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  Subjects -> ELECTRONICS (Total: 207 journals)
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Sensing and Imaging : An International Journal
Journal Prestige (SJR): 0.255
Citation Impact (citeScore): 1
Number of Followers: 2  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1557-2072 - ISSN (Online) 1557-2064
Published by Springer-Verlag Homepage  [2467 journals]
  • Quantitative Measurement of Brain Extracellular Space with
           Three-Dimensional Electron Microscopy Imaging

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      Abstract: Abstract Molecular transport in the extracellular space (ECS) is the functional basis of maintaining brain activities; however, a fine structure imaging method remains lacking. We explored methods for brain ECS imaging, accurate annotation, and quantitative ECS calculation using high-resolution three-dimensional electron microscopy (3D EM). Fresh brain tissue cutting, high-pressure freezing, and freezing substitution methods were tried to preserve natural ECS structure in wild-type rat hippocampal CA1 tissue. 3D EM image stack were obtained by focused ion beam scanning electron microscopy and Amira to explore a suitable scheme for labeling brain ECS. Then, annotated data were used to calculate and characterize ECS volume, surface area, and connectivity properties, which were compared with those obtained through other methods. Using the annotation method that determines intermediate keyframes and extensions to both ends along the Z-axis for adjustments and space-by-space annotation method that optimizes rapidly changing ECS along the Z-axis, ECS annotation in 3D EM images was realized. The ECS brain volume fraction was 6.21%. A large connected ECS network occupied 94.80% and 93.67% of the total ECS volume and surface area, respectively, with some closed microregions, such as cavities in CA1 tissue. Analysis of four local subregions showed that the ECS was 6.26 ± 0.52% of the brain volume fraction; the large connected ECS network occupied 94.23 ± 1.48% and 92.96 ± 1.47% of the total ECS volume and surface area, respectively. Hence, we provide a specific method for obtaining, annotating, and visualizing high-quality 3D EM images of brain ECS using actual data.
      PubDate: 2023-01-03
       
  • GAN-FuzzyNN: Optimization Based Generative Adversarial Network and Fuzzy
           Neural Network Classification for Change Detection in Satellite Images

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      Abstract: Abstract Nowadays, change detection with satellite images plays an essential role in urban planning, resources survey, and understanding global environmental changes. However, numerous satellite images are persistently acquired each and every second and they possess a significant source of data for the assessment of the spatiotemporal case. Moreover, obtaining reference data associated with satellite images dealing with land cover changes still remains a major challenging issue. Besides, traditional techniques for change detection are not valuable because of complex texture features. To cope up with such limitations, an effective mechanism is proposed for change detection by exploiting Fuzzy Neural Network (FNN) classification, which is an integration of the Fuzzy concept with Neural Network (NN), and also segmentation is done using Taylor Shuffled Shepherd Optimization (TSSO)-based Generative Adversarial Network (GAN). The proposed TSSO is obtained by incorporating the Taylor series and Shuffled shepherd Optimization (SSO) and the proposed approach achieved a maximum overall accuracy of 0.932, minimum overall error of 0.0704, and maximum kappa coefficient of 0.911. The accuracy of the devised TSSO-based GAN + Fuzzy NN is 2.28%, 4.78%, 0.33%, and 13.26% improved than the Kernel Principal Component Analysis Convolutional Mapping Network (KPCA-MNet), Multiclass Support Vector Machine (MSVM), Patchlevel and pixel-level change detection network (PPCNET), and Image Fusion Network (IFN), respectively, for Image-1.
      PubDate: 2023-01-03
       
  • An Ultra-Low Frequency and Low-Pressure Capacitive Blood Pressure Sensor
           Based on Micro-Mechanical Resonator

