Time domain computation of flow induced sound

We present a recently developed numerical scheme for computational aeroacoustics (CAA). Therewith, we solve the flow field by a large eddy simulation (LES) and the generation as well as propagation of acoustic noise by Lighthill’s analogy applying the finite element method. The developed scheme allows a direct coupling in time domain as well as a sequential coupling in frequency domain and provides the acoustic sound field not only in the far field but also in the region of the flow. Furthermore, we can directly investigate the acoustic source terms in the flow region. The scheme is well suited for interior aeroacoustic problems with complex geometries as well as for fluid-structure interaction problems. Implementation is validated and a two-dimensional simple application example is used to investigate the acoustic sources and to evaluate the acoustic pressure field from both transient and harmonic analyses.     

Emerging theories and technologies on computational imaging

Computational imaging describes the whole imaging process from the perspective of light transport and information transmission, features traditional optical computing capabilities, and assists in breaking through the limitations of visual information recording. Progress in computational imaging promotes the development of diverse basic and applied disciplines. In this review, we provide an overview of the fundamental principles and methods in computational imaging, the history of this field, and the important roles that it plays in the development of science. We review the most recent and promising advances in computational imaging, from the perspective of different dimensions of visual signals, including spatial dimension, temporal dimension, angular dimension, spectral dimension, and phase. We also discuss some topics worth studying for future developments in computational imaging.     

Computing Information in Neuronal Spikes

This paper provides new insights regarding the transfer of information between input signal and the output of neurons. Simulations of the Hodgkin-Huxley (HH) model combined with computational techniques are used to estimate this transfer of information. Our analysis shows that comparatively, mutual information (MI) between input signal and sodium flux is about two times that between input signal and output spikes during each spike within a millisecond-level time domain. This higher transfer of information provided by ionic fluxes extends the working frequency domain of neural cells beyond those accessible to information transfer within spikes alone.     

Controller synthesis with guaranteed closed-loop phase constraints

In this paper, we present an analysis and synthesis approach for guaranteeing that the phase of a single-input, single-output closed-loop transfer function is contained in the interval [−α,α] for a given α>0 at all frequencies. Specifically, we first derive a sufficient condition involving a frequency domain inequality for guaranteeing a given phase constraint. Next, we use the Kalman–Yakubovich–Popov theorem to derive an equivalent time domain condition. In the case where , we show that frequency and time domain sufficient conditions specialize to the positivity theorem. Furthermore, using linear matrix inequalities, we develop a controller synthesis approach for guaranteeing a phase constraint on the closed-loop transfer function. Finally, we extend this synthesis approach to address mixed gain and phase constraints on the closed-loop transfer function.     

On Plenoptic Multiplexing and Reconstruction

Photography has been striving to capture an ever increasing amount of visual information in a single image. Digital sensors, however, are limited to recording a small subset of the desired information at each pixel. A common approach to overcoming the limitations of sensing hardware is the optical multiplexing of high-dimensional data into a photograph. While this is a well-studied topic for imaging with color filter arrays, we develop a mathematical framework that generalizes multiplexed imaging to all dimensions of the plenoptic function. This framework unifies a wide variety of existing approaches to analyze and reconstruct multiplexed data in either the spatial or the frequency domain. We demonstrate many practical applications of our framework including high-quality light field reconstruction, the first comparative noise analysis of light field attenuation masks, and an analysis of aliasing in multiplexing applications.     

