scholarly journals Using haptic cues to aid nonvisual structure recognition

2008 ◽  
Vol 5 (2) ◽  
pp. 1-14 ◽  
Author(s):  
Caroline Jay ◽  
Robert Stevens ◽  
Roger Hubbold ◽  
Mashhuda Glencross
Keyword(s):  
Structure Recognition ◽  
Haptic Cues

scholarly journals A Magnetorheological Fluids-Based Robot-Assisted Catheter/Guidewire Surgery System for Endovascular Catheterization

Micromachines ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 640
Author(s):  
Linshuai Zhang ◽  
Shuoxin Gu ◽  
Shuxiang Guo ◽  
Takashi Tamiya
Keyword(s):  
Collision Detection ◽  
Endovascular Procedures ◽  
Force Feedback ◽  
Force Measurement ◽  
Occupational Hazards ◽  
Robot Assisted ◽  
X Ray ◽  
Surgical Safety ◽  
Surgical Tool ◽  
Haptic Cues

A teleoperated robotic catheter operating system is a solution to avoid occupational hazards caused by repeated exposure radiation of the surgeon to X-ray during the endovascular procedures. However, inadequate force feedback and collision detection while teleoperating surgical tools elevate the risk of endovascular procedures. Moreover, surgeons cannot control the force of the catheter/guidewire within a proper range, and thus the risk of blood vessel damage will increase. In this paper, a magnetorheological fluid (MR)-based robot-assisted catheter/guidewire surgery system has been developed, which uses the surgeon’s natural manipulation skills acquired through experience and uses haptic cues to generate collision detection to ensure surgical safety. We present tests for the performance evaluation regarding the teleoperation, the force measurement, and the collision detection with haptic cues. Results show that the system can track the desired position of the surgical tool and detect the relevant force event at the catheter. In addition, this method can more readily enable surgeons to distinguish whether the proximal force exceeds or meets the safety threshold of blood vessels.

Exploring Button Designs for Mid-air Interaction in Virtual Reality: A Hexa-metric Evaluation of Key Representations and Multi-modal Cues

2021 ◽  
Vol 5 (EICS) ◽  
pp. 1-26
Author(s):  
Carlos Bermejo ◽  
Lik Hang Lee ◽  
Paul Chojecki ◽  
David Przewozny ◽  
Pan Hui
Keyword(s):  
Virtual Reality ◽  
User Interfaces ◽  
Physical Environment ◽  
Fundamental Issue ◽  
Haptic Cues

The continued advancement in user interfaces comes to the era of virtual reality that requires a better understanding of how users will interact with 3D buttons in mid-air. Although virtual reality owns high levels of expressiveness and demonstrates the ability to simulate the daily objects in the physical environment, the most fundamental issue of designing virtual buttons is surprisingly ignored. To this end, this paper presents four variants of virtual buttons, considering two design dimensions of key representations and multi-modal cues (audio, visual, haptic). We conduct two multi-metric assessments to evaluate the four virtual variants and the baselines of physical variants. Our results indicate that the 3D-lookalike buttons help users with more refined and subtle mid-air interactions (i.e. lesser press depth) when haptic cues are available; while the users with 2D-lookalike buttons unintuitively achieve better keystroke performance than the 3D counterparts. We summarize the findings, and accordingly, suggest the design choices of virtual reality buttons among the two proposed design dimensions.

scholarly journals Nanopore Technology for the Application of Protein Detection

Nanomaterials ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1942
Author(s):  
Xiaoqing Zeng ◽  
Yang Xiang ◽  
Qianshan Liu ◽  
Liang Wang ◽  
Qianyun Ma ◽  
...  
Keyword(s):  
Single Molecule ◽  
Protein Detection ◽  
High Sensitivity ◽  
Single Molecule Detection ◽  
Sequence Detection ◽  
Fast Detection ◽  
Material Basis ◽  
Sensing Technology ◽  
Detection Technology ◽  
Structure Recognition

Protein is an important component of all the cells and tissues of the human body and is the material basis of life. Its content, sequence, and spatial structure have a great impact on proteomics and human biology. It can reflect the important information of normal or pathophysiological processes and promote the development of new diagnoses and treatment methods. However, the current techniques of proteomics for protein analysis are limited by chemical modifications, large sample sizes, or cumbersome operations. Solving this problem requires overcoming huge challenges. Nanopore single molecule detection technology overcomes this shortcoming. As a new sensing technology, it has the advantages of no labeling, high sensitivity, fast detection speed, real-time monitoring, and simple operation. It is widely used in gene sequencing, detection of peptides and proteins, markers and microorganisms, and other biomolecules and metal ions. Therefore, based on the advantages of novel nanopore single-molecule detection technology, its application to protein sequence detection and structure recognition has also been proposed and developed. In this paper, the application of nanopore single-molecule detection technology in protein detection in recent years is reviewed, and its development prospect is investigated.

scholarly journals Convolutional neural network-assisted recognition of nanoscale L12 ordered structures in face-centred cubic alloys

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Yue Li ◽  
Xuyang Zhou ◽  
Timoteo Colnaghi ◽  
Ye Wei ◽  
Andreas Marek ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Chemical Species ◽  
Atom Probe ◽  
Local Order ◽  
Face Centered Cubic ◽  
Ordered Structure ◽  
Ordered Structures ◽  
Distribution Maps ◽  
Structure Recognition ◽  
Fcc Alloys

AbstractNanoscale L12-type ordered structures are widely used in face-centered cubic (FCC) alloys to exploit their hardening capacity and thereby improve mechanical properties. These fine-scale particles are typically fully coherent with matrix with the same atomic configuration disregarding chemical species, which makes them challenging to be characterized. Spatial distribution maps (SDMs) are used to probe local order by interrogating the three-dimensional (3D) distribution of atoms within reconstructed atom probe tomography (APT) data. However, it is almost impossible to manually analyze the complete point cloud (>10 million) in search for the partial crystallographic information retained within the data. Here, we proposed an intelligent L12-ordered structure recognition method based on convolutional neural networks (CNNs). The SDMs of a simulated L12-ordered structure and the FCC matrix were firstly generated. These simulated images combined with a small amount of experimental data were used to train a CNN-based L12-ordered structure recognition model. Finally, the approach was successfully applied to reveal the 3D distribution of L12–type δ′–Al3(LiMg) nanoparticles with an average radius of 2.54 nm in a FCC Al-Li-Mg system. The minimum radius of detectable nanodomain is even down to 5 Å. The proposed CNN-APT method is promising to be extended to recognize other nanoscale ordered structures and even more-challenging short-range ordered phenomena in the near future.

Role of haptic cues in motor learning

2013 ◽  
Author(s):  
Dongwon Kim ◽  
B. J. Johnson ◽  
R. B. Gillespie ◽  
R. D. Seidler
Keyword(s):  
Motor Learning ◽  
Haptic Cues
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