Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of Personality Traits and Body Awareness on the Sense of Embodiment in Virtual Reality

With the increasing use of avatars (i.e. the virtual representation of the user in a virtual environment) in virtual reality, it is important to identify the factors eliciting the sense of embodiment or the factors that can disrupt this feeling. This paper reports an exploratory study aiming at identifying internal factors (personality traits and body awareness) that might cause either a resistance or a predisposition to feel a sense of embodiment towards a virtual avatar. To this purpose, we conducted an experiment (n=123) in which participants were immersed in a virtual environment and embodied in a gender-matched generic virtual avatar through a head-mounted display. After an exposure phase in which they had to perform a number of visuomotor tasks (during 2 minutes) a virtual character entered the virtual scene and stabbed the participants' virtual hand with a knife. The participants' sense of embodiment was measured, as well as several personality traits (Big Five traits and locus of control) and body awareness, to evaluate the influence of participants' personality on the acceptance of the virtual body. The major finding of the experiment is that the locus of control is linked to several components of embodiment: the sense of agency is positively correlated with an internal locus of control and the sense of body ownership is positively correlated with an external locus of control. Interestingly, both components are not influenced by the same traits, which confirms that they can appear independently. Taken together our results suggest that the locus of control could be a good predictor of the sense of embodiment when the user embodies an avatar with a similar physical appearance. Yet, further studies are required to confirm these results.
Analysis of Asymmetrical Series Resonant Converter Using State-plane Diagram

In this paper, analysis of asymmetrical series resonant converter (SRC) operated in continuous conduction mode (CCM) during steady state is presented by using state- plane diagram approach. Expected waveforms of inductor current and capacitor voltage can be obtained, accordingly, from state-plane diagram. The normalized load characteristics for design purpose are included in the analytical results. This paper can show that using state-plane diagram for the analysis is easy to understand and not very complicated. An example of converter design and computer simulation are also given in order to confirm the analytical results.
A machine learning approach to recognize junk food

People are naturally food grains. So they are always looking for appetizing food and junk foods are the largest source of it. Recent time the most observable point is that peoples are attracted to outdoor foods than homemade foods. As a result our dietary is changing and we are leaning to junk food day by day which causes a bad effect on our health and increases the risk of health disorders. Machine learning facts are being used in every inch of our life and recognizing object through image processing is one of those. Although, the reason of foods being different in nature make this process critical where traditional approaches will be the cause of a poor accuracy rate. Deep Neural Network has outperformed all of these problems. In this study, we tried to recognize local junk foods based on a new dataset consist of 2000 data belonging 5 junk food classes. All the data in the data set were collected using Smart-phone camera and believed to be unique in every sense. Convolution Neural Network (CNN) technology was used to reach the goal which is renowned for image processing. Throughout the study we achieve an accuracy of 90.47% which turned out to be satisfying. Furthermore we did test based on real-life scenario and the result was out of the mark. Our ambition is to take this study to the next level which will be implemented on another study later on. Designing such system that will prevent the society not to take junk food and be conscious about health is our final goal.
High Voltage Vertical GaN p-n Diodes With Hydrogen-Plasma Based Guard Rings

This letter demonstrates novel hydrogen-plasma based guard rings (GRs) for high voltage vertical GaN p-n diodes grown on bulk GaN substrates by metalorganic chemical vapor deposition (MOCVD). The GR structure can significantly improve breakdown voltages (<italic>BV</italic>) and critical electric fields (<inline-formula> <tex-math notation="LaTeX">${E}_{c}$ </tex-math></inline-formula>) of the devices. Not having field plates or passivation, the p-n diodes with a <inline-formula> <tex-math notation="LaTeX">$9~\mu \text{m}$ </tex-math></inline-formula> drift layer and 10 GRs showed <italic>BV</italic> /on-resistance (<inline-formula> <tex-math notation="LaTeX">${R}_{\textit {on}}$ </tex-math></inline-formula>) of 1.70 kV/0.65 <inline-formula> <tex-math notation="LaTeX">$\text{m}\Omega \cdot $ </tex-math></inline-formula>cm<sup>2</sup>, which are close to the GaN theoretical limit. Moreover, the device also exhibited good rectifying behaviors with an on-current of ~ 2.6 kA/cm<sup>2</sup>, an on/off ratio of ~ 10<sup>10</sup>, and a turn-on voltage of 3.56 V. This work represents one of the first effective GR techniques for high performance kV-class GaN p-n diodes.
Mobile Edge Computing Based Task Offloading and Resource Allocation in 5G Ultra-Dense Networks

