Advanced Beamformers for Cochlear Implant Users: Acute Measurement of Speech Perception in Challenging Listening Conditions

PubMed Central

Buechner, Andreas; Dyballa, Karl-Heinz; Hehrmann, Phillipp; Fredelake, Stefan; Lenarz, Thomas

2014-01-01

Objective To investigate the performance of monaural and binaural beamforming technology with an additional noise reduction algorithm, in cochlear implant recipients. Method This experimental study was conducted as a single subject repeated measures design within a large German cochlear implant centre. Twelve experienced users of an Advanced Bionics HiRes90K or CII implant with a Harmony speech processor were enrolled. The cochlear implant processor of each subject was connected to one of two bilaterally placed state-of-the-art hearing aids (Phonak Ambra) providing three alternative directional processing options: an omnidirectional setting, an adaptive monaural beamformer, and a binaural beamformer. A further noise reduction algorithm (ClearVoice) was applied to the signal on the cochlear implant processor itself. The speech signal was presented from 0° and speech shaped noise presented from loudspeakers placed at ±70°, ±135° and 180°. The Oldenburg sentence test was used to determine the signal-to-noise ratio at which subjects scored 50% correct. Results Both the adaptive and binaural beamformer were significantly better than the omnidirectional condition (5.3 dB±1.2 dB and 7.1 dB±1.6 dB (p<0.001) respectively). The best score was achieved with the binaural beamformer in combination with the ClearVoice noise reduction algorithm, with a significant improvement in SRT of 7.9 dB±2.4 dB (p<0.001) over the omnidirectional alone condition. Conclusions The study showed that the binaural beamformer implemented in the Phonak Ambra hearing aid could be used in conjunction with a Harmony speech processor to produce substantial average improvements in SRT of 7.1 dB. The monaural, adaptive beamformer provided an averaged SRT improvement of 5.3 dB. PMID:24755864

Speech understanding in background noise with the two-microphone adaptive beamformer BEAM in the Nucleus Freedom Cochlear Implant System.

PubMed

Spriet, Ann; Van Deun, Lieselot; Eftaxiadis, Kyriaky; Laneau, Johan; Moonen, Marc; van Dijk, Bas; van Wieringen, Astrid; Wouters, Jan

2007-02-01

This paper evaluates the benefit of the two-microphone adaptive beamformer BEAM in the Nucleus Freedom cochlear implant (CI) system for speech understanding in background noise by CI users. A double-blind evaluation of the two-microphone adaptive beamformer BEAM and a hardware directional microphone was carried out with five adult Nucleus CI users. The test procedure consisted of a pre- and post-test in the lab and a 2-wk trial period at home. In the pre- and post-test, the speech reception threshold (SRT) with sentences and the percentage correct phoneme scores for CVC words were measured in quiet and background noise at different signal-to-noise ratios. Performance was assessed for two different noise configurations (with a single noise source and with three noise sources) and two different noise materials (stationary speech-weighted noise and multitalker babble). During the 2-wk trial period at home, the CI users evaluated the noise reduction performance in different listening conditions by means of the SSQ questionnaire. In addition to the perceptual evaluation, the noise reduction performance of the beamformer was measured physically as a function of the direction of the noise source. Significant improvements of both the SRT in noise (average improvement of 5-16 dB) and the percentage correct phoneme scores (average improvement of 10-41%) were observed with BEAM compared to the standard hardware directional microphone. In addition, the SSQ questionnaire and subjective evaluation in controlled and real-life scenarios suggested a possible preference for the beamformer in noisy environments. The evaluation demonstrates that the adaptive noise reduction algorithm BEAM in the Nucleus Freedom CI-system may significantly increase the speech perception by cochlear implantees in noisy listening conditions. This is the first monolateral (adaptive) noise reduction strategy actually implemented in a mainstream commercial CI.

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).

Robust Group Sparse Beamforming for Multicast Green Cloud-RAN With Imperfect CSI

NASA Astrophysics Data System (ADS)

Shi, Yuanming; Zhang, Jun; Letaief, Khaled B.

2015-09-01

In this paper, we investigate the network power minimization problem for the multicast cloud radio access network (Cloud-RAN) with imperfect channel state information (CSI). The key observation is that network power minimization can be achieved by adaptively selecting active remote radio heads (RRHs) via controlling the group-sparsity structure of the beamforming vector. However, this yields a non-convex combinatorial optimization problem, for which we propose a three-stage robust group sparse beamforming algorithm. In the first stage, a quadratic variational formulation of the weighted mixed l1/l2-norm is proposed to induce the group-sparsity structure in the aggregated beamforming vector, which indicates those RRHs that can be switched off. A perturbed alternating optimization algorithm is then proposed to solve the resultant non-convex group-sparsity inducing optimization problem by exploiting its convex substructures. In the second stage, we propose a PhaseLift technique based algorithm to solve the feasibility problem with a given active RRH set, which helps determine the active RRHs. Finally, the semidefinite relaxation (SDR) technique is adopted to determine the robust multicast beamformers. Simulation results will demonstrate the convergence of the perturbed alternating optimization algorithm, as well as, the effectiveness of the proposed algorithm to minimize the network power consumption for multicast Cloud-RAN.

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.

An Innovations-Based Noise Cancelling Technique on Inverse Kepstrum Whitening Filter and Adaptive FIR Filter in Beamforming Structure

PubMed Central

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. PMID:22163987

A comparison between temporal and subband minimum variance adaptive beamforming

NASA Astrophysics Data System (ADS)

Diamantis, Konstantinos; Voxen, Iben H.; Greenaway, Alan H.; Anderson, Tom; Jensen, Jørgen A.; Sboros, Vassilis

2014-03-01

This paper compares the performance between temporal and subband Minimum Variance (MV) beamformers for medical ultrasound imaging. Both adaptive methods provide an optimized set of apodization weights but are implemented in the time and frequency domains respectively. Their performance is evaluated with simulated synthetic aperture data obtained from Field II and is quantified by the Full-Width-Half-Maximum (FWHM), the Peak-Side-Lobe level (PSL) and the contrast level. From a point phantom, a full sequence of 128 emissions with one transducer element transmitting and all 128 elements receiving each time, provides a FWHM of 0.03 mm (0.14λ) for both implementations at a depth of 40 mm. This value is more than 20 times lower than the one achieved by conventional beamforming. The corresponding values of PSL are -58 dB and -63 dB for time and frequency domain MV beamformers, while a value no lower than -50 dB can be obtained from either Boxcar or Hanning weights. Interestingly, a single emission with central element #64 as the transmitting aperture provides results comparable to the full sequence. The values of FWHM are 0.04 mm and 0.03 mm and those of PSL are -42 dB and -46 dB for temporal and subband approaches. From a cyst phantom and for 128 emissions, the contrast level is calculated at -54 dB and -63 dB respectively at the same depth, with the initial shape of the cyst being preserved in contrast to conventional beamforming. The difference between the two adaptive beamformers is less significant in the case of a single emission, with the contrast level being estimated at -42 dB for the time domain and -43 dB for the frequency domain implementation. For the estimation of a single MV weight of a low resolution image formed by a single emission, 0.44 * 109 calculations per second are required for the temporal approach. The same numbers for the subband approach are 0.62 * 109 for the point and 1.33 * 109 for the cyst phantom. The comparison demonstrates similar

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.

Double-Stage Delay Multiply and Sum Beamforming Algorithm Applied to Ultrasound Medical Imaging.

PubMed

Mozaffarzadeh, Moein; Sadeghi, Masume; Mahloojifar, Ali; Orooji, Mahdi

2018-03-01

In ultrasound (US) imaging, delay and sum (DAS) is the most common beamformer, but it leads to low-quality images. Delay multiply and sum (DMAS) was introduced to address this problem. However, the reconstructed images using DMAS still suffer from the level of side lobes and low noise suppression. Here, a novel beamforming algorithm is introduced based on expansion of the DMAS formula. We found that there is a DAS algebra inside the expansion, and we proposed use of the DMAS instead of the DAS algebra. The introduced method, namely double-stage DMAS (DS-DMAS), is evaluated numerically and experimentally. The quantitative results indicate that DS-DMAS results in an approximately 25% lower level of side lobes compared with DMAS. Moreover, the introduced method leads to 23%, 22% and 43% improvement in signal-to-noise ratio, full width at half-maximum and contrast ratio, respectively, compared with the DMAS beamformer. Copyright © 2018. Published by Elsevier Inc.

Eigenspace-based minimum variance adaptive beamformer combined with delay multiply and sum: experimental study

NASA Astrophysics Data System (ADS)

Mozaffarzadeh, Moein; Mahloojifar, Ali; Nasiriavanaki, Mohammadreza; Orooji, Mahdi

2018-02-01

Delay and sum (DAS) is the most common beamforming algorithm in linear-array photoacoustic imaging (PAI) as a result of its simple implementation. However, it leads to a low resolution and high sidelobes. Delay multiply and sum (DMAS) was used to address the incapabilities of DAS, providing a higher image quality. However, the resolution improvement is not well enough compared to eigenspace-based minimum variance (EIBMV). In this paper, the EIBMV beamformer has been combined with DMAS algebra, called EIBMV-DMAS, using the expansion of DMAS algorithm. The proposed method is used as the reconstruction algorithm in linear-array PAI. EIBMV-DMAS is experimentally evaluated where the quantitative and qualitative results show that it outperforms DAS, DMAS and EIBMV. The proposed method degrades the sidelobes for about 365 %, 221 % and 40 %, compared to DAS, DMAS and EIBMV, respectively. Moreover, EIBMV-DMAS improves the SNR about 158 %, 63 % and 20 %, respectively.

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.

Smart Acoustic Network Using Combined FSK-PSK, Adaptive Beamforming and Equalization

DTIC Science & Technology

2002-09-30

sonar data transmission from underwater vehicle during mission. The two-year objectives for the high-reliability acoustic network using multiple... sonar laboratory) and used for acoustic networking during underwater vehicle operation. The joint adaptive coherent path beamformer method consists...broadband communications transducer, while the low noise preamplifier conditions received signals for analog to digital conversion. External user

Smart Acoustic Network Using Combined FSK-PSK, Adaptive, Beamforming and Equalization

DTIC Science & Technology

2001-09-30

sonar data transmission from underwater vehicle during mission. The two-year objectives for the high-reliability acoustic network using multiple... sonar laboratory) and used for acoustic networking during underwater vehicle operation. The joint adaptive coherent path beamformer method consists...broadband communications transducer, while the low noise preamplifier conditions received signals for analog to digital conversion. External user

A Covariance Modeling Approach to Adaptive Beamforming and Detection

DTIC Science & Technology

1991-07-30

to achieve the main results of this report. I would especially like to thank Dr. E. J. Kelly for the support he has given me during the past years . His...direction of propagation A,, 0 S, Figure 4. Plane wace propagating through array. The array steering vector d(, E) is d~w d d2 ... dN]T (10) with...the covariance matrix to form a matched-filter beamformer that adapts to the interference environment. This was one of the first papers to propose using

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.

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. Copyright © 2015 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

Double-Stage Delay Multiply and Sum Beamforming Algorithm: Application to Linear-Array Photoacoustic Imaging.

PubMed

Mozaffarzadeh, Moein; Mahloojifar, Ali; Orooji, Mahdi; Adabi, Saba; Nasiriavanaki, Mohammadreza

2018-01-01

Photoacoustic imaging (PAI) is an emerging medical imaging modality capable of providing high spatial resolution of Ultrasound (US) imaging and high contrast of optical imaging. Delay-and-Sum (DAS) is the most common beamforming algorithm in PAI. However, using DAS beamformer leads to low resolution images and considerable contribution of off-axis signals. A new paradigm namely delay-multiply-and-sum (DMAS), which was originally used as a reconstruction algorithm in confocal microwave imaging, was introduced to overcome the challenges in DAS. DMAS was used in PAI systems and it was shown that this algorithm results in resolution improvement and sidelobe degrading. However, DMAS is still sensitive to high levels of noise, and resolution improvement is not satisfying. Here, we propose a novel algorithm based on DAS algebra inside DMAS formula expansion, double stage DMAS (DS-DMAS), which improves the image resolution and levels of sidelobe, and is much less sensitive to high level of noise compared to DMAS. The performance of DS-DMAS algorithm is evaluated numerically and experimentally. The resulted images are evaluated qualitatively and quantitatively using established quality metrics including signal-to-noise ratio (SNR), full-width-half-maximum (FWHM) and contrast ratio (CR). It is shown that DS-DMAS outperforms DAS and DMAS at the expense of higher computational load. DS-DMAS reduces the lateral valley for about 15 dB and improves the SNR and FWHM better than 13% and 30%, respectively. Moreover, the levels of sidelobe are reduced for about 10 dB in comparison with those in DMAS.

Evolutionary Beamforming Optimization for Radio Frequency Charging in Wireless Rechargeable Sensor Networks

PubMed Central

Yao, Ke-Han; Jiang, Jehn-Ruey; Tsai, Chung-Hsien; Wu, Zong-Syun

2017-01-01

This paper investigates how to efficiently charge sensor nodes in a wireless rechargeable sensor network (WRSN) with radio frequency (RF) chargers to make the network sustainable. An RF charger is assumed to be equipped with a uniform circular array (UCA) of 12 antennas with the radius λ, where λ is the RF wavelength. The UCA can steer most RF energy in a target direction to charge a specific WRSN node by the beamforming technology. Two evolutionary algorithms (EAs) using the evolution strategy (ES), namely the Evolutionary Beamforming Optimization (EBO) algorithm and the Evolutionary Beamforming Optimization Reseeding (EBO-R) algorithm, are proposed to nearly optimize the power ratio of the UCA beamforming peak side lobe (PSL) and the main lobe (ML) aimed at the given target direction. The proposed algorithms are simulated for performance evaluation and are compared with a related algorithm, called Particle Swarm Optimization Gravitational Search Algorithm-Explore (PSOGSA-Explore), to show their superiority. PMID:28825648

Evolutionary Beamforming Optimization for Radio Frequency Charging in Wireless Rechargeable Sensor Networks.

PubMed

Yao, Ke-Han; Jiang, Jehn-Ruey; Tsai, Chung-Hsien; Wu, Zong-Syun

2017-08-20

This paper investigates how to efficiently charge sensor nodes in a wireless rechargeable sensor network (WRSN) with radio frequency (RF) chargers to make the network sustainable. An RF charger is assumed to be equipped with a uniform circular array (UCA) of 12 antennas with the radius λ , where λ is the RF wavelength. The UCA can steer most RF energy in a target direction to charge a specific WRSN node by the beamforming technology. Two evolutionary algorithms (EAs) using the evolution strategy (ES), namely the Evolutionary Beamforming Optimization (EBO) algorithm and the Evolutionary Beamforming Optimization Reseeding (EBO-R) algorithm, are proposed to nearly optimize the power ratio of the UCA beamforming peak side lobe (PSL) and the main lobe (ML) aimed at the given target direction. The proposed algorithms are simulated for performance evaluation and are compared with a related algorithm, called Particle Swarm Optimization Gravitational Search Algorithm-Explore (PSOGSA-Explore), to show their superiority.

Implementation of LSCMA adaptive array terminal for mobile satellite communications

NASA Astrophysics Data System (ADS)

Zhou, Shun; Wang, Huali; Xu, Zhijun

2007-11-01

This paper considers the application of adaptive array antenna based on the least squares constant modulus algorithm (LSCMA) for interference rejection in mobile SATCOM terminals. A two-element adaptive array scheme is implemented with a combination of ADI TS201S DSP chips and Altera Stratix II FPGA device, which makes a cooperating computation for adaptive beamforming. Its interference suppressing performance is verified via Matlab simulations. Digital hardware system is implemented to execute the operations of LSCMA beamforming algorithm that is represented by an algorithm flowchart. The result of simulations and test indicate that this scheme can improve the anti-jamming performance of terminals.

Speech Understanding in Noise by Patients with Cochlear Implants Using a Monaural Adaptive Beamformer

ERIC Educational Resources Information Center

Dorman, Michael F.; Natale, Sarah; Spahr, Anthony; Castioni, Erin

2017-01-01

Purpose: The aim of this experiment was to compare, for patients with cochlear implants (CIs), the improvement for speech understanding in noise provided by a monaural adaptive beamformer and for two interventions that produced bilateral input (i.e., bilateral CIs and hearing preservation [HP] surgery). Method: Speech understanding scores for…

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

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

An Improved Adaptive Received Beamforming for Nested Frequency Offset and Nested Array FDA-MIMO Radar.

PubMed

Cheng, Sibei; Zhang, Qingjun; Bian, Mingming; Hao, Xinhong

2018-02-08

For the conventional FDA-MIMO (frequency diversity array multiple-input-multiple-output) Radar with uniform frequency offset and uniform linear array, the DOFs (degrees of freedom) of the adaptive beamformer are limited by the number of elements. A better performance-for example, a better suppression for strong interferences and a more desirable trade-off between the main lobe and side lobe-can be achieved with a greater number of DOFs. In order to obtain larger DOFs, this paper researches the signal model of the FDA-MIMO Radar with nested frequency offset and nested array, then proposes an improved adaptive beamforming method that uses the augmented matrix instead of the covariance matrix to calculate the optimum weight vectors and can be used to improve the output performances of FDA-MIMO Radar with the same element number or reduce the element number while maintain the approximate output performances such as the received beampattern, the main lobe width, side lobe depths and the output SINR (signal-to-interference-noise ratio). The effectiveness of the proposed scheme is verified by simulations.

An Improved Adaptive Received Beamforming for Nested Frequency Offset and Nested Array FDA-MIMO Radar

PubMed Central

Cheng, Sibei; Zhang, Qingjun; Bian, Mingming; Hao, Xinhong

2018-01-01

For the conventional FDA-MIMO (frequency diversity array multiple-input-multiple-output) Radar with uniform frequency offset and uniform linear array, the DOFs (degrees of freedom) of the adaptive beamformer are limited by the number of elements. A better performance—for example, a better suppression for strong interferences and a more desirable trade-off between the main lobe and side lobe—can be achieved with a greater number of DOFs. In order to obtain larger DOFs, this paper researches the signal model of the FDA-MIMO Radar with nested frequency offset and nested array, then proposes an improved adaptive beamforming method that uses the augmented matrix instead of the covariance matrix to calculate the optimum weight vectors and can be used to improve the output performances of FDA-MIMO Radar with the same element number or reduce the element number while maintain the approximate output performances such as the received beampattern, the main lobe width, side lobe depths and the output SINR (signal-to-interference-noise ratio). The effectiveness of the proposed scheme is verified by simulations. PMID:29419814

Frequency-domain beamformers using conjugate gradient techniques for speech enhancement.

PubMed

Zhao, Shengkui; Jones, Douglas L; Khoo, Suiyang; Man, Zhihong

2014-09-01

A multiple-iteration constrained conjugate gradient (MICCG) algorithm and a single-iteration constrained conjugate gradient (SICCG) algorithm are proposed to realize the widely used frequency-domain minimum-variance-distortionless-response (MVDR) beamformers and the resulting algorithms are applied to speech enhancement. The algorithms are derived based on the Lagrange method and the conjugate gradient techniques. The implementations of the algorithms avoid any form of explicit or implicit autocorrelation matrix inversion. Theoretical analysis establishes formal convergence of the algorithms. Specifically, the MICCG algorithm is developed based on a block adaptation approach and it generates a finite sequence of estimates that converge to the MVDR solution. For limited data records, the estimates of the MICCG algorithm are better than the conventional estimators and equivalent to the auxiliary vector algorithms. The SICCG algorithm is developed based on a continuous adaptation approach with a sample-by-sample updating procedure and the estimates asymptotically converge to the MVDR solution. An illustrative example using synthetic data from a uniform linear array is studied and an evaluation on real data recorded by an acoustic vector sensor array is demonstrated. Performance of the MICCG algorithm and the SICCG algorithm are compared with the state-of-the-art approaches.

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.

Adaptive beamforming in a CDMA mobile satellite communications system

NASA Astrophysics Data System (ADS)

Munoz-Garcia, Samuel G.

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.

Cosine beamforming

NASA Astrophysics Data System (ADS)

Ruigrok, Elmer; Wapenaar, Kees

2014-05-01

In various application areas, e.g., seismology, astronomy and geodesy, arrays of sensors are used to characterize incoming wavefields due to distant sources. Beamforming is a general term for phased-adjusted summations over the different array elements, for untangling the directionality and elevation angle of the incoming waves. For characterizing noise sources, beamforming is conventionally applied with a temporal Fourier and a 2D spatial Fourier transform, possibly with additional weights. These transforms become aliased for higher frequencies and sparser array-element distributions. As a partial remedy, we derive a kernel for beamforming crosscorrelated data and call it cosine beamforming (CBF). By applying beamforming not directly to the data, but to crosscorrelated data, the sampling is effectively increased. We show that CBF, due to this better sampling, suffers less from aliasing and yields higher resolution than conventional beamforming. As a flip-side of the coin, the CBF output shows more smearing for spherical waves than conventional beamforming.

Pulse-Inversion Subharmonic Ultrafast Active Cavitation Imaging in Tissue Using Fast Eigenspace-Based Adaptive Beamforming and Cavitation Deconvolution.

PubMed

Bai, Chen; Xu, Shanshan; Duan, Junbo; Jing, Bowen; Yang, Miao; Wan, Mingxi

2017-08-01

Pulse-inversion subharmonic (PISH) imaging can display information relating to pure cavitation bubbles while excluding that of tissue. Although plane-wave-based ultrafast active cavitation imaging (UACI) can monitor the transient activities of cavitation bubbles, its resolution and cavitation-to-tissue ratio (CTR) are barely satisfactory but can be significantly improved by introducing eigenspace-based (ESB) adaptive beamforming. PISH and UACI are a natural combination for imaging of pure cavitation activity in tissue; however, it raises two problems: 1) the ESB beamforming is hard to implement in real time due to the enormous amount of computation associated with the covariance matrix inversion and eigendecomposition and 2) the narrowband characteristic of the subharmonic filter will incur a drastic degradation in resolution. Thus, in order to jointly address these two problems, we propose a new PISH-UACI method using novel fast ESB (F-ESB) beamforming and cavitation deconvolution for nonlinear signals. This method greatly reduces the computational complexity by using F-ESB beamforming through dimensionality reduction based on principal component analysis, while maintaining the high quality of ESB beamforming. The degraded resolution is recovered using cavitation deconvolution through a modified convolution model and compressive deconvolution. Both simulations and in vitro experiments were performed to verify the effectiveness of the proposed method. Compared with the ESB-based PISH-UACI, the entire computation of our proposed approach was reduced by 99%, while the axial resolution gain and CTR were increased by 3 times and 2 dB, respectively, confirming that satisfactory performance can be obtained for monitoring pure cavitation bubbles in tissue erosion.

Uplink transmit beamforming design for SINR maximization with full multiuser channel state information

NASA Astrophysics Data System (ADS)

Xi, Songnan; Zoltowski, Michael D.

2008-04-01

Multiuser multiple-input multiple-output (MIMO) systems are considered in this paper. We continue our research on uplink transmit beamforming design for multiple users under the assumption that the full multiuser channel state information, which is the collection of the channel state information between each of the users and the base station, is known not only to the receiver but also to all the transmitters. We propose an algorithm for designing optimal beamforming weights in terms of maximizing the signal-to-interference-plus-noise ratio (SINR). Through statistical modeling, we decouple the original mathematically intractable optimization problem and achieved a closed-form solution. As in our previous work, the minimum mean-squared error (MMSE) receiver with successive interference cancellation (SIC) is adopted for multiuser detection. The proposed scheme is compared with an existing jointly optimized transceiver design, referred to as the joint transceiver in this paper, and our previously proposed eigen-beamforming algorithm. Simulation results demonstrate that our algorithm, with much less computational burden, accomplishes almost the same performance as the joint transceiver for spatially independent MIMO channel and even better performance for spatially correlated MIMO channels. And it always works better than our previously proposed eigen beamforming algorithm.

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.

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

Enhanced linear-array photoacoustic beamforming using modified coherence factor.

PubMed

Mozaffarzadeh, Moein; Yan, Yan; Mehrmohammadi, Mohammad; Makkiabadi, Bahador

2018-02-01

Photoacoustic imaging (PAI) is a promising medical imaging modality providing the spatial resolution of ultrasound imaging and the contrast of optical imaging. For linear-array PAI, a beamformer can be used as the reconstruction algorithm. Delay-and-sum (DAS) is the most prevalent beamforming algorithm in PAI. However, using DAS beamformer leads to low-resolution images as well as high sidelobes due to nondesired contribution of off-axis signals. Coherence factor (CF) is a weighting method in which each pixel of the reconstructed image is weighted, based on the spatial spectrum of the aperture, to mainly improve the contrast. We demonstrate that the numerator of the formula of CF contains a DAS algebra and propose the use of a delay-multiply-and-sum beamformer instead of the available DAS on the numerator. The proposed weighting technique, modified CF (MCF), has been evaluated numerically and experimentally compared to CF. It was shown that MCF leads to lower sidelobes and better detectable targets. The quantitative results of the experiment (using wire targets) show that MCF leads to for about 45% and 40% improvement, in comparison with CF, in the terms of signal-to-noise ratio and full-width-half-maximum, respectively. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

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. Copyright © 2014 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

Acoustic emission beamforming for enhanced damage detection

NASA Astrophysics Data System (ADS)

McLaskey, Gregory C.; Glaser, Steven D.; Grosse, Christian U.

2008-03-01

As civil infrastructure ages, the early detection of damage in a structure becomes increasingly important for both life safety and economic reasons. This paper describes the analysis procedures used for beamforming acoustic emission techniques as well as the promising results of preliminary experimental tests on a concrete bridge deck. The method of acoustic emission offers a tool for detecting damage, such as cracking, as it occurs on or in a structure. In order to gain meaningful information from acoustic emission analyses, the damage must be localized. Current acoustic emission systems with localization capabilities are very costly and difficult to install. Sensors must be placed throughout the structure to ensure that the damage is encompassed by the array. Beamforming offers a promising solution to these problems and permits the use of wireless sensor networks for acoustic emission analyses. Using the beamforming technique, the azmuthal direction of the location of the damage may be estimated by the stress waves impinging upon a small diameter array (e.g. 30mm) of acoustic emission sensors. Additional signal discrimination may be gained via array processing techniques such as the VESPA process. The beamforming approach requires no arrival time information and is based on very simple delay and sum beamforming algorithms which can be easily implemented on a wireless sensor or mote.

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

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).

An adaptive beamforming method for ultrasound imaging based on the mean-to-standard-deviation factor.

PubMed

Wang, Yuanguo; Zheng, Chichao; Peng, Hu; Chen, Qiang

2018-06-12

The beamforming performance has a large impact on image quality in ultrasound imaging. Previously, several adaptive weighting factors including coherence factor (CF) and generalized coherence factor (GCF) have been proposed to improved image resolution and contrast. In this paper, we propose a new adaptive weighting factor for ultrasound imaging, which is called signal mean-to-standard-deviation factor (SMSF). SMSF is defined as the mean-to-standard-deviation of the aperture data and is used to weight the output of delay-and-sum (DAS) beamformer before image formation. Moreover, we develop a robust SMSF (RSMSF) by extending the SMSF to the spatial frequency domain using an altered spectrum of the aperture data. In addition, a square neighborhood average is applied on the RSMSF to offer a more smoothed square neighborhood RSMSF (SN-RSMSF) value. We compared our methods with DAS, CF, and GCF using simulated and experimental synthetic aperture data sets. The quantitative results show that SMSF results in an 82% lower full width at half-maximum (FWHM) but a 12% lower contrast ratio (CR) compared with CF. Moreover, the SN-RSMSF leads to 15% and 10% improvement, on average, in FWHM and CR compared with GCF while maintaining the speckle quality. This demonstrates that the proposed methods can effectively improve the image resolution and contrast. Copyright © 2018 Elsevier B.V. All rights reserved.

Smart Antenna UKM Testbed for Digital Beamforming System

NASA Astrophysics Data System (ADS)

Islam, Mohammad Tariqul; Misran, Norbahiah; Yatim, Baharudin

2009-12-01

A new design of smart antenna testbed developed at UKM for digital beamforming purpose is proposed. The smart antenna UKM testbed developed based on modular design employing two novel designs of L-probe fed inverted hybrid E-H (LIEH) array antenna and software reconfigurable digital beamforming system (DBS). The antenna is developed based on using the novel LIEH microstrip patch element design arranged into [InlineEquation not available: see fulltext.] uniform linear array antenna. An interface board is designed to interface to the ADC board with the RF front-end receiver. The modular concept of the system provides the capability to test the antenna hardware, beamforming unit, and beamforming algorithm in an independent manner, thus allowing the smart antenna system to be developed and tested in parallel, hence reduces the design time. The DBS was developed using a high-performance [InlineEquation not available: see fulltext.] floating-point DSP board and a 4-channel RF front-end receiver developed in-house. An interface board is designed to interface to the ADC board with the RF front-end receiver. A four-element receiving array testbed at 1.88-2.22 GHz frequency is constructed, and digital beamforming on this testbed is successfully demonstrated.

Scheduling Algorithms for Maximizing Throughput with Zero-Forcing Beamforming in a MIMO Wireless System

NASA Astrophysics Data System (ADS)

Foronda, Augusto; Ohta, Chikara; Tamaki, Hisashi

Dirty paper coding (DPC) is a strategy to achieve the region capacity of multiple input multiple output (MIMO) downlink channels and a DPC scheduler is throughput optimal if users are selected according to their queue states and current rates. However, DPC is difficult to implement in practical systems. One solution, zero-forcing beamforming (ZFBF) strategy has been proposed to achieve the same asymptotic sum rate capacity as that of DPC with an exhaustive search over the entire user set. Some suboptimal user group selection schedulers with reduced complexity based on ZFBF strategy (ZFBF-SUS) and proportional fair (PF) scheduling algorithm (PF-ZFBF) have also been proposed to enhance the throughput and fairness among the users, respectively. However, they are not throughput optimal, fairness and throughput decrease if each user queue length is different due to different users channel quality. Therefore, we propose two different scheduling algorithms: a throughput optimal scheduling algorithm (ZFBF-TO) and a reduced complexity scheduling algorithm (ZFBF-RC). Both are based on ZFBF strategy and, at every time slot, the scheduling algorithms have to select some users based on user channel quality, user queue length and orthogonality among users. Moreover, the proposed algorithms have to produce the rate allocation and power allocation for the selected users based on a modified water filling method. We analyze the schedulers complexity and numerical results show that ZFBF-RC provides throughput and fairness improvements compared to the ZFBF-SUS and PF-ZFBF scheduling algorithms.

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.

Two-dimensional grid-free compressive beamforming.

PubMed

Yang, Yang; Chu, Zhigang; Xu, Zhongming; Ping, Guoli

2017-08-01

Compressive beamforming realizes the direction-of-arrival (DOA) estimation and strength quantification of acoustic sources by solving an underdetermined system of equations relating microphone pressures to a source distribution via compressive sensing. The conventional method assumes DOAs of sources to lie on a grid. Its performance degrades due to basis mismatch when the assumption is not satisfied. To overcome this limitation for the measurement with plane microphone arrays, a two-dimensional grid-free compressive beamforming is developed. First, a continuum based atomic norm minimization is defined to denoise the measured pressure and thus obtain the pressure from sources. Next, a positive semidefinite programming is formulated to approximate the atomic norm minimization. Subsequently, a reasonably fast algorithm based on alternating direction method of multipliers is presented to solve the positive semidefinite programming. Finally, the matrix enhancement and matrix pencil method is introduced to process the obtained pressure and reconstruct the source distribution. Both simulations and experiments demonstrate that under certain conditions, the grid-free compressive beamforming can provide high-resolution and low-contamination imaging, allowing accurate and fast estimation of two-dimensional DOAs and quantification of source strengths, even with non-uniform arrays and noisy measurements.

Optimized Hyper Beamforming of Linear Antenna Arrays Using Collective Animal Behaviour

PubMed Central

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. PMID:23970843

Spatiotemporal Beamforming: A Transparent and Unified Decoding Approach to Synchronous Visual Brain-Computer Interfacing.

PubMed

Wittevrongel, Benjamin; Van Hulle, Marc M

2017-01-01

Brain-Computer Interfaces (BCIs) decode brain activity with the aim to establish a direct communication channel with an external device. Albeit they have been hailed to (re-)establish communication in persons suffering from severe motor- and/or communication disabilities, only recently BCI applications have been challenging other assistive technologies. Owing to their considerably increased performance and the advent of affordable technological solutions, BCI technology is expected to trigger a paradigm shift not only in assistive technology but also in the way we will interface with technology. However, the flipside of the quest for accuracy and speed is most evident in EEG-based visual BCI where it has led to a gamut of increasingly complex classifiers, tailored to the needs of specific stimulation paradigms and use contexts. In this contribution, we argue that spatiotemporal beamforming can serve several synchronous visual BCI paradigms. We demonstrate this for three popular visual paradigms even without attempting to optimizing their electrode sets. For each selectable target, a spatiotemporal beamformer is applied to assess whether the corresponding signal-of-interest is present in the preprocessed multichannel EEG signals. The target with the highest beamformer output is then selected by the decoder (maximum selection). In addition to this simple selection rule, we also investigated whether interactions between beamformer outputs could be employed to increase accuracy by combining the outputs for all targets into a feature vector and applying three common classification algorithms. The results show that the accuracy of spatiotemporal beamforming with maximum selection is at par with that of the classification algorithms and interactions between beamformer outputs do not further improve that accuracy.

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.

Adaptive cockroach swarm algorithm

NASA Astrophysics Data System (ADS)

Obagbuwa, Ibidun C.; Abidoye, Ademola P.

2017-07-01

An adaptive cockroach swarm optimization (ACSO) algorithm is proposed in this paper to strengthen the existing cockroach swarm optimization (CSO) algorithm. The ruthless component of CSO algorithm is modified by the employment of blend crossover predator-prey evolution method which helps algorithm prevent any possible population collapse, maintain population diversity and create adaptive search in each iteration. The performance of the proposed algorithm on 16 global optimization benchmark function problems was evaluated and compared with the existing CSO, cuckoo search, differential evolution, particle swarm optimization and artificial bee colony algorithms.

Minimum Variance Distortionless Response Beamformer with Enhanced Nulling Level Control via Dynamic Mutated Artificial Immune System

PubMed Central

Kiong, Tiong Sieh; Salem, S. Balasem; Paw, Johnny Koh Siaw; Sankar, K. Prajindra

2014-01-01

In smart antenna applications, the adaptive beamforming technique is used to cancel interfering signals (placing nulls) and produce or steer a strong beam toward the target signal according to the calculated weight vectors. Minimum variance distortionless response (MVDR) beamforming is capable of determining the weight vectors for beam steering; however, its nulling level on the interference sources remains unsatisfactory. Beamforming can be considered as an optimization problem, such that optimal weight vector should be obtained through computation. Hence, in this paper, a new dynamic mutated artificial immune system (DM-AIS) is proposed to enhance MVDR beamforming for controlling the null steering of interference and increase the signal to interference noise ratio (SINR) for wanted signals. PMID:25003136

Minimum variance distortionless response beamformer with enhanced nulling level control via dynamic mutated artificial immune system.

PubMed

Kiong, Tiong Sieh; Salem, S Balasem; Paw, Johnny Koh Siaw; Sankar, K Prajindra; Darzi, Soodabeh

2014-01-01

In smart antenna applications, the adaptive beamforming technique is used to cancel interfering signals (placing nulls) and produce or steer a strong beam toward the target signal according to the calculated weight vectors. Minimum variance distortionless response (MVDR) beamforming is capable of determining the weight vectors for beam steering; however, its nulling level on the interference sources remains unsatisfactory. Beamforming can be considered as an optimization problem, such that optimal weight vector should be obtained through computation. Hence, in this paper, a new dynamic mutated artificial immune system (DM-AIS) is proposed to enhance MVDR beamforming for controlling the null steering of interference and increase the signal to interference noise ratio (SINR) for wanted signals.

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.

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.

Highly Reconfigurable Beamformer Stimulus Generator

NASA Astrophysics Data System (ADS)

Vaviļina, E.; Gaigals, G.

2018-02-01

The present paper proposes a highly reconfigurable beamformer stimulus generator of radar antenna array, which includes three main blocks: settings of antenna array, settings of objects (signal sources) and a beamforming simulator. Following from the configuration of antenna array and object settings, different stimulus can be generated as the input signal for a beamformer. This stimulus generator is developed under a greater concept with two utterly independent paths where one is the stimulus generator and the other is the hardware beamformer. Both paths can be complemented in final and in intermediate steps as well to check and improve system performance. This way the technology development process is promoted by making each of the future hardware steps more substantive. Stimulus generator configuration capabilities and test results are presented proving the application of the stimulus generator for FPGA based beamforming unit development and tuning as an alternative to an actual antenna system.

Novel application of windowed beamforming function imaging for FLGPR

NASA Astrophysics Data System (ADS)

Xique, Ismael J.; Burns, Joseph W.; Thelen, Brian J.; LaRose, Ryan M.

2018-04-01

Backprojection of cross-correlated array data, using algorithms such as coherent interferometric imaging (Borcea, et al., 2006), has been advanced as a method to improve the statistical stability of images of targets in an inhomogeneous medium. Recently, the Windowed Beamforming Energy (WBE) function algorithm has been introduced as a functionally equivalent approach, which is significantly less computationally burdensome (Borcea, et al., 2011). WBE produces similar results through the use of a quadratic function summing signals after beamforming in transmission and reception, and windowing in the time domain. We investigate the application of WBE to improve the detection of buried targets with forward looking ground penetrating MIMO radar (FLGPR) data. The formulation of WBE as well the software implementation of WBE for the FLGPR data collection will be discussed. WBE imaging results are compared to standard backprojection and Coherence Factor imaging. Additionally, the effectiveness of WBE on field-collected data is demonstrated qualitatively through images and quantitatively through the use of a CFAR statistic on buried targets of a variety of contrast levels.

Multiline 3D beamforming using micro-beamformed datasets for pediatric transesophageal echocardiography

NASA Astrophysics Data System (ADS)

Bera, D.; Raghunathan, S. B.; Chen, C.; Chen, Z.; Pertijs, M. A. P.; Verweij, M. D.; Daeichin, V.; Vos, H. J.; van der Steen, A. F. W.; de Jong, N.; Bosch, J. G.

2018-04-01

Until now, no matrix transducer has been realized for 3D transesophageal echocardiography (TEE) in pediatric patients. In 3D TEE with a matrix transducer, the biggest challenges are to connect a large number of elements to a standard ultrasound system, and to achieve a high volume rate (>200 Hz). To address these issues, we have recently developed a prototype miniaturized matrix transducer for pediatric patients with micro-beamforming and a small central transmitter. In this paper we propose two multiline parallel 3D beamforming techniques (µBF25 and µBF169) using the micro-beamformed datasets from 25 and 169 transmit events to achieve volume rates of 300 Hz and 44 Hz, respectively. Both the realizations use angle-weighted combination of the neighboring overlapping sub-volumes to avoid artifacts due to sharp intensity changes introduced by parallel beamforming. In simulation, the image quality in terms of the width of the point spread function (PSF), lateral shift invariance and mean clutter level for volumes produced by µBF25 and µBF169 are similar to the idealized beamforming using a conventional single-line acquisition with a fully-sampled matrix transducer (FS4k, 4225 transmit events). For completeness, we also investigated a 9 transmit-scheme (3  ×  3) that allows even higher frame rates but found worse B-mode image quality with our probe. The simulations were experimentally verified by acquiring the µBF datasets from the prototype using a Verasonics V1 research ultrasound system. For both µBF169 and µBF25, the experimental PSFs were similar to the simulated PSFs, but in the experimental PSFs, the clutter level was ~10 dB higher. Results indicate that the proposed multiline 3D beamforming techniques with the prototype matrix transducer are promising candidates for real-time pediatric 3D TEE.

Statistical efficiency of adaptive algorithms.

PubMed

Widrow, Bernard; Kamenetsky, Max

2003-01-01

The statistical efficiency of a learning algorithm applied to the adaptation of a given set of variable weights is defined as the ratio of the quality of the converged solution to the amount of data used in training the weights. Statistical efficiency is computed by averaging over an ensemble of learning experiences. A high quality solution is very close to optimal, while a low quality solution corresponds to noisy weights and less than optimal performance. In this work, two gradient descent adaptive algorithms are compared, the LMS algorithm and the LMS/Newton algorithm. LMS is simple and practical, and is used in many applications worldwide. LMS/Newton is based on Newton's method and the LMS algorithm. LMS/Newton is optimal in the least squares sense. It maximizes the quality of its adaptive solution while minimizing the use of training data. Many least squares adaptive algorithms have been devised over the years, but no other least squares algorithm can give better performance, on average, than LMS/Newton. LMS is easily implemented, but LMS/Newton, although of great mathematical interest, cannot be implemented in most practical applications. Because of its optimality, LMS/Newton serves as a benchmark for all least squares adaptive algorithms. The performances of LMS and LMS/Newton are compared, and it is found that under many circumstances, both algorithms provide equal performance. For example, when both algorithms are tested with statistically nonstationary input signals, their average performances are equal. When adapting with stationary input signals and with random initial conditions, their respective learning times are on average equal. However, under worst-case initial conditions, the learning time of LMS can be much greater than that of LMS/Newton, and this is the principal disadvantage of the LMS algorithm. But the strong points of LMS are ease of implementation and optimal performance under important practical conditions. For these reasons, the LMS

QPSO-Based Adaptive DNA Computing Algorithm

PubMed Central

Karakose, Mehmet; Cigdem, Ugur

2013-01-01

DNA (deoxyribonucleic acid) computing that is a new computation model based on DNA molecules for information storage has been increasingly used for optimization and data analysis in recent years. However, DNA computing algorithm has some limitations in terms of convergence speed, adaptability, and effectiveness. In this paper, a new approach for improvement of DNA computing is proposed. This new approach aims to perform DNA computing algorithm with adaptive parameters towards the desired goal using quantum-behaved particle swarm optimization (QPSO). Some contributions provided by the proposed QPSO based on adaptive DNA computing algorithm are as follows: (1) parameters of population size, crossover rate, maximum number of operations, enzyme and virus mutation rate, and fitness function of DNA computing algorithm are simultaneously tuned for adaptive process, (2) adaptive algorithm is performed using QPSO algorithm for goal-driven progress, faster operation, and flexibility in data, and (3) numerical realization of DNA computing algorithm with proposed approach is implemented in system identification. Two experiments with different systems were carried out to evaluate the performance of the proposed approach with comparative results. Experimental results obtained with Matlab and FPGA demonstrate ability to provide effective optimization, considerable convergence speed, and high accuracy according to DNA computing algorithm. PMID:23935409

Efficient high-performance ultrasound beamforming using oversampling

NASA Astrophysics Data System (ADS)

Freeman, Steven R.; Quick, Marshall K.; Morin, Marc A.; Anderson, R. C.; Desilets, Charles S.; Linnenbrink, Thomas E.; O'Donnell, Matthew

1998-05-01

High-performance and efficient beamforming circuitry is very important in large channel count clinical ultrasound systems. Current state-of-the-art digital systems using multi-bit analog to digital converters (A/Ds) have matured to provide exquisite image quality with moderate levels of integration. A simplified oversampling beamforming architecture has been proposed that may a low integration of delta-sigma A/Ds onto the same chip as digital delay and processing circuitry to form a monolithic ultrasound beamformer. Such a beamformer may enable low-power handheld scanners for high-end systems with very large channel count arrays. This paper presents an oversampling beamformer architecture that generates high-quality images using very simple; digitization, delay, and summing circuits. Additional performance may be obtained with this oversampled system for narrow bandwidth excitations by mixing the RF signal down in frequency to a range where the electronic signal to nose ratio of the delta-sigma A/D is optimized. An oversampled transmit beamformer uses the same delay circuits as receive and eliminates the need for separate transmit function generators.

