Evaluation of NASA Deep Blue/SOAR Aerosol Retrieval Algorithms Applied to AVHRR Measurements

The Deep Blue (DB) and Satellite Ocean Aerosol Retrieval (SOAR) algorithms have previously been applied to observations from sensors like the Moderate Resolution Imaging Spectroradiometers (MODIS) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) to provide records of mid visible aerosol optical depth (AOD) and related quantities over land and ocean surfaces respectively. Recently, DB and SOAR have also been applied to Advanced Very High Resolution Radiometer (AVHRR) observations from several platforms (NOAA11, NOAA14, and NOAA18), to demonstrate the potential for extending the DB and SOAR AOD records. This study provides an evaluation of the initial version (V001) of the resulting AVHRR-based AOD data set, including validation against Aerosol Robotic Network (AERONET)and ship-borne observations, and comparison against both other AVHRR AOD records and MODIS/SeaWiFS products at select long-term AERONET sites. Although it is difficult to distil error characteristics into a simple expression,the results suggest that one standard deviation confidence intervals on retrieved AOD of plus or minus (0.03+15 %) over water and plus or minus (0.05+25 %) over land represent the typical level of uncertainty, with a tendency towards negative biases in high-AOD conditions, caused by a combination of algorithmic assumptions and sensor calibration issues. Most of the available validation data are for NOAA18 AVHRR, although performance appear to be similar for the NOAA11 and NOAA14 sensors as well

Characterization and Testing of the Passive Magnetic Attitude Control System for the 3U AstroBio CubeSat

AstroBio CubeSat is a mission funded by the Italian Space Agency aimed at validating novel lab-on-chip technology, that would enable the use of micro- and nanosatellites as autonomous orbiting laboratories for research in astrobiology. This 3U CubeSat is equipped with a passive magnetic attitude control system (PMACS), including permanent magnets and hysteresis strips, which allows for stabilizing the spacecraft with the longitudinal axis in the direction of the geomagnetic field vector. This work presents the process followed for the experimental characterization of the system, performed on the engineering unit of the satellite by using a Helmholtz cage facility and a spherical air-bearing to recreate environmental conditions similar to the ones experienced during the orbital motion. The hysteresis strips are characterized starting from the determination of the hysteresis loop, from which the energy dissipation per cycle and the apparent magnetic permeability are extracted. Tests performed by using the Helmholtz cage and the air-bearing facility allows for further investigating the damping torque produced by the PMACS and validating the abovementioned parameters. Numerical analysis is then used to select the number of permanent magnets which allows for achieving a pointing accuracy within an error of 10° within 24 h from the deployment. The analysis of the flight data supports the results obtained from the experimental test campaigns, confirming the effectiveness of the proposed methods and of the PMACS design

Retrieving Near-Global Aerosol Loading over Land and Ocean from AVHRR

The spaceborne AVHRR sensors have provided a data record approaching 40 years, which is a crucial asset for studying the long-term trends of aerosol properties on both a global and regional basis. However, due to the limitations on its channels and information content, aerosol optical depth (AOD) data from AVHRR over land are still largely lacking. In this paper, we describe a new physics-based algorithm to retrieve global aerosol properties over both land and ocean from AVHRR for the first time. The over-land algorithm is an extension of our SeaWiFSMODIS Deep Blue algorithm, while a simplified version of the Satellite Ocean Aerosol Retrieval (SOAR) algorithm is used over ocean. We compare the retrieved AVHRR AOD values with those from MODIS collection 6 aerosol products on a daily and seasonal basis, and find in general good agreement between the two. For the satellites with equatorial crossing times within two hours of solar noon, the spatial coverage of the AVHRR aerosol product is comparable to that of MODIS, except over very bright arid regions (such as the Sahara and deserts in the Arabian Peninsula), where the underlying surface reflectance at 630 nm reaches the critical surface reflectance. Based upon comparisons of the AVHRR AOD against the AERONET data, the preliminary results indicate that the expected error is around +/-(0.03+15%) over ocean and +/-(0.05+25%) over land for this first version of the AVHRR aerosol products. Consequently, these new AVHRR aerosol products can contribute important building blocks for constructing a consistent long-term data record for climate studies