Driven by the vision of 5G communication, the demand for mobile communication services has increased explosively. Ultra-dense networks (UDN) is a key technology in 5G. The combination of mobile edge computing (MEC) and UDN can not only cope with access from mass communication devices, but also provide powerful computing capacity for users at the edge of wireless networks. The UDN based on MEC can effectively process computation-intensive and data-intensive tasks. However, when a large number of users offload tasks to the edge server, both the network load and transmission interference would increase. In this paper, the problem of task offloading and channel resource allocation based on MEC in 5G UDN is studied. Specifically, we formulate task offloading as an integer nonlinear programming problem. Due to the coupling of decision variables, we propose an efficient task offloading and channel resource allocation scheme based on differential evolution algorithm. Simulation results show that the proposed scheme can obviously reduce energy consumption and has good convergence.
Impact of Tropospheric Scintillation Models on Earth-Space Path in Southwest, Nigeria

Much investigative studies have been performed over the years on electromagnetic wave signals being attenuated by rain, cloud, gas and tropospheric scintillation. This paper present the impact of tropospheric scintillation on earth-space path in southwest, Nigeria at 12.245 GHz. Twenty-four months of scintillation data from ASTRA 2E/2F/2G on latitude 6.7° N and longitude 3.23° E for two years (2015–2016) were statistically analysed and compare with four existing models. The result reveals that under non-rainy atmosphere, ITU-R model gave the lowest percentage error of about 1.07%, followed closely by Karasawa model with 1.67%. The work also shows that ITU-R is the best fit model of tropospheric scintillation fade for the study area at 1% of time. A modified ITU- R scintillation fade model was also proposed for the tropical region of Ota and its environs.
Black Hole Mass Estimation: Modelling the Biases

Black hole (BH) mass plays a crucial role in understanding the growth and demographics of BHs. However, BH masses based on scaling correlations between host-galaxy properties have been reported to be biased with respect to the measured values. We re-examine this problem by adopting a modelling approach to understand the bias on the BH mass induced by the different broad line region (BLR) geometry, orientation, and line width characterisation in active galactic nuclei. We model the kinematics of the BLR disk-wind with a wide-opening angles outflowing wind to retrieve the virial factor f that relates to the true BH mass. The range of f factors is largely dependent on inclination angle. The f values appear significantly larger at low inclination (close to pole-on). With increasing viewing angle (close to edge-on), the values become smaller. The recovered f factors also differ depending on the spatial location of the emitting lines and the line width measurements. This suggests that considering a fixed f factor may introduce bias in the mass measurements.
Investigation of Program Noise in Charge Trap Based 3D NAND Flash Memory

The mechanisms and characteristics of program noise (PN) in charge trap based 3D NAND flash memory are investigated in this work. Electron injection statistics is found to be primarily responsible for PN. Moreover, it is found that PN gets worse after program and erase (PE) cycling in 3D charge trapping memory devices. A physical model is proposed to explain the observations. After heavy cycling stress, new traps are generated in the tunnel oxide, and more charges can be trapped. As a result, program speed increases and charge centroid shifts towards the tunnel oxide, which lead to worse PN. Furthermore, the upper part of Vt distribution is broadened after heavy PE cycling. The contribution of the worse PN to the upper part broadening after cycling is studied through Monte Carlo simulation.
Multi Sensor Pedestrian Navigation System for Indoor and Outdoor Environments

This paper presents a real-time multi sensor localization system for pedestrians. The system can be separated into a foot-mounted multi sensor platform for robust and precise localization in indoor and outdoor environments and a hand-held device with the same sensor platform and an additional RGB-D camera for mapping purposes. The foot-mounted module is based on an accurate relative inertial positioning system with high precise Zero-velocity-UPdaTes (ZUPTs) motivated on medical research. GNSS, barometric air pressure and OpenStreetMap information is fused to obtain accurate absolute position and orientation estimations. This system can cope with very challenging situations including multiple outdoor-indoor transitions, elevators and escalators. For the hand-held device a barometric altimeter supported Visual Inertial Simultaneous Localization and Mapping (VISLAM) approach is developed. Separate measurements for translational and rotational movement are generated from RGB-D images. The translation measurements are estimated by a visual inertial odometry approach which enables loop closures. The rotation is calculated by a novel visual inertial compass, which robustly integrates the inertial sensor information. Tests show that both modules generate high accurate trajectories with small errors.
Mixed Noise Removal in Hyperspectral Image via Low-Fibered-Rank Regularization