Algorithms for accelerated convergence of adaptive PCA.

PubMed

Chatterjee, C; Kang, Z; Roychowdhury, V P

2000-01-01

We derive and discuss new adaptive algorithms for principal component analysis (PCA) that are shown to converge faster than the traditional PCA algorithms due to Oja, Sanger, and Xu. It is well known that traditional PCA algorithms that are derived by using gradient descent on an objective function are slow to converge. Furthermore, the convergence of these algorithms depends on appropriate choices of the gain sequences. Since online applications demand faster convergence and an automatic selection of gains, we present new adaptive algorithms to solve these problems. We first present an unconstrained objective function, which can be minimized to obtain the principal components. We derive adaptive algorithms from this objective function by using: 1) gradient descent; 2) steepest descent; 3) conjugate direction; and 4) Newton-Raphson methods. Although gradient descent produces Xu's LMSER algorithm, the steepest descent, conjugate direction, and Newton-Raphson methods produce new adaptive algorithms for PCA. We also provide a discussion on the landscape of the objective function, and present a global convergence proof of the adaptive gradient descent PCA algorithm using stochastic approximation theory. Extensive experiments with stationary and nonstationary multidimensional Gaussian sequences show faster convergence of the new algorithms over the traditional gradient descent methods.We also compare the steepest descent adaptive algorithm with state-of-the-art methods on stationary and nonstationary sequences.

Beamforming using subspace estimation from a diagonally averaged sample covariance.

PubMed

Quijano, Jorge E; Zurk, Lisa M

2017-08-01

The potential benefit of a large-aperture sonar array for high resolution target localization is often challenged by the lack of sufficient data required for adaptive beamforming. This paper introduces a Toeplitz-constrained estimator of the clairvoyant signal covariance matrix corresponding to multiple far-field targets embedded in background isotropic noise. The estimator is obtained by averaging along subdiagonals of the sample covariance matrix, followed by covariance extrapolation using the method of maximum entropy. The sample covariance is computed from limited data snapshots, a situation commonly encountered with large-aperture arrays in environments characterized by short periods of local stationarity. Eigenvectors computed from the Toeplitz-constrained covariance are used to construct signal-subspace projector matrices, which are shown to reduce background noise and improve detection of closely spaced targets when applied to subspace beamforming. Monte Carlo simulations corresponding to increasing array aperture suggest convergence of the proposed projector to the clairvoyant signal projector, thereby outperforming the classic projector obtained from the sample eigenvectors. Beamforming performance of the proposed method is analyzed using simulated data, as well as experimental data from the Shallow Water Array Performance experiment.

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

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.

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

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.

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.

Beamforming array techniques for acoustic emission monitoring of large concrete structures

NASA Astrophysics Data System (ADS)

McLaskey, Gregory C.; Glaser, Steven D.; Grosse, Christian U.

2010-06-01

This paper introduces a novel method of acoustic emission (AE) analysis which is particularly suited for field applications on large plate-like reinforced concrete structures, such as walls and bridge decks. Similar to phased-array signal processing techniques developed for other non-destructive evaluation methods, this technique adapts beamforming tools developed for passive sonar and seismological applications for use in AE source localization and signal discrimination analyses. Instead of relying on the relatively weak P-wave, this method uses the energy-rich Rayleigh wave and requires only a small array of 4-8 sensors. Tests on an in-service reinforced concrete structure demonstrate that the azimuth of an artificial AE source can be determined via this method for sources located up to 3.8 m from the sensor array, even when the P-wave is undetectable. The beamforming array geometry also allows additional signal processing tools to be implemented, such as the VESPA process (VElocity SPectral Analysis), whereby the arrivals of different wave phases are identified by their apparent velocity of propagation. Beamforming AE can reduce sampling rate and time synchronization requirements between spatially distant sensors which in turn facilitates the use of wireless sensor networks for this application.

Digital Beamforming Scatterometer

NASA Technical Reports Server (NTRS)

Rincon, Rafael F.; Vega, Manuel; Kman, Luko; Buenfil, Manuel; Geist, Alessandro; Hillard, Larry; Racette, Paul

2009-01-01

This paper discusses scatterometer measurements collected with multi-mode Digital Beamforming Synthetic Aperture Radar (DBSAR) during the SMAP-VEX 2008 campaign. The 2008 SMAP Validation Experiment was conducted to address a number of specific questions related to the soil moisture retrieval algorithms. SMAP-VEX 2008 consisted on a series of aircraft-based.flights conducted on the Eastern Shore of Maryland and Delaware in the fall of 2008. Several other instruments participated in the campaign including the Passive Active L-Band System (PALS), the Marshall Airborne Polarimetric Imaging Radiometer (MAPIR), and the Global Positioning System Reflectometer (GPSR). This campaign was the first SMAP Validation Experiment. DBSAR is a multimode radar system developed at NASA/Goddard Space Flight Center that combines state-of-the-art radar technologies, on-board processing, and advances in signal processing techniques in order to enable new remote sensing capabilities applicable to Earth science and planetary applications [l]. The instrument can be configured to operate in scatterometer, Synthetic Aperture Radar (SAR), or altimeter mode. The system builds upon the L-band Imaging Scatterometer (LIS) developed as part of the RadSTAR program. The radar is a phased array system designed to fly on the NASA P3 aircraft. The instrument consists of a programmable waveform generator, eight transmit/receive (T/R) channels, a microstrip antenna, and a reconfigurable data acquisition and processor system. Each transmit channel incorporates a digital attenuator, and digital phase shifter that enables amplitude and phase modulation on transmit. The attenuators, phase shifters, and calibration switches are digitally controlled by the radar control card (RCC) on a pulse by pulse basis. The antenna is a corporate fed microstrip patch-array centered at 1.26 GHz with a 20 MHz bandwidth. Although only one feed is used with the present configuration, a provision was made for separate corporate

Resolution-Adaptive Hybrid MIMO Architectures for Millimeter Wave Communications

NASA Astrophysics Data System (ADS)

Choi, Jinseok; Evans, Brian L.; Gatherer, Alan

2017-12-01

In this paper, we propose a hybrid analog-digital beamforming architecture with resolution-adaptive ADCs for millimeter wave (mmWave) receivers with large antenna arrays. We adopt array response vectors for the analog combiners and derive ADC bit-allocation (BA) solutions in closed form. The BA solutions reveal that the optimal number of ADC bits is logarithmically proportional to the RF chain's signal-to-noise ratio raised to the 1/3 power. Using the solutions, two proposed BA algorithms minimize the mean square quantization error of received analog signals under a total ADC power constraint. Contributions of this paper include 1) ADC bit-allocation algorithms to improve communication performance of a hybrid MIMO receiver, 2) approximation of the capacity with the BA algorithm as a function of channels, and 3) a worst-case analysis of the ergodic rate of the proposed MIMO receiver that quantifies system tradeoffs and serves as the lower bound. Simulation results demonstrate that the BA algorithms outperform a fixed-ADC approach in both spectral and energy efficiency, and validate the capacity and ergodic rate formula. For a power constraint equivalent to that of fixed 4-bit ADCs, the revised BA algorithm makes the quantization error negligible while achieving 22% better energy efficiency. Having negligible quantization error allows existing state-of-the-art digital beamformers to be readily applied to the proposed system.

Single-station 6C beamforming

NASA Astrophysics Data System (ADS)

Nakata, N.; Hadziioannou, C.; Igel, H.

2017-12-01

Six-component measurements of seismic ground motion provide a unique opportunity to identify and decompose seismic wavefields into different wave types and incoming azimuths, as well as estimate structural information (e.g., phase velocity). By using the relationship between the transverse component and vertical rotational motion for Love waves, we can find the incident azimuth of the wave and the phase velocity. Therefore, when we scan the entire range of azimuth and slownesses, we can process the seismic waves in a similar way to conventional beamforming processing, without using a station array. To further improve the beam resolution, we use the distribution of amplitude ratio between translational and rotational motions at each time sample. With this beamforming, we decompose multiple incoming waves by azimuth and phase velocity using only one station. We demonstrate this technique using the data observed at Wettzell (vertical rotational motion and 3C translational motions). The beamforming results are encouraging to extract phase velocity at the location of the station, apply to oceanic microseism, and to identify complicated SH wave arrivals. We also discuss single-station beamforming using other components (vertical translational and horizontal rotational components). For future work, we need to understand the resolution limit of this technique, suitable length of time windows, and sensitivity to weak motion.

Fractional Programming for Communication Systems—Part I: Power Control and Beamforming

NASA Astrophysics Data System (ADS)

Shen, Kaiming; Yu, Wei

2018-05-01

This two-part paper explores the use of FP in the design and optimization of communication systems. Part I of this paper focuses on FP theory and on solving continuous problems. The main theoretical contribution is a novel quadratic transform technique for tackling the multiple-ratio concave-convex FP problem--in contrast to conventional FP techniques that mostly can only deal with the single-ratio or the max-min-ratio case. Multiple-ratio FP problems are important for the optimization of communication networks, because system-level design often involves multiple signal-to-interference-plus-noise ratio terms. This paper considers the applications of FP to solving continuous problems in communication system design, particularly for power control, beamforming, and energy efficiency maximization. These application cases illustrate that the proposed quadratic transform can greatly facilitate the optimization involving ratios by recasting the original nonconvex problem as a sequence of convex problems. This FP-based problem reformulation gives rise to an efficient iterative optimization algorithm with provable convergence to a stationary point. The paper further demonstrates close connections between the proposed FP approach and other well-known algorithms in the literature, such as the fixed-point iteration and the weighted minimum mean-square-error beamforming. The optimization of discrete problems is discussed in Part II of this paper.

High resolution beamforming on large aperture vertical line arrays: Processing synthetic data

NASA Astrophysics Data System (ADS)

Tran, Jean-Marie Q.; Hodgkiss, William S.

1990-09-01

This technical memorandum studies the beamforming of large aperture line arrays deployed vertically in the water column. The work concentrates on the use of high resolution techniques. Two processing strategies are envisioned: (1) full aperture coherent processing which offers in theory the best processing gain; and (2) subaperture processing which consists in extracting subapertures from the array and recombining the angular spectra estimated from these subarrays. The conventional beamformer, the minimum variance distortionless response (MVDR) processor, the multiple signal classification (MUSIC) algorithm and the minimum norm method are used in this study. To validate the various processing techniques, the ATLAS normal mode program is used to generate synthetic data which constitute a realistic signals environment. A deep-water, range-independent sound velocity profile environment, characteristic of the North-East Pacific, is being studied for two different 128 sensor arrays: a very long one cut for 30 Hz and operating at 20 Hz; and a shorter one cut for 107 Hz and operating at 100 Hz. The simulated sound source is 5 m deep. The full aperture and subaperture processing are being implemented with curved and plane wavefront replica vectors. The beamforming results are examined and compared to the ray-theory results produced by the generic sonar model.

Hardware Acceleration of Adaptive Neural Algorithms.

DOE Office of Scientific and Technical Information (OSTI.GOV)

James, Conrad D.

As tradit ional numerical computing has faced challenges, researchers have turned towards alternative computing approaches to reduce power - per - computation metrics and improve algorithm performance. Here, we describe an approach towards non - conventional computing that strengthens the connection between machine learning and neuroscience concepts. The Hardware Acceleration of Adaptive Neural Algorithms (HAANA) project ha s develop ed neural machine learning algorithms and hardware for applications in image processing and cybersecurity. While machine learning methods are effective at extracting relevant features from many types of data, the effectiveness of these algorithms degrades when subjected to real - worldmore » conditions. Our team has generated novel neural - inspired approa ches to improve the resiliency and adaptability of machine learning algorithms. In addition, we have also designed and fabricated hardware architectures and microelectronic devices specifically tuned towards the training and inference operations of neural - inspired algorithms. Finally, our multi - scale simulation framework allows us to assess the impact of microelectronic device properties on algorithm performance.« less

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.

Performance evaluation of spatial compounding in the presence of aberration and adaptive imaging

NASA Astrophysics Data System (ADS)

Dahl, Jeremy J.; Guenther, Drake; Trahey, Gregg E.

2003-05-01

Spatial compounding has been used for years to reduce speckle in ultrasonic images and to resolve anatomical features hidden behind the grainy appearance of speckle. Adaptive imaging restores image contrast and resolution by compensating for beamforming errors caused by tissue-induced phase errors. Spatial compounding represents a form of incoherent imaging, whereas adaptive imaging attempts to maintain a coherent, diffraction-limited aperture in the presence of aberration. Using a Siemens Antares scanner, we acquired single channel RF data on a commercially available 1-D probe. Individual channel RF data was acquired on a cyst phantom in the presence of a near field electronic phase screen. Simulated data was also acquired for both a 1-D and a custom built 8x96, 1.75-D probe (Tetrad Corp.). The data was compounded using a receive spatial compounding algorithm; a widely used algorithm because it takes advantage of parallel beamforming to avoid reductions in frame rate. Phase correction was also performed by using a least mean squares algorithm to estimate the arrival time errors. We present simulation and experimental data comparing the performance of spatial compounding to phase correction in contrast and resolution tasks. We evaluate spatial compounding and phase correction, and combinations of the two methods, under varying aperture sizes, aperture overlaps, and aberrator strength to examine the optimum configuration and conditions in which spatial compounding will provide a similar or better result than adaptive imaging. We find that, in general, phase correction is hindered at high aberration strengths and spatial frequencies, whereas spatial compounding is helped by these aberrators.

Speech Understanding and Sound Source Localization by Cochlear Implant Listeners Using a Pinna-Effect Imitating Microphone and an Adaptive Beamformer.

PubMed

Dorman, Michael F; Natale, Sarah; Loiselle, Louise

2018-03-01

Sentence understanding scores for patients with cochlear implants (CIs) when tested in quiet are relatively high. However, sentence understanding scores for patients with CIs plummet with the addition of noise. To assess, for patients with CIs (MED-EL), (1) the value to speech understanding of two new, noise-reducing microphone settings and (2) the effect of the microphone settings on sound source localization. Single-subject, repeated measures design. For tests of speech understanding, repeated measures on (1) number of CIs (one, two), (2) microphone type (omni, natural, adaptive beamformer), and (3) type of noise (restaurant, cocktail party). For sound source localization, repeated measures on type of signal (low-pass [LP], high-pass [HP], broadband noise). Ten listeners, ranging in age from 48 to 83 yr (mean = 57 yr), participated in this prospective study. Speech understanding was assessed in two noise environments using monaural and bilateral CIs fit with three microphone types. Sound source localization was assessed using three microphone types. In Experiment 1, sentence understanding scores (in terms of percent words correct) were obtained in quiet and in noise. For each patient, noise was first added to the signal to drive performance off of the ceiling in the bilateral CI-omni microphone condition. The other conditions were then administered at that signal-to-noise ratio in quasi-random order. In Experiment 2, sound source localization accuracy was assessed for three signal types using a 13-loudspeaker array over a 180° arc. The dependent measure was root-mean-score error. Both the natural and adaptive microphone settings significantly improved speech understanding in the two noise environments. The magnitude of the improvement varied between 16 and 19 percentage points for tests conducted in the restaurant environment and between 19 and 36 percentage points for tests conducted in the cocktail party environment. In the restaurant and cocktail party

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.

Adaptive Algorithms for Automated Processing of Document Images

DTIC Science & Technology

2011-01-01

ABSTRACT Title of dissertation: ADAPTIVE ALGORITHMS FOR AUTOMATED PROCESSING OF DOCUMENT IMAGES Mudit Agrawal, Doctor of Philosophy, 2011...2011 4. TITLE AND SUBTITLE Adaptive Algorithms for Automated Processing of Document Images 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM...ALGORITHMS FOR AUTOMATED PROCESSING OF DOCUMENT IMAGES by Mudit Agrawal Dissertation submitted to the Faculty of the Graduate School of the University

An adaptive replacement algorithm for paged-memory computer systems.

NASA Technical Reports Server (NTRS)

Thorington, J. M., Jr.; Irwin, J. D.

1972-01-01

A general class of adaptive replacement schemes for use in paged memories is developed. One such algorithm, called SIM, is simulated using a probability model that generates memory traces, and the results of the simulation of this adaptive scheme are compared with those obtained using the best nonlookahead algorithms. A technique for implementing this type of adaptive replacement algorithm with state of the art digital hardware is also presented.

Code-modulated visual evoked potentials using fast stimulus presentation and spatiotemporal beamformer decoding.

PubMed

Wittevrongel, Benjamin; Van Wolputte, Elia; Van Hulle, Marc M

2017-11-08

When encoding visual targets using various lagged versions of a pseudorandom binary sequence of luminance changes, the EEG signal recorded over the viewer's occipital pole exhibits so-called code-modulated visual evoked potentials (cVEPs), the phase lags of which can be tied to these targets. The cVEP paradigm has enjoyed interest in the brain-computer interfacing (BCI) community for the reported high information transfer rates (ITR, in bits/min). In this study, we introduce a novel decoding algorithm based on spatiotemporal beamforming, and show that this algorithm is able to accurately identify the gazed target. Especially for a small number of repetitions of the coding sequence, our beamforming approach significantly outperforms an optimised support vector machine (SVM)-based classifier, which is considered state-of-the-art in cVEP-based BCI. In addition to the traditional 60 Hz stimulus presentation rate for the coding sequence, we also explore the 120 Hz rate, and show that the latter enables faster communication, with a maximal median ITR of 172.87 bits/min. Finally, we also report on a transition effect in the EEG signal following the onset of the stimulus sequence, and recommend to exclude the first 150 ms of the trials from decoding when relying on a single presentation of the stimulus sequence.

Real-time correction of beamforming time delay errors in abdominal ultrasound imaging

NASA Astrophysics Data System (ADS)

Rigby, K. W.

2000-04-01

The speed of sound varies with tissue type, yet commercial ultrasound imagers assume a constant sound speed. Sound speed variation in abdominal fat and muscle layers is widely believed to be largely responsible for poor contrast and resolution in some patients. The simplest model of the abdominal wall assumes that it adds a spatially varying time delay to the ultrasound wavefront. The adequacy of this model is controversial. We describe an adaptive imaging system consisting of a GE LOGIQ 700 imager connected to a multi- processor computer. Arrival time errors for each beamforming channel, estimated by correlating each channel signal with the beamsummed signal, are used to correct the imager's beamforming time delays at the acoustic frame rate. A multi- row transducer provides two-dimensional sampling of arrival time errors. We observe significant improvement in abdominal images of healthy male volunteers: increased contrast of blood vessels, increased visibility of the renal capsule, and increased brightness of the liver.

An analog integrated circuit beamformer for high-frequency medical ultrasound imaging.

PubMed

Gurun, Gokce; Zahorian, Jaime S; Sisman, Alper; Karaman, Mustafa; Hasler, Paul E; Degertekin, F Levent

2012-10-01

We designed and fabricated a dynamic receive beamformer integrated circuit (IC) in 0.35-μm CMOS technology. This beamformer IC is suitable for integration with an annular array transducer for high-frequency (30-50 MHz) intravascular ultrasound (IVUS) imaging. The beamformer IC consists of receive preamplifiers, an analog dynamic delay-and-sum beamformer, and buffers for 8 receive channels. To form an analog dynamic delay line we designed an analog delay cell based on the current-mode first-order all-pass filter topology, as the basic building block. To increase the bandwidth of the delay cell, we explored an enhancement technique on the current mirrors. This technique improved the overall bandwidth of the delay line by a factor of 6. Each delay cell consumes 2.1-mW of power and is capable of generating a tunable time delay between 1.75 ns to 2.5 ns. We successfully integrated the fabricated beamformer IC with an 8-element annular array. Experimental test results demonstrated the desired buffering, preamplification and delaying capabilities of the beamformer.

Multiscale computations with a wavelet-adaptive algorithm

NASA Astrophysics Data System (ADS)

Rastigejev, Yevgenii Anatolyevich

A wavelet-based adaptive multiresolution algorithm for the numerical solution of multiscale problems governed by partial differential equations is introduced. The main features of the method include fast algorithms for the calculation of wavelet coefficients and approximation of derivatives on nonuniform stencils. The connection between the wavelet order and the size of the stencil is established. The algorithm is based on the mathematically well established wavelet theory. This allows us to provide error estimates of the solution which are used in conjunction with an appropriate threshold criteria to adapt the collocation grid. The efficient data structures for grid representation as well as related computational algorithms to support grid rearrangement procedure are developed. The algorithm is applied to the simulation of phenomena described by Navier-Stokes equations. First, we undertake the study of the ignition and subsequent viscous detonation of a H2 : O2 : Ar mixture in a one-dimensional shock tube. Subsequently, we apply the algorithm to solve the two- and three-dimensional benchmark problem of incompressible flow in a lid-driven cavity at large Reynolds numbers. For these cases we show that solutions of comparable accuracy as the benchmarks are obtained with more than an order of magnitude reduction in degrees of freedom. The simulations show the striking ability of the algorithm to adapt to a solution having different scales at different spatial locations so as to produce accurate results at a relatively low computational cost.

Automated detection of epileptic ripples in MEG using beamformer-based virtual sensors

NASA Astrophysics Data System (ADS)

Migliorelli, Carolina; Alonso, Joan F.; Romero, Sergio; Nowak, Rafał; Russi, Antonio; Mañanas, Miguel A.

2017-08-01

Objective. In epilepsy, high-frequency oscillations (HFOs) are expressively linked to the seizure onset zone (SOZ). The detection of HFOs in the noninvasive signals from scalp electroencephalography (EEG) and magnetoencephalography (MEG) is still a challenging task. The aim of this study was to automate the detection of ripples in MEG signals by reducing the high-frequency noise using beamformer-based virtual sensors (VSs) and applying an automatic procedure for exploring the time-frequency content of the detected events. Approach. Two-hundred seconds of MEG signal and simultaneous iEEG were selected from nine patients with refractory epilepsy. A two-stage algorithm was implemented. Firstly, beamforming was applied to the whole head to delimitate the region of interest (ROI) within a coarse grid of MEG-VS. Secondly, a beamformer using a finer grid in the ROI was computed. The automatic detection of ripples was performed using the time-frequency response provided by the Stockwell transform. Performance was evaluated through comparisons with simultaneous iEEG signals. Main results. ROIs were located within the seizure-generating lobes in the nine subjects. Precision and sensitivity values were 79.18% and 68.88%, respectively, by considering iEEG-detected events as benchmarks. A higher number of ripples were detected inside the ROI compared to the same region in the contralateral lobe. Significance. The evaluation of interictal ripples using non-invasive techniques can help in the delimitation of the epileptogenic zone and guide placement of intracranial electrodes. This is the first study that automatically detects ripples in MEG in the time domain located within the clinically expected epileptic area taking into account the time-frequency characteristics of the events through the whole signal spectrum. The algorithm was tested against intracranial recordings, the current gold standard. Further studies should explore this approach to enable the localization of

Ultrasonic Imaging in Solids Using Wave Mode Beamforming.

PubMed

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

2017-03-01

This paper discusses some improvements to ultrasonic synthetic imaging in solids with primary applications to nondestructive 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 coexisting 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. This 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.

Eigenspace-based minimum variance beamformer combined with Wiener postfilter for medical ultrasound imaging.

PubMed

Zeng, Xing; Chen, Cheng; Wang, Yuanyuan

2012-12-01

In this paper, a new beamformer which combines the eigenspace-based minimum variance (ESBMV) beamformer with the Wiener postfilter is proposed for medical ultrasound imaging. The primary goal of this work is to further improve the medical ultrasound imaging quality on the basis of the ESBMV beamformer. In this method, we optimize the ESBMV weights with a Wiener postfilter. With the optimization of the Wiener postfilter, the output power of the new beamformer becomes closer to the actual signal power at the imaging point than the ESBMV beamformer. Different from the ordinary Wiener postfilter, the output signal and noise power needed in calculating the Wiener postfilter are estimated respectively by the orthogonal signal subspace and noise subspace constructed from the eigenstructure of the sample covariance matrix. We demonstrate the performance of the new beamformer when resolving point scatterers and cyst phantom using both simulated data and experimental data and compare it with the delay-and-sum (DAS), the minimum variance (MV) and the ESBMV beamformer. We use the full width at half maximum (FWHM) and the peak-side-lobe level (PSL) to quantify the performance of imaging resolution and the contrast ratio (CR) to quantify the performance of imaging contrast. The FWHM of the new beamformer is only 15%, 50% and 50% of those of the DAS, MV and ESBMV beamformer, while the PSL is 127.2dB, 115dB and 60dB lower. What is more, an improvement of 239.8%, 232.5% and 32.9% in CR using simulated data and an improvement of 814%, 1410.7% and 86.7% in CR using experimental data are achieved compared to the DAS, MV and ESBMV beamformer respectively. In addition, the effect of the sound speed error is investigated by artificially overestimating the speed used in calculating the propagation delay and the results show that the new beamformer provides better robustness against the sound speed errors. Therefore, the proposed beamformer offers a better performance than the DAS, MV and

Software beamforming: comparison between a phased array and synthetic transmit aperture.

PubMed

Li, Yen-Feng; Li, Pai-Chi

2011-04-01

The data-transfer and computation requirements are compared between software-based beamforming using a phased array (PA) and a synthetic transmit aperture (STA). The advantages of a software-based architecture are reduced system complexity and lower hardware cost. Although this architecture can be implemented using commercial CPUs or GPUs, the high computation and data-transfer requirements limit its real-time beamforming performance. In particular, transferring the raw rf data from the front-end subsystem to the software back-end remains challenging with current state-of-the-art electronics technologies, which offset the cost advantage of the software back end. This study investigated the tradeoff between the data-transfer and computation requirements. Two beamforming methods based on a PA and STA, respectively, were used: the former requires a higher data transfer rate and the latter requires more memory operations. The beamformers were implemente;d in an NVIDIA GeForce GTX 260 GPU and an Intel core i7 920 CPU. The frame rate of PA beamforming was 42 fps with a 128-element array transducer, with 2048 samples per firing and 189 beams per image (with a 95 MB/frame data-transfer requirement). The frame rate of STA beamforming was 40 fps with 16 firings per image (with an 8 MB/frame data-transfer requirement). Both approaches achieved real-time beamforming performance but each had its own bottleneck. On the one hand, the required data-transfer speed was considerably reduced in STA beamforming, whereas this required more memory operations, which limited the overall computation time. The advantages of the GPU approach over the CPU approach were clearly demonstrated.

Star adaptation for two-algorithms used on serial computers

NASA Technical Reports Server (NTRS)

Howser, L. M.; Lambiotte, J. J., Jr.

1974-01-01

Two representative algorithms used on a serial computer and presently executed on the Control Data Corporation 6000 computer were adapted to execute efficiently on the Control Data STAR-100 computer. Gaussian elimination for the solution of simultaneous linear equations and the Gauss-Legendre quadrature formula for the approximation of an integral are the two algorithms discussed. A description is given of how the programs were adapted for STAR and why these adaptations were necessary to obtain an efficient STAR program. Some points to consider when adapting an algorithm for STAR are discussed. Program listings of the 6000 version coded in 6000 FORTRAN, the adapted STAR version coded in 6000 FORTRAN, and the STAR version coded in STAR FORTRAN are presented in the appendices.

Iterative Minimum Variance Beamformer with Low Complexity for Medical Ultrasound Imaging.

PubMed

Deylami, Ali Mohades; Asl, Babak Mohammadzadeh

2018-06-04

Minimum variance beamformer (MVB) improves the resolution and contrast of medical ultrasound images compared with delay and sum (DAS) beamformer. The weight vector of this beamformer should be calculated for each imaging point independently, with a cost of increasing computational complexity. The large number of necessary calculations limits this beamformer to application in real-time systems. A beamformer is proposed based on the MVB with lower computational complexity while preserving its advantages. This beamformer avoids matrix inversion, which is the most complex part of the MVB, by solving the optimization problem iteratively. The received signals from two imaging points close together do not vary much in medical ultrasound imaging. Therefore, using the previously optimized weight vector for one point as initial weight vector for the new neighboring point can improve the convergence speed and decrease the computational complexity. The proposed method was applied on several data sets, and it has been shown that the method can regenerate the results obtained by the MVB while the order of complexity is decreased from O(L 3 ) to O(L 2 ). Copyright © 2018 World Federation for Ultrasound in Medicine and Biology. Published by Elsevier Inc. All rights reserved.

Spherical beamforming for spherical array with impedance surface

NASA Astrophysics Data System (ADS)

Tontiwattanakul, Khemapat

2018-01-01

Spherical microphone array beamforming has been a popular research topic for recent years. Due to their isotropic beam in three dimensional spaces as well as a certain frequency range, the arrays are widely used in many applications such as sound field recording, acoustic beamforming, and noise source localisation. The body of a spherical array is usually considered perfectly rigid. A sound field captured by the sensors on spherical array can be decomposed into a series of spherical harmonics. In noise source localisation, the amplitude density of sound sources is estimated and illustrated by mean of colour maps. In this work, a rigid spherical array covered by fibrous materials is studied via numerical simulation and the performance of the spherical beamforming is discussed.

Adaptive algorithm of magnetic heading detection

NASA Astrophysics Data System (ADS)

Liu, Gong-Xu; Shi, Ling-Feng

2017-11-01

Magnetic data obtained from a magnetic sensor usually fluctuate in a certain range, which makes it difficult to estimate the magnetic heading accurately. In fact, magnetic heading information is usually submerged in noise because of all kinds of electromagnetic interference and the diversity of the pedestrian’s motion states. In order to solve this problem, a new adaptive algorithm based on the (typically) right-angled corridors of a building or residential buildings is put forward to process heading information. First, a 3D indoor localization platform is set up based on MPU9250. Then, several groups of data are measured by changing the experimental environment and pedestrian’s motion pace. The raw data from the attached inertial measurement unit are calibrated and arranged into a time-stamped array and written to a data file. Later, the data file is imported into MATLAB for processing and analysis using the proposed adaptive algorithm. Finally, the algorithm is verified by comparison with the existing algorithm. The experimental results show that the algorithm has strong robustness and good fault tolerance, which can detect the heading information accurately and in real-time.

Improving the Nulling Beamformer Using Subspace Suppression.

PubMed

Rana, Kunjan D; Hämäläinen, Matti S; Vaina, Lucia M

2018-01-01

Magnetoencephalography (MEG) captures the magnetic fields generated by neuronal current sources with sensors outside the head. In MEG analysis these current sources are estimated from the measured data to identify the locations and time courses of neural activity. Since there is no unique solution to this so-called inverse problem, multiple source estimation techniques have been developed. The nulling beamformer (NB), a modified form of the linearly constrained minimum variance (LCMV) beamformer, is specifically used in the process of inferring interregional interactions and is designed to eliminate shared signal contributions, or cross-talk, between regions of interest (ROIs) that would otherwise interfere with the connectivity analyses. The nulling beamformer applies the truncated singular value decomposition (TSVD) to remove small signal contributions from a ROI to the sensor signals. However, ROIs with strong crosstalk will have high separating power in the weaker components, which may be removed by the TSVD operation. To address this issue we propose a new method, the nulling beamformer with subspace suppression (NBSS). This method, controlled by a tuning parameter, reweights the singular values of the gain matrix mapping from source to sensor space such that components with high overlap are reduced. By doing so, we are able to measure signals between nearby source locations with limited cross-talk interference, allowing for reliable cortical connectivity analysis between them. In two simulations, we demonstrated that NBSS reduces cross-talk while retaining ROIs' signal power, and has higher separating power than both the minimum norm estimate (MNE) and the nulling beamformer without subspace suppression. We also showed that NBSS successfully localized the auditory M100 event-related field in primary auditory cortex, measured from a subject undergoing an auditory localizer task, and suppressed cross-talk in a nearby region in the superior temporal sulcus.

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. Copyright © 2013 Elsevier B.V. All rights reserved.

Multicore fiber beamforming network for broadband satellite communications

NASA Astrophysics Data System (ADS)

Zainullin, Airat; Vidal, Borja; Macho, Andres; Llorente, Roberto

2017-02-01

Multi-core fiber (MCF) has been one of the main innovations in fiber optics in the last decade. Reported work on MCF has been focused on increasing the transmission capacity of optical communication links by exploiting space-division multiplexing. Additionally, MCF presents a strong potential in optical beamforming networks. The use of MCF can increase the compactness of the broadband antenna array controller. This is of utmost importance in platforms where size and weight are critical parameters such as communications satellites and airplanes. Here, an optical beamforming architecture that exploits the space-division capacity of MCF to implement compact optical beamforming networks is proposed, being a new application field for MCF. The experimental demonstration of this system using a 4-core MCF that controls a four-element antenna array is reported. An analysis of the impact of MCF on the performance of antenna arrays is presented. The analysis indicates that the main limitation comes from the relatively high insertion loss in the MCF fan-in and fan-out devices, which leads to angle dependent losses which can be mitigated by using fixed optical attenuators or a photonic lantern to reduce MCF insertion loss. The crosstalk requirements are also experimentally evaluated for the proposed MCF-based architecture. The potential signal impairment in the beamforming network is analytically evaluated, being of special importance when MCF with a large number of cores is considered. Finally, the optimization of the proposed MCF-based beamforming network is addressed targeting the scalability to large arrays.

Robust Frequency Invariant Beamforming with Low Sidelobe for Speech Enhancement

NASA Astrophysics Data System (ADS)

Zhu, Yiting; Pan, Xiang

2018-01-01

Frequency invariant beamformers (FIBs) are widely used in speech enhancement and source localization. There are two traditional optimization methods for FIB design. The first one is convex optimization, which is simple but the frequency invariant characteristic of the beam pattern is poor with respect to frequency band of five octaves. The least squares (LS) approach using spatial response variation (SRV) constraint is another optimization method. Although, it can provide good frequency invariant property, it usually couldn’t be used in speech enhancement for its lack of weight norm constraint which is related to the robustness of a beamformer. In this paper, a robust wideband beamforming method with a constant beamwidth is proposed. The frequency invariant beam pattern is achieved by resolving an optimization problem of the SRV constraint to cover speech frequency band. With the control of sidelobe level, it is available for the frequency invariant beamformer (FIB) to prevent distortion of interference from the undesirable direction. The approach is completed in time-domain by placing tapped delay lines(TDL) and finite impulse response (FIR) filter at the output of each sensor which is more convenient than the Frost processor. By invoking the weight norm constraint, the robustness of the beamformer is further improved against random errors. Experiment results show that the proposed method has a constant beamwidth and almost the same white noise gain as traditional delay-and-sum (DAS) beamformer.

Impact of Beamforming on the Path Connectivity in Cognitive Radio Ad Hoc Networks

PubMed Central

Dung, Le The; Hieu, Tran Dinh; Choi, Seong-Gon; Kim, Byung-Seo; An, Beongku

2017-01-01

This paper investigates the impact of using directional antennas and beamforming schemes on the connectivity of cognitive radio ad hoc networks (CRAHNs). Specifically, considering that secondary users use two kinds of directional antennas, i.e., uniform linear array (ULA) and uniform circular array (UCA) antennas, and two different beamforming schemes, i.e., randomized beamforming and center-directed to communicate with each other, we study the connectivity of all combination pairs of directional antennas and beamforming schemes and compare their performances to those of omnidirectional antennas. The results obtained in this paper show that, compared with omnidirectional transmission, beamforming transmission only benefits the connectivity when the density of secondary user is moderate. Moreover, the combination of UCA and randomized beamforming scheme gives the highest path connectivity in all evaluating scenarios. Finally, the number of antenna elements and degree of path loss greatly affect path connectivity in CRAHNs. PMID:28346377

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. © The Author(s) 2014.

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.

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.

An Eye-adapted Beamforming for Axial B-scans Free from Crystalline Lens Aberration: In vitro and ex vivo Results with a 20 MHz Linear Array

NASA Astrophysics Data System (ADS)

Matéo, Tony; Mofid, Yassine; Grégoire, Jean-Marc; Ossant, Frédéric

In ophtalmic ultrasonography, axial B-scans are seriously deteriorated owing to the presence of the crystalline lens. This strongly aberrating medium affects both spatial and contrast resolution and causes important distortions. To deal with this issue, an adapted beamforming (BF) has been developed and experimented with a 20 MHz linear array working with a custom US research scanner. The adapted BF computes focusing delays that compensate for crystalline phase aberration, including refraction effects. This BF was tested in vitro by imaging a wire phantom through an eye phantom consisting of a synthetic gelatin lens, shaped according to the unaccommodated state of an adult human crystalline lens, anatomically set up in an appropriate liquid (turpentine) to approach the in vivo velocity ratio. Both image quality and fidelity from the adapted BF were assessed and compared with conventional delay-and-sum BF over the aberrating medium. Results showed 2-fold improvement of the lateral resolution, greater sensitivity and 90% reduction of the spatial error (from 758 μm to 76 μm) with adapted BF compared to conventional BF. Finally, promising first ex vivo axial B-scans of a human eye are presented.

Estimation of velocity fluctuation in internal combustion engine exhaust systems through beamforming techniques

NASA Astrophysics Data System (ADS)

Piñero, G.; Vergara, L.; Desantes, J. M.; Broatch, A.

2000-11-01

The knowledge of the particle velocity fluctuations associated with acoustic pressure oscillation in the exhaust system of internal combustion engines may represent a powerful aid in the design of such systems, from the point of view of both engine performance improvement and exhaust noise abatement. However, usual velocity measurement techniques, even if applicable, are not well suited to the aggressive environment existing in exhaust systems. In this paper, a method to obtain a suitable estimate of velocity fluctuations is proposed, which is based on the application of spatial filtering (beamforming) techniques to instantaneous pressure measurements. Making use of simulated pressure-time histories, several algorithms have been checked by comparison between the simulated and the estimated velocity fluctuations. Then, problems related to the experimental procedure and associated with the proposed methodology are addressed, making application to measurements made in a real exhaust system. The results indicate that, if proper care is taken when performing the measurements, the application of beamforming techniques gives a reasonable estimate of the velocity fluctuations.

Beam-Forming Concentrating Solar Thermal Array Power Systems

NASA Technical Reports Server (NTRS)

Hoppe, Daniel J. (Inventor); Cwik, Thomas A. (Inventor); Dimotakis, Paul E. (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.

AMOBH: Adaptive Multiobjective Black Hole Algorithm.

PubMed

Wu, Chong; Wu, Tao; Fu, Kaiyuan; Zhu, Yuan; Li, Yongbo; He, Wangyong; Tang, Shengwen

2017-01-01

This paper proposes a new multiobjective evolutionary algorithm based on the black hole algorithm with a new individual density assessment (cell density), called "adaptive multiobjective black hole algorithm" (AMOBH). Cell density has the characteristics of low computational complexity and maintains a good balance of convergence and diversity of the Pareto front. The framework of AMOBH can be divided into three steps. Firstly, the Pareto front is mapped to a new objective space called parallel cell coordinate system. Then, to adjust the evolutionary strategies adaptively, Shannon entropy is employed to estimate the evolution status. At last, the cell density is combined with a dominance strength assessment called cell dominance to evaluate the fitness of solutions. Compared with the state-of-the-art methods SPEA-II, PESA-II, NSGA-II, and MOEA/D, experimental results show that AMOBH has a good performance in terms of convergence rate, population diversity, population convergence, subpopulation obtention of different Pareto regions, and time complexity to the latter in most cases.

Adaptive Two Dimensional RLS (Recursive Least Squares) Algorithms

DTIC Science & Technology

1989-03-01

in Monterey wonderful. IX I. INTRODUCTION Adaptive algorithms have been used successfully for many years in a wide range of digital signal...SIMULATION RESULTS The 2-D FRLS algorithm was tested both on computer-generated data and on digitized images. For a baseline reference the 2-D L:rv1S...Alexander, S. T. Adaptivt Signal Processing: Theory and Applications. Springer- Verlag, New York. 1986. 7. Bellanger, Maurice G. Adaptive Digital

An adaptive inverse kinematics algorithm for robot manipulators

NASA Technical Reports Server (NTRS)

Colbaugh, R.; Glass, K.; Seraji, H.

