Current Search: Lin, Wei (x)
-
-
Title
-
SECOND LANGUAGE LEARNERS' RECOGNITION OF UNKNOWN WORDS.
-
Creator
-
Lin, Chai-Wei, Folse, Keith, University of Central Florida
-
Abstract / Description
-
Recent research has underscored the important role of second language (L2) vocabulary acquisition in the reading process. The present study examined how accurately eighteen learners of English as a Second Language (ESL) were able to identify unknown words within a reading passage. It is assumed that "noticing" unfamiliar words in a text plays an important role in being able to extract meaning from context, which may ultimately result in word learning; thus, whether or not learners are able to...
Show moreRecent research has underscored the important role of second language (L2) vocabulary acquisition in the reading process. The present study examined how accurately eighteen learners of English as a Second Language (ESL) were able to identify unknown words within a reading passage. It is assumed that "noticing" unfamiliar words in a text plays an important role in being able to extract meaning from context, which may ultimately result in word learning; thus, whether or not learners are able to recognize unknown words as unknown is a key step in vocabulary learning. The design of this study was based on previous research (Laufer and Yano, 2001) on the connection between first language background and self-assessment of L2 word knowledge. The first three steps of the Lafuer and Yano study were used in this study. In the first step, ESL learners self-assessed their ability to identify selected words in a text. After this, L2 learners explained or translated the meanings of the words. Finally, the two sets of data were analyzed to measure correlations. The findings of the study showed that teachers, as well as learners, should not underestimate the importance of vocabulary. Instead, they should provide more explicit vocabulary instruction and practice. In addition, L2 learners need to learn to identify words that are unknown. The act of "noticing" unknown words and identifying them as such is the initial step towards building vocabulary through reading. Lastly, L2 learners should not rely solely on context clues for the "guessing" strategy when they have a limited level of vocabulary because they may develop mistaken word knowledge, which would impact reading comprehension. Instead, learners should develop a wide range of strategies to comprehend academic reading.
Show less
-
Date Issued
-
2005
-
Identifier
-
CFE0000645, ucf:46550
-
Format
-
Document (PDF)
-
PURL
-
http://purl.flvc.org/ucf/fd/CFE0000645
-
-
Title
-
Exploring FPGA Implementation for Binarized Neural Network Inference.
-
Creator
-
Yang, Li, Fan, Deliang, Zhang, Wei, Lin, Mingjie, University of Central Florida
-
Abstract / Description
-
Deep convolutional neural network has taken an important role in machine learning algorithm. It is widely used in different areas such as computer vision, robotics, and biology. However, the models of deep neural networks become larger and more computation complexity which is a big obstacle for such huge model to implement on embedded systems. Recent works have shown the binarized neural networks (BNN), utilizing binarized (i.e. +1 and -1) convolution kernel and binarized activation function,...
Show moreDeep convolutional neural network has taken an important role in machine learning algorithm. It is widely used in different areas such as computer vision, robotics, and biology. However, the models of deep neural networks become larger and more computation complexity which is a big obstacle for such huge model to implement on embedded systems. Recent works have shown the binarized neural networks (BNN), utilizing binarized (i.e. +1 and -1) convolution kernel and binarized activation function, can significantly reduce the parameter size and computation cost, which makes it hardware-friendly for Field-Programmable Gate Arrays (FPGAs) implementation with efficient energy cost. This thesis proposes to implement a new parallel convolutional binarized neural network (i.e. PC-BNN) on FPGA with accurate inference. The embedded PC-BNN is designed for image classification on CIFAR-10 dataset and explores the hardware architecture and optimization of customized CNN topology.The parallel-convolution binarized neural network has two parallel binarized convolution layers which replaces the original single binarized convolution layer. It achieves around 86% on CIFAR-10 dataset and owns 2.3Mb parameter size. We implement our PC-BNN inference into the Xilinx PYNQ Z1 FPGA board which only has 4.9Mb on-chip Block RAM. Since the ultra-small network parameter, the whole model parameters can be stored on on-chip memory which can greatly reduce energy consumption and computation latency. Meanwhile, we design a new pipeline streaming architecture for PC-BNN hardware inference which can further increase the performance. The experiment results show that our PC-BNN inference on FPGA achieves 930 frames per second and 387.5 FPS/Watt, which are among the best throughput and energy efficiency compared to most recent works.
