Turbo LMS algorithm: supercharger meets adaptive filter
NASA Astrophysics Data System (ADS)
Meyer-Baese, Uwe
2006-04-01
Adaptive digital filters (ADFs) are, in general, the most sophisticated and resource intensive components of modern digital signal processing (DSP) and communication systems. Improvements in performance or the complexity of ADFs can have a significant impact on the overall size, speed, and power properties of a complete system. The least mean square (LMS) algorithm is a popular algorithm for coefficient adaptation in ADF because it is robust, easy to implement, and a close approximation to the optimal Wiener-Hopf least mean square solution. The main weakness of the LMS algorithm is the slow convergence, especially for non Markov-1 colored noise input signals with high eigenvalue ratios (EVRs). Since its introduction in 1993, the turbo (supercharge) principle has been successfully applied in error correction decoding and has become very popular because it reaches the theoretical limits of communication capacity predicted 5 decades ago by Shannon. The turbo principle applied to LMS ADF is analogous to the turbo principle used for error correction decoders: First, an "interleaver" is used to minimize crosscorrelation, secondly, an iterative improvement which uses the same data set several times is implemented using the standard LMS algorithm. Results for 6 different interleaver schemes for EVR in the range 1-100 are presented.
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
New Approach for IIR Adaptive Lattice Filter Structure Using Simultaneous Perturbation Algorithm
NASA Astrophysics Data System (ADS)
Martinez, Jorge Ivan Medina; Nakano, Kazushi; Higuchi, Kohji
Adaptive infinite impulse response (IIR), or recursive, filters are less attractive mainly because of the stability and the difficulties associated with their adaptive algorithms. Therefore, in this paper the adaptive IIR lattice filters are studied in order to devise algorithms that preserve the stability of the corresponding direct-form schemes. We analyze the local properties of stationary points, a transformation achieving this goal is suggested, which gives algorithms that can be efficiently implemented. Application to the Steiglitz-McBride (SM) and Simple Hyperstable Adaptive Recursive Filter (SHARF) algorithms is presented. Also a modified version of Simultaneous Perturbation Stochastic Approximation (SPSA) is presented in order to get the coefficients in a lattice form more efficiently and with a lower computational cost and complexity. The results are compared with previous lattice versions of these algorithms. These previous lattice versions may fail to preserve the stability of stationary points.
NASA Astrophysics Data System (ADS)
Schneider, Martin; Kellermann, Walter
2016-01-01
Acoustic echo cancellation (AEC) is a well-known application of adaptive filters in communication acoustics. To implement AEC for multichannel reproduction systems, powerful adaptation algorithms like the generalized frequency-domain adaptive filtering (GFDAF) algorithm are required for satisfactory convergence behavior. In this paper, the GFDAF algorithm is rigorously derived as an approximation of the block recursive least-squares (RLS) algorithm. Thereby, the original formulation of the GFDAF algorithm is generalized while avoiding an error that has been in the original derivation. The presented algorithm formulation is applied to pruned transform-domain loudspeaker-enclosure-microphone models in a mathematically consistent manner. Such pruned models have recently been proposed to cope with the tremendous computational demands of massive multichannel AEC. Beyond its generalization, a regularization of the GFDAF is shown to have a close relation to the well-known block least-mean-squares algorithm.
Adaptive RSOV filter using the FELMS algorithm for nonlinear active noise control systems
NASA Astrophysics Data System (ADS)
Zhao, Haiquan; Zeng, Xiangping; He, Zhengyou; Li, Tianrui
2013-01-01
This paper presents a recursive second-order Volterra (RSOV) filter to solve the problems of signal saturation and other nonlinear distortions that occur in nonlinear active noise control systems (NANC) used for actual applications. Since this nonlinear filter based on an infinite impulse response (IIR) filter structure can model higher than second-order and third-order nonlinearities for systems where the nonlinearities are harmonically related, the RSOV filter is more effective in NANC systems with either a linear secondary path (LSP) or a nonlinear secondary path (NSP). Simulation results clearly show that the RSOV adaptive filter using the multichannel structure filtered-error least mean square (FELMS) algorithm can further greatly reduce the computational burdens and is more suitable to eliminate nonlinear distortions in NANC systems than a SOV filter, a bilinear filter and a third-order Volterra (TOV) filter.
Evaluation of an adaptive filtering algorithm for CT cardiac imaging with EKG modulated tube current
NASA Astrophysics Data System (ADS)
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.
NASA Astrophysics Data System (ADS)
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.
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
Adaptive Kalman filter based state of charge estimation algorithm for lithium-ion battery
NASA Astrophysics Data System (ADS)
Zheng, Hong; Liu, Xu; Wei, Min
2015-09-01
In order to improve the accuracy of the battery state of charge (SOC) estimation, in this paper we take a lithium-ion battery as an example to study the adaptive Kalman filter based SOC estimation algorithm. Firstly, the second-order battery system model is introduced. Meanwhile, the temperature and charge rate are introduced into the model. Then, the temperature and the charge rate are adopted to estimate the battery SOC, with the help of the parameters of an adaptive Kalman filter based estimation algorithm model. Afterwards, it is verified by the numerical simulation that in the ideal case, the accuracy of SOC estimation can be enhanced by adding two elements, namely, the temperature and charge rate. Finally, the actual road conditions are simulated with ADVISOR, and the simulation results show that the proposed method improves the accuracy of battery SOC estimation under actual road conditions. Thus, its application scope in engineering is greatly expanded. Project supported by the National Natural Science Foundation of China (Grant Nos. 61004048 and 61201010).
NASA Astrophysics Data System (ADS)
Shams Esfand Abadi, Mohammad; AbbasZadeh Arani, Seyed Ali Asghar
2011-12-01
This paper extends the recently introduced variable step-size (VSS) approach to the family of adaptive filter algorithms. This method uses prior knowledge of the channel impulse response statistic. Accordingly, optimal step-size vector is obtained by minimizing the mean-square deviation (MSD). The presented algorithms are the VSS affine projection algorithm (VSS-APA), the VSS selective partial update NLMS (VSS-SPU-NLMS), the VSS-SPU-APA, and the VSS selective regressor APA (VSS-SR-APA). In VSS-SPU adaptive algorithms the filter coefficients are partially updated which reduce the computational complexity. In VSS-SR-APA, the optimal selection of input regressors is performed during the adaptation. The presented algorithms have good convergence speed, low steady state mean square error (MSE), and low computational complexity features. We demonstrate the good performance of the proposed algorithms through several simulations in system identification scenario.
Adaptive Bloom Filter: A Space-Efficient Counting Algorithm for Unpredictable Network Traffic
NASA Astrophysics Data System (ADS)
Matsumoto, Yoshihide; Hazeyama, Hiroaki; Kadobayashi, Youki
The Bloom Filter (BF), a space-and-time-efficient hashcoding method, is used as one of the fundamental modules in several network processing algorithms and applications such as route lookups, cache hits, packet classification, per-flow state management or network monitoring. BF is a simple space-efficient randomized data structure used to represent a data set in order to support membership queries. However, BF generates false positives, and cannot count the number of distinct elements. A counting Bloom Filter (CBF) can count the number of distinct elements, but CBF needs more space than BF. We propose an alternative data structure of CBF, and we called this structure an Adaptive Bloom Filter (ABF). Although ABF uses the same-sized bit-vector used in BF, the number of hash functions employed by ABF is dynamically changed to record the number of appearances of a each key element. Considering the hash collisions, the multiplicity of a each key element on ABF can be estimated from the number of hash functions used to decode the membership of the each key element. Although ABF can realize the same functionality as CBF, ABF requires the same memory size as BF. We describe the construction of ABF and IABF (Improved ABF), and provide a mathematical analysis and simulation using Zipf's distribution. Finally, we show that ABF can be used for an unpredictable data set such as real network traffic.
Longmire, M S; Milton, A F; Takken, E H
1982-11-01
Several 1-D signal processing techniques have been evaluated by simulation with a digital computer using high-spatial-resolution (0.15 mrad) noise data gathered from back-lit clouds and uniform sky with a scanning data collection system operating in the 4.0-4.8-microm spectral band. Two ordinary bandpass filters and a least-mean-square (LMS) spatial filter were evaluated in combination with a fixed or adaptive threshold algorithm. The combination of a 1-D LMS filter and a 1-D adaptive threshold sensor was shown to reject extreme cloud clutter effectively and to provide nearly equal signal detection in a clear and cluttered sky, at least in systems whose NEI (noise equivalent irradiance) exceeds 1.5 x 10(-13) W/cm(2) and whose spatial resolution is better than 0.15 x 0.36 mrad. A summary gives highlights of the work, key numerical results, and conclusions. PMID:20396326
NASA Astrophysics Data System (ADS)
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.
NASA Astrophysics Data System (ADS)
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.
The algorithm analysis on non-uniformity correction based on LMS adaptive filtering
NASA Astrophysics Data System (ADS)
Zhan, Dongjun; Wang, Qun; Wang, Chensheng; Chen, Huawang
2010-11-01
The traditional least mean square (LMS) algorithm has the performance of good adaptivity to noise, but there are several disadvantages in the traditional LMS algorithm, such as the defect in desired value of pending pixels, undetermined original coefficients, which result in slow convergence speed and long convergence period. Method to solve the desired value of pending pixel has improved based on these problems, also, the correction gain and offset coefficients worked out by the method of two-point temperature non-uniformity correction (NUC) as the original coefficients, which has improved the convergence speed. The simulation with real infrared images has proved that the new LMS algorithm has the advantages of better correction effect. Finally, the algorithm is implemented on the hardware structure of FPGA+DSP.
NASA Astrophysics Data System (ADS)
Stevens, Mark R.; Gutchess, Dan; Checka, Neal; Snorrason, Magnús
2006-05-01
Image exploitation algorithms for Intelligence, Surveillance and Reconnaissance (ISR) and weapon systems are extremely sensitive to differences between the operating conditions (OCs) under which they are trained and the extended operating conditions (EOCs) in which the fielded algorithms are tested. As an example, terrain type is an important OC for the problem of tracking hostile vehicles from an airborne camera. A system designed to track cars driving on highways and on major city streets would probably not do well in the EOC of parking lots because of the very different dynamics. In this paper, we present a system we call ALPS for Adaptive Learning in Particle Systems. ALPS takes as input a sequence of video images and produces labeled tracks. The system detects moving targets and tracks those targets across multiple frames using a multiple hypothesis tracker (MHT) tightly coupled with a particle filter. This tracker exploits the strengths of traditional MHT based tracking algorithms by directly incorporating tree-based hypothesis considerations into the particle filter update and resampling steps. We demonstrate results in a parking lot domain tracking objects through occlusions and object interactions.
Tsanas, Athanasios; Zañartu, Matías; Little, Max A.; Fox, Cynthia; Ramig, Lorraine O.; Clifford, Gari D.
2014-01-01
There has been consistent interest among speech signal processing researchers in the accurate estimation of the fundamental frequency (F0) of speech signals. This study examines ten F0 estimation algorithms (some well-established and some proposed more recently) to determine which of these algorithms is, on average, better able to estimate F0 in the sustained vowel /a/. Moreover, a robust method for adaptively weighting the estimates of individual F0 estimation algorithms based on quality and performance measures is proposed, using an adaptive Kalman filter (KF) framework. The accuracy of the algorithms is validated using (a) a database of 117 synthetic realistic phonations obtained using a sophisticated physiological model of speech production and (b) a database of 65 recordings of human phonations where the glottal cycles are calculated from electroglottograph signals. On average, the sawtooth waveform inspired pitch estimator and the nearly defect-free algorithms provided the best individual F0 estimates, and the proposed KF approach resulted in a ∼16% improvement in accuracy over the best single F0 estimation algorithm. These findings may be useful in speech signal processing applications where sustained vowels are used to assess vocal quality, when very accurate F0 estimation is required. PMID:24815269
Tsanas, Athanasios; Zañartu, Matías; Little, Max A; Fox, Cynthia; Ramig, Lorraine O; Clifford, Gari D
2014-05-01
There has been consistent interest among speech signal processing researchers in the accurate estimation of the fundamental frequency (F(0)) of speech signals. This study examines ten F(0) estimation algorithms (some well-established and some proposed more recently) to determine which of these algorithms is, on average, better able to estimate F(0) in the sustained vowel /a/. Moreover, a robust method for adaptively weighting the estimates of individual F(0) estimation algorithms based on quality and performance measures is proposed, using an adaptive Kalman filter (KF) framework. The accuracy of the algorithms is validated using (a) a database of 117 synthetic realistic phonations obtained using a sophisticated physiological model of speech production and (b) a database of 65 recordings of human phonations where the glottal cycles are calculated from electroglottograph signals. On average, the sawtooth waveform inspired pitch estimator and the nearly defect-free algorithms provided the best individual F(0) estimates, and the proposed KF approach resulted in a ∼16% improvement in accuracy over the best single F(0) estimation algorithm. These findings may be useful in speech signal processing applications where sustained vowels are used to assess vocal quality, when very accurate F(0) estimation is required. PMID:24815269
NASA Astrophysics Data System (ADS)
Yao, Jianjun; Di, Duotao; Jiang, Guilin; Gao, Shuang
2012-10-01
Electro-hydraulic servo shaking table usually requires good control performance for acceleration replication. The poles of the electro-hydraulic servo shaking table are placed by three-variable control method using pole placement theory. The system frequency band is thus extended and the system stability is also enhanced. The phase delay and amplitude attenuation phenomenon occurs in electro-hydraulic servo shaking table corresponding to an acceleration sinusoidal input. The method for phase delay and amplitude attenuation elimination based on LMS adaptive filtering algorithm is proposed here. The task is accomplished by adjusting the weights using LMS adaptive filtering algorithm when there exits phase delay and amplitude attenuation between the input and its corresponding acceleration response. The reference input is weighted in such a way that it makes the system output track the input efficiently. The weighted input signal is inputted to the control system such that the output phase delay and amplitude attenuation are all cancelled. The above concept is used as a basis for the development of amplitude-phase regulation (APR) algorithm. The method does not need to estimate the system model and has good real-time performance. Experimental results demonstrate the efficiency and validity of the proposed APR control scheme.
Feng, Yibo; Li, Xisheng; Zhang, Xiaojuan
2015-01-01
We present an adaptive algorithm for a system integrated with micro-electro-mechanical systems (MEMS) gyroscopes and a compass to eliminate the influence from the environment, compensate the temperature drift precisely, and improve the accuracy of the MEMS gyroscope. We use a simplified drift model and changing but appropriate model parameters to implement this algorithm. The model of MEMS gyroscope temperature drift is constructed mostly on the basis of the temperature sensitivity of the gyroscope. As the state variables of a strong tracking Kalman filter (STKF), the parameters of the temperature drift model can be calculated to adapt to the environment under the support of the compass. These parameters change intelligently with the environment to maintain the precision of the MEMS gyroscope in the changing temperature. The heading error is less than 0.6° in the static temperature experiment, and also is kept in the range from 5° to -2° in the dynamic outdoor experiment. This demonstrates that the proposed algorithm exhibits strong adaptability to a changing temperature, and performs significantly better than KF and MLR to compensate the temperature drift of a gyroscope and eliminate the influence of temperature variation. PMID:25985165
Feng, Yibo; Li, Xisheng; Zhang, Xiaojuan
2015-01-01
We present an adaptive algorithm for a system integrated with micro-electro-mechanical systems (MEMS) gyroscopes and a compass to eliminate the influence from the environment, compensate the temperature drift precisely, and improve the accuracy of the MEMS gyroscope. We use a simplified drift model and changing but appropriate model parameters to implement this algorithm. The model of MEMS gyroscope temperature drift is constructed mostly on the basis of the temperature sensitivity of the gyroscope. As the state variables of a strong tracking Kalman filter (STKF), the parameters of the temperature drift model can be calculated to adapt to the environment under the support of the compass. These parameters change intelligently with the environment to maintain the precision of the MEMS gyroscope in the changing temperature. The heading error is less than 0.6° in the static temperature experiment, and also is kept in the range from 5° to −2° in the dynamic outdoor experiment. This demonstrates that the proposed algorithm exhibits strong adaptability to a changing temperature, and performs significantly better than KF and MLR to compensate the temperature drift of a gyroscope and eliminate the influence of temperature variation. PMID:25985165
Adaptable Iterative and Recursive Kalman Filter Schemes
NASA Technical Reports Server (NTRS)
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.
Objects tracking with adaptive correlation filters and Kalman filtering
NASA Astrophysics Data System (ADS)
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.
NASA Astrophysics Data System (ADS)
Nishimaru, Eiji; Ichikawa, Katsuhiro; Okita, Izumi; Ninomiya, Yuuji; Tomoshige, Yukihiro; Kurokawa, Takehiro; Ono, Yutaka; Nakamura, Yuko; Suzuki, Masayuki
2008-03-01
Recently, several kinds of post-processing image filters which reduce the noise of computed tomography (CT) images have been proposed. However, these image filters are mostly for adults. Because these are not very effective in small (< 20 cm) display fields of view (FOV), we cannot use them for pediatric body images (e.g., premature babies and infant children). We have developed a new noise reduction filter algorithm for pediatric body CT images. This algorithm is based on a 3D post-processing in which the output pixel values are calculated by nonlinear interpolation in z-directions on original volumetric-data-sets. This algorithm does not need the in-plane (axial plane) processing, so the spatial resolution does not change. From the phantom studies, our algorithm could reduce SD up to 40% without affecting the spatial resolution of x-y plane and z-axis, and improved the CNR up to 30%. This newly developed filter algorithm will be useful for the diagnosis and radiation dose reduction of the pediatric body CT images.
Reduction of MPEG ringing artifacts using adaptive sigma filter
NASA Astrophysics Data System (ADS)
Pan, Hao
2006-01-01
In this paper, we propose a novel computationally efficient post-processing algorithm to reduce ringing artifacts in the decoded DCT-coded video without using coding information. While the proposed algorithm is based on edge information as most filtering-based de-ringing algorithms do, this algorithm solely uses one single computationally efficient nonlinear filter, namely sigma filter, for both edge detection and smoothing. Specifically, the sigma filter, which was originally designed for nonlinear filtering, is extended to generate edge proximity information. Different from other adaptive filtering-based methods, whose filters typically use a fixed small window but flexible weights, this sigma filter adaptively switches between small and large windows. The adaptation is designed for removing ringing artifacts only, so the algorithm cannot be used for de-blocking. Overall, the proposed algorithm achieves a good balance among removing ringing artifacts, preserving edges and details, and computational complexity.
Recursive total-least-squares adaptive filtering
NASA Astrophysics Data System (ADS)
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.
A Windowing Frequency Domain Adaptive Filter for Acoustic Echo Cancellation
NASA Astrophysics Data System (ADS)
Wu, Sheng; Qiu, Xiaojun
This letter proposes a windowing frequency domain adaptive algorithm, which reuses the filtering error to apply window function in the filter updating symmetrically. By using a proper window function to reduce the negative influence of the spectral leakage, the proposed algorithm can significantly improve the performance of the acoustic echo cancellation for speech signals.
NASA Astrophysics Data System (ADS)
Kwon, Hyeokjun; Oh, Sechang; Varadan, Vijay K.
2012-04-01
The Electrocardiogram(ECG) signal is one of the bio-signals to check body status. Traditionally, the ECG signal was checked in the hospital. In these days, as the number of people who is interesting with periodic their health check increase, the requirement of self-diagnosis system development is being increased as well. Ubiquitous concept is one of the solutions of the self-diagnosis system. Zigbee wireless sensor network concept is a suitable technology to satisfy the ubiquitous concept. In measuring ECG signal, there are several kinds of methods in attaching electrode on the body called as Lead I, II, III, etc. In addition, several noise components occurred by different measurement situation such as experimenter's respiration, sensor's contact point movement, and the wire movement attached on sensor are included in pure ECG signal. Therefore, this paper is based on the two kinds of development concept. The first is the Zibee wireless communication technology, which can provide convenience and simpleness, and the second is motion artifact remove algorithm, which can detect clear ECG signal from measurement subject. The motion artifact created by measurement subject's movement or even respiration action influences to distort ECG signal, and the frequency distribution of the noises is around from 0.2Hz to even 30Hz. The frequencies are duplicated in actual ECG signal frequency, so it is impossible to remove the artifact without any distortion of ECG signal just by using low-pass filter or high-pass filter. The suggested algorithm in this paper has two kinds of main parts to extract clear ECG signal from measured original signal through an electrode. The first part is to extract motion noise signal from measured signal, and the second part is to extract clear ECG by using extracted motion noise signal and measured original signal. The paper suggests several techniques in order to extract motion noise signal such as predictability estimation theory, low pass filter
Filtering algorithm for dotted interferences
NASA Astrophysics Data System (ADS)
Osterloh, K.; Bücherl, T.; Lierse von Gostomski, Ch.; Zscherpel, U.; Ewert, U.; Bock, S.
2011-09-01
An algorithm has been developed to remove reliably dotted interferences impairing the perceptibility of objects within a radiographic image. This particularly is a major challenge encountered with neutron radiographs collected at the NECTAR facility, Forschungs-Neutronenquelle Heinz Maier-Leibnitz (FRM II): the resulting images are dominated by features resembling a snow flurry. These artefacts are caused by scattered neutrons, gamma radiation, cosmic radiation, etc. all hitting the detector CCD directly in spite of a sophisticated shielding. This makes such images rather useless for further direct evaluations. One approach to resolve this problem of these random effects would be to collect a vast number of single images, to combine them appropriately and to process them with common image filtering procedures. However, it has been shown that, e.g. median filtering, depending on the kernel size in the plane and/or the number of single shots to be combined, is either insufficient or tends to blur sharp lined structures. This inevitably makes a visually controlled processing image by image unavoidable. Particularly in tomographic studies, it would be by far too tedious to treat each single projection by this way. Alternatively, it would be not only more comfortable but also in many cases the only reasonable approach to filter a stack of images in a batch procedure to get rid of the disturbing interferences. The algorithm presented here meets all these requirements. It reliably frees the images from the snowy pattern described above without the loss of fine structures and without a general blurring of the image. It consists of an iterative, within a batch procedure parameter free filtering algorithm aiming to eliminate the often complex interfering artefacts while leaving the original information untouched as far as possible.
Adaptive filtering image preprocessing for smart FPA technology
NASA Astrophysics Data System (ADS)
Brooks, Geoffrey W.
1995-05-01
This paper discusses two applications of adaptive filters for image processing on parallel architectures. The first, based on the results of previously accomplished work, summarizes the analyses of various adaptive filters implemented for pixel-level image prediction. FIR filters, fixed and adaptive IIR filters, and various variable step size algorithms were compared with a focus on algorithm complexity against the ability to predict future pixel values. A gaussian smoothing operation with varying spatial and temporal constants were also applied for comparisons of random noise reductions. The second application is a suggestion to use memory-adaptive IIR filters for detecting and tracking motion within an image. Objects within an image are made of edges, or segments, with varying degrees of motion. An application has been previously published that describes FIR filters connecting pixels and using correlations to determine motion and direction. This implementation seems limited to detecting motion coinciding with FIR filter operation rate and the associated harmonics. Upgrading the FIR structures with adaptive IIR structures can eliminate these limitations. These and any other pixel-level adaptive filtering application require data memory for filter parameters and some basic computational capability. Tradeoffs have to be made between chip real estate and these desired features. System tradeoffs will also have to be made as to where it makes the most sense to do which level of processing. Although smart pixels may not be ready to implement adaptive filters, applications such as these should give the smart pixel designer some long range goals.
Frequency domain FIR and IIR adaptive filters
NASA Technical Reports Server (NTRS)
Lynn, D. W.
