M. BAKHTI Haddi

Water Distribution Networks (WDNs) are essential infrastructures responsible for delivering water to communities. However, they are highly susceptible to leaks, which result in considerable water losses, increased operational costs, and potential damage to the infrastructure. Accurate and efficient leak detection is therefore critical for ensuring the sustainability of these systems. This study introduces the Empirical Mode Decomposition (EMD) method as an advanced approach to enhance pipeline leak detection. Leaks in pipelines generate acoustic and vibration signals that propagate at low frequencies, exhibiting nonlinear and nonstationary characteristics. To address this, we constructed a prototype hydraulic circuit made of Polyethylene High-Density (PEHD) material, measuring 100 m in length and 40 mm in diameter. The circuit was equipped with vibration transducers and an advanced dSPACE-based acquisition system to capture high-resolution data. Traditional signal analysis techniques, such as Fast Fourier Transform (FFT), correlation, and cepstrum analysis, often miss crucial information due to their dependence on narrow frequency bands and the assumption of signal stationarity. EMD, a time-domain decomposition method designed for nonstationary signals, resolves these issues by extracting Intrinsic Mode Functions (IMFs), enabling the separate analysis of signal components. Our experiments involved testing the EMD method on various leak scenarios and comparing it with conventional techniques. The results demonstrate that EMD provides superior leak detection capabilities by effectively analyzing the complex and nonstationary nature of pipeline signals. This approach offers a promising solution for real-time monitoring and maintenance of WDNs, ensuring their
operational efficiency and sustainability.

Water Distribution Networks (WDNs) are critical infrastructure for delivering water to populations. However, leaks in these networks can result in significant water losses, increased operational costs, and damage to infrastructure. Traditional leak detection methods often lack the precision for timely and accurate identification. This study proposes an innovative approach combining Discrete Wavelet Transform (DWT) and Convolutional Neural Networks (CNN) for detecting leaks using pressure signals recorded from WDNs. DWT is employed to decompose pressure signals into multi-scale components, enabling the isolation of features relevant to detecting leaks. By converting raw pressure data into a structured representation. DWT enhances the clarity of signal patterns associated with leakage events. These decomposed signals are subsequently analyzed using a CNN, which extracts spatial and temporal features to classify signals into leaky and non-leaky categories. The integration of DWT and CNN ensures that both fine-grained signal variations and broader contextual patterns are effectively captured. The proposed method was validated on a dataset of pressure signals collected from real-world WDNs, demonstrating high accuracy, precision, and recall. Compared to existing techniques, this approach offers superior performance in distinguishing between leakage and non-leakage scenarios, even in complex environments. The results highlight the potential of combining advanced signal processing and deep learning (DL) methods to improve leak detection capabilities, significantly reduce water losses, and enhance system reliability. Future work will focus on expanding the dataset, integrating additional sensors, and exploring further improvements to detection accuracy and robustness.

Water Distribution Networks (WDNs) are essential for ensuring the reliable delivery of water to communities. However, they are susceptible to leaks, which can result in substantial water losses, increased operational costs, and damage to the system. Early detection and mitiga tion of leaks are therefore crucial for maintaining the efficiency and sustainability of WDNs. To detect leaks using pressure signals recorded from WDNs, this study proposes a new method that combines Convolutional
Neural Networks (CNN) and Continuous Wavelet Transform (CWT). The CNN can extract significant spatial and temporal information by preprocessing the pressure signals and converting them into time-frequency representations using CWT. The proposed model was trained and evaluated on a dataset of pressure signals from real WDNs, achieving high accuracy, precision, and recall. The results demonstrate the efficacy of integrating CWT for signal processing and CNN for feature extraction to enhance leak detection. This approach provides a promising framework for improving urban infrastructure leak management and maintenance strategies. Future work will focus on validating the model on larger datasets and exploring the integration of additional sensors to enhance detection robustness. This approach not only aids in timely leak identification but also supports proactive maintenance strategies, ultimately reducing water losses and enhancing system reliability. Future research will focus
on validating the model on larger and more diverse datasets.

