Explicit Estimation Research Articles

Model-Driven Learning for Generic MIMO Downlink Beamforming With Uplink Channel Information

Accurate downlink channel information is crucial to the beamforming design, but it is difficult to obtain in practice. This paper investigates a deep learning-based optimization approach of the downlink beamforming to maximize the system sum rate, when only the uplink channel information is available. Our main contribution is to propose a model-driven learning technique that exploits the structure of the optimal downlink beamforming to design an effective hybrid learning strategy with the aim to maximize the sum rate performance. This is achieved by jointly considering the learning performance of the downlink channel, the power and the sum rate in the training stage. The proposed approach applies to generic cases in which the uplink channel information is available, but its relation to the downlink channel is unknown and does not require an explicit downlink channel estimation. We further extend the developed technique to massive multiple-input multiple-output scenarios and achieve a distributed learning strategy for multicell systems without an inter-cell signalling overhead. Simulation results verify that our proposed method provides the performance close to the state of the art numerical algorithms with perfect downlink channel information and significantly outperforms existing data-driven methods in terms of the sum rate.

Spatially explicit estimation of soil organic carbon stock of an estuarine mangrove wetland of eastern India using elemental analysis and very-fine resolution satellite data

BackgroundThis study estimated the total soil organic C (SOC) stock of the wetland influence zone of Bichitrapur mangroves in eastern India in a spatially explicit manner. Both spatial and vertical distribution of SOC densities with respect to land use/land cover (LULC) pattern were assessed. Subsequently, some site-specific management strategies were forwarded towards enhancement of C sequestration potential.MethodsThe changing patterns of LULC within the wetland influence zone of the site were analyzed using Landsat TM (30 m) and Pleiades-1A (2 m) imageries from 1988 to 2018. Point-specific SOC measurement was done using samples taken from four core-depth intervals (viz. D1: 0–20 cm, D2: 20–40 cm, D3: 40–70 cm, D4: 70–100 cm) at 89 locations belonging to different LULC categories. Spatial interpolation was applied on this point-based data to produce SOC density and stock models as a whole and at all core-depths. Relationships between SOC density, core-depth and present LULC were evaluated through multivariate statistical analyses.ResultsThe LULC transformations during last three decades suggested the gradual growth of mangrove plantations as well as agricultural and aquacultural activities. Most amount of SOC was concentrated in D1 (37.17%) followed by D3 (26.51%), while D4 had the lowest (10.87%). The highest mean SOC density was observed in the dense mangrove patches (248.92 Mg ha−1) and the lowest mean was in the Casuarina plantations (2.78 Mg ha−1). Here, Spline method emerged as the best-fit interpolation technique to model SOC data (R2 = 0.74) and estimated total SOC stock of the entire wetland influence zone as 169,569.40 Mg and the grand mean as 125.56 Mg ha−1. Overall, LULC was inferred as a major determinant of SOC dynamics with a statistically significant effect (p < 0.001), whereas no such inference could be drawn for soil core-depth.ConclusionsThe C sequestration potential of sites such as the present one could be increased with appropriate zone-wise plantation strategies, restriction on the land conversion to aquaculture and promotion of ecotourism. Periodic monitoring through integration of geospatial techniques and elemental analyses would be immensely beneficial in this regard.Graphic

Estimation of the parameters of the gamma geometric process

There is no doubt that finding the estimators of model parameters accurately and efficiently is very important in many fields. In this study, we obtain the explicit estimators of the unknown model parameters in the gamma geometric process (GP) via the modified maximum likelihood (MML) methodology. These estimators are as efficient as maximum likelihood (ML) estimators. The marginal and joint asymptotic distributions of the MML estimators are also derived and efficiency comparisons between ML and MML estimators are made through an extensive Monte Carlo simulations. Moreover, a real data example is considered to illustrate the performances of the MML estimators together with their ML counterparts. According to simulation results, the performances of MML and ML estimators are close to each other even for small sample sizes.

"Run to the hills": Specific contributions of anticipated energy expenditure during active spatial learning.

