9218 results about "Least squares" patented technology

System for tracking mobile tags. Cell controllers with multiple antenna modules generate a carrier signal which is received by the tags. Tags shift the frequency of the carrier signal, modulate an identification code onto it, and transmit the resulting tag signal at randomized intervals. The antennas receive and process the response, and determine the presence of the tags by proximity and triangulation. The recursive-least squares (RLS) technique is used in filtering received signals. Distance of a tag from an antenna is calculated by measuring the round trip signal time. The cell controllers send data from the antenna to a host computer. The host computer collects the data and resolves them into positional estimates. Data are archived in a data warehouse, such as an SQL Server. Also disclosed is an article tracking system that supports both active and passive tags with a cell controller able to read both passive and active tag signals.

A non-invasive optical sensor which uses the motion signal to calculate the physiological characteristic being measured. For pulse oximetry, a least squares or a ratio-of-ratios technique can be applied to the motion signal itself. This is made possible by selecting a site on the patient where variations in motion produce signals of two wavelengths which are sufficiently correlated. In particular, it has been determined that a sensor placed on a nail, in particular a thumbnail, exhibits the characteristics of having the red and infrared signals correlated when used for pulse oximetry, and the resulting signals correlate to arterial oxygen saturation.

A method of calibrating glucose monitor data includes collecting the glucose monitor data over a period of time at predetermined intervals, obtaining reference glucose values from a reference source that temporally correspond with the glucose monitor data obtained at the predetermined intervals, calculating the calibration characteristics using the reference glucose values and corresponding glucose monitor data to regress the obtained glucose monitor data, and calibrating the obtained glucose monitor data using the calibration characteristics. In additional embodiments, calculation of the calibration characteristics includes linear regression and, in particular embodiments, least squares linear regression. Alternatively, calculation of the calibration characteristics includes non-linear regression. Data integrity may be verified and the data may be filtered. Further, calibration techniques may be modified during a fast rate of change in the patient's blood glucose level to increase sensor accuracy.

An apparatus and method for aligning a line scan camera with a Light Detection and Ranging (LiDAR) scanner for real-time data fusion in three dimensions is provided. Imaging data is captured at a computer processor simultaneously from the line scan camera and the laser scanner from target object providing scanning targets defined in an imaging plane perpendicular to focal axes of the line scan camera and the LiDAR scanner. X-axis and Y-axis pixel locations of a centroid of each of the targets from captured imaging data is extracted. LiDAR return intensity versus scan angle is determined and scan angle locations of intensity peaks which correspond to individual targets is determined. Two axis parallax correction parameters are determined by applying a least squares. The correction parameters are provided to post processing software to correct for alignment differences between the imaging camera and LiDAR scanner for real-time colorization for acquired LiDAR data.

An apparatus and method is disclosed for detecting, identifying, and classifying faults occurring in sensors measuring a process. A variety of process models can be used such as first principles models, dynamic multivariable predictive control models, from data using statistical methods such as partial least squares (PLS) or principal component analysis. If faults are identified in one or more sensors, the apparatus and method provide replacement values for the faulty sensors so that any process controllers and process monitoring systems that use these sensors can remain in operation during the fault period. The identification of faulty sensors is achieved through the use of a set of structured residual transforms that are uniquely designed to be insensitive to specific subsets of sensors, while being maximally sensitive to sensors not in the subset. Identified faults are classified into one of the types Complete Failure, Bias, Drift, Precision Loss, or Unknown.

Techniques for improving treatment delivered to a target site in a patient include delivering a treatment from a treatment delivery device to a target site in a patient supported by a patient support structure. During the delivery of treatment, a state of the patient is measured to produce real-time measurement data. Measuring the state is non-invasive; and the measured state is a correlated surrogate for position of the target site. Compensating movement data is determined based on the real-time measurement data to cause the target site to maintain a particular spatial relationship with the treatment delivery device. Either the treatment delivery device, or the support structure, or both, are moved based on the compensating movement data. When the delivery device alone is moved, the correlation between measured state and target site is based on partial least squares applied to pre-treatment measurements of both.

A control system simultaneously controls a multi-zone process with a self-adaptive model predictive controller (MPC), such as temperature control within a plastic injection molding system. The controller is initialized with basic system information. A pre-identification procedure determines a suggested system sampling rate, delays or “dead times” for each zone and initial system model matrix coefficients necessary for operation of the control predictions. The recursive least squares based system model update, control variable predictions and calculations of the control horizon values are preferably executed in real time by using matrix calculation basic functions implemented and optimized for being used in a S7 environment by a Siemens PLC. The number of predictions and the horizon of the control steps required to achieve the setpoint are significantly high to achieve smooth and robust control. Several matrix calculations, including an inverse matrix procedure performed at each sample pulse and for each individual zone determine the MPC gain matrices needed to bring the system with minimum control effort and variations to the final setpoint. Corrective signals, based on the predictive model and the minimization criteria explained above, are issued to adjust system heating/cooling outputs at the next sample time occurrence, so as to bring the system to the desired set point. The process is repeated continuously at each sample pulse.

A honeycomb structural body comprising: a pillar-shaped ceramic block and a sealing material provided on an outer peripheral portion of said ceramic block, each of them having irregularities formed on an outer peripheral face wherein: when a least square curve is determined by a least square method on the basis of points constituting the contour of a cross-section, a center-of-gravity is defined as c1, a distance between a minimum concentric circumscribed curve having c1 and the center-of-gravity c1 is defined as D1, a distance between a maximum concentric inscribed curve having c1 and the center-of-gravity c1 is defined as D2, and the following inequality is satisfied: about 0.3 mm≦(D1−D2); same definition is applied to said ceramic block, a center-of-gravity thereof is defined as c2, a distance between a minimum concentric circumscribed curve having c2 and the center-of-gravity c2 is defined as D3, a distance between a maximum concentric inscribed curve having c2 and the center-of-gravity c2 is defined as D4, and the following inequality is satisfied: about 0.5 mm≦(D3−D4)≦about 7.0 mm.

