Miracle-dz/UniFAR-FTUs huggingface dataset:
Speed Sensorless Control of Hybrid Excitation Axial Field Flux-Switching Permanent-Magnet Machine Based on Model Reference Adaptive System

A novel 6/13-pole hybrid excitation axial field flux-switching permanent magnet machine (HEAFFSPMM) exhibits strong fault tolerance capability, high efficiency, and large torque density. However, merely few research on speed sensorless control in HEAFFSPMM exists. The speed sensorless control methods based on model reference adaptive system (MRAS) are studied and compared for the machine to improve the stability and reliability of the system and consequently improve the application of machine in control system. Based on the field-oriented control strategy, the MRAS observer of speed is designed and built by applying stator currents, stator flux linkages, and simplified stator currents. The three speed sensorless control algorithms of MRAS are compared and analyzed by using MATLAB/Simulink simulation and dSPACE1104 experimental platform. Results show that the speed sensorless control algorithm based on simplified stator currents has good control performance and high control accuracy.
Deep Learning Aided Signal Detection in OFDM Systems with Time-Varying Channels*

In this paper, we propose a deep learning aided approach for signal detection in orthogonal frequency-division multiplexing (OFDM) systems with time-varying channels. The method simplifies the architecture of OFDM systems by treating OFDM receivers as a black box. We utilize fully-connected deep neural network (FC-DNN) properly and successfully simulate an end-to-end time-varying OFDM system. Compared with two conventional algorithms well-designed to deal with OFDM systems in time-varying environment, the proposed method does not need to estimate channel parameters to detect signals. Simulation results also demonstrate that the trained DNN model has the ability to remember the characteristics of wireless time-varying channels and provide more accurate and robust signal recovery performance.
A Convolutional Neural Network for Prediction of Laser Power Using Melt-Pool Images in Laser Powder Bed Fusion

In laser powder bed fusion, a convolutional neural network could build a good regression model to predict a laser power value from a melt-pool image. To empirically validate it, we used the acquired image data from a monitoring system inside metal additive manufacturing equipment and optimally configured a convolutional network by the grid search of hyper-parameters. The proposed network showed only 0.12 % of test images were out of the criterion for judging the predicted laser power value to be reliable and showed more accurate results than deep feed-forward neural network in the prediction of laser power states unseen in training steps. We expect that the proposed model could be utilized to discover the problematic position in additive-manufactured layers causing defects during a process.
An effective monitored and improved method to control moisture for outgoing FOSB

Quality control of outgoing wafer products is a critical item in the technical industry. An appropriate keeping environment (Humidity/Temperature…etc.) is necessary after the complex wafer manufacturing process to ensure the wafer quality. FOSB is an essential material during the wafer packing process and it can provide an isolated environment to protect outgoing wafers. But each FOSB has different properties, like different used times, clean recipe, moisture content…etc. During vacuum packaging, the humidity and pressure between the FOSB outside wall and packing bag reduces, and causes the moisture inside the FOSB to diffuse out. This then causes a humidity increase inside the FOSB, where wafers are placed (Experiment result humidity increase >5%). But the general method for detecting solid moisture content (like moisture analyser) is not suitable in FOSB, as it is such a big and unbreakable item. In this study, we understood the mechanism for inside humidity change after vacuum packing and found an effective monitored/improved method to control moisture for outgoing FOSB and ensure our packing processes are suitable, uniform, and the environmental status can be controlled precisely. Through this method we can enhance our quality control abilities and provide more assurance for both customers and producers.
AATMS: An Anti-Attack Trust Management Scheme in VANET

Vehicular Ad-hoc Network (VANET) is a significant component of intelligent transportation system, which facilitates vehicles to share sensitive information and corporate with others. However, due to its unique characteristics, such as openness, dynamic topology and high mobility, VANET suffers from various attacks. This paper proposes an anti-attack trust management scheme in VANET called AATMS to evaluate the trustworthiness of vehicles. With the help of AATMS, vehicles in VANET can avoid malicious vehicles and cooperate with trusted vehicles. The idea of AATMS is mainly inspired by TrustRank algorithm, which is used to combat web spams. In this paper, we calculate local trust and global trust, which indicate the local and global trust relationships among vehicles. First, Bayesian inference is adopted to calculate local trust of vehicles based on historical interactions. Then we select a small set of seed vehicles according to local trust and some social factors. Once we identify the reputable seed vehicles, we use the local trust link structure of vehicles to evaluate the global trust of all vehicles. The simulation results show that AATMS can efficiently identify trustworthy and untrustworthy vehicles in VANET even under malicious attacks.
Access Point Switch ON/OFF Strategies for Green Cell-Free Massive MIMO Networking

