Acquisition Unit Research Articles

Modified reptile search algorithm for optimal integration of renewable energy sources in distribution networks

AbstractThis paper introduces a Modified Reptile Search Algorithm (MRSA) designed to optimize the operation of distribution networks (DNs) considering the growing integration of renewable energy sources (RESs). The integration of RESs‐based Distributed Generation (DG) systems, such as wind turbines (WTs) and photovoltaics (PVs), presents a complex challenge due to its significant impact on DN operations and planning, particularly considering uncertainties related to solar irradiance, temperature, wind speed, consumption, and energy prices. The primary objective is cost reduction, encompassing electricity acquisition, PV and WTs unit costs, and annual energy losses. The proposed MRSA incorporates two strategies: the fitness‐distance balance method and Levy flight motion, enhancing its searching capabilities beyond standard Reptile Search Algorithm and mitigating local optima issues. The uncertainties in load demand, energy prices, and renewable energy generation are represented through probability density functions and simulated using Monte Carlo methods. Evaluation involves typical bentchmark functions and a real 112‐bus Algerian DN, comparing MRSA's efficacy with other optimization techniques. Results indicate that the proposed DN optimization program with WTs and PVs integration reduces annual costs by 21.43%, from 6.2715E + 06 to 4.9270E + 06 USD, reduce voltage deviations by 21.67%, from 77.1022 to 60.4007 USD, and enhance system stability by 2.59%, from 2.3699E + 03 to 2.4314E + 03 USD, compared with the base case.

Design of a Data Acquisition, Correction and Retrieval of Na Doppler Lidar for Diurnal Measurement of Temperature and Wind in the Mesosphere and Lower Thermosphere Region

Based on the resonance fluorescence scattering mechanism, a narrowband sodium (Na) lidar can measure temperature and wind in the mesosphere and lower thermosphere (MLT) region. By using a narrowband spectral filter, background light noise during the day can be suppressed, allowing for continuous observations. To obtain full-diurnal-cycle temperature and wind measurement results, a complex and precise retrieval process is required, along with necessary corrections to minimize measurement errors. This paper introduces the design of a data acquisition unit for three frequencies in three directions of the Na lidar system in the Chinese Meridian Project (Phase II) and investigates the calibration and retrieval methods for obtaining diurnal temperature and horizontal wind in the MLT region, using a Na Doppler lidar with Faraday anomalous dispersion optical filter (FADOF). Furthermore, these methods are applied to observations conducted by a Na lidar in Beijing, China. The wind and temperature results over full diurnal cycles obtained from the all-solid-state Na Doppler lidar are reported for the first time and compared with temperature measurements from satellite, as well as wind observations from a meteor radar. The comparison demonstrates a reasonable agreement between the results, indicating the rationality of the lidar-retrieved results and the feasibility and effectiveness of the data correction and retrieval method.

Active Vibration Control Using Loudspeaker-Based Inertial Actuator with Integrated Piezoelectric Sensor

With the evolution of the aerospace industry, structures have become larger and more complex. These structures exhibit significant characteristics such as extensive flexibility, low natural frequencies, numerous modes, and minimal structural damping. Without implementing vibration control measures, the risk of premature structural fatigue failure becomes imminent. In present times, the installation of inertial actuators and control signal acquisition units typically requires independent setups, which can be cumbersome for practical engineering purposes. To address this issue, this study introduces a novel approach: an independent control unit combining a loudspeaker-based inertial actuator (LBIA) with an integrated piezoelectric ceramic sensor. This unit enables autonomous vibration control, offering the advantages of ease of use, low cost, and lightweight construction. Experimental verification was performed to assess the mechanical properties of the LBIA. Additionally, a mathematical model for the LBIA with an integrated piezoelectric ceramic sensor was developed, and its efficacy as a control unit for thin plate structure vibration control was experimentally validated, showing close agreement with numerical results. Furthermore, the LBIA’s benefits as an actuator for low-frequency mode control were verified through experiments using external sensors. To further enhance control effectiveness, a mathematical model of the strain differential feedback controller based on multi-bandpass filtering velocity improvement was established and validated through experiments on the clamp–clamp thin plate structure. The experimental results demonstrate that the designed LBIA effectively reduces vibration in low-frequency bands, achieving vibration energy suppression of up to 12.3 dB and 23.6 dB for the first and second modes, respectively. Moreover, the LBIA completely suppresses the vibration of the fourth mode. Additionally, the improved control algorithm, employing bandpass filtering, enhances the effectiveness of the LBIA-integrated sensor, enabling accurate multimodal damping control of the structure’s vibrations for specified modes.

