Real-time Filtering Research Articles

Context-aware Framework to Support Situation-awareness for Disaster Management

When a crisis event occurs, there is a strong need for any involved decision maker to gather in short time frames relevant situational information from different available data sources, to better understand the caused disruptions. Technological devices proliferation and ICT efficiency in timely information sharing did not leave a choice to responders only to adopt them, supporting their operations. This paper proposes a framework that aims to solve challenges brought by this new paradigm of information sharing. Based on service oriented architecture, our framework relies on web service standard for Devices to make pervasive situation-awareness (SA) environment that allows seamless integration of heterogeneous devices. It also provides solutions to filter in real time received information by taking into account the decision maker's context. This context-aware mechanism plays an important role in making the data source intelligent that delivers personalised view of the situation, relevant to decision maker current needs.

The On-Line Modeling and Filtering of Echo-Data Noise to Immobile Target

Abstract Aiming at the echo-data noise of immobile target in radar data processing, the third order autoregressive model-AR(3) model of echo-data is studied and built by the time series method; a new and improved AR(3) model are designed, by which the echo-data model is set up in real time. According to this model and the Kalman filter arithmetic, the echo-data noise is filtered in real time. The echo-data that are filtered and not filtered are analyzed by the Allan variance analysis method. The experimental and simulating results show that variable range of distance is reduced from 2940-3060m to 2975-3025m and its measuring error is reduced from 25m to 21m by this modeling and filtering method. The correctness of this AR(3) model and the effectiveness of this method are also proved by the results of Allan variance analysis. This method of modeling and filtering has such advantages: on-line modeling, filtering in real time, updating of model and so on. It can be applied in target detection, positioning, imaging, radar's performance test and calibration when the target is immobile.

Parallel context-aware multi-agent tourism recommender system

The presence of millions and millions of users and items makes real time filtering a time consuming process in recommender systems (RS). In context aware recommender systems (CARS), choices of users depend on the contextual information as well as available items. The rapid change in interests of a user under different contexts puts extra load on RS. To address this problem, we present a parallel approach for CARS using a multi-agent system which accelerates the processing time drastically using General Purpose Graphic Processing Unit (GPGPU). Contextual pre-filtering and multi-agent environment update the system with the user context. A prototype of the system is developed using JCuda, JADE and Java technologies for tourism domain. The performance of the presented system is compared with the CARS without parallel processing with respect to processing time, scalability as well as precision, recall and F-measure. The results show a significant speedup for presented system over non-parallel CARS.

Real-Time Human Foot Motion Localization Algorithm With Dynamic Speed

One challenging problem for human–machine systems is to accurately estimate the position, velocity, and attitude of human foot motion, using an inertial measurement unit (IMU) sensor. This is particularly so in large environments affected by local magnetic disturbances. In this paper, we propose an algorithm that not only handles this problem, but also works efficiently in real time. The novelty of this paper lies mainly in two contributions: First, we propose a dynamic gait phase detection (GPD) method that can detect human foot gait phase with high accuracy (2.78% errors) in dynamic speeds of human foot motion, such as walking or running; second, we integrate an inertial navigation system, a GPD, a zero velocity update, and an extended Kalman filter in real time. The system can, thus, handle the IMU drift problem, as well as noise, for high-accuracy localization both indoors (0.375% errors) and outdoors (0.55% errors). To validate the proposed algorithm, we apply the motion-tracking system (MTS-ground truth), and the results show that 93.7% of the proposed algorithm's results converge on the MTS's results within a distance of less than 7.5 cm. Hence, the proposed algorithm can be embedded in wearable sensor devices for practical applications.

Real-time FPGA-based Kalman filter for constant and non-constant velocity periodic error correction

Displacement measuring interferometry has high resolution and high dynamic range, which is widely used in displacement metrology and sensor calibration. Due to beam leakage in the interferometer, imperfect polarization components, and ghost reflections, the displacement measurement suffers from periodic error, whose pitch is multiple harmonics of the Doppler frequency. In dynamic measurements, periodic error is usually on the order of nanometers, which impacts the dynamic measurement accuracy. This paper presents an approach to estimate and correct periodic error in real time based on an extended Kalman filter, which has the capability to deal with both constant and non-constant velocity motions. This algorithm is implemented on an application-specific hardware architecture in an FPGA, which has advantages in throughput and resource usage compared with conventional implementations. The measurement validation shows that this approach can effectively eliminate the periodic error for both constant and non-constant velocity motion, and the residual error reaches to the level of the background noise of the interferometer.