1990-01-01

An adaptive algorithm for solving the inverse kinematics problem for robot manipulators is presented. The algorithm is derived using model reference adaptive control (MRAC) theory and is computationally efficient for online applications. The scheme requires no a priori knowledge of the kinematics of the robot if Cartesian end-effector sensing is available, and it requires knowledge of only the forward kinematics if joint position sensing is used. Computer simulation results are given for the redundant seven-DOF robotics research arm, demonstrating that the proposed algorithm yields accurate joint angle trajectories for a given end-effector position/orientation trajectory.

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.

Genetic algorithms in adaptive fuzzy control

NASA Technical Reports Server (NTRS)

Karr, C. Lucas; Harper, Tony R.

1992-01-01

Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust fuzzy membership functions in response to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific computer-simulated chemical system is used to demonstrate the ideas presented.

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.

A Self Adaptive Differential Evolution Algorithm for Global Optimization

NASA Astrophysics Data System (ADS)

Kumar, Pravesh; Pant, Millie

This paper presents a new Differential Evolution algorithm based on hybridization of adaptive control parameters and trigonometric mutation. First we propose a self adaptive DE named ADE where choice of control parameter F and Cr is not fixed at some constant value but is taken iteratively. The proposed algorithm is further modified by applying trigonometric mutation in it and the corresponding algorithm is named as ATDE. The performance of ATDE is evaluated on the set of 8 benchmark functions and the results are compared with the classical DE algorithm in terms of average fitness function value, number of function evaluations, convergence time and success rate. The numerical result shows the competence of the proposed algorithm.

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).

Properties of an adaptive feedback equalization algorithm.

PubMed

Engebretson, A M; French-St George, M

1993-01-01

This paper describes a new approach to feedback equalization for hearing aids. The method involves the use of an adaptive algorithm that estimates and tracks the characteristic of the hearing aid feedback path. The algorithm is described and the results of simulation studies and bench testing are presented.

Beamforming applied to surface EEG improves ripple visibility.

PubMed

van Klink, Nicole; Mol, Arjen; Ferrier, Cyrille; Hillebrand, Arjan; Huiskamp, Geertjan; Zijlmans, Maeike

2018-01-01

Surface EEG can show epileptiform ripples in people with focal epilepsy, but identification is impeded by the low signal-to-noise ratio of the electrode recordings. We used beamformer-based virtual electrodes to improve ripple identification. We analyzed ten minutes of interictal EEG of nine patients with refractory focal epilepsy. EEGs with more than 60 channels and 20 spikes were included. We computed ∼79 virtual electrodes using a scalar beamformer and marked ripples (80-250 Hz) co-occurring with spikes in physical and virtual electrodes. Ripple numbers in physical and virtual electrodes were compared, and sensitivity and specificity of ripples for the region of interest (ROI; based on clinical information) were determined. Five patients had ripples in the physical electrodes and eight in the virtual electrodes, with more ripples in virtual than in physical electrodes (101 vs. 57, p = .007). Ripples in virtual electrodes predicted the ROI better than physical electrodes (AUC 0.65 vs. 0.56, p = .03). Beamforming increased ripple visibility in surface EEG. Virtual ripples predicted the ROI better than physical ripples, although sensitivity was still poor. Beamforming can facilitate ripple identification in EEG. Ripple localization needs to be improved to enable its use for presurgical evaluation in people with epilepsy. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Photonic beamforming network for multibeam satellite-on-board phased-array antennas

NASA Astrophysics Data System (ADS)

Piqueras, M. A.; Cuesta-Soto, F.; Villalba, P.; Martí, A.; Hakansson, A.; Perdigués, J.; Caille, G.

2017-11-01

The implementation of a beamforming unit based on integrated photonic technologies is addressed in this work. This integrated photonic solution for multibeam coverage will be compared with the digital and the RF solution. Photonic devices show unique characteristics that match the critical requirements of space oriented devices such as low mass/size, low power consumption and easily scalable to big systems. An experimental proof-of-concept of the photonic beamforming structure based on 4x4 and 8x8 Butler matrices is presented. The proof-of-concept is based in the heterodyne generation of multiple phase engineered RF signals for the conformation of 8-4 different beams in an antenna array. Results show the feasibility of this technology for the implementation of optical beamforming with phase distribution errors below σ=10o with big savings in the required mass and size of the beamforming unit.

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.

Systems, Apparatuses and Methods for Beamforming RFID Tags

NASA Technical Reports Server (NTRS)

Fink, Patrick W. (Inventor); Lin, Gregory Y. (Inventor); Ngo, Phong H. (Inventor); Kennedy, Timothy F. (Inventor)

2017-01-01

A radio frequency identification (RFID) system includes an RFID interrogator and an RFID tag having a plurality of information sources and a beamforming network. The tag receives electromagnetic radiation from the interrogator. The beamforming network directs the received electromagnetic radiation to a subset of the plurality of information sources. The RFID tag transmits a response to the received electromagnetic radiation, based on the subset of the plurality of information sources to which the received electromagnetic radiation was directed. Method and other embodiments are also disclosed.

Software-based high-level synthesis design of FPGA beamformers for synthetic aperture imaging.

PubMed

Amaro, Joao; Yiu, Billy Y S; Falcao, Gabriel; Gomes, Marco A C; Yu, Alfred C H

2015-05-01

Field-programmable gate arrays (FPGAs) can potentially be configured as beamforming platforms for ultrasound imaging, but a long design time and skilled expertise in hardware programming are typically required. In this article, we present a novel approach to the efficient design of FPGA beamformers for synthetic aperture (SA) imaging via the use of software-based high-level synthesis techniques. Software kernels (coded in OpenCL) were first developed to stage-wise handle SA beamforming operations, and their corresponding FPGA logic circuitry was emulated through a high-level synthesis framework. After design space analysis, the fine-tuned OpenCL kernels were compiled into register transfer level descriptions to configure an FPGA as a beamformer module. The processing performance of this beamformer was assessed through a series of offline emulation experiments that sought to derive beamformed images from SA channel-domain raw data (40-MHz sampling rate, 12 bit resolution). With 128 channels, our FPGA-based SA beamformer can achieve 41 frames per second (fps) processing throughput (3.44 × 10(8) pixels per second for frame size of 256 × 256 pixels) at 31.5 W power consumption (1.30 fps/W power efficiency). It utilized 86.9% of the FPGA fabric and operated at a 196.5 MHz clock frequency (after optimization). Based on these findings, we anticipate that FPGA and high-level synthesis can together foster rapid prototyping of real-time ultrasound processor modules at low power consumption budgets.

Low-Complexity User Selection for Rate Maximization in MIMO Broadcast Channels with Downlink Beamforming

PubMed Central

Silva, Adão; Gameiro, Atílio

2014-01-01

We present in this work a low-complexity algorithm to solve the sum rate maximization problem in multiuser MIMO broadcast channels with downlink beamforming. Our approach decouples the user selection problem from the resource allocation problem and its main goal is to create a set of quasiorthogonal users. The proposed algorithm exploits physical metrics of the wireless channels that can be easily computed in such a way that a null space projection power can be approximated efficiently. Based on the derived metrics we present a mathematical model that describes the dynamics of the user selection process which renders the user selection problem into an integer linear program. Numerical results show that our approach is highly efficient to form groups of quasiorthogonal users when compared to previously proposed algorithms in the literature. Our user selection algorithm achieves a large portion of the optimum user selection sum rate (90%) for a moderate number of active users. PMID:24574928

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.

Digital phased array beamforming using single-bit delta-sigma conversion with non-uniform oversampling.

PubMed

Kozak, M; Karaman, M

2001-07-01

Digital beamforming based on oversampled delta-sigma (delta sigma) analog-to-digital (A/D) conversion can reduce the overall cost, size, and power consumption of phased array front-end processing. The signal resampling involved in dynamic delta sigma beamforming, however, disrupts synchronization between the modulators and demodulator, causing significant degradation in the signal-to-noise ratio. As a solution to this, we have explored a new digital beamforming approach based on non-uniform oversampling delta sigma A/D conversion. Using this approach, the echo signals received by the transducer array are sampled at time instants determined by the beamforming timing and then digitized by single-bit delta sigma A/D conversion prior to the coherent beam summation. The timing information involves a non-uniform sampling scheme employing different clocks at each array channel. The delta sigma coded beamsums obtained by adding the delayed 1-bit coded RF echo signals are then processed through a decimation filter to produce final beamforming outputs. The performance and validity of the proposed beamforming approach are assessed by means of emulations using experimental raw RF data.

Comparison between iterative wavefront control algorithm and direct gradient wavefront control algorithm for adaptive optics system

NASA Astrophysics Data System (ADS)

Cheng, Sheng-Yi; Liu, Wen-Jin; Chen, Shan-Qiu; Dong, Li-Zhi; Yang, Ping; Xu, Bing

2015-08-01

Among all kinds of wavefront control algorithms in adaptive optics systems, the direct gradient wavefront control algorithm is the most widespread and common method. This control algorithm obtains the actuator voltages directly from wavefront slopes through pre-measuring the relational matrix between deformable mirror actuators and Hartmann wavefront sensor with perfect real-time characteristic and stability. However, with increasing the number of sub-apertures in wavefront sensor and deformable mirror actuators of adaptive optics systems, the matrix operation in direct gradient algorithm takes too much time, which becomes a major factor influencing control effect of adaptive optics systems. In this paper we apply an iterative wavefront control algorithm to high-resolution adaptive optics systems, in which the voltages of each actuator are obtained through iteration arithmetic, which gains great advantage in calculation and storage. For AO system with thousands of actuators, the computational complexity estimate is about O(n2) ˜ O(n3) in direct gradient wavefront control algorithm, while the computational complexity estimate in iterative wavefront control algorithm is about O(n) ˜ (O(n)3/2), in which n is the number of actuators of AO system. And the more the numbers of sub-apertures and deformable mirror actuators, the more significant advantage the iterative wavefront control algorithm exhibits. Project supported by the National Key Scientific and Research Equipment Development Project of China (Grant No. ZDYZ2013-2), the National Natural Science Foundation of China (Grant No. 11173008), and the Sichuan Provincial Outstanding Youth Academic Technology Leaders Program, China (Grant No. 2012JQ0012).

Dual-microphone and binaural noise reduction techniques for improved speech intelligibility by hearing aid users

NASA Astrophysics Data System (ADS)

Yousefian Jazi, Nima

Spatial filtering and directional discrimination has been shown to be an effective pre-processing approach for noise reduction in microphone array systems. In dual-microphone hearing aids, fixed and adaptive beamforming techniques are the most common solutions for enhancing the desired speech and rejecting unwanted signals captured by the microphones. In fact, beamformers are widely utilized in systems where spatial properties of target source (usually in front of the listener) is assumed to be known. In this dissertation, some dual-microphone coherence-based speech enhancement techniques applicable to hearing aids are proposed. All proposed algorithms operate in the frequency domain and (like traditional beamforming techniques) are purely based on the spatial properties of the desired speech source and does not require any knowledge of noise statistics for calculating the noise reduction filter. This benefit gives our algorithms the ability to address adverse noise conditions, such as situations where interfering talker(s) speaks simultaneously with the target speaker. In such cases, the (adaptive) beamformers lose their effectiveness in suppressing interference, since the noise channel (reference) cannot be built and updated accordingly. This difference is the main advantage of the proposed techniques in the dissertation over traditional adaptive beamformers. Furthermore, since the suggested algorithms are independent of noise estimation, they offer significant improvement in scenarios that the power level of interfering sources are much more than that of target speech. The dissertation also shows the premise behind the proposed algorithms can be extended and employed to binaural hearing aids. The main purpose of the investigated techniques is to enhance the intelligibility level of speech, measured through subjective listening tests with normal hearing and cochlear implant listeners. However, the improvement in quality of the output speech achieved by the

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.

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

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.

Adaptive array antenna for satellite cellular and direct broadcast communications

NASA Technical Reports Server (NTRS)

Horton, Charles R.; Abend, Kenneth

1993-01-01

Adaptive phased-array antennas provide cost-effective implementation of large, light weight apertures with high directivity and precise beamshape control. Adaptive self-calibration allows for relaxation of all mechanical tolerances across the aperture and electrical component tolerances, providing high performance with a low-cost, lightweight array, even in the presence of large physical distortions. Beam-shape is programmable and adaptable to changes in technical and operational requirements. Adaptive digital beam-forming eliminates uplink contention by allowing a single electronically steerable antenna to service a large number of receivers with beams which adaptively focus on one source while eliminating interference from others. A large, adaptively calibrated and fully programmable aperture can also provide precise beam shape control for power-efficient direct broadcast from space. Advanced adaptive digital beamforming technologies are described for: (1) electronic compensation of aperture distortion, (2) multiple receiver adaptive space-time processing, and (3) downlink beam-shape control. Cost considerations for space-based array applications are also discussed.

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

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.

Estimating Position of Mobile Robots From Omnidirectional Vision Using an Adaptive Algorithm.

PubMed

Li, Luyang; Liu, Yun-Hui; Wang, Kai; Fang, Mu

2015-08-01

This paper presents a novel and simple adaptive algorithm for estimating the position of a mobile robot with high accuracy in an unknown and unstructured environment by fusing images of an omnidirectional vision system with measurements of odometry and inertial sensors. Based on a new derivation where the omnidirectional projection can be linearly parameterized by the positions of the robot and natural feature points, we propose a novel adaptive algorithm, which is similar to the Slotine-Li algorithm in model-based adaptive control, to estimate the robot's position by using the tracked feature points in image sequence, the robot's velocity, and orientation angles measured by odometry and inertial sensors. It is proved that the adaptive algorithm leads to global exponential convergence of the position estimation errors to zero. Simulations and real-world experiments are performed to demonstrate the performance of the proposed algorithm.

Delay and Standard Deviation Beamforming to Enhance Specular Reflections in Ultrasound Imaging.

PubMed

Bandaru, Raja Sekhar; Sornes, Anders Rasmus; Hermans, Jeroen; Samset, Eigil; D'hooge, Jan

2016-12-01

Although interventional devices, such as needles, guide wires, and catheters, are best visualized by X-ray, real-time volumetric echography could offer an attractive alternative as it avoids ionizing radiation; it provides good soft tissue contrast, and it is mobile and relatively cheap. Unfortunately, as echography is traditionally used to image soft tissue and blood flow, the appearance of interventional devices in conventional ultrasound images remains relatively poor, which is a major obstacle toward ultrasound-guided interventions. The objective of this paper was therefore to enhance the appearance of interventional devices in ultrasound images. Thereto, a modified ultrasound beamforming process using conventional-focused transmit beams is proposed that exploits the properties of received signals containing specular reflections (as arising from these devices). This new beamforming approach referred to as delay and standard deviation beamforming (DASD) was quantitatively tested using simulated as well as experimental data using a linear array transducer. Furthermore, the influence of different imaging settings (i.e., transmit focus, imaging depth, and scan angle) on the obtained image contrast was evaluated. The study showed that the image contrast of specular regions improved by 5-30 dB using DASD beamforming compared with traditional delay and sum (DAS) beamforming. The highest gain in contrast was observed when the interventional device was tilted away from being orthogonal to the transmit beam, which is a major limitation in standard DAS imaging. As such, the proposed beamforming methodology can offer an improved visualization of interventional devices in the ultrasound image with potential implications for ultrasound-guided interventions.

Fast algorithm of adaptive Fourier series

NASA Astrophysics Data System (ADS)

Gao, You; Ku, Min; Qian, Tao

2018-05-01

Adaptive Fourier decomposition (AFD, precisely 1-D AFD or Core-AFD) was originated for the goal of positive frequency representations of signals. It achieved the goal and at the same time offered fast decompositions of signals. There then arose several types of AFDs. AFD merged with the greedy algorithm idea, and in particular, motivated the so-called pre-orthogonal greedy algorithm (Pre-OGA) that was proven to be the most efficient greedy algorithm. The cost of the advantages of the AFD type decompositions is, however, the high computational complexity due to the involvement of maximal selections of the dictionary parameters. The present paper offers one formulation of the 1-D AFD algorithm by building the FFT algorithm into it. Accordingly, the algorithm complexity is reduced, from the original $\\mathcal{O}(M N^2)$ to $\\mathcal{O}(M N\\log_2 N)$, where $N$ denotes the number of the discretization points on the unit circle and $M$ denotes the number of points in $[0,1)$. This greatly enhances the applicability of AFD. Experiments are carried out to show the high efficiency of the proposed algorithm.

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

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.

Adaptive convergence nonuniformity correction algorithm.

PubMed

Qian, Weixian; Chen, Qian; Bai, Junqi; Gu, Guohua

2011-01-01

Nowadays, convergence and ghosting artifacts are common problems in scene-based nonuniformity correction (NUC) algorithms. In this study, we introduce the idea of space frequency to the scene-based NUC. Then the convergence speed factor is presented, which can adaptively change the convergence speed by a change of the scene dynamic range. In fact, the convergence speed factor role is to decrease the statistical data standard deviation. The nonuniformity space relativity characteristic was summarized by plenty of experimental statistical data. The space relativity characteristic was used to correct the convergence speed factor, which can make it more stable. Finally, real and simulated infrared image sequences were applied to demonstrate the positive effect of our algorithm.

Stripline Antenna Beam-Forming Network

NASA Technical Reports Server (NTRS)

Cramer, P. W.

1984-01-01

Stripline antenna beam-forming network includes 87 beam ports and 136 feed-element ports and contained on only two microstrip boards. Both uplink and downlink strips supported on same boards. Originally used for communications coverage of continental United States for Land Mobile Satellite System, structure of interest to antenna designers in other applications.

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.

Flight data processing with the F-8 adaptive algorithm

NASA Technical Reports Server (NTRS)

Hartmann, G.; Stein, G.; Petersen, K.

1977-01-01

An explicit adaptive control algorithm based on maximum likelihood estimation of parameters has been designed for NASA's DFBW F-8 aircraft. To avoid iterative calculations, the algorithm uses parallel channels of Kalman filters operating at fixed locations in parameter space. This algorithm has been implemented in NASA/DFRC's Remotely Augmented Vehicle (RAV) facility. Real-time sensor outputs (rate gyro, accelerometer and surface position) are telemetered to a ground computer which sends new gain values to an on-board system. Ground test data and flight records were used to establish design values of noise statistics and to verify the ground-based adaptive software. The software and its performance evaluation based on flight data are described

Adaptive 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.

Adaptive Beamforming Algorithms for High Resolution Microwave Imaging

DTIC Science & Technology

1991-04-01

frequency- and phase -locked. With a system of radio camera size it must be assumed that oscillators will drift and, similarly, that electronic circuits in...propagation-induced phase errors an array as large as the one under discussion is likely to experience differ- ent weather conditions across it. The nominal...human optical system. Such a passing-scene display with human optical resolving power would be available to the air - man at night as well as during the

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.

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 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. PMID:25397812

Combining DCQGMP-Based Sparse Decomposition and MPDR Beamformer for Multi-Type Interferences Mitigation for GNSS Receivers.

PubMed

Guo, Qiang; Qi, Liangang

2017-04-10

In the coexistence of multiple types of interfering signals, the performance of interference suppression methods based on time and frequency domains is degraded seriously, and the technique using an antenna array requires a large enough size and huge hardware costs. To combat multi-type interferences better for GNSS receivers, this paper proposes a cascaded multi-type interferences mitigation method combining improved double chain quantum genetic matching pursuit (DCQGMP)-based sparse decomposition and an MPDR beamformer. The key idea behind the proposed method is that the multiple types of interfering signals can be excised by taking advantage of their sparse features in different domains. In the first stage, the single-tone (multi-tone) and linear chirp interfering signals are canceled by sparse decomposition according to their sparsity in the over-complete dictionary. In order to improve the timeliness of matching pursuit (MP)-based sparse decomposition, a DCQGMP is introduced by combining an improved double chain quantum genetic algorithm (DCQGA) and the MP algorithm, and the DCQGMP algorithm is extended to handle the multi-channel signals according to the correlation among the signals in different channels. In the second stage, the minimum power distortionless response (MPDR) beamformer is utilized to nullify the residuary interferences (e.g., wideband Gaussian noise interferences). Several simulation results show that the proposed method can not only improve the interference mitigation degree of freedom (DoF) of the array antenna, but also effectively deal with the interference arriving from the same direction with the GNSS signal, which can be sparse represented in the over-complete dictionary. Moreover, it does not bring serious distortions into the navigation signal.

Combining DCQGMP-Based Sparse Decomposition and MPDR Beamformer for Multi-Type Interferences Mitigation for GNSS Receivers

PubMed Central

Guo, Qiang; Qi, Liangang

2017-01-01

In the coexistence of multiple types of interfering signals, the performance of interference suppression methods based on time and frequency domains is degraded seriously, and the technique using an antenna array requires a large enough size and huge hardware costs. To combat multi-type interferences better for GNSS receivers, this paper proposes a cascaded multi-type interferences mitigation method combining improved double chain quantum genetic matching pursuit (DCQGMP)-based sparse decomposition and an MPDR beamformer. The key idea behind the proposed method is that the multiple types of interfering signals can be excised by taking advantage of their sparse features in different domains. In the first stage, the single-tone (multi-tone) and linear chirp interfering signals are canceled by sparse decomposition according to their sparsity in the over-complete dictionary. In order to improve the timeliness of matching pursuit (MP)-based sparse decomposition, a DCQGMP is introduced by combining an improved double chain quantum genetic algorithm (DCQGA) and the MP algorithm, and the DCQGMP algorithm is extended to handle the multi-channel signals according to the correlation among the signals in different channels. In the second stage, the minimum power distortionless response (MPDR) beamformer is utilized to nullify the residuary interferences (e.g., wideband Gaussian noise interferences). Several simulation results show that the proposed method can not only improve the interference mitigation degree of freedom (DoF) of the array antenna, but also effectively deal with the interference arriving from the same direction with the GNSS signal, which can be sparse represented in the over-complete dictionary. Moreover, it does not bring serious distortions into the navigation signal. PMID:28394290

Generalized sidelobe canceler beamforming applied to medical ultrasound imaging

NASA Astrophysics Data System (ADS)

Li, Jiake; Chen, Xiaodong; Wang, Yi; Shi, Yifeng; Yu, Daoyin

2017-03-01

A generalized sidelobe canceler (GSC) approach is proposed for medical ultrasound imaging. The approach uses a set of adaptive weights instead of traditional non-adaptive weights, thus suppressing the interference and noise signal of echo data. In order to verify the validity of the proposed approach, Field II is applied to obtain the echo data of synthetic aperture (SA) for 13 scattering points and circular cysts. The performance of GSC is compared with SA using boxcar weights and Hamming weights, and is quantified by the full width at half maximum (FWHM) and peak signal-to-noise ratio (PSNR). Imaging of scattering point utilizing SA, SA (hamming), GSC provides FWHMs of 1.13411, 1.68910, 0.36195 mm and PSNRs of 60.65, 57.51, 66.72 dB, respectively. The simulation results of circular cyst also show that GSC can perform better lateral resolution than non-adaptive beamformers. Finally, an experiment is conducted on the basis of actual echo data of an ultrasound system, the imaging result after SA, SA (hamming), GSC provides PWHMs of 2.55778, 3.66776, 1.01346 mm at z = 75.6 mm, and 2.65430, 3.76428, 1.27889 mm at z = 77.3 mm, respectively.

A distributed transmit beamforming synchronization strategy for multi-element radar systems

NASA Astrophysics Data System (ADS)

Xiao, Manlin; Li, Xingwen; Xu, Jikang

2017-02-01

The distributed transmit beamforming has recently been discussed as an energy-effective technique in wireless communication systems. A common ground of various techniques is that the destination node transmits a beacon signal or feedback to assist source nodes to synchronize signals. However, this approach is not appropriate for a radar system since the destination is a non-cooperative target of an unknown location. In our paper, we propose a novel synchronization strategy for a distributed multiple-element beamfoming radar system. Source nodes estimate parameters of beacon signals transmitted from others to get their local synchronization information. The channel information of the phase propagation delay is transmitted to nodes via the reflected beacon signals as well. Next, each node generates appropriate parameters to form a beamforming signal at the target. Transmit beamforming signals of all nodes will combine coherently at the target compensating for different propagation delay. We analyse the influence of the local oscillation accuracy and the parameter estimation errors on the performance of the proposed synchronization scheme. The results of numerical simulations illustrate that this synchronization scheme is effective to enable the transmit beamforming in a distributed multi-element radar system.

Compact FPGA-based beamformer using oversampled 1-bit A/D converters.

PubMed

Tomov, Borislav Gueorguiev; Jensen, Jørgen Arendt

2005-05-01

A compact medical ultrasound beamformer architecture that uses oversampled 1-bit analog-to-digital (A/D) converters is presented. Sparse sample processing is used, as the echo signal for the image lines is reconstructed in 512 equidistant focal points along the line through its in-phase and quadrature components. That information is sufficient for presenting a B-mode image and creating a color flow map. The high sampling rate provides the necessary delay resolution for the focusing. The low channel data width (1-bit) makes it possible to construct a compact beamformer logic. The signal reconstruction is done using finite impulse reponse (FIR) filters, applied on selected bit sequences of the delta-sigma modulator output stream. The approach allows for a multichannel beamformer to fit in a single field programmable gate array (FPGA) device. A 32-channel beamformer is estimated to occupy 50% of the available logic resources in a commercially available mid-range FPGA, and to be able to operate at 129 MHz. Simulation of the architecture at 140 MHz provides images with a dynamic range approaching 60 dB for an excitation frequency of 3 MHz.

Implementation of parallel transmit beamforming using orthogonal frequency division multiplexing--achievable resolution and interbeam interference.

PubMed

Demi, Libertario; Viti, Jacopo; Kusters, Lieneke; Guidi, Francesco; Tortoli, Piero; Mischi, Massimo

2013-11-01

The speed of sound in the human body limits the achievable data acquisition rate of pulsed ultrasound scanners. To overcome this limitation, parallel beamforming techniques are used in ultrasound 2-D and 3-D imaging systems. Different parallel beamforming approaches have been proposed. They may be grouped into two major categories: parallel beamforming in reception and parallel beamforming in transmission. The first category is not optimal for harmonic imaging; the second category may be more easily applied to harmonic imaging. However, inter-beam interference represents an issue. To overcome these shortcomings and exploit the benefit of combining harmonic imaging and high data acquisition rate, a new approach has been recently presented which relies on orthogonal frequency division multiplexing (OFDM) to perform parallel beamforming in transmission. In this paper, parallel transmit beamforming using OFDM is implemented for the first time on an ultrasound scanner. An advanced open platform for ultrasound research is used to investigate the axial resolution and interbeam interference achievable with parallel transmit beamforming using OFDM. Both fundamental and second-harmonic imaging modalities have been considered. Results show that, for fundamental imaging, axial resolution in the order of 2 mm can be achieved in combination with interbeam interference in the order of -30 dB. For second-harmonic imaging, axial resolution in the order of 1 mm can be achieved in combination with interbeam interference in the order of -35 dB.

Spatially Controlled Relay Beamforming

NASA Astrophysics Data System (ADS)

Kalogerias, Dionysios

This thesis is about fusion of optimal stochastic motion control and physical layer communications. Distributed, networked communication systems, such as relay beamforming networks (e.g., Amplify & Forward (AF)), are typically designed without explicitly considering how the positions of the respective nodes might affect the quality of the communication. Optimum placement of network nodes, which could potentially improve the quality of the communication, is not typically considered. However, in most practical settings in physical layer communications, such as relay beamforming, the Channel State Information (CSI) observed by each node, per channel use, although it might be (modeled as) random, it is both spatially and temporally correlated. It is, therefore, reasonable to ask if and how the performance of the system could be improved by (predictively) controlling the positions of the network nodes (e.g., the relays), based on causal side (CSI) information, and exploitting the spatiotemporal dependencies of the wireless medium. In this work, we address this problem in the context of AF relay beamforming networks. This novel, cyber-physical system approach to relay beamforming is termed as "Spatially Controlled Relay Beamforming". First, we discuss wireless channel modeling, however, in a rigorous, Bayesian framework. Experimentally accurate and, at the same time, technically precise channel modeling is absolutely essential for designing and analyzing spatially controlled communication systems. In this work, we are interested in two distinct spatiotemporal statistical models, for describing the behavior of the log-scale magnitude of the wireless channel: 1. Stationary Gaussian Fields: In this case, the channel is assumed to evolve as a stationary, Gaussian stochastic field in continuous space and discrete time (say, for instance, time slots). Under such assumptions, spatial and temporal statistical interactions are determined by a set of time and space invariant

Prediction of cardiovascular risk in rheumatoid arthritis: performance of original and adapted SCORE algorithms.

PubMed

Arts, E E A; Popa, C D; Den Broeder, A A; Donders, R; Sandoo, A; Toms, T; Rollefstad, S; Ikdahl, E; Semb, A G; Kitas, G D; Van Riel, P L C M; Fransen, J

2016-04-01

Predictive performance of cardiovascular disease (CVD) risk calculators appears suboptimal in rheumatoid arthritis (RA). A disease-specific CVD risk algorithm may improve CVD risk prediction in RA. The objectives of this study are to adapt the Systematic COronary Risk Evaluation (SCORE) algorithm with determinants of CVD risk in RA and to assess the accuracy of CVD risk prediction calculated with the adapted SCORE algorithm. Data from the Nijmegen early RA inception cohort were used. The primary outcome was first CVD events. The SCORE algorithm was recalibrated by reweighing included traditional CVD risk factors and adapted by adding other potential predictors of CVD. Predictive performance of the recalibrated and adapted SCORE algorithms was assessed and the adapted SCORE was externally validated. Of the 1016 included patients with RA, 103 patients experienced a CVD event. Discriminatory ability was comparable across the original, recalibrated and adapted SCORE algorithms. The Hosmer-Lemeshow test results indicated that all three algorithms provided poor model fit (p<0.05) for the Nijmegen and external validation cohort. The adapted SCORE algorithm mainly improves CVD risk estimation in non-event cases and does not show a clear advantage in reclassifying patients with RA who develop CVD (event cases) into more appropriate risk groups. This study demonstrates for the first time that adaptations of the SCORE algorithm do not provide sufficient improvement in risk prediction of future CVD in RA to serve as an appropriate alternative to the original SCORE. Risk assessment using the original SCORE algorithm may underestimate CVD risk in patients with RA. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Improvement of resolution in full-view linear-array photoacoustic computed tomography using a novel adaptive weighting method

NASA Astrophysics Data System (ADS)

Omidi, Parsa; Diop, Mamadou; Carson, Jeffrey; Nasiriavanaki, Mohammadreza

2017-03-01

Linear-array-based photoacoustic computed tomography is a popular methodology for deep and high resolution imaging. However, issues such as phase aberration, side-lobe effects, and propagation limitations deteriorate the resolution. The effect of phase aberration due to acoustic attenuation and constant assumption of the speed of sound (SoS) can be reduced by applying an adaptive weighting method such as the coherence factor (CF). Utilizing an adaptive beamforming algorithm such as the minimum variance (MV) can improve the resolution at the focal point by eliminating the side-lobes. Moreover, invisibility of directional objects emitting parallel to the detection plane, such as vessels and other absorbing structures stretched in the direction perpendicular to the detection plane can degrade resolution. In this study, we propose a full-view array level weighting algorithm in which different weighs are assigned to different positions of the linear array based on an orientation algorithm which uses the histogram of oriented gradient (HOG). Simulation results obtained from a synthetic phantom show the superior performance of the proposed method over the existing reconstruction methods.

A small hemispherical helical antenna array for two-dimensional GPS beam-forming

NASA Astrophysics Data System (ADS)

Hui, H. T.; Aditya, S.; Mohamed, F. Bin S.; Hafiedz-Ul, A. Bin T.

2005-02-01

A small hemispherical helical antenna array with multibeam output for GPS beam-forming is designed and characterized. A Butler matrix beam-forming network is designed to provide four spatial beams in a two-dimensional directional space. The original design of the hemispherical helical antenna elements is modified in order to match it to the system impedance. Our study shows that even after an ˜30° scan from the normal direction, the maximum change in beam width is only 6°, the maximum change in axial ratio is 1.4 dB, and the maximum change in power gain is 1.1 dB. These characteristics indicate that the array can be potentially used for GPS beam-forming.

Single-snapshot DOA estimation by using Compressed Sensing

NASA Astrophysics Data System (ADS)

Fortunati, Stefano; Grasso, Raffaele; Gini, Fulvio; Greco, Maria S.; LePage, Kevin

2014-12-01

This paper deals with the problem of estimating the directions of arrival (DOA) of multiple source signals from a single observation vector of an array data. In particular, four estimation algorithms based on the theory of compressed sensing (CS), i.e., the classical ℓ 1 minimization (or Least Absolute Shrinkage and Selection Operator, LASSO), the fast smooth ℓ 0 minimization, and the Sparse Iterative Covariance-Based Estimator, SPICE and the Iterative Adaptive Approach for Amplitude and Phase Estimation, IAA-APES algorithms, are analyzed, and their statistical properties are investigated and compared with the classical Fourier beamformer (FB) in different simulated scenarios. We show that unlike the classical FB, a CS-based beamformer (CSB) has some desirable properties typical of the adaptive algorithms (e.g., Capon and MUSIC) even in the single snapshot case. Particular attention is devoted to the super-resolution property. Theoretical arguments and simulation analysis provide evidence that a CS-based beamformer can achieve resolution beyond the classical Rayleigh limit. Finally, the theoretical findings are validated by processing a real sonar dataset.

Adaptive phase k-means algorithm for waveform classification

NASA Astrophysics Data System (ADS)

Song, Chengyun; Liu, Zhining; Wang, Yaojun; Xu, Feng; Li, Xingming; Hu, Guangmin

2018-01-01

Waveform classification is a powerful technique for seismic facies analysis that describes the heterogeneity and compartments within a reservoir. Horizon interpretation is a critical step in waveform classification. However, the horizon often produces inconsistent waveform phase, and thus results in an unsatisfied classification. To alleviate this problem, an adaptive phase waveform classification method called the adaptive phase k-means is introduced in this paper. Our method improves the traditional k-means algorithm using an adaptive phase distance for waveform similarity measure. The proposed distance is a measure with variable phases as it moves from sample to sample along the traces. Model traces are also updated with the best phase interference in the iterative process. Therefore, our method is robust to phase variations caused by the interpretation horizon. We tested the effectiveness of our algorithm by applying it to synthetic and real data. The satisfactory results reveal that the proposed method tolerates certain waveform phase variation and is a good tool for seismic facies analysis.

Fully implicit adaptive mesh refinement MHD algorithm

NASA Astrophysics Data System (ADS)

Philip, Bobby

2005-10-01

In the macroscopic simulation of plasmas, the numerical modeler is faced with the challenge of dealing with multiple time and length scales. The former results in stiffness due to the presence of very fast waves. The latter requires one to resolve the localized features that the system develops. Traditional approaches based on explicit time integration techniques and fixed meshes are not suitable for this challenge, as such approaches prevent the modeler from using realistic plasma parameters to keep the computation feasible. We propose here a novel approach, based on implicit methods and structured adaptive mesh refinement (SAMR). Our emphasis is on both accuracy and scalability with the number of degrees of freedom. To our knowledge, a scalable, fully implicit AMR algorithm has not been accomplished before for MHD. As a proof-of-principle, we focus on the reduced resistive MHD model as a basic MHD model paradigm, which is truly multiscale. The approach taken here is to adapt mature physics-based technologyootnotetextL. Chac'on et al., J. Comput. Phys. 178 (1), 15- 36 (2002) to AMR grids, and employ AMR-aware multilevel techniques (such as fast adaptive composite --FAC-- algorithms) for scalability. We will demonstrate that the concept is indeed feasible, featuring optimal scalability under grid refinement. Results of fully-implicit, dynamically-adaptive AMR simulations will be presented on a variety of problems.

Speed of sound estimation for dual-stage virtual source ultrasound beamforming using point scatterers

NASA Astrophysics Data System (ADS)

Ma, Manyou; Rohling, Robert; Lampe, Lutz

2017-03-01

Synthetic transmit aperture beamforming is an increasingly used method to improve resolution in biomedical ultrasound imaging. Synthetic aperture sequential beamforming (SASB) is an implementation of this concept which features a relatively low computation complexity. Moreover, it can be implemented in a dual-stage architecture, where the first stage only applies simple single receive-focused delay-and-sum (srDAS) operations, while the second, more complex stage is performed either locally or remotely using more powerful processing. However, like traditional DAS-based beamforming methods, SASB is susceptible to inaccurate speed-of-sound (SOS) information. In this paper, we show how SOS estimation can be implemented using the srDAS beamformed image, and integrated into the dual-stage implementation of SASB, in an effort to obtain high resolution images with relatively low-cost hardware. Our approach builds on an existing per-channel radio frequency data-based direct estimation method, and applies an iterative refinement of the estimate. We use this estimate for SOS compensation, without the need to repeat the first stage beamforming. The proposed and previous methods are tested on both simulation and experimental studies. The accuracy of our SOS estimation method is on average 0.38% in simulation studies and 0.55% in phantom experiments, when the underlying SOS in the media is within the range 1450-1620 m/s. Using the estimated SOS, the beamforming lateral resolution of SASB is improved on average 52.6% in simulation studies and 50.0% in phantom experiments.

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

A hybrid adaptive routing algorithm for event-driven wireless sensor networks.

PubMed

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.

An evaluation of kurtosis beamforming in magnetoencephalography to localize the epileptogenic zone in drug resistant epilepsy patients.

PubMed

Hall, Michael B H; Nissen, Ida A; van Straaten, Elisabeth C W; Furlong, Paul L; Witton, Caroline; Foley, Elaine; Seri, Stefano; Hillebrand, Arjan

2018-06-01

Kurtosis beamforming is a useful technique for analysing magnetoencephalograpy (MEG) data containing epileptic spikes. However, the implementation varies and few studies measure concordance with subsequently resected areas. We evaluated kurtosis beamforming as a means of localizing spikes in drug-resistant epilepsy patients. We retrospectively applied kurtosis beamforming to MEG recordings of 22 epilepsy patients that had previously been analysed using equivalent current dipole (ECD) fitting. Virtual electrodes were placed in the kurtosis volumetric peaks and visually inspected to select a candidate source. The candidate sources were compared to the ECD localizations and resection areas. The kurtosis beamformer produced interpretable localizations in 18/22 patients, of which the candidate source coincided with the resection lobe in 9/13 seizure-free patients and in 3/5 patients with persistent seizures. The sublobar accuracy of the kurtosis beamformer with respect to the resection zone was higher than ECD (56% and 50%, respectively), however, ECD resulted in a higher lobar accuracy (75%, 67%). Kurtosis beamforming may provide additional value when spikes are not clearly discernible on the sensors and support ECD localizations when dipoles are scattered. Kurtosis beamforming should be integrated with existing clinical protocols to assist in localizing the epileptogenic zone. Copyright © 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Beamforming approaches for untethered, ultrasonic neural dust motes for cortical recording: a simulation study.

PubMed

Bertrand, Alexander; Seo, Dongjin; Maksimovic, Filip; Carmena, Jose M; Maharbiz, Michel M; Alon, Elad; Rabaey, Jan M

2014-01-01

In this paper, we examine the use of beamforming techniques to interrogate a multitude of neural implants in a distributed, ultrasound-based intra-cortical recording platform known as Neural Dust. We propose a general framework to analyze system design tradeoffs in the ultrasonic beamformer that extracts neural signals from modulated ultrasound waves that are backscattered by free-floating neural dust (ND) motes. Simulations indicate that high-resolution linearly-constrained minimum variance beamforming sufficiently suppresses interference from unselected ND motes and can be incorporated into the ND-based cortical recording system.

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.

An evaluation of the performance of two binaural beamformers in complex and dynamic multitalker environments.

PubMed

Best, Virginia; Mejia, Jorge; Freeston, Katrina; van Hoesel, Richard J; Dillon, Harvey

2015-01-01

Binaural beamformers are super-directional hearing aids created by combining microphone outputs from each side of the head. While they offer substantial improvements in SNR over conventional directional hearing aids, the benefits (and possible limitations) of these devices in realistic, complex listening situations have not yet been fully explored. In this study we evaluated the performance of two experimental binaural beamformers. Testing was carried out using a horizontal loudspeaker array. Background noise was created using recorded conversations. Performance measures included speech intelligibility, localization in noise, acceptable noise level, subjective ratings, and a novel dynamic speech intelligibility measure. Participants were 27 listeners with bilateral hearing loss, fitted with BTE prototypes that could be switched between conventional directional or binaural beamformer microphone modes. Relative to the conventional directional microphones, both binaural beamformer modes were generally superior for tasks involving fixed frontal targets, but not always for situations involving dynamic target locations. Binaural beamformers show promise for enhancing listening in complex situations when the location of the source of interest is predictable.

An evaluation of the performance of two binaural beamformers in complex and dynamic multitalker environments

PubMed Central

Best, Virginia; Mejia, Jorge; Freeston, Katrina; van Hoesel, Richard J.; Dillon, Harvey

2016-01-01

Objective Binaural beamformers are super-directional hearing aids created by combining microphone outputs from each side of the head. While they offer substantial improvements in SNR over conventional directional hearing aids, the benefits (and possible limitations) of these devices in realistic, complex listening situations have not yet been fully explored. In this study we evaluated the performance of two experimental binaural beamformers. Design Testing was carried out using a horizontal loudspeaker array. Background noise was created using recorded conversations. Performance measures included speech intelligibility, localisation in noise, acceptable noise level, subjective ratings, and a novel dynamic speech intelligibility measure. Study sample Participants were 27 listeners with bilateral hearing loss, fitted with BTE prototypes that could be switched between conventional directional or binaural beamformer microphone modes. Results Relative to the conventional directional microphones, both binaural beamformer modes were generally superior for tasks involving fixed frontal targets, but not always for situations involving dynamic target locations. Conclusions Binaural beamformers show promise for enhancing listening in complex situations when the location of the source of interest is predictable. PMID:26140298

A chaos wolf optimization algorithm with self-adaptive variable step-size

NASA Astrophysics Data System (ADS)

Zhu, Yong; Jiang, Wanlu; Kong, Xiangdong; Quan, Lingxiao; Zhang, Yongshun

2017-10-01

To explore the problem of parameter optimization for complex nonlinear function, a chaos wolf optimization algorithm (CWOA) with self-adaptive variable step-size was proposed. The algorithm was based on the swarm intelligence of wolf pack, which fully simulated the predation behavior and prey distribution way of wolves. It possessed three intelligent behaviors such as migration, summons and siege. And the competition rule as "winner-take-all" and the update mechanism as "survival of the fittest" were also the characteristics of the algorithm. Moreover, it combined the strategies of self-adaptive variable step-size search and chaos optimization. The CWOA was utilized in parameter optimization of twelve typical and complex nonlinear functions. And the obtained results were compared with many existing algorithms, including the classical genetic algorithm, the particle swarm optimization algorithm and the leader wolf pack search algorithm. The investigation results indicate that CWOA possess preferable optimization ability. There are advantages in optimization accuracy and convergence rate. Furthermore, it demonstrates high robustness and global searching ability.

