Planar electromagnetic sensor based estimation of nitrate contamination in water sources using independent component analysis

The main advantages of electromagnetic sensors can be listed as low-cost, convenient, suitable for in-situ measurement system, rapid response, and high durability. In this paper, the output parameters of the planar electromagnetic sensor have been observed with different kind of water samples at different concentrations. The output parameters have been derived and tested to be incorporated with independent component analysis (ICA) and used as inputs for an analysis model. The analysis model targeted to estimate the amount of nitrate contamination in water samples with the assistance of ICA based on FastICA fixed point algorithm under the contrast functions of pow3, tanh, gauss, and skew. Nitrates sample in the form of ammonium nitrates (NH 4NO 3), each of different concentration between 5 mg and 20 mg dissolved in 1 litre of deionized water (Milli-Q) was used as one of the main references. The analysis model was tested with eight sets of mixed NH 4NO 3 and (NH 4) 2HPO 4 water samples. It is seen from the results that the model can acceptably detect the presence of nitrate added in Milli-Q water and capable of distinguishing the concentration level in the presence of other type of contamination. The system and approach presented in this paper has the potential to be used as a useful low-cost tool for water sources monitoring

Fire fighting robot

Fire-fighting is an important but dangerous occupation. A fire-fighter must be able to put out fire quickly and safely. This is a common way to prevent fatalities and further damages. So, technology has done its part by bridging up the gap between fire fighters and machineries. So, a robot is invented in order to combine both man kind and technology [1]. Fire Fighting Robot is designed to put out a fire, before it reaches out of control. Robot with these fire-handling abilities is a great advantage to replace fire extinguishers [2]. Water-based robot will be an advantage for users to refill the tank as it goes empty. Users can fill up water and keep the robot in a safe position. Water is a basic non-chemical liquid which will not experience any expiry or damage or corrosion. This invention would be a great contribution to mankind in order to ease their work and minimize the risk during fire put out

An ultrasonic system for profiling bubblers in water

Multi-phase flow occurs as two or more discrete phases flow in a closed pipe or a vessel. Examples of phases include gas, liquid or solid and also different immiscible liquids or solids[1]. Two phase flow of fluids (e.g. gas/liquid, liquid/liquid, etc.) is an important phenomenon in which two immiscible phases coexist in a thermodynamic equilibrium. As a two phase flow regime, bubbly flow column are intensively used as multiphase contactors and reactors in chemical, biochemical and petrochemical industries. Investigation of design parameters characterizing the operation and transport phenomena of bubble columns have led to better understanding of the hydrodynamic properties, heat and mass transfer mechanisms and flow regime characteristics ongoing during the operation[2, 3]. Due to the stringent regulations on precise flow control especially in the case of two phase fluid flow,, there has always been a necessity for developing an easier to use, yet more precise approaches or instrumentation. Accordingly, tomographic measurement is more significant and attractable especially in today's industrial process .

Determination of Pipe Deformity using an Ultrasonic System

Pipe deformation is of major concern as it can be an indication of pipe leakage. This paper presents an investigation using an ultrasonic system to measure the deformity on a pipe. The ultrasonic sensors are connected to aluminum probe cones which can collimate the ultrasonic signal towards the pipe surface. A deformation on the pipe will be represented by a specific voltage signal at the receiver circuit. Different weights were placed at the end of the pipe in order to make the pipe bend and thus causing deformation. Experimental result shows that the system can determine the modulus of elasticity which is identical to the predicted value. The modulus of elasticity represents the amount of deformation experienced by the pipe

An Ultrasonic System for Determining Mango Physiological Properties

There is an increasing requirement for high qualityfruits such as mango. Hence it is vital to have a fast, accurate andreliable method for measuring and monitoring the quality of fruitfrom the field to the consumer. This paper presents aninvestigation on the use of an ultrasonic measurement system fordetermining the quality of mango

Measurement of pipe strain using an ultrasonic system

Ultrasonic sensors can be used to measure strain occurring on an object. In this investigation, an ultrasonic signal utilized the reflected signal as a means of monitoring the condition of a pipe. This is an alternative to the strain gage which is commonly used but has a limited life span. The ultrasonic signal was transmitted to a specific location on the pipe, and then reflected by the pipe surface which experienced strain towards the ultrasonic receiver. Collimation of the transmitted and received signals is performed by aluminum probe cones attached to both ultrasonic transducers. Changes in the strain due to the pipe bending will result in changes in the electric signal due to the changes in the sound intensity. The received electric signal was processed by a signal conditioning circuit consisting of preamplifier, amplifier, band-pass filter and rectifier before being displayed. Two experiments were conducted to establish the relationship between strain on the pipe and the ultrasonic intensity. In order to verify the results, an experiment was conducted using a strain gage and the results were identical. The results show that the system is able to measure strain when the pipe bends

Detecting SIM box fraud by using support vector machine and artificial neural network

Fraud in communication has been increasing dramatically due to the new modern technologies and the global superhighways of communication, resulting in loss of revenues and quality of service in telecommunication providers especially in Africa and Asia. One of the dominant types of fraud is SIM box bypass fraud whereby SIM cards are used to channel national and multinational calls away from mobile operators and deliver as local calls. Therefore it is important to find techniques that can detect this type of fraud efficiently. In this paper, two classification techniques, Artificial Neural Network (ANN) and Support Vector Machine (SVM) were developed to detect this type of fraud. The classification uses nine selected features of data extracted from Customer Database Record. The performance of ANN is compared with SVM to find which model gives the best performance. From the experiments, it is found that SVM model gives higher accuracy compared to ANN by giving the classification accuracy of 99.06% compared with ANN model, 98.71% accuracy. Besides, better accuracy performance, SVM also requires less computational time compared to ANN since it takes lesser amount of time in model building and training

Image reconstruction methods for ultrasonic transmission mode tomography in bubbly flow regime

Image reconstruction from projections plays an important role in monitoring flow regimes by ultrasonic transmission mode tomography (UTMT) system. Fast and more accurate methods are necessary in case of on-line process e.g. bubbly flow regimes. In this work, analytical image reconstruction methods such as linear back projection (LBP), filter back projection (FBP) and convolution back projection (CBP) in bubbly flow regime is investigated and found that CBP is superior to other methods. Furthermore, different filters were applied to CBP to investigate the image quality improvement. Among different types of filters for CBP method, Ram-lack outperforms the others for UTMT. The peak signal to noise ratio (PSNR) of reconstructed images in this particular experiment was improved using Ram-lack in noiseless data

An assessment of stingless beehive climate impact using multivariate recurrent neural networks

A healthy bee colony depends on various elements, including a stable habitat, a sufficient source of food, and favorable weather. This paper aims to assess the stingless beehive climate data and examine the precise short-term forecast model for hive weight output. The dataset was extracted from a single hive, for approximately 36-hours, at every seven seconds time stamp. The result represents the correlation analysis between all variables. The evaluation of root-mean-square error (RMSE), as well as the RMSE performance from various types of topologies, are tested on four different forecasting window sizes. The proposed forecast model considers seven of input vectors such as hive weight, an inside temperature, inside humidity, outside temperature, outside humidity, the dewpoint, and bee count. The various network architecture examined for minimal RMSE are long short-term memory (LSTM) and gated recurrent units (GRU). The LSTM1X50 topology was found to be the best fit while analyzing several forecasting windows sizes for the beehive weight forecast. The results obtained indicate a significant unusual symptom occurring in the stingless bee colonies, which allow beekeepers to make decisions with the main objective of improving the colony’s health and propagation

Classification of SIM box fraud detection using support vector machine and artificial neural network

SIM box fraud is classified as one of the dominant types of fraud instead of subscription and superimposed types of fraud. This fraud activity has been increasing dramatically each year due to the new modern technologies and the global superhighways of communication, resulting the decreasing of the revenue and quality of service in telecommunication providers especially in Africa and Asia. This paper outlines the Artificial Neural Network (ANN) and Support Vector Machine (SVM) to detect Global System for Mobile communication (GSM) gateway bypass in SIM Box fraud. The suitable features of data obtained from the extraction process of Customer Database Record (CDR) are used for classification in the development of ANN and SVM models. The performance of ANN is compared with SVM to find which model gives the best performance. From the experiments, it is found that SVM model gives higher accuracy compared to ANN by giving the classification accuracy of 99.06% compared with ANN model, 98.71% accuracy