Miracle-dz/UniFAR-FTUs huggingface dataset:
Power-Down Mode Verification for Hierarchical Analog Circuits

To reduce the power consumption of modern system-on-chips, parts of the system can be switched off when not required for system operation. Additional circuitry realizes this power-down mode functionality of an analog circuit by shutting off all of its bias currents. When connecting several circuit blocks with individually verified power-down modes together, the correct power-down functionality of the overall circuit cannot be guaranteed anymore as new short-circuit paths might emerge due to the resulting connectivity. In this paper, a new method for power-down mode verification of hierarchical analog circuits is presented. It will be explained how the obtained verification results can be used to revise and correct detected errors. The presented concepts are illustrated for a high input impedance differential amplifier formed by three Miller operational amplifiers and a network of resistors.
Suppression of Harmonics and Instabilities of Single-Phase Inverters Caused by Delay-based Phase-Locked Loop in the Weak Grid

Phase-locked loops (PLLs) are the key technique for inverters to be synchronized with the PCC voltage. Typically, in the single-phase applications, the delay-based PLL is being widely used. However, for nowadays applications where the inverter may be connected to the PCC with large grid impedance, the inverter performance can be endangered, and harmonics or even instabilities are aroused. Although there are some relevant literatures, the solutions for solving the adverse impacts of delay-PLL on the system performance are still not well documented. Therefore, this paper aims to study the limitations of delay-based PLL and provide an improved approach. Through the inverter output impedance modelling, the reason why the delay-based PLL causes the instability in the weak grid case has been clearly explained. Then, the way to solve the instability caused by the PLL has been discussed, and an improved delay-based PLL has been proposed to improve the weak grid compatibility of inverters. The single-phase inverter system can then work well when the grid impedance varies widely.
Would I Lie to You? How Users Evaluate Faked Online Content Depending on Its Publication Type

Since the unexpected outcome of the 2016 US elections and the Brexit referendum, the term fake news mutated to an almost daily mentioned topic in the media. Current research on this topic mostly deals with the effect of fake news on opinion formation, but rarely considers the publication type of the forged content. We investigate the differences in user assessment of faked content between an objectively written news article and an emotionally loaded conversation on a question and answer platform. Our results show, that news articles seem to be more credible and persuasive, but both faked publication types did not impel the users to change their opinion about an environmental topic.
Map-aided Navigation for Emergency Searches

Real-time positioning of emergency personnel has been an active research topic for many years. However, studies on how to improve navigation accuracy by using prior information on the idiosyncratic motion characteristics of firefighters are scarce. This paper presents an algorithm for generating pseudo observations of position and orientation based on standard search patterns used by firefighters. The iterative closest point algorithm is used to compare walking trajectories estimated from inertial odometry with search patterns generated from digital maps. The resulting fitting errors are then used to integrate the pseudo observations into a map-aided navigation filter. Specifically, we present a sequential Monte Carlo solution where the pattern comparison is used to both update particle weights and create new particle samples. Experimental results involving professional firefighters demonstrate that the proposed pseudo observations can achieve a stable localization error of about one meter, and offer increased robustness in the presence of map errors.
A Converse Lyapunov Theorem for p-dominant Switched Linear Systems

We study the path-complete p-contraction property for switched linear systems, which is a generalization of the notion of positive systems. We show on examples that this property is indeed useful for describing convergence properties, like p-dominance, that classical positivity cannot handle. We then provide a Converse Lyapunov Theorem, showing that, contrary to positivity, any p-dominant switched system must possess the path-complete p-contraction property with quadratic cones.
Occurrence of Pseudo-Haptics by Swimming in a Virtual Reality Environment

This study focuses on the investigation of pseudo-haptics while swimming in a highly immersive virtual reality environment created using a head-mounted display to investigate the conditions under which pseudo-haptics occur while performing considerable physical exercises. When the users perform the outward sweep motion of the breaststroke, the spheres floating in the virtual reality space move toward the users. The experimental results confirm that pseudo-haptics can be controlled using this setup by adjusting the ratio of the amount of movement of users' hands and the amount of movement of the virtual reality spheres.
Toward Universal Tangible Objects: Optimizing Haptic Pinching Sensations in 3D Interaction

