Table of contents

ICTEC 2019 is the third international conference organized by Centre for Telecommunication Research and Innovation (CeTRI) together with Faculty of Electronic and Computer Engineering (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM). This conference was organized in collaboration with Photonics Research Centre (PRC), Universiti Malaya (UM), Wireless Communication Centre (WCC), Universiti Teknologi Malaysia (UTM), Universitas Sriwijaya, Indonesia and Universiti Malaysia Sarawak (UNIMAS).

ICTEC 2019 was successfully organized in the heart of the Historical City of Melaka on 22-24 October 2019 with the theme "The 4th Industrial Revolution: Recent and Future Trend in Telecommunication, Electronic & Computer Engineering".

During the conference, a keynote speech entitled "The 4th Industrial Revolution: Disruptive Challenges to Higher Education" has been delivered by Prof. Ts. Ir. Dr Hj. Mohd Nasir Taib, Senior Professor at Faculty of Electrical Engineering, Universiti Teknologi MARA (UiTM).

All papers published in this volume of Journal of Physics: Conference Series have been peer reviewed through processes administered by the proceedings Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing.

This paper presents the performance of a microstrip monopole antenna with and without an Artificial Magnetic Conductor (AMC). The antenna and AMC structures are designed at 2.45 GHz using Rogers RO3003 and RT5880 respectively. The thickness of the substrate of the antenna is 1.52 mm and the permittivity are 3. For the AMC, the thickness and the permittivity of the substrate are 3.18 mm and 2.2. The designed antenna has a hexagonal shape, and it is grounded by 3x3 AMC array. From the simulated results, it is found that integrating the AMC structure to the printed monopole antenna was significantly improved the gain and directivity of the antenna. With the AMC, the achieved gain is 6.54 dB, which exhibited 3.68 dB of increment. This antenna has high gain, as it has both high radiation efficiency and directivity. Its directivity increased from 3.01 dBi to 7.14 dBi. The measured results are in align with the simulated results. The antenna still operate at the desired frequency. The recorded measured gain is 5.33 dB, with 20.3 dB return loss at 2.4 GHz. Thus, the design of the monopole antenna with the AMC is suitable as a RFID tag, because it can improve the RFID reading distance.

To date, Non-Orthogonal Multiple Access (NOMA) is the best and most compatible technique to support for the incoming Fifth Generation (5G). However, the NOMA performance is affected by noise, interference and poor signal strength during transmission. Channel coding is required to correct transmission errors due to error propagation, Therefore, channel coding is an important technique that can detect and correct errors at the receiver side. This paper analyses the performance comparison of different channel coding schemes in order to suggest the optimum channel coding scheme for the 5G mobile communication system. The Bit Error Rate (BER) is being evaluated to assure that better performance can be achieved. The simulation was done using The Vienna 5G Link Level Simulator (LLS). The result of BER shows that LDPC perform better than Turbo and Convolutional which is 3.4×10⁻¹ for cell centred user and 4×10⁻² for cell-edge user.

The ability to serve many users with high-speed transmission has made 5G as the promising candidate for the future generation of the wireless communication system. LTE could no longer support future demand with its current transmission rates. Various multiple access schemes have been proposed for 5G and NOMA as one of the candidates. Thus, this study has analysed the performance of NOMA in comparison to OMA in pedestrian and vehicular environments. To assure the Quality of Service (QoS), the metric of throughput is evaluated. The simulation was done using The Vienna 5G Link Level Simulator (LLS) and the result shows that NOMA cell centred user performed 50% better than OMA. Meanwhile, OMA outperformed NOMA in terms of cell edge user by 56%. The finding is in line with the characteristics of NOMA and OMA where NOMA users have to be in pair and share the same subcarrier while OMA users have their dedicated subcarrier for transmission.

In today's accelerated growth of mobile device technology, resource utilization in access network will continue to draw more attention to the increasing mobile user devices and applications. The main objective is to address the issue of QoS resource utilization efficiency. This paper combines the Ant Colony Optimization (ACO) algorithm and the Particle Swarm Optimization (PSO) algorithm to provide the optimum routing and to improve the QoS resource utilization efficiency. This proposed hybrid ACO-PSO algorithm uses the IEEE 802.11 DCF standard with multi-antenna scheme (MIMO) of Mobile Ad-hoc Network (MANET) to apply into integrated wireless (MANET) optical (PON network) based in Software Defined Network (SDN) with cloud computing. IEEE 802.11 Wireless Local Area Network (WLAN) gives the opportunity to its users to practice the wireless environment and full functionality of "anything, anytime, anywhere" concept. The proposed work is implemented using the OMNeT++ software where it investigates the QoS performance. These metrics include all nodes throughput, bandwidth, and load balance, routing and control overhead improvement with reduction. They also comprise of RSSI, end to end delay, Packet Delivery Ratio, network capacity, packet loss probability, as well as power consumption in all wireless nodes and energy consumption from wireless domain to wired domain.

Visible light communication (VLC) systems which employ visible light for communication is one of a promising technology for 5G due to its high bandwidth, efficient power consumption and higher security. In VLC, typically LEDs are used as a light source for transmitting information and providing illumination simultaneously. The intensity of the light is modulated at a very fast speed which is undetectable by human eyes. This paper explores the performance of two types of single-carrier modulation schemes suitable for VLC, namely OOK and PPM based on power, path-loss and data rates. The performance of unmodulated signal is also included for comparison purposes. The maximum distance achievable for each modulation schemes are recorded. It is shown that PPM has a superior performance compared to OOK in terms of the transmission distance and path loss, while OOK was excellent choice for highest data rates.

Firstly, radar is a system that has a radio transmitter that transmits radio waves, which will be received by a receiver, and it is used for detecting and locating objects. The passive radar system only receives signal from existing transmitters such as telecommunication antenna by using radio signal for tracking, detecting and so on. This experimental study was conducted with a human holding a weapon representing intruders that cross borders illegally into the country. Existing commercial telecommunication antenna was used as the transmitter transmitting the electromagnetic waves, and the system received the waves by using the receiver antenna. There are many techniques for a passive radar but in this case, we used the forward scattering region to detect human with and without weapon. The Doppler signature from human detection is produced when the receiver antenna detects any presence of a human that passes through between the transmitter and receiver. Two LTE frequencies used in this experimental work were 1.8 GHz and 2.63 GHz with several providers and have wider LTE coverage. The data from human detection was collected using LTE based passive forward scattering radar system, and was then analysed using Matlab programme. Principal Component Analysis (PCA) was used to classify the human without weapon and human holding a weapon into two categories. This system successfully classified the human with and without weapon and could be used by the military for border protection.

