Color contamination matrix property assessment for improvement of colored smoke PIV

A single-camera color PIV system that can acquire PIV data of three separated layers has been redesigned, purposing improvement of wind tunnel applicability. We target smoke image that has particle-per-pixel values higher than unity. The system constitutes of a high-power color-coding illuminator and a digital color high-speed video camera. RGB values in recorded image involves severe color contaminations due to five optical and digital sequences (Fig. 1). To quantify this, a snapshot calibration is proposed to describe the contamination matrix equation (Eq. (1)). Taking the inverse matrix (Eq. (2)) allows in-plane PIV in each color layer to be accurately implemented. We also derive mathematical limits to operate the colored smoke PIV, which is explained by the matrix property (Eq (3)). Feasibility of the proposed method has been demonstrated by application to a turbulent wake behind a Delta wing (Fig. 2) and also to a boundary layer flow along heated chocolate.

Investigation of Best Practices in Volumetric Reconstruction for Plenoptic PIV

This paper investigates the effect of smoothing operation in 3D reconstruction using a plenoptic camera. A plenoptic camera - also known as light field camera - features a commercial off the shelf camera with added microlens array (MLA) behind the imaging lens, directly in front of the sensor. The main lens focuses the light to the MLA plane, where each microlens then re-directs the light to small regions of pixels behind, each pixel corresponding to different angle of incident (T. Fahringer (2015)) (Adelson and Wang (1992)). Thus, MLA encodes angular information of incident light rays into the recorded image that assist to acquire 4D information (u,v,s,t) of light-field including both position and angular information of light rays captured by the camera (Ng et al. (2005)) (Adelson and Wang (1992)).

PERSON RE-IDENTIFICATION USING DEEP METRIC LEARNING

Today everyone faces a multitude and variety of threats ranging from robbery, kidnapping and terrorism to murder. To avoid these threats, authorities need to collect real-time information about what's going on in and around the city. New technologies are therefore being developed to make cities safer and more risk-free. Here we have built a reliable system that recognizes the person from every angle from a recorded image. We can get the input into the systems through CCTV cameras installed in public places where these types of life threatening events take place. It is easy to install these cameras in public places, and it is easier to monitor and store the data. The developed system uses deep metric learning and the machine learning platform, Tensor Flow and Keras. It's a type of machine learning where the system iteratively performs calculations to know the patterns. The system processes recorded images and compares them with existing data records in order to identify the person. The comparison is made based on certain selected features. The results are more accurate (98.18%) compared to existing systems.

Wykonywanie obowiązków ustawowych pomimo braku wydanych przepisów wykonawczych w zakresie prowadzenia wizyjnego systemu miejsca magazynowania odpadów. Glosa do wyroku Wojewódzkiego Sądu Administracyjnego w Białymstoku z dnia 9 czerwca 2020 r. (II SA/Bk 16/20, CBOSA)

<p>The Voivodeship Administrative Court in Bialystok, in its judgement of 9 June 2020, dismissing the complaint of the limited liability company in H., on the post-inspection orders of the Podlaskie Voivodeship Inspector for Environmental Protection in Bialystok of 6 November 2019, considered the necessity to fulfill the obligation under Article 25 para. 6a of the Act on Waste, i.e. the operation of a visual control system for the place of waste storage, in force, in the opinion of the Court, from February 2019, despite the lack of executive acts issued by the competent minister. Determining the requirements for the visual control system of the place of storage or storage of waste, the minimum requirements for the technical devices of the vision control system and the requirements for the storage and sharing of the recorded image, guided by the need to enable supervision over the activities in the field of waste management, was established pursuant to the provisions of the Regulation of 29 August 2019 which entered into force in December 2019. In the opinion of the Court, the provisions of the Act on Waste do not have to be met together with the provisions issued on the basis of the Regulation, as the standards of the implementing acts only supplement the general conditions resulting from the statutory provisions.</p>

Application of Innovative Methods of Predictive Control in Projects Involving Intelligent Steel Processing Production Systems

This paper describes the enhancement of the existing predictive system of quality management in the processes of metallurgic manufacturing. Specifically, it addresses steel-strip manufacturing. The main quality management innovation is the transition from the current methodological process of a single-step defect evaluation to a two-step evaluation. A two-step defect check of the strip’s surface involves checking for defects during the hot-rolling process first, and double-checking it during the process of pickling. These defects are detected in a well-established process of camera imaging in the production process. The recorded image is then processed mathematically to find the degree of defect correlation in those processes. The two-step evaluation enables a more detailed focus on a particular defect and its position on the strip. Decisions concerning further processing are based on defect evaluation, for instance, whether a rework is necessary to maximize the product utilization and minimize the eventual negative impact of the defect on production equipment. A crucial aspect is also the reduced probability of failures in the manufacturing process.

Dobutamine Stress Cardiac MRI is Safe and Feasible in Pediatric Patients with Anomalous Aortic Origin of a Coronary Artery (AAOCA)

Background: Risk stratification in anomalous aortic origin of a coronary artery (AAOCA) is challenged by the lack of a reliable method to detect myocardial ischemia. We prospectively studied the safety and feasibility of Dobutamine stress-cardiac magnetic resonance (DSCMR), a test with excellent performance in adults, in pediatric patients with AAOCA. Methods: Consecutive DSCMR from 06/2014-12/2019 in patients 20 years old with AAOCA were included. Hemodynamic response and major/minor events were recorded. Image quality and spatial/temporal resolution were evaluated. Rest and stress first-pass perfusion and wall motion abnormalities (WMA) were assessed. Inter-observer agreement was assessed using kappa coefficient.Results: A total of 224 DSCMR were performed in 182 patients with AAOCA at a median age of 14 years (IQR 12, 16) and median weight of 58.0 kg (IQR 43.3, 73.0). Examinations were completed in 221/224 (98.9%), all studies were diagnostic. Heart rate and blood pressure increased significantly from baseline (p<0.001). No patient had major events and 28 (12.5%) had minor events. Inducible hypoperfusion was noted in 31/221 (14%), associated with WMA in 13/31 (42%). Inter-observer agreement for inducible hypoperfusion was very good (K= 0.87). Asymptomatic patients with inducible hypoperfusion are considered high-risk and those with a negative test are of standard risk.Conclusions: DSCMR is feasible in pediatric patients with AAOCA to assess for inducible hypoperfusion and WMA. It can be performed safely with low incidence of major/minor events. Thus, DSCMR is potentially a valuable test for detection of myocardial ischemia and helpful in the management of this patient population.

