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Device-free localization (DFL) has become a hot topic in the paradigm of the Internet of Things. Traditional localization methods are focused on locating users with attached wearable devices. This involves privacy concerns and physical discomfort especially to users that need to wear and activate those devices daily. DFL makes use of the received signal strength indicator (RSSI) to characterize the user’s location based on their influence on wireless signals. Existing work utilizes statistical features extracted from wireless signals. However, some features may not perform well in different environments. They need to be manually designed for a specific application. Thus, data processing is an important step towards producing robust input data for the classification process. This paper presents experimental procedures using the deep learning approach to automatically learn discriminative features and classify the user’s location. Extensive experiments performed in an indoor laboratory environment demonstrate that the approach can achieve 84.2% accuracy compared to the other basic machine learning algorithms.
Abdul Syafiq Abdull Sukor; Latifah Munirah Kamarudin; Ammar Zakaria; Norasmadi Abdul Rahim; Sukhairi Sudin; Hiromitsu Nishizaki. RSSI-Based for Device-Free Localization Using Deep Learning Technique. Smart Cities 2020, 3, 444 -455.
AMA StyleAbdul Syafiq Abdull Sukor, Latifah Munirah Kamarudin, Ammar Zakaria, Norasmadi Abdul Rahim, Sukhairi Sudin, Hiromitsu Nishizaki. RSSI-Based for Device-Free Localization Using Deep Learning Technique. Smart Cities. 2020; 3 (2):444-455.
Chicago/Turabian StyleAbdul Syafiq Abdull Sukor; Latifah Munirah Kamarudin; Ammar Zakaria; Norasmadi Abdul Rahim; Sukhairi Sudin; Hiromitsu Nishizaki. 2020. "RSSI-Based for Device-Free Localization Using Deep Learning Technique." Smart Cities 3, no. 2: 444-455.
Boon Pin Ooi; Norasmadi Abdul Rahim; Ammar Zakaria; Maz Jamilah Masnan; Shazmin Aniza Abdul Shukor. Random subspace oracle (RSO) ensemble to solve small sample-sized classification problems. Journal of Intelligent & Fuzzy Systems 2019, 36, 3225 -3234.
AMA StyleBoon Pin Ooi, Norasmadi Abdul Rahim, Ammar Zakaria, Maz Jamilah Masnan, Shazmin Aniza Abdul Shukor. Random subspace oracle (RSO) ensemble to solve small sample-sized classification problems. Journal of Intelligent & Fuzzy Systems. 2019; 36 (4):3225-3234.
Chicago/Turabian StyleBoon Pin Ooi; Norasmadi Abdul Rahim; Ammar Zakaria; Maz Jamilah Masnan; Shazmin Aniza Abdul Shukor. 2019. "Random subspace oracle (RSO) ensemble to solve small sample-sized classification problems." Journal of Intelligent & Fuzzy Systems 36, no. 4: 3225-3234.
Norasmadi Abdul Rahim; Mp Paulraj; Abdul Hamid Adom; Shazmin Aniza Abdul Shukor; Maz Jamilah Masnan. Homogeneous multi-classifier system for moving vehicles noise classification based on multilayer perceptron. Journal of Intelligent & Fuzzy Systems 2015, 29, 149 -157.
AMA StyleNorasmadi Abdul Rahim, Mp Paulraj, Abdul Hamid Adom, Shazmin Aniza Abdul Shukor, Maz Jamilah Masnan. Homogeneous multi-classifier system for moving vehicles noise classification based on multilayer perceptron. Journal of Intelligent & Fuzzy Systems. 2015; 29 (1):149-157.
Chicago/Turabian StyleNorasmadi Abdul Rahim; Mp Paulraj; Abdul Hamid Adom; Shazmin Aniza Abdul Shukor; Maz Jamilah Masnan. 2015. "Homogeneous multi-classifier system for moving vehicles noise classification based on multilayer perceptron." Journal of Intelligent & Fuzzy Systems 29, no. 1: 149-157.
