Rex van Olst

Designing a usable mobile application for field data collection

It is envisaged that, particularly in African countries under the NEPAD initiative, mobile technology will play a crucial rote in the collection and maintenance of spatial information about the countrys infrastructure (i.e. water, electricity, roads, communication networks etc). While much of the underlying technology may already be available, usability of the mobile applications remains a serious concern. This work investigates the usability of a mobile application for field data collection in the utility industry. The purpose of the investigation is to gain better understanding of usability requirements for a mobile field data collection application but more importantly, how to meet these requirements from a usability engineering standpoint. Firstly, we give some background information on mobile computing and its role in field data collection. Secondly, we describe the design and implementation of the field data collection prototype. The analysis of usability requirements is based on a field study of underground utility closure sourcing work employed at a telecommunications utility. A usage-centered design approach is used to design the user interface, which is implemented as a working prototype on a pocket computer. And finally, we discuss the usability testing of the prototype.

Automatic classification of combined analog and digital modulation schemes using feedforward neural network

This paper presents an artificial neural network based automatic modulation classifier system which can be used to classify combined analog and digital modulation schemes. Four best known analog modulation schemes and five corresponding digital modulation schemes were considered. An approach that involves three different steps in developing an automatic modulation classification is presented. The first step involves the extraction of the statistical feature keys used as the inputs to the classifier. The statistical feature keys are extracted from instantaneous amplitude, instantaneous frequency and phase of the simulated signals using MATLAB code. The second step involves the development of the automatic modulation classifier based on a backpropagation neural network algorithm. The third step of the methodology involves the performance evaluation of the developed automatic modulation classifier with a related study from the research literature. Results obtained show that the developed classifier is accurate and sensitive to classification of the nine modulation schemes considered with an average success rate above 99.0%.

Narrowband PLC channel modeling using USRP and PSK modulations

The indoor narrowband power line communication (NB-PLC) suffers from noise impairments, which emanate from several end-user electrical devices connected across the PLC channel. These noise impairments result into burst errors, which consequently lead to data corruption. Therefore, in order to implement robust communication techniques that will thrive on the noisy PLC channel, a full knowledge and modeling of the noise that exists on the NB-PLC channel is inevitable. This paper thus reports a First-order Markov modeling of NB-PLC channel noise, based on experimental measurements. For the modeling, BPSK, DBPSK, QPSK and DQPSK modulation schemes were implemented using Universal Software Radio Peripheral (USRP). The resulting channel models are useful for improving the robustness of the above modulation schemes as well as designing forward error correction techniques for mitigating the effect of noise impairments. The results are also useful in optimizing NB-PLC system design, thereby, enhancing the accuracy and improving the overall PLC system performance.

Effect of training algorithms on performance of a developed automatic modulation classification using artificial neural network

As classification has become one of the active areas of applications for artificial neural networks, the major objective of this paper was to verify the impact of training algorithms on classifiers developed using an artificial neural network. This paper presents an algorithm for classifying eight digitally modulated signals using a feature-based approach and a pattern recognition method. The developed automatic modulation classification was trained using two training algorithms often used for supervised neural networks. The performance of the developed automatic modulation classification classifier was evaluated and compared using the two training algorithms. The overall performance evaluation of the classifier using the two training algorithms shows that the developed classifier could successfully classify the eight modulated schemes considered with an average success rate above 97.0% irrespective of the signal-to-noise value. The results of the study also show that training algorithms have an impact on the performance of an artificial neural network classifier. In addition, the result of the comparative analysis carried out between the classifier reported in this paper and the one in surveyed literature shows that the signal recognition rate of this classifier is accurate and reliable.

Enabling g-government in the Gauteng city-region

The term g-government, a subset of e-government, was first introduced in 2000 as the convergence of geographical information systems (GIS) and the Internet to create more effective government interaction with citizens. More recently, it has been revised to describe the combination of GIS and Web 2.0 technologies that can enhance government services and delivery. Most government data is spatially based and can be visualised and interpreted using a Web GIS mapping application, but this data is often not available to other government departments, or the general public, frustratingly so. In South Africa, problems with accessing spatial data continue to exist. The Gauteng City-Region Observatory (GCRO) recognised that g-government remains a challenge within the Gauteng city-region (GCR), and in 2010 developed a GIS website for the GCRO and Gauteng Provincial Government. This article is presented in the context of the global shift to fully connected governments through technologies such as Government 2.0 and g-government. It provides a specific focus on the GCRO GIS website and how it enables g-government by providing local and provincial government with the spatial data and tools required to better understand the city-region, and to make informed decisions about future development in the city-region. The article also reviews Technology Acceptance Model (TAM) scores measured at the launch of the website. Finally, monthly website visits are examined. This confirms that local and provincial government are ready to utilise the g-government website.

