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Tran Thi Thuy Hang

Bio: Tran Thi Thuy Hang is an academic researcher from International University, Cambodia. The author has contributed to research in topics: Home security & Home automation. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

Papers
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Book ChapterDOI
19 Nov 2020
TL;DR: In this article, the authors designed and implemented an IoT-based smart home security system, which not only protects our home from unauthorized access but also saves our life from dangerous situations.
Abstract: A security system is one of the most important applications of a smart home that protects our home from thieves or potential risks. However, a traditional home security system usually suffers from high costs or does not satisfy the user’s needs. Therefore, in this research, we design and implement an IoT-based smart home security system, which not only protects our home from unauthorized access but also saves our life from dangerous situations. In our proposed system, biometric recognition based on the combination of fingerprint and face image is used to identify the homeowners who have permission to access the home. The main door will be opened if the input biometric image matches the one stored in the database. Otherwise, the system will raise an alarm with a doorbell and/or send a notification message to the homeowner. Besides, the system also collects environmental data in the home and notifies the homeowner in case of a dangerous situation, e.g. there was a fire or gas leak. The homeowner can monitor and control their home remotely via a friendly Web-based user interface. All activities happening in the home are recorded in a logging system for further analysis.

4 citations


Cited by
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Journal ArticleDOI
20 Jul 2021-Sensors
TL;DR: In this article, a cost-effective integrated system for smart home based on IoT and edge-computing paradigm is proposed. But, the proposed system is limited to the use of a Raspberry Pi (RPI) as a central controlling unit.
Abstract: Smart home applications are ubiquitous and have gained popularity due to the overwhelming use of Internet of Things (IoT)-based technology. The revolution in technologies has made homes more convenient, efficient, and even more secure. The need for advancement in smart home technology is necessary due to the scarcity of intelligent home applications that cater to several aspects of the home simultaneously, i.e., automation, security, safety, and reducing energy consumption using less bandwidth, computation, and cost. Our research work provides a solution to these problems by deploying a smart home automation system with the applications mentioned above over a resource-constrained Raspberry Pi (RPI) device. The RPI is used as a central controlling unit, which provides a cost-effective platform for interconnecting a variety of devices and various sensors in a home via the Internet. We propose a cost-effective integrated system for smart home based on IoT and Edge-Computing paradigm. The proposed system provides remote and automatic control to home appliances, ensuring security and safety. Additionally, the proposed solution uses the edge-computing paradigm to store sensitive data in a local cloud to preserve the customer's privacy. Moreover, visual and scalar sensor-generated data are processed and held over edge device (RPI) to reduce bandwidth, computation, and storage cost. In the comparison with state-of-the-art solutions, the proposed system is 5% faster in detecting motion, and 5 ms and 4 ms in switching relay on and off, respectively. It is also 6% more efficient than the existing solutions with respect to energy consumption.

40 citations

Journal ArticleDOI
01 May 2022-Sensors
TL;DR: A lossless image compression algorithm based on DAT that is based on a special class of atomic functions generalizing the well-known up-function of V.A. Rvachev is developed, and its performance is studied for different structures of DAT.
Abstract: Digital images are used in various technological, financial, economic, and social processes. Huge datasets of high-resolution images require protected storage and low resource-intensive processing, especially when applying edge computing (EC) for designing Internet of Things (IoT) systems for industrial domains such as autonomous transport systems. For this reason, the problem of the development of image representation, which provides compression and protection features in combination with the ability to perform low complexity analysis, is relevant for EC-based systems. Security and privacy issues are important for image processing considering IoT and cloud architectures as well. To solve this problem, we propose to apply discrete atomic transform (DAT) that is based on a special class of atomic functions generalizing the well-known up-function of V.A. Rvachev. A lossless image compression algorithm based on DAT is developed, and its performance is studied for different structures of DAT. This algorithm, which combines low computational complexity, efficient lossless compression, and reliable protection features with convenient image representation, is the main contribution of the paper. It is shown that a sufficient reduction of memory expenses can be obtained. Additionally, a dependence of compression efficiency measured by compression ratio (CR) on the structure of DAT applied is investigated. It is established that the variation of DAT structure produces a minor variation of CR. A possibility to apply this feature to data protection and security assurance is grounded and discussed. In addition, a structure or file for storing the compressed and protected data is proposed, and its properties are considered. Multi-level structure for the application of atomic functions in image processing and protection for EC in IoT systems is suggested and analyzed.

10 citations

Journal ArticleDOI
TL;DR: Wireless Controlled by Voice Automation in the Home Based on Bluetooth, a project that is moving forward with cell (application) in addition to giving the workspace to the elderly and injured, so they may control household utilities.
Abstract: Wireless Controlled by Voice Automation in the Home Based on Bluetooth, a project that is moving forward with cell (application) in addition to giving the workspace to the elderly and injured, so they may control household utilities. Voice interest is based on their phone. The gadget is disguised in such a manner that it will be quick to pass on, present, figure out, run, and keep up with the dark person. Wires for house automation are also utilized to interconnect the many electrical equipment that are used in a home. This work may be run with a variety of options, such as a fingerprint sensor that will lock and unlock the door, or any other security system that will be installed in a home or business. And a leakage detector will track the system, and if it detects a leak, an automated SMS will be delivered to the user through GSM.

2 citations

Journal ArticleDOI
TL;DR: Unlike the finger vein recognition methods based on traditional machine learning, the artificial neural network technique, especially deep learning, it without relying on feature engineering and have superior performance.
Abstract: Finger vein recognition is an emerging biometric recognition technology. Different from the other biometric features on the body surface, the venous vascular tissue of the fingers is buried deep inside the skin. Due to this advantage, finger vein recognition is highly stable and private. They are almost impossible to be stolen and difficult to interfere with by external conditions. Unlike the finger vein recognition methods based on traditional machine learning, the artificial neural network technique, especially deep learning, it without relying on feature engineering and have superior performance. To summarize the development of finger vein recognition based on artificial neural networks, this paper collects 149 related papers. First, we introduce the background of finger vein recognition and the motivation of this survey. Then, the development history of artificial neural networks and the representative networks on finger vein recognition tasks are introduced. The public datasets that are widely used in finger vein recognition are then described. After that, we summarize the related finger vein recognition tasks based on classical neural networks and deep neural networks, respectively. Finally, the challenges and potential development directions in finger vein recognition are discussed. To our best knowledge, this paper is the first comprehensive survey focusing on finger vein recognition based on artificial neural networks.