Detecting leaks in pipeline networks, particularly in water distribution systems, is a critical yet challenging endeavor. Despite the availability of various detection techniques, many are unsuitable for industrial applications due to limitations such as inefficiency and high costs. This study explores the use of cepstrum analysis, a robust signal processing technique, to detect and locate pipeline leaks by analyzing pressure signal measurements. The method begins with preprocessing pressure signals to eliminate noise using orthogonal wavelets, ensuring the accuracy of subsequent analysis. Cepstrum analysis is then applied to extract key features indicative of leaks. Previous research has shown that this approach can effectively identify leak locations and assess their severity in pipeline systems. Controlled experiments were conducted on a fluid-filled pipeline network, where pressure transducers recorded transient pressure drops caused by simulated leaks. The leaks were initiated using a push-button mechanism that operated a solenoid valve, with pressure signals captured at various pipeline distances from the leakage point. The results demonstrated that the cepstrum-based technique reliably detects and locates leaks with high precision, regardless of the distance from the leak.This study underscores the potential of cepstrum analysis as a valuable tool for improving leak detection methodologies. By providing a cost-effective and efficient solution, this technique can enhance the integrity and operational efficiency of pipeline systems, addressing a critical need in water distribution networks (WDNs).

Detecting leaks in pipeline networks, particularly in water distribution systems, is a critical yet challenging task. Numerous techniques have been proposed for leak detection; however, none have proven entirely suitable for industrial applications due to limitations such as low efficiency or high costs. This study focuses on employing cepstrum analysis as a powerful signal processing technique to detect and locate leaks in pipelines based on pressure signal measurements. The
The proposed method involves preprocessing pressure signals to remove noise using orthogonal wavelets, followed by the application of cepstrum analysis to extract key features indicative of leaks. Earlier studies have demonstrated that this approach can effectively pinpoint the location of leaks and assess their severity in pipeline systems. Experiments were conducted on a controlled fluid-filled pipeline network, where pressure transducers recorded transient pressure drops triggered by
simulated leaks. The analysis demonstrated the ability of the cepstrum-based technique to detect accurately the location of leaks across varying pipeline distances. Pressure transducers were used to record pressure signals, with leakage initiated via a push-button mechanism acting on a solenoid valve. The transient pressure drop was captured during the recordings, which were conducted at varying pipeline distances from the leakage point. This work highlights the potential of cepstrum analysis in advancing leak detection methodologies, offering an effective tool for maintaining the integrity and efficiency of pipeline
systems.

In recent years, the convergence of the Internet of Things (IoT) has driven innovations in pressure sensor development, enhancing accuracy, responsiveness, and efficiency in data handling. IoT connects sensors in real-time, enabling them to transmit vast amounts of pressure data to centralized systems for analysis and decision-making. The development of IoT, the ever-increasing number of internet users, and the advancement of data rates and services have produced enormous amounts of data that can be utilized to enhance service delivery. IoT provides opportunities for both gathering and applying this data. This paper explores the relationship between IoT and sensors, emphasizing their roles in optimizing various sectors such as autonomous vehicles, industrial automation, smart homes, healthcare and medicine, smart cities, and agriculture. By integrating advanced technologies, including Machine Learning (ML) algorithms, we also examine the relationship between pressure sensors and IoT
in leak detection within Water Distribution Networks (WDNs).

The convergence of the Internet of Things (IoT) with pressure sensor technology has revolutionized data acquisition and management, enabling unprecedented accuracy and efficiency in various applications. IoT facilitates real-time connectivity among sensors, allowing them to transmit vast amounts of pressure data to centralized systems for analysis and decision-making. This seamless integration has been fueled by the rapid growth in internet users, advancements in data rates, and enhanced service delivery capabilities. IoT not only enables the collection of large datasets but also opens avenues for their application across diverse industries. This paper highlights the relationship between IoT and pressure sensors, examining their transformative impact on sectors such as autonomous vehicles, industrial automation, smart homes, healthcare, smart cities, and agriculture. By leveraging advanced technologies like machine learning (ML), IoT enhances system optimization and operational efficiency.
A focal point of this study is the application of IoT and pressure sensors in leak detection within water. Distribution Networks (WDNs). Integrating ML algorithms with IoT-connected pressure sensors allows for the identification and localization of leaks, ensuring effective system monitoring and maintenance. This approach significantly reduces water loss, operational costs, and downtime, contributing to the sustainability and reliability of WDNs.

In this research we have proposed an innovative
method called the cepstral method for leak detection in water
distribution network pipes. The cepstral analysis was chosen
because it has a very good performance-complexity ratio. The
application was made at the electronics laboratory. In order to
simulate the distribution network, a short hybrid prototype
channel was used. The acquisition of leak signals was done
using a DSK cardc31. The validation tests of the proposed
technique have shown its effectiveness.

