Calibration Phase Research Articles

CSI Fingerprinting for Device-Free Localization: Phase Calibration and SSIM-Based Augmentation

Fingerprint-based methods have become the mainstream for Device-Free Localization (DFL), and their performances depend heavily on the labeled dataset. In this letter, a novel Channel State Information (CSI) fingerprinting scheme is proposed for fully-exploiting the power of a given dataset. First, a phase calibration approach is applied to eliminate the phase offset due to the imperfection of synchronization and further make the collected phases from the different reference points distinguishable. Then, a Structural Similarity-based (SSIM-based) augmentation method is used to generate artificial samples for expanding the given dataset. Compared to existing GAN-based methods, it is very simple and computationally more efficient. Extensive experiments demonstrate the effectiveness of the proposed method. In comparison with the initial dataset and the AC-GAN based method, using the proposed method can gain approximately 13% and 8% Root-Mean-Square-Error (RMSE) enhancement, respectively.

Human-Robot Interaction Torque Estimation Methods for a Lower Limb Rehabilitation Robotic System with Uncertainties

Lower limb rehabilitation robot (LLRR) users, to successfully conduct isotonic exercises, require real-time feedback on the torque they exert on the robot to meet the goal of the treatment. Still, direct torque measuring is expensive, and indirect encoder-based estimation strategies, such as inverse dynamics (ID) and Nonlinear Disturbance Observers (NDO), are sensitive to Body Segment Inertial Parameters (BSIPs) uncertainties. We envision a way to minimize such parametric uncertainties. This paper proposes two human–robot interaction torque estimation methods: the Identified ID-based method (IID) and the Identified NDO-based method (INDO). Evaluating in simulation the proposal to apply, in each rehabilitation session, a sequential two-phase method: (1) An initial calibration phase will use an online parameter estimation to reduce sensitivity to BSIPs uncertainties. (2) The torque estimation phase uses the estimated parameters to obtain a better result. We conducted simulations under signal-to-noise ratio (SNR) = 40 dB and 20% BSIPs uncertainties. In addition, we compared the effectiveness with two of the best methods reported in the literature via simulation. Both proposed methods obtained the best Coefficient of Correlation, Mean Absolute Error, and Root Mean Squared Error compared to the benchmarks. Moreover, the IID and INDO fulfilled more than 72.2% and 88.9% of the requirements, respectively. In contrast, both methods reported in the literature only accomplish 27.8% and 33.3% of the requirements when using simulations under noise and BSIPs uncertainties. Therefore, this paper extends two methods reported in the literature and copes with BSIPs uncertainties without using additional sensors.

Wireless IoT universal approach based on Allan variance method for detection of artificial vibration signatures of a DC motor's shaft and reconstruction of the reference signal

In this paper, a workbench for remote control of DC motors is proposed. We intend to monitor and control rotating machines. Artificial failures are simulated by injecting different signals into the rotating motor's shaft. A comprehensive, original, and novel approach combining mathematical models, physical phenomena, and smart measurement methods is proposed to eliminate fluctuation faults caused by IoT sensor systems. These consist of a whole set of sensors (Accelerometer in Micro Electro Mechanical Systems [MEMS] technology, etc.) linked to a wireless node which constitutes a monitoring and control generic system of a DC motor running locally or via a remote control through an Internet platform. A reliability test of the IoT sensor system and an exploration of a proposed experimental bench in two measurement environments are successfully achieved. The results prove the reliability of devices for possible detection of acceleration signals corresponding to different speeds and injected vibrations with different forms and frequencies. A universal new approach, based on the Allan VARiance method, that has been proven robust when various disturbing signals are combined, has been successfully implemented to identify artificial vibration signatures. A reconstruction of MEMS sensor outputs using genetic algorithms is successfully fulfilled to get a compromise between the time of data acquisition and the time of processing via a learning phase of our system by means of a pseudo calibration of each motor applying our pseudo real-time control system. After the calibration phase, our control system aims to space out the measurements without degrading the effectiveness of the monitoring process.

