http://hdl.handle.net/123456789/111 2026-04-04T19:12:17Z http://hdl.handle.net/123456789/951 DEVELOPMENT OF A RADIO SPECTRUM UTILISATION TECHNIQUE FOR COGNITIVE RADIOS OLANREWAJU, BABATUNDE SEYI Parts of licensed radio spectrum for data transmission are idle because they are not used at some points in time or location by the user. Cognitive radios which are software radios equipped with sensors and other functionalities are being developed to opportunistically use this idle licensed spectrum. Cognitive radios use dynamic spectrum access methods classified as overlay and underlay to utilise idle television frequency in the radio spectrum. However, simultaneous transmissions of data by licensed and cognitive radio users are not possible in overlay, while restriction is on the usage of the idle spectrum in underlay. Consequently, the potentials of cognitive radios are not effectively maximised. Therefore, this research was aimed at the development of a band access technique in radio frequency to improve the usage of the idle television frequency. The matched filtering technique of overlay for detecting idle licensed signal and interference temperature management of underlay for controlling interference were adapted to develop a new technique referred to as Matched Filtering-Interference Temperature Management (MAFITM). A conceptual transmission model for three different scenarios representing overlay, underlay and MAFITM techniques was created for television frequency access. Simulation of the three techniques in the television frequency band that covered 54 to 862 MHz and three different licensed signal transmission powers was implemented with Java programming language. A cognitive radio access point was simulated to transmit between nodes for the three different scenarios in the presence and absence of licensed television signal. The first, second and third transmission scenarios were simulated using overlay, underlay, and MAFITM techniques respectively. The results generated were compared using the performance metrics of transmission distance, data rates, spectrum efficiency and margin of interference in the presence and absence of licensed signal. In the absence of licensed signal, for both MAFITM and overlay techniques the transmission distance, the maximum achievable data rate and the spectral efficiency (100 km, 163 Mb/s and 9.537 b/s/Hz), respectively were significantly higher compared with those of underlay technique (25 m, 44 Mb/s and 12.429 b/s/Hz), respectively. In the presence of licensed signal, transmission of data was possible in MAFITM and underlay techniques but not in overlay technique. With the presence of licensed signal, for MAFITM technique, the maximum transmission distance, the maximum achievable data rate, the spectral efficiency and margin of interference were (3.5 km, 789 Kb/s, 0.045 b/s/Hz and 0.81), respectively. For underlay technique the maximum transmission distance, the maximum achievable data rate, the spectral efficiency and margin of interference were (25 m, 2.81 Mb/s, 0.001 b/s/Hz and -0.04), respectively. The results reflected a better utilisation of the licensed spectrum using the MAFITM technique. The developed technique produced an improved utilisation of spectrum in areas with stronger presence of licensed signal and allowed data transmission alongside licensed signal without restriction. Matched Filtering-Interference Temperature Management technique is recommended to enhance the effectiveness of cognitive radios in utilising idle licensed frequencies. 2019-10-01T00:00:00Z http://hdl.handle.net/123456789/949 JOINT OPTIMISATION OF FACILITY LOCATION AND TWO-ECHELON INVENTORY CONTROL WITH RESPONSE TIME REQUIREMENT AND LATERAL TRANSSHIPMENT ZELIBE, SAMUEL CHIABOM, Lateral Transshipment (LT) (stock movement between facilities on the same echelon), has been used as an option for reducing the occurrences of stockout and excess stock in many multi-echelon environments. Several LT models have been formulated for many supply chain systems. However, the incorporation of LT into a system which jointly optimises facility location and two-echelon inventory decisions with Response Time Requirement (RTR) has not been considered. Therefore, this study was designed to incorporate LT into a two-echelon system which jointly minimises expected cost emanating from facility location and inventory decisions subject to RTR. The customer arrival at facilities was modelled as a single server queue with Poisson arrivals and exponential service rate. The balance equation of this queue along with the distribution of the number of orders in replenishment (Nvw) was used to derive service center steady state expected level for on-hand inventory (Ivw), backorder (Bvw), and LT (Tvw). The derived steady state expected levels were used to formulate the two echelon LT model. This model was decomposed using Lagrange relaxation. Relaxation of the assignment variable’s integrality was used to further reduce the model. The reduced model was checked for convexity using second order conditions. Karush-Kuhn Tucker (KKT) conditions were used to investigate global optimality, which was also examined for the case of stochastic occurrences. Multiple computational experiments were performed on three data sets using general algebraic modelling system for the values: duvw(max) = 100, 150; ρ = 0.5, 0.9 and τ = 0.2, 0.3, 0.5, where, duvw(max) , ρ and τ are customer distance, utilisation rate and RTR, respectively. The expected number of customers in queue at a service center was: E[Nvw] = P u∈U λuYuvw λ0 ρ S0+1 1−ρ + P u∈U λuYuvwαw. The