Time-Frequency Analysis of GPR Signal Using a Modified Hilbert Huang Approach

In this paper, we will discuss the efficiency of a modified Hilbert Huang transform (HHT) approach [1, 2], which is largely applied to fault diagnosis for rolling bearing and vibration analysis, for the time-frequency analysis of the GPR signals. The aim of this approach is to improve the readability of the HHT spectrum and hence improving the visibility of all the events and reducing the effects of interference terms. An improved Hilbert-Huang transform (HHT) combined with wavelet packet transform (WPT) is proposed for recognizing reflected echoes in A-scans profiles. The HHT consists of Empirical Mode Decomposition (EMD) and Hilbert-Huang spectrum. Firstly, the Wavelet Packet Transform (WPT) decomposes the signal into a set of narrow band signals, then a series of Intrinsic Mode Functions (IMFs) can be obtained after application of the EMD. Where after, a screening processes is conducted on the IMFs of each narrow band signal to remove unrelated IMFs. Hilbert Transform is then employed to calculate the Hilbert amplitude spectrum. The results show that the proposed method has better discriminability than the traditional HHT and the Continuous Wavelet Transform (CWT) among different states. The proposed algorithm has the potentiality to trace the different changes, which paves a way for a better interpretation of radar profiles

Design of Compact Monopole Antenna using Double U-DMS Resonators for WLAN, LTE, and WiMAX Applications

This paper is under in-depth investigation due to suspicion of possible plagiarism on a high similarity indexIn this research, a novel wide-band microstrip antenna for wideband applications is proposed. The proposed antenna consists of a square radiating patch and a partial ground plane with a smal rectangular notch-shape. Two symmetrical U-slots are etched in radiating patch. The defected microstrip U-shapes and the small notch improve the antenna characterestics such impedance wideband and the gain along the transmission area. The proposed antenna is simulated on an FR4 substrate of a dielectric constant of 4.3, thickness 1.6 mm, permittivity 4.4, and loss tangent 0.018. The simulation and optimization results are carried out using CST software.The antenna topology occupies an area of 30 × 40 × 0.8 mm3 or about 0.629λg × 0.839λg × 0.017λg at 3 GHz (the centerresonance frequency). The antenna covers the range of 2.1711 to 4.0531 GHz, which meet the requirements of the wireless local area network (WLAN), worldwide interoperability for microwave access (WiMAX) and LTE (Long Term Evolution) band applications. Good VSWR, return loss and radiation pattern characteristics are obtained in the frequency band of interest. The obtained Simulation results for this antenna depict that it exhibits good radiation behavior within the transmission frequency range

Optimisation des Performances de Rayonnement d'une Nouvelle Structure d'Antenne Patch de Forme Carrée pour un Lecteur RFID

L’objectif principal de ce travail est d’optimiser les performances de rayonnement d’une nouvelle structure d'antenne patch de forme carrée. Cette antenne est excitée par une ligne microruban ayant un port d’alimentation adapté

Non-intrusive load monitoring of household devices using a hybrid deep learning model through convex hull-based data selection

The availability of smart meters and IoT technology has opened new opportunities, ranging from monitoring electrical energy to extracting various types of information related to household occupancy, and with the frequency of usage of different appliances. Non-intrusive load monitoring (NILM) allows users to disaggregate the usage of each device in the house using the total aggregated power signals collected from a smart meter that is typically installed in the household. It enables the monitoring of domestic appliance use without the need to install individual sensors for each device, thus minimizing electrical system complexities and associated costs. This paper proposes an NILM framework based on low frequency power data using a convex hull data selection approach and hybrid deep learning architecture. It employs a sliding window of aggregated active and reactive powers sampled at 1 Hz. A randomized approximation convex hull data selection approach performs the selection of the most informative vertices of the real convex hull. The hybrid deep learning architecture is composed of two models: a classification model based on a convolutional neural network trained with a regression model based on a bidirectional long-term memory neural network. The results obtained on the test dataset demonstrate the effectiveness of the proposed approach, achieving F1 values ranging from 0.95 to 0.99 for the four devices considered and estimation accuracy values between 0.88 and 0.98. These results compare favorably with the performance of existing approaches.This research was funded by Programa Operacional Portugal 2020 and Operational Program CRESC Algarve 2020, grant numbers 39578/2018 and 72581/2020. Antonio Ruano also acknowledges the support of Fundação para a Ciência e Tecnologia, grant UID/EMS/50022/2020, through IDMEC under LAETAinfo:eu-repo/semantics/publishedVersio

Design and Improvement of a Compact Bandpass Filter using DGS Technique for WLAN and WiMAX Applications

This paper is under in-depth investigation due to suspicion of possible plagiarism on a high similarity indexIn this article, A compact size band pass filter based on octagonal resonators is presented to give sharp response at desired frequency bands along with very low insertion loss. The proposed filter structure is composed of octagonal microstrip resonators, backed by Quasi-Yagi slots ‘Quasi-Yagi Defected Ground Structure’ (Quasi-Yagi-DGS). By controlling the electrical coupling between the octagonal–strip and the Quasi-Yagi-DGS, the bandpass filter’s stopband is optimized for better rejection. The proposed BPF has low insertion loss and compact size because of the slow-wave effect. Meanwhile, sharp rejection bands induced by the presence of two transmission zeros. The simulated center frequency and passband insertion loss are 2.4 GHz and 0.6 dB, respectively