The detection of leaks in water distribution
systems is a crucial aspect of conserving and managing water
resources. Drawing inspiration from speech and speaker signal
processing, this paper explores the application of cepstrum
analysis to identify pipeline leaks. The concept is based on the
analogy that water flow in pipes is similar to sound waves
flowing through the vocal tract, with a leak equivalent to an
opening in the vocal tract, such as the mouth or nose.
A laboratory-scale prototype pipeline made of steel was
used to test the proposed method. The results obtained using
cepstrum analysis were compared to those from three
conventional methods: autocorrelation, Fast Fourier
Transform (FFT), and the logarithm of FFT. The comparative
results demonstrate the effectiveness of the proposed method in
tracking leaks in various scenarios, characterized by clear
peaks at the same frequency and moment, outperforming the
three considered methods.

This study uses the empirical mode
decomposition (EMD) method to address the issue of pi peline
water leak detection. Acoustic and vibration signals that
propagate throughout the pipeline at low frequencies are
caused by leaks and have nonlinear and non-stationary
features. Other potential causes of the received signals with
unknown frequencies include the hydraulic pressure, pipe
diameter, kind of pipe, and size of the leak. The choice of the
narrow periods and causes, and consequently the loss of
valuable information, limits the study of such leakage signals
using traditional methods for instance the FFT, the correlation,
and the cepstrum. Multiple stationary components (IMFs) can
be used to treat the received signals independently by allowing
the EMD non-stationary time domain decomposition approach.
The EMD analysis is applied to a few leak positions in the
pipeline and contrasted with the leak methods currently in use.
Our experimental findings, when applied to time domain
analysis, show that the EMD approach offers superior leak
detection capabilities.

The study presents a novel 2-D hybrid analytical
model (HAM) for analyzing dual-rotor permanent-magnet
(PM) synchronous machines, integrating semi-analytical
Maxwell-Fourier equations with finite-element method (FEM)
techniques across all machine regions to provide a thorough and
computationally efficient electromagnetic analysis approach.
The semi-analytical component employs a rigorous formulation
derived from Maxwell's equations, specifically applied for
regions with unitary relative permeability. The model applies a
finite-element method (FEM) to areas with high magnetic
permeability, like the ferromagnetic teeth and yoke components.
This approach excels at precisely capturing intricate shapes and
variable magnetic behaviors typical of these structures. The
analytical model (AM) and finite-element method are connected
in both directions of the periodicity and non-periodicity
direction. It is found that whatever the iron core relative
permeability, the developed hybrid analytical model (HAM)
give accurate results for no-load and on-load conditions. Finiteelement analysis (FEA) shows very good results of the advanced
technique.

This work introduces an innovative two-dimensional (2-D) hybrid analytical model
(HAM) applied for dual-rotor permanent-magnet (PM) synchronous machines. The
proposed HAM seamlessly combines the semi-analytical Maxwell-Fourier
formulations with finite-element method (FEM) across all machine subdomains,
offering a comprehensive and efficient approach to electromagnetic analysis. The
semi-analytical component employs a rigorous formulation derived from Maxwell's
equations, specifically tailored for regions with unitary relative permeability, enabling
high-precision electromagnetic field computations in non-ferromagnetic domains
while maintaining computational efficiency. For regions of high magnetic
permeability, such as the ferromagnetic teeth and yoke structures, the model
employs a finite-element method (FEM) approach, leveraging its superior capability
to accurately represent complex geometries and nonlinear magnetic properties. The
analytical model (AM) and finite-element method are connected in both directions of
the periodicity and non-periodicity direction. It is found that whatever the iron core
relative permeability, the developed hybrid analytical model (HAM) give accurate
results for no-load and on-load conditions. Finite-element analysis (FEA)
demonstrates excellent results of the developed technique. Keywords: Hybrid
magnetic model, exact subdomain technique, magnetic equivalent circuit, finiteelement analysis.

We always face water leakage problems in underground distribution water networks (DWNs). Existing leak detectors suffer from false alarms due to poor leak signal quality affected by external noise, often collected by acoustic or vibratory sensors. This paper introduces a novel Discrete Wavelet Transform Detector (DWTD) that leverages precise pressure signals non-influenced by environmental noise. Using a prototype of a 100m PEHD pipeline and a diameter of 40mm, Data from two pressure
transmitters were collected using a dSPACE MicroLabBox unit.

La présente invention est système de surveillance automatique d’une pompe à eau électrique destinée à l’usage des ménages. Cette invention qui fonctionne sous 220V/50Hz doit être installer avec la pompe à eau et le suppresseur afin qu’elle puisse améliorer la qualité de surveillance de la pompe eau, et tout ça même sous des conditions de fonctionnement sévères comme la perte de la pression de l’eau arrivant du réseau mis en service par l’algérienne des eaux.
L’invention proposée qui fonctionne par l’intelligence artificielle, peut mettre en marche la pompe d’eau sous toutes les conditions imprévues.
Elle peut faire démarrer ou arrêter la pompe à eau, s’assurer qu’il y a de l’eau dans le réseau public, de contrôler le remplissage de la citerne et tant cela sous l’aide d’aucune présence humaine à domicile.

