Science.gov

Sample records for adaptive beamformer algorithm

  1. Improved LMS algorithm for adaptive beamforming

    NASA Technical Reports Server (NTRS)

    Godara, Lal C.

    1990-01-01

    Two adaptive algorithms which make use of all the available samples to estimate the required gradient are proposed and studied. The first algorithm is referred to as the recursive LMS (least mean squares) and is applicable to a general array. The second algorithm is referred to as the improved LMS algorithm and exploits the Toeplitz structure of the ACM (array correlation matrix); it can be used only for an equispaced linear array.

  2. Stochastic Leader Gravitational Search Algorithm for Enhanced Adaptive Beamforming Technique

    PubMed Central

    Darzi, Soodabeh; Islam, Mohammad Tariqul; Tiong, Sieh Kiong; Kibria, Salehin; Singh, Mandeep

    2015-01-01

    In this paper, stochastic leader gravitational search algorithm (SL-GSA) based on randomized k is proposed. Standard GSA (SGSA) utilizes the best agents without any randomization, thus it is more prone to converge at suboptimal results. Initially, the new approach randomly choses k agents from the set of all agents to improve the global search ability. Gradually, the set of agents is reduced by eliminating the agents with the poorest performances to allow rapid convergence. The performance of the SL-GSA was analyzed for six well-known benchmark functions, and the results are compared with SGSA and some of its variants. Furthermore, the SL-GSA is applied to minimum variance distortionless response (MVDR) beamforming technique to ensure compatibility with real world optimization problems. The proposed algorithm demonstrates superior convergence rate and quality of solution for both real world problems and benchmark functions compared to original algorithm and other recent variants of SGSA. PMID:26552032

  3. Adaptive Beamforming with Inadequate Snapshots

    NASA Astrophysics Data System (ADS)

    YU, Jing; LI, Yaan

    2017-01-01

    In array signal processing, the covariance matrix used to calculate the adaptive weights is often poor estimated when the snapshot number is inadequate. The prior environmental knowledge can be used to make the estimation more accuracy. In this paper, an alternative knowledge-aided adaptive beamforming approach that is robust to low sample support environment is proposed. In this algorithm the covariance matrix used to calculate the optimum weights is constructed by blending a sample covariance matrix and a priori structured covariance matrix. Numerical simulations demonstrate the proposed algorithm has the potential for substantial performance improvement.

  4. Adaptive digital beamforming architecture and algorithm for nulling mainlobe and multiple sidelobe jammers

    NASA Astrophysics Data System (ADS)

    Yu, Kai-Bor; Murrow, David J.

    1999-11-01

    This paper describes a digital beamforming architecture for nulling a mainlobe jammer and multiple sidelobe jammers while maintaining the angle estimation accuracy of the monopulse ratio. A sidelobe jammer canceling adaptive array is cascaded with a mainlobe jammer canceller, imposing a mainlobe maintenance technique or constrained adaptation during sidelobe cancellation process so the results of sidelobe jammer cancellation process do not distort subsequent mainlobe cancellation process. The sidelobe jammers and the mainlobe jammer are thus cancelled sequentially in separate processes. This adaptive digital beamforming technique is for improving radar processing for determining the angular location of a target, and specifically to an improvement in the monopulse technique so as to maintain accuracy of the monopulse ratio in the presence of jamming by adaptive suppression of jamming before forming the sum and difference beams.

  5. Constrained Adaptive Beamforming for Improved Contrast in Breast Ultrasound

    DTIC Science & Technology

    2005-06-01

    led us to utilize a recently proposed method, Spatial Processing Optimized and Constrained ( SPOC ). In initial simulations this method not only...6 Review of the Adaptive Beamforming Literature ................... 7 SPOC Progress...given the parameters of in vivo ultrasound imaging. SPOC Progress: Most of the Adaptive Beamforming algorithms previously described fail when applied to

  6. A Nonlinear Adaptive Beamforming Algorithm Based on Least Squares Support Vector Regression

    PubMed Central

    Wang, Lutao; Jin, Gang; Li, Zhengzhou; Xu, Hongbin

    2012-01-01

    To overcome the performance degradation in the presence of steering vector mismatches, strict restrictions on the number of available snapshots, and numerous interferences, a novel beamforming approach based on nonlinear least-square support vector regression machine (LS-SVR) is derived in this paper. In this approach, the conventional linearly constrained minimum variance cost function used by minimum variance distortionless response (MVDR) beamformer is replaced by a squared-loss function to increase robustness in complex scenarios and provide additional control over the sidelobe level. Gaussian kernels are also used to obtain better generalization capacity. This novel approach has two highlights, one is a recursive regression procedure to estimate the weight vectors on real-time, the other is a sparse model with novelty criterion to reduce the final size of the beamformer. The analysis and simulation tests show that the proposed approach offers better noise suppression capability and achieve near optimal signal-to-interference-and-noise ratio (SINR) with a low computational burden, as compared to other recently proposed robust beamforming techniques.

  7. Adaptive beamforming for array signal processing in aeroacoustic measurements.

    PubMed

    Huang, Xun; Bai, Long; Vinogradov, Igor; Peers, Edward

    2012-03-01

    Phased microphone arrays have become an important tool in the localization of noise sources for aeroacoustic applications. In most practical aerospace cases the conventional beamforming algorithm of the delay-and-sum type has been adopted. Conventional beamforming cannot take advantage of knowledge of the noise field, and thus has poorer resolution in the presence of noise and interference. Adaptive beamforming has been used for more than three decades to address these issues and has already achieved various degrees of success in areas of communication and sonar. In this work an adaptive beamforming algorithm designed specifically for aeroacoustic applications is discussed and applied to practical experimental data. It shows that the adaptive beamforming method could save significant amounts of post-processing time for a deconvolution method. For example, the adaptive beamforming method is able to reduce the DAMAS computation time by at least 60% for the practical case considered in this work. Therefore, adaptive beamforming can be considered as a promising signal processing method for aeroacoustic measurements.

  8. Null steering of adaptive beamforming using linear constraint minimum variance assisted by particle swarm optimization, dynamic mutated artificial immune system, and gravitational search algorithm.

    PubMed

    Darzi, Soodabeh; Kiong, Tiong Sieh; Islam, Mohammad Tariqul; Ismail, Mahamod; Kibria, Salehin; Salem, Balasem

    2014-01-01

    Linear constraint minimum variance (LCMV) is one of the adaptive beamforming techniques that is commonly applied to cancel interfering signals and steer or produce a strong beam to the desired signal through its computed weight vectors. However, weights computed by LCMV usually are not able to form the radiation beam towards the target user precisely and not good enough to reduce the interference by placing null at the interference sources. It is difficult to improve and optimize the LCMV beamforming technique through conventional empirical approach. To provide a solution to this problem, artificial intelligence (AI) technique is explored in order to enhance the LCMV beamforming ability. In this paper, particle swarm optimization (PSO), dynamic mutated artificial immune system (DM-AIS), and gravitational search algorithm (GSA) are incorporated into the existing LCMV technique in order to improve the weights of LCMV. The simulation result demonstrates that received signal to interference and noise ratio (SINR) of target user can be significantly improved by the integration of PSO, DM-AIS, and GSA in LCMV through the suppression of interference in undesired direction. Furthermore, the proposed GSA can be applied as a more effective technique in LCMV beamforming optimization as compared to the PSO technique. The algorithms were implemented using Matlab program.

  9. Adaptive phase rolling for opportunistic beamforming in OFDMA systems with a small number of users.

    PubMed

    Rim, Minjoong

    2014-01-01

    The performance of opportunistic beamforming might be degraded if the number of users is small. This paper proposes an adaptive opportunistic beamforming technique for orthogonal frequency division multiple access systems, which can produce good results even with a small number of users. This paper also proposes a modified proportional fairness scheduling algorithm, which can further improve the performance of the proposed opportunistic beamforming technique.

  10. Performance analysis of structured gradient algorithm. [for adaptive beamforming linear arrays

    NASA Technical Reports Server (NTRS)

    Godara, Lal C.

    1990-01-01

    The structured gradient algorithm uses a structured estimate of the array correlation matrix (ACM) to estimate the gradient required for the constrained least-mean-square (LMS) algorithm. This structure reflects the structure of the exact array correlation matrix for an equispaced linear array and is obtained by spatial averaging of the elements of the noisy correlation matrix. In its standard form the LMS algorithm does not exploit the structure of the array correlation matrix. The gradient is estimated by multiplying the array output with the receiver outputs. An analysis of the two algorithms is presented to show that the covariance of the gradient estimated by the structured method is less sensitive to the look direction signal than that estimated by the standard method. The effect of the number of elements on the signal sensitivity of the two algorithms is studied.

  11. Constrained Adaptive Beamforming for Improved Contrast in Breast Ultrasound

    DTIC Science & Technology

    2008-06-01

    us to modify the Spatial Processing Optimized and Constrained algorithm ( SPOC ) to yield the Time-Domain Optimized Near-field Estimator (TONE). In...beamformers - Improvement and evaluation of adaptive imaging algorithms based loosely on SPOC - Invention and testing of the basic TONE algorithm...named the Time-domain Optimized Near-Filed Estimator, or TONE. The novel ABF was developed from Spatial Processing: Optimized and Constrained ( SPOC

  12. Robust adaptive beamforming for MIMO monopulse radar

    NASA Astrophysics Data System (ADS)

    Rowe, William; Ström, Marie; Li, Jian; Stoica, Petre

    2013-05-01

    Researchers have recently proposed a widely separated multiple-input multiple-output (MIMO) radar using monopulse angle estimation techniques for target tracking. The widely separated antennas provide improved tracking performance by mitigating complex target radar cross-section fades and angle scintillation. An adaptive array is necessary in this paradigm because the direct path from any transmitter could act as a jammer at a receiver. When the target-free covariance matrix is not available, it is critical to include robustness into the adaptive beamformer weights. This work explores methods of robust adaptive monopulse beamforming techniques for MIMO tracking radar.

  13. A recurrent neural network for adaptive beamforming and array correction.

    PubMed

    Che, Hangjun; Li, Chuandong; He, Xing; Huang, Tingwen

    2016-08-01

    In this paper, a recurrent neural network (RNN) is proposed for solving adaptive beamforming problem. In order to minimize sidelobe interference, the problem is described as a convex optimization problem based on linear array model. RNN is designed to optimize system's weight values in the feasible region which is derived from arrays' state and plane wave's information. The new algorithm is proven to be stable and converge to optimal solution in the sense of Lyapunov. So as to verify new algorithm's performance, we apply it to beamforming under array mismatch situation. Comparing with other optimization algorithms, simulations suggest that RNN has strong ability to search for exact solutions under the condition of large scale constraints.

  14. A beamforming algorithm for bistatic SAR image formation

    NASA Astrophysics Data System (ADS)

    Jakowatz, Charles V., Jr.; Wahl, Daniel E.; Yocky, David A.

    2010-04-01

    Beamforming is a methodology for collection-mode-independent SAR image formation. It is essentially equivalent to backprojection. The authors have in previous papers developed this idea and discussed the advantages and disadvantages of the approach to monostatic SAR image formation vis-à-vis the more standard and time-tested polar formatting algorithm (PFA). In this paper we show that beamforming for bistatic SAR imaging leads again to a very simple image formation algorithm that requires a minimal number of lines of code and that allows the image to be directly formed onto a three-dimensional surface model, thus automatically creating an orthorectified image. The same disadvantage of beamforming applied to monostatic SAR imaging applies to the bistatic case, however, in that the execution time for the beamforming algorithm is quite long compared to that of PFA. Fast versions of beamforming do exist to help alleviate this issue. Results of image reconstructions from phase history data are presented.

  15. A beamforming algorithm for bistatic SAR image formation.

    SciTech Connect

    Yocky, David Alan; Wahl, Daniel Eugene; Jakowatz, Charles V., Jr.

    2010-03-01

    Beamforming is a methodology for collection-mode-independent SAR image formation. It is essentially equivalent to backprojection. The authors have in previous papers developed this idea and discussed the advantages and disadvantages of the approach to monostatic SAR image formation vis--vis the more standard and time-tested polar formatting algorithm (PFA). In this paper we show that beamforming for bistatic SAR imaging leads again to a very simple image formation algorithm that requires a minimal number of lines of code and that allows the image to be directly formed onto a three-dimensional surface model, thus automatically creating an orthorectified image. The same disadvantage of beamforming applied to monostatic SAR imaging applies to the bistatic case, however, in that the execution time for the beamforming algorithm is quite long compared to that of PFA. Fast versions of beamforming do exist to help alleviate this issue. Results of image reconstructions from phase history data are presented.

  16. Computationally Efficient Adaptive Beamformer for Ultrasound Imaging Based on QR Decomposition.

    PubMed

    Park, Jongin; Wi, Seok-Min; Lee, Jin S

    2016-02-01

    Adaptive beamforming methods for ultrasound imaging have been studied to improve image resolution and contrast. The most common approach is the minimum variance (MV) beamformer which minimizes the power of the beamformed output while maintaining the response from the direction of interest constant. The method achieves higher resolution and better contrast than the delay-and-sum (DAS) beamformer, but it suffers from high computational cost. This cost is mainly due to the computation of the spatial covariance matrix and its inverse, which requires O(L(3)) computations, where L denotes the subarray size. In this study, we propose a computationally efficient MV beamformer based on QR decomposition. The idea behind our approach is to transform the spatial covariance matrix to be a scalar matrix σI and we subsequently obtain the apodization weights and the beamformed output without computing the matrix inverse. To do that, QR decomposition algorithm is used and also can be executed at low cost, and therefore, the computational complexity is reduced to O(L(2)). In addition, our approach is mathematically equivalent to the conventional MV beamformer, thereby showing the equivalent performances. The simulation and experimental results support the validity of our approach.

  17. Observer-based beamforming algorithm for acoustic array signal processing.

    PubMed

    Bai, Long; Huang, Xun

    2011-12-01

    In the field of noise identification with microphone arrays, conventional delay-and-sum (DAS) beamforming is the most popular signal processing technique. However, acoustic imaging results that are generated by DAS beamforming are easily influenced by background noise, particularly for in situ wind tunnel tests. Even when arithmetic averaging is used to statistically remove the interference from the background noise, the results are far from perfect because the interference from the coherent background noise is still present. In addition, DAS beamforming based on arithmetic averaging fails to deliver real-time computational capability. An observer-based approach is introduced in this paper. This so-called observer-based beamforming method has a recursive form similar to the state observer in classical control theory, thus holds a real-time computational capability. In addition, coherent background noise can be gradually rejected in iterations. Theoretical derivations of the observer-based beamforming algorithm are carefully developed in this paper. Two numerical simulations demonstrate the good coherent background noise rejection and real-time computational capability of the observer-based beamforming, which therefore can be regarded as an attractive algorithm for acoustic array signal processing.

  18. The delay multiply and sum beamforming algorithm in ultrasound B-mode medical imaging.

    PubMed

    Matrone, Giulia; Savoia, Alessandro Stuart; Caliano, Giosue; Magenes, Giovanni

    2015-04-01

    Most of ultrasound medical imaging systems currently on the market implement standard Delay and Sum (DAS) beamforming to form B-mode images. However, image resolution and contrast achievable with DAS are limited by the aperture size and by the operating frequency. For this reason, different beamformers have been presented in the literature that are mainly based on adaptive algorithms, which allow achieving higher performance at the cost of an increased computational complexity. In this paper, we propose the use of an alternative nonlinear beamforming algorithm for medical ultrasound imaging, which is called Delay Multiply and Sum (DMAS) and that was originally conceived for a RADAR microwave system for breast cancer detection. We modify the DMAS beamformer and test its performance on both simulated and experimentally collected linear-scan data, by comparing the Point Spread Functions, beampatterns, synthetic phantom and in vivo carotid artery images obtained with standard DAS and with the proposed algorithm. Results show that the DMAS beamformer outperforms DAS in both simulated and experimental trials and that the main improvement brought about by this new method is a significantly higher contrast resolution (i.e., narrower main lobe and lower side lobes), which turns out into an increased dynamic range and better quality of B-mode images.

  19. Robust Adaptive Beamforming Based on Low-Rank and Cross-Correlation Techniques

    NASA Astrophysics Data System (ADS)

    Ruan, Hang; de Lamare, Rodrigo C.

    2016-08-01

    This work presents cost-effective low-rank techniques for designing robust adaptive beamforming (RAB) algorithms. The proposed algorithms are based on the exploitation of the cross-correlation between the array observation data and the output of the beamformer. Firstly, we construct a general linear equation considered in large dimensions whose solution yields the steering vector mismatch. Then, we employ the idea of the full orthogonalization method (FOM), an orthogonal Krylov subspace based method, to iteratively estimate the steering vector mismatch in a reduced-dimensional subspace, resulting in the proposed orthogonal Krylov subspace projection mismatch estimation (OKSPME) method. We also devise adaptive algorithms based on stochastic gradient (SG) and conjugate gradient (CG) techniques to update the beamforming weights with low complexity and avoid any costly matrix inversion. The main advantages of the proposed low-rank and mismatch estimation techniques are their cost-effectiveness when dealing with high dimension subspaces or large sensor arrays. Simulations results show excellent performance in terms of the output signal-to-interference-plus-noise ratio (SINR) of the beamformer among all the compared RAB methods.

  20. Research in Adaptive Beamforming for Satellite Communications.

    DTIC Science & Technology

    1983-05-01

    BEAMFORMING FOR SATELLITE COM MUNICATIONS 1. INTRODUCTION Reported below are the results of a study carried out in the in- terval, November 1, 1981 - October...communication satellite using beam-switching to ground users who may be close to potential interferers. In an earlier exploratory study , [1], the use of...alternative processing schemes was, nevertheless, found and this became the focus of the study reported here. In section (2) below we present a technical

  1. Microwave Photonic Filters for Interference Cancellation and Adaptive Beamforming

    NASA Astrophysics Data System (ADS)

    Chang, John

    beamformer. The solution is two-part. A novel highly-scalable photonic beamformer is first proposed and experimentally verified. A "blind" search algorithm called the guided accelerated random search (GARS) algorithm is then shown. A maximum cancellation of 37 dB is achieved within 50 iterations, a real-world time of 1-3 seconds, while the presence of a signal of interest (SOI) is maintained.

  2. An Overview of Algorithms for Downlink Transmit Beamforming

    DTIC Science & Technology

    2007-11-02

    Swindlehurst Electrical & Computer Engineering Brigham Young University Vector Modulo Pre-coding Use a perturbation of the form SP =,Cc =t(a+ jb) where -c...constellation "spacing" A. Lee Swindlehurst Electrical & Computer Engineering Brigham Young University Vector Modulo Pre-coding (cont.) * Choose c to solve...An Overview of Algorithms for Downlink Transmit Beamforming A. Lee Swindlehurst and Chris Peel Brigham Young University phone: 801-422-4343 email

  3. Constrained Adaptive Beamforming for Improved Contrast in Breast Ultrasound

    DTIC Science & Technology

    2006-06-01

    have led us to utilize a recently proposed method, Spatial Processing Optimized and Constrained ( SPOC ). In initial simulations this method not only...6 Summary of First Year Progress……………………………………6 Conventional Beamformer Optimization………….…………….….7 SPOC Progress...and hundreds of papers published in this area, only the SPOC (Spatial Processing Optimized and Constrained) algorithm could be readily modified to meet

  4. Hybrid RF and Digital Beamformer for Cellular Networks: Algorithms, Microwave Architectures, and Measurements

    NASA Astrophysics Data System (ADS)

    Venkateswaran, Vijay; Pivit, Florian; Guan, Lei

    2016-07-01

    Modern wireless communication networks, particularly cellular networks utilize multiple antennas to improve the capacity and signal coverage. In these systems, typically an active transceiver is connected to each antenna. However, this one-to-one mapping between transceivers and antennas will dramatically increase the cost and complexity of a large phased antenna array system. In this paper, firstly we propose a \\emph{partially adaptive} beamformer architecture where a reduced number of transceivers with a digital beamformer (DBF) is connected to an increased number of antennas through an RF beamforming network (RFBN). Then, based on the proposed architecture, we present a methodology to derive the minimum number of transceivers that are required for marco-cell and small-cell base stations, respectively. Subsequently, in order to achieve optimal beampatterns with given cellular standard requirements and RF operational constraints, we propose efficient algorithms to jointly design DBF and RFBN. Starting from the proposed algorithms, we specify generic microwave RFBNs for optimal marco-cell and small-cell networks. In order to verify the proposed approaches, we compare the performance of RFBN using simulations and anechoic chamber measurements. Experimental measurement results confirm the robustness and performance of the proposed hybrid DBF-RFBN concept eventually ensuring that theoretical multi-antenna capacity and coverage are achieved at a little incremental cost.

  5. An Experience Oriented-Convergence Improved Gravitational Search Algorithm for Minimum Variance Distortionless Response Beamforming Optimum

    PubMed Central

    Darzi, Soodabeh; Tiong, Sieh Kiong; Tariqul Islam, Mohammad; Rezai Soleymanpour, Hassan; Kibria, Salehin

    2016-01-01

    An experience oriented-convergence improved gravitational search algorithm (ECGSA) based on two new modifications, searching through the best experiments and using of a dynamic gravitational damping coefficient (α), is introduced in this paper. ECGSA saves its best fitness function evaluations and uses those as the agents’ positions in searching process. In this way, the optimal found trajectories are retained and the search starts from these trajectories, which allow the algorithm to avoid the local optimums. Also, the agents can move faster in search space to obtain better exploration during the first stage of the searching process and they can converge rapidly to the optimal solution at the final stage of the search process by means of the proposed dynamic gravitational damping coefficient. The performance of ECGSA has been evaluated by applying it to eight standard benchmark functions along with six complicated composite test functions. It is also applied to adaptive beamforming problem as a practical issue to improve the weight vectors computed by minimum variance distortionless response (MVDR) beamforming technique. The results of implementation of the proposed algorithm are compared with some well-known heuristic methods and verified the proposed method in both reaching to optimal solutions and robustness. PMID:27399904

  6. An Experience Oriented-Convergence Improved Gravitational Search Algorithm for Minimum Variance Distortionless Response Beamforming Optimum.

    PubMed

    Darzi, Soodabeh; Tiong, Sieh Kiong; Tariqul Islam, Mohammad; Rezai Soleymanpour, Hassan; Kibria, Salehin

    2016-01-01

    An experience oriented-convergence improved gravitational search algorithm (ECGSA) based on two new modifications, searching through the best experiments and using of a dynamic gravitational damping coefficient (α), is introduced in this paper. ECGSA saves its best fitness function evaluations and uses those as the agents' positions in searching process. In this way, the optimal found trajectories are retained and the search starts from these trajectories, which allow the algorithm to avoid the local optimums. Also, the agents can move faster in search space to obtain better exploration during the first stage of the searching process and they can converge rapidly to the optimal solution at the final stage of the search process by means of the proposed dynamic gravitational damping coefficient. The performance of ECGSA has been evaluated by applying it to eight standard benchmark functions along with six complicated composite test functions. It is also applied to adaptive beamforming problem as a practical issue to improve the weight vectors computed by minimum variance distortionless response (MVDR) beamforming technique. The results of implementation of the proposed algorithm are compared with some well-known heuristic methods and verified the proposed method in both reaching to optimal solutions and robustness.

  7. Adaptive digital beamforming for a CDMA mobile communications payload

    NASA Technical Reports Server (NTRS)

    Munoz-Garcia, Samuel G.; Ruiz, Javier Benedicto

    1993-01-01

    In recent years, Spread-Spectrum Code Division Multiple Access (CDMA) has become a very popular access scheme for mobile communications due to a variety of reasons: excellent performance in multipath environments, high scope for frequency reuse, graceful degradation near saturation, etc. In this way, a CDMA system can support simultaneous digital communication among a large community of relatively uncoordinated users sharing a given frequency band. Nevertheless, there are also important problems associated with the use of CDMA. First, in a conventional CDMA scheme, the signature sequences of asynchronous users are not orthogonal and, as the number of active users increases, the self-noise generated by the mutual interference between users considerably degrades the performance, particularly in the return link. Furthermore, when there is a large disparity in received powers - due to differences in slant range or atmospheric attenuation - the non-zero cross-correlation between the signals gives rise to the so-called near-far problem. This leads to an inefficient utilization of the satellite resources and, consequently, to a drastic reduction in capacity. Several techniques were proposed to overcome this problem, such as Synchronized CDMA - in which the signature sequences of the different users are quasi-orthogonal - and power control. At the expense of increased network complexity and user coordination, these techniques enable the system capacity to be restored by equitably sharing the satellite resources among the users. An alternative solution is presented based upon the use of time-reference adaptive digital beamforming on board the satellite. This technique enables a high number of independently steered beams to be generated from a single phased array antenna, which automatically track the desired user signal and null the unwanted interference source. In order to use a time-reference adaptive antenna in a communications system, the main challenge is to obtain a

  8. Adaptive digital beamforming for a CDMA mobile communications payload

    NASA Astrophysics Data System (ADS)

    Munoz-Garcia, Samuel G.; Ruiz, Javier Benedicto

    In recent years, Spread-Spectrum Code Division Multiple Access (CDMA) has become a very popular access scheme for mobile communications due to a variety of reasons: excellent performance in multipath environments, high scope for frequency reuse, graceful degradation near saturation, etc. In this way, a CDMA system can support simultaneous digital communication among a large community of relatively uncoordinated users sharing a given frequency band. Nevertheless, there are also important problems associated with the use of CDMA. First, in a conventional CDMA scheme, the signature sequences of asynchronous users are not orthogonal and, as the number of active users increases, the self-noise generated by the mutual interference between users considerably degrades the performance, particularly in the return link. Furthermore, when there is a large disparity in received powers - due to differences in slant range or atmospheric attenuation - the non-zero cross-correlation between the signals gives rise to the so-called near-far problem. This leads to an inefficient utilization of the satellite resources and, consequently, to a drastic reduction in capacity. Several techniques were proposed to overcome this problem, such as Synchronized CDMA - in which the signature sequences of the different users are quasi-orthogonal - and power control. At the expense of increased network complexity and user coordination, these techniques enable the system capacity to be restored by equitably sharing the satellite resources among the users. An alternative solution is presented based upon the use of time-reference adaptive digital beamforming on board the satellite. This technique enables a high number of independently steered beams to be generated from a single phased array antenna, which automatically track the desired user signal and null the unwanted interference source. In order to use a time-reference adaptive antenna in a communications system, the main challenge is to obtain a

  9. Adaptive beamforming in a CDMA mobile satellite communications system

    NASA Technical Reports Server (NTRS)

    Munoz-Garcia, Samuel G.

    1993-01-01

    Code-Division Multiple-Access (CDMA) stands out as a strong contender for the choice of multiple access scheme in these future mobile communication systems. This is due to a variety of reasons such as the excellent performance in multipath environments, high scope for frequency reuse and graceful degradation near saturation. However, the capacity of CDMA is limited by the self-interference between the transmissions of the different users in the network. Moreover, the disparity between the received power levels gives rise to the near-far problem, this is, weak signals are severely degraded by the transmissions from other users. In this paper, the use of time-reference adaptive digital beamforming on board the satellite is proposed as a means to overcome the problems associated with CDMA. This technique enables a high number of independently steered beams to be generated from a single phased array antenna, which automatically track the desired user signal and null the unwanted interference sources. Since CDMA is interference limited, the interference protection provided by the antenna converts directly and linearly into an increase in capacity. Furthermore, the proposed concept allows the near-far effect to be mitigated without requiring a tight coordination of the users in terms of power control. A payload architecture will be presented that illustrates the practical implementation of this concept. This digital payload architecture shows that with the advent of high performance CMOS digital processing, the on-board implementation of complex DSP techniques -in particular digital beamforming- has become possible, being most attractive for Mobile Satellite Communications.

  10. Evaluation of a portable two-microphone adaptive beamforming speech processor with cochlear implant patients.

    PubMed

    van Hoesel, R J; Clark, G M

    1995-04-01

    A two-microphone noise reduction technique was tested with four cochlear implant patients. The noise reduction technique, known as adaptive beamforming (ABF), used signals from only two microphones--one behind each ear--to attenuate sounds not arriving from the direction directly in front of the patient. The algorithm was implemented in a portable digital signal processor, and was compared with a strategy in which the two microphone signals were simply added together (two-microphone broadside strategy). Tests with the four patients were conducted in a soundproof booth with target speech arriving from in front of the patient and multitalker babble noise arriving at 90 deg to the left. Results at 0-dB signal-to-noise level (S/N) showed large improvements in speech intelligibility for all patients, when compared to the two-microphone broadside strategy. Precautions were taken to avoid cancellation of the target speech, and, accordingly, subjective tests showed no deterioration in performance for the adaptive beamformer in quiet. Physical measurement of the directional characteristics of the ABF was made with the microphones placed behind the ears of a KEMAR manikin and in the same acoustic environment as used with the patients. Results showed directional gain of approximately 10 dB when the angle of incidence for interfering noise was shifted more than 20 to 30 deg from directly in front of or behind the manikin.(ABSTRACT TRUNCATED AT 250 WORDS)

  11. Effect of element directivity on adaptive beamforming applied to high-frame-rate ultrasound.

    PubMed

    Hasegawa, Hideyuki; Kanai, Hiroshi

    2015-03-01

    High-frame-rate ultrasound is a promising technique for measurement and imaging of cardiovascular dynamics. In high-frame-rate ultrasonic imaging, unfocused ultrasonic beams are used in transmit and multiple focused receiving beams are created by parallel beamforming using the delay and sum (DAS) method. However, the spatial resolution and contrast are degraded compared with conventional beamforming using focused transmit beams. In the present study, the minimum variance beamformer was examined for improvement of the spatial resolution in high-frame-rate ultrasound. In conventional minimum variance beamforming, the spatial covariance matrix of ultrasonic echo signals received by individual transducer elements is obtained without considering the directivity of the transducer element. By omitting the element directivity, the error in estimation of the desired signal (i.e., the echo from the focal point) increases, and as a result, the improvement of the spatial resolution is degraded. In the present study, the element directivity was taken into account in estimation of the spatial covariance matrix used in minimum variance beamforming. The effect of the element directivity on adaptive beamforming was evaluated by computer simulation and basic experiments using a phantom. In parallel beamforming with the conventional DAS beamformer, the lateral spatial resolution, which was evaluated from the lateral full width at half maximum of the echo amplitude profile in the basic experiment, was 0.50 mm. Using conventional amplitude and phase estimation (APES) beamforming, the lateral spatial resolution was improved to 0.37 mm. The lateral spatial resolution was further improved to 0.30 mm using the modified APES beamforming by considering the element directivity. Image contrast and contrast-to-noise ratios, respectively, were -12.3 and 6.5 dB (DAS), -32.8 and -11.3 dB (APES), and -7.0 and 3.1 dB (modified APES).

  12. A low-complexity adaptive beamformer for ultrasound imaging using structured covariance matrix.

    PubMed

    Asl, Babak Mohammadzadeh; Mahloojifar, Ali

    2012-04-01

    In recent years, adaptive beamforming methods have been successfully applied to medical ultrasound imaging, resulting in simultaneous improvement in imaging resolution and contrast. These improvements have been achieved at the expense of higher computational complexity, with respect to the conventional non-adaptive delay-and-sum (DAS) beamformer, in which computational complexity is proportional to the number of elements, O(M). The computational overhead results from the covariance matrix inversion needed for computation of the adaptive weights, the complexity of which is cubic with the subarray size, O(L(3)). This is a computationally intensive procedure, which makes the implementation of adaptive beamformers less attractive in spite of their advantages. Considering that, in medical ultrasound applications, most of the energy is scattered from angles close to the steering angle, assuming spatial stationarity is a good approximation, allowing us to assume the Toeplitz structure for the estimated covariance matrix. Based on this idea, in this paper, we have applied the Toeplitz structure to the spatially smoothed covariance matrix by averaging the entries along all subdiagonals. Because the inverse of the resulting Toeplitz covariance matrix can be computed in O(L(2)) operations, this technique results in a greatly reduced computational complexity. By using simulated and experimental RF data-point targets as well as cyst phantoms-we show that the proposed low-complexity adaptive beamformer significantly outperforms the DAS and its performance is comparable to that of the minimum variance beamformer, with reduced computational complexity.

  13. Development of a Resource Manager Framework for Adaptive Beamformer Selection

    DTIC Science & Technology

    2013-12-27

    users there are interference signals in the environment from appliances such as microwave ovens, radio and television broadcasts from terrestrial and...framework is thus beneficial not only for its potential to increase the performance of electronic support receivers, but also to open up a new research thread...Solbach, “Finite impulse response-filter-based RF- beamforming network for wideband and ultra-wideband antenna arrays,” IET Microwaves , Antennas

  14. Adaptive field-of-view imaging for efficient receive beamforming in medical ultrasound imaging systems.

    PubMed

    Agarwal, Anup; Yoo, Yang Mo; Schneider, Fabio Kurt; Kim, Yongmin

    2008-09-01

    Quadrature demodulation-based phase rotation beamforming (QD-PRBF) is commonly used to support dynamic receive focusing in medical ultrasound systems. However, it is computationally demanding since it requires two demodulation filters for each receive channel. To reduce the computational requirements of QD-PRBF, we have previously developed two-stage demodulation (TSD), which reduces the number of lowpass filters by performing demodulation filtering on summation signals. However, it suffers from image quality degradation due to aliasing at lower beamforming frequencies. To improve the performance of TSD-PRBF with reduced number of beamforming points, we propose a new adaptive field-of-view (AFOV) imaging method. In AFOV imaging, the beamforming frequency is adjusted depending on displayed FOV size and the center frequency of received signals. To study its impact on image quality, simulation was conducted using Field II, phantom data were acquired from a commercial ultrasound machine, and the image quality was quantified using spatial (i.e., axial and lateral) and contrast resolution. The developed beamformer (i.e., TSD-AFOV-PRBF) with 1024 beamforming points provided comparable image resolution to QD-PRBF for typical FOV sizes (e.g., 4.6% and 1.3% degradation in contrast resolution for 160 mm and 112 mm, respectively for a 3.5 MHz transducer). Furthermore, it reduced the number of operations by 86.8% compared to QD-PRBF. These results indicate that the developed TSD-AFOV-PRBF can lower the computational requirement for receive beamforming without significant image quality degradation.

  15. Artifact removal algorithms for stroke detection using a multistatic MIST beamforming algorithm.

    PubMed

    Ricci, E; Di Domenico, S; Cianca, E; Rossi, T

    2015-01-01

    Microwave imaging (MWI) has been recently proved as a promising imaging modality for low-complexity, low-cost and fast brain imaging tools, which could play a fundamental role to efficiently manage emergencies related to stroke and hemorrhages. This paper focuses on the UWB radar imaging approach and in particular on the processing algorithms of the backscattered signals. Assuming the use of the multistatic version of the MIST (Microwave Imaging Space-Time) beamforming algorithm, developed by Hagness et al. for the early detection of breast cancer, the paper proposes and compares two artifact removal algorithms. Artifacts removal is an essential step of any UWB radar imaging system and currently considered artifact removal algorithms have been shown not to be effective in the specific scenario of brain imaging. First of all, the paper proposes modifications of a known artifact removal algorithm. These modifications are shown to be effective to achieve good localization accuracy and lower false positives. However, the main contribution is the proposal of an artifact removal algorithm based on statistical methods, which allows to achieve even better performance but with much lower computational complexity.

  16. Ultrasound Synthetic Aperture Focusing with the Delay Multiply and sum beamforming algorithm.

    PubMed

    Matrone, Giulia; Savoia, Alessandro Stuart; Caliano, Giosue; Magenes, Giovanni

    2015-01-01

    The Delay Multiply and Sum (DMAS) beamforming algorithm was originally conceived for microwave imaging of breast cancer. In a previous work, we demonstrated that, by properly modifying and improving the algorithm processing steps, DMAS can be successfully applied to ultrasound signals for B-mode image formation and that it outperforms standard Delay and Sum (DAS) beamforming in terms of contrast resolution. As previously pointed out, however, DMAS-beamformed B-mode images, in which fixed and dynamic focusing are applied respectively during transmit and receive operations, show an intensity drop away from the transmit focal depth compared to DAS images. This could be due to the fact that DMAS beamforming is based on a measure of backscattered signal coherence, which reaches its maximum only at the transmit focus, where signals are perfectly realigned. The preliminary results presented in this work show that, by employing Synthetic Aperture Focusing (SAF), which allows to achieve dynamic focusing both on transmission and reception, this intensity loss is compensated, as DAS and DMAS images have almost the same maximum amplitude level at all depths.

  17. Fixed-point analysis and realization of a blind beamforming algorithm

    NASA Astrophysics Data System (ADS)

    Xu, Fan; Fu, Dengwei; Willson, Alan N.

    1999-11-01

    We present the fixed-point analysis and realization of a blind beamforming algorithm. This maximum-power beamforming algorithm consists of the computation of a correlation matrix and its dominant eigenvector, and we propose that the later be accomplished by the power method. After analyzing the numerical stability of the power method, we derive a division-free form of the algorithm. Based on a block-Toeplitz assumption, we design an FIR filter based system to realize both the correlation computation and the power method. Our ring processor, which is optimized to implement digital filters, is used as the core of the architecture. A special technique for dynamically switching filter inputs is shown to double the system throughput. Finally we discuss the issue of hardware/software hybrid realization.

  18. Transmission mode adaptive beamforming for planar phased arrays and its application to 3D ultrasonic transcranial imaging

    NASA Astrophysics Data System (ADS)

    Shapoori, Kiyanoosh; Sadler, Jeffrey; Wydra, Adrian; Malyarenko, Eugene; Sinclair, Anthony; Maev, Roman G.

    2013-03-01

    A new adaptive beamforming method for accurately focusing ultrasound behind highly scattering layers of human skull and its application to 3D transcranial imaging via small-aperture planar phased arrays are reported. Due to its undulating, inhomogeneous, porous, and highly attenuative structure, human skull bone severely distorts ultrasonic beams produced by conventional focusing methods in both imaging and therapeutic applications. Strong acoustical mismatch between the skull and brain tissues, in addition to the skull's undulating topology across the active area of a planar ultrasonic probe, could cause multiple reflections and unpredictable refraction during beamforming and imaging processes. Such effects could significantly deflect the probe's beam from the intended focal point. Presented here is a theoretical basis and simulation results of an adaptive beamforming method that compensates for the latter effects in transmission mode, accompanied by experimental verification. The probe is a custom-designed 2 MHz, 256-element matrix array with 0.45 mm element size and 0.1mm kerf. Through its small footprint, it is possible to accurately measure the profile of the skull segment in contact with the probe and feed the results into our ray tracing program. The latter calculates the new time delay patterns adapted to the geometrical and acoustical properties of the skull phantom segment in contact with the probe. The time delay patterns correct for the refraction at the skull-brain boundary and bring the distorted beam back to its intended focus. The algorithms were implemented on the ultrasound open-platform ULA-OP (developed at the University of Florence).

  19. Millimetre Level Accuracy GNSS Positioning with the Blind Adaptive Beamforming Method in Interference Environments.

    PubMed

    Daneshmand, Saeed; Marathe, Thyagaraja; Lachapelle, Gérard

    2016-10-31

    The use of antenna arrays in Global Navigation Satellite System (GNSS) applications is gaining significant attention due to its superior capability to suppress both narrowband and wideband interference. However, the phase distortions resulting from array processing may limit the applicability of these methods for high precision applications using carrier phase based positioning techniques. This paper studies the phase distortions occurring with the adaptive blind beamforming method in which satellite angle of arrival (AoA) information is not employed in the optimization problem. To cater to non-stationary interference scenarios, the array weights of the adaptive beamformer are continuously updated. The effects of these continuous updates on the tracking parameters of a GNSS receiver are analyzed. The second part of this paper focuses on reducing the phase distortions during the blind beamforming process in order to allow the receiver to perform carrier phase based positioning by applying a constraint on the structure of the array configuration and by compensating the array uncertainties. Limitations of the previous methods are studied and a new method is proposed that keeps the simplicity of the blind beamformer structure and, at the same time, reduces tracking degradations while achieving millimetre level positioning accuracy in interference environments. To verify the applicability of the proposed method and analyze the degradations, array signals corresponding to the GPS L1 band are generated using a combination of hardware and software simulators. Furthermore, the amount of degradation and performance of the proposed method under different conditions are evaluated based on Monte Carlo simulations.

  20. Millimetre Level Accuracy GNSS Positioning with the Blind Adaptive Beamforming Method in Interference Environments

    PubMed Central

    Daneshmand, Saeed; Marathe, Thyagaraja; Lachapelle, Gérard

    2016-01-01

    The use of antenna arrays in Global Navigation Satellite System (GNSS) applications is gaining significant attention due to its superior capability to suppress both narrowband and wideband interference. However, the phase distortions resulting from array processing may limit the applicability of these methods for high precision applications using carrier phase based positioning techniques. This paper studies the phase distortions occurring with the adaptive blind beamforming method in which satellite angle of arrival (AoA) information is not employed in the optimization problem. To cater to non-stationary interference scenarios, the array weights of the adaptive beamformer are continuously updated. The effects of these continuous updates on the tracking parameters of a GNSS receiver are analyzed. The second part of this paper focuses on reducing the phase distortions during the blind beamforming process in order to allow the receiver to perform carrier phase based positioning by applying a constraint on the structure of the array configuration and by compensating the array uncertainties. Limitations of the previous methods are studied and a new method is proposed that keeps the simplicity of the blind beamformer structure and, at the same time, reduces tracking degradations while achieving millimetre level positioning accuracy in interference environments. To verify the applicability of the proposed method and analyze the degradations, array signals corresponding to the GPS L1 band are generated using a combination of hardware and software simulators. Furthermore, the amount of degradation and performance of the proposed method under different conditions are evaluated based on Monte Carlo simulations. PMID:27809252

  1. MEG beamforming using Bayesian PCA for adaptive data covariance matrix regularization.

    PubMed

    Woolrich, Mark; Hunt, Laurence; Groves, Adrian; Barnes, Gareth

    2011-08-15

    Beamformers are a commonly used method for doing source localization from magnetoencephalography (MEG) data. A key ingredient in a beamformer is the estimation of the data covariance matrix. When the noise levels are high, or when there is only a small amount of data available, the data covariance matrix is estimated poorly and the signal-to-noise ratio (SNR) of the beamformer output degrades. One solution to this is to use regularization whereby the diagonal of the covariance matrix is amplified by a pre-specified amount. However, this provides improvements at the expense of a loss in spatial resolution, and the parameter controlling the amount of regularization must be chosen subjectively. In this paper, we introduce a method that provides an adaptive solution to this problem by using a Bayesian Principle Component Analysis (PCA). This provides an estimate of the data covariance matrix to give a data-driven, non-arbitrary solution to the trade-off between the spatial resolution and the SNR of the beamformer output. This also provides a method for determining when the quality of the data covariance estimate maybe under question. We apply the approach to simulated and real MEG data, and demonstrate the way in which it can automatically adapt the regularization to give good performance over a range of noise and signal levels.

  2. Synthetic-aperture based photoacoustic re-beamforming (SPARE) approach using beamformed ultrasound data

    PubMed Central

    Zhang, Haichong K.; Bell, Muyinatu A. Lediju; Guo, Xiaoyu; Kang, Hyun Jae; Boctor, Emad M.

    2016-01-01

    Photoacoustic (PA) imaging has been developed for various clinical and pre-clinical applications, and acquiring pre-beamformed channel data is necessary to reconstruct these images. However, accessing these pre-beamformed channel data requires custom hardware to enable parallel beamforming, and is available for a limited number of research ultrasound platforms. To broaden the impact of clinical PA imaging, our goal is to devise a new PA reconstruction approach that uses ultrasound post-beamformed radio frequency (RF) data rather than raw channel data, because this type of data is readily available in both clinical and research ultrasound systems. In our proposed Synthetic-aperture based photoacoustic re-beamforming (SPARE) approach, post-beamformed RF data from a clinical ultrasound scanner are considered as input data for an adaptive synthetic aperture beamforming algorithm. When receive focusing is applied prior to obtaining these data, the focal point is considered as a virtual element, and synthetic aperture beamforming is implemented assuming that the photoacoustic signals are received at the virtual element. The resolution and SNR obtained with the proposed method were compared to that obtained with conventional delay-and-sum beamforming with 99.87% and 91.56% agreement, respectively. In addition, we experimentally demonstrated feasibility with a pulsed laser diode setup. Results indicate that the post-beamformed RF data from any commercially available ultrasound platform can potentially be used to create PA images. PMID:27570697

  3. Synthetic-aperture based photoacoustic re-beamforming (SPARE) approach using beamformed ultrasound data.

    PubMed

    Zhang, Haichong K; Bell, Muyinatu A Lediju; Guo, Xiaoyu; Kang, Hyun Jae; Boctor, Emad M

    2016-08-01

    Photoacoustic (PA) imaging has been developed for various clinical and pre-clinical applications, and acquiring pre-beamformed channel data is necessary to reconstruct these images. However, accessing these pre-beamformed channel data requires custom hardware to enable parallel beamforming, and is available for a limited number of research ultrasound platforms. To broaden the impact of clinical PA imaging, our goal is to devise a new PA reconstruction approach that uses ultrasound post-beamformed radio frequency (RF) data rather than raw channel data, because this type of data is readily available in both clinical and research ultrasound systems. In our proposed Synthetic-aperture based photoacoustic re-beamforming (SPARE) approach, post-beamformed RF data from a clinical ultrasound scanner are considered as input data for an adaptive synthetic aperture beamforming algorithm. When receive focusing is applied prior to obtaining these data, the focal point is considered as a virtual element, and synthetic aperture beamforming is implemented assuming that the photoacoustic signals are received at the virtual element. The resolution and SNR obtained with the proposed method were compared to that obtained with conventional delay-and-sum beamforming with 99.87% and 91.56% agreement, respectively. In addition, we experimentally demonstrated feasibility with a pulsed laser diode setup. Results indicate that the post-beamformed RF data from any commercially available ultrasound platform can potentially be used to create PA images.

  4. Adaptive Beamforming with the Transform Domain LMS (Least Mean-Square).

    DTIC Science & Technology

    1987-02-01

    r --- ". - i,-early corstrained adaptive beamformring to cne cf corstraired multiple -efererne noise carcelling . Applying the TRLMS aigcrth to...Canceller. 2. 1. 1 The Generalized Sidelcbe Carceller Figure 2.2 shows the Generalized Sidelcbe Canceller Broadband Beamfor-mer (GSC), first analyzed by...nalsisanddaignef ’- [13] ~. Widrow et aL., "Adacptve noise carcelling : prlrn±ies ard acpiicatiors," Proc. IEEE, vol. 63, no. .2, pp. 5,2421715, December 10975

  5. Constrained Adaptive Beamforming for Improved Contrast in Breast Ultrasound

    DTIC Science & Technology

    2007-06-01

    us to modify the Spatial Processing Optimized and Constrained algorithm ( SPOC ) toyield the Time-Domain Optimized Near-field Estimator (TONE). In...modified the Spatial Processing Optimized and Constrained ( SPOC ) algorithm [14, 15] to operate on near-field, broadband signals. In the process of writing...about this modified algorithm it became clear that our significant changes to SPOC really made it a new algorithm, deserving a new name. Thus we have

  6. An innovations-based noise cancelling technique on inverse kepstrum whitening filter and adaptive FIR filter in beamforming structure.

    PubMed

    Jeong, Jinsoo

    2011-01-01

    This paper presents an acoustic noise cancelling technique using an inverse kepstrum system as an innovations-based whitening application for an adaptive finite impulse response (FIR) filter in beamforming structure. The inverse kepstrum method uses an innovations-whitened form from one acoustic path transfer function between a reference microphone sensor and a noise source so that the rear-end reference signal will then be a whitened sequence to a cascaded adaptive FIR filter in the beamforming structure. By using an inverse kepstrum filter as a whitening filter with the use of a delay filter, the cascaded adaptive FIR filter estimates only the numerator of the polynomial part from the ratio of overall combined transfer functions. The test results have shown that the adaptive FIR filter is more effective in beamforming structure than an adaptive noise cancelling (ANC) structure in terms of signal distortion in the desired signal and noise reduction in noise with nonminimum phase components. In addition, the inverse kepstrum method shows almost the same convergence level in estimate of noise statistics with the use of a smaller amount of adaptive FIR filter weights than the kepstrum method, hence it could provide better computational simplicity in processing. Furthermore, the rear-end inverse kepstrum method in beamforming structure has shown less signal distortion in the desired signal than the front-end kepstrum method and the front-end inverse kepstrum method in beamforming structure.

  7. Adaptive beamforming for array imaging of plate structures using lamb waves.

    PubMed

    Engholm, Marcus; Stepinski, Tadeusz

    2010-12-01

    Lamb waves are considered a promising tool for the monitoring of plate structures. Large areas of plate structures can be monitored using active arrays employing beamforming techniques. Dispersion and multiple propagating modes are issues that need to be addressed when working with Lamb waves. Previous work has mainly focused on standard delay-and-sum (DAS) beamforming while reducing the effects of multiple modes through frequency selectivity and transducer design. This paper presents a minimum variance distortionless response (MVDR) approach for Lamb waves using a uniform rectangular array (URA) and a single transmitter. Theoretically calculated dispersion curves are used to compensate for dispersion. The combination of the MVDR approach and the two-dimensional array improves the suppression of interfering Lamb modes. The proposed approach is evaluated on simulated and experimental data and compared with the standard DAS beamformer. It is shown that the MVDR algorithm performs better in terms of higher resolution and better side lobe and mode suppression capabilities. Known issues of the MVDR approach, such as signal cancellation in highly correlated environments and poor robustness, are addressed using methods that have proven effective for the purpose in other fields of active imaging.

  8. Enhanced beam-steering-based diagonal beamforming algorithm for binaural hearing support devices.

    PubMed

    Lee, Jun Chang; Nam, Kyoung Won; Cho, Kyeongwon; Lee, Sangmin; Kim, Dongwook; Hong, Sung Hwa; Jang, Dong Pyo; Kim, In Young

    2014-07-01

    In order to improve speech intelligibility for hearing-impaired people in various listening situations, it is necessary to diversify the possible focusing directions of a beamformer. In a previous report, the concept of binaural beam-steering that can focus a beamformer in diagonal directions was applied to a binaural hearing aid; however, in the previously proposed protocol, the effective frequency range for consistent diagonal beam-steering was limited to the 200-750 Hz range, which is far narrower than that of normal speech signals (200-4000 Hz). In this study, we proposed a modified binaural diagonal beam-steering technique that can reduce the focusing-direction deviations at high input frequencies up to 4000 Hz by introducing a new correction factor to the original protocol that can reduce the differences in gradient between the signal and the noise components at frequencies up to 4000 Hz. In simulation tests, the focusing effect of the proposed algorithm was more consistent than conventional algorithms. The deviations between the target and the focusing directions were reduced 27% in the left device and 6% in the right device with 45° steering at a 4000 Hz input signal, and were reduced 3% in the left device and 25% in the right device with 135° steering. On the basis of the experimental results, we believe that the proposed algorithm has the potential to help hearing-impaired people in various listening situations.

  9. An Ultrasonic-Adaptive Beamforming Method and Its Application for Trans-skull Imaging of Certain Types of Head Injuries; Part I: Transmission Mode.

    PubMed

    Shapoori, Kiyanoosh; Sadler, Jeff; Wydra, Adrian; Malyarenko, Eugene V; Sinclair, Anthony N; Maev, Roman Gr

    2015-05-01

    A new adaptive beamforming algorithm for imaging via small-aperture 1-D ultrasonic-phased arrays through composite layered structures is reported. Such structures cause acoustic phase aberration and wave refraction at undulating interfaces and can lead to significant distortion of an ultrasonic field pattern produced by conventional beamforming techniques. This distortion takes the form of defocusing the ultrasonic field transmitted through the barrier and causes loss of resolution and overall degradation of image quality. To compensate for the phase aberration and the refractional effects, we developed and examined an adaptive beamforming algorithm for small-aperture linear-phased arrays. After accurately assessing the barrier's local geometry and sound speed, the method calculates a new timing scheme to refocus the distorted beam at its original location. As a tentative application, implementation of this method for trans-skull imaging of certain types of head injuries through human skull is discussed. Simulation and laboratory results of applying the method on skull-mimicking phantoms are presented. Correction of up to 2.5 cm focal point displacement at up to 10 cm depth under our skull phantom is demonstrated. Quantitative assessment of the method in a variety of temporal focusing scenarios is also reported. Overall temporal deviation on the order of a few nanoseconds was observed between the simulated and experimental results. The single-point adaptive focusing results demonstrate strong potential of our approach for diagnostic imaging through intact human skull. The algorithms were implemented on an ultrasound advanced open-platform controlling 64 active elements on a 128-element phased array.

  10. Adaptive continuous twisting algorithm

    NASA Astrophysics Data System (ADS)

    Moreno, Jaime A.; Negrete, Daniel Y.; Torres-González, Victor; Fridman, Leonid

    2016-09-01

    In this paper, an adaptive continuous twisting algorithm (ACTA) is presented. For double integrator, ACTA produces a continuous control signal ensuring finite time convergence of the states to zero. Moreover, the control signal generated by ACTA compensates the Lipschitz perturbation in finite time, i.e. its value converges to the opposite value of the perturbation. ACTA also keeps its convergence properties, even in the case that the upper bound of the derivative of the perturbation exists, but it is unknown.

  11. A Diagonal-Steering-Based Binaural Beamforming Algorithm Incorporating a Diagonal Speech Localizer for Persons With Bilateral Hearing Impairment.

    PubMed

    Lee, Jun Chang; Nam, Kyoung Won; Jang, Dong Pyo; Kim, In Young

    2015-12-01

    Previously suggested diagonal-steering algorithms for binaural hearing support devices have commonly assumed that the direction of the speech signal is known in advance, which is not always the case in many real circumstances. In this study, a new diagonal-steering-based binaural speech localization (BSL) algorithm is proposed, and the performances of the BSL algorithm and the binaural beamforming algorithm, which integrates the BSL and diagonal-steering algorithms, were evaluated using actual speech-in-noise signals in several simulated listening scenarios. Testing sounds were recorded in a KEMAR mannequin setup and two objective indices, improvements in signal-to-noise ratio (SNRi ) and segmental SNR (segSNRi ), were utilized for performance evaluation. Experimental results demonstrated that the accuracy of the BSL was in the 90-100% range when input SNR was -10 to +5 dB range. The average differences between the γ-adjusted and γ-fixed diagonal-steering algorithms (for -15 to +5 dB input SNR) in the talking in the restaurant scenario were 0.203-0.937 dB for SNRi and 0.052-0.437 dB for segSNRi , and in the listening while car driving scenario, the differences were 0.387-0.835 dB for SNRi and 0.259-1.175 dB for segSNRi . In addition, the average difference between the BSL-turned-on and the BSL-turned-off cases for the binaural beamforming algorithm in the listening while car driving scenario was 1.631-4.246 dB for SNRi and 0.574-2.784 dB for segSNRi . In all testing conditions, the γ-adjusted diagonal-steering and BSL algorithm improved the values of the indices more than the conventional algorithms. The binaural beamforming algorithm, which integrates the proposed BSL and diagonal-steering algorithm, is expected to improve the performance of the binaural hearing support devices in noisy situations.

  12. Fast autodidactic adaptive equalization algorithms

    NASA Astrophysics Data System (ADS)

    Hilal, Katia

    Autodidactic equalization by adaptive filtering is addressed in a mobile radio communication context. A general method, using an adaptive stochastic gradient Bussgang type algorithm, to deduce two low cost computation algorithms is given: one equivalent to the initial algorithm and the other having improved convergence properties thanks to a block criteria minimization. Two start algorithms are reworked: the Godard algorithm and the decision controlled algorithm. Using a normalization procedure, and block normalization, the performances are improved, and their common points are evaluated. These common points are used to propose an algorithm retaining the advantages of the two initial algorithms. This thus inherits the robustness of the Godard algorithm and the precision and phase correction of the decision control algorithm. The work is completed by a study of the stable states of Bussgang type algorithms and of the stability of the Godard algorithms, initial and normalized. The simulation of these algorithms, carried out in a mobile radio communications context, and under severe conditions on the propagation channel, gave a 75% reduction in the number of samples required for the processing in relation with the initial algorithms. The improvement of the residual error was of a much lower return. These performances are close to making possible the use of autodidactic equalization in the mobile radio system.

  13. Interference Suppression for Spread Spectrum Signals Using Adaptive Beamforming and Adaptive Temporal Filter

    DTIC Science & Technology

    1996-12-01

    algorithms for obtaining rapid convergence of the tap weights of a transversal filter to their optimum settings ( Godard , 1974). This algorithm is...1366, Dec. 1989. 10. Godard , D. N. (1974) "Channel equalization using a Kalman filter for fast data transmission," IBM K. Res. Dev., vol. 18, pp. 267

  14. Coordinated Beamforming for MISO Interference Channel: Complexity Analysis and Efficient Algorithms

    DTIC Science & Technology

    2010-01-01

    which the same optimization problem is convex for both the harmonic mean and proportional fairness utility functions [1]. For the min-rate utility, this...converted to a convex optimization problem and solved efficiently to global optimality [1], [6]. However, when the number of tones is more than two, then all... optimal coordinated downlink beamforming problem can be transformed into a convex optimization problem when L = 1, but is NP-hard when L ≥ 2. When there

  15. Adaptive protection algorithm and system

    DOEpatents

    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.

  16. Adaptive beamforming with imperfect arrays: Pattern effects and their partial correction

    NASA Astrophysics Data System (ADS)

    Pettersson, Lars E.

    1992-12-01

    The effects of various types of imperfections on the radiation pattern of adaptive antenna arrays, using the Applebaum and Frost algorithms, are investigated. The imperfections considered are nonuniform noise figures in the channels, gain and phase shift error, IQ imbalance, DC offset, finite number of samples when forming the covariance matrix, quantization of the signals, and the weights and saturation of the signals. In particular the effects on the resulting sidelobe level, null depth, and gain are studied, and simulated results and analytical expressions for the expected pattern parameters are given. The effects of some methods to partly correct for the imperfections are also investigated. A method to obtain low sidelobe pattern when using the Frost algorithm is also discussed.

  17. Adaptive-feedback control algorithm.

    PubMed

    Huang, Debin

    2006-06-01

    This paper is motivated by giving the detailed proofs and some interesting remarks on the results the author obtained in a series of papers [Phys. Rev. Lett. 93, 214101 (2004); Phys. Rev. E 71, 037203 (2005); 69, 067201 (2004)], where an adaptive-feedback algorithm was proposed to effectively stabilize and synchronize chaotic systems. This note proves in detail the strictness of this algorithm from the viewpoint of mathematics, and gives some interesting remarks for its potential applications to chaos control & synchronization. In addition, a significant comment on synchronization-based parameter estimation is given, which shows some techniques proposed in literature less strict and ineffective in some cases.

  18. Flow velocity profiling using acoustic time of flight flow metering based on wide band signals and adaptive beam-forming techniques

    NASA Astrophysics Data System (ADS)

    Murgan, I.; Candel, I.; Ioana, C.; Digulescu, A.; Bunea, F.; Ciocan, G. D.; Anghel, A.; Vasile, G.

    2016-11-01

    velocities are possible to be computed. Analytically defined emitted wide band signals makes possible the identification of signals coming from each transducer. Using the adaptive beam-forming algorithm the receiving transducers can record different signals from the receiver, equivalent to different propagation paths. Therefore, different measurements of time of flight are possible, leading to additional flow velocity measurements. Results carried out in an experiment facility belonging to ICPE-CA, Bucharest - Romania allowed to the validation of the flow velocities computed using this new technique, in symmetric, asymmetric and uneven flow conditions. The acoustic derived values were referenced with those provided from a Pitot tube probe installed in the test channel and the results obtained by the method proposed in this paper are relatively close to this reference.

  19. PHASED ARRAY FEED CALIBRATION, BEAMFORMING, AND IMAGING

    SciTech Connect

    Landon, Jonathan; Elmer, Michael; Waldron, Jacob; Jones, David; Stemmons, Alan; Jeffs, Brian D.; Warnick, Karl F.; Richard Fisher, J.; Norrod, Roger D.

    2010-03-15

    Phased array feeds (PAFs) for reflector antennas offer the potential for increased reflector field of view and faster survey speeds. To address some of the development challenges that remain for scientifically useful PAFs, including calibration and beamforming algorithms, sensitivity optimization, and demonstration of wide field of view imaging, we report experimental results from a 19 element room temperature L-band PAF mounted on the Green Bank 20 Meter Telescope. Formed beams achieved an aperture efficiency of 69% and a system noise temperature of 66 K. Radio camera images of several sky regions are presented. We investigate the noise performance and sensitivity of the system as a function of elevation angle with statistically optimal beamforming and demonstrate cancelation of radio frequency interference sources with adaptive spatial filtering.

  20. IIR algorithms for adaptive line enhancement

    SciTech Connect

    David, R.A.; Stearns, S.D.; Elliott, G.R.; Etter, D.M.

    1983-01-01

    We introduce a simple IIR structure for the adaptive line enhancer. Two algorithms based on gradient-search techniques are presented for adapting the structure. Results from experiments which utilized real data as well as computer simulations are provided.

  1. Comparison between beamforming and super resolution imaging algorithms for non-destructive evaluation

    NASA Astrophysics Data System (ADS)

    Fan, Chengguang; Drinkwater, Bruce W.

    2014-02-01

    In this paper the performance of total focusing method is compared with the widely used time-reversal MUSIC super resolution technique. The algorithms are tested with simulated and experimental ultrasonic array data, each containing different noise levels. The simulated time domain signals allow the effects of array geometry, frequency, scatterer location, scatterer size, scatterer separation and random noise to be carefully controlled. The performance of the imaging algorithms is evaluated in terms of resolution and sensitivity to random noise. It is shown that for the low noise situation, time-reversal MUSIC provides enhanced lateral resolution when compared to the total focusing method. However, for higher noise levels, the total focusing method shows robustness, whilst the performance of time-reversal MUSIC is significantly degraded.

  2. Comparison between beamforming and super resolution imaging algorithms for non-destructive evaluation

    SciTech Connect

    Fan, Chengguang; Drinkwater, Bruce W.

    2014-02-18

    In this paper the performance of total focusing method is compared with the widely used time-reversal MUSIC super resolution technique. The algorithms are tested with simulated and experimental ultrasonic array data, each containing different noise levels. The simulated time domain signals allow the effects of array geometry, frequency, scatterer location, scatterer size, scatterer separation and random noise to be carefully controlled. The performance of the imaging algorithms is evaluated in terms of resolution and sensitivity to random noise. It is shown that for the low noise situation, time-reversal MUSIC provides enhanced lateral resolution when compared to the total focusing method. However, for higher noise levels, the total focusing method shows robustness, whilst the performance of time-reversal MUSIC is significantly degraded.

  3. Hierarchical beamformer and cross-talk reduction in electroneurography

    NASA Astrophysics Data System (ADS)

    Calvetti, Daniela; Wodlinger, Brian; Durand, Dominique M.; Somersalo, Erkki

    2011-10-01

    Electroneurography (ENG) is a method of recording neural activity within nerves. Using nerve electrodes with multiple contacts the activation patterns of individual neuronal fascicles can be estimated by measuring the surface voltages induced by the intraneural activity. The information about neuronal activation can be used for functional electric stimulation (FES) of patients suffering from spinal chord injury, or to control a robotic prosthetic limb of an amputee. However, the ENG signal estimation is a severely ill-posed inverse problem due to uncertainties in the model, low resolution due to limitations of the data, geometric constraints and the difficulty in separating the signal from biological and exogenous noise. In this paper, a reduced computational model for the forward problem is proposed, and the ENG problem is addressed by using beamformer techniques. Furthermore, we show that using a hierarchical statistical model, it is possible to develop an adaptive beamformer algorithm that estimates directly the source variances rather than the voltage source itself. The advantage of this new algorithm, e.g., over a traditional adaptive beamformer algorithm, is that it allows a very stable noise reduction by averaging over a time window. In addition, a new projection technique for separating sources and reducing cross-talk between different fascicle signals is proposed. The algorithms are tested on a computer model of realistic nerve geometry and time series signals.

  4. QPSO-based adaptive DNA computing algorithm.

    PubMed

    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.

  5. Synthesis of an algorithm for interference immunity

    NASA Astrophysics Data System (ADS)

    Kartsan, I. N.; Tyapkin, V. N.; Dmitriev, D. D.; Goncharov, A. E.; Zelenkov, P. V.; Kovalev, I. V.

    2016-11-01

    This paper discusses the synthesis of an algorithm for adaptive interference nulling of an 8-element phased antenna array. An adaptive beamforming system has been built on the basis of the algorithm. The paper discusses results of experimental functioning of navigation satellite systems user equipment fitted with an adaptive phased antenna array in interference environments.

