Miracle-dz/UniFAR-FTUs huggingface dataset:
Demonstration of Adaptive Optical Pre-Compensation in Horizontal Tests

Though long anticipated, adaptive optical (AO) pre-compensation is now experiencing a surge in interest thanks to technological advances and the identification of a real-world application with the advent of satellite laser communication as a viable alternative to radio-frequency. Here, we present an overview of our activities to date in pre-compensation, from the development of our AO system in laboratory experiments using artificial turbulence [1], to the integration into a transportable "AO-box" and testing in first proof-of-principle experiments over horizontal links using real turbulence [2] to the verification in horizontal tests of pre-compensation under large point-ahead-angles beyond the estimated isoplanatic range [3]. We describe the concept and realization of our experimental setup which provides simultaneous bidirectional correction of the incoming beam (post-compensation) and outgoing beam (pre-compensation). We then detail the transfer of this setup into two optical terminals representing a "ground terminal" and "satellite terminal". We describe the results of the horizontal measurement campaigns which have been undertaken to date in which a 7.1 dB increase in the received intensity was demonstrated under a small PAA over 0.5 km and in which a decline in the received laser power over increasingly large point-ahead-angle was demonstrated over a 1.0 km test range. We conclude with the current outlook for pre-compensation technology and indicate future planned experiments.
Experimental Comparison of Velocity Observers: A Scissored Pair Control Moment Gyroscope Case Study

We consider scissored pair gyroscopes as an auxiliary stabilization system for legged robots. A standard state-feedback control can stabilize the system if all states are available; thus, the velocity estimation becomes the key element for the stabilization. To this end, we implement and experimentally compare a model-free linear differentiator, a model-based full-state linear observer with time-varying gains, and a model-based homogeneous nonlinear differentiator. Moreover, we also show that the considered system cannot be partially linearized via a change of coordinates, and thus is not suitable for a recently reported class of nonlinear observers. The proposed designs are tested on an experimental scissored pair control moment gyroscope setup constructed for a walking robot stabilization.
Estimating energy consumption and conservation measures for ESPOL Campus main building model using EnergyPlus

This paper presents the development of an energy model of the ESPOL campus main building, in order to estimate, diagnose, and evaluate the electrical energy consumption and also, to propose energy conservation measures (ECMs). This model used simulation tools, such as OpenStudio and EnergyPlus. The final model provides results as the annual energy performance of the building by end-use, the annual utility bill, and others. These results allowed to model some scenarios of ECMs of the building, mainly, aimed to improve lighting and air-conditioning systems. Finally, the greenhouse gas (GHG) emissions generated by each scenario were calculated and analyzed.
Three-Terminal Bipolar Junction Bottom Cell as Simple as PERC: Towards Lean Tandem Cell Processing

Silicon-based tandem cells are a potential upgrade for the dominating PERC technology and can greatly increase the conversion efficiency. A novel approach, the three-terminal (3T) tandem using interdigitated back-contact (IBC) cells, was shown to combine the advantages of four- and two-terminal tandem cells at the expense of a more complex bottom cell fabrication process. In this paper, we propose a simplified and lean PERC-like 3T bipolar junction bottom cell featuring two minority-carrier selective contacts and a single majority-carrier selective contact, and thus resembling a bipolar junction transistor architecture. We present the lean PERC-like fabrication process and explain the working principle by using current-voltage measurements of illuminated 3T IBC cells.
Reproducing the “Framing” by a Sequential Stress Test

The "Framing" (local discoloration along cell edges) was induced by a simple sequential accelerated stress test (consisting of hygrothermal- and UV-stressors) applied to the PV modules with high OTR (oxygen transmission rate) backsheet, irrespective of the inclusion of UV-absorber in poly(ethylene-co-vinyl acetate) (EVA) encapsulant. UV-fluorescence imaging of the PV modules suggests that the spatially-inhomogeneous degradation of EVA material under UV-irradiating conditions is correlated to this "Framing" indicating an underlying common mechanism. These findings would contribute to the development of test procedures to broadly mimic the actual failures observed in fielded PV.
Diagnosis of Induction Motor Faults Using Frequency Occurrence Image Plots—A Deep Learning Approach

