Analysis of high capacity short reach optical links

Over the last few years, the global Internet traffic has grown exponentially due to the advent of the social networks, high definition streaming, online gaming, high performance computing and cloud services. The network is saturating, facing a challenge to provide enough capacity to such ever-demanding bandwidth expensive applications. Fiber optic communications is the only technology capable of dealing such high demands due to its advantages over the traditional electrical transmission technology. The short haul transmissions currently rely on direct detection due to low cost, low power and low complexity as compared to the coherent detection schemes. In order to increase the bit rate, several advance modulation formats are under investigation for short reach transmissions. Such links mostly use intensity modulation direct detection (IMDD) schemes providing a simple system when compared with the coherent receivers. In this thesis the performance of Multilevel Pulse Amplitude Modulation (MPAM) is studied using IMDD, providing good spectral efficiency as well as able to deal with the limited electronic devices bandwidth. MPAM can address the typical optical channel without the need to go with more complex and higher power modulation schemes. It provides a trade off between sensitivity and the complexity. So a simple communication system using MPAM is implemented using an external modulated laser transmitted over a distance of 2 km. In order to reduce the cost, single laser and single receiver technique is being adopted. The performance of the MPAM system in a bandwidth limited scenarios is studied with a possibility to use equalization techniques to improve the sensitivity. The utility of Forward Error Correction codes is also studied to improve the performance without increasing the latency. By increasing the number of bits per symbol, the system becomes more sensitive to the impairments. Moreover, the components and the connectors in the transmission system also introduces multipath interference (MPI) that is a key limitation to the use of advance modulation formats. Hence a detailed study is carried out to investigate the MPI effects. At the end, a novel idea based on reflective Mach-Zehnder modulator (MZM) is presented that reuses the modulated wavelength eliminating the need for a laser. As a consequent, the cost and power consumption specifically targeted for the optical interconnect environment is reduced. In a nutshell, the thesis provides an overview of the direct detection system targeted to the short optical links. It includes the studies related to the optical transmission systems and provides an insight of the available advance modulation formats and the detection schemes. Finally, the simulations and laboratory results are provided showing that adoption of MPAM is a viable solution that should be employed in high capacity short reach optical links

Does an External Governance Framework Enhance the Performance of Pakistan's Banking Sectors? Foreign Ownership as Moderator

This study would determine how external governance structure improves the performance of listed banks in Pakistan with the return on assets (ROA), return on equity (ROE), earnings per share (EPS) and dividend payout ratio (DPR) estimates. The study concerned external corporate governance with the presence of foreign ownership as a moderator. The sample design of the study is listed banks in Pakistan stock exchange (PSX) from 2009 to 2018 with the availability of foreign ownership data. The data are gathered from financial statements, shareholding trends, and the credit rating agencies Pakistan (PACRA). The panel data approach (fixed and random effect model) was reversed to serve a different research objective and the study goals. The results showed that the external mechanism of governance performs an important part in the transparency and efficiency of the banking sectors. The banks could also increase foreign investment if they get better external governance mechanisms. This work will help commercial banks resolve the issues and improve compliance with the corporate governance code, and devise strategies for better functioning. This research is inconsistent as none defined the governance of the external system with the moderator presence in Pakistan

CAMAL: Context-Aware Multi-scale Attention framework for Lightweight Visual Place Recognition

In the last few years, Deep Convolutional Neural Networks (D-CNNs) have shown state-of-the-art performances for Visual Place Recognition (VPR). Their prestigious generalization power has played a vital role in identifying persistent image regions under changing conditions and viewpoints. However, against the computation intensive D-CNNs based VPR algorithms, lightweight VPR techniques are preferred for resource-constraints mobile robots. This paper presents a lightweight CNN-based VPR technique that captures multi-layer context-aware attentions robust under changing environment and viewpoints. Evaluation of challenging benchmark datasets reveals better performance at low memory and resources utilization over state-of-the-art contemporary VPR methodologies

Levelling the Playing Field: A Comprehensive Comparison of Visual Place Recognition Approaches under Changing Conditions

In recent years there has been significant improvement in the capability of Visual Place Recognition (VPR) methods, building on the success of both hand-crafted and learnt visual features, temporal filtering and usage of semantic scene information. The wide range of approaches and the relatively recent growth in interest in the field has meant that a wide range of datasets and assessment methodologies have been proposed, often with a focus only on precision-recall type metrics, making comparison difficult. In this paper we present a comprehensive approach to evaluating the performance of 10 state-of-the-art recently-developed VPR techniques, which utilizes three standardized metrics: (a) Matching Performance b) Matching Time c) Memory Footprint. Together this analysis provides an up-to-date and widely encompassing snapshot of the various strengths and weaknesses of contemporary approaches to the VPR problem. The aim of this work is to help move this particular research field towards a more mature and unified approach to the problem, enabling better comparison and hence more progress to be made in future research

A Holistic Visual Place Recognition Approach Using Lightweight CNNs for Significant ViewPoint and Appearance Changes

This article presents a lightweight visual place recognition approach, capable of achieving high performance with low computational cost, and feasible for mobile robotics under significant viewpoint and appearance changes. Results on several benchmark datasets confirm an average boost of 13% in accuracy, and 12x average speedup relative to state-of-the-art methods

Are State-of-the-art Visual Place Recognition Techniques any Good for Aerial Robotics?

Visual Place Recognition (VPR) has seen significant advances at the frontiers of matching performance and computational superiority over the past few years. However, these evaluations are performed for ground-based mobile platforms and cannot be generalized to aerial platforms. The degree of viewpoint variation experienced by aerial robots is complex, with their processing power and on-board memory limited by payload size and battery ratings. Therefore, in this paper, we collect $8$ state-of-the-art VPR techniques that have been previously evaluated for ground-based platforms and compare them on $2$ recently proposed aerial place recognition datasets with three prime focuses: a) Matching performance b) Processing power consumption c) Projected memory requirements. This gives a birds-eye view of the applicability of contemporary VPR research to aerial robotics and lays down the the nature of challenges for aerial-VPR

A Generic Framework for Assessing the Performance Bounds of Image Feature Detectors

Since local feature detection has been one of the most active research areas in computer vision during the last decade and has found wide range of applications (such as matching and registration of remotely sensed image data), a large number of detectors have been proposed. The interest in feature-based applications continues to grow and has thus rendered the task of characterizing the performance of various feature detection methods an important issue in vision research. Inspired by the good practices of electronic system design, a generic framework based on the repeatability measure is presented in this paper that allows assessment of the upper and lower bounds of detector performance and finds statistically significant performance differences between detectors as a function of image transformation amount by introducing a new variant of McNemar’s test in an effort to design more reliable and effective vision systems. The proposed framework is then employed to establish operating and guarantee regions for several state-of-the art detectors and to identify their statistical performance differences for three specific image transformations: JPEG compression, uniform light changes and blurring. The results are obtained using a newly acquired, large image database (20,482 images) with 539 different scenes. These results provide new insights into the behavior of detectors and are also useful from the vision systems design perspective. Finally, results for some local feature detectors are presented for a set of remote sensing images to showcase the potential and utility of this framework for remote sensing applications in general