Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Global Irradiance Decomposition by Sky Condition Classification from Measured Spectral Clearness Indices

This manuscript describes a decomposition algorithm for deriving the broadband direct normal (DNI) and diffuse horizontal (DHI) irradiances from one-minute spectral global horizontal irradiance measurements performed by the SolarSIM-G. The algorithm was calibrated and validated at four stations across Canada (Ottawa, Varennes, Egbert, and Devon) and at one station in China (Xianghe), which cumulatively represent a seven-year data set. For the DNI estimation, the root mean square error (RMSE) ranged from 26 W/m2 to 48 W/m2, while the largest mean bias error (MBE) was 4 W/m2. For the DHI estimation, the RMSE ranged from 14 W/m2 to 27 W/m2, while the largest MBE was 3 W/m2. In addition, the integrated DNI and DHI errors for each station were less than 1% and 2%, respectively. The described method is an alternative to other measurement techniques for obtaining all three irradiance components.
Advanced Smoothing for Voxel-based Topologically Optimized 3D Models

Smoothing algorithms are used for mesh refinement and to remove undesired surface. This numerical procedure is recommended and applied on triangulated file coming from 3D scanners or Topology optimization designs based on voxel representation before the optimized structure is manufactured by Additive Manufacturing technologies. In literature, there are several available algorithms, but many of them suffer from mesh shrinkage and do not give to the designer easy procedures to select regions which do not need the application of the smoothing procedure as holes or flat surfaces. For this reason, an improved vertex-based algorithm is presented in this work along with a case study to prove its performances compared with existent algorithms. The algorithm confirms to be efficient and useful. However, user’s intervention is required to guide the procedure to get proper results.
The Influence of Limitation of Sputtered Tin Area Through Mesh Grids on Sn Whiskering

An important question in Sn whisker research concerns the role of lateral Sn diffusion in the film to the whisker root, providing the feedstock for whisker growth. In an elegant tracer experiment on diffusion in Sn films, Woodrow (2006) observed lateral atomic Sn diffusion over distances of hundreds and thousands of microns through Sn grain boundaries during whisker growth. In this work, we examine the effect of limiting the lateral extent of the Sn feedstock available during whiskering by employing extremely thin, micron-sized patterned Sn deposits, and compare to whisker growth from an comparatively large, essentially infinite (1cm x 1cm) thin Sn film. A series of metal, mesh grids of varying sizes were used as masks to define the area of sputtered tin deposited on Si substrates. The defined areas were primarily in the form of squares, with sizes ranging from7. 5µm x 7. 5µm to 153µm x 153µm. Sample areas also included 2 circles, with radii of 1µm and 4µm, and a rectangular strip with dimensions 46µm x 3000µm. A thickness of ~500Å of sputtered Sn was deposited on each specimen. The samples were subsequently incubated for 34 days under three incubation conditions: 23oC (isothermal); 100oC (isothermal); and thermal cycling over -40oCoC. Characteristically, higher whisker densities were observed as the patterned Sn area size increased, e.g., the 153µm patterns had ~ 3X more whiskers compared to the 23µm patterns under the same incubation conditions. There was a discontinuous transition in whisker density at the smallest patterned area sizes, with few whiskers found on the 7. 5µm-23µm dimensioned square patterns and no whiskers found on the 1µm and 4µm patterns. Further, the whiskers obtained from micron-size areas of sputtered Sn are predominantly hillock in nature, in contrast to the high-aspect ratio whiskers normally grown from sputtered Sn on Si using standard sized films (1cm2) under identical growth conditions.
Low loss second-order non-linear optical crosslinked polymers based on a phosphorus-containing maleimide

A series of crossslinked organic and organic/inorganic polymers based on maleimide chemistry have been investigated for second-order non-linear optical (NLO) materials with excellent thermal stability and low optical loss. Two reactive chromophores (maleimide-containing azobenzene dye and alkoxysilane-containing azobenzene dye) were incorporated into a phosphorus-containing maleimide polymer, respectively. The selection of the phosphorus-containing maleimide polymer as the polymeric matrices provides enhanced solubility and thermal stability, and excellent optical quality. Moreover, a full interpenetrating network (IPN) was formed through simultaneous addition reaction of the phosphorus-containing maleimide, and sol-gel process of alkoxysilane dye (ASD). Atomic force microscopy (AFM) results indicate that the inorganic networks are distributed uniformly throughout the polymer matrices on a nano-scale. The silica particle sizes are well under 100 nm. Using in situ contact poling, the r33 coefficients of 2.2–17.0 pm/V have been obtained for the optically clear phosphorus-containing NLO materials. Excellent temporal stability (100°C) and low optical loss (0.99–1.71 dB/cm; 830 nm) were also obtained for these phosphorus-containing materials. Copyright © 2004 John Wiley & Sons, Ltd.
Contactless Palm Vein Authentication Using Deep Learning With Bayesian Optimization

