Miracle-dz/UniFAR-FTUs huggingface dataset:
Partial Discharge and Breakdown Strength of Plasma Treated Nanosilica/LDPE Nanocomposites

Nanocomposites have been actively studied in recent years as an insulating material due to their excellent in electrical, mechanical and thermal properties. Even though, the addition of nanoparticles into polymer matrices showed better performance in relation to partial discharge (PD) and AC breakdown strength tests. However, the introduction of nanoparticles could lead to the formation of agglomeration of the fillers which may nullify the true capabilities of the composites. Therefore, silane coupling agent was introduced for surface functionalization treatment of the nano filler but among the issues associated are toxicity and complexity. In the present study, atmospheric pressure plasma is proposed to enhance the surface functionalization of the nano filler. This proposed method was used to treat the nanosilica (SiO2) surfaces to enhance the interfacial interaction between the host (LDPE) and nano filler. SiO2 nano filler was added into the LDPE at weight percentages of 1, 3 and 5%. The phase-resolved PD behaviour and Weibull analysis of AC breakdown strength of untreated and plasma-treated LDPE nanocomposites were measured to evaluate the performance of the samples. As results, the plasma treated LDPE nanocomposites experience apparent increments of the PD resistance and AC breakdown strength as compared to the untreated nanocomposites. It is implied that the plasma treatment of nanosilica has contributed to the enhancement of the filler dispersion and eventually reducing the agglomeration.
High Performance W/n-Si Schottky Diode using Black Phosphorus as an Interlayer

The novel application of mechanically exfoliated Black Phosphorus (BP) as an interlayer is explored for the first time in W/n-Si Schottky barrier diode (SBD). The SBD performance was significantly enhanced with an increase in Schottky barrier height from ∼. 48 eV to ∼0.59 eV, improvement in ideality factor from ∼2 to ∼1 and increase in ON/OFF ratio from ∼102 to ∼105 with BP interlayer at room temperature. The performance enhancement was found to be across a wide temperature range from 298K to 398K.
Study on asymmetric wavelength division de-multiplexer based on directional coupling in slot waveguides

We propose an asymmetric 1$\times $ 2 wavelength division de-multiplexer(WDDM) based on directional coupling in slot waveguides. And we also analyze the transmission characteristics of the device by the finite difference time domain(FDTD). We can find this 1$\times $ 2 WDDM has a high transmittance at the output ports from the numerical results, which is greater than 93% with no structural optimization is required. Then, we can get an asymmetric 1$\times $ 4 WDDM by cascading three asymmetric 1$\times $ 2 WDDMs. Asymmetric structure makes the device more compact. The WDDMs we proposed are supposed to have more applications in integrated optics.
Role of Drain and Gate Field Plates in Reforming Surface Electric Field of Lateral Power Devices

The Field Plate technique has a sophisticated impact on the surface electric field profile and breakdown characteristics of the lateral power device. Yet, the physical nature of the Field Plate effect remains unclear as a result of the complexity of conventional modeling approaches. In this paper, based on the effective concentration profile (ECP) concept, the field plates induced charge relocation can be equivalent to the variation of drift region doping profile making the complicated 2-D problem reduce to a simple 1-D problem. A novel l-D analytical model is proposed accordingly to qualitatively and quantitatively explore the physical insight of gate and drain field plate effect. The results obtained by the proposed model are found to be sufficiently accurate comparing with TCAD simulation results.
Control of a Three-Phase Cascaded H-Bridge Multilevel Inverter for Solar Energy Injection

CHB multilevel converters play a role for optimising the use of solar electricity as they allow a more efficient energy conversion. In this work, a classic control of a CHB multilevel inverter for PV energy injection is developed, considering grid disturbance compensation and an MPPT strategy for maximising PV energy. In order to prove the proposed control scheme, simulations in MATLAB/Simulink are carried out. The current control strategy, the dc voltage control, and the MPPT controller proved proper steady state operation and proper response in front of external disturbances. Additionally, the inclusion of a dynamic decoupler for the inverters output current control proved to be useful tool for controlling multilevel converters. Thus, solar energy injection to the grid can be maximised using the proposed 7-level CHB multilevel inverter.
An involutive lightweight block cipher for 256-bit block size

As the heavily loaded world of information is coming out with rapid growth, the need for securing specific details also have a great significance. Among the many proposed lightweight block ciphers out there, the main focus nowadays is on their simple design specifications. In this paper, we have proposed a new involutive block cipher of 256-bit block size which is a member of the Extended LS-Designs.
Predicted Congestion Using a Density-based Fast Neural Network Algorithm in Global Routing