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      Abstract: Abstract In this paper, by combining a high dielectric capacitor and a low frequency resonator, a pressure sensor capable of measuring extremely low frequencies is designed. By using a material with a high dielectric coefficient, a high capacitance value is created for the proposed capacitive sensor. Also, in this sensor to record very low frequencies, a material with a low stiffness coefficient is used for the mechanical resonator. The proposed sensor is designed to work at voltages less than 2 volts, and the micrometer dimensions are designed so that it can be used as an implantable pressure sensor with a series of additional tasks. The materials used in this design have been selected according to the possibility of using them in the fabrication of micro-electromechanical systems. HfO2, TiO2, CCTO as materials with high dielectric coefficient have been studied in this work and their results have been reported. Also, PTFE as a material with low Young’s modulus is used as the resonator plate of the proposed microplate sensor. In this work, a special structure is designed according to the standard microsystems fabrication methods and its mathematical model is reported and analyzed. The performance simulation results of the proposed sensor are presented with the help of COMSOL to evaluate it.
      PubDate: 2022-11-08
      DOI: 10.1007/s11220-022-00398-y
       
  • Fibre Optic Silver Plasmonic U-Bent Real Time Sensing Response to
           Accelerated Protein Conformation Kinetics

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      Abstract: Abstract U-bent silver coated fibre optic sensors are reliable tools for monitoring bio-chemical environmental changes in nanometric depth scale. Numerous studies have been performed using such sensors in plasmonic sensing applications and the analysis of the results contribute to the development of sensor-heads for sensing instruments. In this work, accelerated denaturing is introduced to Hen Egg White Lysozyme protein using Guanidine Hydrochloride and silver coated plasmonic U-bents are used to follow the conformational changes in the protein structure. Existing methods for monitoring the protein conformations including fluorescence enhancement studies, AFM imaging, FT-IR spectroscopy and zeta potential measurements are also carried out for the first time to confirm the structural shifts in the protein sample under induced and accelerated denaturing. The results lead to the conclusion that accelerated and induced denaturing of the protein is taking place by 2-h duration and silver coated plasmonic sensor surfaces are efficient to monitor the accelerated protein conformational changes.
      PubDate: 2022-10-28
      DOI: 10.1007/s11220-022-00405-2
       
  • Surface Water Area Extraction by Using Water Indices and DFPS Method
           Applied to Satellites Data

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      Abstract: Abstract Satellite imagesare usedto extract and calculate surface water area and water resource features accurately. Water index and optimum threshold methods are essential for the extraction of surface water area. Various water indices have been developed during the past two decades, such as Normalized difference water index (NDWI), Modified normalized difference water index, Automated Water Extraction Index for shadow and non-shadow, Water Ration Index (WRI) and Normalized difference vegetation index. With application of each of these indices, the surface water area can be extracted by applying the respective threshold values for the indices and computed areas. The present study focuses on the comparing the accuracy of surface water area extraction for respective water indices and obtaining an optimal threshold value to separate water and other features from output images of indices using a Semi-automatic double-window flexible pace search (DFPS) method. The surface water areas of the Kaylana Lake (at Jodhpur, Rajasthan, India)have been extracted for different years with above stated indices and further compared with the extracted values by applying the polygon method on historical images received from Google Earth. The extracted surface water areas with polygon method on historical Google Earth images are 0.88 km2, 0.74 km2, 1.22 km2, 0.67 km2and 1.00 km2for the years 2002, 2008, 2010, 2015, 2019 respectively. The comparison of extracted output results using different indices clearly indicates that the Semi-Automatic DFPS method is the best method to obtain optimum threshold value and to classified water indices output. The final comparison of outputs of indices also shows that overall NDWI provided much better surface water area output results. This study method identifies water bodies using Landsat TM, Lansat ETM + , Landsat OLI, and Sentinel-2A imagery with high accuracy by using NDWI water index and DFPS optimum threshold calculation method.
      PubDate: 2022-10-26
      DOI: 10.1007/s11220-022-00403-4
       
  • Formulation of Geometrically Nonlinear Numerical Model for Design of
           MEMS-Based Piezoresistive Pressure Sensor Operating in the Low-Pressure
           Range