The paper presents the development of a liquid phantom and In-Vitro testing of Compact Meander Line Telemetry Antenna (CMLTA) by using the developed phantom at 402.5 MHz. A liquid phantom for 402.5 MHz has been developed, and the dielectric properties of the developed liquid phantom have been characterized for verification by using an open-ended probe method. Various composite samples of the solution are developed to characterize material properties for comparison with the theoretical properties at 402.5 MHz (ε_r= 58.2, σ = 0.82). The comparison between simulated and theoretical permittivity as well as tangent loss is presented. The measured dielectric properties are verified by using open-ended coaxial dielectric kit. Simulated and measured results can be observed in terms of frequency and return loss performance with a deviation of 0.004 MHz and 2.7 dB, respectively.

This paper presents an antenna for the medical imaging application which can detect unusual tissues on any part of the body. A design of wideband antenna with wearable properties is proposed for the medical imaging application. The wideband antenna is designed with introducing notches to the patch and a t-shaped slot at the partial ground. Polydimethylsiloxane (PDMS) is introduced to the antenna for the implementation of the wearable antenna. The proposed antenna operated in a frequency range of 3GHz to 6GHz. The antenna that embedded with PDMS shows a good agreement to the antenna without PDMS. The overall dimension of the antenna is 24mm (W) x 38mm (L) which consider is a miniature antenna. This proposed design gives an alternative solution for the antenna which cannot be wear on the body and protect the antenna. The introduction of PDMS will reduce the signal reflection cause by the high coupling of the human body.

There are several issues that present in conventional sensor including its accuracy, safety, durability and RF effect. These issues could be minimized with the implementation of fiber optic sensor. This project is to design and implement optical sensor using FBG for various temperature and liquid density sensing. The FBG sensor was submerged in liquid substances to determine the sensing conditions. The available pigtail was connected to the circulator, 1550nm laser source and optical spectrum analyzer (OSA). The data regarding temperature and density sensing captured from OSA were observed and analyzed. It is found that the relationship between the change in temperature and the change in wavelength is virtually linear in both environments making FBG a good candidate for sensing temperature.

Packet scheduling is one of the key schemes used in Radio Resource Management (RRM) for Long Term Evolution (LTE) cellular network. It serves as the resource allocation for each time and frequency dimensions in LTE baseband processing. In previous research, a new scheduling algorithm was developed, namely the Modified-Proportional Fair (PF) scheduler that splits a single subframe into multiple time slots and allocates the resource block (RB) to the identified User Equipment (UE) in some chosen time slots and it is done continuously for each subframe depending on the instantaneous Channel Quality Indicator (CQI) feedback received from UEs. Results have shown that the Modified-PF scheduler produces the most outstanding overall performance in terms of spectral efficiency and throughput with comparable fairness as compared to other state-of-the-art schedulers, namely, Round Robin and Proportional Fair schedulers. However, the performance of the new scheduler is not analysed in UE mobility scenario. Hence, this research will determine the type of response by the scheduler towards different mobility condition by simulating the throughput and the spectral efficiency of the three schedulers for various UE velocities. It is observed that the Modified-PF again shows significant performance improvement for various mobility condition in terms of average UE throughput and average UE spectral efficiency in both single cell omnidirectional and tri-sector eNodeB. This shows that Modified-PF can be considered as one of the packet scheduling options for the LTE cellular network.

This paper presents the feasibility investigation of a flexible antenna made of medical material as the substrate for medical application. A 2.4 GHz antenna is designed on three different medical-friendly materials: cotton-crepe bandage, artificial cotton patch and semi-transparent silicone patch. Water ageing test is conducted to explore the ability of the proposed flexible substrate materials to cope with the wetness issue. The antenna performance in four conditions (original, fully wet, damp and fully dry) are measured and compared. The water-aged antennas' physical condition are observed and the results show that the flexible antenna made of artificial cotton patch retains its radiation performance. Only a minor shift of approximately 40 MHz of resonant frequency is found for the flexible antenna made of cotton-crepe patch. The work presented here has profound effect on the future development of wearable antenna suitable for health monitoring applications.

In the sector of transportation, vehicle to vehicle communication and vehicle to infrastructure communication are becoming a trending topic of studies, as effective information transfer is required for most critical systems. Many protocols were then suggested with better coverage and small end to end delay in restricted bandwidth for data communication purposes. Research on some of the wireless communication technologies are carried out in this paper. The aim of this paper is to define various wireless communication technologies that emerged in the literature and to disclose the possibilities attained once this approach has been applied.

This paper presents the design of a tri-band adhesive antenna for biomedical telemetry. Since human body is lossy in nature, the signal transmitted from an implantable medical device is usually very weak. The adhesive antenna acts like a repeater which increases the strength of the signal. This allows the signal to be sent effectively to the base station. The antenna is designed to resonate at 375 MHz, 2.525 GHz and 5.625 GHz. The measurement results show that the antenna has 10 dB impedance bandwidths of 400 MHz (175 – 575 MHz), 950 MHz (2.125 – 3.075 GHz) and 1.5 GHz (5.125 – 6.625 GHz) to cover the MICS, ISM and IEEE 802.11 WLAN bands, respectively. The proposed antenna is printed on a flexible Rogers RT/duroid 5880 epoxy substrate with a volume of 70 mm × 80 mm × 1 mm. The simulated and measured performance of the antenna confirms its omnidirectional radiation patterns and dual-band operation with high return losses.

