Publications
Two-stage discriminant analysis for improved isolated-word recognition
April 9, 1987
Conference Paper
Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, Vol. 2, 6-9 April 1987, pp. 709-712.
Topic:
R&D area:
Summary
This paper describes a two-stage isolated word search recognition system that uses a Hidden Markov Model (HMM) recognizer in the first stage and a discriminant analysis system in the second stage. During recognition, when the first-stage recognizer is unable to clearly differentiate between acoustically similar words such as "go" and "no" the second-stage discriminator is used. The second-stage system focuses on those parts of the unknown token which are most effective at discriminating the confused words. The system was tested on a 35 word, 10,710 token stress speech isolated word data base created at Lincoln Laboratory. Adding the second-stage discriminating system produced the best results to date on this data base, reducing the overall error rate by more than a factor of two.
Summary
This paper describes a two-stage isolated word search recognition system that uses a Hidden Markov Model (HMM) recognizer in the first stage and a discriminant analysis system in the second stage. During recognition, when the first-stage recognizer is unable to clearly differentiate between acoustically similar words such as "go" and...
READ MORE
An introduction to computing with neural nets
April 1, 1987
Journal Article
Published in:
IEEE ASSP Mag., Vol. 4, No. 2, April 1987, pp. 4-22.
Topic:
R&D area:
R&D group:
Summary
Artificial neural net models have been studied for many years in the hope of achieving human-like performance in the fields of speech and image recognition. These models are composed of many nonlinear computational elements operating in parallel and arranged in patterns reminiscent of biological neural nets. Computational elements or nodes are connected via weights that are typically adapted during use to improve performance. There has been a recent resurgence in the field of artificial neural nets caused by new net topologies and algorithms, analog VLSI implementation techniques, and the belief that massive parallelism is essential for high performance speech and image recognition. This paper provides an introduction to the field of artificial neural nets by reviewing six important neural net models that can be used for pattern classification. These nets are highly parallel building blocks that illustrate neural net components and design principles and can be used to construct more complex systems. In addition to describing these nets, a major emphasis is placed on exploring how some existing classification and clustering algorithms can be performed using simple neuron-like components. Single-layer nets can implement algorithms required by Gaussian maximum-likelihood classifiers and optimum minimum-error classifiers for binary patterns corrupted by noise. More generally, the decision regions required by any classification algorithm can be generated in a straightforward manner by three-layer feed-forward nets.
Summary
Artificial neural net models have been studied for many years in the hope of achieving human-like performance in the fields of speech and image recognition. These models are composed of many nonlinear computational elements operating in parallel and arranged in patterns reminiscent of biological neural nets. Computational elements or nodes...
READ MORE
Recognizing low-altitude wind shear hazards from doppler weather radar: an artificial intelligence approach
March 1, 1987
Journal Article
Published in:
J. Atmos. Oceanic Technol., Vol. 4, No. 1, March 1987, pp. 5-18.
Topic:
R&D area:
R&D group:
Summary
This paper describes an artificial intelligence-based approach for automated recognition of wind shear hazards. The design of a prototype system for recognizing low-altitude wind shear events from Doppler radar displays is presented. This system, called WXI, consists of a conventional expert system augmented by a specialized capability for processing radar images. The radar image processing component of the system employs numerical and computer vision techniques to extract features from radar data. The expert system carries out symbolic reasoning on these features using a set of heuristic rules expressing meteorological knowledge about wind shear recognition. Results are provided demonstrating the ability of the system to recognize microburst and gust front wind shear events.
Summary
This paper describes an artificial intelligence-based approach for automated recognition of wind shear hazards. The design of a prototype system for recognizing low-altitude wind shear events from Doppler radar displays is presented. This system, called WXI, consists of a conventional expert system augmented by a specialized capability for processing radar...
READ MORE
Correcting wind speed measurements for site obstructions
January 16, 1987
Conference Paper
Published in:
Sixth Symp. Meteorological Observations and Instrumentation, 12-16 January 1987, pp. 358-363.
