We address the problem of reconstructing a set of points on a line or a loop from their unassigned noisy pairwise distances. When the points lie on a line, the problem is known as the turnpike; when they are on a loop, it is known as the beltway. We approximate the problem by discretizing the domain and representing the $N$ points via an $N$ -hot encoding, which is a density supported on the discretized

It is shown that, in a line-of-sight setting, a receiver equipped with two antennas may approximately null an arbitrary large number of spatial directions to any desired degree, while maintaining the interference-free signal-to-noise ratio, by judiciously adjusting the distance between the antenna elements, provided that the spacing can be made sufficiently large. The main theoretical result builds

In the field of offshore oil slicks at the sea surface, radar or optical imagery can provide much useful information. Regarding optical imagery, detection relies on the differences between the reflectance of water alone and oil-covered water. As soon as the thickness of the oil layer is large enough to induce a spectral difference between the reflectance of water and oil-covered water, spectral indices

Land use/land cover (LULC) change and climate change are thought to be closely related and mutually influential, especially in contexts where land is converted for urban expansion or agriculture. We represent a first attempt to specify the relationship between LULC change and dryness in a region of Vietnam that is profoundly affected by climate change. Using the temperature–vegetation dryness index

We address unsupervised audio-visual speech enhancement based on variational autoencoders (VAEs), where the prior distribution of clean speech spectrogram is simulated using an encoder-decoder architecture. At enhancement (test) time, the trained generative model (decoder) is combined with a noise model whose parameters need to be estimated. The initialization of the latent variables describing the

In this study, we propose a novel extended target tracking algorithm which is capable of representing the extent of dynamic objects as an ellipsoid with a time-varying orientation angle. A diagonal positive semi-definite matrix is defined to model objects’ extent within the random matrix framework where the diagonal elements have inverse-Gamma priors. The resulting measurement equation is non-linear

Range profiling refers to the measurement of target response along the radar slant range. It plays an important role in automatic target recognition. In this paper, we consider the design of transmit waveform to improve the range profiling performance of radar systems. Two design metrics are adopted for the waveform optimization problem: one is to maximize the mutual information between the received

The chemical sensing process can be understood as the physical process consisting of binding and unbinding reactions on the sensing surface. The random nature of the reactions lead to fluctuations in the response of a sensor. The response of the sensor is modeled in the form of a stochastic differential equation (SDE). The model is developed by formulating the binding and unbinding reactions in the

Molecular Communication (MC) is a promising micro-scale technology that enables wireless connectivity in electromagnetically challenged conditions. The signal processing approaches in MC are different from conventional wireless communications as molecular signals suffer from severe inter-symbol interference (ISI) and signal-dependent counting noise due to the stochastic diffusion process of the information

Graph signal processing (GSP) is a field that deals with data residing on irregular domains, i.e. graph signals. In this field, the graph filter bank is one of the most important developments, owing to its ability to provide multiresolution analysis of graph signals. However, most of the current research on graph filter bank focuses on static graph signals. The research does not exploit the temporal

Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua has been in operation providing continuous global observations for science research and applications since 2002. The long-term stability of thermal emissive bands (TEBs) of Aqua MODIS was monitored through inter-comparisons with measurements by hyperspectral or multi-spectral infrared sensors or through vicarious monitoring over cold targets

Undaria pinnatifida (Harvey) Suringar, native to north-western Asia, is a prolific invasive seaweed species that has established across much of New Zealand, competing and co-existing with native seaweed species. Remote sensing could be used to map both invasive and native seaweeds in New Zealand. The aim of this study is to evaluate the hyperspectral differences (and the wavelengths at which they differ)

Graph signal processing is an emerging field which aims to model processes that exist on the nodes of a network and are explained through diffusion over this structure. Graph signal processing works have heretofore assumed knowledge of the graph shift operator. Our approach is to investigate the question of graph filtering on a graph about which we only know a model. To do this we leverage the theory

Low dimensional nonlinear structure abounds in datasets across computer vision and machine learning. Kernelized matrix factorization techniques have recently been proposed to learn these nonlinear structures for denoising, classification, dictionary learning, and missing data imputation, by observing that the image of the matrix in a sufficiently large feature space is low-rank. However, these nonlinear