1990-01-01
A discussion of the LMS adaptive filter relating to its convergence characteristics and the problems associated with disparate eigenvalues is presented. This is used to introduce the concept of proportional convergence. An approach is used to analyze the convergence characteristics of block frequency-domain adaptive filters. This leads to a development showing how the frequency-domain FIR adaptive filter is easily modified to provide proportional convergence. These ideas are extended to a block frequency-domain IIR adaptive filter and the idea of proportional convergence is applied. Experimental results illustrating proportional convergence in both FIR and IIR frequency-domain block adaptive filters is presented.
Musical noise reduction using an adaptive filter
NASA Astrophysics Data System (ADS)
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.
Estimated spectrum adaptive postfilter and the iterative prepost filtering algirighms
NASA Technical Reports Server (NTRS)
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.
Analysis on Influence Factors of Adaptive Filter Acting on ANC
NASA Astrophysics Data System (ADS)
Zhang, Xiuqun; Zou, Liang; Ni, Guangkui; Wang, Xiaojun; Han, Tao; Zhao, Quanfu
The noise problem has become more and more serious in recent years. The adaptive filter theory which is applied in ANC [1] (active noise control) has also attracted more and more attention. In this article, the basic principle and algorithm of adaptive theory are both researched. And then the influence factor that affects its covergence rate and noise reduction is also simulated.
Adaptive WMMR filters for edge enhancement
NASA Astrophysics Data System (ADS)
Zhou, Jun; Longbotham, Harold G.
1993-05-01
In this paper, an adaptive WMMR filter is introduced, which adaptively changes its window size to accommodate edge width variations. We prove that for any given one dimensional input signal convergence is to fixed points, which are PICO (piecewise constant), by iterative application of the adaptive WMMR filter. An application of the filters to one-D data (non- PICO) and images of printed circuit boards are then provided. Application to images in general is discussed.
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
An improved adaptive deblocking filter for MPEG video decoder
NASA Astrophysics Data System (ADS)
Kwon, Do-Kyoung; Shen, Mei-Yin; Kuo, C.-C. Jay
2005-03-01
A highly adaptive deblocking algorithm is proposed for MPEG video in this research. In comparison with previous work in this area, the proposed deblocking filter improves in three aspects. First, the proposed algorithm is adaptive to the change of the quantization parameter (QP). Since blocking artifacts between two blocks encoded with different QPs tend to be more visible due to quality difference, filters should be able to adapt dynamically to the QP change between blocks. Second, the proposed algorithm classifies the block boundary into three different region modes based on local region characteristics. The three modes are active, smooth and dormant regions. The active region represents a complex region with details and high activities while the smooth and the dormant regions refer to moderately flat and extremely flat regions, respectively. By applying different filters of different strengths to each region mode, the proposed algorithm can minimize the undesirable blur so that both subjective and objective qualities improve for various types of sequences at a wide range of bitrates. Finally, the proposed algorithm also provides a way to determine the threshold values. The proposed adaptive deblocking algorithms require several thresholds in determining proper region modes and filters. Since the quality of image sequences after filtering depends largely on the threshold values, they have to be determined carefully. In the proposed algorithm, thresholds are determined adaptively to the strength of the blocking artifact and, as a result, to various encoding parameters such as QP, absolute difference between QPs, the coding type, and motion vectors. It is shown by experimental results that the proposed algorithm can achieve 0.2-0.4 dB gains for I- and P-frames, and 0.1-0.3 dB gains for the B-frame when bit streams are encoded using the TM5 rate control algorithm.
Dynamic analysis of neural encoding by point process adaptive filtering.
Eden, Uri T; Frank, Loren M; Barbieri, Riccardo; Solo, Victor; Brown, Emery N
2004-05-01
Neural receptive fields are dynamic in that with experience, neurons change their spiking responses to relevant stimuli. To understand how neural systems adapt their representations of biological information, analyses of receptive field plasticity from experimental measurements are crucial. Adaptive signal processing, the well-established engineering discipline for characterizing the temporal evolution of system parameters, suggests a framework for studying the plasticity of receptive fields. We use the Bayes' rule Chapman-Kolmogorov paradigm with a linear state equation and point process observation models to derive adaptive filters appropriate for estimation from neural spike trains. We derive point process filter analogues of the Kalman filter, recursive least squares, and steepest-descent algorithms and describe the properties of these new filters. We illustrate our algorithms in two simulated data examples. The first is a study of slow and rapid evolution of spatial receptive fields in hippocampal neurons. The second is an adaptive decoding study in which a signal is decoded from ensemble neural spiking activity as the receptive fields of the neurons in the ensemble evolve. Our results provide a paradigm for adaptive estimation for point process observations and suggest a practical approach for constructing filtering algorithms to track neural receptive field dynamics on a millisecond timescale. PMID:15070506
Adaptive Mallow's optimization for weighted median filters
NASA Astrophysics Data System (ADS)
Rachuri, Raghu; Rao, Sathyanarayana S.
2002-05-01
This work extends the idea of spectral optimization for the design of Weighted Median filters and employ adaptive filtering that updates the coefficients of the FIR filter from which the weights of the median filters are derived. Mallows' theory of non-linear smoothers [1] has proven to be of great theoretical significance providing simple design guidelines for non-linear smoothers. It allows us to find a set of positive weights for a WM filter whose sample selection probabilities (SSP's) are as close as possible to a SSP set predetermined by Mallow's. Sample selection probabilities have been used as a basis for designing stack smoothers as they give a measure of the filter's detail preserving ability and give non-negative filter weights. We will extend this idea to design weighted median filters admitting negative weights. The new method first finds the linear FIR filter coefficients adaptively, which are then used to determine the weights of the median filter. WM filters can be designed to have band-pass, high-pass as well as low-pass frequency characteristics. Unlike the linear filters, however, the weighted median filters are robust in the presence of impulsive noise, as shown by the simulation results.
NASA Astrophysics Data System (ADS)
Hu, Hongtao; Jing, Zhongliang; Hu, Shiqiang
2006-12-01
A novel adaptive algorithm for tracking maneuvering targets is proposed. The algorithm is implemented with fuzzy-controlled current statistic model adaptive filtering and unscented transformation. A fuzzy system allows the filter to tune the magnitude of maximum accelerations to adapt to different target maneuvers, and unscented transformation can effectively handle nonlinear system. A bearing-only tracking scenario simulation results show the proposed algorithm has a robust advantage over a wide range of maneuvers and overcomes the shortcoming of the traditional current statistic model and adaptive filtering algorithm.
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
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
Infinite impulse response modal filtering in visible adaptive optics
NASA Astrophysics Data System (ADS)
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.
Real time adaptive filtering for digital X-ray applications.
Bockenbach, Olivier; Mangin, Michel; Schuberth, Sebastian
2006-01-01
Over the last decade, many methods for adaptively filtering a data stream have been proposed. Those methods have applications in two dimensional imaging as well as in three dimensional image reconstruction. Although the primary objective of this filtering technique is to reduce the noise while avoiding to blur the edges, diagnostic, automated segmentation and surgery show a growing interest in enhancing the features contained in the image flow. Most of the methods proposed so far emerged from thorough studies of the physics of the considered modality and therefore show only a marginal capability to be extended across modalities. Moreover, adaptive filtering belongs to the family of processing intensive algorithms. Existing technology has often driven to simplifications and modality specific optimization to sustain the expected performances. In the specific case of real time digital X-ray as used surgery, the system has to sustain a throughput of 30 frames per second. In this study, we take a generalized approach for adaptive filtering based on multiple oriented filters. Mapping the filtering part to the embedded real time image processing while a user/application defined adaptive recombination of the filter outputs allow to change the smoothing and edge enhancement properties of the filter without changing the oriented filter parameters. We have implemented the filtering on a Cell Broadband Engine processor and the adaptive recombination on an off-the-shelf PC, connected via Gigabit Ethernet. This implementation is capable of filtering images of 5122 pixels at a throughput in excess of 40 frames per second while allowing to change the parameters in real time. PMID:17354937
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
Demosaicking algorithm for the Kodak-RGBW color filter array
NASA Astrophysics Data System (ADS)
Rafinazari, M.; Dubois, E.
2015-01-01
Digital cameras capture images through different Color Filter Arrays and then reconstruct the full color image. Each CFA pixel only captures one primary color component; the other primary components will be estimated using information from neighboring pixels. During the demosaicking algorithm, the two unknown color components will be estimated at each pixel location. Most of the demosaicking algorithms use the RGB Bayer CFA pattern with Red, Green and Blue filters. The least-Squares Luma-Chroma demultiplexing method is a state of the art demosaicking method for the Bayer CFA. In this paper we develop a new demosaicking algorithm using the Kodak-RGBW CFA. This particular CFA reduces noise and improves the quality of the reconstructed images by adding white pixels. We have applied non-adaptive and adaptive demosaicking method using the Kodak-RGBW CFA on the standard Kodak image dataset and the results have been compared with previous work.
A hybrid method for optimization of the adaptive Goldstein filter
NASA Astrophysics Data System (ADS)
Jiang, Mi; Ding, Xiaoli; Tian, Xin; Malhotra, Rakesh; Kong, Weixue
2014-12-01
The Goldstein filter is a well-known filter for interferometric filtering in the frequency domain. The main parameter of this filter, alpha, is set as a power of the filtering function. Depending on it, considered areas are strongly or weakly filtered. Several variants have been developed to adaptively determine alpha using different indicators such as the coherence, and phase standard deviation. The common objective of these methods is to prevent areas with low noise from being over filtered while simultaneously allowing stronger filtering over areas with high noise. However, the estimators of these indicators are biased in the real world and the optimal model to accurately determine the functional relationship between the indicators and alpha is also not clear. As a result, the filter always under- or over-filters and is rarely correct. The study presented in this paper aims to achieve accurate alpha estimation by correcting the biased estimator using homogeneous pixel selection and bootstrapping algorithms, and by developing an optimal nonlinear model to determine alpha. In addition, an iteration is also merged into the filtering procedure to suppress the high noise over incoherent areas. The experimental results from synthetic and real data show that the new filter works well under a variety of conditions and offers better and more reliable performance when compared to existing approaches.
Tracking filter algorithm for automatic video tracking
NASA Astrophysics Data System (ADS)
McEver, Mark A.; Kimbrell, James E.
2006-05-01
In addition to servo control and power amplification, motion control systems for optical tracking pedestals feature capabilities such as electro-optical tracking using an integrated Automatic Video Tracker (AVT) card. An electro-optical system tracking loop is comprised of sensors mounted on a pointing pedestal, an AVT that detects a target in the sensor imagery, and a tracking filter algorithm that commands the pedestal to follow the target. The tracking filter algorithm receives the target boresight error from the AVT and calculates motion demands for the pedestal servo controller. This paper presents a tracking algorithm based on target state estimation using a Kalman filter. The servo demands are based on calculating the Kalman filter state estimate from absolute line-of-sight angles to the target. Simulations are used to compare its performance to tracking loops without tracking filters, and to other tracking filter algorithms, such as rate feedback loops closed around boresight error. Issues such as data latency and sensor alignment error are discussed.
Enhancement of Electrolaryngeal Speech by Adaptive Filtering.
ERIC Educational Resources Information Center
Espy-Wilson, Carol Y.; Chari, Venkatesh R.; MacAuslan, Joel M.; Huang, Caroline B.; Walsh, Michael J.
1998-01-01
A study tested the quality and intelligibility, as judged by several listeners, of four users' electrolaryngeal speech, with and without filtering to compensate for perceptually objectionable acoustic characteristics. Results indicated that an adaptive filtering technique produced a noticeable improvement in the quality of the Transcutaneous…
Cubit Adaptive Meshing Algorithm Library
Energy Science and Technology Software Center (ESTSC)
2004-09-01
CAMAL (Cubit adaptive meshing algorithm library) is a software component library for mesh generation. CAMAL 2.0 includes components for triangle, quad and tetrahedral meshing. A simple Application Programmers Interface (API) takes a discrete boundary definition and CAMAL computes a quality interior unstructured grid. The triangle and quad algorithms may also import a geometric definition of a surface on which to define the grid. CAMALs triangle meshing uses a 3D space advancing front method, the quadmore » meshing algorithm is based upon Sandias patented paving algorithm and the tetrahedral meshing algorithm employs the GHS3D-Tetmesh component developed by INRIA, France.« less
Adaptive protection algorithm and system
Hedrick, Paul [Pittsburgh, PA; Toms, Helen L [Irwin, PA; Miller, Roger M [Mars, PA
2009-04-28
An adaptive protection algorithm and system for protecting electrical distribution systems traces the flow of power through a distribution system, assigns a value (or rank) to each circuit breaker in the system and then determines the appropriate trip set points based on the assigned rank.
Extended adaptive filtering for wide-angle SAR image formation
NASA Astrophysics Data System (ADS)
Wang, Yanwei; Roberts, William; Li, Jian
2005-05-01
For two-dimensional (2-D) spectral analysis, the adaptive filtering based technologies, such as CAPON and APES (Amplitude and Phase EStimation), are developed under the implicit assumption that the data sets are rectangular. However, in real SAR applications, especially for the wide-angle cases, the collected data sets are always non-rectangular. This raises the problem of how to extend the original adaptive filtering based algorithms for such kind of scenarios. In this paper, we propose an extended adaptive filtering (EAF) approach, which includes Extended APES (E-APES) and Extended CAPON (E-CAPON), for arbitrarily shaped 2-D data. The EAF algorithms adopt a missing-data approach where the unavailable data samples close to the collected data set are assumed missing. Using a group of filter-banks with varying sizes, these algorithms are non-iterative and do not require the estimation of the unavailable samples. The improved imaging results of the proposed algorithms are demonstrated by applying them to two different SAR data sets.
Fault-tolerant adaptive FIR filters using variable detection threshold
NASA Astrophysics Data System (ADS)
Lin, L. K.; Redinbo, G. R.
1994-10-01
Adaptive filters are widely used in many digital signal processing applications, where tap weight of the filters are adjusted by stochastic gradient search methods. Block adaptive filtering techniques, such as block least mean square and block conjugate gradient algorithm, were developed to speed up the convergence as well as improve the tracking capability which are two important factors in designing real-time adaptive filter systems. Even though algorithm-based fault tolerance can be used as a low-cost high level fault-tolerant technique to protect the aforementioned systems from hardware failures with minimal hardware overhead, the issue of choosing a good detection threshold remains a challenging problem. First of all, the systems usually only have limited computational resources, i.e., concurrent error detection and correction is not feasible. Secondly, any prior knowledge of input data is very difficult to get in practical settings. We propose a checksum-based fault detection scheme using two-level variable detection thresholds that is dynamically dependent on the past syndromes. Simulations show that the proposed scheme reduces the possibility of false alarms and has a high degree of fault coverage in adaptive filter systems.
Learning algorithms for stack filter classifiers
Porter, Reid B; Hush, Don; Zimmer, Beate G
2009-01-01
Stack Filters define a large class of increasing filter that is used widely in image and signal processing. The motivations for using an increasing filter instead of an unconstrained filter have been described as: (1) fast and efficient implementation, (2) the relationship to mathematical morphology and (3) more precise estimation with finite sample data. This last motivation is related to methods developed in machine learning and the relationship was explored in an earlier paper. In this paper we investigate this relationship by applying Stack Filters directly to classification problems. This provides a new perspective on how monotonicity constraints can help control estimation and approximation errors, and also suggests several new learning algorithms for Boolean function classifiers when they are applied to real-valued inputs.
NASA Astrophysics Data System (ADS)
Campos Trujillo, Oliver G.; Díaz Blancas, Gerardo
2014-09-01
In recent years, many proposals that consider an adaptive perspective had been developed to solve some drawbacks, such as geometric distortions, background noise and target discrimination. The metrics are based only in the correlation peak output for the filter synthesis. In this paper, the correlation shape is studied to implement adaptive correlation filters guided by the peak and shape of the correlation output. Furthermore, the shape of correlation output is studied to improve the search in the filters bank. In addition, parallel algorithms are developed for accelerated the search in the filters bank. Some results are shown, such as time of synthesis, filter performance and comparisons with other adaptive correlation filter proposals.
Deinterlacing algorithm with an advanced non-local means filter
NASA Astrophysics Data System (ADS)
Wang, Jin; Jeon, Gwanggil; Jeong, Jechang
2012-04-01
The authors introduce an efficient intra-field deinterlacing algorithm using an advanced non-local means filter. The non-local means (NLM) method has received considerable attention due to its high performance and simplicity. The NLM method adaptively obtains the missing pixel by the weighted average of the gray values of all pixels within the image, and then automatically eliminates unrelated neighborhoods from the weighted average. However, spatial location distance is another important issue for the deinterlacing method. Therefore we introduce an advanced NLM (ANLM) filter while consider neighborhood similarity and patch distance. Moreover, the search region of the conventional NLM is the whole image, while, the ANLM can just utilize the limited search region and achieve good performance and high efficiency. When compared with existing deinterlacing algorithms, the proposed algorithm improves the peak signal-to-noise-ratio while maintaining high efficiency.
Adaptive color image watermarking algorithm
NASA Astrophysics Data System (ADS)
Feng, Gui; Lin, Qiwei
2008-03-01
As a major method for intellectual property right protecting, digital watermarking techniques have been widely studied and used. But due to the problems of data amount and color shifted, watermarking techniques on color image was not so widespread studied, although the color image is the principal part for multi-medium usages. Considering the characteristic of Human Visual System (HVS), an adaptive color image watermarking algorithm is proposed in this paper. In this algorithm, HSI color model was adopted both for host and watermark image, the DCT coefficient of intensity component (I) of the host color image was used for watermark date embedding, and while embedding watermark the amount of embedding bit was adaptively changed with the complex degree of the host image. As to the watermark image, preprocessing is applied first, in which the watermark image is decomposed by two layer wavelet transformations. At the same time, for enhancing anti-attack ability and security of the watermarking algorithm, the watermark image was scrambled. According to its significance, some watermark bits were selected and some watermark bits were deleted as to form the actual embedding data. The experimental results show that the proposed watermarking algorithm is robust to several common attacks, and has good perceptual quality at the same time.
Nonlinear filter algorithm for opto-electronic target tracking
NASA Astrophysics Data System (ADS)
Xiong, Mao-tao; Song, Jian-xun; Wu, Qin-zhang
2009-05-01
At present in opto-electronic targets tracking, traditional accepted algorithms inevitably connect with linear errors. To improve the degraded performance of general algorithms, the Adaptive Unscented Particle Kalman Filter (AUPF) algorithm is proposed. The algorithm combines with Unscented Kalman Filter (UKF) to incorporate the most current observation datum and to generate the importance density function. Additionally, the Markov Chain Monte Carlo (MCMC) steps are adopted to counteract the problem of particles impoverishment caused by re-sampling step and thus the diversity of the particles is maintained. The AUPF algorithm reduces the nonlinear influence and improves the tracking accuracy of the opto-electronic targets tracking system. The analytic results of Monte Carlo simulation prove the AUPF algorithm is right and feasible, and it enhances the stability, the convergence rate and the accuracy of tracking system. The simulation results and algorithm provide a new approach for precise tracking of opto-electronic targets, they must have better applicative prospect in various engineering than the traditional tracking algorithms.
A comparison of discrete linear filtering algorithms.
NASA Technical Reports Server (NTRS)
Bierman, G. J.
1973-01-01
Seven filter algorithms were presented in a recent survey paper (Kaminski, 1971), and were compared computationally (operations count) when relatively few observations were to be processed. These algorithms are now elaborated further. Details of the computations are presented, and it is shown that for problems with even moderately large amounts of data, the information matrix and square-root information matrix formulations are computationally more efficient than the other methods considered (conventional Kalman, stabilized Kalman, and square-root covariance mechanizations). It is pointed out that Schmidt's matrix factorization-Householder transformation technique leads to the same equations as those obtained via Potter's method. Several improvements in the equation mechanization are given.
VSP wave separation by adaptive masking filters
NASA Astrophysics Data System (ADS)
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.
A New Method to Cancel RFI---The Adaptive Filter
NASA Astrophysics Data System (ADS)
Bradley, R.; Barnbaum, C.
1996-12-01
An increasing amount of precious radio frequency spectrum in the VHF, UHF, and microwave bands is being utilized each year to support new commercial and military ventures, and all have the potential to interfere with radio astronomy observations. Some radio spectral lines of astronomical interest occur outside the protected radio astronomy bands and are unobservable due to heavy interference. Conventional approaches to deal with RFI include legislation, notch filters, RF shielding, and post-processing techniques. Although these techniques are somewhat successful, each suffers from insufficient interference cancellation. One concept of interference excision that has not been used before in radio astronomy is adaptive interference cancellation. The concept of adaptive interference canceling was first introduced in the mid-1970s as a way to reduce unwanted noise in low frequency (audio) systems. Examples of such systems include the canceling of maternal ECG in fetal electrocardiography and the reduction of engine noise in the passenger compartment of automobiles. Only recently have high-speed digital filter chips made adaptive filtering possible in a bandwidth as large a few megahertz, finally opening the door to astronomical uses. The system consists of two receivers: the main beam of the radio telescope receives the desired signal corrupted by RFI coming in the sidelobes, and the reference antenna receives only the RFI. The reference antenna is processed using a digital adaptive filter and then subtracted from the signal in the main beam, thus producing the system output. The weights of the digital filter are adjusted by way of an algorithm that minimizes, in a least-squares sense, the power output of the system. Through an adaptive-iterative process, the interference canceler will lock onto the RFI and the filter will adjust itself to minimize the effect of the RFI at the system output. We are building a prototype 100 MHz receiver and will measure the cancellation
Birefringent filter design by use of a modified genetic algorithm.
Wen, Mengtao; Yao, Jianping
2006-06-10
A modified genetic algorithm is proposed for the optimization of fiber birefringent filters. The orientation angles and the element lengths are determined by the genetic algorithm to minimize the sidelobe levels of the filters. Being different from the normal genetic algorithm, the algorithm proposed reduces the problem space of the birefringent filter design to achieve faster speed and better performance. The design of 4-, 8-, and 14-section birefringent filters with an improved sidelobe suppression ratio is realized. A 4-section birefringent filter designed with the algorithm is experimentally realized. PMID:16761031
Adaptive Filtering Using Recurrent Neural Networks
NASA Technical Reports Server (NTRS)
Parlos, Alexander G.; Menon, Sunil K.; Atiya, Amir F.
2005-01-01
A method for adaptive (or, optionally, nonadaptive) filtering has been developed for estimating the states of complex process systems (e.g., chemical plants, factories, or manufacturing processes at some level of abstraction) from time series of measurements of system inputs and outputs. The method is based partly on the fundamental principles of the Kalman filter and partly on the use of recurrent neural networks. The standard Kalman filter involves an assumption of linearity of the mathematical model used to describe a process system. The extended Kalman filter accommodates a nonlinear process model but still requires linearization about the state estimate. Both the standard and extended Kalman filters involve the often unrealistic assumption that process and measurement noise are zero-mean, Gaussian, and white. In contrast, the present method does not involve any assumptions of linearity of process models or of the nature of process noise; on the contrary, few (if any) assumptions are made about process models, noise models, or the parameters of such models. In this regard, the method can be characterized as one of nonlinear, nonparametric filtering. The method exploits the unique ability of neural networks to approximate nonlinear functions. In a given case, the process model is limited mainly by limitations of the approximation ability of the neural networks chosen for that case. Moreover, despite the lack of assumptions regarding process noise, the method yields minimum- variance filters. In that they do not require statistical models of noise, the neural- network-based state filters of this method are comparable to conventional nonlinear least-squares estimators.
