Abstract: A method for decoding an encrypted electromagnetic signal W encoded by a first computer with public key N_0=r×s, where N_0, r and s are integers. There is the step of obtaining the electromagnetic signal W from a telecommunications network, or a data network or an Internet or a first non-transient memory. There is the step of storing the electromagnetic signal W in a second non-transient memory. There is the step of decoding with a second computer in communication with the second non-transient memory the electromagnetic signal W in the second non-transient memory by factoring the public key N_0 in at most a time O(log^6 N_0). A non-transitory readable storage medium which includes a computer program stored on the storage medium for decoding an encrypted electromagnetic signal W.

Abstract: A system for measuring blood oxygenation levels and capillary refill time (CRT) of a user includes a camera configured to capture a video stream that includes a series of images representing a process of first squeezing a fingernail of the user to a blanched state, and then releasing to a resting state, and a Convolutional Neural Network (CNN) based processing unit configured to process an input image to create a two-dimensional representation of features, remove spatial relationships in the two-dimensional representation, generate non-spatial metadata, identify the fingernail in the resting state, and regions within a static image of the fingernail, and configured to generate a blood oxygenation value, and a measurement confidence level, and generate a CRT value of the user by applying the blood oxygenation value, and the measurement confidence level to a series of images captured by the camera over time.

Abstract: A system for measuring blood oxygenation levels and capillary refill time (CRT) of a user includes a camera configured to capture a video stream that includes a series of images representing a process of first squeezing a fingernail of the user to a blanched state, and then releasing to a resting state, and a Convolutional Neural Network (CNN) based processing unit configured to process an input image to create a two-dimensional representation of features, remove spatial relationships in the two-dimensional representation, generate non-spatial metadata, identify the fingernail in the resting state, and regions within a static image of the fingernail, and configured to generate a blood oxygenation value, and a measurement confidence level, and generate a CRT value of the user by applying the blood oxygenation value, and the measurement confidence level to a series of images captured by the camera over time.

Abstract: An integrated circuit, comprising a transistor-based cell comprising a set of fin field effect transistors (Fin FETs) chained together in a first direction, wherein the set of Fin FETs include fins extending longitudinally along the first direction and equally-spaced apart in a second direction orthogonal to the first direction by a fin pitch, and a set of polysilicon gates extending longitudinally along the second direction and equally-spaced apart in the first direction by a poly pitch, wherein a first dimension of the transistor-based cell along the first direction is substantially a first integer multiplied by the poly pitch, and wherein a second dimension of the transistor-based cell along the second direction is substantially a second integer multiplied by the fin pitch. The integrated circuit may include other non-transistor-based cells (e.g., passive cells), such as thin-film resistor or capacitor cells, which are arranged in a two-dimensional array with the transistor-based cell.

Abstract: An integrated circuit, comprising a transistor-based cell comprising a set of fin field effect transistors (Fin FETs) chained together in a first direction, wherein the set of Fin FETs include fins extending longitudinally along the first direction and equally-spaced apart in a second direction orthogonal to the first direction by a fin pitch, and a set of polysilicon gates extending longitudinally along the second direction and equally-spaced apart in the first direction by a poly pitch, wherein a first dimension of the transistor-based cell along the first direction is substantially a first integer multiplied by the poly pitch, and wherein a second dimension of the transistor-based cell along the second direction is substantially a second integer multiplied by the fin pitch. The integrated circuit may include other non-transistor-based cells (e.g., passive cells), such as thin-film resistor or capacitor cells, which are arranged in a two-dimensional array with the transistor-based cell.

Abstract: A predictive security camera system includes a camera, an object recognizer for detecting an object in a current frame of the camera, a convolutional encoder for generating a feature representation of the current frame, a primary clustering module for clustering the feature representation in primary clusters of the detected object, a vector generation module for generating an information vector that includes a set of variables such as primary class, sub-class, and one or more external factors associated with the feature representation, an anomaly clustering module for clustering each information vector into one or more secondary clusters wherein each variable in the information vector is a clustering dimension, and a predictive analysis module for receiving at least one variable of an information vector as an input, and predicting at least one another variable of the information vector based on one or more previous secondary clusters formed by the anomaly clustering module.

Abstract: A method for performing real-time recognition of objects includes receiving an input video stream from a camera, pre-processing a current frame of the input video stream using one or more pre-processing layers of a neural network structure, detecting if there is an object in the current pre-processed frame using an auxiliary branch of the neural network structure, recognizing one or more objects in the current pre-processed frame using a primary branch of the neural network structure if an object is detected in the current pre-processed frame, and displaying the one or more recognized objects of the current frame in one or more bounding boxes.

Abstract: A method for performing real-time recognition of objects in motion includes receiving an input video stream from a camera, generating one or more depth maps for one or more frames of the input video stream, recognizing one or more objects in a current frame based on corresponding depth map using a machine learning algorithm, and displaying the one or more recognized objects in the current frame in one or more bounding boxes.

Abstract: A method for detecting temporal pattern anomalies in a video stream includes detecting an object in a current frame of the video stream, generating a processed current frame that contains the detected object, generating a feature representation of the processed current frame, clustering the feature representation in one or more primary clusters in a clustering space of the primary class, generating an information vector of the feature representation, that includes information regarding the primary class, the sub-class and one or more external factors associated with the feature representation, clustering each information vector into one or more secondary clusters, and reporting a next frame as an anomaly when a corresponding information vector is positioned outside a secondary cluster of a feature presentation of a previous frame.