The tensor tubal rank, defined based on the tensor singular value decomposition (t-SVD), has obtained promising results in hyperspectral image (HSI) denoising. However, the framework of the t-SVD lacks flexibility for handling different correlations along different modes of HSIs, leading to suboptimal denoising performance. This article mainly makes three contributions. First, we introduce a new tensor rank named tensor fibered rank by generalizing the t-SVD to the mode- ${k}$ t-SVD, to achieve a more flexible and accurate HSI characterization. Since directly minimizing the fibered rank is NP-hard, we suggest a three-directional tensor nuclear norm (3DTNN) and a three-directional log-based tensor nuclear norm (3DLogTNN) as its convex and nonconvex relaxation to provide an efficient numerical solution, respectively. Second, we propose a fibered rank minimization model for HSI mixed noise removal, in which the underlying HSI is modeled as a low-fibered-rank component. Third, we develop an efficient alternating direction method of multipliers (ADMMs)-based algorithm to solve the proposed model, especially, each subproblem within ADMM is proven to have a closed-form solution, although 3DLogTNN is nonconvex. Extensive experimental results demonstrate that the proposed method has superior denoising performance, as compared with the state-of-the-art competing methods on low-rank matrix/tensor approximation and noise modeling.
LMI-based Synthesis of Quadrotor Guidance and Control System

The problem of quadrotor guidance and control system synthesis based on Linear Matrix Inequalities approach is considered in this paper. Extended quadrotor's mathematical model is proposed, which takes into account the inertia of its motors. Due to the additional state variable appeared in the quadrotor's mathematical model, which is not measured in practice, it became impossible to apply the linear quadratic regulator approach that is quite simple for practical implementation. The chosen Linear Matrix Inequalities approach produces control laws as static output feedback and guarantees H-infinity rejection of external disturbances. The synthesis of the quadrotor control system based on the parametrization of all stabilizing H-infinity static output feedback is presented. The transient processes for one of the quadrotor's axis motion control illustrate the efficiency of proposed control law. Taking into account the symmetry of quadrotor, the same static output feedback coefficients were used for another axis. The simulation results of the quadrotor guidance for the circular reference track following in a horizontal plane for the cases of calm and perturbed atmosphere confirmed that proposed control laws were appropriate.
Land Cover Satellite Image Classification Using NDVI and SimpleCNN

Image classification and prediction is a task which is embedded with quite a lot of challenges. Introduction of deep learning gave a rapid rise in this area of research. The efficient and the simplest deep learning algorithm that has helped researchers to make immense contributions in the field of image classification is Convolutional Neural Network (CNN). One of the important applications of image classification is in remote sensing, where it is used for land cover classification. In this paper we developed a SimpleCNN architecture for the classification of multi-spectral images from SAT-4 and SAT-6 airborne datasets. Two sets of experiments are conducted using the model by feeding it with different features. First level of experiment is done by providing the model with Near-Infrared (NIR) band information as it can sense vegetation health. The domain knowledge of Normalized Difference Vegetation Index (NDVI) motivated us to utilize Red and NIR spectral bands together in the second level of experimentation for the classification. It is observed from the experiment that the two band information gave better results for land cover classification.
Applications of Drones in Emerging Economies: A case study of Malaysia

Drones or unmanned aerial vehicles (UAVs) are becoming increasingly popular for commercial and noncommercial uses - especially in fields of environment, surveillance, aerial photography, digital communications, search and rescue operations and military. Drones are in fact low cost aerial robots, that require little preparation and infrastructure and can be equipped with any number of sensors or cameras making them ideal for monitoring the environment. Environmental monitoring plays a major role in analyzing climate and management impacts on natural, agricultural systems, assessing, forecasting and even preventing natural disasters and enhancing hydrological cycle. Monitoring and data collection systems are based upon a combination of ground-based measurements and remote sensing sensors observations. These data however have spatiotemporal constraints. Drones offer an opportunity to bridge the existing gap between field observations and remote sensing by providing high spatial detail over relatively large areas in a cost-effective way. Drones have become popular in several developed countries in recent years. However, the use of drones is still in the infancy stage of development at developing countries such as Malaysia. This paper attempts to review the development of drone applications in Malaysia in order to identify future directions, applications, developments and challenges. We summarize that, to leverage the full potential of drones approaches in Malaysia, measurement protocols, retrieval algorithms, and processing and evaluation techniques need to be harmonized to ensure the sustainability and resiliency of the implementation.
Uniformizing Routing Hotspots of Ring Oscillators on the Cyclone V FPGA for PUF Applications

Research on the possibility of using Ring Oscillator (RO) as a Physically Unclonable Function (PUF) has been carried out. However, until now, nothing has been realized because none of them meets the expected reliability. One of the factors that influence its reliability is the variety of routing hotspots. This paper proposes a technique for uniformizing routing hotspots in all RO locations. Ten ROs are placed close to each other, and each of them is equipped with a counter. Four variations in the arrangement of RO inside a Logic Array Blocks (LAB) are created. Then, routing hotspots in all RO locations are designed to be uniform. The uniformity process is done by rearranging the location of the logic fan-in and fan-out of the RO and counter. RO is activated vary from 0.4ms to 2ms. The number of pulses generated by ROs are recorded. Based on experiments on four Cyclone V chips, the average uniqueness was 43.79% and 46.04% for 5-stage and 11-stage RO, respectively. Reliability of 11-stage RO is higher when tested between 25°C and 40°C.
An Enhancement Mode MOSFET Based on GaN-on-Silicon Platform for Monolithic OEIC