Simultaneous Primary Calibration on the Infrasonic Amplitude and Phase Sensitivities of Condenser Microphones and Comparison With Theoretical Results

Low frequency primary calibrations of condenser microphones are the basis of accurate measurement of low frequency sound and infrasound. Prior arts on the primary phase calibrations differ greatly, simultaneous primary amplitude and phase calibrations are rarely seen. Recently, two infrasonic sensitivity models of condenser microphones based on the pistonphone technique, for the vent immersed in sound field and outside are derived. The relevant models can explicitly characterize the influence of pistonphone, microphone and environment on the voltage output and sensitivity of the microphone. On this basis, simultaneous primary amplitude and phase calibration tests are evaluable. Based on adequate performance tests of the designed laser pistonphone, this paper carries out in-depth primary microphone calibrations. The calibrated infrasonic amplitude and phase sensitivities are in good agreement with theoretical results, thus proves their values to reveal and quantify the influence of various factors on the calibration results based on the pistonphone technique.

A global view of shocked plasma in the supernova remnant Puppis A provided by SRG/eROSITA

Context. Puppis A is a medium-age supernova remnant (SNR), which is visible as a very bright extended X-ray source. While numerous studies have investigated individual features of the SNR, at this time, no comprehensive study of the entirety of its X-ray emission exists. Aims. Using field-scan data acquired by the SRG/eROSITA telescope during its calibration and performance verification phase, we aim to investigate the physical conditions of shocked plasma and the distribution of elements throughout Puppis A. In doing so, we take advantage of the uniform target coverage, excellent statistics, and decent spatial and spectral resolution of our data set. Methods. Using broad- and narrow-band imaging, we investigate the large-scale distribution of absorption and the plasma temperature as well as that of typical emission lines. This approach is complemented by a spatially resolved spectral analysis of the shocked plasma in Puppis A, for which we divided the SNR into around 700 distinct regions, resulting in maps of key physical quantities over its extent. Results. We find a strong peak of foreground absorption in the southwest quadrant, which in conjunction with high temperatures at the northeast rim creates the well-known strip of hard emission crossing Puppis A. Furthermore, using the observed distribution of ionization ages, we attempt to reconstruct the age of the shock in the individual regions. We find a rather recent shock interaction for the prominent northeast filament and ejecta knot, as well as for the outer edge of the bright eastern knot. Finally, elemental abundance maps reveal only a single clear enhancement of the plasma with ejecta material, consistent with a previously identified region, and no obvious ejecta enrichment in the remainder of the SNR. Within this region, we confirm the spatial separation of silicon-rich ejecta from those dominated by lighter elements. The apparent elemental composition of this ejecta-rich region would imply an unrealistically large silicon-to-oxygen ratio when compared to the integrated yield of a core-collapse supernova. In reality, both the observed ejecta composition and their apparent distribution may be biased by the unknown location and strength of the reverse shock.

The eROSITA Final Equatorial-Depth Survey (eFEDS)

Context.The eROSITA Final Equatorial Depth Survey (eFEDS), completed in survey mode during the calibration and performance verification phase of the eROSITA instrument on Spectrum Roentgen Gamma, delivers data at and beyond the final depth of the 4-yr eROSITA all-sky survey (eRASS:8),f0.5–2 keV= 1.1 × 10−14erg s−1cm−2, over 140 deg2. It provides the first view of normal galaxy X-ray emission from X-ray binaries (XRBs) and the hot interstellar medium at the full depth of eRASS:8.Aims.We used the Heraklion Extragalactic Catalogue (HECATE) of galaxies to correlate with eFEDS X-ray sources and identify X-ray detected normal galaxies. This flux-limited X-ray survey is relatively free from selection effects and enables the study of integrated normal galaxy X-ray emission and its relation to galaxy parameters such as the stellar mass, star formation rate (SFR), and metallicity.Methods.We cross-correlated 32 646 eFEDS X-ray sources to 1181 HECATE normal galaxies and obtained 94 matches. We classified galaxies as star-forming, early-type, composite, and active galactic nuclei (AGN) using Sloan Digital Sky Survey (SDSS) and Six-degree Field (6dF) optical spectroscopy.Results.The eFEDS field harbours 37 normal galaxies: 36 late-type (star-forming) galaxies and one early-type galaxy. There are 1.9 times as many normal galaxies as predicted by scaling relations via simulations, with an overabundance of late-type galaxies and a dearth of early-type galaxies. When compared with empirical relations, eFEDS dwarf galaxies with a high specific SFR have elevatedLX/SFR at a fixed specific SFR and metallicity, indicating an increase in XRB emission due to low metallicity. We expect that eRASS:8 will detect 12 500 normal galaxies, the majority of which will be star-forming, with the caveat that there are unclassified sources in eFEDS and galaxy catalogue incompleteness issues that could increase the actual number of detected galaxies over these current estimates.Conclusions.eFEDS observations detected a rare population of galaxies – the metal-poor dwarf starbursts – that do not follow known scaling relations. eRASS is expected to discover significant numbers of these high-redshift analogues, which are important for studying the heating of the intergalactic medium at high redshift. Further investigation of the hot gas emission from normal galaxies and stochastic effects in the dwarf galaxy population are required to constrain their X-ray output.

