Miracle-dz/UniFAR-FTUs huggingface dataset:
Representation and Classification of Auroral Images Based on Convolutional Neural Networks

Auroral forms are correlated with certain physical processes in the magnetosphere and ionosphere. It is, therefore, desirable to automatically classify the vast amount of observed auroral images and make large statistical studies. The key problem in classification tasks is image representation. In this article, using the adaptive feature learning ability of convolutional neural networks, an end-to-end auroral image classification network is proposed, which automatically classifies the auroral images observed at the Chinese Yellow River Station into four classes: arc, drapery corona, radial corona, and hotspot corona. Based on the AlexNet, our method exploits the advanced spatial transformer network (STN) and large margin Softmax (L-Softmax) loss function to extract auroral features. STN is able to learn invariance to translation, scaling, and rotation, whereas L-Softmax increases the difficulty of auroral feature learning so that it encourages the intraclass compactness and interclass separability between learned features. The proposed method was validated on the auroral image datasets by supervised classification, image retrieval, and statistical analysis of the temporal occurrence distributions of the four auroral categories. Experimental results showed that after trained on the winter auroral observations in 2003, the proposed model achieves an average classification accuracy of 93.7% on the auroral data of the following five winters (2004–2009) while maintaining high efficiency, which is superior to the previously reported articles.
Research and Development of Future Radiation-Resistant Superconducting Magnets With Mineral Insulated REBCO Coils

Research and development of a mineral insulated superconducting magnet based on REBCO coated conductor has been performed at the High Energy Accelerator Research Organization (KEK) to establish technology for a future radiation resistant high field magnet. A goal of the research is an experimental proof of a small-scale demonstration magnet composed of mineral insulated REBCO coils. Application research on ceramic coating technology to REBCO coated conductor is in progress. A short sample study confirms the adaptability of ceramic coating for electrical insulation of REBCO tape. In addition, an electrical insulation film is formed on the surface of a long tape of about 14 m in length by applying the ceramic coating. a first prototype double pancake coil is wound with the ceramic coated REBCO tape with a ceramic adhesive.
Optimal Interleaved Modulation for DC- Link Loss Optimization in Six-Phase Drives

The modulation of a Voltage Source Inverter (VSI) output causes losses and harmonic voltage and current distortions on the DC-Link capacitor due to the discrete switching of the semiconductors. The high-frequent voltage pulses on the output are digitally programmed depending on the desired fundamental wave while additional degrees of freedom allow the optimization of the DC-link voltage distortions. This article describes a method to reduce these distortions for a drive topology with six phases. The topology consists of an asymmetrical six-phase machine with spatially shifted windings by 30°, a six-phase inverter with two independent sets of three-phases, a common DC-Link capacitor and ideal current supply. The pulse pattern of every three-phase set is independently modulated utilizing digital Space Vector Pulse-Width Modulation (SVPWM) to program the desired fundamental wave. The independence of the two sets of drivers offers an additional degree of freedom, namely the shifting of the two pulse patterns. This article shows that an optimal shifting angle can reduce both the current and voltage-distortions significantly, which is directly influencing lifetime and size of the DC-Link capacitor. Simulations and experimental results validated the proposed techniques.
Bandpass Filtering Power Amplifier With Extended Band and High Efficiency

This letter presents a wideband power amplifier (PA) with high efficiency integrated filtering characteristic. A novel bandpass filtering impedance transformer was proposed, which consists of a terminated coupled line structure (TCLS) and impedance tuning network (ITN), to realize the input matching network (IMN) and output impedance matching network (OMN). To enhance the efficiency of PA, the second harmonic has also been controlled perfectly. This design method can not only provide high-selectivity bandpass responses and high efficiency but also broaden the operation band. For demonstration, a wideband filtering PA operating at 2.2 GHz with a bandwidth of 400 MHz by employing a 10-W GaN HEMTs is fabricated. Measured results show the maximum drain efficiency (DE) is 68.3%–78.4% in the design band. Meanwhile, a good filtering performance was obtained. The rejection level was larger than 52.8 and 31 dB for the lower and higher suppression bands, respectively. In addition, simulation results show that the IMD2 and IMD3 were also improved simultaneously. In particular, the IMD2 value was improved about 30–40 dB.
Mutual Coupling Reduction of ±45° Dual-Polarized Closely Spaced MIMO Antenna by Topology Optimization

