Secure Wireless Communications Based on Compressive Sensing: A Survey

IEEE Compressive sensing (CS) has become a popular signal processing technique and has extensive applications in numerous fields such as wireless communications, image processing, magnetic resonance imaging, remote sensing imaging, and anology to information conversion, since it can realize simultaneous sampling and compression. In the information security field, secure CS has received much attention due to the fact that CS can be regarded as a cryptosystem to attain simultaneous sampling, compression and encryption when maintaining the secret measurement matrix. Considering that there are increasing works focusing on secure wireless communications based on CS in recent years, we produce a detailed review for the state-of-the-art in this paper. To be specific, the survey proceeds with two phases. The first phase reviews the security aspects of CS according to different types of random measurement matrices such as Gaussian matrix, circulant matrix, and other special random matrices, which establishes theoretical foundations for applications in secure wireless communications. The second phase reviews the applications of secure CS depending on communication scenarios such as wireless wiretap channel, wireless sensor network, internet of things, crowdsensing, smart grid, and wireless body area networks. Finally, some concluding remarks are given

Investigation of the effects of image compression on the geometric quality of digital protogrammetric imagery

We are living in a decade, where the use of digital images is becoming increasingly important. Photographs are now converted into digital form, and direct acquisition of digital images is becoming increasing important as sensors and associated electronics. Unlike images in analogue form, digital representation of images allows visual information to· be easily manipulated in useful ways. One practical problem of the digital image representation is that, it requires a very large number of bits and hence one encounters a fairly large volume of data in a digital production environment if they are stored uncompressed on the disk. With the rapid advances in sensor technology and digital electronics, the number of bits grow larger in softcopy photogrammetry, remote sensing and multimedia GIS. As a result, it is desirable to find efficient representation for digital images in order to reduce the memory required for storage, improve the data access rate from storage devices, and reduce the time required for transfer across communication channels. The component of digital image processing that deals with this problem is called image compression. Image compression is a necessity for the utilisation of large digital images in softcopy photogrammetry, remote sensing, and multimedia GIS. Numerous image Compression standards exist today with the common goal of reducing the number of bits needed to store images, and to facilitate the interchange of compressed image data between various devices and applications. JPEG image compression standard is one alternative for carrying out the image compression task. This standard was formed under the auspices ISO and CCITT for the purpose of developing an international standard for the compression and decompression of continuous-tone, still-frame, monochrome and colour images. The JPEG standard algorithm &Us into three general categories: the baseline sequential process that provides a simple and efficient algorithm for most image coding applications, the extended DCT-based process that allows the baseline system to satisfy a broader range of applications, and an independent lossless process for application demanding that type of compression. This thesis experimentally investigates the geometric degradations resulting from lossy JPEG compression on photogrammetric imagery at various levels of quality factors. The effects and the suitability of JPEG lossy image compression on industrial photogrammetric imagery are investigated. Examples are drawn from the extraction of targets in close-range photogrammetric imagery. In the experiments, the JPEG was used to compress and decompress a set of test images. The algorithm has been tested on digital images containing various levels of entropy (a measure of information content of an image) with different image capture capabilities. Residual data was obtained by taking the pixel-by-pixel difference between the original data and the reconstructed data. The image quality measure, root mean square (rms) error of the residual was used as a quality measure to judge the quality of images produced by JPEG(DCT-based) image compression technique. Two techniques, TIFF (IZW) compression and JPEG(DCT-based) compression are compared with respect to compression ratios achieved. JPEG(DCT-based) yields better compression ratios, and it seems to be a good choice for image compression. Further in the investigation, it is found out that, for grey-scale images, the best compression ratios were obtained when the quality factors between 60 and 90 were used (i.e., at a compression ratio of 1:10 to 1:20). At these quality factors the reconstructed data has virtually no degradation in the visual and geometric quality for the application at hand. Recently, many fast and efficient image file formats have also been developed to store, organise and display images in an efficient way. Almost every image file format incorporates some kind of compression method to manage data within common place networks and storage devices. The current major file formats used in softcopy photogrammetry, remote sensing and · multimedia GIS. were also investigated. It was also found out that the choice of a particular image file format for a given application generally involves several interdependent considerations including quality; flexibility; computation; storage, or transmission. The suitability of a file format for a given purpose is · best determined by knowing its original purpose. Some of these are widely used (e.g., TIFF, JPEG) and serve as exchange formats. Others are adapted to the needs of particular applications or particular operating systems

