Abstract: A Static Electrostatic Generator (SEG) is disclosed which produces static charges at high voltage and low current. The SEG is capable of generating positive or negative charges on a metal sphere by reversing the polarity of a DC source. The conversion efficiency of the system is about 47% and its design is simple, lightweight, and easy to manufacture. The SEG is a static device and no mechanical movement is required to produce charges. Also, the design is easily scalable.

Abstract: A Static Electrostatic Generator (SEG) is disclosed which produces static charges at high voltage and low current. The SEG is capable of generating positive or negative charges on a metal sphere by reversing the polarity of a DC source. The conversion efficiency of the system is about 47% and its design is simple, lightweight, and easy to manufacture. The SEG is a static device and no mechanical movement is required to produce charges. Also, the design is easily scalable.

Abstract: A microgrid system includes a synchronous generator configured to convert mechanical power into electric power, an energy storage system configured to store and supply electric power, a controller configured to control operation of the energy storage system; and a point of common coupling bus connecting the synchronous generator and the battery energy storage system, wherein a controller parameter of the controller is determined based on a level of a disturbance using a trained artificial neural network in response to occurrence of the disturbance in the synchronous generator.

Abstract: A method and system of accelerating monitoring of network traffic. The method may include receiving, at a network chip of a network device, a network traffic data unit; capturing, by the network chip, the network traffic data unit based on a traffic sampling rate; adding, by the network chip, a sampling header to the network traffic data unit to obtain a sampled network traffic data unit; sending the sampled network traffic data unit from the network chip to a sampling engine; receiving, from the sampling engine, a flow datagram that includes a network traffic data unit portion and a flow datagram header; generating a flow network data traffic unit that includes the flow datagram; and transmitting the flow network data traffic unit towards a collector.

Abstract: Disclosed are systems and methods for automated analysis of facial photographs for improved airway management and patient safety using unsupervised computer algorithms based on feature extraction from facial photographs by deep-learning algorithms. A deep-learning algorithm-based feature extractor uses frontal and/or profile views of the face to identify important information about potential intubation difficulty. This information is used by a trained advanced algorithm to classify faces as easy or difficult to intubate based on the extracted features.

Abstract: A Static Electrostatic Generator (SEG) is disclosed which produces static charges at high voltage and low current. The SEG is capable of generating positive or negative charges on a metal sphere by reversing the polarity of a DC source. The conversion efficiency of the system is about 47% and its design is simple, lightweight, and easy to manufacture. The SEG is a static device and no mechanical movement is required to produce charges. Also, the design is easily scalable.

Abstract: Accelerating monitoring of network traffic by: configuring a first network chip of a non-accelerated line card with a VOQ associated with an internal interface that is connected to a second network chip of a first accelerated line card; receiving, at the first network chip, a data unit; selecting, by the first network chip, the data unit based on a traffic sampling rate; adding information identifying the data unit as having been selected for sampling to obtain a selected data unit; and sending the selected data unit from the first network chip to the second network chip using the VOQ and the internal interface. The second network chip identifies the selected data unit and, based on the identification, appends a sampling header to the data unit to obtain a sampled data unit, and transmits the sampled data unit to the sampling engine of the first accelerated line card.

Abstract: Accelerating monitoring of network traffic by: configuring a first network chip of a non-accelerated line card with a VOQ associated with an internal interface that is connected to a second network chip of a first accelerated line card; receiving, at the first network chip, a data unit; selecting, by the first network chip, the data unit based on a traffic sampling rate; adding information identifying the data unit as having been selected for sampling to obtain a selected data unit; and sending the selected data unit from the first network chip to the second network chip using the VOQ and the internal interface. The second network chip identifies the selected data unit and, based on the identification, appends a sampling header to the data unit to obtain a sampled data unit, and transmits the sampled data unit to the sampling engine of the first accelerated line card.

Abstract: A method and system of accelerating monitoring of network traffic. The method may include receiving, at a network chip of a network device, a network traffic data unit; capturing, by the network chip, the network traffic data unit based on a traffic sampling rate; adding, by the network chip, a sampling header to the network traffic data unit to obtain a sampled network traffic data unit; sending the sampled network traffic data unit from the network chip to a sampling engine; receiving, from the sampling engine, a flow datagram that includes a network traffic data unit portion and a flow datagram header; generating a flow network data traffic unit that includes the flow datagram; and transmitting the flow network data traffic unit towards a collector.