manual-input register

2011 ◽  
Keyword(s):  
Input Register ◽  
Manual Input

manual-input register

2000 ◽  
pp. 973-973
Author(s):  
Martin H. Weik
Keyword(s):  
Input Register ◽  
Manual Input

Test Circuit Conditioning for Soft Defect Localization

2014 ◽  
Author(s):  
Kristopher D. Staller ◽  
Corey Goodrich
Keyword(s):  
Laser Beam ◽  
Failure Analysis ◽  
Signal Frequency ◽  
Dynamic Failure ◽  
The Third ◽  
Analysis Technique ◽  
Test Circuit ◽  
Defect Localization ◽  
Manual Input ◽  
Fast Dynamic

Abstract Soft Defect Localization (SDL) is a dynamic laser-based failure analysis technique that can detect circuit upsets (or cause a malfunctioning circuit to recover) by generation of localized heat or photons from a rastered laser beam. SDL is the third and seldom used method on the LSM tool. Most failure analysis LSM sessions use the endo-thermic mode (TIVA, XIVA, OBIRCH), followed by the photo-injection mode (LIVA) to isolate most of their failures. SDL is seldom used or attempted, unless there is a unique and obvious failure mode that can benefit from the application. Many failure analysts, with a creative approach to the analysis, can employ SDL. They will benefit by rapidly finding the location of the failure mechanism and forgoing weeks of nodal probing and isolation. This paper will cover circuit signal conditioning to allow for fast dynamic failure isolation using an LSM for laser stimulation. Discussions of several cases will demonstrate how the laser can be employed for triggering across a pass/fail boundary as defined by voltage levels, supply currents, signal frequency, or digital flags. A technique for manual input of the LSM trigger is also discussed.

scholarly journals A Model-Driven-to-Sample-Driven Method for Rural Road Extraction

2021 ◽  
Vol 13 (8) ◽  
pp. 1417
Author(s):  
Jiguang Dai ◽  
Rongchen Ma ◽  
Litao Gong ◽  
Zimo Shen ◽  
Jialin Wu
Keyword(s):  
Rural Areas ◽  
Template Matching ◽  
Color Space ◽  
Recall Rate ◽  
Road Extraction ◽  
Rural Roads ◽  
Model Driven ◽  
The Road ◽  
Center Point ◽  
Manual Input

Road extraction in rural areas is one of the most fundamental tasks in the practical application of remote sensing. In recent years, sample-driven methods have achieved state-of-the-art performance in road extraction tasks. However, sample-driven methods are prohibitively expensive and laborious, especially when dealing with rural roads with irregular curvature changes, narrow widths, and diverse materials. The template matching method can overcome these difficulties to some extent and achieve impressive road extraction results. This method also has the advantage of the vectorization of road extraction results, but the automation is limited. Straight line sequences can be substituted for curves, and the use of the color space can increase the recognition of roads and nonroads. A model-driven-to-sample-driven road extraction method for rural areas with a much higher degree of automation than existing template matching methods is proposed in this study. Without prior samples, on the basis of the geometric characteristics of narrow and long roads and using the advantages of straight lines instead of curved lines, the road center point extraction model is established through length constraints and gray mean contrast constraints of line sequences, and the extraction of some rural roads is completed through topological connection analysis. In addition, we take the extracted road center point and manual input data as local samples, use the improved line segment histogram to determine the local road direction, and use the panchromatic and hue, saturation, value (HSV) space interactive matching model as the matching measure to complete the road tracking extraction. Experimental results show that, for different types of data and scenarios on the premise, the accuracy and recall rate of the evaluation indicators reach more than 98%, and, compared with other methods, the automation of this algorithm has increased by more than 40%.

Application of Digital Well Construction Planning Tool During Well Conceptualization Phase

2021 ◽  
Author(s):  
Costeno Hugo ◽  
Kandasamy Rajeswary ◽  
Telles Jose ◽  
Camacho Jacob ◽  
Medina Diego ◽  
...  
Keyword(s):  
Data Storage ◽  
Construction Planning ◽  
Team Collaboration ◽  
Planning Stage ◽  
Risk Levels ◽  
Well Design ◽  
Surface Data ◽  
Well Trajectory ◽  
Ease Of Access ◽  
Manual Input

Abstract Digital well construction tools are becoming more widely considered today for well design planning, enabling automated engineering and simultaneous team collaboration under a single solution. This paper shows the results of using a digital well construction planning solution during a project’s conceptual planning stage. This method shortens the time needed to estimate the well times and risk profile for a drilling campaign by applying smart engines to quickly and accurately perform critical offset analysis for defined well types that is required for project sanction. With this solution, the Offset Well Analysis (OWA) process is done automatically based on the location of the planned well, trajectory and well architecture. Various information and reports (both subsurface and surface data) from neighboring wells is stored in cloud solutions, enabling ease of access and data reliability for both large or smaller scale data storage. The software selects the most relevant offset wells, displays the risk analysis and generates the stick chart. For a conceptual design, the risk levels can be manually set higher due to potential unknowns in surface and subsurface risks which can later be refined. Quick validation of the well design allows the engineer to design a conceptual drilling campaign quickly and more efficiently. The solution minimizes the time to perform probabilistic time and risk estimations. It reduces the risk of biased decision making due to manual input and design. This allows for better-informed decisions on project feasibility, alignment of stakeholders, increased design reliability as well as reducing the amount of time and resources invested in OWA. The work presented here is aimed at sharing the experience of applying a digital well construction planning solution specifically on the conceptual project stage and discuss the value it adds to the well design process.

