Geo-tagging and privacy-preservation in mobile cloud computing

With the emerge of the cloud computing service and the explosive growth of the mobile devices and applications, mobile computing technologies and cloud computing technologies have been drawing significant attentions. Mobile cloud computing, with the synergy between the cloud and mobile technologies, has brought us new opportunities to develop novel and practical systems such as mobile multimedia systems and cloud systems that provide collaborative data-mining services for data from disparate owners (e.g., mobile users). However, it also creates new challenges, e.g., the algorithms deployed in the computationally weak mobile device require higher efficiency, and introduces new problems such as the privacy concern when the private data is shared in the cloud for collaborative data-mining. The main objectives of this dissertation are: 1. to develop practical systems based on the unique features of mobile devices (i.e., all-in-one computing platform and sensors) and the powerful computing capability of the cloud; 2. to propose solutions protecting the data privacy when the data from disparate owners are shared in the cloud for collaborative data-mining. We first propose a mobile geo-tagging system. It is a novel, accurate and efficient image and video based remote target localization and tracking system using the Android smartphone. To cope with the smartphones' computational limitation, we design light-weight image/video processing algorithms to achieve a good balance between estimation accuracy and computational complexity. Our system is first of its kind and we provide first hand real-world experimental results, which demonstrate that our system is feasible and practicable. To address the privacy concern when data from disparate owners are shared in the cloud for collaborative data-mining, we then propose a generic compressive sensing (CS) based secure multiparty computation (MPC) framework for privacy-preserving collaborative data-mining in which data mining is performed in the CS domain. We perform the CS transformation and reconstruction processes with MPC protocols. We modify the original orthogonal matching pursuit algorithm and develop new MPC protocols so that the CS reconstruction process can be implemented using MPC. Our analysis and experimental results show that our generic framework is capable of enabling privacy preserving collaborative data-mining. The proposed framework can be applied to many privacy preserving collaborative data-mining and signal processing applications in the cloud. We identify an application scenario that requires simultaneously performing secure watermark detection and privacy preserving multimedia data storage. We further propose a privacy preserving storage and secure watermark detection framework by adopting our generic framework to address such a requirement. In our secure watermark detection framework, the multimedia data and secret watermark pattern are presented to the cloud for secure watermark detection in a compressive sensing domain to protect the privacy. We also give mathematical and statistical analysis to derive the expected watermark detection performance in the compressive sensing domain, based on the target image, watermark pattern and the size of the compressive sensing matrix (but without the actual CS matrix), which means that the watermark detection performance in the CS domain can be estimated during the watermark embedding process. The correctness of the derived performance has been validated by our experiments. Our theoretical analysis and experimental results show that secure watermark detection in the compressive sensing domain is feasible. By taking advantage of our mobile geo-tagging system and compressive sensing based privacy preserving data-mining framework, we develop a mobile privacy preserving collaborative filtering system. In our system, mobile users can share their personal data with each other in the cloud and get daily activity recommendations based on the data-mining results generated by the cloud, without leaking the privacy and secrecy of the data to other parties. Experimental results demonstrate that the proposed system is effective in enabling efficient mobile privacy preserving collaborative filtering services.Includes bibliographical references (pages 126-133)

Structural health monitoring of in-service tunnels

This work presents an overview of some of the most promising technologies for the structural health monitoring (SHM) of in-service tunnels. The common goal of damage or unusual behaviour detection is best pursued by an integrated approach based on the concurrent deployment of multiple technologies. Typically, traditional SHM systems are installed in problematic or special areas of the tunnels, giving information on conditions and helping manage maintenance. However, these methodologies often have the drawbacks of forcing the interruption of traffic for SHM system installation and monitoring only selected portions. Alternative solutions that would make it possible to keep the tunnel in normal operation and/or to analyse the entire infrastructure development through successive and continuous scanning stages, would be beneficial. In this paper, the authors will briefly review some traditional monitoring technologies for tunnels. Furthermore, the work is aimed at identifying alternative solutions, limiting or avoiding traffic interruptions

A Review of Indoor Millimeter Wave Device-based Localization and Device-free Sensing Technologies and Applications

