This PDF file contains the front matter associated with SPIE Proceedings Volume 8058, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

There are intrinsic wavelet applications, by which we mean mathematical modeling of a physical phenomenon in which wavelet theory is the most natural quantitative means of explaining the phenomenon. This is not the same as the invaluable use of dyadic wavelets, say, as a tool with which to zoom-in or -out with regard to multi-scale phenomena. An example of an intrinsic wavelet application is wavelet auditory modeling (WAM). WAM is analyzed herein, and a natural excursion, one of many possibilities, is taken from WAM to applications of finite frames. This path includes the role of the Discrete Fourier Transform (DFT) in WAM, the emergence of DFT frames, and their use in analyzing Σ▵ quantization, which itself is a staple in audio engineering as well as in a host of other applications.

We describe a smart surveillance strategy for handling novelty changes. Current sensors seem to keep all, redundant or not. The Human Visual System's Hubel-Wiesel (wavelet) edge detection mechanism pays attention to changes in movement, which naturally produce organized sparseness because a stagnant edge is not reported to the brain's visual cortex by retinal neurons. Sparseness is defined as an ordered set of ones (movement or not) relative to zeros that could be pseudo-orthogonal among themselves; then suited for fault tolerant storage and retrieval by means of Associative Memory (AM). The firing is sparse at the change locations. Unlike purely random sparse masks adopted in medical Compressive Sensing, these organized ones have an additional benefit of using the image changes to make retrievable graphical indexes. We coined this organized sparseness as Compressive Sampling; sensing but skipping over redundancy without altering the original image. Thus, we turn illustrate with video the survival tactics which animals that roam the Earth use daily. They acquire nothing but the space-time changes that are important to satisfy specific prey-predator relationships. We have noticed a similarity between the mathematical Compressive Sensing and this biological mechanism used for survival. We have designed a hardware implementation of the Human Visual System's Compressive Sampling scheme. To speed up further, our mixedsignal circuit design of frame differencing is built in on-chip processing hardware. A CMOS trans-conductance amplifier is designed here to generate a linear current output using a pair of differential input voltages from 2 photon detectors for change detection---one for the previous value and the other the subsequent value, ("write" synaptic weight by Hebbian outer products; "read" by inner product & pt. NL threshold) to localize and track the threat targets.

Automatic classification of broadband transient radio frequency (RF) signals is of particular interest in persistent surveillance applications. Because such transients are often acquired in noisy, cluttered environments, and are characterized by complex or unknown analytical models, feature extraction and classification can be difficult. We propose a fast, adaptive classification approach based on non-analytical dictionaries learned from data. Conventional representations using fixed (or analytical) orthogonal dictionaries, e.g., Short Time Fourier and Wavelet Transforms, can be suboptimal for classification of transients, as they provide a rigid tiling of the time-frequency space, and are not specifically designed for a particular signal class. They do not usually lead to sparse decompositions, and require separate feature selection algorithms, creating additional computational overhead. Pursuit-type decompositions over analytical, redundant dictionaries yield sparse representations by design, and work well for target signals in the same function class as the dictionary atoms. The pursuit search however has a high computational cost, and the method can perform poorly in the presence of realistic noise and clutter. Our approach builds on the image analysis work of Mairal et al. (2008) to learn a discriminative dictionary for RF transients directly from data without relying on analytical constraints or additional knowledge about the signal characteristics. We then use a pursuit search over this dictionary to generate sparse classification features. We demonstrate that our learned dictionary is robust to unexpected changes in background content and noise levels. The target classification decision is obtained in almost real-time via a parallel, vectorized implementation.

In this article, we introduce the concept of fractional wavelet transform. Using a two-channel unbalanced lifting structure it is possible to decompose a given discrete-time signal x[n] sampled with period T into two sub-signals x1[n] and x2[n] whose average sampling periods are pT and qT, respectively. Fractions p and q are rational numbers satisfying the condition: 1/p + 1/q = 1. The low-band sub-signal x₁[n] comes from [0, π/p] band and the high-band wavelet signal x₂[n] comes from (π/p, π] band of the original signal x[n]. Filters used in the liftingstructure are designed using the Lagrange interpolation formula. It is straightforward to extend the proposed fractional wavelet transform to two or higher dimensions in a separable or non separable manner.

This work describes a methodology for the recovery of anomalies and their spectral signatures from compressively sensed multi-spectral video using Principal Component Pursuit (PCP). In video surveillance, approaches based on PCP allow the anomaly detection in a cluttered background by modeling a sequence of video frames as a large data matrix composed by a low-rank matrix plus a sparse matrix. The low-rank matrix corresponds to the stationary background and the sparse matrix captures the anomalies in the foreground. The compressive spectral video frames are attained by the use of a Coded Aperture Snapshot Spectral Imaging (CASSI) system. The CASSI system allows the compressive measurement of spectrally rich video content by simply capturing a sequence of 2D coded aperture video frames. This paper describes improved procedures for the reconstruction of the video anomalies and their spectra based on the 2-D, aperture-coded, isolated anomalies.

