V. O. Matthews

Standard Propagation Model Tuning for Path Loss Predictions in Built-Up Environments

This paper provides a simple optimization procedure using ATOLL planning tool for Standard Propagation Model (SPM). Measurement campaigns were conducted to collect Received Signal Strength (RSS) data over commercial base stations operating at 1800 MHz. The prediction accuracy of widely used models were assessed. The models provided high prediction errors. The optimization procedure involves the use of Digital Terrain Model (DTM), clutter classes, clutter heights, vector maps, scanned images, and Web Map Service (WMS). A Logarithmic weighting function was used to calculate the weight of the clutter loss on each pixel from the pixel with the receiver in the direction of the transmitter, up to the defined maximum distance. The approach has proven promising by achieving high accuracy and minimizing the prediction errors by 47.4%.

A queue scheduling approach to quality of service support in Diff-Serv networks using fuzzy logic

In this paper, the fuzzy logic-based decision algorithm is proposed for queue scheduling. This algorithm is employed to determine the optimal values for achieving fairness control for each queue-type serviced. It takes into account the dynamic nature of the internet traffic as regards to its time-varying packet arrival process that affects the network states and performance. In order that the fairness control is achieved some predetermined threshold values are set so as to enforce some level of control in the router for traffics of different quality of service requirements. The threshold values are set for queue priority, drop priority and the number of waiting packets in queue (queue limit). The fuzzy scheduling mechanism is proposed for implementation at the output port of the internet router. Three (3) queues are assumed, low priority queue, medium priority queue and high priority queue, which takes the form q0, q1 and q2, respectively. All the queues are subject to priority, that is, packets are placed in the queues according to their priority class by the classifier. The choice of prioritizing packets influences how queues are served but unlike the priority queue (PQ) scheduling mechanism, which only schedules packets based on priority, the fuzzy scheduler utilizes queue priority, drop priority and the queue limit as parameters for queue scheduling. Through simulation experiments, it is found that the effectiveness of the proposed method proves to offer better improvement for network performance in terms of traffic dropped and throughput.

Path loss dataset for modeling radio wave propagation in smart campus environment

Path loss models are often used by radio network engineers to predict signal coverage, optimize limited network resources, and perform interference feasibility studies. However, the propagation mechanisms of electromagnetic waves depend on the physical characteristics of the wireless channel. Therefore, efficient radio network planning and optimization requires detailed information about the specific propagation environment. In this data article, the path loss data and the corresponding information that are needed for modeling radio wave propagation in smart campus environment are presented and analyzed. Extensive drive test measurements are performed along three different routes (X, Y, and Z) within Covenant University, Ota, Ogun State, Nigeria (Latitude 6°40′30.3″N, Longitude 3°09′46.3″E) to record path loss data as the mobile receiver moves away from each of the three 1800 MHz base station transmitters involved. Also, the longitude, latitude, elevation, altitude, clutter height, and the distance information, which describes the smart campus environment, are obtained from Digital Terrain Map (DTM) in ATOLL radio network planning tool. Results of the first-order descriptive statistics and the frequency distributions of all the seven parameters are presented in tables and graphs respectively. In addition, correlation analyses are performed to understand the relationships between the network parameters and the terrain information. For ease of reuse, the comprehensive data are prepared in Microsoft Excel spreadsheet and attached to this data article. In essence, the availability of these data will facilitate the development of path loss models for efficient radio network planning and optimization in smart campus environment.

Experimental Investigation of Frequency Chaos Game Representation for in Silico and Accurate Classification of Viral Pathogens from Genomic Sequences

This paper presents an experimental investigation to determine the efficacy and the appropriate order of Frequency Chaos Game Representation (FCGR) for accurate and in silico classification of pathogenic viruses. For this study, we curated genomic sequences of selected viral pathogens from the virus pathogen database and analysis resource corpus. The viral genomes were encoded using the first to seventh order FCGRs so as to produce training and testing genomic data features. Thereafter, four different kernels of naive Bayes classifier were experimentally trained and tested with the generated FCGR genomic features. The performance result with the highest average classification accuracy of 98% was returned by the third and fourth order FCGRs. However, due to consideration for memory utilization, computational efficiency vis-a-vis classification accuracy, the third order FCGR is deemed suitable for accurate classification of viral pathogens from genome sequences. This provides a promising foundation for developing genomic based diagnostic toolkit that could be used to promptly address the global incidence of epidemics from pathogenic viruses.