Image denoising for fluorescence microscopy by self-supervised transfer learning

When using fluorescent microscopy to study cellular dynamics, trade-off typically has to be made between light exposure and quality of recorded image to balance phototoxicity and image signal-to-noise ratio. Image denoising is an important tool for retrieving information from dim live cell images. Recently, deep learning based image denoising is becoming the leading method because of its promising denoising performance achieved by leveraging available prior knowledge about noise model and samples at hand. However, the practical application of this method has seen challenges because of the requirement of task relevant big training data. In this work, we show the approach of combining self-supervised learning with transfer learning to address the above challenge. We demonstrate the application of it in subcellular fluorescent imaging, where the light exposure dose can be significantly reduced and the spatial resolution is well restored.

Diagnostic accuracy among trainees to safely confirm peripherally inserted central catheter (PICC) placement using bedside ultrasound

Background Real-time utilization of ultrasound to confirm peripherally inserted central catheter (PICC) placement improves efficacy and reduces patient radiation exposure. We evaluated if novice ultrasound users could accurately confirm appropriate PICC tip location via ultrasound assessment. Methodology A prospective data collection study was conducted in an academic center with an established PICC team. Novice ultrasonography users performed 2 echocardiographic views (subcostal and apical 4 chamber) and noted position of visible wire. The presence of central bubbles (visualized in the heart) after a saline infusion, as well as time to bubbles (push-to-bubbles) seen in all patients, was also recorded. Image quality and confidence in imaging acquisition was also recorded. Results Twenty-eight patients between ages 0 and 18 were enrolled over the study period with mean patient age of 10 years and median weight of 34 kg. The quality of image acquisition was rated as great only 34–44%. The wire was visualized only 25% of the time. The median push-to-bubble time when the PICC was later confirmed to be in appropriate positioning was 1.5 seconds with a delay of greater than 3 seconds 40% of the time when the line was malpositioned. The overall positive predictive value of ultrasound identifying malpositioned lines in this study was 43%. Conclusions With this PICC placement technique, ultrasound confirmation of PICC placement by novice ultrasound users was not superior to confirmation with chest radiograph. There may remain potential for future ultrasound protocols, with pediatric-specific technology or echogenic catheter tips, to reduce radiation exposure from chest radiograph during PICC line positioning verification.

Diagnostic Accuracy Among Trainees to Safely Confirm Peripherally Inserted Central Catheter (PICC) Placement Using Bedside UltrasoundCE

Highlights Abstract Background: Real-time utilization of ultrasound to confirm peripherally inserted central catheter (PICC) placement improves efficacy and reduces patient radiation exposure. We evaluated if novice ultrasound users could accurately confirm appropriate PICC tip location via ultrasound assessment. Methodology: A prospective data collection study was conducted in an academic center with an established PICC team. Novice ultrasonography users performed 2 echocardiographic views (subcostal and apical 4 chamber) and noted position of visible wire. The presence of central bubbles (visualized in the heart) after a saline infusion, as well as time to bubbles (push-to-bubbles) seen in all patients, was also recorded. Image quality and confidence in imaging acquisition was also recorded. Results: Twenty-eight patients between ages 0 and 18 were enrolled over the study period with mean patient age of 10 years and median weight of 34 kg. The quality of image acquisition was rated as great only 34–44%. The wire was visualized only 25% of the time. The median push-to-bubble time when the PICC was later confirmed to be in appropriate positioning was 1.5 seconds with a delay of greater than 3 seconds 40% of the time when the line was malpositioned. The overall positive predictive value of ultrasound identifying malpositioned lines in this study was 43%. Conclusions: With this PICC placement technique, ultrasound confirmation of PICC placement by novice ultrasound users was not superior to confirmation with chest radiograph. There may remain potential for future ultrasound protocols, with pediatric-specific technology or echogenic catheter tips, to reduce radiation exposure from chest radiograph during PICC line positioning verification.

Detection of Flaws in Concrete Using Ultrasonic Tomography and Convolutional Neural Networks

Non-destructive testing of concrete for defects detection, using acoustic techniques, is currently performed mainly by human inspection of recorded images. The images consist of the inside of the examined elements obtained from testing devices such as the ultrasonic tomograph. However, such an automatic inspection is time-consuming, expensive, and prone to errors. To address some of these problems, this paper aims to evaluate a convolutional neural network (CNN) toward an automated detection of flaws in concrete elements using ultrasonic tomography. There are two main stages in the proposed methodology. In the first stage, an image of the inside of the examined structure is obtained and recorded by performing ultrasonic tomography-based testing. In the second stage, a convolutional neural network model is used for automatic detection of defects and flaws in the recorded image. In this work, a large and pre-trained CNN is used. It was fine-tuned on a small set of images collected during laboratory tests. Lastly, the prepared model was applied for detecting flaws. The obtained model has proven to be able to accurately detect defects in examined concrete elements. The presented approach for automatic detection of flaws is being developed with the potential to not only detect defects of one type but also to classify various types of defects in concrete elements.