Sparse representations of multidimensional data have gained more and more prominence in recent years, in response to the need to process large and multi-dimensional data sets arising from a variety of applications in a timely and effective manner. This is especially important in applications such as remote sensing, satellite imagery, scientific simulations and electronic surveillance. Directional multiscale systems such as shearlets are able to provide sparse representations thanks to their ability to approximate anisotropic features much more efficiently than traditional multiscale representations. In this paper, we show that the shearlet approach is essentially optimal in representing a large class of 3D containing discontinuities along surfaces. This is the first nonadaptive approach to achieve provably optimal sparsity properties in the 3D setting.

Volumetric data acquisition and increasingly massive data storage have increased the need to develop better analysis tools for three-dimensional data sets. These volumetric data sets can provide information beyond that contained in standard two-dimensional images. Common strategies to deal with such data sets have been based on sequential use of two-dimensional analysis tools. In this work, we propose using an extension of the wavelet transform known as the shearlet transform for the purpose of edge analysis and detection in three-dimensions. This method takes advantage of the shearlet transform's improved capability to identify edges compared to wavelet-based approaches.

We proposed a novel framework that allows a method optimized for white noise to be used for denoising CT imagery. We considered low-dose x-ray CT imagery where lowering the dose of x-rays results in an increase in quantum noise. We first denoised an image independently several times using different parameters. Then, we selected pixels from those denoised images to form a final composite image. We produced results using blockmatching denoising, but in principle other methods could work within this framework, as well. The proposed method was able to better reproduce regions of low-contrast than the conventional BM3D approach.

The application of wavelet transforms in de-noising optical emission transient, time-domain signals generated from microsamples introduced into an atmospheric-pressure microplasma-on-a-chip is described. The wavelet method of denoising transient signals is compared with traditional noise-filtering methods such as Fast Fourier Transform (FFT) and Fast Hartley Transform (FHT) signal processing. For the transient signals of interest to this work, the wavelet method proved to be superior to both the FFT and the FHT.

Independent component analysis (ICA) for acoustic mixtures has been a challenging problem due to very complex reverberation involved in real-world mixing environments. In an effort to overcome disadvantages of the conventional time domain and frequency domain approaches, this paper describes filterbank-based independent component analysis for acoustic mixtures. In this approach, input signals are split into subband signals and decimated. A simplified network performs ICA on the decimated signals, and finally independent components are synthesized. First, a uniform filterbank is employed in the approach for basic and simple derivation and implementation. The uniform-filterbank-based approach achieves better separation performance than the frequency domain approach and gives faster convergence speed with less computational complexity than the time domain approach. Since most of natural signals have exponentially or more steeply decreasing energy as the frequency increases, the spectral characteristics of natural signals introduce a Bark-scale filterbank which divides low frequency region minutely and high frequency region widely. The Bark-scale-filterbank-based approach shows faster convergence speed than the uniform-filterbank-based one because it has more whitened inputs in low frequency subbands. It also improves separation performance as it has enough data to train adaptive parameters exactly in high frequency subbands.

A method for target speech enhancement based on degenerate unmixing and estimating technique (DUET) has been described. To avoid the requirements of the DUET which need to know the number of sources in advance and to estimate the attenuation and delay parameters for all sources, the method assumes that extraction of only one target signal is required, which is often plausible in real-world applications such as speech enhancement. The method can efficiently recover the target speech with fast convergence by estimating the parameters for the target source only. In addition, it does not need to know the number of sources in advance. In order to accomplish robust speech recognition, we propose an algorithm which employs the cluster-based missing feature reconstruction technique based on log-spectral features of enhanced speech in the process of extracting mel-frequency cepstral coefficients (MFCCs). The algorithm estimates missing time-frequency regions by computing the signal-to-noise ratios (SNRs) from the log-spectral features of the enhanced speech and observed noisy speech and by finding time-frequency segments which have the SNRs smaller than a threshold. The missing time-frequency regions are filled by using bounded estimation based on the log-spectral features that are considered to be reliable and on the knowledge of the log-spectral feature cluster to which the incoming target speech is assumed to belong. Then, the log-spectral features are transformed into cepstral features in the usual fashion of extracting MFCCs. Experimental results show that the proposed algorithm significantly improves recognition performance in noisy environments.

Analysis of bioimaging and biospectra data has received increasingly attention in recent years. Here we will present two experimental results based on independent component analysis (ICA): differentiation of superparamagnetic iron oxide (SPIO) nanoparticles used as contrast agents in magnetic resonance imaging (MRI), and differentiation of mixed chemical analytes by surface-enhanced Raman scattering (SERS). The SPIO nanoparticles have been applied extensively as contrast agent in MRI for tracking of stem cells, targeted detection of cancer, due to its biocompatible and biodegradable features. For differentiation of SPIO from the background signal (e.g. interface between air and tissues), the signal voids from multiple sources makes the task very difficult. To solve this problem, we assume that the number of sensors corresponds to the number of acquisitions with different combinations of MR parameters, i.e., longitudinal and transverse relaxation times. For detection of chemical and biological analytes, the SERS approach has drawn more interest because of its high sensitivity. SERS spectra of mixed analytes were acquired at different locations of a silver nanorod array substrate. Due to the nonuniform diffusion and adsorption of the analytes, these spectra have been successfully used to identify the characteristic SERS spectrum of individual analytes. In both the MRI and SERS data, signal source separation (SPIO or mixed chemical analytes from background signal) was performed on a pixel by pixel basis. The ICA was performed by a spatial analysis using the fast ICA method.

