A new reweighted l1-norm penalized least mean square (LMS) algorithm for sparse channel estimation is proposed and studied in this paper. Since standard LMS algorithm does not take into account the ...sparsity information about the channel impulse response (CIR), sparsity-aware modifications of the LMS algorithm aim at outperforming the standard LMS by introducing a penalty term to the standard LMS cost function which forces the solution to be sparse. Our reweighted l1-norm penalized LMS algorithm introduces in addition a reweighting of the CIR coefficient estimates to promote a sparse solution even more and approximate l0-pseudo-norm closer. We provide in depth quantitative analysis of the reweighted l1-norm penalized LMS algorithm. An expression for the excess mean square error (MSE) of the algorithm is also derived which suggests that under the right conditions, the reweighted l1-norm penalized LMS algorithm outperforms the standard LMS, which is expected. However, our quantitative analysis also answers the question of what is the maximum sparsity level in the channel for which the reweighted l1-norm penalized LMS algorithm is better than the standard LMS. Simulation results showing the better performance of the reweighted l1-norm penalized LMS algorithm compared to other existing LMS-type algorithms are given.
•Reweighted l1-norm penalized least mean square (LMS) algorithm is developed.•The convergence rate and excess mean square error of the proposed LMS are derived.•The performance of the proposed LMS depending on the sparsity level is investigated.•Comparison to other state-of-the-art sparsity aware LMS algorithms is given.
We develop a new tensor model for slow-time multiple-input multiple output (MIMO) radar and apply it for joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation. This tensor ...model aims to exploit the independence of phase modulation matrix and receive array in the received signal for slow-time MIMO radar. Such tensor can be decomposed into two tensors of different ranks, one of which has identical structure to that of the conventional tensor model for MIMO radar, and the other contains all phase modulation values used in the transmit array. We then develop a modification of the alternating least squares algorithm to enable parallel factor decomposition of tensors with extra constants. The Vandermonde structure of the transmit and receive steering matrices (if both arrays are uniform and linear) is then utilized to obtain angle estimates from factor matrices. The multi-linear structure of the received signal is maintained to take advantage of tensor-based angle estimation algorithms, while the shortage of samples in Doppler domain for slow-time MIMO radar is mitigated. As a result, the joint DOD and DOA estimation performance is improved as compared to existing angle estimation techniques for slow-time MIMO radar. Simulation results verify the effectiveness of the proposed method.
ABSTRACT
3D simulations of high mass young stellar object (HMYSO) growth show that their circumstellar discs fragment on to multiple self-gravitating objects. Accretion of these by HMYSO may explain ...episodic accretion bursts discovered recently. We post-process results of a previous 3D simulation of a HMYSO disc with a 1D code that resolves the disc and object dynamics down to the stellar surface. We find that burst-like deposition of material into the inner disc seen in 3D simulations by itself does not always signify powerful accretion bursts. Only high density post-collapse clumps crossing the inner computational boundary may result in observable bursts. The rich physics of the inner disc has a significant impact on the expected accretion bursts: (1) in the standard turbulent viscosity discs, migrating objects can stall at a migration trap at the distance of a few au from the star. However, in discs powered by magnetized winds, the objects are able to cross the trap and produce bursts akin to those observed so far. (2) Migrating objects may interact with and modify the thermal (hydrogen ionization) instability of the inner disc, which can be responsible for longer duration and lower luminosity bursts in HMYSOs. (3) If the central star is bloated to a fraction of an au by a previous episode of high accretion rate, or if the migrating object is particularly dense, a merger rather than a disc-mediated accretion burst results; (4) Object disruption bursts may be super-Eddington, leading to episodic feedback on HMYSO surroundings via powerful outflows.
•A general learning-based decomposition model suitable for fusing images from various imaging modalities is proposed.•The multimodal images are decomposed into correlated and uncorrelated ...components.•A CDL method based on simultaneous sparse approximation is proposed for estimating the correlated features.•The uncorrelated components are estimated using a Pearson correlation-based constraint.