Interaction Design for Personal Photo Management on a Mobile Device

2008 ◽  
pp. 69-85
Author(s):  
Hyowon Lee ◽  
Cathal Gurrin ◽  
Gareth J.F. Jones ◽  
Alan F. Smeaton
Keyword(s):  
Mobile Devices ◽  
Interaction Design ◽  
User Interaction ◽  
Research Area ◽  
Digital Cameras ◽  
Mobile Access ◽  
Camera Phones ◽  
Active Research ◽  
Manual Input ◽  
Photo Management

This chapter explores some of the technological elements that will greatly enhance user interaction with personal photos on mobile devices in the near future. It reviews major technological innovations that have taken place in recent years which are contributing to re-shaping people’s personal photo management behavior and thus their needs, and presents an overview of the major design issues in supporting these for mobile access. It then introduces the currently very active research area of content-based image analysis and context-awareness. These technologies are becoming an important factor in improving mobile interaction by assisting automatic annotation and organization of photos, thus reducing the chore of manual input on mobile devices. Considering the pace of the rapid increases in the number of digital photos stored on our digital cameras, camera phones and online photoware sites, the authors believe that the subsequent benefits from this line of research will become a crucial factor in helping to design efficient and satisfying mobile interfaces for personal photo management systems.

543 Automated detection of mitral annular plane systolic excursion in transoesophageal echocardiography based on deep learning

2020 ◽  
Vol 21 (Supplement_1) ◽  
Author(s):  
T Nordal ◽  
E A R Berg ◽  
G Kiss
Keyword(s):  
Deep Learning ◽  
Left Ventricle ◽  
Vital Signs ◽  
Transoesophageal Echocardiography ◽  
Vertical Movement ◽  
Left Ventricular ◽  
Cardiac Monitoring ◽  
Major Surgery ◽  
Intraobserver Variability ◽  
Manual Input

Abstract Introduction Major surgery and interventions may impact cardiac function. Perioperative monitoring is currently based on vital signs and clinical observations. However, this does not offer a complete monitoring of left ventricular function throughout the intervention. We hypothesize that functional monitoring of the heart can be performed automatically based on transoesphageal echocardiography (TOE) images. One parameter that has been shown to correlate well with ejection fraction is mitral annular plane systolic excursion (MAPSE). To aid functional monitoring of the left ventricle perioperatively, we propose a technique for detecting MAPSE in TOE images of the left ventricle. Purpose The purpose of this study is to automatically track the movement of the mitral annular plane in TOE sequences of the left ventricle and detect MAPSE via a deep learning approach. Method Recordings from 131 consecutive complete TOE exams from the Echocardiography Unit were anonymized and used for training. Recordings from 23 consecutive TOE exams, also anonymized, were used as test set. All recordings were manually annotated with the location of the landmarks indicated in both 4-chamber (4C) and 2-chamber (2C) views. All recordings were made using state-of-the-art clinical scanners. The captures include 3 to 5 heart cycles of standard 4C and 2C views. An approach based on a fully convolutional neural network was implemented and trained in a supervised manner to predict the location of two landmarks on the mitral annular plane in B-mode TOE images from 4C and 2C views. The model was also trained to account for noise by recognizing when detecting the landmarks is not feasible due to poor image quality. We have implemented all necessary post processing calculations to automatically estimate MAPSE based only on raw TOE B-mode sequences. Results Preliminary results on the test data show that the landmark detector is able to track the vertical movement of landmarks on the mitral annular plane with a mean error of 0.88 mm and a standard deviation of 0.27 mm (Fig. 1: Upper left and lower left: tracked mitral attachment points on a sample case presented upper right. Lower right: all measured Y-axis excursion values versus the reference). The classifier for detecting ultrasound frames where landmark detection is not feasible has a sensitivity of 0.82 and a specificity of 0.91. Conclusion The landmark detector is showing promising results in tracking of the mitral annular plane excursion. This can provide a fast calculation of MAPSE and eliminate intraobserver variability. This may be included in a more extensive cardiac monitoring for any type of surgery without the need of manual input from echocardiographers. Further research is ongoing and a comparison with clinical MAPSE values is underway. Abstract 543 Figure 1