In the past, autonomic nervous system response has often been determined through measuring Electrodermal Activity (EDA), sometimes referred to as Skin Conductance (SC). Recent work has shown that high resolution thermal cameras can passively and remotely obtain an analog to EDA by assessing the activation of facial eccrine skin pores. This paper investigates a method to distinguish facial skin from non-skin portions on the face to generate a skin-only Dynamic Mask (DM), validates the DM results, and demonstrates DM performance by removing false pore counts. Moreover, this paper shows results from these techniques using data from 20+ subjects across two different experiments. In the first experiment, subjects were presented with primary screening questions for which some had jeopardy. In the second experiment, subjects experienced standard emotion-eliciting stimuli. The results from using this technique will be shown in relation to data and human perception (ground truth). This paper introduces an automatic end-to-end skin detection approach based on texture feature vectors. In doing so, the paper contributes not only a new capability of tracking facial skin in thermal imagery, but also enhances our capability to provide non-contact, remote, passive, and real-time methods for determining autonomic nervous system responses for medical and security applications.

A common objective with active magnetic, radio frequency, and acoustic sensors is to detect and sense small signals in the presence of large magnitude clutter or interference signals. When the interference to signal-of-interest ratio is greater than the dynamic range of the linear system, the system response may become non-linear. In this paper an adaptive analog cancellation approach is proposed to track and cancel the interference signal. The circuit's linear property is then recovered, enabling detection of the small magnitude signals. The concept was realized in an active RF sensor which demonstrated the effectiveness of the strategy.

Standard unsupervised feature extraction methods such as PCA and ICA provide representative features and latent variables which minimizes the data reconstruction error. These generative features may be common to all data, and may not be optimal for classification tasks. The discriminate ICA (dICA) and discriminant NMF (dNMF) had recently been proposed which jointly maximizes Fisher linear discriminant and Negentropy of the extracted features. Motivated by independence among features and modified Fisher linear discriminant, the new algorithm extracts features with both generative and discriminant powers. Then, the features are further fine-tuned by supervised learning. Experimental results show excellent recognition performance with these features.

Artificial neural networks (ANNs) are being developed for spectral interference correction in optical emission spectrometry using spectral simulations. The networks are being developed for inductively coupled plasma-atomic emission spectrometry (ICP-AES) and for optical emission measurements using microplasmas. In this paper, development of artificial neural networks for spectral interference correction will be described in some detail.

Artificial neural networks are widely used in pattern recognition for sensing systems and other areas. In this paper, we propose to improve the performance of neural networks from the perspectives of output encoding rules, determination of training sample sizes, training performance index and evaluation of generalization error. We propose a new output encoding rule which significantly reduces the training error as compared to classical output encoding methods. Moreover, we develop a new training performance index which is closely relate to the generalization error and is a smooth function suitable for optimization by virtue of nonlinear programming. Furthermore, motivated by the crucial impact of training sample size on the generalization error and the computational complexity of training, we propose a rigorous method for determining appropriate number of training samples. Since the development of a neural network requires many cycles of training and performance evaluation, we introduce adaptive methods for estimating the generalization error. The new techniques of neural network training and evaluation have potential to improve the power of modern sensing systems.

This paper surveys the history of tip based micro/nanomanipulation systems and the contributions of the authors in this topic. Atomic force microscope or scanning tunnerling microscope type of microscopes has been used as nanorobotic manipulation systems since 1990. Using single or multiple tips, many mechanical, electrical, and chemical micro/nanomanipulation applications have demonstrated. The authors contributed to teleoperated and automated control of such systems and also developed new tip based micro/nanomanipulation methods to draw polymer micro/nanofibers and create nanowires on substrates precisely and repeatedly.

Using carbon nanotubes (CNT), high performance infrared detectors have been developed. Since the CNTs have extraordinary optoelectronics properties due to its unique one dimensional geometry and structure, the CNT based infrared detectors have extremely low dark current, low noise equivalent temperature difference (NETD), short response time, and high dynamic range. Most importantly, it can detect 3-5 um middle-wave infrared (MWIR) at room temperature. This unique feature can significantly reduce the size and weight of a MWIR imaging system by eliminating a cryogenic cooling system. However, there are two major difficulties that impede the application of CNT based IR detectors for imaging systems. First, the small diameter of the CNTs results in low fill factor. Secondly, it is difficult to fabricate large scale of detector array for high resolution focal plane due to the limitations on the efficiency and cost of the manufacturing. In this paper, a new CNT based IR imaging system will be presented. Integrating the CNT detectors with photonic crystal resonant cavity, the fill factor of the CNT based IR sensor can reach as high as 0.91. Furthermore, using the compressive sensing technology, a high resolution imaging can be achieved by CNT based IR detectors. The experimental testing results show that the new imaging system can achieve the superb performance enabled by CNT based IR detectors, and, at the same time, overcame its difficulties to achieve high resolution and efficient imaging.

