Journal articles on the topic 'Adaptive snapshots'
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YU, Jing, and Yaan LI. "Adaptive Beamforming with Inadequate Snapshots." Journal of Physics: Conference Series 787 (January 2017): 012025. http://dx.doi.org/10.1088/1742-6596/787/1/012025.
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Liao, Zhipeng, Keqing Duan, Jinjun He, Zizhou Qiu, and Binbin Li. "Robust Adaptive Beamforming Based on a Convolutional Neural Network." Electronics 12, no. 12 (June 20, 2023): 2751. http://dx.doi.org/10.3390/electronics12122751.
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To address the advancements in jamming technology, it is imperative to consider robust adaptive beamforming (RBF) methods with finite snapshots and gain/phase (G/P) errors. This paper introduces an end-to-end RBF approach that utilizes a two-stage convolutional neural network. The first stage includes convolutional blocks and residual blocks without downsampling; the blocks assess the covariance matrix precisely using finite snapshots. The second stage maps the first stage’s output to an adaptive weight vector employing a similar structure to the first stage. The two stages are pre-trained with different datasets and fine-tuned as end-to-end networks, simplifying the network training process. The two-stage structure enables the network to possess practical physical meaning, allowing for satisfying performance even with a few snapshots in the presence of array G/P errors. We demonstrate the resulting beamformer’s performance with numerical examples and compare it to various other adaptive beamformers.
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Wu, Xun, Jie Luo, Guowei Li, Shurui Zhang, and Weixing Sheng. "Fast Wideband Beamforming Using Convolutional Neural Network." Remote Sensing 15, no. 3 (January 25, 2023): 712. http://dx.doi.org/10.3390/rs15030712.
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With the wideband beamforming approaches, the synthetic aperture radar (SAR) could achieve high azimuth resolution and wide swath. However, the performance of conventional adaptive wideband time-domain beamforming is severely affected as the received signal snapshots are insufficient for adaptive approaches. In this paper, a wideband beamformer using convolutional neural network (CNN) method, namely, frequency constraint wideband beamforming prediction network (WBPNet), is proposed to obtain a satisfactory performance in the circumstances of scanty snapshots. The proposed WBPNet successfully estimates the direction of arrival of interference with scanty snapshots and obtains the optimal weights with effectively null for the interference by utilizing the uniqueness of CNN to extract potential nonlinear features of input information. Meanwhile, the novel beamformer has an undistorted response to the wideband signal of interest. Compared with the conventional time-domain wideband beamforming algorithm, the proposed method can fast obtain adaptive weights because of using few snapshots. Moreover, the proposed WBPNet has a satisfactory performance on wideband beamforming with low computational complexity because it avoids the inverse operation of covariance matrix. Simulation results show the meliority and feasibility of the proposed approach.
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Gong, C., L. Huang, D. Xu, and Z. Ye. "Knowledge‐aided robust adaptive beamforming with small snapshots." Electronics Letters 49, no. 20 (September 2013): 1258–59. http://dx.doi.org/10.1049/el.2013.2198.
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Brooker, D. J., Kay L. Gemba, and Laurie T. Fialkowski. "Overcoming snapshot-deficient measurements with knowledge-aided approaches." JASA Express Letters 2, no. 5 (May 2022): 054804. http://dx.doi.org/10.1121/10.0010455.
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The use of knowledge-aided covariance is considered for processing underwater acoustic array data in snapshot-deficient scenarios. The knowledge-aided formalism is a technique that combines array data with a known covariance to produce an invertible estimate. For underwater acoustics, simulations of ambient noise provide the a priori covariance allowing degraded signals to be processed adaptively in situations where the sample covariance matrix is rank-deficient. The method is demonstrated for matched field processing using the 21 element array event S5 from the SWellEx-96 experiment. With five snapshots, the knowledge-aided approach significantly reduces localization ambiguity compared to the adaptive white noise gain constraint processor.
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Sun, Xu, and Ranwei Li. "Robust adaptive beamforming method for active sonar in single snapshot." MATEC Web of Conferences 283 (2019): 03006. http://dx.doi.org/10.1051/matecconf/201928303006.
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Forming narrow beams is a useful way for active sonar to anti-reverberation when it works in the shallow water. High-resolution adaptive beamforming with the performance of narrow beamwidths and low sidelobe levels is a better and more efficient method, particularly in the scenario where the installation space for sonar array is limited, such as hull-mounted sonar. Due to the short duration of target echo signal in the complex and varying acoustic channel, conventional adaptive beamforming methods are invalid. Therefore, this paper proposes a robust adaptive beamforming method for active sonar in single snapshot, also called the steered dominant mode rejection (STDMR). Firstly, STDMR steered the sample covariance matrix (STCM) based on wide-band focusing, which the needed number of snapshots is greatly reduced. Secondly, by partial eigendecomposition, the large eigenvalues of the STCM which are greater than the noise energy and their eigenvectors are used for dominant mode rejection (DMR). DMR is a typical eigenspace-based algorithm which has small computational load and fast convergence speed. Finally, modified with the methods of diagonal loading of 3-5dB over the noise energy and signal mismatch protection, improved the robustness of this method. Simulation and experimental data analysis shows that the STDMR method achieves narrow beams and low-level sidelobes in single snapshot. Hence, the STDMR beamformer is an appropriate implementation to use for active sonar detection systems.
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Wang, He, Ting Zhang, Lei Cheng, and Hangfang Zhao. "Snapshot-deficient active target localization in beam-time domain using multi-frequency expectation-maximization algorithm." Journal of the Acoustical Society of America 153, no. 2 (February 2023): 990–1003. http://dx.doi.org/10.1121/10.0017164.
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The two-dimensional (2D) active target localization is generally hindered by the high temporal and spatial sidelobe levels in snapshot-deficient scenarios, where the adaptive approaches undergo performance degeneration since they require many snapshots to build the sample covariance matrix. Aiming at working robustly in snapshot-deficient active scenarios, a 2D expectation-maximization-based vertical-time-record (EMVTR) approach is proposed to compensate for the snapshot deficiency and achieve the high-resolution active localization by reconstructing the covariance matrix using estimated hyperparameters, i.e., signal powers and noise variance. With the short-time Fourier transform, the proposed approach could reduce echoes' temporal correlation and attain robust beam-time localization in mild reverberation. The multi-frequency EMVTR is derived from the single-frequency case to improve the weak echo localization. The performance is evaluated by considering single and multiple target echoes in simulation and a single moving target with tank experimental data. The results manifest the proposed EMVTR's robustness and effectiveness for the 2D active localization in snapshot-deficient scenarios.
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Ullmann, Sebastian, Marko Rotkvic, and Jens Lang. "POD-Galerkin reduced-order modeling with adaptive finite element snapshots." Journal of Computational Physics 325 (November 2016): 244–58. http://dx.doi.org/10.1016/j.jcp.2016.08.018.
