This PDF file contains the front matter associated with SPIE Proceedings Volume 7703, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

I will present a PhD project were Diffusion Geometry is used in classification of virus particles in cell kernels from electron micrograms. I will give a very short introduction to Diffusion Geometry and discuss the main classification steps. Some preliminary result from a Master Thesis will be presented.

Silver nanorod array substrates are fabricated by oblique angle deposition and characterized for optimal SERS performance. Using UV-visible-NIR spectrophotometry we show that the nanorods have a transverse surface plasmon resonance mode at ~357 nm and a broad absorbance spanning 600-800 nm when excited along the longitudinal direction. We demonstrate that SERS enhancement is optimized using an excitation wavelength of 633 or 785 nm. The large area uniformity in SERS signal (<10% variation) and reproducibility among preparations (<15% variation) provides a unique opportunity for SERS-based whole-organism fingerprinting. Egg prepared avian influenza virus and clinical sputum samples of human influenza virus were investigated to demonstrate SERS-based detection of a virus in a complex sample matrix and to assess the effect of different background matrices on the detection of similar viruses.

Web citation indexes are computed according to a data vector X collected from the frequency of user accesses, citations weighted by other sites' popularities, and modified by the financial sponsorship in a proprietary manner. The indexing determining the information to be retrieved by the public should be made responsible transparently in at least two ways. One shall balance the inbound linkages pointed at the specific i-th site called the popularity (see paper for equation) with the outbound linkages (see paper for equation) called the risk factor before the release of new information as environmental impact analysis. The relationship between these two factors cannot be assumed equivalent (undirected) as in the case of many mainstream Graph Theory (GT) models.

The authority officer relies on facial mug-shots to spot suspects among crowds. Passing through a check point, the facial displays and printouts operate in low resolution fixed poses. Thus, a databases-cuing video is recommended for real-time surveillance with Aided-Target Recognition (AiTR) prompting the inspector taking a closer second look at a specific passenger. Taking advantage of commercial available Face Detection System on Chips (SOC) at 0.04sec, we develop a fast and smart algorithm to sort facial poses among passengers. We can increase the overlapping POFs (pixels on faces) in matching mug shots at arbitrary poses with sorted facial poses. Lemma: We define the long exposure as time average of facial poses and the short exposure as single facial pose in a frame of video in 30 Hz. The fiduciary triangle is defined among two eyes and nose-top. Theorem Self-Reference Matched Filtering (Szu et al. Opt Comm. 1980; JOSA, 1982) to Facial-Pose: If we replace the desirable output of Weiner filter as the long exposure, then the filter can select a short exposure as the normal view. Corollary: Given a short exposure as normal view, the fiduciary triangle can decide all poses from left-to-right and top-to-down.

Iris recognition provides real-time, high confidence identification of persons by analysis of the random patterns that are visible within the iris of an eye from some distance. Because the iris is a protected, internal, organ whose random texture is epigenetic and stable over the lifespan, it can serve as a living password. Recognition decisions are made with confidence levels high enough to support rapid exhaustive searches through national-sized databases. The principle that underlies these algorithms is the failure of an efficient test of statistical independence involving more than 200 degrees-of-freedom, based on phase sequencing each iris pattern with quadrature 2D wavelets. Different persons always pass this test of statistical independence, but images from the same iris almost always fail this test of independence. Database search speeds are around 1 million persons per second per CPU. Data from 200 billion cross-comparisons between different eyes will be presented in this talk, using a database consisting of 632,500 iris images acquired in the United Arab Emirates in a networked national border-crossing security system which performs, every day, about 9 billion iris comparisons using these algorithms. Current research efforts with this technology aim to make it more tolerant of difficult conditions of iris capture, such as "iris on the move," at a distance, and off-axis.

