Engineering topics
Publications and source records attributed to Udomkesmalee, Suraphol.
In this paper, we present an overview of the current technology portfolio for Mars Base Technology Program. Brief descriptions of the awarded technologies and the high-priority areas in both NRAs are provided to show the current focus of MTP. We also present the approach that MTP uses to evaluate technology maturity for each of the technology tasks.
A three-dimensional microelectronic device (3DANN-R) capable of performing general image convolution at the speed of 1012 operations/second (ops) in a volume of less than 1.5 cubic centimeter has been successfully built under the BMDO/JPL VIGILANTE program. 3DANN-R was developed in partnership with Irvine Sensors Corp., Costa Mesa, California. 3DANN-R is a sugar-cube-sized, low power image convolution engine that in its core computation circuitry is capable of performing 64 image convolutions with large (64x64) windows at video frame rates. This paper explores potential applications of 3DANN-R such as target recognition, SAR and hyperspectral data processing, and general machine vision using real data and discuss technical challenges for providing deployable systems for BMDO surveillance and interceptor programs.
VIGILANTE is an ultralfast smart sensor testbed for generic Automatic Target Recognition (ART) applications with a series of capability demonstration focussed on cruise missle defense (CMD).
A number of different strategies are used or have been suggested for identifying star fields atitude determination in space. We offer a general classification of the existing techniques and select three representative algorithms for more comprehensive evaluation. In addition, we describe a software simulation environment we developed for the design, paramter determination, and evaluation of star identification algorithms. The identification rates and performance on the three algorithms are presented over a variety of noise conditions using two different sized onboard catalogs.
An approach to star identification based on comparing observed pattern statistics with the precomputed star catalogued statistics is suggested.
Three algorithms provide for improved postprocessing of outputs of optical correlators based on binary phase-only filters. Detect correlation spots. Function in presence of noise and executed rapidly. First algorithm starts processing correlation-image data while data fed out of video camera and digitized for subsequent analysis. Second involves convolution of correlation image with small-window two-dimensional impulse-response function followed by threshold operation in which negative values of convolution integral set to zero. Third affects generation as well as postprocessing of correlation image.
Although the analytical groundwork for understanding two-dimensional object images and various aspects of computer vision has been laid, we have not yet applied these concepts to automating the process of obtaining science images during space exploration missions. Our current approach in specifying pointing-command sequences relies heavily on target predicts, based on predicted target and spacecraft ephemerides, that propagate the target position as a function of time during pointing operations.
A typical docking target employs a three-point design of retroreflective tape, one at each endpoint of the center-line, and one on the tip of the central post. Scenes, sensed via laser diode illumination, produce pictures with spots corresponding to desired reflection from the retroreflectors and other reflections. Control corrections for each axis of the vehicle can then be properly applied if the desired spots are accurately tracked. However, initial acquisition of these three spots (detection and identification problem) are non-trivial under a severe noise environment. Signal-to-noise enhancement, accomplished by subtracting the non-illuminated scene from the target scene illuminated by laser diodes, can not eliminate every false spot. Hence, minimization of docking failures due to target mistracking would suggest needed inclusion of added processing features pertaining to target locations. In this paper, we present a concurrent processing scheme for a modified docking target scene which could lead to a perfect docking system. Since the non-illuminated target scene is already available, adding another feature to the three-point design by marking two non-reflective lines, one between the two end-points and one from the tip of the central post to the center-line, would allow this line feature to be picked-up only when capturing the background scene (sensor data without laser illumination). Therefore, instead of performing the image subtraction to generate a picture with a high signal-to-noise ratio, a processed line-image based on the robust line detection technique (Hough transform) can be used to fuse with the actively sensed three-point target image to deduce the true locations of the docking target. This dual-channel confirmation scheme is necessary if a fail-safe system is to be realized from both the sensing and processing point-of-views. Detailed algorithms and preliminary results are presented.
A typical hierarchy for a general object recognition problem consists of object detection, classification and identification. This paper establishes necessary building blocks required for high-speed object recognition applications. An architecture that combines digital and optical processing, exploiting current image processing techniques for detection and classification, and optical processing hardware is described. An optical processing scheme is suggested for the identification aspect. Numerical results of each proposed concept are presented.
A major drawback of optical correlator systems is the poor quality of correlation signals. Background noise is one of the many sources of false correlation peaks. However, by reconstructing an input scene with the object's amplitude function, an object-enhanced scene - used as input to the optical correlators - (with respect to background) can be obtained. Thus, the optical correlation of this enhanced image improves the signal-to-noise ratio in the correlation image. Furthermore, construction of a filter plane mask that permits simultaneous scene enhancement and cross-correlation operations is described. Experimental results demonstrate the practicality of this approach.