Miracle-dz/UniFAR-FTUs huggingface dataset:
Study on Mind Controlled Robotic Arms by Collecting and Analyzing Brain Alpha Waves

Assistive robotic technologies that use neural interface systems are designed to allow people with limited mobility to assert control with signals directly from their brains. These robotic systems require detection and analysis of raw brain signals, machine learning methods to extract these signals into useful commands, and the development of an interface between neural signals and robot control. In this paper, a method for controlling a 4-degree of freedom RRRR WAM robotic arm with alpha brain waves of a test subject obtained via electroencephalography (EEG) is presented. The OpenBCI system electrodes and board are ussubed to detect alpha waves and transmit them to digital signal. A robust serial communication interface is developed to convert OpenBCI data into robot commands. An accelerometer embedded in the OpenBCI board is used to implement left-right motion of the robot. To assess the performance of the system, we successfully demonstrate two primary tasks: alpha wave robot control and alpha wave and accelerometer robot control. The methods can be readily extended to include control from other brain regions and additional robotic tasks, paving the way for more complex interactions between robots and human brains. Keywords—mind controlled; robitic arms; brain alpha waves
Reconstitution faciale par imagerie 3d : variabilité morphométrique et mise en oeuvre informatique

La reconstitution faciale a pour but de restituer le visage d’un individu a partir de la morphologie de son crâne. Cette technique est utilisee en anthropologie medico-legale pour apporter de nouveaux temoignages dans l’identification de restes humains. Les objectifs de these sont d’etablir une base de donnees crânio-faciale 3D de Francais grâce a l’imagerie medicale, de tester les regles traditionnelles de reconstitution, et de quantifier les correlations morphologiques entre tissus osseux et cutanes. Des examens hospitaliers par scanner tomodensitometrique (18 – 96 ans ; sexe ratio 1,16:1) ont ete traites avec le logiciel TIVMI pour reconstruire avec precision les surfaces osseuses et cutanees. Plus de 200 points de repere ont pu etre positionnes sur 500 individus, en suivant un protocole precis (repetabilite et reproductibilite verifiees). L’extraction de distances Euclidiennes a permis de tester plusieurs regles traditionnelles, et d’etudier la specificite et la variabilite des epaisseurs de tissus mous. Parallelement, les coordonnees 3D des points ont ete analysees par morphometrie geometrique. Les covariations entre groupes de points osseux et cutanes ont pu etre quantifiees, ainsi que les asymetries, allometries, et influences de l’âge et du sexe sur les variations de conformation. Ces resultats ont permis l’elaboration d’une methode d’estimation de la position des points de repere du visage, et la creation (en collaboration avec le LaBRI) d’un module de reconstitution faciale nomme AFA3D. Base sur le principe de deformation d’un visage synthetique, ce logiciel restitue la forme la plus probable du visage en fonction de la position de 78 points crâniometriques.
Underwater 3D reconstruction based on multi-view stereo

3D measurement of underwater targets could recover 3D morphology of objects/scenes in water, which has extensive application prospects in the fields of submarine map drawing, underwater resource exploration, and marine archaeology et al. 3D reconstruction based on stereoscopic vision is playing a more and more important role in the field of measurement due to its incomparable advantages, such as high automation, rapid accuracy and non-contact. However, its application in underwater target detection is limited by the complex underwater environment, the absorption and scattering of light in the water and so on, which will seriously affect the quality of the image collection. In this paper, a 3D reconstruction method of underwater target based on multi view stereo vision technology was studied. A 3D profilometry system which works underwater was set up. The collection of multi-view image data is completed by a single camera and a rotating device. Firstly, camera’s back projection model is used to calibrate the motion and parameters of the underwater vision system. Secondly, the underwater target is fixed on the rotating device, and a series of images under different viewpoints are collected. Then, feature detection and matching were carried out, and dense surface point clouds were generated by several steps of expansion and filtering operations. Finally, based on the generated dense point cloud, the 3D geometric mesh model of the target is obtained by using the Poisson reconstruction method. Color and texture are fused into the 3D mesh model to get the target with high fidelity.
Use of Machine Learning Algorithms for Prediction of Fetal Risk using Cardiotocographic Data

