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Sathyamoorthy D.,Malaysian Science and Technology Research Institute for Defence
IOP Conference Series: Earth and Environmental Science

The derivation of spatial significance is an important aspect of geospatial analysis and hence, various methods have been proposed to compute the spatial significance of entities based on spatial distances with other entities within the cluster. This paper is aimed at studying the spatial significance of mountain objects extracted from multiscale digital elevation models (DEMs). At each scale, the value of spatial significance index SSI of a mountain object is the minimum number of morphological dilation iterations required to occupy all the other mountain objects in the terrain. The mountain object with the lowest value of SSI is the spatially most significant mountain object, indicating that it has the shortest distance to the other mountain objects. It is observed that as the area of the mountain objects reduce with increasing scale, the distances between the mountain objects increase, resulting in increasing values of SSI. The results obtained indicate that the strategic location of a mountain object at the centre of the terrain is more important than its size in determining its reach to other mountain objects and thus, its spatial significance. © Published under licence by IOP Publishing Ltd. Source

Mohamad Hani A.F.,Petronas University of Technology | Sathyamoorthy D.,Malaysian Science and Technology Research Institute for Defence | Sagayan Asirvadam V.,Petronas University of Technology
Computers and Geosciences

In this paper, a modification of the algorithm proposed by Ahmad Fadzil et al. (2011) for surface roughness computation from digital elevation models (DEMs) via multiscale analysis is presented. The new algorithm takes into account that the three predominant physiographic features of terrains (mountains, basins and piedmont slopes) have distinct curvature region distributions and hence, distinct roughness characteristics. To this end, the surface roughness of individual cells of DEMs is computed by identifying the curvature region distribution and roughness characteristics of each individual mountain, basin and piedmont slope region. The modified algorithm allows for the localisation and quantification curvature regions over varying scales for specific regions, providing a more appropriate surface roughness parameter. © 2011 Elsevier Ltd. Source

Hani A.F.M.,Petronas University of Technology | Sathyamoorthy D.,Malaysian Science and Technology Research Institute for Defence | Asirvadam V.S.,Petronas University of Technology
Computers and Geosciences

In this paper, it is proposed that the mapping of uncertainties of the three predominant physiographic features of terrains, which are mountain, basins and piedmont slopes, using variation in the spatial resolution over which these landforms are defined, can be performed with fuzzy classification. The proposed methodology allows for the generation of fuzzy certainty maps which assign high levels of uncertainty to regions with high levels of change across scales. This paper demonstrates that fuzzy certainty maps provide a better quantification of landform character than Boolean landform maps alone. In terms of sensitivity to noise, the methodology is able to identify narrow bridges, and spurious landforms, and assign these errors with low certainty values. However, it is unable to identify spurious modifications to landform shape, with these errors being assigned high certainty values. Ground truth maps are required to identify these errors. © 2013 Elsevier Ltd. Source

Sathyamoorthy D.,Malaysian Science and Technology Research Institute for Defence
Defence S and T Technical Bulletin

This paper is aimed at conducting a critical assessment of two key defence R&D fi elds that are important in supporting the development of the national defence industry, in particular in supporting the achievement of the objectives of the Fourth Dimension Malaysian Armed Forces (4D MAF) capability plan in terms of operational awareness and mission capability. The fi elds that will be discussed, determined based on the author's literature review and opinions of the MAF's capabilities and requirements, and current and expected future trends of global defence technology development, are Command, Control, Communications, Computers, Intelligence, Surveillance & Reconnaissance (C4ISR) in support of network centric operations (NCO) and unmanned vehicles (UVs).On the whole, the overall capabilities of the national defence industry to produce indigenous equipment and assets that meet the requirements of the 4D MAF plan in these two fi elds of defence R&D are still relatively limited. Nevertheless, signifi cant progress has been, and is being, made through collaborations with relevant agencies, institutes and industries, both local and foreign. Active participation in defence R&D, in these two fi elds, in addition to other fi elds of defence technology, including vehicle & aerospace engineering challenges, emergent naval technology, smart weapons, personnel protection & performance, and biological, nuclear & chemical terrorism countermeasures, is required to further catalyze the development of the national defence industry. Source

Aziz S.A.A.,National University of Malaysia | Aziz S.A.A.,Malaysian Science and Technology Research Institute for Defence | Nuawi M.Z.,National University of Malaysia | Mohd Nor M.J.,National University of Malaysia
International Journal of Industrial Ergonomics

The objective of this study is to present a new method for determination of whole-body vibration (WBV) exposure in the driver's seat of Malaysian Army (MA) three-tonne trucks based on changing vehicle speed using regression models and the statistical analysis method known as Integrated Kurtosis-Based Algorithm for Z-Notch Filter Technique 3D (I-kaz 3D). The study was conducted on two different road conditions; tarmac and dirt roads. WBV exposure was measured using a Brüel & Kjær Type 3649 vibration analyser, which is capable to record WBV exposures from the driver seat and vibration from the truck, and comparisons were made between the two types of roads. The data was analysed using I-kaz 3D to determine the WBV values in relation to varying speeds of the truck and to determine the degree of data scattering for WBV data signals. Based on the results obtained, WBV exposure levels can be presented using frequency weighted root mean square (RMS) accelerations (aw), vibration dose value equivalent to 8 h (VDV(8)), I-kaz 3D coefficient (Z3D∞) and the I-kaz 3D display. The I-kaz 3D displays showed greater scatterings, indicating that the values of Z3D∞ and VDV(8) were getting higher. The prediction of WBV exposure was done using the developed regression models and graphical representations of Z3D∞. The results of the regression models showed that Z3D∞ increased when vehicle speed and WBV exposure increased. For model validation, predicted and measured noise exposures were compared, with high coefficient of correlation (R2) values obtained, indicating that a good agreement was obtained between them. By using the developed regression models, we can easily predict WBV exposure in the driver's seat for WBV exposure monitoring. © 2015 Elsevier B.V. Source

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