Neural Network-based Software Research Articles

EggCountAI: a convolutional neural network-based software for counting of Aedes aegypti mosquito eggs

BackgroundMosquito-borne diseases exert a huge impact on both animal and human populations, posing substantial health risks. The behavioural and fitness traits of mosquitoes, such as locomotion and fecundity, are crucial factors that influence the spread of diseases. In existing egg-counting tools, each image requires separate processing with adjustments to various parameters such as intensity threshold and egg area size. Furthermore, accuracy decreases significantly when dealing with clustered or overlapping eggs. To overcome these issues, we have developed EggCountAI, a Mask Region-based Convolutional Neural Network (RCNN)-based free automatic egg-counting tool for Aedes aegypti mosquitoes.MethodsThe study design involves developing EggCountAI for counting mosquito eggs and comparing its performance with two commonly employed tools—ICount and MECVision—using 10 microscopic and 10 macroscopic images of eggs laid by females on a paper strip. The results were validated through manual egg counting on the strips using ImageJ software. Two different models were trained on macroscopic and microscopic images to enhance egg detection accuracy, achieving mean average precision, mean average recall, and F1-scores of 0.92, 0.90, and 0.91 for the microscopic model, and 0.91, 0.90, and 0.90 for the macroscopic model, respectively. EggCountAI automatically counts eggs in a folder containing egg strip images, offering adaptable filtration for handling impurities of varying sizes.ResultsThe results obtained from EggCountAI highlight its remarkable performance, achieving overall accuracy of 98.88% for micro images and 96.06% for macro images. EggCountAI significantly outperformed ICount and MECVision, with ICount achieving 81.71% accuracy for micro images and 82.22% for macro images, while MECVision achieved 68.01% accuracy for micro images and 51.71% for macro images. EggCountAI also excelled in other statistical parameters, with mean absolute error of 1.90 eggs for micro, 74.30 eggs for macro, and a strong correlation and R-squared value (0.99) for both micro and macro. The superior performance of EggCountAI was most evident when handling overlapping or clustered eggs.ConclusionAccurate detection and counting of mosquito eggs enables the identification of preferred egg-laying sites and facilitates optimal placement of oviposition traps, enhancing targeted vector control efforts and disease transmission prevention. In future research, the tool holds the potential to extend its application to monitor mosquito feeding preferences.Graphical

Automated Intracranial Clot Detection: A Promising Tool for Vascular Occlusion Detection in Non-Enhanced CT.

(1) Background: to test the diagnostic performance of a fully convolutional neural network-based software prototype for clot detection in intracranial arteries using non-enhanced computed tomography (NECT) imaging data. (2) Methods: we retrospectively identified 85 patients with stroke imaging and one intracranial vessel occlusion. An automated clot detection prototype computed clot location, clot length, and clot volume in NECT scans. Clot detection rates were compared to the visual assessment of the hyperdense artery sign by two neuroradiologists. CT angiography (CTA) was used as the ground truth. Additionally, NIHSS, ASPECTS, type of therapy, and TOAST were registered to assess the relationship between clinical parameters, image results, and chosen therapy. (3) Results: the overall detection rate of the software was 66%, while the human readers had lower rates of 46% and 24%, respectively. Clot detection rates of the automated software were best in the proximal middle cerebral artery (MCA) and the intracranial carotid artery (ICA) with 88-92% followed by the more distal MCA and basilar artery with 67-69%. There was a high correlation between greater clot length and interventional thrombectomy and between smaller clot length and rather conservative treatment. (4) Conclusions: the automated clot detection prototype has the potential to detect intracranial arterial thromboembolism in NECT images, particularly in the ICA and MCA. Thus, it could support radiologists in emergency settings to speed up the diagnosis of acute ischemic stroke, especially in settings where CTA is not available.

In-line particle size measurement during granule fluidization using convolutional neural network-aided process imaging

This paper presents a machine learning-based image analysis method to monitor the particle size distribution of fluidized granules. The key components of the direct imaging system are a rigid fiber-optic endoscope, a light source and a high-speed camera, which allow for real-time monitoring of the granules. The system was implemented into a custom-made 3D-printed device that could reproduce the particle movement characteristic in a fluidized-bed granulator. The suitability of the method was evaluated by determining the particle size distribution (PSD) of various granule mixtures within the 100–2000 μm size range. The convolutional neural network-based software was able to successfully detect the granules that were in focus despite the dense flow of the particles. The volumetric PSDs were compared with off-line reference measurements obtained by dynamic image analysis and laser diffraction. Similar trends were observed across the PSDs acquired with all three methods. The results of this study demonstrate the feasibility of performing real‐time particle size analysis using machine vision as an in-line process analytical technology (PAT) tool.

