Publications

An ultrawideband (UWB) antenna has been developed for operation in the 60 MHz to 18 GHz frequency range. This antenna is a new type--an offset bicone/dipole design that allows for vertically polarized omnidirectional coverage over an instantaneous 300:1 bandwidth. Numerical electromagnetic simulations with the finite-element method (FEM) were used to investigate the antenna concept and optimize geometry prior to fabrication. Measurements both outdoors and in an anechoic chamber confirm the antenna's performance.

In this paper, we describe our approach and initial results on modeling, detection, and tracking of terrorist groups and their intents based on multimedia data. While research on automated information extraction from multimedia data has yielded significant progress in areas such as the extraction of entities, links, and events, less progress has been made in the development of automated tools for analyzing the results of information extraction to ?connect the dots.? Hence, our Counter-Terror Social Network Analysis and Intent Recognition (CT-SNAIR) work focuses on development of automated techniques and tools for detection and tracking of dynamically-changing terrorist networks as well as recognition of capability and potential intent. In addition to obtaining and working with real data for algorithm development and test, we have a major focus on modeling and simulation of terrorist attacks based on real information about past attacks. We describe the development and application of a new Terror Attack Description Language (TADL), which is used as a basis for modeling and simulation of terrorist attacks. Examples are shown which illustrate the use of TADL and a companion simulator based on a Hidden Markov Model (HMM) structure to generate transactions for attack scenarios drawn from real events. We also describe our techniques for generating realistic background clutter traffic to enable experiments to estimate performance in the presence of a mix of data. An important part of our effort is to produce scenarios and corpora for use in our own research, which can be shared with a community of researchers in this area. We describe our scenario and corpus development, including specific examples from the September 2004 bombing of the Australian embassy in Jakarta and a fictitious scenario which was developed in a prior project for research in social network analysis. The scenarios can be created by subject matter experts using a graphical editing tool. Given a set of time ordered transactions between actors, we employ social network analysis (SNA) algorithms as a filtering step to divide the actors into distinct communities before determining intent. This helps reduce clutter and enhances the ability to determine activities within a specific group. For modeling and simulation purposes, we generate random networks with structures and properties similar to real-world social networks. Modeling of background traffic is an important step in generating classifiers that can separate harmless activities from suspicious activity. An algorithm for recognition of simulated potential attack scenarios in clutter based on Support Vector Machine (SVM) techniques is presented. We show performance examples, including probability of detection versus probability of false alarm tradeoffs, for a range of system parameters.

There has long been a desire to be able to identify a person on the basis of his or her voice. For many years, judges, lawyers, detectives, and law enforcement agencies have wanted to use forensic voice authentication to investigate a suspect or to confirm a judgment of guilt or innocence. Challenges, realities, and cautions regarding the use of speaker recognition applied to forensic-quality samples are presented.

The dominant trend with conventional image sensors is toward scaled-down pixel sizes to increase spatial resolution and decrease chip size and cost. While highly capable chips, these monolithic image sensors devote substantial perimeter area to signal acquisition and control circuitry and trade off pixel complexity for fill factor. For applications such as wide-area persistent surveillance, reconnaissance, and astronomical sky surveys it is desirable to have simultaneous near-real-time imagery with fast, wide field-of-view coverage. Since the fabrication of a complex large-format sensor on a single piece of silicon is cost and yield-prohibitive and is limited to the wafer size, for these applications many smaller-sized image sensors are tiled together to realize very large arrays. Ideally the tiled image sensor has no missing pixels and the pixel pitch is continuous across the seam to minimize loss of information content. CCD-based imagers have been favored for these large mosaic arrays because of their low noise and high sensitivity, but CMOS-based image sensors bring architectural benefits, including electronic shutters, enhanced radiation tolerance, and higher data-rate digital outputs that are more easily scalable to larger arrays. In this report the first back-illuminated, 1 Mpixel, 3D-integrated CMOS image sensor with 8 mum-pitch 3D via connections. The chip employs a conventional pixel layout and requires 500 mum of perimeter silicon to house the support circuitry and protect the array from saw damage. In this paper we present a back-illuminated 1 Mpixel CMOS image sensor tile that includes a 64-channel vertically integrated ADC chip stack, and requires only a few pixels of silicon perimeter to the pixel array. The tile and system connector design support 4-side abuttability and fast burst data rates.

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.