Show less
-
Date Issued
-
2018
-
Identifier
-
CFE0007384, ucf:52067
-
Format
-
Document (PDF)
-
PURL
-
http://purl.flvc.org/ucf/fd/CFE0007384
-
-
Title
-
Simulation Study of a GPRAM System: Error Control Coding and Connectionism.
-
Creator
-
Schultz, Steven, Wei, Lei, Lin, Mingjie, Yuan, Jiann-Shiun, University of Central Florida
-
Abstract / Description
-
A new computing platform, the General Purpose Reprsentation and Association Machine is studied and simulated. GPRAM machines use vague measurements to do a quick and rough assessment on a task; then use approximated message-passing algorithms to improve assessment; and finally selects ways closer to a solution, eventually solving it. We illustrate concepts and structures using simple examples.
-
Date Issued
-
2012
-
Identifier
-
CFE0004437, ucf:49361
-
Format
-
Document (PDF)
-
PURL
-
http://purl.flvc.org/ucf/fd/CFE0004437
-
-
Title
-
Applied Advanced Error Control Coding for General Purpose Representation and Association Machine Systems.
-
Creator
-
Dai, Bowen, Wei, Lei, Lin, Mingjie, Rahnavard, Nazanin, Turgut, Damla, Sun, Qiyu, University of Central Florida
-
Abstract / Description
-
General-Purpose Representation and Association Machine (GPRAM) is proposed to be focusing on computations in terms of variation and flexibility, rather than precision and speed. GPRAM system has a vague representation and has no predefined tasks. With several important lessons learned from error control coding, neuroscience and human visual system, we investigate several types of error control codes, including Hamming code and Low-Density Parity Check (LDPC) codes, and extend them to...
Show moreGeneral-Purpose Representation and Association Machine (GPRAM) is proposed to be focusing on computations in terms of variation and flexibility, rather than precision and speed. GPRAM system has a vague representation and has no predefined tasks. With several important lessons learned from error control coding, neuroscience and human visual system, we investigate several types of error control codes, including Hamming code and Low-Density Parity Check (LDPC) codes, and extend them to different directions.While in error control codes, solely XOR logic gate is used to connect different nodes. Inspired by bio-systems and Turbo codes, we suggest and study non-linear codes with expanded operations, such as codes including AND and OR gates which raises the problem of prior-probabilities mismatching. Prior discussions about critical challenges in designing codes and iterative decoding for non-equiprobable symbols may pave the way for a more comprehensive understanding of bio-signal processing. The limitation of XOR operation in iterative decoding with non-equiprobable symbols is described and can be potentially resolved by applying quasi-XOR operation and intermediate transformation layer. Constructing codes for non-equiprobable symbols with the former approach cannot satisfyingly perform with regarding to error correction capability. Probabilistic messages for sum-product algorithm using XOR, AND, and OR operations with non-equiprobable symbols are further computed. The primary motivation for the constructing codes is to establish the GPRAM system rather than to conduct error control coding per se. The GPRAM system is fundamentally developed by applying various operations with substantial over-complete basis. This system is capable of continuously achieving better and simpler approximations for complex tasks.The approaches of decoding LDPC codes with non-equiprobable binary symbols are discussed due to the aforementioned prior-probabilities mismatching problem. The traditional Tanner graph should be modified because of the distinction of message passing to information bits and to parity check bits from check nodes. In other words, the message passing along two directions are identical in conventional Tanner graph, while the message along the forward direction and backward direction are different in our case. A method of optimizing signal constellation is described, which is able to maximize the channel mutual information.A simple Image Processing Unit (IPU) structure is proposed for GPRAM system, to which images are inputted. The IPU consists of a randomly constructed LDPC code, an iterative decoder, a switch, and scaling and decision device. The quality of input images has been severely deteriorated for the purpose of mimicking visual information variability (VIV) experienced in human visual systems. The IPU is capable of (a) reliably recognizing digits from images of which quality is extremely inadequate; (b) achieving similar hyper-acuity performance comparing to human visual system; and (c) significantly improving the recognition rate with applying randomly constructed LDPC code, which is not specifically optimized for the tasks.