Software Package for Remote Diagnostics of Agricultural Machinery Condition

Artificial intelligence is stated to be more and more widely used in agriculture, as well as for the diagnostics of the agricultural machinery condition. It was noted that in besides software, new computing devices are developed that enable processing and storing large amounts of data. (Research purpose) To create a neural network-based software package for remote diagnostics of the limit state of machinery individual components and assemblies. (Materials and methods) Foreign studies within the problem area were analysed. It was found out that for data collection for artificial intelligence there exist STM32 and Arduino microcontroller-based devices, and the Nvidia CUDA (Compute Unified Device Architecture) hardware and software platform is used. For the software was developed in the C / C ++ programming language, and the MS SQL Server database were used as a repository. The general software is emphasized to be able to run on all major operating systems such as Windows, Mac OS, Linux. The role of neural network is argued to be important since it integrated all program blocks and provides its own analysis. (Results and discussion) The information from the diagnostics devices is accumulated in a database. The neural network created on the basis of this database is constantly learning and simultaneously analyzing incoming data in real time, automatically issuing its recommendations. It was found that the neural network created by the employees of the Federal Scientific Agroengineering Center VIM has more functional options, for example, it is able to work directly with devices and conduct a more detailed technical analysis. (Conclusions) A neural network for equipment condition diagnostics was created, which increases the efficiency of decision-making in case of repair, and improves forecast and predictability. The criteria for equipment operation were proposed.

Diagnosis of Interproximal Caries Lesions in Bitewing Radiographs Using a Deep Convolutional Neural Network-Based Software

The aim of this study was to evaluate the diagnostic reliability of a web-based artificial intelligence program for the detection of interproximal caries in bitewing radiographs. Three hundred bitewing radiographs of patients were subjected to the evaluation of a convolutional neural network. First, the images were visually evaluated by a previously trained and calibrated operator with radiodiagnosis experience. Then, ground truth was established and was clinically validated. For enamel caries, clinical assessment included a combination of clinical-visual and radiography evaluations. For dentin caries, clinical validation was performed by instrumentally accessing the cavity. Second, the images were uploaded and analyzed by the web-based software. Four different models were established to analyze its evaluations according to the confidence threshold (0–100%) offered by the program: model 1 (values >0% were considered positive and values of 0% were considered negative), model 2 (values ≥25% were considered positive and values <25% were considered negative), model 3 (values ≥50% were considered positive and values <50% were considered negative), and model 4 (values ≥75% were considered positive and values <75% were considered negative). The accuracy rate (A), sensitivity (S), specificity (E), positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and areas under receiver operating characteristic curves (AUC) were calculated for the four models of agreement with the software. Models showed the following results respectively: A = 70.8%, 82%, 85.6%, 86.1%; S = 87%, 69.8%, 57%, 41.6%; E = 66.3%, 85.4%, 93.7%, 98.5%; PPV = 42%, 57.2%, 71.6%, 88.6%; NPV = 94.8%, 91%, 88.6%, 85.8%; PLR = 2.58, 4.78, 9.05, 27.73; NLR = 0.2, 0.35, 0.46, 0.59; AUC = 0.767, 0.777, 0.753, 0.701. Findings in the present study suggest that the artificial intelligence web-based software provides a good diagnostic reliability on the detection of dental caries. Our study highlighted model 2 for showing the best results to differentiate between healthy teeth and decayed teeth.

