Abstract: A tracking system, comprising: multiple beacons, each associated with a different cyclic equivalence class of code-word length n, and each configured to broadcast a bit-stream comprising a repeating code-word, where the code-word belongs to the associated cyclic equivalence class; and a mobile tracking unit, comprising: a sensor, and a processor, wherein the sensor is configured to simultaneously detects at least some of the bit streams, and provide each sensed bit stream in real-time to the processor, wherein for each bit-stream received by the processor from the sensor, the processor is configured to identify the beacon that broadcasted the bit-stream using the first n received bits.

Abstract: A system comprising: a camera configured to capture one or more images of a user's hand; and a computer configured to: receive the one or more captured images, apply a mapping function to the received one or more images, thereby yielding one or more coordinates associated with at least one feature of the user's hand, wherein the mapping function is derived from a set of labeled images that are produced by applying a machine learning algorithm to training data which comprises images of a trainer's hand, wherein the images are labeled with coordinates obtained from multiple magnetic sensors attached to the trainer's hand.

Abstract: A system comprising: a camera configured to capture one or more images of a user's hand; and a computer configured to: receive the one or more captured images, apply a mapping function to the received one or more images, thereby yielding one or more coordinates associated with at least one feature of the user's hand, wherein the mapping function is derived from a set of labeled images that are produced by applying a machine learning algorithm to training data which comprises images of a trainer's hand, wherein the images are labeled with coordinates obtained from multiple magnetic sensors attached to the trainer's hand.

Abstract: A system comprising: a camera configured to capture one or more images of a user's hand; and a computer configured to: receive the one or more captured images, apply a mapping function to the received one or more images, thereby yielding one or more coordinates associated with at least one feature of the user's hand, wherein the mapping function is derived from a set of labeled images that are produced by applying a machine learning algorithm to training data which comprises images of a trainer's hand, wherein the images are labeled with coordinates obtained from multiple magnetic sensors attached to the trainer's hand.

Abstract: A system comprising: a handheld mobile device comprising: a projector unit configured to project an image; a camera; and a prism configured to reflect the image projected by the projector such that the camera can capture the projected image.

Abstract: A method of processing a set of images of a scene is disclosed. The set is represented by a plurality of picture-elements defining an image plane, and the images are characterized by different light intensity spatial distributions but the same viewpoint relative to the scene. The method comprises: for each of at least some picture-elements, independently selecting a direction parallel to the image plane, constructing a weighted vector field based on the selected direction, and calculating a height of a surface in the scene perpendicular to the image plane based on the constructed weighted vector field. The method further comprises reconstructing a three-dimensional spatial representation of the surface based on the heights; and transmitting the spatial representation to a computer readable medium.

Abstract: A tracking system, comprising: multiple beacons, each associated with a different cyclic equivalence class of code-word length n, and each configured to broadcast a bit-stream comprising a repeating code-word, where the code-word belongs to the associated cyclic equivalence class; and a mobile tracking unit, comprising: a sensor, and a processor, wherein the sensor is configured to simultaneously detects at least some of the bit streams, and provide each sensed bit stream in real-time to the processor, wherein for each bit-stream received by the processor from the sensor, the processor is configured to identify the beacon that broadcasted the bit-stream using the first n received bits.

Abstract: A system comprising: a camera configured to capture one or more images of a user's hand; and a computer configured to: receive the one or more captured images, apply a mapping function to the received one or more images, thereby yielding one or more coordinates associated with at least one feature of the user's hand, wherein the mapping function is derived from a set of labeled images that are produced by applying a machine learning algorithm to training data which comprises images of a trainer's hand, wherein the images are labeled with coordinates obtained from multiple magnetic sensors attached to the trainer's hand.

Abstract: A method of processing a set of images of a scene is disclosed. The set is represented by a plurality of picture-elements defining an image plane, and the images are characterized by different light intensity spatial distributions but the same viewpoint relative to the scene. The method comprises: for each of at least some picture-elements, independently selecting a direction parallel to the image plane, constructing a weighted vector field based on the selected direction, and calculating a height of a surface in the scene perpendicular to the image plane based on the constructed weighted vector field. The method further comprises reconstructing a three-dimensional spatial representation of the surface based on the heights; and transmitting the spatial representation to a computer readable medium.

Abstract: A method training a randomized decision tree through multiple iterations, each is based on: a) Receiving multiple data samples that include data subsets, each data subset corresponds to an attribute. b) Distributing the data subsets to slave processing units after sorting the data samples in consecutive ascending order by updating a first index that identifies trajectories of the training data samples through the tree nodes of the previous tree level. c) Simultaneously processing the data subsets to identify split functions for each tree node with respect to each data subset and updating a second index that identifies the trajectories of the training data samples through the tree node of the current tree level. d) Collecting the split functions from the slave processing units and constructing the current tree level by selecting a preferred split function for each tree node of the current tree level.

Abstract: A system comprising: a handheld mobile device comprising: a projector unit configured to project an image; a camera; and a prism configured to reflect the image projected by the projector such that the camera can capture the projected image.

Abstract: A method training a randomized decision tree through multiple iterations, each is based on: a) Receiving multiple data samples that include data subsets, each data subset corresponds to an attribute. b) Distributing the data subsets to slave processing units after sorting the data samples in consecutive ascending order by updating a first index that identifies trajectories of the training data samples through the tree nodes of the previous tree level. c) Simultaneously processing the data subsets to identify split functions for each tree node with respect to each data subset and updating a second index that identifies the trajectories of the training data samples through the tree node of the current tree level. d) Collecting the split functions from the slave processing units and constructing the current tree level by selecting a preferred split function for each tree node of the current tree level.