Manufacturing Process Optimization and Tool Condition Monitoring in Mechanical Engineering

The optimization of manufacturing and production processes with various computer software is essential these days. Solutions on the market allow us to optimize and improve our manufacturing and production processes; one of the most popular software is called Tecnomatrix, which is described in this paper. Tool condition monitoring is a vital part of the manufacturing process in the industry. It requires continuous measurement of the wear of the cutting tool edges to improve the surface quality of the work piece and maintain productivity. Multiple methods are available for the determination of the actual condition of the cutting tool. Vibration diagnostics and acoustic methods are included in this paper. These methods are simple, it requires only high sensitive sensors, microphones, and data acquisition unit to gather the vibration signal and make signal improvement. Extended Taylor equation is applied for tool edge wear ratio. Labview and Matlab software are applied for the measurement and the digital signal processing. Machine learning method with artificial neural network is for the detection and prediction of the edge wear to estimate the remaining useful lifetime (RUL) of the tool.

Real-Time Malfunction Detection of Maglev Suspension Controllers

This study aims to develop a track-side online monitoring system for malfunction detection in the suspension controllers of maglev trains during their in-service operation. The hardware module of the system includes two arrays of accelerometers deployed on an F-type rail and a data acquisition unit. The software module of the system consists of codes for three functions: (i) the identification of time intervals in relation to the passage of each suspension controller via synchrosqueezing transform; (ii) the extraction of a feature index (FI) sequence synthesized by modulating the response amplitude, frequency, and running speed; and (iii) the formulation of a Bayesian dynamic linear model for real-time malfunction detection in maglev suspension controllers. For verification of the proposed monitoring system and malfunction detection algorithm, full-scale tests have been conducted on an maglev test line using the devised system, where a maglev train was run at different speeds with malfunction occurring in the suspension controllers. The malfunction detection results of the proposed approach are exemplified via comparison with the recorded suspension gaps after the trial run of the maglev train. The fidelity of the results obtained using the extracted FI sequence and using the raw monitoring data are compared. The superiority of the proposed malfunction detection algorithm is also discussed via comparison with the results of the different train speeds.

Online hydrodynamic forces estimation system based on the artificial lateral line system

Vehicles that operate beneath or on the surface of water (e.g., boats, remotely operated vehicles, unmanned underwater vehicles, etc.) are subjected to external hydrodynamic forces and require an online hydrodynamic observer for precise autonomous control. The accurate calculation of current forces requires accurate sensor data and computational fluid dynamics, which impose a huge computational burden and cannot be applied in the real-time control of such vehicles. Fish can accurately perceive the effects of ocean currents, a function that can be achieved through an artificial lateral line system. In this study, an online hydrodynamic observer is proposed that uses an artificial lateral line system to estimate the resultant forces acting on water vehicles in real time for the autonomous control of the vehicle. A small three-layer artificial neural network is used to construct the hydrodynamic observer to evaluate various hydrodynamic forces in the form of a six-axis resultant force, such as the damping effects of currents and effects of wind and waves. In this study, an artificial lateral line system is designed for vehicles of any geometry. The system consists of a multipressure sensor array, data acquisition unit, and computational unit. Each pressure sensor is packaged as a separate unit that can be assembled on the surface of an underwater robot. These pressure sensors measure the pressure changes caused by the ocean current forces, while the data acquisition unit collects, collates, and sends the pressure sensor and attitude and heading reference system (AHRS) data to the processing unit. The processing unit uses an artificial neural network to estimate the ocean current forces acting on the vehicle body coordinate system based on the pressure sensor array, and converts them into the inertial reference system in the geoid based on the data acquired from the AHRS. This study explores the effects of the number of sensors and their locations on the accuracy of the estimated results. It investigates whether a scale factor can be used to estimate other available observers of the same size and geometry to enable the rapid deployment of artificial lateral devices to different vehicles. To evaluate the generalization performance of the proposed hydrodynamic observer, tests were conducted in dynamic water environments, such as rivers and oceans.