The invention relates to a remote high precision autonomous integrated navigation and positioning method, which is characterized in that a Strapdown Inertial Navigation System (SINS) is used as a main navigation system during the whole flight course of the aircraft, assisted by 3D high precision position and attitude angle information provided by celestial navigation system (CNS) based on the least square differential correction in boost phase (or middle segment). In reentry phase (terminal), using the characteristics of synthetic aperture radar (SAR), such as strong penetration capability, high resolving precision and all-weather, the SINS can be corrected through accurate location information and course information provided by SAR scene matching after motion compensation when the aircraft reentry into atmospheres, so the impact point (hit) accuracy of the aircraft can be increased and the invention has remarkable effects of eliminating or decreasing non-guidance error. The invention has advantages of autonomy and high precision, which can be used for improving remote ballistic missile, remote cruise missile, navigation and positioning accuracy of remote aircraft, such as long-endurance unmanned aerial vehicle, etc.

A method for estimating unknown parameters (pan angle (ψ), instantaneous tilt angle (τ) and road geometry of an upcoming road segment) for a vehicle object detection system. The vehicle object detection system is preferably a forward looking, radar-cued vision system having a camera, a radar sensor and an processing unit. The method first estimates the pan angle (ψ), then corrects the coordinates from a radar track so that pan angle (ψ) can be treated as zero, and finally solves a least squares problem that determines best estimates for instantaneous tilt angle (τ) and road geometry. Estimating these parameters enables the vehicle object detection system to identify, interpret and locate objects in a more accurate and efficient manner.

Systems and methods for estimating channel response in the presence of interference. Interference and/or noise present on received training symbols is estimated. Based on the measured noise and/or interference, a weighting among training symbols is developed. Channel response is then estimated based on a weighted least squares procedure.

An algorithm for removing motion artifacts from the PPG signal in the time domain to determine heart rate is disclosed. A device for determining a heart rate of a user can include a heart rate sensor configured to generate heart rate signals when positioned on or adjacent to a user's skin, an accelerometer configured to generate one or more acceleration signals, and processing circuitry configured to remove, in a time domain, motion artifacts from the heart rate signals based on the acceleration signals. In some examples, the removal of motion artifacts can also be based on mean-centered, variance-scaled integrated acceleration signals. In some examples, the processing circuitry can be configured to remove motion artifacts using a least squares algorithm to identify a best representation of acceleration and integrated acceleration signals in the heart rate signals.

Signal processing devices and methods estimate transforms between signals using a least squares technique. From a seed set of transform candidates, a direct least squares method applies a seed transform candidate to a reference signal and then measures correlation between the transformed reference signal and a suspect signal. For each candidate, update coordinates of reference signal features are identified in the suspect signal and provided as input to a least squares method to compute an update to the transform candidate. The method iterates so long as the update of the transform provides a better correlation. At the end of the process, the method identifies a transform or set of top transforms based on a further analysis of correlation, as well as other results.

Systems and methods of obtaining and recording fundus images by minimally trained persons, which includes a camera for obtaining images of a fundus of a subject's eye, in combination with mathematical methods to assign real time image quality classification to the images obtained based upon a set of criteria. The classified images will be further processed if the classified images are of sufficient image quality for clinical interpretation by machine-coded and/or human-based methods. Such systems and methods can thus automatically determine whether the quality of a retinal image is sufficient for computer-based eye disease screening. The system integrates global histogram features, textural features, and vessel density, as well as a local non-reference perceptual sharpness metric. A partial least square (PLS) classifier is trained to distinguish low quality images from normal quality images.

The invention discloses a circle center locating method of a circular target, mainly relating to a great deal of target identification and target location. Firstly, rough circle center location is carried out to an image by using a simple contour centroid method, a key square region is extracted as a region of interest according to the information of a rough location circle center and a rough location radius, and pixel-level edge location is carried out to the circular target in the region of interest by a canny operator; then sub-pixel location is carried out to the circular target according to the geometric features and the gray information of the circle, therefore, a precise coordinate of sub-pixel edge points is obtained; after that, a curvature filtering method and an average filtering method are respectively used for filtering 'isolated points' and noise occurring in the sub-pixel edge points; finally, a least squares method is utilized to fit a circle to the filtered sub-pixel edge points so as to obtain the final circular center and radius. The method not only effectively improves the precision of circle center location, but also improves the robustness thereof, thus further improving the measurement precision of a measurement system and perfecting the stability of the measurement system.

The invention provides an SAR image registration method based on SIFT and normalized mutual information. The method includes the steps that firstly, a standard image I1 and an image to be registered I2 are input and are respectively pre-processed; secondly, features of the pre-processed image I1 and features of the pre-processed image I2 are extracted according to the MM-SIFT method to acquire initial feature point pairs Fc and SIFT feature vectors Fv1 and Fv2; thirdly, initial matching is carried out through the Fv1 and the Fv2; fourthly, the Fc is screened for the second time according to the RANSAC strategy of a homography matrix model, final correct matching point pairs Fm are acquired, and a registration parameter pr is worked out according to the least square method; fifthly, I2 is subjected to space conversion through affine transformation, and a roughly-registered image I3 is acquired through interpolation and resampling; sixthly, pr serves as the initial value of normalization information registration, I1 and I2 are subjected to fine registration through the normalized mutual information method, a final registration parameter pr1 is worked out, and a registered image I4 is output. The method can be quickly, effectively and stably carried out, and SAR image registration precision and robustness are improved.