The gain performance of 50-iun core diameter graded-index (GI) multimode europium aluminum benzyl methacrylate (Eu-Al/BzMA) waveguide was investigated by numerically solving rate and propagation equations using MATLAB. At a fixed waveguide length of 10 cm and gain medium concentration of 13 wt.%, optical gain was found to be dependent on pump power and input signal power. This paper utilized a 532 nm wavelength pump with power varied from 100 mW to 500 mW, together with low (-30 dBm) and high (0 dBm) input signal powers, within the amplification range of 580 to 640 nm wavelength. With the highest pumping power of 500 mW and the lowest input signal power of -30 dBm, a 29-dB optical gain with wavelength of 617 nm was observed from forward pumping amplification. For comparison, an identical waveguide in terms of properties was fabricated through an innovative fabrication method for polymer waveguide—the Mosquito method. The fabricated waveguide was then experimentally tested for -30 dBm input signal power with 200 mW pump power in the attempt to realize future real-world applications of short reach networks such as in Local Area Network (LAN) and in-vehicle optical interconnects.

With the adoption of fifth generation (5G) wireless network systems into the fourth Industrial Revolution, the data transfer efficiency and speed will see a significantly improvement, which will lead to systems having the ability to provide a real-time information, and a solution to the capacity problem. Cloud-Based Radio Access Network (C-RAN) is the groundwork on which future 5G networks are based on, and it can increase the spectral efficiency and lower the transmission latency. Moreover, the used of C-RAN in future is expected to decrease the capital, operational and maintenance expenses of mobile operators. This study aims to investigate and validate the joint measures of spectral efficiency and latency optimization by using a quantization process of uplink signal at the remote radio head (RRH) of the C-RAN network. The quantization process is used to convert the received radio frequency signal from mobile user equipment into in-phase and quadrature baseband samples. The uplink transmission power and the number of quantization bits are computed in a way that the performance of spectral efficiency and latency of the RRHs are simultaneously optimized. Results shows that the proposed scheme is capable working with the different types of RRH.

Visible light communication (VLC), where light is used for illumination and communication simultaneously is considered as one of the enabling technology for the fifth-generation (5G) communication standards. Recently energy harvesting capabilities is proposed to be integrated into VLC system by employing a solar panel as the photodetector instead of using the commonly used photodiode. In this paper, an experiment is conducted to analyse the performance of two types of photodetector at the receiver namely a photodiode and a solar panel. First, the transmitter and receiver circuits are built for both unmodulated scheme and modulated scheme using On-off keying (OOK). The performance of unmodulated scheme is included for comparison purposes. It is shown that the transmission distance obtained by using photodiode is better than the solar panel.

The way 5G operators manage infrastructure, enterprise and customers will be radically different compared to preceding generations. This paper aims to provide new perspectives on the 5G operating model by presenting a visual guide on how mobile network operators should deliver value to their prospective 5G customers. The model presented enriches the eTOM framework by elaborating on the implications of virtual network operations, the consequences of an mmWave band, the value of the non-human customer, the security concerns, the role of the content provider in a new ecosystem, and the anticipation of the next generation technological platform.

TV consumption patterns around the world have changed dramatically since the appearance of over the top service. Integrated Broadcast Broadband (IBB) system provide value services that attract customers to enjoy traditional TV services compared to OTT services. ITU-R define IBB system as a combination of broadcast and broadband technology, which provides high quality, flexible, interactive, and personable services. This study aims to determine the challenge and opportunities of implementing IBB in Indonesia. The research method is carried out with a qualitative data approach using PEST analysis. The results showed that IBB has potential opportunities to be applied in Indonesia since the digital terrestrial television coverage has reached more than half of the population. Moreover, the increasing number of digital satellite television subscribers, as well as IPTV subscribers, provide a potential market for IBB services. However, the lack of supporting broadband infrastructure and digital terrestrial television regulations uncertainty will be a challenge for the success of IBB implementation in Indonesia.

Suspension systems are applied in vehicles so as to improve the passengers ride comfort, vehicle stability and better road handling when the vehicle moves on roads with bumps and terrains. In this paper, Linear Matrix Inequality (LMI) and linear quadratic regulator (LQR) controllers were proposed and this was also compared with passive suspension system. Using nonlinear quarter car active suspension system model with hydraulic actuator to reduce the effect of bumpy road surfaces on vehicle and hence improve passenger ride comfort. Vehicle body suspension travel and wheel deflection were measured to determine the performance of the active suspension system. Mean absolute error (MAE) and integral square error (ISE) were used as the performance indexes, thus a MAE and ISE of passive 75.01%, LQR 82.65% and LMI 89.11% and passive 4.11x10⁻⁶, LQR 3.24x10⁻⁶ and LMI 2.19x10⁻⁶ respectively were recorded. Based on the simulation results and analysis, the LMI based control performs better in minimizing vibration effects and also guarantee better ride quality and vehicle stability when compared to the conventional LQR and the passive suspension system.

Most tasks can be done remotely nowadays with the help of the Internet of Things (IoT). IoT can also be applied in the agriculture sector such as aquaponics where water quality is crucial to be kept track at all time to avoid loss of resources. Personnel used for water quality checking on-site is becoming less practical for the technologies available today. Remote monitoring system using IoT is essential for water quality monitoring. However, the lack of attention was given by local farmers for applying it in aquaponics. In this study, an IoT based water quality monitoring system for aquaponics was developed to shift from monitoring water quality on-site to remote monitoring via the Internet. Several open-source software was used in developing the monitoring system. The system covers a web server which is accessible via the Internet for user convenience today. Several tests were carried out on aquaponics in the Malaysian Institute of Sustainable Agriculture (MISA). The result shows that the system can display measurements over time and alert users via the dashboard. Other than that, observation indicates that the reporting system is stable and reliable to be used in an aquaponics system.

Nowadays, automation has become an essential feature in various applications. Agriculture is a crucial sector in which human being heavily depends on. Aquaponics is one of the efficient approaches in agriculture. Human resource allocated for aquaponics maintenance is very inefficient in terms of workload as compared to the time spent especially with the advanced technology we have today. It is necessary to include automation in aquaponics to reduce manpower involvement. However, a lack of attention has been given by local farmers to automate their aquaponics using technology. In this study, an automated aquaponics maintenance system was developed as a prototype to reduce human involvement in the activity. The system covers water level and light-emitting diode (LED) power switch maintenance. Furthermore, the automation system can be controlled via Telegram for user convenient. Moreover, it also measures the pH level of the water as an additional feature. Numerous tests were conducted on aquaponics to observe the reliability of the system at the Malaysian Institute of Sustainable Agriculture (MISA), a non-profit organization focusing on urban farming. Positive results were obtained from the tests which suggested that the system is self-dependent. Therefore, the system is suitable to be used in aquaponics.

