Miracle-dz/UniFAR-FTUs huggingface dataset:
Fab-free, High throughput thin Metal Film Fabrication Method Using Reductive Metal Ion Ink Coating for Diverse Plasmonic and Electronic Applications

Metal thin films can be applied to a wide variety of applications due to their excellent thermal and electrical conduction, as well as unique electrical and optical properties. Conventional thin metal film fabrication methods such as sputtering and vacuum evaporation have limitations especially for scalable and high-throughput production due to area constraint, low speed, and high cost. In this study, we propose a simple metal thin film forming method based on facile coating of reductive metallic (Ag) ion-containing ink followed by quick annealing. This method has a simple and strong controllability for the film thickness and surface morphology by controlling ink concentration, coating speed, and annealing condition. In addition, incorporating nanostructures by lithography, nanoimprinting, etc., further makes it possible to apply Ag thin film to more wider applications involving sensors, plasmonics, photonics, and more. For one vivid example, we demonstrate that it is possible to embed nanopatterns using nanoimprinting in the Ag thin film and zinc oxide nanowires can be selectively grown.
Using Neuromorphic Hardware for the Scalable Execution of Massively Parallel, Communication-Intensive Algorithms

Neuromorphic hardware like SpiNNaker offers massive parallelism and efficient communication of small payloads to accelerate the simulation of spiking neurons in neural networks. In this paper, we demonstrate that this hardware is also beneficial for other for applications which require massive parallelism and the large-scale exchange of small messages. More specifically, we study the scalability of PageRank on SpiNNaker and compare it to an implementation on traditional hardware. In our experiments, we show that PageRank on SpiNNaker scales better than on traditional multicore architectures.
Resource Allocation and Performance Analysis of Cellular-Assisted OFDMA Device-to-Device Communications

Resource allocation of cellular-assisted device-to-device (D2D) communication is very challenging when frequency reuse is considered among multiple D2D pairs within a cell, as intense inter D2D interference is difficult to tackle and generally causes extremely large signaling overhead for channel state information (CSI) acquisition. In this paper, a novel resource allocation framework for cellular-assisted D2D communication is developed with low signaling overhead while maintaining high system capacity. By utilizing the spatial dispersion property of the D2D pairs, a geography-based sub-cell division strategy is proposed to divide the cell into multiple sub-cells and the D2D pairs within one sub-cell are formed into one group. Then, sub-cell resource allocation is performed independently among the sub-cells without the need of any prior knowledge of inter D2D interference. Under the proposed resource allocation framework, a tractable approximation for the inter D2D interference modeling is obtained and a computationally efficient expression for the average ergodic sum capacity of the cell is derived. The expression further allows us to obtain the optimal number of sub-cells, which is an important parameter for maximizing the average ergodic sum capacity of the cell. It is shown that with small CSI feedback, the system capacity can be improved significantly by adopting the proposed resource allocation framework, especially in dense D2D deployed systems.
Symbol Mapping Design for Bit-Interleaved Polar-Coded Modulation With Iterative Decoding

In this letter, we design a bit-interleaved polar-coded modulation system with iterative decoding subject to constraints on complexity, error floor, and waterfall region performance. We show that the hill-like trajectory of polar code extrinsic information transfer curves allows us to predict the gap of the bottleneck region that a polar code achieves at a given received signal-to-noise ratio for a given labeling rule. Using the gap, we propose a 3-D linear objective function to optimize for error floor, waterfall region performance, and complexity. The objective function requires Monte Carlo simulations for only three points on the extrinsic information transfer curve of the demapper for a given labeling rule. Our simulations show that the proposed method designs labeling rule and polar code duos that trade off error-floor and waterfall region performance with complexity and latency.
Using Metadata in Detection Spam Email with Pornography Content

Spam or junk email is a disturbance in email service. They usually have contents that cause inconvenience to users such as electronic news, advertisements, and other things. One of the most troubling contents of spam email is pornography. In this work, we focus on developing a general framework of spam detection which related to the pornographic content. We utilize metadata which has been ignored in detecting spam. The principle of information retrieval is implemented by tracing the URLs which are listed in the email and then obtain the metadata keywords from the page which those URLs pointed. Next, after the similarity calculation and the spam has been detected then the notification is released to dump the email automatically into a spam folder.
Fault Diagnosis of PEMFC Systems in the Model Space Using Reservoir Computing

