Regularized Adaptive Notch Filters for Acoustic Howling Suppression
Gil-Cacho, Pepe; van Waterschoot, Toon; Moonen, Marc;
2009-01-01
In this paper, a method for the suppression of acoustic howling is developed, based on adaptive notch filters (ANF) with regularization (RANF). The method features three RANFs working in parallel to achieve frequency tracking, howling detection and suppression. The ANF-based approach to howling...... components in the source signal are not affected. The RANFs proposed in this paper are implemented in direct form and are updated using a gradient descent type algorithm. Results show that, under certain conditions, the level of suppression and sound quality is similar to what is obtained with frame...
Fast and Flexible Tracking and Mitigating a Jamming Signal with an Adaptive Notch Filter
BORIO DANIELE; O'DRISCOLL CILLIAN; Fortuny Guasch, Joaquim
2014-01-01
GNSS jammers are small portable devices able to broadcast disruptive interference and overpower the much weaker GNSS signals. The authors consider the use of an adaptive notch filter as an effective solution for mitigating jamming effects.
The purpose of this study was to compare the monopolar electromyographic (EMG) amplitude versus isometric force relationships from three signal processing methods (raw versus notch filtering versus adaptive filtering). Seventeen healthy subjects (mean ± SD age = 24.6 ± 4.3 yr) performed incremental isometric muscle actions of the dominant leg extensors in 10% increments from 10% to 100% of the maximum voluntary contraction (MVC). During each muscle action, a monopolar surface EMG signal was recorded from the vastus lateralis and processed with the three signal processing methods. The linear slope coefficients for the EMG amplitude versus isometric force relationships were equivalent for the three signal processing methods and correlated (r = 0.997–0.999). However, the mean amplitude values for the notch-filtered signals were less than those for the raw and adaptive-filtered signals. Thus, adaptive filtering may be the best method for removing electromagnetic noise from monopolar surface EMG signals
Automatic balancing of AMB systems using plural notch filter and adaptive synchronous compensation
Xu, Xiangbo; Chen, Shao; Zhang, Yanan
2016-07-01
To achieve automatic balancing in active magnetic bearing (AMB) system, a control method with notch filters and synchronous compensators is widely employed. However, the control precision is significantly affected by the synchronous compensation error, which is caused by parameter errors and variations of the power amplifiers. Furthermore, the computation effort may become intolerable if a 4-degree-of-freedom (dof) AMB system is studied. To solve these problems, an adaptive automatic balancing control method in the AMB system is presented in this study. Firstly, a 4-dof radial AMB system is described and analyzed. To simplify the controller design, the 4-dof dynamic equations are transferred into two plural functions related to translation and rotation, respectively. Next, to achieve automatic balancing of the AMB system, two synchronous equations are formed. Solution of them leads to a control strategy based on notch filters and feedforward controllers with an inverse function of the power amplifier. The feedforward controllers can be simplified as synchronous phases and amplitudes. Then, a plural phase-shift notch filter which can identify the synchronous components in 2-dof motions is formulated, and an adaptive compensation method that can form two closed-loop systems to tune the synchronous amplitude of the feedforward controller and the phase of the plural notch filter is proposed. Finally, the proposed control strategy is verified by both simulations and experiments on a test rig of magnetically suspended control moment gyro. The results indicate that this method can fulfill the automatic balancing of the AMB system with a light computational load.
Zheng, Shiqiang; Feng, Rui
2016-03-01
This paper introduces a feedforward control strategy combined with a novel adaptive notch filter to solve the problem of rotor imbalance in high-speed Magnetically Suspended Centrifugal Compressors (MSCCs). Unbalance vibration force of rotor in MSCC is mainly composed of current stiffness force and displacement stiffness force. In this paper, the mathematical model of the unbalance vibration with the proportional-integral-derivative (PID) control laws is presented. In order to reduce the unbalance vibration, a novel adaptive notch filter is proposed to identify the synchronous frequency displacement of the rotor as a compensation signal to eliminate the current stiffness force. In addition, a feedforward channel from position component to control output is introduced to compensate displacement stiffness force to achieve a better performance. A simplified inverse model of power amplifier is included in the feedforward channel to reject the degrade performance caused by its low-pass characteristic. Simulation and experimental results on a MSCC demonstrate a significant effect on the synchronous vibration suppression of the magnetically suspended rotor at a high speed.
Park, Chanki; Lee, Boreom
2014-01-01
Background Many researchers have attempted to acquire respiratory rate (RR) information from a photoplethysmogram (PPG) because respiration affects the waveform of the PPG. However, most of these methods were difficult to operate in real-time because of their complexity or computational requirements. From these needs, we attempted to develop a method to estimate RR from a PPG with a light computational burden. Methods To obtain RR information, we adopt a sequential filtering structure and fre...
Wu, Shang-Teh; Lian, Sing-Han; Chen, Sheng-Han
2015-07-01
For a low-stiffness beam driven by a ball-screw stage, the lateral vibrations cannot be adequately controlled by a collocated compensator based on rotary-encoder feedback alone. Acceleration signals at the tip of the flexible beam are measured for active vibration control in addition to the collocated compensator. A second-order bandpass filter (a line enhancer) and two notch filters are included in the acceleration-feedback loop to raise modal dampings for the first and the second flexible modes without exciting higher-frequency resonances. A novel adaptation algorithm is devised to tune the center frequencies of the notch filters in real time. It consists of a second-order low-pass filter, a second-order bandpass filter and a phase detector. Improvement of the control system is elaborated progressively with the root-locus and bode-plot analyses, along with a physical interpretation. Extensive testings are conducted on an experimental device to verify the effectiveness of the control method.
Compact microstrip bandpass filter with tunable notch
Christensen, Silas; Zhurbenko, Vitaliy; Johansen, Tom Keinicke
2014-01-01
Two different designs combining a bandpass and a notch filter are developed to operate in the receiving band from 350–470 MHz. The bandpass filter is designed from a simple structure, by use of only four short circuited stubs and a half wavelength transmission line connecting the stubs. The tunable...... notch filter ensures an attenuation level of 19.3 dB to 27.3 dB in the frequency range from 360–480 MHz. The measured passband ripple of the combined filter is less than 0.5 dB, while the insertion loss for the simplest design is less than 1.7 dB only 10 MHz from the notch frequency. Even though...... the wavelength on the selected substrate (εr = 3.55) is approximately 45 cm, the outer dimensions of the final filter only measure 10×10 cm2....
Digital notch filter based active damping for LCL filters
Yao, Wenli; Yang, Yongheng; Zhang, Xiaobin;
2015-01-01
. In contrast, the active damping does not require any dissipation elements, and thus has become of increasing interest. As a result, a vast of active damping solutions have been reported, among which multi-loop control systems and additional sensors are necessary, leading to increased cost and complexity....... In this paper, a notch filter based active damping without the requirement of additional sensors is proposed, where the inverter current is employed as the feedback variable. Firstly, a design method of the notch filter for active damping is presented. The entire system stability has then been investigated...... in the z-domain. Simulations and experiments are carried out to verify the proposed active damping method. Both results have confirmed that the notch filter based active damping can ensure the entire system stability in the case of resonances with a good system performance....
FM Interference Noise Suppression Based on Adaptive Notch Filter%基于自适应陷波器的噪声调频干扰抑制方法
路翠华; 李国林; 谢鑫
2014-01-01
针对线性调频引信抗噪声干扰能力比较差的问题，提出了基于自适应陷波器的噪声调频干扰抑制方法。该方法根据线性调频引信差频信号的单频特性，将自适应陷波器应用到线性调频引信中，对噪声调频干扰进行抑制。通过自适应调整陷波器的权值，使陷波器在差频信号的频率点具有陷波特性，从而达到噪声调频干扰抑制的目的。仿真结果表明：SJB=-10 dB时，仍然能达到很好的噪声调频干扰抑制效果。%Aiming at the problem that linear frequency-modulated fuze’s ability of anti-noise interference was poor,the method of noise FM interference suppression based on adaptive notch filter was presented.According to the characteristic that the difference frequency signal of linear frequency-modulated fuze was monochromatic, an adaptive notch filter was adopted to suppress FM interference noise in linear frequency-modulated fuze. Through adj usting notch filter’s weights,the notch filter has the notch characteristics in difference frequency signal's frequency,then noise FM interference was suppressed.The simulation results showed that when SJR=-10dB,FM interference noise could be still suppressed effectively.
Notch filter feedback controlled chaos in buck converter
Lu Wei-Guo; Zhou Luo-Wei; Luo Quan-Ming
2007-01-01
A method of controlling chaos in the voltage-mode buck converter is presented by using an improved notch filter feedback control in this paper. The proposed control part comprises a notch filter and a low-pass filter. The discrepancy between the outputs of the two filters is introduced into the control prototype of the power converter. In this way, the system period-1 solution is kept unchanged. The harmonic balance method is applied to analysing the variation law of the system bifurcation point, and then the stable range of the feedback gain is ascertained. The results of simulation and experiment are also given finally.
Broadband notch filter design for millimeter-wave plasma diagnostics
Furtula, Vedran; Michelsen, Poul; Leipold, Frank;
2010-01-01
Notch filters are integrated in plasma diagnostic systems to protect millimeter-wave receivers from intensive stray radiation. Here we present a design of a notch filter with a center frequency of 140 GHz, a rejection bandwidth of ∼ 900 MHz, and a typical insertion loss below 2 dB in the passband...... in the cylindrical cavities is the fundamental TE11. The performance of the constructed filter is measured using a vector network analyzer monitoring a total bandwidth of 30 GHz. We compare the measurements with numerical simulations. © 2010 EURATOM...
Design and Analysis of Robust Active Damping for LCL Filters using Digital Notch Filters
Yao, Wenli; Yang, Yongheng; Zhang, Xiaobin;
2016-01-01
resonance, where the notch frequency should be aligned exactly to the resonant frequency of the LCL filter. However, parameter variations of the LCL filter as well as the time delay appearing in digital control systems will induce resonance drifting, and thus break this alignment, possibly deteriorating the......Resonant poles of LCL filters may challenge the entire system stability especially in digital-controlled Pulse Width Modulation (PWM) inverters. In order to tackle the resonance issues, many active damping solutions have been reported. For instance, a notch filter can be employed to damp the......-connected PWM inverter verify the effectiveness of the proposed design for robust active damping using digital notch filters....
Psaltis, Demetri; Hong, John
1984-01-01
A new adaptive filter utilizing acoustooptic devices in a space integrating architecture is described. Two configurations are presented; one of them, suitable for signal estimation, is shown to approximate the Wiener filter, while the other, suitable for detection, is shown to approximate the matched filter.
A Microwave Photonic Notch Filter Using a Microfiber Ring Resonator
A novel tunable microwave photonic filter based on a microfiber ring resonator is proposed and experimentally demonstrated. A fiber ring laser based on the microfiber ring resonator is employed to generate two single-longitudinal-mode carriers, then the dispersive element introduces the delay between two modulated carriers. By adjusting the diameter of the microfiber ring resonator, the proposed microwave photonic notch filter can be continuously and widely tuned. The measured notch rejection ratio is greater than 35 dB, and there is good agreement between the experimental result and the theoretical analysis. (fundamental areas of phenomenology (including applications))
Ge, Jia; Feng, Hanlin; Scott, Guy; Fok, Mable P
2015-01-01
A high-speed tunable microwave photonic notch filter with ultrahigh rejection ratio is presented, which is achieved by semiconductor optical amplifier (SOA)-based single-sideband modulation and optical spectral filtering with a phase modulator-incorporated Lyot (PM-Lyot) filter. By varying the birefringence of the phase modulator through electro-optic effect, electrically tuning of the microwave photonic notch filter is experimentally achieved at tens of gigahertz speed. The use of SOA-polarizer based single-sideband modulation scheme provides good sideband suppression over a wide frequency range, resulting in an ultrahigh rejection ratio of the microwave photonic notch filter. Stable filter spectrum with bandstop rejection ratio over 60 dB is observed over a frequency tuning range from 1.8 to 10 GHz. Compare with standard interferometric notch filter, narrower bandwidth and sharper notch profile are achieved with the unique PM-Lyot filter, resulting in better filter selectivity. Moreover, bandwidth tuning is also achieved through polarization adjustment inside the PM-Lyot filter, that the 10-dB filter bandwidth is tuned from 0.81 to 1.85 GHz. PMID:25531605
105 GHz Notch Filter Design for Collective Thomson Scattering
Furtula, Vedran; Michelsen, Poul; Leipold, Frank; Johansen, T.; Korsholm, Søren Bang; Meo, Fernando; Moseev, Dmitry; Nielsen, Stefan Kragh; Salewski, Mirko; Stejner Pedersen, Morten
2011-01-01
A millimeter-wave notch filter with 105-GHz center frequency, >20-GHz passband coverage, and 1-GHz rejection bandwidth has been constructed. The design is based on a fundamental rectangular waveguide with cylindrical cavities coupled by narrow iris gaps, i.e., small elongated holes of negligible...
A low-loss, continuously tunable microwave notch filter
Acar, Öncel; Johansen, Tom Keinicke; Zhurbenko, Vitaliy
2016-01-01
The development in high-end microwave transceiver systems toward the software defined radio has brought about the need for tunable frontend filters. Although the problem is being tackled by the microwave community, there still appears to be an unmet demand for practical tunable filter technologies....... With this motivation, this work presents a tuning method that delivers a resonator Q0 of 2000–3621 within a minimum tuning ratio of 1:1.42. A continuously tunable notch filter based on this tuning method is presented. The design is manufactured, measured, and verified. It is shown that the tuning technology compares...
Filter Bank Design for Subband Adaptive Filtering
Haan, Jan Mark de
2001-01-01
Adaptive filtering is an important subject in the field of signal processing and has numerous applications in fields such as speech processing and communications. Examples in speech processing include speech enhancement, echo- and interference- cancellation, and speech coding. Subband filter banks have been introduced in the area of adaptive filtering in order to improve the performance of time domain adaptive filters. The main improvements are faster convergence speed and the reduction of co...
Tunable microwave photonic notch filter based on sliced broadband optical source.
Yu, Yang; Li, Shangyuan; Zheng, Xiaoping; Zhang, Hanyi; Zhou, Bingkun
2015-09-21
A microwave photonic filter is demonstrated with both tunable center frequency and bandwidth. This filter is switchable from all-pass, bandpass to notch filter, and the notch filter is a result of the subtraction of a bandpass filter from an all-pass filter based on a balanced photodetector. The all-pass filter is achieved based on a single wavelength radio over fiber link, and the bandpass one is acquired by using the spectrum-sliced broadband optical source. Theoretical analysis and experimental results show that both the center frequency and the bandwidth of the notch filter can be widely tuned. PMID:26406636
Double-sided printed bow-tie antenna with notch filter for UWB applications
Hirata, Akimasa; ヒラタ, アキマサ; 平田, 晃正
2009-01-01
This letter proposes a double-sided printed bow-tie antenna with a notch band. The notch filter is based on a grounded patch inserted into the feeding microstrip line. The advantage of the structure is its tunability of the notch band.
R P Shukla; Sanjiva Kumar; A K Sinha; Manika Mallick; S Thakur; N K Sahoo
2006-08-01
An indigenously designed and developed micro-Raman spectrograph, consisting of a diode-pumped solid-state green laser for the excitation of Raman scattering, a Raman imaging microscope, CCD as a detector and a notch filter, has been extensively studied to evaluate its performance. A dielectric edge filter (having 27 alternate layers of SiO2 and TiO2) and a holographic notch filter (Oriel make) have been used to block the Rayleigh scattered light from the sample to the entrance slit of the spectrograph. Holographic notch filter is found to be able to record the Raman shifts below 700 cm-1 conveniently whereas dielectric edge filter (27 layers) has enabled the spectrograph to record the Raman spectra very efficiently after a wave-number shift of 700 cm-1. It has also been observed that the instrument using the edge filter provides a peculiar spectrum consisting of three spectral lines having Raman shifts as 569, 1328 and 1393 cm-1 in the Raman spectrum of a weakly scattering sample with large reflectivity. Similarly, a spectrum consisting of multiple lines has been observed when the instrument is being operated using a holographic notch filter. These spectral lines are not observed in the case of liquid samples such as benzene, carbon tetrachloride, ethanol, diethyl ether etc. The origin of these peculiar spectral lines has been briefly discussed in the paper. Additionally, a major motivation for this work is to utilize the results for the selection of an appropriate filter depending on the type of the sample, i.e. weakly scattered and highly reflecting sample or highly scattered and low reflecting sample.
Adaptively robust filtering with classified adaptive factors
CUI Xianqiang; YANG Yuanxi
2006-01-01
The key problems in applying the adaptively robust filtering to navigation are to establish an equivalent weight matrix for the measurements and a suitable adaptive factor for balancing the contributions of the measurements and the predicted state information to the state parameter estimates. In this paper, an adaptively robust filtering with classified adaptive factors was proposed, based on the principles of the adaptively robust filtering and bi-factor robust estimation for correlated observations. According to the constant velocity model of Kalman filtering, the state parameter vector was divided into two groups, namely position and velocity. The estimator of the adaptively robust filtering with classified adaptive factors was derived, and the calculation expressions of the classified adaptive factors were presented. Test results show that the adaptively robust filtering with classified adaptive factors is not only robust in controlling the measurement outliers and the kinematic state disturbing but also reasonable in balancing the contributions of the predicted position and velocity, respectively, and its filtering accuracy is superior to the adaptively robust filter with single adaptive factor based on the discrepancy of the predicted position or the predicted velocity.
Scalable In-Band Optical Notch-Filter Labeling for Ultrahigh Bit Rate Optical Packet Switching
Medhin, Ashenafi Kiros; Galili, Michael; Oxenløwe, Leif Katsuo
2014-01-01
We propose a scalable in-band optical notch-filter labeling scheme for optical packet switching of high-bit-rate data packets. A detailed characterization of the notch-filter labeling scheme and its effect on the quality of the data packet is carried out in simulation and verified by experimental...
Santoso, Isman
2015-01-01
Noiseondigital imagecan be periodicnoisewhich visuallyappearsthere arelines on theimagearespreadevenly. Filtering is one of the mechanism to reduce noise. Periodic noise can be reduced by using Selective Filter. The filter method used by the author to reduce the noise is Optimum Notch Filter and Band Reject Filter which are both a type of selective filter. The use of the filter are processed in the frequency domain, because of that author also use the Fast Fourier Transform method to produce ...
Microwave photonic notch filter based on a dual-Sagnac-loop structure.
Wang, Xudong; Chan, Erwin H W; Minasian, Robert A
2010-11-20
A new single-wavelength, coherence-free microwave photonic notch filter is presented. The concept is based on a dual-Sagnac-loop structure that functions with a new principle in which the two loops operate with different free spectral ranges, and which generate noncommensurate taps. It has the ability to generate a narrow notch response and can operate to high frequencies. Experimental results demonstrate a notch filter with a narrow notch width, a flat passband, and high stop-band attenuation of over 40dB. PMID:21102681
Adaptive filtering and change detection
Gustafsson, Fredrik
2003-01-01
Adaptive filtering is a classical branch of digital signal processing (DSP). Industrial interest in adaptive filtering grows continuously with the increase in computer performance that allows ever more conplex algorithms to be run in real-time. Change detection is a type of adaptive filtering for non-stationary signals and is also the basic tool in fault detection and diagnosis. Often considered as separate subjects Adaptive Filtering and Change Detection bridges a gap in the literature with a unified treatment of these areas, emphasizing that change detection is a natural extensi
Long, Yun; Wang, Jian
2015-07-13
We propose a simple scheme to realize ultra-high peak rejection notch microwave photonic filter (MPF) based on a single silicon microring resonator (MRR). Using the combination of a conventional phase modulator (PM), a tunable bandpass filter (TBF), and a silicon MRR to manipulate the phase and amplitude of optical sidebands resulting in a signal cancellation at the RF notch filter frequency, we experimentally demonstrate a notch MPF with an ultra-high peak rejection beyond 60 dB. The frequency tunability of the proposed ultra-high peak rejection MPF is also demonstrated in the experiment. PMID:26191836
Adaptive filtering prediction and control
Goodwin, Graham C
2009-01-01
Preface1. Introduction to Adaptive TechniquesPart 1. Deterministic Systems2. Models for Deterministic Dynamical Systems3. Parameter Estimation for Deterministic Systems4. Deterministic Adaptive Prediction5. Control of Linear Deterministic Systems6. Adaptive Control of Linear Deterministic SystemsPart 2. Stochastic Systems7. Optimal Filtering and Prediction8. Parameter Estimation for Stochastic Dynamic Systems9. Adaptive Filtering and Prediction10. Control of Stochastic Systems11. Adaptive Control of Stochastic SystemsAppendicesA. A Brief Review of Some Results from Systems TheoryB. A Summary o
Induction machine condition monitoring using notch-filtered motor current
Günal, Serkan; Gökhan Ece, Dog˜an; Nezih Gerek, Ömer
2009-11-01
This paper presents a new approach to induction motor condition monitoring using notch-filtered motor current signature analysis (NFMCSA). Unlike most of the previous work utilizing motor current signature analysis (MCSA) using spectral methods to extract required features for detecting motor fault conditions, here NFMCSA is performed in time-domain to extract features of energy, sample extrema, and third and fourth cumulants evaluated from data within sliding time window. Six identical three-phase induction motors were used for the experimental verification of the proposed method. One healthy machine was used as a reference, while other five with different synthetic faults were used for condition detection and classification. Extracted features obtained from NFMCSA of all motors were employed in three different and popular classifiers. The proposed motor current analysis and the performance of the features used for fault detection and classification are examined at various motor load levels and it is shown that a successful induction motor condition monitoring system is developed. Developed system is also able to indicate the load level and the type of a fault in multi-dimensional feature space representation. In order to test the generality and applicability of the developed method to other induction motors, data acquired from another healthy induction motor with different number of poles and rated power is also incorporated into the system. In spite of the above difference, the proposed feature set successfully locates the healthy motor within the classification cluster of "healthy motors" on the feature space.
160 Gb/s Raman-assisted notch-filtered XPM wavelength conversion and transmission
Galili, Michael; Oxenløwe, Leif Katsuo; Mulvad, Hans Christian Hansen;
2007-01-01
In-line wavelength conversion of 160 Gb/s data by Raman-assisted notch-filtered XPM is demonstrated for 130 km total transmission. The improvement in system performance from applying Raman gain during conversion is shown.......In-line wavelength conversion of 160 Gb/s data by Raman-assisted notch-filtered XPM is demonstrated for 130 km total transmission. The improvement in system performance from applying Raman gain during conversion is shown....
Frequency agile microwave photonic notch filter with anomalously-high stopband rejection
Marpaung, David; Pant, Ravi; Eggleton, Benjamin J
2013-01-01
We report a novel class microwave photonic (MWP) notch filter with a very narrow isolation bandwidth (10 MHz), an ultrahigh stopband rejection (> 60 dB), a wide frequency tuning (1-30 GHz), and flexible bandwidth reconfigurability (10-65 MHz). This record performance is enabled by a new concept of sidebands amplitude and phase controls using an electro-optic modulator and an optical filter. This new concept enables energy efficient operation in active MWP notch filters, and opens up the pathway to enable low-power nanophotonic devices as high performance RF filters.
In this paper, a continuously tunable microwave photonic notch filter is proposed and experimentally demonstrated. This filter is based on the differential group delay generated by a high-birefringence linearly chirped fiber Bragg grating. This microwave photonic filter belongs to the orthogonal polarization approach, polarization maintaining structure ensures the filter free from the random optical interference problem. Its response is induced by the differential group delay (DGD) of the Hi-Bi LCFBG and it can be varied by tuning the grating through adding gradient strength to the grating. Free spectral range tuning by 9.27 GHz with more than 35 dB notch rejection is achieved.
Si3N4 ring resonator-based microwave photonic notch filter with an ultrahigh peak rejection
Marpaung, David; Pant, Ravi; Roeloffzen, Chris; Leinse, Arne; Hoekman, Marcel; Heideman, Rene; Eggleton, Benjamin J
2013-01-01
We report a simple technique in microwave photonic (MWP) signal processing that allows the use of an optical filter with a shallow notch to exhibit a microwave notch filter with anomalously high rejection level. We implement this technique using a low-loss, tunable Si3N4 optical ring resonator as the optical filter, and achieved an MWP notch filter with an ultra-high peak rejection > 60 dB, a tunable high resolution bandwidth of 247-840 MHz, and notch frequency tuning of 2-8 GHz. To our knowledge, this is a record combined peak rejection and resolution for an integrated MWP filter.
Frequency agile microwave photonic notch filter with anomalously-high stopband rejection
Marpaung, David; Morrison, Blair; Pant, Ravi; Eggleton, Benjamin J.
2013-01-01
We report a novel class microwave photonic (MWP) notch filter with a very narrow isolation bandwidth (10 MHz), an ultrahigh stopband rejection (> 60 dB), a wide frequency tuning (1-30 GHz), and flexible bandwidth reconfigurability (10-65 MHz). This record performance is enabled by a new concept of sidebands amplitude and phase controls using an electro-optic modulator and an optical filter. This new concept enables energy efficient operation in active MWP notch filters, and opens up the pathw...
Widely tunable microwave photonic notch filter based on slow and fast light effects
Xue W.; Sales S.; Mork J.; Capmany J.
2009-01-01
A continuously tunable microwave photonic notch filter at around 30 GHz is experimentally demonstrated and 100% fractional tuning over 360 range is achieved without changing the shape of the spectral response. The tuning mechanism is based on the use of slow and fast light effects in semiconductor optical amplifiers assisted by optical filtering.
Widely tunable microwave photonic notch filter based on slow and fast light effects
Xue, Weiqi; Sales, Salvador; Mørk, Jesper;
2009-01-01
A continuously tunable microwave photonic notch filter at around 30 GHz is experimentally demonstrated and 100% fractional tuning over 360 range is achieved without changing the shape of the spectral response. The tuning mechanism is based on the use of slow and fast light effects in semiconductor...... optical amplifiers assisted by optical filtering....
A Novel Digitally Tunable Microwave-Photonic Notch Filter Using Differential Group-Delay Module
Yu, Paul K.L.
2003-01-01
We demonstrate a digitally tunable microwave-photonic notch filter based on a differential group-delay module which produces a relative delay between two orthogonal polarizations. A maximum rejection level greater than 60 dB is obtained. The tenability of the filter is achieved through real-time electronic control of the relative delay time.
Si3N4 ring resonator-based microwave photonic notch filter with an ultrahigh peak rejection
Marpaung, David; Morrison, Blair; Pant, Ravi; Roeloffzen, Chris; Leinse, Arne; Hoekman, Marcel; Heideman, Rene; Eggleton, Benjamin J.
2013-01-01
We report a simple technique in microwave photonic (MWP) signal processing that allows the use of an optical filter with a shallow notch to exhibit a microwave notch filter with anomalously high rejection level. We implement this technique using a low-loss, tunable Si3N4 optical ring resonator as the optical filter, and achieved an MWP notch filter with an ultra-high peak rejection > 60 dB, a tunable high resolution bandwidth of 247-840 MHz, and notch frequency tuning of 2-8 GHz. To our knowl...
Li Xiaoyan
2011-11-01
Full Text Available Abstract Background Notch filtering is the most commonly used technique for suppression of power line and harmonic interference that often contaminate surface electromyogram (EMG signals. Notch filters are routinely included in EMG recording instrumentation, and are used very often during clinical recording sessions. The objective of this study was to quantitatively assess the effects of notch filtering on electrically evoked myoelectric signals and on the related motor unit index measurements. Methods The study was primarily based on an experimental comparison of M wave recordings and index estimates of motor unit number and size, with the notch filter function of the EMG machine (Sierra Wave EMG system, Cadwell Lab Inc, Kennewick, WA, USA turned on and off, respectively. The comparison was implemented in the first dorsal interosseous (FDI muscle from the dominant hand of 15 neurologically intact subjects and bilaterally in 15 hemiparetic stroke subjects. Results On average, for intact subjects, the maximum M wave amplitude and the motor unit number index (MUNIX estimate were reduced by approximately 22% and 18%, respectively, with application of the built-in notch filter function in the EMG machine. This trend held true when examining the paretic and contralateral muscles of the stroke subjects. With the notch filter on vs. off, across stroke subjects, we observed a significant decrease in both maximum M wave amplitude and MUNIX values in the paretic muscles, as compared with the contralateral muscles. However, similar reduction ratios were obtained for both maximum M wave amplitude and MUNIX estimate. Across muscles of both intact and stroke subjects, it was observed that notch filtering does not have significant effects on motor unit size index (MUSIX estimate. No significant difference was found in MUSIX values between the paretic and contralateral muscles of the stroke subjects. Conclusions The notch filter function built in the EMG
Notch Filter Analysis and Its Application in Passive Coherent Location Radar (in English
Li Ji-chuan
2015-01-01
Full Text Available The Normalized Least-Mean-Squares (NLMS algorithm is widely used to cancel the direct and multiple path interferences in Passive Coherent Location (PCL radar systems. This study proposes that the interference cancelation using the NLMS algorithm and the calculation of the radar Cross Ambiguity Function (CAF can be modeled as a notch filter, with the notch located at zero Doppler frequency in the surface of the radar CAF. The analysis shows that the notch’s width and depth are closely related to the step size of the NLMS algorithm. Subsequently, the effect of the notch in PCL radar target detection is analyzed. The results suggest that the detection performance of the PCL radar deteriorates because of the wide notch. Furthermore, the Nonuniform NLMS (NNLMS algorithm is proposed for removing the clutter with the Doppler frequency by using notch filtering. A step-size matrix is adopted to mitigate the low Doppler frequency clutter and lower the floor of the radar CAF. With the step-size matrix, can be obtained notches of different depths and widths in different range units of the CAF, which can filter the low Doppler frequency clutter. In addition, the convergence rate of the NNLMS algorithm is better than that of the traditional NLMS algorithm. The validity of the NNLMS algorithm is verified by experimental results.
R K Mehra; P O Arambel; A M Sampath; R K Prasanth; T C Parham
2000-04-01
New algorithms and results are presented for flutter testing and adaptive notching of structural modes in V-22 tiltrotor aircraft based on simulated and flight-test data from Bell Helicopter Textron, Inc. (BHTI). For flutter testing and the identification of structural mode frequencies, dampings and mode shapes, time domain state space techniques based on Deterministic Stochastic Realization Algorithms (DSRA) are used to accurately identify multiple modessimultaneously from sine sweep and other multifrequency data, resulting in great savings over the conventional Prony method. Two different techniques for adaptive notching are explored in order to design an Integrated Flight Structural Control (IFSC) system. The first technique is based on on-line identification of structural mode parameters using DSRA algorithm and tuning of a notch filter. The second technique is based on decoupling rigid-body and structural modes of the aircraft by means of a Kalman filter and using rigid-body estimates in the feedback control loop. The difference between the two approaches is that on-line identification and adaptive notching in the first approach are entirely based on the knowledge of structural modes, whereas the Kalman filter design in the second approach is based on the rigid-body dynamic model only.In the first IFSC design, on-line identification is necessary for flight envelope expansion and to adjust the notch filter frequencies and suppress aero-servoelastic instabilities due to changing flight conditionssuch as gross weight, sling loads, and airspeed. It isshown that by tuning the notch filterfrequency to the identified frequency, the phase lag is reduced and the corresponding structural mode is effectively suppressed and stability is maintained. In the second IFSC design using Kalman filter design, the structural modes are again effectively suppressed. Furthermore, the rigid-body estimates are found to be fairly insensitive to both natural frequency and damping factor
Adaptive filters: stable but divergent
Rupp, Markus
2015-12-01
The pros and cons of a quadratic error measure in the context of various applications have often been discussed. In this tutorial, we argue that it is not only a suboptimal but definitely the wrong choice when describing the stability behavior of adaptive filters. We take a walk through the past and recent history of adaptive filters and present 14 canonical forms of adaptive algorithms and even more variants thereof contrasting their mean-square with their l 2-stability conditions. In particular, in safety critical applications, the convergence in the mean-square sense turns out to provide wrong results, often not leading to stability at all. Only the robustness concept with its l 2-stability conditions ensures the absence of divergence.
Si3N4 ring resonator-based microwave photonic notch filter with an ultrahigh peak rejection
Marpaung, David; Morrison, Blair; Pant, Ravi; Roeloffzen, Chris; Leinse, Arne; Hoekman, Marcel; Heideman, René; Eggleton, Benjamin J.
2013-01-01
We report a simple technique in microwave photonic (MWP) signal processing that allows the use of an optical filter with a shallow notch to exhibit a microwave notch filter with anomalously high rejection level. We implement this technique using a low-loss, tunable Si3N4 optical ring resonator as th
Fibre Optic Notch Filter For The Antiproton Decelerator Stochastic Cooling System
Simmonds, Max Vincent John
2016-01-01
The project scope included reverse engineering, upgrading, and recovering the operational conditions of an existing fibre optic notch filter. Once operational, tests were to be preformed to confirm the performance of the temperature stabilisation. The end goal is to use said notch filter in the Antiproton Decelerator (AD) facility at CERN to help aid antimatter research. The notch filter was successfully reverse engineered and then documented. Changes were made in order to increase performance and reliability, and also allow easy integration into the AD. An additional phase was added whereby the notch filter was to be controller via a touchscreen computer, situated next to the filter, allowing engineers to set-up each of the electronic devices used. While one of the devices (Motorised Delay Line) can be controlled by the touchscreen computer, the other two cannot.Due to time constraints and difficulties with the Beckhoff TwincatII programming language, the USB devices were not able to be controlled via the To...
Periodic Noise Suppression from ECG Signal using Novel Adaptive Filtering Techniques
Yogesh Sharma
2012-03-01
Full Text Available Electrocardiogram signal most commonly known recognized and used biomedical signal for medical examination of heart. The ECG signal is very sensitive in nature, and even if small noise mixed with original signal, the various characteristics of the signal changes, Data corrupted with noise must either filtered or discarded, filtering is important issue for design consideration of real time heart monitoring systems. Various filters used for removing the noise from ECG signals, most commonly used filters are Notch Filters, FIR filters, IIR filters, Wiener filter, Adaptive filters etc. Performance analysis shows that the best result is obtained by using Adaptive filter to remove various noises from ECG signal and get significant SNR andMSE results. In this paper a novel adaptive approach by using LMS algorithm and delay has shown whichcan be used for pre-processing of ECG signal and give appreciable result.
Design of UWB Bandpass Filter with Notched Band Using Distributed CRLH Transmission Lines
Gyuje Sung
2015-08-01
Full Text Available This study presents an Ultra-Wideband (UWB filter with a notched band. The filter adopts novel Composite Right/Left-Handed (CRLH Transmission Lines (TLs, the unit cell of which is theoretically analyzed to derive the design formulas. A model of the CRLH TLs is composed with distributed elements rather than lumped elements. Based on the results of the analysis, it is confirmed that the proposed structures are CRLH TLs. A UWB bandpass filter with a notched band is designed and fabricated using the induced formulas. The measurement results show that the fabricated UWB bandpass filter has an insertion loss of less than 3 dB, a bandwidth of 2.8-10.5 GHz and a rejection of greater than 27 dB at 5.75 GHz.
Thin-film optical notch filter spectacle coatings for the treatment of migraine and photophobia.
Hoggan, Ryan N; Subhash, Amith; Blair, Steve; Digre, Kathleen B; Baggaley, Susan K; Gordon, Jamison; Brennan, K C; Warner, Judith E A; Crum, Alison V; Katz, Bradley J
2016-06-01
Previous evidence suggests optical treatments hold promise for treating migraine and photophobia. We designed an optical notch filter, centered at 480nm to reduce direct stimulation of intrinsically photosensitive retinal ganglion cells. We used thin-film technology to integrate the filter into spectacle lenses. Our objective was to determine if an optical notch filter, designed to attenuate activity of intrinsically photosensitive retinal ganglion cells, could reduce headache impact in chronic migraine subjects. For this randomized, double-masked study, our primary endpoint was the Headache Impact Test (HIT-6; GlaxoSmithKline, Brentford, Middlesex, UK). We developed two filters: the therapeutic filter blocked visible light at 480nm; a 620nm filter was designed as a sham. Participants were asked to wear lenses with one of the filters for 2weeks; after 2weeks when no lenses were worn, they wore lenses with the other filter for 2weeks. Of 48 subjects, 37 completed the study. Wearing either the 480 or 620nm lenses resulted in clinically and statistically significant HIT-6 reductions. However, there was no significant difference when comparing overall effect of the 480 and 620nm lenses. Although the 620nm filter was designed as a sham intervention, research published following the trial indicated that melanopsin, the photopigment in intrinsically photosensitive retinal ganglion cells, is bi-stable. This molecular property may explain the unexpected efficacy of the 620nm filter. These preliminary findings indicate that lenses outfitted with a thin-film optical notch filter may be useful in treating chronic migraine. PMID:26935748
Review Of Parameter Estimation Using Adaptive Filtering
LALITA RANI, SHALOO KIKAN
2013-07-01
Full Text Available In this paper, a comparative study of different adaptive filter algorithm for channel parameter estimation is described. We presented different parameter estimation approaches of adaptive filtering. An extended Kalman filter is then applied as a near-optimal solution to the adaptive channel parameter estimation problem. Kalman filtering is applied for motion parameters resulting in optimal pose estimation. A parallel Kalman filter is applied for joint estimation of code delay, multipath gains and Doppler shift. In this paper, a complete review of parameter estimation using adaptive filtering is explained.
In-plane deeply-etched optical MEMS notch filter with high-speed tunability
Sabry, Yasser M.; Eltagoury, Yomna M.; Shebl, Ahmed; Soliman, Mostafa; Sadek, Mohamed; Khalil, Diaa
2015-12-01
Notch filters are used in spectroscopy, multi-photon microscopy, fluorescence instrumentation, optical sensors and other life science applications. One type of notch filter is based on a fiber-coupled Fabry-Pérot cavity, which is formed by a reflector (external mirror) facing a dielectric-coated end of an optical fiber. Tailoring this kind of optical filter for different applications is possible because the external mirror has fewer mechanical and optical constraints. In this paper we present optical modeling and implementation of a fiber-coupled Fabry-Pérot filter based on dielectric-coated optical fiber inserted into a micromachined fiber groove facing a metallized micromirror, which is driven by a high-speed MEMS actuator. The optical MEMS chip is fabricated using deep reactive ion etching (DRIE) technology on a silicon on insulator wafer, where the optical axis is parallel to the substrate (in-plane) and the optical/mechanical components are self-aligned by the photolithographic process. The DRIE etching depth is 150 μm, chosen to increase the micromirror optical throughput and improving the out-of-plane stiffness of the MEMS actuator. The MEMS actuator type is closing-gap, while its quality factor is almost doubled by slotting the fixed plate. A low-finesse Fabry-Pérot interferometer is formed by the metallized surface of the micromirror and a cleaved end of a standard single-mode fiber, for characterization of the MEMS actuator stroke and resonance frequency. The actuator achieves a travel distance of 800 nm at a resonance frequency of 89.9 kHz. The notch filter characteristics were measured using an optical spectrum analyzer, and the filter exhibits a free spectral range up to 100 nm and a notch rejection ratio up to 20 dB around a wavelength of 1300 nm. The presented device provides batch processing and low-cost production of the filter.
Characteristics of a Tunable Microwave Photonics Notch Filter Based on Two Fiber Bragg Gratings
YUXianbin; ZHANGXianmin; CHIHao; CHENKangsheng
2005-01-01
We investigate theoretically the characteristic of a tunable microwave fiber-optic notch flter based on two fiber gratings. The microwave frequency response based on the refiectivities of two fiber gratings is analyzed and the optimum filter condition is obtained. The refiectivity of the first fiber grating can be tuned experimentally by adjusting the wavelength of input light. Experimental results are in agreement with the theory. The largest notch depth is more than 15dB. The free-spectral range can be tuned by altering the length of fiber between two fiber gratings.
Hong, Xuezhi; Wang, Dawei; Xu, Lei; He, Sailing
2010-06-01
A novel approach is proposed and experimentally demonstrated for optical steganography transmission in WDM networks using temporal phase coded optical signals with spectral notch filtering. A temporal phase coded stealth channel is temporally and spectrally overlaid onto a public WDM channel. Direct detection of the public channel is achieved in the presence of the stealth channel. The interference from the public channel is suppressed by spectral notching before the detection of the optical stealth signal. The approach is shown to have good compatibility and robustness to the existing WDM network for optical steganography transmission. PMID:20588368
Adaptive Filter in SAR Interferometry Derived DEM
XU Caijun; WANG Hua; WANG Jianglin; GE Linlin
2005-01-01
In this paper, the performance of median filter, elevation dependent adaptive sigma median filter, and directionally dependent adaptive sigma median filter are tested on both InSAR Tandem DEM and simulated high-level noisy DEM. Through the comparison, the directionally dependent adaptive sigma median filter is proved to be the most effective one not only in the noise removing but also in the boundary preserve.
A Review of Bandpass with Tunable Notch Microwave Filter in Wideband Application
Anthony Bruster
2015-06-01
Full Text Available In the last few years, several microwave filter design with band-pass response have been proposed for ultra-wideband (UWB application. Among various microwave filter design, microstrip filter are mostly used by researcher due to their low profile, light weight, easy to fabricate and low cost. Conventional microstrip filter can be in any shape whether circular, rectangular or elliptical but some modification or additional variation in their basic design can be made for different purposes for example notch response and tunable characteristic in order to eliminate undesired signal. This paper proposed a compilation of important review about filter design for band-pass filter and discussion about various design with different method or technique used in order to achieve in wideband application range and tunable capabilities. The previous work will be examined and critically analyzed in terms of insertion and return losses, bandwidth, selectivity and tuning in order to proposed novel design of microwave filter with band-pass and tunable notch response in UWB application for future research work. Through this review, we hope that a better understanding of microwave bandpass filter can be established and therefore can have a huge contribution.
Split quaternion nonlinear adaptive filtering.
Ujang, Bukhari Che; Took, Clive Cheong; Mandic, Danilo P
2010-04-01
A split quaternion learning algorithm for the training of nonlinear finite impulse response adaptive filters for the processing of three- and four-dimensional signals is proposed. The derivation takes into account the non-commutativity of the quaternion product, an aspect neglected in the derivation of the existing learning algorithms. It is shown that the additional information taken into account by a rigorous treatment of quaternion algebra provides improved performance on hypercomplex processes. A rigorous analysis of the convergence of the proposed algorithms is also provided. Simulations on both benchmark and real-world signals support the approach. PMID:19926443
Novel Notched UWB Filter Using Stepped Impedance Stub Loaded Microstrip Resonator and Spurlines
Ramkumar Uikey
2015-01-01
Full Text Available This paper presents a novel ultrawideband (UWB bandpass filter using stepped impedance stub loaded microstrip resonator (SISLMR. The proposed resonator is so formed to allow its four resonant frequencies in the UWB passband, which extends from 3.1 GHz to 10.6 GHz. Moreover, two spurline sections are employed to create a sharp notched-band filter for suppressing the signals of 5 GHz WLAN devices. Experimental results of the fabricated filters are in good agreement with the HFSS simulations and validate the design.
Tunable microwave notch filter created by stimulated Brillouin scattering in a silicon chip
Casas-Bedoya, A.; Morrison, Blair; Pagani, Mattia; Marpaung, David; Eggleton, Benjamin J.
2015-12-01
We show the first functional signal processing device based on forward stimulated Brillouin scattering from a silicon nanowire. We harness 1dB of SBS gain to create a high performance, energy efficient microwave photonic notch filter. The filter possess 48dB of suppression, 98 MHz linewidth, and is tunable within a 6 GHz bandwidth. This demonstration represents a significant advance in integrated microwave photonics with potential applications in on-chip microwave signal processing and establish the foundation towards the first CMOS-compatible high performance RF photonic filter.
Single Molecule DNA Detection with an Atomic Vapor Notch Filter
Uhland, Denis; Widmann, Matthias; Lee, Sang-Yun; Wrachtrup, Jörg; Gerhardt, Ilja
2015-01-01
The detection of single molecules has facilitated many advances in life- and material-sciences. Commonly, it founds on the fluorescence detection of single molecules, which are for example attached to the structures under study. For fluorescence microscopy and sensing the crucial parameters are the collection and detection efficiency, such that photons can be discriminated with low background from a labeled sample. Here we show a scheme for filtering the excitation light in the optical detection of single stranded labeled DNA molecules. We use the narrow-band filtering properties of a hot atomic vapor to filter the excitation light from the emitted fluorescence of a single emitter. The choice of atomic sodium allows for the use of fluorescent dyes, which are common in life-science. This scheme enables efficient photon detection, and a statistical analysis proves an enhancement of the optical signal of more than 15% in a confocal and in a wide-field configuration.
Lee, Kwanil; Lee, Ju Han; Lee, Sang Bae
2009-07-20
A novel photonic microwave notch filter with capability of frequency tuning is proposed and experimentally demonstrated. The scheme is based on a fiber Bragg grating (FBG)-based, single longitudinal mode, wavelength-spacing tunable dual-wavelength fiber laser and a dispersive fiber delay line. By using a symmetrical S-bending technique along the FBGs, the wavelength spacing of the laser can be tuned, which enables the microwave notch frequency tuning. Experimental results show that the notch rejection of more than 30 dB and the flexible tunability of notch frequency can be readily achieved in the range of 1.2 approximately 6.7 GHz. PMID:19654727
Fokker-Planck approach to stochastic momentum cooling with a notch filter
A Fokker-Planck equation that describes the stochastic momentum cooling with a notch filter is formulated. The Fokker-Planck coefficients are calculated for an idealized linear notch filter. The coherent energy correction is expressed explicitly with a parameter representing the difference of the particle's time-of-flight and the signal's transmission time from the pickup to the kicker. This formulation provides us a guide to determine the system passband and a setting accuracy of the signal's transmission time for a given momentum spread of the beam. From the Fokker-Planck equation, a differential equation for time variation of a standard deviation of energy error is derived to obtain the initial cooling time and the final momentum spread. This formulation is useful for analysis of experimental data and for design of the cooling system, i.e. optimization of the system passband and gain, specification of accuracy of the signal's transmission time, etc.. (author)
Single molecule DNA detection with an atomic vapor notch filter
Uhland, Denis; Rendler, Torsten; Widmann, Matthias; Lee, Sang-Yun [University of Stuttgart and Stuttgart Research Center of Photonic Engineering (SCoPE) and IQST, 3rd Physics Institute, Stuttgart (Germany); Wrachtrup, Joerg; Gerhardt, Ilja [University of Stuttgart and Stuttgart Research Center of Photonic Engineering (SCoPE) and IQST, 3rd Physics Institute, Stuttgart (Germany); Max Planck Institute for Solid State Research, Stuttgart (Germany)
2015-12-01
The detection of single molecules has facilitated many advances in life- and material-science. Commonly the fluorescence of dye molecules is detected, which are attached to a non-fluorescent structure under study. For fluorescence microscopy one desires to maximize the detection efficiency together with an efficient suppression of undesired laser leakage. Here we present the use of the narrow-band filtering properties of hot atomic sodium vapor to selectively filter the excitation light from the red-shifted fluorescence of dye labeled single-stranded DNA molecules. A statistical analysis proves an enhancement in detection efficiency of more than 15% in a confocal and in a wide-field configuration. (orig.)
Homemade notch filter to suppress strong FM or DAB - T/DVB - T signals
Monstein, Christian
2016-04-01
Many of the current 116 solar radio spectrometer instruments in the e-Callisto network are suffering from strong interference from FM-radio, DAB-T or DVB-T broadcast stations. With simple surface mount device (SMD) components a cheap notch (trap)filter can be produced to suppress these strong signals that otherwise may saturate the low noise amplifier and/or the receiver.
Xue, Weiqi; Sales, Salvador; Mørk, Jesper;
2009-01-01
We introduce a novel scheme based on slow and fast light effects in semiconductor optical amplifiers, to implement a microwave photonic notch filter with ~100% fractional tuning range at a microwave frequency of 30 GHz.......We introduce a novel scheme based on slow and fast light effects in semiconductor optical amplifiers, to implement a microwave photonic notch filter with ~100% fractional tuning range at a microwave frequency of 30 GHz....
A uniformly convergent adaptive particle filter
Papavasiliou, Anastasia
2005-01-01
Particle filters are Monte Carlo methods that aim to approximate the optimal filter of a partially observed Markov chain. In this paper, we study the case in which the transition kernel of the Markov chain depends on unknown parameters: we construct a particle filter for the simultaneous estimation of the parameter and the partially observed Markov chain (adaptive estimation) and we prove the convergence of this filter to the correct optimal filter, as time and the number...
Adaptive Filtering Algorithms and Practical Implementation
Diniz, Paulo S R
2013-01-01
In the fourth edition of Adaptive Filtering: Algorithms and Practical Implementation, author Paulo S.R. Diniz presents the basic concepts of adaptive signal processing and adaptive filtering in a concise and straightforward manner. The main classes of adaptive filtering algorithms are presented in a unified framework, using clear notations that facilitate actual implementation. The main algorithms are described in tables, which are detailed enough to allow the reader to verify the covered concepts. Many examples address problems drawn from actual applications. New material to this edition includes: Analytical and simulation examples in Chapters 4, 5, 6 and 10 Appendix E, which summarizes the analysis of set-membership algorithm Updated problems and references Providing a concise background on adaptive filtering, this book covers the family of LMS, affine projection, RLS and data-selective set-membership algorithms as well as nonlinear, sub-band, blind, IIR adaptive filtering, and more. Several problems are...
Objects tracking with adaptive correlation filters and Kalman filtering
Ontiveros-Gallardo, Sergio E.; Kober, Vitaly
2015-09-01
Object tracking is commonly used for applications such as video surveillance, motion based recognition, and vehicle navigation. In this work, a tracking system using adaptive correlation filters and robust Kalman prediction of target locations is proposed. Tracking is performed by means of multiple object detections in reduced frame areas. A bank of filters is designed from multiple views of a target using synthetic discriminant functions. An adaptive approach is used to improve discrimination capability of the synthesized filters adapting them to multiple types of backgrounds. With the help of computer simulation, the performance of the proposed algorithm is evaluated in terms of detection efficiency and accuracy of object tracking.
Spoof surface plasmon polaritons based notch filter for ultra-wideband microwave waveguide
Xiao, Binggang; Kong, Sheng; Xiao, Sanshui
2016-09-01
Spoof surface plasmon polaritons based notch filter for ultra-wideband microwave waveguide is proposed. Owing to subwavelength confinement, such a filter has advantage in the structure size without sacrificing the performance. The spoof SPP based notch is introduced to suppress the WLAN and satellite communication interference simultaneously. Both the cutoff frequency and the notch frequency are sensitive to the structure parameters, and the cut-off frequency can reach 20 GHz. An adiabatic transition relying on gradient hole-size and flaring ground is designed to effectively couple energy into spoof SPP waveguide. The result shows its cut-off frequency of 17.4 GHz with the insertion loss better than 3 dB during the whole pass-band, while having more than 20 dB rejections at 5.36 GHz and 9.32 GHz with 10 dB fractional bandwidth 1.07% and 0.74% respectively to avoid the existing WLAN and satellite communication signals. Due to planar structures proposed here, it is easy to integrate in the microwave integrated systems, which can play an important role in the microwave communication circuit and system.
Lu, Lei; Song, Young-Yeal; Bevivino, Joshua; Wu, Mingzhong
2010-10-01
There is a critical need for planar millimeter (mm) wave devices. To meet this need, one important strategy is in the use of high-anisotropy hexagonal ferrite films. The high internal anisotropy field for the hexagonal ferrites can be used to realize low-loss devices in the 30-100 GHz regime without the need for high external magnetic fields. Previous work has demonstrated the use of M-type barium hexagonal ferrite (BaM) films and ferromagnetic resonance therein to make mm-wave notch filters. This presentation reports on a new mm-wave notch filter that uses magnetostatic wave (MSW) resonance in BaM films. The device consists of a BaM film strip positioned on the top of a coplanar waveguide (CPW), with the strip's length along the CPW signal line. The BaM strip was grown by pulsed laser deposition and had uniaxial anisotropy along the strip's length. The device showed a band-stop filtering response centered at 53 GHz in absence of external fields. One can increase this frequency with nonzero external fields. A reduction in the strip's width resulted in an enhancement in peak absorption. This filtering response resulted from MSW resonance across the BaM strip's width. The MSW modes were excited by CPW-produced non-uniform alternating magnetic fields.
Adaptive filtering using Higher Order Statistics (HOS
Abdelghani Manseur
2012-03-01
Full Text Available The performed job, in this study, consists in studying adaptive filters and higher order statistics (HOS to ameliorate their performances, by extension of linear case to non linear filters via Volterra series. This study is, principally, axed on: „ Choice of the adaptation step and convergence conditions. „ Convergence rate. „ Adaptive variation of the convergence factor, according to the input signal. The obtained results, with real signals, have shown computationally efficient and numerically stable algorithms for adaptive nonlinear filtering while keeping relatively simple computational complexity.
Adaptive control of active filter using DSP
In order to reduce output-voltage ripple of power supply, an active filter is necessary. In this paper, the active filter with DSP is proposed. The waveform from active filter can be flexibly improved by DSP programming. The output-voltage ripple can be enough reduced by mixing frequency components of the input-voltage ripple. The result of adaptive control using LMS algorism is presented. The improvement by using filtered-X method is discussed. (author)
ADAPTIVE TRILATERAL FILTER FOR IN-LOOP FILTERING
Akitha Kesireddy
2014-07-01
Full Text Available High Efficiency Video Coding (HEVC has achieved significant coding efficiency improvement beyond existing video coding standard by employing several new coding tools. Deblocking Filter, Sample Adaptive Offset (SAO and Adaptive Loop Filter (ALF for in-loop filtering are currently introduced for the HEVC standard. However, these filters are implemented in spatial domain despite the fact of temporal correlation within video sequences. To reduce the artifacts and better align object boundaries in video, a proposed algorithm in in-loop filtering is proposed. The proposed algorithm is implemented in HM-11.0 software. This proposed algorithm allows an average bitrate reduction of about 0.7% and improves the PSNR of the decoded frame by 0.05%, 0.30% and 0.35% in luminance and chroma.
Adaptive filtering in biological signal processing.
