Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning-Based Gear Pitting Severity Assessment Using Acoustic Emission, Vibration and Currents Signals

A method for gearbox pitting faults severity classification using Deep Learning techniques is reported. The signals are preprocessed for obtaining a 2D time-frequency representation corresponding to the Mel Frequency Cepstral Coefficients. This bi-dimensional representation is the feature space used for classification. A Long Short Term Memory network (LSTM) is used for classifying nine levels of pitting in spur gears. Each signals dataset is used for training and validating a LSTM network. Classification accuracies up to 100 % are obtained during cross-validation with the analyzed signals dataset.
A Central Intrusion Detection System for RPL-Based Industrial Internet of Things

Although Internet-of-Things (IoT) is revolutionizing the IT sector, it is not mature yet as several technologies are still being offered to be candidates for supporting the backbone of this system. IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) is one of those promising candidate technologies to be adopted by IoT and Industrial IoT (IIoT). Attacks against RPL have shown to be possible, as the attackers utilize the unauthorized parent selection system of the RLP protocol. In this work, we are proposing a methodology and architecture to detect intrusions against IIoT. Especially, we are targeting to detect attacks against RPL by using genetic programming. Our results indicate that the developed framework can successfully (with high accuracy, along with high true positive and low false positive rates) detect routing attacks in RPL-based Industrial IoT networks.
Study of a leaky waveguide for the design of filtering antennas

In this paper, we present the possibility to use a leaky waveguide as a radiating structure and to create cavities into this waveguide in order to master the bandwidth of the global structure. We present here the concept of a filtering antenna and the first results about the insertion of resonators into a microstrip line that supports the first higher-order mode of propagation, and about the characterization of the radiation of this mode.
Simultaneous identification and control of a hybrid dynamic model for a mobile robot

A mobile robot moving in an office environment is a hybrid dynamic system. As the robot becomes constrained by obstacles in the environment, the dynamics of the system change. Thus, the robot has a number of distinct states with differing dynamics, corresponding to the different obstacles in the environment. Discrete switches between the states occur when the motion of the robot becomes constrained by a new obstacle or the robot moves away from a previously constraining obstacle. This paper presents a new method for the identification of a model of this hybrid dynamic system. Simulated results are given demonstrating the applicability of this method to identification of hybrid dynamic systems. A simple controller is implemented indicating the possibilities for simultaneous identification and control and demonstrating the ease of controller design in a hybrid dynamic framework.
Real-time expressive rendering of city models

City models have become central elements for visually communicating spatial information related to urban areas and have manifold applications. Our real-time nonphotorealistic rendering technique aims at abstract, comprehensible, and vivid drawings of assemblies of polygonal 3D urban objects. It takes into account related principles in cartography, cognition, and nonphotorealism. Technically, the geometry of a building is rendered using expressive line drawings to enhance the edges, two-tone or three-tone shading to draw the faces, and simulated shadows. The edge enhancement offers several degrees of freedom, such as interactively changing the style, width, tilt, color, transparency, and length of the strokes. Traditional drawings of cities and panoramas inspired the tone shading that achieves a pleasing visual color effect. The rendering technique can be applied not only to city models but to polygonal shapes in general.
Electric Vehicles Charging: Review of Current Status

Electric Vehicles (EVs) will be the next generation in transport. The main reason is the possibility of consuming energy from wide range including fossil and renewable energy sources. Fossil fuel-powered vehicles do not have this privilege. One of the major obstacles for development of EVs is reliable charging infrastructure. This paper describes the current progress in EV charging levels, methods, and topologies of charging stations. Contactless charging as a safe and user friendly approach for charging EVs has been discussed. The influence on the distribution grid in peak hours analyzed. Finally, Hybrid Energy Storage System (HESS) using solar energy is introduced. Multiple energy sources and storage systems to reduce the stress on the grid has been proposed.
Novel Algorithms for Asynchronous Periodic Pattern Mining Based on 2-D Linked List

Periodic pattern mining has gained a great attention in the past decade. Previous studies mostly focus on synchronous periodic patterns. The literature proposes many methods for mining periodic patterns. Nevertheless, asynchronous periodic pattern mining has gradually received more attention recently. In this paper, we propose an efficient 2-D linked structure and the OEOP (One Event One Pattern) algorithm to discover all kinds of valid segments in each single event sequence. Then, referring to the general model of asynchronous periodic pattern mining proposed by Huang and Chang, this study combines these valid segments found by OEOP into 1-patterns with multiple events, multiple patterns with multiple events and asynchronous periodic patterns. The experimental results show that these algorithms have good performance and scalability.
Discriminative Attention-based Convolutional Neural Network for 3D Facial Expression Recognition

