Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel construction of Z-scheme CuO/g-C3N4 heterojunction for visible-light-driven photocatalysis in natural seawater

In the present study a simple and convenient solution route to grow submicron-sized CuO structures onto the surface of g-C3N4 was demonstrated and the upgraded visible-light-driven photocatalytic activity of the as-prepared composite was revealed. The buildup of CuO(4.4 wt%.) /g-C3N4 p-n heterojunction was successfully accomplished via two-step ammonia- evaporation-induced synthetic approach. The resulting inorganic/organic composite was characterized by X-ray diffraction, X-ray photoelectron spectroscopy, transmission electron microscopy, thermogravimetry, ultraviolet-visible diffuse reflectance spectroscopy and photoluminescence spectroscopy to examine its phase structure, valence state, microstructural, thermal and optical properties. Its photocatalytic efficiency was tested using Malachite Green photodegradation in natural seawater and the results indicated that CuO/g-C3N4 is much more active than the individual components in the photodegradation of MG. A plausible photocatalytic mechanism was also proposed.
Permeability Determination of Metallic Wires by a Method Combining Measurement and Computer Simulation

This work is to present a method combing measurement and computer simulation techniques to determine the relative permeability of metallic wires. By constructing a test bed on which the wire under evaluation, it is used to form a two-wire transmission line. The permeability is found out when its set value in the simulation model offers the same simulated characteristic impedance with the measured one on the test bed by a TDR (Time-Domain Reflectometry).
Behavioral Facilitation Tweets with Basis Extraction

There is much information that induces users to take some action on Twitter. In this study, we designate such tweets as behavioral facilitation tweets. There are a large number of behavioral facilitation tweets. In such behavioral facilitation tweets, there is some basis in it. We call such behavioral facilitation tweets as " behavioral facilitation tweets with basis". In this paper, we proposed the method which extracts behavioral facilitation tweets with basis. Our proposed method is that first we extract behavioral facilitation tweets using LSTM, and next we extract behavioral facilitation tweets with basis from them by rule-based. We propose two types of keywords which are general basis keywords and domain dependence keywords by our experiments. Furthermore, we conducted two types of experiments which measure our proposed extracting behavioral facilitation tweets using LSTM and extract behavioral facilitation tweets with basis from them by the rule are good or not. From the results, our proposed methods are good for extracting both behavioral facilitation tweets and behavioral facilitation tweets with basis.
Design of Wideband Microstrip to SICL Transition for Millimeter-Wave Applications

In this communication, the design and working principle of a wideband microstrip to substrate integrated coaxial line (SICL) planar transition for millimeter-wave applications is presented. In the proposed work, along with tapering of microstrip line, two triangular slots in the top ground plane are utilised to compensate for the discontinuity arising at the microstrip-SICL junction & minimize reflections over a wide bandwidth. To confirm the theoretical claims, the proposed microstrip to SICL transition is fabricated using standard multilayer PCB process. The measured results of the prototype indicate return loss better than 14 dB with a maximum insertion loss of 1.78 dB for DC to 40 GHz band (19 dB bandwidth up to 20 GHz with insertion loss less than 0.71 dB). The modeling of proposed transition is presented & versatility of the design is illustrated for different dielectric constant & thickness. Effortless integration of SICL with the widely used microstrip transmission line simplifies the feeding process of SICL based broadband antennas & microwave circuits.
Analysis of Flux Intensifying Effect on Synchronous Motors Applied to Electric Scooter

This paper presents the analysis of a novel type of flux-intensifying synchronous motor for application to electric scooters. The effect of flux intensifying on two types of synchronous motors (SMs) are studied, i.e. the permanent magnet (PM) SM (PMSM) and synchronous reluctance motor (SynRM). Flux intensifying configuration can avoid irreversible demagnetization and enhance the efficiency of EV traction motors for the most operated points in a driving cycle. The analysis indicates that flux intensifying synchronous motors (in particular flux intensifying PMa-SynRM) are a potential and suitable candidate for traction motors of electric scooter.
Low Power Pre-comparison Configuration Strategy for a Logic-based Binary CAM on FPGA

Content-addressable memory (CAM) is considered an efficient searching technology due to the fact that the output is processed in a deterministic time. However, traditional CAM cannot be implemented on field-programmable gate array (FPGA) owing to the lack of architectural support. Thus, researchers use FPGA resources, i.e., random-access memory, lookup tables, and slice registers to emulate CAM. Unfortunately, FPGA-based CAMs have higher power consumption owing to the concurrent activation of the entire CAM components. Therefore, it is indispensable to design such a strategy that can activate merely the required resources and ultimately reduce the power consumption. In this paper, we present a pre-comparison configuration strategy on a logic-based binary CAM. The proposed CAM has two segments such that the first segment when matched with a few bits of the search key enables the second segment. Thus, it results in power reduction during a search operation. A sample of 64 × 36 of the proposed CAM is implemented on a Xilinx Virtex-6 FPGA. Implementation results show a reduction of 31.27% in power consumption when compared to the latest FPGA-based CAMs without sacrificing the throughput.
Privacy Preserving Big data Using Combine Anonymization and Encryption Approach

