Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of Aircraft Using Sentinel-1 SAR Image Series

The work demonstrates the possibility of aircraft detection using Sentinel-1 synthetic aperture radar image series with 10 m resolution using change detection method. Sentinel-1 data can be used for detection of large aircraft as additional “indicators”, effective in combination with high resolution observations. The results of processing are demonstrated using a series of 15 SAR images acquired within the period from January to November 2017. Presented examples show that the results of aircraft detection in many cases clearly correspond to the number of targets that can be detected in the images manually. The probability of correct detection, estimated in real observation conditions for the most of images exceeded 0,8 and the final probability for all series was 0,76.
Proposal of More Accurate Energy Model of Electric Vehicle For SUMO

The attempts to reduce vehicle’s CO2 emissions and fuel consumption are actually motivating the manufacturers in the transport and automotive sectors to focus their interests on Electric Vehicle (EV) concept. Battery autonomy and charging process are still a big issue for that kind of vehicles. The evaluation of EV’s instantaneous energy consumption through realistic scenarios and based on consistent energy models are needed to take step forward on these issues. In this paper, we provide a deep insight on EV consumption dynamics and make a review of the energy model implemented in SUMO, one of the most popular traffic simulation framework used to evaluate vehicular technologies and strategies. We propose a set of energy consumption estimation improvements and show through simulation the achieved improvements in terms of accuracy compared to the sumo implementation.
The P-Value as a New Similarity Function for Spectral Clustering in Sensor Networks

In this paper, we consider spectral clustering over data collected by a network of sensors. In this context, the spatial data distribution is not necessarily uniform and can further be affected by sensor noise. This is why we propose a new similarity function for spectral clustering in sensor networks. This similarity function is derived as the p-value of an hypothesis test that has to decide whether two sensor measurements belong to the same cluster. Unlike other existing similarity functions, the p-value takes into account both the local data densities and the fact that the noise variance can vary from sensor to sensor. Simulation results show that thep-value leads to a better spectral clustering performance than the standard Gaussian kernel when there is some noise in the collected data.
Integrated System for Face Detection, Clustering and Recognition

Recent years, many approaches have achieved remarkable performances in face detection, clustering and recognition. However, real life can barely see systems designed for their combination, and thousands of videos and images need to be handled in a timely manner. In this paper, an end-to-end face detection, clustering and recognition system has been proposed. The input of our system can be videos or images. For videos, we firstly utilize ffmpeg to transfer these collections into separated frames and for images this step can be skipped. Secondly, we employ joint detection and alignment to detect faces in frames. Then the clustering step is conducted to find face relations based on Interpretive Structure Model. Finally, we establish our own dataset and train our own classifier to realize face recognition based on FaceNet. Most important of all, we propose our original clustering method which avoids duplicate feature computation and repetitive face recognition. And it enhances the efficiency of handling data and shortens the runtime of our experiments greatly. This system is evaluated using our designed database of 43 persons' faces with varying scales and poses obtained on different complex backgrounds. The performance of the system is quite good and it achieves average accuracy of 87% to 92%.
Vector-Sensor Array DOA Estimation Using Spatial Time-Frequency Distributions

By making use of the extra particle velocity information, an array of vector sensors can achieve better Direction-of-arrival (DOA) estimation performance than a conventional array of pressure sensors. In this paper, we develop a new approach which exploits the inherent time-frequency-space characteristics of the underlying vector-sensor array signal to achieve better DOA estimation performance even in a noisy and coherent environment with few snapshots. Computer simulations with several frequently encountered scenarios, such as a single source and multiple closely spaced coherent sources, indicate the superior DOA estimation resolution of our proposed approach as compared with existing techniques.
Secret Sharing Application for Two-dimensional QR Barcode

A secret sharing system based upon two-dimensional QR barcode is proposed in this article. The proposed approach generates shadows in a specific way and utilizes the characteristics of QR code to embed shadows into cover QR codes. Each cover QR code itself is a valid QR code which can be scanned and decoded by a general QR code reader. According to the experiments, the proposed approach can satisfy the content readability of the QR code and can be distributed via public channels without raising suspicion.
Deep Learning-Based Sinogram Completion for Low-Dose CT

