Miracle-dz/UniFAR-FTUs huggingface dataset:
Modular Open Hardware Omnidirectional Platform for Mobile Robot Research*

A robotic platform is essential to validate any algorithm developed for mobile robotics research, as usually simulation results does not reflect all the real world circumstances and error sources. Nowadays there are several available commercial solutions that can be used to validate the developed algorithms, but they are usually expensive and closed to hardware modifications. Another option is to build the robotic platform, but while these solutions are normally open hardware and can be easily modified and maintained, many are assembled using low cost components and are intended only for robot education, and for this reason are not suitable for advanced algorithm implementation nor can employ advanced sensors. A possible solution is presented in this paper, in which an open hardware modular omnidirectional platform is developed, with robust hardware implementation using modular commercially available components, at a lower cost that some more expensive available solutions, but with similar performance and capabilities being suitable for mobile robot research and experimentation.
Optimal Machine Learning Algorithms for Cyber Threat Detection

With the exponential hike in cyber threats, organizations are now striving for better data mining techniques in order to analyze security logs received from their IT infrastructures to ensure effective and automated cyber threat detection. Machine Learning (ML) based analytics for security machine data is the next emerging trend in cyber security, aimed at mining security data to uncover advanced targeted cyber threats actors and minimizing the operational overheads of maintaining static correlation rules. However, selection of optimal machine learning algorithm for security log analytics still remains an impeding factor against the success of data science in cyber security due to the risk of large number of false-positive detections, especially in the case of large-scale or global Security Operations Center (SOC) environments. This fact brings a dire need for an efficient machine learning based cyber threat detection model, capable of minimizing the false detection rates. In this paper, we are proposing optimal machine learning algorithms with their implementation framework based on analytical and empirical evaluations of gathered results, while using various prediction, classification and forecasting algorithms.
Multi-Port Patch Antennas for Flexible Power Combining and Feeding Choice

Patch antennas with multiple feeds are proposed in this paper. The antennas consist of a traditional square patch and feeding probes that are set in a line orthogonal to the polarization direction. In addition to radiating EM energy just like the conventional antennas, the proposed multi-port antennas can also realize on-antenna power combining without power combiner. Thanks to this, high-line losses and large system size caused by extra power combiner can be avoided. Moreover, since the location and the number of ports have a limited effect on the antenna performances, flexible feeding strategies can be chosen in different applications. For demonstration, four patch antennas with a different number of ports (one port, two ports, three ports, and four ports) are designed at the center frequency of 1.5 GHz. Compared with the traditional one-port patch antenna, the proposed three multi-port patch antennas can achieve nearly the same antenna performance, including bandwidth, polarization, efficiency, and radiation pattern. Besides, one-port antenna and two-port antenna integrated with a two-way power amplifier are designed and measured, respectively. Then, they are compared to further prove the proposed power combining method in multi-port antennas.
Empirical Analysis of PUCT Algorithm with Evaluation Functions of Different Quality

Monte-Carlo tree search (MCTS) algorithms play an important role in developing computer players for many games. The performance of MCTS players is often leveraged in combination with offline knowledge, i.e., evaluation functions. In particular, recently AlphaGo and AlphaGo Zero achieved a big success in developing strong computer Go player by combining evaluation functions consisting of deep neural networks with a variant of PUCT (Predictor + UCB applied to trees). The effect of evaluation functions on the strength of MCTS algorithms, however, has not been investigated well, especially in terms of the quality of evaluation functions. In this study, we address this issue and empirically analyze the AlphaGo's PUCT algorithm by using Othello (Reversi) as the target game. We investigate the strength of PUCT players using variants of an existing evaluation function of a champion-level computer player. From intensive experiments, we found that the PUCT algorithm works very well especially with a good evaluation function and that the value function has more importance than the policy function in the PUCT algorithm.
A Visual Memory System for Humanoid Robots