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Li, Hongtao, Ke Wang, Chaoyu Wang, Yapeng He, and Xiaohua Zhu. "Robust Adaptive Beamforming Based on Worst-Case and Norm Constraint." International Journal of Antennas and Propagation 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/765385.
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A novel robust adaptive beamforming based on worst-case and norm constraint (RAB-WC-NC) is presented. The proposed beamforming possesses superior robustness against array steering vector (ASV) error with finite snapshots by using the norm constraint and worst-case performance optimization (WCPO) techniques. Simulation results demonstrate the validity and superiority of the proposed algorithm.
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Cui, Weichen, Tong Wang, Degen Wang, and Kun Liu. "An Efficient Sparse Bayesian Learning STAP Algorithm with Adaptive Laplace Prior." Remote Sensing 14, no. 15 (July 22, 2022): 3520. http://dx.doi.org/10.3390/rs14153520.
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Space-time adaptive processing (STAP) encounters severe performance degradation with insufficient training samples in inhomogeneous environments. Sparse Bayesian learning (SBL) algorithms have attracted extensive attention because of their robust and self-regularizing nature. In this study, a computationally efficient SBL STAP algorithm with adaptive Laplace prior is developed. Firstly, a hierarchical Bayesian model with adaptive Laplace prior for complex-value space-time snapshots (CALM-SBL) is formulated. Laplace prior enforces the sparsity more heavily than Gaussian, which achieves a better reconstruction of the clutter plus noise covariance matrix (CNCM). However, similar to other SBL-based algorithms, a large degree of freedom will bring a heavy burden to the real-time processing system. To overcome this drawback, an efficient localized reduced-dimension sparse recovery-based space-time adaptive processing (LRDSR-STAP) framework is proposed in this paper. By using a set of deeply weighted Doppler filters and exploiting prior knowledge of the clutter ridge, a novel localized reduced-dimension dictionary is constructed, and the computational load can be considerably reduced. Numerical experiments validate that the proposed method achieves better performance with significantly reduced computational complexity in limited snapshots scenarios. It can be found that the proposed LRDSR-CALM-STAP algorithm has the potential to be implemented in practical real-time processing systems.
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Lyu, Jiancheng, Jack Xin, and Yifeng Yu. "Computing Residual Diffusivity by Adaptive Basis Learning via Spectral Method." Numerical Mathematics: Theory, Methods and Applications 10, no. 2 (May 2017): 351–72. http://dx.doi.org/10.4208/nmtma.2017.s08.
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AbstractWe study the residual diffusion phenomenon in chaotic advection computationally via adaptive orthogonal basis. The chaotic advection is generated by a class of time periodic cellular flows arising in modeling transition to turbulence in Rayleigh-Bénard experiments. The residual diffusion refers to the non-zero effective (homogenized) diffusion in the limit of zero molecular diffusion as a result of chaotic mixing of the streamlines. In this limit, the solutions of the advection-diffusion equation develop sharp gradients, and demand a large number of Fourier modes to resolve, rendering computation expensive. We construct adaptive orthogonal basis (training) with built-in sharp gradient structures from fully resolved spectral solutions at few sampled molecular diffusivities. This is done by taking snapshots of solutions in time, and performing singular value decomposition of the matrix consisting of these snapshots as column vectors. The singular values decay rapidly and allow us to extract a small percentage of left singular vectors corresponding to the top singular values as adaptive basis vectors. The trained orthogonal adaptive basis makes possible low cost computation of the effective diffusivities at smaller molecular diffusivities (testing). The testing errors decrease as the training occurs at smaller molecular diffusivities. We make use of the Poincaré map of the advection-diffusion equation to bypass long time simulation and gain accuracy in computing effective diffusivity and learning adaptive basis. We observe a non-monotone relationship between residual diffusivity and the amount of chaos in the advection, though the overall trend is that sufficient chaos leads to higher residual diffusivity.
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Liu, Beiyi, Guan Gui, Shin-ya Matsushita, and Li Xu. "Adaptive filtering algorithm for direction-of-arrival (DOA) estimation with small snapshots." Digital Signal Processing 94 (November 2019): 84–95. http://dx.doi.org/10.1016/j.dsp.2019.07.014.
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Xi, Zaifang, Xiao-feng Wu, Shuyue Wu, Zhijun Tang, and Shigang Hu. "Low-Complexity Robust Capon Beamforming Based on Reduced-Rank Technique." Journal of Electrical and Computer Engineering 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/583075.
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Existing robust Capon beamformers achieve robustness against steering vector errors at a high cost in terms of computational complexity. Computationally efficient robust Capon beamforming approach based on the reduced-rank technique is proposed in this paper. The proposed method projects the received data snapshots onto a lower dimensional subspace consisting of the matched filters of the multistage Wiener filter (MSWF). The subsequent adaptive beamforming will then be performed within this subspace. The combination of the benefit of the robust adaptive beamforming and the reduced-rank technique improves the performance on combating steering vector errors and lowering the computational complexity.
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Wang, Wei, Lin Zou, and Xuegang Wang. "A Novel Two-Level Nested STAP Strategy for Clutter Suppression in Airborne Radar." Mathematical Problems in Engineering 2019 (June 4, 2019): 1–16. http://dx.doi.org/10.1155/2019/2540858.
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Nested arrays have been studied recently in array signal processing field because of their closed-form expressions for the sensor locations and achievable degrees of freedom (DOFs). In this paper, the concept of nesting is further extended to space-time adaptive processing (STAP). Different from the traditional uniform-STAP method that calculates the clutter plus noise covariance matrix (CNCM) and performs the STAP filter direct using the data snapshots collected from the uniform linear array (ULA) and the transmitting pulses with uniform pulse repetition interval (PRI), we present a new optimum two-level nested STAP (O2LN-STAP) strategy which employs an optimum two-level nested array (O2LNA) and an optimum two-level nested PRI (O2LN-PRI) to exploit the enhanced DOFs embedded in the space-time O2LN structure. Similar to the difference coarray perspective, we first construct a virtual space-time snapshot from the direct covariance matrix of the received signals. Then, a new CNCM estimation corresponding to the virtual space-time snapshot can be computed by the spatial-temporal smoothing technique for STAP filter. Furthermore, the comparative simulations and analyses with the traditional uniform-STAP and the recently reported coprime-STAP are carried out to verify the effectiveness of the O2LN-STAP approach.
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Shahab, Suhail Najm, Ayib Rosdi Zainun, Balasim S. S., Nurul Hazlina Noordin, and Izzeldin Ibrahim Mohamed. "Evaluation of Minimum Variance Distortionless Response Beamforming Algorithm Based Circular Antenna Arrays." Modern Applied Science 11, no. 1 (September 16, 2016): 1. http://dx.doi.org/10.5539/mas.v11n1p1.