Iyer, V K; Ploysongsang, Y; Ramamoorthy, P A
1990-01-01
The high dependence of conventional optimal filtering methods on the a priori knowledge of the signal and noise statistics render them ineffective in dealing with signals whose statistics cannot be predetermined accurately. Adaptive filtering methods offer a better alternative, since the a priori knowledge of statistics is less critical, real time processing is possible, and the computations are less expensive for this approach. Adaptive filtering methods compute the filter coefficients "on-line", converging to the optimal values in the least-mean square (LMS) error sense. Adaptive filtering is therefore apt for dealing with the "unknown" statistics situation and has been applied extensively in areas like communication, speech, radar, sonar, seismology, and biological signal processing and analysis for channel equalization, interference and echo canceling, line enhancement, signal detection, system identification, spectral analysis, beamforming, modeling, control, etc. In this review article adaptive filtering in the context of biological signals is reviewed. An intuitive approach to the underlying theory of adaptive filters and its applicability are presented. Applications of the principles in biological signal processing are discussed in a manner that brings out the key ideas involved. Current and potential future directions in adaptive biological signal processing are also discussed. PMID:2180633
Adaptable Iterative and Recursive Kalman Filter Schemes
Zanetti, Renato
2014-01-01
Nonlinear filters are often very computationally expensive and usually not suitable for real-time applications. Real-time navigation algorithms are typically based on linear estimators, such as the extended Kalman filter (EKF) and, to a much lesser extent, the unscented Kalman filter. The Iterated Kalman filter (IKF) and the Recursive Update Filter (RUF) are two algorithms that reduce the consequences of the linearization assumption of the EKF by performing N updates for each new measurement, where N is the number of recursions, a tuning parameter. This paper introduces an adaptable RUF algorithm to calculate N on the go, a similar technique can be used for the IKF as well.
Performance Analysis of LMS Adaptive FIR Filter and RLS Adaptive FIR Filter for Noise Cancellation
Jyotsna Yadav
2013-06-01
Full Text Available Interest in adaptive filters continues to grow as they begin to find practical real-time applications in areas such as channel equalization, echo cancellation, noise cancellation and many other adaptive signal processing applications. The key to successful adaptive signal processing understands the fundamental properties of adaptive algorithms such as LMS, RLS etc. Adaptive filter is used for the cancellation of the noise component which is overlap with undesired signal in the same frequency range. This paper presents design, implementation and performance comparison of adaptive FIR filter using LMS and RMS algorithms. MATLAB Simulink environment are used for simulations.
Performance Analysis of LMS Adaptive FIR Filter and RLS Adaptive FIR Filter for Noise Cancellation
Jyotsna Yadav
2013-07-01
Full Text Available Interest in adaptive filters continues to grow as they begin to find practical real-time applications in areassuch as channel equalization, echo cancellation, noise cancellation and many other adaptive signalprocessing applications. The key to successful adaptive signal processing understands the fundamentalproperties of adaptive algorithms such as LMS, RLS etc. Adaptive filter is used for the cancellation of thenoise component which is overlap with undesired signal in the same frequency range. This paper presentsdesign, implementation and performance comparison of adaptive FIR filter using LMS and RMSalgorithms. MATLAB Simulink environment are used for simulations.
Pena-Alzola, Rafael; Liserre, Marco; Blaabjerg, Frede;
2014-01-01
LCL-filters are a cost-effective solution to mitigate harmonic current content in grid-tie converters. In order to avoid stability problems, the resonance frequency of LCL-filters can be damped with active techniques that remove dissipative elements but increase control complexity. A notch filter...... impedance variations due to its ability to tune the grid-tie inverter on-site. Finally, the analysis is validated with both simulation and experiments....
Recursive total-least-squares adaptive filtering
Dowling, Eric M.; DeGroat, Ronald D.
1991-12-01
In this paper a recursive total least squares (RTLS) adaptive filter is introduced and studied. The TLS approach is more appropriate and provides more accurate results than the LS approach when there is error on both sides of the adaptive filter equation; for example, linear prediction, AR modeling, and direction finding. The RTLS filter weights are updated in time O(mr) where m is the filter order and r is the dimension of the tracked subspace. In conventional adaptive filtering problems, r equals 1, so that updates can be performed with complexity O(m). The updates are performed by tracking an orthonormal basis for the smaller of the signal or noise subspaces using a computationally efficient subspace tracking algorithm. The filter is shown to outperform both LMS and RLS in terms of tracking and steady state tap weight error norms. It is also more versatile in that it can adapt its weight in the absence of persistent excitation, i.e., when the input data correlation matrix is near rank deficient. Through simulation, the convergence and tracking properties of the filter are presented and compared with LMS and RLS.
Polyphase Analysis of Subband Adaptive Filters
Weiss, S; Stewart, R W; Harteneck, M; Stenger, A.
1999-01-01
In this paper, we derive a polyphase analysis to determine the optimum filters in a subband adaptive filter (SAF) system. The structure of this optimum solution deviates from the standard SAF approach and presents its best possible solution only as an approximation. Besides this new insight into SAF error sources, the discussed analysis allows to calculate the optimum subband responses and the standard SAF approximation. Examples demonstrating the validity of our analysis and its use for dete...
Medhin, Ashenafi Kiros; Kamchevska, Valerija; Galili, Michael; Oxenløwe, Leif Katsuo
We experimentally perform 1×4 optical packet switching of variable length 640 Gbit/s OTDM data packets using in-band notch-filter labeling with only 2.7-dB penalty. Up to 8 notches are employed to demonstrate scalability of the labeling scheme to 1×256 switching operation.......We experimentally perform 1×4 optical packet switching of variable length 640 Gbit/s OTDM data packets using in-band notch-filter labeling with only 2.7-dB penalty. Up to 8 notches are employed to demonstrate scalability of the labeling scheme to 1×256 switching operation....
Adaptive Filters for Muscle Response Suppression
Sennels, Søren; Biering-Soerensen, Fin; Hansen, Steffen Duus;
1996-01-01
are proposed, based on the observation that the shape of the muscle responses only exhibits moderate changes during a time window of up to 300 ms. The filters are derived and compared with a conventional fixed comb filter on both simulated and real data. For variations in amplitude of the muscle responses......To be able to use the voluntary EMG-signal from an electrically stimulated muscle as control signal for FES-applications, it is necessary to eliminate the muscle response evoked by the stimulation. The muscle response is a non-stationary signal, therefore a set of linear adaptive prediction filters...
Partial update least-square adaptive filtering
Xie, Bei
2014-01-01
Adaptive filters play an important role in the fields related to digital signal processing and communication, such as system identification, noise cancellation, channel equalization, and beamforming. In practical applications, the computational complexity of an adaptive filter is an important consideration. The Least Mean Square (LMS) algorithm is widely used because of its low computational complexity (O(N)) and simplicity in implementation. The least squares algorithms, such as Recursive Least Squares (RLS), Conjugate Gradient (CG), and Euclidean Direction Search (EDS), can converge faster a
Sparse adaptive filters for echo cancellation
Paleologu, Constantin
2011-01-01
Adaptive filters with a large number of coefficients are usually involved in both network and acoustic echo cancellation. Consequently, it is important to improve the convergence rate and tracking of the conventional algorithms used for these applications. This can be achieved by exploiting the sparseness character of the echo paths. Identification of sparse impulse responses was addressed mainly in the last decade with the development of the so-called ``proportionate''-type algorithms. The goal of this book is to present the most important sparse adaptive filters developed for echo cancellati
Nonlinear Adaptive Filters based on Particle Swarm Optimization
Faten BEN ARFIA; Ben Messaoud, Mohamed; Abid, Mohamed
2009-01-01
This paper presents a particle swarm optimization (PSO) algorithm to adjust the parameters of the nonlinear filter and to make this type of the filters more powerful for the elimination of the Gaussian noise and also the impulse noise. In this paper we apply the particle swarm optimization to the rational filters and we completed this work with the comparison between our results and other adaptive nonlinear filters like the LMS adaptive median filters and the no-adaptive rational filter.
A Rapid Introduction to Adaptive Filtering
Vega, Leonardo Rey
2013-01-01
In this book, the authors provide insights into the basics of adaptive filtering, which are particularly useful for students taking their first steps into this field. They start by studying the problem of minimum mean-square-error filtering, i.e., Wiener filtering. Then, they analyze iterative methods for solving the optimization problem, e.g., the Method of Steepest Descent. By proposing stochastic approximations, several basic adaptive algorithms are derived, including Least Mean Squares (LMS), Normalized Least Mean Squares (NLMS) and Sign-error algorithms. The authors provide a general framework to study the stability and steady-state performance of these algorithms. The affine Projection Algorithm (APA) which provides faster convergence at the expense of computational complexity (although fast implementations can be used) is also presented. In addition, the Least Squares (LS) method and its recursive version (RLS), including fast implementations are discussed. The book closes with the discussion of severa...
VSP wave separation by adaptive masking filters
Rao, Ying; Wang, Yanghua
2016-06-01
In vertical seismic profiling (VSP) data processing, the first step might be to separate the down-going wavefield from the up-going wavefield. When using a masking filter for VSP wave separation, there are difficulties associated with two termination ends of the up-going waves. A critical challenge is how the masking filter can restore the energy tails, the edge effect associated with these terminations uniquely exist in VSP data. An effective strategy is to implement masking filters in both τ-p and f-k domain sequentially. Meanwhile it uses a median filter, producing a clean but smooth version of the down-going wavefield, used as a reference data set for designing the masking filter. The masking filter is implemented adaptively and iteratively, gradually restoring the energy tails cut-out by any surgical mute. While the τ-p and the f-k domain masking filters target different depth ranges of VSP, this combination strategy can accurately perform in wave separation from field VSP data.
Kernel adaptive filtering a comprehensive introduction
Liu, Weifeng; Haykin, Simon
2010-01-01
Online learning from a signal processing perspective There is increased interest in kernel learning algorithms in neural networks and a growing need for nonlinear adaptive algorithms in advanced signal processing, communications, and controls. Kernel Adaptive Filtering is the first book to present a comprehensive, unifying introduction to online learning algorithms in reproducing kernel Hilbert spaces. Based on research being conducted in the Computational Neuro-Engineering Laboratory at the University of Florida and in the Cognitive Systems Laboratory at McMaster University, O
Filtering Algebraic Multigrid and Adaptive Strategies
Nagel, A; Falgout, R D; Wittum, G
2006-01-31
Solving linear systems arising from systems of partial differential equations, multigrid and multilevel methods have proven optimal complexity and efficiency properties. Due to shortcomings of geometric approaches, algebraic multigrid methods have been developed. One example is the filtering algebraic multigrid method introduced by C. Wagner. This paper proposes a variant of Wagner's method with substantially improved robustness properties. The method is used in an adaptive, self-correcting framework and tested numerically.
Adaptive Importance Sampling in Particle Filtering
Šmídl, Václav; Hofman, Radek
Istanbul : ISIF, 2013. ISBN 978-605-86311-1-3. [16th International Conference on Information Fusion. Istanbul (TR), 09.07.2013-12.07.2013] R&D Projects: GA MV VG20102013018; GA ČR(CZ) GAP102/11/0437 Keywords : importance sampling * sequential monte carlo * sufficient statistics Subject RIV: BC - Control Systems Theory http://library.utia.cas.cz/separaty/2013/AS/smidl-adaptive importance sampling in particle filtering.pdf
Zhang, Guiju; Cao, Bing; Zhang, Ke; Wang, Chinhua; Sun, Qian
2015-01-01
Developments in micro and nanofabrication technologies have led a variety of grating waveguide structures (GWS) being proposed and implemented in optics and laser application systems. A new design of multilayered nanostructure double-grating is described for reflection notch filter. Thin metal film and dielectric film are used and designed with one-dimensional composite gratings. The results calculated by rigorous coupled-wave analysis (RCWA) present that the thin metal film between substrate...
Musical noise reduction using an adaptive filter
Hanada, Takeshi; Murakami, Takahiro; Ishida, Yoshihisa; Hoya, Tetsuya
2003-10-01
This paper presents a method for reducing a particular noise (musical noise). The musical noise is artificially produced by Spectral Subtraction (SS), which is one of the most conventional methods for speech enhancement. The musical noise is the tin-like sound and annoying in human auditory. We know that the duration of the musical noise is considerably short in comparison with that of speech, and that the frequency components of the musical noise are random and isolated. In the ordinary SS-based methods, the musical noise is removed by the post-processing. However, the output of the ordinary post-processing is delayed since the post-processing uses the succeeding frames. In order to improve this problem, we propose a novel method using an adaptive filter. In the proposed system, the observed noisy signal is used as the input signal to the adaptive filter and the output of SS is used as the reference signal. In this paper we exploit the normalized LMS (Least Mean Square) algorithm for the adaptive filter. Simulation results show that the proposed method has improved the intelligibility of the enhanced speech in comparison with the conventional method.
Adaptive noise Wiener filter for scanning electron microscope imaging system.
Sim, K S; Teh, V; Nia, M E
2016-03-01
Noise on scanning electron microscope (SEM) images is studied. Gaussian noise is the most common type of noise in SEM image. We developed a new noise reduction filter based on the Wiener filter. We compared the performance of this new filter namely adaptive noise Wiener (ANW) filter, with four common existing filters as well as average filter, median filter, Gaussian smoothing filter and the Wiener filter. Based on the experiments results the proposed new filter has better performance on different noise variance comparing to the other existing noise removal filters in the experiments. SCANNING 38:148-163, 2016. © 2015 Wiley Periodicals, Inc. PMID:26235517
We propose a novel notch-filtering scheme for bit-rate transparent all-optical NRZ-to-PRZ format conversion. The scheme is based on a two-degree-of-freedom optimally designed fiber Bragg grating. It is shown that a notch filter optimized for any specific operating bit rate can be used to realize high-Q-factor format conversion over a wide bit rate range without requiring any tuning. (paper)
Design of UWB Filter with Notch Band for WLAN (5.3-5.8 GHz Signal Interference Rejection
Vinay Kumar Sharma
2014-10-01
Full Text Available In this letter, a design of Compact Ultra-Wideband (UWB bandpass filter with a switchable notch band for WLAN (5.3-5.9GHz interference rejection is proposed. As 5.3-5.9GHz wireless local area network (WLAN is existed in UWB spectrum range (3.1 -10.6GHz and may interfere with UWB system operation. The UWB bandpass filter is implemented using a basic multiple mode resonator (MMR structure feed by interdigital coupled lines for achieving higher degree of coupling. The notch band is obtained using a etched slot on main microstrip line. The centre frequency and bandwidth of notch band is optimized. The filter is compact in size with dimension 37.4 X 25 mm2 .The electromagnetic simulation software, Computer Simulation Technology Microwave Studio (CST MWS is used for the simulation and analysis of the designed structure. For fabrication of this structure Rogers RT5880 substrate of thickness 0.4 mm and dielectric constant 2.2 is used. Measured and simulated results show good agreement.
An Efficient Adaptive Filtering for CFA Demosaicking
Dev. R. Newlin,
2010-07-01
Full Text Available Most digital still cameras acquire imagery with a color filter array (CFA, sampling only one color value for each pixel and interpolating the other two color values afterwards. The interpolation process is commonly known as demosaicking. In general, a good demosaicking method should preserve the highfrequency information of imagery as much as possible, since such information is essential for image visual quality. We discuss in this paper two key observations for preserving high-frequency information in CFA demosaicking: 1 the high frequencies are similar across three color components, and 2 the high frequencies along the horizontal and vertical axes are essential for image quality. Our frequency analysis of CFA samplesindicates that filtering a CFA image can better preserve highfrequencies than filtering each color component separately. Thismotivates us to design an efficient filter for estimating theluminance at green pixels of the CFA image and devise an adaptive filtering approach to estimate the luminance at red and blue pixels. Experimental results on simulated images, as well as raw data, verify that the proposed method outperforms the existing methods both visually and in terms of peak signal-tonoise ratio, at a notably lower computational cost.
Adaptive Filtering Queueing for Improving Fairness
Jui-Pin Yang
2015-06-01
Full Text Available In this paper, we propose a scalable and efficient Active Queue Management (AQM scheme to provide fair bandwidth sharing when traffic is congested dubbed Adaptive Filtering Queueing (AFQ. First, AFQ identifies the filtering level of an arriving packet by comparing it with a flow label selected at random from the first level to an estimated level in the filtering level table. Based on the accepted traffic estimation and the previous fair filtering level, AFQ updates the fair filtering level. Next, AFQ uses a simple packet-dropping algorithm to determine whether arriving packets are accepted or discarded. To enhance AFQ’s feasibility in high-speed networks, we propose a two-layer mapping mechanism to effectively simplify the packet comparison operations. Simulation results demonstrate that AFQ achieves optimal fairness when compared with Rotating Preference Queues (RPQ, Core-Stateless Fair Queueing (CSFQ, CHOose and Keep for responsive flows, CHOose and Kill for unresponsive flows (CHOKe and First-In First-Out (FIFO schemes under a variety of traffic conditions.
Quaternion-valued nonlinear adaptive filtering.
Ujang, Bukhari Che; Took, Clive Cheong; Mandic, Danilo P
2011-08-01
A class of nonlinear quaternion-valued adaptive filtering algorithms is proposed based on locally analytic nonlinear activation functions. To circumvent the stringent standard analyticity conditions which are prohibitive to the development of nonlinear adaptive quaternion-valued estimation models, we use the fact that stochastic gradient learning algorithms require only local analyticity at the operating point in the estimation space. It is shown that the quaternion-valued exponential function is locally analytic, and, since local analyticity extends to polynomials, products, and ratios, we show that a class of transcendental nonlinear functions can serve as activation functions in nonlinear and neural adaptive models. This provides a unifying framework for the derivation of gradient-based learning algorithms in the quaternion domain, and the derived algorithms are shown to have the same generic form as their real- and complex-valued counterparts. To make such models second-order optimal for the generality of quaternion signals (both circular and noncircular), we use recent developments in augmented quaternion statistics to introduce widely linear versions of the proposed nonlinear adaptive quaternion valued filters. This allows full exploitation of second-order information in the data, contained both in the covariance and pseudocovariances to cater rigorously for second-order noncircularity (improperness), and the corresponding power mismatch in the signal components. Simulations over a range of circular and noncircular synthetic processes and a real world 3-D noncircular wind signal support the approach. PMID:21712159
Strong tracking adaptive Kalman filters for underwater vehicle dead reckoning
XIAO Kun; FANG Shao-ji; PANG Yong-jie
2007-01-01
To improve underwater vehicle dead reckoning, a developed strong tracking adaptive kalman filter is proposed. The filter is improved with an additional adaptive factor and an estimator of measurement noise covariance. Since the magnitude of fading factor is changed adaptively, the tracking ability of the filter is still enhanced in low velocity condition of underwater vehicles. The results of simulation tests prove the presented filter effective.
Mirmohamadsadeghi, Leila; Vesin, Jean-Marc
2016-09-01
Measuring the instantaneous frequency of a signal rapidly and accurately is essential in many applications. However, the instantaneous frequency by definition is a parameter difficult to determine. Fourier-based methods introduce estimation delays as computations are performed in a time-window. Instantaneous methods based on the Hilbert transform lack robustness. State-of-the-art adaptive filters yield accurate estimates, however, with an adaptation delay. In this study we propose an algorithm based on short length-3 FIR notch filters to estimate the instantaneous frequency of a signal at each sample, in a real-time manner and with very low delay. The output powers of a bank of the above-mentioned filters are used in a recursive weighting scheme to estimate the dominant frequency of the input. This scheme has been extended to process multiple inputs containing a common frequency by introducing an additional weighting scheme upon the inputs. The algorithm was tested on synthetic data and then evaluated on real biomedical data, i.e. the estimation of the respiratory rate from the electrocardiogram. It was shown that the proposed method provided more accurate estimates with less delay than those of state-of-the-art methods. By virtue of its simplicity and good performance, the proposed method is a worthy candidate to be used in biomedical applications, for example in health monitoring developments based on portable and automatic devices. PMID:27510318
Nonlinear Adaptive Filters based on Particle Swarm Optimization
Faten BEN ARFIA
2009-07-01
Full Text Available This paper presents a particle swarm optimization (PSO algorithm to adjust the parameters of the nonlinear filter and to make this type of the filters more powerful for the elimination of the Gaussian noise and also the impulse noise. In this paper we apply the particle swarm optimization to the rational filters and we completed this work with the comparison between our results and other adaptive nonlinear filters like the LMS adaptive median filters and the no-adaptive rational filter.
Adaptive ship autopilot with wave filter
Steinar Sælid
1983-01-01
Full Text Available This paper is concerned with analysis and design of an adaptive autopilot for ships. The design is based on a low and high frequency model of the vessel motion adequate to ship steering. The low frequency model describes the vessel response to rudder control and slowly varying environmental forces. The high frequency model represents the wave induced oscillatory part of the yaw motion. The models are used in a Kalman filter and the rudder control is computed from linear quadratic theory based on the low frequency part of the vector. This yields a very effective filtering of the wave component of the yaw motion. Proper operation of this filter/controller structure requires knowledge of the vessel model parameters and the dominating wave frequency. The vessel parameters are estimated on line by a recursive prediction error method. In order to reduce the computing requirements, the state estimator is operated using scheduled gains. This results in an easy and robust design. The convergence properties are investigated by using the method of Ljung. The performance is confirmed by simulation experiments.
Survey of Sparse Adaptive Filters for Acoustic Echo Cancellation
Krishna Samalla
2013-01-01
Full Text Available This paper reviews the existing developments of adaptive methods of sparse adaptive filters for the identification of sparse impulse response in both network and acoustic echo cancellation from the last decade. A variety of different architectures and novel training algorithms have been proposed in literature. At present most of the work in echo cancellation on using more than one method. Sparse adaptive filters take the advantage of each method and showing good improvement in the sparseness measure performance. This survey gives an overview of existing sparse adaptive filters mechanisms and discusses their advantages over the traditional adaptive filters developed for echo cancellation.
Performance Evaluation of Adaptive Filters Structures for Acoustic Echo Cancellation
Sanjeev Dhull
2011-05-01
Full Text Available We have designed and simulated an acoustic echo cancellation system for conferencing. This system is based upon a least-mean-square (LMS adaptive algorithm and uses multi filter technique. A comparative study of both structure has been carried out and it is found that this new multi-filter converge faster than similar single long adaptive filter. Index Terms: LMS,Multiple sub filter ,Echo cancellation
Adaptive Local Image Registration: Analysis on Filter Size
Vishnukumar S; M.Wilscy
2012-01-01
Adaptive Local Image Registration is a Local Image Registration based on an Adaptive Filtering frame work. A filter of appropriate size convolves with reference image and gives the pixel values corresponding to the distorted image and the filter is updated in each stage of the convolution. When the filter converges to the system model, it provides the registered image. The filter size plays an important role in this method. The analysis on the filter size is done using Peak Signal-to-Noise Ra...
Adaptive information filtering for dynamic recommender systems
Jin, Ci-Hang; Zhang, Yi-Cheng; Zhou, Tao
2009-01-01
The dynamic environment in the real world calls for the adaptive techniques for information filtering, namely to provide real-time responses to the changes of system data. Where many incremental algorithms are designed for this purpose, they are usually challenged by the worse and worse performance resulted from the cumulative errors over time. In this Letter, we propose two incremental diffusion-based algorithms for the personalized recommendations, which integrate some pieces of local and fast updatings to achieve the approximate results. In addition to the fast responses, the errors of the proposed algorithms do not cumulate over time, that is to say, the global recomputing is unnecessary. This remarkable advantage is demonstrated by several metrics on algorithmic accuracy for two movie recommender systems and a social bookmarking system.
Modified Adaptive Weighted Averaging Filtering Algorithm for Noisy Image Sequences
LI Weifeng; YU Daoyin; CHEN Xiaodong
2007-01-01
In order to avoid the influence of noise variance on the filtering performances, a modified adaptive weighted averaging (MAWA) filtering algorithm is proposed for noisy image sequences. Based upon adaptive weighted averaging pixel values in consecutive frames, this algorithm achieves the filtering goal by assigning smaller weights to the pixels with inappropriate estimated motion trajectory for noise. It only utilizes the intensity of pixels to suppress noise and accordingly is independent of noise variance. To evaluate the performance of the proposed filtering algorithm, its mean square error and percentage of preserved edge points were compared with those of traditional adaptive weighted averaging and non-adaptive mean filtering algorithms under different noise variances. Relevant results show that the MAWA filtering algorithm can preserve image structures and edges under motion after attenuating noise, and thus may be used in image sequence filtering.
Adaptive Federal Kalman Filtering for SINS/GPS Integrated System
杨勇; 缪玲娟
2003-01-01
A new adaptive federal Kalman filter for a strapdown integrated navigation system/global positioning system (SINS/GPS) is given. The developed federal Kalman filter is based on the trace operation of parameters estimation's error covariance matrix and the spectral radius of update measurement noise variance-covariance matrix for the proper choice of the filter weight and hence the filter gain factors. Theoretical analysis and results from simulation in which the SINS/GPS was compared to conventional Kalman filter are presented. Results show that the algorithm of this adaptive federal Kalman filter is simpler than that of the conventional one. Furthermore, it outperforms the conventional Kalman filter when the system is undertaken measurement malfunctions because of its possession of adaptive ability. This filter can be used in the vehicle integrated navigation system.
A new adaptive filtering algorithm for systems with multiplicative noise
WANG Hui-li; CHEN Xi-xin; LU Qian-hao
2005-01-01
Presented here is a new adaptive state filtering algorithm for systems with multiplicative noise. This algorithm estimates the vector state of the system and the statistics of noise when all the statistics of noise are unknown. This filtering algorithm is a simple recursive structure. A simulation example is presented which demonstrates the effectiveness of this filtering algorithm.
Performance Evaluation of Adaptive Filters Structures for Acoustic Echo Cancellation
Sanjeev Dhull
2011-05-01
Full Text Available We have designed and simulated an acoustic echo cancellation system for conferencing. Thissystem is based upon a least-mean-square (LMS adaptive algorithm and uses multi filtertechnique. A comparative study of both structure has been carried out and it is found that thisnew multi-filter converge faster than similar single long adaptive filter.
Scheme of adaptive polarization filtering based on Kalman model
Song Lizhong; Qi Haiming; Qiao Xiaolin; Meng Xiande
2006-01-01
A new kind of adaptive polarization filtering algorithm in order to suppress the angle cheating interference for the active guidance radar is presented. The polarization characteristic of the interference is dynamically tracked by using Kalman estimator under variable environments with time. The polarization filter parameters are designed according to the polarization characteristic of the interference, and the polarization filtering is finished in the target cell. The system scheme of adaptive polarization filter is studied and the tracking performance of polarization filter and improvement of angle measurement precision are simulated. The research results demonstrate this technology can effectively suppress the angle cheating interference in guidance radar and is feasible in engineering.
Autonomous Navigation System Using a Fuzzy Adaptive Nonlinear H∞ Filter
Fariz Outamazirt
2014-09-01
Full Text Available Although nonlinear H∞ (NH∞ filters offer good performance without requiring assumptions concerning the characteristics of process and/or measurement noises, they still require additional tuning parameters that remain fixed and that need to be determined through trial and error. To address issues associated with NH∞ filters, a new SINS/GPS sensor fusion scheme known as the Fuzzy Adaptive Nonlinear H∞ (FANH∞ filter is proposed for the Unmanned Aerial Vehicle (UAV localization problem. Based on a real-time Fuzzy Inference System (FIS, the FANH∞ filter continually adjusts the higher order of the Taylor development thorough adaptive bounds and adaptive disturbance attenuation , which significantly increases the UAV localization performance. The results obtained using the FANH∞ navigation filter are compared to the NH∞ navigation filter results and are validated using a 3D UAV flight scenario. The comparison proves the efficiency and robustness of the UAV localization process using the FANH∞ filter.
A hybrid RNS adaptive filter for channel equalization
Bernocchi, Gian Luca; Cardarilli, Gian Carlo; Re, Andrea Del;
2006-01-01
In this work a hybrid Residue Number System (RNS) implementation of an adaptive FIR filter is presented. The used adaptation algorithm is the Least Mean Squares (LMS). The filter has been designed to meet the constraints of specific class of applications. In fact, it is suitable for applications...... requiring a large number of taps where a serial updating of the filter coefficients is feasible (channel equalization or echo cancellation). In the literature, it has been shown that the RNS implementation of FIR filters grants earnings in area ad power consumption due to the introduced arithmetic...... simplifications. Vice versa, the RNS implementation of the adaptation algorithm needs scaling circuits that are complex and expensive in RNS arithmetic. For this reason, a serial binary implementation of the adaptation algorithm is chosen. The advantages in terms of area and speed of the RNS adaptive filter with...
Robust Hammerstein Adaptive Filtering under Maximum Correntropy Criterion
Zongze Wu
2015-10-01
Full Text Available The maximum correntropy criterion (MCC has recently been successfully applied to adaptive filtering. Adaptive algorithms under MCC show strong robustness against large outliers. In this work, we apply the MCC criterion to develop a robust Hammerstein adaptive filter. Compared with the traditional Hammerstein adaptive filters, which are usually derived based on the well-known mean square error (MSE criterion, the proposed algorithm can achieve better convergence performance especially in the presence of impulsive non-Gaussian (e.g., α-stable noises. Additionally, some theoretical results concerning the convergence behavior are also obtained. Simulation examples are presented to confirm the superior performance of the new algorithm.
Spoof surface plasmon polaritons based notch filter for ultra-wideband microwave waveguide
Xiao, Binggang; Li, Sheng-Hua; Xiao, Sanshui
2016-01-01
satellite communication interference simultaneously. Both the cutoff frequency and the notch frequency are sensitive to the structure parameters, and the cut-off frequency can reach 20 GHz. An adiabatic transition relying on gradient hole-size and flaring ground is designed to effectively couple energy into...... satellite communication signals. Due to planar structures proposed here, it is easy to integrate in the microwave integrated systems, which can play an important role in the microwave communication circuit and system....
Sub-band Implementation of Adaptive Nonlinear Filter for Adaptive Nonlinear Echo Cancellation
Dayong Zhou
2007-04-01
Full Text Available The adaptive Volterra filter has been successfully applied in nonlinear acoustic echo cancellation (AEC systems and nonlinear line echo cancellation systems, but its applications are limited by its required computational complexity and slow convergence rate, especially for systems with long memory length. In this paper, we first apply a more general nonlinear filter, the function expansion nonlinear filter, in the acoustic echo cancellation - the Volterra filter can be regarded as special case of the function expansion nonlinear filter. Then by leveraging to a multi-channel configuration of the function expansion nonlinear filter and the sampling theory for nonlinear systems, we extend linear sub-band delay-less adaptive filter techniques to develop an efficient sub-band implementation of the adaptive function expansion nonlinear filter. The developed sub-band configuration of the adaptive nonlinear filter can greatly improve the convergence rate and reduce the computational complexity of nonlinear echo cancellers, which is shown by analyses and simulations.
Adaptive Lattice Filters for CDMA Overlay
Prahatheesan, V; Wang, J.
2000-01-01
This paper presents the behavior of reflection coefficients of a stochastic gradient lattice (SGL) filter applied to a code-division multiple-access overlay system. Analytic expressions for coefficients for a two-stage filter are derived in a Rayleigh fading channel with the presence of narrow-band interference and additive white Gaussian noise. It is shown that the coefficients of the lattice filter exhibit separate tracking and convergent properties,and that compared to an LMS filter, the l...
Guiju ZHANG
2015-11-01
Full Text Available Developments in micro and nanofabrication technologies have led a variety of grating waveguide structures (GWS being proposed and implemented in optics and laser application systems. A new design of multilayered nanostructure double-grating is described for reflection notch filter. Thin metal film and dielectric film are used and designed with one-dimensional composite gratings. The results calculated by rigorous coupled-wave analysis (RCWA present that the thin metal film between substrate and grating can produce significant attenuated reflections and efficiency in a broad reflected spectral range. The behavior of such a reflection filter is evaluated for refractive index sensing, which can be applied inside the integrated waveguide structure while succeeding cycles in measurement. The filter peaks are designed and obtained in a visible range with full width half maximum (FWHM of several nanometers to less than one nanometer. The multilayered structure shows a sensitivity of refractive index of 220nm/RIU as changing the surroundings. The reflection spectra are studied under different periods, depths and duty cycles. The passive structure and its characteristics can achieve practical applications in various fields, such as optical sensing, color filtering, Raman spectroscopy and laser technology.DOI: http://dx.doi.org/10.5755/j01.ms.21.4.9625
A Review of Bandpass with Tunable Notch Microwave Filter in Wideband Application
Anthony Bruster; Zahriladha Zakaria; Eliyana Ruslan; Ariffin Mutalib
2015-01-01
In the last few years, several microwave filter design with band-pass response have been proposed for ultra-wideband (UWB) application. Among various microwave filter design, microstrip filter are mostly used by researcher due to their low profile, light weight, easy to fabricate and low cost. Conventional microstrip filter can be in any shape whether circular, rectangular or elliptical but some modification or additional variation in their basic design can be made for different purposes f...
Adaptive Threshold Median Filter for Multiple-Impulse Noise
JIANG Bo; HUANG Wei
2007-01-01
Attenuating the noises plays an essential role in the image processing. Almost all the traditional median filters concern the removal of impulse noise having a single layer, whose noise gray level value is constant. In this paper, a new adaptive median filter is proposed to handle those images corrupted not only by single layer noise. The adaptive threshold median filter(ATMF) has been developed by combining the adaptive median filter (AMF) and two dynamic thresholds. Because of the dynamic threshold being used, the ATMF is able to balance the removal of the multiple-impulse noise and the quality of image. Comparison of the proposed method with traditional median filters is provided. Some visual examples are given to demonstrate the performance of the proposed Filter.
Low-power adaptive filter based on RNS components
Bernocchi, Gian Luca; Cardarilli, Gian Carlo; Del Re, Andrea;
2007-01-01
least mean squares (LMS) algorithm, is allowed. Previous work showed that the use of the residue number system (RNS) for the variable FIR filter grants advantages both in area and power consumption. On the other hand, the use of a binary serial implementation of the adaptation algorithm eliminates the......In this paper a low-power implementation of an adaptive FIR filter is presented. The filter is designed to meet the constraints of channel equalization for fixed wireless communications that typically requires a large number of taps, but a serial updating of the filter coefficients, based on the...
Decentralized, Adaptive, Look-Ahead Particle Filtering
Ahmed, Mohamed Osama; Bibalan, Pouyan T.; De Freitas, Nando; Fauvel, Simon
2012-01-01
The decentralized particle filter (DPF) was proposed recently to increase the level of parallelism of particle filtering. Given a decomposition of the state space into two nested sets of variables, the DPF uses a particle filter to sample the first set and then conditions on this sample to generate a set of samples for the second set of variables. The DPF can be understood as a variant of the popular Rao-Blackwellized particle filter (RBPF), where the second step is carried out using Monte Ca...
Superresolution restoration of an image sequence: adaptive filtering approach.
Elad, M; Feuer, A
1999-01-01
This paper presents a new method based on adaptive filtering theory for superresolution restoration of continuous image sequences. The proposed methodology suggests least squares (LS) estimators which adapt in time, based on adaptive filters, least mean squares (LMS) or recursive least squares (RLS). The adaptation enables the treatment of linear space and time-variant blurring and arbitrary motion, both of them assumed known. The proposed new approach is shown to be of relatively low computational requirements. Simulations demonstrating the superresolution restoration algorithms are presented. PMID:18262881
Nonlinear Adaptive Filter for MEMS Gyro Error Cancellation Project
National Aeronautics and Space Administration — The Nonlinear adaptive filters (NAF) can learn deterministic gyro errors and cancel the error’s effect from attitude estimates. By completely canceling...
Adaptive Control of Flexible Structures Using Residual Mode Filters
Balas, Mark J.; Frost, Susan
2010-01-01
Flexible structures containing a large number of modes can benefit from adaptive control techniques which are well suited to applications that have unknown modeling parameters and poorly known operating conditions. In this paper, we focus on a direct adaptive control approach that has been extended to handle adaptive rejection of persistent disturbances. We extend our adaptive control theory to accommodate troublesome modal subsystems of a plant that might inhibit the adaptive controller. In some cases the plant does not satisfy the requirements of Almost Strict Positive Realness. Instead, there maybe be a modal subsystem that inhibits this property. This section will present new results for our adaptive control theory. We will modify the adaptive controller with a Residual Mode Filter (RMF) to compensate for the troublesome modal subsystem, or the Q modes. Here we present the theory for adaptive controllers modified by RMFs, with attention to the issue of disturbances propagating through the Q modes. We apply the theoretical results to a flexible structure example to illustrate the behavior with and without the residual mode filter. We have proposed a modified adaptive controller with a residual mode filter. The RMF is used to accommodate troublesome modes in the system that might otherwise inhibit the adaptive controller, in particular the ASPR condition. This new theory accounts for leakage of the disturbance term into the Q modes. A simple three-mode example shows that the RMF can restore stability to an otherwise unstable adaptively controlled system. This is done without modifying the adaptive controller design.
Reducing Background Noise Through a Stethoscope Cup Using Adaptive Filters
Hill, Bryce E.; Christensen, Douglas
2010-01-01
An adaptive filtering algorithm was used to test the validity of adaptively filtering respiratory signals recorded at the trachea with an external reference microphone. Two different setups were tested. The first used a microphone in open air, the second used a microphone that was housed inside a second stethoscope cup. The primary microphone was affixed to a phantom material. External sounds and music were played via aloud speaker to record additive noise data from within the stethoscope cup...
RLS Adaptive Filtering Algorithms Based on Parallel Computations
V. I. Djigan
2005-01-01
The paper presents a family of the sliding window RLS adaptive filtering algorithms with the regularization of adaptive filter correlation matrix. The algorithms are developed in forms, fitted to the implementation by means of parallel computations. The family includes RLS and fast RLS algorithms based on generalized matrix inversion lemma, fast RLS algorithms based on square root free inverse QR decomposition and linearly constrained RLS algorithms. The considered algorithms are mathematical...
Adaptive Filters Revisited - RFI Mitigation in pulsar observations
Kesteven, M.; Hobbs, G.; Clement, R.; Dawson, B.; Manchester, R.; Uppal, T.
2004-01-01
Pulsar detection and timing experiments are applications where adaptive filters seem eminently suitable tools for radio-frequency-interference (RFI) mitigation. We describe a novel variant which works well in field trials of pulsar observations centred on an observing frequency of 675 MHz, a bandwidth of 64 MHz and with 2-bit sampling. Adaptive filters have generally received bad press for RFI mitigation in radio astronomical observations with their most serious drawback being a spectral echo...
Decentralized, Adaptive, Look-Ahead Particle Filtering
Ahmed, Mohamed Osama; de Freitas, Nando; Fauvel, Simon
2012-01-01
The decentralized particle filter (DPF) was proposed recently to increase the level of parallelism of particle filtering. Given a decomposition of the state space into two nested sets of variables, the DPF uses a particle filter to sample the first set and then conditions on this sample to generate a set of samples for the second set of variables. The DPF can be understood as a variant of the popular Rao-Blackwellized particle filter (RBPF), where the second step is carried out using Monte Carlo approximations instead of analytical inference. As a result, the range of applications of the DPF is broader than the one for the RBPF. In this paper, we improve the DPF in two ways. First, we derive a Monte Carlo approximation of the optimal proposal distribution and, consequently, design and implement a more efficient look-ahead DPF. Although the decentralized filters were initially designed to capitalize on parallel implementation, we show that the look-ahead DPF can outperform the standard particle filter even on ...
Reduction of Radiographic Quantum Noise Using Adaptive Weighted Median Filter
Images are easily corrupted by noise during the data transmission, data capture and data processing. A technical method of noise analyzing and adaptive filtering for reducing of quantum noise in radiography is presented. By adjusting the characteristics of the filter according to local statistics around each pixel of the image as moving windowing, it is possible to suppress noise sufficiently while preserve edge and other significant information required in reading. We have proposed adaptive weighted median(AWM) filters based on local statistics. We show two ways of realizing the AWM filters. One is a simple type of AWM filter, whose weights are given by a simple non-linear function of three local characteristics. The other is the AWM filter which is constructed by homogeneous factor(HF). Homogeneous factor(HF) from the quantum noise models that enables the filter to recognize the local structures of the image is introduced, and an algorithm for determining the HF fitted to the detection systems with various inner statistical properties is proposed. We show by the experimented that the performances of proposed method is superior to these of other filters and models in preserving small details and suppressing the noise at homogeneous region. The proposed algorithms were implemented by visual C++ language on a IBM-PC Pentium 550 for testing purposes, the effects and results of the noise filtering were proposed by comparing with images of the other existing filtering methods
Estimated spectrum adaptive postfilter and the iterative prepost filtering algirighms
Linares, Irving (Inventor)
2004-01-01
The invention presents The Estimated Spectrum Adaptive Postfilter (ESAP) and the Iterative Prepost Filter (IPF) algorithms. These algorithms model a number of image-adaptive post-filtering and pre-post filtering methods. They are designed to minimize Discrete Cosine Transform (DCT) blocking distortion caused when images are highly compressed with the Joint Photographic Expert Group (JPEG) standard. The ESAP and the IPF techniques of the present invention minimize the mean square error (MSE) to improve the objective and subjective quality of low-bit-rate JPEG gray-scale images while simultaneously enhancing perceptual visual quality with respect to baseline JPEG images.
FPGA Implementation of Adaptive Filter and its Performance Analysis
J. Malarmannan
2013-06-01
Full Text Available Adaptive filters are used in various real-time applications such as echo cancellation, noise cancellation, system identification and prediction. Field -programmable gate arrays (FPGAs are alsoused most widely for applications where timing requirements are strict. Thus implementation of filter in real-time is needed. The objective of this paper is to design and implement an Adaptive filter which is robust to impulsive noise using hardware description language (HDL design. The design implementation and its performance analysis are presented. The targeted FPGA is Altera CycloneIV. The obtained design results in superior performance, greater data sample frequency and less logic occupation.
Performance Evaluation of 2D Adaptive Bilateral Filter For Removal of Noise From Robust Images
Sridhar, B.; Dr.K.V.V.S.Reddy
2013-01-01
In this paper, we present the performance analysis of adaptive bilateral filter by pixel to noise ratio and mean square errors. It was evaluate changing the parameters of the adaptive filter half width values and standard deviations. In adaptive bilateral filter, the edge slope is enhanced by transforming the histogram via a range filter with adaptive offset and width. The variance of range filter can also be adaptive. The filter is applied to improve the sharpens of a gray level and color im...
Adaptive robust Kalman filtering for precise point positioning
The optimality of precise point postioning (PPP) solution using a Kalman filter is closely connected to the quality of the a priori information about the process noise and the updated mesurement noise, which are sometimes difficult to obtain. Also, the estimation enviroment in the case of dynamic or kinematic applications is not always fixed but is subject to change. To overcome these problems, an adaptive robust Kalman filtering algorithm, the main feature of which introduces an equivalent covariance matrix to resist the unexpected outliers and an adaptive factor to balance the contribution of observational information and predicted information from the system dynamic model, is applied for PPP processing. The basic models of PPP including the observation model, dynamic model and stochastic model are provided first. Then an adaptive robust Kalmam filter is developed for PPP. Compared with the conventional robust estimator, only the observation with largest standardized residual will be operated by the IGG III function in each iteration to avoid reducing the contribution of the normal observations or even filter divergence. Finally, tests carried out in both static and kinematic modes have confirmed that the adaptive robust Kalman filter outperforms the classic Kalman filter by turning either the equivalent variance matrix or the adaptive factor or both of them. This becomes evident when analyzing the positioning errors in flight tests at the turns due to the target maneuvering and unknown process/measurement noises. (paper)
Adaptive monogenic filtering and normalization of ESPI fringe patterns
Guerrero, J. A.; Marroquin, J. L.; Rivera, M.; Quiroga, J. A.
2005-11-01
A technique is presented for filtering and normalizing noisy fringe patterns, which may include closed fringes, so that single-frame demodulation schemes may be successfully applied. It is based on the construction of an adaptive filter as a linear combination of the responses of a set of isotropic bandpass filters. The space-varying coefficients are proportional to the envelope of the response of each filter, which in turn is computed by using the corresponding monogenic image [Felsberg and Sommer, IEEE Trans. Signal Process.49, 3136 (2001)]. Some examples of demodulation of real Electronic Speckle Pattern Interferometry (ESPI) images patterns are presented.
Adaptive Filtering for Non-Gaussian Processes
Kidmose, Preben
2000-01-01
A new stochastic gradient robust filtering method, based on a non-linear amplitude transformation, is proposed. The method requires no a priori knowledge of the characteristics of the input signals and it is insensitive to the signals distribution and to the stationarity of the signals. A...... simulation study, applying both synthetic and real-world signals, shows that the proposed method has overall better robustness performance, in terms of modeling error, compared with state-of-the-art robust filtering methods. A remarkable property of the proposed method is that it can handle double-talk in...
Adaptive Filtering for Aeroservoelastic Response Suppression Project
National Aeronautics and Space Administration — CSA Engineering proposes the design of an adaptive aeroelastic mode suppression for advanced fly-by-wire aircraft, which will partition the modal suppression...
An Efficient Adaptive Filtering for CFA Demosaicking
Dev. R. Newlin,; Elwin Chandra Monie
2010-01-01
Most digital still cameras acquire imagery with a color filter array (CFA), sampling only one color value for each pixel and interpolating the other two color values afterwards. The interpolation process is commonly known as demosaicking. In general, a good demosaicking method should preserve the highfrequency information of imagery as much as possible, since such information is essential for image visual quality. We discuss in this paper two key observations for preserving high-frequency inf...
Adaptive Control Using Residual Mode Filters Applied to Wind Turbines
Frost, Susan A.; Balas, Mark J.
2011-01-01
Many dynamic systems containing a large number of modes can benefit from adaptive control techniques, which are well suited to applications that have unknown parameters and poorly known operating conditions. In this paper, we focus on a model reference direct adaptive control approach that has been extended to handle adaptive rejection of persistent disturbances. We extend this adaptive control theory to accommodate problematic modal subsystems of a plant that inhibit the adaptive controller by causing the open-loop plant to be non-minimum phase. We will augment the adaptive controller using a Residual Mode Filter (RMF) to compensate for problematic modal subsystems, thereby allowing the system to satisfy the requirements for the adaptive controller to have guaranteed convergence and bounded gains. We apply these theoretical results to design an adaptive collective pitch controller for a high-fidelity simulation of a utility-scale, variable-speed wind turbine that has minimum phase zeros.
Junction-type photonic crystal waveguides for notch- and pass-band filtering
Shahid, Naeem
2011-01-01
Evolution of the mode gap and the associated transmission mini stop-band (MSB) as a function of photonic crystal (PhC) waveguide width is theoretically and experimentally investigated. The change of line-defect width is identified to be the most appropriate way since it offers a wide MSB wavelength tuning range. A high transmission narrow-band filter is experimentally demonstrated in a junction-type waveguide composed of two PhC waveguides with slightly different widths. The full width at half maximum is 5.6 nm; the peak transmission is attenuated by only ∼5 dB and is ∼20 dB above the MSBs. Additionally, temperature tuning of the filter were also performed. The results show red-shift of the transmission peak and the MSB edges with a gradient of dλ/dT = 0.1 nm/°C. It is proposed that the transmission MSBs in such junction-type cascaded PhC waveguides can be used to obtain different types of filters. © 2011 Optical Society of America.
Performance Evaluation Of Different Adaptive Filters For ECG Signal Processing
Sachin singh,
2010-08-01
Full Text Available One of the main problem in biomedical data processing like electrocardiography is the separation of the wanted signal from noises caused by power line interference, external electromagnetic fields and random body movements and respiration. Different types of digital filters are used to remove signal components from unwanted frequency ranges. It is difficult to apply filters with fixed coefficients to reduce Biomedical Signal noises, because human behavior is not exact known depending on the time. Adaptive filter technique is required to overcome this problem. In this paper two types of adaptive filters are considered to reduce the ECG signal noises like PLI and Base Line Interference. Results of simulations inMATLAB are presented.
Adaptive Gaussian Mixture Filter Based on Statistical Linearization
Huber, Marco F.
2012-01-01
Gaussian mixtures are a common density representation in nonlinear, non-Gaussian Bayesian state estimation. Selecting an appropriate number of Gaussian components, however, is difficult as one has to trade of computational complexity against estimation accuracy. In this paper, an adaptive Gaussian mixture filter based on statistical linearization is proposed. Depending on the nonlinearity of the considered estimation problem, this filter dynamically increases the number of components via spli...
Multimodal Medical Image Fusion by Adaptive Manifold Filter
Peng Geng; Shuaiqi Liu; Shanna Zhuang
2015-01-01
Medical image fusion plays an important role in diagnosis and treatment of diseases such as image-guided radiotherapy and surgery. The modified local contrast information is proposed to fuse multimodal medical images. Firstly, the adaptive manifold filter is introduced into filtering source images as the low-frequency part in the modified local contrast. Secondly, the modified spatial frequency of the source images is adopted as the high-frequency part in the modified local contrast. Finally,...
A nonlinear neural fir filter with an adaptive activation function
Lee Su Goh
2003-01-01
Full Text Available An adaptive amplitude normalized nonlinear gradient descent (AANNGD algorithm for the class of nonlinear finite impulse response (FIR adaptive filters (dynamical perception is introduced. This is achieved by making the amplitude of the nonlinear activation function gradient adaptive. The proposed learning algorithm is suitable for processing of nonlinear and nonstationary signals with a large dynamical range, and removes the unwanted effect of saturation nonlinearities. For rigor, sensitivity analysis is performed and the improved performance of the AANNGD algorithm over the standard LMS, NGD, NNGD, the fully adaptive NNGD (FANNGD and the sign algorithm is verified by simulations on nonlinear and nonstationary inputs with large dynamics.
Artifact removal from EEG signals using adaptive filters in cascade
Artifacts in EEG (electroencephalogram) records are caused by various factors, like line interference, EOG (electro-oculogram) and ECG (electrocardiogram). These noise sources increase the difficulty in analyzing the EEG and to obtaining clinical information. For this reason, it is necessary to design specific filters to decrease such artifacts in EEG records. In this paper, a cascade of three adaptive filters based on a least mean squares (LMS) algorithm is proposed. The first one eliminates line interference, the second adaptive filter removes the ECG artifacts and the last one cancels EOG spikes. Each stage uses a finite impulse response (FIR) filter, which adjusts its coefficients to produce an output similar to the artifacts present in the EEG. The proposed cascade adaptive filter was tested in five real EEG records acquired in polysomnographic studies. In all cases, line-frequency, ECG and EOG artifacts were attenuated. It is concluded that the proposed filter reduces the common artifacts present in EEG signals without removing significant information embedded in these records
Improvement of the Simplified Fast Transversal Filter Type Algorithm for Adaptive Filtering
Madjid Arezki
2009-01-01
Full Text Available Problem statement: In this study, we proposed a new algorithm M-SMFTF for adaptive filtering with fast convergence and low complexity. Approach: It was the result of a simplified FTF type algorithm, where the adaptation gain was obtained only from the forward prediction variables and using a new recursive method to compute the likelihood variable. Results: The computational complexity was reduced from 7L-6L, where L is the finite impulse response filter length. Furthermore, this computational complexity can be significantly reduced to (2L+4P when used with a reduced P-size forward predictor. Conclusion: This algorithm presented a certain interest, for the adaptation of very long filters, like those used in the problems of echo acoustic cancellation, due to its reduced complexity, its numerical stability and its convergence in the presence of the speech signal.
Design and Efficient Implementation of Oversampled GDFT Filter Banks for Subband Adaptive Filtering
Weiss, S; Harteneck, M; Stewart, R W
1998-01-01
This paper introduces a polyphase implementation and design of an oversampled K-channel generalized DFT (GDFT) filter bank, which can be employed for subband adaptive filtering, and therefore is required to have a low aliasing level in the subband signals. A polyphase structure is derived which can be factorized into a real valued polyphase network and a GDFT modulation. For the latter, an FFT realization may be used, yielding a highly efficient polyphase implementation for arbitrary integer ...
Adaptive Compensation of Reactive Power With Shunt Active Power Filters
Blaabjerg, Frede; Asiminoaei, Lucian; Hansen, Steffan;
2008-01-01
This paper describes an adaptive method for compensating the reactive power with an active power filter (APF), which is initially rated for mitigation of only the harmonic currents given by a nonlinear industrial load. It is proven that, if the harmonic currents do not load the APF at the rated...
An adaptive Kalman filter for speckle reductions in ultrasound images
Speckle is the term used to describe the granular appearance found in ultrasound images. The presence of speckle reduces the diagnostic potential of the echographic technique because it tends to mask small inhomogeneities of the investigated tissue. We developed a new method of speckle reductions that utilizes an adaptive one-dimensional Kalman filter based on the assumption that the observed image can be considered as a superimposition of speckle on a ''true images''. The filter adaptivity, necessary to avoid loss of resolution, has been obtained by statistical considerations on the local signal variations. The results of the applications of this particular Kalman filter, both on A-Mode and B-MODE images, show a significant speckle reduction
BPSK Receiver Based on Recursive Adaptive Filter with Remodulation
N. Milosevic
2011-12-01
Full Text Available This paper proposes a new binary phase shift keying (BPSK signal receiver intended for reception under conditions of significant carrier frequency offsets. The recursive adaptive filter with least mean squares (LMS adaptation is used. The proposed receiver has a constant, defining the balance between the recursive and the nonrecursive part of the filter, whose proper choice allows a simple construction of the receiver. The correct choice of this parameter could result in unitary length of the filter. The proposed receiver has performance very close to the performance of the BPSK receiver with perfect frequency synchronization, in a wide range of frequency offsets (plus/minus quarter of the signal bandwidth. The results obtained by the software simulation are confirmed by the experimental results measured on the receiver realized with the universal software radio peripheral (USRP, with the baseband signal processing at personal computer (PC.
Adaptive training of feedforward neural networks by Kalman filtering
Adaptive training of feedforward neural networks by Kalman filtering is described. Adaptive training is particularly important in estimation by neural network in real-time environmental where the trained network is used for system estimation while the network is further trained by means of the information provided by the experienced/exercised ongoing operation. As result of this, neural network adapts itself to a changing environment to perform its mission without recourse to re-training. The performance of the training method is demonstrated by means of actual process signals from a nuclear power plant. (orig.)