Background: A major contributor to under-five mortality is the death of children in the 1st month of life. Intrapartum complications are one of the major causes of perinatal mortality. Fetal cardiotocograph (CTGs) can be used as a monitoring tool to identify high-risk women during labor. Aim: The objective of this study was to study the precision of machine learning algorithm techniques on CTG data in identifying high-risk fetuses. Methods: CTG data of 2126 pregnant women were obtained from the University of California Irvine Machine Learning Repository. Ten different machine learning classification models were trained using CTG data. Sensitivity, precision, and F1 score for each class and overall accuracy of each model were obtained to predict normal, suspect, and pathological fetal states. Model with best performance on specified metrics was then identified. Results: Determined by obstetricians' interpretation of CTGs as gold standard, 70% of them were normal, 20% were suspect, and 10% had a pathological fetal state. On training data, the classification models generated by XGBoost, decision tree, and random forest had high precision (>96%) to predict the suspect and pathological state of the fetus based on the CTG tracings. However, on testing data, XGBoost model had the highest precision to predict a pathological fetal state (>92%). Conclusion: The classification model developed using XGBoost technique had the highest prediction accuracy for an adverse fetal outcome. Lay health-care workers in low- and middle-income countries can use this model to triage pregnant women in remote areas for early referral and further management.
Research on model calibration method for energy consumption evaluation of cluster motor system

In practical engineering, the conventional energy consumption calculation method of the motor system is facing many problems, such as difficulty in data acquisition, deviation of model parameters and so on. In order to ensure and improve the accuracy and evaluation ability of the energy consumption model, a calibration method based on parameter identification theory is proposed in this paper. Using the Levenberg-Marquardt algorithm, the rated efficiency, rated variable loss and constant loss parameters of the single and cluster motor systems are identified, and the correction model for the calculation of motor energy consumption is established. Taking a typical three machine cluster motor system as an example, a real physical experiment platform is built to obtain real running data. The example shows that the energy consumption model correction method proposed in this paper has strong engineering practicability, which provides an effective technical means for energy consumption assessment and energy saving transformation of motor energy system.
A Bi-Encoder LSTM Model For Learning Unstructured Dialogs

Creating a data-driven model that is trained on a large dataset of unstructured dialogs is a crucial step in developing Retrieval-based Chatbot systems. This paper presents a Long Short Term Memory (LSTM) based architecture that learns unstructured multi-turn dialogs and provides results on the task of selecting the best response from a collection of given responses. Ubuntu Dialog Corpus Version 2 was used as the corpus for training. We show that our model achieves 0.8%, 1.0% and 0.3% higher accuracy for Recall@1, Recall@2 and Recall@5 respectively than the benchmark model. We also show results on experiments performed by using several similarity functions, model hyper-parameters and word embeddings on the proposed architecture
Study of Optical OFDM System for Wireless LAN Applications

The advantages of optical fiber make it possible to extend the data rate transmission and propagation distance. Orthogonal frequency division multiplexing (OFDM) as a multicarrier technique (MC) is used in hybrid optical-wireless system designs because it has the best spectral efficiency to radio frequency (RF) interference and lower multipath distortion. In this dissertation, a study and evaluation of optical OFDM based wireless local area network (W-LAN) systems are presented. The baseband of the OFDM signal is fully transmitted and up-converted to a radio frequency signal. Also, to reduce system costs, simple base stations (BSs) are interconnected to a central office (CO) via an optical fiber. All the required operations are achieved in the CO. The directly modulated laser (DML) and continuous wave (CW) laser are used in the system simulations as optical laser sources. Identical rectangular microstrip patch antennas have been used at the transmitter and the receiver as well. The simulations were carried out for different SMF and MMF lengths, and the variable wireless distance between the transmitting and receiving antennas was in a range of 40 dB to 80 dB. The purpose of this work is to provide a framework for integrating wireless and optical technologies in one system with the presence of OFDM technology. The required microstrip patch antenna parameters for the system are analyzed and designed. The microstrip patch antenna (S-parameters) is loaded into the Optisystem communication software tool in Touchstone format. As a result, this achievement gives a greater impetus iii to design an integrated optical-wireless system, and simulation results validate the proposed technique. Then, the integration of the microstrip patch antenna and optical OFDM system is achieved, and the performance is intensely studied. The entire system has been presented by developing analytical models and simulations. The system performance results are obtained regarding EIRP, SNR, signal constellations and BER. The results show that this integrated optical wireless link is very robust for carrying OFDM based wireless LAN signals over an optical fiber. Moreover, using an active patch antenna in the system helps to increase the coverage service to more than 30 meters when an SMF of 80 km length is utilized.
Map Point Optimization in Keyframe-Based SLAM using Covisibility Graph and Information Fusion

