Non-destructive evaluation of ferromagnetic material thickness using Pulsed Eddy Current sensor detector coil voltage decay rate

© 2018 Elsevier Ltd A ferromagnetic material thickness quantification method based on the decay rate of the Pulsed Eddy Current sensor detector coil voltage is proposed. An expression for the decay rate is derived and the relationship between the decay rate and material thickness is established. Pipe wall thickness estimation is done with a developed circular sensor incorporating the proposed method, and results are evaluated through destructive testing. The decay rate feature has a unique attribute of being lowly dependent on properties such as sensor shape and size, and lift-off, enabling the method to be usable with any detector coil-based sensor. A case study on using the proposed method with a commercial sensor is also presented to demonstrate its versatility

Preliminary estimation of fat depth in the lamb short loin using a hyperspectral camera

© 2018 CSIRO. The objectives of the present study were to describe the approach used for classifying surface tissue, and for estimating fat depth in lamb short loins and validating the approach. Fat versus non-fat pixels were classified and then used to estimate the fat depth for each pixel in the hyperspectral image. Estimated reflectance, instead of image intensity or radiance, was used as the input feature for classification. The relationship between reflectance and the fat/non-fat classification label was learnt using support vector machines. Gaussian processes were used to learn regression for fat depth as a function of reflectance. Data to train and test the machine learning algorithms was collected by scanning 16 short loins. The near-infrared hyperspectral camera captured lines of data of the side of the short loin (i.e. with the subcutaneous fat facing the camera). Advanced single-lens reflex camera took photos of the same cuts from above, such that a ground truth of fat depth could be semi-automatically extracted and associated with the hyperspectral data. A subset of the data was used to train the machine learning model, and to test it. The results of classifying pixels as either fat or non-fat achieved a 96% accuracy. Fat depths of up to 12 mm were estimated, with an R 2 of 0.59, a mean absolute bias of 1.72 mm and root mean square error of 2.34 mm. The techniques developed and validated in the present study will be used to estimate fat coverage to predict total fat, and, subsequently, lean meat yield in the carcass

Gaussian process for interpreting pulsed eddy current signals for ferromagnetic pipe profiling

© 2014 IEEE. This paper describes a Gaussian Process based machine learning technique to estimate the remaining volume of cast iron in ageing water pipes. The method utilizes time domain signals produced by a commercially available pulsed Eddy current sensor. Data produced by the sensor are used to train a Gaussian Process model and perform inference of the remaining metal volume. The Gaussian Process model was learned using sensor data obtained from cast iron calibration plates of various thicknesses. Results produced by the Gaussian Process model were validated against the remaining wall thickness acquired using a high resolution laser scanner after the pipes were sandblasted to remove corrosion. The evaluation shows agreement between model outputs and ground truth. The paper concludes by discussing the implications or results and how the proposed method can potentially advance the current technological setup by facilitating real time pipe profiling

Lean meat yield estimation using a prototype 3D imaging approach.

Lean Meat Yield (LMY, %) of carcass is an important industry trait, which currently is not routinely measured in Australian beef abattoirs. Objective on-line technology to determine LMY is key for wider adoption. This paper presents a proof-of-concept approach for estimating the LMY of beef carcasses from the 3D information provided by RGB-D cameras. Moreover, a specifically designed on-line data acquisition system for abattoir applications is presented, consisting of three cameras moving on a scanning rig to generate 3D carcass side reconstructions. The hindquarter is then segmented consistently across all the 3D models to extract curvature information and LMY estimated via non-linear regression based on Gaussian Process models. Sides from 119 carcasses at two different commercial abattoirs were used to evaluate this approach. Results from this preliminary study (RMSE = 3.91%, R2 = 0.69) using curvature, P8 fat and HSCW indicate that 3D imaging of beef carcasses is a viable and relatively accurate technology to estimate LMY

Subcutaneous Fat Depth Regression Using Hyperspectral and Depth Imaging

Robotic perception is becoming an important component for automation in the meat processing industry. Whether for contaminant detection or automatic cutting, multimodal perception systems, in particular, based on hyperspectral imaging have the ability to provide information that goes beyond the texture and colour of a surface. In this paper, we present a learning-based method to estimate subcutaneous fat depth in meat cuts by leveraging hyperspectral data models that rely on the knowledge of modelled light sources and surface shape information. Data from a fully calibrated hyperspectral and colour depth (RGB-D} camera system is used as input. Fat depth ground truth is recovered via a novel systematic approach that ray casts a computed tomography (CT) mesh of the meat cuts, which is non-rigidly aligned with a depth reconstruction captured by the {RGB-D} camera. We thus evaluate machine learning methods that can handle small datasets, by employing dimensionality reduction and data augmentation to address the limited amount of imbalanced data that is acquired. Our results show that leveraging shape and light models, coupled with machine learning methods that capture nonlinearities and spatial correlations produces the most accurate results

A Fibreoptic Endoscopic Study of Upper Gastrointestinal Bleeding at Bugando Medical Centre in Northwestern Tanzania: a Retrospective Review of 240 Cases.

