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You searched for +publisher:"University of New Mexico" +contributor:("Pollard, Howard"). Showing records 1 – 3 of 3 total matches.

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University of New Mexico

1. Al Zuraiqi, Eyad. Neural network field programmable gate array (FPGA) controllers for reconfigurable antennas.

Degree: Electrical and Computer Engineering, 2012, University of New Mexico

Advantages of reconfigurable antennas are numerous, but limited by the method of controlling their configuration. This dissertation proposes to utilize the advantages of both Neural Networks (NN) and Field Programmable Gate Arrays (FPGAs) to overcome this dilemma. In this work, the methodology of modeling of reconfigurable antennas using neural network embedded on an FPGA board is presented. This work shows a new approach of modeling reconfigurable antennas using neural networks in Matlab, a code is written to generate a NN for any antenna (or any reconfigurable system in general) by providing input/output data of the antenna. An HDL code is generated using Xilinx System Generator and sent to an FPGA board using Xilinx ISE. With a NN embedded on the FPGA board, we have a highly reconfigurable system in real time controller that thinks exactly as the system it models. This brain is connected to the antenna and becomes the decision maker in antenna switching or reconfiguration. Also, with the new approach of using Matlab to generate HDL code; this work opens the door to those who are interested in implementing designs on FPGAs without having enough knowledge in HDL programming. Different types of reconfigurable antennas with different way of reconfigurability are modeled and simulated. NN models show great match with measured antennas data. NN_FPGA controller is built for each antenna. Advisors/Committee Members: Christodoulou, Christos, Pollard, Howard, Simpson, Jamesina, Taha, Mahmoud.

Subjects/Keywords: Adaptive antennas – Computer simulation.; Adaptive antennas – Automatic control.; Programmable controllers.; Field programmable gate arrays.; Neural networks (Computer science)

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APA (6th Edition):

Al Zuraiqi, E. (2012). Neural network field programmable gate array (FPGA) controllers for reconfigurable antennas. (Doctoral Dissertation). University of New Mexico. Retrieved from http://hdl.handle.net/1928/20802

Chicago Manual of Style (16th Edition):

Al Zuraiqi, Eyad. “Neural network field programmable gate array (FPGA) controllers for reconfigurable antennas.” 2012. Doctoral Dissertation, University of New Mexico. Accessed June 20, 2019. http://hdl.handle.net/1928/20802.

MLA Handbook (7th Edition):

Al Zuraiqi, Eyad. “Neural network field programmable gate array (FPGA) controllers for reconfigurable antennas.” 2012. Web. 20 Jun 2019.

Vancouver:

Al Zuraiqi E. Neural network field programmable gate array (FPGA) controllers for reconfigurable antennas. [Internet] [Doctoral dissertation]. University of New Mexico; 2012. [cited 2019 Jun 20]. Available from: http://hdl.handle.net/1928/20802.

Council of Science Editors:

Al Zuraiqi E. Neural network field programmable gate array (FPGA) controllers for reconfigurable antennas. [Doctoral Dissertation]. University of New Mexico; 2012. Available from: http://hdl.handle.net/1928/20802


University of New Mexico

2. Essenmacher, Paul. A real-time, reconfigurable system for energy, error-resilient, and scalable lossless ECG coding.

Degree: Electrical and Computer Engineering, 2011, University of New Mexico

Electrocardiogram (ECG) monitoring systems have evolved to the point where they are now portable and can monitor the patient 24/7 and transmit alerts and ECG data to parents and doctors as soon as a heart irregularity is detected. With the advances in these systems, there is a need for the incorporation of ECG coding systems to reduce the bandwidth used when data is transmitted and to incorporate methods to provide data recovery in the event of a transmission error. However, while ECG encoding systems for hospital or home care settings has been thoroughly researched, the application of ECG encoding systems to portable ECG monitoring systems where there is a much higher likelihood of noise interference during transmission of the data has not been fully investigated. The goal of this work is to develop a real-time ECG encoding system that requires low hardware and power usage, provides lossless signal compression, and provides recovery of as much data as possible in the event of data corruption of packets during transmission. An entropy based compression algorithm is developed based on the Huffman code which is then transformed to reversible variable length codes. This allows the data packets to be both frontward and backwards decodable allowing for greater data recovery in the event that portions of a packet are corrupted. The implementation is designed to be able to encode any sized bit width by utilizing a combination of 4, 6, or 8- bit entropy coders. Two separate encoding systems are investigated using the before-mentioned encoding algorithm. The first system recomputes the Reversible Variable Length Code (RVLC) tables periodically while the signal is being encoded in an effort to adapt to any changes in the signal. The second system uses a pre-calculated RVLC table that minimizes the delay and also significantly reduces the required hardware resources. We provide optimal, reconfigurable implementations for both systems. Furthermore, the effectiveness and error-resilient performance of both systems are validated on 12-bit and 16-bit ECG signals. The performance of the system is shown to be diagnostically lossless in noisy communications channels with significant bit errors. This represents a significant improvement over existing systems that do not employ the proposed error resilient encoding methods. Advisors/Committee Members: Pattichis, Marios, Pollard, Howard, Zarkesh-Ha, Payman.