This paper presents a multimodal image fusion method using a novel decomposition model based on coupled dictionary learning. The proposed method is general and can be used for a variety of imaging modalities. In particular, the images to be fused are decomposed into correlated and uncorrelated components using sparse representations with identical supports and a Pearson correlation constraint, respectively. The resulting optimization problem is solved by an alternating minimization algorithm. Contrary to other learning-based fusion methods, the proposed approach does not require any training data, and the correlated features are extracted online from the data itself. By preserving the uncorrelated components in the fused images, the proposed fusion method significantly improves on current fusion approaches in terms of maintaining the texture details and modality-specific information. The maximum-absolute-value rule is used for the fusion of correlated components only. This leads to an enhanced contrast-resolution without causing intensity attenuation or loss of important information. Experimental results show that the proposed method achieves superior performance in terms of both visual and objective evaluations compared to state-of-the-art image fusion methods.
Various inflammatory stimuli are able to modify or even "re-program" the mitochondrial metabolism that results in generation of reactive oxygen species. In noncommunicable chronic diseases such as ...atherosclerosis and other cardiovascular pathologies, type 2 diabetes and metabolic syndrome, these modifications become systemic and are characterized by chronic inflammation and, in particular, "neuroinflammation" in the central nervous system. The processes associated with chronic inflammation are frequently grouped into "vicious circles" which are able to stimulate each other constantly amplifying the pathological events. These circles are evidently observed in Alzheimer's disease, atherosclerosis, type 2 diabetes, metabolic syndrome and, possibly, other associated pathologies. Furthermore, chronic inflammation in peripheral tissues is frequently concomitant to Alzheimer's disease. This is supposedly associated with some common genetic polymorphisms, for example, Apolipoprotein-E ε4 allele carriers with Alzheimer's disease can also develop atherosclerosis. Notably, in the transgenic mice expressing the recombinant mitochondria targeted catalase, that removes hydrogen peroxide from mitochondria, demonstrates the significant pathology amelioration and health improvements. In addition, the beneficial effects of some natural products from the xanthophyll family, astaxanthin and fucoxanthin, which are able to target the reactive oxygen species at cellular or mitochondrial membranes, have been demonstrated in both animal and human studies. We propose that the normalization of mitochondrial functions could play a key role in the treatment of neurodegenerative disorders and other noncommunicable diseases associated with chronic inflammation in ageing. Furthermore, some prospective drugs based on mitochondria targeted catalase or xanthophylls could be used as an effective treatment of these pathologies, especially at early stages of their development.
We address the problem of tensor decomposition in application to direction-of-arrival (DOA) estimation for two-dimensional transmit beamspace (TB) multiple-input multiple-output (MIMO) radar. A ...general higher-order tensor model that enables computationally efficient DOA estimation is designed. Whereas other tensor decomposition-based methods treat all factor matrices as arbitrary, the essence of the proposed DOA estimation method is to fully exploit the Vandermonde structure of the factor matrices to take advantage of the shift-invariance between and within different transmit subarrays. Specifically, the received signal of TB MIMO radar is expressed as a higher-order tensor. A computationally efficient tensor decomposition method is proposed to decompose the Vandermonde factor matrices. The generators of the Vandermonde factor matrices are computed to estimate the phase rotations between subarrays, which can be utilized as a look-up table for finding target DOAs. The proposed tensor model and the DOA estimation algorithm are also straightforwardly applicable for the one-dimensional TB MIMO radar case. It is further shown that our proposed approach can be used in a more general scenario where the transmit subarrays with arbitrary but identical configuration can be non-uniformly displaced. We also show that both the tensor rank of the signal tensor and the matrix rank of a particular matrix derived from the signal tensor are identical to the number of targets. Thus, the number of targets can be estimated via matrix rank determination. Simulation results illustrate the performance improvement of the proposed DOA estimation method as compared to other tensor decomposition-based techniques for TB MIMO radar.