Upper gastrointestinal (GI) bleeding is recognized as a common and potentially life-threatening abdominal emergency that needs a prompt assessment and aggressive emergency treatment. A retrospective study was undertaken at Bugando Medical Centre in northwestern Tanzania between March 2010 and September 2011 to describe our own experiences with fibreoptic upper GI endoscopy in the management of patients with upper gastrointestinal bleeding in our setting and compare our results with those from other centers in the world. A total of 240 patients representing 18.7% of all patients (i.e. 1292) who had fibreoptic upper GI endoscopy during the study period were studied. Males outnumbered female by a ratio of 2.1:1. Their median age was 37 years and most of patients (60.0%) were aged 40 years and below. The vast majority of the patients (80.4%) presented with haematemesis alone followed by malaena alone in 9.2% of cases. The use of non-steroidal anti-inflammatory drugs, alcohol and smoking prior to the onset of bleeding was recorded in 7.9%, 51.7% and 38.3% of cases respectively. Previous history of peptic ulcer disease was reported in 22(9.2%) patients. Nine (3.8%) patients were HIV positive. The source of bleeding was accurately identified in 97.7% of patients. Diagnostic accuracy was greater within the first 24 h of the bleeding onset, and in the presence of haematemesis. Oesophageal varices were the most frequent cause of upper GI bleeding (51.3%) followed by peptic ulcers in 25.0% of cases. The majority of patients (60.8%) were treated conservatively. Endoscopic and surgical treatments were performed in 30.8% and 5.8% of cases respectively. 140 (58.3%) patients received blood transfusion. The median length of hospitalization was 8 days and it was significantly longer in patients who underwent surgical treatment and those with higher Rockall scores (P < 0.001). Rebleeding was reported in 3.3% of the patients. The overall mortality rate of 11.7% was significantly higher in patients with variceal bleeding, shock, hepatic decompensation, HIV infection, comorbidities, malignancy, age > 60 years and in patients with higher Rockall scores and those who underwent surgery (P < 0.001). Oesophageal varices are the commonest cause of upper gastrointestinal bleeding in our environment and it is associated with high morbidity and mortality. The diagnostic accuracy of fibreoptic endoscopy was related to the time interval between the onset of bleeding and endoscopy. Therefore, it is recommended that early endoscopy should be performed within 24 h of the onset of bleeding

Alpha-1-antitrypsin phenotypes in adult liver disease patients

Alpha-1-antitrypsin (AAT) is an important serine protease inhibitor in humans. Hereditary alpha-1-antitrypsin deficiency (AATD) affects lungs and liver. Liver disease caused by AATD in paediatric patients has been previously well documented. However, the association of liver disease with alpha-1-antitrypsin gene polymorphisms in adults is less clear. Therefore, we aimed to study AAT polymorphisms in adults with liver disease. We performed a case-control study. AAT polymorphisms were investigated by isoelectric focusing in 61 patients with liver cirrhosis and 9 patients with hepatocellular carcinoma. The control group consisted of 218 healthy blood donors. A significant deviation of observed and expected frequency of AAT phenotypes from Hardy-Weinberg equilibrium (chi-square = 34.77, df 11, P = 0.000) in the patient group was caused by a higher than expected frequency of Pi ZZ homozygotes (f = 0.0143 and f = 0.0005, respectively, P = 0.000). In addition, Pi M homozygotes were more frequent in patients than in controls (63% and 46%, respectively, P = 0.025). Our study results show that Pi ZZ homozygosity in adults could be associated with severe liver disease. Presence of Pi M homozygosity could be associated with liver disease via some mechanism different from Z allele-induced liver damage through accumulation of AAT polymers

Blood ammonia levels in liver cirrhosis: a clue for the presence of portosystemic collateral veins

<p>Abstract</p> <p>Background</p> <p>Portal hypertension leads to the formation of portosystemic collateral veins in liver cirrhosis. The resulting shunting is responsible for the development of portosystemic encephalopathy. Although ammonia plays a certain role in determining portosystemic encephalopathy, the venous ammonia level has not been found to correlate with the presence or severity of this entity. So, it has become partially obsolete. Realizing the need for non-invasive markers mirroring the presence of esophageal varices in order to reduce the number of endoscopy screening, we came back to determine whether there was a correlation between blood ammonia concentrations and the detection of portosystemic collateral veins, also evaluating splenomegaly, hypersplenism (thrombocytopenia) and the severity of liver cirrhosis.</p> <p>Methods</p> <p>One hundred and fifty three consecutive patients with hepatic cirrhosis of various etiologies were recruited to participate in endoscopic and ultrasonography screening for the presence of portosystemic collaterals mostly esophageal varices, but also portal hypertensive gastropathy and large spontaneous shunts.</p> <p>Results</p> <p>Based on Child-Pugh classification, the median level of blood ammonia was 45 mcM/L in 64 patients belonging to class A, 66 mcM/L in 66 patients of class B and 108 mcM/L in 23 patients of class C respectively (p < 0.001).</p> <p>The grade of esophageal varices was concordant with venous ammonia levels (rho 0.43, p < 0.001). The best area under the curve was given by ammonia concentrations, i, e., 0.78, when comparing areas of ammonia levels, platelet count and spleen longitudinal diameter at ultrasonography. Ammonia levels predicted hepatic decompensation and ascites presence (Odds Ratio 1.018, p < 0.001).</p> <p>Conclusion</p> <p>Identifying cirrhotic patients with high blood ammonia concentrations could be clinically useful, as high levels would lead to suspicion of being in presence of collaterals, in clinical practice of esophageal varices, and pinpoint those patients requiring closer follow-up and endoscopic screening.</p

Learning Image-Based Contaminant Detection in Wool Fleece from Noisy Annotations

This paper addresses the problem of detecting natural contaminants in freshly shorn wool fleece in RGB images using deep learning-based semantic segmentation. The challenge of inconsistent annotation is overcome by learning the probability of contamination as opposed to a discrete class. From the continuous value predictions, contaminated regions can be extracted by selectively thresholding on the probability of contamination. Furthermore, the imbalance of the class distributions is accounted for by adaptively weighting each pixel’s contribution to the loss function. Results show that the adaptive weight improves the prediction accuracy and overall outperforms learning an approximated representation by quantising the distributions