Electrical Tomography Research Articles

Research on capacitance-coupled electrical impedance imaging fusion algorithm based on wavelet transform.

Capacitively Coupled Electrical Impedance Tomography (CCEIT), as a new electrical tomography method, effectively overcomes the drawbacks of traditional electrical resistance tomography that requires direct contact with liquids, so as to achieve non-contact measurement. In order to make full use of the complete electrical impedance information (real and imaginary) obtained by CCEIT and further improve the quality of image reconstruction, an image fusion algorithm based on wavelet transform was proposed. First, the Landweber algorithm is used to image the real part information and the imaginary part information, and the wavelet transform algorithm is used to decompose the two images at multiple scales, in which the low-frequency coefficient is fused by the improved logic filter algorithm based on the adaptive threshold, the high-frequency coefficient is selected based on the absolute value maximum method for fusion, and finally, the fused image is obtained by wavelet reconstruction. Numerical simulations and experiments verify the feasibility of the proposed method, and the results show that the relative error is reduced by up to 17% and the correlation coefficient is increased by up to 10%.

Lithology identification using electrical imaging logging image: A case study in Jiyang Depression, China

Lithology identification plays a significant role in stratigraphic evaluation and geological analysis. Traditional lithology identification method is by modeling the relationship between well logging and lithology. However, well logging are not always sufficient to identify lithology since sometimes the curves are similar for different lithologies. Recently, electrical imaging logging image (EILI) with high resolution plays an increasingly important role in logging interpretation since EILI can intuitively reflect the characteristics of lithology. Unlike traditional lithology identification method by using well logging, in this paper, we propose a novel multi-dimensional automatic lithology identification method by applying deep learning to EILI. First, Filtersim algorithm is employed to fill the blank strip of the EILI. Then, an integrated convolutional neural networks (CNNs) model is designed to extract the resistivity feature, texture feature, and holistic feature of the EILI, respectively. Specifically, the integrated CNNs model can realize automatic recognition for different geological structures (massive, bedded, lamellar) and lithology (mudstone, sand-mudstone, lime-mudstone). Finally, lithology identification can be achieved by combining with multi-dimensional features. The efficacy of proposed integrated model is validated experimentally on the EILI of shale oil reservoir in the Jiyang Depression of China. Experimental results show the effectiveness and superiority of the integrated CNNs method for lithology identification.

Climate-driven versus anthropogenic induced erosion of the last 3000 years from an ancient lake in the Southern Phokis Plain (Desfina), Greece

The timing and causes for erosional events at the Kastrouli (Greece) archeological site – a Late Myceneaen with reuse in later periods – are presented. Two borehole sediment cores (max 6 m depth), collected from the footsteps of the settlement hill plain, were studied. Sedimentary analysis and luminescence dating techniques investigate and identify periods of soil aggradation in this record. Moreover, optically stimulated luminescence (OSL) dating confirmed the concept of an ancient lake during Kastrouli settlement times. Macroscopic overview of the stratigraphic structure for each core, included lithological and textural evaluation of the core sediments, assessments of grain size, and determination of the geochemical and mineralogical composition of the sediments. X-ray powder diffraction (XRPD), X-ray fluorescence (XRF), and Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS) techniques were applied for the mineralogical and geochemical analysis. OSL ages from seven sediment horizons spanned from the Ottoman period to archaic times. All lithological findings correlate with the results of the electrical tomography survey conducted in same area. Extrapolation of the Logarithmic fitting of the data back in time of prehistoric Late Mycenaean era of Kastrouli verifies a lake deeper than 10 m, with a considerable enhancement in soil aggradation of 1.2 cm/year. Attempts to dry out the area are evidenced by the hydraulic works found in two engineering sinkholes. Sedimentation changes had occurred in the local environment over the last 2500 years, and soil aggradation underwent significant fluctuations in the two studied cores. The Roman period (a wet period) and the Byzantine period experienced high aggradation rates. By contrast, soil aggradation rates in southern Phokis (Kastrouli region) have remained exceedingly low from the Ottoman era (circa 13th century) to the present. In the context of the Phokis case study, it appears that the sedimentation rates, driven by climate, have strengthened anthropogenic activities.