Accurate diagnosis of induction motor faults is important for reliable and safe operation of industrial processes. Majority of the faults which occur in induction motors are mainly diagnosed using motor current signature analysis. However, the accuracy of fault detection depends on selection of suitable features from motor current, the failure of which may result in incorrect interpretation. Considering the aforesaid fact, this paper presents an image processing aided deep learning framework for reliable diagnosis of induction motor faults, which eliminates the need of separate feature extraction stage. To this end, the motor current signals under different types of fault conditions were procured and were subsequently processed into frequency occurrence plots. The frequency occurrence image plots for different fault scenarios were finally used as inputs to a deep convolution neural network for the purpose of classification. Transfer learning technique was adopted to reduce the computation time of Convolution Neural Network and classification of motor faults was done at five different loading conditions. Four types of classification tasks have been addressed here and comprehensive analysis was done using a variety of CNN architectures. It has been observed that the proposed method returns a highest mean classification accuracy of 96.67% in segregating different types of faults which can be implemented in real-life for condition monitoring of induction motors.
EFFECT OF CULTIVARS, PLANT SPACING AND AL-GAMIX ON GROWTH AND GREEN YIELD OF BROAD BEAN (Vicia faba L.)

The experiment was amid to tested the effects of three Cultivars (Reina, Giza & Local), two plant spacing (15&20cm) and Al-gamix at concentration (0& 2 ml.L -1 ) on growth, green yield and green seed yield of Broad bean (Vicia faba.). Results showed that the cultivars had affected on vegetative, green yield and seed yield characters; The Reina cultivar had significant increase in (No. of branches , Pod Yield g. plant -1 , Total Yield ton. donum -1 , No. of seed per pod, no. of seed. Plant -1 , seed weight g, seed yield.plant -1 , Total seed yield ton. Donum -1 and weight of 100 seed g). While Giza cultivar had a significant increase in (plant high, pod length cm, pod weight g). The local cultivar had a significant increase in (No. of pods per plant and No, of seed per plant)There were significant effect of plant spacing on vegetative and yield characters, 15 cm spacing increased (plant high cm, No. of branches, pod weight g, plant yield g. plant -1 , total yield ton.donum-1). But 20cm spacing significantly increased (No. of pods per plant, pod length cm, No. of seed per pod, seed weight g, weight of 100 seed g), While treating of broad bean plant with Al-gamix especially (2ml.L -1 ) led to significant increases in all parameters like vegetative, green yield and green seed yield characters. KEY WORD:Faba bean, cultivars, plant spacing and Al-gamix
Design and Simulation of Novel Perovskite/Mg2Si Based Monolithic Tandem Solar Cell With 25.5% Conversion Efficiency

High thermalization losses associated with the single junction based solar cells demand the investigation of multilayer junction solar cell. Multilayer junction solar cell which comprises of top cell (high bandgap)/bottom cell (low bandgap) efficiently utilizes the solar spectrum. Perovskite/Si based tandem solar cells often require thick (>100 µm) and high quality (large lifetime) Si wafer-based bottom subcell, which eventually increases overall module cost. In this work, relatively a much thinner layer of magnesium silicide (Mg2Si) of thickness 2µm is used to propose a novel perovskite/Mg2Si based tandem solar cell. Proposed device exhibits open circuit voltage (VOC) = 1.67 V, short circuit current density (JSC) = 19.9 mA. cm-2, fill factor (FF) = 76 % and power conversion efficiency (PCE) of 25.5%. The results suggest the Mg2Si as an optimum choice with the band gap of 0.77 eV for the bottom cell of the tandem solar cell.
The Computer Nose Best

