Miracle-dz/UniFAR-FTUs huggingface dataset:
Reducing Sensors in Mental Imagery Based Cognitive Task for Brain Computer Interface

The performance of mental imagery based brain-computer-interfaces (BCI) can be enhanced by improving Electroencephalography (EEG) signal classification. It is known that the optimal sensors/electrodes for mental imagery applications are the C3, Cz and C4 that are not present in low cost EEG acquisition devices like the Emotiv EPOC+ headset. Hence in this paper a framework is proposed to classify mental imagery tasks using alternative and reduced number of sensors available in Emotiv EPOC+ headset. In this paper four features are extracted from EEG signals which are Band Power (BP), Approximate Entropy (ApEn), statistical features, and wavelet-based features. For classification, Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) are used. 100% cross validation accuracy is achieved by using BP and ApEn features and KNN Classifier with all the 14 electrodes. It is further observed that most of the information necessary for the mental imagery classification is present at the FC5, FC6, P7, P8, AF3 and AF4 electrodes. By classifying the Band Power and ApEn features from the electrodes mentioned above and using the KNN classifier, an average cross validation accuracy of 99.75% is achieved. If the same features from the FC5, FC6, AF3 and AF4 electrodes are classified using KNN, an average cross validation accuracy of 98.55% can be achieved. Hence reduced number of sensors can be used successfully for motor imagery classification. Also based on the model selected, it can be concluded that out of the four mental imagery tasks (LEFT, RIGHT, PUSH and PULL), the PULL mental imagery task is the hardest to be classified, with a classification error of 2.4%.
Performance Analysis of BDRF based Reflectors, MFOT and Fixed Tilt PV

Solar energy is one of the most promising renewable energy that has accelerated and globally about 630 GW has been installed so far. And it projected to reach 8500 GW by 2050 as per IRENA. Solar photovoltaic panels have typically 15-20% efficiency in capturing the incident sunlight. And as the market is maturing, new avenues of research are focused on improving solar PV efficiency at cell level, module level and by adopting solar tracking. Solar photovoltaic panels (PV) are typically mounted at an optimum tilt angle according to the latitude and generally known as Fixed tit deployment. Solar tracking is an alternative methodology to maximize energy captured by a PV panel and is achieved by tracking the sun. This can give an efficiency gain of up to 25% w.r. t fixed tilt but at an additional capital cost of 20%. At Renkube we have developed an innovative PV panel design called MFOT (Motion Free Optical Tracking) that can increase the energy generation of module by the same quantum of 20% as physical trackers but at an additional capital cost of 5-10%. The MFOT panel uses prismatic structures that work on the principal of Total Internal Reflection to increase sunlight incident on solar cell. BDRF (Bi-Directional Reflectance Function) based mirrors is another alternative technology built at Michigan Technology University to boost the illumination on solar cells using mirrors and improve energy generation by up to 20% more. In this paper, we compare the pros and cons of BDRF and MFOT technologies and take a deep dive on various factors such as annual performance gain, geographic constraints, and highlight the seasonal improvements of these systems.
Global Adaptive Input Normalization for Short-Term Electric Load Forecasting

Recent advances in Deep Learning (DL) provided immensely powerful tools for various time series forecasting tasks. However, DL models are often quite sensitive to the used input normalization method, requiring extensive experimentation and fine-tuning to identify and apply the most appropriate method for different tasks. Even though trainable adaptive normalization methods have been recently proposed to overcome this to a certain extend, these methods also tend to remove useful information from the data, ignoring the global statistics of the input time series. To overcome this limitation, in this paper we propose a trainable adaptive normalization approach that is capable of both maintaining important mode information, since global statistics are employed for the normalization, as well as taking into account the current behavior of the time series and adjusting the normalization scheme to this. The effectiveness of the proposed method over baseline and state-of the-art normalization methods is demonstrated using extensive experiments on two different electric load forecasting datasets.
Brain Computer Interface for Neurorehabilitation with Kinesthetic Feedback

