Video-Based Wetting Detection For Blended Fabrics

Miccrorheology of Sickle Hemoglobin Gels

This article describes a general formalism for obtaining spatially localized ("sparse") solutions to a class of problems in mathematical physics, which can be recast as variational optimization problems, such as the important case of Schrödinger's equation in quantum mechanics. Sparsity is achieved by adding an regularization term to the variational principle, which is shown to yield solutions with compact support ("compressed modes"). Linear combinations of these modes approximate the eigenvalue spectrum and eigenfunctions in a systematically improvable manner, and the localization properties of compressed modes make them an attractive choice for use with efficient numerical algorithms that scale linearly with the problem size.

Publication in the conference proceedings of EUSIPCO, Lisbon, Portugal, 2014

Systems driven out of equilibrium can often exhibit behaviour not seen in systems in thermal equilibrium- for example phase transitions in one-dimensional systems. In this talk I will review several `condensation' transitions that occur when a conserved quantity is driven through the system. Although the condensation is spatial, i.e. a finite fraction of the conserved quantity condenses into a small spatial region, useful comparison can be made with usual Bose-Einstein condensation. Amongst some one-dimensional examples I will discuss the `Bus Route Model' where the condensation corresponds to the clustering together of buses moving along a bus-route.

Estimation of signal parameters via rotational invariant techniques (ESPRIT) based high-resolution parameter estimation algorithms in discrete Fourier transform (DFT) beamspace are efficient gridless schemes for hybrid beamforming architectures. Since the number of coherent DFT measurements is equal to the number of radio frequency (RF) chains that is much smaller than the number of antennas, only a small spatial region can be scanned coherently. The number of coherent measurements can be increased by generating virtual RF chains through time-domain-multiplexed (TDM) measurements. However, due to hardware imperfections, random phase jump errors between the TDM measurements degrade the direction of arrival (DoA) estimation accuracy. This paper proposes a new 2-dimensional (2-D) beamspace phase noise parameter compensation (PNPC) algorithm, called 2-D PNPC-DFT-ESPRIT, which jointly estimates the random phase jump errors and the DoAs for uniform rectangular arrays (URAs). The 2-D PNPC-DFT-ESPRIT algorithm modifies the beamspace shift invariance equations for URAs, which consider the contributions of the random phase jump errors and the DoAs. An iterative procedure is developed to estimate both jointly. The simulations show that, even in the presence of random phase jump errors, the 2-D PNPC-DFT-ESPRIT algorithm can achieve a similar performance as 2-D DFT ESPRIT without these phase jump errors.

A miniature laser with linear polarization is a long sought-after component of photonic integrated circuits. In particular, for multiwavelength polarization lasers, it supports simultaneous access to multiple, widely varying laser wavelengths in a small spatial region, which is of great significance for advancing applications such as optical computing, optical storage, and optical sensing. However, there is a trade-off between the size of small-scale lasers and laser performance, and multiwavelength co-gain of laser media and multicavity micromachining in the process of laser miniaturization remain as significant challenges. Herein, room-temperature linearly polarized multiwavelength lasers in the visible and near-infrared wavelength ranges are demonstrated, by fabricating random cavities scattered with silica in an Er-doped Cs2Ag0.4Na0.6In0.98Bi0.02Cl6 double-perovskite quantum dots gain membrane. By regulating the local symmetry and enabling effective energy transfer in nanocrystals, multiwavelength lasers with ultralow thresholds are achieved at room temperature. The maximum degree of polarization reaches 0.89. With their advantages in terms of miniaturization, ultralow power consumption, and adaptability for integration, these lasers offer a prospective light source for future photonic integrated circuits aimed at high-capacity optical applications.

Anomalies in many video surveillance applications have local spatio-temporal signatures, namely, they occur over a small time window or a small spatial region. The distinguishing feature of these scenarios is that outside this spatio-temporal anomalous region, activities appear normal. We develop a probabilistic framework to account for such local spatio-temporal anomalies. We show that our framework admits elegant characterization of optimal decision rules. A key insight of the paper is that if anomalies are local optimal decision rules are local even when the nominal behavior exhibits global spatial and temporal statistical dependencies. This insight helps collapse the large ambient data dimension for detecting local anomalies. Consequently, consistent data-driven local empirical rules with provable performance can be derived with limited training data. Our empirical rules are based on scores functions derived from local nearest neighbor distances. These rules aggregate statistics across spatio-temporal locations & scales, and produce a single composite score for video segments. We demonstrate the efficacy of our scheme on several video surveillance datasets and compare with existing work.

Classifying motor imagery brain signals where the signals are obtained based on imagined movement of the limbs is a major, yet very challenging, step in developing Brain Computer Interfaces (BCIs). Of primary importance is to use less data and computationally efficient algorithms to support real-time BCI. To this end, an algorithm that exploits the sparse characteristics of EEGs is proposed to classify these signals. Different feature vectors are extracted based on the energies in different frequency sub-bands of the Wavelet Packet decomposition of EEG trials recorded only by five electrodes near the sensorimotor cortex. Features from a small spatial region are approximated by a sparse linear combination of few atoms from a multi-class dictionary constructed from the wavelet features of the projected EEG training signals for each class. This is used to classify the signals based on the pattern of their sparse representation using a minimum-residual decision rule. The results obtained from real data demonstrate that the combination of energy and entropy features enables efficient classification of motor imagery EEG trials related to hand and foot movements. Also, the decomposition used is shown to provide a frequency resolution consistent with the existence of different levels within the alpha band.