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Wireless data traffic is in a continuous growth, and there are increasing demands for wireless systems that provide deep interference suppression and noise mitigation. In this paper, adaptive beamforming (ABF) technique for Smart Antenna System (SAS) based on Minimum Variance Distortionless Response (MVDR) algorithm connected toCircular Antenna Array (CAA) is discussed and analyzed. The MVDR performance is evaluated by varying various parameters; namely the number of antenna elements, space separation between the elements, the number of interference sources, noise power label, and a number of snapshots. LTE networks allocate a spectrum band of 2.6 GHz is used for evaluating the MVDR performance. The MVDR performance is evaluated with two important metrics; beampattern and SINR. Simulation results demonstrate that as the antenna elements increase, the performance of the MVDR improves dramatically. This means the performance of MVDR greatly relies upon the number of the elements. Half of the wavelength is considered the best interelement spacing, the performance degraded as noise power increased, and more accurately resolution occurred when the number of snapshots increased. The proposed method was found to be performed better than some existing techniques. According to the result, the beampattern relies on the number of element and the separation between array elements. Also, the SINR strongly depends on noise power label and the number of snapshots.
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Bonizzoni, Francesca, Davide Pradovera, and Michele Ruggeri. "Rational-approximation-based model order reduction of Helmholtz frequency response problems with adaptive finite element snapshots." Mathematics in Engineering 5, no. 4 (2023): 1–38. http://dx.doi.org/10.3934/mine.2023074.
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<abstract><p>We introduce several spatially adaptive model order reduction approaches tailored to non-coercive elliptic boundary value problems, specifically, parametric-in-frequency Helmholtz problems. The offline information is computed by means of adaptive finite elements, so that each snapshot lives in a different discrete space that resolves the local singularities of the analytical solution and is adjusted to the considered frequency value. A rational surrogate is then assembled adopting either a least-squares or an interpolatory approach, yielding a function-valued version of the the standard rational interpolation method ($ \mathcal{V} $-SRI) and the minimal rational interpolation method (MRI). In the context of building an approximation for linear or quadratic functionals of the Helmholtz solution, we perform several numerical experiments to compare the proposed methodologies. Our simulations show that, for interior resonant problems (whose singularities are encoded by poles on the real axis), the spatially adaptive $ \mathcal{V} $-SRI and MRI work comparably well. Instead, when dealing with exterior scattering problems, whose frequency response is mostly smooth, the $ \mathcal{V} $-SRI method seems to be the best-performing one.</p></abstract>
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Kilic, Zeliha, Max Schweiger, Camille Moyer, and Steve Pressé. "Monte Carlo samplers for efficient network inference." PLOS Computational Biology 19, no. 7 (July 18, 2023): e1011256. http://dx.doi.org/10.1371/journal.pcbi.1011256.
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Accessing information on an underlying network driving a biological process often involves interrupting the process and collecting snapshot data. When snapshot data are stochastic, the data’s structure necessitates a probabilistic description to infer underlying reaction networks. As an example, we may imagine wanting to learn gene state networks from the type of data collected in single molecule RNA fluorescence in situ hybridization (RNA-FISH). In the networks we consider, nodes represent network states, and edges represent biochemical reaction rates linking states. Simultaneously estimating the number of nodes and constituent parameters from snapshot data remains a challenging task in part on account of data uncertainty and timescale separations between kinetic parameters mediating the network. While parametric Bayesian methods learn parameters given a network structure (with known node numbers) with rigorously propagated measurement uncertainty, learning the number of nodes and parameters with potentially large timescale separations remain open questions. Here, we propose a Bayesian nonparametric framework and describe a hybrid Bayesian Markov Chain Monte Carlo (MCMC) sampler directly addressing these challenges. In particular, in our hybrid method, Hamiltonian Monte Carlo (HMC) leverages local posterior geometries in inference to explore the parameter space; Adaptive Metropolis Hastings (AMH) learns correlations between plausible parameter sets to efficiently propose probable models; and Parallel Tempering takes into account multiple models simultaneously with tempered information content to augment sampling efficiency. We apply our method to synthetic data mimicking single molecule RNA-FISH, a popular snapshot method in probing transcriptional networks to illustrate the identified challenges inherent to learning dynamical models from these snapshots and how our method addresses them.
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Peng, Xing, Xinwu Li, Changcheng Wang, Haiqiang Fu, and Yanan Du. "A Maximum Likelihood Based Nonparametric Iterative Adaptive Method of Synthetic Aperture Radar Tomography and Its Application for Estimating Underlying Topography and Forest Height." Sensors 18, no. 8 (July 30, 2018): 2459. http://dx.doi.org/10.3390/s18082459.
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Synthetic aperture radar tomography (TomoSAR) is an important way of obtaining underlying topography and forest height for long-wavelength datasets such as L-band and P-band radar. It is usual to apply nonparametric spectral estimation methods with a large number of snapshots over forest areas. The nonparametric iterative adaptive approach for amplitude and phase estimation (IAA-APES) can obtain a high resolution; however, it only tends to work well with a small number of snapshots. To overcome this problem, this paper proposes the nonparametric iterative adaptive approach based on maximum likelihood estimation (IAA-ML) for the application over forest areas. IAA-ML can be directly used in forest areas, without any prior information or preprocessing. Moreover, it can work well in the case of a large number of snapshots. In addition, it mainly focuses on the backscattered power around the phase centers, helping to detect their locations. The proposed IAA-ML estimator was tested in simulated experiments and the results confirmed that IAA-ML obtains a higher resolution than IAA-APES. Moreover, six P-band fully polarimetric airborne SAR images were applied to acquire the structural parameters of a forest area. It was found that the results of the HH polarization are suitable for analyzing the ground contribution and the results of the HV polarization are beneficial when studying the canopy contribution. Based on this, the underlying topography and forest height of a test site in Paracou, French Guiana, were estimated. With respect to the Light Detection and Ranging (LiDAR) measurements, the standard deviation of the estimations of the IAA-ML TomoSAR method was 2.11 m for the underlying topography and 2.80 m for the forest height. Furthermore, compared to IAA-APES, IAA-ML obtained a higher resolution and a higher estimation accuracy. In addition, the estimation accuracy of IAA-ML was also slightly higher than that of the SKP-beamforming technique in this case study.
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Yang, Zhaocheng, Rui Fa, Yuliang Qin, Xiang Li, and Hongqiang Wang. "Direct Data Domain Sparsity-Based STAP Utilizing Subaperture Smoothing Techniques." International Journal of Antennas and Propagation 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/171808.
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We propose a novel direct data domain (D3) sparsity-based space-time adaptive processing (STAP) algorithm utilizing subaperture smoothing techniques for airborne radar applications. Different from either normal sparsity-based STAP or D3 sparsity-based STAP, the proposed algorithm firstly uses only the snapshot in the cell under test (CUT) to generate multiple subsnapshots by exploiting the space-time structure of the steering vector and the uncorrelated nature of the components of the interference covariance matrix. Since the interference spectrum is sparse in the whole angle-Doppler plane, by employing a sparse regularization, the generated multiple subsnapshots are jointly used to recover the interference spectrum. The interference covariance matrix is then estimated from the interference spectrum, followed by the space-time filtering and the target detection. Simulation results illustrate that the proposed algorithm outperforms the generalized forward/backward method, the conventional D3 least squares STAP algorithm, and the existing D3 sparsity-based STAP algorithm. Furthermore, compared with the normal sparsity-based STAP algorithm using multiple snapshots, the proposed algorithm can also avoid the performance degradation caused by discrete interferers merely appearing in the CUT.