The fabrication of integrated nanomachinary systems can enable break-through applications in nanoelectronics, photonics, bioengineering, and drug delivery or disease treatment. Naturally occurring nanomotors are biological motor proteins powered by catalytic reactions, which convert the chemical energy from the environment into mechanical energy directly. It has been demonstrated recently that using a simple catalytic reaction and an asymmetric bimetallic nanorod, one can produce catalytic nanomotors that mimic the autonomous motions of bionanomotors. Yet the construction of artificial nanomachines remains a major contemporary challenge due to the lack of a flexible fabrication technique that can design the desired dynamic components. We use a design technique called dynamic shadowing growth that allows for the fabrication of a wide range of various geometries and the asymmetric placement of the catalyst is easily accomplished as well which is necessary for directed propulsion. Programming nanomotor behavior is possible through geometrically-focused design and by incorporating different materials into the nanomotor structure is a simple process as well. A propulsion mechanism based upon bubble ejection from the catalyst surface is introduced to explain the driving force, and the comparison of this driving mechanism with the self-electrophoresis mechanism is also studied. We have also successfully incorporated multiple parts to form complex nanomotor assemblies which exhibit motions not observed from individual parts by using magnetic interactions.

For EOIR nanotechnology sensors, we elucidated the quantum mechanical nature of the Einstein photoelectric effect in terms of a field-effect transistor (FET) made of Carbon NanoTube (CNT) semiconductors. Consequently, we discovered a surprising low-pass band gap property, as opposed to the traditional sharp band-pass band-gaps. In other words, the minimum amount of photon energy shining in the middle of CNT is necessary to excite the semiconductor CNT electrons. The conduction electron will spiral in a steady over the surface to minimize the collision recombination when travelling from the cathode end to the anode end by the asymmetric semiconductor-metal (using Pd or Al) Schottky interface effect for read out.

A smart sensor network is composed of a number of sensor nodes to extract meaningful and actionable information to system users in a timely, practical and intuitive manner. It requires sophisticated and geospatially-distributed infrastructures, centralized supervision, and deployment of large-scale security and surveillance networks to provide a 24/7 and all weather security operation in heavily-populated environments as well as restricted areas. Technically, development of sensor networks requires advanced technologies from four different areas: sensing technology, communication network, on-board Digital Signal Processing (DSP) capability, and sensing data management. These four key technologies have practical difficulties in various areas including communication covertness, network discovery, control and routing, collaborative signal and information processing, tasking and querying, and data management and security. In this paper, a brief history of sensor network development will be addressed first. Then technology trends and impacts of the sensing networks will be reviewed. This paper also describes the concept, design guideline and industrial standards of sensor networks which have been made viable by the convergence of sensor technology, wireless communications, digital electronics, and sensing system management to make all types of sensors, transducers and sensor data discoverable, accessible, manageable, and useable via the Web.

The next generation surveillance system will equip with versatile sensor devices and information focus capable of conducting regular and irregular surveillance and security environments worldwide. The community of the persistent surveillance must invest the limited energy and money effectively into researching enabling technologies such as nanotechnology, wireless networks, and micro-electromechanical systems (MEMS) to develop persistent surveillance applications for the future. Wireless sensor networks can be used by the military for a number of purposes such as monitoring militant activity in remote areas and force protection. Being equipped with appropriate sensors these networks can enable detection of enemy movement, identification of enemy force and analysis of their movement and progress. Among these sensor network technologies, covert communication is one of the challenging tasks in the persistent surveillance because it is highly demanded to provide secured sensor nodes and linkage for fear of deliberate sabotage. Due to the matured VLSI/DSP technologies, affordable COTS of UWB technology with noise-like direct sequence (DS) time-domain pulses is a potential solution to support low probability of intercept and low probability of detection (LPI/LPD) data communication and transmission. This paper will describe a number of technical challenges in wireless persistent surveillance development include covert communication, network control and routing, collaborating signal and information processing, and etc. The paper concludes by presenting Hermitian Wavelets to enhance SNR in support of secured communication.

The Principal Component Analysis (PCA) is an optimal method for approximating a set of vectors or images, which was used in image processing and computer vision for a number of tasks including face and object recognition. The computational complexity and its batch calculation nature have limited its applications. Here we discuss the two different effective solutions to sequentially calculate the principal bases in terms of the eigenvectors with respective eigenvalues using the covariance (or covariance estimate), which is faster in typical applications and is especially advantageous for image sequences. This principal component basis calculation is processed with much lower delay and allows for dynamic updating of image databases.

We mathematically model the mammalian Visual System's (VS) capability of spotting objects. How can a hawk see a tiny running rabbit from miles above ground? How could that rabbit see the approaching hawk? This predatorprey interaction draws parallels with spotting a familiar person in a crowd. We assume that mammal eyes use peripheral vision to perceive unexpected changes from our memory, and then use our central vision (fovea) to pay attention. The difference between an image and our memory of that image is usually small, mathematically known as a 'sparse representation'. The VS communicates with the brain using a finite reservoir of neurotransmittents, which produces an on-center and thus off-surround Hubel/Wiesel Mexican hat receptive field. This is the basis of our model. This change detection mechanism could drive our attention, allowing us to hit a curveball. If we are about to hit a baseball, what information extracted by our HVS tells us where to swing? Physical human features such as faces, irises, and fingerprints have been successfully used for identification (Biometrics) for decades, recently including voice and walking style for identification from further away. Biologically, humans must use a change detection strategy to achieve an ordered sparseness and use a sigmoid threshold for noisy measurements in our Hetero-Associative Memory [HAM] classifier for fault tolerant recall. Human biometrics is dynamic, and therefore involves more than just the surface, requiring a 3 dimensional measurement (i.e. Daugman/Gabor iris features). Such a measurement can be achieved using the partial coherence of a laser's reflection from a 3-D biometric surface, creating more degrees of freedom (d.o.f.) to meet the Army's challenge of distant Biometrics. Thus, one might be able to increase the standoff loss of less distinguished degrees of freedom (DOF).