As the feature size of devices decreases and the number of transistor interconnect exceeds billions, the runtime of global router becomes a problem in very large-scale integrated circuit design. The congestion prediction in the global routing, which can cost huge time, is one of the most important and challenging problems. In this paper, we propose a density and pins peaks-based fast neural network algorithm (NNA) to predict congestion map. In order to save runtime, traditional prediction methods are replaced by a machine learning method with local density and pins as features. Furthermore, to improve the performance of proposed method, the predictive identification method to specify order of the rip-up and reroute for congested regions is also proposed. Compared with state-of-the-art router, NTHU-Route 2.0, on ISPD08 benchmarks, our method greatly improves the speed of predicting routing congestion information by 1094% in test cases, and saves runtime of the global router.
A Decoupled Linear Model Predictive Control-based Motion Cueing Algorithm for Simulation-based Motion Platform with Limitted Workspace

The simulation-based motion platforms (SBMPs) are useful devices to reproduce the motion feeling for a driver/piolet. The SBMPs are restricted due to limitations of the platforms’ structures. These limitations lead discrepancy between visual and motion cues which can cause motion sickness. The motion cueing algorithm (MCA) is employed to reproduce motion cues of a real land and air vehicle in the SBMP within its physical limitations aiming to regenerate the realistic feeling for the SBMP’s driver. Recently, coupled linear model predictive control-based motion cueing algorithms (MPC-based MCA) have been proposed with consideration of the human perception model including semicircular canals and otolith organs. The decoupled linear model predictive control including semicircular canals and otolith organs have been designed in this study for the first time to use the limited linear workspace of the Hexapod-based motion simulation platform more effectively. The results of both the existing coupled and new decoupled MPC-based MCAs are compared. Finally, it is shown that the decoupled linear MPC-based MCA is able to reduce the motion sensation error between the real vehicle and SBMP drivers and also use the limited linear motion simulation platform workspace more efficiently compared to the existing coupled linear MPC-based MCA.
Supporting Computational Thinking Development in K-6

This paper describes the CSER Digital Technologies MOOC curriculum, which aims to build confidence and awareness of fundamental computational thinking skills for teachers within the K-6 context. The CSER MOOC builds upon recent advances in the development of the Australian Curriculum Digital Technologies learning area, which explicitly addresses the development of computational thinking skills, moving ICT education away from digital literacy towards embedding the skills needed for the next generation to become creators of technology. The CSER MOOC presents a broad coverage of core computational thinking concepts, with a focus on integrating with existing learning areas and appropriate pedagogy for the K-6 context. In this paper, we discuss our course curriculum, its alignment with key concepts from the formal curriculum statements, example teacher assessments, and discuss key challenges and concerns.
Highly Sensitive Refractive Index Sensor Using Dual Resonance in Subwavelength Grating/Waveguide With Normally Incident Optical Geometry

A highly sensitive refractive index sensor with normally incident optical geometry was experimentally demonstrated using dual resonance in a subwavelength grating (SWG) on a waveguide. Two eigenmodes within the SWG and waveguide were utilized for refractive index sensing with the reduction of noise such as background noise. The finite-difference time-domain numerical method was used to estimate the refractive index sensing performance of our sensor. The calculated electric field clarified that the incident light could excite modes in the SWG and waveguide with normally incident optical geometry, and the modes formed two resonance reflection peaks. The calculation showed that these dual resonance peaks depended differently on the refractive index of the ambient around the sensor, and the intensity difference between both resonance peaks varied with the slight change in refractive index of the ambient. The sensor was fabricated using traditional electron-beam lithography techniques, and two reflection resonant peaks were experimentally obtained. The difference between the dual resonance peak intensities varied significantly with the minuscule change in refractive index of the ambient around the sensor, and the resolution of the refractive index reached $7.65\times 10^{\mathbf {-4}}$ , assuming a spectrometer intensity sensitivity of 1%.
A Performance Improvement Approach for Second-Order Optimization in Large Mini-batch Training