Phase-resolved X-ray spectroscopy of PSR B0656+14 with SRG/eROSITA andXMM-Newton

We present a detailed spectroscopic and timing analysis of X-ray observations of the bright pulsar PSR B0656+14. The observations were obtained simultaneously with eROSITA andXMM-Newtonduring the calibration and performance verification phase of the Spektrum-Roentgen-Gamma mission (SRG). The analysis of the 100 ks deep observation of eROSITA is supported by archival observations of the source, includingXMM-Newton, NuSTAR, and NICER. UsingXMM-Newtonand NICER, we first established an X-ray ephemeris for the time interval 2015 to 2020, which connects all X-ray observations in this period without cycle count alias and phase shifts. The mean eROSITA spectrum clearly reveals an absorption feature originating from the star at 570 eV with a Gaussianσof about 70 eV that was tentatively identified in a previous longXMM-Newtonobservation. A second previously discussed absorption feature occurs at 260–265 eV and is described here as an absorption edge. It could be of atmospheric or of instrumental origin. These absorption features are superposed on various emission components that are phenomenologically described here as the sum of hot (120 eV) and cold (65 eV) blackbody components, both of photospheric origin, and a power law with photon index Γ = 2 from the magnetosphere. We created energy-dependent light curves and phase-resolved spectra with a high signal-to-noise ratio. The phase-resolved spectroscopy reveals that the Gaussian absorption line at 570 eV is clearly present throughout ~60% of the spin cycle, but it is otherwise undetected. Likewise, its parameters were found to be dependent on phase. The visibility of the line strength coincides in phase with the maximum flux of the hot blackbody. If the line originates from the stellar surface, it nevertheless likely originates from a different location than the hot polar cap. We also present three families of model atmospheres: a magnetized atmosphere, a condensed surface, and a mixed model. They were applied to the mean observed spectrum, whose continuum fit the observed data well. The atmosphere model, however, predicts distances that are too short. For the mixed model, the Gaussian absorption may be interpreted as proton cyclotron absorption in a field as high as 1014G, which is significantly higher than the field derived from the moderate observed spin-down.

EROSITA calibration and performance verification phase: High-mass X-ray binaries in the Magellanic Clouds

Context.During its performance verification phase, the soft X-ray instrument eROSITA on board the Spektrum-Roentgen-Gamma (SRG) spacecraft observed large regions in the Magellanic Clouds in which almost 40 known high-mass X-ray binaries (HMXBs, including candidates) are located.Aims.We looked for new HMXBs in the eROSITA data, searched for pulsations in HMXB candidates, and investigated the long-term behaviour of the full sample using archival X-ray and optical data.Methods.For sufficiently bright sources, we performed a detailed spectral and temporal analysis of their eROSITA data. A source detection analysis of the eROSITA images in different energy bands provided count rates and upper limits for the remaining sources.Results.We report the discovery of a new Be/X-ray binary in the Large Magellanic Cloud. The transient SRGEt J052829.5–690345 was detected with a 0.2–8.0 keV luminosity of~1035erg s−1. It exhibits a hard X-ray spectrum, typical for this class of HMXBs. The OGLEI-band light curve of theV~15.7mag counterpart shows large variations up to 0.75 mag, which occur with a quasi-period of~511 days. The eROSITA observations of the Small Magellanic Cloud covered 16 Be/X-ray binary pulsars, 5 of which were bright enough for an accurate determination of their current pulse period. The pulse periods for SXP 726 and SXP 1323 measured from eROSITA data are~800 s and~1006 s, respectively, which is very different from their discovery periods. Including archivalXMM-Newtonobservations, we update the spin-period history of the two long-period pulsars, which have shown nearly linear trends in their period evolution for more than 15 yr. The corresponding average spin-down rate for SXP 726 is 4.3 s yr−1, while SXP 1323 has a spin-up rate of –23.2 s yr−1. We discuss the spin evolution of the two pulsars in the framework of quasi-spherical accretion.