  6. Photoacoustic image reconstruction from ultrasound post-beamformed B-mode image

    NASA Astrophysics Data System (ADS)

    Zhang, Haichong K.; Guo, Xiaoyu; Kang, Hyun Jae; Boctor, Emad M.

    2016-03-01

    A requirement to reconstruct photoacoustic (PA) image is to have a synchronized channel data acquisition with laser firing. Unfortunately, most clinical ultrasound (US) systems don't offer an interface to obtain synchronized channel data. To broaden the impact of clinical PA imaging, we propose a PA image reconstruction algorithm utilizing US B-mode image, which is readily available from clinical scanners. US B-mode image involves a series of signal processing including beamforming, followed by envelope detection, and end with log compression. Yet, it will be defocused when PA signals are input due to incorrect delay function. Our approach is to reverse the order of image processing steps and recover the original US post-beamformed radio-frequency (RF) data, in which a synthetic aperture based PA rebeamforming algorithm can be further applied. Taking B-mode image as the input, we firstly recovered US postbeamformed RF data by applying log decompression and convoluting an acoustic impulse response to combine carrier frequency information. Then, the US post-beamformed RF data is utilized as pre-beamformed RF data for the adaptive PA beamforming algorithm, and the new delay function is applied by taking into account that the focus depth in US beamforming is at the half depth of the PA case. The feasibility of the proposed method was validated through simulation, and was experimentally demonstrated using an acoustic point source. The point source was successfully beamformed from a US B-mode image, and the full with at the half maximum of the point improved 3.97 times. Comparing this result to the ground-truth reconstruction using channel data, the FWHM was slightly degraded with 1.28 times caused by information loss during envelope detection and convolution of the RF information.

  7. (A new time of flight) Acoustic flow meter using wide band signals and adaptive beamforming techniques

    NASA Astrophysics Data System (ADS)

    Murgan, I.; Ioana, C.; Candel, I.; Anghel, A.; Ballester, J. L.; Reeb, B.; Combes, G.

    2016-11-01

    In this paper we present the result of our research concerning the improvement of acoustic time of flight flow metering for water pipes. Current flow meters are based on the estimation of direct time of flight by matched filtering of the received and emitted signals by acoustic transducers. Currently, narrow band signals are used, as well as a single emitter/receptor transducer configuration. Although simple, this configuration presents a series of limitations such as energy losses due to pipe wall/water interface, pressure/flow transients, sensitivity to flow induced vibrations, acoustic beam deformations and shift due to changes in flow velocity and embedded turbulence in the flow. The errors associated with these limitations reduce the overall robustness of existing flow meters, as well as the measured flow rate range and lower accuracy. In order to overcome these limitations, two major innovations were implemented at the signal processing level. The first one concerns the use of wide band signals that optimise the power transfer throughout the acoustic path and also increase the number of velocity/flow readings per second. Using wide band signals having a high duration-bandwidth product increases the precision in terms of time of flight measurements and, in the same time, improves the system robustness. The second contribution consists in the use of a multiple emitter - multiple receivers configuration (for one path) in order to compensate the emitted acoustic beam shift, compensate the time of flight estimation errors and thus increase the flow meter's robustness in case of undesired effects such as the “flow blow” and transient/rapid flow rate/velocity changes. Using a new signal processing algorithm that take advantage of the controlled wide band content coming from multiple receivers, the new flow meters achieves a higher accuracy in terms of flow velocity over a wider velocity range than existing systems. Tests carried out on real scale experimental

  8. A beamformer post-filter for cochlear implant noise reduction.

    PubMed

    Hersbach, Adam A; Grayden, David B; Fallon, James B; McDermott, Hugh J

    2013-04-01

    Cochlear implant users have limited ability to understand speech in noisy conditions. Signal processing methods to address this issue that use multiple microphones typically use beamforming to perform noise reduction. However, the effectiveness of the beamformer is diminished as the number of interfering noises increases and the acoustic environment becomes more diffuse. A multi-microphone noise reduction algorithm that aims to address this issue is presented in this study. The algorithm uses spatial filtering to estimate the signal-to-noise ratio (SNR) and attenuates time-frequency elements that have poor SNR. The algorithm was evaluated by measuring intelligibility of speech embedded in 4-talker babble where the interfering talkers were spatially separated and changed location during the test. Twelve cochlear implant users took part in the evaluation, which demonstrated a significant mean improvement of 4.6 dB (standard error 0.4, P < 0.001) in speech reception threshold compared to an adaptive beamformer. The results suggest that a substantial improvement in performance can be gained for cochlear implant users in noisy environments where the noise is spatially separated from the target speech.

  9. 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.

  10. Optimal Hops-Based Adaptive Clustering Algorithm

    NASA Astrophysics Data System (ADS)

    Xuan, Xin; Chen, Jian; Zhen, Shanshan; Kuo, Yonghong

    This paper proposes an optimal hops-based adaptive clustering algorithm (OHACA). The algorithm sets an energy selection threshold before the cluster forms so that the nodes with less energy are more likely to go to sleep immediately. In setup phase, OHACA introduces an adaptive mechanism to adjust cluster head and load balance. And the optimal distance theory is applied to discover the practical optimal routing path to minimize the total energy for transmission. Simulation results show that OHACA prolongs the life of network, improves utilizing rate and transmits more data because of energy balance.

  11. Adaptive Cuckoo Search Algorithm for Unconstrained Optimization

    PubMed Central

    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

  12. Adaptive cuckoo search algorithm for unconstrained optimization.

    PubMed

    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.

  13. Beamforming using compressive sensing.

    PubMed

    Edelmann, Geoffrey F; Gaumond, Charles F

    2011-10-01

    Compressive sensing (CS) is compared with conventional beamforming using horizontal beamforming of at-sea, towed-array data. They are compared qualitatively using bearing time records and quantitatively using signal-to-interference ratio. Qualitatively, CS exhibits lower levels of background interference than conventional beamforming. Furthermore, bearing time records show increasing, but tolerable, levels of background interference when the number of elements is decreased. For the full array, CS generates signal-to-interference ratio of 12 dB, but conventional beamforming only 8 dB. The superiority of CS over conventional beamforming is much more pronounced with undersampling.

  14. Cross-correlation beamforming

    NASA Astrophysics Data System (ADS)

    Ruigrok, Elmer; Gibbons, Steven; Wapenaar, Kees

    2016-10-01

    An areal distribution of sensors can be used for estimating the direction of incoming waves through beamforming. Beamforming may be implemented as a phase-shifting and stacking of data recorded on the different sensors (i.e., conventional beamforming). Alternatively, beamforming can be applied to cross-correlations between the waveforms on the different sensors. We derive a kernel for beamforming cross-correlated data and call it cross-correlation beamforming (CCBF). We point out that CCBF has slightly better resolution and aliasing characteristics than conventional beamforming. When auto-correlations are added to CCBF, the array response functions are the same as for conventional beamforming. We show numerically that CCBF is more resilient to non-coherent noise. Furthermore, we illustrate that with CCBF individual receiver-pairs can be removed to improve mapping to the slowness domain. An additional flexibility of CCBF is that cross-correlations can be time-windowed prior to beamforming, e.g., to remove the directionality of a scattered wavefield. The observations on synthetic data are confirmed with field data from the SPITS array (Svalbard). Both when beamforming an earthquake arrival and when beamforming ambient noise, CCBF focuses more of the energy to a central beam. Overall, the main advantage of CCBF is noise suppression and its flexibility to remove station pairs that deteriorate the signal-related beampower.

  15. Forward-backward generalized sidelobe canceler beamforming applied to medical ultrasound imaging

    NASA Astrophysics Data System (ADS)

    Li, Jiake; Chen, Xiaodong; Wang, Yi; Chen, Xiaoshuai; Yu, Daoyin

    2017-01-01

    For adaptive ultrasound imaging, accurate estimation of the covariance matrix is of great importance, and it has a fundamental influence on the performance of the adaptive beamformer. In this paper, a new forward-backward generalized sidelobe canceler (FBGSC) approach is proposed for medical ultrasound imaging, which uses forward and backward subaperture averaging to accurate estimate the covariance matrix. And resulted from accurate estimating of covariance matrix, FBGSC can achieve better lateral resolution and contrast without preprocessing algorithms. Field II is applied to obtain the simulated echo data of scattering points and a circular cyst. Beamforming responses of scattering points show that FBGSC can improve the lateral resolution by 55.7% and 66.6% compared with synthetic aperture (SA) and delay-and-sum (DS), respectively. Similarly, the simulated results of circular cyst show that FBGSC can obtain better beamforming responses than traditional adaptive beamformers. Finally, an experiment is conducted based on the real echo data of a medical ultrasound system. Results demonstrate that the FBGSC can improve the imaging quality of medical ultrasound imaging system, with lower computational demand and higher reliability.

  16. Adaptive Algorithms for HF Antenna Arrays.

    DTIC Science & Technology

    1987-07-01

    SUBJECT TERMS (Contnue on reverse dfnoceaq and identiy by bkICk numnber) FIELD GROUP SUB-GROUP HP Adaptive Arrays HrF Comunications Systems 4 HP...Although their heavy computational load renders them impractical *1 for many applications, the advancements in cheap, fast digital hardware have...or digital form. For many applications, the LMS algorithm represents a good trade off between speed of convergence* and implementational The speed of

  17. 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.

  18. Adaptive Routing Algorithm for Priority Flows in a Network

    DTIC Science & Technology

    2012-06-14

    ADAPTIVE ROUTING ALGORITHM FOR PRIORITY FLOWS IN A NETWORK THESIS Timothy J. Carbino, Captain...ADAPTIVE ROUTING ALGORITHM FOR PRIORITY FLOWS IN A NETWORK THESIS Presented to the Faculty Department of Electrical and Computer... Thesis 20 Aug 10 – 14 Jun 12 Adaptive Routing Algorithm for Priority Flows in a Network 12629PCarbino, Timothy J, Captain, USAF Air Force Institute of

  19. Adaptive path planning: Algorithm and analysis

    SciTech Connect

    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.

  20. High-Resolution Ultrasound Imaging With Unified Pixel-Based Beamforming.

    PubMed

    Nguyen, Nghia Q; Prager, Richard W

    2016-01-01

    This paper describes the development and evaluation of a new beamforming strategy based on pixel-based focusing for ultrasound linear array systems. We first implement conventional pixel-based beamforming in which the transmitted wave is assumed as spherical and diverging from the centre of the transmit subaperture. This assumed wave-shape is only valid within a limited angle on each side of the beam and this restricts the number of different subaperture positions from which data can be combined to improve image quality. By analyzing the field patterns, we propose a new unified pixel-based beamforming algorithm that better adapts to the non-spherical wave-shape of the transmit beam. This approach enables us to select the best-possible signal from each transducer waveform for data superposition. In simulations and a phantom study, we show that the unified pixel-based beamformer offers significant improvements in image quality compared to other delay-and-sum methods but at a higher computational cost. The new algorithm also demonstrates robust performance in a limited in vivo study. Overall, the results show that it is potentially of value in clinical applications.

  1. 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.

  2. Multi-channel pre-beamformed data acquisition system for research on advanced ultrasound imaging methods.

    PubMed

    Cheung, Chris C P; Yu, Alfred C H; Salimi, Nazila; Yiu, Billy Y S; Tsang, Ivan K H; Kerby, Benjamin; Azar, Reza Zahiri; Dickie, Kris

    2012-02-01

    The lack of open access to the pre-beamformed data of an ultrasound scanner has limited the research of novel imaging methods to a few privileged laboratories. To address this need, we have developed a pre-beamformed data acquisition (DAQ) system that can collect data over 128 array elements in parallel from the Ultrasonix series of research-purpose ultrasound scanners. Our DAQ system comprises three system-level blocks: 1) a connector board that interfaces with the array probe and the scanner through a probe connector port; 2) a main board that triggers DAQ and controls data transfer to a computer; and 3) four receiver boards that are each responsible for acquiring 32 channels of digitized raw data and storing them to the on-board memory. This system can acquire pre-beamformed data with 12-bit resolution when using a 40-MHz sampling rate. It houses a 16 GB RAM buffer that is sufficient to store 128 channels of pre-beamformed data for 8000 to 25 000 transmit firings, depending on imaging depth; corresponding to nearly a 2-s period in typical imaging setups. Following the acquisition, the data can be transferred through a USB 2.0 link to a computer for offline processing and analysis. To evaluate the feasibility of using the DAQ system for advanced imaging research, two proof-of-concept investigations have been conducted on beamforming and plane-wave B-flow imaging. Results show that adaptive beamforming algorithms such as the minimum variance approach can generate sharper images of a wire cross-section whose diameter is equal to the imaging wavelength (150 μm in our example). Also, planewave B-flow imaging can provide more consistent visualization of blood speckle movement given the higher temporal resolution of this imaging approach (2500 fps in our example).

  3. An environment-adaptive management algorithm for hearing-support devices incorporating listening situation and noise type classifiers.

    PubMed

    Yook, Sunhyun; Nam, Kyoung Won; Kim, Heepyung; Hong, Sung Hwa; Jang, Dong Pyo; Kim, In Young

    2015-04-01

    In order to provide more consistent sound intelligibility for the hearing-impaired person, regardless of environment, it is necessary to adjust the setting of the hearing-support (HS) device to accommodate various environmental circumstances. In this study, a fully automatic HS device management algorithm that can adapt to various environmental situations is proposed; it is composed of a listening-situation classifier, a noise-type classifier, an adaptive noise-reduction algorithm, and a management algorithm that can selectively turn on/off one or more of the three basic algorithms-beamforming, noise-reduction, and feedback cancellation-and can also adjust internal gains and parameters of the wide-dynamic-range compression (WDRC) and noise-reduction (NR) algorithms in accordance with variations in environmental situations. Experimental results demonstrated that the implemented algorithms can classify both listening situation and ambient noise type situations with high accuracies (92.8-96.4% and 90.9-99.4%, respectively), and the gains and parameters of the WDRC and NR algorithms were successfully adjusted according to variations in environmental situation. The average values of signal-to-noise ratio (SNR), frequency-weighted segmental SNR, Perceptual Evaluation of Speech Quality, and mean opinion test scores of 10 normal-hearing volunteers of the adaptive multiband spectral subtraction (MBSS) algorithm were improved by 1.74 dB, 2.11 dB, 0.49, and 0.68, respectively, compared to the conventional fixed-parameter MBSS algorithm. These results indicate that the proposed environment-adaptive management algorithm can be applied to HS devices to improve sound intelligibility for hearing-impaired individuals in various acoustic environments.

  4. Advanced Beamformers

    DTIC Science & Technology

    2008-09-01

    adaptatif dans des systèmes à ultrasons et des sonars actifs -passifs intégrés déployant des réseaux de capteurs à plusieurs dimensions. ii DRDC...de prochaine génération et des sonars actifs et passifs intégrés. Le présent rapport porte principalement sur la mise au point d’une structure...de configurations adaptatives dans des réseaux de capteurs à deux dimensions (2D) et à trois dimensions (3D), comme des réseaux planaires

  5. Beamforming in noninvasive brain-computer interfaces.

    PubMed

    Grosse-Wentrup, Moritz; Liefhold, Christian; Gramann, Klaus; Buss, Martin

    2009-04-01

    Spatial filtering (SF) constitutes an integral part of building EEG-based brain-computer interfaces (BCIs). Algorithms frequently used for SF, such as common spatial patterns (CSPs) and independent component analysis, require labeled training data for identifying filters that provide information on a subject's intention, which renders these algorithms susceptible to overfitting on artifactual EEG components. In this study, beamforming is employed to construct spatial filters that extract EEG sources originating within predefined regions of interest within the brain. In this way, neurophysiological knowledge on which brain regions are relevant for a certain experimental paradigm can be utilized to construct unsupervised spatial filters that are robust against artifactual EEG components. Beamforming is experimentally compared with CSP and Laplacian spatial filtering (LP) in a two-class motor-imagery paradigm. It is demonstrated that beamforming outperforms CSP and LP on noisy datasets, while CSP and beamforming perform almost equally well on datasets with few artifactual trials. It is concluded that beamforming constitutes an alternative method for SF that might be particularly useful for BCIs used in clinical settings, i.e., in an environment where artifact-free datasets are difficult to obtain.

  6. 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

  7. 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

  8. An SDR-Based Real-Time Testbed for GNSS Adaptive Array Anti-Jamming Algorithms Accelerated by GPU.

    PubMed

    Xu, Hailong; Cui, Xiaowei; Lu, Mingquan

    2016-03-11

    Nowadays, software-defined radio (SDR) has become a common approach to evaluate new algorithms. However, in the field of Global Navigation Satellite System (GNSS) adaptive array anti-jamming, previous work has been limited due to the high computational power demanded by adaptive algorithms, and often lack flexibility and configurability. In this paper, the design and implementation of an SDR-based real-time testbed for GNSS adaptive array anti-jamming accelerated by a Graphics Processing Unit (GPU) are documented. This testbed highlights itself as a feature-rich and extendible platform with great flexibility and configurability, as well as high computational performance. Both Space-Time Adaptive Processing (STAP) and Space-Frequency Adaptive Processing (SFAP) are implemented with a wide range of parameters. Raw data from as many as eight antenna elements can be processed in real-time in either an adaptive nulling or beamforming mode. To fully take advantage of the parallelism resource provided by the GPU, a batched method in programming is proposed. Tests and experiments are conducted to evaluate both the computational and anti-jamming performance. This platform can be used for research and prototyping, as well as a real product in certain applications.

  9. An SDR-Based Real-Time Testbed for GNSS Adaptive Array Anti-Jamming Algorithms Accelerated by GPU

    PubMed Central

    Xu, Hailong; Cui, Xiaowei; Lu, Mingquan

    2016-01-01

    Nowadays, software-defined radio (SDR) has become a common approach to evaluate new algorithms. However, in the field of Global Navigation Satellite System (GNSS) adaptive array anti-jamming, previous work has been limited due to the high computational power demanded by adaptive algorithms, and often lack flexibility and configurability. In this paper, the design and implementation of an SDR-based real-time testbed for GNSS adaptive array anti-jamming accelerated by a Graphics Processing Unit (GPU) are documented. This testbed highlights itself as a feature-rich and extendible platform with great flexibility and configurability, as well as high computational performance. Both Space-Time Adaptive Processing (STAP) and Space-Frequency Adaptive Processing (SFAP) are implemented with a wide range of parameters. Raw data from as many as eight antenna elements can be processed in real-time in either an adaptive nulling or beamforming mode. To fully take advantage of the parallelism resource provided by the GPU, a batched method in programming is proposed. Tests and experiments are conducted to evaluate both the computational and anti-jamming performance. This platform can be used for research and prototyping, as well as a real product in certain applications. PMID:26978363

  10. Effect of atmospherics on beamforming accuracy

    NASA Technical Reports Server (NTRS)

    Alexander, Richard M.

    1990-01-01

    Two mathematical representations of noise due to atmospheric turbulence are presented. These representations are derived and used in computer simulations of the Bartlett Estimate implementation of beamforming. Beamforming is an array processing technique employing an array of acoustic sensors used to determine the bearing of an acoustic source. Atmospheric wind conditions introduce noise into the beamformer output. Consequently, the accuracy of the process is degraded and the bearing of the acoustic source is falsely indicated or impossible to determine. The two representations of noise presented here are intended to quantify the effects of mean wind passing over the array of sensors and to correct for these effects. The first noise model is an idealized case. The effect of the mean wind is incorporated as a change in the propagation velocity of the acoustic wave. This yields an effective phase shift applied to each term of the spatial correlation matrix in the Bartlett Estimate. The resultant error caused by this model can be corrected in closed form in the beamforming algorithm. The second noise model acts to change the true direction of propagation at the beginning of the beamforming process. A closed form correction for this model is not available. Efforts to derive effective means to reduce the contributions of the noise have not been successful. In either case, the maximum error introduced by the wind is a beam shift of approximately three degrees. That is, the bearing of the acoustic source is indicated at a point a few degrees from the true bearing location. These effects are not quite as pronounced as those seen in experimental results. Sidelobes are false indications of acoustic sources in the beamformer output away from the true bearing angle. The sidelobes that are observed in experimental results are not caused by these noise models. The effects of mean wind passing over the sensor array as modeled here do not alter the beamformer output as

  11. Fronthaul Compression and Transmit Beamforming Optimization for Multi-Antenna Uplink C-RAN

    NASA Astrophysics Data System (ADS)

    Zhou, Yuhan; Yu, Wei

    2016-08-01

    This paper considers the joint fronthaul compression and transmit beamforming design for the uplink cloud radio access network (C-RAN), in which multi-antenna user terminals communicate with a cloud-computing based centralized processor (CP) through multi-antenna base-stations (BSs) serving as relay nodes. A compress-and-forward relaying strategy, named the VMAC scheme, is employed, in which the BSs can either perform single-user compression or Wyner-Ziv coding to quantize the received signals and send the quantization bits to the CP via capacity-limited fronthaul links; the CP performs successive decoding with either successive interference cancellation (SIC) receiver or linear minimum-mean-square-error (MMSE) receiver. Under this setup, this paper investigates the joint optimization of the transmit beamformers at the users and the quantization noise covariance matrices at the BSs for maximizing the network utility. A novel weighted minimum-mean-square-error successive convex approximation (WMMSE-SCA) algorithm is first proposed for maximizing the weighted sum rate under the user transmit power and fronthaul capacity constraints with single-user compression. Assuming a heuristic decompression order, the proposed algorithm is then adapted for optimizing the transmit beamforming and fronthaul compression under Wyner-Ziv coding. This paper also proposes a low-complexity separate design consisting of optimizing transmit beamformers for the Gaussian vector multiple-access channel along with per-antenna quantizers with uniform quantization noise levels across the antennas at each BS. Numerical results show that with optimized beamforming and fronthaul compression, C-RAN can significantly outperform conventional cellular networks. Furthermore, the low complexity separate design already performs very close to the optimized joint design in regime of practical interest.

  12. Adaptive RED algorithm based on minority game

    NASA Astrophysics Data System (ADS)

    Wei, Jiaolong; Lei, Ling; Qian, Jingjing

    2007-11-01

    With more and more applications appearing and the technology developing in the Internet, only relying on terminal system can not satisfy the complicated demand of QoS network. Router mechanisms must be participated into protecting responsive flows from the non-responsive. Routers mainly use active queue management mechanism (AQM) to avoid congestion. In the point of interaction between the routers, the paper applies minority game to describe the interaction of the users and observes the affection on the length of average queue. The parameters α, β of ARED being hard to confirm, adaptive RED based on minority game can depict the interactions of main body and amend the parameter α, β of ARED to the best. Adaptive RED based on minority game optimizes ARED and realizes the smoothness of average queue length. At the same time, this paper extends the network simulator plat - NS by adding new elements. Simulation has been implemented and the results show that new algorithm can reach the anticipative objects.

  13. Eigenvector pruning method for high resolution beamforming.

    PubMed

    Quijano, Jorge E; Zurk, Lisa M

    2015-10-01

    This paper introduces an eigenvector pruning algorithm for the estimation of the signal-plus-interference eigenspace, required as a preliminary step to subspace beamforming. The proposed method considers large-aperture passive array configurations operating in environments with multiple maneuvering targets in background noise, in which the available data for estimation of sample covariances and eigenvectors are limited. Based on statistical properties of scalar products between deterministic and complex random vectors, this work defines a statistically justified threshold to identify target-related features embedded in the sample eigenvectors, leading to an estimator for the signal-bearing eigenspace. It is shown that data projection into this signal subspace results in sharpening of beamforming outputs corresponding to closely spaced targets and provides better target separation compared to current subspace beamformers. In addition, the proposed threshold gives the user control over the worst-case scenario for the number of false detections by the beamformer. Simulated data are used to quantify the performance of the subspace estimator according to the distance between estimated and true signal subspaces. Beamforming resolution using the proposed method is analyzed with simulated data corresponding to a horizontal line array, as well as experimental data from the Shallow Water Array Performance experiment.

  14. Adaptive Mesh and Algorithm Refinement Using Direct Simulation Monte Carlo

    NASA Astrophysics Data System (ADS)

    Garcia, Alejandro L.; Bell, John B.; Crutchfield, William Y.; Alder, Berni 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.

  15. An Adaptive Unified Differential Evolution Algorithm for Global Optimization

    SciTech Connect

    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.

  16. Beamforming and power control in sensor arrays using reinforcement learning.

    PubMed

    Almeida, Náthalee C; Fernandes, Marcelo A C; Neto, Adrião D D

    2015-03-19

    The use of beamforming and power control, combined or separately, has advantages and disadvantages, depending on the application. The combined use of beamforming and power control has been shown to be highly effective in applications involving the suppression of interference signals from different sources. However, it is necessary to identify efficient methodologies for the combined operation of these two techniques. The most appropriate technique may be obtained by means of the implementation of an intelligent agent capable of making the best selection between beamforming and power control. The present paper proposes an algorithm using reinforcement learning (RL) to determine the optimal combination of beamforming and power control in sensor arrays. The RL algorithm used was Q-learning, employing an ε-greedy policy, and training was performed using the offline method. The simulations showed that RL was effective for implementation of a switching policy involving the different techniques, taking advantage of the positive characteristics of each technique in terms of signal reception.

  17. An Adaptive Cauchy Differential Evolution Algorithm for Global Numerical Optimization

    PubMed Central

    Choi, Tae Jong; Ahn, Chang Wook; An, Jinung

    2013-01-01

    Adaptation of control parameters, such as scaling factor (F), crossover rate (CR), and population size (NP), appropriately is one of the major problems of Differential Evolution (DE) literature. Well-designed adaptive or self-adaptive parameter control method can highly improve the performance of DE. Although there are many suggestions for adapting the control parameters, it is still a challenging task to properly adapt the control parameters for problem. In this paper, we present an adaptive parameter control DE algorithm. In the proposed algorithm, each individual has its own control parameters. The control parameters of each individual are adapted based on the average parameter value of successfully evolved individuals' parameter values by using the Cauchy distribution. Through this, the control parameters of each individual are assigned either near the average parameter value or far from that of the average parameter value which might be better parameter value for next generation. The experimental results show that the proposed algorithm is more robust than the standard DE algorithm and several state-of-the-art adaptive DE algorithms in solving various unimodal and multimodal problems. PMID:23935445

  18. An adaptive Cauchy differential evolution algorithm for global numerical optimization.

    PubMed

    Choi, Tae Jong; Ahn, Chang Wook; An, Jinung

    2013-01-01

    Adaptation of control parameters, such as scaling factor (F), crossover rate (CR), and population size (NP), appropriately is one of the major problems of Differential Evolution (DE) literature. Well-designed adaptive or self-adaptive parameter control method can highly improve the performance of DE. Although there are many suggestions for adapting the control parameters, it is still a challenging task to properly adapt the control parameters for problem. In this paper, we present an adaptive parameter control DE algorithm. In the proposed algorithm, each individual has its own control parameters. The control parameters of each individual are adapted based on the average parameter value of successfully evolved individuals' parameter values by using the Cauchy distribution. Through this, the control parameters of each individual are assigned either near the average parameter value or far from that of the average parameter value which might be better parameter value for next generation. The experimental results show that the proposed algorithm is more robust than the standard DE algorithm and several state-of-the-art adaptive DE algorithms in solving various unimodal and multimodal problems.

  19. Adaptive phase aberration correction based on imperialist competitive algorithm.

    PubMed

    Yazdani, R; Hajimahmoodzadeh, M; Fallah, H R

    2014-01-01

    We investigate numerically the feasibility of phase aberration correction in a wavefront sensorless adaptive optical system, based on the imperialist competitive algorithm (ICA). Considering a 61-element deformable mirror (DM) and the Strehl ratio as the cost function of ICA, this algorithm is employed to search the optimum surface profile of DM for correcting the phase aberrations in a solid-state laser system. The correction results show that ICA is a powerful correction algorithm for static or slowly changing phase aberrations in optical systems, such as solid-state lasers. The correction capability and the convergence speed of this algorithm are compared with those of the genetic algorithm (GA) and stochastic parallel gradient descent (SPGD) algorithm. The results indicate that these algorithms have almost the same correction capability. Also, ICA and GA are almost the same in convergence speed and SPGD is the fastest of these algorithms.

  20. Self-Adaptive Differential Evolution Algorithm With Zoning Evolution of Control Parameters and Adaptive Mutation Strategies.

    PubMed

    Fan, Qinqin; Yan, Xuefeng

    2016-01-01

    The performance of the differential evolution (DE) algorithm is significantly affected by the choice of mutation strategies and control parameters. Maintaining the search capability of various control parameter combinations throughout the entire evolution process is also a key issue. A self-adaptive DE algorithm with zoning evolution of control parameters and adaptive mutation strategies is proposed in this paper. In the proposed algorithm, the mutation strategies are automatically adjusted with population evolution, and the control parameters evolve in their own zoning to self-adapt and discover near optimal values autonomously. The proposed algorithm is compared with five state-of-the-art DE algorithm variants according to a set of benchmark test functions. Furthermore, seven nonparametric statistical tests are implemented to analyze the experimental results. The results indicate that the overall performance of the proposed algorithm is better than those of the five existing improved algorithms.

  1. 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.

  2. Classification of adaptive memetic algorithms: a comparative study.

    PubMed

    Ong, Yew-Soon; Lim, Meng-Hiot; Zhu, Ning; Wong, Kok-Wai

    2006-02-01

    Adaptation of parameters and operators represents one of the recent most important and promising areas of research in evolutionary computations; it is a form of designing self-configuring algorithms that acclimatize to suit the problem in hand. Here, our interests are on a recent breed of hybrid evolutionary algorithms typically known as adaptive memetic algorithms (MAs). One unique feature of adaptive MAs is the choice of local search methods or memes and recent studies have shown that this choice significantly affects the performances of problem searches. In this paper, we present a classification of memes adaptation in adaptive MAs on the basis of the mechanism used and the level of historical knowledge on the memes employed. Then the asymptotic convergence properties of the adaptive MAs considered are analyzed according to the classification. Subsequently, empirical studies on representatives of adaptive MAs for different type-level meme adaptations using continuous benchmark problems indicate that global-level adaptive MAs exhibit better search performances. Finally we conclude with some promising research directions in the area.

  3. Beamforming in an acoustic shadow

    NASA Technical Reports Server (NTRS)

    Havelock, David; Stinson, Michael; Daigle, Gilles

    1993-01-01

    The sound field deep within an acoustic shadow region is less well understood than that outside the shadow region. Signal levels are substantially lower within the shadow, but beamforming difficulties arise for other reasons such as loss of spatial coherence. Based on analysis of JAPE-91 data, and other data, three types of characteristic signals within acoustic shadow regions are identified. These signal types may correspond to different, intermittent signal propagation conditions. Detection and classification algorithms might take advantage of the signal characteristics. Frequency coherence is also discussed. The extent of coherence across frequencies is shown to be limited, causing difficulties for source classification based on harmonic amplitude relationships. Discussions emphasize short-term characteristics on the order of one second. A video presentation on frequency coherence shows the similarity, in the presence of atmospheric turbulence, between the received signal from a stable set of harmonics generated by a loudspeaker and that received from a helicopter hovering behind a hill.

  4. The PCNN adaptive segmentation algorithm based on visual perception

    NASA Astrophysics Data System (ADS)

    Zhao, Yanming

    To solve network adaptive parameter determination problem of the pulse coupled neural network (PCNN), and improve the image segmentation results in image segmentation. The PCNN adaptive segmentation algorithm based on visual perception of information is proposed. Based on the image information of visual perception and Gabor mathematical model of Optic nerve cells receptive field, the algorithm determines adaptively the receptive field of each pixel of the image. And determines adaptively the network parameters W, M, and β of PCNN by the Gabor mathematical model, which can overcome the problem of traditional PCNN parameter determination in the field of image segmentation. Experimental results show that the proposed algorithm can improve the region connectivity and edge regularity of segmentation image. And also show the PCNN of visual perception information for segmentation image of advantage.

  5. Minimum Variance Approaches to Ultrasound Pixel-Based Beamforming.

    PubMed

    Nguyen, Nghia Q; Prager, Richard W

    2017-02-01

    We analyze the principles underlying minimum variance distortionless response (MVDR) beamforming in order to integrate it into a pixel-based algorithm. There is a challenge posed by the low echo signal-to-noise ratio (eSNR) when calculating beamformer contributions at pixels far away from the beam centreline. Together with the well-known scarcity of samples for covariance matrix estimation, this reduces the beamformer performance and degrades the image quality. To address this challenge, we implement the MVDR algorithm in two different ways. First, we develop the conventional minimum variance pixel-based (MVPB) beamformer that performs the MVDR after the pixel-based superposition step. This involves a combination of methods in the literature, extended over multiple transmits to increase the eSNR. Then we propose the coherent MVPB beamformer, where the MVDR is applied to data within individual transmits. Based on pressure field analysis, we develop new algorithms to improve the data alignment and matrix estimation, and hence overcome the low-eSNR issue. The methods are demonstrated on data acquired with an ultrasound open platform. The results show the coherent MVPB beamformer substantially outperforms the conventional MVPB in a series of experiments, including phantom and in vivo studies. Compared to the unified pixel-based beamformer, the newest delay-and-sum algorithm in [1], the coherent MVPB performs well on regions that conform to the diffuse scattering assumptions on which the minimum variance principles are based. It produces less good results for parts of the image that are dominated by specular reflections.

  6. 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.

  7. Adaptive wavelet transform algorithm for image compression applications

    NASA Astrophysics Data System (ADS)

    Pogrebnyak, Oleksiy B.; Manrique Ramirez, Pablo

    2003-11-01

    A new algorithm of locally adaptive wavelet transform is presented. The algorithm implements the integer-to-integer lifting scheme. It performs an adaptation of the wavelet function at the prediction stage to the local image data activity. The proposed algorithm is based on the generalized framework for the lifting scheme that permits to obtain easily different wavelet coefficients in the case of the (N~,N) lifting. It is proposed to perform the hard switching between (2, 4) and (4, 4) lifting filter outputs according to an estimate of the local data activity. When the data activity is high, i.e., in the vicinity of edges, the (4, 4) lifting is performed. Otherwise, in the plain areas, the (2,4) decomposition coefficients are calculated. The calculations are rather simples that permit the implementation of the designed algorithm in fixed point DSP processors. The proposed adaptive transform possesses the perfect restoration of the processed data and possesses good energy compactation. The designed algorithm was tested on different images. The proposed adaptive transform algorithm can be used for image/signal lossless compression.

  8. Automatic DarkAdaptation Threshold Detection Algorithm.

    PubMed

    G de Azevedo, Dario; Helegda, Sergio; Glock, Flavio; Russomano, Thais

    2005-01-01

    This paper describes an algorithm used to automatically determine the threshold sensitivity in a new dark adaptometer. The new instrument is controlled by a personal computer and can be used in the investigation of several retinal diseases. The stimulus field is delivered to the eye through the modified optics of a fundus camera. An automated light stimulus source was developed to operate together with this fundus camera. New control parameters were developed in this instrument to improve the traditional Goldmann-Weekers dark adaptometer.

  9. Beamforming Using Compressive Sensing

    DTIC Science & Technology

    2011-10-01

    Am. 130 (4), October 2011 VC 2011 Acoustical Society of America G. F. Edelmann and C. F. Gaumond: JASA Express Letters [DOI: 10.1121/1.3632046...arbitrarily spaced array, the rank of A may be insufficient, G. F. Edelmann and C. F. Gaumond: JASA Express Letters [DOI: 10.1121/1.3632046] Published Online...09 September 2011 J. Acoust. Soc. Am. 130 (4), October 2011 G. F. Edelmann and C. F. Gaumond: Beamforming using compressive sensing EL233 Downloaded

  10. Low complex subspace minimum variance beamformer for medical ultrasound imaging.

    PubMed

    Deylami, Ali Mohades; Asl, Babak Mohammadzadeh

    2016-03-01

    Minimum variance (MV) beamformer enhances the resolution and contrast in the medical ultrasound imaging at the expense of higher computational complexity with respect to the non-adaptive delay-and-sum beamformer. The major complexity arises from the estimation of the L×L array covariance matrix using spatial averaging, which is required to more accurate estimation of the covariance matrix of correlated signals, and inversion of it, which is required for calculating the MV weight vector which are as high as O(L(2)) and O(L(3)), respectively. Reducing the number of array elements decreases the computational complexity but degrades the imaging resolution. In this paper, we propose a subspace MV beamformer which preserves the advantages of the MV beamformer with lower complexity. The subspace MV neglects some rows of the array covariance matrix instead of reducing the array size. If we keep η rows of the array covariance matrix which leads to a thin non-square matrix, the weight vector of the subspace beamformer can be achieved in the same way as the MV obtains its weight vector with lower complexity as high as O(η(2)L). More calculations would be saved because an η×L covariance matrix must be estimated instead of a L×L. We simulated a wire targets phantom and a cyst phantom to evaluate the performance of the proposed beamformer. The results indicate that we can keep about 16 from 43 rows of the array covariance matrix which reduces the order of complexity to 14% while the image resolution is still comparable to that of the standard MV beamformer. We also applied the proposed method to an experimental RF data and showed that the subspace MV beamformer performs like the standard MV with lower computational complexity.

  11. Performance of an Adaptive Matched Filter Using the Griffiths Algorithm

    DTIC Science & Technology

    1988-12-01

    Simon. Introduction to Adaptive Filters. New York: Macmillan Publishing Company, 1984. 11. Sklar , Bernard . Digital Communications Fundamentals and...York: Harper and Row, 1986. 8. Widrow, Bernard and Samuel D. Stearns. Adaptive Signal Processing. Englewood Cliffs, N.J.: Prentice-Hall, 1985. 9...Fourier Transforms. and Optics. New York: John Wiley and Sons, 1978. 15. Widrow, Bernard and others. "The Complex LMS Algorithm," Proceedings of the IEEE

  12. Spatially adaptive regularized iterative high-resolution image reconstruction algorithm

    NASA Astrophysics Data System (ADS)

    Lim, Won Bae; Park, Min K.; Kang, Moon Gi

    2000-12-01

    High resolution images are often required in applications such as remote sensing, frame freeze in video, military and medical imaging. Digital image sensor arrays, which are used for image acquisition in many imaging systems, are not dense enough to prevent aliasing, so the acquired images will be degraded by aliasing effects. To prevent aliasing without loss of resolution, a dense detector array is required. But it may be very costly or unavailable, thus, many imaging systems are designed to allow some level of aliasing during image acquisition. The purpose of our work is to reconstruct an unaliased high resolution image from the acquired aliased image sequence. In this paper, we propose a spatially adaptive regularized iterative high resolution image reconstruction algorithm for blurred, noisy and down-sampled image sequences. The proposed approach is based on a Constrained Least Squares (CLS) high resolution reconstruction algorithm, with spatially adaptive regularization operators and parameters. These regularization terms are shown to improve the reconstructed image quality by forcing smoothness, while preserving edges in the reconstructed high resolution image. Accurate sub-pixel motion registration is the key of the success of the high resolution image reconstruction algorithm. However, sub-pixel motion registration may have some level of registration error. Therefore, a reconstruction algorithm which is robust against the registration error is required. The registration algorithm uses a gradient based sub-pixel motion estimator which provides shift information for each of the recorded frames. The proposed algorithm is based on a technique of high resolution image reconstruction, and it solves spatially adaptive regularized constrained least square minimization functionals. In this paper, we show that the reconstruction algorithm gives dramatic improvements in the resolution of the reconstructed image and is effective in handling the aliased information. The

  13. Adapting Eclat algorithm to parallel environments with Charm++ library

    NASA Astrophysics Data System (ADS)

    Puścian, Marek; Grabski, Waldemar

    2016-09-01

    In this paper we describe Eclat algorithm that is adapted to deal with growing data repositories. The presented solution utilizes Master-Slave scheme to distribute data mining tasks among available computation nodes. Several improvements have been proposed and successfully implemented using Charm++ library. This paper introduces optimization techniques to reduce communication cost and synchronization overhead. It also discusses results of the performance of parallel Eclat algorithm against different databases and compares it with parallel Apriori algorithm. The proposed approach has been illustrated with many experiments and measurements performed using multiprocessor and multithreaded computer platform.

  14. An Adaptive Tradeoff Algorithm for Multi-issue SLA Negotiation

    NASA Astrophysics Data System (ADS)

    Son, Seokho; Sim, Kwang Mong

    Since participants in a Cloud may be independent bodies, mechanisms are necessary for resolving different preferences in leasing Cloud services. Whereas there are currently mechanisms that support service-level agreement negotiation, there is little or no negotiation support for concurrent price and timeslot for Cloud service reservations. For the concurrent price and timeslot negotiation, a tradeoff algorithm to generate and evaluate a proposal which consists of price and timeslot proposal is necessary. The contribution of this work is thus to design an adaptive tradeoff algorithm for multi-issue negotiation mechanism. The tradeoff algorithm referred to as "adaptive burst mode" is especially designed to increase negotiation speed and total utility and to reduce computational load by adaptively generating concurrent set of proposals. The empirical results obtained from simulations carried out using a testbed suggest that due to the concurrent price and timeslot negotiation mechanism with adaptive tradeoff algorithm: 1) both agents achieve the best performance in terms of negotiation speed and utility; 2) the number of evaluations of each proposal is comparatively lower than previous scheme (burst-N).

  15. Adaptive wavelet transform algorithm for lossy image compression

    NASA Astrophysics Data System (ADS)

    Pogrebnyak, Oleksiy B.; Ramirez, Pablo M.; Acevedo Mosqueda, Marco Antonio

    2004-11-01

    A new algorithm of locally adaptive wavelet transform based on the modified lifting scheme is presented. It performs an adaptation of the wavelet high-pass filter at the prediction stage to the local image data activity. The proposed algorithm uses the generalized framework for the lifting scheme that permits to obtain easily different wavelet filter coefficients in the case of the (~N, N) lifting. Changing wavelet filter order and different control parameters, one can obtain the desired filter frequency response. It is proposed to perform the hard switching between different wavelet lifting filter outputs according to the local data activity estimate. The proposed adaptive transform possesses a good energy compaction. The designed algorithm was tested on different images. The obtained simulation results show that the visual and quantitative quality of the restored images is high. The distortions are less in the vicinity of high spatial activity details comparing to the non-adaptive transform, which introduces ringing artifacts. The designed algorithm can be used for lossy image compression and in the noise suppression applications.

  16. Adaptation algorithms for 2-D feedforward neural networks.

    PubMed

    Kaczorek, T

    1995-01-01

    The generalized weight adaptation algorithms presented by J.G. Kuschewski et al. (1993) and by S.H. Zak and H.J. Sira-Ramirez (1990) are extended for 2-D madaline and 2-D two-layer feedforward neural nets (FNNs).

  17. A Procedure for Empirical Initialization of Adaptive Testing Algorithms.

    ERIC Educational Resources Information Center

    van der Linden, Wim J.

    In constrained adaptive testing, the numbers of constraints needed to control the content of the tests can easily run into the hundreds. Proper initialization of the algorithm becomes a requirement because the presence of large numbers of constraints slows down the convergence of the ability estimator. In this paper, an empirical initialization of…

  18. GPU-Powered Coherent Beamforming

    NASA Astrophysics Data System (ADS)

    Magro, A.; Adami, K. Zarb; Hickish, J.

    2015-03-01

    Graphics processing units (GPU)-based beamforming is a relatively unexplored area in radio astronomy, possibly due to the assumption that any such system will be severely limited by the PCIe bandwidth required to transfer data to the GPU. We have developed a CUDA-based GPU implementation of a coherent beamformer, specifically designed and optimized for deployment at the BEST-2 array which can generate an arbitrary number of synthesized beams for a wide range of parameters. It achieves ˜1.3 TFLOPs on an NVIDIA Tesla K20, approximately 10x faster than an optimized, multithreaded CPU implementation. This kernel has been integrated into two real-time, GPU-based time-domain software pipelines deployed at the BEST-2 array in Medicina: a standalone beamforming pipeline and a transient detection pipeline. We present performance benchmarks for the beamforming kernel as well as the transient detection pipeline with beamforming capabilities as well as results of test observation.

  19. An Adaptive Immune Genetic Algorithm for Edge Detection

    NASA Astrophysics Data System (ADS)

    Li, Ying; Bai, Bendu; Zhang, Yanning

    An adaptive immune genetic algorithm (AIGA) based on cost minimization technique method for edge detection is proposed. The proposed AIGA recommends the use of adaptive probabilities of crossover, mutation and immune operation, and a geometric annealing schedule in immune operator to realize the twin goals of maintaining diversity in the population and sustaining the fast convergence rate in solving the complex problems such as edge detection. Furthermore, AIGA can effectively exploit some prior knowledge and information of the local edge structure in the edge image to make vaccines, which results in much better local search ability of AIGA than that of the canonical genetic algorithm. Experimental results on gray-scale images show the proposed algorithm perform well in terms of quality of the final edge image, rate of convergence and robustness to noise.

  20. An Adaptive Homomorphic Aperture Photometry Algorithm for Merging Galaxies

    NASA Astrophysics Data System (ADS)

    Huang, J. C.; Hwang, C. Y.

    2017-03-01

    We present a novel automatic adaptive aperture photometry algorithm for measuring the total magnitudes of merging galaxies with irregular shapes. First, we use a morphological pattern recognition routine for identifying the shape of an irregular source in a background-subtracted image. Then, we extend the shape of the source by using the Dilation image operation to obtain an aperture that is quasi-homomorphic to the shape of the irregular source. The magnitude measured from the homomorphic aperture would thus have minimal contamination from the nearby background. As a test of our algorithm, we applied our technique to the merging galaxies observed by the Sloan Digital Sky Survey and the Canada–France–Hawaii Telescope. Our results suggest that the adaptive homomorphic aperture algorithm can be very useful for investigating extended sources with irregular shapes and sources in crowded regions.

  1. 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

  2. A new adaptive GMRES algorithm for achieving high accuracy

    SciTech Connect

    Sosonkina, M.; Watson, L.T.; Kapania, R.K.; Walker, H.F.

    1996-12-31

    GMRES(k) is widely used for solving nonsymmetric linear systems. However, it is inadequate either when it converges only for k close to the problem size or when numerical error in the modified Gram-Schmidt process used in the GMRES orthogonalization phase dramatically affects the algorithm performance. An adaptive version of GMRES (k) which tunes the restart value k based on criteria estimating the GMRES convergence rate for the given problem is proposed here. The essence of the adaptive GMRES strategy is to adapt the parameter k to the problem, similar in spirit to how a variable order ODE algorithm tunes the order k. With FORTRAN 90, which provides pointers and dynamic memory management, dealing with the variable storage requirements implied by varying k is not too difficult. The parameter k can be both increased and decreased-an increase-only strategy is described next followed by pseudocode.

  3. Adaptive process control using fuzzy logic and genetic algorithms

    NASA Technical Reports Server (NTRS)

    Karr, C. L.

    1993-01-01

    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, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.

  4. Adaptive Process Control with Fuzzy Logic and Genetic Algorithms

    NASA Technical Reports Server (NTRS)

    Karr, C. L.

    1993-01-01

    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 to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.

  5. Adaptive optics image restoration algorithm based on wavefront reconstruction and adaptive total variation method

    NASA Astrophysics Data System (ADS)

    Li, Dongming; Zhang, Lijuan; Wang, Ting; Liu, Huan; Yang, Jinhua; Chen, Guifen

    2016-11-01

    To improve the adaptive optics (AO) image's quality, we study the AO image restoration algorithm based on wavefront reconstruction technology and adaptive total variation (TV) method in this paper. Firstly, the wavefront reconstruction using Zernike polynomial is used for initial estimated for the point spread function (PSF). Then, we develop our proposed iterative solutions for AO images restoration, addressing the joint deconvolution issue. The image restoration experiments are performed to verify the image restoration effect of our proposed algorithm. The experimental results show that, compared with the RL-IBD algorithm and Wiener-IBD algorithm, we can see that GMG measures (for real AO image) from our algorithm are increased by 36.92%, and 27.44% respectively, and the computation time are decreased by 7.2%, and 3.4% respectively, and its estimation accuracy is significantly improved.

  6. Adaptive bad pixel correction algorithm for IRFPA based on PCNN

    NASA Astrophysics Data System (ADS)

    Leng, Hanbing; Zhou, Zuofeng; Cao, Jianzhong; Yi, Bo; Yan, Aqi; Zhang, Jian

    2013-10-01

    Bad pixels and response non-uniformity are the primary obstacles when IRFPA is used in different thermal imaging systems. The bad pixels of IRFPA include fixed bad pixels and random bad pixels. The former is caused by material or manufacture defect and their positions are always fixed, the latter is caused by temperature drift and their positions are always changing. Traditional radiometric calibration-based bad pixel detection and compensation algorithm is only valid to the fixed bad pixels. Scene-based bad pixel correction algorithm is the effective way to eliminate these two kinds of bad pixels. Currently, the most used scene-based bad pixel correction algorithm is based on adaptive median filter (AMF). In this algorithm, bad pixels are regarded as image noise and then be replaced by filtered value. However, missed correction and false correction often happens when AMF is used to handle complex infrared scenes. To solve this problem, a new adaptive bad pixel correction algorithm based on pulse coupled neural networks (PCNN) is proposed. Potential bad pixels are detected by PCNN in the first step, then image sequences are used periodically to confirm the real bad pixels and exclude the false one, finally bad pixels are replaced by the filtered result. With the real infrared images obtained from a camera, the experiment results show the effectiveness of the proposed algorithm.

  7. Combined phase screen aberration correction and minimum variance beamforming in medical ultrasound.

    PubMed

    Ziksari, Mahsa Sotoodeh; Asl, Babak Mohammadzadeh

    2017-03-01

    In recent years, applying adaptive beamforming to ultrasound imaging improves image quality in terms of resolution and contrast. One of the best adaptive beamformers in this field is the minimum variance (MV) beamformer which presents better resolution and edge definition compared to the traditional delay-and-sum (DAS) beamformer. However, in real situations, sound-velocity inhomogeneities cause phase aberration which leads to ambiguity in targets' location and degradation in resolution. This effect is a fundamental obstacle to utilize advantages of MV beamformer, although, in aberrating medium MV beamformer results in better performance compared to DAS. In this paper, two different levels of phase screens have been applied to simulate aberrator layers located close to the transducer. Also, prior to beamforming process, a conventional correction technique based on phase screen model is used. Simulations are performed in majority resolution of MV which has the lowest robustness. The results demonstrate that applying this correction method can retrieve the efficiency of the MV beamformer. Moreover, the method improves the performance of the MV in both terms of resolution and contrast. As corrected MV achieved at least 22% improvement in sidelobe reduction and 24% increase in contrast to noise ratio (CNR) with respect to the DAS corrected data. Also, according to experimental dataset 17% enhancement in CNR is yielded by MV.

  8. Terahertz plasmonic Bessel beamformer

    SciTech Connect

    Monnai, Yasuaki; Shinoda, Hiroyuki; Jahn, David; Koch, Martin; Withayachumnankul, Withawat

    2015-01-12

    We experimentally demonstrate terahertz Bessel beamforming based on the concept of plasmonics. The proposed planar structure is made of concentric metallic grooves with a subwavelength spacing that couple to a point source to create tightly confined surface waves or spoof surface plasmon polaritons. Concentric scatterers periodically incorporated at a wavelength scale allow for launching the surface waves into free space to define a Bessel beam. The Bessel beam defined at 0.29 THz has been characterized through terahertz time-domain spectroscopy. This approach is capable of generating Bessel beams with planar structures as opposed to bulky axicon lenses and can be readily integrated with solid-state terahertz sources.

  9. Adaptive Flocking of Robot Swarms: Algorithms and Properties

    NASA Astrophysics Data System (ADS)

    Lee, Geunho; Chong, Nak Young

    This paper presents a distributed approach for adaptive flocking of swarms of mobile robots that enables to navigate autonomously in complex environments populated with obstacles. Based on the observation of the swimming behavior of a school of fish, we propose an integrated algorithm that allows a swarm of robots to navigate in a coordinated manner, split into multiple swarms, or merge with other swarms according to the environment conditions. We prove the convergence of the proposed algorithm using Lyapunov stability theory. We also verify the effectiveness of the algorithm through extensive simulations, where a swarm of robots repeats the process of splitting and merging while passing around multiple stationary and moving obstacles. The simulation results show that the proposed algorithm is scalable, and robust to variations in the sensing capability of individual robots.

  10. An Adaptive Hybrid Genetic Algorithm for Improved Groundwater Remediation Design

    NASA Astrophysics Data System (ADS)

    Espinoza, F. P.; Minsker, B. S.; Goldberg, D. E.

    2001-12-01

    Identifying optimal designs for a groundwater remediation system is computationally intensive, especially for complex, nonlinear problems such as enhanced in situ bioremediation technology. To improve performance, we apply a hybrid genetic algorithm (HGA), which is a two-step solution method: a genetic algorithm (GA) for global search using the entire population and then a local search (LS) to improve search speed for only a few individuals in the population. We implement two types of HGAs: a non-adaptive HGA (NAHGA), whose operations are invariant throughout the run, and a self-adaptive HGA (SAHGA), whose operations adapt to the performance of the algorithm. The best settings of the two HGAs for optimal performance are then investigated for a groundwater remediation problem. The settings include the frequency of LS with respect to the normal GA evaluation, probability of individual selection for LS, evolution criterion for LS (Lamarckian or Baldwinian), and number of local search iterations. A comparison of the algorithms' performance under different settings will be presented.

  11. An adaptive multimeme algorithm for designing HIV multidrug therapies.

    PubMed

    Neri, Ferrante; Toivanen, Jari; Cascella, Giuseppe Leonardo; Ong, Yew-Soon

    2007-01-01

    This paper proposes a period representation for modeling the multidrug HIV therapies and an Adaptive Multimeme Algorithm (AMmA) for designing the optimal therapy. The period representation offers benefits in terms of flexibility and reduction in dimensionality compared to the binary representation. The AMmA is a memetic algorithm which employs a list of three local searchers adaptively activated by an evolutionary framework. These local searchers, having different features according to the exploration logic and the pivot rule, have the role of exploring the decision space from different and complementary perspectives and, thus, assisting the standard evolutionary operators in the optimization process. Furthermore, the AMmA makes use of an adaptation which dynamically sets the algorithmic parameters in order to prevent stagnation and premature convergence. The numerical results demonstrate that the application of the proposed algorithm leads to very efficient medication schedules which quickly stimulate a strong immune response to HIV. The earlier termination of the medication schedule leads to lesser unpleasant side effects for the patient due to strong antiretroviral therapy. A numerical comparison shows that the AMmA is more efficient than three popular metaheuristics. Finally, a statistical test based on the calculation of the tolerance interval confirms the superiority of the AMmA compared to the other methods for the problem under study.

  12. An adaptive grid algorithm for one-dimensional nonlinear equations

    NASA Technical Reports Server (NTRS)

    Gutierrez, William E.; Hills, Richard G.

    1990-01-01

    Richards' equation, which models the flow of liquid through unsaturated porous media, is highly nonlinear and difficult to solve. Step gradients in the field variables require the use of fine grids and small time step sizes. The numerical instabilities caused by the nonlinearities often require the use of iterative methods such as Picard or Newton interation. These difficulties result in large CPU requirements in solving Richards equation. With this in mind, adaptive and multigrid methods are investigated for use with nonlinear equations such as Richards' equation. Attention is focused on one-dimensional transient problems. To investigate the use of multigrid and adaptive grid methods, a series of problems are studied. First, a multigrid program is developed and used to solve an ordinary differential equation, demonstrating the efficiency with which low and high frequency errors are smoothed out. The multigrid algorithm and an adaptive grid algorithm is used to solve one-dimensional transient partial differential equations, such as the diffusive and convective-diffusion equations. The performance of these programs are compared to that of the Gauss-Seidel and tridiagonal methods. The adaptive and multigrid schemes outperformed the Gauss-Seidel algorithm, but were not as fast as the tridiagonal method. The adaptive grid scheme solved the problems slightly faster than the multigrid method. To solve nonlinear problems, Picard iterations are introduced into the adaptive grid and tridiagonal methods. Burgers' equation is used as a test problem for the two algorithms. Both methods obtain solutions of comparable accuracy for similar time increments. For the Burgers' equation, the adaptive grid method finds the solution approximately three times faster than the tridiagonal method. Finally, both schemes are used to solve the water content formulation of the Richards' equation. For this problem, the adaptive grid method obtains a more accurate solution in fewer work units and

  13. Selective Frequency Invariant Uniform Circular Broadband Beamformer

    NASA Astrophysics Data System (ADS)

    Zhang, Xin; Ser, Wee; Zhang, Zhang; Krishna, AnoopKumar

    2010-12-01

    Frequency-Invariant (FI) beamforming is a well known array signal processing technique used in many applications. In this paper, an algorithm that attempts to optimize the frequency invariant beampattern solely for the mainlobe, and relax the FI requirement on the sidelobe is proposed. This sacrifice on performance in the undesired region is traded off for better performance in the desired region as well as reduced number of microphones employed. The objective function is designed to minimize the overall spatial response of the beamformer with a constraint on the gain being smaller than a pre-defined threshold value across a specific frequency range and at a specific angle. This problem is formulated as a convex optimization problem and the solution is obtained by using the Second Order Cone Programming (SOCP) technique. An analysis of the computational complexity of the proposed algorithm is presented as well as its performance. The performance is evaluated via computer simulation for different number of sensors and different threshold values. Simulation results show that, the proposed algorithm is able to achieve a smaller mean square error of the spatial response gain for the specific FI region compared to existing algorithms.

  14. Estimating meme fitness in adaptive memetic algorithms for combinatorial problems.

    PubMed

    Smith, J E

    2012-01-01

    Among the most promising and active research areas in heuristic optimisation is the field of adaptive memetic algorithms (AMAs). These gain much of their reported robustness by adapting the probability with which each of a set of local improvement operators is applied, according to an estimate of their current value to the search process. This paper addresses the issue of how the current value should be estimated. Assuming the estimate occurs over several applications of a meme, we consider whether the extreme or mean improvements should be used, and whether this aggregation should be global, or local to some part of the solution space. To investigate these issues, we use the well-established COMA framework that coevolves the specification of a population of memes (representing different local search algorithms) alongside a population of candidate solutions to the problem at hand. Two very different memetic algorithms are considered: the first using adaptive operator pursuit to adjust the probabilities of applying a fixed set of memes, and a second which applies genetic operators to dynamically adapt and create memes and their functional definitions. For the latter, especially on combinatorial problems, credit assignment mechanisms based on historical records, or on notions of landscape locality, will have limited application, and it is necessary to estimate the value of a meme via some form of sampling. The results on a set of binary encoded combinatorial problems show that both methods are very effective, and that for some problems it is necessary to use thousands of variables in order to tease apart the differences between different reward schemes. However, for both memetic algorithms, a significant pattern emerges that reward based on mean improvement is better than that based on extreme improvement. This contradicts recent findings from adapting the parameters of operators involved in global evolutionary search. The results also show that local reward schemes

  15. Efficient implementation of the adaptive scale pixel decomposition algorithm

    NASA Astrophysics Data System (ADS)

    Zhang, L.; Bhatnagar, S.; Rau, U.; Zhang, M.

    2016-08-01

    Context. Most popular algorithms in use to remove the effects of a telescope's point spread function (PSF) in radio astronomy are variants of the CLEAN algorithm. Most of these algorithms model the sky brightness using the delta-function basis, which results in undesired artefacts when used to image extended emission. The adaptive scale pixel decomposition (Asp-Clean) algorithm models the sky brightness on a scale-sensitive basis and thus gives a significantly better imaging performance when imaging fields that contain both resolved and unresolved emission. Aims: However, the runtime cost of Asp-Clean is higher than that of scale-insensitive algorithms. In this paper, we identify the most expensive step in the original Asp-Clean algorithm and present an efficient implementation of it, which significantly reduces the computational cost while keeping the imaging performance comparable to the original algorithm. The PSF sidelobe levels of modern wide-band telescopes are significantly reduced, allowing us to make approximations to reduce the computational cost, which in turn allows for the deconvolution of larger images on reasonable timescales. Methods: As in the original algorithm, scales in the image are estimated through function fitting. Here we introduce an analytical method to model extended emission, and a modified method for estimating the initial values used for the fitting procedure, which ultimately leads to a lower computational cost. Results: The new implementation was tested with simulated EVLA data and the imaging performance compared well with the original Asp-Clean algorithm. Tests show that the current algorithm can recover features at different scales with lower computational cost.

  16. PHURBAS: AN ADAPTIVE, LAGRANGIAN, MESHLESS, MAGNETOHYDRODYNAMICS CODE. I. ALGORITHM

    SciTech Connect

    Maron, Jason L.; McNally, Colin P.; Mac Low, Mordecai-Mark E-mail: cmcnally@amnh.org

    2012-05-01

    We present an algorithm for simulating the equations of ideal magnetohydrodynamics and other systems of differential equations on an unstructured set of points represented by sample particles. Local, third-order, least-squares, polynomial interpolations (Moving Least Squares interpolations) are calculated from the field values of neighboring particles to obtain field values and spatial derivatives at the particle position. Field values and particle positions are advanced in time with a second-order predictor-corrector scheme. The particles move with the fluid, so the time step is not limited by the Eulerian Courant-Friedrichs-Lewy condition. Full spatial adaptivity is implemented to ensure the particles fill the computational volume, which gives the algorithm substantial flexibility and power. A target resolution is specified for each point in space, with particles being added and deleted as needed to meet this target. Particle addition and deletion is based on a local void and clump detection algorithm. Dynamic artificial viscosity fields provide stability to the integration. The resulting algorithm provides a robust solution for modeling flows that require Lagrangian or adaptive discretizations to resolve. This paper derives and documents the Phurbas algorithm as implemented in Phurbas version 1.1. A following paper presents the implementation and test problem results.

  17. Landsat ecosystem disturbance adaptive processing system (LEDAPS) algorithm description

    USGS Publications Warehouse

    Schmidt, Gail; Jenkerson, Calli; Masek, Jeffrey; Vermote, Eric; Gao, Feng

    2013-01-01

    The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) software was originally developed by the National Aeronautics and Space Administration–Goddard Space Flight Center and the University of Maryland to produce top-of-atmosphere reflectance from LandsatThematic Mapper and Enhanced Thematic Mapper Plus Level 1 digital numbers and to apply atmospheric corrections to generate a surface-reflectance product.The U.S. Geological Survey (USGS) has adopted the LEDAPS algorithm for producing the Landsat Surface Reflectance Climate Data Record.This report discusses the LEDAPS algorithm, which was implemented by the USGS.

  18. A Novel Adaptive Frequency Estimation Algorithm Based on Interpolation FFT and Improved Adaptive Notch Filter

    NASA Astrophysics Data System (ADS)

    Shen, Ting-ao; Li, Hua-nan; Zhang, Qi-xin; Li, Ming

    2017-02-01

    The convergence rate and the continuous tracking precision are two main problems of the existing adaptive notch filter (ANF) for frequency tracking. To solve the problems, the frequency is detected by interpolation FFT at first, which aims to overcome the convergence rate of the ANF. Then, referring to the idea of negative feedback, an evaluation factor is designed to monitor the ANF parameters and realize continuously high frequency tracking accuracy. According to the principle, a novel adaptive frequency estimation algorithm based on interpolation FFT and improved ANF is put forward. Its basic idea, specific measures and implementation steps are described in detail. The proposed algorithm obtains a fast estimation of the signal frequency, higher accuracy and better universality qualities. Simulation results verified the superiority and validity of the proposed algorithm when compared with original algorithms.

  19. Self-adaptive incremental Newton-Raphson algorithms

    NASA Technical Reports Server (NTRS)

    Padovan, J.

    1980-01-01

    Multilevel self-adaptive Newton-Raphson type strategies are developed to improve the solution efficiency of nonlinear finite element simulations of statically loaded structures. The overall strategy involves three basic levels. The first level involves preliminary solution tunneling via primative operators. Secondly, the solution is constantly monitored via quality/convergence/nonlinearity tests. Lastly, the third level involves self-adaptive algorithmic update procedures aimed at improving the convergence characteristics of the Newton-Raphson strategy. Numerical experiments are included to illustrate the results of the procedure.

