A nanoliter volume nuclear magnetic resonance (NMR) system using tunneling magneto-resistive (TMR) sensors to recognize biomolecules

The need to incorporate advanced engineering tools in biology, biochemistry and medicine is in great demand. Many of the existing instruments and tools are usually expensive and require special facilities.^ With the advent of nanotechnology in the past decade, new approaches to develop devices and tools have been generated by academia and industry. ^ One such technology, NMR spectroscopy, has been used by biochemists for more than 2 decades to study the molecular structure of chemical compounds. However, NMR spectrometers are very expensive and require special laboratory rooms for their proper operation. High magnetic fields with strengths in the order of several Tesla make these instruments unaffordable to most research groups.^ This doctoral research proposes a new technology to develop NMR spectrometers that can operate at field strengths of less than 0.5 Tesla using an inexpensive permanent magnet and spin dependent nanoscale magnetic devices. This portable NMR system is intended to analyze samples as small as a few nanoliters.^ The main problem to resolve when downscaling the variables is to obtain an NMR signal with high Signal-To-Noise-Ratio (SNR). A special Tunneling Magneto-Resistive (TMR) sensor design was developed to achieve this goal. The minimum specifications for each component of the proposed NMR system were established. A complete NMR system was designed based on these minimum requirements. The goat was always to find cost effective realistic components. The novel design of the NMR system uses technologies such as Direct Digital Synthesis (DDS), Digital Signal Processing (DSP) and a special Backpropagation Neural Network that finds the best match of the NMR spectrum. The system was designed, calculated and simulated with excellent results.^ In addition, a general method to design TMR Sensors was developed. The technique was automated and a computer program was written to help the designer perform this task interactively.^

VLSI testing and characterization system for micro electro-mechanical (MEM) sensors

The purpose of this investigation was to develop and implement a general purpose VLSI (Very Large Scale Integration) Test Module based on a FPGA (Field Programmable Gate Array) system to verify the mechanical behavior and performance of MEM sensors, with associated corrective capabilities; and to make use of the evolving System-C, a new open-source HDL (Hardware Description Language), for the design of the FPGA functional units. System-C is becoming widely accepted as a platform for modeling, simulating and implementing systems consisting of both hardware and software components. In this investigation, a Dual-Axis Accelerometer (ADXL202E) and a Temperature Sensor (TMP03) were used for the test module verification. Results of the test module measurement were analyzed for repeatability and reliability, and then compared to the sensor datasheet. Further study ideas were identified based on the study and results analysis. ASIC (Application Specific Integrated Circuit) design concepts were also being pursued.

Structural Health Monitoring using Index Based Reasoning for Unmanned Aerial Vehicles

Unmanned Aerial Vehicles (UAVs) may develop cracks, erosion, delamination or other damages due to aging, fatigue or extreme loads. Identifying these damages is critical for the safe and reliable operation of the systems. ^ Structural Health Monitoring (SHM) is capable of determining the conditions of systems automatically and continually through processing and interpreting the data collected from a network of sensors embedded into the systems. With the desired awareness of the systems’ health conditions, SHM can greatly reduce operational cost and speed up maintenance processes. ^ The purpose of this study is to develop an effective, low-cost, flexible and fault tolerant structural health monitoring system. The proposed Index Based Reasoning (IBR) system started as a simple look-up-table based diagnostic system. Later, Fast Fourier Transformation analysis and neural network diagnosis with self-learning capabilities were added. The current version is capable of classifying different health conditions with the learned characteristic patterns, after training with the sensory data acquired from the operating system under different status. ^ The proposed IBR systems are hierarchy and distributed networks deployed into systems to monitor their health conditions. Each IBR node processes the sensory data to extract the features of the signal. Classifying tools are then used to evaluate the local conditions with health index (HI) values. The HI values will be carried to other IBR nodes in the next level of the structured network. The overall health condition of the system can be obtained by evaluating all the local health conditions. ^ The performance of IBR systems has been evaluated by both simulation and experimental studies. The IBR system has been proven successful on simulated cases of a turbojet engine, a high displacement actuator, and a quad rotor helicopter. For its application on experimental data of a four rotor helicopter, IBR also performed acceptably accurate. The proposed IBR system is a perfect fit for the low-cost UAVs to be the onboard structural health management system. It can also be a backup system for aircraft and advanced Space Utility Vehicles. ^