Multiantenna Relay Beamforming Design for QoS Discrimination in Two-Way Relay Networks

PubMed Central

Xiong, Ke; Zhang, Yu; Li, Dandan; Zhong, Zhangdui

2013-01-01

This paper investigates the relay beamforming design for quality of service (QoS) discrimination in two-way relay networks. The purpose is to keep legitimate two-way relay users exchange their information via a helping multiantenna relay with QoS guarantee while avoiding the exchanged information overhearing by unauthorized receiver. To this end, we propose a physical layer method, where the relay beamforming is jointly designed with artificial noise (AN) which is used to interfere in the unauthorized user's reception. We formulate the joint beamforming and AN (BFA) design into an optimization problem such that the received signal-to-interference-ratio (SINR) at the two legitimate users is over a predefined QoS threshold while limiting the received SINR at the unauthorized user which is under a certain secure threshold. The objective of the optimization problem is to seek the optimal AN and beamforming vectors to minimize the total power consumed by the relay node. Since the optimization problem is nonconvex, we solve it by using semidefinite program (SDP) relaxation. For comparison, we also study the optimal relay beamforming without using AN (BFO) under the same QoS discrimination constraints. Simulation results show that both the proposed BFA and BFO can achieve the QoS discrimination of the two-way transmission. However, the proposed BFA yields significant power savings and lower infeasible rates compared with the BFO method. PMID:24391459

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.

Phased Array Beamforming and Imaging in Composite Laminates Using Guided Waves

NASA Technical Reports Server (NTRS)

Tian, Zhenhua; Leckey, Cara A. C.; Yu, Lingyu

2016-01-01

This paper presents the phased array beamforming and imaging using guided waves in anisotropic composite laminates. A generic phased array beamforming formula is presented, based on the classic delay-and-sum principle. The generic formula considers direction-dependent guided wave properties induced by the anisotropic material properties of composites. Moreover, the array beamforming and imaging are performed in frequency domain where the guided wave dispersion effect has been considered. The presented phased array method is implemented with a non-contact scanning laser Doppler vibrometer (SLDV) to detect multiple defects at different locations in an anisotropic composite plate. The array is constructed of scan points in a small area rapidly scanned by the SLDV. Using the phased array method, multiple defects at different locations are successfully detected. Our study shows that the guided wave phased array method is a potential effective method for rapid inspection of large composite structures.

Fault detection in rotating machines with beamforming: Spatial visualization of diagnosis features

NASA Astrophysics Data System (ADS)

Cardenas Cabada, E.; Leclere, Q.; Antoni, J.; Hamzaoui, N.

2017-12-01

Rotating machines diagnosis is conventionally related to vibration analysis. Sensors are usually placed on the machine to gather information about its components. The recorded signals are then processed through a fault detection algorithm allowing the identification of the failing part. This paper proposes an acoustic-based diagnosis method. A microphone array is used to record the acoustic field radiated by the machine. The main advantage over vibration-based diagnosis is that the contact between the sensors and the machine is no longer required. Moreover, the application of acoustic imaging makes possible the identification of the sources of acoustic radiation on the machine surface. The display of information is then spatially continuous while the accelerometers only give it discrete. Beamforming provides the time-varying signals radiated by the machine as a function of space. Any fault detection tool can be applied to the beamforming output. Spectral kurtosis, which highlights the impulsiveness of a signal as function of frequency, is used in this study. The combination of spectral kurtosis with acoustic imaging makes possible the mapping of the impulsiveness as a function of space and frequency. The efficiency of this approach lays on the source separation in the spatial and frequency domains. These mappings make possible the localization of such impulsive sources. The faulty components of the machine have an impulsive behavior and thus will be highlighted on the mappings. The study presents experimental validations of the method on rotating machines.

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

Generalized sidelobe canceller beamforming method for ultrasound imaging.

PubMed

Wang, Ping; Li, Na; Luo, Han-Wu; Zhu, Yong-Kun; Cui, Shi-Gang

2017-03-01

A modified generalized sidelobe canceller (IGSC) algorithm is proposed to enhance the resolution and robustness against the noise of the traditional generalized sidelobe canceller (GSC) and coherence factor combined method (GSC-CF). In the GSC algorithm, weighting vector is divided into adaptive and non-adaptive parts, while the non-adaptive part does not block all the desired signal. A modified steer vector of the IGSC algorithm is generated by the projection of the non-adaptive vector on the signal space constructed by the covariance matrix of received data. The blocking matrix is generated based on the orthogonal complementary space of the modified steer vector and the weighting vector is updated subsequently. The performance of IGSC was investigated by simulations and experiments. Through simulations, IGSC outperformed GSC-CF in terms of spatial resolution by 0.1 mm regardless there is noise or not, as well as the contrast ratio respect. The proposed IGSC can be further improved by combining with CF. The experimental results also validated the effectiveness of the proposed algorithm with dataset provided by the University of Michigan.

Detecting an atomic clock frequency anomaly using an adaptive Kalman filter algorithm

NASA Astrophysics Data System (ADS)

Song, Huijie; Dong, Shaowu; Wu, Wenjun; Jiang, Meng; Wang, Weixiong

2018-06-01

The abnormal frequencies of an atomic clock mainly include frequency jump and frequency drift jump. Atomic clock frequency anomaly detection is a key technique in time-keeping. The Kalman filter algorithm, as a linear optimal algorithm, has been widely used in real-time detection for abnormal frequency. In order to obtain an optimal state estimation, the observation model and dynamic model of the Kalman filter algorithm should satisfy Gaussian white noise conditions. The detection performance is degraded if anomalies affect the observation model or dynamic model. The idea of the adaptive Kalman filter algorithm, applied to clock frequency anomaly detection, uses the residuals given by the prediction for building ‘an adaptive factor’ the prediction state covariance matrix is real-time corrected by the adaptive factor. The results show that the model error is reduced and the detection performance is improved. The effectiveness of the algorithm is verified by the frequency jump simulation, the frequency drift jump simulation and the measured data of the atomic clock by using the chi-square test.

Fast frequency acquisition via adaptive least squares algorithm

NASA Technical Reports Server (NTRS)

Kumar, R.

1986-01-01

A new least squares algorithm is proposed and investigated for fast frequency and phase acquisition of sinusoids in the presence of noise. This algorithm is a special case of more general, adaptive parameter-estimation techniques. The advantages of the algorithms are their conceptual simplicity, flexibility and applicability to general situations. For example, the frequency to be acquired can be time varying, and the noise can be nonGaussian, nonstationary and colored. As the proposed algorithm can be made recursive in the number of observations, it is not necessary to have a priori knowledge of the received signal-to-noise ratio or to specify the measurement time. This would be required for batch processing techniques, such as the fast Fourier transform (FFT). The proposed algorithm improves the frequency estimate on a recursive basis as more and more observations are obtained. When the algorithm is applied in real time, it has the extra advantage that the observations need not be stored. The algorithm also yields a real time confidence measure as to the accuracy of the estimator.

Smart algorithms and adaptive methods in computational fluid dynamics

NASA Astrophysics Data System (ADS)

Tinsley Oden, J.

1989-05-01

A review is presented of the use of smart algorithms which employ adaptive methods in processing large amounts of data in computational fluid dynamics (CFD). Smart algorithms use a rationally based set of criteria for automatic decision making in an attempt to produce optimal simulations of complex fluid dynamics problems. The information needed to make these decisions is not known beforehand and evolves in structure and form during the numerical solution of flow problems. Once the code makes a decision based on the available data, the structure of the data may change, and criteria may be reapplied in order to direct the analysis toward an acceptable end. Intelligent decisions are made by processing vast amounts of data that evolve unpredictably during the calculation. The basic components of adaptive methods and their application to complex problems of fluid dynamics are reviewed. The basic components of adaptive methods are: (1) data structures, that is what approaches are available for modifying data structures of an approximation so as to reduce errors; (2) error estimation, that is what techniques exist for estimating error evolution in a CFD calculation; and (3) solvers, what algorithms are available which can function in changing meshes. Numerical examples which demonstrate the viability of these approaches are presented.

Microcomb-Based True-Time-Delay Network for Microwave Beamforming With Arbitrary Beam Pattern Control

NASA Astrophysics Data System (ADS)

Xue, Xiaoxiao; Xuan, Yi; Bao, Chengying; Li, Shangyuan; Zheng, Xiaoping; Zhou, Bingkun; Qi, Minghao; Weiner, Andrew M.

2018-06-01

Microwave phased array antennas (PAAs) are very attractive to defense applications and high-speed wireless communications for their abilities of fast beam scanning and complex beam pattern control. However, traditional PAAs based on phase shifters suffer from the beam-squint problem and have limited bandwidths. True-time-delay (TTD) beamforming based on low-loss photonic delay lines can solve this problem. But it is still quite challenging to build large-scale photonic TTD beamformers due to their high hardware complexity. In this paper, we demonstrate a photonic TTD beamforming network based on a miniature microresonator frequency comb (microcomb) source and dispersive time delay. A method incorporating optical phase modulation and programmable spectral shaping is proposed for positive and negative apodization weighting to achieve arbitrary microwave beam pattern control. The experimentally demonstrated TTD beamforming network can support a PAA with 21 elements. The microwave frequency range is $\\mathbf{8\\sim20\\ {GHz}}$, and the beam scanning range is $\\mathbf{\\pm 60.2^\\circ}$. Detailed measurements of the microwave amplitudes and phases are performed. The beamforming performances of Gaussian, rectangular beams and beam notch steering are evaluated through simulations by assuming a uniform radiating antenna array. The scheme can potentially support larger PAAs with hundreds of elements by increasing the number of comb lines with broadband microcomb generation.

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.

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.

Multi-objective Optimization Design of Gear Reducer Based on Adaptive Genetic Algorithms

NASA Astrophysics Data System (ADS)

Li, Rui; Chang, Tian; Wang, Jianwei; Wei, Xiaopeng; Wang, Jinming

2008-11-01

An adaptive Genetic Algorithm (GA) is introduced to solve the multi-objective optimized design of the reducer. Firstly, according to the structure, strength, etc. in a reducer, a multi-objective optimized model of the helical gear reducer is established. And then an adaptive GA based on a fuzzy controller is introduced, aiming at the characteristics of multi-objective, multi-parameter, multi-constraint conditions. Finally, a numerical example is illustrated to show the advantages of this approach and the effectiveness of an adaptive genetic algorithm used in optimized design of a reducer.

Intermediate view reconstruction using adaptive disparity search algorithm for real-time 3D processing

NASA Astrophysics Data System (ADS)

Bae, Kyung-hoon; Park, Changhan; Kim, Eun-soo

2008-03-01

In this paper, intermediate view reconstruction (IVR) using adaptive disparity search algorithm (ASDA) is for realtime 3-dimensional (3D) processing proposed. The proposed algorithm can reduce processing time of disparity estimation by selecting adaptive disparity search range. Also, the proposed algorithm can increase the quality of the 3D imaging. That is, by adaptively predicting the mutual correlation between stereo images pair using the proposed algorithm, the bandwidth of stereo input images pair can be compressed to the level of a conventional 2D image and a predicted image also can be effectively reconstructed using a reference image and disparity vectors. From some experiments, stereo sequences of 'Pot Plant' and 'IVO', it is shown that the proposed algorithm improves the PSNRs of a reconstructed image to about 4.8 dB by comparing with that of conventional algorithms, and reduces the Synthesizing time of a reconstructed image to about 7.02 sec by comparing with that of conventional algorithms.

Techniques for detection and localization of weak hippocampal and medial frontal sources using beamformers in MEG.

PubMed

Mills, Travis; Lalancette, Marc; Moses, Sandra N; Taylor, Margot J; Quraan, Maher A

2012-07-01

Magnetoencephalography provides precise information about the temporal dynamics of brain activation and is an ideal tool for investigating rapid cognitive processing. However, in many cognitive paradigms visual stimuli are used, which evoke strong brain responses (typically 40-100 nAm in V1) that may impede the detection of weaker activations of interest. This is particularly a concern when beamformer algorithms are used for source analysis, due to artefacts such as "leakage" of activation from the primary visual sources into other regions. We have previously shown (Quraan et al. 2011) that we can effectively reduce leakage patterns and detect weak hippocampal sources by subtracting the functional images derived from the experimental task and a control task with similar stimulus parameters. In this study we assess the performance of three different subtraction techniques. In the first technique we follow the same post-localization subtraction procedures as in our previous work. In the second and third techniques, we subtract the sensor data obtained from the experimental and control paradigms prior to source localization. Using simulated signals embedded in real data, we show that when beamformers are used, subtraction prior to source localization allows for the detection of weaker sources and higher localization accuracy. The improvement in localization accuracy exceeded 10 mm at low signal-to-noise ratios, and sources down to below 5 nAm were detected. We applied our techniques to empirical data acquired with two different paradigms designed to evoke hippocampal and frontal activations, and demonstrated our ability to detect robust activations in both regions with substantial improvements over image subtraction. We conclude that removal of the common-mode dominant sources through data subtraction prior to localization further improves the beamformer's ability to project the n-channel sensor-space data to reveal weak sources of interest and allows more accurate

Uncoordinated MAC for Adaptive Multi Beam Directional Networks: Analysis and Evaluation

DTIC Science & Technology

2016-08-01

control (MAC) policies for emerging systems that are equipped with fully digital antenna arrays which are capable of adaptive multi-beam directional...Adaptive Beam- forming, Multibeam, Directional Networking, Random Access, Smart Antennas I. INTRODUCTION Fully digital beamforming antenna arrays that...are capable of adaptive multi-beam communications are quickly becoming a reality. These antenna arrays allow users to form multiple simultaneous

A Circular Polarizer with Beamforming Feature Based on Frequency Selective Surfaces

NASA Astrophysics Data System (ADS)

Yin, Jia Yuan; Wan, Xiang; Ren, Jian; Cui, Tie Jun

2017-01-01

We propose a circular polarizer with beamforming features based on frequency selective surface (FSS), in which a modified anchor-shaped unit cell is used to reach the circular polarizer function. The beamforming characteristic is realized by a particular design of the unit-phase distribution, which is obtained by varying the scale of the unit cell. Instead of using plane waves, a horn antenna is designed to feed the phase-variant FSS. The proposed two-layer FSS is fabricated and measured to verify the design. The measured results show that the proposed structure can convert the linearly polarized waves to circularly polarized waves. Compared with the feeding horn antenna, the transmitted beam of the FSS-added horn is 14.43° broader in one direction, while 3.77° narrower in the orthogonal direction. To our best knowledge, this is the first time to realize circular polarizer with beamforming as the extra function based on FSS, which is promising in satellite and communication systems for potential applications due to its simple design and good performance.

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.

SIMULATION OF A REACTING POLLUTANT PUFF USING AN ADAPTIVE GRID ALGORITHM

EPA Science Inventory

A new dynamic solution adaptive grid algorithm DSAGA-PPM, has been developed for use in air quality modeling. In this paper, this algorithm is described and evaluated with a test problem. Cone-shaped distributions of various chemical species undergoing chemical reactions are rota...

Three-dimensional geoelectric modelling with optimal work/accuracy rate using an adaptive wavelet algorithm

NASA Astrophysics Data System (ADS)

Plattner, A.; Maurer, H. R.; Vorloeper, J.; Dahmen, W.

2010-08-01

Despite the ever-increasing power of modern computers, realistic modelling of complex 3-D earth models is still a challenging task and requires substantial computing resources. The overwhelming majority of current geophysical modelling approaches includes either finite difference or non-adaptive finite element algorithms and variants thereof. These numerical methods usually require the subsurface to be discretized with a fine mesh to accurately capture the behaviour of the physical fields. However, this may result in excessive memory consumption and computing times. A common feature of most of these algorithms is that the modelled data discretizations are independent of the model complexity, which may be wasteful when there are only minor to moderate spatial variations in the subsurface parameters. Recent developments in the theory of adaptive numerical solvers have the potential to overcome this problem. Here, we consider an adaptive wavelet-based approach that is applicable to a large range of problems, also including nonlinear problems. In comparison with earlier applications of adaptive solvers to geophysical problems we employ here a new adaptive scheme whose core ingredients arose from a rigorous analysis of the overall asymptotically optimal computational complexity, including in particular, an optimal work/accuracy rate. Our adaptive wavelet algorithm offers several attractive features: (i) for a given subsurface model, it allows the forward modelling domain to be discretized with a quasi minimal number of degrees of freedom, (ii) sparsity of the associated system matrices is guaranteed, which makes the algorithm memory efficient and (iii) the modelling accuracy scales linearly with computing time. We have implemented the adaptive wavelet algorithm for solving 3-D geoelectric problems. To test its performance, numerical experiments were conducted with a series of conductivity models exhibiting varying degrees of structural complexity. Results were compared

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.

Improved Plane-Wave Ultrasound Beamforming by Incorporating Angular Weighting and Coherent Compounding in Fourier Domain.

PubMed

Chen, Chuan; Hendriks, Gijs A G M; van Sloun, Ruud J G; Hansen, Hendrik H G; de Korte, Chris L

2018-05-01

In this paper, a novel processing framework is introduced for Fourier-domain beamforming of plane-wave ultrasound data, which incorporates coherent compounding and angular weighting in the Fourier domain. Angular weighting implies spectral weighting by a 2-D steering-angle-dependent filtering template. The design of this filter is also optimized as part of this paper. Two widely used Fourier-domain plane-wave ultrasound beamforming methods, i.e., Lu's f-k and Stolt's f-k methods, were integrated in the framework. To enable coherent compounding in Fourier domain for the Stolt's f-k method, the original Stolt's f-k method was modified to achieve alignment of the spectra for different steering angles in k-space. The performance of the framework was compared for both methods with and without angular weighting using experimentally obtained data sets (phantom and in vivo), and data sets (phantom) provided by the IEEE IUS 2016 plane-wave beamforming challenge. The addition of angular weighting enhanced the image contrast while preserving image resolution. This resulted in images of equal quality as those obtained by conventionally used delay-and-sum (DAS) beamforming with apodization and coherent compounding. Given the lower computational load of the proposed framework compared to DAS, to our knowledge it can, therefore, be concluded that it outperforms commonly used beamforming methods such as Stolt's f-k, Lu's f-k, and DAS.

An adaptive tensor voting algorithm combined with texture spectrum

NASA Astrophysics Data System (ADS)

Wang, Gang; Su, Qing-tang; Lü, Gao-huan; Zhang, Xiao-feng; Liu, Yu-huan; He, An-zhi

2015-01-01

An adaptive tensor voting algorithm combined with texture spectrum is proposed. The image texture spectrum is used to get the adaptive scale parameter of voting field. Then the texture information modifies both the attenuation coefficient and the attenuation field so that we can use this algorithm to create more significant and correct structures in the original image according to the human visual perception. At the same time, the proposed method can improve the edge extraction quality, which includes decreasing the flocculent region efficiently and making image clear. In the experiment for extracting pavement cracks, the original pavement image is processed by the proposed method which is combined with the significant curve feature threshold procedure, and the resulted image displays the faint crack signals submerged in the complicated background efficiently and clearly.

An adaptive clustering algorithm for image matching based on corner feature

NASA Astrophysics Data System (ADS)

Wang, Zhe; Dong, Min; Mu, Xiaomin; Wang, Song

2018-04-01

The traditional image matching algorithm always can not balance the real-time and accuracy better, to solve the problem, an adaptive clustering algorithm for image matching based on corner feature is proposed in this paper. The method is based on the similarity of the matching pairs of vector pairs, and the adaptive clustering is performed on the matching point pairs. Harris corner detection is carried out first, the feature points of the reference image and the perceived image are extracted, and the feature points of the two images are first matched by Normalized Cross Correlation (NCC) function. Then, using the improved algorithm proposed in this paper, the matching results are clustered to reduce the ineffective operation and improve the matching speed and robustness. Finally, the Random Sample Consensus (RANSAC) algorithm is used to match the matching points after clustering. The experimental results show that the proposed algorithm can effectively eliminate the most wrong matching points while the correct matching points are retained, and improve the accuracy of RANSAC matching, reduce the computation load of whole matching process at the same time.

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

Fast Adapting Ensemble: A New Algorithm for Mining Data Streams with Concept Drift

PubMed Central

Ortíz Díaz, Agustín; Ramos-Jiménez, Gonzalo; Frías Blanco, Isvani; Caballero Mota, Yailé; Morales-Bueno, Rafael

2015-01-01

The treatment of large data streams in the presence of concept drifts is one of the main challenges in the field of data mining, particularly when the algorithms have to deal with concepts that disappear and then reappear. This paper presents a new algorithm, called Fast Adapting Ensemble (FAE), which adapts very quickly to both abrupt and gradual concept drifts, and has been specifically designed to deal with recurring concepts. FAE processes the learning examples in blocks of the same size, but it does not have to wait for the batch to be complete in order to adapt its base classification mechanism. FAE incorporates a drift detector to improve the handling of abrupt concept drifts and stores a set of inactive classifiers that represent old concepts, which are activated very quickly when these concepts reappear. We compare our new algorithm with various well-known learning algorithms, taking into account, common benchmark datasets. The experiments show promising results from the proposed algorithm (regarding accuracy and runtime), handling different types of concept drifts. PMID:25879051

A Demons algorithm for image registration with locally adaptive regularization.

PubMed

Cahill, Nathan D; Noble, J Alison; Hawkes, David J

2009-01-01

Thirion's Demons is a popular algorithm for nonrigid image registration because of its linear computational complexity and ease of implementation. It approximately solves the diffusion registration problem by successively estimating force vectors that drive the deformation toward alignment and smoothing the force vectors by Gaussian convolution. In this article, we show how the Demons algorithm can be generalized to allow image-driven locally adaptive regularization in a manner that preserves both the linear complexity and ease of implementation of the original Demons algorithm. We show that the proposed algorithm exhibits lower target registration error and requires less computational effort than the original Demons algorithm on the registration of serial chest CT scans of patients with lung nodules.

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.

Performance study of LMS based adaptive algorithms for unknown system identification

DOE Office of Scientific and Technical Information (OSTI.GOV)

Javed, Shazia; Ahmad, Noor Atinah

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) signalmore » 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.« less

Mobile Ultrasound Plane Wave Beamforming on iPhone or iPad using Metal- based GPU Processing

NASA Astrophysics Data System (ADS)

Hewener, Holger J.; Tretbar, Steffen H.

Mobile and cost effective ultrasound devices are being used in point of care scenarios or the drama room. To reduce the costs of such devices we already presented the possibilities of consumer devices like the Apple iPad for full signal processing of raw data for ultrasound image generation. Using technologies like plane wave imaging to generate a full image with only one excitation/reception event the acquisition times and power consumption of ultrasound imaging can be reduced for low power mobile devices based on consumer electronics realizing the transition from FPGA or ASIC based beamforming into more flexible software beamforming. The massive parallel beamforming processing can be done with the Apple framework "Metal" for advanced graphics and general purpose GPU processing for the iOS platform. We were able to integrate the beamforming reconstruction into our mobile ultrasound processing application with imaging rates up to 70 Hz on iPad Air 2 hardware.

Formulation and implementation of nonstationary adaptive estimation algorithm with applications to air-data reconstruction

NASA Technical Reports Server (NTRS)

Whitmore, S. A.

1985-01-01

The dynamics model and data sources used to perform air-data reconstruction are discussed, as well as the Kalman filter. The need for adaptive determination of the noise statistics of the process is indicated. The filter innovations are presented as a means of developing the adaptive criterion, which is based on the true mean and covariance of the filter innovations. A method for the numerical approximation of the mean and covariance of the filter innovations is presented. The algorithm as developed is applied to air-data reconstruction for the space shuttle, and data obtained from the third landing are presented. To verify the performance of the adaptive algorithm, the reconstruction is also performed using a constant covariance Kalman filter. The results of the reconstructions are compared, and the adaptive algorithm exhibits better performance.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sheng, Zheng, E-mail: 19994035@sina.com; Wang, Jun; Zhou, Bihua

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 tomore » 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.« less

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.

A kernel adaptive algorithm for quaternion-valued inputs.

PubMed

Paul, Thomas K; Ogunfunmi, Tokunbo

2015-10-01

The use of quaternion data can provide benefit in applications like robotics and image recognition, and particularly for performing transforms in 3-D space. Here, we describe a kernel adaptive algorithm for quaternions. A least mean square (LMS)-based method was used, resulting in the derivation of the quaternion kernel LMS (Quat-KLMS) algorithm. Deriving this algorithm required describing the idea of a quaternion reproducing kernel Hilbert space (RKHS), as well as kernel functions suitable with quaternions. A modified HR calculus for Hilbert spaces was used to find the gradient of cost functions defined on a quaternion RKHS. In addition, the use of widely linear (or augmented) filtering is proposed to improve performance. The benefit of the Quat-KLMS and widely linear forms in learning nonlinear transformations of quaternion data are illustrated with simulations.

Radar wideband digital beamforming based on time delay and phase compensation

NASA Astrophysics Data System (ADS)

Fu, Wei; Jiang, Defu

2018-07-01

In conventional phased array radars, analogue time delay devices and phase shifters have been used for wideband beamforming. These methods suffer from insertion losses, gain mismatches and delay variations, and they occupy a large chip area. To solve these problems, a compact architecture of digital array antennas based on subarrays was considered. In this study, the receiving beam patterns of wideband linear frequency modulation (LFM) signals were constructed by applying analogue stretch processing via mixing with delayed reference signals at the subarray level. Subsequently, narrowband digital time delaying and phase compensation of the tone signals were implemented with reduced arithmetic complexity. Due to the differences in amplitudes, phases and time delays between channels, severe performance degradation of the beam patterns occurred without corrections. To achieve good beamforming performance, array calibration was performed in each channel to adjust the amplitude, frequency and phase of the tone signal. Using a field-programmable gate array, wideband LFM signals and finite impulse response filters with continuously adjustable time delays were implemented in a polyphase structure. Simulations and experiments verified the feasibility and effectiveness of the proposed digital beamformer.

Dynamic Transmit-Receive Beamforming by Spatial Matched Filtering for Ultrasound Imaging with Plane Wave Transmission.

PubMed

Chen, Yuling; Lou, Yang; Yen, Jesse

2017-07-01

During conventional ultrasound imaging, the need for multiple transmissions for one image and the time of flight for a desired imaging depth limit the frame rate of the system. Using a single plane wave pulse during each transmission followed by parallel receive processing allows for high frame rate imaging. However, image quality is degraded because of the lack of transmit focusing. Beamforming by spatial matched filtering (SMF) is a promising method which focuses ultrasonic energy using spatial filters constructed from the transmit-receive impulse response of the system. Studies by other researchers have shown that SMF beamforming can provide dynamic transmit-receive focusing throughout the field of view. In this paper, we apply SMF beamforming to plane wave transmissions (PWTs) to achieve both dynamic transmit-receive focusing at all imaging depths and high imaging frame rate (>5000 frames per second). We demonstrated the capability of the combined method (PWT + SMF) of achieving two-way focusing mathematically through analysis based on the narrowband Rayleigh-Sommerfeld diffraction theory. Moreover, the broadband performance of PWT + SMF was quantified in terms of lateral resolution and contrast from both computer simulations and experimental data. Results were compared between SMF beamforming and conventional delay-and-sum (DAS) beamforming in both simulations and experiments. At an imaging depth of 40 mm, simulation results showed a 29% lateral resolution improvement and a 160% contrast improvement with PWT + SMF. These improvements were 17% and 48% for experimental data with noise.

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.

A MISO UCA Beamforming Dimmable LED System for Indoor Positioning

PubMed Central

Taparugssanagorn, Attaphongse; Siwamogsatham, Siwaruk; Pomalaza-Ráez, Carlos

2014-01-01

The use of a multiple input single output (MISO) transmit beamforming system using dimmable light emitting arrays (LEAs) in the form of a uniform circular array (UCA) of transmitters is proposed in this paper. With this technique, visible light communications between a transmitter and a receiver (LED reader) can be achieved with excellent performance and the receiver's position can be estimated. A hexagonal lattice alignment of LED transmitters is deployed to reduce the coverage holes and the areas of overlapping radiation. As a result, the accuracy of the position estimation is better than when using a typical rectangular grid alignment. The dimming control is done with pulse width modulation (PWM) to obtain an optimal closed loop beamforming and minimum energy consumption with acceptable lighting. PMID:24481234

Fully implicit moving mesh adaptive algorithm

NASA Astrophysics Data System (ADS)

Chacon, Luis

2005-10-01

In many problems of interest, the numerical modeler is faced with the challenge of dealing with multiple time and length scales. The former is best dealt with with fully implicit methods, which are able to step over fast frequencies to resolve the dynamical time scale of interest. The latter requires grid adaptivity for efficiency. Moving-mesh grid adaptive methods are attractive because they can be designed to minimize the numerical error for a given resolution. However, the required grid governing equations are typically very nonlinear and stiff, and of considerably difficult numerical treatment. Not surprisingly, fully coupled, implicit approaches where the grid and the physics equations are solved simultaneously are rare in the literature, and circumscribed to 1D geometries. In this study, we present a fully implicit algorithm for moving mesh methods that is feasible for multidimensional geometries. A crucial element is the development of an effective multilevel treatment of the grid equation.ootnotetextL. Chac'on, G. Lapenta, A fully implicit, nonlinear adaptive grid strategy, J. Comput. Phys., accepted (2005) We will show that such an approach is competitive vs. uniform grids both from the accuracy (due to adaptivity) and the efficiency standpoints. Results for a variety of models 1D and 2D geometries, including nonlinear diffusion, radiation-diffusion, Burgers equation, and gas dynamics will be presented.

Transform-Based Channel-Data Compression to Improve the Performance of a Real-Time GPU-Based Software Beamformer.

PubMed

Lok, U-Wai; Li, Pai-Chi

2016-03-01

Graphics processing unit (GPU)-based software beamforming has advantages over hardware-based beamforming of easier programmability and a faster design cycle, since complicated imaging algorithms can be efficiently programmed and modified. However, the need for a high data rate when transferring ultrasound radio-frequency (RF) data from the hardware front end to the software back end limits the real-time performance. Data compression methods can be applied to the hardware front end to mitigate the data transfer issue. Nevertheless, most decompression processes cannot be performed efficiently on a GPU, thus becoming another bottleneck of the real-time imaging. Moreover, lossless (or nearly lossless) compression is desirable to avoid image quality degradation. In a previous study, we proposed a real-time lossless compression-decompression algorithm and demonstrated that it can reduce the overall processing time because the reduction in data transfer time is greater than the computation time required for compression/decompression. This paper analyzes the lossless compression method in order to understand the factors limiting the compression efficiency. Based on the analytical results, a nearly lossless compression is proposed to further enhance the compression efficiency. The proposed method comprises a transformation coding method involving modified lossless compression that aims at suppressing amplitude data. The simulation results indicate that the compression ratio (CR) of the proposed approach can be enhanced from nearly 1.8 to 2.5, thus allowing a higher data acquisition rate at the front end. The spatial and contrast resolutions with and without compression were almost identical, and the process of decompressing the data of a single frame on a GPU took only several milliseconds. Moreover, the proposed method has been implemented in a 64-channel system that we built in-house to demonstrate the feasibility of the proposed algorithm in a real system. It was found

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

DTIC Science & Technology

2010-01-01

Algorithm The cyclic coordinate descent algorithm is also known as the nonlinear Gauss - Seidel iteration [32]. There are several studies of this type of...vkρ(vi−1). It can be shown that the above BB gradient projection direction is always a descent direction. The R-linear convergence of the BB method has...KKT solution ) of the inexact pricing algorithm for MISO interference channel. The latter is interesting since the convergence of the original pricing

Real-time algorithm for acoustic imaging with a microphone array.

PubMed

Huang, Xun

2009-05-01

Acoustic phased array has become an important testing tool in aeroacoustic research, where the conventional beamforming algorithm has been adopted as a classical processing technique. The computation however has to be performed off-line due to the expensive cost. An innovative algorithm with real-time capability is proposed in this work. The algorithm is similar to a classical observer in the time domain while extended for the array processing to the frequency domain. The observer-based algorithm is beneficial mainly for its capability of operating over sampling blocks recursively. The expensive experimental time can therefore be reduced extensively since any defect in a testing can be corrected instantaneously.

Impedance computed tomography using an adaptive smoothing coefficient algorithm.

PubMed

Suzuki, A; Uchiyama, A

2001-01-01

In impedance computed tomography, a fixed coefficient regularization algorithm has been frequently used to improve the ill-conditioning problem of the Newton-Raphson algorithm. However, a lot of experimental data and a long period of computation time are needed to determine a good smoothing coefficient because a good smoothing coefficient has to be manually chosen from a number of coefficients and is a constant for each iteration calculation. Thus, sometimes the fixed coefficient regularization algorithm distorts the information or fails to obtain any effect. In this paper, a new adaptive smoothing coefficient algorithm is proposed. This algorithm automatically calculates the smoothing coefficient from the eigenvalue of the ill-conditioned matrix. Therefore, the effective images can be obtained within a short computation time. Also the smoothing coefficient is automatically adjusted by the information related to the real resistivity distribution and the data collection method. In our impedance system, we have reconstructed the resistivity distributions of two phantoms using this algorithm. As a result, this algorithm only needs one-fifth the computation time compared to the fixed coefficient regularization algorithm. When compared to the fixed coefficient regularization algorithm, it shows that the image is obtained more rapidly and applicable in real-time monitoring of the blood vessel.

Dual stage beamforming in the absence of front-end receive focusing

NASA Astrophysics Data System (ADS)

Bera, Deep; Bosch, Johan G.; Verweij, Martin D.; de Jong, Nico; Vos, Hendrik J.

2017-08-01

Ultrasound front-end receive designs for miniature, wireless, and/or matrix transducers can be simplified considerably by direct-element summation in receive. In this paper we develop a dual-stage beamforming technique that is able to produce a high-quality image from scanlines that are produced with focused transmit, and simple summation in receive (no delays). We call this non-delayed sequential beamforming (NDSB). In the first stage, low-resolution RF scanlines are formed by simple summation of element signals from a running sub-aperture. In the second stage, delay-and-sum beamforming is performed in which the delays are calculated considering the transmit focal points as virtual sources emitting spherical waves, and the sub-apertures as large unfocused receive elements. The NDSB method is validated with simulations in Field II. For experimental validation, RF channel data were acquired with a commercial research scanner using a 5 MHz linear array, and were subsequently processed offline. For NDSB, good average lateral resolution (0.99 mm) and low grating lobe levels (<-40 dB) were achieved by choosing the transmit {{F}\\#} as 0.75 and the transmit focus at 15 mm. NDSB was compared with conventional dynamic receive focusing (DRF) and synthetic aperture sequential beamforming (SASB) with their own respective optimal settings. The full width at half maximum of the NDSB point spread function was on average 20% smaller than that of DRF except for at depths  <30 mm and 10% larger than SASB considering all the depths. NDSB showed only a minor degradation in contrast-to-noise ratio and contrast ratio compared to DRF and SASB when measured on an anechoic cyst embedded in a tissue-mimicking phantom. In conclusion, using simple receive electronics front-end, NDSB can attain an image quality better than DRF and slightly inferior to SASB.

An adaptive bit synchronization algorithm under time-varying environment.

NASA Technical Reports Server (NTRS)

Chow, L. R.; Owen, H. A., Jr.; Wang, P. P.

1973-01-01

This paper presents an adaptive estimation algorithm for bit synchronization, assuming that the parameters of the incoming data process are time-varying. Experiment results have proved that this synchronizer is workable either judged by the amount of data required or the speed of convergence.

Sidelobe reduction and capacity improvement of open-loop collaborative beamforming in wireless sensor networks

PubMed Central

2017-01-01

Collaborative beamforming (CBF) with a finite number of collaborating nodes (CNs) produces sidelobes that are highly dependent on the collaborating nodes’ locations. The sidelobes cause interference and affect the communication rate of unintended receivers located within the transmission range. Nulling is not possible in an open-loop CBF since the collaborating nodes are unable to receive feedback from the receivers. Hence, the overall sidelobe reduction is required to avoid interference in the directions of the unintended receivers. However, the impact of sidelobe reduction on the capacity improvement at the unintended receiver has never been reported in previous works. In this paper, the effect of peak sidelobe (PSL) reduction in CBF on the capacity of an unintended receiver is analyzed. Three meta-heuristic optimization methods are applied to perform PSL minimization, namely genetic algorithm (GA), particle swarm algorithm (PSO) and a simplified version of the PSO called the weightless swarm algorithm (WSA). An average reduction of 20 dB in PSL alongside 162% capacity improvement is achieved in the worst case scenario with the WSA optimization. It is discovered that the PSL minimization in the CBF provides capacity improvement at an unintended receiver only if the CBF cluster is small and dense. PMID:28464000

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.;

Control algorithms and applications of the wavefront sensorless adaptive optics

NASA Astrophysics Data System (ADS)

Ma, Liang; Wang, Bin; Zhou, Yuanshen; Yang, Huizhen

2017-10-01

Compared with the conventional adaptive optics (AO) system, the wavefront sensorless (WFSless) AO system need not to measure the wavefront and reconstruct it. It is simpler than the conventional AO in system architecture and can be applied to the complex conditions. Based on the analysis of principle and system model of the WFSless AO system, wavefront correction methods of the WFSless AO system were divided into two categories: model-free-based and model-based control algorithms. The WFSless AO system based on model-free-based control algorithms commonly considers the performance metric as a function of the control parameters and then uses certain control algorithm to improve the performance metric. The model-based control algorithms include modal control algorithms, nonlinear control algorithms and control algorithms based on geometrical optics. Based on the brief description of above typical control algorithms, hybrid methods combining the model-free-based control algorithm with the model-based control algorithm were generalized. Additionally, characteristics of various control algorithms were compared and analyzed. We also discussed the extensive applications of WFSless AO system in free space optical communication (FSO), retinal imaging in the human eye, confocal microscope, coherent beam combination (CBC) techniques and extended objects.

Computational Acoustic Beamforming for Noise Source Identification for Small Wind Turbines.

PubMed

Ma, Ping; Lien, Fue-Sang; Yee, Eugene

2017-01-01

This paper develops a computational acoustic beamforming (CAB) methodology for identification of sources of small wind turbine noise. This methodology is validated using the case of the NACA 0012 airfoil trailing edge noise. For this validation case, the predicted acoustic maps were in excellent conformance with the results of the measurements obtained from the acoustic beamforming experiment. Following this validation study, the CAB methodology was applied to the identification of noise sources generated by a commercial small wind turbine. The simulated acoustic maps revealed that the blade tower interaction and the wind turbine nacelle were the two primary mechanisms for sound generation for this small wind turbine at frequencies between 100 and 630 Hz.

Computational Acoustic Beamforming for Noise Source Identification for Small Wind Turbines

PubMed Central

Lien, Fue-Sang

2017-01-01

This paper develops a computational acoustic beamforming (CAB) methodology for identification of sources of small wind turbine noise. This methodology is validated using the case of the NACA 0012 airfoil trailing edge noise. For this validation case, the predicted acoustic maps were in excellent conformance with the results of the measurements obtained from the acoustic beamforming experiment. Following this validation study, the CAB methodology was applied to the identification of noise sources generated by a commercial small wind turbine. The simulated acoustic maps revealed that the blade tower interaction and the wind turbine nacelle were the two primary mechanisms for sound generation for this small wind turbine at frequencies between 100 and 630 Hz. PMID:28378012

CHAMP: a locally adaptive unmixing-based hyperspectral anomaly detection algorithm

NASA Astrophysics Data System (ADS)

Crist, Eric P.; Thelen, Brian J.; Carrara, David A.

1998-10-01

Anomaly detection offers a means by which to identify potentially important objects in a scene without prior knowledge of their spectral signatures. As such, this approach is less sensitive to variations in target class composition, atmospheric and illumination conditions, and sensor gain settings than would be a spectral matched filter or similar algorithm. The best existing anomaly detectors generally fall into one of two categories: those based on local Gaussian statistics, and those based on linear mixing moles. Unmixing-based approaches better represent the real distribution of data in a scene, but are typically derived and applied on a global or scene-wide basis. Locally adaptive approaches allow detection of more subtle anomalies by accommodating the spatial non-homogeneity of background classes in a typical scene, but provide a poorer representation of the true underlying background distribution. The CHAMP algorithm combines the best attributes of both approaches, applying a linear-mixing model approach in a spatially adaptive manner. The algorithm itself, and teste results on simulated and actual hyperspectral image data, are presented in this paper.

Terahertz plasmonic Bessel beamformer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Monnai, Yasuaki; Shinoda, Hiroyuki; Jahn, David

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 integratedmore » with solid-state terahertz sources.« less

Landsat ecosystem disturbance adaptive processing system (LEDAPS) algorithm description

USGS Publications Warehouse

Schmidt, Gail; Jenkerson, Calli B.; 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.

An adaptive displacement estimation algorithm for improved reconstruction of thermal strain.

