Modelling And Simulation of Voice Over Internet Protocol (Voip) Over Wireless Local Area Network (WLAN)

The adoption of Voice over Wireless Local Area Network is on tremendous increase due to its ease, non-intrusive and inexpensive deployment, low maintenance cost, universal coverage and basic roaming capabilities. However, deploying Voice over Internet Protocol (VoIP) over Wireless Local Area Network (WLAN) is a challenging task for many network managers, architects, planners, designers and engineers, hence the need for a guideline to design, model and simulate the network before deployment. This work analyzed parameters such as latency, jitter, packet loss, codec, bandwidth, throughput, voice data length and de-jitter buffer size, which quantify the quality of degradation over the network. The analytical mathematical E-model was used to predict the readiness of the existing network to support VoIP. The Transmission Rating Factor R was calculated as 85.08 indicating a high speech quality and excellent user satisfaction. Riverbed Modeller Academic Edition was used to model and simulate the network. Results from this project work show that VoIP can be successfully deployed on WLAN with perceived high speech quality, user’s satisfaction, low delay and high throughput

Performance Evaluation of The Quality of VoIP Over WLAN Codecs

The adoption of Voice over Wireless Local Area Network is on tremendous increase due its ease, non-intrusive, inexpensive deployment, low maintenance cost, universal coverage and basic roaming capabilities. However, deploying Voice over Internet Protocol (VoIP) over Wireless Local Area Network (WLAN) is a challenging task for many network managers, architects, planners, designers and engineers. Voice codec is one of the most critical components of a VoIP system. This work evaluates the effects of various codecs such as G.711, G.723.1, G.729A, G.728, G.726, Adaptive MultiRate (AMR) and Global System for Mobile communication (GSM) codecs on a VoIP over WLAN. Result from simulated network shows that the GSM codec offers the best quality of service for VoIP over WLA

Large-scale radio propagation path loss measurements and predictions in the VHF and UHF bands

For decades now, a lot of radio wave path loss propagation models have been developed for predictions across different environmental terrains. Amongst these models, empirical models are practically the most popular due to their ease of application. However, their prediction accuracies are not as high as required. Therefore, extensive path loss measurement data are needed to develop novel measurement-oriented path loss models with suitable correction factors for varied frequency, capturing both local terrain and clutter information, this have been found to be relatively expensive. In this paper, a large-scale radio propagation path loss measurement campaign was conducted across the VHF and UHF frequencies. A multi-transmitter propagation set-up was employed to measure the strengths of radio signals from seven broadcasting transmitters (operating at 89.30, 103.5, 203.25, 479.25, 615.25, 559.25 and 695.25 MHz respectively) at various locations covering a distance of 145.5 km within Nigerian urban environments. The measurement procedure deployed ensured that the data obtained strictly reflect the shadowing effects on radio signal propagation by filtering out the small-scale fading components. The paper also, examines the feasibilities of applying Kriging method to predict distanced-based path losses in the VHF and UHF bands. This method was introduced to minimize the cost of measurements, analysis and predictions of path losses in built-up propagation environment

A Genomic Signal Processing-Based Coronavirus Classification Model Using Deep Learning with Web-Based Console

Various strains of Coronavirus have led to numerous deaths worldwide with CoViD-19 being the most recent. Hence, the need for various research studies to determine and develop technologies that would reduce the spread of this virus as well as aid in the early diagnosis of the disease. The Severe Acute Respiratory Syndrome CoV (SARS-CoV), which emerged in 2003, Middle East Respiratory Syndrome CoV (MERS-CoV) in 2012 and Severe Acute Respiratory Syndrome CoV 2 (SARS-CoV-2) which is generally regarded as CoViD-19, in 2019 have very similar symptoms and genetics. Without proper diagnosis of these strains, they may be mistaken for one another. Therefore, there is a need to distinguish CoViD-19 from the other two Coronaviruses to enhance prompt and specific treatment. In this study, we developed a deep learning model with a web console for the classification of genomic sequences of the three Coronavirus strains using genomic signal processing. The DNA sequences harvested from the Virus Pathogen Database and Analysis Resource (ViPR) was used as dataset and these sequences were transformed to RGB images using Voss and Z-curve encodings. A convolutional neural network (CNN) model was consequently used for classification and incorporated in a web application platform developed with the Django framework. The results of the transformation of the images highlights the similarities of the three coronaviruses in terms of visual and genetic characteristics with the CNN model distinctly classifying SARS-CoV-2, SARS-CoV and MERS-CoV with a training and validation accuracies of 95.58% and 85% respectively which compares favourably with other results in the literature

Development of an IoT Based Data Acquisition and Automatic Irrigation System for Precision Agriculture

