Miracle-dz/UniFAR-FTUs huggingface dataset:
Study of a Conical DRA Array with Enhanced Bandwidth and Gain for C-Band Applications

In this paper, a conical dielectric resonator antenna (DRA) array with two linear elements and circular polarization is proposed for C-band applications. The proposed elementary antenna is composed of two layers of the substrate arranged vertically and excited from the feed slots. The first layer is the Rogers RO3010 with $\pmb{\varepsilon_{1}}=\pmb{10.2}$ and tan $\pmb{\delta=0.0035}$, and the second one is the Teflon with $\pmb{\varepsilon_{2}=2.1}$ and tan $\pmb{\delta=0.001}$. The inclination of the two layers relative to the slot feed is equal to 45° thus creating a circular polarization. The main goal of this technique is to increase the antenna performance in terms of bandwidth and gain. The reflection coefficient, radiation patterns, gain and axial ratio of the proposed antenna are simulated using Ansoft HFSS and validated by CST Microwave Studio.
Learning How to Drive in Blind Intersections from Human Data

In this paper we present a method to learn how to drive in different types of blind intersections using expert driving data. We cluster different intersections based on the velocity of how drivers approach them, and train a linear SVM classifier for each class of intersection. Through clustering we found that there were three different classes of intersections in typical residential areas in Japan. We used inverse reinforcement learning (IRL) to build a driving model for each type of intersection. The models were trained from 308 trajectories traversed by 5 different drivers. The models and policies were implemented and evaluated in a ROS simulator where the agent is provided a global path, and upon it reaching an intersection, it selects the appropriate trained policy. By doing this, the simulated autonomous vehicle can perform proactive safe driving behaviors when approaching blind intersections.
A User Study on MR Remote Collaboration Using Live 360 Video

Sharing and watching live 360 panorama video is available on modern social networking platforms, yet the communication is often a passive one-directional experience. This research investigates how to further improve live 360 panorama based remote collaborative experiences by adding Mixed Reality (MR) cues. SharedSphere is a wearable MR remote collaboration system that enriches a live captured immersive panorama based collaboration through MR visualisation of non-verbal communication cues (e.g., view awareness and gestures cues). We describe the design and implementation details of the prototype system, and report on a user study investigating how MR live panorama sharing affects the user's collaborative experience. The results showed that providing view independence through sharing live panorama enhances co-presence in collaboration, and the MR cues help users understanding each other. Based on the study results we discuss design implications and future research direction.
Novelty Detection and Analysis with a Βeta-DVAE Network

In this paper we apply generative modeling to Gaussian Mixture Models (GMM) as a solution to high dimensional novel event detection and analysis on radio frequency (RF) power generators. A family of Denoising and β-Disentangling Variational Autoencoders β-DVAE) is used to encode datasets into lower and more salient dimensions. The reduced feature sets are modeled using GMMs where model parameters are learned using the Expectation Maximization (EM) algorithm. The data is obtained from two different generator models operating under normal as well as known not-normal conditions. This approach is also tested on standard classification sets. Robust testing is reported to achieve a target class accuracy of 98.16% for the target RF generator. Additionally, the GMM parameters are decoded by the generative model to the original data space for calculating per-variable fitness in order to provide an initial novel event analysis for engineers and technicians. This per-variable fitness is very critical for determining each variable's contribution to a novelty so that engineers can perform informed trouble shooting and maintenance of the RF generators.
Similar Gesture Recognition using Hierarchical Classification Approach in RGB Videos

Recognizing human actions from the video streams has become one of the very popular research areas in computer vision and deep learning in the recent years. Action recognition is wildly used in different scenarios in real life, such as surveillance, robotics, healthcare, video indexing and human-computer interaction. The challenges and complexity involved in developing a video-based human action recognition system are manifold. In particular, recognizing actions with similar gestures and describing complex actions is a very challenging problem. To address these issues, we study the problem of classifying human actions using Convolutional Neural Networks (CNN) and develop a hierarchical 3DCNN architecture for similar gesture recognition. The proposed model firstly combines similar gesture pairs into one class, and classify them along with all other class, as a stage-1 classification. In stage-2, similar gesture pairs are classified individually, which reduces the problem to binary classification. We apply and evaluate the developed models to recognize the similar human actions on the HMDB51 dataset. The result shows that the proposed model can achieve high performance in comparison to the state-of-the-art methods.
Why and How VCO-based ADCs can improve instrumentation applications