The combination of user-centric network densification and distributed massive multiple-input multiple-output (MIMO) operation has recently brought along a new paradigm in the wireless communications arena, referred to as cell-free massive MIMO networking. In these networks, a large number of distributed access points (APs), coordinated by a central processing unit (CPU), cooperate to coherently serve a large number of mobile stations (MSs) in the same time/frequency resource. Similar to what has been traditionally done with conventional cellular networks, cell-free massive MIMO networks will be dimensioned to provide the required quality of service (QoS) to MSs under heavy traffic load conditions, and thus they might be underutilized during low traffic load periods, leading to an inefficient use of both spectral and energy resources. Aiming at the implementation of green cell-free massive MIMO networks, this paper proposes and analyzes the performance of different AP switch ON/OFF (ASO) strategies designed to dynamically turn ON/OFF some of the APs based on the number and/or location of the active MSs in the network. The proposed framework considers line-of-sight (LOS) and non-line-of-sight (NLOS) links between APs and MSs, the use of different antenna array architectures at the access points (APs), suitably characterized by array-dependent spatial correlation matrices, and specific power consumption models for APs, MSs and fronthaul links between the APs and the CPU. Numerical results show that the use of properly designed ASO strategies in cell-free massive MIMO networks clearly improve the achievable energy efficiency. Moreover, they also reveal the existing trade-offs among the achievable energy efficiency, the available network-state information, and the hardware configuration (i.e., number of APs, number of transmit antennas per AP, and number of MSs).
Social Software and Knowledge Sharing: A Systematic Literature Review

Some researchers have linked the social software usage like social media, wikis, blogs, instant messaging, forums and corporate social networks with the process of knowledge sharing. They also found that the application of social software that can provide positive results in the knowledge sharing process. The purpose of this research is to provide insights into state-of-the-art research on the use of social software to support the knowledge sharing process. This research is conducted to answer (i) how the general description of the knowledge sharing process is and (ii) how social software helps the process of knowledge sharing. The method used in this research is the Systematic Literature Review (SLR) where synthesizing related papers sourced from the ScienceDirect journal database. Searching related papers using certain keywords obtained 865 papers. Then 85 papers were selected after applying the inclusion and exclusion criteria. Following a more rigorous papers quality assessment, 20 papers are selected for further analyses. Based on the analysis, we found a general description of knowledge sharing. This paper also describes how social software can support the process of sharing knowledge, and some influencing factors. That factors influencing the level of orientation and knowledge sharing activities on social media, and divided into individual and social factors. Besides that, there are several other factors related but have not been clearly classified yet. Also, we found Social Networking Sites (SNS) as the type of social software that often used and this topic frequently discussed in organization scope.
The effect of hydroiodic (HI) acid on the optoelectronic properties of CsPbI3 films and their photovoltaic performance

The cubic phase CsPbI3 with a suitable direct band gap of 1.73 eV has been widely explored as the light absorption layer for pure inorganic perovskite solar cells. However, the phase transition of CsPbI3 will seriously affect its application prospect. As an effective method, the phase transition of CsPbI3 in cubic phase can be significantly suppressed by using hydroiodic (HI) acid as a precursor additive. Here, we have systematically studied the mechanism of promoting the film formation of cubic phase CsPbI3 and the effect on its photovoltaic performance by regulating the addition of HI acid. The results show that the addition of HI acid has an obvious effect on the film morphology, grain size, crystal structure, light absorption and band structure of CsPbI3. The optimized perovskite solar cells with ITO/PEDOT:PSS/CsPbI3/PCBM/Ag as the device structure achieved the highest efficiency of 4.8%.
Development of Dual Band Power Divider Using Meander Line Technique for Local Oscillator System

This paper presents the development of Wilkinson-based dual band power divider working at the center frequencies of 2400 MHz and 4900 MHz for local oscillator system. The dual band power divider is designed and deployed on a 0.787 mm thick RT/Duroid® 5880 dielectric substrate. To reduce the overall size, a meander pattern is applied in the design process. The proposed power divider is characterized by use of a commercialized simulation software in dealing with the specifications required by a local oscillator system such as low insertion loss and good impedance matching. Based on the design result, the dual band power divider which takes the dimension of 55 mm × 42 mm is realized through wet etching technique. The measured insertion losses at the center frequencies of 2400 MHz and 4900 MHz are less than 1.6 dB. Meanwhile, the measured Voltage Standing Wave Ratio (VSWR) at the first center frequency of 2400 MHz is less than 1.5 at both input and output ports. However, at the second center frequency of 4900 MHz, the measured VSWR is 1.253 at the input port and 2.777 at the output port. The measured insertion loss and VSWR for the first frequency band have good agreements with the simulated ones, whereas for the second frequency band the measured VSWR at the output port shifted to the frequency of 4440 MHz.
Towards Secure FinTech: A Survey, Taxonomy, and Open Research Challenges