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.
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
Adaptive noise Wiener filter for scanning electron microscope imaging system.
Sim, K S; Teh, V; Nia, M E
2016-01-01
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. PMID:26235517
An Efficient Adaptive Weighted Switching Median Filter for Removing High Density Impulse Noise
NASA Astrophysics Data System (ADS)
Nair, Madhu S.; Ameera Mol, P. M.
2014-09-01
Restoration of images corrupted by impulse noise is a very active research area in image processing. In this paper, an Efficient Adaptive Weighted Switching Median filter for restoration of images that are corrupted by high density impulse noise is proposed. The filtering is performed as a two phase process—a detection phase followed by a filtering phase. In the proposed method, noise detection is done by HEIND algorithm proposed by Duan et al. The filtering algorithm is then applied to the pixels which are detected as noisy by the detection algorithm. All uncorrupted pixels in the image are left unchanged. The filtering window size is chosen adaptively depending on the local noise distribution around each corrupted pixels. Noisy pixels are replaced by a weighted median value of uncorrupted pixels in the filtering window. The weight value assigned to each uncorrupted pixels depends on its closeness to the central pixel.
An adaptive filtered back-projection for photoacoustic image reconstruction
Huang, He; Bustamante, Gilbert; Peterson, Ralph; Ye, Jing Yong
2015-01-01
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
An adaptive filtered back-projection for photoacoustic image reconstruction
Huang, He; Bustamante, Gilbert; Peterson, Ralph; Ye, Jing Yong
2015-05-15
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
NASA Astrophysics Data System (ADS)
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.
Belyakov, A.A.; Mal`tsev, A.A.; Medvedev, S.Yu.
1995-04-01
A modified least squares algorithm, preventing the overflow of the discharge grid of weight coefficients of an adaptive transverse filter and guaranteeing stable system operation, is suggested for the tuning of an adaptive system of an actively quenched sound field. Experimental results are provided for an adaptive filter with a modified algorithm in a system of several harmonic components of an actively quenched sound field.
Modeling of Rate-Dependent Hysteresis Using a GPO-Based Adaptive Filter.
Zhang, Zhen; Ma, Yaopeng
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) algorithm is used to adjust the weights of the nonlinear filter. The modeling results of four adaptive filter methods are compared: GPO-based adaptive filter, Volterra filter, backlash filter and linear adaptive filter. Moreover, a phenomenological operator-based model, the rate-dependent generalized Prandtl-Ishlinskii (RDGPI) model, is compared to the proposed adaptive filter. The various rate-dependent modeling methods are applied to model the rate-dependent hysteresis of a giant magnetostrictive actuator (GMA). It is shown from the modeling results that the GPO-based adaptive filter can describe the rate-dependent hysteresis nonlinear of the GMA more accurately and effectively. PMID:26861349
Modeling of Rate-Dependent Hysteresis Using a GPO-Based Adaptive Filter
Zhang, Zhen; Ma, Yaopeng
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) algorithm is used to adjust the weights of the nonlinear filter. The modeling results of four adaptive filter methods are compared: GPO-based adaptive filter, Volterra filter, backlash filter and linear adaptive filter. Moreover, a phenomenological operator-based model, the rate-dependent generalized Prandtl-Ishlinskii (RDGPI) model, is compared to the proposed adaptive filter. The various rate-dependent modeling methods are applied to model the rate-dependent hysteresis of a giant magnetostrictive actuator (GMA). It is shown from the modeling results that the GPO-based adaptive filter can describe the rate-dependent hysteresis nonlinear of the GMA more accurately and effectively. PMID:26861349
Hernandez, Wilmar; de Vicente, Jesús; Sergiyenko, Oleg Y; Fernández, Eduardo
2010-01-01
In this paper, the fast least-mean-squares (LMS) algorithm was used to both eliminate noise corrupting the important information coming from a piezoresisitive accelerometer for automotive applications, and improve the convergence rate of the filtering process based on the conventional LMS algorithm. The response of the accelerometer under test was corrupted by process and measurement noise, and the signal processing stage was carried out by using both conventional filtering, which was already shown in a previous paper, and optimal adaptive filtering. The adaptive filtering process relied on the LMS adaptive filtering family, which has shown to have very good convergence and robustness properties, and here a comparative analysis between the results of the application of the conventional LMS algorithm and the fast LMS algorithm to solve a real-life filtering problem was carried out. In short, in this paper the piezoresistive accelerometer was tested for a multi-frequency acceleration excitation. Due to the kind of test conducted in this paper, the use of conventional filtering was discarded and the choice of one adaptive filter over the other was based on the signal-to-noise ratio improvement and the convergence rate. PMID:22315579
Vectorization of linear discrete filtering algorithms
NASA Technical Reports Server (NTRS)
Schiess, J. R.
1977-01-01
Linear filters, including the conventional Kalman filter and versions of square root filters devised by Potter and Carlson, are studied for potential application on streaming computers. The square root filters are known to maintain a positive definite covariance matrix in cases in which the Kalman filter diverges due to ill-conditioning of the matrix. Vectorization of the filters is discussed, and comparisons are made of the number of operations and storage locations required by each filter. The Carlson filter is shown to be the most efficient of the filters on the Control Data STAR-100 computer.
NASA Technical Reports Server (NTRS)
Kelly, D. A.; Fermelia, A.; Lee, G. K. F.
1990-01-01
An adaptive Kalman filter design that utilizes recursive maximum likelihood parameter identification is discussed. At the center of this design is the Kalman filter itself, which has the responsibility for attitude determination. At the same time, the identification algorithm is continually identifying the system parameters. The approach is applicable to nonlinear, as well as linear systems. This adaptive Kalman filter design has much potential for real time implementation, especially considering the fast clock speeds, cache memory and internal RAM available today. The recursive maximum likelihood algorithm is discussed in detail, with special attention directed towards its unique matrix formulation. The procedure for using the algorithm is described along with comments on how this algorithm interacts with the Kalman filter.
Adaptive noise cancellation based on beehive pattern evolutionary digital filter
NASA Astrophysics Data System (ADS)
Zhou, Xiaojun; Shao, Yimin
2014-01-01
Evolutionary digital filtering (EDF) exhibits the advantage of avoiding the local optimum problem by using cloning and mating searching rules in an adaptive noise cancellation system. However, convergence performance is restricted by the large population of individuals and the low level of information communication among them. The special beehive structure enables the individuals on neighbour beehive nodes to communicate with each other and thus enhance the information spread and random search ability of the algorithm. By introducing the beehive pattern evolutionary rules into the original EDF, this paper proposes an improved beehive pattern evolutionary digital filter (BP-EDF) to overcome the defects of the original EDF. In the proposed algorithm, a new evolutionary rule which combines competing cloning, complete cloning and assistance mating methods is constructed to enable the individuals distributed on the beehive to communicate with their neighbours. Simulation results are used to demonstrate the improved performance of the proposed algorithm in terms of convergence speed to the global optimum compared with the original methods. Experimental results also verify the effectiveness of the proposed algorithm in extracting feature signals that are contaminated by significant amounts of noise during the fault diagnosis task.
A New Adaptive Framework for Collaborative Filtering Prediction.
Almosallam, Ibrahim A; Shang, Yi
2008-06-01
Collaborative filtering is one of the most successful techniques for recommendation systems and has been used in many commercial services provided by major companies including Amazon, TiVo and Netflix. In this paper we focus on memory-based collaborative filtering (CF). Existing CF techniques work well on dense data but poorly on sparse data. To address this weakness, we propose to use z-scores instead of explicit ratings and introduce a mechanism that adaptively combines global statistics with item-based values based on data density level. We present a new adaptive framework that encapsulates various CF algorithms and the relationships among them. An adaptive CF predictor is developed that can self adapt from user-based to item-based to hybrid methods based on the amount of available ratings. Our experimental results show that the new predictor consistently obtained more accurate predictions than existing CF methods, with the most significant improvement on sparse data sets. When applied to the Netflix Challenge data set, our method performed better than existing CF and singular value decomposition (SVD) methods and achieved 4.67% improvement over Netflix's system. PMID:21572924
Adaptive box filters for removal of random noise from digital images
Eliason, E.M.; McEwen, A.S.
1990-01-01
We have developed adaptive box-filtering algorithms to (1) remove random bit errors (pixel values with no relation to the image scene) and (2) smooth noisy data (pixels related to the image scene but with an additive or multiplicative component of noise). For both procedures, we use the standard deviation (??) of those pixels within a local box surrounding each pixel, hence they are adaptive filters. This technique effectively reduces speckle in radar images without eliminating fine details. -from Authors
Low-Complexity Lossless Compression of Hyperspectral Imagery Via Adaptive Filtering
NASA Technical Reports Server (NTRS)
Klimesh, Matthew A.
2005-01-01
A low-complexity, adaptive predictive technique for lossless compression of hyperspectral data is presented. The technique relies on the sign algorithm from the repertoire of adaptive filtering. The compression effectiveness obtained with the technique is competitive with that of the best of previously described techniques with similar complexity.
Low-Complexity Lossless Compression of Hyperspectral Imagery via Adaptive Filtering
NASA Technical Reports Server (NTRS)
Klimesh, M.
2005-01-01
A low-complexity, adaptive predictive technique for lossless compression of hyperspectral data is presented. The technique relies on the sign algorithm from the repertoire of adaptive filtering. The compression effectiveness obtained with the technique is competitive with that of the best of previously described techniques with similar complexity.
NASA Technical Reports Server (NTRS)
Whitmore, S. A.
1985-01-01
The dynamics model and data sources used to perform air-data reconstruction are discussed, as well as the Kalman filter. The need for adaptive determination of the noise statistics of the process is indicated. The filter innovations are presented as a means of developing the adaptive criterion, which is based on the true mean and covariance of the filter innovations. A method for the numerical approximation of the mean and covariance of the filter innovations is presented. The algorithm as developed is applied to air-data reconstruction for the space shuttle, and data obtained from the third landing are presented. To verify the performance of the adaptive algorithm, the reconstruction is also performed using a constant covariance Kalman filter. The results of the reconstructions are compared, and the adaptive algorithm exhibits better performance.
NASA Technical Reports Server (NTRS)
Whitmore, S. A.
1985-01-01
The dynamics model and data sources used to perform air-data reconstruction are discussed, as well as the Kalman filter. The need for adaptive determination of the noise statistics of the process is indicated. The filter innovations are presented as a means of developing the adaptive criterion, which is based on the true mean and covariance of the filter innovations. A method for the numerical approximation of the mean and covariance of the filter innovations is presented. The algorithm as developed is applied to air-data reconstruction for the Space Shuttle, and data obtained from the third landing are presented. To verify the performance of the adaptive algorithm, the reconstruction is also performed using a constant covariance Kalman filter. The results of the reconstructions are compared, and the adaptive algorithm exhibits better performance.
Geometric-Algebra LMS Adaptive Filter and Its Application to Rotation Estimation
NASA Astrophysics Data System (ADS)
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.
The Kernel Adaptive Autoregressive-Moving-Average Algorithm.
Li, Kan; Príncipe, José C
2016-02-01
In this paper, we present a novel kernel adaptive recurrent filtering algorithm based on the autoregressive-moving-average (ARMA) model, which is trained with recurrent stochastic gradient descent in the reproducing kernel Hilbert spaces. This kernelized recurrent system, the kernel adaptive ARMA (KAARMA) algorithm, brings together the theories of adaptive signal processing and recurrent neural networks (RNNs), extending the current theory of kernel adaptive filtering (KAF) using the representer theorem to include feedback. Compared with classical feedforward KAF methods, the KAARMA algorithm provides general nonlinear solutions for complex dynamical systems in a state-space representation, with a deferred teacher signal, by propagating forward the hidden states. We demonstrate its capabilities to provide exact solutions with compact structures by solving a set of benchmark nondeterministic polynomial-complete problems involving grammatical inference. Simulation results show that the KAARMA algorithm outperforms equivalent input-space recurrent architectures using first- and second-order RNNs, demonstrating its potential as an effective learning solution for the identification and synthesis of deterministic finite automata. PMID:25935049
QPSO-Based Adaptive DNA Computing Algorithm
Karakose, Mehmet; Cigdem, Ugur
2013-01-01
DNA (deoxyribonucleic acid) computing that is a new computation model based on DNA molecules for information storage has been increasingly used for optimization and data analysis in recent years. However, DNA computing algorithm has some limitations in terms of convergence speed, adaptability, and effectiveness. In this paper, a new approach for improvement of DNA computing is proposed. This new approach aims to perform DNA computing algorithm with adaptive parameters towards the desired goal using quantum-behaved particle swarm optimization (QPSO). Some contributions provided by the proposed QPSO based on adaptive DNA computing algorithm are as follows: (1) parameters of population size, crossover rate, maximum number of operations, enzyme and virus mutation rate, and fitness function of DNA computing algorithm are simultaneously tuned for adaptive process, (2) adaptive algorithm is performed using QPSO algorithm for goal-driven progress, faster operation, and flexibility in data, and (3) numerical realization of DNA computing algorithm with proposed approach is implemented in system identification. Two experiments with different systems were carried out to evaluate the performance of the proposed approach with comparative results. Experimental results obtained with Matlab and FPGA demonstrate ability to provide effective optimization, considerable convergence speed, and high accuracy according to DNA computing algorithm. PMID:23935409
Adaptive Wiener filter super-resolution of color filter array images.
Karch, Barry K; Hardie, Russell C
2013-08-12
Digital color cameras using a single detector array with a Bayer color filter array (CFA) require interpolation or demosaicing to estimate missing color information and provide full-color images. However, demosaicing does not specifically address fundamental undersampling and aliasing inherent in typical camera designs. Fast non-uniform interpolation based super-resolution (SR) is an attractive approach to reduce or eliminate aliasing and its relatively low computational load is amenable to real-time applications. The adaptive Wiener filter (AWF) SR algorithm was initially developed for grayscale imaging and has not previously been applied to color SR demosaicing. Here, we develop a novel fast SR method for CFA cameras that is based on the AWF SR algorithm and uses global channel-to-channel statistical models. We apply this new method as a stand-alone algorithm and also as an initialization image for a variational SR algorithm. This paper presents the theoretical development of the color AWF SR approach and applies it in performance comparisons to other SR techniques for both simulated and real data. PMID:23938797
A Simple and Fast Spline Filtering Algorithm for Surface Metrology
Zhang, Hao; Ott, Daniel; Song, John; Tong, Mingsi; Chu, Wei
2015-01-01
Spline filters and their corresponding robust filters are commonly used filters recommended in ISO (the International Organization for Standardization) standards for surface evaluation. Generally, these linear and non-linear spline filters, composed of symmetric, positive-definite matrices, are solved in an iterative fashion based on a Cholesky decomposition. They have been demonstrated to be relatively efficient, but complicated and inconvenient to implement. A new spline-filter algorithm is proposed by means of the discrete cosine transform or the discrete Fourier transform. The algorithm is conceptually simple and very convenient to implement. PMID:26958443
Adaptive filters for detection of gravitational waves from coalescing binaries
Eleuteri, Antonio; Milano, Leopoldo; De Rosa, Rosario; Garufi, Fabio; Acernese, Fausto; Barone, Fabrizio; Giordano, Lara; Pardi, Silvio
2006-06-15
In this work we propose use of infinite impulse response adaptive line enhancer (IIR ALE) filters for detection of gravitational waves from coalescing binaries. We extend our previous work and define an adaptive matched filter structure. Filter performance is analyzed in terms of the tracking capability and determination of filter parameters. Furthermore, following the Neyman-Pearson strategy, receiver operating characteristics are derived, with closedform expressions for detection threshold, false alarm, and detection probability. Extensive tests demonstrate the effectiveness of adaptive filters both in terms of small computational cost and robustness.
Adaptive sensor fusion using genetic algorithms
Fitzgerald, D.S.; Adams, D.G.
1994-08-01
Past attempts at sensor fusion have used some form of Boolean logic to combine the sensor information. As an alteniative, an adaptive ``fuzzy`` sensor fusion technique is described in this paper. This technique exploits the robust capabilities of fuzzy logic in the decision process as well as the optimization features of the genetic algorithm. This paper presents a brief background on fuzzy logic and genetic algorithms and how they are used in an online implementation of adaptive sensor fusion.
An adaptive algorithm for low contrast infrared image enhancement
NASA Astrophysics Data System (ADS)
Liu, Sheng-dong; Peng, Cheng-yuan; Wang, Ming-jia; Wu, Zhi-guo; Liu, Jia-qi
2013-08-01
An adaptive infrared image enhancement algorithm for low contrast is proposed in this paper, to deal with the problem that conventional image enhancement algorithm is not able to effective identify the interesting region when dynamic range is large in image. This algorithm begin with the human visual perception characteristics, take account of the global adaptive image enhancement and local feature boost, not only the contrast of image is raised, but also the texture of picture is more distinct. Firstly, the global image dynamic range is adjusted from the overall, the dynamic range of original image and display grayscale form corresponding relationship, the gray scale of bright object is raised and the the gray scale of dark target is reduced at the same time, to improve the overall image contrast. Secondly, the corresponding filtering algorithm is used on the current point and its neighborhood pixels to extract image texture information, to adjust the brightness of the current point in order to enhance the local contrast of the image. The algorithm overcomes the default that the outline is easy to vague in traditional edge detection algorithm, and ensure the distinctness of texture detail in image enhancement. Lastly, we normalize the global luminance adjustment image and the local brightness adjustment image, to ensure a smooth transition of image details. A lot of experiments is made to compare the algorithm proposed in this paper with other convention image enhancement algorithm, and two groups of vague IR image are taken in experiment. Experiments show that: the contrast ratio of the picture is boosted after handled by histogram equalization algorithm, but the detail of the picture is not clear, the detail of the picture can be distinguished after handled by the Retinex algorithm. The image after deal with by self-adaptive enhancement algorithm proposed in this paper becomes clear in details, and the image contrast is markedly improved in compared with Retinex
Real-time 3D adaptive filtering for portable imaging systems
NASA Astrophysics Data System (ADS)
Bockenbach, Olivier; Ali, Murtaza; Wainwright, Ian; Nadeski, Mark
2015-03-01
Portable imaging devices have proven valuable for emergency medical services both in the field and hospital environments and are becoming more prevalent in clinical settings where the use of larger imaging machines is impractical. 3D adaptive filtering is one of the most advanced techniques aimed at noise reduction and feature enhancement, but is computationally very demanding and hence often not able to run with sufficient performance on a portable platform. In recent years, advanced multicore DSPs have been introduced that attain high processing performance while maintaining low levels of power dissipation. These processors enable the implementation of complex algorithms like 3D adaptive filtering, improving the image quality of portable medical imaging devices. In this study, the performance of a 3D adaptive filtering algorithm on a digital signal processor (DSP) is investigated. The performance is assessed by filtering a volume of size 512x256x128 voxels sampled at a pace of 10 MVoxels/sec.
Block-adaptive filtering and its application to seismic-event detection
Clark, G.A.
1981-04-01
Block digital filtering involves the calculation of a block or finite set of filter output samples from a block of input samples. The motivation for block processing arises from computational advantages of the technique. Block filters take good advantage of parallel processing architectures, which are becoming more and more attractive with the advent of very large scale integrated (VLSI) circuits. This thesis extends the block technique to Wiener and adaptive filters, both of which are statistical filters. The key ingredient to this extension turns out to be the definition of a new performance index, block mean square error (BMSE), which combines the well known sum square error (SSE) and mean square error (MSE). A block adaptive filtering procedure is derived in which the filter coefficients are adjusted once per each output block in accordance with a generalized block least mean-square (BLMS) algorithm. Convergence properties of the BLMS algorithm are studied, including conditions for guaranteed convergence, convergence speed, and convergence accuracy. Simulation examples are given for clarity. Convergence properties of the BLMS and LMS algorithms are analyzed and compared. They are shown to be analogous, and under the proper circumstances, equivalent. The block adaptive filter was applied to the problem of detecting small seismic events in microseismic background noise. The predictor outperformed the world-wide standardized seismograph network (WWSSN) seismometers in improving signal-to-noise ratio (SNR).
A backtracking algorithm that deals with particle filter degeneracy
NASA Astrophysics Data System (ADS)
Baarsma, Rein; Schmitz, Oliver; Karssenberg, Derek
2016-04-01
Particle filters are an excellent way to deal with stochastic models incorporating Bayesian data assimilation. While they are computationally demanding, the particle filter has no problem with nonlinearity and it accepts non-Gaussian observational data. In the geoscientific field it is this computational demand that creates a problem, since dynamic grid-based models are often already quite computationally demanding. As such it is of the utmost importance to keep the amount of samples in the filter as small as possible. Small sample populations often lead to filter degeneracy however, especially in models with high stochastic forcing. Filter degeneracy renders the sample population useless, as the population is no longer statistically informative. We have created an algorithm in an existing data assimilation framework that reacts to and deals with filter degeneracy based on Spiller et al. [2008]. During the Bayesian updating step of the standard particle filter, the algorithm tests the sample population for filter degeneracy. If filter degeneracy has occurred, the algorithm resets to the last time the filter did work correctly and recalculates the failed timespan of the filter with an increased sample population. The sample population is then reduced to its original size and the particle filter continues as normal. This algorithm was created in the PCRaster Python framework, an open source tool that enables spatio-temporal forward modelling in Python [Karssenberg et al., 2010] . The framework already contains several data assimilation algorithms, including a standard particle filter and a Kalman filter. The backtracking particle filter algorithm has been added to the framework, which will make it easy to implement in other research. The performance of the backtracking particle filter is tested against a standard particle filter using two models. The first is a simple nonlinear point model, and the second is a more complex geophysical model. The main testing
Burst noise reduction of image by decimation and adaptive weighted median filter
NASA Astrophysics Data System (ADS)
Nakayama, Fumitaka; Meguro, Mitsuhiko; Hamada, Nozomu
2000-12-01
The removal of noise in image is one of the important issues, and useful as a preprocessing for edge detection, motion estimation and so on. Recently, many studies on the nonlinear digital filter for impulsive noise reduction have been reported. The median filter, the representative of the nonlinear filters, is very effective for removing impulsive noise and preserving sharp edge. In some cases, burst (i.e., successive) impulsive noise is added to image, and this type of noise is difficult to remove by using the median filter. In this paper, we propose an Adaptive Weighted Median (AWM) filter with Decimation (AWM-D filter) for burst noise reduction. This method can also be applied to recover large destructive regions, such as blotch and scratch. The proposed filter is an extension of the Decimated Median (DM) filter, which is useful for reducing successive impulsive noise. The DM filter can split long impulsive noise sequences into short ones, and remove burst noise in spite of the short filter window. Nevertheless, the DM filter also has two disadvantages. One is that the signals without added noise is unnecessary filtered. The other is that the position information in the window is not considered in the weight determinative process, as common in the median type filter. To improve detail-preserving property of the DM filter, we use the noise detection procedure and the AWM-D filter, which can be tuned by Least Mean Absolute (LMA) algorithm. The AWM-D filter preserves details more precisely than the median-type filter, because the AWM-D filter has the weights that can control the filter output. Through some simulations, the higher performance of the proposed filter is shown compared with the simple median, the WM filter, and the DM filter.