Machine Learning Empowered Software Prediction System

Prediction of software defects is one of the most active study fields in software engineering today. Using a defect prediction model, a list of code prone to defects may be compiled. Using a defect prediction model, software may be made more reliable by identifying and discovering faults before or during the software enhancement process. Defect prediction will play an increasingly important role in the design process as the scope of software projects grows. Bugs or the number of bugs used to measure the performance of a defect prediction procedure are referred to as "bugs" in this context. Defect prediction models can incorporate a wide range of metrics, including source code and process measurements. Defects are determined using a variety of models. Using machine learning, the defect prediction model may be developed. Machine inclining in the second and third levels is dependent on the preparation and assessment of data (to break down model execution). Defect prediction models typically use 90 percent preparation information and 10 percent testing information. Improve prediction performance with the use of dynamic/semi-directed taking in, a machine learning approach. So that the results and conclusion may be sharply defined under many circumstances and factors, it is possible to establish a recreated domain to house the entire method. Computer-aided engineering (CAE) is being used to identify software defects in the context of neural networks. Neural network-based software fault prediction is compared to fuzzy logic fundamental results in this research paper. On numerous parameters, neural network training provides better and more effective outcomes, according to the recommended findings and outputs.

Predicting Drosha and Dicer Cleavage Sites with DeepMirCut

MicroRNAs are a class of small RNAs involved in post-transcriptional gene silencing with roles in disease and development. Many computational tools have been developed to identify novel microRNAs. However, there have been no attempts to predict cleavage sites for Drosha from primary sequence, or to identify cleavage sites using deep neural networks. Here, we present DeepMirCut, a recurrent neural network-based software that predicts both Dicer and Drosha cleavage sites. We built a microRNA primary sequence database including flanking genomic sequences for 34,713 microRNA annotations. We compare models trained on sequence data, sequence and secondary structure data, as well as input data with annotated structures. Our best model is able to predict cuts within closer average proximity than results reported for other methods. We show that a guanine nucleotide before and a uracil nucleotide after Dicer cleavage sites on the 3′ arm of the microRNA precursor had a positive effect on predictions while the opposite order (U before, G after) had a negative effect. Our analysis was also able to predict several positions where bulges had either positive or negative effects on the score. We expect that our approach and the data we have curated will enable several future studies.

Role of Wide-Area Transepithelial Sampling With 3D Computer-Assisted Analysis in the Diagnosis and Management of Barrett's Esophagus.

Barrett's esophagus (BE) is a premalignant condition in which cancer prevention is performed by endoscopic surveillance combined with Seattle protocol mucosal biopsies. The Seattle protocol has significant limitations, including a high rate of sampling error due to the focality of dysplasia/carcinoma, low endoscopist adherence to the protocol, and a high degree of variability in pathologic interpretation. These factors all contribute to a high incidence of cancers missed within 1 year of surveillance endoscopy. Wide-area transepithelial sampling with computer-assisted three-dimensional analysis (WATS3D) is a relatively new technique that minimizes sampling error by using a brush biopsy device that extensively samples “at risk” mucosa and helps pathologists diagnose dysplasia/neoplasia by generating three-dimensional images of whole crypts using a neural network-based software program. Several large prospective trials (involving both academic and community practices) have shown significantly increased rates of detection of dysplasia and intestinal metaplasia in both screening and surveillance in patients with BE when used as an adjunct to Seattle protocol-based forceps biopsies. The WATS3D diagnostic platform was included in the most recent American Society for Gastrointestinal Endoscopy Barrett's guideline as an adjunct to forceps biopsies (conditional recommendation and low quality of evidence). This review summarizes the scientific and pathologic basis of WATS3D technology, its potential impact on BE surveillance and management, and its limitations and future directions.

Development of in-house fully residual deep convolutional neural network-based segmentation software for the male pelvic CT

