Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a low cost mobile system for monitoring cardiac activity based on noncontact electrodes

Here we present the design, implementation and testing of a mobile system for ambulatory monitoring of cardiac activity, it uses capacitive electrodes that do not need of direct contact with the skin. The system uses the material between the skin and the electrodes as a dielectric for the capacitor formed between them. The device pretends to monitor heart activity in people with high skin sensitivity or with injuries, it could also be used as part of the attire of professionals such as pilots. Our hypotheses were the following: 1) It is possible to develop a system that allows monitoring heart activity through a mobile device, without the need conductor gel or even in direct contact to the skin. 2) The implemented electrodes will be able to take data with and without direct contact with the skin. 3) The system will be able to get electrocardiographic signal through at least three different type of textures. All the hypotheses were satisfied. The performance of the system was evaluated by acquiring measurements, using different textures as dielectric and then performing a paired t-test comparing them with a signal of wet Ag/Cl electrodes that are considered the gold standard now, the results suggested that there is not significative difference between the signals taken with the implemented electrodes and the ones taken with the Ag/Cl electrodes. Connectivity and performance of the App were also tested by using it and obtaining satisfactory results.
Face Recognition and Smile Detection as an Appraisal of Customer Satisfaction Instrument

The spreading of cafes, restaurant, and food courts in a small market area can create a high-level competitive situation among food serving industries. As customer satisfaction is the main key to reach the glory, making up or keeping up a service or product quality is a critical decision. In this research, we presented a steady and easy way out to solve this problem based on face recognition and smile detection method. We implemented an Adaboost (adaptive boosting) algorithm by Viola-Jones which is the robust algorithm for recognizing the face and detecting a smile. It works completely by cascade classifier step to ensure the object detected is a face. Our system performance attained 96,43% of accuracy in real time condition. It is able to detect a face in a range of 25–100 cm, and it can detect a smile from 25–45 cm in a certain position and conditions.
Plasmonic Gold Nanoparticles for Combined Photoacoustic Imaging and Plasmonic Photothermal Therapy Using a Pulsed Laser

Plasmonic gold nanoparticles show potential for use in a range of cancer diagnostics and therapeutics, such as photoacoustic imaging (PAI) and plasmonic photothermal therapy (PPTT). Generally in PPTT, continuous wave (CW) lasers are used to destroy cancerous tissue. However, in order to add a diagnostic component through PAI, a pulsed wave (PW) laser is needed. If PPTT can be achieved using PW lasers then combined theranostic applications with the same laser system is possible. Additionally, AuNRs can be many different sizes but exhibit equivalent surface plasmon resonances, so the size may be significant in the efficacy of these modalities. We have demonstrated the potential for gold nanorods to be used for both PAI and PPTT. The Au10s displayed the highest photoacoustic signal amplitude and PPTT efficacy. A PW laser was shown to induce significant cell death to a lung cancer cell line with a fluence below the maximum permissible exposure, indicating the possibility for cancer theranostics with a PW laser.
Analyzing Learners Behavior in MOOCs: An Examination of Performance and Motivation Using a Data-Driven Approach

Massive open online courses (MOOCs) have been experiencing increasing use and popularity in highly ranked universities in recent years. The opportunity of accessing high quality courseware content within such platforms, while eliminating the burden of educational, financial, and geographical obstacles has led to a rapid growth in participant numbers. The increasing number and diversity of participating learners has opened up new horizons to the research community for the investigation of effective learning environments. Learning Analytics has been used to investigate the impact of engagement on student performance. However, the extensive literature review indicates that there is little research on the impact of MOOCs, particularly in analyzing the link between behavioral engagement and motivation as predictors of learning outcomes. In this paper, we consider a dataset, which originates from online courses provided by Harvard University and the Massachusetts Institute of Technology, delivered through the edX platform. Two sets of empirical experiments are conducted using both statistical and machine learning techniques. Statistical methods are used to examine the association between engagement level and performance, including the consideration of learner educational backgrounds. The results indicate a significant gap between success and failure outcome learner groups, where successful learners are found to read and watch course material to a higher degree. Machine learning algorithms are used to automatically detect learners who are lacking in motivation at an early time in the course, thus providing instructors with insight in regards to student withdrawal.
Dimension and Control Design of a Permanent Magnet Synchronous Generator with Outer Rotor for Wind Power Application