Artificial neuron network provides a promising solution for fault diagnosis of fuel cell systems. A recently proposed novel framework of recurrent neuron network named Reservoir Computing is focused with only its output weights to be trained, which is rather advantageous for online adaption in real-time applications. In a previous work, its simplicity and efficiency has been demonstrated. This paper focus on a novel attempt of performing fault diagnosis directly in the reservoir computing based model space (current-voltage model) instead of the original data space (voltage signal). No additional feature extraction procedure is needed and abnormal health states could be detected directly in the model space (in the form of evolution of output weights).
Wideband Transition From Microstrip Line-to- Empty Substrate-Integrated Waveguide Without Sharp Dielectric Taper

In this letter, a wideband transition from microstrip line-to-empty substrate-integrated waveguide (ESIW) without sharp dielectric taper is proposed. A section of ESIW with an expanded width is employed to replace the commonly used dielectric taper. The dielectric loss of the taper is eliminated, and the low reflection is achieved over a broad bandwidth. A design procedure is presented to provide initial dimensions of the transitions for different frequency bands and different substrates. A tolerance analysis shows that the reflection performance is insensitive to the dimensional parameters related to the printed circuit board cutting process. A Ku-band back-to-back transition prototype has been designed and manufactured using RO4350B substrate of 0.508-mm thickness to validate its performance. The measured return loss is better than 21 dB with an insertion loss of 0.83 ± 0.25 dB (0.415 ± 0.125 dB for a single transition) over the entire Ku-band.
Conceptual Models of HTS Levitation and Linear Propulsion System

High-temperature superconductor (HTS) materials, including HTS wires and bulks, become available to apply in transportation applications such as various hybrid levitation and linear propulsion systems. In this paper, for the evacuated tube transportation and electromagnetic aircraft launch system applications, two different types of hybrid HTS levitation and linear propulsion systems have been designed and modeled, respectively. The levitation force and electromagnetic thrust performance of the systems have been studied by a numerical and finite element method. With the principal parameters’ verification, this work forms a basis for the structural and electromagnetic design of a hybrid HTS levitation and linear propulsion systems for practical hyper flight applications.
Edge Computing and Caching based Blockchain IoT Network

With the development of internet of things (IoT) network in recent years, while the centralization data management brings longer delay while has more safety troubles. Moreover, once a single device is attacked, the system crashes. So, blockchain technique which could realize safety distributed data management is introduced. However, the consensus mechanism of blockchain consumes huge computation resources. And the storage of entire ledger also consumes huge storage resources of the blockchain nodes. Due to the limited capacity of IoT devices, the nodes cannot afford the computation resources to reach consensus and caching resources. This paper introduces an architecture of edge computing based blockchain network which makes use of the computation and caching capacity of edge server to help the IoT devices in reaching consensus and storing data.
Design of Neuron Net Function using Modified Radix-4 Booth Multiplier with a Flipped Logic Parallel Prefix Adder

This paper presents a full custom implementation of net function as a building block of neuron in an Artificial Neural Network system. The net function is implemented using radix-4 booth multiplier architecture. The main challenge or bottle neck in a booth multiplier architecture is designing an efficient adder. In this work, we propose a flipped logic parallel prefix adder to optimize the performance of the multiplier. By using this adder, the processing stages are able to be reduced more than a half, which resulting in a significant improvement of the propagation delay. The system has a total layout area of 0.027 mm2 with maximum operating frequency of 3.4 MHz. The circuit and layout design are implemented on 130nm CMOS technology.
Full State Tracking and Formation Control for Under-Actuated VTOL UAVs

In this paper, a coupled-attitude based trajectory tracking scheme is proposed to track both the position and attitude of under-actuated unmanned aerial vehicles, and its application on formation control is further demonstrated. An intermediate attitude which is composed of the desired attitude and position information is designed in a two-stage framework, wherein the first stage is the controller design of a translational subsystem and the second stage is that of an attitude subsystem. By virtue of the intermediate attitude, trajectory tracking which includes both attitude and position is realized. The proposed intermediate attitude can be viewed as a bridge connecting the position and attitude motion, and it is a new approach for both single and multiple under-actuated rigid bodies’ control. Based on the approach of coupled-attitude-based trajectory tracking, both the set point stabilization and formation tracking tasks for under-actuated vertical takeoff and landing vehicles over a directed acyclic graph can be achieved. The performances of the proposed control laws are illustrated through numerical simulations.
Relation Extraction in Vietnamese Text via Piecewise Convolution Neural Network with Word-Level Attention

