Miracle-dz/UniFAR-FTUs huggingface dataset:
Pre-processing of EEG signal using Independent Component Analysis

Biometric systems play vital role in everyday life. After signal acquisition, first step for any biometric system is to process the signal and extract valuable information from that signal. Pre-processing the EEG signal, which is essentially a collection of signal processing techniques before the primary EEG data analysis, is required to extract just brain activity from noisy EEG data. The selection of pre-processing techniques can influence the results of future EEG data analysis. Pre-processing of EEG signal includes various techniques like reduction of line noise, removal of redundant channels and reducing artefacts from EEG signal. EEG data is highly contaminated from various artefacts. This paper implements ICA as a pre-processing technique to remove eyeblink artefacts from signals which is then followed by Auto Regressive model features and results are obtained using Support Vector Machine classifier. Results shows that EEG signals performs better after applying ICA to raw data.
MPC for Propofol Anesthesia: the Noise Issue

The design of automatic control systems for general anesthesia is a challenging task due to the severe safety requirements and process constraints. This is even more complex when model-based control techniques are used due to the significant variability of the process model. Additionally, issues like noisy measurements and interference also influence the control system overall performance. In this context, adequate filtering and control system sampling period selection should be analyzed to test their influence on the controller. In this paper, an MPC system for the depth of hypnosis, where the BIS signal is used as a controlled variable, is analyzed. The main purpose is to test and evaluate how the process noise affects the performance of the control system. The analysis is performed in a simulation study using a dataset of virtual patients representative of a wide population. Results show that a satisfactory performance is obtained when the noise is explicitly taken into account in the controller tuning procedure for a specific sampling period.
On accurate differential measurements with electrochemical impedance spectroscopy

This paper describes the impedance spectroscopy adapted for analysis of small electrochemical changes in fluids. To increase accuracy of measurements the differential approach with temperature stabilization of fluid samples and electronics is used. The impedance analysis is performed by the single point DFT, signal correlation, calculation of RMS amplitudes and interference phase shift. For test purposes the samples of liquids and colloids are treated by fully shielded electromagnetic generators and passive cone-shaped structures. Fluidic samples collected from different geological locations are also analysed. In all tested cases we obtained different results for impacted and non-impacted samples, moreover, a degradation of electrochemical stability after treatment is observed. This method is used in laboratory analysis of weak emissions and ensures a high repeatability of results.
Design of Digital-controlled Two-stage AC/DC Converter Based on GaN HEMT

In this paper, a two-stage AC/DC converter based on GaN devices is investigated and designed. The bridgeless totem pole power factor correction (PFC) converter and PFM control mode based half-bridge LLC resonant converter are applied to the first stage AC/DC conversion part and the second stage DC/DC conversion part, respectively. The absence of reverse recovery in GaN devices enables bridgeless totem pole PFC converter to operate in continuous conduction mode. Meanwhile, a mixed-mode soft-start control strategy is proposed for the LLC resonant converter to solve the issue of large start-up inrush current. In addition, the burst control strategy is employed to improve the gain adjustment range of the LLC circuit. Finally, a 200W digital controlled experimental prototype is designed to verify the feasibility of the control system design.
LRFM Model Analysis for Customer Segmentation Using K-Means Clustering

Customer segmentation is one method used by businesses to obtain a better knowledge of their consumers and improve the quality of their connections with them. To achieve good and quantifiable customer relationship management results, it is crucial to adopt systematic data analysis methodologies to understand client characteristics. Customer segmentation is used to develop a retention strategy in UD. Antar Berkah Group that matches the potential of each client segment. The LRFM (Length, Recency, Frequency, and Monetary) model is used, and the K-Means algorithm is used as a clustering technique. To segment customers, both methods are applied. The Elbow method is used to determine the ideal number of clusters. The value of the LRFM variable in each customer is multiplied by the weights previously obtained using the Analytical Hierarchy Process (AHP) method to produce the Customer Lifetime Value (CLV) for each cluster. The results of customer clustering are then used in cooperation with UD. Antar Berkah Group to create a retention strategy. Using daily transaction data, information on the number of visits made, and data on the amount of money spent by consumers, this study was successful in making it easier to identify UD. Antar Berkah Gorup customer characteristics. Two clusters based on the LRFM variable are the outcome of the clustering method. The retention strategy is developed for each cluster in accordance with its unique characteristics because the two clusters differ from one another in many ways.
A Lithium-ion Battery SOC Estimation Method Involving Battery Internal Temperature