La communication entre objets est devenue de nos jours l’une des principaux soucis des chercheurs vue la demande croissante des individués à se communiquer soit par téléphone ou par internet et la liaison des objets intelligents par internet. Pour se communiquer entre ce grand nombre d’objets on a besoin de dispositifs spéciales. L’élément principale de communication tourne autour des antennes. Ces dernières pour qu’elles soient intégrées dans les dispositifs, il faut qu’elles soient miniatures pour éviter l’encombrement. De plus travailler dans les bandes de fréquences autour de 5GHz et 28 GHz signifie liée un très grand nombre d’éléments ou objets communiquant.

Water is a precious, sometimes scarce resource that is crucial for all kinds of life. In the last two decades, the water demand has far exceeded the supply in many countries. It is also worth mentioning that distribution networks are constantly increasing. According to an international study 20-50% of the produced quantities are lost due to leaks. These leaks can cause significant economic losses and multiple water contaminations that are carried as a major health risk for the citizen. Therefore, network managers are always looking for fast and inexpensive harmless leak detection systems. The rapid detection of a leak in underground pipes is widely taken into account in the performance evaluation of water supply systems. In addition, detection methods should not interfere with the normal operation of water transport. To minimize the effect of false alarms (FA) that have a costly effect on infrastructure. These false alarms are produced by the use of the most widely used acoustic detectors in the world, which are usually based on the signal correlation technique to know the exact location of the leak in relation to one of the sensors. In this work, we present a new leak detector applied to a prototype pipe using highly sensitive pressure transmitters. For this purpose, we have applied the wavelet technique (DWT) for denoising our non-stationary signals from these transmitters. The STFT (Short time Fourier transform) will be used for the analysis of these non-stationary and non-linear signals coming from the leaks to know the exact position of the leak. Validation tests have proven the efficiency of our detector.
Keywords: Leak, DWT: STFT, Detection, WDNS.

Water distribution networks around the world suffer from leaks. These are due to the movements of various agents such as ground movements, and vibrations caused by road traffic without forgetting the nature of the ground. For this, permanent control is needed. In this work, we have developed a LabVIEW interface for
the acquisition and processing of signals received from pressure transmitters. A Wifi system for transmitting and receiving signals from the pressure transmitters is used. To analyze signals to remotely detect anomalies that may occur on the network. The work is divided into two parts: a practical or hard part and a soft part. The hard one is used to obtain the signals containing the leakage information. The soft part is based on the application of signal processing techniques for the detection and location of the exact position of the leakage and therefore minimize the amount of water that is lost.
Keywords: Leak, LabVIEW, WiFi, Detection, WDNS.

La présente invention est un détecteur de fuite d’eau dans les canalisations de distribution d’eau. Il permet de détecter et de localiser la position de la fuite par rapport à l’un des capteurs. Ce dernier remplace efficacement les détecteurs acoustiques existants dans la plupart des marchés internationaux et qui ont des problèmes des fausses alarmes à cause des bruits environnants.
Notre détecteur fonctionne à base d’un réseau de capteurs de pressions qui transmettent les signaux en temps réel à distance à une centrale de traitement et qui permet de surveiller les fuites. Une alarme se déclenche dès qu’une ou plusieurs fuites apparaissent.

Water distribution systems (WDNs) suffer from leakage problems. The latter can cause damage to infrastructure and also act on public health by the penetration of microbes of waterborne diseases transmissible through the orifices of leaks as soon as a drop in pressure occurs. For this purpose, the network managers always look for the best devices to detect and locate in time the anomaly of the networks as soon as it appears. In order to realize a system of detection and location of leakage
a prototype circuit was realized equipped with pressure sensors and a system of acquisition based on a Digital Signal Processor (DSP). Pressure measurements for specific distances were carried out. A digital filter was applied for denoising. Pairs of pressure values before and after leakage are applied to a mathematical model for localization. We opted to use nonlinear regression (RNL) for the determination of nonmeasurable physical parameters based on pre-localized leak positions. Validation tests are performed to demonstrate the effectiveness of the model. In our study, a divergence of the model used was found.

