초록 ▼

Aircraft flight data collection is enhanced by changing the data sampling rate as a function of an operational condition of the aircraft, such as its flight phase. An algorithm is used to determine the flight phase. Then, the data is collected at a first sampling rate when the flight phase is one of

Aircraft flight data collection is enhanced by changing the data sampling rate as a function of an operational condition of the aircraft, such as its flight phase. An algorithm is used to determine the flight phase. Then, the data is collected at a first sampling rate when the flight phase is one of a first set of flight phases and collected at a second sampling rate when the flight phase is one of a second set of flight phases. The second sampling rate is greater than the first sampling rate to maximize the data storage capacity of the system. Generally, the higher sampling rate is used with transient flight phases such as takeoff and thrust reverse.

대표청구항 ▼

Aircraft flight data collection is enhanced by changing the data sampling rate as a function of an operational condition of the aircraft, such as its flight phase. An algorithm is used to determine the flight phase. Then, the data is collected at a first sampling rate when the flight phase is one of

Aircraft flight data collection is enhanced by changing the data sampling rate as a function of an operational condition of the aircraft, such as its flight phase. An algorithm is used to determine the flight phase. Then, the data is collected at a first sampling rate when the flight phase is one of a first set of flight phases and collected at a second sampling rate when the flight phase is one of a second set of flight phases. The second sampling rate is greater than the first sampling rate to maximize the data storage capacity of the system. Generally, the higher sampling rate is used with transient flight phases such as takeoff and thrust reverse. at has matrix elements representing coordinates of feature points of the object tracked through the sequence, and that has each row representing a corresponding image frame and each column representing a corresponding feature point, wherein some of the matrix elements are missing; an estimation-matrix-generation unit which generates an estimation matrix as a sub-matrix of the tracking matrix by selecting rows and by selecting a column of a given feature point and columns of a predetermined number of feature points closest to the given feature point, such that the estimation matrix has matrix elements thereof missing only for the given feature point in a single image frame; a missing-matrix-element-estimation unit which calculates estimates of the missing matrix elements of the estimation matrix, and further obtain estimates of remaining missing matrix elements of the tracking matrix based on estimation matrixes generated by said estimation-matrix-generation unit; and a 3-dimensional-data operation unit which obtains a 3-dimensional shape of the object from the tracking matrix having the missing matrix elements thereof estimated. 8. The device as claimed in claim 7, wherein said estimation-matrix-generation unit compares the coordinates of the given feature point with the coordinates of the feature points in an image frame immediately preceding said single image frame, so as to select said feature points closest to the given feature point. 9. The device as claimed in claim 7, wherein repeats estimation of the missing matrix elements of the estimation matrix until the estimation is successful while said estimation-matrix-generation unit increases size of the estimation matrix at each attempt of the estimation of the missing matrix elements of the estimation matrix. 10. The device as claimed in claim 7, wherein said estimation-matrix-generation unit reshapes the tracking matrix by rearranging columns of the tracking matrix according to classification of the feature points into groups based on how the feature points are tracked through the sequence, and generates the estimation matrix as a sub-matrix of the reshaped tracking matrix. 11. The device as claimed in claim 10, wherein said estimation-matrix-generation unit turns the tracking matrix upside down when the estimation matrix cannot be generated despite presence of still remaining missing matrix elements in the tracking matrix. 12. The device as claimed in claim 7, further comprising a unit which generates the sequence of image frames by taking pictures of the object while the object rotates relative to a camera view, wherein said 3-dimensional-data operation unit obtains motion of the feature points from the tracking matrix having the missing matrix elements thereof estimated, and reverses a convex surface to a concave surface or reversing a concave surface to a convex surface with regard to said 3-dimensional shape of the object if the motion of the feature points is in a direction opposite to a rotational direction of the object. 13. A computer-readable medium having a program embodied therein for causing a computer to obtain a 3-dimensional shape of an object from a sequence of image frames, said program comprising: a tracking-matrix-generation unit which generates a tracking matrix that has matrix elements representing coordinates of feature points of the object tracked through the sequence, and that has each row representing a corresponding image frame and each column representing a corresponding feature point, wherein some of the matrix elements are missing; an estimation-matrix-generation unit which generates an estimation matrix as a sub-matrix of the tracking matrix by selecting rows and by selecting a column of a given feature point and columns of a predetermined number of feature points closest to the given feature point, such that the estimation matrix has matrix elements thereof missing only for the given feature point in a single image frame; a missin