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Chan, Ho Yuen, Eric Chung, and Yalchin Efendiev. "Adaptive Mixed GMsFEM for Flows in Heterogeneous Media." Numerical Mathematics: Theory, Methods and Applications 9, no. 4 (November 2016): 497–527. http://dx.doi.org/10.4208/nmtma.2016.m1603.
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AbstractIn this paper, we present two adaptive methods for the basis enrichment of the mixed Generalized Multiscale Finite Element Method (GMsFEM) for solving the flow problem in heterogeneous media. We develop an a-posteriori error indicator which depends on the norm of a local residual operator. Based on this indicator, we construct an offline adaptive method to increase the number of basis functions locally in coarse regions with large local residuals. We also develop an online adaptive method which iteratively enriches the function space by adding new functions computed based on the residual of the previous solution and special minimum energy snapshots. We show theoretically and numerically the convergence of the two methods. The online method is, in general, better than the offline method as the online method is able to capture distant effects (at a cost of online computations), and both methods have faster convergence than a uniform enrichment. Analysis shows that the online method should start with a certain number of initial basis functions in order to have the best performance. The numerical results confirm this and show further that with correct selection of initial basis functions, the convergence of the online method can be independent of the contrast of the medium. We consider cases with both very high and very low conducting inclusions and channels in our numerical experiments.
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Orr, Alastair, Jason Donovan, and Dietmar Stoian. "Smallholder value chains as complex adaptive systems: a conceptual framework." Journal of Agribusiness in Developing and Emerging Economies 8, no. 1 (March 12, 2018): 14–33. http://dx.doi.org/10.1108/jadee-03-2017-0031.
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Purpose Smallholder value chains are dynamic, changing over time in sudden, unpredictable ways as they adapt to shocks. Understanding these dynamics and adaptation is essential for these chains to remain competitive in turbulent markets. Many guides to value chain development, though they focus welcome attention on snapshots of current structure and performance, pay limited attention to the dynamic forces affecting these chains or to adaptation. The paper aims to discuss these issues. Design/methodology/approach This paper develops an expanded conceptual framework to understand value chain performance based on the theory of complex adaptive systems. The framework combines seven common properties of complex systems: time, uncertainty, sensitivity to initial conditions, endogenous shocks, sudden change, interacting agents and adaptation. Findings The authors outline how the framework can be used to ask new research questions and analyze case studies in order to improve our understanding of the development of smallholder value chains and their capacity for adaptation. Research limitations/implications The framework highlights the need for greater attention to value chain dynamics. Originality/value The framework offers a new perspective on the dynamics of smallholder value chains.
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NGWA, GIDEON A., WOLDEGEBRIEL A. WOLDEGERIMA, and MIRANDA I. TEBOH-EWUNGKEM. "A MATHEMATICAL STUDY OF THE IMPLICIT ROLE OF INNATE AND ADAPTIVE IMMUNE RESPONSES ON WITHIN-HUMAN PLASMODIUM FALCIPARUM PARASITE LEVELS." Journal of Biological Systems 28, no. 02 (June 2020): 377–429. http://dx.doi.org/10.1142/s0218339020400069.
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A within-human-host malaria parasite model, integrating key variables that influence parasite evolution-progression-advancement, under innate and adaptive immune responses, is analyzed. The implicit role of immunity on the steady state parasite loads and parasitemia reproduction number ([Formula: see text]), a threshold parameter measuring the parasite’s annexing ability of healthy red blood cells (HRBCs), eventually rendering a human infectious to mosquitoes, is investigated. The impact of the type of recruitment function used to model HRBC growth is also investigated. The model steady states and [Formula: see text], both obtained as functions of immune system variables, are analyzed at snapshots of immune sizes. Model results indicate that the more the immune cells, innate and adaptive, the more efficient they are at inhibiting parasite development and progression; consequently, the less severe the malaria disease in a patient. Our analysis also illustrates the existence of a Hopf bifurcation leading to a limit cycle, observable only for the nonlinear recruitment functions, at reasonably large [Formula: see text].
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Liu, Cheng, Tong Wang, Kun Liu, and Xinying Zhang. "A Novel Sparse Bayesian Space-Time Adaptive Processing Algorithm to Mitigate Off-Grid Effects." Remote Sensing 14, no. 16 (August 11, 2022): 3906. http://dx.doi.org/10.3390/rs14163906.
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Space-time adaptive processing (STAP) algorithms based on sparse recovery (SR) have been researched because of their low requirement of training snapshots. However, once some portion of clutter is not located on the grids, i.e., off-grid problems, the performances of most SR-STAP algorithms degrade significantly. Reducing the grid interval can mitigate off-grid effects, but brings strong column coherence of the dictionary, heavy computational load, and heavy storage load. A sparse Bayesian learning approach is proposed to mitigate the off-grid effects in the paper. The algorithm employs an efficient sequential addition and deletion of dictionary atoms to estimate the clutter subspace, which means that strong column coherence has no effect on the performance of the proposed algorithm. Besides, the proposed algorithm does not require much computational load and storage load. Off-grid effects can be mitigated with the proposed algorithm when the grid-interval is sufficiently small. The excellent performance of the novel algorithm is demonstrated on the simulated data.
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Lin, Weimin, Erlong Ning, Lingyi Yang, Yin Rao, Shuyin Peng, and Qiaowei Li. "Snapshots of Postsynthetic Modification in a Layered Metal–Organic Framework: Isometric Linker Exchange and Adaptive Linker Installation." Inorganic Chemistry 60, no. 16 (July 9, 2021): 11756–63. http://dx.doi.org/10.1021/acs.inorgchem.1c01341.
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Yang, Yanfang, Mohammadreza Ghasemi, Eduardo Gildin, Yalchin Efendiev, and Victor Calo. "Fast Multiscale Reservoir Simulations With POD-DEIM Model Reduction." SPE Journal 21, no. 06 (June 30, 2016): 2141–54. http://dx.doi.org/10.2118/173271-pa.
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Summary We present a global/local model reduction for fast multiscale reservoir simulations in highly heterogeneous porous media. Our approach identifies a low-dimensional structure in the solution space. We introduce an auxiliary variable (the velocity field) in our model reduction that achieves a high compression of the model. This compression is achieved because the velocity field is conservative for any low-order reduced model in our framework, whereas a typical global model reduction that is based on proper-orthogonal-decomposition (POD) Galerkin projection cannot guarantee local mass conservation. The lack of mass conservation can be observed in numerical simulations that use finite-volume-based approaches. The discrete empirical interpolation method (DEIM) approximates fine-grid nonlinear functions in Newton iterations. This approach delivers an online computational cost that is independent of the fine-grid dimension. POD snapshots are inexpensively computed with local model-reduction techniques that are based on the generalized multiscale finite-element method (GMsFEM) that provides (1) a hierarchical approximation of the snapshot vectors, (2) adaptive computations with coarse grids, and (3) inexpensive global POD operations in small dimensional spaces on a coarse grid. By balancing the errors of the global and local reduced-order models, our new methodology provides an error bound in simulations. Our numerical results, by use of a two-phase immiscible flow, show a substantial speedup, and we compare our results with the standard POD-DEIM in a finite-volume setup.