This study aims to record and translate the process of motor imagery to enable orthotic extension and flexion of the finger. This study probes the feasibility of developing a Brain Computer Interface system by prioritizing - Data Acquisition, Deep Learning, and End Effector of the system. The optimal channels to record MI data were deduced using Recursive Feature Elimination from a sixty four channel online dataset. Eight electrode channels of the OpenBCI Cyton kit were used, covering the sensorimotor cortex region of five subjects to record electroencephalographic data by following a standardized EEG acquisition protocol. Classification of tasks was carried out on a custom deep learning architecture using a convolutional layer and LSTM. The results were passed to an orthotic brace that provided a kinesthetic feedback mechanism to improve grip strength and support the neurorehabilitation of its user.
A Rule and Graph-Based Approach for Targeted Identity Resolution on Policing Data

In criminal records, intentional manipulation of data is prevalent to create ambiguous identity and mislead authorities. Registering data electronically can result in misspelled data, variations in naming order, case sensitive data and inconsistencies in abbreviations and terminology. Therefore, trying to obtain the true identity (or identities) of a suspect can be a challenge for law enforcement agencies. We have developed a targeted approach to identity resolution which uses a rule-based scoring system on physical and official identity attributes and a graph-based analysis on social identity attributes to interrogate policing data and resolve whether a specific target is using multiple identities. The approach has been tested on an anonymized policing dataset, used in the SPIRIT project, funded by the European Union’s Horizon 2020. The dataset contains four ‘known’ identities using a total of five false identities. 23 targets were inputted into the methodology with no knowledge of how many or which had false identities. The rule-based scoring system ranked four of the five false identities with the joint highest score for the relevant target name with the remaining false identity holding the joint second highest score for its target. Moreover, when using graph analysis, 51 suspected false identities were found for the 23 targets with four of the five false identities linked through the crimes they had been involved in. Therefore, an identity resolution approach using both a rule-based scoring system and graph analysis, could be effective in facilitating the investigation process for law enforcement agencies and assisting them in finding criminals using false identities.
Neural Information Bottleneck Decoding

Receiver-sided channel decoding is a crucial, but computationally very demanding task. Recently, information-bottleneck-based decoding received considerable attention in the literature, as it achieves very good performance with coarse quantization and low complexity. Information bottleneck de-coders replace the conventional node operations in the channel decoders with relevant-information-preserving mappings. In the literature, these mappings are typically represented as lookup tables. Unfortunately, the lookup tables are fairly large and the logic circuits needed to implement the lookup tables occupy significant chip area. This paper proposes a novel approach to overcome these impairments, which is based on a trainable function, i.e., a neural network. We show that a trained neural network can represent the mappings more compactly than the frequently used lookup tables. Moreover, our results show that even multiple-input-multiple-output mappings can be learned to replace lookup tables with prohibitive complexity. We investigate a coarsely quantized low-density parity-check decoder that realizes the relevant-information-preserving mappings for all decoding iterations with a single neural network to illustrate the practical benefits of the proposed concept.
Effect of Enclosed Lighting Environment on Work Performance and Visual Perception

This study aimed to validate non-visual effect of light on work performance and give recommendation for enclosed environment. Based on literature review, nine light conditions with three high correlated color temperature (4000 K, 6500 K, 10000 K) × three illuminance (300 lx, 500 lx, 750 lx) were compared in this study to select the best lighting for work. Two tests were used to measure work performance, including cognition test of STROOP and attention test of PVT. KSS and subjective evaluation were used to assessed alertness and visual perception as well. 12 males at their twenties were recruited and completed experiment in the windowless lab. Results: The results showed better cognition of STROOP task under 6500 K and 10000 K vs. 4000 K and promoted sustained attention with shorter reaction time of PVT under 500 lx and 750 lx vs. 300 x. Subjective alertness showed no significant differences between any two of the lighting conditions. Visual perceptions reported the best visual comfort, satisfaction, and preference under 500 lx. Subjective evaluations for three CCT levels were not significantly different from each other. Conclusion: As a result, office lighting with CCT of 6500 K or 10000 K with illuminance at 500 lx was recommended to ensure both task performance and visual perception in the enclosed working space.
Generating Scene Descriptor from Indonesian Narrative Text

