Abdul Rahman Mohd Saad

Classifying Sources Influencing Indoor Air Quality (IAQ) Using Artificial Neural Network (ANN)

Monitoring indoor air quality (IAQ) is deemed important nowadays. A sophisticated IAQ monitoring system which could classify the source influencing the IAQ is definitely going to be very helpful to the users. Therefore, in this paper, an IAQ monitoring system has been proposed with a newly added feature which enables the system to identify the sources influencing the level of IAQ. In order to achieve this, the data collected has been trained with artificial neural network or ANN—a proven method for pattern recognition. Basically, the proposed system consists of sensor module cloud (SMC), base station and service-oriented client. The SMC contain collections of sensor modules that measure the air quality data and transmit the captured data to base station through wireless network. The IAQ monitoring system is also equipped with IAQ Index and thermal comfort index which could tell the users about the room’s conditions. The results showed that the system is able to measure the level of air quality and successfully classify the sources influencing IAQ in various environments like ambient air, chemical presence, fragrance presence, foods and beverages and human activity.

Indoor air quality monitoring system using wireless sensor network (WSN) with web interface

Indoor air quality (IAQ) is a term that describes the air quality of the building occupants. IAQ can be influenced by physical factors like dust particles, chemical or gaseous pollutants (including carbon dioxide, carbon monoxide and volatile organic compounds) and biological factors like molds and bacteria growth which largely depend on temperature and humidity condition of a room. These kinds of pollutants can induce a bad health effects to building occupants. In this study, a web-based system for monitoring indoor air quality is presented. The system has been developed to monitor the environmental parameters of air quality like gaseous pollutants, particulate matter, temperature and humidity that can affect the occupants comfort, health and indoor work environment if not properly maintained. Generally, the system consists of a desktop computer acting as base station, an array of metal oxide gas sensor, temperature and humidity sensor, particle dust sensor and wireless sensor network (WSN) node as a wireless transmitter. The measured data from sensors are sent to base station via WSN node. The computer will log the collected data using a self-developed server program which can be accessed through the web.

Modelling indoor propagation for WSN deployment in smart building

This paper models the signal strength measurements at 2.4 GHz in indoor environment. The received signal strength indicator (RSSI) measurement is used to investigate the wireless network coverage in a real office environment where obstacles such as furniture are present. From this experiment, a mapping is created to determine the suitable positions for a short range sensor nodes deployment for sensing humidity, temperature, human movement, etc. The purpose is to evaluate the suitable area for WSN deployments using RF signal and to minimize the number of sensor nodes required for data gathering and monitoring applications. The result shows that through adequate planning of WSN nodes, good radio coverage and efficient monitoring can be achieved for greener building.

Implementation of index for real-time monitoring indoor air quality system

Indoor air quality is a rising issue these days. People are becoming are of the importance of indoor air quality. This paper further advances the technology for indoor air quality surveillance by adding an index feature to it. The index feature, accompanied with real-time monitoring system is very useful in giving the real-time alert to the users on the conditions of the current indoor air quality. This index is very handy because human beings ability to detect the changes in the air quality is limited while the changes in the air quality may be rapid. The index helps the users to recognize the conditions of the air instantly by giving four signals, good, normal, unhealthy and hazardous. The index is developed based on the index used in the outside environment but by emphasizing on the internal air quality elements.

Effects of the Window Size and Feature Extraction Approach for Arrhythmia Classification

Arrhythmia, a common form of heart disease, can be detected from an electrocardiogram (ECG) signal. This research work presents a comparative study between five feature extraction methods applied separately on two window sizes for detecting three ECG pulse types, namely normal and two arrhythmia variations. The library support vector machine (LIBSVM) was used to classify the three classes of the ECG pulses. The ECG signals were obtained from MIT-BIH database. The ECG dataset was normalized and filtered to remove any noise and after that the signals were windowed into two window sizes (long window and short window). Five approaches were used to extract the features from the ECG signals. These approaches are scalar Autoregressive model coefficients, Haar discrete wavelet transform (DWT), Daubechies (db) DWT, Biorthogonal (bior) DWT, and principal components analysis (PCA). Each approach was applied separately on the two window sizes. The results of the classification show that scalar Autoregressive model coefficients, Haar, db, and bior are better approaches to catch the ECG features for short window than the long window. However, PCA gave the closest and highest results for the two window sizes than other approaches. That mean the PCA is the better feature extraction approach for both window sizes.