Competitions like RoboCup, require a robot that is capable of playing soccer competitively. Therefore the robot must have functional control, vision, localization and decision systems. This project proposes the implementation of an object tracking system, using the vision system of a robot in the domain of RoboCup KidSize Soccer. The proposed system detects multiple objects like: robots, landmarks and the ball; transmit these informations to the tracking system and keep track of their positions, even when the vision system loses the objects. It also considers the movement of the robot in order to predict the objects positions relative to the robot. Real humanoid robots were used in order to implement and test the proposal. Experiments showed that the system was able to track the position of a second moving robot in the field.
Outdoors Trajectory Tracking Control for a Four Wheel Skid-Steering Vehicle*

The motivation of this project is to develop a high precision autonomous navigation system for precision agriculture robots. Therefore, in this paper an outdoor trajectory tracking system for a four wheel skid-steering vehicle is presented. The systems integrates an outdoor geolocation system with a tracking trajectory control system. The vehicle’s outdoor geolocation system was developed by fusing GPS (Global Positioning System), magetometer and wheel encoders position information. The sensor fusion algorithm was based on the EKF (Exended Kalman Filter) and the vehicle’s kinematic model. Afterwards, from top to bottom a tracking trajectory control system was designed and implemented as follows; first, a linear transfer function identification was carried out in order to model the response of the vehicle’s angular direction, then, a PID controller with a feedforward compensator for vehicle’s angular direction was designed. Finally, a trajectory generator as well as the trajectory tracking system were developed. The systems was implemented in a Raspberry PI-3 with an excellent processing performance. The results showed a good performance of the control tracking system, with trajectory errors of less than one meter respect to the reference trajectory, which are very promising results for tasks related to precision agriculture.
Deep Learning Based Exposure Correction for Image Exposure Correction with Application in Computer Vision for Robotics

This paper presents a convolutional neural network that is able to minimize the effects of poorly exposed digital images in post processing, outperforming state-of-the-art networks on two different conditions: images that are too dark, and images where the irradiance exceeds both the lower and upper bounds of the sensor. The scene's illumination, camera aperture, exposure time, and ISO sensitivity present a direct impact on the quality of the resulting image. Optimize these parameters, however, is a challenging task, which often leads to images that are either too bright, too dark or out of focus. In order to restore over-exposed and under-exposed images, we propose a Generative Adversarial Network (GAN) which learns how to restore an image, preserving and enhancing the contents of the input image, as well as complete regions where data was completely lost due to clipping. Our results show the proposed network is able to enhance color, fulfill regions with missing data, and preserve structural details.
Diyabetli Bireylerin İnsülin Uygulama Bilgi-Beceri Düzeyleri: Doğru ve Yanlışlar

Ozet Giris: Diyabet, bireylerin ve ailelerinin yasamlari­nin tum yonlerini etkileyen, yasam kalitesini azaltan ve yasam boyu suren bir hastaliktir. Tip 2 diyabet eriskin toplumda en yaygin gorulen metabolizma hastaligidir. TURDEP-II sonuclarina gore Turk eriskin toplumunda diyabet sikligi %13,7’dir. Amerikan Diyabet Birligi, diyabetin dogrudan tibbi ve dolayli maliyetinin 132 milyar dolar oldugunu bildirmektedir. Uygun enjeksiyon tekniginin kullanilmasi, iyi diyabet kontrolu saglamak ve komplikasyonlari onlemek acisindan buyuk onem tasir. Amac: Bu calisma, diyabetli hastalarin insulin kullanim bilgi-becerileri ve yanlislarini degerlendirmek amaciyla yapilmistir. Yontem: Tanimlayici tipte planlanan bu calisma, Endokrinoloji Anabilim Dali Polikliniginde ve Dahiliye kliniginde tedavi goren 110 diyabetli hasta ile gerceklestirilmistir. Veri toplama araci olarak “Hasta Bilgi Formu” ve “Insulin Uygulamasi Gozlem Formu” kullanilmistir. Verilerin degerlendirilmesinde sayi, yuzde, ortalama, standart sapma yontemleri kullanilmistir. Bulgular: Hastalarin %68.2’si gunde 4 kez insulin kullanmakta ve kullandigi insulinin adini bilmemekte, %97.3’unun diyabetle ilgili herhangi bir dergi/dernege uyeligi bulunmamaktadir. Insulin kullananlarin %10’u oruc tutmaktadir. Hastalarin %32.7’si insulin bolgesinde sertlik/sislik, %54.5’i morluk, %79.1’i hipoglisemi, %66.4’u hiperglisemi yasadigini soylemistir. Hastalarin %59.1’i insulin yapmayi unutmakta, %31.8’i insulini bilerek atlamaktadir. Hastalarin %56.4’u insulin bolgeleri arasinda rotasyon yapmamaktadir. Sonuc: Kapsamli ve duzenli bir diyabet ve insulin uygulama egitimi ile hastalarin yanlis yapma oranlari azaltilabilir. Anahtar Sozcukler: Diyabetes mellitus, insulin kullanimi, uygulama becerisi
Fully Convolutional Networks for Multisource Building Extraction From an Open Aerial and Satellite Imagery Data Set