This paper presents the design of a permanent magnet synchronous generator with outer rotor for wind power application. The proposed model incorporates a control system for the injected active power and also to avoid permanent magnet demagnetization. In addition, it considers a high-frequency harmonics filter to mitigate wave distortions at the load side. In order to test the control system stability, the model is subject to abrupt wind speed variations. The results reveal that the proposed modeling can be used in future research for distributed generation applications since it has the capacity to control the power and voltage generated.
Transient Stability Analysis for Offshore Wind Power Plant Integration Planning Studies—Part I: Short-Term Faults

Power system planning is the process of forecasting load demand and ensuring sufficient generation and reserve capacities over some planning horizon. This process includes investigation of steady-state and dynamic operations of the power system of interest. This paper addresses the transient stability (also known as large-signal stability) analysis of power systems for offshore wind power plant integration planning studies. In particular, this study develops a comprehensive practical methodology to assess the transient stability of power systems, including rotor angle stability, voltage stability, and frequency response for large scale power systems. This methodology considers variability of the offshore wind power plants as well as the type of any faulted system's components present. Additionally, this methodology is applicable to the study of both short-term and long-term faults, though only short-term faults are considered here (long-term faults are treated sequentially in a companion paper). This paper considers the integration of offshore wind power plants into existing power systems and demonstrates the utility of this methodology through the examination of the specific case of integrating 1000 MW of offshore wind power into the FirstEnergy/PJM service territory using a realistic model of 63k-bus test system that represents the U.S. Eastern Interconnection.
Detection and Following of Moving Targets by an Indoor Mobile Robot using Microsoft Kinect and 2D Lidar Data

The mobile robot following behavior is frequently considered as an important pre-requisite while developing autonomous service robots intended to co-exist with humans in a shared environment. It can also simplify the development of autonomous navigation and obstacle avoidance behavior as well as the robot ability to operate within multi-agent formations. In this investigation, an approach is proposed that allows a KUKA youBot omnidirectional mobile platform to detect and follow people carrying different objects, such as suitcases, in a shared indoor environment. Using a Kinect sensor, the mobile robot can recognize standing human with a suitcase and will begin to consider him as a potential dynamic target. The 2D lidar of the mobile platform can further detect when the above target starts to move and will begin to track it. Meanwhile, the robot will start following the human by controlling its own velocity while maintaining a pre-specified orientation and distance.
Neural Text Line Segmentation of Multilingual Print and Handwriting with Recognition-Based Evaluation

We present a novel method for detecting text lines in historical handwritten and printed document images. Our hybrid technique begins by leveraging deep neural networks to perform multi-class pixel-wise prediction. The predictor not only discovers text and graphics pixels in the document, but it is also designed to automatically adhere contiguous regions from the same text line while also predicting buffers that prevent disassociated text lines from merging. The system breaks neural "ties" through dynamic programming. To the best of our knowledge, our system is the first neural system to predict the entire perimeters of full text lines. Also, to aid in scaling and full-scope awareness, the network during training is initially given small regions of the image to study and then expands its scope to full images as training continues. Our goal for line segmentation is to enable automatic transcription on huge heterogenous collections of historical images, so we use transcription accuracy as our metric. We document and leverage our state-of-the-art transcription system as an evaluation harness for scoring our segmenter along with various other competitor segmenters. We then show the effectiveness of our system as it relates to other systems by comparing it to both known data sets (IAM) and to three 50K-word "in the wild" test sets consisting of (a) US handwritten wills and deeds, (b) US historical newsprint images, and (c) Spanish Church and Government records.
UWB Planar Antenna with Dual Band Notch Characteristics for WLAN and WIMAX

UWB planar antennas with dual slits in the conductor planes are presented. The reference antenna has frequency bandwidth between 3.1 and 10.6 GHz while the proposed antenna has dual band notch characteristics between the frequency bandwidths 3.3~3.7GHz and 5~6GHz, respectively. Dual band notch characteristics are realized by dual slits etched onto the conductor elements. Simplicity and low-profile are features attributes the proposed antenna band rejections for World Interoperable Microwave Access (WIMAX) and Wireless Local Area Network (WLAN) are designed. Band notch characteristics are simply manifested by changing the structural parameters of the slits.
Sign Language Recognition Based on Intelligent Glove Using Machine Learning Techniques

