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New approaches for optical data storage (ODS) applications are needed to meet the future requirements of applications in multimedia, archiving, security, and many others. Commercial data storage technologies are moving to threedimensional (3D) materials, but the capacity is limited by the fabrication cost and the number of layers that can be addressed using the reflection-based storage mechanism. We demonstrate here storage systems based on co-extrusion of multilayer (ML) films that can overcome these problems. The organic roll-to-roll films produced can easily be produced hundreds of meters in length, in a far simpler and cheaper manner than current manufacturing methods such as spin coating and lamination. The medium consists of alternating active and buffer layers, and data storage is demonstrated by writing images in 23 layers of 78 μm thick films by fluorescence (FL) quenching of an organic dye. The areal data resolution is at the diffraction limit of the CW Blu-ray (BR) laser employed, and the co-extrusion technique allows for small layer spacings, leading to a total bit density 1.2 Tb/cm3. We anticipate materials already demonstrated successful for 3D ODS will be adapted to this technique, as well as new systems developed, to take full advantage of this medium.

Uncalibrated stereo imagery experimental and analytical results are presented for path planning and navigation. An Army Research and Development Engineering Command micro-size UAV was outfitted with two commercial cameras and flown over varied landscapes. Polaris Sensor Technologies processed the data post flight with an image correspondence algorithm of their own design. Stereo disparity (depth) was computed despite a quick assembly, image blur, intensity saturation, noise and barrel distortion. No camera calibration occurred. Disparity maps were computed at a processing rate of approximately 5 seconds per frame to improve perception. Disparity edges (treeline to ground, voids and plateaus) were successfully observed and confirmed to be properly identified. Despite the success of localizing this disparity edges sensitivity to saturated pixels, lens distortion and defocus were strong enough to overwhelm more subtle features such as the contours of the trees, which should be possible to extract using this algorithm. These factors are being addressed. The stereo data is displayed on a flat panel 3D display well suited for a human machine interface in field applications. Future work will entail extraction of intelligence from acquired data and the overlay of such data on the 3D image as displayed.

Stereo imagery has been a goal in optics research since the invention of the stereoscope in 1834. While the market has been inundated with displays of various types, sizes, and formats, no general purpose, easy to use, inexpensive method for the display of imagery in stereo has been developed. The benefits of stereo vision are numerous and quickly become apparent when attempting to perform simple tasks without the aid of stereo cues. Numerous approaches to the display of stereo imagery have been demonstrated. Stereoscopic displays typically require the user to wear special headgear. Autostereoscopic displays, so named because they do not require the headgear, typically have tight limitations on the position of the viewer's head. The research proposed here will investigate the application of two readily available, inexpensive liquid crystal panels sandwiched together to form a compact, rugged stereoscopic display. The appropriate drive signals are provided to the two stacked panels, which encode in polarization, the left and right images. Standard polarized 3D glasses can then be used to view the image in stereo. The proposed display will provide stereo pairs without loss of resolution or frame rate and without strict limitations on the placement of the viewer’s head.

A stereoscopic or autostereoscopic display based on this technology provides full resolution and freedom of movement, but with no flicker. Simply put, the display is neither spatially nor temporally multiplexed. It sounds unbelievable, but it's true -- an autostereoscopic display where each eye sees every displayable pixel on the screen at all times. This technology is designed for flat-panel displays, such as LCDs and has the following characteristics: (1) The display is not spatially multiplexed. Each eye sees the full native resolution of the entire screen. (2) The display is not temporally multiplexed. The image for each eye is visible continuously, i.e., at all times. (3) In its simplest form, this technology provides a full-time, full-resolution stereoscopic display for multiple viewers wearing passive polarizing glasses. (4) A variation of this technology can be used to make a full-time, full-resolution stereoscopic projection system for viewers wearing passive polarizing glasses using just a single projector. (5) With the addition of a dynamic aiming mechanism, and an adjustment in the display's output, we can create a single-user, full-time, full-resolution autostereoscopic display requiring no glasses and providing full freedom of movement. Software applications can use the same information about viewwer position to provide natural, full "look-around." (6) A hybrid version of the display can alternate between autostereoscopic (single-user, no glasses) and stereoscopic modes (multi-user, passive glasses).

Perception has both unconscious and conscious aspects. In all cases, however, what we perceive is a model of reality. By brain construction through evolution, we divide the world into two parts--our body and the outside world. But the process is the same in both cases. We perceive a construct usually governed by sensed data but always involving memory, goals, fears, expectations, etc. As a first step toward Artificial Perception in man-made systems, we examine perception in general here.

