Two-step Filter Research Articles

LiDAR-Based Glass Detection for Improved Occupancy Grid Mapping.

Creating an accurate awareness of the environment using laser scanners is a major challenge in robotics and auto industries. LiDAR (light detection and ranging) is a powerful laser scanner that provides a detailed map of the environment. However, efficient and accurate mapping of the environment is yet to be obtained, as most modern environments contain glass, which is invisible to LiDAR. In this paper, a method to effectively detect and localise glass using LiDAR sensors is proposed. This new approach is based on the variation of range measurements between neighbouring point clouds, using a two-step filter. The first filter examines the change in the standard deviation of neighbouring clouds. The second filter uses a change in distance and intensity between neighbouring pules to refine the results from the first filter and estimate the glass profile width before updating the cartesian coordinate and range measurement by the instrument. Test results demonstrate the detection and localisation of glass and the elimination of errors caused by glass in occupancy grid maps. This novel method detects frameless glass from a long range and does not depend on intensity peak with an accuracy of 96.2%.

Impulse noise removal in color image sequences using fuzzy logic

This paper presents a two-step fuzzy filter to remove impulse noise from a color image sequence in RGB color space. The primary filter recognizes the pixels corrupted by impulse noise. It also computes the extent of noise and afterward rectifies them. The output of the first filter acts as an input to the secondary filter and further refines the outcome to give the final output. Excellent alignment is seen between noise removal and structure conservation of an image due to the classifying nature of the algorithm. To minimize blurring, noisy pixels are exclusively rectified and noise-free pixels remain intact. The proposed filter is a 3-D Spatio-temporal filter that considers spatial, temporal as well as color information. A pixel of one color component is compared to its neighboring pixels within the same frame and with the corresponding pixels in neighboring frames. It is likewise compared with the pixels of the other two color components. Peak Signal to Noise Ratio (PSNR) and structural similarity index (SSIM), Miss Detection (MD), and False Alarm (FA) rates are utilized as a performance metric. The experimental result of several color image sequences demonstrates the efficacy of the proposed fuzzy filter both qualitatively and quantitatively.

Power Systems Dynamic State Estimation With the Two-Step Fault Tolerant Extended Kalman Filtering

Bad data may lead to performance degradation or even instability of a power system, which can be caused by various factors: unintentional PMU abnormalities, topology error, malicious cyber-attacks, electromagnetic interference, temporary loss of communication links, external disturbances, extraneous noise biases, etc. In order to develop a more resilient and reliable state estimation technique, this manuscript presents a novel two-step fault tolerant extended Kalman filter framework for discrete-time stochastic power systems, under bad data, PMU failures, external disturbances, extraneous noise, and bounded observer-gain perturbation conditions. The failure mechanisms of multiple phasor measurement units are assumed to be independent of each other with various bad data or malfunction rates. The benchmark IEEE standard test systems are utilized as a demonstrative example to carry out computer simulation studies and to examine different estimation algorithms. Experimental results demonstrates that the proposed second-order fault tolerant extended Kalman filter provides more accurate estimation results, in comparison with traditional first- and second-order extended Kalman filter, and the unscented Kalman filter. The proposed two-step fault-tolerant extended Kalman filter can serve as a powerful alternative to the existing dynamic power system state estimation techniques.

Motion Analysis of Deadlift for Trainers With Different Levels Based on Body Sensor Network

Deadlift is one of the most common weight training exercises that can help build waist muscles. However, many trainers with less experience may not receive the expected effect or even damage themselves. To exercise in a healthier way, a portable and quantifiable deadlift evaluation system is proposed in this article. The system employs a body sensor network (BSN), including the inertial and surface electromyography (sEMG) information, to reconstruct the human motion and segment the deadlift into certain phases to realize the detailed analysis. A two-step extended Kalman filter (EKF) that can reduce the magnetic interference is presented to estimate the attitude and the accuracy of the method is validated by the OptiTrack system. We also carry out the kinematic and sEMG analysis among trainers with different levels. The experiment results reveal that our system can significantly discriminate the technical differences for those trainers, which demonstrated that the system can provide valuable information for the deadlift training.