Gain and bandwidth enhancement of New Planar microstrip array antennas geometry for C band weather radar applications

International audienceA 4–8 GHz ultra wide band microstrip array antennas with improved gain for weather Radar applications was designed and fabricated. The microstrip radiating elements of the proposed array antennas are powered using a T-junction power divider with quarter wave-transformer impedance for a best matching. The design of this array antennas is based on the geometry of a linear array antennas well studied [ref (8)], which is based on minimizing of the electromagnetic (EM) coupling between the radiating elements and as decreasing the number of side lobes. The array antennas with dimension of (110 × 214 × 1.58 mm3) is fabricated on FR-4 epoxy dielectric with relative permittivity of 4.4 and thickness h = 1.58 mm, it is performed in the full-wave EM solver High Frequency Structure Simulator, CST and verified by measurement. The proposed broadband array antennas show a better performance than the references antennas in deferent parameter which cited after

Ultra Wideband Planar Microstrip Array Antennas for C-Band Aircraft Weather Radar Applications

A miniaturized ultra wideband (UWB) planar array antennas for C-band aircraft weather RADAR applications is presented. Firstly, the effect of the ground plane is studied. Later, the realization and experimental validation of the geometry that has an UWB characteristic are discussed. This array antennas is composed of a twenty-four radiating element that is etched onto FR-4 substrate with an overall size of 162×100×1.58 mm3 and a dielectric constant of εr=4.4. The results show that this miniaturized array antennas gives us a bandwidth which is about 115% and a gain greater than 13 dB which are required in aircraft weather radar applications

Overview of Non-Intrusive Load Monitoring: Probabilistic and Artificial Intelligence approaches

Reduction and conservation of electrical energy consumption in residential buildings is the main objective of Non-Intrusive Load Monitoring (NILM) techniques. NILM detects events and estimate the power consumption of individual appliances by analyzing the aggregate power consumption measured at the service entry. Indeed, our major contribution in this paper is to update research works on NILM methodologies by adding the most recent NILM methods proposed in the literature. In this paper we present an overview of NILM and energy disaggregation methods. Then, we discuss the challenges of this technique to provide useful recommendations for future research

A comprehensive review of solar irradiation estimation and forecasting using artificial neural networks: data, models and trends

Solar irradiation data are imperatively required for any solar energy-based project. The non-accessibility and uncertainty of these data can greatly affect the implementation, management, and performance of photovoltaic or thermal systems. Developing solar irradiation estimation and forecasting approaches is an effective way to overcome these issues. Practically, prediction approaches can help anticipate events by ensuring good operation of the power network and maintaining a precise balance between the demand and supply of the power at every moment. In the literature, various estimation and forecasting methods have been developed. Artificial Neural Network (ANN) models are the most commonly used methods in solar irradiation prediction. This paper aims to firstly review, analyze, and provide an overview of different aspects required to develop an ANN model for solar irradiation prediction, such as data types, data horizon, data preprocessing, forecasting horizon, feature selection, and model type. Secondly, a highly detailed state of the art of ANN-based approaches including deep learning and hybrid ANN models for solar irradiation estimation and forecasting is presented. Finally, the factors influencing prediction model performances are discussed in order to propose recommendations, trends, and outlooks for future research in this field.info:eu-repo/semantics/publishedVersio

Energy Disaggregation Using Multi-Objective Genetic Algorithm Designed Neural Networks

Energy-saving schemes are nowadays a major worldwide concern. As the building sector is a major energy consumer, and hence greenhouse gas emitter, research in home energy management systems (HEMS) has increased substantially during the last years. One of the primary purposes of HEMS is monitoring electric consumption and disaggregating this consumption across different electric appliances. Non-intrusive load monitoring (NILM) enables this disaggregation without having to resort in the profusion of specific meters associated with each device. This paper proposes a low-complexity and low-cost NILM framework based on radial basis function neural networks designed by a multi-objective genetic algorithm (MOGA), with design data selected by an approximate convex hull algorithm. Results of the proposed framework on residential house data demonstrate the designed models’ ability to disaggregate the house devices with excellent performance, which was consistently better than using other machine learning algorithms, obtaining F1 values between 68% and 100% and estimation accuracy values ranging from 75% to 99%. The proposed NILM approach enabled us to identify the operation of electric appliances accounting for 66% of the total consumption and to recognize that 60% of the total consumption could be schedulable, allowing additional flexibility for the HEMS operation. Despite reducing the data sampling from one second to one minute, to allow for low-cost meters and the employment of low complexity models and to enable its real-time implementation without having to resort to specific hardware, the proposed technique presented an excellent ability to disaggregate the usage of devices