Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting Popularity of Electric Vehicle Charging Infrastructure in Urban Context

The availability of charging infrastructure is essential for large-scale adoption of electric vehicles (EV). Charging patterns and the utilization of infrastructure have consequences not only for the energy demand by loading local power grids, but influence the economic returns, parking policies and further adoption of EVs. We develop a data-driven approach that exploits predictors compiled from Geographic Information Systems data describing the urban context and urban activities near charging infrastructure to explore correlations with a comprehensive set of indicators that measure the performance of charging infrastructure. The best fit was identified for the size of the unique group of visitors (popularity) attracted by the charging infrastructure. Consecutively, charging infrastructure is ranked by popularity. The question of whether or not a given charging spot belongs to the top tier is posed as a binary classification problem and predictive performance of logistic regression regularized with an $\mathit {l}_{1}$ penalty, random forests and gradient boosted regression trees is evaluated. Obtained results indicate that the collected predictors contain information that can be used to predict the popularity of charging infrastructure. The significance of predictors and how they are linked with the popularity are explored as well. The proposed methodology can be used to inform charging infrastructure deployment strategies.
Towards Responsibility-Sensitive Safety of Automated Vehicles with Reachable Set Analysis

One of the most critical, unsolved challenges for the introduction of automated vehicles is safety verification of planned trajectories. The most promising concepts approaching this topic are worst-case occupancy predictions based on reachable set analysis and the definition of Responsibility-Sensitive Safety (RSS) that formalizes dangerous situations and proper responses. Worst-case predictions result in over-conservative behavior while the RSS approach makes strong assumptions w.r. t. lateral and longitudinal behavior. Our contribution is a first step in bringing both worlds together, to benefit from respective advantages. First, we define RSS-motivated safe-states for merge and crossing scenarios, that ensure absolute safety towards leading vehicles, appropriate time gaps towards following vehicles in merging lanes and minimum clearance time of conflict zones with crossing lanes. We then show how to integrate these safety constraints in a trajectory and behavior planner using reachable sets and finally illustrate its usefulness in various simulative evaluations.
Attention-Guided Version of 2D UNet for Automatic Brain Tumor Segmentation

Gliomas are the most common and aggressive among brain tumors, which cause a short life expectancy in their highest grade. Therefore, treatment assessment is a key stage to enhance the quality of the patients’ lives. Recently, deep convolutional neural networks (DCNNs) have achieved a remarkable performance in brain tumor segmentation, but this task is still difficult owing to high varying intensity and appearance of gliomas. Most of the existing methods, especially UNet-based networks, integrate low-level and high-level features in a naive way, which may result in confusion for the model. Moreover, most approaches employ 3D architectures to benefit from 3D contextual information of input images. These architectures contain more parameters and computational complexity than 2D architectures. On the other hand, using 2D models causes not to benefit from 3D contextual information of input images. In order to address the mentioned issues, we design a low-parameter network based on 2D UNet in which we employ two techniques. The first technique is an attention mechanism, which is adopted after concatenation of low-level and high-level features. This technique prevents confusion for the model by weighting each of the channels adaptively. The second technique is the Multi-View Fusion. By adopting this technique, we can benefit from 3D contextual information of input images despite using a 2D model. Experimental results demonstrate that our method performs favorably against 2017 and 2018 state-of-the-art methods.
Beam Management in Millimeter-Wave Communications for 5G and Beyond

Massive MIMO is one of the promising techniques to improve spectral efficiency and network performance for reaching its targeted multi-gigabit throughput in 5G systems. For 5G New Radio (NR) systems, one of the key differences compared to 4G systems is the utilization of high frequency millimeter wave (mmWave) bands in addition to sub-6GHz bands. To keep the complexity and implementation cost low, hybrid analog-digital beam-forming with large-scale antenna array has become a common design approach to address the issue of higher propagation loss as well as to improve spectral efficiency in mmWave communication in 5G NR. The 5G NR standard is designed to adapt to different beam-forming architecture and deployment scenarios. This paper provides the overview on beam management procedure according to the current 5G standardization progress. We discuss some major challenges of millimeter-wave communications encountered in the current 5G NR standard and present some expected enhancements considered for the future beyond-5G standard.
An SEB Hardened AlGaN/GaN HEMT With Barrier Interlayer