3D Facial Expression Recognition (FER) is an active research area in computer vision. Although previous methods report promising results, two key issues still remain to be solved. On the one hand, different facial areas contribute unequally to performing various expressions, but most existing methods extract features from the entire 3D surface. On the other hand, the difference between expressions varies, while previous methods generally treat different emotions equally, making some of them extremely hard to be distinguished. To solve these problems, we propose a novel approach for 3D FER, namely Discriminative Attention-based Convolution Neural Network (DA-CNN), to generate more comprehensive expression related representations. DA-CNN introduces an attention module to the CNN models, which helps the deep model selectively focus on emotional salient regions in a learnable way. Furthermore, a novel loss named Dimensional Distribution (DD) loss is proposed to model the inter-expression relationship. Supervised by DD loss, DA-CNN can generate more discriminative expression representation. Extensive experiments are conducted on BU-3DFE dataset, and the results show that DA-CNN achieves significant improvement over the state-of-the-art.
Sub-Nanosecond Delay CMOS Active Gate Driver for Closed-Loop $\mathrm{d}v/\mathrm{d}t$ Control of GaN Transistors

This paper presents an AGD (active gate driver) implemented with a low voltage CMOS technology to control the $\mathbf{d}\boldsymbol{v}/\mathbf{d}\boldsymbol{t}$ sequence of low voltage (100V) and high voltage (650V) GaN power transistors. Such an AGD can control and reduce the $\mathbf{d}\boldsymbol{v}/\mathbf{d}\boldsymbol{t}$ of fast switching GaN devices with a reduced impact on switching losses. In the case of both low voltage and high voltage GaN fast switching transistors, such an AGD must have a total response time lower than 1ns. Therefore, introducing a feedback loop to control the $\mathbf{d}\boldsymbol{v}/\mathbf{d}\boldsymbol{t}$ requires a specific design with a very high bandwidth (550MHz). Moreover, probing the $\boldsymbol{v}_{\mathbf{DS}}$ voltage and its derivative is quite challenging, as the voltage level is higher than the low voltage gate driver supply. The purpose of this work is to optimize a low voltage CMOS AGD with fully integrated functions, and implement such a solution in GaN-based power converters.
Powering 5G Era Computing Platforms – the Road toward Integrated Power Delivery

The projected core density in future processor chip creates pressing demand to improve computing energy efficiency. The challenge lies in delivering more accurate on-die supply voltage. The conventional discrete voltage regulator based power delivery becomes very inadequate. Breakthroughs in high frequency power stage design, integrated magnetic inductors, and silicon capacitors enables IVR based integrated power delivery architecture for future processor's energy efficiency need.
Integrating Visual and Geometric Consistency for Pose Estimation

In this work, we tackle the problem of estimating the relative pose between two cameras in urban environments in the presence of additional information provided by low quality localization and orientation sensors. An M-estimator based approach provides an elegant solution for the fusion between inertial and vision data, but it is sensitive to the prior importance of the visual matches between the two views. In addition to using cues extracted from local visual similarity, we propose to rely at the same time on learned associations provided by the global geometrical coherence. A conservative weighting scheme for combining the two types of cues has been proposed and validated successfully on an urban dataset.
Effects of the HV-BIGT Design Elements on the High-Frequency Oscillation Instability during Short Circuit Transients

The design elements of the Bi-mode Insulated Gate Transistor BIGT show that the combination of the high hole injection levels supplied from the collector together with the presence of a localized lifetime control at the MOS cells have brought improvements on the short circuit capability, strongly minimizing the high-frequency oscillations observed in IGBTs. The BIGT concept and the traditional IGBT structures have been compared under short circuit conditions to investigate the charge-field interactions at the MOS cells, triggering the oscillation mechanism. The effect of the lifetime control and the irradiation method on the short circuit capability is investigated.
A Total Ionizing Dose Detecting Circuit Based on Off-state Leakage Current of NLDMOS in Power IC

Due to the demand of aerospace applications, radiation-hard techniques have become critical for power IC. In order to compensate the total ionizing dose (TID) effect on the circuits, detecting it is the precondition. The TID detecting circuit proposed in this paper is able to monitor the TID effect on power IC accurately in the radiated environment. Moreover, it can be integrated with conventional power IC in an ordinary commercial process. Namely, the signals generated by the detecting circuit can be applied to TID compensation for power IC flexibly. The simulation results of the detecting circuit can meet the design requirements accurately from radiation experiment.
Computer-based methods for assessing information security competencies

With the growing importance of information security and cybersecurity, there is a widely recognized need for more security experts and a higher level of security education among all members of society. Effective methods for assessing information security competencies are a necessary prerequisite for developing, evaluating and improving security education. The goal of this paper is to provide groundwork for developing assessment methods of information security competencies that are both effective and feasible for use in formal education. To accomplish this, a list of requirements is proposed, and existing assessment methods are surveyed through the lens of these requirements.
Map building and localization using 2D range scanner