Accurate estimating the state of charge(SOC) of lithium-ion batteries is important for battery management systems. The SOC estimation generally considers ambient temperature instead of internal temperature parameter due to the difficulty of capturing internal temperature. Thanks to a recent work capturing internal temperature, this work proposes a method of SOC estimation for lithium-ion batteries based on internal temperature which has not been investigated in the existing studies. Three kinds of tests are carried out at 0°C, 15°C, 25 °C, 35°C and 45 °C, including maximum capacity test, open circuit voltage test, and dynamic stress test (DST). Subsequently, the second-order RC equivalent circuit model of the battery is established. And the parameters of the battery model are identified by the forgetting factor recursive least squares (FFRLS) algorithm considering the ambient temperature and the internal temperature. Finally, the SOC of the lithium-ion battery is estimated using the DST test data at 0 °C, 25 °C and 45 °C, the model parameter identification results at the two temperatures, and the EKF algorithm. The experimental results show that the SOC estimation accuracy involving the internal temperature of the battery is higher than the SOC estimation accuracy considering the ambient temperature.
Techno-Economic Selection of Energy Storage Providing Multiple Services

Utilizing energy storage systems (ESSs) to perform multiple grid supporting services is an effective way to rationalize the investment of ESS. It is crucial to choose the matching energy storage technologies (ESTs) for achieving the specific stackable services as well as reducing investment costs and increasing revenue. Therefore, the EST suitable for providing stackable services, which includes energy arbitrage and frequency regulation, is proposed in this paper based on a framework. The framework considers the influence of technical, economical, and lifetime parameters. It mainly consists of technical preselection and economic analysis. At first, the requirements of provided services to ESS are adopted as the hard constraints to select the technically feasible ESTs. For economic analysis, a cost-benefit evaluation model for stackable services is proposed. It can quantitatively describe the influence of ESS parameters on cost and revenue. The analysis is based on the estimated parameters of ESTs in 2022. The load data and frequency regulation data are from the IEEE 33-bus distribution system and the PJM market.
Usability of Cloud-based Learning Management Systems Among the Selected Higher Education Institutions in Negros Occidental: A Comparative Analysis

The current public health emergency led many Higher Education Institutions (HEIs) to use online solutions, such as cloud-based Learning Management Systems (LMSs), in delivering instructions. With this abrupt transition from traditional classrooms to online learning, it is necessary to explore and understand users’ perceptions, especially the students, about the usability of LMSs that HEIs are using. The primary purpose of this descriptive study is to determine the subjective usability of Google Classroom, Canvas, and Edmodo using the Usefulness, Satisfaction, and Ease of Use (USE) questionnaire. These are the three topmost LMSs preferred by HEIs in the province of Negros Occidental, Philippines, according to the initial survey of 40 college teachers. For usability assessment, 165 college students were recruited, of which 88 (53.3%) were male and 77 (46.7%) were female. The student participants were divided equally into three groups with 55 members each, and each group was assigned to evaluate one of the three selected LMSs. The collected data were processed and analyzed using descriptive statistics and one-way ANOVA. The results show that the usability of the three LMSs is acceptable based on the perception of the student participants, with Google Classroom achieving the highest usability score, followed by Edmodo and Canvas in respective order. In addition, this study found that the differences between the three LMSs in terms of usefulness, ease of use, learnability, and satisfaction are statistically significant. This result implies that the three LMSs are equally acceptable in subjective usability; however, the student participants highly preferred using Google Classroom. This preference is mainly due to the growing perception that Google Classroom is more practical, easier to utilize and learn, and more satisfying.
A Systematic Study for Organizing Research Topics in Ontology Data Mining Domain

Ontology has played a significant role in various domains for the organization of computer-managed knowledge in recent years. Ontology is used to organize and classify objects in certain domains so that ontology can maximize new knowledge at a more detailed level. One of the domains that apply ontology is the data mining domain. Combining these two knowledge fields is targeted to represent the formal knowledge generated in the data mining process. One of them is that the ontology can help choose the right algorithm or technology so that the ontology can improve the data mining process results. To research in this field, it is necessary to compile, map and analyze the literature on the current state of the data mining ontology by looking at the contributions made from research journals and conferences originating from several relevant electronic databases. Systematic mapping study is a method to achieve this goal by integrating automatic and manual searches from various sources by developing a classification scheme. In this article, the Systematic mapping study starts with 12,819 articles from 1998 to 2019, and then the inclusion and exclusion criteria are carried out into 74 articles selected for review. The review has examined how papers are distributed annually. The material has been generated with the greatest contribution, an overview of the present state of the research, and the identification of gaps and prospective future avenues. The review provides an integrated and consolidated body of knowledge in the data mining ontology domain that can be used as a starting point for research to explore possible ideas from the topic of data mining ontology research.
Negative capacitor paves the way to ultra-broadband metamaterials