A new single event burnout (SEB) hardened AlGaN/GaN structure with a thin barrier interlayer (IL) is presented in this work. The proposed hardened structure is compared with the simulation results of the conventional structure. The comparative analysis demonstrates that the IL lifts the conduction band energy in buffer layer and a new quantum well is formed. The quantum well limits the electrons induced by heavy ion below the second channel into the first channel. Thus, better SEB performance is achieved compared with the conventional structure. Moreover, the breakdown characteristic of the new structure is significantly improved, while the output characteristic is slightly reduced. Under the condition that the two structures are irradiated vertically by heavy ion having the linear energy transfer (LET) value of 0.6 pC/ $\mu \text{m}$ , the SEB threshold voltage of the conventional structure is 280 V, while the hardened structure can achieve 375 V.
A Tasseled Cap Transformation for GF-2 Fused Multispectral Images

Compared with spectral bands and vegetation indices, the tasseled cap transformation (TCT) components can associate remote sensing spectral information with physical characteristics of vegetation classes, and can yield slightly better classification results. However, the TCT parameters must be recalculated for a new sensor or a different spectral feature data (digital number (DN), reflectance factor, or at-satellite reflectance). In this paper, the TCT parameters appropriate for the spectral reflectance of fused high spatial resolution multispectral images from Gaofen 2 (GF-2) were derived based on the optimal seasonal image (April 30, 2016) for classifying the quasi-circular vegetation patches sparsely distributed in the Yellow River Delta, China. The tasseled cap parameters from this study will enhance the applications of GF-2 images in detecting vegetation patch pattern, classifying crop types, mapping the dieback of forest and forest health, and monitoring land use and land cover changes.
Malware Detection Using Honeypot and Machine Learning

Malware is one of the threats to information security that continues to increase. In 2014 nearly six million new malware was recorded. The highest number of malware is in Trojan Horse malware while in Adware malware is the most significantly increased malware. Security system devices such as antivirus, firewall, and IDS signature-based are considered to fail to detect malware. This happens because of the very fast spread of computer malware and the increasing number of signatures. Besides signature-based security systems it is difficult to identify new methods, viruses or worms used by attackers. One other alternative in detecting malware is to use honeypot with machine learning. Honeypot can be used as a trap for packages that are suspected while machine learning can detect malware by classifying classes. Decision Tree and Support Vector Machine (SVM) are used as classification algorithms. In this paper, we propose architectural design as a solution to detect malware. We presented the architectural proposal and explained the experimental method to be used.
Finite-Time Fault-Tolerant Control for a Nonlinear SISO System with Actuator Faults

This article focuses on output feedback finite-time adaptive fuzzy fault-tolerant control (FTC) scheme for a single-input single-output (SISO) nonlinear system. By introducing parameter estimation technique and a state observer, the actuator faults and estimate the immeasurable states are compensated, respectively. Combining finite-time theory with backstepping design technique, a finite-time FTC scheme is proposed. The presented control scheme not only guarantees that the closed-loop system is stable, and also ensures that tracking error converge to the zero in finite-time. The simulation example is given to elaborate the effectiveness of the developed control scheme.
Image Recognition Using Manifold Constrained Collaborative Representation

Image recognition is still a challenging task due to the existed illumination and view variations. Manifold learning and representation based classifiers (RCs) are two widely utilized methods to treat the image recognition. The common RCs only emphasize the representation by the training samples globally, while the geometric manifold structure of samples is not fully considered. In this letter, a novel manifold constrained collaborative representation is proposed, which aims to make the representation of query sample be similar with the codes of their nearby-points. Thus, the obtained representations can be more discriminative for recognition. Extensive experiments on several popular databases show that the our proposed method is promising in recognizing various images.
Combined Triangle Quarter-Wavelength Patches and Their Application to High-Gain CP Antenna

A modified triangle quarter-wavelength patch is proposed in this letter, and a compact circularly polarized (CP) antenna is designed by combining two of them. The evolution process of the combined modified triangle quarter-wavelength patches is depicted, and the current distribution is analyzed to illustrate the right-hand circularly polarized radiation generated by the designed antenna. In order to achieve better performance, a subarray with high-gain radiation characteristics consisting of four radiators and a power distribution network is designed and fabricated within a compact size of 0.176 <inline-formula><tex-math notation="LaTeX">$\lambda _0^2$</tex-math></inline-formula> and thickness of 0.026 <inline-formula><tex-math notation="LaTeX">$\lambda _0$</tex-math></inline-formula>. The measured maximum gain is up to 8.4 dBic at 1.935 GHz, and the gain is flat in the operating frequency band with maximum ripple less than 0.9 dB.
Residential Community Load Management Based on Optimal Design of Standalone HRES With Model Predictive Control