This paper presents a global localization method based on map matching. A local map is built by successive splitting and merging scan points. A global map is built by merging all local maps. Local and global map are represented with line segments that are sorted counterclockwise. Complete line segments are selected to match between two maps. Matching is based on relative position relation of complete line segment. The localization algorithm based on an ordinal map and relative position improves the matching efficiency and lowers computational cost. All these techniques have been implemented on our mobile robot ATRVII equipped with 2D laser range scanner SICK.
Complex System Fault Diagnostic Method Based on Convolutional Neural Network

As a branch of machine learning, deep learning is characterized by multi-level learning to obtain different abstraction levels of raw data, thus improving the accuracy of the tasks such as classification and prediction. It brings a new idea of the complex system fault diagnostics and prognostics. Combining the characteristics of complex system test data and the advantages of deep learning, a fault diagnostics method based on convolutional neural network is proposed, including preprocessing, model training and optimization. Then a complex system fault diagnostic algorithm platform based-on deep learning method is realized. The simulation method of an aero-engine gas path test proves that the proposed method has good feasibility and effect, can fully utilize the advantages of deep learning, and is more suitable for characterizing the complex and varied characteristics hidden inside the complex system data. It can provide the technical support for the design, test and management of the complex system, and improve the safety and effectively reduce the life cycle costs of the complex system.
Lesion Attributes Segmentation for Melanoma Detection with Multi-Task U-Net

Melanoma is the most deadly form of skin cancer worldwide. Many efforts have been made for early detection of melanoma with deep learning based on dermoscopic images. It is crucial to identify the specific lesion patterns for accurate diagnosis of melanoma. However, the common lesion patterns are not consistently present and cause sparse label problems in the data. In this paper, we propose a multi-task U-Net model to automatically detect lesion attributes of melanoma. The network includes two tasks, one is the classification task to classify if the lesion attributes present, and the other is the segmentation task to segment the attributes in the images. Our multi-task U-Net model achieves a Jaccard index of 0.433 on official test data of ISIC 2018 Challenges task 2, which ranks the 5th place on the final leaderboard.
A Probabilistic Assume-Guarantee Reasoning Framework Based on Genetic Algorithm

Probabilistic assume-guarantee reasoning is a theoretically feasible way to alleviate the state space explosion problem in stochastic model checking. The key to probabilistic assume-guarantee reasoning is how to generate the assumption. At present, the main way to automatically generate assumption is the L* (or symbolic L*) learning algorithm. An important limitation of it is that too many intermediate results are produced and need to be stored. To overcome this, we propose a novel assumption generation method by a genetic algorithm and present a probabilistic assume-guarantee reasoning framework for a Markov decision process (MDP). The genetic algorithm is a randomized algorithm essentially, and there are no intermediate results that need to be stored in the process of assumption generation, except the encoding of the problem domain and the training set. It can obviously reduce the space complexity of the probabilistic assume-guarantee reasoning framework. In order to improve the efficiency further, we combine the probabilistic assume-guarantee reasoning framework with interface alphabet refinement orthogonally. Moreover, we employ the diagnostic submodel as a counterexample for the guidance of augmenting training set. We implement a prototype tool for the probabilistic assume-guarantee reasoning framework and report the encouraging results.
Seml: A Semantic LSTM Model for Software Defect Prediction

Software defect prediction can assist developers in finding potential bugs and reducing maintenance cost. Traditional approaches usually utilize software metrics (Lines of Code, Cyclomatic Complexity, etc.) as features to build classifiers and identify defective software modules. However, software metrics often fail to capture programs’ syntax and semantic information. In this paper, we propose Seml, a novel framework that combines word embedding and deep learning methods for defect prediction. Specifically, for each program source file, we first extract a token sequence from its abstract syntax tree. Then, we map each token in the sequence to a real-valued vector using a mapping table, which is trained with an unsupervised word embedding model. Finally, we use the vector sequences and their labels (defective or non-defective) to build a Long Short Term Memory (LSTM) network. The LSTM model can automatically learn the semantic information of programs and perform defect prediction. The evaluation results on eight open source projects show that Seml outperforms three state-of-the-art defect prediction approaches on most of the datasets for both within-project defect prediction and cross-project defect prediction.
An SIMD architecture for texture mapping

This paper presents a novel SIMD architecture for texture mapping using affine transformation that can be used in mobile applications. It is used in MPEG video object motion tracking and texture warping processors. The architecture explores the properties of the affine transformation to reduce the computational cost. It is prototyped using 0.35 /spl mu/m CMOS technology with three layers of metal.
Visualizations of the training process of neural networks

