Miracle-dz/UniFAR-FTUs huggingface dataset:
Proposal and Prototyping of Self-Excited Pneumatic Actuator Using Automatic-Flow-Path-Switching-Mechanism

Robots currently have a wide range of practical applications. However, their widespread use is limited by long design and manufacturing times as well as increasingly complex drive system electronics and software, which have led to high development costs. Therefore, simpler manufacturing, driving, and control methods are required. In this study, we design a pneumatic actuator drive system that combines the printing technique and self-excited vibration. In the proposed actuator, a mechanism for automatically switching the airflow path is used to induce self-excited vibration. Moreover, the actuator is integrally molded by a 3D printer; therefore, no assembly process is required. This actuator can be used to easily build robots in a short time, contributing to more widespread use of robots. In this study, we also calculate the theoretical value of the moving frequency by modeling the actuator and verify the validity of this value through experiments using a prototype actuator. Based on the results, we were able to freely design the operating frequency of the actuator; by using this knowledge, we designed a flapping robot. The robot is also integrally molded by a 3D printer. Finally, we validate its motion through experiments, in order to illustrate one of the many applications of the proposed actuator.
A Survey of Self-Interference in LTE-Advanced and 5G New Radio Wireless Transceivers

This article presents a comprehensive survey of the literature on self-interference (SI) in long-term evolution advanced (LTE-A) and fifth-generation (5G) new radio transceivers and should serve the reader as a guide and starting point for further work on SI management. Current trends in cellular transceiver designs are discussed, and reasons why new technologies, such as carrier aggregation, cause potential sensitivity degradation due to self-interfering signals are highlighted. The survey provides an overview of the most common interference mechanisms and continues with a taxonomy on SI mitigation architectures by comparing the strengths and weaknesses of various techniques.
Flipping Rectifiers for Piezoelectric Vibration Energy Harvesting

Piezoelectric is a vibration source which can generate energy out of ambient vibrations. Four approaches of energy harvesting circuits in 0.35-µm CMOS process are discussed in the paper, including 1) the full bridge rectifier (FBR), 2) the switch only rectifier (SOR), 3) the parallel-synchronized-switch harvesting-on-inductor (P-SSHI), and 4) the flipping-capacitor rectifier (FCR). Compared to the transferred-power ability of the conventional FBR, the improved factors of SOR, P-SSHI and FCR can reach up to 1.58x, 1.93x and 1.96x at an excitation frequency of 100 Hz, respectively.
Fast Point Cloud Feature Extraction for Real-time SLAM

Automated driving has been developed rapidly in the past five years. Many automakers have popularized SAE Level 2 on self-driving cars, some even on regular vehicles. To implement Level 3 or Level 4, it’s necessary to rely on a good localization system when GPS signal is not available. For this need, this paper proposes two effective methods to increase the accuracy of the real-time SLAM: The first method: When the original point cloud is input, the point cloud is divided into the short-range group, the medium-range group and the long-range group. An adaptive parameter adjustment method is then used to obtain the optimal parameters for each of these point cloud groups. However, Lidar’s physical characteristics can cause the point cloud to be insufficient, making an important part of points misjudged as outliers for the medium-range and long-range cases. Thanks to the help of the adaptive parameters, these point clouds, which were originally misjudged as outliers can be preserved in this paper. The second method: In point clouds, the same object in different ranges is represented with different point clouds. Hence, features, such as the roughness and density, can dramatically change with the variation of distance even when it is the same object. To solve this problem, we have designed three different range point cloud feature extraction methods to get more accurate point cloud features, such as the planes or edges. By combining these two steps, the LeGO-LOAM accuracy can be effectively increased by more than 30% while achieving the performance of the real-time SLAM, which is more accurate and faster than the NDT-Mapping used in Autoware.
Multi-Type Interdependent Feature Analysis Based on Hybrid Neural Networks for Computer-Aided Diagnosis of Epidermal Growth Factor Receptor Mutations

The mutation status of the epidermal growth factor receptor (EGFR) is an important clinical reference indicator for lung cancer diagnosis and treatment. However, the extraction of effective discriminative features for non-invasive computer-aided EGFR mutation prediction still poses a big challenge. In this paper, multiple types of features are designed and analyzed to address this problem. These features include clinical features based on prior medical knowledge and quantitative image features extracted by convolutional neural networks (CNN). A long short-term memory (LSTM) network is also introduced to exploit the dependency between these feature types and then fuse them. In particular, a CNN is constructed to extract quantitative features of computed-tomography (CT) images. Furthermore, a LSTM is utilized to analyze the dependency between these clinical and CT image features and generate a new feature representation for computer-aided diagnosis. For samples from the same category, the proposed method deal with feature representation variabilities arising from interdependencies in multi-type features and patient specificity. The multiple feature types of the collected clinical data are used to assess the proposed approach and other relevant algorithms. Our results demonstrate that the multi-type dependency-based feature representation shows superior performance (Accuracy = 75%, AUC = 0.78) compared to single-type feature representations. The proposed method is reliable to apply for diagnosing of the EGFR mutation status.
End-to-End Dynamic Bandwidth Resource Allocation Based on QoS Demand in SDN

