IMPROVING SOIL ORGANIC CARBON (SOC) PREDICTION BY FIELD SPECTROMETRY IN BARE CROPLAND BY REDUCING THE DISTURBING EFFECT OF SOIL ROUGHNESS

in Geoscience and Remote Sensing Symposium, 2009 IEEE International, IGARSS 2009 (2009, July 17)

The spatial estimation of Soil Organic Carbon (SOC) at large scale in outdoor condition is an important issue. It has been largely demonstrated that diffuse reflectance spectroscopic techniques, are ... [more ▼]

The spatial estimation of Soil Organic Carbon (SOC) at large scale in outdoor condition is an important issue. It has been largely demonstrated that diffuse reflectance spectroscopic techniques, are efficient for SOC determination in field conditions. However these methods are influenced by disturbing factors such as soil water content, vegetation residues and surface roughness, the later being the object of this study. Our laboratory experiments showed that the accuracy of SOC prediction from shadowed soil samples with spectroscopy techniques decreases with increasing soil shadow. In this study a new methodology using a digital camera for identifying and correcting the effect of soil shadow on field reflectance spectra measured with an Analytical Spectral Devices (ASD) during field campaign in bare crop lands has been elaborated and tested. Results showed that the proposed shadow correction method enables improving significantly SOC prediction accuracy and performs better than traditionally used methods consisting in automatic signal processing. [less ▲]

Improving spatial information extraction for local and regional authorities using Very-High-Resolution data - geometric aspects.

in International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences (2003), 34(XXX),

The purpose of the presented research project is to investigate how present-day EO-technology can support local and regional decision-making, particularly in Belgium, and to develop prototype versions of ... [more ▼]

The purpose of the presented research project is to investigate how present-day EO-technology can support local and regional decision-making, particularly in Belgium, and to develop prototype versions of value-added products that fulfil some of the actual information needs, as expressed by Belgian authorities at the local and at the regional level. To accomplish this goal the proposal addresses both technical and user-oriented issues, and builds on the experience of five research teams, each with its own background and particular know-how. The most important objectives of the project are: - to define optimal methods for improved spatial information extraction from high- and very-high resolution data, based on innovative processing techniques; - to identify useful EO-applications at the level of local and regional decision making that are made possible by applying the information extraction methods that will be developed; in this case especially 3D information - to define and develop value-added core data products that will support these applications, and that can be consulted with easy-to-use, widely available IT-tools (CD-ROM, Internet, …). One of the technical research modules is focussing on the geometrical aspects of VHR data processing. In terms of spatial resolution, VHR data have the required level of detail to resolve individual objects in the landscape, in a similar way as airborne data. As such, they have the potential of becoming a useful data source for the extraction of detailed, object-related information, and for the production of large-scale cartographic reference data. The geometry of VHR sensor data, however, completely differs from the geometry of aerial photographs (Bethel et al., 2001). Research is going on to evaluate the geometric qualities of present and future VHR standard data products, from the moment they become available, and to examine what level of geometric accuracy is attained with these data, depending on image parameters, processing strategies, and terrain characteristics (Zhou and Li, 2000). The main objectives of the research module on geometric aspects of VHR data processing are: a) to define an optimal procedure for the derivation of envelope DSMs (Digital Surface Models) and ortho-photoplans from VHR data; b) to develop a theoretical model to study the effect of the oblique viewing angle of VHR data on image displacements caused by building height and relief; c) to evaluate the geometric accuracy of satellite-derived envelope DSMs and ortho-corrected image data in comparison with similar products obtained by means of large-scale aerial photography. The module about geometrical aspects is divided into six specific tasks. a) GPS database creation b) Creation of reference envelope DSMs based on aerial photography c) Ortho-rectification of VHR images and aerial photography based on reference DSMs d) Evaluation of the quality of VHR geo-referenced data e) Modelling of displacements caused by terrain morphology and sensor viewing angle f) Development of envelope DEMs from VHR stereoscopic imagery The project is now in the stage where the quality of the georeferenced and orthorectified data is evaluated. [less ▲]

