This PDF file contains the front matter associated with SPIE Proceedings Volume 9118 including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

The largest biometric deployment in history is now underway in India, where the Government is enrolling the iris patterns (among other data) of all 1.2 billion citizens. The purpose of the Unique Identification Authority of India (UIDAI) is to ensure fair access to welfare benefits and entitlements, to reduce fraud, and enhance social inclusion. Only a minority of Indian citizens have bank accounts; only 4 percent possess passports; and less than half of all aid money reaches its intended recipients. A person who lacks any means of establishing their identity is excluded from entitlements and does not officially exist; thus the slogan of UIDAI is: To give the poor an identity." This ambitious program enrolls a million people every day, across 36,000 stations run by 83 agencies, with a 3-year completion target for the entire national population. The halfway point was recently passed with more than 600 million persons now enrolled. In order to detect and prevent duplicate identities, every iris pattern that is enrolled is first compared against all others enrolled so far; thus the daily workflow now requires 600 trillion (or 600 million-million) iris cross-comparisons. Avoiding identity collisions (False Matches) requires high biometric entropy, and achieving the tremendous match speed requires phase bit coding. Both of these requirements are being delivered operationally by wavelet methods developed by the author for encoding and comparing iris patterns, which will be the focus of this Large Data Award" presentation.

We exam the historical remote digital video and in situ analog acoustic data analyses from the modern Large Data Analysis standpoint. We discussed a potential automation from traditional search engine to modern one. We exam the mathematic theory to answer where the nonlinear dimensional analysis assume a local flat space where linear eigenvalue provided the independent components, then the component is extrapolated to original nonlinear space and assume the local flat reduction remains meaningful at global nonlinear domain.

Almost all consumer and firm transactions are achieved using computers and as a result gives rise to increasingly large amounts of data available for analysts. The gold standard in Economic data manipulation techniques matured during a period of limited data access, and the new Large Data Analysis (LDA) paradigm we all face may quickly obfuscate most tools used by Economists. When coupled with an increased availability of numerous unstructured, multi-modal data sets, the impending 'data tsunami' could have serious detrimental effects for Economic forecasting, analysis, and research in general. Given this reality we propose a decision-aid framework for Augmented-LDA (A-LDA) – a synergistic approach to LDA which combines traditional supervised, rule-based Machine Learning (ML) strategies to iteratively uncover hidden sources in large data, the artificial neural network (ANN) Unsupervised Learning (USL) at the minimum Helmholtz free energy for isothermal dynamic equilibrium strategies, and the Economic intuitions required to handle problems encountered when interpreting large amounts of Financial or Economic data. To make the ANN USL framework applicable to economics we define the temperature, entropy, and energy concepts in Economics from non-equilibrium molecular thermodynamics of Boltzmann viewpoint, as well as defining an information geometry, on which the ANN can operate using USL to reduce information saturation. An exemplar of such a system representation is given for firm industry equilibrium. We demonstrate the traditional ML methodology in the economics context and leverage firm financial data to explore a frontier concept known as behavioral heterogeneity. Behavioral heterogeneity on the firm level can be imagined as a firm's interactions with different types of Economic entities over time. These interactions could impose varying degrees of institutional constraints on a firm's business behavior. We specifically look at behavioral heterogeneity for firms that are operating with the label of ‘Going-Concern’ and firms labeled according to institutional influence they may be experiencing, such as constraints on firm hiring/spending while in a Bankruptcy or a Merger procedure. Uncovering invariant features, or behavioral data metrics from observable firm data in an economy can greatly benefit the FED, World Bank, etc. We find that the ML/LDA communities can benefit from Economic intuitions just as much as Economists can benefit from generic data exploration tools. The future of successful Economic data understanding, modeling, simulation, and visualization can be amplified by new A-LDA models and approaches for new and analogous models of Economic system dynamics. The potential benefits of improved economic data analysis and real time decision aid tools are numerous for researchers, analysts, and federal agencies who all deal with increasingly large amounts of complex data to support their decision making.

Software-defined Cameras (SDC) based on Boltzmann’s molecular thermodynamics can “see” through visually-degraded fields such as fire, fog, and dust in some situations. This capability is possible by means of unsupervised learning implemented on a neuromorphic algo-tecture. This paper describes the SDC algorithm design strategy with respect to nontrivial solutions, stability, and accuracy. An example neuromorphic learning algorithm is presented along with unsupervised learning stopping criteria.