In this paper, we propose an end-to-end dynamic bandwidth allocation scheme based on QoS demand in Software Defined Networking (SDN). The proposed scheme aims to provide high performance of different network traffic and efficient use of network resources by providing network resources to network traffic according to their QoS demands and the current network status. We demonstrate the effectiveness of the proposed scheme on our emulated SDN network by experiment. The proposed scheme is compared with the traditional single path and flow-based multipath routing schemes. Experimental results demonstrate that the proposed scheme provides higher performance in terms of packet loss rate and average network delay for high priority flows than the other two schemes.
A Convolution Neural Network-based Method for Designing Honeycomb Absorbing Material

In this paper, we propose a fast method for optimizing multiple size parameters of honeycomb absorbing material including honeycomb height and dip coating thickness, based on deep convolutional neural networks (CNN). The reflection coeﬃcients (S11) of honeycomb absorbing material are simulated to generate CNN training data as inputs to the CNN in this paper. The network consists of six concatenated convolution-maximum pooling layers and three fully-connected layers. The convolutional layers and the fully-connected layers perform feature extraction and data regression, respectively. When the correlation coeﬃcient between the predicted value and the true value of the cellular absorbing material using CNN inversion is close to 1.0, it means CNN performs well in inversion. The trained CNN model is used to optimize the honeycomb absorbing material in a given size range. Numerical results show that the optimized S11 of honeycomb absorbing material is even about 10 dB lower than that of the honeycomb without optimizing at some frequencies. This CNN based method provides a faster and more eﬃcient approach for electromagnetic analysis.
A Circularly Polarized SIW Slot Antenna Based on High-Order Dual-Mode Cavity

In this letter, a novel circularly polarized (CP) slotted substrate integrated waveguide (SIW) cavity antenna using a hybrid TE202 mode (HTM) and the eigen TE202 mode (ETM) is proposed and studied. The antenna is designed for Ku-band satellite wireless link applications. It utilizes two different and orthogonal TE202 modes (ETM and HTM) with slots etched on the surface to form left-hand CP radiation. It is directly fed by a coaxial probe with a low profile. The working mechanism and the design approach are elaborated. The current distribution for the two TE202 high-order modes is analyzed. Shorting pins and slots are applied to generate the required CP radiation. To verify the feasibility, the antenna is simulated and fabricated using the conventional printed circuit board (PCB) process. It has a measured –10 dB impedance bandwidth of 4.2% and a 3 dB axial ratio (AR) bandwidth of 1.6%. The peak gain is over 7 dBic in the operation band. The antenna has a size of 0. 92λ0 × 0. 90λ0, where λ0 is the free-space wavelength at 12.24 GHz. Good agreement between the simulation and measurement results is observed.
User Selection and Power Allocation in Massive multiuser MIMO Systems

In this paper, we investigate a user selection and power allocation scheme to decrease the system complexity and power consumption of downlink massive multiuser multipleinput multiple-output (MIMO) systems. Our target is to achieve the optimal sets of user and power allocation to approach the maximum cell capacity. To this end, we proposed user selection (US) and power allocation (PA) schemes, which selects users and allocates power iteratively with the adaptive Markov Chain Monte Carlo (MCMC) method and the Lagrange algorithm. Performance analysis and simulation results demonstrate the effectiveness of the proposed algorithm. At the same time, the simulation of the computational complexity is decreased significantly by the proposed algorithm.
Learning a generative model for robot control using visual feedback

We introduce a novel formulation for incorporating visual feedback in controlling robots. We define a generative model from actions to image observations of features on the end-effector. Inference in the model allows us to infer the robot state corresponding to target locations of the features. This, in turn, guides motion of the robot and allows for matching the target locations of the features in significantly fewer steps than state-of-the-art visual servoing methods. The training procedure for our model enables effective learning of the kinematics, feature structure, and camera parameters, simultaneously. This can be done with no prior information about the robot, structure, and cameras that observe it. Learning is done sample-efficiently and shows strong generalization to test data. Since our formulation is modular, we can modify components of our setup, like cameras and objects, and relearn them quickly online. Our method can handle noise in the observed state and noise in the controllers that we interact with. We demonstrate the effectiveness of our method by executing grasping and tight-fit insertions on robots with inaccurate controllers.
Use as essential factor for Technology Adoption: Case study of electric vehi cle’s uses in French regional Ecosystem