Anti-islanding image detection and optimization of distributed power supply using neural network architecture search

Anti-islanding detection (AILD) for distributed power sources plays an important role on the stable operation of power grid systems and the safety of electrical systems. In order to improve the detection accuracy, we propose neural network architecture search (NAS) based approach for anti-islanding image detection and optimization of distributed power sources. It combines the fast region-based convolutional neural network (Faster-R-CNN) with the differentiable architecture search (Darts), utilizing electrical signal image data acquired from cameras or photovoltaic (PV) inverters, thereby enhancing the accuracy and efficiency of detection. We conduct a comparative analysis of two methods for obtaining distributed power supply (DPS) anti-islanding image data: camera-based acquisition and PV inverter-based acquisition. Our observation reveals that the image data acquired through cameras is more conducive for the learning process of DCNN. The proposed algorithm was compared with other convolutional neural network (CNN) models, validating its performance superiority. This provides a novel perspective and methodology for the advancement of AILD for distributed power sources, and enhances the security and stability of DPS.

Non-Optical, Label-free Electrical Capacitance Imaging of Microorganisms.

Microbes live in diverse environments, and occupy biological roles across many timescales. Investigating the full scope of microbial activity requires imaging systems appropriate to each context. Though optical microscopy is powerful, the use of light, lenses, and other hardware limits where it can be applied. At the same time, existing non-optical imaging methods are frequently destructive to samples and require extensive equipment. In this paper we present a non-optical imaging system that is small, cheap, requires no sample labeling, and is compatible with a variety of microbial species. Our system uses semiconductor chips to measure the inherent material properties of a sample with spatial sensitivity, producing images of microbes contrasted against their environment and each other. Our technique captures label-free, micrometer-resolution images with a pocket-sized device, enabling microbiological imaging experiments in new environments with new species.

Investigating Arctic Permafrost Dynamics Using Electrical Resistivity Imaging and Borehole Measurement in Svalbard

This study utilized electrical resistivity imaging (ERI) to investigate subsurface characteristics near Nicolaus Copernicus University Polar Station on the western Spitsbergen-Kaffiøyra Plain island in the Svalbard archipelago. Surveys along two lines, LN (148 m) collected in 2022 and 2023, and ST (40 m) collected in 2023, were conducted to assess resistivity and its correlation with ground temperatures. The LN line revealed a 1- to 2-m-thick resistive unsaturated outwash sediment layer, potentially indicative of permafrost. Comparing the LN resistivity result between 2022 and 2023, a 600 Ohm.m decrease in the unsaturated active layer in 2023 was observed, attributed to a 5.8 °C temperature increase, suggesting a link to global warming. ERI along the ST line depicted resistivity, reaching its minimum at approximately 1.6 m, rising to over 200 Ohm.m at 4 m, and slightly decreasing to around 150 Ohm.m at 7 m. Temperature measurements from the ST line’s monitoring strongly confirmed that the active layer extends to around 1.6 m, with permafrost located at greater depths. Additionally, water content distribution in the ST line was estimated after temperature correction, revealing a groundwater depth of approximately 1.06 m, consistent with measurements from the S4 borehole on the ST line. This study provides valuable insights into Arctic subsurface dynamics, emphasizing the sensitivity of resistivity patterns to climate change and offering a comprehensive understanding of permafrost behavior in the region.

