Abstract: Disclosed is a rotor track and balance system for rotorcraft that includes a data processing unit, a tachometer sensor and at least one accelerometer. The tachometer sensor is located remotely from the data processing unit and is mounted proximate to the rotating blades of the rotorcraft. The tachometer sensor is adapted to measure the speed and position of the rotating blades and to wirelessly transmit speed and position data to the data processor. The at least one accelerometer is also located remotely from the data processing unit and is mounted proximate the rotating blades of the rotorcraft. Each accelerometer is adapted to measure vibration anomalies in the rotating blades and to wirelessly transmit vibration data to the data processor. The data processing unit synchronizes the wireless data transmitted from the tachometer sensor and the wireless accelerometer(s) and determines necessary adjustments to be made in order to reduce the vibration anomalies in the rotor blades.

Abstract: Detecting video phenomena, such as fire in an aircraft cargo bay, includes receiving a plurality of video images from a plurality of sources, compensating the images to provide enhanced images, extracting features from the enhanced images, and combining the features from the plurality of sources to detect the video phenomena. Extracting features may include determining an energy indicator for each of a subset of the plurality of frames. Detecting video phenomena may also include comparing energy indicators for each of the subset of the plurality of frames to a reference frame. The reference frame corresponds to a video frame taken when no fire is present, video frame immediately preceding each of the subset of the plurality of frames, or a video frame immediately preceding a frame that is immediately preceding each of the subset of the plurality of frames. Image-based and non-image based techniques are described herein in connection with fire detection and/or verification and other applications.

Abstract: A wireless sensor system for an aircraft including a central access point and a plurality of slave nodes arranged in a star network in which the central access point wirelessly exchanges data with the slave nodes in a bidirectional manner. At least one of the antenna at the central access point and the antenna of each of the slave nodes is circularly polarized.

Abstract: Disclosed is a rotor track and balance system for rotorcraft that includes a data processing unit, a tachometer sensor and at least one accelerometer. The tachometer sensor is located remotely from the data processing unit and is mounted proximate to the rotating blades of the rotorcraft. The tachometer sensor is adapted to measure the speed and position of the rotating blades and to wirelessly transmit speed and position data to the data processor. The at least one accelerometer is also located remotely from the data processing unit and is mounted proximate the rotating blades of the rotorcraft. Each accelerometer is adapted to measure vibration anomalies in the rotating blades and to wirelessly transmit vibration data to the data processor. The data processing unit synchronizes the wireless data transmitted from the tachometer sensor and the wireless accelerometer(s) and determines necessary adjustments to be made in order to reduce the vibration anomalies in the rotor blades.

Abstract: A wireless sensor system for an aircraft including a central access point and a plurality of slave nodes arranged in a star network in which the central access point wirelessly exchanges data with the slave nodes in a bidirectional manner. At least one of the antenna at the central access point and the antenna of each of the slave nodes is circularly polarized.

Abstract: Detecting video phenomena, such as fire in an aircraft cargo bay, includes receiving a plurality of video images from a plurality of sources, compensating the images to provide enhanced images, extracting features from the enhanced images, and combining the features from the plurality of sources to detect the video phenomena. Extracting features may include determining an energy indicator for each of a subset of the plurality of frames. Detecting video phenomena may also include comparing energy indicators for each of the subset of the plurality of frames to a reference frame. The reference frame corresponds to a video frame taken when no fire is present, video frame immediately preceding each of the subset of the plurality of frames, or a video frame immediately preceding a frame that is immediately preceding each of the subset of the plurality of frames. Image-based and non-image based techniques are described herein in connection with fire detection and/or verification and other applications.

Abstract: Described are techniques used in a wire diagnostics system to detect events, such as defects, that may occur within a wire or cable under test. An incident voltage signal is sent out on the wire and a measured voltage signal is obtained which includes the incident voltage and the reflected voltage. Compensation processing is performed on the measured waveform to remove unwanted reflective components. Additionally, the waveform is then subject to attenuation processing and event detection processing. Detected events, such as defects, are classified and output as results. Events are classified by parametric classification using a library of known events or faults. The library of known events or faults is previously generated using empirical analysis and modeling techniques. Additionally, joint time-frequency domain reflectometry (TFDR) techniques are described for event identification and classification for a wire under test.

