Browsing by Author "Alabi, O. O."
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Item Design of a Bimodal Home Automation System using ESP8266 and ATMEGA328 Microcontroller(Department of Computer Engineering, Faculty of Computer Science, University of Sriwijaya, Indonesia, 2017-10-31) Zakariyya, Olayinka Sikiru; Salami, Abdulazeez Femi; Alabi, O. O.; Usman, A. M.Home automation systems are garnering increasing popularity and widespread use due to the relative ease of domestic management and comparatively high return on technology investment tied to its adoption. However, Nigeria and other emerging ICT economies are yet to fully actualize and maximize the inherent potential of these smart home technologies due to endemic challenges associated with poor infrastructure, erratic power supply and unreliable Internet connectivity. These challenges necessitate an innovative paradigmatic shift that could provide a pragmatic technological solution suitable to the context of Nigeria and other developing climes. For most smart home systems in this research context, the status quo is based on choosing whether the design would be for short- or long-range communication network. Short-range designs which are usually realized with Bluetooth technology suffer from limited range issues while poor connectivity, bandwidth and latency issues are some of the problems plaguing Wi-Fi-based long-range designs. Consequently, this research presents a hybrid adaptive architecture that combines desirable features of both short- and long-range modes. The proposed smart home system is based on using embedded systems which use mobile application to send messages to ESP8266 Wi-Fi module. Together with notifications received from the monitoring unit, these messages are parsed by Arduino's ATMEGA328 microcontroller from where instruction codes are sent for controlling the load by switching ON or OFF various relays connected to the load.Item Generalized Lynch Multivariate Regression Estimators with k-Parameters of Order 1/n(International Center for Advance Studies, India, 2008) Adewara, A. A.; Job, O.; Abidoye, A. O.; Oyeyemi, G. M.; Gali, M. O.; Alabi, O. O.In this paper several authors have proposed ratio type estimator which utilized data from several auxiliary variates that involves the use of unknown weights which have to be estimated but Lynch proposed a multivariate regression estimator when there are two auxiliary variates (x-varaiates) which was found to be better and preferred, even to the conventional mean estimate. In this paper, we proposed multivariate regression estimators with k -parameters of order 1/n as used by Lynch. Two data sets called Populations 1 and 2 were used to justify this research work. While Population I is based on the total monthly income and total monthly expenditure on food, rent, clothing, transportation and miscellaneous of 86 occupants of Kubwa Federal Housing Authority, Phase lV Estate Abuja, Nigeria, Population 2 is based on the total monthly income and total monthly expenditure on food, rent, clothing, transportation and miscellaneous of 95 occupants of Gwarinpa II Estate, Abuja Nigeria and it was observed that as, K-parameters increases, our k multivariate regression estimate becomes smaller and better which makes the highest k -parameter multivariate regression estimator to be preferredItem On Statistical Analysis and Modeling of Rare Events with Autoregressive integrated Moving Average (Arima) Model.(Journal of the Nigerian Association of Mathematical Physics, 2014-11) Olatayo, T. O.; Mayor, Andrew; Alabi, O. O.; Afolayan, RazaqForecasting methods to produced numerical estimates range from relative simple techniques to complex and sophisticated techniques are discussed in this paper. Among forecasting methods were extrapolative or projective techniques i.e. moving averages and exponential smoothing. A moving average is a trend method wherein each point of a moving average of a time series is the arithmetic mean of a number of consecutive observations of the series. The number of observations in the moving average computation is chosen to minimize the effect of seasonality or other disturbances in the series [l-4]. Exponential smoothing is a flexible trend whereby past data observations arc given different weights in computing the forecast. It has the advantages of providing a simple up-to-date forecast where the new forecast is equal to the previous one plus some stated proportion of the previous periods of a forecasting error. Exponential smoothing methods are adaptable to adjustment to include trend and seasonal projections with adaptive types of optimum weighting procedures. Exponential smoothing methods are used to forecast large numbers of items. Time series decomposition methods are widely used to identify the systematic components of a time scries, trend cycle and seasonal pattern and the non systematic or random component. The seasonal Pattern is identified by first determining the seasonal indexes for each month or quarter of year and in turn these patterns arc projected ahead. The cyclical forecast may be prepared by other systematic projection or by economic judgment. Non systematic or irregular variation is usually assumed zero in a forecast but irregular adjustments may be needed for an anticipated stoppage in production or some other casual factors in the time period of the forecast. A highly analytical method for measuring seasonal fluctuations is called census I l or X-l l variant. In Regression models express the past relationships among the item being forecasted. These models are useful when adequate history of data are available on the major factors associated with variations in the item being forecasted [5,6].