Tangible objects are a simple yet effective way for providing haptic sensations in Virtual Reality. For achieving a compelling illusion, there should be a good correspondence between what users see in the virtual environment and what they touch in the real world. The haptic features of the tangible object should indeed match those of the corresponding virtual one in terms of, e.g., size, local shape, mass, texture. A straightforward solution is to create perfect tangible replicas of all the virtual objects in the scene. However, this is often neither feasible nor desirable. This paper presents an innovative approach enabling the use of few tangible objects to render many virtual ones. The proposed algorithm analyzes the available tangible and virtual objects to find the best grasps in terms of matching haptic sensations. It starts by identifying several suitable pinching poses on the considered tangible and virtual objects. Then, for each pose, it evaluates a series of haptically-salient characteristics. Next, it identifies the two most similar pinching poses according to these metrics, one on the tangible and one on the virtual object. Finally, it highlights the chosen pinching pose, which provides the best matching sensation between what users see and touch. The effectiveness of our approach is evaluated through a user study. Results show that the algorithm is able to well combine several haptically-salient object features to find convincing pinches between the given tangible and virtual objects.
Preliminary Experiment on Shareable and Portable Voice Sticky using Sound Orientation

In this research, we considered a scheme for recording, browsing, and sharing personal voice memos using three-dimensional (3D) auditory space, and we proposed a voice memo system that can achieve auditory localization of multiple voice memos. In the system, the users can record the voice memos and place them on the surrounding relative directions. The direction of each memo is determined by head direction during the recording. After the recording, the users can browse numerous voice memos using head directions and can define the sharing attribute of each voice memo in the edit mode so that personal and public voice memos are appropriately shared with the permitted users. In this paper, we evaluate the effectiveness of the proposed scheme when the participants conduct a series flow, from recording the voice memos to browsing the recorded voice memos, especially focusing on the effects of the user's relative directions and head motions.
Immersive EEG: Evaluating Electroencephalography in Virtual Reality

We investigate the feasibility of combining off-the-shelf virtual reality headsets and electroencephalography. EEG is a highly sensitive tool and subject to strong distortions when exerting physical force like mounting a VR headset on top of it that twists sensors and cables. Our study compares the signal quality of EEG in VR against immersive dome environments and traditional displays using an oddball paradigm experimental design. Furthermore, we compare the signal quality of EEG when combined with a commodity VR headset without modification against a modified version that reduces physical strain on the EEG headset. Our results indicate, that it is possible to combine EEG and VR even without modification under certain conditions. VR headset customisation improves signal quality results. Additionally, display latency of the different modalities is visible on a neurological level.
A Novel Switched-Capacitor Converter With High Voltage Gain

In this paper, a novel topology of switched-capacitor converter is proposed. Compared with the existing switched-capacitor topologies, such as the Dickson-based switched-capacitor converter or the ladder switched-capacitor converter, it limits both voltage stress and current stress of components under lower level, which reduces the volume of capacitor banks and the conduction loss. These characteristics can help high-power density and high-efficiency design, especially in high-voltage-gain applications. The phase shift modulation strategy corresponding to the proposed switched-capacitor converter is also presented in this paper. With phase shift modulation, voltage gain can be adjusted as continuous value. The voltage regulation ability makes up the defect of conventional switched-capacitor converters, and the ZVS operation of all the switching devices is also achieved with the proposed modulation method so that the WBG devices can play more strength in the converter with higher switching frequency. The derivation of the circuit topology and phase shift modulation will be introduced in this paper. The steady analysis and performance comparison will prove the advantage of the proposed converter. A 600-W prototype is built, and the experiment results are presented to validate the proposed converter.
A Navigation Scheme for a Random Maze using Reinforcement Learning with Quadrotor Vision

In this paper, the maze navigation problem for the unmanned ground vehicle (UGV) is considered. A new maze navigation scheme with Reinforcement Learning (RL) is proposed to find the optimal path from the entrance to the exit for the UGV. First, the quadrotor with a camera at its bottom is used to capture the image data of a random maze in a 3D view, and an image processing approach is implemented to reconstruct the maze in a virtual platform. Then, a novel exploration algorithm, Q-Learning ($\lambda$) with improved e-greedy ($\text{i}\epsilon$-greedy), is proposed to find the shortest path in the reconstructed maze. The advantages of this paper are that the navigation scheme can provide an optimal path to the UGV without experiencing the maze cell by cell, and the proposed exploration algorithm can greatly reduce the randomness comparing to the traditional RL method. The effectiveness of both the proposed navigation scheme and the proposed exploration algorithm has been verified by simulations.
A Single Stage AC-DC Converter for Wireless Power Transfer System