Fire can overwhelm the field of view of a thermal imaging sensor with intensive radiation, and when presented to observers can cause important cues in the scene to go unnoticed due to the limited dynamic range of displays and of the human visual system. Here we propose a computational method, called software-defined camera (SDC), to improve the image quality for an un-cooled thermal imager seeing through fire that obscures lower-temperature objects in the background. To that end, we developed a novel theory for the arbitrary manipulation of optical radiation sources, which is based on rigorous application of Boltzmann’s molecular thermodynamics. On this framework it is possible to formulate the problem of identification and selective removal/suppression of the optical radiation sources, and thus to design a blind source separation algorithm. Application of the developed theory should make it possible to design a low-cost specialty SDC that is able to see through high temperature fire for locating a relatively low temperature objects such as a human body.

Under the Department of Homeland Security-sponsored Sensor-smart Affordable Autonomous Robotic Platforms (SAARP) project, Robotic Research, LLC is developing an affordable and adaptable method to provide disaster response robots developed with 3D printer technology. The SAARP Store contains a library of robots, a developer storefront, and a user storefront. The SAARP Store allows the user to select, print, assemble, and operate the robot. In addition to the SAARP Store, two platforms are currently being developed. They use a set of common non-printed components that will allow the later design of other platforms that share non-printed components. During disasters, new challenges are faced that require customized tools or platforms. Instead of prebuilt and prepositioned supplies, a library of validated robots will be catalogued to satisfy various challenges at the scene. 3D printing components will allow these customized tools to be deployed in a fraction of the time that would normally be required. While the current system is focused on supporting disaster response personnel, this system will be expandable to a range of customers, including domestic law enforcement, the armed services, universities, and research facilities.

The cost and scope of additive printing machines range from several hundred to hundreds of thousands of dollars. For the extra money, one can get improvements in build size, selection of material properties, resolution, and consistency. However, temperature control during build and fusing predicts outcome and protects the IP by large high cost machines. Support material options determine geometries that can be accomplished which drives cost and complexity of printing heads. Historically, 3D printers have been used for design and prototyping efforts. Recent advances and cost reduction sparked new interest in developing printed products and consumables such as NASA who is printing food, printing consumer parts (e.g. cell phone cases, novelty toys), making tools and fixtures in manufacturing, and recursively print a self-similar printer (c.f. makerbot). There is a near term promise of the capability to print on demand products at the home or office... directly from the printer to use.

We present a new method of highway 3-D model construction developed based on feature extraction in highway images and LIDAR data. We describe the processing road coordinate data that connect the image frames to the coordinates of the elevation data. Image processing methods are used to extract sky, road, and ground regions as well as significant objects (such as signs and building fronts) in the roadside for the 3D model. LIDAR data are interpolated and processed to extract the road lanes as well as other features such as trees, ditches, and elevated objects to form the 3D model. 3D geometry reasoning is used to match the image features to the 3D model. Results from successive frames are integrated to improve the final model.

The theory of compressive sampling (CS) was reintroduced by Candes, Romberg and Tao, and D. Donoho in 2006. Using a priori knowledge that a signal is sparse, it has been mathematically proven that CS can defY Nyquist sampling theorem. Theoretically, reconstruction of a CS image relies on the minimization and optimization techniques to solve this complex almost NP-complete problem. There are many paths to consider when compressing and reconstructing an image but these methods have remained untested and unclear on natural images, such as underwater sonar images. The goal of this research is to perfectly reconstruct the original sonar image from a sparse signal while maintaining pertinent information, such as mine-like object, in Side-scan sonar (SSS) images. Goldstein and Osher have shown how to use an iterative method to reconstruct the original image through a method called Split Bregman's iteration. This method "decouples" the energies using portions of the energy from both the !1 and !2 norm. Once the energies are split, Bregman iteration is used to solve the unconstrained optimization problem by recursively solving the problems simultaneously. The faster these two steps or energies can be solved then the faster the overall method becomes. While the majority of CS research is still focused on the medical field, this paper will demonstrate the effectiveness of the Split Bregman's methods on sonar images.