We present an intelligent electronic glove system able to detect numbers of sign language in order to automate the process of communication between a deaf-mute person and others. This is done by translating the hands move sign language into an oral language. The system is inside to a glove with flex sensors in each finger that we are used to collect data that are analyzed through a methodology involving the following stages: (i) Data balancing with the Kennard-Stone (KS), (ii) Comparison of prototypes selection between CHC evolutionary Algorithm and Decremental Reduction Optimization Procedure 3 (DROP3) to define the best one. Subsequently, the K-Nearest Neighbors (kNN) as classifier (iii) is implemented. As a result, the amount of data reduced from stage (i) from storage within the system is 98%. Also, a classification performance of 85% is achieved with CHC evolutionary algorithm.
Minimizing Inverter Capacity Design and Comparative Performance Evaluation of SVC-Coupling Hybrid Active Power Filters

In this study, a minimizing inverter capacity design is proposed, and the characteristics and performance of static VAR compensator (SVC) coupling hybrid active power filters (SVC-HAPFs) are analyzed. The cost of the SVC part is much lower than that of the active inverter part, and thus, the reduction of inverter part capacity can lead to a decrease in the total cost of SVC-HAPF. To demonstrate the advantages of the proposed minimizing inverter capacity design of SVC-HAPF, comparisons are given between the conventional and the proposed SVC-HAPF designs. Comparisons are also provided among active power filters (APFs), HAPFs, and the proposed SVC-HAPF in terms of dc-link voltage, compensation range, and performance. Finally, simulation and laboratory-scale experimental results are given to validate the minimizing inverter capacity design, and to verify the characteristics and compensation performances of APF, HAPF, and the proposed SVC-HAPF.
Telematics and Road Safety

Road Safety is a major concern around the world. Telematic solutions have been available for more than a decade, and several studies have been done in the use of telematics data in road safety. However, these studies are scattered on different topics. There is a need to find the best possible ways of using telematics data for safe driving. This paper presents the review made with the aim of finding the evidence on the effective use of telematics data for road safety. Summary of the data collection devices, sensors, features, algorithms, feedback types used are discussed in this paper.
A Framework For Autonomous Heterogeneous Robot Teams

This paper discusses the key features of autonomous heterogeneous teams. Autonomy in this sense entails both the ability to overcome unforeseen circumstances and failures as well as the capability of enhancing performance over time. The paper presents a framework that provides a comprehensive solution for handling faults and dynamically configuring the team in response to varying situations, while improving its performance over time, on both individual and team levels, within a modular and expandable architecture. The performance of the framework is studied in a set of simulated foraging scenarios, under conditions of both software and hardware failure.
Sigma-Delta Pulse Width Modulator: Jitter Analysis

High accuracy pulse width modulation (PWM) is crucial for saturating devices such as optical communication systems, control and energy efficient amplification. In this paper we propose a closed-loop PWM architecture, called Sigma-Delta ($\Sigma\Delta$) PWM, showing jitter robustness and compensation for some PWM inaccuracies arising from clock imprecision. The $\Sigma\Delta$-PWM architecture trades oversampling and some increases in architectural complexity to gain in signal quality, leveraging on the noise shaping capability of the $\Sigma\Delta$ loop. Analytical results as well as numerical simulations assess the impact of timing jitter as key parameter in PW modulators.
$k^{3}$-Sparse Graph Convolutional Networks for Face Recognition

In recent years, deep learning networks have substantially improved the performance of face recognition. Although deep learning networks have been very successful, there are limited to underlying Euclidean structure data. When dealing with complex signals such as medical imaging, genetics, social networks and computer vision, recently there has been a growing interest in trying to apply learning on non-Euclidean geometric data. Graph convolutional networks are a new deep learning architecture for analyzing non-Euclidean geometric data. In computer vision, a human face image is modeled as a graph in the irregular domain. A major technical challenge is how to optimize the structured face graph. Because, classification performance critically depends on the quality of the graph. In this paper, we explore an undirected graph convolutional network called $k^{3}$-SGCNs ($k^{3}$ - sparse graph convolutional networks). The main idea is to use sparsity-constrained optimization that obtain connected sparse subgraphs. A sparse graph of face image is composed of connected sparse subgraphs. Experiments demonstrate that the learned sparse graph has better performance than mutual $k$-nearest neighbor graph and $l1$ graph.
Computer Tools for Supporting of Constructive Tasks in Discrete Mathematics for Engineering Education

