A Novel Gesomin Detection Method Based on Microwave Spectroscopy

Geosmin contamination in water is a leading cause of odor related complaints to water companies in UK, tainting water with an earthy smell that is detectable by humans in quantities as low as 4 nanograms per liter. Current Geosmin detection methods depend on lab-based equipment, requiring samples to be collected and transported before Geosmin can be tested. This research presents a novel method for the detection of Geosmin in water using Microwave spectroscopy capable of detecting differentiating between four levels of Geosmin contamination: 5 ng/L, 10 ng/L, 0.5 mg/L and 1 mg/L as well as control samples. Frequencies within the 5.4 GHz to 5.9, 6.4 GHz to 6.5 GHz and 7.2 GHz to 7.5 GHz ranges showed significant separation between the sample classe

An Implementation of a Multi-Hop Underwater Wireless Sensor Network using Bowtie Antenna

Water quality is a growing area of research, with more and more focus in the UK and globally on environmental issues and water quality. Current methods of monitoring environmental data such as air quality have continued to develop, spurred on by the growth of the Internet of Things. However, water quality monitoring mainly still depends on manual sample collection. This research presents the first implementation of a multi-hop underwater radio frequency sensor network using bowtie antennas combined with the 433 MHz frequency and a controlled flooding routing approach. The experimental work was conducted in the water reservoir and demonstrates the potential of multi-hop routing in underwater sensor networks to extend range to 19 meters as well as improvements on communication distances from 7 meters previously to 17 meters using radio frequency communications in an underwater environment. Simulated results show that the experimental platform used could enable the long-term deployment of an underwater wireless sensor network that used RF for periods of over a year with support for a 100 sensor node network broadcasting twice daily remaining active for 418 days or a 100 sensor node network broadcasting hourly remaining active for 406 days before any node deaths

Identification of Optimal Frequencies to Determine Alpha-Cypermethrin using Machine Learning Feature Selection Techniques

Machine learning feature space reduction techniques allow for vast feature spaces to be reduced with little loss or even significant improvements in the reliability of predictions of models. Microwave spectroscopy with feature spaces of over 8000 are not uncommon when considering magnitude and phase. Applying Machine learning techniques to this type of feature space allows for a quicker reduction and helps to identify the most suitable predictive features. The control of insect vectors that transmit diseases including malaria, visceral leishmaniasis and zika rely on the use of insecticide. These diseases affect millions, malaria alone accounted for 214 million new cases resulting in 438, 000 deaths in 2015. One method used in controlling the vectors is through indoor residual spraying, applying insecticide to the wall surface inside houses. Alpha-cypermethrin is one of the insecticides that is currently sprayed in several countries for vector control. Quality assurance and monitoring of the control activities is challenging relying on the use of laboratory-reared insects. This was improved with a chemical based Insecticide Quantification Kit, but these assays have been challenging to operationalise. An electromagnetic sensor is being developed to investigate the potential to detect alpha-cypermethrin. Preliminary experiments were carried out to differentiate tiles sprayed with Technical Grade alpha-cypermethrin, wettable powder containing 5% alpha-cypermethrin and wettable powder with over dose of alpha-cypermethrin using a horn antenna at a frequency range between 1 GHz to 6 GHz. The experimental results indicated the potential use of electromagnetic waves to determine alpha-cypermethrin in a non-destructive manner