31 results about "Variance Criterion" patented technology

Visual information regarding intensity and intensity-based gradient information is used in a multi-step process for accurately locating and decoding an encoded visual symbol matrix. A symbol is preprocessed with filtering and morphology. A set of coarse locations of candidate symbols is filtered according to coarse geometric criteria to determine which image locations are most viable. Depending upon the symbol type, and information about the edges of the symbols, additional symbol-specific filtering and refinements can be applied or skipped. For 2D matrices filtering according to intensity variance criteria is followed by an edge-locating refinement and by identifying finder patterns and timing patterns. Once finder patterns and timing patterns are precisely located, the data region is decoded. Where a data region does not decode validly, another pass is run, using the non-morphed image, resulting in an overall saving of time, as rejections may require operator intervention.

A sensor data processing system and method is described. Contemplated systems and methods derive a first recognition trait of an object from a first data set that represents the object in a first environmental state. A second recognition trait of the object is then derived from a second data set that represents the object in a second environmental state. The sensor data processing systems and methods then identifies a mapping of elements of the first and second recognition traits in a new representation space. The mapping of elements satisfies a variance criterion for corresponding elements, which allows the mapping to be used for object recognition. The sensor data processing systems and methods described herein provide new object recognition techniques that are computationally efficient and can be performed in real-time by the mobile phone technology that is currently available.

The invention relates to a signal-noise-ratio-post-filtering-and-characteristic-space-fusion minimum-variance ultrasonic imaging method. The signal-noise-ratio-post-filtering-and-characteristic-space-fusion minimum-variance ultrasonic imaging method includes the steps that sampling signals received by array elements are delayed, subjected to forward-backward smoothing and subjected to diagonal loading treatment, and an estimated sample covariance matrix is obtained; the estimated covariance matrix is subjected to characteristic decomposition, and signal subspace is constructed; in the desired signal subspace, according to the minimum-variance criterion, an adaptive beam-forming weight value is calculated; then a post-filtering coefficient is designed according to the signal coherence, a noise weighting coefficient is introduced according to the input signal noise ratio, and a signal-noise-ratio-filtering coefficient is calculated; the adaptive beam-forming weight value and the signal-noise-ratio-filtering coefficient are fused to obtain the novel weight vector; finally, multiple pieces of data subjected to forward-backward smoothing treatment are weighted and summed through the obtained minimum-variance weight value fusing the signal-noise-ratio post filtering and the characteristic space, and an adaptive beam signal is obtained. By means of the signal-noise-ratio-post-filtering-and-characteristic-space-fusion minimum-variance ultrasonic imaging method, the properties of ultrasonic images in the resolution ratio aspect, the contrast ratio aspect, the noise robustness aspect and the like can be improved, and therefore the quality of ultrasonic imaging is improved as a whole.

The invention discloses an independent sorting method of a fabric sorting robot. Digital images, acquired by the robot, of fabric on an assembly line are adopted as research objects, a weighting form is applied to a color component H and a color component S through image color spatial switching, and an Otsu self-adaption threshold value method is applied to achieve image segmentation; the contours of the fabric are extracted by applying wavelet edge detection and morphological contour compensation; and the actual positions of the fabric are obtained according to the calibration result of a camera. Meanwhile, the weighting form of the color component H and the color component S is adopted as a feature, and a minimum variance criterion is used for classifying K-mean clustering methods. Different kinds of fabric are arranged at corresponding placing positions, the robot reversely solves kinematical parameters of all joints according to the placing positions and a kinematical equation, and finally grabbing actions are implemented. According to the independent sorting method of the fabric sorting robot, manual intervention is not needed, and the fabric can be sorted automatically, efficiently, accurately, stably and enduringly.

The invention discloses a linearly constrained minimum variance diagonal loaded robust frequency-invariant beam forming method. On the basis of a linearly constrained minimum variance criterion, a balanced matrix of an array spatial response derivation function is defined through a spatial response derivation function; the matrix is introduced into a linearly constrained minimum variance beam forming method, such that a linearly constrained minimum variance wideband frequency-invariant beam forming method is obtained; and an iterative global optimal solution is obtained on a constrained boundary through a convex optimization toolbox CVX. By means of the method disclosed by the invention, the robustness when the microphone characteristic error exists is improved; the mismatch error problem due to uncertainty of a microphone, such as the gain, the phase and the position, is solved; and the method disclosed by the invention is widely applied in many fields, such as multi-channel voice enhancement, human-computer voice interactive systems, hearing-aids, vehicular hand-free voice communication, remote television conference systems and robot hearing.

The invention discloses a method for automatic identification of modal parameters for mechanical equipment structures. The method includes the following steps: dividing the entire frequency domain interval into frequency sub-sections with the same width according to the interval width, and counting the natural frequencies falling in each frequency sub-section. The total number of poles in the interval; the poles in the frequency sub-area whose total number of poles is less than the threshold value are removed as calculated poles, and the remaining poles are reserved as physical poles; then, using the minimum variance criterion, from the remaining poles in the above steps Automatically identify the modal parameters of each order. This identification method can effectively distinguish the calculation poles from the structural physical poles, automatically identify the modal parameters of the structure, obtain the dynamic characteristic parameters of the mechanical structure in the working state, effectively reduce the manual participation in the modal analysis, and contribute to the online detection of the mechanical structure. , damage identification, etc. provide a favorable way.