Auto-calibration Algorithm Research Articles

Cotton Gin Stand Machine-Vision Inspection and Removal System for Plastic Contamination: Hand Intrusion Sensor Design

Plastic contamination in cotton lint poses significant challenges to the U.S. cotton industry, with plastic wrap from John Deere round module harvesters being a primary contaminant. Despite efforts to manually remove this plastic during module unwrapping, some inevitably enters the cotton gin’s processing system. To address this, a machine-vision detection and removal system has been developed. This system uses inexpensive color cameras to identify plastic on the gin stand feeder apron, triggering a mechanism that expels the plastic from the cotton stream. However, the system, composed of 30–50 Linux-based ARM computers, requires substantial effort for calibration and tuning and presents a technological barrier for typical cotton gin workers. This research aims to transition the system to a more user-friendly, plug-and-play model by implementing an auto-calibration function. The proposed function dynamically tracks cotton colors while excluding plastic images that could hinder performance. A critical component of this auto-calibration algorithm is the hand intrusion detector, or “HID”, which is discussed in this paper. In the normal operation of a cotton gin, the gin personnel periodically have to clear the machine, which entails running a stick or their arm/hand under the detection cameras. This results in the system capturing a false positive, which interferes with the ability of auto-calibration algorithms to function correctly. Hence, there is a critical need for an HID to remove these false positives from the record. The anticipated benefits of the auto-calibration function include reduced setup and maintenance overhead, less reliance on skilled personnel, and enhanced adoption of the plastic removal system within the cotton ginning industry.

Development of a behaviour pattern-based testing approach for coupled socioeconomic and environmental models

Understanding the interactions between human and environmental systems is key to sustainable environmental management. Dynamically Coupled Socioeconomic system dynamics models integrated with physically-based Environmental Models (DCSEMs) are promising tools to appropriately capture the non-linear relationships between complex socioeconomic and biophysical systems, thereby supporting sustainable environmental management. However, existing approaches for testing integrated models are commonly based on the point-to-point analysis of model outputs, which is not suitable for DCSEMs that are behaviour pattern oriented. Consequently, the lack of well-defined behaviour pattern-based approaches has limited the adaptability of DCSEMs. To address this gap, this study proposes a novel behaviour pattern-based model testing approach that includes global sensitivity analysis (GSA), auto-calibration algorithms, and evaluation to assess behaviour pattern similarities between model outputs and real-world trends. The proposed approach is demonstrated through a real-world case study, in which an existing DCSEM is calibrated and evaluated to simulate water table depth in the Rechna Doab region of Pakistan. Compared to the conventional numerical point approach, the proposed approach is better suited for DCSEMs, as it replicates observed system behaviour patterns (as opposed to observed point values) over time. Furthermore, the outcomes of the Theil inequality statistical analysis and parameter distribution analysis provide evidence that the suggested approach is effective in testing and improving the performance of the DCSEM by capturing the spatial heterogeneity within the study area. The proposed behaviour-pattern testing procedure is a useful approach for model testing in data-limited, spatially-distributed DCSEMs.

A Size, Weight, Power, and Cost-Efficient 32-Channel Time to Digital Converter Using a Novel Wave Union Method.

We present a Tapped Delay Line (TDL)-based Time to Digital Converter (TDC) using Wave Union type A (WU-A) architecture for applications that require high-precision time interval measurements with low size, weight, power, and cost (SWaP-C) requirements. The proposed TDC is implemented on a low-cost Field-Programmable Gate Array (FPGA), Artix-7, from Xilinx. Compared to prior works, our high-precision multi-channel TDC has the lowest SWaP-C requirements. We demonstrate an average time precision of less than 3 ps and a Root Mean Square resolution of about 1.81 ps. We propose a novel Wave Union type A architecture where only the first multiplexer is used to generate the wave union pulse train at the arrival of the start signal to minimize the required computational processing. In addition, an auto-calibration algorithm is proposed to help improve the TDC performance by improving the TDC Differential Non-Linearity and Integral Non-Linearity.

