Laser-to-radar sensing redundancy for resilient perception in adverse environmental conditions

This paper presents an approach to promote the integrity of perception systems for outdoor unmanned ground vehicles (UGV) operating in challenging environmental conditions (presence of dust or smoke). The proposed technique automatically evaluates the consistency of the data provided by two sensing modalities: a 2D laser range finder and a millimetre-wave radar, allowing for perceptual failure mitigation. Experimental results, obtained with a UGV operating in rural environments, and an error analysis validate the approach.

An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions

An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Strain engineering of selective chemical adsorption on monolayer MoS2

Nanomaterials are prone to influence by chemical adsorption because of their large surface to volume ratios. This enables sensitive detection of adsorbed chemical species which, in turn, can tune the property of the host material. Recent studies discovered that single and multi-layer molybdenum disulfide (MoS2) films are ultra-sensitive to several important environmental molecules. Here we report new findings from ab inito calculations that reveal substantially enhanced adsorption of NO and NH3 on strained monolayer MoS2 with significant impact on the properties of the adsorbates and the MoS2 layer. The magnetic moment of adsorbed NO can be tuned between 0 and 1 μB; strain also induces an electronic phase transition between half-metal and metal. Adsorption of NH3 weakens the MoS2 layer considerably, which explains the large discrepancy between the experimentally measured strength and breaking strain of MoS2 films and previous theoretical predictions. On the other hand, adsorption of NO2, CO, and CO2 is insensitive to the strain condition in the MoS2 layer. This contrasting behavior allows sensitive strain engineering of selective chemical adsorption on MoS2 with effective tuning of mechanical, electronic, and magnetic properties. These results suggest new design strategies for constructing MoS2-based ultrahigh-sensitivity nanoscale sensors and electromechanical devices.

Handheld 3D thermography using range sensing and computer vision

This thesis developed a method for real-time and handheld 3D temperature mapping using a combination of off-the-shelf devices and efficient computer algorithms. It contributes a new sensing and data processing framework to the science of 3D thermography, unlocking its potential for application areas such as building energy auditing and industrial monitoring. New techniques for the precise calibration of multi-sensor configurations were developed, along with several algorithms that ensure both accurate and comprehensive surface temperature estimates can be made for rich 3D models as they are generated by a non-expert user.

Toxic state–corporate crimes, Neo-liberalism and green criminology : the hazards and legacies of the oil, chemical and mineral industries

This paper uses examples from the history and practices of multi-national and large companies in the oil, chemical and asbestos industries to examine their legal and illegal despoiling and destruction of the environment and impact on human and non-human life. The discussion draws on the literature on green criminology and state-corporate crime and considers measures and arrangements that might mitigate or prevent such damaging acts. This paper is part of ongoing work on green criminology and crimes of the economy. It places these actions and crimes in the context of a global neo-liberal economic system and considers and critiques the distorting impact of the GDP model of ‘economic health’ and its consequences for the environment.

Estimation of biodiesel properties from chemical composition – An artificial neural network (ANN) approach

Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture and learning algorithm were optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Expression, regulation and putative nutrient-sensing function of taste GPCRs in the heart

G protein-coupled receptors (GPCRs) are critical for cardiovascular physiology. Cardiac cells express >100 nonchemosensory GPCRs, indicating that important physiological and potential therapeutic targets remain to be discovered. Moreover, there is a growing appreciation that members of the large, distinct taste and odorant GPCR families have specific functions in tissues beyond the oronasal cavity, including in the brain, gastrointestinal tract and respiratory system. To date, these chemosensory GPCRs have not been systematically studied in the heart. We performed RT-qPCR taste receptor screens in rodent and human heart tissues that revealed discrete subsets of type 2 taste receptors (TAS2/Tas2) as well as Tas1r1 and Tas1r3 (comprising the umami receptor) are expressed. These taste GPCRs are present in cultured cardiac myocytes and fibroblasts, and are enriched in myocytes, which we corroborated using in situ hybridization. Tas1r1 gene-targeted mice (Tas1r1Cre/Rosa26tdRFP) strikingly recapitulated these data. In vivo taste receptor expression levels were developmentally regulated in the postnatal period. Intriguingly, several Tas2rs were upregulated in cultured rat myocytes and in mouse heart in vivo following starvation. The discovery of taste GPCRs in the heart opens an exciting new field of cardiac research. We predict that these taste receptors may function as nutrient sensors in the heart.

