Miracle-dz/UniFAR-FTUs huggingface dataset:
Robotito: programming robots from preschool to undergraduate school level

Computational thinking is a skill that is considered essential for the future generations. Because of this it should be incorporated into the curricula as soon as possible. Many robots can be programmed using graphical languages or physical blocks instead of writing code. This makes programming more accessible for the youngest programmers. Looking to extend the programming activities to preschool students, we present a novel approach that allows to program a mobile robot, Robotito, by changing its environment. We describe the architecture of Robotito, software used to program its interaction with the environment, and developed behaviours. Moreover, Robotito exports his sensors and actuators using ROS standard mechanisms and is modelled in Gazebo allowing it to be used in research and undergraduate school courses providing researchers an autonomous and safety mobile platform, which can be integrated with any system using ROS.
Comparison of EEG Pattern Recognition of Motor Imagery for Finger Movement Classification

The detection of a hand movement beforehand can be a beneficent tool to control a prosthetic hand for upper extremity rehabilitation. To be able to achieve smooth control, the intention detection is acquired from the human body, especially from brain signal or electroencephalogram (EEG) signal. However, many constraints hamper the development of this brain-computer interface (BCI), especially for finger movement detection. Most of the researchers have focused on the detection of the left and right-hand movement. This article presents the comparison of various pattern recognition method for recognizing five individual finger movements, i.e., the thumb, index, middle, ring, and pinky finger movements. The EEG pattern recognition utilized common spatial pattern (CSP) for feature extraction. As for the classifier, four classifiers, i.e., random forest (RF), support vector machine (SVM), k-nearest neighborhood (kNN), and linear discriminant analysis (LDA) were tested and compared to each other. The experimental results indicated that the EEG pattern recognition with RF achieved the best accuracy of about 54%. Other published publication reported that the classification of the individual finger movement is still challenging and need more efforts to achieve better performance.
Design of Battery Charging System as Supply of Rice Threshers in Tractor

This paper discusses about the design of a battery charging system on thresher. The proposed system uses a power-boost converter type converter using a constant voltage method. The battery charger uses a constant voltage method by maintaining the voltage output boost converter of 60 volts using the PI control. Testing was conducted using 400WP solar panels and an battery of 45 AH. The charging battery 3.09 ampere with constant voltage according to the set of predefined points. The charging process was carried out for 3.47 hours with an initial SOC of 70% to end at SOC 100% gained SOC increase by 30%.
Multi-FPGA Solution for Large Power Systems and Microgrids Real Time Simulation

This paper presents a Multi FPGA based solution for large power systems and Microgrids real-time simulation. The proposed platform allows control and protection devices to be designed and tested in virtual power system, before they can be implemented in a physical system. This solution promotes flexibility of operation with no risk of components failure under any contingency analysis. To demonstrate the effectiveness of the OPAL-RT platform, a large distribution network (DN) of 210 bus bars and a Microgrid (MG) are considered. The MG includes a solar panel and a battery energy storage system (BESS). The complete power system including the DN and MG is simulated using only two Kintex-7 FPGA boards. The controllers of the MG distributed energy resources (DER) are compiled and real time simulated using a 3.5 GHz Intel processor. The performance of this overall Processor-In-The-Loop application (PIL) is validated based on two criteria: 1) Evaluation of the results accuracy compared to the reference offline simulation under steady state and transient conditions. 2) Evaluation of the losses introduced by the power interface used to split the DN into two FPGA boards.
BLDC Motor Based Solar Water Pumping System with Grid Interface

This work mainly focuses on solar PV fed water pumping system using Brushless DC (BLDC) motor. When compared to induction motor, BLDC offers higher efficiency, improved power factor, reduced size and better power density. Since solar PV is unreliable source for continuous power flow instead of using batteries, grid is chosen as other source of supply. As the output generated by solar PV changes in proportion to input irradiation, improved P&O based maximum power point tracking technique is implemented. To utilize the motor pump to its full capacity and to share the load demand between the two sources power factor controlled converter circuit using dual loop average current control approach is implemented here with improved reliability. MATLAB/Simulink environment is used for simulation of the chosen system under various test conditions namely at constant, varying and zero irradiation.
Insights into the Buried-Metal-Layer Impact on GIDL and ON-State Characteristics in Junctionless Transistor

