Crustal Deformation and Seismic History Associated with the 2004 Indian Ocean Earthquake: A Perspective from the Andaman–Nicobar Islands

The Indian Ocean earthquake of 26 December 2004 led to significant ground deformation in the Andaman and Nicobar region, accounting for ~800 km of the rupture. Part of this article deals with coseismic changes along these islands, observable from coastal morphology, biological indicators, and Global Positioning System (GPS) data. Our studies indicate that the islands south of 10° N latitude coseismically subsided by 1–1.5 m, both on their eastern and western margins, whereas those to the north showed a mixed response. The western margin of the Middle Andaman emerged by >1 m, and the eastern margin submerged by the same amount. In the North Andaman, both western and eastern margins emerged by >1 m. We also assess the pattern of long-term deformation (uplift/subsidence) and attempt to reconstruct earthquake/tsunami history, with the available data. Geological evidence for past submergence includes dead mangrove vegetation dating to 740 ± 100 yr B.P., near Port Blair and peat layers at 2–4 m and 10–15 m depths observed in core samples from nearby locations. Preliminary paleoseismological/tsunami evidence from the Andaman and Nicobar region and from the east coast of India, suggest at least one predecessor for the 2004 earthquake 900–1000 years ago. The history of earthquakes, although incomplete at this stage, seems to imply that the 2004-type earthquakes are infrequent and follow variable intervals

Medium-Coupled Bus-Based INS-GPS Sensor Fusion for Accurate and Reliable Positioning

A scheme for integration of stand-alone INS and GPS sensors is presented, with data interchange over an external bus. This ensures modularity and sensor interchangeability. Use of a medium-coupled scheme reduces data flow and computation, facilitating use in surface vehicles. Results show that the hybrid navigation system is capable of delivering high positioning accuracy.

Urban Growth Analysis Using Spatial and Temporal Data

Urban growth identification, quantification, knowledge of rate and the trends of growth would help in regional planning for better infrastructure provision in environmentally sound way. This requires analysis of spatial and temporal data, which help in quantifying the trends of growth on spatial scale. Emerging technologies such as Remote Sensing, Geographic Information System (GIS) along with Global Positioning System (GPS) help in this regard. Remote sensing aids in the collection of temporal data and GIS helps in spatial analysis. This paper focuses on the analysis of urban growth pattern in the form of either radial or linear sprawl along the Bangalore - Mysore highway. Various GIS base layers such as builtup areas along the highway, road network, village boundary etc. were generated using collateral data such as the Survey of India toposheet, etc. Further, this analysis was complemented with the computation of Shannon's entropy, which helped in identifying prevalent sprawl zone, rate of growth and in delineating potential sprawl locations. The computation Shannon's entropy helped in delineating regions with dispersed and compact growth. This study reveals that the Bangalore North and South taluks contributed mainly to the sprawl with 559% increase in built-up area over a period of 28 years and high degree of dispersion. The Mysore and Srirangapatna region showed 128% change in built-up area and a high potential for sprawl with slightly high dispersion. The degree of sprawl was found to be directly proportional to the distances from the cities.

Energy Efficient GPS Acquisition with Sparse-GPS

Following rising demands in positioning with GPS, low-cost receivers are becoming widely available; but their energy demands are still too high. For energy efficient GPS sensing in delay-tolerant applications, the possibility of offloading a few milliseconds of raw signal samples and leveraging the greater processing power of the cloud for obtaining a position fix is being actively investigated. In an attempt to reduce the energy cost of this data offloading operation, we propose Sparse-GPS(1): a new computing framework for GPS acquisition via sparse approximation. Within the framework, GPS signals can be efficiently compressed by random ensembles. The sparse acquisition information, pertaining to the visible satellites that are embedded within these limited measurements, can subsequently be recovered by our proposed representation dictionary. By extensive empirical evaluations, we demonstrate the acquisition quality and energy gains of Sparse-GPS. We show that it is twice as energy efficient than offloading uncompressed data, and has 5-10 times lower energy costs than standalone GPS; with a median positioning accuracy of 40 m.