Abstract: A method for detecting temporal pattern anomalies in a video stream includes detecting an object in a current frame of the video stream, generating a processed current frame that contains the detected object, generating a feature representation of the processed current frame, clustering the feature representation in one or more primary clusters in a clustering space of the primary class, generating an information vector of the feature representation, that includes information regarding the primary class, the sub-class and one or more external factors associated with the feature representation, clustering each information vector into one or more secondary clusters, and reporting a next frame as an anomaly when a corresponding information vector is positioned outside a secondary cluster of a feature presentation of a previous frame.

Abstract: A predictive security camera system includes a camera, an object recognizer for detecting an object in a current frame of the camera, a convolutional encoder for generating a feature representation of the current frame, a primary clustering module for clustering the feature representation in primary clusters of the detected object, a vector generation module for generating an information vector that includes a set of variables such as primary class, sub-class, and one or more external factors associated with the feature representation, an anomaly clustering module for clustering each information vector into one or more secondary clusters wherein each variable in the information vector is a clustering dimension, and a predictive analysis module for receiving at least one variable of an information vector as an input, and predicting at least one another variable of the information vector based on one or more previous secondary clusters formed by the anomaly clustering module.

Abstract: A method for performing real-time detection of objects in motion includes receiving an input video stream from a camera, detecting if a motion has occurred in a current frame of the input video stream, providing the current frame for object detection if the motion has been detected therein, detecting a moving object in the current frame, displaying the detected moving object, simultaneously tracking a location of the detected moving object within corresponding frame, while the object detection continues for one or more moving objects, and generating a tracking box and overlaying the tracking box on the detected moving object and then transmitting the video to the display, and continuing the tracking of the detected moving object till the object detection continues for corresponding one or more moving objects.

Abstract: A method for performing real-time recognition of objects includes receiving an input video stream from a camera, pre-processing a current frame of the input video stream using one or more pre-processing layers of a neural network structure, detecting if there is an object in the current pre-processed frame using an auxiliary branch of the neural network structure, recognizing one or more objects in the current pre-processed frame using a primary branch of the neural network structure if an object is detected in the current pre-processed frame, and displaying the one or more recognized objects of the current frame in one or more bounding boxes.

Abstract: A method for performing real-time recognition of objects in motion includes receiving an input video stream from a camera, generating one or more depth maps for one or more frames of the input video stream, recognizing one or more objects in a current frame based on corresponding depth map using a machine learning algorithm, and displaying the one or more recognized objects in the current frame in one or more bounding boxes.

Abstract: A method for performing real-time detection of objects in motion includes receiving an input video stream from a camera, detecting if a motion has occurred in a current frame of the input video stream, providing the current frame for object detection if the motion has been detected therein, detecting a moving object in the current frame, displaying the detected moving object, simultaneously tracking a location of the detected moving object within corresponding frame, while the object detection continues for one or more moving objects, and generating a tracking box and overlaying the tracking box on the detected moving object and then transmitting the video to the display, and continuing the tracking of the detected moving object till the object detection continues for corresponding one or more moving objects.

Abstract: An IC includes an array of cells and a first set of endcap cells. The array of cells includes a first set of Mx layer power interconnects coupled to a first voltage, a first set of Mx layer interconnects, a second set of Mx layer power interconnects coupled to a second voltage source, and a second set of Mx layer interconnects. The first set of endcap cells includes first and second sets of Mx+1 layer interconnects. The first set of Mx+1 layer interconnects is coupled to the first set of Mx layer power interconnects and to the second set of Mx layer interconnects to provide a first set of decoupling capacitors. The second set of Mx+1 layer interconnects is coupled to the second set of Mx layer power interconnects and to the first set of Mx layer interconnects to provide a second set of decoupling capacitors.

Abstract: A switch module mountable in a switch box including a switch with a first terminal connectable to the live wire input and a second terminal, a driver circuit with input from the second terminal of the switch and with an output connected to the live wire output. The driver circuit outputs electrical power to the lighting circuit over the live wire output to LED lamp fixtures. The switch module includes a controller connected to the driver circuit. The controller is configured to control at least one of voltage, current, pulse width and pulse duty cycle of the electrical power to the lighting circuit.

Abstract: A switch module mountable in a switch box including a switch with a first terminal connectable to the live wire input and a second terminal, a driver circuit with input from the second terminal of the switch and with an output connected to the live wire output. The driver circuit outputs electrical power to the lighting circuit over the live wire output to LED lamp fixtures. The switch module includes a controller connected to the driver circuit. The controller is configured to control at least one of voltage, current, pulse width and pulse duty cycle of the electrical power to the lighting circuit.

Abstract: A ballast circuit adapted for converting an input alternating current (AC) mains power received at input terminals to an output alternating current (AC) to supply a load, e.g. a gas discharge lamp. The output terminals of the ballast connect to the load in parallel with another ballast circuit. The ballast circuit is configured to supply the output AC current to the load in parallel with an AC current output of the other ballast circuit. A synchronization module attached at the output is adapted for synchronizing the output alternating current (AC) of the ballast circuit with the AC current output of the other ballast circuit. The synchronization module is configured to synchronize phase of the output alternating current with phase of the AC current output of the other ballast circuit.

Abstract: A high power lighting fixture which includes a first multiple of first light emitting diodes (LEDs) mounted and series connected to form a first serial string of LEDs on a first circuit board. A second multiple of second light emitting diodes (LEDs) mounted and series connected to form a second serial string of LEDs on a second circuit board. Mating circuit board connectors are mounted on the first and second circuit boards. The first and second LEDs, the number of first and second LEDs and the current supplied when operating the first and second LEDs are selected so that the lighting fixture is operable at a minimum electrical power rating of 100 Watts. A control signal is received which determines current division between the first and second LEDs.