Developing a novel permeability prediction method for tight carbonate reservoirs using borehole electrical image logging

Predicting permeability accurately is crucial for effective hydrocarbon extraction, but the intricate pore structures of tight carbonates resulting from sedimentation, diagenesis, and tectonic activity present significant challenges. Based on borehole electrical image logging and fractal theory, we develop a method to calculate the fractal dimension of the porosity spectrum to characterize the complexity of the pore structure of the reservoir. Fractal features of the porosity spectra are studied, and fractal parameters are calculated, such as the left ([Formula: see text]), middle ([Formula: see text]), and right fractal dimensions ([Formula: see text]). A permeability prediction model is developed based on fractal parameters by investigating the linear relationship between fractal parameters and core permeability. The results indicate that [Formula: see text] and permeability have a coefficient of determination ([Formula: see text]) of 0.78, whereas [Formula: see text] between porosity and permeability is only 0.03. The [Formula: see text] and [Formula: see text] have little correlation with core permeability. The prediction results of the [Formula: see text]-based permeability model are in good agreement with the experimental data with Pearson product-moment correlation coefficient of 0.93 in the field applications. Our findings suggest that large pores primarily contribute to the permeability of tight carbonates because [Formula: see text] corresponds to the macroporous part of the porosity spectrum. This study enhances our understanding of the factors that influence permeability and provides a useful tool for predicting permeability in tight carbonate reservoirs.

Oil spills characterization and modeling using remote sensing and geophysical techniques to protect the highly vulnerable coastal zones in Alexandria, Egypt

Nowadays, oil spills threaten both aquatic and terrestrial environments, especially in regions with intensive oil refining and shipping activities and high environmental sensitivity, such as Alexandria city, Egypt. Oil spill characterization in coastal populous cities is particularly difficult due to large chemical/physical soil heterogeneities and saltwater intrusion, which represent a major challenges for soil remediation and restoration. Recently, the development of inversion algorithms enables electrical resistivity imaging (ERI) to perform detailed characterization of near-surface soil pollution. The study implements an interdisciplinary approach using remote sensing and an advanced time-lapse 2D-inversion scheme for detailed characterization of oil spill patterns around oil refinery sites in the Alexandria coastal zone. The implemented scheme was able to improve the depth of investigation while maintaining the shallow lateral model resolution. The findings indicate that the mapped oil spills constitute a wedge-like form where the oil moves gradually downward, and it then shifts horizontally towards the shoreline with thinning in oil-contaminated zones under control of tidal action and ground surface slope. Consequently, guided by remote sensing observations, in-situ trenches/wells are suggested to withdraw the oil-contaminated water at the maximum deduced oil-contaminated soil thickness. The applied procedures in this study are replicable and can be effectively used as a pre-requisite to remedy oil spills along terrestrial coastal environments worldwide.

Electromagnetic Source Imaging in Presurgical Evaluation of Children with Drug-Resistant Epilepsy.

For children with drug-resistant epilepsy (DRE), seizure freedom relies on the delineation and resection (or ablation/disconnection) of the epileptogenic zone (EZ) while preserving the eloquent brain areas. The development of a reliable and noninvasive localization method that provides clinically useful information for the localization of the EZ is, therefore, crucial to achieving successful surgical outcomes. Electric and magnetic source imaging (ESI and MSI) have been increasingly utilized in the presurgical evaluation of these patients showing promising findings in the delineation of epileptogenic as well as eloquent brain areas. Moreover, the combination of ESI and MSI into a single solution, namely electromagnetic source imaging (EMSI), performed on simultaneous high-density electroencephalography (HD-EEG) and magnetoencephalography (MEG) recordings has shown higher source localization accuracy than either modality alone. Despite these encouraging findings, such techniques are performed in only a few tertiary epilepsy centers, are rarely recorded simultaneously, and are underutilized in pediatric cohorts. This study illustrates the experimental setup for recording simultaneous MEG and HD-EEG data as well as the methodological framework for analyzing these data aiming to localize the irritative zone, the seizure onset zone, and eloquent brain areas in children with DRE. More specifically, the experimental setups are presented for (i) recording and localizing interictal and ictal epileptiform activity during sleep and (ii) recording visual-, motor-, auditory-, and somatosensory-evoked responses and mapping relevant eloquent brain areas (i.e., visual, motor, auditory, and somatosensory) during visuomotor task, as well as auditory and somatosensory stimulations. Detailed steps of the data analysis pipeline are further presented for performing EMSI as well as individual ESI and MSI using equivalent current dipole (ECD) and dynamic statistical parametric mapping (dSPM).