The nose is the most central feature on the face which is known to exhibit both gender and ethnic differences. It is a robust feature, invariant to expression and known to contain depth information. In this paper we address the topic of binary ethnicity classificiation from images of the nose, using a novel dataset of South Asian, Pakistani images. To the best of our knowledge, we are one of the first to attempt demographic (ethnicity) based identification based solely on information from the nose.A two-category (Pakistani vs Non-Pakistani) task was used in combination with Deep learning (ResNet) based and VGG-based pre-trained models. A series of experiments were conducted using ResNet-50, ResNet-101, ResNet-152, VGG-Face, VGG-16 and VGG-19, for feature extraction and a Linear Support Vector Machine for classification. The experimental results demonstrate ResNet-50 achieves the highest performance accuracy of 94.1%. In comparison, the highest score for the VGG-based models (VGG-16) was 90.8%. These results demonstrate that information from the nose is sufficient for deep learning models to achieve >90% accuracy on judgements of ethnicity.
Experimental Efficiency and Thermal Parameters Evaluation in Full-SiC Quasi-Z-Source Inverter

The paper presents results of the experimental investigation of full-SiC quasi-z-source (QZS) inverter. The study is devoted to a single-phase dc-ac solution which comprises SiC diode and SiC MOSFETs and demonstrates 97.1% efficiency. The experimental results that confirm efficient performance of the converter were presented and discussed. Due to its high efficiency and safe operation under high semiconductor devices temperatures the developed solution is feasible for PV applications. CEC efficiency characteristic was measured. The temperature behavior of semiconductors and heatsinks was investigated and detailed results were presented. The study aimed 1.8 kW as nominal power, however inverter was verified for secure operation and demonstrated acceptable efficiency for up to +33% from nominal power.
Internet of Farming: Channel Characterization of Organic Communication Channels

Internet of Farming (IoF) is the application of Internet of Things (IoT) to agriculture use cases. Organic channels are any hydrocarbon based media. When organic channels are used to communicate data, they are termed Organic Communication Channels (OCCs). The accomplishments of Human Body Communication (HBC) in Body Area Networks (BANs) suggests OCCs’ great potential when applied to cyber-physical systems. For example, soil-based and vegetation-based OCCs can be integrated into IoF scenarios. However other OCCs are less studied. This paper characterizes vegetation-based and soil-based OCCs through analysis of channel measurements and derivation of path loss equations. Presented results suggest log-distance path median path loss models hold for herbaceous vegetation-based channels. Furthermore, the results suggest vegetation-based channels were more frequency-selective than the soil-based channels examined.
Development of Linear Regression Models to Estimate the Margin of Stability Based on Spatio-Temporal Gait Parameters

Spatio-temporal gait parameters such as step width, cadence, stride length, and walking speed contribute to dynamic stability. Several studies have investigated the role of gait parameters in maintaining balance. However, in these studies, subjects were instructed to alter their gait. This intentional alteration has the potential to create error in the results, as subjects are not walking with a natural and comfortable gait. In consideration of this, the sample chosen in this study consisted of patients who had undergone a knee replacement. Such individuals naturally have gait parameters that differ from normal subjects. The primary objective of this study was to develop regression models that predict and measure gait stability in both the anterior-posterior and medio-lateral directions based on gait parameters. The maximum deviation of the extrapolated center of mass from the border of the base of support was the measure of gait stability. A forward stepwise multiple regression analysis was conducted to develop both models. In testing the goodness of fit of models, the values of coefficient of determination, standard error of estimates, and root mean square error were calculated. Both models showed sufficient values of goodness of fit. To improve walking stability and minimize falls, fall-prone people should walk with an adequate base-of-support area, and with lower cadence and speed. The results of this study contribute to an understanding of gait patterns and their relationship to walking stability and to how gait strategies might be taught in physical therapy programs to minimize the risk of falls.
Machine Learning and End-to-End Deep Learning for the Detection of Chronic Heart Failure From Heart Sounds