Cotton Gin Stand Machine-Vision Inspection and Removal System for Plastic Contamination: Auto-Calibration Design

Plastic contamination in marketable cotton bales, predominantly from plastic wraps used in John Deere round module harvesters, poses a significant challenge to the U.S. cotton industry. Despite rigorous manual efforts, the intrusion of plastic into the cotton gin’s processing system persists. We have developed a machine-vision detection and removal system aimed at mitigating this problem. This system employs inexpensive color cameras to detect plastic on the gin-stand feeder apron and subsequently removes it, reducing contamination. This system, built around low-cost ARM computers running Linux, comprises 30–50 machines and requires considerable effort to calibrate and tune. Moreover, its operation represents a technological challenge to typical cotton gin workers. This research presents a solution to this calibration operational hurdle by introducing an auto-calibration algorithm that has potential to simplify the system into a plug-and-play device. The auto-calibration system is designed to dynamically track the cotton color and utilizes frequency statistics to avoid plastic images that could compromise the system’s performance if incorporated into the auto-calibration process. We detail the design of the auto-calibration algorithm, which is expected to significantly reduce the setup overhead and facilitate the system’s continuous use. This innovation minimizes the need for skilled personnel and, therefore, is expected to expedite the system’s adoption across the cotton ginning industry.

Highly Productive 3D Printing Process to Transcend Intractability in Materials and Geometries via Interactive Machine‐Learning‐Based Technique

Herein, a highly productive and defect‐free 3D‐printing system enforced by deep‐learning (DL)‐based anomaly detection and reinforcement‐learning (RL)‐based optimization processes is developed. Unpredictable defect factors, such as machine setting errors or unexpected material flow, are analyzed by image‐based anomaly detection implemented using a variational autoencoder DL model. Real‐time detection and in situ correction of defects are implemented by an autocalibration algorithm in conjunction with the DL system. In view of productivity enhancement, the optimized set of diversified printing speeds can be generated from virtual simulation of RL, which is established using a physics‐based engineering model. The RL‐simulated parameter set maximizes printing speed and minimizes deflection‐related failures throughout the 3D‐printing process. With the synergistic assistance of DL and RL techniques, the developed system can overcome the inherent challenging intractability of 3D printing in terms of material property and geometry, achieving high process productivity.

An Auto-Calibration Algorithm for Hybrid Guided Wave Tomography Based on Full Waveform Inversion

Combining with the dispersion characteristics of ultrasonic guided waves, full waveform inversion method shows great application potential in the quantificational detections of defects in plate- and pipe- like structures. Owing to the inversion efficiency problem, the forward modeling is generally based on the acoustic equation approximation, and a reference signal unaffected by the defects needs to be artificially selected to correct the approximation forward simulation results. This study presents an auto-calibration (without artificial selection of the reference signal) and high-precision imaging method based on the combination of the full waveform inversion and ray tomography algorithm. The ray tomography results are not only used to automatically select the reference signal based on an auto-calibration process, but also used as the macro initial model for the full inversion method, which decreases the possibility of losing in local minimum values during the inversion process and enhances the robustness of the inversion method. Therefore, compared with the classical guided wave tomography method based on full waveform inversion, relatively high-frequency transducers can be used to acquire high-frequency signals, and thus, the imaging accuracy could be effectively improved. Simulation and experiment results have verified that the global relative error of the auto-calibration method is smaller than the classical method. The good imaging results of irregular complex defects confirmed the effectiveness and applicability of the new method.

Combining remote sensing-derived management zones and an auto-calibrated crop simulation model to determine optimal nitrogen fertilizer rates

CONTEXTCotton is an economically important crop in Australia that requires high resource application, particularly that of nitrogen (N) fertilizers. Determining optimal N fertilizer rates that reach both economic and environmental objectives is a key challenge in cotton systems because of the inherent within-field variability and relatively low N fertilizer use efficiency (NFUE). OBJECTIVEThis study aimed to model optimal N fertilizer rates by accounting for within-field variability through management zone (MZ) delineation across a cotton field in Queensland, Australia. METHODSMZs were delineated using satellite-derived normalized difference vegetation index (NDVI) and the crop simulation model, Decision Support System for Agrotechnology Transfer (DSSAT), was automatically calibrated with a grid-search optimization algorithm and validated across two cotton seasons. A total of 336 different N fertilizer scenarios were subsequently evaluated at pre-planting and top-dressing to observe the effect on profit margin and NFUE. RESULTS AND CONCLUSIONSThe MZ delineation analysis determined that within-field variability could be best represented by two MZs, one of which displaying subsoil constraints due to high carbonate concentrations. The use of the auto-calibration algorithm led to a successful validation of the model with a Wilmott d-index of agreement ranging between 0.75 (soil nitrate) and 0.96 (aboveground biomass and plant N), respectively. The subsequent N scenario simulations indicated that by reducing N fertilizer rates by 80 and 30 kg N ha−1 across the two MZs, respectively, compared to the current industry average, profits could be maximized through maintaining yields while reducing N inputs. SIGNIFICANCEOverall, these results demonstrate the potential of combining remote sensing-derived MZs and crop model auto-calibration techniques to support the cotton industry in achieving improved resource efficiency and profit margins.