Among various biometric methods, palm vein authentication has taken significant attention because of its uniqueness, stability and non-intrusiveness. In this paper, we propose a palm vein authentication model using convolutional neural networks (CNN), which is the most popular deep learning architecture and Bayesian optimization. First and foremost, region of interest (ROI) of the palm vein is extracted as an image and filtered by Jerman enhancement filter to enhance the gray levels of the vein patterns. The proposed CNN model allows different numbers of convolutional layers to be added to optimize the network structure. Furthermore, the model is trained with training data to extract the highly representative features of the different classes. The training process is performed at every objective function evaluation, each with a different network structure and training options using a Bayesian optimization algorithm to find the optimal network structure and training options in a search space of possible solutions. The CNN model serves as the palm vein template creator or feature extractor for our identification and verification experiments. Receiver operating characteristic (ROC) curve and equal error rate (EER) were plotted for evaluating the performance of the proposed model. Our proposed method attained an average identification accuracy of 99.4 % and average EER of 0.0683%, which outperforms state-of-the-art palm vein authentication approaches.
Internet of Things in the Field of Smart Farming: Benefits and Challenges

The increase in population at the global level results in an increase in food demand and a shift towards smart agricultural practices. This is supported by the reduced availability of agricultural land, reduced natural resources, and difficulty in predicting climate, making food security a significant problem in several countries. Therefore, the technology trend of using the Internet of Things (IoT) with case studies in agriculture is a solution to increase the efficiency of the crop cultivation process and increase agricultural productivity. Currently, the use of IoT technology in the agricultural sector uses Wireless Sensor Network (WSN) technology and Data Analytics for data processing. This IoT platform combines several other technologies, such as WSN, cloud computing, middleware architecture, end-user application, and radio frequency identification (RFID). There are several benefits and challenges regarding the use of IoT technology for the agricultural sector. This paper will discuss the IoT platform and architecture and enable the application of the concept of smart agriculture.
Modelling and Performance analysis of InGaN/GaN based Multiple Quantum Well solar cells

In this work, an analytical model is proposed as a convenient tool for the performance analysis of multiple quantum well (MQW) solar cells (MQWSC). Significantly, the American Society for Testing and Materials (ASTM) standards data sheets are utilized for obtaining photon flux instead of Plank's blackbody radiation law. Further, the performance parameters are evaluated using the obtained photon flux density. The MQWSC is analyzed based on the composition of indium in InGaN material of the intrinsic region (active region), the operating temperature and the number of MQWs used in MQWSC.
Improving power grid transient stability and transfer capability using HVDC emergency power controls

In the future, with high levels of renewable energy sources penetration, the transmission corridors will have to be flexible to operate under faster transients and variable power flows. Fast power ramping and overloading capabilities of HVDC system can be utilized to ensure transient stability of the interconnected power grid. In this paper, HVDC auxiliary controllers have been studied that can provide the power grid with additional synchronizing torques through fast DC power changes in response to arising system conditions. The effectiveness of the auxiliary controllers in improving transient stability is demonstrated by performing dynamic simulations on the detailed model of the Western Interconnection system (WECC).
Discovering Action Regions for Solving the Bin Packing Problem through Hyper-heuristics

Hyper-heuristics represent an innovative method for solving hard combinatorial problems such as the Bin Packing Problem. In this work, we propose a solution model that incorporates insights from unsupervised learning to produce solvers that base their decisions on maximizing a reward. The solution model takes the form of a hyper-heuristic. As the search progresses, this strategy chooses among different heuristics, adapting the solution process to the current problem state under exploration. The proposed model relies on the k-means clustering algorithm to identify the centroids of what we define as “action regions” (regions where we can recommend one particular heuristic). To recommend a heuristic in such action regions, we use a simple yet useful reward-based approach that analyzes the performance of individual heuristics. Then, the hyper-heuristic (a collection of action regions) decides which heuristic is the most suitable one to apply given one specific problem state. The experimental setup was carried out on a total of 580 bin packing instances with promising results.
Synthesis of New Resorcinarenes Under Alkaline Conditions

