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Full-Text Articles in Computer Engineering

Computationally Efficient Mixed Pixel Decomposition Using Constrained Optimizations, Lidan Miao Dec 2007

Computationally Efficient Mixed Pixel Decomposition Using Constrained Optimizations, Lidan Miao

Doctoral Dissertations

Sensors with spatial resolution larger than targets yield mixed pixel, i.e., pixel whose measurement is a composite of different sources (endmembers). The analysis of mixed pixels demands subpixel methods to perform source separation and quantification, which is a problem of blind source separation (BSS). Although various algorithms have been proposed, several important issues remain unresolved. First, assuming the endmembers are known, the abundance estimation is commonly performed by employing a least squares criterion, which however makes the estimation sensitive to noise and outliers, and the endmembers with very similar signatures are difficult to differentiate. In addition, the nonnegative con- ...


Fabric-On-A-Chip: Toward Consolidating Packet Switching Functions On Silicon, William B. Matthews Dec 2007

Fabric-On-A-Chip: Toward Consolidating Packet Switching Functions On Silicon, William B. Matthews

Doctoral Dissertations

The switching capacity of an Internet router is often dictated by the memory bandwidth required to bu¤er arriving packets. With the demand for greater capacity and improved service provisioning, inherent memory bandwidth limitations are encountered rendering input queued (IQ) switches and combined input and output queued (CIOQ) architectures more practical. Output-queued (OQ) switches, on the other hand, offer several highly desirable performance characteristics, including minimal average packet delay, controllable Quality of Service (QoS) provisioning and work-conservation under any admissible traffic conditions. However, the memory bandwidth requirements of such systems is O(NR), where N denotes the number of ports ...


Hardware-Efficient Scalable Reinforcement Learning Systems, Zhenzhen Liu Dec 2007

Hardware-Efficient Scalable Reinforcement Learning Systems, Zhenzhen Liu

Doctoral Dissertations

Reinforcement Learning (RL) is a machine learning discipline in which an agent learns by interacting with its environment. In this paradigm, the agent is required to perceive its state and take actions accordingly. Upon taking each action, a numerical reward is provided by the environment. The goal of the agent is thus to maximize the aggregate rewards it receives over time. Over the past two decades, a large variety of algorithms have been proposed to select actions in order to explore the environment and gradually construct an e¤ective strategy that maximizes the rewards. These RL techniques have been successfully ...


Automated Genome-Wide Protein Domain Exploration, Bhanu Prasad Rekepalli Dec 2007

Automated Genome-Wide Protein Domain Exploration, Bhanu Prasad Rekepalli

Doctoral Dissertations

Exploiting the exponentially growing genomics and proteomics data requires high quality, automated analysis. Protein domain modeling is a key area of molecular biology as it unravels the mysteries of evolution, protein structures, and protein functions. A plethora of sequences exist in protein databases with incomplete domain knowledge. Hence this research explores automated bioinformatics tools for faster protein domain analysis. Automated tool chains described in this dissertation generate new protein domain models thus enabling more effective genome-wide protein domain analysis. To validate the new tool chains, the Shewanella oneidensis and Escherichia coli genomes were processed, resulting in a new peptide domain ...


Self-Certified Public Key Cryptographic Methodologies For Resource-Constrained Wireless Sensor Networks, Ortal Arazi Dec 2007

Self-Certified Public Key Cryptographic Methodologies For Resource-Constrained Wireless Sensor Networks, Ortal Arazi

Doctoral Dissertations

As sensor networks become one of the key technologies to realize ubiquitous computing, security remains a growing concern. Although a wealth of key-generation methods have been developed during the past few decades, they cannot be directly applied to sensor network environments. The resource-constrained characteristics of sensor nodes, the ad-hoc nature of their deployment, and the vulnerability of wireless media pose a need for unique solutions.

A fundamental requisite for achieving security is the ability to provide for data con…dential- ity and node authentication. However, the scarce resources of sensor networks have rendered the direct applicability of existing public key ...


Personalized Health Monitoring Using Evolvable Block-Based Neural Networks, Wei Jiang Aug 2007

Personalized Health Monitoring Using Evolvable Block-Based Neural Networks, Wei Jiang

Doctoral Dissertations

This dissertation presents personalized health monitoring using evolvable block-based neural networks. Personalized health monitoring plays an increasingly important role in modern society as the population enjoys longer life. Personalization in health monitoring considers physiological variations brought by temporal, personal or environmental differences, and demands solutions capable to reconfigure and adapt to specific requirements. Block-based neural networks (BbNNs) consist of 2-D arrays of modular basic blocks that can be easily implemented using reconfigurable digital hardware such as field programmable gate arrays (FPGAs) that allow on-line partial reorganization. The modular structure of BbNNs enables easy expansion in size by adding more blocks ...


Energy-Aware And Secure Routing With Trust Levels For Wireless Ad Hoc And Sensor Networks, Eyad Taqieddin Jan 2007

Energy-Aware And Secure Routing With Trust Levels For Wireless Ad Hoc And Sensor Networks, Eyad Taqieddin

Doctoral Dissertations

"This dissertation focuses on the development of routing algorithms for secure and trusted routing in wireless ad hoc and sensor network. The first paper presents the Trust Level Routing (TLR) protocol, an extension of the optimized energy-delay routing (OEDR) protocol, focusing on the integrity, reliability and survivability of the wireless network...The second paper analyzes both OLSR and TLR in terms of survivability and reliability to emphasize the improved performance of the network in terms of lifetime and proper delivery of data...The third paper proposes a statistical reputation model that uses the watchdog mechanism to observe the cooperation of ...