K. R. Kashwan

Tea quality prediction using a tin oxide-based electronic nose: an artificial intelligence approach

In this paper, we have (analyzed using a metal oxide sensor (MOS)-based electronic nose (EN)) five tea samples with different qualities, namely, drier month, drier month again over-fired, well-fermented normal fired in oven, well-fermented over-fired in oven, and under-fermented normal fired in oven. The flavour of tea is determined mainly by its taste and smell, which are determined by hundreds of volatile organic compounds (VOC) and non-volatile organic compounds present in tea. Tea flavour is traditionally measured through the use of a combination of conventional analytical instrumentation and human organoleptic profiling panels. These methods are expensive in terms of for example time and labour. The methods are also inaccurate because of a lack of either sensitivity or quantitative information. In this paper an investigation has been made to determine the flavours of different tea samples using an EN and thus to explore the possibility of replacing existing analytical and profiling panel methods. The technique uses an array of four MOSs, each of, which has an electrical resistance that has partial sensitivity to the headspace of tea. The signals from the sensor array are then conditioned by suitable interface circuitry resulting in our tea data-set. The data were processed using principal component analysis (PCA), fuzzy C means (FCM) algorithm. The data were then analyzed following the neural network paradigms, following the self-organizing map (SOM) method along with radial basis function (RBF) network and probabilistic neural network (PNN) classifier. Using FCM and SOM feature extraction techniques along with RBF neural network, we achieved 100% correct classification for the five different tea samples, each of which have different qualities. These results prove that our EN is capable of discriminating between the flavours of teas manufactured under different processing conditions, viz. over-fermented, over-fired, under-fermented, etc.

Electronic nose based tea quality standardization

In this paper we have used a metal oxide sensor (MOS) based electronic nose (EN) to analyze five tea samples with different qualities, namely, drier month, drier month again over-fired, well fermented normal fired in oven, well fermented overfired in oven, and under fermented normal fired in oven. The flavour of tea is determined mainly by its taste and smell, which is generated by hundreds of Volatile Organic Compounds (VOCs) and Non-Volatile Organic Compounds present in tea. These VOCs are present in different ratios and determine the quality of the tea. For example Assamica (Sri Lanka and Assam Tea) and Assamica Sinesis (Darjeeling and Japanese Tea) are two different species of tea giving different flavour notes. Tea flavour is traditionally measured through the use of a combination of conventional analytical instrumentation and human or ganoleptic profiling panels. These methods are expensive in terms of time and labour and also inaccurate because of a lack of either sensitivity or quantitative information. In this paper an investigation has been made to determine the flavours of different tea samples using an EN and to explore the possibility of replacing existing analytical and profiling panel methods. The technique uses an array of 4 MOSs, each of, which has an electrical resistance that has partial sensitivity to the headspace of tea. The signals from the sensor array are then conditioned by suitable interface circuitry. The data were processed using Principal Components Analysis (PCA), Fuzzy C Means algorithm (FCM). We also explored the use of a Self-Organizing Map (SOM) method along with a Radial Basis Function network (RBF) and a Probabilistic Neural Network classifier. Using FCM and SOM feature extraction techniques along with RBF neural network we achieved 100% correct classification for the five different tea samples with different qualities. These results prove that our EN is capable of discriminating between the flavours of teas manufactured under different processing conditions, viz. over-fermented, over-fired, under fermented, etc.

Robust electronic-nose system with temperature and humidity drift compensation for tea and spice flavour discrimination

The aim of this paper is to determine aroma and flavour of the tea and spices by using Electronic-nose (E-nose) system with temperature and humidity drift compensation techniques. E-nose sensors are used with variable temperature and humidity conditions. Compensation for drift is an important factor and that generally is neglected. Therefore, we have put an effort to compensate the drifts in E-nose response data. Firstly, drift coefficients for E-noses sensors due to temperature and humidity variations in samples are determined and subsequently, these coefficients are used to eliminate drift in E-nose response data during online capturing and processing. We have described the results and experiments conducted by using four metal oxide semiconductor (MOS) based E-nose sensors. Artificial neural network (ANN) based pattern recognition techniques are used for discrimination and classification of electronic nose response data for different flavour terms of tea and spice.

A new routing protocol for energy efficient mobile applications for ad hoc networks

Display Omitted A new Energy-Aware Span Routing Protocol (EASRP) for wireless ad hoc networks is proposed.Proposed protocol can minimize utilization of energy source by combining energy saving approaches Span and AFECA.It uses the Remote Activated Switch and wakes up the sleeping nodes during inactive time for reduce latency problem.The performance parameter of proposed protocol is tested under Network Simulator-2. A Mobile Ad hoc Network (MANET) is an infrastructure-less collection of nodes that are powered by portable batteries. Consumption of energy is the major constraint in a wireless network. This paper presents a new algorithm called Energy-Aware Span Routing Protocol (EASRP) that uses energy-saving approaches such as Span and the Adaptive Fidelity Energy Conservation Algorithm (AFECA). Energy consumption is further optimized by using a hardware circuit called the Remote Activated Switch (RAS) to wake up sleeping nodes. These energy-saving approaches are well-established in reactive protocols. However, there are certain issues to be addressed when using EASRP in a hybrid protocol, especially a proactive protocol. Simulation results for the EASRP protocol show an increase in energy efficiency of 12.2% and 17.45% compared with EAZRP and ZRP, respectively. The EASRP protocol also proves to be effective in by producing a better packet delivery ratio for low- and high-density networks as measured by the NS-2 simulation tool.

Determination of tea quality by using a neural network based electronic nose

In these paper we have used a metal oxide sensor based electronic nose (EN) to analyse five tea samples with different qualities, namely, drier month, drier month again over fired, well fermented normal fired in oven, well fermented over fired in oven, and under fermented normal fired in oven. The flavour of team is determined mainly by its taste and smell, which generated by hundreds of volatile organic compounds (VOCs) and non-volatile organic compounds present in tea. These VOCs are present in different ratios and determine the quality of the tea. For example Assamica (Sri Lanka and Assam tea) and Assamica Sinesis (Dajeeling and Japanese tea) are two different species of tea giving different flavour notes. Tea flavour is traditionally measured through the use of a combination of conventional analytical instrumentation and human organoleptic profiling panels. These methods are expensive in terms of time and labour and also inaccurate because of lack of either sensitivity or quantitative information. In this paper an investigation has been made to determine the flavours of different tea samples using an EN and to explore the possibility of replacing existing analytical and profiling panel methods. The technique uses as array of 4 metal oxide sensors (MOS), each of, which has an electrical resistance that has partial sensitivity to the headspace of tea. The signals from the sensor array are then conditioned by suitable interface circuitry. The data were processed using principal component analysis (PCA), fuzzy C means algorithm (FCM). We also explored the use of self-organizing map (SOM) method along with a radial basis function network (RBF) and a probabilistic neural network (PNN) classifier. Using FCM and SOM feature extraction techniques along with RBF neural network we achieved 100% correct classification for the five different tea samples with different qualities. These results prove that our EN is capable of discriminating between the flavours of teas manufactured under different processing conditions, viz. over-fermented, over-fired, under fermented etc.

Design and characterization of Pin fed microstrip patch antennae

The ever-increasing need for mobile communication and the emergence of newer technologies require an efficient design of antennae of smaller size for wider frequency range applications such as Wi-MAX. It is an enormous challenge. Microstrip patch antennae have found extensive application in compact wireless communication system. They have advantages of low-profile, conformability, low-cost fabrication and ease of integration with feed networks. This research analyses different feeding techniques and the effect of dielectric constant used for microstrip patch design. The main objective is to design and characterize pin-fed rectangular microstrip patch antenna. The characterization includes the effect of antennae dimensions (length and width), substrate dielectric constant (εr) and substrate thickness (t) on the radiation parameters of gain, return loss and bandwidth. The simulations have been carried out on high frequency simulation software. Two different materials of Teflon (dielectric constant 2.2) and glass epoxy (dielectric constant 4.4) are analyzed for substrate design. The simulated results are compared for analysis. The compact rectangular microstrip patch antenna design procedure for cellular phones (Wi-Max) is illustrated.

Performance Analysis for Visual Data Mining Classification Techniques of Decision Tree, Ensemble and SOM

This research paper is a comprehensive report on experimental setup, data collection methods, implementation and result analyses of market segmentation and forecasting using neural network based artificial intelligence tools. The main focus of the paper is on visual data mining applications for enhancing business decisions. The software based system is implemented as a fully automated and intelligent enough to take into effect of each sales transaction. It updates and instantly modifies forecasting statistics by receiving input sales data directly from sales counter through networked connectivity. The connectivity may be wired or wireless. Three artificial intelligence tools, namely decision tree, ensemble classifier and Self Organizing Maps (SOM) are used for data processing and data analysis. The visual data mining concept is implemented by presenting results in the form of visual interpretation in as simple as possible way to understand very complex statistics. The current research results are mapped to interactive visualization by using multilevel pie charts, multi bar charts, histograms, scatter plots, tree maps and dataflow diagrams. The different visualization techniques help in understanding different levels of information hidden in very large data sets. The results analysis show that decision tree has classified data correctly up to a 86.0 %, ensemble techniques produced an average of 88.0 % and the predictions using SOM has accuracy of 90.0 %. The survey carried out after implementation and use of the system shows that the system is very easy to understand and can be interpreted quickly with minimum efforts. General Terms Computer science and engineering, information technology, data mining, market, business.

Customer Segmentation Using Clustering and Data Mining Techniques

—Clustering technique is critically important step in data mining process. It is a multivariate procedure quite suitable for segmentation applications in the market forecasting and planning research. This research paper is a comprehensive report of k-means clustering technique and SPSS Tool to develop a real time and online system for a particular super market to predict sales in various annual seasonal cycles. The model developed was an intelligent tool which received inputs directly from sales data records and automatically updated segmentation statistics at the end of days business. The model was successfully implemented and tested over a period of three months. A total of n = 2138, customer, were tested for observations which were then divided into k = 4 similar groups. The classification was based on nearest mean. An ANOVA analysis was also carried out to test the stability of the clusters. The actual day to day sales statistics were compared with predicted statistics by the model. Results were quite encouraging and had shown high accuracy.

Design of CSRR loaded MIMO antenna for ISM band application

In this paper a novel micro strip patch of 2×2 Multiple Input Multiple Output (MIMO) antenna system is designed and simulated. The micro-strip patch antenna may designed by using Complementary Split Ring Resonator (CSRR). The micro strip antenna in its original design, resonates at 5.04 GHz. Further, the miniaturization of patch is achieved by loaded CSRR at ground plane to reduce resonating frequency. The size of single patch antenna is reduced by 76%. CSRR allows to decrease the resonator Q-factor easily without affecting any other parameters including shape and volume. The final design of patch antenna operates at 2.45 GHz by varying the CSRR. The radiation pattern, gain and return loss are measured. The proposed antenna is simulated through advanced design system (ADS) software.

Effective power utilization and conservation in smart homes using IoT

Overuse of energy has caused many environmental and economic crises. Home appliances consume high energy. Energy consumption by home appliances is considered as one of the most critical areas for the attention to the researchers. Energy saving is a big challenging. Energy can be saved effectively by proper management of electricity distribution for home appliances based on the activities of the users. Recognizing human activities and providing energy supply for those appliances that are related to that activity can provide effective power utilization and conservation. The existing system uses multiple sensors and servers which monitors the human activities, causing discomfort to users. Thus a simple technique, based on Internet of Things (IoT), for recognizing human activity through image processing is proposed in this paper. It is a real time approach for energy management in which a machine to machine communication takes place.