Chronic heart failure (CHF) affects over 26 million of people worldwide, and its incidence is increasing by 2% annually. Despite the significant burden that CHF poses and despite the ubiquity of sensors in our lives, methods for automatically detecting CHF are surprisingly scarce, even in the research community. We present a method for CHF detection based on heart sounds. The method combines classic Machine-Learning (ML) and end-to-end Deep Learning (DL). The classic ML learns from expert features, and the DL learns from a spectro-temporal representation of the signal. The method was evaluated on recordings from 947 subjects from six publicly available datasets and one CHF dataset that was collected for this study. Using the same evaluation method as a recent PhysoNet challenge, the proposed method achieved a score of 89.3, which is 9.1 higher than the challenge’s baseline method. The method’s aggregated accuracy is 92.9% (error of 7.1%); while the experimental results are not directly comparable, this error rate is relatively close to the percentage of recordings labeled as “unknown” by experts (9.7%). Finally, we identified 15 expert features that are useful for building ML models to differentiate between CHF phases (i.e., in the decompensated phase during hospitalization and in the recompensated phase) with an accuracy of 93.2%. The proposed method shows promising results both for the distinction of recordings between healthy subjects and patients and for the detection of different CHF phases. This may lead to the easier identification of new CHF patients and the development of home-based CHF monitors for avoiding hospitalizations.
Analysis of Server-Based Electronic Money Acceptance Using Partial Least Square Method

Electronic money based on servers, known as e-wallet, is a non-cash payment device. The number and amount of electronic money distributed in Indonesia has increased since the Gerakan Nasional Non Tunai (GNNT) launched by Bank Indonesia Governor on August 14, 2014. This study discussed factors influencing consumer behavioral intent to use in Indonesia's e-wallet adoption of Technology Acceptance Model (TAM), which is perceived as useful and user-friendly, as well as other variables of trust, enjoyment perception, feature, and m-payment experience. Data collection method used was quantitative method. Data were collected with questionnaires through google form provided that respondents have and have used GoPay, OVO and or DANA. The number of respondents were 343. Using the SmartPLS application, data were analyzed using the Structural Equation Modeling (SEM) method using Partial Least Square (PLS). The results showed that perceived enjoyment, m-payment experience and trust had a positive and significant influence on the behavioral intention to use in using e-wallet. Perceived ease of use also influenced trust and usefulness positively and significantly. Whereas the perceived ease of use and perceived usefulness had a positive but insignificant influence on behavioral intention to use, feature had a negative and insignificant influence on the behavioral intention to use in using the e-wallet.
Image Denoising Using Graph-Based Frequency Domain Low-pass Filtering

Image denoising is always a very challenging task in image processing, which requires appropriate signal priori to regulate the problem. In recent years, the development of spectrum theory and graph signal has given us a new direction. By taking the image as a graph signal, we can construct a suitable underlying image to make the image relatively smooth, and combine the signal priori to image denoising. Based on this idea, and combined with graph Laplacian matrix，a method of image low-pass filtering in frequency domain is proposed in this paper. Experiments show that the proposed method is effective in image denoising, and superior to the traditional Wiener filtering and Gaussian filtering.
Estimating Video Popularity From Past Request Arrival Times in a VoD System

Efficient provision of Video-on-Demand (VoD) services requires that popular videos are stored in a cache close to users. Video popularity (defined by requested count) prediction is, therefore, important for optimal choice of videos to be cached. The popularity of a video depends on many factors and, as a result, changes dynamically with time. Accurate video popularity estimation that can promptly respond to the variations in video popularity then becomes crucial. In this paper, we analyze a method, called Minimal Inverted Pyramid Distance (MIPD), to estimate a video popularity measure called the Inverted Pyramid Distance (IPD). MIPD requires choice of a parameter, $k$ , representing the number of past requests from each video used to calculate its IPD. We derive, analytically, expressions to determine an optimal value for $k$ , given the requirement on ranking a certain number of videos with specified confidence. In order to assess the prediction efficiency of MIPD, we have compared it by simulations against four other prediction methods: Least Recency Used (LRU), Least Frequency Used (LFU), Least Recently/Frequently Used (LRFU), and Exponential Weighted Moving Average (EWMA). Lacking real data, we have, based on an extensive literature review of real-life VoD system, designed a model of VoD system to provide a realistic simulation of videos with different patterns of popularity variation, using the Zipf (heavy-tailed) distribution of popularity and a non-homogeneous Poisson process for requests. From a large number of simulations, we conclude that the performance of MIPD is, in general, superior to all of the other four methods.
Performance Degradation in aSi/cSi Heterojunction Solar Cells by Glassy Dynamics