A hybrid topology optimization (HTO) method is adopted to reduce the mutual coupling of a ±45° dual-polarized closely spaced MIMO antenna. In order to shorten the optimization time, only the isolation structure region of the two-element MIMO antenna is selected for optimization. More importantly, by exploiting the symmetry of both the MIMO antenna and the isolation structure, only a quarter of the isolation structure needs to be optimized to achieve good antenna performance. In this way, not only the optimization variables, but also the optimization sub-objectives are greatly reduced, and the optimization time is further diminished. The performance of the dual-polarized MIMO antenna with the optimized isolation structure (OIS) is validated by both simulation and measurement. The OIS resonates in both polarizations, thus lowering the mutual coupling between co-polarization ports (<inline-formula> <tex-math notation="LaTeX">$\vert S_{31}\vert $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\vert S_{42}\vert$ </tex-math></inline-formula>) and between cross-polarization ports (<inline-formula> <tex-math notation="LaTeX">$\vert S_{41}\vert $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\vert S_{32}\vert$ </tex-math></inline-formula>) simultaneously. With the center distance of only <inline-formula> <tex-math notation="LaTeX">$0.35\lambda _{0}$ </tex-math></inline-formula>, the measured mutual coupling between different ports are reduced by 6~11 dB within the 10-dB impedance bandwidth (Reflection coefficients < −10 dB) by adding the OIS, and the highest mutual coupling between different ports is reduced from −15 to −21 dB. Besides, the antenna maintains a low profile (<inline-formula> <tex-math notation="LaTeX">$0.026\lambda _{0}$ </tex-math></inline-formula>), low cross-polarization and excellent radiation pattern performance.
On-line Estimation of Electrolytic Capacitor Parameters using Recursive Least Squares and Sliding Mode Differentiators

Electrolytic capacitors may experience a gradual deterioration that results in an increase in the equivalent series resistance (ESR) and a decrease in capacitance over time. An indication of the condition of a capacitor may be obtained by the on-line monitoring of these two electrical properties. In this paper, an on-line capacitor parameter estimation scheme that can be used for degradation monitoring is proposed. The approach involves the recursive least squares (RLS) algorithm with forgetting factor and robust exact differentiators based on sliding mode concepts. This scheme allows for on-line estimation of the ESR and capacitance under noisy conditions. Computer simulation results involving a DC-DC converter, provide evidence of the effectiveness of the scheme proposed in this paper.
A Deep Learning Approach to Distributed Anomaly Detection for Edge Computing

One of the multiplier effects of the boom in mobile technologies ranging from cell phones to computers and wearables like smart watches is that every public and private common spaces are now dotted with Wi-Fi hotspots. These hotspots provide the convenience of accessing the internet on-the-go for either play or work. Also, with the increased automation of our daily routines by our mobile devices via a multitude of applications, our vulnerability to cyber fraud or attacks becomes higher too. Hence, the need for heightened security that is capable of detecting anomalies on-the-fly. However, these edge devices connected to the local area network come with diverse capabilities with varying degrees of limitations in compute and energy resources. Therefore, running a process-based anomaly detector is not given a high priority in these devices because; a) the primary functions of the applications running on the devices is not security; therefore, the device allocates much of its resources into satisfying the primary duty of the applications. b) the volume and velocity of the data are high. Therefore, in this paper, we introduce a multi-node (nodes and devices are used interchangeably in the paper) ad-hoc network that uses a novel offloading scheme to bring an online anomaly detection capability on the kernel events to the nodes in the network. We test the framework in a Wi-Fi-based ad-hoc network made up of several devices, and the results confirm our hypothesis that the scheme can reduce latency and increase the throughput of the anomaly detector, thereby making online anomaly detection in the edge possible without sacrificing the accuracy of the deep recurrent neural network.
Exact Multistatic Interferometric Imaging via Generalized Wirtinger Flow

We present a novel, exact method to address the interferometric inversion problem for multistatic wave-based imaging based on Generalized Wirtinger Flow (GWF) [1]. Interferometric imaging is a relative of phase retrieval, which arises from cross-correlating measurements from pairs of receivers. GWF provides a theoretical framework to process scattering data satisfying the Born approximation, and guarantees exact recovery of the underlying scene reflectivity vector from interferometric measurements if the discretized lifted forward model satisfies the restricted isometry property over rank-1, positive semi-definite matrices with a sufficiently small restricted isometry constant (RIC). To this end, we design a linear deterministic discrete lifted forward model for interferometric multistatic radar measurements such that the exact recovery conditions of GWF are satisfied. Our results identify a lower limit on the pixel spacing and the sample complexity for exact multistatic radar imaging. We provide a numerical study of our RIC and pixel spacing bounds on synthetic single scattering data, which show that GWF can achieve exact recovery with super-resolution. While our primary interest lies in radar imaging, our results are applicable to other multistatic wave-based imaging problems such as those arising in acoustics and geophysics.
Maximum Torque per Ampere and Field-weakening Controls for the High-Speed Operation of Permanent-Magnet Assisted Synchronous Reluctance Motors