Prioritizing Content of Interest in Multimedia Data Compression

Image and video compression techniques make data transmission and storage in digital multimedia systems more efficient and feasible for the system's limited storage and bandwidth. Many generic image and video compression techniques such as JPEG and H.264/AVC have been standardized and are now widely adopted. Despite their great success, we observe that these standard compression techniques are not the best solution for data compression in special types of multimedia systems such as microscopy videos and low-power wireless broadcast systems. In these application-specific systems where the content of interest in the multimedia data is known and well-defined, we should re-think the design of a data compression pipeline. We hypothesize that by identifying and prioritizing multimedia data's content of interest, new compression methods can be invented that are far more effective than standard techniques. In this dissertation, a set of new data compression methods based on the idea of prioritizing the content of interest has been proposed for three different kinds of multimedia systems. I will show that the key to designing efficient compression techniques in these three cases is to prioritize the content of interest in the data. The definition of the content of interest of multimedia data depends on the application. First, I show that for microscopy videos, the content of interest is defined as the spatial regions in the video frame with pixels that don't only contain noise. Keeping data in those regions with high quality and throwing out other information yields to a novel microscopy video compression technique. Second, I show that for a Bluetooth low energy beacon based system, practical multimedia data storage and transmission is possible by prioritizing content of interest. I designed custom image compression techniques that preserve edges in a binary image, or foreground regions of a color image of indoor or outdoor objects. Last, I present a new indoor Bluetooth low energy beacon based augmented reality system that integrates a 3D moving object compression method that prioritizes the content of interest.Doctor of Philosoph

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

IoT Anomaly Detection Methods and Applications: A Survey

Ongoing research on anomaly detection for the Internet of Things (IoT) is a rapidly expanding field. This growth necessitates an examination of application trends and current gaps. The vast majority of those publications are in areas such as network and infrastructure security, sensor monitoring, smart home, and smart city applications and are extending into even more sectors. Recent advancements in the field have increased the necessity to study the many IoT anomaly detection applications. This paper begins with a summary of the detection methods and applications, accompanied by a discussion of the categorization of IoT anomaly detection algorithms. We then discuss the current publications to identify distinct application domains, examining papers chosen based on our search criteria. The survey considers 64 papers among recent publications published between January 2019 and July 2021. In recent publications, we observed a shortage of IoT anomaly detection methodologies, for example, when dealing with the integration of systems with various sensors, data and concept drifts, and data augmentation where there is a shortage of Ground Truth data. Finally, we discuss the present such challenges and offer new perspectives where further research is required.Comment: 22 page

Machine learning based anomaly detection for industry 4.0 systems.

223 p.This thesis studies anomaly detection in industrial systems using technologies from the Fourth Industrial Revolution (4IR), such as the Internet of Things, Artificial Intelligence, 3D Printing, and Augmented Reality. The goal is to provide tools that can be used in real-world scenarios to detect system anomalies, intending to improve production and maintenance processes. The thesis investigates the applicability and implementation of 4IR technology architectures, AI-driven machine learning systems, and advanced visualization tools to support decision-making based on the detection of anomalies. The work covers a range of topics, including the conception of a 4IR system based on a generic architecture, the design of a data acquisition system for analysis and modelling, the creation of ensemble supervised and semi-supervised models for anomaly detection, the detection of anomalies through frequency analysis, and the visualization of associated data using Visual Analytics. The results show that the proposed methodology for integrating anomaly detection systems in new or existing industries is valid and that combining 4IR architectures, ensemble machine learning models, and Visual Analytics tools significantly enhances theanomaly detection processes for industrial systems. Furthermore, the thesis presents a guiding framework for data engineers and end-users

Deep learning in food category recognition

Integrating artificial intelligence with food category recognition has been a field of interest for research for the past few decades. It is potentially one of the next steps in revolutionizing human interaction with food. The modern advent of big data and the development of data-oriented fields like deep learning have provided advancements in food category recognition. With increasing computational power and ever-larger food datasets, the approach’s potential has yet to be realized. This survey provides an overview of methods that can be applied to various food category recognition tasks, including detecting type, ingredients, quality, and quantity. We survey the core components for constructing a machine learning system for food category recognition, including datasets, data augmentation, hand-crafted feature extraction, and machine learning algorithms. We place a particular focus on the field of deep learning, including the utilization of convolutional neural networks, transfer learning, and semi-supervised learning. We provide an overview of relevant studies to promote further developments in food category recognition for research and industrial applicationsMRC (MC_PC_17171)Royal Society (RP202G0230)BHF (AA/18/3/34220)Hope Foundation for Cancer Research (RM60G0680)GCRF (P202PF11)Sino-UK Industrial Fund (RP202G0289)LIAS (P202ED10Data Science Enhancement Fund (P202RE237)Fight for Sight (24NN201);Sino-UK Education Fund (OP202006)BBSRC (RM32G0178B8