This article explores the interactions between electric vehicles and their users, compiling determinants for the use of electric vehicles from international studies, as well as a qualitative and contextual field study with a focus group. The valid determinants of utilization have been identified among users in northeastern France, setting a methodological template for understanding the French use of electric vehicles. This research helps to understand the influence that these different uses can have on the purchase of electric vehicles, and allows establishing a critical reading of the diffusion of electric vehicles through their use.
Defining News Authenticity on Social Media Using Machine Learning Approach

Social network and online news media are becoming popular in today’s era. Due to low cost, easy access and rapid diffusion, social media platform becomes a source to distribute false information. Fake news propagation on social media can cause serious negative effects on human society especially in politic, reputation and finance. So, automatic fake news detection plays a vital role to robust news media platform on social network. Defining news authenticity is insufficient based on news content only. It also needs to analyze social features of news. In this paper, we propose an approach to detect fake news on social media that covers both news content and social context. We use synonym-based feature extraction method and three different classifiers based on multidimensional dataset. Experimental result shows the effective as an accuracy way to define news authenticity on online news media.
Research on a Multi-Motor Coordinated Control Strategy Based on Fuzzy Ring Network Control

Aiming at the multi-motor coordinated control of intelligent robots, cross-coupling ring control based on fuzzy theory (CRCF) is proposed. Each motor is cross-coupled with two adjacent motors to form a multi-motor ring network structure that eliminates lag in speed tracking between motors and enhances synchronization between motors. Applying fuzzy control to the cross-coupling control of the proposed control strategy, the speed error and change in speed error of two adjacent motors are used as the input of the fuzzy controller, and its output is used as the input of the second of the two adjacent motors. The fuzzy controller achieves real-time tracking adjustment of the speed error between motors. The controllability and observability of the proposed control strategy are verified by the theorem of the Kalman matrix rank criterion. The external stability and internal stability are verified using an impulse response matrix. The theoretical analysis of the lag is verified by establishing a physical and mathematical model. Finally, a four-motor coordination control system model is built. By comparison with the non-ring network proportional cross-coupling control method, the effectiveness of the proposed control strategy is experimentally verified by the MATLAB/Simulink and the RT-LAB real-time simulation platform.
Investigating the Developments on the Frequency Compensation Techniques of the Two-Stage OTAs - A Brief Guide and Updated Review -

Frequency compensation techniques (FCTs) of the two-stage Operational Transconductance Amplifiers (OTAs) are of critical importance in analog design. They are considered essential for all other FCTs of multi-stage OTAs. In this work, the developments of the major FCTs of the two-stage OTAs are investigated. This brief investigation includes tracking the original work on each major FCT. The description of these original works is discussed along with the possibility of improvements on each major technique, as some of these techniques have not been fully developed. Also, this work shows the gradual developments of each major FCT, which might help predicting the future of these FCTs. This work can be used as a guide for the design engineers as well as the universities teaching graduate level advanced electronics and OTAs/Op-amps courses.
A 2D Tunable Frequency Selective Absorber Based on Liquid Metal

This paper describes a 2D tunable frequency selective absorber that incorporates a liquid metal and NaOH solution. The structure consists of circular chambers and small steps on a 3D printed substrate, where the radius of liquid metal [eutectic gallium indium (EGaIn)] can be controlled by electrochemical method. The novelty of the proposed design lies in its reconfigurable response, depending on the size of EGaIn on a 2D surface. By changing the radius of EGaIn, the unit cell of frequency selective absorber is capable of achieving four tunable states in simulation. Experimental results show the circular EGaIn patch can be easily created and controlled by DC bias, and produces a tunable absorption peak of 25-30 dB.
Malware Classification of Portable Executables using Tree-Based Ensemble Machine Learning

The impact of malware and its role in cyber attacks is well known in this current day and age where there is a consistent barrage of cyber attacks on a daily basis. Techniques that can identify the malware that is obfuscated, or has the capabilities to stay hidden, are needed to combat these malware based cyber attacks. In the current study, an attempt is made to study different tree-based ensemble machine learning techniques that can identify malware among windows portable executable (PE) files and the features of importance in the identification of malware. The datasets used in the present research includes both malicious (489 files) and benign (700) portable executable files. The PE file metadata extracted as raw and calculated features (54 features) from the PE file headers. Six different Tree-based ensemble Machine Learning techniques, Bagging Decision Tree Classifier (BDT), Random Forest Classifier (RFC), Extra Trees Classifier (ETC), AdaBoost Classifier (ABC), Gradient Boosting Classifier (GBC), and Voting Ensemble Classifier (VEC) are used to classify the PE files. All the tested classifiers achieved above 95% accuracy and VEC, a voting based ensemble of five tree-based methods performed the best.
Inductive Fault Current Limiters in VSC-HVDC Systems: A Review