An effective automatic tracking algorithm based on Camshift and Kalman filter
NASA Astrophysics Data System (ADS)
Liang, Juan; Hou, Jianhua; Xiang, Jun; Da, Bangyou; Chen, Shaobo
2011-11-01
An automatic tracking algorithm based on Camshift and Kalman filter is proposed in this paper to deal with the problems in traditional Camshift algorithm, such as artificial orientation and increasing possibility of tracking failure under occlusion. The inter-frame difference and canny edge detection are combined to segment perfect moving object region accurately, and the center point of the region is obtained as the initial position of the object. With regard to tracking under occlusion, Kalman filter is used to predict the position and velocity of the target. Specifically, the initial iterative position of Camshift algorithm is obtained by Kalman filter in every frame, and then Camshift algorithm is utilized to track the target position. Finally, the parameters of adaptive Kalman filter are updated by the optimal position. However, when severe occlusion appears, the optimal position calculated by Camshift algorithm is inaccurate, and the Kalman filter fails to estimate the coming state effectively. In this situation, the Kalman filter is updated by the Kalman predictive value instead of the value calculated by the Camshift algorithm. The experiment results demonstrate that the proposed algorithm can detect and track the target object accurately and has better robustness to occlusion.
Flight data processing with the F-8 adaptive algorithm
NASA Technical Reports Server (NTRS)
Hartmann, G.; Stein, G.; Petersen, K.
1977-01-01
An explicit adaptive control algorithm based on maximum likelihood estimation of parameters has been designed for NASA's DFBW F-8 aircraft. To avoid iterative calculations, the algorithm uses parallel channels of Kalman filters operating at fixed locations in parameter space. This algorithm has been implemented in NASA/DFRC's Remotely Augmented Vehicle (RAV) facility. Real-time sensor outputs (rate gyro, accelerometer and surface position) are telemetered to a ground computer which sends new gain values to an on-board system. Ground test data and flight records were used to establish design values of noise statistics and to verify the ground-based adaptive software. The software and its performance evaluation based on flight data are described
Adaptive NUC algorithm for uncooled IRFPA based on neural networks
NASA Astrophysics Data System (ADS)
Liu, Ziji; Jiang, Yadong; Lv, Jian; Zhu, Hongbin
2010-10-01
With developments in uncooled infrared plane array (UFPA) technology, many new advanced uncooled infrared sensors are used in defensive weapons, scientific research, industry and commercial applications. A major difference in imaging techniques between infrared IRFPA imaging system and a visible CCD camera is that, IRFPA need nonuniformity correction and dead pixel compensation, we usually called it infrared image pre-processing. Two-point or multi-point correction algorithms based on calibration commonly used may correct the non-uniformity of IRFPAs, but they are limited by pixel linearity and instability. Therefore, adaptive non-uniformity correction techniques are developed. Two of these adaptive non-uniformity correction algorithms are mostly discussed, one is based on temporal high-pass filter, and another is based on neural network. In this paper, a new NUC algorithm based on improved neural networks is introduced, and involves the compare result between improved neural networks and other adaptive correction techniques. A lot of different will discussed in different angle, like correction effects, calculation efficiency, hardware implementation and so on. According to the result and discussion, it could be concluding that the adaptive algorithm offers improved performance compared to traditional calibration mode techniques. This new algorithm not only provides better sensitivity, but also increases the system dynamic range. As the sensor application expended, it will be very useful in future infrared imaging systems.
Resampling Algorithms for Particle Filters: A Computational Complexity Perspective
NASA Astrophysics Data System (ADS)
Bolić, Miodrag; Djurić, Petar M.; Hong, Sangjin
2004-12-01
Newly developed resampling algorithms for particle filters suitable for real-time implementation are described and their analysis is presented. The new algorithms reduce the complexity of both hardware and DSP realization through addressing common issues such as decreasing the number of operations and memory access. Moreover, the algorithms allow for use of higher sampling frequencies by overlapping in time the resampling step with the other particle filtering steps. Since resampling is not dependent on any particular application, the analysis is appropriate for all types of particle filters that use resampling. The performance of the algorithms is evaluated on particle filters applied to bearings-only tracking and joint detection and estimation in wireless communications. We have demonstrated that the proposed algorithms reduce the complexity without performance degradation.
Self-adaptive parameters in genetic algorithms
NASA Astrophysics Data System (ADS)
Pellerin, Eric; Pigeon, Luc; Delisle, Sylvain
2004-04-01
Genetic algorithms are powerful search algorithms that can be applied to a wide range of problems. Generally, parameter setting is accomplished prior to running a Genetic Algorithm (GA) and this setting remains unchanged during execution. The problem of interest to us here is the self-adaptive parameters adjustment of a GA. In this research, we propose an approach in which the control of a genetic algorithm"s parameters can be encoded within the chromosome of each individual. The parameters" values are entirely dependent on the evolution mechanism and on the problem context. Our preliminary results show that a GA is able to learn and evaluate the quality of self-set parameters according to their degree of contribution to the resolution of the problem. These results are indicative of a promising approach to the development of GAs with self-adaptive parameter settings that do not require the user to pre-adjust parameters at the outset.
Autonomous navigation system using a fuzzy adaptive nonlinear H∞ filter.
Outamazirt, Fariz; Li, Fu; Yan, Lin; Nemra, Abdelkrim
2014-01-01
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. PMID:25244587
Algorithme d'adaptation du filtre de Kalman aux variations soudaines de bruit
NASA Astrophysics Data System (ADS)
Canciu, Vintila
This research targets the case of Kalman filtering as applied to linear time-invariant systems having unknown process noise covariance and measurement noise covariance matrices and addresses the problem represented by the incomplete a priori knowledge of these two filter initialization parameters. The goal of this research is to determine in realtime both the process covariance matrix and the noise covariance matrix in the context of adaptive Kalman filtering. The resultant filter, called evolutionary adaptive Kalman filter, is able to adapt to sudden noise variations and constitutes a hybrid solution for adaptive Kalman filtering based on metaheuristic algorithms. MATLAB/Simulink simulation using several processes and covariance matrices plus comparison with other filters was selected as validation method. The Cramer-Rae Lower Bound (CRLB) was used as performance criterion. The thesis begins with a description of the problem under consideration (the design of a Kalman filter that is able to adapt to sudden noise variations) followed by a typical application (INS-GPS integrated navigation system) and by a statistical analysis of publications related to adaptive Kalman filtering. Next, the thesis presents the current architectures of the adaptive Kalman filtering: the innovation adaptive estimator (IAE) and the multiple model adaptive estimator (MMAE). It briefly presents their formulation, their behavior, and the limit of their performances. The thesis continues with the architectural synthesis of the evolutionary adaptive Kalman filter. The steps involved in the solution of the problem under consideration is also presented: an analysis of Kalman filtering and sub-optimal filtering methods, a comparison of current adaptive Kalman and sub-optimal filtering methods, the emergence of evolutionary adaptive Kalman filter as an enrichment of sub-optimal filtering with the help of biological-inspired computational intelligence methods, and the step-by-step architectural
Adaptive box filters for removal of random noise from digital images
NASA Technical Reports Server (NTRS)
Eliason, Eric M.; Mcewen, Alfred S.
1990-01-01
Adaptive box-filtering algorithms to remove random bit errors and to smooth noisy data have been developed. For both procedures, the standard deviation of those pixels within a local box surrounding each pixel is used. A series of two or three filters with decreasing box sizes can be run to clean up extremely noisy images and to remove bit errors near sharp edges. The second filter, for noise smoothing, is similar to the 'sigma filter' of Lee (1983). The technique effectively reduces speckle in radar images without eliminating fine details.
Adaptive link selection algorithms for distributed estimation
NASA Astrophysics Data System (ADS)
Xu, Songcen; de Lamare, Rodrigo C.; Poor, H. Vincent
2015-12-01
This paper presents adaptive link selection algorithms for distributed estimation and considers their application to wireless sensor networks and smart grids. In particular, exhaustive search-based least mean squares (LMS) / recursive least squares (RLS) link selection algorithms and sparsity-inspired LMS / RLS link selection algorithms that can exploit the topology of networks with poor-quality links are considered. The proposed link selection algorithms are then analyzed in terms of their stability, steady-state, and tracking performance and computational complexity. In comparison with the existing centralized or distributed estimation strategies, the key features of the proposed algorithms are as follows: (1) more accurate estimates and faster convergence speed can be obtained and (2) the network is equipped with the ability of link selection that can circumvent link failures and improve the estimation performance. The performance of the proposed algorithms for distributed estimation is illustrated via simulations in applications of wireless sensor networks and smart grids.
Reduced-Rank Adaptive Filtering Using Krylov Subspace
NASA Astrophysics Data System (ADS)
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 Cuckoo Search Algorithm for Unconstrained Optimization
2014-01-01
Modification of the intensification and diversification approaches in the recently developed cuckoo search algorithm (CSA) is performed. The alteration involves the implementation of adaptive step size adjustment strategy, and thus enabling faster convergence to the global optimal solutions. The feasibility of the proposed algorithm is validated against benchmark optimization functions, where the obtained results demonstrate a marked improvement over the standard CSA, in all the cases. PMID:25298971
Adaptive cuckoo search algorithm for unconstrained optimization.
Ong, Pauline
2014-01-01
Modification of the intensification and diversification approaches in the recently developed cuckoo search algorithm (CSA) is performed. The alteration involves the implementation of adaptive step size adjustment strategy, and thus enabling faster convergence to the global optimal solutions. The feasibility of the proposed algorithm is validated against benchmark optimization functions, where the obtained results demonstrate a marked improvement over the standard CSA, in all the cases. PMID:25298971
A comparison of filtering algorithms for GPS satellite navigation application
NASA Technical Reports Server (NTRS)
Tapley, B. D.; Peters, J. G.; Schutz, B. E.
1979-01-01
A comparison of three square root filter formulations with the standard extended Kalman filter is described. The characteristics of the algorithms were compared by simulating the application of a phase one GPS system to the determination of a LANDSAT-D Satellite.
NASA Astrophysics Data System (ADS)
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.
A kernel adaptive algorithm for quaternion-valued inputs.
Paul, Thomas K; Ogunfunmi, Tokunbo
2015-10-01
The use of quaternion data can provide benefit in applications like robotics and image recognition, and particularly for performing transforms in 3-D space. Here, we describe a kernel adaptive algorithm for quaternions. A least mean square (LMS)-based method was used, resulting in the derivation of the quaternion kernel LMS (Quat-KLMS) algorithm. Deriving this algorithm required describing the idea of a quaternion reproducing kernel Hilbert space (RKHS), as well as kernel functions suitable with quaternions. A modified HR calculus for Hilbert spaces was used to find the gradient of cost functions defined on a quaternion RKHS. In addition, the use of widely linear (or augmented) filtering is proposed to improve performance. The benefit of the Quat-KLMS and widely linear forms in learning nonlinear transformations of quaternion data are illustrated with simulations. PMID:25594982
Genetic algorithms in adaptive fuzzy control
NASA Technical Reports Server (NTRS)
Karr, C. Lucas; Harper, Tony R.
1992-01-01
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust fuzzy membership functions in response to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific computer-simulated chemical system is used to demonstrate the ideas presented.
Filter model based dwell time algorithm for ion beam figuring
NASA Astrophysics Data System (ADS)
Li, Yun; Xing, Tingwen; Jia, Xin; Wei, Haoming
2010-10-01
The process of Ion Beam Figuring (IBF) can be described by a two-dimensional convolution equation which including dwell time. Solving the dwell time is a key problem in IBF. Theoretically, the dwell time can be solved from a two-dimensional deconvolution. However, it is often ill-posed]; the suitable solution of that is hard to get. In this article, a dwell time algorithm is proposed, depending on the characters of IBF. Usually, the Beam Removal Function (BRF) in IBF is Gaussian, which can be regarded as a headstand Gaussian filter. In its stop-band, the filter has various filtering abilities for various frequencies. The dwell time algorithm proposed in this article is just based on this concept. The Curved Surface Smooth Extension (CSSE) method and Fast Fourier Transform (FFT) algorithm are also used. The simulation results show that this algorithm is high precision, effective, and suitable for actual application.
Information filtering via weighted heat conduction algorithm
NASA Astrophysics Data System (ADS)
Liu, Jian-Guo; Guo, Qiang; Zhang, Yi-Cheng
2011-06-01
In this paper, by taking into account effects of the user and object correlations on a heat conduction (HC) algorithm, a weighted heat conduction (WHC) algorithm is presented. We argue that the edge weight of the user-object bipartite network should be embedded into the HC algorithm to measure the object similarity. The numerical results indicate that both the accuracy and diversity could be improved greatly compared with the standard HC algorithm and the optimal values reached simultaneously. On the Movielens and Netflix datasets, the algorithmic accuracy, measured by the average ranking score, can be improved by 39.7% and 56.1% in the optimal case, respectively, and the diversity could reach 0.9587 and 0.9317 when the recommendation list equals to 5. Further statistical analysis indicates that, in the optimal case, the distributions of the edge weight are changed to the Poisson form, which may be the reason why HC algorithm performance could be improved. This work highlights the effect of edge weight on a personalized recommendation study, which maybe an important factor affecting personalized recommendation performance.
Performance Evaluation of Different Ground Filtering Algorithms for Uav-Based Point Clouds
NASA Astrophysics Data System (ADS)
Serifoglu, C.; Gungor, O.; Yilmaz, V.
2016-06-01
Digital Elevation Model (DEM) generation is one of the leading application areas in geomatics. Since a DEM represents the bare earth surface, the very first step of generating a DEM is to separate the ground and non-ground points, which is called ground filtering. Once the point cloud is filtered, the ground points are interpolated to generate the DEM. LiDAR (Light Detection and Ranging) point clouds have been used in many applications thanks to their success in representing the objects they belong to. Hence, in the literature, various ground filtering algorithms have been reported to filter the LiDAR data. Since the LiDAR data acquisition is still a costly process, using point clouds generated from the UAV images to produce DEMs is a reasonable alternative. In this study, point clouds with three different densities were generated from the aerial photos taken from a UAV (Unmanned Aerial Vehicle) to examine the effect of point density on filtering performance. The point clouds were then filtered by means of five different ground filtering algorithms as Progressive Morphological 1D (PM1D), Progressive Morphological 2D (PM2D), Maximum Local Slope (MLS), Elevation Threshold with Expand Window (ETEW) and Adaptive TIN (ATIN). The filtering performance of each algorithm was investigated qualitatively and quantitatively. The results indicated that the ATIN and PM2D algorithms showed the best overall ground filtering performances. The MLS and ETEW algorithms were found as the least successful ones. It was concluded that the point clouds generated from the UAVs can be a good alternative for LiDAR data.
Filter. Remix. Make.: Cultivating Adaptability through Multimodality
ERIC Educational Resources Information Center
Dusenberry, Lisa; Hutter, Liz; Robinson, Joy
2015-01-01
This article establishes traits of adaptable communicators in the 21st century, explains why adaptability should be a goal of technical communication educators, and shows how multimodal pedagogy supports adaptability. Three examples of scalable, multimodal assignments (infographics, research interviews, and software demonstrations) that evidence…
Adaptive Control of Flexible Structures Using Residual Mode Filters
NASA Technical Reports Server (NTRS)
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.
Adaptive median filtering for preprocessing of time series measurements
NASA Technical Reports Server (NTRS)
Paunonen, Matti
1993-01-01
A median (L1-norm) filtering program using polynomials was developed. This program was used in automatic recycling data screening. Additionally, a special adaptive program to work with asymmetric distributions was developed. Examples of adaptive median filtering of satellite laser range observations and TV satellite time measurements are given. The program proved to be versatile and time saving in data screening of time series measurements.
Likelihood Methods for Adaptive Filtering and Smoothing. Technical Report #455.
ERIC Educational Resources Information Center
Butler, Ronald W.
The dynamic linear model or Kalman filtering model provides a useful methodology for predicting the past, present, and future states of a dynamic system, such as an object in motion or an economic or social indicator that is changing systematically with time. Recursive likelihood methods for adaptive Kalman filtering and smoothing are developed.…
On recursive least-squares filtering algorithms and implementations. Ph.D. Thesis
NASA Technical Reports Server (NTRS)
Hsieh, Shih-Fu
1990-01-01
In many real-time signal processing applications, fast and numerically stable algorithms for solving least-squares problems are necessary and important. In particular, under non-stationary conditions, these algorithms must be able to adapt themselves to reflect the changes in the system and take appropriate adjustments to achieve optimum performances. Among existing algorithms, the QR-decomposition (QRD)-based recursive least-squares (RLS) methods have been shown to be useful and effective for adaptive signal processing. In order to increase the speed of processing and achieve high throughput rate, many algorithms are being vectorized and/or pipelined to facilitate high degrees of parallelism. A time-recursive formulation of RLS filtering employing block QRD will be considered first. Several methods, including a new non-continuous windowing scheme based on selectively rejecting contaminated data, were investigated for adaptive processing. Based on systolic triarrays, many other forms of systolic arrays are shown to be capable of implementing different algorithms. Various updating and downdating systolic algorithms and architectures for RLS filtering are examined and compared in details, which include Householder reflector, Gram-Schmidt procedure, and Givens rotation. A unified approach encompassing existing square-root-free algorithms is also proposed. For the sinusoidal spectrum estimation problem, a judicious method of separating the noise from the signal is of great interest. Various truncated QR methods are proposed for this purpose and compared to the truncated SVD method. Computer simulations provided for detailed comparisons show the effectiveness of these methods. This thesis deals with fundamental issues of numerical stability, computational efficiency, adaptivity, and VLSI implementation for the RLS filtering problems. In all, various new and modified algorithms and architectures are proposed and analyzed; the significance of any of the new method depends
Object tracking with adaptive HOG detector and adaptive Rao-Blackwellised particle filter
NASA Astrophysics Data System (ADS)
Rosa, Stefano; Paleari, Marco; Ariano, Paolo; Bona, Basilio
2012-01-01
Scenarios for a manned mission to the Moon or Mars call for astronaut teams to be accompanied by semiautonomous robots. A prerequisite for human-robot interaction is the capability of successfully tracking humans and objects in the environment. In this paper we present a system for real-time visual object tracking in 2D images for mobile robotic systems. The proposed algorithm is able to specialize to individual objects and to adapt to substantial changes in illumination and object appearance during tracking. The algorithm is composed by two main blocks: a detector based on Histogram of Oriented Gradient (HOG) descriptors and linear Support Vector Machines (SVM), and a tracker which is implemented by an adaptive Rao-Blackwellised particle filter (RBPF). The SVM is re-trained online on new samples taken from previous predicted positions. We use the effective sample size to decide when the classifier needs to be re-trained. Position hypotheses for the tracked object are the result of a clustering procedure applied on the set of particles. The algorithm has been tested on challenging video sequences presenting strong changes in object appearance, illumination, and occlusion. Experimental tests show that the presented method is able to achieve near real-time performances with a precision of about 7 pixels on standard video sequences of dimensions 320 × 240.
An Efficient Conflict Detection Algorithm for Packet Filters
NASA Astrophysics Data System (ADS)
Lee, Chun-Liang; Lin, Guan-Yu; Chen, Yaw-Chung
Packet classification is essential for supporting advanced network services such as firewalls, quality-of-service (QoS), virtual private networks (VPN), and policy-based routing. The rules that routers use to classify packets are called packet filters. If two or more filters overlap, a conflict occurs and leads to ambiguity in packet classification. This study proposes an algorithm that can efficiently detect and resolve filter conflicts using tuple based search. The time complexity of the proposed algorithm is O(nW+s), and the space complexity is O(nW), where n is the number of filters, W is the number of bits in a header field, and s is the number of conflicts. This study uses the synthetic filter databases generated by ClassBench to evaluate the proposed algorithm. Simulation results show that the proposed algorithm can achieve better performance than existing conflict detection algorithms both in time and space, particularly for databases with large numbers of conflicts.
NASA Astrophysics Data System (ADS)
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.
NASA Astrophysics Data System (ADS)
Lee, Seunghee; Bae, Kwanghyuk; Kyung, Kyu-min; Kim, Tae-Chan
2012-03-01
In this work, we present an adaptive switching filter for noise reduction and sharpness preservation in depth maps provided by Time-of-Flight (ToF) image sensors. Median filter and bilateral filter are commonly used in cost-sensitive applications where low computational complexity is needed. However, median filter blurs fine details and edges in depth map while bilateral filter works poorly with impulse noise present in the image. Since the variance of depth is inversely proportional to amplitude, we suggest an adaptive filter that switches between median filter and bilateral filter based on the level of amplitude. If a region of interest has low amplitude indicating low confidence level of measured depth data, then median filter is applied on the depth at the position while regions with high level of amplitude is processed with bilateral filter using Gaussian kernel with adaptive weights. Results show that the suggested algorithm performs surface smoothing and detail preservation as well as median filter and bilateral filter, respectively. By using the suggested algorithm, significant gain in visual quality is obtained in depth maps while low computational cost is maintained.
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
Method and system for training dynamic nonlinear adaptive filters which have embedded memory
NASA Technical Reports Server (NTRS)
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.
Biohybrid Control of General Linear Systems Using the Adaptive Filter Model of Cerebellum
Wilson, Emma D.; Assaf, Tareq; Pearson, Martin J.; Rossiter, Jonathan M.; Dean, Paul; Anderson, Sean R.; Porrill, John
2015-01-01
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 cancelation of reafferent noise. It has also been successfully applied to problems in robotics, such as adaptive camera stabilization and sensor noise cancelation. 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 stabilizes 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. PMID:26257638
An adaptive guidance algorithm for aerospace vehicles
NASA Astrophysics Data System (ADS)
Bradt, J. E.; Hardtla, J. W.; Cramer, E. J.
The specifications for proposed space transportation systems are placing more emphasis on developing reusable avionics subsystems which have the capability to respond to vehicle evolution and diverse missions while at the same time reducing the cost of ground support for mission planning, contingency response and verification and validation. An innovative approach to meeting these goals is to specify the guidance problem as a multi-point boundary value problen and solve that problem using modern control theory and nonlinear constrained optimization techniques. This approach has been implemented as Gamma Guidance (Hardtla, 1978) and has been successfully flown in the Inertial Upper Stage. The adaptive guidance algorithm described in this paper is a generalized formulation of Gamma Guidance. The basic equations are presented and then applied to four diverse aerospace vehicles to demonstrate the feasibility of using a reusable, explicit, adaptive guidance algorithm for diverse applications and vehicles.
On some limitations of adaptive feedback measurement algorithm
NASA Astrophysics Data System (ADS)
Opalski, Leszek J.
2015-09-01
The brilliant idea of Adaptive Feedback Control Systems (AFCS) makes possible creation of highly efficient adaptive systems for estimation, identification and filtering of signals and physical processes. The research problem considered in this paper is: how performance of AFCS changes if some of the assumptions used to formulate iterative estimation algorithm are not fulfilled exactly. To limit the scope of research a particular implementation of the AFCS concept was considered, i.e. an adaptive feedback measurement system (AFMS). The iterative measurement algorithm used was derived under some idealized conditions, notably with perfect knowledge of the system model and Gaussian communication channels. The selected non-idealities of interest are non-zero mean value of noise processes and non-ideal calibration of transmission gain in the forward channel - because they are related to intrinsic non-idealities of analog building blocks, used for the AFMS implementation. The presented original analysis of the iterative measurement algorithm provides quantitative information on speed of convergence and limit behavior. The analysis should be useful for AFCS implementors in the measurement area - since the results are presented in terms of accuracy and precision of iterative measurement process.
Optimization of phononic filters via genetic algorithms
NASA Astrophysics Data System (ADS)
Hussein, M. I.; El-Beltagy, M. A.