Patient radiation dose associated with $X$-ray CT is a significant concern in the medical community. One of the ways to reduce patient dose is to acquire projection data at fewer angles. Using conventional reconstruction methods with such sparsely sampled data introduces severe streaking artifacts in the reconstructions, that reduces their diagnostic utility. Conventional methods in this domain have focused on postprocessing the artifact-filled images or the use of model-based inversion techniques with image-domain priors. In this work, we examine the potential of a deep-learning-based method to construct a mapping from the observed, sparsely sampled, CT projection data to a set of densely sampled projection estimates directly in the original projection domain. We cast the problem as one of sinogram in-painting and focus on completing the projection data prior to reconstructing the images. As compared to existing work, we focus on “correcting” the data rather than the subsequent images and avoid costly iterative tomographic inversion. Our initial results on a simulated dataset demonstrate the potential effectiveness of this new approach in suppressing artifacts.
Application of data mining for young children education using emotion information

The current preschool education is still facing many difficulties. First, unlike primary and secondary schools which have test scores, there are few good ways to assess the learning situation of young children. Second, it is hard for teachers and parents to give thoughtful care all the time to each child. In particular, recent incidents of child abuse have been exposed frequently, causing social panic. However, as research of young children's mental health and emotion is still in its infancy due to the lack of relevant data, methods that focus on measuring and analyzing young children's mental health and emotional states are lacking. Furthermore, on the one hand, the principal goal of preschool education is to stimulate interest and enhance cognitive ability. On the other hand, assimilation and accommodation which are two specific stages of cognitive development require an active learner, not a passive one. Thus, emotion analysis can solve these problems to some extent. In this paper, we design an intelligent system, obtaining video clips in a kindergarten's classroom and managing to leverage the emotion data to portray cognitive learning rules and mental states of young children. At the same time, with the augmentation by data analysis, it brings broader applications developing educational policy and teaching practice. For example, children with abnormal behaviors such as violent mood swings and unusual time nodes during courses can be detected and notified to parents and teachers. And it is meaningful to measure the children's acceptance and preference of course activities and content which has been explained in our experiments.
Quad Band SRR Loaded Square Slot Antenna

This paper proposes a Split Ring Resonator (SRR) loaded quad band microstrip slot antenna designed to operate at 2.42 GHz, 4.33 GHz, 8.46 GHz and 10.9 GHz. The additional resonant bands are generated by loading the square slot antenna with two pairs of SRR structures on either side of the feed line. The bands centered at 8.46 GHz and 10.9 GHz are found to exhibit wideband and circular polarization properties respectively. The resonance band at 8.46 GHz provides a wide bandwidth of 2.43 GHz. One pair of the loaded SRR structure consists of two spiral shaped connected SRR's. The excitation of these structures gives rise to a circularly polarized band at 10.9 GHz with 3-dB axial ratio bandwidth of 390 MHz. Maximum gains of 3.82 dB, 3.95 dB, 7.5 dB and 4.2 dB are obtained at the four resonant frequencies, respectively. A minimum bandwidth of 270MHz is observed for all the bands of operation.
An SDN QoE Monitoring Framework for VoIP and Video Applications

Over the past years there has been a rapid rise of the mobile communications industry which, combined with the limitations of the current communications networks' structure, necessitates the development of new networks with increased capabilities, so that users can be served with optimal quality of service and network resources utilization. Software Defined Networking (SDN) is a networking approach which transforms the network elements to simple forwarding devices, making decisions centrally. The quality of service perceived by the user, or Quality of Experience (QoE), is considered to be a matter of great importance in software defined networks. This paper presents the SDN QoE Monitoring Framework (SQMF), an SDN application that monitors and preserves the QoE for VoIP and video applications, by periodically monitoring various network parameters and using QoE estimation models.
DAS Modifications for More Efficient Network Cost in 5G

Demanding goals have already been described concerning the next generation of mobile networks. In order to address these requirements, novel technologies are introduced or new concepts are integrated into conventional ones. In this paper, a modified Distributed Antenna System (DAS) architecture is described and a Sensitivity Analysis (SA) indicates which of the network cost parameters are the most influential ones concerning this model. Several hardware components are replaced with Network Function Virtualization (NFV) techniques, which are introduced, so as to contribute in reducing capital and operational expenses. The model is described mathematically, is compared and contrasted with the conventional model proposed by authors in past research activity. Simulation scenarios help concluding into fundamental results concerning the most dissuasive cost factors e.g. bandwidth, frequency, capacity, coverage, etc.
Two Tier Hox Collinearity Mediates Vertebrate Axial Patterning