Development of Real Time Implementation of 5/5 Rule based Fuzzy Logic Controller Shunt Active Power Filter for Power Quality Improvement

Abstract This paper presents the effectiveness of 5/5 Fuzzy rule implementation in Fuzzy Logic Controller conjunction with indirect control technique to enhance the power quality in single phase system, An indirect current controller in conjunction with Fuzzy Logic Controller is applied to the proposed shunt active power filter to estimate the peak reference current and capacitor voltage. Current Controller based pulse width modulation (CCPWM) is used to generate the switching signals of voltage source inverter. Various simulation results are presented to verify the good behaviour of the Shunt active Power Filter (SAPF) with proposed two levels Hysteresis Current Controller (HCC). For verification of Shunt Active Power Filter in real time, the proposed control algorithm has been implemented in laboratory developed setup in dSPACE platform.

Development of a digital method for neutron/gamma-ray discrimination based on matched filtering

Neutron/gamma-ray discrimination is crucial for measurements with detectors sensitive to both neutron and gamma-ray radiation. Different techniques to discriminate between neutrons and gamma-rays based on pulse shape analysis are widely used in many applications, e.g., homeland security, radiation dosimetry, environmental monitoring, fusion experiments, nuclear spectroscopy. A common requirement is to improve a radiation detection level with a high detection reliability. Modern electronic components, such as high speed analog to digital converters and powerful programmable digital circuits for signal processing, allow us to develop a fully digital measurement system. With this solution it is possible to optimize digital signal processing algorithms without changing any electronic components in an acquisition signal path. We report on results obtained with a digital acquisition system DNG@NCBJ designed at the National Centre for Nuclear Research. A 2′′ × 2′′ EJ309 liquid scintillator was used to register mixed neutron and gamma-ray radiation from PuBe sources. A dedicated algorithm for pulse shape discrimination, based on real-time filtering, was developed and implemented in hardware.

Real-time scale-adaptive correlation filters tracker with depth information to handle occlusion

In visual object tracking, occlusions significantly undermine the performance of tracking algorithms. RGB-D cameras, such as Microsoft Kinect or the related PrimeSense camera, are widely available to consumers. Great attention has been focused on exploiting depth information for object tracking in recent years. We propose an algorithm that improves the existing correlation filter-based tracker for scale-adaptive tracking. Moreover, we utilize depth information provided by the Kinect camera to handle various types of occlusions. First, the optimal location of the target is obtained by the conventional kernelized correlation filter tracker. Then, we make use of the discriminative correlation filter for scale estimation as an independent part. At last, to further improve the tracking performance under occlusions, we present a simple yet effective occlusion handling mechanism to detect occlusion and recovery. In this mechanism, cluster analysis and object segmentation by K-means method have been applied to depth data. Numerous experiments on Princeton RGB-D tracking dataset demonstrate that the proposed algorithm outperforms several state-of-the-art trackers by successfully dealing with occlusions.

FOG Random Drift Signal Denoising Based on the Improved AR Model and Modified Sage-Husa Adaptive Kalman Filter.

In order to reduce the influence of fiber optic gyroscope (FOG) random drift error on inertial navigation systems, an improved auto regressive (AR) model is put forward in this paper. First, based on real-time observations at each restart of the gyroscope, the model of FOG random drift can be established online. In the improved AR model, the FOG measured signal is employed instead of the zero mean signals. Then, the modified Sage-Husa adaptive Kalman filter (SHAKF) is introduced, which can directly carry out real-time filtering on the FOG signals. Finally, static and dynamic experiments are done to verify the effectiveness. The filtering results are analyzed with Allan variance. The analysis results show that the improved AR model has high fitting accuracy and strong adaptability, and the minimum fitting accuracy of single noise is 93.2%. Based on the improved AR(3) model, the denoising method of SHAKF is more effective than traditional methods, and its effect is better than 30%. The random drift error of FOG is reduced effectively, and the precision of the FOG is improved.