Since the Brain Order Disorder (BOD) group reported on a high density Electroencephalogram (EEG) to capture the neuronal information using EEG to wirelessly interface with a Smartphone [1,2], a larger BOD group has been assembled, including the Obama BRAIN program, CUA Brain Computer Interface Lab and the UCSD Swartz Computational Neuroscience Center. We can implement the pair-electrodes correlation functions in order to operate in a real time daily environment, which is of the computation complexity of O(N³) for N=10^2~3 known as functional f-EEG. The daily monitoring requires two areas of focus. Area #(1) to quantify the neuronal information flow under arbitrary daily stimuli-response sources. Approach to #1: (i) We have asserted that the sources contained in the EEG signals may be discovered by an unsupervised learning neural network called blind sources separation (BSS) of independent entropy components, based on the irreversible Boltzmann cellular thermodynamics(ΔS < 0), where the entropy is a degree of uniformity. What is the entropy? Loosely speaking, sand on the beach is more uniform at a higher entropy value than the rocks composing a mountain – the internal binding energy tells the paleontologists the existence of information. To a politician, landside voting results has only the winning information but more entropy, while a non-uniform voting distribution record has more information. For the human’s effortless brain at constant temperature, we can solve the minimum of Helmholtz free energy (H = E − TS) by computing BSS, and then their pairwise-entropy source correlation function. (i) Although the entropy itself is not the information per se, but the concurrence of the entropy sources is the information flow as a functional-EEG, sketched in this 2^nd BOD report. Area #(2) applying EEG bio-feedback will improve collective decision making (TBD). Approach to #2: We introduce a novel performance quality metrics, in terms of the throughput rate of faster (Δt) & more accurate (ΔA) decision making, which applies to individual, as well as team brain dynamics. Following Nobel Laureate Daniel Kahnmen’s novel “Thinking fast and slow”, through the brainwave biofeedback we can first identify an individual’s “anchored cognitive bias sources”. This is done in order to remove the biases by means of individually tailored pre-processing. Then the training effectiveness can be maximized by the collective product Δt * ΔA. For Area #1, we compute a spatiotemporally windowed EEG in vitro average using adaptive time-window sampling. The sampling rate depends on the type of neuronal responses, which is what we seek. The averaged traditional EEG measurements and are further improved by BSS decomposition into finer stimulus-response source mixing matrix [A] having finer & faster spatial grids with rapid temporal updates. Then, the functional EEG is the second order co-variance matrix defined as the electrode-pair fluctuation correlation function C(s~, s~’) of independent thermodynamic source components. (1) We define a 1-D Space filling curve as a spiral curve without origin. This pattern is historically known as the Peano-Hilbert arc length a. By taking the most significant bits of the Cartesian product a≡ O(x * y * z), it represents the arc length in the numerical size with values that map the 3-D neighborhood proximity into a 1-D neighborhood arc length representation. (2) 1-D Fourier coefficients spectrum have no spurious high frequency contents, which typically arise in lexicographical (zig-zag scanning) discontinuity [Hsu & Szu, “Peano-Hilbert curve,” SPIE 2014]. A simple Fourier spectrum histogram fits nicely with the Compressive Sensing CRDT Mathematics. (3) Stationary power spectral density is a reasonable approximation of EEG responses in striate layers in resonance feedback loops capable of producing a 100, 000 neuronal collective Impulse Response Function (IRF). The striate brain layer architecture represents an ensemble <IRF< e.g. at V1-V4 of Brodmann areas 17-19 of the Cortex, i.e. stationary Wiener-Kintchine-Einstein Theorem. Goal#1: functional-EEG: After taking the 1-D space-filling curve, we compute the ensemble averaged 1-D Power Spectral Density (PSD) and then make use of the inverse FFT to generate f-EEG. (ii) Goal#2 individual wellness baseline (IWB): We need novel change detection, so we derive the ubiquitous fat-tail distributions for healthy brains PSD in outdoor environments (Signal=310°C; Noise=27°C: SNR=310/300; 300°K=(1/40)eV). The departure from IWB might imply stress, fever, a sports injury, an unexpected fall, or numerous midnight excursions which may signal an onset of dementia in Home Alone Senior (HAS), discovered by telemedicine care-giver networks. Aging global villagers need mental healthcare devices that are affordable, harmless, administrable (AHA) and user-friendly, situated in a clothing article such as a baseball hat and able to interface with pervasive Smartphones in daily environment.