aSi/cSi heterojunction solar cells hold the efficiency world record around 27%, yet their market pentration is delayed. One concern is the presence of an amorphous Si layer that some suspect may speed up the degradation of their performance. To address this concern, we developed a structural simulation platform that is capable of capturing extremely slow degradation processes in aSi. One of our main results is the implementation of the nudged elastic band method to capture the defect generation time scales, which we showed is capable of capturing a time scale distribution that stretches over 8 orders of magnitude.
Dynamic economic dispatch of microgrid system using hybrid intelligence techniques

This paper performs optimal sizing of the distributed energy resources (DERs) of an islanded microgrid test system to minimize the generation cost. Two fossil fueled generators, two wind turbines and three fuel cells are the DERs involved to be sized for the minimization of generation cost. Utilization percentage shows the maximum usage of DERs in obtaining the minimal value of objective function. Grey Wolf Optimizer (GWO), its modified version (MGWO) and MGWO amalgamated with particle swarm optimization (PSO), sine cosine algorithm (SCA) and crow search algorithm (CSA) to perform the optimization. A comparative study is then made to prove the efficiency of the proposed hybrid MGWOCSA among the four other optimizers used and also among those available in literature.
Wider Stability Region for Discrete-time Systems with Structured Saturation Nonlinearities and Time-varying Delay

This paper utilizes the improved Wirtinger-based inequality method by introducing free matrices in Wirtinger-based inequality. In addition to time-varying delays, we consider the system in which the structural properties of multiple saturation nonlinearities are taken into account. With the use of an augmented Lyapunov-Krasovskii functional and a more relaxed method, delay-dependent stability criterion is established. Numerical example shows the efficacy of the presented criterion.
Leakage Current Degradation in SiC Junction Barrier Schottky Diodes under Heavy Ion Microbeam

Leakage current degradation of SiC junction barrier Schottky diodes were studied under heavy ion microbeam. Leakage current increased linearly with fluence and was positively related to bias voltage. It was proposed that leakage current occurred as a result of the accumulation of multiple leakage paths. The explanation that the increased leakage current was related to leakage paths formed by damage in the mechanism of "micro-SEB" was verified.
User’s Intentions in Health Information Exchange in Social Media

The activity of exchanging information on social media has become widespread and more open in Indonesia with the increasing number of Internet users. This study analyzes the factors that influence users to exchange information related to health on social media, and users’ intention to implement or use information that has been obtained. This study used quantitative methods and an online questionnaire for data collection. There were 1,456 respondents, who had searched for and shared health information on social media. The collected data were analyzed by Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS 3.0 tools. This study found that the outcome of the expectation of social relationship did not affect health information usage intention, and human-to-human interaction did not affect health information exchange.
Discrete Phase Shift Design for Practical Large Intelligent Surface Communication

In this paper, we investigate a downlink channel of a large intelligent surface (LIS) communication system. The LIS is equipped with B-bit discrete phase shifts while base station (BS) exploits low-resolution digital-to-analog converters (DACs). Without the knowledge of channel state information (CSI) related to the LIS, we propose a practical phase shift design method, whose computational complexity increases by 2B independent of the number of reflecting elements N. A tight lower bound for the asymptotic rate of the user is obtained in closed form. As N increases, we observe that the asymptotic rate becomes saturated because both the received signal power and the DAC quantization noise increase. Compared to the optimal continuous phase shift design with perfect CSI, our proposed method asymptotically approaches the ideal benchmark performance for moderate to high values of B. The derived results and observations are verified by simulation results.