Recently, an interest of thermoelectric generator (TEG) to manipulate and change heat waste into electrical energy has increased. The heat from electrical appliances, sun, human body, and natural environments can convert into electrical energy using TEG. However, typical conventional TEGs in the market have a hard and solid construction structure, hence difficult to bend according to curved surfaces of the heat sources. To overcome this problem, polymer-based material is proposing as the new packaging and substrate structure for the TEG. Besides, the thermoelectric conductor layer also changed using different types of pyroelectric for better heat absorption performance with low cost in mass-scale fabrication. Therefore, the simulation of eight pairs segmented conductive layer insulated with thin-film polymer due to standard modelling equation is present. The comparison of simulation with reference TEG to get the optimum output of temperature difference were also explaining. At the end of the simulation, polyimide as a packaging substrate with a conductive layer of Graphene (P-type legs) and Bismuth Telluride (N-type legs) has chosen for the best performance material for the flexible thermoelectric generator. The highest temperature difference produced by this design is 542˚C for 0.945V input voltage and 120˚C input temperature at the hot side.

In this era of the internet with the latest technology and rapid communication, the various fields of processes and business are led to the road of automation in recent days. The major part of this automation is that they help in the cost reduction with increased safety measures and also results in absolute portability of the equipment. To indulge in continuous processing, and also to identify the best technology, the analytical methods must be directly involved in decision making. This revolution in analytical methods is named as Analytics 4.0, which has a significant role in healthcare and diagnostics. The self-assessment and decentralization of the data acquiring can be motivated with the help of the microfluidic biosensors and biochip. It also helps in the immediate access of the information without resulting in delay. Diagnostic 4.0 helps in storing the patient details as cloud files providing high security and also analyze them through the deep learning algorithms. It also helps in constant monitoring on a broad scale and reliable assessment through monitoring apps that can shift the health care efficiency to the next notch. Hence the evolution of the healthcare sector has multiple generations listed with numerous advances which stand as the boon in this era.

In this paper, a PID and PID-liked fuzzy logic controller is designed for optimal control of inverted pendulum. An inverted pendulum is highly a nonlinear and unstable system. Thus, in this paper, the system is modeled, linearized and controlled. The control objective is to keep the pendulum in an upright position despite external disturbances. The stability analysis of the system is performed, which proves the instability of the system. Two separate controllers were developed to cater for the nonlinear behavior of the system. In the PID controller, the PID gains were fine-tuned through trial and error in MATLAB to obtain optimal control of the system. On the other hand, fuzzy logic is used to tune the PID in the PID-like fuzzy logic controller. Control performances of the system by PID and PID-like fuzzy control method were compared. The result indicated PID-like fuzzy control method performs better in respect of rise time, settling time, peak overshoot and steady-state error.

This paper defines a low-cost tool for calculating the distance from a vehicle to the front. The motive of this study is to define a method for range estimation of vehicles with small price application, particularly in Malaysia. The measuring tool utilizes an ultrasonic sensor and a raspberry pi to determine the range. In addition, the data is stored in Firebase in real-time. The tool is evaluated in real-time, and all outcomes are tabulated. Finally, the study results underline that the measuring tool is possible, but economic variables and communication are of concerns.

This paper presents the design and analysis of a dual function of a switchable resonator for Radio Frequency (RF) switch. The most common configuration of RF switch is Single Pole Double Throw (SPDT) switch that widely used in the wireless communication system. In order to achieve the dual function between isolation improvement and band-stop filter in SPDT switch, two switchable resonators were used that resonated at 2.3 and 5.2 GHz bands. PIN diodes were used as switching elements in the SPDT switch. The main advantages of the dual function of the switchable resonator are minimum usage of switching elements for isolation and integrated band-stop filter in a single device of SPDT switch. As a result, the design showed almost 40 dB of isolation at 2.3 GHz and 39 dB of the notch response of the band-stop filter at 5.2 GHz.

In recent years, in order to solve critical problems such as global warming and climate change that have been occurring in the world, research on the application of power generation methods which are harmless to the environment has been underway. In this paper, a fault diagnosis method using the self-powered sensing system based on vibration power generation is proposed. Frequency analysis is known as a general failure diagnosis method. However, due to the limitation of the generated power, the sampling period of data acquisition is as large as a few-ten millisecond. For this reason, it is difficult to use frequency analysis for fault diagnosis using vibration power generation. Therefore, a fault diagnosis system corresponding to an increase in the sampling period is constructed by introducing machine learning. An acceleration sensor used for data acquisition is driven by the vibration power generator attached to factory equipment. The diagnosis is performed by wireless-transmitted acceleration data. By introducing a machine learning strategy into the diagnosis, accurate diagnosis can be performed even for data with low-sampling rate. The effectiveness of the proposed diagnosis method is experimentally evaluated by using the factory equipment.

A PZT device is an element that generates electric power when an impact is applied by vibration. Currently, PZT devices are installed on the floor near the ticket gates to supply power to station equipment, and on the floor of bridges to turn on LEDs at night. Since the current generated by the PZT device is small, there is a disadvantage that the electric power obtained is small. In this paper, we developed a power-generating shoe using PZT devices for use in disaster-affected areas where power shortages tend to occur. The validity of the developed power-generating shoes is experimentally evaluated under the variation of PZT device, shoes and secondary batteries. At present, the result is that it is possible to charge about 1.5% of mobile phones at the maximum. In order to reduce loss and obtain more power, it is necessary to improve shoes and to build a charge / discharge circuit.

The Sunda Strait is a region that has the potential for tsunamis due to tectonic and volcanic activities. Anak Krakatau landslide is one of the causes of Tsunamis that require accurate early detection by combining data from seismometers and other sensors. One of the instruments that can be implemented in the verification of early detection of Sunda Strait Tsunami waves is a network of automatic water levels that can measure changes in water level every one minute. The system is designed using ultrasonic water level sensors and rainfall sensors. The sensors are connected to a data logger for processing data with a solar power supply panel. The automatic water level network is installed in 3 locations around Anak Krakatau. Measurement data from sensors is sent to BMKG servers for further processing using 2G communication modules. The results of the system design show that the network of automatic water level works properly, where the pattern of water level patterns every minute from each station can be seen.

