, such that no information about input bits may be lost on parallel paths in a trellis diagram.

Abstract: The present techniques provide systems and methods for decoding an optical data signal in an optical system to retrieve source information while decreasing errors resulting from optical and electronic noise in the optical system. The techniques involve using decoding algorithms to estimate the a posteriori state probabilities and the a posteriori transition probabilities of the data encoding, and estimating bit state probabilities. The probability density function used to estimate bit states is parameterized by the expected optical and electronic noise in the optical system. Different optical and electronic noise variances, or different probability densities, may be stored in registers or look-up tables to be accessed by a decoder while decoding the optical data signal.