In this paper we present an approach to the visualizations of the neural networks training process. The main goal of such visualizations is to better understand the training process of neural networks, and to help with the creation and optimization of new neural networks. We implemented a tool which can be used to create a large number of visualizations by using different combinations of created transformations (for example: diff, plot2, angle-path, vec-dist-dev). To be able to visualize the learning process, we had to record all the weights from every nth iteration. For experiments we used three neural networks: a neural network for digit recognition (DR), a neural network for word2vec representation of words (WV), and a neural network which learned the simple logical gate XOR, (XR). For the purpose of further comparisons, we also constructed one example of a damaged network (DR’, WV’, XR’) for each of the three original networks (DR, WV, XR). We constructed over 30 visualizations and then chose 6 visualizations which could be easily interpreted and present them in this paper. The results indicate that there may exist some regularities in the visualizations of original networks and that damaged network visualizations differ from the original networks.
Exploiting localization systems for LoRaWAN transmission scheduling in industrial applications

The Internet of Things (IoT) paradigm contaminated the industrial world. Wireless communications seem to be particularly attracting, especially when complement indoor and outdoor Real Time Location Systems (RTLS) for geo-referencing smart objects (e.g. for asset tracking). In this paper, the LoRaWAN solution is considered for long range transmission of RTLS data (LoRaWAN is an example of Low Power Wide Area Network). Given that the RTLSs use time synchronization, this work proposes to opportunistically obtain LoRaWAN Class A node time synchronization using the RTLS ranging devices. Once a common sense of time is shared in the LoRaWAN network, more efficient scheduled medium access strategies can be implemented. The experimental testbed, based on commercially available solutions, demonstrates the affordability and feasibility of the proposed approach. When low-cost GPS (outdoor) and UWB (indoor) ranging devices are considered, synchronization error of few microseconds can be easily obtained. The experimental results show the that time reference pulses disciplined by GPS have a maximum jitter of 180 ns and a standard deviation of 40 ns whereas, if time reference pulses disciplined by UWB are considered, the maximum jitter is 3.3 μs and the standard deviation is 0.7 μs.
Repetitive Short Circuit Energy Dependent $V_{\text{TH}}$ Instability of 1.2kV SiC Power MOSFETs

Repetitive short circuit energy (<tex>$E_{\text{SC}}$</tex>) dependent threshold voltage <tex>$\boldsymbol{V}_{\mathbf{TH}}$</tex> instabilities of 1.2kV SiC power MOSFETs are characterized. A bidirectional <tex>$\boldsymbol{V}_{\mathbf{TH}}$</tex> shift behavior, i.e. negative shift at lower (<tex>$E_{\text{SC}}$</tex>) and positive shift at higher (<tex>$E_{\text{SC}}$</tex>), was revealed. The <tex>$\boldsymbol{V}_{\mathbf{TH}}$</tex> shifts under repetitive SC tests are attributed to the SC pulse phase according to the results of high temperature reverse bias (HTRB) and dynamic high temperature gate bias (HTGB) tests. The underlying mechanisms of the complex <tex>$\boldsymbol{V}_{\mathbf{TH}}$</tex> shift behavior are explained in a unified framework by taking into account the junction temperature (<tex>$\boldsymbol{T}_{\mathbf{j}}$</tex>) increase with higher <tex>$\boldsymbol{E}_{\mathbf{SC}}$</tex>. TCAD device simulation is used to help analyze the mechanisms.
An educational tool for visualising actor programs

Object-oriented programming (OOP) is probably the most popular programming paradigm today. Because of this, it is often taught at faculties associated with computer science. The general idea of OOP is to break a complex problem into abstractions called objects which interact with each other via method invocation.On the other hand, the actor model provides a computation model based on agents that are called actors. Actors in the actor model are similar to objects in OOP, but unlike objects are concurrent and communicate by message passing.At our faculty, a game-based approach is used to teach students about OOP. The actors in a game can be taught as agents that interact with an environment. Furthermore, students program agent behaviour in our artificial intelligence course. Therefore, students have experience with both objects and agents.However, students still find it difficult to understand the actor model and build applications with it. In order to help students, we have developed a visualisation system for actor-based applications. We collected responses on the system after presenting it to students that were taught the model. In this paper, we i) present the developed visualisations system and ii) report the results on the responses collected.
Ultrasound Elastography Utilizing Pre-Beam-Formed Data

Herein, a novel algorithm is proposed for strain estimation in free-hand palpation quasi-static ultrasound elastography. In all previously proposed methods for strain estimation, post-beam-formed ultrasound Radio-Frequency (RF) data have been utilized. However, we propose to exploit pre-beam-formed RF data (often called channel data) for displacement estimation in ultrasound elastography in this paper. Our novel technique builds on our recently proposed GLobal Ultrasound Elastography (GLUE) method, a regularized optimization-based approach for displacement estimation. Here, we propose Channel data based GLUE (CGLUE), which uses channel data instead of the post-beam-formed RF data. Phantom experiments show that considering channel data improves the displacement estimation results substantially.