Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of an automatic shutdown system for lighting and air conditioning by using Esp8266 to meet energy efficiency requirements in buildings

The energy efficiency is a subject that has been discussed worldwide. The technological advances allow the production of equipment that consumes less energy than old ones that perform the same work. Research on energy efficiency in buildings has gained a lot of attention lately and it can be optimized by evaluating parameters such as the installed equipment, the physical construction of the building or the habits of users. This article studies the construction of a system using the Esp8266 microcontroller, the Node-RED platform, among other devices. This system was based on RQT-C manual to provide an automatic shutdown of the lighting and air conditioning systems. It was installed at a research laboratory at Federal Institute Fluminense campus Campos Centro. The results obtained in 30 days after the equipment installation shows that approximately 20% of the energy was saved when it compared to the same period without the system.
Automatic parameter tuning framework for performance diagnosis report

For ICT system operators, performance diagnosis reports that summarize the status and problems of systems in an easy-to-understand manner are important information for operating systems stably and maintaining normal conditions. In making the report, each analysis technique used for diagnosis needs its parameters tuned properly in accordance with the environment of the system. However, it is difficult for system operators to tune these parameters because they usually seem to not have enough knowledge on analysis. In this paper, we propose a framework for automatic parameter tuning that updates the parameters of analysis techniques by analyzing comments corrected by system operators in reports written in natural language. By using the framework, we confirmed that a system operator who does not have enough knowledge on analysis can easily tune the parameters of analysis techniques in accordance with the system environment.
Analysis of shielding enclosures based on CFRP materials

The main topic of this paper is the analysis of shielding enclosures based on CFRP materials. The analysis starts on material level with a determination of the electrical properties of CFRP. Also the magnetic shielding effectiveness of a CFRP material sample was measured and discussed. For the analysis on enclosure level, a test enclosure was prepared and also a numerical model for further investigations and parameter studies. At last, measurement and simulation results for parameter studies were developed and compared to an Aluminium enclosure. The varied parameter was the number of screws between the enclosure parts. A huge influence of the parameters and materials was observed in the measurement and simulation results. The simulation model could be used for a quick analysis of an enclosure based on CFRP material.
Reconstruction based Fault Prognosis in Dynamic Processes using Canonical Variate Analysis

Incipient fault prognosis is an important task in industrial process monitoring, as it deals with the prediction of the degradation of a faulty process until a failure threshold is reached. For multivariate processes working under varying operating conditions, the existing challenge is to distinguish the non-stationary fault trend from the usual process dynamic trends present in the measured signals. In this paper, canonical variate analysis with Kalman filtering is proposed to reconstruct the fault signal against a reference signal predicted from a state-space model of the normal process. Vector autoregressive (VAR) modelling is then used for the long-term prediction of fault signals in order to compute the remaining useful life (RUL) of the process. Hence, the approach constitutes an efficient fault prognosis method for dynamic multivariate processes. The effectiveness of the proposed method is demonstrated using incipient fault case studies in a chemical reactor process.
An Ultra-wideband Battery-less Positioning System for Space Applications

An ultra-wide bandwidth (UWB) remote-powered positioning system for potential use in tracking floating objects inside space stations is presented. It makes use of battery-less tags that are powered-up and addressed through wireless power transfer in the UHF band and embed an energy efficient pulse generator in the 3-5 GHz UWB band. The system has been mounted on the ESA Mars Rover prototype to demonstrate its functionality and performance. Experimental results show the feasibility of centimeter-level localization accuracy at distances larger than 10 meters, with the capability of determining the position of multiple tags using a 2W-ERP power source in the UHF RFID frequency band.
Multi-core Fiber Technology supporting MIMO and Photonic Beamforming in 5G Multi-Antenna Systems : (Invited paper)

