Physical interpretation of the correlation between multi-angle spectral data and canopy height

Recent empirical studies have shown that multi-angle spectral data can be useful for predicting canopy height, but the physical reason for this correlation was not understood. We follow the concept of canopy spectral invariants, specifically escape probability, to gain insight into the observed correlation. Airborne Multi-Angle Imaging Spectrometer (AirMISR) and airborne Laser Vegetation Imaging Sensor (LVIS) data acquired during a NASA Terrestrial Ecology Program aircraft campaign underlie our analysis. Two multivariate linear regression models were developed to estimate LVIS height measures from 28 AirMISR multi-angle spectral reflectances and from the spectrally invariant escape probability at 7 AirMISR view angles. Both models achieved nearly the same accuracy, suggesting that canopy spectral invariant theory can explain the observed correlation. We hypothesize that the escape probability is sensitive to the aspect ratio (crown diameter to crown height). The multi-angle spectral data alone therefore may not provide enough information to retrieve canopy height globally.

Autonomous ship collision free trajectory navigation and control algorithms

A new autonomous ship collision free (ASCF) trajectory navigation and control system has been introduced with a new recursive navigation algorithm based on analytic geometry and convex set theory for ship collision free guidance. The underlying assumption is that the geometric information of ship environment is available in the form of a polygon shaped free space, which may be easily generated from a 2D image or plots relating to physical hazards or other constraints such as collision avoidance regulations. The navigation command is given as a heading command sequence based on generating a way point which falls within a small neighborhood of the current position, and the sequence of the way points along the trajectory are guaranteed to lie within a bounded obstacle free region using convex set theory. A neurofuzzy network predictor which in practice uses only observed input/output data generated by on board sensors or external sensors (or a sensor fusion algorithm), based on using rudder deflection angle for the control of ship heading angle, is utilised in the simulation of an ESSO 190000 dwt tanker model to demonstrate the effectiveness of the system.

A wind-tunnel study of flow distortion at a meteorological sensor on top of the BT Tower, London, UK

High quality wind measurements in cities are needed for numerous applications including wind engineering. Such data-sets are rare and measurement platforms may not be optimal for meteorological observations. Two years' wind data were collected on the BT Tower, London, UK, showing an upward deflection on average for all wind directions. Wind tunnel simulations were performed to investigate flow distortion around two scale models of the Tower. Using a 1:160 scale model it was shown that the Tower causes a small deflection (ca. 0.5°) compared to the lattice on top on which the instruments were placed (ca. 0–4°). These deflections may have been underestimated due to wind tunnel blockage. Using a 1:40 model, the observed flow pattern was consistent with streamwise vortex pairs shed from the upstream lattice edge. Correction factors were derived for different wind directions and reduced deflection in the full-scale data-set by <3°. Instrumental tilt caused a sinusoidal variation in deflection of ca. 2°. The residual deflection (ca. 3°) was attributed to the Tower itself. Correction of the wind-speeds was small (average 1%) therefore it was deduced that flow distortion does not significantly affect the measured wind-speeds and the wind climate statistics are reliable.

Visible-near infrared spectroscopy sensor for predicting curd and whey composition during cheese processing

The potential of visible-near infrared spectra, obtained using a light backscatter sensor, in conjunction with chemometrics, to predict curd moisture and whey fat content in a cheese vat was examined. A three-factor (renneting temperature, calcium chloride, cutting time), central composite design was carried out in triplicate. Spectra (300–1,100 nm) of the product in the cheese vat were captured during syneresis using a prototype light backscatter sensor. Stirring followed upon cutting the gel, and samples of curd and whey were removed at 10 min intervals and analyzed for curd moisture and whey fat content. Spectral data were used to develop models for predicting curd moisture and whey fat contents using partial least squares regression. Subjecting the spectral data set to Jack-knifing improved the accuracy of the models. The whey fat models (R = 0.91, 0.95) and curd moisture model (R = 0.86, 0.89) provided good and approximate predictions, respectively. Visible-near infrared spectroscopy was found to have potential for the prediction of important syneresis indices in stirred cheese vats.