Functional MRI study with conductivity signal changes during visual stimulation

BackgroundAlthough blood oxygen level-dependent (BOLD) functional MRI (fMRI) is a standard method, major BOLD signals primarily originate from intravascular sources. Magnetic resonance electrical properties tomography (MREPT)-based fMRI signals may provide additional insights into electrical activity caused by alterations in ion concentrations and mobilities. PurposeThis study aimed to investigate the neuronal response of conductivity during visual stimulation and compare it with BOLD. Materials and methodsA total of 30 young, healthy volunteers participated in two independent experiments using BOLD and MREPT techniques with a visual stimulation paradigm at 3 T MRI. The first set of MREPT fMRI data was obtained using a multi-echo spin-echo (SE) echo planar imaging (EPI) sequence from 14 participants. The second set of MREPT fMRI data was collected from 16 participants using both a single-echo SE-EPI and a single-echo three-dimensional (3D) balanced fast-field-echo (bFFE) sequence. We reconstructed the time-course Larmor frequency conductivity to evaluate hemodynamics. ResultsConductivity values slightly increased during visual stimulation. Activation strengths were consistently stronger with BOLD than with conductivity for both SE-EPI MREPT and bFFE MREPT. Additionally, the activated areas were always larger with BOLD than MREPT. Some participants also exhibited decreased conductivity values during visual stimulations. In Experiment 1, conductivity showed significant differences between the fixation and visual stimulation blocks in the secondary visual cortex (SVC) and cuneus, with conductivity differences of 0.43 % and 0.47 %, respectively. No significant differences in conductivity were found in the cerebrospinal fluid (CSF) areas between the two blocks. In Experiment 2, significant conductivity differences were observed between the two blocks in the SVC, cuneus, and lingual gyrus for SE-EPI MREPT, with differences of 0.90 %, 0.67 %, and 0.24 %, respectively. Again, no significant differences were found in the CSF areas. ConclusionConductivity values increased slightly during visual stimulation in the visual cortex areas but were much weaker than BOLD responses. The conductivity change during visual stimulation was less than 1 % compared to the fixation block. No significant differences in conductivity were observed between the primary visual cortex (PVC)-CSF and SVC-CSF during fixation and visual stimulations, suggesting that the observed conductivity changes may not be related to CSF changes in the visual cortex but rather to diffusion changes. Future research should explore the potential of MREPT to detect neuronal electrical activity and hemodynamic changes, with further optimization of the MREPT technique.

A 1024-Channel Simultaneous Electrophysiological and Electrochemical Neural Recording System with In-Pixel Digitization and Crosstalk Compensation.

Simultaneous electrophysiological and chemical recording allows for multi-modal neural instrumentation and provides insights into chemical synapses and ion channels across the cell membrane. However, intermodal interference can hinder highly synchronized recording in large-scale systems with high temporal and spatial resolution. In this work, we propose a 1024-channel lab-on-CMOS system for dual-modal neural recording with in-pixel digitization and interference suppression. A foreground calibration scheme with tunable capacitance is implemented in-pixel to compensate for the crosstalk between electrical and chemical recording. Active pixels for both electrical and chemical modalities are designed based on a pulse width modulation (PWM) analog-to-digital conversion scheme. CMOS-compatible post-processing is implemented to realize in-pixel electrodes and chemical sensing membranes. The prototype, implemented in a 180nm CMOS technology, occupies a total area of 33mm2 with 1024 pixels, and each unit pixel includes one electrical recording site and two chemical recording sites, with dimensions of 150μm×130 μm. The total system power consumption is 19.68mW at a frame rate of 9k and 3k for electrical and chemical imaging respectively. The in-vitro experiment demonstrated the concurrent high density electrophysilogical and electrochemical recording with sub millisecond temporal resolution.

