Advanced optical modulation and fast reconfigurable en/decoding techniques for OCDMA application

With the explosive growth of bandwidth requirement in optical fiber communication networks, optical code division multiple access (OCDMA) has witnessed tremendous achievements as one of the promising technologies for optical access networks over the past decades. In an OCDMA system, optical code processing is one of the key techniques. Rapid optical code reconfiguration can improve flexibility and security of the OCDMA system. This thesis focuses on advanced optical modulations and en/decoding techniques for applications in fast reconfigurable OCDMA systems and secure optical communications. A novel time domain spectral phase encoding (SPE) scheme which can rapidly reconfigure the optical code and is compatible with conventional spectral domain phase en/decoding by using a pair of dispersive devices and a high speed phase modulator is proposed. Based on this scheme, a novel advanced modulation technique that can simultaneously generate both the optical code and the differential-phase-shift-keying (DPSK) data using a single phase modulator is experimentally demonstrated. A symmetric time domain spectral phase encoding and decoding (SPE/SPD) scheme using a similar setup for both the transmitter and receiver is further proposed, based on which a bit-by-bit optical code scrambling and DPSK data modulation technique for secure optical communications has been successfully demonstrated. By combining optical encoding and optical steganography, a novel approach for secure transmission of time domain spectral phase encoded on-off-keying (OOK)/DPSK-OCDMA signal over public wavelength-division multiplexing (WDM) network has also been proposed and demonstrated. To enable high speed operation of the time domain SPE/SPD scheme and enhance the system security, a rapid programmable, code-length variable bit-by-bit optical code shifting technique is proposed. Based on this technique, security improvements for OOK/DPSK OCDMA systems at data rates of 10Gb/s and 40Gb/s using reconfigurable optical codes of up to 1024-chip have been achieved. Finally, a novel tunable two-dimensional coherent optical en/decoder which can simultaneously perform wavelength hopping and spectral phase encoding based on coupled micro-ring resonator is proposed and theoretically investigated. The techniques included in this thesis could be potentially used for future fast reconfigurable and secure optical code based communication systems

Underwater Wireless Video Transmission using Acoustic OFDM

The current project aims to design and implement an acoustic OFDM system for underwater video transmissions. The thesis work combines a theoretical part, whose objective is to choose the appropriate techniques to deal with the characteristics of the targeted channel, and a practical part regarding the system deployment and experimental test

Adaptive implementation of turbo multi-user detection architecture

MULTI-access techniques have been adopted widely for communications in underwater acoustic channels, which present many challenges to the development of reliable and practical systems. In such an environment, the unpredictable and complex ocean conditions cause the acoustic waves to be affected by many factors such as limited bandwidth, large propagation losses, time variations and long latency, which limit the usefulness of such techniques. Additionally, multiple access interference (MAI) signals and poor estimation of the unknown channel parameters in the presence of limited training sequences are two of the major problems that degrade the performance of such technologies. In this thesis, two different single-element multi-access schemes, interleave division multiple access (IDMA) and code division multiple access (CDMA), employing decision feedback equalization (DFE) and soft Rake-based architectures, are proposed for multi-user underwater communication applications. By using either multiplexing pilots or continuous pilots, these adaptive turbo architectures with carrier phase tracking are jointly optimized based on the minimum mean square error (MMSE) criterion and adapted iteratively by exchanging soft information in terms of Log-Likelihood Ratio (LLR) estimates with the single-user’s channel decoders. The soft-Rake receivers utilize developed channel estimation and the detection is implemented using parallel interference cancellation (PIC) to remove MAI effects between users. These architectures are investigated and applied to simulated data and data obtained from realistic underwater communication trials using off-line processing of signals acquired during sea-trials in the North Sea. The results of different scenarios demonstrate the penalty in performance as the fading induces irreducible error rates that increase with channel delay spread and emphasize the benefits of using coherent direct adaptive receivers in such reverberant channels. The convergence behaviour of the detectors is evaluated using EXIT chart analyses and issues such as the adaptation parameters and their effects on the performance are also investigated. However, in some cases the receivers with partial knowledge of the interleavers’ patterns or codes can still achieve performance comparable to those with full knowledge. Furthermore, the thesis describes implementation issues of these algorithms using digital signal processors (DSPs), such as computational complexity and provides valuable guidelines for the design of real time underwater communication systems.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Adaptive implementation of turbo multi-user detection architecture

