Multi-Scale Attention Networks for Pavement Defect Detection

Pavement defects such as cracks, net cracks, and pit slots can cause potential traffic safety problems. The timely detection and identification play a key role in reducing the harm of various pavement defects. Particularly, the recent development in deep learning-based CNNs has shown competitive performance in image detection and classification. To detect pavement defects automatically and improve effects, a multi-scale mobile attention-based network, which we termed MANet, is proposed to perform the detection of pavement defects. The architecture of the encoder-decoder is used in MANet, where the encoder adopts the MobileNet as the backbone network to extract pavement defect features. Instead of the original 3×3 convolution, the multi-scale convolution kernels are utilized in depth-wise separable convolution layers of the network. Further, the hybrid attention mechanism is separately incorporated into the encoder and decoder modules to infer the significance of spatial points and inter-channel relationship features for the input intermediate feature maps. The proposed approach achieves state-of-the-art performance on two publicly-available benchmark datasets, i.e., the Crack500 (500 crack images with 2,000×1,500 pixels) and CFD (118 crack images with 480×320 pixels) datasets. The mean intersection over union ( MIoU ) of the proposed approach on these two datasets reaches 0.7219 and 0.7788, respectively. Ablation experiments show that the multi-scale convolution and hybrid attention modules can effectively help the model extract high-level feature representations and generate more accurate pavement crack segmentation results. We further test the model on locally collected pavement crack images (131 images with 1024×768 pixels) and it achieves a satisfactory result. The proposed approach realizes the MIoU of 0.6514 on the local dataset and outperforms other compared baseline methods. Experimental findings demonstrate the validity and feasibility of the proposed approach and it provides a viable solution for pavement crack detection in practical application scenarios. Our code is available at https://github.com/xtu502/pavement-defects

Improving the Performance of Wireless LANs

This book quantifies the key factors of WLAN performance and describes methods for improvement. It provides theoretical background and empirical results for the optimum planning and deployment of indoor WLAN systems, explaining the fundamentals while supplying guidelines for design, modeling, and performance evaluation. It discusses environmental effects on WLAN systems, protocol redesign for routing and MAC, and traffic distribution; examines emerging and future network technologies; and includes radio propagation and site measurements, simulations for various network design scenarios, numerous illustrations, practical examples, and learning aids

Coherent, automatic address resolution for vehicular ad hoc networks

Published in: Int. J. of Ad Hoc and Ubiquitous Computing, 2017 Vol.25, No.3, pp.163 - 179. DOI: 10.1504/IJAHUC.2017.10001935The interest in vehicular communications has increased notably. In this paper, the use of the address resolution (AR) procedures is studied for vehicular ad hoc networks (VANETs). We analyse the poor performance of AR transactions in such networks and we present a new proposal called coherent, automatic address resolution (CAAR). Our approach inhibits the use of AR transactions and instead increases the usefulness of routing signalling to automatically match the IP and MAC addresses. Through extensive simulations in realistic VANET scenarios using the Estinet simulator, we compare our proposal CAAR to classical AR and to another of our proposals that enhances AR for mobile wireless networks, called AR+. In addition, we present a performance evaluation of the behaviour of CAAR, AR and AR+ with unicast traffic of a reporting service for VANETs. Results show that CAAR outperforms the other two solutions in terms of packet losses and furthermore, it does not introduce additional overhead.Postprint (published version

Very Deep Convolutional Neural Networks for Robust Speech Recognition

This paper describes the extension and optimization of our previous work on very deep convolutional neural networks (CNNs) for effective recognition of noisy speech in the Aurora 4 task. The appropriate number of convolutional layers, the sizes of the filters, pooling operations and input feature maps are all modified: the filter and pooling sizes are reduced and dimensions of input feature maps are extended to allow adding more convolutional layers. Furthermore appropriate input padding and input feature map selection strategies are developed. In addition, an adaptation framework using joint training of very deep CNN with auxiliary features i-vector and fMLLR features is developed. These modifications give substantial word error rate reductions over the standard CNN used as baseline. Finally the very deep CNN is combined with an LSTM-RNN acoustic model and it is shown that state-level weighted log likelihood score combination in a joint acoustic model decoding scheme is very effective. On the Aurora 4 task, the very deep CNN achieves a WER of 8.81%, further 7.99% with auxiliary feature joint training, and 7.09% with LSTM-RNN joint decoding.Comment: accepted by SLT 201