2007-12-01
A phononic crystal is commonly characterized by its dispersive frequency spectrum. With appropriate spatial distribution of the constituent material phases, spectral stop bands could be generated. Moreover, it is possible to control the number, the width, and the location of these bands within a frequency range of interest. This study aims at exploring the relationship between unit cell configuration and frequency spectrum characteristics. Focusing on 1D layered phononic crystals, and longitudinal wave propagation in the direction normal to the layering, the unit cell features of interest are the number of layers and the material phase and relative thickness of each layer. An evolutionary search for binary- and ternary-phase cell designs exhibiting a series of stop bands at predetermined frequencies is conducted. A specially formulated representation and set of genetic operators that break the symmetries in the problem are developed for this purpose. An array of optimal designs for a range of ratios in Young's modulus and density are obtained and the corresponding objective values (the degrees to which the resulting bands match the predetermined targets) are examined as a function of these ratios. It is shown that a rather complex filtering objective could be met with a high degree of success. Structures composed of the designed phononic crystals are excellent candidates for use in a wide range of applications including sound and vibration filtering.
Filtered refocusing: a volumetric reconstruction algorithm for plenoptic-PIV
NASA Astrophysics Data System (ADS)
Fahringer, Timothy W.; Thurow, Brian S.
2016-09-01
A new algorithm for reconstruction of 3D particle fields from plenoptic image data is presented. The algorithm is based on the technique of computational refocusing with the addition of a post reconstruction filter to remove the out of focus particles. This new algorithm is tested in terms of reconstruction quality on synthetic particle fields as well as a synthetically generated 3D Gaussian ring vortex. Preliminary results indicate that the new algorithm performs as well as the MART algorithm (used in previous work) in terms of the reconstructed particle position accuracy, but produces more elongated particles. The major advantage to the new algorithm is the dramatic reduction in the computational cost required to reconstruct a volume. It is shown that the new algorithm takes 1/9th the time to reconstruct the same volume as MART while using minimal resources. Experimental results are presented in the form of the wake behind a cylinder at a Reynolds number of 185.
Lossless compression of weight vectors from an adaptive filter
Bredemann, M.V.; Elliott, G.R.; Stearns, S.D.
1994-08-01
Techniques for lossless waveform compression can be applied to the transmission of weight vectors from an orbiting satellite. The vectors, which are a part of a hybrid analog/digital adaptive filter, are a representation of the radio frequency background seen by the satellite. An approach is used which treats each adaptive weight as a time-varying waveform.
A Nonlinear Adaptive Filter for Gyro Thermal Bias Error Cancellation
NASA Technical Reports Server (NTRS)
Galante, Joseph M.; Sanner, Robert M.
2012-01-01
Deterministic errors in angular rate gyros, such as thermal biases, can have a significant impact on spacecraft attitude knowledge. In particular, thermal biases are often the dominant error source in MEMS gyros after calibration. Filters, such as J\\,fEKFs, are commonly used to mitigate the impact of gyro errors and gyro noise on spacecraft closed loop pointing accuracy, but often have difficulty in rapidly changing thermal environments and can be computationally expensive. In this report an existing nonlinear adaptive filter is used as the basis for a new nonlinear adaptive filter designed to estimate and cancel thermal bias effects. A description of the filter is presented along with an implementation suitable for discrete-time applications. A simulation analysis demonstrates the performance of the filter in the presence of noisy measurements and provides a comparison with existing techniques.
Algorithmic and architectural optimizations for computationally efficient particle filtering.
Sankaranarayanan, Aswin C; Srivastava, Ankur; Chellappa, Rama
2008-05-01
In this paper, we analyze the computational challenges in implementing particle filtering, especially to video sequences. Particle filtering is a technique used for filtering nonlinear dynamical systems driven by non-Gaussian noise processes. It has found widespread applications in detection, navigation, and tracking problems. Although, in general, particle filtering methods yield improved results, it is difficult to achieve real time performance. In this paper, we analyze the computational drawbacks of traditional particle filtering algorithms, and present a method for implementing the particle filter using the Independent Metropolis Hastings sampler, that is highly amenable to pipelined implementations and parallelization. We analyze the implementations of the proposed algorithm, and, in particular, concentrate on implementations that have minimum processing times. It is shown that the design parameters for the fastest implementation can be chosen by solving a set of convex programs. The proposed computational methodology was verified using a cluster of PCs for the application of visual tracking. We demonstrate a linear speed-up of the algorithm using the methodology proposed in the paper. PMID:18390378
A parallel adaptive mesh refinement algorithm
NASA Technical Reports Server (NTRS)
Quirk, James J.; Hanebutte, Ulf R.
1993-01-01
Over recent years, Adaptive Mesh Refinement (AMR) algorithms which dynamically match the local resolution of the computational grid to the numerical solution being sought have emerged as powerful tools for solving problems that contain disparate length and time scales. In particular, several workers have demonstrated the effectiveness of employing an adaptive, block-structured hierarchical grid system for simulations of complex shock wave phenomena. Unfortunately, from the parallel algorithm developer's viewpoint, this class of scheme is quite involved; these schemes cannot be distilled down to a small kernel upon which various parallelizing strategies may be tested. However, because of their block-structured nature such schemes are inherently parallel, so all is not lost. In this paper we describe the method by which Quirk's AMR algorithm has been parallelized. This method is built upon just a few simple message passing routines and so it may be implemented across a broad class of MIMD machines. Moreover, the method of parallelization is such that the original serial code is left virtually intact, and so we are left with just a single product to support. The importance of this fact should not be underestimated given the size and complexity of the original algorithm.
Adaptive filters for suppressing irregular hostile jamming in direct sequence spread-spectrum system
NASA Astrophysics Data System (ADS)
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.
A study of infrared spectroscopy de-noising based on LMS adaptive filter
NASA Astrophysics Data System (ADS)
Mo, Jia-qing; Lv, Xiao-yi; Yu, Xiao
2015-12-01
Infrared spectroscopy has been widely used, but which often contains a lot of noise, so the spectral characteristic of the sample is seriously affected. Therefore the de-noising is very important in the spectrum analysis and processing. In the study of infrared spectroscopy, the least mean square (LMS) adaptive filter was applied in the field firstly. LMS adaptive filter algorithm can reserve the detail and envelope of the effective signal when the method was applied to infrared spectroscopy of breast cancer which signal-to-noise ratio (SNR) is lower than 10 dB, contrast and analysis the result with result of wavelet transform and ensemble empirical mode decomposition (EEMD). The three evaluation standards (SNR, root mean square error (RMSE) and the correlation coefficient (ρ)) fully proved de-noising advantages of LMS adaptive filter in infrared spectroscopy of breast cancer.
Fully implicit adaptive mesh refinement MHD algorithm
NASA Astrophysics Data System (ADS)
Philip, Bobby
2005-10-01
In the macroscopic simulation of plasmas, the numerical modeler is faced with the challenge of dealing with multiple time and length scales. The former results in stiffness due to the presence of very fast waves. The latter requires one to resolve the localized features that the system develops. Traditional approaches based on explicit time integration techniques and fixed meshes are not suitable for this challenge, as such approaches prevent the modeler from using realistic plasma parameters to keep the computation feasible. We propose here a novel approach, based on implicit methods and structured adaptive mesh refinement (SAMR). Our emphasis is on both accuracy and scalability with the number of degrees of freedom. To our knowledge, a scalable, fully implicit AMR algorithm has not been accomplished before for MHD. As a proof-of-principle, we focus on the reduced resistive MHD model as a basic MHD model paradigm, which is truly multiscale. The approach taken here is to adapt mature physics-based technologyootnotetextL. Chac'on et al., J. Comput. Phys. 178 (1), 15- 36 (2002) to AMR grids, and employ AMR-aware multilevel techniques (such as fast adaptive composite --FAC-- algorithms) for scalability. We will demonstrate that the concept is indeed feasible, featuring optimal scalability under grid refinement. Results of fully-implicit, dynamically-adaptive AMR simulations will be presented on a variety of problems.
A novel fast median filter algorithm without sorting
NASA Astrophysics Data System (ADS)
Yang, Weiping; Zhang, Zhilong; Lu, Xinping; Li, Jicheng; Chen, Dong; Yang, Guopeng
2016-04-01
As one of widely applied nonlinear smoothing filtering methods, median filter is quite effective for removing salt-andpepper noise and impulsive noise while maintaining image edge information without blurring its boundaries, but its computation load is the maximal drawback while applied in real-time processing systems. In order to solve the issue, researchers have proposed many effective fast algorithms and published many papers. However most of the algorithms are based on sorting operations so as to make real-time implementation difficult. In this paper considering the large scale Boolean calculation function and convenient shift operation which are two of the advantages of FPGA(Field Programmable Gate Array), we proposed a novel median value finding algorithm without sorting, which can find the median value effectively and its performing time almost keeps changeless despite how large the filter radius is. Based on the algorithm, a real-time median filter has been realized. A lot of tests demonstrate the validity and correctness of proposed algorithm.
Improving nonlinear modeling capabilities of functional link adaptive filters.
Comminiello, Danilo; Scarpiniti, Michele; Scardapane, Simone; Parisi, Raffaele; Uncini, Aurelio
2015-09-01
The functional link adaptive filter (FLAF) represents an effective solution for online nonlinear modeling problems. In this paper, we take into account a FLAF-based architecture, which separates the adaptation of linear and nonlinear elements, and we focus on the nonlinear branch to improve the modeling performance. In particular, we propose a new model that involves an adaptive combination of filters downstream of the nonlinear expansion. Such combination leads to a cooperative behavior of the whole architecture, thus yielding a performance improvement, particularly in the presence of strong nonlinearities. An advanced architecture is also proposed involving the adaptive combination of multiple filters on the nonlinear branch. The proposed models are assessed in different nonlinear modeling problems, in which their effectiveness and capabilities are shown. PMID:26057613
Comparative study of adaptive-noise-cancellation algorithms for intrusion detection systems
Claassen, J.P.; Patterson, M.M.
1981-01-01
Some intrusion detection systems are susceptible to nonstationary noise resulting in frequent nuisance alarms and poor detection when the noise is present. Adaptive inverse filtering for single channel systems and adaptive noise cancellation for two channel systems have both demonstrated good potential in removing correlated noise components prior detection. For such noise susceptible systems the suitability of a noise reduction algorithm must be established in a trade-off study weighing algorithm complexity against performance. The performance characteristics of several distinct classes of algorithms are established through comparative computer studies using real signals. The relative merits of the different algorithms are discussed in the light of the nature of intruder and noise signals.
Adaptive algorithm for cloud cover estimation from all-sky images over the sea
NASA Astrophysics Data System (ADS)
Krinitskiy, M. A.; Sinitsyn, A. V.
2016-05-01
A new algorithm for cloud cover estimation has been formulated and developed based on the synthetic control index, called the grayness rate index, and an additional algorithm step of adaptive filtering of the Mie scattering contribution. A setup for automated cloud cover estimation has been designed, assembled, and tested under field conditions. The results shows a significant advantage of the new algorithm over currently commonly used procedures.
A novel iris localization algorithm using correlation filtering
NASA Astrophysics Data System (ADS)
Pohit, Mausumi; Sharma, Jitu
2015-06-01
Fast and efficient segmentation of iris from the eye images is a primary requirement for robust database independent iris recognition. In this paper we have presented a new algorithm for computing the inner and outer boundaries of the iris and locating the pupil centre. Pupil-iris boundary computation is based on correlation filtering approach, whereas iris-sclera boundary is determined through one dimensional intensity mapping. The proposed approach is computationally less extensive when compared with the existing algorithms like Hough transform.
Adaptive path planning: Algorithm and analysis
Chen, Pang C.
1993-03-01
Path planning has to be fast to support real-time robot programming. Unfortunately, current planning techniques are still too slow to be effective, as they often require several minutes, if not hours of computation. To alleviate this problem, we present a learning algorithm that uses past experience to enhance future performance. The algorithm relies on an existing path planner to provide solutions to difficult tasks. From these solutions, an evolving sparse network of useful subgoals is learned to support faster planning. The algorithm is suitable for both stationary and incrementally-changing environments. To analyze our algorithm, we use a previously developed stochastic model that quantifies experience utility. Using this model, we characterize the situations in which the adaptive planner is useful, and provide quantitative bounds to predict its behavior. The results are demonstrated with problems in manipulator planning. Our algorithm and analysis are sufficiently general that they may also be applied to task planning or other planning domains in which experience is useful.
Adaptive Control Using Residual Mode Filters Applied to Wind Turbines
NASA Technical Reports Server (NTRS)
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.
Adaptive Trajectory Prediction Algorithm for Climbing Flights
NASA Technical Reports Server (NTRS)
Schultz, Charles Alexander; Thipphavong, David P.; Erzberger, Heinz
2012-01-01
Aircraft climb trajectories are difficult to predict, and large errors in these predictions reduce the potential operational benefits of some advanced features for NextGen. The algorithm described in this paper improves climb trajectory prediction accuracy by adjusting trajectory predictions based on observed track data. It utilizes rate-of-climb and airspeed measurements derived from position data to dynamically adjust the aircraft weight modeled for trajectory predictions. In simulations with weight uncertainty, the algorithm is able to adapt to within 3 percent of the actual gross weight within two minutes of the initial adaptation. The root-mean-square of altitude errors for five-minute predictions was reduced by 73 percent. Conflict detection performance also improved, with a 15 percent reduction in missed alerts and a 10 percent reduction in false alerts. In a simulation with climb speed capture intent and weight uncertainty, the algorithm improved climb trajectory prediction accuracy by up to 30 percent and conflict detection performance, reducing missed and false alerts by up to 10 percent.
Local adaptive filtering of images corrupted by nonstationary noise
NASA Astrophysics Data System (ADS)
Lukin, Vladimir V.; Fevralev, Dmitriy V.; Ponomarenko, Nikolay N.; Pogrebnyak, Oleksiy B.; Egiazarian, Karen O.; Astola, Jaakko T.
2009-02-01
In various practical situations of remote sensing image processing it is assumed that noise is nonstationary and no a priory information on noise dependence on local mean or about local properties of noise statistics is available. It is shown that in such situations it is difficult to find a proper filter for effective image processing, i.e., for noise removal with simultaneous edge/detail preservation. To deal with such images, a local adaptive filter based on discrete cosine transform in overlapping blocks is proposed. A threshold is set locally based on a noise standard deviation estimate obtained for each block. Several other operations to improve performance of the locally adaptive filter are proposed and studied. The designed filter effectiveness is demonstrated for simulated data as well as for real life radar remote sensing and marine polarimetric radar images.
NASA Technical Reports Server (NTRS)
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.
Acoustic Echo Cancellation Using Sub-Adaptive Filter
NASA Astrophysics Data System (ADS)
Ohta, Satoshi; Kajikawa, Yoshinobu; Nomura, Yasuo
In the acoustic echo canceller (AEC), the step-size parameter of the adaptive filter must be varied according to the situation if double talk occurs and/or the echo path changes. We propose an AEC that uses a sub-adaptive filter. The proposed AEC can control the step-size parameter according to the situation. Moreover, it offers superior convergence compared to the conventional AEC even when the double talk and the echo path change occur simultaneously. Simulations demonstrate that the proposed AEC can achieve higher ERLE and faster convergence than the conventional AEC. The computational complexity of the proposed AEC can be reduced by reducing the number of taps of the sub-adaptive filter.
Image reconstruction algorithms with wavelet filtering for optoacoustic imaging
NASA Astrophysics Data System (ADS)
Gawali, S.; Leggio, L.; Broadway, C.; González, P.; Sánchez, M.; Rodríguez, S.; Lamela, H.
2016-03-01
Optoacoustic imaging (OAI) is a hybrid biomedical imaging modality based on the generation and detection of ultrasound by illuminating the target tissue by laser light. Typically, laser light in visible or near infrared spectrum is used as an excitation source. OAI is based on the implementation of image reconstruction algorithms using the spatial distribution of optical absorption in tissues. In this work, we apply a time-domain back-projection (BP) reconstruction algorithm and a wavelet filtering for point and line detection, respectively. A comparative study between point detection and integrated line detection has been carried out by evaluating their effects on the image reconstructed. Our results demonstrate that the back-projection algorithm proposed is efficient for reconstructing high-resolution images of absorbing spheres embedded in a non-absorbing medium when it is combined with the wavelet filtering.
A matched filter algorithm for acoustic signal detection
NASA Astrophysics Data System (ADS)
Jordan, D. W.
1985-06-01
This thesis is a presentation of several alternative acoustic filter designs which allow Space Shuttle payload experiment initiation prior to launch. This initiation is accomplished independently of any spacecraft services by means of a matched band-pass filter tuned to the acoustic signal characteristic of the Auxiliary Power Unit (APU) which is brought up to operating RPM's approximately five minutes prior to launch. These alternative designs include an analog filter built around operational amplifiers, a digital IIR design implemented with an INTEL 2920 Signal Processor, and an Adaptive FIR Weiner design. Working prototypes of the first two filters are developed and a discussion of the advantage of the 2920 digital design is presented.
Robust Wiener filtering for Adaptive Optics
Poyneer, L A
2004-06-17
In many applications of optical systems, the observed field in the pupil plane has a non-uniform phase component. This deviation of the phase of the field from uniform is called a phase aberration. In imaging systems this aberration will degrade the quality of the images. In the case of a large astronomical telescope, random fluctuations in the atmosphere lead to significant distortion. These time-varying distortions can be corrected using an Adaptive Optics (AO) system, which is a real-time control system composed of optical, mechanical and computational parts. Adaptive optics is also applicable to problems in vision science, laser propagation and communication. For a high-level overview, consult this web site. For an in-depth treatment of the astronomical case, consult these books.
Seasonal signal capturing in time series of up coordinates by means of adaptive filters
NASA Astrophysics Data System (ADS)
Yalvac, S.; Ustun, A.
2013-12-01
Digital filters, is a system that performs mathematical operations on a sampled or discrete time signals. Adaptive filters designed for noise canceling are capable tools of decomposing correlated parts of data sets. This kind of filters which optimize itself using Least Mean Square (LMS) algorithm is a powerful tool for understand the truth hidden into the complex data sets like time series in Geosciences. The complex data sets such as CGPS (Continuously operating reference station) station's time series can be understood better with adaptive noise canceling by means of decompose coherent (seasonal effect, tectonic plate motion) and incoherent (noise; site-specific effects) parts of data. In this study, it is aimed to model the subsidence caused by groundwater withdrawal based on the seasonal correlation between consecutive years of CGPS time series. For this purpose, two stations where located into subsidence area of 3 year time series have analyzed with adaptive noise canceling filter. According to the results, the annual movement of these two stations have strong relationship. Also, subsidence behavior are correlated with annual rainfall data. BELD station one year filtered movement KAMN station one year filtered movements
The new approach for infrared target tracking based on the particle filter algorithm
NASA Astrophysics Data System (ADS)
Sun, Hang; Han, Hong-xia
2011-08-01
Target tracking on the complex background in the infrared image sequence is hot research field. It provides the important basis in some fields such as video monitoring, precision, and video compression human-computer interaction. As a typical algorithms in the target tracking framework based on filtering and data connection, the particle filter with non-parameter estimation characteristic have ability to deal with nonlinear and non-Gaussian problems so it were widely used. There are various forms of density in the particle filter algorithm to make it valid when target occlusion occurred or recover tracking back from failure in track procedure, but in order to capture the change of the state space, it need a certain amount of particles to ensure samples is enough, and this number will increase in accompany with dimension and increase exponentially, this led to the increased amount of calculation is presented. In this paper particle filter algorithm and the Mean shift will be combined. Aiming at deficiencies of the classic mean shift Tracking algorithm easily trapped into local minima and Unable to get global optimal under the complex background. From these two perspectives that "adaptive multiple information fusion" and "with particle filter framework combining", we expand the classic Mean Shift tracking framework .Based on the previous perspective, we proposed an improved Mean Shift infrared target tracking algorithm based on multiple information fusion. In the analysis of the infrared characteristics of target basis, Algorithm firstly extracted target gray and edge character and Proposed to guide the above two characteristics by the moving of the target information thus we can get new sports guide grayscale characteristics and motion guide border feature. Then proposes a new adaptive fusion mechanism, used these two new information adaptive to integrate into the Mean Shift tracking framework. Finally we designed a kind of automatic target model updating strategy
Maier, Andreas; Wigström, Lars; Hofmann, Hannes G.; Hornegger, Joachim; Zhu, Lei; Strobel, Norbert; Fahrig, Rebecca
2011-01-01
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
Maier, Andreas; Wigstroem, Lars; Hofmann, Hannes G.; Hornegger, Joachim; Zhu Lei; Strobel, Norbert; Fahrig, Rebecca
2011-11-15
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
An information theoretic approach of designing sparse kernel adaptive filters.
Liu, Weifeng; Park, Il; Principe, José C
2009-12-01
This paper discusses an information theoretic approach of designing sparse kernel adaptive filters. To determine useful data to be learned and remove redundant ones, a subjective information measure called surprise is introduced. Surprise captures the amount of information a datum contains which is transferable to a learning system. Based on this concept, we propose a systematic sparsification scheme, which can drastically reduce the time and space complexity without harming the performance of kernel adaptive filters. Nonlinear regression, short term chaotic time-series prediction, and long term time-series forecasting examples are presented. PMID:19923047
Yun, Jong Pil; Jeon, Yong-Ju; Choi, Doo-chul; Kim, Sang Woo
2012-05-01
We propose a new defect detection algorithm for scale-covered steel wire rods. The algorithm incorporates an adaptive wavelet filter that is designed on the basis of lattice parameterization of orthogonal wavelet bases. This approach offers the opportunity to design orthogonal wavelet filters via optimization methods. To improve the performance and the flexibility of wavelet design, we propose the use of the undecimated discrete wavelet transform, and separate design of column and row wavelet filters but with a common cost function. The coefficients of the wavelet filters are optimized by the so-called univariate dynamic encoding algorithm for searches (uDEAS), which searches the minimum value of a cost function designed to maximize the energy difference between defects and background noise. Moreover, for improved detection accuracy, we propose an enhanced double-threshold method. Experimental results for steel wire rod surface images obtained from actual steel production lines show that the proposed algorithm is effective. PMID:22561939
Eigenvector spatial filtering for image analysis: An efficient algorithm
NASA Astrophysics Data System (ADS)
Rura, Melissa J.
Eigenvector Spatial Filtering (ESF) is an established method in social science literature for incorporating spatial information in model specifications. ESF computes spatial eigenvectors, which are defined by the spatial structure associated with a variable. One important limitation of this technique is that it becomes computationally intensive in image analysis because of the massive number of image pixels. This research develops an algorithm, which makes ESF more efficient, by using the analytical solution for the eigenvalues and spatial eigenvectors, which are essentially a series of orthogonal, uncorrelated map patterns that describe positively spatial autocorrelated patterns through negatively spatially autocorrelated patterns, and global, regional, and local patterns of spatial dependencies in a surface. A reformulation of the analytical solution reduces the required computations and allows the eigenvectors to be computed sequentially. Finally, a series of sampling methods are explored. This algorithm is applied to three example multispectral images of different sizes: small (i.e., ˜200,000 pixels), medium (i.e., ˜1,000,000 pixels) and large (i.e., ˜110,000,000 pixels) and is evaluated in terms of output for each sampling technique and the complete spectral information. The output spatial filters of these sampling techniques compare to the filter generated with the complete spectral information. In terms of efficiency evaluation, the time is required to construct filters through sampling versus through analysis of the complete image surface is evaluated and the complexity of set-up and execution of the sampled and distributed algorithms are assessed.