SRG/eROSITA discovery of 164 s pulsations from the SMC Be/X-ray binary XMMU J010429.4-723136

Context.The Small Magellanic Cloud (SMC) hosts many known high-mass X-ray binaries (HMXBs), and all but one (SMC X-1) have a Be companion star. Through the calibration and verification phase of eROSITA on board the Spektrum-Roentgen-Gamma (SRG) spacecraft, the Be/X-ray binary XMMU J010429.4-723136 was in the field of view during observations of the supernova remnant, 1E0102.2-7219, used as a calibration standard.Aims.We report timing and spectral analyses of XMMU J010429.4-723136 based on three eROSITA observations of the field, two of which were performed on 2019 November 7-9, with the third on 2020 June 18-19. We also reanalyse the OGLE-IV light curve for that source in order to determine the orbital period.Methods.We performed a Lomb-Scargle periodogram analysis to search for pulsations (from the X-ray data) and for the orbital period (from the OGLE data). X-ray spectral parameters and fluxes were retrieved from the best-fit model.Results.We detect, for the first time, the pulsations of XMMU J010429.4-723136 at a period of -164 s, and therefore designate the source as SXP 164. From the spectral fitting, we derive a source flux of ~1 × 10−12erg s−1cm−2for all three observations, corresponding to a luminosity of ~4 × 1035erg s−1at the distance of the SMC. Furthermore, reanalysing the OGLE light curve, including the latest observations, we find a significant periodic signal that we believe is likely be the orbital period; at 22.3 days, this is shorter than the previously reported values. TheSwift/XRTlight curve, extracted from two long monitorings of the field and folded at the same period, suggests that a modulation is also present in the X-ray data.

Taking Account of Seasonality in a Regional Flood Frequency Estimation Approach Based on Event Simulations

Regional flood estimation is an important issue in hydrology to anticipate and reduce the damages caused by extreme rainfall events. Approaches based on event simulation are particularly suitable to address this. As research has demonstrated the seasonality of rainfall characteristics, many flood frequency estimation approaches take into account rainfall seasonality to include seasonal fluctuations. For an event-based approach, since its hydrological model is initialized for each rainfall event, its performance is very sensitive to the initial states of the model. The seasonality of its hydrological model could thus become a decisive factor. Due to the complexity of the regionalization method, very few flood frequency estimation approaches based on event simulation have been regionalized at a large scale and do not consider the seasonality of hydrological parameters. This is the case for the SHYREG method studied in this article. Using data from HYDRO French database and SAFRAN, we discuss several adapted configurations considering the seasonality of both rainfall and hydrological parameters during its calibration and regionalization phase. Tests were carried out on 1929 catchments throughout France. Rather than calibrating a constant annual parameter for the hydrological model, we calibrated “winter” and “summer” parameters based on different observed flow quantiles (“seasonal”, “annual”, or “both”). Criteria on flood quantiles were calculated for different samplings. We also discuss the representativeness of seasonal parameters for the regionalization procedure and hydrological coherence observed from this seasonal parameterization. It seems that calibrating parameters based on seasonal flow quantiles helps reproduce annual quantiles, while the opposite is not possible. Among all the calibration configurations, calibration performed on both seasonal and annual flow quantiles makes the largest improvement compared to the initial annual parameterization method. It can correctly restitute seasonal flood quantiles for both calibration and validation catchments, with an obvious improvement in terms of estimating flood frequency in ungauged sites. It shows that the seasonality of hydrological parameters is worth considering for a regional flood estimation approach.