The human iris is a circular curtain over the light entrance pupil which is controlled directly by the intensity of blue light from photosensitive ganglions in the retina within the eye. The human iris dynamic is remarkable in that it is capable of shrinking concentrically along the radial direction by a factor 4 from 8mm to 2mm, and constantly oscillates in 1/2 second periodicity. Pupil dilation and contraction causes the iris texture to undergo nonlinear deformation with discrete components and minutia features. Thus, iris recognition must be scale invariant due to the pupil dynamics. We propose the Mandelbrot fractal dimension count of minutia iris details, at different intensity thresholds, in dilation-invariant wedge-boxes, formed at specific angular sizes, but spatially varying over 4 90° quadrants due to the cellular growth under the gravity. Despite the concentric dynamic, we have sought an invariant fractal dimensionality in the circular direction and discovered the non-isotropic effect, departed from the simple Richardson fractal law. Furthermore, we choose an optimum Rayleigh criterion λ/D matching the robust fine resolution scale for the given lens aperture D and the illumination wavelength λ for a potential application from a distant, with the help of comprehensive biometric including iris.

In this work, we present a new approach for the problem of interferometric phase noise reduction in synthetic aperture radar interferometry based on the shearlet representation. Shearlets provide a multidirectional and multiscale decomposition that have advantages when dealing with noisy phase fringes over standard filtering methods. Using a shearlet decomposition of a noisy phase image, we can adaptively estimate a phase representation in a multiscale and anisotropic fashion. Such denoised phase interferograms can be used to provide much better digital elevation maps (DEM). Experiments show that this method performs significantly better than many competitive methods.

The Artificial Cognitive Systems (ACS) will be developed for human-like functions such as vision, auditory, inference, and behavior. Especially, computational models and artificial HW/SW systems will be devised for Proactive Learning (PL) and Self-Identity (SI). The PL model provides bilateral interactions between robot and unknown environment (people, other robots, cyberspace). For the situation awareness in unknown environment it is required to receive audiovisual signals and to accumulate knowledge. If the knowledge is not enough, the PL should improve by itself though internet and others. For human-oriented decision making it is also required for the robot to have self-identify and emotion. Finally, the developed models and system will be mounted on a robot for the human-robot co-existing society. The developed ACS will be tested against the new Turing Test for the situation awareness. The Test problems will consist of several video clips, and the performance of the ACSs will be compared against those of human with several levels of cognitive ability.

This paper presents an electronic tongue system with blind source separation (BSS) and wireless sensor network (WSN) for remote multi-ion sensing applications. Electrochemical sensors, such as ion-sensitive field-effect transistor (ISFET) and extended-gate field-effect transistor (EGFET), only provide the combined concentrations of all ions in aqueous solutions. Mixed hydrogen and sodium ions in chemical solutions are observed by means of H⁺ ISFET and H+ EGFET sensor array. The BSS extracts the concentration of individual ions using independent component analysis (ICA). The parameters of ISFET and EGFET sensors serve as a priori knowledge that helps solve the BSS problem. Using wireless transceivers, the ISFET/EGFET modules are realized as wireless sensor nodes. The integration of WSN technology into our electronic tongue system with BSS capability makes distant multi-ion measurement viable for environment and water quality monitoring.

Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory regionof- interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.

In this paper we describe a prototype surveillance system that leverages smart sensor motes, intelligent video, and Sensor Web technologies to aid in large area monitoring operations and to enhance the security of borders and critical infrastructures. Intelligent video has emerged as a promising tool amid growing concern about border security and vulnerable entry points. However, numerous barriers exist that limit the effectiveness of surveillance video in large area protection; such as the number of cameras needed to provide coverage, large volumes of data to be processed and disseminated, lack of smart sensors to detect potential threats and limited bandwidth to capture and distribute video data. We present a concept prototype that addresses these obstacles by employing a Smart Video Node in a Sensor Web framework. Smart Video Node (SVN) is an IP video camera with automated event detection capability. SVNs are cued by inexpensive sensor motes to detect the existence of humans or vehicles. Based on sensor motes' observations cameras are slewed in to observe the activity and automated video analysis detects potential threats to be disseminated as "alerts". Sensor Web framework enables quick and efficient identification of available sensors, collects data from disparate sensors, automatically tasks various sensors based on observations or events received from other sensors, and receives and disseminates alerts from multiple sensors. The prototype system is implemented by leveraging intuVision's intelligent video, Northrop Grumman's sensor motes and SensorWeb technologies. Implementation of a deployable system with Smart Video Nodes and sensor motes within the SensorWeb platform is currently underway. The final product will have many applications in commercial, government and military systems.