The application of the convolutional neural network has shown to greatly improve the accuracy of building extraction from remote sensing imagery. In this paper, we created and made open a high-quality multisource data set for building detection, evaluated the accuracy obtained in most recent studies on the data set, demonstrated the use of our data set, and proposed a Siamese fully convolutional network model that obtained better segmentation accuracy. The building data set that we created contains not only aerial images but also satellite images covering 1000 km2 with both raster labels and vector maps. The accuracy of applying the same methodology to our aerial data set outperformed several other open building data sets. On the aerial data set, we gave a thorough evaluation and comparison of most recent deep learning-based methods, and proposed a Siamese U-Net with shared weights in two branches, and original images and their down-sampled counterparts as inputs, which significantly improves the segmentation accuracy, especially for large buildings. For multisource building extraction, the generalization ability is further evaluated and extended by applying a radiometric augmentation strategy to transfer pretrained models on the aerial data set to the satellite data set. The designed experiments indicate our data set is accurate and can serve multiple purposes including building instance segmentation and change detection; our result shows the Siamese U-Net outperforms current building extraction methods and could provide valuable reference.
Day-ahead electricity consumption prediction of a population of households: analyzing different machine learning techniques based on real data from RTE in France

Forecasting of power consumption has been a topic of great interest for the stakeholders of electricity markets. It has an essential role in decision making, including purchasing and generating electric power, load switching, and demand side management. Different algorithms are tested and used for balancing the demand and supply of energy. This research work focuses on predicting power consumption using time series forecasting methods for the Île-de-France region with publicly available energy data from RTE, France. The two machine learning algorithms Support Vector Machine (SVM) and Recurrent Neural Network (RNN) are implemented and tested for their accuracy in predicting day-ahead half-hourly power consumption data. This paper provides brief insights on the algorithms used and further explains the data handling for its implementation. The Mean Absolute Percentage Error (MAPE) is used as the performance measure. The results indicate a higher accuracy of the RNN at the cost of longer computation times.
Low-Cost Bio-Impedance Analysis System for the Evaluation of Fruit Ripeness

Novel non-destructive sensing techniques for fast and reliable fruit quality assessment are strongly needed to meet both production and consumer demands. In this direction, Electrical Impedance Spectroscopy (EIS) has shown a great potential in correlating the electrical parameters of fruits with their quality attributes. In this paper, the effect of aging on 9 bananas during a 12 days period was studied using a microcontroller-based EIS system. The bio-impedance was measured in the 100 Hz - 85 kHz frequency range and then correlated to the image analysis of the color evolution of such fruits. The results showed a good potential in monitoring the ripening process of bananas, with a good correlation of the low frequency points with the impedance evolution. Finally, both the impedance and the image analysis indicated the presence of a turning point in the ripening process.
Comparing Features Extraction and Classification Methods to Recognize ErrP Signals