A Self-organizing Deep Auto-Encoder approach for Classification of Complex Diseases using SNP Genomics Data

Recently, many Machine Learning algorithms have been utilized to identify significant Single Nucleotide Polymorphisms (SNPs) in various human diseases. However, some principal obstacles are challenging in the field of SNP detection and healthy-patient classification. The curse of dimensionality is the main challenge. On the other hand, the number of samples is decidedly smaller than the number of SNPs. In addition, the number of healthy and patient samples can be unequal. These challenges make the feature selection and classification very difficult. The main goal of the current study is the combination of the various algorithms to find out the most effective way of SNP data analysis. Therefore, an efficient method is proposed to identify significant SNPs and classify healthy and patient samples. In this regard, firstly, the Mean Encoding, as an intelligent method, is utilized to convert the nominal SNP data to numeric. Then a two-step filter method is used for feature selection, which removes the irrelevant and redundant features. Finally, the proposed deep auto-encoder is employed to classify so that it can construct its structure based on input data, automatically. To evaluate, we apply the proposed approach to five different SNP datasets, including thyroid cancer, mental retardation, breast cancer, colorectal cancer, and autism, which obtained from the Gene Expression Omnibus (GEO) dataset. The proposed method has succeeded in feature selection and classification so that it can classify healthy and patient samples based on selected features in thyroid cancer, mental retardation, breast cancer, colorectal cancer, and autism with 100%, 94.4%, 100%, 96%, and 99.1% accuracy, respectively. The results indicate that it has succeeded with high efficiency, compared with other published works.

Compositional knockoff filter for high-dimensional regression analysis of microbiome data.

A critical task in microbiome data analysis is to explore the association between a scalar response of interest and a large number of microbial taxa that are summarized as compositional data at different taxonomic levels. Motivated by fine-mapping of the microbiome, we propose a two-step compositional knockoff filter to provide the effective finite-sample false discovery rate (FDR) control in high-dimensional linear log-contrast regression analysis of microbiome compositional data. In the first step, we propose a new compositional screening procedure to remove insignificant microbial taxa while retaining the essential sum-to-zero constraint. In the second step, we extend the knockoff filter to identify the significant microbial taxa in the sparse regression model for compositional data. Thereby, a subset of the microbes is selected from the high-dimensional microbial taxa as related to the response under a prespecified FDR threshold. We study the theoretical properties of the proposed two-step procedure, including both sure screening and effective false discovery control. We demonstrate these properties in numerical simulation studies to compare our methods to some existing ones and show power gain of the new method while controlling the nominal FDR. The potential usefulness of the proposed method is also illustrated with application to an inflammatory bowel disease data set to identify microbial taxa that influence host geneexpressions.

A two-step Fabry-Perot filter array in mid-infrared wavelength range for integration with photochemical microreactors

We present the design, fabrication and experimental verification of a two-step Fabry-Perot filter array for online monitoring of liquid phase photochemical reactions in a microreactor by spectral analysis in the mid-infrared wavelength range (3–5 μm). Without any moving parts, the monitoring system is highly compact and can realize extremely fast and robust detection. The two-step Fabry-Perot (FP) filter array has a pixel size of 2 × 2 mm2 and the transmission wavelengths are designed according to the relevant chemical absorption peaks, providing both spectral and spatial resolutions. The filters have a spectral width with full width at half maximum (FWHM) of 110 nm and 145 nm at 4.7 μm and 4.27 μm, respectively. Experimental results show that the system can monitor the photolysis of 2-azidobenzoic acid in a microreactor and is able to detect different solution concentrations.

A Robust Indirect Kalman Filter Based on the Gradient Descent Algorithm for Attitude Estimation During Dynamic Conditions

The real-time response and accuracy of the attitude (i.e., roll and pitch) estimation from low-cost inertial measurement unit (IMU) have become the key issues restricting related applications. This paper proposes a robust attitude estimation scheme which can perform well under dynamic conditions. When only accelerometers are used to calculate and correct the attitude, the external acceleration becomes the main source of attitude estimation errors. Moreover, the truncation error in the linearization process of the nonlinear system also affects the attitude estimation. As our first contribution, the external acceleration is modeled as a first-order Gauss Markov model, and its value is calculated under the indirect Kalman filter (IKF) framework. The measurement noise covariance matrix of the IKF is adaptively adjusted to enhance its robustness and reduce the negative impact caused by inaccurate modeling. In the second part of our work, the two-step cascade filter method is used for attitude estimation. The attitude obtained from the gravity field based on the gradient descent (GD) algorithm shows fast response capabilities, and hence, it is embedded as a measurement in the IKF by using the chain-derivation rule. The truncation error introduced into the linearization process of the nonlinear system is effectively avoided. Both simulation and experiments are carried out to verify the feasibility and accuracy of the proposed algorithm. The results show that the approach proposed in this paper can meet the accuracy requirements of consumer products.