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      Abstract: Abstract Micro-fabricated pressure sensors are presently one of the most used micro-electromechanical system devices in the industry. Notably, they have gained popularity in medical, automotive and aeronautical applications. In the present work, a sensor operating in the low-pressure range with piezoresistive sensing and having a bossed-diaphragm structure has been designed. The structure has been characterized through numerical simulations using a custom-made software featuring geometrically nonlinear 2D elements. This simulation tool enables fast iterative design along with capturing key features related to the drift in sensitivity with respect to doping concentration and temperature. The simulation results show that the designed sensor has a full scale output of 2.2 µV/V/Pa, a linear error of 0.05% over its operating range of 5 kPa and a thermal sensitivity shift of − 0.1%/oC.
      PubDate: 2022-10-25
      DOI: 10.1007/s11220-022-00401-6
       
  • Image Captioning using Hybrid LSTM-RNN with Deep Features

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      Abstract: Abstract Automated image captioning is the process of creating textual, human-like subtitles or explanations for photos based on their content. Throughout the image captioning problem, getting good results and consistency on par with humans always has been difficult for machines. The captioning framework used in this research is an efficient hybrid deep learning framework. Deep feature extraction and automatic image captioning are the two main parts of the proposed method. The inceptionv3 model first extracts the deep characteristics from the gathered photos. The automatic Image captioning step is then modeled using a hybrid classifier, which blends LSTM and RNN from two deep learning models. The captured deep features obtained during the feature extraction stage are used to train these two deep learning models. Additionally, we will fine-tune the weight of RNN using a novel self-improved Rock Hyraxes Swarm Optimization (SI-RHSO), which is a conceptual enhancement of conventional RHSO, to increase the Auto Image Captioning precision. As a result, the final result was obtained with a precise Image caption. A comparison analysis has been conducted to confirm the effectiveness of the suggested work.
      PubDate: 2022-10-10
      DOI: 10.1007/s11220-022-00400-7
       
  • Learned Anomaly Detection with Terahertz Radiation in Inline Process
           Monitoring

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      Abstract: Abstract Terahertz tomographic imaging as well as machine learning tasks represent two emerging fields in the area of nondestructive testing. Detecting outliers in measurements that are caused by defects is the main challenge in inline process monitoring. An efficient inline control enables to intervene directly during the manufacturing process and, consequently, to reduce product discard. We focus on plastics and ceramics, for which terahertz radiation is perfectly suited because of its characteristics, and propose a density based technique to automatically detect anomalies in the measured radiation data. The algorithm relies on a classification method based on machine learning. For a verification, supervised data are generated by a measuring system that approximates an inline process. The experimental results show that the use of terahertz radiation, combined with the classification algorithm, has great potential for a real inline manufacturing process. In a further investigation additional data are simulated to enlarge the data set, especially the variety of defects. We model the propagation of terahertz radiation by means of the Eikonal equation.
      PubDate: 2022-09-18
      DOI: 10.1007/s11220-022-00402-5
       
  • A Novel Non-maximum Suppression Strategy via Frame Bounding Box Smooth for
           Video Aerobics Target Location

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      Abstract: Abstract At present, a large number of studies in object detection from video focus on improving the accuracy of location prediction frame, but few studies research the improvement of location stability. However, the location stability of prediction frame has important influence on multi-target tracking and motion recognition etc. In order to improve the aerobics location stability of the prediction frame, a novel non-maximum suppression strategy via frame bounding box smooth is proposed in this paper. In the stage of target detection, spatial pyramid pooling network (SPP-Net) is used. In the stage of non-maximum suppression, results are obtained by integrating information of multiple prediction frames to enhance the stability of prediction frames in continuous video streams. Subsequently, by using the information association of adjacent frames, the prediction frame is smoothed to further improve the positioning stability of the prediction frame. Self-made aerobics data set is selected for analysis experiment, and the results show that the proposed method has greatly improved the location stability, and the corresponding tracking quality has been significantly improved. The mAP of proposed method exceeds 92.6% compared other methods, which has a better effect in object detection area.
      PubDate: 2022-09-02
      DOI: 10.1007/s11220-022-00399-x
       
  • Investigation of Sensing Characteristics for the Detection of Formaldehyde
           Using a Multichannel Data Acquisition System and Semiconductor Metal Oxide
           Sensor