Comparison of Different Signal Processing Methodologies and Their Impact on the Range of Acceleration Amplitudes Experienced by Preschool-Aged Children

ABSTRACT This study explored 1) the effect of applying an autocalibration algorithm on accelerometer data analyzed using different signal processing techniques and 2) how these techniques changed the signal composition. Using a dataset of preschoolers (n = 137; 4.31 ± 0.87 years; Actigraph accelerometers (100 Hz) on right hip 24 hours/7 days) the vector magnitude (VM) was calculated as a reference; highpass, bandpass and lowpass filters were applied and the “Euclidean Norm Minus One” (ENMO) was calculated. Amplitude probability distribution functions were created to compare the 1) uncalibrated versus calibrated data and, 2) amplitude distribution across the techniques. Results showed that the calibration correction significantly impacted ENMO, VM lowpass and VM data, but the impact on VM highpass and bandpass data was minimal. Comparing across the techniques, results showed the lowest outputs were derived according to ENMO, followed by the VM highpass/VM bandpass and the VM lowpass/VM which yielded very similar results.

Fast Alignment Algorithm for Autocalibration in Digital Monopulse Radars

Calibration is important for digital monopulse radars since the target tracking performance would be degraded by the amplitude and phase imbalance among channels. However, the alignment between the radar to be calibrated and the test probe is a challenge, especially for antennas with high gain and narrow beamwidth. In this letter, we propose a fast alignment algorithm for the autocalibration of digital monopulse radars using the gradient ascent method based on the tentative radiation pattern gradient model. We verify the feasibility and the high efficiency of the proposed algorithm by simulations and an experiment of a W-band digital monopulse radar. The elapsed time of the proposed algorithm is 0.01 <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</b> of the existing method and requires lower equipment complexity compared with the scanning-based method.

A Functional Electrical Stimulation System of High-Density Electrodes With Auto-Calibration for Optimal Selectivity

Surface functional electrical stimulation (FES) is a well-recognized technique for motor function rehabilitation of some paralyzed patients. Finding the proper positions of the electrodes on the forearm for selective control of finger movements is a challenge for FES applications. This work developed a FES system towards hand rehabilitation based on customised high-density electrodes in order to achieve optimal selective stimulation. The proposed system is composed of a customised multi-channel electronic stimulator, an electrode array of 32 electrode pads with each electrode pad size of 1.2 cm ×1.5 cm, and motion feedback sensors. An auto-calibration algorithm was developed to determine the optimal stimulation parameters and electrode pads automatically. The algorithm was based on the prior knowledge gained through manual stimulation. The FES system was tested on eight healthy subjects. The results indicated that the auto-calibration algorithm could obtain the optimal electrode pads for the activation of at least three fingers for six subjects in an isolated manner, and the time required for optimal electrode pads and stimulation parameters detection was significantly reduced, demonstrating the potential for finger movement control in hand rehabilitation.

Optimised Autocalibration Algorithm of Weigh-In-Motion Systems for Direct Mass Enforcement.

Dynamic vehicle weighing systems, also known as Weigh-In-Motion (WIM), are sensitive to factors which interfere with the measurement, including weather and climate conditions. This is a result of the sensitivity of the axle load sensors used in the systems. As a result, a significant change in the precision of weighing can be observed over short periods of time (even less than 1 h). This fact is a deterrent to the use of such systems for direct mass enforcement. In this article, we present a solution for this problem using an optimised autocalibration algorithm. We show the results of simulation studies which we conducted on the proposed algorithm. These were then verified experimentally at an in-road site. We demonstrated that autocalibration of the WIM system allows for effective limitation of the sensitivity of weighing results to interfering factors. This is, however, conditioned on a sufficiently high frequency of reference vehicles crossing the WIM site. The required frequency depends on the speed of change in the concentration of influencing factors.

