Procrustes analysis is a useful technique useful to measure, compare shape differences and estimate a mean shape for objects; however it is based on a least squares criterion and is affected by some outliers. Therefore, we propose two generalized Procrustes analysis methods based on M-estimation and least median of squares estimation that are resistant to object outliers. In addition, two algorithms are given for practical implementation. A simulation study and some examples are used to examine and compared the performances of the algorithms with the least square method. Moreover since these resistant GPA methods are available for higher dimensions, we need some methods to visualize the objects and mean shape effectively. Also since we have concentrated on resistant fitting methods without considering shape distributions, we wish to shape analysis not be sensitive to particular model.

Distances or dissimilarities among units are assumed to be symmetric in most cases of multidimensional scaling(MDS); consequently, it is not an easy task to deal with asymmetric distances. Current asymmetric MDS still face difficulties in the interpretation of results. This study proposes a simpler asymmetric MDS that utilizes the order statistic of an asymmetric matrix. The proposed Web method demonstrates that some influences among objects are visualized by direction, size and shape of arrow to ease the interpretability of users.

An algorithm for enhancing the throughput of CAS (Cooperation-Aware Scheme) over a WSN (wireless sensor network) is presented in this article. The WSNs are classified as a task-oriented framework due to the fact that the power supply, memory and computing power of wireless sensor nodes are greatly hampered in WSNs. This study focused on exploring problems caused by traffic congestion on the WSNs with a large amount of flow, such as packet loss, bandwidth reduction, and waste of energy on the sensor nodes. On the other hand, the proposed CAS is a cooperative strong node mechanism in which a threshold is preset in order to determine whether the node traffic is over or not. The privilege of corresponding sensor nodes is upgraded when the load exceeds the threshold. Therefore, the sensor node can command its child nodes to change the transmission path for distributing the traffic effectively. Moreover, once the traffic is over the overall network flow threshold, it is necessary to add the other new sensor nodes into the network for relieving the traffic.