Due to the delay of sequential 3-D Lidar image acquisition while an uncooperative human target is in motion, the image may generate missing or occlusion pixels. We wish to minimize the impact of image acquisition of a moving target for aided target recognition. We apply the standard Fourier transform algorithms for an error resilience restoration to minimize the impact to the Human Visual System (HVS) which tends to overly emphasize the edge and the artificially generated discontinuity in missing pixels. We compared (i) classical phase retrieval scheme: Gerchburg-Saxon-Hayes-Papoulis (GSHP) and (ii) the Compressive Sensing scheme: Candes-Romberg-Donohoe-Tao (CRDT). The following two lessons were learned: The mechanism is based on Gibbs overshooting of a step-discontinuity. It is based on relocating the sparsely sampled zeros at missing pixel locations a la spatial and spatial frequency inner product conformal mapping property.

A data-driven unsupervised learning such as an independent component analysis was gainfully applied to bloodoxygenation- level-dependent (BOLD) functional magnetic resonance imaging (fMRI) data compared to a model-based general linear model (GLM). This is due to an ability of this unsupervised learning method to extract a meaningful neuronal activity from BOLD signal that is a mixture of confounding non-neuronal artifacts such as head motions and physiological artifacts as well as neuronal signals. In this study, we support this claim by identifying neuronal underpinnings of cigarette craving and cigarette resistance. The fMRI data were acquired from heavy cigarette smokers (n = 14) while they alternatively watched images with and without cigarette smoking. During acquisition of two fMRI runs, they were asked to crave when they watched cigarette smoking images or to resist the urge to smoke. Data driven approaches of group independent component analysis (GICA) method based on temporal concatenation (TC) and TCGICA with an extension of iterative dual-regression (TC-GICA-iDR) were applied to the data. From the results, cigarette craving and cigarette resistance related neuronal activations were identified in the visual area and superior frontal areas, respectively with a greater statistical significance from the TC-GICA-iDR method than the TC-GICA method. On the other hand, the neuronal activity levels in many of these regions were not statistically different from the GLM method between the cigarette craving and cigarette resistance due to potentially aberrant BOLD signals.

We propose to use EEG signals to make user authentication for requiring high security. EEG signals were measured while the subjects saw several images in sequences. Since subjects‘ EEG signals are different for known and unknown images, these EEG sequences may be used to identify each subject. Correlation analysis and classification results show the feasibility of user authentication from EEG signals.

The theory behind compressive sampling pre-supposes that a given sequence of observations may be exactly represented by a linear combination of a small number of vectors. In practice, however, even small deviations from an exact signal model can result in dramatic increases in estimation error; this is the so-called basis mismatch" problem. This work provides one possible solution to this problem in the form of an iterative, biconvex search algorithm. The approach uses standard ℓ₁-minimization to find the signal model coefficients followed by a maximum likelihood estimate of the signal model. The process is repeated until a convergence criteria is met. The algorithm is illustrated on harmonic signals of varying sparsity and outperforms the current state-of-the-art.

Space-Filling Curves (SFCs) are encountered in different fields of engineering and computer science, especially where it is important to linearize multidimensional data for effective and robust interpretation of the information. Examples of multidimensional data are matrices, images, tables, computational grids, and Electroencephalography (EEG) sensor data resulting from the discretization of partial differential equations (PDEs). Data operations like matrix multiplications, load/store operations and updating and partitioning of data sets can be simplified when we choose an efficient way of going through the data. In many applications SFCs present just this optimal manner of mapping multidimensional data onto a one dimensional sequence. In this report, we begin with an example of a space-filling curve and demonstrate how it can be used to find the most similarity using Fast Fourier transform (FFT) through a set of points. Next we give a general introduction to space-filling curves and discuss properties of them. Finally, we consider a discrete version of space-filling curves and present experimental results on discrete space-filling curves optimized for special tasks.

Ground moving target indication (GMTI) using multiple resolutions of synthetic aperture radar (SAR) images to estimate the clutter scattering statistics can help transform Intelligence, Surveillance, and Reconnaissance (ISR), a mission area essential to the U.S. battlefield information advantage, because it can depict vehicular movement of enemy forces in near-real time throughout a large area in all-weather situations including fires, sand storm, and brown out, etc. Significant progress has been made in many aspects of GMTI since 1960s, but many challenges remain. These challenges include sensor development, signal processing, and target-recognition technology. This article describes in GMTI technologies that were developed for both airborne and ground-based surface surveillance.