The pipeline system is the most essential part of transporting potable water. In other words, pipes are the most crucial component of the water distribution system. Because of several factors, they suffer from holes in which water is lost, and therefore an economic loss to states and humanity. Responsible for the water distribution networks always try to minimize the damage. By working to find the most effective way to detect leaks in these networks. This article centers on a mathematical model of a hydraulic system that aims to locate leakage in water transmission pipes. Several optimization techniques can be used. We opted for the BBO (biogeography-based optimization) method because of its performance, in particular the execution time of its algorithm. This method is applied to determine the two parameters of the model (friction and dynamic viscosity) to obtain a good localization. We take GA as a method compared method with BBO.

Nowadays, the generalization of wireless communication systems and the need to miniaturize antennas to allow their integration into small objects at ultra-high
frequencies is growing. At these frequencies, the antenna is the bulkiest component and its miniaturization constitutes one of the most important current challenges for designers of communicating objects. The main objective of our work is the design of a dual-band patch antenna working for fifth-generation systems. For this purpose, we designed a miniature antenna having two resonance frequencies, one around 7GHz and the other around 28GHz. For this, our work is based on a classic rectangular patch antenna. Miniaturization techniques and ground plane deformation (GDS) are used to obtain the best performance. A parametric study was performed to see the most influential parameters to achieve the desired requirements. The influence of the geometrical parameters of the antenna on the resonance frequency, bandwidth, and directivity has been taken into consideration. The results obtained showed a good performance of the designed antenna.

The design of leak detection systems that occur on water pipes is a priority area of applied research that has an economic and health impact on the future of any nation. The various control systems and tools that currently exist throughout the world are designed to ensure permanent and effective monitoring of natural resources that have become rare and precious. The determining factor for the choice of a good detector is the cost in the first place, flexibility, and speed of processing. In this work, the basic idea is to simultaneously acquire from a new inexpensive electronic device two signals from two pressure transmitters installed on a prototype pipe carried out at the laboratory. These signals are usually immersed in noise. For this, denoising by an appropriate digital filter is indispensable. In our case, the Savitzky-Golay filter
(S.G) presents its efficiency. The denoising performances are obtained from the calculation of SNR. The denoised signals are analysed to confirm the presence of the leak in the case of its existence. Mathematical equations are applied to determine the exact position of the leak with regards to one of the sensors. Validation tests are required to determine the position of the leak when the difference time between the signals is known.

The dimensioning of photovoltaic systems is the major concern of researchers and power industry practitioners. This aims to improve energy efficiency and protect the conversion units by a consistent assessment of power conditioning circuits and interconnections for the PV application. In this context, this paper sets out to fulfill detailed modeling and control steps of a standalone photovoltaic (PV) power system with energy storage, according to practical specifications of the load, PV generation unit, and battery pack. The main goal is to estimate all unknown parameters, as the diode ideality factor and revers saturation current, the controller, and the PV link. The PV link interfacing the PV source circuit to the PV-side converter (PVSC) provides a filtering function to maintain a steady voltage at the link. The charge controller used in the PV-side converter is a DC/DC buck converter. It transfers the PV power to the battery and supplies the load. Using pulse- width modulation (PWM) technical, of which the switching duty cycle is the control-input variable; the PVSC power-conditioning circuit is permanently controlled by the maximum power point tracking (MPPT) algorithm to achieve the maximum energy. The battery pack voltage is properly maintained by the charge controller and specified to match the load voltage rating, to avoid a high ratio of voltage conversion. A method is proposed to integrate both the MPPT function and the battery cycle charge. The PV generator output and the power conditioning circuits, mainly constructed from switching- mode power converters, are nonlinear. An averaged model is then derived for dynamic analysis and controller synthesis, using the state-space averaging and linearization method. A PV array of nine PV modules configured into three strings is used in this application to demonstrate the effectiveness of modeling, design, control, and simulation. Simulation model for the controller and power interface is built and developed in short term, using the fundamental blocks of Matlab Simulink.

In the present work, a dynamic stochastic method is proposed and used for the synthesis of uniform linear antenna arrays. The proposed method combines the classical invasive weeds (IWO) and the mutation process, which makes it robust, simple and shows flexibility to be adapted. The dynamic IWO applies the mutation process in the calculation of standard deviation during the spatial dispersal process of produced seeds while keeping the mean at the parent plants. In the mutation process, if special conditions were achieved, the standard deviation would be re-initialized. This proposed method tries to achieve an optimal array pattern by acting on the relative amplitude excitation of each element in the linear array for an optimal inter-element spacing. The optimal array pattern has deep or broad nulls in some directions of interferences with low sidelobes level. The objective of the synthesis is to get amplitude excitations with low dynamic range ratio (DRR), which facilitates the design of beamforming feed network. To illustrate the robustness of the proposed method, numerical examples are presented and compared with the obtained results using bees algorithm (BA), bacterial foraging algorithm (BFA), real genetic algorithm (RGA), and the corresponding reference array pattern for each example.

