Guillochon
Didier Guillochon, Marcq En Baroeul FR
Patent application number | Description | Published |
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20120043649 | Method for making microchannels on a substrate, and substrate including such microchannels - The present invention relates to a process for fabricating microchannels on a substrate and to a substrate comprising these microchannels, the invention being especially applicable to the fabrication of microstructured substrates for microelectronic, microfluidic and/or micromechanical systems. | 02-23-2012 |
Florian Guillochon, Grenoble FR
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20150067655 | Dynamic Debugging Method of a Software Module and Corresponding Device - When a module is loaded by the operating system kernel, dynamic information of the module, such as the memory addresses of the different sections of the module allocated by the operating system, is stored in a known variable, which is subsequently accessible by the debugging tool. Furthermore, an interrupt instruction that will allow the debugger to interrupt the running of the operating system following the complete loading of the module is inserted into the debugging tool in such a way as to retrieve the dynamic information necessary for the debugging of the module. | 03-05-2015 |
James Guillochon, San Diego, CA US
Patent application number | Description | Published |
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20080235318 | Information Processing System for Classifying and/or Tracking an Object - According to one embodiment, a computing system includes a computing node coupled to a number of sensors. The sensors are operable to generate records from received information and transmit these records to the computing node. The computing node is operable to bind the plurality of records in a plurality of classifications using a multiple level classifier such that each classification has a differing level of specificity. | 09-25-2008 |
20080250875 | Sparse sampling planner for sensor resource management - A method and system of a sparse sampling planner uses a finite number of measurements to determine a track's expected intermediate kinematic and classification state for a specific sensor action. It uses the expected track state to compute a reward function. The expected states are further propagated for actions at the next time step to determine the next states and so on. The sampling becomes sparse and the reward function is discounted as one propagates further in time. This produces a state-action tree that is more top-heavy while providing greater accuracy at times closer to the decision point. By doing so, the planner creates a plan comprising a sequence of actions that result in the highest reward. By employing various heuristics to further prune the tree gives highly accurate results with significant savings in computational processor time. | 10-16-2008 |