Grounded views of cognition consider that space perception is shaped by the body and its potential for action. These views are substantiated by observations such as the distance-on-hill effect, described as the overestimation of visually perceived uphill distances. An interpretation of this phenomenon is that slanted distances are overestimated because of the integration of energy expenditure cues. The visual perceptual processes involved are, however, usually tackled through explicit estimation tasks in passive situations. The goal of this study was to consider instead more ecological active spatial processing. Using immersive virtual reality and an omnidirectional treadmill, we investigated the effect of anticipated implicit physical locomotion cost by comparing spatial learning for uphill and downhill routes, while maintaining actual physical cost and walking speed constant. In the first experiment, participants learnt city layouts by exploring uphill or downhill routes. They were then tested using a landmark positioning task on a map. In the second experiment, the same protocol was used with participants who wore loaded ankle weights. The results from the first experiment showed that walking uphill routes led to a global underestimation of distances compared with downhill routes. This inverted distance-of-hill effect was not observed in the second experiment, where an additional effort was applied. These results suggest that the underestimation of distances observed in Experiment 1 emerged from recalibration processes whose function was to solve the transgression of proprioceptive predictions linked with uphill energy expenditure. The results are discussed in relation to constructivist approaches on spatial representations and predictive coding theories.

Fixed-Time Containment Control for Nonlinear Multi-Agent Systems With External Disturbances

This brief investigates the fixed-time containment control (FTCC) of second-order disturbed nonlinear multi-agent systems (MASs). A new nonsingular terminal sliding mode (NTSM) control protocol is devised to ensure the FTCC for disturbed nonlinear MASs. A novel NTSM surface is correspondingly proposed to ensure the fixed-time convergence of the considered systems. The explicit estimation of settling time is given as well, which is irrespective of system initial values. Finally, one numerical example is presented to demonstrate the correctness of the proposed results.

Online Beam Current Estimation in Particle Beam Microscopy

In conventional particle beam microscopy, knowledge of the beam current is essential for accurate micrograph formation and sample milling. This generally necessitates offline calibration of the instrument. In this work, we establish that beam current can be estimated online, from the same secondary electron count data that is used to form micrographs. Our methods depend on the recently introduced time-resolved measurement concept, which combines multiple short measurements at a single pixel and has previously been shown to partially mitigate the effect of beam current variation on micrograph accuracy. We analyze the problem of jointly estimating beam current and secondary electron yield using the Cram&#x00E9;r&#x2013;Rao bound. Joint estimators operating at a single pixel and estimators that exploit models for inter-pixel correlation and Markov beam current variation are proposed and tested on synthetic microscopy data. Our estimates of secondary electron yield that incorporate explicit beam current estimation beat state-of-the-art methods, resulting in micrograph accuracy nearly indistinguishable from what is obtained with perfect beam current knowledge. Our novel beam current estimation could help improve milling outcomes, prevent sample damage, and enable online instrument diagnostics.

Grouped Spatio-Temporal Alignment Network for Video Super-Resolution

Video super-resolution based on CNNs has recently achieved significant progress. The existing popular super-resolution methods usually impose the optical flow between neighboring frames for temporal alignment. However, estimation of accurate optical flow is hard and expends greater computation. To address this problem, we propose a novel grouped spatio-temporal alignment network (GSTAN) that effectively incorporates spatio-temporal information in a hierarchical way. The input sequence is divided into several groups corresponding to different frame rates. These groups provide complementary information, which is helpful to restore missing textures for the reference frame. Specifically, each group employs deformable 3D convolution to incorporate spatio-temporal information, which avoids artifacts from explicit motion estimation. In addition, a Gated-Dconv information filter is proposed to control information flow to focus on the fine details. Finally, these groups provide complementary information, which is integrated with the inter-group fusion module. Extensive experiments have demonstrated our method achieves state-of-the-art SR performance on several benchmark datasets.

Approximate Maximum Likelihood Estimations for the Parameters of the Generalized Gudermannian Distribution and Its Characterizations

Generalized Gudermannian distribution is a symmetric distribution with location and scale parameters as an alternative to the well‐known symmetric distributions such as normal, Laplace, and Cauchy. This distribution has a simple closed form for the probability density function (pdf) and cumulative distribution function (cdf) and is more flexible than normal distribution based on kurtosis criterion. Certain characterizations of this distribution are presented. For this distribution, due to the nonlinear form of the likelihood equations, the maximum likelihood estimations (MLEs) of the location and scale parameters do not have closed forms and need a numerical approximation method with suitable starting values. A simple method of deriving explicit estimators by approximating the likelihood equations is presented. The bias and variance of these estimators are examined numerically and are shown that these estimators are as efficient as the maximum likelihood estimators. Some pivotal quantities are proposed for finding confidence intervals for location and scale parameters based on asymptotic normality. From the coverage probability, the MLEs do not work well especially for the small sample sizes; thus, to improve the coverage probability, simulated percentiles based on the Monte Carlo method are used. Finally, a real data set is presented to illustrate the suggested method and its inference related to this data set.