Monthly Streamflow Modeling Based on Self-Organizing Maps and Satellite-Estimated Rainfall Data

Hydrological data provide valuable information for the decision-making process in water resources management, where long and complete time series are always desired. However, it is common to deal with missing data when working on streamflow time series. Rainfall-streamflow modeling is an alternative to overcome such a difficulty. In this paper, self-organizing maps (SOM) were developed to simulate monthly inflows to a reservoir based on satellite-estimated gridded precipitation time series. Three different calibration datasets from Três Marias Reservoir, composed of inflows (targets) and 91 TRMM-estimated rainfall data (inputs), from 1998 to 2019, were used. The results showed that the inflow data homogeneity pattern influenced the rainfall-streamflow modeling. The models generally showed superior performance during the calibration phase, whereas the outcomes varied depending on the data homogeneity pattern and the chosen SOM structure in the testing phase. Regardless of the input data homogeneity, the SOM networks showed excellent results for the rainfall-runoff modeling, presenting Nash–Sutcliffe coefficients greater than 0.90.Graphical

ICatcher: A neural network approach for automated coding of young children's eye movements.

Infants' looking behaviors are often used for measuring attention, real‐time processing, and learning—often using low‐resolution videos. Despite the ubiquity of gaze‐related methods in developmental science, current analysis techniques usually involve laborious post hoc coding, imprecise real‐time coding, or expensive eye trackers that may increase data loss and require a calibration phase. As an alternative, we propose using computer vision methods to perform automatic gaze estimation from low‐resolution videos. At the core of our approach is a neural network that classifies gaze directions in real time. We compared our method, called iCatcher, to manually annotated videos from a prior study in which infants looked at one of two pictures on a screen. We demonstrated that the accuracy of iCatcher approximates that of human annotators and that it replicates the prior study's results. Our method is publicly available as an open‐source repository at https://github.com/yoterel/iCatcher.

Comparison in dynamic primary calibration of digital-output accelerometer between CEM and PTB

In the framework of the EU-funded project Met4FoF Metrology for the Factory of the Future a method was developed to perform primary magnitude and phase calibration of accelerometers. The validation of the developed associated hard- and software, which is designed to complement conventional primary calibration systems, is demonstrated based on a comparison measurement between the project partners CEM and PTB. The results show that the statistical distribution of results is only a minor contribution to uncertainty. An evaluation of the comparison based on the conventional procedure and established CMCs proofed a good consistency between the two laboratories, although the utilized sensor was (only) industrial grade quality.

Numerical Analysis of Masonry Structures through a Modified Composite Interface (MCI) Model

This paper presents an innovative modelling approach for evaluating the structural response of new and existing masonry buildings characterized by a periodic arrangement of units and mortar joints. The aim is to provide a calculation tool that allows to model the non-linear behaviour of masonry structures under lateral and vertical loads with a reduced numerical effort, without compromising the accuracy of the results. The proposed model is a typical D-FEM (Discontinuum - Finite Element Model) consisting of deformable blocks, which incorporates several masonry units. The blocks are separated by interface elements placed along predetermined surfaces on which cracks, sliding or crushing planes can develop. The surface layout is conceived to replicate all the fundamental failure mechanisms that can occur in real masonry structures. To adequately describe the non-linear behaviour of the interface elements, a Modified Composite Interface (MCI) model is formulated by modifying the "Combined Cracking-Shearing-Crushing" model originally proposed by Lourenço to simulate the cracking mechanisms along the interface elements of simplified micro-models in FEM analysis. The proposed D-FE model has been already calibrated and validated by the Authors on different masonry panels and, through an Evolutionary Polynomial Regression (EPR), expressions in closed form have been defined to calculate the mechanical parameters of the new MCI model starting from the original CI. Particularly, for each mechanical parameter, six formulas of increasing complexity have been identified through non-linear regression, progressively including a greater number of input variables to increase the precision of results. The aim of this study is to determine the accuracy of these formulas. Numerical analyses are carried out on a population of sixty-four masonry walls already used in the previous calibration phase, modeling each panel with all available equations. This allows to evaluate the average accuracy of each formula and to understand their efficiency in terms of calculation effort and correctness of the results.