Particle motion characteristics on the rotational flow pneumatic conveying of horizontal-vertical pipeline

In this study, a rotational flow device (rotational blade) is developed and installed in the upstream of the particle inlet with the aim of improving the efficiency and capacity of pneumatic conveying. Firstly, this study analyzed the energy-saving effect of rotational flow based on the pressure drop and power consumption. The results shown that the optimal velocity can be reduced by a maximum of 18.7 % and the power consumption coefficient can be reduced by a maximum of 9.8 %. Furthermore, the distributions of particle concentration, velocity and pulsation velocity are analyzed by using electrical capacitance tomography (ECT) and particle image velocimetry (PIV) system. It is found that the particle velocity and velocity pulsation intensity for rotational flow are higher, and they have the ability to enhance particle suspension. Then, the power spectrum of the particle pulsation velocity shown that the rotational flow exhibited higher peak value at lower frequencies, indicating the particles are not easily deposited at pipe bottom. Finally, the auto-correlation of particle pulsation velocity indicated that the particle motion is more stable and has a longer period at low particle concentrations. The skewness factor and probability density function of particle pulsation velocity indicated that the use of rotational blades makes the particle pulsation velocity to deviate from the Gaussian distribution.

Electrical resistivity imaging of crude oil contaminant in coastal soils – A laboratory sandbox study

Characterizing the subsurface distribution of crude oil after a spill in a coastal environment is challenging due to variations in the soil and fluid properties. In situ sampling is limited in capturing the lateral and vertical migration of the crude oil within the vadose and saturated zones. This study presents a laboratory sandbox framework used to assess the effectiveness of electrical resistivity imaging for investigating the spatiotemporal distribution of crude oil in coastal sandy soils. A sandbox with dimensions L = 240 cm, W = 60 cm, and H = 60 cm was constructed using a 10 mm plexiglass and filled to a 40 cm height with 2 mm medium to fine-grained sand. At each stage of the experiment, 20 kg of sand was mixed with 1 l of water to create moist sand, after which the mixture was flushed over 12 h to remove suspended fine particles. Both saturated and unsaturated conditions were simulated by setting the water table at 10 cm and draining a fully saturated system overnight. Two liters of crude oil were spilled and monitored for 30 h. A surface array of 98 electrodes, with a unit electrode spacing of 2 cm, was installed along two transects 12 cm apart. Resistivity measurements were collected using a dipole-dipole array before, during, and after the simulated crude oil spill. The time-lapse electrical resistivity results revealed an initial gravity-induced vertical migration under both saturated and unsaturated conditions; over time, lateral migration of crude oil became apparent. In the saturated zone, there was a noticeable reduction in the percentage difference in resistivity from 700 % to 400 % after 24 h, depicting a spatial and temporal redistribution of the crude oil attributed to variation in pore geometry. This highlights the sensitivity of electrical resistivity measurements to subtle but measurable anisotropy in the distribution of soil pores. Overall, electrical resistivity proved successful in imaging the non-ideal behavior of crude oil pollutants and the associated spatial changes in the pore-size distribution of subsurface sediments.

Functional conductivity imaging: quantitative mapping of brain activity.

Theory and modelling suggest that detection of neuronal activity may be feasible using phase sensitive MRI methods. Successful detection of neuronal activity both in vitro and in vivo has been described while others have reported negative results. Magnetic resonance electrical properties tomography may be a route by which signal changes can be identified. Here, we report successful and repeatable detection at 3 Tesla of human brain activation in response to visual and somatosensory stimuli using a functional version of tissue conductivity imaging (funCI). This detects activation in both white and grey matter with apparent tissue conductivity changes of 0.1 S/m (17-20%, depending on the tissue baseline conductivity measure) allowing visualization of complete system circuitry. The degree of activation scales with the degree of the stimulus (duration or contrast). The conductivity response functions show a distinct timecourse from that of traditional fMRI haemodynamic (BOLD or Blood Oxygenation Level Dependent) response functions, peaking within milliseconds of stimulus cessation and returning to baseline within 3-4s. We demonstrate the utility of the funCI approach by showing robust activation of the lateral somatosensory circuitry on stimulation of an index finger, on stimulation of a big toe or of noxious (heat) stimulation of the face as well as activation of visual circuitry on visual stimulation in up to five different individuals. The sensitivity and repeatability of this approach provides further evidence that magnetic resonance imaging approaches can detect brain activation beyond changes in blood supply.