A comparison study on hydrogen sensing performance of MoO3 nanoplatelets coated with a thin layer of Ta2O5 or La2O3

There has been significant interest in developing metal oxide films with high surface area-to-volume ratio nanostructures particularly in substantially increasing the performance of Pt/oxide/semiconductor Schottky-diode gas sensors. While retaining the surface morphology of these devices, they can be further improved by modifying their nanostructured surface with a thin metal oxide layer. In this work, we analyse and compare the electrical and hydrogen-sensing properties of MoO3 nanoplatelets coated with a 4 nm layer of tantalum oxide (Ta2O5) or lanthanum oxide (La2O3). We explain in our study, that the presence of numerous defect traps at the surface (and the bulk) of the thin high-� layer causes a substantial trapping of charge during hydrogen adsorption. As a result, the interface between the Pt electrode and the thin oxide layer becomes highly polarised. Measurement results also show that the nanoplatelets coated with Ta2O5 can enable the device to be more sensitive (a larger voltage shift under hydrogen exposure) than those coated with La2O3.

Hybrid multi-channel cooperative spectrum sensing to satisfy channel target

Spectrum sensing of multiple primary user channels is a crucial function in cognitive radio networks. In this paper we propose an optimal, sensing resource allocation algorithm for multi-channel cooperative spectrum sensing. The channel target is implemented as an objective and constraint to ensure a pre-determined number of empty channels are detected for secondary user network operations. Based on primary user traffic parameters, we calculate the minimum number of primary user channels that must be sensed to satisfy the channel target. We implement a hybrid sensing structure by grouping secondary user nodes into clusters and assign each cluster to sense a different primary user channels. We then solve the resource allocation problem to find the optimal sensing configuration and node allocation to minimise sensing duration. Simulation results show that the proposed algorithm requires the shortest sensing duration to achieve the channel target compared to existing studies that require long sensing and cannot guarantee the target.

Development of a Remote Sensing Core Curriculum submitted for the sessions on educational initiatives in Geoscience and Remote Sensing

A Remote Sensing Core Curriculum (RSCC) development project is currently underway. This project is being conducted under the auspices of the National Center for Geographic Information and Analysis (NCGIA). RSCC is an outgrowth of the NCGIA GIS Core Curriculum project. It grew out of discussions begun at NCGIA, Initiative 12 (I-12): 'Integration of Remote Sensing and Geographic Information Systems'. This curriculum development project focuses on providing professors, teachers and instructors in undergraduate and graduate institutions with course materials from experts in specific subject matter for areas use in the class room.

Remote sensing utility expansion through Earth Portal internet browser

A modular, graphic-oriented Internet browser has been developed to enable non-technical client access to a literal spinning world of information and remotely sensed. The Earth Portal (www.earthportal.net) uses the ManyOne browser (www.manyone.net) to provide engaging point and click views of the Earth fully tessellated with remotely sensed imagery and geospatial data. The ManyOne browser technology use Mozilla with embedded plugins to apply multiple 3-D graphics engines, e.g. ArcGlobe or GeoFusion, that directly link with the open-systems architecture of the geo-spatial infrastructure. This innovation allows for rendering of satellite imagery directly over the Earth's surface and requires no technical training by the web user. Effective use of this global distribution system for the remote sensing community requires a minimal compliance with protocols and standards that have been promoted by NSDI and other open-systems standards organizations.