Unattended ground sensor (UGS) networks have been widely used in remote battlefield and other tactical applications over the last few decades due to the advances of the digital signal processing. The UGS network can be applied in a variety of areas including border surveillance, special force operations, perimeter and building protection, target acquisition, situational awareness, and force protection. In this paper, a highly-distributed, fault-tolerant, and energyefficient Smart Sensing Surveillance System (S4) is presented to efficiently provide 24/7 and all weather security operation in a situation management environment. The S4 is composed of a number of distributed nodes to collect, process, and disseminate heterogeneous sensor data. Nearly all S4 nodes have passive sensors to provide rapid omnidirectional detection. In addition, Pan- Tilt- Zoom- (PTZ) Electro-Optics EO/IR cameras are integrated to selected nodes to track the objects and capture associated imagery. These S4 camera-connected nodes will provide applicable advanced on-board digital image processing capabilities to detect and track the specific objects. The imaging detection operations include unattended object detection, human feature and behavior detection, and configurable alert triggers, etc. In the S4, all the nodes are connected with a robust, reconfigurable, LPI/LPD (Low Probability of Intercept/ Low Probability of Detect) wireless mesh network using Ultra-wide band (UWB) RF technology, which can provide an ad-hoc, secure mesh network and capability to relay network information, communicate and pass situational awareness and messages. The S4 utilizes a Service Oriented Architecture such that remote applications can interact with the S4 network and use the specific presentation methods. The S4 capabilities and technologies have great potential for both military and civilian applications, enabling highly effective security support tools for improving surveillance activities in densely crowded environments and near perimeters and borders. The S4 is compliant with Open Geospatial Consortium - Sensor Web Enablement (OGC-SWE®) standards. It would be directly applicable to solutions for emergency response personnel, law enforcement, and other homeland security missions, as well as in applications requiring the interoperation of sensor networks with handheld or body-worn interface devices.

In noisy environment the human speech perception utilizes visual lip-reading as well as audio phonetic classification. This audio-visual integration may be done by combining the two sensory features at the early stage. Also, the top-down attention may integrate the two modalities. For the sensory feature fusion we introduce mapping functions between the audio and visual manifolds. Especially, we present an algorithm to provide one-to-many mapping function for the videoto- audio mapping. The top-down attention is also presented to integrate both the sensory features and classification results of both modalities, which is able to explain McGurk effect. Each classifier is separately implemented by the Hidden-Markov Model (HMM), but the two classifiers are combined at the top level and interact by the top-down attention.

A batch training algorithm for feed-forward networks is proposed which uses Newton's method to estimate a vector of optimal learning factors, one for each hidden unit. Backpropagation, using this learning factor vector, is used to modify the hidden unit's input weights. Linear equations are then solved for the network's output weights. Elements of the new method's Gauss-Newton Hessian matrix are shown to be weighted sums of elements from the total network's Hessian. In several examples, the new method performs better than backpropagation and conjugate gradient, with similar numbers of required multiplies. The method performs as well as or better than Levenberg-Marquardt, with several orders of magnitude fewer multiplies due to the small size of its Hessian.

Support Vector Machines are currently one of the best classification algorithms used in a wide number of applications. The ability to extract a classification function from a limited number of learning examples keeping in the structural risk low has demonstrated to be a clear alternative to other neural networks. However, the calculations involved in computing the kernel and the repetition of the process for all support vectors in the classification problem are certainly intensive, requiring time or power consumption in order to function correctly. This problem could be a drawback in certain applications with limited resources or time. Therefore simple algorithms circumventing this problem are needed. In this paper we analyze an FPGA implementation of a SVM which uses a CORDIC algorithm for simplifying the calculation of as specific kernel greatly reducing the time and hardware requirements needed for the classification, allowing for powerful in-field portable applications. The algorithm is and its calculation capabilities are shown. The full SVM classifier using this algorithm is implemented in an FPGA and its in-field use assessed for high speed low power classification.