  20. Guided wave phased array beamforming and imaging in composite plates.

    PubMed

    Yu, Lingyu; Tian, Zhenhua

    2016-05-01

    This paper describes phased array beamforming using guided waves in anisotropic composite plates. A generic phased array algorithm is presented, in which direction dependent guided wave parameters and the energy skew effect are considered. This beamforming at an angular direction is achieved based on the classic delay-and-sum principle by applying phase delays to signals received at array elements and adding up the delayed signals. The phase delays are determined with the goal to maximize the array output at the desired direction and minimize it otherwise. For array characterization, the beam pattern of rectangular grid arrays in composite plates is derived. In addition to the beam pattern, the beamforming factor in terms of wavenumber distribution is defined to provide intrinsic explanations for phased array beamforming. The beamforming and damage detection in a composite plate are demonstrated using rectangular grid arrays made by a non-contact scanning laser Doppler vibrometer. Detection images of the composite plate with multiple surface defects at various directions are obtained. The results show that the guided wave phased array method is a potential effective method for rapid inspection of large composite structures.

  1. Advanced Dynamically Adaptive Algorithms for Stochastic Simulations on Extreme Scales

    SciTech Connect

    Xiu, Dongbin

    2016-06-21

    The focus of the project is the development of mathematical methods and high-performance com- putational tools for stochastic simulations, with a particular emphasis on computations on extreme scales. The core of the project revolves around the design of highly e cient and scalable numer- ical algorithms that can adaptively and accurately, in high dimensional spaces, resolve stochastic problems with limited smoothness, even containing discontinuities.

  2. Adaptive primal-dual genetic algorithms in dynamic environments.

    PubMed

    Wang, Hongfeng; Yang, Shengxiang; Ip, W H; Wang, Dingwei

    2009-12-01

    Recently, there has been an increasing interest in applying genetic algorithms (GAs) in dynamic environments. Inspired by the complementary and dominance mechanisms in nature, a primal-dual GA (PDGA) has been proposed for dynamic optimization problems (DOPs). In this paper, an important operator in PDGA, i.e., the primal-dual mapping (PDM) scheme, is further investigated to improve the robustness and adaptability of PDGA in dynamic environments. In the improved scheme, two different probability-based PDM operators, where the mapping probability of each allele in the chromosome string is calculated through the statistical information of the distribution of alleles in the corresponding gene locus over the population, are effectively combined according to an adaptive Lamarckian learning mechanism. In addition, an adaptive dominant replacement scheme, which can probabilistically accept inferior chromosomes, is also introduced into the proposed algorithm to enhance the diversity level of the population. Experimental results on a series of dynamic problems generated from several stationary benchmark problems show that the proposed algorithm is a good optimizer for DOPs.

  3. Adaptive Load-Balancing Algorithms Using Symmetric Broadcast Networks

    NASA Technical Reports Server (NTRS)

    Das, Sajal K.; Biswas, Rupak; Chancellor, Marisa K. (Technical Monitor)

    1997-01-01

    In a distributed-computing environment, it is important to ensure that the processor workloads are adequately balanced. Among numerous load-balancing algorithms, a unique approach due to Dam and Prasad defines a symmetric broadcast network (SBN) that provides a robust communication pattern among the processors in a topology-independent manner. In this paper, we propose and analyze three novel SBN-based load-balancing algorithms, and implement them on an SP2. A thorough experimental study with Poisson-distributed synthetic loads demonstrates that these algorithms are very effective in balancing system load while minimizing processor idle time. They also compare favorably with several other existing load-balancing techniques. Additional experiments performed with real data demonstrate that the SBN approach is effective in adaptive computational science and engineering applications where dynamic load balancing is extremely crucial.

  4. An adaptive gyroscope-based algorithm for temporal gait analysis.

    PubMed

    Greene, Barry R; McGrath, Denise; O'Neill, Ross; O'Donovan, Karol J; Burns, Adrian; Caulfield, Brian

    2010-12-01

    Body-worn kinematic sensors have been widely proposed as the optimal solution for portable, low cost, ambulatory monitoring of gait. This study aims to evaluate an adaptive gyroscope-based algorithm for automated temporal gait analysis using body-worn wireless gyroscopes. Gyroscope data from nine healthy adult subjects performing four walks at four different speeds were then compared against data acquired simultaneously using two force plates and an optical motion capture system. Data from a poliomyelitis patient, exhibiting pathological gait walking with and without the aid of a crutch, were also compared to the force plate. Results show that the mean true error between the adaptive gyroscope algorithm and force plate was -4.5 ± 14.4 ms and 43.4 ± 6.0 ms for IC and TC points, respectively, in healthy subjects. Similarly, the mean true error when data from the polio patient were compared against the force plate was -75.61 ± 27.53 ms and 99.20 ± 46.00 ms for IC and TC points, respectively. A comparison of the present algorithm against temporal gait parameters derived from an optical motion analysis system showed good agreement for nine healthy subjects at four speeds. These results show that the algorithm reported here could constitute the basis of a robust, portable, low-cost system for ambulatory monitoring of gait.

  5. Adaptive Firefly Algorithm: Parameter Analysis and its Application

    PubMed Central

    Shen, Hong-Bin

    2014-01-01

    As a nature-inspired search algorithm, firefly algorithm (FA) has several control parameters, which may have great effects on its performance. In this study, we investigate the parameter selection and adaptation strategies in a modified firefly algorithmadaptive firefly algorithm (AdaFa). There are three strategies in AdaFa including (1) a distance-based light absorption coefficient; (2) a gray coefficient enhancing fireflies to share difference information from attractive ones efficiently; and (3) five different dynamic strategies for the randomization parameter. Promising selections of parameters in the strategies are analyzed to guarantee the efficient performance of AdaFa. AdaFa is validated over widely used benchmark functions, and the numerical experiments and statistical tests yield useful conclusions on the strategies and the parameter selections affecting the performance of AdaFa. When applied to the real-world problem — protein tertiary structure prediction, the results demonstrated improved variants can rebuild the tertiary structure with the average root mean square deviation less than 0.4Å and 1.5Å from the native constrains with noise free and 10% Gaussian white noise. PMID:25397812

  6. Adaptive firefly algorithm: parameter analysis and its application.

    PubMed

    Cheung, Ngaam J; Ding, Xue-Ming; Shen, Hong-Bin

    2014-01-01

    As a nature-inspired search algorithm, firefly algorithm (FA) has several control parameters, which may have great effects on its performance. In this study, we investigate the parameter selection and adaptation strategies in a modified firefly algorithm - adaptive firefly algorithm (AdaFa). There are three strategies in AdaFa including (1) a distance-based light absorption coefficient; (2) a gray coefficient enhancing fireflies to share difference information from attractive ones efficiently; and (3) five different dynamic strategies for the randomization parameter. Promising selections of parameters in the strategies are analyzed to guarantee the efficient performance of AdaFa. AdaFa is validated over widely used benchmark functions, and the numerical experiments and statistical tests yield useful conclusions on the strategies and the parameter selections affecting the performance of AdaFa. When applied to the real-world problem - protein tertiary structure prediction, the results demonstrated improved variants can rebuild the tertiary structure with the average root mean square deviation less than 0.4Å and 1.5Å from the native constrains with noise free and 10% Gaussian white noise.

  7. Generalized pattern search algorithms with adaptive precision function evaluations

    SciTech Connect

    Polak, Elijah; Wetter, Michael

    2003-05-14

    In the literature on generalized pattern search algorithms, convergence to a stationary point of a once continuously differentiable cost function is established under the assumption that the cost function can be evaluated exactly. However, there is a large class of engineering problems where the numerical evaluation of the cost function involves the solution of systems of differential algebraic equations. Since the termination criteria of the numerical solvers often depend on the design parameters, computer code for solving these systems usually defines a numerical approximation to the cost function that is discontinuous with respect to the design parameters. Standard generalized pattern search algorithms have been applied heuristically to such problems, but no convergence properties have been stated. In this paper we extend a class of generalized pattern search algorithms to a form that uses adaptive precision approximations to the cost function. These numerical approximations need not define a continuous function. Our algorithms can be used for solving linearly constrained problems with cost functions that are at least locally Lipschitz continuous. Assuming that the cost function is smooth, we prove that our algorithms converge to a stationary point. Under the weaker assumption that the cost function is only locally Lipschitz continuous, we show that our algorithms converge to points at which the Clarke generalized directional derivatives are nonnegative in predefined directions. An important feature of our adaptive precision scheme is the use of coarse approximations in the early iterations, with the approximation precision controlled by a test. Such an approach leads to substantial time savings in minimizing computationally expensive functions.

  8. Adaptive Mesh Refinement Algorithms for Parallel Unstructured Finite Element Codes

    SciTech Connect

    Parsons, I D; Solberg, J M

    2006-02-03

    This project produced algorithms for and software implementations of adaptive mesh refinement (AMR) methods for solving practical solid and thermal mechanics problems on multiprocessor parallel computers using unstructured finite element meshes. The overall goal is to provide computational solutions that are accurate to some prescribed tolerance, and adaptivity is the correct path toward this goal. These new tools will enable analysts to conduct more reliable simulations at reduced cost, both in terms of analyst and computer time. Previous academic research in the field of adaptive mesh refinement has produced a voluminous literature focused on error estimators and demonstration problems; relatively little progress has been made on producing efficient implementations suitable for large-scale problem solving on state-of-the-art computer systems. Research issues that were considered include: effective error estimators for nonlinear structural mechanics; local meshing at irregular geometric boundaries; and constructing efficient software for parallel computing environments.

  9. Beamforming-Enhanced Inverse Scattering for Microwave Breast Imaging.

    PubMed

    Burfeindt, Matthew J; Shea, Jacob D; Van Veen, Barry D; Hagness, Susan C

    2014-10-01

    We present a focal-beamforming-enhanced formulation of the distorted Born iterative method (DBIM) for microwave breast imaging. Incorporating beamforming into the imaging algorithm has the potential to mitigate the effect of noise on the image reconstruction. We apply the focal-beamforming-enhanced DBIM algorithm to simulated array measurements from two MRI-derived, anatomically realistic numerical breast phantoms and compare its performance to that of the DBIM formulated with two non-focal schemes. The first scheme simply averages scattered field data from reciprocal antenna pairs while the second scheme discards reciprocal pairs. Images of the dielectric properties are reconstructed for signal-to-noise ratios (SNR) ranging from 35 dB down to 0 dB. We show that, for low SNR, the focal beamforming algorithm creates reconstructions that are of higher fidelity with respect to the exact dielectric profiles of the phantoms as compared to reconstructions created using the non-focal schemes. At high SNR, the focal and non-focal reconstructions are of comparable quality.

  10. Beamforming-Enhanced Inverse Scattering for Microwave Breast Imaging

    PubMed Central

    Burfeindt, Matthew J.; Shea, Jacob D.; Van Veen, Barry D.; Hagness, Susan C.

    2015-01-01

    We present a focal-beamforming-enhanced formulation of the distorted Born iterative method (DBIM) for microwave breast imaging. Incorporating beamforming into the imaging algorithm has the potential to mitigate the effect of noise on the image reconstruction. We apply the focal-beamforming-enhanced DBIM algorithm to simulated array measurements from two MRI-derived, anatomically realistic numerical breast phantoms and compare its performance to that of the DBIM formulated with two non-focal schemes. The first scheme simply averages scattered field data from reciprocal antenna pairs while the second scheme discards reciprocal pairs. Images of the dielectric properties are reconstructed for signal-to-noise ratios (SNR) ranging from 35 dB down to 0 dB. We show that, for low SNR, the focal beamforming algorithm creates reconstructions that are of higher fidelity with respect to the exact dielectric profiles of the phantoms as compared to reconstructions created using the non-focal schemes. At high SNR, the focal and non-focal reconstructions are of comparable quality. PMID:26663930

  11. FLATDAN beamformer enhancements and testing

    NASA Astrophysics Data System (ADS)

    Racca, Roberto; Schumacher, Ian; Kraeutner, P.

    1992-07-01

    FLATDAN is a program that implements a Farfield Line Array Time Domain beamformer for processing large amounts of acoustic data. Upsampling is used to increase the time delay resolution beyond that of the input sampling frequency. However, decimation after the beamforming operation ensures that the output data rate per beam is the same as the input data rate per sensor. The beamformer can accommodate up to 256 input sensor channels and can provide output for up to 256 beams. The beamformer expects input data for all sensors to be in a single file written using the Defence Research Establishment Pacific Ocean Acoustics .DAT format. Beam output data is written in the same format. An information manual for FLATDAN users is presented, describing the beamformer design, program operation, and program structure. Modifications to the file input/output routines since the original introduction of FLATDAN are noted. A performance analysis of the beamformer code was conducted using a VAX performance and coverage analyzer utility. Results are given, showing how much total execution time is spent at given locations in the program. Sample plots from test runs using simulated input data are included.

  12. G/SPLINES: A hybrid of Friedman's Multivariate Adaptive Regression Splines (MARS) algorithm with Holland's genetic algorithm

    NASA Technical Reports Server (NTRS)

    Rogers, David

    1991-01-01

    G/SPLINES are a hybrid of Friedman's Multivariable Adaptive Regression Splines (MARS) algorithm with Holland's Genetic Algorithm. In this hybrid, the incremental search is replaced by a genetic search. The G/SPLINE algorithm exhibits performance comparable to that of the MARS algorithm, requires fewer least squares computations, and allows significantly larger problems to be considered.

  13. Analysis of adaptive algorithms for an integrated communication network

    NASA Technical Reports Server (NTRS)

    Reed, Daniel A.; Barr, Matthew; Chong-Kwon, Kim

    1985-01-01

    Techniques were examined that trade communication bandwidth for decreased transmission delays. When the network is lightly used, these schemes attempt to use additional network resources to decrease communication delays. As the network utilization rises, the schemes degrade gracefully, still providing service but with minimal use of the network. Because the schemes use a combination of circuit and packet switching, they should respond to variations in the types and amounts of network traffic. Also, a combination of circuit and packet switching to support the widely varying traffic demands imposed on an integrated network was investigated. The packet switched component is best suited to bursty traffic where some delays in delivery are acceptable. The circuit switched component is reserved for traffic that must meet real time constraints. Selected packet routing algorithms that might be used in an integrated network were simulated. An integrated traffic places widely varying workload demands on a network. Adaptive algorithms were identified, ones that respond to both the transient and evolutionary changes that arise in integrated networks. A new algorithm was developed, hybrid weighted routing, that adapts to workload changes.

  14. Statistical behaviour of adaptive multilevel splitting algorithms in simple models

    NASA Astrophysics Data System (ADS)

    Rolland, Joran; Simonnet, Eric

    2015-02-01

    Adaptive multilevel splitting algorithms have been introduced rather recently for estimating tail distributions in a fast and efficient way. In particular, they can be used for computing the so-called reactive trajectories corresponding to direct transitions from one metastable state to another. The algorithm is based on successive selection-mutation steps performed on the system in a controlled way. It has two intrinsic parameters, the number of particles/trajectories and the reaction coordinate used for discriminating good or bad trajectories. We investigate first the convergence in law of the algorithm as a function of the timestep for several simple stochastic models. Second, we consider the average duration of reactive trajectories for which no theoretical predictions exist. The most important aspect of this work concerns some systems with two degrees of freedom. They are studied in detail as a function of the reaction coordinate in the asymptotic regime where the number of trajectories goes to infinity. We show that during phase transitions, the statistics of the algorithm deviate significatively from known theoretical results when using non-optimal reaction coordinates. In this case, the variance of the algorithm is peaking at the transition and the convergence of the algorithm can be much slower than the usual expected central limit behaviour. The duration of trajectories is affected as well. Moreover, reactive trajectories do not correspond to the most probable ones. Such behaviour disappears when using the optimal reaction coordinate called committor as predicted by the theory. We finally investigate a three-state Markov chain which reproduces this phenomenon and show logarithmic convergence of the trajectory durations.

  15. Statistical behaviour of adaptive multilevel splitting algorithms in simple models

    SciTech Connect

    Rolland, Joran Simonnet, Eric

    2015-02-15

    Adaptive multilevel splitting algorithms have been introduced rather recently for estimating tail distributions in a fast and efficient way. In particular, they can be used for computing the so-called reactive trajectories corresponding to direct transitions from one metastable state to another. The algorithm is based on successive selection–mutation steps performed on the system in a controlled way. It has two intrinsic parameters, the number of particles/trajectories and the reaction coordinate used for discriminating good or bad trajectories. We investigate first the convergence in law of the algorithm as a function of the timestep for several simple stochastic models. Second, we consider the average duration of reactive trajectories for which no theoretical predictions exist. The most important aspect of this work concerns some systems with two degrees of freedom. They are studied in detail as a function of the reaction coordinate in the asymptotic regime where the number of trajectories goes to infinity. We show that during phase transitions, the statistics of the algorithm deviate significatively from known theoretical results when using non-optimal reaction coordinates. In this case, the variance of the algorithm is peaking at the transition and the convergence of the algorithm can be much slower than the usual expected central limit behaviour. The duration of trajectories is affected as well. Moreover, reactive trajectories do not correspond to the most probable ones. Such behaviour disappears when using the optimal reaction coordinate called committor as predicted by the theory. We finally investigate a three-state Markov chain which reproduces this phenomenon and show logarithmic convergence of the trajectory durations.

  16. Ultrasonic Multipath and Beamforming Clutter Reduction: A Chirp Model Approach

    PubMed Central

    Byram, Brett; Jakovljevic, Marko

    2014-01-01

    In vivo ultrasonic imaging with transducer arrays suffers from image degradation due to beamforming limitations, which includes diffraction limited beamforming as well as beamforming degradation due to tissue inhomogeneity. Additionally, based on recent studies, multipath scattering also causes significant image degradation. To reduce degradation from both sources, we propose a model-based, signal decomposition scheme. The proposed algorithm identifies spatial frequency signatures to decompose received wavefronts into their most significant scattering sources. Scattering sources originating from a region of interest are used to reconstruct decluttered wavefronts, which are beamformed into decluttered radio frequency (RF) scan lines or A-lines. To test the algorithm, ultrasound system channel data were acquired during liver scans from 8 patients. Multiple data sets were acquired from each patient, with 55 total data sets, 43 of which had identifiable hypoechoic regions on normal B-mode images. The data sets with identifiable hypoechoic regions were analyzed. The results show the decluttered B-mode images have an average improvement in contrast over normal images of 7.3±4.6 dB. The CNR changed little on average between normal and decluttered B-mode, −0.4±5.9 dB. The in vivo speckle SNR decreased; the change was −0.65±0.28. Phantom speckle SNR also decreased but only by −0.40±0.03. PMID:24569248

  17. Beamforming and Power Control in Sensor Arrays Using Reinforcement Learning

    PubMed Central

    Almeida, Náthalee C.; Fernandes, Marcelo A.C.; Neto, Adrião D.D.

    2015-01-01

    The use of beamforming and power control, combined or separately, has advantages and disadvantages, depending on the application. The combined use of beamforming and power control has been shown to be highly effective in applications involving the suppression of interference signals from different sources. However, it is necessary to identify efficient methodologies for the combined operation of these two techniques. The most appropriate technique may be obtained by means of the implementation of an intelligent agent capable of making the best selection between beamforming and power control. The present paper proposes an algorithm using reinforcement learning (RL) to determine the optimal combination of beamforming and power control in sensor arrays. The RL algorithm used was Q-learning, employing an ε-greedy policy, and training was performed using the offline method. The simulations showed that RL was effective for implementation of a switching policy involving the different techniques, taking advantage of the positive characteristics of each technique in terms of signal reception. PMID:25808769

  18. Ultrasonic multipath and beamforming clutter reduction: a chirp model approach.

    PubMed

    Byram, Brett; Jakovljevic, Marko

    2014-03-01

    In vivo ultrasonic imaging with transducer arrays suffers from image degradation resulting from beamforming limitations, including diffraction-limited beamforming and beamforming degradation caused by tissue inhomogeneity. Additionally, based on recent studies, multipath scattering also causes significant image degradation. To reduce degradation from both sources, we propose a model-based signal decomposition scheme. The proposed algorithm identifies spatial frequency signatures to decompose received wavefronts into their most significant scattering sources. Scattering sources originating from a region of interest are used to reconstruct decluttered wavefronts, which are beamformed into decluttered RF scan lines or A-lines. To test the algorithm, ultrasound system channel data were acquired during liver scans from 8 patients. Multiple data sets were acquired from each patient, with 55 total data sets, 43 of which had identifiable hypoechoic regions on normal B-mode images. The data sets with identifiable hypoechoic regions were analyzed. The results show the decluttered B-mode images have an average improvement in contrast over normal images of 7.3 ± 4.6 dB. The contrast-to-noise ratio (CNR) changed little on average between normal and decluttered Bmode, -0.4 ± 5.9 dB. The in vivo speckle SNR decreased; the change was -0.65 ± 0.28. Phantom speckle SNR also decreased, but only by -0.40 ± 0.03.

  19. Adaptivity and smart algorithms for fluid-structure interaction

    NASA Technical Reports Server (NTRS)

    Oden, J. Tinsley

    1990-01-01

    This paper reviews new approaches in CFD which have the potential for significantly increasing current capabilities of modeling complex flow phenomena and of treating difficult problems in fluid-structure interaction. These approaches are based on the notions of adaptive methods and smart algorithms, which use instantaneous measures of the quality and other features of the numerical flowfields as a basis for making changes in the structure of the computational grid and of algorithms designed to function on the grid. The application of these new techniques to several problem classes are addressed, including problems with moving boundaries, fluid-structure interaction in high-speed turbine flows, flow in domains with receding boundaries, and related problems.

  20. ADART: an adaptive algebraic reconstruction algorithm for discrete tomography.

    PubMed

    Maestre-Deusto, F Javier; Scavello, Giovanni; Pizarro, Joaquín; Galindo, Pedro L

    2011-08-01

    In this paper we suggest an algorithm based on the Discrete Algebraic Reconstruction Technique (DART) which is capable of computing high quality reconstructions from substantially fewer projections than required for conventional continuous tomography. Adaptive DART (ADART) goes a step further than DART on the reduction of the number of unknowns of the associated linear system achieving a significant reduction in the pixel error rate of reconstructed objects. The proposed methodology automatically adapts the border definition criterion at each iteration, resulting in a reduction of the number of pixels belonging to the border, and consequently of the number of unknowns in the general algebraic reconstruction linear system to be solved, being this reduction specially important at the final stage of the iterative process. Experimental results show that reconstruction errors are considerably reduced using ADART when compared to original DART, both in clean and noisy environments.

  1. An adaptive penalty method for DIRECT algorithm in engineering optimization

    NASA Astrophysics Data System (ADS)

    Vilaça, Rita; Rocha, Ana Maria A. C.

    2012-09-01

    The most common approach for solving constrained optimization problems is based on penalty functions, where the constrained problem is transformed into a sequence of unconstrained problem by penalizing the objective function when constraints are violated. In this paper, we analyze the implementation of an adaptive penalty method, within the DIRECT algorithm, in which the constraints that are more difficult to be satisfied will have relatively higher penalty values. In order to assess the applicability and performance of the proposed method, some benchmark problems from engineering design optimization are considered.

  2. Adaptive testing for psychological assessment: how many items are enough to run an adaptive testing algorithm?

    PubMed

    Wagner-Menghin, Michaela M; Masters, Geoff N

    2013-01-01

    Although the principles of adaptive testing were established in the psychometric literature many years ago (e.g., Weiss, 1977), and practice of adaptive testing is established in educational assessment, it not yet widespread in psychological assessment. One obstacle to adaptive psychological testing is a lack of clarity about the necessary number of items to run an adaptive algorithm. The study explores the relationship between item bank size, test length and measurement precision. Simulated adaptive test runs (allowing a maximum of 30 items per person) out of an item bank with 10 items per ability level (covering .5 logits, 150 items total) yield a standard error of measurement (SEM) of .47 (.39) after an average of 20 (29) items for 85-93% (64-82%) of the simulated rectangular sample. Expanding the bank to 20 items per level (300 items total) did not improve the algorithm's performance significantly. With a small item bank (5 items per ability level, 75 items total) it is possible to reach the same SEM as with a conventional test, but with fewer items or a better SEM with the same number of items.

  3. Path Planning Algorithms for the Adaptive Sensor Fleet

    NASA Technical Reports Server (NTRS)

    Stoneking, Eric; Hosler, Jeff

    2005-01-01

    The Adaptive Sensor Fleet (ASF) is a general purpose fleet management and planning system being developed by NASA in coordination with NOAA. The current mission of ASF is to provide the capability for autonomous cooperative survey and sampling of dynamic oceanographic phenomena such as current systems and algae blooms. Each ASF vessel is a software model that represents a real world platform that carries a variety of sensors. The OASIS platform will provide the first physical vessel, outfitted with the systems and payloads necessary to execute the oceanographic observations described in this paper. The ASF architecture is being designed for extensibility to accommodate heterogenous fleet elements, and is not limited to using the OASIS platform to acquire data. This paper describes the path planning algorithms developed for the acquisition phase of a typical ASF task. Given a polygonal target region to be surveyed, the region is subdivided according to the number of vessels in the fleet. The subdivision algorithm seeks a solution in which all subregions have equal area and minimum mean radius. Once the subregions are defined, a dynamic programming method is used to find a minimum-time path for each vessel from its initial position to its assigned region. This path plan includes the effects of water currents as well as avoidance of known obstacles. A fleet-level planning algorithm then shuffles the individual vessel assignments to find the overall solution which puts all vessels in their assigned regions in the minimum time. This shuffle algorithm may be described as a process of elimination on the sorted list of permutations of a cost matrix. All these path planning algorithms are facilitated by discretizing the region of interest onto a hexagonal tiling.

  4. A Competency-Based Guided-Learning Algorithm Applied on Adaptively Guiding E-Learning

    ERIC Educational Resources Information Center

    Hsu, Wei-Chih; Li, Cheng-Hsiu

    2015-01-01

    This paper presents a new algorithm called competency-based guided-learning algorithm (CBGLA), which can be applied on adaptively guiding e-learning. Computational process analysis and mathematical derivation of competency-based learning (CBL) were used to develop the CBGLA. The proposed algorithm could generate an effective adaptively guiding…

  5. Anatomically constrained minimum variance beamforming applied to EEG.

    PubMed

    Murzin, Vyacheslav; Fuchs, Armin; Kelso, J A Scott

    2011-10-01

    Neural activity as measured non-invasively using electroencephalography (EEG) or magnetoencephalography (MEG) originates in the cortical gray matter. In the cortex, pyramidal cells are organized in columns and activated coherently, leading to current flow perpendicular to the cortical surface. In recent years, beamforming algorithms have been developed, which use this property as an anatomical constraint for the locations and directions of potential sources in MEG data analysis. Here, we extend this work to EEG recordings, which require a more sophisticated forward model due to the blurring of the electric current at tissue boundaries where the conductivity changes. Using CT scans, we create a realistic three-layer head model consisting of tessellated surfaces that represent the cerebrospinal fluid-skull, skull-scalp, and scalp-air boundaries. The cortical gray matter surface, the anatomical constraint for the source dipoles, is extracted from MRI scans. EEG beamforming is implemented on simulated sets of EEG data for three different head models: single spherical, multi-shell spherical, and multi-shell realistic. Using the same conditions for simulated EEG and MEG data, it is shown (and quantified by receiver operating characteristic analysis) that EEG beamforming detects radially oriented sources, to which MEG lacks sensitivity. By merging several techniques, such as linearly constrained minimum variance beamforming, realistic geometry forward solutions, and cortical constraints, we demonstrate it is possible to localize and estimate the dynamics of dipolar and spatially extended (distributed) sources of neural activity.

  6. Adaptively wavelet-based image denoising algorithm with edge preserving

    NASA Astrophysics Data System (ADS)

    Tan, Yihua; Tian, Jinwen; Liu, Jian

    2006-02-01

    A new wavelet-based image denoising algorithm, which exploits the edge information hidden in the corrupted image, is presented. Firstly, a canny-like edge detector identifies the edges in each subband. Secondly, multiplying the wavelet coefficients in neighboring scales is implemented to suppress the noise while magnifying the edge information, and the result is utilized to exclude the fake edges. The isolated edge pixel is also identified as noise. Unlike the thresholding method, after that we use local window filter in the wavelet domain to remove noise in which the variance estimation is elaborated to utilize the edge information. This method is adaptive to local image details, and can achieve better performance than the methods of state of the art.

  7. Algorithms and data structures for adaptive multigrid elliptic solvers

    NASA Technical Reports Server (NTRS)

    Vanrosendale, J.

    1983-01-01

    Adaptive refinement and the complicated data structures required to support it are discussed. These data structures must be carefully tuned, especially in three dimensions where the time and storage requirements of algorithms are crucial. Another major issue is grid generation. The options available seem to be curvilinear fitted grids, constructed on iterative graphics systems, and unfitted Cartesian grids, which can be constructed automatically. On several grounds, including storage requirements, the second option seems preferrable for the well behaved scalar elliptic problems considered here. A variety of techniques for treatment of boundary conditions on such grids are reviewed. A new approach, which may overcome some of the difficulties encountered with previous approaches, is also presented.

  8. A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation.

    PubMed

    Jiang, Chen; Zhang, Shu-Bi; Zhang, Qiu-Zhao

    2016-12-19

    The Kalman filter is an optimal estimator with numerous applications in technology, especially in systems with Gaussian distributed noise. Moreover, the adaptive Kalman filtering algorithms, based on the Kalman filter, can control the influence of dynamic model errors. In contrast to the adaptive Kalman filtering algorithms, the H-infinity filter is able to address the interference of the stochastic model by minimization of the worst-case estimation error. In this paper, a novel adaptive H-infinity filtering algorithm, which integrates the adaptive Kalman filter and the H-infinity filter in order to perform a comprehensive filtering algorithm, is presented. In the proposed algorithm, a robust estimation method is employed to control the influence of outliers. In order to verify the proposed algorithm, experiments with real data of the Global Positioning System (GPS) and Inertial Navigation System (INS) integrated navigation, were conducted. The experimental results have shown that the proposed algorithm has multiple advantages compared to the other filtering algorithms.

  9. A low power, area efficient fpga based beamforming technique for 1-D CMUT arrays.

    PubMed

    Joseph, Bastin; Joseph, Jose; Vanjari, Siva Rama Krishna

    2015-08-01

    A low power area efficient digital beamformer targeting low frequency (2MHz) 1-D linear Capacitive Micromachined Ultrasonic Transducer (CMUT) array is developed. While designing the beamforming logic, the symmetry of the CMUT array is well exploited to reduce the area and power consumption. The proposed method is verified in Matlab by clocking an Arbitrary Waveform Generator(AWG). The architecture is successfully implemented in Xilinx Spartan 3E FPGA kit to check its functionality. The beamforming logic is implemented for 8, 16, 32, and 64 element CMUTs targeting Application Specific Integrated Circuit (ASIC) platform at Vdd 1.62V for UMC 90nm technology. It is observed that the proposed architecture consumes significantly lesser power and area (1.2895 mW power and 47134.4 μm(2) area for a 64 element digital beamforming circuit) compared to the conventional square root based algorithm.

  10. An adaptive /N-body algorithm of optimal order

    NASA Astrophysics Data System (ADS)

    Pruett, C. David; Rudmin, Joseph W.; Lacy, Justin M.

    2003-05-01

    Picard iteration is normally considered a theoretical tool whose primary utility is to establish the existence and uniqueness of solutions to first-order systems of ordinary differential equations (ODEs). However, in 1996, Parker and Sochacki [Neural, Parallel, Sci. Comput. 4 (1996)] published a practical numerical method for a certain class of ODEs, based upon modified Picard iteration, that generates the Maclaurin series of the solution to arbitrarily high order. The applicable class of ODEs consists of first-order, autonomous systems whose right-hand side functions (generators) are projectively polynomial; that is, they can be written as polynomials in the unknowns. The class is wider than might be expected. The method is ideally suited to the classical N-body problem, which is projectively polynomial. Here, we recast the N-body problem in polynomial form and develop a Picard-based algorithm for its solution. The algorithm is highly accurate, parameter-free, and simultaneously adaptive in time and order. Test cases for both benign and chaotic N-body systems reveal that optimal order is dynamic. That is, in addition to dependency upon N and the desired accuracy, optimal order depends upon the configuration of the bodies at any instant.

  11. Design of infrasound-detection system via adaptive LMSTDE algorithm

    NASA Technical Reports Server (NTRS)

    Khalaf, C. S.; Stoughton, J. W.

    1984-01-01

    A proposed solution to an aviation safety problem is based on passive detection of turbulent weather phenomena through their infrasonic emission. This thesis describes a system design that is adequate for detection and bearing evaluation of infrasounds. An array of four sensors, with the appropriate hardware, is used for the detection part. Bearing evaluation is based on estimates of time delays between sensor outputs. The generalized cross correlation (GCC), as the conventional time-delay estimation (TDE) method, is first reviewed. An adaptive TDE approach, using the least mean square (LMS) algorithm, is then discussed. A comparison between the two techniques is made and the advantages of the adaptive approach are listed. The behavior of the GCC, as a Roth processor, is examined for the anticipated signals. It is shown that the Roth processor has the desired effect of sharpening the peak of the correlation function. It is also shown that the LMSTDE technique is an equivalent implementation of the Roth processor in the time domain. A LMSTDE lead-lag model, with a variable stability coefficient and a convergence criterion, is designed.

  12. The Adaptive Analysis of Visual Cognition using Genetic Algorithms

    PubMed Central

    Cook, Robert G.; Qadri, Muhammad A. J.

    2014-01-01

    Two experiments used a novel, open-ended, and adaptive test procedure to examine visual cognition in animals. Using a genetic algorithm, a pigeon was tested repeatedly from a variety of different initial conditions for its solution to an intermediate brightness search task. On each trial, the animal had to accurately locate and peck a target element of intermediate brightness from among a variable number of surrounding darker and lighter distractor elements. Displays were generated from six parametric variables, or genes (distractor number, element size, shape, spacing, target brightness, distractor brightness). Display composition changed over time, or evolved, as a function of the bird’s differential accuracy within the population of values for each gene. Testing three randomized initial conditions and one set of controlled initial conditions, element size and number of distractors were identified as the most important factors controlling search accuracy, with distractor brightness, element shape, and spacing making secondary contributions. The resulting changes in this multidimensional stimulus space suggested the existence of a set of conditions that the bird repeatedly converged upon regardless of initial conditions. This psychological “attractor” represents the cumulative action of the cognitive operations used by the pigeon in solving and performing this search task. The results are discussed regarding their implications for visual cognition in pigeons and the usefulness of adaptive, subject-driven experimentation for investigating human and animal cognition more generally. PMID:24000905

  13. An Adaptive Mesh Algorithm: Mesh Structure and Generation

    SciTech Connect

    Scannapieco, Anthony J.

    2016-06-21

    The purpose of Adaptive Mesh Refinement is to minimize spatial errors over the computational space not to minimize the number of computational elements. The additional result of the technique is that it may reduce the number of computational elements needed to retain a given level of spatial accuracy. Adaptive mesh refinement is a computational technique used to dynamically select, over a region of space, a set of computational elements designed to minimize spatial error in the computational model of a physical process. The fundamental idea is to increase the mesh resolution in regions where the physical variables are represented by a broad spectrum of modes in k-space, hence increasing the effective global spectral coverage of those physical variables. In addition, the selection of the spatially distributed elements is done dynamically by cyclically adjusting the mesh to follow the spectral evolution of the system. Over the years three types of AMR schemes have evolved; block, patch and locally refined AMR. In block and patch AMR logical blocks of various grid sizes are overlaid to span the physical space of interest, whereas in locally refined AMR no logical blocks are employed but locally nested mesh levels are used to span the physical space. The distinction between block and patch AMR is that in block AMR the original blocks refine and coarsen entirely in time, whereas in patch AMR the patches change location and zone size with time. The type of AMR described herein is a locally refi ned AMR. In the algorithm described, at any point in physical space only one zone exists at whatever level of mesh that is appropriate for that physical location. The dynamic creation of a locally refi ned computational mesh is made practical by a judicious selection of mesh rules. With these rules the mesh is evolved via a mesh potential designed to concentrate the nest mesh in regions where the physics is modally dense, and coarsen zones in regions where the physics is modally

  14. Evaluating Knowledge Structure-Based Adaptive Testing Algorithms and System Development

    ERIC Educational Resources Information Center

    Wu, Huey-Min; Kuo, Bor-Chen; Yang, Jinn-Min

    2012-01-01

    In recent years, many computerized test systems have been developed for diagnosing students' learning profiles. Nevertheless, it remains a challenging issue to find an adaptive testing algorithm to both shorten testing time and precisely diagnose the knowledge status of students. In order to find a suitable algorithm, four adaptive testing…

  15. Adaptive reference update (ARU) algorithm. A stochastic search algorithm for efficient optimization of multi-drug cocktails

    PubMed Central

    2012-01-01

    Background Multi-target therapeutics has been shown to be effective for treating complex diseases, and currently, it is a common practice to combine multiple drugs to treat such diseases to optimize the therapeutic outcomes. However, considering the huge number of possible ways to mix multiple drugs at different concentrations, it is practically difficult to identify the optimal drug combination through exhaustive testing. Results In this paper, we propose a novel stochastic search algorithm, called the adaptive reference update (ARU) algorithm, that can provide an efficient and systematic way for optimizing multi-drug cocktails. The ARU algorithm iteratively updates the drug combination to improve its response, where the update is made by comparing the response of the current combination with that of a reference combination, based on which the beneficial update direction is predicted. The reference combination is continuously updated based on the drug response values observed in the past, thereby adapting to the underlying drug response function. To demonstrate the effectiveness of the proposed algorithm, we evaluated its performance based on various multi-dimensional drug functions and compared it with existing algorithms. Conclusions Simulation results show that the ARU algorithm significantly outperforms existing stochastic search algorithms, including the Gur Game algorithm. In fact, the ARU algorithm can more effectively identify potent drug combinations and it typically spends fewer iterations for finding effective combinations. Furthermore, the ARU algorithm is robust to random fluctuations and noise in the measured drug response, which makes the algorithm well-suited for practical drug optimization applications. PMID:23134742

  16. Multi-element array signal reconstruction with adaptive least-squares algorithms

    NASA Technical Reports Server (NTRS)

    Kumar, R.

    1992-01-01

    Two versions of the adaptive least-squares algorithm are presented for combining signals from multiple feeds placed in the focal plane of a mechanical antenna whose reflector surface is distorted due to various deformations. Coherent signal combining techniques based on the adaptive least-squares algorithm are examined for nearly optimally and adaptively combining the outputs of the feeds. The performance of the two versions is evaluated by simulations. It is demonstrated for the example considered that both of the adaptive least-squares algorithms are capable of offsetting most of the loss in the antenna gain incurred due to reflector surface deformations.

  17. Sparse diffraction imaging method using an adaptive reweighting homotopy algorithm

    NASA Astrophysics Data System (ADS)

    Yu, Caixia; Zhao, Jingtao; Wang, Yanfei; Qiu, Zhen

    2017-02-01

    Seismic diffractions carry valuable information from subsurface small-scale geologic discontinuities, such as faults, cavities and other features associated with hydrocarbon reservoirs. However, seismic imaging methods mainly use reflection theory for constructing imaging models, which means a smooth constraint on imaging conditions. In fact, diffractors occupy a small account of distributions in an imaging model and possess discontinuous characteristics. In mathematics, this kind of phenomena can be described by the sparse optimization theory. Therefore, we propose a diffraction imaging method based on a sparsity-constraint model for studying diffractors. A reweighted L 2-norm and L 1-norm minimization model is investigated, where the L 2 term requests a least-square error between modeled diffractions and observed diffractions and the L 1 term imposes sparsity on the solution. In order to efficiently solve this model, we use an adaptive reweighting homotopy algorithm that updates the solutions by tracking a path along inexpensive homotopy steps. Numerical examples and field data application demonstrate the feasibility of the proposed method and show its significance for detecting small-scale discontinuities in a seismic section. The proposed method has an advantage in improving the focusing ability of diffractions and reducing the migration artifacts.

  18. Rate-Constrained Beamforming in Binaural Hearing Aids

    NASA Astrophysics Data System (ADS)

    Srinivasan, Sriram; den Brinker, Albertus C.

    2009-12-01

    Recently, hearing aid systems where the left and right ear devices collaborate with one another have received much attention. Apart from supporting natural binaural hearing, such systems hold great potential for improving the intelligibility of speech in the presence of noise through beamforming algorithms. Binaural beamforming for hearing aids requires an exchange of microphone signals between the two devices over a wireless link. This paper studies two problems: which signal to transmit from one ear to the other, and at what bit-rate. The first problem is relevant as modern hearing aids usually contain multiple microphones, and the optimal choice for the signal to be transmitted is not obvious. The second problem is relevant as the capacity of the wireless link is limited by stringent power consumption constraints imposed by the limited battery life of hearing aids.

  19. Accounting for microsaccadic artifacts in the EEG using independent component analysis and beamforming.

    PubMed

    Craddock, Matt; Martinovic, Jasna; Müller, Matthias M

    2016-04-01

    Neuronal activity in the gamma-band range was long considered a marker of object representation. However, scalp-recorded EEG activity in this range is contaminated by a miniature saccade-related muscle artifact. Independent component analysis (ICA) has been proposed as a method of removal of such artifacts. Alternatively, beamforming, a source analysis method in which potential sources of activity across the whole brain are scanned independently through the use of adaptive spatial filters, offers a promising method of accounting for the artifact without relying on its explicit removal. We present here the application of ICA-based correction to a previously published dataset. Then, using beamforming, we examine the effect of ICA correction on the scalp-recorded EEG signal and the extent to which genuine activity is recoverable before and after ICA correction. We find that beamforming attributes much of the scalp-recorded gamma-band signal before correction to deep frontal sources, likely the eye muscles, which generate the artifact related to each miniature saccade. Beamforming confirms that what is removed by ICA is predominantly this artifactual signal, and that what remains after correction plausibly originates in the visual cortex. Thus, beamforming allows researchers to confirm whether their removal procedures successfully removed the artifact. Our results demonstrate that ICA-based correction brings about general improvements in signal-to-noise ratio suggesting it should be used along with, rather than be replaced by, beamforming.

  20. Eigenspace based minimum variance beamforming applied to ultrasound imaging of acoustically hard tissues.

    PubMed

    Mehdizadeh, Saeed; Austeng, Andreas; Johansen, Tonni F; Holm, Sverre

    2012-10-01

    Minimum variance (MV) based beamforming techniques have been successfully applied to medical ultrasound imaging. These adaptive methods offer higher lateral resolution, lower sidelobes, and better definition of edges compared to delay and sum beamforming (DAS). In standard medical ultrasound, the bone surface is often visualized poorly, and the boundaries region appears unclear. This may happen due to fundamental limitations of the DAS beamformer, and different artifacts due to, e.g., specular reflection, and shadowing. The latter can degrade the robustness of the MV beamformers as the statistics across the imaging aperture is violated because of the obstruction of the imaging beams. In this study, we employ forward/backward averaging to improve the robustness of the MV beamforming techniques. Further, we use an eigen-spaced minimum variance technique (ESMV) to enhance the edge detection of hard tissues. In simulation, in vitro, and in vivo studies, we show that performance of the ESMV beamformer depends on estimation of the signal subspace rank. The lower ranks of the signal subspace can enhance edges and reduce noise in ultrasound images but the speckle pattern can be distorted.

  1. Parameter estimation for chaotic systems using a hybrid adaptive cuckoo search with simulated annealing algorithm.

    PubMed

    Sheng, Zheng; Wang, Jun; Zhou, Shudao; Zhou, Bihua

    2014-03-01

    This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.

  2. Parameter estimation for chaotic systems using a hybrid adaptive cuckoo search with simulated annealing algorithm

    NASA Astrophysics Data System (ADS)

    Sheng, Zheng; Wang, Jun; Zhou, Shudao; Zhou, Bihua

    2014-03-01

    This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.

  3. Parameter estimation for chaotic systems using a hybrid adaptive cuckoo search with simulated annealing algorithm

    SciTech Connect

    Sheng, Zheng; Wang, Jun; Zhou, Bihua; Zhou, Shudao

    2014-03-15

    This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.

  4. F-k Domain Imaging for Synthetic Aperture Sequential Beamforming.

    PubMed

    Vos, Hendrik J; van Neer, Paul L M J; Mota, Mariana Melo; Verweij, Martin D; van der Steen, Antonius F W; Volker, Arno W F

    2016-01-01

    beamforming. The signal-to-noise ratio increased by 6 dB in both the near field and far field. These results show that the second-stage processing algorithm effectively produces a focused image over the entire depth range from a single-focused ultrasound field.

  5. Adaptive Estimation and Parameter Identification Using Multiple Model Estimation Algorithm

    DTIC Science & Technology

    1976-06-23

    Point Continuous Linear Smoothing ," Proc. Joint Automatic Control Conf., June 1967, pp. 249-257. [26] J. S. Meditch , "On Optimal Linear Smoothing ...Theory," Infor- mation and Control, 10, 598-615 (1967). [27] J. S. Meditch , "A Successive Approximation Procedure for Nonlinear Data Smoothing ," Proc...algorithm Kalman filter algorithms multiple model smoothing algorithm 70. ABSTRACT (Coensnia• en rever.e side if eceossuy Adidonilty by block nu.wbe

  6. Fast Minimum Variance Beamforming Based on Legendre Polynomials.

    PubMed

    Bae, MooHo; Park, Sung Bae; Kwon, Sung Jae

    2016-09-01

    Currently, minimum variance beamforming (MV) is actively investigated as a method that can improve the performance of an ultrasound beamformer, in terms of the lateral and contrast resolution. However, this method has the disadvantage of excessive computational complexity since the inverse spatial covariance matrix must be calculated. Some noteworthy methods among various attempts to solve this problem include beam space adaptive beamforming methods and the fast MV method based on principal component analysis, which are similar in that the original signal in the element space is transformed to another domain using an orthonormal basis matrix and the dimension of the covariance matrix is reduced by approximating the matrix only with important components of the matrix, hence making the inversion of the matrix very simple. Recently, we proposed a new method with further reduced computational demand that uses Legendre polynomials as the basis matrix for such a transformation. In this paper, we verify the efficacy of the proposed method through Field II simulations as well as in vitro and in vivo experiments. The results show that the approximation error of this method is less than or similar to those of the above-mentioned methods and that the lateral response of point targets and the contrast-to-speckle noise in anechoic cysts are also better than or similar to those methods when the dimensionality of the covariance matrices is reduced to the same dimension.

  7. Selective Reverberation Cancellation via Adaptive Beamforming

    DTIC Science & Technology

    1985-12-01

    Dr. Milstein for his constant encouragement and support. I also thank John Mclnerney for his expert management of the MPLVAX computer system and Eric...Rick Brienzo for painstakingly review- ing an early draft of this thesis and Allan Sauter, Barbara Sotirin and Robin Williams for providing me with...Professors Victor C. Anderson and Fred N. Spiess Studies in Digital Signal Processing. Professor William S. Hodgkiss XIV ABSTRACT OF THE

  8. Subspace Estimation Without Eigenvectors for Adaptive Beamforming

    DTIC Science & Technology

    1993-12-01

    rdsultats lorsque le nombre de brouilleurs est trts faible par rapport au nombre d𔃾ldments, la performance de la technique de Yeh se d6grade rapidement...lorsque le nombre de brouilleurs augmente. Nous am~liorons la mithode en lu ajoutant tin scuil cc qui permet d’optimiser la valeur de N en augmentant le

  9. Experiences with an adaptive mesh refinement algorithm in numerical relativity.

    NASA Astrophysics Data System (ADS)

    Choptuik, M. W.

    An implementation of the Berger/Oliger mesh refinement algorithm for a model problem in numerical relativity is described. The principles of operation of the method are reviewed and its use in conjunction with leap-frog schemes is considered. The performance of the algorithm is illustrated with results from a study of the Einstein/massless scalar field equations in spherical symmetry.

  10. A novel algorithm for real-time adaptive signal detection and identification

    SciTech Connect

    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.

  11. Binocular self-calibration performed via adaptive genetic algorithm based on laser line imaging

    NASA Astrophysics Data System (ADS)

    Apolinar Muñoz Rodríguez, J.; Mejía Alanís, Francisco Carlos

    2016-07-01

    An accurate technique to perform binocular self-calibration by means of an adaptive genetic algorithm based on a laser line is presented. In this calibration, the genetic algorithm computes the vision parameters through simulated binary crossover (SBX). To carry it out, the genetic algorithm constructs an objective function from the binocular geometry of the laser line projection. Then, the SBX minimizes the objective function via chromosomes recombination. In this algorithm, the adaptive procedure determines the search space via line position to obtain the minimum convergence. Thus, the chromosomes of vision parameters provide the minimization. The approach of the proposed adaptive genetic algorithm is to calibrate and recalibrate the binocular setup without references and physical measurements. This procedure leads to improve the traditional genetic algorithms, which calibrate the vision parameters by means of references and an unknown search space. It is because the proposed adaptive algorithm avoids errors produced by the missing of references. Additionally, the three-dimensional vision is carried out based on the laser line position and vision parameters. The contribution of the proposed algorithm is corroborated by an evaluation of accuracy of binocular calibration, which is performed via traditional genetic algorithms.

  12. Design and analysis of closed-loop decoder adaptation algorithms for brain-machine interfaces.

    PubMed

    Dangi, Siddharth; Orsborn, Amy L; Moorman, Helene G; Carmena, Jose M

    2013-07-01

    Closed-loop decoder adaptation (CLDA) is an emerging paradigm for achieving rapid performance improvements in online brain-machine interface (BMI) operation. Designing an effective CLDA algorithm requires making multiple important decisions, including choosing the timescale of adaptation, selecting which decoder parameters to adapt, crafting the corresponding update rules, and designing CLDA parameters. These design choices, combined with the specific settings of CLDA parameters, will directly affect the algorithm's ability to make decoder parameters converge to values that optimize performance. In this article, we present a general framework for the design and analysis of CLDA algorithms and support our results with experimental data of two monkeys performing a BMI task. First, we analyze and compare existing CLDA algorithms to highlight the importance of four critical design elements: the adaptation timescale, selective parameter adaptation, smooth decoder updates, and intuitive CLDA parameters. Second, we introduce mathematical convergence analysis using measures such as mean-squared error and KL divergence as a useful paradigm for evaluating the convergence properties of a prototype CLDA algorithm before experimental testing. By applying these measures to an existing CLDA algorithm, we demonstrate that our convergence analysis is an effective analytical tool that can ultimately inform and improve the design of CLDA algorithms.

  13. Adaptive Load-Balancing Algorithms using Symmetric Broadcast Networks

    NASA Technical Reports Server (NTRS)

    Das, Sajal K.; Harvey, Daniel J.; Biswas, Rupak; Biegel, Bryan A. (Technical Monitor)

    2002-01-01

    In a distributed computing environment, it is important to ensure that the processor workloads are adequately balanced, Among numerous load-balancing algorithms, a unique approach due to Das and Prasad defines a symmetric broadcast network (SBN) that provides a robust communication pattern among the processors in a topology-independent manner. In this paper, we propose and analyze three efficient SBN-based dynamic load-balancing algorithms, and implement them on an SGI Origin2000. A thorough experimental study with Poisson distributed synthetic loads demonstrates that our algorithms are effective in balancing system load. By optimizing completion time and idle time, the proposed algorithms are shown to compare favorably with several existing approaches.

  14. Comparison of several stochastic parallel optimization algorithms for adaptive optics system without a wavefront sensor

    NASA Astrophysics Data System (ADS)

    Yang, Huizhen; Li, Xinyang

    2011-04-01

    Optimizing the system performance metric directly is an important method for correcting wavefront aberrations in an adaptive optics (AO) system where wavefront sensing methods are unavailable or ineffective. An appropriate "Deformable Mirror" control algorithm is the key to successful wavefront correction. Based on several stochastic parallel optimization control algorithms, an adaptive optics system with a 61-element Deformable Mirror (DM) is simulated. Genetic Algorithm (GA), Stochastic Parallel Gradient Descent (SPGD), Simulated Annealing (SA) and Algorithm Of Pattern Extraction (Alopex) are compared in convergence speed and correction capability. The results show that all these algorithms have the ability to correct for atmospheric turbulence. Compared with least squares fitting, they almost obtain the best correction achievable for the 61-element DM. SA is the fastest and GA is the slowest in these algorithms. The number of perturbation by GA is almost 20 times larger than that of SA, 15 times larger than SPGD and 9 times larger than Alopex.

  15. A Hybrid Adaptive Routing Algorithm for Event-Driven Wireless Sensor Networks

    PubMed Central

    Figueiredo, Carlos M. S.; Nakamura, Eduardo F.; Loureiro, Antonio A. F.

    2009-01-01

    Routing is a basic function in wireless sensor networks (WSNs). For these networks, routing algorithms depend on the characteristics of the applications and, consequently, there is no self-contained algorithm suitable for every case. In some scenarios, the network behavior (traffic load) may vary a lot, such as an event-driven application, favoring different algorithms at different instants. This work presents a hybrid and adaptive algorithm for routing in WSNs, called Multi-MAF, that adapts its behavior autonomously in response to the variation of network conditions. In particular, the proposed algorithm applies both reactive and proactive strategies for routing infrastructure creation, and uses an event-detection estimation model to change between the strategies and save energy. To show the advantages of the proposed approach, it is evaluated through simulations. Comparisons with independent reactive and proactive algorithms show improvements on energy consumption. PMID:22423207

  16. Inversion for Refractivity Parameters Using a Dynamic Adaptive Cuckoo Search with Crossover Operator Algorithm.

    PubMed

    Zhang, Zhihua; Sheng, Zheng; Shi, Hanqing; Fan, Zhiqiang

    2016-01-01

    Using the RFC technique to estimate refractivity parameters is a complex nonlinear optimization problem. In this paper, an improved cuckoo search (CS) algorithm is proposed to deal with this problem. To enhance the performance of the CS algorithm, a parameter dynamic adaptive operation and crossover operation were integrated into the standard CS (DACS-CO). Rechenberg's 1/5 criteria combined with learning factor were used to control the parameter dynamic adaptive adjusting process. The crossover operation of genetic algorithm was utilized to guarantee the population diversity. The new hybrid algorithm has better local search ability and contributes to superior performance. To verify the ability of the DACS-CO algorithm to estimate atmospheric refractivity parameters, the simulation data and real radar clutter data are both implemented. The numerical experiments demonstrate that the DACS-CO algorithm can provide an effective method for near-real-time estimation of the atmospheric refractivity profile from radar clutter.

  17. Inversion for Refractivity Parameters Using a Dynamic Adaptive Cuckoo Search with Crossover Operator Algorithm

    PubMed Central

    Zhang, Zhihua; Sheng, Zheng; Shi, Hanqing; Fan, Zhiqiang

    2016-01-01

    Using the RFC technique to estimate refractivity parameters is a complex nonlinear optimization problem. In this paper, an improved cuckoo search (CS) algorithm is proposed to deal with this problem. To enhance the performance of the CS algorithm, a parameter dynamic adaptive operation and crossover operation were integrated into the standard CS (DACS-CO). Rechenberg's 1/5 criteria combined with learning factor were used to control the parameter dynamic adaptive adjusting process. The crossover operation of genetic algorithm was utilized to guarantee the population diversity. The new hybrid algorithm has better local search ability and contributes to superior performance. To verify the ability of the DACS-CO algorithm to estimate atmospheric refractivity parameters, the simulation data and real radar clutter data are both implemented. The numerical experiments demonstrate that the DACS-CO algorithm can provide an effective method for near-real-time estimation of the atmospheric refractivity profile from radar clutter. PMID:27212938

  18. Efficient Algorithm for Optimizing Adaptive Quantum Metrology Processes

    NASA Astrophysics Data System (ADS)

    Hentschel, Alexander; Sanders, Barry C.

    2011-12-01

    Quantum-enhanced metrology infers an unknown quantity with accuracy beyond the standard quantum limit (SQL). Feedback-based metrological techniques are promising for beating the SQL but devising the feedback procedures is difficult and inefficient. Here we introduce an efficient self-learning swarm-intelligence algorithm for devising feedback-based quantum metrological procedures. Our algorithm can be trained with simulated or real-world trials and accommodates experimental imperfections, losses, and decoherence.

  19. A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation

    PubMed Central

    Jiang, Chen; Zhang, Shu-Bi; Zhang, Qiu-Zhao

    2016-01-01

    The Kalman filter is an optimal estimator with numerous applications in technology, especially in systems with Gaussian distributed noise. Moreover, the adaptive Kalman filtering algorithms, based on the Kalman filter, can control the influence of dynamic model errors. In contrast to the adaptive Kalman filtering algorithms, the H-infinity filter is able to address the interference of the stochastic model by minimization of the worst-case estimation error. In this paper, a novel adaptive H-infinity filtering algorithm, which integrates the adaptive Kalman filter and the H-infinity filter in order to perform a comprehensive filtering algorithm, is presented. In the proposed algorithm, a robust estimation method is employed to control the influence of outliers. In order to verify the proposed algorithm, experiments with real data of the Global Positioning System (GPS) and Inertial Navigation System (INS) integrated navigation, were conducted. The experimental results have shown that the proposed algorithm has multiple advantages compared to the other filtering algorithms. PMID:27999361

  20. SIMULATION OF DISPERSION OF A POWER PLANT PLUME USING AN ADAPTIVE GRID ALGORITHM. (R827028)

    EPA Science Inventory

    A new dynamic adaptive grid algorithm has been developed for use in air quality modeling. This algorithm uses a higher order numerical scheme––the piecewise parabolic method (PPM)––for computing advective solution fields; a weight function capable o...

  1. SIMULATION OF DISPERSION OF A POWER PLANT PLUME USING AN ADAPTIVE GRID ALGORITHM

    EPA Science Inventory

    A new dynamic adaptive grid algorithm has been developed for use in air quality modeling. This algorithm uses a higher order numerical scheme?the piecewise parabolic method (PPM)?for computing advective solution fields; a weight function capable of promoting grid node clustering ...

  2. Beamformer for simultaneous magnetoencephalography and electroencephalography analysis

    NASA Astrophysics Data System (ADS)

    Ko, Seokha; Jun, Sung Chan

    2010-05-01

    We proposed the beamformer for simultaneous magnetoencephalography (MEG)/electroencephalography (EEG) analysis which has the synergy effects such as high spatial resolution, low localization bias and robustness for orientation of brain sources. Through Monte Carlo simulation study, it was found that the localization performance of our proposed beamformer was far superior to those of MEG-only and EEG-only. For the given specific sensor geometry (160 MEG, 50 EEG sensors), we investigated comparative localization performance of our proposed beamformer over various weighting factors of MEG data, while weighting factor of EEG keeps fixed. Furthermore, we demonstrated its capability for simulated two dipole problem and empirical somatosensory median nerve stimulation data.

  3. Adaptive Filtering in the Wavelet Transform Domain via Genetic Algorithms

    DTIC Science & Technology

    2004-08-06

    identification. Figure 1 shows a very basic example of this type of system . x(n) Figure 1. Basic system identification using adaptive filters block diagram...block diagram of adaptive wavelet filtering system . The main objective of the system shown in Figure 2 is to minimize the error signal, e(k), which is...in Table 1. Daub4 wavelets use filter banks (Vaidyanathan 1992) containing exactly four elements. 5 Figure 4. Time-Domain Representation of

  4. Improving GPU-accelerated adaptive IDW interpolation algorithm using fast kNN search.

    PubMed

    Mei, Gang; Xu, Nengxiong; Xu, Liangliang

    2016-01-01

    This paper presents an efficient parallel Adaptive Inverse Distance Weighting (AIDW) interpolation algorithm on modern Graphics Processing Unit (GPU). The presented algorithm is an improvement of our previous GPU-accelerated AIDW algorithm by adopting fast k-nearest neighbors (kNN) search. In AIDW, it needs to find several nearest neighboring data points for each interpolated point to adaptively determine the power parameter; and then the desired prediction value of the interpolated point is obtained by weighted interpolating using the power parameter. In this work, we develop a fast kNN search approach based on the space-partitioning data structure, even grid, to improve the previous GPU-accelerated AIDW algorithm. The improved algorithm is composed of the stages of kNN search and weighted interpolating. To evaluate the performance of the improved algorithm, we perform five groups of experimental tests. The experimental results indicate: (1) the improved algorithm can achieve a speedup of up to 1017 over the corresponding serial algorithm; (2) the improved algorithm is at least two times faster than our previous GPU-accelerated AIDW algorithm; and (3) the utilization of fast kNN search can significantly improve the computational efficiency of the entire GPU-accelerated AIDW algorithm.

  5. An Adaptive Data Collection Algorithm Based on a Bayesian Compressed Sensing Framework

    PubMed Central

    Liu, Zhi; Zhang, Mengmeng; Cui, Jian

    2014-01-01

    For Wireless Sensor Networks, energy efficiency is always a key consideration in system design. Compressed sensing is a new theory which has promising prospects in WSNs. However, how to construct a sparse projection matrix is a problem. In this paper, based on a Bayesian compressed sensing framework, a new adaptive algorithm which can integrate routing and data collection is proposed. By introducing new target node selection metrics, embedding the routing structure and maximizing the differential entropy for each collection round, an adaptive projection vector is constructed. Simulations show that compared to reference algorithms, the proposed algorithm can decrease computation complexity and improve energy efficiency. PMID:24818659

  6. Feature Selection for Natural Language Call Routing Based on Self-Adaptive Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Koromyslova, A.; Semenkina, M.; Sergienko, R.

    2017-02-01

    The text classification problem for natural language call routing was considered in the paper. Seven different term weighting methods were applied. As dimensionality reduction methods, the feature selection based on self-adaptive GA is considered. k-NN, linear SVM and ANN were used as classification algorithms. The tasks of the research are the following: perform research of text classification for natural language call routing with different term weighting methods and classification algorithms and investigate the feature selection method based on self-adaptive GA. The numerical results showed that the most effective term weighting is TRR. The most effective classification algorithm is ANN. Feature selection with self-adaptive GA provides improvement of classification effectiveness and significant dimensionality reduction with all term weighting methods and with all classification algorithms.

  7. Study on adaptive PID algorithm of hydraulic turbine governing system based on fuzzy neural network

    NASA Astrophysics Data System (ADS)

    Tang, Liangbao; Bao, Jumin

    2006-11-01

    The conventional hydraulic turbine governing system can't automatically modulate PID parameters according to the dynamic process of the system, the generator speed is unstable and the mains frequency fluctuation results in. To solve the above problem, the fuzzy neural network (FNN) and the adaptive control are combined to design an adaptive PID algorithm based on the fuzzy neural network which can effectively control the hydraulic turbine governing system. Finally, the improved mathematic model is simulated. The simulation results are compared with the conventional hydraulic turbine's. Thus the validity and superiority of the fuzzy neural network PID algorithm have been proved. The simulation results show that the algorithm not only retains the functions of fuzzy control, but also provides the ability to approach to the non-linear system. Also the dynamic process of the system can be reflected more precisely and the on-line adaptive control is implemented. The algorithm is superior to other methods in response and control effect.

  8. An Adaptive Inpainting Algorithm Based on DCT Induced Wavelet Regularization

    DTIC Science & Technology

    2013-01-01

    differentiable and its gradient is Lipschitz continuous. This property is particularly important in developing a fast and efficient numerical algorithm for...with Lipschitz continuous gra- dient L(ψ), i.e., ∥∇ψ(f1) − ∇ψ(f2)∥2 ≤ L(ψ)∥f1 − f2∥2 for every f1, f2 ∈ Rn. The corresponding APG algorithm proposed in...entries are uniformly distributed on the interval [0, 255]; 2) Take u1 = f0 and L = L(ψ) as a Lipschitz constant of ∇ψ; 3) For k = 1, 2, . . ., compute a

  9. Establishing a Dynamic Self-Adaptation Learning Algorithm of the BP Neural Network and Its Applications

    NASA Astrophysics Data System (ADS)

    Li, Xiaofeng; Xiang, Suying; Zhu, Pengfei; Wu, Min

    2015-12-01

    In order to avoid the inherent deficiencies of the traditional BP neural network, such as slow convergence speed, that easily leading to local minima, poor generalization ability and difficulty in determining the network structure, the dynamic self-adaptive learning algorithm of the BP neural network is put forward to improve the function of the BP neural network. The new algorithm combines the merit of principal component analysis, particle swarm optimization, correlation analysis and self-adaptive model, hence can effectively solve the problems of selecting structural parameters, initial connection weights and thresholds and learning rates of the BP neural network. This new algorithm not only reduces the human intervention, optimizes the topological structures of BP neural networks and improves the network generalization ability, but also accelerates the convergence speed of a network, avoids trapping into local minima, and enhances network adaptation ability and prediction ability. The dynamic self-adaptive learning algorithm of the BP neural network is used to forecast the total retail sale of consumer goods of Sichuan Province, China. Empirical results indicate that the new algorithm is superior to the traditional BP network algorithm in predicting accuracy and time consumption, which shows the feasibility and effectiveness of the new algorithm.