Spatio-Temporal Generative Adversarial Network Based Power Distribution Network State Estimation With Multiple Time-Scale Measurements

The increasing penetration of distributed renewable generation has introduced significant uncertainties and randomness to the power distribution network operation. Accurate and timely awareness of the network operation is of paramount importance to ensure system safety and reliability and is considered non-trivial and costly as substantial network reinforcement with advanced measurement devices is generally required. Also, the existing state estimation methods, e.g., weighted least square (WLS), may not converge in the presence of incomplete and inaccurate measurements. This paper proposes a spatio-temporal estimation generative adversarial network (ST-EGAN) consisting of feature extraction, information completion, data reconstruction and fake data discrimination to generate high-resolution pseudo-measurements to promote the accuracy and robustness of state estimation. The task of high-resolution power distribution network state estimation is carried out based on the mixed dataset of multiple time-scale measurements obtained from Supervisory Control and Data Acquisition (SCADA) and Phasor Measurement Units (PMU). The proposed solution is extensively assessed using the IEEE 33-bus test network compared with the existing solutions for a range of scenarios with different resolutions and noise intensities. The numerical results demonstrated that the proposed ST-EGAN can reduce the mean RMSE by 4.78% compared to interpolation algorithms, and reduce the RMSE by 0.14% and 0.21% compared with deep convolutional generative adversarial networks (DCGAN) and super-resolution convolutional networks (SRCNN) in the presence of noises with different intensities. The proposed method can be generalized to cases with different topological structures and measurement assembly conditions.

Frequency-domain synchronization of structural health monitoring data

Since the advent of structural health monitoring (SHM), vibration analysis has been one of the most popular strategies for yielding modal information of structures, such as eigenfrequencies and mode shapes. As a result of the relatively high sampling rates adopted in vibration analysis, obtaining accurate modal information requires absolute synchronization of structural response data. Despite the advances in sensing technologies and synchronization protocols, synchronization discrepancies in structural response data may occur due to drifting in internal clocks of independent data acquisition units. Particularly in wireless SHM, in which each wireless sensor node essentially operates as an independent data acquisition unit, mitigating clock drifting has been an area of intensive research. This paper addresses the issue of data synchronization from a physics-based perspective, aiming to add an additional synchronization check at the post-sampling stage. Specifically, a frequency-domain synchronization approach is reported, which builds upon the relationship between time lags in structural response data and the cross spectral density phase angles. Validation tests are conducted using simulations of a multi-degree-of-freedom oscillator and data collected from a full-scale road bridge, demonstrating the capability of the approach to estimate time lags between SHM data even in the presence of non-proportional damping, thus overcoming a common limitation of other synchronization methods. The frequency-domain synchronization approach is intended to complement synchronization protocols in extracting accurate modal information.

Fatigue Strength Analysis of a Prototype Francis Turbine in a Multilevel Lifetime Assessment Procedure Part III: Instrumentation and Prototype Site Measurement

Part I of this series of publications addressed the background and fundamentals of the lifetime assessment of prototype Francis turbines. Part II concentrated on the developed methods of numerical calculation and assessment procedures. The present contribution (Part III) deals with the instrumentation and the metrological range of the assessment procedure. The most important sensors, measurement tools, and data acquisition units are presented (background). The instrumentation of the prototype Francis turbine is used, on the one hand, for machine unit monitoring and plant operating and, on the other hand, for generating measurement data to validate and adjust/correct the numerical simulations. Measurement data form the basis for further evaluations at various levels. A wide variety of measured variables are required to carry out the remaining lifetime of a component using fatigue analysis. Those variables include pressure and acceleration signals, vibration monitoring, and strain gauge applications for mechanical stress analysis. The available measurement signals are divided into groups based on the developed method. Thus, already-available data from the control room are compared with machine monitoring and temporarily measured data. The correlation of all available data is essential today to determine an exact idea of the occurring flow phenomena and their effects on the mechanical stresses on the component. This interaction of the different data sources and, subsequently, the use of selected quantities for the numerical calculation are part of the newly developed concept for fatigue strength analysis of mechanical components of a turbine unit (methods). The results of this journal article are divided into the discussion of the necessary instrumentation and mounting of the sensors and into the evaluation, presentation, and interpretation of the measurement data. In addition, a fatigue strength assessment is made at the position of the strain gauges. These results serve as a basis for validating the numerical stress calculation. It is worth mentioning that the validation of the numerical results and the discussion of the deviations and error consideration is carried out in Part IV of this publication series (results). This journal article of the series on condition assessment of prototype Francis turbines ends with a discussion of the results and conclusions for further data processing (conclusion).

