Amelia Wong Azman

Bio: Amelia Wong Azman is an academic researcher from International Islamic University Malaysia. The author has contributed to research in topics: Smart camera & Capacitive sensing. The author has an hindex of 8, co-authored 69 publications receiving 286 citations. Previous affiliations of Amelia Wong Azman include Universiti Teknikal Malaysia Melaka & University of Kuala Lumpur.

Indoor UAV Positioning Using Stereo Vision Sensor

Abstract: The UAV system has becoming increasingly popular in the application such as surveillance, reconnaissance, mapping and many more. Different from guided vehicles, which rely on the pilot to navigate the system, UAV relies on autonomous control to provide this functionality. Hence, precise feedback on the position of the UAV is very important. Unlike outdoor positioning, there are no standard, low cost indoor positioning systems available. Hence, we proposed to utilize a stereo vision sensor as an indoor positioning system for our UAVs. The system utilizes two video cameras for stereo vision capture and set of fast algorithms so that position information can be obtained in real-time. Experiment conducted shows that the system could provide a reliable accuracy in real-time.

Mechanisms for doped PEDOT:PSS electrical conductivity improvement

Abstract: Due to their good electrical conductivity and versatility, conductive polymers (CPs), in particular, poly(3,4-ethylene dioxythiophene) (PEDOT):poly(styrene sulphonate) (PSS), have recently attracted considerable research interest in bioelectronics applications. This study provides insight into the mechanisms in PEDOT:PSS for increasing electrical conductivity. As such, the preparation of doped PEDOT:PSS using distinctive approaches, such as undergoing treatment and using secondary dopants is focused primarily on improving its electrical efficiency. It also systematically addresses various primary parameters that have significant effects on its conductivity. We present the potential of doped PEDOT:PSS for many promising applications in fields such as bioelectronics, through an in-depth analysis of the most remarkable studies recorded by various research groups over the past decade. Therefore, this review is expected to be significantly helpful in promoting further studies, as well as paving the way for increased qualification and productivity for future revolutions of organic CP materials.

Assessment of knowledge, attitude and practice of food allergies among food handlers in the state of Penang, Malaysia.

Abstract: Objectives Food handler's knowledge, attitude and practice regarding food allergies are important to prevent debilitating and sometimes fatal reactions. This study aimed to assess their food allergy knowledge, attitude and practice, which could help to maintain the safety and hygiene of food consumed by the public. Study design Cross-sectional survey. Methods A cross-sectional survey involving 121 conveniently sampled (81.76% response rate) respondents among the food handlers in the state of Penang, Malaysia, was conducted using a validated self-administered questionnaire. Their knowledge, attitude and work practice were captured using a 37-item questionnaire that elicited their responses using a mixture of closed-ended and Likert scale techniques. Results The mean knowledge score for respondents was 50.23 (SD = 14.03), attitude score was 54.26 (SD = 11.67) and practice score was 45.90 (SD = 24.78). Only 1.79% of the respondents were considered to have excellent knowledge, 21.37% had a low risk practice and 4.27% had positive attitudes towards food allergies. Most of the respondents (70%) knew about food allergies and their seriousness. There was a statistically significant correlation between the attitude and practice of respondents ( r = 0.51). The type of establishment was the only characteristic significantly associated ( P Conclusions More than half of food handlers in Penang have moderate levels of food allergy knowledge, practice and attitude. Only a small proportion of them have excellent food allergy knowledge, practice and attitude. Integration of food allergy training into compulsory food safety training programmes is needed to reduce food allergy risks and prevent fatal allergic reactions to food among their customers.

An Automated Face Recognition System for Intelligence Surveillance: Smart Camera Recognizing Faces in the Crowd

Abstract: Smart cameras are rapidly finding their way into intelligent surveillance systems. Recognizing faces in the crowd in real-time is one of the key features that will significantly enhance intelligent surveillance systems. The main challenge is the fact that the high volumes of data generated by high-resolution sensors make it computationally impossible for mainstream computers to process. In our proposed technique, the smart camera extracts all the faces from the full-resolution frame and sends the pixel information from these face areas to the main processing unit as a auxiliary video stream - potentially achieving massive data rate reduction. Face recognition software running on the main processing unit then performs the required pattern recognition.

Face detection system design for real time high resolution smart camera

Abstract: Recognizing faces in a crowd in real-time is a key feature which would significantly enhance Intelligent Surveillance Systems. Using a smart camera as a tool to extract faces for recognition would greatly reduce the computational load on the main processing unit. Main processing unit would not be overloaded by the demands of the high data rates of the video and could be designed solely for face recognition. The challenge is with the increasing speed and resolution of the camera sensors, a fast and robust face detection system is required for real time operation. In this paper we report on a multiple-stage face detection system that is designed for implementation on an FPGA based high resolution smart camera system. The system consist of filter stages to greatly reduce the region of interest in video image, followed by a face detection stage to accurately locate the faces. For filter stage, the algorithm is designed to be very fast so that it can be processed in real time. Meanwhile, for face detection stage, a hardware and software co-design technique is utilised to accelerate it.

Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges

Abstract: The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civil application domains, including real-time monitoring, providing wireless coverage, remote sensing, search and rescue, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection. Smart UAVs are the next big revolution in the UAV technology promising to provide new opportunities in different applications, especially in civil infrastructure in terms of reduced risks and lower cost. Civil infrastructure is expected to dominate more than $45 Billion market value of UAV usage. In this paper, we present UAV civil applications and their challenges. We also discuss the current research trends and provide future insights for potential UAV uses. Furthermore, we present the key challenges for UAV civil applications, including charging challenges, collision avoidance and swarming challenges, and networking and security-related challenges. Based on our review of the recent literature, we discuss open research challenges and draw high-level insights on how these challenges might be approached.

Handbook of Medical Imaging—Processing and Analysis

Abstract: In summary, this large, edited volume would provide a valuable addition to the shelf of anyone interested in the rapidly-developing field of medical image processing. At £79.95 it appears expensive, but given the coverage of the field, the page count and the quality of the illustrations, it would appear to be well worth the outlay.

A low-complexity algorithm for static background estimation from cluttered image sequences in surveillance contexts

Abstract: For the purposes of foreground estimation, the true background model is unavailable in many practical circumstances and needs to be estimated from cluttered image sequences. We propose a sequential technique for static background estimation in such conditions, with low computational and memory requirements. Image sequences are analysed on a block-by-block basis. For each block location a representative set is maintained which contains distinct blocks obtained along its temporal line. The background estimation is carried out in a Markov Random Field framework, where the optimal labelling solution is computed using iterated conditional modes. The clique potentials are computed based on the combined frequency response of the candidate block and its neighbourhood. It is assumed that the most appropriate block results in the smoothest response, indirectly enforcing the spatial continuity of structures within a scene. Experiments on real-life surveillance videos demonstrate that the proposedmethod obtains considerably better background estimates (both qualitatively and quantitatively) than median filtering and the recently proposed "intervals of stable intensity" method. Further experiments on the Wallflower dataset suggest that the combination of the proposed method with a foreground segmentation algorithm results in improved foreground segmentation.

Epidemiology of restaurant-associated foodborne disease outbreaks, United States, 1998-2013.

Abstract: Although contamination of food can occur at any point from farm to table, restaurant food workers are a common source of foodborne illness. We describe the characteristics of restaurant-associated foodborne disease outbreaks and explore the role of food workers by analysing outbreaks associated with restaurants from 1998 to 2013 reported to the Centers for Disease Control and Prevention's Foodborne Disease Outbreak Surveillance System. We identified 9788 restaurant-associated outbreaks. The median annual number of outbreaks was 620 (interquartile range 618-629). In 3072 outbreaks with a single confirmed aetiology reported, norovirus caused the largest number of outbreaks (1425, 46%). Of outbreaks with a single food reported and a confirmed aetiology, fish (254 outbreaks, 34%) was most commonly implicated, and these outbreaks were commonly caused by scombroid toxin (219 outbreaks, 86% of fish outbreaks). Most outbreaks (79%) occurred at sit-down establishments. The most commonly reported contributing factors were those related to food handling and preparation practices in the restaurant (2955 outbreaks, 61%). Food workers contributed to 2415 (25%) outbreaks. Knowledge of the foods, aetiologies, and contributing factors that result in foodborne disease restaurant outbreaks can help guide efforts to prevent foodborne illness.

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

Abstract: In the last few years, there has been a growing expectation created about the analysis of large amounts of data often available in organizations, which has been both scrutinized by the academic world and successfully exploited by industry. Nowadays, two of the most common terms heard in scientific circles are Big Data and Deep Learning. In this double review, we aim to shed some light on the current state of these different, yet somehow related branches of Data Science, in order to understand the current state and future evolution within the healthcare area. We start by giving a simple description of the technical elements of Big Data technologies, as well as an overview of the elements of Deep Learning techniques, according to their usual description in scientific literature. Then, we pay attention to the application fields that can be said to have delivered relevant real-world success stories, with emphasis on examples from large technology companies and financial institutions, among others. The academic effort that has been put into bringing these technologies to the healthcare sector are then summarized and analyzed from a twofold view as follows: first, the landscape of application examples is globally scrutinized according to the varying nature of medical data, including the data forms in electronic health recordings, medical time signals, and medical images; second, a specific application field is given special attention, in particular the electrocardiographic signal analysis, where a number of works have been published in the last two years. A set of toy application examples are provided with the publicly-available MIMIC dataset, aiming to help the beginners start with some principled, basic, and structured material and available code. Critical discussion is provided for current and forthcoming challenges on the use of both sets of techniques in our future healthcare.