Shazmin Aniza Abdul Shukor
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Preferred name
Shazmin Aniza Abdul Shukor
Official Name
Shazmin Aniza, Abdul Shukor
Alternative Name
Shukor, S. A.
Shukor, S. A.A
S. A.A, Shukor
Shukor, Shazmin Aniza Abdul
Abdul Shukor, Shazmin Aniza
Main Affiliation
Pusat Kecemerlangan Kecerdikan Robotik (COFRI)
Scopus Author ID
57214325384
Researcher ID
GSD-2143-2022

Research Output

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ORIENTATION-BASED PAIRWISE COARSE REGISTRATION with MARKERLESS TERRESTRIAL LASER SCANS

2019-10-01 , Mohd Isa S.N. , Shazmin Aniza Abdul Shukor , Rahim N.A. , Maarof I. , Yahya Z.R. , Zakaria A. , Abdullah A.H. , Wong R.

In this paper, pairwise coarse registration is presented using real world point cloud data obtained by terrestrial laser scanner and without information on reference marker on the scene. The challenge in the data is because of multi-scanning which caused large data size in millions of points due to limited range about the scene generated from side view. Furthermore, the data have a low percentage of overlapping between two scans, and the point cloud data were acquired from structures with geometrical symmetry which leads to minimal transformation during registration process. To process the data, 3D Harris keypoint is used and coarse registration is done by Iterative Closest Point (ICP). Different sampling methods were applied in order to evaluate processing time for further analysis on different voxel grid size. Then, Root Means Squared Error (RMSE) is used to determine the accuracy of the approach and to study its relation to relative orientation of scan by pairwise registration. The results show that the grid average downsampling method gives shorter processing time with reasonable RMSE in finding the exact scan pair. It can also be seen that grid step size is having an inverse relationship with downsampling points. This setting is used to test on smaller overlapping data set of other heritage building. Evaluation on relative orientation is studied from transformation parameter for both data set, where Data set I, which higher overlapping data gives better accuracy which may be due to the small distance between the two point clouds compared to Data set II.

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Development of Artificial Stingless Bee Hive Monitoring using IoT System on Developing Colony

2024-01-01 , Ali M.A.A.C. , Bukhari Ilias , Norasmadi Abdul Rahim , Shazmin Aniza Abdul Shukor , Abdul Hamid Adom , Saad M.A.H. , Hassan M.F.A.

The trend of stingless bees’ farm in Malaysia has increased recently as it has been proven that its honey gives various benefits to human beings. This trend requires beekeepers to do more frequent inspections of beehives. However, the current practice of opening the cover to inspect the colony and honey will disrupt colony activity. According to a recent study, these stingless bees can only survive between 22 and 38 degrees Celsius, and harsh weather conditions might lead to the collapse of bee colonies. In order to ensure a consistent honey production, the IoT monitoring system will be implemented on an artificial stingless beehive. The system is equipped with an embedded system that utilizes a NodeMCU ESP8266, temperature and humidity sensors, and load cell sensors. Next, honey compartment weight, temperature and humidity inside stingless beehive, and temperature and humidity outside stingless beehive will be uploaded to the Internet of Things (IoT) platforms, namely Thingspeak and Cayenne. The data is sent to Thingspeak via the REST API while to Cayenne by the MQTT API. All data from the artificial stingless bee hive indicating the occurrence of colony rising and has been uploaded to the IoT platform. By analysing the data that were recorded for 13 days, all of the input data such as the weight of the honey compartment, the temperature in the hive, and the humidity in the hive, display its respective characteristics. For the honey compart weight, it has been found that the stingless bee colony is rising as a result of the increasing honey and colony in the compartment weight. Regarding the hive temperature, it has been determined that the temperature inside the hive is stable around 26°C to 38°C in normal weather conditions. Whereas for humidity inside the hive, it is remained between 76.5% and 85.6% due to the moisture from the honey inside the compartment. Lastly, these results indicate that the colony living in the artificial hive of stingless bees is healthy and growing.