CONTACT or Consortium for Nanomaterials for Aerospace Commerce and Technology is a cooperative program between the Air Force Research Laboratory and seven Texas universities focused on four research areas in aerospace. This paper summarizes recent developments in one of those areas, sensors, for eventual use in aircraft and spacecraft. We report direct measurement of spectrally selective absorption properties of PbSe and PbS colloidal quantum dots (CQDs) in Si nanomembrane photonic crystal cavities on flexible plastic polyethylene terephthalate (PET) substrates. The interaction of CQD absorption with photonic crystal Fano resonances is presented both analytically and experimentally for use in wavelength selective sensors.

Nanotech is at the scale of 10^-9 meters, located at the mesocopic transition phase, which can take both classical mechanics (CM) and quantum mechanics (QM) descriptions bridging ten orders of magnitude phenomena, between the microscopic world of a single atom at 10^-10 meters with the macroscopic world at meters. However, QM principles aid the understanding of any unusual property at the nanotech level. The other major difference between nano-photonics and other forms of optics is that the nano-scale is not very 'hands on'. For the most part, we will not be able to see the components with our naked eyes, but will be required to use some nanotech imaging tools, as follows:

We discuss the principle of superposition relative to the definition of extensivity in thermodynamics and discuss how it can be extended to systems that do not obey the linear principle of superposition. This leads to the concept of generalized superposition, of which, there are multiple types defined in the paper. Generalized superposition can be used to explore the breakdown of linearity at the nano-scale, which leads to deeper understanding of nano-materials and nano-thermodynamics.

Reuse-based design has emerged as one of the most important methodologies for integrated circuit design, with reusable Intellectual Property (IP) cores enabling the optimization of company resources due to reduced development time and costs. This is of special interest in the Field-Programmable Logic (FPL) domain, which mainly relies on automatic synthesis tools. However, this design methodology has brought to light the intellectual property protection (IPP) of those modules, with most forms of protection in the EDA industry being difficult to translate to this domain. However, IP core watermarking has emerged as a tool for IP core protection. Although watermarks may be inserted at different levels of the design flow, watermarking Hardware Description Language (HDL) descriptions has been proved to be a robust and secure option. In this paper, a new framework for the protection of μP cores is presented. The protection scheme is derived from the IPP@HDL procedure and it has been adapted to the singularities of μP cores, overcoming the problems for the digital signature extraction in such systems. Additionally, the feature of hardware activation has been introduced, allowing the distribution of μP cores in a "demo" mode and a later activation that can be easily performed by the customer executing a simple program. Application examples show that the additional hardware introduced for protection and/or activation has no effect over the performance, and showing an assumable area increase.

The quadratic sieve (QS) algorithm is one of the most powerful algorithms to factor large composite primes used to break RSA cryptographic systems. The hardware structure of the QS algorithm seems to be a good fit for FPGA acceleration. Our new ε-QS algorithm further simplifies the hardware architecture making it an even better candidate for C2H acceleration. This paper shows our design results in FPGA resource and performance when implementing very long arithmetic on the Nios microprocessor platform with C2H acceleration for different libraries (GMP, LIP, FLINT, NRMP) and QS architecture choices for factoring 32-2048 bit RSA numbers.

Pulse Coupled Neural Networks are a very useful tool for image processing and visual applications, since it has the advantages of being invariant to image changes as rotation, scale, or certain distortion. Among other characteristics, the PCNN changes a given image input into a temporal representation which can be easily later analyzed for pattern recognition. The structure of a PCNN though, makes it necessary to determine all of its parameters very carefully in order to function optimally, so that the responses to the kind of inputs it will be subjected are clearly discriminated allowing for an easy and fast post-processing yielding useful results. This tweaking of the system is a taxing process. In this paper we analyze and compare two methods for modeling PCNNs. A purely mathematical model is programmed and a similar circuital model is also designed. Both are then used to determine the optimal values of the several parameters of a PCNN: gain, threshold, time constants for feed-in and threshold and linking leading to an optimal design for image recognition. The results are compared for usefulness, accuracy and speed, as well as the performance and time requirements for fast and easy design, thus providing a tool for future ease of management of a PCNN for different tasks.