We propose a new approach for deriving probabilistic inequalities based on bounding likelihood ratios. We demonstrate that this approach is more general and powerful than the classical method frequently used for deriving concentration inequalities such as Chernoff bounds. We discover that the proposed approach is inherently related to statistical concepts such as monotone likelihood ratio, maximum likelihood, and the method of moments for parameter estimation. A connection between the proposed approach and the large deviation theory is also established. We show that, without using moment generating functions, tightest possible concentration inequalities may be readily derived by the proposed approach. The applications of the new probabilistic techniques to statistical machine learning theory are demonstrated.

We describe an affordable, harmless, and administrative (AHA) metabolic biomarker (MBM) for homecare cancer screening. It may save hundreds of thousands of women’s and thousands of men’s lives every year from breast cancer and melanoma. The goal is to increase the specificity of infrared (IR) imagery to reduce the false alarm rate (FAR). The patient’s hands are immersed in icy cold water, about 11oC, for 30 seconds. We then compare two IR images, taken before and after the cold stimulus, and the difference reveals an enhanced signal and noise ratio (SNR) at tumorigenesis sites since the contraction of capillaries under cold challenge is natural to healthy capillaries, except those newly built capillaries during angiogenesis (Folkman, Nature 1995). Concomitant with the genome and the phenome (molecular signaling by phosphor-mediate protein causing inflammation by platelet activating factor (PAF) that transform cells from benign to malignant is the amplification of nitric oxide (NO) syntheses, a short-lived reactive oxygen species (ROS) that dilates regional blood vessels; superseding normal autonomic nervous system regulation. A rapidly growing tumor site might implicate accumulation of ROS, for which NO can rapidly stretch the capillary bed system usually having thinning muscular lining known as Neo-Angiogenesis (NA) that could behave like Leaky In-situ Faucet Effect (LIFE) in response to cold challenge. To emphasize the state of art knowledge of NA, we mentioned in passing the first generation of an anticapillary growth drug, Avastin by Genetech; it is an antibody protein that is injected for metastasis, while the second generation drug; Sorafenib by Bayers (2001) and Sutent by Pfizer (2000) both target molecular signaling loci to block receptor associated tyrosine kinase induced protein phosphorylation in order to reverse the angiogenesis. Differentiating benign from malignant in a straightforward manner is required to achieve the wellness protocol, yet would become prohibitively expensive and impossible to follow through. For example, given the probability of detection (PD) about 0.1% over unspecified number of years (e.g. menopause years for breast cancer), one might need hundred thousand volunteers. We suggested a Time Reversal Invariant Paradigm (TRIP) (a private communication with Vatican) for gathering equivalent cancer symptom imagery from recovery histories of dozens of patients. We further mixed it with few % of recovered/non-sick cases for negative controls. Creating Virtual images and running videos of these, frame by frame, in two directions (forward and backward in time) resulted in identical Receiver Operation Characteristics (ROC) for both the computer Aided Target Recognition (AiTR) algorithm and the human radiological experts; namely PD versus FAR within the standard deviation; even though the physiology could be entirely different. Such a TRIP would be true taken by any memory-less instantaneous imagery devices (IR, ultrasound, X-rays, MRI excluding magnetic hysteresis memory). In summary, such an affordable, harmless, and administrative, neo-angiogenesis metabolic biomarker can help monitor the transitioning from benign to malignant states of high-risk home alone seniors and also monitor the progress of home alone seniors treatment at home. Therefore, Smartphone equipped with a day camera having IR spectral filtering for a contact self imaging called joystick, when augmented with AHA NA MBM, may be suited for HAS homecare.