We consider the problem of inferring the conditional independence graph (CIG) of high-dimensional Gaussian vectors from multi-attribute data. Most existing methods for graph estimation are based on single-attribute models where one associates a scalar random variable with each node. In multi-attribute graphical models, each node represents a random vector. In this paper, we present a sparse-group lasso

This paper considers a layered coding framework with a relaxed hierarchical structure, tailored to serve content at multiple quality levels, where a key challenge is the conflict between coding optimality at each layer and efficient use of storage and networking resources. The prevalent approach of storing and transmitting independent copies for each quality level, is highly wasteful in resources.

Smoothness priors and quadratic variation (QV) regularization are widely used techniques in many applications ranging from signal and image processing, computer vision, pattern recognition, and many other fields of engineering and science. In this contribution, an extension of such algorithms to band-stop smoothing filters (BSSFs) is investigated. For designing a BSSF, the most important parameters

This paper considers the popular minimax robust hypothesis testing problem—seeking the optimal decision rule with a minimum error probability for the least favorable distributions (LFDs) lying within an uncertainty set, which is characterized by an upper bound on the distance between actual and nominal densities. First, we convert the minimax robust hypothesis testing problem to a convex minimization

In this paper, we study decentralized online stochastic non-convex optimization over a network of nodes. Integrating a technique called gradient tracking in decentralized stochastic gradient descent, we show that the resulting algorithm, GT-DSGD , enjoys certain desirable characteristics towards minimizing a sum of smooth non-convex functions. In particular, for general smooth non-convex functions

The general Cauchy loss (GCL) criterion has been successfully proposed to improve the performance of the Cauchy loss (CL) criterion for linear adaptive filtering in the presence of complex non-Gaussian noise. However, the commonly used adaptive filtering algorithms based on the GCL criterion utilize the stochastic gradient descent (SGD) method to update their weights with slow convergence rate and

Selection of sparse sensors to recover the global signal field is a crucial task in many areas. Most of the existing algorithms tackle this problem by optimizing the surrogates of reconstruction criterion which relies on structural assumptions or low-dimensional models. In this paper, we propose a novel sensor placement method using signal reconstruction error as the cost function, sequentially minimize

The efficient and rapid extraction of information about the growth period of the Zanthoxylum bungeanum maxim is one of the prerequisites for analyzing and mastering its growth trend. The 5-cm high-resolution visible-light red, green, blue (RGB) image of the Zanthoxylum bungeanum growing period is obtained based on the low-altitude aerial photography of a quad-rotor unmanned aerial vehicle (UAV) platform

Control of orbital angular momentum (OAM) offers the potential for increases in control and sensitivity for high-performance microwave systems. EM waves with properties dependent on spatial distribution are said to be “structured.” Control of OAM in microwave systems is an example of a wave structure that exploits EM degrees of freedom, which most conventional systems do not use. OAM is characterized

In this paper, we analyze the accuracy of target localization in multiple-input multiple-output (MIMO) radars with widely-separated antennas. The relative target-antennas geometry plays an important role in target localization. We investigate the optimal placement of transmit and receive antennas for coherent and non-coherent processing, based on maximizing the determinant of the Fisher information

This paper develops a new mathematical framework for denoising in blind two-dimensional (2D) super-resolution upon using the atomic norm. The framework denoises a signal that consists of a weighted sum of an unknown number of time-delayed and frequency-shifted unknown waveforms from its noisy measurements. Moreover, the framework also provides an approach for estimating the unknown parameters in the

Sparse arrays can resolve significantly more scatterers or sources than sensor by utilizing the co-array — a virtual array structure consisting of pairwise differences or sums of sensor positions. Although several sparse array configurations have been developed for passive sensing applications, far fewer active array designs exist. In active sensing, the sum co-array is typically more relevant than

Distributed Bayesian inference provides a full quantification of uncertainty offering numerous advantages over point estimates that autonomous sensor networks are able to exploit. However, fully-decentralized Bayesian inference often requires large communication overheads and low network latency, resources that are not typically available in practical applications. In this paper, we propose a decentralized