Upgrade of EAST data acquisition system

After the 2021 spring campaign, EAST (Experimental Advanced Superconducting Tokamak) facility carried out maintenance. EAST data acquisition system, which is responsible for the unified diagnostic data acquisition and long-term data storage, also has been upgraded. This article will present an overview of the upgrade data acquisition system. The EAST data acquisition system consists of a DAQ console, DAQ units, and data storage database. The DAQ console is used to manage the data acquisition configuration information and control the data acquisition workflow. The old DAQ console has been developed for many years, however, it has become inadequate with increasing of data acquisition units. After upgrade, it is replaced by a data acquisition workflow control program and a configuration management module. The configuration management module provides a website based on Spring Boot framework for administrator and diagnostic users to manage the DAQ units, data storage servers, and signal conditioning equipment, while the data acquisition workflow control program runs as a Linux service. Up to 65 DAQ units with about 4000 channels are distributed in different physical positions for different diagnostic systems. The raw diagnostic data acquired by DAQ units is quasi-real-time transferred to the data storage database. The data acquisition driver plugin has been updated for various kinds of data acquisition cards. The data server cluster keeps these data with MDSPlus for long-term storage. The upgraded data acquisition system has been used in the EAST campaign.

A Rigorous and Integrated On-Water Monitoring System for Performance and Technique Improvement in Rowing.

This paper presents a prototype, on-water rowing monitoring system and its testing results for a single scull boat. The proposed system aims at recording critical kinetic (athlete biomechanics and oar/seat movements) and kinematic (boat position, velocity, acceleration, and attitude) parameters for sport performance evaluation and rowing technique improvement. The data acquisition unit is organized in two parts: the first part aims at logging boat kinematics based on GNSS/INS filtering, while the second one facilitates kinetics data recording using a series of analog sensors (potentiometers, strain gauges) installed on the athlete's body and the boat seat and oars. Both parts are connected to a central unit featuring analog voltage digitizers and a micro-PC for device handling and data storing. In order to test the performance of the system a series of field trials were undertaken featuring different observation scenarios as well as intentionally induced errors in the rowing technique. Analysis revealed the high performance of the system in terms of sensor completeness and setup procedures as well as operational efficiency. Moreover, system performance evaluation exercised through studying raw data recordings and resultant parameters at stroke cycle and average (standardized) stroke cycle level confirmed the fruitfulness of the proposed approach and system and its potential for implementation on a broad scale. Finally, the data acquired from the proposed system were used to compute the adopted input parameters and performance indicators to characterize the system in terms of functionality and operational efficiency.

The principles of a new line surge arrester's transient current measurement system

Power systems are exposed to different types of electromagnetic transients, which involve a large spectrum of signals whose duration can range from several nanoseconds to several milliseconds, and whose peak values can also cover a wide range of values. Therefore, measuring electromagnetic transients in power systems is a challenging task. Fast front transients caused by lightning events are especially dangerous, and improved knowledge about them is essential for better protection design. The development of measuring systems for such phenomenon is important. This paper is dedicated to the development of a measuring system for lightning and line surge arrester currents. Modern electronic tools and equipment, as well as cutting-edge communication and information technologies, constitute the foundation of the proposed system. This includes the most recent models of acquisition units and GPS devices that are now available on the market, as well as communication networks including 4 G, 5 G, and WiFi. Software and hardware components of the system are described. The paper presents laboratory tests conceived to prove that the system operates correctly and provides accurate results. The measurements are compared to EMTP and MATLAB simulations. Both are in agreement.