Improving TCP in wireless networks with an adaptive machine-learnt classifier of packet loss causes

in Lecture Notes in Computer Science (2005, May), 3462

TCP understands all packet losses as buffer overflows and reacts to such congestions by reducing its rate. In hybrid wired/wireless networks where a non negligible number of packet losses are due to link ... [more ▼]

TCP understands all packet losses as buffer overflows and reacts to such congestions by reducing its rate. In hybrid wired/wireless networks where a non negligible number of packet losses are due to link errors, TCP is unable to sustain a reasonable rate. In this paper, we propose to extend TCP Newreno with a packet loss classifier built by a supervised learning algorithm called 'decision tree boosting'. The learning set of the classifier is a database of 25,000 packet loss events in a thousand of random topologies. Since a limited percentage of wrong classifications of congestions as link errors is allowed to preserve TCP-Friendliness, our protocol computes this constraint dynamically and tunes a parameter of the classifier accordingly to maximise the TCP rate. Our classifier outperforms the Veno and Westwood classifiers by achieving a higher rate in wireless networks while remaining TCP-Friendly. [less ▲]

Improving the action of insulin.

in Clinical & Investigative Medicine = Médecine Clinique et Experimentale (1995), 18(4), 340-7

Improving the action of insulin is a relatively new concept in diabetes management. Insulin sensitivity can be improved by reduction of excessive body weight, regular physical activity and, possibly, by ... [more ▼]

Improving the action of insulin is a relatively new concept in diabetes management. Insulin sensitivity can be improved by reduction of excessive body weight, regular physical activity and, possibly, by correcting a subclinical magnesium deficiency. Pharmacological means of improving insulin action include metformin, antiobesity serotoninergic agents and, possibly, benfluorex. New compounds aiming at improving the action of insulin are in development and include thiazolidinedione derivatives (known as "insulin sensitizers"), inhibitors of adipose tissue lipolysis (e.g. acipimox), and inhibitors of free fatty acid oxidation (e.g. etomoxir). Avoidance of drugs that reduce insulin sensitivity, such as beta blockers and thiazide diuretics, is recommended. Finally, cigarette smoking is associated with resistance to insulin but it remains to be demonstrated that cessation of cigarette smoking does in fact increase sensitivity to insulin. [less ▲]

Improving the bias/variance tradeoff of decision trees - towards soft tree induction

in Engineering intelligent systems (2001), 9

One of the main difficulties with standard top down induction of decision trees comes from the high variance of these methods. High variance means that, for a given problem and sample size, the resulting ... [more ▼]

One of the main difficulties with standard top down induction of decision trees comes from the high variance of these methods. High variance means that, for a given problem and sample size, the resulting tree is strongly dependent on the random nature of the particular sample used for training. Consequently, these algorithms tend to be suboptimal in terms of accuracy and interpretability. This paper analyses this problem in depth and proposes a new method, relying on threshold softening, able to significantly improve the bias/variance tradeoff of decision trees. The algorithm is validated on a number of benchmark problems and its relationship with fuzzy decision tree induction is discussed. This sheds some light on the success of fuzzy decision tree induction and improves our understanding of machine learning, in general. [less ▲]

Improving the catabolic functions in dessication-tolerant soil bacteria in view of their use in bioaugmentation

Poster (1998, May)

Improving the classification of plant functional types in dynamic vegetation modelling for the Neogene

Poster (2010, May)

Improving the Design of Hydraulic Structures : A Unified 1D Simulation Tool for Highly Transient Water Flow

Poster (2008)

Improving the performance of low-gain designs for bounded control of linear systems

in Automatica (2002), 38(11), 1777-1782

Improving the quality and nutritional status of Gari trough the use of starter cultures and fortification with soybean, palm oil and coconut milk

Poster (2006, September)