Image processing for smarter browsing of ocean color data products: investigating algal blooms

Remote sensing technology continues to play a significant role in the understanding of our environment and the investigation of the Earth. Ocean color is the water hue due to the presence of tiny plants containing the pigment chlorophyll, sediments, and colored dissolved organic material and so can provide valuable information on coastal ecosystems. We propose to make the browsing of Ocean Color data more efficient for users by using image processing techniques to extract useful information which can be accessible through browser searching. Image processing is applied to chlorophyll and sea surface temperature images. The automatic image processing of the visual level 1 and level 2 data allow us to investigate the occurrence of algal blooms. Images with colors in a certain range (red, orange etc.) are used to address possible algal blooms and allow us to examine the seasonal variation of algal blooms in Europe (around Ireland and in the Baltic Sea). Yearly seasonal variation of algal blooms in Europe based on image processing for smarting browsing of Ocean Color are presented

Aggregating multiple body sensors for analysis in sports

Real time monitoring of the wellness of sportspersons, during their sporting activity and training, is important in order to maximise performance during the sporting event itself and during training, as well as being important for the health of the sportsperson overall. We have combined a suite of common, off-the-shelf sensors with specialist body sensing technology we are developing ourselves and constructed a software system for recording, analysing and presenting sensed data gathered from a single player during a sporting activity, a football match. We gather readings for heart rate, galvanic skin response, motion, heat flux, respiration, and location (GPS) using on-body sensors, while simultaneously tracking player activity using a combination of a playercam video and pitch-wide video recording. We have aggregated all this sensed data into a single overview of player performance and activity which can be reviewed, post-event. We are currently working on integrating other non-invasive methods for real-time on-body monitoring of sweat electrolytes and pH via a textile-based sweat sampling and analysis platform. Our work is heading in two directions; firstly from post-event data aggregation to real-time monitoring, and secondly, to convert raw sensor readings into performance indicators that are meaningful to practitioners in the field

Semi-Analytic Techniques for Solving Quasi-Normal Modes

In this chapter, we discuss an approach to obtaining black hole quasi-normal modes known as the asymptotic iteration method, which was initially developed in mathematics as a new way to solve for eigenvalues in differential equations. Furthermore, we demonstrate that the asymptotic iteration method allows one to also solve for the radial quasi-normal modes on a variety of black hole spacetimes for a variety of perturbing fields. A specific example for Dirac fields in a general dimensional Schwarzschild black hole spacetime is given, as well as for spin-3/2 field quasi-normal modes

Sensor node localisation using a stereo camera rig

In this paper, we use stereo vision processing techniques to detect and localise sensors used for monitoring simulated environmental events within an experimental sensor network testbed. Our sensor nodes communicate to the camera through patterns emitted by light emitting diodes (LEDs). Ultimately, we envisage the use of very low-cost, low-power, compact microcontroller-based sensing nodes that employ LED communication rather than power hungry RF to transmit data that is gathered via existing CCTV infrastructure. To facilitate our research, we have constructed a controlled environment where nodes and cameras can be deployed and potentially hazardous chemical or physical plumes can be introduced to simulate environmental pollution events in a controlled manner. In this paper we show how 3D spatial localisation of sensors becomes a straightforward task when a stereo camera rig is used rather than a more usual 2D CCTV camera

A Large-Deviation Analysis of the Maximum-Likelihood Learning of Markov Tree Structures

The problem of maximum-likelihood (ML) estimation of discrete tree-structured distributions is considered. Chow and Liu established that ML-estimation reduces to the construction of a maximum-weight spanning tree using the empirical mutual information quantities as the edge weights. Using the theory of large-deviations, we analyze the exponent associated with the error probability of the event that the ML-estimate of the Markov tree structure differs from the true tree structure, given a set of independently drawn samples. By exploiting the fact that the output of ML-estimation is a tree, we establish that the error exponent is equal to the exponential rate of decay of a single dominant crossover event. We prove that in this dominant crossover event, a non-neighbor node pair replaces a true edge of the distribution that is along the path of edges in the true tree graph connecting the nodes in the non-neighbor pair. Using ideas from Euclidean information theory, we then analyze the scenario of ML-estimation in the very noisy learning regime and show that the error exponent can be approximated as a ratio, which is interpreted as the signal-to-noise ratio (SNR) for learning tree distributions. We show via numerical experiments that in this regime, our SNR approximation is accurate.Comment: Accepted to the IEEE Transactions on Information Theory on Nov 18, 201

Modelling extreme concentration from a source in a turbulent flow over rough wall

The concentration fluctuations in passive plumes from an elevated and a groundlevel source in a turbulent boundary layer over a rough wall were studied using large eddy simulation and wind tunnel experiment. The predictions of statistics up to second order moments were thereby validated. In addition, the trend of relative fluctuations far downstream for a ground level source was estimated using dimensional analysis. The techniques of extreme value theory were then applied to predict extreme concentrations by modelling the upper tail of the probability density function of the concentration time series by the Generalised Pareto Distribution. Data obtained from both the simulations and experiments were analysed in this manner. The predicted maximum concentration (?0) normalized by the local mean concentration (Cm) or by the local r.m.s of concentration fluctuation (crms), was extensively investigated. Values for ?0/Cm and ?0/crms as large as 50 and 20 respectively were found for the elevated source and 10 and 15 respectively for the ground-level source

COVID-19 Heterogeneity in Islands Chain Environment

As 2021 dawns, the COVID-19 pandemic is still raging strongly as vaccines finally appear and hopes for a return to normalcy start to materialize. There is much to be learned from the pandemic's first year data that will likely remain applicable to future epidemics and possible pandemics. With only minor variants in virus strain, countries across the globe have suffered roughly the same pandemic by first glance, yet few locations exhibit the same patterns of viral spread, growth, and control as the state of Hawai'i. In this paper, we examine the data and compare the COVID-19 spread statistics between the counties of Hawai'i as well as examine several locations with similar properties to Hawai'i

Nocardiopsis deserti sp. nov., isolated from a high altitude Atacama Desert soil

The taxonomic status of a Nocardiopsis strain, designated H13T, isolated from a high altitude Atacama Desert soil, was established by using a polyphasic approach. The strain was found to have chemotaxonomic, cultural and morphological characteristics consistent with its classification within the genus Nocardiopsis and formed a well-supported clade in the Nocardiopsis phylogenomic tree together with the type strains of Nocardiopsis alborubida, Nocardiopsis dassonvillei and Nocardiopsis synnematoformans. Strain H13T was distinguished from its closest relatives by low average nucleotide identity (93.2–94.9 %) and in silico DNA–DNA hybridization (52.5–62.4 %) values calculated from draft genome assemblies and by a range of phenotypic properties. On the basis of these results, it is proposed that the isolate be assigned to the genus Nocardiopsis as Nocardiopsis deserti sp. nov. with isolate H13T (=CGMCC 4.7585T=KCTC 49249T) as the type strai