TECHNIQUES FOR SPECIAL FOLLOWING OF THE DEGRADATIONS DEVELOPMENT FOR A RAILWAY PASSAGE

Nowadays, most of the transportation infrastructure is already built, the main challenge being the maintenance of these structures in optimal conditions of safety and comfort for users. Therefore, in recent years, research in the field of bridge maintenance has focused on the development and implementation of special monitoring programs that can identify and warn transportation infrastructure managers of the appearance and development of degradation, and especially the possibility of collapse of the construction element in the near future. In addition to familiarizing readers with the structural design of the Constanta Bridge, this work proposes the presentation of the main technologies used for implementing the in-service behavior monitoring program, along with the arrangement of data acquisition units and a brief interpretation of the information provided by the monitoring system. In the conclusions of the article, the authors will draw attention to the bearing capacity of the structure, affected by the development of degradation processes and the need for emergency implementation of rehabilitation and strengthening works of the passage.

Tiny Deep Learning Architectures Enabling Sensor-Near Acoustic Data Processing and Defect Localization

The timely diagnosis of defects at their incipient stage of formation is crucial to extending the life-cycle of technical appliances. This is the case of mechanical-related stress, either due to long aging degradation processes (e.g., corrosion) or in-operation forces (e.g., impact events), which might provoke detrimental damage, such as cracks, disbonding or delaminations, most commonly followed by the release of acoustic energy. The localization of these sources can be successfully fulfilled via adoption of acoustic emission (AE)-based inspection techniques through the computation of the time of arrival (ToA), namely the time at which the induced mechanical wave released at the occurrence of the acoustic event arrives to the acquisition unit. However, the accurate estimation of the ToA may be hampered by poor signal-to-noise ratios (SNRs). In these conditions, standard statistical methods typically fail. In this work, two alternative deep learning methods are proposed for ToA retrieval in processing AE signals, namely a dilated convolutional neural network (DilCNN) and a capsule neural network for ToA (CapsToA). These methods have the additional benefit of being portable on resource-constrained microprocessors. Their performance has been extensively studied on both synthetic and experimental data, focusing on the problem of ToA identification for the case of a metallic plate. Results show that the two methods can achieve localization errors which are up to 70% more precise than those yielded by conventional strategies, even when the SNR is severely compromised (i.e., down to 2 dB). Moreover, DilCNN and CapsNet have been implemented in a tiny machine learning environment and then deployed on microcontroller units, showing a negligible loss of performance with respect to offline realizations.

Data acquisition unit for road vehicle or aircraft

Data acquisition unit for road vehicle or aircraft

A CMOS Synchronized Sample-and-Hold Artifact Blanking Analog Front-End Local Field Potential Acquisition Unit With ±3.6-V Stimulation Artifact Tolerance and Monopolar Electrode-Tissue Impedance Measurement Circuit for Closed-Loop Deep Brain Stimulation SoCs

In this paper, an analog front-end (AFE) local-field potential (LFP) acquisition unit with real-time stimulation artifact removal is proposed and verified for closed-loop deep brain stimulation (DBS) applications. The proposed acquisition unit is called the synchronized sample-and-hold stimulation artifact blanking (SSAB) AFE LFP acquisition unit. Both right-leg-driven (RLD) circuit and monopolar electrode-tissue impedance (ETI) measurement circuit associated with the AFE amplifier are also proposed. During closed-loop stimulations, the artifact removal is realized through the SSAB-IPC by blanking the AR-CCIA with a clock synchronized to the stimulation-enable signal and holding the amplifier at its state before stimulation through a sample-and-hold operation. After stimulation, the acquisition unit can quickly recover from the holding state back to the LFP recording state to reduce the discontinuity in LFP recording. The proposed acquisition unit was fabricated in 0.18-um CMOS technology. With the RLD circuit, the measured CMRR is 124 – 145 dB in the signal bandwidth. The fabricated monopolar ETI measurement circuit has a measurement error less than 8.3% with an extra power consumption of 2.65 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $</tex-math> </inline-formula> W. The experimental results have shown that the proposed SSAB AFE LFP acquisition unit is feasible for the integration of SoCs in real-time closed-loop DBS systems for various applications.

