Machine Learning Techniques To Mitigate Nonlinear Impairments In Optical Fiber System

The upcoming deployment of 5/6G networks, online services like 4k/8k HDTV (streamers and online games), the development of the Internet of Things concept, connecting billions of active devices, as well as the high-speed optical access networks, impose progressively higher and higher requirements on the underlying optical networks infrastructure. With current network infrastructures approaching almost unsustainable levels of bandwidth utilization/ data traffic rates, and the electrical power consumption of communications systems becoming a serious concern in view of our achieving the global carbon footprint targets, network operators and system suppliers are now looking for ways to respond to these demands while also maximizing the returns of their investments. The search for a solution to this predicted ªcapacity crunchº led to a renewed interest in alternative approaches to system design, including the usage of high-order modulation formats and high symbol rates, enabled by coherent detection, development of wideband transmission tools, new fiber types (such as multi-mode and ±core), and finally, the implementation of advanced digital signal processing (DSP) elements to mitigate optical channel nonlinearities and improve the received SNR. All aforementioned options are intended to boost the available optical systems’ capacity to fulfill the new traffic demands. This thesis focuses on the last of these possible solutions to the ªcapacity crunch," answering the question: ªHow can machine learning improve existing optical communications by minimizing quality penalties introduced by transceiver components and fiber media nonlinearity?". Ultimately, by identifying a proper machine learning solution (or a bevy of solutions) to act as a nonlinear channel equalizer for optical transmissions, we can improve the system’s throughput and even reduce the signal processing complexity, which means we can transmit more using the already built optical infrastructure. This problem was broken into four parts in this thesis: i) the development of new machine learning architectures to achieve appealing levels of performance; ii) the correct assessment of computational complexity and hardware realization; iii) the application of AI techniques to achieve fast reconfigurable solutions; iv) the creation of a theoretical foundation with studies demonstrating the caveats and pitfalls of machine learning methods used for optical channel equalization. Common measures such as bit error rate, quality factor, and mutual information are considered in scrutinizing the systems studied in this thesis. Based on simulation and experimental results, we conclude that neural network-based equalization can, in fact, improve the channel quality of transmission and at the same time have computational complexity close to other classic DSP algorithms

RIO Country Report 2015: Czech Republic

The 2015 series of RIO Country Reports analyse and assess the policy and the national research and innovation system developments in relation to national policy priorities and the EU policy agenda with special focus on ERA and Innovation Union. The executive summaries of these reports put forward the main challenges of the research and innovation systems.JRC.J.6-Innovation Systems Analysi

The Boeing / McDonnell Douglas and EADS mergers: ethnocentric vs. regiocentric consolidation in the aerospace and defence industry and the implications for international relations

This thesis relies on realist and neo-mercantilist approaches to explain the consolidation of the US and European aerospace and defence industry during the second half of the 1990s. Based on two case studies, the Boeing / McDonnell Douglas (BMD) merger in 1997 and the EADS merger in 1999, the thesis analyses the different political and economic motivations that led these aerospace and defence companies as well as their respective home governments to pursue either ethnocentric consolidation (in the case of the US) or regiocentric consolidation (in the case of France, Germany, and Spain) strategies. The BMD merger is interpreted as an attempt by the American hegemon to ensure that the important military, economic, and technological benefits derived from this strategic sector continue to accrue, above all, to the United States and its aerospace and defence industrial base. The cross-border EADS merger, in contrast, is viewed as a Franco-German-led counterbalancing attempt to guarantee the survival and autonomy of the European aerospace and defence industry, including Airbus, in the face of growing competitive pressures from the rapidlyconsolidating US mega-primes like Boeing. The thesis contrasts several high-profile transatlantic M&A deals in a variety of business sectors with the marked absence of similar transactions between US and European aerospace and defence companies. It thus highlights the strategic nature of this particular sector as well as American concerns about the proliferation of advanced US technologies to third countries, including to European NATO allies. Ultimately, realist and neo-mercantilist arguments prevailed over liberal-institutionalist / globalisation arguments among policymakers and business leaders on both sides of the Atlantic (especially in Washington, DC and Paris) – thus paving the way for the BMD and EADS mergers

Combined in silico approaches towards the identification of novel malarial cysteine protease inhibitors

Malaria an infectious disease caused by a group of parasitic organisms of the Plasmodium genus remains a severe public health problem in Africa, South America and parts of Asia. The leading causes for the persistence of malaria are the emergence of drug resistance to common antimalarial drugs, lack of effective vaccines and the inadequate control of mosquito vectors. Worryingly, accumulating evidence shows that the parasite has developed resistant to the current first-line treatment based on artemisinin. Hence, the identification and characterization of novel drug targets and drugs with unique mode of action remains an urgent priority. The successful sequencing and assembly of genomes from several Plasmodium species has opened an opportune window for the identification of new drug targets. Cysteine proteases are one of the major drug targets to be identified so far. The use of cysteine protease inhibitors coupled with gene manipulation studies has defined specific and putative roles of cysteine proteases which include hemoglobin degradation, erythrocyte rupture, immune evasion and erythrocyte invasion, steps which are central for the completion of the Plasmodium parasite life cycle. In an aim to discover potential novel antimalarials, this thesis focussed on falcipains (FPs), a group of four papain-like cysteine proteases from Plasmodium falciparum. Two of these enzymes, FP-2 and FP-3 are the major hemoglobinases and have been validated as drug targets. For the successful elimination of malaria, drugs must be safe and target both human and wild Plasmodium infective forms. Thus, an incipient aim was to identify protein homologs of these two proteases from other Plasmodium species and the host (human). From BLASTP analysis, up to 16 FP-2 and FP-3 homologs were identified (13 plasmodial proteases and 3 human cathepsins). Using in silico characterization approaches, the intra and inter group sequence, structural, phylogenetic and physicochemical differences were determined. To extend previous work (MSc student) involving docking studies on the identified proteins using known FP-2 and FP-3 inhibitors, a South African natural compound and its ZINC analogs, molecular dynamics and binding free energy studies were performed to determine the stabilities and quantification of the strength of interactions between the different protein-ligand complexes. From the results, key structural elements that regulate the binding and selectivity of non-peptidic compounds onto the different proteins were deciphered. Interaction fingerprints and energy decomposition analysis identified key residues and energetic terms that are central for effective ligand binding. This research presents novel insight essential for the structure-based molecular drug design of more potent antimalarial drugs

Massachusetts Domestic and Foreign Corporations Subject to an Excise: For the Use of Assessors (2004)

International audienc