The present work investigates the OFF-state lateral band-to-band-tunneling (L-BTBT) phenomena in a buried-metal-layer (BML) based junctionless (JL) FET (BML-JL-FET). The impact of BML-workfunction and BML-voltage on the L-BTBT component of the gate-induced-drain leakage (GIDL) is studied via the 2-D TCAD simulations. The nature of Schottky junction formed at the bottom of the semiconductor device layer has also been investigated. We observe that the sufficiently high BML-workfunction, or high BML-workfunction with negative voltage, allows for excessive band bending at the bottom Si/BML interface, resulting in the accumulation of the opposite charge carriers (holes). If BML is not optimized, this accumulated holes contribute adversely to the L-BTBT current component of the device. In addition, the impact of BML acting as a secondary gate on the device characteristics has also been studied.
A 402 ∼614 MHz CMOS Transceiver for IEEE 802.15.6 Wireless Body Area Networks

A 402 ∼614 MHz CMOS transceiver is proposed and designed in standard 0.18$\mu$m CMOS technology, complying with the IEEE 802.15. 6 Wireless Body Area Network (WBAN) standard. A current-reused differential common-gate low noise amplifier (CG-LNA), a high precision image rejection active-RC complex filter, a four-stage programmable gain amplifier (PGA), a high linearity power amplifier (PA) and so on, are employed. The simulation results show that, within the desired bandwidth from 402 MHz to 614MHz, the input/output power reflection coefficients of less than -16dB, the receiver gain range of $38 \sim 87$ dB and the output 1 dB compression point of over 2.11dBm are achieved. From a single voltage supply of 1.8V, 8mW and 16mW are consumed by the receiver and the transmitter, respectively.
A 3-level Inverter based Induction Motor Drive for Cane Preparation in Sugar Industry

The sugar making process has broadly four steps, namely, cane preparation, milling, clarification and crystallization. In a sugar factory, a share of around 25-30% of total installed power is in Cane Preparation station. The prime mover of cane preparatory devices is invariably a slip ring induction motor (SRIM). For providing load equalization and to mitigate the effect of high swing in load current a flywheel is installed at the far end of the shaft of the preparatory devices and, additionally, a residual resistance (also known as buffer/slip resistance in the industry) is left in-line in rotor circuit throughout the operation. Conventionally, the value of this buffer/slip resistance is kept such so that a 15% slip (or 7.5% slip at some of the installations) is realized at full load. This kind of set up results in huge power loss in cane preparation. In this paper, a simulation study of proposed 3-level inverter based induction motor drive is carried out and the performance is compared with the existing system having SRIM drive and with the recently introduced 2-level inverter based squirrel cage induction motor (SCIM) drive in the industry. The 3-level inverter based induction motor drive is presented for an Open-End Winding Induction Motor (OEWIM) for the application.
Handoff Mechanism in Wireless Mesh Network Using Deficit Round Robin (DRR) Algorithm

Wireless Mesh Network (WMN) is a wireless network consisting of several connected wireless nodes. The performance of WMN will be optimal if the routing protocol used on the network can work properly. OLSR is routing that is widely used in WMN. WMN is also enlarged wireless coverage areas with the interconnection between wireless routers, in its scope the mobile node can move and connect to nearby wireless routers, shift and movement of nodes are very influential during handoff. Handoff is a mechanism for transferring channels among access points as the mobile station (MS) moves within a cellular system coverage, meanwhile time of displacement there are delays and throughput. The algorithm used in this research is Deficit Round Robin (DRR) where the algorithm able to fulfill the demand for stable throughput and delay values. In this study, handoff quality testing refers to Quality of Services (QoS) mechanism based on the value throughput and delay from OLSR protocol measurement. The results show a larger throughput value of 37% obtained at wider range and a delay value of 12 % smaller.
Sumatra Traditional Food Image Classification Using Classical Machine Learning

Indonesia is a country rich in culture. One of Indonesia's culturaldiversity is on traditional foods. Traditional food not only has a role in the cultural aspect, but also has an influence on biodiversity. Unfortunately, the current diet of people endangers the existence of traditional foods, which indirectly will also affect Indonesia's food security. Indonesia Local Food Database is one solution proposed to prevent this problem, where the database will play a role to monitor food systems in Indonesia. In this research, database development will focus on collecting data for Sumatra traditionalfood, and also building a model for image classification which will later become one of the main features of the database. Some features like color and texture are extracted from the image. These features are used for classification using 5 classical machine learning models. Evaluation results show performance that as good as deep learning approach.
Patient-centric medication history recording system using blockchain

Medication errors are one of the problems to be solved not only in Korea but also across the world. To prevent a medication accident in advance, the comprehensive management of individual medication history is essential. Currently, in the hospital-centric Personal Health Record system, if the patient is prescribed medicines at multiple hospitals, he/she needs to go to the hospital where a medicine was prescribed to get the corresponding needs such as prescription or medical certification to record the prescription information on his/her own. Alternatively, the patient needs to record the prescription information on his/her own. This is very time consuming, inconvenient, and even worse, not reliable method because of input errors. Therefore, in this study, we developed Patient-centric medication history recording system using blockchain, which is directly capturing QR code printed on the envelop by drug store based on prescription. All the information are stored using the hash value of the data in a blockchain and it prevents the tampering of the data. In addition, this system adopted Fast Healthcare Interoperability Resources, which is the international health information exchange standard, as way to improve interoperability.
Investigation of Accuracy of various STC Correction Procedures for I-V Characteristics of PV Modules Measured at Different Temperature and Irradiances