Abstract
The FU Orionis–type objects (FUors) are low-mass pre-main-sequence stars undergoing a temporary but significant increase of mass accretion rate from the circumstellar disk onto the ...protostar. It is not yet clear what triggers the accretion bursts and whether the disks of FUors are in any way different from the disks of nonbursting young stellar objects. Motivated by this, we conducted a 1.3 mm continuum survey of 10 FUors and FUor-like objects with ALMA, using both the 7 m array and the 12 m array in two different configurations to recover emission at the widest possible range of spatial scales. We detected all targeted sources and several nearby objects as well. To constrain the disk structure, we fit the data with models of increasing complexity from 2D Gaussian to radiative transfer, enabling comparison with other samples modeled in a similar way. The radiative transfer modeling gives disk masses that are significantly larger than what is obtained from the measured millimeter fluxes assuming optically thin emission, suggesting that the FUor disks are optically thick at this wavelength. In comparison with samples of regular class II and class I objects, the disks of FUors are typically a factor of 2.9–4.4 more massive and a factor of 1.5–4.7 smaller in size. A significant fraction of them (65%–70%) may be gravitationally unstable.
This paper studies optimal bandwidth and power allocation in a cognitive radio network where multiple secondary users (SUs) share the licensed spectrum of a primary user (PU) under fading channels ...using the frequency division multiple access scheme. The sum ergodic capacity of all the SUs is taken as the performance metric of the network. Besides the peak/average transmit power constraints at the SUs and the peak/average interference power constraint imposed by the PU, total bandwidth constraint of the licensed spectrum is also taken into account. Optimal bandwidth allocation is derived in closed-form for any given power allocation. The structures of optimal power allocations are also derived under all possible combinations of the aforementioned power constraints. These structures indicate the possible numbers of users that transmit at nonzero power but below their corresponding peak powers, and show that other users do not transmit or transmit at their corresponding peak powers. Based on these structures, efficient algorithms are developed for finding the optimal power allocations.
In this paper, we investigate the downlink throughput performance of a massive multiple-input multiple-output system that employs superimposed pilots for channel estimation. The component of downlink ...(DL) interference that results from transmitting data alongside pilots in the uplink (UL) is shown to decrease at a rate proportional to the square root of the number of antennas at the BS when the least-squares channel estimate is employed in a matched-filter precoder. The normalized mean-squared error (NMSE) of the channel estimate is compared with the Bayesian Cramér-Rao lower bound that is derived for the system, and the former is also shown to diminish with increasing number of antennas at the base station. Furthermore, we show that staggered pilots are a particular case of superimposed pilots and offer the downlink throughput of superimposed pilots while retaining the UL spectral and energy efficiency of regular pilots. We also extend the framework for designing a hybrid system, consisting of users that transmit either regular or superimposed pilots, to minimize both the UL and DL interference. The improved NMSE and DL rates of the channel estimator based on superimposed pilots are demonstrated by means of simulations.
The problem of estimating the parameters of a moving target in multiple-input multiple-output (MIMO) radar is considered and a new approach for estimating the moving target parameters by making use ...of the phase information associated with each transmit-receive path is introduced. It is required for this technique that different receive antennas have the same time reference, but no synchronization of initial phases of the receive antennas is needed and, therefore, the estimation process is noncoherent. We model the target motion within a certain processing interval as a polynomial of general order. The first three coefficients of such a polynomial correspond to the initial location, velocity, and acceleration of the target, respectively. A new maximum likelihood (ML) technique for estimating the target motion coefficients is developed. It is shown that the considered ML problem can be interpreted as the classic "overdetermined" nonlinear least-squares problem. The proposed ML estimator requires multidimensional search over the unknown polynomial coefficients. The Cramér-Rao bound (CRB) for the proposed parameter estimation problem is derived. The performance of the proposed estimator is validated by simulation results and is shown to achieve the CRB.
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