After introducing the basic concepts, we will describe the different VCO-based ADC architectures for instrumentation and biomedical applications published in the recent years. Both open loop and closed loop, high order architectures will be analyzed and compared to other standard instrumentation ADCs. The main solutions given to problems associated with VCO-based ADCs in instrumentation, like VCO's phase noise, linearity and sampling metastability will be reviewed. The comparison with other architectures reveals that the FoM of VCO-based ADCs lags that of conventional $\Delta\Sigma$ modulators. Nevertheless the total power consumption, area and performance make them fairly superior when we consider the total efficiency of the instrumentation system, including signal conditioning pre-amplifiers and references.
Robust Discrete-Time Sliding Mode Control with Arbitrarily Small Boundary Layer

The robust discrete-time sliding mode control problem is addressed. A novel approach to estimate the matched disturbance is presented. This approach is based on the approximation of a discrete-time function actual value by using various previous steps of itself. The matched disturbance previous step is calculated from the system model and then, using the mentioned approach, its actual value is estimated with an error of exponential order w.r. t. the sampling time and the used number of previous steps. Simulations show the effectiveness of the proposed approach to estimate a discrete time function and to robustly stabilize a dynamic system.
Application of Characteristic Mode Theory in HF Band Aircraft-Integrated Multiantenna System Designs

The large wavelength at high-frequency (HF) band imposes critical challenges in platform conformal HF antenna designs, especially when multiple HF antennas are required in multifunction aircraft electronic systems. This paper presents an approach for the design of the aircraft-integrated multiantenna system based on the theory of characteristic modes (CMs). By investigating the CMs of the aircraft body in the entire HF band, three types of radiation patterns are synthesized at three frequency bands centered at 8, 18, and 28 MHz, respectively. The synthesis is achieved through the linear weighting combinations of the first few dominant CMs, and a multiobjective evolutionary algorithm is implemented to seek the complex weighting coefficients in each band. Following the theoretically synthesized radiating currents, three pairs of exciters are then designed and flush mounted on optimal locations over the aircraft. Furthermore, properly designed LC matching networks are introduced in each exciter to get good impedance matching in each band. Because of the large physical size of the aircraft, scaled model of the proposed aircraft-integrated multiantenna system was fabricated and experimentally verified. Measurement results are in good agreement with simulated results and demonstrate the effectiveness of the proposed approach in the designs of HF aircraft-integrated multiantenna systems.
Microwave Characteristics of a Conductor Backed CPW by a Home Printed Electronics Technology with Silver Nanoparticle Ink

Various printed electronics with conductive ink are proposed for IoT technology in the radio frequency region. Recently, we can be available a home ink-jet printer with silver nanoparticle ink, readily. However, there are few reports to apply this technology in microwave and millimeter wave regions. In order to investigate the possibility of this technology, transmission characteristics of a conductor backed coplanar waveguide by the home ink-jet printer with silver nanoparticle ink are evaluated in frequency ranges from 50 MHz to 18 GHz. Moreover, complex permittivity of special paper and PET film and conductivity of a printed silver conductor are evaluated in millimeter wave region. As a result, the printed transmission line has been characterized with a loss of 0.2 dB/mm at 18 GHz.
Analysis of Factors that Influence Purchase Intention on Omni-channel Services

The emergence of online and new digital media such as mobile applications and social media changed the retail business model and its consumers behaviors. These changes bring new challenges for companies and their stores to manage various channels. This phenomenon creates new idea to integrate them into Omni-channel services. It requires the readiness and complex maintenance because each channel has different characteristics. The consumers find difficulties in ensuring their transactions to be accurate and trustable. This study aims to analyze the factors that influence consumers to use Omni-channel services. It adopts the UTAUT2 with two new variables, such as personal innovativeness and perceived security. The quantitative approach is demonstrated using PLS-SEM with 168 respondents. As the results, social influence variable becomes the most influencing factor on purchasing intention in Omni-channel services, followed by performance expectation, and security perception. These factors become priority when a company formulates strategies for its omni-channel.
Selective Hardening for Neural Networks in FPGAs

Neural networks are becoming an attractive solution for automatizing vehicles in the automotive, military, and aerospace markets. Thanks to their low-cost, low-power consumption, and flexibility, field-programmable gate arrays (FPGAs) are among the promising devices to implement neural networks. Unfortunately, FPGAs are also known to be susceptible to radiation-induced errors. In this paper, we evaluate the effects of radiation-induced errors in the output correctness of two neural networks [Iris Flower artificial neural network (ANN) and Modified National Institute of Standards and Technology (MNIST) convolutional neural network (CNN)] implemented in static random-access memory-based FPGAs. In particular, we notice that radiation can induce errors that modify the output of the network with or without affecting the neural network’s functionality. We call the former critical errors and the latter tolerable errors. Through exhaustive fault injection, we identify the portions of Iris Flower ANN and MNIST CNN implementation on FPGAs that are more likely, once corrupted, to generate a critical or a tolerable error. Based on this analysis, we propose a selective hardening strategy that triplicates only the most vulnerable layers of the neural network. With neutron radiation testing, our selective hardening solution was able to mask 40% of faults with a marginal 8% overhead in one of our tested neural networks.
An Environmental Perception and Navigational Assistance System for Visually Impaired Persons Based on Semantic Stixels and Sound Interaction