Because of the high-torque's capability and power factor as well as high-speed flux-weakening region operation, the permanent-magnet assisted synchronous motor (PMA-SynRM) is a right candidate for the next generations of electric vehicles (EVs) and hybrid electric vehicles (HEVs). The maximum torque per ampere (MTPA) control scheme of the PMA-SynRM presented in this paper can be used to minimize the copper losses of the system and utilize the field-weakening control scheme. A small-scale “1 kW prototype PMA-SynRM” was designed and manufactured to test and examine the proposed control in the laboratory. The proposed algorithm was digitally carried out using the MicroLab Box dSPACE. Based on the simulation and experimental results, the performance of the proposed control scheme was determined.
The Joint Optimization of Online Traffic Matrix Measurement and Traffic Engineering For Software-Defined Networks

Software-Defined Networking (SDN) provides programmable, flexible and fine-grained traffic control capability, which paves the way for realizing dynamic and high-performance traffic measurement and traffic engineering. In the SDN paradigm, the traffic forwarding and measurement strategies are realized through flow tables stored in the Tenantry Content Addressable Memories (TCAM) of SDN switches. However, the number of TCAM entries in SDN switches is limited. In this paper, we aim to jointly optimize the Traffic Matrix Measurement (TMM) and Traffic Engineering (TE) process under the TCAM capacity and flow aggregation constraints in software-defined networks. We first formulate the joint optimization problem as a Mixed Integer Linear Programming (MILP) model. Then to get an initial traffic matrix for the joint optimization problem, we propose a simple flow rule generation strategy named Maximum Load Rule First (MLRF) to efficiently generate feasible flow rules, which are used to provide direct measurements for the traffic matrix measurement problem. At last, to solve the joint optimization efficiently, we propose two efficient heuristic algorithms named Traffic Matrix Measurement First (TMMF) and Traffic Engineering First (TEF), respectively. TMMF and TEF can generate feasible flow rules for realizing TMM and TE strategies. Our evaluations on real network topologies and traffic traces verify that by jointly optimizing the TMM and TE strategies, both TMMF and TEF can significantly improve TMM accuracy and TE objective (i.e., load balancing) with limited TCAM resource.
Olfactory-Based Augmented Reality Support for Industrial Maintenance

Augmented reality (AR) applications have opened innovative ways for performance improvement in the IoT industry. It can enhance user perception of the real-world by providing valuable information about an industrial environment and provide visual virtual information onto a head-mounted device (HMD). Such information is important for maintainers to quickly detect abnormalities, reduce nugatory routines and facilitate preventive maintenance activities. Since odors are made up of volatile compounds at low concentration, they can be used for olfactory-based identification. The article outlines the development of an olfactory-based AR system to help with the identification of maintenance issues using smell. The prototype comprises of three components: an electronic nose, a database and an AR application integrated with Microsoft HoloLens. After diagnosing an odor, the data is sent wirelessly through a local network to the HMD worn by the user. To validate the technology, four odors have been used, including engine oil, sun lotion, medical alcohol and perfume, to record behaviors and demonstrate the repeatability of the process. The presented technology incorporates sampling methods, cleaning processes and statistical analysis that can be further scrutinized to allow better smell augmentation for diagnosis.
Broadband 180° Hybrid for 5G Applications

A broadband and compact 180° hybrid that covers 3–5 GHz is proposed for the fifth generation (5G) new radio (NR) N77/N78/N79 applications. The broadband characteristic is obtained by using cascaded two-section topology and edge-coupled lines at each input/output (I/O) port. Miniaturization of two-section rings is achieved using meander microstrip lines. A fractional bandwidth (FBW) of 50% and a size reduction of 50% are both reached. Good in-phase and out-of-phase characteristics are obtained as well. The circuit can be easily fabricated using conventional printed circuit board (PCB) process and suitable for mass production.
Insights into the complexities of communication and automated sign language translation from the I-ACE Project

Deafness is a recognized disability across Europe with equality in education being a requirement of legislated Accessibility Acts. The I-ACE Project was a European Union Erasmus KA2 project which explored the potential use of an automatic sign language translator in the school education system. In addition to the schools' application, the project constructed a demonstrator system to capture a lot of views from general users through a Porto Metro Station pilot installation and through focus group meetings with the deaf community across Europe. This paper presents one aspect of the findings of this project, that relating to the complexities of communicating with the deaf. The findings are used to review the generic communication model between any two individuals, and, as a consequence, the general communication model for education. One specific update is made to this generic model, that of the addition of technical context. Examples from the pilot study and focus group meetings are used to illustrate why technical context is appropriate and relevant to include. The paper concludes with some general recommendations for educators.
MILP Based Deployment of Micro-PMU in Reconfigurable Active Distribution Network