This paper presents a wideband planar antenna integrated with a frequency selective surface (FSS). Initially, the FSS array screen is investigated using a basic square ring to construct a unit cell of the FSS periodical structure with a (12x12) array. FR4 with a permittivity of 4.7 and a thickness of 1.6 mm is used as the substrate material for both FSS structure and antenna. Then, the antenna is placed closely parallel over the FSS configuration at a distance of 28 mm. After analyzing the performance of the antenna in terms of return loss, directivity and antenna gain, the design is integrated with the FSS structure in order to achieve a stable frequency response. Directivity and gain improvement of 6.5 dB and 5.1 dB respectively are observed along broadside direction.

A planar bridge multiple split-ring resonators (BMSRR) with a microfluidic sensor is a real-time nondestructive sensor designed based on bridge split ring topology. This sensor has the ability to characterize liquid solvents using the extraction of polynomial fitting technique and bridge connector elements were added to enhance the quality factor and sensitivity. The characterization of liquid samples is analysed and numerically expressed based on their dielectric properties and loss tangent with a minimum volume of samples. The unloaded Q-factor improves more than 400 over the bandwidth at an operating frequency of 2.3 GHz.

In this paper, the implementation of localizing and recognizing license plate in real time environment with a neural network using a mobile device is described. The neural networks used in this research are Convolutional Neural Network (CNN) and Backpropagation Feed Forward Neural Network (BPFFNN). Image processing algorithm for pre-processing, localization and segmentation is chosen based on its ability to cope with limited computational resource in mobile device. The proposed license plate localization steps include combination of Sobel edge detection method and morphological based method. Detected license plate image is segmented using connected component analysis (CCA) and bounding box method. Each cropped character is fed into CNN or BPFFNN model for character recognition process. The neural network model was pretrained using desktop computer and then later exported and implemented in Android mobile device. The experiment was conducted in a moving vehicle on selected driving routes. The results obtained showed that CNN performed better compared to BPFFNN in a real time environment.

The internet has been one of the greatest advancements in technologies. It has brought many advantages to today's society in many domains such as e-commerce, entertainment and supply chain, amongst others. However, it is also a double-edged sword which has brought many threats to the computer systems and devices known as cyber-attack, and one of these threats would be phishing attack. A phishing attack is where the scammer tries to impose or clone the legitimate email or website in order to deceive the victim to key in their personal information such as username and password. Phishing attack has been one of the most common attacks that happens every day on the Internet especially through email. Many methods have been devised to encounter phishing attack, and one of approaches is through training and monitoring team. These manual approaches, however, are user's experience-dependent and cost-inefficient. Therefore, many have adopted AI approach instead to detect phishing attack. This paper is one of the many efforts to detect the phishing attack through email by adopting AI method. The objective of this paper is to investigate the performance of feedforward neural network, recurrent neural network and ensemble neural network in phishing email detection. The result of this comparison is empirically evaluated.

A phishing attack remains popular among security attacks, which has specific characteristics. The attacks have an impact on the economy and involve user behaviour that makes the attack successful. Many solution framework and program has developed but fails for eliminating the attack except minimizing the impact only. Mitigating and cope processes for reducing phishing attacks involve user education and training to raise higher user awareness. The mitigating process itself has two main steps that are prevention and detection. Many algorithms employ machine learning to overthrow the attack intelligently. The remaining problem of this process is detection time is slower than using blacklist filtering. The paper review social and technical aspects to conclude and identify the potential solution that will be proposed for the next stage of the research. The potential solution will include future data analytics such as an immersive and augmented analytic regardless of computing issues. An immersive and augmented analytics should have the ability to learn from the past data of user and attack behaviour to direct the system and user in combating the attacks. The learning result should give preventive suggestions and evaluate user awareness level.

The mobile application is in rapid growth and dissemination in business and enhancement of customer satisfaction has emerged as a core issue. In Customer Relationship Management (CRM), customer reviews are recognized as fruitful information sources for monitoring and enhancing customer satisfaction levels, in particular, feedback of actual customers expressing their opinion reflects on core business. Customer knowledge about products or services also reflects on the business. Knowledge is one of the key factors for customer service success - most companies would empower employees with knowledge which is deemed reasonable. The knowledge of technical intricacies and variables in finding an accurate Uninterruptable Power Supply (UPS) by customers has been a challenging task. This challenge via using a UPS calculator that computes upon different back-up time and technical calculations involved, allows customers to find an accurate UPS easily and gives the customers the ownership of the knowledge. This UPS calculator has created a niche market for UPS manufacturing and distributing companies. The feedback from the telephone interview conducted in this study shows that the customer using this UPS calculator can make an accurate decision-making process of purchasing a UPS and electrical wholesalers can give advice to customers to suitable UPS and prices with an improved business process. In the future, this research would like to extend its study towards customer interaction, customer satisfaction and customer value.

RSA is a well-known and widely used cryptosystem. One major limitation of RSA is its maximum input length depends on the chosen key size and the selected padding. This paper proposed a hybrid data compression algorithm to increase the input data length to be encrypted by RSA. Firstly, a plain text is compressed by the LZW algorithm. The compressed data is padded. After that, the size of the compressed data is further reduced by using continued fraction. Double-compressed data is, then, used as an input of RSA. When using a combination of continued fraction and LZW algorithm, the results showed that, on average, the input data length is increased by 18.91 per cent. In most cases, the processing time is much better than not doing any compression. In the case of decryption, a cipher text is decrypted by RSA first. The padding is removed. Then, the Euclidean algorithm and LZW are used respectively to restore the original data. The hybrid data compression is not only relieved the limitation of RSA but also enhances the security level.

