This book introduces the point cloud; its applications in industry, and the most frequently used datasets. It mainly focuses on three computer vision tasks -- point cloud classification, segmentation, and registration -- which are fundamental to any point cloud-based system. An overview of traditional point cloud processing methods helps readers build background knowledge quickly, while the deep learning on point clouds methods include comprehensive analysis of the breakthroughs from the past few years. Brand-new explainable machine learning methods for point cloud learning, which are lightweight and easy to train, are then thoroughly introduced. Quantitative and qualitative performance evaluations are provided. The comparison and analysis between the three types of methods are given to help readers have a deeper understanding. With the rich deep learning literature in 2D vision, a natural inclination for 3D vision researchers is to develop deep learning methods for point cloud processing. Deep learning on point clouds has gained popularity since 2017, and the number of conference papers in this area continue to increase. Unlike 2D images, point clouds do not have a specific order, which makes point cloud processing by deep learning quite challenging. In addition, due to the geometric nature of point clouds, traditional methods are still widely used in industry. Therefore, this book aims to make readers familiar with this area by providing comprehensive overview of the traditional methods and the state-of-the-art deep learning methods. A major portion of this book focuses on explainable machine learning as a different approach to deep learning. The explainable machine learning methods offer a series of advantages over traditional methods and deep learning methods. This is a main highlight and novelty of the book. By tackling three research tasks -- 3D object recognition, segmentation, and registration using our methodology -- readers will have a sense of how to solve problems in a different way and can apply the frameworks to other 3D computer vision tasks, thus give them inspiration for their own future research. Numerous experiments, analysis and comparisons on three 3D computer vision tasks (object recognition, segmentation, detection and registration) are provided so that readers can learn how to solve difficult Computer Vision problems.

Cooperative and relay communications have recently become the most widely explored topics in communications, whereby users cooperate in transmitting their messages to the destination, instead of conventional networks which operate independently and compete among each other for channel resources. As the field has progressed, cooperative communications have become a design concept rather than a specific transmission technology. This concept has revolutionized the design of wireless networks, allowing increased coverage, throughput, and transmission reliability even as conventional transmission techniques gradually reach their limits. Cooperative and relay technologies have also made their way toward next generation wireless standards, such as IEEE802.16 (WiMAX) or LTE, and have been incorporated into many modern wireless applications, such as cognitive radio and secret communications.

"Cooperative Communications and Networking: Technologies and System Design" provides a systematic introduction to the fundamental concepts of cooperative communications and relays technology to enable engineers, researchers or graduate students to conduct advanced research and development in this area.

"Cooperative Communications and Networking: Technologies and System Design" provides researchers, graduate students, and practical engineers with sufficient knowledge of both the background of cooperative communications and networking, and potential research directions.

With the fast growth ofmultimedia information, content-based video anal- ysis, indexing and representation have attracted increasing attention in re- cent years. Many applications have emerged in these areas such as video- on-demand, distributed multimedia systems, digital video libraries, distance learning/education, entertainment, surveillance and geographical information systems. The need for content-based video indexing and retrieval was also rec- ognized by ISOIMPEG, and a new international standard called "Multimedia Content Description Interface" (or in short, MPEG-7)was initialized in 1998 and finalized in September 2001. In this context, a systematic and thorough review ofexisting approaches as well as the state-of-the-art techniques in video content analysis, indexing and representation areas are investigated and studied in this book. In addition, we will specifically elaborate on a system which analyzes, indexes and abstracts movie contents based on the integration ofmultiple media modalities. Content ofeach part ofthis book is briefly previewed below. In the first part, we segment a video sequence into a set ofcascaded shots, where a shot consistsofone or more continuouslyrecorded image frames. Both raw and compressedvideo data will beinvestigated. Moreover, consideringthat there are always non-story units in real TV programs such as commercials, a novel commercial break detection/extraction scheme is developed which ex- ploits both audio and visual cues to achieve robust results. Specifically, we first employ visual cues such as the video data statistics, the camera cut fre- quency, and the existenceofdelimiting black frames between commercials and programs, to obtain coarse-level detection results.

Content-Based Audio Classification and Retrieval for Audiovisual Data Parsing is an up-to-date overview of audio and video content analysis. Included is extensive treatment of audiovisual data segmentation, indexing and retrieval based on multimodal media content analysis, and content-based management of audio data. In addition to the commonly studied audio types such as speech and music, the authors have included hybrid types of sounds that contain more than one kind of audio component such as speech or environmental sound with music in the background. Emphasis is also placed on semantic-level identification and classification of environmental sounds. The authors introduce a new generic audio retrieval system on top of the audio archiving schemes. Both theoretical analysis and implementation issues are presented. The developing MPEG-7 standards are explored. Content-Based Audio Classification and Retrieval for Audiovisual Data Parsing will be especially useful to researchers and graduate level students designing and developing fully functional audiovisual systems for audio/video content parsing of multimedia streams.