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Hu, Panhe, Xiaolong Su, and Zhen Liu. "Direction Finding for Passive Bistatic Radar in the Presence of Multipath Propagation." Wireless Communications and Mobile Computing 2022 (December 15, 2022): 1–9. http://dx.doi.org/10.1155/2022/7562517.
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In the case of multipath propagation for passive bistatic radar (PBR) using uncooperative frequency agile-phased array radar as an illuminator, a new direction-finding method is proposed to deal with the scenario where the coherent and uncorrelated signals are closely spaced or in the same direction. Firstly, spatial difference technique is used to eliminate uncorrelated signals. Then, in order to avoid the cross-terms effect and improve the resolution of coherent signal, the iterative adaptive method (IAA) is adopted for the rearranged spatial difference matrix. Finally, the direction of arrival (DOA) of the target signal is obtained by the reconstruction of the interference-plus-noise covariance matrix. Compared with previous studies, this method has better performance in the case of low signal-to-noise ratio (SNR) and limited number of snapshots.
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Hinkley, Sasha. "Adaptive Optics Observations of Exoplanets, Brown Dwarfs, and Binary Stars." Proceedings of the International Astronomical Union 7, S282 (July 2011): 181–88. http://dx.doi.org/10.1017/s1743921311027293.
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AbstractThe current direct observations of brown dwarfs and exoplanets have been obtained using instruments not specifically designed for overcoming the large contrast ratio between the host star and any wide-separation faint companions. However, we are about to witness the birth of several new dedicated observing platforms specifically geared towards high contrast imaging of these objects. The Gemini Planet Imager, VLT-SPHERE, Subaru HiCIAO, and Project 1640 at the Palomar 5m telescope will return images of numerous exoplanets and brown dwarfs over hundreds of observing nights in the next five years. Along with diffraction-limited coronagraphs and high-order adaptive optics, these instruments also will return spectral and polarimetric information on any discovered targets, giving clues to their atmospheric compositions and characteristics. Such spectral characterization will be key to forming a detailed theory of comparative exoplanetary science which will be widely applicable to both exoplanets and brown dwarfs. Further, the prevalence of aperture masking interferometry in the field of high contrast imaging is also allowing observers to sense massive, young planets at solar system scales (~3–30 AU)— separations out of reach to conventional direct imaging techniques. Such observations can provide snapshots at the earliest phases of planet formation—information essential for constraining formation mechanisms as well as evolutionary models of planetary mass companions. As a demonstration of the power of this technique, I briefly review recent aperture masking observations of the HR 8799 system. Moreover, all of the aforementioned techniques are already extremely adept at detecting low-mass stellar companions to their target stars, and I present some recent highlights.
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Garaldi, Enrico, Matteo Nori, and Marco Baldi. "Dynamic zoom simulations: A fast, adaptive algorithm for simulating light-cones." Monthly Notices of the Royal Astronomical Society 499, no. 2 (September 23, 2020): 2685–700. http://dx.doi.org/10.1093/mnras/staa2064.
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ABSTRACT The advent of a new generation of large-scale galaxy surveys is pushing cosmological numerical simulations in an uncharted territory. The simultaneous requirements of high resolution and very large volume pose serious technical challenges, due to their computational and data storage demand. In this paper, we present a novel approach dubbed dynamic zoom simulations – or dzs – developed to tackle these issues. Our method is tailored to the production of light-cone outputs from N-body numerical simulations, which allow for a more efficient storage and post-processing compared to standard comoving snapshots, and more directly mimic the format of survey data. In dzs, the resolution of the simulation is dynamically decreased outside the light-cone surface, reducing the computational work load, while simultaneously preserving the accuracy inside the light-cone and the large-scale gravitational field. We show that our approach can achieve virtually identical results to traditional simulations at half of the computational cost for our largest box. We also forecast this speedup to increase up to a factor of 5 for larger and/or higher resolution simulations. We assess the accuracy of the numerical integration by comparing pairs of identical simulations run with and without dzs. Deviations in the light-cone halo mass function, in the sky-projected light-cone, and in the 3D matter light-cone always remain below 0.1 per cent. In summary, our results indicate that the dzs technique may provide a highly valuable tool to address the technical challenges that will characterize the next generation of large-scale cosmological simulations.
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Wang, Chen, Junqiang Bai, Jan S. Hesthaven, Yasong Qiu, Lei Qiao, and Xiao Han. "POD-Kriging Reduced Method's Application in Tandem Cylinders' Flow." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, no. 2 (April 2018): 220–28. http://dx.doi.org/10.1051/jnwpu/20183620220.
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The configuration of Tandem Cylinders is a typical one in researching noise and interactions between different components, where the distance-diameter ratio is a very significant parameter with which the flow form varies a lot. To clearly research the ratio's effect on tandem cylinders flow as well as the unsteady flow physics, a reduced-order model is built where Proper orthogonal Decomposition(POD) is usedto extract reduced basis modes, and Kriging model is used to train sample data and interpolate projection coefficents for each reduced basis. To ensure the snapshots' accuracy, the scale-adaptive simulation (SAS) model based on SST turbulence model is used to predict time-dependent flow field. By comparing with solutions provided by the numerical solver as well as experimental data, the reduced model presents a good accuracy and efficiency. And then based on the reduced basis, the change regulation of the time-average flow is summarized.
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Han, Chuang, Haoyang Lei, Yanyun Gong, and Ling Wang. "Sparse Covariance Matrix Reconstruction-Based Nulling Broadening for UAV 2D Antenna Arrays." Wireless Communications and Mobile Computing 2022 (May 10, 2022): 1–19. http://dx.doi.org/10.1155/2022/4987990.
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Since the antennas on UAVs may have slight vibrations or the interference source is in the state of rapid movement in practice, the interference suppression performance and robustness of the traditional methods may suffer a decline. In this paper, we propose a flexible asymmetric null widening technique, which allows flexible adjustment of the null width to accommodate the variation of the interference source. This method has a good effect of spreading zero trap on the two-dimensional array and can effectively reduce the waste of degrees of freedom. Firstly, the flexible asymmetric null widening method is extended to two-dimensional arrays to accommodate 2D array antennas of UAVs. Secondly, when the SMI algorithm is applied in adaptive beamforming, the desired signal appears in sampling snapshots or using data samples, resulting in a model mismatch. To solve the model mismatch problem of UAV antenna arrays, this paper applies a sparsity-based interference plus noise covariance matrix reconstruction technique. Finally, for the application scenario that the UAV may receive signals from multiple directions, we apply the linear constrained minimum variance criterion (LCMV) to achieve the main beam gain formation in multiple directions. The simulation results show that we can generate a wide null and adjust the null width asymmetrically. The results also show that the model mismatch problem is avoided, and the performance of the adaptive beamforming is almost optimal. For the UAV antenna, we also implemented multiple beams to receive multiple signals .