Animation is one of media that are frequently used in the entertainment world. Production of animation starts from scripts which then used to create storyboards which in turn used to create animation scenes. This paper aims to find an automatic way to generate animation scenes from text input. No existing research has been conducted for generating animation automatically from Indonesian narrative text. As preliminary study, this paper focused on preprocessing component in automatic generating animation scenes from text input. Our system employs Constituency Parsing as the main Parsing technique, with supporting techniques such as Dependency Parsing and Word Embedding. Testing was conducted to the 420 narrative text datasets gained from Wattpad for Indonesian. The performance results show that this system can generate scene description from narrative text with F1 scores for object, object count, pose and relation are 0.9541, 0.8294, 0.8294 and 0.5216.
Rear homo and hetero junctions III-V n-on-p solar cells grown with high speed MOVPE

This study focuses on how the performances of III-V cells with high-speed metalorganic vapor phase epitaxy (MOVPE) are affected by the cell structure: a conventional n-on-p (a thin highly doped n-emitter on a thick lightly-doped p-base), a rear junction (RJ) n-on-p (a thick lightly-doped n-emitter on a thin highly-doped p-base) and a rear hetero junction (RHJ; a thick lightly-doped n-emitter on a highly-doped p-BSF). For the 120-μm/h grown GaAs solar cells, dark current density (Jdark) corresponding to non-radiative recombination in a space charge region (n=2) and the consequent open circuit voltage (Voc) were significantly improved with RJ and RHJ structures. The reason was the lower defect density, most likely native point defects, in n-GaAs compared with that in p-GaAs. However, both short circuit current density and fill-factor were decreased in RJ and RHJ samples, and more accented in the latter. In the case of 30-μm/h grown InGaP solar cells, the RHJ (n-InGaP/p-InAIGaP) structure showed no advantage over the normal n-on-p device in terms of Jdark and Voc, probably due to the abruptness/roughness of hetero interface. Nevertheless, the RHJ structure has a good potential for high speed MOVPE growth of multiple junction solar cells because of its simple structure with smaller number of interfaces.
Negative and Positive Effect of Giant Magnetoresistance in The Magnetically Ordered Sandwich

Using the modified Fuchs-Sondheimer model [1,2] in the framework of the two-current model [3–5], the analysis of the effect of giant magnetoresistance in the magnetically ordered sandwich was theoretically performed. The conditions under which either negative or positive giant magnetoresitive effect is observed and their amplitude reachs maximum were determined.
Hybrid Approach and Architecture to Detect Fake News on Twitter in Real-Time using Neural Networks

Fake news has been a key issue since the dawn of social media. Currently, we are at a stage where it is merely impossible to differentiate between real and fake news. This directly and indirectly affects people’s decision patterns and makes us question the credibility of the news shared via social media platforms. Twitter is one of the leading social networks in the world by active users. There has been an exponential spread of fake news on Twitter in the recent past. In this paper, we will discuss the implementation of a browser extension which will identify fake news on Twitter using deep learning models with a focus on real-world applicability, architectural stability and scalability of such a solution. Experimental results show that the proposed browser extension has an accuracy of 86% accuracy in fake news detection. To the best of our knowledge, our work is the first of its kind to detect fake news on Twitter real-time using a hybrid approach and evaluate using real users.
Target Range Selection of FMCW Radar for Accurate Vital Information Extraction

Frequency modulated continuous wave (FMCW) radar, which can detect the range and small displacement of a target, has been used for contactless vital information extraction. For accurate vital sign (respiration and heartbeat) measurement, a precise selection of the target range bin, where vital information exists, is important. In this paper, an effective method for selecting the range bin with accurate vital information is proposed. The proposed method is based on the newly introduced magnitude-phase coherency (MPC) index. The experimental results show that the vital information extracted by the proposed method is more accurate than those by conventional methods, indicating that the proposed range bin selection based on MPC is an effective method for extracting accurate respiration and heartbeat rates.
Why and How to Measure the Non-Metallized Contact Resistivity of a Passivating Contact