  10. Time-sequenced adaptive filtering using a modified P-vector algorithm

    NASA Astrophysics Data System (ADS)

    Williams, Robert L.

    1996-10-01

    An adaptive algorithm and two stage filter structure were developed for adaptive filtering of certain classes of signals that exhibit cyclostationary characteristics. The new modified P-vector algorithm (mPa) eliminates the need for a separate desired signal which is typically required by conventional adaptive algorithms. It is then implemented in a time-sequenced manner to counteract the nonstationary characteristics typically found in certain radar and bioelectromagnetic signals. Initial algorithm testing is performed on evoked responses generated by the visual cortex of the human brain with the objective, ultimately, to transition the results to radar signals. Each sample of the evoked response is modeled as the sum of three uncorrelated signal components, a time-varying mean (M), a noise component (N), and a random jitter component (Q). A two stage single channel time-sequenced adaptive filter structure was developed which improves convergence characteristics by de coupling the time-varying mean component from the `Q' and noise components in the first stage. The EEG statistics must be known a priori and are adaptively estimated from the pre stimulus data. The performance of the two stage mPa time-sequenced adaptive filter approaches the performance for the ideal case of an adaptive filter having a noiseless desired response.

  11. apGA: An adaptive parallel genetic algorithm

    SciTech Connect

    Liepins, G.E. ); Baluja, S. )

    1991-01-01

    We develop apGA, a parallel variant of the standard generational GA, that combines aggressive search with perpetual novelty, yet is able to preserve enough genetic structure to optimally solve variably scaled, non-uniform block deceptive and hierarchical deceptive problems. apGA combines elitism, adaptive mutation, adaptive exponential scaling, and temporal memory. We present empirical results for six classes of problems, including the DeJong test suite. Although we have not investigated hybrids, we note that apGA could be incorporated into other recent GA variants such as GENITOR, CHC, and the recombination stage of mGA. 12 refs., 2 figs., 2 tabs.

  12. Infrared image gray adaptive adjusting enhancement algorithm based on gray redundancy histogram-dealing technique

    NASA Astrophysics Data System (ADS)

    Hao, Zi-long; Liu, Yong; Chen, Ruo-wang

    2016-11-01

    In view of the histogram equalizing algorithm to enhance image in digital image processing, an Infrared Image Gray adaptive adjusting Enhancement Algorithm Based on Gray Redundancy Histogram-dealing Technique is proposed. The algorithm is based on the determination of the entire image gray value, enhanced or lowered the image's overall gray value by increasing appropriate gray points, and then use gray-level redundancy HE method to compress the gray-scale of the image. The algorithm can enhance image detail information. Through MATLAB simulation, this paper compares the algorithm with the histogram equalization method and the algorithm based on gray redundancy histogram-dealing technique , and verifies the effectiveness of the algorithm.

  13. A fast partitioning algorithm using adaptive Mahalanobis clustering with application to seismic zoning

    NASA Astrophysics Data System (ADS)

    Morales-Esteban, Antonio; Martínez-Álvarez, Francisco; Scitovski, Sanja; Scitovski, Rudolf

    2014-12-01

    In this paper we construct an efficient adaptive Mahalanobis k-means algorithm. In addition, we propose a new efficient algorithm to search for a globally optimal partition obtained by using the adoptive Mahalanobis distance-like function. The algorithm is a generalization of the previously proposed incremental algorithm (Scitovski and Scitovski, 2013). It successively finds optimal partitions with k = 2 , 3 , … clusters. Therefore, it can also be used for the estimation of the most appropriate number of clusters in a partition by using various validity indexes. The algorithm has been applied to the seismic catalogues of Croatia and the Iberian Peninsula. Both regions are characterized by a moderate seismic activity. One of the main advantages of the algorithm is its ability to discover not only circular but also elliptical shapes, whose geometry fits the faults better. Three seismogenic zonings are proposed for Croatia and two for the Iberian Peninsula and adjacent areas, according to the clusters discovered by the algorithm.

  14. Leaky-wave multiple dichroic beamformers

    NASA Astrophysics Data System (ADS)

    James, J. R.; Kinany, S. J. A.; Peel, P. D.; Andrasic, G.

    1989-08-01

    It is demonstrated that multiple frequency selective surfaces (FSSs) placed parallel to a low-directivity aperture exhibit useful beamforming properties at the expense of a narrowing of the radiation pattern bandwidth and a thicker structure. The beam-forming techniques are very compatible with printed technology employing layers of metal foil and low-permittivity dielectric materials. The application of the technique to a microstrip patch antenna is used as an example.

  15. The new image segmentation algorithm using adaptive evolutionary programming and fuzzy c-means clustering

    NASA Astrophysics Data System (ADS)

    Liu, Fang

    2011-06-01

    Image segmentation remains one of the major challenges in image analysis and computer vision. Fuzzy clustering, as a soft segmentation method, has been widely studied and successfully applied in mage clustering and segmentation. The fuzzy c-means (FCM) algorithm is the most popular method used in mage segmentation. However, most clustering algorithms such as the k-means and the FCM clustering algorithms search for the final clusters values based on the predetermined initial centers. The FCM clustering algorithms does not consider the space information of pixels and is sensitive to noise. In the paper, presents a new fuzzy c-means (FCM) algorithm with adaptive evolutionary programming that provides image clustering. The features of this algorithm are: 1) firstly, it need not predetermined initial centers. Evolutionary programming will help FCM search for better center and escape bad centers at local minima. Secondly, the spatial distance and the Euclidean distance is also considered in the FCM clustering. So this algorithm is more robust to the noises. Thirdly, the adaptive evolutionary programming is proposed. The mutation rule is adaptively changed with learning the useful knowledge in the evolving process. Experiment results shows that the new image segmentation algorithm is effective. It is providing robustness to noisy images.

  16. Three component microseism analysis in Australia from deconvolution enhanced beamforming

    NASA Astrophysics Data System (ADS)

    Gal, Martin; Reading, Anya; Ellingsen, Simon; Koper, Keith; Burlacu, Relu; Tkalčić, Hrvoje; Gibbons, Steven

    2016-04-01

    Ocean induced microseisms in the range 2-10 seconds are generated in deep oceans and near coastal regions as body and surface waves. The generation of these waves can take place over an extended area and in a variety of geographical locations at the same time. It is therefore common to observe multiple arrivals with a variety of slowness vectors which leads to the desire to measure multiple arrivals accurately. We present a deconvolution enhanced direction of arrival algorithm, for single and 3 component arrays, based on CLEAN. The algorithm iteratively removes sidelobe contributions in the power spectrum, therefore improves the signal-to-noise ratio of weaker sources. The power level on each component (vertical, radial and transverse) can be accurately estimated as the beamformer decomposes the power spectrum into point sources. We first apply the CLEAN aided beamformer to synthetic data to show its performance under known conditions and then evaluate real (observed) data from a range of arrays with apertures between 10 and 70 km (ASAR, WRA and NORSAR) to showcase the improvement in resolution. We further give a detailed analysis of the 3 component wavefield in Australia including source locations, power levels, phase ratios, etc. by two spiral arrays (PSAR and SQspa). For PSAR the analysis is carried out in the frequency range 0.35-1Hz. We find LQ, Lg and fundamental and higher mode Rg wave phases. Additionally, we also observe the Sn phase. This is the first time this has been achieved through beamforming on microseism noise and underlines the potential for extra seismological information that can be extracted using the new implementation of CLEAN. The fundamental mode Rg waves are dominant in power for low frequencies and show equal power levels with LQ towards higher frequencies. Generation locations between Rg and LQ are mildly correlated for low frequencies and uncorrelated for higher frequencies. Results from SQspa will discuss lower frequencies around the

  17. Antenna beamforming using optical processing

    NASA Technical Reports Server (NTRS)

    Anderson, L. P., Jr.; Boldissar, F.; Chang, D. C. D.

    1987-01-01

    This work concerns itself with the analytical investigation into the feasibility of optical processor based beamforming for microwave array antennas. The primary focus is on systems utilizing the 20 and 30 GHz communications band and a transmit configuration exclusively to serve this band. A mathematical model is developed for computation of candidate design configurations. The model is capable of determination of the necessary design parameters required for both spatial aspects of the microwave footprint (beam) formation as well as transmitted signal quality. Computed example beams transmitted from geosynchronous orbit are presented to demonstrate network capabilities. A comprehensive device/component survey is also conducted in parallel to determine the feasibility of breadboarding a transmit processor. Recommendations are made for the configuration of such a processor and the components which would comprise such a network.

  18. Simple and Effective Algorithms: Computer-Adaptive Testing.

    ERIC Educational Resources Information Center

    Linacre, John Michael

    Computer-adaptive testing (CAT) allows improved security, greater scoring accuracy, shorter testing periods, quicker availability of results, and reduced guessing and other undesirable test behavior. Simple approaches can be applied by the classroom teacher, or other content specialist, who possesses simple computer equipment and elementary…

  19. Ultrasound beamforming using compressed data.

    PubMed

    Li, Yen-Feng; Li, Pai-Chi

    2012-05-01

    The rapid advancements in electronics technologies have made software-based beamformers for ultrasound array imaging feasible, thus facilitating the rapid development of high-performance and potentially low-cost systems. However, one challenge to realizing a fully software-based system is transferring data from the analog front end to the software back end at rates of up to a few gigabits per second. This study investigated the use of data compression to reduce the data transfer requirements and optimize the associated trade-off with beamforming quality. JPEG and JPEG2000 compression techniques were adopted. The acoustic data of a line phantom were acquired with a 128-channel array transducer at a center frequency of 3.5 MHz, and the acoustic data of a cyst phantom were acquired with a 64-channel array transducer at a center frequency of 3.33 MHz. The receive-channel data associated with each transmit event are separated into 8 × 8 blocks and several tiles before JPEG and JPEG2000 data compression is applied, respectively. In one scheme, the compression was applied to raw RF data, while in another only the amplitude of baseband data was compressed. The maximum compression ratio of RF data compression to produce an average error of lower than 5 dB was 15 with JPEG compression and 20 with JPEG2000 compression. The image quality is higher with baseband amplitude data compression than with RF data compression; although the maximum overall compression ratio (compared with the original RF data size), which was limited by the data size of uncompressed phase data, was lower than 12, the average error in this case was lower than 1 dB when the compression ratio was lower than 8.

  20. Adaptive quasi-Newton algorithm for source extraction via CCA approach.

    PubMed

    Zhang, Wei-Tao; Lou, Shun-Tian; Feng, Da-Zheng

    2014-04-01

    This paper addresses the problem of adaptive source extraction via the canonical correlation analysis (CCA) approach. Based on Liu's analysis of CCA approach, we propose a new criterion for source extraction, which is proved to be equivalent to the CCA criterion. Then, a fast and efficient online algorithm using quasi-Newton iteration is developed. The stability of the algorithm is also analyzed using Lyapunov's method, which shows that the proposed algorithm asymptotically converges to the global minimum of the criterion. Simulation results are presented to prove our theoretical analysis and demonstrate the merits of the proposed algorithm in terms of convergence speed and successful rate for source extraction.

  1. Passification based simple adaptive control of quadrotor attitude: Algorithms and testbed results

    NASA Astrophysics Data System (ADS)

    Tomashevich, Stanislav; Belyavskyi, Andrey; Andrievsky, Boris

    2017-01-01

    In the paper, the results of the Passification Method with the Implicit Reference Model (IRM) approach are applied for designing the simple adaptive controller for quadrotor attitude. The IRM design technique makes it possible to relax the matching condition, known for habitual MRAC systems, and leads to simple adaptive controllers, ensuring fast tuning the controller gains, high robustness with respect to nonlinearities in the control loop, to the external disturbances and the unmodeled plant dynamics. For experimental evaluation of the adaptive systems performance, the 2DOF laboratory setup has been created. The testbed allows to safely test new control algorithms in the laboratory area with a small space and promptly make changes in cases of failure. The testing results of simple adaptive control of quadrotor attitude are presented, demonstrating efficacy of the applied simple adaptive control method. The experiments demonstrate good performance quality and high adaptation rate of the simple adaptive control system.

  2. Simulated annealing algorithm applied in adaptive near field beam shaping

    NASA Astrophysics Data System (ADS)

    Yu, Zhan; Ma, Hao-tong; Du, Shao-jun

    2010-11-01

    Laser beam shaping is required in many applications for improving the efficiency of the laser systems. In this paper, the near field beam shaping based on the combination of simulated annealing algorithm and Zernike polynomials is demonstrated. Considering phase distribution can be represented by the expansion of Zernike polynomials, the problem of searching appropriate phase distribution can be changed into a problem of optimizing a vector made up of Zernike coefficients. The feasibility of this method is validated theoretically by translating the Gaussian beam into square quasi-flattop beam in the near field. Finally, the closed control loop system constituted by phase only liquid crystal spatial light modulator and simulated annealing algorithm is used to prove the validity of the technique. The experiment results show that the system can generate laser beam with desired intensity distributions.

  3. An algorithmic approach to adaptive state filtering using recurrent neural networks.

    PubMed

    Parlos, A G; Menon, S K; Atiya, A

    2001-01-01

    Practical algorithms are presented for adaptive state filtering in nonlinear dynamic systems when the state equations are unknown. The state equations are constructively approximated using neural networks. The algorithms presented are based on the two-step prediction-update approach of the Kalman filter. The proposed algorithms make minimal assumptions regarding the underlying nonlinear dynamics and their noise statistics. Non-adaptive and adaptive state filtering algorithms are presented with both off-line and online learning stages. The algorithms are implemented using feedforward and recurrent neural network and comparisons are presented. Furthermore, extended Kalman filters (EKFs) are developed and compared to the filter algorithms proposed. For one of the case studies, the EKF converges but results in higher state estimation errors that the equivalent neural filters. For another, more complex case study with unknown system dynamics and noise statistics, the developed EKFs do not converge. The off-line trained neural state filters converge quite rapidly and exhibit acceptable performance. Online training further enhances the estimation accuracy of the developed adaptive filters, effectively decoupling the eventual filter accuracy from the accuracy of the process model.

  4. Astronomical image denoising by means of improved adaptive backtracking-based matching pursuit algorithm.

    PubMed

    Liu, Qianshun; Bai, Jian; Yu, Feihong

    2014-11-10

    In an effort to improve compressive sensing and spare signal reconstruction by way of the backtracking-based adaptive orthogonal matching pursuit (BAOMP), a new sparse coding algorithm called improved adaptive backtracking-based OMP (ABOMP) is proposed in this study. Many aspects have been improved compared to the original BAOMP method, including replacing the fixed threshold with an adaptive one, adding residual feedback and support set verification, and others. Because of these ameliorations, the proposed algorithm can more precisely choose the atoms. By adding the adaptive step-size mechanism, it requires much less iteration and thus executes more efficiently. Additionally, a simple but effective contrast enhancement method is also adopted to further improve the denoising results and visual effect. By combining the IABOMP algorithm with the state-of-art dictionary learning algorithm K-SVD, the proposed algorithm achieves better denoising effects for astronomical images. Numerous experimental results show that the proposed algorithm performs successfully and effectively on Gaussian and Poisson noise removal.

  5. Almost Sure Convergence of Adaptive Identification Prediction and Control Algorithms.

    DTIC Science & Technology

    1981-03-01

    achievable with known plant parameters, in the Cesaro sense. An additional regularity assumption on the signal model establishes the result that the...the Cesaro sense. Under an additional regularity assumption, the convergence of these errors and also that of the tracking error for the adaptive con...The 4- convergence in all these references is established in the Cesaro sense. The above schemes of [7-10] leave the question unanswered as to

  6. Simulation of Biochemical Pathway Adaptability Using Evolutionary Algorithms

    SciTech Connect

    Bosl, W J

    2005-01-26

    The systems approach to genomics seeks quantitative and predictive descriptions of cells and organisms. However, both the theoretical and experimental methods necessary for such studies still need to be developed. We are far from understanding even the simplest collective behavior of biomolecules, cells or organisms. A key aspect to all biological problems, including environmental microbiology, evolution of infectious diseases, and the adaptation of cancer cells is the evolvability of genomes. This is particularly important for Genomes to Life missions, which tend to focus on the prospect of engineering microorganisms to achieve desired goals in environmental remediation and climate change mitigation, and energy production. All of these will require quantitative tools for understanding the evolvability of organisms. Laboratory biodefense goals will need quantitative tools for predicting complicated host-pathogen interactions and finding counter-measures. In this project, we seek to develop methods to simulate how external and internal signals cause the genetic apparatus to adapt and organize to produce complex biochemical systems to achieve survival. This project is specifically directed toward building a computational methodology for simulating the adaptability of genomes. This project investigated the feasibility of using a novel quantitative approach to studying the adaptability of genomes and biochemical pathways. This effort was intended to be the preliminary part of a larger, long-term effort between key leaders in computational and systems biology at Harvard University and LLNL, with Dr. Bosl as the lead PI. Scientific goals for the long-term project include the development and testing of new hypotheses to explain the observed adaptability of yeast biochemical pathways when the myosin-II gene is deleted and the development of a novel data-driven evolutionary computation as a way to connect exploratory computational simulation with hypothesis

  7. Sequential Insertion Heuristic with Adaptive Bee Colony Optimisation Algorithm for Vehicle Routing Problem with Time Windows.

    PubMed

    Jawarneh, Sana; Abdullah, Salwani

    2015-01-01

    This paper presents a bee colony optimisation (BCO) algorithm to tackle the vehicle routing problem with time window (VRPTW). The VRPTW involves recovering an ideal set of routes for a fleet of vehicles serving a defined number of customers. The BCO algorithm is a population-based algorithm that mimics the social communication patterns of honeybees in solving problems. The performance of the BCO algorithm is dependent on its parameters, so the online (self-adaptive) parameter tuning strategy is used to improve its effectiveness and robustness. Compared with the basic BCO, the adaptive BCO performs better. Diversification is crucial to the performance of the population-based algorithm, but the initial population in the BCO algorithm is generated using a greedy heuristic, which has insufficient diversification. Therefore the ways in which the sequential insertion heuristic (SIH) for the initial population drives the population toward improved solutions are examined. Experimental comparisons indicate that the proposed adaptive BCO-SIH algorithm works well across all instances and is able to obtain 11 best results in comparison with the best-known results in the literature when tested on Solomon's 56 VRPTW 100 customer instances. Also, a statistical test shows that there is a significant difference between the results.

  8. Sequential Insertion Heuristic with Adaptive Bee Colony Optimisation Algorithm for Vehicle Routing Problem with Time Windows

    PubMed Central

    Jawarneh, Sana; Abdullah, Salwani

    2015-01-01

    This paper presents a bee colony optimisation (BCO) algorithm to tackle the vehicle routing problem with time window (VRPTW). The VRPTW involves recovering an ideal set of routes for a fleet of vehicles serving a defined number of customers. The BCO algorithm is a population-based algorithm that mimics the social communication patterns of honeybees in solving problems. The performance of the BCO algorithm is dependent on its parameters, so the online (self-adaptive) parameter tuning strategy is used to improve its effectiveness and robustness. Compared with the basic BCO, the adaptive BCO performs better. Diversification is crucial to the performance of the population-based algorithm, but the initial population in the BCO algorithm is generated using a greedy heuristic, which has insufficient diversification. Therefore the ways in which the sequential insertion heuristic (SIH) for the initial population drives the population toward improved solutions are examined. Experimental comparisons indicate that the proposed adaptive BCO-SIH algorithm works well across all instances and is able to obtain 11 best results in comparison with the best-known results in the literature when tested on Solomon’s 56 VRPTW 100 customer instances. Also, a statistical test shows that there is a significant difference between the results. PMID:26132158

  9. Adaptive Waveform Correlation Detectors for Arrays: Algorithms for Autonomous Calibration

    DTIC Science & Technology

    2007-09-01

    correlation coefficient , or some comparable detection statistic, exceeds a given threshold. Since these methods exploit characteristic details of the full waveform, they provide exquisitely sensitive detectors with far lower detection thresholds than typical short-term average/long-term average (STA/LTA) algorithms. The drawback is that the form of the sought-after signal needs to be known quite accurately a priori, which limits such methods to instances of seismicity whereby a very similar signal has already been observed by every station used. Such instances include

  10. Adaptive merit function in SPGD algorithm for beam combining

    NASA Astrophysics Data System (ADS)

    Yang, Guo-qing; Liu, Li-sheng; Jiang, Zhen-hua; Wang, Ting-feng; Guo, Jin

    2016-09-01

    The beam pointing is the most crucial issue for beam combining to achieve high energy laser output. In order to meet the turbulence situation, a beam pointing method that cooperates with the stochastic parallel gradient descent (SPGD) algorithm is proposed. The power-in-the-bucket ( PIB) is chosen as the merit function, and its radius changes gradually during the correction process. The linear radius and the exponential radius are simulated. The results show that the exponential radius has great promise for beam pointing.

  11. A geometry-based adaptive unstructured grid generation algorithm for complex geological media

    NASA Astrophysics Data System (ADS)

    Bahrainian, Seyed Saied; Dezfuli, Alireza Daneh

    2014-07-01

    In this paper a novel unstructured grid generation algorithm is presented that considers the effect of geological features and well locations in grid resolution. The proposed grid generation algorithm presents a strategy for definition and construction of an initial grid based on the geological model, geometry adaptation of geological features, and grid resolution control. The algorithm is applied to seismotectonic map of the Masjed-i-Soleiman reservoir. Comparison of grid results with the “Triangle” program shows a more suitable permeability contrast. Immiscible two-phase flow solutions are presented for a fractured porous media test case using different grid resolutions. Adapted grid on the fracture geometry gave identical results with that of a fine grid. The adapted grid employed 88.2% less CPU time when compared to the solutions obtained by the fine grid.

  12. Adaptive Sampling Algorithms for Probabilistic Risk Assessment of Nuclear Simulations

    SciTech Connect

    Diego Mandelli; Dan Maljovec; Bei Wang; Valerio Pascucci; Peer-Timo Bremer

    2013-09-01

    Nuclear simulations are often computationally expensive, time-consuming, and high-dimensional with respect to the number of input parameters. Thus exploring the space of all possible simulation outcomes is infeasible using finite computing resources. During simulation-based probabilistic risk analysis, it is important to discover the relationship between a potentially large number of input parameters and the output of a simulation using as few simulation trials as possible. This is a typical context for performing adaptive sampling where a few observations are obtained from the simulation, a surrogate model is built to represent the simulation space, and new samples are selected based on the model constructed. The surrogate model is then updated based on the simulation results of the sampled points. In this way, we attempt to gain the most information possible with a small number of carefully selected sampled points, limiting the number of expensive trials needed to understand features of the simulation space. We analyze the specific use case of identifying the limit surface, i.e., the boundaries in the simulation space between system failure and system success. In this study, we explore several techniques for adaptively sampling the parameter space in order to reconstruct the limit surface. We focus on several adaptive sampling schemes. First, we seek to learn a global model of the entire simulation space using prediction models or neighborhood graphs and extract the limit surface as an iso-surface of the global model. Second, we estimate the limit surface by sampling in the neighborhood of the current estimate based on topological segmentations obtained locally. Our techniques draw inspirations from topological structure known as the Morse-Smale complex. We highlight the advantages and disadvantages of using a global prediction model versus local topological view of the simulation space, comparing several different strategies for adaptive sampling in both

  13. [Curvelet denoising algorithm for medical ultrasound image based on adaptive threshold].

    PubMed

    Zhuang, Zhemin; Yao, Weike; Yang, Jinyao; Li, FenLan; Yuan, Ye

    2014-11-01

    The traditional denoising algorithm for ultrasound images would lost a lot of details and weak edge information when suppressing speckle noise. A new denoising algorithm of adaptive threshold based on curvelet transform is proposed in this paper. The algorithm utilizes differences of coefficients' local variance between texture and smooth region in each layer of ultrasound image to define fuzzy regions and membership functions. In the end, using the adaptive threshold that determine by the membership function to denoise the ultrasound image. The experimental text shows that the algorithm can reduce the speckle noise effectively and retain the detail information of original image at the same time, thus it can greatly enhance the performance of B ultrasound instrument.

  14. Detection of Human Impacts by an Adaptive Energy-Based Anisotropic Algorithm

    PubMed Central

    Prado-Velasco, Manuel; Ortiz Marín, Rafael; del Rio Cidoncha, Gloria

    2013-01-01

    Boosted by health consequences and the cost of falls in the elderly, this work develops and tests a novel algorithm and methodology to detect human impacts that will act as triggers of a two-layer fall monitor. The two main requirements demanded by socio-healthcare providers—unobtrusiveness and reliability—defined the objectives of the research. We have demonstrated that a very agile, adaptive, and energy-based anisotropic algorithm can provide 100% sensitivity and 78% specificity, in the task of detecting impacts under demanding laboratory conditions. The algorithm works together with an unsupervised real-time learning technique that addresses the adaptive capability, and this is also presented. The work demonstrates the robustness and reliability of our new algorithm, which will be the basis of a smart falling monitor. This is shown in this work to underline the relevance of the results. PMID:24157505

  15. 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.

  16. Performance study of LMS based adaptive algorithms for unknown system identification

    SciTech Connect

    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.

  17. 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.

  18. Self-adaptive differential evolution algorithm incorporating local search for protein-ligand docking

    NASA Astrophysics Data System (ADS)

    Chung, Hwan Won; Cho, Seung Joo; Lee, Kwang-Ryeol; Lee, Kyu-Hwan

    2013-02-01

    Differential Evolution (DE) algorithm is powerful in optimization problems over several real parameters. DE depends on strategies to generate new trial solutions and the associated parameter values for searching performance. In self-adaptive DE, the automatic learning about previous evolution was used to determine the best mutation strategy and its parameter settings. By combining the self-adaptive DE and Hooke Jeeves local search, we developed a new docking method named SADock (Strategy Adaptation Dock) with the help of AutoDock4 scoring function. As the accuracy and performance of SADock was evaluated in self-docking using the Astex diverse set, the introduced SADock showed better success ratio (89%) than the success ratio (60%) of the Lamarckian genetic algorithm (LGA) of AutoDock4. The self-adapting scheme enabled our new docking method to converge fast and to be robust through the various docking problems.

  19. A bit-level image encryption algorithm based on spatiotemporal chaotic system and self-adaptive

    NASA Astrophysics Data System (ADS)

    Teng, Lin; Wang, Xingyuan

    2012-09-01

    This paper proposes a bit-level image encryption algorithm based on spatiotemporal chaotic system which is self-adaptive. We use a bit-level encryption scheme to reduce the volume of data during encryption and decryption in order to reduce the execution time. We also use the adaptive encryption scheme to make the ciphered image dependent on the plain image to improve performance. Simulation results show that the performance and security of the proposed encryption algorithm can encrypt plaintext effectively and resist various typical attacks.

  20. A Mass Conservation Algorithm for Adaptive Unrefinement Meshes Used by Finite Element Methods

    DTIC Science & Technology

    2012-01-01

    dimensional mesh generation. In: Proc. 4th ACM-SIAM Symp. on Disc. Algorithms. (1993) 83–92 [9] Weatherill, N., Hassan, O., Marcum, D., Marchant, M.: Grid ...Conference on Computational Science, ICCS 2012 A Mass Conservation Algorithm For Adaptive Unrefinement Meshes Used By Finite Element Methods Hung V. Nguyen...velocity fields, and chemical distribution, as well as conserve mass, especially for water quality applications. Solution accuracy depends highly on mesh

  1. Comparison of adaptive algorithms for the control of tonal disturbances in mechanical systems

    NASA Astrophysics Data System (ADS)

    Zilletti, M.; Elliott, S. J.; Cheer, J.

    2016-09-01

    This paper presents a study on the performance of adaptive control algorithms designed to reduce the vibration of mechanical systems excited by a harmonic disturbance. The mechanical system consists of a mass suspended on a spring and a damper. The system is equipped with a force actuator in parallel with the suspension. The control signal driving the actuator is generated by adjusting the amplitude and phase of a sinusoidal reference signal at the same frequency as the excitation. An adaptive feedforward control algorithm is used to adapt the amplitude and phase of the control signal, to minimise the mean square velocity of the mass. Two adaptation strategies are considered in which the control signal is either updated after each period of the oscillation or at every time sample. The first strategy is traditionally used in vibration control in helicopters for example; the second strategy is normally referred to as the filtered-x least mean square algorithm and is often used to control engine noise in cars. The two adaptation strategies are compared through a parametric study, which investigates the influence of the properties of both the mechanical system and the control system on the convergence speed of the two algorithms.

  2. Multiobjective Image Color Quantization Algorithm Based on Self-Adaptive Hybrid Differential Evolution

    PubMed Central

    Xia, Xuewen

    2016-01-01

    In recent years, some researchers considered image color quantization as a single-objective problem and applied heuristic algorithms to solve it. This paper establishes a multiobjective image color quantization model with intracluster distance and intercluster separation as its objectives. Inspired by a multipopulation idea, a multiobjective image color quantization algorithm based on self-adaptive hybrid differential evolution (MoDE-CIQ) is then proposed to solve this model. Two numerical experiments on four common test images are conducted to analyze the effectiveness and competitiveness of the multiobjective model and the proposed algorithm. PMID:27738423

  3. Low-power metabolic equivalents estimation algorithm using adaptive acceleration sampling.

    PubMed

    Tsukahara, Mio; Nakanishi, Motofumi; Izumi, Shintaro; Nakai, Yozaburo; Kawaguchi, Hiroshi; Yoshimoto, Masahiko; Tsukahara, Mio; Nakanishi, Motofumi; Izumi, Shintaro; Nakai, Yozaburo; Kawaguchi, Hiroshi; Yoshimoto, Masahiko; Izumi, Shintaro; Nakai, Yozaburo; Kawaguchi, Hiroshi; Yoshimoto, Masahiko; Tsukahara, Mio; Nakanishi, Motofumi

    2016-08-01

    This paper describes a proposed low-power metabolic equivalent estimation algorithm that can calculate the value of metabolic equivalents (METs) from triaxial acceleration at an adaptively changeable sampling rate. This algorithm uses four rates of 32, 16, 8 and 4 Hz. The mode of switching them is decided from synthetic acceleration. Applying this proposed algorithm to acceleration measured for 1 day, we achieved the low root mean squared error (RMSE) of calculated METs, with current consumption that was 41.5 % of the value at 32 Hz, and 75.4 % of the value at 16 Hz.

  4. An adaptive prediction and detection algorithm for multistream syndromic surveillance

    PubMed Central

    Najmi, Amir-Homayoon; Magruder, Steve F

    2005-01-01

    Background Surveillance of Over-the-Counter pharmaceutical (OTC) sales as a potential early indicator of developing public health conditions, in particular in cases of interest to biosurvellance, has been suggested in the literature. This paper is a continuation of a previous study in which we formulated the problem of estimating clinical data from OTC sales in terms of optimal LMS linear and Finite Impulse Response (FIR) filters. In this paper we extend our results to predict clinical data multiple steps ahead using OTC sales as well as the clinical data itself. Methods The OTC data are grouped into a few categories and we predict the clinical data using a multichannel filter that encompasses all the past OTC categories as well as the past clinical data itself. The prediction is performed using FIR (Finite Impulse Response) filters and the recursive least squares method in order to adapt rapidly to nonstationary behaviour. In addition, we inject simulated events in both clinical and OTC data streams to evaluate the predictions by computing the Receiver Operating Characteristic curves of a threshold detector based on predicted outputs. Results We present all prediction results showing the effectiveness of the combined filtering operation. In addition, we compute and present the performance of a detector using the prediction output. Conclusion Multichannel adaptive FIR least squares filtering provides a viable method of predicting public health conditions, as represented by clinical data, from OTC sales, and/or the clinical data. The potential value to a biosurveillance system cannot, however, be determined without studying this approach in the presence of transient events (nonstationary events of relatively short duration and fast rise times). Our simulated events superimposed on actual OTC and clinical data allow us to provide an upper bound on that potential value under some restricted conditions. Based on our ROC curves we argue that a biosurveillance system can

  5. An Adaptive Weighting Algorithm for Interpolating the Soil Potassium Content

    PubMed Central

    Liu, Wei; Du, Peijun; Zhao, Zhuowen; Zhang, Lianpeng

    2016-01-01

    The concept of spatial interpolation is important in the soil sciences. However, the use of a single global interpolation model is often limited by certain conditions (e.g., terrain complexity), which leads to distorted interpolation results. Here we present a method of adaptive weighting combined environmental variables for soil properties interpolation (AW-SP) to improve accuracy. Using various environmental variables, AW-SP was used to interpolate soil potassium content in Qinghai Lake Basin. To evaluate AW-SP performance, we compared it with that of inverse distance weighting (IDW), ordinary kriging, and OK combined with different environmental variables. The experimental results showed that the methods combined with environmental variables did not always improve prediction accuracy even if there was a strong correlation between the soil properties and environmental variables. However, compared with IDW, OK, and OK combined with different environmental variables, AW-SP is more stable and has lower mean absolute and root mean square errors. Furthermore, the AW-SP maps provided improved details of soil potassium content and provided clearer boundaries to its spatial distribution. In conclusion, AW-SP can not only reduce prediction errors, it also accounts for the distribution and contributions of environmental variables, making the spatial interpolation of soil potassium content more reasonable. PMID:27051998

  6. The new adaptive enhancement algorithm on the degraded color images

    NASA Astrophysics Data System (ADS)

    Xue, Rong Kun; He, Wei; Li, Yufeng

    2016-10-01

    Based on the scene characteristics of frequency distribution in the degraded color images, the MSRCR method and wavelet transform in the paper are introduced respectively to enhance color images and the advantages and disadvantages of them are analyzed combining with the experiment, then the combination of improved MSRCR method and wavelet transform are proposed to enhance color images, it uses wavelet to decompose color images in order to increase the coefficient of low-level details and reduce top-level details to highlight the scene information, meanwhile, the method of improved MSRCR is used to enhance the low-frequency components of degraded images processed by wavelet, then the adaptive equalization is carried on to further enhance images, finally, the enhanced color images are acquired with the reconstruction of all the coefficients brought by the wavelet transform. Through the evaluation of the experimental results and data analysis, it shows that the method proposed in the paper is better than the separate use of wavelet transform and MSRCR method.

  7. Adaptive motion artifact reducing algorithm for wrist photoplethysmography application

    NASA Astrophysics Data System (ADS)

    Zhao, Jingwei; Wang, Guijin; Shi, Chenbo

    2016-04-01

    Photoplethysmography (PPG) technology is widely used in wearable heart pulse rate monitoring. It might reveal the potential risks of heart condition and cardiopulmonary function by detecting the cardiac rhythms in physical exercise. However the quality of wrist photoelectric signal is very sensitive to motion artifact since the thicker tissues and the fewer amount of capillaries. Therefore, motion artifact is the major factor that impede the heart rate measurement in the high intensity exercising. One accelerometer and three channels of light with different wavelengths are used in this research to analyze the coupled form of motion artifact. A novel approach is proposed to separate the pulse signal from motion artifact by exploiting their mixing ratio in different optical paths. There are four major steps of our method: preprocessing, motion artifact estimation, adaptive filtering and heart rate calculation. Five healthy young men are participated in the experiment. The speeder in the treadmill is configured as 12km/h, and all subjects would run for 3-10 minutes by swinging the arms naturally. The final result is compared with chest strap. The average of mean square error (MSE) is less than 3 beats per minute (BPM/min). Proposed method performed well in intense physical exercise and shows the great robustness to individuals with different running style and posture.

  8. An adaptive solution domain algorithm for solving multiphase flow equations

    NASA Astrophysics Data System (ADS)

    Katyal, A. K.; Parker, J. C.

    1992-01-01

    An adaptive solution domain (ASD) finite-element model for simulating hydrocarbon spills has been developed that is computationally more efficient than conventional numerical methods. Coupled flow of water and oil with an air phase at constant pressure is considered. In the ASD formulation, the solution domain for water- and oil-flow equations is restricted by eliminating elements from the global matrix assembly which are not experiencing significant changes in fluid saturations or pressures. When any nodes of an element exhibit changes in fluid pressures more than a stipulated tolerance τ, or changes in fluid saturations greater than tolerance τ 2 during the current time step, it is labeled active and included in the computations for the next iteration. This formulation achieves computational efficiency by solving the flow equations for only the part of the domain where changes in fluid pressure or the saturations take place above stipulated tolerances. Examples involving infiltration and redistribution of oil in 1- and 2-D spatial domains are described to illustrate the application of the ASD method and the savings in the processor time achieved by this formulation. Savings in the computational effort up to 84% during infiltration and 63% during redistribution were achieved for the 2-D example problem.

  9. Optimization of heterogeneous Bin packing using adaptive genetic algorithm

    NASA Astrophysics Data System (ADS)

    Sridhar, R.; Chandrasekaran, M.; Sriramya, C.; Page, Tom

    2017-03-01

    This research is concentrates on a very interesting work, the bin packing using hybrid genetic approach. The optimal and feasible packing of goods for transportation and distribution to various locations by satisfying the practical constraints are the key points in this project work. As the number of boxes for packing can not be predicted in advance and the boxes may not be of same category always. It also involves many practical constraints that are why the optimal packing makes much importance to the industries. This work presents a combinational of heuristic Genetic Algorithm (HGA) for solving Three Dimensional (3D) Single container arbitrary sized rectangular prismatic bin packing optimization problem by considering most of the practical constraints facing in logistic industries. This goal was achieved in this research by optimizing the empty volume inside the container using genetic approach. Feasible packing pattern was achieved by satisfying various practical constraints like box orientation, stack priority, container stability, weight constraint, overlapping constraint, shipment placement constraint. 3D bin packing problem consists of ‘n’ number of boxes being to be packed in to a container of standard dimension in such a way to maximize the volume utilization and in-turn profit. Furthermore, Boxes to be packed may be of arbitrary sizes. The user input data are the number of bins, its size, shape, weight, and constraints if any along with standard container dimension. This user input were stored in the database and encoded to string (chromosomes) format which were normally acceptable by GA. GA operators were allowed to act over these encoded strings for finding the best solution.

  10. Modified fast frequency acquisition via adaptive least squares algorithm

    NASA Technical Reports Server (NTRS)

    Kumar, Rajendra (Inventor)

    1992-01-01

    A method and the associated apparatus for estimating the amplitude, frequency, and phase of a signal of interest are presented. The method comprises the following steps: (1) inputting the signal of interest; (2) generating a reference signal with adjustable amplitude, frequency and phase at an output thereof; (3) mixing the signal of interest with the reference signal and a signal 90 deg out of phase with the reference signal to provide a pair of quadrature sample signals comprising respectively a difference between the signal of interest and the reference signal and a difference between the signal of interest and the signal 90 deg out of phase with the reference signal; (4) using the pair of quadrature sample signals to compute estimates of the amplitude, frequency, and phase of an error signal comprising the difference between the signal of interest and the reference signal employing a least squares estimation; (5) adjusting the amplitude, frequency, and phase of the reference signal from the numerically controlled oscillator in a manner which drives the error signal towards zero; and (6) outputting the estimates of the amplitude, frequency, and phase of the error signal in combination with the reference signal to produce a best estimate of the amplitude, frequency, and phase of the signal of interest. The preferred method includes the step of providing the error signal as a real time confidence measure as to the accuracy of the estimates wherein the closer the error signal is to zero, the higher the probability that the estimates are accurate. A matrix in the estimation algorithm provides an estimate of the variance of the estimation error.

  11. Synthetic Aperture Ultrasound Fourier Beamformation Using Virtual Sources.

    PubMed

    Moghimirad, Elahe; Villagomez Hoyos, Carlos A; Mahloojifar, Ali; Mohammadzadeh Asl, Babak; Jensen, Jorgen Arendt

    2016-12-01

    An efficient Fourier beamformation algorithm is presented for multistatic synthetic aperture ultrasound imaging using virtual sources. The concept is based on the frequency domain wavenumber algorithm from radar and sonar and is extended to a multielement transmit/receive configuration using virtual sources. Window functions are used to extract the azimuth processing bandwidths and weight the data to reduce side lobes in the final image. Field II simulated data and SARUS (Synthetic Aperture Real-time Ultrasound System) measured data are used to evaluate the results in terms of point spread function, resolution, contrast, signal-to-noise ratio, and processing time. Lateral resolutions of 0.53 and 0.66 mm are obtained for Fourier Beamformation Using Virtual Sources (FBV) and delay and sum (DAS) on point target simulated data. Corresponding axial resolutions are 0.21 mm for FBV and 0.20 mm for DAS. The results are also consistent over different depths evaluated using a simulated phantom containing several point targets at different depths. FBV shows a better lateral resolution at all depths, and the axial and cystic resolutions of -6, -12, and -20 dB are almost the same for FBV and DAS. To evaluate the cyst phantom metrics, three different criteria of power ratio, contrast ratio, and contrast-to-noise ratio have been used. Results show that the algorithms have a different performance in the cyst center and near the boundary. FBV has a better performance near the boundary; however, DAS is better in the more central area of the cyst. Measured data from phantoms are also used for evaluation. The results confirm applicability of FBV in ultrasound, and 20 times less processing time is attained in comparison with DAS. Evaluating the results over a wide variety of parameters and having almost the same results for simulated and measured data demonstrates the ability of FBV in preserving the quality of image as DAS, while providing a more efficient algorithm with 20 times less

  12. Investigation of Adaptive Robust Kalman Filtering Algorithms for GPS/DR Navigation System Filters

    NASA Astrophysics Data System (ADS)

    Elzoghby, MOSTAFA; Arif, USMAN; Li, FU; Zhi Yu, XI

    2017-03-01

    The conventional Kalman filter (KF) algorithm is suitable if the characteristic noise covariance for states as well as measurements is readily known but in most cases these are unknown. Similarly robustness is required instead of smoothing if states are changing abruptly. Such an adaptive as well as robust Kalman filter is vital for many real time applications, like target tracking and navigating aerial vehicles. A number of adaptive as well as robust Kalman filtering methods are available in the literature. In order to investigate the performance of some of these methods, we have selected three different Kalman filters, namely Sage Husa KF, Modified Adaptive Robust KF and Adaptively Robust KF, which are easily simulate able as well as implementable for real time applications. These methods are simulated for land based vehicle and the results are compared with conventional Kalman filter. Results show that the Modified Adaptive Robust KF is best amongst the selected methods and can be used for Navigation applications.

  13. A High Fuel Consumption Efficiency Management Scheme for PHEVs Using an Adaptive Genetic Algorithm

    PubMed Central

    Lee, Wah Ching; Tsang, Kim Fung; Chi, Hao Ran; Hung, Faan Hei; Wu, Chung Kit; Chui, Kwok Tai; Lau, Wing Hong; Leung, Yat Wah

    2015-01-01

    A high fuel efficiency management scheme for plug-in hybrid electric vehicles (PHEVs) has been developed. In order to achieve fuel consumption reduction, an adaptive genetic algorithm scheme has been designed to adaptively manage the energy resource usage. The objective function of the genetic algorithm is implemented by designing a fuzzy logic controller which closely monitors and resembles the driving conditions and environment of PHEVs, thus trading off between petrol versus electricity for optimal driving efficiency. Comparison between calculated results and publicized data shows that the achieved efficiency of the fuzzified genetic algorithm is better by 10% than existing schemes. The developed scheme, if fully adopted, would help reduce over 600 tons of CO2 emissions worldwide every day. PMID:25587974

  14. Optimized adaptation algorithm for HEVC/H.265 dynamic adaptive streaming over HTTP using variable segment duration

    NASA Astrophysics Data System (ADS)

    Irondi, Iheanyi; Wang, Qi; Grecos, Christos

    2016-04-01

    Adaptive video streaming using HTTP has become popular in recent years for commercial video delivery. The recent MPEG-DASH standard allows interoperability and adaptability between servers and clients from different vendors. The delivery of the MPD (Media Presentation Description) files in DASH and the DASH client behaviours are beyond the scope of the DASH standard. However, the different adaptation algorithms employed by the clients do affect the overall performance of the system and users' QoE (Quality of Experience), hence the need for research in this field. Moreover, standard DASH delivery is based on fixed segments of the video. However, there is no standard segment duration for DASH where various fixed segment durations have been employed by different commercial solutions and researchers with their own individual merits. Most recently, the use of variable segment duration in DASH has emerged but only a few preliminary studies without practical implementation exist. In addition, such a technique requires a DASH client to be aware of segment duration variations, and this requirement and the corresponding implications on the DASH system design have not been investigated. This paper proposes a segment-duration-aware bandwidth estimation and next-segment selection adaptation strategy for DASH. Firstly, an MPD file extension scheme to support variable segment duration is proposed and implemented in a realistic hardware testbed. The scheme is tested on a DASH client, and the tests and analysis have led to an insight on the time to download next segment and the buffer behaviour when fetching and switching between segments of different playback durations. Issues like sustained buffering when switching between segments of different durations and slow response to changing network conditions are highlighted and investigated. An enhanced adaptation algorithm is then proposed to accurately estimate the bandwidth and precisely determine the time to download the next

  15. Beamforming Based Full-Duplex for Millimeter-Wave Communication.

    PubMed

    Liu, Xiao; Xiao, Zhenyu; Bai, Lin; Choi, Jinho; Xia, Pengfei; Xia, Xiang-Gen

    2016-07-21

    In this paper, we study beamforming based full-duplex (FD) systems in millimeter-wave (mmWave) communications. A joint transmission and reception (Tx/Rx) beamforming problem is formulated to maximize the achievable rate by mitigating self-interference (SI). Since the optimal solution is difficult to find due to the non-convexity of the objective function, suboptimal schemes are proposed in this paper. A low-complexity algorithm, which iteratively maximizes signal power while suppressing SI, is proposed and its convergence is proven. Moreover, two closed-form solutions, which do not require iterations, are also derived under minimum-mean-square-error (MMSE), zero-forcing (ZF), and maximum-ratio transmission (MRT) criteria. Performance evaluations show that the proposed iterative scheme converges fast (within only two iterations on average) and approaches an upper-bound performance, while the two closed-form solutions also achieve appealing performances, although there are noticeable differences from the upper bound depending on channel conditions. Interestingly, these three schemes show different robustness against the geometry of Tx/Rx antenna arrays and channel estimation errors.

  16. Beamforming Based Full-Duplex for Millimeter-Wave Communication

    PubMed Central

    Liu, Xiao; Xiao, Zhenyu; Bai, Lin; Choi, Jinho; Xia, Pengfei; Xia, Xiang-Gen

    2016-01-01

    In this paper, we study beamforming based full-duplex (FD) systems in millimeter-wave (mmWave) communications. A joint transmission and reception (Tx/Rx) beamforming problem is formulated to maximize the achievable rate by mitigating self-interference (SI). Since the optimal solution is difficult to find due to the non-convexity of the objective function, suboptimal schemes are proposed in this paper. A low-complexity algorithm, which iteratively maximizes signal power while suppressing SI, is proposed and its convergence is proven. Moreover, two closed-form solutions, which do not require iterations, are also derived under minimum-mean-square-error (MMSE), zero-forcing (ZF), and maximum-ratio transmission (MRT) criteria. Performance evaluations show that the proposed iterative scheme converges fast (within only two iterations on average) and approaches an upper-bound performance, while the two closed-form solutions also achieve appealing performances, although there are noticeable differences from the upper bound depending on channel conditions. Interestingly, these three schemes show different robustness against the geometry of Tx/Rx antenna arrays and channel estimation errors. PMID:27455256

  17. Search Control Algorithm Based on Random Step Size Hill-Climbing Method for Adaptive PMD Compensation

    NASA Astrophysics Data System (ADS)

    Tanizawa, Ken; Hirose, Akira

    Adaptive polarization mode dispersion (PMD) compensation is required for the speed-up and advancement of the present optical communications. The combination of a tunable PMD compensator and its adaptive control method achieves adaptive PMD compensation. In this paper, we report an effective search control algorithm for the feedback control of the PMD compensator. The algorithm is based on the hill-climbing method. However, the step size changes randomly to prevent the convergence from being trapped at a local maximum or a flat, unlike the conventional hill-climbing method. The randomness depends on the Gaussian probability density functions. We conducted transmission simulations at 160Gb/s and the results show that the proposed method provides more optimal compensator control than the conventional hill-climbing method.

  18. An improved cooperative adaptive cruise control (CACC) algorithm considering invalid communication

    NASA Astrophysics Data System (ADS)

    Wang, Pangwei; Wang, Yunpeng; Yu, Guizhen; Tang, Tieqiao

    2014-05-01

    For the Cooperative Adaptive Cruise Control (CACC) Algorithm, existing research studies mainly focus on how inter-vehicle communication can be used to develop CACC controller, the influence of the communication delays and lags of the actuators to the string stability. However, whether the string stability can be guaranteed when inter-vehicle communication is invalid partially has hardly been considered. This paper presents an improved CACC algorithm based on the sliding mode control theory and analyses the range of CACC controller parameters to maintain string stability. A dynamic model of vehicle spacing deviation in a platoon is then established, and the string stability conditions under improved CACC are analyzed. Unlike the traditional CACC algorithms, the proposed algorithm can ensure the functionality of the CACC system even if inter-vehicle communication is partially invalid. Finally, this paper establishes a platoon of five vehicles to simulate the improved CACC algorithm in MATLAB/Simulink, and the simulation results demonstrate that the improved CACC algorithm can maintain the string stability of a CACC platoon through adjusting the controller parameters and enlarging the spacing to prevent accidents. With guaranteed string stability, the proposed CACC algorithm can prevent oscillation of vehicle spacing and reduce chain collision accidents under real-world circumstances. This research proposes an improved CACC algorithm, which can guarantee the string stability when inter-vehicle communication is invalid.

  19. A self adaptive hybrid enhanced artificial bee colony algorithm for continuous optimization problems.

    PubMed

    Shan, Hai; Yasuda, Toshiyuki; Ohkura, Kazuhiro

    2015-06-01

    The artificial bee colony (ABC) algorithm is one of popular swarm intelligence algorithms that inspired by the foraging behavior of honeybee colonies. To improve the convergence ability, search speed of finding the best solution and control the balance between exploration and exploitation using this approach, we propose a self adaptive hybrid enhanced ABC algorithm in this paper. To evaluate the performance of standard ABC, best-so-far ABC (BsfABC), incremental ABC (IABC), and the proposed ABC algorithms, we implemented numerical optimization problems based on the IEEE Congress on Evolutionary Computation (CEC) 2014 test suite. Our experimental results show the comparative performance of standard ABC, BsfABC, IABC, and the proposed ABC algorithms. According to the results, we conclude that the proposed ABC algorithm is competitive to those state-of-the-art modified ABC algorithms such as BsfABC and IABC algorithms based on the benchmark problems defined by CEC 2014 test suite with dimension sizes of 10, 30, and 50, respectively.

  20. Particle velocity gradient based acoustic mode beamforming for short linear vector sensor arrays.

    PubMed

    Gur, Berke

    2014-06-01

    In this paper, a subtractive beamforming algorithm for short linear arrays of two-dimensional particle velocity sensors is described. The proposed method extracts the highly directional acoustic modes from the spatial gradients of the particle velocity field measured at closely spaced sensors along the array. The number of sensors in the array limits the highest order of modes that can be extracted. Theoretical analysis and numerical simulations indicate that the acoustic mode beamformer achieves directivity comparable to the maximum directivity that can be obtained with differential microphone arrays of equivalent aperture. When compared to conventional delay-and-sum beamformers for pressure sensor arrays, the proposed method achieves comparable directivity with 70%-85% shorter apertures. Moreover, the proposed method has additional capabilities such as high front-back (port-starboard) discrimination, frequency and steer direction independent response, and robustness to correlated ambient noise. Small inter-sensor spacing that results in very compact apertures makes the proposed beamformer suitable for space constrained applications such as hearing aids and short towed arrays for autonomous underwater platforms.

  1. Combination of MVDR beamforming and single-channel spectral processing for enhancing noisy and reverberant speech

    NASA Astrophysics Data System (ADS)

    Cauchi, Benjamin; Kodrasi, Ina; Rehr, Robert; Gerlach, Stephan; Jukić, Ante; Gerkmann, Timo; Doclo, Simon; Goetze, Stefan

    2015-12-01

    This paper presents a system aiming at joint dereverberation and noise reduction by applying a combination of a beamformer with a single-channel spectral enhancement scheme. First, a minimum variance distortionless response beamformer with an online estimated noise coherence matrix is used to suppress noise and reverberation. The output of this beamformer is then processed by a single-channel spectral enhancement scheme, based on statistical room acoustics, minimum statistics, and temporal cepstrum smoothing, to suppress residual noise and reverberation. The evaluation is conducted using the REVERB challenge corpus, designed to evaluate speech enhancement algorithms in the presence of both reverberation and noise. The proposed system is evaluated using instrumental speech quality measures, the performance of an automatic speech recognition system, and a subjective evaluation of the speech quality based on a MUSHRA test. The performance achieved by beamforming, single-channel spectral enhancement, and their combination are compared, and experimental results show that the proposed system is effective in suppressing both reverberation and noise while improving the speech quality. The achieved improvements are particularly significant in conditions with high reverberation times.

  2. Advances in Digital Calibration Techniques Enabling Real-Time Beamforming SweepSAR Architectures

    NASA Technical Reports Server (NTRS)

    Hoffman, James P.; Perkovic, Dragana; Ghaemi, Hirad; Horst, Stephen; Shaffer, Scott; Veilleux, Louise

    2013-01-01

    Real-time digital beamforming, combined with lightweight, large aperture reflectors, enable SweepSAR architectures, which promise significant increases in instrument capability for solid earth and biomass remote sensing. These new instrument concepts require new methods for calibrating the multiple channels, which are combined on-board, in real-time. The benefit of this effort is that it enables a new class of lightweight radar architecture, Digital Beamforming with SweepSAR, providing significantly larger swath coverage than conventional SAR architectures for reduced mass and cost. This paper will review the on-going development of the digital calibration architecture for digital beamforming radar instrument, such as the proposed Earth Radar Mission's DESDynI (Deformation, Ecosystem Structure, and Dynamics of Ice) instrument. This proposed instrument's baseline design employs SweepSAR digital beamforming and requires digital calibration. We will review the overall concepts and status of the system architecture, algorithm development, and the digital calibration testbed currently being developed. We will present results from a preliminary hardware demonstration. We will also discuss the challenges and opportunities specific to this novel architecture.

  3. Transform Domain Robust Variable Step Size Griffiths' Adaptive Algorithm for Noise Cancellation in ECG

    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.

  4. Adaptive search range adjustment and multiframe selection algorithm for motion estimation in H.264/AVC

    NASA Astrophysics Data System (ADS)

    Liu, Yingzhe; Wang, Jinxiang; Fu, Fangfa

    2013-04-01

    The H.264/AVC video standard adopts a fixed search range (SR) and fixed reference frame (RF) for motion estimation. These fixed settings result in a heavy computational load in the video encoder. We propose a dynamic SR and multiframe selection algorithm to improve the computational efficiency of motion estimation. By exploiting the relationship between the predicted motion vector and the SR size, we develop an adaptive SR adjustment algorithm. We also design a RF selection scheme based on the correlation between the different block sizes of the macroblock. Experimental results show that our algorithm can significantly reduce the computational complexity of motion estimation compared with the JM15.1 reference software, with a negligible decrease in peak signal-to-noise ratio and a slight increase in bit rate. Our algorithm also outperforms existing methods in terms of its low complexity and high coding quality.

  5. Super-resolution reconstruction algorithm based on adaptive convolution kernel size selection

    NASA Astrophysics Data System (ADS)

    Gao, Hang; Chen, Qian; Sui, Xiubao; Zeng, Junjie; Zhao, Yao

    2016-09-01

    Restricted by the detector technology and optical diffraction limit, the spatial resolution of infrared imaging system is difficult to achieve significant improvement. Super-Resolution (SR) reconstruction algorithm is an effective way to solve this problem. Among them, the SR algorithm based on multichannel blind deconvolution (MBD) estimates the convolution kernel only by low resolution observation images, according to the appropriate regularization constraints introduced by a priori assumption, to realize the high resolution image restoration. The algorithm has been shown effective when each channel is prime. In this paper, we use the significant edges to estimate the convolution kernel and introduce an adaptive convolution kernel size selection mechanism, according to the uncertainty of the convolution kernel size in MBD processing. To reduce the interference of noise, we amend the convolution kernel in an iterative process, and finally restore a clear image. Experimental results show that the algorithm can meet the convergence requirement of the convolution kernel estimation.

  6. Error bounds of adaptive dynamic programming algorithms for solving undiscounted optimal control problems.

    PubMed

    Liu, Derong; Li, Hongliang; Wang, Ding

    2015-06-01

    In this paper, we establish error bounds of adaptive dynamic programming algorithms for solving undiscounted infinite-horizon optimal control problems of discrete-time deterministic nonlinear systems. We consider approximation errors in the update equations of both value function and control policy. We utilize a new assumption instead of the contraction assumption in discounted optimal control problems. We establish the error bounds for approximate value iteration based on a new error condition. Furthermore, we also establish the error bounds for approximate policy iteration and approximate optimistic policy iteration algorithms. It is shown that the iterative approximate value function can converge to a finite neighborhood of the optimal value function under some conditions. To implement the developed algorithms, critic and action neural networks are used to approximate the value function and control policy, respectively. Finally, a simulation example is given to demonstrate the effectiveness of the developed algorithms.

  7. On an adaptive preconditioned Crank-Nicolson MCMC algorithm for infinite dimensional Bayesian inference

    NASA Astrophysics Data System (ADS)

    Hu, Zixi; Yao, Zhewei; Li, Jinglai

    2017-03-01

    Many scientific and engineering problems require to perform Bayesian inference for unknowns of infinite dimension. In such problems, many standard Markov Chain Monte Carlo (MCMC) algorithms become arbitrary slow under the mesh refinement, which is referred to as being dimension dependent. To this end, a family of dimensional independent MCMC algorithms, known as the preconditioned Crank-Nicolson (pCN) methods, were proposed to sample the infinite dimensional parameters. In this work we develop an adaptive version of the pCN algorithm, where the covariance operator of the proposal distribution is adjusted based on sampling history to improve the simulation efficiency. We show that the proposed algorithm satisfies an important ergodicity condition under some mild assumptions. Finally we provide numerical examples to demonstrate the performance of the proposed method.

  8. A Constrained Genetic Algorithm with Adaptively Defined Fitness Function in MRS Quantification

    NASA Astrophysics Data System (ADS)

    Papakostas, G. A.; Karras, D. A.; Mertzios, B. G.; Graveron-Demilly, D.; van Ormondt, D.

    MRS Signal quantification is a rather involved procedure and has attracted the interest of the medical engineering community, regarding the development of computationally efficient methodologies. Significant contributions based on Computational Intelligence tools, such as Neural Networks (NNs), demonstrated a good performance but not without drawbacks already discussed by the authors. On the other hand preliminary application of Genetic Algorithms (GA) has already been reported in the literature by the authors regarding the peak detection problem encountered in MRS quantification using the Voigt line shape model. This paper investigates a novel constrained genetic algorithm involving a generic and adaptively defined fitness function which extends the simple genetic algorithm methodology in case of noisy signals. The applicability of this new algorithm is scrutinized through experimentation in artificial MRS signals interleaved with noise, regarding its signal fitting capabilities. Although extensive experiments with real world MRS signals are necessary, the herein shown performance illustrates the method's potential to be established as a generic MRS metabolites quantification procedure.

  9. Self-adaptive predictor-corrector algorithm for static nonlinear structural analysis

    NASA Technical Reports Server (NTRS)

    Padovan, J.

    1981-01-01

    A multiphase selfadaptive predictor corrector type algorithm was developed. This algorithm enables the solution of highly nonlinear structural responses including kinematic, kinetic and material effects as well as pro/post buckling behavior. The strategy involves three main phases: (1) the use of a warpable hyperelliptic constraint surface which serves to upperbound dependent iterate excursions during successive incremental Newton Ramphson (INR) type iterations; (20 uses an energy constraint to scale the generation of successive iterates so as to maintain the appropriate form of local convergence behavior; (3) the use of quality of convergence checks which enable various self adaptive modifications of the algorithmic structure when necessary. The restructuring is achieved by tightening various conditioning parameters as well as switch to different algorithmic levels to improve the convergence process. The capabilities of the procedure to handle various types of static nonlinear structural behavior are illustrated.

  10. An adaptive metamodel-based global optimization algorithm for black-box type problems

    NASA Astrophysics Data System (ADS)

    Jie, Haoxiang; Wu, Yizhong; Ding, Jianwan

    2015-11-01

    In this article, an adaptive metamodel-based global optimization (AMGO) algorithm is presented to solve unconstrained black-box problems. In the AMGO algorithm, a type of hybrid model composed of kriging and augmented radial basis function (RBF) is used as the surrogate model. The weight factors of hybrid model are adaptively selected in the optimization process. To balance the local and global search, a sub-optimization problem is constructed during each iteration to determine the new iterative points. As numerical experiments, six standard two-dimensional test functions are selected to show the distributions of iterative points. The AMGO algorithm is also tested on seven well-known benchmark optimization problems and contrasted with three representative metamodel-based optimization methods: efficient global optimization (EGO), GutmannRBF and hybrid and adaptive metamodel (HAM). The test results demonstrate the efficiency and robustness of the proposed method. The AMGO algorithm is finally applied to the structural design of the import and export chamber of a cycloid gear pump, achieving satisfactory results.

  11. A parallel second-order adaptive mesh algorithm for incompressible flow in porous media.

    PubMed

    Pau, George S H; Almgren, Ann S; Bell, John B; Lijewski, Michael J

    2009-11-28

    In this paper, we present a second-order accurate adaptive algorithm for solving multi-phase, incompressible flow in porous media. We assume a multi-phase form of Darcy's law with relative permeabilities given as a function of the phase saturation. The remaining equations express conservation of mass for the fluid constituents. In this setting, the total velocity, defined to be the sum of the phase velocities, is divergence free. The basic integration method is based on a total-velocity splitting approach in which we solve a second-order elliptic pressure equation to obtain a total velocity. This total velocity is then used to recast component conservation equations as nonlinear hyperbolic equations. Our approach to adaptive refinement uses a nested hierarchy of logically rectangular grids with simultaneous refinement of the grids in both space and time. The integration algorithm on the grid hierarchy is a recursive procedure in which coarse grids are advanced in time, fine grids are advanced multiple steps to reach the same time as the coarse grids and the data at different levels are then synchronized. The single-grid algorithm is described briefly, but the emphasis here is on the time-stepping procedure for the adaptive hierarchy. Numerical examples are presented to demonstrate the algorithm's accuracy and convergence properties and to illustrate the behaviour of the method.

  12. A Parallel Second-Order Adaptive Mesh Algorithm for Incompressible Flow in Porous Media

    SciTech Connect

    Pau, George Shu Heng; Almgren, Ann S.; Bell, John B.; Lijewski, Michael J.

    2008-04-01

    In this paper we present a second-order accurate adaptive algorithm for solving multiphase, incompressible flows in porous media. We assume a multiphase form of Darcy's law with relative permeabilities given as a function of the phase saturation. The remaining equations express conservation of mass for the fluid constituents. In this setting the total velocity, defined to be the sum of the phase velocities, is divergence-free. The basic integration method is based on a total-velocity splitting approach in which we solve a second-order elliptic pressure equation to obtain a total velocity. This total velocity is then used to recast component conservation equations as nonlinear hyperbolic equations. Our approach to adaptive refinement uses a nested hierarchy of logically rectangular grids with simultaneous refinement of the grids in both space and time. The integration algorithm on the grid hierarchy is a recursive procedure in which coarse grids are advanced in time, fine grids areadvanced multiple steps to reach the same time as the coarse grids and the data atdifferent levels are then synchronized. The single grid algorithm is described briefly,but the emphasis here is on the time-stepping procedure for the adaptive hierarchy. Numerical examples are presented to demonstrate the algorithm's accuracy and convergence properties and to illustrate the behavior of the method.

  13. Construction of point process adaptive filter algorithms for neural systems using sequential Monte Carlo methods.

    PubMed

    Ergün, Ayla; Barbieri, Riccardo; Eden, Uri T; Wilson, Matthew A; Brown, Emery N

    2007-03-01

    The stochastic state point process filter (SSPPF) and steepest descent point process filter (SDPPF) are adaptive filter algorithms for state estimation from point process observations that have been used to track neural receptive field plasticity and to decode the representations of biological signals in ensemble neural spiking activity. The SSPPF and SDPPF are constructed using, respectively, Gaussian and steepest descent approximations to the standard Bayes and Chapman-Kolmogorov (BCK) system of filter equations. To extend these approaches for constructing point process adaptive filters, we develop sequential Monte Carlo (SMC) approximations to the BCK equations in which the SSPPF and SDPPF serve as the proposal densities. We term the two new SMC point process filters SMC-PPFs and SMC-PPFD, respectively. We illustrate the new filter algorithms by decoding the wind stimulus magnitude from simulated neural spiking activity in the cricket cercal system. The SMC-PPFs and SMC-PPFD provide more accurate state estimates at low number of particles than a conventional bootstrap SMC filter algorithm in which the state transition probability density is the proposal density. We also use the SMC-PPFs algorithm to track the temporal evolution of a spatial receptive field of a rat hippocampal neuron recorded while the animal foraged in an open environment. Our results suggest an approach for constructing point process adaptive filters using SMC methods.