Agriculture has benefited greatly from improvements in Internet of Things based technology. Farm data can be sent to farmers in real-time through the advent of Internet of Things based technology which integrates data collection, transmission, storage and other essential components that provide for great user experience. This work involves the development of a system that enable the transmission of sensor field data to the Internet, via a microcontroller, a transceiver and a Wi-Fi module. In this work, an Internet of Things based data acquisition and automatic irrigation system for precision agriculture was designed and implemented using Arduino Uno, Soil Moisture and Temperature sensors, Proteus design suite, and the Arduino integrated development environment software. The significance of this work is evident as it, enables farmers perform specified functionalities at the comfort of their home, minimize wastage of water during irrigation and most importantly reduce the maintainability cost of the farm through minimal physical supervision. This work also elicits requirements for better improvements on the IoT-based data acquisition and automatic irrigation system

System Identification Model to Predict Output Current and Voltage of Solar Photovoltaic Panels

Solar irradiance is the energy per unit area received by the Sun as electromagnetic radiation. It is one of the most important renewable energy sources. Photovoltaic or other solar technologies are used to generate power more accurately than direct sun irradiation. Solar irradiance research and measurement have a variety of critical applications, including forecasting power generation from solar power plants, climate modeling, and weather forecasting. This paper presents a neural network-based system identification model developed using measured parameters from solar panels with various wattage specifications, namely, 10W, 20W, 40W, and 100W. The parameters that were measured to train the ANN model for the prediction of the output current and voltage include the angle of panel orientation, panel temperature, ambient temperature, irradiance, and wattage. Several training experiments were conducted and the best ANN model produced at 500 epochs gave an accuracy of 99.81% and a loss of 0.1940. The model was deployed into an intelligent Web App that was also developed in this study. This app could be a potential tool for renewable energy engineers and researchers

A Neural Network-based System Identification Model to Predict Output Current and Voltage of Solar Photovoltaic Panels

Solar irradiance is the energy per unit area received by the Sun as electromagnetic radiation. It is one of the most important renewable energy sources. Photovoltaic or other solar technologies are used to generate power more accurately than direct sun irradiation. Solar irradiance research and measurement have a variety of critical applications, including forecasting power generation from solar power plants, climate modeling, and weather forecasting. This paper presents a neural network-based system identification model developed using measured parameters from solar panels with various wattage specifications, namely, 10W, 20W, 40W, and 100W. The parameters that were measured to train the ANN model for the prediction of the output current and voltage include the angle of panel orientation, panel temperature, ambient temperature, irradiance, and wattage. Several training experiments were conducted and the best ANN model produced at 500 epochs gave an accuracy of 99.81% and a loss of 0.1940. The model was deployed into an intelligent Web App that was also developed in this study. This app could be a potential tool for renewable energy engineers and researchers

A Neural Network-based System Identification Model to Predict Output Current and Voltage of Solar Photovoltaic Panels

Solar irradiance is the energy per unit area received by the Sun as electromagnetic radiation. It is one of the most important renewable energy sources. Photovoltaic or other solar technologies are used to generate power more accurately than direct sun irradiation. Solar irradiance research and measurement have a variety of critical applications, including forecasting power generation from solar power plants, climate modeling, and weather forecasting. This paper presents a neural network-based system identification model developed using measured parameters from solar panels with various wattage specifications, namely, 10W, 20W, 40W, and 100W. The parameters that were measured to train the ANN model for the prediction of the output current and voltage include the angle of panel orientation, panel temperature, ambient temperature, irradiance, and wattage. Several training experiments were conducted and the best ANN model produced at 500 epochs gave an accuracy of 99.81% and a loss of 0.1940. The model was deployed into an intelligent Web App that was also developed in this study. This app could be a potential tool for renewable energy engineers and researchers

Interference Detection Among Secondary Users Deployed in Television Whitespace

Interference is one of the significant issues in television white space (TVWS) that limits the scalability of secondary user networks, lowers the quality of service, and causes harmful destruction to primary users. Interference among secondary users is one of the severe problems in TVWS because there is no legal rule that governs the coexistence of secondary nodes in the available white space channels. Many studies have been conducted to recognize the presence of primary signals in order to identify spectrum gaps and avoid interference between primary and secondary users, but the majority of them failed to detect interference among secondary users. Furthermore, the few works that mitigate interference among secondary users, rather than detecting it, assume interference. Therefore, in this paper, we develop an interference detection algorithm using an energy detector. To enhance the energy detector’s functionality, we consider dynamic thresholds rather than static ones. We also modify the binary hypothesis to account for interference between two non-cooperative users coexisting in TVWS. We simulate the energy detector technique in MATLAB R2020a environment and utilised various signal-to-noise ratios (SNR) values. With an SNR of −8 dB, the proposed algorithm attains a maximum performance of 95.35% as the probability of detection and meets the standard set by IEEE 802.22 which requires the probability of detection to surpass or equal to 90%