Abstract: A modular neural network system comprising: a plurality of neural network modules; and a controller configured to select a combination of at least one of the neural network modules to construct a neural network, dedicated for a specific task.

Abstract: A modular neural network system comprising: a plurality of neural network modules; and a controller configured to select a combination of at least one of the neural network modules to construct a neural network, dedicated for a specific task.

Abstract: A modular neural network system comprising: a plurality of neural network modules; and a controller configured to select a combination of at least one of the neural network modules to construct a neural network, dedicated for a specific task.

Abstract: A modular neural network system comprising: a plurality of neural network modules; and a controller configured to select a combination of at least one of the neural network modules to construct a neural network, dedicated for a specific task.

Abstract: A modular neural network system comprising: a plurality of neural network modules: and a controller configured to select a combination of at least one of the neural network modules to construct a neural network, dedicated for a specific task.

Abstract: A method for AI training at an end device placed in an end-user premises, the method comprising: receiving by a processor of the end device sensor data captured by a sensor bundle of the end device; deciding if the sensor data included a data unit that requires external feedback, and in case external feedback is required obtaining external feedback about classification of the data unit; updating a decision module controlled by the processor of the end device based on the obtained feedback; and making a decision about the received sensor data, by the decision module. The external feedback is obtained from an end user or from another device. The method includes obtaining instructions about the type of decision to make, from a user or by pre-configuration. External feedback about the decision is obtained and the decision module is updated based on the feedback.

Abstract: A modular neural network system comprising: a plurality of neural network modules; and a controller configured to select a combination of at least one of the neural network modules to construct a neural network, dedicated for a specific task.

Abstract: A robotic system (“new robot”) operative for performing at least one task in an environment, the system comprising: learn-from-predecessor functionality governed by a data exchange protocol, which controls short-range wireless knowledge transfer from a short-range wireless transmitter in a predecessor robot system (“old robot”) to a short-range wireless receiver in said robotic system, said knowledge comprising at least one environment-specific datum previously stored by the predecessor robot.

Abstract: A robotic system (“new robot”) operative for performing at least one task in an environment, the system comprising: learn-from-predecessor functionality governed by a data exchange protocol, which controls short-range wireless knowledge transfer from a short-range wireless transmitter in a predecessor robot system (“old robot”) to a short-range wireless receiver in said robotic system, said knowledge comprising at least one environment-specific datum previously stored by the predecessor robot.

Abstract: A system and method for accent invariant speech recognition comprising: maintaining a database scoring a set of language units in a given language, and for each of the language units, scoring audio samples of pronunciation variations of the language unit pronounced by a plurality of speakers; extracting and storing m the database a feature vector for locating each of the audio samples in a feature space; identifying pronunciation variation distances, which are distances between locations of audio samples of the same language unit in the feature space, and inter-unit distances, which are distances between locations of audio samples of different language units in the feature space; calculating a transformation applicable on the feature space to reduce the pronunciation variation distances relative to the inter-unit distances; and based on the calculated transformation, training a processor to classify as a same language unit pronunciation variations of the same language unit.

Abstract: A modular neural network system comprising: a plurality of neural network modules; and a controller configured to select a combination of at least one of the neural network modules to construct a neural network, dedicated for a specific task.

Abstract: A system and method for implementation of attention mechanism in artificial neural networks, the method comprising: receiving sensor data from at least one sensor sensing properties of an environment, classifying the received data by a multi-regional neural network, wherein each region of the network is trained to classify sensor data with a different property of the environment, and wherein each region has an individually adjustable contribution to the classification, calculating based on the classification a current environment state including at least one property of the environment, and based on the at least one property, selecting corresponding regions of the network and adjusting contribution of the selected regions to the classification.

Abstract: A system and method for accent invariant speech recognition comprising: maintaining a database scoring a set of language units in a given language, and for each of the language units, scoring audio samples of pronunciation variations of the language unit pronounced by a plurality of speakers; extracting and storing m the database a feature vector for locating each of the audio samples in a feature space; identifying pronunciation variation distances, which are distances between locations of audio samples of the same language unit in the feature space, and inter-unit distances, which are distances between locations of audio samples of different language units in the feature space; calculating a transformation applicable on the feature space to reduce the pronunciation variation distances relative to the inter-unit distances; and based on the calculated transformation, training a processor to classify as a same language unit pronunciation variations of the same language unit.

Abstract: A robotic system (“new robot”) operative for performing at least one task in an environment, the system comprising: learn-from-predecessor functionality governed by a data exchange protocol, which controls short-range wireless knowledge transfer from a short-range wireless transmitter in a predecessor robot system (“old robot”) to a short-range wireless receiver in said robotic system, said knowledge comprising at least one environment-specific datum previously stored by the predecessor robot.

Abstract: The present invention includes computer vision based driver assistance devices, systems, methods and associated computer executable code (hereinafter collectively referred to as: “ADAS”). According to some embodiments, an ADAS may include one or more fixed image/video sensors and one or more adjustable or otherwise movable image/video sensors, characterized by different dimensions of fields of view. According to some embodiments of the present invention, an ADAS may include improved image processing. According to some embodiments, an ADAS may also include one or more sensors adapted to monitor/sense an interior of the vehicle and/or the persons within. An ADAS may include one or more sensors adapted to detect parameters relating to the driver of the vehicle and processing circuitry adapted to assess mental conditions/alertness of the driver and directions of driver gaze. These may be used to modify ADAS operation/thresholds.

Abstract: The present invention includes computer vision based driver assistance devices, systems, methods and associated computer executable code (hereinafter collectively referred to as: “ADAS”). According to some embodiments, an ADAS may include one or more fixed image/video sensors and one or more adjustable or otherwise movable image/video sensors, characterized by different dimensions of fields of view. According to some embodiments of the present invention, an ADAS may include improved image processing. According to some embodiments, an ADAS may also include one or more sensors adapted to monitor/sense an interior of the vehicle and/or the persons within. An ADAS may include one or more sensors adapted to detect parameters relating to the driver of the vehicle and processing circuitry adapted to assess mental conditions/alertness of the driver and directions of driver gaze. These may be used to modify ADAS operation/thresholds.

Abstract: A method of reducing print-density variations in printers, particularly multi-deflection continuous-jet printers, including printing elements arrayed along the X-axis for printing on a substrate moving relative to the printing elements along the Y-axis. The method includes controlling the printer to print a test pattern having a plurality of strips extending along the X-axis, wherein the gray-level of each printed strip is the same along the X-axis, but varies from one strip to the next along the Y-axis; analyzing the printed test pattern to detect gray-level variations in the printed strips; preparing a density correction table of gray-level corrections for each X-coordinate; and controlling the printing elements in accordance with the density correction table to reduce the detected gray-level variations.

Abstract: A method and a system for color converting an HT image according to a desired color conversion. The method includes the steps of dilating the HT image to form a dilated HT image, converting the original HT image and the dilated HT image to CT images, color converting the CT image from the original HT image to form a converted CT image, and then using the CT images as a guide to interpolate between the dilated and the original HT images.