As the nodes of AWSN (Aerial Wireless Sensor Networks) fly around, the network topology changes frequently with high energy consumption and high cluster head mortality, and some sensor nodes may fly away from the original cluster and interrupt network communication. To ensure the normal communication of the network, this paper proposes an improved LEACH-M protocol for aerial wireless sensor networks. The protocol is improved based on the traditional LEACH-M protocol and MCR protocol. A Cluster head selection method based on maximum energy and an efficient solution for outlier nodes is proposed to ensure that cluster heads can be replaced prior to their death and ensure outlier nodes re-home quickly and efficiently. The experiments show that, compared with the LEACH-M protocol and MCR protocol, the improved LEACH-M protocol performance is significantly optimized, increasing network data transmission efficiency, improving energy utilization, and extending network lifetime.

As a first step to develop a system to analyze or recognize patterns contained in mages, it is important to provide a good base representation that can facilitate efficiently the interpretation of such patterns. Since structural features of basic patterns in document images such as characters or tables are horizontal and vertical stroke components, we propose a new expression of document image based on the MCR expression that can express well such features of text and tabular components of an image.

In this paper we introduce a new feature called stroke index for document image analysis. It is based on the minimum covering run expression method (MCR). This stroke index is a function of the number of horizontal and vertical runs in the original image and of number of runs by the MCR expression. As document images may present a variety of patterns such as graph, text or picture, it is necessary for image understanding to classify these different patterns into categories beforehand. Here we show how one could use this stroke index for such applications as classification or segmentation. It also gives an insight on the possibility of stroke extraction from document images in addition to classifying different patterns in a compound image.

The Minimum Covering Run (MCR) expression used for representing binary images has been proposed [1]-[3]. The MCR expression is an adaptation from horizontal and vertical run expression. In the expression, some horizontal and vertical runs are used together for representing binary images in which total number of them is minimized. It was shown that, sets of horizontal and vertical runs representing any binary image could be viewed as partite sets of a bipartite graph, then the MCR expression of binary images was found analogously by constructing a maximum matching as well as a minimum covering in the corresponding graph. In the original algorithm, the most efficient algorithm, proposed by Hopcroft, solving the graph-theoretical problems mentioned above, associated with the Rectangular Segment Analysis (RSA) was used for finding the MCR expression. However, the original algorithm still suffers from a long processing time. In this paper, we propose two new efficient MCR algorithms that are beneficial to a practical implementation. The new algorithms are composed of two main procedures; i.e., Partial Segment Analysis (PSA) and construction of a maximum matching. It is shown in this paper that the first procedure which is directly an improvement to the RSA, appoints well a lot of representative runs of the MCR expression in regions of text and line drawing. Due to the PSA, the new algorithms reduce the number of runs used in the technique of solving the matching problem in corresponding graphs so that satisfactory processing time can be obtained. To clarify the validity of new algorithms proposed in this paper, the experimental results show the comparative performance of the original and new algorithms in terms of processing time.

An efficient technique for expressing document image is required as part of a unified approach to document image processing. This paper presents a new method, Minimum Covering Run (MCR), for expressing binary images. The name being adapted from horizontal or vertical run representation. The proposed technique uses some horizontal and vertical runs together to represent binary images in which the total number of representative runs is minimized. Considering the characteristic of above run types precisely, it is shown that horizontal and vertical runs of any binary image could be thought of as partite sets of a bipartite graph. Consequently, the MCR expression that corresponds to the construction of one of the most interesting problems in graphs; i.e., maximum matching, is analogously found by using an algorithm which solves this problem in a corresponding graph. The most efficient algorithm takes at most O(n5/2) computations for solving the problem where n is the sum of cardinalities of both partite sets. However, some patterns in images like tables or line drowings, generally, have a large number of runs representing them which results in a long processing time. Therefore, we provide the Rectangular Segment Analysis (RSA) as a pre-processing to define runs representing such patterns beforehand. We also show that horizontal and vertical covering parts of the proposed expression are able to represent stroke components of characters in document images. As an implementation, an efficient algorithm including arrangement for run data structure of the MCR expression is presented. The experimental results show the possibility of stroke extraction of characters in document images. As an application, some patterns such as tables can be extracted from document images.