A two tier mechanism mediates Hox collinearity. Besides the familiar collinear chromatin modification within each Hox cluster (nanocollinearity), there is also a macrocollinearity tier. Individual Hox clusters and individual cells are coordinated and synchronized to generate multiscale (macro and nano) collinearity in the early vertebrate embryo. Macro-collinearity is mediated by three non-cell autonomous Hox–Hox interactions. These mediate temporal collinearity in early NOM (non-organizer mesoderm), time space translation where temporal collinearity is translated to spatial collinearity along the early embryo’s main body axis and neural transformation, where Hox expression is copied monospecifically from NOM mesoderm to overlying neurectoderm in the late gastrula. Unlike nanocollinearity, which is Hox cluster restricted, axial macrocollinearity extends into the head and EAD domains, thus covering the whole embryonic anterior-posterior (A-P) axis. EAD: extreme anterior domain, the only A-P axial domain anterior to the head. The whole time space translation mechanism interacts with A-P signaling pathways via “decision points,” separating different domains on the axis.
Evaluating the Effectiveness of Head-Mounted Display Virtual Reality (HMD VR) Environment on Students' Learning for a Virtual Collaborative Engineering Assembly Task

The emerging VR social networks (e.g., Facebook Spaces, Rec Room) provide opportunities for engineering faculties to design collaborative virtual engineering tasks in their classroom instruction with HMD VR system. However, we do not how this capacity will affect students' learning and their professional skills (e.g., communication and collaboration). The proposed study is expected to fill this research gap and will use a mixed-methods design to explore students' performance and learning outcomes in a virtual collaborative automotive assembly task. The quantitative data will be collected from the pre-and-post task survey and the task itself. This data will be used to analyze the differences among experiment and control groups. Students' responses to the open questions in the post-task survey will serve as triangulation and provide deep insight for the quantitative results. The study is expected to not only contribute to the research field but also benefit different stakeholders in the engineering education systems.
Countering illegal UAV flights : passive DVB radar potentiality

Since a few years, illegal UAV(Unmanned Aerial Vehicle) flights have been observed in different countries and under various environments[1]. Following these malicious fligths, most of the countries decided to counter such illegal activities asking their main industrial and laboratories to provide an already existing global solution against such UAV activities. However, it seems that up to now, none active system has been validated as the universal ideal solution. This paper will analyse the main system requirements. Then the potential of a passive DVB-T (Digital Video Broadcasting- Terrestrial) component will be illustrated using experimental data for both the detection, identification, location and alert functions:•The expected benefits of such a passive DVB-T solution will be analysed by considering experimental results on multi-rotors UAV as well as on fixed wings UAV. •Furthermore, some limitations of such a passive countering UAV component/system will be outlined.In summary, the passive DVB-T component offers very attractive capabilities for struggling against UAV at ranges up to three kilometres and even more in terms of detection, identification, location and alert.The interest of passive component for struggling against UAV is quite new [2] and it has to be noticed that several papers were dedicated to such an application during the last Passive Coherent Location Meeting in Bonn Wachtberg in 2017[3][4][5].
Sensitivity Analysis of Primary-Side LCC and Secondary-Side Series Compensated Wireless Charging System

This paper presents the sensitivity analysis of primary-side LCC tuned and secondary-side series tuned wireless charging system with experimental validation. The primary and secondary coils have been modeled as a loosely coupled transformer to obtain a circuit model which can be analyzed for parametric sensitivity. Furthermore, sensitivity to variation in coupling coefficient and load are presented. To prove the effectiveness of the theoretical analyses, a test setup was built and tested up to 10kW, demonstrating the operation of the primary-side LCC and secondary-side series wireless power transfer system. The circuit model derived using the loosely coupled transformer model is verified experimentally by using a frequency response analyzer. The experimental results confirmed that the sensitivity analysis can be accurately used for future system designs.
Entropy-based spammer detection

In recent years, many studies have been conducted to detect spam email or spammer (the member of spamming botnets). Machine learning is the most common method for solving this problem. For spammer detection, however, the accuracy of discriminating legitimate users and spammers is heavily dependent on the selection of features used by machine learning algorithms. In this paper, we propose a novel group of features and combine these features with the recently proposed deep forest learning algorithm called gcForest to train a classification model to detect spammer. These features used by our method to represent a mail user include: entropy of subject size of sent mails, entropy of content size of sent mails, ratio of received mail count to sent mail count. To evaluate the effectiveness of our proposed features on classification, we conduct combinational tests for our proposed features and another group of features used by SpaDes method, which has achieved an impressive performance on spammer detection, together with three learning algorithms, i.e. gcForest, SVM (Support Vector Machine) and ALAC (Active Lazy Association Classification) which is used by the later version of SpaDes. The results show that the combination of our proposed features and gcForest approach performs well on classification accuracy to the point of 93.28%. In the same time, the performance achieved by SpaDes's features with gcForest is very close to ours. Nevertheless, our features have the obvious advantage on processing time.
A Project-Based Learning Method Applied to an Introductory Course in Electronics Engineering