Single-Image Specular Highlight Removal via Real-World Dataset Construction

Specular reflections pose great challenges on various multimedia and computer vision tasks, <i>e.g.</i>, image segmentation, detection and matching. In this paper, we build a large-scale Paired Specular-Diffuse (PSD) image dataset, where the images are carefully captured by using real-world objects and the ground-truth specular-free diffuse images are provided. To the best of our knowledge, this is the first real-world benchmark dataset for specular highlight removal task, which is useful for evaluating and encouraging new deep learning-based approaches. Given this dataset, we present a novel Generative Adversarial Network (GAN) for specular highlight removal from a single image by introducing the detection of specular reflection information as a guidance. Our network also makes full use of the attention mechanism and is able to directly model the mapping relation between the diffuse area and the specular highlight area without any explicit estimation of the illumination. Experimental results demonstrate that the proposed network is more effective to remove specular reflection components with the guidance of specular highlight detection than recent state-of-the-art methods.

Łojasiewicz exponents of certain analytic functions

We consider the exponent of Łojasiewicz inequality ‖∂f(z)‖≥c|f(z|θ for two classes of analytic functions and we will give an explicit estimation for θ. First we consider certain non-degenerate functions which is not convenient. In §3.4, we give an example of a polynomial for which θ0(f) is not constant on the moduli space and in §3.5, we show that the behaviors of the Łojasiewicz exponents are not similar as the Milnor numbers by an example.In the last section (§4), we give also an estimation for product functions f(z)=f1(z)⋯fk(z) associated to a family of a certain convenient non-degenerate complete intersection varieties. In either class, the singularity is not isolated. We will give explicit estimations of the Łojasiewicz exponent θ0(f) using combinatorial data of the Newton boundary of f. We generalize this estimation for non-reduced function g=f1m1⋯fkmk.

Topography-dependent eikonal tomography based on the fast-sweeping scheme and the adjoint-state technique

First-arrival traveltime tomography has been widely used for upper crustal velocity modeling, but it usually suffers from the problem of complex surface topography. To overcome this problem, we have developed a new topography-dependent eikonal tomography scheme that combines a developed accurate and efficient traveltime modeling method and introduces a flexible and robust adjoint inversion scheme in the presence of irregular topography. A surface-flattening scheme is used to handle the irregular surface, where the real model is discretized by curvilinear grids and the irregular free surface is mathematically flattened through the transformation from Cartesian to curvilinear coordinates. Based on this parameterization, the forward traveltime modeling is conducted by a monotone fast-sweeping method that discretizes the factored topography-dependent eikonal equation with a point-source condition. This algorithm can circumvent the source-singularity problem and decrease the numerical error in the vicinity of a point source in the curvilinear system. Then, the gradient-based inversion is used to minimize the misfit function, which is achieved by a matrix-free adjoint-state method without cumbersome ray tracing and explicit estimation of the Fréchet derivative matrix in the curvilinear coordinate system. The new tomographic scheme is evaluated through numerical examples with different seismic structures with complex topography, and then applied to a wide-angle profile acquired in the northeastern Tibetan Plateau. The results validate the effectiveness and efficiency of our tomography scheme in constructing shallow crustal velocity models with irregular topography.

A review of spatial capture-recapture: Ecological insights, limitations, and prospects.

First described by Efford (2004), spatial capture–recapture (SCR) has become a popular tool in ecology. Like traditional capture–recapture, SCR methods account for imperfect detection when estimating ecological parameters. In addition, SCR methods use the information inherent in the spatial configuration of individual detections, thereby allowing spatially explicit estimation of population parameters, such as abundance, survival, and recruitment. Paired with advances in noninvasive survey methods, SCR has been applied to a wide range of species across different habitats, allowing for population‐ and landscape‐level inferences with direct consequences for conservation and management. I conduct a literature review of SCR studies published since the first description of the method and provide an overview of their scope in terms of the ecological questions answered with this tool, taxonomic groups targeted, geography, spatio‐temporal extent of analyses, and data collection methods. In addition, I review approaches for analytical implementation and provide an overview of parameters targeted by SCR studies and conclude with current limitations and future directions in SCR methods.