Capability of electrocardiogram (ECG) signal in contributing to the daily application keeps developing days by days. As technology advances, ECG marks the possibility as a potential mechanism towards the drowsiness detection system. Driver drowsiness is a state between sleeping and being awake due to body fatigue while driving. This condition has become a common issue that leads to road accidents and death. It is proven in previous studies that biological signals are closely related to a person's reaction. Electrocardiogram (ECG) is an electrical indicator of the heart, provides such criteria as it reflects the heart activity that can detect changes in human response which relates to our emotions and reactions. Thus, this study proposed a non-intrusive detector to detect driver drowsiness by using the ECG. This study obtained ECG data from the ULg multimodality drowsiness database to simulate the different stages of sleep, which are PVT1 as early sleep while PVT2 as deep sleep. The signals are later processed in MATLAB using Savitzky-Golay filter to remove artifacts in the signal. Then, QRS complexes are extracted from the acquired ECG signal. The process was followed by classifying the ECG signal using Machine Learning (ML) tools. The classification techniques that include Multilayer Perceptron (MLP), k-Nearest Neighbour (IBk) and Bayes Network (BN) algorithms proved to support the argument made in both PVT1 and PVT2 to measure the accuracy of the data acquired. As a result, PVT1 and PVT2 are correctly classified as the result shown with higher percentage accuracy on each PVTs. Hence, this paper present and prove the reliability of ECG signal for drowsiness detection in classifying high accuracy ECG data using different classification algorithms.

Acute Lymphoblastic Leukemia (ALL) is a disease that is defined by the uncontrollable growth of abnormal cell of lymphocyte, which is called lymphoblast. ALL patients cannot be left untreated as it can be fatal, and hence, early detection is very crucial for proper treatment suggestion. Conventionally, ALL analysis is manually done, which is time-consuming and highly dependent on the pathologist's skills. Furthermore, it will be hard to pathologist as the number of sample increases, and also there are other cells inside the blood smear image, which will create confusion to them. Haematology counter is great to assist the process, but unfortunately, the cost is unbearable for some countries. For that reason, in this project, a computer-aided ALL detection with fast response is proposed. The system consists of five main modules which are image acquisition, pre-processing, segmentation, feature extraction and classification. For the process flow, firstly the color space correction based on l*a*b* color space is applied to standardize the color of the input image. Next, WBC segmentation is employed to locate the WBC region and consequently divided it into two parts which are the nucleus and cytoplasm based on a combination of color space analysis and Otsu thresholding. However, the segmented image contains noises and hence, is eliminated by using a combination of morphological filter and Connected Component Labelling (CCL). Then, the feature extraction process is made to study the nature of each individual cell using features derive from color, texture and geometrical properties. Lastly, lymphoblast classification is incurred to categorized the lymphoblast and the non-lymphoblast cell by employing Support Vector Machine (SVM) with a linear kernel function. As mentioned before, WBC segmentation is divided into two parts, and nucleus detection accuracy is higher than cytoplasm detection accuracy, which is 98.87% and 74.12% respectively. The presence of color space correction is analyzed, and the result is better with 96.92% accuracy for the presence of color space correction compared to 93.55% accuracy for without color space correction result. Classification performance is able to achieve 98.72% of accuracy.

With the forestry and logging activities contributing to 5.6% of the agricultural sector in Malaysia's 2018 GDP growth, this had thus implied the forest as having a significant role in national growth and the critical need of a precise tree volume estimation. Although regression has been the most common method used for this form of estimation, the expansion of information technology had, however, led to the use of a machine learning technique that is capable of overcoming the issues posed by the regression analysis. In this paper, the estimation of the tree volume was not only conducted via the regression method but had also involved the use of two machine learning techniques, namely the artificial neural network (ANN) and that of the epsilon-Support Vector Regression (ε-SVR). By comparing the root mean square error (RMSE) and standard deviation (SD) values from each of the volume model that had been obtained in this study, the machine learning technique was thus found to have demonstrated a better precision and accuracy level than that of the regression method.

Garbage management is one of the primary problem faced by cleaners in terms of the duration of the cleaning process. The design of a smart green environment of garbage monitoring systems is developed based on an IoT (Internet of Things) application is believed to overcome this waste issue. The main contribution of this project is demonstrated by a system based on IoT that allows the waste management to monitor based on the garbage depth inside the dustbin and also the notification of its full condition by using a mobile phone and the Blynk apps. The proposed system consisted of the ultrasonic sensor, which measures the garbage level inside the dustbin. The system shows the status of the garbage through LCD, and a WiFi module (ESP8266) used to send the information to the smartphone. Thus, it is expected that this system can build a greener environment by monitoring and controlling the collection of garbage smartly through an IoT application.

The design of a smart green environment of home automation for appliances monitoring systems is developed based on an IoT(Internet of Things) application. The smart home concept represents a motivating platform for innovation of information technology services to produce more operative house devices and system that can improve the standard of life. This project aims at controlling home appliances via Smartphone using Wi-Fi as a communication protocol and the Raspberry Pi as a server system. The user here can move directly with the system through a web-based interface over the net. The designed system not solely monitors the sensor data but also actuates a process according to the need. Therefore, globally accessible automation of electronic appliances is created attainable with the utilization of a Raspberry Pi micro-controller board, a web affiliation and relay switches in a user-friendly way for the users to regulate home electronic appliances with high flexibility and security.

A 16 nm double-gate FinFET (DG-FinFET) designed are optimized with a mathematical modelling using a response surface method-central composite design (RSM-CCD), with the relationship between parameters and output responses are investigated and examined. The threshold voltage (V_TH), drive current (I_ON), leakage current (I_OFF) and subthreshold swing (SS) ramifications towards the adjustment of six process parameter that integrates polysilicon doping dose, polysilicon doping tilt, Source/Drain doping dose, Source/Drain doping tilt, V_TH doping dose and V_TH doping tilt is studied using the RSM-CCD using half-factorial of 86 experimental runs, which totals to 52 runs, consisting of 8 centre points, 12 axial points, and 32 factorials. Ultimately, the V_TH after the result is optimized with RSM-CCD showcased an improvement at 0.1785 V, with I_OFF achieved at 958.71 pA/μm despite performing less favourably after optimized. That said, an improvement towards I_ON/I_OFF ratio at 2.049×10⁶ compared to 1.666×10⁶ proves that both optimization techniques have met the predictions of International Technology Roadmap for Semiconductors (ITRS) 2013.

