Design of a smart system for rapid bacterial test

In this article, we present our initial findings to support the design of an advanced field test to detect bacterial contamination in water samples. The system combines the use of image processing and neural networks to detect an early presence of bacterial activity. We present here a proof of concept with some tests results. Our initial findings are very promising and indicate detection of viable bacterial cells within a period of 2 h. To the authors' knowledge this is the first attempt to quantify viable bacterial cells in a water sample using cell splitting. We also present a detailed design of the complete system that uses the time lapse images from a microscope to complete the design of a neural network based smart system

A Compact Size Implantable Antenna for Bio-medical Applications

Implantable antennas play a vital role in implantable sensors and medical devices. In this paper, we present the design of a compact size implantable antenna for biomedical applications. The antenna is designed to operate in ISM band at 915 MHz and the overall size of the antenna is 4×4×0.3mm3. A shorting pin is used to lower the operating frequency of the antenna. For excitation purpose a 50-ohm coaxial probe feed is used in the design. A superstrate layer is placed on the patch to prevent the direct contact between the radiating patch and body tissues. The antenna is simulated in skin layer model. The designed antenna demonstrates a gain of 3.22 dBi while having a -10 dB bandwidth of 240 MHz with good radiation characteristics at 915 MHz. The simulated results show that this antenna is an excellent candidate for implantable applications

Evaluating urban surface water quality in Luton

Using a single numerical value to indicate the quality of water, a so-called Water Quality Index (WQI) is a well-established way of rating the overall water quality status of a given water body. During the last few years, researchers in the water sector have developed different such indices to address their specific needs. In this study, we attempt to obtain a WQI formula suited for evaluating the water quality of the River Lea. We have selected four different sites on the River Lea and explore the possibility of monitoring using a minimum number of parameters only. The results obtained are very encouraging and provide a strong indication that only three parameters are enough to indicate water quality of a water body

A 'Human-in-the-Loop' Mobile Image Recognition Application for Rapid Scanning of Water Quality Test Results

This paper describes an interactive system for drinking water quality testing in small community supplies, particularly in the developing world. The system combines a lowcost field test (the Aquatest field kit), a mobile phone for data processing and communications, and a human operator who is able to react immediately to a test result. Once a water sample has been collected and incubated, the mobile phone camera is used to 'scan' the test and obtain the result, which is displayed to the user along with information about the health implications of the water quality. Initial prototypes, while not yet sufficiently robust for real-world use, demonstrate that the system is technically feasible. This opens up interesting possibilities for wider use of 'human-in-the-loop' sensor systems in environmental monitoring

Implantable antennas for bio-medical applications

Biomedical telemetry has gained a lot of attention with the development in the healthcare industry. This technology has made it feasible to monitor the physiological signs of patient remotely without traditional hospital appointments and follow up routine check-ups. Implantable Medical Devices(IMDs) play an important role to monitor the patients through wireless telemetry. IMDs consist of nodes and implantable sensors in which antenna is a major component. The implantable sensors suffer a lot of limitations. Various factors need to be considered for the implantable sensors such as miniaturization, patient safety, bio-compatibility, low power consumption, lower frequency band of operation and dual-band operation to have a robust and continuous operation. The selection of the antenna is a challenging task in implantable sensor design as it dictates performance of the whole implant. In this paper a critical review on implantable antennas for biomedical applications is presented

Using IoT Sensor Technologies to Reduce Waste and Improve Sustainability in Artisanal Fish Farming in Southern Brazil

Modern digital technologies have the great potential to improve the sustainability of fish farming in artisanal fisheries. However, in spite of the popularity of these technologies for fish farming in other parts of the world, Brazil still lags behind. To fill this gap, this study has conducted the first field study in implementing the IoT sensor technologies in Southern Brazil and documents the experiences in this paper. More specifically, it discusses developing sustainable artisanal fisheries infrastructure using these technologies with reference to southern Brazil, where the study explores the use of sensor technology in aquaculture and its effectiveness in reducing waste and improving productivity. The overarching goal of the project is to demonstrate how simple data collection using IoT sensors and its analysis can support artisanal freshwater fish farms in Brazil and beyond to increase production, reduce waste, and thereby improve their sustainability. The pilot implementation of these technologies has demonstrated the potential of increasing the productivity of the artisanal fisheries, reducing waste (e.g., loss of farmed fish, optimised feeding to reduce waste of feeds), and improving the sustainability of aquaculture. This paper documents the valuable firsthand experiences of selecting, adapting, and implementing the IoT sensor technologies with close cooperation from local research institutions and artisanal fish farmers. The paper describes the different implementation stages and use interviews with stakeholders as a testimony of the effectiveness of the IoT technology adoption