Experimental demonstration of the overcoming of basic dispersion-energy constraints in metamaterials with the help of active non-Foster negative capacitors is reported. The experimental metamaterial operates in RF regime, and it is based on air transmission line loaded with negative capacitors. Measurement results clearly show almost dispersionless Epsilon-Near-Zero behavior, accompanied with superluminal both phase and group velocities, over a bandwidth of more than four octaves (2 MHz-40 MHz). The principle of periodic loading of transmission line with negative capacitors may find applications in ultra-broadband active metamaterials for antennas and cloaking technology.
A Variable-Period Inertia Identification Strategy Based on Landau Adaptive Method for PMSM Drives Under Low-Acceleration Conditions

An accurate inertia identification is particularly important for the controller self-tuning to achieve high-performance permanent magnet synchronous motor (PMSM) drives. In this paper, a variable-period inertia identification strategy based on Landau adaptive method is proposed to improve the identification accuracy under low-acceleration conditions. According to the speed measurement rule of low-resolution encoder, the speed updating period of the Landau adaptive inertia identification method is redesigned and auto-adjusted under low-acceleration conditions. Finally, the proposed method is verified on a 2.2-kW PMSM drive platform.
Profiling the Spatial and Temporal Properties of Earthquake Occurrences Using ST-DBSCAN Algorithm

Earthquakes are a special phenomenon that cannot be prevented or avoided. However this natural disaster can be mitigated to reduce its risk of impact. Earthquakes often occur sequentially and converge at a location and time of occurrence. Data having these characteristics can be analyzed precisely using density-based clustering. This paper presents knowledge discovery from the spatial and temporal properties of earthquake data set by applying ST-DBSCAN algorithm, a spatial temporal density-based clustering. Data clustering is done by considering the location of earthquake coherently with its time of occurrence. The main goal of data clustering is to identify the earthquake prone areas and recognize the pattern of earthquake activity in Nusa Tenggara Barat (NTB), a province in the southeast of Indonesia. Research was conducted in four main steps. First is determining the algorithm hyperparameters which include spatial threshold based on the earthquake location, temporal threshold based on the date of occurrence, and minimum number of a cluster density. The Silhouette index is used to validate the results. Second is data clustering with ST-DBSCAN algorithm. Third is cluster profiling to analyze data distribution spatially and temporally. Fourth is recognizing the pattern of earthquake occurrences. Research shows a one year of data has the optimal value of algorithm hyperparameter with spatial threshold is 0.25 degree (27.75 km), temporal threshold is 3 days, and minimum points of cluster density is 9 occurrences. Data profiling identified there are three districts of NTB were prone to the earthquakes, i.e. East Lombok, North Lombok, and Central Lombok, with the highest earthquakes occurring between July 28 to September 27, 2018. Additionally, research revealed that throughout that time frame, stationary patterns were mostly observed.
Influence of Core/Shell Nanoparticles on the Fretting Behavior of Electrical Contacts

In order to guarantee long lifetime and high performance of electrical contacts, a plating is usually applied on the base material. Silver is a promising plating material because of a good balance between performance and costs. The conventional silver plating is soft; therefore, a thick silver plating should be used to prevent the wear through during the operation. In order to enhance the wear resistance and prolong the lifetime of the silver plating, silver platings are modified by co-depositing nanoparticles with a core/shell structure into the silver matrix. A novel method to prepare the Ag (shell)@Al2O3 (core) nanoparticles by galvanic process is introduced in this paper. The influence of fabrication parameters in the galvanic process such as the concentration of silver nitrate solution and the plating voltage on the silver content in the Ag@Al2O3 nanoparticles is investigated. Afterwards, different concentrations of core/shell nanoparticles are co-deposited into the silver plating to study the effect of nanoparticles on the microhardness, microstructure and the lifetime of the silver plating. As a result, the microhardness and the lifetime of the silver plating are significantly improved and a favorable nanoparticle concentration exists for the longest lifetime. Moreover, the mechanism of the lifetime improvement is determined.
Short-interval SMS wind vector determinations for a severe local storms area

Abstract Short-interval SMS-2 visible digital image data are used to derive wind vectors from cloud tracking on time-lapsed sequences of geosynchronous satellite images. The cloud tracking areas are located in the Central Plains, where on 6 May 1975 hail-producing thunderstorms occurred ahead of a well-defined dry line. Cloud tracking is performed on the Goddard Space Flight Center Atmospheric and Oceanographic Information Processing System (AOIPS). Lower tropospheric cumulus tracers are selected with the assistance of a cloud-top height algorithm. Divergence is derived from the cloud motions using a modified Cressman (1959) objective analysis technique which is designed to organize irregularly spaced wind vectors into uniformly gridded wind fields. The results demonstrate the feasibility of using satellite-derived wind vectors and their associated divergence fields in describing the conditions preceding severe local storm development. For this case, an area of convergence appeared ahead of the dry line a.
Robust MPC-based Synergy Control of a Hybrid Neuroprosthesis for Foot Placement