The S-transform (ST) is a method of time-frequency analysis of time series. We develop here the Multi-taper S-transform (MTST) for spectral estimation of multi-variate stationary processes through replacing the scalable Gaussian window of the ST with scalable, adjustable orthogonal time-frequency Hermite functions. The MTST is shown to reduce bias and variance of power spectral density (PSD) estimates

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The hyperspectral-image (HSI) noise-reduction step is a very significant preprocessing phase of data-quality enhancement. It has been attracting immense research attention in the remote sensing and image processing domains. Many methods have been developed for HSI restoration, the goal of which is to remove noise from the whole HSI cube simultaneously without considering the spectral–spatial similarity

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The global interaction of CO2 flux was highly dynamic under seasonal and inter-annual variability. Thus, precise estimation of gross primary productivity (GPP) and net photosynthesis (PsnNet) under seasonal variations was important to understand the global carbon cycle and ecosystem response to climate variability. Considering this significance, in our paper, we have conducted a comparative investigation

In this paper, an efficient power allocation (PA) strategy is developed for maneuvering target tracking (MTT) in the collocated MIMO radar. The mechanism of our strategy is to implement the optimal PA based on the prior target maneuvering information in the tracking cycle. The predicted conditional Cramer–Rao lower bound (PC-CRLB) is derived, normalized and adopted as the optimization criterion, since

In this paper, power allocation is examined for the coexistence of a radar and a communication system that employ multicarrier waveforms. We propose two designs for the considered spectrum sharing problem by maximizing the output signal-to-interference-plus-noise ratio (SINR) at the radar receiver while maintaining certain communication throughput and power constraints. The first is a joint design

Non-negative matrix factorization (NMF) has become a well-established class of methods for the analysis of non-negative data. In particular, a lot of effort has been devoted to probabilistic NMF, namely estimation or inference tasks in probabilistic models describing the data, based for example on Poisson or exponential likelihoods. When dealing with time series data, several works have proposed to

Hidden Markov models are widely used for target tracking, where the process and measurement noises are usually modeled as independent Gaussian distributions for mathematical simplicity. However, the independence and Gaussian assumptions do not always hold in practice. For example, in a typical target tracking application, a radar is utilized to track a non-cooperative target. Measurement noise is correlated

Time-frequency (TF) representations of time series are intrinsically subject to the boundary effects. As a result, the structures of signals that are highlighted by the representations are garbled when approaching the boundaries of the TF domain. In this paper, for the purpose of real-time TF information acquisition of nonstationary oscillatory time series, we propose a numerically efficient approach

The problem of identification of nonstationary stochastic processes (systems or signals) is considered and a new class of identification algorithms, combining the basis functions approach with local estimation technique, is described. Unlike the classical basis function estimation schemes, the proposed regularized local basis function estimators are not used to obtain interval approximations of the

Terra and Aqua MODIS have successfully operated for more than 20 and 18 years, respectively, and far exceeded their designed lifetimes of 6 years. MODIS has 36 spectral bands, among which 16 are the thermal emissive bands (TEB) covering a wavelength range from 3.75 to 14.24 μm. Observations from the MODIS TEB have been used to generate a number of data products, such as surface/cloud/atmospheric temperatures

Detecting and monitoring surface water has received much attention in recent decades. Surface water is one of the most critical water resources for both human and ecological systems. Remote sensing technology has made it possible to have accurate and frequent updates of surface water. We propose a remote sensing multisensor fusion system using optical data including Landsat-8 and Sentinel-2 and RADAR

Windthrows induced by strong winds pose a major threat to both transport infrastructure and road users. Therefore, an exposure analysis of trees along the federal trunk road network was carried out and exemplarily applied for the federal state of North Rhine-Westphalia, Germany. The aim of this project was the development of a GIS-based method on the basis of freely accessible high-resolution airborne

In this paper, we analyze the performance guarantee of the orthogonal least squares (OLS) algorithm expressed in terms of the restricted isometry property (RIP). We show the optimality of the proposed guarantee by showing that there exists an example for which OLS fails to work when the proposed condition is violated. In addition, we show that if the columns of a sampling matrix are $\ell _{2}$ -normalized