Performance Analysis of Gas Wells Based on the Conventional Decline Parameters and the Flow Integral Equation

The estimation of reserves and performance prediction are two vital tasks for the development of gas reservoirs where the evaluation of gas in place or well-controlled reserves, as the foundation of the performance analysis of gas wells, turns to be exceedingly significant. Advanced production data analysis or modern rate transient analysis (RTA) methods mainly depend on the iterative calculations of material balance quasitime (tca) and type curve fitting, the essence of which is to update the average reservoir pressure data time and again. The traditional Arps’ decline models are of empirical nature despite the convenience and applicability to the constant bottomhole pressure (BHP) condition. In order to avoid the implicit iteration, this paper develops an explicit method for estimating the average reservoir pressure on the basis of dynamic material balance equation (DMBE), termed “flow integral method,” which can be applied to various gas production systems under boundary-dominated flow (BDF). Based on the flow integral method and the decline parameter evaluation, we employ the hyperbolic decline model to model the gas well performance at a constant BHP. The analytical formulations of decline rate and decline exponent are deduced from the DMBE and the static material balance equation (SMBE) considering the elastic compressibilities of rock pore and bound water. The resulting decline parameter method for explicit estimation of gas reserves boasts a solid and rigorous theory foundation that production rate, decline rate, and average reservoir pressure profiles have reference to each other, and its implementation steps are explained in the paper. The SMBE can, combined with the estimated pressure profile by the flow integral method, also be used to determine gas reserves which is not limited to the constant-BHP condition and can calibrate the estimates of the decline parameter method. The proposed methods are proven effective and reliable with several numerical cases at different BHPs and a field example.

Asymptotics of the Hausdorff dimensions of the Julia sets of McMullen maps with error bounds* *This work was supported by NSFC Grant Nos. 11731003 and 12071118 for the second author, NSFC Grant No. 12071210 and NSF of Jiangsu Province Grant No. BK20191246 for the third author.

For McMullen maps f λ (z) = z p + λ/z p , where , it is known that if p ⩾ 3 and λ is small enough, then the Julia set J(f λ ) of f λ is a Cantor set of circles. In this paper we show that the Hausdorff dimension of J(f λ ) has the following asymptotic behavior An explicit error estimation of the remainder is also obtained. We also observe a ‘dimension paradox’ for the Julia set of Cantor set of circles.

Restoration of Hardware Function of Spectral Colorimeters Using Regularization Methods

The problem of determining the spectral characteristic of a controlled sample under conditions of limited a priori information using regularization methods is considered in the paper. A change in the state of the surface of optical elements significantly increases the light scattering, so it is necessary regularly to take into account the amount of scattered light in the light flux reflected from the surface and the measured and comparative samples. The conversion of the light flux into the electrical signal of the photodetector can also occur non-linearly. This requires the development of such measurement method that considers both the scattered light and various non-linearities of the measuring circuit. It is known that the mathematical model of measurement is described by the Fredholm integral equation of the first kind, its solution under the accepted assumptions is recommended to be sought in the form of a matrix equation using a recurring procedure. With regard to the fact that the estimation of the initial data errors in the equation is associated with certain difficulties, in the case under consideration, it is advisable to determine the regularization parameter based on the method of quasi-optimality. A characteristic disadvantage of the known analytical and experimental methods for determining the hardware function of a spectral device is that they do not take into account its change during operation. Since the actual hardware function of the device usually differs from the Gaussian curve, the use of hardware functions in the form of analytical dependencies does not always give the desired result, and for experimental methods, special equipment with a quasi-monochromatic radiation source is required. An algorithm for restoring the hardware function of a spectral device based on regular methods for solving ill-posed problems is proposed. The estimation of the matrix operator of the hardware function is proposed to be obtained on the basis of explicit least squares estimation algorithms. The expediency of choosing a value of the regularization parameter that minimizes the accepted characteristic of the accuracy of the solution is indicated.

Estimated Biomass Loss Caused by the Vaia Windthrow in Northern Italy: Evaluation of Active and Passive Remote Sensing Options

Windstorms are a major disturbance factor for European forests. The 2018 Vaia storm, felled large volumes of timber in Italy causing serious ecological and financial losses. Remote sensing is fundamental for primary assessment of damages and post-emergency phase. An explicit estimation of the timber loss caused by Vaia using satellite remote sensing was not yet undertaken. In this investigation, three different estimates of timber loss were compared in two study sites in the Alpine area: pre-existing local growing stock volume maps based on lidar data, a recent national-level forest volume map, and an novel estimation of AGB values based on active and passive remote sensing. The compared datasets resemble the type of information that a forest manager might potentially find or produce. The results show a significant disagreement in the different biomass estimates, related to the methods used to produce them, the study areas characteristics, and the size of the damaged areas. These sources of uncertainty highlight the difficulty of estimating timber loss, unless a unified national or regional European strategy to improve preparedness to forest hazards is defined. Considering the frequent impacts on forest resources that occurred in the last years in the European Union, remote sensing-based surveys targeting forests is urgent, particularly for the many European countries that still lack reliable forest stocks data.

