ABSTRACT
Random Data with Applications to the Field of Neuroscience.............. 297
(W. W. Weiner)
6.1.1. Introduction to the Ozturk Algorithm........................................... 297
6.1.1.1. Overview.......................................................................... 298
6.1.1.2. Sample Simulation........................................................... 299
6.1.2. Detailed Description of the Ozturk Algorithm............................. 306
6.1.2.1. The Standardized Order Statistic .................................... 306
6.1.2.2. The Goodness-of-Fit Test................................................ 307
6.1.2.3. Calculation of Linked Vectors in the U-V Plane ........... 308
6.1.2.4. Calculation of Confidence Ellipses ................................. 311
6.1.2.5. The Best-Fit Test ............................................................. 312
6.1.2.6. Estimation of Location and Scale Parameters ................ 314
6.1.3. Analysis of Spontaneous Auditory Nerve Activity
of Chinchillas................................................................................. 316
6.1.3.1. Analysis of Two Fibers with Different
Spontaneous Rates ........................................................... 325
6.1.3.2. Analysis of Pulse-Number Distributions......................... 327
6.1.4. Analysis of Efferent Optic-Nerve Activity
in the Horseshoe Crab ................................................................... 333
6.1.4.1. Characterization of Interburst Intervals .......................... 335
6.1.4.2. Trends in the Shape Parameter........................................ 339
6.1.5. Analysis of the Visual Field of the Horseshoe Crab.................... 341
6.1.5.1. Total Interommatidial Angles ......................................... 344
6.1.5.2. Horizontal and Vertical Interommatidial Angles............ 346
6.1.6. Applications of the Ozturk Algorithm in Neuroscience............... 348
6.2. Use of Image Processing to Partition a Radar Surveillance
Volume into Background Noise and Clutter Patches ............................ 349
(M. A. Slamani and D. D. Weiner)
6.2.1. Introduction ................................................................................... 349
6.2.2. Observations about BN and CL .................................................... 350
6.2.2.1. Observations about BN.................................................... 351
6.2.2.2. Observations about CL .................................................... 351
6.2.3. Mapping Procedure ....................................................................... 351
6.2.3.2. Detection of Clutter Patch Edges .................................... 354
6.2.3.3. Enhancement of Clutter Patch Edges.............................. 355
6.2.4. Example ......................................................................................... 355
6.3. Probabilistic Insight into the Application of Image Processing
to the Mapping of Clutter and Noise Regions in a Radar
Surveillance Volume ............................................................................... 359
(M. A. Slamani and D. D. Weiner)
6.3.1. Introduction ................................................................................... 359
6.3.2. Separation between BN and CL Patches ...................................... 360
6.3.3. Summary........................................................................................ 368
6.4. A New Approach to the Analysis of IR Images ..................................... 368
(M. A. Slamani, D. Ferris, and V. Vannicola)
6.4.1. Introduction ................................................................................... 368
6.4.2. ASCAPE ........................................................................................ 369
6.4.3. Mapping Procedure ....................................................................... 371
6.4.3.1. Identification of Lowest Average Power
Level (LP)........................................................................ 371
6.4.3.2. Detection of Patch Edges ................................................ 371
6.4.4. Statistical Procedure ...................................................................... 372
6.4.4.1. Introduction to Ozturk Algorithm ................................... 372
6.4.4.2. Outliers............................................................................. 374
6.4.4.3. Strategy to SubPatch Investigation Using
the Statistical Procedure .................................................. 374
6.4.5. Expert System Shell IPUS ............................................................ 375
6.4.6. Example: Application of ASCAPE to Real IR Data.................... 376
6.4.7. Conclusion ..................................................................................... 381
6.5. Automatic Statistical Characterization and Partitioning
of Environments (ASCAPE) ................................................................... 382
(M. A. Slamani, D. D. Weiner, and V. Vannicola)
6.5.1. Problem Statement ........................................................................ 382
6.5.2. ASCAPE Process........................................................................... 385
6.5.3. Application of ASCAPE to Real IR Data .................................... 385
6.5.4. Conclusion ..................................................................................... 386
6.6. Statistical Characterization of Nonhomogeneous
and Nonstationary Backgrounds.............................................................. 386
(A. D. Keckler, D. L. Stadelman, D. D. Weiner,
and M. A. Slamani)
6.6.1. Introduction ................................................................................... 386
6.6.2. Application of ASCAPE to Concealed Weapon Detection ......... 387
6.6.3. The SIRV Radar Clutter Model .................................................... 390
6.6.4. Distribution Approximation Using the Ozturk Algorithm ........... 392
6.6.5. Approximation of SIRVs .............................................................. 395
6.6.6. NonGaussian Receiver Performance............................................. 398
(KBMapSTAP) ........................................................................................ 400
(C. T. Capraro, G. T. Capraro, D. D. Weiner, and M. C. Wicks)
6.7.1. Introduction ................................................................................... 400
6.7.2. Clutter Model................................................................................. 401
6.7.3. Representative Secondary Clutter ................................................. 402
6.7.4. Airborne Radar Data ..................................................................... 402
6.7.5. A Priori Data ................................................................................. 403
6.7.6. Research Problem, Hypothesis, and Preliminary Findings .......... 403
6.7.7. Conclusions and Future Work....................................................... 407
6.8. Improved STAP Performance Using Knowledge-Aided
Secondary Data Selection........................................................................ 408
(C. T. Capraro, G. T. Capraro, D. D. Weiner, M. C. Wicks,
and W. J. Baldygo)
6.8.1. Introduction ................................................................................... 408
6.8.2. Radar and Terrain Data ................................................................. 409
6.8.3. Approach........................................................................................ 410
6.8.3.1. STAP Algorithm.............................................................. 410
6.8.3.2. Registration of the Radar with the Earth ........................ 411
6.8.3.3. Data Selection.................................................................. 412
6.8.3.4. Corrections for Visibility................................................. 412
6.8.3.5. Secondary Data Guard Cells ........................................... 413
6.8.4. Results............................................................................................ 413
6.8.5. Conclusion ..................................................................................... 415
The purpose of this project is to introduce a new algorithm for analyzing random
data. This algorithm is now referred to as the Ozturk Algorithm, named after
the person who invented it (Ozturk
). In addition to describing this algorithm,
this chapter will also present three detailed applications that illustrate the power
of this algorithm. In the first part of this chapter, a brief description of the Ozturk
Algorithm and its advantages over classical techniques will be discussed. The
reader will also be introduced to the basic operation of this algorithm. In the
next section of this chapter, a detailed description of how the Ozturk Algorithm
works will be given. For those readers who are only interested in applications of
of its
three applications of the Ozturk Algorithm:
1) Analysis of auditory nerve activity in the chinchilla
2) Analysis of efferent optic nerve activity in the horseshoe crab
3) Analysis of the visual field of the horseshoe crab.