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      Abstract: Abstract A multichannel data acquisition setup was designed and developed uniquely for detection of low concentration of formaldehyde using semiconductor metal oxide sensor. The selectivity towards formaldehyde was achieved by choosing optimized temperature of sensor operation. The gas sensing studies proved useful for sensing formaldehyde in the concentration range of 20–100 ppm. The mean and standard deviation of baseline stability of sensor were 14.8 kΩ and 0.21 kΩ respectively.
      PubDate: 2022-08-29
      DOI: 10.1007/s11220-022-00397-z
       
  • Carcinogenic Chromium (VI) Sensing Using Transducing Characteristics of
           Fiber Bragg Grating and Physical Swelling of Hydrogel

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      Abstract: Abstract A Chemo-mechanical-optical sensing approach for the detection of hexagonal chromium (Cr6+) metal ion is demonstrated. A new sensor head is designed by epoxying fiber Bragg grating (FBG) on a thin silicon membrane beneath which a Chromium (VI) responsive hydrogel is embedded. When the gel is exposed to chromium spiked solutions, it suffers a volume change due to its stimulus responsive property and deforms a silicon membrane which in turn causes a wavelength peak shift of FBG. Hydrogel synthesized from the blends of (3-Acrylamidopropyl)—trimethylammonium chloride is used for the purpose. The relation between FBG peak shifts with change in volume of hydrogel due to it swelling is experimentally established. The FBG wavelength peak shift is directly correlated with the concentration of the Cr (VI) metal ion. The estimated sensitivity and resolution of the sensor are 0.1 nm/ppb with a limit of detection of the sensor is 0.75 ppb. The sensor has demonstrated good sensitivity, selectivity, and repeatability.
      PubDate: 2022-08-05
      DOI: 10.1007/s11220-022-00396-0
       
  • Simulation of Optical Hollow Microbottle Resonator for Sensing
           Applications

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      Abstract: Abstract A silica hollow microbottle resonator (HMBR) combined with a pair of curved silicon micro-mirrors on the outside wall of the microbottle is proposed and numerically investigated using the Finite-Difference Time-Domain (FDTD) algorithm. The microbottle has only 32 μm length, 26 μm width and 1.5 μm wall thickness. The curved micro-mirrors overcome the light diffraction loss through light focusing, while the microbottle, in which gas analytes are introduced, provides additional light confinement and hence improves the performance of the sensor. The obtained Q-factor is about 4590 at 1543.21 nm and the free spectral range (FSR) is more than 31 nm. An internal sensitivity of 1567 nm per refractive index unit (RIU) is achieved in the near-infrared (NIR), which is the highest ever reported for an refractive index (RI) gas sensor based on HMBR. With the introduction of an air gap layer between the silica HMBR and the silicon micro-mirrors, both the Q-factor and sensitivity have been improved to 6729 and 1730 nmRIU− 1 respectively. We believe that the proposed architecture will be used in future sensing applications.
      PubDate: 2022-08-03
      DOI: 10.1007/s11220-022-00395-1
       
  • Correction to: Adaptive Higher‑Order Spectral Analysis for Image
           Recovery Under Distortion of Moving Water Surface Using
           Dragonfly‑Colliding Bodies Optimization

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      PubDate: 2022-07-20
      DOI: 10.1007/s11220-022-00393-3
       
  • Motion Capture Sensing Technologies and Techniques: A Sensor Agnostic
           Approach to Address Wearability Challenges

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      Abstract: Abstract Body area sensing systems specifically designed for motion capture need to consider the wearer’s comfort and wearability criteria. In this paper, after studying body models and their approximation by link-segment models, the kinematics and inverse kinematics problems for determining motion are explored. Different sensor technologies and related motion capture systems are then discussed within the context of wearability and portability challenges of the systems. For such systems, the weight and size of the system need to be kept small and the system should not interfere with the user’s movements. The requirements will be considered in terms of portability: portable motion capture systems should be less sensitive in accurate positioning of sensors and have more battery lifetime or less power consumption for their wider adoption as an assisted rehabilitation platform. Therefore, a proposed signal processing technique is validated in a controlled setting to address the challenges. By reducing sampling frequency, the power consumption will be reduced but there would be more variability in data whereas by utilising an adaptive filtering approach the variation can be compensated for. It is shown how by using the technique it is possible to reduce the energy consumption; therefore, the potential to decrease the battery size leading to a less bulky on-body sensing system with more comfort to the wearer.
      PubDate: 2022-07-20
      DOI: 10.1007/s11220-022-00394-2
       