Equivalence models (EM) advantages of neural networks (NN) are shown in paper. EMs are based on vectormatrix procedures with basic operations of continuous neurologic: normalized vector operations "equivalence", "nonequivalence", "autoequivalence", "autononequivalence". The capacity of NN on the basis of EM and of its modifications, including auto-and heteroassociative memories for 2D images, exceeds in several times quantity of neurons. Such neuroparadigms are very perspective for processing, recognition, storing large size and strongly correlated images. A family of "normalized equivalence-nonequivalence" neuro-fuzzy logic operations on the based of generalized operations fuzzy-negation, t-norm and s-norm is elaborated. A biologically motivated concept and time pulse encoding principles of continuous logic photocurrent reflexions and sample-storage devices with pulse-width photoconverters have allowed us to design generalized structures for realization of the family of normalized linear vector operations "equivalence"-"nonequivalence". Simulation results show, that processing time in such circuits does not exceed units of micro seconds. Circuits are simple, have low supply voltage (1-3 V), low power consumption (milliwatts), low levels of input signals (microwatts), integrated construction, satisfy the problem of interconnections and cascading.

First, we describe an automated procedure for segmenting an MR image of a human brain based on fuzzy logic for diagnosing Alzheimer's disease. The intensity thresholds for segmenting the whole brain of a subject are automatically determined by finding the peaks of the intensity histogram. After these thresholds are evaluated in a region growing, the whole brain can be identified. Next, we describe a procedure for decomposing the obtained whole brain into the left and right cerebral hemispheres, the cerebellum and the brain stem. Our method then identified the whole brain, the left cerebral hemisphere, the right cerebral hemisphere, the cerebellum and the brain stem. Secondly, we describe a transskull sonography system that can visualize the shape of the skull and brain surface from any point to examine skull fracture and some brain diseases. We employ fuzzy signal processing to determine the skull and brain surface. The phantom model, the animal model with soft tissue, the animal model with brain tissue, and a human subjects' forehead is applied in our system. The all shapes of the skin surface, skull surface, skull bottom, and brain tissue surface are successfully determined.

Although contemporary, contact methods of measuring cardiovascular health are accurate and applicable, a noncontact optical sensor that detects these same parameters of health and eliminates the inconvenience of patient contact would be useful to the medical community. Techniques of mapping and imaging blood flow with laser speckle contrast imaging have shown promise as a non-contact health sensor. This paper explores using a laser speckle detector to detect blood pressure, pulse pressure waves, and pulse wave velocity at a standoff. The laser speckle detector was able to detect pulse pressure waves and with further development, may be able to measure pulse wave velocity and blood pressure.

Laser speckle imaging (LSI) has been gaining popularity for the past few years. Like other optical imaging modalities such as optical coherence tomography (OCT), orthogonal polarization spectroscopy (OPS), and laser Doppler imaging (LDI), LSI utilizes nonionizing radiation. In LSI, blood flow velocity is obtained by analyzing, temporally or spatially, laser speckle (LS) patterns generated when an expanded laser beam illuminates the tissue. The advantages of LSI are that it is fast, does not require scanning, and provides full-field LS images to extract realtime, quantitative hemodynamic information of subtle changes in the tissue vasculature. For medical applications, LSI has been used for obtaining blood velocities in human retina, skin flaps, wounds, and cerebral and sublingual areas. When coupled with optical fibers, LSI can be used for endoscopic measurements for a variety of applications. This paper describes the application of LSI in retinal, sublingual, and skin flap measurements. Evaluation of retinal hemodynamics provides very important diagnostic information, since the human retina offers direct optical access to both the central nervous system (CNS) and afferent and efferent CNS vasculature. The performance of an LSI-based fundus imager for measuring retinal hemodynamics is presented. Sublingual microcirculation may have utility for sepsis indication, since inherent in organ injury caused by sepsis is a profound change in microvascular hemodynamics. Sublingual measurement results using an LSI scope are reported. A wound imager for imaging LS patterns of wounds and skin flaps is described, and results are presented.