In recent years, data breach and sensitive information leak is main issue created. Data analysis is done on large amount of data to integrate and analysed data. But it increase data violation in data. To overcome this privacy is need of any particular data and it plays a vital role. Anonymization is a technique which hide the sensitive information of that particular data using generalization and suppression. It increases the privacy of any information. But it is not sufficient as in today’s world many tools are available to break privacy.so we can use protection too to protect the data and by providing dual approach. For this various algorithms are tested among them Paillier encryption proved to be more efficient.
Bistable Hydrogel-based Sensor Switch for Monitoring Relative Humidity

This contribution presents a hydrogel-based bistable sensor switch with a switching hysteresis for the measurement of the relative humidity via a moisture-sensitive swellable material in ambient air. The sensor enables the continuous inline monitoring and control of the relative humidity, for example in buildings and containers. Since the required energy is taken from the environment, the sensor does not need any internal electrical energy supply for the monitoring of the relative humidity and the operation. To protect the sensor from arcing and damage the contacts have to close or open very fast, i.e. the sensor has to show a switching hysteresis. This contribution deals with the principle concept of a sensor switch and the implementation of such a switching hysteresis based on a mono-/bimorph beam partly covered with hydrogel. For that, it provides a simple mathematical model to describe the switching in dependence on the geometry parameters and the material properties of the sensor. The switching hysteresis is implemented by a pre-deflection due to an axial force and by buckling resulting from the hydrogel swelling. To prove the switching hysteresis a macroscopic sensor switch was studied and, from the results, the mathematical model was validated. Based on the obtained results a miniaturized silicon-based sensor switch is proposed.
Proposal of Scalable Vector Extension for Embedded RISC-V Soft-Core Processor

Recently, the opportunity to use FPGA in the field of embedded computer system has increased. The cost for development of system using FPGA is quite high. Therefore, to easily develop the embedded system using FPGA, the development cost should be reduced, and it can be reduced by limiting the development of the dedicated circuit to the truly required part of the system and by using soft-core processor. If the performance of the soft-core processor improves, the development cost can be suppressed, since we can expect that the amount of the dedicated circuit can be reduced. Furthermore, even in the field of embedded system, the opportunity for data parallel processing such as image processing is increasing. We consider that it is important merit of soft-core processor with SIMD extension that the computation performance can be optimized to the system by changing the number of parallel computing units according to the application. However, when optimizing the performance of soft-core processor, it is an obstacle that the software for soft-core processor needs to be changed every time we change the number of computing units. Thus, vector processing facility should provide the scalability that means the number of computing units can be changed without changing the program code. In this paper, we propose a scalable vector extension for RISC-V ISA and its programming method.
Analysis of Point-to-Point Robotic Arm Control using PID controller

Point-to-point robotic arm has been used in manufacturing industry to assist company in increasing its production rate. However, the biggest concern in robotic arm is the accuracy for the robot to complete the task of pick-and-place of an object which is dependent on the designed controller. This study investigates the accuracy of the 5 degrees of freedom (DOF) robotic arm model with PID controller. The forward kinematic is analysed systematically in order to determine the required movements for each of the robotic arm joint for its desired operations. The objective of this paper is to simulate the movements of the robotic arm with and without controller by utilizing MATLAB GUI, a microcontroller and servo motors in order to demonstrate the effectiveness of the system. Performance comparison of the robotic arm in completing the pick-and-place task with and without PID is presented in this paper. The preliminary results showed that the difference errors between the desired angular displacement of with and without controller are 0.65%, 3.61%, 10.74%, 82.18% and 44.44% for the five movements.
Perceptual Tolerance to Motion-To-Photon Latency with Head Movement in Virtual Reality

Since Motion-To-Photon (MTP) latency is inevitable and can be perceived in virtual reality, quantifying perception of MTP latency becomes necessary. In this paper, we investigate perceptual tolerance to MTP latency, including perception threshold of the latency and user acceptance of delays above the threshold. It is affected by different head motion events, such as Motion-Static-Alternate (MSA, i.e., an acceleration or deceleration in one direction) and Motion-To-Reverse (MTR, i.e., a movement reverses direction). In each motion event, rotation angle and angular velocity also influence perception of MTP latency. Experimental results show that subjects are more intolerant of MTP latency in MTR than MSA. The latency perception threshold is about 23 ms when subjects turn their heads at the maximum speed of human limits. When the angular velocity decreases, the perception threshold increases. The maximum threshold is ~41 ms at 20 °/s in this study. Inversely proportional models are established to describe the relationship between threshold and angular velocity. Besides, MTP latency over the threshold is harder to be accepted with the rotation angle decreasing or the angular velocity increasing.
Analysis of a Single-Frequency Multi-Channel Ambient RF Energy Harvesting in CMOS Technology

