다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

The PASCAL Visual Object Classes Challenge

http://www.cs.unibo.it/~montesi/CBD/Articoli/SurvyBigData2.pdf

Data-intensive applications, challenges, techniques and technologies: A survey on Big Data

Two people with long-standing tetraplegia use neural interface system-based control of a robotic arm to perform three-dimensional reach and grasp movements. John Donoghue and colleagues have previously demonstrated that people with tetraplegia can learn to use neural signals from the motor cortex to control a computer cursor. Work from another lab has also shown that monkeys can learn to use such signals to feed themselves with a robotic arm. Now, Donoghue and colleagues have advanced the technology to a level at which two people with long-standing paralysis — a 58-year-old woman and a 66-year-old man — are able to use a neural interface to direct a robotic arm to reach for and grasp objects. One subject was able to learn to pick up and drink from a bottle using a device implanted 5 years earlier, demonstrating not only that subjects can use the brain–machine interface, but also that it has potential longevity. Paralysis following spinal cord injury, brainstem stroke, amyotrophic lateral sclerosis and other disorders can disconnect the brain from the body, eliminating the ability to perform volitional movements. A neural interface system1,2,3,4,5 could restore mobility and independence for people with paralysis by translating neuronal activity directly into control signals for assistive devices. We have previously shown that people with long-standing tetraplegia can use a neural interface system to move and click a computer cursor and to control physical devices6,7,8. Able-bodied monkeys have used a neural interface system to control a robotic arm9, but it is unknown whether people with profound upper extremity paralysis or limb loss could use cortical neuronal ensemble signals to direct useful arm actions. Here we demonstrate the ability of two people with long-standing tetraplegia to use neural interface system-based control of a robotic arm to perform three-dimensional reach and grasp movements. Participants controlled the arm and hand over a broad space without explicit training, using signals decoded from a small, local population of motor cortex (MI) neurons recorded from a 96-channel microelectrode array. One of the study participants, implanted with the sensor 5 years earlier, also used a robotic arm to drink coffee from a bottle. Although robotic reach and grasp actions were not as fast or accurate as those of an able-bodied person, our results demonstrate the feasibility for people with tetraplegia, years after injury to the central nervous system, to recreate useful multidimensional control of complex devices directly from a small sample of neural signals.

/pdf/reach-and-grasp-by-people-with-tetraplegia-using-a-neurally-u1xyn3on0d.pdf

Reach and grasp by people with tetraplegia using a neurally controlled robotic arm

In this paper we propose the creation of context-aware middleware to solve the challenge of integrating disparate incompatible systems involved in the teaching of human action skills to robots. Context-aware middleware provides the solution to retrofitting capabilities onto existing robots (agents) and bridges the technology differences between systems. The experimental results demonstrate a framework for handling situational and contextual data for robot Learning from Demonstration.

/pdf/accelerating-humanoid-robot-learning-from-human-action-cdh3uxqyz3.pdf

Accelerating Humanoid Robot Learning from Human Action Skills Using Context-Aware Middleware

Ultrasound can non-invasively detect muscle deformations and has great potential applications in prosthetic hand control. Traditional ultrasound equipment was usually too bulky to be applied in wearable scenarios. This research presented a compact ultrasound device that could be integrated into a prosthetic hand socket. The miniaturized ultrasound system included four A-mode ultrasound transducers for sensing musculature deformations, a signal excitation/acquisition module, and a prosthetic hand control module. The size of the ultrasound system was 65*75*25 mm, weighing only 85 g. For the first time, we integrated the ultrasound system into a prosthetic hand socket to evaluate its performance in practical prosthetic hand control. We designed an experiment requiring twenty subjects to perform six commonly used gestures. The performance of decoding ultrasound signals was analyzed offline using four classification algorithms and then was assessed in online control. The average values of online classification accuracy with and without wearing the physical prosthetic were 91.5 $\pm\;\text{6.4}\%$ and 96.5 $\pm \;\text{3.0}\%$, respectively. We found that wearing the prosthetic hand influenced the ultrasound gestures classification accuracy, but remarkable online classification performance could still be maintained. These experimental results demonstrated the efficacy of the designed integrated ultrasound system for practical use, paving the way for an effective HMI system that could be widely used in prosthetic hand control.

A Wearable Ultrasound Interface for Prosthetic Hand Control

The viewpoint issue has been one of the bottlenecks for research development and practical implementation of human motion analysis. In this paper, we introduce a new method, e.g., hidden conditional random fields(HCRFs) to achieve viewpoint insensitive human action recognition. The HCRF model can relax the independence assumption of the generative models. So it is very suitable to model the human actions fromdifferent actors and different viewpoints.Experiment results on a public dataset demonstrate the effectiveness and robustness of our method.

Viewpoint insensitive actions recognition using hidden conditional random fields

It is crucial to model skeletal muscles for muscle-centered health care, such as prosthetics. Here we hypothesize that the acoustic non-linearity parameter (B/A) can be utilized to partially represent the contraction state of skeletal muscles. Although previous work commonly measured the B/A value of tissues in vitro, the present study targets the biceps brachii muscle to investigate the relationship between the B/A value and the dynamics of the elbow. Furthermore, it is proposed that a correction method based on the angular spectrum theory be applied in vivo, and the dynamic metrics of the B/A value and its feasibility be verified through an underwater experiment. Seven participants were invited for the in vivo experiment, in which elbow torque and B/A values were measured simultaneously. The non-plane reflection was approximately treated through an integral method, leading to a modified B/A value. Then, linear regression was applied to characterize the B/A-torque relationship, with the calculated coefficient of determination (R2) ranging from 0.85-0.93. Experimental results indicate that the modified B/A value of the biceps brachii correlates well with elbow torque. This study not only paves the way to dynamic measurement of the B/A value of skeletal muscles in vivo, but also confirms that B/A can be used as a more comprehensive assessment criterion for muscle functions.

Dynamically Characterizing Skeletal Muscles via Acoustic Non-linearity Parameter: In Vivo Assessment for Upper Arms

It is a challenge to precisely predict hand grasps based on EMG signals given practical scenarios, due to its inherent nature. This paper proposes a solution to tackle the challenge with a force-driven granular model (FDGM). The problem of n-class hand grasp classification has been represented as force-based granular modelling, in which a number of granules are constructed for each class relying on the synchronically captured grasping force. A rule based mechanism is formed for granule generation of each class, and a cross-testing algorithm is proposed to optimise the number of granules. The experiment based on 8-case grasp recognition reveals that the proposed method performs better in terms of motion recognition accuracy of multiple EMG channel combination, and is more insensitive to signal interferences. In comparison with other rules of information granulation, it is confirmed that the force-driven rule is of the most efficiency with comparable classification accuracy. The research outcomes pave the way for real-time prediction of grasps and corresponding force in human-centred environments.

/pdf/a-force-driven-granular-model-for-emg-based-grasp-gy29v4gysa.pdf

Honghai Liu

Papers

Accelerating Humanoid Robot Learning from Human Action Skills Using Context-Aware Middleware

A Wearable Ultrasound Interface for Prosthetic Hand Control

Viewpoint insensitive actions recognition using hidden conditional random fields

Dynamically Characterizing Skeletal Muscles via Acoustic Non-linearity Parameter: In Vivo Assessment for Upper Arms

A force-driven granular model for EMG based grasp recognition