Bridging the ensemble Kalman filter and particle filters: the adaptive Gaussian mixture filter
Stordal, Andreas Størksen; Karlsen, Hans A.; Nævdal, Geir; Skaug, Hans J.; Vallès, Brice
2010-01-01
The nonlinear filtering problem occurs in many scientific areas. Sequential Monte Carlo solutions with the correct asymptotic behavior such as particle filters exist, but they are computationally too expensive when working with high-dimensional systems. The ensemble Kalman filter (EnKF) is a more robust method that has shown promising results with a small sample size, but the samples are not guaranteed to come from the true posterior distribution. By approximating the model error with a Gauss...
This paper describes ORNL's development of an environment for the simulation of robotic manipulators. Simulation includes the modeling of kinematics, dynamics, sensors, actuators, control systems, operators, and environments. Models will be used for manipulator design, proposal evaluation, control system design and analysis, graphical preview of proposed motions, safety system development, and training. Of particular interest is the development of models for robotic manipulators having at least one flexible link. As a first application, models have been developed for the Pacific Northwest Laboratory's Flexible Beam Test Bed (PNL FBTB), which is a 1-Degree-of-Freedom, flexible arm with a hydraulic base actuator. ORNL transferred control algorithms developed for the PNL FBTB to controlling IGRIP models. A robust notch filter is running in IGRIP controlling a full dynamics model of the PNL test bed. Model results provide a reasonable match to the experimental results (quantitative results are being determined) and can run on ORNL's Onyx machine in approximately realtime. The flexible beam is modeled as six rigid sections with torsional springs between each segment. The spring constants were adjusted to match the physical response of the flexible beam model to the experimental results. The controller is able to improve performance on the model similar to the improvement seen on the experimental system. Some differences are apparent, most notably because the IGRIP model presently uses a different trajectory planner than the one used by ORNL on the PNL test bed. In the future, the trajectory planner will be modified so that the experiments and models are the same. The successful completion of this work provides the ability to link C code with IGRIP, thus allowing controllers to be developed, tested, and tuned in simulation and then ported directly to hardware systems using the C language
Paulchamy Balaiah
2012-01-01
Full Text Available Problem statement: This study presents an effective method for removing mixed artifacts (EOG-Electro-ocular gram, ECG-Electrocardiogram, EMG-Electromyogram from the EEG-Electroencephalogram records. The noise sources increases the difficulty in analyzing the EEG and obtaining clinical information. EEG signals are multidimensional, non-stationary (i.e., statistical properties are not invariant in time, time domain biological signals, which are not reproducible. It is supposed to contain information about what is going on in the ensemble of excitatory pyramidal neuron level, at millisecond temporal resolution scale. Since scalp EEG contains considerable amount of noise and artifacts and exactly where it is coming from is poorly determined, extracting information from it is extremely challenging. For this reason it is necessary to design specific filters to decrease such artifacts in EEG records. Approach: Some of the other methods that are really appealing are artifact removal through Independent Component Analysis (ICA, Wavelet Transforms, Linear filtering and Artificial Neural Networks. ICA method could be used in situations, where large numbers of noises need to be distinguished, but it is not suitable for on-line real time application like Brain Computer Interface (BCI. Wavelet transforms are suitable for real-time application, but there all success lies in the selection of the threshold function. Linear filtering is best when; the frequency of noises does not interfere or overlap with each other. In this study we proposed adaptive filtering and neuro-fuzzy filtering method to remove artifacts from EEG. Adaptive filter performs linear filtering. Neuro-fuzzy approaches are very promising for non-linear filtering of noisy image. The multiple-output structure is based on recursive processing. It is able to adapt the filtering action to different kinds of corrupting noise. Fuzzy reasoning embedded into the network structure aims at reducing errors
Wagner D.
2015-01-01
Full Text Available Sensitive millimeter wave diagnostics need often to be protected against unwanted radiation like, for example, stray radiation from high power Electron Cyclotron Heating applied in nuclear fusion plasmas. A notch filter based on a waveguide Bragg reflector (photonic band-gap may provide several stop bands of defined width within up to two standard waveguide frequency bands. A Bragg reflector that reflects an incident fundamental TE11 into a TM1n mode close to cutoff is combined with two waveguide tapers to fundamental waveguide diameter. Here the fundamental TE11 mode is the only propagating mode at both ends of the reflector. The incident TE11 mode couples through the taper and is converted to the high order TM1n mode by the Bragg structure at the specific Bragg resonances. The TM1n mode is trapped in the oversized waveguide section by the tapers. Once reflected at the input taper it will be converted back into the TE11 mode which then can pass through the taper. Therefore at higher order Bragg resonances, the filter acts as a reflector for the incoming TE11 mode. Outside of the Bragg resonances the TE11 mode can propagate through the oversized waveguide structure with only very small Ohmic attenuation compared to propagating in a fundamental waveguide. Coupling to other modes is negligible in the non-resonant case due to the small corrugation amplitude (typically 0.05·λ0, where λ0 is the free space wavelength. A Bragg reflector for 105 and 140 GHz was optimized by mode matching (scattering matrix simulations and manufactured by SWISSto12 SA, where the required mechanical accuracy of ± 5 μm could be achieved by stacking stainless steel rings, manufactured by micro-machining, in a high precision guiding pipe. The two smooth-wall tapers were fabricated by electroforming. Several measurements were performed using vector network analyzers from Agilent (E8362B, ABmm (MVNA 8-350 and Rohde&Schwarz (ZVA24 together with frequency multipliers. The
Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems
Chien-Hao Tseng
2016-07-01
Full Text Available This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF and fuzzy logic adaptive system (FLAS for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF, unscented Kalman filter (UKF, and CKF approaches.
Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems.
Tseng, Chien-Hao; Lin, Sheng-Fuu; Jwo, Dah-Jing
2016-01-01
This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF) and fuzzy logic adaptive system (FLAS) for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system) integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF) is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD) parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF), unscented Kalman filter (UKF), and CKF approaches. PMID:27472336
Demystifying the Digital Adaptive Filters Conducts in Acoustic Echo Cancellation
Mohammad Anamul Haque
2010-12-01
Full Text Available A digital sound system resembles the wireless network link transmission system. A wireless network link is affected by several disturbing factors: fading, attenuation, non-linear distortion and noise. These factors are also unavoidable in acoustic echo cancellation. A few number of digital adaptive filter algorithms are approached to detect and cancel the noise in a system. Among these algorithms Least Mean Square, Block Frequency Domain Adaptive Filter and Kalman Filter are widely used to predict and remove the noise or unwanted signals. It is found that performances of these filters varied and depends upon the system of application. Therefore, a detailed performance analysis is exigent. The goal of this paper is to use an approach to analyze and figure out the best performed filter by performance evaluation in the context of acoustic echo cancellation.
Chen, Yangkang
2016-07-01
The seislet transform has been demonstrated to have a better compression performance for seismic data compared with other well-known sparsity promoting transforms, thus it can be used to remove random noise by simply applying a thresholding operator in the seislet domain. Since the seislet transform compresses the seismic data along the local structures, the seislet thresholding can be viewed as a simple structural filtering approach. Because of the dependence on a precise local slope estimation, the seislet transform usually suffers from low compression ratio and high reconstruction error for seismic profiles that have dip conflicts. In order to remove the limitation of seislet thresholding in dealing with conflicting-dip data, I propose a dip-separated filtering strategy. In this method, I first use an adaptive empirical mode decomposition based dip filter to separate the seismic data into several dip bands (5 or 6). Next, I apply seislet thresholding to each separated dip component to remove random noise. Then I combine all the denoised components to form the final denoised data. Compared with other dip filters, the empirical mode decomposition based dip filter is data-adaptive. One only needs to specify the number of dip components to be separated. Both complicated synthetic and field data examples show superior performance of my proposed approach than the traditional alternatives. The dip-separated structural filtering is not limited to seislet thresholding, and can also be extended to all those methods that require slope information.
Chen, Yangkang
2016-04-01
The seislet transform has been demonstrated to have a better compression performance for seismic data compared with other well-known sparsity promoting transforms, thus it can be used to remove random noise by simply applying a thresholding operator in the seislet domain. Since the seislet transform compresses the seismic data along the local structures, the seislet thresholding can be viewed as a simple structural filtering approach. Because of the dependence on a precise local slope estimation, the seislet transform usually suffers from low compression ratio and high reconstruction error for seismic profiles that have dip conflicts. In order to remove the limitation of seislet thresholding in dealing with conflicting-dip data, I propose a dip-separated filtering strategy. In this method, I first use an adaptive empirical mode decomposition based dip filter to separate the seismic data into several dip bands (5 or 6). Next, I apply seislet thresholding to each separated dip component to remove random noise. Then I combine all the denoised components to form the final denoised data. Compared with other dip filters, the empirical mode decomposition based dip filter is data-adaptive. One only need to specify the number of dip components to be separated. Both complicated synthetic and field data examples show superior performance of my proposed approach than the traditional alternatives. The dip-separated structural filtering is not limited to seislet thresholding, and can also be extended to all those methods that require slope information.
Cahit Tağı ÇELİK
2004-01-01
Full Text Available Monitoring the Crustal Movement in Geodesy is performed by the deformation survey and analysis. If monitoring the crustal movements involves more than two epochs of survey campaign then from the plate tectonic theory, stations do not move randomly from one epoch to the other, therefore Kalman Filter may be suitable to use. However, if sudden movements happened in the crust in particular earthquake happened, the crust moves very fast in a very short period of time. When Kalman Filter used for monitoring these movements, from associated epoch, for a number of epochs the results may be biased. In the paper, comparison of two methods for elimination of the above mentioned biases have been performed. These methods are Fading Memory Filter and Adaptive Kalman Filter for an unknown bias.
Market Risk Beta Estimation using Adaptive Kalman Filter
Atanu Das,
2010-06-01
Full Text Available Market risk of an asset or portfolio is recognized through beta in Capital Asset Pricing Model (CAPM. Traditional estimation techniques emerge poor results when beta in CAPM assumed to be dynamic and follows auto regressive model. Kalman Filter (KF can optimally estimate dynamic beta where measurement noise covariance and state noise covariance are assumed to be known in a state-space framework. This paper applied Adaptive Kalman Filter (AKF for beta estimation when the above covariances are not known and estimated dynamically. The technique is first characterized through simulation study and then applied to empirical data from Indian security market. A odification of the used AKF is also proposed to take care of the problems of AKF implementation onbeta estimation and simulations show that modified method improves the performance of the filter measured by RMSE.
Kalman filtering to suppress spurious signals in Adaptive Optics control
Poyneer, L; Veran, J P
2010-03-29
In many scenarios, an Adaptive Optics (AO) control system operates in the presence of temporally non-white noise. We use a Kalman filter with a state space formulation that allows suppression of this colored noise, hence improving residual error over the case where the noise is assumed to be white. We demonstrate the effectiveness of this new filter in the case of the estimated Gemini Planet Imager tip-tilt environment, where there are both common-path and non-common path vibrations. We discuss how this same framework can also be used to suppress spatial aliasing during predictive wavefront control assuming frozen flow in a low-order AO system without a spatially filtered wavefront sensor, and present experimental measurements from Altair that clearly reveal these aliased components.
Infinite impulse response modal filtering in visible adaptive optics
Agapito, G; Quirós-Pacheco, F; Puglisi, A; Esposito, S
2012-01-01
Diffraction limited resolution adaptive optics (AO) correction in visible wavelengths requires a high performance control. In this paper we investigate infinite impulse response filters that optimize the wavefront correction: we tested these algorithms through full numerical simulations of a single-conjugate AO system comprising an adaptive secondary mirror with 1127 actuators and a pyramid wavefront sensor (WFS). The actual practicability of the algorithms depends on both robustness and knowledge of the real system: errors in the system model may even worsen the performance. In particular we checked the robustness of the algorithms in different conditions, proving that the proposed method can reject both disturbance and calibration errors.
Infinite impulse response modal filtering in visible adaptive optics
Agapito, G.; Arcidiacono, C.; Quirós-Pacheco, F.; Puglisi, A.; Esposito, S.
2012-07-01
Diffraction limited resolution adaptive optics (AO) correction in visible wavelengths requires a high performance control. In this paper we investigate infinite impulse response filters that optimize the wavefront correction: we tested these algorithms through full numerical simulations of a single-conjugate AO system comprising an adaptive secondary mirror with 1127 actuators and a pyramid wavefront sensor (WFS). The actual practicability of the algorithms depends on both robustness and knowledge of the real system: errors in the system model may even worsen the performance. In particular we checked the robustness of the algorithms in different conditions, proving that the proposed method can reject both disturbance and calibration errors.
Adaptive Neuro-Fuzzy Extended Kalman Filtering for Robot Localization
Havangi, Ramazan; Teshnehlab, Mohammad
2010-01-01
Extended Kalman Filter (EKF) has been a popular approach to localization a mobile robot. However, the performance of the EKF and the quality of the estimation depends on the correct a priori knowledge of process and measurement noise covariance matrices (Qk and Rk, respectively). Imprecise knowledge of these statistics can cause significant degradation in performance. This paper proposed the development of an Adaptive Neuro- Fuzzy Extended Kalman Filtering (ANFEKF) for localization of robot. The Adaptive Neuro-Fuzzy attempts to estimate the elements of Qk and Rk matrices of the EKF algorithm, at each sampling instant when measurement update step is carried out. The ANFIS supervises the performance of the EKF with the aim of reducing the mismatch between the theoretical and actual covariance of the innovation sequences. The free parameters of ANFIS are trained using the steepest gradient descent (SD) to minimize the differences of the actual value of the covariance of the residual with its theoretical value as...
Adaptive control of large space structures using recursive lattice filters
Sundararajan, N.; Goglia, G. L.
1985-01-01
The use of recursive lattice filters for identification and adaptive control of large space structures is studied. Lattice filters were used to identify the structural dynamics model of the flexible structures. This identification model is then used for adaptive control. Before the identified model and control laws are integrated, the identified model is passed through a series of validation procedures and only when the model passes these validation procedures is control engaged. This type of validation scheme prevents instability when the overall loop is closed. Another important area of research, namely that of robust controller synthesis, was investigated using frequency domain multivariable controller synthesis methods. The method uses the Linear Quadratic Guassian/Loop Transfer Recovery (LQG/LTR) approach to ensure stability against unmodeled higher frequency modes and achieves the desired performance.
Adaptive Kalman filtering for anomaly detection in software appliances
Knorn, Florian; Leith, Douglas J.
2008-01-01
Availability and reliability are often important features of key software appliances such as firewalls, web servers, etc. In this paper we seek to go beyond the simple heartbeat monitoring that is widely used for failover control. We do this by integrating more fine grained measurements that are readily available on most platforms to detect possible faults or the onset of failures. In particular, we evaluate the use of adaptive Kalman Filtering for automated CPU usage prediction that...
Reduced-Rank Adaptive Filtering Using Krylov Subspace
Sergueï Burykh; Karim Abed-Meraim
2003-01-01
A unified view of several recently introduced reduced-rank adaptive filters is presented. As all considered methods use Krylov subspace for rank reduction, the approach taken in this work is inspired from Krylov subspace methods for iterative solutions of linear systems. The alternative interpretation so obtained is used to study the properties of each considered technique and to relate one reduced-rank method to another as well as to algorithms used in computational linear algebra. Practical...
Reduced-Rank Adaptive Filtering Using Krylov Subspace
Burykh Sergueï; Abed-Meraim Karim
2002-01-01
A unified view of several recently introduced reduced-rank adaptive filters is presented. As all considered methods use Krylov subspace for rank reduction, the approach taken in this work is inspired from Krylov subspace methods for iterative solutions of linear systems. The alternative interpretation so obtained is used to study the properties of each considered technique and to relate one reduced-rank method to another as well as to algorithms used in computational linear algebra. Practica...
Model Adaptation for Prognostics in a Particle Filtering Framework
Bhaskar Saha
2011-01-01
Full Text Available One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the “curse of dimensionality”, i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for “well-designed” particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion and Li-Polymer batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well-designed prognostic model that can take advantage of the model adaptation properties of a particle filter.
Yingsong Li
2013-01-01
Full Text Available A printed reconfigurable ultra-wideband (UWB monopole antenna with triple narrow band-notched characteristics is proposed for cognitive radio applications in this paper. The triple narrow band-notched frequencies are obtained using a defected microstrip structure (DMS band stop filter (BSF embedded in the microstrip feed line and an inverted π-shaped slot etched in the rectangular radiation patch, respectively. Reconfigurable characteristics of the proposed cognitive radio antenna (CRA are achieved by means of four ideal switches integrated on the DMS-BSF and the inverted π-shaped slot. The proposed UWB CRA can work at eight modes by controlling switches ON and OFF. Moreover, impedance bandwidth, design procedures, and radiation patterns are presented for analysis and explanation of this antenna. The designed antenna operates over the frequency band between 3.1 GHz and 14 GHz (bandwidth of 127.5%, with three notched bands from 4.2 GHz to 6.2 GHz (38.5%, 6.6 GHz to 7.0 GHz (6%, and 12.2 GHz to 14 GHz (13.7%. The antenna is successfully simulated, fabricated, and measured. The results show that it has wide impedance bandwidth, multimodes characteristics, stable gain, and omnidirectional radiation patterns.
Adaptive distributed Kalman filtering with wind estimation for astronomical adaptive optics.
Massioni, Paolo; Gilles, Luc; Ellerbroek, Brent
2015-12-01
In the framework of adaptive optics (AO) for astronomy, it is a common assumption to consider the atmospheric turbulent layers as "frozen flows" sliding according to the wind velocity profile. For this reason, having knowledge of such a velocity profile is beneficial in terms of AO control system performance. In this paper we show that it is possible to exploit the phase estimate from a Kalman filter running on an AO system in order to estimate wind velocity. This allows the update of the Kalman filter itself with such knowledge, making it adaptive. We have implemented such an adaptive controller based on the distributed version of the Kalman filter, for a realistic simulation of a multi-conjugate AO system with laser guide stars on a 30 m telescope. Simulation results show that this approach is effective and promising and the additional computational cost with respect to the distributed filter is negligible. Comparisons with a previously published slope detection and ranging wind profiler are made and the impact of turbulence profile quantization is assessed. One of the main findings of the paper is that all flavors of the adaptive distributed Kalman filter are impacted more significantly by turbulence profile quantization than the static minimum mean square estimator which does not incorporate wind profile information. PMID:26831389
Switched Band-Pass Filters for Adaptive Transceivers
Wang, Ray
2007-01-01
Switched band-pass filters are key components of proposed adaptive, software- defined radio transceivers that would be parts of envisioned digital-data-communication networks that would enable real-time acquisition and monitoring of data from geographically distributed sensors. Examples of sensors to be connected to such networks include security cameras, radio-frequency identification units, and geolocation units based on the Global Positioning System. Through suitable software configuration and without changing hardware, these transceivers could be made to operate according to any of a number of complex wireless-communication standards that could be characterized by diverse modulation schemes, bandwidths, and data-handling protocols. The adaptive transceivers would include field-programmable gate arrays (FPGAs) and digital signal-processing hardware. In the receiving path of a transceiver, the incoming signal would be amplified by a low-noise amplifier (LNA). The output spectrum of the LNA would be processed by a band-pass filter operating in the frequency range between 900 MHz and 2.4 GHz. Then a down-converter would translate the signal to a lower frequency range to facilitate analog-to-digital conversion, which would be followed by baseband processing by one or more FPGAs. In the transmitting path, a digital stream would first be converted to an analog signal, which would then be up-converted to a selected frequency band before being applied to a transmitting power amplifier. The aforementioned band-pass filter in the receiving path would be a combination of resonant inductor-and-capacitor filters and switched band-pass filters. The overall combination would implement a switch function designed mathematically to exhibit desired frequency responses and to switch the signal in each frequency band to an analog-to-digital converter appropriate for that band to produce a digital intermediate-frequency signal for digital signal processing.
Coevolution-Based Adaptive Particle Filters for Global Localization
LUORonghua; HONGBingrong; PIAOSonghao; DAIHuming
2005-01-01
A coevolution mechanism derived from competition relationships between ecological species is merged into Particle filters (PF). The new version of particle filters is termed Coevolutionbased adaptive particle filters (CEAPF). In CEAPF, samples are clustered into species, each of which represents a hypothesis of state of the system in a higher level than a single sample. Since the coevolution between the species ensures that the multiple distinct hypotheses can be tracked stably, the problem of premature convergence of PF can be solved. And the number of samples can be adjusted adaptively over time according to the uncertainty of the state of the system by using the population growth model. In addition, by using the crossover and mutation operators in evolutionary computation, intra-species evolution can drive the samples move towards the regions where the desired posterior density is large. So a small number of samples can represent the desired density well enough. And CEAPF is applied to robot localization in highly symmetric environments. Experiments prove that CEAPF can considerably improve the success rate and precision of localization.
Meng, Yang; Gao, Shesheng; Zhong, Yongmin; Hu, Gaoge; Subic, Aleksandar
2016-03-01
The use of the direct filtering approach for INS/GNSS integrated navigation introduces nonlinearity into the system state equation. As the unscented Kalman filter (UKF) is a promising method for nonlinear problems, an obvious solution is to incorporate the UKF concept in the direct filtering approach to address the nonlinearity involved in INS/GNSS integrated navigation. However, the performance of the standard UKF is dependent on the accurate statistical characterizations of system noise. If the noise distributions of inertial instruments and GNSS receivers are not appropriately described, the standard UKF will produce deteriorated or even divergent navigation solutions. This paper presents an adaptive UKF with noise statistic estimator to overcome the limitation of the standard UKF. According to the covariance matching technique, the innovation and residual sequences are used to determine the covariance matrices of the process and measurement noises. The proposed algorithm can estimate and adjust the system noise statistics online, and thus enhance the adaptive capability of the standard UKF. Simulation and experimental results demonstrate that the performance of the proposed algorithm is significantly superior to that of the standard UKF and adaptive-robust UKF under the condition without accurate knowledge on system noise, leading to improved navigation precision.
Samuel Boudet
2014-01-01
Full Text Available Muscle artifacts constitute one of the major problems in electroencephalogram (EEG examinations, particularly for the diagnosis of epilepsy, where pathological rhythms occur within the same frequency bands as those of artifacts. This paper proposes to use the method dual adaptive filtering by optimal projection (DAFOP to automatically remove artifacts while preserving true cerebral signals. DAFOP is a two-step method. The first step consists in applying the common spatial pattern (CSP method to two frequency windows to identify the slowest components which will be considered as cerebral sources. The two frequency windows are defined by optimizing convolutional filters. The second step consists in using a regression method to reconstruct the signal independently within various frequency windows. This method was evaluated by two neurologists on a selection of 114 pages with muscle artifacts, from 20 clinical recordings of awake and sleeping adults, subject to pathological signals and epileptic seizures. A blind comparison was then conducted with the canonical correlation analysis (CCA method and conventional low-pass filtering at 30 Hz. The filtering rate was 84.3% for muscle artifacts with a 6.4% reduction of cerebral signals even for the fastest waves. DAFOP was found to be significantly more efficient than CCA and 30 Hz filters. The DAFOP method is fast and automatic and can be easily used in clinical EEG recordings.
Statistical-uncertainty-based adaptive filtering of lidar signals
An adaptive filter signal processing technique is developed to overcome the problem of Raman lidar water-vapor mixing ratio (the ratio of the water-vapor density to the dry-air density) with a highly variable statistical uncertainty that increases with decreasing photomultiplier-tube signal strength and masks the true desired water-vapor structure. The technique, applied to horizontal scans, assumes only statistical horizontal homogeneity. The result is a variable spatial resolution water-vapor signal with a constant variance out to a range limit set by a specified signal-to-noise ratio. The technique was applied to Raman water-vapor lidar data obtained at a coastal pier site together with in situ instruments located 320 m from the lidar. The micrometerological humidity data were used to calibrate the ratio of the lidar gains of the H2O and the N2 photomultiplier tubes and set the water-vapor mixing ratio variance for the adaptive filter. For the coastal experiment the effective limit of the lidar range was found to be approximately 200 m for a maximum noise-to-signal variance ratio of 0.1 with the implemented data-reduction procedure. The technique can be adapted to off-horizontal scans with a small reduction in the constraints and is also applicable to other remote-sensing devices that exhibit the same inherent range-dependent signal-to-noise ratio problem. (c) 2000 Optical Society of America
Reduced rank adaptive filtering in impulsive noise environments
Soury, Hamza
2014-11-01
An impulsive noise environment is considered in this paper. A new aspect of signal truncation is deployed to reduce the harmful effect of the impulsive noise to the signal. A full rank direct solution is derived followed by an iterative solution. The reduced rank adaptive filter is presented in this environment by using two methods for rank reduction, while the minimized objective function is defined using the L
Attitude determination using an adaptive multiple model filtering Scheme
Lam, Quang; Ray, Surendra N.
1995-05-01
Attitude determination has been considered as a permanent topic of active research and perhaps remaining as a forever-lasting interest for spacecraft system designers. Its role is to provide a reference for controls such as pointing the directional antennas or solar panels, stabilizing the spacecraft or maneuvering the spacecraft to a new orbit. Least Square Estimation (LSE) technique was utilized to provide attitude determination for the Nimbus 6 and G. Despite its poor performance (estimation accuracy consideration), LSE was considered as an effective and practical approach to meet the urgent need and requirement back in the 70's. One reason for this poor performance associated with the LSE scheme is the lack of dynamic filtering or 'compensation'. In other words, the scheme is based totally on the measurements and no attempts were made to model the dynamic equations of motion of the spacecraft. We propose an adaptive filtering approach which employs a bank of Kalman filters to perform robust attitude estimation. The proposed approach, whose architecture is depicted, is essentially based on the latest proof on the interactive multiple model design framework to handle the unknown of the system noise characteristics or statistics. The concept fundamentally employs a bank of Kalman filter or submodel, instead of using fixed values for the system noise statistics for each submodel (per operating condition) as the traditional multiple model approach does, we use an on-line dynamic system noise identifier to 'identify' the system noise level (statistics) and update the filter noise statistics using 'live' information from the sensor model. The advanced noise identifier, whose architecture is also shown, is implemented using an advanced system identifier. To insure the robust performance for the proposed advanced system identifier, it is also further reinforced by a learning system which is implemented (in the outer loop) using neural networks to identify other unknown
SIMULATION AND PERFORMANCE ANALYASIS OF ADAPTIVE FILTER IN NOISE CANCELLATION
RAJ KUMAR THENUA,
2010-09-01
Full Text Available Noise problems in the environment have gained attention due to the tremendous growth of technology that has led to noisy engines, heavy machinery, high speed wind buffeting and other noise sources. The problem of controlling the noise level has become the focus of a tremendous amount of research over the years. In last few years various adaptive algorithms are developed for noise cancellation. In this paper we present an implementation of LMS (Least Mean Square, NLMS (Normalized Least Mean Square and RLS (Recursive Least Square algorithms on MATLAB platform with the intention to compare their performance in noise cancellation. We simulate the adaptive filter in MATLAB with a noisy tone signal and white noise signal and analyze the performance of algorithms in terms of MSE (Mean Squared Error, percentage noise removal, computational complexity and stability. The obtained results shows that RLS has the best performance but at thecost of large computational complexity and memory requirement.
Jinsoo Jeong
2011-06-01
Full Text Available This paper presents an acoustic noise cancelling technique using an inverse kepstrum system as an innovations-based whitening application for an adaptive finite impulse response (FIR filter in beamforming structure. The inverse kepstrum method uses an innovations-whitened form from one acoustic path transfer function between a reference microphone sensor and a noise source so that the rear-end reference signal will then be a whitened sequence to a cascaded adaptive FIR filter in the beamforming structure. By using an inverse kepstrum filter as a whitening filter with the use of a delay filter, the cascaded adaptive FIR filter estimates only the numerator of the polynomial part from the ratio of overall combined transfer functions. The test results have shown that the adaptive FIR filter is more effective in beamforming structure than an adaptive noise cancelling (ANC structure in terms of signal distortion in the desired signal and noise reduction in noise with nonminimum phase components. In addition, the inverse kepstrum method shows almost the same convergence level in estimate of noise statistics with the use of a smaller amount of adaptive FIR filter weights than the kepstrum method, hence it could provide better computational simplicity in processing. Furthermore, the rear-end inverse kepstrum method in beamforming structure has shown less signal distortion in the desired signal than the front-end kepstrum method and the front-end inverse kepstrum method in beamforming structure.
Multimodal Medical Image Fusion by Adaptive Manifold Filter.
Geng, Peng; Liu, Shuaiqi; Zhuang, Shanna
2015-01-01
Medical image fusion plays an important role in diagnosis and treatment of diseases such as image-guided radiotherapy and surgery. The modified local contrast information is proposed to fuse multimodal medical images. Firstly, the adaptive manifold filter is introduced into filtering source images as the low-frequency part in the modified local contrast. Secondly, the modified spatial frequency of the source images is adopted as the high-frequency part in the modified local contrast. Finally, the pixel with larger modified local contrast is selected into the fused image. The presented scheme outperforms the guided filter method in spatial domain, the dual-tree complex wavelet transform-based method, nonsubsampled contourlet transform-based method, and four classic fusion methods in terms of visual quality. Furthermore, the mutual information values by the presented method are averagely 55%, 41%, and 62% higher than the three methods and those values of edge based similarity measure by the presented method are averagely 13%, 33%, and 14% higher than the three methods for the six pairs of source images. PMID:26664494
Multimodal Medical Image Fusion by Adaptive Manifold Filter
Peng Geng
2015-01-01
Full Text Available Medical image fusion plays an important role in diagnosis and treatment of diseases such as image-guided radiotherapy and surgery. The modified local contrast information is proposed to fuse multimodal medical images. Firstly, the adaptive manifold filter is introduced into filtering source images as the low-frequency part in the modified local contrast. Secondly, the modified spatial frequency of the source images is adopted as the high-frequency part in the modified local contrast. Finally, the pixel with larger modified local contrast is selected into the fused image. The presented scheme outperforms the guided filter method in spatial domain, the dual-tree complex wavelet transform-based method, nonsubsampled contourlet transform-based method, and four classic fusion methods in terms of visual quality. Furthermore, the mutual information values by the presented method are averagely 55%, 41%, and 62% higher than the three methods and those values of edge based similarity measure by the presented method are averagely 13%, 33%, and 14% higher than the three methods for the six pairs of source images.
Immune adaptive Gaussian mixture par ticle filter for state estimation
Wenlong Huang; Xiaodan Wang; Yi Wang; Guohong Li
2015-01-01
The particle filter (PF) is a flexible and powerful sequen-tial Monte Carlo (SMC) technique capable of modeling nonlinear, non-Gaussian, and nonstationary dynamical systems. However, the generic PF suffers from particle degeneracy and sample im-poverishment, which greatly affects its performance for nonlinear, non-Gaussian tracking problems. To deal with those issues, an improved PF is proposed. The algorithm consists of a PF that uses an immune adaptive Gaussian mixture model (IAGM) based immune algorithm to re-approximate the posterior density. At the same time, three immune antibody operators are embed in the new filter. Instead of using a resample strategy, the newest obser-vation and conditional likelihood are integrated into those immune antibody operators to update the particles, which can further im-prove the diversity of particles, and drive particles toward their close local maximum of the posterior probability. The improved PF algorithm can produce a closed-form expression for the posterior state distribution. Simulation results show the proposed algorithm can maintain the effectiveness and diversity of particles and avoid sample impoverishment, and its performance is superior to several PFs and Kalman filters.
Adaptive Neuro-Fuzzy Extended Kalman Filtering for Robot Localization
Ramazan Havangi
2010-03-01
Full Text Available Extended Kalman Filter (EKF has been a popular approach to localization a mobile robot. However, the performance of the EKF and the quality of the estimation depends on the correct a priori knowledge of process and measurement noise covariance matrices (Qk and Rk , respectively. Imprecise knowledge of these statistics can cause significant degradation in performance. This paper proposed the development of an Adaptive Neuro- Fuzzy Extended Kalman Filtering (ANFEKF for localization of robot. The Adaptive Neuro-Fuzzy attempts to estimate the elements of Qk and Rk matrices of the EKF algorithm, at each sampling instant when measurement update step is carried out. The ANFIS supervises the performance of the EKF with the aim of reducing the mismatch between the theoretical and actual covariance of the innovation sequences. The free parameters of ANFIS are trained using the steepest gradient descent (SD to minimize the differences of the actual value of the covariance of the residual with its theoretical value as much possible. The simulation results show the effectiveness of the proposed algorithm.
A wavelet packet adaptive filtering algorithm for enhancing manatee vocalizations.
Gur, M Berke; Niezrecki, Christopher
2011-04-01
Approximately a quarter of all West Indian manatee (Trichechus manatus latirostris) mortalities are attributed to collisions with watercraft. A boater warning system based on the passive acoustic detection of manatee vocalizations is one possible solution to reduce manatee-watercraft collisions. The success of such a warning system depends on effective enhancement of the vocalization signals in the presence of high levels of background noise, in particular, noise emitted from watercraft. Recent research has indicated that wavelet domain pre-processing of the noisy vocalizations is capable of significantly improving the detection ranges of passive acoustic vocalization detectors. In this paper, an adaptive denoising procedure, implemented on the wavelet packet transform coefficients obtained from the noisy vocalization signals, is investigated. The proposed denoising algorithm is shown to improve the manatee detection ranges by a factor ranging from two (minimum) to sixteen (maximum) compared to high-pass filtering alone, when evaluated using real manatee vocalization and background noise signals of varying signal-to-noise ratios (SNR). Furthermore, the proposed method is also shown to outperform a previously suggested feedback adaptive line enhancer (FALE) filter on average 3.4 dB in terms of noise suppression and 0.6 dB in terms of waveform preservation. PMID:21476661
An adaptive filtered back-projection for photoacoustic image reconstruction
Purpose: The purpose of this study is to develop an improved filtered-back-projection (FBP) algorithm for photoacoustic tomography (PAT), which allows image reconstruction with higher quality compared to images reconstructed through traditional algorithms. Methods: A rigorous expression of a weighting function has been derived directly from a photoacoustic wave equation and used as a ramp filter in Fourier domain. The authors’ new algorithm utilizes this weighting function to precisely calculate each photoacoustic signal’s contribution and then reconstructs the image based on the retarded potential generated from the photoacoustic sources. In addition, an adaptive criterion has been derived for selecting the cutoff frequency of a low pass filter. Two computational phantoms were created to test the algorithm. The first phantom contained five spheres with each sphere having different absorbances. The phantom was used to test the capability for correctly representing both the geometry and the relative absorbed energy in a planar measurement system. The authors also used another phantom containing absorbers of different sizes with overlapping geometry to evaluate the performance of the new method for complicated geometry. In addition, random noise background was added to the simulated data, which were obtained by using an arc-shaped array of 50 evenly distributed transducers that spanned 160° over a circle with a radius of 65 mm. A normalized factor between the neighbored transducers was applied for correcting measurement signals in PAT simulations. The authors assumed that the scanned object was mounted on a holder that rotated over the full 360° and the scans were set to a sampling rate of 20.48 MHz. Results: The authors have obtained reconstructed images of the computerized phantoms by utilizing the new FBP algorithm. From the reconstructed image of the first phantom, one can see that this new approach allows not only obtaining a sharp image but also showing
Rossi, V
2002-01-01
In the framework of the LHC project and the modifications of the SPS as its injector, I present the concept of global digital signal processing applied to a particle accelerator, using Field Programmable Gate Array (FPGA) technology. The approach of global digital synthesis implements in numerical form the architecture of a system, from the start up of a project and the very beginning of the signal flow. It takes into account both the known parameters and the future evolution, whenever possible. Due to the increased performance requirements of today's projects, the CAE design methodology becomes more and more necessary to handle successfully the added complexity and speed of modern electronic circuits. Simulation is performed both for behavioural analysis, to ensure conformity to functional requirements, and for time signal analysis (speed requirements). The digital notch filter with programmable delay for the SPS Transverse Damper is now fully operational with fixed target and LHC-type beams circulating in t...
An Adaptive Unscented Kalman Filtering Algorithm for MEMS/GPS Integrated Navigation Systems
Jianhua Cheng; Daidai Chen; Rene Jr. Landry; Lin Zhao; Dongxue Guan
2014-01-01
MEMS/GPS integrated navigation system has been widely used for land-vehicle navigation. This system exhibits large errors because of its nonlinear model and uncertain noise statistic characteristics. Based on the principles of the adaptive Kalman filtering (AKF) and unscented Kalman filtering (AUKF) algorithms, an adaptive unscented Kalman filtering (AUKF) algorithm is proposed. By using noise statistic estimator, the uncertain noise characteristics could be online estimated to adaptively com...
Adaptive integrated navigation filtering based on accelerometer calibration
Qifan Zhou
2012-11-01
Full Text Available In this paper, a novel GPS (Global Positioning System and DR (Dead Reckoning system which was based on the accelerometer and gyroscope integrated system was designed and implemented. In this system, the odometer used in traditional DR system was replaced by a MEMS tri-axis accelerometer in order to decrease the cost and the volume of the system. The system was integrated by the Kalman filter and a new mathematical model was introduced. In order to reasonably use the GPS information, an adaptive algorithm based on single measurement system which could estimate the measurement noise covariance was obtained. On the purpose of reducing the effect of the accumulated error caused by drift and bias of accelerometer, the accelerometer was calibrated online when GPS performed well. In this way, the integrated system could not only obtain the high-precision positioning in real time, but also perform stably in practice.
Channel Estimation using Adaptive Filtering for LTE-Advanced
Saqib Saleem
2011-05-01
Full Text Available For demand of high data rates, enhanced system capacity and coverage, ITU made proposal for the standardization of next generation wireless communication systems, known as IMT-Advanced. To achieve these targets, a priori knowledge of the channel is required at the transmitter side. In this paper, three adaptive channel estimation techniques: Least Mean Square (LMS, Recursive Least Square (RLS and Kalman-Filtering Based, are compared in terms of performance and complexity. For performance, Mean Square Error (MSE and Symbol Error Rate (SER while for complexity, computational time is measured for variable channel impulse response (CIR lengths and channel taps. MATLAB Monte-Carlo Simulations are used to evaluate these techniques.
Reduced-Rank Adaptive Filtering Using Krylov Subspace
Burykh Sergueï
2002-01-01
Full Text Available A unified view of several recently introduced reduced-rank adaptive filters is presented. As all considered methods use Krylov subspace for rank reduction, the approach taken in this work is inspired from Krylov subspace methods for iterative solutions of linear systems. The alternative interpretation so obtained is used to study the properties of each considered technique and to relate one reduced-rank method to another as well as to algorithms used in computational linear algebra. Practical issues are discussed and low-complexity versions are also included in our study. It is believed that the insight developed in this paper can be further used to improve existing reduced-rank methods according to known results in the domain of Krylov subspace methods.
Reduced-Rank Adaptive Filtering Using Krylov Subspace
Burykh, Sergueï; Abed-Meraim, Karim
2003-12-01
A unified view of several recently introduced reduced-rank adaptive filters is presented. As all considered methods use Krylov subspace for rank reduction, the approach taken in this work is inspired from Krylov subspace methods for iterative solutions of linear systems. The alternative interpretation so obtained is used to study the properties of each considered technique and to relate one reduced-rank method to another as well as to algorithms used in computational linear algebra. Practical issues are discussed and low-complexity versions are also included in our study. It is believed that the insight developed in this paper can be further used to improve existing reduced-rank methods according to known results in the domain of Krylov subspace methods.
Adaptive Data Filtering of Inertial Sensors with Variable Bandwidth
Mushfiqul Alam
2015-02-01
Full Text Available MEMS (micro-electro-mechanical system-based inertial sensors, i.e., accelerometers and angular rate sensors, are commonly used as a cost-effective solution for the purposes of navigation in a broad spectrum of terrestrial and aerospace applications. These tri-axial inertial sensors form an inertial measurement unit (IMU, which is a core unit of navigation systems. Even if MEMS sensors have an advantage in their size, cost, weight and power consumption, they suffer from bias instability, noisy output and insufficient resolution. Furthermore, the sensor’s behavior can be significantly affected by strong vibration when it operates in harsh environments. All of these constitute conditions require treatment through data processing. As long as the navigation solution is primarily based on using only inertial data, this paper proposes a novel concept in adaptive data pre-processing by using a variable bandwidth filtering. This approach utilizes sinusoidal estimation to continuously adapt the filtering bandwidth of the accelerometer’s data in order to reduce the effects of vibration and sensor noise before attitude estimation is processed. Low frequency vibration generally limits the conditions under which the accelerometers can be used to aid the attitude estimation process, which is primarily based on angular rate data and, thus, decreases its accuracy. In contrast, the proposed pre-processing technique enables using accelerometers as an aiding source by effective data smoothing, even when they are affected by low frequency vibration. Verification of the proposed concept is performed on simulation and real-flight data obtained on an ultra-light aircraft. The results of both types of experiments confirm the suitability of the concept for inertial data pre-processing.
Adaptive data filtering of inertial sensors with variable bandwidth.
Alam, Mushfiqul; Rohac, Jan
2015-01-01
MEMS (micro-electro-mechanical system)-based inertial sensors, i.e., accelerometers and angular rate sensors, are commonly used as a cost-effective solution for the purposes of navigation in a broad spectrum of terrestrial and aerospace applications. These tri-axial inertial sensors form an inertial measurement unit (IMU), which is a core unit of navigation systems. Even if MEMS sensors have an advantage in their size, cost, weight and power consumption, they suffer from bias instability, noisy output and insufficient resolution. Furthermore, the sensor's behavior can be significantly affected by strong vibration when it operates in harsh environments. All of these constitute conditions require treatment through data processing. As long as the navigation solution is primarily based on using only inertial data, this paper proposes a novel concept in adaptive data pre-processing by using a variable bandwidth filtering. This approach utilizes sinusoidal estimation to continuously adapt the filtering bandwidth of the accelerometer's data in order to reduce the effects of vibration and sensor noise before attitude estimation is processed. Low frequency vibration generally limits the conditions under which the accelerometers can be used to aid the attitude estimation process, which is primarily based on angular rate data and, thus, decreases its accuracy. In contrast, the proposed pre-processing technique enables using accelerometers as an aiding source by effective data smoothing, even when they are affected by low frequency vibration. Verification of the proposed concept is performed on simulation and real-flight data obtained on an ultra-light aircraft. The results of both types of experiments confirm the suitability of the concept for inertial data pre-processing. PMID:25648711
G Huang; Q. Zhang;
2012-01-01
In order to estimate the satellite clock offset in a real-time mode, a new algorithm of adaptively robust Kalman filter with classified adaptive factors for clock offset estimation is proposed. Compared with standard Kalman filter clock offset model, the new method can detect and control outliers and clock jumps automatically in real-time. Moreover, the clock model parameters, which contain the clock offset, clock speed and clock shift, are classified to decide the adaptive factors in the new...
Theory of affine projection algorithms for adaptive filtering
Ozeki, Kazuhiko
2016-01-01
This book focuses on theoretical aspects of the affine projection algorithm (APA) for adaptive filtering. The APA is a natural generalization of the classical, normalized least-mean-squares (NLMS) algorithm. The book first explains how the APA evolved from the NLMS algorithm, where an affine projection view is emphasized. By looking at those adaptation algorithms from such a geometrical point of view, we can find many of the important properties of the APA, e.g., the improvement of the convergence rate over the NLMS algorithm especially for correlated input signals. After the birth of the APA in the mid-1980s, similar algorithms were put forward by other researchers independently from different perspectives. This book shows that they are variants of the APA, forming a family of APAs. Then it surveys research on the convergence behavior of the APA, where statistical analyses play important roles. It also reviews developments of techniques to reduce the computational complexity of the APA, which are important f...
Ida Bagus Ketut Surya Arnawa
2015-08-01
Full Text Available Balinese papyrus (Lontar is one of the most popular media to write for more than a hundred years in Indonesia. Balinese papyrus are used to document things that are considered important in the past. Most of the balinese papyrus suffered damage caused by weathering, edible fungus and insects making it is difficult to read. One of the efforts made to preserve the existence of balinese papyrus is to perform digitization of it. The problems most often encountered in the process of digitizing the image of the balinese papyrus is less good results as there is noise caused by its conditions that have been damaged and the uneven distribution illumination in this part of the image. In this study the authors propose to combine homomorphic filtering with adaptive median filtering to perform image enhancement. Surve results obtained show the percentage of the average respondents stated that the image enhancement results are good is 83.4%, the percentage of the average respondents stated that the image enhancement results are very good is 4% and the percentage of the average respondents stated that the image enhancement results are enough is 12, 6%.
Biparametric Adaptive Filter: detection of compact sources in complex microwave backgrounds
López-Caniego, M.; Vielva, P.
2012-01-01
In this article we consider the detection of compact sources in maps of the Cosmic Microwave Background radiation (CMB) following the philosophy behind the Mexican Hat Wavelet Family (MHWn) of linear filters. We present a new analytical filter, the Biparametric Adaptive Filter (BAF), that is able to adapt itself to the statistical properties of the background as well as to the profile of the compact sources, maximizing the amplification and improving the detection process. We have tested the ...
Fault diagnosis of rolling element bearings is important for improving mechanical system reliability and performance. Vibration signals contain a wealth of complex information useful for state monitoring and fault diagnosis. However, any fault-related impulses in the original signal are often severely tainted by various noises and the interfering vibrations caused by other machine elements. Narrow-band amplitude demodulation has been an effective technique to detect bearing faults by identifying bearing fault characteristic frequencies. To achieve this, the key step is to remove the corrupting noise and interference, and to enhance the weak signatures of the bearing fault. In this paper, a new method based on adaptive wavelet filtering and spectral subtraction is proposed for fault diagnosis in bearings. First, to eliminate the frequency associated with interfering vibrations, the vibration signal is bandpass filtered with a Morlet wavelet filter whose parameters (i.e. center frequency and bandwidth) are selected in separate steps. An alternative and efficient method of determining the center frequency is proposed that utilizes the statistical information contained in the production functions (PFs). The bandwidth parameter is optimized using a local ‘greedy’ scheme along with Shannon wavelet entropy criterion. Then, to further reduce the residual in-band noise in the filtered signal, a spectral subtraction procedure is elaborated after wavelet filtering. Instead of resorting to a reference signal as in the majority of papers in the literature, the new method estimates the power spectral density of the in-band noise from the associated PF. The effectiveness of the proposed method is validated using simulated data, test rig data, and vibration data recorded from the transmission system of a helicopter. The experimental results and comparisons with other methods indicate that the proposed method is an effective approach to detecting the fault-related impulses
Zhang, Yan; Tang, Baoping; Liu, Ziran; Chen, Rengxiang
2016-02-01
Fault diagnosis of rolling element bearings is important for improving mechanical system reliability and performance. Vibration signals contain a wealth of complex information useful for state monitoring and fault diagnosis. However, any fault-related impulses in the original signal are often severely tainted by various noises and the interfering vibrations caused by other machine elements. Narrow-band amplitude demodulation has been an effective technique to detect bearing faults by identifying bearing fault characteristic frequencies. To achieve this, the key step is to remove the corrupting noise and interference, and to enhance the weak signatures of the bearing fault. In this paper, a new method based on adaptive wavelet filtering and spectral subtraction is proposed for fault diagnosis in bearings. First, to eliminate the frequency associated with interfering vibrations, the vibration signal is bandpass filtered with a Morlet wavelet filter whose parameters (i.e. center frequency and bandwidth) are selected in separate steps. An alternative and efficient method of determining the center frequency is proposed that utilizes the statistical information contained in the production functions (PFs). The bandwidth parameter is optimized using a local ‘greedy’ scheme along with Shannon wavelet entropy criterion. Then, to further reduce the residual in-band noise in the filtered signal, a spectral subtraction procedure is elaborated after wavelet filtering. Instead of resorting to a reference signal as in the majority of papers in the literature, the new method estimates the power spectral density of the in-band noise from the associated PF. The effectiveness of the proposed method is validated using simulated data, test rig data, and vibration data recorded from the transmission system of a helicopter. The experimental results and comparisons with other methods indicate that the proposed method is an effective approach to detecting the fault-related impulses
The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation.
Gao, Siwei; Liu, Yanheng; Wang, Jian; Deng, Weiwen; Oh, Heekuck
2016-01-01
This paper proposes a multi-sensory Joint Adaptive Kalman Filter (JAKF) through extending innovation-based adaptive estimation (IAE) to estimate the motion state of the moving vehicles ahead. JAKF views Lidar and Radar data as the source of the local filters, which aims to adaptively adjust the measurement noise variance-covariance (V-C) matrix 'R' and the system noise V-C matrix 'Q'. Then, the global filter uses R to calculate the information allocation factor 'β' for data fusion. Finally, the global filter completes optimal data fusion and feeds back to the local filters to improve the measurement accuracy of the local filters. Extensive simulation and experimental results show that the JAKF has better adaptive ability and fault tolerance. JAKF enables one to bridge the gap of the accuracy difference of various sensors to improve the integral filtering effectivity. If any sensor breaks down, the filtered results of JAKF still can maintain a stable convergence rate. Moreover, the JAKF outperforms the conventional Kalman filter (CKF) and the innovation-based adaptive Kalman filter (IAKF) with respect to the accuracy of displacement, velocity, and acceleration, respectively. PMID:27438835
The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation
Gao, Siwei; Liu, Yanheng; Wang, Jian; Deng, Weiwen; Oh, Heekuck
2016-01-01
This paper proposes a multi-sensory Joint Adaptive Kalman Filter (JAKF) through extending innovation-based adaptive estimation (IAE) to estimate the motion state of the moving vehicles ahead. JAKF views Lidar and Radar data as the source of the local filters, which aims to adaptively adjust the measurement noise variance-covariance (V-C) matrix ‘R’ and the system noise V-C matrix ‘Q’. Then, the global filter uses R to calculate the information allocation factor ‘β’ for data fusion. Finally, the global filter completes optimal data fusion and feeds back to the local filters to improve the measurement accuracy of the local filters. Extensive simulation and experimental results show that the JAKF has better adaptive ability and fault tolerance. JAKF enables one to bridge the gap of the accuracy difference of various sensors to improve the integral filtering effectivity. If any sensor breaks down, the filtered results of JAKF still can maintain a stable convergence rate. Moreover, the JAKF outperforms the conventional Kalman filter (CKF) and the innovation-based adaptive Kalman filter (IAKF) with respect to the accuracy of displacement, velocity, and acceleration, respectively. PMID:27438835
Jan eKneissler
2015-04-01
Full Text Available Predictive coding appears to be one of the fundamental working principles of brain processing. Amongst other aspects, brains often predict the sensory consequences of their own actions. Predictive coding resembles Kalman filtering, where incoming sensory information is filtered to produce prediction errors for subsequent adaptation and learning. However, to generate prediction errors given motor commands, a suitable temporal forward model is required to generate predictions. While in engineering applications, it is usually assumed that this forward model is known, the brain has to learn it. When filtering sensory input and learning from the residual signal in parallel, a fundamental problem arises: the system can enter a delusional loop when filtering the sensory information using an overly trusted forward model. In this case, learning stalls before accurate convergence because uncertainty about the forward model is not properly accommodated. We present a Bayes-optimal solution to this generic and pernicious problem for the case of linear forward models, which we call Predictive Inference and Adaptive Filtering (PIAF. PIAF filters incoming sensory information and learns the forward model simultaneously. We show that PIAF is formally related to Kalman filtering and to the Recursive Least Squares linear approximation method, but combines these procedures in a Bayes optimal fashion. Numerical evaluations confirm that the delusional loop is precluded and that the learning of the forward model is more than ten-times faster when compared to a naive combination of Kalman filtering and Recursive Least Squares.
Mean-square performance of a convex combination of two adaptive filters
Garcia, Jeronimo; Figueiras-Vidal, A.R.; Sayed, A.H.
2006-01-01
Combination approaches provide an interesting way to improve adaptive filter performance. In this paper, we study the mean-square performance of a convex combination of two transversal filters. The individual filters are independently adapted using their own error signals, while the combination is...... adapted by means of a stochastic gradient algorithm in order to minimize the error of the overall structure. General expressions are derived that show that the method is universal with respect to the component filters, i.e., in steady-state, it performs at least as well as the best component filter....... Furthermore, when the correlation between the a priori errors of the components is low enough, their combination is able to outperform both of them. Using energy conservation relations, we specialize the results to a combination of least mean-square filters operating both in stationary and in nonstationary...
Geometric-Algebra LMS Adaptive Filter and Its Application to Rotation Estimation
Lopes, Wilder B.; Al-Nuaimi, Anas; Lopes, Cassio G.
2016-06-01
This paper exploits Geometric (Clifford) Algebra (GA) theory in order to devise and introduce a new adaptive filtering strategy. From a least-squares cost function, the gradient is calculated following results from Geometric Calculus (GC), the extension of GA to handle differential and integral calculus. The novel GA least-mean-squares (GA-LMS) adaptive filter, which inherits properties from standard adaptive filters and from GA, is developed to recursively estimate a rotor (multivector), a hypercomplex quantity able to describe rotations in any dimension. The adaptive filter (AF) performance is assessed via a 3D point-clouds registration problem, which contains a rotation estimation step. Calculating the AF computational complexity suggests that it can contribute to reduce the cost of a full-blown 3D registration algorithm, especially when the number of points to be processed grows. Moreover, the employed GA/GC framework allows for easily applying the resulting filter to estimating rotors in higher dimensions.
Benardini, James N.; Koukol, Robert C.; Schubert, Wayne W.; Morales, Fabian; Klatte, Marlin F.
2012-01-01
A report describes an adaptation of a filter assembly to enable it to be used to filter out microorganisms from a propulsion system. The filter assembly has previously been used for particulates greater than 2 micrometers. Projects that utilize large volumes of nonmetallic materials of planetary protection concern pose a challenge to their bioburden budget, as a conservative specification value of 30 spores per cubic centimeter is typically used. Helium was collected utilizing an adapted filtration approach employing an existing Millipore filter assembly apparatus used by the propulsion team for particulate analysis. The filter holder on the assembly has a 47-mm diameter, and typically a 1.2-5 micrometer pore-size filter is used for particulate analysis making it compatible with commercially available sterilization filters (0.22 micrometers) that are necessary for biological sampling. This adaptation to an existing technology provides a proof-of-concept and a demonstration of successful use in a ground equipment system. This adaptation has demonstrated that the Millipore filter assembly can be utilized to filter out microorganisms from a propulsion system, whereas in previous uses the filter assembly was utilized for particulates greater than 2 micrometers.