This paper proposes a single frequency multichannel RF energy harvesting (RFEH) system capable of increasing the power harvesting density and sensitivity performance. Alternatively, the power summation of the multichannel RFEH system is investigated to determine the electrical characteristics in the output power density and output voltage with respect to varying input power level conditions. The RFEH system was simulated on a standard 65 nm CMOS technology and the simulation results show that by combining the power from each RF harvesting branch of the same frequency, the output power density is increased by 1.74x. In addition, better performance in the sensitivity down to -20.4 dBm for a Vout of 1V of the multi-channel RF harvesting is observed when compared to single channel harvesting.
A 30MHz Delay-Line-Based Buck Converter with 5.7%-94.8% Switching Duty Cycle

As the switching frequency of DC-DC converters continues to increase, the influence of circuit delays is also increasing, which poses a great challenge to the design of a comparator. A 30MHz delay-line-based buck converter is proposed in this paper, which eliminates the need for a traditional PWM comparator, simplifies the complexity of the circuit and logic and solves a series of problems caused by comparator delays at high switching frequency. A delay-line-based voltage-to-duty-cycle (V2D) controller is used to replace the traditional ramp-comparator-based V2D controller to achieve a wider duty cycle range. The proposed DC-DC converter has been designed and simulated by Cadence software based on the 0.18-µm mixed signal CMOS process. The tunable duty cycle ranges from 5.7% to 94.8%, allowing the converter to regulate the output from 0.1V to 1.7V with 1.8V input. With a step of 400mA in the load current, the setting time is around 2µs. The maximum load current is 1A and the peak efficiency is as high as 94.5% with 1.5V output.
Pairwise Matching for 3D Fragment Reassembly Based on Boundary Curves and Concave-Convex Patches

We present a simple pairwise matching method for 3D fragment reassembly that uses boundary curves and concave-convex patches to accelerate and optimize the matching. Given the boundary curves of two fracture surfaces that completely or partially coincide, we can quickly exclude the fracture surface pairs with low boundary curve similarities, which can substantially reduce the computational cost of the subsequent patch matching, where we extract and delineate concave-convex patches of the selected fracture surfaces. A modified iterative closest point algorithm is applied on these concave-convex patches to refine the alignment. Finally, we determine the matched 3D fragments according to the overlap ratio of their fracture surfaces. The results of experiments on real-world examples demonstrate that our proposed algorithm is both accurate and efficient.
Bone age assessment using general use convolutional neural networks

Deep Learning methods have been applied to different medical imaging analysis tasks like, e.g., lesion classification and tissue segmentation. Bone age assessment is traditionally performed on an x-ray of the non-dominant hand applying the Greulich and Pyle or the Tanner Whitehouse methods. In this work, we first have tested several state-of-the-art Convolution Neural Networks models for assessing bone age that previously has shown great results in general computer vision tasks. Based on these results, we have developed/optimized a new model, which is presented here. For this purpose, we used transfer learning methods and trained the selected networks from scratch achieving a 7.89-month error rate when assessing bone age in females.
Development of Automatic Driving Position Information Positioning System using TACAN Technology

In this paper, we propose a highly accurate position information positioning system using aircraft technology. Automatic driving requires recognition, judgment and operation on behalf of human beings. Therefore, advanced information processing and travel control are required. At present, technological development is being pursued aiming at practical application of automatic driving. In this paper, we propose a new control system to further improve the accuracy and processing speed of position information in automatic operation. We verified the proposed system using model vehicle. It was possible to detect and record position information by multiple cameras, and we confirmed that the position can be specified by obtaining longitude/latitude.
Remote Quantification of Nearshore Wave Energy Dissipation Rates from Coherent X-Band Radar Backscatter

We present an efficient, new methodology to remotely estimate nearshore wave energy dissipation using a shore based coherent-on-receive marine radar (CMR). The CMR measures the backscatter intensity as well as the Doppler shift velocity from the sea surface scattering. We show by comparisons to simulations that the spatial difference of the Doppler velocity is related to local roller dissipation rates. From the spatial distribution of the radar derived dissipation, the offshore significant wave height can be estimated with a root-mean-square error of 23 cm with bias of 4 cm when compared to a moored wave rider buoy at a distance of 1.1 km from the shore line.
Fault-Tolerant Modular Stator Concentrated Winding Permanent Magnet Machine