Implementation of Oxy-Fuel Combustion (OFC) technology in a Gasoline Direct Injection (GDI) engine fuelled with gasoline-ethanol blends

Nowadays, to mitigate the global warming problem, the requirement of carbon neutrality has become more urgent. Oxy-Fuel Combustion (OFC) has been proposed as a promising way of Carbon Capture and Storage (CCS) to eliminate Carbon Dioxide (CO2) emissions. This article explores the implementation of OFC technology in a practical Gasoline Direct Injection (GDI) engine fuelled with gasoline-ethanol blends, including E0 (gasoline), E25 (25% ethanol, 75% is gasoline in mass fraction) and E50 (50% ethanol, 50% is gasoline in mass fraction). The results show that with a fixed spark timing

Comparative investigation on macroscopic and microscopic characteristics of impingement spray of gasoline and ethanol from a GDI injector under injection pressure up to 50 MPa

Particulate Matter (PM) emissions from passenger vehicles have attracted considerable interest over the last decade. In order to reduce PM emissions, improving maximum injection pressure has been a developing trend for new generation GDI engines. However, comparing gasoline and ethanol impingement spray characteristics from a GDI injector under high injection pressure is still unclear. In this paper, a comparative investigation on both the macroscopic and microscopic characteristics of impingement spray from a GDI injector fuelled with gasoline and ethanol was performed under injection pressure up to 50 MPa, providing new findings to promote a more homogeneous air-fuel mixture and reduce PM emissions. The experimental results show that under the sam

Exploring the potential benefits of Ethanol Direct Injection (EDI) timing and pressure on particulate emission characteristics in a Dual-Fuel Spark Ignition (DFSI) engine

Nowadays, particulate matter emitted by vehicles severely impacts environmental quality and human health. In this paper, the potential benefits of Ethanol Direct Injection (EDI) timing and pressure on particulate emission characteristics in a Dual-Fuel Spark Ignition (DFSI) engine were initially and systematically explored. The experimental results illustrate that by delaying EDI timing from -340 ºCA to -300 ºCA, there is a significant benefit in both particulate number and mass concentration. Furthermore, the size distribution curve of particulate number changes from bimodal to unimodal, meantime size distribution curves of particulate mass consistently concentrate on the accumulation mode. By increasing EDI pressure from 5.5 MPa to 18 MPa, the droplet size of ethanol spray can be effectively reduced. The benefit of increasing EDI pressure is more apparent in reducing particulate number is than particulate mass. The concentration of number and mass for total particulates have a reduction of 51.15% and 22.64%, respectively. In summary, it was demonstrated that an appropriate EDI timing or high EDI pressure could be a practical and efficient way to reduce particulate emissions in a DFSI engine

Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach

Dissolved oxygen (DO) concentration is a vital parameter that indicates water quality. We present here DO short term forecasting using time series analysis on data collected from an aquaculture pond. This can provide the basis of data support for an early warning system, for an improved management of the aquaculture farm. The conventional forecasting approaches are commonly characterized by low accuracy and poor generalization problems. In this article, we present a novel hybrid DO concentration forecasting method with ensemble empirical mode decomposition (EEMD)-based LSTM (long short-term memory) neural network (NN). With this method, first, the sensor data integrity is improved through linear interpolation and moving average filtering methods of data preprocessing. Next, the EEMD algorithm is applied to decompose the original sensor data into multiple intrinsic mode functions (IMFs). Finally, the feature selection is used to carefully select IMFs that strongly correlate with the original sensor data, and integrate into both inputs for the NN. The hybrid EEMD-based LSTM forecasting model is then constructed. The performance of this proposed model in training and validation sets was compared with the observed real sensor data. To obtain the exact evaluation accuracy of the forecasted results of the hybrid EEMD-based LSTM forecasting model, four statistical performance indices were adopted: mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE). Results are presented for the short term (12-h) and the long term (1-month) that are encouraging, indicating suitability of this technique for forecasting DO values