Abstract: Detecting video phenomena, such as fire in an aircraft cargo bay, includes receiving a plurality of video images from a plurality of sources, compensating the images to provide enhanced images, extracting features from the enhanced images, and combining the features from the plurality of sources to detect the video phenomena. Extracting features may include determining an energy indicator for each of a subset of the plurality of frames. Detecting video phenomena may also include comparing energy indicators for each of the subset of the plurality of frames to a reference frame. The reference frame corresponds to a video frame taken when no fire is present, video frame immediately preceding each of the subset of the plurality of frames, or a video frame immediately preceding a frame that is immediately preceding each of the subset of the plurality of frames. Image-based and non-image based techniques are described herein in connection with fire detection and/or verification and other applications.

Abstract: Detecting video phenomena, such as fire in an aircraft cargo bay, includes receiving a plurality of video images from a plurality of sources, compensating the images to provide enhanced images, extracting features from the enhanced images, and combining the features from the plurality of sources to detect the video phenomena. Extracting features may include determining an energy indicator for each of a subset of the plurality of frames. Detecting video phenomena may also include comparing energy indicators for each of the subset of the plurality of frames to a reference frame. The reference frame corresponds to a video frame taken when no fire is present, video frame immediately preceding each of the subset of the plurality of frames, or a video frame immediately preceding a frame that is immediately preceding each of the subset of the plurality of frames. Image-based and non-image based techniques are described herein in connection with fire detection and/or verification and other applications.

Abstract: Detecting video phenomena, such as fire in an aircraft cargo bay, includes receiving a plurality of video images from a plurality of sources, compensating the images to provide enhanced images, extracting features from the enhanced images, and combining the features from the plurality of sources to detect the video phenomena. Extracting features may include determining an energy indicator for each of a subset of the plurality of frames. Detecting video phenomena may also include comparing energy indicators for each of the subset of the plurality of frames to a reference frame. The reference frame corresponds to a video frame taken when no fire is present, video frame immediately preceding each of the subset of the plurality of frames, or a video frame immediately preceding a frame that is immediately preceding each of the subset of the plurality of frames. Image-based and non-image based techniques are described herein in connection with fire detection and/or verification and other applications.

Abstract: Detecting video phenomena, such as fire in an aircraft cargo bay, includes receiving a plurality of video images from a plurality of sources, compensating the images to provide enhanced images, extracting features from the enhanced images, and combining the features from the plurality of sources to detect the video phenomena. The plurality of sources may include cameras having a sensitivity of between 400 nm and 1000 nm and/or may include cameras having a sensitivity of between 7 and 14 micrometers. Extracting features may include determining an energy indicator for each of a subset of the plurality of frames. Detecting video phenomena may also include comparing energy indicators for each of the subset of the plurality of frames to a reference frame.

Abstract: Detecting video phenomena, such as fire in an aircraft cargo bay, includes receiving a plurality of video images from a plurality of sources, compensating the images to provide enhanced images, extracting features from the enhanced images, and combining the features from the plurality of sources to detect the video phenomena. The plurality of sources may include cameras having a sensitivity of between 400 nm and 1000 nm and/or may include cameras having a sensitivity of between 7 and 14 micrometers. Extracting features may include determining an energy indicator for each of a subset of the plurality of frames. Detecting video phenomena may also include comparing energy indicators for each of the subset of the plurality of frames to a reference frame.

Abstract: Detecting a fire, such as a fire in an aircraft cargo bay, includes receiving a plurality of frames of video information, determining an energy indicator for each of a subset of the plurality of frames, and detecting the presence of fire in response to the energy indicator for each of the subset of the plurality of frames corresponding to a predetermined pattern as a function of time. Detecting a fire may also include comparing energy indicators for each of the subset of the plurality of frames to a reference frame. The reference frame may correspond to a video frame taken when no fire is present or a video frame immediately preceding each of the subset of the plurality of frames. At least some of the subset of the plurality of frames may be provided by a camera having a sensitivity of between 400 nm and 1000 nm that may generates 640×480 pixels per frame. At least some of the subset of the plurality of frames may be provided by a CCD camera or a CMOS camera.

Abstract: Detecting fire and non-fire conditions includes receiving a plurality of frames of video information, determining an energy indicator for each of a subset of the plurality of frames, detecting a fire condition in response to the energy indicator for each of the subset of the plurality of frames forming a pattern as a function of time corresponding to a fire condition, and detecting a non-fire condition in response to the energy indicator for each of the subset of the plurality of frames forming a pattern as a function of time corresponding to a non-fire condition. Detecting fire and non-fire conditions may also include comparing energy indicators for each of the subset of the plurality of frames to a reference frame. The reference frame may correspond to a video frame taken when no fire is present, a video frame immediately preceding each of the subset of the plurality of frames, or a video frame immediately preceding a frame that is immediately preceding each of the subset of the plurality of frames.