Efficient DPCA SAR imaging with fast iterative spectrum reconstruction method

The displaced phase center antenna(DPCA)technique is an effective strategy to achieve wide-swath synthetic aperture radar(SAR)imaging with high azimuth resolution.However,traditionally,it requires strict limitation of the pulse repetition frequency(PRF)to avoid non-uniform sampling.Otherwise,any deviation could bring serious ambiguity if the data are directly processed using a matched filter.To break this limitation,a recently proposed spectrum reconstruction method is capable of recovering the true spectrum from the nonuniform samples.However,the performance is sensitive to the selection of the PRF.Sparse regularization based imaging may provide a way to overcome this sensitivity.The existing time-domain method,however,requires a large-scale observation matrix to be built,which brings a high computational cost.In this paper,we propose a frequency domain method,called the iterative spectrum reconstruction method,through integration of the sparse regularization technique with spectrum analysis of the DPCA signal.By approximately expressing the observation in the frequency domain,which is realized via a series of decoupled linear operations,the method performs SAR imaging which is then not directly based on the observation matrix,which reduces the computational cost from O(N2)to O(N log N)(where N is the number of range cells),and is therefore more efficient than the time domain method.The sparse regularization scheme,realized via a fast thresholding iteration,has been adopted in this method,which brings the robustness of the imaging process to the PRF selection.We provide a series of simulations and ground based experiments to demonstrate the high efficiency and robustness of the method.The simulations show that the new method is almost as fast as the traditional mono-channel algorithm,and works well almost independently of the PRF selection.Consequently,the suggested method can be accepted as a practical and efficient wide-swath SAR imaging technique.     

On the Fourier Properties of Discontinuous Motion

Retinal image motion and optical flow as its approximation are fundamental concepts in the field of vision, perceptual and computational. However, the computation of optical flow remains a challenging problem as image motion includes discontinuities and multiple values mostly due to scene geometry, surface translucency and various photometric effects such as reflectance. In this contribution, we analyze image motion in the frequency space with respect to motion discontinuities and translucence. We derive the frequency structure of motion discontinuities due to occlusion and we demonstrate its various geometrical properties. The aperture problem is investigated and we show that the information content of an occlusion almost always disambiguates the velocity of an occluding signal suffering from the aperture problem. In addition, the theoretical framework can describe the exact frequency structure of Non-Fourier motion and bridges the gap between Non-Fourier visual phenomena and their understanding in the frequency domain.     

On the Implementation of the Discrete Fourier Transform in the Encrypted Domain

Signal-processing modules working directly on encrypted data provide an elegant solution to application scenarios where valuable signals must be protected from a malicious processing device. In this paper, we investigate the implementation of the discrete Fourier transform (DFT) in the encrypted domain by using the homomorphic properties of the underlying cryptosystem. Several important issues are considered for the direct DFT: the radix-2 and the radix-4 fast Fourier algorithms, including the error analysis and the maximum size of the sequence that can be transformed. We also provide computational complexity analyses and comparisons. The results show that the radix-4 fast Fourier transform is best suited for an encrypted domain implementation in the proposed scenarios.      

基于H无穷范数的颤振分析方法

颤振是一种典型的气动弹性动不稳定现象, 求解颤振临界点是气动弹性稳定性分析的重要任务之一.从H_∞控制理论观点出发, 将气动弹性系统视为多输入多输出系统, 并导出其传递函数矩阵. 在颤振临界点附近, 根据系统传递函数矩阵的H_∞–范数趋于无穷大的特点, 发展了相应的颤振临界点搜索方法. 与传统的颤振分析方法相比, 该方法属于完全频域方法, 算法更为简洁, 且具有更高的自动化程度. 数值算例表明, 该方法可以获得正确的颤振临界点.     

Robust non-negative matrix factorization via joint sparse and graph regularization for transfer learning

In real-world applications, we often have to deal with some high-dimensional, sparse, noisy, and non-independent identically distributed data. In this paper, we aim to handle this kind of complex data in a transfer learning framework, and propose a robust non-negative matrix factorization via joint sparse and graph regularization model for transfer learning. First, we employ robust non-negative matrix factorization via sparse regularization model (RSNMF) to handle source domain data and then learn a meaningful matrix, which contains much common information between source domain and target domain data. Second, we treat this learned matrix as a bridge and transfer it to target domain. Target domain data are reconstructed by our robust non-negative matrix factorization via joint sparse and graph regularization model (RSGNMF). Third, we employ feature selection technique on new sparse represented target data. Fourth, we provide novel efficient iterative algorithms for RSNMF model and RSGNMF model and also give rigorous convergence and correctness analysis separately. Finally, experimental results on both text and image data sets demonstrate that our REGTL model outperforms existing start-of-art methods.     