NASA Astrophysics Data System (ADS)
Liu, Delian; Li, Zhaohui; Wang, Xiaorui; Zhang, Jianqi
2015-11-01
Target detection is of great importance both in civil and military fields. Here a new moving target detection approach is proposed, which employs a nonlinear adaptive filter to remove large fluctuations on temporal profiles that are produced by evolving clutters. Initially, this paper discusses the temporal behaviors of different pixels in infrared sequences. Then, the new nonlinear adaptive filter that is a variation of the median-modified Wiener filter is given to extract pulse signals on temporal profiles that relate to moving targets. Next, the variance of each temporal profile is estimated by segmenting each temporal profile into several segments to normalize the amplitude of the pulse signals. Finally, the proposed approach is tested via two infrared image sequences and compared with several conventional target detection algorithms. The results show our approach has a high effectiveness in extracting target temporal profiles amidst heavy and slowly evolving clutters.
Synaptic dynamics: linear model and adaptation algorithm.
Yousefi, Ali; Dibazar, Alireza A; Berger, Theodore W
2014-08-01
In this research, temporal processing in brain neural circuitries is addressed by a dynamic model of synaptic connections in which the synapse model accounts for both pre- and post-synaptic processes determining its temporal dynamics and strength. Neurons, which are excited by the post-synaptic potentials of hundred of the synapses, build the computational engine capable of processing dynamic neural stimuli. Temporal dynamics in neural models with dynamic synapses will be analyzed, and learning algorithms for synaptic adaptation of neural networks with hundreds of synaptic connections are proposed. The paper starts by introducing a linear approximate model for the temporal dynamics of synaptic transmission. The proposed linear model substantially simplifies the analysis and training of spiking neural networks. Furthermore, it is capable of replicating the synaptic response of the non-linear facilitation-depression model with an accuracy better than 92.5%. In the second part of the paper, a supervised spike-in-spike-out learning rule for synaptic adaptation in dynamic synapse neural networks (DSNN) is proposed. The proposed learning rule is a biologically plausible process, and it is capable of simultaneously adjusting both pre- and post-synaptic components of individual synapses. The last section of the paper starts with presenting the rigorous analysis of the learning algorithm in a system identification task with hundreds of synaptic connections which confirms the learning algorithm's accuracy, repeatability and scalability. The DSNN is utilized to predict the spiking activity of cortical neurons and pattern recognition tasks. The DSNN model is demonstrated to be a generative model capable of producing different cortical neuron spiking patterns and CA1 Pyramidal neurons recordings. A single-layer DSNN classifier on a benchmark pattern recognition task outperforms a 2-Layer Neural Network and GMM classifiers while having fewer numbers of free parameters and
Automatic Data Filter Customization Using a Genetic Algorithm
NASA Technical Reports Server (NTRS)
Mandrake, Lukas
2013-01-01
This work predicts whether a retrieval algorithm will usefully determine CO2 concentration from an input spectrum of GOSAT (Greenhouse Gases Observing Satellite). This was done to eliminate needless runtime on atmospheric soundings that would never yield useful results. A space of 50 dimensions was examined for predictive power on the final CO2 results. Retrieval algorithms are frequently expensive to run, and wasted effort defeats requirements and expends needless resources. This algorithm could be used to help predict and filter unneeded runs in any computationally expensive regime. Traditional methods such as the Fischer discriminant analysis and decision trees can attempt to predict whether a sounding will be properly processed. However, this work sought to detect a subsection of the dimensional space that can be simply filtered out to eliminate unwanted runs. LDAs (linear discriminant analyses) and other systems examine the entire data and judge a "best fit," giving equal weight to complex and problematic regions as well as simple, clear-cut regions. In this implementation, a genetic space of "left" and "right" thresholds outside of which all data are rejected was defined. These left/right pairs are created for each of the 50 input dimensions. A genetic algorithm then runs through countless potential filter settings using a JPL computer cluster, optimizing the tossed-out data s yield (proper vs. improper run removal) and number of points tossed. This solution is robust to an arbitrary decision boundary within the data and avoids the global optimization problem of whole-dataset fitting using LDA or decision trees. It filters out runs that would not have produced useful CO2 values to save needless computation. This would be an algorithmic preprocessing improvement to any computationally expensive system.
Adaptive Numerical Algorithms in Space Weather Modeling
NASA Technical Reports Server (NTRS)
Toth, Gabor; vanderHolst, Bart; Sokolov, Igor V.; DeZeeuw, Darren; Gombosi, Tamas I.; Fang, Fang; Manchester, Ward B.; Meng, Xing; Nakib, Dalal; Powell, Kenneth G.; Stout, Quentin F.; Glocer, Alex; Ma, Ying-Juan; Opher, Merav
2010-01-01
Space weather describes the various processes in the Sun-Earth system that present danger to human health and technology. The goal of space weather forecasting is to provide an opportunity to mitigate these negative effects. Physics-based space weather modeling is characterized by disparate temporal and spatial scales as well as by different physics in different domains. A multi-physics system can be modeled by a software framework comprising of several components. Each component corresponds to a physics domain, and each component is represented by one or more numerical models. The publicly available Space Weather Modeling Framework (SWMF) can execute and couple together several components distributed over a parallel machine in a flexible and efficient manner. The framework also allows resolving disparate spatial and temporal scales with independent spatial and temporal discretizations in the various models. Several of the computationally most expensive domains of the framework are modeled by the Block-Adaptive Tree Solar wind Roe Upwind Scheme (BATS-R-US) code that can solve various forms of the magnetohydrodynamics (MHD) equations, including Hall, semi-relativistic, multi-species and multi-fluid MHD, anisotropic pressure, radiative transport and heat conduction. Modeling disparate scales within BATS-R-US is achieved by a block-adaptive mesh both in Cartesian and generalized coordinates. Most recently we have created a new core for BATS-R-US: the Block-Adaptive Tree Library (BATL) that provides a general toolkit for creating, load balancing and message passing in a 1, 2 or 3 dimensional block-adaptive grid. We describe the algorithms of BATL and demonstrate its efficiency and scaling properties for various problems. BATS-R-US uses several time-integration schemes to address multiple time-scales: explicit time stepping with fixed or local time steps, partially steady-state evolution, point-implicit, semi-implicit, explicit/implicit, and fully implicit numerical
Adaptive numerical algorithms in space weather modeling
NASA Astrophysics Data System (ADS)
Tóth, Gábor; van der Holst, Bart; Sokolov, Igor V.; De Zeeuw, Darren L.; Gombosi, Tamas I.; Fang, Fang; Manchester, Ward B.; Meng, Xing; Najib, Dalal; Powell, Kenneth G.; Stout, Quentin F.; Glocer, Alex; Ma, Ying-Juan; Opher, Merav
2012-02-01
Space weather describes the various processes in the Sun-Earth system that present danger to human health and technology. The goal of space weather forecasting is to provide an opportunity to mitigate these negative effects. Physics-based space weather modeling is characterized by disparate temporal and spatial scales as well as by different relevant physics in different domains. A multi-physics system can be modeled by a software framework comprising several components. Each component corresponds to a physics domain, and each component is represented by one or more numerical models. The publicly available Space Weather Modeling Framework (SWMF) can execute and couple together several components distributed over a parallel machine in a flexible and efficient manner. The framework also allows resolving disparate spatial and temporal scales with independent spatial and temporal discretizations in the various models. Several of the computationally most expensive domains of the framework are modeled by the Block-Adaptive Tree Solarwind Roe-type Upwind Scheme (BATS-R-US) code that can solve various forms of the magnetohydrodynamic (MHD) equations, including Hall, semi-relativistic, multi-species and multi-fluid MHD, anisotropic pressure, radiative transport and heat conduction. Modeling disparate scales within BATS-R-US is achieved by a block-adaptive mesh both in Cartesian and generalized coordinates. Most recently we have created a new core for BATS-R-US: the Block-Adaptive Tree Library (BATL) that provides a general toolkit for creating, load balancing and message passing in a 1, 2 or 3 dimensional block-adaptive grid. We describe the algorithms of BATL and demonstrate its efficiency and scaling properties for various problems. BATS-R-US uses several time-integration schemes to address multiple time-scales: explicit time stepping with fixed or local time steps, partially steady-state evolution, point-implicit, semi-implicit, explicit/implicit, and fully implicit
Enhancing Adaptive Filtering Approaches for Land Data Assimilation Systems
Technology Transfer Automated Retrieval System (TEKTRAN)
Recent work has presented the initial application of adaptive filtering techniques to land surface data assimilation systems. Such techniques are motivated by our current lack of knowledge concerning the structure of large-scale error in either land surface modeling output or remotely-sensed estima...
Neural Network Aided Adaptive Extended Kalman Filtering Approach for DGPS Positioning
NASA Astrophysics Data System (ADS)
Jwo, Dah-Jing; Huang, Hung-Chih
2004-09-01
The extended Kalman filter, when employed in the GPS receiver as the navigation state estimator, provides optimal solutions if the noise statistics for the measurement and system are completely known. In practice, the noise varies with time, which results in performance degradation. The covariance matching method is a conventional adaptive approach for estimation of noise covariance matrices. The technique attempts to make the actual filter residuals consistent with their theoretical covariance. However, this innovation-based adaptive estimation shows very noisy results if the window size is small. To resolve the problem, a multilayered neural network is trained to identify the measurement noise covariance matrix, in which the back-propagation algorithm is employed to iteratively adjust the link weights using the steepest descent technique. Numerical simulations show that based on the proposed approach the adaptation performance is substantially enhanced and the positioning accuracy is substantially improved.
An Adaptive Path Planning Algorithm for Cooperating Unmanned Air Vehicles
Cunningham, C.T.; Roberts, R.S.
2000-09-12
An adaptive path planning algorithm is presented for cooperating Unmanned Air Vehicles (UAVs) that are used to deploy and operate land-based sensor networks. The algorithm employs a global cost function to generate paths for the UAVs, and adapts the paths to exceptions that might occur. Examples are provided of the paths and adaptation.
Adaptive path planning algorithm for cooperating unmanned air vehicles
Cunningham, C T; Roberts, R S
2001-02-08
An adaptive path planning algorithm is presented for cooperating Unmanned Air Vehicles (UAVs) that are used to deploy and operate land-based sensor networks. The algorithm employs a global cost function to generate paths for the UAVs, and adapts the paths to exceptions that might occur. Examples are provided of the paths and adaptation.
NASA Astrophysics Data System (ADS)
Dasgupta, Udayan; Ali, Murtaza
2011-03-01
Low dose X-ray image sequences, as obtained in fluoroscopy, exhibit high levels of noise that must be suppressed in real-time, while preserving diagnostic structures. Multi-step adaptive filtering approaches, often involving spatio-temporal filters, are typically used to achieve this goal. In this work typical fluoroscopic image sequences, corrupted with Poisson noise, were processed using various filtering schemes. The noise suppression of the schemes was evaluated using objective image quality measures. Two adaptive spatio-temporal schemes, the first one using object detection and the second one using unsharp masking, were chosen as representative approaches for different fluoroscopy procedures and mapped on to Texas Instrument's (TI) high performance digital signal processors (DSP). The paper explains the fixed point design of these algorithms and evaluates its impact on overall system performance. The fixed point versions of these algorithms are mapped onto the C64x+TM core using instruction-level parallelism to effectively use its VLIW architecture. The overall data flow was carefully planned to reduce cache and data movement overhead, while working with large medical data sets. Apart from mapping these algorithms on to TI's single core DSP architecture, this work also distributes the operations to leverage multi-core DSP architectures. The data arrangement and flow were optimized to minimize inter-processor messaging and data movement overhead.
An adaptive replacement algorithm for paged-memory computer systems.
NASA Technical Reports Server (NTRS)
Thorington, J. M., Jr.; Irwin, J. D.
1972-01-01
A general class of adaptive replacement schemes for use in paged memories is developed. One such algorithm, called SIM, is simulated using a probability model that generates memory traces, and the results of the simulation of this adaptive scheme are compared with those obtained using the best nonlookahead algorithms. A technique for implementing this type of adaptive replacement algorithm with state of the art digital hardware is also presented.
NASA Astrophysics Data System (ADS)
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.
NASA Astrophysics Data System (ADS)
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.
A novel retinal vessel extraction algorithm based on matched filtering and gradient vector flow
NASA Astrophysics Data System (ADS)
Yu, Lei; Xia, Mingliang; Xuan, Li
2013-10-01
The microvasculature network of retina plays an important role in the study and diagnosis of retinal diseases (age-related macular degeneration and diabetic retinopathy for example). Although it is possible to noninvasively acquire high-resolution retinal images with modern retinal imaging technologies, non-uniform illumination, the low contrast of thin vessels and the background noises all make it difficult for diagnosis. In this paper, we introduce a novel retinal vessel extraction algorithm based on gradient vector flow and matched filtering to segment retinal vessels with different likelihood. Firstly, we use isotropic Gaussian kernel and adaptive histogram equalization to smooth and enhance the retinal images respectively. Secondly, a multi-scale matched filtering method is adopted to extract the retinal vessels. Then, the gradient vector flow algorithm is introduced to locate the edge of the retinal vessels. Finally, we combine the results of matched filtering method and gradient vector flow algorithm to extract the vessels at different likelihood levels. The experiments demonstrate that our algorithm is efficient and the intensities of vessel images exactly represent the likelihood of the vessels.
Ham, Bumsub; Min, Dongbo; Sohn, Kwanghoon
2013-03-01
Anisotropic diffusion has been known to be closely related to adaptive smoothing and discretized in a similar manner. This paper revisits a fundamental relationship between two approaches. It is shown that adaptive smoothing and anisotropic diffusion have different theoretical backgrounds by exploring their characteristics with the perspective of normalization, evolution step size, and energy flow. Based on this principle, adaptive smoothing is derived from a second order partial differential equation (PDE), not a conventional anisotropic diffusion, via the coupling of Fick's law with a generalized continuity equation where a "source" or "sink" exists, which has not been extensively exploited. We show that the source or sink is closely related to the asymmetry of energy flow as well as the normalization term of adaptive smoothing. It enables us to analyze behaviors of adaptive smoothing, such as the maximum principle and stability with a perspective of a PDE. Ultimately, this relationship provides new insights into application-specific filtering algorithm design. By modeling the source or sink in the PDE, we introduce two specific diffusion filters, the robust anisotropic diffusion and the robust coherence enhancing diffusion, as novel instantiations which are more robust against the outliers than the conventional filters. PMID:23193236
Microwave Photonic Filters for Interference Cancellation and Adaptive Beamforming
NASA Astrophysics Data System (ADS)
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
Infrared gas detection based on an adaptive Savitzky-Golay algorithm
NASA Astrophysics Data System (ADS)
Deng, Hao; Li, Jingsong; Li, Pengfei; Liu, Yu; Yu, Benli
2015-08-01
We have developed a simple but robust method based on the Savitzky-Golay filter for real-time processing tunable diode laser absorption spectroscopy (TDLAS) signal. Our method was developed to resolve the blindness of selecting the input filter parameters and potential signal distortion induced in digital signal processing. By applying the developed adaptive Savitzky-Golay filter algorithm to the simulated and experimentally observed signal and comparing with the wavelet-based de-noising technique, the results indicate that the new developed method is effective in obtaining high-quality TDLAS data for a wide variety of applications including atmospheric environmental monitoring and industrial processing control.
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
A unified set-based test with adaptive filtering for gene-environment interaction analyses
Liu, Qianying; Chen, Lin S.; Nicolae, Dan L.; Pierce, Brandon L.
2015-01-01
Summary 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
Adaptive non-local means filtering based on local noise level for CT denoising
NASA Astrophysics Data System (ADS)
Li, Zhoubo; Yu, Lifeng; Trzasko, Joshua D.; Fletcher, Joel G.; McCollough, Cynthia H.; Manduca, Armando
2012-03-01
Radiation dose from CT scans is an increasing health concern in the practice of radiology. Higher dose scans can produce clearer images with high diagnostic quality, but may increase the potential risk of radiation-induced cancer or other side effects. Lowering radiation dose alone generally produces a noisier image and may degrade diagnostic performance. Recently, CT dose reduction based on non-local means (NLM) filtering for noise reduction has yielded promising results. However, traditional NLM denoising operates under the assumption that image noise is spatially uniform noise, while in CT images the noise level varies significantly within and across slices. Therefore, applying NLM filtering to CT data using a global filtering strength cannot achieve optimal denoising performance. In this work, we have developed a technique for efficiently estimating the local noise level for CT images, and have modified the NLM algorithm to adapt to local variations in noise level. The local noise level estimation technique matches the true noise distribution determined from multiple repetitive scans of a phantom object very well. The modified NLM algorithm provides more effective denoising of CT data throughout a volume, and may allow significant lowering of radiation dose. Both the noise map calculation and the adaptive NLM filtering can be performed in times that allow integration with the clinical workflow.
Fuel-flow filter for internal combustion engine, adaptable for use with a by-pass filter
Schmidt, R.
1987-06-16
This patent describes a filter apparatus for an internal combustion engine to replace a spin-on, full-flow oil filter threadably connected to an oil filter bushing. The engine has an oil system with an oil pump, an oil pan, and an oil cap at a low pressure side of the oil system. The apparatus comprises: a full-flow filter to be connected to the oil filter bushing to permit oil within the oil system to flow into the full-flow filter. The full-flow filter is of such density and filtering capacity that the oil flows from the oil pump through the full-flow filter with a minimum pressure drop; adapter means to permit use of the full-flow filter either with or without a by-pass filter. The adapter means is a nut located at the forward end of the full-flow filter opposite the oil filter bushing and extending outwardly. The nut defines an area that can be either left intact, permitting all of the oil flow outward from the full-flow filter after filtering, or punctured, permitting most of the oil to flow outward from the full-flow filter after filtering. A small portion of the oil to flows outward therefrom prior to filtering. The nut is within a specific range of depth and circumference so as to provide a means for controlling the size of the hole. The nut is inwardly threaded.
Adaptive Spatial Filtering of Interferometric Data Stack Oriented to Distributed Scatterers
NASA Astrophysics Data System (ADS)
Zhang, Y.; Xie, C.; Shao, Y.; Yuan, M.
2013-07-01
Standard interferometry poses a challenge in non-urban areas due to temporal and spatial decorrelation of the radar signal, where there is high signal noise. Techniques such as Small Baseline Subset Algorithm (SBAS) have been proposed to make use of multiple interferometric combinations to alleviate the problem. However, the interferograms used in SBAS are multilooked with a boxcar (rectangle) filter to reduce phase noise, resulting in a loss of resolution and signal superstition from different objects. In this paper, we proposed a modified adaptive spatial filtering algorithm for accurate estimation of interferogram and coherence without resolution loss even in rural areas, to better support the deformation monitoring with time series interferometric synthetic aperture radar (InSAR) technique. The implemented method identifies the statistically homogenous pixels in a neighbourhood based on the goodness-of-fit test, and then applies an adaptive spatial filtering of interferograms. Three statistical tests for the identification of distributed targets will be presented, applied to real data. PALSAR data of the yellow river delta in China is used for demonstrating the effectiveness of this algorithm in rural areas.
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; Yu, Lifeng; Fletcher, Joel G.; McCollough, Cynthia H.
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
A novel algorithm for real-time adaptive signal detection and identification
Sleefe, G.E.; Ladd, M.D.; Gallegos, D.E.; Sicking, C.W.; Erteza, I.A.
1998-04-01
This paper describes a novel digital signal processing algorithm for adaptively detecting and identifying signals buried in noise. The algorithm continually computes and updates the long-term statistics and spectral characteristics of the background noise. Using this noise model, a set of adaptive thresholds and matched digital filters are implemented to enhance and detect signals that are buried in the noise. The algorithm furthermore automatically suppresses coherent noise sources and adapts to time-varying signal conditions. Signal detection is performed in both the time-domain and the frequency-domain, thereby permitting the detection of both broad-band transients and narrow-band signals. The detection algorithm also provides for the computation of important signal features such as amplitude, timing, and phase information. Signal identification is achieved through a combination of frequency-domain template matching and spectral peak picking. The algorithm described herein is well suited for real-time implementation on digital signal processing hardware. This paper presents the theory of the adaptive algorithm, provides an algorithmic block diagram, and demonstrate its implementation and performance with real-world data. The computational efficiency of the algorithm is demonstrated through benchmarks on specific DSP hardware. The applications for this algorithm, which range from vibration analysis to real-time image processing, are also discussed.
An adaptive algorithm for motion compensated color image coding
NASA Technical Reports Server (NTRS)
Kwatra, Subhash C.; Whyte, Wayne A.; Lin, Chow-Ming
1987-01-01
This paper presents an adaptive algorithm for motion compensated color image coding. The algorithm can be used for video teleconferencing or broadcast signals. Activity segmentation is used to reduce the bit rate and a variable stage search is conducted to save computations. The adaptive algorithm is compared with the nonadaptive algorithm and it is shown that with approximately 60 percent savings in computing the motion vector and 33 percent additional compression, the performance of the adaptive algorithm is similar to the nonadaptive algorithm. The adaptive algorithm results also show improvement of up to 1 bit/pel over interframe DPCM coding with nonuniform quantization. The test pictures used for this study were recorded directly from broadcast video in color.
Adaptive filtering of radar images for autofocus applications
NASA Technical Reports Server (NTRS)
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.
Adaptive gain and filtering circuit for a sound reproduction system
NASA Technical Reports Server (NTRS)
Engebretson, A. Maynard (Inventor); O'Connell, Michael P. (Inventor)
1998-01-01
Adaptive compressive gain and level dependent spectral shaping circuitry for a hearing aid include a microphone to produce an input signal and a plurality of channels connected to a common circuit output. Each channel has a preset frequency response. Each channel includes a filter with a preset frequency response to receive the input signal and to produce a filtered signal, a channel amplifier to amplify the filtered signal to produce a channel output signal, a threshold register to establish a channel threshold level, and a gain circuit. The gain circuit increases the gain of the channel amplifier when the channel output signal falls below the channel threshold level and decreases the gain of the channel amplifier when the channel output signal rises above the channel threshold level. A transducer produces sound in response to the signal passed by the common circuit output.
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.
Adaptive control of large space structures using recursive lattice filters
NASA Technical Reports Server (NTRS)
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 control of large space structures using recursive lattice filters
NASA Technical Reports Server (NTRS)
Goglia, G. L.
1985-01-01
The use of recursive lattice filters for identification and adaptive control of large space structures was studied. Lattice filters are used widely in the areas of speech and signal processing. Herein, they are used to identify the structural dynamics model of the flexible structures. This identified 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 control is engaged. This type of validation scheme prevents instability when the overall loop is closed. The results obtained from simulation were compared to those obtained from experiments. In this regard, the flexible beam and grid apparatus at the Aerospace Control Research Lab (ACRL) of NASA Langley Research Center were used as the principal candidates for carrying out the above tasks. Another important area of research, namely that of robust controller synthesis, was investigated using frequency domain multivariable controller synthesis methods.
Spitzer Instrument Pointing Frame (IPF) Kalman Filter Algorithm
NASA Technical Reports Server (NTRS)
Bayard, David S.; Kang, Bryan H.
2004-01-01
This paper discusses the Spitzer Instrument Pointing Frame (IPF) Kalman Filter algorithm. The IPF Kalman filter is a high-order square-root iterated linearized Kalman filter, which is parametrized for calibrating the Spitzer Space Telescope focal plane and aligning the science instrument arrays with respect to the telescope boresight. The most stringent calibration requirement specifies knowledge of certain instrument pointing frames to an accuracy of 0.1 arcseconds, per-axis, 1-sigma relative to the Telescope Pointing Frame. In order to achieve this level of accuracy, the filter carries 37 states to estimate desired parameters while also correcting for expected systematic errors due to: (1) optical distortions, (2) scanning mirror scale-factor and misalignment, (3) frame alignment variations due to thermomechanical distortion, and (4) gyro bias and bias-drift in all axes. The resulting estimated pointing frames and calibration parameters are essential for supporting on-board precision pointing capability, in addition to end-to-end 'pixels on the sky' ground pointing reconstruction efforts.