We present an application of a modified version of the project-based learning (PBL) method in the context of an introductory course in electronics engineering. The project is aimed at solving a real-life communication problem to boost the students' enthusiasm and participation, which are essential motivational aspects to succeed at university. Therefore, the students play an active role in learning the concepts of electronics involving design, construction, and testing of circuits and embedded software. The presented case covers a broader area of knowledge and skills than previously reported PBL introductory courses. We briefly address the criteria used to organize the experiment-oriented lectures and the discovery-oriented labs, which in turn are driven by the project itself. Moreover, the students feedback is analyzed to improve actual PBL implementations and encourage the application of the PBL method from the beginning of the engineering education curricula.
Data-Driven Evaluation of Anticipatory Networking in LTE Networks

Anticipatory networking is a recent branch of network optimization based on prediction of the system state. Our work specifically tackles prediction-driven resource allocation for mobile networks. While some anticipatory networking concepts have been proposed in the literature, understanding of the potential real world gains is so far very limited. Future mobile networks will likely integrate such mechanisms, and thus it is of paramount importance to understand the actual performance improvements and in which scenarios they can be realized. Analyzing a month of LTE control channel information collected in four locations, we show how anticipatory networking can enhance current LTE networks. First, we propose a comprehensive optimization framework encompassing different forecasting solutions. Then, we provide a thorough analysis of the aggregated network traffic and the contributions of individual users. In particular, we show that predictable traffic accounts for more than 95 percent of the total traffic volume and that simple prediction and optimization techniques allow network operators to save 50 percent of the resources and/or on average more than double the offered data rate in our data set.
Peri-Implant Diseases

Abstract Osseointegrated dental implants have become an increasingly popular modality of treatment for the replacement of absent or lost teeth because of its high rates of long-term survival when used to support various types of dental prostheses. However, complications and implant failure can still occur and are considered by many clinicians as a major obstacle for implant treatment. Biological complications mainly refer to inflammatory conditions of the soft tissues and bone surrounding implants and their restorative components, which are induced by the accumulation of bacterial biofilm. Two clinical varieties may be distinguished: peri-implant mucositis and peri-implantitis. Peri-implant mucositis is a reversible, plaque-induced inflammatory lesion confined to the peri-implant soft tissue unit, whereas peri-implantitis is an extension of peri-implant mucositis to involve the bone supporting the implant. Diagnosing and managing these biological complications is of utmost importance for the implant surgeon and dental practitioner. This review encompasses the etiology, diagnostic aspects, prevention, and management of biological complications.
3-DOF planar parallel-wire driven robot with an active balancer and its model-based adaptive control

ABSTRACT This paper has proposed a parallel-wire driven robot (PWDR) with an active balancer, which is notably useful for such applications as ceiling maintenance and object conveyance near a ceiling in a factory. Because this robot is an under-actuated system, the uncertainty of the inertial parameters of the load strongly affects the resultant motion and reduces the control accuracy because of the dynamics interference. However, to date, the dynamics of this robot has not been thoroughly elucidated. Thus, this study analyzes the dynamics of a PWDR that controls three degree-of-freedom using two wires and an active balancer. Moreover, based on the dynamic analysis, a model-based adaptive controller for the parameter uncertainty of a load is proposed, and its effectiveness is demonstrated through simulation. GRAPHICAL ABSTRACT
Assessing Security of Cloud Services in Malaysian Universities A review

Currently, The cloud computing emerged due to developing rapidly of technology, an Internet-based development in which dynamically scalable and often virtualized resources are provided as a service over the Internet, universities are reviewing IT services and considering common service options. Studies suggest that a wide range of IT services can be shared in higher education that offer several potential features. Nowadays, the security of information becomes more substantial. The aim of this paper analyzes the security issue in cloud computing and review the effect of cloud data privacy on the transition to the cloud computing system in the Malaysian Universities.
Formación y antecedentes de la lealtad del paciente en el contexto de los servicios médicos privados

Due to the increasing competitiveness in the medical activity, patients loyalty to doctors have become as a key factor in medical services management. The purpose of this study is to establish how satisfaction, trust and commitment affect patients’ loyalty to their doctor in the private health services context. Valid and reliable scales are built and a proposed theoretical model is proved through structural equations method. Results show that the best model to explain the formation of loyalty starts with satisfaction and it is mediated by trust and commitment, building a chain relationship. ESTUDIOS DE ADMINISTRACIÓN, VOL. 18, No 2 (2011), PP. 49-78 50 These results are supported by contrasting the proposed model with many other alternative models, concluding an optimal structure for loyalty formation based on the studied variables.