Multi-joint hybrid neuroprostheses provide ther-apeutic benefits for people with spinal cord injury, but they are typically overactuated, which makes input dimensional-ity reduction desirable. This can be done through artificial synergies that function like the biological synergies utilized by the human neuromuscular system. Bio-inspired synergistic control is here merged with a model predictive control (MPC) scheme for cooperative control of a hybrid neuroprosthesis that combines functional electrical stimulation (FES) with a powered exoskeleton. The controller assigns activation coefficients to artificial synergies to produce walking-like movement in a leg model that includes activation and muscle fatigue dynamics. The tube-based MPC scheme employs a sliding mode controller to ensure robustness and is formulated in task space to guide foot placement. The formulation is employed on a 2-degree of freedom hybrid neuroprosthesis model with 6 actuators.
System Level IR Drop Impact on Chip Power Performance Signoff for RISC-V System on Chip

Understanding of how a chip power performance is impacted by the IR drop of a power delivery network (PDN) enables system operating conditions to be optimized. It allows IR drop impacts to a silicon chip to be identified and fixed early during design phases. Conventionally, the static and dynamic IR drop analysis for a silicon chip only considers the impact of silicon level power switching gates and metal routing. Such an analysis approach assumes the power supply from voltage regulator module (VRM) to silicon bumps is noise-free and steady, without taking the impacts of system level components into considerations [1]–[4]. In our opinions, such an analysis approach is unduly optimistic and causes IR drop signoff to be less reliable. This paper uses a RISC-V CPU as a case study to illustrate the importance of the external components in an IR drop analysis. The characterization results show that an IR drop of 100mV caused by PDN impedance can result in the targeted clock speed to be reduced by up to 500MHz, which is equivalent to a 50% performance degradation for a 1GHz CPU and is significant. Then, the impact of system level IR drop caused by package, board and VRM to a silicon chip power performance is further shown. A comparison between the conventional Redhawk chip IR drop simulation and our approach is presented. Our modelling methodology, which uses a system-level distributed PDN to increase IR drop analysis accuracy has also been described in detail. It is shown that external factors including package, board and VRM can induce up to 5.8% additional IR drop, or, 0. 058V for a 1V power supply. This may cause the targeted clock speed to be reduced by up to 250MHz in the actual silicon. It is demonstrated that the intrinsic resistance and impedance of PDN if not being managed properly could negate the power delivery efficiency and impacting silicon performance.
Algoritmos Numéricos para el Problema de Restauración de Imágenes usando el Método de las Proyecciones Alternantes

The projection algorithms have evolved from the alternating projection method proposed by J. von Neumann in 1933, who treated the problem of finding the projection of a given point in a Hilbert space onto the intersection of two closed subspaces. Recent researches have been centered in techniques for accelerate the convergence ofthe method and to exploit the multiprocessing. In this work we considered the image restoration problem. In most techniques developed to solved it have used iterative algorithms; one of them consists of using alternating orthogonal projections. We carried out one chronological looking back of different techniques in which has been applied the method of the alternating orthogonal projections to the problem of imagen restoration, until arriving at the recent approach of Combettes (1997-1999), on where the restoration process is based on the computation of approximate projections (i.e., subgradient projections), instead of exact projections
Human-Robot Interaction Force based Power Assistive Algorithm of Upper Limb Exoskeleton Robots Driven by a Series Elastic Actuator

Upper limb exoskeleton robots have been widely used to assist humans in industry and rehabilitation. Human-robot interaction control consists of two modes. A compliant mode and an assistive mode. The compliant mode is manipulating the robot compliantly according to the movement of the human. At this time, the human must not receive impedance from the robot. Therefore, in this paper, a novel external force control which makes the robot follow human movement without causing resistance is proposed. The assistive mode is to generate assistive power according to human intention. In this paper, the algorithm for recognizing the human intention for primitive movement and generating assistive forces adaptive to real-time work environments is proposed. The proposed two modes are also integrated into a novel human-robot interaction control framework. The performance of the proposed methods is verified through experiments.
The Canarias Database of Nearby Type II Supernovae

Our aim is to present an atlas containing 35 low-resolution spectra that correspond to 19 Type II supernovae. These spectra cover different phases of postmaximum supernova evolution, ranging from ∼2 weeks to ≳1 yr after maximum brightness. The Canarias Database of Nearby Type II Supernovae contains spectra obtained from two different programs: the first, the "Supernova Monitoring Project," was carried out from 1990 April to 1992 July; the second takes advantage of the ORM Service Time facility and is currently active. In this paper we present the first results, mainly compiled from the Supernova Monitoring Project.