Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Jothish, M.

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    Algorithmic approach for strategic cell tower placement
    (IEEE Computer Society help@computer.org, 2015) Kashyap, R.; Bhuvan, B.M.; Chamarti, S.; Bhat, T.; Jothish, M.; Annappa, B.
    The increasing number of cell phone users and the usage of cell phones in remote areas have demanded the network service providers to increase their coverage and extend it to all places. Cost of placing a cell tower depends on the height and location, and as it can be very expensive, they have to be placed strategically to minimize the cost. The research aims to find a simple implementable algorithm which effectively determines the strategic positions of the cell towers. Given a satellite image and population density, and obtaining topographical information from GIS (Geographic Information Systems), potential tower locations can be determined. Applying the proposed three stage algorithm, out of many potential tower locations only the indispensible and optimal locations can be chosen. In addition, this algorithm helps to find out the optimal height of the tower at a chosen potential tower location. Hence, the proposal will provide cost-effective way for tower placement specifying their optimal position and height to cover any area and population. © 2014 IEEE.
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    Green routing algorithm for wireless networks
    (Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2016) Deshmukh, A.A.; Jothish, M.; Chandrasekaran, K.
    With wireless devices gaining greater prevalence, there is a growing need for energy conservation for these devices. We propose a routing algorithm that reduces energy consumption at these mobile devices by modifying Optimized Link State Routing Protocol (OLSR). The protocol we propose is energy aware and reduces traffic to those nodes in the network that are low on battery life by using a modified Dijkstra’s algorithm. © Springer International Publishing Switzerland 2016.
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    Optimized cryptographic algorithm for embedded systems
    (Institute of Electrical and Electronics Engineers Inc., 2016) Deshmukh, A.A.; Jothish, M.; Chandrasekaran, K.
    Cryptographic hash algorithms have gained widespread popularity over its algorithmic complexity and the impossibility of recreation of the original input from the message digest. Embedded systems employ such algorithms for its security after substantial modifications in order to meet the hardware specifications due to the parsimonious capacity of such systems. Efficacious hashing algorithms lucidly adhere to all performance constraints and therein lies its popularity. We propose an optimization of the Secure Hashing Algorithm 1 (SHA-1) in memory requirements to suit the environment of an embedded system. We explore the idea of simplifying SHA-1's complicated set of steps to quicken its execution through loop reduction and lookup buffers stored in the main memory. © 2015 IEEE.
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    Detection of similarity in music files using signal level analysis
    (Institute of Electrical and Electronics Engineers Inc., 2017) Thomas, M.; Jothish, M.; Thomas, N.; Koolagudi, S.G.; Vishnu Srinivasa Murthy, Y.V.
    In today's age of digital media, the collection of music files available to the general public is extremely diverse. As with any such set of data, efforts must be made to classify and categorize these files in order to facilitate easy access and searching. Songs can be classified based on attributes available in the music file's metadata such as artist, album, year of release, length, etc. However, if the similarity between two songs is to be determined, a simple comparison of metadata is not only unsatisfactory, the metadata itself might not be available. Therefore, a method of comparison independent of the availability of metadata is required. In this work, a comparison method has been proposed involving the use of musical parameters such as tempo, key and signal envelope, which are extracted from the music file through signal level analysis. Genre is also computed using a support vector machine (SVM) classifier and used to estimate the similarity between two songs. © 2016 IEEE.