Analysis of feature selection with Probabilistic Neural Network (PNN) to classify sources influencing indoor air quality

There are various sources influencing indoor air quality (IAQ) which 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 sources that trigger the indoor air pollution. In this project, an IAQMS that able to classify sources influencing IAQ has been developed. This IAQMS applies a classification method based on Probabilistic Neural Network (PNN). It is used to classify the sources of indoor air pollution 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 accur...

Development of attitude control system on RCM3400 microcontroller

This paper describes the development of a nano-satellite altitude control system (ACS) which employ a filter base controller with comparison with a simple adaptive predictive fuzzy logic controller (APFLC) for a 1, 2 and 3 axis orientation using RCM3400 controller. For this the paper describe about the different performance both controller apply in the hardware. The physical interface, module and configuration with several key features are described. The result of the controller apply in to the hardware is shown in this paper. The interface, communication protocol and data handling for the microcontroller are described in this paper.

Wireless Heart Abnormality Monitoring Kit Based on Raspberry Pi

Heart monitoring kits are only available for bedridden patients and the traditional heart monitoring kits have many wires that are obstacle patients’ mobility. Most of the existing heart monitoring kits can not detect heart diseases. Thus, the current study proposed a wireless heart monitoring kit to monitor patients with a heart abnormality. The proposed kit can detect and classify four arrhythmia types as well as normal ECG with high accuracy. The design and development of the wireless heart abnormality monitoring kit (WHAMK) in this research were divided into three stages. These stages are the development of an arrhythmias detection and classification method using artificial intelligence approach, design and implementation of the kit hardware, and design and coding of the kit software. Arrhythmias classification approach is divided into four stages, namely obtaining the electrocardiograph (ECG) signals, preprocessing, features extraction and classification. The features extraction method are based on statistical features. The library support vector machine (LIBSVM) was used to classify the ECG signals. The hardware of the kit is divided into two parts, namely ECG body sensor (EBS), and processing and displaying unit (PDU). EBS working on acquiring the ECG signal from patients body. PDU working on processing the collected ECG signal, plotting it and detecting the arrhythmias. Arrhythmias classification approach was developed by using statistical features and LIBSVM. They were implemented in the software of the kit to enable it to detect the arrhythmias in the real-time and fully automatically. The kit can detect and classify four arrhythmia types as well as normal sinus rhythm (NSR). These types of arrhythmia are premature atrial contraction (PAC), premature ventricles contraction (PVC), Bradycardia and Tachycardia. The proposed kit gave a good accuracy for detecting and classifying Arrhythmia with the overall accuracy of 96.2%.

Development of indoor environmental index: Air quality index and thermal comfort index

In this paper, index for indoor air quality (also known as IAQI) and thermal comfort index (TCI) have been developed. The IAQI was actually modified from previous outdoor air quality index (AQI) designed by the United States Environmental Protection Agency (US EPA). In order to measure the index, a real-time monitoring system to monitor indoor air quality level was developed. The proposed system consists of three parts: sensor module cloud, base station and service-oriented client. The sensor module cloud (SMC) contains collections of sensor modules that measures the air quality data and transmit the captured data to base station through wireless. Each sensor modules includes an integrated sensor array that can measure indoor air parameters like Carbon Dioxide, Carbon Monoxide, Ozone, Nitrogen Dioxide, Oxygen, Volatile Organic Compound and Particulate Matter. Temperature and humidity were also being measured in order to determine comfort condition in indoor environment. The result from several experiments...

Development of wireless sensor network for monitoring indoor air pollutant

The air that we breathe with everyday contains variety of contaminants and particles. Some of these contaminants and particles are hazardous to human health. Most of the people don’t realize that the content of air they being exposed to whether it was a good or bad air quality. The air quality whether in indoor or outdoor environment can be influenced by physical factors like dust particles, gaseous pollutants (including carbon dioxide, carbon monoxide and volatile organic compounds) and biological like molds and bacteria growth which largely depend on temperature and humidity condition of a room. These kinds of pollutants can affect human health, physical reaction, comfort or work performance. In this study, a wireless sensor network (WSN) monitoring system for monitor air pollutant in indoor environment was developed. The system was divided into three parts: web-based interface program, sensing module and a base station. The measured data was displayed on the web which is can be accessed by the user. Th...