Detection of Periodic Disturbances in LOFAR Calibration Solutions

The Earth’s ionosphere is a highly variable medium on a wide range of spatio-temporal scales. The responsiveness of plasma to the geomagnetic field and its changes gives rise to anisotropy, which may introduce wave-like characteristics while scanning the ionosphere with a line-of-sight towards a radio source. Previous studies of LOw Frequency ARray (LOFAR) calibration phase solutions report that the estimated beta parameter of a structure function calculated over 6–8 h of astronomical observation timespan has a range of values from 1.6 to 2.0, with an average of 1.89. Such difference between the observations could result from transient wave-like disturbances within the data. This study aims to present a method of signal processing of ionospheric calibration datasets that allows the extraction of a transient wave-like signal and discuss its possible origin. We use complex Morlet wavelet analysis applied to two 8 h observations corresponding to very quiet geomagnetic conditions. We find a wave-like signal in the interferometric Total Electron Content data even during periods of no geomagnetic activity. We suggest it results from the relative velocity changes between the LOFAR line-of-sight and a convection pattern in the ionospheric F layer. Establishing the relationship between quiet time ionosphere, geomagnetic field changes and LOFAR’s calibration solutions may prove beneficial to determination of the dominant signals in the more disturbed conditions, which we leave for future study.

Usability Assessment of Different Contact Models for Modelling the Failure Behaviour of Sedimentary Rocks Under Unconfined Stress Conditions

The methods applied in rock material and rock mass studies related to rock mechanics have begun to differentiate with computational and software technology development in recent years. Particle Flow Code software, which is based on the discrete element method, is used in many studies on rock mechanics. With Particle Flow Code, rocks can be modeled in both two and three dimensions, and the behavior of rocks under different conditions can be analyzed. Rocks can be formed using disks in two dimensions and spherical particles in three dimensions. These particles are bonded to each other by contact models with different micro-mechanical properties. The failure of the model occurs as a result of the fracturing with the rupture of these bonds. Using parameters such as density, strength, deformation, which can be defined as the mathematical expression of the natural properties of rock samples, studies can be carried out for different stress conditions through models created after multi-stage calibration steps. In order to have a calibrated and representative model, chosen contact models should be mimic the failure of the rock material. For this purpose, a calibrated model was created for both PBM (Parallel-bonded model) and FJC (Flat-jointed model) using the results of the unconfined compressive strength test of Hawkesbury sandstone. In the calibration phase, the results obtained from the models were matched from the laboratory experiments by using elasticity modulus, uniaxial compressive strength, tensile strength, and Poisson ratio parameters. Results were evaluated by considering the displacement vectors, force chains, and crack resolution parameters. PBM and FJM were similar in terms of displacement vectors and force chains. However, in terms of fracture resolution, FJM stands out as it can be used to analyze failure behavior and modeling studies for sedimentary rocks by providing data up to four times that of PBM.

Calibration and validation of the Angstrom\u2013Prescott model in solar radiation estimation using optimization algorithms

The Angstrom–Prescott (A–P) model is widely suggested for estimating solar radiation (Rs) in areas without measured or deficiency of data. The aim of this research was calibration and validation of the coefficients of the A–P model at six meteorological stations across arid and semi-arid regions of Iran. This model has improved by adding the air temperature and relative humidity terms. Besides, the coefficients of the A–P model and improved models have calibrated using some optimization algorithms including Harmony Search (HS) and Shuffled Complex Evolution (SCE). Performance indices, i.e., Root Mean Square Error (RMSE), Mean Bias Error, and coefficient of determination (R2) have used to analyze the models ability in estimating Rs. The results indicated that the performance of the A–P model had more precision and less error than improved models in all the stations. In addition, the best results have obtained for the A–P model with the SCE algorithm. The RMSE varies between 0.82 and 2.67 MJ m−2 day−1 for the A–P model with the SCE algorithm in the calibration phase. In the SCE algorithm, the values of RMSE had decreased about 4% and 7% for Mashhad and Kerman stations in the calibration phase compared to the HS algorithm, respectively.