  • Modeling Electrical Resistance Behavior of Soft and Flexible
           Piezoresistive Sensors Based on Carbon-Black/Silicone Elastomer Composites
           

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      Abstract: Abstract Soft and flexible strain piezoresistive sensors are gaining interest in wearable and robotic applications, but resistance relaxation limits the widespread use of the sensors. As soft, flexible, and stretchable sensors, they can easily be retrofitted into any existing robotic hand. To understand the resistance relaxation of stretchable sensors, three different elastomers were used to fabricate soft piezoresistive sensors. The experimental results showed that the sensor has good linearity and scalability while their resistance is strongly influenced by the stretching speed and modulus of the elastomer. Thus, the Kevin Voigt model was adopted to describe the sensor’s change of resistance during the stretching process. The model is sufficient to describe the change of resistance of the carbon black/elastomer filler when the sensors are stretched before the fracturing of the conductive filler. However, when the filler fractures, the model is invalid. The behavior indicates that the elongation of the sensor must not exceed the strain that causes the filler to fracture.
      PubDate: 2022-07-01
      DOI: 10.1007/s11220-022-00392-4
       
  • BI-LSTM Based Encoding and GAN for Text-to-Image Synthesis

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      Abstract: Abstract Synthesizing images from text is to produce images with reliable content as specified text depiction that is an extremely demanding task with the most important problems like: content consistency and visual realism. Owing to considerable progression of GAN, it is now possible to produce images with good visual certainty. The translation of text descriptions to images with higher content reliability, on the other hand, is still a work in progress. This paper intends to frame a novel text-to-image synthesis approach, which includes two major phases namely; (1) Text to image encoding and (2) GAN. Initially, during text to image encoding, cross modal feature alignment takes place including text and image features. Consequently, BI-LSTM is deployed to transfer the text embedding to feature vector. At second stage, the image is synthesized based on the encoding. Consequently, text feature group are given as input to GAN, which offers the final synthesized images. Finally, the supremacy of developed approach is examined via evaluation over extant techniques.
      PubDate: 2022-07-01
      DOI: 10.1007/s11220-022-00390-6
       
  • Efficient Multi-focus Image Fusion Using Parameter Adaptive Pulse Coupled
           Neural Network Based Consistency Verification

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      Abstract: Abstract Multi-focus image fusion technique is an important approach to generate a composite image with all objects in focus. Accurate focused pixel detection from multiple source images is crucial for multi-focus image fusion. However, false detection of focused pixels is inevitably due to the low-level image features being usually used to achieve focus pixel classification in most fusion methods. Consistency verification operation is frequently used to revise the falsely detected focused pixels in many fusion schemes. However, most consistency verification strategies cannot achieve the desired results. In this paper, we modify the parameter adaptive pulse coupled neural network (PA-PCNN) by introducing a new strategy to measure the linking strength of neurons. Thus, the PA-PCNN can greatly improve the accuracy of identifying focused pixels. The proposed method contains four steps. Firstly, the residual between an image and its filtered version by efficient mean filter is used to calculate the sharpness of a source image. Then, a new consistency verification method based on adaptive pulse coupled neural network (PA-PCNN) is adopted to improve the accuracy of the initial sharpness. Next, the focus detection maps are constructed by comparing the refined sharpness of two source images. Finally, the fused image is constructed according to the focus detection map. Experimental results show that the proposed method has achieved comparable or even better results compared with the state-of-the-art approaches in both visual quality and objective evaluation.
      PubDate: 2022-06-29
      DOI: 10.1007/s11220-022-00391-5
       
  • Floating Point Implementation of the Improved QRD and OMP for Compressive
           Sensing Signal Reconstruction