  14. A structured multi-block solution-adaptive mesh algorithm with mesh quality assessment

    NASA Technical Reports Server (NTRS)

    Ingram, Clint L.; Laflin, Kelly R.; Mcrae, D. Scott

    1995-01-01

    The dynamic solution adaptive grid algorithm, DSAGA3D, is extended to automatically adapt 2-D structured multi-block grids, including adaption of the block boundaries. The extension is general, requiring only input data concerning block structure, connectivity, and boundary conditions. Imbedded grid singular points are permitted, but must be prevented from moving in space. Solutions for workshop cases 1 and 2 are obtained on multi-block grids and illustrate both increased resolution of and alignment with the solution. A mesh quality assessment criteria is proposed to determine how well a given mesh resolves and aligns with the solution obtained upon it. The criteria is used to evaluate the grid quality for solutions of workshop case 6 obtained on both static and dynamically adapted grids. The results indicate that this criteria shows promise as a means of evaluating resolution.

  15. Adaptive Neural Network Algorithm for Power Control in Nuclear Power Plants

    NASA Astrophysics Data System (ADS)

    Masri Husam Fayiz, Al

    2017-01-01

    The aim of this paper is to design, test and evaluate a prototype of an adaptive neural network algorithm for the power controlling system of a nuclear power plant. The task of power control in nuclear reactors is one of the fundamental tasks in this field. Therefore, researches are constantly conducted to ameliorate the power reactor control process. Currently, in the Department of Automation in the National Research Nuclear University (NRNU) MEPhI, numerous studies are utilizing various methodologies of artificial intelligence (expert systems, neural networks, fuzzy systems and genetic algorithms) to enhance the performance, safety, efficiency and reliability of nuclear power plants. In particular, a study of an adaptive artificial intelligent power regulator in the control systems of nuclear power reactors is being undertaken to enhance performance and to minimize the output error of the Automatic Power Controller (APC) on the grounds of a multifunctional computer analyzer (simulator) of the Water-Water Energetic Reactor known as Vodo-Vodyanoi Energetichesky Reaktor (VVER) in Russian. In this paper, a block diagram of an adaptive reactor power controller was built on the basis of an intelligent control algorithm. When implementing intelligent neural network principles, it is possible to improve the quality and dynamic of any control system in accordance with the principles of adaptive control. It is common knowledge that an adaptive control system permits adjusting the controller’s parameters according to the transitions in the characteristics of the control object or external disturbances. In this project, it is demonstrated that the propitious options for an automatic power controller in nuclear power plants is a control system constructed on intelligent neural network algorithms.

  16. Unsupervised Cryo-EM Data Clustering through Adaptively Constrained K-Means Algorithm

    PubMed Central

    Xu, Yaofang; Wu, Jiayi; Yin, Chang-Cheng; Mao, Youdong

    2016-01-01

    In single-particle cryo-electron microscopy (cryo-EM), K-means clustering algorithm is widely used in unsupervised 2D classification of projection images of biological macromolecules. 3D ab initio reconstruction requires accurate unsupervised classification in order to separate molecular projections of distinct orientations. Due to background noise in single-particle images and uncertainty of molecular orientations, traditional K-means clustering algorithm may classify images into wrong classes and produce classes with a large variation in membership. Overcoming these limitations requires further development on clustering algorithms for cryo-EM data analysis. We propose a novel unsupervised data clustering method building upon the traditional K-means algorithm. By introducing an adaptive constraint term in the objective function, our algorithm not only avoids a large variation in class sizes but also produces more accurate data clustering. Applications of this approach to both simulated and experimental cryo-EM data demonstrate that our algorithm is a significantly improved alterative to the traditional K-means algorithm in single-particle cryo-EM analysis. PMID:27959895

  17. Unsupervised Cryo-EM Data Clustering through Adaptively Constrained K-Means Algorithm.

    PubMed

    Xu, Yaofang; Wu, Jiayi; Yin, Chang-Cheng; Mao, Youdong

    2016-01-01

    In single-particle cryo-electron microscopy (cryo-EM), K-means clustering algorithm is widely used in unsupervised 2D classification of projection images of biological macromolecules. 3D ab initio reconstruction requires accurate unsupervised classification in order to separate molecular projections of distinct orientations. Due to background noise in single-particle images and uncertainty of molecular orientations, traditional K-means clustering algorithm may classify images into wrong classes and produce classes with a large variation in membership. Overcoming these limitations requires further development on clustering algorithms for cryo-EM data analysis. We propose a novel unsupervised data clustering method building upon the traditional K-means algorithm. By introducing an adaptive constraint term in the objective function, our algorithm not only avoids a large variation in class sizes but also produces more accurate data clustering. Applications of this approach to both simulated and experimental cryo-EM data demonstrate that our algorithm is a significantly improved alterative to the traditional K-means algorithm in single-particle cryo-EM analysis.

  18. Dependence of Adaptive Cross-correlation Algorithm Performance on the Extended Scene Image Quality

    NASA Technical Reports Server (NTRS)

    Sidick, Erkin

    2008-01-01

    Recently, we reported an adaptive cross-correlation (ACC) algorithm to estimate with high accuracy the shift as large as several pixels between two extended-scene sub-images captured by a Shack-Hartmann wavefront sensor. It determines the positions of all extended-scene image cells relative to a reference cell in the same frame using an FFT-based iterative image-shifting algorithm. It works with both point-source spot images as well as extended scene images. We have demonstrated previously based on some measured images that the ACC algorithm can determine image shifts with as high an accuracy as 0.01 pixel for shifts as large 3 pixels, and yield similar results for both point source spot images and extended scene images. The shift estimate accuracy of the ACC algorithm depends on illumination level, background, and scene content in addition to the amount of the shift between two image cells. In this paper we investigate how the performance of the ACC algorithm depends on the quality and the frequency content of extended scene images captured by a Shack-Hatmann camera. We also compare the performance of the ACC algorithm with those of several other approaches, and introduce a failsafe criterion for the ACC algorithm-based extended scene Shack-Hatmann sensors.

  19. A fast image super-resolution algorithm using an adaptive Wiener filter.

    PubMed

    Hardie, Russell

    2007-12-01

    A computationally simple super-resolution algorithm using a type of adaptive Wiener filter is proposed. The algorithm produces an improved resolution image from a sequence of low-resolution (LR) video frames with overlapping field of view. The algorithm uses subpixel registration to position each LR pixel value on a common spatial grid that is referenced to the average position of the input frames. The positions of the LR pixels are not quantized to a finite grid as with some previous techniques. The output high-resolution (HR) pixels are obtained using a weighted sum of LR pixels in a local moving window. Using a statistical model, the weights for each HR pixel are designed to minimize the mean squared error and they depend on the relative positions of the surrounding LR pixels. Thus, these weights adapt spatially and temporally to changing distributions of LR pixels due to varying motion. Both a global and spatially varying statistical model are considered here. Since the weights adapt with distribution of LR pixels, it is quite robust and will not become unstable when an unfavorable distribution of LR pixels is observed. For translational motion, the algorithm has a low computational complexity and may be readily suitable for real-time and/or near real-time processing applications. With other motion models, the computational complexity goes up significantly. However, regardless of the motion model, the algorithm lends itself to parallel implementation. The efficacy of the proposed algorithm is demonstrated here in a number of experimental results using simulated and real video sequences. A computational analysis is also presented.

  20. Robust Multi-Frame Adaptive Optics Image Restoration Algorithm Using Maximum Likelihood Estimation with Poisson Statistics.

    PubMed

    Li, Dongming; Sun, Changming; Yang, Jinhua; Liu, Huan; Peng, Jiaqi; Zhang, Lijuan

    2017-04-06

    An adaptive optics (AO) system provides real-time compensation for atmospheric turbulence. However, an AO image is usually of poor contrast because of the nature of the imaging process, meaning that the image contains information coming from both out-of-focus and in-focus planes of the object, which also brings about a loss in quality. In this paper, we present a robust multi-frame adaptive optics image restoration algorithm via maximum likelihood estimation. Our proposed algorithm uses a maximum likelihood method with image regularization as the basic principle, and constructs the joint log likelihood function for multi-frame AO images based on a Poisson distribution model. To begin with, a frame selection method based on image variance is applied to the observed multi-frame AO images to select images with better quality to improve the convergence of a blind deconvolution algorithm. Then, by combining the imaging conditions and the AO system properties, a point spread function estimation model is built. Finally, we develop our iterative solutions for AO image restoration addressing the joint deconvolution issue. We conduct a number of experiments to evaluate the performances of our proposed algorithm. Experimental results show that our algorithm produces accurate AO image restoration results and outperforms the current state-of-the-art blind deconvolution methods.

  1. A novel adaptive multi-focus image fusion algorithm based on PCNN and sharpness

    NASA Astrophysics Data System (ADS)

    Miao, Qiguang; Wang, Baoshu

    2005-05-01

    A novel adaptive multi-focus image fusion algorithm is given in this paper, which is based on the improved pulse coupled neural network(PCNN) model, the fundamental characteristics of the multi-focus image and the properties of visual imaging. Compared with the traditional algorithm where the linking strength, βij, of each neuron in the PCNN model is the same and its value is chosen through experimentation, this algorithm uses the clarity of each pixel of the image as its value, so that the linking strength of each pixel can be chosen adaptively. A fused image is produced by processing through the compare-select operator the objects of each firing mapping image taking part in image fusion, deciding in which image the clear parts is and choosing the clear parts in the image fusion process. By this algorithm, other parameters, for example, Δ, the threshold adjusting constant, only have a slight effect on the new fused image. It therefore overcomes the difficulty in adjusting parameters in the PCNN. Experiments show that the proposed algorithm works better in preserving the edge and texture information than the wavelet transform method and the Laplacian pyramid method do in multi-focus image fusion.

  2. Algorithm for localized adaptive diffuse optical tomography and its application in bioluminescence tomography

    NASA Astrophysics Data System (ADS)

    Naser, Mohamed A.; Patterson, Michael S.; Wong, John W.

    2014-04-01

    A reconstruction algorithm for diffuse optical tomography based on diffusion theory and finite element method is described. The algorithm reconstructs the optical properties in a permissible domain or region-of-interest to reduce the number of unknowns. The algorithm can be used to reconstruct optical properties for a segmented object (where a CT-scan or MRI is available) or a non-segmented object. For the latter, an adaptive segmentation algorithm merges contiguous regions with similar optical properties thereby reducing the number of unknowns. In calculating the Jacobian matrix the algorithm uses an efficient direct method so the required time is comparable to that needed for a single forward calculation. The reconstructed optical properties using segmented, non-segmented, and adaptively segmented 3D mouse anatomy (MOBY) are used to perform bioluminescence tomography (BLT) for two simulated internal sources. The BLT results suggest that the accuracy of reconstruction of total source power obtained without the segmentation provided by an auxiliary imaging method such as x-ray CT is comparable to that obtained when using perfect segmentation.

  3. Parallel paving: An algorithm for generating distributed, adaptive, all-quadrilateral meshes on parallel computers

    SciTech Connect

    Lober, R.R.; Tautges, T.J.; Vaughan, C.T.

    1997-03-01

    Paving is an automated mesh generation algorithm which produces all-quadrilateral elements. It can additionally generate these elements in varying sizes such that the resulting mesh adapts to a function distribution, such as an error function. While powerful, conventional paving is a very serial algorithm in its operation. Parallel paving is the extension of serial paving into parallel environments to perform the same meshing functions as conventional paving only on distributed, discretized models. This extension allows large, adaptive, parallel finite element simulations to take advantage of paving`s meshing capabilities for h-remap remeshing. A significantly modified version of the CUBIT mesh generation code has been developed to host the parallel paving algorithm and demonstrate its capabilities on both two dimensional and three dimensional surface geometries and compare the resulting parallel produced meshes to conventionally paved meshes for mesh quality and algorithm performance. Sandia`s {open_quotes}tiling{close_quotes} dynamic load balancing code has also been extended to work with the paving algorithm to retain parallel efficiency as subdomains undergo iterative mesh refinement.

  4. Multiuser Transmit Beamforming for Maximum Sum Capacity in Tactical Wireless Multicast Networks

    DTIC Science & Technology

    2006-08-01

    method of the same computational complexity based on simple zero-forcing beamforming. Our results indicate that the proposed method attains a...Simulations indicate that the performance of our algorithm is close to the CRB when the network is (close to) fully connected, in the sense that every node can...throughput of gZF-DP and sum capacity, at a low coding and on-line computation cost. The simulation results also indicate that, at high SNR, ZFS achieves

  5. Adaptive-mesh-based algorithm for fluorescence molecular tomography using an analytical solution

    NASA Astrophysics Data System (ADS)

    Wang, Daifa; Song, Xiaolei; Bai, Jing

    2007-07-01

    Fluorescence molecular tomography (FMT) has become an important method for in-vivo imaging of small animals. It has been widely used for tumor genesis, cancer detection, metastasis, drug discovery, and gene therapy. In this study, an algorithm for FMT is proposed to obtain accurate and fast reconstruction by combining an adaptive mesh refinement technique and an analytical solution of diffusion equation. Numerical studies have been performed on a parallel plate FMT system with matching fluid. The reconstructions obtained show that the algorithm is efficient in computation time, and they also maintain image quality.

  6. Genetic algorithm based adaptive neural network ensemble and its application in predicting carbon flux

    USGS Publications Warehouse

    Xue, Y.; Liu, S.; Hu, Y.; Yang, J.; Chen, Q.

    2007-01-01

    To improve the accuracy in prediction, Genetic Algorithm based Adaptive Neural Network Ensemble (GA-ANNE) is presented. Intersections are allowed between different training sets based on the fuzzy clustering analysis, which ensures the diversity as well as the accuracy of individual Neural Networks (NNs). Moreover, to improve the accuracy of the adaptive weights of individual NNs, GA is used to optimize the cluster centers. Empirical results in predicting carbon flux of Duke Forest reveal that GA-ANNE can predict the carbon flux more accurately than Radial Basis Function Neural Network (RBFNN), Bagging NN ensemble, and ANNE. ?? 2007 IEEE.

  7. 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

  8. Diverging Wave Volumetric Imaging Using Subaperture Beamforming.

    PubMed

    Santos, Pedro; Haugen, Geir Ultveit; Lovstakken, Lasse; Samset, Eigil; D'hooge, Jan

    2016-12-01

    Several clinical settings could benefit from 3-D high frame rate (HFR) imaging and, in particular, HFR 3-D tissue Doppler imaging (TDI). To date, the proposed methodologies are based mostly on experimental ultrasound platforms, making their translation to clinical systems nontrivial as these have additional hardware constraints. In particular, clinically used 2-D matrix array transducers rely on subaperture (SAP) beamforming to limit cabling between the ultrasound probe and the back-end console. Therefore, this paper is aimed at assessing the feasibility of HFR 3-D TDI using diverging waves (DWs) on a clinical transducer with SAP beamforming limitations. Simulation studies showed that the combination of a single DW transmission with SAP beamforming results in severe imaging artifacts due to grating lobes and reduced penetration. Interestingly, a promising tradeoff between image quality and frame rate was achieved for scan sequences with a moderate number of transmit beams. In particular, a sparse sequence with nine transmissions showed good imaging performance for an imaging sector of 70 (°)×70 (°) at volume rates of approximately 600 Hz. Subsequently, this sequence was implemented in a clinical system and TDI was recorded in vivo on healthy subjects. Velocity curves were extracted and compared against conventional TDI (i.e., with focused transmit beams). The results showed similar velocities between both beamforming approaches, with a cross-correlation of 0.90 ± 0.11 between the traces of each mode. Overall, this paper indicates that HFR 3-D TDI is feasible in systems with clinical 2-D matrix arrays, despite the limitations of SAP beamforming.

  9. Beamforming of joint polarization-space matched filtering for conformal array.

    PubMed

    Liu, Lutao; Jiang, Yilin; Wan, Liangtian; Tian, Zuoxi

    2013-01-01

    Due to the polarization mismatch of the antenna, the received signal suffers from energy loss. The conventional beamforming algorithms could not be applied to the conformal array because of the varying curvature. In order to overcome the energy loss of the received signal, a novel joint polarization-space matched filtering algorithm for cylindrical conformal array is proposed. First, the snapshot data model of the conformal polarization sensitive array is analyzed. Second, the analytical expression of polarization sensitive array beamforming is derived. Linearly constrained minimum variance (LCMV) beamforming technique is facilitated for the cylindrical conformal array. Third, the idea of joint polarization-space matched filtering is presented, and the principle of joint polarization-space matched filtering is discussed in detail. Theoretical analysis and computer simulation results verify that the conformal polarization sensitive array is more robust than the ordinary conformal array. The proposed algorithm can improve the performance when signal and interference are too close. It can enhance the signal-to-noise ratio (SNR) by adjusting the polarization of the elements of the conformal array, which matches the polarization of the incident signal.

  10. Active control of passive acoustic fields: passive synthetic aperture/Doppler beamforming with data from an autonomous vehicle.

    PubMed

    D'Spain, Gerald L; Terrill, Eric; Chadwell, C David; Smith, Jerome A; Lynch, Stephen D

    2006-12-01

    The maneuverability of autonomous underwater vehicles (AUVs) equipped with hull-mounted arrays provides the opportunity to actively modify received acoustic fields to optimize extraction of information. This paper uses ocean acoustic data collected by an AUV-mounted two-dimensional hydrophone array, with overall dimension one-tenth wavelength at 200-500 Hz, to demonstrate aspects of this control through vehicle motion. Source localization is performed using Doppler shifts measured at a set of receiver velocities by both single elements and a physical array. Results show that a source in the presence of a 10-dB higher-level interferer having exactly the same frequency content (as measured by a stationary receiver) is properly localized and that white-noise-constrained adaptive beamforming applied to the physical aperture data in combination with Doppler beamforming provides greater spatial resolution than physical-aperture-alone beamforming and significantly lower sidelobes than single element Doppler beamforming. A new broadband beamformer that adjusts for variations in vehicle velocity on a sample by sample basis is demonstrated with data collected during a high-acceleration maneuver. The importance of including the cost of energy expenditure in determining optimal vehicle motion is demonstrated through simulation, further illustrating how the vehicle characteristics are an integral part of the signal/array processing structure.

  11. A DVH-guided IMRT optimization algorithm for automatic treatment planning and adaptive radiotherapy replanning

    SciTech Connect

    Zarepisheh, Masoud; Li, Nan; Long, Troy; Romeijn, H. Edwin; Tian, Zhen; Jia, Xun; Jiang, Steve B.

    2014-06-15

    Purpose: To develop a novel algorithm that incorporates prior treatment knowledge into intensity modulated radiation therapy optimization to facilitate automatic treatment planning and adaptive radiotherapy (ART) replanning. Methods: The algorithm automatically creates a treatment plan guided by the DVH curves of a reference plan that contains information on the clinician-approved dose-volume trade-offs among different targets/organs and among different portions of a DVH curve for an organ. In ART, the reference plan is the initial plan for the same patient, while for automatic treatment planning the reference plan is selected from a library of clinically approved and delivered plans of previously treated patients with similar medical conditions and geometry. The proposed algorithm employs a voxel-based optimization model and navigates the large voxel-based Pareto surface. The voxel weights are iteratively adjusted to approach a plan that is similar to the reference plan in terms of the DVHs. If the reference plan is feasible but not Pareto optimal, the algorithm generates a Pareto optimal plan with the DVHs better than the reference ones. If the reference plan is too restricting for the new geometry, the algorithm generates a Pareto plan with DVHs close to the reference ones. In both cases, the new plans have similar DVH trade-offs as the reference plans. Results: The algorithm was tested using three patient cases and found to be able to automatically adjust the voxel-weighting factors in order to generate a Pareto plan with similar DVH trade-offs as the reference plan. The algorithm has also been implemented on a GPU for high efficiency. Conclusions: A novel prior-knowledge-based optimization algorithm has been developed that automatically adjust the voxel weights and generate a clinical optimal plan at high efficiency. It is found that the new algorithm can significantly improve the plan quality and planning efficiency in ART replanning and automatic treatment

  12. Adjoint-Based Algorithms for Adaptation and Design Optimizations on Unstructured Grids

    NASA Technical Reports Server (NTRS)

    Nielsen, Eric J.

    2006-01-01

    Schemes based on discrete adjoint algorithms present several exciting opportunities for significantly advancing the current state of the art in computational fluid dynamics. Such methods provide an extremely efficient means for obtaining discretely consistent sensitivity information for hundreds of design variables, opening the door to rigorous, automated design optimization of complex aerospace configuration using the Navier-Stokes equation. Moreover, the discrete adjoint formulation provides a mathematically rigorous foundation for mesh adaptation and systematic reduction of spatial discretization error. Error estimates are also an inherent by-product of an adjoint-based approach, valuable information that is virtually non-existent in today's large-scale CFD simulations. An overview of the adjoint-based algorithm work at NASA Langley Research Center is presented, with examples demonstrating the potential impact on complex computational problems related to design optimization as well as mesh adaptation.

  13. A robust face recognition algorithm under varying illumination using adaptive retina modeling

    NASA Astrophysics Data System (ADS)

    Cheong, Yuen Kiat; Yap, Vooi Voon; Nisar, Humaira

    2013-10-01

    Variation in illumination has a drastic effect on the appearance of a face image. This may hinder the automatic face recognition process. This paper presents a novel approach for face recognition under varying lighting conditions. The proposed algorithm uses adaptive retina modeling based illumination normalization. In the proposed approach, retina modeling is employed along with histogram remapping following normal distribution. Retina modeling is an approach that combines two adaptive nonlinear equations and a difference of Gaussians filter. Two databases: extended Yale B database and CMU PIE database are used to verify the proposed algorithm. For face recognition Gabor Kernel Fisher Analysis method is used. Experimental results show that the recognition rate for the face images with different illumination conditions has improved by the proposed approach. Average recognition rate for Extended Yale B database is 99.16%. Whereas, the recognition rate for CMU-PIE database is 99.64%.

  14. A Study on Adapting the Zoom FFT Algorithm to Automotive Millimetre Wave Radar

    NASA Astrophysics Data System (ADS)

    Kuroda, Hiroshi; Takano, Kazuaki

    The millimetre wave radar has been developed for automotive application such as ACC (Adaptive Cruise Control) and CWS (Collision Warning System). The radar uses MMIC (Monolithic Microwave Integrated Circuits) devices for transmitting and receiving 76 GHz millimetre wave signals. The radar is FSK (Frequency Shift Keying) monopulse type. The radar transmits 2 frequencies in time-duplex manner, and measures distance and relative speed of targets. The monopulse feature detects the azimuth angle of targets without a scanning mechanism. The Zoom FFT (Fast Fourier Transform) algorithm, which analyses frequency domain precisely, has adapted to the radar for discriminating multiple stationary targets. The Zoom FFT algorithm is evaluated in test truck. The evaluation results show good performance on discriminating two stationary vehicles in host lane and adjacent lane.

  15. Photorefractive phased array antenna beam-forming processor

    NASA Astrophysics Data System (ADS)

    Sarto, Anthony W.; Wagner, Kelvin H.; Weverka, Robert T.; Blair, Steven M.; Weaver, Samuel P.

    1996-11-01

    A high bandwidth, large degree-of-freedom photorefractive phased-array antenna beam-forming processor which uses 3D dynamic volume holograms in photorefractive crystals to time integrate the adaptive weights to perform beam steering and jammer-cancellation signal-processing tasks is described. The processor calculates the angle-of-arrival of a desired signal of interest and steers the antenna pattern in the direction of this desired signal by forming a dynamic holographic grating proportional to the correlation between the incoming signal of interest from the antenna array and the temporal waveform of the desired signal. Experimental results of main-beam formation and measured array-functions are presented in holographic index grating and the resulting processor output.

  16. Multi-line transmission combined with minimum variance beamforming in medical ultrasound imaging.

    PubMed

    Rabinovich, Adi; Feuer, Arie; Friedman, Zvi

    2015-05-01

    Increasing medical ultrasound imaging frame rate is important in several applications such as cardiac diagnostic imaging, where it is desirable to be able to examine the temporal behavior of fast phases in the cardiac cycle. This is particularly true in 3-D imaging, where current frame rate is still much slower than standard 2-D, B-mode imaging. Recently, a method that increases frame rate, labeled multi-line transmission (MLT), was reintroduced and analyzed. In MLT scanning, the transmission is simultaneously focused at several directions. This scan mode introduces artifacts that stem from the overlaps of the receive main lobe with the transmit side lobes of additional transmit directions besides the one of interest. Similar overlaps occur between the transmit main lobe with receive side lobes. These artifacts are known in the signal processing community as cross-talk. Previous studies have concentrated on proper transmit and receive apodization, as well as transmit directions arrangement in the transmit event, to reduce the cross-talk artifacts. This study examines the possibility of using adaptive beamforming, specifically, minimum variance (MV) and linearly constrained minimum variance (LCMV) beamforming, to reduce the cross-talk artifacts, and maintain or even improve image quality characteristics. Simulation results, as well as experimental phantom and in vivo cardiac data, demonstrate the feasibility of reducing cross-talk artifacts with MV beamforming. The MV and LCMV results achieve superior spatial resolution, not only over other MLT methods with data-independent apodization, but even over that of single-line transmission (SLT) without receive apodization. The MV beamformer is shown to be less sensitive to wider transmit profiles required to reduce the transmit crosstalk artifacts. MV beamforming, combined with the wider transmit profiles, can provide a good approach for MLT scanning with reduced cross-talk artifacts, without compromising spatial

  17. Incrementing data quality of multi-frequency echograms using the Adaptive Wiener Filter (AWF) denoising algorithm

    NASA Astrophysics Data System (ADS)

    Peña, M.

    2016-10-01

    Achieving acceptable signal-to-noise ratio (SNR) can be difficult when working in sparsely populated waters and/or when species have low scattering such as fluid filled animals. The increasing use of higher frequencies and the study of deeper depths in fisheries acoustics, as well as the use of commercial vessels, is raising the need to employ good denoising algorithms. The use of a lower Sv threshold to remove noise or unwanted targets is not suitable in many cases and increases the relative background noise component in the echogram, demanding more effectiveness from denoising algorithms. The Adaptive Wiener Filter (AWF) denoising algorithm is presented in this study. The technique is based on the AWF commonly used in digital photography and video enhancement. The algorithm firstly increments the quality of the data with a variance-dependent smoothing, before estimating the noise level as the envelope of the Sv minima. The AWF denoising algorithm outperforms existing algorithms in the presence of gaussian, speckle and salt & pepper noise, although impulse noise needs to be previously removed. Cleaned echograms present homogenous echotraces with outlined edges.

  18. Adaptive local backlight dimming algorithm based on local histogram and image characteristics

    NASA Astrophysics Data System (ADS)

    Nadernejad, Ehsan; Burini, Nino; Korhonen, Jari; Forchhammer, Søren; Mantel, Claire

    2013-02-01

    Liquid Crystal Display (LCDs) with Light Emitting Diode (LED) backlight is a very popular display technology, used for instance in television sets, monitors and mobile phones. This paper presents a new backlight dimming algorithm that exploits the characteristics of the target image, such as the local histograms and the average pixel intensity of each backlight segment, to reduce the power consumption of the backlight and enhance image quality. The local histogram of the pixels within each backlight segment is calculated and, based on this average, an adaptive quantile value is extracted. A classification into three classes based on the average luminance value is performed and, depending on the image luminance class, the extracted information on the local histogram determines the corresponding backlight value. The proposed method has been applied on two modeled screens: one with a high resolution direct-lit backlight, and the other screen with 16 edge-lit backlight segments placed in two columns and eight rows. We have compared the proposed algorithm against several known backlight dimming algorithms by simulations; and the results show that the proposed algorithm provides better trade-off between power consumption and image quality preservation than the other algorithms representing the state of the art among feature based backlight algorithms.

  19. An adaptive scale factor based MPPT algorithm for changing solar irradiation levels in outer space

    NASA Astrophysics Data System (ADS)

    Kwan, Trevor Hocksun; Wu, Xiaofeng

    2017-03-01

    Maximum power point tracking (MPPT) techniques are popularly used for maximizing the output of solar panels by continuously tracking the maximum power point (MPP) of their P-V curves, which depend both on the panel temperature and the input insolation. Various MPPT algorithms have been studied in literature, including perturb and observe (P&O), hill climbing, incremental conductance, fuzzy logic control and neural networks. This paper presents an algorithm which improves the MPP tracking performance by adaptively scaling the DC-DC converter duty cycle. The principle of the proposed algorithm is to detect the oscillation by checking the sign (ie. direction) of the duty cycle perturbation between the current and previous time steps. If there is a difference in the signs then it is clear an oscillation is present and the DC-DC converter duty cycle perturbation is subsequently scaled down by a constant factor. By repeating this process, the steady state oscillations become negligibly small which subsequently allows for a smooth steady state MPP response. To verify the proposed MPPT algorithm, a simulation involving irradiances levels that are typically encountered in outer space is conducted. Simulation and experimental results prove that the proposed algorithm is fast and stable in comparison to not only the conventional fixed step counterparts, but also to previous variable step size algorithms.

  20. An adaptive importance sampling algorithm for Bayesian inversion with multimodal distributions

    SciTech Connect

    Li, Weixuan; Lin, Guang

    2015-08-01

    Parametric uncertainties are encountered in the simulations of many physical systems, and may be reduced by an inverse modeling procedure that calibrates the simulation results to observations on the real system being simulated. Following Bayes' rule, a general approach for inverse modeling problems is to sample from the posterior distribution of the uncertain model parameters given the observations. However, the large number of repetitive forward simulations required in the sampling process could pose a prohibitive computational burden. This difficulty is particularly challenging when the posterior is multimodal. We present in this paper an adaptive importance sampling algorithm to tackle these challenges. Two essential ingredients of the algorithm are: 1) a Gaussian mixture (GM) model adaptively constructed as the proposal distribution to approximate the possibly multimodal target posterior, and 2) a mixture of polynomial chaos (PC) expansions, built according to the GM proposal, as a surrogate model to alleviate the computational burden caused by computational-demanding forward model evaluations. In three illustrative examples, the proposed adaptive importance sampling algorithm demonstrates its capabilities of automatically finding a GM proposal with an appropriate number of modes for the specific problem under study, and obtaining a sample accurately and efficiently representing the posterior with limited number of forward simulations.

  1. An adaptive image enhancement technique by combining cuckoo search and particle swarm optimization algorithm.

    PubMed

    Ye, Zhiwei; Wang, Mingwei; Hu, Zhengbing; Liu, Wei

    2015-01-01

    Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO) for low contrast images to enhance image adaptively. In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three factors which are threshold, entropy value, and gray-level probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CS-PSO is utilized to maximize the objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The performance of the proposed method has been compared with other existing techniques such as linear contrast stretching, histogram equalization, and evolutionary computing based image enhancement methods like backtracking search algorithm, differential search algorithm, genetic algorithm, and particle swarm optimization in terms of processing time and image quality. Experimental results demonstrate that the proposed method is robust and adaptive and exhibits the better performance than other methods involved in the paper.

  2. An adaptive importance sampling algorithm for Bayesian inversion with multimodal distributions

    SciTech Connect

    Li, Weixuan; Lin, Guang

    2015-03-21

    Parametric uncertainties are encountered in the simulations of many physical systems, and may be reduced by an inverse modeling procedure that calibrates the simulation results to observations on the real system being simulated. Following Bayes’ rule, a general approach for inverse modeling problems is to sample from the posterior distribution of the uncertain model parameters given the observations. However, the large number of repetitive forward simulations required in the sampling process could pose a prohibitive computational burden. This difficulty is particularly challenging when the posterior is multimodal. We present in this paper an adaptive importance sampling algorithm to tackle these challenges. Two essential ingredients of the algorithm are: 1) a Gaussian mixture (GM) model adaptively constructed as the proposal distribution to approximate the possibly multimodal target posterior, and 2) a mixture of polynomial chaos (PC) expansions, built according to the GM proposal, as a surrogate model to alleviate the computational burden caused by computational-demanding forward model evaluations. In three illustrative examples, the proposed adaptive importance sampling algorithm demonstrates its capabilities of automatically finding a GM proposal with an appropriate number of modes for the specific problem under study, and obtaining a sample accurately and efficiently representing the posterior with limited number of forward simulations.

  3. An adaptive importance sampling algorithm for Bayesian inversion with multimodal distributions

    DOE PAGES

    Li, Weixuan; Lin, Guang

    2015-03-21

    Parametric uncertainties are encountered in the simulations of many physical systems, and may be reduced by an inverse modeling procedure that calibrates the simulation results to observations on the real system being simulated. Following Bayes’ rule, a general approach for inverse modeling problems is to sample from the posterior distribution of the uncertain model parameters given the observations. However, the large number of repetitive forward simulations required in the sampling process could pose a prohibitive computational burden. This difficulty is particularly challenging when the posterior is multimodal. We present in this paper an adaptive importance sampling algorithm to tackle thesemore » challenges. Two essential ingredients of the algorithm are: 1) a Gaussian mixture (GM) model adaptively constructed as the proposal distribution to approximate the possibly multimodal target posterior, and 2) a mixture of polynomial chaos (PC) expansions, built according to the GM proposal, as a surrogate model to alleviate the computational burden caused by computational-demanding forward model evaluations. In three illustrative examples, the proposed adaptive importance sampling algorithm demonstrates its capabilities of automatically finding a GM proposal with an appropriate number of modes for the specific problem under study, and obtaining a sample accurately and efficiently representing the posterior with limited number of forward simulations.« less

  4. A family of variable step-size affine projection adaptive filter algorithms using statistics of channel impulse response

    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.

  5. A biomimetic adaptive algorithm and low-power architecture for implantable neural decoders.

    PubMed

    Rapoport, Benjamin I; Wattanapanitch, Woradorn; Penagos, Hector L; Musallam, Sam; Andersen, Richard A; Sarpeshkar, Rahul

    2009-01-01

    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.

  6. Binary 3D image interpolation algorithm based global information and adaptive curves fitting

    NASA Astrophysics Data System (ADS)

    Zhang, Tian-yi; Zhang, Jin-hao; Guan, Xiang-chen; Li, Qiu-ping; He, Meng

    2013-08-01

    Interpolation is a necessary processing step in 3-D reconstruction because of the non-uniform resolution. Conventional interpolation methods simply use two slices to obtain the missing slices between the two slices .when the key slice is missing, those methods may fail to recover it only employing the local information .And the surface of 3D object especially for the medical tissues may be highly complicated, so a single interpolation can hardly get high-quality 3D image. We propose a novel binary 3D image interpolation algorithm. The proposed algorithm takes advantages of the global information. It chooses the best curve adaptively from lots of curves based on the complexity of the surface of 3D object. The results of this algorithm are compared with other interpolation methods on artificial objects and real breast cancer tumor to demonstrate the excellent performance.

  7. An adaptive support driven reweighted L1-regularization algorithm for fluorescence molecular tomography

    PubMed Central

    Shi, Junwei; Liu, Fei; Pu, Huangsheng; Zuo, Simin; Luo, Jianwen; Bai, Jing

    2014-01-01

    Fluorescence molecular tomography (FMT) is a promising in vivo functional imaging modality in preclinical study. When solving the ill-posed FMT inverse problem, L1 regularization can preserve the details and reduce the noise in the reconstruction results effectively. Moreover, compared with the regular L1 regularization, reweighted L1 regularization is recently reported to improve the performance. In order to realize the reweighted L1 regularization for FMT, an adaptive support driven reweighted L1-regularization (ASDR-L1) algorithm is proposed in this work. This algorithm has two integral parts: an adaptive support estimate and the iteratively updated weights. In the iteratively reweighted L1-minimization sub-problem, different weights are equivalent to different regularization parameters at different locations. Thus, ASDR-L1 can be considered as a kind of spatially variant regularization methods for FMT. Physical phantom and in vivo mouse experiments were performed to validate the proposed algorithm. The results demonstrate that the proposed reweighted L1-reguarization algorithm can significantly improve the performance in terms of relative quantitation and spatial resolution. PMID:25426329

  8. A Self-Adaptive Fuzzy c-Means Algorithm for Determining the Optimal Number of Clusters

    PubMed Central

    Wang, Zhihao; Yi, Jing

    2016-01-01

    For the shortcoming of fuzzy c-means algorithm (FCM) needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the empirical rule n and obtained the optimal initial cluster centroids, improving the limitation of FCM that randomly selected cluster centroids lead the convergence result to the local minimum. Secondly, this paper, by introducing a penalty function, proposed a new fuzzy clustering validity index based on fuzzy compactness and separation, which ensured that when the number of clusters verged on that of objects in the dataset, the value of clustering validity index did not monotonically decrease and was close to zero, so that the optimal number of clusters lost robustness and decision function. Then, based on these studies, a self-adaptive FCM algorithm was put forward to estimate the optimal number of clusters by the iterative trial-and-error process. At last, experiments were done on the UCI, KDD Cup 1999, and synthetic datasets, which showed that the method not only effectively determined the optimal number of clusters, but also reduced the iteration of FCM with the stable clustering result. PMID:28042291

  9. A Self-Adaptive Fuzzy c-Means Algorithm for Determining the Optimal Number of Clusters.

    PubMed

    Ren, Min; Liu, Peiyu; Wang, Zhihao; Yi, Jing

    2016-01-01

    For the shortcoming of fuzzy c-means algorithm (FCM) needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the empirical rule [Formula: see text] and obtained the optimal initial cluster centroids, improving the limitation of FCM that randomly selected cluster centroids lead the convergence result to the local minimum. Secondly, this paper, by introducing a penalty function, proposed a new fuzzy clustering validity index based on fuzzy compactness and separation, which ensured that when the number of clusters verged on that of objects in the dataset, the value of clustering validity index did not monotonically decrease and was close to zero, so that the optimal number of clusters lost robustness and decision function. Then, based on these studies, a self-adaptive FCM algorithm was put forward to estimate the optimal number of clusters by the iterative trial-and-error process. At last, experiments were done on the UCI, KDD Cup 1999, and synthetic datasets, which showed that the method not only effectively determined the optimal number of clusters, but also reduced the iteration of FCM with the stable clustering result.

  10. An adaptive left-right eigenvector evolution algorithm for vibration isolation control

    NASA Astrophysics Data System (ADS)

    Wu, T. Y.

    2009-11-01

    The purpose of this research is to investigate the feasibility of utilizing an adaptive left and right eigenvector evolution (ALREE) algorithm for active vibration isolation. As depicted in the previous paper presented by Wu and Wang (2008 Smart Mater. Struct. 17 015048), the structural vibration behavior depends on both the disturbance rejection capability and mode shape distributions, which correspond to the left and right eigenvector distributions of the system, respectively. In this paper, a novel adaptive evolution algorithm is developed for finding the optimal combination of left-right eigenvectors of the vibration isolator, which is an improvement over the simultaneous left-right eigenvector assignment (SLREA) method proposed by Wu and Wang (2008 Smart Mater. Struct. 17 015048). The isolation performance index used in the proposed algorithm is defined by combining the orthogonality index of left eigenvectors and the modal energy ratio index of right eigenvectors. Through the proposed ALREE algorithm, both the left and right eigenvectors evolve such that the isolation performance index decreases, and therefore one can find the optimal combination of left-right eigenvectors of the closed-loop system for vibration isolation purposes. The optimal combination of left-right eigenvectors is then synthesized to determine the feedback gain matrix of the closed-loop system. The result of the active isolation control shows that the proposed method can be utilized to improve the vibration isolation performance compared with the previous approaches.

  11. A optimized context-based adaptive binary arithmetic coding algorithm in progressive H.264 encoder

    NASA Astrophysics Data System (ADS)

    Xiao, Guang; Shi, Xu-li; An, Ping; Zhang, Zhao-yang; Gao, Ge; Teng, Guo-wei

    2006-05-01

    Context-based Adaptive Binary Arithmetic Coding (CABAC) is a new entropy coding method presented in H.264/AVC that is highly efficient in video coding. In the method, the probability of current symbol is estimated by using the wisely designed context model, which is adaptive and can approach to the statistic characteristic. Then an arithmetic coding mechanism largely reduces the redundancy in inter-symbol. Compared with UVLC method in the prior standard, CABAC is complicated but efficiently reduce the bit rate. Based on thorough analysis of coding and decoding methods of CABAC, This paper proposed two methods, sub-table method and stream-reuse methods, to improve the encoding efficiency implemented in H.264 JM code. In JM, the CABAC function produces bits one by one of every syntactic element. Multiplication operating times after times in the CABAC function lead to it inefficient.The proposed algorithm creates tables beforehand and then produce every bits of syntactic element. In JM, intra-prediction and inter-prediction mode selection algorithm with different criterion is based on RDO(rate distortion optimization) model. One of the parameter of the RDO model is bit rate that is produced by CABAC operator. After intra-prediction or inter-prediction mode selection, the CABAC stream is discard and is recalculated to output stream. The proposed Stream-reuse algorithm puts the stream in memory that is created in mode selection algorithm and reuses it in encoding function. Experiment results show that our proposed algorithm can averagely speed up 17 to 78 MSEL higher speed for QCIF and CIF sequences individually compared with the original algorithm of JM at the cost of only a little memory space. The CABAC was realized in our progressive h.264 encoder.

  12. Flutter signal extracting technique based on FOG and self-adaptive sparse representation algorithm

    NASA Astrophysics Data System (ADS)

    Lei, Jian; Meng, Xiangtao; Xiang, Zheng

    2016-10-01

    Due to various moving parts inside, when a spacecraft runs in orbits, its structure could get a minor angular vibration, which results in vague image formation of space camera. Thus, image compensation technique is required to eliminate or alleviate the effect of movement on image formation and it is necessary to realize precise measuring of flutter angle. Due to the advantages such as high sensitivity, broad bandwidth, simple structure and no inner mechanical moving parts, FOG (fiber optical gyro) is adopted in this study to measure minor angular vibration. Then, movement leading to image degeneration is achieved by calculation. The idea of the movement information extracting algorithm based on self-adaptive sparse representation is to use arctangent function approximating L0 norm to construct unconstrained noisy-signal-aimed sparse reconstruction model and then solve the model by a method based on steepest descent algorithm and BFGS algorithm to estimate sparse signal. Then taking the advantage of the principle of random noises not able to be represented by linear combination of elements, useful signal and random noised are separated effectively. Because the main interference of minor angular vibration to image formation of space camera is random noises, sparse representation algorithm could extract useful information to a large extent and acts as a fitting pre-process method of image restoration. The self-adaptive sparse representation algorithm presented in this paper is used to process the measured minor-angle-vibration signal of FOG used by some certain spacecraft. By component analysis of the processing results, we can find out that the algorithm could extract micro angular vibration signal of FOG precisely and effectively, and can achieve the precision degree of 0.1".

  13. Robustness of continuous-time adaptive control algorithms in the presence of unmodeled dynamics

    NASA Technical Reports Server (NTRS)

    Rohrs, C. E.; Valavani, L.; Athans, M.; Stein, G.

    1985-01-01

    This paper examines the robustness properties of existing adaptive control algorithms to unmodeled plant high-frequency dynamics and unmeasurable output disturbances. It is demonstrated that there exist two infinite-gain operators in the nonlinear dynamic system which determines the time-evolution of output and parameter errors. The pragmatic implications of the existence of such infinite-gain operators is that: (1) sinusoidal reference inputs at specific frequencies and/or (2) sinusoidal output disturbances at any frequency (including dc), can cause the loop gain to increase without bound, thereby exciting the unmodeled high-frequency dynamics, and yielding an unstable control system. Hence, it is concluded that existing adaptive control algorithms as they are presented in the literature referenced in this paper, cannot be used with confidence in practical designs where the plant contains unmodeled dynamics because instability is likely to result. Further understanding is required to ascertain how the currently implemented adaptive systems differ from the theoretical systems studied here and how further theoretical development can improve the robustness of adaptive controllers.

  14. Analysis of Wideband Beamformers Designed with Artificial Neural Networks

    DTIC Science & Technology

    1990-12-01

    TECHNICAL REPORT 0-90-1 ANALYSIS OF WIDEBAND BEAMFORMERS DESIGNED WITH ARTIFICIAL NEURAL NETWORKS by Cary Cox Instrumentation Services Division...included. A briel tutorial on beamformers and neural networks is also provided. 14. SUBJECT TERMS 15, NUMBER OF PAGES Artificial neural networks Fecdforwa:,l...Beamformers Designed with Artificial Neural Networks ". The study was conducted under the general supervision of Messrs. George P. Bonner, Chief

  15. A New Modified Artificial Bee Colony Algorithm with Exponential Function Adaptive Steps.

    PubMed

    Mao, Wei; Lan, Heng-You; Li, Hao-Ru

    2016-01-01

    As one of the most recent popular swarm intelligence techniques, artificial bee colony algorithm is poor at exploitation and has some defects such as slow search speed, poor population diversity, the stagnation in the working process, and being trapped into the local optimal solution. The purpose of this paper is to develop a new modified artificial bee colony algorithm in view of the initial population structure, subpopulation groups, step updating, and population elimination. Further, depending on opposition-based learning theory and the new modified algorithms, an improved S-type grouping method is proposed and the original way of roulette wheel selection is substituted through sensitivity-pheromone way. Then, an adaptive step with exponential functions is designed for replacing the original random step. Finally, based on the new test function versions CEC13, six benchmark functions with the dimensions D = 20 and D = 40 are chosen and applied in the experiments for analyzing and comparing the iteration speed and accuracy of the new modified algorithms. The experimental results show that the new modified algorithm has faster and more stable searching and can quickly increase poor population diversity and bring out the global optimal solutions.

  16. A surrogate-primary replacement algorithm for response-adaptive randomization in stroke clinical trials.

    PubMed

    Nowacki, Amy S; Zhao, Wenle; Palesch, Yuko Y

    2015-01-12

    Response-adaptive randomization (RAR) offers clinical investigators benefit by modifying the treatment allocation probabilities to optimize the ethical, operational, or statistical performance of the trial. Delayed primary outcomes and their effect on RAR have been studied in the literature; however, the incorporation of surrogate outcomes has not been fully addressed. We explore the benefits and limitations of surrogate outcome utilization in RAR in the context of acute stroke clinical trials. We propose a novel surrogate-primary (S-P) replacement algorithm where a patient's surrogate outcome is used in the RAR algorithm only until their primary outcome becomes available to replace it. Computer simulations investigate the effect of both the delay in obtaining the primary outcome and the underlying surrogate and primary outcome distributional discrepancies on complete randomization, standard RAR and the S-P replacement algorithm methods. Results show that when the primary outcome is delayed, the S-P replacement algorithm reduces the variability of the treatment allocation probabilities and achieves stabilization sooner. Additionally, the S-P replacement algorithm benefit proved to be robust in that it preserved power and reduced the expected number of failures across a variety of scenarios.

  17. A New Modified Artificial Bee Colony Algorithm with Exponential Function Adaptive Steps

    PubMed Central

    Mao, Wei; Li, Hao-ru

    2016-01-01

    As one of the most recent popular swarm intelligence techniques, artificial bee colony algorithm is poor at exploitation and has some defects such as slow search speed, poor population diversity, the stagnation in the working process, and being trapped into the local optimal solution. The purpose of this paper is to develop a new modified artificial bee colony algorithm in view of the initial population structure, subpopulation groups, step updating, and population elimination. Further, depending on opposition-based learning theory and the new modified algorithms, an improved S-type grouping method is proposed and the original way of roulette wheel selection is substituted through sensitivity-pheromone way. Then, an adaptive step with exponential functions is designed for replacing the original random step. Finally, based on the new test function versions CEC13, six benchmark functions with the dimensions D = 20 and D = 40 are chosen and applied in the experiments for analyzing and comparing the iteration speed and accuracy of the new modified algorithms. The experimental results show that the new modified algorithm has faster and more stable searching and can quickly increase poor population diversity and bring out the global optimal solutions. PMID:27293426

  18. Fast parallel MR image reconstruction via B1-based, adaptive restart, iterative soft thresholding algorithms (BARISTA).

    PubMed

    Muckley, Matthew J; Noll, Douglas C; Fessler, Jeffrey A

    2015-02-01

    Sparsity-promoting regularization is useful for combining compressed sensing assumptions with parallel MRI for reducing scan time while preserving image quality. Variable splitting algorithms are the current state-of-the-art algorithms for SENSE-type MR image reconstruction with sparsity-promoting regularization. These methods are very general and have been observed to work with almost any regularizer; however, the tuning of associated convergence parameters is a commonly-cited hindrance in their adoption. Conversely, majorize-minimize algorithms based on a single Lipschitz constant have been observed to be slow in shift-variant applications such as SENSE-type MR image reconstruction since the associated Lipschitz constants are loose bounds for the shift-variant behavior. This paper bridges the gap between the Lipschitz constant and the shift-variant aspects of SENSE-type MR imaging by introducing majorizing matrices in the range of the regularizer matrix. The proposed majorize-minimize methods (called BARISTA) converge faster than state-of-the-art variable splitting algorithms when combined with momentum acceleration and adaptive momentum restarting. Furthermore, the tuning parameters associated with the proposed methods are unitless convergence tolerances that are easier to choose than the constraint penalty parameters required by variable splitting algorithms.

  19. Optimized hyper beamforming of linear antenna arrays using collective animal behaviour.

    PubMed

    Ram, Gopi; Mandal, Durbadal; Kar, Rajib; Ghoshal, Sakti Prasad

    2013-01-01

    A novel optimization technique which is developed on mimicking the collective animal behaviour (CAB) is applied for the optimal design of hyper beamforming of linear antenna arrays. Hyper beamforming is based on sum and difference beam patterns of the array, each raised to the power of a hyperbeam exponent parameter. The optimized hyperbeam is achieved by optimization of current excitation weights and uniform interelement spacing. As compared to conventional hyper beamforming of linear antenna array, real coded genetic algorithm (RGA), particle swarm optimization (PSO), and differential evolution (DE) applied to the hyper beam of the same array can achieve reduction in sidelobe level (SLL) and same or less first null beam width (FNBW), keeping the same value of hyperbeam exponent. Again, further reductions of sidelobe level (SLL) and first null beam width (FNBW) have been achieved by the proposed collective animal behaviour (CAB) algorithm. CAB finds near global optimal solution unlike RGA, PSO, and DE in the present problem. The above comparative optimization is illustrated through 10-, 14-, and 20-element linear antenna arrays to establish the optimization efficacy of CAB.

  20. A Novel Monopulse Technique for Adaptive Phased Array Radar.

    PubMed

    Zhang, Xinyu; Li, Yang; Yang, Xiaopeng; Zheng, Le; Long, Teng; Baker, Christopher J

    2017-01-08

    The monopulse angle measuring technique is widely adopted in radar systems due to its simplicity and speed in accurately acquiring a target's angle. However, in a spatial adaptive array, beam distortion, due to adaptive beamforming, can result in serious deterioration of monopulse performance. In this paper, a novel constrained monopulse angle measuring algorithm is proposed for spatial adaptive arrays. This algorithm maintains the ability to suppress the unwanted signals without suffering from beam distortion. Compared with conventional adaptive monopulse methods, the proposed algorithm adopts a new form of constraint in forming the difference beam with the merit that it is more robust in most practical situations. At the same time, it also exhibits the simplicity of one-dimension monopulse, helping to make this algorithm even more appealing to use in adaptive planar arrays. The theoretical mean and variance of the proposed monopulse estimator is derived for theoretical analysis. Mathematical simulations are formulated to demonstrate the effectiveness and advantages of the proposed algorithm. Both theoretical analysis and simulation results show that the proposed algorithm can outperform the conventional adaptive monopulse methods in the presence of severe interference near the mainlobe.

  1. A Novel Monopulse Technique for Adaptive Phased Array Radar

    PubMed Central

    Zhang, Xinyu; Li, Yang; Yang, Xiaopeng; Zheng, Le; Long, Teng; Baker, Christopher J.

    2017-01-01

    The monopulse angle measuring technique is widely adopted in radar systems due to its simplicity and speed in accurately acquiring a target’s angle. However, in a spatial adaptive array, beam distortion, due to adaptive beamforming, can result in serious deterioration of monopulse performance. In this paper, a novel constrained monopulse angle measuring algorithm is proposed for spatial adaptive arrays. This algorithm maintains the ability to suppress the unwanted signals without suffering from beam distortion. Compared with conventional adaptive monopulse methods, the proposed algorithm adopts a new form of constraint in forming the difference beam with the merit that it is more robust in most practical situations. At the same time, it also exhibits the simplicity of one-dimension monopulse, helping to make this algorithm even more appealing to use in adaptive planar arrays. The theoretical mean and variance of the proposed monopulse estimator is derived for theoretical analysis. Mathematical simulations are formulated to demonstrate the effectiveness and advantages of the proposed algorithm. Both theoretical analysis and simulation results show that the proposed algorithm can outperform the conventional adaptive monopulse methods in the presence of severe interference near the mainlobe. PMID:28075348

  2. Distributed Beamforming in a Swarm UAV Network

    DTIC Science & Technology

    2008-03-01

    opportunistic random arrays with the concept of swarm UAVs. A considerable amount of research has already been done about the feasibility and advantages of...a widely dispersed wirelessly networked opportunistic array may anticipate many advantages over single platform-borne opportunistic arrays. Major...distribution is unlimited DISTRIBUTED BEAMFORMING IN A SWARM UAV NETWORK İbrahim KOCAMAN 1st Lieutenant, Turkish Air Force B.S., Turkish Air Force

  3. GPU-based minimum variance beamformer for synthetic aperture imaging of the eye.

    PubMed

    Yiu, Billy Y S; Yu, Alfred C H

    2015-03-01

    Minimum variance (MV) beamforming has emerged as an adaptive apodization approach to bolster the quality of images generated from synthetic aperture ultrasound imaging methods that are based on unfocused transmission principles. In this article, we describe a new high-speed, pixel-based MV beamforming framework for synthetic aperture imaging to form entire frames of adaptively apodized images at real-time throughputs and document its performance in swine eye imaging case examples. Our framework is based on parallel computing principles, and its real-time operational feasibility was realized on a six-GPU (graphics processing unit) platform with 3,072 computing cores. This framework was used to form images with synthetic aperture imaging data acquired from swine eyes (based on virtual point-source emissions). Results indicate that MV-apodized image formation with video-range processing throughput (>20 fps) can be realized for practical aperture sizes (128 channels) and frames with λ/2 pixel spacing. Also, in a corneal wound detection experiment, MV-apodized images generated using our framework revealed apparent contrast enhancement of the wound site (10.8 dB with respect to synthetic aperture images formed with fixed apodization). These findings indicate that GPU-based MV beamforming can, in real time, potentially enhance image quality when performing synthetic aperture imaging that uses unfocused firings.

  4. An Energy Aware Adaptive Sampling Algorithm for Energy Harvesting WSN with Energy Hungry Sensors.

    PubMed

    Srbinovski, Bruno; Magno, Michele; Edwards-Murphy, Fiona; Pakrashi, Vikram; Popovici, Emanuel

    2016-03-28

    Wireless sensor nodes have a limited power budget, though they are often expected to be functional in the field once deployed for extended periods of time. Therefore, minimization of energy consumption and energy harvesting technology in Wireless Sensor Networks (WSN) are key tools for maximizing network lifetime, and achieving self-sustainability. This paper proposes an energy aware Adaptive Sampling Algorithm (ASA) for WSN with power hungry sensors and harvesting capabilities, an energy management technique that can be implemented on any WSN platform with enough processing power to execute the proposed algorithm. An existing state-of-the-art ASA developed for wireless sensor networks with power hungry sensors is optimized and enhanced to adapt the sampling frequency according to the available energy of the node. The proposed algorithm is evaluated using two in-field testbeds that are supplied by two different energy harvesting sources (solar and wind). Simulation and comparison between the state-of-the-art ASA and the proposed energy aware ASA (EASA) in terms of energy durability are carried out using in-field measured harvested energy (using both wind and solar sources) and power hungry sensors (ultrasonic wind sensor and gas sensors). The simulation results demonstrate that using ASA in combination with an energy aware function on the nodes can drastically increase the lifetime of a WSN node and enable self-sustainability. In fact, the proposed EASA in conjunction with energy harvesting capability can lead towards perpetual WSN operation and significantly outperform the state-of-the-art ASA.

  5. An adaptive reconstruction algorithm for spectral CT regularized by a reference image

    NASA Astrophysics Data System (ADS)

    Wang, Miaoshi; Zhang, Yanbo; Liu, Rui; Guo, Shuxu; Yu, Hengyong

    2016-12-01

    The photon counting detector based spectral CT system is attracting increasing attention in the CT field. However, the spectral CT is still premature in terms of both hardware and software. To reconstruct high quality spectral images from low-dose projections, an adaptive image reconstruction algorithm is proposed that assumes a known reference image (RI). The idea is motivated by the fact that the reconstructed images from different spectral channels are highly correlated. If a high quality image of the same object is known, it can be used to improve the low-dose reconstruction of each individual channel. This is implemented by maximizing the patch-wise correlation between the object image and the RI. Extensive numerical simulations and preclinical mouse study demonstrate the feasibility and merits of the proposed algorithm. It also performs well for truncated local projections, and the surrounding area of the region- of-interest (ROI) can be more accurately reconstructed. Furthermore, a method is introduced to adaptively choose the step length, making the algorithm more feasible and easier for applications.

  6. Investigation of model based beamforming and Bayesian inversion signal processing methods for seismic localization of underground sources.

    PubMed

    Oh, Geok Lian; Brunskog, Jonas

    2014-08-01

    Techniques have been studied for the localization of an underground source with seismic interrogation signals. Much of the work has involved defining either a P-wave acoustic model or a dispersive surface wave model to the received signal and applying the time-delay processing technique and frequency-wavenumber processing to determine the location of the underground tunnel. Considering the case of determining the location of an underground tunnel, this paper proposed two physical models, the acoustic approximation ray tracing model and the finite difference time domain three-dimensional (3D) elastic wave model to represent the received seismic signal. Two localization algorithms, beamforming and Bayesian inversion, are developed for each physical model. The beam-forming algorithms implemented are the modified time-and-delay beamformer and the F-K beamformer. Inversion is posed as an optimization problem to estimate the unknown position variable using the described physical forward models. The proposed four methodologies are demonstrated and compared using seismic signals recorded by geophones set up on ground surface generated by a surface seismic excitation. The examples show that for field data, inversion for localization is most advantageous when the forward model completely describe all the elastic wave components as is the case of the FDTD 3D elastic model.

  7. Comparison of a new rapid convergent adaptive control algorithm to least mean square on an active noise control system

    NASA Astrophysics Data System (ADS)

    Koshigoe, Shozo; Gordon, Alan; Teagle, Allen; Tsay, Ching-Hsu

    1995-04-01

    In this paper, an efficient rapid convergent control algorithm will be developed and will be compared with other adaptive control algorithms using an experimental active noise control system. Other control algorithms are Widrow's finite impulse response adaptive control algorithm, and a modified Godard's algorithm. Comparisons of the random noise attenuation capability, transient and convergence performance, and computational requirements of each algorithm will be made as the order of the controller and relevant convergence parameters are varied. The system used for these experiments is a test bed of noise suppression technology for expendable launch vehicles. It consists of a flexible plate backed by a rigid cavity. Piezoelectric actuators are mounted on the plate and polyvinylidene fluoride is used both for microphones and pressure sensors within the cavity. The plate is bombarded with an amplified random noise signal, and the control system is used to suppress the noise inside the cavity generated by the outside sound source.

  8. An adaptive multi-level simulation algorithm for stochastic biological systems.

    PubMed

    Lester, C; Yates, C A; Giles, M B; Baker, R E

    2015-01-14

    Discrete-state, continuous-time Markov models are widely used in the modeling of biochemical reaction networks. Their complexity often precludes analytic solution, and we rely on stochastic simulation algorithms (SSA) to estimate system statistics. The Gillespie algorithm is exact, but computationally costly as it simulates every single reaction. As such, approximate stochastic simulation algorithms such as the tau-leap algorithm are often used. Potentially computationally more efficient, the system statistics generated suffer from significant bias unless tau is relatively small, in which case the computational time can be comparable to that of the Gillespie algorithm. The multi-level method [Anderson and Higham, "Multi-level Monte Carlo for continuous time Markov chains, with applications in biochemical kinetics," SIAM Multiscale Model. Simul. 10(1), 146-179 (2012)] tackles this problem. A base estimator is computed using many (cheap) sample paths at low accuracy. The bias inherent in this estimator is then reduced using a number of corrections. Each correction term is estimated using a collection of paired sample paths where one path of each pair is generated at a higher accuracy compared to the other (and so more expensive). By sharing random variables between these paired paths, the variance of each correction estimator can be reduced. This renders the multi-level method very efficient as only a relatively small number of paired paths are required to calculate each correction term. In the original multi-level method, each sample path is simulated using the tau-leap algorithm with a fixed value of τ. This approach can result in poor performance when the reaction activity of a system changes substantially over the timescale of interest. By introducing a novel adaptive time-stepping approach where τ is chosen according to the stochastic behaviour of each sample path, we extend the applicability of the multi-level method to such cases. We demonstrate the

  9. Adaptation of a Fast Optimal Interpolation Algorithm to the Mapping of Oceangraphic Data

    NASA Technical Reports Server (NTRS)

    Menemenlis, Dimitris; Fieguth, Paul; Wunsch, Carl; Willsky, Alan

    1997-01-01

    A fast, recently developed, multiscale optimal interpolation algorithm has been adapted to the mapping of hydrographic and other oceanographic data. This algorithm produces solution and error estimates which are consistent with those obtained from exact least squares methods, but at a small fraction of the computational cost. Problems whose solution would be completely impractical using exact least squares, that is, problems with tens or hundreds of thousands of measurements and estimation grid points, can easily be solved on a small workstation using the multiscale algorithm. In contrast to methods previously proposed for solving large least squares problems, our approach provides estimation error statistics while permitting long-range correlations, using all measurements, and permitting arbitrary measurement locations. The multiscale algorithm itself, published elsewhere, is not the focus of this paper. However, the algorithm requires statistical models having a very particular multiscale structure; it is the development of a class of multiscale statistical models, appropriate for oceanographic mapping problems, with which we concern ourselves in this paper. The approach is illustrated by mapping temperature in the northeastern Pacific. The number of hydrographic stations is kept deliberately small to show that multiscale and exact least squares results are comparable. A portion of the data were not used in the analysis; these data serve to test the multiscale estimates. A major advantage of the present approach is the ability to repeat the estimation procedure a large number of times for sensitivity studies, parameter estimation, and model testing. We have made available by anonymous Ftp a set of MATLAB-callable routines which implement the multiscale algorithm and the statistical models developed in this paper.

  10. RZA-NLMF algorithm-based adaptive sparse sensing for realizing compressive sensing

    NASA Astrophysics Data System (ADS)

    Gui, Guan; Xu, Li; Adachi, Fumiyuki

    2014-12-01

    Nonlinear sparse sensing (NSS) techniques have been adopted for realizing compressive sensing in many applications such as radar imaging. Unlike the NSS, in this paper, we propose an adaptive sparse sensing (ASS) approach using the reweighted zero-attracting normalized least mean fourth (RZA-NLMF) algorithm which depends on several given parameters, i.e., reweighted factor, regularization parameter, and initial step size. First, based on the independent assumption, Cramer-Rao lower bound (CRLB) is derived as for the performance comparisons. In addition, reweighted factor selection method is proposed for achieving robust estimation performance. Finally, to verify the algorithm, Monte Carlo-based computer simulations are given to show that the ASS achieves much better mean square error (MSE) performance than the NSS.