Segmenting the Uterus in Monocular Laparoscopic Images without Manual Input

2015 ◽  
pp. 181-189 ◽  
Author(s):  
Toby Collins ◽  
Adrien Bartoli ◽  
Nicolas Bourdel ◽  
Michel Canis
Keyword(s):  
Manual Input

scholarly journals Comparison of galaxy spiral arm pitch angle measurements using manual and automated techniques

2020 ◽  
Vol 493 (3) ◽  
pp. 3854-3865
Author(s):  
Ian B Hewitt ◽  
Patrick Treuthardt
Keyword(s):  
Pitch Angle ◽  
Spiral Galaxies ◽  
Small Sample ◽  
Automated Method ◽  
Angle Measurements ◽  
Barred Spiral Galaxy ◽  
Manual Input ◽  
Automated Methods ◽  
Disc Galaxies ◽  
Spiral Arm

ABSTRACT The pitch angle (PA) of arms in spiral galaxies has been found to correlate with a number of important parameters that are normally time intensive and difficult to measure. Accurate PA measurements are therefore important in understanding the underlying physics of disc galaxies. We introduce a semi-automated method that improves upon a parallelized two-dimensional fast Fourier transform algorithm (p2dfft) to estimate PA. Rather than directly inputting deprojected, star subtracted, and galaxy centred images into p2dfft, our method (p2dfft:traced) takes visually traced spiral arms from deprojected galaxy images as input. The tracings do not require extensive expertise to complete. This procedure ignores foreground stars, bulge and/or bar structures, and allows for better discrimination between arm and interarm regions, all of which reduce noise in the results. We compare p2dfft:traced to other manual and automated methods of measuring PA using both simple barred and non-barred spiral galaxy models and a small sample of observed spiral galaxies with different representative morphologies. We find that p2dfft:traced produces results that, in general, are more accurate and precise than the other tested methods and it strikes a balance between total automation and time-consuming manual input to give reliable PA measurements.

scholarly journals Opioids, Polypharmacy, and Drug Interactions: A Technological Paradigm Shift Is Needed to Ameliorate the Ongoing Opioid Epidemic

Pharmacy ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 154
Author(s):  
Adriana Matos ◽  
David L. Bankes ◽  
Kevin T. Bain ◽  
Tyler Ballinghoff ◽  
Jacques Turgeon
Keyword(s):  
Drug Interactions ◽  
Paradigm Shift ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Health Records ◽  
Technological Paradigm ◽  
Life Threatening ◽  
Drug Regimens ◽  
Manual Input ◽  
Complex Drug

Polypharmacy is a common phenomenon among adults using opioids, which may influence the frequency, severity, and complexity of drug–drug interactions (DDIs) experienced. Clinicians must be able to easily identify and resolve DDIs since opioid-related DDIs are common and can be life-threatening. Given that clinicians often rely on technological aids—such as clinical decision support systems (CDSS) and drug interaction software—to identify and resolve DDIs in patients with complex drug regimens, this narrative review provides an appraisal of the performance of existing technologies. Opioid-specific CDSS have several system- and content-related limitations that need to be overcome. Specifically, we found that these CDSS often analyze DDIs in a pairwise manner, do not account for relevant pharmacogenomic results, and do not integrate well with electronic health records. In the context of polypharmacy, existing systems may encourage inadvertent serious alert dismissal due to the generation of multiple incoherent alerts. Future technological systems should minimize alert fatigue, limit manual input, allow for simultaneous multidrug interaction assessments, incorporate pharmacogenomic data, conduct iterative risk simulations, and integrate seamlessly with normal workflow.

scholarly journals Enabling Automatic LCA at Any Stage of the Building Based on Its BIM Model

2020 ◽  
Vol 51 (1) ◽  
pp. 15-22
Author(s):  
Mathieu Dupuis ◽  
Alain April ◽  
Daniel Forgues
Keyword(s):  
Life Cycle Assessment ◽  
Early Stage ◽  
File Format ◽  
Extended Version ◽  
Building Information Modelling ◽  
Information Gap ◽  
Building Information ◽  
Manual Input ◽  
Creative Construction ◽  
Share Information

The Life Cycle Assessment (LCA) of a whole building is a well-known process used to assess its environmental impact. The construction domain does not use this process at this time because it requires too much information and collecting it is very labor intensive. This paper identifies the information needed to perform an LCA at any level of development of a Building Information Modelling (BIM) model and proposes some solutions to fill the information gap of an early stage BIM model. After the required information is identified, the interoperability strategy is analyzed to propose a framework introducing a way to organize the LCA of a whole building, as well as a new file format to share information between BIM and LCA software. The proposed framework enables an LCA to be performed, without manual input, at every iteration of the BIM model. This framework was previously presented at the Creative Construction Conference 2019 and this paper is an extended version of that paper.

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