Fast emerging fault current levels in high voltage direct current (HVDC) systems has always been a significant obstacle to building a safer and more reliable grid. Inductive fault current limiters (FCLs), compared with its resistive counterpart, are very effective in restricting the fault rising speed that causes major damage during the whole fault process. In this study, a comprehensive review of the methods used for building inductive type FCLs in HVDC systems is presented. The fault current characteristics of the modern HVDC system is discussed to obtain the basic requirements for the FCL. On the basis of different technological domains, various inductive-type FCL topologies and devices are well categorized and analyzed. After the overall discussion and comparison, representative works, as well as new progress and future prospects, are conveyed in detail. One may find the content of this study helpful as a detailed literature review or a practical technical guidance of this field.
Information Security Risk Analysis of Airborne Communication Network

Under the conditions of informationization and networked operations, the airborne communication network faces serious threats. Any unit under the network system needs to acquire and share real-time information. In this process, the information security problem of the airborne communication network is particularly important, ensuring the confidentiality, integrity and availability of the transmitted information can largely guarantee the smooth completion of the task. In view of this, this paper uses the decision of the key nodes of complex networks and the risk analysis method of the attack tree model to evaluate the information security of the airborne communication network. This method helps decision-makers find important communication nodes in the airborne communication network and the attack methods most likely to be exploited by attackers, then taking targeted defense measures.
Augmented Textual Features-Based Stock Market Prediction

Due to its dynamics, non-linearity and complexity nature, stock market is inherently difficult to predict. One of the attractive objectives is to predict stock market movement direction by using public sentiments analysis. However, there is an active debate about the usefulness of this approach and the strength of causality between stock market trends and sentiments. The opinions of researchers range from rejecting the relationship to confirming a clear causality between sentiments and trading in stock markets. Nevertheless, many advanced computational methods have adopted sentiment-based features, yet did not attain maturity and performance. In this paper, we are contributing constructively in this debate by empirically investigating the predictability of stock market movement direction using an enhanced method of sentiments analysis. Precisely, we experiment on stock prices history, sentiments polarity, subjectivity, N-grams, customized text-based features in addition to features lags that are used for a finer-grained analysis. Five research questions have been investigated towards answering issues associated with stock market movement prediction using sentiment analysis. We have collected and studied the stocks of ten influential companies belonging to different stock domains in NASDAQ. Our analysis approach is complemented by a sophisticated causality analysis, an algorithmic feature selection and a variety of machine learning techniques including regularized models stacking. A comparison of our approach with other sentiment-based stock market prediction approaches including Deep learning, establishes that our proposed model is performing adequately and predicting stock movements with a higher accuracy of 60%.
Thermal Analysis of 2.5D Package in High Power Application

Thermal analyses have been conducted on 2.5D package in high performance application. 3D simulation model with heat sink and air fan for forced convection cooling has been constructed in limited device space. Thermal effects of heat sink length, material, and fin pattern have been investigated and compared. With 80cm air fan, a heat sink of length 120mm and width 80mm, with Cu base and Al fin array structure Type A demonstrates higher heat removal capability. Package heating power of >100W can be dissipated, while maintaining all chips under temperature limit, suggesting the developed 2.5D package integrated with multiple chips can be used in the high power application.
Experimental Characterization of SRR-based Multilayer X-band Wave Absorber

In last two decades, researches on electromagnetics (EM) materials particularly which have unique properties have grown tremendously. The structure of ring resonator such as split ring resonator (SRR) as well as its complementary could sometimes implemented for fulfilling specification of EM wave absorber requirement. In this paper, the experimental characterization of SRR-based multilayer X-band wave absorber is presented. The working frequency of wave absorber is focused on 9.5 GHz intended for X-band radar application. The proposed X-band wave absorber which is developed on multilayer of 1.6mm thick FR4 epoxy dielectric substrates is designed using a unit cell composed of SRR and thin strip with the dimension of 3.5mm × 3.5 mm. Some attempts to enhance the characteristics of X-band wave absorber are carried out by inserting an air gap between the SRR structure on the top of first layer and the thin strip on the top of third layer. The characterization result shows that realized X-band wave absorber could achieve the reflection coefficient value up to −42.5dB which is similar to the performance of commercialized absorbent material with the reflection coefficient of −42.9 dB.