The paper presents the final results of the restructuring of the course of discrete mathematics and, as its continuation, the course of mathematical logic and the theory of algorithms in a technical university based on a constructive approach. Modernization has affected both methodical aspects of courses composition, as well as forms of organization of students' academic work. Among others, the connection of computer support of training with pedagogical methods of organization of “computer-free” work of students is regarded. Part of the methodological and technical aspects, such as the development by students' teams of computer algebra systems, the verification of constructive solutions, including the use of self-verified tasks, were presented in earlier works with the participation of authors. Based on post-questioning it is shown that students of technical universities are inclined to constructive activity and positively assess the influence of constructive tasks on mastering mathematical concepts. The developed materials have also been tested in the framework of Olympiad in Discrete Mathematics and Theoretical Informatics for entrants.
Contention & Energy-Aware Real-Time Task Mapping on NoC Based Heterogeneous MPSoCs

Network-on-Chip (NoC)-based multiprocessor system-on-chips (MPSoCs) are becoming the de-facto computing platform for computationally intensive real-time applications in the embedded systems due to their high performance, exceptional quality-of-service (QoS) and energy efficiency over superscalar uniprocessor architectures. Energy saving is important in the embedded system because it reduces the operating cost while prolongs lifetime and improves the reliability of the system. In this paper, contention-aware energy efficient static mapping using NoC-based heterogeneous MPSoC for real-time tasks with an individual deadline and precedence constraints is investigated. Unlike other schemes task ordering, mapping, and voltage assignment are performed in an integrated manner to minimize the processing energy while explicitly reduce contention between the communications and communication energy. Furthermore, both dynamic voltage and frequency scaling and dynamic power management are used for energy consumption optimization. The developed contention-aware integrated task mapping and voltage assignment (CITM-VA) static energy management scheme performs tasks ordering using earliest latest finish time first (ELFTF) strategy that assigns priorities to the tasks having shorter latest finish time (LFT) over the tasks with longer LFT. It remaps every task to a processor and/or discrete voltage level that reduces processing energy consumption. Similarly, the communication energy is minimized by assigning discrete voltage levels to the NoC links. Further, total energy efficiency is achieved by putting the processor into a low-power state when feasible. Moreover, this approach resolves the contention between communications that traverse the same link by allocating links to communications with higher priority. The results obtained through extensive simulations of real-world benchmarks demonstrate that CITM-VA approach outperforms state-of-the-art technique and achieves an average ~30% total energy improvement. Additionally, it maintains high QoS and robustness for real-time applications.
Person Re-Identification by Multi-Camera Networks for Internet of Things in Smart Cities

As one of the most important areas of public safety and security, intelligent video surveillance is an indispensable part of the urban Internet of Things infrastructure. Person re-identification (person re-ID), which aims to track and recognize a person in a multi-camera scene, is mostly viewed as an image retrieval problem, and this task has been greatly boosted by deep convolutional neural networks (CNNs) in recent years. In practice, person re-ID usually adopts automatic detectors to obtain cropped pedestrian images, and CNNs are inherently limited to model geometric transformations due to the fixed geometric structures in their building modules. We incorporate the deformable convolution module to the traditional baseline to enhance the transformation modeling capability without additional supervision. The new module can readily replace their plain counterparts in the existing CNNs and can be easily trained end-to-end by standard backpropagation. Experiments on two large-scale re-ID datasets confirm the performance of our approach. The experiments also show that learning dense spatial transformation in deep CNNs is effective for person re-ID task and has a bright future in the intelligent video surveillance area.
Magnetic Optical FBG Sensors Using Optical Frequency-Domain Reflectometry

The optical frequency-domain reflectometry (OFDR) sensing system and signal processing method, which is based on the short-time Fourier transform, enable us to interrogate reflected wavelengths of fiber Bragg gratings (FBGs) and their relative position along the optical fiber with high spatial resolution. An FBG is an optical fiber with an appropriately inscribed periodic refractive index modulation. When a broadband optical signal is launched into the FBG, narrow wavelength bandwidth is reflected. Due to an attached transducer, such as Terfenol-D, the reflected wavelength changes with the applied magnetic field. The proposed magnetic FBG sensor consists of a small rod of magnetostrictive alloy TbDyFe, also known as Terfenol-D, which has large magnetostriction. The FBG is attached to the Terfenol-D rod with UV glue. Consequently, the external magnetic field is applied to the sensor and as a result, the Terfenol-D rod length increases. The reflected wavelength shift is observed and measured by the OFDR sensing system. Therefore, we determine the external magnetic field along the optical fiber from the reflected Bragg wavelength shift. The measurement results show that the proposed magnetic field sensors based on Terfenol-D using OFDR can be successfully used for measurement of the external magnetic field along an optical fiber.