Nonlinearly regenerated long haul data transmission

This thesis experimentally examines the use of different techniques for optical fibre transmission over ultra long haul distances. Its format firstly examines the use of dispersion management as a means of achieving long haul communications. Secondly, examining the use concatenated NOLMs for DM autosoliton ultra long haul propagation, by comparing their performance with a generic system without NOLMs. Thirdly, timing jitter in concatenated NOLM system is examined and compared to the generic system and lastly issues of OTDM amplitude non-uniformity from channel to channel in a saturable absorber, specifically a NOLM, are raised. Transmission at a rate of 40Gbit/s is studied in an all-Raman amplified standard fibre link with amplifier spacing of the order of 80km. We demonstrate in this thesis that the detrimental effects associated with high power Raman amplification can be minimized by dispersion map optimization. As a result, a transmission distance of 1600 km (2000km including dispersion compensating fibre) has been achieved in standard single mode fibre. The use of concatenated NOLMs to provide a stable propagation regime has been proposed theoretically. In this thesis, the observation experimentally of autosoliton propagation is shown for the first time in a dispersion managed optical transmission system. The system is based on a strong dispersion map with large amplifier spacing. Operation at transmission rates of 10, 40 and 80Gbit/s is demonstrated. With an insertion of a stabilizing element to the NOLM, the transmission of a 10 and 20Gbit/s data stream was extended and demonstrated experimentally. Error-free propagation over 100 and 20 thousand kilometres has been achieved at 10 and 20Gbit/s respectively, with terrestrial amplifier spacing. The monitor of timing jitter is of importance to all optical systems. Evolution of timing jitter in a DM autosoliton system has been studied in this thesis and analyzed at bit ranges from 10Gbit/s to 80Gbit/s. Non-linear guiding by in-line regenerators considerably changes the dynamics of jitter accumulation. As transmission systems require higher data rates, the use of OTDM will become more prolific. The dynamics of switching and transmission of an optical signal comprising individual OTDM channels of unequal amplitudes in a dispersion-managed link with in-line non-linear fibre loop mirrors is investigated.

A new method for microwave generation and data transmission using DFB laser based on fiber Bragg gratings

A novel architecture for microwave/millimeter-wave signal generation and data modulation using a fiber-grating-based distributed feedback laser has been proposed in this letter. For demonstration, a 155.52-Mb/s data stream on a 16.9-GHz subcarrier has been transmitted and recovered successfully. It has been proved that this technology would be of benefit to future microwave data transmission systems.

1000 Km transmission of 40 Gbit/s single channel RZ data over dispersion managed standard (non-dispersion shifted) fibre

Error-free transmission of a single polarization optical time division multiplexed 40 Gbit/s dispersion managed pulse data stream over 1009 km has been achieved in dispersion-compensated standard (non-dispersion shifted) fibre. This distance is twice the previous record at this data rate.

Using secondary knowledge to support decision tree classification of retrospective clinical data

Retrospective clinical data presents many challenges for data mining and machine learning. The transcription of patient records from paper charts and subsequent manipulation of data often results in high volumes of noise as well as a loss of other important information. In addition, such datasets often fail to represent expert medical knowledge and reasoning in any explicit manner. In this research we describe applying data mining methods to retrospective clinical data to build a prediction model for asthma exacerbation severity for pediatric patients in the emergency department. Difficulties in building such a model forced us to investigate alternative strategies for analyzing and processing retrospective data. This paper describes this process together with an approach to mining retrospective clinical data by incorporating formalized external expert knowledge (secondary knowledge sources) into the classification task. This knowledge is used to partition the data into a number of coherent sets, where each set is explicitly described in terms of the secondary knowledge source. Instances from each set are then classified in a manner appropriate for the characteristics of the particular set. We present our methodology and outline a set of experiential results that demonstrate some advantages and some limitations of our approach. © 2008 Springer-Verlag Berlin Heidelberg.

A new method for microwave generation and data transmission using DFB laser based on fiber bragg gratings

A novel architecture for microwave/millimeter-wave signal generation and data modulation using a fiber-grating-based distributed feedback laser has been proposed in this letter. For demonstration, a 155.52-Mb/s data stream on a 16.9-GHz subcarrier has been transmitted and recovered successfully. It has been proved that this technology would be of benefit to future microwave data transmission systems. © 2006 IEEE.