This paper describes and experimentally validates different multi-antenna system applications supported by a multicore fiber (MCF) optical fronthaul. The MCF fronthaul enables the simultaneous transmission of different optical data streams at the same wavelength by the spatial multiplexing of multiple-input multiple-output (MIMO) signals. In addition, the MCF enables simultaneous radio beamforming by transmitting the same optical data signal with different phase or time delays to each antenna element. Both MIMO and beamforming capabilities are required for 5G multi-antenna systems. Experimental demonstrations of $4 \times 4$ MIMO transmission and multi-beam $4 \times 1$ beamforming using a commercially available 4-core fiber are reported in this work. Optical beam-steering in 5G is achieved using a Si3 N4 photonic chip based on optical ring resonators (ORRs) that enables the continuous and centralized tuning of the time delay applied to each antenna element.
One-step Prediction of Air Pollution Control Parameters using Neural-Like Structure Based on Geometric Data Transformations

The article has aimed at the investigation of a one-step prediction by neural-like structures using the Model of Geometric Data Transformations. The work describes the timestamp method for a one-step prediction. It has indicated that the smaller the time window for the selected parameters for environmental monitoring, the more accurate the prediction error. Moreover, in the article is proved that non-iterative linear neural-like structures of Geometric Data Transformations do better one-step prediction than Radial Bases Function neural network and linear neural-like structure combined with Radial Bases Function.
An Incident Field Receiving Antenna Based on Microstrip Structure for EMP Measurement

This paper proposes a new type of incident field receiving antenna for EMP measurement based on microstrip structure. The beginning of the microstrip structure designed can be excited and the end has no response. The two-coaxial feeder connected to a shielding shell is employed as the feeding line to eliminate the common mode interferences by just difference operation of the two outputs. Simulated results demonstrate the microstrip structure has a wide bandwidth of more than 1 GHz, good fidelity and high front/back ratio. The receiving antenna provides good performances with simulated fidelity coefficient of 0.999 and front/back ratio of 42.2 dB. It has the ability to recover the incident field of EMP while rejecting a reflected field.
Experimental Analysis of a Developed I-V Curve Tracer under Partially Shading Conditions

Photovoltaic technologies stand out for their reliability and durability. However, in order to verify the perfect conditions of a PV installation, it is necessary to carry out the commissioning process. One of the most important equipment for the commissioning process is the I-V trace curve. The I-V trace curve has a high cost, making it difficult for small installers of PV systems. At this point, the paper presents an I-V curve trace solution, with low cost and high precision. In order to do so, an I-V curve trace was designed and tested in situations of partial shading that generate curves of more difficult tracing. As a result, it was able to obtain values close to high-cost commercial equipment. Therefore, the developed I-V curve trace can be a solution for small PV system installers.
Wireless Sensors Converged Network Enhancement

This work deals with the proposition of machine-to-machine communications enhancement. Thus, a convergence between the wireless sensor network and the wireless mobile network has been studied. It is divided into three main parts; making use of one of our previously published work to enhance the wireless mobile network based on combatting the peak-to-average power ratio problem, building an actual wireless sensor network to observe a real data, and proposing a converged network as an enhancement. To validate the proposition, an extensive simulation has been performed based on observing some cretin performance parameters such as the round, the dying time, the consumed energy. Thus, the comparison has been made between the conventional wireless sensor network and the converged one. It shows a remarkable results and reaches 78% delay reduction. However, further studies should be made to investigate the routing algorithms in order to enhance the consumed energy by the sensor nodes.
Design Optimization of Resource Allocation in OFDMA-based Cognitive Radio-enabled Internet of Vehicles (IoVs)*