We consider the problem of finding the minimum sum-rate strategy in cooperative data exchange systems that do not allow packet-splitting (NPS-CDE). In an NPS-CDE system, there are a number of geographically close cooperative clients who send packets to help the others recover a packet set. A minimum sum-rate strategy is the strategy that achieves universal recovery (the situation when all the clients recover the whole packet set) with the the minimal sum-rate (the total number of transmissions). We propose an iterative merging (IM) algorithm that recursively merges client sets based on a lower estimate of the minimum sum-rate and updates to the value of the minimum sum-rate. We also show that a minimum sum-rate strategy can be learned by allocating rates for the local recovery in each merged client set in the IM algorithm. We run an experiment to show that the complexity of the IM algorithm is lower than that of the existing deterministic algorithm when the number of clients is lower than 94.

This article develops a spatial partition-based particle filtering framework to support data assimilation for large-scale wildfire spread simulation effectively. The developed spatial partition-based particle filtering framework breaks the system state and observation data into smaller spatial regions and then carries out localized particle filtering based on these spatial regions. Particle Filters (PFs) hold great promise to support data assimilation for spatial temporal simulations, such as wildfire spread simulation, to achieve more accurate simulation or prediction results. However, PFs face major challenges to work effectively for complex spatial temporal simulations due to the high-dimensional state space of the simulation models, which typically cover large areas and have a large number of spatially dependent state variables. The developed framework exploits the spatial locality property of system state and observation data and employs the divide-and-conquer principle to reduce state dimension and data complexity. This framework is especially developed for a discrete event cellular space model (the wildfire simulation model), which significantly differs from prior works that use numerical models specified by partial differential equations (PDEs) with continuous variables. Within this framework, a two-level automated spatial partitioning method is presented to provide automated and balanced spatial partitions with fewer boundary sensors. The developed framework is applied to a wildfire spread simulation and achieved improved results compared to using standard PF-based data assimilation methods.

Nestedness of faunal assemblages is a multi-scale phenomenon, potentially influenced by a variety of factors. Prior small-scale studies have found freshwater fish species assemblages to be nested along stream courses as a result of either selective colonization or extinction. However, within-stream gradients in temperature and other factors are correlated with the distributions of many fish species and may also contribute to nestedness. At a regional level, strongly nested patterns would require a consistent set of structuring mechanisms across streams, and correlation among species' tolerances of the environmental factors that influence distribution. Thus, nestedness should be negatively associated with the spatial extent of the region analyzed and positively associated with elevational gradients (a correlate of temperature and other environmental factors). We examined these relationships for the freshwater fishes of Virginia. Regions were defined within a spatial hierarchy and included whole river drainages, portions of drainages within physiographic provinces, and smaller subdrainages. In most cases, nestedness was significantly stronger in regions of smaller spatial extent and in regions characterized by greater topographic relief. Analysis of hydrologic variability and patterns of faunal turnover provided no evidence that inter-annual colonization/extinction dynamics contributed to elevational differences in nestedness. These results suggest that, at regional scales, nestedness is influenced by interactions between biotic and abiotic factors, and that the strongest nestedness is likely to occur where a small number of organizational processes predominate, i.show $132#e., over small spatial extents and regions exhibiting strong environmental gradients.

In this paper, energy efficiency (EE) was investigated for a cognitive radio network (CRN) where massive multiple-input multiple-output (MIMO) was combined with femto cells. A heterogeneous network was considered to maximise EE, while massive MIMO was implemented to increase spatial reuse and focus energy into smaller spatial regions. Cooperation between femto cell based relay stations (RS) and a micro cell based secondary base station (SBS) allowed for secondary user (SU) quality-of-service (QoS) requirements to be met and also for control over primary user (PU) interference. Two key issues were addressed by the proposed model, namely improved CRN EE and reduced PU interference. The formulated EE optimisation problem was non-convex and thus converted into a semi-definite problem. Simulation results show that combining massive MIMO at the micro cell level with femto cells, lead to a CRN EE improvement. The addition of femto cells also lead to a reduction in PU interference.

Selective sweeps reduce neutral genetic diversity. In sexual populations, this "hitchhiking" effect is thought to be limited to the local genomic region of the sweeping allele. While this is true in panmictic populations, we find that in spatially extended populations the combined effects of many unlinked sweeps can affect patterns of ancestry (and therefore neutral genetic diversity) across the whole genome. Even low rates of sweeps can be enough to skew the spatial locations of ancestors such that neutral mutations that occur in an individual living outside a small region in the center of the range have virtually no chance of fixing in the population. The fact that nearly all ancestry rapidly traces back to a small spatial region also means that relatedness between individuals falls off very slowly as a function of the spatial distance between them.

Forest cover change and its drivers in the upstream area of the Minjiang River, China

In this paper, we present morphological processing using median operation for small object detection. First, we perform median morphological operation on the gray-scale image with structuring element A and make all scene regions of size equal to the central A's area or larger brighter (for bright objects) or darker (for dark objects) and other regions approximate invariably. Second, we perform median morphological operation on the gray-scale image with larger structuring element B and make all scene regions of size equal to the central B's area or smaller darker (for bright objects) or brighter (for dark objects) and other regions approximate invariably. Third, we calculate the absolute difference of above two outputs. All object regions between the smallest and largest will be enhanced and all background regions will be weakened. Then a simple threshold can extract all objects with some smaller background regions. Finally, those smaller background regions whose areas are smaller than structuring element A can be eliminated by region labeling processing. We find that if (1) contrary to background, the object regions have the signature of discontinuity with their neighbor regions. (2) Each object concentrates relatively in a small region, which can be considered as a homogeneous compact region, our algorithm can achieve satisfactory detection performance.