Show less
-
Date Issued
-
2016
-
Identifier
-
CFE0006449, ucf:51413
-
Format
-
Document (PDF)
-
PURL
-
http://purl.flvc.org/ucf/fd/CFE0006449
-
-
Title
-
RF Circuit Designs for Reliability and Process Variability Resilience.
-
Creator
-
Kritchanchai, Ekavut, Yuan, Jiann-Shiun, Sundaram, Kalpathy, Wei, Lei, Lin, Mingjie, Chow, Lee, University of Central Florida
-
Abstract / Description
-
Complementary metal oxide semiconductor (CMOS) radio frequency (RF) circuit design has been an ever-lasting research field. It has gained so much attention since RF circuits offer high mobility and wide-band efficiency, while CMOS technology provides the advantage of low cost and high integration capability. At the same time, CMOS device size continues to scale to the nanometer regime. Reliability issues with RF circuits have become more challenging than ever before. Reliability mechanisms,...
Show moreComplementary metal oxide semiconductor (CMOS) radio frequency (RF) circuit design has been an ever-lasting research field. It has gained so much attention since RF circuits offer high mobility and wide-band efficiency, while CMOS technology provides the advantage of low cost and high integration capability. At the same time, CMOS device size continues to scale to the nanometer regime. Reliability issues with RF circuits have become more challenging than ever before. Reliability mechanisms, such as gate oxide breakdown, hot carrier injection, negative bias temperature instability, have been amplified as the device size shrinks. In addition, process variability becomes a new design paradigm in modern RF circuits.In this Ph.D. work, a class F power amplifier (PA) was designed and analyzed using TSMC 180nm process technology. Its pre-layout and post-layout performances were compared. Post-layout parasitic effect decreases the output power and power-added efficiency. Physical insight of hot electron impact ionization and device self-heating was examined using the mixed-mode device and circuit simulation to mimic the circuit operating environment. Hot electron effect increases the threshold voltage and decreases the electron mobility of an n-channel transistor, which in turn decreases the output power and power-added efficiency of the power amplifier, as evidenced by the RF circuit simulation results. The device self-heating also reduces the output power and power-added efficiency of the PA. The process, voltage, and temperature (PVT) effects on a class AB power amplifier were studied. A PVT compensation technique using a current-source as an on-chip sensor was developed. The adaptive body bias design with the current sensing technique makes the output power and power-added efficiency much less sensitive to process variability, supply voltage variation, and temperature fluctuation, predicted by our derived analytical equations which are also verified by Agilent Advanced Design System (ADS) circuit simulation.Process variations and hot electron reliability on the mixer performance were also evaluated using different process corner models. The conversion gain and noise figure were modeled using analytical equations, supported by ADS circuit simulation results. A process invariant current source circuit was developed to eliminate process variation effect on circuit performance. Our conversion gain, noise figure, and output power show robust performance against PVT variations compared to those of a traditional design without using the current sensor, as evidenced by Monte Carlo statistical simulation.Finally, semiconductor process variations and hot electron reliability on the LC-voltage controlled oscillator (VCO) performance was evaluated using different process models. In our newly designed VCO, the phase noise and power consumptions are resilient against process variation effect due to the use of on-chip current sensing and compensation. Our Monte-Carlo simulation and analysis demonstrate that the standard deviation of phase noise in the new VCO design reduces about five times than that of the conventional design.