Closed-Form Differential Hierarchical Demodulator of 2-Source Hierarchical-MAC Channel

This letter addresses a problem of differential demodulation in a 2-source Hierarchical MAC (H-MAC) in the context of a Wireless Physical Layer Network Coded system with Hierarchical Decode and Forward strategy. We assume a parametric H-MAC with known attenuations but unknown phases. Component phases have generally strong impact on the resulting superposed constellation which poses high demands on the accuracy of their estimation. In this letter we propose multiple suboptimal closed-form differential demodulation methods which do not require explicit phase estimation and thus substantially simplify the receiver processing. We analyze and compare their performance. Particularly we measure the resulting hierarchical bit error rate for all proposed demodulation methods for different scenarios of a particular symbol alphabet and channel attenuation values. For comparison we include a reference scenario of a fully coherent demodulation.

Distributed Beam Training for Intelligent Reflecting Surface Enabled Multi-Hop Routing

Intelligent reflecting surface (IRS) is an emerging technology to enhance the spectral and energy efficiency of wireless communications cost-effectively. This letter considers a new multi-IRS aided wireless network where a cascaded line-of-sight (LoS) link is established between the base station (BS) and a remote user by leveraging the multi-hop signal reflection of selected IRSs. As compared to the conventional single-/double-hop IRS system, multi-hop IRS system provides more pronounced path diversity and cooperative passive beamforming gains, especially in the environment with dense obstacles. However, a more challenging joint active/passive beamforming and multi-hop beam routing problem also arises for maximizing the end-to-end channel gain. Furthermore, the number of IRS-associated channel coefficients increases drastically with the number of IRS hops. To tackle the above issues, in this letter we propose a new and efficient beam training based solution by considering the use of practical codebook-based BS/IRS active/passive beamforming without the need of explicit channel estimation. Instead of exhaustively or sequentially searching over all combinations of active and passive beam patterns for each beam route, a distributed beam training scheme is proposed to reduce the complexity, by exploiting the (nearly) time-invariant BS-IRS and inter-IRS channels and the cooperative training among the BS and IRSs’ controllers. Simulation results show that our proposed design achieves the end-to-end channel gain close to that of the sequential beam search, but at a much lower training overhead and complexity.

Use of infrared thermography for the explicit heat flux estimation employing regularized measurements with truncated eigenfunction expansions

This work addresses the experimental validation of the explicit estimation of timewise varying boundary heat fluxes in thermally thin plates employing temperature measurements obtained by infrared thermography. The measurements were regularized with the controlled truncation of the eigenfunction expansions that are employed to overcome the ill-posed nature of the inverse problem. In order to allow for a critical analysis of the obtained estimates, reference results were obtained from Bayesian inference, implemented with the Markov Chain Monte Carlo (MCMC) method and the total variation prior. The good results achieved with the explicit technique, with very little computational effort, demonstrate the feasibility of the developed approach for practical applications.

Probabilistic properties and parametric inference of small variance nonlinear self-stabilizing stochastic differential equations

We consider a process (Xt) solution of a one-dimensional nonlinear self-stabilizing stochastic differential equation, with classical drift term V(α,x) depending on an unknown parameter α, self-stabilizing term Φ(β,x) depending on another unknown parameter β and small noise amplitude ɛ. Self-Stabilization is the effect of including a mean-field interaction in addition to the state-dependent drift. Adding this term leads to a nonlinear or McKean-Vlasov Markov process with transitions depending on the distribution of Xt. We study the probabilistic properties of (Xt) as ɛ tends to 0 and exhibit a Gaussian approximating process for (Xt). Next, we study the estimation of (α,β) from a continuous observation of (Xt,t∈[0,T]). We build explicit estimators using an approximate log-likelihood function obtained from the exact log-likelihood function of a proxi-model. We prove that, for fixed T, as ɛ tends to 0, α can be consistently estimated with rate ɛ−1but notβ. Then, considering ni.i.d. sample paths (Xti,i=1,…,n), we build consistent and asymptotically Gaussian estimators of (α,β) with rates nɛ−1 for α and n for β. Finally, we prove that the statistical experiments generated by (Xt) and the proxi-model are asymptotically equivalent in the sense of the Le Cam Δ-distance both for the continuous observation of one path and for ni.i.d. paths under the condition nɛ→0, which justifies our statistical method.