We seek to augment the current Common Access Control (CAC) card and Personal Identification Number (PIN) verification systems with an additional layer of classified access biometrics. Among proven devices such as fingerprint readers and cameras that can sense the human eye's iris pattern, we introduced a number of users to a sequence of 'grandmother images', or emotionally evoked stimuli response images from other users, as well as one of their own, for the purpose of authentication. We performed testing and evaluation of the Authenticity Privacy and Security (APS) brainwave biometrics, similar to the internal organ of the human eye’s iris which cannot easily be altered. ‘Aha’ recognition through stimulus-response habituation can serve as a biomarker, similar to keystroke dynamics analysis for inter and intra key fluctuation time of a memorized PIN number (FIST). Using a non-tethered Electroencephalogram (EEG) wireless smartphone/pc monitor interface, we explore the appropriate stimuli-response biomarker present in DTAB low frequency group waves. Prior to login, the user is shown a series of images on a computer display. They have been primed to click their mouse when the image is presented. DTAB waves are collected with a wireless EEG and are sent via Smartphone to a cloud based processing infrastructure. There, we measure fluctuations in DTAB waves from a wireless, non-tethered, single node EEG device between the Personal Graphic Image Number (PGIN) stimulus image and the response time from an individual’s mental performance baseline. Towards that goal, we describe an infrastructure that supports distributed verification for web-based EEG authentication. The performance of machine learning on the relative Power Spectral Density EEG data may uncover features required for subsequent access to web or media content. Our approach provides a scalable framework wrapped into a robust Neuro-Informatics toolkit, viable for use in the Biomedical and mental health communities, as well as numerous consumer applications.

Heart rate variability (HRV) can be an important indicator of several conditions that affect the autonomic nervous system, including traumatic brain injury, post-traumatic stress disorder and peripheral neuropathy [3], [4], [10] & [11]. Recent work has shown that some of the HRV features can potentially be used for distinguishing a subject’s normal mental state from a stressed one [4], [13] & [14]. In all of these past works, although processing is done in both frequency and time domains, few classification algorithms have been explored for classifying normal from stressed RRintervals. In this paper we used 30 s intervals from the Electrocardiogram (ECG) time series collected during normal and stressed conditions, produced by means of a modified version of the Trier social stress test, to compute HRV-driven features and subsequently applied a set of classification algorithms to distinguish stressed from normal conditions. To classify RR-intervals, we explored classification algorithms that are commonly used for medical applications, namely 1) logistic regression (LR) [16] and 2) linear discriminant analysis (LDA) [6]. Classification performance for various levels of stress over the entire test was quantified using precision, accuracy, sensitivity and specificity measures. Results from both classifiers were then compared to find an optimal classifier and HRV features for stress detection. This work, performed under an IRB-approved protocol, not only provides a method for developing models and classifiers based on human data, but also provides a foundation for a stress indicator tool based on HRV. Further, these classification tools will not only benefit many civilian applications for detecting stress, but also security and military applications for screening such as: border patrol, stress detection for deception [3],[17], and wounded-warrior triage [12].

Emotional states of humans and their impact on physiological and neurological characteristics are discussed in this paper. This problem is the goal of many teams who have dealt with this topic. Nowadays, it is necessary to increase the accuracy of methods for obtaining information about correlations between emotional state and physiological changes. To be able to record these changes, we focused on two majority emotional states. Studied subjects were psychologically stimulated to neutral - calm and then to the stress state. Electrocardiography, Electroencephalography and blood pressure represented neurological and physiological samples that were collected during patient’s stimulated conditions. Speech activity was recording during the patient was reading selected text. Feature extraction was calculated by speech processing operations. Classifier based on Gaussian Mixture Model was trained and tested using Mel-Frequency Cepstral Coefficients extracted from the patient's speech. All measurements were performed in a chamber with electromagnetic compatibility. The article discusses a method for determining the influence of stress emotional state on the human and his physiological and neurological changes.

Various types of monitoring mechanism allow us to detect and monitor behavior of attackers in VoIP networks. Analysis of detected malicious traffic is crucial for further investigation and hardening the network. This analysis is typically based on statistical methods and the article brings a solution based on neural network. The proposed algorithm is used as a classifier of attacks in a distributed monitoring network of independent honeypot probes. Information about attacks on these honeypots is collected on a centralized server and then classified. This classification is based on different mechanisms. One of them is based on the multilayer perceptron neural network. The article describes inner structure of used neural network and also information about implementation of this network. The learning set for this neural network is based on real attack data collected from IP telephony honeypot called Dionaea. We prepare the learning set from real attack data after collecting, cleaning and aggregation of this information. After proper learning is the neural network capable to classify 6 types of most commonly used VoIP attacks. Using neural network classifier brings more accurate attack classification in a distributed system of honeypots. With this approach is possible to detect malicious behavior in a different part of networks, which are logically or geographically divided and use the information from one network to harden security in other networks. Centralized server for distributed set of nodes serves not only as a collector and classifier of attack data, but also as a mechanism for generating a precaution steps against attacks.