Subjects/Keywords: Electrocardiography – Data processing.; Error-correcting codes (Information theory)

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APA (6th Edition):

Essenmacher, P. (2011). A real-time, reconfigurable system for energy, error-resilient, and scalable lossless ECG coding. (Masters Thesis). University of New Mexico. Retrieved from http://hdl.handle.net/1928/13084

Chicago Manual of Style (16th Edition):

Essenmacher, Paul. “A real-time, reconfigurable system for energy, error-resilient, and scalable lossless ECG coding.” 2011. Masters Thesis, University of New Mexico. Accessed June 20, 2019. http://hdl.handle.net/1928/13084.

MLA Handbook (7th Edition):

Essenmacher, Paul. “A real-time, reconfigurable system for energy, error-resilient, and scalable lossless ECG coding.” 2011. Web. 20 Jun 2019.

Vancouver:

Essenmacher P. A real-time, reconfigurable system for energy, error-resilient, and scalable lossless ECG coding. [Internet] [Masters thesis]. University of New Mexico; 2011. [cited 2019 Jun 20]. Available from: http://hdl.handle.net/1928/13084.

Council of Science Editors:

Essenmacher P. A real-time, reconfigurable system for energy, error-resilient, and scalable lossless ECG coding. [Masters Thesis]. University of New Mexico; 2011. Available from: http://hdl.handle.net/1928/13084


University of New Mexico

3. Laros, James Howard, III. Measuring and tuning energy efficiency on large scale high performance computing platforms.

Degree: Electrical and Computer Engineering, 2012, University of New Mexico

Recognition of the importance of power in the field of High Performance Computing, whether it be as an obstacle, expense or design consideration, has never been greater and more pervasive. Research has been conducted in a number of areas related to power. Little, if any, existing research has focused on large scale High Performance Computing. Part of the reason is the lack of measurement capability currently available on small or large platforms. Typically, research is conducted using coarse methods of measurement such as inserting a power meter between the power source and the platform, or fine grained measurements using custom instrumented boards (with obvious limitations in scale). To collect the measurements necessary to analyze real scientific computing applications at large scale, an in-situ measurement capability must exist on a large scale capability class platform. In response to this challenge, the unique power measurement capabilities of the Cray XT architecture were exploited to gain an understanding of power use and the effects of tuning both CPU and network bandwidth. Modifications were made at the operating system level to deterministically halt cores when idle. Additionally, capabilities to alter operating P-state were added. At the application level, an understanding of the power requirements of a range of important DOE/NNSA production scientific computing applications running at large scale (thousands of nodes) is gained, by simultaneously collecting current and voltage measurements on the hosting nodes. The effects of both CPU and network bandwidth tuning are examined and energy savings opportunities of up to 39% with little or no impact on run-time performance is demonstrated. Capturing scale effects was key. This thesis provides strong evidence that next generation large-scale platforms should not only approach CPU frequency scaling differently, but could also benefit from the capability to tune other platform components, such as the network, to achieve energy efficient performance. Advisors/Committee Members: Shu, Wei, Shu, Wei, Pollard, Howard, Ang, James.

Subjects/Keywords: Computer platforms – Energy consumption – Measurement; High performance processors – Energy consumption – Measurement; High performance computing.

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Laros, James Howard, I. (2012). Measuring and tuning energy efficiency on large scale high performance computing platforms. (Masters Thesis). University of New Mexico. Retrieved from http://hdl.handle.net/1928/20773

Chicago Manual of Style (16th Edition):

Laros, James Howard, III. “Measuring and tuning energy efficiency on large scale high performance computing platforms.” 2012. Masters Thesis, University of New Mexico. Accessed June 20, 2019. http://hdl.handle.net/1928/20773.

MLA Handbook (7th Edition):

Laros, James Howard, III. “Measuring and tuning energy efficiency on large scale high performance computing platforms.” 2012. Web. 20 Jun 2019.

Vancouver:

Laros, James Howard I. Measuring and tuning energy efficiency on large scale high performance computing platforms. [Internet] [Masters thesis]. University of New Mexico; 2012. [cited 2019 Jun 20]. Available from: http://hdl.handle.net/1928/20773.

Council of Science Editors:

Laros, James Howard I. Measuring and tuning energy efficiency on large scale high performance computing platforms. [Masters Thesis]. University of New Mexico; 2012. Available from: http://hdl.handle.net/1928/20773

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