This paper will give background information on the structure of different influenza viruses and address the remote detection of viral particles using Surface Enhanced Raman Spectroscopy (SERS). Also, we will mathematically predict how small to create the silver nanorods, measured by the diameter of the rod, in order for there to be a discernable enhancement in the Raman signal due to quantum properties of the rods. Finally, the future of nanotechnology in optics as it relates to medical applications will be addressed, highlighting a few of the most important possible future applications.

Multi-modality diagnosis techniques are more and more replacing traditional medical imaging for breast cancer detection. Newly emerging advances in both intelligent cancer detection systems and lipidomics technologies offer an excellent opportunity to detect tumors and to understand regulation at the molecular level in many diseases such as cancer. In this paper, we present a detailed computer-aided diagnosis (CAD) systems combining motion artefact reduction and automated feature extraction and classification, and a novel data mining approach for visualization of gene therapy leading to apoptosis in U87 MG glioblastoma cells, a secondary tumor of breast cancer. The achieved results show that the CAD system represents a robust and integrative tool for reliable small contrast enhancing lesions. Graph-clustering methods are introduced as powerful correlation networks which enable a simultaneous exploration and visualization of co-regulation in glioblastoma data. These new paradigms are providing unique "fingerprints" by revealing how the intricate interactions at the lipidome level can be employed to induce apoptosis (cell death) and are thus opening a new window to biomedical frontiers.

We described a way to use a block-matching 3-D denoising algorithm to reduce noise in x-ray imagery. We first filtered an image multiple times using different estimates of the noise variance. From a simple estimate of the denoised image, we then estimated the noise variance at each pixel of the image. Using this approach, we obtained improved results when compared to using a single value of estimate for the noise variance. Even a small number of quantization levels of the estimates of the noise showed improved results.

Various types of cancer remain the second leading cause of death in the world. As a consequence, the detection of these tumors has a vital importance. Optoacoustic imaging (OA), a novel imaging technique, offers high contrast and resolution to detect them by measuring the pressure waves generated by tissues exposed to optical energy. Several algorithms, based on Back Projection (BP) techniques, have been suggested to process OA images in conjunction with signal filtering. In this paper, we compare several BP techniques in combination with different classes of filtering. We apply these techniques first directly to a numerical generated sample image and then to the laser-digitalized image of a tissue phantom, obtaining in both cases the best results in resolution and contrast for a wavelet-based filter.

An analysis of the degree of genomic variation between two individual genomes suggests that there may be considerable biochemical differences among individuals. Examination of DNA sequence variations in 14 canonical signaling pathways and Monte-Carlo simulation modeling suggest that the kinetic and quantitative behavior of signaling pathways in many individuals may be significantly perturbed from the 'healthy' norm. Signal transduction pathways in some individuals may suffer context-specific failures, or they may function normally but fail easily in the face of additional environmental perturbations or somatic mutations. These findings argue for new systems biology approaches that can predict pathway status in individuals using personal genome sequences and biomarker data.

The analysis and interpretation of large lipidomic data sets requires the development of new dynamical systems, data mining and visualization approaches. Traditional techniques are insufficient to study corregulations and stochastic fluctuations observed in lipidomic networks and resulting experimental data. The emphasis of this paper lies in the presentation of novel approaches for the dynamical analysis and projection representation. Different paradigms describing kinetic models and providing context-based information are described and at the same time their interrelations are revealed. These qualitative and quantitative methods are applied to the lipidomic analysis of U87 MG glioblastoma cells. The achieved provide a more detailed insight into the data structure of the lipidomic system.