BackgroundThis study aimed to (1) develop a fully residual deep convolutional neural network (CNN)-based segmentation software for computed tomography image segmentation of the male pelvic region and (2) demonstrate its efficiency in the male pelvic region.MethodsA total of 470 prostate cancer patients who had undergone intensity-modulated radiotherapy or volumetric-modulated arc therapy were enrolled. Our model was based on FusionNet, a fully residual deep CNN developed to semantically segment biological images. To develop the CNN-based segmentation software, 450 patients were randomly selected and separated into the training, validation and testing groups (270, 90, and 90 patients, respectively). In Experiment 1, to determine the optimal model, we first assessed the segmentation accuracy according to the size of the training dataset (90, 180, and 270 patients). In Experiment 2, the effect of varying the number of training labels on segmentation accuracy was evaluated. After determining the optimal model, in Experiment 3, the developed software was used on the remaining 20 datasets to assess the segmentation accuracy. The volumetric dice similarity coefficient (DSC) and the 95th-percentile Hausdorff distance (95%HD) were calculated to evaluate the segmentation accuracy for each organ in Experiment 3.ResultsIn Experiment 1, the median DSC for the prostate were 0.61 for dataset 1 (90 patients), 0.86 for dataset 2 (180 patients), and 0.86 for dataset 3 (270 patients), respectively. The median DSCs for all the organs increased significantly when the number of training cases increased from 90 to 180 but did not improve upon further increase from 180 to 270. The number of labels applied during training had a little effect on the DSCs in Experiment 2. The optimal model was built by 270 patients and four organs. In Experiment 3, the median of the DSC and the 95%HD values were 0.82 and 3.23 mm for prostate; 0.71 and 3.82 mm for seminal vesicles; 0.89 and 2.65 mm for the rectum; 0.95 and 4.18 mm for the bladder, respectively.ConclusionsWe have developed a CNN-based segmentation software for the male pelvic region and demonstrated that the CNN-based segmentation software is efficient for the male pelvic region.

The Clinical Influence after Implementation of Convolutional Neural Network-Based Software for Diabetic Retinopathy Detection in the Primary Care Setting.

Deep learning-based software is developed to assist physicians in terms of diagnosis; however, its clinical application is still under investigation. We integrated deep-learning-based software for diabetic retinopathy (DR) grading into the clinical workflow of an endocrinology department where endocrinologists grade for retinal images and evaluated the influence of its implementation. A total of 1432 images from 716 patients and 1400 images from 700 patients were collected before and after implementation, respectively. Using the grading by ophthalmologists as the reference standard, the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) to detect referable DR (RDR) were 0.91 (0.87–0.96), 0.90 (0.87–0.92), and 0.90 (0.87–0.93) at the image level; and 0.91 (0.81–0.97), 0.84 (0.80–0.87), and 0.87 (0.83–0.91) at the patient level. The monthly RDR rate dropped from 55.1% to 43.0% after implementation. The monthly percentage of finishing grading within the allotted time increased from 66.8% to 77.6%. There was a wide range of agreement values between the software and endocrinologists after implementation (kappa values of 0.17–0.65). In conclusion, we observed the clinical influence of deep-learning-based software on graders without the retinal subspecialty. However, the validation using images from local datasets is recommended before clinical implementation.

SSSCPreds: Deep Neural Network-Based Software for the Prediction of Conformational Variability and Application to SARS-CoV-2.

Amino acid mutations that improve protein stability and rigidity can accompany increases in binding affinity. Therefore, conserved amino acids located on a protein surface may be successfully targeted by antibodies. The quantitative deep mutational scanning approach is an excellent technique to understand viral evolution, and the obtained data can be utilized to develop a vaccine. However, the application of the approach to all of the proteins in general is difficult in terms of cost. To address this need, we report the construction of a deep neural network-based program for sequence-based prediction of supersecondary structure codes (SSSCs), called SSSCPrediction (SSSCPred). Further, to predict conformational flexibility or rigidity in proteins, a comparison program called SSSCPreds that consists of three deep neural network-based prediction systems (SSSCPred, SSSCPred100, and SSSCPred200) has also been developed. Using our algorithms we calculated here shows the degree of flexibility for the receptor-binding motif of SARS-CoV-2 spike protein and the rigidity of the unique motif (SSSC: SSSHSSHHHH) at the S2 subunit and has a value independent of the X-ray and Cryo-EM structures. The fact that the sequence flexibility/rigidity map of SARS-CoV-2 RBD resembles the sequence-to-phenotype maps of ACE2-binding affinity and expression, which were experimentally obtained by deep mutational scanning, suggests that the identical SSSC sequences among the ones predicted by three deep neural network-based systems correlate well with the sequences with both lower ACE2-binding affinity and lower expression. The combined analysis of predicted and observed SSSCs with keyword-tagged datasets would be helpful in understanding the structural correlation to the examined system.

