A Software Approach to Manage and Maintain Warfighter Training Systems

The use of simulation technologies in Warfighter training will need to increase as resource constraints tighten on the US military. Multiple studies have shown that simulation-based training promotes faster skills acquisition and assessment at greatly reduced cost and time. However, as these technologies become more pervasive, in a wider variety of training tasks, integrating them into a cohesive environment is increasingly complex. Industries such as medical and construction use single purpose trainers to teach welding or suturing, but Warfighter training requires multiple systems, with unique capabilities, to interoperate. For example, it may be necessary for a ground-based Soldier trainer to be connected to fighter and tank trainers to adequately prepare for actual missions. The setup, running, debugging, and maintenance of these systems is a very difficult task that can use up resource savings achieved through simulation. As each training system is composed of multiple components (e.g., projectors, computers, and motion trackers), it is imperative to understand items such as: 1) input parameter initialization (e.g., scenario, number of Soldiers, etc.), 2) the real-time status of individual systems, 3) data flow and timing between systems, and 4) software and hardware malfunctions. In this paper, the Mixed Reality Toolbox (MRT), developed to perform these operations, is presented. A review of current research and products available to the training community will be presented. In addition, the issues involved with combining physical, mixed-reality, and virtual reality environments into a single training system will be discussed. Lastly, results will be presented showing the effectiveness of the MRT when used to run two simulated training exercises with systems comprised of five to fifteen different technology and/or real components. In both exercises, the MRT allowed a significant reduction in system setup and runtime compared to comparable setups done previously

FPGA BASED IMPLEMENTATION OF A POSITION ESTIMATOR FOR CONTROLLING A SWITCHED RELUCTANCE MOTOR

Rotor Position information is essential in the operation of the Switched Reluctance Motor (SRM) for properly controlling its phase currents. This thesis uses Field Programmable Gate Array (FPGA) technology to implement a method to estimate the SRMs rotor position using the inverse inductance value of the SRMs phases. The estimated rotor position is given as input to the Commutator circuit, also implemented in the FPGA, to determine when torque-producing currents should be input in the SRM phase windings. The Estimator and Commutator design is coded using Verilog HDL and is simulated using Xilinx tools. This circuit is implemented on a Xilinx Virtex XCV800 FPGA system. The experimentally generated output is validated by comparing it with simulation results from a Simulink model of the Estimator. The performance of the FPGA based SRM rotor position estimator in terms of calculation time is compared to a digital signal processor (DSP) implementation of the same position estimator algorithm. It is found that the FPGA rotor position Estimator with a 5MHz clock can update its rotor position estimate every 7s compared to an update time of 50s for a TMS320C6701-150 DSP implementation using a commercial DSP board. This is a greater than 7 to one reduction in the update time

Research Naval Postgraduate School, v.12, no.3, October 2002

NPS Research is published by the Research and Sponsored Programs, Office of the Vice President and Dean of Research, in accordance with NAVSOP-35. Views and opinions expressed are not necessarily those of the Department of the Navy.Approved for public release; distribution is unlimited

Techniques for Wireless Channel Modeling in Harsh Environments

With the rapid growth in the networked environments for different industrial, scientific and defense applications, there is a vital need to assure the user or application a certain level of Quality of Service (QoS). Environments like the industrial environment are particularly harsh with interference from metal structures (as found in the manufacturing sector), interference generated during wireless propagation, and multipath fading of the radio frequency (RF) signal all invite novel mitigation techniques. The challenge of achieving the benefits like improved energy efficiency using wireless is closely coupled with maintaining network QoS requirements. Assessment and management of QoS needs to occur, allowing the network to adapt to changes in the RF, information, and operational environments. The capacity to adapt is paramount to maintaining the required operational performance (throughput, latency, reliability and security). This thesis address the need for accurate radio channel modeling techniques to improve the performance of the wireless communication systems. Multiple different channel modeling techniques are considered including statistical models, ray tracing techniques, finite time-difference technique, transmission line matrix method (TLM), and stochastic differential equation-based (SDE) dynamic channel models. Measurement of ambient RF is performed at several harsh industrial environments to demonstrate the existence of uncertainty in channel behavior. Comparison of various techniques is performed with metrics including accuracy, applicability, and computational efficiency. SDE- and TLM-based methods are validated using indoor and outdoor measurements. Fast, accurate techniques for modeling multipath fading in harsh environments is explored. Application of dynamic channel models is explored for improving QoS of wireless communication system. The TLM-based models provide accurate site-specific path loss calculations taking into consideration materials and propagation characteristics of propagating environment. The validation studies confirm the technique is comparable with existing channel models. The TLM-based channel models is extended to compute the site-specific multipath characteristics of the radio channel eliminating the need for experimental measurement. The TLM-based simulator is also integrated with packet-level network simulator to perform end to end-to-end site specific calculation of wireless network performance. The SDE-channel models provide accurate online estimations of the channel performance along with accurate one-step prediction of the signal strength. The validation studies confirm the accuracy of the technique. Application of the SDE-based models for adaptive antenna control is formulated using online recursive estimation

Information Processing with Electron Spins

Information processors process information in a variety of ways. The human brain processes information through a highly interconnected system of neurons and synapses, while a digital computer processes information by having a binary switch toggle on and off in response to a stream of binary bits. The “switch” is the most primitive unit of the modern computer. The better it is (faster, more energy efficient, more reliable, etc.), the more advanced is the computer hardware. Energy efficiency, however, is more important than any other attribute, not so much because energy is costly, but because too much energy dissipation prevents increasing the density of switches on a chip that is necessary to make the chip increasingly more powerful. Reducing dissipation entails radically new and often revolutionary approaches for implementing the switch. One such approach is to encode digital bit information in the spin polarization of a single electron (or ensemble of electrons) and then using two mutually antiparallel polarizations to represent the binary bits 0 and 1. Switching between the bits can be accomplished by simply flipping the polarizations of the spins, which takes very little energy. Such switches are extremely energy efficient if designed properly, but they are somewhat slower than traditional transistor-based switches and can be more error prone. This paper discusses the pros and cons of spin-based switches and introduces the reader to the most recent advancements in information processing predicated on encoding information in electron spin polarization

A PUF based Lightweight Hardware Security Architecture for IoT

With an increasing number of hand-held electronics, gadgets, and other smart devices, data is present in a large number of platforms, thereby increasing the risk of security, privacy, and safety breach than ever before. Due to the extreme lightweight nature of these devices, commonly referred to as IoT or `Internet of Things\u27, providing any kind of security is prohibitive due to high overhead associated with any traditional and mathematically robust cryptographic techniques. Therefore, researchers have searched for alternative intuitive solutions for such devices. Hardware security, unlike traditional cryptography, can provide unique device-specific security solutions with little overhead, address vulnerability in hardware and, therefore, are attractive in this domain. As Moore\u27s law is almost at its end, different emerging devices are being explored more by researchers as they present opportunities to build better application-specific devices along with their challenges compared to CMOS technology. In this work, we have proposed emerging nanotechnology-based hardware security as a security solution for resource constrained IoT domain. Specifically, we have built two hardware security primitives i.e. physical unclonable function (PUF) and true random number generator (TRNG) and used these components as part of a security protocol proposed in this work as well. Both PUF and TRNG are built from metal-oxide memristors, an emerging nanoscale device and are generally lightweight compared to their CMOS counterparts in terms of area, power, and delay. Design challenges associated with designing these hardware security primitives and with memristive devices are properly addressed. Finally, a complete security protocol is proposed where all of these different pieces come together to provide a practical, robust, and device-specific security for resource-limited IoT systems