Distributed H∞Filtering Over Sensor Networks Subject to Randomly Occurred Nonlinearity and Time-Varying Topology

This article is concerned with the distributed $H_{\infty } $ filtering problems with consensus for a class of systems subject to randomly occurred nonlinearity and time-varying topology in sensor networks where the nonlinearity considered can include the cyber attacks as a special case. A group of Bernoulli distributed random variables are utilized to describe the varying topology and missing measurements in a unified framework. In terms of two-dimensional (2-D) system theory in Rosser model, a nonlinear two-step filter is designed with consensus protocol where a multiple data strategy is used to improve the system performance. The desired filter parameters are obtained by LMI technique such that the filtering error system is asymptotically stable in the sense of mean square and meets a prescribed average $H_{\infty } $ performance level. Finally, a simulation example demonstrates the effectiveness of the proposed algorithm.

Косвенный иск как механизм защиты миноритариев по праву Англии и Уэльса

This article considers the features of derivative actions under the English and Welsh law and its efficiency depending on certain circumstances. The chronological development and judicial practice outcomes of the question in consideration were analyzed from the 19th century to nowadays including the contemporary provisions of Companies act 2006 such as the two-step filter system for derivative claims. The need for assessing the specific circumstances of the dispute, such as the form of a legal entity, the percentage of participation in the capital of the company, as well as the distribution of the burden of legal expenses, is identified and justified in order to decide on the most effective way to protect the rights of a minority shareholder.

LimbMotion

Wearable-based human-computer interaction is a promising technology to enable various applications. This paper aims to track the 3D posture of the entire limb, both wrist/ankle and elbow/knee, of a user wearing a smart device. This limb tracking technology can trace the geometric motion of the limb, without introducing any training stage usually required in gesture recognition approaches. Nonetheless, the tracked limb motion can also be used as a generic input for gesture-based applications. The 3D posture of a limb is defined by the relative positions among main joints, e.g., shoulder, elbow, and wrist for an arm. When a smartwatch is worn on the wrist of a user, its position is affected by both elbow and shoulder motions. It is challenging to infer the entire 3D posture when only given a single point of sensor data from the smartwatch. In this paper, we propose LimbMotion, an accurate and real-time limb tracking system. The performance gain of LimbMotion comes from multiple key technologies, including an accurate attitude estimator based on a novel two-step filter, fast acoustic ranging, and point clouds-based positioning. We implemented LimbMotion and evaluated its performance using extensive experiments, including different gestures, moving speeds, users, and limbs. Results show that LimbMotion achieves real-time tracking with a median error of 7.5cm to 8.9cm, which outperforms the state-of-the-art approach by about 32%.

Minimum-Variance Recursive Filtering for Two-Dimensional Systems With Degraded Measurements: Boundedness and Monotonicity

This paper addresses the recursive minimum-variance filtering problem for a class of two-dimensional shift-varying systems with stochastic nonlinearity and degraded measurements. The stochastic nonlinearity is governed by its statistical characteristics and the degraded measurements reflect the signal degradation obeying certain prescribed probabilistic distributions. The main objective of this paper is to construct a two-step recursive filter that achieves the minimum error variance of the state estimation at each step. Utilizing an inductive approach, unbiasedness of the proposed filter is first ensured and the parameters of the filter are then designed by resorting to the completing squares method. Subsequently, the filtering performances including the boundedness and the monotonicity are investigated with respect to the measurement degradations through mathematically rigorous analysis. Moreover, a computational algorithm is presented to facilitate the online implementation of the designed filter. Finally, numerical simulation illustrates the effectiveness and applicability of the proposed filtering scheme in the state estimation problem for monitoring a long transmission line in circuit systems.

Online isolation of near-inertial internal waves in ocean general circulation models

Near-inertial internal waves (NIWs) play an important role in furnishing ocean diapycnal mixing. However, parameterization of NIW-induced mixing in high-resolution ocean general circulation models (OGCMs) remains a challenging problem partially due to the lack of appropriate tools for isolating NIWs during the model integration. In this study, we propose two new methods feasible for online isolation of NIWs in high-resolution OGCMs, i.e., the two-step filter and three-step filter.The two-step filter only requires the velocity records to be stored at two time steps. It performs reasonably well in isolating NIWs poleward of 30° if the time step is set as 6 h. By utilizing the velocity information at one additional time step, the three-step filter with a time step of 6 h generally outperforms the two-step filter in the global ocean. Specifically, the three-step filter results in reasonable isolation of NIWs poleward of 15° where the NIW-induced mixing is strongest.

