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One important feature of geography phenomenon or spatial object is that there exists the spatial autocorrelation between them. As for a spatial sampling scheme for crop acreage estimation, the spatial autocorrelation of the sampling units has an important effect on the design of the sampling scheme and the improvement of the sampling efficiency. While the related researches on spatial autocorrelation...
Early information on crops spatial extent is useful from food security standpoint. Growing number of remote sensing satellites are making it easier to map the crop areas in a rapid and cost-effective way. Grape being an commercial fruit crop of India, it is useful to map the grape areas for acreage estimation and monitor its health continuously to get optimum yield. The main contribution of this paper...
This paper presents novel constrained consensus least mean square (cLMS) algorithms with adjustable constraints that can improve the learning performance of distributed estimation problems in sensor networks by exploiting the spatial diversity of the estimates. For the first algorithm, the constraint vectors are adjusted by combining the components of the estimate orthogonal to its neighbor estimates...
Fast and accurate measuring depth of anesthesia (DoA) during heavy surgeries (e.g. orthopedic or neurosurgery) is still a challenge. Late estimation of DoA in critical conditions may lead to severe effects such as a comma or conscious state, and jeopardize patient's life accordingly. Recently, several attempts have been made to elicit an accurate DoA index by analyzing electroencephalogram (EEG) signals,...
Granger causality approaches have been widely used to estimate effective connectivity in complex dynamic systems. These techniques are based on the building of predictive models which not only depend on a proper selection of the predictive vectors size but also on the chosen class of regression functions. The question addressed in this paper is the estimation of the model order in the computation...
Sparse arrays can generate a larger aperture than traditional uniform linear arrays (ULA) and offer enhanced degrees-of-freedom (DOFs) which can be exploited in both beamforming and direction-of-arrival (DOA) estimation. One class of sparse arrays is the coprime array, composed of two uniform linear subarrays which yield an effective difference co-array with higher number of DOFs. In this work, we...
This paper proposes a novel algorithm for Time of Arrival (ToA) estimation in Orthogonal Frequency Division Multiplexing (OFDM) systems. The algorithm performs the estimation starting from the channel frequency samples, in a fully opportunistic way when some known reference signals are already available for operations different from ToA estimation itself. The developed solution, named Difference-Based...
This study focuses on how the cumulative excess returns (CER) of corporate bonds in the Japanese market respond to simultaneous publications of current net earnings and management’s net earnings forecast. The estimation results using a regression model generalizing the interaction of the current net earnings and management’s net earnings forecast show that the CER of corporate bonds is influenced...
In this paper, a precise and intelligent deconvolution process is developed in order to further extend its applications in discovering bearing faults and characteristics. A new index, harmonics-to-noise ratio (HNR), is introduced to extract the period. At the same time by combining HNR with the characteristic of kurtosis which is sensitive to the impulsivity of the fault signal, a novel deconvolution...
This paper investigates the problem of glacier flow estimation using Synthetic Aperture Radar (SAR) image data. Our motivation is to exploit a weighted graph model constructed from characteristic points (i.e. keypoints) to measure the displacement vectors located at their positions. In fact, characteristic points are capable of capturing the image's radiometric and contextual information. Then, by...
Flood maps are indispensable to regional prioritization and effective resource distribution, and are required by policy makers, insurance firms, and disaster-relief agencies. SAR (Synthetic Aperture Radar) image classification is widely used for flood mapping, although the utilization of image texture has not been well explored. This study proposes a novel SAR-based flood mapping technique that uses...
This paper presents a ship-detection study with Synthetic Aperture Radar (SAR) images acquired at two different frequencies: X- and C-band. The detection procedure relies on a novel algorithm based on the likelihood functions of both canonical ship target and sea clutter. Spaceborne images were acquired over the same area in the Solent Channel in UK at approximately the same time on the 7th June 2016...
Recently, multi-source remote sensing data and their derived features such as vegetation indices, texture metrics have been frequently applied to quantitatively estimate forest above-ground biomass (AGB). However, it is still challenging to efficiently select the optimal features for modeling the forest AGB. In this study, a fast, efficient and automatic method has been proposed, called as k-nearest...
Red tides have frequently occurred in the Arabian Gulf in the last three decades, which have caused massive fish mortalities, degradation of the water quality, and interruption the operation of the coastal desalination plants. Several studies have been undertaken to monitor this phenomenon from space by developing ocean color models that can quantify red tide by Chlorophyll a (Chl a) parameter. However,...
A lot of spectral indices were developed based on the relationship between the spectral reflectance of the upper leaf surface and chlorophyll content. But the lower leaf surface may influence reflectance spectra because of canopy structure or the inclination of leaves. The results of this study showed that structural differences of the two leaf sides may result in differences in reflectance and spectral...
Most of the decision fusion techniques developed for the remote sensing applications have the drawback of assuming the conditional independence between the classification results, whereas, usually the correlation exists due to the same measuring instrument or same area under study. Fusion of Correlated Probabilities (FCP) method has a potential to deal with conditional dependence only for two data...
Leaf Area Index (LAI) is an important parameter in describing leaf density and canopy structure of plants, which could be estimated by remote sensing data conveniently by empirical methods with vegetation indexes. Due to the saturation of Normalized Difference Vegetation Index (NDVI) in high LAI value, Inverted Difference Vegetation Index (IDVI), which possessed a robust insensitivity on leaf water...
The traditional damping controller design method is difficult to adapt to the complex operating conditions, and may lead to poor damping effect due to improper control signal selection. A wide-area multi-HVDC (High Voltage Direct Current) damping controller design method is proposed based on auto regressive exogenous power system state space model identification. First, a low-order power system state...
This paper presents a computational framework for accurately estimating the disparity map of plenoptic images. The proposed framework is based on the variational principle and provides intrinsic sub-pixel precision. The light-field motion tensor introduced in the framework allows us to combine advanced robust data terms as well as provides explicit treatments for different color channels. A warping...
We propose a diffusion expectation-maximization algorithm with adaptive combiner for distributed estimation over sensor networks. Due to the spatial distribution of the nodes, variation of node profile across the network is a common phenomena in real applications. The unreliable nodes exist and provide inaccurate estimates, which may be caused by high levels of noise or malicious attacks. Instead...
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