This work deals with the preparation and characterization of pure and Nickel-doped porous Tin oxide (SnO2) thin films, using Spray pyrolysis technique for an eventual application in optoelectronics. The structural, morphological, optical, and electrical properties of these prepared samples were investigated using various techniques. X-ray diffraction analysis confirmed the tetragonal structure of pure SnO2 and Ni-doped films. The preferred orientation of the crystallites changed from (110) to (200) with the film doped at 15.3 at.% Ni, with crystallite size of about 10 and 18 nm for 15.3 at.% Ni-doped and 5.6 at.% Ni-doped SnO2, respectively. Scanning Electron Microscopy shows that the 15.3 at.% Ni-doped SnO2 film surface is relatively homogeneous, while the 5.6 at.% Ni-doped film is highly porous. The measured contact angles are less than 90° for all the prepared samples confirming the hydrophilic character of all the films. Transmittance as high as 85% in the visible region was observed for 5.6 at.% Ni-doped films, while lower transmittance is observed for 15.3 at.% Ni-doped films. A decrease of the optical band gap and the resistivity with increasing Ni dopant concentration were also observed.

The main objective of this paper is to study the dielectric behavior of a ternary composite, made from a mixture of barium titanate (BT), copper oxide (CuO) in the same epoxy resin matrix (RE) maintained at 70% by volume fraction, while those of the other constituents are variable and completing each other in a way to achieve the remaining proportion, i.e. 30%. Random mixtures are made at room temperature and under atmospheric pressure. A dielectric characterization of this mixture type was performed by time-domain spectroscopy (TDS) over a frequency wide band (DC–3GHz). This has been carried out to illustrate the effect of copper oxide (CuO) simultaneously at low frequency (500MHz), in the presence of (BaTiO3), on the composite dielectric behavior. This has led consequently to make a comparison between the present acquired results and those of the ternary composite, where (CuO) act separately. The results obtained so far confirm the AC conductivity frequencies dependence, and fit the Jonscher’s law. In addition, It has been reported an increase of this conductivity σ_ACwhile the frequency goes up. However, the Static conductivity is affected clearly by the presence of CuO particles amount and these results show σ_(DC )plateau in the low frequency range. In fact, these characteristics are useful in many pragmatic engineering areas like microelectronics and telecommunications components manufacturing.

Water is a vital resource for life as well as for the majority of energy industries. Finding a way to manage this water sustainably is essential at a time when the world’s water resources are already under increasing stress due to population growth and global warming. As a direct implication, saving water is coming very crucial. This work deals with the application of the empirical mode decomposition method to the detection and the localization of the leaks using acoustic signals issued from STM32F4 acquisition card. Firstly, the empirical mode decomposition method has been applied in the denoising of acquired signals leading to two estimated components on which we apply secondly the correlation method to detect and localize the leakage in water distribution network. Experimental obtained results using a developed hydraulic plant as well as the acquisition system confirm the efficiency of the proposed method to detect accurately the leaks compared to conventional leaks detection methods.

The battery charging control within wind turbine applications is the most important challenge in standalone exploitation mode because of the random nature of wind energy and irregular use of electricity. Within this problematic, this paper deals with monitoring and control of a permanent magnet synchronous generator voltage for battery charging. In the proposed topology, the output direct drive PMSG is linked to the battery through a vector-controlled pulse modulated (PWM) rectifier. Most wind energy conversion systems are based on precise knowledge of the wind speed, which makes the adjustment of the charge voltage relatively complicated, and degrades the powers captured with erroneous measurements of this wind speed. In this context, all the efforts of this work are meant to simplify and improve the dynamic performance of the system. The technique used in this work is devoid of wind measurement. By neglecting different losses, the approach adopted leads us to the reference electromagnetic torque, assuming that the wind speed and consequently the rotational speed of the turbine vary slowly in steady state. Using the load current and voltage measures, the proposed controllers calculate permanently the power reference that corresponds to the fixed battery voltage. A vector control approach is adopted to achieve decoupled power control on the supply side power converter by generating the direct and quadrature control voltages. These voltages are necessary to generate the corresponding duty cycles of the PWM active switching devices. Hence, the PWM rectifier maintains the battery voltage at a fixed value by balancing both the rectifier output current and the load input current. To activate the vector control, two dynamic controllers are designed, simulated, and compared. The first controller is based on simple PI regulators. Meanwhile, the second one, which is a sliding mode technique, is applied to control the battery voltage. In order to assess the effectiveness of the used techniques, simulation models have been subjected to critical conditions of changing in speed and load. Simulation results revealed a poor accuracy of the first technique during transients and its close correlation under load disturbance. Whereas the sliding mode controller has high precision and strong robustness in steady state and during transient phases. The system simulation is performed using MATLAB/SIMULINK.