The preparation of functionalized resorcinarenes is described. Thus, 2-nitroresorcinol (= 2-nitrobenzene-1,2-diol), 2-acetylresorcinol (= 1-(2,6-dihydroxyphenyl)ethanone), and 2,6-dihydroxybenzoic acid were treated with formaldehyde in alkaline medium to give the corresponding resorcinarenes 1–3 (Scheme 1). This method is also applicable for resorcinol (= benzene-1,3-diol) itself, but the yields are poorer. In this case, the molecule formed is the simplest resorcinarene 4 on which a number of substituents can be inserted between the two OH groups. Thus, bromation of 4 yields 5 (Scheme 2). Some properties and conformations of these new products are discussed, and the X-ray crystal structures of the nitro and bromo compounds 1 and 5, respectively, are presented.
InGaAsP Radiation Hardness and Post Irradiation Regeneration Behavior

As an answer to increased demand for more radiation tolerant solar cells for space applications, we investigate the dependence of radiation hardness of III-V compounds on composition of InGaAsP. Four solar cells with different composition were grown on InP and metamorphically on GaAs and irradiated with 1 MeV electrons. A simulation-based analysis was used to determine the material specific irradiation damage. In addition, the regeneration ability is investigated under typical operating conditions (60 °C and AM0 illumination) and under the ECSS standard. While a clear decrease in radiation damage compared to GaAs is observed in all samples, the effect is stronger with increasing InP-fraction. The irradiation induced defect recombination coefficient can be described with a linear function of InP-fraction.
A Spiking Neural Network Based Auto-encoder for Anomaly Detection in Streaming Data

Anomaly Detection (AD) is useful for a range of applications including cyber security, health analytics, robotics, defense and big data. Automating the detection of anomalies is necessary to deal with large volumes of data and to satisfy real time processing constraints. Current Machine Learning (ML) methods have had some success in the automated detection of anomalies, but no ideal ML solutions have been found for any domain. Spiking Neural Networks (SNNs), an emerging ML technique, have the potential to do AD well, especially for Edge applications where it needs to be low power, readily adaptable, autonomous and reliable. Here we investigate SNNs doing anomaly detection on streams of text. We show that SNNs are well suited for detecting anomalous character sequences, that they can learn rapidly, and that there are many optimizations to the SNN architecture and training that can improve AD performance.
Fast-Chirp FDMA MIMO Radar System Using Range-Division Multiple-Access and Doppler-Division Multiple-Access

Frequency-division multiple-access (FDMA) in frequency-modulated continuous-wave (FMCW) radar comprises range-division multiple-access (RDMA) and Doppler-division multiple-access (DDMA). In this article, we present a method using RDMA and DDMA to realize multiple-input multiple-output (MIMO) functionality for FMCW radars. We use single-sideband modulation and quadrature phase shift keying (QPSK) modulation to implement RDMA and DDMA, respectively. These two approaches enable the simultaneous transmission of fast chirp waveforms from multiple transmitting (TX) antennas. Compared to existing works, our proposed system shows the following advantages. First, in RDMA FMCW radar, we apply overlapping binary masking to improve the unambiguous range. Second, in DDMA FMCW radar, we introduce a phase compensation method to reduce the image signals from the nonideality of QPSK symbols. Finally, we leverage these advantages of combining both RDMA and DDMA to balance the performance for FMCW MIMO radar in angular resolution, unambiguous range, and unambiguous velocity. In particular, the FMCW radar system combining RDMA and DDMA allows us to increase the numbers of TX antennas in MIMO applications. We verify our proposed radar system with measurement data on a prototype using three TX and four receiving antennas.
Nonlinear Analysis of Oscillator Mutual Injection Locking Through Inductor Coupling

This work presents an in-depth investigation of the nonlinear behavior of two mutually injection-locked oscillators through inductor coupling. An analytical formulation, solved through an innovative procedure, facilitates the understanding of the qualitative transformations in the system solutions when increasing the coupling factor. The analysis demonstrates that, in a manner similar to the unilaterally injection-locked oscillators, families of disconnected/connected curves are obtained when increasing this factor, although the patterns, associated with distinct operation modes, are more complex. Then, an accurate numerical method to predict the behavior of coupled transistor-based oscillators is presented, based on nonlinear admittance models of the individual oscillators. Mathematical conditions are derived to solve the coupled system through a two-level contour-intersection technique. In this way, all the solutions coexisting for a given set of element and parameter values are calculated simultaneously, in an exhaustive manner. The cases of two coupled oscillators at the fundamental frequency and at 1:3 frequency ratio are considered. Possible applications include the oscillator phase-noise reduction and the implementation of sensors using the phase shift between the two oscillator elements.
All-in-one “HairNet”: A Deep Neural Model for Joint Hair Segmentation and Characterization