两种渐消滤波与自适应抗差滤波的综合比较分析%Comparison of Two Fading Filters and Adaptively Robust Filter
杨元喜; 高为广
2007-01-01
Two kinds of fading filters and their principles are introduced. An adaptive robust filter is given with corresponding principle. The basic abilities of the fading filters and adaptively robust filter in controlling the influences of the kinematic model errors are analyzed. A practical example is given. The results of the fading filter and adaptively robust filter are compared and analyzed.
Nababan, Sunfirst Lady Jeanfera
2015-01-01
Basically, every image acquisition can cause to the presence of noise in the resulting image. Uniform Noise, Salt & Pepper Noise, and Speckle Noise are three of many model noises that are present in the image. Digital image that contained noise can cause problems in the form of an image that cannot be interpreted properly by human, however noise can be reduce through image restoration called filtering. Filter method that can be used to reduce the noises are Alpha-Trimmed Mean Filter and Adapt...
Application of Adaptive Divided Difference Filter on GPS/IMU Integrated Navigation System
ZHAO Pei-pei; LI Shi-xin; XIAO Zhi-tao
2009-01-01
The efficient and accurate approximate nonlinear filters have been widely used in the estimation of states and parameters of dynamical systems.In this paper,an adaptive divided difference filter is designed for precise estimation of states and parameters of mieromechanical gyro navigation system.Based on the investigation of nonlinear divided difference filter the adaptive divided difference filter(ADDF)was designed,which takes account of the incorrect time-varying noise statistics of dynamical systems and compensation of the nonlinearity effects neglected by linearization.And its performance is superior to that of DDF and extended Kalman filter (EKF). Simulation results indicate that the advantages of the proposed nonlinear filters make them attractive alternatives to the extended Kalman filter.
An Adjoint-Based Adaptive Ensemble Kalman Filter
Song, Hajoon
2013-10-01
A new hybrid ensemble Kalman filter/four-dimensional variational data assimilation (EnKF/4D-VAR) approach is introduced to mitigate background covariance limitations in the EnKF. The work is based on the adaptive EnKF (AEnKF) method, which bears a strong resemblance to the hybrid EnKF/three-dimensional variational data assimilation (3D-VAR) method. In the AEnKF, the representativeness of the EnKF ensemble is regularly enhanced with new members generated after back projection of the EnKF analysis residuals to state space using a 3D-VAR [or optimal interpolation (OI)] scheme with a preselected background covariance matrix. The idea here is to reformulate the transformation of the residuals as a 4D-VAR problem, constraining the new member with model dynamics and the previous observations. This should provide more information for the estimation of the new member and reduce dependence of the AEnKF on the assumed stationary background covariance matrix. This is done by integrating the analysis residuals backward in time with the adjoint model. Numerical experiments are performed with the Lorenz-96 model under different scenarios to test the new approach and to evaluate its performance with respect to the EnKF and the hybrid EnKF/3D-VAR. The new method leads to the least root-mean-square estimation errors as long as the linear assumption guaranteeing the stability of the adjoint model holds. It is also found to be less sensitive to choices of the assimilation system inputs and parameters.
Analysis of dynamic deformation processes with adaptive KALMAN-filtering
Eichhorn, Andreas
2007-05-01
In this paper the approach of a full system analysis is shown quantifying a dynamic structural ("white-box"-) model for the calculation of thermal deformations of bar-shaped machine elements. The task was motivated from mechanical engineering searching new methods for the precise prediction and computational compensation of thermal influences in the heating and cooling phases of machine tools (i.e. robot arms, etc.). The quantification of thermal deformations under variable dynamic loads requires the modelling of the non-stationary spatial temperature distribution inside the object. Based upon FOURIERS law of heat flow the high-grade non-linear temperature gradient is represented by a system of partial differential equations within the framework of a dynamic Finite Element topology. It is shown that adaptive KALMAN-filtering is suitable to quantify relevant disturbance influences and to identify thermal parameters (i.e. thermal diffusivity) with a deviation of only 0,2%. As result an identified (and verified) parametric model for the realistic prediction respectively simulation of dynamic temperature processes is presented. Classifying the thermal bend as the main deformation quantity of bar-shaped machine tools, the temperature model is extended to a temperature deformation model. In lab tests thermal load steps are applied to an aluminum column. Independent control measurements show that the identified model can be used to predict the columns bend with a mean deviation (r.m.s.) smaller than 10 mgon. These results show that the deformation model is a precise predictor and suitable for realistic simulations of thermal deformations. Experiments with modified heat sources will be necessary to verify the model in further frequency spectra of dynamic thermal loads.
Farsani, Mahsa Saffari; Sahhaf, Masoud Reza Aghabozorgi; Abootalebi, Vahid
2016-01-01
The aim of this paper is to improve the performance of the conventional Goertzel algorithm in determining the protein coding regions in deoxyribonucleic acid (DNA) sequences. First, the symbolic DNA sequences are converted into numerical signals using electron ion interaction potential method. Then by combining the modified anti-notch filter and linear predictive coding model, we proposed an efficient algorithm to achieve the performance improvement in the Goertzel algorithm for estimating genetic regions. Finally, a thresholding method is applied to precisely identify the exon and intron regions. The proposed algorithm is applied to several genes, including genes available in databases BG570 and HMR195 and the results are compared to other methods based on the nucleotide level evaluation criteria. Results demonstrate that our proposed method reduces the number of incorrect nucleotides which are estimated to be in the noncoding region. In addition, the area under the receiver operating characteristic curve has improved by the factor of 1.35 and 1.12 in HMR195 and BG570 datasets respectively, in comparison with the conventional Goertzel algorithm. PMID:27563569
Development of adaptive IIR filtered-e LMS algorithm for active noise control
SUN Xu; MENG Guang; TENG Pengxiao; CHEN Duanshi
2003-01-01
Compared to finite impulse response (FIR) filters, infinite impulse response (IIR)filters can match the system better with much fewer coefficients, and hence the computationload is saved and the performance improves. Therefore, it is attractive to use IIR filters insteadof FIR filters in active noise control (ANC). However, filtered-U LMS (FULMS) algorithm, theIIR filter-based algorithm commonly used so far cannot ensure global convergence. A new IIRfilter based adaptive algorithm, which can ensure global convergence with computation loadonly slightly increasing, is proposed in this paper. The new algorithm is called as filtered-eLMS algorithm since the error signal of which need to be filtered. Simulation results show thatthe FELMS algorithm presents better performance than the FULMS algorithm.
Slice image pretreatment for cone-beam computed tomography based on adaptive filter
According to the noise properties and the serial slice image characteristics in Cone-Beam Computed Tomography (CBCT) system, a slice image pretreatment for CBCT based on adaptive filter was proposed. The judging criterion for the noise is established firstly. All pixels are classified into two classes: adaptive center weighted modified trimmed mean (ACWMTM) filter is used for the pixels corrupted by Gauss noise and adaptive median (AM) filter is used for the pixels corrupted by impulse noise. In ACWMTM filtering algorithm, the estimated Gauss noise standard deviation in the current slice image with offset window is replaced by the estimated standard deviation in the adjacent slice image to the current with the corresponding window, so the filtering accuracy of the serial images is improved. The pretreatment experiment on CBCT slice images of wax model of hollow turbine blade shows that the method makes a good performance both on eliminating noises and on protecting details. (authors)
Wang, Yunlong; Soltani, Mohsen; Hussain, Dil muhammed Akbar
2016-01-01
adaptive Multiplicative Extended Kalman Filter (MEKF) for attitude estimation of Marine Satellite Tracking Antenna (MSTA) is presented with the measurement noise covariance matrix adjusted according to the norm of accelerometer measurements, which can significantly reduce the slamming influence from waves...
An Adaptive Unscented Kalman Filtering Algorithm for MEMS/GPS Integrated Navigation Systems
Jianhua Cheng
2014-01-01
Full Text Available MEMS/GPS integrated navigation system has been widely used for land-vehicle navigation. This system exhibits large errors because of its nonlinear model and uncertain noise statistic characteristics. Based on the principles of the adaptive Kalman filtering (AKF and unscented Kalman filtering (AUKF algorithms, an adaptive unscented Kalman filtering (AUKF algorithm is proposed. By using noise statistic estimator, the uncertain noise characteristics could be online estimated to adaptively compensate the time-varying noise characteristics. Employing the adaptive filtering principle into UKF, the nonlinearity of system can be restrained. Simulations are conducted for MEMS/GPS integrated navigation system. The results show that the performance of estimation is improved by the AUKF approach compared with both conventional AKF and UKF.
Macroblock-based adaptive loop filter for all intra-coding
Jo, Hyun-Ho; Nam, Jung-Hak; Jung, Kwang-Soo; Sim, Dong-Gyu; Choi, Byeong-Doo
2013-03-01
A macroblock-based adaptive loop filter (MBALF) to improve coding gain for all intra-coding was presented. The proposed method adaptively applies the adaptive loop filter for each reconstructed macroblock (MB) in order to reduce the blocking artifact caused by the quantization process. The MBALF can improve coding efficiency of intra-predicted MBs as it is conducted prior to intra-prediction for the subsequent MBs in a slice. We found that the proposed MBALF yields a coding gain of approximately 7.1% compared to the H.264/AVC High profile. In addition, we achieved a coding gain of approximately 8.9%, on average, by combining the conventional in-loop filter, block-based, adaptive filter control.
Microwave Photonic Filters for Interference Cancellation and Adaptive Beamforming
Chang, John
Wireless communication has experienced an explosion of growth, especially in the past half- decade, due to the ubiquity of wireless devices, such as tablets, WiFi-enabled devices, and especially smartphones. Proliferation of smartphones with powerful processors and graphic chips have given an increasing amount of people the ability to access anything from anywhere. Unfortunately, this ease of access has greatly increased mobile wireless bandwidth and have begun to stress carrier networks and spectra. Wireless interference cancellation will play a big role alongside the popularity of wire- less communication. In this thesis, we will investigate optical signal processing methods for wireless interference cancellation methods. Optics provide the perfect backdrop for interference cancellation. Mobile wireless data is already aggregated and transported through fiber backhaul networks in practice. By sandwiching the signal processing stage between the receiver and the fiber backhaul, processing can easily be done locally in one location. Further, optics offers the advantages of being instantaneously broadband and size, weight, and power (SWAP). We are primarily concerned with two methods for interference cancellation, based on microwave photonic filters, in this thesis. The first application is for a co-channel situation, in which a transmitter and receiver are co-located and transmitting at the same frequency. A novel analog optical technique extended for multipath interference cancellation of broadband signals is proposed and experimentally demonstrated in this thesis. The proposed architecture was able to achieve a maximum of 40 dB of cancellation over 200 MHz and 50 dB of cancellation over 10 MHz. The broadband nature of the cancellation, along with its depth, demonstrates both the precision of the optical components and the validity of the architecture. Next, we are interested in a scenario with dynamically changing interference, which requires an adaptive photonic
Baseline adaptive Kalman filter estimation method for nuclear radiation pulse height analysis
To estimate the baseline of sampled pulse signal serial and then complete the analysis of the nuclear radiation pulse height, the present study concerns in baseline adaptive kalman filtering estimation method. The present study concerns in the application effect of using adaptive kalman filtering estimation method to establish the mathematical model and then estimate the baseline. The validity of this method was certified by experiment, this method is an effective way of data pretreatment for the pulse shaping and so on. (authors)
Edited Adaptive Hypermedia: Combining Human and Machine Intelligence to Achieve Filtered Information
Höök, Kristina; Rudström, Åsa; Waern, Annika
1997-01-01
We discuss a novel approach to filtering of hypermedia information based on an information broker and user environment coupled together. The advantage of the proposed approach, edited adaptive hypermedia, is that it combines human expertise with machine intelligence in order to achieve high quality of the filtered information provided to the end users.
A New Adaptive Square-Root Unscented Kalman Filter for Nonlinear Systems with Additive Noise
Yong Zhou
2015-01-01
Full Text Available The Kalman filter (KF, extended KF, and unscented KF all lack a self-adaptive capacity to deal with system noise. This paper describes a new adaptive filtering approach for nonlinear systems with additive noise. Based on the square-root unscented KF (SRUKF, traditional Maybeck’s estimator is modified and extended to nonlinear systems. The square root of the process noise covariance matrix Q or that of the measurement noise covariance matrix R is estimated straightforwardly. Because positive semidefiniteness of Q or R is guaranteed, several shortcomings of traditional Maybeck’s algorithm are overcome. Thus, the stability and accuracy of the filter are greatly improved. In addition, based on three different nonlinear systems, a new adaptive filtering technique is described in detail. Specifically, simulation results are presented, where the new filter was applied to a highly nonlinear model (i.e., the univariate nonstationary growth model (UNGM. The UNGM is compared with the standard SRUKF to demonstrate its superior filtering performance. The adaptive SRUKF (ASRUKF algorithm can complete direct recursion and calculate the square roots of the variance matrixes of the system state and noise, which ensures the symmetry and nonnegative definiteness of the matrixes and greatly improves the accuracy, stability, and self-adaptability of the filter.
Multi-template Scale-Adaptive Kernelized Correlation Filters
Bibi, Adel Aamer
2015-12-07
This paper identifies the major drawbacks of a very computationally efficient and state-of-the-art-tracker known as the Kernelized Correlation Filter (KCF) tracker. These drawbacks include an assumed fixed scale of the target in every frame, as well as, a heuristic update strategy of the filter taps to incorporate historical tracking information (i.e. simple linear combination of taps from the previous frame). In our approach, we update the scale of the tracker by maximizing over the posterior distribution of a grid of scales. As for the filter update, we prove and show that it is possible to use all previous training examples to update the filter taps very efficiently using fixed-point optimization. We validate the efficacy of our approach on two tracking datasets, VOT2014 and VOT2015.
Adaptive high temperature superconducting filters for interference rejection
An optically switched high temperature superconducting (HTS) band-reject filter bank is presented. Fast low loss switching of high quality (Q) factor HTS filter elements enables digital selection of arbitrary pass-bands and stop-bands. Patterned pieces of GaAs and silicon are used in the manufacture of the photosensitive switches. Fiber optic cabling is used to transfer the optical energy from an LED to the switch. The fiber optic cable minimizes the thermal loading of the filter package and de-couples the switch's power source from the RF circuit. This paper will discuss the development of a computer-controlled HTS bank of optically switchable, narrow band, high Q bandstop filters which incorporates a cryocooler to maintain the 77 K operating temperature of the HTS microwave circuit
Model Adaptation for Prognostics in a Particle Filtering Framework
National Aeronautics and Space Administration — One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated....
Performance Evaluation Of Different Adaptive Filters For ECG Signal Processing
Sachin singh,; Dr K. L. Yadav
2010-01-01
One of the main problem in biomedical data processing like electrocardiography is the separation of the wanted signal from noises caused by power line interference, external electromagnetic fields and random body movements and respiration. Different types of digital filters are used to remove signal components from unwanted frequency ranges. It is difficult to apply filters with fixed coefficients to reduce Biomedical Signal noises, because human behavior is not exact known depending on the t...
Multiple Adaptive Fading Schmidt-Kalman Filter for Unknown Bias
Tai-Shan Lou; Zhi-Hua Wang; Meng-Li Xiao; Hui-Min Fu
2014-01-01
Unknown biases in dynamic and measurement models of the dynamic systems can bring greatly negative effects to the state estimates when using a conventional Kalman filter algorithm. Schmidt introduces the “consider” analysis to account for errors in both the dynamic and measurement models due to the unknown biases. Although the Schmidt-Kalman filter “considers” the biases, the uncertain initial values and incorrect covariance matrices of the unknown biases still are not considered. To solve th...
PSO Algorithm based Adaptive Median Filter for Noise Removal in Image Processing Application
Ruby Verma
2016-07-01
Full Text Available A adaptive Switching median filter for salt and pepper noise removal based on genetic algorithm is presented. Proposed filter consist of two stages, a noise detector stage and a noise filtering stage. Particle swarm optimization seems to be effective for single objective problem. Noise Dictation stage works on it. In contrast to the standard median filter, the proposed algorithm generates the noise map of corrupted Image. Noise map gives information about the corrupted and non-corrupted pixels of Image. In filtering, filter calculates the median of uncorrupted neighbouring pixels and replaces the corrupted pixels. Extensive simulations are performed to validate the proposed filter. Simulated results show refinement both in Peak signal to noise ratio (PSNR and Image Quality Index value (IQI. Experimental results shown that proposed method is more effective than existing methods.
IAE-adaptive Kalman filter for INS/GPS integrated navigation system
Bian Hongwei; Jin Zhihua; Tian Weifeng
2006-01-01
A marine INS/GPS adaptive navigation system is presented in this paper. GPS with two antenna providing vessel's altitude is selected as the auxiliary system fusing with INS to improve the performance of the hybrid system. The Kalman filter is the most frequently used algorithm in the integrated navigation system, which is capable of estimating INS errors online based on the measured errors between INS and GPS. The standard Kalman filter (SKF) assumes that the statistics of the noise on each sensor are given. As long as the noise distributions do not change, the Kalman filter will give the optimal estimation. However GPS receiver will be disturbed easily and thus temporally changing measurement noise will join into the outputs of GPS, which will lead to performance degradation of the Kalman filter. Many researchers introduce fuzzy logic control method into innovation-based adaptive estimation adaptive Kalman filtering (IAE-AKF) algorithm, and accordingly propose various adaptive Kalman filters. However how to design the fuzzy logic controller is a very complicated problem still without a convincing solution. A novel IAE-AKF is proposed herein, which is based on the maximum likelihood criterion for the proper computation of the filter innovation covariance and hence of the filter gain. The approach is direct and simple without having to establish fuzzy inference rules. After having deduced the proposed IAE-AKF algorithm theoretically in detail, the approach is tested by the simulation based on the system error model of the developed INS/GPS integrated marine navigation system. Simulation results show that the adaptive Kalman filter outperforms the SKF with higher accuracy, robustness and less computation. It is demonstrated that this proposed approach is a valid solution for the unknown changing measurement noise exited in the Kalman filter.
Adaptive filtering of radar images for autofocus applications
Stiles, J. A.; Frost, V. S.; Gardner, J. S.; Eland, D. R.; Shanmugam, K. S.; Holtzman, J. C.
1981-01-01
Autofocus techniques are being designed at the Jet Propulsion Laboratory to automatically choose the filter parameters (i.e., the focus) for the digital synthetic aperture radar correlator; currently, processing relies upon interaction with a human operator who uses his subjective assessment of the quality of the processed SAR data. Algorithms were devised applying image cross-correlation to aid in the choice of filter parameters, but this method also has its drawbacks in that the cross-correlation result may not be readily interpretable. Enhanced performance of the cross-correlation techniques of JPL was hypothesized given that the images to be cross-correlated were first filtered to improve the signal-to-noise ratio for the pair of scenes. The results of experiments are described and images are shown.
Qiguang Zhu
2014-05-01
Full Text Available To resolve the difficulty in establishing accurate priori noise model for the extended Kalman filtering algorithm, propose the fractional-order Darwinian particle swarm optimization (PSO algorithm has been proposed and introduced into the fuzzy adaptive extended Kalman filtering algorithm. The natural selection method has been adopted to improve the standard particle swarm optimization algorithm, which enhanced the diversity of particles and avoided the premature. In addition, the fractional calculus has been used to improve the evolution speed of particles. The PSO algorithm after improved has been applied to train fuzzy adaptive extended Kalman filter and achieve the simultaneous localization and mapping. The simulation results have shown that compared with the geese particle swarm optimization training of fuzzy adaptive extended Kalman filter localization and mapping algorithm, has been greatly improved in terms of localization and mapping.
Adaptive Kalman Filter of Transfer Alignment with Un-modeled Wing Flexure of Aircraft
无
2008-01-01
The alignment accuracy of the strap-down inertial navigation system (SINS) of airborne weapon is greatly degraded by the dynamic wing flexure of the aircraft. An adaptive Kalman filter uses innovation sequences based on the maximum likelihood estimated criterion to adapt the system noise covariance matrix and the measurement noise covariance matrix on line, which is used to estimate the misalignment if the model of wing flexure of the aircraft is unknown. From a number of simulations, it is shown that the accuracy of the adaptive Kalman filter is better than the conventional Kalman filter, and the erroneous misalignment models of the wing flexure of aircraft will cause bad estimation results of Kalman filter using attitude match method.
We present design optimization of wavelength filters based on long period waveguide gratings (LPWGs) using the adaptive particle swarm optimization (APSO) technique. We demonstrate optimization of the LPWG parameters for single-band, wide-band and dual-band rejection filters for testing the convergence of APSO algorithms. After convergence tests on the algorithms, the optimization technique has been implemented to design more complicated application specific filters such as erbium doped fiber amplifier (EDFA) amplified spontaneous emission (ASE) flattening, erbium doped waveguide amplifier (EDWA) gain flattening and pre-defined broadband rejection filters. The technique is useful for designing and optimizing the parameters of LPWGs to achieve complicated application specific spectra. (paper)
An Adaptive Kalman Filter Using a Simple Residual Tuning Method
Harman, Richard R.
1999-01-01
One difficulty in using Kalman filters in real world situations is the selection of the correct process noise, measurement noise, and initial state estimate and covariance. These parameters are commonly referred to as tuning parameters. Multiple methods have been developed to estimate these parameters. Most of those methods such as maximum likelihood, subspace, and observer Kalman Identification require extensive offline processing and are not suitable for real time processing. One technique, which is suitable for real time processing, is the residual tuning method. Any mismodeling of the filter tuning parameters will result in a non-white sequence for the filter measurement residuals. The residual tuning technique uses this information to estimate corrections to those tuning parameters. The actual implementation results in a set of sequential equations that run in parallel with the Kalman filter. A. H. Jazwinski developed a specialized version of this technique for estimation of process noise. Equations for the estimation of the measurement noise have also been developed. These algorithms are used to estimate the process noise and measurement noise for the Wide Field Infrared Explorer star tracker and gyro.
In this report we describe the concept of adaptive noise canceling, an alternative method of estimating signals corrupted by additive noise of interference. The method uses 'primary' input containing the corrupted signal and a 'reference' input containing noise correlated in some unknown way with the primary noise, the reference input is adaptively filtered and subtracted from the primary input to obtain the signal estimate. Adaptive filtering before subtraction allows the treatment of inputs that are deterministic or stochastic, stationary or time variable. When the reference input is free of signal and certain other conditions are met then noise in the primary input can be essentially eliminated without signal distortion. It is further shown that the adaptive filter also acts as notch filter. Simulated results illustrate the usefulness of the adaptive noise canceling technique. (author)
Cahit Tağı ÇELİK
2004-01-01
Monitoring the Crustal Movement in Geodesy is performed by the deformation survey and analysis. If monitoring the crustal movements involves more than two epochs of survey campaign then from the plate tectonic theory, stations do not move randomly from one epoch to the other, therefore Kalman Filter may be suitable to use. However, if sudden movements happened in the crust in particular earthquake happened, the crust moves very fast in a very short period of time. When Kalman Filter used for ...
Adaptive RBF Neural Network Control for Three-Phase Active Power Filter
Juntao Fei; Zhe Wang
2013-01-01
An adaptive radial basis function (RBF) neural network control system for three‐phase active power filter (APF) is proposed to eliminate harmonics. Compensation current is generated to track command current so as to eliminate the harmonic current of non‐linear load and improve the quality of the power system. The asymptotical stability of the APF system can be guaranteed with the proposed adaptive neural network strategy. The parameters of the neural network can be adaptively updated to achie...
Delay Estimator and Improved Proportionate Multi-Delay Adaptive Filtering Algorithm
E. Verteletskaya
2012-04-01
Full Text Available This paper pertains to speech and acoustic signal processing, and particularly to a determination of echo path delay and operation of echo cancellers. To cancel long echoes, the number of weights in a conventional adaptive filter must be large. The length of the adaptive filter will directly affect both the degree of accuracy and the convergence speed of the adaptation process. We present a new adaptive structure which is capable to deal with multiple dispersive echo paths. An adaptive filter according to the present invention includes means for storing an impulse response in a memory, the impulse response being indicative of the characteristics of a transmission line. It also includes a delay estimator for detecting ranges of samples within the impulse response having relatively large distribution of echo energy. These ranges of samples are being indicative of echoes on the transmission line. An adaptive filter has a plurality of weighted taps, each of the weighted taps having an associated tap weight value. A tap allocation/control circuit establishes the tap weight values in response to said detecting means so that only taps within the regions of relatively large distributions of echo energy are turned on. Thus, the convergence speed and the degree of estimation in the adaptation process can be improved.
An Improved Variable Structure Adaptive Filter Design and Analysis for Acoustic Echo Cancellation
A. Kar
2015-04-01
Full Text Available In this research an advance variable structure adaptive Multiple Sub-Filters (MSF based algorithm for single channel Acoustic Echo Cancellation (AEC is proposed and analyzed. This work suggests a new and improved direction to find the optimum tap-length of adaptive filter employed for AEC. The structure adaptation, supported by a tap-length based weight update approach helps the designed echo canceller to maintain a trade-off between the Mean Square Error (MSE and time taken to attain the steady state MSE. The work done in this paper focuses on replacing the fixed length sub-filters in existing MSF based AEC algorithms which brings refinements in terms of convergence, steady state error and tracking over the single long filter, different error and common error algorithms. A dynamic structure selective coefficient update approach to reduce the structural and computational cost of adaptive design is discussed in context with the proposed algorithm. Simulated results reveal a comparative performance analysis over proposed variable structure multiple sub-filters designs and existing fixed tap-length sub-filters based acoustic echo cancellers.
An adaptive filter bank for motor imagery based Brain Computer Interface.
Thomas, Kavitha P; Guan, Cuntai; Tong, Lau Chiew; Prasad, Vinod A
2008-01-01
Brain Computer Interface (BCI) provides an alternative communication and control method for people with severe motor disabilities. Motor imagery patterns are widely used in Electroencephalogram (EEG) based BCIs. These motor imagery activities are associated with variation in alpha and beta band power of EEG signals called Event Related Desynchronization/synchronization (ERD/ERS). The dominant frequency bands are subject-specific and therefore performance of motor imagery based BCIs are sensitive to both temporal filtering and spatial filtering. As the optimum filter is strongly subject-dependent, we propose a method that selects the subject-specific discriminative frequency components using time-frequency plots of Fisher ratio of two-class motor imagery patterns. We also propose a low complexity adaptive Finite Impulse Response (FIR) filter bank system based on coefficient decimation technique which can realize the subject-specific bandpass filters adaptively depending on the information of Fisher ratio map. Features are extracted only from the selected frequency components. The proposed adaptive filter bank based system offers average classification accuracy of about 90%, which is slightly better than the existing fixed filter bank system. PMID:19162856
Secure Tracking in Sensor Networks using Adaptive Extended Kalman Filter
Fard, Ali P
2012-01-01
Location information of sensor nodes has become an essential part of many applications in Wireless Sensor Networks (WSN). The importance of location estimation and object tracking has made them the target of many security attacks. Various methods have tried to provide location information with high accuracy, while lots of them have neglected the fact that WSNs may be deployed in hostile environments. In this paper, we address the problem of securely tracking a Mobile Node (MN) which has been noticed very little previously. A novel secure tracking algorithm is proposed based on Extended Kalman Filter (EKF) that is capable of tracking a Mobile Node (MN) with high resolution in the presence of compromised or colluding malicious beacon nodes. It filters out and identifies the malicious beacon data in the process of tracking. The proposed method considerably outperforms the previously proposed secure algorithms in terms of either detection rate or MSE. The experimental data based on different settings for the netw...
Adaptive Non-Linear Bayesian Filter for ECG Denoising
Mitesh Kumar Sao; Anurag Shrivastava
2014-01-01
The cycles of an electrocardiogram (ECG) signal contain three components P-wave, QRS complex and the T-wave. Noise is present in cardiograph as signals being measured in which biological resources (muscle contraction, base line drift, motion noise) and environmental resources (power line interference, electrode contact noise, instrumentation noise) are normally pollute ECG signal detected at the electrode. Visu-Shrink thresholding and Bayesian thresholding are the two filters based technique ...
Adaptive filtering enhances information transmission in visual cortex
Sharpee, Tatyana O.; Sugihara, Hiroki; Kurgansky, Andrei V.; Rebrik, Sergei P.; Stryker, Michael P.; Miller, Kenneth D.
2006-01-01
Sensory neuroscience seeks to understand how the brain encodes natural environments. However, neural coding has largely been studied using simplified stimuli. In order to assess whether the brain’s coding strategy depends on the stimulus ensemble, we apply a new information-theoretic method that allows unbiased calculation of neural filters (receptive fields) from responses to natural scenes or other complex signals with strong multipoint correlations. In the cat primary visual cortex we comp...
Adaptive high-gain extended kalman filter and applications
Boizot, Nicolas Richard
2010-01-01
The work concerns the ``observability problem” --- the reconstruction of a dynamic process's full state from a partially measured state--- for nonlinear dynamic systems. The Extended Kalman Filter (EKF) is a widely-used observer for such nonlinear systems. However it suffers from a lack of theoretical justifications and displays poor performance when the estimated state is far from the real state, e.g. due to large perturbations, a poor initial state estimate, etc… We propose a solution to...
Steady State Analysis of Convex Combination of Affine Projection Adaptive Filters
S. Radhika
2015-05-01
Full Text Available The aim of the study is to propose an adaptive algorithm using convex combinational approach to have both fast convergence and less steady state error simultaneously. For this purpose, we have used two affine projection adaptive filters with complementary nature (both in step size and projection order as the component filters. The first component filter has high projection order and large step size which makes it to have fast convergence at the cost of more steady state error. The second component filter has slow convergence and less steady state error due to the selection of small step size and projection order. Both are combined using convex combiner so as to have best final output with fast convergence and less steady state error. Each of the component filters are updated using their own error signals and stochastic gradient approach is used to update the convex combiner so as to have minimum overall error. By using energy conservation argument, analytical treatment of the combination stage is made in stationary environment. It is found that during initial stage the proposed scheme converges to the fast filter which has good convergence later it converges to either of the two (whichever has less steady state error and towards the end, the final output converges to slow filter which is superior in lesser steady state error. Experimental results proved that the proposed algorithm has adopted the best features of the component filters.
Intra-fraction tumor tracking methods can improve radiation delivery during radiotherapy sessions. Image acquisition for tumor tracking and subsequent adjustment of the treatment beam with gating or beam tracking introduces time latency and necessitates predicting the future position of the tumor. This study evaluates the use of multi-dimensional linear adaptive filters and support vector regression to predict the motion of lung tumors tracked at 30 Hz. We expand on the prior work of other groups who have looked at adaptive filters by using a general framework of a multiple-input single-output (MISO) adaptive system that uses multiple correlated signals to predict the motion of a tumor. We compare the performance of these two novel methods to conventional methods like linear regression and single-input, single-output adaptive filters. At 400 ms latency the average root-mean-square-errors (RMSEs) for the 14 treatment sessions studied using no prediction, linear regression, single-output adaptive filter, MISO and support vector regression are 2.58, 1.60, 1.58, 1.71 and 1.26 mm, respectively. At 1 s, the RMSEs are 4.40, 2.61, 3.34, 2.66 and 1.93 mm, respectively. We find that support vector regression most accurately predicts the future tumor position of the methods studied and can provide a RMSE of less than 2 mm at 1 s latency. Also, a multi-dimensional adaptive filter framework provides improved performance over single-dimension adaptive filters. Work is underway to combine these two frameworks to improve performance.
Gear Fault Signal Detection based on an Adaptive Fractional Fourier Transform Filter
Vibration-based fault diagnosis is widely used for gearbox monitoring. However, it often needs considerable effort to extract effective diagnostic feature signal from noisy vibration signals because of rich signal components contained in a complex gear transmission system. In this paper, an adaptive fractional Fourier transform filter is proposed to suppress noise in gear vibration signals and hence to highlight signal components originated from gear fault dynamic characteristics. The approach relies on the use of adaptive filters in the fractional Fourier transform domain with the optimised fractional transform order and the filter parameters, while the transform orders are selected when the signal have the highest energy gathering and the filter parameters are determined by evolutionary rules. The results from the simulation and experiments have verified the performance of the proposed algorithm in extracting the gear failure signal components from the noisy signals based on a multistage gearbox system.
A proposal for an Adaptive Information Filtering and Control Concept
Maas, H.L.M.M.; Meiler, P.P.
1998-01-01
This paper describes a concept to manage the information exchange between the operators and their consoles (the interface to the computer system) within a Command and Control (C2) centre. Application of his concept will result in a more effective and efficient information exchange, using adaptive in
Adaptive integrated navigation filtering based on accelerometer calibration
Qifan Zhou; Hai Zhang; Yanran Wang
2012-01-01
In this paper, a novel GPS (Global Positioning System) and DR (Dead Reckoning) system which was based on the accelerometer and gyroscope integrated system was designed and implemented. In this system, the odometer used in traditional DR system was replaced by a MEMS tri-axis accelerometer in order to decrease the cost and the volume of the system. The system was integrated by the Kalman filter and a new mathematical model was introduced. In order to reasonably use the GPS information, an adap...
Adaptive filter for a miniature MEMS based attitude and heading reference system
WANG Mei; WANG Yong-quan; ZHANG Yan-hua
2006-01-01
An extended Kalman filter with adaptive gain was used to build a miniature attitude and heading reference system based on a stochastic model. The adaptive filter has six states with a time variable transition matrix. When the system is in the non-acceleration mode, the accelerometer measurements of the gravity and the compass measurements of the heading have observability and yield good estimates of the states. When the system is in the high dynamic mode and the bias has converged to an accurate estimate, the attitude calculation will be maintained for a long interval of time. The adaptive filter tunes its gain automatically based on the system dynamics sensed by the accelerometers to yield optimal performance.
Adaptive filters for suppressing irregular hostile jamming in direct sequence spread-spectrum system
Lee, Jung Hoon; Lee, Choong Woong
A stable and high-performance adaptive filter for suppressing irregular hostile jamming in direct-sequence (DS) spread-spectrum systems is designed. A gradient-search fast converging algorithm (GFC) is suggested. For the case of a sudden parameter jump or incoming of an interference, the transient behaviors of the receiver using a GFC adaptive filter are investigated and compared with those of the receiver using a least-mean-square (LMS) or a lattice adaptive filter. The results are shown in the response graphs of the simulated receiver during the short period when the characteristic of a jammer is suddenly changed. Steady-state performances of those receivers are also evaluated in the sense of the excess mean-square error over that of an optimum receiver for suppressing stationary interferences.
Predicting Hyper-Chaotic Time Series Using Adaptive Higher-Order Nonlinear Filter
Zhang, Jia-Shu; Xiao, Xian-Ci
2001-03-01
A newly proposed method, i.e. the adaptive higher-order nonlinear finite impulse response (HONFIR) filter based on higher-order sparse Volterra series expansions, is introduced to predict hyper-chaotic time series. The effectiveness of using the adaptive HONFIR filter for making one-step and multi-step predictions is tested based on very few data points by computer-generated hyper-chaotic time series including the Mackey-Glass equation and four-dimensional nonlinear dynamical system. A comparison is made with some neural networks for predicting the Mackey-Glass hyper-chaotic time series. Numerical simulation results show that the adaptive HONFIR filter proposed here is a very powerful tool for making prediction of hyper-chaotic time series.
Weighted Robust Adaptive Filtering in Krein Space and Its Application in Active Noise Control
Jayawardhana, Bayu; Yuan, Shuqing; Xie, Lihua
2002-01-01
Robust adaptive filtering ensures the minimization of the transfer function from the disturbance to the estimation error and thus, guarantees the robustness against the worst-case disturbance in the system. However, a more general approach will be given in this paper hy employing frequency weighting, which offers flexibility in determining robustness sensitivity in certain frequency of interest. Using the projection in Krein space, we developed a weighted recursive H∞ filtering that computes ...
Ökzan, E.; Šmídl, Václav; Saha, S.; Lundquist, C.; Gustafsson, F.
2013-01-01
Roč. 49, č. 6 (2013), s. 1566-1575. ISSN 0005-1098 R&D Projects: GA ČR(CZ) GAP102/11/0437 Keywords : Unknown Noise Statistics * Adaptive Filtering * Marginalized Particle Filter * Bayesian Conjugate prior Subject RIV: BC - Control Systems Theory Impact factor: 3.132, year: 2013 http://library.utia.cas.cz/separaty/2013/AS/smidl-0393047.pdf
On Implementation and Design of Filter Banks for Subband Adaptive Systems
Weiss, S; Harteneck, M; Stewart, R W
1998-01-01
This paper introduces a polyphase implementation and design of an oversampled K-channel generalized DFT (GDFT) filter bank, which can be employed for subband adaptive filtering, and therefore is required to have a low aliasing level in the subband signals. A polyphase structure is derived which can be factorized into a real valued polyphase network and a GDFT modulation. For the latter, an FFT realization may be used, yielding a highly efficient polyphase implementation for arbitrary integer ...
Li Shu; Zhuo Jiashou; Ren Qingwen
2000-01-01
In this paper, an optimal criterion is presented for adaptive Kalman filter in a control sys tem with unknown variances of stochastic vibration by constructing a function of noise variances and minimizing the function. We solve the model and measure variances by using DFP optimal method to guarantee the results of Kalman filter to be optimized. Finally, the control of vibration can be implemented by LQG method.
Optimizing Cost of Continuous Overlapping Queries over Data Streams by Filter Adaption
Xie, Qing
2016-01-12
The problem we aim to address is the optimization of cost management for executing multiple continuous queries on data streams, where each query is defined by several filters, each of which monitors certain status of the data stream. Specially the filter can be shared by different queries and expensive to evaluate. The conventional objective for such a problem is to minimize the overall execution cost to solve all queries, by planning the order of filter evaluation in shared strategy. However, in streaming scenario, the characteristics of data items may change in process, which can bring some uncertainty to the outcome of individual filter evaluation, and affect the plan of query execution as well as the overall execution cost. In our work, considering the influence of the uncertain variation of data characteristics, we propose a framework to deal with the dynamic adjustment of filter ordering for query execution on data stream, and focus on the issues of cost management. By incrementally monitoring and analyzing the results of filter evaluation, our proposed approach can be effectively adaptive to the varied stream behavior and adjust the optimal ordering of filter evaluation, so as to optimize the execution cost. In order to achieve satisfactory performance and efficiency, we also discuss the trade-off between the adaptivity of our framework and the overhead incurred by filter adaption. The experimental results on synthetic and two real data sets (traffic and multimedia) show that our framework can effectively reduce and balance the overall query execution cost and keep high adaptivity in streaming scenario.
Li, Wei; Haese-Coat, Veronique; Ronsin, Joseph
1996-03-01
An adaptive GA scheme is adopted for the optimal morphological filter design problem. The adaptive crossover and mutation rate which make the GA avoid premature and at the same time assure convergence of the program are successfully used in optimal morphological filter design procedure. In the string coding step, each string (chromosome) is composed of a structuring element coding chain concatenated with a filter sequence coding chain. In decoding step, each string is divided into 3 chains which then are decoded respectively into one structuring element with a size inferior to 5 by 5 and two concatenating morphological filter operators. The fitness function in GA is based on the mean-square-error (MSE) criterion. In string selection step, a stochastic tournament procedure is used to replace the simple roulette wheel program in order to accelerate the convergence. The final convergence of our algorithm is reached by a two step converging strategy. In presented applications of noise removal from texture images, it is found that with the optimized morphological filter sequences, the obtained MSE values are smaller than those using corresponding non-adaptive morphological filters, and the optimized shapes and orientations of structuring elements take approximately the same shapes and orientations as those of the image textons.
Image restoration using regularized inverse filtering and adaptive threshold wavelet denoising
Mr. Firas Ali
2007-01-01
Full Text Available Although the Wiener filtering is the optimal tradeoff of inverse filtering and noise smoothing, in the case when the blurring filter is singular, the Wiener filtering actually amplify the noise. This suggests that a denoising step is needed to remove the amplified noise .Wavelet-based denoising scheme provides a natural technique for this purpose .In this paper a new image restoration scheme is proposed, the scheme contains two separate steps : Fourier-domain inverse filtering and wavelet-domain image denoising. The first stage is Wiener filtering of the input image , the filtered image is inputted to adaptive threshold wavelet denoising stage . The choice of the threshold estimation is carried out by analyzing the statistical parameters of the wavelet sub band coefficients like standard deviation, arithmetic mean and geometrical mean . The noisy image is first decomposed into many levels to obtain different frequency bands. Then soft thresholding method is used to remove the noisy coefficients, by fixing the optimum thresholding value by this method .Experimental results on test image by using this method show that this method yields significantly superior image quality and better Peak Signal to Noise Ratio (PSNR. Here, to prove the efficiency of this method in image restoration , we have compared this with various restoration methods like Wiener filter alone and inverse filter.
Method and system for training dynamic nonlinear adaptive filters which have embedded memory
Rabinowitz, Matthew (Inventor)
2002-01-01
Described herein is a method and system for training nonlinear adaptive filters (or neural networks) which have embedded memory. Such memory can arise in a multi-layer finite impulse response (FIR) architecture, or an infinite impulse response (IIR) architecture. We focus on filter architectures with separate linear dynamic components and static nonlinear components. Such filters can be structured so as to restrict their degrees of computational freedom based on a priori knowledge about the dynamic operation to be emulated. The method is detailed for an FIR architecture which consists of linear FIR filters together with nonlinear generalized single layer subnets. For the IIR case, we extend the methodology to a general nonlinear architecture which uses feedback. For these dynamic architectures, we describe how one can apply optimization techniques which make updates closer to the Newton direction than those of a steepest descent method, such as backpropagation. We detail a novel adaptive modified Gauss-Newton optimization technique, which uses an adaptive learning rate to determine both the magnitude and direction of update steps. For a wide range of adaptive filtering applications, the new training algorithm converges faster and to a smaller value of cost than both steepest-descent methods such as backpropagation-through-time, and standard quasi-Newton methods. We apply the algorithm to modeling the inverse of a nonlinear dynamic tracking system 5, as well as a nonlinear amplifier 6.
New cardiac MRI gating method using event-synchronous adaptive digital filter.
Park, Hodong; Park, Youngcheol; Cho, Sungpil; Jang, Bongryoel; Lee, Kyoungjoung
2009-11-01
When imaging the heart using MRI, an artefact-free electrocardiograph (ECG) signal is not only important for monitoring the patient's heart activity but also essential for cardiac gating to reduce noise in MR images induced by moving organs. The fundamental problem in conventional ECG is the distortion induced by electromagnetic interference. Here, we propose an adaptive algorithm for the suppression of MR gradient artefacts (MRGAs) in ECG leads of a cardiac MRI gating system. We have modeled MRGAs by assuming a source of strong pulses used for dephasing the MR signal. The modeled MRGAs are rectangular pulse-like signals. We used an event-synchronous adaptive digital filter whose reference signal is synchronous to the gradient peaks of MRI. The event detection processor for the event-synchronous adaptive digital filter was implemented using the phase space method-a sort of topology mapping method-and least-squares acceleration filter. For evaluating the efficiency of the proposed method, the filter was tested using simulation and actual data. The proposed method requires a simple experimental setup that does not require extra hardware connections to obtain the reference signals of adaptive digital filter. The proposed algorithm was more effective than the multichannel approach. PMID:19644754
Adaptive filtering algorithms for channel equalization and echo cancellation
Swati Dhamija
2011-09-01
Full Text Available In this paper, we will be addressing the major concerns in telecommunication nowadays which are channel equalization and echo cancellation using different adaptive algorithms in order to identify the most efficient methodology. There are a number of conventional algorithms available in literature and every algorithm has its own properties, however the aim of every adaptive algorithm is to achieve minimum mean square error at a high rate of convergence and with less complexity. The experimental results prove that Least Mean Square algorithm (LMS is the best for channel equalization and Recursive Least Square (RLS is most efficient for echo cancellation. Moreover, LMS algorithms work efficiently in case of stochastic processes and on the contrary RLS is good for deterministic signals
Sudeep, P V; Issac Niwas, S; Palanisamy, P; Rajan, Jeny; Xiaojun, Yu; Wang, Xianghong; Luo, Yuemei; Liu, Linbo
2016-04-01
Optical coherence tomography (OCT) has continually evolved and expanded as one of the most valuable routine tests in ophthalmology. However, noise (speckle) in the acquired images causes quality degradation of OCT images and makes it difficult to analyze the acquired images. In this paper, an iterative approach based on bilateral filtering is proposed for speckle reduction in multiframe OCT data. Gamma noise model is assumed for the observed OCT image. First, the adaptive version of the conventional bilateral filter is applied to enhance the multiframe OCT data and then the bias due to noise is reduced from each of the filtered frames. These unbiased filtered frames are then refined using an iterative approach. Finally, these refined frames are averaged to produce the denoised OCT image. Experimental results on phantom images and real OCT retinal images demonstrate the effectiveness of the proposed filter. PMID:26907572
Modeling of Rate-Dependent Hysteresis Using a GPO-Based Adaptive Filter
Zhen Zhang; Yaopeng Ma
2016-01-01
A novel generalized play operator-based (GPO-based) nonlinear adaptive filter is proposed to model rate-dependent hysteresis nonlinearity for smart actuators. In the proposed filter, the input signal vector consists of the output of a tapped delay line. GPOs with various thresholds are used to construct a nonlinear network and connected with the input signals. The output signal of the filter is composed of a linear combination of signals from the output of GPOs. The least-mean-square (LMS) al...
Performance Analysis of Adaptive Volterra Filters in the Finite-Alphabet Input Case
Jaïdane Mériem
2004-01-01
Full Text Available This paper deals with the analysis of adaptive Volterra filters, driven by the LMS algorithm, in the finite-alphabet inputs case. A tailored approach for the input context is presented and used to analyze the behavior of this nonlinear adaptive filter. Complete and rigorous mean square analysis is provided without any constraining independence assumption. Exact transient and steady-state performances expressed in terms of critical step size, rate of transient decrease, optimal step size, excess mean square error in stationary mode, and tracking nonstationarities are deduced.
Xiaogu ZHENG
2009-01-01
An adaptive estimation of forecast error covariance matrices is proposed for Kalman filtering data assimilation. A forecast error covariance matrix is initially estimated using an ensemble of perturbation forecasts. This initially estimated matrix is then adjusted with scale parameters that are adaptively estimated by minimizing -2log-likelihood of observed-minus-forecast residuals. The proposed approach could be applied to Kalman filtering data assimilation with imperfect models when the model error statistics are not known. A simple nonlinear model (Burgers' equation model) is used to demonstrate the efficacy of the proposed approach.
Stent enhancement using a locally adaptive unsharp masking filter in digital x-ray fluoroscopy
Jiang, Yuhao; Ekanayake, Eranda
2014-03-01
Low exposure X-ray fluoroscopy is used to guide some complicate interventional procedures. Due to the inherent high levels of noise, improving the visibility of some interventional devices such as stent will greatly benefit those interventional procedures. Stent, which is made up of tiny steel wires, is also suffered from contrast dilutions of large flat panel detector pixels. A novel adaptive unsharp masking filter has been developed to improve stent contrast in real-time applications. In unsharp masking processing, the background is estimated and subtracted from the original input image to create a foreground image containing objects of interest. A background estimator is therefore critical in the unsharp masking processing. In this specific study, orientation filter kernels are used as the background estimator. To make the process simple and fast, the kernels average along a line of pixels. A high orientation resolution of 18° is used. A nonlinear operator is then used to combine the information from the images generated from convolving the original background and noise only images with orientation filters. A computerized Monte Carlo simulation followed by ROC study is used to identify the best nonlinear operator. We then apply the unsharp masking filter to the images with stents present. It is shown that the locally adaptive unsharp making filter is an effective filter for improving stent visibility in the interventional fluoroscopy. We also apply a spatio-temporal channelized human observer model to quantitatively optimize and evaluate the filter.
Adaptive Non-Linear Bayesian Filter for ECG Denoising
Mitesh Kumar Sao
2014-06-01
Full Text Available The cycles of an electrocardiogram (ECG signal contain three components P-wave, QRS complex and the T-wave. Noise is present in cardiograph as signals being measured in which biological resources (muscle contraction, base line drift, motion noise and environmental resources (power line interference, electrode contact noise, instrumentation noise are normally pollute ECG signal detected at the electrode. Visu-Shrink thresholding and Bayesian thresholding are the two filters based technique on wavelet method which is denoising the PLI noisy ECG signal. So thresholding techniques are applied for the effectiveness of ECG interval and compared the results with the wavelet soft and hard thresholding methods. The outputs are evaluated by calculating the root mean square (RMS, signal to noise ratio (SNR, correlation coefficient (CC and power spectral density (PSD using MATLAB software. The clean ECG signal shows Bayesian thresholding technique is more powerful algorithm for denoising.
GAN Yu
2015-09-01
Full Text Available Attitude determination directly by carrier phase observation makes optimal use of observation and attitude constraints. The phase models based on misalignment angle and multiplicative quaternion error are derived. The state models for attitude estimation with and without external angular rate sensors are both erected. The attitude errors are estimated by adaptively robust filtering, in which the adaptive factors of ambiguity and attitude error are decided respectively following the idea of multi adaptive factor filtering. The factor of attitude is determined by a three-section function containing Ratio. Adaptively robust filtering makes the best use of constraint and historical information, fusing them in the calculation of float solution. As the accuracy of float solution and the structure of covariance matrix are improved greatly, the fix solution can be searched efficiently using LAMBDA (least-squares ambiguity decorrelation adjustment method merely, perfectly fulfilling the real-time requirement. Field test of a ship-based three-antenna attitude system is used to validate the proposed method. It is showed that direct attitude determination based on adaptively robust filtering has obvious advantages in efficiency and reliability.
Application of Adaptive Filters to Active Noise Control
PEI Bingnan; LI Chuanguang
2001-01-01
A modified LMS algorithm for noise-control is suggested after a mathematical model ofsound-cancellation is established, on the basis of thesound wave interference principle and the physicalmodel of progressive waves in a duct. Its applicationin controlling noise with the frequency range from 100to 800 Hz can be implemented by using the adaptivedigital signal processing technique. The experimentson a pink noise, a broadband noise and a noise takenfrom a tank were made, which show that there existsan attenuation of 11 dB at the frequency of 500 Hzor so, and that the proposed adaptive noise controltechnique is very effective and valid.
Cannistraci, Carlo Vittorio
2015-01-26
Denoising multidimensional NMR-spectra is a fundamental step in NMR protein structure determination. The state-of-the-art method uses wavelet-denoising, which may suffer when applied to non-stationary signals affected by Gaussian-white-noise mixed with strong impulsive artifacts, like those in multi-dimensional NMR-spectra. Regrettably, Wavelet\\'s performance depends on a combinatorial search of wavelet shapes and parameters; and multi-dimensional extension of wavelet-denoising is highly non-trivial, which hampers its application to multidimensional NMR-spectra. Here, we endorse a diverse philosophy of denoising NMR-spectra: less is more! We consider spatial filters that have only one parameter to tune: the window-size. We propose, for the first time, the 3D extension of the median-modified-Wiener-filter (MMWF), an adaptive variant of the median-filter, and also its novel variation named MMWF*. We test the proposed filters and the Wiener-filter, an adaptive variant of the mean-filter, on a benchmark set that contains 16 two-dimensional and three-dimensional NMR-spectra extracted from eight proteins. Our results demonstrate that the adaptive spatial filters significantly outperform their non-adaptive versions. The performance of the new MMWF* on 2D/3D-spectra is even better than wavelet-denoising. Noticeably, MMWF* produces stable high performance almost invariant for diverse window-size settings: this signifies a consistent advantage in the implementation of automatic pipelines for protein NMR-spectra analysis.
For all segments and tests, a modified Kalman filter and a quasi-static sensor fusion algorithm were equally accurate (precision and accuracy ∼2–3°) compared to normalized least mean squares filtering, recursive least-squares filtering and standard Kalman filtering. The aims were to: (1) compare adaptive filtering techniques used for sensor fusion and (2) evaluate the precision and accuracy for a chosen adaptive filter. Motion sensors (based on inertial measurement units) are limited by accumulative integration errors arising from sensor bias. This drift can partly be handled with adaptive filtering techniques. To advance the measurement technique in this area, a new modified Kalman filter is developed. Differences in accuracy were observed during different tests especially drift in the internal/external rotation angle. This drift can be minimized if the sensors include magnetometers. (paper)
The Application of Adaptive Federated Filter in GPS-INS-Odometer Integrated Navigation
LI Zengke; WANG, Jian; GAO Jingxiang; YAO Yifei
2016-01-01
In multi-sensor integrated navigation, extensive observation information, low computational efficiency and weak robust ability will lead to poor navigation performance. An adaptive federated filter is proposed and applied in GPS-INS-Odometer integrated navigation. First the dynamical model and observation model of GPS-INS-Odometer integrated navigation are introduced. Information allocation factor and adaptive factor are compared to find out their common characteristic. The equivalence proper...
Adaptive error covariances estimation methods for ensemble Kalman filters
This paper presents a computationally fast algorithm for estimating, both, the system and observation noise covariances of nonlinear dynamics, that can be used in an ensemble Kalman filtering framework. The new method is a modification of Belanger's recursive method, to avoid an expensive computational cost in inverting error covariance matrices of product of innovation processes of different lags when the number of observations becomes large. When we use only product of innovation processes up to one-lag, the computational cost is indeed comparable to a recently proposed method by Berry–Sauer's. However, our method is more flexible since it allows for using information from product of innovation processes of more than one-lag. Extensive numerical comparisons between the proposed method and both the original Belanger's and Berry–Sauer's schemes are shown in various examples, ranging from low-dimensional linear and nonlinear systems of SDEs and 40-dimensional stochastically forced Lorenz-96 model. Our numerical results suggest that the proposed scheme is as accurate as the original Belanger's scheme on low-dimensional problems and has a wider range of more accurate estimates compared to Berry–Sauer's method on L-96 example
Robust and Adaptive Block Tracking Method Based on Particle Filter
Bin Sun
2015-10-01
Full Text Available In the field of video analysis and processing, object tracking is attracting more and more attention especially in traffic management, digital surveillance and so on. However problems such as objects’ abrupt motion, occlusion and complex target structures would bring difficulties to academic study and engineering application. In this paper, a fragmentsbased tracking method using the block relationship coefficient is proposed. In this method, we use particle filter algorithm and object region is divided into blocks initially. The contribution of this method is that object features are not extracted just from a single block, the relationship between current block and its neighbor blocks are extracted to describe the variation of the block. Each block is weighted according to the block relationship coefficient when the block is voted on the most matched region in next frame. This method can make full use of the relationship between blocks. The experimental results demonstrate that our method can provide good performance in condition of occlusion and abrupt posture variation.