On the Minimal Realizations of a Finite Sequence

We develop a theory of minimal realizations of a finite sequence over an integral domain R, from first principles. Our notion of a minimal realization is closely related to that of a linear recurring sequence and of a partial realization (as in Mathematical Systems Theory). From this theory, we derive Algorithm MR. which computes a minimal realization of a sequence of L elements using at most L(5L + 1)/2 R-multiplications. We also characterize all minimal realizations of a given sequence in terms of the computed minimal realization.This algorithm computes the linear complexity of an R sequence, solves non-singular linear systems over R (extending Wiedemann's method), computes the minimal polynomial of an R-matrix, transfer/growth functions and symbolic Padé approximations. There are also a number of applications to Coding Theory.We thus provide a common framework for solving some well-known problems in Systems Theory, Symbolic/Algebraic Computation and Coding Theory.     

Interplay of spatial aggregation and computational geometry in extracting diagnostic features from cardiac activation data

Functional imaging plays an important role in the assessment of organ functions, as it provides methods to represent the spatial behavior of diagnostically relevant variables within reference anatomical frameworks. The salient physical events that underly a functional image can be unveiled by appropriate feature extraction methods capable to exploit domain-specific knowledge and spatial relations at multiple abstraction levels and scales. In this work we focus on general feature extraction methods that can be applied to cardiac activation maps, a class of functional images that embed spatio-temporal information about the wavefront propagation. The described approach integrates a qualitative spatial reasoning methodology with techniques borrowed from computational geometry to provide a computational framework for the automated extraction of basic features of the activation wavefront kinematics and specific sets of diagnostic features that identify an important class of rhythm pathologies.     

Bregmanized Domain Decomposition for Image Restoration

Computational problems of large-scale data are gaining attention recently due to better hardware and hence, higher dimensionality of images and data sets acquired in applications. In the last couple of years non-smooth minimization problems such as total variation minimization became increasingly important for the solution of these tasks. While being favorable due to the improved enhancement of images compared to smooth imaging approaches, non-smooth minimization problems typically scale badly with the dimension of the data. Hence, for large imaging problems solved by total variation minimization domain decomposition algorithms have been proposed, aiming to split one large problem into N>1 smaller problems which can be solved on parallel CPUs. The N subproblems constitute constrained minimization problems, where the constraint enforces the support of the minimizer to be the respective subdomain. In this paper we discuss a fast computational algorithm to solve domain decomposition for total variation minimization. In particular, we accelerate the computation of the subproblems by nested Bregman iterations. We propose a Bregmanized Operator Splitting–Split Bregman (BOS-SB) algorithm, which enforces the restriction onto the respective subdomain by a Bregman iteration that is subsequently solved by a Split Bregman strategy. The computational performance of this new approach is discussed for its application to image inpainting and image deblurring. It turns out that the proposed new solution technique is up to three times faster than the iterative algorithm currently used in domain decomposition methods for total variation minimization.     

Arbitrarily fast and robust tracking by feedback

The output of a singe-input-single-output linear feedback system with more than one pole in excess over the zeros in the loop transmission cannot track arbitrarily fast its input (by the root locus). In this work we extend the linear feedback so that some of the open loop poles may depend on the open loop gain; we call this new class quasi-linear feedback systems. We then derive time domain, pole-zero, and frequency domain conditions which ensure arbitrarily fast and robust tracking by quasi-linear feedback, for an arbitrary number of poles in excess over the zeros. We prove that in a particular case these conditions are equivalent, and that the boundedness in frequency of the closed loop transfer function is no longer necessary for achieving arbitrarily fast tracking. The robustness is to external disturbances and initial conditions, and the open loop has to be minimum phase. Some examples are presented which illustrate these results. They also show that this good performance can be obtained with a reduced control effort, and that quasi-linear feedback can alleviate the limitation on performance of non-minimum phase open loops.     

The novel and robust watermarking method based on q-logarithm frequency domain

In general, watermarking techniques on transform domain are always mainly researched in literature since it is robust against several attacks. In this paper, we continue to focus on the transform domain and extend it to the novel frequency domain, called q-logarithm frequency domain (q-LFD), for robust watermarking. In order to achieve the robustness of embedding method, we embed the scrambled watermark information into the low-band frequency of q-LFD by using the quantization index modulation (QIM) technique. According to the simulation results, our method can improve the quality of embedded image and also the robustness of watermark based on the optimized parameters: q of q-LFD and Q of QIM. Experimental results show that our proposed method is also robust against geometric attacks, processing attacks, filtering attacks, and so on.     