Abstract: Described are techniques used in a wire diagnostics system to detect events, such as defects, that may occur within a wire or cable under test. An incident voltage signal is sent out on the wire and a measured voltage signal is obtained which includes the incident voltage and the reflected voltage. Compensation processing is performed on the measured waveform to remove unwanted reflective components. Additionally, the waveform is then subject to attenuation processing and event detection processing. Detected events, such as defects, are classified and output as results. Events are classified by parametric classification using a library of known events or faults. The library of known events or faults is previously generated using empirical analysis and modeling techniques. Additionally, joint time-frequency domain reflectometry (TFDR) techniques are described for event identification and classification for a wire under test.

Abstract: Detecting video phenomena, such as fire in an aircraft cargo bay, includes receiving a plurality of video images from a plurality of sources, compensating the images to provide enhanced images, extracting features from the enhanced images, and combining the features from the plurality of sources to detect the video phenomena. Extracting features may include determining an energy indicator for each of a subset of the plurality of frames. Detecting video phenomena may also include comparing energy indicators for each of the subset of the plurality of frames to a reference frame. The reference frame corresponds to a video frame taken when no fire is present, video frame immediately preceding each of the subset of the plurality of frames, or a video frame immediately preceding a frame that is immediately preceding each of the subset of the plurality of frames. Image-based and non-image based techniques are described herein in connection with fire detection and/or verification and other applications.

Abstract: Described are techniques used in a wire diagnostics system to detect events, such as defects, that may occur within a wire or cable under test. An incident voltage signal is sent out on the wire and a measured voltage signal is obtained which includes the incident voltage and the reflected voltage. Compensation processing is performed on the measured waveform to remove unwanted reflective components. Additionally, the waveform is then subject to attenuation processing and event detection processing. Detected events, such as defects, are classified and output as results. Events are classified by parametric classification using a library of known events or faults. The library of known events or faults is previously generated using empirical analysis and modeling techniques. Additionally, joint time-frequency domain reflectometry (TFDR) techniques are described for event identification and classification for a wire under test.

Abstract: Described are techniques used in a wire diagnostics system to detect events, such as defects, that may occur within a wire or cable under test. An incident voltage signal is sent out on the wire and a measured voltage signal is obtained which includes the incident voltage and the reflected voltage. Compensation processing is performed on the measured waveform to remove unwanted reflective components. Additionally, the waveform is then subject to attenuation processing and event detection processing. Detected events, such as defects, are classified and output as results. Events are classified by parametric classification using a library of known events or faults. The library of known events or faults is previously generated using empirical analysis and modeling techniques. Additionally, joint time-frequency domain reflectometry (TFDR) techniques are described for event identification and classification for a wire under test.

Abstract: Detecting a fire, such as a fire in an aircraft cargo bay, includes receiving a plurality of frames of video information, determining an energy indicator for each of a subset of the plurality of frames, and detecting the presence of fire in response to the energy indicator for each of the subset of the plurality of frames corresponding to a predetermined pattern as a function of time. Detecting a fire may also include comparing energy indicators for each of the subset of the plurality of frames to a reference frame. The reference frame may correspond to a video frame taken when no fire is present or a video frame immediately preceding each of the subset of the plurality of frames. At least some of the subset of the plurality of frames may be provided by a camera having a sensitivity of between 400 nm and 1000 nm that may generates 640×480 pixels per frame. At least some of the subset of the plurality of frames may be provided by a CCD camera or a CMOS camera.

Abstract: Detecting video phenomena, such as fire in an aircraft cargo bay, includes receiving a plurality of video images from a plurality of sources, compensating the images to provide enhanced images, extracting features from the enhanced images, and combining the features from the plurality of sources to detect the video phenomena. The plurality of sources may include cameras having a sensitivity of between 400 nm and 1000 nmand/or may include cameras having a sensitivity of between 7 and 14 micrometers. Extracting features may include determining an energy indicator for each of a subset of the plurality of frames. Detecting video phenomena may also include comparing energy indicators for each of the subset of the plurality of frames to a reference frame.