Abstract: A method of designing a radar, comprises receiving data pertaining to a set of reflected signals received from a distribution of objects by a respective set of receiving antennas at a respective set of locations, and feeding the data and the locations as training data to a machine learning procedure. The machine learning procedure calculates, simultaneously, a set of learned antenna locations and a set of learned parameters associating the signals with the objects, thereby providing a trained machine learning procedure parametrized by the set of learned parameters.

Abstract: Provided herein are methods for EEG monitoring of a patient, which includes: positioning an array comprising a plurality of EEG electrodes on the patient's head; recording, during a first time period, a first data set of EEG signals from a first set of spatial scalp locations; manipulating the array to record, during a second time period, a second data set of EEG signals from a second set of spatial locations including at least some spatial scalp locations that are different from the first set of spatial scalp locations; integrating the first and second data sets, thereby generating a combined data set with a higher spatial density; and processing the combined data set to determine EEG events.

Abstract: A method for localization of a drone within a building, the method may include (a) detecting, by an acoustic detection unit of the drone, sounds generated by a propulsion unit of the drone while the drone is located within the building; and (b) determining, by a processing unit of the drone, the location of the drone within the building, based on the sounds and on building acoustic information regarding sounds detected at different portions of the building.

Abstract: A method of processing an image is disclosed. The method comprises decomposing the image into a plurality of channels, each being characterized by a different depth-of-field, and accessing a computer readable medium storing an in-focus dictionary defined over a plurality of dictionary atoms, and an out-of-focus dictionary defined over a plurality of sets of dictionary atoms, each set corresponding to a different out-of-focus condition. The method also comprises computing one or more sparse representations of the decomposed image over the dictionaries.

Abstract: There is provided an ultrasound device comprising: ultrasound transducer(s), memory storing code, hardware processor(s) coupled to the ultrasound transducer(s) and the memory for executing stored code comprising: activating the transducer(s) for simultaneously transmitting at least one line with a predefined focus width, and receiving an indication of a plurality of narrow-focused received lines, inputting the indication of the narrow-focused received lines into a convolutional neural network (CNN) trained on pairs of ultrasound imaging data capturing rapid motion of the certain tissue of sample patient(s), including a single-line ultrasound imaging data captured based on a single line in response to a transmitted single narrow-focused pulse, and a multiple-line ultrasound imaging data captured based on receiving narrow-focused lines in response to simultaneously transmitted at least one line, outputting adjusted narrow-focused received lines, and computing an adjusted ultrasound image according to the adjust

Abstract: There is provided an ultrasound device comprising: ultrasound transducer(s), memory storing code, hardware processor(s) coupled to the ultrasound transducer(s) and the memory for executing stored code comprising: activating the transducer(s) for simultaneously transmitting at least one line with a predefined focus width, and receiving an indication of a plurality of narrow-focused received lines, inputting the indication of the narrow-focused received lines into a convolutional neural network (CNN) trained on pairs of ultrasound imaging data capturing rapid motion of the certain tissue of sample patient(s), including a single-line ultrasound imaging data captured based on a single line in response to a transmitted single narrow-focused pulse, and a multiple-line ultrasound imaging data captured based on receiving narrow-focused lines in response to simultaneously transmitted at least one line, outputting adjusted narrow-focused received lines, and computing an adjusted ultrasound image according to the adjust

Abstract: A method of processing an input image comprises receiving the input image, storing the image in a memory, and accessing, by an image processor, a computer readable medium storing a trained deep learning network. A first part of the deep learning network has convolutional layers providing low-level features extracted from the input image, and convolutional layers providing a residual image. A second part of the deep learning network has convolutional layers for receiving the low-level features and extracting high-level features based on the low-level features. The method feeds the input image to the trained deep learning network, and applies a transformation to the residual image based on the extracted high-level features.

Abstract: A system for training a quantized neural network dataset, comprising at least one hardware processor adapted to: receive input data comprising a plurality of training input value sets and a plurality of target value sets; in each of a plurality of training iterations: for each layer, comprising a plurality of weight values, of one or more of a plurality of layers of a neural network: compute a set of transformed values by applying to a plurality of layer values one or more emulated non-uniformly quantized transformations by adding to each of the plurality of layer values one or more uniformly distributed random noise values; and compute a plurality of output values; compute a plurality of training output values; and update one or more of the plurality of weight values to decrease a value of a loss function; and output the updated plurality of weight values of the plurality of layers.