A heuristic analysis approach to admission control for voice in packet switched wireless networks

In considering Fourth Generation (4G) wireless networks and beyond, such have evolved from circuit switch technology to packet switched technology. This inherently implies that the flaws of both wireless and IP work concurrently to degrade voice quality. The following paper proposes the application of an admission control scheme utilising heuristic analysis for the recognition of voice calls at various levels of the OSI stack. The philosophy is deemed to recognise and prioritise voice admission in a packet switched wireless network. The proposed scheme is investigated so as to provide improved voice quality by accurately recognising and admitting voice traffic over other services. In the solution presented voice classifiers using Deep Packet Inspection (DPI) at the e-Node-B level are proposed for operators and manufacturers to analyse and consider. This is further combined with a variable admission control policy so as to provide a combined heuristic analysis and admission control scheme. SIP and Google Talk are analysed to devise a pattern of recognition. The probability of recognition in combination with the admission control policy is simulated to determine the blocking probability of the combined scheme.

Effect of tunnel geometry and antenna parameters on through-the-air communication systems in underground mines: Survey and open research areas

Abstract In the mining industry, communication systems are important for ensuring personnel safety and optimizing the mining processes underground. The need for through-the-air (TTA) in the underground mining industry has been evolved from man-to-man, man-to-machine and machine-to-machine real-time voice, video and data transmission. Reliable communication has always been a challenge in the underground environment due to harsh and dynamic conditions. This article surveys the effect of tunnel geometry and antenna parameters on TTA communication system performance in underground mines. It provides a comprehensive review of measurement campaigns that have been published to date by systematic organization of literature. The open research areas for future investigation are also discussed. Finally, digital system’s findings in the University of Witwatersrand (WITS) mock-mine are presented.

Optimal Channel Selection and Power Allocation for Channel Assembling in Cognitive Radio Networks

Channel assembling (ChA) has been proposed to maximize capacity in wireless communications. This paper determines the optimal channel selection and power distribution for secondary user (SU) transmissions under varying channel conditions, subject to power constraint, minimum quality of service (QoS) requirement for capacity, and collision probability threshold to protect primary user (PU) services. The nature of the optimization problem turns out to be in a form of mixed integer nonlinear programming (MINLP), which is generally nondeterministic in polynomial time. A Lagrangian framework is therefore employed to reformulate the optimization problem, based on which dual decomposition with subgradient and Newton-Raphson methods are used to determine a relaxed optimal solution. Thereafter, accelerated branch-and-bound (BnB) with sequential fixing (SF) is applied to determine an optimal solution to the original MINLP problem. The simulation results presented in this paper demonstrate that adaptive ChA with channel characterization improves performance in terms of channel capacity, outage probability and collision probability.

Dynamic spectrum access as an alternative radio spectrum regulation system

Since the beginning of the 20th century when the possibility of wireless communication via radio waves was first demonstrated, radio spectrum usage has been centrally regulated on a fixed allocation policy. However, over the last decade, as the usage of and demand for radio spectrum has dramatically increased, the disadvantages of fixed allocation policy have become increasingly apparent. As an alternative, various flexible radio spectrum allocation policies have been proposed. In an attempt to contribute to the possibility of adopting dynamic spectrum access as an alternative radio spectrum regulation system, this paper presents on-going research on the sensing and detection of licensed user signals. The paper proposes the adoption of dynamic spectrum access based on cognitive radio technology as an alternative spectrum regulation scheme. It is envisioned that such a scheme will reduce the problems underlying the current spectrum allocation policy without compromising the performance of existing radio systems.

Maximising user acceptance as the underlying strategy for the development of an interactive website for South Africa's citizens

This paper describes the development of an interactive GIS website for the Gauteng City-Region Observatory (GCRO), using User- and Usage-centric design methodologies. User acceptance was tested with the Technology Acceptance Model (TAM) to ensure that the targeted users of the website would benefit from the information presented within the website and find the technology easy to assimilate.