Assistive technologies aim at enhancing personal mobility of individuals with disabilities to improve their independence and access to social life. For the visually impaired, perception during navigation comprises a major ingredient of independent living. With the development of computer vision, it is possible to meet the richer needs of visually impaired people. However, research on navigation assistance for the visually impaired is still relatively unexplored when compared with the active progress in autonomous driving which is already in full swing. In respond to this issue, we aim to leverage the study of the Stixel-World for automotive systems and transfer it to develop assistive technology for visually impaired people. The impressive research results of deep learning also suppose benefits for vision-based technology. Precisely, semantic segmentation is a task that enables identification of different objects uniformly. Inspired by these observations, we design a set of wearable visual aids, while the core algorithm is based on the stixel representations for three-dimensional world combined with pixel-wise semantic segmentation. Predetermined conditions for stixels in automotive research, such as camera angles, position fixes, and unsuitable assumptions made about the real world are optimized to fit the needs of navigation assistance in our algorithm, along with the incorporation of traversability-related semantic information. We also propose a sound mapping scheme, so that the environmental awareness about geographic and semantic information are conveyed to the visually impaired through acoustic feedback.
Two Wheels Line Following Balancing Robot Control using Fuzzy Logic and PID on Sloping Surface

Balancing robot proposed in this paper is a robot that can follow the line and also be balanced on the sloping area while moving only by two wheels on the right and left side of robot. Distance controller is used for the movement of the robot that uses rotary encoder sensor to calculate its traveled distance and uses Proportional Integrated Derivative controller to convert it to angle. That angle will change the center of gravity of robot to make the robot can keep moving forward on the flat or sloping area. The balancing control used in this robot is Fuzzy Logic to maintain the robot stable on the set point position. An angle error for Fuzzy Logic input combines the tilt feedback from IMU and the output of the distance controller. To be able to follow the line, robot uses Proportional Derivative controller. The output of line following control will be combined with the output of balancing control to make robot balanced while following the line. By combining three controllers above, robot is able to balance not only on flat area but also on the sloping area while moving to follow the line.
Survey on Advances in Magnetic Induction-Based Wireless Underground Sensor Networks

Underground communication systems present a variety of new research challenges. Here, the goal is to establish an efficient wireless connection between the transceivers in the challenging underground medium. Typical applications for this type of communication systems include soil condition monitoring, earthquake prediction, communication in mines/tunnels, etc. These applications require a gathering of relevant information from multiple locations, which suggests the use of multiple sensor nodes that would be organized in wireless underground sensor networks (WUSNs). Due to the harsh propagation conditions in the soil medium (including rock, sand, and water sheds), traditional wireless signal propagation techniques using electromagnetic waves can only be applied for very short transmission ranges. In recent years, magnetic induction (MI)-based transmission has been proposed to overcome these issues. In this approach, induction coils are utilized as antennas in the transceivers in order to reduce the vulnerability of signal propagation to the soil properties, in particular the soil conductivity. Correspondingly, the design rules for optimum MI-WUSNs have been shown to substantially differ from the design rules for the traditional wireless communication systems due to unique properties of the transmission channel. In this survey paper, the recent advances in the area of MI-WUSNs are discussed, which range from signal transmission techniques and network design to wireless power transfer and localization. Also, new research challenges in this area are provided.
Vision Memory for Target Object Navigation Using Deep Reinforcement Learning: An Empirical Study

Recently, a number of methods have been conducted by combining deep neural network and reinforcement learning to solve problems. Neural networks have strong ability to deal with high dimensional data and a good mean to learn features while reinforcement learning allows a system to learn by experience optimal action control and adjust its behavior to new environments. It thus may be used in mobile robot navigation tasks with perceptron as deep neural network and learning to control by reinforcement learning. We first investigate some issues in agents learning to navigate in indoor environments such as how robot memorize vision information and how it discovers the environments. We show that agents can rely on some position with vision information in navigation task such as reduce training time and locating object efficiently. Some approaches based on deep reinforcement learning are step by step discussed and proposed to deal with each problem in target object navigation task. The key ideas include adding checking point as vision memory and using auxiliary learning task to enhance agents to discover the environment.
Data Flow in Low-Power Wide-Area IoT Applications