All-optical passive 2R regeneration of dispersion-managed RZ data at 40 and 80 Gbit/s using in-line NOLMs

We demonstrate that the use of in-line nonlinear optical loop mirrors (NOLMs) in dispersion-managed (DM) transmission systems dominated by amplitude noise can achieve passive 2R regeneration of a 40 and 80 Gbit/s RZ data stream. This is an indication that the use of this approach could obviate the need for full-regeneration in high data rate, strong DM systems, when intra-channel four-wave mixing poses serious problems.

Study of the operating regime for all-optical passive 2R regeneration of dispersion-managed RZ data at 40 Gb/s using in-line NOLMs

In this letter, we numerically demonstrate that the use of inline nonlinear optical loop mirrors in strongly dispersion-managed transmission systems dominated by pulse distortion and amplitude noise can achieve all-optical passive 2R regeneration of a 40-Gb/s return-to-zero data stream. We define the tolerance limits of this result to the parameters of the input pulses.

1220km propagation of 40Gbit/s single channel RZ data over dispersion managed standard (non-dispersion shifted) fibre

Error free propagation of a single polarisation optical time division multiplexed 40 Gbit/s dispersion managed pulsed data stream over dispersion (non-shifted) fibre. This distance is twice the previous record at this data rate.

Forming of Learning Set for Neural Networks in Problems of Losless Data Compression

questions of forming of learning sets for artificial neural networks in problems of lossless data compression are considered. Methods of construction and use of learning sets are studied. The way of forming of learning set during training an artificial neural network on the data stream is offered.

Upgrading legacy long-haul WDM systems through unbalancing the proportion of 1s and 0s in the transmitted data

We present experimental results for wavelength-division multiplexed (WDM) transmission performance using unbalanced proportions of 1s and 0s in pseudo-random bit sequence (PRBS) data. This investigation simulates the effect of local, in time, data unbalancing which occurs in some coding systems such as forward error correction when extra bits are added to the WDM data stream. We show that such local unbalancing, which would practically give a time-dependent error-rate, can be employed to improve the legacy long-haul WDM system performance if the system is allowed to operate in the nonlinear power region. We use a recirculating loop to simulate a long-haul fibre system.

Mitigating inconsistencies by coupling data cleaning, filtering, and contextual data validation in wireless sensor networks

With the advent of peer to peer networks, and more importantly sensor networks, the desire to extract useful information from continuous and unbounded streams of data has become more prominent. For example, in tele-health applications, sensor based data streaming systems are used to continuously and accurately monitor Alzheimer's patients and their surrounding environment. Typically, the requirements of such applications necessitate the cleaning and filtering of continuous, corrupted and incomplete data streams gathered wirelessly in dynamically varying conditions. Yet, existing data stream cleaning and filtering schemes are incapable of capturing the dynamics of the environment while simultaneously suppressing the losses and corruption introduced by uncertain environmental, hardware, and network conditions. Consequently, existing data cleaning and filtering paradigms are being challenged. This dissertation develops novel schemes for cleaning data streams received from a wireless sensor network operating under non-linear and dynamically varying conditions. The study establishes a paradigm for validating spatio-temporal associations among data sources to enhance data cleaning. To simplify the complexity of the validation process, the developed solution maps the requirements of the application on a geometrical space and identifies the potential sensor nodes of interest. Additionally, this dissertation models a wireless sensor network data reduction system by ascertaining that segregating data adaptation and prediction processes will augment the data reduction rates. The schemes presented in this study are evaluated using simulation and information theory concepts. The results demonstrate that dynamic conditions of the environment are better managed when validation is used for data cleaning. They also show that when a fast convergent adaptation process is deployed, data reduction rates are significantly improved. Targeted applications of the developed methodology include machine health monitoring, tele-health, environment and habitat monitoring, intermodal transportation and homeland security.

Mitigating Inconsistencies by Coupling Data Cleaning, Filtering, and Contextual Data Validation in Wireless Sensor Networks