Keyframe-based monocular SLAM (Simultaneous Localization and Mapping) is one of the main visual SLAM approaches, used to estimate the camera motion together with the map reconstruction over selected frames. These techniques represent the environment by map points located in the three-dimensional space, that can be recognized and located in the frame. However, these techniques usually cannot decide when a map point is an outlier or obsolete information and can be discarded. Another problem is to decide when combining map points corresponding to the same three-dimensional point. In this paper, we present a robust method to maintain a refined map. This approach uses the covisibility graph and an algorithm based on information fusion to build a probabilistic map, that explicitly models outlier measurements. In addition, we incorporate a pruning mechanism to reduce redundant information and remove outliers. In this way, our approach manages to reduce the map size maintaining essential information of the environment. Finally, in order to evaluate the performance of our method, we incorporate it into an ORB-SLAM system and measure the accuracy achieved on publicly available benchmark datasets which contain indoor images sequences recorded with a hand-held monocular camera.
A Method to Support Diagnostics of Dynamic Faults in Networks of Interconnections

The article is devoted to the method facilitating the diagnostics of dynamic faults in networks of interconnection in systems-on-chips. It shows how to reconstruct the erroneous test response sequence coming from the faulty connection based on the set of signatures obtained as a result of multiple compaction of this sequence in the MISR register with programmable feedback. The Chinese reminder theorem is used for this purpose. The article analyzes in detail the various hardware realizations of the discussed method. The testing time associated with each proposed solution was also estimated. Presented method can be used with any type of test sequence and test pattern generator. It is also easily scalable to any number of nets in the network of interconnections. Moreover, it supports finding a trade-off between area overhead and testing time.
The Design and Validation of a Rhythm Span Task

iii List of Tables . v Chapter 1: Introduction . 1 Chapter 2: Literature Review . 5 Chapter 3: Methodology . 26 Chapter 4: Results . 35 Chapter 5: Discussion . 44 Appendix A . 50 Appendix B . 52 References . 54 Curriculum Vitae . 59
Online Social Networks:Measurements, Analysis and Solutions forMining Challenges

In the last decade, online social networks showed enormous growth. With the rise of these networks and the consequent availability of wealth social network data, Social Network Analysis (SNA) led researchers to get the opportunity to access, analyse and mine the social behaviour of millions of people, explore the way they communicate and exchange information. Despite the growing interest in analysing social networks, there are some challenges and implications accompanying the analysis and mining of these networks. For example, dealing with large-scale and evolving networks is not yet an easy task and still requires a new mining solution. In addition, finding communities within these networks is a challenging task and could open opportunities to see how people behave in groups on a large scale. Also, the challenge of validating and optimizing communities without knowing in advance the structure of the network due to the lack of ground truth is yet another challenging barrier for validating the meaningfulness of the resulting communities. In this thesis, we started by providing an overview of the necessary background and key concepts required in the area of social networks analysis. Our main focus is to provide solutions to tackle the key challenges in this area. For doing so, first, we introduce a predictive technique to help in the prediction of the execution time of the analysis tasks for evolving networks through employing predictive modeling techniques to the problem of evolving and large-scale networks. Second, we study the performance of existing community detection approaches to derive high quality community structure using a real email network through analysing the exchange of emails and exploring community dynamics. The aim is to study the community behavioral patterns and evaluate their quality within an actual network. Finally, we propose an ensemble technique for deriving communities using a rich internal enterprise real network in IBM that reflects real collaborations and communications between employees. The technique aims to improve the community detection process through the fusion of different algorithms.
A New Horizo-Vertical Distributed Feature Selection Approach

Abstract Feature selection technique has been a very active research topic that addresses the problem of reducing the dimensionality. Whereas, datasets are continuously growing over time both in samples and features number. As a result, handling both irrelevant and redundant features has become a real challenge. In this paper we propose a new straightforward framework which combines the horizontal and vertical distributed feature selection technique, called Horizo-Vertical Distributed Feature Selection approach (HVDFS), aimed at achieving good performances as well as reducing the number of features. The effectiveness of our approach is demonstrated on three well-known datasets compared to the centralized and the previous distributed approach, using four well-known classifiers.
Study on Cultivation of Intercultural Communication Competence of College English Based on Modern Information Technology

Economic globalization, integration of the world, and wide application and advanced development of modern information technology (MIT) demand high requirements for talents. Traditional teaching methods and means cannot satisfy the requirements of students for real language communication environment and language output. Consequently, students exhibit low intercultural communication ability and fail to satisfy the ultimate goal of foreign language teaching. A novel method to cultivate students’ intercultural communication competence (ICC) in college English based on MIT was proposed in this study. The method was used to examine students’ intercultural sensitivity (IS) and ICC. The concept of ICC was redefined. A model of ICC in college English based on MIT and a teaching mode for cultivating students’ ICC in college English based on MIT were constructed. The proposed teaching mode was applied to a teaching experiment, and interviews and questionnaires were used to estimate students’ IS and ICC. Results demonstrate that this mode is superior to traditional teaching methods, surpasses the limitations of time and space, realizes global information sharing, and integrates the language and cultural learning among students organically. The constructed teaching mode meets the needs and demands of most students and provides a resource-rich learning platform where students can improve their IS and ICC.
Malicious Website Detection Based on URLs Static Features