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Hu, Panhe, Qinglong Bao, and Zengping Chen. "DOA Estimation Method in Multipath Environment for Passive Bistatic Radar." International Journal of Antennas and Propagation 2019 (June 20, 2019): 1–9. http://dx.doi.org/10.1155/2019/7419156.
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Direction-of-arrival (DOA) estimation in multipath environment is an important issue for passive bistatic radar (PBR) using frequency agile phased array VHF radar as illuminator of opportunity. Under such scenario, the main focus of this paper is to cope with the closely spaced uncorrelated and coherent signals in low signal-to-noise ratio and limited snapshots. Making full use of the characteristics of moduli of eigenvalues, the DOAs of the uncorrelated signals are firstly estimated. Afterwards, their contributions are eliminated by means of spatial difference technique. Finally, in order to improve resolution and accuracy DOA estimation of remaining coherent signals while avoiding the cross-terms effect, a new beamforming solution based iterative adaptive approach (IAA) is proposed to deal with a reconstructed covariance matrix. The proposed method combines the advantages of both spatial difference method and the IAA algorithm while avoiding their shortcomings. Simulation results validate its effectiveness; meanwhile, the good performances of the proposed method in terms of resolution probability, detection probability, and estimation accuracy are demonstrated by comparison with the existing methods.
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Tian, Songyuan, Sheng Zhang, Hongmei Mao, Rui Liu, and Xiaowu Xiong. "Temporal Network Link Prediction Based on the Optimized Exponential Smoothing Model and Node Interaction Entropy." Symmetry 15, no. 6 (June 1, 2023): 1182. http://dx.doi.org/10.3390/sym15061182.
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Link prediction accuracy in temporal networks is easily affected by the time granularity of network snapshots. This is due to the insufficient information conveyed by snapshots and the lack of temporal continuity between snapshots. We propose a temporal network link prediction method based on the optimized exponential smoothing model and node interaction entropy (OESMNIE). This method utilizes fine-grained interaction information between nodes within snapshot periods and incorporates the information entropy theory to improve the construction of node similarity in the gravity model as well as the prediction process of node similarity. Experiment results on several real-world datasets demonstrate the superiority and reliability of this proposed method in adapting to link prediction requirements over other methods across different time granularities of snapshots, which is essential for studying the evolution of temporal networks.
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Fiorentino, G., M. Bellazzini, M. Spera, N. B. Sabha, M. Mapelli, L. Schreiber, M. Dall’Ora, et al. "Deep into the core of dense star clusters: an astrometric and photometric test case for ELT." Monthly Notices of the Royal Astronomical Society 494, no. 3 (March 29, 2020): 4413–25. http://dx.doi.org/10.1093/mnras/staa869.
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ABSTRACT We present a novel analysis of a young star cluster in the Large Magellanic cloud, R136-like, as seen by the Extremely Large Telescope (ELT). The main aim of this study is to quantify precision and accuracy of stellar proper motion measurements in crowded field when using an ELT working at its diffraction limit. This can serve as a reference study for future development of ELT scientific cases. In particular, we investigate our future ability to detect the dynamical signature of intermediate-mass black holes (IMBHs) with mass ∼104 M⊙ through detailed measurements of stellar proper motions. We have simulated two N-body dynamical cluster models with and without an IMBH. For each model, we have chosen two snapshots temporally spaced by 5 yr. Stellar fluxes in IJHK filters and star positions have been used to create ELT mock images for both single- and multiconjugate adaptive optics observing modes following the requierements given by ESO technical specifications for the first light imager. These images have been analysed using a classical software for seeing-limited data reduction, daophot/allstar. We make accurate photometry till the very faint pre-main-sequence stars, i.e. depending on the adaptive optics (AO) mode, magnitudes down to K ∼ 24 mag (single-conjugate AO) or K ∼ 22 mag (multiconjugate AO) in a total integration time of 20 min on target. Although daophot suite of programs is not devoted to precise astrometry, the astrometric accuracy is impressive, reaching few μas yr−1 or km s−1. In these assumptions, we are able to detect the IMBH signature at the centre of the cluster.
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Mackey, Jonathan, Samuel Green, Maria Moutzouri, Thomas J. Haworth, Robert D. Kavanagh, Davit Zargaryan, and Maggie Celeste. "pion: simulating bow shocks and circumstellar nebulae." Monthly Notices of the Royal Astronomical Society 504, no. 1 (March 19, 2021): 983–1008. http://dx.doi.org/10.1093/mnras/stab781.
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ABSTRACT Expanding nebulae are produced by mass-loss from stars, especially during late stages of evolution. Multidimensional simulation of these nebulae requires high resolution near the star and permits resolution that decreases with distance from the star, ideally with adaptive time-steps. We report the implementation and testing of static mesh-refinement in the radiation-magnetohydrodynamics (R-MHD) code pion, and document its performance for 2D and 3D calculations. The bow shock produced by a hot, magnetized, slowly rotating star as it moves through the magnetized ISM is simulated in 3D, highlighting differences compared with 2D calculations. Latitude-dependent, time-varying magnetized winds are modelled and compared with simulations of ring nebulae around blue supergiants from the literature. A 3D simulation of the expansion of a fast wind from a Wolf–Rayet star into the slow wind from a previous red supergiant phase of evolution is presented, with results compared with results in the literature and analytic theory. Finally, the wind–wind collision from a binary star system is modelled with 3D MHD, and the results compared with previous 2D hydrodynamic calculations. A python library is provided for reading and plotting simulation snapshots, and the generation of synthetic infrared emission maps using torus is also demonstrated. It is shown that state-of-the-art 3D MHD simulations of wind-driven nebulae can be performed using pion with reasonable computational resources. The source code and user documentation is made available for the community under a BSD3 licence.
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Ivkovic, Mladen, and Romain Teyssier. "ACACIA: a new method to produce on-the-fly merger trees in the ramses code." Monthly Notices of the Royal Astronomical Society 510, no. 1 (November 23, 2021): 959–79. http://dx.doi.org/10.1093/mnras/stab3329.
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ABSTRACT The implementation of ACACIA, a new algorithm to generate dark matter halo merger trees with the Adaptive Mesh Refinement code RAMSES, is presented. The algorithm is fully parallel and based on the Message Passing Interface. As opposed to most available merger tree tools, it works on the fly during the course of the N-body simulation. It can track dark matter substructures individually using the index of the most bound particle in the clump. Once a halo (or a sub-halo) merges into another one, the algorithm still tracks it through the last identified most bound particle in the clump, allowing to check at later snapshots whether the merging event was definitive, or whether it was only temporary, with the clump only traversing another one. The same technique can be used to track orphan galaxies that are not assigned to a parent clump anymore because the clump dissolved due to numerical overmerging. We study in detail the impact of various parameters on the resulting halo catalogues and corresponding merger histories. We then compare the performance of our method using standard validation diagnostics, demonstrating that we reach a quality similar to the best available and commonly used merger tree tools. As a proof of concept, we use our merger tree algorithm together with a parametrized stellar-mass-to-halo-mass relation and generate a mock galaxy catalogue that shows good agreement with observational data.