En este trabajo, investigamos y modelamos el comportamiento dieléctrico de un compuesto ternario preparado
a temperatura ambiente, con una mezcla de resina epoxi (RE), titanato de bario (BaTiO3) y óxido de cobre
(Cu2O) a varias proporciones y posteriormente sinterizado a tres temperaturas diferentes (150 °C, 200 °C y
250 °C) con el fin de determinar el efecto de la temperatura desinterizado sobre estos medios.
La caracterización experimental de las muestras, en el rango DC- 6GHz, se realiza por reflectometría en el
dominio de tiempo (TDR) utilizando el protocolo de múltiples reflexiones. Los compuestos sinterizados
muestran una buena estabilidad dieléctrica con respecto a la frecuencia y una fuerte dependencia con la
concentración de BaTiO3.
Un proceso de relajación dieléctrica tiene lugar a baja frecuencia atribuible principalmente a la resina. Para
describir y predecir el comportamiento dieléctrico de estos medios materiales en función de la concentración
de cada una de los diferentes componentes, así como con la temperatura de sinterizado se utiliza finalmente
un modelo modificado de Lichtenecker.

A lot of progress in computing systems components are devoted today to grant more support for parallelism. This, is likely affording much opportunities for High Performance Computing (HPC) applications developers who become now able to accelerate run-times progressively. Adapting algorithmic writings for parallelism paradigm likely leads to additional improvement in run-times. This paper deals with this matter. We carry an empirical measures to assess how interesting is to re-adapt algorithmic thinking for parallel processing context. We provide thorough comparisons of achievable accelerations among a number of different sorting algorithm kinds. We use a proprietary framework meant previously to serve as a front-end kernel in an automatic parallelization compiler and we populated it with interpolation to make performance predictions in large-scale parallelization. Sequential, semi parallel and parallel algorithms for sorting problem are all involved in the empiric tests considering different distributions for randomized input records. The results let to estimate how much the innovation of specific parallel algorithms could be more profitable than parallelization of serial programs.

The main goal of the present paper is the study of transmitted power control (TPC) algorithms for mobile cellular systems (UMTS). A novel power control algorithm and some already existing ones have been studied focusing on resolving the near-far problem that affects the performance of the system a whole. Unlike the other existing power control algorithms, and besides the dynamic step size, our Modified Adaptive Power Control algorithm (MAPC) shows its uniqueness in the intelligence added to restrict the oscillations of the estimated signal to interference ratio (SIR) around its target value by using a stabilization zone. Simulations were carried out to evaluate the robustness of the novel algorithm. The results show that the proposed algorithm outperforms the other existing power control algorithms.

This paper describes the design of an automatic parallelization framework. The kernel supplied at its front end was suggested as an instrument for parallel potential assessment. It was used to measure the maximum achievable speedups in the major set of the CHStone benchmark suite programs. In such framework, we suggested the liberation of parallelism incrementally. We proposed a data dependency heuristic-based transformation method to make true dependences dissociation. We generated an internal representation (IR2), where the Banerjee test conditions are met. Two among three of Banerjee test conditions came to be committed. In shared memory many/multicore platforms, the third condition could be satisfied by privatization. We would be able to choose the safe and the opportune pairwise (mapping-privatization) scheme among a number of threads mapping scenarios that become available in the IR2 structure. Instrumentation on a subset of CHStone benchmark was carried out as a validity proof of our proposal, and the results confirmed that our framework kernel is robust.

This paper describes control system design of a small size vertical axis wind turbine for battery charging, using permanent magnet synchronous generator (PMSG). The direct drive PMSG is connected to the battery through a switch mode rectifier where a DC-DC buck converter is used to optimize the wind power. The use of speed sensor to the control system design complicates and adds more costs to the system. To resolve this problem, a sensorless maximum powertracking algorithm is proposed to calculate the current command that corresponds to maximum power output of the turbine.
The DC-DC converter uses this current command to calculate the duty cycle which is necessary to control the pulse width modulated (PWM) active switching device. The system overview and modeling is presented, including characteristics of wind turbine, generator, batteries, power converter, control system, and supervisory system. A simulation of the system is performed using MATLAB/SIMULINK.