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Surface Reconstruction from Unstructured Point Cloud Data for Building Digital Twin

2023-01-01 , Ismail F.A. , Shazmin Aniza Abdul Shukor , Norasmadi Abdul Rahim , Wong R.

This study highlights on the methods used for surface reconstruction from unstructured point cloud data, characterized by simplicity, robustness and broad applicability from 3D point cloud data. The input data consists of unstructured 3D point cloud data representing a building. The reconstruction methods tested here are Poisson Reconstruction Algorithm, Ball Pivoting Algorithm, Alpha Shape Algorithm and 3D surface refinement, employing mesh refinement through Laplacian smoothing and Simple Smoothing techniques. Analysis on the algorithm parameters and their influence on reconstruction quality, as well as their impact on computational time are discussed. The findings offer valuable insights into parameter behavior and its effects on computational efficiency and level of detail in the reconstruction process, contributing to enhanced 3D modeling and digital twin for buildings.

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A Review on the Stingless Beehive Conditions and Parameters Monitoring using IoT and Machine Learning

2021-12-01 , Ali M.A.A.C. , Bukhari Ilias , Norasmadi Abdul Rahim , Shazmin Aniza Abdul Shukor , Abdul Hamid Adom , Saad M.A.H.

One of the stingless bee types named Heterotrigona Itama are widespread in the tropics and subtropics especially in Malaysia. Due to its excellent nutritional content, stingless bee honey has gained favour in recent years. According to some studies, stingless bee honey has been used to cure eye infections, open wounds, diabetes, hypertension, and a variety of other diseases. Additionally, this stingless bee is non-venomous and smaller in size than common bees. Nevertheless, beekeepers may encounter a number of obstacles that may result in colony failure and under-production. These problems can be attributed to a variety of factors such as surrounding temperature, surrounding humidity and predators. Numerous stingless bee colonies and other bee species lost in 2006 due to Colony Collapse Disorder as a result of this problem. Therefore, this article will review previous research on optimizing stingless beehive conditions via the use of the Internet of Things (IoT) and machine learning to minimise this issue. To begin, a review of existing research on the characteristics of stingless bees, particularly the Heterotrigona Itama species, has been conducted to understand the natural habitat of Heterotrigona Itama. Following that, the articles on colony division was reviewed in order to transition the colony from the conventional hive to the artificial hive which also reviewed its design from the past article to simplify the sensors installation, IoT monitoring system and honey harvesting. Then, the prior article on sensors and IoT deployment was examined to monitor and analysis the data online without disturbing the colony activity inside the beehives. Finally, the article on the application of machine learning with the beehive dataset was reviewed the most precise and accurate machine learning method to predict the existence of bee activity in the hives and the future condition of beehive.

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Improved mobile robot based gas distribution mapping through propagated distance transform for structured indoor environment

2020-05-18 , Visvanathan R. , Kamarulzaman Kamarudin , Syed Muhammad Mamduh Syed Zakaria , Toyoura M. , Ali Yeon A.S. , Ammar Zakaria , Latifah Munirah Kamarudin , Mao X. , Shazmin Aniza Abdul Shukor

Mobile robot carrying gas sensors have been widely used in mobile olfaction applications. One of the challenging tasks in this research field is Gas Distribution Mapping (GDM). GDM is a representation of how volatile organic compound is spatially dispersed within an environment. This paper addresses the effect of obstacles towards GDM for indoor environment. This work proposes a solution by improvising the Kernel DM + V technique using propagated distance transform (DT) as the weighing function. Since DT computations are CPU heavy, parallel computing, using Compute Unified Device Architecture (CUDA) available in Graphics Processing Unit (GPU), is used to accelerate the DT computation. The proposed solution is compared with the Kernel DM + V algorithm, presenting that the proposed method drastically improves the quality of GDM under various kernel sizes. The study is also further extended towards the effect of obstacles on gas source localization task. The outcome of this work proves that the proposed method shows better accuracy for GDM estimation and gas source localization if obstacle information is considered.

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