Modeling of transient bending wave in an infinite plate and its coupling to arbitrary shaped piezoelements

Estimating the location and energy of impacts is of primary importance for assessing the condition of structures. Particularly, such estimation can be easily obtained from the energy flow in the structures, which is usually derived from the Poynting vector. In order to measure the Poynting vector in a thin plate using piezoelements bonded on the plate, an analytical formulation of the impulse response in thin infinite plates is presented. The knowledge of the impulse response of any linear time invariant (LTI) system is precious information for the determination of its behavior under arbitrary inputs. When dealing with propagation, and especially mechanical wave propagation, a common approach consists in using numerical methods that are often time-consuming, especially for multi-coupled systems. This paper proposes a new approach for modeling the impulse response of an infinite plate with surface-bonded piezoelectric elements. The proposed analytical formulation allows bypassing numerical analysis drawbacks, in particular instabilities occurring at high frequencies, case-dependent systems and computational requirements, while giving the response for any time and space domain values using a simple convolution. The proposed model relies on flexural wave decomposition over the spatial frequency domain and corresponds to a time generalization of the angular spectrum theory, thus introducing flexural wave propagation as a time-varying spatial filter. Once the impulse is know in the spatial frequency domain, the inverse Fourier transform is applied and leads to the impulse response in the physical domain. From this model, an analytical expression of the impulse voltage response of the piezoelectric transducers and the Poynting vector can be derived quite easily. The predicted impulse response is then compared to FEM simulation results and experimental measurements in order to assess the model.     

几种提高分布式雷达方位向分辨率方法的分析与比较

分析比较了三种提高分布式合成孔径雷达方位向分辨率的方法:时域扩频,展宽等效多普勒带宽,分辨率虽有提高,但存在频谱混叠,影响成像效果;提高频谱利用率,实现全孔径成像,达到理论分辨率,但多普勒分量提取以及模糊抑制的计算量过大;利用统计分析及估计理论对成像进行空域滤波虽然扩展了清晰成像面积,但没有提高绝对分辨率,且由于矩阵的伪逆操作,计算量惊人。针对上述算法的不足,借鉴扩频思想,提出空间频率滤波方法以改善合成信号的频域特性,从而提高方位向分辨率。给出了仿真结果以说明所提出方法的有效性。     

Two-stage Resampling for Bidirectional Path Tracing with Multiple Light Sub-paths

Recent advances in bidirectional path tracing (BPT) reveal that the use of multiple light sub-paths and the resampling of a small number of these can improve the efficiency of BPT. By increasing the number of pre-sampled light sub-paths, the possibility of generating light paths that provide large contributions can be better explored and this can alleviate the correlation of light paths due to the reuse of pre-sampled light sub-paths by all eye sub-paths. The increased number of pre-sampled light subpaths, however, also incurs a high computational cost. In this paper, we propose a two-stage resampling method for BPT to efficiently handle a large number of pre-sampled light sub-paths. We also derive a weighting function that can treat the changes in path probability due to the two-stage resampling. Our method can handle a two orders of magnitude larger number of presampled light sub-paths than previous methods in equal-time rendering, resulting in stable and better noise reduction than state-of-the-art methods.     

An a posteriori error estimator for model adaptivity in electrocardiology

We introduce an a posteriori modeling error estimator for the effective computation of electric potential propagation in the heart. Starting from the Bidomain problem and an extended formulation of the simplified Monodomain system, we build a hybrid model, called Hybridomain, which is dynamically adapted to be either Bi- or Monodomain ones in different regions of the computational domain according to the error estimator. We show that accurate results can be obtained with the adaptive Hybridomain model with a reduced computational cost compared to the full Bidomain model. We discuss the effectivity of the estimator and the reliability of the results on simulations performed on real human left ventricle geometries retrieved from healthy subjects.     

Maximal infinite-valued constraint languages

We systematically investigate the computational complexity of constraint satisfaction problems for constraint languages over an infinite domain. In particular, we study a generalization of the well-established notion of maximal constraint languages   from finite to infinite domains. If the constraint language can be defined with an ω$\omega$-categorical structure, then maximal constraint languages are in one-to-one correspondence to minimal oligomorphic clones. Based on this correspondence, we derive general tractability and hardness criteria for the corresponding constraint satisfaction problems.