In this paper, we investigate and model the dielectric behavior of a ternary composite prepared at room temperature with a mixture of epoxy resin (RE), barium titanate (BT) and copper oxide (Cu2O), sintered at three different temperatures (150 °C, 200 °C, and 250 °C). Time domain spectroscopy (TDS) is used to characterize samples in the range [DC to 2 GHz] by performing a particular study at low frequency (500 MHz). The latter focused on both the sintering and the Cu2O addition effects on a ternary composite dielectric behavior. These effects were quantified as a function of the BT volume fraction. For this purpose, we used an optimization method based on nonlinear regressions to determine the permittivity, to minimize systematic errors of this dielectric parameter, and to show the effect of Cu2O on it. Moreover, we attempt to explain the sintering temperature effect on this kind of mixtures through the modified Lichtenecker model. As a matter of fact, the importance of this law is allocated on one hand to the validation and concordance of the experimental results with those of the theory and on the other hand to the temperature effect investigation on the form factor given by the modified Lichtenecker law.

In this study, the authors propose and analyse a novel leak-detection method based on the ‘Haar’ continuous wavelet transform (CWT) and a double thresholding, i.e. CWTDT. Inspired by the idea of the binary integration technique in radar target detection, the algorithm analyses the non-stationary vibration signal issued from a water pipeline through which it decides whether or not there exists a leak in the water conveyance. To achieve this, the signal is first divided into several segments. Partial binary decisions within each segment are then obtained through the use of two preselected thresholds. The final binary decision is obtained by means of the ‘K out of L’ fusion rule. In doing this, the hardware leak system prototype is designed and a number of desirable leak positions in the water pipeline are first created to achieve the two best thresholds and ‘K out of L’ fusion rule. For comparison purposes, the performances of the proposed CWTDT method are assessed experimentally against those of the existing fast Fourier transform- and CWT-based methods under real operating conditions.

In the development of dielectric composite,it is
interesting to know the structure-property relationship at
nanoscale level and the effect of each phase in the global
properties of the material. In this context, we present an
experimental analysisin the case of a ternary composite material.
Several homogeneous microstructures samples with copper (II)
oxide (CuO) and barium titanate (BaTiO
3) particles in powder
form with various amounts dispersed in a host matrix of Epoxy
Resin are prepared. Their dielectric permittivity spectra are
measured using time domain Reflectometry (TDR)
technique.Theexperimental data isanalyzed by means of electric
modulus formalisms.In high frequency, the electrical modulus
confirms the presence of two relaxations process.The real part of
such composite presents a stability before the frequency where
the relaxation phenomena appeared.The addition of the CuO
does not affect the real part of permittivity significantly, but its
effect is clear in the imaginary part and hence the AC
conductivity.The conductivity indicates that the conduction is
ionic and due to the ion of CuO primarily. According to results
obtained from this study, we can made composite materials with
desired characteristics using these two phases in the limits of
their dielectric intrinsic permittivity.The manufacturing process
of such composite is simple andcan be used in manufacture.In
addition, the reproducibility of such composite ischecked in this
study.

Leak detection of water distribution networks is a problem that arouses theresponsibleauthorities for distribution all around the word. The reason, which involved researchers representing various disciplines to try to find a solution to the problem by implementing efficient devices in this field.Distributions networks are being degraded over time, which can cause leakage.To detect a leakequipment and techniquesare needed. They are constant progress with the development of technology. In this paper, the CWT (continuous wavelet transform) wassuggested as a technique for detecting and locating leaks in water distribution networks. The treatment using the CWT technique was foundas thebest solution for confirmation of the presence of a leakafter trying different signal analysis tools such as transforms (FFT, STFT, etc ....). The application was carried out in the laboratory with a short channel hybrid prototype (a part of steel followed by a part in PVC). Validations tests have shown the effectiveness of the method.

In this work, we present an experimental study on a novel ternary composite material. In this case, several samples with barium titanate (BaTiO3) and copper oxide (Cu2O) particles in powder form in various amounts dispersed in a host matrix of epoxy resin (Re/BT/Cu2O) are carried out and sintered at 150°C. Their dielectric constants spectra were measured in the frequency range DC–3GHz by time domain spectroscopy (TDS). Low frequency has also been performed throughout this work, and it has primarily concentrated on conductivity behaviour which may be attributed to the effects of a percolation process. Experimental data were analyzed by means of dielectric permittivity and electric modulus formalisms. The functionality of the composite systems is related to the abrupt variation of the real part of permittivity, and to the relaxation process of the Cu2O particles. In addition, the behaviour obtained experimentally has been validated by the random mixture law of Lichtenecker in order to predict the electromagnetic behaviour of such composite material.