Microgrids being an important entity in the distribution system, and to get their full advantages by incorporating maximum distributed generation, standalone hybrid renewable energy systems (HRESs), being environmentally-safe and economically-efficient, are considered as the promising solution to electrify remote areas where the grid power is not available. In this work, a techno-economic investigation with an optimal design of HRES is presented to fulfill the domestic electricity need for a residential area of the Sherani district in the Province of Baluchistan, Pakistan. Nine case studies based on PV/wind/diesel/battery are analyzed based on net present cost (NPC), cost of energy (COE), and emission to decide the feasible solution. HOMER tool is utilized to accomplish modeling and simulation for economic analysis and optimal sizing. Simulation results demonstrated that HRES with PV-wind-battery is the most viable option for the specified area, and the optimal sizing of components are also obtained with $ 28,620 NPC and $ 0.311/kWh COE which shows 81.65 % reduction in cost and 100 % preserving in toxic emission while fulfilling 100 % energy demand with 67.3 % of excess energy. Furthermore, MATLAB/Simulink modeling for the optimally designed system is built for technical analysis while its effectiveness is proved by keeping dc and ac buses voltage constant, safe operating range of battery state of charge (SOC) with active power balance between HRES components, as well as efficient ac voltage quality, regardless of generation disturbances and load fluctuations. The output signal has total harmonic distortion (THD) of 0.30 % as compared to 5.44 % with the conventional control scheme. The novelty lies in the sequential application of both HOMER and MATLAB simulations of the proposed HRES model and validation of the proposition for the studied area; by using and implementing model predictive control (MPC) of a reconfigurable inverter.
Detecting Falls with Recurrent Autoencoders and Body Acceleration Data

According to the world population statistics, the elderly proportion is continuously growing. With this growth, the concerns of this population increase. Falls are a common problem within the elderly population causing considerable health and financial impacts. In order to mitigate such impacts, automatic fall detection systems are more and more employed by the elderly. In this work, we propose a fall detection system based on the body acceleration data, where we investigate the use of recurrent-autoencoders to distinguish between normal activities of daily living and falls. We study different training strategies combined with either the use of the autoencoder as feature extractor or employing its reconstruction error for decision. Experiments on the UniMiB SHARE fall data set provides interesting insights about the designed system.
Haze weather recognition based on multiple features and Random Forest

A single image based haze weather recognition is the fundamental operation of the applications of outdoor computer vision. Currently, the recognition results are remains undesirable and most existing methods are only for the fixed scene. In this paper, we propose multiple features and Random Forest based haze weather classification method for any scenario to improve the detection accuracy. First, through systematically investigation, multiple features are extracted and properly processed. Then, these features are combined into high dimension vectors and the Random Forest is adopted to lean an adaptive classifier for haze recognition. In the experiment, an outdoor image set which contains around 4000 images is collected. Form the experimental results is can be seen that the proposed method achieves 97.4% recognition accuracy of the haze weather on the collected dataset.
Multifrequency Band Synthesis of Modulated Metasurface Antennas

We present and discuss the synthesis of modulated metasurface (MTS) antennas capable of radiating multiple beams at multiple frequencies. The proposed technique is built on a recently developed integral equation formalism. Assuming that the MTS can be homogenized over the whole frequency band, the sheet impedance describing the MTS can be accurately modeled w.r. t. the frequency. This allows us to simultaneously establish multiple systems of equations standing for a design at different frequencies, and possibly different feed points, and to solve the overall problem in the least-squares sense. The method can be used either for improving the antenna bandwidth (if the frequencies of interest are relatively close) or for multiplexing several applications operating at different frequencies. Several synthesis examples are provided and numerically validated.
A New Vehicular Fog Computing Architecture for Cooperative Sensing of Autonomous Driving

The sensing coverage and accuracy of vehicles are vital for autonomous driving. However, the current sensing capability of a single autonomous vehicle is quite limited in the complicated road traffic environment, which leads to many sensing dead zones or frequent misdetection. In this paper, we propose to develop a Vehicular Fog Computing (VFC) architecture to implement cooperative sensing among multiple adjacent vehicles driving in the form of a platoon. Based on our VFC architecture greedy and Support Vector Machine (SVM) algorithms are adopted respectively to enhance the sensing coverage and accuracy in the platoon. Furthermore, the distributed deep learning is processed for trajectory prediction by applying the Light Gated Recurrent Unit (Li-GRU) neural network algorithm. Simulation results based on real-world traffic datasets indicate the sensing coverage and accuracy by the proposed algorithms can be significantly improved with low computational complexity.
A Survey on Cooperative Longitudinal Motion Control of Multiple Connected and Automated Vehicles