Image registration plays an important role in many image understanding applications, such as stereo vision for depth recovery and biometric recognition. Correlation-based matches are often used in such registration applications. The performance of correlation-based matching algorithms such as the Normalized Cross Correlation (NCC) depends on the image patch size used in the computation. We present an algorithm that adapts the patch size so that the patch is distinguishable from other patches from the same image. The new method seeks the best balance point between matching performance and computational cost in NCC computation. Experimental results demonstrate the performance of our method.

The fusion of multi-spectral images is an important pre-processing operation for scientists and engineers seeking to design robust detection, recognition and identification (DRI) systems. Due to the multitude of pixellevel fusion algorithms available, there is an extreme need for reliable metrics to analyze their performance. Most recently, subspace learning methods have been applied to the field of information fusion for object recognition and classification. This paper aims to extend the capabilities of existing nonlinear dimensionality reduction algorithms to a new area, evaluating the performance of image fusion algorithms. We prove that distances between points in the low dimensional embedding are essentially equivalent to the results given by estimating the amount of information transfered from source images to resultant fused images (normalized mutual information).

Can the background affect a foreground target in distant, low-quality imagery? If it does, it might occur in our mind, or perhaps it may represent a snapshot of our early vision. An affirmative answer, one way or another, may affect our current understanding of this phenomena and potentially for related applications. How can we be sure about this in the psycho-physical sense? We begin with the physiology of our brain's homeostasis, of which an isothermal equilibrium is characterized by the minimum of Helmholtz isothermal Free Energy: A = U - T₀S ≥ 0, where T0 = 37°C, the Boltzmann Entropy S = K_B1n(W), and U is the unknown internal energy to be computed.

An algorithm of distinguishing rock from coal based on statistical analysis of Fast Fourier Transform (FFT) is presented which can be used in the mechanized caving coal locales. First, eight groups of sound signals sampled with the speed 8192 samples/sec during caving are transformed by FFT. Second, the FFT results are analyzed and the ratios of the low frequency energy to the high frequency energy(ER) defined in the FFT results are calculated by using the variance analytical method (Var). Third, the typical values of the sound of the coal bumping the transporting coal armor plate, the rock bumping the armor plate and the mixing of coal and rock bumping the armor plate are calculated with the variances and the ratios(EV= ER *Var). Finally, the threshold of distinguishing rock from coal is evaluated by using the typical values and used to direct the opportunity for caving. We can learn by the experimental results that the proposed technique can depict effectively the different characteristics of the sampled signals. The experimental results also show that we can distinguish effectively different bumping sounds of coal, rock and the mixing of them by the characteristics when adjusting the threshold value. Therefore the algorithm can be used to improve the miners' productivity and promote the construction of digital mine.

To evaluate the implementation results of mine safety production law, the evaluation model based on neural network is presented. In this model, 63 indicators which can describe the mine law effectively are proposed. The evaluation system is developed by using the model and the 63 indicators. The evaluation results show that the proposed method has high accuracy. We can effectively estimate the score of one mine for its carrying out the safety law. The estimate results are of scientific credibility and impartiality.

In the crowded rain forest, how do animals locate camouflaged prey that resemble the environment, such as walking sticks? Birds will observe the suspected stick over a period of time to determine if its behavior matches that of a tree. If the stick's behavior exceeds normal tree behavior, such as moving faster than the other branches, the bird will determine that it is actually a walking stick and not a tree branch. Once this determination is made, they will prey on the insect. Studying this natural process of novelty detection, present in a variety of animals from birds to turkey vultures, can be beneficial for numerous human applications. The spatiotemporal novelty detector will be developed using self-referencing matched filters to go beyond auto-regression. This algorithm is more than the usual change detector for it will detect a novelty behavior that exceeds its predicted value based on past data, e.g. the bird who identifies a walking stick in the forest. A scalar model is presented; however, the application can be expanded to multiple modalities for more detailed applications. Some of these applications include gas pipeline leak detection, persistent surveillance, and the creation of a smart sensor web.