Emerging new technologies have brought a paradigm shift to the world of wireless sensor networks. Whereas in the previous decade the majority of research effort was focused on “traditional” multi-hop ad-hoc networking due to the underlying low-power (and consequently short-range) communication, the emergence of long-range low-power wireless technologies such as LoRaWAN and NB-IoT changed the way we think about the massive scale M2M networking. Consequently, a change to the rules and the philosophy introduced a new scope of problems and challenges, when it comes to the adoption of new technologies, as well as their integration into a broader IoT ecosystem. In this paper, we give an overview of the various ways to establish reliable and secure data path between wireless sensor nodes on one side, and web-or cloud-based IoT applications on the other side.
Integrated Telegram and Web-based Forum with Automatic Assessment of Questions and Answers for Collaborative Learning

We designed, developed and implemented an integrated Telegram and Web-based Forum capable of automatic thoughtfulness assessment of questions and answers, using text mining and Natural Language Processing (NLP) methodologies, for collaborative learning. We trained and applied the Random Forest algorithm to provide instant thoughtfulness score prediction for the new posts contributed by the students, and prompted the students to improve on their posts, thereby invoking deeper learning. In addition, the platform is designed to ensure that students remain engaged on the platform. Our initial implementation for a university course on spreadsheets modeling using Excel, shows some promising results which strengthened our belief that through the process of asking good questions, and providing good answers, students' learning can be improved beyond the traditional classroom, and beyond instructor-student relationship.
Mean Payoff Supervisory Control Under Partial Observation

The problem under investigation is mean payoff supervisory control on a partially observed quantitative discrete event system modeled by a finite state weighted automaton. We intend to design a partial-observation supervisor such that the limit-average weights of all infinite sequences in the supervised system remain nonnegative. This problem may be viewed as a two-player quantitative game between the supervisor and the environment, with asymmetric information and a mean payoff objective. To cope with partial observation of the supervisor, we introduce the energy information state which incorporates information about both state estimate and energy change for supervisor's decision making. Based on that, we transfer the supervisory control problem into a two-player reachability game under full observation and propose a finite bipartite structure called First Cycle Energy Inclusive Controller (FCEIC). Further analysis demonstrates that winning strategies in the FCEIC lead to solutions to the original control problem.
A New Morphological Annotation Tool for Arabic Texts

We present a new Arabic annotation web application that aims to associate tags to the words of the vocalized Hadiths corpus. The application assures a collaborative space between many annotators. Each annotator treats one or more Hadith from his/her personal session. We analyze each Hadith using AlKhalil morphological analyzer. The annotator rectifies the analysis and assigns the accurate annotation values. Annotation includes the segmentation of a word, the POS tag, the root and the pattern. The final annotation results of the corpus are strcutured in XML files. We can display, also, the annotation results in a Treebank. Our tool is considered as a crowdsourcing solution that uses contributions from various annotators to obtain an annotated Arabic coprus. This resource may be used as a standard collection to experiment NLP researches.
RETUNES: Reliable and Energy-Efficient Network-on-Chip Architecture

As the number of cores integrated on the chip increases, the design of reliable and energy-efficient Network-on-Chip (NoC) to support the data movement needed by the multicores is becoming a critical challenge. Reliability of NoC is affected by several aging effects such as Hot carrier injection (HCI) and Negative Bias Temperature Instability (NBTI) which vary the threshold voltage of the transistor, causing timing errors. Dynamic Frequency and Voltage Scaling (DVFS) along with Near Threshold Voltage (NTV) scaling allows the transistor to operate close to the threshold voltage, thereby aggressively minimizing dynamic power consumption by reducing voltage/frequency and minimizing the threshold voltage variation mitigating aging process. However, the trade-off is increased latency and reduced reliability due to lower voltage margins. In this paper, we propose a unified approach called RETUNES: Reliable and Energy-Efficient NoC where NTV scaling and reliability are both achieved while improving performance. RETUNES is a five-level voltage/frequency scaling scheme, which decreases power consumption and threshold voltage variation (? Vth) during low network load with higher reliability and increases the network performance during high network load with reduced reliability. In order to even out the wear out and minimize the impact of aging in NoC, we propose an adaptive routing algorithm in our design. RETUNES improves, total power savings by nearly 2.5x and the energy-delay product (EDP) of the NoC by 3x for Splash-2 and PARSEC benchmarks on a 4 x 4 concentrated mesh architecture.