The commercial availability of low-cost millimeter wave (mmWave) communication and radar devices is starting to improve the penetration of such technologies in consumer markets, paving the way for large-scale and dense deployments in fifth-generation (5G)-and-beyond as well as 6G networks. At the same time, pervasive mmWave access will enable device localization and device-free sensing with unprecedented accuracy, especially with respect to sub-6 GHz commercial-grade devices. This paper surveys the state of the art in device-based localization and device-free sensing using mmWave communication and radar devices, with a focus on indoor deployments. We first overview key concepts about mmWave signal propagation and system design. Then, we provide a detailed account of approaches and algorithms for localization and sensing enabled by mmWaves. We consider several dimensions in our analysis, including the main objectives, techniques, and performance of each work, whether each research reached some degree of implementation, and which hardware platforms were used for this purpose. We conclude by discussing that better algorithms for consumer-grade devices, data fusion methods for dense deployments, as well as an educated application of machine learning methods are promising, relevant and timely research directions.Comment: 43 pages, 13 figures. Accepted in IEEE Communications Surveys & Tutorials (IEEE COMST

Arrayed LiDAR signal analysis for automotive applications

Light detection and ranging (LiDAR) is one of the enabling technologies for advanced driver assistance and autonomy. Advances in solid-state photon detector arrays offer the potential of high-performance LiDAR systems but require novel signal processing approaches to fully exploit the dramatic increase in data volume an arrayed detector can provide. This thesis presents two approaches applicable to arrayed solid-state LiDAR. First, a novel block independent sparse depth reconstruction framework is developed, which utilises a random and very sparse illumination scheme to reduce illumination density while improving sampling times, which further remain constant for any array size. Compressive sensing (CS) principles are used to reconstruct depth information from small measurement subsets. The smaller problem size of blocks reduces the reconstruction complexity, improves compressive depth reconstruction performance and enables fast concurrent processing. A feasibility study of a system proposal for this approach demonstrates that the required logic could be practically implemented within detector size constraints. Second, a novel deep learning architecture called LiDARNet is presented to localise surface returns from LiDAR waveforms with high throughput. This single data driven processing approach can unify a wide range of scenarios, making use of a training-by-simulation methodology. This augments real datasets with challenging simulated conditions such as multiple returns and high noise variance, while enabling rapid prototyping of fast data driven processing approaches for arrayed LiDAR systems. Both approaches are fast and practical processing methodologies for arrayed LiDAR systems. These retrieve depth information with excellent depth resolution for wide operating ranges, and are demonstrated on real and simulated data. LiDARNet is a rapid approach to determine surface locations from LiDAR waveforms for efficient point cloud generation, while block sparse depth reconstruction is an efficient method to facilitate high-resolution depth maps at high frame rates with reduced power and memory requirements.Engineering and Physical Sciences Research Council (EPSRC

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing

Internationales Kolloquium über Anwendungen der Informatik und Mathematik in Architektur und Bauwesen : 20. bis 22.7. 2015, Bauhaus-Universität Weimar

The 20th International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering will be held at the Bauhaus University Weimar from 20th till 22nd July 2015. Architects, computer scientists, mathematicians, and engineers from all over the world will meet in Weimar for an interdisciplinary exchange of experiences, to report on their results in research, development and practice and to discuss. The conference covers a broad range of research areas: numerical analysis, function theoretic methods, partial differential equations, continuum mechanics, engineering applications, coupled problems, computer sciences, and related topics. Several plenary lectures in aforementioned areas will take place during the conference. We invite architects, engineers, designers, computer scientists, mathematicians, planners, project managers, and software developers from business, science and research to participate in the conference

Enhanced life-size holographic telepresence framework with real-time three-dimensional reconstruction for dynamic scene