This paper presents a simulation study to estimate the accuracy of various STC correction procedures as a function of irradiance and temperature in field. The normalized errors in maximum power (Pmax) in STC correction for the IEC 60891 Procedure 1, Modified IEC 60891 Procedure 1 and Standard Irradiance and Desired Temperature (SIDT) procedures were compared at various irradiances and temperatures. Also, the most accurate method to predict I-V characteristics of PV modules based on single diode model was identified by comparing eight different methods. The IEC 60891 Procedure 1 was found to have least normalized error, however it is not always suitable in field as it requires I-V data at various temperatures and irradiances. Out of the remaining two procedures Modified IEC 60891 Procedure 1 is having lowest normalized error compared to SIDT in the range of temperature (50 0C to 75 0C) and irradiance (700 W/m2 to 1000 W/m2) which are typically encountered in outdoor measurements. The primary experimental validation of simulation results has performed at National Centre for Photovoltaic Research and Education (NCPRE).
Device Discovery for D2D Communication Using Belief Space Search in Dense Area

A device to device (D2D) communication is a direct link between the cellular devices in the next generation networks. The probability of D2D links in dense areas like in a stadium, shopping malls, and housing societies is high due to the existence of large number of devices. However, an accurate, fast, and energy efficient discovery algorithm is needed to perform D2D communication. The device discovery is a primary task to initiate D2D communication. If it is not legitimate properly may present noteworthy latency to access the system. This paper presents the belief space search algorithm for in-band device discovery cellular network. The circular network coverage area is divided into small rings and each ring belongs to a belief space. This strategy reduces the exhaustive search for the devices in the whole coverage area (space). It searches only belief space where devices are. Therefore, it saves time and energy with higher accuracy. A mathematical model for the belief space is made and verified by the simulations. It has 90% accuracy, saves 13% energy and 28% efficient than whole space search.
Low-power wireless sensor network system for early diagnostic of subacute rumen acidosis in cows

High-yielding cow rations under intensive production conditions contribute to the development of subacute rumen acidosis (SARA), leading to pathologies such as rumenitis, laminitis, reproductive disorders, loss of productivity and reduced longevity. The aim of this work is to develop a reticulo-ruminal long-acting cyber-physical system for monitoring rumen parameters. Three scientific institutions in cooperation with two farmers conduct the research in order to create a prototype of a low-power wireless sensor network system for early diagnostic of subacute rumen acidosis of dairy cows. The new system architecture includes, reticulo-ruminal bolus with pH and temperature sensors, a microcontroller, a radio transmitter and a power supply module. The system includes a base station for data collection from boluses, an MQTT broker, a web server and a database. Data communication solution has been developed and tested in the laboratory, and micro-controllers have been selected and adapted for data processing. In addition, research is under way to create an autonomous long-term power supply system. Work shall be conducted in two directions: (a) stand-alone battery-powered electricity supply system; (b) an autonomous power supply system based on the generation of an electrostatic generator. The results of the initial stage of the research are discussed in this paper.
Meter-transformer connection relationship detection algorithm based on smart meter data

In this paper, we designed an algorithm to detect the meter-transformer relationship. First, the voltage distortion data will be used for preliminary judgment. The voltage distortion data of the central coordinator is compared with the voltage distortion data of each smart meter, and the similarity value between the data will be calculated. Second, we will use the voltage amplitude data to calculate the correlation coefficient between the meter that may be recorded incorrectly. The meter-transformer relationship of the current record is detected by combining the similarity and the correlation coefficient. This algorithm can effectively correct the meter-transformer connection relationship which is incorrectly stored. Simulation and experiment results show that the algorithm has strong versatility.
Towards Guided Summarization of Scientific Articles: Selection of Important Update Sentences

Guided summarization is aimed to distinguish the information from the prior and later documents for a given domain that are read by readers. Update summary is a second-crucial component to be generated from this task, after the initial summary. The existing techniques of guided summarization for news article cannot be directly adapted to the scientific article due to the different location structure of important information between these domains. In this research, we try to conduct the selection of important update sentences which is part of steps in guided summarization for the domain of scientific articles. We employ and compare some selection algorithms, such as Maximum Marginal Relevance (MMR) and TextRank. Because there is an initial summary that will become a reference to generate non- redundant information for update summary, so we modified their algorithms to minimize the similarity between the initial summary and candidates sentences for update summary. The result shows that the feature of ‘TextRank+ROUGE-SU4’ fully gives the best ROUGE-2 recall performance that is 15.21% and the modified MMR and TextRank equation is better than baseline.
$S^{3}$ Net: Trained on a Small Sample Segmentation Network for Biomedical Image Analysis