In the field of electric vehicles, researches on wireless power transfer (WPT)charging system have been developed recently because of environmental problems and convenience. In this paper, we proposed a wireless power transfer charging system using a single stage AC/DC converter that can be reduced in size and weight, as well as ensuring convenience. The proposed single-stage AC/DC converter can control the wide output voltage $(250\ \mathrm{V}_{\text{DC}}\sim 350\ \mathrm{V}_{\text{DC}})$ by the link voltage and pulsewidth voltage applied to CLLC resonant circuit through the phase-shifted modulation at a fixed switching frequency. Also, the input power factor (PF)and the total harmonic distortion (THDi)by using the proposed converter can be improved. A 1 kW prototype was fabricated and validated through experimental results and analysis
Credit Evaluation Ensemble Model with Self-Contained Shunt

In the actual loan scenario, the proportion of “good” and “bad” samples is usually extremely imbalanced. Therefore, this paper constructs an ensemble model with a pre-judging mechanism for the imbalanced datasets of small enterprise. It can learn the structure of data set independently and adjust the structure spontaneously through the “data shunt”, which can improve the efficiency of model, simplify the complexity of model and optimize the indicators to achieve the goal of reducing bank operating costs and bad debt rates. (1) Supervised data splitter: In this paper, we select the density-based outlier test algorithm Local Outlier Factor (LOF) as the main algorithm of the shunt and the raw data will be reduced and clustered by Principal Component Analysis (PCA) and K-Means++ clustering algorithm, and the outlier test pipeline will be constructed for each cluster data. Finally, the samples identified as abnormal in each cluster will be sent to the “strict channel” to simplify the whole model, speed up the calculation and improve the recall rate and other indicators, whereas the samples identified as normal will be sent to the “loose channel” to speed up the approval of loans to normal customers. (2) Construction of the two-channel model: It has been verified that the “strict channel” has the best performance when using the Bagging model as the main model based on eXtreme Gradient Boosting (XGBoost), and the “loose channel” has the best performance when using the Gradient Boosting Decision Tree (GBDT) single model. In general, the empirical results show that the dual-channel credit evaluation model with shunt could achieve the effect of simplifying the whole model, speeding up the calculation, and optimizing the model indicators. In particular, it is possible to control the loan pass rate under different loan policies (loose or tighten) automatically by setting multiple sets of parameters for the “shunt”.
Survey on Standards and Regulations for Wireless Charging of Electric Vehicles

In the next decades, the number of electric vehicles (EVs) for private use is predicted to increase considerably. Charging the EVs through wireless power transfer (WPT), i.e. wireless charging, has the potential to shorten charging duration of EVs batteries through dynamic charging and it would also improve the safety of static charging in case of wet weather conditions. From 2015, standards and regulations have been released such that common constraints can be defined. Therefore, it is essential to be aware of these regulations before designing an EV wireless charging system, such that it can become an actual application. This paper focuses on the available regulations and standards on EVs charging through WPT. These regulations are explained individually and then, they are compared and discussed mainly in terms of the electromagnetic compatibility (EMC) and exposure of humans to the radiated magnetic field limits. To gain a better understanding, a quantitative and qualitative interpretation of these limits is also given.
Face Anti-Spoofing Based on Multi-layer Domain Adaptation

With the popularity of face recognition technology, people have put forward higher requirements for the security of face recognition system. Face anti-spoofing detection attracts extensive attention and many methods been proposed. However, these methods perform poorly in cross scenes. To solve this problem, we propose a face anti-spoofing detection algorithm based on domain adaptation. We apply Maximum Mean Discrepancy (MMD) to multi-layer network distribution adaptation, which improves the generalization ability of the model. To further improve the performance of face anti-spoofing detection, we fuse the low-level features with the high-level features of convolutional neural network for face anti-spoofing detection. Two widely used datasets are used to test the proposed method. The experimental results show that the proposed algorithm outperforms state-of-the-art approaches.
Sensor Selection under Unknown but Bounded Disturbances in Multi- Target Tracking Problem

The problem of sensor selection arises in various applications. In multi-target tracking, the main challenge is to select sensors for each target in such a way as to minimize the estimation error, taking into account the limitations of the computation and communication resources of the sensors. In this paper, we deal with two problems arising in sensor selection. First, we try to reduce the situations, in which selected sensors might be loaded more than the rest of the nodes. Secondly, we discard the assumption, requiring the measurement noise to have the Gaussian distribution. Instead of that, we consider the measurements corrupted by the unknown but bounded noise. We present a sensor selection strategy based on linear matrix inequalities and show its performance.
Robustification of sample-and-hold controllers for the consensus problem

Robustification of controllers for the consensus problem of multi-agent systems (MASs) with actuator disturbances is analysed in this work. If a general consensus protocol is available for the disturbances-free system, a new sampled-data control law guarantees the agreement, in the sample-and-hold sense, with arbitrarily large and bounded actuator disturbance. Also the observation error is investigated. The agent drift dynamics are required to be globally Lipschitz and bounded. A numerical simulation example is finally proposed to validate the results.
Innovative Filters in Partially Air-Filled in Substrate Integrated Waveguide Technology

