An Energy Efficient Android Application

Statistics demonstrate that Android is the mostly used operating system on mobile phones and tablets across the world. These mobile devices operate using batteries which have limited size and capacity. Therefore, energy management when running mobile applications become of vital importance. In this paper, an energy-efficient android application for goods delivery management system has been developed. The major aim of this application was to monitor and minimise the energy consumption while running the application. Two applications have been built: a normal prototype and an energy-optimised prototype. Seven best practices have been identified and analysis has been performed on energy consumption by both applications developed for four different scenarios over a period of 60 minutes.  Results demonstrate that the energy-optimised prototype consumes less energy. For every hour, 597.6 J of energy and 8% of the battery level can be saved with the proper application development and energy optimisation techniques.Keywords: Energy consumption, Mobile phones, Android application, Best practices

Analysing Transportation Data with Open Source Big Data Analytic Tools

Big data analytics allows a vast amount of structured and unstructured data to be effectively processed so that correlations, hidden patterns, and other useful information can be mined from the data. Several open source big data analytic tools that can perform tasks such as dimensionality reduction, feature extraction, transformation, optimization, are now available. One interesting area where such tools can provide effective solutions is transportation. Big data analytics can be used to efficiently manage transport infrastructure assets such as roads, airports, bus stations or ports. In this paper an overview of two open source big data analytic tools is first provided followed by a simple demonstration of application of these tools on transport dataset

SYMBOL LEVEL DECODING FOR DUO-BINARY TURBO CODES

This paper investigates the performance of three different symbol level decoding algorithms for Duo-Binary Turbo codes. Explicit details of the computations involved in the three decoding techniques, and a computational complexity analysis are given. Simulation results with different couple lengths, code-rates, and QPSK modulation reveal that the symbol level decoding with bit-level information outperforms the symbol level decoding by 0.1 dB on average in the error floor region. Moreover, a complexity analysis reveals that symbol level decoding with bit-level information reduces the decoding complexity by 19.6 % in terms of the total number of computations required for each half-iteration as compared to symbol level decoding