Effective bootstrapping of Peer-to Peer networks over Mobile Ad-hoc networks

Mobile Ad-hoc Networks (MANETs) and Peer-to-Peer (P2P) networks are vigorous, revolutionary communication technologies in the 21st century. They lead the trend of decentralization. Decentralization will ultimately win clients over client/server model, because it gives ordinary network users more control, and stimulates their active participation. It is a determinant factor in shaping the future of networking. MANETs and P2P networks are very similar in nature. Both are dynamic, distributed. Both use multi-hop broadcast or multicast as major pattern of traffic. Both set up connection by self-organizing and maintain connection by self-healing. Embodying the slogan networking without networks, both abandoned traditional client/server model and disclaimed pre-existing infrastructure. However, their status quo levels of real world application are widely divergent. P2P networks are now accountable for about 50 ~ 70% internet traffic, while MANETs are still primarily in the laboratory. The interesting and confusing phenomenon has sparked considerable research effort to transplant successful approaches from P2P networks into MANETs. While most research in the synergy of P2P networks and MANETs focuses on routing, the network bootstrapping problem remains indispensable for any such transplantation to be realized. The most pivotal problems in bootstrapping are: (1) automatic configuration of nodes addresses and IDs, (2) topology discovery and transformation in different layers and name spaces. In this dissertation research, we have found novel solutions for these problems. The contributions of this dissertation are: (1) a non-IP, flat address automatic configuration scheme, which integrates lower layer addresses and P2P IDs in application layer and makes simple cryptographical assignment possible. A related paper entitled Pastry over Ad-Hoc Networks with Automatic Flat Address Configuration was submitted to Elsevier Journal of Ad Hoc Networks in May. (2) an effective ring topology construction algorithm which builds perfect ring in P2P ID space using only simplest multi-hop unicast or multicast. Upon this ring, popular structured P2P networks like Chord, Pastry could be built with great ease. A related paper entitled Chord Bootstrapping on MANETs - All Roads lead to Rome will be ready for submission after defense of the dissertation

Mobile Ad Hoc Networks

Guiding readers through the basics of these rapidly emerging networks to more advanced concepts and future expectations, Mobile Ad hoc Networks: Current Status and Future Trends identifies and examines the most pressing research issues in Mobile Ad hoc Networks (MANETs). Containing the contributions of leading researchers, industry professionals, and academics, this forward-looking reference provides an authoritative perspective of the state of the art in MANETs. The book includes surveys of recent publications that investigate key areas of interest such as limited resources and the mobility of mobile nodes. It considers routing, multicast, energy, security, channel assignment, and ensuring quality of service. Also suitable as a text for graduate students, the book is organized into three sections: Fundamentals of MANET Modeling and Simulation—Describes how MANETs operate and perform through simulations and models Communication Protocols of MANETs—Presents cutting-edge research on key issues, including MAC layer issues and routing in high mobility Future Networks Inspired By MANETs—Tackles open research issues and emerging trends Illustrating the role MANETs are likely to play in future networks, this book supplies the foundation and insight you will need to make your own contributions to the field. It includes coverage of routing protocols, modeling and simulations tools, intelligent optimization techniques to multicriteria routing, security issues in FHAMIPv6, connecting moving smart objects to the Internet, underwater sensor networks, wireless mesh network architecture and protocols, adaptive routing provision using Bayesian inference, and adaptive flow control in transport layer using genetic algorithms

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Smart Sensor Technologies for IoT

The recent development in wireless networks and devices has led to novel services that will utilize wireless communication on a new level. Much effort and resources have been dedicated to establishing new communication networks that will support machine-to-machine communication and the Internet of Things (IoT). In these systems, various smart and sensory devices are deployed and connected, enabling large amounts of data to be streamed. Smart services represent new trends in mobile services, i.e., a completely new spectrum of context-aware, personalized, and intelligent services and applications. A variety of existing services utilize information about the position of the user or mobile device. The position of mobile devices is often achieved using the Global Navigation Satellite System (GNSS) chips that are integrated into all modern mobile devices (smartphones). However, GNSS is not always a reliable source of position estimates due to multipath propagation and signal blockage. Moreover, integrating GNSS chips into all devices might have a negative impact on the battery life of future IoT applications. Therefore, alternative solutions to position estimation should be investigated and implemented in IoT applications. This Special Issue, “Smart Sensor Technologies for IoT” aims to report on some of the recent research efforts on this increasingly important topic. The twelve accepted papers in this issue cover various aspects of Smart Sensor Technologies for IoT