The relationship between geophysical and geotechnical data: a temporal analysis of an iron ore tailings dam

Iron ore tailings dams must be constantly monitored for safety purposes. Among the aspects traditionally monitored are geotechnical protocols, such as piezometric levels along the dam. Recently, geophysics, a science that studies, among other things, dynamic processes in the Earth, has contributed to non-invasive monitoring using electro-resistivity methods. Geophysical methods provide a large amount of data in relatively quick samples, but there is little information on the integration of traditional geotechnical and geophysical techniques. Therefore, this work aimed to evaluate the relationship between geophysical data, monitored every 15 days between May 2022 and March 2023, and geotechnical data from an iron ore tailings dam located in the Iron Quadrangle. The geophysical method used was two-dimensional electrical imaging with a Schlumberger array. Analysis of variance and multiple regression allowed us to evaluate the relationship between resistivity values, piezometer level, sampling time, and rainfall. Piezometry data explained the electroresistivity significantly, while sampling time and rainfall did not significantly explain electro-resistivity. Therefore, the dam’s electro-resistivity patterns have not changed significantly over the period, and local rainfall does not imply an immediate response in resistivity. These conclusions can support new directions in decisions about monitoring iron ore tailings dams and may contribute to advances in dam safety in the mining sector.

Geoelectrical Evaluation of Contamination in and around Ori-Ile Battery Wastes Dumpsite in Ibadan, Southwestern Nigeria

Geoelectrical surveys involving vertical electrical sounding (VES) and 2D resistivity imaging were integrated to assess subsurface contamination by heavy metals in and around Orile Battery Waste Dumpsite, Kumapayi in Ibadan, Southwestern Nigeria. The objectives are to determine the lateral and vertical variations of resistivity/conductivity in and around the dumpsite, determine the nature and thickness of the overburden, determine the depth to bedrock, and delineate subsurface zones of anomalously high conductivity/low resistivity which may have resulted from contamination by heavy metals. Thirty-three VES stations were occupied using the Schlumberger electrode array with electrode spacing (AB/2) varied from 1 m to 100 m. The 2D resistivity survey was carried out along six East–West trending traverses established across and around the dumpsite using the Dipole-Dipole electrode array with station spacing of 10 m and expansion factor, n=1-5. The VES data were quantitatively interpreted using initial partial curve matching technique and 1D forward modelling while the dipole-dipole data were interpreted using 2D inversion procedures. The geoelctric sections delineated three layers comprising topsoil with layer resistivity ranging from 18 Ωm to 264 Ωm and thickness varying from 0.4 m to 1.4 m, weathered layer with resistivity and thickness ranging from 1 Ωm to 219 Ωm and 0.6 m to 9.4 m respectively, and weathered/fractured/fresh bedrock with resistivity ranging from 132 Ωm and 6500 Ωm. The 2D inverted resistivity sections revealed anomalous resistivity lows suggesting contamination at depths ranging from 5 m to 10 m beneath the traverses. The contamination zones are characterized by resistivity values less than 10 Ωm. The study revealed that the soil beneath the study area has been contaminated by the battery wastes. The suspected fractures and relatively shallow water table observed in the study area may have predisposed the groundwater to contamination which could constitute serious health risk to the inhabitants. It is recommended that geochemical analysis for heavy metals be conducted on the soil and groundwater from wells in the study area to assess the level of contamination.

