Humairah Mansor
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Preferred name
Humairah Mansor
Official Name
Mansor, Humairah
Alternative Name
Mansor, Humairah
Mansor, H.
Main Affiliation
Scopus Author ID
57008126300

Research Output
Now showing 1 - 6 of 6
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Detection of indoor building lighting fixtures in point cloud data using SDBSCAN

2025-06 , Humairah Mansor , Shazmin Aniza Abdul Shukor , Nurul Syahirah Khalid , Razak Wong Chen Keng

Building fixtures like lighting are very important to be modelled, especially when a higher level of modelling details is required for planning indoor renovation. LIDAR is often used to capture these details due to its capability to produce dense information. However, this led to the high amount of data that needs to be processed and requires a specific method, especially to detect lighting fixtures. This work proposed a method named Size Density-Based Spatial Clustering of Applications with Noise (SDBSCAN) to detect the lighting fixtures by calculating the size of the clusters and classifying them by extracting the clusters that belong to lighting fixtures. It works based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN), where geometrical features like size are incorporated to detect and classify these lighting fixtures. The final results of the detected lighting fixtures to the raw point cloud data are validated by using F1-score and IoU to determine the accuracy of the predicted object classification and the positions of the detected fixtures. The results show that the proposed method has successfully detected the lighting fixtures with scores of over 0.9. It is expected that the developed algorithm can be used to detect and classify fixtures from any 3D point cloud data representing buildings.

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Development of a swarming algorithm for mobile robots

2014 , Humairah Mansor

Swarming robots basically consist of a group of several simple robots that interact and collaborate with each other to achieve shared goals. It is inspired by social insects, which can perform tasks that are beyond the capability of an individual. In a navigation task, a single robot system is not suitable to be used as an agent for the navigation usually covers a wide range of area. Furthermore, a single robot system is more complicated and requires a higher cost to build since the mobile robots need to be more complex in order to enable their abilities. Therefore, a group of simple robots is introduced. A group of robots can perform their tasks together in a more efficient way compared to a single robot, hence develop a more robust system. This thesis presents an approach for swarming algorithm using autonomous mobile robots. This project implements the swarming algorithm by supplementing the ability of mobile robot platforms with autonomy and odour detection. The work focused on the localization of chemical odour source in the testing environment and the leader and follower swarm formation through wireless communication. The project was developed in stages, namely hardware implementation where the mobile robots were given the ability to detect obstacles. A TGS 2600 Figaro sensor was utilized to provide the ability to detect odour. To enable the mobile robots to communicate with each other and able to perform leader and follower designation once the target has been found, the robots were installed with X-Bee module. The robot which found the odour source first will be the leader and the other will automatically become a follower. The Received Signal Strength Indicator (RSSI) of X-Bee is used as the parameter to estimate the distance between the leader and the follower robots. The algorithm was developed using Arduino development environment. By combining these three algorithm stages, a simple swarming system is tested. In this research, the leader-follower designation has been proposed as the method of swarming searching behaviour. The results show that the searching method provides a centralized communication between all the mobile robots. This communication leads to a better wireless data exchange between mobile robots compared to the distributed communication approach which decision making is based on each agent in the testing environment. The RSSI used in this research shows the reliability as an estimation parameter between mobile robots. The use of RSSI is a new method of estimating the distance between two wireless communication nodes despite the widely use of Bluetooth, ultrasonic sensors and Global Positioning System (GPS). Based on the RSSI value, the swarming system experiment is demonstrated. From the results, future work on the stabilization of the RSSI value during the wireless data transmission can be further investigated.

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IoT-based Carbon Monoxide (CO) Real-Time Warning System Application in Vehicles

2021-12-01 , Kamarudin A.A.A. , Nurul Syahirah Khalid , Ismail Ishaq Ibrahim , Muhammad Aizat Abu Bakar , Mahadi M.Z. , Shazmin Aniza Abdul Shukor , Sukhairi Sudin , Mohd Zamri Hasan , Humairah Mansor

The project is about develop a system and application for detect the presence of Carbon Monoxide(CO) in car, since recently there are many cases of drowning while sleeping in car due to inhaling CO. The build system are able to detect the presence of CO and provide warning about level of CO to the users. It uses Blynk application to monitors level of CO inside the vehicle, MQ-9 gas sensor as the input sensor, ESP 8266 as medium to send data to the application via IoT-based and the level concentration of CO is displayed on the LCD in real-time displayed. For the output, it has 3 different condition based on the level concentration of CO. This project has been testing in six different situation. Based on the result, ambience air and in car with open window situation have lowest of CO level. Meanwhile, the highest of CO level is detect in smoke that are produced from fuel combustion of the car exhaust at distance 5 cm. Additionally, Principal Component Analysis (PCA) is used to analysed the ability of this system in clustering for each situation. As a result, PCA have clearly clustering data for every situation with the value of PC1 is 71.82% and PC2 is 28.18%, hence it is verified that the build system is able to applied in detecting the presence of CO. This project is believed able in helping to reduce the numbers of cases people drowning while sleeping due to inhaling CO in the car.