Due to the great success and enormous impact of IP networks, In ternet access (such as sending and receiving e-mails) and web brows ing have become the ruling paradigm for next generation wireless systems. On the other hand, great technological and commercial success of services and applications is being witnessed in mobile wire less communications with examples of cellular, pes voice telephony and wireless LANs. The service paradigm has thus shifted from the conventional voice service to seamlessly integrated high quality mul timedia transmission over broadband wireless mobile networks. The multimedia content may include data, voice, audio, image, video and so on. With availability of more powerful portable devices, such as PDA, portable computer and cellular phone, coupled with the easier access to the core network (using a mobile device), the number of mobile users and the demand for multimedia-based applications is increasing rapidly. As a result, there is an urgent need for a sys tem that supports heterogeneous multimedia services and provides seamless access to the desired resources via wireless connections. Therefore, the convergence of multimedia communication and wireless mobile networking technologies into the next generation wireless multimedia (WMM) networks with the vision of "anytime, anywhere, anyform" information system is the certain trend in the foreseeable future. However, successful combination of these two technologies presents many challenges such as available spectral bandwidth, energy efficiency, seamless end-to-end communication, robustness, security, etc."

Semantic Video Object Segmentation for Content-Based Multimedia Applications provides a thorough review of state-of-the-art techniques as well as describing several novel ideas and algorithms for semantic object extraction from image sequences. Semantic object extraction is an essential element in content-based multimedia services, such as the newly developed MPEG4 and MPEG7 standards. An interactive system called SIVOG (Smart Interactive Video Object Generation) is presented, which converts user's semantic input into a form that can be conveniently integrated with low-level video processing. Thus, high-level semantic information and low-level video features are integrated seamlessly into a smart segmentation system. A region and temporal adaptive algorithm was further proposed to improve the efficiency of the SIVOG system so that it is feasible to achieve nearly real-time video object segmentation with robust and accurate performances. Also included is an examination of the shape coding problem and the object segmentation problem simultaneously. Semantic Video Object Segmentation for Content-Based Multimedia Applications will be of great interest to research scientists and graduate-level students working in the area of content-based multimedia representation and applications and its related fields.

Cooperative and relay communications have recently become the most widely explored topics in communications, whereby users cooperate in transmitting their messages to the destination, instead of conventional networks which operate independently and compete among each other for channel resources. As the field has progressed, cooperative communications have become a design concept rather than a specific transmission technology. This concept has revolutionized the design of wireless networks, allowing increased coverage, throughput, and transmission reliability even as conventional transmission techniques gradually reach their limits. Cooperative and relay technologies have also made their way toward next generation wireless standards, such as IEEE802.16 (WiMAX) or LTE, and have been incorporated into many modern wireless applications, such as cognitive radio and secret communications. Cooperative Communications and Networking: Technologies and System Design provides a systematic introduction to the fundamental concepts of cooperative communications and relays technology to enable engineers, researchers or graduate students to conduct advanced research and development in this area. Cooperative Communications and Networking: Technologies and System Design provides researchers, graduate students, and practical engineers with sufficient knowledge of both the background of cooperative communications and networking, and potential research directions.

This book introduces various signal processing approaches to enhance physical layer secrecy in multi-antenna wireless systems. Wireless physical layer secrecy has attracted much attention in recent years due to the broadcast nature of the wireless medium and its inherent vulnerability to eavesdropping. While most articles on physical layer secrecy focus on the information-theoretic aspect, we focus specifically on the signal processing aspects, including beamforming and precoding techniques for data transmission and discriminatory training schemes for channel estimation. The discussions will cover cases with collocated and with distributed antennas, i.e., relays. The topics covered will be of interest to researchers in the signal processing community as well to practitioners and engineers working in this area. This book will also review recent works that apply these signal processing approaches to more advanced wireless systems, such as OFDM systems, multicell systems, cognitive radio, multihop networks etc. This will draw interest from researchers that wish to pursue the topic further in these new directions. This book is divided into three parts: (i) data transmission, (ii) channel estimation and (iii) advanced applications. Even though many works exist in the literature on these topics, the approaches and perspectives taken were largely diverse. This book provides a more organized and systematic view of these designs and to lay a solid foundation for future work in these areas. Moreover, by presenting the work from a signal processing perspective, this book will also trigger more research interest from the signal processing community and further advance the field of physical layer secrecy along the described directions. This book allows readers to gain basic understanding of works on physical layer secrecy, knowledge of how signal processing techniques can be applied to this area, and the application of these techniques in advanced wireless applications.