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Cui, Guanghai, Zhen Wang, Yancun Yang, Jiahao Xu, and Hongyong Yang. "Evolution of cooperation under the influence of environments on individual-performed interactions." International Journal of Modern Physics C 29, no. 08 (August 2018): 1850070. http://dx.doi.org/10.1142/s0129183118500705.
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Traditionally, individual intensities to perform games are always assumed to be fixed in networks (e.g. to depend on the number of their neighbors). However, to increase their own fitness or payoffs, individuals may adjust their intensities in reaction to external environment changes in real scenarios. With this motivation, we have studied this adjustment by considering the average payoff of individual neighbors to be the network environment in a spatial prisoner’s dilemma game. An individual will unilaterally increase (decrease) its intensity to perform games between itself and its neighbors when its payoff is greater than or equal to (lower than) the average payoff of its neighbors. Compared with the normal situation, we find that individual cooperation is significantly facilitated either on the cooperator fraction or the effective cooperation fraction when the environment-induced intensity adjustment is considered, and the value of intensity adjustment per time has a positive influence on the maintenance of cooperation. Evolution snapshots and a formulated typical schematic are used to explain the results. We find that cooperation behaviors are enhanced because of the existence of defectors with lower intensities who are near the boundaries between cooperator and defector clusters. Finally, the promotion is also validated in random networks. We hope that our results may shed light on a greater understanding of the role of individual adaptive behaviors in reaction to network environments in the maintenance of cooperation in societies.
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Goris, N., and H. Elbern. "Singular vector decomposition for sensitivity analyses of tropospheric chemical scenarios." Atmospheric Chemistry and Physics Discussions 11, no. 6 (June 16, 2011): 16745–99. http://dx.doi.org/10.5194/acpd-11-16745-2011.
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Abstract. Observations of the chemical state of the atmosphere typically provide only sparse snapshots of the state of the system due to their insufficient temporal and spatial density. Therefore the measurement configurations need to be optimised to get a best possible state estimate. One possibility to optimise the state estimate is provided by observation targeting of sensitive system states, to identify measurement configurations of best value for forecast improvements. In recent years, numerical weather prediction adapted singular vector analysis with respect to initial values as a novel method to identify sensitive states. In the present work, this technique is transferred from meteorological to chemical forecast. Besides initial values, emissions are investigated as controlling variables. More precisely uncertainties in the amplitude of the diurnal profile of emissions are analysed, yielding emission factors as target variables. Singular vector analysis is extended to allow for projected target variables not only at final time but also at initial time. Further, special operators are introduced, which consider the combined influence of groups of chemical species. As a preparation for targeted observation calculations, the concept of adaptive observations is studied with a chemistry box model. For a set of six different scenarios, the VOC versus NOx limitation of the ozone formation is investigated. Results reveal, that the singular vectors are strongly dependent on start time and length of the simulation. As expected, singular vectors with initial values as target variables tend to be more sensitive to initial values, while emission factors as target variables are more sensitive to simulation length. Further, the particular importance of chemical compounds differs strongly between absolute and relative error growth.
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Reich-Zeliger, Shlomit, Manu Setty, Michelle Tadmor, Tomer Meir Salame, Dana Pe’er, and Nir Friedman. "Multiparameter mass cytometry of T cell development and selection." Journal of Immunology 196, no. 1_Supplement (May 1, 2016): 121.12. http://dx.doi.org/10.4049/jimmunol.196.supp.121.12.
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Abstract T cell development and selection in the thymus play key roles in shaping the adaptive immune system and maintaining self-tolerance. Previous studies characterizing thymic development have largely relied on genetic perturbations and subsequent cell sorting that invariably perturb specific developmental compartments. Using mass-cytometry, allow us to capture the entire developmental progression of T cells, from the double-negative (DN) stages, through double-positive (DP) and the CD4 and CD8 branches, without perturbation. With 42 channels simultaneously measured, we collected a mass cytometry dataset profiling the mouse thymus with T-cell surface markers and transcription factors, chosen based on their broad functionality in T cell development. Using the newly developed “Wishbone” algorithm, we recovered the known stages in T cell development with high accuracy and developmental resolution. This allowed us to place DN, DP, CD4+ and CD8+ cells from a single thymus along a unified bifurcating trajectory. Our data allows for precise ordering of multiple events along the trajectory using un-sorted cells from mice that were not perturbed genetically. To characterize the selection process along the development trajectory we used Nur77-GFP mice in which GFP is induced by TCR signaling that occurs during positive and negative selection. By following GFP strength together with cell trajectory we were able to recognize and characterize the behavior of proteins that participate in selection and that are involved in the transition from the DP to SP stage. This approach enables us for the first time to place and follow coordinated changes in multiple markers that take part in T cell development and selection in the thymus using single snapshots
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Goris, N., and H. Elbern. "Singular vector decomposition for sensitivity analyses of tropospheric chemical scenarios." Atmospheric Chemistry and Physics 13, no. 9 (May 15, 2013): 5063–87. http://dx.doi.org/10.5194/acp-13-5063-2013.
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Abstract. Observations of the chemical state of the atmosphere typically provide only sparse snapshots of the state of the system due to their insufficient temporal and spatial density. One possibility for optimisation of the state estimate is to target the observation of those parameters that have the largest potential of resulting in forecast improvements. In the present work, the technique of singular vector analysis is applied to atmospheric chemical modelling in order to identify the most sensitive chemical compounds during a given time period and prioritise them for measurement. Novel to the current work is the fact that, in the application of singular vector analysis, not only the initial values but also the emissions are considered as target variables for adaptive observation strategies. This specific application of singular vector analysis is studied in the context of a chemistry box model allowing for validation of its new features for two chemical regimes. The time and regime dependence of the ozone (O3) and peroxyacetyl nitrate (PAN) formation potential of individual volatile organic compounds (VOCs) is investigated. Results show that the combined sensitivity of O3 and PAN to individual VOCs is strongly dependent on the photochemical scenario and simulation interval used. Particularly the alkanes show increasing sensitivities with increasing simulation length. Classifying the VOCs as being of high, medium, little or negligible importance for the formation of O3 and PAN allows for the identification of those VOCs that may be omitted from measurement. We find that it is possible to omit 6 out of 18 VOCs considered for initial value measurement and 4 out of 12 VOCs considered for emission measurement. The omission of these VOCs is independent of photochemical regime and simulation length. The VOCs selected for measuring account for more than 96% and 90% of the O3 and PAN sensitivity to VOCs, respectively.