In this paper the predictive model for speech quality estimation is described. This model allows its user to gain the information about the speech quality in VoIP networks without the need of performing the actual call and the consecutive time consuming sound file evaluation. This rapidly increases usability of the speech quality measurement especially in high load networks, where the actual processing of all calls is rendered difficult or even impossible. This model can reach its results that are highly conformant with the PESQ algorithm only based on the network state parameters that are easily obtainable by the commonly used software tools. Experiments were carried out to investigate whether different languages (English, Czech) have an effect on perceived voice quality for the same network conditions and the language factor was incorporated directly into the model.

Biologic processes are represented as Boolean networks, in a discrete time. The dynamics within these networks are approached with the help of SMT Solvers and the use of computer algebra. Software such as Maple and Z3 was used in this case. The number of stationary states for each network was calculated. The network studied here corresponds to the immune system under the effects of drastic mood changes. Mood is considered as a Boolean variable that affects the entire dynamics of the immune system, changing the Boolean satisfiability and the number of stationary states of the immune network. Results obtained show Z3’s great potential as a SMT Solver. Some of these results were verified in Maple, even though it showed not to be as suitable for the problem approach. The solving code was constructed using Z3-Python and Z3-SMT-LiB. Results obtained are important in biology systems and are expected to help in the design of immune therapies. As a future line of research, more complex Boolean network representations of the immune system as well as the whole psychological apparatus are suggested.

To ensure a self-consistent scaling approximation, the underlying microscopic fluctuation components can naturally influence macroscopic means, which may give rise to emergent observable phenomena. In this paper, we describe a consistent macroscopic (cm-scale), mesoscopic (micron-scale), and microscopic (nano-scale) approach to introduce Telescopic Multi-Resolution (TMR) into current Augmented Reality (AR) visualization technology. We propose to couple TMR-AR by introducing an energy-matter interaction engine framework that is based on known Physics, Biology, Chemistry principles. An immediate payoff of TMR-AR is a self-consistent approximation of the interaction between microscopic observables and their direct effect on the macroscopic system that is driven by real-world measurements. Such an interdisciplinary approach enables us to achieve more than multiple scale, telescopic visualization of real and virtual information but also conducting thought experiments through AR. As a result of the consistency, this framework allows us to explore a large dimensionality parameter space of measured and unmeasured regions. Towards this direction, we explore how to build learnable libraries of biological, physical, and chemical mechanisms. Fusing analytical sensors with TMR-AR libraries provides a robust framework to optimize testing and evaluation through data-driven or virtual synthetic simulations. Visualizing mechanisms of interactions requires identification of observable image features that can indicate the presence of information in multiple spatial and temporal scales of analog data. The AR methodology was originally developed to enhance pilot-training as well as ‘make believe’ entertainment industries in a user-friendly digital environment We believe TMR-AR can someday help us conduct thought experiments scientifically, to be pedagogically visualized in a zoom-in-and-out, consistent, multi-scale approximations.

Interference from overlaps of spectral lines is a key concern in optical emission spectrometry even when a spectrometer with relatively high resolution and a long focal length (e.g., 1 m) is used. The problem becomes more complex when a portable spectrometer (e.g., with a focal length of 12.5 cm) with low resolution is used. Such a spectrometer is better suited for “taking part of the lab to the sample” types of applications. We used Artificial Neural Networks (ANNs) and Partial Least Squares (PLS) to address spectral interference correction and our efforts are described here in some detail.