Auto-calibration method for high concentration sun trackers

High concentration photovoltaic modules allow a significant increase in the electric power produced per unit area. Nevertheless, in order to reach the maximum possible increase in performance, the sun tracker must meet severe specifications regarding sun-pointing accuracy, because the lens-cell sets of the solar modules only work in a very small angle. For the sun tracker to meet these requirements, a precise procedure of installation is necessary in order to minimize errors from different sources such as misalignment of the structure with respect to geographical North, misalignment of the solar modules with respect to the wing of the sun tracker and other structural tolerances. This paper presents an auto-calibration algorithm which allows to work with higher tolerances in the installation process by estimating some sources of error instead of spending installation time trying to minimize them. Simulation and experimental results with a testing sun tracker are exposed showing the benefits of the proposed method with respect to a manual calibration in terms of power performance.

CO Removal Catalysts and CO Tolerant Materials in Fuel Cell Systems

Multimodal electrochemical technique incorporating both open circuit potential (OCP) and amperometric techniques have been conceptualized and implemented to improve the detection of specific analyte in systems where more than one analyte is present. This approach has been demonstrated through the detection of ethanol while eliminating the contribution of water in a micro fuel cell sensor system. The sensor was interfaced with LMP91000 potentiostat, controlled through MSP430F5529LP microcontroller to implement an auto-calibration algorithm tailored to improve the detection of alcohol. The sensor was designed and fabricated as a three electrode system with Nafion as a proton exchange membrane (PEM). The electrochemical signal of the interfering phase (water) was eliminated by implementing the multimodal electrochemical detection technique. The results were validated by comparing sensor and potentiostat performances with a commercial sensor and potentiostat respectively. The results suggest that such a sensing system can detect ethanol at concentrations as low as 5 ppm. The structure and properties such as low detection limit, selectivity and miniaturized size enables potential application of this device in wearable transdermal alcohol measurements.

A fast-converge, real-time auto-calibration algorithm for triaxial accelerometer

Online parameter estimation for nonlinear systems are challenging, especially when only limited computational powers are available. The auto-calibration of triaxial accelerometers is essentially a nonlinear parameter estimation problem. In this paper, a simple but efficient auto-calibration method for micro triaxial accelerometers is proposed. In particular, this method is an online calibration approach based on the six-parameter auto-calibration model, which can be implemented in a wearable health-monitoring device to compensate the estimation errors due to parameter drift. To achieve online calibration, the nonlinear model together with its cost function for auto-calibration is linearized, and an online recursive method is exploited to identify the unknown parameters and remove the bias caused by linearization. This online recursive method is developed by modifying the damped recursive least square estimation (MDRLS), where the estimated unknown parameter can converge stably in a short period. The MDRLS-based method can significantly reduce the calculation complexity comparing to the commonly used nonlinear optimization method. Additionally, the proposed method is validated by both simulation and experiment. The simulation results show the estimated parameters are very close to their true values. By comparing the output results before and after calibration in different experimental conditions, the effectiveness of the proposed method is further verified by real-time experiments.

Array auto-calibration using a generalized least-squares method

This paper introduces a new class of auto-calibration algorithms, based on a generalized least-squares (GLS) cost function. This class supports both gain-phase calibration and mutual coupling calibration and it is independent of array shape. The GLS cost function is minimized cyclically with respect to the direction of arrivals (DOA) and calibration parameters. These two minimization steps are repeated until convergence. In the DOA estimation step, minimization leads to the SPICE (SParse Iterative Covariance-based Estimation) DOA estimation algorithm. In fact, the innovation of this paper mainly lies in the calibration step. In the DOA estimation step, a generalized form of the SPICE algorithm should be used in the coherent sources case. Consequently, the proposed auto-calibration algorithm is able to support coherent source cases. Moreover, the proposed algorithm does not need multi-dimensional search. The necessary condition for the existence of a solution is studied as well. Using simulations, the performance of the proposed algorithm is compared with several other auto-calibration algorithms. Simulation results show that the proposed algorithm is more robust than the other algorithms and usually has better performance.