  11. Comparison of Control Algorithms for a MEMS-based Adaptive Optics Scanning Laser Ophthalmoscope

    PubMed Central

    Li, Kaccie Y.; Mishra, Sandipan; Tiruveedhula, Pavan; Roorda, Austin

    2010-01-01

    We compared four algorithms for controlling a MEMS deformable mirror of an adaptive optics (AO) scanning laser ophthalmoscope. Interferometer measurements of the static nonlinear response of the deformable mirror were used to form an equivalent linear model of the AO system so that the classic integrator plus wavefront reconstructor type controller can be implemented. The algorithms differ only in the design of the wavefront reconstructor. The comparisons were made for two eyes (two individuals) via a series of imaging sessions. All four controllers performed similarly according to estimated residual wavefront error not reflecting the actual image quality observed. A metric based on mean image intensity did consistently reflect the qualitative observations of retinal image quality. Based on this metric, the controller most effective for suppressing the least significant modes of the deformable mirror performed the best. PMID:20454552

  12. Validation of elastic registration algorithms based on adaptive irregular grids for medical applications

    NASA Astrophysics Data System (ADS)

    Franz, Astrid; Carlsen, Ingwer C.; Renisch, Steffen; Wischmann, Hans-Aloys

    2006-03-01

    Elastic registration of medical images is an active field of current research. Registration algorithms have to be validated in order to show that they fulfill the requirements of a particular clinical application. Furthermore, validation strategies compare the performance of different registration algorithms and can hence judge which algorithm is best suited for a target application. In the literature, validation strategies for rigid registration algorithms have been analyzed. For a known ground truth they assess the displacement error at a few landmarks, which is not sufficient for elastic transformations described by a huge number of parameters. Hence we consider the displacement error averaged over all pixels in the whole image or in a region-of-interest of clinical relevance. Using artificially, but realistically deformed images of the application domain, we use this quality measure to analyze an elastic registration based on transformations defined on adaptive irregular grids for the following clinical applications: Magnetic Resonance (MR) images of freely moving joints for orthopedic investigations, thoracic Computed Tomography (CT) images for the detection of pulmonary embolisms, and transmission images as used for the attenuation correction and registration of independently acquired Positron Emission Tomography (PET) and CT images. The definition of a region-of-interest allows to restrict the analysis of the registration accuracy to clinically relevant image areas. The behaviour of the displacement error as a function of the number of transformation control points and their placement can be used for identifying the best strategy for the initial placement of the control points.

  13. Enabling the extended compact genetic algorithm for real-parameter optimization by using adaptive discretization.

    PubMed

    Chen, Ying-ping; Chen, Chao-Hong

    2010-01-01

    An adaptive discretization method, called split-on-demand (SoD), enables estimation of distribution algorithms (EDAs) for discrete variables to solve continuous optimization problems. SoD randomly splits a continuous interval if the number of search points within the interval exceeds a threshold, which is decreased at every iteration. After the split operation, the nonempty intervals are assigned integer codes, and the search points are discretized accordingly. As an example of using SoD with EDAs, the integration of SoD and the extended compact genetic algorithm (ECGA) is presented and numerically examined. In this integration, we adopt a local search mechanism as an optional component of our back end optimization engine. As a result, the proposed framework can be considered as a memetic algorithm, and SoD can potentially be applied to other memetic algorithms. The numerical experiments consist of two parts: (1) a set of benchmark functions on which ECGA with SoD and ECGA with two well-known discretization methods: the fixed-height histogram (FHH) and the fixed-width histogram (FWH) are compared; (2) a real-world application, the economic dispatch problem, on which ECGA with SoD is compared to other methods. The experimental results indicate that SoD is a better discretization method to work with ECGA. Moreover, ECGA with SoD works quite well on the economic dispatch problem and delivers solutions better than the best known results obtained by other methods in existence.

  14. An Adaptive Reputation-Based Algorithm for Grid Virtual Organization Formation

    NASA Astrophysics Data System (ADS)

    Cui, Yongrui; Li, Mingchu; Ren, Yizhi; Sakurai, Kouichi

    A novel adaptive reputation-based virtual organization formation is proposed. It restrains the bad performers effectively based on the consideration of the global experience of the evaluator and evaluates the direct trust relation between two grid nodes accurately by consulting the previous trust value rationally. It also consults and improves the reputation evaluation process in PathTrust model by taking account of the inter-organizational trust relationship and combines it with direct and recommended trust in a weighted way, which makes the algorithm more robust against collusion attacks. Additionally, the proposed algorithm considers the perspective of the VO creator and takes required VO services as one of the most important fine-grained evaluation criterion, which makes the algorithm more suitable for constructing VOs in grid environments that include autonomous organizations. Simulation results show that our algorithm restrains the bad performers and resists against fake transaction attacks and badmouth attacks effectively. It provides a clear advantage in the design of a VO infrastructure.

  15. Three-dimensional microscope vision system based on micro laser line scanning and adaptive genetic algorithms

    NASA Astrophysics Data System (ADS)

    Apolinar, J.; Rodríguez, Muñoz

    2017-02-01

    A microscope vision system to retrieve small metallic surface via micro laser line scanning and genetic algorithms is presented. In this technique, a 36 μm laser line is projected on the metallic surface through a laser diode head, which is placed to a small distance away from the target. The micro laser line is captured by a CCD camera, which is attached to the microscope. The surface topography is computed by triangulation by means of the line position and microscope vision parameters. The calibration of the microscope vision system is carried out by an adaptive genetic algorithm based on the line position. In this algorithm, an objective function is constructed from the microscope geometry to determine the microscope vision parameters. Also, the genetic algorithm provides the search space to calculate the microscope vision parameters with high accuracy in fast form. This procedure avoids errors produced by the missing of references and physical measurements, which are employed by the traditional microscope vision systems. The contribution of the proposed system is corroborated by an evaluation via accuracy and speed of the traditional microscope vision systems, which retrieve micro-scale surface topography.

  16. A baseline correction algorithm for Raman spectroscopy by adaptive knots B-spline

    NASA Astrophysics Data System (ADS)

    Wang, Xin; Fan, Xian-guang; Xu, Ying-jie; Wang, Xiu-fen; He, Hao; Zuo, Yong

    2015-11-01

    The Raman spectroscopy technique is a powerful and non-invasive technique for molecular fingerprint detection which has been widely used in many areas, such as food safety, drug safety, and environmental testing. But Raman signals can be easily corrupted by a fluorescent background, therefore we presented a baseline correction algorithm to suppress the fluorescent background in this paper. In this algorithm, the background of the Raman signal was suppressed by fitting a curve called a baseline using a cyclic approximation method. Instead of the traditional polynomial fitting, we used the B-spline as the fitting algorithm due to its advantages of low-order and smoothness, which can avoid under-fitting and over-fitting effectively. In addition, we also presented an automatic adaptive knot generation method to replace traditional uniform knots. This algorithm can obtain the desired performance for most Raman spectra with varying baselines without any user input or preprocessing step. In the simulation, three kinds of fluorescent background lines were introduced to test the effectiveness of the proposed method. We showed that two real Raman spectra (parathion-methyl and colza oil) can be detected and their baselines were also corrected by the proposed method.

  17. An adaptive guidance algorithm for an aerodynamically assisted orbital plane change maneuver. M.S. Thesis

    NASA Technical Reports Server (NTRS)

    Blissit, J. A.

    1986-01-01

    Using analysis results from the post trajectory optimization program, an adaptive guidance algorithm is developed to compensate for density, aerodynamic and thrust perturbations during an atmospheric orbital plane change maneuver. The maneuver offers increased mission flexibility along with potential fuel savings for future reentry vehicles. Although designed to guide a proposed NASA Entry Research Vehicle, the algorithm is sufficiently generic for a range of future entry vehicles. The plane change analysis provides insight suggesting a straight-forward algorithm based on an optimized nominal command profile. Bank angle, angle of attack, and engine thrust level, ignition and cutoff times are modulated to adjust the vehicle's trajectory to achieve the desired end-conditions. A performance evaluation of the scheme demonstrates a capability to guide to within 0.05 degrees of the desired plane change and five nautical miles of the desired apogee altitude while maintaining heating constraints. The algorithm is tested under off-nominal conditions of + or -30% density biases, two density profile models, + or -15% aerodynamic uncertainty, and a 33% thrust loss and for various combinations of these conditions.

  18. Adaptive Fault Detection on Liquid Propulsion Systems with Virtual Sensors: Algorithms and Architectures

    NASA Technical Reports Server (NTRS)

    Matthews, Bryan L.; Srivastava, Ashok N.

    2010-01-01

    Prior to the launch of STS-119 NASA had completed a study of an issue in the flow control valve (FCV) in the Main Propulsion System of the Space Shuttle using an adaptive learning method known as Virtual Sensors. Virtual Sensors are a class of algorithms that estimate the value of a time series given other potentially nonlinearly correlated sensor readings. In the case presented here, the Virtual Sensors algorithm is based on an ensemble learning approach and takes sensor readings and control signals as input to estimate the pressure in a subsystem of the Main Propulsion System. Our results indicate that this method can detect faults in the FCV at the time when they occur. We use the standard deviation of the predictions of the ensemble as a measure of uncertainty in the estimate. This uncertainty estimate was crucial to understanding the nature and magnitude of transient characteristics during startup of the engine. This paper overviews the Virtual Sensors algorithm and discusses results on a comprehensive set of Shuttle missions and also discusses the architecture necessary for deploying such algorithms in a real-time, closed-loop system or a human-in-the-loop monitoring system. These results were presented at a Flight Readiness Review of the Space Shuttle in early 2009.

  19. Nonlinear systems modeling based on self-organizing fuzzy-neural-network with adaptive computation algorithm.

    PubMed

    Han, Honggui; Wu, Xiao-Long; Qiao, Jun-Fei

    2014-04-01

    In this paper, a self-organizing fuzzy-neural-network with adaptive computation algorithm (SOFNN-ACA) is proposed for modeling a class of nonlinear systems. This SOFNN-ACA is constructed online via simultaneous structure and parameter learning processes. In structure learning, a set of fuzzy rules can be self-designed using an information-theoretic methodology. The fuzzy rules with high spiking intensities (SI) are divided into new ones. And the fuzzy rules with a small relative mutual information (RMI) value will be pruned in order to simplify the FNN structure. In parameter learning, the consequent part parameters are learned through the use of an ACA that incorporates an adaptive learning rate strategy into the learning process to accelerate the convergence speed. Then, the convergence of SOFNN-ACA is analyzed. Finally, the proposed SOFNN-ACA is used to model nonlinear systems. The modeling results demonstrate that this proposed SOFNN-ACA can model nonlinear systems effectively.

  20. Ultrasonic Imaging in Solids Using Wave Mode Beamforming.

    PubMed

    Lanza di Scalea, Francesco; Sternini, Simone; Nguyen, Thompson

    2016-12-07

    This paper discusses some improvements to ultrasonic synthetic imaging in solids with primary applications to non-destructive testing of materials and structures. Specifically, the study proposes new adaptive weights applied to the beamforming array that are based on the physics of the propagating waves, specifically the displacement structure of the propagating longitudinal (L) mode and shear (S) mode that are naturally co-existing in a solid. The wave mode structures can be combined with the wave geometrical spreading to better filter the array (in a matched filter approach) and improve its focusing ability compared to static array weights. The paper also proposes compounding, or summing, images obtained from the different wave modes to further improve the array gain without increasing its physical aperture. The wave mode compounding can be performed either incoherently or coherently, in analogy with compounding multiple frequencies or multiple excitations. Numerical simulations and experimental testing demonstrate the potential improvements obtainable by the wave structure adaptive weights compared to either static weights in conventional delay-and-sum focusing, or adaptive weights based on geometrical spreading alone in minimum-variance distortionless response focusing.

  1. Envelope analysis with a genetic algorithm-based adaptive filter bank for bearing fault detection.

    PubMed

    Kang, Myeongsu; Kim, Jaeyoung; Choi, Byeong-Keun; Kim, Jong-Myon

    2015-07-01

    This paper proposes a fault detection methodology for bearings using envelope analysis with a genetic algorithm (GA)-based adaptive filter bank. Although a bandpass filter cooperates with envelope analysis for early identification of bearing defects, no general consensus has been reached as to which passband is optimal. This study explores the impact of various passbands specified by the GA in terms of a residual frequency components-to-defect frequency components ratio, which evaluates the degree of defectiveness in bearings and finally outputs an optimal passband for reliable bearing fault detection.

  2. Reliability Optimization Design for Contact Springs of AC Contactors Based on Adaptive Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Zhao, Sheng; Su, Xiuping; Wu, Ziran; Xu, Chengwen

    The paper illustrates the procedure of reliability optimization modeling for contact springs of AC contactors under nonlinear multi-constraint conditions. The adaptive genetic algorithm (AGA) is utilized to perform reliability optimization on the contact spring parameters of a type of AC contactor. A method that changes crossover and mutation rates at different times in the AGA can effectively avoid premature convergence, and experimental tests are performed after optimization. The experimental result shows that the mass of each optimized spring is reduced by 16.2%, while the reliability increases to 99.9% from 94.5%. The experimental result verifies the correctness and feasibility of this reliability optimization designing method.

  3. Experimental Evaluation of a Braille-Reading-Inspired Finger Motion Adaptive Algorithm.

    PubMed

    Ulusoy, Melda; Sipahi, Rifat

    2016-01-01

    Braille reading is a complex process involving intricate finger-motion patterns and finger-rubbing actions across Braille letters for the stimulation of appropriate nerves. Although Braille reading is performed by smoothly moving the finger from left-to-right, research shows that even fluent reading requires right-to-left movements of the finger, known as "reversal". Reversals are crucial as they not only enhance stimulation of nerves for correctly reading the letters, but they also show one to re-read the letters that were missed in the first pass. Moreover, it is known that reversals can be performed as often as in every sentence and can start at any location in a sentence. Here, we report experimental results on the feasibility of an algorithm that can render a machine to automatically adapt to reversal gestures of one's finger. Through Braille-reading-analogous tasks, the algorithm is tested with thirty sighted subjects that volunteered in the study. We find that the finger motion adaptive algorithm (FMAA) is useful in achieving cooperation between human finger and the machine. In the presence of FMAA, subjects' performance metrics associated with the tasks have significantly improved as supported by statistical analysis. In light of these encouraging results, preliminary experiments are carried out with five blind subjects with the aim to put the algorithm to test. Results obtained from carefully designed experiments showed that subjects' Braille reading accuracy in the presence of FMAA was more favorable then when FMAA was turned off. Utilization of FMAA in future generation Braille reading devices thus holds strong promise.

  4. Experimental Evaluation of a Braille-Reading-Inspired Finger Motion Adaptive Algorithm

    PubMed Central

    2016-01-01

    Braille reading is a complex process involving intricate finger-motion patterns and finger-rubbing actions across Braille letters for the stimulation of appropriate nerves. Although Braille reading is performed by smoothly moving the finger from left-to-right, research shows that even fluent reading requires right-to-left movements of the finger, known as “reversal”. Reversals are crucial as they not only enhance stimulation of nerves for correctly reading the letters, but they also show one to re-read the letters that were missed in the first pass. Moreover, it is known that reversals can be performed as often as in every sentence and can start at any location in a sentence. Here, we report experimental results on the feasibility of an algorithm that can render a machine to automatically adapt to reversal gestures of one’s finger. Through Braille-reading-analogous tasks, the algorithm is tested with thirty sighted subjects that volunteered in the study. We find that the finger motion adaptive algorithm (FMAA) is useful in achieving cooperation between human finger and the machine. In the presence of FMAA, subjects’ performance metrics associated with the tasks have significantly improved as supported by statistical analysis. In light of these encouraging results, preliminary experiments are carried out with five blind subjects with the aim to put the algorithm to test. Results obtained from carefully designed experiments showed that subjects’ Braille reading accuracy in the presence of FMAA was more favorable then when FMAA was turned off. Utilization of FMAA in future generation Braille reading devices thus holds strong promise. PMID:26849058

  5. Faster P300 Classifier Training Using Spatiotemporal Beamforming.

    PubMed

    Wittevrongel, Benjamin; Van Hulle, Marc M

    2016-05-01

    The linearly-constrained minimum-variance (LCMV) beamformer is traditionally used as a spatial filter for source localization, but here we consider its spatiotemporal extension for P300 classification. We compare two variants and show that the spatiotemporal LCMV beamformer is at par with state-of-the-art P300 classifiers, but several orders of magnitude faster in training the classifier.

  6. Adaptive ILC algorithms of nonlinear continuous systems with non-parametric uncertainties for non-repetitive trajectory tracking

    NASA Astrophysics Data System (ADS)

    Li, Xiao-Dong; Lv, Mang-Mang; Ho, John K. L.

    2016-07-01

    In this article, two adaptive iterative learning control (ILC) algorithms are presented for nonlinear continuous systems with non-parametric uncertainties. Unlike general ILC techniques, the proposed adaptive ILC algorithms allow that both the initial error at each iteration and the reference trajectory are iteration-varying in the ILC process, and can achieve non-repetitive trajectory tracking beyond a small initial time interval. Compared to the neural network or fuzzy system-based adaptive ILC schemes and the classical ILC methods, in which the number of iterative variables is generally larger than or equal to the number of control inputs, the first adaptive ILC algorithm proposed in this paper uses just two iterative variables, while the second even uses a single iterative variable provided that some bound information on system dynamics is known. As a result, the memory space in real-time ILC implementations is greatly reduced.

  7. An Adaptive Defect Weighted Sampling Algorithm to Design Pseudoknotted RNA Secondary Structures

    PubMed Central

    Zandi, Kasra; Butler, Gregory; Kharma, Nawwaf

    2016-01-01

    Computational design of RNA sequences that fold into targeted secondary structures has many applications in biomedicine, nanotechnology and synthetic biology. An RNA molecule is made of different types of secondary structure elements and an important RNA element named pseudoknot plays a key role in stabilizing the functional form of the molecule. However, due to the computational complexities associated with characterizing pseudoknotted RNA structures, most of the existing RNA sequence designer algorithms generally ignore this important structural element and therefore limit their applications. In this paper we present a new algorithm to design RNA sequences for pseudoknotted secondary structures. We use NUPACK as the folding algorithm to compute the equilibrium characteristics of the pseudoknotted RNAs, and describe a new adaptive defect weighted sampling algorithm named Enzymer to design low ensemble defect RNA sequences for targeted secondary structures including pseudoknots. We used a biological data set of 201 pseudoknotted structures from the Pseudobase library to benchmark the performance of our algorithm. We compared the quality characteristics of the RNA sequences we designed by Enzymer with the results obtained from the state of the art MODENA and antaRNA. Our results show our method succeeds more frequently than MODENA and antaRNA do, and generates sequences that have lower ensemble defect, lower probability defect and higher thermostability. Finally by using Enzymer and by constraining the design to a naturally occurring and highly conserved Hammerhead motif, we designed 8 sequences for a pseudoknotted cis-acting Hammerhead ribozyme. Enzymer is available for download at https://bitbucket.org/casraz/enzymer. PMID:27499762

  8. How can computerized interpretation algorithms adapt to gender/age differences in ECG measurements?

    PubMed

    Xue, Joel; Farrell, Robert M

    2014-01-01

    It is well known that there are gender differences in 12 lead ECG measurements, some of which can be statistically significant. It is also an accepted practice that we should consider those differences when we interpret ECGs, by either a human overreader or a computerized algorithm. There are some major gender differences in 12 lead ECG measurements based on automatic algorithms, including global measurements such as heart rate, QRS duration, QT interval, and lead-by-lead measurements like QRS amplitude, ST level, etc. The interpretation criteria used in the automatic algorithms can be adapted to the gender differences in the measurements. The analysis of a group of 1339 patients with acute inferior MI showed that for patients under age 60, women had lower ST elevations at the J point in lead II than men (57±91μV vs. 86±117μV, p<0.02). This trend was reversed for patients over age 60 (lead aVF: 102±126μV vs. 84±117μV, p<0.04; lead III: 130±146μV vs. 103±131μV, p<0.007). Therefore, the ST elevation thresholds were set based on available gender and age information, which resulted in 25% relative sensitivity improvement for women under age 60, while maintaining a high specificity of 98%. Similar analyses were done for prolonged QT interval and LVH cases. The paper uses several design examples to demonstrate (1) how to design a gender-specific algorithm, and (2) how to design a robust ECG interpretation algorithm which relies less on absolute threshold-based criteria and is instead more reliant on overall morphology features, which are especially important when gender information is unavailable for automatic analysis.

  9. Optical Cluster-Finding with an Adaptive Matched-Filter Technique: Algorithm and Comparison with Simulations

    SciTech Connect

    Dong, Feng; Pierpaoli, Elena; Gunn, James E.; Wechsler, Risa H.

    2007-10-29

    We present a modified adaptive matched filter algorithm designed to identify clusters of galaxies in wide-field imaging surveys such as the Sloan Digital Sky Survey. The cluster-finding technique is fully adaptive to imaging surveys with spectroscopic coverage, multicolor photometric redshifts, no redshift information at all, and any combination of these within one survey. It works with high efficiency in multi-band imaging surveys where photometric redshifts can be estimated with well-understood error distributions. Tests of the algorithm on realistic mock SDSS catalogs suggest that the detected sample is {approx} 85% complete and over 90% pure for clusters with masses above 1.0 x 10{sup 14}h{sup -1} M and redshifts up to z = 0.45. The errors of estimated cluster redshifts from maximum likelihood method are shown to be small (typically less that 0.01) over the whole redshift range with photometric redshift errors typical of those found in the Sloan survey. Inside the spherical radius corresponding to a galaxy overdensity of {Delta} = 200, we find the derived cluster richness {Lambda}{sub 200} a roughly linear indicator of its virial mass M{sub 200}, which well recovers the relation between total luminosity and cluster mass of the input simulation.

  10. Adaptive filter design based on the LMS algorithm for delay elimination in TCR/FC compensators.

    PubMed

    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.

  11. Clustering of tethered satellite system simulation data by an adaptive neuro-fuzzy algorithm

    NASA Technical Reports Server (NTRS)

    Mitra, Sunanda; Pemmaraju, Surya

    1992-01-01

    Recent developments in neuro-fuzzy systems indicate that the concepts of adaptive pattern recognition, when used to identify appropriate control actions corresponding to clusters of patterns representing system states in dynamic nonlinear control systems, may result in innovative designs. A modular, unsupervised neural network architecture, in which fuzzy learning rules have been embedded is used for on-line identification of similar states. The architecture and control rules involved in Adaptive Fuzzy Leader Clustering (AFLC) allow this system to be incorporated in control systems for identification of system states corresponding to specific control actions. We have used this algorithm to cluster the simulation data of Tethered Satellite System (TSS) to estimate the range of delta voltages necessary to maintain the desired length rate of the tether. The AFLC algorithm is capable of on-line estimation of the appropriate control voltages from the corresponding length error and length rate error without a priori knowledge of their membership functions and familarity with the behavior of the Tethered Satellite System.

  12. Strategies to overcome photobleaching in algorithm-based adaptive optics for nonlinear in-vivo imaging.

    PubMed

    Caroline Müllenbroich, M; McGhee, Ewan J; Wright, Amanda J; Anderson, Kurt I; Mathieson, Keith

    2014-01-01

    We have developed a nonlinear adaptive optics microscope utilizing a deformable membrane mirror (DMM) and demonstrated its use in compensating for system- and sample-induced aberrations. The optimum shape of the DMM was determined with a random search algorithm optimizing on either two photon fluorescence or second harmonic signals as merit factors. We present here several strategies to overcome photobleaching issues associated with lengthy optimization routines by adapting the search algorithm and the experimental methodology. Optimizations were performed on extrinsic fluorescent dyes, fluorescent beads loaded into organotypic tissue cultures and the intrinsic second harmonic signal of these cultures. We validate the approach of using these preoptimized mirror shapes to compile a robust look-up table that can be applied for imaging over several days and through a variety of tissues. In this way, the photon exposure to the fluorescent cells under investigation is limited to imaging. Using our look-up table approach, we show signal intensity improvement factors ranging from 1.7 to 4.1 in organotypic tissue cultures and freshly excised mouse tissue. Imaging zebrafish in vivo, we demonstrate signal improvement by a factor of 2. This methodology is easily reproducible and could be applied to many photon starved experiments, for example fluorescent life time imaging, or when photobleaching is a concern.

  13. Adaptive phase extraction: incorporating the Gabor transform in the matching pursuit algorithm.

    PubMed

    Wacker, Matthias; Witte, Herbert

    2011-10-01

    Short-time Fourier transform (STFT), Gabor transform (GT), wavelet transform (WT), and the Wigner-Ville distribution (WVD) are just some examples of time-frequency analysis methods which are frequently applied in biomedical signal analysis. However, all of these methods have their individual drawbacks. The STFT, GT, and WT have a time-frequency resolution that is determined by algorithm parameters and the WVD is contaminated by cross terms. In 1993, Mallat and Zhang introduced the matching pursuit (MP) algorithm that decomposes a signal into a sum of atoms and uses a cross-term free pseudo-WVD to generate a data-adaptive power distribution in the time-frequency space. Thus, it solved some of the problems of the GT and WT but lacks phase information that is crucial e.g., for synchronization analysis. We introduce a new time-frequency analysis method that combines the MP with a pseudo-GT. Therefore, the signal is decomposed into a set of Gabor atoms. Afterward, each atom is analyzed with a Gabor analysis, where the time-domain gaussian window of the analysis matches that of the specific atom envelope. A superposition of the single time-frequency planes gives the final result. This is the first time that a complete analysis of the complex time-frequency plane can be performed in a fully data-adaptive and frequency-selective manner. We demonstrate the capabilities of our approach on a simulation and on real-life magnetoencephalogram data.

  14. Jacobi-like method for a control algorithm in adaptive-optics imaging

    NASA Astrophysics Data System (ADS)

    Pitsianis, Nikos P.; Ellerbroek, Brent L.; Van Loan, Charles; Plemmons, Robert J.

    1998-10-01

    A study is made of a non-smooth optimization problem arising in adaptive-optics, which involves the real-time control of a deformable mirror designed to compensate for atmospheric turbulence and other dynamic image degradation factors. One formulation of this problem yields a functional f(U) equals (Sigma) iequals1n maxj[(UTMjU)ii] to be maximized over orthogonal matrices U for a fixed collection of n X n symmetric matrices Mj. We consider first the situation which can arise in practical applications where the matrices Mj are nearly pairwise commutative. Besides giving useful bounds, results for this case lead to a simple corollary providing a theoretical closed-form solution for globally maximizing f if the Mj are simultaneously diagonalizable. However, even here conventional optimization methods for maximizing f are not practical in a real-time environment. The genal optimization problem is quite difficult and is approached using a heuristic Jacobi-like algorithm. Numerical test indicate that the algorithm provides an effective means to optimize performance for some important adaptive-optics systems.

  15. Short-Lag Spatial Coherence Imaging on Matrix Arrays, Part I: Beamforming Methods and Simulation Studies

    PubMed Central

    Hyun, Dongwoon; Trahey, Gregg E.; Jakovljevic, Marko; Dahl, Jeremy J.

    2014-01-01

    Short-lag spatial coherence (SLSC) imaging is a beamforming technique that has demonstrated improved imaging performance compared with conventional B-mode imaging in previous studies. Thus far, the use of 1-D arrays has limited coherence measurements and SLSC imaging to a single dimension. Here, the SLSC algorithm is extended for use on 2-D matrix array transducers and applied in a simulation study examining imaging performance as a function of subaperture configuration and of incoherent channel noise. SLSC images generated with a 2-D array yielded superior contrast-to-noise ratio (CNR) and texture SNR measurements over SLSC images made on a corresponding 1-D array and over B-mode imaging. SLSC images generated with square subapertures were found to be superior to SLSC images generated with subapertures of equal surface area that spanned the whole array in one dimension. Subaperture beamforming was found to have little effect on SLSC imaging performance for subapertures up to 8 × 8 elements in size on a 64 × 64 element transducer. Additionally, the use of 8 × 8, 4 × 4, and 2 × 2 element subapertures provided 8, 4, and 2 times improvement in channel SNR along with 2640-, 328-, and 25-fold reduction in computation time, respectively. These results indicate that volumetric SLSC imaging is readily applicable to existing 2-D arrays that employ subaperture beamforming. PMID:24960700

  16. Point focusing using loudspeaker arrays from the perspective of optimal beamforming.

    PubMed

    Bai, Mingsian R; Hsieh, Yu-Hao

    2015-06-01

    Sound focusing is to create a concentrated acoustic field in the region surrounded by a loudspeaker array. This problem was tackled in the previous research via the Helmholtz integral approach, brightness control, acoustic contrast control, etc. In this paper, the same problem was revisited from the perspective of beamforming. A source array model is reformulated in terms of the steering matrix between the source and the field points, which lends itself to the use of beamforming algorithms such as minimum variance distortionless response (MVDR) and linearly constrained minimum variance (LCMV) originally intended for sensor arrays. The beamforming methods are compared with the conventional methods in terms of beam pattern, directional index, and control effort. Objective tests are conducted to assess the audio quality by using perceptual evaluation of audio quality (PEAQ). Experiments of produced sound field and listening tests are conducted in a listening room, with results processed using analysis of variance and regression analysis. In contrast to the conventional energy-based methods, the results have shown that the proposed methods are phase-sensitive in light of the distortionless constraint in formulating the array filters, which helps enhance audio quality and focusing performance.

  17. Short-lag spatial coherence imaging on matrix arrays, part 1: Beamforming methods and simulation studies.

    PubMed

    Hyun, Dongwoon; Trahey, Gregg E; Jakovljevic, Marko; Dahl, Jeremy J

    2014-07-01

    Short-lag spatial coherence (SLSC) imaging is a beamforming technique that has demonstrated improved imaging performance compared with conventional B-mode imaging in previous studies. Thus far, the use of 1-D arrays has limited coherence measurements and SLSC imaging to a single dimension. Here, the SLSC algorithm is extended for use on 2-D matrix array transducers and applied in a simulation study examining imaging performance as a function of subaperture configuration and of incoherent channel noise. SLSC images generated with a 2-D array yielded superior contrast-to-noise ratio (CNR) and texture SNR measurements over SLSC images made on a corresponding 1-D array and over B-mode imaging. SLSC images generated with square subapertures were found to be superior to SLSC images generated with subapertures of equal surface area that spanned the whole array in one dimension. Subaperture beamforming was found to have little effect on SLSC imaging performance for subapertures up to 8 x 8 elements in size on a 64 × 64 element transducer. Additionally, the use of 8 x 8, 4 x 4, and 2 x 2 element subapertures provided 8, 4, and 2 times improvement in channel SNR along with 2640-, 328-, and 25-fold reduction in computation time, respectively. These results indicate that volumetric SLSC imaging is readily applicable to existing 2-D arrays that employ subaperture beamforming.

  18. A sidelobe suppressing near-field beamforming approach for ultrasound array imaging.

    PubMed

    He, Zhengyao; Zheng, Fan; Ma, Yuanliang; Kim, Hyung Ham; Zhou, Qifa; Shung, K Kirk

    2015-05-01

    A method is proposed to suppress sidelobe level for near-field beamforming in ultrasound array imaging. An optimization problem is established, and the second-order cone algorithm is used to solve the problem to obtain the weight vector based on the near-field response vector of a transducer array. The weight vector calculation results show that the proposed method can be used to suppress the sidelobe level of the near-field beam pattern of a transducer array. Ultrasound images following the application of weight vector to the array of a wire phantom are obtained by simulation with the Field II program, and the images of a wire phantom and anechoic sphere phantom are obtained experimentally with a 64-element 26 MHz linear phased array. The experimental and simulation results agree well and show that the proposed method can achieve a much lower sidelobe level than the conventional delay and sum beamforming method. The wire phantom image is demonstrated to focus much better and the contrast of the anechoic sphere phantom image improved by applying the proposed beamforming method.

  19. Self-adapting root-MUSIC algorithm and its real-valued formulation for acoustic vector sensor array

    NASA Astrophysics Data System (ADS)

    Wang, Peng; Zhang, Guo-jun; Xue, Chen-yang; Zhang, Wen-dong; Xiong, Ji-jun

    2012-12-01

    In this paper, based on the root-MUSIC algorithm for acoustic pressure sensor array, a new self-adapting root-MUSIC algorithm for acoustic vector sensor array is proposed by self-adaptive selecting the lead orientation vector, and its real-valued formulation by Forward-Backward(FB) smoothing and real-valued inverse covariance matrix is also proposed, which can reduce the computational complexity and distinguish the coherent signals. The simulation experiment results show the better performance of two new algorithm with low Signal-to-Noise (SNR) in direction of arrival (DOA) estimation than traditional MUSIC algorithm, and the experiment results using MEMS vector hydrophone array in lake trails show the engineering practicability of two new algorithms.

  20. An Energy Aware Adaptive Sampling Algorithm for Energy Harvesting WSN with Energy Hungry Sensors

    PubMed Central

    Srbinovski, Bruno; Magno, Michele; Edwards-Murphy, Fiona; Pakrashi, Vikram; Popovici, Emanuel

    2016-01-01

    Wireless sensor nodes have a limited power budget, though they are often expected to be functional in the field once deployed for extended periods of time. Therefore, minimization of energy consumption and energy harvesting technology in Wireless Sensor Networks (WSN) are key tools for maximizing network lifetime, and achieving self-sustainability. This paper proposes an energy aware Adaptive Sampling Algorithm (ASA) for WSN with power hungry sensors and harvesting capabilities, an energy management technique that can be implemented on any WSN platform with enough processing power to execute the proposed algorithm. An existing state-of-the-art ASA developed for wireless sensor networks with power hungry sensors is optimized and enhanced to adapt the sampling frequency according to the available energy of the node. The proposed algorithm is evaluated using two in-field testbeds that are supplied by two different energy harvesting sources (solar and wind). Simulation and comparison between the state-of-the-art ASA and the proposed energy aware ASA (EASA) in terms of energy durability are carried out using in-field measured harvested energy (using both wind and solar sources) and power hungry sensors (ultrasonic wind sensor and gas sensors). The simulation results demonstrate that using ASA in combination with an energy aware function on the nodes can drastically increase the lifetime of a WSN node and enable self-sustainability. In fact, the proposed EASA in conjunction with energy harvesting capability can lead towards perpetual WSN operation and significantly outperform the state-of-the-art ASA. PMID:27043559

  1. A local anisotropic adaptive algorithm for the solution of low-Mach transient combustion problems

    NASA Astrophysics Data System (ADS)

    Carpio, Jaime; Prieto, Juan Luis; Vera, Marcos

    2016-02-01

    A novel numerical algorithm for the simulation of transient combustion problems at low Mach and moderately high Reynolds numbers is presented. These problems are often characterized by the existence of a large disparity of length and time scales, resulting in the development of directional flow features, such as slender jets, boundary layers, mixing layers, or flame fronts. This makes local anisotropic adaptive techniques quite advantageous computationally. In this work we propose a local anisotropic refinement algorithm using, for the spatial discretization, unstructured triangular elements in a finite element framework. For the time integration, the problem is formulated in the context of semi-Lagrangian schemes, introducing the semi-Lagrange-Galerkin (SLG) technique as a better alternative to the classical semi-Lagrangian (SL) interpolation. The good performance of the numerical algorithm is illustrated by solving a canonical laminar combustion problem: the flame/vortex interaction. First, a premixed methane-air flame/vortex interaction with simplified transport and chemistry description (Test I) is considered. Results are found to be in excellent agreement with those in the literature, proving the superior performance of the SLG scheme when compared with the classical SL technique, and the advantage of using anisotropic adaptation instead of uniform meshes or isotropic mesh refinement. As a more realistic example, we then conduct simulations of non-premixed hydrogen-air flame/vortex interactions (Test II) using a more complex combustion model which involves state-of-the-art transport and chemical kinetics. In addition to the analysis of the numerical features, this second example allows us to perform a satisfactory comparison with experimental visualizations taken from the literature.

  2. Dimensionality Reduction in Complex Medical Data: Improved Self-Adaptive Niche Genetic Algorithm.

    PubMed

    Zhu, Min; Xia, Jing; Yan, Molei; Cai, Guolong; Yan, Jing; Ning, Gangmin

    2015-01-01

    With the development of medical technology, more and more parameters are produced to describe the human physiological condition, forming high-dimensional clinical datasets. In clinical analysis, data are commonly utilized to establish mathematical models and carry out classification. High-dimensional clinical data will increase the complexity of classification, which is often utilized in the models, and thus reduce efficiency. The Niche Genetic Algorithm (NGA) is an excellent algorithm for dimensionality reduction. However, in the conventional NGA, the niche distance parameter is set in advance, which prevents it from adjusting to the environment. In this paper, an Improved Niche Genetic Algorithm (INGA) is introduced. It employs a self-adaptive niche-culling operation in the construction of the niche environment to improve the population diversity and prevent local optimal solutions. The INGA was verified in a stratification model for sepsis patients. The results show that, by applying INGA, the feature dimensionality of datasets was reduced from 77 to 10 and that the model achieved an accuracy of 92% in predicting 28-day death in sepsis patients, which is significantly higher than other methods.

  3. Dimensionality Reduction in Complex Medical Data: Improved Self-Adaptive Niche Genetic Algorithm

    PubMed Central

    Zhu, Min; Xia, Jing; Yan, Molei; Cai, Guolong; Yan, Jing; Ning, Gangmin

    2015-01-01

    With the development of medical technology, more and more parameters are produced to describe the human physiological condition, forming high-dimensional clinical datasets. In clinical analysis, data are commonly utilized to establish mathematical models and carry out classification. High-dimensional clinical data will increase the complexity of classification, which is often utilized in the models, and thus reduce efficiency. The Niche Genetic Algorithm (NGA) is an excellent algorithm for dimensionality reduction. However, in the conventional NGA, the niche distance parameter is set in advance, which prevents it from adjusting to the environment. In this paper, an Improved Niche Genetic Algorithm (INGA) is introduced. It employs a self-adaptive niche-culling operation in the construction of the niche environment to improve the population diversity and prevent local optimal solutions. The INGA was verified in a stratification model for sepsis patients. The results show that, by applying INGA, the feature dimensionality of datasets was reduced from 77 to 10 and that the model achieved an accuracy of 92% in predicting 28-day death in sepsis patients, which is significantly higher than other methods. PMID:26649071

  4. Development and evaluation of a data-adaptive alerting algorithm for univariate temporal biosurveillance data.

    PubMed

    Elbert, Yevgeniy; Burkom, Howard S

    2009-11-20

    This paper discusses further advances in making robust predictions with the Holt-Winters forecasts for a variety of syndromic time series behaviors and introduces a control-chart detection approach based on these forecasts. Using three collections of time series data, we compare biosurveillance alerting methods with quantified measures of forecast agreement, signal sensitivity, and time-to-detect. The study presents practical rules for initialization and parameterization of biosurveillance time series. Several outbreak scenarios are used for detection comparison. We derive an alerting algorithm from forecasts using Holt-Winters-generalized smoothing for prospective application to daily syndromic time series. The derived algorithm is compared with simple control-chart adaptations and to more computationally intensive regression modeling methods. The comparisons are conducted on background data from both authentic and simulated data streams. Both types of background data include time series that vary widely by both mean value and cyclic or seasonal behavior. Plausible, simulated signals are added to the background data for detection performance testing at signal strengths calculated to be neither too easy nor too hard to separate the compared methods. Results show that both the sensitivity and the timeliness of the Holt-Winters-based algorithm proved to be comparable or superior to that of the more traditional prediction methods used for syndromic surveillance.

  5. [A spatial adaptive algorithm for endmember extraction on multispectral remote sensing image].

    PubMed

    Zhu, Chang-Ming; Luo, Jian-Cheng; Shen, Zhan-Feng; Li, Jun-Li; Hu, Xiao-Dong

    2011-10-01

    Due to the problem that the convex cone analysis (CCA) method can only extract limited endmember in multispectral imagery, this paper proposed a new endmember extraction method by spatial adaptive spectral feature analysis in multispectral remote sensing image based on spatial clustering and imagery slice. Firstly, in order to remove spatial and spectral redundancies, the principal component analysis (PCA) algorithm was used for lowering the dimensions of the multispectral data. Secondly, iterative self-organizing data analysis technology algorithm (ISODATA) was used for image cluster through the similarity of the pixel spectral. And then, through clustering post process and litter clusters combination, we divided the whole image data into several blocks (tiles). Lastly, according to the complexity of image blocks' landscape and the feature of the scatter diagrams analysis, the authors can determine the number of endmembers. Then using hourglass algorithm extracts endmembers. Through the endmember extraction experiment on TM multispectral imagery, the experiment result showed that the method can extract endmember spectra form multispectral imagery effectively. What's more, the method resolved the problem of the amount of endmember limitation and improved accuracy of the endmember extraction. The method has provided a new way for multispectral image endmember extraction.

  6. Automatic classification of schizophrenia using resting-state functional language network via an adaptive learning algorithm

    NASA Astrophysics Data System (ADS)

    Zhu, Maohu; Jie, Nanfeng; Jiang, Tianzi

    2014-03-01

    A reliable and precise classification of schizophrenia is significant for its diagnosis and treatment of schizophrenia. Functional magnetic resonance imaging (fMRI) is a novel tool increasingly used in schizophrenia research. Recent advances in statistical learning theory have led to applying pattern classification algorithms to access the diagnostic value of functional brain networks, discovered from resting state fMRI data. The aim of this study was to propose an adaptive learning algorithm to distinguish schizophrenia patients from normal controls using resting-state functional language network. Furthermore, here the classification of schizophrenia was regarded as a sample selection problem where a sparse subset of samples was chosen from the labeled training set. Using these selected samples, which we call informative vectors, a classifier for the clinic diagnosis of schizophrenia was established. We experimentally demonstrated that the proposed algorithm incorporating resting-state functional language network achieved 83.6% leaveone- out accuracy on resting-state fMRI data of 27 schizophrenia patients and 28 normal controls. In contrast with KNearest- Neighbor (KNN), Support Vector Machine (SVM) and l1-norm, our method yielded better classification performance. Moreover, our results suggested that a dysfunction of resting-state functional language network plays an important role in the clinic diagnosis of schizophrenia.

  7. Effects of directional microphone and adaptive multichannel noise reduction algorithm on cochlear implant performance.

    PubMed

    Chung, King; Zeng, Fan-Gang; Acker, Kyle N

    2006-10-01

    Although cochlear implant (CI) users have enjoyed good speech recognition in quiet, they still have difficulties understanding speech in noise. We conducted three experiments to determine whether a directional microphone and an adaptive multichannel noise reduction algorithm could enhance CI performance in noise and whether Speech Transmission Index (STI) can be used to predict CI performance in various acoustic and signal processing conditions. In Experiment I, CI users listened to speech in noise processed by 4 hearing aid settings: omni-directional microphone, omni-directional microphone plus noise reduction, directional microphone, and directional microphone plus noise reduction. The directional microphone significantly improved speech recognition in noise. Both directional microphone and noise reduction algorithm improved overall preference. In Experiment II, normal hearing individuals listened to the recorded speech produced by 4- or 8-channel CI simulations. The 8-channel simulation yielded similar speech recognition results as in Experiment I, whereas the 4-channel simulation produced no significant difference among the 4 settings. In Experiment III, we examined the relationship between STIs and speech recognition. The results suggested that STI could predict actual and simulated CI speech intelligibility with acoustic degradation and the directional microphone, but not the noise reduction algorithm. Implications for intelligibility enhancement are discussed.

  8. Fitting of adaptive neuron model to electrophysiological recordings using particle swarm optimization algorithm

    NASA Astrophysics Data System (ADS)

    Shan, Bonan; Wang, Jiang; Zhang, Lvxia; Deng, Bin; Wei, Xile

    2017-02-01

    In order to fit neural model’s spiking features to electrophysiological recordings, in this paper, a fitting framework based on particle swarm optimization (PSO) algorithm is proposed to estimate the model parameters in an augmented multi-timescale adaptive threshold (AugMAT) model. PSO algorithm is an advanced evolutionary calculation method based on iteration. Selecting a reasonable criterion function will ensure the effectiveness of PSO algorithm. In this work, firing rate information is used as the main spiking feature and the estimation error of firing rate is selected as the criterion for fitting. A series of simulations are presented to verify the performance of the framework. The first step is model validation; an artificial training data is introduced to test the fitting procedure. Then we talk about the suitable PSO parameters, which exhibit adequate compromise between speediness and accuracy. Lastly, this framework is used to fit the electrophysiological recordings, after three adjustment steps, the features of experimental data are translated into realistic spiking neuron model.

  9. Adaptive particle-cell algorithm for Fokker-Planck based rarefied gas flow simulations

    NASA Astrophysics Data System (ADS)

    Pfeiffer, M.; Gorji, M. H.

    2017-04-01

    Recently, the Fokker-Planck (FP) kinetic model has been devised on the basis of the Boltzmann equation (Jenny et al., 2010; Gorji et al., 2011). Particle Monte-Carlo schemes are then introduced for simulations of rarefied gas flows based on the FP kinetics. Here the particles follow independent stochastic paths and thus a spatio-temporal resolution coarser than the collisional scales becomes possible. In contrast to the direct simulation Monte-Carlo (DSMC), the computational cost is independent of the Knudsen number resulting in efficient simulations at moderate/low Knudsen flows. In order to further exploit the efficiency of the FP method, the required particle-cell resolutions should be found, and a cell refinement strategy has to be developed accordingly. In this study, an adaptive particle-cell scheme applicable to a general unstructured mesh is derived for the FP model. Virtual sub cells are introduced for the adaptive mesh refinement. Moreover a sub cell-merging algorithm is provided to honor the minimum required number of particles per cell. For assessments, the 70 degree blunted cone reentry flow (Allgre et al., 1997) is studied. Excellent agreement between the introduced adaptive FP method and DSMC is achieved.

  10. Phased array antenna beamforming using optical processor

    NASA Technical Reports Server (NTRS)

    Anderson, L. P.; Boldissar, F.; Chang, D. C. D.

    1991-01-01

    The feasibility of optical processor based beamforming for microwave array antennas is investigated. The primary focus is on systems utilizing the 20/30 GHz communications band and a transmit configuration exclusively to serve this band. A mathematical model is developed for computation of candidate design configurations. The model is capable of determination of the necessary design parameters required for spatial aspects of the microwave 'footprint' (beam) formation. Computed example beams transmitted from geosynchronous orbit are presented to demonstrate network capabilities. The effect of the processor on the output microwave signal to noise quality at the antenna interface is also considered.

  11. Sequential beamforming for synthetic aperture imaging.

    PubMed

    Kortbek, Jacob; Jensen, Jørgen Arendt; Gammelmark, Kim Løkke

    2013-01-01

    Synthetic aperture sequential beamforming (SASB) is a novel technique which allows to implement synthetic aperture beamforming on a system with a restricted complexity, and without storing RF-data. The objective is to improve lateral resolution and obtain a more depth independent resolution compared to conventional ultrasound imaging. SASB is a two-stage procedure using two separate beamformers. The initial step is to construct and store a set of B-mode image lines using a single focal point in both transmit and receive. The focal points are considered virtual sources and virtual receivers making up a virtual array. The second stage applies the focused image lines from the first stage as input data, and take advantage of the virtual array in the delay and sum beamforming. The size of the virtual array is dynamically expanded and the image is dynamically focused in both transmit and receive and a range independent lateral resolution is obtained. The SASB method has been investigated using simulations in Field II and by off-line processing of data acquired with a commercial scanner. The lateral resolution increases with a decreasing F#. Grating lobes appear if F#≤2 for a linear array with λ-pitch. The performance of SASB with the virtual source at 20mm and F#=1.5 is compared with conventional dynamic receive focusing (DRF). The axial resolution is the same for the two methods. For the lateral resolution there is improvement in FWHM of at least a factor of 2 and the improvement at -40dB is at least a factor of 3. With SASB the resolution is almost constant throughout the range. For DRF the FWHM increases almost linearly with range and the resolution at -40dB is fluctuating with range. The theoretical potential improvement in SNR of SASB over DRF has been estimated. An improvement is attained at the entire range, and at a depth of 80mm the improvement is 8dB.

  12. Adapted Prescription Dose for Monte Carlo Algorithm in Lung SBRT: Clinical Outcome on 205 Patients

    PubMed Central

    Bibault, Jean-Emmanuel; Mirabel, Xavier; Lacornerie, Thomas; Tresch, Emmanuelle; Reynaert, Nick; Lartigau, Eric

    2015-01-01

    Purpose SBRT is the standard of care for inoperable patients with early-stage lung cancer without lymph node involvement. Excellent local control rates have been reported in a large number of series. However, prescription doses and calculation algorithms vary to a great extent between studies, even if most teams prescribe to the D95 of the PTV. Type A algorithms are known to produce dosimetric discrepancies in heterogeneous tissues such as lungs. This study was performed to present a Monte Carlo (MC) prescription dose for NSCLC adapted to lesion size and location and compare the clinical outcomes of two cohorts of patients treated with a standard prescription dose calculated by a type A algorithm or the proposed MC protocol. Patients and Methods Patients were treated from January 2011 to April 2013 with a type B algorithm (MC) prescription with 54 Gy in three fractions for peripheral lesions with a diameter under 30 mm, 60 Gy in 3 fractions for lesions with a diameter over 30 mm, and 55 Gy in five fractions for central lesions. Clinical outcome was compared to a series of 121 patients treated with a type A algorithm (TA) with three fractions of 20 Gy for peripheral lesions and 60 Gy in five fractions for central lesions prescribed to the PTV D95 until January 2011. All treatment plans were recalculated with both algorithms for this study. Spearman’s rank correlation coefficient was calculated for GTV and PTV. Local control, overall survival and toxicity were compared between the two groups. Results 205 patients with 214 lesions were included in the study. Among these, 93 lesions were treated with MC and 121 were treated with TA. Overall survival rates were 86% and 94% at one and two years, respectively. Local control rates were 79% and 93% at one and two years respectively. There was no significant difference between the two groups for overall survival (p = 0.785) or local control (p = 0.934). Fifty-six patients (27%) developed grade I lung fibrosis without

  13. Synthetic aperture ultrasound Fourier beamformation using virtual sources.

    PubMed

    Moghimirad, Elahe; Villagomez-Hoyos, Carlos A; Mahloojifar, Ali; Mohammadzadeh Asl, Babak; Jensen, Jorgen

    2016-09-07

    An efficient Fourier beamformation algorithm is presented for multistatic synthetic aperture ultrasound imaging using virtual sources (FBV). The concept is based on the frequency domain wavenumber algorithm from radar and sonar and is extended to a multi-element transmit/receive configuration using virtual sources. Window functions are used to extract the azimuth processing bandwidths and weight the data to reduce sidelobes in the final image. Field II simulated data and SARUS measured data are used to evaluate the results in terms of point spread function, resolution, contrast, SNR, and processing time. Lateral resolutions of 0.53 mm and 0.66 mm are obtained for FBV and DAS on point target simulated data. Corresponding axial resolutions are 0.21 mm for FBV and 0.20 mm for DAS. The results are also consistent over different depths evaluated using a simulated phantom containing several point targets at different depths. FBV shows a better lateral resolution at all depths, and the axial and cystic resolutions of -6 dB, -12 dB and -20 dB are almost the same for FBV and DAS. To evaluate the cyst phantom metrics, three different criteria of Power Ratio (PR), Contrast Ratio (CR), and contrast to noise ratio (CNR) have been used. Results show that the algorithms have a different performance in the cyst center and near the boundary. FBV has a better performance near the boundary, however, DAS is better in the more central area of the cyst. Measured data from phantoms are also used for evaluation. The results confirm the applicability of FBV in ultrasound and 20 times less processing time in comparison with DAS is attained. Evaluating the results over a wide variety of parameters and having almost the same results for simulated and measured data demonstrates the ability of FBV in preserving the quality of image as DAS, while providing a more efficient algorithm with 20 times less computations.

  14. A fast minimum variance beamforming method using principal component analysis.

    PubMed

    Kim, Kyuhong; Park, Suhyun; Kim, Jungho; Park, Sung-Bae; Bae, MooHo

    2014-06-01

    Minimum variance (MV) beamforming has been studied for improving the performance of a diagnostic ultrasound imaging system. However, it is not easy for the MV beamforming to be implemented in a real-time ultrasound imaging system because of the enormous amount of computation time associated with the covariance matrix inversion. In this paper, to address this problem, we propose a new fast MV beamforming method that almost optimally approximates the MV beamforming while reducing the computational complexity greatly through dimensionality reduction using principal component analysis (PCA). The principal components are estimated offline from pre-calculated conventional MV weights. Thus, the proposed method does not directly calculate the MV weights but approximates them by a linear combination of a few selected dominant principal components. The combinational weights are calculated in almost the same way as in MV beamforming, but in the transformed domain of beamformer input signal by the PCA, where the dimension of the transformed covariance matrix is identical to the number of some selected principal component vectors. Both computer simulation and experiment were carried out to verify the effectiveness of the proposed method with echo signals from simulation as well as phantom and in vivo experiments. It is confirmed that our method can reduce the dimension of the covariance matrix down to as low as 2 × 2 while maintaining the good image quality of MV beamforming.

  15. A solution-adaptive mesh algorithm for dynamic/static refinement of two and three dimensional grids

    NASA Technical Reports Server (NTRS)

    Benson, Rusty A.; Mcrae, D. S.

    1991-01-01

    An adaptive grid algorithm has been developed in two and three dimensions that can be used dynamically with a solver or as part of a grid refinement process. The algorithm employs a transformation from the Cartesian coordinate system to a general coordinate space, which is defined as a parallelepiped in three dimensions. A weighting function, independent for each coordinate direction, is developed that will provide the desired refinement criteria in regions of high solution gradient. The adaptation is performed in the general coordinate space and the new grid locations are returned to the Cartesian space via a simple, one-step inverse mapping. The algorithm for relocation of the mesh points in the parametric space is based on the center of mass for distributed weights. Dynamic solution-adaptive results are presented for laminar flows in two and three dimensions.

  16. High resolution beamforming for small aperture arrays

    NASA Astrophysics Data System (ADS)

    Clark, Chris; Null, Tom; Wagstaff, Ronald A.

    2003-04-01

    Achieving fine resolution bearing estimates for multiple sources using acoustic arrays with small apertures, in number of wavelengths, is a difficult challenge. It requires both large signal-to-noise ratio (SNR) gains and very narrow beam responses. High resolution beamforming for small aperture arrays is accomplished by exploiting acoustical fluctuations. Acoustical fluctuations in the atmosphere are caused by wind turbulence along the propagation path, air turbulence at the sensor, source/receiver motion, unsteady source level, and fine scale temperature variations. Similar environmental and source dependent phenomena cause fluctuations in other propagation media, e.g., undersea, optics, infrared. Amplitude fluctuations are exploited to deconvolve the beam response functions from the beamformed data of small arrays to achieve high spatial resolution, i.e., fine bearing resolution, and substantial SNR gain. Results are presented for a six microphone low-frequency array with an aperture of less than three wavelengths. [Work supported by U.S. Army Armament Research Development and Engineering Center.

  17. An adaptive alignment algorithm for quality-controlled label-free LC-MS.

    PubMed

    Sandin, Marianne; Ali, Ashfaq; Hansson, Karin; Månsson, Olle; Andreasson, Erik; Resjö, Svante; Levander, Fredrik

    2013-05-01

    Label-free quantification using precursor-based intensities is a versatile workflow for large-scale proteomics studies. The method however requires extensive computational analysis and is therefore in need of robust quality control during the data mining stage. We present a new label-free data analysis workflow integrated into a multiuser software platform. A novel adaptive alignment algorithm has been developed to minimize the possible systematic bias introduced into the analysis. Parameters are estimated on the fly from the data at hand, producing a user-friendly analysis suite. Quality metrics are output in every step of the analysis as well as actively incorporated into the parameter estimation. We furthermore show the improvement of this system by comprehensive comparison to classical label-free analysis methodology as well as current state-of-the-art software.

  18. Adaptive polarimetric sensing for optimum radar signature classification using a genetic search algorithm.

    PubMed

    Sadjadi, Firooz A

    2006-08-01

    An automated technique for adaptive radar polarimetric pattern classification is described. The approach is based on a genetic algorithm that uses a probabilistic pattern separation distance function and searches for those transmit and receive states of polarization sensing angles that optimize this function. Seven pattern separation distance functions--the Rayleigh quotient, the Bhattacharyya, divergence, Kolmogorov, Matusta, Kullback-Leibler distances, and the Bayesian probability of error--are used on real, fully polarimetric synthetic aperture radar target signatures. Each of these signatures is represented as functions of transmit and receive polarization ellipticity angles and the angle of polarization ellipse. The results indicate that, based on the majority of the distance functions used, there is a unique set of state of polarization angles whose use will lead to improved classification performance.

  19. Aberration correction in an adaptive free-space optical interconnect with an error diffusion algorithm

    NASA Astrophysics Data System (ADS)

    Gil-Leyva, Diego; Robertson, Brian; Wilkinson, Timothy D.; Henderson, Charley J.

    2006-06-01

    Aberration correction within a free-space optical interconnect based on a spatial light modulator for beam steering and holographic wavefront correction is presented. The wavefront sensing technique is based on an extension of a modal wavefront sensor described by Neil et al. [J. Opt. Soc. Am. A 17, 1098 (2000)], which uses a diffractive element. In this analysis such a wavefront sensor is adapted with an error diffusion algorithm that yields a low reconstruction error and fast reconfigurability. Improvement of the beam propagation quality (Strehl ratio) for different channels across the input plane is achieved. However, due to the space invariancy of the system, a trade-off among the beam propagation quality for channels is obtained. Experimental results are presented and discussed.

  20. Quantitative analysis of terahertz spectra for illicit drugs using adaptive-range micro-genetic algorithm

    NASA Astrophysics Data System (ADS)

    Chen, Yi; Ma, Yong; Lu, Zheng; Peng, Bei; Chen, Qin

    2011-08-01

    In the field of anti-illicit drug applications, many suspicious mixture samples might consist of various drug components—for example, a mixture of methamphetamine, heroin, and amoxicillin—which makes spectral identification very difficult. A terahertz spectroscopic quantitative analysis method using an adaptive range micro-genetic algorithm with a variable internal population (ARVIPɛμGA) has been proposed. Five mixture cases are discussed using ARVIPɛμGA driven quantitative terahertz spectroscopic analysis in this paper. The devised simulation results show agreement with the previous experimental results, which suggested that the proposed technique has potential applications for terahertz spectral identifications of drug mixture components. The results show agreement with the results obtained using other experimental and numerical techniques.

  1. An Adaptive Numeric Predictor-corrector Guidance Algorithm for Atmospheric Entry Vehicles. M.S. Thesis - MIT, Cambridge

    NASA Technical Reports Server (NTRS)

    Spratlin, Kenneth Milton

    1987-01-01

    An adaptive numeric predictor-corrector guidance is developed for atmospheric entry vehicles which utilize lift to achieve maximum footprint capability. Applicability of the guidance design to vehicles with a wide range of performance capabilities is desired so as to reduce the need for algorithm redesign with each new vehicle. Adaptability is desired to minimize mission-specific analysis and planning. The guidance algorithm motivation and design are presented. Performance is assessed for application of the algorithm to the NASA Entry Research Vehicle (ERV). The dispersions the guidance must be designed to handle are presented. The achievable operational footprint for expected worst-case dispersions is presented. The algorithm performs excellently for the expected dispersions and captures most of the achievable footprint.

  2. Demonstration of the use of ADAPT to derive predictive maintenance algorithms for the KSC central heat plant

    NASA Technical Reports Server (NTRS)

    Hunter, H. E.

    1972-01-01

    The Avco Data Analysis and Prediction Techniques (ADAPT) were employed to determine laws capable of detecting failures in a heat plant up to three days in advance of the occurrence of the failure. The projected performance of algorithms yielded a detection probability of 90% with false alarm rates of the order of 1 per year for a sample rate of 1 per day with each detection, followed by 3 hourly samplings. This performance was verified on 173 independent test cases. The program also demonstrated diagnostic algorithms and the ability to predict the time of failure to approximately plus or minus 8 hours up to three days in advance of the failure. The ADAPT programs produce simple algorithms which have a unique possibility of a relatively low cost updating procedure. The algorithms were implemented on general purpose computers at Kennedy Space Flight Center and tested against current data.

  3. Flight Testing of the Space Launch System (SLS) Adaptive Augmenting Control (AAC) Algorithm on an F/A-18

    NASA Technical Reports Server (NTRS)

    Dennehy, Cornelius J.; VanZwieten, Tannen S.; Hanson, Curtis E.; Wall, John H.; Miller, Chris J.; Gilligan, Eric T.; Orr, Jeb S.

    2014-01-01

    The Marshall Space Flight Center (MSFC) Flight Mechanics and Analysis Division developed an adaptive augmenting control (AAC) algorithm for launch vehicles that improves robustness and performance on an as-needed basis by adapting a classical control algorithm to unexpected environments or variations in vehicle dynamics. This was baselined as part of the Space Launch System (SLS) flight control system. The NASA Engineering and Safety Center (NESC) was asked to partner with the SLS Program and the Space Technology Mission Directorate (STMD) Game Changing Development Program (GCDP) to flight test the AAC algorithm on a manned aircraft that can achieve a high level of dynamic similarity to a launch vehicle and raise the technology readiness of the algorithm early in the program. This document reports the outcome of the NESC assessment.

  4. Beamforming and holography image formation methods: an analytic study.

    PubMed

    Solimene, Raffaele; Cuccaro, Antonio; Ruvio, Giuseppe; Tapia, Daniel Flores; O'Halloran, Martin

    2016-04-18

    Beamforming and holographic imaging procedures are widely used in many applications such as radar sensing, sonar, and in the area of microwave medical imaging. Nevertheless, an analytical comparison of the methods has not been done. In this paper, the Point Spread Functions pertaining to the two methods are analytically determined. This allows a formal comparison of the two techniques, and to easily highlight how the performance depends on the configuration parameters, including frequency range, number of scatterers, and data discretization. It is demonstrated that the beamforming and holography basically achieve the same resolution but beamforming requires a cheaper (less sensors) configuration..

  5. Beamforming using spatial matched filtering with annular arrays (L).

    PubMed

    Kim, Kang-Sik; Liu, Jie; Insana, Michael F

    2007-04-01

    A linear array beamforming method for ultrasonic B-mode imaging using spatial matched filtering (SMF) and a rectangular aperture geometry was recently proposed Kim et al., [J. Acoust. Soc. Am. 120, 852-861 (2006)]. This letter extends those results to include circularly symmetric apertures. SMF applied to annular arrays can improve the lateral resolution and echo signal-to-noise ratio as compared with conventional dynamic-receive delay-sum beamforming. At high frequencies, where delay and sum beamforming is problematic, SMF showed greatly improved target contrast over an extended field of view.

  6. Robust image transmission using a new joint source channel coding algorithm and dual adaptive OFDM

    NASA Astrophysics Data System (ADS)

    Farshchian, Masoud; Cho, Sungdae; Pearlman, William A.

    2004-01-01

    In this paper we consider the problem of robust image coding and packetization for the purpose of communications over slow fading frequency selective channels and channels with a shaped spectrum like those of digital subscribe lines (DSL). Towards this end, a novel and analytically based joint source channel coding (JSCC) algorithm to assign unequal error protection is presented. Under a block budget constraint, the image bitstream is de-multiplexed into two classes with different error responses. The algorithm assigns unequal error protection (UEP) in a way to minimize the expected mean square error (MSE) at the receiver while minimizing the probability of catastrophic failure. In order to minimize the expected mean square error at the receiver, the algorithm assigns unequal protection to the value bit class (VBC) stream. In order to minimizes the probability of catastrophic error which is a characteristic of progressive image coders, the algorithm assigns more protection to the location bit class (LBC) stream than the VBC stream. Besides having the advantage of being analytical and also numerically solvable, the algorithm is based on a new formula developed to estimate the distortion rate (D-R) curve for the VBC portion of SPIHT. The major advantage of our technique is that the worst case instantaneous minimum peak signal to noise ratio (PSNR) does not differ greatly from the averge MSE while this is not the case for the optimal single stream (UEP) system. Although both average PSNR of our method and the optimal single stream UEP are about the same, our scheme does not suffer erratic behavior because we have made the probability of catastrophic error arbitarily small. The coded image is sent via orthogonal frequency division multiplexing (OFDM) which is a known and increasing popular modulation scheme to combat ISI (Inter Symbol Interference) and impulsive noise. Using dual adaptive energy OFDM, we use the minimum energy necessary to send each bit stream at a

  7. Heart Motion Prediction Based on Adaptive Estimation Algorithms for Robotic Assisted Beating Heart Surgery

    PubMed Central

    Tuna, E. Erdem; Franke, Timothy J.; Bebek, Özkan; Shiose, Akira; Fukamachi, Kiyotaka; Çavuşoğlu, M. Cenk

    2013-01-01

    Robotic assisted beating heart surgery aims to allow surgeons to operate on a beating heart without stabilizers as if the heart is stationary. The robot actively cancels heart motion by closely following a point of interest (POI) on the heart surface—a process called Active Relative Motion Canceling (ARMC). Due to the high bandwidth of the POI motion, it is necessary to supply the controller with an estimate of the immediate future of the POI motion over a prediction horizon in order to achieve sufficient tracking accuracy. In this paper, two least-square based prediction algorithms, using an adaptive filter to generate future position estimates, are implemented and studied. The first method assumes a linear system relation between the consecutive samples in the prediction horizon. On the contrary, the second method performs this parametrization independently for each point over the whole the horizon. The effects of predictor parameters and variations in heart rate on tracking performance are studied with constant and varying heart rate data. The predictors are evaluated using a 3 degrees of freedom test-bed and prerecorded in-vivo motion data. Then, the one-step prediction and tracking performances of the presented approaches are compared with an Extended Kalman Filter predictor. Finally, the essential features of the proposed prediction algorithms are summarized. PMID:23976889

  8. SHARE: an adaptive algorithm to select the most informative set of SNPs for candidate genetic association.

    PubMed

    Dai, James Y; Leblanc, Michael; Smith, Nicholas L; Psaty, Bruce; Kooperberg, Charles

    2009-10-01

    Association studies have been widely used to identify genetic liability variants for complex diseases. While scanning the chromosomal region 1 single nucleotide polymorphism (SNP) at a time may not fully explore linkage disequilibrium, haplotype analyses tend to require a fairly large number of parameters, thus potentially losing power. Clustering algorithms, such as the cladistic approach, have been proposed to reduce the dimensionality, yet they have important limitations. We propose a SNP-Haplotype Adaptive REgression (SHARE) algorithm that seeks the most informative set of SNPs for genetic association in a targeted candidate region by growing and shrinking haplotypes with 1 more or less SNP in a stepwise fashion, and comparing prediction errors of different models via cross-validation. Depending on the evolutionary history of the disease mutations and the markers, this set may contain a single SNP or several SNPs that lay a foundation for haplotype analyses. Haplotype phase ambiguity is effectively accounted for by treating haplotype reconstruction as a part of the learning procedure. Simulations and a data application show that our method has improved power over existing methodologies and that the results are informative in the search for disease-causal loci.