The hair appearance is among the most valuable soft biometric traits when performing human recognition at-a-distance. Even in degraded data, the hair's appearance is instinctively used by humans to distinguish between individuals. In this paper we propose a multi-task deep neural model capable of segmenting the hair region, while also inferring the hair color, shape and style, all from in-the-wild images. Our main contributions are two-fold: 1) the design of an all-in-one neural network, based on depthwise separable convolutions to extract the features; and 2) the use convolutional feature masking layer as an attention mechanism that enforces the analysis only within the ‘hair’ regions. In a conceptual perspective, the strength of our model is that the segmentation mask is used by the other tasks to perceive - at feature-map level - only the regions relevant to the attribute characterization task. This paradigm allows the network to analyze features from nonrectangular areas of the input data, which is particularly important, considering the irregularity of hair regions. Our experiments showed that the proposed approach reaches a hair segmentation performance comparable to the state-of-the-art, having as main advantage the fact of performing multiple levels of analysis in a single-shot paradigm.
Engineering Resilience: Predicting The Change Impact on Performance and Availability of Reconfigurable Systems

Modern distributed systems are supposed to be resilience and continue to operate according to agreed-on Quality of Service (QoS) despite the failure of few services or variations in workload. Real-world incidents show that systems still undergo unacceptable QoS degradations or significant service outages. The main reasons are updates of the system or infrastructural services, and subsequently, faulty recovery logic. Frequent updates and faulty recovery logic result in a correlated set of failure modes that impact the system’s QoS. Software architects need assurance that the system satisfies agreed-on QoS despite updates in the system or infrastructural services. In this research, we propose systematic identification of the risk of a correlated set of failure modes due to updates that cause unacceptable performance degradation or service outage. According to the Architecture Tradeoff Analysis Method (ATAM), we propose to formulate collected risks into a scenario structure for a precise resilience requirement characterization. Furthermore, we propose model-based prediction methods for scenario-based resilience evaluation of the system. Therefore, the software architect has a measurement-based evaluation of system resilience and can incorporate the evaluation result for further system resilience improvement or specifying a precise service level agreement.
On the Performance of the Two-Diode Model for Photovoltaic Cells Under Indoor Artificial Lighting

Models of photovoltaic devices are an important tool for the estimation of their I-V characteristics. These characteristics, in turn, can be used to optimize production, compare devices, or predict the output power under different illumination conditions. Equivalent circuit models are the most common model types utilized. Although these models and the estimation of their parameters are thoroughly investigated, little is known about their performance under indoor illumination conditions. This, however, is essential for applications where photovoltaic devices are used indoors, such as for PV-powered sensors, wearables or Internet of Things devices. In this paper, a comprehensive and quantitative study of parameter estimation methods for the two-diode model is conducted, focusing particularly on the performance at indoor illumination levels. We reviewed and implemented a set of six common parameter estimation methods, and evaluate the performance of the estimated parameters on a typical photovoltaic module utilized in indoor scenarios. The results of this investigation demonstrate that there is a large performance variation between different parameter estimation methods, and that many methods have difficulties to estimate accurate parameters at low illumination conditions. Moreover, the majority of methods result in physically infeasible parameters, at least under some of the evaluated conditions. When applying physically motivated parameter scaling methods to these parameters, large estimation errors are observed, which limits the model’s applicability for power estimation purposes.
Averaging Methods using Dynamic Time Warping for Time Series Classification

Averaging is an important step in time series classi-fication or clustering, to create representative sequences for each category of data. A global averaging method for Dynamic Time Warping (DTW) based time series analysis is DTW Barycenter Averaging (DBA). In this paper, we propose a recursive tree based implementation of DBA, for faster computation of an average sequence, using the divide-and-conquer strategy. We also propose to automate the termination of DBA using a data-driven approach. The performance of the proposed methods is evaluated using accuracy, precision and recall as performance metrics, in a simple DTW-distance based classification method on ten standard time series datasets. Experimental results demonstrate that the proposed approaches are significantly faster than DBA, while achieving similar performance.
Inverse Biometrics: Reconstructing Grayscale Finger Vein Images from Binary Features

In this work, we investigate the possibility of generating a grayscale image of the finger vein from its binary template. This exercise would allow us to determine the invertibility of finger vein templates, and this has implications in biometric security and privacy. While such an analysis has been undertaken in the context of face, fingerprint and iris templates, this is the first work involving the finger vein biometric trait. The transformation from binary features to a grayscale image is accomplished using a Pix2Pix Convolutional Neural Network (CNN). The reversibility of 6 different types of binary features is evaluated using this CNN. Further, a number of experiments are conducted using 7 distinct finger vein datasets. Results indicate that (a) it is possible to reconstruct finger vein images from their binary templates; (b) the reconstructed images can be used for biometric recognition purposes; (c) the CNN trained on one dataset can be successfully used for reconstructing images in a different dataset (cross-dataset reconstruction); and (d) the images reconstructed from one set of features can be successfully used to extract a different set of features for biometric recognition (cross-feature-set generalization).