Evaluation of Multi-objective PSO Algorithm for SWAT Auto-Calibration

Evaluation of Multi-objective PSO Algorithm for SWAT Auto-Calibration

Autocalibration of gyroscope based two-step regulator for autonomous vehicles.

In this article was presented an auto calibration algorithm based on a two-step regulator and gyroscope readings. Unable to use PID, as the PWM signal causes the motors to overheat, it was decided to introduce a two-step algorithm. This type of controller reaches the setpoint much faster, though an error is present. Gyroscope is used to sample angular speed and then integrate these values to get the angle. To calibrate the offsets the robot performs a calibration run during which it measures and averages the error on each gyroscope axis while no movement is present. Afterwards it makes a characteristics of braking angle and angular speed. Interpolated polynomial from 13 samples allows to designate the point in time where the braking has to start to achieve the desired angle. The calibration aims for adjusting the parameters of movement depending on the surface it runs on. Additional check is made using encoders to estimate the accuracy of gyroscope output.

Modeling Photo-multiplier Gain and Regenerating Pulse Height Data for Application Development

Systems that adopt organic scintillation detector arrays often require a calibration process prior to the intended measurement campaign to correct for significant performance variances between detectors within the array. These differences exist because of low tolerances associated with photo-multiplier tube technology and environmental influences. Differences in detector response can be corrected for by adjusting the supplied photo-multiplier tube voltage to control its gain and the effect that this has on the pulse height spectra from a gamma-only calibration source with a defined photo-peak. Automated methods that analyze these spectra and adjust the photo-multiplier tube bias accordingly are emerging for hardware that integrate acquisition electronics and high voltage control. However, development of such algorithms require access to the hardware, multiple detectors and calibration source for prolonged periods, all with associated constraints and risks. In this work, we report on a software function and related models developed to rescale and regenerate pulse height data acquired from a single scintillation detector. Such a function could be used to generate significant and varied pulse height data that can be used to integration-test algorithms that are capable of automatically response matching multiple detectors using pulse height spectra analysis. Furthermore, a function of this sort removes the dependence on multiple detectors, digital analyzers and calibration source. Results show a good match between the real and regenerated pulse height data. The function has also been used successfully to develop auto-calibration algorithms.

System-Level Design of a 64-Channel Low Power Neural Spike Recording Sensor.

This paper reports an integrated 64-channel neural spike recording sensor, together with all the circuitry to process and configure the channels, process the neural data, transmit via a wireless link the information and receive the required instructions. Neural signals are acquired, filtered, digitized and compressed in the channels. Additionally, each channel implements an auto-calibration algorithm which individually configures the transfer characteristics of the recording site. The system has two transmission modes; in one case the information captured by the channels is sent as uncompressed raw data; in the other, feature vectors extracted from the detected neural spikes are released. Data streams coming from the channels are serialized by the embedded digital processor. Experimental results, including in vivo measurements, show that the power consumption of the complete system is lower than 330μW.

DrivingSense: Dangerous Driving Behavior Identification Based on Smartphone Autocalibration

Since pervasive smartphones own advanced computing capability and are equipped with various sensors, they have been used for dangerous driving behaviors detection, such as drunk driving. However, sensory data gathered by smartphones are noisy, which results in inaccurate driving behaviors estimations. Some existing works try to filter noise from sensor readings, but usually only the outlier data are filtered. The noises caused by hardware of the smartphone cannot be removed from the sensor reading. In this paper, we propose DrivingSense, a reliable dangerous driving behavior identification scheme based on smartphone autocalibration. We first theoretically analyze the impact of the sensor error on the vehicle driving behavior estimation. Then, we propose a smartphone autocalibration algorithm based on sensor noise distribution determination when a vehicle is being driven. DrivingSense leverages the corrected sensor parameters to identify three kinds of dangerous behaviors: speeding, irregular driving direction change, and abnormal speed control. We evaluate the effectiveness of our scheme under realistic environments. The results show that DrivingSense, on average, is able to detect the driving direction change event and abnormal speed control event with 93.95% precision and 90.54% recall, respectively. In addition, the speed estimation error is less than 2.1 m/s, which is an acceptable range.