IoT-Enabled Alcohol Detection System for Road Transportation Safety in Smart City

In this paper, an alcohol detection system was developed for road transportation safety in smart city using Internet of Things (IoT) technology. Two Blood Alcohol Content (BAC) thresholds are set and monitored with the use of a microcontroller. When the first threshold is reached, the developed system transmits the BAC level of the driver and the position coordinates of the vehicle to the central monitoring unit. At the reach of the second BAC threshold, the IoT-enabled alcohol detection system shuts down the vehicle’s engine, triggers an alarm and puts on the warning light indicator. A prototype of this scenario is designed and implemented such that a Direct Current (DC) motor acted as the vehicle’s engine while a push button served as its ignition system. The efficiency of this system is tested to ensure proper functionality. The deployment of this system will help in reducing the incidence of drunk driving-related road accidents in smart cities.

Alignment-Free Z-Curve Genomic Cepstral Coefficients and Machine Learning for Classification of Viruses

Accurate detection of pathogenic viruses has become highly imperative. This is because viral diseases constitute a huge threat to human health and wellbeing on a global scale. However, both traditional and recent techniques for viral detection suffer from various setbacks. In codicil, some of the existing alignment-free methods are also limited with respect to viral detection accuracy. In this paper, we present the development of an alignment-free, digital signal processing based method for pathogenic viral detection named Z-Curve Genomic Cesptral Coefficients (ZCGCC). To evaluate the method, ZCGCC were computed from twenty six pathogenic viral strains extracted from the ViPR corpus. Naïve Bayesian classifier, which is a popular machine learning method was experimentally trained and validated using the extracted ZCGCC and other alignment-free methods in the literature. Comparative results show that the proposed ZCGCC gives good accuracy (93.0385%) and improved performance to existing alignment-free methods.

NomadicBTS: Evolving cellular communication networks with software-defined radio architecture and open-source technologies

Abstract Utilization of software-defined radio (SDR) and open-source technologies in the next evolution of cellular communication networks may significantly reduce the capital expenditure (CAPEX) and operating expenditure (OPEX) of mobile network operators in emerging economies. In this paper, a novel cellular communication architecture named NomadicBTS was proposed, designed, and prototyped based on SDR paradigm to achieve an efficient, dynamic, and cost-effective wireless communication. The architecture comprises of hardware front-end and software that are compatible with general-purpose processor. Open-source technologies were adopted to implement a proof-of-concept architype of the architecture. A prototype femtocell (of approximately 7.54-m cell radius) was set up and two different commercially available smart phones were used to scan, detect, and measure the strength and quality of radio signals that were transmitted by the NomadicBTS prototype. Experimentation results showed that the network signal is visible to the smart phones, and its strength and quality are sufficient for good transmission and reception of Short Messaging Services (SMS). Unlike the conventional cellular networks which are capital-intensive, the results obtained in this study provide a basis to deploy low-cost cellular communication networks for the underserved areas in developing countries.

Image Enhancement of Lemon Grasses Using Image Processing Techniques (Histogram Equalization)

Lemon grass a type of medicinal plants has been part of human existence and has been applied in so many ways, like for healing, for drugs and for protection. In this paper, the conventional Histogram Equalization has been used to improve the images of the lemon grasses. MATLAB software was used to display the Histogram as well as the Histogram Equalization of the image of the lemon grasses. Image processing techniques to be used here is Histogram Equalization. The Histogram Equalization is considered, since it is one of the techniques in the enhancement of images and as such is being applied to the medicinal herb which in particular is the lemon grasses. The Histogram Equalization technique used may be seen as a conventional technique but the results obtained demonstrates its capability to improve the appearance of images by bringing out hidden details. The performance of the technique also shows that it is a better method in comparison to other types of Histogram Equalization methods.

Performance Evaluation of Dynamic Guard Channels Assignment with Queue and Prioritized Schemes

It has been said that dynamic guard channel (DGC) assignment scheme based on traffic intensities averages QoS and performs better than the prioritized guard channel assignment scheme with queue (QPGC) during traffic congestion. This work has extended the investigation to DGC with queue (QDGC) and carried out its comparison with other call traffic channel assignment schemes. Mathematical analysis of the models was done using Markov chain and simulation was carried out in MATLAB. When traffic arrival rates were symmetric, the QPGC had the lower call blocking probability than the QDGC but became the same when the system was congested. However, the performance of the two queuing schemes was the same when handover call traffic was more than new calls traffic.