Effective management of patients with diabetic foot infection is a crucial concern. A delay in prescribing appropriate antimicrobial agent can lead to amputation or life threatening complications. Thus, this electronic nose (e-nose) technique will provide a diagnostic tool that will allow for rapid and accurate identification of a pathogen. This study investigates the performance of e-nose technique performing direct measurement of static headspace with algorithm and data interpretations which was validated by Headspace SPME-GC-MS, to determine the causative bacteria responsible for diabetic foot infection. The study was proposed to complement the wound swabbing method for bacterial culture and to serve as a rapid screening tool for bacteria species identification. The investigation focused on both single and poly microbial subjected to different agar media cultures. A multi-class technique was applied including statistical approaches such as Support Vector Machine (SVM), K Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA) as well as neural networks called Probability Neural Network (PNN). Most of classifiers successfully identified poly and single microbial species with up to 90% accuracy. The results obtained from this study showed that the e-nose was able to identify and differentiate between poly and single microbial species comparable to the conventional clinical technique. It also indicates that even though poly and single bacterial species in different agar solution emit different headspace volatiles, they can still be discriminated and identified using multivariate techniques.
Nurlisa Yusuf; Ammar Zakaria; Mohammad Iqbal Omar; Ali Yeon Md Shakaff; Maz Jamilah Masnan; Latifah Munirah Kamarudin; Norasmadi Abdul Rahim; Nur Zawatil Isqi Zakaria; Azian Azamimi Abdullah; Amizah Othman; Mohd Sadek Yasin. In-vitro diagnosis of single and poly microbial species targeted for diabetic foot infection using e-nose technology. BMC Bioinformatics 2015, 16, 1 -12.
AMA StyleNurlisa Yusuf, Ammar Zakaria, Mohammad Iqbal Omar, Ali Yeon Md Shakaff, Maz Jamilah Masnan, Latifah Munirah Kamarudin, Norasmadi Abdul Rahim, Nur Zawatil Isqi Zakaria, Azian Azamimi Abdullah, Amizah Othman, Mohd Sadek Yasin. In-vitro diagnosis of single and poly microbial species targeted for diabetic foot infection using e-nose technology. BMC Bioinformatics. 2015; 16 (1):1-12.
Chicago/Turabian StyleNurlisa Yusuf; Ammar Zakaria; Mohammad Iqbal Omar; Ali Yeon Md Shakaff; Maz Jamilah Masnan; Latifah Munirah Kamarudin; Norasmadi Abdul Rahim; Nur Zawatil Isqi Zakaria; Azian Azamimi Abdullah; Amizah Othman; Mohd Sadek Yasin. 2015. "In-vitro diagnosis of single and poly microbial species targeted for diabetic foot infection using e-nose technology." BMC Bioinformatics 16, no. 1: 1-12.
Commercial mobile robots provide good platform for the study and development of algorithms for wireless mobile communication of devices. E-Puck is a good example, with wireless communication utilising Bluetooth among others. However, the limitations of Bluetooth communications lead to the investigation of using X-Bee module as an alternative. This is to allow the E-Puck to communicate with a computer and other mobile robots using specified Zigbee protocol. This paper presents X-Bee module as wireless communication method between computer and E-Pucks and the way they exchange data.
Humairah Mansor; Abdul Hamid Adom; Norasmadi Abdul Rahim. Wireless Communication for Mobile Robots Using Commercial System. International Journal on Advanced Science, Engineering and Information Technology 2012, 2, 53 -55.
AMA StyleHumairah Mansor, Abdul Hamid Adom, Norasmadi Abdul Rahim. Wireless Communication for Mobile Robots Using Commercial System. International Journal on Advanced Science, Engineering and Information Technology. 2012; 2 (1):53-55.
Chicago/Turabian StyleHumairah Mansor; Abdul Hamid Adom; Norasmadi Abdul Rahim. 2012. "Wireless Communication for Mobile Robots Using Commercial System." International Journal on Advanced Science, Engineering and Information Technology 2, no. 1: 53-55.