Brain Computer Interface systems allow communication between the user and the computer using information provided from brain waves. This control is possible through the interpretation of electrical records and allows the system to analyze the conditions of human attention and engagement in a task. One of the problems in attention during learning is the moment when the student makes a mistake, which is possible to catch through a specific signal of the brain using those systems. However, this data is quite complicated and noisy, and low accuracy was found so far. In this paper, it was compared methods to extract features and classify this signal to enhance the accuracy in error detection. Both wavelets and Fourier transform are used to feature extraction and a MultiLayer Perceptron as compared to Deep Learning Neural Network in classification. The results shown that wavelet extraction are better to extract the error and Deep Learning approach is better to classify.
Attention-based Multi-hop Reasoning for Knowledge Graph

Knowledge graph plays an important role in detection, prediction, early warning, and other security related applications. A fundamental task in applying knowledge graph is the so-called multi-hop reasoning, which focuses on inferring new relations between entities. In this paper, we introduce attention mechanism to the classic compositional method. After finding reasoning paths between entities, we aggregate these paths’ embeddings into one according to their attentions, and infer the relation of entities based on the combined embedding. Two experiments on NELL-995 dataset, fact prediction and link prediction, validated that our method outperforms all baselines.
Design of User-Friendly Virtual Thai Keyboard Based on Eye-Tracking Controlled System

Eye trackers are noninvasive and portable devices allowing the application of various assistive technologies. We aim to improve the quality of life of Thai people with disabilities whose eyes are the only mean of communication. Therefore, this paper presents a gaze-controlled virtual Thai keyboard. We compare two typing modes. automatic typing and free-form typing. A survey of often used words for Thai patients is conducted for word prediction system. The system is evaluated with 10 adult participants. The results show that the automatic typing provides faster responses and obtains higher user satisfaction than free-form typing.
Humidity Sensing of Zinc Oxide Nanorods Based Prototype Using Arduino Uno Microcontroller Platform

Here we report development of a zinc oxide (ZnO) nanorods based humidity sensor prototype using Arduino Uno microcontroller platform. A commercial humidity sensor (DHT11) was also connected with the microcontroller to calibrate ZnO sensor. ZnO nanorods were grown hydrothermally on indium tin oxide (ITO) coated PET substrate. Nanorods were characterised with field emission electron microscopy and X-ray diffraction. Sensors were exposed to 50%–80% relative humidity (RH) at room temperature. The sensor response was found to be 0–76%. Such low cost simple prototype development can open up opportunities for future realization of internet of things (IOT).
Fabrication and Evaluation of an Uniform Piezoelectric Film for a Robot Skin Sensor

In this paper, we propose a novel method to form a robot skin sensor by a piezoelectric material spraying technique. By spraying piezoelectric material, the robot skin sensor can be fabricated on a robot with 3D free form. Our fabrication method is based on the sol-gel spraying technique and we improve it for fabricating a larger and more uniform piezoelectric film with reproducibility. We conducted experiments to evaluate the film fabrication quality and the prototype sensor's characteristics. According to the results, the film fabricated by the proposed method has enough thickness uniformity on 100 [mm] square plane. Additionally, the prototype sensor responses well to the applied force and its sensitivity is 25 [mV/ N] with the bandwidth of 1 [kHz].
Reflectometer with Complete Error Correction

A reflectometer operating at 2.4 GHz with complete error correction is described in this paper. The reflectometer utilizes two gain/phase detectors AD8302 from Analog Devices to measure the reflection coefficient in the full phase range of 360 degrees. Incident and reflected signal are measured and split up into equal power to fed the detectors inputs with the help of two directional couplers and two Wilkinson power dividers. The circuits are realized in microstrip line technique. A micro-controller board using MSP430G2553 from Texas Instrument is connected to the system in order to take measurement samples and send them to a computer. In spite of collecting and calibrating data, graphical user interface (GUI) software is also designed. In addition, calibration technique using a one-port error model was programmed in GUI to perform the error correction in order to overcome systematic errors of the reflectometer. The final result points out that this reflectometer has the best accuracy in th region between 0 dB and −12 dB, in which total error is lower than 2 dB. The reachable limit of this reflectometer is −25 dB.
Effects of Sound Volume Change When Squeezing a Virtual Soft Object with a Bare Hand