The Design on the Real-Time Wavelet Filter for ITER PF AC/DC Converter Control System

Since noise degrades the control precision of International Thermonuclear Experimental Reactor (ITER) poloidal field (PF) ac/dc converter control system, a real-time wavelet filter algorithm for feedback signal denoising is studied. According to the requirements of ITER PF ac/dc converter controller, a real-time digital filter based on a wavelet transform is designed in this paper. The Daubechies wavelet is used as the wavelet basis and the mandatory denoising method is taken to remove the noise. In order to improve the boundary effects of wavelet transform when the width of sliding window and decomposition scale is quite small, the left and right symmetric boundary extension method is proposed. Three different boundary extension methods are compared, and the effectiveness of the proposed boundary extension method for denoising are described. The experiments are carried out on ITER PF ac/dc converter sequential control test platform to prove that the designed real-time wavelet filter eliminates the noise effectively and guarantees the real-time processing of feedback signal.

UAV State Estimation Using Adaptive Complementary Filters

We address the complete state estimation problem of unmanned aerial vehicles, even under high-dynamic 3-D aerobatic maneuvers, while using low-cost sensors with bias variations and higher levels of noise. In such conditions, the control demand, for a robust real-time data fusion filter with minimal lag and noise, is addressed with the efficiency of a complementary filter scheme. First, the attitude is directly estimated in Special Orthogonal Group (SO(3)) by complementing the noisy accelerometer/magnetometer vector basis with a gyro propagated vector basis. Data fusion follows a least square minimization in SO(3) (Wahba’s problem) solved in an analytic nonrecursive manner. Stability of the proposed filter is shown and performance metrics are extracted, whereas the computational complexity has been minimized with an appropriate reference frame and a custom singular value decomposition algorithm. An adaptation scheme is proposed to allow unhindered operation of the filter to erroneous inputs introduced by the high dynamics of a 3-D flight. Finally, the velocity/position estimation is mainly constructed by complementary filters combining multiple sensors. In addition to the low complexity and the filtering of the noise, the proposed observer is aided through a developed vision algorithm, enabling the use of the filter in Global-Positioning-System-denied environments. Extensive experimental results and comparative studies with state-of-the-art filters, either in the laboratory or in the field using high-performance autonomous helicopters, demonstrate the efficacy of the proposed scheme in demanding conditions.

Ensuring safety in human-robot coexisting environment based on two-level protection

Purpose The purpose of this paper is to provide a novel methodology based on two-level protection for ensuring safety of the moving human who enters the robot’s workspace, which is significant for dealing with the problem of human security in a human-robot coexisting environment. Design/methodology/approach In this system, anyone who enters the robot’s working space is detected by using the Kinect and their skeletons are calculated by the interval Kalman filter in real time. The first-level protection is mainly based on the prediction of the human motion, which used Gaussian mixture model and Gaussian Mixture Regression. However, even in cases where the prediction of human motion is incorrect, the system can still safeguard the human by enlarging the initial bounding volume of the human as the second-level early warning areas. Finally, an artificial potential field with some additional avoidance strategies is used to plan a path for a robot manipulator. Findings Experimental studies on the GOOGOL GRB3016 robot show that the robot manipulator can accomplish the predetermined tasks by circumventing the human, and the human does not feel dangerous. Originality/value This study presented a new framework for ensuring human security in a human-robot coexisting environment, and thus can improve the reliability of human-robot cooperation.

An extremely fast adaptive high-performance filter to remove salt and pepper noise using overlapping medians in images

A new fast adaptive high-performance filter (FAHPF) has been proposed to remove salt-and-pepper noise in images. ‘Maximize the speed without compromising denoising performance’ is the fundamental intention to build up the FAHPF algorithm. Among diverse phases of filtering employed in the FAHPF, overlapping medians (OM) is our newly proposed frame-based filtering concept which is the basis for speed of FAHPF and running averages embedded with OM is an idea behind excellent denoising performance of FAHPF at the same pace. Simulation experiments have been conducted and denoising results of FAHPF has been investigated against very recently developed filtering methods. It is proved that the FAHPF excellently outperforms many of state-of-the-art filters considered for comparison, in terms of peak signal to noise ratio, structural similarity index, and visual representation and requires the extremely shortest execution time among all, which could make it as a real time filter.

