Multilevel multistate hybrid voltage regulator

In this work, a new set of voltage regulators as well as some controlling methods and schemes are proposed. While normal switched capacitor voltage regulators are easy integrable, they are suffering from charge sharing losses as well as fast degradation of efficiency when deviating from target operation point. On the other hand, conventional buck converters use bulky magnetic components that introduce challenges to integrate them on chip. The new set of voltage regulators covers the gap between inductor-based and capacitor-based voltage regulators by taking the advantages of both of them while avoiding or minimizing their disadvantages. The voltage regulator device consists of a switched capacitor circuit that is periodically switching its output between different voltage levels followed by a low pass filter to give a regulated output voltage. The voltage regulator is capable of converting an input voltage to a wide range of output voltage with a high efficiency that is roughly constant over the whole operation range. By switching between adjacent voltage levels, the voltage drop on the inductor is limited allowing for the use of smaller inductor sizes while maintaining the same performance. The general concept of the proposed voltage regulator as well as some operating conditions and techniques are explained. A phase interleaving technique to operate the multilevel multistate voltage regulator has been proposed. In this technique, the phases of two or more voltage levels are interleaved which enhances the effective switching frequency of the charge transferring components. This results in a further boost in the proposed regulator\u27s performance. A 4-level 4-state hybrid voltage regulator has been introduced as an application on the proposed concepts and techniques. It shows better performance compared to both integrated inductor-based and capacitor-based voltage regulators. The results prove that the proposed set of voltage regulators offers a potential move towards easing the integration of voltage regulators on chip with a performance that approaches that of off-chip voltage regulators

MMVR: ジェスチャを利用した仮想空間上での多人数向けマインドマップツールの実装と評価

本研究では、発想法の1つであるマインドマップ(以下MM)を多人数で効率よく扱うため、ヘッドマウントディスプレイ(以下HMD)とハンドジェスチャを用い、バーチャルリアリティ(以下VR)空間で行うシステム「MMVR」を提案、作成し、その有用性を評価実験を通じ評価した。MMとは発想の中心となるキーワードや画像等を中央に置き、キーワードやキーイメージが付随したブランチを放射状に伸ばしていく事によって発想を促す方法である。MMは紙上の他ディスプレイ上でも行われるが、作業空間が限られる。そこでVR空間の広大な作業領域を取り入れることでより効率的な作業が可能か、さらにその操作にハンドジェスチャという直接操作がどれほど有効に行えるかの調査を目的とした。本研究では二次元的な画面表示だけでなく、三次元情報や手による直接的なジェスチャ、加えて複数人で机を囲んで行う作業を実現し、更なる想起を促す事を目的としたMM支援システムを作成した。結果として、個人でMMを行う際は既存のソフトウェアであるiMindMapに比べキーイメージの作成時間が短縮され、また二人で既存のMM支援ソフトウェアとMMVRを使う場合どちらのほうが総合的に考え使いやすいか実験を通し尋ねた所、被験者全員から本研究で作成したMMVRの方が使いやすいという回答が得られた。電気通信大学201

SOFA: A modular yet efficient simulation framework

International audienceSOFA is a new open source framework primarily targeted at medical simulation research and industry. It is based on a scene graph data structure extended to physical models and abstract algorithms. Additionally, multiple models of the same objects can easily be used to optimize different tasks such as force computation, collision handling, and rendering. This results in a highly flexible architecture able to model and animate a wide range of simulated objects. We explain the main concepts of SOFA and detail an example of application to a surgery procedure

Universal charts for optical difference frequency generation in the terahertz domain

We present a universal and rigorous approach to study difference frequency generation in the terahertz domain, keeping the number of degrees of freedom to a minimum, through the definition of a suitable figure of merit. The proposed method relies on suitably normalized charts, that enable to predict the optical-to-terahertz conversion efficiency of any system based on wave propagation in quadratic nonlinear materials. The predictions of our approach are found to be in good agreement with the best experimental results reported to date, enabling also to estimate the d22 nonlinear coefficient of high quality GaSe.Comment: 3 pages in 2 columns format, 3 figures. GaSe analysis has been corrected. Fig. 3 has been replace