With the advent of peer to peer networks, and more importantly sensor networks, the desire to extract useful information from continuous and unbounded streams of data has become more prominent. For example, in tele-health applications, sensor based data streaming systems are used to continuously and accurately monitor Alzheimer's patients and their surrounding environment. Typically, the requirements of such applications necessitate the cleaning and filtering of continuous, corrupted and incomplete data streams gathered wirelessly in dynamically varying conditions. Yet, existing data stream cleaning and filtering schemes are incapable of capturing the dynamics of the environment while simultaneously suppressing the losses and corruption introduced by uncertain environmental, hardware, and network conditions. Consequently, existing data cleaning and filtering paradigms are being challenged. This dissertation develops novel schemes for cleaning data streams received from a wireless sensor network operating under non-linear and dynamically varying conditions. The study establishes a paradigm for validating spatio-temporal associations among data sources to enhance data cleaning. To simplify the complexity of the validation process, the developed solution maps the requirements of the application on a geometrical space and identifies the potential sensor nodes of interest. Additionally, this dissertation models a wireless sensor network data reduction system by ascertaining that segregating data adaptation and prediction processes will augment the data reduction rates. The schemes presented in this study are evaluated using simulation and information theory concepts. The results demonstrate that dynamic conditions of the environment are better managed when validation is used for data cleaning. They also show that when a fast convergent adaptation process is deployed, data reduction rates are significantly improved. Targeted applications of the developed methodology include machine health monitoring, tele-health, environment and habitat monitoring, intermodal transportation and homeland security.

Dynamically managing sensed context data

This thesis reports on an investigation of the feasibility and usefulness of incorporating dynamic management facilities for managing sensed context data in a distributed contextaware mobile application. The investigation focuses on reducing the work required to integrate new sensed context streams in an existing context aware architecture. Current architectures require integration work for new streams and new contexts that are encountered. This means of operation is acceptable for current fixed architectures. However, as systems become more mobile the number of discoverable streams increases. Without the ability to discover and use these new streams the functionality of any given device will be limited to the streams that it knows how to decode. The integration of new streams requires that the sensed context data be understood by the current application. If the new source provides data of a type that an application currently requires then the new source should be connected to the application without any prior knowledge of the new source. If the type is similar and can be converted then this stream too should be appropriated by the application. Such applications are based on portable devices (phones, PDAs) for semi-autonomous services that use data from sensors connected to the devices, plus data exchanged with other such devices and remote servers. Such applications must handle input from a variety of sensors, refining the data locally and managing its communication from the device in volatile and unpredictable network conditions. The choice to focus on locally connected sensory input allows for the introduction of privacy and access controls. This local control can determine how the information is communicated to others. This investigation focuses on the evaluation of three approaches to sensor data management. The first system is characterised by its static management based on the pre-pended metadata. This was the reference system. Developed for a mobile system, the data was processed based on the attached metadata. The code that performed the processing was static. The second system was developed to move away from the static processing and introduce a greater freedom of handling for the data stream, this resulted in a heavy weight approach. The approach focused on pushing the processing of the data into a number of networked nodes rather than the monolithic design of the previous system. By creating a separate communication channel for the metadata it is possible to be more flexible with the amount and type of data transmitted. The final system pulled the benefits of the other systems together. By providing a small management class that would load a separate handler based on the incoming data, Dynamism was maximised whilst maintaining ease of code understanding. The three systems were then compared to highlight their ability to dynamically manage new sensed context. The evaluation took two approaches, the first is a quantitative analysis of the code to understand the complexity of the relative three systems. This was done by evaluating what changes to the system were involved for the new context. The second approach takes a qualitative view of the work required by the software engineer to reconfigure the systems to provide support for a new data stream. The evaluation highlights the various scenarios in which the three systems are most suited. There is always a trade-o↵ in the development of a system. The three approaches highlight this fact. The creation of a statically bound system can be quick to develop but may need to be completely re-written if the requirements move too far. Alternatively a highly dynamic system may be able to cope with new requirements but the developer time to create such a system may be greater than the creation of several simpler systems.

Uma arquitetura de referência para o processamento distribuído de stream de dados em soluções analíticas de near real-time

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2015.

Filtering redundant data from RFID data streams

Radio Frequency Identification (RFID) enabled systems are evolving in many applications that need to know the physical location of objects such as supply chain management. Naturally, RFID systems create large volumes of duplicate data. As the duplicate data wastes communication, processing, and storage resources as well as delaying decision-making, filtering duplicate data from RFID data stream is an important and challenging problem. Existing Bloom Filter-based approaches for filtering duplicate RFID data streams are complex and slow as they use multiple hash functions. In this paper, we propose an approach for filtering duplicate data from RFID data streams. The proposed approach is based on modified Bloom Filter and uses only a single hash function. We performed extensive empirical study of the proposed approach and compared it against the Bloom Filter, d-Left Time Bloom Filter, and the Count Bloom Filter approaches. The results show that the proposed approach outperforms the baseline approaches in terms of false positive rate, execution time, and true positive rate.