bluuebunny/crossref_metadata_2025_split huggingface dataset:
Doping a lattice-trapped bosonic species with impurities: from ground state properties to correlated tunneling dynamics
Authors: Keiler Kevin , Mistakidis Simeon I , Schmelcher Peter 
Published: June 2020

Abstract
We investigate the ground state properties and the nonequilibrium dynamics of a lattice trapped bosonic mixture consisting of an impurity species and a finite-sized medium. For the case of one as well as two impurities we observe that, depending on the lattice depth and the interspecies interaction strength, a transition from a strongly delocalized to a localized impurity distribution occurs. In the latter regime the two species phase separate, thereby forming a particle–hole pair. For two impurities we find that below a critical lattice depth they are delocalized among two neighboring outer lattice wells and are two-body correlated. This transition is characterized by a crossover from strong to a suppressed interspecies entanglement for increasing impurity-medium repulsion. Turning to the dynamical response of the mixture, upon quenching the interspecies repulsion to smaller values, we reveal that the predominant tunneling process for a single impurity corresponds to that of a particle–hole pair, whose dynamical stability depends strongly on the quench amplitude. During the time-evolution a significant increase of the interspecies entanglement is observed, caused by the build-up of a superposition of states and thus possesses a many-body nature. In the case of two bosonic impurities the particle–hole pair process becomes unstable in the course of the dynamics with the impurities aggregating in adjacent lattice sites while being strongly correlated.
On the transport of edge localized mode filaments in the tokamak scrape-off layer
Authors: Adamek J. , Tskhakaya D. , Devitre A. , Cavalier J. , Horacek J. , Komm M. , Sos M. , Bilkova P. , Böhm P. , Seidl J. , Weinzettl V. , Vondracek P. , Markovic T. , Hron M. , Panek R. 
Published: June 2020

Abstract
Microsecond probe measurements of the electron temperature during the tokamak edge localised mode (ELM) instability show that the peak values significantly exceed those obtained by conventional techniques. The temperatures measured at the plasma facing component (divertor) are around 80% of the initial value (at the pedestal). This challenges the current understanding, where only several percent of the pedestal value are measured at the divertor. Our results imply a negligible energy transfer from the electrons to the ions during the ELM instability, and therefore no associated increase of the ion power loads on the divertor. This observation is supported by the simple analytic free-streaming model, as well as by full kinetic simulations. The energetic ELM ion loads are expected to be one of the main divertor damaging factors; therefore, the obtained results give an optimistic prediction for next generation fusion devices.
Simulation on wideband antenna based on Polydimethylsiloxane (PDMS) for medical imaging application
Authors: Haszerila Wan Hassan Wan , Yan Li Nga , Mohd Azize Aziean , Zakaria Zahriladha , Azwan Shairi Noor , Hakim Abdul Hamid Mohd 
Published: June 2020

Abstract
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.
Solar panel receiver characterisation for indoor visible light communication system
Authors: Zaiton AM , Muhammad HR , Jasman F 
Published: June 2020

Abstract
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.
Challenges and opportunities for Integrated Broadcast Broadband (IBB) implementation in Indonesia
Authors: Ariyanti Sri , Yuniarti Diah 
Published: June 2020

Abstract
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.
Optimized state feedback control of quarter car active suspension system based on LMI algorithm
Authors: Babawuro A Y , Tahir N M , Muhammed M , Sambo A U 
Published: June 2020

Abstract
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.
Simulation and analysis of flexible TEG using polymer based and pyroelectric material for microdevice energy harvesting
Authors: Bakhari N A , Hamid N A , Syafeeza A R , Wong Y C , Ibrahim M 
Published: June 2020

Abstract
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.
Design and analysis of a dual function of switchable resonator for RF switch
Authors: Shairi N. A. , Zobilah A. M. , Zakaria Z. , Othman A. , Weng S. Y. 
Published: June 2020

Abstract
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.
Low-sampling rate data-based failure diagnosis by using self-powered system
Authors: Okada S , Hashimoto S , Basari A A 
Published: June 2020

Abstract
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.
Design of Sunda Straits automatic water level station network
Authors: Sugiarto  , Wijaya S K , Rosid M S 
Published: June 2020

Abstract
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.
Real time mobile based license plate recognition system with neural networks
Authors: Liew Connie , Kim On Chin , Alfred Rayner , Tse Guan Tan , Anthony Patricia 
Published: June 2020

Abstract
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.
Comparison of simple feedforward neural network, recurrent neural network and ensemble neural networks in phishing detection
Authors: Kim Soon Gan , Kim On Chin , Mohd Rusli Nordaliela , Soo Fun Tan , Alfred Rayner , Tse Guan Tan 
Published: June 2020

Abstract
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.
The initial socio-technical solution for phishing attack
Authors: Fajar Abdullah , Yazid Setiadi 
Published: June 2020

Abstract
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.
Garbage monitoring systems based on Internet-of-Things application
Authors: Ali N. A. , Syafeeza A.R. , Ja’afar A. S. , Abdul Hamid Norihan , Ridzuan M. 
Published: June 2020

Abstract
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.
Impact of strained channel on electrical properties of Junctionless Double Gate MOSFET
Authors: Kaharudin K E , Salehuddin F. , Zain A S M , Roslan Ameer F , Ahmad I 
Published: June 2020

Abstract
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 (ION), on-off ratio, transconductance (gm) 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 (τint) has significantly reduced by approximately 52% as the strained channel is applied. Since the variation of intrinsic gate capacitances (Cint) 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 (Pdyn) for approximately 19% mainly due to slightly increased intrinsic gate capacitances.
Region-based segmentation and classification of Mandibular First Molar Tooth based on Demirjian’s method
Authors: Mohammad N , Yusof M Y P M , Ahmad R , Muad A M 
Published: June 2020

Abstract
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.
Comparison of global and local features for author’s identification by using geometrical and zoning methods
Authors: Jalil I E A , Shamsuddin S M , Muda A K , Azmi M S , Hasan S , Ahmad S 
Published: June 2020

Abstract
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%.
Classification and detection of chili and its flower using deep learning approach
Authors: Saad W H M , Karim S A A , Razak M S J A , Radzi S A , Yussof Z M 
Published: June 2020

Abstract
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.