Brain volumes quantification from MRI in healthy controls: Assessing correlation, agreement and robustness of a convolutional neural network-based software against FreeSurfer, CAT12 and FSL

Background and purposeThere are instances in which an estimate of the brain volume should be obtained from MRI in clinical practice. Our objective is to calculate cross-sectional robustness of a convolutional neural network (CNN) based software (Entelai Pic) for brain volume estimation and compare it to traditional software such as FreeSurfer, CAT12 and FSL in healthy controls (HC). Materials and MethodsSixteen HC were scanned four times, two different days on two different MRI scanners (1.5 T and 3 T). Volumetric T1-weighted images were acquired and post-processed with FreeSurfer v6.0.0, Entelai Pic v2, CAT12 v12.5 and FSL v5.0.9. Whole-brain, grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) volumes were calculated. Correlation and agreement between methods was assessed using intraclass correlation coefficient (ICC) and Bland Altman plots. Robustness was assessed using the coefficient of variation (CV). ResultsWhole-brain volume estimation had better correlation between FreeSurfer and Entelai Pic (ICC (95% CI) 0.96 (0.94−0.97)) than FreeSurfer and CAT12 (0.92 (0.88−0.96)) and FSL (0.87 (0.79−0.91)). WM, GM and CSF showed a similar trend. Compared to FreeSurfer, Entelai Pic provided similarly robust segmentations of brain volumes both on same-scanner (mean CV 1.07, range 0.20–3.13% vs. mean CV 1.05, range 0.21–3.20%, p = 0.86) and on different-scanner variables (mean CV 3.84, range 2.49–5.91% vs. mean CV 3.84, range 2.62–5.13%, p = 0.96). Mean post-processing times were 480, 5, 40 and 5 min for FreeSurfer, Entelai Pic, CAT12 and FSL respectively. ConclusionBased on robustness and processing times, our CNN-based model is suitable for cross-sectional volumetry on clinical practice.

Digital Medicine – The New Frontier for AI in Healthcare

Digital Medicine – The New Frontier for AI in Healthcare

Neural network-based software sensors for the estimation of key components in brewery wastewater anaerobic digester: an experimental validation.

This work focused on the experimental validation of software sensors with a view to improving on-line anaerobic digester monitoring. Based on cheaply available measurements such as conductivity, temperature, pH, redox potential, total suspended solids concentration and digester inflows and outflows, an intelligent estimator was built to reproduce the evolutions of key components such as volatile fatty acid, carbonate and alkalinity concentrations, as well as biogas composition (methane and carbon dioxide). The proposed solution considers a principal component pre-processing of the data selected as inputs of a radial basis function neural network (RBF-ANN) structure, using a particular sequential learning algorithm. Process dynamics were also taken into account, introducing a moving horizon version of this network (MH-RBF-ANN). Experimental results demonstrated the capacity of the MH-RBF-ANN to correctly predict the key-component evolutions and to improve the estimation accuracy, compared to the classical RBF-ANN.

A neural network-based software to recognise blepharospasm symptoms and to measure eye closure time

Blepharospasm (BSP) is an adult-onset focal dystonia with phenomenologically heterogeneous effects, including, but not limited to, blinks, brief or prolonged spasms, and a narrowing or closure of the eyelids. In spite of the clear and well-known symptomatology, objectively rating the severity of this dystonia is a rather complex task since BSP symptoms are so subtle and hardly perceptible that even expert neurologists can rate the gravity of the pathology differently in the same patients. Software tools have been developed to help clinicians in the rating procedure. Currently, a computerised video-based system is available that is capable of objectively determining the eye closure time, however, it cannot distinguish the typical symptoms of the pathology. In this study, we attempt to take a step forward by proposing a neural network-based software able not only to measure the eye closure, time but also to recognise and count the typical blepharospasm symptoms. The software, after detecting the state of the eyes (open or closed), the movement of specific facial landmarks, and properly implementing artificial neural networks with an optimised topology, can recognise blinking, and brief and prolonged spasms. Comparing the software predictions with the observations of an expert neurologist allowed assessment of the sensitivity and specificity of the proposed software. The levels of sensitivity were high for recognising brief and prolonged spasms but were lower in the case of blinks. The proposed software is an automatic tool capable of making objective ‘measurements’ of blepharospasm symptoms.