Show less
-
Date Issued
-
2016
-
Identifier
-
CFE0006131, ucf:51182
-
Format
-
Document (PDF)
-
PURL
-
http://purl.flvc.org/ucf/fd/CFE0006131
-
-
Title
-
Fabrication of Metallic Antenna Arrays using Nanoimprint Lithography.
-
Creator
-
Lin, Yu-wei, Kik, Pieter, Schoenfeld, Winston, Fathpour, Sasan, University of Central Florida
-
Abstract / Description
-
This Thesis describes the development of a cost-effective process for patterning nanoscale metal antenna arrays. Soft ultraviolet (UV) Nanoimprint Lithography (NIL) into bilayer resist was chosen since it enables repeatable large-scale replication of nanoscale patterns with good lift-off properties using a simple low-cost process. Nanofabrication often involves the use of Electron Beam Lithography (EBL) which enables the definition of nanoscale patterns on small sample regions, typically (
Show moreThis Thesis describes the development of a cost-effective process for patterning nanoscale metal antenna arrays. Soft ultraviolet (UV) Nanoimprint Lithography (NIL) into bilayer resist was chosen since it enables repeatable large-scale replication of nanoscale patterns with good lift-off properties using a simple low-cost process. Nanofabrication often involves the use of Electron Beam Lithography (EBL) which enables the definition of nanoscale patterns on small sample regions, typically (<) 1 mm2. However its sequential nature makes the large scale production of nanostructured substrates using EBL cost-prohibitive. NIL is a pattern replication method that can reproduce nanoscale patterns in a parallel fashion, allowing the low-cost and rapid production of a large number of nano-patterned samples based on a single nanostructured master mold.Standard NIL replicates patterns by pressing a nanostructured hard mold into a soft resist layer on a substrate resulting in exposed substrate regions, followed by an optional Reactive Ion Etching (RIE) step and the subsequent deposition of e.g. metal onto the exposed substrate area. However, non-vertical sidewalls of the features in the resist layer resulting from an imperfect hard mold, from reflow of the resist layer, or from isotropic etching in the RIE step may cause imperfect lift-off. To overcome this problem, a bilayer resist method can be used. Using stacked resist layers with different etch rates, undercut structures can be obtained after the RIE step, allowing for easy lift-off even when using a mold with non-vertical sidewalls. Experiments were carried out using a nanostructured negative SiO2 master mold. Various material combinations and processing methods were explored. The negative master mold was transferred to a positive soft mold, leaving the original master mold unaltered. The soft mold consisted of a 5 ?m thick top Poly(methyl methacrylate) (PMMA), or Polyvinyl alcohol (PVA) layer, a 1.5 mm thick Polydimethylsiloxane (PDMS) buffer layer, and a glass supporting substrate. The soft mold was pressed into a bilayer of 300 nm PMMA and 350 nm of silicon based UV-curable resist that was spin-coated onto a glass slide, and cured using UV radiation. The imprinted patterns were etched using RIE, exposing the substrate, followed by metal deposition and lift-off. The experiments show that the use of soft molds enables successful pattern transfer even in the presence of small dust particles between the mold and the resist layer. Feature sizes down to 280 nm were replicated successfully.
Show less
-
Date Issued
-
2013
-
Identifier
-
CFE0005026, ucf:49990
-
Format
-
Document (PDF)
-
PURL
-
http://purl.flvc.org/ucf/fd/CFE0005026
-
-
Title
-
Improving the performance of data-intensive computing on Cloud platforms.
-
Creator
-
Dai, Wei, Bassiouni, Mostafa, Zou, Changchun, Wang, Jun, Lin, Mingjie, Bai, Yuanli, University of Central Florida
-
Abstract / Description
-
Big Data such as Terabyte and Petabyte datasets are rapidly becoming the new norm for various organizations across a wide range of industries. The widespread data-intensive computing needs have inspired innovations in parallel and distributed computing, which has been the effective way to tackle massive computing workload for decades. One significant example is MapReduce, which is a programming model for expressing distributed computations on huge datasets, and an execution framework for data...
