Performance Analysis of ANN on Dataset Allocations for Pattern Recognition of Bivariate Process

Several approaches to identifying the out-of-control variables after the detection of abnormal pattern has been most intensively studied and used in practice. One of the several approaches is the Artificial Neural Network (ANN) based model for diagnosis of out-of-control signal of multivariate process mean shift. In spite of the number of years of research in neural network, limited research (if any) have been done on the effect of dataset allocations in percentages for training and testing on the performance of ANN. In this paper, we investigate the use of different percentages of dataset allocation into training, validation and testing on the performance of ANN in pattern recognition of bivariate process using six selected training algorithms. The result of study showed that large allocation of dataset for training was found suitable, having higher recognition accuracy for ANN learning and perform better for pattern recognition of bivariate process. Keywords: Bivariate Process; Pattern Recognition; Recognition accuracy; Multivariate quality control charts, training algorith

RAAC: A bandwidth estimation technique for admission control in MANET

The widespread of wireless mobile network have increased the demand for its applications. Providing a reliable QoS in wireless medium, especially mobile ad-hoc network (MANET), is quite challenging and remains an ongoing research trend. One of the key issues of MANET is its inability to accurately predict the needed and available resources to avoid interference with already transmitting traffic flow. In this work, we propose a resource allocation and admission control (RAAC) solution. RAAC is an admission control scheme that estimates the available bandwidth needed within a network, using a robust and accurate resource estimation technique. Simulation results obtained show that our proposed scheme for MANET can efficiently estimate the available bandwidth and outperforms other existing approaches for admission control with bandwidth estimation

Impact of Employee Training on Organizational Performance A Study of Selected Insurance Firms in Abuja-Nigeria

Performance of organization depends on the knowledge and ability of its employees toward understanding the dynamism in the market. Insurance firms spent huge amount of money annually on training employees and the excess of such investment on employees is to improve the employee knowledge on the work and achieve organizational goals better than the competitors. The study is on the impact of employee training on organizational performance of selected insurance firms in Abuja. Questionnaire was administered to population sample of one hundred and twenty (120) employees. Hypothesis formulated for the study were analyzed using t-test statistical technique to determine the relationship that exist between employee training and organisational performance. The study observed that induction and orientation have significant impact on employee job effectives. It was also discovered that on-the-job training has significant relationship with employee productivities while off-the-job training has significant relationship with employees’ innovativeness in insurance industry. The study recommends that insurance organizations should set up regular training and development programmes that are capable of improving the skills, morale and productivity of employees. Personnel managers of insurance firms should also involve experts to determine the appropriate training for the employees. Insurance industry should prioritize training programmes to ensure high level of productivity. Keywords: training, employee, performance, insurance, productivity, innovativenes

Cubic interpolation based channel estimator for rectangular M-QAM

This paper propose a Pilot Symbol Assisted Modulation (PSAM) channel estimation and compensation technique for rectangular M-QAM based on Cubic interpolation in order to determine the channel state information (CSI) over flat Rayleigh fading channels. The proposed Cubic channel estimator technique is based on sliding window approach and pilot symbol estimates adjustment in order to reduce its computational time complexity. The Cubic estimator is combined with the Koetter and Vardy (KV) Reed-Solomon (RS) decoder to test its performance. The simulation results show that the Cubic interpolation gives the same performance as the Linear, and Sinc interpolators over slow flat Rayleigh fading channel; however, it achieves significant performance improvement of +1.0dB in symbol error rate (SER) over fast flat Rayleigh fading channel.Keywords: CSI, cubic estimator, fading channel, M-QAM, PSA

Improved distance metric technique for deriving soft reliability information over Rayleigh Fading Channel

This paper presents an improved Distance Metric (DM) technique for deriving soft reliability information over Rayleigh fading channel. We compared this proposed DM technique with the conventionally used DM technique in the literature. The conventional DM method derives the soft reliability information from the output received symbols while the proposed DM method derives the soft reliability information from the Channel State Information (CSI) which result due to variations in the channel gain. Performance analysis of these DM methods are verified over flat Rayleigh fading channels, and on time-varying frequency-selective Rayleigh fading channels using rectangular M-QAM and OFDM systems respectively. Also, two channel estimation techniques are used to derived the CSI assuming different normalized Doppler frequency and frame length size. The performance of the conventional and proposed soft reliability derivation methods are documented through computer simulations assuming Koetter and Vardy, Reed-Solomon (KV-RS) soft decision decoding algorithm as the Forward Error Correction (FEC) scheme. From the computer simulation results, the proposed DM method offers significant improvement in Codeword Error Rate (CER) performance in comparison with the conventional DM method with no significant increase in computational delay and time complexity.Keywords: CSI, cubic estimators, DM, fading channels, KV-RS decoder, LMMSE, OFDM, R-matrix, 16QA