  9. Refinement and evaluation of helicopter real-time self-adaptive active vibration controller algorithms

    NASA Technical Reports Server (NTRS)

    Davis, M. W.

    1984-01-01

    A Real-Time Self-Adaptive (RTSA) active vibration controller was used as the framework in developing a computer program for a generic controller that can be used to alleviate helicopter vibration. Based upon on-line identification of system parameters, the generic controller minimizes vibration in the fuselage by closed-loop implementation of higher harmonic control in the main rotor system. The new generic controller incorporates a set of improved algorithms that gives the capability to readily define many different configurations by selecting one of three different controller types (deterministic, cautious, and dual), one of two linear system models (local and global), and one or more of several methods of applying limits on control inputs (external and/or internal limits on higher harmonic pitch amplitude and rate). A helicopter rotor simulation analysis was used to evaluate the algorithms associated with the alternative controller types as applied to the four-bladed H-34 rotor mounted on the NASA Ames Rotor Test Apparatus (RTA) which represents the fuselage. After proper tuning all three controllers provide more effective vibration reduction and converge more quickly and smoothly with smaller control inputs than the initial RTSA controller (deterministic with external pitch-rate limiting). It is demonstrated that internal limiting of the control inputs a significantly improves the overall performance of the deterministic controller.

  10. An Adaptive Multigrid Algorithm for Simulating Solid Tumor Growth Using Mixture Models

    PubMed Central

    Wise, S.M.; Lowengrub, J.S.; Cristini, V.

    2010-01-01

    In this paper we give the details of the numerical solution of a three-dimensional multispecies diffuse interface model of tumor growth, which was derived in (Wise et al., J. Theor. Biol. 253 (2008)) and used to study the development of glioma in (Frieboes et al., NeuroImage 37 (2007) and tumor invasion in (Bearer et al., Cancer Research, 69 (2009)) and (Frieboes et al., J. Theor. Biol. 264 (2010)). The model has a thermodynamic basis, is related to recently developed mixture models, and is capable of providing a detailed description of tumor progression. It utilizes a diffuse interface approach, whereby sharp tumor boundaries are replaced by narrow transition layers that arise due to differential adhesive forces among the cell-species. The model consists of fourth-order nonlinear advection-reaction-diffusion equations (of Cahn-Hilliard-type) for the cell-species coupled with reaction-diffusion equations for the substrate components. Numerical solution of the model is challenging because the equations are coupled, highly nonlinear, and numerically stiff. In this paper we describe a fully adaptive, nonlinear multigrid/finite difference method for efficiently solving the equations. We demonstrate the convergence of the algorithm and we present simulations of tumor growth in 2D and 3D that demonstrate the capabilities of the algorithm in accurately and efficiently simulating the progression of tumors with complex morphologies. PMID:21076663

  11. Multiple Adaptive Neuro-Fuzzy Inference System with Automatic Features Extraction Algorithm for Cervical Cancer Recognition

    PubMed Central

    Subhi Al-batah, Mohammad; Mat Isa, Nor Ashidi; Klaib, Mohammad Fadel; Al-Betar, Mohammed Azmi

    2014-01-01

    To date, cancer of uterine cervix is still a leading cause of cancer-related deaths in women worldwide. The current methods (i.e., Pap smear and liquid-based cytology (LBC)) to screen for cervical cancer are time-consuming and dependent on the skill of the cytopathologist and thus are rather subjective. Therefore, this paper presents an intelligent computer vision system to assist pathologists in overcoming these problems and, consequently, produce more accurate results. The developed system consists of two stages. In the first stage, the automatic features extraction (AFE) algorithm is performed. In the second stage, a neuro-fuzzy model called multiple adaptive neuro-fuzzy inference system (MANFIS) is proposed for recognition process. The MANFIS contains a set of ANFIS models which are arranged in parallel combination to produce a model with multi-input-multioutput structure. The system is capable of classifying cervical cell image into three groups, namely, normal, low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL). The experimental results prove the capability of the AFE algorithm to be as effective as the manual extraction by human experts, while the proposed MANFIS produces a good classification performance with 94.2% accuracy. PMID:24707316

  12. An adaptive enhancement algorithm for infrared video based on modified k-means clustering

    NASA Astrophysics Data System (ADS)

    Zhang, Linze; Wang, Jingqi; Wu, Wen

    2016-09-01

    In this paper, we have proposed a video enhancement algorithm to improve the output video of the infrared camera. Sometimes the video obtained by infrared camera is very dark since there is no clear target. In this case, infrared video should be divided into frame images by frame extraction, in order to carry out the image enhancement. For the first frame image, which can be divided into k sub images by using K-means clustering according to the gray interval it occupies before k sub images' histogram equalization according to the amount of information per sub image, we used a method to solve a problem that final cluster centers close to each other in some cases; and for the other frame images, their initial cluster centers can be determined by the final clustering centers of the previous ones, and the histogram equalization of each sub image will be carried out after image segmentation based on K-means clustering. The histogram equalization can make the gray value of the image to the whole gray level, and the gray level of each sub image is determined by the ratio of pixels to a frame image. Experimental results show that this algorithm can improve the contrast of infrared video where night target is not obvious which lead to a dim scene, and reduce the negative effect given by the overexposed pixels adaptively in a certain range.

  13. A memory structure adapted simulated annealing algorithm for a green vehicle routing problem.

    PubMed

    Küçükoğlu, İlker; Ene, Seval; Aksoy, Aslı; Öztürk, Nursel

    2015-03-01

    Currently, reduction of carbon dioxide (CO2) emissions and fuel consumption has become a critical environmental problem and has attracted the attention of both academia and the industrial sector. Government regulations and customer demands are making environmental responsibility an increasingly important factor in overall supply chain operations. Within these operations, transportation has the most hazardous effects on the environment, i.e., CO2 emissions, fuel consumption, noise and toxic effects on the ecosystem. This study aims to construct vehicle routes with time windows that minimize the total fuel consumption and CO2 emissions. The green vehicle routing problem with time windows (G-VRPTW) is formulated using a mixed integer linear programming model. A memory structure adapted simulated annealing (MSA-SA) meta-heuristic algorithm is constructed due to the high complexity of the proposed problem and long solution times for practical applications. The proposed models are integrated with a fuel consumption and CO2 emissions calculation algorithm that considers the vehicle technical specifications, vehicle load, and transportation distance in a green supply chain environment. The proposed models are validated using well-known instances with different numbers of customers. The computational results indicate that the MSA-SA heuristic is capable of obtaining good G-VRPTW solutions within a reasonable amount of time by providing reductions in fuel consumption and CO2 emissions.

  14. An adaptive Bayesian inference algorithm to estimate the parameters of a hazardous atmospheric release

    NASA Astrophysics Data System (ADS)

    Rajaona, Harizo; Septier, François; Armand, Patrick; Delignon, Yves; Olry, Christophe; Albergel, Armand; Moussafir, Jacques

    2015-12-01

    In the eventuality of an accidental or intentional atmospheric release, the reconstruction of the source term using measurements from a set of sensors is an important and challenging inverse problem. A rapid and accurate estimation of the source allows faster and more efficient action for first-response teams, in addition to providing better damage assessment. This paper presents a Bayesian probabilistic approach to estimate the location and the temporal emission profile of a pointwise source. The release rate is evaluated analytically by using a Gaussian assumption on its prior distribution, and is enhanced with a positivity constraint to improve the estimation. The source location is obtained by the means of an advanced iterative Monte-Carlo technique called Adaptive Multiple Importance Sampling (AMIS), which uses a recycling process at each iteration to accelerate its convergence. The proposed methodology is tested using synthetic and real concentration data in the framework of the Fusion Field Trials 2007 (FFT-07) experiment. The quality of the obtained results is comparable to those coming from the Markov Chain Monte Carlo (MCMC) algorithm, a popular Bayesian method used for source estimation. Moreover, the adaptive processing of the AMIS provides a better sampling efficiency by reusing all the generated samples.

  15. Adaptive algorithm for corner detecting based on the degree of sharpness of the contour

    NASA Astrophysics Data System (ADS)

    Xiao, Jianli; Xiang, Zongjie; Wang, Bin; Liu, Yuncai

    2011-04-01

    We propose an adaptive corner detector based on the degree of sharpness of the contour, which can detect the corners efficiently and accurately. First, we extract edges from an image using an edge detector, such as Canny, Sobel, etc. Second, we define a degree-of-sharpness variable and calculate its value for each point on the edge. Then we use an adaptive threshold of the degree of sharpness to find the initial corner candidates for each edge. Third, we propose a method of making a projection for an edge segment toward the edge's fitting line to remove salient points. Finally, if some neighboring corner candidates are detected in a support area, we offer an effective method to merge them into one point, and the final corners are reserved. The proposed detector is compared to a classical corner detector and two state-of-the-art methods on planar curves and gray-level images, respectively. The experimental results show that the proposed detector has high correctness in finding true corners. In addition, the algorithm is robust to the additive noise and can detect the circular contour well.

  16. Application of an adaptive blade control algorithm to a gust alleviation system

    NASA Technical Reports Server (NTRS)

    Saito, S.

    1984-01-01

    The feasibility of an adaptive control system designed to alleviate helicopter gust induced vibration was analytically investigated for an articulated rotor system. This control system is based on discrete optimal control theory, and is composed of a set of measurements (oscillatory hub forces and moments), an identification system using a Kalman filter, a control system based on the minimization of the quadratic performance function, and a simulation system of the helicopter rotor. The gust models are step and sinusoidal vertical gusts. Control inputs are selected at the gust frequency, subharmonic frequency, and superharmonic frequency, and are superimposed on the basic collective and cyclic control inputs. The response to be reduced is selected to be that at the gust frequency because this is the dominant response compared with sub- and superharmonics. Numerical calculations show that the adaptive blade pitch control algorithm satisfactorily alleviates the hub gust response. Almost 100 percent reduction of the perturbation thrust response to a step gust and more than 50 percent reduction to a sinusoidal gust are achieved in the numerical simulations.

  17. Application of an adaptive blade control algorithm to a gust alleviation system

    NASA Technical Reports Server (NTRS)

    Saito, S.

    1983-01-01

    The feasibility of an adaptive control system designed to alleviate helicopter gust induced vibration was analytically investigated for an articulated rotor system. This control system is based on discrete optimal control theory, and is composed of a set of measurements (oscillatory hub forces and moments), an identification system using a Kalman filter, a control system based on the minimization of the quadratic performance function, and a simulation system of the helicopter rotor. The gust models are step and sinusoidal vertical gusts. Control inputs are selected at the gust frequency, subharmonic frequency, and superharmonic frequency, and are superimposed on the basic collective and cyclic control inputs. The response to be reduced is selected to be that at the gust frequency because this is the dominant response compared with sub- and superharmonics. Numerical calculations show that the adaptive blade pitch control algorithm satisfactorily alleviates the hub gust response. Almost 100% reduction of the perturbation thrust response to a step gust and more than 50% reduction to a sinusoidal gust are achieved in the numerical simulations.

  18. Enhancing Artificial Bee Colony Algorithm with Self-Adaptive Searching Strategy and Artificial Immune Network Operators for Global Optimization

    PubMed Central

    Chen, Tinggui; Xiao, Renbin

    2014-01-01

    Artificial bee colony (ABC) algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA), artificial colony optimization (ACO), and particle swarm optimization (PSO). However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments. PMID:24772023

  19. Effect of subaperture beamforming on phase coherence imaging.

    PubMed

    Hasegawa, Hideyuki; Kanai, Hiroshi

    2014-11-01

    High-frame-rate echocardiography using unfocused transmit beams and parallel receive beamforming is a promising method for evaluation of cardiac function, such as imaging of rapid propagation of vibration of the heart wall resulting from electrical stimulation of the myocardium. In this technique, high temporal resolution is realized at the expense of spatial resolution and contrast. The phase coherence factor has been developed to improve spatial resolution and contrast in ultrasonography. It evaluates the variance in phases of echo signals received by individual transducer elements after delay compensation, as in the conventional delay-andsum beamforming process. However, the phase coherence factor suppresses speckle echoes because phases of speckle echoes fluctuate as a result of interference of echoes. In the present study, the receiving aperture was divided into several subapertures, and conventional delay-and-sum beamforming was performed with respect to each subaperture to suppress echoes from scatterers except for that at a focal point. After subaperture beamforming, the phase coherence factor was obtained from beamformed RF signals from respective subapertures. By means of this procedure, undesirable echoes, which can interfere with the echo from a focal point, can be suppressed by subaperture beamforming, and the suppression of the phase coherence factor resulting from phase fluctuation caused by such interference can be avoided. In the present study, the effect of subaperture beamforming in high-frame-rate echocardiography with the phase coherence factor was evaluated using a phantom. By applying subaperture beamforming, the average intensity of speckle echoes from a diffuse scattering medium was significantly higher (-39.9 dB) than that obtained without subaperture beamforming (-48.7 dB). As for spatial resolution, the width at half-maximum of the lateral echo amplitude profile obtained without the phase coherence factor was 1.06 mm. By using the phase

  20. Digital Beamforming Synthetic Aperture Radar (DBSAR) Polarimetric Upgrade

    NASA Technical Reports Server (NTRS)

    Rincon, Rafael F.; Perrine, Martin; McLinden, Matthew; Valett, Susan

    2011-01-01

    The Digital Beamforming Synthetic Aperture Radar (DBSAR) is a state-of-the-art radar system developed at NASA/Goddard Space Flight Center for the development and implementation of digital beamforming radar techniques. DBSAR was recently upgraded to polarimetric operation in order to enhance its capability as a science instrument. Two polarimetric approaches were carried out which will be demonstrated in upcoming flight campaigns.

  1. Fourier-domain beamforming: the path to compressed ultrasound imaging.

    PubMed

    Chernyakova, Tanya; Eldar, Yonina

    2014-08-01

    Sonography techniques use multiple transducer elements for tissue visualization. Signals received at each element are sampled before digital beamforming. The sampling rates required to perform high-resolution digital beamforming are significantly higher than the Nyquist rate of the signal and result in considerable amount of data that must be stored and processed. A recently developed technique, compressed beamforming, based on the finite rate of innovation model, compressed sensing (CS), and Xampling ideas, allows a reduction in the number of samples needed to reconstruct an image comprised of strong reflectors. A drawback of this method is its inability to treat speckle, which is of significant importance in medical imaging. Here, we build on previous work and extend it to a general concept of beamforming in frequency. This allows exploitation of the low bandwidth of the ultrasound signal and bypassing of the oversampling dictated by digital implementation of beamforming in time. By using beamforming in frequency, the same image quality is obtained from far fewer samples. We next present a CS technique that allows for further rate reduction, using only a portion of the beamformed signal's bandwidth. We demonstrate our methods on in vivo cardiac data and show that reductions up to 1/28 of the standard beamforming rates are possible. Finally, we present an implementation on an ultrasound machine using sub-Nyquist sampling and processing. Our results prove that the concept of sub-Nyquist processing is feasible for medical ultrasound, leading to the potential of considerable reduction in future ultrasound machines' size, power consumption, and cost.

  2. Beamforming for Radar Systems on COTS Heterogeneous Computing Platforms

    DTIC Science & Technology

    2004-08-20

    Beamforming for Radar Systems on COTS Heterogeneous Computing Platforms Mr. Jeffrey Rudin Mercury Computer Systems, Inc. Phone: (978) 967-1686...ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Mercury ...allocation and the resulting system topologies. © 2003 Mercury Computer Systems, Inc. Beamforming for Radar Systems on COTS Heterogeneous

  3. Adapting iterative algorithms for solving large sparse linear systems for efficient use on the CDC CYBER 205

    NASA Technical Reports Server (NTRS)

    Kincaid, D. R.; Young, D. M.

    1984-01-01

    Adapting and designing mathematical software to achieve optimum performance on the CYBER 205 is discussed. Comments and observations are made in light of recent work done on modifying the ITPACK software package and on writing new software for vector supercomputers. The goal was to develop very efficient vector algorithms and software for solving large sparse linear systems using iterative methods.

  4. EEG/ERP adaptive noise canceller design with controlled search space (CSS) approach in cuckoo and other optimization algorithms.

    PubMed

    Ahirwal, M K; Kumar, Anil; Singh, G K

    2013-01-01

    This paper explores the migration of adaptive filtering with swarm intelligence/evolutionary techniques employed in the field of electroencephalogram/event-related potential noise cancellation or extraction. A new approach is proposed in the form of controlled search space to stabilize the randomness of swarm intelligence techniques especially for the EEG signal. Swarm-based algorithms such as Particles Swarm Optimization, Artificial Bee Colony, and Cuckoo Optimization Algorithm with their variants are implemented to design optimized adaptive noise canceler. The proposed controlled search space technique is tested on each of the swarm intelligence techniques and is found to be more accurate and powerful. Adaptive noise canceler with traditional algorithms such as least-mean-square, normalized least-mean-square, and recursive least-mean-square algorithms are also implemented to compare the results. ERP signals such as simulated visual evoked potential, real visual evoked potential, and real sensorimotor evoked potential are used, due to their physiological importance in various EEG studies. Average computational time and shape measures of evolutionary techniques are observed 8.21E-01 sec and 1.73E-01, respectively. Though, traditional algorithms take negligible time consumption, but are unable to offer good shape preservation of ERP, noticed as average computational time and shape measure difference, 1.41E-02 sec and 2.60E+00, respectively.

  5. Wireless rake-receiver using adaptive filter with a family of partial update algorithms in noise cancellation applications

    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.

  6. Fresnel-based beamforming for low-cost portable ultrasound.

    PubMed

    Nguyen, Man Minh; Mung, Jay; Yen, Jesse T

    2011-01-01

    In this paper, we propose a modified electronic Fresnel-based beamforming method for low-cost portable ultrasound systems. This method uses a unique combination of analog and digital beamforming methods. Two versions of Fresnel beamforming are presented in this paper: 4-phase (4 different time delays or phase shifts) and 8-phase (8 different time delays or phase shifts). The advantage of this method is that a system with 4 to 8 transmit channels and 2 receive channels with a network of switches can be used to focus an array with 64 to 128 elements. The simulation and experimental results show that Fresnel beamforming image quality is comparable to traditional delay-and-sum (DAS) beamforming in terms of spatial resolution and contrast-to-noise ratio (CNR) under certain system parameters. With an f-number of 2 and 50% signal bandwidth, the experimental lateral beamwidths are 0.54, 0.67, and 0.66 mm and the axial pulse lengths are 0.50, 0.51, and 0.50 mm for DAS, 8-phase, and 4-phase Fresnel beamforming, respectively. The experimental CNRs are 4.66, 4.42, and 3.98, respectively. These experimental results are in good agreement with simulation results.

  7. Time-multiplexed beamforming for noninvasive microwave hyperthermia treatment.

    PubMed

    Zastrow, Earl; Hagness, Susan C; Van Veen, Barry D; Medow, Joshua E

    2011-06-01

    A noninvasive microwave beamforming strategy is proposed for selective localized heating of biological tissue. The proposed technique is based on time multiplexing of multiple beamformers. We investigate the effectiveness of the time-multiplexed beamforming in the context of brain hyperthermia treatment by using a high-fidelity numerical head phantom of an adult female from the Virtual Family (IT'IS Foundation) as our testbed. An operating frequency of 1 GHz is considered to balance the improved treatment resolution afforded by higher frequencies against the increased penetration through the brain afforded by lower frequencies. The exact head geometry and dielectric properties of biological tissues in the head are assumed to be available for the creation of patient-specific propagation models used in beamformer design. Electromagnetic and thermal simulations based on the finite-difference time-domain method are used to evaluate the hyperthermia performance of time-multiplexed beamforming and conventional beamforming strategies. The proposed time-multiplexing technique is shown to reduce the unintended heating of healthy tissue without affecting the treatment temperature or volume. The efficacy of the method is demonstrated for target locations in three different regions of the brain. This approach has the potential to improve microwave-induced localized heating for cancer treatment via hyperthermia or heat-activated chemotherapeutic drug release.

  8. A novel kernel extreme learning machine algorithm based on self-adaptive artificial bee colony optimisation strategy

    NASA Astrophysics Data System (ADS)

    Ma, Chao; Ouyang, Jihong; Chen, Hui-Ling; Ji, Jin-Chao

    2016-04-01

    In this paper, we propose a novel learning algorithm, named SABC-MKELM, based on a kernel extreme learning machine (KELM) method for single-hidden-layer feedforward networks. In SABC-MKELM, the combination of Gaussian kernels is used as the activate function of KELM instead of simple fixed kernel learning, where the related parameters of kernels and the weights of kernels can be optimised by a novel self-adaptive artificial bee colony (SABC) approach simultaneously. SABC-MKELM outperforms six other state-of-the-art approaches in general, as it could effectively determine solution updating strategies and suitable parameters to produce a flexible kernel function involved in SABC. Simulations have demonstrated that the proposed algorithm not only self-adaptively determines suitable parameters and solution updating strategies learning from the previous experiences, but also achieves better generalisation performances than several related methods, and the results show good stability of the proposed algorithm.

  9. A Novel Clinical Decision Support System Using Improved Adaptive Genetic Algorithm for the Assessment of Fetal Well-Being

    PubMed Central

    Jambek, Asral Bahari; Neoh, Siew-Chin

    2015-01-01

    A novel clinical decision support system is proposed in this paper for evaluating the fetal well-being from the cardiotocogram (CTG) dataset through an Improved Adaptive Genetic Algorithm (IAGA) and Extreme Learning Machine (ELM). IAGA employs a new scaling technique (called sigma scaling) to avoid premature convergence and applies adaptive crossover and mutation techniques with masking concepts to enhance population diversity. Also, this search algorithm utilizes three different fitness functions (two single objective fitness functions and multi-objective fitness function) to assess its performance. The classification results unfold that promising classification accuracy of 94% is obtained with an optimal feature subset using IAGA. Also, the classification results are compared with those of other Feature Reduction techniques to substantiate its exhaustive search towards the global optimum. Besides, five other benchmark datasets are used to gauge the strength of the proposed IAGA algorithm. PMID:25793009

  10. A Similarity-Based Adaptation of Naive Bayes for Label Ranking: Application to the Metalearning Problem of Algorithm Recommendation

    NASA Astrophysics Data System (ADS)

    Aiguzhinov, Artur; Soares, Carlos; Serra, Ana Paula

    The problem of learning label rankings is receiving increasing attention from several research communities. A number of common learning algorithms have been adapted for this task, including k-Nearest Neighbours (k-NN) and decision trees. Following this line, we propose an adaptation of the naive Bayes classification algorithm for the label ranking problem. Our main idea lies in the use of similarity between the rankings to replace the concept of probability. We empirically test the proposed method on some metalearning problems that consist of relating characteristics of learning problems to the relative performance of learning algorithms. Our method generally performs better than the baseline indicating that it is able to identify some of the underlying patterns in the data.

  11. Descent algorithms on oblique manifold for source-adaptive ICA contrast.

    PubMed

    Selvan, Suviseshamuthu Easter; Amato, Umberto; Gallivan, Kyle A; Qi, Chunhong; Carfora, Maria Francesca; Larobina, Michele; Alfano, Bruno

    2012-12-01

    A Riemannian manifold optimization strategy is proposed to facilitate the relaxation of the orthonormality constraint in a more natural way in the course of performing independent component analysis (ICA) that employs a mutual information-based source-adaptive contrast function. Despite the extensive development of manifold techniques catering to the orthonormality constraint, only a limited number of works have been dedicated to oblique manifold (OB) algorithms to intrinsically handle the normality constraint, which has been empirically shown to be superior to other Riemannian and Euclidean approaches. Imposing the normality constraint implicitly, in line with the ICA definition, essentially guarantees a substantial improvement in the solution accuracy, by way of increased degrees of freedom while searching for an optimal unmixing ICA matrix, in contrast with the orthonormality constraint. Designs of the steepest descent, conjugate gradient with Hager-Zhang or a hybrid update parameter, quasi-Newton, and cost-effective quasi-Newton methods intended for OB are presented in this paper. Their performance is validated using natural images and systematically compared with the popular state-of-the-art approaches in order to assess the performance effects of the choice of algorithm and the use of a Riemannian rather than Euclidean framework. We surmount the computational challenge associated with the direct estimation of the source densities using the improved fast Gauss transform in the evaluation of the contrast function and its gradient. The proposed OB schemes may find applications in the offline image/signal analysis, wherein, on one hand, the computational overhead can be tolerated, and, on the other, the solution quality holds paramount interest.

  12. A new adaptive algorithm for image denoising based on curvelet transform

    NASA Astrophysics Data System (ADS)

    Chen, Musheng; Cai, Zhishan

    2013-10-01

    The purpose of this paper is to study a method of denoising images corrupted with additive white Gaussian noise. In this paper, the application of the time invariant discrete curvelet transform for noise reduction is considered. In curvelet transform, the frame elements are indexed by scale, orientation and location parameters. It is designed to represent edges and the singularities along curved paths more efficiently than the wavelet transform. Therefore, curvelet transform can get better results than wavelet method in image denoising. In general, image denoising imposes a compromise between noise reduction and preserving significant image details. To achieve a good performance in this respect, an efficient and adaptive image denoising method based on curvelet transform is presented in this paper. Firstly, the noisy image is decomposed into many levels to obtain different frequency sub-bands by curvelet transform. Secondly, efficient and adaptive threshold estimation based on generalized Gaussian distribution modeling of sub-band coefficients is used to remove the noisy coefficients. The choice of the threshold estimation is carried out by analyzing the standard deviation and threshold. Ultimately, invert the multi-scale decomposition to reconstruct the denoised image. Here, to prove the performance of the proposed method, the results are compared with other existent algorithms such as hard and soft threshold based on wavelet. The simulation results on several testing images indicate that the proposed method outperforms the other methods in peak signal to noise ratio and keeps better visual in edges information reservation as well. The results also suggest that curvelet transform can achieve a better performance than the wavelet transform in image denoising.

  13. Adaptive GDDA-BLAST: fast and efficient algorithm for protein sequence embedding.

    PubMed

    Hong, Yoojin; Kang, Jaewoo; Lee, Dongwon; van Rossum, Damian B

    2010-10-22

    A major computational challenge in the genomic era is annotating structure/function to the vast quantities of sequence information that is now available. This problem is illustrated by the fact that most proteins lack comprehensive annotations, even when experimental evidence exists. We previously theorized that embedded-alignment profiles (simply "alignment profiles" hereafter) provide a quantitative method that is capable of relating the structural and functional properties of proteins, as well as their evolutionary relationships. A key feature of alignment profiles lies in the interoperability of data format (e.g., alignment information, physio-chemical information, genomic information, etc.). Indeed, we have demonstrated that the Position Specific Scoring Matrices (PSSMs) are an informative M-dimension that is scored by quantitatively measuring the embedded or unmodified sequence alignments. Moreover, the information obtained from these alignments is informative, and remains so even in the "twilight zone" of sequence similarity (<25% identity). Although our previous embedding strategy was powerful, it suffered from contaminating alignments (embedded AND unmodified) and high computational costs. Herein, we describe the logic and algorithmic process for a heuristic embedding strategy named "Adaptive GDDA-BLAST." Adaptive GDDA-BLAST is, on average, up to 19 times faster than, but has similar sensitivity to our previous method. Further, data are provided to demonstrate the benefits of embedded-alignment measurements in terms of detecting structural homology in highly divergent protein sequences and isolating secondary structural elements of transmembrane and ankyrin-repeat domains. Together, these advances allow further exploration of the embedded alignment data space within sufficiently large data sets to eventually induce relevant statistical inferences. We show that sequence embedding could serve as one of the vehicles for measurement of low-identity alignments

  14. Robust fundamental frequency estimation in sustained vowels: detailed algorithmic comparisons and information fusion with adaptive Kalman filtering.

    PubMed

    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.

  15. Robust fundamental frequency estimation in sustained vowels: Detailed algorithmic comparisons and information fusion with adaptive Kalman filtering

    PubMed Central

    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

  16. Multi-focus image fusion algorithm based on adaptive PCNN and wavelet transform

    NASA Astrophysics Data System (ADS)

    Wu, Zhi-guo; Wang, Ming-jia; Han, Guang-liang

    2011-08-01

    Being an efficient method of information fusion, image fusion has been used in many fields such as machine vision, medical diagnosis, military applications and remote sensing. In this paper, Pulse Coupled Neural Network (PCNN) is introduced in this research field for its interesting properties in image processing, including segmentation, target recognition et al. and a novel algorithm based on PCNN and Wavelet Transform for Multi-focus image fusion is proposed. First, the two original images are decomposed by wavelet transform. Then, based on the PCNN, a fusion rule in the Wavelet domain is given. This algorithm uses the wavelet coefficient in each frequency domain as the linking strength, so that its value can be chosen adaptively. Wavelet coefficients map to the range of image gray-scale. The output threshold function attenuates to minimum gray over time. Then all pixels of image get the ignition. So, the output of PCNN in each iteration time is ignition wavelet coefficients of threshold strength in different time. At this moment, the sequences of ignition of wavelet coefficients represent ignition timing of each neuron. The ignition timing of PCNN in each neuron is mapped to corresponding image gray-scale range, which is a picture of ignition timing mapping. Then it can judge the targets in the neuron are obvious features or not obvious. The fusion coefficients are decided by the compare-selection operator with the firing time gradient maps and the fusion image is reconstructed by wavelet inverse transform. Furthermore, by this algorithm, the threshold adjusting constant is estimated by appointed iteration number. Furthermore, In order to sufficient reflect order of the firing time, the threshold adjusting constant αΘ is estimated by appointed iteration number. So after the iteration achieved, each of the wavelet coefficient is activated. In order to verify the effectiveness of proposed rules, the experiments upon Multi-focus image are done. Moreover

  17. Real-time MRI-guided hyperthermia treatment using a fast adaptive algorithm

    NASA Astrophysics Data System (ADS)

    Stakhursky, Vadim L.; Arabe, Omar; Cheng, Kung-Shan; MacFall, James; Maccarini, Paolo; Craciunescu, Oana; Dewhirst, Mark; Stauffer, Paul; Das, Shiva K.

    2009-04-01

    temperature in the tumor to integral temperature in normal tissue) by up to six-fold, compared to the first iteration. The integrated MR-HT treatment algorithm successfully steered the focus of heating into the desired target volume for both the simple homogeneous and the more challenging muscle equivalent phantom with tumor insert models of human extremity sarcomas after 16 and 2 iterations, correspondingly. The adaptive method for MR thermal image guided focal steering shows promise when tested in phantom experiments on a four-antenna phased array applicator.

  18. A Fast, Locally Adaptive, Interactive Retrieval Algorithm for the Analysis of DIAL Measurements

    NASA Astrophysics Data System (ADS)

    Samarov, D. V.; Rogers, R.; Hair, J. W.; Douglass, K. O.; Plusquellic, D.

    2010-12-01

    Differential absorption light detection and ranging (DIAL) is a laser-based tool which is used for remote, range-resolved measurement of particular gases in the atmosphere, such as carbon-dioxide and methane. In many instances it is of interest to study how these gases are distributed over a region such as a landfill, factory, or farm. While a single DIAL measurement only tells us about the distribution of a gas along a single path, a sequence of consecutive measurements provides us with information on how that gas is distributed over a region, making DIAL a natural choice for such studies. DIAL measurements present a number of interesting challenges; first, in order to convert the raw data to concentration it is necessary to estimate the derivative along the path of the measurement. Second, as the distribution of gases across a region can be highly heterogeneous it is important that the spatial nature of the measurements be taken into account. Finally, since it is common for the set of collected measurements to be quite large it is important for the method to be computationally efficient. Existing work based on Local Polynomial Regression (LPR) has been developed which addresses the first two issues, but the issue of computational speed remains an open problem. In addition to the latter, another desirable property is to allow user input into the algorithm. In this talk we present a novel method based on LPR which utilizes a variant of the RODEO algorithm to provide a fast, locally adaptive and interactive approach to the analysis of DIAL measurements. This methodology is motivated by and applied to several simulated examples and a study out of NASA Langley Research Center (LaRC) looking at the estimation of aerosol extinction in the atmosphere. A comparison study of our method against several other algorithms is also presented. References Chaudhuri, P., Marron, J.S., Scale-space view of curve estimation, Annals of Statistics 28 (2000) 408-428. Duong, T., Cowling

  19. Policy iteration adaptive dynamic programming algorithm for discrete-time nonlinear systems.

    PubMed

    Liu, Derong; Wei, Qinglai

    2014-03-01

    This paper is concerned with a new discrete-time policy iteration adaptive dynamic programming (ADP) method for solving the infinite horizon optimal control problem of nonlinear systems. The idea is to use an iterative ADP technique to obtain the iterative control law, which optimizes the iterative performance index function. The main contribution of this paper is to analyze the convergence and stability properties of policy iteration method for discrete-time nonlinear systems for the first time. It shows that the iterative performance index function is nonincreasingly convergent to the optimal solution of the Hamilton-Jacobi-Bellman equation. It is also proven that any of the iterative control laws can stabilize the nonlinear systems. Neural networks are used to approximate the performance index function and compute the optimal control law, respectively, for facilitating the implementation of the iterative ADP algorithm, where the convergence of the weight matrices is analyzed. Finally, the numerical results and analysis are presented to illustrate the performance of the developed method.

  20. 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.

  1. Vibration suppression in cutting tools using collocated piezoelectric sensors/actuators with an adaptive control algorithm

    SciTech Connect

    Radecki, Peter P; Farinholt, Kevin M; Park, Gyuhae; Bement, Matthew T

    2008-01-01

    The machining process is very important in many engineering applications. In high precision machining, surface finish is strongly correlated with vibrations and the dynamic interactions between the part and the cutting tool. Parameters affecting these vibrations and dynamic interactions, such as spindle speed, cut depth, feed rate, and the part's material properties can vary in real-time, resulting in unexpected or undesirable effects on the surface finish of the machining product. The focus of this research is the development of an improved machining process through the use of active vibration damping. The tool holder employs a high bandwidth piezoelectric actuator with an adaptive positive position feedback control algorithm for vibration and chatter suppression. In addition, instead of using external sensors, the proposed approach investigates the use of a collocated piezoelectric sensor for measuring the dynamic responses from machining processes. The performance of this method is evaluated by comparing the surface finishes obtained with active vibration control versus baseline uncontrolled cuts. Considerable improvement in surface finish (up to 50%) was observed for applications in modern day machining.

  2. Intelligent Modeling Combining Adaptive Neuro Fuzzy Inference System and Genetic Algorithm for Optimizing Welding Process Parameters

    NASA Astrophysics Data System (ADS)

    Gowtham, K. N.; Vasudevan, M.; Maduraimuthu, V.; Jayakumar, T.

    2011-04-01

    Modified 9Cr-1Mo ferritic steel is used as a structural material for steam generator components of power plants. Generally, tungsten inert gas (TIG) welding is preferred for welding of these steels in which the depth of penetration achievable during autogenous welding is limited. Therefore, activated flux TIG (A-TIG) welding, a novel welding technique, has been developed in-house to increase the depth of penetration. In modified 9Cr-1Mo steel joints produced by the A-TIG welding process, weld bead width, depth of penetration, and heat-affected zone (HAZ) width play an important role in determining the mechanical properties as well as the performance of the weld joints during service. To obtain the desired weld bead geometry and HAZ width, it becomes important to set the welding process parameters. In this work, adaptative neuro fuzzy inference system is used to develop independent models correlating the welding process parameters like current, voltage, and torch speed with weld bead shape parameters like depth of penetration, bead width, and HAZ width. Then a genetic algorithm is employed to determine the optimum A-TIG welding process parameters to obtain the desired weld bead shape parameters and HAZ width.

  3. Two-layer and Adaptive Entropy Coding Algorithms for H.264-based Lossless Image Coding

    DTIC Science & Technology

    2008-04-01

    adaptive binary arithmetic coding (CABAC) [7], and context-based adaptive variable length coding (CAVLC) [3], should be adaptively adopted for advancing...Sep. 2006. [7] H. Schwarz, D. Marpe and T. Wiegand, Context-based adaptive binary arithmetic coding in the H.264/AVC video compression standard, IEEE

  4. Optimal beamforming in ultrasound using the ideal observer.

    PubMed

    Abbey, Craig K; Nguyen, Nghia Q; Insana, Michael F

    2010-08-01

    Beamforming of received pulse-echo data generally involves the compression of signals from multiple channels within an aperture. This compression is irreversible, and therefore allows the possibility that information relevant for performing a diagnostic task is irretrievably lost. The purpose of this study was to evaluate information transfer in beamforming using a previously developed ideal observer model to quantify diagnostic information relevant to performing a task. We describe an elaborated statistical model of image formation for fixed-focus transmission and single-channel reception within a moving aperture, and we use this model on a panel of tasks related to breast sonography to evaluate receive-beamforming approaches that optimize the transfer of information. Under the assumption that acquisition noise is well described as an additive wide-band Gaussian white-noise process, we show that signal compression across receive-aperture channels after a 2-D matched-filtering operation results in no loss of diagnostic information. Across tasks, the matched-filter beamformer results in more information than standard delay-and-sum beamforming in the subsequent radio-frequency signal by a factor of two. We also show that for this matched filter, 68% of the information gain can be attributed to the phase of the matched-filter and 21% can be attributed to the amplitude. A 1-D matched filtering along axial lines shows no advantage over delay-andsum, suggesting an important role for incorporating correlations across different aperture windows in beamforming. We also show that a post-compression processing before the computation of an envelope is necessary to pass the diagnostic information in the beamformed radio-frequency signal to the final envelope image.

  5. Variable Is Better Than Invariable: Sparse VSS-NLMS Algorithms with Application to Adaptive MIMO Channel Estimation

    PubMed Central

    Gui, Guan; Chen, Zhang-xin; Xu, Li; Wan, Qun; Huang, Jiyan; Adachi, Fumiyuki

    2014-01-01

    Channel estimation problem is one of the key technical issues in sparse frequency-selective fading multiple-input multiple-output (MIMO) communication systems using orthogonal frequency division multiplexing (OFDM) scheme. To estimate sparse MIMO channels, sparse invariable step-size normalized least mean square (ISS-NLMS) algorithms were applied to adaptive sparse channel estimation (ACSE). It is well known that step-size is a critical parameter which controls three aspects: algorithm stability, estimation performance, and computational cost. However, traditional methods are vulnerable to cause estimation performance loss because ISS cannot balance the three aspects simultaneously. In this paper, we propose two stable sparse variable step-size NLMS (VSS-NLMS) algorithms to improve the accuracy of MIMO channel estimators. First, ASCE is formulated in MIMO-OFDM systems. Second, different sparse penalties are introduced to VSS-NLMS algorithm for ASCE. In addition, difference between sparse ISS-NLMS algorithms and sparse VSS-NLMS ones is explained and their lower bounds are also derived. At last, to verify the effectiveness of the proposed algorithms for ASCE, several selected simulation results are shown to prove that the proposed sparse VSS-NLMS algorithms can achieve better estimation performance than the conventional methods via mean square error (MSE) and bit error rate (BER) metrics. PMID:25089286

  6. Influence of reconstruction settings on the performance of adaptive thresholding algorithms for FDG-PET image segmentation in radiotherapy planning.

    PubMed

    Matheoud, Roberta; Della Monica, Patrizia; Loi, Gianfranco; Vigna, Luca; Krengli, Marco; Inglese, Eugenio; Brambilla, Marco

    2011-01-30

    The purpose of this study was to analyze the behavior of a contouring algorithm for PET images based on adaptive thresholding depending on lesions size and target-to-background (TB) ratio under different conditions of image reconstruction parameters. Based on this analysis, the image reconstruction scheme able to maximize the goodness of fit of the thresholding algorithm has been selected. A phantom study employing spherical targets was designed to determine slice-specific threshold (TS) levels which produce accurate cross-sectional areas. A wide range of TB ratio was investigated. Multiple regression methods were used to fit the data and to construct algorithms depending both on target cross-sectional area and TB ratio, using various reconstruction schemes employing a wide range of iteration number and amount of postfiltering Gaussian smoothing. Analysis of covariance was used to test the influence of iteration number and smoothing on threshold determination. The degree of convergence of ordered-subset expectation maximization (OSEM) algorithms does not influence TS determination. Among these approaches, the OSEM at two iterations and eight subsets with a 6-8 mm post-reconstruction Gaussian three-dimensional filter provided the best fit with a coefficient of determination R² = 0.90 for cross-sectional areas ≤ 133 mm² and R² = 0.95 for cross-sectional areas > 133 mm². The amount of post-reconstruction smoothing has been directly incorporated in the adaptive thresholding algorithms. The feasibility of the method was tested in two patients with lymph node FDG accumulation and in five patients using the bladder to mimic an anatomical structure of large size and uniform uptake, with satisfactory results. Slice-specific adaptive thresholding algorithms look promising as a reproducible method for delineating PET target volumes with good accuracy.

  7. Harmonic imaging with fresnel beamforming in the presence of phase aberration.

    PubMed

    Nguyen, Man Minh; Shin, Junseob; Yen, Jesse

    2014-10-01

    Fresnel beamforming is a beamforming method with a delay profile similar in shape to a physical Fresnel lens. The advantage of Fresnel beamforming is the reduced channel count, which consists of four to eight transmit and two analog-to-digital receive channels. Fresnel beamforming was found to perform comparably to conventional delay-and-sum beamforming. However, the performance of Fresnel beamforming is highly dependent on focal errors. These focal errors result in high side-lobe levels and further reduce the performance of Fresnel beamforming in the presence of phase aberration. With the advantages of lower side-lobe levels and suppression of aberration effects, harmonic imaging offers an effective solution to the limitations of Fresnel beamforming. We describe the implementation of tissue harmonic imaging and pulse inversion harmonic imaging in Fresnel beamforming, followed by dual apodization with cross-correlation, to improve image quality. Compared with conventional delay-and-sum beamforming, experimental results indicated contrast-to-noise ratio improvements of 10%, 49% and 264% for Fresnel beamforming using tissue harmonic imaging in the cases of no aberrator, 5-mm pork aberrator and 12-mm pork aberrator, respectively. These improvements were 22%, 57% and 352% for Fresnel beamforming using pulse inversion harmonic imaging. Moreover, dual apodization with cross-correlation was found to further improve the contrast-to-noise ratios in all cases. Harmonic imaging was also found to narrow the lateral beamwidth and shorten the axial pulse length by at least 25% and 21%, respectively, for Fresnel beamforming at different aberration levels. These results suggest the effectiveness of harmonic imaging in improving image quality for Fresnel beamforming, especially in the presence of phase aberration. Even though this combination of Fresnel beamforming and harmonic imaging does not outperform delay-and-sum beamforming combined with harmonic imaging, it provides the

  8. Lossless data compression for improving the performance of a GPU-based beamformer.

    PubMed

    Lok, U-Wai; Fan, Gang-Wei; Li, Pai-Chi

    2015-04-01

    The powerful parallel computation ability of a graphics processing unit (GPU) makes it feasible to perform dynamic receive beamforming However, a real time GPU-based beamformer requires high data rate to transfer radio-frequency (RF) data from hardware to software memory, as well as from central processing unit (CPU) to GPU memory. There are data compression methods (e.g. Joint Photographic Experts Group (JPEG)) available for the hardware front end to reduce data size, alleviating the data transfer requirement of the hardware interface. Nevertheless, the required decoding time may even be larger than the transmission time of its original data, in turn degrading the overall performance of the GPU-based beamformer. This article proposes and implements a lossless compression-decompression algorithm, which enables in parallel compression and decompression of data. By this means, the data transfer requirement of hardware interface and the transmission time of CPU to GPU data transfers are reduced, without sacrificing image quality. In simulation results, the compression ratio reached around 1.7. The encoder design of our lossless compression approach requires low hardware resources and reasonable latency in a field programmable gate array. In addition, the transmission time of transferring data from CPU to GPU with the parallel decoding process improved by threefold, as compared with transferring original uncompressed data. These results show that our proposed lossless compression plus parallel decoder approach not only mitigate the transmission bandwidth requirement to transfer data from hardware front end to software system but also reduce the transmission time for CPU to GPU data transfer.

  9. A comparison study between Wiener and adaptive state estimation (STAP-ASE) algorithms for space time adaptive radar processing

    NASA Astrophysics Data System (ADS)

    Malek, Obaidul; Venetsanopoulos, Anastasios; Anpalagan, Alagan

    2010-08-01

    Space Time Adaptive Processing (STAP) is a multi-dimensional adaptive signal processing technique, which processes the signal in spatial and Doppler domains for which a target detection hypothesis is to be formed. It is a sample based technique and based on the assumption of adequate number of Independent and Identically Distributed (i.i.d.) training data set in the surrounding environment. The principal challenge of the radar processing lies when it violates these underlying assumptions due to severe dynamic heterogeneous clutter (hot clutter) and jammer effects. This in turn degrades the Signal to Interference-plus-Noise Ratio (SINR), hence signal detection performance. Classical Wiener filtering theory is inadequate to deal with nonlinear and nonstationary interferences, however Wiener filtering approach is optimal for stationary and linear systems. But, these challenges can be overcome by Adaptive Sequential State Estimation (ASSE) filtering technique.

  10. A self-adaptive genetic algorithm-artificial neural network algorithm with leave-one-out cross validation for descriptor selection in QSAR study.

    PubMed

    Wu, Jingheng; Mei, Juan; Wen, Sixiang; Liao, Siyan; Chen, Jincan; Shen, Yong

    2010-07-30

    Based on the quantitative structure-activity relationships (QSARs) models developed by artificial neural networks (ANNs), genetic algorithm (GA) was used in the variable-selection approach with molecule descriptors and helped to improve the back-propagation training algorithm as well. The cross validation techniques of leave-one-out investigated the validity of the generated ANN model and preferable variable combinations derived in the GAs. A self-adaptive GA-ANN model was successfully established by using a new estimate function for avoiding over-fitting phenomenon in ANN training. Compared with the variables selected in two recent QSAR studies that were based on stepwise multiple linear regression (MLR) models, the variables selected in self-adaptive GA-ANN model are superior in constructing ANN model, as they revealed a higher cross validation (CV) coefficient (Q(2)) and a lower root mean square deviation both in the established model and biological activity prediction. The introduced methods for validation, including leave-multiple-out, Y-randomization, and external validation, proved the superiority of the established GA-ANN models over MLR models in both stability and predictive power. Self-adaptive GA-ANN showed us a prospect of improving QSAR model.

  11. Development of a deformable dosimetric phantom to verify dose accumulation algorithms for adaptive radiotherapy

    PubMed Central

    Zhong, Hualiang; Adams, Jeffrey; Glide-Hurst, Carri; Zhang, Hualin; Li, Haisen; Chetty, Indrin J.

    2016-01-01

    Adaptive radiotherapy may improve treatment outcomes for lung cancer patients. Because of the lack of an effective tool for quality assurance, this therapeutic modality is not yet accepted in clinic. The purpose of this study is to develop a deformable physical phantom for validation of dose accumulation algorithms in regions with heterogeneous mass. A three-dimensional (3D) deformable phantom was developed containing a tissue-equivalent tumor and heterogeneous sponge inserts. Thermoluminescent dosimeters (TLDs) were placed at multiple locations in the phantom each time before dose measurement. Doses were measured with the phantom in both the static and deformed cases. The deformation of the phantom was actuated by a motor driven piston. 4D computed tomography images were acquired to calculate 3D doses at each phase using Pinnacle and EGSnrc/DOSXYZnrc. These images were registered using two registration software packages: VelocityAI and Elastix. With the resultant displacement vector fields (DVFs), the calculated 3D doses were accumulated using a mass-and energy congruent mapping method and compared to those measured by the TLDs at four typical locations. In the static case, TLD measurements agreed with all the algorithms by 1.8% at the center of the tumor volume and by 4.0% in the penumbra. In the deformable case, the phantom's deformation was reproduced within 1.1 mm. For the 3D dose calculated by Pinnacle, the total dose accumulated with the Elastix DVF agreed well to the TLD measurements with their differences <2.5% at four measured locations. When the VelocityAI DVF was used, their difference increased up to 11.8%. For the 3D dose calculated by EGSnrc/DOSXYZnrc, the total doses accumulated with the two DVFs were within 5.7% of the TLD measurements which are slightly over the rate of 5% for clinical acceptance. The detector-embedded deformable phantom allows radiation dose to be measured in a dynamic environment, similar to deforming lung tissues, supporting

  12. Beamforming of Ultrasound Signals from 1-D and 2-D Arrays under Challenging Imaging Conditions

    NASA Astrophysics Data System (ADS)

    Jakovljevic, Marko

    Beamforming of ultrasound signals in the presence of clutter, or partial aperture blockage by an acoustic obstacle can lead to reduced visibility of the structures of interest and diminished diagnostic value of the resulting image. We propose new beamforming methods to recover the quality of ultrasound images under such challenging conditions. Of special interest are the signals from large apertures, which are more susceptible to partial blockage, and from commercial matrix arrays that suffer from low sensitivity due to inherent design/hardware limitations. A coherence-based beamforming method designed for suppressing the in vivo clutter, namely Short-lag Spatial Coherence (SLSC) Imaging, is first implemented on a 1-D array to enhance visualization of liver vasculature in 17 human subjects. The SLSC images show statistically significant improvements in vessel contrast and contrast-to-noise ratio over the matched B-mode images. The concept of SLSC imaging is then extended to matrix arrays, and the first in vivo demonstration of volumetric SLSC imaging on a clinical ultrasound system is presented. The effective suppression of clutter via volumetric SLSC imaging indicates it could potentially compensate for the low sensitivity associated with most commercial matrix arrays. The rest of the dissertation assesses image degradation due to elements blocked by ribs in a transthoracic scan. A method to detect the blocked elements is demonstrated using simulated, ex vivo, and in vivo data from the fully-sampled 2-D apertures. The results show that turning off the blocked elements both reduces the near-field clutter and improves visibility of anechoic/hypoechoic targets. Most importantly, the ex vivo data from large synthetic apertures indicates that the adaptive weighing of the non-blocked elements can recover the loss of focus quality due to periodic rib structure, allowing large apertures to realize their full resolution potential in transthoracic ultrasound.

  13. Localization of coherent sources by simultaneous MEG and EEG beamformer.

    PubMed

    Hong, Jun Hee; Ahn, Minkyu; Kim, Kiwoong; Jun, Sung Chan

    2013-10-01

    Simultaneous magnetoencephalography (MEG) and electroencephalography (EEG) analysis is known generally to yield better localization performance than a single modality only. For simultaneous analysis, MEG and EEG data should be combined to maximize synergistic effects. Recently, beamformer for simultaneous MEG/EEG analysis was proposed to localize both radial and tangential components well, while single modality analyses could not detect them, or had relatively higher location bias. In practice, most interesting brain sources are likely to be activated coherently; however, conventional beamformer may not work properly for such coherent sources. To overcome this difficulty, a linearly constrained minimum variance (LCMV) beamformer may be used with a source suppression strategy. In this work, simultaneous MEG/EEG LCMV beamformer using source suppression was formulated firstly to investigate its capability over various suppression strategies. The localization performance of our proposed approach was examined mainly for coherent sources and compared thoroughly with the conventional simultaneous and single modality approaches, over various suppression strategies. For this purpose, we used numerous simulated data, as well as empirical auditory stimulation data. In addition, some strategic issues of simultaneous MEG/EEG analysis were discussed. Overall, we found that our simultaneous MEG/EEG LCMV beamformer using a source suppression strategy is greatly beneficial in localizing coherent sources.

  14. An Automated Reference Frame Selection (ARFS) Algorithm for Cone Imaging with Adaptive Optics Scanning Light Ophthalmoscopy

    PubMed Central

    Salmon, Alexander E.; Cooper, Robert F.; Langlo, Christopher S.; Baghaie, Ahmadreza; Dubra, Alfredo; Carroll, Joseph

    2017-01-01

    Purpose To develop an automated reference frame selection (ARFS) algorithm to replace the subjective approach of manually selecting reference frames for processing adaptive optics scanning light ophthalmoscope (AOSLO) videos of cone photoreceptors. Methods Relative distortion was measured within individual frames before conducting image-based motion tracking and sorting of frames into distinct spatial clusters. AOSLO images from nine healthy subjects were processed using ARFS and human-derived reference frames, then aligned to undistorted AO-flood images by nonlinear registration and the registration transformations were compared. The frequency at which humans selected reference frames that were rejected by ARFS was calculated in 35 datasets from healthy subjects, and subjects with achromatopsia, albinism, or retinitis pigmentosa. The level of distortion in this set of human-derived reference frames was assessed. Results The average transformation vector magnitude required for registration of AOSLO images to AO-flood images was significantly reduced from 3.33 ± 1.61 pixels when using manual reference frame selection to 2.75 ± 1.60 pixels (mean ± SD) when using ARFS (P = 0.0016). Between 5.16% and 39.22% of human-derived frames were rejected by ARFS. Only 2.71% to 7.73% of human-derived frames were ranked in the top 5% of least distorted frames. Conclusion ARFS outperforms expert observers in selecting minimally distorted reference frames in AOSLO image sequences. The low success rate in human frame choice illustrates the difficulty in subjectively assessing image distortion. Translational Relevance Manual reference frame selection represented a significant barrier to a fully automated image-processing pipeline (including montaging, cone identification, and metric extraction). The approach presented here will aid in the clinical translation of AOSLO imaging. PMID:28392976

  15. Influence of adaptive statistical iterative reconstruction algorithm on image quality in coronary computed tomography angiography

    PubMed Central

    Thygesen, Jesper; Gerke, Oke; Egstrup, Kenneth; Waaler, Dag; Lambrechtsen, Jess

    2016-01-01

    Background Coronary computed tomography angiography (CCTA) requires high spatial and temporal resolution, increased low contrast resolution for the assessment of coronary artery stenosis, plaque detection, and/or non-coronary pathology. Therefore, new reconstruction algorithms, particularly iterative reconstruction (IR) techniques, have been developed in an attempt to improve image quality with no cost in radiation exposure. Purpose To evaluate whether adaptive statistical iterative reconstruction (ASIR) enhances perceived image quality in CCTA compared to filtered back projection (FBP). Material and Methods Thirty patients underwent CCTA due to suspected coronary artery disease. Images were reconstructed using FBP, 30% ASIR, and 60% ASIR. Ninety image sets were evaluated by five observers using the subjective visual grading analysis (VGA) and assessed by proportional odds modeling. Objective quality assessment (contrast, noise, and the contrast-to-noise ratio [CNR]) was analyzed with linear mixed effects modeling on log-transformed data. The need for ethical approval was waived by the local ethics committee as the study only involved anonymously collected clinical data. Results VGA showed significant improvements in sharpness by comparing FBP with ASIR, resulting in odds ratios of 1.54 for 30% ASIR and 1.89 for 60% ASIR (P = 0.004). The objective measures showed significant differences between FBP and 60% ASIR (P < 0.0001) for noise, with an estimated ratio of 0.82, and for CNR, with an estimated ratio of 1.26. Conclusion ASIR improved the subjective image quality of parameter sharpness and, objectively, reduced noise and increased CNR.

  16. AIDA: an adaptive image deconvolution algorithm with application to multi-frame and three-dimensional data

    PubMed Central

    Hom, Erik F. Y.; Marchis, Franck; Lee, Timothy K.; Haase, Sebastian; Agard, David A.; Sedat, John W.

    2011-01-01

    We describe an adaptive image deconvolution algorithm (AIDA) for myopic deconvolution of multi-frame and three-dimensional data acquired through astronomical and microscopic imaging. AIDA is a reimplementation and extension of the MISTRAL method developed by Mugnier and co-workers and shown to yield object reconstructions with excellent edge preservation and photometric precision [J. Opt. Soc. Am. A 21, 1841 (2004)]. Written in Numerical Python with calls to a robust constrained conjugate gradient method, AIDA has significantly improved run times over the original MISTRAL implementation. Included in AIDA is a scheme to automatically balance maximum-likelihood estimation and object regularization, which significantly decreases the amount of time and effort needed to generate satisfactory reconstructions. We validated AIDA using synthetic data spanning a broad range of signal-to-noise ratios and image types and demonstrated the algorithm to be effective for experimental data from adaptive optics–equipped telescope systems and wide-field microscopy. PMID:17491626

  17. Adaptive Guidance and Control Algorithms applied to the X-38 Reentry Mission

    NASA Astrophysics Data System (ADS)

    Graesslin, M.; Wallner, E.; Burkhardt, J.; Schoettle, U.; Well, K. H.

    International Space Station's Crew Return/Rescue Vehicle (CRV) is planned to autonomously return the complete crew of 7 astronauts back to earth in case of an emergency. As prototype of such a vehicle, the X-38, is being developed and built by NASA with European participation. The X-38 is a lifting body with a hyper- sonic lift to drag ratio of about 0.9. In comparison to the Space Shuttle Orbiter, the X-38 has less aerodynamic manoeuvring capability and less actuators. Within the German technology programme TETRA (TEchnologies for future space TRAnsportation systems) contributing to the X-38 program, guidance and control algorithms have been developed and applied to the X-38 reentry mission. The adaptive guidance concept conceived combines an on-board closed-loop predictive guidance algorithm with flight load control that temporarily overrides the attitude commands of the predictive component if the corre- sponding load constraints are violated. The predictive guidance scheme combines an optimization step and a sequence of constraint restoration cycles. In order to satisfy on-board computation limitations the complete scheme is performed only during the exo-atmospheric flight coast phase. During the controlled atmospheric flight segment the task is reduced to a repeatedly solved targeting problem based on the initial optimal solution, thus omitting in-flight constraints. To keep the flight loads - especially the heat flux, which is in fact a major concern of the X-38 reentry flight - below their maximum admissible values, a flight path controller based on quadratic minimization techniques may override the predictive guidance command for a flight along the con- straint boundary. The attitude control algorithms developed are based on dynamic inversion. This methodology enables the designer to straightforwardly devise a controller structure from the system dynamics. The main ad- vantage of this approach with regard to reentry control design lies in the fact that

  18. A new algorithm for high-dimensional uncertainty quantification based on dimension-adaptive sparse grid approximation and reduced basis methods

    NASA Astrophysics Data System (ADS)

    Chen, Peng; Quarteroni, Alfio

    2015-10-01

    In this work we develop an adaptive and reduced computational algorithm based on dimension-adaptive sparse grid approximation and reduced basis methods for solving high-dimensional uncertainty quantification (UQ) problems. In order to tackle the computational challenge of "curse of dimensionality" commonly faced by these problems, we employ a dimension-adaptive tensor-product algorithm [16] and propose a verified version to enable effective removal of the stagnation phenomenon besides automatically detecting the importance and interaction of different dimensions. To reduce the heavy computational cost of UQ problems modelled by partial differential equations (PDE), we adopt a weighted reduced basis method [7] and develop an adaptive greedy algorithm in combination with the previous verified algorithm for efficient construction of an accurate reduced basis approximation. The efficiency and accuracy of the proposed algorithm are demonstrated by several numerical experiments.

  19. NORSAR Final Scientific Report Adaptive Waveform Correlation Detectors for Arrays: Algorithms for Autonomous Calibration

    SciTech Connect

    Gibbons, S J; Ringdal, F; Harris, D B

    2009-04-16

    Correlation detection is a relatively new approach in seismology that offers significant advantages in increased sensitivity and event screening over standard energy detection algorithms. The basic concept is that a representative event waveform is used as a template (i.e. matched filter) that is correlated against a continuous, possibly multichannel, data stream to detect new occurrences of that same signal. These algorithms are therefore effective at detecting repeating events, such as explosions and aftershocks at a specific location. This final report summarizes the results of a three-year cooperative project undertaken by NORSAR and Lawrence Livermore National Laboratory. The overall objective has been to develop and test a new advanced, automatic approach to seismic detection using waveform correlation. The principal goal is to develop an adaptive processing algorithm. By this we mean that the detector is initiated using a basic set of reference ('master') events to be used in the correlation process, and then an automatic algorithm is applied successively to provide improved performance by extending the set of master events selectively and strategically. These additional master events are generated by an independent, conventional detection system. A periodic analyst review will then be applied to verify the performance and, if necessary, adjust and consolidate the master event set. A primary focus of this project has been the application of waveform correlation techniques to seismic arrays. The basic procedure is to perform correlation on the individual channels, and then stack the correlation traces using zero-delay beam forming. Array methods such as frequency-wavenumber analysis can be applied to this set of correlation traces to help guarantee the validity of detections and lower the detection threshold. In principle, the deployment of correlation detectors against seismically active regions could involve very large numbers of very specific detectors. To

  20. Estimation of Evoked Fields Using a Time-Sequenced Adaptive Filter with the Modified P-Vector Algorithm

    DTIC Science & Technology

    1990-12-01

    the ensemble average given a perfect adaptation. The last algorithm is the calculation of /. 3-5 As stated earlier, the gain constant /s is determined...Vita .. .. .. .. ... ... .. ... ... ... ... ... ... ... .. VITA-i vii List of Figures Figure Page 2.1. Ensemble Average of SDAT, M...2-8 2.6. Ensemble Average of N1 . . . . . . . . . . . . . . . . . . . . . . . . . 2-10 2.7. Variance of N

  1. An Adaptive Compensation Algorithm for Temperature Drift of Micro-Electro-Mechanical Systems Gyroscopes Using a Strong Tracking Kalman Filter

    PubMed Central

    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

  2. An adaptive compensation algorithm for temperature drift of micro-electro-mechanical systems gyroscopes using a strong tracking Kalman filter.

    PubMed

    Feng, Yibo; Li, Xisheng; Zhang, Xiaojuan

    2015-05-13

    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.

  3. A correction algorithm to simultaneously control dual deformable mirrors in a woofer-tweeter adaptive optics system

    PubMed Central

    Li, Chaohong; Sredar, Nripun; Ivers, Kevin M.; Queener, Hope; Porter, Jason

    2010-01-01

    We present a direct slope-based correction algorithm to simultaneously control two deformable mirrors (DMs) in a woofer-tweeter adaptive optics system. A global response matrix was derived from the response matrices of each deformable mirror and the voltages for both deformable mirrors were calculated simultaneously. This control algorithm was tested and compared with a 2-step sequential control method in five normal human eyes using an adaptive optics scanning laser ophthalmoscope. The mean residual total root-mean-square (RMS) wavefront errors across subjects after adaptive optics (AO) correction were 0.128 ± 0.025 μm and 0.107 ± 0.033 μm for simultaneous and 2-step control, respectively (7.75-mm pupil). The mean intensity of reflectance images acquired after AO convergence was slightly higher for 2-step control. Radially-averaged power spectra calculated from registered reflectance images were nearly identical for all subjects using simultaneous or 2-step control. The correction performance of our new simultaneous dual DM control algorithm is comparable to 2-step control, but is more efficient. This method can be applied to any woofer-tweeter AO system. PMID:20721058

  4. Experimental Results for a Photonic Time Reversal Processor for Adaptive Control of an Ultra Wideband Phased Array Antenna

    DTIC Science & Technology

    2008-03-01

    Radar , Boston: Artech House, 1994. 2. H. Zmuda, “ Optical Beamforming for Phased Array Antennas,” Chapter 19, R...Beamforming, Phased Array Antennas, Time Reversal, Ultra Wideband Radar 1 INTRODUCTION 1.1 Photonic Processing for Microwave Phased Array ...Architecture for Broadband Adaptive Nulling with Linear and Conformal Phased Array Antennas”, Fiber and Integrated Optics , vol. 19, no. 2, March 2000, pp.