With the explosion of information technology, the Internet now contains enormous amounts of data, so the role of information extraction systems becomes very important. Relation Extraction is a sub-task of Information Extraction, which focuses on classifying the relationship between the entity pairs mentioned in the text. In recent years, despite the many new methods have been introduced, Relation Extraction still receives attention from researchers for languages in general and Vietnamese in particular.Relation Extraction can be addressed in a variety of ways, including supervised learning methods, unsupervised and semi-supervised methods. Recent studies in the English language have shown that Relation Extraction using deep learning method in the supervised or semi-supervised domains is achieving optimal and superior results over traditional non-deep learning methods. However, researches in Vietnamese are few and in the process of searching documents, the results of deep learning applying for Relation Extraction in Vietnamese are not found. Therefore, the research focuses on studying and research the method of using deep learning to solve Relation Extraction task in Vietnamese. In order to solve the Relation Extraction task, the research proposes and constructs a deep learning model named Piecewise Convolution Neural Network with Word-Level Attention.
Implementation of Model Predictive Control on The Pitch Motion of Train Simulator Platform with Load Torque Uncertain

This paper presents an analysis of the non-linearity of the torque load on the pitch platform movement of train simulator. Angle modelling of pitch available in many researches. However, for the changes of torque loads in the train simulator there has been no research that examines it. Then, this is one of the novelty we provided in this research. Beside that, we simulate Model Predictive Control (MPC) with a plant that contains non-linearity using simulation tool. In order to show the performance, we analyze its data and make a conclusion. As a Linear Quadratic Regulator (LQR), MPC produces an optimal control signal that can overcome constraints such as saturation. In MPC implementation, we determine the number of horizons (N), weighting Q and R based on the desired specifications. The simulation results showed that the MPC design can handle non-linearity well. However, it is merely a simulation. We have not applied it to a actual plant yet.
CoLPUF : A Novel Configurable LFSR-based PUF

In this paper, a new configurable LFSR-based PUF is proposed. This proposed design uses the LFSR states to generate different control signals for selecting the response bits from the ring oscillator frequencies. The main advantage of the design is by generating control signal from the LFSR with the given challenge, one can extend the length of the response by increasing number of states of LFSR without any corresponding increase in the hardware. The concept of constant-resource scalability can be adopted to any delay-based PUF design. The PUF design (128-bit response) is tested on an Artix-7 Nexys 4 FPGA board and results in uniqueness of 49.2%, uniformity of 48.5% and 47.8% bit-aliasing. The noisy bits are recovered using BCH error correction scheme and we obtained final response with a reliability of 99.99 %.
Reed–Muller Network Coded-Cooperation With Joint Decoding

This letter proposes a Reed–Muller network coded-cooperative (RMNCC) scheme with the joint decoding. The Kronecker product (KP) construction is utilized to construct RM codes. The KP construction is effectively employed to accomplish network coded-cooperation between two users. An asymptotic error performance bound is presented to validate the average BER performance of the RMNCC scheme over fast Rayleigh fading channel. Monte Carlo simulated results for the RMNCC scheme agree with the analytical framework at higher SNR. Furthermore, a joint bit selection algorithm for the optimum selection of information bits at the relay node is also proposed, which results in an improved BER performance.
A Community Merger of Optimization Algorithm to Extract Overlapping Communities in Networks

A community in networks is a subset of vertices primarily connecting internal components, yet less connecting to the external vertices. The existing algorithms aim to extract communities of the topological features in networks. However, the edges of practical complex networks involving a weight that represents the tightness degree of connection and robustness, which leads a significant influence on the accuracy of community detection. In our study, we propose an overlapping community detection method based on the seed expansion strategy applying to both the unweighted and the weighted networks, called OCSE. First, it redefines the edge weight and the vertex weight depending on the influence of the network topology and the original edge weight, and then selects the seed vertices and updates the edges weight. Comparisons between OCSE approach and existing community detection methods on synthetic and real-world networks, the results of the experiment show that our proposed approach has the significantly better performance in terms of the accuracy.
Improving the 3D model classification based on selecting proper features

Classifying 3D models helps to organise databases according to categories. As a result, models are quickly search to recommend models when designing virtual scenes in movies and games. Today, number of 3D models increases sharply and entertainment needs develop quickly. Therefore, classifying 3D models is essential task. Previous studies used many features to improve the accuracy of classifying. It takes a long time for both extracting features and classifying models. In this paper, we select three features and find a suitable classifier to drop the time for computing in classifying 3D models. Proposed features are eigenvalues associated with the principal axes of 3D models. We compare available classifiers to select the best one, Support Vector Machine, to classify models. Experiments are conducted on two benchmark databases including travel means in Princeton Shape Benchmark and animals in Shape Retrieval Contest 2010. Experiment results show our approach is useful for recommendation applications and roughly classifying 3D models.
Parameterized Data Reduction Framework of Thermal Sensing for Gait Velocity Measurements