In this paper, we propose the vector SMT, a new decorrelating transform suitable for performing distributed anomaly detection in wireless sensor networks (WSN). Here, we assume that each sensor in the network performs vector measurements, instead of a scalar ones. The proposed transform decorrelates a sequence of pairs of vector sensor measurements, until the vectors from all sensors are completely decorrelated. We perform simulations with a network of cameras, where each camera records an image of the monitored environment from its particular viewpoint. Results show that the proposed transform effectively decorrelates image measurements from the multiple cameras in the network. Because it enables joint processing of the multiple images, our method provides significant improvements to anomaly detection accuracy when compared to the baseline case when we process the images independently.

Law enforcement agencies need a quick decision in May 1 2010 NY Time Square for quickly spot wanted car bomb individuals in a crowd. This goal requires real-time smart firmware and a smart search algorithm to know how to order the faces in a geometric way. We demonstrate such a sorting problem of N facial poses time ordering is like the TSP of N cities, NP-complete, having no exact deterministic solution. Here we demonstrated a heuristic working solution to answer the ONR grand challenge called Empire 2010. How could the N boxes of faces detected and cut by the efficient COST parallel color-hue-algorithm, without time mark, at a single CPU (or even with the time marks but collected videos from multiple vintage points) to determine automatically who speaks what, where, and when? There must be a cross platform sensory association from our different sensors.

As a simple observation of the world, it is composed of human beings, artifacts, and natural environment. As all of their healths are issues like expansion of healthy aging, low maintenance cost, and low energy consumption the notion of health management can be extended to be applicable to all the entities. In this article, health management technology is proposed as a general solution framework. Its important aspect is cyclic evolution based on causality which illustrates conditions of target systems. The causality can be used as problem-solving knowledge, which is composed of feature attributes extracted from sensory data and intermediate characteristics. The causality should evolve to be updated according to sophistication of sensing and control mechanisms. It also provides the important nature of transparency to humans and machines bidirectionally, which enhances human-machine collaboration. Besides the idea of health management technology, the applications of human health, manufacturing, and energy consumption are also introduced and discussed. All applications were realized by multiple sensory networking to require multivariate time series analysis. Some experiments were conducted to investigate the performance of the proposed method.

This paper discusses a data analysis by YURAGI for a heart rate non-constraining monitoring system Three signals are employed: primary signal is obtained by a mat-type sensor, which is placed between a bed and subject, the second one is obtained by an ultrasonic vibration senor attached to bed frame, and third one is Gaussian noise. We compare the results from the synthesized data of the first and second signals with those of first signal and the noise. We employ weighted sum as the synthesized method. We consider Gaussian noise as YURAGI. The extraction algorithm was developed based on fuzzy logic. The comparison was done on 10 healthy volunteers and we evaluated the accuracy for various weight ratio. Here, we must concern the accuracy because the tiny accuracy difference causes large difference in the autonomic nerve system assessment. As the result, the results obtained from both synthesized signals were superior to that from mat-type sensor signal only. Thus, YURAGI analysis is useful to for detecting heart rate by mat-type sensor.

It has been clarified that abdominal visceral fat accumulation is closely associated to the lifestyle disease and metabolic syndrome. The gold standard in medical fields is visceral fat area measured by an X-ray computer tomography (CT) scan or magnetic resonance imaging. However, their measurements are high invasive and high cost; especially a CT scan causes X-ray exposure. They are the reasons why medical fields need an instrument for viscera fat measurement with low invasive, ease of use, and low cost. The article proposes a simple and practical method of visceral fat estimation by employing bioelectrical impedance analysis and causal analysis. In the method, abdominal shape and dual impedances of abdominal surface and body total are measured to estimate a visceral fat area based on the cause-effect structure. The structure is designed according to the nature of abdominal body composition to be fine-tuned by statistical analysis. The experiments were conducted to investigate the proposed model. 180 subjects were hired to be measured by both a CT scan and the proposed method. The acquired model explained the measurement principle well and the correlation coefficient is 0.88 with the CT scan measurements.

Small blood vessels may be difficult to detect in magnetic resonance angiography due to the lack of blood flow caused by disease or injury. Our method, which uses a block-matching denoising approach to segment blood vessels, works well in the presence of noise. We examined extended regions of an image to determine whether they contained blood vessels by fitting a Gaussian mixture model to a region's histogram. Then, dissimilar regions were denoised separately. This approach was beneficial in low-contrast settings. It can be used to detect higher-order blood vessels that may be difficult to detect under normal conditions.

Among lots of vital signals, heart-rate (HR) is an important index for diagnose human's health condition. For instance, HR provides an early stage of cardiac disease, autonomic nerve behavior, and so forth. However, currently, HR is measured only in medical checkups and clinical diagnosis during the rested state by using electrocardiograph (ECG). Thus, some serious cardiac events in daily life could be lost. Therefore, a continuous HR monitoring during 24 hours is desired. Considering the use in daily life, the monitoring should be noninvasive and low intrusive. Thus, in this paper, an HR monitoring in sleep by using air pressure sensors is proposed. The HR monitoring is realized by employing the causal analysis among air pressure and HR. The causality is described by employing fuzzy logic. According to the experiment on 7 males at age 22-25 (23 on average), the correlation coefficient against ECG is 0.73-0.97 (0.85 on average). In addition, the cause-effect structure for HR monitoring is arranged by employing causal decomposition, and the arranged causality is applied to HR monitoring in a setting posture. According to the additional experiment on 6 males, the correlation coefficient is 0.66-0.86 (0.76 on average). Therefore, the proposed method is suggested to have enough accuracy and robustness for some daily use cases.

