Miracle-dz/UniFAR-FTUs huggingface dataset:
Navigation based - Intelligent Parking Management System using Queuing theory and IOT

The development of devices that can connect to the Internet and transmit data has been a source of inspiration for smart city designs. in a metropolitan cities where the smart cities project are working in progress there are various approaches for making the cities smarter, one of the key features is smart parking, at a peak hour rush it is v difficult for driver to find a parking spaces with an increase and order of traffic, causing traffic congestion and result in wastage of fuel, energy etc. there are various work done,. With liberalization of economy number of vehicle on the road have increased leading to illegal, unorganized parking behavior in at public place, private building, educational campus, corporate campus. Thus, Our main objective is to avoid this unorganized parking system and so for this we can provide the intelligences to parking system which can solved the real time problem of organized and unorganized parking. In this study we have developed a smart parking proposal using queuing theory approach which can be model the parking system as a queue and the evaluation parameter like wait time for user in a queue or in a system can be estimated. Also by using IOT concept, by using wireless sensor node and infrared sensor to provide the real time status of parking lot for user on IOT and suggest the parking lot to avoid wait time in peak hour. The parking space closest to the current location can be found using multiple server single queue system and optimizing the existing system for better user experiences.
Online Monitoring of Large Events

In this paper, we describe an approach that can be used to monitor activity online that concerns large events. We propose six different tasks that can be used separately or in combination. The different tasks include analyzing messages from various actors, understanding the impact of messages to receivers, studying online discussions, analyzing hate and threats directed towards people and threats towards the execution of the large event and finally if there are any ongoing influential operations directed towards the general public. To illustrate how the approach can be used, we provide some examples of the different steps when monitoring online environments a few months before the Swedish general election in 2018.
Didactic Switchgear for Teaching Thermographic Inspection for Electrical Maintenance

Infrared thermography is one of the main techniques of predictive maintenance. It consists of measurements of temperature at a distance and analysis of thermograms. Thermography is widely used in the electrical sector because most electrical equipment presents thermal disturbances before a failure. Currently, there is difficulty in teaching and learning thermographic inspection because practical lessons are usually performed in no suitable teaching equipment. The using actual electrical installations for thermography practical class are not recommended due to the need for the insertion of failures, which is risky for people’s safety and can cause arcing and interruption of electricity supply. Thus, in this paper, a prototype was developed for use as a didactic resource for thermography. For this, a set of switchgear was designed and built that simulated common electrical failures: open circuit, poor contact, undersizing of conductors, and interruption of parallel conductors. The assembled didactic switchgear was used in pilot classes with technical and upper-level students from the electricity area who had already taken the class through a real electrical switchgear. All students considered the didactic switchgear better than the real one, with a 100% satisfaction index. From the proposed switchgear and the pilot classes, a tutorial for practical classes and a teaching manual were developed. It was concluded that the didact resource proposed, classes with the proposed set of switchgear, is better than classes in actual facilities, considering the control of teaching resources and the operational safety, as well as people’s safety.
Research on Face Recognition Based on Small Samples of CNN

The data enhancement related technology was used to augment the small sample data set, and the multi-sample nature of the sample was enhanced. Then the face recognition model has been established based on the siamese neural network, and the face recognition model was trained by using the expanded data set. Combined with the simulation calculation, the paper tests the trained face recognition model and obtains a face recognition rate of over 90%. The result proves that the appropriate face recognition model can be established under the small sample data, and the recognition model with good recognition rate can also be trained. At the same time, the paper also analyzes the effects of different activation and loss functions on face recognition rate.
Microwave Sensing and Sleep: Noncontact Sleep-Monitoring Technology With Microwave Biomedical Radar

A good night's sleep is an important part of a healthy life. Sleep can affect quality of life as much as eating and exercise do. Unfortunately, the accelerated pace and pressure of life today often result in people losing sleep and/or suffering from poor sleep quality [1]. Poor sleep quality can contribute to weight gain [2], depression [3], heart disease [4], long-term inflammation [5], emotional problems [6], energy imbalance [7], and social interaction disorders [8]. In the past few years, researchers in sleep medicine and biomedical engineering have paid increasing attention to sleep quality analysis [9]-[12].
An Adaptive Remaining Useful Life Estimation Approach for Newly Developed System Based on Nonlinear Degradation Model