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      Abstract: Abstract In this paper, the Floating-Point Core Architecture based QR decomposition is proposed for solving least square problems in the Orthogonal Matching Pursuit algorithm (OMP-FPCA-QRD). To improve the computational performance of Orthogonal Matching Pursuit (OMP), it is necessary to modify the Orthogonal Matching Pursuit algorithm for analysing a wide range of signals in field programmable gate array (FPGA). As a result, it highly benefits from the available resources and acquires a scalable computational complexity. Since the solution of least square problem involves some iterative parts, like square root and division units, the processing time of the proposed QR Decomposition (QRD) approach is decreased by increasing parallelism using processing element driven systolic array implementation across all data-dependent operations. The hardware implementation on the ALTERA field programmable gate array shows optimal performance depends on hardware complexity and frequency of operation with the improved computational accuracy over existing QR Decomposition implementations. Moreover, the implementation of Orthogonal Matching Pursuit algorithm for signal reconstruction is also proposed to validate the performance metrics of floating point unit (FPU). The experimental results show that the optimization of floating point unit offers significant resource optimization in QR decomposition, and also better performance of high peak signal-to-noise ratio of 32.99 dB, which outperforms all other fixed point Orthogonal Matching Pursuit systems.
      PubDate: 2022-06-26
      DOI: 10.1007/s11220-022-00389-z
       
  • Adaptive Higher-Order Spectral Analysis for Image Recovery Under
           Distortion of Moving Water Surface using Dragonfly-Colliding Bodies
           Optimization

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      Abstract: Abstract Reconstruction of an underwater object from a sequence of images distorted by moving water waves is a challenging task. Most of the environmental research has been employing image data in recent days. The precision of this research is often dependent on the superiority of image data. In the existing approaches, the problem of analyzing video sequences when the water surface is disturbed by waves. The water waves will affect the appearance of the individual video frames such that no single frame is completely free of geometric distortion. Thus, the image acquisition from the environmental condition is more complex and crucial, but it must be focused on getting the high spectral and spatial quality. The primary intent of this paper is to plan for the intelligent higher-order spectral analysis for recovering the images from the moving water surface. The three main phases of the proposed image recovery model are (a) image pre-processing, (b) lucky region selection, and (c) image recovery. Once the pre-processing of the image is carried out, the lucky region selection is performed by computing the dice coefficient method. As a modification to the existing methods, the proposed model adopts optimized bispectra to enhance the quality of the recovered image. A hybrid algorithm with Dragonfly-Colliding Body Optimization (D-CBO) is used for enhancing the bispectra method. The proposed model has been tested on distorted underwater images. From the experimental analysis, in terms of PSNR measure, the suggested D-CBO-bispectra gets better efficiency than other conventional models, in which D-CBO-bispectra is 10.7%, 8.7%, 19%, 6.8% and 5% progressed than Blind deconv, Bispectra, Bispectra with Fourier, and Radon transform, respectively. Finally, the comparison of the proposed model with the existing approaches proves the method's efficiency.
      PubDate: 2022-05-17
      DOI: 10.1007/s11220-022-00388-0
       
  • Electric Resistance Tomograph (ERT): a review as non-destructive Tool
           (NDT) in deciphering interiors of standing trees

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      Abstract: Abstract The Electric Resistance Tomograph (ERT) is a customized tree specific novel German-technology which was developed to monitor and estimate the tree growth development by looking into the inner structure of the tree to analyse the growth and health status. This technique contributes to detect and study the internal assembly of a tree for the mapping of decay, hollowness, and also to distinguish the sapwood and heartwood demarcation, this way of discovering the internal growth at an early stage, mainly for the timber trees which are economically important can help to regulate thereafter to check whether the growth is not hindered. This paper highlights the device operational methods, electric resistance testing of trees and its applications, also reviewed the various successful application of this equipment in various tree species through-out the world to estimate non-destructively for accurate quantification of standing trees. The performance of this instrument has created breakthrough among various studies and methodologies to spot the internal condition in a standing tree with less invasive ways.
      PubDate: 2022-05-11
      DOI: 10.1007/s11220-022-00385-3
       
 
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