This letter proposes an enhancement mode MOSFET on GaN-on-silicon LED epitaxial wafer for the first time. The fabrication processes of the MOSFET are fully compatible with InGaN/GaN multiple-quantum-wells (MQWs) diode and include no ion implantation or additional epitaxial growth. Gate-recess structure is formed by well-controlled etching to define the channel area. The bottom and side wall of the recess are fully covered by the gate metal to improve the control ability of gate voltage on the channel conductance. The measurement results indicate acceptable performance of the MOSFET with threshold voltage of 6.01 V. Finally, the MOSFET is serially connected with an LED based on the same platform and the brightness of LED can be effectively controlled by the gate voltage according to the experimental results. Therefore the MOSFET and LED have been fabricated on the same GaN-on-silicon platform, which paves way for the monolithic optical electronic integrated circuit (OEIC).
Un-Rectifying Non-Linear Networks for Signal Representation

We consider deep neural networks with rectifier activations and max-pooling from a signal representation perspective. In this view, such representations mark the transition from using a single linear representation for all signals to utilizing a large collection of affine linear representations that are tailored to particular regions of the signal space. We propose a novel technique to “un-rectify” the nonlinear activations into data-dependent linear equations and constraints, from which we derive explicit expressions for the affine linear operators, their domains and ranges in terms of the network parameters. We show how increasing the depth of the network refines the domain partitioning and derive atomic decompositions for the corresponding affine mappings that process data belonging to the same partitioning region. In each atomic decomposition the connections over all hidden network layers are summarized and interpreted in a single matrix. We apply the decompositions to study the Lipschitz regularity of the networks and give sufficient conditions for network-depth-independent stability of the representation, drawing a connection to compressible weight distributions. Such analyses may facilitate and promote further theoretical insight and exchange from both the signal processing and machine learning communities.
Analyzing Learning Effectiveness and Students’ Perceptions of an Educational Escape Room in a Programming Course in Higher Education

In recent years, educational escape rooms have started to gain momentum in the academic community. Prior research has reported on the use of educational escape rooms in several fields. However, earlier works have failed to assess the impact of this sort of activities for teaching programming in terms of learning effectiveness. This work fills the existing gap in the literature by examining an educational escape room for teaching programming in a higher education setting by means of three different instruments: (1) a pre-test and a post-test for measuring learning gains, (2) a survey for assessing students’ perceptions, and (3) a web platform for recording student interaction data during the activity. The results of this work provide, for the first time, empirical evidence that educational escape rooms are an effective and engaging way of teaching programming.
Extended Gate Reference-FET (REFET) Using 2D h-BN Sensing Layer for pH Sensing Applications

Though ISFET (Ion-Sensitive Field Effect Transistor) has been widely used in measuring pH or other ions in electrolyte, one of main challenges it faces is its large volume of fluidic reference electrode. The use of REFET is one approach to address the above issue where a pH inert material such as hydrophobic polymer is used as sensing layer. However, REFETs with polymer sensing film may suffer from instability and non-uniformity. In this letter, an extended gate ISFET with 2D hexagonal boron nitride (h-BN) as sensing layer is proposed and demonstrated as a REFET. The device with as-exfoliated 2D h-BN flake shows a low pH sensitivity of less than 5 mV/pH and low drift around 0.50 mV/hour. All these suggest 2D h-BN a highly potential candidate to be used in making a stable and high performance REFET.
A 2 $\times$ 30k-Spin Multi-Chip Scalable CMOS Annealing Processor Based on a Processing-in-Memory Approach for Solving Large-Scale Combinatorial Optimization Problems

The world’s first 2 <inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 30k-spin multi-chip CMOS annealing processor (AP)—based on the processing-in-memory approach for solving large-scale combinatorial optimization problem—was developed. To expand the bit width of coefficients and enhance the scalability of the AP, it has three key features: an expandable and high-accuracy spin operator for local communication, a highly integrated spin circuit using direct access to SRAM, and a low-latency inter-chip interface that does not affect the runtime or results of the annealing process. The AP is fabricated on the basis of 40-nm CMOS technology. It was experimentally demonstrated that the spin-flip ratio of the processor agrees well with theoretical values based on the Gibbs distribution over a wide temperature range. As a result, under two-chip operation with 2 <inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 30k spins, the AP achieves an annealing time of 22 <inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula>, which is 455 times and 2.6 <inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 10<sup>4</sup> times faster than those achieved by our previous CMOS-AP and a conventional CPU, respectively. Moreover, its energy efficiency is 1.75 <inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 10<sup>5</sup> times higher than that of a conventional CPU-based algorithm.