Toy models for the Arabidopsis Thaliana flower and the Drosophila are analyzed using Microsoft SMT-Solver Z3 with the SMT-LIB language. The models are formulated as Boolean networks which describe the metabolic cycles for Arabidopsis and Drosophila. The dynamic activation of the different bio macromolecules is described by the variables and laws of Boolean transition. Specifically, bitvectors and assertions, which describe the change of state of bitvectors from a sampling time to the next, are used. The dynamic feasibility problem of the biological network is translated to a Boolean satisfiability problem. The corresponding dynamic attractors are represented as a model of satisfiability. The Z3 software allows all required computations in a friendly and efficient manner. It is expected that the SMT-solvers, such as Z3, will become a routine tool in system biology and that they will provide bio-nanosystem design techniques. As a line for future research, the study of the models for Arabidopsis and Drosophila using different SMT-solvers such as CVC4, Mathsat and Yices, is proposed.

A discrete model for the Lcac Operon is solved using the SMT-solver Z3. Traditionally the Lac Operon is formulated in a continuous math model. This model is a system of ordinary differential equations. Here, it was considerated as a discrete model, based on a Boolean red. The biological problem of Lac Operon is enunciated as a problem of Boolean satisfiability, and it is solved using an STM-solver named Z3. Z3 is a powerful solver that allows understanding the basic dynamic of the Lac Operon in an easier and more efficient way. The multi-stability of the Lac Operon can be easily computed with Z3. The code that solves the Boolean red can be written in Python language or SMT-Lib language. Both languages were used in local version of the program as online version of Z3. For future investigations it is proposed to solve the Boolean red of Lac Operon using others SMT-solvers as cvc4, alt-ergo, mathsat and yices.

Graph theory in general and Tutte polynomials in particular, are implemented for analyzing the chemical structure of the benzodiazepines. Similarity analysis are used with the Tutte polynomials for finding other molecules that are similar to the benzodiazepines and therefore that might show similar psycho-active actions for medical purpose, in order to evade the drawbacks associated to the benzodiazepines based medicine. For each type of benzodiazepines, Tutte polynomials are computed and some numeric characteristics are obtained, such as the number of spanning trees and the number of spanning forests. Computations are done using the computer algebra Maple’s GraphTheory package. The obtained analytical results are of great importance in pharmaceutical engineering. As a future research line, the usage of the chemistry computational program named Spartan, will be used to extent and compare it with the obtained results from the Tutte polynomials of benzodiazepines.

Transient electronic devices are a new technology development whose main characteristic is that its components can disappear in a programmed and controlled way, which means such devices have a pre-engineered service life. Nowadays, transient electronics have a large application field, involving from the reduction of e-waste in the planet until the development of medical instruments and implants that can be discarded when the patients do not need it anymore, avoiding the trouble of having an extra procedure for them. These devices must be made from biocompatible materials avoiding long-term adverse effects in the environment and patients. It is fundamental to develop an analytical model that allows describing the behavior of these materials considering cases which its porosity may be constant or not, in presence of water or any other biofluid. In order to accomplish this analysis was solve the reactive diffusion equation based on Bromwich’s integral and the Residue theorem for two material cases, those whose porosity is constant, and those whose porosity increases linearly in terms of its thickness, where was found a general expression. This allows to the analysis of the relation of the electric resistance (per unit length) and the rate of dissolution of the material.

We simulate the immune compatibility using the Install Problem: The idea is to define a Boolean variable for each antibody. This variable is true if the antibody must be in the immune system. The Install Problem refers to the incompatibility that some programs may have with an specific operative system making it impossible to be installed. The analysis was implemented using SMT-solvers, specifically Z3, and the code was wrote using the commands “DependsOn”, “Conflict” and “Compatibility_check”, making it possible to check the antibody compatibility. The programming languages used to build up the code were Z3-Python and Z3-SMT-Lib. The results can be used in systems biology and in the analysis of immunological therapies. As future line of research it could be developed a more complex algorithm to verify the immunological compatibility.

A problem of improving the efficiency of nonparametric entropy estimation for discrete stationary ergodic processes is considered. The estimation depends on selection of underlying metric on the space of right-sided infinite sequences. Proposed is a new family of metrics which depend on a set of parameters. The estimator is linearly dependent on the parameters, and the best accuracy is achieved by solution of a system of linear equations.

A problem of nonparametric entropy estimation for discrete stationary ergodic processes is considered. The estimation is based on so-called ”nearest-neighbor method”. It is shown that, for Bernoulli measures, the estimator is unbiased, i.e. converges to the (inverse) entropy of the process. Moreover, for symmetric Bernoulli measures, the unbiased estimator can be explicitly constructed.