Tai-chi chuan is popular worldwide especially in China. People practice tai-chi chuan daily with faith believing that they will be rewarded with health and varieties of other rewords. The Tai Chi Chuan Theory by Master Chang and the Tai Chi Chuan Theory by Master Wang are translated to be the baseline of the tai-chi chuan. The theory described in these two papers clearly shows that the tai-chi power source is the combination of the two antigravity forces distinction by each foot. The ying, yang and hollowed, solid discussed in the papers are the properties and body relationship of the two antigravity forces. The antigravity forces presented inside of body are as air to the balloon termed chi. However chi could be generated by any muscle pressing; only the antigravity forces from feet are called nature chi that has the maximum strength of the person. When a person is soft, as an infant the nature chi will fulfill entire body with no time and effort. The sequence forms were designed for deploying the nature chi in speed and power. The combination of chi and tai-chi form make tai chi chuan supreme than other kinds of martial art. In the training process chi massages whole body many time for a sequence form practice that stimulate all organs and could lead to cure body diseases, lose weight, postpone aging process, and remove the aging symptoms. For the people practicing in the park daily with proper guidance they will fulfill their wishes. Tai chi exercise could also apply to other sports as in dancing and golfing they are discussed at the end of the paper.

This paper proposes a YURAGI-Analysis for brain imaging under the skull. In it, we employ 1.0MHz and 0.5MHz ultrasonic waves. We consider the weighted sum of these waves and attempt to extract the skull depth and image the sulcus under it. We add 1.0MHz and 0.5MHz, and we add the waves of 1.0MHz and Gaussian noise as the YURAGI analysis. We visualize the sulcus and skull. First, we calculate the thickness of the skull from the each of two synthesized waves. The thickness is determined from the surface and bottom points determined from the wave based on fuzzy inference. The sulcus surface was extracted from B-mode images for the each of two synthesized waves. As the result using a cow scapula as the skull and steel ditch as the human sulcus, we successfully calculated skull thickness. We extracted the sulcus width within the error of 5.86 mm and depth within the error of 1.94 mm. As for imaging the sulcus under the skull, the highest effectiveness of the synthesized wave is 96.30% when the weight of 0.5MHz waves is 0.60, and the one of YURAGI-Analysis wave is 97.15% when the weight is 0.003. Thus, YURAGI-Analysis is useful to this study.

This paper describes a biometric personal authentication method based on fuzzy logic using dynamics of sole pressure distribution while walking. The method employs a pair of right and left sole pressure data. These data are acquired by a mat type load distribution sensor. The proposed method has two processes. First, we calculate a fuzzy degree of each sole pressure data. In this process, we extract several gait features based on weight shift and shape of footprint. Fuzzy ifthen rules for each registered person are introduced. In it, their parameters are statistically optimized in learning process. Second, we combine fuzzy degrees of right and left sole. In this process, we employ five operators. The method authenticates walking person with the combined fuzzy degree. We calculate the fuzzy degree of an interest person for all registered persons, and identify the interest person as the registered person with the highest fuzzy degree. While, we verify the interest person as the target person if the fuzzy degree of the interest person calculated for a target person is higher than a threshold. In an experiment on 50 volunteers, we obtained low false rejection and false acceptance rates.

Home security in night is very important, and the system that watches a person's movements is useful in the security. This paper describes a classification system of adult, child and the other object from distance distribution measured by an infrared laser camera. This camera radiates near infrared waves and receives reflected ones. Then, it converts the time of flight into distance distribution. Our method consists of 4 steps. First, we do background subtraction and noise rejection in the distance distribution. Second, we do fuzzy clustering in the distance distribution, and form several clusters. Third, we extract features such as the height, thickness, aspect ratio, area ratio of the cluster. Then, we make fuzzy if-then rules from knowledge of adult, child and the other object so as to classify the cluster to one of adult, child and the other object. Here, we made the fuzzy membership function with respect to each features. Finally, we classify the clusters to one with the highest fuzzy degree among adult, child and the other object. In our experiment, we set up the camera in room and tested three cases. The method successfully classified them in real time processing.

Communication of intent usually requires motor function, which can be limiting during military missions. Determining a soldier's intent from brain signals rather than using muscles would have numerous applications for tactical combat. Brain-computer interfaces (BCIs) translate brain signals into machine readable form and could optimize a soldier's interaction with the surrounding environment. However, current BCI devices have largely remained laboratory curiosities, because current techniques either require extended training or do not have the requisite signal fidelity, because they are highly invasive and thus not safe or practical for use in humans, or because they rely on equipment (such as magnetic resonance imaging scanners) that do not allow for real-time applications and/or field deployment. The objective of our research program is to create a prototype of a system for communication and monitoring of orientation that uses brain signals to provide, in real time, an accurate assessment of the users intentional focus and imagined speech. We expect that our efforts will provide a prototype of the first intuitive brain-based communication and orientation system for human use.