Improving the accuracy of wearable sensor orientation using a two-step complementary filter with state machine-based adaptive strategy

Magnetic and inertial sensors have been widely used in human motion analysis, where accurate orientation estimation is important. Regardless of methods, sensor fusion process faces two major challenges: external acceleration and magnetic disturbance. By analyzing the existing literature, we found that a suitable base sensor fusion algorithm integrated with proper adaptive strategies is essential. In this paper, we first implemented a quaternion-based two-step complementary filter as the base sensor fusion algorithm. Its attitude estimation is immune to magnetic disturbance, and it contains two separate tuning parameters for different conditions of external acceleration and magnetic disturbance. With this base algorithm, we proposed a novel finite state machine-based adaptive strategy. Two state machines were developed to cope with external acceleration and magnetic disturbance. To validate the performance of the proposed sensor fusion method systematically, we developed a battery of tests representing daily-living environments, including acceleration distorted condition, magnetically distorted condition, and combined distorted condition. Also, a real-world experiment was performed to validate the orientation estimation accuracy on foot trajectory calculation. The results demonstrate that the proposed sensor fusion method performs well against external acceleration and magnetic disturbance compared with the existing methods. Especially, the proposed sensor fusion method showed a very high accuracy in the 60 s continuous combined distorted condition, where the root mean square errors of the roll, pitch and yaw were 0.63°, 0.83° and 0.96°, respectively. The accuracy of foot trajectory estimation was significantly improved with the orientation estimated by the proposed method. In conclusion, the proposed sensor fusion method is encouraging for human motion-related applications in daily-living environments. In addition, the proposed state machine based adaptive strategy is simple and robust, and can be easily integrated into other base sensor fusion algorithms.

Novel dual vector quaternions based adaptive extended two-step filter for pose and inertial parameters estimation of a free-floating tumbling space target

Estimating the parameters of an uncooperative space target is essential to the on-orbit service missions. A good parameter estimation can provide sufficient prior knowledge for the further operations. This paper proposes a novel dual vector quaternions based adaptive extended two-step filter (DVQ-AETSF) to estimate the pose and inertial parameters of a free-floating tumbling space target. Firstly, both of the rotational and translational motions are modeled by the dual vector quaternions (DVQ). Then, by using the DVQ-based system model, the DVQ-AETSF is designed. The proposed DVQ-AETSF mainly consists of a traditional Kalman filter prediction procedure in the first step and an adaptive regularized Newton iteration technique in the second step. The new proposed two-step filter aims to deal with the high nonlinearities in the measurements equations. By using the proposed DVQ-AETSF, both of the pose and initial parameters of a free floating tumbling space target under large errors of initial guesses and high measurement noise can be well estimated. Finally, the proposed DVQ-AETSF is validated by mathematical simulations to show its performance.

Design of adaptive filter for the wheel force transducer

Wheel force transducers (WFTs) are widely used in the car and commercial vehicle industry to determine forces of vehicles in operation, relevant for multiple tasks within the development process of the manufacturers (Sankarganesh and Pawar, 2015). Due to the uncertainty and randomness of the wheel forces, it is crucial to design a suitable real-time filter for the WFT to obtain the high-accuracy forces data. Considering the diversity of the wheel forces, models for different dynamic ranges are established. And a two-step adaptive filter is proposed in this paper. In the first step, the wheel forces’ actual dynamic is evaluated by the interacting multiple model (IMM) algorithm, then a suitable model is chosen to match the evaluated dynamic of the wheel force for data denoising in the second step. Simulations in different driving conditions are carried out to demonstrate the effectiveness of the proposed filter. Furthermore, the results of data test show that the adaptive filter can be successfully used in practice to get high-accuracy wheel forces.

Robust Microbial Markers for Non-Invasive Inflammatory Bowel Disease Identification.

Inflammatory Bowel Disease (IBD) is an umbrella term for a group of inflammatory diseases of the gastrointestinal tract, including Crohn's Disease and ulcerative colitis. Changes to the intestinal microbiome, the community of micro-organisms that resides in the human gut, have been shown to contribute to the pathogenesis of IBD. IBD diagnosis is often delayed due to its non-specific symptoms and because an invasive colonoscopy is required for confirmation, which leads to poor growth in children and worse treatment outcomes. Feature selection algorithms are often applied to microbial communities to identify bacterial groups that drive disease. It has been shown that aggregating Ensemble Feature Selection (EFS) can improve the robustness of feature selection algorithms, which is defined as the variation of feature selector output caused by small changes to the dataset. In this work, we apply a two-step filter and an EFS process to generate robust feature subsets that can non-invasively predict IBD subtypes from high-resolution microbiome data. The predictive power of the robust feature subsets is the highest reported in literature to date. Furthermore, we identify five biologically plausible bacterial species that have not previously been implicated in IBD aetiology.