Intelligent Workplace Activity Monitoring and Detection Using Self-Powered Triboelectric/Piezoelectric Sensor Augmented Machine Learning

In this letter, we propose a method for monitoring workplace activity using a self-powered, flexible triboelectric/piezoelectric tactile sensor. The flexible sensor is applied to the forefinger of the participants while they perform typical workplace activities like keyboard typing, mouse scrolling, scribbling, and finger stretching. The electrical output signal corresponding to triboelectric and piezoelectric sensors for each activity is recorded by a data acquisition unit (DAQ) and analyzed using an efficient convolution neural network (CNN) algorithm to precisely distinguish between the different activities. The classification results demonstrate that the proposed strategy of incorporating dual transduction mechanisms results in an accuracy higher than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$98\%$</tex-math></inline-formula> , and the precision and recall of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$98.13\%$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$98.14\%$</tex-math></inline-formula> , respectively. Due to the simple and inexpensive design of the sensor and the remarkable accuracy of the proposed machine-learning architecture, the reported work has immense potential in a wide range of workplace monitoring applications.

Data acquisition unit for suborbital rocket vibration measurement

Suborbital rockets and space launch vehicles experience high levels of vibration. They are caused by various factors, such as aerodynamic effects on the external surfaces and engine combustion instability. The second factor is especially pronounced in hybrid rocket engines, such as the main engine of the ILR-33 AMBER 2K rocket. In order to ensure the correct operation of all the equipment and payload on board, vibration testing of the devices is necessary as part of pre-flight qualification. For such tests to be truly indicative of in-flight performance, the vibration environment of the vehicle must be analysed. A conceptual measurement method was proposed, along with a prototype measurement system. The system is adapted to be integrated into the modular ILR-33 AMBER 2K on-board computer or work independently as a separate module. The device measures vibration in three axes in several key points onboard the rocket, with the possibility of connecting multiple units in a daisy-chain configuration. This paper presents the design of the module, including the key aspects of integrating it as part of the avionics package. The means of acquiring and storing of measurement data are presented. The method of validating the system’s data acquisition capabilities is described in detail. Qualification testing results and identified issues are discussed. Possible future improvements are proposed.

Finger gesture recognition with smart skin technology and deep learning

Finger gesture recognition (FGR) was extensively studied in recent years for a wide range of human-machine interface applications. Surface electromyography (sEMG), in particular, is an attractive, enabling technique in the realm of FGR, and both low and high-density sEMG were previously studied. Despite the clear potential, cumbersome electrode wiring and electronic instrumentation render contemporary sEMG-based finger gestures recognition to be performed under unnatural conditions. Recent developments in smart skin technology provide an opportunity to collect sEMG data in more natural conditions. Here we report on a novel approach based on soft 16 electrode array, a miniature and wireless data acquisition unit and neural network analysis, in order to achieve gesture recognition under natural conditions. FGR accuracy values, as high as 93.1%, were achieved for 8 gestures when the training and test data were from the same session. For the first time, high accuracy values are also reported for training and test data from different sessions for three different hand positions. These results demonstrate an important step towards sEMG based gesture recognition in non-laboratory settings, such as in gaming or Metaverse.

Design, Development and Testing of a Monitoring System for the Study of Proton Exchange Fuel Cells and Stacks.

This article is about the design, development and validation of a new monitoring architecture for individual cells and stacks to facilitate the study of proton exchange fuel cells. The system consists of four main elements: input signals, signal processing boards, analogue-to-digital converters (ADCs) and a master terminal unit (MTU). The latter integrates a high-level graphic user interface (GUI) software developed by National Instruments LABVIEW, while the ADCs are based on three digital acquisition units (DAQs). Graphs showing the temperature, currents and voltages in individual cells as well as stacks are integrated for ease of reference. The system validation was carried out both in static and dynamic modes of operation using a Ballard Nexa 1.2 kW fuel cell fed by a hydrogen cylinder, with a Prodigit 32612 electronic load at the output. The system was able to measure the voltage distributions of individual cells, and temperatures at different equidistant points of the stack both with and without an external load, validating its use as an indispensable tool for the study and characterization of these systems.