Dipole acoustic remote detection (ARD) logging and its application in fracture-cave reservoir

Abstract Oil and gas from deep reservoirs are mostly enriched in the fracture-cave reservoirs. For this reason, fine evaluation of large-scale fracture-cave and effective detection of small scale fracture-cave are the key issues to be solved in the exploration of fracture-cave type carbonate reservoirs. The acoustic remote detection logging technology is based on acoustic reflection method to continuously image the formation around the well. The cross dipole array sonic data can be processed to image geological anomalies within a range of about 20 meters outside the well. Dipole S wave remote detection has the remarkable advantages of low emission frequency and larger investigation depth, but it has 180 degrees’ azimuthal ambiguity. This study is based on previously developed far field dipole S wave remote detection tool, finite difference is performed on formation models with and without are obtained for representative fracture-cave formations and remote detection processing and interpretation software is updated. These are applied on a well in the northwestern area. Through the comprehensive analysis of remote logging, conventional logging, electrical imaging logging and seismic data of the fracture-cave geological anomalies, a fine picture of the morphology and development of the geological anomalies from the near wellbore to the far end is formed

A model for energy predictions and diagnostics of large-scale photovoltaic systems based on electric data and thermal imaging of the PV fields

The aim of this investigation is the development of robust models for the performance prediction and automatic monitoring of large photovoltaic systems, based on historical and real-time electric and thermal data. This issue is increasingly important due to the worldwide diffusion of large photovoltaic systems and their need to identify and predict failures and malfunctions, in order to promptly assess the convenience of maintenance actions. The present model describes the response to irradiance and temperature conditions of both modules and inverters and also it is able to predict shading conditions able to affect the energy yield. The model has been validated against real electric measurements in 6 large PV plants located in southern Italy and it demonstrated to be able to predict the real time power production within a 4.1 % error. Even more importantly, the model and its comparison with subhourly measurements over several years has demonstrated its effectiveness in detecting downtime conditions caused by inverter or string problems. Simulations and measurements revealed that missed energy production due to electrical grid coupling downtime can exceed 50 % on certain days and that the shading conditions (up to 5 % of the daily energy production) can be easily detected and separated from component problems, thus avoiding false alarms. Finally, the analysis of aerial infrared images allowed to further test the model in failure detection capability, assess the relationship between thermal anomalies and underperformance conditions and in predicting the yearly deterioration rate at the PV plants.

Implementation of electrical tomography in a medical measurement system for innovative imaging and area monitoring

LETS (Lung Electrical Tomography System) is an innovative medical system enabling comprehensive heart and lung function monitoring. It utilizes advanced techniques such as electrical impedance tomography (EIT), body surface potential mapping (BSPM), and electrical capacitance tomography (ECT) to provide accurate information about physiological parameters. The LETS system allows one-time examinations and continuous monitoring 24 hours a day. Remotely measuring and monitoring parameters such as the electrical activity of the heart muscle, blood flow, blood pressure, pulse, and lung aeration enables rapid identification and intervention in case of abnormalities. The system also allows for the detection of cardiac arrhythmias and other heart disorders, as well as the assessment of lung respiratory capacity and optimization of ventilation strategies. However, it's important to note that the LETS system has some limitations. For example, it may not be suitable for patients with certain pacemakers or other implanted devices. Overall, LETS represents a breakthrough in medical imaging, opening up new possibilities in diagnosing and monitoring heart and lung diseases. Its integration may contribute to improving patient care and increasing the effectiveness of medical interventions.

Image reconstruction and bladder stimulation using electrical impedance tomography

Impedance tomography (EIT) is an imaging technique that harnesses differences in electrical conductivity to visualize the interior of objects. Despite limitations such as low resolution and nonlinearity of current distribution, its potential in medicine and industry is a source of fascination. The research in this work is a step towards unlocking this potential, focusing on improving the quality of EIT image reconstruction, particularly in bladder modeling. A key element is regularization techniques, including Laplace matrices and the iterative Gauss-Newton algorithm, which balance matching accuracy and image smoothness. In the practical part, simulations were conducted on dense and sparse meshes. Modeling the urinary bladder as a rotational ellipse significantly influences the interpretation of data by algorithms, a crucial factor for the accuracy of reconstruction. Various electrode configurations were also analyzed, revealing the impact of their arrangement on electrical properties and imaging quality. Iterative testing led to identifying optimal electrode placement and grid configuration, underscoring the importance of precise modeling for obtaining high-quality images. The results underscore the critical role of appropriately selecting the regularization parameter in minimizing reconstruction errors.