Enhanced indoor positioning utilising wi-fi fingerprint and QR calibration techniques

The growing interest in location-based services (LBS), due to the demand for its application in personal navigation, billing and information enquiries, has expedited the research development for indoor positioning techniques. The widely used global positioning system (GPS) is a proven technology for positioning, navigation, but it performs poorly indoors. Hence, researchers seek alternative solutions, including the concept of signal of opportunity (SoOP) for indoor positioning. This research planned to use cheap solutions by utilizing available communication system infrastructure without the need to deploy new transmitters or beacons for positioning purposes. Wi-Fi fingerprinting has been identified for potential indoor positioning due to its availability in most buildings. In unplanned building conditions where the available number of APs is limited and the locations of APs are predesignated, certain positioning algorithms do not perform well consistently. In addition, there are several other factors that influence positioning accuracy, such as different path movements of users and different Wi-Fi chipset manufacturers. To overcome these challenges, many techniques have been proposed, such as collaborative positioning techniques, data fusion of radio-based positioning and mobile-based positioning that uses sensors to sense the physical movement activity of users. A few researchers have proposed combining radio-based positioning with vision-based positioning while utilizing image sensors. This work proposed integrated layers of positioning techniques, which is based on enhanced deterministic method; Bayesian estimation and Kalman filter utilising dynamic localisation region. Here, accumulated accuracy is proposed with distribution of error location by estimation at each test point on path movement. The error distribution and accumulated accuracy have been presented in graphs and tables for each result. The proposed algorithm has been enhanced by location based calibration with additional QR calibration. It allows not only correction of the actual position but the control of the errors from being accumulated by utilizing the Bayesian technique and dynamic localisation region. The position of calibration point is determined by analysing the error distribution region. In the last part, modification on Kalman filter step for calibration algorithm did further improve the location error compared to other deterministic algorithms with calibration point. The CDF plots have shown all developed techniques that provide accuracy improvement for indoor positioning based on Wi-Fi fingerprinting and QR calibration

LSTM-based Electroencephalogram Classification on Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is categorized as a neurodevelopmental disability. Having an automated technology system to classify the ASD trait would have a huge influence on paediatricians, which can aid them in diagnosing ASD in children using a quantifiable method. A novel autism diagnosis method based on a bidirectional long-short-term-memory (LSTM) network's deep learning algorithm is proposed. This multi-layered architecture merges two LSTM blocks with the other direction of propagation to classify the output state on the brain signal data from an electroencephalogram (EEG) on individuals; normal and autism obtained from the Simon Foundation Autism Research Initiative (SFARI) database. The accuracy of 99.6% obtained for 90:10 train:test data distribution, while the accuracy of 97.3% was achieved for 70:30 distribution. The result shows that the proposed approach had better autism classification with upgraded efficiency compared to single LSTM network method and potentially giving a significant contribution in neuroscience research

Graphical user interface for wireless patient monitoring system using zigbee communication

Nowadays, heart related diseases are on the rise situation. In Malaysia, the proportion of patients is increasing day by day but the number of doctor and nurse slightly different situation. For this reason, the new propose graphical user interface for wireless patient monitoring system is proposed in order to help doctors and nurses to monitor their patient wirelessly for 24 hours by using a designated proposed device. This system runs as prototype to minimize the costing issue in the hospital. This system consists of software and hardware. Visual Basic Net 2010 software is used to design the graphical user interface (GUI) and Peripheral Interface Controller (PIC) 16F877A microcontroller is used as the hardware to implement the whole proposed system. This system is can be divided into three parts. There are three stages that involved in completing the system. The first is developing a program for the microcontroller, the second is transmitting the data from microcontroller to the personal computer (PC) using XBee module and the third is designing the GUI. In conclusion, the proposed GUI for wireless patient monitoring system facilitated the doctor and nurse in monitoring the patient and increased the efficiency of patient monitoring. For the future recommendation

Child in car alarm system using various sensors

The network service system is increasingly extended as the demand from various of usage is growing. Although many products had been invented, there are still the incidents that involve to death of children which been left in cars often occur. The system is designed in order to overcome this unwanted incident from happening. The proposed system is designed to detect sound or voice and any movement made by the children that had been left behind in a vehicle. The main target of the system is to create a complete system which uses Global System for Mobile Communication (GSM) that can communicate with human. GSM modem is the medium to interact and communicate with the module. It is used to send and receive Short Messaging System (SMS) based on which appropriate actions taken by the user. PIC microcontroller performs as heart of whole controlling system. The system at the final stage can be used to detect the sound that had been produced by a human at optimum strength. In addition, it was also able to detect motion that performed by a person and can detect any sounds that produced from inside the car. The system that has generated is expected to continue to expand with concomitant change in time with the developed and equipped with a great technology

ConVnet BiLSTM for ASD Classification on EEG Brain Signal

As a neurodevelopmental disability, Autism Spectrum Disorder (ASD) is classified as a spectrum disorder.  The availability of an automated technology system to classify the ASD trait would have a significant impact on paediatricians, as it would assist them in diagnosing ASD in children using a quantifiable method. In this paper, we propose a novel autism diagnosis method that is based on a hybrid of the deep learning algorithms. This hybrid consists of a convolutional neural network (ConVnet) architecture that merges two LSTM blocks (BiLSTM) with the other direction of propagation to classify the output state on the brain signal data from electroencephalogram (EEG) on individuals; typically development (TD) and autism (ASD) obtained from the Simon Foundation Autism Research Initiative (SFARI) database to classify the output state. For a 70:30 data distribution, an accuracy of 97.7 percent was achieved. Proposed methods outperformed the current state-of-the art in terms of autism classification efficiency and have the potential to make a significant contribution to neuroscience research, as demonstrated by the results

Characterization of chemically treated new natural cellulosic fibers from peduncle of Cocos nucifera l. Var typica

The aim of this study is to look into the effect of chemical treatments on fibers extracted from the unbranched portion of the peduncle of the coconut tree (Cocos nucifera L. Var typica) for use as reinforcement in polymer composites. The extracted coconut tree peduncle (CTP) fibers were treated with 5% alkali, 6% benzoyl peroxide, 0.5% potassium permanganate, and 1% stearic acid. The chemical composition, surface morphology, mechanical properties, crystallinity, and thermal decomposition of chemically treated CTP fibers were thoroughly investigated. The chemical analysis shows that fibers treated with 0.5% potassium permanganate had a maximum cellulose content of 58.05 wt% after hemicellulose, lignin, and wax were removed from the fiber. This has been due to the chemically treated fiber's improved crystallinity index, crystalline size, tensile strength, kinetic activation energy, and thermal stability. The existence of chemical functional groups is confirmed by Fourier transform infra-red analysis, and major elements such as carbon, nitrogen, and oxygen are quantified by energy dispersive X-ray spectroscopy analysis in chemically treated fibers. The surface of the fibers has become roughened as a result of chemical treatments, as shown by the morphological analysis performed using scanning electron microscopy. Among the chemical treatments tested, fibers treated with 0.5% potassium permanganate demonstrated superior thermo-mechanical properties for use as bio-reinforcement in high performance polymer composites