An efficient implementation of lattice-ladder multilayer perceptrons in field programmable gate arrays

The implementation efficiency of electronic systems is a combination of conflicting requirements, as increasing volumes of computations, accelerating the exchange of data, at the same time increasing energy consumption forcing the researchers not only to optimize the algorithm, but also to quickly implement in a specialized hardware. Therefore in this work, the problem of efficient and straightforward implementation of operating in a real-time electronic intelligent systems on field-programmable gate array (FPGA) is tackled. The object of research is specialized FPGA intellectual property (IP) cores that operate in a real-time. In the thesis the following main aspects of the research object are investigated: implementation criteria and techniques. The aim of the thesis is to optimize the FPGA implementation process of selected class dynamic artificial neural networks. In order to solve stated problem and reach the goal following main tasks of the thesis are formulated: rationalize the selection of a class of Lattice-Ladder Multi-Layer Perceptron (LLMLP) and its electronic intelligent system test-bed – a speaker dependent Lithuanian speech recognizer, to be created and investigated; develop dedicated technique for implementation of LLMLP class on FPGA that is based on specialized efficiency criteria for a circuitry synthesis; develop and experimentally affirm the efficiency of optimized FPGA IP cores used in Lithuanian speech recognizer. The dissertation contains: introduction, four chapters and general conclusions. The first chapter reveals the fundamental knowledge on computer-aideddesign, artificial neural networks and speech recognition implementation on FPGA. In the second chapter the efficiency criteria and technique of LLMLP IP cores implementation are proposed in order to make multi-objective optimization of throughput, LLMLP complexity and resource utilization. The data flow graphs are applied for optimization of LLMLP computations. The optimized neuron processing element is proposed. The IP cores for features extraction and comparison are developed for Lithuanian speech recognizer and analyzed in third chapter. The fourth chapter is devoted for experimental verification of developed numerous LLMLP IP cores. The experiments of isolated word recognition accuracy and speed for different speakers, signal to noise ratios, features extraction and accelerated comparison methods were performed. The main results of the thesis were published in 12 scientific publications: eight of them were printed in peer-reviewed scientific journals, four of them in a Thomson Reuters Web of Science database, four articles – in conference proceedings. The results were presented in 17 scientific conferences

Distributed XML Query Processing

While centralized query processing over collections of XML data stored at a single site is a well understood problem, centralized query evaluation techniques are inherently limited in their scalability when presented with large collections (or a single, large document) and heavy query workloads. In the context of relational query processing, similar scalability challenges have been overcome by partitioning data collections, distributing them across the sites of a distributed system, and then evaluating queries in a distributed fashion, usually in a way that ensures locality between (sub-)queries and their relevant data. This thesis presents a suite of query evaluation techniques for XML data that follow a similar approach to address the scalability problems encountered by XML query evaluation. Due to the significant differences in data and query models between relational and XML query processing, it is not possible to directly apply distributed query evaluation techniques designed for relational data to the XML scenario. Instead, new distributed query evaluation techniques need to be developed. Thus, in this thesis, an end-to-end solution to the scalability problems encountered by XML query processing is proposed. Based on a data partitioning model that supports both horizontal and vertical fragmentation steps (or any combination of the two), XML collections are fragmented and distributed across the sites of a distributed system. Then, a suite of distributed query evaluation strategies is proposed. These query evaluation techniques ensure locality between each fragment of the collection and the parts of the query corresponding to the data in this fragment. Special attention is paid to scalability and query performance, which is achieved by ensuring a high degree of parallelism during distributed query evaluation and by avoiding access to irrelevant portions of the data. For maximum flexibility, the suite of distributed query evaluation techniques proposed in this thesis provides several alternative approaches for evaluating a given query over a given distributed collection. Thus, to achieve the best performance, it is necessary to predict and compare the expected performance of each of these alternatives. In this work, this is accomplished through a query optimization technique based on a distribution-aware cost model. The same cost model is also used to fine-tune the way a collection is fragmented to the demands of the query workload evaluated over this collection. To evaluate the performance impact of the distributed query evaluation techniques proposed in this thesis, the techniques were implemented within a production-quality XML database system. Based on this implementation, a thorough experimental evaluation was performed. The results of this evaluation confirm that the distributed query evaluation techniques introduced here lead to significant improvements in query performance and scalability both when compared to centralized techniques and when compared to existing distributed query evaluation techniques

Impact of peripheral inflammation in the brain : new roles for the anti-inflammatory molecule Annexin A1

Growing evidence has shown that peripheral inflammation can trigger a central nervous system response, sometimes worsening pre-existing neurological conditions, breaking down the concept of brain as an immune-privileged organ. Understanding which components contribute to periphery-to-brain communication may help identify molecules exploitable for therapeutic intervention. Usually, inflammation is followed by resolution: one of the main effectors in this process during peripheral inflammation is Annexin A1, while its implications in the CNS are still unclear. This thesis provides evidence for a new face for the molecule: we observed a well-defined expression at blood brain barrier (BBB) at endothelial level and we detected, in vivo, significantly higher BBB permeability in the AnxA1 null mice due to disrupted inter-endothelial cell tight junctions, essentially as a consequence to changes in the actin cytoskeleton. Such changes are reminiscent of early MS pathology, a relationship confirmed by detecting a selective loss of ANXA1 in the plasma and cerebrovascular endothelium of MS patients. Under peripheral inflammatory conditions (i.p. lipopolysaccharide, LPS), in vivo data suggested an inherent sex difference in BBB response, while in vitro studies confirmed the protective action of sex hormone 17β-Estradiol on the endothelium through ANXA1 modulation. Within the CNS, we detected a constitutively higher microglial density and pro-inflammatory environment in the Anxa1 null mouse, which worsened upon peripheral inflammation. In a neurodegeneration model (6-hydroxydopamine), genotype-related differences in microglial invasion occurred, while subsequent peripheral inflammatory challenges synergised and caused worse dopaminergic neuronal loss only in the knock-out model. These original data unveil a novel functional paradigm for ANXA1 as a “translator” between peripheral immune system and CNS through novel pathways compared to its well-characterized peripheral role. In addition, this study opens up a novel path to find therapeutic applications against disorders characterized by central and peripheral inflammation