The problem of joint optimal subcarrier and transmit power allocation with quality of service (QoS) guarantee for enhanced packet transmission over a cognitive radio-enabled IoVs network system is considered in this paper. A novel Symmetric Nash bargaining solution (SNBS) based wireless radio resource scheduling scheme in orthogonal frequency division multiple access (OFDMA) cognitive radio (CR)-enabled IoVs network systems is proposed. Furthermore, a novel approach which uses Lambert-W function characteristics is introduced and by applying time-sharing variable transformation, closed-form analytical solutions were obtained. To avoid the iterative processes associated with searching the optimal solution numerically through iterative programming methods, this study developed a low-complexity algorithm. Theoretical analysis and simulation results demonstrate that under similar conditions, the proposed solutions outperform reference scheduler schemes.
Deep Learning Based Anomaly Detection Scheme in Software-Defined Networking

Software Defined Networking (SDN) has attracted more and more attention due to its prominent features that are different from the traditional network. SDN is programmable through which controller can modify the rules in the switch. However, security was not considered in its initial design, and many manufacturers no longer support Transport Layer Security (TLS) due to the cost. Although many machine learning based approaches have been implemented in SDN, they all need features that experts extract from original data. However, the manual extraction increases the level of human interaction and decreases detection accurate. This paper presents a malicious network traffic classification method based on Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) to address these concerns. Our proposed method is implemented in Graphic Process Unit (GPU) enabled TensorFlow. We evaluated our proposal on three datasets. The results demonstrate that our proposal achieves improvements in term of detection accuracy and stability over existing approaches and strong potential for user in SDN security.
Asynchronous Time Synchronization Protocol for WSNs

The simplicity of time synchronization is a necessary requirement for decentralized Wireless Sensor Networks (WSNs) to operate well in harsh environments. The averaging consensus control protocol (ACCP), that uses single hop communication, performs well in such environments. However, it works based on synchronous time synchronization mode which is unrealistic since the nodes in WSNs in real environment cannot exchange/update information at exactly same time. This paper proposes asynchronous realization/protocol for WSNs based on averaging consensus control. It works on a sequential manner and therefor called Timed Sequential Asynchronous Update (TSAU) protocol. The proposed protocol is tested by simulations and by experiments, and shown to achieve a small time synchronization error. Finally, TSAU is shown to be reliable since it maintains good time synchronization even when connectivity is not available all the time or when the network is under attack by a malicious node.
Building a V2X Simulation Framework for Future Autonomous Driving

Collecting surrounding vehicles' motion information is one of the key issues for accident prevention and autonomous driving. Although multi-vehicle simulation frameworks are widely provided, We need a platform that enable inter-vehicle V2X communications. In this work, based on the open source simulation platform, CARLA, we extend and implement several modules to build a V2X simulation framework. In the proposed framework, vehicles are allowed to share their profiles and sensory data through V2X communications. With the motion information of other vehicles, a car can thus make more intelligent decisions. To validate the effectiveness of the framework, we run simulations in variose scenarios. Each time, a primary vehicle is selected and then both its sensory data and received surrounding vehicles' information are output and recorded in a simulated dataset. It is shown that with the dataset and our multi-vehicle data fusion algorithm, the primary vehicle can visually see the driving status of surrounding cars, which can greatly help a vehicle to choose a better driving strategy. This work not only proposes a V2X communication-enabled multi-vehicle simulation framework based on CARLA, but also provides a low cost way to generate simulated V2X datasets.
Development of the Hyperband HPEM simulator satisfied with IEC61 000-4-36 standard

This paper describes a hyperband HPEM simulator to be capable of IEMI radiation immunity test satisfied with IEC61000-4-36 standard. The simulator has 90% energy bandwidth from 200MHz to 2.1GHz. And also, it can produce far-voltage of the 217kV and pulse repetition rate of 100Hz. It can be operated stand-alone with charging battery and has compact size of 1450mm χ 400mm χ 400mm in length, width and height, respectively.
A Capacitive SIW Discontinuity for Impedance Matching