Three-dimensional (3D) reconstruction has the ability to capture and reproduce 3D representation of a real object or scene. 3D telepresence allows the user to feel the presence of remote user that was remotely transferred in a digital representation. Holographic display is one of alternatives to discard wearable hardware restriction, it utilizes light diffraction to display 3D images to the viewers. However, to capture a real-time life-size or a full-body human is still challenging since it involves a dynamic scene. The remaining issue arises when dynamic object to be reconstructed is always moving and changes shapes and required multiple capturing views. The life-size data captured were multiplied exponentially when working with more depth cameras, it can cause the high computation time especially involving dynamic scene. To transfer high volume 3D images over network in real-time can also cause lag and latency issue. Hence, the aim of this research is to enhance life-size holographic telepresence framework with real-time 3D reconstruction for dynamic scene. There are three stages have been carried out, in the first stage the real-time 3D reconstruction with the Marching Square algorithm is combined during data acquisition of dynamic scenes captured by life-size setup of multiple Red Green Blue-Depth (RGB-D) cameras. Second stage is to transmit the data that was acquired from multiple RGB-D cameras in real-time and perform double compression for the life-size holographic telepresence. The third stage is to evaluate the life-size holographic telepresence framework that has been integrated with the real-time 3D reconstruction of dynamic scenes. The findings show that by enhancing life-size holographic telepresence framework with real-time 3D reconstruction, it has reduced the computation time and improved the 3D representation of remote user in dynamic scene. By running the double compression for the life-size holographic telepresence, 3D representations in life-size is smooth. It has proven can minimize the delay or latency during acquired frames synchronization in remote communications

DNA computing based stream cipher for internet of things using MQTT protocol

Internet of Things (IoT) is a rapidly developing technology that enables “devices” to communicate and share information amongst them without human control. The devices have the features of internet connectivity and networking. Due to the increasing demands of a secure environment in IoT application, security has become a crucial aspect on which researchers have been increasingly focused. Connecting devices to the internet can facilitate intruders to attack devices as they can access the data from anywhere in the globe. In this work, an encryption–decryption process-based stream cipher has been used. The messages between IoT nodes were encrypted using One Time Pad (OTP) and DNA computing. Furthermore, the required key sequence was generated using a linear feedback shift register (LFSR) as a pseudo number key generator. This key sequence was combined to generate a unique key for each message. The algorithm was implemented using source python and tested on a Raspberry pi under Linux open operation system

Optical measurement of shape and deformation fields on challenging surfaces

A multiple-sensor optical shape measurement system (SMS) based on the principle of white-light fringe projection has been developed and commercialised by Loughborough University and Phase Vision Ltd for over 10 years. The use of the temporal phase unwrapping technique allows precise and dense shape measurements of complex surfaces; and the photogrammetry-based calibration technique offers the ability to calibrate multiple sensors simultaneously in order to achieve 360° measurement coverage. Nevertheless, to enhance the applicability of the SMS in industrial environments, further developments are needed (i) to improve the calibration speed for quicker deployment, (ii) to broaden the application range from shape measurement to deformation field measurement, and (iii) to tackle practically-challenging surfaces of which specular components may disrupt the acquired data and result in spurious measurements. The calibration process typically requires manual positioning of an artefact (i.e., reference object) at many locations within the view of the sensors. This is not only timeconsuming but also complicated for an operator with average knowledge of metrology. This thesis introduces an automated artefact positioning system which enables automatic and optimised distribution of the artefacts, automatic prediction of their whereabouts to increase the artefact detection speed and robustness, and thereby greater overall calibration performance. This thesis also describes a novel technique that integrates the digital image correlation (DIC) technique into the present fringe projection SMS for the purpose of simultaneous shape and deformation field measurement. This combined technique offers three key advantages: (a) the ability to deal with geometrical discontinuities which are commonly present on mechanical surfaces and currently challenging to most deformation measurement methods, (b) the ability to measure 3D displacement fields with a basic single-camera single-projector SMS with no additional hardware components, and (c) the simple implementation on a multiple-sensor hardware platform to achieve complete coverage of large-scale and complex samples, with the resulting displacement fields automatically lying in a single global coordinate system. A displacement measurement iii accuracy of ≅1/12,000 of the measurement volume, which is comparable to that of an industry-standard DIC system, has been achieved. The applications of this novel technique to several structural tests of aircraft wing panels on-site at the research centre of Airbus UK in Filton are also presented. Mechanical components with shiny surface finish and complex geometry may introduce another challenge to present fringe projection techniques. In certain circumstances, multiple reflections of the projected fringes on an object surface may cause ambiguity in the phase estimation process and result in incorrect coordinate measurements. This thesis presents a new technique which adopts a Fourier domain ranging (FDR) method to correctly identifying multiple phase signals and enables unambiguous triangulation for a measured coordinate. Experiments of the new FDR technique on various types of surfaces have shown promising results as compared to the traditional phase unwrapping techniques

Review : Deep learning in electron microscopy

Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy