Shape Classification of Harumanis Mango using Discriminant Analysis (DA) and Support Vector Machine (SVM)

The perceived quality of fruits, such as mangoes, is greatly dependent on many parameters such as ripeness, shape, size, and is influenced by other factors such as harvesting time. Unfortunately, a manual fruit grading has several drawbacks such as subjectivity, tediousness and inconsistency. By automating the procedure, as well as developing new classification technique, it may solve these problems. This paper presents the novel work on the using visible Imaging as a Tool in Quality Monitoring of Harumanis Mangoes. A Fourier-Descriptor method was developed from CCD camera images to grade mango by its shape. Discriminant analysis (DA) and Support vector machine (SVM) were applied for classification process and able to correctly classify 98.3% for DA and 100% for SVM

Interference issues and mitigation method in WSN 2.4GHz ISM band: A survey

Current lifestyles promote the development and advancement in wireless technologies, especially in Wireless Sensor Networks (WSN) due to its several benefits.WSN offers a low cost, low data rate, flexible routing, longer lifetime, and low-energy consumption suitable for unmanned and long term monitoring.Among huge WSN applications, some key applications are smart houses, environmental monitoring, military applications, and other monitoring applications.As a result, ubiquitous increase in the number of wireless devices occupying the 2.4GHz frequency band.This causes a dense wireless connection followed by interference problem to WSN in the 2.4GHz frequency band. WSN is most affected by the interference issue because it has a lower data rate and transmission power compared to WLAN.Despite efforts made by researchers, to the author's knowledge, the interference issue is still a major problem in wireless networks.This paper aims to review the coexistence and interference issues of existing wireless technologies in the 2.4GHz Industrial, Scientific and Medical (ISM) band.Keywords— Coexistence, Frequency Spectrum, IEEE 802.15.4, Interference, WSN, 2.4GHz ISM ban

Smell index for indoor air quality system based on multilayer perceptron (MLP)

Indoor air quality index (IAQI) is developed to help users understand the effect of air pollutants to human’s health, with respect to each band. It also gives an overall idea about the condition of air in the locations or rooms being measured. However, most of the IAQ indices presented by previous researchers in their works is calculated based on single pollutatant parameter only like carbon monoxide (CO) and sulfur dioxide (SO2). Since smell is also part of indoor air contaminants, thus it is important to calculated the IAQ indices based on an array of pollutant parameters. This study proposes a smell index (SI) that can inform the user about the perception of smell presents either the smell is “Neutral”, “Pleasant” or “Unpleasant”. In contrast with the IAQI which generates its index based on single pollutant parameter, SI is generated based on an array of pollutant parameters. It generates the smell perceptions based on all pollutants input from six gas sensors, which are parts of an indoor air quality monitoring system (IAQMS). In order to classify the perception smell, multilayer perceptron (MLP) classifier with a back propagation learning algorithm has been used. The results show that the classifier has successfully classify the perception of smell for each pollutant present in indoor environment like ambient air, human activity, presence of chemical products, presence of food and beverage, and presence of fragrance. The model for smell classification which is used to produce smell index (SI) is assigned with the following weightage: “Pleasant” - 1, “Neutral” – 0 and “Unpleasant - 2”. This SI is embedded to the IAQMS system

Analysis of feature selection with K-nearest neighbour (KNN) to classify indoor air pollutants

Indoor air may be polluted by various types of pollutants which may come from cleaning products, construction activities, perfumes, cigarette smoke and outdoor pollutants. This type of pollutants could emit dangerous gases such as carbon monoxide (CO), carbon dioxide (CO2), ozone (O3) and particulate matter. These gases are usually safe for us to breathe in if they are emitted in safe quantity but if the amount of these gases exceeded the safe level, they might be hazardous to human being especially children and people with asthmatic problem. Therefore, a smart indoor air quality monitoring system (IAQMS) is needed that able to tell the occupants about which pollutant that trigger the indoor air pollution. In this study, an IAQMS that able to classify the air pollutants has been developed. This IAQMS applies a classification method based on K-Nearest Neighbour (KNN). It is used to classify the air pollutants based on five conditions: ambient air, human activity, presence of chemical products, presence of food and beverage and presence of fragrance. In order to get good and best classification accuracy, an analysis of several feature selection based on data pre-processing method is done to discriminate among of sources. The output from each data pre-processing method has been used as the input for the classification. The result shows that KNN analysis with the data pre-processing method for most of the features obtained remarkably high classification accuracy of above 97% and able to classify the air pollutants at high classification rate

Pollutant recognition based on supervised machine learning for Indoor Air Quality monitoring systems

Indoor air may be polluted by various types of pollutants which may come from cleaning products, construction activities, perfumes, cigarette smoke, water-damaged building materials and outdoor pollutants. Although these gases are usually safe for humans, they could be hazardous if their amount exceeded certain limits of exposure for human health. A sophisticated indoor air quality (IAQ) monitoring system which could classify the specific type of pollutants is very helpful. This study proposes an enhanced indoor air quality monitoring system (IAQMS) which could recognize the pollutants by utilizing supervised machine learning algorithms: multilayer perceptron (MLP), K-nearest neighbour (KNN) and linear discrimination analysis (LDA). Five sources of indoor air pollutants have been tested: ambient air, combustion activity, presence of chemicals, presence of fragrances and presence of food and beverages. The results showed that the three algorithms successfully classify the five sources of indoor air pollution (IAP) with a classification rate of up to 100 percent. An MLP classifier with a model structure of 9-3-5 has been chosen to be embedded into the IAQMS. The system has also been tested with all sources of IAP presented together. The result shows that the system is able to classify when single and two mixed sources are presented together. However, when more than two sources of IAP are presented at the same period, the system will classify the sources as 'unknown', because the system cannot recognize the input of the new pattern

Disposable array sensor strip for quantification of sinensetin in orthosiphon stamineus benth samples

A disposable screen printed array sensor strip based on self-plasticized lipid membranes combined with chemometric algorithm has been developed and applied for quantification of Orthosiphon stamineus Benth extracts. Sinensetin, a pharmacologically active flavonoid in Orthosiphon stamineus Benth, was quantified with the sensor system using standard addition method. The method was compared with high performance thin layer chromatography (HPTLC). Partial least square (PLS) and principal component regression (PCR) were applied to the array sensor output to determine the sinensetin in O. stamineus samples from different suppliers. Comparison between the PLS and PCR models presented in the quantitative analysis showed that PLS have substantially better predictive capability than PCR. The root mean square error (RMSE) of Prediction for PLS and PCR were 0.17 ppm and 0.19 ppm, respectively. The concentration of sinensetin by PLS fell within the range of 0.25%–0.30% in six different batches of extracts that were supplied by Hovid Sdn Bhd (HV) while a range 0.18%–0.24% was obtained in ten different batches of extracts supplied by Nusantara Herbs Sdn Bhd (NH). The array sensor showed good correlation (0.9902) with the HPTLC method