Financial Technology (FinTech) has attracted a wide range of attention and is rapidly proliferating. As a result of its consistent growth new terms have been introduced in this domain. The term ‘FinTech’ is one such terminology. This term is used for describing various operations that are being frequently employed in the financial technology sector. These operations are usually practiced in enterprises or organizations and provide requested services by using Information Technology based applications. The term does take into account various other sensitive issues, like, security, privacy, threats, cyber-attacks, etc. This is important to note that the development of FinTech is indebted to the mutual integration of different state of the art technologies, for example, technologies related to a mobile embedded system, mobile networks, mobile cloud computing, big data, data analytics techniques, and cloud computing etc. However, this technology is facing several security and privacy issues that are much needed to be addressed in order to improve the acceptability of this new technology among its users. In an effort to secure FinTech, this article provides a comprehensive survey of FinTech by reviewing the most recent as well as anticipated financial industry privacy and security issues. It provides a comprehensive analysis of current security issues, detection mechanisms and security solutions proposed for FinTech. Finally, it discusses future challenges to ensure the security and privacy of financial technology applications.
Room temperature photoluminescence analysis of alkali treated single-stage thin Cu(In,Ga)Se2 absorber layers

Room temperature photoluminescence (PL) and intensity dependent PL measurements were used as a tool to investigate alkali treatment (KF and RbF) of thin (< 500 nm) single-stage absorber layers and compared with similar 3-stage samples. Single-stage absorber layers have a clear distinguishable PL peak below the band gap, which is absent in 3-stage absorber layers. We attribute this peak to a defect band arising due to the planar defects and the small grains. We find that this low energy peak can be largely reduced with alkali treatment, leading to similar decay times and PL yield as thin 3 stage samples.
Epilepsy seizure detection using SWT features and ANN classifier

Epilepsy is a common life-threatening neurological disorder, which is unpredictable and affects around 50 million people worldwide. Electroencephalography (EEG) is a monitoring method used to diagnose epilepsy. It records fluctuations in electrical activities of brain. In this study, Stationary Wavelet Transform (SWT) is used to analyse the EEG signal for classification of focal and normal EEG signal. SWT decomposes EEG signal into approximate and detailed coefficients. From each coefficient the statistical features are extracted and are fed into Artificial Neural Network (ANN) classifier for the discrimination of focal and normal EEG signal. These methods are verified by using Karunya EEG database. This method is achieved an overall accuracy of 98.75%, sensitivity of 98.39%, specificity of 99.16%, positive predictive value (PPV) of 99.16%, negative predictive value (NPV) of 98.33%.
Game Pedagogy with TPACK Model: A Learner Centric Method

in this paper, Game pedagogy in technical learning is implemented effectively on open source moodle: GNOMIO. Subject specific technical questions are asked while designing the game. This will provide the knowledge enhancement of students. It will be helpful in improving skill set of students. The game pedagogy can attract much concentration of learners. Higher thinking order questions can be framed while designing the game, which can achieve the competency in the learners. In this paper, crossword game is explained as a case study. If the game is to be designed in moodle: Gnomio, it should be equipped with glossary which must have all the questions with respect to Bloom's Taxonomy. The questions can be fetched from glossary for game design purpose.
Color image fusion utilizing NSCT and YUV transform

A new color image fusion method utilizing NSCT and YUV transform is proposed. The two source images are changed into gray images by gray transform and color visible light image is decomposed into three components which are Y, U and V by YUV transform. Gray infrared image and gray visible light image are fused by NSCT to obtain fused image. In the fusion process, the low frequency components are fused based on region energy criterion and the high frequency components are fused utilizing absolute value criterion. The Y componenet of color visible light image is replaced by the fused image. Then The final fused color image is obtained by inverse YUV transform from U,V and Y above. Simulation results show that the method can maintain the details of the image texture and color information well, and the objective evaluation parameters such as mean, variance, information entropy and spatial frequency for R, G, B channels are greatly improved. The proposed method is an effective color image fusion algorithm.
Multi-Modal Sentiment Classification With Independent and Interactive Knowledge via Semi-Supervised Learning