PubMed

Ding, Xuan; Dutta, Debaditya; Mahmoud, Ahmed M; Tillman, Bryan; Leers, Steven A; Kim, Kang

2015-01-01

Thermal strain imaging (TSI) can be used to differentiate between lipid and water-based tissues in atherosclerotic arteries. However, detecting small lipid pools in vivo requires accurate and robust displacement estimation over a wide range of displacement magnitudes. Phase-shift estimators such as Loupas' estimator and time-shift estimators such as normalized cross-correlation (NXcorr) are commonly used to track tissue displacements. However, Loupas' estimator is limited by phase-wrapping and NXcorr performs poorly when the SNR is low. In this paper, we present an adaptive displacement estimation algorithm that combines both Loupas' estimator and NXcorr. We evaluated this algorithm using computer simulations and an ex vivo human tissue sample. Using 1-D simulation studies, we showed that when the displacement magnitude induced by thermal strain was >λ/8 and the electronic system SNR was >25.5 dB, the NXcorr displacement estimate was less biased than the estimate found using Loupas' estimator. On the other hand, when the displacement magnitude was ≤λ/4 and the electronic system SNR was ≤25.5 dB, Loupas' estimator had less variance than NXcorr. We used these findings to design an adaptive displacement estimation algorithm. Computer simulations of TSI showed that the adaptive displacement estimator was less biased than either Loupas' estimator or NXcorr. Strain reconstructed from the adaptive displacement estimates improved the strain SNR by 43.7 to 350% and the spatial accuracy by 1.2 to 23.0% (P < 0.001). An ex vivo human tissue study provided results that were comparable to computer simulations. The results of this study showed that a novel displacement estimation algorithm, which combines two different displacement estimators, yielded improved displacement estimation and resulted in improved strain reconstruction.

An Adaptive Displacement Estimation Algorithm for Improved Reconstruction of Thermal Strain

PubMed Central

Ding, Xuan; Dutta, Debaditya; Mahmoud, Ahmed M.; Tillman, Bryan; Leers, Steven A.; Kim, Kang

2014-01-01

Thermal strain imaging (TSI) can be used to differentiate between lipid and water-based tissues in atherosclerotic arteries. However, detecting small lipid pools in vivo requires accurate and robust displacement estimation over a wide range of displacement magnitudes. Phase-shift estimators such as Loupas’ estimator and time-shift estimators like normalized cross-correlation (NXcorr) are commonly used to track tissue displacements. However, Loupas’ estimator is limited by phase-wrapping and NXcorr performs poorly when the signal-to-noise ratio (SNR) is low. In this paper, we present an adaptive displacement estimation algorithm that combines both Loupas’ estimator and NXcorr. We evaluated this algorithm using computer simulations and an ex-vivo human tissue sample. Using 1-D simulation studies, we showed that when the displacement magnitude induced by thermal strain was >λ/8 and the electronic system SNR was >25.5 dB, the NXcorr displacement estimate was less biased than the estimate found using Loupas’ estimator. On the other hand, when the displacement magnitude was ≤λ/4 and the electronic system SNR was ≤25.5 dB, Loupas’ estimator had less variance than NXcorr. We used these findings to design an adaptive displacement estimation algorithm. Computer simulations of TSI using Field II showed that the adaptive displacement estimator was less biased than either Loupas’ estimator or NXcorr. Strain reconstructed from the adaptive displacement estimates improved the strain SNR by 43.7–350% and the spatial accuracy by 1.2–23.0% (p < 0.001). An ex-vivo human tissue study provided results that were comparable to computer simulations. The results of this study showed that a novel displacement estimation algorithm, which combines two different displacement estimators, yielded improved displacement estimation and results in improved strain reconstruction. PMID:25585398

Early Breast Cancer Diagnosis Using Microwave Imaging via Space-Frequency Algorithm

NASA Astrophysics Data System (ADS)

Vemulapalli, Spandana

The conventional breast cancer detection methods have limitations ranging from ionizing radiations, low specificity to high cost. These limitations make way for a suitable alternative called Microwave Imaging, as a screening technique in the detection of breast cancer. The discernible differences between the benign, malignant and healthy breast tissues and the ability to overcome the harmful effects of ionizing radiations make microwave imaging, a feasible breast cancer detection technique. Earlier studies have shown the variation of electrical properties of healthy and malignant tissues as a function of frequency and hence stimulates high bandwidth requirement. A Ultrawideband, Wideband and Narrowband arrays have been designed, simulated and optimized for high (44%), medium (33%) and low (7%) bandwidths respectively, using the EM (electromagnetic software) called FEKO. These arrays are then used to illuminate the breast model (phantom) and the received backscattered signals are obtained in the near field for each case. The Microwave Imaging via Space-Time (MIST) beamforming algorithm in the frequency domain, is next applied to these near field backscattered monostatic frequency response signals for the image reconstruction of the breast model. The main purpose of this investigation is to access the impact of bandwidth and implement a novel imaging technique for use in the early detection of breast cancer. Earlier studies show the implementation of the MIST imaging algorithm on the time domain signals via a frequency domain beamformer. The performance evaluation of the imaging algorithm on the frequency response signals has been carried out in the frequency domain. The energy profile of the breast in the spatial domain is created via the frequency domain Parseval's theorem. The beamformer weights calculated using these the MIST algorithm (not including the effect of the skin) has been calculated for Ultrawideband, Wideband and Narrowband arrays, respectively

Study on a low complexity adaptive modulation algorithm in OFDM-ROF system with sub-carrier grouping technology

NASA Astrophysics Data System (ADS)

Liu, Chong-xin; Liu, Bo; Zhang, Li-jia; Xin, Xiang-jun; Tian, Qing-hua; Tian, Feng; Wang, Yong-jun; Rao, Lan; Mao, Yaya; Li, Deng-ao

2018-01-01

During the last decade, the orthogonal frequency division multiplexing radio-over-fiber (OFDM-ROF) system with adaptive modulation technology is of great interest due to its capability of raising the spectral efficiency dramatically, reducing the effects of fiber link or wireless channel, and improving the communication quality. In this study, according to theoretical analysis of nonlinear distortion and frequency selective fading on the transmitted signal, a low-complexity adaptive modulation algorithm is proposed in combination with sub-carrier grouping technology. This algorithm achieves the optimal performance of the system by calculating the average combined signal-to-noise ratio of each group and dynamically adjusting the origination modulation format according to the preset threshold and user's requirements. At the same time, this algorithm takes the sub-carrier group as the smallest unit in the initial bit allocation and the subsequent bit adjustment. So, the algorithm complexity is only 1 /M (M is the number of sub-carriers in each group) of Fischer algorithm, which is much smaller than many classic adaptive modulation algorithms, such as Hughes-Hartogs algorithm, Chow algorithm, and is in line with the development direction of green and high speed communication. Simulation results show that the performance of OFDM-ROF system with the improved algorithm is much better than those without adaptive modulation, and the BER of the former achieves 10e1 to 10e2 times lower than the latter when SNR values gets larger. We can obtain that this low complexity adaptive modulation algorithm is extremely useful for the OFDM-ROF system.

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.

General purpose graphic processing unit implementation of adaptive pulse compression algorithms

NASA Astrophysics Data System (ADS)

Cai, Jingxiao; Zhang, Yan

2017-07-01

This study introduces a practical approach to implement real-time signal processing algorithms for general surveillance radar based on NVIDIA graphical processing units (GPUs). The pulse compression algorithms are implemented using compute unified device architecture (CUDA) libraries such as CUDA basic linear algebra subroutines and CUDA fast Fourier transform library, which are adopted from open source libraries and optimized for the NVIDIA GPUs. For more advanced, adaptive processing algorithms such as adaptive pulse compression, customized kernel optimization is needed and investigated. A statistical optimization approach is developed for this purpose without needing much knowledge of the physical configurations of the kernels. It was found that the kernel optimization approach can significantly improve the performance. Benchmark performance is compared with the CPU performance in terms of processing accelerations. The proposed implementation framework can be used in various radar systems including ground-based phased array radar, airborne sense and avoid radar, and aerospace surveillance radar.

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

An adaptive importance sampling algorithm for Bayesian inversion with multimodal distributions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Weixuan; Lin, Guang, E-mail: guanglin@purdue.edu

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 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

Sparse matrix beamforming and image reconstruction for real-time 2D HIFU monitoring using Harmonic Motion Imaging for Focused Ultrasound (HMIFU) with in vitro validation

PubMed Central

Hou, Gary Y.; Provost, Jean; Grondin, Julien; Wang, Shutao; Marquet, Fabrice; Bunting, Ethan; Konofagou, Elisa E.

2015-01-01

Harmonic Motion Imaging for Focused Ultrasound (HMIFU) is a recently developed High-Intensity Focused Ultrasound (HIFU) treatment monitoring method. HMIFU utilizes an Amplitude-Modulated (fAM = 25 Hz) HIFU beam to induce a localized focal oscillatory motion, which is simultaneously estimated and imaged by confocally-aligned imaging transducer. HMIFU feasibilities have been previously shown in silico, in vitro, and in vivo in 1-D or 2-D monitoring of HIFU treatment. The objective of this study is to develop and show the feasibility of a novel fast beamforming algorithm for image reconstruction using GPU-based sparse-matrix operation with real-time feedback. In this study, the algorithm was implemented onto a fully integrated, clinically relevant HMIFU system composed of a 93-element HIFU transducer (fcenter = 4.5MHz) and coaxially-aligned 64-element phased array (fcenter = 2.5MHz) for displacement excitation and motion estimation, respectively. A single transmit beam with divergent beam transmit was used while fast beamforming was implemented using a GPU-based delay-and-sum method and a sparse-matrix operation. Axial HMI displacements were then estimated from the RF signals using a 1-D normalized cross-correlation method and streamed to a graphic user interface. The present work developed and implemented a sparse matrix beamforming onto a fully-integrated, clinically relevant system, which can stream displacement images up to 15 Hz using a GPU-based processing, an increase of 100 fold in rate of streaming displacement images compared to conventional CPU-based conventional beamforming and reconstruction processing. The achieved feedback rate is also currently the fastest and only approach that does not require interrupting the HIFU treatment amongst the acoustic radiation force based HIFU imaging techniques. Results in phantom experiments showed reproducible displacement imaging, and monitoring of twenty two in vitro HIFU treatments using the new 2D system showed a

Spectrum Sharing in an ISM Band: Outage Performance of a Hybrid DS/FH Spread Spectrum System with Beamforming

NASA Astrophysics Data System (ADS)

Li, Hanyu; Syed, Mubashir; Yao, Yu-Dong; Kamakaris, Theodoros

2009-12-01

This paper investigates spectrum sharing issues in the unlicensed industrial, scientific, and medical (ISM) bands. It presents a radio frequency measurement setup and measurement results in 2.4 GHz. It then develops an analytical model to characterize the coexistence interference in the ISM bands, based on radio frequency measurement results in the 2.4 GHz. Outage performance using the interference model is examined for a hybrid direct-sequence frequency-hopping spread spectrum system. The utilization of beamforming techniques in the system is also investigated, and a simplified beamforming model is proposed to analyze the system performance using beamforming. Numerical results show that beamforming significantly improves the system outage performance. The work presented in this paper provides a quantitative evaluation of signal outages in a spectrum sharing environment. It can be used as a tool in the development process for future dynamic spectrum access models as well as engineering designs for applications in unlicensed bands.

Mutual coupling, channel model, and BER for curvilinear antenna arrays

NASA Astrophysics Data System (ADS)

Huang, Zhiyong

This dissertation introduces a wireless communications system with an adaptive beam-former and investigates its performance with different antenna arrays. Mutual coupling, real antenna elements and channel models are included to examine the system performance. In a beamforming system, mutual coupling (MC) among the elements can significantly degrade the system performance. However, MC effects can be compensated if an accurate model of mutual coupling is available. A mutual coupling matrix model is utilized to compensate mutual coupling in the beamforming of a uniform circular array (UCA). Its performance is compared with other models in uplink and downlink beamforming scenarios. In addition, the predictions are compared with measurements and verified with results from full-wave simulations. In order to accurately investigate the minimum mean-square-error (MSE) of an adaptive array in MC, two different noise models, the environmental and the receiver noise, are modeled. The minimum MSEs with and without data domain MC compensation are analytically compared. The influence of mutual coupling on the convergence is also examined. In addition, the weight compensation method is proposed to attain the desired array pattern. Adaptive arrays with different geometries are implemented with the minimum MSE algorithm in the wireless communications system to combat interference at the same frequency. The bit-error-rate (BER) of systems with UCA, uniform rectangular array (URA) and UCA with center element are investigated in additive white Gaussian noise plus well-separated signals or random direction signals scenarios. The output SINR of an adaptive array with multiple interferers is analytically examined. The influence of the adaptive algorithm convergence on the BER is investigated. The UCA is then investigated in a narrowband Rician fading channel. The channel model is built and the space correlations are examined. The influence of the number of signal paths, number of the

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.

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

Real-time spectrum estimation–based dual-channel speech-enhancement algorithm for cochlear implant

PubMed Central

2012-01-01

Background Improvement of the cochlear implant (CI) front-end signal acquisition is needed to increase speech recognition in noisy environments. To suppress the directional noise, we introduce a speech-enhancement algorithm based on microphone array beamforming and spectral estimation. The experimental results indicate that this method is robust to directional mobile noise and strongly enhances the desired speech, thereby improving the performance of CI devices in a noisy environment. Methods The spectrum estimation and the array beamforming methods were combined to suppress the ambient noise. The directivity coefficient was estimated in the noise-only intervals, and was updated to fit for the mobile noise. Results The proposed algorithm was realized in the CI speech strategy. For actual parameters, we use Maxflat filter to obtain fractional sampling points and cepstrum method to differentiate the desired speech frame and the noise frame. The broadband adjustment coefficients were added to compensate the energy loss in the low frequency band. Discussions The approximation of the directivity coefficient is tested and the errors are discussed. We also analyze the algorithm constraint for noise estimation and distortion in CI processing. The performance of the proposed algorithm is analyzed and further be compared with other prevalent methods. Conclusions The hardware platform was constructed for the experiments. The speech-enhancement results showed that our algorithm can suppresses the non-stationary noise with high SNR. Excellent performance of the proposed algorithm was obtained in the speech enhancement experiments and mobile testing. And signal distortion results indicate that this algorithm is robust with high SNR improvement and low speech distortion. PMID:23006896

Application of X-Y Separable 2-D Array Beamforming for Increased Frame Rate and Energy Efficiency in Handheld Devices

PubMed Central

Owen, Kevin; Fuller, Michael I.; Hossack, John A.

2015-01-01

Two-dimensional arrays present significant beamforming computational challenges because of their high channel count and data rate. These challenges are even more stringent when incorporating a 2-D transducer array into a battery-powered hand-held device, placing significant demands on power efficiency. Previous work in sonar and ultrasound indicates that 2-D array beamforming can be decomposed into two separable line-array beamforming operations. This has been used in conjunction with frequency-domain phase-based focusing to achieve fast volume imaging. In this paper, we analyze the imaging and computational performance of approximate near-field separable beamforming for high-quality delay-and-sum (DAS) beamforming and for a low-cost, phaserotation-only beamforming method known as direct-sampled in-phase quadrature (DSIQ) beamforming. We show that when high-quality time-delay interpolation is used, separable DAS focusing introduces no noticeable imaging degradation under practical conditions. Similar results for DSIQ focusing are observed. In addition, a slight modification to the DSIQ focusing method greatly increases imaging contrast, making it comparable to that of DAS, despite having a wider main lobe and higher side lobes resulting from the limitations of phase-only time-delay interpolation. Compared with non-separable 2-D imaging, up to a 20-fold increase in frame rate is possible with the separable method. When implemented on a smart-phone-oriented processor to focus data from a 60 × 60 channel array using a 40 × 40 aperture, the frame rate per C-mode volume slice increases from 16 to 255 Hz for DAS, and from 11 to 193 Hz for DSIQ. Energy usage per frame is similarly reduced from 75 to 4.8 mJ/ frame for DAS, and from 107 to 6.3 mJ/frame for DSIQ. We also show that the separable method outperforms 2-D FFT-based focusing by a factor of 1.64 at these data sizes. This data indicates that with the optimal design choices, separable 2-D beamforming can

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 ...

An adaptive DPCM algorithm for predicting contours in NTSC composite video signals

NASA Astrophysics Data System (ADS)

Cox, N. R.

An adaptive DPCM algorithm is proposed for encoding digitized National Television Systems Committee (NTSC) color video signals. This algorithm essentially predicts picture contours in the composite signal without resorting to component separation. The contour parameters (slope thresholds) are optimized using four 'typical' television frames that have been sampled at three times the color subcarrier frequency. Three variations of the basic predictor are simulated and compared quantitatively with three non-adaptive predictors of similar complexity. By incorporating a dual-word-length coder and buffer memory, high quality color pictures can be encoded at 4.0 bits/pel or 42.95 Mbit/s. The effect of channel error propagation is also investigated.

Digital Beamforming Synthetic Aperture Radar Developments at NASA Goddard Space Flight Center

NASA Technical Reports Server (NTRS)

Rincon, Rafael; Fatoyinbo, Temilola; Osmanoglu, Batuhan; Lee, Seung Kuk; Du Toit, Cornelis F.; Perrine, Martin; Ranson, K. Jon; Sun, Guoqing; Deshpande, Manohar; Beck, Jaclyn;

Performance of velocity vector estimation using an improved dynamic beamforming setup

NASA Astrophysics Data System (ADS)

Munk, Peter; Jensen, Joergen A.

2001-05-01

Estimation of velocity vectors using transverse spatial modulation has previously been presented. Initially, the velocity estimation was improved using an approximated dynamic beamformer setup instead of a static combined with a new velocity estimation scheme. A new beamformer setup for dynamic control of the acoustic field, based on the Pulsed Plane Wave Decomposition (PPWD), is presented. The PPWD gives an unambiguous relation between a given acoustic field and the time functions needed on an array transducer for transmission. Applying this method for the receive beamformation results in a setup of the beamformer with different filters for each channel for each estimation depth. The method of the PPWD is illustrated by analytical expressions of the decomposed acoustic field and these results are used for simulation. Results of velocity estimates using the new setup are given on the basis of simulated and experimental data. The simulation setup is an attempt to approximate the situation present when performing a scanning of the carotid artery with a linear array. Measurement of the flow perpendicular to the emission direction is possible using the approach of transverse spatial modulation. This is most often the case in a scanning of the carotid artery, where the situation is handled by an angled Doppler setup in the present ultrasound scanners. The modulation period of 2 mm is controlled for a range of 20-40 mm which covers the typical range of the carotid artery. A 6 MHz array on a 128-channel system is simulated. The flow setup in the simulation is based on a vessel with a parabolic flow profile for a 60 and 90-degree flow angle. The experimental results are based on the backscattered signal from a sponge mounted in a stepping device. The bias and std. Dev. Of the velocity estimate are calculated for four different flow angles (50,60,75 and 90 degrees). The velocity vector is calculated using the improved 2D estimation approach at a range of depths.

Development of NASA's Next Generation L-Band Digital Beamforming Synthetic Aperture Radar (DBSAR-2)

NASA Technical Reports Server (NTRS)

Rincon, Rafael; Fatoyinbo, Temilola; Osmanoglu, Batuhan; Lee, Seung-Kuk; Ranson, K. Jon; Marrero, Victor; Yeary, Mark

2014-01-01

NASA's Next generation Digital Beamforming SAR (DBSAR-2) is a state-of-the-art airborne L-band radar developed at the NASA Goddard Space Flight Center (GSFC). The instrument builds upon the advanced architectures in NASA's DBSAR-1 and EcoSAR instruments. The new instrument employs a 16-channel radar architecture characterized by multi-mode operation, software defined waveform generation, digital beamforming, and configurable radar parameters. The instrument has been design to support several disciplines in Earth and Planetary sciences. The instrument was recently completed, and tested and calibrated in a anechoic chamber.

Compressive spherical beamforming for localization of incipient tip vortex cavitation.

PubMed

Choo, Youngmin; Seong, Woojae

2016-12-01

Noises by incipient propeller tip vortex cavitation (TVC) are generally generated at regions near the propeller tip. Localization of these sparse noises is performed using compressive sensing (CS) with measurement data from cavitation tunnel experiments. Since initial TVC sound radiates in all directions as a monopole source, a sensing matrix for CS is formulated by adopting spherical beamforming. CS localization is examined with known source acoustic measurements, where the CS estimated source position coincides with the known source position. Afterwards, CS is applied to initial cavitation noise cases. The result of cavitation localization was detected near the upper downstream area of the propeller and showed less ambiguity compared to Bartlett spherical beamforming. Standard constraint in CS was modified by exploiting the physical features of cavitation to suppress remaining ambiguity. CS localization of TVC using the modified constraint is shown according to cavitation numbers and compared to high-speed camera images.

Lateral velocity estimation bias due to beamforming delay errors (Conference Presentation)

NASA Astrophysics Data System (ADS)

Rodriguez-Molares, Alfonso; Fadnes, Solveig; Swillens, Abigail; Løvstakken, Lasse

2017-03-01

An artefact has recently been reported [1,2] in the estimation of the lateral blood velocity using speckle tracking. This artefact shows as a net velocity bias in presence of strong spatial velocity gradients such as those that occur at the edges of the filling jets in the heart. Even though this artifact has been found both in vitro and in simulated data, its causes are still undescribed. Here we demonstrate that a potential source of this artefact can be traced to smaller errors in the beamforming setup. By inserting a small offset in the beamforming delay, one can artificially create a net lateral movement in the speckle in areas of high velocity gradient. That offset does not have a strong impact in the image quality and can easily go undetected.

Noise-shaping gradient descent-based online adaptation algorithms for digital calibration of analog circuits.

PubMed

Chakrabartty, Shantanu; Shaga, Ravi K; Aono, Kenji

2013-04-01

Analog circuits that are calibrated using digital-to-analog converters (DACs) use a digital signal processor-based algorithm for real-time adaptation and programming of system parameters. In this paper, we first show that this conventional framework for adaptation yields suboptimal calibration properties because of artifacts introduced by quantization noise. We then propose a novel online stochastic optimization algorithm called noise-shaping or ΣΔ gradient descent, which can shape the quantization noise out of the frequency regions spanning the parameter adaptation trajectories. As a result, the proposed algorithms demonstrate superior parameter search properties compared to floating-point gradient methods and better convergence properties than conventional quantized gradient-methods. In the second part of this paper, we apply the ΣΔ gradient descent algorithm to two examples of real-time digital calibration: 1) balancing and tracking of bias currents, and 2) frequency calibration of a band-pass Gm-C biquad filter biased in weak inversion. For each of these examples, the circuits have been prototyped in a 0.5-μm complementary metal-oxide-semiconductor process, and we demonstrate that the proposed algorithm is able to find the optimal solution even in the presence of spurious local minima, which are introduced by the nonlinear and non-monotonic response of calibration DACs.

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.

Comparing Binaural Pre-processing Strategies II: Speech Intelligibility of Bilateral Cochlear Implant Users.

PubMed

Baumgärtel, Regina M; Hu, Hongmei; Krawczyk-Becker, Martin; Marquardt, Daniel; Herzke, Tobias; Coleman, Graham; Adiloğlu, Kamil; Bomke, Katrin; Plotz, Karsten; Gerkmann, Timo; Doclo, Simon; Kollmeier, Birger; Hohmann, Volker; Dietz, Mathias

2015-12-30

Several binaural audio signal enhancement algorithms were evaluated with respect to their potential to improve speech intelligibility in noise for users of bilateral cochlear implants (CIs). 50% speech reception thresholds (SRT50) were assessed using an adaptive procedure in three distinct, realistic noise scenarios. All scenarios were highly nonstationary, complex, and included a significant amount of reverberation. Other aspects, such as the perfectly frontal target position, were idealized laboratory settings, allowing the algorithms to perform better than in corresponding real-world conditions. Eight bilaterally implanted CI users, wearing devices from three manufacturers, participated in the study. In all noise conditions, a substantial improvement in SRT50 compared to the unprocessed signal was observed for most of the algorithms tested, with the largest improvements generally provided by binaural minimum variance distortionless response (MVDR) beamforming algorithms. The largest overall improvement in speech intelligibility was achieved by an adaptive binaural MVDR in a spatially separated, single competing talker noise scenario. A no-pre-processing condition and adaptive differential microphones without a binaural link served as the two baseline conditions. SRT50 improvements provided by the binaural MVDR beamformers surpassed the performance of the adaptive differential microphones in most cases. Speech intelligibility improvements predicted by instrumental measures were shown to account for some but not all aspects of the perceptually obtained SRT50 improvements measured in bilaterally implanted CI users. © The Author(s) 2015.

Comparing Binaural Pre-processing Strategies II

PubMed Central

Hu, Hongmei; Krawczyk-Becker, Martin; Marquardt, Daniel; Herzke, Tobias; Coleman, Graham; Adiloğlu, Kamil; Bomke, Katrin; Plotz, Karsten; Gerkmann, Timo; Doclo, Simon; Kollmeier, Birger; Hohmann, Volker; Dietz, Mathias

2015-01-01

Several binaural audio signal enhancement algorithms were evaluated with respect to their potential to improve speech intelligibility in noise for users of bilateral cochlear implants (CIs). 50% speech reception thresholds (SRT50) were assessed using an adaptive procedure in three distinct, realistic noise scenarios. All scenarios were highly nonstationary, complex, and included a significant amount of reverberation. Other aspects, such as the perfectly frontal target position, were idealized laboratory settings, allowing the algorithms to perform better than in corresponding real-world conditions. Eight bilaterally implanted CI users, wearing devices from three manufacturers, participated in the study. In all noise conditions, a substantial improvement in SRT50 compared to the unprocessed signal was observed for most of the algorithms tested, with the largest improvements generally provided by binaural minimum variance distortionless response (MVDR) beamforming algorithms. The largest overall improvement in speech intelligibility was achieved by an adaptive binaural MVDR in a spatially separated, single competing talker noise scenario. A no-pre-processing condition and adaptive differential microphones without a binaural link served as the two baseline conditions. SRT50 improvements provided by the binaural MVDR beamformers surpassed the performance of the adaptive differential microphones in most cases. Speech intelligibility improvements predicted by instrumental measures were shown to account for some but not all aspects of the perceptually obtained SRT50 improvements measured in bilaterally implanted CI users. PMID:26721921

Improved adaptive genetic algorithm with sparsity constraint applied to thermal neutron CT reconstruction of two-phase flow

NASA Astrophysics Data System (ADS)

Yan, Mingfei; Hu, Huasi; Otake, Yoshie; Taketani, Atsushi; Wakabayashi, Yasuo; Yanagimachi, Shinzo; Wang, Sheng; Pan, Ziheng; Hu, Guang

2018-05-01

Thermal neutron computer tomography (CT) is a useful tool for visualizing two-phase flow due to its high imaging contrast and strong penetrability of neutrons for tube walls constructed with metallic material. A novel approach for two-phase flow CT reconstruction based on an improved adaptive genetic algorithm with sparsity constraint (IAGA-SC) is proposed in this paper. In the algorithm, the neighborhood mutation operator is used to ensure the continuity of the reconstructed object. The adaptive crossover probability P c and mutation probability P m are improved to help the adaptive genetic algorithm (AGA) achieve the global optimum. The reconstructed results for projection data, obtained from Monte Carlo simulation, indicate that the comprehensive performance of the IAGA-SC algorithm exceeds the adaptive steepest descent-projection onto convex sets (ASD-POCS) algorithm in restoring typical and complex flow regimes. It especially shows great advantages in restoring the simply connected flow regimes and the shape of object. In addition, the CT experiment for two-phase flow phantoms was conducted on the accelerator-driven neutron source to verify the performance of the developed IAGA-SC algorithm.

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.

An adaptive SVSF-SLAM algorithm to improve the success and solving the UGVs cooperation problem

NASA Astrophysics Data System (ADS)

Demim, Fethi; Nemra, Abdelkrim; Louadj, Kahina; Hamerlain, Mustapha; Bazoula, Abdelouahab

2018-05-01

This paper aims to present a Decentralised Cooperative Simultaneous Localization and Mapping (DCSLAM) solution based on 2D laser data using an Adaptive Covariance Intersection (ACI). The ACI-DCSLAM algorithm will be validated on a swarm of Unmanned Ground Vehicles (UGVs) receiving features to estimate the position and covariance of shared features before adding them to the global map. With the proposed solution, a group of (UGVs) will be able to construct a large reliable map and localise themselves within this map without any user intervention. The most popular solutions to this problem are the EKF-SLAM, Nonlinear H-infinity ? SLAM and the FAST-SLAM. The former suffers from two important problems which are the poor consistency caused by the linearization problem and the calculation of Jacobian. The second solution is the ? which is a very promising filter because it doesn't make any assumption about noise characteristics, while the latter is not suitable for real time implementation. Therefore, a new alternative solution based on the smooth variable structure filter (SVSF) is adopted. Cooperative adaptive SVSF-SLAM algorithm is proposed in this paper to solve the UGVs SLAM problem. Our main contribution consists in adapting the SVSF filter to solve the Decentralised Cooperative SLAM problem for multiple UGVs. The algorithms developed in this paper were implemented using two mobile robots Pioneer ?, equiped with 2D laser telemetry sensors. Good results are obtained by the Cooperative adaptive SVSF-SLAM algorithm compared to the Cooperative EKF/?-SLAM algorithms, especially when the noise is colored or affected by a variable bias. Simulation results confirm and show the efficiency of the proposed algorithm which is more robust, stable and adapted to real time applications.

Real time optimization algorithm for wavefront sensorless adaptive optics OCT (Conference Presentation)

NASA Astrophysics Data System (ADS)

Verstraete, Hans R. G. W.; Heisler, Morgan; Ju, Myeong Jin; Wahl, Daniel J.; Bliek, Laurens; Kalkman, Jeroen; Bonora, Stefano; Sarunic, Marinko V.; Verhaegen, Michel; Jian, Yifan

2017-02-01

Optical Coherence Tomography (OCT) has revolutionized modern ophthalmology, providing depth resolved images of the retinal layers in a system that is suited to a clinical environment. A limitation of the performance and utilization of the OCT systems has been the lateral resolution. Through the combination of wavefront sensorless adaptive optics with dual variable optical elements, we present a compact lens based OCT system that is capable of imaging the photoreceptor mosaic. We utilized a commercially available variable focal length lens to correct for a wide range of defocus commonly found in patient eyes, and a multi-actuator adaptive lens after linearization of the hysteresis in the piezoelectric actuators for aberration correction to obtain near diffraction limited imaging at the retina. A parallel processing computational platform permitted real-time image acquisition and display. The Data-based Online Nonlinear Extremum seeker (DONE) algorithm was used for real time optimization of the wavefront sensorless adaptive optics OCT, and the performance was compared with a coordinate search algorithm. Cross sectional images of the retinal layers and en face images of the cone photoreceptor mosaic acquired in vivo from research volunteers before and after WSAO optimization are presented. Applying the DONE algorithm in vivo for wavefront sensorless AO-OCT demonstrates that the DONE algorithm succeeds in drastically improving the signal while achieving a computational time of 1 ms per iteration, making it applicable for high speed real time applications.

Principles of Adaptive Array Processing

DTIC Science & Technology

2006-09-01

ACE with and without tapering (homogeneous case). These analytical results are less suited to predict the detection performance of a real system ...Nickel: Adaptive Beamforming for Phased Array Radars. Proc. Int. Radar Symposium IRS’98 (Munich, Sept. 1998), DGON and VDE /ITG, pp. 897-906.(Reprint also...strategies for airborne radar. Asilomar Conf. on Signals, Systems and Computers, Pacific Grove, CA, 1998, IEEE Cat.Nr. 0-7803-5148-7/98, pp. 1327-1331. [17

Analysis on accuracy improvement of rotor-stator rubbing localization based on acoustic emission beamforming method.

PubMed

He, Tian; Xiao, Denghong; Pan, Qiang; Liu, Xiandong; Shan, Yingchun

2014-01-01

This paper attempts to introduce an improved acoustic emission (AE) beamforming method to localize rotor-stator rubbing fault in rotating machinery. To investigate the propagation characteristics of acoustic emission signals in casing shell plate of rotating machinery, the plate wave theory is used in a thin plate. A simulation is conducted and its result shows the localization accuracy of beamforming depends on multi-mode, dispersion, velocity and array dimension. In order to reduce the effect of propagation characteristics on the source localization, an AE signal pre-process method is introduced by combining plate wave theory and wavelet packet transform. And the revised localization velocity to reduce effect of array size is presented. The accuracy of rubbing localization based on beamforming and the improved method of present paper are compared by the rubbing test carried on a test table of rotating machinery. The results indicate that the improved method can localize rub fault effectively. Copyright © 2013 Elsevier B.V. All rights reserved.

Cobalt: A GPU-based correlator and beamformer for LOFAR

NASA Astrophysics Data System (ADS)

Broekema, P. Chris; Mol, J. Jan David; Nijboer, R.; van Amesfoort, A. S.; Brentjens, M. A.; Loose, G. Marcel; Klijn, W. F. A.; Romein, J. W.

2018-04-01

For low-frequency radio astronomy, software correlation and beamforming on general purpose hardware is a viable alternative to custom designed hardware. LOFAR, a new-generation radio telescope centered in the Netherlands with international stations in Germany, France, Ireland, Poland, Sweden and the UK, has successfully used software real-time processors based on IBM Blue Gene technology since 2004. Since then, developments in technology have allowed us to build a system based on commercial off-the-shelf components that combines the same capabilities with lower operational cost. In this paper, we describe the design and implementation of a GPU-based correlator and beamformer with the same capabilities as the Blue Gene based systems. We focus on the design approach taken, and show the challenges faced in selecting an appropriate system. The design, implementation and verification of the software system show the value of a modern test-driven development approach. Operational experience, based on three years of operations, demonstrates that a general purpose system is a good alternative to the previous supercomputer-based system or custom-designed hardware.

Delay-and-sum beamforming for direction of arrival estimation applied to gunshot acoustics

NASA Astrophysics Data System (ADS)

Ramos, António L. L.; Holm, Sverre; Gudvangen, Sigmund; Otterlei, Ragnvald

2011-06-01

Sniper positioning systems described in the literature use a two-step algorithm to estimate the sniper's location. First, the shockwave and the muzzle blast acoustic signatures must be detected and recognized, followed by an estimation of their respective direction-of-arrival (DOA). Second, the actual sniper's position is calculated based on the estimated DOA via an iterative algorithm that varies from system to system. The overall performance of such a system, however, is highly compromised when the first step is not carried out successfully. Currently available systems rely on a simple calculation of differences of time-of-arrival to estimate angles-of-arrival. This approach, however, lacks robustness by not taking full advantage of the array of sensors. This paper shows how the delay-and-sum beamforming technique can be applied to estimate the DOA for both the shockwave and the muzzle blast. The method has the twofold advantage of 1) adding an array gain of 10 logM, i.e., an increased SNR of 6 dB for a 4-microphone array, which is equivalent to doubling the detection range assuming free-field propagation; and 2) offering improved robustness in handling single- and multi-shots events as well as reflections by taking advantage of the spatial filtering capability.

Adaptive optics compensation of orbital angular momentum beams with a modified Gerchberg-Saxton-based phase retrieval algorithm

NASA Astrophysics Data System (ADS)

Chang, Huan; Yin, Xiao-li; Cui, Xiao-zhou; Zhang, Zhi-chao; Ma, Jian-xin; Wu, Guo-hua; Zhang, Li-jia; Xin, Xiang-jun

2017-12-01

Practical orbital angular momentum (OAM)-based free-space optical (FSO) communications commonly experience serious performance degradation and crosstalk due to atmospheric turbulence. In this paper, we propose a wave-front sensorless adaptive optics (WSAO) system with a modified Gerchberg-Saxton (GS)-based phase retrieval algorithm to correct distorted OAM beams. We use the spatial phase perturbation (SPP) GS algorithm with a distorted probe Gaussian beam as the only input. The principle and parameter selections of the algorithm are analyzed, and the performance of the algorithm is discussed. The simulation results show that the proposed adaptive optics (AO) system can significantly compensate for distorted OAM beams in single-channel or multiplexed OAM systems, which provides new insights into adaptive correction systems using OAM beams.

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.

Software-based approach toward vendor independent real-time photoacoustic imaging using ultrasound beamformed data

NASA Astrophysics Data System (ADS)

Zhang, Haichong K.; Huang, Howard; Lei, Chen; Kim, Younsu; Boctor, Emad M.

2017-03-01

Photoacoustic (PA) imaging has shown its potential for many clinical applications, but current research and usage of PA imaging are constrained by additional hardware costs to collect channel data, as the PA signals are incorrectly processed in existing clinical ultrasound systems. This problem arises from the fact that ultrasound systems beamform the PA signals as echoes from the ultrasound transducer instead of directly from illuminated sources. Consequently, conventional implementations of PA imaging rely on parallel channel acquisition from research platforms, which are not only slow and expensive, but are also mostly not approved by the FDA for clinical use. In previous studies, we have proposed the synthetic-aperture based photoacoustic re-beamformer (SPARE) that uses ultrasound beamformed radio frequency (RF) data as the input, which is readily available in clinical ultrasound scanners. The goal of this work is to implement the SPARE beamformer in a clinical ultrasound system, and to experimentally demonstrate its real-time visualization. Assuming a high pulsed repetition frequency (PRF) laser is used, a PZT-based pseudo PA source transmission was synchronized with the ultrasound line trigger. As a result, the frame-rate increases when limiting the image field-of-view (FOV), with 50 to 20 frames per second achieved for FOVs from 35 mm to 70 mm depth, respectively. Although in reality the maximum PRF of laser firing limits the PA image frame rate, this result indicates that the developed software is capable of displaying PA images with the maximum possible frame-rate for certain laser system without acquiring channel data.

Performance Evaluation of Analog Beamforming with Hardware Impairments for mmW Massive MIMO Communication in an Urban Scenario.

PubMed

Gimenez, Sonia; Roger, Sandra; Baracca, Paolo; Martín-Sacristán, David; Monserrat, Jose F; Braun, Volker; Halbauer, Hardy

2016-09-22

The use of massive multiple-input multiple-output (MIMO) techniques for communication at millimeter-Wave (mmW) frequency bands has become a key enabler to meet the data rate demands of the upcoming fifth generation (5G) cellular systems. In particular, analog and hybrid beamforming solutions are receiving increasing attention as less expensive and more power efficient alternatives to fully digital precoding schemes. Despite their proven good performance in simple setups, their suitability for realistic cellular systems with many interfering base stations and users is still unclear. Furthermore, the performance of massive MIMO beamforming and precoding methods are in practice also affected by practical limitations and hardware constraints. In this sense, this paper assesses the performance of digital precoding and analog beamforming in an urban cellular system with an accurate mmW channel model under both ideal and realistic assumptions. The results show that analog beamforming can reach the performance of fully digital maximum ratio transmission under line of sight conditions and with a sufficient number of parallel radio-frequency (RF) chains, especially when the practical limitations of outdated channel information and per antenna power constraints are considered. This work also shows the impact of the phase shifter errors and combiner losses introduced by real phase shifter and combiner implementations over analog beamforming, where the former ones have minor impact on the performance, while the latter ones determine the optimum number of RF chains to be used in practice.

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

Fully implicit moving mesh adaptive algorithm

NASA Astrophysics Data System (ADS)

Serazio, C.; Chacon, L.; Lapenta, G.

2006-10-01

In many problems of interest, the numerical modeler is faced with the challenge of dealing with multiple time and length scales. The former is best dealt with with fully implicit methods, which are able to step over fast frequencies to resolve the dynamical time scale of interest. The latter requires grid adaptivity for efficiency. Moving-mesh grid adaptive methods are attractive because they can be designed to minimize the numerical error for a given resolution. However, the required grid governing equations are typically very nonlinear and stiff, and of considerably difficult numerical treatment. Not surprisingly, fully coupled, implicit approaches where the grid and the physics equations are solved simultaneously are rare in the literature, and circumscribed to 1D geometries. In this study, we present a fully implicit algorithm for moving mesh methods that is feasible for multidimensional geometries. Crucial elements are the development of an effective multilevel treatment of the grid equation, and a robust, rigorous error estimator. For the latter, we explore the effectiveness of a coarse grid correction error estimator, which faithfully reproduces spatial truncation errors for conservative equations. We will show that the moving mesh approach is competitive vs. uniform grids both in accuracy (due to adaptivity) and efficiency. Results for a variety of models 1D and 2D geometries will be presented. L. Chac'on, G. Lapenta, J. Comput. Phys., 212 (2), 703 (2006) G. Lapenta, L. Chac'on, J. Comput. Phys., accepted (2006)

Identification of robust adaptation gene regulatory network parameters using an improved particle swarm optimization algorithm.

PubMed

Huang, X N; Ren, H P

2016-05-13

Robust adaptation is a critical ability of gene regulatory network (GRN) to survive in a fluctuating environment, which represents the system responding to an input stimulus rapidly and then returning to its pre-stimulus steady state timely. In this paper, the GRN is modeled using the Michaelis-Menten rate equations, which are highly nonlinear differential equations containing 12 undetermined parameters. The robust adaption is quantitatively described by two conflicting indices. To identify the parameter sets in order to confer the GRNs with robust adaptation is a multi-variable, multi-objective, and multi-peak optimization problem, which is difficult to acquire satisfactory solutions especially high-quality solutions. A new best-neighbor particle swarm optimization algorithm is proposed to implement this task. The proposed algorithm employs a Latin hypercube sampling method to generate the initial population. The particle crossover operation and elitist preservation strategy are also used in the proposed algorithm. The simulation results revealed that the proposed algorithm could identify multiple solutions in one time running. Moreover, it demonstrated a superior performance as compared to the previous methods in the sense of detecting more high-quality solutions within an acceptable time. The proposed methodology, owing to its universality and simplicity, is useful for providing the guidance to design GRN with superior robust adaptation.