Particle filter-based track before detect algorithms
NASA Astrophysics Data System (ADS)
Boers, Yvo; Driessen, Hans
2003-12-01
In this paper we will give a general system setup, that allows the formulation of a wide range of Track Before Detect (TBD) problems. A general basic particle filter algorithm for this system is also provided. TBD is a technique, where tracks are produced directly on the basis of raw (radar) measurements, e.g. power or IQ data, without intermediate processing and decision making. The advantage over classical tracking is that the full information is integrated over time, this leads to a better detection and tracking performance, especially for weak targets. In this paper we look at the filtering and the detection aspect of TBD. We will formulate a detection result, that allows the user to implement any optimal detector in terms of the weights of a running particle filter. We will give a theoretical as well as a numerical (experimental) justification for this. Furthermore, we show that the TBD setup, that is chosen in this paper, allows a straightforward extension to the multi-target case. This easy extension is also due to the fact that the implementation of the solution is by means of a particle filter.
Particle filter-based track before detect algorithms
NASA Astrophysics Data System (ADS)
Boers, Yvo; Driessen, Hans
2004-01-01
In this paper we will give a general system setup, that allows the formulation of a wide range of Track Before Detect (TBD) problems. A general basic particle filter algorithm for this system is also provided. TBD is a technique, where tracks are produced directly on the basis of raw (radar) measurements, e.g. power or IQ data, without intermediate processing and decision making. The advantage over classical tracking is that the full information is integrated over time, this leads to a better detection and tracking performance, especially for weak targets. In this paper we look at the filtering and the detection aspect of TBD. We will formulate a detection result, that allows the user to implement any optimal detector in terms of the weights of a running particle filter. We will give a theoretical as well as a numerical (experimental) justification for this. Furthermore, we show that the TBD setup, that is chosen in this paper, allows a straightforward extension to the multi-target case. This easy extension is also due to the fact that the implementation of the solution is by means of a particle filter.
Model Adaptation for Prognostics in a Particle Filtering Framework
NASA Technical Reports Server (NTRS)
Saha, Bhaskar; Goebel, Kai Frank
2011-01-01
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 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.
NASA Astrophysics Data System (ADS)
Singh, R.; Verma, H. K.
2013-12-01
This paper presents a teaching-learning-based optimization (TLBO) algorithm to solve parameter identification problems in the designing of digital infinite impulse response (IIR) filter. TLBO based filter modelling is applied to calculate the parameters of unknown plant in simulations. Unlike other heuristic search algorithms, TLBO algorithm is an algorithm-specific parameter-less algorithm. In this paper big bang-big crunch (BB-BC) optimization and PSO algorithms are also applied to filter design for comparison. Unknown filter parameters are considered as a vector to be optimized by these algorithms. MATLAB programming is used for implementation of proposed algorithms. Experimental results show that the TLBO is more accurate to estimate the filter parameters than the BB-BC optimization algorithm and has faster convergence rate when compared to PSO algorithm. TLBO is used where accuracy is more essential than the convergence speed.
A 3D approach for object recognition in illuminated scenes with adaptive correlation filters
NASA Astrophysics Data System (ADS)
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.
NASA Astrophysics Data System (ADS)
Fang, Hao; Li, Qian; Huang, Zhenghua
2015-12-01
Denoising algorithms based on gradient dependent energy functionals, such as Perona-Malik, total variation and adaptive total variation denoising, modify images towards piecewise constant functions. Although edge sharpness and location is well preserved, important information, encoded in image features like textures or certain details, is often compromised in the process of denoising. In this paper, We propose a novel Spatially Adaptive Guide-Filtering Total Variation (SAGFTV) regularization with image restoration algorithm for denoising images. The guide-filter is extended to the variational formulations of imaging problem, and the spatially adaptive operator can easily distinguish flat areas from texture areas. Our simulating experiments show the improvement of peak signal noise ratio (PSNR), root mean square error (RMSE) and structure similarity increment measurement (SSIM) over other prior algorithms. The results of both simulating and practical experiments are more appealing visually. This type of processing can be used for a variety of tasks in PDE-based image processing and computer vision, and is stable and meaningful from a mathematical viewpoint.
Image super-resolution via adaptive filtering and regularization
NASA Astrophysics Data System (ADS)
Ren, Jingbo; Wu, Hao; Dong, Weisheng; Shi, Guangming
2014-11-01
Image super-resolution (SR) is widely used in the fields of civil and military, especially for the low-resolution remote sensing images limited by the sensor. Single-image SR refers to the task of restoring a high-resolution (HR) image from the low-resolution image coupled with some prior knowledge as a regularization term. One classic method regularizes image by total variation (TV) and/or wavelet or some other transform which introduce some artifacts. To compress these shortages, a new framework for single image SR is proposed by utilizing an adaptive filter before regularization. The key of our model is that the adaptive filter is used to remove the spatial relevance among pixels first and then only the high frequency (HF) part, which is sparser in TV and transform domain, is considered as the regularization term. Concretely, through transforming the original model, the SR question can be solved by two alternate iteration sub-problems. Before each iteration, the adaptive filter should be updated to estimate the initial HF. A high quality HF part and HR image can be obtained by solving the first and second sub-problem, respectively. In experimental part, a set of remote sensing images captured by Landsat satellites are tested to demonstrate the effectiveness of the proposed framework. Experimental results show the outstanding performance of the proposed method in quantitative evaluation and visual fidelity compared with the state-of-the-art methods.
Blended particle methods with adaptive subspaces for filtering turbulent dynamical systems
NASA Astrophysics Data System (ADS)
Qi, Di; Majda, Andrew J.
2015-04-01
It is a major challenge throughout science and engineering to improve uncertain model predictions by utilizing noisy data sets from nature. Hybrid methods combining the advantages of traditional particle filters and the Kalman filter offer a promising direction for filtering or data assimilation in high dimensional turbulent dynamical systems. In this paper, blended particle filtering methods that exploit the physical structure of turbulent dynamical systems are developed. Non-Gaussian features of the dynamical system are captured adaptively in an evolving-in-time low dimensional subspace through particle methods, while at the same time statistics in the remaining portion of the phase space are amended by conditional Gaussian mixtures interacting with the particles. The importance of both using the adaptively evolving subspace and introducing conditional Gaussian statistics in the orthogonal part is illustrated here by simple examples. For practical implementation of the algorithms, finding the most probable distributions that characterize the statistics in the phase space as well as effective resampling strategies is discussed to handle realizability and stability issues. To test the performance of the blended algorithms, the forty dimensional Lorenz 96 system is utilized with a five dimensional subspace to run particles. The filters are tested extensively in various turbulent regimes with distinct statistics and with changing observation time frequency and both dense and sparse spatial observations. In real applications perfect dynamical models are always inaccessible considering the complexities in both modeling and computation of high dimensional turbulent system. The effects of model errors from imperfect modeling of the systems are also checked for these methods. The blended methods show uniformly high skill in both capturing non-Gaussian statistics and achieving accurate filtering results in various dynamical regimes with and without model errors.
Frequency-shift low-pass filtering and least mean square adaptive filtering for ultrasound imaging
NASA Astrophysics Data System (ADS)
Wang, Shanshan; Li, Chunyu; Ding, Mingyue; Yuchi, Ming
2016-04-01
Ultrasound image quality enhancement is a problem of considerable interest in medical imaging modality and an ongoing challenge to date. This paper investigates a method based on frequency-shift low-pass filtering (FSLF) and least mean square adaptive filtering (LMSAF) for ultrasound image quality enhancement. FSLF is used for processing the ultrasound signal in the frequency domain, while LMSAPF in the time domain. Firstly, FSLF shifts the center frequency of the focused signal to zero. Then the real and imaginary part of the complex data are filtered respectively by finite impulse response (FIR) low-pass filter. Thus the information around the center frequency are retained while the undesired ones, especially background noises are filtered. Secondly, LMSAF multiplies the signals with an automatically adjusted weight vector to further eliminate the noises and artifacts. Through the combination of the two filters, the ultrasound image is expected to have less noises and artifacts and higher resolution, and contrast. The proposed method was verified with the RF data of the CIRS phantom 055A captured by SonixTouch DAQ system. Experimental results show that the background noises and artifacts can be efficiently restrained, the wire object has a higher resolution and the contrast ratio (CR) can be enhanced for about 12dB to 15dB at different image depth comparing to delay-and-sum (DAS).
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
Adaptive mesh and algorithm refinement using direct simulation Monte Carlo
Garcia, A.L.; Bell, J.B.; Crutchfield, W.Y.; Alder, B.J.
1999-09-01
Adaptive mesh and algorithm refinement (AMAR) embeds a particle method within a continuum method at the finest level of an adaptive mesh refinement (AMR) hierarchy. The coupling between the particle region and the overlaying continuum grid is algorithmically equivalent to that between the fine and coarse levels of AMR. Direct simulation Monte Carlo (DSMC) is used as the particle algorithm embedded within a Godunov-type compressible Navier-Stokes solver. Several examples are presented and compared with purely continuum calculations.
Switched Band-Pass Filters for Adaptive Transceivers
NASA Technical Reports Server (NTRS)
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.
An Adaptive Unified Differential Evolution Algorithm for Global Optimization
Qiang, Ji; Mitchell, Chad
2014-11-03
In this paper, we propose a new adaptive unified differential evolution algorithm for single-objective global optimization. Instead of the multiple mutation strate- gies proposed in conventional differential evolution algorithms, this algorithm employs a single equation unifying multiple strategies into one expression. It has the virtue of mathematical simplicity and also provides users the flexibility for broader exploration of the space of mutation operators. By making all control parameters in the proposed algorithm self-adaptively evolve during the process of optimization, it frees the application users from the burden of choosing appro- priate control parameters and also improves the performance of the algorithm. In numerical tests using thirteen basic unimodal and multimodal functions, the proposed adaptive unified algorithm shows promising performance in compari- son to several conventional differential evolution algorithms.
Application of an adaptive plan to the configuration of nonlinear image-processing algorithms
NASA Astrophysics Data System (ADS)
Chu, Chee-Hung H.
1990-07-01
The application of an adaptive plan to the design of a class of nonlinear digital image processing operators known as stack filters is presented in this paper. The adaptive plan is based on the mechanics found in genetics and natural selection. Such learning mechanisms have become known as genetic algorithms. A stack filter is characterized by the coefficients of its underlying positive Boolean function. This set of coefficients constitute a binary string, referred to as a chromosome in a genetic algorithm, that represents that particular filter configuration. A fitness value for each chromosome is computed based on the performance of the associated filter in specific tasks such as noise suppression. A population of chromosomes is maintained by the genetic algorithm, and new generations are formed by selecting mating pairs based on their fitness values. Genetic operators such as crossover or mutation are applied to the mating pairs to form offsprings. By exchanging some substrings of the two parent-chromosomes, the crossover operator can bring different blocks of genes that result in good performance together into one chromosome that yields the best performance. Empirical results show that this method is capable of configuring stack filters that are effective in impulsive noise suppression.
A new algorithm of inter-frame filtering in IR image based on threshold value
NASA Astrophysics Data System (ADS)
Liu, Wei; Leng, Hanbing; Chen, Weining; Yang, Hongtao; Xie, Qingsheng; Yi, Bo; Zhang, Haifeng
2013-09-01
This paper proposed a new algorithm of inter-frame filtering in IR image based on threshold value for the purpose of solving image blur and smear brought by traditional inter-frame filtering algorithm. At first, it finds out causes of image blur and smear by analyzing general inter-frame filtering algorithm and dynamic inter-frame filtering algorithm, hence to bring up a new kind of time-domain filter. In order to obtain coefficients of the filter, it firstly gets difference image of present image and previous image, and then, it gets noisy threshold value by analyzing difference image with probability analysis method. The relationship between difference image and threshold value helps obtaining the coefficients of filter. At last, inter-frame filtering method is adopted to process pixels interrupted by noise. The experimental result shows that this algorithm has successfully repressed IR image blur and smear, and NETD tested by traditional inter filtering algorithm and the new algorithm are respectively 78mK and 70mK, which shows it has a better noise reduction performance than traditional ones. The algorithm is not only applied to still image, but also to sports image. As a new algorithm with great practical value, it is easy to achieve on FPGA, of excellent real-time performance and it effectively extends application scope of time domain filtering algorithm.
Improving the Response of Accelerometers for Automotive Applications by Using LMS Adaptive Filters
Hernandez, Wilmar; de Vicente, Jesús; Sergiyenko, Oleg; Fernández, Eduardo
2010-01-01
In this paper, the least-mean-squares (LMS) algorithm was used to eliminate noise corrupting the important information coming from a piezoresisitive accelerometer for automotive applications. This kind of accelerometer is designed to be easily mounted in hard to reach places on vehicles under test, and they usually feature ranges from 50 to 2,000 g (where is the gravitational acceleration, 9.81 m/s2) and frequency responses to 3,000 Hz or higher, with DC response, durable cables, reliable performance and relatively low cost. However, here we show that the response of the sensor under test had a lot of noise and we carried out the signal processing stage by using both conventional and optimal adaptive filtering. Usually, designers have to build their specific analog and digital signal processing circuits, and this fact increases considerably the cost of the entire sensor system and the results are not always satisfactory, because the relevant signal is sometimes buried in a broad-band noise background where the unwanted information and the relevant signal sometimes share a very similar frequency band. Thus, in order to deal with this problem, here we used the LMS adaptive filtering algorithm and compare it with others based on the kind of filters that are typically used for automotive applications. The experimental results are satisfactory. PMID:22315542
High performance 3D adaptive filtering for DSP based portable medical imaging systems
NASA Astrophysics Data System (ADS)
Bockenbach, Olivier; Ali, Murtaza; Wainwright, Ian; Nadeski, Mark
2015-03-01
Portable medical imaging devices have proven valuable for emergency medical services both in the field and hospital environments and are becoming more prevalent in clinical settings where the use of larger imaging machines is impractical. Despite their constraints on power, size and cost, portable imaging devices must still deliver high quality images. 3D adaptive filtering is one of the most advanced techniques aimed at noise reduction and feature enhancement, but is computationally very demanding and hence often cannot be run with sufficient performance on a portable platform. In recent years, advanced multicore digital signal processors (DSP) have been developed that attain high processing performance while maintaining low levels of power dissipation. These processors enable the implementation of complex algorithms on a portable platform. In this study, the performance of a 3D adaptive filtering algorithm on a DSP is investigated. The performance is assessed by filtering a volume of size 512x256x128 voxels sampled at a pace of 10 MVoxels/sec with an Ultrasound 3D probe. Relative performance and power is addressed between a reference PC (Quad Core CPU) and a TMS320C6678 DSP from Texas Instruments.
An Adaptive Kalman Filter using a Simple Residual Tuning Method
NASA Technical Reports Server (NTRS)
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. 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.
An Adaptive Kalman Filter Using a Simple Residual Tuning Method
NASA Technical Reports Server (NTRS)
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.
Zhu, Xinjun; Chen, Zhanqing; Tang, Chen; Mi, Qinghua; Yan, Xiusheng
2013-03-20
In this paper, we are concerned with denoising in experimentally obtained electronic speckle pattern interferometry (ESPI) speckle fringe patterns with poor quality. We extend the application of two existing oriented partial differential equation (PDE) filters, including the second-order single oriented PDE filter and the double oriented PDE filter, to two experimentally obtained ESPI speckle fringe patterns with very poor quality, and compare them with other efficient filtering methods, including the adaptive weighted filter, the improved nonlinear complex diffusion PDE, and the windowed Fourier transform method. All of the five filters have been illustrated to be efficient denoising methods through previous comparative analyses in published papers. The experimental results have demonstrated that the two oriented PDE models are applicable to low-quality ESPI speckle fringe patterns. Then for solving the main shortcoming of the two oriented PDE models, we develop the numerically fast algorithms based on Gauss-Seidel strategy for the two oriented PDE models. The proposed numerical algorithms are capable of accelerating the convergence greatly, and perform significantly better in terms of computational efficiency. Our numerically fast algorithms are extended automatically to some other PDE filtering models. PMID:23518722
Adaptive DNA Computing Algorithm by Using PCR and Restriction Enzyme
NASA Astrophysics Data System (ADS)
Kon, Yuji; Yabe, Kaoru; Rajaee, Nordiana; Ono, Osamu
In this paper, we introduce an adaptive DNA computing algorithm by using polymerase chain reaction (PCR) and restriction enzyme. The adaptive algorithm is designed based on Adleman-Lipton paradigm[3] of DNA computing. In this work, however, unlike the Adleman- Lipton architecture a cutting operation has been introduced to the algorithm and the mechanism in which the molecules used by computation were feedback to the next cycle devised. Moreover, the amplification by PCR is performed in the molecule used by feedback and the difference concentration arisen in the base sequence can be used again. By this operation the molecules which serve as a solution candidate can be reduced down and the optimal solution is carried out in the shortest path problem. The validity of the proposed adaptive algorithm is considered with the logical simulation and finally we go on to propose applying adaptive algorithm to the chemical experiment which used the actual DNA molecules for solving an optimal network problem.
Statistical-uncertainty-based adaptive filtering of lidar signals
Fuehrer, P. L.; Friehe, C. A.; Hristov, T. S.; Cooper, D. I.; Eichinger, W. E.
2000-02-10
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 H{sub 2}O and the N{sub 2} 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.
Fast Source Camera Identification Using Content Adaptive Guided Image Filter.
Zeng, Hui; Kang, Xiangui
2016-03-01
Source camera identification (SCI) is an important topic in image forensics. One of the most effective fingerprints for linking an image to its source camera is the sensor pattern noise, which is estimated as the difference between the content and its denoised version. It is widely believed that the performance of the sensor-based SCI heavily relies on the denoising filter used. This study proposes a novel sensor-based SCI method using content adaptive guided image filter (CAGIF). Thanks to the low complexity nature of the CAGIF, the proposed method is much faster than the state-of-the-art methods, which is a big advantage considering the potential real-time application of SCI. Despite the advantage of speed, experimental results also show that the proposed method can achieve comparable or better performance than the state-of-the-art methods in terms of accuracy. PMID:27404627
An adaptive filter method for spacecraft using gravity assist
NASA Astrophysics Data System (ADS)
Ning, Xiaolin; Huang, Panpan; Fang, Jiancheng; Liu, Gang; Ge, Shuzhi Sam
2015-04-01
Celestial navigation (CeleNav) has been successfully used during gravity assist (GA) flyby for orbit determination in many deep space missions. Due to spacecraft attitude errors, ephemeris errors, the camera center-finding bias, and the frequency of the images before and after the GA flyby, the statistics of measurement noise cannot be accurately determined, and yet have time-varying characteristics, which may introduce large estimation error and even cause filter divergence. In this paper, an unscented Kalman filter (UKF) with adaptive measurement noise covariance, called ARUKF, is proposed to deal with this problem. ARUKF scales the measurement noise covariance according to the changes in innovation and residual sequences. Simulations demonstrate that ARUKF is robust to the inaccurate initial measurement noise covariance matrix and time-varying measurement noise. The impact factors in the ARUKF are also investigated.
Despeckling algorithm on ultrasonic image using adaptive block-based singular value decomposition
NASA Astrophysics Data System (ADS)
Sae-Bae, Napa; Udomhunsakul, Somkait
2008-03-01
Speckle noise reduction is an important technique to enhance the quality of ultrasonic image. In this paper, a despeckling algorithm based on an adaptive block-based singular value decomposition filtering (BSVD) applied on ultrasonic images is presented. Instead of applying BSVD directly to ultrasonic image, we propose to apply BSVD on the noisy edge image version obtained from the difference between the logarithmic transformations of the original image and blur image version of its. The recovered image is performed by combining the speckle noise-free edge image with blur image version of its. Finally, exponential transformation is applied in order to get the reconstructed image. To evaluate our algorithm compared with well-know algorithms such as Lee filter, Kuan filter, Homomorphic Wiener filter, median filter and wavelet soft thresholding, four image quality measurements, which are Mean Square Error (MSE), Signal to MSE (S/MSE), Edge preservation (β), and Correlation measurement (ρ), are used. From the results, it clearly shows that the proposed algorithm outperforms other methods in terms of quantitative and subjective assessments.
Adaptive control and noise suppression by a variable-gain gradient algorithm
NASA Technical Reports Server (NTRS)
Merhav, S. J.; Mehta, R. S.
1987-01-01
An adaptive control system based on normalized LMS filters is investigated. The finite impulse response of the nonparametric controller is adaptively estimated using a given reference model. Specifically, the following issues are addressed: The stability of the closed loop system is analyzed and heuristically established. Next, the adaptation process is studied for piecewise constant plant parameters. It is shown that by introducing a variable-gain in the gradient algorithm, a substantial reduction in the LMS adaptation rate can be achieved. Finally, process noise at the plant output generally causes a biased estimate of the controller. By introducing a noise suppression scheme, this bias can be substantially reduced and the response of the adapted system becomes very close to that of the reference model. Extensive computer simulations validate these and demonstrate assertions that the system can rapidly adapt to random jumps in plant parameters.
Performance study of LMS based adaptive algorithms for unknown system identification
Javed, Shazia; Ahmad, Noor Atinah
2014-07-10
Adaptive filtering techniques have gained much popularity in the modeling of unknown system identification problem. These techniques can be classified as either iterative or direct. Iterative techniques include stochastic descent method and its improved versions in affine space. In this paper we present a comparative study of the least mean square (LMS) algorithm and some improved versions of LMS, more precisely the normalized LMS (NLMS), LMS-Newton, transform domain LMS (TDLMS) and affine projection algorithm (APA). The performance evaluation of these algorithms is carried out using adaptive system identification (ASI) model with random input signals, in which the unknown (measured) signal is assumed to be contaminated by output noise. Simulation results are recorded to compare the performance in terms of convergence speed, robustness, misalignment, and their sensitivity to the spectral properties of input signals. Main objective of this comparative study is to observe the effects of fast convergence rate of improved versions of LMS algorithms on their robustness and misalignment.
Performance study of LMS based adaptive algorithms for unknown system identification
NASA Astrophysics Data System (ADS)
Javed, Shazia; Ahmad, Noor Atinah
2014-07-01
Adaptive filtering techniques have gained much popularity in the modeling of unknown system identification problem. These techniques can be classified as either iterative or direct. Iterative techniques include stochastic descent method and its improved versions in affine space. In this paper we present a comparative study of the least mean square (LMS) algorithm and some improved versions of LMS, more precisely the normalized LMS (NLMS), LMS-Newton, transform domain LMS (TDLMS) and affine projection algorithm (APA). The performance evaluation of these algorithms is carried out using adaptive system identification (ASI) model with random input signals, in which the unknown (measured) signal is assumed to be contaminated by output noise. Simulation results are recorded to compare the performance in terms of convergence speed, robustness, misalignment, and their sensitivity to the spectral properties of input signals. Main objective of this comparative study is to observe the effects of fast convergence rate of improved versions of LMS algorithms on their robustness and misalignment.
Attitude determination using an adaptive multiple model filtering Scheme
NASA Technical Reports Server (NTRS)
Lam, Quang; Ray, Surendra N.