A new method of classifying objects is presented. Rather than trying to form the classifier in one step or in one training algorithm, it is done in a series of small steps, or nibbles. This leads to an efficient and versatile system that is trained in series with single one-shot examples but applied in parallel, is implemented with single layer perceptrons, yet maintains its fully sequential hierarchical structure. Based on the nibbling algorithm, a basic new method of target reference filter management is described.

Pulsed-Coupled Neural Network (PCNN) is an oscillatory model neural network where grouping of cells and grouping among the groups that form the output time series (number of cells that fires in each input presentation also called `icon'). This is based on the synchronicity of oscillations. Recent work by Johnson and others demonstrated the functional capabilities of networks containing such elements for invariant feature extraction using intensity maps. PCNN thus presents itself as a more biologically plausible model with solid functional potential. This paper will present the summary of several projects and their results where we successfully applied PCNN. In project one, the PCNN was applied for object recognition and classification through a robotic vision system. The features (icons) generated by the PCNN were then fed into a feedforward neural network for classification. In project two, we developed techniques for sensory data fusion. The PCNN algorithm was implemented and tested on a B14 mobile robot. The PCNN-based features were extracted from the images taken from the robot vision system and used in conjunction with the map generated by data fusion of the sonar and wheel encoder data for the navigation of the mobile robot. In our third project, we applied the PCNN for speaker recognition. The spectrogram image of speech signals are fed into the PCNN to produce invariant feature icons which are then fed into a feedforward neural network for speaker identification.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The Pulsed Coupled Neural Network (PCNN) algorithm, when modified for use as an image processor, provides a unique method of multiplicative image decomposition (PCNN factorization). Because the factorization is ordered by levels of scene contrast, the first few factors contain the strong contrasts generally associated with shadows. The PCNN factorization effectively and automatically finds scene shadows. This is further developed here as a computationally effective shadow compensation algorithm with illustrative examples given, and is shown to be significantly more effective than histogram equalization. The advantage and disadvantages are discussed.

Summary.A 32x32 electronic PCNN array was tested. It had an awkward and innefficient pulsesample rate due to the imposition of a raster scan output format, it required high levels ofillumination to initiate any pulse activity, and it had an extremely limited operating regime.Most of the pixels were inoperable even under very bright input intensities. Due to theomission of a normalization circuit it was not possible to relate the performance of the chipto its simulated performance. There was no indication of any linking activity, and so there was no pulse synchronization in the chip. In comparison to software versions of the PCNN algorithm, the experimental PCNN array gave abysmal results. It is noted that thedesign choices for the array incorporated none of the features of the Eckhorn linking fieldmodel: the coupling was additive and the pulse generator was a nonlinear version of anintegrate and fire algorithm. In the Eckhorn model5, the coupling is modulatory and thepulse generator is a neuromime6. The poor performance of the array illustrates the needand importance of careful, thoughtful design in any future implementation of the PCNNalgorithm as an electronic chip.References.

The first problem confronting the developers of algorithms for reliable automatic object recognition systems is basic intensity segmentation and noise smoothing. The benefits of using PCNNs for this are described. The next issue for the developer is the mixture of syntactical and statistical techniques. For many, only the latter is included due to the lack of abundance of fast, simple and effective syntactical algorithms. Relational maps and model-based algorithms are generally computationally intensive as compared to a straightforward statistical method such as a classifier net. It is described how the time signals of a nonadaptive PCNN incorporate some syntactical information which in turn has been shown to be compatible with a statistical classifier.

We propose an unbiased multifeature fusion Pulse Coupled Neural Network (PCNN) algorithm. The method shares linking between several PCNNs running in parallel. We illustrate the PCNN fusion technique with a clean and noisy three-band color image example.

The new image factorization algorithm, the k-factor method, is applied to the problem of shadow removal. Unlike the other technique, PCNN factorization, the k-factor method will completely remove shadows. It has several features that allow automatic or manual control that tailor it to specific applications. Unlike histogram equalization which globally remaps rather than remove shadows, the k-factor method eliminates regional shadows without remapping other regions. The algorithm is presented, numerous examples of its performance shown, and future plans discussed.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