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.

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.

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

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

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.

An Adaptive Cultural Algorithm with Improved Quantum-behaved Particle Swarm Optimization for Sonar Image Detection.

PubMed

Wang, Xingmei; Hao, Wenqian; Li, Qiming

2017-12-18

This paper proposes an adaptive cultural algorithm with improved quantum-behaved particle swarm optimization (ACA-IQPSO) to detect the underwater sonar image. In the population space, to improve searching ability of particles, iterative times and the fitness value of particles are regarded as factors to adaptively adjust the contraction-expansion coefficient of the quantum-behaved particle swarm optimization algorithm (QPSO). The improved quantum-behaved particle swarm optimization algorithm (IQPSO) can make particles adjust their behaviours according to their quality. In the belief space, a new update strategy is adopted to update cultural individuals according to the idea of the update strategy in shuffled frog leaping algorithm (SFLA). Moreover, to enhance the utilization of information in the population space and belief space, accept function and influence function are redesigned in the new communication protocol. The experimental results show that ACA-IQPSO can obtain good clustering centres according to the grey distribution information of underwater sonar images, and accurately complete underwater objects detection. Compared with other algorithms, the proposed ACA-IQPSO has good effectiveness, excellent adaptability, a powerful searching ability and high convergence efficiency. Meanwhile, the experimental results of the benchmark functions can further demonstrate that the proposed ACA-IQPSO has better searching ability, convergence efficiency and stability.

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.

Ka-Band Digital Beamforming and SweepSAR Demonstration for Ice and Solid Earth Topography

NASA Technical Reports Server (NTRS)

Sadowy, Gregory; Ghaemi, Hirad; Heavy, Brandon; Perkovic, Dragana; Quddus, Momin; Zawadzki, Mark; Moller, Delwyn

2010-01-01

GLISTIN is an instrument concept for a single-pass interferometric SAR operating at 35.6 GHz. To achieve large swath widths using practical levels of transmitter power, a digitally-beamformed planar waveguide array is used. This paper describes results from a ground-based demonstration of a 16-receiver prototype. Furthermore, SweepSAR is emerging as promising technique for achieving very wide swaths for surface change detection. NASA and DLR are studying this approach for the DESDynI and Tandem-L missions. SweepSAR employs a reflector with a digitally-beamformed array feed. We will describe development of an airborne demonstration of SweepSAR using the GLISTIN receiver array and a reflector.

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.

Robust Adaptive Modified Newton Algorithm for Generalized Eigendecomposition and Its Application

NASA Astrophysics Data System (ADS)

Yang, Jian; Yang, Feng; Xi, Hong-Sheng; Guo, Wei; Sheng, Yanmin

2007-12-01

We propose a robust adaptive algorithm for generalized eigendecomposition problems that arise in modern signal processing applications. To that extent, the generalized eigendecomposition problem is reinterpreted as an unconstrained nonlinear optimization problem. Starting from the proposed cost function and making use of an approximation of the Hessian matrix, a robust modified Newton algorithm is derived. A rigorous analysis of its convergence properties is presented by using stochastic approximation theory. We also apply this theory to solve the signal reception problem of multicarrier DS-CDMA to illustrate its practical application. The simulation results show that the proposed algorithm has fast convergence and excellent tracking capability, which are important in a practical time-varying communication environment.

Adaptive lesion formation using dual mode ultrasound array system

NASA Astrophysics Data System (ADS)

Liu, Dalong; Casper, Andrew; Haritonova, Alyona; Ebbini, Emad S.

2017-03-01

We present the results from an ultrasound-guided focused ultrasound platform designed to perform real-time monitoring and control of lesion formation. Real-time signal processing of echogenicity changes during lesion formation allows for identification of signature events indicative of tissue damage. The detection of these events triggers the cessation or the reduction of the exposure (intensity and/or time) to prevent overexposure. A dual mode ultrasound array (DMUA) is used for forming single- and multiple-focus patterns in a variety of tissues. The DMUA approach allows for inherent registration between the therapeutic and imaging coordinate systems providing instantaneous, spatially-accurate feedback on lesion formation dynamics. The beamformed RF data has been shown to have high sensitivity and specificity to tissue changes during lesion formation, including in vivo. In particular, the beamformed echo data from the DMUA is very sensitive to cavitation activity in response to HIFU in a variety of modes, e.g. boiling cavitation. This form of feedback is characterized by sudden increase in echogenicity that could occur within milliseconds of the application of HIFU (see http://youtu.be/No2wh-ceTLs for an example). The real-time beamforming and signal processing allowing the adaptive control of lesion formation is enabled by a high performance GPU platform (response time within 10 msec). We present results from a series of experiments in bovine cardiac tissue demonstrating the robustness and increased speed of volumetric lesion formation for a range of clinically-relevant exposures. Gross histology demonstrate clearly that adaptive lesion formation results in tissue damage consistent with the size of the focal spot and the raster scan in 3 dimensions. In contrast, uncontrolled volumetric lesions exhibit significant pre-focal buildup due to excessive exposure from multiple full-exposure HIFU shots. Stopping or reducing the HIFU exposure upon the detection of such an

Adaptive Swarm Balancing Algorithms for rare-event prediction in imbalanced healthcare data

PubMed Central

Wong, Raymond K.; Mohammed, Sabah; Fiaidhi, Jinan; Sung, Yunsick

2017-01-01

Clinical data analysis and forecasting have made substantial contributions to disease control, prevention and detection. However, such data usually suffer from highly imbalanced samples in class distributions. In this paper, we aim to formulate effective methods to rebalance binary imbalanced dataset, where the positive samples take up only the minority. We investigate two different meta-heuristic algorithms, particle swarm optimization and bat algorithm, and apply them to empower the effects of synthetic minority over-sampling technique (SMOTE) for pre-processing the datasets. One approach is to process the full dataset as a whole. The other is to split up the dataset and adaptively process it one segment at a time. The experimental results reported in this paper reveal that the performance improvements obtained by the former methods are not scalable to larger data scales. The latter methods, which we call Adaptive Swarm Balancing Algorithms, lead to significant efficiency and effectiveness improvements on large datasets while the first method is invalid. We also find it more consistent with the practice of the typical large imbalanced medical datasets. We further use the meta-heuristic algorithms to optimize two key parameters of SMOTE. The proposed methods lead to more credible performances of the classifier, and shortening the run time compared to brute-force method. PMID:28753613

Analysis of Online DBA Algorithm with Adaptive Sleep Cycle in WDM EPON

NASA Astrophysics Data System (ADS)

Pajčin, Bojan; Matavulj, Petar; Radivojević, Mirjana

2018-05-01

In order to manage Quality of Service (QoS) and energy efficiency in the optical access network, an online Dynamic Bandwidth Allocation (DBA) algorithm with adaptive sleep cycle is presented. This DBA algorithm has the ability to allocate an additional bandwidth to the end user within a single sleep cycle whose duration changes depending on the current buffers occupancy. The purpose of this DBA algorithm is to tune the duration of the sleep cycle depending on the network load in order to provide service to the end user without violating strict QoS requests in all network operating conditions.

An Adaptive Evolutionary Algorithm for Traveling Salesman Problem with Precedence Constraints

PubMed Central

Sung, Jinmo; Jeong, Bongju

2014-01-01

Traveling sales man problem with precedence constraints is one of the most notorious problems in terms of the efficiency of its solution approach, even though it has very wide range of industrial applications. We propose a new evolutionary algorithm to efficiently obtain good solutions by improving the search process. Our genetic operators guarantee the feasibility of solutions over the generations of population, which significantly improves the computational efficiency even when it is combined with our flexible adaptive searching strategy. The efficiency of the algorithm is investigated by computational experiments. PMID:24701158

An adaptive evolutionary algorithm for traveling salesman problem with precedence constraints.

PubMed

Sung, Jinmo; Jeong, Bongju

2014-01-01

Traveling sales man problem with precedence constraints is one of the most notorious problems in terms of the efficiency of its solution approach, even though it has very wide range of industrial applications. We propose a new evolutionary algorithm to efficiently obtain good solutions by improving the search process. Our genetic operators guarantee the feasibility of solutions over the generations of population, which significantly improves the computational efficiency even when it is combined with our flexible adaptive searching strategy. The efficiency of the algorithm is investigated by computational experiments.

Wavefront sensorless adaptive optics OCT with the DONE algorithm for in vivo human retinal imaging [Invited].

PubMed

Verstraete, Hans R G W; Heisler, Morgan; Ju, Myeong Jin; Wahl, Daniel; Bliek, Laurens; Kalkman, Jeroen; Bonora, Stefano; Jian, Yifan; Verhaegen, Michel; Sarunic, Marinko V

2017-04-01

In this report, which is an international collaboration of OCT, adaptive optics, and control research, we demonstrate the Data-based Online Nonlinear Extremum-seeker (DONE) algorithm to guide the image based optimization for wavefront sensorless adaptive optics (WFSL-AO) OCT for in vivo human retinal imaging. The ocular aberrations were corrected using a multi-actuator adaptive lens after linearization of the hysteresis in the piezoelectric actuators. The DONE algorithm succeeded in drastically improving image quality and the OCT signal intensity, up to a factor seven, while achieving a computational time of 1 ms per iteration, making it applicable for many high speed applications. We demonstrate the correction of five aberrations using 70 iterations of the DONE algorithm performed over 2.8 s of continuous volumetric OCT acquisition. Data acquired from an imaging phantom and in vivo from human research volunteers are presented.

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...

Analysis of convergence of an evolutionary algorithm with self-adaptation using a stochastic Lyapunov function.

PubMed

Semenov, Mikhail A; Terkel, Dmitri A

2003-01-01

This paper analyses the convergence of evolutionary algorithms using a technique which is based on a stochastic Lyapunov function and developed within the martingale theory. This technique is used to investigate the convergence of a simple evolutionary algorithm with self-adaptation, which contains two types of parameters: fitness parameters, belonging to the domain of the objective function; and control parameters, responsible for the variation of fitness parameters. Although both parameters mutate randomly and independently, they converge to the "optimum" due to the direct (for fitness parameters) and indirect (for control parameters) selection. We show that the convergence velocity of the evolutionary algorithm with self-adaptation is asymptotically exponential, similar to the velocity of the optimal deterministic algorithm on the class of unimodal functions. Although some martingale inequalities have not be proved analytically, they have been numerically validated with 0.999 confidence using Monte-Carlo simulations.

Advanced Dynamically Adaptive Algorithms for Stochastic Simulations on Extreme Scales

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xiu, Dongbin

2017-03-03

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

EMD self-adaptive selecting relevant modes algorithm for FBG spectrum signal

NASA Astrophysics Data System (ADS)

Chen, Yong; Wu, Chun-ting; Liu, Huan-lin

2017-07-01

Noise may reduce the demodulation accuracy of fiber Bragg grating (FBG) sensing signal so as to affect the quality of sensing detection. Thus, the recovery of a signal from observed noisy data is necessary. In this paper, a precise self-adaptive algorithm of selecting relevant modes is proposed to remove the noise of signal. Empirical mode decomposition (EMD) is first used to decompose a signal into a set of modes. The pseudo modes cancellation is introduced to identify and eliminate false modes, and then the Mutual Information (MI) of partial modes is calculated. MI is used to estimate the critical point of high and low frequency components. Simulation results show that the proposed algorithm estimates the critical point more accurately than the traditional algorithms for FBG spectral signal. While, compared to the similar algorithms, the signal noise ratio of the signal can be improved more than 10 dB after processing by the proposed algorithm, and correlation coefficient can be increased by 0.5, so it demonstrates better de-noising effect.

Wavefront sensorless adaptive optics OCT with the DONE algorithm for in vivo human retinal imaging [Invited

PubMed Central

Verstraete, Hans R. G. W.; Heisler, Morgan; Ju, Myeong Jin; Wahl, Daniel; Bliek, Laurens; Kalkman, Jeroen; Bonora, Stefano; Jian, Yifan; Verhaegen, Michel; Sarunic, Marinko V.

2017-01-01

In this report, which is an international collaboration of OCT, adaptive optics, and control research, we demonstrate the Data-based Online Nonlinear Extremum-seeker (DONE) algorithm to guide the image based optimization for wavefront sensorless adaptive optics (WFSL-AO) OCT for in vivo human retinal imaging. The ocular aberrations were corrected using a multi-actuator adaptive lens after linearization of the hysteresis in the piezoelectric actuators. The DONE algorithm succeeded in drastically improving image quality and the OCT signal intensity, up to a factor seven, while achieving a computational time of 1 ms per iteration, making it applicable for many high speed applications. We demonstrate the correction of five aberrations using 70 iterations of the DONE algorithm performed over 2.8 s of continuous volumetric OCT acquisition. Data acquired from an imaging phantom and in vivo from human research volunteers are presented. PMID:28736670

Research on adaptive optics image restoration algorithm based on improved joint maximum a posteriori method

NASA Astrophysics Data System (ADS)

Zhang, Lijuan; Li, Yang; Wang, Junnan; Liu, Ying

2018-03-01

In this paper, we propose a point spread function (PSF) reconstruction method and joint maximum a posteriori (JMAP) estimation method for the adaptive optics image restoration. Using the JMAP method as the basic principle, we establish the joint log likelihood function of multi-frame adaptive optics (AO) images based on the image Gaussian noise models. To begin with, combining the observed conditions and AO system characteristics, a predicted PSF model for the wavefront phase effect is developed; then, we build up iterative solution formulas of the AO image based on our proposed algorithm, addressing the implementation process of multi-frame AO images joint deconvolution method. We conduct a series of experiments on simulated and real degraded AO images to evaluate our proposed algorithm. Compared with the Wiener iterative blind deconvolution (Wiener-IBD) algorithm and Richardson-Lucy IBD algorithm, our algorithm has better restoration effects including higher peak signal-to-noise ratio ( PSNR) and Laplacian sum ( LS) value than the others. The research results have a certain application values for actual AO image restoration.

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.

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

Sparse matrix beamforming and image reconstruction for 2-D HIFU monitoring using harmonic motion imaging for focused ultrasound (HMIFU) with in vitro validation.

PubMed

Hou, Gary Y; Provost, Jean; Grondin, Julien; Wang, Shutao; Marquet, Fabrice; Bunting, Ethan; Konofagou, Elisa E

2014-11-01

Harmonic motion imaging for focused ultrasound (HMIFU) utilizes an amplitude-modulated HIFU beam to induce a localized focal oscillatory motion simultaneously estimated. The objective of this study is to develop and show the feasibility of a novel fast beamforming algorithm for image reconstruction using GPU-based sparse-matrix operation with real-time feedback. In this study, the algorithm was implemented onto a fully integrated, clinically relevant HMIFU system. A single divergent transmit beam was used while fast beamforming was implemented using a GPU-based delay-and-sum method and a sparse-matrix operation. Axial HMI displacements were then estimated from the RF signals using a 1-D normalized cross-correlation method and streamed to a graphic user interface with frame rates up to 15 Hz, a 100-fold increase compared to conventional CPU-based processing. The real-time feedback rate does not require interrupting the HIFU treatment. Results in phantom experiments showed reproducible HMI images and monitoring of 22 in vitro HIFU treatments using the new 2-D system demonstrated reproducible displacement imaging, and monitoring of 22 in vitro HIFU treatments using the new 2-D system showed a consistent average focal displacement decrease of 46.7 ±14.6% during lesion formation. Complementary focal temperature monitoring also indicated an average rate of displacement increase and decrease with focal temperature at 0.84±1.15%/(°)C, and 2.03±0.93%/(°)C , respectively. These results reinforce the HMIFU capability of estimating and monitoring stiffness related changes in real time. Current ongoing studies include clinical translation of the presented system for monitoring of HIFU treatment for breast and pancreatic tumor applications.

A globally convergent MC algorithm with an adaptive learning rate.

PubMed

Peng, Dezhong; Yi, Zhang; Xiang, Yong; Zhang, Haixian

2012-02-01

This brief deals with the problem of minor component analysis (MCA). Artificial neural networks can be exploited to achieve the task of MCA. Recent research works show that convergence of neural networks based MCA algorithms can be guaranteed if the learning rates are less than certain thresholds. However, the computation of these thresholds needs information about the eigenvalues of the autocorrelation matrix of data set, which is unavailable in online extraction of minor component from input data stream. In this correspondence, we introduce an adaptive learning rate into the OJAn MCA algorithm, such that its convergence condition does not depend on any unobtainable information, and can be easily satisfied in practical applications.

Angular-contact ball-bearing internal load estimation algorithm using runtime adaptive relaxation

NASA Astrophysics Data System (ADS)

Medina, H.; Mutu, R.

2017-07-01

An algorithm to estimate internal loads for single-row angular contact ball bearings due to externally applied thrust loads and high-operating speeds is presented. A new runtime adaptive relaxation procedure and blending function is proposed which ensures algorithm stability whilst also reducing the number of iterations needed to reach convergence, leading to an average reduction in computation time in excess of approximately 80%. The model is validated based on a 218 angular contact bearing and shows excellent agreement compared to published results.

Reconstruction of sparse-view X-ray computed tomography using adaptive iterative algorithms.

PubMed

Liu, Li; Lin, Weikai; Jin, Mingwu

2015-01-01

In this paper, we propose two reconstruction algorithms for sparse-view X-ray computed tomography (CT). Treating the reconstruction problems as data fidelity constrained total variation (TV) minimization, both algorithms adapt the alternate two-stage strategy: projection onto convex sets (POCS) for data fidelity and non-negativity constraints and steepest descent for TV minimization. The novelty of this work is to determine iterative parameters automatically from data, thus avoiding tedious manual parameter tuning. In TV minimization, the step sizes of steepest descent are adaptively adjusted according to the difference from POCS update in either the projection domain or the image domain, while the step size of algebraic reconstruction technique (ART) in POCS is determined based on the data noise level. In addition, projection errors are used to compare with the error bound to decide whether to perform ART so as to reduce computational costs. The performance of the proposed methods is studied and evaluated using both simulated and physical phantom data. Our methods with automatic parameter tuning achieve similar, if not better, reconstruction performance compared to a representative two-stage algorithm. Copyright © 2014 Elsevier Ltd. All rights reserved.

Should the parameters of a BCI translation algorithm be continually adapted?

PubMed

McFarland, Dennis J; Sarnacki, William A; Wolpaw, Jonathan R

2011-07-15

People with or without motor disabilities can learn to control sensorimotor rhythms (SMRs) recorded from the scalp to move a computer cursor in one or more dimensions or can use the P300 event-related potential as a control signal to make discrete selections. Data collected from individuals using an SMR-based or P300-based BCI were evaluated offline to estimate the impact on performance of continually adapting the parameters of the translation algorithm during BCI operation. The performance of the SMR-based BCI was enhanced by adaptive updating of the feature weights or adaptive normalization of the features. In contrast, P300 performance did not benefit from either of these procedures. Copyright © 2011 Elsevier B.V. All rights reserved.

A General, Adaptive, Roadmap-Based Algorithm for Protein Motion Computation.

PubMed

Molloy, Kevin; Shehu, Amarda

2016-03-01

Precious information on protein function can be extracted from a detailed characterization of protein equilibrium dynamics. This remains elusive in wet and dry laboratories, as function-modulating transitions of a protein between functionally-relevant, thermodynamically-stable and meta-stable structural states often span disparate time scales. In this paper we propose a novel, robotics-inspired algorithm that circumvents time-scale challenges by drawing analogies between protein motion and robot motion. The algorithm adapts the popular roadmap-based framework in robot motion computation to handle the more complex protein conformation space and its underlying rugged energy surface. Given known structures representing stable and meta-stable states of a protein, the algorithm yields a time- and energy-prioritized list of transition paths between the structures, with each path represented as a series of conformations. The algorithm balances computational resources between a global search aimed at obtaining a global view of the network of protein conformations and their connectivity and a detailed local search focused on realizing such connections with physically-realistic models. Promising results are presented on a variety of proteins that demonstrate the general utility of the algorithm and its capability to improve the state of the art without employing system-specific insight.

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.

An improved contrast enhancement algorithm for infrared images based on adaptive double plateaus histogram equalization

NASA Astrophysics Data System (ADS)

Li, Shuo; Jin, Weiqi; Li, Li; Li, Yiyang

2018-05-01

Infrared thermal images can reflect the thermal-radiation distribution of a particular scene. However, the contrast of the infrared images is usually low. Hence, it is generally necessary to enhance the contrast of infrared images in advance to facilitate subsequent recognition and analysis. Based on the adaptive double plateaus histogram equalization, this paper presents an improved contrast enhancement algorithm for infrared thermal images. In the proposed algorithm, the normalized coefficient of variation of the histogram, which characterizes the level of contrast enhancement, is introduced as feedback information to adjust the upper and lower plateau thresholds. The experiments on actual infrared images show that compared to the three typical contrast-enhancement algorithms, the proposed algorithm has better scene adaptability and yields better contrast-enhancement results for infrared images with more dark areas or a higher dynamic range. Hence, it has high application value in contrast enhancement, dynamic range compression, and digital detail enhancement for infrared thermal images.

Adaptive Cross-correlation Algorithm and Experiment of Extended Scene Shack-Hartmann Wavefront Sensing

NASA Technical Reports Server (NTRS)

Sidick, Erkin; Morgan, Rhonda M.; Green, Joseph J.; Ohara, Catherine M.; Redding, David C.

2007-01-01

We have developed a new, adaptive cross-correlation (ACC) algorithm to estimate with high accuracy the shift as large as several pixels in two extended-scene images captured by a Shack-Hartmann wavefront sensor (SH-WFS). It determines the positions of all of the extended-scene image cells relative to a reference cell using an FFT-based iterative image shifting algorithm. It works with both point-source spot images as well as extended scene images. We have also set up a testbed for extended0scene SH-WFS, and tested the ACC algorithm with the measured data of both point-source and extended-scene images. In this paper we describe our algorithm and present out experimental results.

An implicit adaptation algorithm for a linear model reference control system

NASA Technical Reports Server (NTRS)

Mabius, L.; Kaufman, H.

1975-01-01

This paper presents a stable implicit adaptation algorithm for model reference control. The constraints for stability are found using Lyapunov's second method and do not depend on perfect model following between the system and the reference model. Methods are proposed for satisfying these constraints without estimating the parameters on which the constraints depend.

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.

3-component beamforming analysis of ambient seismic noise field for Love and Rayleigh wave source directions

NASA Astrophysics Data System (ADS)

Juretzek, Carina; Hadziioannou, Céline

2014-05-01

Our knowledge about common and different origins of Love and Rayleigh waves observed in the microseism band of the ambient seismic noise field is still limited, including the understanding of source locations and source mechanisms. Multi-component array methods are suitable to address this issue. In this work we use a 3-component beamforming algorithm to obtain source directions and polarization states of the ambient seismic noise field within the primary and secondary microseism bands recorded at the Gräfenberg array in southern Germany. The method allows to distinguish between different polarized waves present in the seismic noise field and estimates Love and Rayleigh wave source directions and their seasonal variations using one year of array data. We find mainly coinciding directions for the strongest acting sources of both wave types at the primary microseism and different source directions at the secondary microseism.

Frequency Domain Beamforming for a Deep Space Network Downlink Array

NASA Technical Reports Server (NTRS)

Navarro, Robert

2012-01-01

This paper describes a frequency domain beamformer to array up to 8 antennas of NASA's Deep Space Network currently in development. The objective of this array is to replace and enhance the capability of the DSN 70m antennas with multiple 34m antennas for telemetry, navigation and radio science use. The array will coherently combine the entire 500 MHz of usable bandwidth available to DSN receivers. A frequency domain beamforming architecture was chosen over a time domain based architecture to handle the large signal bandwidth and efficiently perform delay and phase calibration. The antennas of the DSN are spaced far enough apart that random atmospheric and phase variations between antennas need to be calibrated out on an ongoing basis in real-time. The calibration is done using measurements obtained from a correlator. This DSN Downlink Array expands upon a proof of concept breadboard array built previously to develop the technology and will become an operational asset of the Deep Space Network. Design parameters for frequency channelization, array calibration and delay corrections will be presented as well a method to efficiently calibrate the array for both wide and narrow bandwidth telemetry.

Dynamic game balancing implementation using adaptive algorithm in mobile-based Safari Indonesia game

NASA Astrophysics Data System (ADS)

Yuniarti, Anny; Nata Wardanie, Novita; Kuswardayan, Imam

2018-03-01

In developing a game there is one method that should be applied to maintain the interest of players, namely dynamic game balancing. Dynamic game balancing is a process to match a player’s playing style with the behaviour, attributes, and game environment. This study applies dynamic game balancing using adaptive algorithm in scrolling shooter game type called Safari Indonesia which developed using Unity. The game of this type is portrayed by a fighter aircraft character trying to defend itself from insistent enemy attacks. This classic game is chosen to implement adaptive algorithms because it has quite complex attributes to be developed using dynamic game balancing. Tests conducted by distributing questionnaires to a number of players indicate that this method managed to reduce frustration and increase the pleasure factor in playing.

A parallel adaptive quantum genetic algorithm for the controllability of arbitrary networks.

PubMed

Li, Yuhong; Gong, Guanghong; Li, Ni

2018-01-01

In this paper, we propose a novel algorithm-parallel adaptive quantum genetic algorithm-which can rapidly determine the minimum control nodes of arbitrary networks with both control nodes and state nodes. The corresponding network can be fully controlled with the obtained control scheme. We transformed the network controllability issue into a combinational optimization problem based on the Popov-Belevitch-Hautus rank condition. A set of canonical networks and a list of real-world networks were experimented. Comparison results demonstrated that the algorithm was more ideal to optimize the controllability of networks, especially those larger-size networks. We demonstrated subsequently that there were links between the optimal control nodes and some network statistical characteristics. The proposed algorithm provides an effective approach to improve the controllability optimization of large networks or even extra-large networks with hundreds of thousands nodes.

A Controlled Study of the Effectiveness of an Adaptive Closed-Loop Algorithm to Minimize Corticosteroid-Induced Stress Hyperglycemia in Type 1 Diabetes

PubMed Central

Youssef, Joseph El; Castle, Jessica R; Branigan, Deborah L; Massoud, Ryan G; Breen, Matthew E; Jacobs, Peter G; Bequette, B Wayne; Ward, W Kenneth

2011-01-01

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. PMID:22226248

An Adaptive Image Enhancement Technique by Combining Cuckoo Search and Particle Swarm Optimization Algorithm

PubMed Central

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. PMID:25784928

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.

Asynchronous Incremental Stochastic Dual Descent Algorithm for Network Resource Allocation

NASA Astrophysics Data System (ADS)

Bedi, Amrit Singh; Rajawat, Ketan

2018-05-01

Stochastic network optimization problems entail finding resource allocation policies that are optimum on an average but must be designed in an online fashion. Such problems are ubiquitous in communication networks, where resources such as energy and bandwidth are divided among nodes to satisfy certain long-term objectives. This paper proposes an asynchronous incremental dual decent resource allocation algorithm that utilizes delayed stochastic {gradients} for carrying out its updates. The proposed algorithm is well-suited to heterogeneous networks as it allows the computationally-challenged or energy-starved nodes to, at times, postpone the updates. The asymptotic analysis of the proposed algorithm is carried out, establishing dual convergence under both, constant and diminishing step sizes. It is also shown that with constant step size, the proposed resource allocation policy is asymptotically near-optimal. An application involving multi-cell coordinated beamforming is detailed, demonstrating the usefulness of the proposed algorithm.

Acoustic Emission Beamforming for Detection and Localization of Damage

NASA Astrophysics Data System (ADS)

Rivey, Joshua Callen

The aerospace industry is a constantly evolving field with corporate manufacturers continually utilizing innovative processes and materials. These materials include advanced metallics and composite systems. The exploration and implementation of new materials and structures has prompted the development of numerous structural health monitoring and nondestructive evaluation techniques for quality assurance purposes and pre- and in-service damage detection. Exploitation of acoustic emission sensors coupled with a beamforming technique provides the potential for creating an effective non-contact and non-invasive monitoring capability for assessing structural integrity. This investigation used an acoustic emission detection device that employs helical arrays of MEMS-based microphones around a high-definition optical camera to provide real-time non-contact monitoring of inspection specimens during testing. The study assessed the feasibility of the sound camera for use in structural health monitoring of composite specimens during tensile testing for detecting onset of damage in addition to nondestructive evaluation of aluminum inspection plates for visualizing stress wave propagation in structures. During composite material monitoring, the sound camera was able to accurately identify the onset and location of damage resulting from large amplitude acoustic feedback mechanisms such as fiber breakage. Damage resulting from smaller acoustic feedback events such as matrix failure was detected but not localized to the degree of accuracy of larger feedback events. Findings suggest that beamforming technology can provide effective non-contact and non-invasive inspection of composite materials, characterizing the onset and the location of damage in an efficient manner. With regards to the nondestructive evaluation of metallic plates, this remote sensing system allows us to record wave propagation events in situ via a single-shot measurement. This is a significant improvement over

An Energy Efficient Adaptive Sampling Algorithm in a Sensor Network for Automated Water Quality Monitoring.

PubMed

Shu, Tongxin; Xia, Min; Chen, Jiahong; Silva, Clarence de

2017-11-05

Power management is crucial in the monitoring of a remote environment, especially when long-term monitoring is needed. Renewable energy sources such as solar and wind may be harvested to sustain a monitoring system. However, without proper power management, equipment within the monitoring system may become nonfunctional and, as a consequence, the data or events captured during the monitoring process will become inaccurate as well. This paper develops and applies a novel adaptive sampling algorithm for power management in the automated monitoring of the quality of water in an extensive and remote aquatic environment. Based on the data collected on line using sensor nodes, a data-driven adaptive sampling algorithm (DDASA) is developed for improving the power efficiency while ensuring the accuracy of sampled data. The developed algorithm is evaluated using two distinct key parameters, which are dissolved oxygen (DO) and turbidity. It is found that by dynamically changing the sampling frequency, the battery lifetime can be effectively prolonged while maintaining a required level of sampling accuracy. According to the simulation results, compared to a fixed sampling rate, approximately 30.66% of the battery energy can be saved for three months of continuous water quality monitoring. Using the same dataset to compare with a traditional adaptive sampling algorithm (ASA), while achieving around the same Normalized Mean Error (NME), DDASA is superior in saving 5.31% more battery energy.

An Energy Efficient Adaptive Sampling Algorithm in a Sensor Network for Automated Water Quality Monitoring

PubMed Central

Shu, Tongxin; Xia, Min; Chen, Jiahong; de Silva, Clarence

2017-01-01

Power management is crucial in the monitoring of a remote environment, especially when long-term monitoring is needed. Renewable energy sources such as solar and wind may be harvested to sustain a monitoring system. However, without proper power management, equipment within the monitoring system may become nonfunctional and, as a consequence, the data or events captured during the monitoring process will become inaccurate as well. This paper develops and applies a novel adaptive sampling algorithm for power management in the automated monitoring of the quality of water in an extensive and remote aquatic environment. Based on the data collected on line using sensor nodes, a data-driven adaptive sampling algorithm (DDASA) is developed for improving the power efficiency while ensuring the accuracy of sampled data. The developed algorithm is evaluated using two distinct key parameters, which are dissolved oxygen (DO) and turbidity. It is found that by dynamically changing the sampling frequency, the battery lifetime can be effectively prolonged while maintaining a required level of sampling accuracy. According to the simulation results, compared to a fixed sampling rate, approximately 30.66% of the battery energy can be saved for three months of continuous water quality monitoring. Using the same dataset to compare with a traditional adaptive sampling algorithm (ASA), while achieving around the same Normalized Mean Error (NME), DDASA is superior in saving 5.31% more battery energy. PMID:29113087

An Adaptive Cross-Correlation Algorithm for Extended-Scene Shack-Hartmann Wavefront Sensing

NASA Technical Reports Server (NTRS)

Sidick, Erkin; Green, Joseph J.; Ohara, Catherine M.; Redding, David C.

2007-01-01

This viewgraph presentation reviews the Adaptive Cross-Correlation (ACC) Algorithm for extended scene-Shack Hartmann wavefront (WF) sensing. A Shack-Hartmann sensor places a lenslet array at a plane conjugate to the WF error source. Each sub-aperture lenslet samples the WF in the corresponding patch of the WF. A description of the ACC algorithm is included. The ACC has several benefits; amongst them are: ACC requires only about 4 image-shifting iterations to achieve 0.01 pixel accuracy and ACC is insensitive to both background light and noise much more robust than centroiding,

Non-Orthogonal Random Access in MIMO Cognitive Radio Networks: Beamforming, Power Allocation, and Opportunistic Transmission

PubMed Central

Lin, Huifa; Shin, Won-Yong

2017-01-01

We study secondary random access in multi-input multi-output cognitive radio networks, where a slotted ALOHA-type protocol and successive interference cancellation are used. We first introduce three types of transmit beamforming performed by secondary users, where multiple antennas are used to suppress the interference at the primary base station and/or to increase the received signal power at the secondary base station. Then, we show a simple decentralized power allocation along with the equivalent single-antenna conversion. To exploit the multiuser diversity gain, an opportunistic transmission protocol is proposed, where the secondary users generating less interference are opportunistically selected, resulting in a further reduction of the interference temperature. The proposed methods are validated via computer simulations. Numerical results show that increasing the number of transmit antennas can greatly reduce the interference temperature, while increasing the number of receive antennas leads to a reduction of the total transmit power. Optimal parameter values of the opportunistic transmission protocol are examined according to three types of beamforming and different antenna configurations, in terms of maximizing the cognitive transmission capacity. All the beamforming, decentralized power allocation, and opportunistic transmission protocol are performed by the secondary users in a decentralized manner, thus resulting in an easy implementation in practice. PMID:28076402

Ultrasonic Fingerprint Sensor With Transmit Beamforming Based on a PMUT Array Bonded to CMOS Circuitry.

PubMed

Jiang, Xiaoyue; Tang, Hao-Yen; Lu, Yipeng; Ng, Eldwin J; Tsai, Julius M; Boser, Bernhard E; Horsley, David A

2017-09-01

In this paper, we present a single-chip 65 ×42 element ultrasonic pulse-echo fingerprint sensor with transmit (TX) beamforming based on piezoelectric micromachined ultrasonic transducers directly bonded to a CMOS readout application-specific integrated circuit (ASIC). The readout ASIC was realized in a standard 180-nm CMOS process with a 24-V high-voltage transistor option. Pulse-echo measurements are performed column-by-column in sequence using either one column or five columns to TX the ultrasonic pulse at 20 MHz. TX beamforming is used to focus the ultrasonic beam at the imaging plane where the finger is located, increasing the ultrasonic pressure and narrowing the 3-dB beamwidth to [Formula: see text], a factor of 6.4 narrower than nonbeamformed measurements. The surface of the sensor is coated with a poly-dimethylsiloxane (PDMS) layer to provide good acoustic impedance matching to skin. Scanning laser Doppler vibrometry of the PDMS surface was used to map the ultrasonic pressure field at the imaging surface, demonstrating the expected increase in pressure, and reduction in beamwidth. Imaging experiments were conducted using both PDMS phantoms and real fingerprints. The average image contrast is increased by a factor of 1.5 when beamforming is used.

Non-Orthogonal Random Access in MIMO Cognitive Radio Networks: Beamforming, Power Allocation, and Opportunistic Transmission.

PubMed

Lin, Huifa; Shin, Won-Yong

2017-01-01

We study secondary random access in multi-input multi-output cognitive radio networks, where a slotted ALOHA-type protocol and successive interference cancellation are used. We first introduce three types of transmit beamforming performed by secondary users, where multiple antennas are used to suppress the interference at the primary base station and/or to increase the received signal power at the secondary base station. Then, we show a simple decentralized power allocation along with the equivalent single-antenna conversion. To exploit the multiuser diversity gain, an opportunistic transmission protocol is proposed, where the secondary users generating less interference are opportunistically selected, resulting in a further reduction of the interference temperature. The proposed methods are validated via computer simulations. Numerical results show that increasing the number of transmit antennas can greatly reduce the interference temperature, while increasing the number of receive antennas leads to a reduction of the total transmit power. Optimal parameter values of the opportunistic transmission protocol are examined according to three types of beamforming and different antenna configurations, in terms of maximizing the cognitive transmission capacity. All the beamforming, decentralized power allocation, and opportunistic transmission protocol are performed by the secondary users in a decentralized manner, thus resulting in an easy implementation in practice.

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.

[Application of an Adaptive Inertia Weight Particle Swarm Algorithm in the Magnetic Resonance Bias Field Correction].

PubMed

Wang, Chang; Qin, Xin; Liu, Yan; Zhang, Wenchao

2016-06-01

An adaptive inertia weight particle swarm algorithm is proposed in this study to solve the local optimal problem with the method of traditional particle swarm optimization in the process of estimating magnetic resonance(MR)image bias field.An indicator measuring the degree of premature convergence was designed for the defect of traditional particle swarm optimization algorithm.The inertia weight was adjusted adaptively based on this indicator to ensure particle swarm to be optimized globally and to avoid it from falling into local optimum.The Legendre polynomial was used to fit bias field,the polynomial parameters were optimized globally,and finally the bias field was estimated and corrected.Compared to those with the improved entropy minimum algorithm,the entropy of corrected image was smaller and the estimated bias field was more accurate in this study.Then the corrected image was segmented and the segmentation accuracy obtained in this research was 10% higher than that with improved entropy minimum algorithm.This algorithm can be applied to the correction of MR image bias field.

Matched-filtering generalized phase contrast using LCoS pico-projectors for beam-forming.

PubMed

Bañas, Andrew; Palima, Darwin; Glückstad, Jesper

2012-04-23

We report on a new beam-forming system for generating high intensity programmable optical spikes using so-called matched-filtering Generalized Phase Contrast (mGPC) applying two consumer handheld pico-projectors. Such a system presents a low-cost alternative for optical trapping and manipulation, optical lattices and other beam-shaping applications usually implemented with high-end spatial light modulators. Portable pico-projectors based on liquid crystal on silicon (LCoS) devices are used as binary phase-only spatial light modulators by carefully setting the appropriate polarization of the laser illumination. The devices are subsequently placed into the object and Fourier plane of a standard 4f-setup according to the mGPC spatial filtering configuration. Having a reconfigurable spatial phase filter, instead of a fixed and fabricated one, allows the beam shaper to adapt to different input phase patterns suited for different requirements. Despite imperfections in these consumer pico-projectors, the mGPC approach tolerates phase aberrations that would have otherwise been hard to overcome by standard phase projection. © 2012 Optical Society of America

STAR adaptation of QR algorithm. [program for solving over-determined systems of linear equations

NASA Technical Reports Server (NTRS)

Shah, S. N.

1981-01-01

The QR algorithm used on a serial computer and executed on the Control Data Corporation 6000 Computer was adapted to execute efficiently on the Control Data STAR-100 computer. How the scalar program was adapted for the STAR-100 and why these adaptations yielded an efficient STAR program is described. Program listings of the old scalar version and the vectorized SL/1 version are presented in the appendices. Execution times for the two versions applied to the same system of linear equations, are compared.

Fully implicit adaptive mesh refinement algorithm for reduced MHD

NASA Astrophysics Data System (ADS)

Philip, Bobby; Pernice, Michael; Chacon, Luis

2006-10-01

In the macroscopic simulation of plasmas, the numerical modeler is faced with the challenge of dealing with multiple time and length scales. Traditional approaches based on explicit time integration techniques and fixed meshes are not suitable for this challenge, as such approaches prevent the modeler from using realistic plasma parameters to keep the computation feasible. We propose here a novel approach, based on implicit methods and structured adaptive mesh refinement (SAMR). Our emphasis is on both accuracy and scalability with the number of degrees of freedom. As a proof-of-principle, we focus on the reduced resistive MHD model as a basic MHD model paradigm, which is truly multiscale. The approach taken here is to adapt mature physics-based technology to AMR grids, and employ AMR-aware multilevel techniques (such as fast adaptive composite grid --FAC-- algorithms) for scalability. We demonstrate that the concept is indeed feasible, featuring near-optimal scalability under grid refinement. Results of fully-implicit, dynamically-adaptive AMR simulations in challenging dissipation regimes will be presented on a variety of problems that benefit from this capability, including tearing modes, the island coalescence instability, and the tilt mode instability. L. Chac'on et al., J. Comput. Phys. 178 (1), 15- 36 (2002) B. Philip, M. Pernice, and L. Chac'on, Lecture Notes in Computational Science and Engineering, accepted (2006)

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.

Processing of fetal heart rate through non-invasive adaptive system based on recursive least squares algorithm

NASA Astrophysics Data System (ADS)

Fajkus, Marcel; Nedoma, Jan; Martinek, Radek; Vasinek, Vladimir

2017-10-01

In this article, we describe an innovative non-invasive method of Fetal Phonocardiography (fPCG) using fiber-optic sensors and adaptive algorithm for the measurement of fetal heart rate (fHR). Conventional PCG is based on a noninvasive scanning of acoustic signals by means of a microphone placed on the thorax. As for fPCG, the microphone is placed on the maternal abdomen. Our solution is based on patent pending non-invasive scanning of acoustic signals by means of a fiber-optic interferometer. Fiber-optic sensors are resistant to technical artifacts such as electromagnetic interferences (EMI), thus they can be used in situations where it is impossible to use conventional EFM methods, e.g. during Magnetic Resonance Imaging (MRI) examination or in case of delivery in water. The adaptive evaluation system is based on Recursive least squares (RLS) algorithm. Based on real measurements provided on five volunteers with their written consent, we created a simplified dynamic signal model of a distribution of heartbeat sounds (HS) through the human body. Our created model allows us to verification of the proposed adaptive system RLS algorithm. The functionality of the proposed non-invasive adaptive system was verified by objective parameters such as Sensitivity (S+) and Signal to Noise Ratio (SNR).