Communication mechanisms and middleware for distributed video surveillance

A new generation of advanced surveillance systems is being conceived as a collection of multi-sensor components such as video, audio and mobile robots interacting in a cooperating manner to enhance situation awareness capabilities to assist surveillance personnel. The prominent issues that these systems face are: the improvement of existing intelligent video surveillance systems, the inclusion of wireless networks, the use of low power sensors, the design architecture, the communication between different components, the fusion of data emerging from different type of sensors, the location of personnel (providers and consumers) and the scalability of the system. This paper focuses on the aspects pertaining to real-time distributed architecture and scalability. For example, to meet real-time requirements, these systems need to process data streams in concurrent environments, designed by taking into account scheduling and synchronisation. The paper proposes a framework for the design of visual surveillance systems based on components derived from the principles of Real Time Networks/Data Oriented Requirements Implementation Scheme (RTN/DORIS). It also proposes the implementation of these components using the well-known middleware technology Common Object Request Broker Architecture (CORBA). Results using this architecture for video surveillance are presented through an implemented prototype.

A low-cost wireless temperature sensor: evaluation for use in environmental applications

With a wide range of applications benefiting from dense network air temperature observations but with limitations of costs, existing siting guidelines and risk of damage to sensors, new methods are required to gain a high resolution understanding of the spatio-temporal patterns of urban meteorological phenomena such as the urban heat island or precision farming needs. With the launch of a new generation of low cost sensors it is possible to deploy a network to monitor air temperature at finer spatial resolutions. Here we investigate the Aginova Sentinel Micro (ASM) sensor with a bespoke radiation shield (together < US$150) which can provide secure near-real-time air temperature data to a server utilising existing (or user deployed) Wireless Fidelity (Wi-Fi) networks. This makes it ideally suited for deployment where wireless communications readily exist, notably urban areas. Assessment of the performance of the ASM relative to traceable standards in a water bath and atmospheric chamber show it to have good measurement accuracy with mean errors < ± 0.22 °C between -25 and 30 °C, with a time constant in ambient air of 110 ± 15 s. Subsequent field tests of it within the bespoke shield also had excellent performance (root-mean-square error = 0.13 °C) over a range of meteorological conditions relative to a traceable operational UK Met Office platinum resistance thermometer. These results indicate that the ASM and bespoke shield are more than fit-for-purpose for dense network deployment in urban areas at relatively low cost compared to existing observation techniques.

Data stream mining in ubiquitous environments: state-of-the-art and current directions

In this article, we review the state-of-the-art techniques in mining data streams for mobile and ubiquitous environments. We start the review with a concise background of data stream processing, presenting the building blocks for mining data streams. In a wide range of applications, data streams are required to be processed on small ubiquitous devices like smartphones and sensor devices. Mobile and ubiquitous data mining target these applications with tailored techniques and approaches addressing scarcity of resources and mobility issues. Two categories can be identified for mobile and ubiquitous mining of streaming data: single-node and distributed. This survey will cover both categories. Mining mobile and ubiquitous data require algorithms with the ability to monitor and adapt the working conditions to the available computational resources. We identify the key characteristics of these algorithms and present illustrative applications. Distributed data stream mining in the mobile environment is then discussed, presenting the Pocket Data Mining framework. Mobility of users stimulates the adoption of context-awareness in this area of research. Context-awareness and collaboration are discussed in the Collaborative Data Stream Mining, where agents share knowledge to learn adaptive accurate models.