Evaluation of a reconfigurable portable instrument for copper determination based on luminescent carbon dots.

A portable reconfigurable platform for copper (Cu(II)) determination based on luminescent carbon dot (Cdots) quenching is described. The electronic setup consists of a light-emitting diode (LED) as the carbon dot optical exciter and a photodiode as a light-to-current converter integrated in the same instrument. Moreover, the overall analog conditioning is simply performed with one integrated solution, a field-programmable analog array (FPAA), which makes it possible to reconfigure the filter and gain stages in real time. This feature provides adaptability to use the platform as an analytical probe for carbon dots coming from different batches with some variations in luminescence characteristics. The calibration functions obtained that fit a modified Stern-Volmer equation were obtained using luminescence signals from Cdots quenching by Cu(II). The analytical applicability of the reconfigurable portable instrument for Cu(II) using Cdots has been successfully demonstrated in tap water analysis.

An improved real-time adaptive Kalman filter with recursive noise covariance updating rules

Kalman filter (KF) is used extensively for state estimation. Among its requirements are the process and observation noise covariances which are unknown or partially known in real life applications. Biased initializations of the covariances result in performance degradation of KF or divergence. Therefore, an extensive research is carried on to improve its performance however, relying on a moving window, heavy computations, and the availability of the exact model are the fundamental problems in most of the proposed techniques. In this paper, we are using the idea of the recursive estimation of KF to propose two recursive updating rules for the process and observation covariances respectively designed based on the covariance matching principles. Each rule is a tuned scaled version of the previous covariance in addition to a tuned correction term derived based on the most recent available data. The proposed adaptive Kalman filter AKF avoided the aforementioned problems and proved itself to have an improved performance over the conventional KF. The results show that the AKF estimates are more accurate, have less noise and more stable against biased covariance initializations.

Optimal real-time filters for linear prediction problems

The classic model-based paradigm in time series analysis is rooted in the Wold decomposition of the data-generating process into an uncorrelated white noise process. By design, this universal decomposition is indifferent to particular features of a specific prediction problem (e. g., forecasting or signal extraction) – or features driven by the priorities of the data-users. A single optimization principle (one-step ahead forecast error minimization) is proposed by this classical paradigm to address a plethora of prediction problems. In contrast, this paper proposes to reconcile prediction problem structures, user priorities, and optimization principles into a general framework whose scope encompasses the classic approach. We introduce the linear prediction problem (LPP), which in turn yields an LPP objective function. Then one can fit models via LPP minimization, or one can directly optimize the linear filter corresponding to the LPP, yielding the Direct Filter Approach. We provide theoretical results and practical algorithms for both applications of the LPP, and discuss the merits and limitations of each. Our empirical illustrations focus on trend estimation (low-pass filtering) and seasonal adjustment in real-time, i. e., constructing filters that depend only on present and past data.

