Throughput Analysis of CSMA Wireless Networks with Finite Offered-load

This paper proposes an approximate method, equivalent access intensity (EAI), for the throughput analysis of CSMA wireless networks in which links have finite offered-load and their MAC-layer transmit buffers may be empty from time to time. Different from prior works that mainly considered the saturated network, we take into account in our analysis the impacts of empty transmit buffers on the interactions and dependencies among links in the network that is more common in practice. It is known that the empty transmit buffer incurs extra waiting time for a link to compete for the channel airtime usage, since when it has no packet waiting for transmission, the link will not perform channel competition. The basic idea behind EAI is that this extra waiting time can be mapped to an equivalent "longer" backoff countdown time for the unsaturated link, yielding a lower link access intensity that is defined as the mean packet transmission time divided by the mean backoff countdown time. That is, we can compute the "equivalent access intensity" of an unsaturated link to incorporate the effects of the empty transmit buffer on its behavior of channel competition. Then, prior saturated ideal CSMA network (ICN) model can be adopted for link throughput computation. Specifically, we propose an iterative algorithm, "Compute-and-Compare", to identify which links are unsaturated under current offered-load and protocol settings, compute their "equivalent access intensities" and calculate link throughputs. Simulation shows that our algorithm has high accuracy under various offered-load and protocol settings. We believe the ability to identify unsaturated links and compute links throughputs as established in this paper will serve an important first step toward the design and optimization of general CSMA wireless networks with offered-load control.Comment: 6 pages. arXiv admin note: text overlap with arXiv:1007.5255 by other author

A study of a clothing image segmentation method in complex conditions using a features fusion model

According to a priori knowledge in complex conditions, this paper proposes an unsupervised image segmentation algorithm to be used for clothing images that combines colour and texture features. First, block truncation encoding is used to divide the traditional three-dimensional colour space into a six-dimensional colour space so that more fine colour features can be obtained. Then, a texture feature based on the improved local binary pattern (LBP) algorithm is designed and used to describe the clothing image with the colour features. After that, according to the statistical appearance law of the object region and background information in the clothing image, a bisection method is proposed for the segmentation operation. Since the image is divided into several subimage blocks, bisection image segmentation will be accomplished more efficiently. The experimental results show that the proposed algorithm can quickly and effectively extract effective clothing regions from complex circumstances without any artificial parameters. The proposed clothing image segmentation method will play an important role in computer vision, machine learning applications, pattern recognition and intelligent systems

National Mangrove Restoration Project in Malaysia

Mangrove forests are unique ecosystem in the world. The importance of mangrove forests in providing invaluable goods and services in economics, social and environmental terms are well understood. However, there is a trend that the mangrove forests were lost due to human activities such as direct conversion of mangroves to aquaculture, agriculture and urban land uses. In realizing the importance of mangrove and the threat towards mangrove forests, there is a need to restore and conserve mangrove areas. This paper highlights the overview, progress and challenges in the implementation of national mangrove restoration projects in Malaysia through “Tree Planting Program with Mangroves and Other Suitable Species Along National Coastlines”, which has been implemented since 2005 and involved a strategic integrated approach. As of December 2015, a total area of about 2,605 hectares had been planted with more than 6.3 million trees. To ensure the sustainability and success of this project, a continuous financial support is needed to help in implementing and maintaining the existing restoration areas. Realizing that this project will enhance the livelihood and socioeconomic well-being of the local communities, the paper recommends that the areas involved in mangrove restoration project to be gazette as a Permanent Reserve Forest. Keywords: mangrove forest, mangrove Malaysia, mangrove restoration project

[5-Chloro-2-hy­droxy-N′-(2-oxidobenzyl­idene)benzohydrazidato]dimethyl­tin(IV)

In the title compound, [Sn(CH3)2(C14H9ClN2O3)], the SnIV ion is coordinated by one N and two O atoms from the tridentate 5-chloro-2-hy­droxy-N′-(2-oxidobenzyl­idene)benzohydrazidate (L) ligand and two methyl groups in a distorted trigonal–bipyramidal geometry. In the ligand, the hy­droxy group is involved in an intra­molecular O—H⋯N hydrogen bond and the two aromatic rings form a dihedral angle of 5.5 (1)°. In the crystal, weak inter­molecular C—H⋯O hydrogen bonds and π–π inter­actions between the aromatic rings [centroid–centroid distance = 3.816 (3) Å] link the mol­ecules into centrosymmetric dimers

Identifying Influential Users Of Micro-Blogging Services: A Dynamic Action-Based Network Approach

In this paper, we present a dynamic model to identify influential users of micro-blogging services. Micro-blogging services, such as Twitter, allow their users (twitterers) to publish tweets and choose to follow other users to receive tweets. Previous work on user influence on Twitter, concerns more on following link structure and the contents user published, seldom emphasizes the importance of interactions among users. We argue that, by emphasizing on user actions in micro-blogging platform, user influence could be measured more accurately. Since micro-blogging is a powerful social media and communication platform, identifying influential users according to user interactions has more practical meanings, e.g., advertisers may concern how many actions – buying, in this scenario – the influential users could initiate rather than how many advertisements they spread. By introducing the idea of PageRank algorithm, innovatively, we propose our model using action-based network which could capture the ability of influential users when they interacting with micro-blogging platform. Taking the evolving prosperity of micro-blogging into consideration, we extend our action-based user influence model into a dynamic one, which could distinguish influential users in different time periods. Simulation results demonstrate that our models could support and give reasonable explanations for the scenarios that we considered

Neural Network Observer-Based Finite-Time Formation Control of Mobile Robots

This paper addresses the leader-following formation problem of nonholonomic mobile robots. In the formation, only the pose (i.e., the position and direction angle) of the leader robot can be obtained by the follower. First, the leader-following formation is transformed into special trajectory tracking. And then, a neural network (NN) finite-time observer of the follower robot is designed to estimate the dynamics of the leader robot. Finally, finite-time formation control laws are developed for the follower robot to track the leader robot in the desired separation and bearing in finite time. The effectiveness of the proposed NN finite-time observer and the formation control laws are illustrated by both qualitative analysis and simulation results