MULTI-access techniques have been adopted widely for communications in underwater acoustic channels, which present many challenges to the development of reliable and practical systems. In such an environment, the unpredictable and complex ocean conditions cause the acoustic waves to be affected by many factors such as limited bandwidth, large propagation losses, time variations and long latency, which limit the usefulness of such techniques. Additionally, multiple access interference (MAI) signals and poor estimation of the unknown channel parameters in the presence of limited training sequences are two of the major problems that degrade the performance of such technologies. In this thesis, two different single-element multi-access schemes, interleave division multiple access (IDMA) and code division multiple access (CDMA), employing decision feedback equalization (DFE) and soft Rake-based architectures, are proposed for multi-user underwater communication applications. By using either multiplexing pilots or continuous pilots, these adaptive turbo architectures with carrier phase tracking are jointly optimized based on the minimum mean square error (MMSE) criterion and adapted iteratively by exchanging soft information in terms of Log-Likelihood Ratio (LLR) estimates with the single-user’s channel decoders. The soft-Rake receivers utilize developed channel estimation and the detection is implemented using parallel interference cancellation (PIC) to remove MAI effects between users. These architectures are investigated and applied to simulated data and data obtained from realistic underwater communication trials using off-line processing of signals acquired during sea-trials in the North Sea. The results of different scenarios demonstrate the penalty in performance as the fading induces irreducible error rates that increase with channel delay spread and emphasize the benefits of using coherent direct adaptive receivers in such reverberant channels. The convergence behaviour of the detectors is evaluated using EXIT chart analyses and issues such as the adaptation parameters and their effects on the performance are also investigated. However, in some cases the receivers with partial knowledge of the interleavers’ patterns or codes can still achieve performance comparable to those with full knowledge. Furthermore, the thesis describes implementation issues of these algorithms using digital signal processors (DSPs), such as computational complexity and provides valuable guidelines for the design of real time underwater communication systems.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

VISION AND NATURAL LANGUAGE FOR CREATIVE APPLICATIONS, AND THEIR ANALYSIS

Recent advances in machine learning, specifically problems in Computer Vision and Natural Language, have involved training deep neural networks with enormous amounts of data. The first frontier for deep networks was in uni-modal classification and detection problems (which were directed more towards ”intelligent robotics” and surveillance applications), while the next wave involves deploying deep networks on more creative tasks and common-sense reasoning. We provide two applications of these, interspersed by an analysis on these deep models. Automatic colorization is the process of adding color to greyscale images. We condition this process on language, allowing end users to manipulate a colorized image by feeding in different captions. We present two different architectures for language-conditioned colorization, both of which produce more accurate and plausible colorizations than a language-agnostic version. Through this language-based framework, we can dramatically alter colorizations by manipulating descriptive color words in captions. Researchers have observed that Visual Question Answering(VQA) models tend to answer questions by learning statistical biases in the data. (for example, the answer to the question “What is the color of the sky?” is usually “Blue”). It is of interest to the community to explicitly discover such biases, both for understanding the behavior of such models, and towards debugging them. In a database, we store the words of the question, answer and visual words corresponding to regions of interest in attention maps. By running simple rule mining algorithms on this database, we discover human-interpretable rules which give us great insight into the behavior of such models. Our results also show examples of unusual behaviors learned by the model in attempting VQA tasks. Visual narrative is often a combination of explicit information and judicious omissions, relying on the viewer to supply missing details. In comics, most movements in time and space are hidden in the gutters between panels. To follow the story, readers logically connect panels together by inferring unseen actions through a process called closure. While computers can now describe what is explicitly depicted in natural images, in this paper we examine whether they can understand the closure-driven narratives conveyed by stylized artwork and dialogue in comic book panels. We construct a dataset, COMICS, that consists of over 1.2 million panels (120 GB) paired with automatic textbox transcriptions. An in-depth analysis of COMICS demonstrates that neither text nor image alone can tell a comic book story, so a computer must understand both modalities to keep up with the plot. We introduce three cloze-style tasks that ask models to predict narrative and character-centric aspects of a panel given n preceding panels as context. Various deep neural architectures underperform human baselines on these tasks, suggesting that COMICS contains fundamental challenges for both vision and language. For many NLP tasks, ordered models, which explicitly encode word order information, do not significantly outperform unordered (bag-of-words) models. One potential explanation is that the tasks themselves do not require word order to solve. To test whether this explanation is valid, we perform several time-controlled human experiments with scrambled language inputs. We compare human accuracies to those of both ordered and unordered neural models. Our results contradict the initial hypothesis, suggesting instead that humans may be less robust to word order variation than computers