Internet teaching foundation for the Remote Sensing Core Curriculum program

The Remote Sensing Core Curriculum (RSCC) was initiated in 1993 to meet the demands for a college-level set of resources to enhance the quality of education across national and international campuses. The American Society of Photogrammetry and Remote Sensing adopted the RSCC in 1996 to sustain support of this educational initiative for its membership and collegiate community. A series of volumes, containing lectures, exercises, and data, is being created by expert contributors to address the different technical fields of remote sensing. The RSCC program is designed to operate on the Internet taking full advantage of the World Wide Web (WWW) technology for distance learning. The issues of curriculum development related to the educational setting, with demands on faculty, students, and facilities, is considered to understand the new paradigms for WWW-influenced computer-aided learning. The WWW is shown to be especially appropriate for facilitating remote sensing education with requirements for addressing image data sets and multimedia learning tools. The RSCC is located at http://www.umbc.edu/rscc. The Remote Sensing Core Curriculum (RSCC) was initiated in 1993 to meet the demands for a college-level set of resources to enhance the quality of education across national and international campuses. The American Society of Photogrammetry and Remote Sensing adopted the RSCC in 1996 to sustain support of this educational initiative for its membership and collegiate community. A series of volumes, containing lectures, exercises, and data, is being created by expert contributors to address the different technical fields of remote sensing. The RSCC program is designed to operate on the Internet taking full advantage of the World Wide Web (WWW) technology for distance learning. The issues of curriculum development related to the educational setting, with demands on faculty, students, and facilities, is considered to understand the new paradigms for WWW-influenced computer-aided learning. The WWW is shown to be especially appropriate for facilitating remote sensing education with requirements for addressing image data sets and multimedia learning tools. The RSCC is located at http://www.umbc.edu/rscc.

Remote sensing and core data needed to support planning and policy decision making

A variety of sustainable development research efforts and related activities are attempting to reconcile the issues of conserving our natural resources without limiting economic motivation while also improving our social equity and quality of life. Land use/land cover change, occurring on a global scale, is an aggregate of local land use decisions and profoundly impacts our environment. It is therefore the local decision making process that should be the eventual target of many of the ongoing data collection and research efforts which strive toward supporting a sustainable future. Satellite imagery data is a primary source of data upon which to build a core data set for use by researchers in analyzing this global change. A process is necessary to link global change research, utilizing satellite imagery, to the local land use decision making process. One example of this is the NASA-sponsored Regional Data Center (RDC) prototype. The RDC approach is an attempt to integrate science and technology at the community level. The anticipated result of this complex interaction between research and the decision making communities will be realized in the form of long-term benefits to the public.

Mandate for remote sensing education and the remote sensing core curriculum

Consensus was developed by the remote sensing community during the 1980s and early 1990s regarding the need for an organized approach to teaching remote sensing fundamentals for collegiate institutions. Growth of the remote sensing industry might be seriously hampered without concerted efforts to bolster the capacity to teach state-of-the-practice remote sensing theory and practice to the next generation of professionals. A concerted effort of educators, researchers, government, and industry began in 1992 to meet these demands leading to the creation of the Remote Sensing Core Curriculum. The RSCC is currently sustained by cooperative efforts of the ASPRS, ICRSE, NASA, NCGIA, and others in the remote sensing community. Growth of the RSCC into the K-12 community resulted from its Internet teaching foundation that enables comprehensive and response reference links to the whole of the education community.

Gas sensing of ruthenium implanted tungsten oxide thin films

Different amounts of Ru were implanted into thermally evaporated WO3 thin films by ion implantation. The films were subsequently annealed at 600oC for 2 hours in air to remove defects generated during the ion implantation. The Ru concentrations of four samples have been quantified by Rutherford Backscattering Spectrometry as 0.8, 5.5, 9 and 11.5 at%. The un-implanted WO3 films were highly porous but the porosity decreased significantly after ion implantation as observed by Transmission Electron Microscopy and Scanning Electron Microscopy. The thickness of the films also decreased with increasing Ru-ion dose, which is mainly due to densification of the porous films during ion implantation. From Raman spectroscopy two peaks at 408 and 451 cm-1 (in addition to the typical vibrational peaks of the monoclinic WO3 phase) associated with Ru were observed. Their intensity increased with increasing Ru concentration. X-Ray Photoelectron Spectroscopy showed a metallic state of Ru with binding energy of Ru 3d5/2 at 280.1 eV. This peak position remained almost unchanged with increasing Ru concentration. The resistances of the Ru-implanted films were found to increase in the presence of NO2 and NO with higher sensor response to NO2. The effect of Ru concentration on the sensing performance of the films was not explicitly observed due to reduced film thickness and porosity with increasing Ru concentration. However, the results indicate that the implantation of Ru into WO3 films with sufficient film porosity and film thickness can be beneficial for NO2 sensing at temperatures in the range of 250°C to 350°C.