This paper presents a novel class of substrate integrated waveguide filters, based on partially air-filled rectangular cavities. These partially-air filled cavity implements a doublet, that exhibits a frequency response with two poles and two transmission zeros. Two different topologies are proposed: in the first one, the partially air-filled region is located in the center of the rectangular cavity and this allows to locate two zeros below the pass band; in the second topology, two partially air-filled regions are located at the sides of the rectangular cavity and this allows to locate arbitrarily the two zeros, either both below, or both above, or one below and one above the pass band. Prototypes of the two topologies have been designed and experimentally verified.
Municipal Solid Waste Segregation with CNN

Pollution from municipal solid waste has been a problem in Thailand for a long time. People generate waste in every minute. Ineffective waste segregation does increase difficulties in solid waste management. The Pollution Control Department of Thailand provides segregation guideline for municipal solid waste. Household wastes should be separated into four types—general waste, compostable waste, recyclable waste and hazardous waste. This paper explored performance of CNN-based waste-type classifiers (VGG-16, ResNet-50, MobileNet V2 and DenseNet-121) in classifying waste types of 9,200 municipal solid waste images. Waste type can be identified directly from waste-type classifier or derived from waste-item class. Derived classifiers outperformed their corresponding direct classifiers in the experiment. The highest waste-type classification accuracy was 94.86% from the derived ResNet-50 classifier.
Vulnerability Severity Prediction With Deep Neural Network

High frequency of network security incidents has also brought a lot of negative effects and even huge economic losses to countries, enterprises and individuals in recent years. Therefore, more and more attention has been paid to the problem of network security. In order to evaluate the newly included vulnerability text information accurately, and to reduce the workload of experts and the false negative rate of the traditional method. Multiple deep learning methods for vulnerability text classification evaluation are proposed in this paper. The standard Cross Site Scripting (XSS) vulnerability text data is processed first, and then classified using three kinds of deep neural networks (CNN, LSTM, TextRCNN) and one kind of traditional machine learning method (XGBoost). The dropout ratio of the optimal CNN network, the epoch of all deep neural networks and training set data were tuned via experiments to improve the fit on our target task. The results show that the deep learning methods evaluate vulnerability risk levels better, compared with traditional machine learning methods, but cost more time. We train our models in various training sets and test with the same testing set. The performance and utility of recurrent convolutional neural networks (TextRCNN) is highest in comparison to all other methods, which classification accuracy rate is 93.95%.
From Vision to Content: Construction of Domain-Specific Multi-Modal Knowledge Graph

Knowledge graphs are usually constructed to describe the various concepts that exist in real world as well as the relationships between them. There are many knowledge graphs in specific fields, but they usually pay more attention on text or structured data, ignoring the image vision information, and cannot play an adequate role in the emerging visualization applications. Aiming at this issue, we design a method that integrates image vision information and text information derived from Wikimedia Commons to construct a domain-specific multi-modal knowledge graph, taking the metallic materials domain as an example to illustrate the method. The text description of each image is regarded as its context semantic to acquire the image’s context semantic labels based on the DBpedia resource. Furthermore, we adopt deep neural network model instead of simple visual descriptors to acquire the image’s visual semantic labels using the concepts from WordNet. In order to fuse the visual semantic labels and context semantic labels, a path-based concept extension and fusion strategy is proposed based on the conceptual hierarchies of WordNet and DBpedia to obtain the effective extension concepts as well as the links between them, increasing the scale of the knowledge graph and enhancing the correlation between images. The experimental results show that the maximum extension level has a significant impact on the quality of the generated domain knowledge graph, and the best extension level number is respectively determined for both DBpedia and WordNet. In addition, the results of this paper are compared with IMGpedia to further show the effectiveness of the proposed method.
Modeling of Circularly Polarized Stacked Patch Antenna

In this paper, a coupled-resonator circuit model is proposed for a circularly polarized (CP) stacked patch antenna. The x- and y-polarized far-field radiations are regarded as two virtual output ports while the degenerate resonant modes of both the lower and upper patches are considered. Then, a three-port fourth-order filtering network is constructed. The coupling matrix can be established, with the definite physical meanings for all the elements, to predict the performance of the CP stacked patch antenna, including the port reflection, antenna efficiency and axial ratio. The modeling method is verified numerically. It is expected that this method will provide an efficient way for the design and optimization of CP stacked patch antennas.