Topic:
R&D area:
R&D group:
Summary
The FLOWS (FAA-Lincoln Laboratory Operational Weather Studies) Project is developing methods for automatically detecting and warning against aviation weather hazards, such as low-altitude wind shear, in airport terminal areas using NEXRAD-like Doppler weather radars. Currently, the FAA uses the Low Level Wind Shear Alert System (LLWAS), an anemometer array situated within and around an airport terminal area, for real-time detection of wind shear events. Even with the installation of Terminal Doppler Weather Radars (TDWRs) at some airports, the LLWAS systems there could still play an important role in the accurate detection of wind shear events, and at airports without TDWRs, the LLWAS will remain the primary detection system. The slowing or obstruction of wind by local obstacles is a well know n problem to those wishing to make accurate wind speed measurements. Anemometers should always be located where there will be, as nearly as passible, an unobstructed wind flow free from turbulent eddies in all directions. Because of the fairly precise required sensor configuration of the anemometers in an LLWAS system, it can occasionally be difficult or impossible to find sites with good exposure in all directions. The FLOWS project is interested in the unobstructed wind speed measurements for two main reasons. First, when analyzing a snapshot of the wind field over a mesonet (or LLWAS) for horizontal wind shear and/or for comparison with Doppler radar data, use of the measured, uncorrected winds would reveal spurious patterns of divergence or vorticity that depend little on time but greatly on the prevailing wind direction and that would, in some cases, obscure the true wind shear pattern. Second, when using surface wind measurements to estimate winds aloft that might be encountered by an aircraft on take-off or landing, an· appropriate power law can be accurately used if the original surface wind speed measurements are representative of the unobstructed flow.
Summary
The FLOWS (FAA-Lincoln Laboratory Operational Weather Studies) Project is developing methods for automatically detecting and warning against aviation weather hazards, such as low-altitude wind shear, in airport terminal areas using NEXRAD-like Doppler weather radars. Currently, the FAA uses the Low Level Wind Shear Alert System (LLWAS), an anemometer array situated...
READ MORE
The FLOWS automatic weather station network
January 12, 1987
Conference Paper
Published in:
Proc. Sixth Symp. on Meterological Observations and Instrumentation, 12-16 January 1987, pp. 294-9.
Topic:
R&D area:
R&D group:
Summary
Lincoln Laboratory is operating a network of 30 automatic weather stations for the FAA as part of the ongoing FLOWS (FAA-Lincoln Laboratory Operational Weather Studies) Project, which focuses on developing techniques for automated hazardous weather detection in airport terminal areas using NEXRAD-like Doppler weather radars. The stations, designed to measure temperature, relative humidity, barometric pressure, wind speed, wind direction, and precipitation amounts, originally used one of the first commercially available data collection platforms (DCPs) to transmit 5-min averaged data to the GOES satellites. Under FAA sponsorship, Lincoln has procured modern DCPs and has refurbished amd modified the sensors to create a reliable 30 station network capable of transmitting one minute averages of the variables mentioned above, as well as the peak wind speed each minute and some internal diagnostic variables, on a single GOES satellite channel. The complete system is described and some performance results from the FLOWS 1984-1985 Memphis operation are presented.
Summary
Lincoln Laboratory is operating a network of 30 automatic weather stations for the FAA as part of the ongoing FLOWS (FAA-Lincoln Laboratory Operational Weather Studies) Project, which focuses on developing techniques for automated hazardous weather detection in airport terminal areas using NEXRAD-like Doppler weather radars. The stations, designed to measure...
READ MORE
Speech transformations based on a sinusoidal representation
December 1, 1986
Journal Article
Published in:
IEEE Trans. Acoust. Speech Signal Process., Vol. ASSP-34, No. 6, December 1986, pp. 1449-1464.
Topic:
R&D area:
Summary
In this paper a new speech analysis/synthesis technique is presented which provides the basis for a general class of speech transformations including time-scale modification, frequency scaling, and pitch modification. These modifications can be performed with a time-varying change, permitting continuous adjustment of a speaker's fundamental frequency rate of articulation. The method is based on a sinusoidal representation of the speech production mechanism which has been shown to produce synthetic speech that preserves the waveform shape and is perceptually indistinguishable from the original. Although the analysis/synthesis system was originally designed for single speaker signals, it is also capable ot recovering and modifying non-speech signals such as music, multiple speakers, marine biologic sounds, and speakers in the presence of interferences such as noise and musical backgrounds.
Summary
In this paper a new speech analysis/synthesis technique is presented which provides the basis for a general class of speech transformations including time-scale modification, frequency scaling, and pitch modification. These modifications can be performed with a time-varying change, permitting continuous adjustment of a speaker's fundamental frequency rate of articulation. The...
READ MORE
Clutter suppression for low altitude wind shear detection by doppler weather radars
October 26, 1986
Conference Paper
Published in:
23rd Conf. on Radar Meteorology, Vol. 1, 22-26 September 1986, pp. 9-13.
Topic:
R&D area:
R&D group:
Summary
Low altitude wind shear (LAWS) has been recognized as a major cause of commercial airline aircraft accidents in the United States. The FAA is actively conducting the Terminal Doppler Weather Radar (TDWR) program to detect and identify dangerous wind fields at and around airports using Doppler radar techniques. Clutter poses a major challenge to successful operation of such a system due to the need to measure the return from low cross section wind tracers in the presence of close-in clutter from stationary objects. The paper describes the overall LAWS detection scenario with particular emphasis on microburst and gust front detection before presenting detailed experimental and analytical results on the suppression of ground clutter using a combination of: 1) subclutter visibility in excess of 50 dB by the use of high pass digital filters with narrow stopbands, and 2) interclutter visibility (ICV) algorithms which utilize the spatially distributed nature of the weather phenomena being measured, and 3) pencil beam antennas with readily achievable sidelobes.