When a silicon solar cell passivating contact is combined with a metal grid, the lateral resistance of the wafer and of the passivating contact layer, as well as their interface resistances, are decisive for a high fill factor (FF). A particular challenge for characterization and modelling arises for industrial bifacial TOPCon solar cells featuring fire-through metallization on the rear side, in which case three contact resistivities are of relevance: (i) the contact resistivity over the thin oxide under the metallization, (ii) the same in the non-metallized region, and (iii) the contact resistivity between the poly-Si and the metal. We show that a common approach to determine a single lumped contact resistivity via transfer length method (TLM) may result in large errors when predicting its influence on FF. We then present a new approach to determine the three contact resistivities via a modified TLM structure and fitting of Quokka3 simulations, which we call biTLM.
Evaluation of unsupervised anomaly detection approaches on photovoltaic monitoring data

Fault detection in photovoltaic is a challenging task, our project „PV Digital 4.0” addreses this challenge by using machine learning techniques on monitoring data from different systems to detect faults on photovoltaic modules. This paper is embedded in this project and addresses faults for which we have little data or which may occur in the future. For this we use different state of the art unsupervised anomaly detection approaches (e.g. LSCP, VAE or MOGAAL) and compare them in terms of their applicability to our problem. We combine the monitoring data of 70 PV plants with weather data and preprocess them for the usage in multiple models. Each model is elaborated with the same dataset and afterwards compared according to its accuracy. In our scenario we only want to detect new occurring faults and not long term existing faults like shadows. The models should be able to detect the actual behavior of the PV system.
Integrating Decision Trees with Metaheuristic Search Optimization Algorithm for a Student’s Performance Prediction

The main objective of educational institutions these days is to refine their student’s performance in the overall grind. One way to achieve the zenith for these is to identify factors including curricular and extracurricular activities of students. The basic aim of the proposed work is to predict a learner’s performance based on certain parameters such as the number of absences, family background, eating and sleeping habits, etc. This kind of performance prediction can be used by facilitators or faculties to help learners improve their academic conduct. In this paper, the Classification and Regression Tress(CART) algorithm is used for the construction of the initial population of decision trees. The dataset is pursued in two ways for construction of decision tree: 1) It is divided into five different sub-datasets depending on the similarities of attributes and 2) Original one single dataset is considered. To make an optimal prediction, a metaheuristic optimization algorithm that is, a genetic algorithm has been used. According to genetic algorithms, the best chromosome(solution) to the problem survives amongst all possible chromosomes(solutions). However, its performance might get deeply affected by the choice of the genetic operators used in various steps of the genetic algorithm. There are four different classes namely Class 1, Class 2, Class 3, Class 4 based on the different attribute values of the instances. The proposed approach yields 91% accuracy for the final prediction of classes, which is quite encouraging as compared to the other existing works in the same domain.
Effect of Water on the Electrochemical Window and Potential Limits of Room-Temperature Ionic Liquids

The effect of water content on room-temperature ionic liquids (RTILs) was studied by Karl Fischer titration and cyclic voltammetry in the following ionic liquids: tris(P-hexyl)tetradecylphosphonium trifluorotris(pentafluoroethyl)phosphate [P14,6,6,6][NTf2], N-butyl-N-methyl-pyrrolidinium bis(trifluoromethylsulfonyl)imide [C4mpyrr][NTf2], 1-hexyl-3-methylimidazolium tris(perfluoroethyl)trifluorophosphate [C6mim][FAP], 1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide [C4mim][NTf2], 1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide [C4dmim][NTf2], N-hexyltriethylammonium bis(trifluoromethylsolfonyl)imide [N6,2,2,2][NTf2], 1-butyl-3-methylimidazolium hexafluorophosphate [C4mim][PF6], 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide [C2mim][NTf2], 1-butyl-3-methylimidazolium tetrafluoroborate [C4mim][BF4], 1-hexyl-3-methylimidazolium iodide [C4mim][I], 1-butyl-3-methylimidazolium trifluoromethylsulfonate [C4mim][OTf], and 1-hexyl-3-methylimidazolium chloride [C6mim][Cl]. I.
Solubility and Rheological Properties of Live Crude Oils

