Abstract: Exception event recorders and analysis systems include: vehicle mounted sensors arranged as a vehicle event recorder to capture both discrete and non-discrete data; a discretization facility; a database; and an analysis server all coupled together as a computer network. Motor vehicles with video cameras and onboard diagnostic systems capture data when the vehicle is involved in a crash or other anomaly (an ‘event’). In station where interpretation of non-discrete data is rendered, i.e. a discretization facility, captured data is used as a basis for production of supplemental discrete data to further characterize the event. Such interpreted data is joined to captured data and inserted into a database in a structure which is searchable and which supports logical or mathematical analysis by automated machines. A coupled analysis server is arranged to test stored data for prescribed conditions and upon finding such, to initiate further actions appropriate for the detected condition.

Abstract: Example method includes: receiving, by a network device, a plurality of signal strength values associated with a particular timestamp corresponding to a particular client device from a plurality of access points in a wireless network at a site; determining a mobility pattern based on the plurality of signal strength values associated with the particular timestamp corresponding to the particular client device; comparing the mobility pattern associated with the particular timestamp corresponding to the particular client device with mobility patterns associated with other timestamps or corresponding to other client devices to identify a set of frequent mobility patterns; determining whether an unassociated client device exhibits a threshold number of frequent mobility patterns for a threshold amount of time; and determining that the unassociated client device presents in the site in response to the unassociated client device exhibiting the threshold number of the frequent mobility patterns for the threshold amou

Abstract: A service providing device includes a storage unit configured to store driver management information including at least driver identification information, a reception unit configured to receive the driver identification information from the in-vehicle device, a determination unit configured to determine whether the driver management information including the driver identification information received by the reception unit is stored in the storage unit, an acquisition unit configured to acquire the driver management information from one or more other service providing devices connected to the service providing device over a network the determination unit determines that the driver management information is not stored in the storage unit, an authentication unit configured to authenticate the in-vehicle device based on the acquired driver management information, and a providing unit configured to provide the service to the in-vehicle device when the authentication has been successful.

Abstract: Provided is a positioning method of a user equipment performed by the user equipment, the method including receiving first and second reference signals from at least two transmitters installed at positions with different heights; acquiring first snapshot vectors based on received data samples of the first reference signal and acquiring second snapshot vectors based on received data samples of the second reference signal; calculating a first covariance matrix based on the first snapshot vectors and calculating a second covariance matrix based on the second snapshot vectors; estimating a difference between a reception delay time of the first reference signal and a reception delay time of the second reference signal based on the first and second covariance matrices; and estimating a vertical position of the user equipment based on the difference between the reception delay time of the first reference signal and the reception delay time of the second reference signal.

Abstract: The present disclosure is directed to a robotic surgical system that includes a robotic surgical device having a robotic arm and an end effector with a pair of jaw members. A handpiece includes a pinch interface to control the arm or end effector, optical marker(s), an accelerometer, and a transmitter to transmit data from the pinch interface or accelerometer to the robotic surgical device. The system further includes a tracking system, to track the marker and provide a position or orientation of the handpiece. A processor receives: (i) the position or orientation of the handpiece from the tracking system; and (ii) the measured acceleration of the handpiece from the accelerometer. The processor integrates the measured acceleration to establish a second position beyond that of the tracking system. The processor controls movement of the robotic arm and end effector based on the received data from the camera or the accelerometer.

Abstract: Example method includes: receiving, by a network device, a plurality of signal strength values associated with a particular timestamp corresponding to a particular client device from a plurality of access points in a wireless network at a site; determining a mobility pattern based on the plurality of signal strength values associated with the particular timestamp corresponding to the particular client device; comparing the mobility pattern associated with the particular timestamp corresponding to the particular client device with mobility patterns associated with other timestamps or corresponding to other client devices to identify a set of frequent mobility patterns; determining whether an unassociated client device exhibits a threshold number of frequent mobility patterns for a threshold amount of time; and determining that the unassociated client device presents in the site in response to the unassociated client device exhibiting the threshold number of the frequent mobility patterns for the threshold amou

Abstract: A prediction method that estimates the real-time position of a mobile device based on previously observed data is provided. The present invention can be used in real-time navigation, including providing real-time alerts of an upcoming destination and notifications of emergency events in close geographic proximity. The prediction method utilizes neural networks and/or functions generated using genetic algorithms in estimating the mobile device's real-time position. The prediction method provides reliable Location-Based Services (LBS) in events where traditional positioning technologies become unreliable. It is also seamless, as the user remains unaware of any interruption in accessing the positioning technology.

Abstract: A prediction method that estimates the real-time position of a mobile device based on previously observed data is provided. The present invention can be used in real-time navigation, including providing real-time alerts of an upcoming destination and notifications of emergency events in close geographic proximity. The prediction method utilizes neural networks and/or functions generated using genetic algorithms in estimating the mobile device's real-time position. The prediction method provides reliable Location-Based Services (LBS) in events where traditional positioning technologies become unreliable. It is also seamless, as the user remains unaware of any interruption in accessing the positioning technology.

Abstract: A navigation device and computer implemented method for predicting the destination of a trip, the method being executed by a navigation device, the method comprising the steps of: determining starting parameters, the starting parameters comprising at least the starting point, starting time and date of the trip, executing a destination prediction algorithm, the destination prediction algorithm taking the starting parameters as input and predicting a destination, wherein the destination prediction algorithm is generated by using information of a trip history; determining, upon arrival at the predicted or another destination, the actual destination.

Abstract: A computer controlled object oriented programming system for distributive program development over networks such as the internet with means for interfacing a plurality of programming objects with each other to provide combination objects combining programming functions of said objects, each object including predetermined interface data defining a required common interface with the other programming objects as well as a framework of events and attributes and methods for manipulating the attributes. These objects may be combined with each other via their common interfaces to form combination objects, and such combination objects may in turn be further combined with other objects and combination objects to form objects of increasing complexity which function as program routine versions.