Continuous Interaction with a Virtual Human

Attentive Speaking and Active Listening require that a Virtual Human be capable of simultaneous perception/interpretation and production of communicative behavior. A Virtual Human should be able to signal its attitude and attention while it is listening to its interaction partner, and be able to attend to its interaction partner while it is speaking – and modify its communicative behavior on-the-fly based on what it perceives from its partner. This report presents the results of a four week summer project that was part of eNTERFACE’10. The project resulted in progress on several aspects of continuous interaction such as scheduling and interrupting multimodal behavior, automatic classification of listener responses, generation of response eliciting behavior, and models for appropriate reactions to listener responses. A pilot user study was conducted with ten participants. In addition, the project yielded a number of deliverables that are released for public access

Investigating behavioural and computational approaches for defining imprecise regions

People often communicate with reference to informally agreedplaces, such as “the city centre”. However, views of the spatial extent of such areas may vary, resulting in imprecise regions. We compare perceptions of Sheffield’s City Centre from a street survey to extents derived from various web-based sources. Such automated approaches have advantages of speed, cost and repeatability. We show that footprints from web sources are often in concordance with models derived from more labour-intensive methods. Notable exceptions however were found with sources advertising or selling residential property. Agreement between sources was measured by aggregating them to identify locations of consensus

NeuroPlace: categorizing urban places according to mental states

Urban spaces have a great impact on how people’s emotion and behaviour. There are number of factors that impact our brain responses to a space. This paper presents a novel urban place recommendation approach, that is based on modelling in-situ EEG data. The research investigations leverages on newly affordable Electroencephalogram (EEG) headsets, which has the capability to sense mental states such as meditation and attention levels. These emerging devices have been utilized in understanding how human brains are affected by the surrounding built environments and natural spaces. In this paper, mobile EEG headsets have been used to detect mental states at different types of urban places. By analysing and modelling brain activity data, we were able to classify three different places according to the mental state signature of the users, and create an association map to guide and recommend people to therapeutic places that lessen brain fatigue and increase mental rejuvenation. Our mental states classifier has achieved accuracy of (%90.8). NeuroPlace breaks new ground not only as a mobile ubiquitous brain monitoring system for urban computing, but also as a system that can advise urban planners on the impact of specific urban planning policies and structures. We present and discuss the challenges in making our initial prototype more practical, robust, and reliable as part of our on-going research. In addition, we present some enabling applications using the proposed architecture

The MATCH Corpus: A Corpus of Older and Younger Users' Interactions With Spoken Dialogue Systems.

We present the MATCH corpus, a unique data set of 447 dialogues in which 26 older and 24 younger adults interact with nine different spoken dialogue systems. The systems varied in the number of options presented and the confirmation strategy used. The corpus also contains information about the users’ cognitive abilities and detailed usability assessments of each dialogue system. The corpus, which was collected using a Wizard-of-Oz methodology, has been fully transcribed and annotated with dialogue acts and ‘‘Information State Update’’ (ISU) representations of dialogue context. Dialogue act and ISU annotations were performed semi-automatically. In addition to describing the corpus collection and annotation, we present a quantitative analysis of the interaction behaviour of older and younger users and discuss further applications of the corpus. We expect that the corpus will provide a key resource for modelling older people’s interaction with spoken dialogue systems

An Ensemble Analysis of Electromyographic Activity during Whole Body Pointing with the Use of Support Vector Machines

We explored the use of support vector machines (SVM) in order to analyze the ensemble activities of 24 postural and focal muscles recorded during a whole body pointing task. Because of the large number of variables involved in motor control studies, such multivariate methods have much to offer over the standard univariate techniques that are currently employed in the field to detect modifications. The SVM was used to uncover the principle differences underlying several variations of the task. Five variants of the task were used. An unconstrained reaching, two constrained at the focal level and two at the postural level. Using the electromyographic (EMG) data, the SVM proved capable of distinguishing all the unconstrained from the constrained conditions with a success of approximately 80% or above. In all cases, including those with focal constraints, the collective postural muscle EMGs were as good as or better than those from focal muscles for discriminating between conditions. This was unexpected especially in the case with focal constraints. In trying to rank the importance of particular features of the postural EMGs we found the maximum amplitude rather than the moment at which it occurred to be more discriminative. A classification using the muscles one at a time permitted us to identify some of the postural muscles that are significantly altered between conditions. In this case, the use of a multivariate method also permitted the use of the entire muscle EMG waveform rather than the difficult process of defining and extracting any particular variable. The best accuracy was obtained from muscles of the leg rather than from the trunk. By identifying the features that are important in discrimination, the use of the SVM permitted us to identify some of the features that are adapted when constraints are placed on a complex motor task