A new computational paradigm is introduced. Other image representation such as Fourier transforms and wavelet decompositions depend on linear superposition of basis functions. The k-factor image factorization reduces an image into a finite or infinite set of contrast-ordered images whose joint product reproduces the original image. It is experimentally found that shadows and noise often fall into factors disjoint from the 'pure' image. The analytical foundations of the k-factor method are given, followed by full factorizations and reconstructions, and future research directions are described that include shadow removal, speckle reduction, medical and military image analysis, and commercial applications.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Recent developments in pulse coupled neural networks techniques provide an opportunity to extend the toolbox available for exploring new approaches to biosensor applications. This paper presents a demonstration of properties and limitations of new computational intelligence (CI) techniques as shown by and related to an application. New pulse coupled neural networks (PCNN) techniques are supplemented by combination with wavelet analysis and fine- tuned by radial basis functions. This toolbox is exercised to demonstrate its properties and limitations as related to the development of biosensor applications. The approach selected employs abstractions of biological models of peripheral vision and relates them to analysis of time series generated by biosensors such as chemosensors or motion detectors. Detection of targets (rare or interesting events) is facilitated by PCNN multi-scale image factorization. Interpretation of the resulting image set is aided by contrast enhancement and by segmentation using standard PCNNs. Wavelet coefficients provide supplemental discrimination and lead to characteristic sets of numbers useful in identifying image factors of interest. To complete the transition from acquisition of a complex, noisy image to recognition of targets of interest, radial basis function (RBF) analysis is appended. This five- step process (odor image generation, image factoring, PCNN analysis, wavelet analysis and RBF interpretation) was recently suggested, but is expanded and fully implemented here for the first time. This paper explores the properties and limitations of this approach for simulation of biosensors using small, incomplete sets of real-world data. The relationship between selection of appropriate design parameters and the need for supplementing the available data by simulation is investigated. Evolutionary computation is employed off line to explore and evaluate the possibilities and limitations. Sensor fault detection and RBF training vector generation are addressed. Results are analyzed to provide recommendations for further experimentation and collection of needed additional data without extraneous effort. This methodology is recommended for use in real-world applications where experimental data is difficult, expensive or time consuming to obtain.

Perception is assisted by sensed impressions of the outside world but not determined by them. The primary organ of perception is the brain and, in particular, the cortex. With that in mind, we have sought to see how a computer-modeled cortex--the PCNN or Pulse Coupled Neural Network--performs as a sensor fusing element. In essence, the PCNN is comprised of an array of integrate-and-fire neurons with one neuron for each input pixel. In such a system, the neurons corresponding to bright pixels reach firing threshold faster than the neurons corresponding to duller pixels. Thus, firing rate is proportional to brightness. In PCNNs, when a neuron fires it sends some of the resulting signal to its neighbors. This linking can cause a near-threshold neuron to fire earlier than it would have otherwise. This leads to synchronization of the pulses across large regions of the image. We can simplify the 3D PCNN output by integrating out the time dimension. Over a long enough time interval, the resulting 2D (x,y) pattern IS the input image. The PCNN has taken it apart and put it back together again. The shorter- term time integrals are interesting in themselves and will be commented upon in the paper. The main thrust of this paper is the use of multiple PCNNs mutually coupled in various ways to assemble a single 2D pattern or fused image. Results of experiments on PCNN image fusion and an evaluation of its advantages are our primary objectives.

A parent has been awarded for a new CRT design that eliminates the internal shadow mask, and promises a bright, rugged, high-resolution/large-format, multiscan/multi-sync display with potentially low manufacturing costs. The design incorporates a light-valve within the CRT. Modulation of the electron gun and the function of the shadow mask are replaced by two chemical reactions typical of phosphors: decay stimulation and quenching. These reactions are controlled through illumination of the phosphors by non- visible light projected through the light-valve. The image is generated in traditional raster form, but with a single, non-modulated electron gun; control of each pixel is made through modulation of the light-valve. The design decreases manufacturing alignment requirements and eliminates the need to focus the electron gun. Advantages include: high-G resistance due to elimination of the shadow mask; brighter displays because the electron beam is not occluded or absorbed by the shadow mask; elimination of decay rate as a parameter in phosphor choice and frame state; and digital interfacing to image sources. Applications include: large- format displays without the physical constraints of the shadow mask and by elimination of distortion control requirements of the electron beam at the image edges; variable rates by modulating the decay rate of the phosphors through quenching; and stereoscopic displays without crosstalk through control of left-right phosphor decay rates.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

This paper focuses on use of a new image filtering technique, Pulsed Coupled Neural Network factoring to enhance both the analysis and visual interpretation of noisy sinusoidal time signals, such as those produced by LLNL's Microwave Impulse Radar motion sensor. Separation of a slower, carrier wave from faster, finer detailed signals and from scattered noise is illustrated. The resulting images clearly illustrate the changes over time of simulated heart motion patterns. Such images can potentially assist a field medic in interpretation of the extent of combat injuries. These images can also be transmitted or stored and retrieved for later analysis.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

In the many years that pattern recognition has been of interest, there have ben many clever advances. One recent advance is the pulse-coupled neural network (PCNN). Due to recent developments in PCNNs, it is becoming increasingly possible to recognize images in space regardless of scale, rotation, translation. A continuation of this has been investigated which will allow images to be recognized audibly. In this paper, a general method for converting 2D spatial patterns into pattern-specific sound patterns will be discussed, along with some background information on PCNNs, and projections for his image-to-sound conversion.