Summary
Low altitude wind shear (LAWS) has been recognized as a major cause of commercial airline aircraft accidents in the United States. The FAA is actively conducting the Terminal Doppler Weather Radar (TDWR) program to detect and identify dangerous wind fields at and around airports using Doppler radar techniques. Clutter poses...
READ MORE
Microburst recognition: an expert system approach
September 26, 1986
Conference Paper
Published in:
Proc. 23rd Conf. on Radar Meteorology, Vol. 1, 22-26 September 1986, pp. 26-29.
Summary
Expert systems have gained much recent attention as a means for capturing the performance of human experts in specialized fields of knowledge. Areas in which expert systems have been successfully developed include such varied applications as mass spectrogram interpretation, disease diagnosis, geological data analysis and computer configuration (Hayes-Roth et al, 1983). The assumption behind these applications is that a body of specialized knowledge is possessed by the human expert. Expert systems attempt to capture this knowledge in an explicit form, each as a set of heuristic rules, and employ mechanisms to apply this knowledge to solve problems in the domain of expertise. Using this approach, expert systems have been able to successfully perform tasks which previously could only be carried out by human specialists. Moreover, expert systems have in some cases been able to attain levels of performance equaling that of humans (Buchanan and Shortliffe, 1984). This paper describes an expert system-based approach to the problem of recognizing microbursts from Doppler weather radar data. A prototype system based on this approach is currently being developed at Lincoln Laboratory for automated recognition of low-altitude wind shear hazards. This system, called WX1, employs artificial intelligence and computer vision techniques to emulate the symbolic reasoning and visual processing capabilities of a radar meteorologist.
Summary
Expert systems have gained much recent attention as a means for capturing the performance of human experts in specialized fields of knowledge. Areas in which expert systems have been successfully developed include such varied applications as mass spectrogram interpretation, disease diagnosis, geological data analysis and computer configuration (Hayes-Roth et al...
READ MORE
Mode S beacon system: functional description (revision D)
August 29, 1986
Project Report
Published in:
MIT Lincoln Laboratory Report ATC-42-D
Topic:
R&D area:
R&D group:
Summary
This document provides a functional description of the Mode S Beacon System, a combined secondary surveillance radar (beacon) and ground-air-ground data link system capable of providing the aircraft surveillance and communications necessary to support ATC automation in future traffic environments. Mode S is capable of common-channel interoperation with the current ATC beacon system, and may be implemented at low user cost over an extended transition period. Mode S will provide the surveillance and communication performance required by the ATC automation, the reliable communications needed to support data link services, and the capability of operating with a terminal or enroute, radar digitizer-equipped, ATC surveillance radar. The material contained in this document updates and expands the information presented in "Mode S Beacon System: Functional Description", DOT/FAA/PM-83/8, 215 July 1983.
Summary
This document provides a functional description of the Mode S Beacon System, a combined secondary surveillance radar (beacon) and ground-air-ground data link system capable of providing the aircraft surveillance and communications necessary to support ATC automation in future traffic environments. Mode S is capable of common-channel interoperation with the current...
READ MORE
A coordinate conversion algorithm for multisensor data processing
August 5, 1986
Project Report
Published in:
MIT Lincoln Laboratory Report ATC-139
Topic:
R&D area:
R&D group:
Summary
Processing of aircraft surveillance data from several geographically separated radars is most easily accomplished using a common coordinate system to represent data from all sensors. The Multisensor Data Processing system currently being developed for the FAA in support of the Advanced Automation System (AAS) requires a degree of accuracy and consistency that is not available from the current NAS implementation of coordinate conversion. A study has been undertaken to design a coordinate covnersion algorithm that meets the needs of Multisensor Data Processing. The process of projection of the ellipsoidal surface of the earth onto a planar surface is examined in light of teh requirements of air traffic control systems. The effects of the non-spherical nature of the earth and of limited computational resources are considered. Several standard cartographic projection techniques are examined, and the sterographic projection is found to be the projection of choice. A specific implementation of stereographic projection that makes the needs of Multisensor Data Processing is described. This implementation makes use of several approximations to decrease the computational load. The systemic errors introduced by these approximations are removed by the addition of a correction term determined from a precomputed error surface. The performance of this conversion system is demonstrated using realistic test data.
Summary
Processing of aircraft surveillance data from several geographically separated radars is most easily accomplished using a common coordinate system to represent data from all sensors. The Multisensor Data Processing system currently being developed for the FAA in support of the Advanced Automation System (AAS) requires a degree of accuracy and...
READ MORE