This paper presents the use of wavelet cores for a full reconfigurable electrocardiogram signal (ECG) acquisition system. The system is compound by two reconfigurable devices, a FPGA and a FPAA. The FPAA is in charge of the ECG signal acquisition, since this device is a versatile and reconfigurable analog front-end for biosignals. The FPGA is in charge of FPAA configuration, digital signal processing and information extraction such as heart beat rate and others. Wavelet analysis has become a powerful tool for ECG signal processing since it perfectly fits ECG signal shape. The use of these cores has been integrated in the LabVIEW FPGA module development tool that makes possible to employ VHDL cores within the usual LabVIEW graphical programming environment, thus freeing the designer from tedious and time consuming design of communication interfaces. This enables rapid test and graphical representation of results.

In this paper, we evaluate the feature extraction technique of Recoursing Energy Efficiency on electroencephalograph data for human emotion recognition. A protocol has been established to elicit five distinct emotions (joy, sadness, disgust, fear, surprise, and neutral). EEG signals are collected using a 256-channel system, preprocessed using band-pass filters and Laplacian Montage, and decomposed into five frequency bands using Discrete Wavelet Transform. The Recoursing Energy Efficiency (REE) is calculated and applied to a Multi-Layer Perceptron network for classification. We compare the performance of REE features with conventional energy based features.

Advances in high-throughput measurements of biological specimens necessitate the development of biologically driven computational techniques. To understand the molecular level of many human diseases, such as cancer, lipid quantifications have been shown to offer an excellent opportunity to reveal disease-specific regulations. The data analysis of the cell lipidome, however, remains a challenging task and cannot be accomplished solely based on intuitive reasoning. We have developed a method to identify a lipid correlation network which is entirely disease-specific. A powerful method to correlate experimentally measured lipid levels across the various samples is a Gaussian Graphical Model (GGM), which is based on partial correlation coefficients. In contrast to regular Pearson correlations, partial correlations aim to identify only direct correlations while eliminating indirect associations. Conventional GGM calculations on the entire dataset can, however, not provide information on whether a correlation is truly disease-specific with respect to the disease samples and not a correlation of control samples. Thus, we implemented a novel differential GGM approach unraveling only the disease-specific correlations, and applied it to the lipidome of immortal Glioblastoma tumor cells. A large set of lipid species were measured by mass spectrometry in order to evaluate lipid remodeling as a result to a combination of perturbation of cells inducing programmed cell death, while the other perturbations served solely as biological controls. With the differential GGM, we were able to reveal Glioblastoma-specific lipid correlations to advance biomedical research on novel gene therapies.

Although "gut feeling" is a cliché in English parlance, there are neuro-physiological basis for registration of emotions in the gut. Control of the gastro-intestinal (GI) tract is by an integration of neuro-hormonal factors from the local myogenic to the central nervous system. Gastric contractile activity, which is responsible for the motor properties of the stomach, is regulated by this integrated complex. Signatures of the activity include gastric electrical activity (GEA) and bowel sounds. GEA has two distinct components: a high-frequency spike activity or post depolarization potential termed the electrical response activity superimposed on a lower frequency, rhythmic depolarization termed the control activity. These signatures are measured in the clinic with contact sensors and well understood for diagnosis of gut dysmotility. Can these signatures be measured at standoff and employed for purposes of biometrics, malintent and wellness assessment?

Optoacoustic Imaging (OAI), a novel hybrid imaging technology, offers high contrast, molecular specificity and excellent resolution to overcome limitations of the current clinical modalities for detection of solid tumors. The exact time-domain reconstruction formula produces images with excellent resolution but poor contrast. Some approximate time-domain filtered back-projection reconstruction algorithms have also been reported to solve this problem. A wavelet transform implementation filtering can be used to sharpen object boundaries while simultaneously preserving high contrast of the reconstructed objects. In this paper, several algorithms, based on Back Projection (BP) techniques, have been suggested to process OA images in conjunction with signal filtering for ultrasonic point detectors and integral detectors. We apply these techniques first directly to a numerical generated sample image and then to the laserdigitalized image of a tissue phantom, obtaining in both cases the best results in resolution and contrast for a waveletbased filter.

This paper focuses on the hardware acceleration of motion compensation techniques suitable for the MPEG video compression. A plethora of representative motion estimation search algorithms and the new perspectives are introduced. The methods and designs described here are qualified for medical imaging area where are involved larger images. The structure of the processing systems considered has a good fit for reconfigurable acceleration. The system is based in a platform like FPGA working with the Nios II Microprocessor platform applying C2H acceleration. The paper shows the results in terms of performance and resources needed.

This paper presents a method for detecting mispronunciations with the aim of improving Computer Assisted Language Learning (CALL) tools used by foreign language learners. The algorithm is based on Principle Component Analysis (PCA). It is hierarchical with each successive step refining the estimate to classify the test word as being either mispronounced or correct. Preprocessing before detection, like normalization and time-scale modification, is implemented to guarantee uniformity of the feature vectors input to the detection system. The performance using various features including spectrograms and Mel-Frequency Cepstral Coefficients (MFCCs) are compared and evaluated. Best results were obtained using MFCCs, achieving up to 99% accuracy in word verification and 93% in native/non-native classification. Compared with Hidden Markov Models (HMMs) which are used pervasively in recognition application, this particular approach is computational efficient and effective when training data is limited.