Multi-modal sentiment analysis extends conventional text-based definition of sentiment analysis to a multi-modal setup where multiple relevant modalities are leveraged to perform sentiment analysis. In real applications, however, acquiring annotated multi-modal data is normally labor expensive and time-consuming. In this paper, we aim to reduce the annotation effort for multi-modal sentiment classification via semi-supervised learning. The key idea is to leverage the semi-supervised variational autoencoders to mine more information from unlabeled data for multi-modal sentiment analysis. Specifically, the mined information includes both the independent knowledge within single modality and the interactive knowledge among different modalities. Empirical evaluation demonstrates the great effectiveness of the proposed semi-supervised approach to multi-modal sentiment classification.
Efficient Route Discovery and Link Failure Detection Mechanisms for Source Routing Protocol in Mobile Ad-Hoc Networks

The rapid advances in the wireless communication industry have paved the way for the enhancement of wireless mobile ad-hoc networks (MANETs) to support various domains including civilian environments, emergency operations, and military affairs. Source routing in MANETs is subject to some issues such as changes in the network topology, which lead to frequent link breakages that may increase the requests of route discoveries. Thus, this paper aims to enhance on-demand source routing protocols by proposing two mechanisms, a zone-based route discovery mechanism (ZRDM) and a link failure prediction mechanism (LFPM). ZRDM aims to control the flooding of route requests, and LFPM aims to avoid route breakages caused by node mobility. The performance of the proposed mechanisms was evaluated using network simulator 3 in terms of normalized routing load, average end-to-end delay, and packet delivery ratio. The experimental results showed that the proposed mechanisms outperform well-known mechanisms such as the dynamic source routing (DSR) protocol, reliable DSR, and zone-based DSR and segment-based DSR.
A radiation resistant library based on DICE and fault-tolerant delay filtering techniques in CMOS 0.18μm technology

A radiation resistant standard cell library is completed based on SMIC 0.18 μm technology in this paper. Standard cell library includes combinatorial logic cells and sequential logic cells. SET (Single Event Transient) effect always occurs in combinatorial logic cells, and SEU (Single Event Upset) effect always occurs in sequential logic cells. SEL(Single Event Latchup) and TID(Total Ionizing Dose) effects affect all logic cells. According to radiation effects, logic cells in the standard cell library are designed with harden methods at the circuit level and the layout level. Combination logic cells adopt fault-tolerant delay filtering method in circuit level, and sequential logic cells use DICE structure in circuit design. A guard ring has been added to the layout design for the SEL and TID effects. Then physical information and timing information are extracted for all cells, and the library building process is completed. Finally we use EDA tools to verify the availability of the anti-radiation library.
Analysis of Low Power SRAM Sense Amplifier

Rapid increase in technology for faster and smarter innovations that smoothens the needs of humans resulting in use of super tech gadgets, which use memory, such as, SRAM. In the present time we want that the data to be written and Read from SRAM at very high speed and with low power consumption. Sense Amplifier is the key circuit used for reading the data in memory. The major issue in modern integrated circuit design is power consumption which is an important parameter to design memory element and digital system. In this work we have analyzed the 6T SRAM by using different sense amplifiers namely Foot Switch PMOS Access Voltage Latch Sense Amplifier (FSPA-VLSA), Current Latch Sense Amplifier (CLSA) & Voltage Latch Sense Amplifier (VLSA) in terms of power. Furthermore, comparison of CLSA and VLSA in terms of power, area, delay, power delay product (PDP) and static noise margin has been performed. In this paper, several latch type sense amplifiers have been analyzed and simulated using 45nm CMOS technology with supply voltage of 1.2V. The simulation results show that when this technique is applied with SRAM circuit during read operation, overall power consumption has been reduced. The overall power consumption has been reduced to approximately 20.63% whereas in case of Current Latch and Voltage Latch Sense Amplifier it is up to 10.37% and 2.37%respectively and the delay of SRAM FSPA has been reduced by 243.8%. Also, the static power consumption in SRAM with body bias technique has been reduced by91%. As the body biasing of transistor will lead to a decrement in leakage of the device, hence the static power consumption.
Time-Optimal Trajectory Planning for Flexible Joint Robots

In this letter, a new approach is proposed to optimally plan the motion along a parametrized path for flexible joint robots, i.e., robots whose structure is purposefully provided with compliant elements. State-of-the-art methods efficiently solve the problem in case of torque-controlled rigid robots via a translation of the optimal control problem into a convex optimization problem. Recently, we showed that, for jerk-controlled rigid robots, the problem could be recast into a non-convex optimization problem. The non-convexity is given by bilinear constraints that can be efficiently handled through McCormick relaxations and spatial Branch-and-Bound techniques. In this letter, we show that, even in case of robots with flexible joints, the time-optimal trajectory planning problem can be recast into a non-convex problem in which the non-convexity is still given by bilinear constraints. We performed experimental tests on a planar 2R elastic manipulator to validate the benefits of the proposed approach. The scalability of the method for robots with multiple degrees of freedom is also discussed.