Abstract: A method of processing an input image comprises receiving the input image, storing the image in a memory, and accessing, by an image processor, a computer readable medium storing a trained deep learning network. A first part of the deep learning network has convolutional layers providing low-level features extracted from the input image, and convolutional layers providing a residual image. A second part of the deep learning network has convolutional layers for receiving the low-level features and extracting high-level features based on the low-level features. The method feeds the input image to the trained deep learning network, and applies a transformation to the residual image based on the extracted high-level features.

Abstract: A method for planning a current medical procedure to be performed on a body part of a current patient includes obtaining a current first representation of a surface of the body part of the current patient, and either obtaining a representation of a desired result surface for the current medical procedure or selecting parameters for the current medical procedure. The method further includes retrieving at least one best matching record from a database of records of previously performed medical procedures of previous patients, based on a similarity criterion. Each record includes: parameters of a previously performed procedure of the previously performed medical procedures, a first representation of the body part of the previous patient prior to performance of the previously performed procedure, and a second representation of a body part of the previous patient after the performance of the previously performed procedure.

Abstract: A method of designing an element for the manipulation of waves, comprises: accessing a computer readable medium storing a machine learning procedure, having a plurality of learnable weight parameters. A first plurality of the weight parameters corresponds to the element, and a second plurality of the weight parameters correspond to an image processing. The method comprises accessing a computer readable medium storing training imaging data, and training the machine learning procedure on the training imaging data, so as to obtain values for at least the first plurality of the weight parameters.

Abstract: A method of processing an input image comprises receiving the input image, storing the image in a memory, and accessing, by an image processor, a computer readable medium storing a trained deep learning network. A first part of the deep learning network has convolutional layers providing low-level features extracted from the input image, and convolutional layers providing a residual image. A second part of the deep learning network has convolutional layers for receiving the low-level features and extracting high-level features based on the low-level features. The method feeds the input image to the trained deep learning network, and applies a transformation to the residual image based on the extracted high-level features.

Abstract: In accordance with disclosed embodiments, there are provided systems, methods, and apparatuses for implementing maximum likelihood image binarization in a coded light range camera.

Abstract: A method of processing an image is disclosed. The method comprises decomposing the image into a plurality of channels, each being characterized by a different depth-of-field, and accessing a computer readable medium storing an in-focus dictionary defined over a plurality of dictionary atoms, and an out-of-focus dictionary defined over a plurality of sets of dictionary atoms, each set corresponding to a different out-of-focus condition. The method also comprises computing one or more sparse representations of the decomposed image over the dictionaries.

Abstract: A method for multidimensional scaling (MDS) of a data set comprising a plurality of data elements is provided, wherein each data element is identified by its coordinates, the method comprising the steps of: (i) applying an iterative optimization technique, such as SMACOF, a predetermined amount of times on a coordinates vector, said coordinates vector representing the coordinates of a plurality of said data elements, and obtaining a modified coordinates vector; (ii) applying a vector extrapolation technique, such as Minimal Polynomial Extrapolation (MPE) or reduced Rank Extrapolation (RRE) on said modified coordinates vector obtaining a further modified coordinates vector; and (iii) repeating steps (i) and (ii) until one or more predefined conditions are met.

Abstract: Methods and systems for large-scale face recognition. The system includes an electronic processor to receive at least one image of a subject of interest and apply at least one subspace model as a splitting binary decision function on the at least one image of the subject of interest. The electronic processor is further configured to generate at least one binary code from the at least one splitting binary decision function. The electronic processor is further configured to apply a code aggregation model to combine the at least one binary codes generated by the at least one subspace model. The electronic processor is further configured to generate an aggregated binary code from the code aggregation model and use the aggregated binary code to provide a hashing scheme.

Abstract: Various embodiments are generally directed to an apparatus, method, system, and/or other techniques to determine estimated ambient electromagnetic (EM) radiation information based at least partially on ambient recovery sensor measurement information, determine estimated albedo information based at least partially on albedo recovery sensor measurement information, albedo recovery emitter modulation information, sensing matrix information, and the estimated ambient EM radiation information, and determine estimated range information based at least partially on range recovery sensor measurement information, the estimated albedo information, range recovery emitter modulation information, and sensing matrix information.

Abstract: In accordance with disclosed embodiments, there are provided systems, methods, and apparatuses for implementing maximum likelihood image binarization in a coded light range camera.

Abstract: In accordance with disclosed embodiments, there are provided systems, methods, and apparatuses for implementing maximum likelihood image binarization in a coded light range camera.

Abstract: In accordance with disclosed embodiments, there are provided systems, methods, and apparatuses for implementing maximum likelihood image binarization in a coded light range camera.