Show moreBig Data such as Terabyte and Petabyte datasets are rapidly becoming the new norm for various organizations across a wide range of industries. The widespread data-intensive computing needs have inspired innovations in parallel and distributed computing, which has been the effective way to tackle massive computing workload for decades. One significant example is MapReduce, which is a programming model for expressing distributed computations on huge datasets, and an execution framework for data-intensive computing on commodity clusters as well. Since it was originally proposed by Google, MapReduce has become the most popular technology for data-intensive computing. While Google owns its proprietary implementation of MapReduce, an open source implementation called Hadoop has gained wide adoption in the rest of the world. The combination of Hadoop and Cloud platforms has made data-intensive computing much more accessible and affordable than ever before.This dissertation addresses the performance issue of data-intensive computing on Cloud platforms from three different aspects: task assignment, replica placement, and straggler identification. Both task assignment and replica placement are subjects closely related to load balancing, which is one of the key issues that can significantly affect the performance of parallel and distributed applications. While task assignment schemes strive to balance data processing load among cluster nodes to achieve minimum job completion time, replica placement policies aim to assign block replicas to cluster nodes according to their processing capabilities to exploit data locality to the maximum extent. Straggler identification is also one of the crucial issues data-intensive computing has to deal with, as the overall performance of parallel and distributed applications is often determined by the node with the lowest performance. The results of extensive evaluation tests confirm that the schemes/policies proposed in this dissertation can improve the performance of data-intensive applications running on Cloud platforms.
Show less
-
Date Issued
-
2017
-
Identifier
-
CFE0006731, ucf:51896
-
Format
-
Document (PDF)
-
PURL
-
http://purl.flvc.org/ucf/fd/CFE0006731
-
-
Title
-
Multi-Physics Model of Key Components In High Efficiency Vehicle Drive.
-
Creator
-
Lin, Shao Hua, Wu, Xinzhang, Sundaram, Kalpathy, Wahid, Parveen, Wei, Lei, Chow, Louis, University of Central Florida
-
Abstract / Description
-
Hybrid Electric Vehicles (HEVs) and Electric Vehicles (EVs) are crucial technologies for the automotive industry to meet society's demands for cleaner, more energy efficient transportation. Meeting the need to provide power which sustains HEVs and EVs is an immediate area of concern that research and development within the automotive community must address. Electric batteries and electrical motors are the key components in HEV and EV power generation and transmission, and their performance...
Show moreHybrid Electric Vehicles (HEVs) and Electric Vehicles (EVs) are crucial technologies for the automotive industry to meet society's demands for cleaner, more energy efficient transportation. Meeting the need to provide power which sustains HEVs and EVs is an immediate area of concern that research and development within the automotive community must address. Electric batteries and electrical motors are the key components in HEV and EV power generation and transmission, and their performance plays very important role in the overall performance of the modern high efficiency vehicles. Therefore, in this dissertation, we are motivated to study the electric batteries, interior permanent motor (IPM), in the context of modern hybrid electric/electric drive systems, from both multi-physics and system level perspectives. Electrical circuit theory, electromagnetic Finite Element Analysis (FEA), and Computational Fluid Dynamic (CFD) finite volume method will be used primarily in this work. The work has total of five parts, and they are introduced in the following.Firstly, Battery thermal management design is critical in HEV and EV development. Accurate temperature distribution of the battery cells during vehicle operation is required for achieving optimized design. We propose a novel electrical-thermal battery modeling technique that couples a temperature dependent battery circuit model and a physics-based CFD model to meet this need. The electrical circuit model serves as a heat generation mechanism for the CFD model, and the CFD model provides the temperature distribution of the battery cells, which can also impact the heat generation of the electrical battery model. In this part of work, simulation data has been derived from the model respective to electrical performance of the battery as well as the temperature distribution simultaneously in consideration of the physical dimensions, material properties, and cooling conditions. The proposed model is validated against a