Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 207 for “"Network Architectures"”.
-
Boolean Weightless Neural Network Architectures
… pertinence to the field of weightless neural networks. They have also been shown to have merit in their own right for the design of robust architectures. A major element of this is a collection of weightless Boolean sum and threshold techniques. These are fundamental building blocks which can …
-
Exploring neural network architectures for acoustic modeling
Deep neural network (DNN)-based acoustic models (AMs) have significantly improved automatic speech recognition (ASR) on many tasks. However, ASR performance still suffers from speaker and environment variability, especially under low-resource, distant microphone, noisy, and reverberant conditions. …
-
Evaluating Modern Neural Network Architectures for Suicide Prediction
… Machine learning algorithms, including neural networks, have previously been employed for this task, yet it remains an understudied domain.</p> <p>This research aims to evaluate the capabilities of Multi-Layer Perceptron (MLP) and a selection of its successors, ResNet and MLP with a category …
-
Demystifying deep network architectures : from theory to applications
Deep neural networks significantly power the success of machine learning and artificial intelligence. Over the past decade, the community keeps designing architectures of deep layers and complicated connections. Many works in deep learning theory tried to understand deep networks from different …
-
Neural network architectures for Prepositional Phrase attachment disambiguation
… model is defined using a recursive neural network. Word vector representations are obtained from large amounts of raw text and fed into the neural network. The vectors are first forward propagated up the network in order to create a composite representation, which is used to score all …
-
Decentralized detection in sensor network architectures with feedback
… decentralized detection problem for different network configurations of interest under both the Neyman-Pearson and the Bayesian criteria. In a configuration with feedback, the fusion center would make a preliminary decision which it would pass on back to the local sensors; a related …
-
Decentralized detection in resource-limited sensor network architectures
… problem of decentralized binary detection in a network consisting of a large number of nodes arranged as a tree of bounded height. We show that the error probability decays exponentially fast with the number of nodes under both a Neyman-Pearson criterion and a Bayesian criterion, and provide …
-
Efficient on-chip Network architectures for multicore VLSI systems.
… (PEs) to be placed onto a chip. As a result, the Network-on-Chip (NoC) architecture, which provides packet-based routing, is emerging as a solution which can provide a scalable communication platform. In this thesis, we propose a multicasting and bandwidth-reusable Code Division Multiple Access …
-
Genetically Engineered Adaptive Resonance Theory (art) Neural Network Architectures
… considered to be one of the premier neural network architectures in solving classification problems. One of the limitations of Fuzzy ARTMAP that has been extensively reported in the literature is the category proliferation problem. That is Fuzzy ARTMAP has the tendency of increasing its …
-
Nonlinear Interference Generation in Wideband and Disaggregated Optical Network Architectures
L'abstract è presente nell'allegato / the abstract is in the attachment
-
Dynamic node clustering in hierarchical optical data center network architectures
… decade an increasing trend in the Data Center Network's traffic has been observed. This traffic is characterized mostly by many small bursty flows (mice) that last for less than few milliseconds as well as a few heavier more persistent (elephant) flows between certain number of nodes. As a …
-
On the Design and Analysis of Cloud Data Center Network Architectures
… are the cloud data centers. The Data Center Network (DCN) defines what networking devices are used and how different devices are interconnected in a cloud data center; thus, it has great impacts on the total cost, performances, and power consumption of the entire data center. Conventional …
-
An experimental investigation of dynamically reconfigurable computer network architectures through simulation
… of dynamically reconfigurable computer network architectures, (2) a comparative study of the standardized time series method of simulation output analysis, and (3) an experimental comparison of the effects of dynamic reconfigurability on message transmission delays and network …
-
Development and application of an analysis methodology for satellite broadband network architectures
… and economic performance of a satellite network system has been developed and implemented. It was applied to a set of satellite broadband network system designs based on the five systems in Ku-band recently proposed to the Federal Communications Commission. The considered systems …
-
A reliability method for the analysis of turboelectric distributed propulsion electrical network architectures
… reliance on the electrical system, requiring architectures that meet specified thrust requirements at a minimum associated weight as well as providing the greatest performance against the proposed emissions targets. This thesis presents a method for evaluating the power and reliability …
-
Efficient embeddings of meshes and hypercubes on a group of future network architectures.
… structures used in parallel computing. Network embedding problems for meshes and hypercubes on traditional network architectures have been intensively studied during the past years. With the emergence of new network architectures, the traditional network embedding results are not enough …
-
Universal approximation of input-output maps and dynamical systems by neural network architectures
It is well known that feedforward neural networks can approximate any continuous function supported on a finite-dimensional compact set to arbitrary accuracy. However, many engineering applications require modeling infinite-dimensional functions, such as sequence-to-sequence transformations or …
Page 1 of 11