This paper studies a modular permanent magnet synchronous machine (MPMSM) converted from a traditional single-core 24-slot 20-pole tooth-coil winding permanent magnet synchronous machine (TCW PMSM). The performance of the TCW PMSM is compared with the performance of the MPMSM (rearranged from this TCW PMSM) by the finite element method (FEM). It is found that if an electrical machine with a modular structure is designed, the efficiency of the MPMSM may be degraded compared with the conventional structure, especially, if a solid rotor yoke is used. However, the advantages of the proposed MPMSM are related to an option to scale up the machine by applying a certain number of modules, or removing faulty modules (while keeping the healthy modules working). This makes this type of a motor design applicable in certain life-critical applications, or in applications where scaling of the motor power might be needed. As a prototype, a low-power, low-speed generator added to a hook block of a hoist to supply power to measurement electronics is studied.
Large-Scale IoT Devices Firmware Identification Based on Weak Password

The growth of Internet-connected IoT devices brings many security issues, such as DDoS, weak password and embedded malware. The vulnerability analysis is a critical strategy to prevent security issues. Due to technical exclusivity of diverse manufacturers, their firmware is hard to patch timely and respectively. Therefore, the vulnerability of the device is closely related to the device firmware version. The identification of the firmware version is an essential prerequisite for protecting these devices from attack. With the increasing of IoT devices, device firmware identification is still a critical challenge. In this paper, we propose a new firmware identification method by analyzing webpages content directly based on a weak password. We extract the characteristics of the login page to identify the device type and brand, and then use classification and page segmentation to identify the model and firmware version of the device. We evaluated 74,307 devices to verify the effectiveness of our proposed method. Experimental results show that our method achieves an accuracy of 95.97%, superior to the other methods.
Conquering the Worst Case of Infections in Networks

We develop algorithms to control the scope of an infection spread on a network by allocating a fixed immunization budget to edges of the graph. We assume that the infection propagates according to an independent cascade model and interventions operate by reducing the propensities of edges to transmit the infection. We formulate this problem as a constrained min-max optimization problem with respect to the placements of interventions and the location of the worst-case seed nodes. However, the result is a challenging bilevel mixed integer optimization problem. Furthermore, gradients of the objective function with respect to the continuous variables are unavailable in closed form. We employ tools from derivative-free optimization and stochastic optimization to optimize the objective by iterating between the outer minimization and inner maximization problems. In the inner loop, we use a weighted degree discount (WDD) method to select the seed set of the influence maximization problem. In the outer loop, we utilize two methods: a sample-based simultaneous perturbation Nelder-Mead (SBSP-NM) algorithm and a simultaneous perturbation stochastic approximation (SPSA) algorithm. We perform simulations on synthetic graphs and three larger-scale real-world datasets and illustrate the computational feasibility of our algorithms and their efficacy at controlling epidemic spreads.
CLOSE: Local Community Detection by LOcal Structure Expansion in a Complex Network

There have been more and more researches on community discovery in complex networks. Expanding of a source node into a community is one of the most successful methods for local community detection, especially when the global structure of the network is not accessible. In this paper, we propose CLOSE algorithm, Local Community Detection by LOcal Structure Expansion, based on the local expansion technique in the community detection. In CLOSE, we propose a novel connective function to identify a better source node. The node is in the center of a highly connected component of a graph. CLOSE selects a group of nodes instead of a single node to be the seed for the expansion of a local community. In addition, using the neighboring group can identify a suitable community for a hub node. Moreover, the expansion strategy is based on the label propagation technique instead of local community measurements. In experiments, we compare the performance of CLOSE with previous methods both on synthetic networks from the LFR Benchmark and real-world networks. We also examine the merit of the source node selection strategy. Both source node selection and community detection in CLOSE outperform previous algorithms.
Topological Data Analysis for Portfolio Management of Cryptocurrencies

Portfolio management is essential for any investment decision. Yet, traditional methods in the literature are ill-suited for the characteristics and dynamics of cryptocurrencies. This work presents a method to build an investment portfolio consisting of more than 1500 cryptocurrencies covering 6 years of market data. It is centred around Topological Data Analysis (TDA), a recent approach to analyze data sets from the perspective of their topological structure. This publication proposes a system combining persistence landscapes to identify suitable investment opportunities in cryptocurrencies. Using a novel and comprehensive data set of cryptocurrency prices, this research shows that the proposed system enables analysts to outperform a classic method from the literature without requiring any feature engineering or domain knowledge in TDA. This work thus introduces TDA-based portfolio management of cryptocurrencies as a viable tool for the practitioner.