The Orbita hyperspectral satellite (OHS) is the first commercial hyperspectral satellite in China that completed launching and networking. It can collect world-class hyperspectral data and obtain aerial hyperspectral imagery with 32 bands covering the spectrum range from 400 to 1000 nm at a 10-m resolution, which are of great significance for the quantitative analyses of remote sensing and refined

Semantic segmentation is an important and foundational task in the application of high-resolution remote sensing images (HRRSIs). However, HRRSIs feature large differences within categories and minor variances across categories, posing a significant challenge to the high-accuracy semantic segmentation of HRRSIs. To address this issue and obtain powerful feature expressiveness, a deep conditional generative

The imbalance between in-phase (I) and quadrature (Q) branches of quadrature down-conversion receivers creates interference between the mirror frequencies in baseband. We introduce two new adaptive algorithms for compensating frequency-dependent IQ imbalance in orthogonal frequency-division multiplexing (OFDM) systems. The proposed algorithms utilize frequency-domain observations to update filter weights

This paper proposes a framework for optimizing cost functions of orthonormal basis learning problems, such as principal component analysis (PCA), subspace recovery, orthogonal dictionary learning, etc. The optimization algorithm is derived using the majorization-minimization framework in conjunction with orthogonal projection reformulations to deal with the orthonormality constraint in a systematic

Bark beetle outbreaks are a significant cause of loss of vegetation cover, for which accurate monitoring of forest areas is required to detect and control bark beetle outbreaks as early as possible. A tool that processes aerial imagery from an unmanned aerial vehicle to automatically detect levels of damage caused by bark beetle outbreaks is proposed and evaluated. The true-color RGB flight imagery

This paper addresses adaptive detection of a range distributed target in the presence of dominant heterogeneous clutter, which is (possibly) low-rank and lies in a known subspace, plus Gaussian thermal noise. First, this problem is transformed into an equivalent binary hypothesis test with observations having block-diagonal covariance matrices. Then an invariance analysis is conducted on the resulting

The Split-Radix Fast Fourier Transform has the same low arithmetic complexity as the related Conjugate Pair Fast Fourier Transform. Both transforms have an irregular datapath structure which is straightforwardly expressed only in recursive forms. Furthermore, the conjugate pair variant has a complicated input indexing pattern which requires existing iterative implementations to rely on precomputed

This paper investigates the problem of wideband multiple-input multiple-output radar transmit beampattern synthesis in a spectrally dense environment, and two optimization problems are formulated according to practical consideration. The first one is beampattern matching design under the scenario of known interference spectral band while the second addresses minimum peak sidelobe beampattern design

Wide-sense cyclostationary processes are an important class of non-stationary processes that have a periodic structure in their first- and second-order moments. This article extends the notion of cyclostationarity (in the wide sense) to processes where the mean and covariance functions might depart from strict periodicities and constant amplitudes. Specifically, we propose a novel and flexible class

Urban agglomerations (UA) are the fastest growing regional types during recent years, especially in developing countries. Monitoring and evaluating the eco-environment quality of UA is significant for sustainability. The previous remote sensing models of urban eco-environment are generally based on the vegetation-impervious surface-soil framework, which neglects the air quality in urban areas. We constructed

Stellar map denoising and centroid positioning, which directly determine the postpositioning accuracy of star trackers, are key technologies in stellar map processing. Due to the influence of a complex starry sky background, there is often a large amount of noise in stellar maps, which makes it difficult to accurately locate the stellar centroid. A stellar map processing method based on dark channel

Randomized stepped frequency radar (RSFR) is very attractive for tasks under complex electromagnetic environment. Due to the synthetic high range resolution in RSFRs, a target usually occupies a series of range cells and is called an extended target . To reconstruct the range-Doppler information in a RSFR, previous studies based on sparse recovery mainly exploit the sparsity of the target scene but

Phase retrieval is in general a non-convex and non-linear task and the corresponding algorithms struggle with the issue of local minima. We consider the case where the measurement samples within typically very small and disconnected subsets are coherently linked to each other — which is a reasonable assumption for our objective of antenna measurements. Two classes of measurement setups are discussed