The biological neuronal model is an electrically polarized membrane capacitor with numerous ionicchannels for synaptic inputs and leakage currents. It is generally a complex system whose geometrical,electronic, and chemical substructures synergestically determine its temporal dynamics in response to itsinput distributions. The integrate-and-fire pulse generator resets a local internal voltage activity, which inturn interacts with the other voltage distributions in the dendrites. A recent closed-form solution to acompartmental neuronal model shows that it is a fully chaotic system with algorithmic properties that provide a new approach to object-oriented data processing.Introduction. The biological neuron is an electro-chemical system enclosed by an electrically polarized membrane thatmaintains a small potential difference between the interior and the surrounding sea of chemicals. The neuronis modelled as a simple capacitor. The membrane is not perfect, and so there are leakage currents. Thecellular chemistry works to compensate for these leaks and maintain the potential difference, but the manycomplex chemical interactions within and around the cell constantly generate leakage channels. Sometimesthe membrane fails altogether and a current of ions flows into the cell, and the electronic dynamics as thecell attempts to regain its stability (and its potential difference) causes a wave of voltage to flow over themembrane. The wave interacts with the delicate chemical balance around the cell. If another cell is nearby,it can be affected by these transient dynamics and develop a temporary leak in its membrane also. Worse,even when the cell manages to recover, it usually suffers some damage in the form of a change in itsleakage current capacity at the point of breakdown. The situation is complicated by multiple contactsamong adjacent cells, by the complex geometrical structure of the cells, and the all-prevading chemicalreactions and dynamical interactions.The biological neuronal model's task is to make sense of all this, which it does. However, there are furtherlevels of abstraction and generalization which interpert the biological system in terms of its functional

We describe and illustrate a hybrid statistical-syntactic pattern recognition system which seems to offer the simplicity of statistical pattern recognition with the power of syntactic pattern recognition.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

A system employing three acousto-optic cells and a small laser has been constructed which transforms a stereo image, provided by two identical cameras, into a range sensitive translation of an optical beam.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

A pulse-coupled neural network using the Eckhorn linking field coupling [1] is shown to contain invariant spatial information in the phase structure of the output pulse trains. The time domain signals axe directly related to the intensity histogram of an input spatial distribution and have complex phase factors that specify the spatial location of the histogram elements. Two time scales are identified. On the fast time scale the linking produces dynamic, quasi-periodic, fringe-like traveling waves [2] that can carry information beyond the physical limits of the receptive fields. These waves contain the morphological connectivity structure of image elements. The slow time scale is set by the pulse generator, and on that scale the image is segmented into multineuron time-synchronous groups. These groups act as giant neurons, firing together, and by the same linking field mechanism as for the linking waves can form quasi-periodic pulse structures whose relative phases encode the location of the groups with respect to one another. These time signals are a unique, object-specific, and roughly invariant time signature for their corresponding input spatial image or distribution [3]. The details of the model are discussed, giving the basic Eckhorn linking field, extensions, generation of time series in the limit of very weak linking, invariances from the symmetries of the receptive fields, time scales, waves, and signatures. Multirule logical systems are shown to exist on single neurons. Adaptation is discussed. The pulse-coupled nets are compatible with standard nonpulsed adaptive nets rather than competitive with them in the sense that any learning law can be used. Their temporal nature results in adaptive associations in time as well as over space, and they are similar to the time-sequence learning models of Reiss and Taylor [4]. Hardware implementations, optical and electronic, are reviewed. Segmentation, object identification, and location methods are discussed and current results given. The conjugate basic problem of transforming a time signal into a spatial distribution, comparable in importance to the transformation of a spatial distribution into a time signal, is discussed. It maps the invariant time signature into a phase versus frequency spatial distribution and is the spatial representation of the complex histogram. A method of generating this map is discussed. Image pattern recognition using this network is shown to have the power of syntactical pattern recognition and the simplicity of statistical pattern recognition.