Publications

Systems currently being developed to operate across wide bandwidths with high sensitivity requirements are limited by the inherent dynamic range of a receiver's analog and mixed-signal components. To increase a receiver's overall linearity, we have developed a digital NonLinear EQualization (NLEQ) processor which is capable of extending a receiver's dynamic range from one to three orders of magnitude. In this paper we describe the NLEQ architecture and present measurements of its performance.

We demonstrate the transfer of the largest (150 mm in diameter) available InP-based epitaxial structure to the silicon-on-insulator substrate through a direct wafer-bonding process. Over 95% bonding yield and a void-free bonding interface was obtained. A multiple quantum-well diode laser structure is well-preserved after bonding, as indicated by the high-resolution X-ray diffraction measurement and photoluminescence (PL) map. A bowing of 64.12 um is measured, resulting in a low bonding-induced strain of 17 MPa. PL measurement shows a standard deviation of 1.09% across the entire bonded area with less than 1.1 nm wavelength shift from the as-grown wafer.

This paper will discuss the progress the Multi-function Phased Array Radar (MPAR) research program has made over the last 18 months as well as insight into the program strategy for moving forward. Current research activities include evaluating the impact of MPAR's faster scanning rates to aviation weather algorithms (e.g., how it will help in predicting storm growth and decay) and exploring dual polarization for phased array radars. Additionally, the Department of Homeland Security (DHS) has expanded the MPAR multi-agency partnership and is sponsoring research into the mitigation of wind-farm interference on weather sensing. Significant research in semi-conductor technology and advances in transmit/receive module design and phased array architectures are beginning to create a pathway towards system affordability. The MPAR program plan calls for a technology demonstration phase followed by the initiation of a prototype development effort within the next five years. This paper will provide the updates on these and other program activities.

Dozens of Ground Delay Programs (GDPs) are implemented each summer for San Francisco International Airport (SFO) in order to cope with reduced capacity caused by the presence of warm-season stratus in the approach zone. The stratus prevents the use of dual approaches to SFO's closely-spaced parallel runways, which essentially reduces the arrival capacity by half. In 2004, a prototype system for providing probabilistic stratus forecast guidance was transitioned from the research community to NWS Monterey. This system was intended to be used as a tool for improving the daily forecast of stratus clearing time from the approach zone, and correspondingly improve the efficiency of GDP implementation strategy. Since its transition to the NWS in 2004, the automated forecast guidance system has continued to produce reliable forecasts of daily stratus clearing time. However, this success has not adequately translated to a marked improvement in GDP efficiency. Analysis by the NWS indicates that the existing mechanisms for introducing the forecast guidance information into the GDP decision process, as well as the GDP implementation strategy itself, are not suited for taking full advantage of the forecast skill demonstrated by the system. A historical examination of SFO GDP implementation based on the probabilistic forecasts provided by the automated forecast guidance system is currently in process, with the objective being a recommendation for a more effective GDP strategy. An important consideration is understanding the risk/reward associated with the decision process. In this instance, the reward is increased efficiency seen as reduced aircraft delays, at the risk of creating increased delay, aircraft diversions, and controller workload in the event that an incorrect optimistic forecast results in the premature release of ground-held aircraft. This investigation is being performed in concert with the weather-integration objectives of the current FAA modernization program, particularly the integration of weather information that is delivered in a probabilistic format. Shortcomings within the current GDP strategy are described to provide context for potential improvements that exploit the probabilistic forecasts currently emerging from the research community.

Air traffic congestion caused by convective weather in the US has become a serious national problem. Several studies have shown that there is a critical need for timely, reliable and high quality forecasts of precipitation and echo tops with forecast time horizons of up to 12 hours in order to predict airspace capacity (Robinson et al. 2008, Evans et al. 2006 and FAA REDAC Report 2007). Yet, there are currently several forecast systems available to strategic planners across the National Airspace System (NAS) that are not fully meeting Air Traffic Management (ATM) needs. Furthermore, the use of many forecasting systems increases the potential for conflicting information in the planning process, which can cause situational awareness problems between operational facilities. One of the goals of the Next Generation Air Transportation System (NextGen) is to consolidate these redundant and sometimes conflicting forecast systems into a Single Authoritative Source (SAS) for aviation uses. The FAA initiated an effort to begin consolidating these systems in 2006, which led to the establishment of a collaboration between MIT Lincoln Laboratory (MIT LL), the National Center for Atmospheric Research (NCAR) Research Applications Laboratory (RAL), the NOAA Earth Systems Research Laboratory (ESRL) Global Systems Division (GSD) and NASA, called the Consolidated Storm Prediction for Aviation (CoSPA; Wolfson et al. 2008). The on-going collaboration is structured to leverage the expertise and technologies of each laboratory to build a CoSPA forecast capability that not only exceeds all current operational forecast capabilities and skill, but that provides enough resolution and skill to meet the demands of the envisioned NextGen decision support technology. The current CoSPA prototype for 0-6 hour forecasts is planned for operation as part of the NextGen Initial Operational Capability (IOC) in 2013. CoSPA is funded under the FAA's Aviation Weather Research Program (AWRP). The first CoSPA research prototype demonstration was conducted during the summer of 2008. Technologies from the Corridor Integrated Weather System (CIWS; Evans and Ducot 2006), National Convective Weather Forecast (NCWF; Megenhardt et al. 2004), and NOAA’s Rapid Update Cycle (RUC; Benjamin et al. 2004) and High Resolution Rapid Refresh (HRRR; Benjamin et al. 2009) models were consolidated along with new technologies into a single high-resolution forecast and display system. Historically, forecasts based on heuristics and extrapolation have performed well in the 0-2 hour window, whereas forecasts based on Numerical Weather Prediction (NWP) models have shown better performance than heuristics past 3-4 hours (Figure 1). One of the goals of CoSPA is to optimally blend heuristics and NWP models into a unified set of aviation-specific storm forecast products with the best overall performance possible. The CoSPA prototype demonstration began in July 2008 with 2-6 hr forecasts of Vertically-Integrated Liquid water (VIL) that seamlessly matched with the 0-2 hr VIL forecasts available in CIWS. These real-time forecasts have been made available to the research team and FAA management only through a web-based interface. This paper discusses the system infrastructure, the forecast display, the forecast technology and performance of the 2-6 hr VIL forecast. Our early assessment based on the 2008 demonstration is that CoSPA is showing tremendous promise for greatly improving strategic storm forecasts for the NAS. Early user feedback during CoSPA briefings suggested that the 6 hr forecast time horizon be extended to 8 hours to better meet their planning functions, and that forecasts of Echo Tops must also be included.

