New LWD Technology Provides High-Resolution Images in Oil- and Water-Based Muds for Improved Decision Making in Real Time

2018 ◽  
Author(s):  
M. E. Gillen ◽  
B. Moody ◽  
S. Dymmock
Keyword(s):  
Decision Making ◽  
High Resolution ◽  
Real Time ◽  
Water Based ◽  
High Resolution Images

Electrical Probing and Surface Imaging of Deep Sub-Micron Integrated Circuits

1999 ◽  
Author(s):  
Kenneth Krieg ◽  
Richard Qi ◽  
Douglas Thomson ◽  
Greg Bridges
Keyword(s):  
High Resolution ◽  
Integrated Circuits ◽  
Real Time ◽  
High Speed ◽  
Time Signal ◽  
Surface Imaging ◽  
Atomic Force ◽  
Submicron Structures ◽  
High Resolution Images ◽  
Capacitive Loading

Abstract A contact probing system for surface imaging and real-time signal measurement of deep sub-micron integrated circuits is discussed. The probe fits on a standard probe-station and utilizes a conductive atomic force microscope tip to rapidly measure the surface topography and acquire real-time highfrequency signals from features as small as 0.18 micron. The micromachined probe structure minimizes parasitic coupling and the probe achieves a bandwidth greater than 3 GHz, with a capacitive loading of less than 120 fF. High-resolution images of submicron structures and waveforms acquired from high-speed devices are presented.

Real-time, parallel segmentation of high-resolution images on multi-core platforms

2014 ◽  
Vol 13 (4) ◽  
pp. 685-702 ◽  
Author(s):  
Dana Forsthoefel Fitzgerald ◽  
D. Scott Wills ◽  
Linda M. Wills
Keyword(s):  
High Resolution ◽  
Real Time ◽  
High Resolution Images

scholarly journals Real-Time Moving Object Detection in High-Resolution Video Sensing

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3591 ◽  
Author(s):  
Haidi Zhu ◽  
Haoran Wei ◽  
Baoqing Li ◽  
Xiaobing Yuan ◽  
Nasser Kehtarnavaz
Keyword(s):  
High Resolution ◽  
Object Detection ◽  
Real Time ◽  
Moving Object Detection ◽  
Moving Object ◽  
Computationally Efficient ◽  
Real Time Processing ◽  
Time Processing ◽  
Video Frames ◽  
High Resolution Images

This paper addresses real-time moving object detection with high accuracy in high-resolution video frames. A previously developed framework for moving object detection is modified to enable real-time processing of high-resolution images. First, a computationally efficient method is employed, which detects moving regions on a resized image while maintaining moving regions on the original image with mapping coordinates. Second, a light backbone deep neural network in place of a more complex one is utilized. Third, the focal loss function is employed to alleviate the imbalance between positive and negative samples. The results of the extensive experimentations conducted indicate that the modified framework developed in this paper achieves a processing rate of 21 frames per second with 86.15% accuracy on the dataset SimitMovingDataset, which contains high-resolution images of the size 1920 × 1080.

Horizontal Well Placement Benefits of High Resolution Images in Real Time, Case Study from UAE

2018 ◽  
Author(s):  
A. B. Abdelkarim ◽  
S. AL-Hassani ◽  
S. Ahmed ◽  
H. Abiujmeih ◽  
A. Serry ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Horizontal Well ◽  
Well Placement ◽  
High Resolution Images

Faster-PPN: Towards Real-Time Semantic Segmentation with Dual Mutual Learning for Ultra-High Resolution Images

2021 ◽  
Author(s):  
Bicheng Dai ◽  
Kaisheng Wu ◽  
Tong Wu ◽  
Kai Li ◽  
Yanyun Qu ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Semantic Segmentation ◽  
Mutual Learning ◽  
High Resolution Images

P09.03 Fully integrating functional Ultrasound (fUS) into the onco-neurosurgical operating room: Towards a new real-time, high-resolution image-guided resection tool with multimodal potential

2021 ◽  
Vol 23 (Supplement_2) ◽  
pp. ii26-ii27
Author(s):  
S Soloukey ◽  
L Verhoef ◽  
F Mastik ◽  
B S Generowicz ◽  
E M Bos ◽  
...  
Keyword(s):  
Decision Making ◽  
High Resolution ◽  
Real Time ◽  
Cerebral Blood Volume ◽  
Imaging Technique ◽  
Power Doppler ◽  
Frame Rate ◽  
Neurosurgical Procedures ◽  
Vascular Morphology ◽  
Image Guided

Abstract BACKGROUND Onco-neurosurgical practice still relies heavily on pre-operatively acquired images to guide intra-operative decision-making for safe tumor removal, a practice with inherent pitfalls such as registration inaccuracy due to brain shift, and lack of real-time (functional) feedback. Exploiting the opportunity for real-time imaging of the exposed brain can improve intra-operative decision-making, neurosurgical safety and patient outcomes. Previously, we described functional Ultrasound (fUS) as a high-resolution, depth-resolved imaging technique able to detect functional regions and vascular morphology during awake resections. Here, we present for the first time fUS as a fully integrated, MRI/CT-registered imaging modality in the OR. MATERIAL AND METHODS fUS relies on high-frame-rate (HFR) ultrasound, making the technique sensitive for very small motions caused by vascular dynamics (µDoppler) and allowing measurements of changes in cerebral blood volume (CBV) with micrometer-millisecond precision. This opens up the possibility to 1) detect functional response, as CBV-changes reflect changes in metabolism of activated neurons through neurovascular coupling and 2) visualize in-vivo vascular morphology of tumor and healthy tissue. During a range of anesthetized and awake onco-neurosurgical procedures we acquired images of brain and spinal cord using conventional linear ultrasound probes connected to an experimental acquisition unit. Building on Brainlab’s ‘Cranial Navigation’ and ‘Intra-Operative Ultrasound’ modules, we could co-register our intra-operative Power Doppler Images (PDIs) to patient-registered MRI/CT-data. Using the ‘IGTLink’ research interface, we could access and store real-time tracking data for informed volume reconstructions in post-processing. RESULTS Intra-operative fUS could be registered to MRI/CT-images in real-time, showing overlays of PDIs at imaging depths of >5 centimeters. During meningioma resections, these co-registered PDIs revealed fUS’ ability to visualize the tumor’s feeding vessels and surrounding healthy vasculature prior to durotomy, with a level of detail unprecedented by conventional MRI-sequences. Comparing post-operatively reconstructed 3D-vascular maps of pre- and post-durotomy acquisitions, further confirmed the dural dependency of the vascular network feeding the tumor. During awake resections, fUS revealed distinct functional areas as activated during motor and language tasks. CONCLUSION fUS is a new real-time, high-resolution and depth-resolved imaging technique, combining characteristics uniquely beneficial for a potential image-guided resection tool. The successful integration of fUS in the onco-neurosurgical OR demonstrated by our team, is an essential step towards clinical integration of fUS, as well as the technique’s validation against modalities such as MRI and CT.

scholarly journals ICNet for Real-Time Semantic Segmentation on High-Resolution Images

2018 ◽  
pp. 418-434 ◽  
Author(s):  
Hengshuang Zhao ◽  
Xiaojuan Qi ◽  
Xiaoyong Shen ◽  
Jianping Shi ◽  
Jiaya Jia
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Semantic Segmentation ◽  
High Resolution Images
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