Palm vein authentication technology which reads the features of palm vein has been widely used in recent years as it offers high accuracy identification and difficult to be forged or impersonated. This paper demonstrates a palm vein recognition system using uniform Local Binary Pattern (LBP) through the Python language and R. Python language was used for contrast enhancement, noise reduction and LBP implementation while R was used for classifying palm vein pattern using K-Nearest Neighbour (KNN) classifier. The palm samples come from two datasets which are from the Chinese Academy of Sciences Institute of Automation (CASIA) and self-dataset. The outcomes were the extracted and classified palm vein pattern based on subjects and their accuracy based on each dataset. The accuracy for all uniform LBP bins and selected uniform LBP bins from self-dataset were 87% and 53% respectively; while for CASIA dataset were 60% and 27% respectively. The results show that the accuracy is higher if all uniform LBP bins are used for the recognition purpose.

Nowadays, a lot of equipment and machines in factories generating a high volume of production data at high speed which are then stored in a data warehouse. These data are used for analytic reporting purposes, to assist the decision-maker in strategizing their business. To speed up the report generation queries, materialized views (MVs) are used. An MV is a database object that contains the results of a query. However, each MV has different query time and maintenance cost. Therefore, it is crucial to select the best set of MVs in order to generate the report while minimizing the incurred cost. Genetic Algorithm is proposed to find the best set of MVs with minimum cost and time, and at the same time maximizing the number MVs selected. Experimental results demonstrate that the proposed GA delivered better solutions compared to the conventional approach

Application of strained channel in Metal-oxide-semiconductor Field Effect Transistors (MOSFET) technology influences the electrical properties due to the significant changes in the energy band structure of silicon lattices. Thus, in this paper, a comprehensive analysis is conducted to investigate the impact of strained channel towards several electrical properties of junctionless double-gate MOSFET. The comparative analysis is carried out by simulating two different sets of device structure which are JLDGM device (without strain) and junctionless double-gate strained MOSFET (JLDGSM) device. The results show that the strained channel has improved the on-state current (I_ON), on-off ratio, transconductance (g_m) and transconductance generation factor (TGF) by approximately 58 %, 98%, 98%, and 44% respectively. The significant improvement is mainly attributed to the presence of biaxial strain boosting the electron mobility in the channel. The intrinsic gate delay (τ_int) has significantly reduced by approximately 52% as the strained channel is applied. Since the variation of intrinsic gate capacitances (C_int) is very minimal (4%) as the strained channel is applied, the gate delay is dominantly governed by the drain current. However, the application of strain channel has increased the dynamic power dissipation (P_dyn) for approximately 19% mainly due to slightly increased intrinsic gate capacitances.

Dental age estimation is applied in various clinical and scientific disciplines such as pediatric dentistry, orthodontics, archaeology, palaeontology and forensic dentistry. Dental age assessment, particularly in forensic dentistry application for the purpose of human identification, has led to the fastest and cheapest procedure in the estimation process of the undocumented and unrecognized individuals. Traditionally, dental age assessment is done by applying the scoring system based on tooth development from the x-ray image. However, this method can be intricate and time consuming for massive incidents that involve a large number of victims such as a natural disaster. Thus, a number of experts are required during the identification process. Therefore, in this research, the application of digital image processing techniques is used to perform the segmentation and classification of the first molar tooth in every stage of its development. A region-based segmentation method known as Watershed Transform associated with several image enhancement algorithms is implemented. A marker image is automatically computed to control the operation of a watershed transform. Features are extracted based on the area of a segmented tooth, size of crown, the distance of left crown to root and distance of right crown to root. As a result, 94% and 92.8% of segmentation and classification accuracy are obtained.

To acquire the optimal value of the performance parameter, a bilayer graphene with silicon on insulator (SOI) was enhanced and analyzed on 22 nm NMOS device. The device is made of Hafnium Dioxide (HfO₂) as a high-k material whereas Tungsten Silicide (WSi_x) as a metal gate. The Silvaco software ATHENA and ATLAS modules were applied to simulate the fabrication process of virtual devices and to verify the device's electrical properties accordingly. The Taguchi L9 orthogonal array method was then used to enhance the device process parameters minimum leakage current (I_LEAK) according to the International Technology Roadmap Semiconductor (ITRS) specification of 100nA/μm maximum range. The result from smaller-the-better (STB) for I_LEAK is then reviewed by the percentage affecting the process parameter. The simulation result shows that the halo tilting angle is the most dominant factor for I_LEAK optimization process. The optimized results indicate excellent device performance with I_LEAK = 9.29746 nA/µm which is far lower than the prediction.

This paper reports an ultrahigh-sensitive ISFET sensor that is developed using TCAD model of the industrial 22-nm ultrathin body and buried oxide (UTBB) fully depleted silicon-on-insulator (FDSOI) transistors. The modeling method is exploited by changing the potential surface charge depending on the electrolyte pH change and investigating how will it cause the threshold voltage shift of ISFET device and other transfer characteristic parameters. The properties of a user-defined material offered by Silvaco are exploited to simulate the electrolyte behavior. The parameters of silicon semiconductor material (i.e., energy bandgap, permittivity, affinity, and density of states) are set to reconstruct an electrolyte solution. The electrostatic solution of the electrolyte area is investigated by giving a numerical solution for the semiconductor equation inside this area. On the other hand, the strong electrostatic coupling between the front gate and the back gate of FDSOI devices provide an intrinsic signal amplification feature for sensing applications. Utilizing a layer deposited Titanium dioxide (TiO2) as a pH sensing film, pH sensors having a sensitivity ∼1250 mV/pH is reported. The small sensing area and the FDSOI-based technology of the device make the sensors ideal for the IoT market.

Identification analysis for author's handwriting image in forensic investigation is still an important research area in this current big data era. Images feature extraction can lead to an issue of high dimensionality of data. The process of feature extraction is the most crucial process in author's identification. It is important to choose the best method to represent the image. This study compared two feature extraction methods, namely Higher-Order United Moment Invariant (HUMI) and the Edge-based Directional (ED) method that construct the Global and Local Features respectively. The additional process of discretization was implemented before the training and testing phase to represent the generalized features for the classifier models. This process induced a better performance accuracy for both methods where the discretized Local Features achieved 99.95% accuracy rate that slightly outperforms the discretized Global Features with only 99.91%.