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Goris, N., and H. Elbern. "Singular vector-based targeted observations of chemical constituents: description and first application of the EURAD-IM-SVA v1.0." Geoscientific Model Development 8, no. 12 (December 10, 2015): 3929–45. http://dx.doi.org/10.5194/gmd-8-3929-2015.
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Abstract. Measurements of the large-dimensional chemical state of the atmosphere provide only sparse snapshots of the state of the system due to their typically insufficient temporal and spatial density. In order to optimize the measurement configurations despite those limitations, the present work describes the identification of sensitive states of the chemical system as optimal target areas for adaptive observations. For this purpose, the technique of singular vector analysis (SVA), which has proven effective for targeted observations in numerical weather prediction, is implemented in the EURAD-IM (EURopean Air pollution and Dispersion – Inverse Model) chemical transport model, yielding the EURAD-IM-SVA v1.0. Besides initial values, emissions are investigated as critical simulation controlling targeting variables. For both variants, singular vectors are applied to determine the optimal placement for observations and moreover to quantify which chemical compounds have to be observed with preference. Based on measurements of the airship based ZEPTER-2 campaign, the EURAD-IM-SVA v1.0 has been evaluated by conducting a comprehensive set of model runs involving different initial states and simulation lengths. For the sake of brevity, we concentrate our attention on the following chemical compounds, O3, NO, NO2, HCHO, CO, HONO, and OH, and focus on their influence on selected O3 profiles. Our analysis shows that the optimal placement for observations of chemical species is not entirely determined by mere transport and mixing processes. Rather, a combination of initial chemical concentrations, chemical conversions, and meteorological processes determines the influence of chemical compounds and regions. We furthermore demonstrate that the optimal placement of observations of emission strengths is highly dependent on the location of emission sources and that the benefit of including emissions as target variables outperforms the value of initial value optimization with growing simulation length. The obtained results confirm the benefit of considering both initial values and emission strengths as target variables and of applying the EURAD-IM-SVA v1.0 for measurement decision guidance with respect to chemical compounds.
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Goris, N., and H. Elbern. "Singular vector based targeted observations of chemical constituents: description and first application of the EURAD-IM-SVA." Geoscientific Model Development Discussions 8, no. 8 (August 10, 2015): 6267–307. http://dx.doi.org/10.5194/gmdd-8-6267-2015.
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Abstract. Measurements of the large dimensional chemical state of the atmosphere provide only sparse snapshots of the state of the system due to their typically insufficient temporal and spatial density. In order to optimize the measurement configurations despite those limitations, the present work describes the identification of sensitive states of the chemical system as optimal target areas for adaptive observations. For this purpose, the technique of singular vector analysis (SVA), which has been proved effective for targeted observations in numerical weather predication, is implemented into the chemical transport model EURAD-IM (EURopean Air pollution and Dispersion – Inverse Model) yielding the EURAD-IM-SVA. Besides initial values, emissions are investigated as critical simulation controlling targeting variables. For both variants, singular vectors are applied to determine the optimal placement for observations and moreover to quantify which chemical compounds have to be observed with preference. Based on measurements of the airship based ZEPTER-2 campaign, the EURAD-IM-SVA has been evaluated by conducting a comprehensive set of model runs involving different initial states and simulation lengths. Since the considered cases are restricted in terms of considered chemical compounds and selected areas, they allow for a retracing of the results and a confirmation of their correctness. Our analysis shows that the optimal placement for observations of chemical species is not entirely determined by mere transport and mixing processes. Rather, a combination of initial chemical concentrations, chemical conversions, and meteorological processes determine the influence of chemical compounds and regions. We furthermore demonstrate that the optimal placement of observations of emission strengths is highly dependent on the location of emission sources and that the benefit of including emissions as target variables outperforms the value of initial value optimisation with growing simulation length. The obtained results confirm the benefit of considering both initial values and emission strengths as target variables and of applying the EURAD-IM-SVA for measurement decision guidance with respect to chemical compounds.
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McGregor, S., S. Lacroix, and A. Monmayrant. "AN ADAPTIVE PROGRAMMABLE HYPERSPECTRAL IMAGER." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W3 (August 19, 2015): 419–25. http://dx.doi.org/10.5194/isprsarchives-xl-3-w3-419-2015.
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An adaptive, hyperspectral imager is presented. We propose a system with easily adaptable spectral resolution, adjustable acquisition time, and high spatial resolution which is independent of spectral resolution. The system yields the possibility to define a variety of acquisition schemes, and in particular near snapshot acquisitions that may be used to measure the spectral content of given or automatically detected regions of interest. The proposed system is modelled and simulated, and tests on a first prototype validate the approach to achieve near snapshot spectral acquisitions without resorting to any computationally heavy post-processing, nor cumbersome calibration.
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Ali, Mazen, Kristina Steih, and Karsten Urban. "Reduced basis methods with adaptive snapshot computations." Advances in Computational Mathematics 43, no. 2 (October 22, 2016): 257–94. http://dx.doi.org/10.1007/s10444-016-9485-9.
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Carter, Joshua, and Blake Wiedenheft. "SnapShot: CRISPR-RNA-Guided Adaptive Immune Systems." Cell 163, no. 1 (September 2015): 260–260. http://dx.doi.org/10.1016/j.cell.2015.09.011.
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Song, Heechun. "Some approaches to robust, snapshot‐deficient, adaptive processing." Journal of the Acoustical Society of America 113, no. 4 (April 2003): 2262. http://dx.doi.org/10.1121/1.4780486.
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Song, Jiawei, Rasit Abay, J. Scott Tyo, and Andrey S. Alenin. "Transcending conventional snapshot polarimeter performance via neuromorphically adaptive filters." Optics Express 29, no. 12 (May 24, 2021): 17758. http://dx.doi.org/10.1364/oe.426072.
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Oxberry, Geoffrey M., Tanya Kostova-Vassilevska, William Arrighi, and Kyle Chand. "Limited-memory adaptive snapshot selection for proper orthogonal decomposition." International Journal for Numerical Methods in Engineering 109, no. 2 (July 14, 2016): 198–217. http://dx.doi.org/10.1002/nme.5283.
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Tsuchida, Eiji, Yoong-Kee Choe, and Takahiro Ohkubo. "An adaptive finite-element method for large-scale ab initio molecular dynamics simulations." Physical Chemistry Chemical Physics 17, no. 47 (2015): 31444–52. http://dx.doi.org/10.1039/c5cp00320b.
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Tabassum, Muhammad Naveed, and Esa Ollila. "Sequential adaptive elastic net approach for single-snapshot source localization." Journal of the Acoustical Society of America 143, no. 6 (June 2018): 3873–82. http://dx.doi.org/10.1121/1.5042363.
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Qin, Lilong, Manqing Wu, and Zhen Dong. "Robust adaptive beamforming using multi-snapshot direct data domain approach." AEU - International Journal of Electronics and Communications 75 (May 2017): 124–29. http://dx.doi.org/10.1016/j.aeue.2017.03.004.
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