1995-01-01
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
Attitude determination using an adaptive multiple model filtering Scheme
NASA Astrophysics Data System (ADS)
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
NASA Astrophysics Data System (ADS)
Li, Yan; He, Lin; Shuai, Chang-geng; Wang, Fei
2016-04-01
A time-domain filtered-x Newton narrowband algorithm (the Fx-Newton algorithm) is proposed to address three major problems in active isolation of machinery vibration: multiple narrowband components, MIMO coupling, and amplitude and frequency fluctuations. In this algorithm, narrowband components are extracted by narrowband-pass filters (NBPF) and independently controlled by multi-controllers, and fast convergence of the control algorithm is achieved by inverse secondary-path filtering of the extracted sinusoidal reference signal and its orthogonal component using L×L numbers of 2nd-order filters in the time domain. Controller adapting and control signal generation are also implemented in the time domain, to ensure good real-time performance. The phase shift caused by narrowband filter is compensated online to improve the robustness of control system to frequency fluctuations. A double-reference Fx-Newton algorithm is also proposed to control double sinusoids in the same frequency band, under the precondition of acquiring two independent reference signals. Experiments are conducted with an MIMO single-deck vibration isolation system on which a 200 kW ship diesel generator is mounted, and the algorithms are tested under the vibration alternately excited by the diesel generator and inertial shakers. The results of control over sinusoidal vibration excited by inertial shakers suggest that the Fx-Newton algorithm with NBPF have much faster convergence rate and better attenuation effect than the Fx-LMS algorithm. For swept, frequency-jumping, double, double frequency-swept and double frequency-jumping sinusoidal vibration, and multiple high-level harmonics in broadband vibration excited by the diesel generator, the proposed algorithms also demonstrate large vibration suppression at fast convergence rate, and good robustness to vibration with frequency fluctuations.
NASA Astrophysics Data System (ADS)
Hegde, Veena; Deekshit, Ravishankar; Satyanarayana, P. S.
2011-12-01
The electrocardiogram (ECG) is widely used for diagnosis of heart diseases. Good quality of ECG is utilized by physicians for interpretation and identification of physiological and pathological phenomena. However, in real situations, ECG recordings are often corrupted by artifacts or noise. Noise severely limits the utility of the recorded ECG and thus needs to be removed, for better clinical evaluation. In the present paper a new noise cancellation technique is proposed for removal of random noise like muscle artifact from ECG signal. A transform domain robust variable step size Griffiths' LMS algorithm (TVGLMS) is proposed for noise cancellation. For the TVGLMS, the robust variable step size has been achieved by using the Griffiths' gradient which uses cross-correlation between the desired signal contaminated with observation or random noise and the input. The algorithm is discrete cosine transform (DCT) based and uses symmetric property of the signal to represent the signal in frequency domain with lesser number of frequency coefficients when compared to that of discrete Fourier transform (DFT). The algorithm is implemented for adaptive line enhancer (ALE) filter which extracts the ECG signal in a noisy environment using LMS filter adaptation. The proposed algorithm is found to have better convergence error/misadjustment when compared to that of ordinary transform domain LMS (TLMS) algorithm, both in the presence of white/colored observation noise. The reduction in convergence error achieved by the new algorithm with desired signal decomposition is found to be lower than that obtained without decomposition. The experimental results indicate that the proposed method is better than traditional adaptive filter using LMS algorithm in the aspects of retaining geometrical characteristics of ECG signal.
Self-adaptive genetic algorithms with simulated binary crossover.
Deb, K; Beyer, H G
2001-01-01
Self-adaptation is an essential feature of natural evolution. However, in the context of function optimization, self-adaptation features of evolutionary search algorithms have been explored mainly with evolution strategy (ES) and evolutionary programming (EP). In this paper, we demonstrate the self-adaptive feature of real-parameter genetic algorithms (GAs) using a simulated binary crossover (SBX) operator and without any mutation operator. The connection between the working of self-adaptive ESs and real-parameter GAs with the SBX operator is also discussed. Thereafter, the self-adaptive behavior of real-parameter GAs is demonstrated on a number of test problems commonly used in the ES literature. The remarkable similarity in the working principle of real-parameter GAs and self-adaptive ESs shown in this study suggests the need for emphasizing further studies on self-adaptive GAs. PMID:11382356
Zhu, Wei; Wang, Wei; Yuan, Gannan
2016-01-01
In order to improve the tracking accuracy, model estimation accuracy and quick response of multiple model maneuvering target tracking, the interacting multiple models five degree cubature Kalman filter (IMM5CKF) is proposed in this paper. In the proposed algorithm, the interacting multiple models (IMM) algorithm processes all the models through a Markov Chain to simultaneously enhance the model tracking accuracy of target tracking. Then a five degree cubature Kalman filter (5CKF) evaluates the surface integral by a higher but deterministic odd ordered spherical cubature rule to improve the tracking accuracy and the model switch sensitivity of the IMM algorithm. Finally, the simulation results demonstrate that the proposed algorithm exhibits quick and smooth switching when disposing different maneuver models, and it also performs better than the interacting multiple models cubature Kalman filter (IMMCKF), interacting multiple models unscented Kalman filter (IMMUKF), 5CKF and the optimal mode transition matrix IMM (OMTM-IMM). PMID:27258285
Zhu, Wei; Wang, Wei; Yuan, Gannan
2016-01-01
In order to improve the tracking accuracy, model estimation accuracy and quick response of multiple model maneuvering target tracking, the interacting multiple models five degree cubature Kalman filter (IMM5CKF) is proposed in this paper. In the proposed algorithm, the interacting multiple models (IMM) algorithm processes all the models through a Markov Chain to simultaneously enhance the model tracking accuracy of target tracking. Then a five degree cubature Kalman filter (5CKF) evaluates the surface integral by a higher but deterministic odd ordered spherical cubature rule to improve the tracking accuracy and the model switch sensitivity of the IMM algorithm. Finally, the simulation results demonstrate that the proposed algorithm exhibits quick and smooth switching when disposing different maneuver models, and it also performs better than the interacting multiple models cubature Kalman filter (IMMCKF), interacting multiple models unscented Kalman filter (IMMUKF), 5CKF and the optimal mode transition matrix IMM (OMTM-IMM). PMID:27258285
Residual mode filters and adaptive control in large space structures
NASA Technical Reports Server (NTRS)
Davidson, Roger A.; Balas, Mark J.
1989-01-01
One of the most difficult problems in controlling large systems and structures is compensating for the destructive interaction which can occur between the reduced-order model (ROM) of the plant, which is used by the controller, and the unmodeled dynamics of the plant, often called the residual modes. The problem is more significant in the case of large space structures because their naturally light damping and high performance requirements lead to more frequent, destructive residual mode interaction (RMI). Using the design/compensation technique of residual mode filters (RMF's), effective compensation of RMI can be accomplished in a straightforward manner when using linear controllers. The use of RMF's has been shown to be effective for a variety of large structures, including a space-based laser and infinite dimensional systems. However, the dynamics of space structures is often uncertain and may even change over time due to on-orbit erosion from space debris and corrosive chemicals in the upper atmosphere. In this case, adaptive control can be extremely beneficial in meeting the performance requirements of the structure. Adaptive control for large structures is also based on ROM's and so destructive RMI may occur. Unfortunately, adaptive control is inherently nonlinear, and therefore the known results of RMF's cannot be applied. The purpose is to present the results of new research showing the effects of RMI when using adaptive control and the work which will hopefully lead to RMF compensation of this problem.
Independent motion detection with a rival penalized adaptive particle filter
NASA Astrophysics Data System (ADS)
Becker, Stefan; Hübner, Wolfgang; Arens, Michael
2014-10-01
Aggregation of pixel based motion detection into regions of interest, which include views of single moving objects in a scene is an essential pre-processing step in many vision systems. Motion events of this type provide significant information about the object type or build the basis for action recognition. Further, motion is an essential saliency measure, which is able to effectively support high level image analysis. When applied to static cameras, background subtraction methods achieve good results. On the other hand, motion aggregation on freely moving cameras is still a widely unsolved problem. The image flow, measured on a freely moving camera is the result from two major motion types. First the ego-motion of the camera and second object motion, that is independent from the camera motion. When capturing a scene with a camera these two motion types are adverse blended together. In this paper, we propose an approach to detect multiple moving objects from a mobile monocular camera system in an outdoor environment. The overall processing pipeline consists of a fast ego-motion compensation algorithm in the preprocessing stage. Real-time performance is achieved by using a sparse optical flow algorithm as an initial processing stage and a densely applied probabilistic filter in the post-processing stage. Thereby, we follow the idea proposed by Jung and Sukhatme. Normalized intensity differences originating from a sequence of ego-motion compensated difference images represent the probability of moving objects. Noise and registration artefacts are filtered out, using a Bayesian formulation. The resulting a posteriori distribution is located on image regions, showing strong amplitudes in the difference image which are in accordance with the motion prediction. In order to effectively estimate the a posteriori distribution, a particle filter is used. In addition to the fast ego-motion compensation, the main contribution of this paper is the design of the probabilistic
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
On application of adaptive decorrelation filtering to assistive listening
NASA Astrophysics Data System (ADS)
Zhao, Yunxin; Yen, Kuan-Chieh; Soli, Sig; Gao, Shawn; Vermiglio, Andy
2002-02-01
This paper describes an application of the multichannel signal processing technique of adaptive decorrelation filtering to the design of an assistive listening system. A simulated ``dinner table'' scenario was studied. The speech signal of a desired talker was corrupted by three simultaneous speech jammers and by a speech-shaped diffusive noise. The technique of adaptive decorrelation filtering processing was used to extract the desired speech from the interference speech and noise. The effectiveness of the assistive listening system was evaluated by observing improvements in A-weighted signal-to-noise ratio (SNR) and in sentence intelligibility, where the latter was evaluated in a listening test with eight normal hearing subjects and three subjects with hearing impairments. Significant improvements in SNR and sentence intelligibility were achieved with the use of the assistive listening system. For subjects with normal hearing, the speech reception threshold was improved by 3 to 5 dBA, and for subjects with hearing impairments, the threshold was improved by 4 to 8 dBA.
A Biomimetic Adaptive Algorithm and Low-Power Architecture for Implantable Neural Decoders
Rapoport, Benjamin I.; Wattanapanitch, Woradorn; Penagos, Hector L.; Musallam, Sam; Andersen, Richard A.; Sarpeshkar, Rahul
2010-01-01
Algorithmically and energetically efficient computational architectures that operate in real time are essential for clinically useful neural prosthetic devices. Such devices decode raw neural data to obtain direct control signals for external devices. They can also perform data compression and vastly reduce the bandwidth and consequently power expended in wireless transmission of raw data from implantable brain-machine interfaces. We describe a biomimetic algorithm and micropower analog circuit architecture for decoding neural cell ensemble signals. The decoding algorithm implements a continuous-time artificial neural network, using a bank of adaptive linear filters with kernels that emulate synaptic dynamics. The filters transform neural signal inputs into control-parameter outputs, and can be tuned automatically in an on-line learning process. We provide experimental validation of our system using neural data from thalamic head-direction cells in an awake behaving rat. PMID:19964345
A DSP-Based Beam Current Monitoring System for Machine Protection Using Adaptive Filtering
J. Musson; H. Dong; R. Flood; C. Hovater; J. Hereford
2001-06-01
The CEBAF accelerator at Jefferson Lab is currently using an analog beam current monitoring (BCM) system for its machine protection system (MPS), which has a loss accuracy of 2 micro-amps. Recent burn-through simulations predict catastrophic beam line component failures below 1 micro-amp of loss, resulting in a blind spot for the MPS. Revised MPS requirements target an ultimate beam loss accuracy of 250 nA. A new beam current monitoring system has been developed which utilizes modern digital receiver technology and digital signal processing concepts. The receiver employs a direct-digital down converter integrated circuit, mated with a Jefferson Lab digital signal processor VME card. Adaptive filtering is used to take advantage of current-dependent burn-through rates. Benefits of such a system include elimination of DC offsets, generic algorithm development, extensive filter options, and interfaces to UNIX-based control systems.
Controller-structure interaction compensation using adaptive residual mode filters
NASA Technical Reports Server (NTRS)
Davidson, Roger A.; Balas, Mark J.
1990-01-01
It is not feasible to construct controllers for large space structures or large scale systems (LSS's) which are of the same order as the structures. The complexity of the dynamics of these systems is such that full knowledge of its behavior cannot by processed by today's controller design methods. The controller for system performance of such a system is therefore based on a much smaller reduced-order model (ROM). Unfortunately, the interaction between the LSS and the ROM-based controller can produce instabilities in the closed-loop system due to the unmodeled dynamics of the LSS. Residual mode filters (RMF's) allow the systematic removal of these instabilities in a matter which does not require a redesign of the controller. In addition RMF's have a strong theoretical basis. As simple first- or second-order filters, the RMF CSI compensation technique is at once modular, simple and highly effective. RMF compensation requires knowledge of the dynamics of the system modes which resulted in the previous closed-loop instabilities (the residual modes), but this information is sometimes known imperfectly. An adaptive, self-tuning RMF design, which compensates for uncertainty in the frequency of the residual mode, has been simulated using continuous-time and discrete-time models of a flexible robot manipulator. Work has also been completed on the discrete-time experimental implementation on the Martin Marietta flexible robot manipulator experiment. This paper will present the results of that work on adaptive, self-tuning RMF's, and will clearly show the advantage of this adaptive compensation technique for controller-structure interaction (CSI) instabilities in actively-controlled LSS's.
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
NASA Astrophysics Data System (ADS)
Xu, Dexiang
This dissertation presents a novel method of designing finite word length Finite Impulse Response (FIR) digital filters using a Real Parameter Parallel Genetic Algorithm (RPPGA). This algorithm is derived from basic Genetic Algorithms which are inspired by natural genetics principles. Both experimental results and theoretical studies in this work reveal that the RPPGA is a suitable method for determining the optimal or near optimal discrete coefficients of finite word length FIR digital filters. Performance of RPPGA is evaluated by comparing specifications of filters designed by other methods with filters designed by RPPGA. The parallel and spatial structures of the algorithm result in faster and more robust optimization than basic genetic algorithms. A filter designed by RPPGA is implemented in hardware to attenuate high frequency noise in a data acquisition system for collecting seismic signals. These studies may lead to more applications of the Real Parameter Parallel Genetic Algorithms in Electrical Engineering.
Adaptive path planning: Algorithm and analysis
Chen, Pang C.
1995-03-01
To address the need for a fast path planner, we present a learning algorithm that improves path planning by using past experience to enhance future performance. The algorithm relies on an existing path planner to provide solutions difficult tasks. From these solutions, an evolving sparse work of useful robot configurations is learned to support faster planning. More generally, the algorithm provides a framework in which a slow but effective planner may be improved both cost-wise and capability-wise by a faster but less effective planner coupled with experience. We analyze algorithm by formalizing the concept of improvability and deriving conditions under which a planner can be improved within the framework. The analysis is based on two stochastic models, one pessimistic (on task complexity), the other randomized (on experience utility). Using these models, we derive quantitative bounds to predict the learning behavior. We use these estimation tools to characterize the situations in which the algorithm is useful and to provide bounds on the training time. In particular, we show how to predict the maximum achievable speedup. Additionally, our analysis techniques are elementary and should be useful for studying other types of probabilistic learning as well.
Optimal Pid Controller Design Using Adaptive Vurpso Algorithm
NASA Astrophysics Data System (ADS)
Zirkohi, Majid Moradi
2015-04-01
The purpose of this paper is to improve theVelocity Update Relaxation Particle Swarm Optimization algorithm (VURPSO). The improved algorithm is called Adaptive VURPSO (AVURPSO) algorithm. Then, an optimal design of a Proportional-Integral-Derivative (PID) controller is obtained using the AVURPSO algorithm. An adaptive momentum factor is used to regulate a trade-off between the global and the local exploration abilities in the proposed algorithm. This operation helps the system to reach the optimal solution quickly and saves the computation time. Comparisons on the optimal PID controller design confirm the superiority of AVURPSO algorithm to the optimization algorithms mentioned in this paper namely the VURPSO algorithm, the Ant Colony algorithm, and the conventional approach. Comparisons on the speed of convergence confirm that the proposed algorithm has a faster convergence in a less computation time to yield a global optimum value. The proposed AVURPSO can be used in the diverse areas of optimization problems such as industrial planning, resource allocation, scheduling, decision making, pattern recognition and machine learning. The proposed AVURPSO algorithm is efficiently used to design an optimal PID controller.
An adaptive inverse kinematics algorithm for robot manipulators
NASA Technical Reports Server (NTRS)
Colbaugh, R.; Glass, K.; Seraji, H.
1990-01-01
An adaptive algorithm for solving the inverse kinematics problem for robot manipulators is presented. The algorithm is derived using model reference adaptive control (MRAC) theory and is computationally efficient for online applications. The scheme requires no a priori knowledge of the kinematics of the robot if Cartesian end-effector sensing is available, and it requires knowledge of only the forward kinematics if joint position sensing is used. Computer simulation results are given for the redundant seven-DOF robotics research arm, demonstrating that the proposed algorithm yields accurate joint angle trajectories for a given end-effector position/orientation trajectory.
Adaptive UAV Attitude Estimation Employing Unscented Kalman Filter, FOAM and Low-Cost MEMS Sensors
de Marina, Héctor García; Espinosa, Felipe; Santos, Carlos
2012-01-01
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. PMID:23012559
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
An adaptive Kalman filter technique for context-aware heart rate monitoring.
Xu, Min; Goldfain, Albert; Dellostritto, Jim; Iyengar, Satish
2012-01-01
Traditional physiological monitoring systems convert a person's vital sign waveforms, such as heart rate, respiration rate and blood pressure, into meaningful information by comparing the instant reading with a preset threshold or a baseline without considering the contextual information of the person. It would be beneficial to incorporate the contextual data such as activity status of the person to the physiological data in order to obtain a more accurate representation of a person's physiological status. In this paper, we proposed an algorithm based on adaptive Kalman filter that describes the heart rate response with respect to different activity levels. It is towards our final goal of intelligent detection of any abnormality in the person's vital signs. Experimental results are provided to demonstrate the feasibility of the algorithm. PMID:23367423
Adaptive UAV attitude estimation employing unscented Kalman Filter, FOAM and low-cost MEMS sensors.
de Marina, Héctor García; Espinosa, Felipe; Santos, Carlos
2012-01-01
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. PMID:23012559
Adaptive Fuzzy Hysteresis Band Current Controller for Four-Wire Shunt Active Filter
NASA Astrophysics Data System (ADS)
Hamoudi, F.; Chaghi, A.; Amimeur, H.; Merabet, E.
2008-06-01
This paper presents an adaptive fuzzy hysteresis band current controller for four-wire shunt active power filters to eliminate harmonics and to compensate reactive power in distribution systems in order to keep currents at the point of common coupling sinusoidal and in phase with the corresponding voltage and the cancel neutral current. The conventional hysteresis band known for its robustness and its advantage in current controlled applications is adapted with a fuzzy logic controller to change the bandwidth according to the operating point in order to keep the frequency modulation at tolerable limits. The algorithm used to identify the reference currents is based on the synchronous reference frame theory (dqγ). Finally, simulation results using Matlab/Simulink are given to validate the proposed control.
NASA Astrophysics Data System (ADS)
Wang, Bin; Dong, Lili; Zhao, Ming; Xu, Wenhai
2015-12-01
In order to realize accurate detection for small dim infrared maritime target, this paper proposes a target detection algorithm based on local peak detection and pipeline-filtering. This method firstly extracts some suspected targets through local peak detection and removes most of non-target peaks with self-adaptive threshold process. And then pipeline-filtering is used to eliminate residual interferences so that only real target can be retained. The experiment results prove that this method has high performance on target detection, and its missing alarm rate and false alarm rate can basically meet practical requirements.
NASA Astrophysics Data System (ADS)
Lardière, Olivier; Conan, Rodolphe; Clare, Richard; Bradley, Colin; Hubin, Norbert
2010-07-01
Variations of the sodium layer altitude and atom density profile induce errors on laser-guide-star (LGS) adaptive optics systems. These errors must be mitigated by (i), optimizing the LGS wavefront sensor (WFS) and the centroiding algorithm, and (ii), by adding a high-pass filter on the LGS path and a low-bandwidth natural-guide-star WFS. In the context of the ESO E-ELT project, five centroiding algorithms, namely the centre-of-gravity (CoG), the weighted CoG, the matched filter, the quad-cell and the correlation, have been evaluated in closedloop on the University of Victoria LGS wavefront sensing test bed. Each centroiding algorithm performance is compared for a central versus side-launch laser, different fields of view, pixel sampling, and LGS flux.
Adaptive inpainting algorithm based on DCT induced wavelet regularization.
Li, Yan-Ran; Shen, Lixin; Suter, Bruce W
2013-02-01
In this paper, we propose an image inpainting optimization model whose objective function is a smoothed l(1) norm of the weighted nondecimated discrete cosine transform (DCT) coefficients of the underlying image. By identifying the objective function of the proposed model as a sum of a differentiable term and a nondifferentiable term, we present a basic algorithm inspired by Beck and Teboulle's recent work on the model. Based on this basic algorithm, we propose an automatic way to determine the weights involved in the model and update them in each iteration. The DCT as an orthogonal transform is used in various applications. We view the rows of a DCT matrix as the filters associated with a multiresolution analysis. Nondecimated wavelet transforms with these filters are explored in order to analyze the images to be inpainted. Our numerical experiments verify that under the proposed framework, the filters from a DCT matrix demonstrate promise for the task of image inpainting. PMID:23060331
Adaptively resizing populations: Algorithm, analysis, and first results
NASA Technical Reports Server (NTRS)
Smith, Robert E.; Smuda, Ellen
1993-01-01
Deciding on an appropriate population size for a given Genetic Algorithm (GA) application can often be critical to the algorithm's success. Too small, and the GA can fall victim to sampling error, affecting the efficacy of its search. Too large, and the GA wastes computational resources. Although advice exists for sizing GA populations, much of this advice involves theoretical aspects that are not accessible to the novice user. An algorithm for adaptively resizing GA populations is suggested. This algorithm is based on recent theoretical developments that relate population size to schema fitness variance. The suggested algorithm is developed theoretically, and simulated with expected value equations. The algorithm is then tested on a problem where population sizing can mislead the GA. The work presented suggests that the population sizing algorithm may be a viable way to eliminate the population sizing decision from the application of GA's.
A numerical comparison of discrete Kalman filtering algorithms - An orbit determination case study
NASA Technical Reports Server (NTRS)
Thornton, C. L.; Bierman, G. J.
1976-01-01
An improved Kalman filter algorithm based on a modified Givens matrix triangularization technique is proposed for solving a nonstationary discrete-time linear filtering problem. The proposed U-D covariance factorization filter uses orthogonal transformation technique; measurement and time updating of the U-D factors involve separate application of Gentleman's fast square-root-free Givens rotations. Numerical stability and accuracy of the algorithm are compared with those of the conventional and stabilized Kalman filters and the Potter-Schmidt square-root filter, by applying these techniques to a realistic planetary navigation problem (orbit determination for the Saturn approach phase of the Mariner Jupiter-Saturn Mission, 1977). The new algorithm is shown to combine the numerical precision of square root filtering with the efficiency of the original Kalman algorithm.
A Novel Hybrid Self-Adaptive Bat Algorithm
Fister, Iztok; Brest, Janez
2014-01-01
Nature-inspired algorithms attract many researchers worldwide for solving the hardest optimization problems. One of the newest members of this extensive family is the bat algorithm. To date, many variants of this algorithm have emerged for solving continuous as well as combinatorial problems. One of the more promising variants, a self-adaptive bat algorithm, has recently been proposed that enables a self-adaptation of its control parameters. In this paper, we have hybridized this algorithm using different DE strategies and applied these as a local search heuristics for improving the current best solution directing the swarm of a solution towards the better regions within a search space. The results of exhaustive experiments were promising and have encouraged us to invest more efforts into developing in this direction. PMID:25187904
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
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