Multiphase transportation of the wellhead product in a single pipeline is widely used in oilfields. The oil samples transported in the multiphase pipeline are often live oils saturated by light alkanes. To support the multiphase pipelining more efficiently, in this paper, the solubility and rheology of Chang10 live crude oils are studied at the pressures range from 0 to 2.0 MPa. It was found that the dissolved gas in the live oils is mainly composed of methane (32.70 vol% at 1MPa), ethane (19.43 vol% at 1MPa), propane (21.42 vol% at 1MPa) and C4+ component (22.50 vol% at 1 MPa). The gas-oil ratio, Rs, of the live oils increases rapidly with the increase of saturation pressure. However, the effect of saturation temperature on Rs is very small. The pour point, abnormal point, WAT, viscosity/equilibrium viscosity and yield stress of the live oils decrease obviously with the increase of saturation pressure, meaning that the rheological properties of the live oils improve greatly by gas dissolving. The rheological properties of the live oils are improved mainly by the dilution effect of the dissolved gas.
Tris(1,4-dimethyl­pyridinium) aqua­hepta­kis(dimethyl­formamide-κO)lanthanum(III) diphosphatoocta­deca­tungsten dimethyl­formamide disolvate

In the title compound, (C7H10N)3[La(P2W18O62)(C3H7NO)7(H2O)]·2C3H7NO, 1,4-dimethyl­pyridinium cations, [La(DMF)7(H2O)]3+ (DMF = dimethyl­formamide) complex cations and octa­deca­tungstodiphosphate (P2W18O626−) anions form an infinite three-dimensional assembly via La—O—W coordination bonds, electrostatic forces, and C—H⋯O and O—H⋯O hydrogen bonds.
In situ investigation of 1,2-dibromoethane sorption/desorption processes on clay mineral surfaces by diffuse reflectance infrared spectroscopy

Sorption/desorption processes for 1,2-dibromoethane (EDB) at clay surfaces were investigated in situ by means of diffuse reflectance infrared spectroscopy. Spectra were collected during the sorption phase wile N 2 saturated with EDB flowed through the clay samples. Desorption processes were similarly followed by flowing N 2 alone through the samples after the sorption period was completed. The spectral results indicated the presence of EDB in two conformations, anti and gauche, within a liquid phase
Purification of Carbon Nano-Onions Fabricated by Arc Discharge

Soot containing carbon nano-onions was synthesized by traditional electric arc-discharge method with nickel powder as catalyst. After heating at 400°C for 2 h, the arc discharged soot was soaked in hydrochloric acid solution 8 h, and then ultrasonically dispersed into sodium cholate solution. Such obtained dispersion was filtrated with a medium-speed filter paper, and the filtrate was further filtered with a 0.2 μm polytetrafluoroethylene membrane to collect the purified carbon nano-onions. SEM (scanning electron microscope) and TEM (transmission electron microscope) observation showed that carbon nano-onions with hollow or nickel-encapsulated cavities were observed and they had a wide size distribution with an averaged diameter of 24.4 nm. XRD (X-ray diffraction) and Raman scattering investigations evidenced a high degree graphitization of such obtained carbon nano-onions. The XPS (X-ray photoemission spectroscopy) analysis, survey to surface chemistry, demonstrated that oxygen-containing and nitrogen-containing functional groups and the metal catalyst were removed from the surface of carbon nano-onions after purification.
Amperometric Detection of Peroxides with Poly(anilinomethylferrocene)-Modified Enzyme Electrodes

An amperometric enzyme electrode employing covalently attached horseradish peroxidase to glassy carbon electrode and a ferrocene-modified polyaniline film deposited by the electrochemical polymerization of N-(ferrocenylmethyl)aniline monomer is reported. The electrode responded rapidly to micromolar concentrations of peroxides, in accordance with the following trend: hydrogen peroxide>cumene hydroperoxide>tert-butyl hydroperoxide. The effect of operational parameters, such as methods of enzyme immobilization, operating potential of the working electrode, pH, and effect of molecular oxygen, were explored for optimum analytical performance