Classical learning theory states that when the number of parameters of the model is too large compared to the data, the model will overfit and the generalization performance deteriorates. However, it has been empirically shown that deep neural networks (DNN) can achieve high generalization capability by training with extremely large amount of data and model parameters, which exceeds the predictions of classical learning theory. One drawback of this is that training of DNN requires enormous calculation time. Therefore, it is necessary to reduce the training time through large scale parallelization. Straightforward data-parallelization of DNN degrades convergence and generalization. In the present work, we investigate the possibility of using second order methods to solve this generalization gap in large-batch training. This is motivated by our observation that each mini-batch becomes more statistically stable, and thus the effect of considering the curvature plays a more important role in large-batch training. We have also found that naively adapting the natural gradient method causes the generalization performance to deteriorate further due to the lack of regularization capability. We propose an improved second order method by smoothing the loss function, which allows second-order methods to generalize as well as mini-batch SGD.
Smart Farm Monitoring Using LoRa Enabled IoT

Agricultural practices need to be transformed in order to overcome future food scarcity due to overpopulation across the globe. By employing emerging, disruptive technologies like IoT in the agricultural sector, it is possible to monitor farm fields using low-cost and low-power consuming devices, to automate irrigation systems for efficient usage of water resources. Weather forecast using IoT can help to plan farm filed activities like sowing, harrowing, harvesting, etc. This reduces negative impacts like yield losses due to uncertain weather changes. LPWAN technologies serve IoT applications in a better possible way so that these applications can overcome bandwidth, power and coverage constraints which are main drawbacks in other wireless communication technologies. In agricultural fields, LoRaWAN protocol or LoRa in LPWAN space gives additional advantages like scalability, security and robustness in designing IoT applications. In this paper, a smart farm monitoring model is proposed. This model utilizes LoRa communication mechanism to send sensor data like temperature (°C), humidity (%) and soil moisture (%) from the transmitter node to receiver node. The receiving node which is Wi-Fi enabled uses MQTT services to monitor the data in IBM Watson IoT platform and to store the same data in IBM cloud DB service.
Fog Computing for Smart Grid Systems in the 5G Environment: Challenges and Solutions

Currently, the demand for electricity is increasing day by day, which necessitates upgrading of the existing power grid system. The conventional power grid has already been replaced with modern ICT-based infrastructure, which is known as smart grid (SG). In SG, smart meters generate a huge amount of data, and it is a challenging task to store, process, and analyze the data, which varies with respect to volume, velocity, and variety. The data generated in an SG system is generally stored and analyzed using cloud computing (CC), which provides real-time response for various applications. However, to handle the latency issue during the data analytics in SG, fog computing (FC) has emerged as a new technology that provides most of the computing resources in proximity of the end users. It acts as a bridge between SG and CC to fill the gap between processing power of remote data centers and smart devices in SG systems. To handle the aforementioned issues, there is a requirement to set up advanced sensors and measurement systems having communication network backbones in the upcoming fifth generation (5G). In this article, we discuss the architecture of SG in the context of FC for making the decision about energy requirements by the smart devices at the fog layer. Moreover, the communication and computing aspects are also explored in the context of 5G network infrastructure. We examine the influence of FC on response time, transmission delay, and energy management costs for delay-sensitive applications.
Binary Convolutional Neural Network for Brain Computer Interfaces

Recent years, there is increasing interest in using deep neural network for EEG analysis. In this paper, we have innovatively applied the data augmentation commonly used in CNN image classification to EEG signals classification, which can improve classification performance: motor imagery (MI) tasks (left hand and right hand) accuracy rate can reach 93.1%. Then, we innovatively use BCNN (Binary Convolutional Neural Network) to analyze the MI tasks (left hand and right hand movement). BCNN can reduce power consumption and reduce the time complexity by 60% in dedicated hardware. BCNN is also easy to map to general hardware. In addition, we use BCNN as a classification module and use Emotiv EPOC as collection device to build BCI platform.
A Generic Inertial Navigation System

In order to control the movement of a vehicle, precise position and movement information is required. This information is usually obtained through a data fusion of different sensors. This paper presents a new approach of a generic fusion filter for integrated navigation systems, which can be integrated for different types of vehicles. In addition, the fusion filter offers the possibility to add additional sensors with minimal development effort to improve the accuracy of the estimation. For a generic approach a quaternion-based multi-sensor Square-Root Spherical Simplex Unscented Kalman Filter is used. In this way, the nonlinear process and measurement models can be directly applied to the state estimation without the need for analytic derivations or Jacobians compared to conventional Extended Kalman Filter approaches. The presented method was evaluated by sea trials using an unmanned surface vehicle.
User Driven Multiclass Cell Association in 5G HetNets for Mobile & IoT Devices