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Hand-held shelf life decay detector for non-destructive fruits quality assessment

2024 , Nurul Syahirah Khalid , Nordiana Shariffudiin , Shazmin Aniza Abdul Shukor , Humairah Mansor , Masturah Tunnur Mohamad Talib , Sukhairi Sudin , Ismail I. Ibrahim , Mohd Zamri Hasan , Siti Marhainis Othman , N.D.N Dalila , M.Thaqif B.N Ashimi

Perishable food such as fruits have a limited shelf life and can quickly degrade if not properly stored. One method for detecting decay in these foods is the use of ethylene gas. Ethylene is a naturally occurring hormone that is released by fruits as they ripen. By measuring the levels of ethylene in the storage area, it is possible to detect when fruits and vegetables are starting to degrade. This information can then be used to act, such as removing spoiled produce and adjusting storage conditions, to extend the shelf life of the remaining products. By utilizing ethylene gas for early detection of decay, it is possible to improve food safety and reduce food waste. The project aims to utilized ethylene gas from perishable food such as fruits before decay. This project proposed portable or hand-held detection ethylene gas by including temperature and humidity. The sensor will be measuring the level of ethylene gas, temperature and humidity. Next, machine learning method; K-Nearest Neighbour(KNN) were used to evaluate the accuracy of the proposed system. This project, a hand-held decay detector for perishable food products is believed can help to prevent food waste by detecting early signs of spoilage in fruits.

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An overview of object detection from building point cloud data

2021-06-11 , Humairah Mansor , Shazmin Aniza Abdul Shukor , Wong R.

3D laser scanner, also known as LiDAR (Light Detection and Ranging), is a device that able to collect dense representation of its surroundings. Its data in point cloud form is commonly used to monitor complex environments like the highways, infrastructures and buildings. The rapid development of 3D laser scanner nowadays has assisted the process of managing complicated and huge areas, especially in building and facility management. As the advancement in architectural and civil engineering increases, building spaces change frequently, as well as renovations work which consists of several items like structures (walls, ceilings, floors) and building fixtures (doors, windows). This has contributed towards complex and huge data to be processed which usually involves tedious and complicated work. Therefore, this data needs to be handled efficiently. Object recognition and classification is one of the most important process in point cloud data since it provides a full detail of building information. Object recognition is used to recognize multiple objects in point cloud data and classification process is used to classify the objects into a class based on the criteria of the objects. These processes reduce the noise and size of point cloud data to be processed. This paper provides an overview on data processing approaches, which focused on the process of object detection and classification, especially for buildings, as part of Building Information Management (BIM) and the possibility of future research in BIM modelling.

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Detection of building fixtures in 3D point cloud data

2021-12-01 , Humairah Mansor , Shazmin Aniza Abdul Shukor , Wong R.

Building architectural and civil engineering are constantly changing, causes the increases of building spaces as well as renovation works which includes structures such as walls, ceilings and floors, and building fixtures. Building fixtures are objects which is secured to the building, such as lighting fixtures, plug and socket, ceiling fan and so on. It is considered as one of the complex structures in building as the size of the fixtures are small and sometimes are hardly seen immediately. When a certain building changes, the building information need to be updated along with the changes of the building. The process to update the changes has contributed towards complex and huge data to be processed which usually involves tedious and complicated work. Therefore, to recognize the fixtures in building environment before renovation, an object recognition method is applied. This investigation focused on the recognition of lighting fixtures in the environments. By using MATLAB, an algorithm is developed to detect the point cloud data that belongs to the lighting fixtures. The investigation shows that the lighting fixtures can be identified by using Region of Interest (ROI) method within an environment. From the results, the accuracy of the dimensions of the lighting fixtures detected in point cloud data compared to the real one in the environment is 75% and 72% match, which is good but still need an improvement to be closely match with the real dimensions. The finding is hoped to simplify the tasks of determining the fixtures in the building before any changes is done.

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