Adaptive Calibration Algorithm Research Articles

An Adaptive Calibration Algorithm Based on RSSI and LDPLM for Indoor Ranging and Positioning.

The positioning algorithm based on received signal strength indication (RSSI) and the logarithmic distance path loss model (LDPLM) is widely used in indoor positioning scenarios due to its convenient detection and low costs. However, the classic LDPLM with fixed coefficients and fixed error estimation usually reduces the ranging accuracy, but it is rarely studied in previous literature. This study proposes an adaptive calibration ranging algorithm based on LDPLM, which consists of two parts: coefficient adaptive algorithm and error correction algorithm. The coefficient adaptive algorithm is derived by utilizing the error theory and the least squares method. The error correction algorithm is defined as the linear regression equation, in which coefficients are determined by the least squares method. In addition, to reduce the influence of RSSI’s fluctuation on ranging accuracy, we propose a simple but effective filtering algorithm based on Gaussian. The experimental results show that compared with the classic LDPLM and polynomial fitting model, the ranging accuracy of the proposed algorithm is improved by 58% and 51%, respectively, and the positioning cumulative prediction error of the proposed model is reduced by 69% and 80%, respectively.

An Adaptive Image Calibration Algorithm for Steganalysis

In this paper, a new adaptive calibration algorithm for image steganalysis is proposed. Steganography disturbs the dependence between neighboring pixels and decreases the neighborhood node degree. Firstly, we analyzed the effect of steganography on the neighborhood node degree of cover images. Then, the calibratable pixels are marked by the analysis of neighborhood node degree. Finally, the strong correlation calibration image is constructed by revising the calibratable pixels. Experimental results reveal that compared with secondary steganography the image calibration method significantly increased the detection accuracy for LSB matching steganography on low embedding ratio. The proposed method also has a better performance against spatial steganography.

Kalman-Based Real-Time Functional Decomposition for the Spectral Calibration in Swept Source Optical Coherence Tomography.

This paper presents a real-time functional decomposition adaptive algorithm for the optimal sampling of the interferometric signal in Swept-Source Optical Coherence Tomography imaging systems, which completely eliminates the input signal dependent nonlinearities that are problematic in current state-of-the-art OCT realizations that use interpolation and resampling. The proposed adaptive calibration algorithm uses the Kalman approach to estimate the wavenumber index parameter k from the Mach-Zender Interferometer signal which is then applied to an adaptive level crossing sampler to generate a sampling clock that k-linearizes the data on real-time during the sampling process. Such a system implements an artifact-free realization of the technology removing the need for classical interpolation and resampling. The new real-time linearization scheme has the additional capability of increasing the imaging acquisition speed by 10X while providing robustness to noise, properties that are demonstrated through mathematical analysis and simulation results throughout the paper.

ACA-Pro: calibration protocol for quantitative diffuse reflectance spectroscopy. Validation on contact and noncontact probe- and CCD-based systems.

We have developed an adaptive calibration algorithm and protocol (ACA-Pro) that corrects from the instrumental response of various spatially resolved diffuse reflectance spectroscopy (DRSsr) systems to enable the quantification of absorption and scattering properties based on a Monte Carlo-based look-up-table approach. The protocol involves the use of a calibration reference base built with measurements of a range of different diffusive intralipid phantoms. Moreover, an advanced strategy was established to take into account the experimental variations with an additional measurement of a common solid material, allowing the use of a single calibration reference base for all experiments. The ACA-Pro is validated in contact and noncontact probe-based DRSsr systems. Furthermore, the first results of a setup replacing the probe with a CCD detector are shown to confirm the robustness of the approach.

Efficient and flexible Focused Ion Beam micromachining of Solid Immersion Lenses in various bulk semiconductor materials – An adaptive calibration algorithm

Focused Ion Beam (FIB) micromachining of Solid Immersion Lenses (SILs) in substrate material offers optical analysis solutions for current and future technologies without the limitations of external SIL systems. This work presents an efficient single iteration calibration algorithm. This algorithm enables the implementation of FIB created SILs using a variety of substrate materials, process chemistries and most importantly different SIL shapes to match sample thicknesses. The successful application on silicon and silicon carbide is presented by creating a 50μm wide refractive lens segment with a radius of curvature of 60μm. Laser scanning microscope images of a silicon sample demonstrate the optical benefit with a measured resolution of 274nm.

Adaptive Calibration Algorithm for Plasma Glucose Estimation in Continuous Glucose Monitoring

Minimally or noninvasive continuous glucose monitors estimate plasma glucose from compartments alternative to blood, and may revolutionize the management of diabetes. However, the accuracy of current devices is still poor and it may partly depend on low performance of the implemented calibration algorithm. Here, a new adaptive calibration algorithm based on a population local-model-based intercompartmental glucose dynamic model is proposed. The novelty consists in the adaptation of data normalization parameters in real time to estimate and compensate patient's sensitivity variations. Adaptation is performed to minimize mean absolute relative deviation at the calibration points with a time window forgetting strategy. Four calibrations are used: preprandial and 1.5 h postprandial at two different meals. Two databases are used for validation: 1) a 9-h CGMS Gold (Medtronic, Northridge, USA) time series with paired reference glucose values from a clinical study in 17 subjects with type 1 diabetes; 2) data from 30 virtual patients (UVa simulator, Virginia, USA), where inter- and intrasubject variability of sensor's sensitivity were simulated. Results show how the adaptation of the normalization parameters improves the performance of the calibration algorithm since it counteracts sensor sensitivity variations. This improvement is more evident in one-week simulations.

An Adaptive Algorithm for Eye-Gaze-Tracking-Device Calibration

Eye-gaze tracking has been an important and interesting area of research for quite some time now. In laboratory experiments for psychological research, eye-gaze tracking constitutes a helpful tool to probe the perceptual or the cognitive processes of the subjects. In day-to-day applications, eye-gaze tracking can be used as a computer interface for both industrial and nonindustrial applications, which require hands-free installations. It can also be used to help disabled people to use computers for communication and for environmental control. This paper aims to develop a computer system to facilitate the study of visual scanning and eye fixation during image exploration. The system uses an infrared device to detect horizontal and vertical eye movements. This paper mainly focuses its attention on the calibration procedure that can be effectively utilized for correction of the nonlinearities in the oculomotor for the eyes of each individual subject. An adaptive calibration algorithm has been developed by employing a polynomial model. Several experiments have been carried out to demonstrate that the proposed approach is a rapid one and is accurate for the study of visual perception and recognition processes.

Performance of a Computer System for Recording and Analysing Eye Gaze Position Using an Infrared Light Device

Our aim is to develop a computer system in order to study the visual scanning and eye fixation during exploration of image. The system uses an infrared device to detect the horizontal and vertical eye movements. In this paper, our interest is concentrated mainly on the calibration procedure. More precisely, on the correction of the nonlinearity in the oculomotor for individual subject's eyes. An adaptive calibration algorithm is developed. The algorithm uses a polynomial model and the optimal correction is obtained by using a mean square error criterion. The paper presents also a method to correct head movements. Experiments show that the proposed approach is fast and accurate for the study of visual perception and recognition processes.