System-on-a-Chip (SOC) based environmental monitoring platform for the detection of hazardous materials

The purpose of this research is design considerations for environmental monitoring platforms for the detection of hazardous materials using System-on-a-Chip (SoC) design. Design considerations focus on improving key areas such as: (1) sampling methodology; (2) context awareness; and (3) sensor placement. These design considerations for environmental monitoring platforms using wireless sensor networks (WSN) is applied to the detection of methylmercury (MeHg) and environmental parameters affecting its formation (methylation) and deformation (demethylation). ^ The sampling methodology investigates a proof-of-concept for the monitoring of MeHg using three primary components: (1) chemical derivatization; (2) preconcentration using the purge-and-trap (P&T) method; and (3) sensing using Quartz Crystal Microbalance (QCM) sensors. This study focuses on the measurement of inorganic mercury (Hg) (e.g., Hg2+) and applies lessons learned to organic Hg (e.g., MeHg) detection. ^ Context awareness of a WSN and sampling strategies is enhanced by using spatial analysis techniques, namely geostatistical analysis (i.e., classical variography and ordinary point kriging), to help predict the phenomena of interest in unmonitored locations (i.e., locations without sensors). This aids in making more informed decisions on control of the WSN (e.g., communications strategy, power management, resource allocation, sampling rate and strategy, etc.). This methodology improves the precision of controllability by adding potentially significant information of unmonitored locations.^ There are two types of sensors that are investigated in this study for near-optimal placement in a WSN: (1) environmental (e.g., humidity, moisture, temperature, etc.) and (2) visual (e.g., camera) sensors. The near-optimal placement of environmental sensors is found utilizing a strategy which minimizes the variance of spatial analysis based on randomly chosen points representing the sensor locations. Spatial analysis is employed using geostatistical analysis and optimization occurs with Monte Carlo analysis. Visual sensor placement is accomplished for omnidirectional cameras operating in a WSN using an optimal placement metric (OPM) which is calculated for each grid point based on line-of-site (LOS) in a defined number of directions where known obstacles are taken into consideration. Optimal areas of camera placement are determined based on areas generating the largest OPMs. Statistical analysis is examined by using Monte Carlo analysis with varying number of obstacles and cameras in a defined space. ^

Optimum wireless power transmission for sensors embedded in concrete

Various nondestructive testing (NDT) technologies for construction and performance monitoring have been studied for decades. Recently, the rapid evolution of wireless sensor network (WSN) technologies has enabled the development of sensors that can be embedded in concrete to monitor the structural health of infrastructure. Such sensors can be buried inside concrete and they can collect and report valuable volumetric data related to the health of a structure during and/or after construction. Wireless embedded sensors monitoring system is also a promising solution for decreasing the high installation and maintenance cost of the conventional wire based monitoring systems. Wireless monitoring sensors need to operate for long time. However, sensor batteries have finite life-time. Therefore, in order to enable long operational life of wireless sensors, novel wireless powering methods, which can charge the sensors’ rechargeable batteries wirelessly, need to be developed. The optimization of RF wireless powering of sensors embedded in concrete is studied here. First, our analytical results focus on calculating the transmission loss and propagation loss of electromagnetic waves penetrating into plain concrete at different humidity conditions for various frequencies. This analysis specifically leads to the identification of an optimum frequency range within 20–80 MHz that is validated through full-wave electromagnetic simulations. Second, the effects of various reinforced bar configurations on the efficiency of wireless powering are investigated. Specifically, effects of the following factors are studied: rebar types, rebar period, rebar radius, depth inside concrete, and offset placement. This analysis leads to the identification of the 902–928 MHz ISM band as the optimum power transmission frequency range for sensors embedded in reinforced concrete, since antennas working in this band are less sensitive to the effects of varying humidity as well as rebar configurations. Finally, optimized rectennas are designed for receiving and/or harvesting power in order to charge the rechargeable batteries of the embedded sensors. Such optimized wireless powering systems exhibit significantly larger efficiencies than the efficiencies of conventional RF wireless powering systems for sensors embedded in plain or reinforced concrete.