Software-defined cognitive radios (CRs) use voice as a primary input/output (I/O) modality and are expected to have substantial computational resources capable of supporting advanced speech- and audio-processing applications. This chapter extends previous work on speech applications (e.g., [1]) to cognitive services that enhance military mission capability by capitalizing on automatic processes, such as speech information extraction and understanding the environment. Such capabilities go beyond interaction with the intended user of the software-defined radio (SDR) - they extend to speech and audio applications that can be applied to information that has been extracted from voice and acoustic noise gathered from other users and entities in the environment. For example, in a military environment, situational awareness and understanding could be enhanced by informing users based on processing voice and noise from both friendly and hostile forces operating in a given battle space. This chapter provides a survey of a number of speech- and audio-processing technologies and their potential applications to CR, including: - A description of the technology and its current state of practice. - An explanation of how the technology is currently being applied, or could be applied, to CR. - Descriptions and concepts of operations for how the technology can be applied to benefit users of CRs. - A description of relevant future research directions for both the speech and audio technologies and their applications to CR. A pictorial overview of many of the core technologies with some applications presented in the following sections is shown in Figure 10.1. Also shown are some overlapping components between the technologies. For example, Gaussian mixture models (GMMs) and support vector machines (SVMs) are used in both speaker and language recognition technologies [2]. These technologies and components are described in further detail in the following sections. Speech and concierge cognitive services and their corresponding applications are covered in the following sections. The services covered include speaker recognition, language identification (LID), text-to-speech (TTS) conversion, speech-to-text (STT) conversion, machine translation (MT), background noise suppression, speech coding, speaker characterization, noise management, noise characterization, and concierge services. These technologies and their potential applications to CR are discussed at varying levels of detail commensurate with their innovation and utility.

A Gaussian Mixture Model (GMM) is a parametric probability density function represented as a weighted sum of Gaussian component densities. GMMs are commonly used as a parametric model of the probability distribution of continuous measurements or features in a biometric system, such as vocal-tract related spectral features in a speaker recognition system. GMM parameters are estimated from training data using the iterative Expectation-Maximization (EM) algorithm or Maximum A Posteriori (MAP) estimation from a well-trained prior model.

In this paper, we explore the ease of tackling a communication-intensive parallel computing task - namely, the 2D fast Fourier transform (FFT). We start with a simple serial Matlab code, explore in detail a ID parallel FFT, and illustrate how it can be extended to multidimensional FFTs.

This paper describes the construction of ASR and MT systems for translation of speech from Urdu into English. As both Urdu pronunciation lexicons and Urdu-English bitexts are sparse, we employ several techniques that make use of semi-supervised annotation to improve ASR and MT training. Specifically, we describe 1) the construction of a semi-supervised HMM-based part-of-speech tagger that is used to train factored translation models and 2) the use of an HMM-based transliterator from which we derive a spelling-to-pronunciation model for Urdu used in ASR training. We describe experiments performed for both ASR and MT training in the context of the Urdu-to-English task of the NIST MT08 Evaluation and we compare methods making use of additional annotation with standard statistical MT and ASR baselines.

Airspace capacity constraints caused by adverse weather are a major driver for enhanced Traffic Flow Management (TFM) capabilities. One of the most prominent TFM initiatives introduced in recent years is the Airspace Flow Program (AFP). AFPs are used to plan and manage flights through airspace constrained by severe weather. An AFP is deployed using "strategic" (i.e., 4-6 hour) weather forecasts to determine AFP traffic throughput rates. These rates are set for hourly periods. However, as convective weather continuously evolves, the achievable en route airspace throughput can fluctuate significantly over periods as short as 15-30 minutes. Thus, without tactical AFP adjustments, inefficiencies in available airspace usage can arise, often resulting in increased air traffic delay. An analysis of AFP usage in 2007 was conducted in order to (1) better understand the relationship between AFP parameters and convective weather characteristics, and (2) assess the potential use of an objective model for forecasting tactical AFP throughput. An en route airway blockage-based algorithm, using tactical forecast information from the Corridor Integrated Weather System (CIWS), has been developed in order to objectively forecast achievable flow rates through AFP boundaries during convective weather. A description of the model and preliminary model results are presented.