This paper proposes a robust biometric identification system using compressed electrocardiogram (ECG) signal by varying physiological conditions. The ECG data were obtained by recording a total of 30 healthy subjects where they performed six regular daily activities repeatedly at a sampling frequency of 1000 Hz. Then, the QRS complexes are segmented by implementing Amplitude Based Technique (ABT) where it compares the amplitudes of ECG points to determine the R peak. The segmented QRS is then compressed for various levels by using Discrete Wavelet Transform (DWT) algorithms and first 3 Daubechies (db) wavelet are computed. Next, a Cardioid graph is generated. In order to verify the matching process, the classification is performed by using the Multilayer Perceptron (MLP) technique. The results show that by applying this method, the accuracy of the identification rate can be achieved as high as 96.4% even when the data file is compressed up to 73.3%. When the data file is compressed, the outcomes also demonstrate that the execution time is less compare to non-compressed data. Therefore, the biometric identification system can be implemented efficiently as there will be a lesser issue regarding the data storage, execution time and accuracy based on the outcome of the study.

In this paper, we present a gait recognition method using convolutional features and histograms of temporal gradients. The method comprises three stages, namely the convolutional stage, temporal gradient stage and classification stage. In the convolutional stage, the video frames are convolved with a set of pre-learned filters. This is followed by the temporal gradient stage. In this stage, the gradient of each convolved frame in time axis is calculated. Unlike histograms of oriented gradients that accumulate the gradients in the spatial domain, the proposed histogram of temporal gradients encodes the gradients in the spatial and temporal domain. The histogram of temporal gradients captures the gradient patterns of every pixel over the temporal axis throughout the video sequence. By doing so, the feature encodes both spatial and temporal information in the gait cycle. Finally, in the classification stage, a majority voting classification with Euclidean distance is performed for gait recognition. Experimental results show that the proposed method outperforms the state-of-the-art methods.

The electroencephalogram (EEG) is a device for measuring the electrical activity of the brain; it has the ability to detect the waves at various frequencies. The device uses a small electrode to record the measurements. The EEG waves can be used to detect many activities in the brain, such as stress. This study identifies stress using EEG signals. Stress causes a certain range of frequencies in the range to change their activities, in which the changes can be analyzed. Test results were filtered properly, and the frequency bands measured. The data shows the difference in the ratio of beta waves and alpha waves in the brain as a result of stress. The changes in the ratio will be able to show the degree of stress encountered.

Occupancy grid map is a map representation that shows the occupancy of spaces, whether there is any object in a particular area or it is a free space. This map representation is also commonly known as a grid map. However, the accuracy of the occupancy grid map is highly dependent on the accuracy of the sensors. In this paper, low cost and noisy sensors such as infrared sensors were used with the occupancy grid map algorithm integrated with a neural network. The neural network was used to interpret adjacent sensor measurements into cell's occupancy value in the grid map. From the simulation experiments, it is observed that, that neural network-integrated algorithm has a better map estimate throughout robot's navigation with mean of 28% more accurate compared to occupancy grid map algorithm without neural network. This finding is beneficial for implementation with simultaneous localization and mapping or commonly known as SLAM problem. This is because SLAM algorithm makes use of both estimations of environment's map and robot's state. Thus, a better map estimate throughout the robot's journey can improve a robot's state estimate as well.

Foreign exchange trading activities are one of the businesses that can generate big profits, and provide freedom for business people without the need to provide a large capital. Traders often suffer losses due to uncertainty in the market. One of them is market manipulation carried out by brokers or banks. For this reason, this research was conducted to detect any manipulation that occurred in the foreign exchange market. This research tries to combine trading systems, indicators and expert advisors that aim to help traders detect fake market price movements to minimize losses that occur due to errors in making transaction decisions. The results of the study produce an indicator that is able to detect the potential of certain patterns used by the market maker to reverse the direction of market prices and is supported by the presence of expert advisors who are able to pinpoint potential market manipulation, so traders can avoid large losses.

This study presents an application of using Deep Neural Network (DNN) based detector to detect chili and its flower in the chili plant image. Detecting chili on its plant is important for the development of a robotic vision to automatically picking the chili. Only one type of local chili variety is used in this study from the species of Capsicum frustecens. Five hundred of chili plant images were captured from multiple angles and each of images was marked and labelled for any present of chili and its flower. These images were divided into 70-30 per cent proportion for training with validation and testing purposes accordingly. This project uses Faster Regions with Convolutional Neural Networks (R-CNN) as a deep learning model for training that contains around 177 numbers of layers including input and output layers. The classifier network was trained to optimize all parameters involved in chili and its flower classification and detection. The classification and detection accuracy are measured on the tested images. The result shows very good accuracy in validation and testing for classification and detection especially with the image of chili plan is upright position.

The increasing use of mobile platforms for detecting a disease challenge the researcher for designing a more efficient application than previously. Skin diseases can be recognized visually by an expert. However, the diseases are often ignored, especially by they who live in the remote area. For this reason, it requires a system worked solely independent of medic expert through a simple device like a smartphone. The study designs a system for detecting the healthiness of skin through its image. The proposed design is the improved model of the previously published work. The system requires suspected unhealthy and reference images. Both images will be compared after passing the edge process. The result is displayed on the platform containing a suggestion for the user to have medical treatment. For detecting the skin diseases accurately, an adjustment in the edge process and comparing process (similarity check) is required. Based on the experiment, a suitable threshold for edge detection are 0 for low and 50 for high. Regarding the edge process, a suitable similarity threshold will be estimated. The similarity threshold that suits to the designed application is 6. Lastly, the decision making to determine the healthiness of an image is 45.35%.

At present, the development of citizen journalism in the era of globalization and information technology is increasingly developing because of the presence of the internet and application technology that are in demand by readers and writers to develop their potential. The technology that is developing at this time is to use an Android technology mobile application that is very supportive of its operation. However, Citizen Journalism must be careful in using and filtering information from irresponsible blogs so that they can deceive the informant and easily incite an information that has not been proven by the truth of the news. Therefore, this research will make a citizen journalism application based on mobile technology in the platform of an Android and AJAX programming mobile. The results of the research show that the Mobile Technology Citizen Journalism application can be tested by sending or accessing news or information content on the server using a GPRS connection. Tests carried out include features, reliability, suitability, easy to use, and perception of the quality of this Mobile application.