BodyCloud: a SaaS approach for community body sensor networks

Body Sensor Networks (BSNs) have been recently introduced for the remote monitoring of human activities in a broad range of application domains, such as health care, emergency management, fitness and behaviour surveillance. BSNs can be deployed in a community of people and can generate large amounts of contextual data that require a scalable approach for storage, processing and analysis. Cloud computing can provide a flexible storage and processing infrastructure to perform both online and offline analysis of data streams generated in BSNs. This paper proposes BodyCloud, a SaaS approach for community BSNs that supports the development and deployment of Cloud-assisted BSN applications. BodyCloud is a multi-tier application-level architecture that integrates a Cloud computing platform and BSN data streams middleware. BodyCloud provides programming abstractions that allow the rapid development of community BSN applications. This work describes the general architecture of the proposed approach and presents a case study for the real-time monitoring and analysis of cardiac data streams of many individuals.

Sources, sinks, and transatlantic transport of North African dust aerosol: a multimodel analysis and comparison with remote sensing data

This study evaluates model-simulated dust aerosols over North Africa and the North Atlantic from five global models that participated in the Aerosol Comparison between Observations and Models phase II model experiments. The model results are compared with satellite aerosol optical depth (AOD) data from Moderate Resolution Imaging Spectroradiometer (MODIS), Multiangle Imaging Spectroradiometer (MISR), and Sea-viewing Wide Field-of-view Sensor, dust optical depth (DOD) derived from MODIS and MISR, AOD and coarse-mode AOD (as a proxy of DOD) from ground-based Aerosol Robotic Network Sun photometer measurements, and dust vertical distributions/centroid height from Cloud Aerosol Lidar with Orthogonal Polarization and Atmospheric Infrared Sounder satellite AOD retrievals. We examine the following quantities of AOD and DOD: (1) the magnitudes over land and over ocean in our study domain, (2) the longitudinal gradient from the dust source region over North Africa to the western North Atlantic, (3) seasonal variations at different locations, and (4) the dust vertical profile shape and the AOD centroid height (altitude above or below which half of the AOD is located). The different satellite data show consistent features in most of these aspects; however, the models display large diversity in all of them, with significant differences among the models and between models and observations. By examining dust emission, removal, and mass extinction efficiency in the five models, we also find remarkable differences among the models that all contribute to the discrepancies of model-simulated dust amount and distribution. This study highlights the challenges in simulating the dust physical and optical processes, even in the best known dust environment, and stresses the need for observable quantities to constrain the model processes.

Case study on the maintenance of a construction monitoring using USN-based data acquisition

In recent years, there has been an increasing interest in the adoption of emerging ubiquitous sensor network (USN) technologies for instrumentation within a variety of sustainability systems. USN is emerging as a sensing paradigm that is being newly considered by the sustainability management field as an alternative to traditional tethered monitoring systems. Researchers have been discovering that USN is an exciting technology that should not be viewed simply as a substitute for traditional tethered monitoring systems. In this study, we investigate how a movement monitoring measurement system of a complex building is developed as a research environment for USN and related decision-supportive technologies. To address the apparent danger of building movement, agent-mediated communication concepts have been designed to autonomously manage large volumes of exchanged information. In this study, we additionally detail the design of the proposed system, including its principles, data processing algorithms, system architecture, and user interface specifics. Results of the test and case study demonstrate the effectiveness of the USN-based data acquisition system for real-time monitoring of movement operations.

Routing protocols and quality of services for security based applications using wireless video sensor networks

Wireless video sensor networks have been a hot topic in recent years; the monitoring capability is the central feature of the services offered by a wireless video sensor network can be classified into three major categories: monitoring, alerting, and information on-demand. These features have been applied to a large number of applications related to the environment (agriculture, water, forest and fire detection), military, buildings, health (elderly people and home monitoring), disaster relief, area and industrial monitoring. Security applications oriented toward critical infrastructures and disaster relief are very important applications that many countries have identified as critical in the near future. This paper aims to design a cross layer based protocol to provide the required quality of services for security related applications using wireless video sensor networks. Energy saving, delay and reliability for the delivered data are crucial in the proposed application. Simulation results show that the proposed cross layer based protocol offers a good performance in term of providing the required quality of services for the proposed application.