Fifth generation (5G) needs to support plenty of applications and services with a wide variety of quality of service requirements. The deployment of ultra-dense small cells’ networks as a part of the heterogeneous networks architecture is one of the key technologies to achieve this. In such a dense architecture, associating devices with the network is challenging. The traditional cell association algorithms use signal-to-interference-plus-noise ratio metric. However, this is not appropriate for 5G, especially with the reduction in the cells size and the growing number of the user equipment (UE) and the Internet-of-Things (IoT) devices. In this paper, we propose a distributed multiclass user-driven cell association algorithm based on the multi armed bandit game (CA-MAB) to connect devices with different requirements to the network. Here, we focus on two classes of devices: UE devices and low-power IoT devices. The proposed algorithm is evaluated in static and mobile environments, where the convergence and equilibrium are achieved. Our performance results are validated against the central cell association method that is complex and requires a huge amount of information exchange. The results show that CA-MAB throughput and energy efficiency are within 10% of the centralized solution. These values increase by less than 5% in the case of mobility. However, they reduce with more network densification.
Thickness-Dominated Forming Conditions of TaOx-Based Memristor

As one of appealing NAND flash replacements, resistance switching random access memory (RRAM) has been hotly investigated due to its simple structure, high read speed and considerable retention. TaOx is a promising candidate as the insulators in the resistance switching random access memory. The switching uniformity and the associated reliability are particularly important for industrialization. In this paper, we fabricate ITO/TaOx/TiN memory device with four groups of different thickness TaOx films: 15nm, 20nm, 25nm and 30nm. The results show that the electroforming voltages of the devices are approximately proportional to the thickness of the TaOx films. On the basis of the conducting filament theory, we infer that it is more difficult for devices to hard breakdown with film thicknesses increasing. This will further affect the uniformity and window (RHRS/RLRS) of the device. However, the endurance of the 30nm TaOx is relatively poor. Therefore, 25nm is the best thickness in terms of uniformity and endurance of the devices.
An Adaptative Ratio-Metric Analog-To-Digital Interface For Weakly-Coupled Resonant Sensors Based On Mutually Injection-Locked Oscillators

In this paper, we propose a new ratio-metric interface for amplitude-modulated MEMS sensors based on mutually injection-locked oscillators (MILO). The two outputs are down-converted by a peak demodulator to improve the immunity to phase variation and noise. The peak values are then fed into a second-order discrete-time delta-sigma modulator that directly produces a digital ratio-metric output with a worst-case resolution greater than 16 bits and with input full scale of 0.6. The interface is clocked by the resonators output signal and is adapted for a sampling frequency up to 1MHz.
A high speed fully self-timed SAR ADC with an on-chip adaptative reference buffer

This paper presents a 12bit 100Ms/s fully-self timed SAR ADC. A timer circuit is introduced in order to self-time the sub-ADC settlement. Redundancy and monotonic switching techniques are adopted to further enhance the conversion speed. A source follower based high speed reference buffer is included on the chip. To adaptively adjust the drive strength under different PVT condition, a bias current adjustment feedback loop is designed. The proposed SAR ADC is designed with 55nm CMOS process, the active core circuit occupies an area of 560μm*320μm. The simulated result indicates that 75.2dB SFDR, 70.7dB SNR, 69dB SNDR are achieved.
Low power CNN Accelerator for mobile lensless imaging system

This paper presents a CNN accelerator structure for lensless microfluidic acquisition of biological image processing. We propose a low power optimized convolution circuit which utilized the sparse characteristics of lensless cell image acquisition system. And a reconfigurable dual-register group structure is also developed for improving the CNN processing efficiency. The CNN accelerator structure can accelerate various cell image analysis algorithm, including image edge segmentation and super-resolution reconstruction. The circuit is implemented with UMC 110nm process, the circuit area is 2.66 mm2 and the maximal frequency is 300 MHz.
In-lab validation of jammer detection and direction finding algorithms for GNSS

Our paper focuses on the detection and direction finding of jamming signals in Global Navigation Satellite System (GNSS)bands. A methodology for in-lab validation for three selected jammer detection algorithms and two selected jammer direction finding algorithms is presented and measurement-based results, for nine combinations of GNSS and jamming signals, are shown. Both chirp jammers and amplitude-modulated single-tone jammers are considered in our in-lab validation process. The algorithm selection was done based on literature studies. Power-based detectors and direction finding algorithms are considered in this paper. It is shown that the considered detectors have similar performance and good detection probabilities for Jammer-to-Signal ratio (JSR)above −10 dB and that the Minimum Variance Distortionless Response (MVDR)beamformer can estimate quite accurately the jammer's Angle of Arrival (AoA)with JSR above 10 dB.