As the central component of prognostic and health management (PHM) field, remaining useful life (RUL) estimation approaches based on degradation modeling have played an extremely significant role in recent years. For the newly developed systems working in complex environments, the associated degradation processes not only lack historical data and prior information but also have strong nonlinearity and three-source variability. Therefore, this paper proposes an adaptive RUL estimation approach for the newly developed system based on a nonlinear model. Specifically, a general nonlinear Wiener-process-based degradation model is established to simultaneously characterize three-source variability and nonlinearity, and the associated RUL distribution is derived with an explicit form. In order to utilize the condition monitoring (CM) data of the service system up to date, we present a parameter estimation method based on the expectation maximization algorithm to adaptively estimate and update the model parameters online. As such, the RUL distribution can be updated once the new CM data are available. Finally, the effectiveness and superiority of the proposed method are demonstrated by the numerical example an empirical study for battery data. The results show that the proposed method can provide accurate and robust RUL prediction for the newly developed system.
A Circular Radiating Patch Filtering UWB Antenna With Improved Band-edge Selectivity

In this study, a circular radiating patch filtering UWB antenna with good selectivity is proposed. By designing a compact bandpass filter to replace the 50 ohm feeding line of the circular monopole antenna, the selectivity of the upper and lower sidebands of the antenna are improved, thereby enabling the antenna to have a filtering function. The proposed design provides a 116.8% passband bandwidth, ranging from 3 to 11 GHz. In order to better evaluate the sideband selection characteristics of the antenna reflection coefficient, the shape factor is defined. Both reflection coefficient and gain of the proposed design show good filtering characteristics. The size of the filtering antenna is 32 mm $\times25$ mm, which is still smaller than most UWB antennas without filtering function.
Comparative Performance Study of Linear and Gaussian Kernel SVM Implementations for Phase Scintillation Detection

The aim of this paper is to analyze the design of support vector machine (SVM)algorithm that belongs to the class of supervised machine learning algorithms for phase scintillation detection and to discuss the performance comparison of linear and Gaussian kernel implementations by considering the design parameter's effects. The algorithm processes the phase scintillation indices computed for GPS L1 signals through the designed linear and Gaussian kernel SVM models. The study is based on the real GNSS signals which are affected by phase scintillations, collected at South African Antarctic research base (SANAE IV).
A Study of Deep Silicon Etching for Power Device Fabrication

High density plasma etch equipment of NAURA has been widely used for power devices manufacture. For IGBT (Insulated Gate Bipolar Transistor) si-trench etch, NAURA proposes a practical process method for improving the trench profile, and develops an in-situ TCR (top corner rounding) process optimization method which simplifies the process flow of IGBT, improves the production efficiency, and saves the manufacturing cost. Test results show that the optimized process method can achieve comparable electrical performance of IGBT with conventional process.
Application of Data Warehouse in Decision Support and Business Intelligence System

Since many decades, organizations have been using Business Intelligence (BI) as a foundation to organizational growth and credibility. This awareness has risen because of increased market competition and aspiration to make organizational processes more efficient. Data Warehouse (DW) emerged as a system used for reporting and data analysis. This paper scratches the surface of application of Data Warehouse (DW) in Business Intelligence (BI) and Decision Support Systems. The research community is encouraged to acknowledge the effectiveness of Business Intelligence (BI) applied to organizations at large as well as small scale.
Electronic glossing – is it worth the effort?

This article is based on a PhD thesis that investigates how electronic glossing can support active vocabulary learning in English for Academic Purposes (EAP). Electronic glossing is generally acknowledged as contributing to vocabulary learning. Electronic glosses have been investigated in recent years in the areas of incidental vocabulary acquisition and reading comprehension. Despite a considerable number of studies showing the positive effects of electronic glosses on reading comprehension and vocabulary learning, only a few studies have attempted to look into learner attitudes toward this means of vocabulary learning and how electronic glosses support vocabulary acquisition. The aim of this paper is to investigate three questions: When do learners use electronic glosses? How do they use electronic glosses? What attitudes do learners have towards electronic glossing as a means of vocabulary learning? The motivation of the study was the need to design materials for a course in EAP for students of economics. To provide students with appropriate input custom-made exercises were created. These consisted of authentic texts that were annotated with ready-made electronic glosses and included an accompanying reading task. A longitudinal qualitative study with upper-intermediate and advanced learners (levels B2 and C1 according to the Common European Framework of Reference for Languages) in an authentic EAP class was conducted with electronically glossed reading tasks. The study reported here took place in an authentic EAP classroom over a period of four consecutive academic terms. The main research instruments were screen observations during class periods, Likert scale questionnaires and semi-structured interviews on the basis of the questionnaires, conducted after the end of each term. The main result of this study is that electronic glosses are acknowledged as useful and appropriate by learners. Learners are particularly motivated to use the glosses if the words are perceived as relevant to their future careers. When authentic texts are combined with accompanying tasks, glossing enables active vocabulary learning.
Predictive current control for SynRM drives under low dc link voltage