Wireless Sensor Networks: MAC protocols and real time applications

Wireless Senor Networks(WSNs) detect events using one or more sensors, then collect data from detected events using these sensors. This data is aggregated and forwarded to a base station(sink) through wireless communication to provide the required operations. Different kinds of MAC and routing protocols need to be designed for WSN in order to guarantee data delivery from the source nodes to the sink. Some of the proposed MAC protocols for WSN with their techniques, advantages and disadvantages in the terms of their suitability for real time applications are discussed in this paper. We have concluded that most of these protocols can not be applied to real time applications without improvement

Big data architecture for pervasive healthcare: a literature review

Pervasive healthcare aims to deliver deinstitutionalised healthcare services to patients anytime and anywhere. Pervasive healthcare involves remote data collection through mobile devices and sensor network which the data is usually in large volume, varied formats and high frequency. The nature of big data such as volume, variety, velocity and veracity, together with its analytical capabilities com-plements the delivery of pervasive healthcare. However, there is limited research in intertwining these two domains. Most research focus mainly on the technical context of big data application in the healthcare sector. Little attention has been paid to a strategic role of big data which impacts the quality of healthcare services provision at the organisational level. Therefore, this paper delivers a conceptual view of big data architecture for pervasive healthcare via an intensive literature review to address the aforementioned research problems. This paper provides three major contributions: 1) identifies the research themes of big data and pervasive healthcare, 2) establishes the relationship between research themes, which later composes the big data architecture for pervasive healthcare, and 3) sheds a light on future research, such as semiosis and sense-making, and enables practitioners to implement big data in the pervasive healthcare through the proposed architecture.

Segmentation-level fusion for iris recognition

This paper investigates the potential of fusion at normalisation/segmentation level prior to feature extraction. While there are several biometric fusion methods at data/feature level, score level and rank/decision level combining raw biometric signals, scores, or ranks/decisions, this type of fusion is still in its infancy. However, the increasing demand to allow for more relaxed and less invasive recording conditions, especially for on-the-move iris recognition, suggests to further investigate fusion at this very low level. This paper focuses on the approach of multi-segmentation fusion for iris biometric systems investigating the benefit of combining the segmentation result of multiple normalisation algorithms, using four methods from two different public iris toolkits (USIT, OSIRIS) on the public CASIA and IITD iris datasets. Evaluations based on recognition accuracy and ground truth segmentation data indicate high sensitivity with regards to the type of errors made by segmentation algorithms.

Evidence of chromosome fusion in Gymnotus sylvius Albert & Fernandes-Matoli, 1999 (Teleostei: Gymnotiformes) detected by telomeric probes and R-banding

Gymnotus cf. carapo and Gynznotus sylvius are two fish species inhabiting the Upper Parana River Basin, presenting respectively 2n =54 and 2n = 40 chromosomes. In the present cytogenetic analysis, R-banding and telomere-sequence hybridization were carried out in order to determine the possible relationship between the karyotipes of these two species. Incorporation bands (R-bands) obtained for the two species allowed the identification of chromosome similarities, showing to be an usefull alternative to the G-banding methods, which fail in producing satisfying results in most of analyzed fish species. This approach, associated with the hybridization of telomeric sequences, permited to identify chromosomal rearrangements that could be used as indicators of karyotypic evolution within the group. In the present case, telomeric sequences were detected in the centromeric region of two metacentric chromosome pairs of Gymnotus sylvius. The results obtained after hybridization with the telomere sequences, coupled with the chromosome homeologies detected by R-banding, showed that G. cf carapo and G. sylvius should present a common ancestor, and this may also be corroborated by the similarities found in three chromosome pairs, that seem to have been conserved during the evolution of the two species. Based on the data here presented we propose that G. sylvius may have undergone a recent process of chromosome fusion that resulted in the diminution of its chromosome number.