This book focuses on interactive segmentation techniques, which have been extensively studied in recent decades. Interactive segmentation emphasizes clear extraction of objects of interest, whose locations are roughly indicated by human interactions based on high level perception. This book will first introduce classic graph-cut segmentation algorithms and then discuss state-of-the-art techniques, including graph matching methods, region merging and label propagation, clustering methods, and segmentation methods based on edge detection. A comparative analysis of these methods will be provided with quantitative and qualitative performance evaluation, which will be illustrated using natural and synthetic images. Also, extensive statistical performance comparisons will be made. Pros and cons of these interactive segmentation methods will be pointed out, and their applications will be discussed. There have been only a few surveys on interactive segmentation techniques, and those surveys do not cover recent state-of-the art techniques. By providing comprehensive up-to-date survey on the fast developing topic and the performance evaluation, this book can help readers learn interactive segmentation techniques quickly and thoroughly.

Semantic Video Object Segmentation for Content-Based Multimedia Applications provides a thorough review of state-of-the-art techniques as well as describing several novel ideas and algorithms for semantic object extraction from image sequences. Semantic object extraction is an essential element in content-based multimedia services, such as the newly developed MPEG4 and MPEG7 standards. An interactive system called SIVOG (Smart Interactive Video Object Generation) is presented, which converts user's semantic input into a form that can be conveniently integrated with low-level video processing. Thus, high-level semantic information and low-level video features are integrated seamlessly into a smart segmentation system. A region and temporal adaptive algorithm was further proposed to improve the efficiency of the SIVOG system so that it is feasible to achieve nearly real-time video object segmentation with robust and accurate performances. Also included is an examination of the shape coding problem and the object segmentation problem simultaneously. Semantic Video Object Segmentation for Content-Based Multimedia Applications will be of great interest to research scientists and graduate-level students working in the area of content-based multimedia representation and applications and its related fields.

This book constitutes the refereed proceedings of the 4th International Symposium on Cyberspace Safety and Security (CSS 2012), held in Melbourne, Australia, in December 2012. The 30 revised full papers presented together with 7 invited talks were carefully reviewed and selected from 105 submissions. The papers cover the following topics: mobile security, cyberspace attacks and defense, security application adn systems, network and cloud security, wireless security, security protocols and models.

Due to the great success and enormous impact of IP networks, In ternet access (such as sending and receiving e-mails) and web brows ing have become the ruling paradigm for next generation wireless systems. On the other hand, great technological and commercial success of services and applications is being witnessed in mobile wire less communications with examples of cellular, pes voice telephony and wireless LANs. The service paradigm has thus shifted from the conventional voice service to seamlessly integrated high quality mul timedia transmission over broadband wireless mobile networks. The multimedia content may include data, voice, audio, image, video and so on. With availability of more powerful portable devices, such as PDA, portable computer and cellular phone, coupled with the easier access to the core network (using a mobile device), the number of mobile users and the demand for multimedia-based applications is increasing rapidly. As a result, there is an urgent need for a sys tem that supports heterogeneous multimedia services and provides seamless access to the desired resources via wireless connections. Therefore, the convergence of multimedia communication and wireless mobile networking technologies into the next generation wireless multimedia (WMM) networks with the vision of "anytime, anywhere, anyform" information system is the certain trend in the foreseeable future. However, successful combination of these two technologies presents many challenges such as available spectral bandwidth, energy efficiency, seamless end-to-end communication, robustness, security, etc.

Content-Based Audio Classification and Retrieval for Audiovisual Data Parsing is an up-to-date overview of audio and video content analysis. Included is extensive treatment of audiovisual data segmentation, indexing and retrieval based on multimodal media content analysis, and content-based management of audio data. In addition to the commonly studied audio types such as speech and music, the authors have included hybrid types of sounds that contain more than one kind of audio component such as speech or environmental sound with music in the background. Emphasis is also placed on semantic-level identification and classification of environmental sounds. The authors introduce a new generic audio retrieval system on top of the audio archiving schemes. Both theoretical analysis and implementation issues are presented. The developing MPEG-7 standards are explored. Content-Based Audio Classification and Retrieval for Audiovisual Data Parsing will be especially useful to researchers and graduate level students designing and developing fully functional audiovisual systems for audio/video content parsing of multimedia streams.

