In this paper we discuss extraction of anomalous events based on the theory of catastrophes, a mathematical theory of
continuous geometrical manifolds with discrete singularities called catastrophes. Intelligence exploitation systems and
technologies include such novel data mining techniques as automatic extraction of discrete anomalous events by
software algorithms based on the theory of catastrophes, that can reduce complex problems to a few essential so-called
state variables. This paper discusses mostly corank-1 catastrophes with only one state variable, for simplicity. As an
example we discuss mostly avionics platforms and catastrophic failures that can be recorded by flight instruments.
KEYWORDS: Video, Video compression, Sensors, Video processing, Video surveillance, Homeland security, Automatic target recognition, RGB color model, Digital watermarking, Image compression
KEYWORDS: Image processing, Digital signal processing, Acoustics, Signal processing, Sensors, Cameras, Video surveillance, Video, Computer programming, Combined lens-mirror systems
The authors have invented and are developing a new Real-Time Stereoscopic Catadioptric Omni-Detection (RSCO)
system based on a high-resolution stereoscopic vision system, an acoustic array with high-fidelity/sensitivity, real-time
image processing hardware based on evolutionary stream processing hardware architecture based on the processors (SPs) with unprecedented digital signal processing
(DSP) performance, and proprietary unwrapping and operating software. The RSCO development involves optimizing
(1) the omnidirectional multimodal (visible/MWIR) sensor system configuration and mechanical hardening to survive
harsh conditions and to minimize maintenance; (2) a video/image
ultrahigh-performance SPs, (3) an acoustic array with high-performance DSP technology; and (4) real-time unwrapping
and operating software. SPs form a new class of image processors scalable to teraOPS, with efficiency comparable to
ASICs, and completely programmable in high-level languages. SPs innovatively combine a new programming model,
tool automation, and hardware to exploit the high data parallelism and processing locality that are inherent in a wide
range of applications, especially media processing. The excellent performance, efficiency, and programmability of SPs
make them ideal for implementation of the RSCO system, with its unprecedented omnidirectional, multiple-modality
sensors, range, accuracy, size, and robustness.
In this paper we discuss the utilization of the Projection-Slice Theorem, PST, to reduce a data set that display each multiple spectral band representation of an image and to extract variant features from those representation. Noise is removed from each of the one-dimensional projections of the images via PST and a wavelet transform thresholding process. The extracted features emphasize differences in spectral information from the same image and are combined through synthesis via the inverse PST. This sensor fusion method facilitates the design of filters to recognize an image with characteristics similar to the relevant features from each of the bands that have been incorporated in a combined multispectral/fused image. We present our method of feature extraction, wavelet noise removal, and data synthesis.
KEYWORDS: Video, Video compression, Chromium, Error control coding, Global system for mobile communications, Image compression, Image quality, Error analysis, Information operations, Multimedia
A new approach to low-bandwidth network packet video quality maximization has been proposed, based on software agent and global optimization algorithm, including: environmental factors (noise, multi-path fading); compression ratio; bit-error-correction; maximum available bandwidth; video format; and encryption. This is important for 2G-wireless RF cellular GSM visual communication, and other low-bandwidth homeland security visibility, and civilian RF WLANs.
KEYWORDS: Video, Video compression, Sensors, Visualization, Local area networks, Sensor networks, Network architectures, Prisms, Video surveillance, RGB color model
A new type of wireless sensor network is discussed for the digital battlefield and network-centric warfare. This network is rapidly deployable, and has unique features specifically suited to imaging sensors (visible, IR, imaging radar, low-light) and wireless local area network applications.
The progress in soft computing and soft communication (SC2) is reviewed in detail including video/imaging compression, communications, processing, sensing and networking, based on 8 B0PS distributed hardware, allowing for full video frame evaluation, in real time.
This paper presents experimental results of hyperspectral image compression by means of soft computing. Compressions and transmission of hyperspectral data requires intensive computation and sophisticated processing that have been incompatible with on-board real-time operation. Soft computing with intelligent processing optimizes the compression parameters of MPEG 1, tuning them to the specific video content to deliver the highest hyperspectral video compression quality. This soft computing approach is compared with compression based on wavelet transform.
This paper describes all-optical packet header processing by content-addressable memory and optical joint transform correlation. The results of proof-of-principle computer simulation and experiment are presented.
This work addresses the problem faced by an aircraft that is off its nominal flight path. The goal is to find the optimal trajectory to safely and efficiently return the aircraft to its proper path in rugged terrain. The authors approach to this problem is to consider the space of possible trajectories as a series of linked maneuvers, so that a particular trajectory can be described by the ordered list of parameters specifying the maneuvers. A penalty function is minimized with respect to variations of the maneuver parameter list. The work considered trajectories of up to three straight flight segments linked by turns. The penalty function includes terms penalizing elapsed time for the measure, distance climbed, and closest approach to the ground as well as distance from the nominal flight path at the end of the maneuver. Minimization is performed by means of an adaptive fuzzy-logic-enhanced genetic algorithm.
A method of automatically reducing uncertainties and calibrating possible biases involved in sensed data and extracted features by a system based on the geometric data fusion is presented. The perception net, as a structural representation of the sensing capabilities of a system, connects features of various levels of abstraction, referred to here as logical sensors, with their functional relationships such as feature transformations, data fusions, and constraints to be satisfied. The net maintains the consistency of logical sensors based on the forward propagation of uncertainties as well as the backward propagation of constraint errors. A novel geometric data fusion algorithm is presented as a unified framework for computing forward and backward propagation through which the net achieves the self-reduction of uncertainties and self- calibration of biases. The effectiveness of the proposed method is validated through simulation.
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