3D convolutional neural networks applied to CT angiography in the detection of acute ischemic stroke

BackgroundThe aim of this study was to investigate the feasibility of ischemic stroke detection from computed tomography angiography source images (CTA-SI) using three-dimensional convolutional neural networks.MethodsCTA-SI of 60 patients with a suspected acute ischemic stroke of the middle cerebral artery were randomly selected for this study; 30 patients were used in the neural network training, and the subsequent testing was performed using the remaining 30 patients. The training and testing were based on manually segmented lesions. Cerebral hemispheric comparison CTA and non-contrast computed tomography (NCCT) were studied as additional input features.ResultsAll ischemic lesions in the testing data were correctly lateralized, and a high correspondence to manual segmentations was achieved. Patients with a diagnosed stroke had clinically relevant regions labeled infarcted with a 0.93 sensitivity and 0.82 specificity. The highest achieved voxel-wise area under receiver operating characteristic curve was 0.93, and the highest Dice similarity coefficient was 0.61. When cerebral hemispheric comparison was used as an input feature, the algorithm performance improved. Only a slight effect was seen when NCCT was included.ConclusionThe results support the hypothesis that an acute ischemic stroke lesion can be detected with 3D convolutional neural network-based software from CTA-SI. Utilizing information from the contralateral hemisphere appears to be beneficial for reducing false positive findings.

Development of real-time state estimators for reaction–separation processes: A continuous flash fermentation as a study case

The development of reliable on-line state estimators applicable to reaction–separation processes is addressed in this work. Artificial Neural Network-based software sensors (ANN-SS) are proposed to allow on-line measurement of key variables, with an estimation algorithm that uses secondary variables as inputs. A continuous laboratory-scale flash fermentation for bioethanol production is considered as a case study. The process consists of three interconnected units: fermentor, filter (tangential microfiltration for cell recycling) and vacuum flash vessel (for the continuous separation of ethanol from the broth). The concentrations of ethanol in the fermentor and of ethanol condensed from the flash are successfully monitored on-line using ANN-SS. The proposed model contributes to improve the understanding of the complex relationships between process variables in the reaction and separation units, which is of major importance to allow the operation of the ethanol production process near its optimum performance.

Real-time remote monitoring of small-scaled biological wastewater treatment plants by a multivariate statistical process control and neural network-based software sensors

A real-time remote monitoring system for wastewater treatment plants (WWTPs) has been developed to give local operators a guideline that would allow them to arrive at the optimum operational strategy in the early stage of a process disturbance. Especially, small-scaled WWTPs in Korea's rural areas show a large fluctuation in their influent loading and, therefore, they require an efficient operation for treatment of organic matter, nitrogen and phosphorus. However, under requirements to lower running costs, most of the small-scaled WWTPs are being forced to operate with a minimum number of operators. It is too costly for them to employ a local expert to maintain plant systems properly. In recent years, recognition of these problems has raised great interests in real-time remote monitoring systems. They serve the key information needed for efficient operation, and help to transfer knowledge from the experts at a remote control center to local operators in real-time. In this study, both operation data and measurement values from a novel mobile multi-sensor system were transmitted on-line by a telecommunication system. Then multivariate statistical process controls and software sensor techniques were applied to supervise local WWTPs. The developed remote monitoring system makes it possible to monitor the current plants’ statuses and to support the operation of local wastewater systems.

An empirical validation of a neural network model for software effort estimation

As software becomes more complex and its scope dramatically increases, the importance of research on developing methods for estimating software development efforts has perpetually increased. Such accurate estimation has a prominent impact on the success of projects. Out of the numerous methods for estimating software development efforts that have been proposed, line of code (LOC)-based constructive cost model (COCOMO), function point-based regression model (FP), neural network model (NN), and case-based reasoning (CBR) are among the most popular models. Recent research has tended to focus on the use of function points (FPs) in estimating the software development efforts, however, a precise estimation should not only consider the FPs, which represent the size of the software, but should also include various elements of the development environment for its estimation. Therefore, this study is designed to analyze the FPs and the development environments of recent software development cases. The primary purpose of this study is to propose a precise method of estimation that takes into account and places emphasis on the various software development elements. This research proposes and evaluates a neural network-based software development estimation model.

A new methodology for in situ calibration of a neural network-based software sensor for S-parameter prediction in six-port reflectometers

In this work, a neural network-based software sensor is proposed for determining the reflection coefficient from measurements obtained by a six-port reflectometer. The proposed software sensor is able to cope with the nonlinearities and noise inherent to the measurement electronics, without needing additional calibration. To extract data for the calibration, a new method that allows in situ calibration is applied. Experimental evidence of the feasibility of the proposed method is given using a simulation testbench.