battery model that couples the same electrical model with a known equivalent thermal model.Secondly, we propose an accurate system level Foster network thermal model. The parameters of the model are extracted from step responses of the CFD battery thermal model. The Foster network model and the CFD model give the same results. The Foster network can couple with battery circuit model to form an electric-thermal battery model for system simulation.Thirdly, IPM electric machines are important in high performance drive systems. During normal operations, irreversible demagnetization can occur due to temperature rise and various loading conditions. We investigate the performance of an IPM using 3d time stepping electromagnetic FEA considering magnet's temperature dependency. Torque, flux linkage, induced voltage, inductance and saliency of the IPM will be studied in details. Finally, we use CFD to predict the non-uniform temperature distribution of the IPM machine and the impact of this distribution on motor performance. Fourthly, we will switch gear to investigate the IPM motor on the system level. A reduced order IPM model is proposed to consider the effect of demagnetization of permanent magnet due to temperature effect. The proposed model is validated by comparing its results to the FEA results.Finally, a HEV is a vehicle that has both conventional mechanical (i.e. internal combustion engine) and electrical propulsion systems. The electrical powertrain is used to work with the conventional powertrain to achieve higher fuel economy and lower emissions. Computer based modeling and simulation techniques are therefore essential to help reduce the design cost and optimize system performance. Due to the complexity of hybrid vehicles, multi-domain modeling ability is preferred for both component modeling and system simulation. We present a HEV library developed using VHDL-AMS.
Show less
-
Date Issued
-
2013
-
Identifier
-
CFE0005024, ucf:50016
-
Format
-
Document (PDF)
-
PURL
-
http://purl.flvc.org/ucf/fd/CFE0005024
-
-
Title
-
Differential Games for Multi-Agent Systems under Distributed Information.
-
Creator
-
Lin, Wei, Qu, Zhihua, Simaan, Marwan, Haralambous, Michael, Das, Tuhin, Yong, Jiongmin, University of Central Florida
-
Abstract / Description
-
In this dissertation, we consider differential games for multi-agent systems under distributed information where every agent is only able to acquire information about the others according to a directed information graph of local communication/sensor networks. Such games arise naturally from many applications including mobile robot coordination, power system optimization, multi-player pursuit-evasion games, etc. Since the admissible strategy of each agent has to conform to the information...
Show moreIn this dissertation, we consider differential games for multi-agent systems under distributed information where every agent is only able to acquire information about the others according to a directed information graph of local communication/sensor networks. Such games arise naturally from many applications including mobile robot coordination, power system optimization, multi-player pursuit-evasion games, etc. Since the admissible strategy of each agent has to conform to the information graph constraint, the conventional game strategy design approaches based upon Riccati equation(s) are not applicable because all the agents are required to have the information of the entire system. Accordingly, the game strategy design under distributed information is commonly known to be challenging. Toward this end, we propose novel open-loop and feedback game strategy design approaches for Nash equilibrium and noninferior solutions with a focus on linear quadratic differential games. For the open-loop design, approximate Nash/noninferior game strategies are proposed by integrating distributed state estimation into the open-loop global-information Nash/noninferior strategies such that, without global information, the distributed game strategies can be made arbitrarily close to and asymptotically converge over time to the global-information strategies. For the feedback design, we propose the best achievable performance indices based approach under which the distributed strategies form a Nash equilibrium or noninferior solution with respect to a set of performance indices that are the closest to the original indices. This approach overcomes two issues in the classical optimal output feedback approach: the simultaneous optimization and initial state dependence. The proposed open-loop and feedback design approaches are applied to an unmanned aerial vehicle formation control problem and a multi-pursuer single-evader differential game problem, respectively. Simulation results of several scenarios are presented for illustration.
Show less
-
Date Issued
-
2013
-
Identifier
-
CFE0005025, ucf:49991
-
Format
-
Document (PDF)
-
PURL
-
http://purl.flvc.org/ucf/fd/CFE0005025