Fully convolutional networks (FCNs) are powerful methods to extract hierarchies of features that have achieved remarkable success in various biomedical image analysis tasks. However, the successful training of FCN requires more than hundreds of pixel-level annotated training samples, which poses a challenge for biomedical image processing tasks. In this paper, we present $S^{3}$ Net, a network that makes more efficient use of available annotated samples on biomedical image segmentation. $S^{3}$ Net is essentially a deeply-supervised encoder-decoder network where the decoder has been redesigned to efficiently restore multi-level encoded feature resolution in a single step. We have conducted extensive experiments on the 2015 MICCAI Gland Challenge dataset. Compared with other methods, $S^{3}$ Net achieves a 0.781 dice-score using 11 images for training, higher than U-Net++ 18.6% and U-Net 12.9%, which verifies the performance of $S^{3}$ Net trained on a small sample. Further, with training on 85 images, $S^{3}$ Net achieves a 0.910 dice-score by using resnet50 as the encoder, which is higher than state-of-the-art semantic segmentation results by 4%.
Light-Illumination-Induced Degradation and Its Long-Term Recovery in Indium-Tin-Zinc Oxide Thin-Film Transistors

Light-illumination-induced degradation and its long-term recovery in indium-tin-zinc oxide (ITZO) thin-film transistors (TFTs) are investigated. Negative threshold voltage shift and subthreshold degradation are observed under light of 546.1 nm with 14.92 mW/cm2. Short-term recovery and long-term recovery of ITZO TFT exhibit two different characteristics. Incorporated with TCAD simulations, the degradation mechanism and recovery mechanism are tentatively discussed.
Impact of Battery Scheduling on the Operation of Microgrid System having Dynamic Load Demand

This paper presents a mathematical model for optimal scheduling problem of grid connected microgrid system with different distributed generation sources operating with thermal and electrical loads. The dynamic natured distributed generating plants and the loads can be smoothened by the properly sized quick response energy storage system. Microgrid systems operating with storage system are preferred in those areas where power system expansion may lead to undesirable interferences with the existing system. The charging and discharging time of the energy storage system has a great impact on the optimal scheduling of the microgrid system operating in grid connected mode. Frequent charging and discharging operations reduces the life expectancy of the energy storage system. The simulation result shows the impact of minimum charging and discharging time constraint on the cost of operation for grid connected microgrid system.
Implementation of Distributed Processing Using a PC-FPGA Hybrid System

Recent research has focused on decreasing processing times and power consumption by combined use PCs and FPGAs. Here describe a system that performs distributed processing across multiple PCs and FPGAs using ethernet for PC connections and an in-house designed FPGA network to connect FPGAs. For processing software, we used Apache Spark on the PCs and an in-house application programming interface (API) for the FPGAs. Experimental results show that both PC and FPGA are effectively used for image compression calculation.
Room Map Estimation from Two-Dimensional Lidar's Point Cloud Data

An automatic digital map estimation is not only essential for the repository of the architectural plans but also crucial for providing clear paths for a trajectory finder system, an autonomous vehicle, or a robot. Therefore, it is useful for reconstructing heritage buildings or finding a location lying in ruin after an earthquake. Rebuilding a digital map of a room can be done by using information from two-dimensional (2D) images. However, it seems to be complicated to obtain an accurate dimension directly from those images. One potential breakthrough is to make use of light detecting and ranging (LIDAR) technology. This state-of-the-art device can detect the solid surface of an object, as well as presenting an easiness of the distance measurements. This research focused on the reconstruction of a room map by utilizing 2D point cloud data obtained from a Lidar device. The results showed that by applying the conditioned random sample consensus (RANSAC) method, the 2D map of a room could be identified accurately from the 2D point cloud data.
The observation of thermal photon gain in quantum well solar cells

Photon gain, which indicates the breakdown of the Principle of Detailed Balance, has been observed in radiatively dominated quantum well solar cells (QWSCs) at illumination levels consistent with around 200x concentration. Evidence comes from three types of experiment. The radiative dark-current extracted from a short-circuit current versus open circuit voltage plot becomes steeper than expected for radiative recombination. The photoluminescence and electro-luminescence spectra from the lowest exciton broaden as illumination increases. We report evidence demonstrating that photon gain results from hot-carrier recombination. Our results support the hypothesis that hot carriers are responsible for the reduced radiative recombination in QWSCs and the voltage enhancement observed in triple-junction QWSCs.