Array signal recovery algorithm for a single-RF-channel DBF array

NASA Astrophysics Data System (ADS)

Zhang, Duo; Wu, Wen; Fang, Da Gang

2016-12-01

An array signal recovery algorithm based on sparse signal reconstruction theory is proposed for a single-RF-channel digital beamforming (DBF) array. A single-RF-channel antenna array is a low-cost antenna array in which signals are obtained from all antenna elements by only one microwave digital receiver. The spatially parallel array signals are converted into time-sequence signals, which are then sampled by the system. The proposed algorithm uses these time-sequence samples to recover the original parallel array signals by exploiting the second-order sparse structure of the array signals. Additionally, an optimization method based on the artificial bee colony (ABC) algorithm is proposed to improve the reconstruction performance. Using the proposed algorithm, the motion compensation problem for the single-RF-channel DBF array can be solved effectively, and the angle and Doppler information for the target can be simultaneously estimated. The effectiveness of the proposed algorithms is demonstrated by the results of numerical simulations.

An improved self-adaptive ant colony algorithm based on genetic strategy for the traveling salesman problem

NASA Astrophysics Data System (ADS)

Wang, Pan; Zhang, Yi; Yan, Dong

2018-05-01

Ant Colony Algorithm (ACA) is a powerful and effective algorithm for solving the combination optimization problem. Moreover, it was successfully used in traveling salesman problem (TSP). But it is easy to prematurely converge to the non-global optimal solution and the calculation time is too long. To overcome those shortcomings, a new method is presented-An improved self-adaptive Ant Colony Algorithm based on genetic strategy. The proposed method adopts adaptive strategy to adjust the parameters dynamically. And new crossover operation and inversion operation in genetic strategy was used in this method. We also make an experiment using the well-known data in TSPLIB. The experiment results show that the performance of the proposed method is better than the basic Ant Colony Algorithm and some improved ACA in both the result and the convergence time. The numerical results obtained also show that the proposed optimization method can achieve results close to the theoretical best known solutions at present.

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.

A comparison of two adaptive algorithms for the control of active engine mounts

NASA Astrophysics Data System (ADS)

Hillis, A. J.; Harrison, A. J. L.; Stoten, D. P.

2005-08-01

This paper describes work conducted in order to control automotive active engine mounts, consisting of a conventional passive mount and an internal electromagnetic actuator. Active engine mounts seek to cancel the oscillatory forces generated by the rotation of out-of-balance masses within the engine. The actuator generates a force dependent on a control signal from an algorithm implemented with a real-time DSP. The filtered-x least-mean-square (FXLMS) adaptive filter is used as a benchmark for comparison with a new implementation of the error-driven minimal controller synthesis (Er-MCSI) adaptive controller. Both algorithms are applied to an active mount fitted to a saloon car equipped with a four-cylinder turbo-diesel engine, and have no a priori knowledge of the system dynamics. The steady-state and transient performance of the two algorithms are compared and the relative merits of the two approaches are discussed. The Er-MCSI strategy offers significant computational advantages as it requires no cancellation path modelling. The Er-MCSI controller is found to perform in a fashion similar to the FXLMS filter—typically reducing chassis vibration by 50-90% under normal driving conditions.

An arrhythmia classification algorithm using a dedicated wavelet adapted to different subjects.

PubMed

Kim, Jinkwon; Min, Se Dong; Lee, Myoungho

2011-06-27

Numerous studies have been conducted regarding a heartbeat classification algorithm over the past several decades. However, many algorithms have also been studied to acquire robust performance, as biosignals have a large amount of variation among individuals. Various methods have been proposed to reduce the differences coming from personal characteristics, but these expand the differences caused by arrhythmia. In this paper, an arrhythmia classification algorithm using a dedicated wavelet adapted to individual subjects is proposed. We reduced the performance variation using dedicated wavelets, as in the ECG morphologies of the subjects. The proposed algorithm utilizes morphological filtering and a continuous wavelet transform with a dedicated wavelet. A principal component analysis and linear discriminant analysis were utilized to compress the morphological data transformed by the dedicated wavelets. An extreme learning machine was used as a classifier in the proposed algorithm. A performance evaluation was conducted with the MIT-BIH arrhythmia database. The results showed a high sensitivity of 97.51%, specificity of 85.07%, accuracy of 97.94%, and a positive predictive value of 97.26%. The proposed algorithm achieves better accuracy than other state-of-the-art algorithms with no intrasubject between the training and evaluation datasets. And it significantly reduces the amount of intervention needed by physicians.

An arrhythmia classification algorithm using a dedicated wavelet adapted to different subjects

PubMed Central

2011-01-01

Background Numerous studies have been conducted regarding a heartbeat classification algorithm over the past several decades. However, many algorithms have also been studied to acquire robust performance, as biosignals have a large amount of variation among individuals. Various methods have been proposed to reduce the differences coming from personal characteristics, but these expand the differences caused by arrhythmia. Methods In this paper, an arrhythmia classification algorithm using a dedicated wavelet adapted to individual subjects is proposed. We reduced the performance variation using dedicated wavelets, as in the ECG morphologies of the subjects. The proposed algorithm utilizes morphological filtering and a continuous wavelet transform with a dedicated wavelet. A principal component analysis and linear discriminant analysis were utilized to compress the morphological data transformed by the dedicated wavelets. An extreme learning machine was used as a classifier in the proposed algorithm. Results A performance evaluation was conducted with the MIT-BIH arrhythmia database. The results showed a high sensitivity of 97.51%, specificity of 85.07%, accuracy of 97.94%, and a positive predictive value of 97.26%. Conclusions The proposed algorithm achieves better accuracy than other state-of-the-art algorithms with no intrasubject between the training and evaluation datasets. And it significantly reduces the amount of intervention needed by physicians. PMID:21707989

Performance Evaluation of Multichannel Adaptive Algorithms for Local Active Noise Control

NASA Astrophysics Data System (ADS)

DE DIEGO, M.; GONZALEZ, A.

2001-07-01

This paper deals with the development of a multichannel active noise control (ANC) system inside an enclosed space. The purpose is to design a real practical system which works well in local ANC applications. Moreover, the algorithm implemented in the adaptive controller should be robust, of low computational complexity and it should manage to generate a uniform useful-size zone of quite in order to allow the head motion of a person seated on a seat inside a car. Experiments were carried out under semi-anechoic and listening room conditions to verify the successful implementation of the multichannel system. The developed prototype consists of an array of up to four microphones used as error sensors mounted on the headrest of a seat place inside the enclosure. One loudspeaker was used as single primary source and two secondary sources were placed facing the seat. The aim of this multichannel system is to reduce the sound pressure levels in an area around the error sensors, following a local control strategy. When using this technique, the cancellation points are not only the error sensor positions but an area around them, which is measured by using a monitoring microphone. Different multichannel adaptive algorithms for ANC have been analyzed and their performance verified. Multiple error algorithms are used in order to cancel out different types of primary noise (engine noise and random noise) with several configurations (up to four channels system). As an alternative to the multiple error LMS algorithm (multichannel version of the filtered-X LMS algorithm, MELMS), the least maximum mean squares (LMMS) and the scanning error-LMS algorithm have been developed in this work in order to reduce computational complexity and achieve a more uniform residual field. The ANC algorithms were programmed on a digital signal processing board equipped with a TMS320C40 floating point DSP processor. Measurements concerning real-time experiments on local noise reduction in two

Novel Near-Lossless Compression Algorithm for Medical Sequence Images with Adaptive Block-Based Spatial Prediction.

PubMed

Song, Xiaoying; Huang, Qijun; Chang, Sheng; He, Jin; Wang, Hao

2016-12-01

To address the low compression efficiency of lossless compression and the low image quality of general near-lossless compression, a novel near-lossless compression algorithm based on adaptive spatial prediction is proposed for medical sequence images for possible diagnostic use in this paper. The proposed method employs adaptive block size-based spatial prediction to predict blocks directly in the spatial domain and Lossless Hadamard Transform before quantization to improve the quality of reconstructed images. The block-based prediction breaks the pixel neighborhood constraint and takes full advantage of the local spatial correlations found in medical images. The adaptive block size guarantees a more rational division of images and the improved use of the local structure. The results indicate that the proposed algorithm can efficiently compress medical images and produces a better peak signal-to-noise ratio (PSNR) under the same pre-defined distortion than other near-lossless methods.

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.

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

PubMed Central

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

2017-01-01

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. PMID:28383503

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.

A systematic review of gait analysis methods based on inertial sensors and adaptive algorithms.

PubMed

Caldas, Rafael; Mundt, Marion; Potthast, Wolfgang; Buarque de Lima Neto, Fernando; Markert, Bernd

2017-09-01

The conventional methods to assess human gait are either expensive or complex to be applied regularly in clinical practice. To reduce the cost and simplify the evaluation, inertial sensors and adaptive algorithms have been utilized, respectively. This paper aims to summarize studies that applied adaptive also called artificial intelligence (AI) algorithms to gait analysis based on inertial sensor data, verifying if they can support the clinical evaluation. Articles were identified through searches of the main databases, which were encompassed from 1968 to October 2016. We have identified 22 studies that met the inclusion criteria. The included papers were analyzed due to their data acquisition and processing methods with specific questionnaires. Concerning the data acquisition, the mean score is 6.1±1.62, what implies that 13 of 22 papers failed to report relevant outcomes. The quality assessment of AI algorithms presents an above-average rating (8.2±1.84). Therefore, AI algorithms seem to be able to support gait analysis based on inertial sensor data. Further research, however, is necessary to enhance and standardize the application in patients, since most of the studies used distinct methods to evaluate healthy subjects. Copyright © 2017 Elsevier B.V. All rights reserved.

Near-field self-interference cancellation and quality of service multicast beamforming in full-duplex

NASA Astrophysics Data System (ADS)

Wu, Fei; Shao, Shihai; Tang, Youxi

2016-10-01

To enable simultaneous multicast downlink transmit and receive operations on the same frequency band, also known as full-duplex links between an access point and mobile users. The problem of minimizing the total power of multicast transmit beamforming is considered from the viewpoint of ensuring the suppression amount of near-field line-of-sight self-interference and guaranteeing prescribed minimum signal-to-interference-plus-noise-ratio (SINR) at each receiver of the multicast groups. Based on earlier results for multicast groups beamforming, the joint problem is easily shown to be NP-hard. A semidefinite relaxation (SDR) technique with linear program power adjust method is proposed to solve the NP-hard problem. Simulation shows that the proposed method is feasible even when the local receive antenna in nearfield and the mobile user in far-filed are in the same direction.

Overview of implementation of DARPA GPU program in SAIC

NASA Astrophysics Data System (ADS)

Braunreiter, Dennis; Furtek, Jeremy; Chen, Hai-Wen; Healy, Dennis

2008-04-01

This paper reviews the implementation of DARPA MTO STAP-BOY program for both Phase I and II conducted at Science Applications International Corporation (SAIC). The STAP-BOY program conducts fast covariance factorization and tuning techniques for space-time adaptive process (STAP) Algorithm Implementation on Graphics Processor unit (GPU) Architectures for Embedded Systems. The first part of our presentation on the DARPA STAP-BOY program will focus on GPU implementation and algorithm innovations for a prototype radar STAP algorithm. The STAP algorithm will be implemented on the GPU, using stream programming (from companies such as PeakStream, ATI Technologies' CTM, and NVIDIA) and traditional graphics APIs. This algorithm will include fast range adaptive STAP weight updates and beamforming applications, each of which has been modified to exploit the parallel nature of graphics architectures.

Fast adaptive diamond search algorithm for block-matching motion estimation using spatial correlation

NASA Astrophysics Data System (ADS)

Park, Sang-Gon; Jeong, Dong-Seok

2000-12-01

In this paper, we propose a fast adaptive diamond search algorithm (FADS) for block matching motion estimation. Many fast motion estimation algorithms reduce the computational complexity by the UESA (Unimodal Error Surface Assumption) where the matching error monotonically increases as the search moves away from the global minimum point. Recently, many fast BMAs (Block Matching Algorithms) make use of the fact that global minimum points in real world video sequences are centered at the position of zero motion. But these BMAs, especially in large motion, are easily trapped into the local minima and result in poor matching accuracy. So, we propose a new motion estimation algorithm using the spatial correlation among the neighboring blocks. We move the search origin according to the motion vectors of the spatially neighboring blocks and their MAEs (Mean Absolute Errors). The computer simulation shows that the proposed algorithm has almost the same computational complexity with DS (Diamond Search), but enhances PSNR. Moreover, the proposed algorithm gives almost the same PSNR as that of FS (Full Search), even for the large motion with half the computational load.

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.

Massively parallel algorithms for real-time wavefront control of a dense adaptive optics system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fijany, A.; Milman, M.; Redding, D.

1994-12-31

In this paper massively parallel algorithms and architectures for real-time wavefront control of a dense adaptive optic system (SELENE) are presented. The authors have already shown that the computation of a near optimal control algorithm for SELENE can be reduced to the solution of a discrete Poisson equation on a regular domain. Although, this represents an optimal computation, due the large size of the system and the high sampling rate requirement, the implementation of this control algorithm poses a computationally challenging problem since it demands a sustained computational throughput of the order of 10 GFlops. They develop a novel algorithm,more » designated as Fast Invariant Imbedding algorithm, which offers a massive degree of parallelism with simple communication and synchronization requirements. Due to these features, this algorithm is significantly more efficient than other Fast Poisson Solvers for implementation on massively parallel architectures. The authors also discuss two massively parallel, algorithmically specialized, architectures for low-cost and optimal implementation of the Fast Invariant Imbedding algorithm.« less

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.

Wake acoustic analysis and image decomposition via beamforming of microphone signal projections on wavelet subspaces

DOT National Transportation Integrated Search

2006-05-08

This paper describes the integration of wavelet analysis and time-domain beamforming : of microphone array output signals for analyzing the acoustic emissions from airplane : generated wake vortices. This integrated process provides visual and quanti...

Motion Cueing Algorithm Development: Initial Investigation and Redesign of the Algorithms

NASA Technical Reports Server (NTRS)

Telban, Robert J.; Wu, Weimin; Cardullo, Frank M.; Houck, Jacob A. (Technical Monitor)

2000-01-01

In this project four motion cueing algorithms were initially investigated. The classical algorithm generated results with large distortion and delay and low magnitude. The NASA adaptive algorithm proved to be well tuned with satisfactory performance, while the UTIAS adaptive algorithm produced less desirable results. Modifications were made to the adaptive algorithms to reduce the magnitude of undesirable spikes. The optimal algorithm was found to have the potential for improved performance with further redesign. The center of simulator rotation was redefined. More terms were added to the cost function to enable more tuning flexibility. A new design approach using a Fortran/Matlab/Simulink setup was employed. A new semicircular canals model was incorporated in the algorithm. With these changes results show the optimal algorithm has some advantages over the NASA adaptive algorithm. Two general problems observed in the initial investigation required solutions. A nonlinear gain algorithm was developed that scales the aircraft inputs by a third-order polynomial, maximizing the motion cues while remaining within the operational limits of the motion system. A braking algorithm was developed to bring the simulator to a full stop at its motion limit and later release the brake to follow the cueing algorithm output.

Layer-oriented multigrid wavefront reconstruction algorithms for multi-conjugate adaptive optics

NASA Astrophysics Data System (ADS)

Gilles, Luc; Ellerbroek, Brent L.; Vogel, Curtis R.

2003-02-01

Multi-conjugate adaptive optics (MCAO) systems with 104-105 degrees of freedom have been proposed for future giant telescopes. Using standard matrix methods to compute, optimize, and implement wavefront control algorithms for these systems is impractical, since the number of calculations required to compute and apply the reconstruction matrix scales respectively with the cube and the square of the number of AO degrees of freedom. In this paper, we develop an iterative sparse matrix implementation of minimum variance wavefront reconstruction for telescope diameters up to 32m with more than 104 actuators. The basic approach is the preconditioned conjugate gradient method, using a multigrid preconditioner incorporating a layer-oriented (block) symmetric Gauss-Seidel iterative smoothing operator. We present open-loop numerical simulation results to illustrate algorithm convergence.

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.

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.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fan, Chengguang; Drinkwater, Bruce W.

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.more » However, for higher noise levels, the total focusing method shows robustness, whilst the performance of time-reversal MUSIC is significantly degraded.« less

An Adaptive and Time-Efficient ECG R-Peak Detection Algorithm.

PubMed

Qin, Qin; Li, Jianqing; Yue, Yinggao; Liu, Chengyu

2017-01-01

R-peak detection is crucial in electrocardiogram (ECG) signal analysis. This study proposed an adaptive and time-efficient R-peak detection algorithm for ECG processing. First, wavelet multiresolution analysis was applied to enhance the ECG signal representation. Then, ECG was mirrored to convert large negative R-peaks to positive ones. After that, local maximums were calculated by the first-order forward differential approach and were truncated by the amplitude and time interval thresholds to locate the R-peaks. The algorithm performances, including detection accuracy and time consumption, were tested on the MIT-BIH arrhythmia database and the QT database. Experimental results showed that the proposed algorithm achieved mean sensitivity of 99.39%, positive predictivity of 99.49%, and accuracy of 98.89% on the MIT-BIH arrhythmia database and 99.83%, 99.90%, and 99.73%, respectively, on the QT database. By processing one ECG record, the mean time consumptions were 0.872 s and 0.763 s for the MIT-BIH arrhythmia database and QT database, respectively, yielding 30.6% and 32.9% of time reduction compared to the traditional Pan-Tompkins method.

An Adaptive and Time-Efficient ECG R-Peak Detection Algorithm

PubMed Central

Qin, Qin

2017-01-01

R-peak detection is crucial in electrocardiogram (ECG) signal analysis. This study proposed an adaptive and time-efficient R-peak detection algorithm for ECG processing. First, wavelet multiresolution analysis was applied to enhance the ECG signal representation. Then, ECG was mirrored to convert large negative R-peaks to positive ones. After that, local maximums were calculated by the first-order forward differential approach and were truncated by the amplitude and time interval thresholds to locate the R-peaks. The algorithm performances, including detection accuracy and time consumption, were tested on the MIT-BIH arrhythmia database and the QT database. Experimental results showed that the proposed algorithm achieved mean sensitivity of 99.39%, positive predictivity of 99.49%, and accuracy of 98.89% on the MIT-BIH arrhythmia database and 99.83%, 99.90%, and 99.73%, respectively, on the QT database. By processing one ECG record, the mean time consumptions were 0.872 s and 0.763 s for the MIT-BIH arrhythmia database and QT database, respectively, yielding 30.6% and 32.9% of time reduction compared to the traditional Pan-Tompkins method. PMID:29104745

Moving microphone arrays to reduce spatial aliasing in the beamforming technique: theoretical background and numerical investigation.

PubMed

Cigada, Alfredo; Lurati, Massimiliano; Ripamonti, Francesco; Vanali, Marcello

2008-12-01

This paper introduces a measurement technique aimed at reducing or possibly eliminating the spatial aliasing problem in the beamforming technique. Beamforming main disadvantages are a poor spatial resolution, at low frequency, and the spatial aliasing problem, at higher frequency, leading to the identification of false sources. The idea is to move the microphone array during the measurement operation. In this paper, the proposed approach is theoretically and numerically investigated by means of simple sound propagation models, proving its efficiency in reducing the spatial aliasing. A number of different array configurations are numerically investigated together with the most important parameters governing this measurement technique. A set of numerical results concerning the case of a planar rotating array is shown, together with a first experimental validation of the method.

A new interferential multispectral image compression algorithm based on adaptive classification and curve-fitting

NASA Astrophysics Data System (ADS)

Wang, Ke-Yan; Li, Yun-Song; Liu, Kai; Wu, Cheng-Ke

2008-08-01

A novel compression algorithm for interferential multispectral images based on adaptive classification and curve-fitting is proposed. The image is first partitioned adaptively into major-interference region and minor-interference region. Different approximating functions are then constructed for two kinds of regions respectively. For the major interference region, some typical interferential curves are selected to predict other curves. These typical curves are then processed by curve-fitting method. For the minor interference region, the data of each interferential curve are independently approximated. Finally the approximating errors of two regions are entropy coded. The experimental results show that, compared with JPEG2000, the proposed algorithm not only decreases the average output bit-rate by about 0.2 bit/pixel for lossless compression, but also improves the reconstructed images and reduces the spectral distortion greatly, especially at high bit-rate for lossy compression.

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.

Experiment on a three-beam adaptive array for EHF frequency-hopped signals using a fast algorithm, phase-D

NASA Astrophysics Data System (ADS)

Yen, J. L.; Kremer, P.; Amin, N.; Fung, J.

1989-05-01

The Department of National Defence (Canada) has been conducting studies into multi-beam adaptive arrays for extremely high frequency (EHF) frequency hopped signals. A three-beam 43 GHz adaptive antenna and a beam control processor is under development. An interactive software package for the operation of the array, capable of applying different control algorithms is being written. A maximum signal to jammer plus noise ratio (SJNR) was found to provide superior performance in preventing degradation of user signals in the presence of nearby jammers. A new fast algorithm using a modified conjugate gradient approach was found to be a very efficient way to implement anti-jamming arrays based on maximum SJNR criterion. The present study was intended to refine and simplify this algorithm and to implement the algorithm on an experimental array for real-time evaluation of anti-jamming performance. A three-beam adaptive array was used. A simulation package was used in the evaluation of multi-beam systems using more than three beams and different user-jammer scenarios. An attempt to further reduce the computation burden through continued analysis of maximum SJNR met with limited success. A method to acquire and track an incoming laser beam is proposed.

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

Accelerating adaptive inverse distance weighting interpolation algorithm on a graphics processing unit

PubMed Central

Xu, Liangliang; Xu, Nengxiong

2017-01-01

This paper focuses on designing and implementing parallel adaptive inverse distance weighting (AIDW) interpolation algorithms by using the graphics processing unit (GPU). The AIDW is an improved version of the standard IDW, which can adaptively determine the power parameter according to the data points’ spatial distribution pattern and achieve more accurate predictions than those predicted by IDW. In this paper, we first present two versions of the GPU-accelerated AIDW, i.e. the naive version without profiting from the shared memory and the tiled version taking advantage of the shared memory. We also implement the naive version and the tiled version using two data layouts, structure of arrays and array of aligned structures, on both single and double precision. We then evaluate the performance of parallel AIDW by comparing it with its corresponding serial algorithm on three different machines equipped with the GPUs GT730M, M5000 and K40c. The experimental results indicate that: (i) there is no significant difference in the computational efficiency when different data layouts are employed; (ii) the tiled version is always slightly faster than the naive version; and (iii) on single precision the achieved speed-up can be up to 763 (on the GPU M5000), while on double precision the obtained highest speed-up is 197 (on the GPU K40c). To benefit the community, all source code and testing data related to the presented parallel AIDW algorithm are publicly available. PMID:28989754

Accelerating adaptive inverse distance weighting interpolation algorithm on a graphics processing unit.

PubMed

Mei, Gang; Xu, Liangliang; Xu, Nengxiong

2017-09-01

This paper focuses on designing and implementing parallel adaptive inverse distance weighting (AIDW) interpolation algorithms by using the graphics processing unit (GPU). The AIDW is an improved version of the standard IDW, which can adaptively determine the power parameter according to the data points' spatial distribution pattern and achieve more accurate predictions than those predicted by IDW. In this paper, we first present two versions of the GPU-accelerated AIDW, i.e. the naive version without profiting from the shared memory and the tiled version taking advantage of the shared memory. We also implement the naive version and the tiled version using two data layouts, structure of arrays and array of aligned structures, on both single and double precision. We then evaluate the performance of parallel AIDW by comparing it with its corresponding serial algorithm on three different machines equipped with the GPUs GT730M, M5000 and K40c. The experimental results indicate that: (i) there is no significant difference in the computational efficiency when different data layouts are employed; (ii) the tiled version is always slightly faster than the naive version; and (iii) on single precision the achieved speed-up can be up to 763 (on the GPU M5000), while on double precision the obtained highest speed-up is 197 (on the GPU K40c). To benefit the community, all source code and testing data related to the presented parallel AIDW algorithm are publicly available.

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.

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.

Adaptive filtering in biological signal processing.

PubMed

Iyer, V K; Ploysongsang, Y; Ramamoorthy, P A

1990-01-01

The high dependence of conventional optimal filtering methods on the a priori knowledge of the signal and noise statistics render them ineffective in dealing with signals whose statistics cannot be predetermined accurately. Adaptive filtering methods offer a better alternative, since the a priori knowledge of statistics is less critical, real time processing is possible, and the computations are less expensive for this approach. Adaptive filtering methods compute the filter coefficients "on-line", converging to the optimal values in the least-mean square (LMS) error sense. Adaptive filtering is therefore apt for dealing with the "unknown" statistics situation and has been applied extensively in areas like communication, speech, radar, sonar, seismology, and biological signal processing and analysis for channel equalization, interference and echo canceling, line enhancement, signal detection, system identification, spectral analysis, beamforming, modeling, control, etc. In this review article adaptive filtering in the context of biological signals is reviewed. An intuitive approach to the underlying theory of adaptive filters and its applicability are presented. Applications of the principles in biological signal processing are discussed in a manner that brings out the key ideas involved. Current and potential future directions in adaptive biological signal processing are also discussed.

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

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.

Improving acoustic beamforming maps in a reverberant environment by modifying the cross-correlation matrix

NASA Astrophysics Data System (ADS)

Fischer, J.; Doolan, C.

2017-12-01

A method to improve the quality of acoustic beamforming in reverberant environments is proposed in this paper. The processing is based on a filtering of the cross-correlation matrix of the microphone signals obtained using a microphone array. The main advantage of the proposed method is that it does not require information about the geometry of the reverberant environment and thus it can be applied to any configuration. The method is applied to the particular example of aeroacoustic testing in a hard-walled low-speed wind tunnel; however, the technique can be used in any reverberant environment. Two test cases demonstrate the technique. The first uses a speaker placed in the hard-walled working section with no wind tunnel flow. In the second test case, an airfoil is placed in a flow and acoustic beamforming maps are obtained. The acoustic maps have been improved, as the reflections observed in the conventional maps have been removed after application of the proposed method.

The wavenumber algorithm for full-matrix imaging using an ultrasonic array.

PubMed

Hunter, Alan J; Drinkwater, Bruce W; Wilcox, Paul D

2008-11-01

Ultrasonic imaging using full-matrix capture, e.g., via the total focusing method (TFM), has been shown to increase angular inspection coverage and improve sensitivity to small defects in nondestructive evaluation. In this paper, we develop a Fourier-domain approach to full-matrix imaging based on the wavenumber algorithm used in synthetic aperture radar and sonar. The extension to the wavenumber algorithm for full-matrix data is described and the performance of the new algorithm compared with the TFM, which we use as a representative benchmark for the time-domain algorithms. The wavenumber algorithm provides a mathematically rigorous solution to the inverse problem for the assumed forward wave propagation model, whereas the TFM employs heuristic delay-and-sum beamforming. Consequently, the wavenumber algorithm has an improved point-spread function and provides better imagery. However, the major advantage of the wavenumber algorithm is its superior computational performance. For large arrays and images, the wavenumber algorithm is several orders of magnitude faster than the TFM. On the other hand, the key advantage of the TFM is its flexibility. The wavenumber algorithm requires a regularly sampled linear array, while the TFM can handle arbitrary imaging geometries. The TFM and the wavenumber algorithm are compared using simulated and experimental data.

Complexity control algorithm based on adaptive mode selection for interframe coding in high efficiency video coding

NASA Astrophysics Data System (ADS)

Chen, Gang; Yang, Bing; Zhang, Xiaoyun; Gao, Zhiyong

2017-07-01

The latest high efficiency video coding (HEVC) standard significantly increases the encoding complexity for improving its coding efficiency. Due to the limited computational capability of handheld devices, complexity constrained video coding has drawn great attention in recent years. A complexity control algorithm based on adaptive mode selection is proposed for interframe coding in HEVC. Considering the direct proportionality between encoding time and computational complexity, the computational complexity is measured in terms of encoding time. First, complexity is mapped to a target in terms of prediction modes. Then, an adaptive mode selection algorithm is proposed for the mode decision process. Specifically, the optimal mode combination scheme that is chosen through offline statistics is developed at low complexity. If the complexity budget has not been used up, an adaptive mode sorting method is employed to further improve coding efficiency. The experimental results show that the proposed algorithm achieves a very large complexity control range (as low as 10%) for the HEVC encoder while maintaining good rate-distortion performance. For the lowdelayP condition, compared with the direct resource allocation method and the state-of-the-art method, an average gain of 0.63 and 0.17 dB in BDPSNR is observed for 18 sequences when the target complexity is around 40%.

Finite element analysis and genetic algorithm optimization design for the actuator placement on a large adaptive structure

NASA Astrophysics Data System (ADS)

Sheng, Lizeng

The dissertation focuses on one of the major research needs in the area of adaptive/intelligent/smart structures, the development and application of finite element analysis and genetic algorithms for optimal design of large-scale adaptive structures. We first review some basic concepts in finite element method and genetic algorithms, along with the research on smart structures. Then we propose a solution methodology for solving a critical problem in the design of a next generation of large-scale adaptive structures---optimal placements of a large number of actuators to control thermal deformations. After briefly reviewing the three most frequently used general approaches to derive a finite element formulation, the dissertation presents techniques associated with general shell finite element analysis using flat triangular laminated composite elements. The element used here has three nodes and eighteen degrees of freedom and is obtained by combining a triangular membrane element and a triangular plate bending element. The element includes the coupling effect between membrane deformation and bending deformation. The membrane element is derived from the linear strain triangular element using Cook's transformation. The discrete Kirchhoff triangular (DKT) element is used as the plate bending element. For completeness, a complete derivation of the DKT is presented. Geometrically nonlinear finite element formulation is derived for the analysis of adaptive structures under the combined thermal and electrical loads. Next, we solve the optimization problems of placing a large number of piezoelectric actuators to control thermal distortions in a large mirror in the presence of four different thermal loads. We then extend this to a multi-objective optimization problem of determining only one set of piezoelectric actuator locations that can be used to control the deformation in the same mirror under the action of any one of the four thermal loads. A series of genetic algorithms

Using patient-specific phantoms to evaluate deformable image registration algorithms for adaptive radiation therapy

PubMed Central

Stanley, Nick; Glide-Hurst, Carri; Kim, Jinkoo; Adams, Jeffrey; Li, Shunshan; Wen, Ning; Chetty, Indrin J.; Zhong, Hualiang

2014-01-01

The quality of adaptive treatment planning depends on the accuracy of its underlying deformable image registration (DIR). The purpose of this study is to evaluate the performance of two DIR algorithms, B-spline–based deformable multipass (DMP) and deformable demons (Demons), implemented in a commercial software package. Evaluations were conducted using both computational and physical deformable phantoms. Based on a finite element method (FEM), a total of 11 computational models were developed from a set of CT images acquired from four lung and one prostate cancer patients. FEM generated displacement vector fields (DVF) were used to construct the lung and prostate image phantoms. Based on a fast-Fourier transform technique, image noise power spectrum was incorporated into the prostate image phantoms to create simulated CBCT images. The FEM-DVF served as a gold standard for verification of the two registration algorithms performed on these phantoms. The registration algorithms were also evaluated at the homologous points quantified in the CT images of a physical lung phantom. The results indicated that the mean errors of the DMP algorithm were in the range of 1.0 ~ 3.1 mm for the computational phantoms and 1.9 mm for the physical lung phantom. For the computational prostate phantoms, the corresponding mean error was 1.0–1.9 mm in the prostate, 1.9–2.4 mm in the rectum, and 1.8–2.1 mm over the entire patient body. Sinusoidal errors induced by B-spline interpolations were observed in all the displacement profiles of the DMP registrations. Regions of large displacements were observed to have more registration errors. Patient-specific FEM models have been developed to evaluate the DIR algorithms implemented in the commercial software package. It has been found that the accuracy of these algorithms is patient-dependent and related to various factors including tissue deformation magnitudes and image intensity gradients across the regions of interest. This may

Experiment on a three-beam adaptive array for EHF frequency-hopped signals using a fast algorithm, phase E

NASA Astrophysics Data System (ADS)

Yen, J. L.; Kremer, P.; Fung, J.

1990-05-01

The Department of National Defence (Canada) has been conducting studies into multi-beam adaptive arrays for extremely high frequency (EHF) frequency hopped signals. A three-beam 43 GHz adaptive antenna and a beam control processor is under development. An interactive software package for the operation of the array, capable of applying different control algorithms is being written. A maximum signal to jammer plus noise ratio (SJNR) has been found to provide superior performance in preventing degradation of user signals in the presence of nearby jammers. A new fast algorithm using a modified conjugate gradient approach has been found to be a very efficient way to implement anti-jamming arrays based on maximum SJNR criterion. The present study was intended to refine and simplify this algorithm and to implement the algorithm on an experimental array for real-time evaluation of anti-jamming performance. A three-beam adaptive array was used. A simulation package was used in the evaluation of multi-beam systems using more than three beams and different user-jammer scenarios. An attempt to further reduce the computation burden through further analysis of maximum SJNR met with limited success. The investigation of a new angle detector for spatial tracking in heterodyne laser space communications was completed.

A new adaptive algorithm for automated feature extraction in exponentially damped signals for health monitoring of smart structures

NASA Astrophysics Data System (ADS)

Qarib, Hossein; Adeli, Hojjat

2015-12-01

In this paper authors introduce a new adaptive signal processing technique for feature extraction and parameter estimation in noisy exponentially damped signals. The iterative 3-stage method is based on the adroit integration of the strengths of parametric and nonparametric methods such as multiple signal categorization, matrix pencil, and empirical mode decomposition algorithms. The first stage is a new adaptive filtration or noise removal scheme. The second stage is a hybrid parametric-nonparametric signal parameter estimation technique based on an output-only system identification technique. The third stage is optimization of estimated parameters using a combination of the primal-dual path-following interior point algorithm and genetic algorithm. The methodology is evaluated using a synthetic signal and a signal obtained experimentally from transverse vibrations of a steel cantilever beam. The method is successful in estimating the frequencies accurately. Further, it estimates the damping exponents. The proposed adaptive filtration method does not include any frequency domain manipulation. Consequently, the time domain signal is not affected as a result of frequency domain and inverse transformations.

Modified artificial fish school algorithm for free space optical communication with sensor-less adaptive optics system

NASA Astrophysics Data System (ADS)

Cao, Jingtai; Zhao, Xiaohui; Li, Zhaokun; Liu, Wei; Gu, Haijun

2017-11-01

The performance of free space optical (FSO) communication system is limited by atmospheric turbulent extremely. Adaptive optics (AO) is the significant method to overcome the atmosphere disturbance. Especially, for the strong scintillation effect, the sensor-less AO system plays a major role for compensation. In this paper, a modified artificial fish school (MAFS) algorithm is proposed to compensate the aberrations in the sensor-less AO system. Both the static and dynamic aberrations compensations are analyzed and the performance of FSO communication before and after aberrations compensations is compared. In addition, MAFS algorithm is compared with artificial fish school (AFS) algorithm, stochastic parallel gradient descent (SPGD) algorithm and simulated annealing (SA) algorithm. It is shown that the MAFS algorithm has a higher convergence speed than SPGD algorithm and SA algorithm, and reaches the better convergence value than AFS algorithm, SPGD algorithm and SA algorithm. The sensor-less AO system with MAFS algorithm effectively increases the coupling efficiency at the receiving terminal with fewer numbers of iterations. In conclusion, the MAFS algorithm has great significance for sensor-less AO system to compensate atmospheric turbulence in FSO communication system.

FPGA implementation of ICA algorithm for blind signal separation and adaptive noise canceling.

PubMed

Kim, Chang-Min; Park, Hyung-Min; Kim, Taesu; Choi, Yoon-Kyung; Lee, Soo-Young

2003-01-01

An field programmable gate array (FPGA) implementation of independent component analysis (ICA) algorithm is reported for blind signal separation (BSS) and adaptive noise canceling (ANC) in real time. In order to provide enormous computing power for ICA-based algorithms with multipath reverberation, a special digital processor is designed and implemented in FPGA. The chip design fully utilizes modular concept and several chips may be put together for complex applications with a large number of noise sources. Experimental results with a fabricated test board are reported for ANC only, BSS only, and simultaneous ANC/BSS, which demonstrates successful speech enhancement in real environments in real time.

Clustering and Beamforming for Efficient Communication in Wireless Sensor Networks

PubMed Central

Porcel-Rodríguez, Francisco; Valenzuela-Valdés, Juan; Padilla, Pablo; Luna-Valero, Francisco; Luque-Baena, Rafael; López-Gordo, Miguel Ángel

2016-01-01

Energy efficiency is a critical issue for wireless sensor networks (WSNs) as sensor nodes have limited power availability. In order to address this issue, this paper tries to maximize the power efficiency in WSNs by means of the evaluation of WSN node networks and their performance when both clustering and antenna beamforming techniques are applied. In this work, four different scenarios are defined, each one considering different numbers of sensors: 50, 20, 10, five, and two nodes per scenario, and each scenario is randomly generated thirty times in order to statistically validate the results. For each experiment, two different target directions for transmission are taken into consideration in the optimization process (φ = 0° and θ = 45°; φ = 45°, and θ = 45°). Each scenario is evaluated for two different types of antennas, an ideal isotropic antenna and a conventional dipole one. In this set of experiments two types of WSN are evaluated: in the first one, all of the sensors have the same amount of power for communications purposes; in the second one, each sensor has a different amount of power for its communications purposes. The analyzed cases in this document are focused on 2D surface and 3D space for the node location. To the authors’ knowledge, this is the first time that beamforming and clustering are simultaneously applied to increase the network lifetime in WSNs. PMID:27556463

Digital Beamforming Synthetic Aperture Radar (DBSAR): Performance Analysis During the Eco-3D 2011 and Summer 2012 Flight Campaigns

NASA Technical Reports Server (NTRS)

Rincon, Rafael F.; Fatoyinbo, Temilola; Carter, Lynn; Ranson, K. Jon; Vega, Manuel; Osmanoglu, Batuhan; Lee, SeungKuk; Sun, Guoqing

2014-01-01

The Digital Beamforming Synthetic Aperture radar (DBSAR) is a state-of-the-art airborne radar developed at NASA/Goddard for the implementation, and testing of digital beamforming techniques applicable to Earth and planetary sciences. The DBSAR measurements have been employed to study: The estimation of vegetation biomass and structure - critical parameters in the study of the carbon cycle; The measurement of geological features - to explore its applicability to planetary science by measuring planetary analogue targets. The instrument flew two test campaigns over the East coast of the United States in 2011, and 2012. During the campaigns the instrument operated in full polarimetric mode collecting data from vegetation and topography features.

An adaptive multi-level simulation algorithm for stochastic biological systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lester, C., E-mail: lesterc@maths.ox.ac.uk; 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 Montemore » 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

Improved Adaptive LSB Steganography Based on Chaos and Genetic Algorithm

NASA Astrophysics Data System (ADS)

Yu, Lifang; Zhao, Yao; Ni, Rongrong; Li, Ting

2010-12-01

We propose a novel steganographic method in JPEG images with high performance. Firstly, we propose improved adaptive LSB steganography, which can achieve high capacity while preserving the first-order statistics. Secondly, in order to minimize visual degradation of the stego image, we shuffle bits-order of the message based on chaos whose parameters are selected by the genetic algorithm. Shuffling message's bits-order provides us with a new way to improve the performance of steganography. Experimental results show that our method outperforms classical steganographic methods in image quality, while preserving characteristics of histogram and providing high capacity.

A dynamic fuzzy genetic algorithm for natural image segmentation using adaptive mean shift

NASA Astrophysics Data System (ADS)

Arfan Jaffar, M.

2017-01-01

In this paper, a colour image segmentation approach based on hybridisation of adaptive mean shift (AMS), fuzzy c-mean and genetic algorithms (GAs) is presented. Image segmentation is the perceptual faction of pixels based on some likeness measure. GA with fuzzy behaviour is adapted to maximise the fuzzy separation and minimise the global compactness among the clusters or segments in spatial fuzzy c-mean (sFCM). It adds diversity to the search process to find the global optima. A simple fusion method has been used to combine the clusters to overcome the problem of over segmentation. The results show that our technique outperforms state-of-the-art methods.

A Fiber Bragg Grating Interrogation System with Self-Adaption Threshold Peak Detection Algorithm.

PubMed

Zhang, Weifang; Li, Yingwu; Jin, Bo; Ren, Feifei; Wang, Hongxun; Dai, Wei

2018-04-08

A Fiber Bragg Grating (FBG) interrogation system with a self-adaption threshold peak detection algorithm is proposed and experimentally demonstrated in this study. This system is composed of a field programmable gate array (FPGA) and advanced RISC machine (ARM) platform, tunable Fabry-Perot (F-P) filter and optical switch. To improve system resolution, the F-P filter was employed. As this filter is non-linear, this causes the shifting of central wavelengths with the deviation compensated by the parts of the circuit. Time-division multiplexing (TDM) of FBG sensors is achieved by an optical switch, with the system able to realize the combination of 256 FBG sensors. The wavelength scanning speed of 800 Hz can be achieved by a FPGA+ARM platform. In addition, a peak detection algorithm based on a self-adaption threshold is designed and the peak recognition rate is 100%. Experiments with different temperatures were conducted to demonstrate the effectiveness of the system. Four FBG sensors were examined in the thermal chamber without stress. When the temperature changed from 0 °C to 100 °C, the degree of linearity between central wavelengths and temperature was about 0.999 with the temperature sensitivity being 10 pm/°C. The static interrogation precision was able to reach 0.5 pm. Through the comparison of different peak detection algorithms and interrogation approaches, the system was verified to have an optimum comprehensive performance in terms of precision, capacity and speed.