In IoT systems, many sensing devices will periodically transmit data. However, most of the sensing data are either similar or redundant and the sensing device itself may not have effective rules standard to decide to send or not. Static rules maybe effective on specific scenario, and become ineffective when the use scenario of the sensor changes. Hence, we design an algorithm to solve the problem of data redundant for IoT devices. The algorithm iteratively separates a data region into smaller regions. In each round, it chooses a region with highest variability, and separates it into smaller regions. Finally, each region has different sizes and uses its average value to represent itself. If an area has more dynamical, diverse data, more data points will be used to present the data. In this paper, we present a method to reduce the file size of thermal sensor which can sense the temperature of a surface and outputs a two dimension gray scale image. In our evaluation result, we can reduce the file size to be 50% of JPEG format when 0:5% of distortion is allowed, and up to 93% less when 2% of distortion is allowed.
A Heart Rate Measurement System Based on Ballistocardiogram for Smart Furniture

This paper presents a novel on-chair heart rate detection system based on ballistocardiogram (BCG) using sensor cores placed symmetrically on steel plate. The sensor cores are designed based on piezoelectric effect to detect weak vibrations from body. A 3-stage circuit is designed with cutoff frequency of 0.1-10Hz and a gain of 10 to interface with piezoelectric sensor core. The symmetrically placed sensors produce signals in opposite direction that cancel the motion artifacts when the body swings from left-to-right or vice versus. Measurement results show that the proposed system enhances the signal-to-noise ratio (SNR) by around 2 times. The experimental results also demonstrated that the proposed system detects high-quality BCG signals when small body movement is induced during measurement.
Modeling and simulation of a spherical vehicle for underwater surveillance

This paper presents the modeling and simulation of a spherical autonomous underwater vehicle. The robot was developed under the European Union H2020 innovation action UNEXMIN for the exploration of underground flooded mines, and is a small spherical robot with thrusters and an internal pendulum for pitch control. A model of the vehicle is presented, initially without the pendulum, then an extended formulation is derived accounting for a multibody dynamic description of the system. Experimental identification results for the determination of drag parameters are presented as well. A Modelica based simulator is developed for dynamic simulation of the vehicle, and is integrated with the Matlab/Simulink environment. The simulator is then validated based on preliminary experimental results.
Analytical Modeling of Electrostatic Characteristics of Enhancement Mode GaN Double Channel HEMT

In this paper, the electrostatic characteristics of enhancement mode GaN double channel MOS-HEMT has been investigated. A polynomial analytical expression establishing a relationship between the Fermi level and the two-dimensional electron gas density has been developed for this device for the first time. This analytical expression has been utilized to develop a relationship between sheet carrier density and applied gate voltage. A self-consistent simulation incorporating quantum mechanical effect has been carried out to obtain the charge density profile and capacitance-voltage profile. The variation of conduction band profile with the variation of gate voltage has been inspected. The impact of variation of GaN channel thickness on the conduction band profile and the charge density profile is also explored. The comparison of results obtained from the analytical model and simulation confirms that the analytical model produces satisfactory result in both subthreshold and saturation region. We have extracted threshold voltage which corroborates enhancement mode operation of the device. Finally it has been showed that our results are congruent with published results found in the literature.
A Method for Finding the Routes of Mazes

In this paper, we propose a potential modified route-searching algorithm to find all feasible paths in a new place which is like an unknown maze. The maze-like place is assumed to be a region constructed by grids and each grid is supposed to have at most four directions for moving. A searching algorithm is developed to accomplish the required tasks for patrolling every path in the new place. Based on the traditional depth-first graph traversal method, we build up a local-maze coordinate system to record the visited pathway. The main results of this paper will give an experiment simulation to demonstrate the success of proposed design.
BER Analysis of spatial multiplexing and STBC MIMO-OFDM System

Higher data rates and reliability have been problems in the wireless communication systems from the outset. It is essential and necessary even by deal with multiple antennas at both ends; simultaneously fading occurs to each link can be considered due to multipath propagation this leads to increase BER performance at the receiver. We can achieve reliability and high data rates through MIMO-OFDM with STBC and spatial multiplexing techniques are used respectively. This paper explores to analyze different fading channels (Rayleigh and Nakagami) under AWGN environment on spatial multiplexing (SM) based MIMO systems, Zero forcing with Successive interference Cancellation (ZF-SIC), MMSE-SIC and ML equalization techniques are used to channel equalization. And of transmit diversity, STBC based MIMO-OFDM systems, Maximum Ratio Combining (MRC) technique is used. Simulation results showed with various modulation techniques (BPSK/QPSK/64QAM/32PSK) both Systems. Furthermore, BER performance of coded MIMO-Wi-Max systems also describes to transmit diversity.