Connected and automated vehicles (CAVs) have the potential to address a number of safety, mobility, and sustainability issues of our current transportation systems. Cooperative longitudinal motion control is one of the key CAV technologies that allows vehicles to be driven in a cooperative manner to achieve system-wide benefits. In this paper, we provide a literature survey on the progress accomplished by researchers worldwide regarding cooperative longitudinal motion control systems of multiple CAVs. Specifically, the architecture of various cooperative CAV systems is reviewed to answer how cooperative longitudinal motion control can work with the help of multiple system modules. Next, different operational concepts of cooperative longitudinal motion control applications are reviewed to answer where they can be implemented in today's transportation systems. Different cooperative longitudinal motion control methodologies and their major characteristics are then described to answer what the critical design issues are. This paper concludes by describing an overall landscape of cooperative longitudinal motion control of CAVs, as well as pointing out opportunities and challenges in the future research and experimental implementations.
A Novel Tuning Approach of the H∞ Filter for Longitudinal Tracking of Automated Vehicles

This paper contributes to the challenging field of reliable vehicle tracking as a pivotal part of automated driving. The Kalman filter is an optimal state estimator based on the assumption of an accurate dynamical model and known noise statistic. In order to achieve a fast, robust and efficient vehicle state estimation in the omnipresence of model imperfections and measurement noise, a synergetic combination of the Kalman filter and H∞ filter is proposed, making optimal usage of their advantages. The performance of the filters and their combination is analyzed throughout a sensor data fusion example. Based on the determination of the position and velocity of a vehicle, the improvements, but also the limits of the different approaches are discussed. Additionally, the possibility to detect sensor faults such as time-varying offsets by augmenting the state-space model is explained.
Deep Learning Based In-Loop Filter for Video Coding

In lossy video coding, in-loop filter can improve the image reconstruction by reducing compression artifacts and distortions. Recently, several CNN-based in-loop filtering algorithms are proposed to improve HEVC. However, something deficient is still limiting their capability to further boost the coding efficiency. Specifically, things like prediction residuals and coding-unit boundaries, which is quite relevant to the compression artifacts, were usually neglected; moreover, the loss functions adopted in these methods usually haven’t been regularized properly. To improve these inadequacies, we propose a deep learning based in-loop filter for HEVC to improve its rate-distortion performance. Firstly, prediction residuals, the compression of which is actually where the encoding noise directly results from, are skillfully considered to help filtering. Then, we unify a block-based measurement-reconstruction process and a neighborhood-based filtering process into one end-to-end all-convolutional architecture to catch the relevance between compression noise and the pixel’s related position to the coding block boundaries. Finally, for network training, total-variation model is adopted to derive the loss function by MAP (maximum a posteriori) estimation for better regularization. Experimentally, our proposed in-loop filtering method brings average 6.5% and 4.3% BD-rate reduction under AI and RA configuration respectively.
Unpaired Image-to-Sketch Translation Network for Sketch Synthesis

Image-to-sketch translation is to learn the mapping between an image and a corresponding human drawn sketch. Machine can be trained to mimic the human drawing process using a training set of aligned image-sketch pairs. However, to collect such paired data is quite expensive or even unavailable for many cases since sketches exhibit various level of abstractness and drawing preferences. Hence we present an approach for learning an image-to-sketch translation network via unpaired examples. A translation network, which can translate the representation in image latent space to sketch domain, is trained in unsupervised setting. To prevent the problem of representation shifting in cross-domain translation, a novel cycle+ consistency loss is explored. Experimental results on sketch recognition and sketch-based image retrieval demonstrate the effectiveness of our approach.
Identifying Compatible Syndrome Elements of Traditional Chinese Medicine Based on Three-Way Decisions

Quantitative diagnosis of Traditional Chinese Medicine (TCM) is a hot issue in the study of modernization of TCM. Quantitative identification of syndrome elements is the key problem of quantitative diagnosis. The relationship between syndrome elements of TCM can be summarized as compatibility and incompatibility. Aiming at the identification of syndrome elements compatibility relationship, this paper presents an identification model based on Three-Way Decision. By constructing a double threshold evaluation function, the membership degree between samples and branches can be calculated, and the optimal decision function is finally provided. The method which this paper proposed has been tested in small sample clinical trials. 30 clinical cases have identified and analyzed by the method and a medical team of three traditional Chinese physicians, and the result shows that match degree has over 93 percent between the method and the medical team. Clinical preliminary experiment and result show that the proposed method is feasible and effective. The research provides a new approach for quantitative and artificial intelligence research of TCM