In order to improve the perception of the core part of a virtual soft object when a user squeezes it with his/her bare hand, we apply multisensory integration of visual and auditory stimuli to our proposed AR system. The visual stimuli are real-time stereoscopic images in which the user's bare hand is squeezing the virtual soft object in the actual scene. As the auditory stimuli, we focus on the sound volume change linked to the movements of the user's thumb and index finger. The present paper reports the effects of sound volume change in enhancing the pseudo-softness and the perception of the core part of a virtual soft object. The experimental results of our study statistically show that when a user squeezes a virtual soft object with his/her bare hand, multisensory integration of the visual and auditory stimuli effectively increases the feeling of grasping and facilitates handling of the virtual soft object. In addition, auditory stimuli that have a clearly audible sound volume change at the core part naturally enhance the perception of the core part of the virtual soft object.
Research on Open-loop Soft-start Strategy of CLLLC Bi-directional Resonant Converter

The CLLLC resonant converter inherits the soft switching characteristics, impedance characteristics and gain characteristics of the LLC resonant converter in forward and reverse directions. There are problems and difficulties in the start-up of CLLLC resonant converter, and it has more complex gain characteristics and impedance characteristics than the LLC resonant converters. By discussing the factors affecting the current and voltage stress at the start-up moment and during the start-up process, the different soft-start strategies are proposed in the paper. The feasibility of the proposed soft-start strategy is proved by comparing the start-up current and voltage stress with the current and voltage stress at the steady-state resonant point in the simulation waveform.
An Open Source Framework for Educational Applications Using Cozmo Mobile Robot

Cozmo is a real-life robot designed to interact with people playing games, making sounds, expressing emotions on a LCD screen and many other pre-programmable functions. We present the development and implementation of an educational platform for Cozmo mobile robot, with several features, including web server for user interface, computer vision, voice recognition, robot trajectory tracking control, among others. Functions for educational purposes were implemented, including mathematical operations, spelling, directions, and questions functions that gives more flexibility for the teachers to create their own scripts. In this system, a cloud voice recognition tool was implemented to improve the interactive system between Cozmo and the users. Also, a cloud computing vision system was used to perform object recognition using Cozmo's camera, to be applied on educational games. Other functions were created with the purpose of controlling the emotions and the motors of Cozmo to create more sophisticated scripts. To apply the functions on Cozmo robot, an interpreter algorithm was developed to translate the functions into Cozmo's programming language. To validate this work, the proposed framework was presented to several elementary school teachers (classes with students between 4 and 12). Students and teacher's impressions are reported in this text, and indicate that the proposed system can be a useful educational tool.
Extracting a Sequence of Cause-Effect Concept Pairs from Texts

This research aims to extract event-pairs, particularly cause-effect concept pairs from disease documents downloaded from hospital-web-boards. Where the event pairs express in a sequence of cause-effect concept pairs lined up to produce a causal chain of certain diseases. This causal chain containing its root-cause benefits for solving system. Each causative/effect event concept is expressed by a verb phrase of an elementary discourse unit (EDU) which is a simple sentence. The research has three problems; how to determine each adjacent-EDU pair having the cause-effect relation, how to determine the causal-chain expression on the document having adjacent-EDU pairs with the cause-effect relation blended with non-cause-effect relation, and how to simply collect and represent the sequence of cause-effect pairs. Therefore, we extract an nWordCo concept set having the causative/effect concepts from EDUs' verb phrases including a Bayesian network to solve each nWordCo size. We apply the Naïve Bayes classifier to learn and extract a cause-effect-relation template of nWordCo concept pairs from the documents. We then propose using this template to extract the causal chain. We apply the ArrayList data structure to collect and represent the sequence of cause-effect pairs. The research results provide the high-percent correctness of the extracted causal-chain from the documents.