利用曲线拟合对触觉数据的压缩

为减轻离线触觉数据压缩带来的失真变形,提出利用曲线拟合的压缩方法.在假定触觉数据所对应原触觉信号能用分段二次参数曲线表示的前提下,通过反向采样过程来构造触觉数据拟合曲线,该曲线由分段二次曲线组成,在连接处达到切线连续,且每段二次曲线对原触觉信号具有二次多项式逼近精度.为避免保存拟合曲线的所有系数以进一步提高数据减少率,对拟合曲线系数进行了迭代求解,从而只需编码保存部分系数即可解码.实验结果表明,该方法在保持高数据减少率的前提下减轻了压缩失真变形.     

Strong Convergence Analysis of Batch Gradient-Based Learning Algorithm for Training Pi-Sigma Network Based on TSK Fuzzy Models

By combining of the benefits of high-order network and TSK (Tagaki-Sugeno-Kang) inference system, Pi-Sigma network is capable to dispose with the nonlinear problems much more effectively, which means it has a compacter construction, and quicker computational speed. The aim of this paper is to present a gradient-based learning method for Pi-Sigma network to train TSK fuzzy inference system. Moreover, some strong convergence results are established based on the weak convergence outcomes, which indicates that the sequence of weighted fuzzy parameters gets to a fixed point. Simulation results show the modified learning algorithm is effective to support the theoretical results.     

Recover Fault Services via Complex Service-to-Node Mappings in Wireless Sensor Networks

With the motivation of seamlessly extending wireless sensor networks to the external environment, service-oriented architecture comes up as a promising solution. However, as sensor nodes are failure prone, this consequently renders the whole wireless sensor network to seriously faulty. When a particular node is faulty, the service on it should be migrated into those substitute sensor nodes that are in a normal status. Currently, two kinds of approaches exist to identify the substitute sensor nodes: the most common approach is to prepare redundancy nodes, though the involved tasks such as maintaining redundancy nodes, i.e., relocating the new node, lead to an extra burden on the wireless sensor networks. More recently, other approaches without using redundancy nodes are emerging, and they merely select the substitute nodes in a sensor node’s perspective i.e., migrating the service of faulty node to it’s nearest sensor node, though usually neglecting the requirements of the application level. Even a few work consider the need of the application level, they perform at packets granularity and don’t fit well at service granularity. In this paper, we aim to remove these limitations in the wireless sensor network with the service-oriented architecture. Instead of deploying redundancy nodes, the proposed mechanism replaces the faulty sensor node with consideration of the similarity on the application level, as well as on the sensor level. On the application level, we apply the Bloom Filter for its high efficiency and low space costs. While on the sensor level, we design an objective solution via the coefficient of a variation as an evaluation for choosing the substitute on the sensor level.     

依托网络教学平台 提高课堂教学效果

网络教学作为一种新的辅助教学手段,是目前教学改革主要研究的方向之一。通过对网络教学平台的功能分析,探讨了在网络教学平台上进行教学资源的建设、交流互动教学活动的开展和虚拟实验室的建设,通过应用实践证明,充分利用网络教学平台的优势,能有效地提高课堂教学质量和效率,培养学生的学习能力、实践能力和创新能力。