A Fiber Bragg Grating Interrogation System with Self-Adaption Threshold Peak Detection Algorithm

PubMed Central

Zhang, Weifang; Li, Yingwu; Jin, Bo; Ren, Feifei

2018-01-01

A Fiber Bragg Grating (FBG) interrogation system with a self-adaption threshold peak detection algorithm is proposed and experimentally demonstrated in this study. This system is composed of a field programmable gate array (FPGA) and advanced RISC machine (ARM) platform, tunable Fabry–Perot (F–P) filter and optical switch. To improve system resolution, the F–P filter was employed. As this filter is non-linear, this causes the shifting of central wavelengths with the deviation compensated by the parts of the circuit. Time-division multiplexing (TDM) of FBG sensors is achieved by an optical switch, with the system able to realize the combination of 256 FBG sensors. The wavelength scanning speed of 800 Hz can be achieved by a FPGA+ARM platform. In addition, a peak detection algorithm based on a self-adaption threshold is designed and the peak recognition rate is 100%. Experiments with different temperatures were conducted to demonstrate the effectiveness of the system. Four FBG sensors were examined in the thermal chamber without stress. When the temperature changed from 0 °C to 100 °C, the degree of linearity between central wavelengths and temperature was about 0.999 with the temperature sensitivity being 10 pm/°C. The static interrogation precision was able to reach 0.5 pm. Through the comparison of different peak detection algorithms and interrogation approaches, the system was verified to have an optimum comprehensive performance in terms of precision, capacity and speed. PMID:29642507

Implementation of a rapid correction algorithm for adaptive optics using a plenoptic sensor

NASA Astrophysics Data System (ADS)

Ko, Jonathan; Wu, Chensheng; Davis, Christopher C.

2016-09-01

Adaptive optics relies on the accuracy and speed of a wavefront sensor in order to provide quick corrections to distortions in the optical system. In weaker cases of atmospheric turbulence often encountered in astronomical fields, a traditional Shack-Hartmann sensor has proved to be very effective. However, in cases of stronger atmospheric turbulence often encountered near the surface of the Earth, atmospheric turbulence no longer solely causes small tilts in the wavefront. Instead, lasers passing through strong or "deep" atmospheric turbulence encounter beam breakup, which results in interference effects and discontinuities in the incoming wavefront. In these situations, a Shack-Hartmann sensor can no longer effectively determine the shape of the incoming wavefront. We propose a wavefront reconstruction and correction algorithm based around the plenoptic sensor. The plenoptic sensor's design allows it to match and exceed the wavefront sensing capabilities of a Shack-Hartmann sensor for our application. Novel wavefront reconstruction algorithms can take advantage of the plenoptic sensor to provide a rapid wavefront reconstruction necessary for real time turbulence. To test the integrity of the plenoptic sensor and its reconstruction algorithms, we use artificially generated turbulence in a lab scale environment to simulate the structure and speed of outdoor atmospheric turbulence. By analyzing the performance of our system with and without the closed-loop plenoptic sensor adaptive optics system, we can show that the plenoptic sensor is effective in mitigating real time lab generated atmospheric turbulence.

A novel clinical decision support system using improved adaptive genetic algorithm for the assessment of fetal well-being.

PubMed

Ravindran, Sindhu; Jambek, Asral Bahari; Muthusamy, Hariharan; 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.

Opportunistic Beamforming with Wireless Powered 1-bit Feedback Through Rectenna Array

NASA Astrophysics Data System (ADS)

Krikidis, Ioannis

2015-11-01

This letter deals with the opportunistic beamforming (OBF) scheme for multi-antenna downlink with spatial randomness. In contrast to conventional OBF, the terminals return only 1-bit feedback, which is powered by wireless power transfer through a rectenna array. We study two fundamental topologies for the combination of the rectenna elements; the direct-current combiner and the radio-frequency combiner. The beam outage probability is derived in closed form for both combination schemes, by using high order statistics and stochastic geometry.

Testing for a slope-based decoupling algorithm in a woofer-tweeter adaptive optics system.

PubMed

Cheng, Tao; Liu, WenJin; Yang, KangJian; He, Xin; Yang, Ping; Xu, Bing

2018-05-01

It is well known that using two or more deformable mirrors (DMs) can improve the compensation ability of an adaptive optics (AO) system. However, to keep the stability of an AO system, the correlation between the multiple DMs must be suppressed during the correction. In this paper, we proposed a slope-based decoupling algorithm to simultaneous control the multiple DMs. In order to examine the validity and practicality of this algorithm, a typical woofer-tweeter (W-T) AO system was set up. For the W-T system, a theory model was simulated and the results indicated in theory that the algorithm we presented can selectively make woofer and tweeter correct different spatial frequency aberration and suppress the cross coupling between the dual DMs. At the same time, the experimental results for the W-T AO system were consistent with the results of the simulation, which demonstrated in practice that this algorithm is practical for the AO system with dual DMs.

A novel adaptive, real-time algorithm to detect gait events from wearable sensors.

PubMed

Chia Bejarano, Noelia; Ambrosini, Emilia; Pedrocchi, Alessandra; Ferrigno, Giancarlo; Monticone, Marco; Ferrante, Simona

2015-05-01

A real-time, adaptive algorithm based on two inertial and magnetic sensors placed on the shanks was developed for gait-event detection. For each leg, the algorithm detected the Initial Contact (IC), as the minimum of the flexion/extension angle, and the End Contact (EC) and the Mid-Swing (MS), as minimum and maximum of the angular velocity, respectively. The algorithm consisted of calibration, real-time detection, and step-by-step update. Data collected from 22 healthy subjects (21 to 85 years) walking at three self-selected speeds were used to validate the algorithm against the GaitRite system. Comparable levels of accuracy and significantly lower detection delays were achieved with respect to other published methods. The algorithm robustness was tested on ten healthy subjects performing sudden speed changes and on ten stroke subjects (43 to 89 years). For healthy subjects, F1-scores of 1 and mean detection delays lower than 14 ms were obtained. For stroke subjects, F1-scores of 0.998 and 0.944 were obtained for IC and EC, respectively, with mean detection delays always below 31 ms. The algorithm accurately detected gait events in real time from a heterogeneous dataset of gait patterns and paves the way for the design of closed-loop controllers for customized gait trainings and/or assistive devices.

Optimization of IBF parameters based on adaptive tool-path algorithm

NASA Astrophysics Data System (ADS)

Deng, Wen Hui; Chen, Xian Hua; Jin, Hui Liang; Zhong, Bo; Hou, Jin; Li, An Qi

2018-03-01

As a kind of Computer Controlled Optical Surfacing(CCOS) technology. Ion Beam Figuring(IBF) has obvious advantages in the control of surface accuracy, surface roughness and subsurface damage. The superiority and characteristics of IBF in optical component processing are analyzed from the point of view of removal mechanism. For getting more effective and automatic tool path with the information of dwell time, a novel algorithm is proposed in this thesis. Based on the removal functions made through our IBF equipment and the adaptive tool-path, optimized parameters are obtained through analysis the residual error that would be created in the polishing process. A Φ600 mm plane reflector element was used to be a simulation instance. The simulation result shows that after four combinations of processing, the surface accuracy of PV (Peak Valley) value and the RMS (Root Mean Square) value was reduced to 4.81 nm and 0.495 nm from 110.22 nm and 13.998 nm respectively in the 98% aperture. The result shows that the algorithm and optimized parameters provide a good theoretical for high precision processing of IBF.

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.

Using patient‐specific phantoms to evaluate deformable image registration algorithms for adaptive radiation therapy

PubMed Central

Stanley, Nick; Glide‐Hurst, Carri; Kim, Jinkoo; Adams, Jeffrey; Li, Shunshan; Wen, Ning; Chetty, Indrin J

2013-01-01

The quality of adaptive treatment planning depends on the accuracy of its underlying deformable image registration (DIR). The purpose of this study is to evaluate the performance of two DIR algorithms, B‐spline‐based deformable multipass (DMP) and deformable demons (Demons), implemented in a commercial software package. Evaluations were conducted using both computational and physical deformable phantoms. Based on a finite element method (FEM), a total of 11 computational models were developed from a set of CT images acquired from four lung and one prostate cancer patients. FEM generated displacement vector fields (DVF) were used to construct the lung and prostate image phantoms. Based on a fast‐Fourier transform technique, image noise power spectrum was incorporated into the prostate image phantoms to create simulated CBCT images. The FEM‐DVF served as a gold standard for verification of the two registration algorithms performed on these phantoms. The registration algorithms were also evaluated at the homologous points quantified in the CT images of a physical lung phantom. The results indicated that the mean errors of the DMP algorithm were in the range of 1.0~3.1mm for the computational phantoms and 1.9 mm for the physical lung phantom. For the computational prostate phantoms, the corresponding mean error was 1.0–1.9 mm in the prostate, 1.9–2.4 mm in the rectum, and 1.8–2.1 mm over the entire patient body. Sinusoidal errors induced by B‐spline interpolations were observed in all the displacement profiles of the DMP registrations. Regions of large displacements were observed to have more registration errors. Patient‐specific FEM models have been developed to evaluate the DIR algorithms implemented in the commercial software package. It has been found that the accuracy of these algorithms is patient‐dependent and related to various factors including tissue deformation magnitudes and image intensity gradients across the regions of interest. This

A hybrid skull-stripping algorithm based on adaptive balloon snake models

NASA Astrophysics Data System (ADS)

Liu, Hung-Ting; Sheu, Tony W. H.; Chang, Herng-Hua

2013-02-01

Skull-stripping is one of the most important preprocessing steps in neuroimage analysis. We proposed a hybrid algorithm based on an adaptive balloon snake model to handle this challenging task. The proposed framework consists of two stages: first, the fuzzy possibilistic c-means (FPCM) is used for voxel clustering, which provides a labeled image for the snake contour initialization. In the second stage, the contour is initialized outside the brain surface based on the FPCM result and evolves under the guidance of the balloon snake model, which drives the contour with an adaptive inward normal force to capture the boundary of the brain. The similarity indices indicate that our method outperformed the BSE and BET methods in skull-stripping the MR image volumes in the IBSR data set. Experimental results show the effectiveness of this new scheme and potential applications in a wide variety of skull-stripping applications.

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.

A dual-adaptive support-based stereo matching algorithm

NASA Astrophysics Data System (ADS)

Zhang, Yin; Zhang, Yun

2017-07-01

Many stereo matching algorithms use fixed color thresholds and a rigid cross skeleton to segment supports (viz., Cross method), which, however, does not work well for different images. To address this issue, this paper proposes a novel dual adaptive support (viz., DAS)-based stereo matching method, which uses both appearance and shape information of a local region to segment supports automatically, and, then, integrates the DAS-based cost aggregation with the absolute difference plus census transform cost, scanline optimization and disparity refinement to develop a stereo matching system. The performance of the DAS method is also evaluated in the Middlebury benchmark and by comparing with the Cross method. The results show that the average error for the DAS method 25.06% lower than that for the Cross method, indicating that the proposed method is more accurate, with fewer parameters and suitable for parallel computing.

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

Angular dependence of multiangle dynamic light scattering for particle size distribution inversion using a self-adapting regularization algorithm

NASA Astrophysics Data System (ADS)

Li, Lei; Yu, Long; Yang, Kecheng; Li, Wei; Li, Kai; Xia, Min

2018-04-01

The multiangle dynamic light scattering (MDLS) technique can better estimate particle size distributions (PSDs) than single-angle dynamic light scattering. However, determining the inversion range, angular weighting coefficients, and scattering angle combination is difficult but fundamental to the reconstruction for both unimodal and multimodal distributions. In this paper, we propose a self-adapting regularization method called the wavelet iterative recursion nonnegative Tikhonov-Phillips-Twomey (WIRNNT-PT) algorithm. This algorithm combines a wavelet multiscale strategy with an appropriate inversion method and could self-adaptively optimize several noteworthy issues containing the choices of the weighting coefficients, the inversion range and the optimal inversion method from two regularization algorithms for estimating the PSD from MDLS measurements. In addition, the angular dependence of the MDLS for estimating the PSDs of polymeric latexes is thoroughly analyzed. The dependence of the results on the number and range of measurement angles was analyzed in depth to identify the optimal scattering angle combination. Numerical simulations and experimental results for unimodal and multimodal distributions are presented to demonstrate both the validity of the WIRNNT-PT algorithm and the angular dependence of MDLS and show that the proposed algorithm with a six-angle analysis in the 30-130° range can be satisfactorily applied to retrieve PSDs from MDLS measurements.

An unbiased adaptive sampling algorithm for the exploration of RNA mutational landscapes under evolutionary pressure.

PubMed

Waldispühl, Jérôme; Ponty, Yann

2011-11-01

The analysis of the relationship between sequences and structures (i.e., how mutations affect structures and reciprocally how structures influence mutations) is essential to decipher the principles driving molecular evolution, to infer the origins of genetic diseases, and to develop bioengineering applications such as the design of artificial molecules. Because their structures can be predicted from the sequence data only, RNA molecules provide a good framework to study this sequence-structure relationship. We recently introduced a suite of algorithms called RNAmutants which allows a complete exploration of RNA sequence-structure maps in polynomial time and space. Formally, RNAmutants takes an input sequence (or seed) to compute the Boltzmann-weighted ensembles of mutants with exactly k mutations, and sample mutations from these ensembles. However, this approach suffers from major limitations. Indeed, since the Boltzmann probabilities of the mutations depend of the free energy of the structures, RNAmutants has difficulties to sample mutant sequences with low G+C-contents. In this article, we introduce an unbiased adaptive sampling algorithm that enables RNAmutants to sample regions of the mutational landscape poorly covered by classical algorithms. We applied these methods to sample mutations with low G+C-contents. These adaptive sampling techniques can be easily adapted to explore other regions of the sequence and structural landscapes which are difficult to sample. Importantly, these algorithms come at a minimal computational cost. We demonstrate the insights offered by these techniques on studies of complete RNA sequence structures maps of sizes up to 40 nucleotides. Our results indicate that the G+C-content has a strong influence on the size and shape of the evolutionary accessible sequence and structural spaces. In particular, we show that low G+C-contents favor the apparition of internal loops and thus possibly the synthesis of tertiary structure motifs. On

A lane line segmentation algorithm based on adaptive threshold and connected domain theory

NASA Astrophysics Data System (ADS)

Feng, Hui; Xu, Guo-sheng; Han, Yi; Liu, Yang

2018-04-01

Before detecting cracks and repairs on road lanes, it's necessary to eliminate the influence of lane lines on the recognition result in road lane images. Aiming at the problems caused by lane lines, an image segmentation algorithm based on adaptive threshold and connected domain is proposed. First, by analyzing features like grey level distribution and the illumination of the images, the algorithm uses Hough transform to divide the images into different sections and convert them into binary images separately. It then uses the connected domain theory to amend the outcome of segmentation, remove noises and fill the interior zone of lane lines. Experiments have proved that this method could eliminate the influence of illumination and lane line abrasion, removing noises thoroughly while maintaining high segmentation precision.

Benefit of the UltraZoom beamforming technology in noise in cochlear implant users.

PubMed

Mosnier, Isabelle; Mathias, Nathalie; Flament, Jonathan; Amar, Dorith; Liagre-Callies, Amelie; Borel, Stephanie; Ambert-Dahan, Emmanuèle; Sterkers, Olivier; Bernardeschi, Daniele

2017-09-01

The objectives of the study were to demonstrate the audiological and subjective benefits of the adaptive UltraZoom beamforming technology available in the Naída CI Q70 sound processor, in cochlear-implanted adults upgraded from a previous generation sound processor. Thirty-four adults aged between 21 and 89 years (mean 53 ± 19) were prospectively included. Nine subjects were unilaterally implanted, 11 bilaterally and 14 were bimodal users. The mean duration of cochlear implant use was 7 years (range 5-15 years). Subjects were tested in quiet with monosyllabic words and in noise with the adaptive French Matrix test in the best-aided conditions. The test setup contained a signal source in front of the subject and three noise sources at +/-90° and 180°. The noise was presented at a fixed level of 65 dB SPL and the level of speech signal was varied to obtain the speech reception threshold (SRT). During the upgrade visit, subjects were tested with the Harmony and with the Naída CI sound processors in omnidirectional microphone configuration. After a take-home phase of 2 months, tests were repeated with the Naída CI processor with and without UltraZoom. Subjective assessment of the sound quality in daily environments was recorded using the APHAB questionnaire. No difference in performance was observed in quiet between the two processors. The Matrix test in noise was possible in the 21 subjects with the better performance. No difference was observed between the two processors for performance in noise when using the omnidirectional microphone. At the follow-up session, the median SRT with the Naída CI processor with UltraZoom was -4 dB compared to -0.45 dB without UltraZoom. The use of UltraZoom improved the median SRT by 3.6 dB (p < 0.0001, Wilcoxon paired test). When looking at the APHAB outcome, improvement was observed for speech understanding in noisy environments (p < 0.01) and in aversive situations (p < 0.05) in the group of 21 subjects who were

Performance comparisons on spatial lattice algorithm and direct matrix inverse method with application to adaptive arrays processing

NASA Technical Reports Server (NTRS)

An, S. H.; Yao, K.

1986-01-01

Lattice algorithm has been employed in numerous adaptive filtering applications such as speech analysis/synthesis, noise canceling, spectral analysis, and channel equalization. In this paper the application to adaptive-array processing is discussed. The advantages are fast convergence rate as well as computational accuracy independent of the noise and interference conditions. The results produced by this technique are compared to those obtained by the direct matrix inverse method.

Three-component ambient noise beamforming in the Parkfield area

NASA Astrophysics Data System (ADS)

Löer, Katrin; Riahi, Nima; Saenger, Erik H.

2018-06-01

We apply a three-component beamforming algorithm to an ambient noise data set recorded at a seismic array to extract information about both isotropic and anisotropic surface wave velocities. In particular, we test the sensitivity of the method with respect to the array geometry as well as to seasonal variations in the distribution of noise sources. In the earth's crust, anisotropy is typically caused by oriented faults or fractures and can be altered when earthquakes or human activities cause these structures to change. Monitoring anisotropy changes thus provides time-dependent information on subsurface processes, provided they can be distinguished from other effects. We analyse ambient noise data at frequencies between 0.08 and 0.52 Hz recorded at a three-component array in the Parkfield area, California (US), between 2001 November and 2002 April. During this time, no major earthquakes were identified in the area and structural changes are thus not expected. We compute dispersion curves of Love and Rayleigh waves and estimate anisotropy parameters for Love waves. For Rayleigh waves, the azimuthal source coverage is too limited to perform anisotropy analysis. For Love waves, ambient noise sources are more widely distributed and we observe significant and stable surface wave anisotropy for frequencies between 0.2 and 0.4 Hz. Synthetic data experiments indicate that the array geometry introduces apparent anisotropy, especially when waves from multiple sources arrive simultaneously at the array. Both the magnitude and the pattern of apparent anisotropy, however, differ significantly from the anisotropy observed in Love wave data. Temporal variations of anisotropy parameters observed at frequencies below 0.2 Hz and above 0.4 Hz correlate with changes in the source distribution. Frequencies between 0.2 and 0.4 Hz, however, are less affected by these variations and provide relatively stable results over the period of study.

A proposed adaptive step size perturbation and observation maximum power point tracking algorithm based on photovoltaic system modeling

NASA Astrophysics Data System (ADS)

Huang, Yu

Solar energy becomes one of the major alternative renewable energy options for its huge abundance and accessibility. Due to the intermittent nature, the high demand of Maximum Power Point Tracking (MPPT) techniques exists when a Photovoltaic (PV) system is used to extract energy from the sunlight. This thesis proposed an advanced Perturbation and Observation (P&O) algorithm aiming for relatively practical circumstances. Firstly, a practical PV system model is studied with determining the series and shunt resistances which are neglected in some research. Moreover, in this proposed algorithm, the duty ratio of a boost DC-DC converter is the object of the perturbation deploying input impedance conversion to achieve working voltage adjustment. Based on the control strategy, the adaptive duty ratio step size P&O algorithm is proposed with major modifications made for sharp insolation change as well as low insolation scenarios. Matlab/Simulink simulation for PV model, boost converter control strategy and various MPPT process is conducted step by step. The proposed adaptive P&O algorithm is validated by the simulation results and detail analysis of sharp insolation changes, low insolation condition and continuous insolation variation.

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.

Theoretical and experimental comparative analysis of beamforming methods for loudspeaker arrays under given performance constraints

NASA Astrophysics Data System (ADS)

Olivieri, Ferdinando; Fazi, Filippo Maria; Nelson, Philip A.; Shin, Mincheol; Fontana, Simone; Yue, Lang

2016-07-01

Methods for beamforming are available that provide the signals used to drive an array of sources for the implementation of systems for the so-called personal audio. In this work, performance of the delay-and-sum (DAS) method and of three widely used methods for optimal beamforming are compared by means of computer simulations and experiments in an anechoic environment using a linear array of sources with given constraints on quality of the reproduced field at the listener's position and limit to input energy to the array. Using the DAS method as a benchmark for performance, the frequency domain responses of the loudspeaker filters can be characterized in three regions. In the first region, at low frequencies, input signals designed with the optimal methods are identical and provide higher directivity performance than that of the DAS. In the second region, performance of the optimal methods are similar to the DAS method. The third region starts above the limit due to spatial aliasing. A method is presented to estimate the boundaries of these regions.

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

A Space-Time Signal Decomposition Algorithm for Downlink MIMO DS-CDMA Receivers

NASA Astrophysics Data System (ADS)

Wang, Yung-Yi; Fang, Wen-Hsien; Chen, Jiunn-Tsair

We propose a dimension reduction algorithm for the receiver of the downlink of direct-sequence code-division multiple access (DS-CDMA) systems in which both the transmitters and the receivers employ antenna arrays of multiple elements. To estimate the high order channel parameters, we develop a layered architecture using dimension-reduced parameter estimation algorithms to estimate the frequency-selective multipath channels. In the proposed architecture, to exploit the space-time geometric characteristics of multipath channels, spatial beamformers and constrained (or unconstrained) temporal filters are adopted for clustered-multipath grouping and path isolation. In conjunction with the multiple access interference (MAI) suppression techniques, the proposed architecture jointly estimates the direction of arrivals, propagation delays, and fading amplitudes of the downlink fading multipaths. With the outputs of the proposed architecture, the signals of interest can then be naturally detected by using path-wise maximum ratio combining. Compared to the traditional techniques, such as the Joint-Angle-and-Delay-Estimation (JADE) algorithm for DOA-delay joint estimation and the space-time minimum mean square error (ST-MMSE) algorithm for signal detection, computer simulations show that the proposed algorithm substantially mitigate the computational complexity at the expense of only slight performance degradation.

[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.

SASS: A symmetry adapted stochastic search algorithm exploiting site symmetry

NASA Astrophysics Data System (ADS)

Wheeler, Steven E.; Schleyer, Paul v. R.; Schaefer, Henry F.

2007-03-01

A simple symmetry adapted search algorithm (SASS) exploiting point group symmetry increases the efficiency of systematic explorations of complex quantum mechanical potential energy surfaces. In contrast to previously described stochastic approaches, which do not employ symmetry, candidate structures are generated within simple point groups, such as C2, Cs, and C2v. This facilitates efficient sampling of the 3N-6 Pople's dimensional configuration space and increases the speed and effectiveness of quantum chemical geometry optimizations. Pople's concept of framework groups [J. Am. Chem. Soc. 102, 4615 (1980)] is used to partition the configuration space into structures spanning all possible distributions of sets of symmetry equivalent atoms. This provides an efficient means of computing all structures of a given symmetry with minimum redundancy. This approach also is advantageous for generating initial structures for global optimizations via genetic algorithm and other stochastic global search techniques. Application of the SASS method is illustrated by locating 14 low-lying stationary points on the cc-pwCVDZ ROCCSD(T) potential energy surface of Li5H2. The global minimum structure is identified, along with many unique, nonintuitive, energetically favorable isomers.

Adaptive twisting sliding mode algorithm for hypersonic reentry vehicle attitude control based on finite-time observer.

PubMed

Guo, Zongyi; Chang, Jing; Guo, Jianguo; Zhou, Jun

2018-06-01

This paper focuses on the adaptive twisting sliding mode control for the Hypersonic Reentry Vehicles (HRVs) attitude tracking issue. The HRV attitude tracking model is transformed into the error dynamics in matched structure, whereas an unmeasurable state is redefined by lumping the existing unmatched disturbance with the angular rate. Hence, an adaptive finite-time observer is used to estimate the unknown state. Then, an adaptive twisting algorithm is proposed for systems subject to disturbances with unknown bounds. The stability of the proposed observer-based adaptive twisting approach is guaranteed, and the case of noisy measurement is analyzed. Also, the developed control law avoids the aggressive chattering phenomenon of the existing adaptive twisting approaches because the adaptive gains decrease close to the disturbance once the trajectories reach the sliding surface. Finally, numerical simulations on the attitude control of the HRV are conducted to verify the effectiveness and benefit of the proposed approach. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

High-frequency ultrasound annular array imaging. Part II: digital beamformer design and imaging.

PubMed

Hu, Chang-Hong; Snook, Kevin A; Cao, Pei-Jie; Shung, K Kirk

2006-02-01

This is the second part of a two-paper series reporting a recent effort in the development of a high-frequency annular array ultrasound imaging system. In this paper an imaging system composed of a six-element, 43 MHz annular array transducer, a six-channel analog front-end, a field programmable gate array (FPGA)-based beamformer, and a digital signal processor (DSP) microprocessor-based scan converter will be described. A computer is used as the interface for image display. The beamformer that applies delays to the echoes for each channel is implemented with the strategy of combining the coarse and fine delays. The coarse delays that are integer multiples of the clock periods are achieved by using a first-in-first-out (FIFO) structure, and the fine delays are obtained with a fractional delay (FD) filter. Using this principle, dynamic receiving focusing is achieved. The image from a wire phantom obtained with the imaging system was compared to that from a prototype ultrasonic backscatter microscope with a 45 MHz single-element transducer. The improved lateral resolution and depth of field from the wire phantom image were observed. Images from an excised rabbit eye sample also were obtained, and fine anatomical structures were discerned.

An adaptive algorithm for the detection of microcalcifications in simulated low-dose mammography.

PubMed

Treiber, O; Wanninger, F; Führ, H; Panzer, W; Regulla, D; Winkler, G

2003-02-21

This paper uses the task of microcalcification detection as a benchmark problem to assess the potential for dose reduction in x-ray mammography. We present the results of a newly developed algorithm for detection of microcalcifications as a case study for a typical commercial film-screen system (Kodak Min-R 2000/2190). The first part of the paper deals with the simulation of dose reduction for film-screen mammography based on a physical model of the imaging process. Use of a more sensitive film-screen system is expected to result in additional smoothing of the image. We introduce two different models of that behaviour, called moderate and strong smoothing. We then present an adaptive, model-based microcalcification detection algorithm. Comparing detection results with ground-truth images obtained under the supervision of an expert radiologist allows us to establish the soundness of the detection algorithm. We measure the performance on the dose-reduced images in order to assess the loss of information due to dose reduction. It turns out that the smoothing behaviour has a strong influence on detection rates. For moderate smoothing. a dose reduction by 25% has no serious influence on the detection results. whereas a dose reduction by 50% already entails a marked deterioration of the performance. Strong smoothing generally leads to an unacceptable loss of image quality. The test results emphasize the impact of the more sensitive film-screen system and its characteristics on the problem of assessing the potential for dose reduction in film-screen mammography. The general approach presented in the paper can be adapted to fully digital mammography.

An adaptive algorithm for the detection of microcalcifications in simulated low-dose mammography

NASA Astrophysics Data System (ADS)

Treiber, O.; Wanninger, F.; Führ, H.; Panzer, W.; Regulla, D.; Winkler, G.

2003-02-01

This paper uses the task of microcalcification detection as a benchmark problem to assess the potential for dose reduction in x-ray mammography. We present the results of a newly developed algorithm for detection of microcalcifications as a case study for a typical commercial film-screen system (Kodak Min-R 2000/2190). The first part of the paper deals with the simulation of dose reduction for film-screen mammography based on a physical model of the imaging process. Use of a more sensitive film-screen system is expected to result in additional smoothing of the image. We introduce two different models of that behaviour, called moderate and strong smoothing. We then present an adaptive, model-based microcalcification detection algorithm. Comparing detection results with ground-truth images obtained under the supervision of an expert radiologist allows us to establish the soundness of the detection algorithm. We measure the performance on the dose-reduced images in order to assess the loss of information due to dose reduction. It turns out that the smoothing behaviour has a strong influence on detection rates. For moderate smoothing, a dose reduction by 25% has no serious influence on the detection results, whereas a dose reduction by 50% already entails a marked deterioration of the performance. Strong smoothing generally leads to an unacceptable loss of image quality. The test results emphasize the impact of the more sensitive film-screen system and its characteristics on the problem of assessing the potential for dose reduction in film-screen mammography. The general approach presented in the paper can be adapted to fully digital mammography.

Design of a Synthetic Aperture Array to Support Experiments in Active Control of Scattering

DTIC Science & Technology

1990-06-01

becomes necessary to validate the theory and test the control system algorithms . While experiments in open water would be most like the anticipated...mathematical development of the beamforming algorithms used as well as an estimate of their applicability to the specifics of beamforming in a reverberant...Chebyshev array have been proposed. The method used in ARRAY, a nested product algorithm , proposed by Bresler [21] is recommended by Pozar [19] and

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.

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.

Efficiently sampling conformations and pathways using the concurrent adaptive sampling (CAS) algorithm

NASA Astrophysics Data System (ADS)

Ahn, Surl-Hee; Grate, Jay W.; Darve, Eric F.

2017-08-01

Molecular dynamics simulations are useful in obtaining thermodynamic and kinetic properties of bio-molecules, but they are limited by the time scale barrier. That is, we may not obtain properties' efficiently because we need to run microseconds or longer simulations using femtosecond time steps. To overcome this time scale barrier, we can use the weighted ensemble (WE) method, a powerful enhanced sampling method that efficiently samples thermodynamic and kinetic properties. However, the WE method requires an appropriate partitioning of phase space into discrete macrostates, which can be problematic when we have a high-dimensional collective space or when little is known a priori about the molecular system. Hence, we developed a new WE-based method, called the "Concurrent Adaptive Sampling (CAS) algorithm," to tackle these issues. The CAS algorithm is not constrained to use only one or two collective variables, unlike most reaction coordinate-dependent methods. Instead, it can use a large number of collective variables and adaptive macrostates to enhance the sampling in the high-dimensional space. This is especially useful for systems in which we do not know what the right reaction coordinates are, in which case we can use many collective variables to sample conformations and pathways. In addition, a clustering technique based on the committor function is used to accelerate sampling the slowest process in the molecular system. In this paper, we introduce the new method and show results from two-dimensional models and bio-molecules, specifically penta-alanine and a triazine trimer.

Text grouping in patent analysis using adaptive K-means clustering algorithm

NASA Astrophysics Data System (ADS)

Shanie, Tiara; Suprijadi, Jadi; Zulhanif

2017-03-01

Patents are one of the Intellectual Property. Analyzing patent is one requirement in knowing well the development of technology in each country and in the world now. This study uses the patent document coming from the Espacenet server about Green Tea. Patent documents related to the technology in the field of tea is still widespread, so it will be difficult for users to information retrieval (IR). Therefore, it is necessary efforts to categorize documents in a specific group of related terms contained therein. This study uses titles patent text data with the proposed Green Tea in Statistical Text Mining methods consists of two phases: data preparation and data analysis stage. The data preparation phase uses Text Mining methods and data analysis stage is done by statistics. Statistical analysis in this study using a cluster analysis algorithm, the Adaptive K-Means Clustering Algorithm. Results from this study showed that based on the maximum value Silhouette, generate 87 clusters associated fifteen terms therein that can be utilized in the process of information retrieval needs.

Quick fuzzy backpropagation algorithm.

PubMed

Nikov, A; Stoeva, S

2001-03-01

A modification of the fuzzy backpropagation (FBP) algorithm called QuickFBP algorithm is proposed, where the computation of the net function is significantly quicker. It is proved that the FBP algorithm is of exponential time complexity, while the QuickFBP algorithm is of polynomial time complexity. Convergence conditions of the QuickFBP, resp. the FBP algorithm are defined and proved for: (1) single output neural networks in case of training patterns with different targets; and (2) multiple output neural networks in case of training patterns with equivalued target vector. They support the automation of the weights training process (quasi-unsupervised learning) establishing the target value(s) depending on the network's input values. In these cases the simulation results confirm the convergence of both algorithms. An example with a large-sized neural network illustrates the significantly greater training speed of the QuickFBP rather than the FBP algorithm. The adaptation of an interactive web system to users on the basis of the QuickFBP algorithm is presented. Since the QuickFBP algorithm ensures quasi-unsupervised learning, this implies its broad applicability in areas of adaptive and adaptable interactive systems, data mining, etc. applications.

Speech Intelligibility Advantages using an Acoustic Beamformer Display

NASA Technical Reports Server (NTRS)

Begault, Durand R.; Sunder, Kaushik; Godfroy, Martine; Otto, Peter

2015-01-01

A speech intelligibility test conforming to the Modified Rhyme Test of ANSI S3.2 "Method for Measuring the Intelligibility of Speech Over Communication Systems" was conducted using a prototype 12-channel acoustic beamformer system. The target speech material (signal) was identified against speech babble (noise), with calculated signal-noise ratios of 0, 5 and 10 dB. The signal was delivered at a fixed beam orientation of 135 deg (re 90 deg as the frontal direction of the array) and the noise at 135 deg (co-located) and 0 deg (separated). A significant improvement in intelligibility from 57% to 73% was found for spatial separation for the same signal-noise ratio (0 dB). Significant effects for improved intelligibility due to spatial separation were also found for higher signal-noise ratios (5 and 10 dB).

Genetic algorithms

NASA Technical Reports Server (NTRS)

Wang, Lui; Bayer, Steven E.

1991-01-01

Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology.

An Adaptive Impedance Matching Network with Closed Loop Control Algorithm for Inductive Wireless Power Transfer

PubMed Central

Miao, Zhidong; Liu, Dake

2017-01-01

For an inductive wireless power transfer (IWPT) system, maintaining a reasonable power transfer efficiency and a stable output power are two most challenging design issues, especially when coil distance varies. To solve these issues, this paper presents a novel adaptive impedance matching network (IMN) for IWPT system. In our adaptive IMN IWPT system, the IMN is automatically reconfigured to keep matching with the coils and to adjust the output power adapting to coil distance variation. A closed loop control algorithm is used to change the capacitors continually, which can compensate mismatches and adjust output power simultaneously. The proposed adaptive IMN IWPT system is working at 125 kHz for 2 W power delivered to load. Comparing with the series resonant IWPT system and fixed IMN IWPT system, the power transfer efficiency of our system increases up to 31.79% and 60% when the coupling coefficient varies in a large range from 0.05 to 0.8 for 2 W output power. PMID:28763011

An Adaptive Impedance Matching Network with Closed Loop Control Algorithm for Inductive Wireless Power Transfer.

PubMed

Miao, Zhidong; Liu, Dake; Gong, Chen

2017-08-01

For an inductive wireless power transfer (IWPT) system, maintaining a reasonable power transfer efficiency and a stable output power are two most challenging design issues, especially when coil distance varies. To solve these issues, this paper presents a novel adaptive impedance matching network (IMN) for IWPT system. In our adaptive IMN IWPT system, the IMN is automatically reconfigured to keep matching with the coils and to adjust the output power adapting to coil distance variation. A closed loop control algorithm is used to change the capacitors continually, which can compensate mismatches and adjust output power simultaneously. The proposed adaptive IMN IWPT system is working at 125 kHz for 2 W power delivered to load. Comparing with the series resonant IWPT system and fixed IMN IWPT system, the power transfer efficiency of our system increases up to 31.79% and 60% when the coupling coefficient varies in a large range from 0.05 to 0.8 for 2 W output power.

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.

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

Performance evaluation of GPU parallelization, space-time adaptive algorithms, and their combination for simulating cardiac electrophysiology.

PubMed

Sachetto Oliveira, Rafael; Martins Rocha, Bernardo; Burgarelli, Denise; Meira, Wagner; Constantinides, Christakis; Weber Dos Santos, Rodrigo

2018-02-01

The use of computer models as a tool for the study and understanding of the complex phenomena of cardiac electrophysiology has attained increased importance nowadays. At the same time, the increased complexity of the biophysical processes translates into complex computational and mathematical models. To speed up cardiac simulations and to allow more precise and realistic uses, 2 different techniques have been traditionally exploited: parallel computing and sophisticated numerical methods. In this work, we combine a modern parallel computing technique based on multicore and graphics processing units (GPUs) and a sophisticated numerical method based on a new space-time adaptive algorithm. We evaluate each technique alone and in different combinations: multicore and GPU, multicore and GPU and space adaptivity, multicore and GPU and space adaptivity and time adaptivity. All the techniques and combinations were evaluated under different scenarios: 3D simulations on slabs, 3D simulations on a ventricular mouse mesh, ie, complex geometry, sinus-rhythm, and arrhythmic conditions. Our results suggest that multicore and GPU accelerate the simulations by an approximate factor of 33×, whereas the speedups attained by the space-time adaptive algorithms were approximately 48. Nevertheless, by combining all the techniques, we obtained speedups that ranged between 165 and 498. The tested methods were able to reduce the execution time of a simulation by more than 498× for a complex cellular model in a slab geometry and by 165× in a realistic heart geometry simulating spiral waves. The proposed methods will allow faster and more realistic simulations in a feasible time with no significant loss of accuracy. Copyright © 2017 John Wiley & Sons, Ltd.

Practical Considerations in the Implementation of Collaborative Beamforming on Wireless Sensor Networks.

PubMed

Felici-Castell, Santiago; Navarro, Enrique A; Pérez-Solano, Juan J; Segura-García, Jaume; García-Pineda, Miguel

2017-01-26

Wireless Sensor Networks (WSNs) are composed of spatially distributed autonomous sensor devices, named motes. These motes have their own power supply, processing unit, sensors and wireless communications However with many constraints, such as limited energy, bandwidth and computational capabilities. In these networks, at least one mote called a sink, acts as a gateway to connect with other networks. These sensor networks run monitoring applications and then the data gathered by these motes needs to be retrieved by the sink. When this sink is located in the far field, there have been many proposals in the literature based on Collaborative Beamforming (CB), also known as Distributed or Cooperative Beamforming, for these long range communications to reach the sink. In this paper, we conduct a thorough study of the related work and analyze the requirements to do CB. In order to implement these communications in real scenarios, we will consider if these requirements and the assumptions made are feasible from the point of view of commercial motes and their constraints. In addition, we will go a step further and will consider different alternatives, by relaxing these requirements, trying to find feasible assumptions to carry out these types of communications with commercial motes. This research considers the nonavailability of a central clock that synchronizes all motes in the WSN, and all motes have identical hardware. This is a feasibility study to do CB on WSN, using a simulated scenario with randomized delays obtained from experimental data from commercial motes.

Passive acoustic mapping of cavitation using eigenspace-based robust Capon beamformer in ultrasound therapy.

PubMed

Lu, Shukuan; Hu, Hong; Yu, Xianbo; Long, Jiangying; Jing, Bowen; Zong, Yujin; Wan, Mingxi

2018-03-01

Pulse-echo imaging technique can only play a role when high intensity focused ultrasound (HIFU) is turned off due to the interference between the primary HIFU signal and the transmission pulse. Passive acoustic mapping (PAM) has been proposed as a tool for true real-time monitoring of HIFU therapy. However, the most-used PAM algorithm based on time exposure acoustic (TEA) limits the quality of cavitation image. Recently, robust Capon beamformer (RCB) has been used in PAM to provide improved resolution and reduced artifacts over TEA-based PAM, but the presented results have not been satisfactory. In the present study, we applied an eigenspace-based RCB (EISRCB) method to further improve the PAM image quality. The optimal weighting vector of the proposed method was found by projecting the RCB weighting vector onto the desired vector subspace constructed from the eigenstructure of the covariance matrix. The performance of the proposed PAM was validated by both simulations and in vitro histotripsy experiments. The results suggested that the proposed PAM significantly outperformed the conventionally used TEA and RCB-based PAM. The comparison results between pulse-echo images of the residual bubbles and cavitation images showed the potential of our proposed PAM in accurate localization of cavitation activity during HIFU therapy. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

Development of a real-time, high-frequency ultrasound digital beamformer for high-frequency linear array transducers.

PubMed

Hu, Chang-Hong; Xu, Xiao-Chen; Cannata, Jonathan M; Yen, Jesse T; Shung, K Kirk

2006-02-01

A real-time digital beamformer for high-frequency (>20 MHz) linear ultrasonic arrays has been developed. The system can handle up to 64-element linear array transducers and excite 16 channels and receive simultaneously at 100 MHz sampling frequency with 8-bit precision. Radio frequency (RF) signals are digitized, delayed, and summed through a real-time digital beamformer, which is implemented using a field programmable gate array (FPGA). Using fractional delay filters, fine delays as small as 2 ns can be implemented. A frame rate of 30 frames per second is achieved. Wire phantom (20 microm tungsten) images were obtained and -6 dB axial and lateral widths were measured. The results showed that, using a 30 MHz, 48-element array with a pitch of 100 microm produced a -6 dB width of 68 microm in the axial and 370 microm in the lateral direction at 6.4 mm range. Images from an excised rabbit eye sample also were acquired, and fine anatomical structures, such as the cornea and lens, were resolved.

Variable is better than invariable: sparse VSS-NLMS algorithms with application to adaptive MIMO channel estimation.

PubMed

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.

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

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.

Beamforming strategy of ULA and UCA sensor configuration in multistatic passive radar

NASA Astrophysics Data System (ADS)

Hossa, Robert

2009-06-01

A Beamforming Network (BN) concept of Uniform Linear Array (ULA) and Uniform Circular Array (UCA) dipole configuration designed to multistatic passive radar is considered in details. In the case of UCA configuration, computationally efficient procedure of beamspace transformation from UCA to virtual ULA configuration with omnidirectional coverage is utilized. If effect, the idea of the proposed solution is equivalent to the techniques of antenna array factor shaping dedicated to ULA structure. Finally, exemplary results from the computer software simulations of elaborated spatial filtering solutions to reference and surveillance channels are provided and discussed.