Lab Meeting September 25th, 2014(Bang-Cheng Wang): Strategies for Adjusting the ZMP Reference Trajectory for Maintaining Balance in Humanoid Walking

Title: Strategies for Adjusting the ZMP Reference Trajectory for Maintaining Balance in Humanoid Walking

Authors: Koichi Nishiwaki and Satoshi Kagami

Abstract:
The present paper addresses strategies of changing the reference trajectories of the future ZMP that are used for online repetitive walking pattern generation. Walking pattern generation operates with a cycle of 20 [ms], and the reference ZMP trajectory is adjusted according to the current actual motion status in order to maintain the current balance. Three different strategies are considered for adjusting the ZMP. The first strategy is to change the reference ZMP inside the sole area. The second strategy is to change the position of the next step, and the third strategy is to change the duration of the current step. The manner in which these changes affect the current balance and how to combine the three strategies are discussed. The proposed methods are implemented as part of an online walking control system with short cycle pattern generation and are evaluated using the HRP-2 full-sized humanoid robot.

2010 IEEE International Conference on Robotics and Automation

http://ieeexplore.ieee.org/xpl/abstractMultimedia.jsp?arnumber=5510002

Lab Meeting September 18th, 2014(Gene): A Survey on Clustering Algorithms for Wireless Sensor Networks

Title:  A Survey on Clustering Algorithms for Wireless Sensor Networks
Authors: Boyinbode, Olutayo, Hanh Le, and Makoto Takizawa

Abstract: 
A wireless sensor network (WSN) consisting of a large number of tiny sensors can be an effective tool for gathering data in diverse kinds of environments. The data collected by each sensor is communicated to the base station, which forwards the data to the end user. Clustering is introduced to WSNs because it hasproven to be an effective approach to provide better data aggregation and scalability for large WSNs. Clustering also conserves the limited energy resources of the sensors. This paper synthesises existing clustering algorithms in WSNs and highlights the challenges in clustering.

2010 13th International Conference on Network-Based Information Systems

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5636152

Lab Meeting September 11th, 2014(Zhi-Qiang): DeepFlow Large displacement optical flow with deep matching

Title:  DeepFlow Large displacement optical flow with deep matching

Authors:  Philippe Weinzaepfel, Jerome Revaud, Zaid Harchaoui, Cordelia Schmid

Abstract: 

Optical flow computation is a key component in many computer vision systems designed for tasks such as action detection or activity recognition. However, despite several major advances over the last decade, handling large displacement in optical flow remains an open problem. Inspired by the large displacement optical flow of Brox and Malik, our approach, termed Deep Flow, blends a matching algorithm with a variational approach for optical flow. We propose a descriptor matching algorithm, tailored to the optical flow problem, that allows to boost performance on fast motions. The matching algorithm builds upon a multi-stage architecture with 6 layers, interleaving convolutions and max-pooling, a construction akin to deep convolutional nets. Using dense sampling, it allows to efficiently retrieve quasi-dense correspondences, and enjoys a built-in smoothing effect on descriptors matches, a valuable asset for integration into an energy minimization framework for optical flow estimation. Deep Flow efficiently handles large displacements occurring in realistic videos, and shows competitive performance on optical flow benchmarks. Furthermore, it sets a new state-of-the-art on the MPI-Sintel dataset.

Computer Vision (ICCV), 2013 IEEE International Conference on

http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6751282