We present a shunt capacitive substrate-integrated waveguide (SIW) discontinuity for the first time. The discontinuity is parametrically characterized in terms of its reactive impedance, and an equivalent circuit model is presented and validated. <inline-formula> <tex-math notation="LaTeX">$C$ </tex-math></inline-formula>-band prototypes are manufactured and measured, confirming the simulated results. The utility of the discontinuity is then demonstrated as a part of a matching network for an <inline-formula> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula>-band amplifier. A simple synthesis method for the matching networks is presented, which requires minimal numerical optimization. The amplifier is prototyped and measured and found to be in good agreement with simulation.
Maximum Power Point Tracking and Power/Voltage Regulation for Inductive Wireless Battery Charging

The imperfect coupling (or misalignment) between the inductively coupled coils in an inductive power transfer (IPT) system is a major problem of the wireless battery charging. This problem causes decreasing and fluctuations of the system efficiency and load power (or load voltage). In this paper, a new approach was introduced to deals with such a problem. The proposed approach based on the maximum power point tracking (MPPT) and load power/voltage regulation (PR/VR). The MPPT technology based on the impedance matching (IM), where the tuning capacitances are still varying to keep the resonant frequencies of the coils equal to the inverter switching frequency (fs). The proposed PR/VR was achieved based on the duty cycle control of the inverter; the fuzzy logic controller (FLC) was used to achieve that.
An Adaptive Perturb and Observe Method with Clustering for Photovoltaic Module with Smart Bypass Diode under Partial Shading

The photovoltaic maximum power point tracking using perturb and observe method has a fixed step size, where a small step size has a slower time response and more accurate steady-state, while a large step size is the opposite. This work proposes an adaptive step size, proportional to the difference between actual and previous power sample, providing a fast time response and reducing the oscillations at steady-state. The oscillations are smaller with adaptive step size, but they are not annulled and the method presents loss by power oscillations. The clustering is used to eliminate this loss, setting the result of the simple average of the last five voltage samples. The enhanced method has been tested on a 72-cell photovoltaic module with a smart bypass diode per cell under partial shading. Modeling and simulation have been implemented using MATLAB/Simulink. The proposal obtained a faster time response and elimination of oscillations at steady-state.
Integration of Camera and Inertial Measurement Unit for Entire Human Robot Interaction Using Machine Learning Algorithm

Importance of robots in industrial applications is a well-known fact. There are many tasks in industries, which can't be done alone by a robot or alone by a worker. Therefore, there is a need to establish a reliable and safe interaction environment between robots and workers. To do so, some information about the worker should be conveyed to the robot. This article focuses on industrial Human-Robot Interaction. For a safe and efficient Human-Robot Interaction, a robot needs to know about worker's position, posture and gesture. This paper proposes integration of an Inertial Measurement Unit (IMU) and a 3D camera as an image sensor to eliminate each other drawbacks and applies machine learning algorithm to detect postures and gestures of worker. Some experimental results during the interaction with heavy-duty robot will be presented
Testbed Implementation of a Fuzzy based Energy Efficient Clustering Algorithm for Wireless Sensor Networks

Energy constraint is a prominent feature for real time monitoring Wireless Sensor Networks (WSNs). Indeed, communication and data transmission present an increasing demand for power consumption. Different clustering routing protocols are introduced to reduce the energy consumption while enhancing the network lifetime. In this paper, a real implementation of an unequal clustering based fuzzy logic algorithm is carried out using the PanStamp NRG 2.0 sensor node to analyze the actual performance of the network. The energy consumption of the nodes, their lifetime and the packets transmitted are evaluated. A comparison between the simulation and experimental results is conducted to highlight the importance of the testbed implementation.
FPGA implementation of simplified Fuzzy LRU replacement algorithm

