Abstract: A global navigation satellite system (GNSS) signal tracking system (GNSSSTS), deployed in a tracking channel of a GNSS baseband engine, includes a piecewise down sampling module for generating code bit accumulated values (CBAVs) at different time instants at a reduced rate from samples of intermediate frequency data received at a high rate, and a pseudo random noise (PRN) code generation module for generating a PRN code bit sequence (PRNCBS) corresponding to a GNSS signal and storing arms of the PRNCBS. The GNSSSTS includes a primary mixer for generating a despread value for a selected arm of the PRNCBS and a phase component generation module (PCGM) for generating inphase and quadrature phase correlation components of the despread value for storage in a storage array. The primary mixer, the PCGM, and the storage array perform their functions continuously for each CBAV generated at a corresponding time instant in a time multiplexed manner.

Abstract: Drowsiness onset detection implementations are presented that predict when a person transitions from a state of wakefulness to a state of drowsiness based on heart rate information. Appropriate action is then taken to stimulate the person to a state of wakefulness or notify other people of their state (with respect to drowsiness/alertness). This generally involves capturing a person's heart rate information over time using one or more heart rate (HR) sensors and then computing a heart-rate variability (HRV) signal from the captured heart rate information. The HRV signal is analyzed to extract features that are indicative of an individual's transition from a wakeful state to a drowsy state. The extracted features are input into an artificial neural net (ANN) that has been trained using the same features to identify when an individual makes the aforementioned transition to drowsiness. Whenever an onset of drowsiness is detected, a warning is initiated.

Abstract: A global navigation satellite system (GNSS) signal tracking system (GNSSSTS), deployed in a tracking channel of a GNSS baseband engine, includes a piecewise down sampling module for generating code bit accumulated values (CBAVs) at different time instants at a reduced rate from samples of intermediate frequency data received at a high rate, and a pseudo random noise (PRN) code generation module for generating a PRN code bit sequence (PRNCBS) corresponding to a GNSS signal and storing arms of the PRNCBS. The GNSSSTS includes a primary mixer for generating a despread value for a selected arm of the PRNCBS and a phase component generation module (PCGM) for generating inphase and quadrature phase correlation components of the despread value for storage in a storage array. The primary mixer, the PCGM, and the storage array perform their functions continuously for each CBAV generated at a corresponding time instant in a time multiplexed manner.

Abstract: Drowsiness onset detection implementations are presented that predict when a person transitions from a state of wakefulness to a state of drowsiness based on heart rate information. Appropriate action is then taken to stimulate the person to a state of wakefulness or notify other people of their state (with respect to drowsiness/alertness). This generally involves capturing a person's heart rate information over time using one or more heart rate (HR) sensors and then computing a heart-rate variability (HRV) signal from the captured heart rate information. The HRV signal is analyzed to extract features that are indicative of an individual's transition from a wakeful state to a drowsy state. The extracted features are input into an artificial neural net (ANN) that has been trained using the same features to identify when an individual makes the aforementioned transition to drowsiness. Whenever an onset of drowsiness is detected, a warning is initiated.

Abstract: Various technologies described herein pertain to smart fabric that includes a sensor array, which includes sensor nodes arranged at respective sites in the fabric. The sensor nodes are configured to output signals indicative of detected conditions at the respective sites in the fabric. The fabric can further include a transmitter and a control unit. The control unit can be configured to receive the signals indicative of the detected conditions at the respective sites in the fabric from the sensor nodes, and transmit, using the transmitter, data specifying the detected conditions at the respective sites in the fabric. The data can be transmitted to a computing system for analyzing a state of the fabric to detect an occurrence of an event. The event can be a tear of the fabric, a torsion in the fabric greater than a threshold torsion, a strain in the fabric greater than a threshold strain.

Abstract: Drowsiness onset detection implementations are presented that predict when a person transitions from a state of wakefulness to a state of drowsiness based on heart rate information. Appropriate action is then taken to stimulate the person to a state of wakefulness or notify other people of their state (with respect to drowsiness/alertness). This generally involves capturing a person's heart rate information over time using one or more heart rate (HR) sensors and then computing a heart-rate variability (HRV) signal from the captured heart rate information. The HRV signal is analyzed to extract features that are indicative of an individual's transition from a wakeful state to a drowsy state. The extracted features are input into an artificial neural net (ANN) that has been trained using the same features to identify when an individual makes the aforementioned transition to drowsiness. Whenever an onset of drowsiness is detected, a warning is initiated.

Abstract: Drowsiness onset detection implementations are presented that predict when a person transitions from a state of wakefulness to a state of drowsiness based on heart rate information. Appropriate action is then taken to stimulate the person to a state of wakefulness or notify other people of their state (with respect to drowsiness/alertness). This generally involves capturing a person's heart rate information over time using one or more heart rate (HR) sensors and then computing a heart-rate variability (HRV) signal from the captured heart rate information. The HRV signal is analyzed to extract features that are indicative of an individual's transition from a wakeful state to a drowsy state. The extracted features are input into an artificial neural net (ANN) that has been trained using the same features to identify when an individual makes the aforementioned transition to drowsiness. Whenever an onset of drowsiness is detected, a warning is initiated.

Abstract: Various technologies described herein pertain to smart fabric that includes a sensor array, which includes sensor nodes arranged at respective sites in the fabric. The sensor nodes are configured to output signals indicative of detected conditions at the respective sites in the fabric. The fabric can further include a transmitter and a control unit. The control unit can be configured to receive the signals indicative of the detected conditions at the respective sites in the fabric from the sensor nodes, and transmit, using the transmitter, data specifying the detected conditions at the respective sites in the fabric. The data can be transmitted to a computing system for analyzing a state of the fabric to detect an occurrence of an event. The event can be a tear of the fabric, a torsion in the fabric greater than a threshold torsion, a strain in the fabric greater than a threshold strain.

Abstract: This invention is developed using all the laws of hydraulics and fluid dynamics by digging a tunnel, placing a pipe, etc in the earth surrounding the water body at various levels at least starting from the surface of the water body and going to the very bottom of the water bed in various geometrical figures, angles and degrees suitable for an individual project and individual site and individual generation requirements any where in the world for the water to come out of the water body either from the bottom, below the bottom of the water bed and projecting into the water body or from the sides surrounding the water body and run into the facility from the inlet and various inlets which is constructed below the main level of the water body or mean sea level or main water body level on the earth and required in the water body also so that the various turbine-generators of various capacities, shapes, designs, available in the global markets are erected in a cascading manner or any other system designed for the ge