Automatic Clearance Anomaly Detection for Transmission Line Corridors Utilizing UAV-Borne LIDAR Data

Transmission line corridor (i.e., Right-of-Ways (ROW)) clearance management plays a critically important role in power line risk management and is an important task of the routine power line inspection of the grid company. The clearance anomaly detection measures the distance between the power lines and the surrounding non-power-facility objects in the corridor such as trees, and buildings, to judge whether the clearance is within the safe range. To find the clearance hazards efficiently and flexibly, this study thus proposed an automatic clearance anomaly detection method utilizing LiDAR point clouds collected by unmanned aerial vehicle (UAV). Firstly, the terrain points were filtered out using two-step adaptive terrain filter and the pylons were detected in the non-terrain points following a feature map method. After dividing the ROW point clouds into spans based on the pylon detection results, the power line point clouds were extracted according to their geometric distribution in local span point clouds slices, and were further segmented into clusters by applying conditional Euclidean clustering with linear feature constraints. Secondly, the power line point clouds segments were iteratively fitted with 3D catenary curve model that is represented by a horizontal line and a vertical catenary curve defined by a hyperbolic cosine function, resulting in a continuous mathematical model of the discretely sampled points of the power line. Finally, a piecewise clearance calculation method which converts the point-to-catenary curve distance measurements to minimal distance calculation based on differential geometry was used to calculate the distance between the power line and the non-power-facility objects in the ROW. The clearance measurements were compared with the standard safe threshold to find the clearance anomalies in the ROWs. Multiple LiDAR point clouds datasets collected by a large-scale UAV power line inspection system were used to validate the effectiveness and accuracy of the proposed method. The detected results were validated through qualitatively visual inspection, quantitatively manual measurements in raw point clouds and on-site field survey. The experiments show that the automatic clearance anomaly detection method proposed in this paper effectively detects the clearance hazards such as tree encroachment, and the clearance measurement accuracy is decimeter level for the LiDAR point clouds collected by our UAV inspection system.

Two-Step Adaptive Augmented Unscented Kalman Filter for Roll Angles of Spinning Missiles Based on Magnetometer Measurements

It is critical to measure the roll angle of a spinning missile quickly and accurately. Magnetometers are commonly used to implement these measurements. At present, the estimation of roll angle parameters is usually performed with the unscented Kalman filter algorithm. In this paper, the two-step adaptive augmented unscented Kalman filter algorithm is proposed to calibrate the biaxial magnetometer and circuit measurements quickly, which allows accurate estimates of the missile roll angle. Unlike the existing algorithms, the state vector of the algorithm is based on the missile roll angle parameters and the error factors caused by the magnetometer and the measurement circuit errors. Next, a two-step fast fitting algorithm is used to fit the initial value. After satisfying the stop rule, the state vector of the filter is configured to estimate the roll angle parameters and the calibration parameters. This method is evaluated by running numerous simulations. In the experiment, the algorithm completes the calibration of the magnetometer and the measurement circuit 1 s after the missile launches, with a sampling rate of 1 ms and an output roll attitude angle with a 0.0015 rad precision. The conventional unscented Kalman filter algorithm requires more time to achieve such a high accuracy. The simulation results demonstrate that the proposed two-step augmented unscented Kalman filter outperforms the conventional unscented Kalman filter in its estimation accuracy and convergence characteristics.

Water End Use Disaggregation Based on Soft Computing Techniques

Disaggregating residential water end use events through the available commercial tools needs a great investment in time to manually process smart metering data. Therefore, it is extremely difficult to achieve a homogenous and sufficiently large corpus of classified single-use events capable of accurately describe residential water consumption. The main goal of the present paper is to develop an automatic tool that facilitates the disaggregation of the individual water consumptions events from the raw flow trace. The proposed disaggregation methodology is conducted through two actions that are iteratively performed: first, the use of an advanced two-step filter, whose calibration is automatically conducted by the Elitist Non-Dominated Sorting Genetic Algorithm NSGA-II; and second, a cropping algorithm based on the filtered water consumption flow traces. As a secondary goal, yet complementary to the main one, a semiautomatic massive classification process has been developed, so that the resulting single-use events can be easily categorized in the different water end uses in a household. This methodology was tested using water consumption data from two different case studies. The characteristics of the households taken as reference and their occupants were unequivocally dissimilar from each other. In addition, the monitoring equipment used to obtain the consumption flow traces had completely different technical specifications. The results obtained from the processing of the two studies show that the automatic disaggregation is both robust and accurate, and produces significant time saving compared to the standard manual analysis.