Minghui Liwang

A novel retinex based approach for image enhancement with illumination adjustment

Retinex based algorithms have been widely used among in image enhancement. Since many retinex based algorithms remove illumination and regard the reflectance as enhancement, over-enhancement and unnaturalness are inevitable. In this paper, a novel retinex based image enhancement using illumination adjustment is proposed. Different from existing variational retinex models, a new model without the logarithmic transformation is established and can well preserve the edge. A fast alternating direction optimization method is used to solve this problem. After the decomposition of illumination and reflectance, a simple and effective post-processing method for illumination adjustment is adopted for the enhancement to make the result more natural. The proposed method can deal with many kinds of image, such as high dynamic range (HDR) images and non-uniform illumination images. Experimental results illustrate that the naturalness can be preserved while details are enhanced by the presented new approach.

An enhanced GPSR routing protocol based on the buffer length of nodes for the congestion problem in VANETs

Vehicular ad hoc networks technology is an important part of intelligent transportation system which has wide developmental and applied prospect. The routing protocol is a key technology of vehicular ad hoc networks and largely determines the performance of the vehicle network communication system. Vehicular ad hoc networks (VANETs) makes great requests to routing protocol due to several reasons: firstly, vehicle nodes moving into tubular mobile states which are limited in road. Then, nodes moves fast, network topology changes frequently, the relationship among nodes becomes unstable. Aiming at the network congestion problem exists in commonly used GPSR routing protocols of current vehicular ad hoc networks, this paper proposed an improved project of GPSR routing protocol which controls network congestion based on the buffer of nodes. Performance simulation for relevant parameters is tested on platform NS2. From the results of the analysis, the enhanced GPSR protocol in vehicles environment has lower packet loss rate and less time delay than the traditional GPSR protocol.

OOP-based Device-to-Device Communication simulator design of LTE network

Device-to-Device (D2D) Communication is an innovative mobile network technology, which is one of the key technologies in LTE-Advanced system. D2D communication can realize cellular users directly connect to transfer data under the eNodeBs control. This technology brings higher system capacity and mitigates the eNodeBs load. In order to evaluate the performance of this new technology, D2D communication system level simulation based in LTE network is needed. In this paper, we design D2D simulator module based on LTE system level simulator which is developed by Vienna University of Technology. This module realizes initialization of the LTE-D2D network, measurement of interference to cellular users caused by D2D users and link quality for D2D users. We can design algorithm of radio resource allocation including power and frequency for D2D users and valuate the LTE-D2D network performance in this simulator.

A new multipath AODV routing protocol for VANET based on expected link lifetime

In Vehicular Ad Hoc Networks (VANETs), the node mobility leads to network topology changed frequently. Due to the vehicle movement, the limited wireless resources and channel fading, the routing protocols in Mobile Ad Hoc Networks MANETs are not suitable for VANETs. In this paper we propose a new multipath route protocol based on Ad hoc On-Demand Distance Vector Routing (AODV) for adapting VANETs environment called Enhanced Multipath AODV (EMP-AODV). EMP-AODV use a node-disjoint multipath strategy to set up routing and choose routing to transmission based on a link expected link lifetime algorithm. The simulation results show that our proposed protocol reduces the end-to-end delay and packet loss ratio while improving the network throughput in VANETs.

Hybrid Quantum-Behaved Particle Swarm Optimization for Mobile-Edge Computation Offloading in Internet of Things

Mobile edge computing (MEC) is a technology that transfers resource to the edge of network, which spares more attention to giving users easier access to network and computation resources. Due to the large amount of data computation needed for devices in Internet of Things, MEC technology is applied to improve computing efficiency. Though MEC can be applied to the Internet of Things, it needs further consideration on how to efficiently and reasonably allocate computing resources, and how to minimize the computing time of all users. This paper proposes a computing resources allocation scheme based on hybrid quantum-behaved particle swarm optimization. Simulation experiments with the network environment based on the Internet of Things is carried out. The results show that this algorithm can accelerate the whole computing process and reduce the number of iterations.

Real-time detection algorithm of abnormal behavior in crowds based on Gaussian mixture model

Recently, abnormal evens detection in crowds has received considerable attention in the field of public safety. Most existing studies do not account for the processing time and the continuity of abnormal behavior characteristics. In this paper, we present a new motion feature descriptor, called the sensitive movement point (SMP). Gaussian Mixture Model (GMM) is used for modeling the abnormal crowd behavior with full consideration of the characteristics of crowd abnormal behavior. First, we analyze the video with GMM, to extract sensitive movement point in certain speed by setting update threshold value of GMM. Then, analyze the sensitive movement point of video frame with temporal and spatial modeling. Identify abnormal behavior through the analysis of mutation duration occurs in temporal and spatial model, and the density, distribution and mutative acceleration of sensitive movement point in blocks. The algorithm can be implemented with automatic adapt to environmental change and online learning, without tracking individuals of crowd and large scale training in detection process. Experiments involving the UMN datasets and the videos taken by us show that the proposed algorithm can real-time effectively identify various types of anomalies and that the recognition results and processing time are better than existing algorithms.

A routing protocol based on improved Ad-hoc on demand multi-path distance vector in multi-radio network

Vehicular Ad Hoc Network(VANET) plays an important and indispensable part in the Intelligent Transport System(ITS). The QoS required by data transmission becomes higher due to the moving characteristic of nodes and the increasing of business types in VANET. Hence, the technology of Multi-Radio Multi-Channel has been applied in VANET in order to provide a more efficient network performance. On account of the difference between link channel qualities in the same route, a routing protocol based on IAOMDV(Improved Ad-hoc on Demand Multipath Distance Vector Routing) is proposed in this paper to make the routing mechanism in Multi-Radio network fairer and more reasonable. The simulation platform of Network performance is set based on the Manhattan Grid Model thus to build a real simulation scenario. The indicators like time delay, packet loss rate, network throughput and routing overhead of the proposed method are compared with other frequently-used routing protocols. As a result, the improved routing protocol is obviously better suited for the Multi-Radio VANET and can provide better performance for data transmission.