Adaptive error covariances estimation methods for ensemble Kalman filters
Zhen, Yicun, E-mail: zhen@math.psu.edu [Department of Mathematics, The Pennsylvania State University, University Park, PA 16802 (United States); Harlim, John, E-mail: jharlim@psu.edu [Department of Mathematics and Department of Meteorology, The Pennsylvania State University, University Park, PA 16802 (United States)
2015-08-01
This paper presents a computationally fast algorithm for estimating, both, the system and observation noise covariances of nonlinear dynamics, that can be used in an ensemble Kalman filtering framework. The new method is a modification of Belanger's recursive method, to avoid an expensive computational cost in inverting error covariance matrices of product of innovation processes of different lags when the number of observations becomes large. When we use only product of innovation processes up to one-lag, the computational cost is indeed comparable to a recently proposed method by Berry–Sauer's. However, our method is more flexible since it allows for using information from product of innovation processes of more than one-lag. Extensive numerical comparisons between the proposed method and both the original Belanger's and Berry–Sauer's schemes are shown in various examples, ranging from low-dimensional linear and nonlinear systems of SDEs and 40-dimensional stochastically forced Lorenz-96 model. Our numerical results suggest that the proposed scheme is as accurate as the original Belanger's scheme on low-dimensional problems and has a wider range of more accurate estimates compared to Berry–Sauer's method on L-96 example.
Balas, Mark; Frost, Susan
2012-01-01
Flexible structures containing a large number of modes can benefit from adaptive control techniques which are well suited to applications that have unknown modeling parameters and poorly known operating conditions. In this paper, we focus on a direct adaptive control approach that has been extended to handle adaptive rejection of persistent disturbances. We extend our adaptive control theory to accommodate troublesome modal subsystems of a plant that might inhibit the adaptive controller. In some cases the plant does not satisfy the requirements of Almost Strict Positive Realness. Instead, there maybe be a modal subsystem that inhibits this property. This section will present new results for our adaptive control theory. We will modify the adaptive controller with a Residual Mode Filter (RMF) to compensate for the troublesome modal subsystem, or the Q modes. Here we present the theory for adaptive controllers modified by RMFs, with attention to the issue of disturbances propagating through the Q modes. We apply the theoretical results to a flexible structure example to illustrate the behavior with and without the residual mode filter.
Boz, Utku; Basdogan, Ipek
2015-12-01
Structural vibrations is a major cause for noise problems, discomfort and mechanical failures in aerospace, automotive and marine systems, which are mainly composed of plate-like structures. In order to reduce structural vibrations on these structures, active vibration control (AVC) is an effective approach. Adaptive filtering methodologies are preferred in AVC due to their ability to adjust themselves for varying dynamics of the structure during the operation. The filtered-X LMS (FXLMS) algorithm is a simple adaptive filtering algorithm widely implemented in active control applications. Proper implementation of FXLMS requires availability of a reference signal to mimic the disturbance and model of the dynamics between the control actuator and the error sensor, namely the secondary path. However, the controller output could interfere with the reference signal and the secondary path dynamics may change during the operation. This interference problem can be resolved by using an infinite impulse response (IIR) filter which considers feedback of the one or more previous control signals to the controller output and the changing secondary path dynamics can be updated using an online modeling technique. In this paper, IIR filtering based filtered-U LMS (FULMS) controller is combined with online secondary path modeling algorithm to suppress the vibrations of a plate-like structure. The results are validated through numerical and experimental studies. The results show that the FULMS with online secondary path modeling approach has more vibration rejection capabilities with higher convergence rate than the FXLMS counterpart.
Purpose: The combination of quickly rotating C-arm gantry with digital flat panel has enabled the acquisition of three-dimensional data (3D) in the interventional suite. However, image quality is still somewhat limited since the hardware has not been optimized for CT imaging. Adaptive anisotropic filtering has the ability to improve image quality by reducing the noise level and therewith the radiation dose without introducing noticeable blurring. By applying the filtering prior to 3D reconstruction, noise-induced streak artifacts are reduced as compared to processing in the image domain. Methods: 3D anisotropic adaptive filtering was used to process an ensemble of 2D x-ray views acquired along a circular trajectory around an object. After arranging the input data into a 3D space (2D projections + angle), the orientation of structures was estimated using a set of differently oriented filters. The resulting tensor representation of local orientation was utilized to control the anisotropic filtering. Low-pass filtering is applied only along structures to maintain high spatial frequency components perpendicular to these. The evaluation of the proposed algorithm includes numerical simulations, phantom experiments, and in-vivo data which were acquired using an AXIOM Artis dTA C-arm system (Siemens AG, Healthcare Sector, Forchheim, Germany). Spatial resolution and noise levels were compared with and without adaptive filtering. A human observer study was carried out to evaluate low-contrast detectability. Results: The adaptive anisotropic filtering algorithm was found to significantly improve low-contrast detectability by reducing the noise level by half (reduction of the standard deviation in certain areas from 74 to 30 HU). Virtually no degradation of high contrast spatial resolution was observed in the modulation transfer function (MTF) analysis. Although the algorithm is computationally intensive, hardware acceleration using Nvidia's CUDA Interface provided an 8.9-fold
Fuzzy adaptive Kalman filter for indoor mobile target positioning with INS/WSN integrated method
杨海; 李威; 罗成名
2015-01-01
Pure inertial navigation system (INS) has divergent localization errors after a long time. In order to compensate the disadvantage, wireless sensor network (WSN) associated with the INS was applied to estimate the mobile target positioning. Taking traditional Kalman filter (KF) as the framework, the system equation of KF was established by the INS and the observation equation of position errors was built by the WSN. Meanwhile, the observation equation of velocity errors was established by the velocity difference between the INS and WSN, then the covariance matrix of Kalman filter measurement noise was adjusted with fuzzy inference system (FIS), and the fuzzy adaptive Kalman filter (FAKF) based on the INS/WSN was proposed. The simulation results show that the FAKF method has better accuracy and robustness than KF and EKF methods and shows good adaptive capacity with time-varying system noise. Finally, experimental results further prove that FAKF has the fast convergence error, in comparison with KF and EKF methods.
A multi-stage noise adaptive switching filter for extremely corrupted images
Dinh, Hai; Adhami, Reza; Wang, Yi
2015-07-01
A multi-stage noise adaptive switching filter (MSNASF) is proposed for the restoration of images extremely corrupted by impulse and impulse-like noise. The filter consists of two steps: noise detection and noise removal. The proposed extrema-based noise detection scheme utilizes the false contouring effect to get better over detection rate at low noise density. It is adaptive and will detect not only impulse but also impulse-like noise. In the noise removal step, a novel multi-stage filtering scheme is proposed. It replaces corrupted pixel with the nearest uncorrupted median to preserve details. When compared with other methods, MSNASF provides better peak signal to noise ratio (PSNR) and structure similarity index (SSIM). A subjective evaluation carried out online also demonstrates that MSNASF yields higher fidelity.
Becker, J.C. [Technical Univ. Braunschweig (Germany). Inst. of Control Engineering
2000-07-01
This paper describes an adaptive information filter for the fusion of sensor data of an autonomous vehicle. The vehicle sensor system for object detection consists of a stereo vision sensor, four laserscanners and a radar sensor and provides a high redundancy in the observed area in front of the vehicle. The derivation of the information filter as well as its application to sensor data fusion is presented. Maneuver of observed targets are detected and the filter parameter are adapted accordingly. The information filter fusion is compared to the Kalman filter based measurement fusion. (orig.)
Point cloud filtering is the basic and key step in LiDAR data processing. Adaptive Triangle Irregular Network Modelling (ATINM) algorithm and Threshold Segmentation on Elevation Statistics (TSES) algorithm are among the mature algorithms. However, few researches concentrate on the parameter selections of ATINM and the iteration condition of TSES, which can greatly affect the filtering results. First the paper presents these two key problems under two different terrain environments. For a flat area, small height parameter and angle parameter perform well and for areas with complex feature changes, large height parameter and angle parameter perform well. One-time segmentation is enough for flat areas, and repeated segmentations are essential for complex areas. Then the paper makes comparisons and analyses of the results by these two methods. ATINM has a larger I error in both two data sets as it sometimes removes excessive points. TSES has a larger II error in both two data sets as it ignores topological relations between points. ATINM performs well even with a large region and a dramatic topology while TSES is more suitable for small region with flat topology. Different parameters and iterations can cause relative large filtering differences
A new learning statistic for adaptive filter based on predicted residuals
YANG Yuanxi; GAO Weiguang
2006-01-01
A key problem for an adaptive filter is to establish a suitable adaptive factor for balancing the contributions of the measurements and the predicted state information from some kinematic models. The reasonable adaptive factor needs a reliable learning statistics to judge the state kinematic model errors. After analyzing the existing two kinds of learning statistics based on the state discrepancy and variance component ratio, a new learning statistic based on predicted residuals is set up, which is different from the exiting learning statistics. The new learning statistic does not need to estimate the kinemetic state parameters before the filtering process, Of course, it does not need necessary measurements to estimate state parameters for all observation epochs. The new learning statistic can be applied together with the learning factor constructed by the state discrepancy. The advantages and shortcomings of the new learning factor are analyzed, and an example is given.
Adaptive Current Control with PI-Fuzzy Compound Controller for Shunt Active Power Filter
Juntao Fei
2013-01-01
Full Text Available An adaptive control technology and PI-fuzzy compound control technology are proposed to control an active power filter (APF. AC side current compensation and DC capacitor voltage tracking control strategy are discussed and analyzed. Model reference adaptive controller for the AC side current compensation is derived and established based on Lyapunov stability theory; proportional and integral (PI fuzzy compound controller is designed for the DC side capacitor voltage control. The adaptive current controller based on PI-fuzzy compound system is compared with the conventional PI controller for active power filter. Simulation results demonstrate the feasibility and satisfactory performance of the proposed control strategies. It is shown that the proposed control method has an excellent dynamic performance such as small current tracking error, reduced total harmonic distortion (THD, and strong robustness in the presence of parameters variation and nonlinear load.
XU TianHe; JIANG Nan; SUN ZhangZhen
2012-01-01
The shortcomings of an adaptive Sage filter are analyzed in this paper.An improved adaptive Sage filter is developed by using a weighted average quadratic form of the historical residuals of observations and predicted states to evaluate the covariance matrices of observations and dynamic model errors at the present epoch.The weight function is constructed based on the variances of observational residuals or predicted state residuals and the space distance between the previous and the present epoch.In order to balance the contributions of the measurements and the dynamic model information,an adaptive factor is applied by using a two-segment function and predicted state discrepancy statistics.Two applications,orbit determination of a maneuvered GEO satellite and GPS kinematic positioning,are conducted to verify the performance of the proposed method.
Guo, Qing; Sun, Ping; Yin, Jing-Min; Yu, Tian; Jiang, Dan
2016-05-01
Some unknown parameter estimation of electro-hydraulic system (EHS) should be considered in hydraulic controller design due to many parameter uncertainties in practice. In this study, a parametric adaptive backstepping control method is proposed to improve the dynamic behavior of EHS under parametric uncertainties and unknown disturbance (i.e., hydraulic parameters and external load). The unknown parameters of EHS model are estimated by the parametric adaptive estimation law. Then the recursive backstepping controller is designed by Lyapunov technique to realize the displacement control of EHS. To avoid explosion of virtual control in traditional backstepping, a decayed memory filter is presented to re-estimate the virtual control and the dynamic external load. The effectiveness of the proposed controller has been demonstrated by comparison with the controller without adaptive and filter estimation. The comparative experimental results in critical working conditions indicate the proposed approach can achieve better dynamic performance on the motion control of Two-DOF robotic arm. PMID:26920086
Automatic speech signal segmentation based on the innovation adaptive filter
Makowski Ryszard
2014-06-01
Full Text Available Speech segmentation is an essential stage in designing automatic speech recognition systems and one can ﬁnd several algorithms proposed in the literature. It is a difﬁcult problem, as speech is immensely variable. The aim of the authors’ studies was to design an algorithm that could be employed at the stage of automatic speech recognition. This would make it possible to avoid some problems related to speech signal parametrization. Posing the problem in such a way requires the algorithm to be capable of working in real time. The only such algorithm was proposed by Tyagi et al., (2006, and it is a modiﬁed version of Brandt’s algorithm. The article presents a new algorithm for unsupervised automatic speech signal segmentation. It performs segmentation without access to information about the phonetic content of the utterances, relying exclusively on second-order statistics of a speech signal. The starting point for the proposed method is time-varying Schur coefﬁcients of an innovation adaptive ﬁlter. The Schur algorithm is known to be fast, precise, stable and capable of rapidly tracking changes in second order signal statistics. A transfer from one phoneme to another in the speech signal always indicates a change in signal statistics caused by vocal track changes. In order to allow for the properties of human hearing, detection of inter-phoneme boundaries is performed based on statistics deﬁned on the mel spectrum determined from the reﬂection coefﬁcients. The paper presents the structure of the algorithm, deﬁnes its properties, lists parameter values, describes detection efﬁciency results, and compares them with those for another algorithm. The obtained segmentation results, are satisfactory.
A unified set-based test with adaptive filtering for gene-environment interaction analyses.
Liu, Qianying; Chen, Lin S; Nicolae, Dan L; Pierce, Brandon L
2016-06-01
In genome-wide gene-environment interaction (GxE) studies, a common strategy to improve power is to first conduct a filtering test and retain only the SNPs that pass the filtering in the subsequent GxE analyses. Inspired by two-stage tests and gene-based tests in GxE analysis, we consider the general problem of jointly testing a set of parameters when only a few are truly from the alternative hypothesis and when filtering information is available. We propose a unified set-based test that simultaneously considers filtering on individual parameters and testing on the set. We derive the exact distribution and approximate the power function of the proposed unified statistic in simplified settings, and use them to adaptively calculate the optimal filtering threshold for each set. In the context of gene-based GxE analysis, we show that although the empirical power function may be affected by many factors, the optimal filtering threshold corresponding to the peak of the power curve primarily depends on the size of the gene. We further propose a resampling algorithm to calculate P-values for each gene given the estimated optimal filtering threshold. The performance of the method is evaluated in simulation studies and illustrated via a genome-wide gene-gender interaction analysis using pancreatic cancer genome-wide association data. PMID:26496228
Developing de-noising methods for ultrasonic NDT based on wavelet transform and adaptive filtering
Digital signal processing methods based on the advanced wavelet transform and adaptive filtering were developed to deal with the problem of material's grain noise in ultrasonic Non Destructive Testing applications. The developed methods were implemented in lab View (Laboratory Virtual Instruments Engineering Workbench) programming environment. The experimental ultrasonic signals were obtained by inspecting stainless steel blocks with side-drilled holes, and carbon steel welded plates contain three types of welding flaws: root crack, centerline crack and slag inclusion. The simulations were carried out using CIVA Non Destructive Evaluation modeling software. Wavelet transform has introduced innovative changes in different fields of science and engineering. One of its important applications is in de-noising of signals and images. Wavelet packet is an efficient de-noising method, which has been used for ultrasonic Non Destructive Testing signals de-noising, wavelet packet is generalizations of the discrete wavelet transform. The first part of this research proposes the use of the un decimated wavelet transform in implementing wavelet packets to overcome the limitation of the shift variance encountered in discrete wavelet transform. Simulations and experiments were carried out on flaw's echo signals contaminated with material's grain noise, various wavelet transform processing parameters were investigated, including the number of decomposition levels, analyzing wavelets, and threshold setting. The results showed superior de-noising effect of the developed method over the conventional one. In the second part of the research, improvements are proposed to the multi-stage adaptive filter, which has been reported in a previous study as an advanced adaptive noise cancellation system for ultrasonic None Destructive Testing applications. The multi stage adaptive filter is limited by the slow convergence speed of the least-mean-squares algorithm as well as
Usefulness of noise adaptive non-linear Gaussian filter in FDG-PET study
In positron emission tomography (PET) studies, shortening transmission (TR) scan time can improve patient comfort and increase scanner throughput. However, PET images from short TR scans may be degraded due to the statistical noise included in the TR image. The purpose of this study was to apply non-linear Gaussian (NLG) and noise adaptive NLG (ANLG) filters to TR images, and to evaluate the extent of noise reduction by the ANLG filter in comparison with that by the NLG filter using phantom and clinical studies. In phantom studies, pool phantoms of various diameters and injected doses of 2-deoxy-2-[18F]fluoro-D-glucose (FDG) were used and the coefficients of variation (CVs) of the counts in the TR images processed with the NLG and ANLG filters were compared. In clinical studies, two normal volunteers and 13 patients with tumors were studied. In volunteer studies, the CV values in the liver were compared. In patient studies, the standardized uptake values (SUVs) of tumors in the emission images were obtained after processing the TR images using the NLG and ANLG filters. In phantom studies, the CV values in the TR images processed with the ANLG filter were smaller than those in the images processed with the NLG filter. When using the ANLG filter, their dependency on the phantom size, injected dose of FDG and TR scan time was smaller than when using the NLG filter. In volunteer studies, the CV values in the images processed with the ANLG filter were smaller than those in the images processed with the NLG filter, and were almost constant regardless of the TR scan time. In patient studies, there was an excellent correlation between the SUVs obtained from the images with a TR scan time of 7 min processed with the NLG filter (x) and those obtained from the images with a TR scan time of 4 min processed with the ANLG filter (y) (r=0.995, y=1.034x-0.075). Our results suggest that the ANLG filter is effective and useful for noise reduction in TR images and shortening TR scan
Filter accuracy for the Lorenz 96 model: Fixed versus adaptive observation operators
Law, K. J. H.; Sanz-Alonso, D.; Shukla, A.; Stuart, A. M.
2016-06-01
In the context of filtering chaotic dynamical systems it is well-known that partial observations, if sufficiently informative, can be used to control the inherent uncertainty due to chaos. The purpose of this paper is to investigate, both theoretically and numerically, conditions on the observations of chaotic systems under which they can be accurately filtered. In particular, we highlight the advantage of adaptive observation operators over fixed ones. The Lorenz '96 model is used to exemplify our findings. We consider discrete-time and continuous-time observations in our theoretical developments. We prove that, for fixed observation operator, the 3DVAR filter can recover the system state within a neighbourhood determined by the size of the observational noise. It is required that a sufficiently large proportion of the state vector is observed, and an explicit form for such sufficient fixed observation operator is given. Numerical experiments, where the data is incorporated by use of the 3DVAR and extended Kalman filters, suggest that less informative fixed operators than given by our theory can still lead to accurate signal reconstruction. Adaptive observation operators are then studied numerically; we show that, for carefully chosen adaptive observation operators, the proportion of the state vector that needs to be observed is drastically smaller than with a fixed observation operator. Indeed, we show that the number of state coordinates that need to be observed may even be significantly smaller than the total number of positive Lyapunov exponents of the underlying system.
Keel, Byron M.
1989-01-01
An optimum adaptive clutter rejection filter for use with airborne Doppler weather radar is presented. The radar system is being designed to operate at low-altitudes for the detection of windshear in an airport terminal area where ground clutter returns may mask the weather return. The coefficients of the adaptive clutter rejection filter are obtained using a complex form of a square root normalized recursive least squares lattice estimation algorithm which models the clutter return data as an autoregressive process. The normalized lattice structure implementation of the adaptive modeling process for determining the filter coefficients assures that the resulting coefficients will yield a stable filter and offers possible fixed point implementation. A 10th order FIR clutter rejection filter indexed by geographical location is designed through autoregressive modeling of simulated clutter data. Filtered data, containing simulated dry microburst and clutter return, are analyzed using pulse-pair estimation techniques. To measure the ability of the clutter rejection filters to remove the clutter, results are compared to pulse-pair estimates of windspeed within a simulated dry microburst without clutter. In the filter evaluation process, post-filtered pulse-pair width estimates and power levels are also used to measure the effectiveness of the filters. The results support the use of an adaptive clutter rejection filter for reducing the clutter induced bias in pulse-pair estimates of windspeed.
Speed Estimation of Induction Motor Using Model Reference Adaptive System with Kalman Filter
Pavel Brandstetter
2013-01-01
Full Text Available The paper deals with a speed estimation of the induction motor using observer with Model Reference Adaptive System and Kalman Filter. For simulation, Hardware in Loop Simulation method is used. The first part of the paper includes the mathematical description of the observer for the speed estimation of the induction motor. The second part describes Kalman filter. The third part describes Hardware in Loop Simulation method and its realization using multifunction card MF 624. In the last section of the paper, simulation results are shown for different changes of the induction motor speed which confirm high dynamic properties of the induction motor drive with sensorless control.
Bayesian Estimation of Forgetting Factor in Adaptive Filtering and Change Detection
Šmídl, Václav; Gustafsson, F.
Ann Arbor: IEEE, 2012, s. 197-200. ISBN 978-1-4673-0182-4. [2012 IEEE Statistical Signal Processing Workshop. Ann Arbor (US), 05.08.2012-08.08.2012] R&D Projects: GA ČR(CZ) GAP102/11/0437 Institutional support: RVO:67985556 Keywords : Marginalized particle filter * Rao-Blackwellization * maximum entropy Subject RIV: BD - Theory of Information http://library.utia.cas.cz/separaty/2012/AS/Smidl-bayesian estimation of forgetting factor in adaptive filtering and change detection.pdf
Kikuchi, Kazuro
2011-03-14
We analyze the clock-recovery process based on adaptive finite-impulse-response (FIR) filtering in digital coherent optical receivers. When the clock frequency is synchronized between the transmitter and the receiver, only five taps in half-symbol-spaced FIR filters can adjust the sampling phase of analog-to-digital conversion optimally, enabling bit-error rate performance independent of the initial sampling phase. Even if the clock frequency is not synchronized between them, the clock-frequency misalignment can be adjusted within an appropriate block interval; thus, we can achieve an asynchronous clock mode of operation of digital coherent receivers with block processing of the symbol sequence. PMID:21445201
Parameter estimation with an iterative version of the adaptive Gaussian mixture filter
Stordal, A.; Lorentzen, R.
2012-04-01
The adaptive Gaussian mixture filter (AGM) was introduced in Stordal et. al. (ECMOR 2010) as a robust filter technique for large scale applications and an alternative to the well known ensemble Kalman filter (EnKF). It consists of two analysis steps, one linear update and one weighting/resampling step. The bias of AGM is determined by two parameters, one adaptive weight parameter (forcing the weights to be more uniform to avoid filter collapse) and one pre-determined bandwidth parameter which decides the size of the linear update. It has been shown that if the adaptive parameter approaches one and the bandwidth parameter decrease with increasing sample size, the filter can achieve asymptotic optimality. For large scale applications with a limited sample size the filter solution may be far from optimal as the adaptive parameter gets close to zero depending on how well the samples from the prior distribution match the data. The bandwidth parameter must often be selected significantly different from zero in order to make large enough linear updates to match the data, at the expense of bias in the estimates. In the iterative AGM we take advantage of the fact that the history matching problem is usually estimation of parameters and initial conditions. If the prior distribution of initial conditions and parameters is close to the posterior distribution, it is possible to match the historical data with a small bandwidth parameter and an adaptive weight parameter that gets close to one. Hence the bias of the filter solution is small. In order to obtain this scenario we iteratively run the AGM throughout the data history with a very small bandwidth to create a new prior distribution from the updated samples after each iteration. After a few iterations, nearly all samples from the previous iteration match the data and the above scenario is achieved. A simple toy problem shows that it is possible to reconstruct the true posterior distribution using the iterative version of
Adaptive nonlocal means filtering based on local noise level for CT denoising
Purpose: To develop and evaluate an image-domain noise reduction method based on a modified nonlocal means (NLM) algorithm that is adaptive to local noise level of CT images and to implement this method in a time frame consistent with clinical workflow. Methods: A computationally efficient technique for local noise estimation directly from CT images was developed. A forward projection, based on a 2D fan-beam approximation, was used to generate the projection data, with a noise model incorporating the effects of the bowtie filter and automatic exposure control. The noise propagation from projection data to images was analytically derived. The analytical noise map was validated using repeated scans of a phantom. A 3D NLM denoising algorithm was modified to adapt its denoising strength locally based on this noise map. The performance of this adaptive NLM filter was evaluated in phantom studies in terms of in-plane and cross-plane high-contrast spatial resolution, noise power spectrum (NPS), subjective low-contrast spatial resolution using the American College of Radiology (ACR) accreditation phantom, and objective low-contrast spatial resolution using a channelized Hotelling model observer (CHO). Graphical processing units (GPU) implementation of this noise map calculation and the adaptive NLM filtering were developed to meet demands of clinical workflow. Adaptive NLM was piloted on lower dose scans in clinical practice. Results: The local noise level estimation matches the noise distribution determined from multiple repetitive scans of a phantom, demonstrated by small variations in the ratio map between the analytical noise map and the one calculated from repeated scans. The phantom studies demonstrated that the adaptive NLM filter can reduce noise substantially without degrading the high-contrast spatial resolution, as illustrated by modulation transfer function and slice sensitivity profile results. The NPS results show that adaptive NLM denoising preserves the
Adaptive nonlocal means filtering based on local noise level for CT denoising
Li, Zhoubo; Trzasko, Joshua D.; Lake, David S.; Blezek, Daniel J.; Manduca, Armando, E-mail: manduca.armando@mayo.edu [Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota 55905 (United States); Yu, Lifeng; Fletcher, Joel G.; McCollough, Cynthia H. [Department of Radiology, Mayo Clinic, Rochester, Minnesota 55905 (United States)
2014-01-15
Purpose: To develop and evaluate an image-domain noise reduction method based on a modified nonlocal means (NLM) algorithm that is adaptive to local noise level of CT images and to implement this method in a time frame consistent with clinical workflow. Methods: A computationally efficient technique for local noise estimation directly from CT images was developed. A forward projection, based on a 2D fan-beam approximation, was used to generate the projection data, with a noise model incorporating the effects of the bowtie filter and automatic exposure control. The noise propagation from projection data to images was analytically derived. The analytical noise map was validated using repeated scans of a phantom. A 3D NLM denoising algorithm was modified to adapt its denoising strength locally based on this noise map. The performance of this adaptive NLM filter was evaluated in phantom studies in terms of in-plane and cross-plane high-contrast spatial resolution, noise power spectrum (NPS), subjective low-contrast spatial resolution using the American College of Radiology (ACR) accreditation phantom, and objective low-contrast spatial resolution using a channelized Hotelling model observer (CHO). Graphical processing units (GPU) implementation of this noise map calculation and the adaptive NLM filtering were developed to meet demands of clinical workflow. Adaptive NLM was piloted on lower dose scans in clinical practice. Results: The local noise level estimation matches the noise distribution determined from multiple repetitive scans of a phantom, demonstrated by small variations in the ratio map between the analytical noise map and the one calculated from repeated scans. The phantom studies demonstrated that the adaptive NLM filter can reduce noise substantially without degrading the high-contrast spatial resolution, as illustrated by modulation transfer function and slice sensitivity profile results. The NPS results show that adaptive NLM denoising preserves the
S. KALAVATHY
2012-02-01
Full Text Available The image de-noising naturally corrupted by noise is a classical problem in the field of signal or image processing. Image denoising has become an essential exercise in medical imaging especially the Magnetic Resonance Imaging (MRI..We propose a new method for MRI restoration. Because MR magnitude images suffer from a contrast-reducing signal-dependent bias. Also the noise is often assumed to be white, however a widely used acquisition technique to decrease the acquisition time gives rise to correlated noise. Subband adaptive thresholding technique based on wavelet coefficient along with Neighbourhood Pixel Filtering Algorithm (NPFA for noise suppression of Magnetic Resonance Images (MRI is presented in this paper. Astatistical model is proposed to estimate the noise variance for each coefficient based on the subband using Maximum Likelihood (ML estimator or a Maximum a Posterior (MAP estimator. Also this model describes a new method for suppression of noise by fusing the wavelet denoising technique with optimized thresholding function. This is achieved by including a multiplying factor (α to make the threshold value dependent on decomposition level. By finding Neighbourhood Pixel Difference (NPD and adding NPFA along with subband thresholding the clarity of the image is improved. The filtered value is generated by minimizing NPD and Weighted Mean Square Error (WMSE using method of leastsquare.Areduction in noise pixel is well observedon replacing the optimal weight namely NPFA filter solution with the noisy value of the current pixel. Due to this NPFA filter gains the effect of both high pass and low pass filter. Hence the proposed technique yields significantly superior image quality by preserving the edges, producing a better PSNR value. To confirm the efficiency this is further compared with Median filter, Weiner Filter, Subband thresholding technique along with NPFA filter.
Structural adaptive and optimal speckle filtering in multilook full polarimetric SAR images
Sun Nan; Zhang Bingchen; Wang Yanfei
2007-01-01
A novel approach is proposed for speckle reduction in multilook full polarimetric SAR images.In contrast to others, this approach adopts an enhanced structure detection method to estimate the parameters of the polarimetric covariance matrix for the multilook polarimetric whitening filtering (MPWF) algorithm and thus a structural adaptive and optimal speckle filter is developed.To evaluate the present approach, NASA SIR-C/X-SAR, L band, four-look processed polarimetric SAR data of the Tian-Mountain Forest is used for simulation.Experimental results demonstrate the effectiveness of this novel filtering algorithm in case of both speckle reduction and preservation of texture information.Comparisons with other methods are also made.
A 3D approach for object recognition in illuminated scenes with adaptive correlation filters
Picos, Kenia; Díaz-Ramírez, Víctor H.
2015-09-01
In this paper we solve the problem of pose recognition of a 3D object in non-uniformly illuminated and noisy scenes. The recognition system employs a bank of space-variant correlation filters constructed with an adaptive approach based on local statistical parameters of the input scene. The position and orientation of the target are estimated with the help of the filter bank. For an observed input frame, the algorithm computes the correlation process between the observed image and the bank of filters using a combination of data and task parallelism by taking advantage of a graphics processing unit (GPU) architecture. The pose of the target is estimated by finding the template that better matches the current view of target within the scene. The performance of the proposed system is evaluated in terms of recognition accuracy, location and orientation errors, and computational performance.
Adaptive filtering for reduction of speckle in ultrasonic pulse-echo images.
Bamber, J C; Daft, C
1986-01-01
Current medical ultrasonic scanning instrumentation permits the display of fine image detail (speckle) which does not transfer useful information but degrades the apparent low contrast resolution in the image. An adaptive two-dimensional filter has been developed which uses local features of image texture to recognize and maximally low-pass filter those parts of the image which correspond to fully developed speckle, while substantially preserving information associated with resolved-object structure. A first implementation of the filter is described which uses the ratio of the local variance and the local mean as the speckle recognition feature. Preliminary results of applying this form of display processing to medical ultrasound images are very encouraging; it appears that the visual perception of features such as small discrete structures, subtle fluctuations in mean echo level and changes in image texture may be enhanced relative to that for unprocessed images. PMID:3510500
Biohybrid control of general linear systems using the adaptive filter model of cerebellum
Emma D. Wilson
2015-07-01
Full Text Available The adaptive filter model of the cerebellar microcircuit has been successfully applied to biological motor control problems such as the vestibulo-ocular reflex (VOR and to sensory processing problems such as the adaptive cancellation of reafferent noise. It has also been successfully applied to problems in robotics such as adaptive camera stabilisation and sensor noise cancellation. In previous applications to inverse control problems the algorithm was applied to the velocity control of a plant dominated by viscous and elastic elements. Naive application of the adaptive filter model to the displacement (as opposed to velocity control of this plant results in unstable learning and control. To be more generally useful in engineering problems it is essential to remove this restriction to enable the stable control of plants of any order. We address this problem here by developing a biohybrid model reference adaptive control (MRAC scheme, which stabilises the control algorithm for strictly proper plants. We evaluate the performance of this novel cerebellar inspired algorithm with MRAC scheme in the experimental control of a dielectric electroactive polymer, a class of artificial muscle. The results show that the augmented cerebellar algorithm is able to accurately control the displacement response of the artificial muscle. The proposed solution not only greatly extends the practical applicability of the cerebellar-inspired algorithm, but may also shed light on cerebellar involvement in a wider range of biological control tasks.
Ma, Shaokang; Wu, Peijun; Ji, Jinhu; Li, Xuchun
2016-02-01
This article presents a sensorless control approach of salient PMSM with an online parameter identifier. Adaptive Integrator is proposed and utilised for the estimation of active flux and rotor position. As a result, integrator overflow caused by DC offset is avoided. Meanwhile, an online stator resistance identification algorithm using strong tracking filter is employed, and the identified stator resistance is fed back to the estimating algorithm. Thus, the estimating algorithm can calculate the rotor position correctly. Simulations and experimental results validate the feasibility of both adaptive integrator and the parameter identification method.
Video Enhancement Using Adaptive Spatio-Temporal Connective Filter and Piecewise Mapping
Wang Chao
2008-01-01
Full Text Available This paper presents a novel video enhancement system based on an adaptive spatio-temporal connective (ASTC noise filter and an adaptive piecewise mapping function (APMF. For ill-exposed videos or those with much noise, we first introduce a novel local image statistic to identify impulse noise pixels, and then incorporate it into the classical bilateral filter to form ASTC, aiming to reduce the mixture of the most two common types of noises—Gaussian and impulse noises in spatial and temporal directions. After noise removal, we enhance the video contrast with APMF based on the statistical information of frame segmentation results. The experiment results demonstrate that, for diverse low-quality videos corrupted by mixed noise, underexposure, overexposure, or any mixture of the above, the proposed system can automatically produce satisfactory results.
Sealing Clay Text Segmentation Based on Radon-Like Features and Adaptive Enhancement Filters
Xia Zheng
2015-01-01
Full Text Available Text extraction is a key issue in sealing clay research. The traditional method based on rubbings increases the risk of sealing clay damage and is unfavorable to sealing clay protection. Therefore, using digital image of sealing clay, a new method for text segmentation based on Radon-like features and adaptive enhancement filters is proposed in this paper. First, adaptive enhancement LM filter bank is used to get the maximum energy image; second, the edge image of the maximum energy image is calculated; finally, Radon-like feature images are generated by combining maximum energy image and its edge image. The average image of Radon-like feature images is segmented by the image thresholding method. Compared with 2D Otsu, GA, and FastFCM, the experiment result shows that this method can perform better in terms of accuracy and completeness of the text.
Command Filtered Adaptive Fuzzy Neural Network Backstepping Control for Marine Power System
Xin Zhang
2014-01-01
Full Text Available In order to retrain chaotic oscillation of marine power system which is excited by periodic electromagnetism perturbation, a novel command-filtered adaptive fuzzy neural network backstepping control method is designed. First, the mathematical model of marine power system is established based on the two parallel nonlinear model. Then, main results of command-filtered adaptive fuzzy neural network backstepping control law are given. And the Lyapunov stability theory is applied to prove that the system can remain closed-loop asymptotically stable with this controller. Finally, simulation results indicate that the designed controller can suppress chaotic oscillation with fast convergence speed that makes the system return to the equilibrium point quickly; meanwhile, the parameter which induces chaotic oscillation can also be discriminated.
Wentao Yu
2013-01-01
high. In order to reduce the computation cost of UPF and meanwhile maintain the accuracy, we propose an adaptive unscented particle filter (AUPF algorithm through relative entropy. AUPF can adaptively adjust the number of particles during filtering to reduce the necessary computation and hence improve the real-time capability of UPF. In AUPF, the relative entropy is used to measure the distance between the empirical distribution and the true posterior distribution. The least number of particles for the next step is then decided according to the relative entropy. In order to offset the difference between the proposal distribution, and the true distribution the least number is adjusted thereafter. The ideal performance of AUPF in real robot self-localization is demonstrated.
Direction-Based Adaptive Switching Filter for Removing High-Density Impulse Noise
刘会刚; 孙菁; 张福海; 任立儒
2014-01-01
A direction-based adaptive switching (DBAS) filter is presented for the removal of high-density impulse noise in images. The extrema detection and 28-directional detection are employed to discriminate the pixels as noisy or noise-free. If a pixel is classified as noisy, it will be replaced by a median or a mean value within an adaptive filter window with respect to different noise densities. Simulation results show that the miss-detection ratio and false-alarm ratio are both very low even at noise level as high as 90%. At the same time, better results are obtained in terms of the qualitative and quantitative measures. The peak signal-to-noise ratios increase by nearly 1 dB compared with other existing algorithms. In addition, the computation time is around 10 s for test images with resolutions of 512´512 since the proposed approach has low complexity.
Particle filter based visual tracking with multi-cue adaptive fusion
Anping Li; Zhongliang Jing; Shiqiang Hu
2005-01-01
@@ To improve the robustness of visual tracking in complex environments such as: cluttered backgrounds, partial occlusions, similar distraction and pose variations, a novel tracking method based on adaptive fusion and particle filter is proposed in this paper. In this method, the image color and shape cues are adaptively fused to represent the target observation; fuzzy logic is applied to dynamically adjust each cue weight according to its associated reliability in the past frame; particle filter is adopted to deal with non-linear and non-Gaussian problems in visual tracking. The method is demonstrated to be robust to illumination changes, pose variations, partial occlusions, cluttered backgrounds and camera motion for a test image sequence.
Manish Jaiswal
2015-03-01
Full Text Available An energy efficient high-speed adaptive finite impulse response filter with novel architecture is developed. Synthesis results along with novel architecture on different complementary metal–oxide semiconductor (CMOS families are presented. Analysis is performed using Artix-7, Spartan-6 and Virtex-4 for most popular adaptive least mean square filter for different orders such as N = 8, 16, 32. The presented work is done using MATLAB (2013b and Xilinx (14.2. From the synthesis results, it can be found that CMOS (28 nm achieves the lowest power and critical path delay compared to others, and thus proves its efficiency in terms of energy. Different parameters are considered such as look up tables and input–output blocks, along with their optimised results.
Ensembles of adaptive spatial filters increase BCI performance: an online evaluation
Sannelli, Claudia; Vidaurre, Carmen; Müller, Klaus-Robert; Blankertz, Benjamin
2016-08-01
Objective: In electroencephalographic (EEG) data, signals from distinct sources within the brain are widely spread by volume conduction and superimposed such that sensors receive mixtures of a multitude of signals. This reduction of spatial information strongly hampers single-trial analysis of EEG data as, for example, required for brain–computer interfacing (BCI) when using features from spontaneous brain rhythms. Spatial filtering techniques are therefore greatly needed to extract meaningful information from EEG. Our goal is to show, in online operation, that common spatial pattern patches (CSPP) are valuable to counteract this problem. Approach: Even though the effect of spatial mixing can be encountered by spatial filters, there is a trade-off between performance and the requirement of calibration data. Laplacian derivations do not require calibration data at all, but their performance for single-trial classification is limited. Conversely, data-driven spatial filters, such as common spatial patterns (CSP), can lead to highly distinctive features; however they require a considerable amount of training data. Recently, we showed in an offline analysis that CSPP can establish a valuable compromise. In this paper, we confirm these results in an online BCI study. In order to demonstrate the paramount feature that CSPP requires little training data, we used them in an adaptive setting with 20 participants and focused on users who did not have success with previous BCI approaches. Main results: The results of the study show that CSPP adapts faster and thereby allows users to achieve better feedback within a shorter time than previous approaches performed with Laplacian derivations and CSP filters. The success of the experiment highlights that CSPP has the potential to further reduce BCI inefficiency. Significance: CSPP are a valuable compromise between CSP and Laplacian filters. They allow users to attain better feedback within a shorter time and thus reduce BCI
An Observer-Based Adaptive Iterative Learning Control Using Filtered-FNN Design for Robotic Systems
Ying-Chung Wang; Chiang-Ju Chien
2014-01-01
An observer-based adaptive iterative learning control using a filtered fuzzy neural network is proposed for repetitive tracking control of robotic systems. A state tracking error observer is introduced to design the iterative learning controller using only the measurement of joint position. We first derive an observation error model based on the state tracking error observer. Then, by introducing some auxiliary signals, the iterative learning controller is proposed based on the use of an aver...
Real-time adaptive filtering of dental drill noise using a digital signal processor
Kaymak, E; Atherton, MA; Rotter, KRG; Millar, B.
2006-01-01
The application of noise reduction methods requires the integration of acoustics engineering and digital signal processing, which is well served by a mechatronic approach as described in this paper. The Normalised Least Mean Square (NLMS) algorithm is implemented on the Texas Instruments TMS320C6713 DSK Digital Signal Processor (DSP) as an adaptive digital filter for dental drill noise. Blocks within the Matlab/Simulink Signal Processing Blockset and the Embedded Target for TI C6000 DSP famil...
Adaptive filter design based on the LMS algorithm for delay elimination in TCR/FC compensators.
Hooshmand, Rahmat Allah; Torabian Esfahani, Mahdi
2011-04-01
Thyristor controlled reactor with fixed capacitor (TCR/FC) compensators have the capability of compensating reactive power and improving power quality phenomena. Delay in the response of such compensators degrades their performance. In this paper, a new method based on adaptive filters (AF) is proposed in order to eliminate delay and increase the response of the TCR compensator. The algorithm designed for the adaptive filters is performed based on the least mean square (LMS) algorithm. In this design, instead of fixed capacitors, band-pass LC filters are used. To evaluate the filter, a TCR/FC compensator was used for nonlinear and time varying loads of electric arc furnaces (EAFs). These loads caused occurrence of power quality phenomena in the supplying system, such as voltage fluctuation and flicker, odd and even harmonics and unbalancing in voltage and current. The above design was implemented in a realistic system model of a steel complex. The simulation results show that applying the proposed control in the TCR/FC compensator efficiently eliminated delay in the response and improved the performance of the compensator in the power system. PMID:21193194
State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter
Bizhong Xia
2015-06-01
Full Text Available Accurate state of charge (SOC estimation is of great significance for a lithium-ion battery to ensure its safe operation and to prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner sate of a battery cell, which cannot be directly measured. This paper presents an Adaptive Cubature Kalman filter (ACKF-based SOC estimation algorithm for lithium-ion batteries in electric vehicles. Firstly, the lithium-ion battery is modeled using the second-order resistor-capacitor (RC equivalent circuit and parameters of the battery model are determined by the forgetting factor least-squares method. Then, the Adaptive Cubature Kalman filter for battery SOC estimation is introduced and the estimated process is presented. Finally, two typical driving cycles, including the Dynamic Stress Test (DST and New European Driving Cycle (NEDC are applied to evaluate the performance of the proposed method by comparing with the traditional extended Kalman filter (EKF and cubature Kalman filter (CKF algorithms. Experimental results show that the ACKF algorithm has better performance in terms of SOC estimation accuracy, convergence to different initial SOC errors and robustness against voltage measurement noise as compared with the traditional EKF and CKF algorithms.
Man, Jun; Li, Weixuan; Zeng, Lingzao; Wu, Laosheng
2016-06-01
The ensemble Kalman filter (EnKF) has gained popularity in hydrological data assimilation problems. As a Monte Carlo based method, a sufficiently large ensemble size is usually required to guarantee the accuracy. As an alternative approach, the probabilistic collocation based Kalman filter (PCKF) employs the polynomial chaos expansion (PCE) to represent and propagate the uncertainties in parameters and states. However, PCKF suffers from the so-called "curse of dimensionality". Its computational cost increases drastically with the increasing number of parameters and system nonlinearity. Furthermore, PCKF may fail to provide accurate estimations due to the joint updating scheme for strongly nonlinear models. Motivated by recent developments in uncertainty quantification and EnKF, we propose a restart adaptive probabilistic collocation based Kalman filter (RAPCKF) for data assimilation in unsaturated flow problems. During the implementation of RAPCKF, the important parameters are identified and active PCE basis functions are adaptively selected at each assimilation step; the "restart" scheme is utilized to eliminate the inconsistency between updated model parameters and states variables. The performance of RAPCKF is systematically tested with numerical cases of unsaturated flow models. It is shown that the adaptive approach and restart scheme can significantly improve the performance of PCKF. Moreover, RAPCKF has been demonstrated to be more efficient than EnKF with the same computational cost.
An accurate battery State of Charge estimation is of great significance for battery electric vehicles and hybrid electric vehicles. This paper presents an adaptive unscented Kalman filtering method to estimate State of Charge of a lithium-ion battery for battery electric vehicles. The adaptive adjustment of the noise covariances in the State of Charge estimation process is implemented by an idea of covariance matching in the unscented Kalman filter context. Experimental results indicate that the adaptive unscented Kalman filter-based algorithm has a good performance in estimating the battery State of Charge. A comparison with the adaptive extended Kalman filter, extended Kalman filter, and unscented Kalman filter-based algorithms shows that the proposed State of Charge estimation method has a better accuracy. -- Highlights: → Adaptive unscented Kalman filtering is proposed to estimate State of Charge of a lithium-ion battery for electric vehicles. → The proposed method has a good performance in estimating the battery State of Charge. → A comparison with three other Kalman filtering algorithms shows that the proposed method has a better accuracy.
Ship detection for high resolution optical imagery with adaptive target filter
Ju, Hongbin
2015-10-01
Ship detection is important due to both its civil and military use. In this paper, we propose a novel ship detection method, Adaptive Target Filter (ATF), for high resolution optical imagery. The proposed framework can be grouped into two stages, where in the first stage, a test image is densely divided into different detection windows and each window is transformed to a feature vector in its feature space. The Histograms of Oriented Gradients (HOG) is accumulated as a basic feature descriptor. In the second stage, the proposed ATF highlights all the ship regions and suppresses the undesired backgrounds adaptively. Each detection window is assigned a score, which represents the degree of the window belonging to a certain ship category. The ATF can be adaptively obtained by the weighted Logistic Regression (WLR) according to the distribution of backgrounds and targets of the input image. The main innovation of our method is that we only need to collect positive training samples to build the filter, while the negative training samples are adaptively generated by the input image. This is different to other classification method such as Support Vector Machine (SVM) and Logistic Regression (LR), which need to collect both positive and negative training samples. The experimental result on 1-m high resolution optical images shows the proposed method achieves a desired ship detection performance with higher quality and robustness than other methods, e.g., SVM and LR.
Li, Xiaofan; Zhao, Yubin; Zhang, Sha; Fan, Xiaopeng
2016-01-01
Particle filters (PFs) are widely used for nonlinear signal processing in wireless sensor networks (WSNs). However, the measurement uncertainty makes the WSN observations unreliable to the actual case and also degrades the estimation accuracy of the PFs. In addition to the algorithm design, few works focus on improving the likelihood calculation method, since it can be pre-assumed by a given distribution model. In this paper, we propose a novel PF method, which is based on a new likelihood fusion method for WSNs and can further improve the estimation performance. We firstly use a dynamic Gaussian model to describe the nonparametric features of the measurement uncertainty. Then, we propose a likelihood adaptation method that employs the prior information and a belief factor to reduce the measurement noise. The optimal belief factor is attained by deriving the minimum Kullback–Leibler divergence. The likelihood adaptation method can be integrated into any PFs, and we use our method to develop three versions of adaptive PFs for a target tracking system using wireless sensor network. The simulation and experimental results demonstrate that our likelihood adaptation method has greatly improved the estimation performance of PFs in a high noise environment. In addition, the adaptive PFs are highly adaptable to the environment without imposing computational complexity. PMID:27249002
Li, Xiaofan; Zhao, Yubin; Zhang, Sha; Fan, Xiaopeng
2016-01-01
Particle filters (PFs) are widely used for nonlinear signal processing in wireless sensor networks (WSNs). However, the measurement uncertainty makes the WSN observations unreliable to the actual case and also degrades the estimation accuracy of the PFs. In addition to the algorithm design, few works focus on improving the likelihood calculation method, since it can be pre-assumed by a given distribution model. In this paper, we propose a novel PF method, which is based on a new likelihood fusion method for WSNs and can further improve the estimation performance. We firstly use a dynamic Gaussian model to describe the nonparametric features of the measurement uncertainty. Then, we propose a likelihood adaptation method that employs the prior information and a belief factor to reduce the measurement noise. The optimal belief factor is attained by deriving the minimum Kullback-Leibler divergence. The likelihood adaptation method can be integrated into any PFs, and we use our method to develop three versions of adaptive PFs for a target tracking system using wireless sensor network. The simulation and experimental results demonstrate that our likelihood adaptation method has greatly improved the estimation performance of PFs in a high noise environment. In addition, the adaptive PFs are highly adaptable to the environment without imposing computational complexity. PMID:27249002
A. Konstantaras
2006-01-01
Full Text Available The method of Hybrid Adaptive Filtering (HAF aims to recover the recorded electric field signals from anomalies of magnetotelluric origin induced mainly by magnetic storms. An adaptive filter incorporating neuro-fuzzy technology has been developed to remove any significant distortions from the equivalent magnetic field signal, as retrieved from the original electric field signal by reversing the magnetotelluric method. Testing with further unseen data verifies the reliability of the model and demonstrates the effectiveness of the HAF method.
Yushkov, Konstantin B.; Molchanov, Vladimir Y.; Belousov, Pavel V.; Abrosimov, Aleksander Y.
2016-01-01
We report a method for edge enhancement in the images of transparent samples using analog image processing in coherent light. The experimental technique is based on adaptive spatial filtering with an acousto-optic tunable filter in a telecentric optical system. We demonstrate processing of microscopic images of unstained and stained histological sections of human thyroid tumor with improved contrast.
Automated detection scheme of architectural distortion in mammograms using adaptive Gabor filter
Yoshikawa, Ruriha; Teramoto, Atsushi; Matsubara, Tomoko; Fujita, Hiroshi
2013-03-01
Breast cancer is a serious health concern for all women. Computer-aided detection for mammography has been used for detecting mass and micro-calcification. However, there are challenges regarding the automated detection of the architectural distortion about the sensitivity. In this study, we propose a novel automated method for detecting architectural distortion. Our method consists of the analysis of the mammary gland structure, detection of the distorted region, and reduction of false positive results. We developed the adaptive Gabor filter for analyzing the mammary gland structure that decides filter parameters depending on the thickness of the gland structure. As for post-processing, healthy mammary glands that run from the nipple to the chest wall are eliminated by angle analysis. Moreover, background mammary glands are removed based on the intensity output image obtained from adaptive Gabor filter. The distorted region of the mammary gland is then detected as an initial candidate using a concentration index followed by binarization and labeling. False positives in the initial candidate are eliminated using 23 types of characteristic features and a support vector machine. In the experiments, we compared the automated detection results with interpretations by a radiologist using 50 cases (200 images) from the Digital Database of Screening Mammography (DDSM). As a result, true positive rate was 82.72%, and the number of false positive per image was 1.39. There results indicate that the proposed method may be useful for detecting architectural distortion in mammograms.
Man, Jun; Li, Weixuan; Zeng, Lingzao; Wu, Laosheng
2016-06-01
The ensemble Kalman filter (EnKF) has gained popularity in hydrological data assimilation problems. As a Monte Carlo based method, a relatively large ensemble size is usually required to guarantee the accuracy. As an alternative approach, the probabilistic collocation based Kalman filter (PCKF) employs the polynomial chaos to approximate the original system. In this way, the sampling error can be reduced. However, PCKF suffers from the so-called "curse of dimensionality". When the system nonlinearity is strong and number of parameters is large, PCKF could be even more computationally expensive than EnKF. Motivated by most recent developments in uncertainty quantification, we propose a restart adaptive probabilistic collocation based Kalman filter (RAPCKF) for data assimilation in unsaturated flow problems. During the implementation of RAPCKF, the important parameters are identified and active PCE basis functions are adaptively selected. The "restart" technology is used to eliminate the inconsistency between model parameters and states. The performance of RAPCKF is tested with numerical cases of unsaturated flow models. It is shown that RAPCKF is more efficient than EnKF with the same computational cost. Compared with the traditional PCKF, the RAPCKF is more applicable in strongly nonlinear and high dimensional problems.
Superconducting magnetometry for cardiovascular studies and an application of adaptive filtering
Leifer, M.C.
1981-01-01
Sensitive magnetic detectors utilizing Superconducting Quantum Interference Devices (SQUID's) have been developed and used for studying the cardiovascular system. The theory of magnetic detection of cardiac currents is discussed, and new experimental data supporting the validity of the theory is presented. Measurements on both humans and dogs, in both health and diseased states, are presented using the new technique, which is termed vector magnetocardiography. In the next section, a new type of superconducting magnetometer with a room temperature pickup is analyzed, and techniques for optimizing its sensitivity to low-frequency sub-microamp currents are presented. Performance of the actual device displays significantly improved sensitivity in this frequency range, and the ability to measure currents in intact, in vivo biological fibers. The final section reviews the theoretical operation of a digital self-optimizing filter, and presents a four-channel software implementation of the system. The application of the adaptive filter to enhancement of geomagnetic signals for earthquake forecasting is discussed, and the adaptive filter is shown to outperform existing techniques in suppressing noise from geomagnetic records.
Ben Youssef, C.; Dahhou, B.; Roux, G. [Centre National de la Recherche Scientifique (CNRS), 31 - Toulouse (France); Rols, J.L. [Institut National des Sciences Appliquees (INSA), 31 - Toulouse (France)
1995-12-31
Controlling the process of a fixed bed bioreactor imply solving filtering and adaptative control problems. Estimation processes have been developed for unmeasurable parameters. An adaptative non linear control has been built, instead of conventional approaches trying to linearize the system and apply a linear control system. (D.L.) 10 refs.