A Wearable, Multi-Frequency Device to Measure Muscle Activity Combining Simultaneous Electromyography and Electrical Impedance Myography.

The detection of muscle contraction and the estimation of muscle force are essential tasks in robot-assisted rehabilitation systems. The most commonly used method to investigate muscle contraction is surface electromyography (EMG), which, however, shows considerable disadvantages in predicting the muscle force, since unpredictable factors may influence the detected force but not necessarily the EMG data. Electrical impedance myography (EIM) investigates the change in electrical impedance during muscle activities and is another promising technique to investigate muscle functions. This paper introduces the design, development, and evaluation of a device that performs EMG and EIM simultaneously for more robust measurement of muscle conditions subject to artifacts. The device is light, wearable, and wireless and has a modular design, in which the EMG, EIM, micro-controller, and communication modules are stacked and interconnected through connectors. As a result, the EIM module measures the bioimpedance between 20 and 200 with an error of less than 5% at 140 SPS. The settling time during the calibration phase of this module is less than 1000 ms. The EMG module captures the spectrum of the EMG signal between 20–150 Hz at 1 kSPS with an SNR of 67 dB. The micro-controller and communication module builds an ARM-Cortex M3 micro-controller which reads and transfers the captured data every 1 ms over RF (868 Mhz) with a baud rate of 500 kbps to a receptor connected to a PC. Preliminary measurements on a volunteer during leg extension, walking, and sit-to-stand showed the potential of the system to investigate muscle function by combining simultaneous EMG and EIM.

Hydrological simulation of the Laja River basin with the WEAP model

The Laja River basin is located between parallels 21° 32' 58", 20° 16'14" N, and meridians - 100° 6' 28'' - 101° 30'44" W. In 2014, the basin was classified with a high degree of pressure on the water resource (Conagua, 2014). It also ranked second as an administrative hydrological region with the highest population density, and it presents strong problems with supply and demand. All the previous reasons motivated this work. The objective was to calibrate, validate and estimate runoff surface, considering the effects of changes in precipitation, Therefore, hydrological modeling was performed in WEAP (Water Evaluation and Planning) using the soil moisture method. The information used was on soil use and physical characteristics; crop coefficients; average monthly data on precipitation; average temperatures; relative humidity; and wind speed. The calibration and validation processes were carried out at four points in the basin, for five years on a monthly period. The hydrometric stations were Puente Dolores, Begoña II, Tres Guerras and Pericos. For each station, the coefficient of determination (r2) and Nash-Sutcliffe efficiency index (NSE) were estimated. The values obtained in the calibration phase were an r2 of 0.8 to 0.82 and NSE from 0.55 to 0.77. In the validation phase, the values for r2 range from 0.65 to 0.86 and NSE from 0.57 to 0.75. Climate information was generated with WGEN for 10 years, to generate runoff scenarios, with the RCP8.5 projections, for the period 2012-2022. In conclusion, WEAP is a software capable of correctly simulating, with the soil moisture method, the response of the Laja River basin. WEAP correctly simulated flows measured in the gauging station with an average absolute error of less than 10 %.

Gain and Phase Calibration of Uniform Rectangular Arrays Based on Convex Optimization and Neural Networks

A calibration method based on convex optimization (CVX) and neural networks is proposed for the large planar arrays of phased array three-dimensional imaging sonar systems. The method only needs an acoustic calibration source at an unknown position in the far field, and the direction of arrival (DOA) and gain and phase error are jointly estimated. The method uses a CVX algorithm to solve an optimization problem and initially estimates the DOA of the calibration source robustly. Subsequently, according to the estimation results, a neural network is used for fitting to obtain off-grid DOA estimation of the calibration source. Thereafter, spatial matched filtering is performed to obtain the gain and phase residual estimations. The root mean square error (RMSE) of the beam pattern calibrated by the method for uniform planar arrays can reach a value of 4.9542 × 10−5. The experimental results demonstrate the efficiency of the proposed method for gain and phase calibration.