Although it has an excellent steady performance, traditional proportion integration (PI) current control still needs PI parameters tuning, which is a time-consuming. In addition, because there are no permanent magnets in synchronous reluctance motor (SynRM) drives, the large stator inductance will affect the stator current variation, which actually limits the response of the current controller. This problem gets worse if dc link of an inverter is low. To deal with these problems, other current control strategies for SynRM will be introduced, namely deadbeat predictive current control (DPCC) and model predictive current control (MPCC). In addition, this paper analyzes the advantages and drawback about the PI current control and predictive control in SynRM drives. Simulation results are demonstrated to verify the effectiveness of the four control strategies.
Multispectral Imaging and Convolutional Neural Network for Photosynthetic Pigments Prediction

The evaluation of photosynthetic pigments composition is an essential task in agricultural studies. This is due to the fact that pigments composition could well represent the plant characteristics such as age and varieties. It could also describe the plant conditions, for example, nutrient deficiency, senescence, and responses under stress. Pigment role as light absorber makes it visually colorful. This colorful appearance provides benefits to the researcher on conducting a nondestructive analysis through a plant color digital image. In this research, a multispectral digital image was used to analyze three main photosynthetic pigments, i.e., chlorophyll, carotenoid, and anthocyanin in a plant leaf. Moreover, Convolutional Neural Network (CNN) model was developed to deliver a real-time analysis system. Input of the system is a plant leaf multispectral digital image, and the output is a content prediction of the pigments. It is proven that the CNN model could well recognize the relationship pattern between leaf digital image and pigments content. The best CNN architecture was found on ShallowNet model using Adaptive Moment Estimation (Adam) optimizer, batch size 30 and trained with 15 epoch. It performs satisfying prediction with MSE 0.0037 for in sample and 0.0060 for out sample prediction (actual data range -0.1 up to 2.2).
Design of a Peripheral-Circuit-Compensation Adjustable-Gamma-Voltage Driving Chip for OLED-on-Silicon Microdisplay

In this paper, a chip of driving integrated circuit with a resolution of $640\times 480$ and a refresh rate of 60 Hz for microdisplay is proposed. The pixel circuit can detect and feed back the potential of OLED anode to determine the compensation data signal, thereby solving the problem of threshold voltage drift caused by OLED aging. The gamma voltage is adjustable to accommodate the brightness and contrast requirements of the microdisplay in different environments, since the gamma voltage is generated by additional DAC. Based on $0.18 \mu {m}$ process, the simulation results show that the proposed driving chip can achieve a grayscale of 256 levels and a refresh rate of 60Hz at 25MHz clock frequency. The output OLED pixel current range is $27 pA \sim 48.3 {n}{A}$, the microdisplay brightness can reach $7400 cd/{m}^{2}$, and the contrast ratio can reach 1400:1.
Design of an Artificial Neural Network Control Based on Levenberg-Marquart Algorithm for Grid-Connected Packed U-Cell Inverter

In this paper an Artificial Neural Network (ANN) controller based on Levenberg-Marquart algorithm is designed and applied to a five-level sensor-less Packed U-cell (PUC5) inverter operating as grid connected mode. The advantage of the proposed design is that there is no longer required to the conventional parameters tuning snag confronted with the traditional Proportional Integral (PI) method. An offline algorithm takes in action of the controller parameters tuning calculation. The proposed ANN regulates the grid line current while the DC bus voltage is maintained constant with no need of regulator as the capacitor is self-voltage balanced in PUC5 topology. The overall design is simple as less tuning, sensors and regulators are requested compared to the conventional PI compensator case. Simulation results on MATLAB/Simulink are conducted to validate the performance of the controller in both steady and dynamic state where low Total Harmonic Distortion (THD) of the line current and high dynamic system response can be identified.
Impact of Forming Voltage Polarity on HfO2-based RRAM Performance

In this paper, the impact of forming voltage polarity on Pt/HfO2/TiN RRAM performance is investigated. Experimental results show that performance including forming voltage and resistance ratio can be improved by tuning the forming voltage polarity at the inert electrode. The underlying mechanism may be the suppression of the filament overgrowing by the reverse forming voltage.
A New Single-Input Dual-Output DC-DC Converter Modelled Using Small-Signal AC Approach for Micro-Grid Applications