Ah counting is not a satisfactory method for the estimation of the State of Charge (SOC) of a battery, as the initial SOC and coulombic efficiency are difficult to measure. To address this issue, a new SOC estimation method, denoted as 'AEKFAh', is proposed. This method uses the adaptive Kalman filtering method which can avoid filtering divergence resulting from uncertainty to correct for the initial value used in the Ah counting method. A Ni/MH battery test procedure, consisting of 8.08 continuous Federal Urban Driving Schedule (FUDS) cycles, is carried out to verify the method. The SOC estimation error is 2.4% when compared with the real SOC obtained from a discharge test. This compares favorably with an estimation error of 11.4% when using Ah counting.
Li, Jing; Song, Ningfang; Yang, Gongliu; Jiang, Rui
2016-07-01
In the initial alignment process of strapdown inertial navigation system (SINS), large misalignment angles always bring nonlinear problem, which can usually be processed using the scaled unscented Kalman filter (SUKF). In this paper, the problem of large misalignment angles in SINS alignment is further investigated, and the strong tracking scaled unscented Kalman filter (STSUKF) is proposed with fixed parameters to improve convergence speed, while these parameters are artificially constructed and uncertain in real application. To further improve the alignment stability and reduce the parameters selection, this paper proposes a fuzzy adaptive strategy combined with STSUKF (FUZZY-STSUKF). As a result, initial alignment scheme of large misalignment angles based on FUZZY-STSUKF is designed and verified by simulations and turntable experiment. The results show that the scheme improves the accuracy and convergence speed of SINS initial alignment compared with those based on SUKF and STSUKF.
Cardiac fiber tracking using adaptive particle filtering based on tensor rotation invariant in MRI
Kong, Fanhui; Liu, Wanyu; Magnin, Isabelle E.; Zhu, Yuemin
2016-03-01
Diffusion magnetic resonance imaging (dMRI) is a non-invasive method currently available for cardiac fiber tracking. However, accurate and efficient cardiac fiber tracking is still a challenge. This paper presents a probabilistic cardiac fiber tracking method based on particle filtering. In this framework, an adaptive sampling technique is presented to describe the posterior distribution of fiber orientations by adjusting the number and status of particles according to the fractional anisotropy of diffusion. An observation model is then proposed to update the weight of particles by rotating diffusion tensor from the primary eigenvector to a given fiber orientation while keeping the shape of the tensor invariant. The results on human cardiac dMRI show that the proposed method is robust to noise and outperforms conventional streamline and particle filtering techniques.
Li, Jing; Song, Ningfang; Yang, Gongliu; Jiang, Rui
2016-07-01
In the initial alignment process of strapdown inertial navigation system (SINS), large misalignment angles always bring nonlinear problem, which can usually be processed using the scaled unscented Kalman filter (SUKF). In this paper, the problem of large misalignment angles in SINS alignment is further investigated, and the strong tracking scaled unscented Kalman filter (STSUKF) is proposed with fixed parameters to improve convergence speed, while these parameters are artificially constructed and uncertain in real application. To further improve the alignment stability and reduce the parameters selection, this paper proposes a fuzzy adaptive strategy combined with STSUKF (FUZZY-STSUKF). As a result, initial alignment scheme of large misalignment angles based on FUZZY-STSUKF is designed and verified by simulations and turntable experiment. The results show that the scheme improves the accuracy and convergence speed of SINS initial alignment compared with those based on SUKF and STSUKF. PMID:27475606
CHEN Shi-hai; WANG En-yuan
2012-01-01
The electromagnetic radiation (EMR) signal collected by monitoring system during coal or rock dynamic disaster may be interferred easily by electromagnetic noises in mines.The noises have a direct influence on the recognition and analysis of the EMR signal features during the disaster.With the aim of removing these noises,an ensemble empirical mode decomposition (EEMD) adaptive morphological filter was proposed.From the result of the simulation and the experiment,it is shown that the method can restrain the random noise and white Gaussian noise mixed with EMR signal effectively.The filter is highly useful for improving the robustness of the coal or rock dynamic disaster monitoring system.
Hofman, Radek; Šmídl, Václav
Americam Geophzsical Society, 2010. s. 1-2. [2010 The Meeting of the Americas. 08.08.2010-12.08.2010, Foz do Iguaçu] R&D Projects: GA MŠk(CZ) 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : ensemble Kalman filter * inflation factor * adaptive tunning * observation error * Rao-Blackwellized particle filter Subject RIV: BC - Control Systems Theory http://library.utia.cas.cz/separaty/2010/AS/hofman-enkf within the marginalized particle filter and its application for adaptive estimation of inflation factor.pdf
Evaluation of an adaptive filtering algorithm for CT cardiac imaging with EKG modulated tube current
Li, Jianying; Hsieh, Jiang; Mohr, Kelly; Okerlund, Darin
2005-04-01
We have developed an adaptive filtering algorithm for cardiac CT scans with EKG-modulated tube current to optimize resolution and noise for different cardiac phases and to provide safety net for cases where end-systole phase is used for coronary imaging. This algorithm has been evaluated using patient cardiac CT scans where lower tube currents are used for the systolic phases. In this paper, we present the evaluation results. The results demonstrated that with the use of the proposed algorithm, we could improve image quality for all cardiac phases, while providing greater noise and streak artifact reduction for systole phases where lower CT dose were used.
Realizing of the Kalman filtering and self-tuning adaptive process
The Kalman filtering is part of the modern control theory, and widely applied in industry, aviation, control science, and some random data process. How to realize this method is introduced, through 137Cs spectrum is given as an example which was measured in lab with NaI(Tl) detector because it is a random process with γ-ray energy. Its futures and limit in practice is analyzed and two self-tuning adaptive methods are introduced: parameter and error self-tuning methods
de Lamare, R C; Fa, R
2012-01-01
This paper presents reduced-rank linearly constrained minimum variance (LCMV) beamforming algorithms based on joint iterative optimization of filters. The proposed reduced-rank scheme is based on a constrained joint iterative optimization of filters according to the minimum variance criterion. The proposed optimization procedure adjusts the parameters of a projection matrix and an adaptive reducedrank filter that operates at the output of the bank of filters. We describe LCMV expressions for the design of the projection matrix and the reduced-rank filter. We then describe stochastic gradient and develop recursive least-squares adaptive algorithms for their efficient implementation along with automatic rank selection techniques. An analysis of the stability and the convergence properties of the proposed algorithms is presented and semi-analytical expressions are derived for predicting their mean squared error (MSE) performance. Simulations for a beamforming application show that the proposed scheme and algorit...
ICA Based Speckle Filtering for Target Extraction in SAR Images Using Adaptive Space Separation
LI Yu-tong; ZHOU Yue; YANG Lei
2008-01-01
A novel approach based on independent component analysis (ICA) for speckle filtering and target extraction of synthetic aperture radar (SAR) images is proposed using adaptive space separation with weighted information entropy (WIE) incorporated. First the basis and the independent components are respectively obtained by ICA technique, and WIE of the image is computed; then based on the threshold computed from function T-WIE (threshold versus weighted-information-entropy), independent components are adaptively separated and the bases are classified accordingly. Thus, the image space is separated into two subspaces: "clean" and "noise". Then, a proposed nonlinear operator ABO is applied on each component of the 'clean' subspace for further optimization. Finally, recovery image is obtained reconstructing this subspace and target is easily extracted with binarisation. Note that here T-WIE is an interpolated function based on several representative target SAR images using proposed space separation algorithm.
Temporal Scalability through Adaptive -Band Filter Banks for Robust H.264/MPEG-4 AVC Video Coding
Pau G
2006-01-01
Full Text Available This paper presents different structures that use adaptive -band hierarchical filter banks for temporal scalability. Open-loop and closed-loop configurations are introduced and illustrated using existing video codecs. In particular, it is shown that the H.264/MPEG-4 AVC codec allows us to introduce scalability by frame shuffling operations, thus keeping backward compatibility with the standard. The large set of shuffling patterns introduced here can be exploited to adapt the encoding process to the video content features, as well as to the user equipment and transmission channel characteristics. Furthermore, simulation results show that this scalability is obtained with no degradation in terms of subjective and objective quality in error-free environments, while in error-prone channels the scalable versions provide increased robustness.
Gil-Cacho, Jose M.; van Waterschoot, Toon; Moonen, Marc;
2014-01-01
In this paper, we propose a new framework to tackle the double-talk (DT) problem in acoustic echo cancellation (AEC). It is based on a frequency-domain adaptive filter (FDAF) implementation of the so-called prediction error method adaptive filtering using row operations (PEM-AFROW) leading to the...... FDAF-PEM-AFROW algorithm. We show that FDAF-PEM-AFROW is by construction related to the best linear unbiased estimate (BLUE) of the echo path. We depart from this framework to show an improvement in performance with respect to other adaptive filters minimizing the BLUE criterion, namely the PEM......-AFROW and the FDAF-NLMS with near-end signal normalization. One of the contributions is to propose the instantaneous pseudo-correlation (IPC) measure between the near-end signal and the loudspeaker signal. The IPC measure serves as an indication of the effect of a DT situation occurring during adaptation...
An Adaptive Approach to Mitigate Background Covariance Limitations in the Ensemble Kalman Filter
Song, Hajoon
2010-07-01
A new approach is proposed to address the background covariance limitations arising from undersampled ensembles and unaccounted model errors in the ensemble Kalman filter (EnKF). The method enhances the representativeness of the EnKF ensemble by augmenting it with new members chosen adaptively to add missing information that prevents the EnKF from fully fitting the data to the ensemble. The vectors to be added are obtained by back projecting the residuals of the observation misfits from the EnKF analysis step onto the state space. The back projection is done using an optimal interpolation (OI) scheme based on an estimated covariance of the subspace missing from the ensemble. In the experiments reported here, the OI uses a preselected stationary background covariance matrix, as in the hybrid EnKF–three-dimensional variational data assimilation (3DVAR) approach, but the resulting correction is included as a new ensemble member instead of being added to all existing ensemble members. The adaptive approach is tested with the Lorenz-96 model. The hybrid EnKF–3DVAR is used as a benchmark to evaluate the performance of the adaptive approach. Assimilation experiments suggest that the new adaptive scheme significantly improves the EnKF behavior when it suffers from small size ensembles and neglected model errors. It was further found to be competitive with the hybrid EnKF–3DVAR approach, depending on ensemble size and data coverage.
Parametric adaptive filtering and data validation in the bar GW detector AURIGA
We report on our experience gained in the signal processing of the resonant GW detector AURIGA. Signal amplitude and arrival time are estimated by means of a matched-adaptive Wiener filter. The detector noise, entering in the filter set-up, is modelled as a parametric ARMA process; to account for slow non-stationarity of the noise, the ARMA parameters are estimated on an hourly basis. A requirement of the set-up of an unbiased Wiener filter is the separation of time spans with 'almost Gaussian' noise from non-Gaussian and/or strongly non-stationary time spans. The separation algorithm consists basically of a variance estimate with the Chauvenet convergence method and a threshold on the Curtosis index. The subsequent validation of data is strictly connected with the separation procedure: in fact, by injecting a large number of artificial GW signals into the 'almost Gaussian' part of the AURIGA data stream, we have demonstrated that the effective probability distributions of the signal-to-noise ratio χ2 and the time of arrival are those that are expected
Three-State Locally Adaptive Texture Preserving Filter for Radar and Optical Image Processing
Jaakko T. Astola
2005-05-01
Full Text Available Textural features are one of the most important types of useful information contained in images. In practice, these features are commonly masked by noise. Relatively little attention has been paid to texture preserving properties of noise attenuation methods. This stimulates solving the following tasks: (1 to analyze the texture preservation properties of various filters; and (2 to design image processing methods capable to preserve texture features well and to effectively reduce noise. This paper deals with examining texture feature preserving properties of different filters. The study is performed for a set of texture samples and different noise variances. The locally adaptive three-state schemes are proposed for which texture is considered as a particular class. For Ã¢Â€ÂœdetectionÃ¢Â€Â of texture regions, several classifiers are proposed and analyzed. As shown, an appropriate trade-off of the designed filter properties is provided. This is demonstrated quantitatively for artificial test images and is confirmed visually for real-life images.
Quaternion-based Kalman Filter for AHRS Using an Adaptive-step Gradient Descent Algorithm
Li Wang
2015-09-01
Full Text Available This paper presents a quaternion-based Kalman filter for real-time estimation of the orientation of a quadrotor. Quaternions are used to represent rotation relationship between navigation frame and body frame. Processing of a 3-axis accelerometer using Adaptive-Step Gradient Descent (ASGD produces a computed quaternion input to the Kalman filter. The step-size in GD is set in direct proportion to the physical orientation rate. Kalman filter combines 3-axis gyroscope and computed quaternion to determine pitch and roll angles. This combination overcomes linearization error of the measurement equations and reduces the calculation cost. 3-axis magnetometer is separated from ASGD to independently calculate yaw angle for Attitude Heading Reference System (AHRS. This AHRS algorithm is able to remove the magnetic distortion impact. Experiments are carried out in the small-size flight controller and the real world flying test shows the proposed AHRS algorithm is adequate for the real-time estimation of the orientation of a quadrotor.
Bai, Mingsian R; Chi, Li-Wen; Liang, Li-Huang; Lo, Yi-Yang
2016-02-01
In this paper, an evolutionary exposition is given in regard to the enhancing strategies for acoustic echo cancellers (AECs). A fixed beamformer (FBF) is utilized to focus on the near-end speaker while suppressing the echo from the far end. In reality, the array steering vector could differ considerably from the ideal freefield plane wave model. Therefore, an experimental procedure is developed to interpolate a practical array model from the measured frequency responses. Subband (SB) filtering with polyphase implementation is exploited to accelerate the cancellation process. Generalized sidelobe canceller (GSC) composed of an FBF and an adaptive blocking module is combined with AEC to maximize cancellation performance. Another enhancement is an internal iteration (IIT) procedure that enables efficient convergence in the adaptive SB filters within a sample time. Objective tests in terms of echo return loss enhancement (ERLE), perceptual evaluation of speech quality (PESQ), word recognition rate for automatic speech recognition (ASR), and subjective listening tests are conducted to validate the proposed AEC approaches. The results show that the GSC-SB-AEC-IIT approach has attained the highest ERLE without speech quality degradation, even in double-talk scenarios. PMID:26936567
Dynamic Adaptive Median Filter (DAMF for Removal of High Density Impulse Noise
Punyaban Patel
2012-10-01
Full Text Available This paper proposes a novel adaptive filtering scheme to remove impulse noise from images. The scheme replaces the corrupted test pixel with the median value of non-corrupted neighboring pixels selected from a window dynamically. If the number of non-corrupted pixels in the selected window is not sufficient, a window of next higher size is chosen. Thus window size is automatically adapted based on the density of noise in the image as well as the density of corruption local to a window. As a result window size may vary pixel to pixel while filtering. The scheme is simple to implement and do not require multiple iterations. The efficacy of the proposed scheme is evaluated with respect to subjective as well as objective parameters on standard images on various noise densities. Comparative analysis reveals that the proposed scheme has improved performance over other schemes, preferably in high density impulse noise cases. Further, the computational overhead is also less as compared its competent scheme.
Dong, Gangqi; Zhu, Zheng H.
2016-05-01
This paper presents a real-time, vision-based algorithm for the pose and motion estimation of non-cooperative targets and its application in visual servo robotic manipulator to perform autonomous capture. A hybrid approach of adaptive extended Kalman filter and photogrammetry is developed for the real-time pose and motion estimation of non-cooperative targets. Based on the pose and motion estimates, the desired pose and trajectory of end-effector is defined and the corresponding desired joint angles of the robotic manipulator are derived by inverse kinematics. A close-loop visual servo control scheme is then developed for the robotic manipulator to track, approach and capture the target. Validating experiments are designed and performed on a custom-built six degrees of freedom robotic manipulator with an eye-in-hand configuration. The experimental results demonstrate the feasibility, effectiveness and robustness of the proposed adaptive extended Kalman filter enabled pose and motion estimation and visual servo strategy.
Reduction of EEG artifacts in simultaneous EEG-fMRI: Reference layer adaptive filtering (RLAF).
Steyrl, David; Patz, Franz; Krausz, Gunther; Edlinger, Günter; Müller-Putz, Gernot R
2015-08-01
Although simultaneous measurement of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is one of the most valuable methods for studying human brain activity non-invasively, it remains challenging to measure high quality EEG inside the MRI scanner. Recently, a new approach for minimizing residual MRI scanner artifacts in the EEG was presented: reference layer artifact subtraction (RLAS). Here, reference electrodes capture only the artifacts, which are subsequently subtracted from the measurement electrodes. With the present work we demonstrate that replacing the subtraction by adaptive filtering statistically significantly outperforms RLAS. Reference layer adaptive filtering (RLAF) attenuates the average artifact root-mean-square (RMS) voltage of the passive MRI scanner to 0.7 μV (-14.4 dB). RLAS achieves 0.78 μV (-13.5 dB). The combination of average artifact subtraction (AAS) and RLAF reduces the residual average gradient artifact RMS voltage to 2.3 μV (-49.2 dB). AAS alone achieves 5.7 μV (-39.0 dB). All measurements were conducted with an MRI phantom, as the reference layer cap available to us was a prototype. PMID:26737122
A Multidelay Double-Talk Detector Combined with the MDF Adaptive Filter
Gänsler Tomas
2003-01-01
Full Text Available The multidelay block frequency-domain (MDF adaptive filter is an excellent candidate for both acoustic and network echo cancellation. There is a need for a very good double-talk detector (DTD to be combined efficiently with the MDF algorithm. Recently, a DTD based on a normalized cross-correlation vector was proposed and it was shown that this DTD performs much better than the Geigel algorithm and other DTDs based on the cross-correlation coefficient. In this paper, we show how to extend the definition of a normalized cross-correlation vector in the frequency domain for the general case where the block size of the Fourier transform is smaller than the length of the adaptive filter. The resulting DTD has an MDF structure, which makes it easy to implement, and a good fit with an echo canceler based on the MDF algorithm. We also analyze resource requirements (computational complexity and memory requirement and compare the MDF algorithm with the normalized least mean square algorithm (NLMS from this point of view.
Shanechi, Maryam M; Orsborn, Amy L; Carmena, Jose M
2016-04-01
Much progress has been made in brain-machine interfaces (BMI) using decoders such as Kalman filters and finding their parameters with closed-loop decoder adaptation (CLDA). However, current decoders do not model the spikes directly, and hence may limit the processing time-scale of BMI control and adaptation. Moreover, while specialized CLDA techniques for intention estimation and assisted training exist, a unified and systematic CLDA framework that generalizes across different setups is lacking. Here we develop a novel closed-loop BMI training architecture that allows for processing, control, and adaptation using spike events, enables robust control and extends to various tasks. Moreover, we develop a unified control-theoretic CLDA framework within which intention estimation, assisted training, and adaptation are performed. The architecture incorporates an infinite-horizon optimal feedback-control (OFC) model of the brain's behavior in closed-loop BMI control, and a point process model of spikes. The OFC model infers the user's motor intention during CLDA-a process termed intention estimation. OFC is also used to design an autonomous and dynamic assisted training technique. The point process model allows for neural processing, control and decoder adaptation with every spike event and at a faster time-scale than current decoders; it also enables dynamic spike-event-based parameter adaptation unlike current CLDA methods that use batch-based adaptation on much slower adaptation time-scales. We conducted closed-loop experiments in a non-human primate over tens of days to dissociate the effects of these novel CLDA components. The OFC intention estimation improved BMI performance compared with current intention estimation techniques. OFC assisted training allowed the subject to consistently achieve proficient control. Spike-event-based adaptation resulted in faster and more consistent performance convergence compared with batch-based methods, and was robust to parameter
Mazumder, Ria; Clymer, Bradley D; Mo, Xiaokui; White, Richard D; Kolipaka, Arunark
2016-06-01
Diffusion tensor imaging (DTI) is used to quantify myocardial fiber orientation based on helical angles (HA). Accurate HA measurements require multiple excitations (NEX) and/or several diffusion encoding directions (DED). However, increasing NEX and/or DED increases acquisition time (TA). Therefore, in this study, we propose to reduce TA by implementing a 3D adaptive anisotropic Gaussian filter (AAGF) on the DTI data acquired from ex-vivo healthy and infarcted porcine hearts. DTI was performed on ex-vivo hearts [9-healthy, 3-myocardial infarction (MI)] with several combinations of DED and NEX. AAGF, mean (AVF) and median filters (MF) were applied on the primary eigenvectors of the diffusion tensor prior to HA estimation. The performance of AAGF was compared against AVF and MF. Root mean square error (RMSE), concordance correlation-coefficients and Bland-Altman's technique was used to determine optimal combination of DED and NEX that generated the best HA maps in the least possible TA. Lastly, the effect of implementing AAGF on the infarcted porcine hearts was also investigated. RMSE in HA estimation for AAGF was lower compared to AVF or MF. Post-filtering (AAGF) fewer DED and NEX were required to achieve HA maps with similar integrity as those obtained from higher NEX and/or DED. Pathological alterations caused in HA orientation in the MI model were preserved post-filtering (AAGF). Our results demonstrate that AAGF reduces TA without affecting the integrity of the myocardial microstructure. PMID:26843150
Mahmood, Muhammad Tariq; Chu, Yeon-Ho; Choi, Young-Kyu
2016-06-01
This paper proposes a Rician noise reduction method for magnetic resonance (MR) images. The proposed method is based on adaptive non-local mean and guided image filtering techniques. In the first phase, a guidance image is obtained from the noisy image through an adaptive non-local mean filter. Sobel operators are applied to compute the strength of edges which is further used to control the spread of the kernel in non-local mean filtering. In the second phase, the noisy and the guidance images are provided to the guided image filter as input to restore the noise-free image. The improved performance of the proposed method is investigated using the simulated and real data sets of MR images. Its performance is also compared with the previously proposed state-of-the art methods. Comparative analysis demonstrates the superiority of the proposed scheme over the existing approaches.
Research of Design Method of the Power Notch Filter Based on FPAA%基于FPAA技术的工频滤波器设计方法研究
朱正伟; 孙广辉; 张丹; 鲍海虹
2013-01-01
随着电子技术的不断发展,电力电子给人类生活带来便捷时同时也带来许多困扰.交流电路传输时对日常仪器产生电磁干扰,降低了仪器的性能.在此背景下以基于开关电容技术的FPAA芯片AN221E04为例对FPAA技术在工频滤波方面展开研究.首先介绍了FPAA技术特点及软件,然后对设计流程做了详细描述,最后应用软件进行仿真验证设计.软件仿真验证得滤波器阻带为0.4Hz、通带为2Hz,在50Hz在处衰减为80dB的巴特沃斯工频滤波器,滤波效果达到预期设计要求.%With the continuous development of the electronic technology,power electronics not only brought convenience to human life,but also a lot of troubles.During AC circuit transmission,the instruments suffer from electromagnetic interference in daily life,lowering the performance.In this context,the study of the FPAA technology in terms of power frequency filtering on the FPAA chip AN221E04 was carried out,which was based on switched capacitor technology.Firstly,the FPAA technical characteristics and the software were introduced.Secondly,a detailed description of the design process was described.Finally,the application software design was verified by simulation.The 50Hz Butterworth notch filter with the passband 3.01dB,stopband 30dB,and the bandwidth of stop band 0.4Hz was successfully developed.The results by software simulation showed the stopband of the filter was 40mHz,the passband was 2Hz and decreased to 80dB at 50Hz,and the filtering effect achieved the desired requirement.
Liu Yu; Dong Kai; Wang Haipeng; Liu Jun; He You; Pan Lina
2014-01-01
The paper deals with state estimation problem of nonlinear non-Gaussian discrete dynamic systems for improvement of accuracy and consistency. An efficient new algorithm called the adaptive Gaussian-sum square-root cubature Kalman filter (AGSSCKF) with a split-merge scheme is proposed. It is developed based on the squared-root extension of newly introduced cubature Kalman filter (SCKF) and is built within a Gaussian-sum framework. Based on the condition that the probability density functions o...
Singh, Omkar; Sunkaria, Ramesh Kumar
2015-01-01
Separating an information-bearing signal from the background noise is a general problem in signal processing. In a clinical environment during acquisition of an electrocardiogram (ECG) signal, The ECG signal is corrupted by various noise sources such as powerline interference (PLI), baseline wander and muscle artifacts. This paper presents novel methods for reduction of powerline interference in ECG signals using empirical wavelet transform (EWT) and adaptive filtering. The proposed methods are compared with the empirical mode decomposition (EMD) based PLI cancellation methods. A total of six methods for PLI reduction based on EMD and EWT are analysed and their results are presented in this paper. The EWT-based de-noising methods have less computational complexity and are more efficient as compared with the EMD-based de-noising methods. PMID:25412942
Yoon, Paul K; Zihajehzadeh, Shaghayegh; Bong-Soo Kang; Park, Edward J
2015-08-01
This paper proposes a novel indoor localization method using the Bluetooth Low Energy (BLE) and an inertial measurement unit (IMU). The multipath and non-line-of-sight errors from low-power wireless localization systems commonly result in outliers, affecting the positioning accuracy. We address this problem by adaptively weighting the estimates from the IMU and BLE in our proposed cascaded Kalman filter (KF). The positioning accuracy is further improved with the Rauch-Tung-Striebel smoother. The performance of the proposed algorithm is compared against that of the standard KF experimentally. The results show that the proposed algorithm can maintain high accuracy for position tracking the sensor in the presence of the outliers. PMID:26736389
P. Hari Krishnan
2014-08-01
Full Text Available Image segmentation is the foremost process in medical image processing. It aids the diagnostic and clinical analysis of MRI (Magnetic Resonance Imaging images that were acquired through the most complex procedures of medical diagnostics. The earliest soft computing techniques in segmenting images were carried out through Fuzzy C-Means (FCM and similar extensions of various clustering algorithms. In this paper, we introduced an innovative method that uses Gabor energy filter with adaptive features to pre-extract the information of various regions of a brain image, obtained either from a MRI or CT scanner. The noise-reduced image with blurred features was then made to undergo modifications by applying unsupervised learning methods such as FCM technique, whose output has efficient exclusion of certain strength of noise elements resulting in better classified pixels.
Adaptation of Gabor filters for simulation of human preattentive mechanism for a mobile robot
Kulkarni, Naren; Naghdy, Golshah A.
1993-08-01
Vision guided mobile robot navigation is complex and requires analysis of tremendous amounts of information in real time. In order to simplify the task and reduce the amount of information, human preattentive mechanism can be adapted [Nag90]. During the preattentive search the scene is analyzed rapidly but in sufficient detail for the attention to be focused on the `area of interest.' The `area of interest' can further be scrutinized in more detail for recognition purposes. This `area of interest' can be a text message to facilitate navigation. Gabor filters and an automated turning mechanism are used to isolate the `area of interest.' These regions are subsequently processed with optimal spatial resolution for perception tasks. This method has clear advantages over the global operators in that, after an initial search, it scans each region of interest with optimum resolution. This reduces the volume of information for recognition stages and ensures that no region is over or under estimated.
Adaptive UAV Attitude Estimation Employing Unscented Kalman Filter, FOAM and Low-Cost MEMS Sensors
Carlos Santos
2012-07-01
Full Text Available Navigation employing low cost MicroElectroMechanical Systems (MEMS sensors in Unmanned Aerial Vehicles (UAVs is an uprising challenge. One important part of this navigation is the right estimation of the attitude angles. Most of the existent algorithms handle the sensor readings in a fixed way, leading to large errors in different mission stages like take-off aerobatic maneuvers. This paper presents an adaptive method to estimate these angles using off-the-shelf components. This paper introduces an Attitude Heading Reference System (AHRS based on the Unscented Kalman Filter (UKF using the Fast Optimal Attitude Matrix (FOAM algorithm as the observation model. The performance of the method is assessed through simulations. Moreover, field experiments are presented using a real fixed-wing UAV. The proposed low cost solution, implemented in a microcontroller, shows a satisfactory real time performance.
Detection of failure in the operational status of a NPP is described. The method uses lattice form of the signal modelling established by means of Kalman filtering methodology. In this approach each lattice parameter is considered to be a state and the minimum variance estimate of the states is performed adaptively by optimal parameter estimation together with fast convergence and favourable statistical properties. In particular, the state covariance is also the covariance of the error committed by that estimate of the state value and the Mahalanobis distance formed for pattern comparison takes x2 distribution for normally distributed signals. The failure detection is performed after a decision making process by probabilistic assessments based on the statistical information provided. The failure detection system is implemented in multi-channel signal environment of Borssele NPP and its favourable features are demonstrated. (author). 29 refs.; 7 figs
Propagating adaptive-weighted vector median filter for motion-field smoothing
林梦冬; 余松煜
2004-01-01
In the field of predictive video coding and format conversion, there is an increasing attention towards estimation of the true inter-frame motion. The restoration of motion vector field computed by 3-D RS is addressed and a propagating adaptive-weighted vector median (PAWVM) post-filter is proposed. This approach decomposes blocks to make a betteres timation on object borders and propagates good vectors in the scanning direction. Furthermore, a hard-thresholding method is introduced into calculating vector weights to improve the propagating. By exploiting both the spatial correlation of the vector field and the matching error of candidate vectors, PAWVM makes a good balance between the smoothness of vector field and the prediction error, and the output vector field is more valid to reflect the true motion.
Doo Yong Choi
2016-04-01
Full Text Available Rapid detection of bursts and leaks in water distribution systems (WDSs can reduce the social and economic costs incurred through direct loss of water into the ground, additional energy demand for water supply, and service interruptions. Many real-time burst detection models have been developed in accordance with the use of supervisory control and data acquisition (SCADA systems and the establishment of district meter areas (DMAs. Nonetheless, no consideration has been given to how frequently a flow meter measures and transmits data for predicting breaks and leaks in pipes. This paper analyzes the effect of sampling interval when an adaptive Kalman filter is used for detecting bursts in a WDS. A new sampling algorithm is presented that adjusts the sampling interval depending on the normalized residuals of flow after filtering. The proposed algorithm is applied to a virtual sinusoidal flow curve and real DMA flow data obtained from Jeongeup city in South Korea. The simulation results prove that the self-adjusting algorithm for determining the sampling interval is efficient and maintains reasonable accuracy in burst detection. The proposed sampling method has a significant potential for water utilities to build and operate real-time DMA monitoring systems combined with smart customer metering systems.
Gruneisen, Mark T.; Sickmiller, Brett A.; Flanagan, Michael B.; Black, James P.; Stoltenberg, Kurt E.; Duchane, Alexander W.
2016-02-01
Spatial filtering is an important technique for reducing sky background noise in a satellite quantum key distribution downlink receiver. Atmospheric turbulence limits the extent to which spatial filtering can reduce sky noise without introducing signal losses. Using atmospheric propagation and compensation simulations, the potential benefit of adaptive optics (AO) to secure key generation (SKG) is quantified. Simulations are performed assuming optical propagation from a low-Earth-orbit satellite to a terrestrial receiver that includes AO. Higher-order AO correction is modeled assuming a Shack-Hartmann wavefront sensor and a continuous-face-sheet deformable mirror. The effects of atmospheric turbulence, tracking, and higher-order AO on the photon capture efficiency are simulated using statistical representations of turbulence and a time-domain wave-optics hardware emulator. SKG rates are calculated for a decoy-state protocol as a function of the receiver field of view for various strengths of turbulence, sky radiances, and pointing angles. The results show that at fields of view smaller than those discussed by others, AO technologies can enhance SKG rates in daylight and enable SKG where it would otherwise be prohibited as a consequence of background optical noise and signal loss due to propagation and turbulence effects.
A novel nonlinear adaptive filter using a pipelined second-order Volterra recurrent neural network.
Zhao, Haiquan; Zhang, Jiashu
2009-12-01
To enhance the performance and overcome the heavy computational complexity of recurrent neural networks (RNN), a novel nonlinear adaptive filter based on a pipelined second-order Volterra recurrent neural network (PSOVRNN) is proposed in this paper. A modified real-time recurrent learning (RTRL) algorithm of the proposed filter is derived in much more detail. The PSOVRNN comprises of a number of simple small-scale second-order Volterra recurrent neural network (SOVRNN) modules. In contrast to the standard RNN, these modules of a PSOVRNN can be performed simultaneously in a pipelined parallelism fashion, which can lead to a significant improvement in its total computational efficiency. Moreover, since each module of the PSOVRNN is a SOVRNN in which nonlinearity is introduced by the recursive second-order Volterra (RSOV) expansion, its performance can be further improved. Computer simulations have demonstrated that the PSOVRNN performs better than the pipelined recurrent neural network (PRNN) and RNN for nonlinear colored signals prediction and nonlinear channel equalization. However, the superiority of the PSOVRNN over the PRNN is at the cost of increasing computational complexity due to the introduced nonlinear expansion of each module. PMID:19523787
Wang, Xin; Wu, Linhui; Yi, Xi; Zhang, Yanqi; Zhang, Limin; Zhao, Huijuan; Gao, Feng
2015-01-01
Due to both the physiological and morphological differences in the vascularization between healthy and diseased tissues, pharmacokinetic diffuse fluorescence tomography (DFT) can provide contrast-enhanced and comprehensive information for tumor diagnosis and staging. In this regime, the extended Kalman filtering (EKF) based method shows numerous advantages including accurate modeling, online estimation of multiparameters, and universal applicability to any optical fluorophore. Nevertheless the performance of the conventional EKF highly hinges on the exact and inaccessible prior knowledge about the initial values. To address the above issues, an adaptive-EKF scheme is proposed based on a two-compartmental model for the enhancement, which utilizes a variable forgetting-factor to compensate the inaccuracy of the initial states and emphasize the effect of the current data. It is demonstrated using two-dimensional simulative investigations on a circular domain that the proposed adaptive-EKF can obtain preferable estimation of the pharmacokinetic-rates to the conventional-EKF and the enhanced-EKF in terms of quantitativeness, noise robustness, and initialization independence. Further three-dimensional numerical experiments on a digital mouse model validate the efficacy of the method as applied in realistic biological systems. PMID:26089975
Dong, Feng; Pierpaoli, Elena; Gunn, James E.; Wechsler, Risa H.
2007-10-29
We present a modified adaptive matched filter algorithm designed to identify clusters of galaxies in wide-field imaging surveys such as the Sloan Digital Sky Survey. The cluster-finding technique is fully adaptive to imaging surveys with spectroscopic coverage, multicolor photometric redshifts, no redshift information at all, and any combination of these within one survey. It works with high efficiency in multi-band imaging surveys where photometric redshifts can be estimated with well-understood error distributions. Tests of the algorithm on realistic mock SDSS catalogs suggest that the detected sample is {approx} 85% complete and over 90% pure for clusters with masses above 1.0 x 10{sup 14}h{sup -1} M and redshifts up to z = 0.45. The errors of estimated cluster redshifts from maximum likelihood method are shown to be small (typically less that 0.01) over the whole redshift range with photometric redshift errors typical of those found in the Sloan survey. Inside the spherical radius corresponding to a galaxy overdensity of {Delta} = 200, we find the derived cluster richness {Lambda}{sub 200} a roughly linear indicator of its virial mass M{sub 200}, which well recovers the relation between total luminosity and cluster mass of the input simulation.
Hongjian Wang
2014-01-01
Full Text Available We present a support vector regression-based adaptive divided difference filter (SVRADDF algorithm for improving the low state estimation accuracy of nonlinear systems, which are typically affected by large initial estimation errors and imprecise prior knowledge of process and measurement noises. The derivative-free SVRADDF algorithm is significantly simpler to compute than other methods and is implemented using only functional evaluations. The SVRADDF algorithm involves the use of the theoretical and actual covariance of the innovation sequence. Support vector regression (SVR is employed to generate the adaptive factor to tune the noise covariance at each sampling instant when the measurement update step executes, which improves the algorithm’s robustness. The performance of the proposed algorithm is evaluated by estimating states for (i an underwater nonmaneuvering target bearing-only tracking system and (ii maneuvering target bearing-only tracking in an air-traffic control system. The simulation results show that the proposed SVRADDF algorithm exhibits better performance when compared with a traditional DDF algorithm.
Shih, Cheng-Ting; Lin, Hsin-Hon; Chuang, Keh-Shih [Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu 30013, Taiwan (China); Wu, Jay, E-mail: jwu@mail.cmu.edu.tw [Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung 40402, Taiwan (China); Chang, Shu-Jun [Health Physics Division, Institute of Nuclear Energy Research, Atomic Energy Council, Taoyuan 32546, Taiwan (China)
2014-08-15
Purpose: Several positron emission tomography (PET) scanners with special detector block arrangements have been developed in recent years to improve the resolution of PET images. However, the discontinuous detector blocks cause gaps in the sinogram. This study proposes an adaptive discrete cosine transform-based (aDCT) filter for gap-inpainting. Methods: The gap-corrupted sinogram was morphologically closed and subsequently converted to the DCT domain. A certain number of the largest coefficients in the DCT spectrum were identified to determine the low-frequency preservation region. The weighting factors for the remaining coefficients were determined by an exponential weighting function. The aDCT filter was constructed and applied to two digital phantoms and a simulated phantom introduced with various levels of noise. Results: For the Shepp-Logan head phantom, the aDCT filter filled the gaps effectively. For the Jaszczak phantom, no secondary artifacts were induced after aDCT filtering. The percent mean square error and mean structure similarity of the aDCT filter were superior to those of the DCT2 filter at all noise levels. For the simulated striatal dopamine innervation study, the aDCT filter recovered the shape of the striatum and restored the striatum to reference activity ratios to the ideal value. Conclusions: The proposed aDCT filter can recover the missing gap data in the sinogram and improve the image quality and quantitative accuracy of PET images.
Adaptive Control of Non-Minimum Phase Modal Systems Using Residual Mode Filters2. Parts 1 and 2
Balas, Mark J.; Frost, Susan
2011-01-01
Many dynamic systems containing a large number of modes can benefit from adaptive control techniques, which are well suited to applications that have unknown parameters and poorly known operating conditions. In this paper, we focus on a direct adaptive control approach that has been extended to handle adaptive rejection of persistent disturbances. We extend this adaptive control theory to accommodate problematic modal subsystems of a plant that inhibit the adaptive controller by causing the open-loop plant to be non-minimum phase. We will modify the adaptive controller with a Residual Mode Filter (RMF) to compensate for problematic modal subsystems, thereby allowing the system to satisfy the requirements for the adaptive controller to have guaranteed convergence and bounded gains. This paper will be divided into two parts. Here in Part I we will review the basic adaptive control approach and introduce the primary ideas. In Part II, we will present the RMF methodology and complete the proofs of all our results. Also, we will apply the above theoretical results to a simple flexible structure example to illustrate the behavior with and without the residual mode filter.
Liu Yu
2014-10-01
Full Text Available The paper deals with state estimation problem of nonlinear non-Gaussian discrete dynamic systems for improvement of accuracy and consistency. An efficient new algorithm called the adaptive Gaussian-sum square-root cubature Kalman filter (AGSSCKF with a split-merge scheme is proposed. It is developed based on the squared-root extension of newly introduced cubature Kalman filter (SCKF and is built within a Gaussian-sum framework. Based on the condition that the probability density functions of process noises and initial state are denoted by a Gaussian sum using optimization method, a bank of SCKF are used as the sub-filters to estimate state of system with the corresponding weights respectively, which is adaptively updated. The new algorithm consists of an adaptive splitting and merging procedure according to a proposed split-decision model based on the nonlinearity degree of measurement. The results of two simulation scenarios (one-dimensional state estimation and bearings-only tracking show that the proposed filter demonstrates comparable performance to the particle filter with significantly reduced computational cost.
Liu Yu; Dong Kai; Wang Haipeng; Liu Jun; He You; Pan Lina
2014-01-01
The paper deals with state estimation problem of nonlinear non-Gaussian discrete dynamic systems for improvement of accuracy and consistency. An efficient new algorithm called the adaptive Gaussian-sum square-root cubature Kalman filter (AGSSCKF) with a split-merge scheme is proposed. It is developed based on the squared-root extension of newly introduced cuba-ture Kalman filter (SCKF) and is built within a Gaussian-sum framework. Based on the condition that the probability density functions of process noises and initial state are denoted by a Gaussian sum using optimization method, a bank of SCKF are used as the sub-filters to estimate state of sys-tem with the corresponding weights respectively, which is adaptively updated. The new algorithm consists of an adaptive splitting and merging procedure according to a proposed split-decision model based on the nonlinearity degree of measurement. The results of two simulation scenarios (one-dimensional state estimation and bearings-only tracking) show that the proposed filter demon-strates comparable performance to the particle filter with significantly reduced computational cost.
Pham, Mai Quyen; Chaux, Caroline; Pesquet, Jean-Christophe
2014-01-01
Unveiling meaningful geophysical information from seismic data requires to deal with both random and structured "noises". As their amplitude may be greater than signals of interest (primaries), additional prior information is especially important in performing efficient signal separation. We address here the problem of multiple reflections, caused by wave-field bouncing between layers. Since only approximate models of these phenomena are available, we propose a flexible framework for time-varying adaptive filtering of seismic signals, using sparse representations, based on inaccurate templates. We recast the joint estimation of adaptive filters and primaries in a new convex variational formulation. This approach allows us to incorporate plausible knowledge about noise statistics, data sparsity and slow filter variation in parsimony-promoting wavelet frames. The designed primal-dual algorithm solves a constrained minimization problem that alleviates standard regularization issues in finding hyperparameters. Th...
Improved characterization of slow-moving landslides by means of adaptive NL-InSAR filtering
Albiol, David; Iglesias, Rubén.; Sánchez, Francisco; Duro, Javier
2014-10-01
Advanced remote sensing techniques based on space-borne Synthetic Aperture Radar (SAR) have been developed during the last decade showing their applicability for the monitoring of surface displacements in landslide areas. This paper presents an advanced Persistent Scatterer Interferometry (PSI) processing based on the Stable Point Network (SPN) technique, developed by the company Altamira-Information, for the monitoring of an active slowmoving landslide in the mountainous environment of El Portalet, Central Spanish Pyrenees. For this purpose, two TerraSAR-X data sets acquired in ascending mode corresponding to the period from April to November 2011, and from August to November 2013, respectively, are employed. The objective of this work is twofold. On the one hand, the benefits of employing Nonlocal Interferomtric SAR (NL-InSAR) adaptive filtering techniques over vegetated scenarios to maximize the chances of detecting natural distributed scatterers, such as bare or rocky areas, and deterministic point-like scatterers, such as man-made structures or poles, is put forward. In this context, the final PSI displacement maps retrieved with the proposed filtering technique are compared in terms of pixels' density and quality with classical PSI, showing a significant improvement. On the other hand, since SAR systems are only sensitive to detect displacements in the line-of-sight (LOS) direction, the importance of projecting the PSI displacement results retrieved along the steepest gradient of the terrain slope is discussed. The improvements presented in this paper are particularly interesting in these type of applications since they clearly allow to better determine the extension and dynamics of complex landslide phenomena.
Adaptive partial median filter for early CT signs of acute cerebral infarction
Purpose: Detection of early CT signs of infarct in non- enhanced CT image is mandatory in patients with acute ischemic stroke. Loss of the gray-white matter interface at the lentiform nucleus or the insular ribbon has been an important early CT sign of acute cerebral infarction, which affects decisions on thrombolytic therapy. However, its detection is difficult, since the principal early CT sign is subtle hypoattenuation. An image processing method to reduce local noise with edges preserved was developed to improve infarct detection. Rationale: An adaptive partial median filter (APMF) was selected for this application, since the APMF can markedly improve the visibility of the normal gray-white matter interface. APMF should enhance the conspicuity of gray-white matter interface changes due to hypoattenuation that accompanies cerebral infarction. Method: In a criterion referenced performance study using simulated CT images with gray-white matter interfaces, a total of 14 conventional smoothing filters were also used for comparison to validate the usefulness of the proposed APMF. The APMF indicated the highest performance among the compared methods. Then, observer performance study by receiver operator characteristic (ROC) analysis was performed with 4 radiologist observers using a database with 18 abnormal and 33 normal head CT images. The average Az values of ROC curves for all radiologists increased from 0.876 without the APMF images to 0.926 with the APMF images, and this difference was statistically significant (P = 0.04). The results from the two observer performance studies demonstrated that APMF has significant potential to improve the diagnosis of acute cerebral infarction using non-enhanced CT images. (orig.)
Adaptive partial median filter for early CT signs of acute cerebral infarction
Lee, Yongbum; Tsai, Du-Yih [Niigata University, Department of Radiological Technology, School of Health Sciences, Niigata (Japan); Takahashi, Noriyuki; Ishii, Kiyoshi [Sendai City Hospital, Department of Radiology, Sendai (Japan)
2007-08-15
Purpose: Detection of early CT signs of infarct in non- enhanced CT image is mandatory in patients with acute ischemic stroke. Loss of the gray-white matter interface at the lentiform nucleus or the insular ribbon has been an important early CT sign of acute cerebral infarction, which affects decisions on thrombolytic therapy. However, its detection is difficult, since the principal early CT sign is subtle hypoattenuation. An image processing method to reduce local noise with edges preserved was developed to improve infarct detection. Rationale: An adaptive partial median filter (APMF) was selected for this application, since the APMF can markedly improve the visibility of the normal gray-white matter interface. APMF should enhance the conspicuity of gray-white matter interface changes due to hypoattenuation that accompanies cerebral infarction. Method: In a criterion referenced performance study using simulated CT images with gray-white matter interfaces, a total of 14 conventional smoothing filters were also used for comparison to validate the usefulness of the proposed APMF. The APMF indicated the highest performance among the compared methods. Then, observer performance study by receiver operator characteristic (ROC) analysis was performed with 4 radiologist observers using a database with 18 abnormal and 33 normal head CT images. The average A{sub z} values of ROC curves for all radiologists increased from 0.876 without the APMF images to 0.926 with the APMF images, and this difference was statistically significant (P = 0.04). The results from the two observer performance studies demonstrated that APMF has significant potential to improve the diagnosis of acute cerebral infarction using non-enhanced CT images. (orig.)
Wen-Yu Wang; An-Wen Shen
2012-01-01
A novel method for middle frequency resonance detection and reduction is proposed for speed control in industrial servo systems. Defects of traditional resonance reduction method based on adaptive notch filter in middle frequency range are analyzed. And the main reason is summarized as the difference between the resonance frequency and the oscillation frequency. A self-tuning low-pass filter is introduced in the speed feedback path, whose corner frequency is determined by FFT results and seve...
Gray, Morgan; Rodionov, Sergey; Bertino, Laurent; Bocquet, Marc; Fusco, Thierry
2013-01-01
We propose a new algorithm for an adaptive optics system control law which allows to reduce the computational burden in the case of an Extremely Large Telescope (ELT) and to deal with non-stationary behaviors of the turbulence. This approach, using Ensemble Transform Kalman Filter and localizations by domain decomposition is called the local ETKF: the pupil of the telescope is split up into various local domains and calculations for the update estimate of the turbulent phase on each domain are performed independently. This data assimilation scheme enables parallel computation of markedly less data during this update step. This adapts the Kalman Filter to large scale systems with a non-stationary turbulence model when the explicit storage and manipulation of extremely large covariance matrices are impossible. First simulation results are given in order to assess the theoretical analysis and to demonstrate the potentiality of this new control law for complex adaptive optics systems on ELTs.
The electromagnetic part of an extensive air shower developing in the atmosphere provides significant information complementary to that obtained by water Cherenkov detectors which are predominantly sensitive to the muonic content of an air shower at ground. The emissions can be observed in the frequency band between 10 and 100 MHz. However, this frequency range is significantly contaminated by narrow-band RFI and other human-made distortions. The Auger Engineering Radio Array currently suppresses the RFI by multiple time-to-frequency domain conversions using an FFT procedure as well as by a set of manually chosen IIR notch filters in the time-domain. An alternative approach developed in this paper is an adaptive FIR filter based on linear prediction (LP). The coefficients for the linear predictor are dynamically refreshed and calculated in the virtual NIOS processor. The radio detector is an autonomous system installed on the Argentinean pampas and supplied from a solar panel. Powerful calculation capacity inside the FPGA is a factor. Power consumption versus the degree of effectiveness of the calculation inside the FPGA is a figure of merit to be minimized. Results show that the RFI contamination can be significantly suppressed by the LP FIR filter for 64 or less stages. -- Highlights: • We propose an adaptive method using linear prediction for periodic RFI suppression. • Requirements are the detection of short transient signals powered by solar panels. • The RFI is significantly suppressed by ∼70%, even in a very contaminated environment. • This method consumes less energy than the current method based on FFT used in AERA. • Distortion of the short transient signals is negligible
Szadkowski, Zbigniew, E-mail: zszadkow@kfd2.phys.uni.lodz.pl [University of Lodz, Department of Physics and Applied Informatics (Poland); Fraenkel, E.D. [Kernfysisch Versneller Instituut of the University of Groningen, Groningen (Netherlands); Glas, Dariusz; Legumina, Remigiusz [University of Lodz, Department of Physics and Applied Informatics (Poland)
2013-12-21
The electromagnetic part of an extensive air shower developing in the atmosphere provides significant information complementary to that obtained by water Cherenkov detectors which are predominantly sensitive to the muonic content of an air shower at ground. The emissions can be observed in the frequency band between 10 and 100 MHz. However, this frequency range is significantly contaminated by narrow-band RFI and other human-made distortions. The Auger Engineering Radio Array currently suppresses the RFI by multiple time-to-frequency domain conversions using an FFT procedure as well as by a set of manually chosen IIR notch filters in the time-domain. An alternative approach developed in this paper is an adaptive FIR filter based on linear prediction (LP). The coefficients for the linear predictor are dynamically refreshed and calculated in the virtual NIOS processor. The radio detector is an autonomous system installed on the Argentinean pampas and supplied from a solar panel. Powerful calculation capacity inside the FPGA is a factor. Power consumption versus the degree of effectiveness of the calculation inside the FPGA is a figure of merit to be minimized. Results show that the RFI contamination can be significantly suppressed by the LP FIR filter for 64 or less stages. -- Highlights: • We propose an adaptive method using linear prediction for periodic RFI suppression. • Requirements are the detection of short transient signals powered by solar panels. • The RFI is significantly suppressed by ∼70%, even in a very contaminated environment. • This method consumes less energy than the current method based on FFT used in AERA. • Distortion of the short transient signals is negligible.
Rodríguez-Caballero, E.; Afana, A.; Chamizo, S.; Solé-Benet, A.; Canton, Y.
2016-07-01
Terrestrial laser scanning (TLS), widely known as light detection and ranging (LiDAR) technology, is increasingly used to provide highly detailed digital terrain models (DTM) with millimetric precision and accuracy. In order to generate a DTM, TLS data has to be filtered from undesired spurious objects, such as vegetation, artificial structures, etc., Early filtering techniques, successfully applied to airborne laser scanning (ALS), fail when applied to TLS data, as they heavily smooth the terrain surface and do not retain their real morphology. In this article, we present a new methodology for filtering TLS data based on the geometric and radiometric properties of the scanned surfaces. This methodology was built on previous morphological filters that select the minimum point height within a sliding window as the real surface. However, contrary to those methods, which use a fixed window size, the new methodology operates under different spatial scales represented by different window sizes, and can be adapted to different types and sizes of plants. This methodology has been applied to two study areas of differing vegetation type and density. The accuracy of the final DTMs was improved by ∼30% under dense canopy plants and over ∼40% on the open spaces between plants, where other methodologies drastically underestimated the real surface heights. This resulted in more accurate representation of the soil surface and microtopography than up-to-date techniques, eventually having strong implications in hydrological and geomorphological studies.
In-cylinder pressure analysis is a key tool for engine research and diagnosis and it has been object of study from the beginning of the internal combustion engines. One of its most useful application is combustion analysis on the basis of the First Law of Thermodynamics. However, heat release law calculations use the in-cylinder pressure derivative signal. Hence, the noise is increased and pressure filtering becomes necessary to remove high frequency noise, thus allowing for accurate combustion analyses. In this work, a methodology to set the cut-off frequency of a low-pass filter is proposed. Statistical criteria are used to separate the signal from the noise through the calculation of the Discrete Fourier Transform of several consecutive in-cylinder pressures cycles. Thus, only physically meaningful information is preserved. The proposed methodology is compared with some adaptive and non-adaptive algorithms used to select the cut-off frequencies, and it shows a good ability to adapt to different engine operating conditions. - Highlights: →Combustion analysis is performed by means of in-cylinder pressure measurement. →Filtering is necessary due to high noise and dispersion in the measurement. →A proposed methodology to select cut-off frequencies is proposed. →The methodology is compared with other filters, showing a better behaviour.
Inertial sensors are widely used in human body motion monitoring systems since they permit us to determine the position of the subject's limbs. Limb angle measurement is carried out through the integration of the angular velocity measured by a rate sensor and the decomposition of the components of static gravity acceleration measured by an accelerometer. Different factors derived from the sensors' nature, such as the angle random walk and dynamic bias, lead to erroneous measurements. Dynamic bias effects can be reduced through the use of adaptive filtering based on sensor fusion concepts. Most existing published works use a Kalman filtering sensor fusion approach. Our aim is to perform a comparative study among different adaptive filters. Several least mean squares (LMS), recursive least squares (RLS) and Kalman filtering variations are tested for the purpose of finding the best method leading to a more accurate and robust limb angle measurement. A new angle wander compensation sensor fusion approach based on LMS and RLS filters has been developed
This paper concerns the active vibration control of a rectangular panel using smart sensors from the viewpoint of an active wave control theory. The objective of this paper is to present a new type of filter which enables the measurement of the wave amplitude of a rectangular panel in real time for the application of an adaptive feedforward control system which inactivates vibration modes. Firstly, a novel wave filtering method using smart PVDF sensors is proposed. It is found that the shaping function of smart sensors is a complex function. To realize the smart sensor in a practical situation, a Hilbert transformer is utilized to implement a phase shifter of 90° for broadband frequencies. Then, from the viewpoint of a numerical analysis, the characteristics of the proposed wave filter and the performance of the adaptive feedforward control system using the wave filter are discussed. Finally, experiments implementing the active wave control theory which uses the proposed wave filter are conducted, demonstrating the validity of the proposed method in suppressing the vibration of a rectangular panel
Pipino, A; Pierpaoli, E; MacKenzie, S M; Dong, F
2010-01-01
We study the properties of Brightest Cluster Galaxies (BCGs) drawn from a catalogue of more than 69000 clusters in the SDSS DR6 based on the adaptive matched filter technique (AMF, Szabo et al., 2010). Our sample consists of more than 14300 galaxies in the redshift range 0.1-0.3. We test the catalog by showing that it includes well-known BCGs which lie in the SDSS footprint. We characterize the BCGs in terms of r-band luminosities and optical colours as well as their trends with redshift. In particular, we define and study the fraction of blue BCGs, namely those that are likely to be missed by either colour-based cluster surveys and catalogues. Richer clusters tend to have brighter BCGs, however less dominant than in poorer systems. 4-9% of our BCGs are at least 0.3 mag bluer in the g-r colour than the red-sequence at their given redshift. Such a fraction decreases to 1-6% for clusters above a richness of 50, where 3% of the BCGs are 0.5 mag below the red-sequence. A preliminary morphological study suggests t...
Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing
2016-01-01
A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method. PMID:26761006
Mitchell, D A; Sault, R J
2010-01-01
In radio astronomy, reference signals from auxiliary antennas that receive only the radio frequency interference (RFI) can be modified to model the RFI environment at the astronomy receivers. The RFI can then be canceled from the astronomy signal paths. However, astronomers typically only require signal statistics. If the RFI statistics are changing slowly, the cancellation can be applied to the signal correlations at a much lower rate than is required for standard adaptive filters. In this paper we describe five canceler setups; precorrelation and postcorrelation cancelers that use one or two reference signals in different ways. The theoretical residual RFI and added noise levels are examined and are demonstrated using microwave television RFI at the Australia Telescope Compact Array. The RFI is attenuated to below the system noise, a reduction of at least 20 dB. While dual-reference cancelers add more reference noise than single-reference cancelers, this noise is zero-mean and only adds to the system noise,...
S. Saravanakumar
2014-01-01
Full Text Available A Morphological based Adaptive Unsymmetrical Trimmed Mid-Point Filter (MAUTMPF for the restoration of gray scale images corrupted by salt and pepper noise for varying noise densities is proposed in this study. Images corrupted by impulsive noise severely hinder subsequent image processing tasks, such as edge detection, image segmentation, object recognition, etc. Therefore, it is absolutely essential to restore the original image from the corrupted image. The proposed algorithm replaces the corrupted pixel by mid point value out of the retained pixels other than 0’s and 255’s in a 3×3 window. The essential condition for the validity of the window is that at least two pixels in the selected window should be uncorrupted; if not the window size is incremented by 2. The iteration stops when the window size reaches 7. In particular case, when the condition for validity doesn’t hold in 7×7 window then the original 3×3 window is chosen and midpoint of minimum and maximum values of already processed pixels is replaced with the centre pixel. experimental evaluation using MATLAB reveals that our MAUTMPF shows better performance compared to the previous de-noising algorithms in terms of Peak Signal-to-Noise Ratio (PSNR and Mean Square Error (MSE for noise densities up to 90%. The validity of the proposed algorithm is verified by testing it for different gray scale images.
Stability of the adaptive fading extended Kalman filter with the matrix forgetting factor
BİÇER, Cenker; BABACAN, Esin KÖKSAL; ÖZBEK, Levent
2012-01-01
The extended Kalman filter is extensively used in nonlinear state estimation problems. As long as the system characteristics are correctly known, the extended Kalman filter gives the best performance. However, when the system information is partially known or incorrect, the extended Kalman filter may diverge or give biased estimates. An extensive number of works has been published to improve the performance of the extended Kalman filter. Many researchers have proposed the introductio...
Wells, Gregg B.; Ricci, Anthony J.
2011-11-01
In the auditory system, mechanotransduction occurs in the hair cell sensory hair bundle and is the first major step in the translation of mechanical energy into electrical. Tonotopic variations in the activation kinetics of this process are posited to provide a low pass filter to the input. An adaptation process, also associated with mechanotransduction, is postulated to provide a high pass filter to the input in a tonotopic manner. Together a bandpass filter is created at the hair cell input. Corresponding mechanical components to both activation and adaptation are also suggested to be involved in generating cochlear amplification. A paradox to this story is that hair cells where the mechanotransduction properties are most robust possess an intrinsic electrical resonance mechanism proposed to account for all required tuning and amplification. A simple Hodgkin-Huxley type model is presented to attempt to determine the role of the activation and adaptation kinetics in further tuning hair cells that exhibit electrical resonance. Results further support that steady state mechanotransduction properties are critical for setting the resting potential of the hair cell while the kinetics of activation and adaptation are important for sharpening tuning around the characteristic frequency of the hair cell.
Collagen represses canonical Notch signaling and binds to Notch ectodomain
Zhang, Xiaojie; Meng, He; Michael M Wang
2013-01-01
The Notch signaling system features a growing number of modulators that include extracellular proteins that bind to the Notch ectodomain. Collagens are a complex, heterogeneous family of secreted proteins that serve both structural and signaling functions, most prominently through binding to integrins and DDR. The shared widespread tissue distribution of Notch and collagen prompted us to investigate the effects of collagen on Notch signaling. In a cell co-culture signaling assay, we found tha...
黄龙君; 王筠华; 郑凯; 杨永良; 周泽然; 陈园博
2008-01-01
合肥光源横向束流反馈系统已经建成,着重介绍了系统中矢量运算单元和光纤陷波滤波器的研制.矢量运算单元中使用混频器控制信号的衰减,调节控制电压的大小以控制反馈信号的相位;光纤陷波滤波器创新性地提出用光纤延时制作陷波滤波器,很好地滤除了信号中的回旋频率分量,节省了反馈功率.%The transverse bunchbybunch feedback system has been constructed at Hefei light source to cure and damp the coupled bunch instability. There are two important modules in the system: the vector calculation module and the notch filter. The vector calculation module is a signal processing module used to adjust the phase of the feedback signals and the notch filter can filter the revolution frequency component in a signal, which will save the feedback power.
Aelterman, Jan; Goossens, Bart; De Vylder, Jonas; Pizurica, Aleksandra; Philips, Wilfried
2013-01-01
Most digital cameras use an array of alternating color filters to capture the varied colors in a scene with a single sensor chip. Reconstruction of a full color image from such a color mosaic is what constitutes demosaicing. In this paper, a technique is proposed that performs this demosaicing in a way that incurs a very low computational cost. This is done through a (dual-tree complex) wavelet interpretation of the demosaicing problem. By using a novel locally adaptive approach for demosaici...
Purpose: To compare image quality and visualization of normal structures and lesions in brain computed tomography (CT) with adaptive statistical iterative reconstruction (ASIR) and filtered back projection (FBP) reconstruction techniques in different X-ray tube current–time products. Materials and methods: In this IRB-approved prospective study, forty patients (nineteen men, twenty-one women; mean age 69.5 ± 11.2 years) received brain scan at different tube current–time products (300 and 200 mAs) in 64-section multi-detector CT (GE, Discovery CT750 HD). Images were reconstructed with FBP and four levels of ASIR-FBP blending. Two radiologists (please note that our hospital is renowned for its geriatric medicine department, and these two radiologists are more experienced in chronic cerebral vascular disease than in neoplastic disease, so this research did not contain cerebral tumors but as a discussion) assessed all the reconstructed images for visibility of normal structures, lesion conspicuity, image contrast and diagnostic confidence in a blinded and randomized manner. Volume CT dose index (CTDIvol) and dose-length product (DLP) were recorded. All the data were analyzed by using SPSS 13.0 statistical analysis software. Results: There was no statistically significant difference between the image qualities at 200 mAs with 50% ASIR blending technique and 300 mAs with FBP technique (p > .05). While between the image qualities at 200 mAs with FBP and 300 mAs with FBP technique a statistically significant difference (p < .05) was found. Conclusion: ASIR provided same image quality and diagnostic ability in brain imaging with greater than 30% dose reduction compared with FBP reconstruction technique
Ren, Qingguo, E-mail: renqg83@163.com [Department of Radiology, Hua Dong Hospital of Fudan University, Shanghai 200040 (China); Dewan, Sheilesh Kumar, E-mail: sheilesh_d1@hotmail.com [Department of Geriatrics, Hua Dong Hospital of Fudan University, Shanghai 200040 (China); Li, Ming, E-mail: minli77@163.com [Department of Radiology, Hua Dong Hospital of Fudan University, Shanghai 200040 (China); Li, Jianying, E-mail: Jianying.Li@med.ge.com [CT Imaging Research Center, GE Healthcare China, Beijing (China); Mao, Dingbiao, E-mail: maodingbiao74@163.com [Department of Radiology, Hua Dong Hospital of Fudan University, Shanghai 200040 (China); Wang, Zhenglei, E-mail: Williswang_doc@yahoo.com.cn [Department of Radiology, Shanghai Electricity Hospital, Shanghai 200050 (China); Hua, Yanqing, E-mail: cjr.huayanqing@vip.163.com [Department of Radiology, Hua Dong Hospital of Fudan University, Shanghai 200040 (China)
2012-10-15
Purpose: To compare image quality and visualization of normal structures and lesions in brain computed tomography (CT) with adaptive statistical iterative reconstruction (ASIR) and filtered back projection (FBP) reconstruction techniques in different X-ray tube current–time products. Materials and methods: In this IRB-approved prospective study, forty patients (nineteen men, twenty-one women; mean age 69.5 ± 11.2 years) received brain scan at different tube current–time products (300 and 200 mAs) in 64-section multi-detector CT (GE, Discovery CT750 HD). Images were reconstructed with FBP and four levels of ASIR-FBP blending. Two radiologists (please note that our hospital is renowned for its geriatric medicine department, and these two radiologists are more experienced in chronic cerebral vascular disease than in neoplastic disease, so this research did not contain cerebral tumors but as a discussion) assessed all the reconstructed images for visibility of normal structures, lesion conspicuity, image contrast and diagnostic confidence in a blinded and randomized manner. Volume CT dose index (CTDI{sub vol}) and dose-length product (DLP) were recorded. All the data were analyzed by using SPSS 13.0 statistical analysis software. Results: There was no statistically significant difference between the image qualities at 200 mAs with 50% ASIR blending technique and 300 mAs with FBP technique (p > .05). While between the image qualities at 200 mAs with FBP and 300 mAs with FBP technique a statistically significant difference (p < .05) was found. Conclusion: ASIR provided same image quality and diagnostic ability in brain imaging with greater than 30% dose reduction compared with FBP reconstruction technique.
Variational Bayesian Adaptation of Noise Covariances in Non-Linear Kalman Filtering
Hartikainen, Simo Särkkä Jouni
2013-01-01
This paper is considered with joint estimation of state and time-varying noise covariance matrices in non-linear stochastic state space models. We present a variational Bayes and Gaussian filtering based algorithm for efficient computation of the approximate filtering posterior distributions. The Gaussian filtering based formulation of the non-linear state space model computation allows usage of efficient Gaussian integration methods such as unscented transform, cubature integration and Gauss...
Multiple Order, Multiple-Time Constant Self-Adaptive Tracking Filter
Thomas, Edison
1995-01-01
The algorithm described provides simultaneous availability of the state estimates corresponding to many orders of filters through the use of the fading memory (discounted) averages of the residuals of each lower order to obtain the estimates of a higher order. These averages are also used to provide maneuver parameters at different levels in order to obtain a gracefully changing hybrid combination of the filter estimates. Further, as the state estimate of a higher order filter is generally be...
Noise Reduction and Gap Filling of fAPAR Time Series Using an Adapted Local Regression Filter
Álvaro Moreno
2014-08-01
Full Text Available Time series of remotely sensed data are an important source of information for understanding land cover dynamics. In particular, the fraction of absorbed photosynthetic active radiation (fAPAR is a key variable in the assessment of vegetation primary production over time. However, the fAPAR series derived from polar orbit satellites are not continuous and consistent in space and time. Filtering methods are thus required to fill in gaps and produce high-quality time series. This study proposes an adapted (iteratively reweighted local regression filter (LOESS and performs a benchmarking intercomparison with four popular and generally applicable smoothing methods: Double Logistic (DLOG, smoothing spline (SSP, Interpolation for Data Reconstruction (IDR and adaptive Savitzky-Golay (ASG. This paper evaluates the main advantages and drawbacks of the considered techniques. The results have shown that ASG and the adapted LOESS perform better in recovering fAPAR time series over multiple controlled noisy scenarios. Both methods can robustly reconstruct the fAPAR trajectories, reducing the noise up to 80% in the worst simulation scenario, which might be attributed to the quality control (QC MODIS information incorporated into these filtering algorithms, their flexibility and adaptation to the upper envelope. The adapted LOESS is particularly resistant to outliers. This method clearly outperforms the other considered methods to deal with the high presence of gaps and noise in satellite data records. The low RMSE and biases obtained with the LOESS method (|rMBE| < 8%; rRMSE < 20% reveals an optimal reconstruction even in most extreme situations with long seasonal gaps. An example of application of the LOESS method to fill in invalid values in real MODIS images presenting persistent cloud and snow coverage is also shown. The LOESS approach is recommended in most remote sensing applications, such as gap-filling, cloud-replacement, and observing temporal
NOTCH SIGNALING REGULATES MOUSE AND HUMAN TH17 DIFFERENTIATION
Keerthivasan, Shilpa; Suleiman, Reem; Lawlor, Rebecca; Roderick, Justine; Bates, Tonya; Minter, Lisa; Anguita, Juan; Juncadella, Ignacio; Nickoloff, Brian J; Le Poole, I. Caroline; Miele, Lucio; Osborne, Barbara A.
2011-01-01
T helper17 (Th17) cells are known to play a critical role in adaptive immune responses to several important extracellular pathogens. Additionally, Th17 cells are implicated in the pathogenesis of several autoimmune and inflammatory disorders as well as in cancer. Therefore, it is essential to understand the mechanisms that regulate Th17 differentiation. Notch signaling is known to be important at several stages of T cell development and differentiation. Here we report that Notch1 is activated...
Analysis of ECG Using Filter Bank Approach
S. Thulasi Prasad
2014-01-01
Full Text Available In recent years scientists and engineers are facing several problems in the biomedical field. However Digital Signal Processing is solving many of those problems easily and effectively. The signal processing of ECG is very useful in detecting selected arrhythmia conditions from a patient’s electrocardiograph (ECG signals. In this paper we performed analysis of noisy ECG by filtering of 50 Hz power line interference using an adaptive LMS notch filter. This is very meaningful in the measurement of biomedical events, particularly when the recorded ECG signal is very weak. The basic ECG has the frequency range from 5 Hz to 100 Hz. It becomes difficult for the Specialist to diagnose the diseases if the artifacts are present in the ECG signal. Methods of noise reduction have decisive influence on performance of all electro-cardio-graphic (ECG signal processing systems. After removing 50/60 Hz powerline interference, the ECG is lowpass filtered in a digital FIR filter. We designed a Filter Bank to separate frequency ranges of ECG signal to enhance the occurrences QRS complexes. Later the positions of R-peaks are identified and shown plotted. The result shows the ECG signal before filtering and after filtering with their frequency spectrums which clearly indicates the reduction of the power line interference in the ECG signal and a filtered ECG with identified R-peaks.
Sun, Jin; Xu, Xiaosu; Liu, Yiting; Zhang, Tao; Li, Yao
2016-01-01
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. PMID:27420062
Sun, Jin; Xu, Xiaosu; Liu, Yiting; Zhang, Tao; Li, Yao
2016-01-01
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. PMID:27420062
Adaptive filtering in spatial vision: evidence from feature marking in plaids.
Georgeson, M A; Meese, T S
1999-01-01
Much evidence shows that early vision employs an array of spatial filters tuned for different spatial frequencies and orientations. We suggest that for moderately low spatial frequencies these preliminary filters are not treated independently, but are used to perform grouping and segmentation in the patchwise Fourier domain. For example, consider a stationary plaid made from two superimposed sinusoidal gratings of the same contrast and spatial frequency oriented +/- 45 degrees from vertical. Most of the energy in a wavelet-like (e.g. simple-cell) transform of this stimulus is in the oblique orientations, but typically it looks like a compound structure containing blurred vertical and horizontal edges. This checkerboard structure corresponds with the locations of zero crossings in the output of an isotropic (circular) filter, synthesised from the linear sum of a set of oriented basis-filters (Georgeson, 1992 Proceedings of the Royal Society of London, Series B 249 235-245). However, the addition of a third harmonic in square-wave phase causes almost complete perceptual segmentation of the plaid into two overlapping oblique gratings. Here we confirm this result psychophysically using a feature-marking technique, and argue that this perceptual segmentation cannot be understood in terms of the zero crossings marked in the output of any static linear filter that is sensitive to all of the plaid's components. If it is assumed that zero crossings or similar are an appropriate feature-primitive in human vision, our results require a flexible process that combines and segments early basis-filters according to prevailing image conditions. Thus, we suggest that combination and segmentation of spatial filters in the patchwise Fourier domain underpins the perceptual segmentation observed in our experiments. Under this kind of image-processing scheme, registration across spatial scales occurs at the level of spatial filters, before features are extracted. This contrasts with
Fayadh, Rashid A.; Malek, F.; Fadhil, Hilal A.; Aldhaibani, Jaafar A.; Salman, M. K.; Abdullah, Farah Salwani
2015-05-01
For high data rate propagation in wireless ultra-wideband (UWB) communication systems, the inter-symbol interference (ISI), multiple-access interference (MAI), and multiple-users interference (MUI) are influencing the performance of the wireless systems. In this paper, the rake-receiver was presented with the spread signal by direct sequence spread spectrum (DS-SS) technique. The adaptive rake-receiver structure was shown with adjusting the receiver tap weights using least mean squares (LMS), normalized least mean squares (NLMS), and affine projection algorithms (APA) to support the weak signals by noise cancellation and mitigate the interferences. To minimize the data convergence speed and to reduce the computational complexity by the previous algorithms, a well-known approach of partial-updates (PU) adaptive filters were employed with algorithms, such as sequential-partial, periodic-partial, M-max-partial, and selective-partial updates (SPU) in the proposed system. The simulation results of bit error rate (BER) versus signal-to-noise ratio (SNR) are illustrated to show the performance of partial-update algorithms that have nearly comparable performance with the full update adaptive filters. Furthermore, the SPU-partial has closed performance to the full-NLMS and full-APA while the M-max-partial has closed performance to the full-LMS updates algorithms.
Sheng-Hui, Rong; Hui-Xin, Zhou; Han-Lin, Qin; Rui, Lai; Kun, Qian
2016-05-01
Imaging non-uniformity of infrared focal plane array (IRFPA) behaves as fixed-pattern noise superimposed on the image, which affects the imaging quality of infrared system seriously. In scene-based non-uniformity correction methods, the drawbacks of ghosting artifacts and image blurring affect the sensitivity of the IRFPA imaging system seriously and decrease the image quality visibly. This paper proposes an improved neural network non-uniformity correction method with adaptive learning rate. On the one hand, using guided filter, the proposed algorithm decreases the effect of ghosting artifacts. On the other hand, due to the inappropriate learning rate is the main reason of image blurring, the proposed algorithm utilizes an adaptive learning rate with a temporal domain factor to eliminate the effect of image blurring. In short, the proposed algorithm combines the merits of the guided filter and the adaptive learning rate. Several real and simulated infrared image sequences are utilized to verify the performance of the proposed algorithm. The experiment results indicate that the proposed algorithm can not only reduce the non-uniformity with less ghosting artifacts but also overcome the problems of image blurring in static areas.
Gray, Morgan; Petit, Cyril; Rodionov, Sergey; Bertino, Laurent; Bocquet, Marc; Fusco, Thierry
2013-12-01
We propose a new algorithm for an AO control law which allows to reduce the computation burden in the case of an Extremely Large Telescope and to deal with a non stationary behavior of the atmospheric turbulence. This approach uses Ensemble Transform Kalman Filter (ETKF) and localizations by domains decomposition: the assimilation is split into local domains on the pupil of the telescope and each of the update data assimilation for each domain is performed independently. This kind of assimilation enables parallel computation of much less data during the update stage. This is a Kalman Filter adaptation for large scale systems with a non stationary turbulence when the explicit storage and manipulation of extremely large covariance matrices are impossible. This distributed parallel environment implementation is highlighted and studied in the context of an ELT application. First simulation results are proposed to assess our theoretical analysis and to demonstrate the potentiality of this new approach for an AO control law on ELTs.
Mihajlovic, Vojkan; Patki, Shrishail; Grundlehner, Bernard
2014-01-01
Designing and developing a comfortable and convenient EEG system for daily usage that can provide reliable and robust EEG signal, encompasses a number of challenges. Among them, the most ambitious is the reduction of artifacts due to body movements. This paper studies the effect of head movement artifacts on the EEG signal and on the dry electrode-tissue impedance (ETI), monitored continuously using the imec's wireless EEG headset. We have shown that motion artifacts have huge impact on the EEG spectral content in the frequency range lower than 20 Hz. Coherence and spectral analysis revealed that ETI is not capable of describing disturbances at very low frequencies (below 2 Hz). Therefore, we devised a motion artifact reduction (MAR) method that uses a combination of a band-pass filtering and multi-channel adaptive filtering (AF), suitable for real-time MAR. This method was capable of substantially reducing artifacts produced by head movements. PMID:25571131
Optical axis jitter rejection for double overlapped adaptive optics systems
Luo, Qi; Luo, Xi; Li, Xinyang
2016-04-01
Optical axis jitters, or vibrations, which arise from wind shaking and structural oscillations of optical platforms, etc., cause a deleterious impact on the performance of adaptive optics systems. When conventional integrators are utilized to reject such high frequency and narrow-band disturbance, the benefits are quite small despite their acceptable capabilities to reject atmospheric turbulence. In our case, two suits of complete adaptive optics systems called double overlapped adaptive optics systems (DOAOS) are used to counteract both optical jitters and atmospheric turbulence. A novel algorithm aiming to remove vibrations is proposed by resorting to combine the Smith predictor and notch filer. With the help of loop shaping method, the algorithm will lead to an effective and stable controller, which makes the characteristics of error transfer function close to notch filters. On the basis of the spectral analysis of observed data, the peak frequency and bandwidth of vibrations can be identified in advance. Afterwards, the number of notch filters and their parameters will be determined using coordination descending method. The relationship between controller parameters and filtering features is discussed, and the robustness of the controller against varying parameters of the control object is investigated. Preliminary experiments are carried out to validate the proposed algorithms. The overall control performance of DOAOS is simulated. Results show that time delays are a limit of the performance, but the algorithm can be successfully implemented on our systems, which indicate that it has a great potential to reject jitters.
Kazuhiro Katada
2008-11-01
Full Text Available Objective. For the multislice CT (MSCT systems with a larger number of detector rows, it is essential to employ dose-reduction techniques. As reported in previous studies, edge-preserving adaptive image filters, which selectively eliminate only the noise elements that are increased when the radiation dose is reduced without affecting the sharpness of images, have been developed. In the present study, we employed receiver operating characteristic (ROC analysis to assess the effects of the quantum denoising system (QDS, which is an edge-preserving adaptive filter that we have developed, on low-contrast resolution, and to evaluate to what degree the radiation dose can be reduced while maintaining acceptable low-contrast resolution. Materials and Methods. The low-contrast phantoms (Catphan 412 were scanned at various tube current settings, and ROC analysis was then performed for the groups of images obtained with/without the use of QDS at each tube current to determine whether or not a target could be identified. The tube current settings for which the area under the ROC curve (Az value was approximately 0.7 were determined for both groups of images with/without the use of QDS. Then, the radiation dose reduction ratio when QDS was used was calculated by converting the determined tube current to the radiation dose. Results. The use of the QDS edge-preserving adaptive image filter allowed the radiation dose to be reduced by up to 38%. Conclusion. The QDS was found to be useful for reducing the radiation dose without affecting the low-contrast resolution in MSCT studies.
Sun, W Y [Lawrence Berkeley Lab., CA (United States)
1993-04-01
This thesis solves the problem of finding the optimal linear noise-reduction filter for linear tomographic image reconstruction. The optimization is data dependent and results in minimizing the mean-square error of the reconstructed image. The error is defined as the difference between the result and the best possible reconstruction. Applications for the optimal filter include reconstructions of positron emission tomographic (PET), X-ray computed tomographic, single-photon emission tomographic, and nuclear magnetic resonance imaging. Using high resolution PET as an example, the optimal filter is derived and presented for the convolution backprojection, Moore-Penrose pseudoinverse, and the natural-pixel basis set reconstruction methods. Simulations and experimental results are presented for the convolution backprojection method.
Target Tracking for Visual Servoing Systems Based on an Adaptive Kalman Filter
Chang Liu; Xinhan Huang; Min Wang
2012-01-01
Visual servoing has been around for decades, but time delay is still one of the most troublesome problems to achieve target tracking. To circumvent the problem, in this paper, the Kalman filter is employed to estimate the future position of the object. In order to introduce the Kalman filter, accurate time delays, which include the processing lag and the motion lag, need to be obtained. Thus, the delays of the visual control servoing systems are discussed and a generic timing model for the sy...
Marie Ramon
2009-01-01
Full Text Available Systematic lossy error protection (SLEP is a robust error resilient mechanism based on principles of Wyner-Ziv (WZ coding for video transmission over error-prone networks. In an SLEP scheme, the video bitstream is separated into two parts: a systematic part consisting of a video sequence transmitted without channel coding, and additional information consisting of a WZ supplementary stream. This paper presents an adaptive SLEP scheme in which the WZ stream is obtained by frequency filtering in the transform domain. Additionally, error resilience varies adaptively depending on the characteristics of compressed video. We show that the proposed SLEP architecture achieves graceful degradation of reconstructed video quality in the presence of increasing transmission errors. Moreover, it provides good performances in terms of error protection as well as reconstructed video quality if compared to solutions based on coarser quantization, while offering an interesting embedded scheme to apply digital video format conversion.
Akihiko Murata; Shin-Ichi Hayashi
2016-01-01
Notch family members are generally recognized as signaling molecules that control various cellular responses in metazoan organisms. Early fly studies and our mammalian studies demonstrated that Notch family members are also cell adhesion molecules; however, information on the physiological roles of this function and its origin is limited. In this review, we discuss the potential present and ancestral roles of Notch-mediated cell adhesion in order to explore its origin and the initial roles of...
Hassani, V.; Sorensen, A.J.; Pascoal, A.M.
This paper addresses a filtering problem that arises in the design of dynamic positioning systems for ships and offshore rigs subjected to the influence of sea waves. The dynamic model of the vessel captures explicitly the sea state as an uncertain...
A novel methodology for adaptive wave filtering of marine vessels: Theory and experiments
Hassani, V.; Pascoal, A.M.; Sorensen, A.J.
This paper addresses a filtering problem that arises in the design of dynamic positioning systems for ships and offshore rigs subjected to the influence of sea waves. The vessel`s dynamic model adopted captures the sea state as an uncertain...
Improved prediction error filters for adaptive feedback cancellation in hearing aids
Ngo, Kim; van Waterschoot, Toon; Christensen, Mads Græsbøll;
2013-01-01
a number of improved PEF designs that are inspired by harmonic sinusoidal modeling and pitch prediction of speech signals. The resulting PEM-based AFC algorithms are evaluated in terms of the maximum stable gain (MSG), filter misadjustment, and computational complexity. Simulation results for a...
Dai, Haifeng; Zhu, Letao; Zhu, Jiangong; Wei, Xuezhe; Sun, Zechang
2015-10-01
The accurate monitoring of battery cell temperature is indispensible to the design of battery thermal management system. To obtain the internal temperature of a battery cell online, an adaptive temperature estimation method based on Kalman filtering and an equivalent time-variant electrical network thermal (EENT) model is proposed. The EENT model uses electrical components to simulate the battery thermodynamics, and the model parameters are obtained with a least square algorithm. With a discrete state-space description of the EENT model, a Kalman filtering (KF) based internal temperature estimator is developed. Moreover, considering the possible time-varying external heat exchange coefficient, a joint Kalman filtering (JKF) based estimator is designed to simultaneously estimate the internal temperature and the external thermal resistance. Several experiments using the hard-cased LiFePO4 cells with embedded temperature sensors have been conducted to validate the proposed method. Validation results show that, the EENT model expresses the battery thermodynamics well, the KF based temperature estimator tracks the real central temperature accurately even with a poor initialization, and the JKF based estimator can simultaneously estimate both central temperature and external thermal resistance precisely. The maximum estimation errors of the KF- and JKF-based estimators are less than 1.8 °C and 1 °C respectively.
Han Wenhua; Que Peiwen
2006-01-01
With the widespread application and fast development of gas and oil pipeline network in China, the pipeline inspection technology has been used more extensively. The magnetic flux leakage (MFL) method has established itself as the most widely used in-line inspection technique for the evaluation of gas and oil pipelines. The MFL data obtained from seamless pipeline inspection is usually contaminated by the seamless pipe noise (SPN). SPN can in some cases completely mask MFL signals from certain type of defects,and therefore considerably reduces the detectability of the defect signals. In this paper, a new de-noising algorithm called wavelet domain adaptive filtering is proposed for removing the SPN contained in the MFL data. The new algorithm results from combining the wavelet transform with the adaptive filtering technique. Results from application of the proposed algorithm to the MFL data from field tests show that the proposed algorithm has good performance and considerably improves the detectability of the defect signals in the MFL data.
Xin Li
2016-02-01
Full Text Available Wireless signal strength is susceptible to the phenomena of interference, jumping, and instability, which often appear in the positioning results based on Wi-Fi field strength fingerprint database technology for indoor positioning. Therefore, a Wi-Fi and PDR (pedestrian dead reckoning real-time fusion scheme is proposed in this paper to perform fusing calculation by adaptively determining the dynamic noise of a filtering system according to pedestrian movement (straight or turning, which can effectively restrain the jumping or accumulation phenomena of wireless positioning and the PDR error accumulation problem. Wi-Fi fingerprint matching typically requires a quite high computational burden: To reduce the computational complexity of this step, the affinity propagation clustering algorithm is adopted to cluster the fingerprint database and integrate the information of the position domain and signal domain of respective points. An experiment performed in a fourth-floor corridor at the School of Environment and Spatial Informatics, China University of Mining and Technology, shows that the traverse points of the clustered positioning system decrease by 65%–80%, which greatly improves the time efficiency. In terms of positioning accuracy, the average error is 4.09 m through the Wi-Fi positioning method. However, the positioning error can be reduced to 2.32 m after integration of the PDR algorithm with the adaptive noise extended Kalman filter (EKF.
Zhang, Weige; Shi, Wei; Ma, Zeyu
2015-09-01
Accurate estimations of battery energy and available power capability are of great of importance for realizing an efficient and reliable operation of electric vehicles. To improve the estimation accuracy and reliability for battery state of energy and power capability, a novel model-based joint estimation approach has been proposed against uncertain external operating conditions and internal degradation status of battery cells. Firstly, it proposes a three-dimensional response surface open circuit voltage model to calibrate the estimation inaccuracies of battery state of energy. Secondly, the adaptive unscented Kalman filter (AUKF) is employed to develop a novel model-based joint state estimator for battery state of energy and power capability. The AUKF algorithm utilizes the well-known features of the Kalman filter but employs the method of unscented transform (UT) and adaptive error covariance matching technology to improve the state estimation accuracy. Thirdly, the proposed joint estimator has been verified by a LiFePO4 lithium-ion battery cell under different operating temperatures and aging levels. The result indicates that the estimation errors of battery voltage and state-of-energy are less than 2% even if given a large erroneous initial value, which makes the state of available power capability predict more accurate and reliable for the electric vehicles application.
Simulation and Performance Analysis of Adaptive Filtering Algorithms in Noise Cancellation
Ferdouse, Lilatul; Nipa, Tamanna Haque; Jaigirdar, Fariha Tasmin
2011-01-01
Noise problems in signals have gained huge attention due to the need of noise-free output signal in numerous communication systems. The principal of adaptive noise cancellation is to acquire an estimation of the unwanted interfering signal and subtract it from the corrupted signal. Noise cancellation operation is controlled adaptively with the target of achieving improved signal to noise ratio. This paper concentrates upon the analysis of adaptive noise canceller using Recursive Least Square (RLS), Fast Transversal Recursive Least Square (FTRLS) and Gradient Adaptive Lattice (GAL) algorithms. The performance analysis of the algorithms is done based on convergence behavior, convergence time, correlation coefficients and signal to noise ratio. After comparing all the simulated results we observed that GAL performs the best in noise cancellation in terms of Correlation Coefficient, SNR and Convergence Time. RLS, FTRLS and GAL were never evaluated and compared before on their performance in noise cancellation in ...
Bruckner, Tim A; Saxton, Katherine B.; Pearl, Michelle; Currier, Robert; Kharrazi, Martin
2012-01-01
The risk of abnormalities and morbidity among live births increases with advanced maternal age. Explanations for this elevated morbidity invoke several maternal mechanisms. The relaxed filter stringency (RFS) hypothesis asserts that mothers, nearing the end of their reproductive lifespan, reduce the stringency of a screen of offspring quality in utero based on life-history traits of parity and interbirth interval (IBI). A separate line of research implicates human chorionic gonadotropin (hCG)...
Songer, Jocelyn E.; Eatock, Ruth Anne
2011-11-01
The mammalian saccule detects head tilt and low-frequency head accelerations as well as higher-frequency bone vibrations and sounds. It has two different hair cell types, I and II, dispersed throughout two morphologically distinct regions, the striola and extrastriola. Afferents from the two zones have distinct response dynamics which may arise partly from zonal differences in hair cell properties. We find that type II hair cells in the rat saccular epithelium adapt with a time course appropriate for influencing afferent responses to head motions. Moreover, striolar type II hair cells adapted by a greater extent than extrastriolar type II hair cells and had greater phase leads in the mid-frequency range (5-50 Hz). These differences suggest that hair cell transduction may contribute to zonal differences in the adaptation of vestibular afferents to head motions.
Ubiquitination of Notch1 is regulated by MAML1-mediated p300 acetylation of Notch1
Popko-Scibor, Anita E.; Lindberg, Mikael J.; Hansson, Magnus L.; Holmlund, Teresa [Division of Molecular Toxicology, Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm (Sweden); Wallberg, Annika E., E-mail: Annika.Wallberg@ki.se [Division of Molecular Toxicology, Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm (Sweden)
2011-12-16
Highlights: Black-Right-Pointing-Pointer p300 acetylates conserved lysines within Notch1 C-terminal nuclear localization signal. Black-Right-Pointing-Pointer MAML1 and CSL, components of Notch transcription complex, increase Notch acetylation. Black-Right-Pointing-Pointer MAML1-dependent acetylation of Notch1 by p300 decreases the ubiquitination of Notch1. Black-Right-Pointing-Pointer CDK8 inhibits Notch acetylation and Notch transcription enhanced by p300. -- Abstract: Earlier studies demonstrated the involvement of the p300 histone acetyltransferase in Notch signaling but the precise mechanisms by which p300 might modulate Notch function remains to be investigated. In this study, we show that p300 acetylates Notch1 ICD in cell culture assay and in vitro, and conserved lysines located within the Notch C-terminal nuclear localization signal are essential for Notch acetylation. MAML1 and CSL, which are components of the Notch transcription complex, enhance Notch acetylation and we suggest that MAML1 increases Notch acetylation by potentiating p300 autoacetylation. Furthermore, MAML1-dependent acetylation of Notch1 ICD by p300 decreases the ubiquitination of Notch1 ICD in cellular assays. CDK8 has been shown to target Notch1 for ubiquitination and proteosomal degradation. We show that CDK8 inhibits Notch acetylation and Notch transcription enhanced by p300. Therefore, we speculate that acetylation of Notch1 might be a mechanism to regulate Notch activity by interfering with ubiquitin-dependent pathways.
Sakata-Yanagimoto, Mamiko; Chiba, Shigeru
2012-01-01
Notch2 is expressed in many cell types of most lineages in the hematolymphoid compartment and has specific roles in differentiation and function of various immune cells. Notch2 is required for development of splenic marginal zone B cells and regulates differentiation of dendritic cells (DCs) in the spleen. Notch2 appears to play some specific roles in the intestinal immunity, given that the fate of mast cells and a subset of DCs is regulated by Notch2 in the intestine. Notch2 also has important roles in helper T cell divergence from naïve CD4 T cells and activation of cytotoxic T cells. Moreover, recent genetic evidence suggests that both gain-and loss-of-function abnormalities of Notch2 cause transformation of immune cells. Inactivating mutations are found in Notch2 signaling pathways in chronic myelomonocytic leukemia, while activating mutations are found in mature B cell lymphomas, which reflects the role of Notch2 in the developmental process of these cells. PMID:22695918
Nadernejad, Ehsan; Forchhammer, Søren; Korhonen, Jari
2011-01-01
and ringing artifacts, we have applied directional anisotropic diffusion. Besides that, the selection of the adaptive threshold parameter for the diffusion coefficient has also improved the performance of the algorithm. Experimental results on JPEG compressed images as well as MJPEG and H.264...
A tunable electrochromic fabry-perot filter for adaptive optics applications.
Blaich, Jonathan David; Kammler, Daniel R.; Ambrosini, Andrea; Sweatt, William C.; Verley, Jason C.; Heller, Edwin J.; Yelton, William Graham
2006-10-01
The potential for electrochromic (EC) materials to be incorporated into a Fabry-Perot (FP) filter to allow modest amounts of tuning was evaluated by both experimental methods and modeling. A combination of chemical vapor deposition (CVD), physical vapor deposition (PVD), and electrochemical methods was used to produce an ECFP film stack consisting of an EC WO{sub 3}/Ta{sub 2}O{sub 5}/NiO{sub x}H{sub y} film stack (with indium-tin-oxide electrodes) sandwiched between two Si{sub 3}N{sub 4}/SiO{sub 2} dielectric reflector stacks. A process to produce a NiO{sub x}H{sub y} charge storage layer that freed the EC stack from dependence on atmospheric humidity and allowed construction of this complex EC-FP stack was developed. The refractive index (n) and extinction coefficient (k) for each layer in the EC-FP film stack was measured between 300 and 1700 nm. A prototype EC-FP filter was produced that had a transmission at 500 nm of 36%, and a FWHM of 10 nm. A general modeling approach that takes into account the desired pass band location, pass band width, required transmission and EC optical constants in order to estimate the maximum tuning from an EC-FP filter was developed. Modeling shows that minor thickness changes in the prototype stack developed in this project should yield a filter with a transmission at 600 nm of 33% and a FWHM of 9.6 nm, which could be tuned to 598 nm with a FWHM of 12.1 nm and a transmission of 16%. Additional modeling shows that if the EC WO{sub 3} absorption centers were optimized, then a shift from 600 nm to 598 nm could be made with a FWHM of 11.3 nm and a transmission of 20%. If (at 600 nm) the FWHM is decreased to 1 nm and transmission maintained at a reasonable level (e.g. 30%), only fractions of a nm of tuning would be possible with the film stack considered in this study. These tradeoffs may improve at other wavelengths or with EC materials different than those considered here. Finally, based on our limited investigation and material set
A Small UWB Antenna with Dual Band-Notched Characteristics
J. Xu
2012-01-01
Full Text Available A small novel ultrawideband (UWB antenna with dual band-notched functions is proposed. The dual band rejection is achieved by etching two C-shaped slots on the radiation patch with limited area. A single band-notched antenna is firstly presented, and then an optimized dual band-notched antenna is presented and analyzed. The measured VSWR shows that the proposed antenna could operate from 3.05 to 10.7 GHz with VSWR less than 2, except two stopbands at 3.38 to 3.82 GHz and 5.3 to 5.8 GHz for filtering the WiMAX and WLAN signals. Radiation patterns are simulated by HFSS and verified by CST, and quasiomnidirectional radiation patterns in the H-plane could be observed. Moreover, the proposed antenna has a very compact size and could be easily integrated into portable UWB devices.
Duval, L.
2000-11-01
Wavelet and wavelet packet transforms are the most commonly used algorithms for seismic data compression. Wavelet coefficients are generally quantized and encoded by classical entropy coding techniques. We first propose in this work a compression algorithm based on the wavelet transform. The wavelet transform is used together with a zero-tree type coding, with first use in seismic applications. Classical wavelet transforms nevertheless yield a quite rigid approach, since it is often desirable to adapt the transform stage to the properties of each type of signal. We thus propose a second algorithm using, instead of wavelets, a set of so called 'extended transforms'. These transforms, originating from the filter bank theory, are parameterized. Classical examples are Malvar's Lapped Orthogonal Transforms (LOT) or de Queiroz et al. Generalized Lapped Orthogonal Transforms (GenLOT). We propose several optimization criteria to build 'extended transforms' which are adapted the properties of seismic signals. We further show that these transforms can be used with the same zero-tree type coding technique as used with wavelets. Both proposed algorithms provide exact compression rate choice, block-wise compression (in the case of extended transforms) and partial decompression for quality control or visualization. Performances are tested on a set of actual seismic data. They are evaluated for several quality measures. We also compare them to other seismic compression algorithms. (author)
Design of blind adaptive filter based on blind deconvolution%基于盲反卷积的盲自适应滤波器设计
陈善继; 苏建萍
2012-01-01
The blind adaptive filtering was realized mainly based on the blind deconvolution. The operating principle and basic structure model of the blind deconvolution filter are described. The filtering is achieved by adjusting the filter coefficients, so as to track the signals' changes, and implement the adaptive filtering ultimately. The adaptive filter was designed by means of Matlab simulation platform,and its design performance was verified.%通过盲反卷积的算法来实现盲自适应滤波,阐述了盲反卷积滤波器的工作原理及基本结构模型,通过调整滤波器系数来实现滤波,以便更好地跟踪信号的变化,最终实现自适应滤波,并借用Matlab仿真平台设计出自适应滤波器,验证了它的设计性能.
无
2010-01-01
Accurate assignment of model and observation errors is crucial for the successful application of land surface data assimilation algorithms. Poorly-specified model and observation errors can significantly degrade assimilation results. In 2008, Reichle et al. developed an operational procedure to adaptively tune model and observation errors. In this paper, we modified and applied Reichle’s procedure in the Noah land surface model to assimilate observed surface soil moisture data. Numerical simulations showed that: (1) the best estimate of model and observation errors appears when the empirical factor β equals 1.02; (2) the Reichle procedure can be deployed to adaptively tune errors if their true values change slowly; and (3) convergence of the Reichle procedure was improved using better initial errors achieved by iterative computations.
Aortic pressure wave reconstruction during exercise is improved by adaptive filtering: a pilot study
Stok, W.J.; Westerhof, B E; Guelen, I.; Karemaker, J. M.
2011-01-01
Reconstruction of central aortic pressure from a peripheral measurement by a generalized transfer function (genTF) works well at rest and mild exercise at lower heart rates, but becomes less accurate during heavy exercise. Particularly, systolic and pulse pressure estimations deteriorate, thereby underestimating central pressure. We tested individualization of the TF (indTF) by adapting its resonance frequency at the various levels of exercise. In seven males (age 44–57) with coronary artery ...
Flad, David; Beck, Andrea; Munz, Claus-Dieter
2016-05-01
Scale-resolving simulations of turbulent flows in complex domains demand accurate and efficient numerical schemes, as well as geometrical flexibility. For underresolved situations, the avoidance of aliasing errors is a strong demand for stability. For continuous and discontinuous Galerkin schemes, an effective way to prevent aliasing errors is to increase the quadrature precision of the projection operator to account for the non-linearity of the operands (polynomial dealiasing, overintegration). But this increases the computational costs extensively. In this work, we present a novel spatially and temporally adaptive dealiasing strategy by projection filtering. We show this to be more efficient for underresolved turbulence than the classical overintegration strategy. For this novel approach, we discuss the implementation strategy and the indicator details, show its accuracy and efficiency for a decaying homogeneous isotropic turbulence and the transitional Taylor-Green vortex and compare it to the original overintegration approach and a state of the art variational multi-scale eddy viscosity formulation.
Active Optical Lattice Filters
Gary Evans; MacFarlane, Duncan L.; Govind Kannan; Jian Tong; Issa Panahi; Vishnupriya Govindan; L. Roberts Hunt
2005-01-01
Optical lattice filter structures including gains are introduced and analyzed. The photonic realization of the active, adaptive lattice filter is described. The algorithms which map between gains space and filter coefficients space are presented and studied. The sensitivities of filter parameters with respect to gains are derived and calculated. An example which is relevant to adaptive signal processing is also provided.
The authors investigated a new method to optimize artificial neural networks (ANNs) with adaptive filtering used in computer-assisted detection schemes in digitized mammograms and to assess performance changes when averaging classification scores from three sets of optimized schemes. Two independent training and testing image databases involving 978 and 830 digitized mammograms, respectively, were used in this study. In the training data set, initial filtering and subtraction resulted in the identification of 592 mass regions and 3790 suspicious, but actually negative regions. These regions (including both true-positive and negative regions) were segmented into three subsets three times based on the calculation of the values of three features as segmentation indices. The indices were 'mass' size multiplied by their digital value contrast, conspicuity, and circularity. Nine ANN-based classifiers were separately optimized using a genetic algorithm for each subset of regions. Each region was assigned three classification scores after applying the three adaptive ANNs. The performance gain of the CAD scheme after averaging the three scores for each suspicious region was tested using an independent data set and a ROC methodology. The experimental results showed that the areas under ROC curves (Az) for the testing database using three sets of optimized ANNs individually were 0.84±0.01, 0.83±0.01, and 0.84±0.01, respectively. The between-index correlations of three Az values were 0.013, -0.007, and 0.086. Similar to averaging diagnostic ratings from independent observers, by averaging three ANN-generated scores for each testing region, the performance of the CAD scheme was significantly improved (pz value of 0.95±0.01
Mosbeh R. Kaloop
2015-10-01
Full Text Available This study describes the performance assessment of the Huangpu Bridge in Guangzhou, China based on long-term monitoring in real-time by the kinematic global positioning system (RTK-GPS technique. Wavelet transformde-noising is applied to filter the GPS measurements, while the adaptive neuro-fuzzy inference system (ANFIS time series output-only model is used to predict the deformations of GPS-bridge monitoring points. In addition, GPS and accelerometer monitoring systems are used to evaluate the bridge oscillation performance. The conclusions drawn from investigating the numerical results show that: (1the wavelet de-noising of the GPS measurements of the different recording points on the bridge is a suitable tool to efficiently eliminate the signal noise and extract the different deformation components such as: semi-static and dynamic displacements; (2 the ANFIS method with two multi-input single output model is revealed to powerfully predict GPS movement measurements and assess the bridge deformations; and (3 The installed structural health monitoring system and the applied ANFIS movement prediction performance model are solely sufficient to assure bridge safety based on the analyses of the different filtered movement components.
Adaptation of TSH filter paper method for regionalized screening for congenital hypothyroidism.
Foley, T P; Klein, A H; Agustin, A V
1977-07-01
A sensitive, specific, rapid radioimmunoassay is described for the determination of thyrotropin (TSH) in eluates from two 3 mm discs punched from dried blood filter-paper specimens. This method is sufficiently sensitive to easily discriminate between normal infants and infants with primary hypothyroidism. The use of two 3 mm discs enables screening laboratories to easily incorporate this methodology into the currently available, fully automated systems to screen for several metabolic disorders. Since mental retardation occurs in untreated infants with primary hypothyroidism, our TSH method as the primary screening test alone, or in association with a thyroxine (T4) screening test, should detect all infants with primary hypothyroidism with a very acceptable low false-positive recall rate. PMID:874362
Signal quality improvement of holographic data storage using adaptive two-dimensional filter
Takahata, Yosuke; Kondo, Yo; Yoshida, Shuhei; Yamamoto, Manabu
2010-05-01
Holographic data storage is being widely studied for the purpose of developing next-generation large optical memories. A prospective use of this type of memory is in building image archives in large-scale data centers. In particular, demand for energy conservation at data centers, and therefore for holographic data storage, is growing. In holographic data storage, interference between bits occurs owing to wave aberration in the optical system, shrinkage of the medium, and crosstalk noise from neighboring holograms during multiplex recording; as a result of the interference, the reproduced image deteriorates and the bit error rate (BER) increases. In this study, to reduce the BER in both off-axis-type recording and coaxial-type recording, a two-dimensional finite impulse response (FIR) filter is applied to a reproduced image that has been recorded by angle multiplex recording and shift multiplex recording. First, for the optimization of the FIR filter coefficients, the linear minimum mean square error (LMMSE) method is applied; this method optimizes the coefficients by reducing the BER. Furthermore, for evaluating the optimization performance of the LMMSE method, the optimization performance is compared with that of the real-coded genetic algorithm (RCGA), which has the capability to search a wide range of coefficients. The optimization by the LMMSE method has been found to be excellent for off-axis-type recording but not for coaxial-type recording. It is speculated that this is because of the brightness irregularity in the reproduced image, resulting from crosstalk. On the other hand, a marked reduction in the BER is observed using the RCGA, despite the brightness irregularity. In this study, the effectiveness of the LMMSE method for signals recorded by coaxial-type recording, in which large brightness irregularity is expected, is examined using automatic gain control (AGC). It is found that the application of AGC reduces the BER even in the case of coaxial
Electronic transport properties on V-shaped-notched zigzag graphene nanoribbons junctions
Using nonequilibrium Green's functions in combination with the density functional theory, the spin-dependent electronic transport properties on V-shaped notched zigzag-edged graphene nanoribbons junctions have been calculated. The results show that the electronic transport properties are strongly depending on the type of notch and the symmetry of ribbon. The spin-filter phenomenon and negative differential resistance behaviors can be observed. A physical analysis of these results is given. -- Highlights: → Spin-dependent electronic transport on V-shaped notched ZGNRs junctions. → The effects of armchair and zigzag notches on ZGNRs have been considered. → The currents will be reduced for notched systems. → The spin-up and spin-down display different behaviors. → The transport will be suppressed and NDR behaviors can be observed.
Gray, Morgan; Rodionov, Sergey; Bocquet, Marc; Bertino, Laurent; Ferrari, Marc; Fusco, Thierry
2014-01-01
We propose a new algorithm for an adaptive optics system control law, based on the Linear Quadratic Gaussian approach and a Kalman Filter adaptation with localizations. It allows to handle non-stationary behaviors, to obtain performance close to the optimality defined with the residual phase variance minimization criterion, and to reduce the computational burden with an intrinsically parallel implementation on the Extremely Large Telescopes (ELTs).
Notch signaling and the developing hair follicle
Aubin Houzelstein, Geneviève
2012-01-01
Notch function in the hair follicle has been mainly studied by use of transgenic mice carrying either loss or gain of function mutations in various members of the pathway. These studies revealed that whereas embryonic development of the hair follicle can be achieved without Notch, its postnatal development requires an intact Notch signaling in the hair bulb and the outer root sheath. Among the many roles played by Notch in the hair follicle, two can be highlighted: in the bulge, Notch control...
The Design of a Compact Bow-tie UWB Antenna with Band-notch Filter%便携设备中带陷蝶形UWB天线的设计
蔡文新; 李少甫; 潘建
2011-01-01
This paper presents a bow-tie UWB printed antenna with band-notch function.The antenna uses bow-tie patch as the radiating element and is matched with 50 Ω feeder line by the gradient line. Moreover, by cutting the C-shaped slot at the radiating element, the antenna achievs the band-notch function. At last, the results of the simulation and measure are put forward. The antenna with a bandwidth covering the 3.1 ～ 10. 6 GHz can escap the bandwidth of WLAN at 5. 15～5. 825 GHz, and fits for the application of the UWB communication system.%提出了一种应用于便携设备中具有带陷特性的平面蝶形UWB天线.该天线采用蝶形贴片作为辐射单元,并由渐变线作为阻抗变换器与50 Ω馈线进行匹配.通过在辐射面上挖C形槽实现带陷功能,并给出仿真和实测结果.该天线的工作频带覆盖3.1～10.6 GHz,并有效避免了5.15～5.825 GHz的无线局域网(WLAN)频段,适于便携式超宽带无线通信系统的应用.
Xu, Yuan; Chen, Xiyuan; Li, Qinghua
2014-01-01
As the core of the integrated navigation system, the data fusion algorithm should be designed seriously. In order to improve the accuracy of data fusion, this work proposed an adaptive iterated extended Kalman (AIEKF) which used the noise statistics estimator in the iterated extended Kalman (IEKF), and then AIEKF is used to deal with the nonlinear problem in the inertial navigation systems (INS)/wireless sensors networks (WSNs)-integrated navigation system. Practical test has been done to evaluate the performance of the proposed method. The results show that the proposed method is effective to reduce the mean root-mean-square error (RMSE) of position by about 92.53%, 67.93%, 55.97%, and 30.09% compared with the INS only, WSN, EKF, and IEKF. PMID:24693225
Yuan Xu
2014-01-01
Full Text Available As the core of the integrated navigation system, the data fusion algorithm should be designed seriously. In order to improve the accuracy of data fusion, this work proposed an adaptive iterated extended Kalman (AIEKF which used the noise statistics estimator in the iterated extended Kalman (IEKF, and then AIEKF is used to deal with the nonlinear problem in the inertial navigation systems (INS/wireless sensors networks (WSNs-integrated navigation system. Practical test has been done to evaluate the performance of the proposed method. The results show that the proposed method is effective to reduce the mean root-mean-square error (RMSE of position by about 92.53%, 67.93%, 55.97%, and 30.09% compared with the INS only, WSN, EKF, and IEKF.
Svenson, Björn; Larsson, Lars; Båth, Magnus
2016-04-01
Objective The purpose of the present study was to investigate the potential of using advanced external adaptive image processing for maintaining image quality while reducing exposure in dental panoramic storage phosphor plate (SPP) radiography. Materials and methods Thirty-seven SPP radiographs of a skull phantom were acquired using a Scanora panoramic X-ray machine with various tube load, tube voltage, SPP sensitivity and filtration settings. The radiographs were processed using General Operator Processor (GOP) technology. Fifteen dentists, all within the dental radiology field, compared the structural image quality of each radiograph with a reference image on a 5-point rating scale in a visual grading characteristics (VGC) study. The reference image was acquired with the acquisition parameters commonly used in daily operation (70 kVp, 150 mAs and sensitivity class 200) and processed using the standard process parameters supplied by the modality vendor. Results All GOP-processed images with similar (or higher) dose as the reference image resulted in higher image quality than the reference. All GOP-processed images with similar image quality as the reference image were acquired at a lower dose than the reference. This indicates that the external image processing improved the image quality compared with the standard processing. Regarding acquisition parameters, no strong dependency of the image quality on the radiation quality was seen and the image quality was mainly affected by the dose. Conclusions The present study indicates that advanced external adaptive image processing may be beneficial in panoramic radiography for increasing the image quality of SPP radiographs or for reducing the exposure while maintaining image quality. PMID:26478956
Szadkowski, Zbigniew; Fraenkel, E. D.; van den Berg, Ad M.