Assessment of joystick and wrist control in hand-held articulated laparoscopic prototypes

Various steerable instruments with flexible distal tip have been developed for laparoscopic surgery. The problem of steering such instruments, however, remains a challenge, because no study investigated which control method is the most suitable. This study was designed to examine whether thumb (joystick) or wrist control method is designated for prototypes of steerable instruments by means of motion analysis. Methods: Five experts and 12 novices participated. Each participant performed a needle-driving task in three directions with two prototypes (wrist and thumb) and a conventional instrument. Novices performed the tasks in three sessions, whereas experts performed one session only. The order of performing the tasks was determined by Latin squares design. Assessment of performance was done by means of five motion analysis parameters, a newly developed matrix for assigning penalty points, and a questionnaire. Results: The thumb-controlled prototype outperformed the wrist-controlled prototype. Comparison of the results obtained in each task showed that regarding penalty points, the up ? down task was the most difficult to perform. Conclusions: The thumb control is more suitable for steerable instruments than the wrist control. To avoid uncontrolled movements and difficulties with applying forces to the tissue while keeping the tip of the instrument at the constant angle, adding a ‘‘locking’’ feature is necessary. It is advisable not to perform the needle driving task in the up down directionBiomechanical EngineeringMechanical, Maritime and Materials Engineerin

Multi-Modal Deep Learning to Understand Vision and Language

Developing intelligent agents that can perceive and understand the rich visual world around us has been a long-standing goal in the field of artificial intelligence. In the last few years, significant progress has been made towards this goal and deep learning has been attributed to recent incredible advances in general visual and language understanding. Convolutional neural networks have been used to learn image representations while recurrent neural networks have demonstrated the ability to generate text from visual stimuli. In this thesis, we develop methods and techniques using hybrid convolutional and recurrent neural network architectures that connect visual data and natural language utterances. Towards appreciating these methods, this work is divided into two broad groups. Firstly, we introduce a general purpose attention mechanism modeled using a continuous function for video understanding. The use of an attention based hierarchical approach along with automatic boundary detection advances state-of-the-art video captioning results. We also develop techniques for summarizing and annotating long videos. In the second part, we introduce architectures along with training techniques to produce a common connection space where natural language sentences are efficiently and accurately connected with visual modalities. In this connection space, similar concepts lie close, while dissimilar concepts lie far apart, irrespective` of their modality. We discuss four modality transformations: visual to text, text to visual, visual to visual and text to text. We introduce a novel attention mechanism to align multi-modal embeddings which are learned through a multi-modal metric loss function. The common vector space is shown to enable bidirectional generation of images and text. The learned common vector space is evaluated on multiple image-text datasets for cross-modal retrieval and zero-shot retrieval. The models are shown to advance the state-of-the-art on tasks that require joint processing of images and natural language

Visuo-spatial ability in colonoscopy simulator training

Visuo-spatial ability is associated with a quality of performance in a variety of surgical and medical skills. However, visuo-spatial ability is typically assessed using Visualization tests only, which led to an incomplete understanding of the involvement of visuo-spatial ability in these skills. To remedy this situation, the current study investigated the role of a broad range of visuo-spatial factors in colonoscopy simulator training. Fifteen medical trainees (no clinical experience in colonoscopy) participated in two psycho-metric test sessions to assess four visuo-spatial ability factors. Next, participants trained flexible endoscope manipulation, and navigation to the cecum on the GI Mentor II simulator, for four sessions within 1 week. Visualization, and to a lesser degree Spatial relations were the only visuo-spatial ability factors to correlate with colonoscopy simulator performance. Visualization additionally covaried with learning rate for time on task on both simulator tasks. High Visualization ability indicated faster exercise completion. Similar to other endoscopic procedures, performance in colonoscopy is positively associated with Visualization, a visuo-spatial ability factor characterized by the ability to mentally manipulate complex visuo-spatial stimuli. The complexity of the visuo-spatial mental transformations required to successfully perform colonoscopy is likely responsible for the challenging nature of this technique, and should inform training- and assessment design. Long term training studies, as well as studies investigating the nature of visuo-spatial complexity in this domain are needed to better understand the role of visuo-spatial ability in colonoscopy, and other endoscopic techniques