  5. A feature extraction method of the particle swarm optimization algorithm based on adaptive inertia weight and chaos optimization for Brillouin scattering spectra

    NASA Astrophysics Data System (ADS)

    Zhang, Yanjun; Zhao, Yu; Fu, Xinghu; Xu, Jinrui

    2016-10-01

    A novel particle swarm optimization algorithm based on adaptive inertia weight and chaos optimization is proposed for extracting the features of Brillouin scattering spectra. Firstly, the adaptive inertia weight parameter of the velocity is introduced to the basic particle swarm algorithm. Based on the current iteration number of particles and the adaptation value, the algorithm can change the weight coefficient and adjust the iteration speed of searching space for particles, so the local optimization ability can be enhanced. Secondly, the logical self-mapping chaotic search is carried out by using the chaos optimization in particle swarm optimization algorithm, which makes the particle swarm optimization algorithm jump out of local optimum. The novel algorithm is compared with finite element analysis-Levenberg Marquardt algorithm, particle swarm optimization-Levenberg Marquardt algorithm and particle swarm optimization algorithm by changing the linewidth, the signal-to-noise ratio and the linear weight ratio of Brillouin scattering spectra. Then the algorithm is applied to the feature extraction of Brillouin scattering spectra in different temperatures. The simulation analysis and experimental results show that this algorithm has a high fitting degree and small Brillouin frequency shift error for different linewidth, SNR and linear weight ratio. Therefore, this algorithm can be applied to the distributed optical fiber sensing system based on Brillouin optical time domain reflection, which can effectively improve the accuracy of Brillouin frequency shift extraction.

  6. Discrete-Time Nonzero-Sum Games for Multiplayer Using Policy-Iteration-Based Adaptive Dynamic Programming Algorithms.

    PubMed

    Zhang, Huaguang; Jiang, He; Luo, Chaomin; Xiao, Geyang

    2016-10-03

    In this paper, we investigate the nonzero-sum games for a class of discrete-time (DT) nonlinear systems by using a novel policy iteration (PI) adaptive dynamic programming (ADP) method. The main idea of our proposed PI scheme is to utilize the iterative ADP algorithm to obtain the iterative control policies, which not only ensure the system to achieve stability but also minimize the performance index function for each player. This paper integrates game theory, optimal control theory, and reinforcement learning technique to formulate and handle the DT nonzero-sum games for multiplayer. First, we design three actor-critic algorithms, an offline one and two online ones, for the PI scheme. Subsequently, neural networks are employed to implement these algorithms and the corresponding stability analysis is also provided via the Lyapunov theory. Finally, a numerical simulation example is presented to demonstrate the effectiveness of our proposed approach.

  7. Beamforming design with proactive interference cancelation in MISO interference channels

    NASA Astrophysics Data System (ADS)

    Li, Yang; Tian, Yafei; Yang, Chenyang

    2015-12-01

    In this paper, we design coordinated beamforming at base stations (BSs) to facilitate interference cancelation at users in interference networks, where each BS is equipped with multiple antennas and each user is with a single antenna. By assuming that each user can select the best decoding strategy to mitigate the interference, either canceling the interference after decoding when it is strong or treating it as noise when it is weak, we optimize the beamforming vectors that maximize the sum rate for the networks under different interference scenarios and find the solutions of beamforming with closed-form expressions. The inherent design principles are then analyzed, and the performance gain over passive interference cancelation is demonstrated through simulations in heterogeneous cellular networks.

  8. Near-field beamforming analysis for acoustic emission source localization.

    PubMed

    He, Tian; Pan, Qiang; Liu, Yaoguang; Liu, Xiandong; Hu, Dayong

    2012-07-01

    This paper attempts to introduce a near-field acoustic emission (AE) beamforming method to estimate the AE source locations by using a small array of sensors closely placed in a local region. The propagation characteristics of AE signals are investigated based on guided wave theory to discuss the feasibility of using beamforming techniques in AE signal processing. To validate the effectiveness of the AE beamforming method, a series of pencil lead break tests at various regions of a thin steel plate are conducted. The potential of this method for engineering applications are explored through rotor-stator rubbing tests. The experimental results demonstrate that the proposed method can effectively determine the region where rubbing occurs. It is expected that the work of this paper may provide a helpful analysis tool for near-field AE source localization.

  9. Introduction to the SKA low correlator and beamformer system

    NASA Astrophysics Data System (ADS)

    Hampson, Grant A.; Bunton, John D.; Gunst, Andre W.; Baillie, Peter; bij de Vaate, Jan-Geralt

    2016-07-01

    The Square Kilometre Array (SKA) organisation is building a low frequency (50-350 MHz) aperture array to be located in remote Western Australia. The array consists of 512-stations, each consisting of 256-dual polarisation log-periodic antennas. The stations are distributed over a distance of 80km, with the greatest density of stations located in the central core. The input bandwidth is processed in a two stage polyphase filterbank, with the first stage channeliser producing 384 x 781 kHz narrow-band channels. Each station beamforms the antennas together to form a single dual polarisation beam with a bandwidth of 300 MHz (additional beams can also be traded for bandwidth). The second stage polyphase filterbank is located in a system called the Correlator and BeamFormer (CBF) which is the topic of this paper. In the CBF the station signals are first aligned in time. Thereafter the signals are simultaneously correlated and beamformed.

  10. Beam-Forming Concentrating Solar Thermal Array Power Systems

    NASA Technical Reports Server (NTRS)

    Cwik, Thomas A. (Inventor); Dimotakis, Paul E. (Inventor); Hoppe, Daniel J. (Inventor)

    2016-01-01

    The present invention relates to concentrating solar-power systems and, more particularly, beam-forming concentrating solar thermal array power systems. A solar thermal array power system is provided, including a plurality of solar concentrators arranged in pods. Each solar concentrator includes a solar collector, one or more beam-forming elements, and one or more beam-steering elements. The solar collector is dimensioned to collect and divert incoming rays of sunlight. The beam-forming elements intercept the diverted rays of sunlight, and are shaped to concentrate the rays of sunlight into a beam. The steering elements are shaped, dimensioned, positioned, and/or oriented to deflect the beam toward a beam output path. The beams from the concentrators are converted to heat at a receiver, and the heat may be temporarily stored or directly used to generate electricity.

  11. True-time-delay photonic beamformer for an L-band phased array radar

    NASA Astrophysics Data System (ADS)

    Zmuda, Henry; Toughlian, Edward N.; Payson, Paul M.; Malowicki, John E.

    1995-10-01

    The problem of obtaining a true-time-delay photonic beamformer has recently been a topic of great interest. Many interesting and novel approaches to this problem have been studied. This paper examines the design, construction, and testing of a dynamic optical processor for the control of a 20-element phased array antenna operating at L-band (1.2-1.4 GHz). The approach taken here has several distinct advantages. The actual optical control is accomplished with a class of spatial light modulator known as a segmented mirror device (SMD). This allows for the possibility of controlling an extremely large number (tens of thousands) of antenna elements using integrated circuit technology. The SMD technology is driven by the HDTV and laser printer markets so ultimate cost reduction as well as technological improvements are expected. Optical splitting is efficiently accomplished using a diffractive optical element. This again has the potential for use in antenna array systems with a large number of radiating elements. The actual time delay is achieved using a single acousto-optic device for all the array elements. Acousto-optic device technologies offer sufficient delay as needed for a time steered array. The topological configuration is an optical heterodyne system, hence high, potentially millimeter wave center frequencies are possible by mixing two lasers of slightly differing frequencies. Finally, the entire system is spatially integrated into a 3D glass substrate. The integrated system provides the ruggedness needed in most applications and essentially eliminates the drift problems associated with free space optical systems. Though the system is presently being configured as a beamformer, it has the ability to operate as a general photonic signal processing element in an adaptive (reconfigurable) transversal frequency filter configuration. Such systems are widely applicable in jammer/noise canceling systems, broadband ISDN, and for spread spectrum secure communications

  12. Ultrasound plane-wave imaging with delay multiply and sum beamforming and coherent compounding.

    PubMed

    Matrone, Giulia; Savoia, Alessandro S; Caliano, Giosue; Magenes, Giovanni

    2016-08-01

    Improving the frame rate is an important aspect in medical ultrasound imaging, particularly in 3D/4D cardiac applications. However, an accurate trade-off between the higher frame rate and image contrast and resolution should be performed. Plane-Wave Imaging (PWI) can potentially achieve frame rates in the order of 10 kHz, as it uses a single unfocused plane wave (and thus a single transmit event) to acquire the image of the entire region of interest. The lack of transmit focusing however causes a significant drop of image quality, which can be restored by coherently compounding several tilted plane-wave frames, at the expense of the frame rate. PWI together with the use of a beamforming algorithm able to achieve a higher image contrast resolution, such as the Delay Multiply And Sum (DMAS), could thus allow to improve image quality achieving a high frame rate at the same time. This paper presents the first simulation results obtained by employing DMAS beamforming and PWI with different transmission angles and coherent compounding. The simulated Point Spread Function (PSF) and cyst-phantom images show that DMAS makes it possible to achieve a high image quality with a reduced number of compounded frames compared to standard Delay And Sum (DAS), and hence it can be used to improve the contrast and resolution of plane-wave images still achieving a very high frame rate.

  13. Dipole localization using beamforming and RAP-MUSIC on simulated intracerebral recordings.

    PubMed

    Chang, N; Gotman, J; Gulrajani, R

    2004-01-01

    Interpreting intracerebral recordings in the search of an epileptic focus can be difficult because the amplitude of the potentials are misleading. Small generators located near the electrode site generate large potentials, which could swamp the signal of a nearby epileptic focus. In order to address this problem, two inverse problem algorithms, beamforming and recursively applied and projected multiple signal classification (RAP-MUSIC), were used with simulated intracerebral potentials to calculate equivalent dipole positions. Three dipoles were positioned in an infinite plane medium near three intracerebral electrodes. The potentials generated by the dipoles were simulated and contaminated with white noise. Initial localization simulations showed that both methods detected the sources accurately with RAP-MUSIC reporting lower orientation errors. A spatial resolution analysis for both methods was undertaken in which two dipoles were placed on a plane with the same orientation and overlapping time-courses. Beamforming was able to adequately distinguish the sources for separation distances of 1.2 cm, whereas RAP-MUSIC managed to separate the sources for dipoles as close as 0.4-0.6 cm.

  14. Prototype Parts of a Digital Beam-Forming Wide-Band Receiver

    NASA Technical Reports Server (NTRS)

    Kaplan, Steven B.; Pylov, Sergey V.; Pambianchi, Michael

    2003-01-01

    Some prototype parts of a digital beamforming (DBF) receiver that would operate at multigigahertz carrier frequencies have been developed. The beam-forming algorithm in a DBF receiver processes signals from multiple antenna elements with appropriate time delays and weighting factors chosen to enhance the reception of signals from a specific direction while suppressing signals from other directions. Such a receiver would be used in the directional reception of weak wideband signals -- for example, spread-spectrum signals from a low-power transmitter on an Earth-orbiting spacecraft or other distant source. The prototype parts include superconducting components on integrated-circuit chips, and a multichip module (MCM), within which the chips are to be packaged and connected via special inter-chip-communication circuits. The design and the underlying principle of operation are based on the use of the rapid single-flux quantum (RSFQ) family of logic circuits to obtain the required processing speed and signal-to-noise ratio. RSFQ circuits are superconducting circuits that exploit the Josephson effect. They are well suited for this application, having been proven to perform well in some circuits at frequencies above 100 GHz. In order to maintain the superconductivity needed for proper functioning of the RSFQ circuits, the MCM must be kept in a cryogenic environment during operation.

  15. Computed Tomography Images De-noising using a Novel Two Stage Adaptive Algorithm.

    PubMed

    Fadaee, Mojtaba; Shamsi, Mousa; Saberkari, Hamidreza; Sedaaghi, Mohammad Hossein

    2015-01-01

    In this paper, an optimal algorithm is presented for de-noising of medical images. The presented algorithm is based on improved version of local pixels grouping and principal component analysis. In local pixels grouping algorithm, blocks matching based on L (2) norm method is utilized, which leads to matching performance improvement. To evaluate the performance of our proposed algorithm, peak signal to noise ratio (PSNR) and structural similarity (SSIM) evaluation criteria have been used, which are respectively according to the signal to noise ratio in the image and structural similarity of two images. The proposed algorithm has two de-noising and cleanup stages. The cleanup stage is carried out comparatively; meaning that it is alternately repeated until the two conditions based on PSNR and SSIM are established. Implementation results show that the presented algorithm has a significant superiority in de-noising. Furthermore, the quantities of SSIM and PSNR values are higher in comparison to other methods.

  16. Computed Tomography Images De-noising using a Novel Two Stage Adaptive Algorithm

    PubMed Central

    Fadaee, Mojtaba; Shamsi, Mousa; Saberkari, Hamidreza; Sedaaghi, Mohammad Hossein

    2015-01-01

    In this paper, an optimal algorithm is presented for de-noising of medical images. The presented algorithm is based on improved version of local pixels grouping and principal component analysis. In local pixels grouping algorithm, blocks matching based on L2 norm method is utilized, which leads to matching performance improvement. To evaluate the performance of our proposed algorithm, peak signal to noise ratio (PSNR) and structural similarity (SSIM) evaluation criteria have been used, which are respectively according to the signal to noise ratio in the image and structural similarity of two images. The proposed algorithm has two de-noising and cleanup stages. The cleanup stage is carried out comparatively; meaning that it is alternately repeated until the two conditions based on PSNR and SSIM are established. Implementation results show that the presented algorithm has a significant superiority in de-noising. Furthermore, the quantities of SSIM and PSNR values are higher in comparison to other methods. PMID:26955565

  17. Characterization of adaptive statistical iterative reconstruction algorithm for dose reduction in CT: A pediatric oncology perspective

    SciTech Connect

    Brady, S. L.; Yee, B. S.; Kaufman, R. A.

    2012-09-15

    Purpose: This study demonstrates a means of implementing an adaptive statistical iterative reconstruction (ASiR Trade-Mark-Sign ) technique for dose reduction in computed tomography (CT) while maintaining similar noise levels in the reconstructed image. The effects of image quality and noise texture were assessed at all implementation levels of ASiR Trade-Mark-Sign . Empirically derived dose reduction limits were established for ASiR Trade-Mark-Sign for imaging of the trunk for a pediatric oncology population ranging from 1 yr old through adolescence/adulthood. Methods: Image quality was assessed using metrics established by the American College of Radiology (ACR) CT accreditation program. Each image quality metric was tested using the ACR CT phantom with 0%-100% ASiR Trade-Mark-Sign blended with filtered back projection (FBP) reconstructed images. Additionally, the noise power spectrum (NPS) was calculated for three common reconstruction filters of the trunk. The empirically derived limitations on ASiR Trade-Mark-Sign implementation for dose reduction were assessed using (1, 5, 10) yr old and adolescent/adult anthropomorphic phantoms. To assess dose reduction limits, the phantoms were scanned in increments of increased noise index (decrementing mA using automatic tube current modulation) balanced with ASiR Trade-Mark-Sign reconstruction to maintain noise equivalence of the 0% ASiR Trade-Mark-Sign image. Results: The ASiR Trade-Mark-Sign algorithm did not produce any unfavorable effects on image quality as assessed by ACR criteria. Conversely, low-contrast resolution was found to improve due to the reduction of noise in the reconstructed images. NPS calculations demonstrated that images with lower frequency noise had lower noise variance and coarser graininess at progressively higher percentages of ASiR Trade-Mark-Sign reconstruction; and in spite of the similar magnitudes of noise, the image reconstructed with 50% or more ASiR Trade-Mark-Sign presented a more

  18. Constraining ecosystem model with adaptive Metropolis algorithm using boreal forest site eddy covariance measurements

    NASA Astrophysics Data System (ADS)

    Mäkelä, Jarmo; Susiluoto, Jouni; Markkanen, Tiina; Aurela, Mika; Järvinen, Heikki; Mammarella, Ivan; Hagemann, Stefan; Aalto, Tuula

    2016-12-01

    We examined parameter optimisation in the JSBACH (Kaminski et al., 2013; Knorr and Kattge, 2005; Reick et al., 2013) ecosystem model, applied to two boreal forest sites (Hyytiälä and Sodankylä) in Finland. We identified and tested key parameters in soil hydrology and forest water and carbon-exchange-related formulations, and optimised them using the adaptive Metropolis (AM) algorithm for Hyytiälä with a 5-year calibration period (2000-2004) followed by a 4-year validation period (2005-2008). Sodankylä acted as an independent validation site, where optimisations were not made. The tuning provided estimates for full distribution of possible parameters, along with information about correlation, sensitivity and identifiability. Some parameters were correlated with each other due to a phenomenological connection between carbon uptake and water stress or other connections due to the set-up of the model formulations. The latter holds especially for vegetation phenology parameters. The least identifiable parameters include phenology parameters, parameters connecting relative humidity and soil dryness, and the field capacity of the skin reservoir. These soil parameters were masked by the large contribution from vegetation transpiration. In addition to leaf area index and the maximum carboxylation rate, the most effective parameters adjusting the gross primary production (GPP) and evapotranspiration (ET) fluxes in seasonal tuning were related to soil wilting point, drainage and moisture stress imposed on vegetation. For daily and half-hourly tunings the most important parameters were the ratio of leaf internal CO2 concentration to external CO2 and the parameter connecting relative humidity and soil dryness. Effectively the seasonal tuning transferred water from soil moisture into ET, and daily and half-hourly tunings reversed this process. The seasonal tuning improved the month-to-month development of GPP and ET, and produced the most stable estimates of water use

  19. Using Subdivision Surfaces and Adaptive Surface Simplification Algorithms for Modeling Chemical Heterogeneities in Geophysical Flows

    NASA Astrophysics Data System (ADS)

    Schmalzl, J.

    2003-04-01

    Convective flows govern much of the dynamics of the Earth. Examples of such flows are convection in the Earth's mantle, convection in magma chambers and much of the dynamics of the world oceans. Nowadays these time-dependent flows are often studied by means of three dimensional (3D) numerical models which solve the equations for the transport of heat and momentum alternatingly. These flows are often driven by a temperature difference. But for many flows there is also an active or passive chemical component that has to be considered. One characteristics of these flows is that the chemical diffusivity is very small. Implementing such a chemical field with a very low diffusivity into a numerical model using a field approach is difficult due to numerical diffusion introduced by the Eulerian schemes. Using Lagrangian tracers is also difficult in 3D flow since a massive amount of tracers is needed. We therefore have implemented a tracer-mesh method which tracks only the position of the interface between the two different components. Compared to a 2D tracer-line the insertion of new 3D surface-elements in highly deformed regions is however more complex. This is due to the topology of the mesh which changes because of the adaptive refinement. Luckily the refinement of polygonal meshes is a very active field of research in computer graphics and has been termed "Subdivision Surfaces". There is a wealth of different subdivision schemes with different properties. We applied the Butterfly scheme and the Loop scheme for the refinement of tracer meshes. When a density difference is connected to a chemical component it often acts as a restoring force. In many cases, the governing flow is spatially heterogenous and the spatial location of the heterogeneities is varying in time (e.g. the location of an upwelling plume). The restoring force of the density contrast may result in a situation where a highly deformed, and therefore highly refined region, returns to a simple geometry. In

  20. Using Subdivision Surfaces and Adaptive Surface Simplification Algorithms for Modeling Chemical Heterogeneities in Geophysical Flows

    NASA Astrophysics Data System (ADS)

    Schmalzl, J.; Loddoch, A.

    2003-12-01

    Convective flows govern much of the dynamics of the Earth. Examples of such flows are convection in the Earth's mantle, convection in magma chambers and much of the dynamics of the world oceans. Nowadays these time-dependent flows are often studied by means of three dimensional (3D) numerical models which solve the equations for the transport of heat and momentum alternatingly. These flows are often driven by a temperature difference. But for many flows there is also an active or passive chemical component that has to be considered. One characteristics of these flows is that the chemical diffusivity is very small. Implementing such a chemical field with a very low diffusivity into a numerical model using a field approach is difficult due to numerical diffusion introduced by the Eulerian schemes. Using Lagrangian tracers is also difficult in 3D flow since a massive amount of tracers is needed. We therefore have implemented a tracer-mesh method which tracks only the position of the interface between the two different components. Compared to a 2D tracer-line the insertion of new 3D surface-elements in highly deformed regions is however more complex. This is due to the topology of the mesh which changes because of the adaptive refinement. Luckily the refinement of polygonal meshes is a very active field of research in computer graphics and has been termed "Subdivision Surfaces". There is a wealth of different subdivision schemes with different properties. We applied the Butterfly scheme and the Loop scheme for the refinement of tracer meshes. When a density difference is connected to a chemical component it often acts as a restoring force. In many cases, the governing flow is spatially heterogenous and the spatial location of the heterogeneities is varying in time (e.g. the location of an upwelling plume). The restoring force of the density contrast may result in a situation where a highly deformed, and therefore highly refined region, returns to a simple geometry. In

  1. Beamforming effects on generalized Nakagami imaging.

    PubMed

    Yu, Xue; Guo, Yuexin; Huang, Sheng-Min; Li, Meng-Lin; Lee, Wei-Ning

    2015-10-07

    Ultrasound tissue characterization is crucial for the detection of tissue abnormalities. Since the statistics of the backscattered ultrasound signals strongly depend on density and spatial arrangement of local scatterers, appropriate modeling of the backscattered signals may be capable of providing unique physiological information on local tissue properties. Among various techniques, the Nakagami imaging, realized in a window-based estimation scheme, has a good performance in assessing different scatterer statistics in tissues. However, inconsistent m values have been reported in literature and obtained only from a local tissue region, abating the reliability of Nakagami imaging in tissue characterization. The discrepancies in m values in relevant literature may stem from the nonuniformity of the ultrasound image resolution, which is often neglected. We therefore hypothesized that window-based Nakagami m estimation was highly associated with the regional spatial resolution of ultrasound imaging. To test this hypothesis, our study investigated the effect of beamforming methods, including synthetic aperture (SA), coherent plane wave compounding (CPWC), multi-focusing (MF), and single-focusing (SF), on window-based m parameter estimation from the perspective of the resolution cell. The statistics of m parameter distribution as a function of imaging depth were characterized by their mean, variance, and skewness. The phantom with a low scatterer density (16 scatterers mm(-3)) had significantly lower m values compared to the ones with high scatterer densities (32 and 64 scatterers mm(-3)). Results from the homogeneous phantom with 64 scatterers mm(-3) showed that SA, MF, and CPWC had relatively uniform lateral resolutions compared to SF and thus relatively constant m estimates at different imaging depths. Our findings suggest that an ultrasound imaging regime exhibiting invariant spatial resolution throughout the entire imaging field of view would be the most appropriate

  2. Beamforming effects on generalized Nakagami imaging

    NASA Astrophysics Data System (ADS)

    Yu, Xue; Guo, Yuexin; Huang, Sheng-Min; Li, Meng-Lin; Lee, Wei-Ning

    2015-10-01

    Ultrasound tissue characterization is crucial for the detection of tissue abnormalities. Since the statistics of the backscattered ultrasound signals strongly depend on density and spatial arrangement of local scatterers, appropriate modeling of the backscattered signals may be capable of providing unique physiological information on local tissue properties. Among various techniques, the Nakagami imaging, realized in a window-based estimation scheme, has a good performance in assessing different scatterer statistics in tissues. However, inconsistent m values have been reported in literature and obtained only from a local tissue region, abating the reliability of Nakagami imaging in tissue characterization. The discrepancies in m values in relevant literature may stem from the nonuniformity of the ultrasound image resolution, which is often neglected. We therefore hypothesized that window-based Nakagami m estimation was highly associated with the regional spatial resolution of ultrasound imaging. To test this hypothesis, our study investigated the effect of beamforming methods, including synthetic aperture (SA), coherent plane wave compounding (CPWC), multi-focusing (MF), and single-focusing (SF), on window-based m parameter estimation from the perspective of the resolution cell. The statistics of m parameter distribution as a function of imaging depth were characterized by their mean, variance, and skewness. The phantom with a low scatterer density (16 scatterers mm-3) had significantly lower m values compared to the ones with high scatterer densities (32 and 64 scatterers mm-3). Results from the homogeneous phantom with 64 scatterers mm-3 showed that SA, MF, and CPWC had relatively uniform lateral resolutions compared to SF and thus relatively constant m estimates at different imaging depths. Our findings suggest that an ultrasound imaging regime exhibiting invariant spatial resolution throughout the entire imaging field of view would be the most appropriate for

  3. Estimation of key parameters in adaptive neuron model according to firing patterns based on improved particle swarm optimization algorithm

    NASA Astrophysics Data System (ADS)

    Yuan, Chunhua; Wang, Jiang; Yi, Guosheng

    2017-03-01

    Estimation of ion channel parameters is crucial to spike initiation of neurons. The biophysical neuron models have numerous ion channel parameters, but only a few of them play key roles in the firing patterns of the models. So we choose three parameters featuring the adaptation in the Ermentrout neuron model to be estimated. However, the traditional particle swarm optimization (PSO) algorithm is still easy to fall into local optimum and has the premature convergence phenomenon in the study of some problems. In this paper, we propose an improved method that uses a concave function and dynamic logistic chaotic mapping mixed to adjust the inertia weights of the fitness value, effectively improve the global convergence ability of the algorithm. The perfect predicting firing trajectories of the rebuilt model using the estimated parameters prove that only estimating a few important ion channel parameters can establish the model well and the proposed algorithm is effective. Estimations using two classic PSO algorithms are also compared to the improved PSO to verify that the algorithm proposed in this paper can avoid local optimum and quickly converge to the optimal value. The results provide important theoretical foundations for building biologically realistic neuron models.

  4. DARAL: A Dynamic and Adaptive Routing Algorithm for Wireless Sensor Networks

    PubMed Central

    Estévez, Francisco José; Glösekötter, Peter; González, Jesús

    2016-01-01

    The evolution of Smart City projects is pushing researchers and companies to develop more efficient embedded hardware and also more efficient communication technologies. These communication technologies are the focus of this work, presenting a new routing algorithm based on dynamically-allocated sub-networks and node roles. Among these features, our algorithm presents a fast set-up time, a reduced overhead and a hierarchical organization, which allows for the application of complex management techniques. This work presents a routing algorithm based on a dynamically-allocated hierarchical clustering, which uses the link quality indicator as a reference parameter, maximizing the network coverage and minimizing the control message overhead and the convergence time. The present work based its test scenario and analysis in the density measure, considered as a node degree. The routing algorithm is compared with some of the most well known routing algorithms for different scenario densities. PMID:27347962

  5. Performance improvement of Fresnel beamforming using dual apodization with cross-correlation.

    PubMed

    Nguyen, Man M; Yen, Jesse T

    2013-03-01

    Fresnel beamforming is a beamforming method that has a delay profile with a shape similar to a physical Fresnel lens. With 4 to 8 transmit channels, 2 receive channels, and a network of single-pole/single-throw switches, Fresnel beamforming can reduce the size, cost, and complexity of a beamformer. The performance of Fresnel beamforming is highly dependent on focal errors resulting from phase wraparound and quantization of its delay profile. Previously, we demonstrated that the performance of Fresnel beamforming relative to delayand- sum (DAS) beamforming is comparable for linear arrays at f-number = 2 and 50% bandwidth. However, focal errors for Fresnel beamforming are larger because of larger path length differences between elements, as in the case of curvilinear arrays compared with linear arrays. In this paper, we present the concept and performance evaluation of Fresnel beamforming combined with a novel clutter suppression method called dual apodization with cross-correlation (DAX) for curvilinear arrays. The contrast-to-noise ratios (CNRs) of Fresnel beamforming followed by DAX are highest at f-number = 3. At f-number = 3, the experimental results show that using DAX, the CNR for Fresnel beamforming improves from 3.7 to 10.6, compared with a CNR of 5.2 for DAS beamforming. Spatial resolution is shown to be unaffected by DAX. At f-number = 3, the lateral beamwidth and axial pulse length for Fresnel beamforming with DAX are 1.44 and 1.00 mm larger than those for DAS beamforming (about 14% and 21% larger), respectively. These experimental results are in good agreement with simulation results.

  6. Performance Improvement of Fresnel Beamforming Using Dual Apodization With Cross-Correlation

    PubMed Central

    Nguyen, Man M.; Yen, Jesse T.

    2013-01-01

    Fresnel beamforming is a beamforming method that has a delay profile with a shape similar to a physical Fresnel lens. With 4 to 8 transmit channels, 2 receive channels, and a network of single-pole/single-throw switches, Fresnel beamforming can reduce the size, cost, and complexity of a beam-former. The performance of Fresnel beamforming is highly dependent on focal errors resulting from phase wraparound and quantization of its delay profile. Previously, we demonstrated that the performance of Fresnel beamforming relative to delay-and-sum (DAS) beamforming is comparable for linear arrays at f-number = 2 and 50% bandwidth. However, focal errors for Fresnel beamforming are larger because of larger path length differences between elements, as in the case of curvilinear arrays compared with linear arrays. In this paper, we present the concept and performance evaluation of Fresnel beamforming combined with a novel clutter suppression method called dual apodization with cross-correlation (DAX) for curvilinear arrays. The contrast-to-noise ratios (CNRs) of Fresnel beamforming followed by DAX are highest at f-number = 3. At f-number = 3, the experimental results show that using DAX, the CNR for Fresnel beamforming improves from 3.7 to 10.6, compared with a CNR of 5.2 for DAS beamforming. Spatial resolution is shown to be unaffected by DAX. At f-number = 3, the lateral beamwidth and axial pulse length for Fresnel beamforming with DAX are 1.44 and 1.00 mm larger than those for DAS beamforming (about 14% and 21% larger), respectively. These experimental results are in good agreement with simulation results. PMID:23475913

  7. On the estimation algorithm used in adaptive performance optimization of turbofan engines

    NASA Technical Reports Server (NTRS)

    Espana, Martin D.; Gilyard, Glenn B.

    1993-01-01

    The performance seeking control algorithm is designed to continuously optimize the performance of propulsion systems. The performance seeking control algorithm uses a nominal model of the propulsion system and estimates, in flight, the engine deviation parameters characterizing the engine deviations with respect to nominal conditions. In practice, because of measurement biases and/or model uncertainties, the estimated engine deviation parameters may not reflect the engine's actual off-nominal condition. This factor has a necessary impact on the overall performance seeking control scheme exacerbated by the open-loop character of the algorithm. The effects produced by unknown measurement biases over the estimation algorithm are evaluated. This evaluation allows for identification of the most critical measurements for application of the performance seeking control algorithm to an F100 engine. An equivalence relation between the biases and engine deviation parameters stems from an observability study; therefore, it is undecided whether the estimated engine deviation parameters represent the actual engine deviation or whether they simply reflect the measurement biases. A new algorithm, based on the engine's (steady-state) optimization model, is proposed and tested with flight data. When compared with previous Kalman filter schemes, based on local engine dynamic models, the new algorithm is easier to design and tune and it reduces the computational burden of the onboard computer.

  8. Worst-case-based robust beamforming for wireless cooperative networks

    NASA Astrophysics Data System (ADS)

    Zhang, Li; Chen, Haihua; He, Ming

    2014-12-01

    It is known that distributed beamforming techniques can improve the performance of relay networks by using channel state information (CSI). In practical applications, there exist unavoidably estimation errors of the CSI, which results in outage of quality of service (QoS) or overconsumption of transmit power. In this paper, we propose two worst-case-based distributed beamforming techniques that are robust to the channel estimation errors. In the worst-case-based approaches, the worst case in a set that includes the actual case is optimized. Therefore, the performance of the actual case can be guaranteed. In our first approach, the maximal total relay transmit power in the set is minimized subject to the QoS constraint. This distributed beamforming problem can be approximately solved using second-order cone programming (SOCP). In our second method, the worst QoS in the set is maximized subject to the constraints of individual relay transmit powers. It is shown that the resultant problem can be approximately formulated as a quasi-convex problem and can be solved by using a bisection search method. Simulation results show that the proposed beamforming techniques are robust to the CSI errors and there is no outage of QoS or power in the proposed methods.

  9. Flux projection beamforming for monochromatic source localization in enclosed space.

    PubMed

    Li, Xiaolei; Yu, Gaokun; Wang, Ning; Gao, Dazhi; Wang, Haozhong

    2017-01-01

    Monochromatic sound source localization becomes difficult in enclosed space. According to the reciprocity theorem, a self-consistent method of source localization in enclosed space, referred to as the flux projection beamforming, is proposed, only using the measurement of the sound pressure and normal velocity on the closed boundary at a single frequency. Its validity is examined both by experiment and simulation.

  10. Beamforming through regularized inverse problems in ultrasound medical imaging.

    PubMed

    Szasz, Teodora; Basarab, Adrian; Kouame, Denis

    2016-09-13

    Beamforming in ultrasound imaging has significant impact on the quality of the final image, controlling its resolution and contrast. Despite its low spatial resolution and contrast, delay-and-sum is still extensively used nowadays in clinical applications, due to its real-time capabilities. The most common alternatives are minimum variance method and its variants, which overcome the drawbacks of delay-and-sum, at the cost of higher computational complexity that limits its utilization in real-time applications. In this paper, we propose to perform beamforming in ultrasound imaging through a regularized inverse problem based on a linear model relating the reflected echoes to the signal to be recovered. Our approach presents two major advantages: i) its flexibility in the choice of statistical assumptions on the signal to be beamformed (Laplacian and Gaussian statistics are tested herein) and ii) its robustness to a reduced number of pulse emissions. The proposed framework is flexible and allows for choosing the right trade-off between noise suppression and sharpness of the resulted image. We illustrate the performance of our approach on both simulated and experimental data, with in vivo examples of carotid and thyroid. Compared to delay-and-sum, minimimum variance and two other recently published beamforming techniques, our method offers better spatial resolution, respectively contrast, when using Laplacian and Gaussian priors.

  11. Beamforming Arrays with Faulty Sensors in Dynamic Environments

    DTIC Science & Technology

    2004-01-01

    slightly outperforms MMSE at all SNR’s. • ALPINEX ( Swinger and Walker) degrades significantly in correlated multipath. • Single snapshot ROC for...MMSE at all SNR’s. • ALPINEX ( Swinger and Walker) degrades significantly in correlated multipath. 14 Simulated BTR for ACC vs. Clairvoyant Beamformer

  12. Numerical analysis of biosonar beamforming mechanisms and strategies in bats.

    PubMed

    Müller, Rolf

    2010-09-01

    Beamforming is critical to the function of most sonar systems. The conspicuous noseleaf and pinna shapes in bats suggest that beamforming mechanisms based on diffraction of the outgoing and incoming ultrasonic waves play a major role in bat biosonar. Numerical methods can be used to investigate the relationships between baffle geometry, acoustic mechanisms, and resulting beampatterns. Key advantages of numerical approaches are: efficient, high-resolution estimation of beampatterns, spatially dense predictions of near-field amplitudes, and the malleability of the underlying shape representations. A numerical approach that combines near-field predictions based on a finite-element formulation for harmonic solutions to the Helmholtz equation with a free-field projection based on the Kirchhoff integral to obtain estimates of the far-field beampattern is reviewed. This method has been used to predict physical beamforming mechanisms such as frequency-dependent beamforming with half-open resonance cavities in the noseleaf of horseshoe bats and beam narrowing through extension of the pinna aperture with skin folds in false vampire bats. The fine structure of biosonar beampatterns is discussed for the case of the Chinese noctule and methods for assessing the spatial information conveyed by beampatterns are demonstrated for the brown long-eared bat.

  13. A controlled study of the effectiveness of an adaptive closed-loop algorithm to minimize corticosteroid-induced stress hyperglycemia in type 1 diabetes.

    PubMed

    El Youssef, Joseph; Castle, Jessica R; Branigan, Deborah L; Massoud, Ryan G; Breen, Matthew E; Jacobs, Peter G; Bequette, B Wayne; Ward, W Kenneth

    2011-11-01

    To be effective in type 1 diabetes, algorithms must be able to limit hyperglycemic excursions resulting from medical and emotional stress. We tested an algorithm that estimates insulin sensitivity at regular intervals and continually adjusts gain factors of a fading memory proportional-derivative (FMPD) algorithm. In order to assess whether the algorithm could appropriately adapt and limit the degree of hyperglycemia, we administered oral hydrocortisone repeatedly to create insulin resistance. We compared this indirect adaptive proportional-derivative (APD) algorithm to the FMPD algorithm, which used fixed gain parameters. Each subject with type 1 diabetes (n = 14) was studied on two occasions, each for 33 h. The APD algorithm consistently identified a fall in insulin sensitivity after hydrocortisone. The gain factors and insulin infusion rates were appropriately increased, leading to satisfactory glycemic control after adaptation (premeal glucose on day 2, 148 ± 6 mg/dl). After sufficient time was allowed for adaptation, the late postprandial glucose increment was significantly lower than when measured shortly after the onset of the steroid effect. In addition, during the controlled comparison, glycemia was significantly lower with the APD algorithm than with the FMPD algorithm. No increase in hypoglycemic frequency was found in the APD-only arm. An afferent system of duplicate amperometric sensors demonstrated a high degree of accuracy; the mean absolute relative difference of the sensor used to control the algorithm was 9.6 ± 0.5%. We conclude that an adaptive algorithm that frequently estimates insulin sensitivity and adjusts gain factors is capable of minimizing corticosteroid-induced stress hyperglycemia.

  14. Adaptive passive fathometer processing.

    PubMed

    Siderius, Martin; Song, Heechun; Gerstoft, Peter; Hodgkiss, William S; Hursky, Paul; Harrison, Chris

    2010-04-01

    Recently, a technique has been developed to image seabed layers using the ocean ambient noise field as the sound source. This so called passive fathometer technique exploits the naturally occurring acoustic sounds generated on the sea-surface, primarily from breaking waves. The method is based on the cross-correlation of noise from the ocean surface with its echo from the seabed, which recovers travel times to significant seabed reflectors. To limit averaging time and make this practical, beamforming is used with a vertical array of hydrophones to reduce interference from horizontally propagating noise. The initial development used conventional beamforming, but significant improvements have been realized using adaptive techniques. In this paper, adaptive methods for this process are described and applied to several data sets to demonstrate improvements possible as compared to conventional processing.

  15. Adaptive Grouping Cloud Model Shuffled Frog Leaping Algorithm for Solving Continuous Optimization Problems

    PubMed Central

    Liu, Haorui; Yi, Fengyan; Yang, Heli

    2016-01-01

    The shuffled frog leaping algorithm (SFLA) easily falls into local optimum when it solves multioptimum function optimization problem, which impacts the accuracy and convergence speed. Therefore this paper presents grouped SFLA for solving continuous optimization problems combined with the excellent characteristics of cloud model transformation between qualitative and quantitative research. The algorithm divides the definition domain into several groups and gives each group a set of frogs. Frogs of each region search in their memeplex, and in the search process the algorithm uses the “elite strategy” to update the location information of existing elite frogs through cloud model algorithm. This method narrows the searching space and it can effectively improve the situation of a local optimum; thus convergence speed and accuracy can be significantly improved. The results of computer simulation confirm this conclusion. PMID:26819584

  16. Adaptive Grouping Cloud Model Shuffled Frog Leaping Algorithm for Solving Continuous Optimization Problems.

    PubMed

    Liu, Haorui; Yi, Fengyan; Yang, Heli

    2016-01-01

    The shuffled frog leaping algorithm (SFLA) easily falls into local optimum when it solves multioptimum function optimization problem, which impacts the accuracy and convergence speed. Therefore this paper presents grouped SFLA for solving continuous optimization problems combined with the excellent characteristics of cloud model transformation between qualitative and quantitative research. The algorithm divides the definition domain into several groups and gives each group a set of frogs. Frogs of each region search in their memeplex, and in the search process the algorithm uses the "elite strategy" to update the location information of existing elite frogs through cloud model algorithm. This method narrows the searching space and it can effectively improve the situation of a local optimum; thus convergence speed and accuracy can be significantly improved. The results of computer simulation confirm this conclusion.

  17. Higher-Order, Space-Time Adaptive Finite Volume Methods: Algorithms, Analysis and Applications

    SciTech Connect

    Minion, Michael

    2014-04-29

    The four main goals outlined in the proposal for this project were: 1. Investigate the use of higher-order (in space and time) finite-volume methods for fluid flow problems. 2. Explore the embedding of iterative temporal methods within traditional block-structured AMR algorithms. 3. Develop parallel in time methods for ODEs and PDEs. 4. Work collaboratively with the Center for Computational Sciences and Engineering (CCSE) at Lawrence Berkeley National Lab towards incorporating new algorithms within existing DOE application codes.

  18. Biologically inspired binaural hearing aid algorithms: Design principles and effectiveness

    NASA Astrophysics Data System (ADS)

    Feng, Albert

    2002-05-01

    Despite rapid advances in the sophistication of hearing aid technology and microelectronics, listening in noise remains problematic for people with hearing impairment. To solve this problem two algorithms were designed for use in binaural hearing aid systems. The signal processing strategies are based on principles in auditory physiology and psychophysics: (a) the location/extraction (L/E) binaural computational scheme determines the directions of source locations and cancels noise by applying a simple subtraction method over every frequency band; and (b) the frequency-domain minimum-variance (FMV) scheme extracts a target sound from a known direction amidst multiple interfering sound sources. Both algorithms were evaluated using standard metrics such as signal-to-noise-ratio gain and articulation index. Results were compared with those from conventional adaptive beam-forming algorithms. In free-field tests with multiple interfering sound sources our algorithms performed better than conventional algorithms. Preliminary intelligibility and speech reception results in multitalker environments showed gains for every listener with normal or impaired hearing when the signals were processed in real time with the FMV binaural hearing aid algorithm. [Work supported by NIH-NIDCD Grant No. R21DC04840 and the Beckman Institute.

  19. Adaptive algorithms of position and energy reconstruction in Anger-camera type detectors: experimental data processing in ANTS

    NASA Astrophysics Data System (ADS)

    Morozov, A.; Defendi, I.; Engels, R.; Fraga, F. A. F.; Fraga, M. M. F. R.; Gongadze, A.; Guerard, B.; Jurkovic, M.; Kemmerling, G.; Manzin, G.; Margato, L. M. S.; Niko, H.; Pereira, L.; Petrillo, C.; Peyaud, A.; Piscitelli, F.; Raspino, D.; Rhodes, N. J.; Sacchetti, F.; Schooneveld, E. M.; Solovov, V.; Van Esch, P.; Zeitelhack, K.

    2013-05-01

    The software package ANTS (Anger-camera type Neutron detector: Toolkit for Simulations), developed for simulation of Anger-type gaseous detectors for thermal neutron imaging was extended to include a module for experimental data processing. Data recorded with a sensor array containing up to 100 photomultiplier tubes (PMT) or silicon photomultipliers (SiPM) in a custom configuration can be loaded and the positions and energies of the events can be reconstructed using the Center-of-Gravity, Maximum Likelihood or Least Squares algorithm. A particular strength of the new module is the ability to reconstruct the light response functions and relative gains of the photomultipliers from flood field illumination data using adaptive algorithms. The performance of the module is demonstrated with simulated data generated in ANTS and experimental data recorded with a 19 PMT neutron detector. The package executables are publicly available at http://coimbra.lip.pt/~andrei/

  20. Exploration of the optimisation algorithms used in the implementation of adaptive optics in confocal and multiphoton microscopy.

    PubMed

    Wright, Amanda J; Burns, David; Patterson, Brett A; Poland, Simon P; Valentine, Gareth J; Girkin, John M

    2005-05-01

    We report on the introduction of active optical elements into confocal and multiphoton microscopes in order to reduce the sample-induced aberration. Using a flexible membrane mirror as the active element, the beam entering the rear of the microscope objective is altered to produce the smallest point spread function once it is brought to a focus inside the sample. The conventional approach to adaptive optics, commonly used in astronomy, is to utilise a wavefront sensor to determine the required mirror shape. We have developed a technique that uses optimisation algorithms to improve the returned signal without the use of a wavefront sensor. We have investigated a number of possible optimisation methods, covering hill climbing, genetic algorithms, and more random search methods. The system has demonstrated a significant enhancement in the axial resolution of a confocal microscope when imaging at depth within a sample. We discuss the trade-offs of the various approaches adopted, comparing speed with resolution enhancement.

  1. Eutectic pattern transition under different temperature gradients: A phase field study coupled with the parallel adaptive-mesh-refinement algorithm

    NASA Astrophysics Data System (ADS)

    Zhang, A.; Guo, Z.; Xiong, S.-M.

    2017-03-01

    Eutectic pattern transition under an externally imposed temperature gradient was studied using the phase field method coupled with a novel parallel adaptive-mesh-refinement (Para-AMR) algorithm. Numerical tests revealed that the Para-AMR algorithm could improve the computational efficiency by two orders of magnitude and thus made it possible to perform large-scale simulations without any compromising accuracy. Results showed that the direction of the temperature gradient played a crucial role in determining the eutectic patterns during solidification, which agreed well with experimental observations. In particular, the presence of the transverse temperature gradient could tilt the eutectic patterns, and in 3D simulations, the eutectic microstructure would alter from lamellar to rod-like and/or from rod-like to dumbbell-shaped. Furthermore, under a radial temperature gradient, the eutectic would evolve from a dumbbell-shaped or clover-shaped pattern to an isolated rod-like pattern.

  2. A new adaptive self-tuning Fourier coefficients algorithm for periodic torque ripple minimization in permanent magnet synchronous motors (PMSM).

    PubMed

    Gómez-Espinosa, Alfonso; Hernández-Guzmán, Víctor M; Bandala-Sánchez, Manuel; Jiménez-Hernández, Hugo; Rivas-Araiza, Edgar A; Rodríguez-Reséndiz, Juvenal; Herrera-Ruíz, Gilberto

    2013-03-19

    A New Adaptive Self-Tuning Fourier Coefficients Algorithm for Periodic Torque Ripple Minimization in Permanent Magnet Synchronous Motors (PMSM) Torque ripple occurs in Permanent Magnet Synchronous Motors (PMSMs) due to the non-sinusoidal flux density distribution around the air-gap and variable magnetic reluctance of the air-gap due to the stator slots distribution. These torque ripples change periodically with rotor position and are apparent as speed variations, which degrade the PMSM drive performance, particularly at low speeds, because of low inertial filtering. In this paper, a new self-tuning algorithm is developed for determining the Fourier Series Controller coefficients with the aim of reducing the torque ripple in a PMSM, thus allowing for a smoother operation. This algorithm adjusts the controller parameters based on the component's harmonic distortion in time domain of the compensation signal. Experimental evaluation is performed on a DSP-controlled PMSM evaluation platform. Test results obtained validate the effectiveness of the proposed self-tuning algorithm, with the Fourier series expansion scheme, in reducing the torque ripple.

  3. Adaptation of a cubic smoothing spline algorithm for multi-channel data stitching at the National Ignition Facility

    NASA Astrophysics Data System (ADS)

    Brown, Charles G., Jr.; Adcock, Aaron B.; Azevedo, Stephen G.; Liebman, Judith A.; Bond, Essex J.

    2011-03-01

    Some diagnostics at the National Ignition Facility (NIF), including the Gamma Reaction History (GRH) diagnostic, require multiple channels of data to achieve the required dynamic range. These channels need to be stitched together into a single time series, and they may have non-uniform and redundant time samples. We chose to apply the popular cubic smoothing spline technique to our stitching problem because we needed a general non-parametric method. We adapted one of the algorithms in the literature, by Hutchinson and deHoog, to our needs. The modified algorithm and the resulting code perform a cubic smoothing spline fit to multiple data channels with redundant time samples and missing data points. The data channels can have different, timevarying, zero-mean white noise characteristics. The method we employ automatically determines an optimal smoothing level by minimizing the Generalized Cross Validation (GCV) score. In order to automatically validate the smoothing level selection, the Weighted Sum-Squared Residual (WSSR) and zero-mean tests are performed on the residuals. Further, confidence intervals, both analytical and Monte Carlo, are also calculated. In this paper, we describe the derivation of our cubic smoothing spline algorithm. We outline the algorithm and test it with simulated and experimental data.

  4. Ultrasound phase rotation beamforming on multi-core DSP.

    PubMed

    Ma, Jieming; Karadayi, Kerem; Ali, Murtaza; Kim, Yongmin

    2014-01-01

    Phase rotation beamforming (PRBF) is a commonly-used digital receive beamforming technique. However, due to its high computational requirement, it has traditionally been supported by hardwired architectures, e.g., application-specific integrated circuits (ASICs) or more recently field-programmable gate arrays (FPGAs). In this study, we investigated the feasibility of supporting software-based PRBF on a multi-core DSP. To alleviate the high computing requirement, the analog front-end (AFE) chips integrating quadrature demodulation in addition to analog-to-digital conversion were defined and used. With these new AFE chips, only delay alignment and phase rotation need to be performed by DSP, substantially reducing the computational load. We implemented the delay alignment and phase rotation modules on a Texas Instruments C6678 DSP with 8 cores. We found it takes 200 μs to beamform 2048 samples from 64 channels using 2 cores. With 4 cores, 20 million samples can be beamformed in one second. Therefore, ADC frequencies up to 40 MHz with 2:1 decimation in AFE chips or up to 20 MHz with no decimation can be supported as long as the ADC-to-DSP I/O requirement can be met. The remaining 4 cores can work on back-end processing tasks and applications, e.g., color Doppler or ultrasound elastography. One DSP being able to handle both beamforming and back-end processing could lead to low-power and low-cost ultrasound machines, benefiting ultrasound imaging in general, particularly portable ultrasound machines.

  5. Adaptation.

    PubMed

    Broom, Donald M

    2006-01-01

    The term adaptation is used in biology in three different ways. It may refer to changes which occur at the cell and organ level, or at the individual level, or at the level of gene action and evolutionary processes. Adaptation by cells, especially nerve cells helps in: communication within the body, the distinguishing of stimuli, the avoidance of overload and the conservation of energy. The time course and complexity of these mechanisms varies. Adaptive characters of organisms, including adaptive behaviours, increase fitness so this adaptation is evolutionary. The major part of this paper concerns adaptation by individuals and its relationships to welfare. In complex animals, feed forward control is widely used. Individuals predict problems and adapt by acting before the environmental effect is substantial. Much of adaptation involves brain control and animals have a set of needs, located in the brain and acting largely via motivational mechanisms, to regulate life. Needs may be for resources but are also for actions and stimuli which are part of the mechanism which has evolved to obtain the resources. Hence pigs do not just need food but need to be able to carry out actions like rooting in earth or manipulating materials which are part of foraging behaviour. The welfare of an individual is its state as regards its attempts to cope with its environment. This state includes various adaptive mechanisms including feelings and those which cope with disease. The part of welfare which is concerned with coping with pathology is health. Disease, which implies some significant effect of pathology, always results in poor welfare. Welfare varies over a range from very good, when adaptation is effective and there are feelings of pleasure or contentment, to very poor. A key point concerning the concept of individual adaptation in relation to welfare is that welfare may be good or poor while adaptation is occurring. Some adaptation is very easy and energetically cheap and

  6. Adaptive implicit-explicit finite element algorithms for fluid mechanics problems

    NASA Technical Reports Server (NTRS)

    Tezduyar, T. E.; Liou, J.

    1988-01-01

    The adaptive implicit-explicit (AIE) approach is presented for the finite-element solution of various problems in computational fluid mechanics. In the AIE approach, the elements are dynamically (adaptively) arranged into differently treated groups. The differences in treatment could be based on considerations such as the cost efficiency, the type of spatial or temporal discretization employed, the choice of field equations, etc. Several numerical tests are performed to demonstrate that this approach can achieve substantial savings in CPU time and memory.

  7. MITRE Adaptive Processing Capability

    DTIC Science & Technology

    1994-06-01

    gathering, Funded Research and Development transfer, processing , and interpretation of Center (FFRDC) under the primary data are provided. A strong state-of...1988: Unisys Reston Technology Center, Reston, VA Dr. Bronez was a Member of the Technical Staff. He performed research on signal processing and... processing , mathematical research , and sensor array processing . He was Project Leader and Principal Investigator for projects in adaptive beamforming

  8. Novel adaptive playout algorithm for voice over IP applications and performance assessment over WANs

    NASA Astrophysics Data System (ADS)

    Hintoglu, Mustafa H.; Ergul, Faruk R.

    2001-07-01

    Special purpose hardware and application software have been developed to implement and test Voice over IP protocols. The hardware has interface units to which ISDN telephone sets can be connected. It has Ethernet and RS-232 interfaces for connections to LANs and controlling PCs. The software has modules which are specific to telephone operations and simulation activities. The simulator acts as a WAN environment, generating delays in delivering speech packets according to delay distribution specified. By using WAN simulator, different algorithms can be tested and their performances can be compared. The novel algorithm developed correlates silence periods with received voice packets and delays play out until confidence is established that a significant phrase or sentence is stored in the playout buffer. The performance of this approach has been found to be either superior or comparable to performances of existing algorithms tested. This new algorithm has the advantage that at least a complete phrase or sentence is played out, thereby increasing the intelligibility considerably. The penalty of having larger delays compared to published algorithms operating under bursty traffic conditions is compensated by higher quality of service offered. In the paper, details of developed system and obtained test results will be presented.

  9. An approximate waves-bordering algorithm for adaptive finite elements analysis

    NASA Astrophysics Data System (ADS)

    Morandi Cecchi, M.; Marcuzzi, F.

    1999-09-01

    In this paper an Approximate Waves-Bordering algorithm (AWB) is presented. It computes the finite elements linear system solution-update after a refinement/unrefinement step. This is done taking into consideration only the equations that correspond to the nodes whose solution is modified above a certain tolerance and it appears to be very efficient. The algorithm considers an increasing set of equations that updates recursively and stops when the norm of the residual has gone under a user-defined threshold.

  10. An implementable digital adaptive flight controller designed using stabilized single stage algorithms

    NASA Technical Reports Server (NTRS)

    Kaufman, H.; Alag, G.

    1975-01-01

    Simple mechanical linkages have not solved the many control problems associated with high performance aircraft maneuvering throughout a wide flight envelope. One procedure for retaining uniform handling qualities over such an envelope is to implement a digital adaptive controller. Towards such an implementation an explicit adaptive controller which makes direct use of on-line parameter identification, has been developed and applied to both linearized and nonlinear equations of motion for a typical fighter aircraft. This controller is composed of an on-line weighted least squares parameter identifier, a Kalman state filter, and a model following control law designed using single stage performance indices. Simulation experiments with realistic measurement noise indicate that the proposed adaptive system has the potential for on-board implementation.

  11. FASART: An iterative reconstruction algorithm with inter-iteration adaptive NAD filter.

    PubMed

    Zhou, Ziying; Li, Yugang; Zhang, Fa; Wan, Xiaohua

    2015-01-01

    Electron tomography (ET) is an essential imaging technique for studying structures of large biological specimens. These structures are reconstructed from a set of projections obtained at different sample orientations by tilting the specimen. However, most of existing reconstruction methods are not appropriate when the data are extremely noisy and incomplete. A new iterative method has been proposed: adaptive simultaneous algebraic reconstruction with inter-iteration adaptive non-linear anisotropic diffusion (NAD) filter (FASART). We also adopted an adaptive parameter and discussed the step for the filter in this reconstruction method. Experimental results show that FASART can restrain the noise generated in the process of iterative reconstruction and still preserve the more details of the structure edges.

  12. Autonomous atmospheric entry on mars: Performance improvement using a novel adaptive control algorithm

    NASA Astrophysics Data System (ADS)

    Ulrich, Steve; de Lafontaine, Jean

    2007-12-01

    Upcoming landing missions to Mars will require on-board guidance and control systems in order to meet the scientific requirement of landing safely within hundreds of meters to the target of interest. More specifically, in the longitudinal plane, the first objective of the entry guidance and control system is to bring the vehicle to its specified velocity at the specified altitude (as required for safe parachute deployment), while the second objective is to reach the target position in the longitudinal plane. This paper proposes an improvement to the robustness of the constant flight path angle guidance law for achieving the first objective. The improvement consists of combining this guidance law with a novel adaptive control scheme, derived from the so-called Simple Adaptive Control (SAC) technique. Monte-Carlo simulation results are shown to demonstrate the accuracy and the robustness of the proposed guidance and adaptive control system.

  13. Irradiation of the prostate and pelvic lymph nodes with an adaptive algorithm

    SciTech Connect

    Hwang, A. B.; Chen, J.; Nguyen, T. B.; Gottschalk, A. G.; Roach, M. R. III; Pouliot, J.

    2012-02-15

    Purpose: The simultaneous treatment of pelvic lymph nodes and the prostate in radiotherapy for prostate cancer is complicated by the independent motion of these two target volumes. In this work, the authors study a method to adapt intensity modulated radiation therapy (IMRT) treatment plans so as to compensate for this motion by adaptively morphing the multileaf collimator apertures and adjusting the segment weights. Methods: The study used CT images, tumor volumes, and normal tissue contours from patients treated in our institution. An IMRT treatment plan was then created using direct aperture optimization to deliver 45 Gy to the pelvic lymph nodes and 50 Gy to the prostate and seminal vesicles. The prostate target volume was then shifted in either the anterior-posterior direction or in the superior-inferior direction. The treatment plan was adapted by adjusting the aperture shapes with or without re-optimizing the segment weighting. The dose to the target volumes was then determined for the adapted plan. Results: Without compensation for prostate motion, 1 cm shifts of the prostate resulted in an average decrease of 14% in D-95%. If the isocenter is simply shifted to match the prostate motion, the prostate receives the correct dose but the pelvic lymph nodes are underdosed by 14% {+-} 6%. The use of adaptive morphing (with or without segment weight optimization) reduces the average change in D-95% to less than 5% for both the pelvic lymph nodes and the prostate. Conclusions: Adaptive morphing with and without segment weight optimization can be used to compensate for the independent motion of the prostate and lymph nodes when combined with daily imaging or other methods to track the prostate motion. This method allows the delivery of the correct dose to both the prostate and lymph nodes with only small changes to the dose delivered to the target volumes.

  14. Adaptive partitioning by local density-peaks: An efficient density-based clustering algorithm for analyzing molecular dynamics trajectories.

    PubMed

    Liu, Song; Zhu, Lizhe; Sheong, Fu Kit; Wang, Wei; Huang, Xuhui

    2017-01-30

    We present an efficient density-based adaptive-resolution clustering method APLoD for analyzing large-scale molecular dynamics (MD) trajectories. APLoD performs the k-nearest-neighbors search to estimate the density of MD conformations in a local fashion, which can group MD conformations in the same high-density region into a cluster. APLoD greatly improves the popular density peaks algorithm by reducing the running time and the memory usage by 2-3 orders of magnitude for systems ranging from alanine dipeptide to a 370-residue Maltose-binding protein. In addition, we demonstrate that APLoD can produce clusters with various sizes that are adaptive to the underlying density (i.e., larger clusters at low-density regions, while smaller clusters at high-density regions), which is a clear advantage over other popular clustering algorithms including k-centers and k-medoids. We anticipate that APLoD can be widely applied to split ultra-large MD datasets containing millions of conformations for subsequent construction of Markov State Models. © 2016 Wiley Periodicals, Inc.

  15. The application of Firefly algorithm in an Adaptive Emergency Evacuation Centre Management (AEECM) for dynamic relocation of flood victims

    NASA Astrophysics Data System (ADS)

    ChePa, Noraziah; Hashim, Nor Laily; Yusof, Yuhanis; Hussain, Azham

    2016-08-01

    Flood evacuation centre is defined as a temporary location or area of people from disaster particularly flood as a rescue or precautionary measure. Gazetted evacuation centres are normally located at secure places which have small chances from being drowned by flood. However, due to extreme flood several evacuation centres in Kelantan were unexpectedly drowned. Currently, there is no study done on proposing a decision support aid to reallocate victims and resources of the evacuation centre when the situation getting worsens. Therefore, this study proposes a decision aid model to be utilized in realizing an adaptive emergency evacuation centre management system. This study undergoes two main phases; development of algorithm and models, and development of a web-based and mobile app. The proposed model operates using Firefly multi-objective optimization algorithm that creates an optimal schedule for the relocation of victims and resources for an evacuation centre. The proposed decision aid model and the adaptive system can be applied in supporting the National Security Council's respond mechanisms for handling disaster management level II (State level) especially in providing better management of the flood evacuating centres.

  16. A fully-automatic locally adaptive thresholding algorithm for blood vessel segmentation in 3D digital subtraction angiography.

    PubMed

    Boegel, Marco; Hoelter, Philip; Redel, Thomas; Maier, Andreas; Hornegger, Joachim; Doerfler, Arnd

    2015-01-01

    Subarachnoid hemorrhage due to a ruptured cerebral aneurysm is still a devastating disease. Planning of endovascular aneurysm therapy is increasingly based on hemodynamic simulations necessitating reliable vessel segmentation and accurate assessment of vessel diameters. In this work, we propose a fully-automatic, locally adaptive, gradient-based thresholding algorithm. Our approach consists of two steps. First, we estimate the parameters of a global thresholding algorithm using an iterative process. Then, a locally adaptive version of the approach is applied using the estimated parameters. We evaluated both methods on 8 clinical 3D DSA cases. Additionally, we propose a way to select a reference segmentation based on 2D DSA measurements. For large vessels such as the internal carotid artery, our results show very high sensitivity (97.4%), precision (98.7%) and Dice-coefficient (98.0%) with our reference segmentation. Similar results (sensitivity: 95.7%, precision: 88.9% and Dice-coefficient: 90.7%) are achieved for smaller vessels of approximately 1mm diameter.

  17. A Fuzzy Genetic Algorithm Approach to an Adaptive Information Retrieval Agent.

    ERIC Educational Resources Information Center

    Martin-Bautista, Maria J.; Vila, Maria-Amparo; Larsen, Henrik Legind

    1999-01-01

    Presents an approach to a Genetic Information Retrieval Agent Filter (GIRAF) that filters and ranks documents retrieved from the Internet according to users' preferences by using a Genetic Algorithm and fuzzy set theory to handle the imprecision of users' preferences and users' evaluation of the retrieved documents. (Author/LRW)

  18. Path Tracking for Unmanned Ground Vehicle Navigation: Implementation and Adaptation of the Pure Pursuit Algorithm

    DTIC Science & Technology

    2005-12-01

    a user for a patrol mission. To increase the vehicle’s abilities, other behaviours such as obstacle avoidance, path planning or leader / follower augment...15 5.4 Leader / Follower Behaviour . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 5.5 Waypoint Following...Navigation Behaviour - Provide goal directedness in concert with an obstacle avoid- ance algorithm. 3. Leader / Follower - Allow a follower vehicle to

  19. Robustness of Adaptive Control Algorithms in the Presence of Unmodeled Dynamics,

    DTIC Science & Technology

    1982-09-01

    result, two possible noch - tt (t (3a) anisms of instability are isolated and discussed. It is argued, that the destabilizing effects in the presence L t [J...to Modeling Errors, Ph.D. Thesis, Dept. of Elec. Eng., Univ. of Illinois at 2. A. uer mad A.S. Norse, *Adaptive Control of Urbana -ahampaign, Report

  20. Controlling chaos in a defined trajectory using adaptive fuzzy logic algorithm

    NASA Astrophysics Data System (ADS)

    Sadeghi, Maryam; Menhaj, Bagher

    2012-09-01

    Chaos is a nonlinear behavior of chaotic system with the extreme sensitivity to the initial conditions. Chaos control is so complicated that solutions never converge to a specific numbers and vary chaotically from one amount to the other next. A tiny perturbation in a chaotic system may result in chaotic, periodic, or stationary behavior. Modern controllers are introduced for controlling the chaotic behavior. In this research an adaptive Fuzzy Logic Controller (AFLC) is proposed to control the chaotic system with two equilibrium points. This method is introduced as an adaptive progressed fashion with the full ability to control the nonlinear systems even in the undertrained conditions. Using AFLC designers are released to determine the precise mathematical model of system and satisfy the vast adaption that is needed for a rapid variation which may be caused in the dynamic of nonlinear system. Rules and system parameters are generated through the AFLC and expert knowledge is downright only in the initialization stage. So if the knowledge was not assuring the dynamic of system it could be changed through the adaption procedure of parameters values. AFLC methodology is an advanced control fashion in control yielding to both robustness and smooth motion in nonlinear system control.