This study presents the µPMU deployment technique in the reconfigurable medium voltage active distribution network. For this, the multi-objective function-based approach is utilized targetting the complete observability in all the realtime configuration with minimum numbers of µPMU and maximum observability redundancy for the base configuration. The effectiveness of the proposed method is demonstrated on standard IEEE 33 and 118-node network considering multiple configurations. The results validate that the proposed method successfully locate the µPMU in the reconfigurable active distribution network.
An Empirical Comparison of Classification Algorithms for Imbalanced Credit Scoring Datasets

The profitability of banks is highly dependent on credit scoring models, which support decision making to approve a loan to a customer. State-of-the-art credit scoring models are based on learning methods. These methods need to cope with the problem of imbalanced classes since credit scoring datasets usually contain mainly paid loans and few defaults (unpaid ones). Recently, new imbalanced learning techniques have been proposed in the literature, and they can improve the credit scoring results. Motivated by this scenario, we evaluate several classification approaches to credit scoring. Besides, we also assess some preprocessing methods to overcome skewed datasets. To achieve it, we use three public real-world credit scoring datasets. In our experiments, we progressively increase the class imbalance in each of these datasets by randomly undersampling the minority class of defaulters to identify how the predictive power is affected. The results indicate that random forest, extreme gradient boosting perform very well in all imbalance levels. We also find that a complete grid search step can increase the prediction power of classification approaches in high imbalanced datasets.
Automatic Pavement Crack Detection using Multi-Scale Image & Neighborhoods Information

Pavement cracks are dangerous because they can cause accidents such as tire punctures, slips, and collapses. Therefore, it is necessary to repair them properly. In recent years, various crack detection methods using pavement images have been proposed. However, in many cases, there are problems with accuracy and processing time. In this paper, we propose a new crack detection method using multi-scale image and neighborhood information. Experimental results show that the proposed method is superior to the most advanced crack detection methods in both accuracy and processing time.
Brain Tumor Classification with Fisher Vector and Linear Classifier for T1-Weighted Contrast-Enhanced MRI Images

This paper presents the development of a computational method for classifying three types of brain tumors - i.e. meningioma, glioma and pituitary - from T1-weighted contrast-enhanced MRI images. The proposed method performs feature extraction on a specified set of tumor pixel intensity and uses the extracted information to determine the corresponding type of brain tumor. In feature extraction, the specified tumor area was first augmented to incorporate the sample of the surrounding tissue, prior to intensity extraction with dense local patches. Afterwards, the extracted intensity from each patch was fitted to a Gaussian Mixture Model (GMM) and processed into Fisher Vector representation. Furthermore, we applied four linear classifiers to the Fisher Vector representation and evaluated their classification performance. Our experiments showed that the logistic regression gave the best performance with average accuracy, sensitivity and specificity of 89.9%, 95.2%, and 89.0% respectively.
Deep Neuronal Based Classifiers for Wireless Multi-hop Network Mobility Models

Mobility plays an important role in the performance of wireless multi-hop networks. Since communications are established in a multi-hop fashion, the mobility of nodes can cause a significant degradation of the performance. Therefore, the analysis of nodes' mobility is relevant to improve the performance of the applications implemented over wireless multi-hop networks. This work evaluates two neuronal network models, such as fully connected or multi-layer perceptron and 1D convolutional models, for the classification of up to four widely used mobility models for wireless multi-hop networks. Several architectures are evaluated and parametrized for both models. The results indicate a considerable better performance of an architecture with 1D convolutional layers. The test results show that the best convolutional 1D model is able to reach an accuracy level of 0.91, outperforming the best multi-layer perceptron model in 13,9 %.
Characterization of a Silicon Carbide BCD Process for 300°C Circuits

This paper describes a silicon carbide (SiC) Bipolar-CMOS-DMOS (BCD) process technology and presents the corresponding characterization results. The process enables the design of integrated circuits (ICs) capable of high temperature operation and heterogeneous integration into SiC power modules. The paper showcases the cross-section of the triple-well, single metal layer SiC BCD process and details the key process challenges. Characterization results for NMOS, PMOS, LDMOS, and BJT structures are presented. DC output characteristics of the standard 1.5 μm NMOS and PMOS are shown for temperatures of 25°C and 300°C. The drain-to-source breakdown voltages observed for the LDMOS (at 1 nA leakage) is 178 V at 25°C. The integrated passives available in the process include N-diffusion resistors with a sheet resistance of approximately 1.2 kΩ/□ and poly-to-N-diffusion capacitors with 0.612 fF/μm2 capacitance.