The Least Recently Used (LRU) policy is often used as a page replacement algorithm for virtual memory, and it is considered to be good. The major problem is that it is time consuming algorithm. This paper presents an efficient hardware FPGA implementation for LRU algorithm based on fuzzy logic with reduced inference rules. In this proposal, the page's recency of reference is used as input crisp variable to calculate the fuzzy value. Then a knowledge base that composed of five inference rules are used to calculate the page replacement weights for the pages in the memory. These pages' weights are used to select the victim page for replacement when there is a page fault and no empty page frame available in the memory. All stages starting from the crisp input values until the decision of selection the victim page to be replaced is implemented using very careful designed FPGA circuits. The main features of the design are focusing on the time speed of decision and the FPGA area. That is done in two ways, reducing the fuzzy rules to five only and using the best of the FPGA capability. The results show that the merging between fuzzy logic and FPGA implementation is very promising to achieve the LRU page replacement algorithm in real time.
Multi-Parallel Sending Coils for Movable Receivers in Inductive Charging Systems

The design of the sending side is critical in inductive power transmission systems. Multi-coil systems can be an interesting solution to reach larger and more flexible charging area. The challenge thereby is that during movements the voltage at the receiving side gets amplitude ripple. In this paper, a comparison between different coil systems such as Single Input Single Output (SISO), Parallel Multi Input Single Output (P-MISO) and a Controlled Parallel Multi Input Single Output (CP-MISO) coils systems is carried out. The aim of the investigation is to select a suitable architecture, where the resulting magnitude ripple of the receiver voltage due to coil movement are minimized. Results shows that a big receiving coil radius and a small sending coil radius in a CP-MISO coil system delivers the lowest ripples having a magnitude of around 40 mVp-p and a suitable maximal load voltage of 250 mVp-p.
Effect of Magnetic Field on Heat TransferEnhancement Using Fe3O4 Nanofluids In A Heated Pipe

The effects of various magnetic field intensity on heat transfer enhancement, hence an increase in Nusselt number (Nu), in a horizontal pipe heated with constant heating flux of 420W for different turbulent flow rates Fe3O4/water nanofluids were experimentally investigated. Fe3O4/water nanofluids of different volume concentrations of 1.2, 0.6, 0.3 vol.% will be used as the test fluid. Experiments were conducted for Reynolds number range of $2180 < \mathbf{Re} < 9160$ and for three different magnetic field intensity 15.1, 30.3, 45.5 mT. Initial results using Fe3O4/water nanofluid of 1.2 vol.% showed that Nu increases in the presence and absence of a magnetic field compared with water. Also, Nu increases with higher values of Re for aforementioned nanoofluid concentration and water. However, in the absence of a magnetic field, the average increase in Nu for Fe3O4-nanofluids was 16.7% relative to water, while, the average increase in Nu was 8.8%, 13.1%, and 23.9% under the application of magnetic field intensity of 15.1, 30.3, 45.5 mT relative to the absence of magnetic field, respectively. Based on that, it can be concluded that in the presence of magnetic field using Fe3O4-nanofluids, higher values of Nu is achieved and consequently an increase in heat transfer rate. These preliminary results can be useful for enhancing heat transfer in many engineering applications such as heat exchangers and electronic devices.
Concurrent Chaining Hash Maps for Software Model Checking

Stateful model checking creates numerous states which need to be stored and checked if already visited. One option for such storage is a hash map and this has been used in many model checkers. In particular, we are interested in the performance of concurrent hash maps for use in multi-core model checkers with a variable state vector size. Previous research claimed that open addressing was the best performing method for the parallel speedup of concurrent hash maps. However, here we demonstrate that chaining lends itself perfectly for use in a concurrent setting.We implemented 12 hash map variants, all aiming at multicore efficiency. 8 of our implementations support variable-length key-value pairs. We compare our implementations and 22 other hash maps by means of an extensive test suite. Of these 34 hash maps, we show the representative performance of 11 hash maps. Our implementations not only support state vectors of variable length, but also feature superior scalability compared with competing hash maps. Our benchmarks show that on 96 cores, our best hash map is between 1.3 and 2.6 times faster than competing hash maps, for a load factor under 1. For higher load factors, it is an order of magnitude faster.