Real-time and effectiveness are two basic requirements in URL-based malicious website detection. Based on the analysis of malicious URLs construction pattern, this paper puts forward a method to detect malicious website based on URLs static features. All features used in classification are extracted using statistic and there is no need to online access to obtain additional information about website, which reduces detection time greatly. At the same time, a feature reduction method is given in feature vector construction. Combined with reasonable selection of machine learning algorithm, our method can achieve both high accuracy and high efficiency. A series of comparative experiments proved the effectiveness of our method.
Wireless Transceiver Circuits

Modern transceiver systems require diversified design aspects as various radio and sensor applications have emerged. Choosing the right architecture and understanding interference and linearity issues are important for multi-standard cellular transceivers and software-defined radios. A millimeter-wave complementary metal–oxide–semiconductor (CMOS) transceiver design for multi-Gb/s data transmission is another challenging area. Energy-efficient short-range radios for body area networks and sensor networks have recently received great attention. To meet different design requirements, gaining good system perspectives is important.
Using anonymity and rounds-based structure for effective online discussions in STEM courses

Using anonymity and rounds-based structure for effective online discussions in STEM courses Mr. Swaroop Joshi, The Ohio State University Swaroop Joshi is a Senior Lecturer of Computer Science and a Ph.D. candidate in Computer Science and Engineering at Ohio State University. He is interested in a range of topics in Education Technology and Software Engineering, including but not limited to: Computer Supported Collaborative Learning, Game Based Learning, Programming Languages, Compiler Construction and Optimization. Dr. Neelam Soundarajan, Ohio State University Dr. Neelam Soundarajan is an Associate Professor in the Computer Science and Engineering Department at Ohio State University. His interests include software engineering as well as innovative approaches to engineering education.
HDFS File Formats: Study and Performance Comparison

The distributed system Hadoop has become very popular for storing and process large amounts of data (Big Data). As it is composed of many machines, its file system, called HDFS (Hadoop Distributed File System), is also distributed. But as HDFS is not a traditional storage system, plenty of new file formats have been developed, to take advantage of its features. In this work we study that new formats to find out their characteristics, and being able to decide which ones can be better knowing the needs of our data. For that goal, we have made a theoretical framework to compare them, and easily recognize which formats fit our needs. Also we have made an experimental study to find out how the formats work in some specific situations, selecting two very different datasets and a set of simple queries, resolved with MapReduce jobs, written with Java or run using Hive tool. The final goal of this work is to be able to identify the different strengths and weakenesses of the file formats.
Reliable and efficient methods for identification of parameter and measurement errors in power networks

of the Dissertation Reliable and Efficient Methods for Identification of Parameter and Measurement Errors in Power Networks by Yuzhang Lin Doctor of Philosophy in Electrical and Computer Engineering Northeastern University, August 2018 Dr. Ali Abur, Advisor The detection, identification, and correction of parameter and measurement errors (referred to as ”error processing” below) are one of the core problems in the modeling and monitoring of electric power networks. In this dissertation, analyses and methodologies are proposed in order to tackle different aspects of this problem. To start with, the theoretical foundation of error processing is strengthened. The fundamental relations between the errors and their corresponding indicators (Lagrange multipliers and residuals) which are provided in the output of SE, are derived. Along with the development of this result, several remaining theoretical issues regarding error processing are addressed. Closely following the aforementioned results, avenues to enhance the power of parameter error detection and estimation methods are presented. They are aimed at reliably processing the ”weakly detectable errors” which are likely to be missed by existing approaches. The proposed solutions include exploitation of multiple measurement scans, and strategic use of phasor measurement units (PMUs). A critical consideration in real-world implementation, namely the computational efficiency of error processing approaches is subsequently addressed in this dissertation. Solutions are presented to tackle the computational bottlenecks at different stages of error processing. They include efficient computation of the covariance matrix associated with Lagrange multipliers, linear correction of parameter errors, and simultaneous identification of multiple non-interacting measurement errors. This dissertation also seeks another avenue to process the errors by directly using robust state estimation. A novel approach named Extended Least Absolute Value State Estimator (ELAV SE) is proposed for this purpose. It is the first state estimation approach for power systems that remains