Preliminary studies on ethanol production from Garcinia kola (bitter kola) pod: Effect of sacharification and different treatments on ethanol yield

A study on yeast fermentation of bitter kola pod( agricultural waste) was carried out using dried active bakers’ yeast (Saccharomyces cerevisiae)and brewer’s yeast (Saccharomyces carlsbergensis) Effects of saccharification using Aspergillus niger and different treatments on the waste as they relate to the optimization of the ethanol production were investigated. The reducing sugar in the waste was increasedafter 24-48 hours of sacharification using Aspergillus niger, fresh bitter kola waste recorded 4.4 -8.0g/100g of the waste, while the partially fermented bitter kola waste gave the value in the range of 5.6 -6.8g/100g. Baker’s yeast gave a higher ethanol yield than Brewer’s yeast. Different treatments of bitter kola pod revealed that addition of nutrient supplement enhanced the ethanol yield; however, 48 hours of saccharification significantly improved the ethanol yield at p £ 0.05

Production of ethanol from Carica papaya (pawpaw) agro waste: effect of saccharification and different treatments on ethanol yield

A study was carried out on yeast fermentation of carica papaya (pawpaw) agricultural waste using dried active baker\'s yeast and brewer\'s yeast strain (Sacchromyces cerevisiae). The pawpaw considered as an agricultural waste was the tapped ripe pawpaw fruit harvested after the tapping of papain. Effect of different yeast strains on the percentage yield of ethanol was investigated. The effects of yeast concentration, saccharification and different nutrient supplements as they relate to the optimization of the ethanol yield were also carried out. The fermented pawpaw yielded ethanol contents of 3.83 to 5.19% (v/v). The reducing sugar in the pawpaw was determined before and after saccharification. The reducing sugar was highest after 48 h of saccharification using Aspergillus niger. The value recorded was 7.6 to 13.6 g/100g. Brewers yeast gave a higher ethanol yield than bakers yeast. Saccharification for 48 h coupled with nutrient supplements significantly increased the ethanol yield.African Journal of Biotechnology Vol. 4 (7), pp. 657-659, 200

A GSM module-based smart electric meter reader

The traditional and estimated billing system of electric energy consumed in most part of Sub-Saharan Africa has become a lingering issue to the electricity consumers. This has therefore necessitated the advent of smart electric meters. In this work, we propose a smart electric meter reader that provides an efficient and economically viable technique for measuring the consumption of electricity. This proposed method tends to solve many issues of the traditional reading system, such as reading efficiency, accuracy, and the elimination of human interface. Our proposed method, consisting of a GSM module, is used to wirelessly communicate the smart meter readings to the electricity provider and the consumer in form of a text message. The results obtained from the evaluation of this work show that our proposed method has improved the accuracy of the meter reading process for proper accountability

FEATURE SELECTION FOR INTRUSION DETECTION SYSTEM IN A CLUSTER-BASED HETEROGENEOUS WIRELESS SENSOR NETWORK

Wireless sensor network (WSN) has become one of the most promising networking solutions with exciting new applications for the near future. Notwithstanding the resource constrain of WSNs, it has continued to enjoy widespread deployment.  Security in WSN, however, remains an ongoing research trend as the deployed sensor nodes (SNs) are susceptible to various security challenges due to its architecture, hostile deployment environment and insecure routing protocols. In this work, we propose a feature selection method by combining three filter methods; Gain ratio, Chi-squared and ReliefF (triple-filter) in a cluster-based heterogeneous WSN prior to classification. This will increase the classification accuracy and reduce system complexity by extracting 14 important features from the 41 original features in the dataset. An intrusion detection benchmark dataset, NSL-KDD, is used for performance evaluation by considering detection rate, accuracy and the false alarm rate. Results obtained show that our proposed method can effectively reduce the number of features with a high classification accuracy and detection rate in comparison with other filter methods. In addition, this proposed feature selection method tends to reduce the total energy consumed by SNs during intrusion detection as compared with other filter selection methods, thereby extending the network lifetime and functionality for a reasonable period