2013-01-01
We present the FPGA/NIOS implementation of an adaptive finite impulse response (FIR) filter based on linear prediction to suppress radio frequency interference (RFI). This technique will be used for experiments that observe coherent radio emission from extensive air showers induced by ultra-high-ene
José Velázquez
2008-01-01
Full Text Available Se presenta el análisis de una variante del algoritmo de mínimos cuadrados para reducir la complejidad de diseño para su implementación en filtros adaptativos digitales. Dicha variante consiste en cambiar la codificación del error en el algoritmo, ya que dicho error es un valor de tipo entero. Los resultados obtenidos en las pruebas realizadas en aplicaciones de filtros adaptativos tales como predictor lineal e identificador de sistema, demuestran que la velocidad de convergencia aumenta. Esto permite que el algoritmo propuesto pueda ser aplicado en filtros adaptativos donde se requiere una velocidad de convergencia alta. Además la modificación propuesta es compatible con los filtros adaptativos existentes, debido a que la codificación del error se puede realizar por separado.Analysis of a modified least mean square algorithm to reduce its design complexity for digital adaptive filter implementation, is presented. Such a modification consists of changing the error codification of the algorithm, because such error is an integer number. Results obtained during the testing of the method in applications to adaptive filters such as linear prediction and system identifier, show that the speed convergence increases. This allows the algorithm to be applied to adaptive filter for which high speed is required. Also, the proposed modification is compatible with existing adaptive filters, since error codification can be separately done.
Stok, Wim J; Westerhof, Berend E; Guelen, Ilja; Karemaker, John M
2011-08-01
Reconstruction of central aortic pressure from a peripheral measurement by a generalized transfer function (genTF) works well at rest and mild exercise at lower heart rates, but becomes less accurate during heavy exercise. Particularly, systolic and pulse pressure estimations deteriorate, thereby underestimating central pressure. We tested individualization of the TF (indTF) by adapting its resonance frequency at the various levels of exercise. In seven males (age 44-57) with coronary artery disease, central and peripheral pressures were measured simultaneously. The optimal resonance frequency was predicted from regression formulas using variables derived from the individual's peripheral pressure pulse, including a pulse contour estimation of cardiac output (pcCO). In addition, reconstructed pressures were calibrated to central mean and diastolic pressure at each exercise level. Using a genTF and without calibration, the error in estimated aortic pulse pressure was -7.5 ± 6.4 mmHg, which was reduced to 0.2 ± 5.7 mmHg with the indTFs using pcCO for prediction. Calibration resulted in less scatter at the cost of a small bias (2.7 mmHg). In exercise, the indTFs predict systolic and pulse pressure better than the genTF. This pilot study shows that it is possible to individualize the peripheral to aortic pressure transfer function, thereby improving accuracy in central blood pressure assessment during exercise. PMID:21720842
Correia, Carlos M
2014-01-01
Computationally-efficient wave-front reconstruction techniques for astronomical adaptive optics systems have seen a great development in the past decade. Algorithms developed in the spatial-frequency (Fourier) domain have gathered large attention specially for high-contrast imaging systems. In this paper we present the Wiener filter (resulting in the maximization of the Strehl-ratio) and further develop formulae for the anti-aliasing Wiener filter that optimally takes into account high-order wave-front terms folded in-band during the sensing (i.e. discrete sampling) process. We employ a continuous spatial-frequency representation for the forward measurement operators and derive the Wiener filter when aliasing is explicitly taken into account. We further investigate and compare to classical estimates using least-squares filters the reconstructed wave-front, measurement noise and aliasing propagation coefficients as a function of the system order. Regarding high-contrast systems, we provide achievable performan...
Purpose: To evaluate the effects of a 2D non-linear adaptive post-processing filter (2D-NLAF) on image quality in dose-reduced multi-detector CT (MDCT) of the upper abdomen. Materials and Methods: MDCT of the upper abdomen was simulated on a 64-slice scanner using a multi-modal anthropomorphic phantom (CIRS, Norfolk, USA). While keeping the collimation (64 x 0.6 mm) and pitch (p = 1) unchanged, the tube current (100 - 500 mAs) and tube potential (80 - 140 kVp) were varied to perform MDCT as high dose (CTDI > 20), middle dose (CTDI 10-20) and low dose (CTDI < 10) level protocols. Four independent blinded radiologists evaluated axial images with a thickness of 7 and 3 mm with respect to the presentation of ''mesenteric low contrast lesions'', ''liver veins'', ''liver cysts'', ''renal cysts'' and ''big vessels''. The subjective image quality of original data and post-processed images using a 2D-NLAF (SharpViewCT, Linkoeping, Sweden) was graded on a 5-point scale (from ''1'' not visible to ''5'' excellent) and statistically analyzed. The effective dose (E) was estimated using commercial software (CT-EXPO). Results: For all protocol groups, 2D-NLAF led to a significant improvement in subjective image quality for all examined lesions (p < 0.01), particularly at the protocols of middle dose (E: 5 - 8 mSv) and low dose level (E: 1-5 mSv). A maximum effect was seen in middle dose protocols for ''low contrast lesions'' (score ''3.3'' with filter versus ''2.5'' without) and ''liver veins'' (''4.5'' versus ''3.9''). Conclusion: The phantom study indicates a potential dose reduction of up to 50% in MDCT of the upper abdomen by use of a 2D-NLAF, which should be further examined in clinical trails. (orig.)
Nina Nikolic
Full Text Available Effects of soil on vegetation patterns are commonly obscured by other environmental factors; clear and general relationships are difficult to find. How would community assembly processes be affected by a substantial change in soil characteristics when all other relevant factors are held constant? In particular, can we identify some functional adaptations which would underpin such soil-induced vegetation response?Eastern Serbia: fields partially damaged by long-term and large-scale fluvial deposition of sulphidic waste from a Cu mine; subcontinental/submediterranean climate.We analysed the multivariate response of cereal weed assemblages (including biomass and foliar analyses to a strong man-made soil gradient (from highly calcareous to highly acidic, nutrient-poor soils over short distances (field scale.The soil gradient favoured a substitution of calcicoles by calcifuges, and an increase in abundance of pseudometallophytes, with preferences for Atlantic climate, broad geographical distribution, hemicryptophytic life form, adapted to low-nutrient and acidic soils, with lower concentrations of Ca, and very narrow range of Cu concentrations in leaves. The trends of abundance of the different ecological groups of indicator species along the soil gradient were systematically reflected in the maintenance of leaf P concentrations, and strong homeostasis in biomass N:P ratio.Using annual weed vegetation at the field scale as a fairly simple model, we demonstrated links between gradients in soil properties (pH, nutrient availability and floristic composition that are normally encountered over large geographic distances. We showed that leaf nutrient status, in particular the maintenance of leaf P concentrations and strong homeostasis of biomass N:P ratio, underpinned a clear functional response of vegetation to mineral stress. These findings can help to understand assembly processes leading to unusual, novel combinations of species which are typically
Lu, Lu; Zhao, Haiquan
2016-03-01
The filtered-x least mean lp-norm (FxLMP) algorithm is proven to be useful for nonlinear active noise control (NANC) systems. However, its performance deteriorates when the impulsive noises are presented in NANC systems. To surmount this shortcoming, a new nonlinear adaptive algorithm based on Volterra expansion model (VFxlogLMP) is developed in this paper, which is derived by minimizing the lp-norm of logarithmic cost. It is found that the FxLMP and VFxlogLMP require to select an appropriate value of p according to the prior information on noise characteristics, which prohibit their practical applications. Based on VFxlogLMP algorithm, we proposed a continuous lp-norm algorithm with logarithmic cost (VFxlogCLMP), which does not need the parameter selection and thresholds estimation. Benefiting from the various error norms for 1≤p≤2, it remains the robustness of VFxlogLMP. Moreover, the convergence behavior of VFxlogCLMP for moving average secondary paths and stochastic input signals is performed. Compared to the existing algorithms, two versions of the proposed algorithms have much better convergence and stability in impulsive noise environments.
Notch effects in uniaxial tension specimens
Results of a literature survey on the effect of notches on the time-dependent failure of uniaxial tension specimens at elevated temperatures are presented. Particular attention is paid to the failure of notched specimens containing weldments
Purpose: Evaluation of subjective image quality in dose-reduced multi-detector CT (MDCT) of paranasal sinuses using a 2D non-linear adaptive post-processing filter (2D-NLAF). Materials and Methods: MDCT of paranasal sinuses was simulated using a human head phantom at a Somatom Sensation Cardiac 64 (Siemens, Erlangen). At constant collimation (64 x 0.6 mm) und pitch (p = 1), the tube current (50, 100, 200 mAs) and tube potential (80, 100, 120 kVp) were modified. The radiation exposure was represented by CTDIvol. Four independent blinded radiologists evaluated the image quality of axial 2 mm images and coronal reformations concerning the assessment of 'fractures' and 'soft tissue processes'. The subjective image quality of original and post-processed images using a 2D-NLAF (SharpViewCT registered, Sweden) was graded on a 5-point scale ('1' excellent - '5' not adequate) and compared. Results: Compared to the protocol with the best image quality (120kVp/ 200 mAs) 2D-NLAF led to a significant improvement in the subjective image quality at 100 kVp/ 100 mAs (score '1.4' with filter versus '2.2' without) and 120 kVp/ 50 mAs ('1.6' versus '2.0') (p < 0.03) particularly for high contrasts ('fractures', p < 0.001). In 'soft tissue processes', 2D-NLAF provided improved quality from '2.1' to '1.4' (p < 0.04) at 100 kVp/ 100 mAs. Down to a CTDIvol of 8 mGy, the image quality was rated 'good', and down to 5 mGy 'diagnostic'. Conclusion: The phantom study indicates a dose reduction potential in MDCT of paranasal sinuses up to 58 % compared to a standard dose protocol using a 2D-NLAF without an essential loss of image quality. 2D-NLAF is particularly effective at 100 kVp/ 100 mAs and 120 kVp/ 50 mAs. (orig.)
Lukas eBauer
2015-04-01
Full Text Available Background: Notch signaling can exert oncogenic or tumor suppressive functions and can contribute to chemotherapy resistance in cancer. In this study, we aimed to clarify the clinicopathological significance and the prognostic and predictive value of NOTCH1 and NOTCH2 expression in gastric carcinoma (GC. Methods: NOTCH1 and NOTCH2 expression was determined immunohistochemically in 142 primarily resected GCs using tissue microarrays and in 84 pretherapeutic biopsies from patients treated by neoadjuvant chemotherapy. The results were correlated with survival, response to therapy and clinico-pathological features.Results: Primarily resected patients with NOTCH1-negative tumors demonstrated worse survival. High NOTCH1 expression was associated with early-stage tumors and with significantly increased survival in this subgroup.Higher NOTCH2 expression was associated with early-stage and intestinal-type tumors and with better survival in the subgroup of intestinal-type tumors.In pretherapeutic biopsies, higher NOTCH1 and NOTCH2 expression was more frequent in nonresponding patients, but these differences were statistically not significant. Conclusion: Our findings suggested that, in particular NOTCH1 expression indicated good prognosis in GC. The close relationship of high NOTCH1 and NOTCH2 expression with early tumor stages may indicate a tumor-suppressive role of Notch signaling in GC. The role of NOTCH1 and NOTCH2 in neoadjuvantly treated GC is limited.
Compact electromagnetic bandgap structures for notch band in ultra-wideband applications.
Rotaru, Mihai; Sykulski, Jan
2010-01-01
This paper introduces a novel approach to create notch band filters in the front-end of ultra-wideband (UWB) communication systems based on electromagnetic bandgap (EBG) structures. The concept presented here can be implemented in any structure that has a microstrip in its configuration. The EBG structure is first analyzed using a full wave electromagnetic solver and then optimized to work at WLAN band (5.15-5.825 GHz). Two UWB passband filters are used to demonstrate the applicability and effectiveness of the novel EBG notch band feature. Simulation results are provided for two cases studied. PMID:22163430
Notch Signaling Pathway and Human Placenta
Wei-Xiu Zhao, Jian-Hua Lin
2012-01-01
Notch signaling was evolutionarily conserved and critical for cell-fate determination, differentiation and many other biological processes. Growing evidences suggested that Notch signaling pathway played an important role in the mammalian placental development. All of the mammalian Notch family proteins had been identified in human placenta except Delta-like 3, which appeared to affect the axial skeletal system. However the molecular mechanisms that regulated the Notch signaling pathway remai...
J. Mar; Chen-Chih Liu; Basnet, M. B.
2015-01-01
Beam pointing error caused by ship motion over the ocean affects the tracking performance of the ship-borne phased array radar. Due to the dynamic nature of the sea environments, the ship-borne phased array radar must be able to compensate for the ship’s motion adaptively. In this paper, the adaptive α-β-γ filter is proposed for the ship-borne phased array radar to compensate for the beam pointing error and to track the air target. The genetic algorithm (GA) and the particle swarm optimizatio...
Mark Frogley
2013-01-01
Full Text Available To reduce the maintenance cost, avoid catastrophic failure, and improve the wind transmission system reliability, online condition monitoring system is critical important. In the real applications, many rotating mechanical faults, such as bearing surface defect, gear tooth crack, chipped gear tooth and so on generate impulsive signals. When there are these types of faults developing inside rotating machinery, each time the rotating components pass over the damage point, an impact force could be generated. The impact force will cause a ringing of the support structure at the structural natural frequency. By effectively detecting those periodic impulse signals, one group of rotating machine faults could be detected and diagnosed. However, in real wind turbine operations, impulsive fault signals are usually relatively weak to the background noise and vibration signals generated from other healthy components, such as shaft, blades, gears and so on. Moreover, wind turbine transmission systems work under dynamic operating conditions. This will further increase the difficulties in fault detection and diagnostics. Therefore, developing advanced signal processing methods to enhance the impulsive signals is in great needs.In this paper, an adaptive filtering technique will be applied for enhancing the fault impulse signals-to-noise ratio in wind turbine gear transmission systems. Multiple statistical features designed to quantify the impulsive signals of the processed signal are extracted for bearing fault detection. The multiple dimensional features are then transformed into one dimensional feature. A minimum error rate classifier will be designed based on the compressed feature to identify the gear transmission system with defect. Real wind turbine vibration signals will be used to demonstrate the effectiveness of the presented methodology.
Necking and notch strengthening in metallic glass with symmetric sharp-and-deep notches
Sha, Z. D.; Pei, Q. X.; Z. S. Liu; Zhang, Y W; Wang, T J
2015-01-01
Notched metallic glasses (MGs) have received much attention recently due to their intriguing mechanical properties compared to their unnotched counterparts, but so far no fundamental understanding of the correlation between failure behavior and notch depth/sharpness exists. Using molecular dynamics simulations, we report necking and large notch strengthening in MGs with symmetric sharp-and-deep notches. Our work reveals that the failure mode and strength of notched MGs are strongly dependent ...
自适应滤波在地震次声波信号中的应用研究%Application of adaptive filtering in seismic infrasound signals
左明成; 武云
2015-01-01
针对地震次声波信号次声波干扰严重，有价值的次声波信号埋没在复杂的噪音背景之中而难以分析的情况，采用将自适应滤波应用于次声波信号的处理，并将传统方法进行简单的改进。通过实际地震数据的去噪处理，将自适应滤波处理的信号与原信号进行对比的试验，得出自适应滤波完全可以完美的应用于地震次声波数据处理的结论，并使地震监测系统更加完美。%According to the seismic signal infrasonic wave interference, signal buried valuable and difficult to analyze in complex noise background conditions, the adaptive filtering is applied to the treatment of signal, and the traditional method is simple. Through the actual seismic data denoising, contrast tests were carried out with the original signal adaptive filtering, the adaptive filter can be perfect in the earthquake infrasound data processing results, and make the earthquake monitoring system more perfect.
Lai, Jonathan Y.
1994-01-01
This dissertation focuses on the signal processing problems associated with the detection of hazardous windshears using airborne Doppler radar when weak weather returns are in the presence of strong clutter returns. In light of the frequent inadequacy of spectral-processing oriented clutter suppression methods, we model a clutter signal as multiple sinusoids plus Gaussian noise, and propose adaptive filtering approaches that better capture the temporal characteristics of the signal process. This idea leads to two research topics in signal processing: (1) signal modeling and parameter estimation, and (2) adaptive filtering in this particular signal environment. A high-resolution, low SNR threshold maximum likelihood (ML) frequency estimation and signal modeling algorithm is devised and proves capable of delineating both the spectral and temporal nature of the clutter return. Furthermore, the Least Mean Square (LMS) -based adaptive filter's performance for the proposed signal model is investigated, and promising simulation results have testified to its potential for clutter rejection leading to more accurate estimation of windspeed thus obtaining a better assessment of the windshear hazard.
Jeong, Jong Seob; Cannata, Jonathan Matthew; Shung, K Kirk [Department of Biomedical Engineering, NIH Resource Center for Medical Ultrasonic Transducer Technology, University of Southern California, Los Angeles, CA (United States)], E-mail: jongsjeo@usc.edu
2010-04-07
It was previously demonstrated that it is feasible to simultaneously perform ultrasound therapy and imaging of a coagulated lesion during treatment with an integrated transducer that is capable of high intensity focused ultrasound (HIFU) and B-mode ultrasound imaging. It was found that coded excitation and fixed notch filtering upon reception could significantly reduce interference caused by the therapeutic transducer. During HIFU sonication, the imaging signal generated with coded excitation and fixed notch filtering had a range side-lobe level of less than -40 dB, while traditional short-pulse excitation and fixed notch filtering produced a range side-lobe level of -20 dB. The shortcoming is, however, that relatively complicated electronics may be needed to utilize coded excitation in an array imaging system. It is for this reason that in this paper an adaptive noise canceling technique is proposed to improve image quality by minimizing not only the therapeutic interference, but also the remnant side-lobe 'ripples' when using the traditional short-pulse excitation. The performance of this technique was verified through simulation and experiments using a prototype integrated HIFU/imaging transducer. Although it is known that the remnant ripples are related to the notch attenuation value of the fixed notch filter, in reality, it is difficult to find the optimal notch attenuation value due to the change in targets or the media resulted from motion or different acoustic properties even during one sonication pulse. In contrast, the proposed adaptive noise canceling technique is capable of optimally minimizing both the therapeutic interference and residual ripples without such constraints. The prototype integrated HIFU/imaging transducer is composed of three rectangular elements. The 6 MHz center element is used for imaging and the outer two identical 4 MHz elements work together to transmit the HIFU beam. Two HIFU elements of 14.4 mm x 20.0 mm dimensions
It was previously demonstrated that it is feasible to simultaneously perform ultrasound therapy and imaging of a coagulated lesion during treatment with an integrated transducer that is capable of high intensity focused ultrasound (HIFU) and B-mode ultrasound imaging. It was found that coded excitation and fixed notch filtering upon reception could significantly reduce interference caused by the therapeutic transducer. During HIFU sonication, the imaging signal generated with coded excitation and fixed notch filtering had a range side-lobe level of less than -40 dB, while traditional short-pulse excitation and fixed notch filtering produced a range side-lobe level of -20 dB. The shortcoming is, however, that relatively complicated electronics may be needed to utilize coded excitation in an array imaging system. It is for this reason that in this paper an adaptive noise canceling technique is proposed to improve image quality by minimizing not only the therapeutic interference, but also the remnant side-lobe 'ripples' when using the traditional short-pulse excitation. The performance of this technique was verified through simulation and experiments using a prototype integrated HIFU/imaging transducer. Although it is known that the remnant ripples are related to the notch attenuation value of the fixed notch filter, in reality, it is difficult to find the optimal notch attenuation value due to the change in targets or the media resulted from motion or different acoustic properties even during one sonication pulse. In contrast, the proposed adaptive noise canceling technique is capable of optimally minimizing both the therapeutic interference and residual ripples without such constraints. The prototype integrated HIFU/imaging transducer is composed of three rectangular elements. The 6 MHz center element is used for imaging and the outer two identical 4 MHz elements work together to transmit the HIFU beam. Two HIFU elements of 14.4 mm x 20.0 mm dimensions could
Notch Signaling and Brain Tumors
Stockhausen, Marie; Kristoffersen, Karina; Poulsen, Hans Skovgaard
2011-01-01
Human brain tumors are a heterogenous group of neoplasms occurring inside the cranium and the central spinal cord. In adults and children, astrocytic glioma and medulloblastoma are the most common subtypes of primary brain tumors. These tumor types are thought to arise from cells in which Notch...
Regulation of Notch Signaling by Glycosylation
Stanley, Pamela
2007-01-01
Notch receptors are ~300 kd cell surface glycoproteins whose activation by Notch ligands regulates cell fate decisions in the metazoa. The extracellular domain of Notch receptors has many epidermal growth factor-like repeats that are glycosylated with O-fucose and O-glucose glycans as well as N-glycans. Disruption of O-fucose glycan synthesis leads to severe Notch signaling defects in Drosophila and mammals. Removal or addition of O-fucose glycan consensus sites on Notch receptors also leads ...
Yoshida Teruhiko
2006-09-01
Full Text Available Abstract Background Recent advances in genome technologies have provided an excellent opportunity to determine the complete biological characteristics of neoplastic tissues, resulting in improved diagnosis and selection of treatment. To accomplish this objective, it is important to establish a sophisticated algorithm that can deal with large quantities of data such as gene expression profiles obtained by DNA microarray analysis. Results Previously, we developed the projective adaptive resonance theory (PART filtering method as a gene filtering method. This is one of the clustering methods that can select specific genes for each subtype. In this study, we applied the PART filtering method to analyze microarray data that were obtained from soft tissue sarcoma (STS patients for the extraction of subtype-specific genes. The performance of the filtering method was evaluated by comparison with other widely used methods, such as signal-to-noise, significance analysis of microarrays, and nearest shrunken centroids. In addition, various combinations of filtering and modeling methods were used to extract essential subtype-specific genes. The combination of the PART filtering method and boosting – the PART-BFCS method – showed the highest accuracy. Seven genes among the 15 genes that are frequently selected by this method – MIF, CYFIP2, HSPCB, TIMP3, LDHA, ABR, and RGS3 – are known prognostic marker genes for other tumors. These genes are candidate marker genes for the diagnosis of STS. Correlation analysis was performed to extract marker genes that were not selected by PART-BFCS. Sixteen genes among those extracted are also known prognostic marker genes for other tumors, and they could be candidate marker genes for the diagnosis of STS. Conclusion The procedure that consisted of two steps, such as the PART-BFCS and the correlation analysis, was proposed. The results suggest that novel diagnostic and therapeutic targets for STS can be extracted by
Owen, Harry
2007-01-01
Volume phase holographic (VPH) optical elements have made a major contribution to Raman spectroscopy by providing notch filters, and VPH gratings that provide remarkable performance advantages over previous technologies. Holographic notch filters have eliminated Rayleigh scattered laser light from single monochromators, thereby contributing to the…
Wen-Yu Wang
2012-01-01
Full Text Available A novel method for middle frequency resonance detection and reduction is proposed for speed control in industrial servo systems. Defects of traditional resonance reduction method based on adaptive notch filter in middle frequency range are analyzed. And the main reason is summarized as the difference between the resonance frequency and the oscillation frequency. A self-tuning low-pass filter is introduced in the speed feedback path, whose corner frequency is determined by FFT results and several self-tuning rules. With the proposed method the effective range of the adaptive filter is extended across the middle frequency range. Simulation and Experiment results show that the frequency detection is accurate and resonances during the speed steady states and dynamics are successfully reduced.
Mechanical behaviors of notched composite laminates
无
2000-01-01
Presents the study on the mechanical behaviors of composite laminates with both static and fatigue tests per formed with different notched specimens and concludes with experimental results that ultimate strength and initial stiff ness of various notched composite laminates is almost as same as un-notched ones but the fatigue life of notched speci mens is much higher than un-notched ones. Compared with metals, composite materials are notch insensitive. The properties measured by using bar type specimens can not represent the real properties of composite laminates. Notches on the free edge may be helpful to the structure. The fatigue life can be predicted through theoretical models estab lished using the residual stiffness model.
Randriamparany, T; Kouakou, K V; Michaud, V; Fernández-Pinero, J; Gallardo, C; Le Potier, M-F; Rabenarivahiny, R; Couacy-Hymann, E; Raherimandimby, M; Albina, E
2016-08-01
The performance of Whatman 3-MM filter papers for the collection, drying, shipment and long-term storage of blood at ambient temperature, and for the detection of African swine fever virus and antibodies was assessed. Conventional and real-time PCR, viral isolation and antibody detection by ELISA were performed on paired samples (blood/tissue versus dried-blood 3-MM filter papers) collected from experimentally infected pigs and from farm pigs in Madagascar and Côte d'Ivoire. 3-MM filter papers were used directly in the conventional and real-time PCR without previous extraction of nucleic acids. Tests that performed better with 3-MM filter papers were in descending order: virus isolation, real-time UPL PCR and conventional PCR. The analytical sensitivity of real-time UPL PCR on filter papers was similar to conventional testing (virus isolation or conventional PCR) on organs or blood. In addition, blood-dried filter papers were tested in ELISA for antibody detection and the observed sensitivity was very close to conventional detection on serum samples and gave comparable results. Filter papers were stored up to 9 months at 20-25°C and for 2 months at 37°C without significant loss of sensitivity for virus genome detection. All tests on 3-MM filter papers had 100% specificity compared to the gold standards. Whatman 3-MM filter papers have the advantage of being cheap and of preserving virus viability for future virus isolation and characterization. In this study, Whatman 3-MM filter papers proved to be a suitable support for the collection, storage and use of blood in remote areas of tropical countries without the need for a cold chain and thus provide new possibilities for antibody testing and virus isolation. PMID:25430732
Investigation into the Performance of Bamboo Using the Notched and the Un-Notched Specimen
L. Gyansah; S. Kwofie
2011-01-01
This study investigates the performance of bamboo (Bambusa vulgaris) using the notched and the un-notched specimen. Double v-notched bamboo and un-notched bamboo specimens were used to carry out the experiment. Notched- angles of 20, 30, 60, 80 and 90º were made on each specimen. These were done to ascertain the effect of the notched-angle on the performance of the bamboo. Each specimen was placed in a Uniaxial Compression Machine and was crushed with respect to time. The results are plotted ...
Haoqian Huang
2014-12-01
Full Text Available High accuracy attitude and position determination is very important for underwater gliders. The cross-coupling among three attitude angles (heading angle, pitch angle and roll angle becomes more serious when pitch or roll motion occurs. This cross-coupling makes attitude angles inaccurate or even erroneous. Therefore, the high accuracy attitude and position determination becomes a difficult problem for a practical underwater glider. To solve this problem, this paper proposes backing decoupling and adaptive extended Kalman filter (EKF based on the quaternion expanded to the state variable (BD-AEKF. The backtracking decoupling can eliminate effectively the cross-coupling among the three attitudes when pitch or roll motion occurs. After decoupling, the adaptive extended Kalman filter (AEKF based on quaternion expanded to the state variable further smoothes the filtering output to improve the accuracy and stability of attitude and position determination. In order to evaluate the performance of the proposed BD-AEKF method, the pitch and roll motion are simulated and the proposed method performance is analyzed and compared with the traditional method. Simulation results demonstrate the proposed BD-AEKF performs better. Furthermore, for further verification, a new underwater navigation system is designed, and the three-axis non-magnetic turn table experiments and the vehicle experiments are done. The results show that the proposed BD-AEKF is effective in eliminating cross-coupling and reducing the errors compared with the conventional method.
Huang, Haoqian; Chen, Xiyuan; Zhou, Zhikai; Xu, Yuan; Lv, Caiping
2014-01-01
High accuracy attitude and position determination is very important for underwater gliders. The cross-coupling among three attitude angles (heading angle, pitch angle and roll angle) becomes more serious when pitch or roll motion occurs. This cross-coupling makes attitude angles inaccurate or even erroneous. Therefore, the high accuracy attitude and position determination becomes a difficult problem for a practical underwater glider. To solve this problem, this paper proposes backing decoupling and adaptive extended Kalman filter (EKF) based on the quaternion expanded to the state variable (BD-AEKF). The backtracking decoupling can eliminate effectively the cross-coupling among the three attitudes when pitch or roll motion occurs. After decoupling, the adaptive extended Kalman filter (AEKF) based on quaternion expanded to the state variable further smoothes the filtering output to improve the accuracy and stability of attitude and position determination. In order to evaluate the performance of the proposed BD-AEKF method, the pitch and roll motion are simulated and the proposed method performance is analyzed and compared with the traditional method. Simulation results demonstrate the proposed BD-AEKF performs better. Furthermore, for further verification, a new underwater navigation system is designed, and the three-axis non-magnetic turn table experiments and the vehicle experiments are done. The results show that the proposed BD-AEKF is effective in eliminating cross-coupling and reducing the errors compared with the conventional method. PMID:25479331
Ragot, Hélène; Monfort, Astrid; Baudet, Mathilde; Azibani, Fériel; Fazal, Loubina; Merval, Régine; Polidano, Evelyne; Cohen-Solal, Alain; Delcayre, Claude; Vodovar, Nicolas; Chatziantoniou, Christos; Samuel, Jane-Lise
2016-08-01
Hypertension, which is a risk factor of heart failure, provokes adaptive changes at the vasculature and cardiac levels. Notch3 signaling plays an important role in resistance arteries by controlling the maturation of vascular smooth muscle cells. Notch3 deletion is protective in pulmonary hypertension while deleterious in arterial hypertension. Although this latter phenotype was attributed to renal and cardiac alterations, the underlying mechanisms remained unknown. To investigate the role of Notch3 signaling in the cardiac adaptation to hypertension, we used mice with either constitutive Notch3 or smooth muscle cell-specific conditional RBPJκ knockout. At baseline, both genotypes exhibited a cardiac arteriolar rarefaction associated with oxidative stress. In response to angiotensin II-induced hypertension, the heart of Notch3 knockout and SM-RBPJκ knockout mice did not adapt to pressure overload and developed heart failure, which could lead to an early and fatal acute decompensation of heart failure. This cardiac maladaptation was characterized by an absence of media hypertrophy of the media arteries, the transition of smooth muscle cells toward a synthetic phenotype, and an alteration of angiogenic pathways. A subset of mice exhibited an early fatal acute decompensated heart failure, in which the same alterations were observed, although in a more rapid timeframe. Altogether, these observations indicate that Notch3 plays a major role in coronary adaptation to pressure overload. These data also show that the hypertrophy of coronary arterial media on pressure overload is mandatory to initially maintain a normal cardiac function and is regulated by the Notch3/RBPJκ pathway. PMID:27296994
A pseudo-matched filter for chaos
Cohen, Seth D.; Gauthier, Daniel J
2012-01-01
A matched filter maximizes the signal-to-noise ratio of a signal. In the recent work of Corron et al. [Chaos 20, 023123 (2010)], a matched filter is derived for the chaotic waveforms produced by a piecewise-linear system. Motivated by these results, we describe a pseudo-matched filter, which removes noise from the same chaotic signal. It consists of a notch filter followed by a first-order, low-pass filter. We compare quantitatively the matched filter's performance to that of our pseudo-match...
The Daala Directional Deringing Filter
Valin, Jean-Marc
2016-01-01
This paper presents the deringing filter used in the Daala royalty-free video codec. The filter is based on a non-linear conditional replacement filter and is designed for vectorization efficiency. It takes into account the direction of edges and patterns being filtered. The filter works by identifying the direction of each block and then adaptively filtering along the identified direction. In a second pass, the blocks are also filtered in a different direction, with more conservative thresho...
基于小波和自适应滤波的ECG基线漂移校正%ECG Baseline Shift Correction Based on Wavelet and Adaptive Filtering
史健婷; 黄剑华; 张英涛; 唐降龙
2013-01-01
为校正ECG信号的基陑漂移，提出小波变换和自适应滤波陒结合的方法。利用小波变换对原始ECG信号进行分解，将得到的高频分量作为参考信号输入，采用基于幂函数的最小均方算法(P-LMS)进行自适应滤噪处理。通过与传统的归一化最小均方算法(NLMS)进行对比，验证该算法的准确性。仿真实验和MIT-BIH数据库中的实际数据检验结果表明，基于幂函数的最小均方算法和小波变换陒结合的方法能够有效校正基陑漂移，并较好地保持心电信号的几何特征。%In order to calibrate the baseline shift of ECG signal, the combination methods of wavelet transform and adaptive filtering are proposed. The wavelet transform method is used to decompose the original ECG signal and the high-frequency components are used as reference input data. A new adaptive filtering algorithm, P-LMS based on the power function is proposed to conduct adaptive noise filtering. Compared with the traditional Normalized Least Mean Square(NLMS) algorithm, the proposed algorithm is precise. Using the simulated experiment and actual data in the MIT-BIH database, the method of combining P-LMS and wavelet transform is verified that can effectively correct the baseline shift and maintain the geometric characteristics of the ECG signal.
Junichi Susaki
2012-06-01
Full Text Available A filtering algorithm is proposed that accurately extracts ground data from airborne light detection and ranging (LiDAR measurements and generates an estimated digital terrain model (DTM. The proposed algorithm utilizes planar surface features and connectivity with locally lowest points to improve the extraction of ground points (GPs. A slope parameter used in the proposed algorithm is updated after an initial estimation of the DTM, and thus local terrain information can be included. As a result, the proposed algorithm can extract GPs from areas where different degrees of slope variation are interspersed. Specifically, along roads and streets, GPs were extracted from urban areas, from hilly areas such as forests, and from flat area such as riverbanks. Validation using reference data showed that, compared with commercial filtering software, the proposed algorithm extracts GPs with higher accuracy. Therefore, the proposed filtering algorithm effectively generates DTMs, even for dense urban areas, from airborne LiDAR data.
Bargatze, L. F.
2015-12-01
Active Data Archive Product Tracking (ADAPT) is a collection of software routines that permits one to generate XML metadata files to describe and register data products in support of the NASA Heliophysics Virtual Observatory VxO effort. ADAPT is also a philosophy. The ADAPT concept is to use any and all available metadata associated with scientific data to produce XML metadata descriptions in a consistent, uniform, and organized fashion to provide blanket access to the full complement of data stored on a targeted data server. In this poster, we present an application of ADAPT to describe all of the data products that are stored by using the Common Data File (CDF) format served out by the CDAWEB and SPDF data servers hosted at the NASA Goddard Space Flight Center. These data servers are the primary repositories for NASA Heliophysics data. For this purpose, the ADAPT routines have been used to generate data resource descriptions by using an XML schema named Space Physics Archive, Search, and Extract (SPASE). SPASE is the designated standard for documenting Heliophysics data products, as adopted by the Heliophysics Data and Model Consortium. The set of SPASE XML resource descriptions produced by ADAPT includes high-level descriptions of numerical data products, display data products, or catalogs and also includes low-level "Granule" descriptions. A SPASE Granule is effectively a universal access metadata resource; a Granule associates an individual data file (e.g. a CDF file) with a "parent" high-level data resource description, assigns a resource identifier to the file, and lists the corresponding assess URL(s). The CDAWEB and SPDF file systems were queried to provide the input required by the ADAPT software to create an initial set of SPASE metadata resource descriptions. Then, the CDAWEB and SPDF data repositories were queried subsequently on a nightly basis and the CDF file lists were checked for any changes such as the occurrence of new, modified, or deleted
Evidence of non-canonical NOTCH signaling
Traustadóttir, Gunnhildur Ásta; Jensen, Charlotte H; Thomassen, Mads;
2016-01-01
Dlk1(+/+) and Dlk1(-/-) mouse tissues at E16.5, we demonstrated that several NOTCH signaling pathways indeed are affected by DLK1 during tissue development, and this was supported by a lower activation of NOTCH1 protein in Dlk1(+/+) embryos. Likewise, but using a distinct Dlk1-manipulated (si......Canonical NOTCH signaling, known to be essential for tissue development, requires the Delta-Serrate-LAG2 (DSL) domain for NOTCH to interact with its ligand. However, despite lacking DSL, Delta-like 1 homolog (DLK1), a protein that plays a significant role in mammalian development, has been...
Characterization of EMI filters based on metamaterials
Gil Galí, Ignacio; Fernández García, Raúl; Vives, Yolanda; Jauregui Tellería, Ricardo; Silva Martínez, Fernando
2009-01-01
This paper analyzes the behavior of EMI filters based on metamaterials. The filters are developed by means of sub-wavelength resonators and designed to have notch-type attenuation in the 2.45 GHz band. Two types of filters based on SRR and CSRR rings are presented. The simulated responses by MoM and FDTD are compared with the measurement data obtained from the developed prototypes.
Prince, Kamau; Presi, Marco; Chiuchiarelli, Andrea;
2009-01-01
on a directly-modulated reflective emiconductor amplifier (R-SOA) and exploits the interplay between transmission-line dispersion and tunable optical filtering to achieve flexible true time delay, with $2pi$ beam steering at the different antennas. The system was characterized, then successfully...
A NOVEL ULTRA WIDEBAND MONOPOLE ANTENNA WITH BAND-NOTCHED CHARACTERISTICS
Deng Hongwei; He Xiaoxiang; Yao Binyan; Zhou Yonggang
2009-01-01
A simple and compact microstrip-fed Ultra WideBand (UWB) printed monopole antenna with band-notched characteristic is proposed in this paper. The antenna is composed of a square ring with a small strip bar, so that the antenna occupies about 7.69 GHz bandwidth covering 3.11～10.8 GHz with expected band rejection from 5.12 GHz to 5.87 GHz. A quasi-omnidirectional and quasi-symmetrical radiation pattern is also obtained. This kind of band-notched UWB antenna requires no external filters and thus greatly simplifies the system design of UWB wireless communication.
Tunable band-notched line-defect waveguide in a surface-wave photonic crystal
Gao, Zhen; Zhang, Youming; Xu, Hongyi; Zhang, Baile
2016-01-01
We propose and experimentally demonstrate a tunable band-notched line-defect waveguide in a surface-wave photonic crystal, which consists of a straight line-defect waveguide and side-coupled defect cavities. A tunable narrow stopband can be observed in the broadband transmission spectra. We also demonstrate that both the filtering levels and filtering frequencies of the band-notched line-defect waveguide can be conveniently tuned through changing the total number and the pillar height of the side-coupled defect cavities. The band-notch function is based on the idea that the propagating surface modes with the resonance frequencies of the side-coupled defect cavities will be tightly localized around the defect sites, being filtered from the waveguide output. Transmission spectra measurements and direct near-field profiles imaging are performed at microwave frequencies to verify our idea and design. These results may enable new band-notched devices design and provide routes for the realization of tunable surface...
Alvaro Glavic
2011-01-01
Full Text Available The Notch signaling pathway plays an important role in development and physiology. In Drosophila, Notch is activated by its Delta or Serrate ligands, depending in part on the sugar modifications present in its extracellular domain. O-fucosyltransferase-1 (OFUT1 performs the first glycosylation step in this process, O-fucosylating various EGF repeats at the Notch extracellular domain. Besides its O-fucosyltransferase activity, OFUT1 also behaves as a chaperone during Notch synthesis and is able to down regulate Notch by enhancing its endocytosis and degradation. We have reevaluated the roles that O-fucosylation and the synthesis of GDP-fucose play in the regulation of Notch protein stability. Using mutants and the UAS/Gal4 system, we modified in developing tissues the amount of GDP-mannose-deshydratase (GMD, the first enzyme in the synthesis of GDP-fucose. Our results show that GMD activity, and likely the levels of GDP-fucose and O-fucosylation, are essential to stabilize the Notch protein. Notch degradation observed under low GMD expression is absolutely dependent on OFUT1 and this is also observed in Notch Abruptex mutants, which have mutations in some potential O-fucosylated EGF domains. We propose that the GDP-fucose/OFUT1 balance determines the ability of OFUT1 to endocytose and degrade Notch in a manner that is independent of the residues affected by Abruptex mutations in Notch EGF domains.
Notching on cancer’s door: Notch signaling in brain tumors
Marcin eTeodorczyk
2015-01-01
Full Text Available Notch receptors play an essential role in the regulation of central cellular processes during embryonic and postnatal development. The mammalian genome encodes for four Notch paralogs (Notch 1-4, which are activated by three Delta-like (Dll1/3/4 and two Serrate-like (Jagged1/2 ligands. Further, non-canonical Notch ligands such as EGFL7 have been identified and serve mostly as antagonists of Notch signaling. The Notch pathway prevents neuronal differentiation in the central nervous system by driving neural stem cell maintenance and commitment of neural progenitor cells into the glial lineage. Notch is therefore often implicated in the development of brain tumors, as tumor cells share various characteristics with neural stem and progenitor cells. Notch receptors are overexpressed in gliomas and their oncogenicity has been confirmed by gain- and loss-of-function studies in vitro and in vivo. To this end, special attention is paid to the impact of Notch signaling on stem-like brain tumor-propagating cells as these cells contribute to growth, survival, invasion and recurrence of brain tumors. Based on the outcome of ongoing studies in vivo, Notch-directed therapies such as γ secretase inhibitors and blocking antibodies have entered and completed various clinical trials. This review summarizes the current knowledge on Notch signaling in brain tumor formation and therapy.
Junichi Susaki
2012-01-01
A filtering algorithm is proposed that accurately extracts ground data from airborne light detection and ranging (LiDAR) measurements and generates an estimated digital terrain model (DTM). The proposed algorithm utilizes planar surface features and connectivity with locally lowest points to improve the extraction of ground points (GPs). A slope parameter used in the proposed algorithm is updated after an initial estimation of the DTM, and thus local terrain information can be included. As a ...
Noise Reduction and Gap Filling of fAPAR Time Series Using an Adapted Local Regression Filter
Álvaro Moreno; Francisco Javier García-Haro; Beatriz Martínez; María Amparo Gilabert
2014-01-01
Time series of remotely sensed data are an important source of information for understanding land cover dynamics. In particular, the fraction of absorbed photosynthetic active radiation (fAPAR) is a key variable in the assessment of vegetation primary production over time. However, the fAPAR series derived from polar orbit satellites are not continuous and consistent in space and time. Filtering methods are thus required to fill in gaps and produce high-quality time series. This study propose...
Payri González, Francisco; Olmeda González, Pablo Cesar; Guardiola García, Carlos; Martín Díaz, Jaime
2011-01-01
Abstract In-cylinder pressure analysis is a key tool for engine research and diagnosis and it has been object of study from the beginning of the internal combustion engines. One of its most useful application is combustion analysis on the basis of the First Law of Thermodynamics. However, heat release law calculations use the in-cylinder pressure derivative signal. Hence, the noise is increased and pressure filtering becomes necessary to remove high frequency noise, thus allowing f...
Targeting Notch Signaling in Colorectal Cancer.
Suman, Suman; Das, Trinath P; Ankem, Murali K; Damodaran, Chendil
2014-12-01
The activation of Notch signaling is implicated in tumorigenesis in the colon due to the induction of pro-survival signaling in colonic epithelial cells. Chemoresistance is a major obstacle for treatment and for the complete eradication of colorectal cancer (CRC), hence, the inhibition of Notch is an attractive target for CRC and several groups are working to identify small molecules or monoclonal antibodies that inhibit Notch or its downstream events; however, toxicity profiles in normal cells and organs often impede the clinical translation of these molecules. Dietary agents have gained momentum for targeting several pro-survival signaling cascades, and recent studies demonstrated that agents that inhibit Notch signaling result in growth inhibition in preclinical models of CRC. In this review, we focus on the importance of Notch as a preventive and therapeutic target for colon cancer and on the effect of WA on this signaling pathway in the context of colon cancer. PMID:25395896
Canonical Notch activation in osteocytes causes osteopetrosis.
Canalis, Ernesto; Bridgewater, David; Schilling, Lauren; Zanotti, Stefano
2016-01-15
Activation of Notch1 in cells of the osteoblastic lineage inhibits osteoblast differentiation/function and causes osteopenia, whereas its activation in osteocytes causes a distinct osteopetrotic phenotype. To explore mechanisms responsible, we established the contributions of canonical Notch signaling (Rbpjκ dependent) to osteocyte function. Transgenics expressing Cre recombinase under the control of the dentin matrix protein-1 (Dmp1) promoter were crossed with Rbpjκ conditional mice to generate Dmp1-Cre(+/-);Rbpjκ(Δ/Δ) mice. These mice did not have a skeletal phenotype, indicating that Rbpjκ is dispensable for osteocyte function. To study the Rbpjκ contribution to Notch activation, Rosa(Notch) mice, where a loxP-flanked STOP cassette is placed between the Rosa26 promoter and the NICD coding sequence, were crossed with Dmp1-Cre transgenic mice and studied in the context (Dmp1-Cre(+/-);Rosa(Notch);Rbpjκ(Δ/Δ)) or not (Dmp1-Cre(+/-);Rosa(Notch)) of Rbpjκ inactivation. Dmp1-Cre(+/-);Rosa(Notch) mice exhibited increased femoral trabecular bone volume and decreased osteoclasts and bone resorption. The phenotype was reversed in the context of the Rbpjκ inactivation, demonstrating that Notch canonical signaling was accountable for the phenotype. Notch activation downregulated Sost and Dkk1 and upregulated Axin2, Tnfrsf11b, and Tnfsf11 mRNA expression, and these effects were not observed in the context of the Rbpjκ inactivation. In conclusion, Notch activation in osteocytes suppresses bone resorption and increases bone volume by utilization of canonical signals that also result in the inhibition of Sost and Dkk1 and upregulation of Wnt signaling. PMID:26578715
Bolea, Mario; Mora, José; Ortega, Beatriz; Capmany, José
2009-03-30
We propose theoretically and demonstrate experimentally an optical architecture for flexible Ultra-Wideband pulse generation. It is based on an N-tap reconfigurable microwave photonic filter fed by a laser array by using phase inversion in a Mach-Zehnder modulator. Since a large number of positive and negative coefficients can be easily implemented, UWB pulses fitted to the FCC mask requirements can be generated. As an example, a four tap pulse generator is experimentally demonstrated which complies with the FCC regulation. The proposed pulse generator allows different pulse modulation formats since the amplitude, polarity and time delay of generated pulse is controlled. PMID:19333263
Reconfigurable microwave photonic filter based on polarization modulation
Xu, Enming; Pan, Shilong; Li, Peili
2016-03-01
A reconfigurable microwave photonic filter based on a polarization modulator (PolM) is proposed and experimentally demonstrated. The PolM together with a polarization controller (PC) and a polarization beam splitter (PBS) implements two complementary intensity modulations in two separated branches. Then, optical components are inserted in the two branches to realize a bandpass filter and an allpass filter, respectively. When the two branches are combined by a second PBS, a filter with a frequency response that equals the subtraction of the frequency responses of the allpass filter and bandpass filter is achieved. By adjusting the PCs placed before the second PBS, a notch filter with a tunable notch depth or a bandpass filter can be achieved.
Kwon, Hyeokjun; Oh, Sechang; Varadan, Vijay K.
2012-04-01
, a filter including a moving weighted factor, peak to peak detection, and interpolation techniques. In addition, this paper introduces an adaptive filter in order to extract clear ECG signal by using extracted baseline noise signal and measured signal from sensor.
周勇; 张玉峰; 张超; 张举中
2013-01-01
Aiming at the flaws of the standard Kalman Filter(KF) and Extended Kalman Filter (EKF) , and based on the square-root filtering algorithm, we modify traditional Sage-Husa adaptive filter and present a novel algorithm of Linear Adaptive Square-Root Kalman Filtering( LASRKF) in this paper. With this new filter, the square root of system state covariance matrix is calculated recursively and the estimation of the square root of the system noise co-variance matrix is obtained straightforwardly. Then the positive semi-definiteness of system state and noise covariance matrix are guaranteed; the stability and the adaptability of filter are also enhanced. Compared with the traditional Sage-Husa adaptive filtering algorithm, LASRKF algorithm improves the anti-divergence capability. Simulation results show preliminarily that the stability, accuracy and adaptability of the filter are improved greatly.%针对标准卡尔曼滤波和扩展卡尔曼滤波存在的局限性,结合平方根滤波的思想,对传统Sage-Husa估计器进行改进,提出了一种新的线性自适应平方根卡尔曼滤波(Linear Adaptive Square-Root Kalman Filtering,LASRKF)算法.该算法直接对系统状态方差阵和噪声方差阵的平方根进行递推与估算,确保了状态和噪声方差阵的对称性和非负定性；算法还增添了对系统噪声统计特性估计的计算,强化了滤波器的稳定性和自适应能力；与传统Sage-Husa自适应滤波算法相比LASRKF可提高滤波器抗发散的能力.仿真实验表明,LASRKF可有效提高滤波器的精确性、稳定性和自适应能力.
沈云峰; 朱海; 莫军; 宋裕农
2001-01-01
讨论一种简化的Sage-Husa自适应Kalman滤波算法，它可对系统的量测噪声和系统干扰进行实时估计，同时在工程中又比较易实现与调整，通过在组合导航舰船运动模型的仿真发现，可以明显提高滤波精度与稳定性。%A simplified adaptive Sage-Husa filter is discussed.Generally the method of increasing the adaptive ability of normal Kalman filter is to do optimal estimation of the statistical feature of measurement noise and inteference.This way the complication of filter is enhanced also.This influences the real-time chasasteristics of the filtering.The simplified Sage-Husa filter can solve this problem partly because of its simple structure.The result of computer simulation shows the simplified filter is useful,effective and adaptive when it is applied in the ship integrated navigation system.
Bonde, Casper Stork; Graversen, Carina; Gregersen, Andreas Gregers;
2005-01-01
appearance of the speech signal which require noise robust voice activity detection and assumptions of stationary noise. However, both of these requirements are often not met and it is therefore of particular interest to investigate methods like the Quantile Based Noise Estimation (QBNE) mehtod which......An important topic in Automatic Speech Recognition (ASR) is to reduce the effect of noise, in particular when mismatch exists between the training and application conditions. Many noise robutness schemes within the feature processing domain use as a prerequisite a noise estimate prior to the...... estimates the noise during speech and non-speech sections without the use of a voice activity detector. While the standard QBNE-method uses a fixed pre-defined quantile accross all frequency bands, this paper suggests adaptive QBNE (AQBNE) which adapts the quantile individually to each frequency band...
D Panigrahy; P K Sahu
2015-06-01
Fetal electrocardiogram (ECG) gives information about the health status of fetus and so, an early diagnosis of any cardiac defect before delivery increases the effectiveness of appropriate treatment. In this paper, authors investigate the use of adaptive neuro-fuzzy inference system (ANFIS) with extended Kalman filter for fetal ECG extraction from one ECG signal recorded at the abdominal areas of the mother’s skin. The abdominal ECG is considered to be composite as it contains both mother’s and fetus’ ECG signals. We use extended Kalman filter framework to estimate the maternal component from abdominal ECG. The maternal component in the abdominal ECG signal is a nonlinear transformed version of maternal ECG. ANFIS network has been used to identify this nonlinear relationship, and to align the estimated maternal ECG signal with the maternal component in the abdominal ECG signal. Thus, we extract the fetal ECG component by subtracting the aligned version of the estimated maternal ECG from the abdominal signal. Our results demonstrate the effectiveness of the proposed technique in extracting the fetal ECG component from abdominal signal at different noise levels. The proposed technique is also validated on the extraction of fetal ECG from both actual abdominal recordings and synthetic abdominal recording.
2011-04-22
... Surface Transportation Board Three Notch Railway, LLC--Acquisition and Operation Exemption-- Three Notch Railroad Co., Inc. Three Notch Railway, LLC (TNRW), a noncarrier, has filed a verified notice of exemption under 49 CFR 1150.31 to acquire from Three Notch Railroad Co., Inc. (TNHR) and to operate...
Poyneer, Lisa A; Macintosh, Bruce; Palmer, David W; Perrin, Marshall D; Sadakuni, Naru; Savransky, Dmitry; Bauman, Brian; Cardwell, Andrew; Chilcote, Jeffrey K; Dillon, Daren; Gavel, Donald; Goodsell, Stephen J; Hartung, Markus; Hibon, Pascale; Rantakyro, Fredrik T; Thomas, Sandrine; Veran, Jean-Pierre
2014-01-01
The Gemini Planet Imager instrument's adaptive optics (AO) subsystem was designed specifically to facilitate high-contrast imaging. It features several new technologies, including computationally efficient wavefront reconstruction with the Fourier transform, modal gain optimization every 8 seconds, and the spatially filtered wavefront sensor. It also uses a Linear-Quadratic-Gaussian (LQG) controller (aka Kalman filter) for both pointing and focus. We present on-sky performance results from verification and commissioning runs from December 2013 through May 2014. The efficient reconstruction and modal gain optimization are working as designed. The LQG controllers effectively notch out vibrations. The spatial filter can remove aliases, but we typically use it oversized by about 60% due to stability problems.
郝钢; 叶秀芬
2011-01-01
For the multiaensor nonlinear systems which have the same measurement function, an adaptive unscented Kalman filter is presented based on the Sage-Husa estimator. This algorithm can estimate the measurement noise variances R（J） of the subsystems by the correlated functions matrix of these educed sequences, and its convergence is also proved. The algorithm avoids the disadvantage of classic Sage-Husa estimator when the Q and R are all unknown. To take full advantage of the information of multisensor systems and improve the filtering accuracy, the adaptive weighted measurement fusion unscented Kalman filter is obtained by using the weighted least squares ( WLS) method. A simulation example for a nonlinear system with 3 sensors shows its effectiveness.%对于带有相同观测方程和未知噪声统计的非线性多传感器系统,提出了一种基于Sage-Husa估计的自适应UKF滤波算法.该算法利用导出的平稳随机序列的相关函数估计系统观测噪声方差统计R(j),并证明了其收敛性.进而利用Sage-Husa估计算法得到自适应UKF滤波算法.该方法避免了传统Sage和Husa的自适应滤波算法不能处理Q和R均未知的系统的局限性.为了将多传感器信息加以充分利用,提高滤波精度,本文利用加权最小二乘法(WLS),实现了多传感器加权观测融合自适应UKF滤波器.一个带3传感器非线性系统的仿真例子说明了该算法的有效性.