In this paper, a new structure of a non-isolated Single-Input Dual-Output (SIDO) DC-DC converter suitable to medium voltage applications is proposed. This structure is based on a Buck-Boost converter stage associated with Two Output Stages (BBTOS) composed, in addition, by a power switch and a power diode. The new structure of the SIDO Buck-Boost converter is providing two outputs and presents several appealing advantages, in particular a reduced number of power devices and the ability to step-up and step-down the output voltage. In this work the principle of the operating mode and the control strategy of the proposed structure are described (Multi-Input Multi-Output (MIMO) controller). A thorough ac small signal study of the SIDO Buck-Boost converter is also introduced. In addition, the simulation results under MATLAB/SIMULINK environment with different load conditions and voltage input are presented. The benefits and limits of the suggested SIDO Buck-Boost converter are also discussed.
Semi-reactive Switch Based Proxy ARP in SDN

In order to achieve high scalability during the network discovery process in software-defined networking (SDN), an extensive method for generating switch-based proxy is essential. This paper investigated the semi reactive solution for guiding the controller to build an OFPT_FLOW_MOD message that allowed SDN switch to reply an Address Resolution Protocol (ARP) request directly by deploying the semi-reactive switch-based proxy ARP application in northbound application programming interface (API). We conduct the experiment by using Open Networking Operating System (ONOS) an open-source SDN controller simulated in Mininet environment. As can be seen from the evaluation result, the installed application can reduce the ARP reaction time up to 95% calculated from the sender host. The final result also indicates that our approach can decrease the controller's loads significantly.
Resonant Polysilicon Antenna for Terahertz Detection

Resonant antennas have garnered considerable interest in terahertz and visible regimes owing to its extraordinary localized field concentration capability. In this work, we experimentally demonstrate a novel metal-free terahertz detector, which is fashioned by heavily doped polysilicon using standard CMOS process. The computational results show that the incident field can be largely enhanced within the antenna gap area at the resonance of 650 GHz. The room temperature measured results show that the responsivity and noise equivalence power of the detector integrated with the optimized antenna design is 600 V/W and 200 pW/Hz0.5 at 650 GHz, respectively. Aided by an equivalent circuit model, theoretical analysis is carried out to elucidate that the field enhancement ratio of the resonant polysilicon antenna is comparable to gold made antenna at terahertz frequencies. Our study provides a new approach for designing a low-cost and CMOS-compatible terahertz detector characterized with strongly localized field, projecting more insights on light-matter interaction at terahertz frequencies.
How to Hack Java Like a Functional Programmer

class Game { protected int playersCount; abstract void initializeGame();void initializeGame(); abstract void makePlay(int player);void makePlay(int player); abstract boolean endOfGame();boolean endOfGame(); abstract void printWinner();void printWinner(); /* A template method : */ final void playOneGame(int playersCount) { this.playersCount = playersCount; initializeGame(); int j = 0; while (! endOfGame()) { makePlay(j); j = (j + 1) % playersCount; } printWinner(); } } Figure 2.4: An abstract game with a template method. Concrete games must implement each step of the game-playing process.
Research on Low Resolution Cell Image Feature Fusion Algorithm Based on Convolutional Neural Network

In this paper, a low-resolution image fusion method based on convolutional neural network is proposed for the problem of low resolution, few detail features and difficulty in feature extraction of cell images collected by lensless cell acquisition system. Firstly, the image of the cell collected by the medical microscope is segmented into a single white blood cell image with a resolution of 90 × 90 by image threshold segmentation algorithm, and then downsample it to 9 × 9 and input it into the feature fusion network for training. After the training is converged, a feature fusion model is obtained, and then the white blood cell image collected by the lensless cell collection system is input into the model to synthesize the fused cell image with a resolution of 36 × 36. Further, using image binarization and other algorithms, the nucleoplasmic ratio of the fused cell image can be obtained. Finally, the simulated vacuolar white blood cell image with a resolution of 9 × 9 is mixed with the normal white blood cell test image in different proportions and then tested. The test results show that the fused cell image shows a similar topographical feature to the larger part of the mixed test images. This is of great significance for the diagnosis of medical diseases.
Analysis of High Frequency Harmonics in Distribution Networks: 9 – 150 kHz

Improvement of power electronic technologies can also introduce various new types of distortions in electrical power systems due to high switching frequency of power converters. The emissions from one device can create noticeable impact on other devices connected to the same system, which may raise immunity concerns. Moreover, power line communication and ripple control signaling may have implications due to the presence of these harmonics. This paper demonstrates analysis of harmonic distortions in the frequency range of 9 – 150 kHz in low voltage distribution networks. Detailed study of voltage and current harmonics is performed based on field measurements with different load characteristics and solar inverter integration. A simulation study of 5 kW inverter control is shown to demonstrate the effect of controller strategy for current harmonics. The results could be useful in development of high frequency harmonic regulations for grid connected inverters in distribution networks.