With the fast growth ofmultimedia information, content-based video anal- ysis, indexing and representation have attracted increasing attention in re- cent years. Many applications have emerged in these areas such as video- on-demand, distributed multimedia systems, digital video libraries, distance learning/education, entertainment, surveillance and geographical information systems. The need for content-based video indexing and retrieval was also rec- ognized by ISOIMPEG, and a new international standard called "Multimedia Content Description Interface" (or in short, MPEG-7)was initialized in 1998 and finalized in September 2001. In this context, a systematic and thorough review ofexisting approaches as well as the state-of-the-art techniques in video content analysis, indexing and representation areas are investigated and studied in this book. In addition, we will specifically elaborate on a system which analyzes, indexes and abstracts movie contents based on the integration ofmultiple media modalities. Content ofeach part ofthis book is briefly previewed below. In the first part, we segment a video sequence into a set ofcascaded shots, where a shot consistsofone or more continuouslyrecorded image frames. Both raw and compressedvideo data will beinvestigated. Moreover, consideringthat there are always non-story units in real TV programs such as commercials, a novel commercial break detection/extraction scheme is developed which ex- ploits both audio and visual cues to achieve robust results. Specifically, we first employ visual cues such as the video data statistics, the camera cut fre- quency, and the existenceofdelimiting black frames between commercials and programs, to obtain coarse-level detection results.

This book introduces the point cloud; its applications in industry, and the most frequently used datasets. It mainly focuses on three computer vision tasks -- point cloud classification, segmentation, and registration -- which are fundamental to any point cloud-based system. An overview of traditional point cloud processing methods helps readers build background knowledge quickly, while the deep learning on point clouds methods include comprehensive analysis of the breakthroughs from the past few years. Brand-new explainable machine learning methods for point cloud learning, which are lightweight and easy to train, are then thoroughly introduced. Quantitative and qualitative performance evaluations are provided. The comparison and analysis between the three types of methods are given to help readers have a deeper understanding. With the rich deep learning literature in 2D vision, a natural inclination for 3D vision researchers is to develop deep learning methods for point cloud processing. Deep learning on point clouds has gained popularity since 2017, and the number of conference papers in this area continue to increase. Unlike 2D images, point clouds do not have a specific order, which makes point cloud processing by deep learning quite challenging. In addition, due to the geometric nature of point clouds, traditional methods are still widely used in industry. Therefore, this book aims to make readers familiar with this area by providing comprehensive overview of the traditional methods and the state-of-the-art deep learning methods. A major portion of this book focuses on explainable machine learning as a different approach to deep learning. The explainable machine learning methods offer a series of advantages over traditional methods and deep learning methods. This is a main highlight and novelty of the book. By tackling three research tasks -- 3D object recognition, segmentation, and registration using our methodology -- readers will have a sense of how to solve problems in a different way and can apply the frameworks to other 3D computer vision tasks, thus give them inspiration for their own future research. Numerous experiments, analysis and comparisons on three 3D computer vision tasks (object recognition, segmentation, detection and registration) are provided so that readers can learn how to solve difficult Computer Vision problems.

This book offers an overview of traditional big visual data analysis approaches and provides state-of-the-art solutions for several scene comprehension problems, indoor/outdoor classification, outdoor scene classification, and outdoor scene layout estimation. It is illustrated with numerous natural and synthetic color images, and extensive statistical analysis is provided to help readers visualize big visual data distribution and the associated problems. Although there has been some research on big visual data analysis, little work has been published on big image data distribution analysis using the modern statistical approach described in this book. By presenting a complete methodology on big visual data analysis with three illustrative scene comprehension problems, it provides a generic framework that can be applied to other big visual data analysis tasks.

This book constitutes the proceedings of the 8th International Conference on Network and System Security, NSS 2014, held in Xi'an, China, in October 2014. The 35 revised full papers and 12 revised short papers presented were carefully reviewed and selected from 155 initial submissions. The papers are organized in topical sections on cloud computing, access control, network security, security analysis, public key cryptography, system security, privacy-preserving systems and biometrics, and key management and distribution.

The book encompasses the state-of-the-art visual quality assessment (VQA) and learning based visual quality assessment (LB-VQA) by providing a comprehensive overview of the existing relevant methods. It delivers the readers the basic knowledge, systematic overview and new development of VQA. It also encompasses the preliminary knowledge of Machine Learning (ML) to VQA tasks and newly developed ML techniques for the purpose. Hence, firstly, it is particularly helpful to the beginner-readers (including research students) to enter into VQA field in general and LB-VQA one in particular. Secondly, new development in VQA and LB-VQA particularly are detailed in this book, which will give peer researchers and engineers new insights in VQA.