DCT와 Guided 필터를 이용한 실시간 영상 분류

In this paper, we present a novel segmentation method that can extract new foreground objects from a current frame in real-time. It is performed by detecting differences between the current frame and reference frame taken from a fixed camera. We minimize computing complexity for real-time video processing. First DCT (Discrete Cosine Transform) is utilized to generate rough binary segmentation maps where foreground and background regions are separated. DCT shows better result of texture analysis than previous methods where texture analysis is performed in spatial domain. It is because texture analysis in frequency domain is easier than that in special domain and intensity and texture in DCT are taken into account at the same time. We maximize run-time efficiency of DCT by considering color information to analyze object region prior to DCT process. Last we use Guided filter for natural matting of the generated binary segmentation map. In general, Guided filter can enhance quality of intermediate result by incorporating guidance information. However, it shows some limitations in homogeneous area. Therefore, we present an additional method which can overcome them. Keyword : DCT, matting, segmentation, Guided Filter a) 경희대학교 전자정보대학 (Kyung Hee University) b) kt 융합기술원 (kt fusion technology institute) ‡Corresponding Author : 김원하(Wonha Kim) E-mail: hjk1017@ssu.ac.kr Tel: +82-2-826-9063 ※ 본 연구는 산업통상지원부의 재원으로 산업핵심기술개발사업의 지원을 받아 수행 되었습니다.(10052967, 재난재해 대응용 특수목적기계 통합제어시스 템 개발) ・Manuscript received August 17, 2015; revised September 22, 2015; accepted September 22, 2015. 특집논문 (Special Paper) 방송공학회논문지 제20권 제5호, 2015년 9월 (JBE Vol. 20, No. 5, September 2015) http://dx.doi.org/10.5909/JBE.2015.20.5.718 ISSN 2287-9137 (Online) ISSN 1226-7953 (Print) 신현학 외 2인 : DCT와 Guided 필터를 이용한 실시간 영상 분류 719 (Hyunhak Shin et al.: Video Segmentation Using DCT and Guided Filter in real time)

A new set of asymmetric filters for tracking the short-term trend in real-time

For assessing in real time the short-term trend of major economic indicators, official statistical agencies generally rely on asymmetric filters that were developed by Musgrave in 1964. However, the use of the latter introduces revisions as new observations are added to the series and, from a policy-making viewpoint, they are too slow in detecting true turning points. In this paper, we use a reproducing kernel methodology to derive asymmetric filters that converge quickly and monotonically to the corresponding symmetric one. We show theoretically that proposed criteria for time-varying bandwidth selection produce real-time trend-cycle filters to be preferred to the Musgrave filters from the viewpoint of revisions and time delay to detect true turning points. We use a set of leading, coincident and lagging indicators of the US economy to illustrate the potential gains statistical agencies could have by also using our methods in their practice.

An Efficient FPGA based Real-Time Implementation Shunt Active Power Filter for Current Harmonic Elimination and Reactive Power Compensation

This paper proposes a new approach of Field Programmable Gate Array (FPGA) controlled digital implementation of shunt active power filter (SAPF) under steady state and dynamic operations. Typical implementations of SAPF uses microprocessor and digital signal processor (DSP) but it limited for complex algorithm structure, absence of feedback loop delays and their cost can be exceed the benefit they bring. In this paper, the hardware resources of an FPGA are configured and implemented in order to overcome conventional microcontroller or digital signal processor implementations. This proposed FPGA digital implementation scheme has very less execution time and boosts the overall performance of the system. The FPGA controller integrates the entire control algorithm of an SAPF, including synchronous reference frame transformation, phase locked loop, low pass filter and inverter current controller etc. All these required algorithms are implemented with a single all-on chip FPGA module which provides freedom to reconfigure for any other applications. The entire algorithm is coded, processed and simulated using Xilinx 12.1 ISE suite to estimate the advantages of the proposed system. The coded algorithm is also defused on a single all-on-chip Xilinx Spartan 3A DSP-XC3SD1800 laboratory prototype and experimental results thus obtained match with simulated counterparts under the dynamic state and steady state operating conditions.

Real‐time state estimator without noise covariance matrices knowledge – fast minimum norm filtering algorithm

The digital filtering technology has been widely applied in a majority of signal processing applications. For the linear systems with state-space model, Kalman filter provides optimal state estimates in the sense of minimum-mean-squared errors and maximum-likelihood estimation. However, only with accurate system parameters and noise statistical properties, the estimation obtained by standard Kalman filter is the optimal state estimate. Most of time, the exact noise statistical properties could not be obtained as a priori information or even wrong statistical properties may be captured by the offline method. This may lead to a poor performance (even divergence) of Kalman filtering algorithm. In this study, a novel real-time filter, named as fast minimum norm filtering algorithm, has been proposed to deal with the case when the covariance matrices of the process and measurement noises were unknown in the linear time-invariant systems with state-space model. Tests have been performed on numerical examples to illustrate that the fast minimum norm filtering algorithm could be used to obtain acceptable precision state estimation in comparison with the standard Kalman filter for the discrete-time linear time-invariant systems.