derived steady state expected levels were: Ivw = PSvw−1 s=0 (Svw − s)P{Nvw = s}, Bvw = P u∈U λuYuvw λ0 ρ S0+1 1−ρ + P u∈U λuYuvwαw + P u∈U λuYuvw λw P|w|Svw−1 s=0 Fw(s) − |w|Svw and Tvw = PSvw−1 s=0 Fvw(s) − Svw − P u∈U λuYuvw λw P|w|Svw−1 s=0 Fw(s) − |w|Svw ii The two-echelon LT model formulated was: min X w∈W X v∈V fvwXvw + hvwIvw + pvwBvw + qvwTvw + X u∈U λuYuvwduvw! + h0S0 Subject to P v∈V Yuvw = 1 Yuvw ≤ auvwXvw Svw ≤ Cvw S0 ≤ C0 h ρ S0+1 λ0(1−ρ) + αw − τ i ≤ P|w|Svw−1 s=0 [1−Fw(s)] λw Xvw, Yuvw ∈ {0, 1}. The Lagrange dual problem was: max θ,π≥0 min X,Y,S X w∈W X v∈V ( fvwXvw + (hvw + qvw) S Xvw−1 s=0 Fvw(s) − qvwSvw + (pvw − qvw + θvw) P u∈U λuYuvw λw + (pvw − qvw) P u∈U λuYuvw λw ( |w| X Svw−1 s=0 Fw(s) − |w|Svw) +X u∈U λuYuvw (pvw + θvw)ρ S0+1 λ0(1 − ρ) + X u∈U (((pvw + θvw)αw + duvw − θvwτ )λu − πu) Yuvw) + X u∈U πu Subject to Yuvw ≤ auvwXvw Svw ≤ Cvw S0 ≤ C0 Xvw, Yuvw ∈ {0, 1} iii The reduced model obtained was: min 0≤Yuvw (hvw + qvw) S Xvw−1 s=0 Fvw(s) − qvwSvw + (pvw − qvw + θvw) P u∈U λuYuvw λw + (pvw − qvw) P u∈U λuYuvw λw ( |w| X Svw−1 s=0 Fw(s) − |w|Svw) +X u∈U λuYuvw (pvw + θvw)ρ S0+1 λ0(1 − ρ) + X u∈U (((pvw + θvw)αw + duvw − θvwτ )λu − πu) Yuvw where λu, λw, λ0, Yuvw, Lw,(Svw, S0),(Cvw, C0), Xvw, auvw, τ, fvw, hvw, pvw, qvw and duvw are, customer demand, pool demand, plant demand, assignment variable, lead time, base stock levels, capacity, location variable, distance variable, facility, holding, backorder, LT and transportation costs, while, θvw, πu are Lagrange multipliers and Fvw, Fw are facility and pool distribution functions, respectively. The reduced model was convex and satisfied KKT conditions, establishing the existence of global minimum for the two echelon LT model. The stochastic case was also shown to be convex. The computational experiment showed that expected cost remained stable with increasing RTR, and that the model resulted to lower cost when compared with the model without LT. The two-echelon joint location-inventory model with response time requirement and lateral transshipment obtained lower expected cost than the model without lateral trans shipment. Stability of expected cost with varying response time requirement was also established. 2019-12-01T00:00:00Z http://hdl.handle.net/123456789/290 NEURO-GENETIC ALGORITHM APPROACH FOR CLASSIFICATION AND PREDICTION MODELLING ADEYEMO, OMOWUNMI OMOBOLA Artificial Neural Network (ANN) is a data mining algorithm that is used for classification and prediction of any set of data. Previous studies on classification and prediction of data were based only on using MLP, a type of ANN algorithm. However, there are problems of low prediction, low classification accuracy and over-training with the MLP. Genetic Algorithm (GA) is well known for improving prediction and reducing over-training. This work was therefore designed to minimise the problem of low accuracy and over-training by embedding GA into Multi-Layer Perceptron ANN (MLP-ANN). The model, hereafter named Neuro-Genetic Model (NEGEM) was developed using feed forward ANN that trains MLP with search optimisation ability of GA. The MLP-Delta learning algorithm was used to implement ANN while GA was used to avoid low accuracy and overtraining of MLP-ANN. Demographic and treatment data on HIV/AIDS patients from 2000-2011 were collected where available from selected tertiary and general hospitals, primary health care and non-governmental organisations in southwestern Nigeria. The data was used to create medical database on HIV/AIDS using a two-tier architecture that allows multi-dimensional analysis. Three different MLP-hidden layers (one, two and three) network implemented in C# programming language and Microsoft Structured Query Language (SQL) server were used for the database to predict and classify HIV/AIDS data. Precisely 12,000 and 2,230 data were respectively imported for training and testing into the model. Mutation and crossover operators with 2000 training epochs in GA were used as operating parameters to avoid low prediction accuracy and over-training. The ability of the model to classify and predict was compared with Waikato Engineering Knowledge Analysis (WEKA), an existing MLP software. Classification and predictive accuracies were measured using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Recall and precision were used to measure the level of true positive prediction/classification and over-training. The RMSE for NEGEM with one, two and three hidden layers were 2.9x10-16, 9.87x10-15 and 7.0x10-8 respectively, while for WEKA were 1.0x10-2, 8.0x10-3 and 1.37x10-1 respectively. The MAE for NEGEM was 9.0x10-4, 0.0x10-4 and 3.0x10-4 while for WEKA were 1.0x10-3, 1.0x10-3and 7.0x10-4 for one, two and three hidden layers respectively. These showed higher level of accuracy in NEGEM prediction and classification than WEKA. The NEGEM recall values for one, two, and three hidden layers were respectively 9.8x10-1, 1.0x10-3 and 9.8x10-1, while for WEKA they were 1.0, 1.0 and 1.0. This showed that WEKA over-trained in its predictive/classification values. Precision values of NEGEM were. 9.6x10-1,9.6x10-1and 9.8x10-1for one, two, and three hidden layers respectively, while for WEKA they were 1.0, 1.0 and 1.0. This showed a high level of positive prediction/classification of NEGEM than WEKA. Precision/recall values showed that NEGEM avoids over-training whereas WEKA over-trained because the values exceeded the standard precision/recall range value in MLP-ANN algorithm. The developed model could be used to mine database more efficiently than Waikato Engineering Knowledge Analysis 2013-01-01T00:00:00Z