A new approach to optimize the parameters of a gradient-based optical flow model using a parallel genetic algorithm (GA) is proposed. The main characteristics of the optical flow algorithm are its bio-inspiration and robustness against contrast, static patterns and noise, besides working consistently with several optical illusions where other algorithms fail. This model depends on many parameters which conform the number of channels, the orientations required, the length and shape of the kernel functions used in the convolution stage, among many more. The GA is used to find a set of parameters which improve the accuracy of the optical flow on inputs where the ground-truth data is available. This set of parameters helps to understand which of them are better suited for each type of inputs and can be used to estimate the parameters of the optical flow algorithm when used with videos that share similar characteristics. The proposed implementation takes into account the embarrassingly parallel nature of the GA and uses the OpenMP Application Programming Interface (API) to speedup the process of estimating an optimal set of parameters. The information obtained in this work can be used to dynamically reconfigure systems, with potential applications in robotics, medical imaging and tracking.

One of the big challenges in the design of embedded systems today is how to combine design reuse and intellectual property protection (IPP). Strong IP schemes such as hardware dongle or layout watermarking usually have a very limited design reuse for different FPGA/ASIC design platforms. Some techniques also do not fit well with protection of software in embedded microprocessors. Another approach to IPP that allows an easy design reuse and has low costs but a somehow reduced security is code "obfuscation." Obfuscation is a method to hide the design concept, or program algorithm included in the C or HDL source by using one or more transformations of the original code. Obfuscation methods include, for instance, renaming identifiers, removing comments or formatting of the code. More sophisticated obfuscation methods include data splitting or merging, and control flow changes. This paper shows strength and weakness of method obfuscating C, VHDL and Verilog code.

The detection of slowly moving/stationary targets in a heavy-clutter environment is a challenging problem in a surveillance system. Recent researches [1-3] illustrate that polarization diversity can provide a measure to detect the symmetry of a target in inhomogeneous clutter, especially when discrimination by Doppler effects is not possible, and detection performance could be further enhanced if the polarization of transmitted signal is optimally selected to match the target polarimetric aspects. In this paper, we address the challenges of threat detection in inhomogeneous clutter such as in the riverine wetland environment. Second, a local sequential polarimetric diversity algorithm using dual (horizontal and vertical) polarizations is presented to calculate the singularity of polarization diversity for potential threat detection. The singularity of polarization diversity is a measure to discriminate the difference (less similarity) of targets from the neighborhood (background), which area can be decided by the size of the sliding processing window. Next, a field test using a Vector Network Analyzer collected dual-polarized scatterings of targets and accomplished a multiple frequency band (200 MHz - 18 GHz from UHF to Ku bands) threat characterization and detection on the same stationary threats. Finally, we show the testing result using the local sequential polarimetric diversity algorithm to detect potential threats in inhomogeneous environments.

In this paper, an image fusion technique based on weighted average of Daubechies wavelet transform (db2) coefficients from visual face image and their corresponding thermal images have been presented. Further, a comparative study has been conducted for dimensionality reduction based on Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Fused images thus obtained are classified using a multi-layer perceptron (MLP). For experiments IRIS Thermal/Visual Face Database has been used. Experimental results show that the performance of ICA architecture-I is better than the other two approaches i.e. PCA and ICA-II. The average success rate for PCA, ICA-I and ICA-II are 91.13%, 94.44% and 89.72% respectively. However, approaches presented here achieves maximum success rate of 100% in some cases, especially in case of varying illumination.

Vertebrates are constantly threatened by the invasion of microorganisms and have evolved systems of immunity to eliminate infectious pathogens in the body. Initial sensing of microbial agents is mediated by the recognition of pathogens by means of molecular structures expressed uniquely by microbes of a given type. So-called 'Toll-like receptors' are expressed on host epithelial barrier cells play an essential role in the host defense against microbial pathogens by inducing cell responses (e.g., proliferation, death, cytokine secretion) via activation of intracellular signaling networks. As these networks, comprising multiple interconnecting dynamic pathways, represent highly complex multi-variate "information processing" systems, the signaling activities particularly critical for governing the host cell responses are poorly understood and not easily ascertained by a priori theoretical notions. We have developed over the past half-decade a "data-driven" computational modeling approach, on a 'cue-signal-response' combined experiment/computation paradigm, to elucidate key multi-variate signaling relationships governing the cell responses. In an example presented here, we study how a canonical set of six kinase pathways combine to effect microbial agent-induced apoptotic death of a macrophage cell line. One modeling technique, partial least-squares regression, yielded the following key insights: {a} signal combinations most strongly correlated to apoptotic death are orthogonal to those most strongly correlated with release of inflammatory cytokines; {b} the ratio of two key pathway activities is the most powerful predictor of microbe-induced macrophage apoptotic death; {c} the most influential time-window of this signaling activity ratio is surprisingly fast: less than one hour after microbe stimulation.

This paper presents two aordable low-tack system for household biomedical wellness monitoring. The rst system, JIKIMI (pronounced caregiver in Korean), is a remote monitoring system that analyzes the behavior patterns of elders that live alone. JIKIMI is composed of an in-house sensing system, a set of wireless sensor nodes containing a pyroelectric infrared sensor to detect the motion of elders, an emergency button and a magnetic sensor that detects the opening and closing of doors. The system is also equipped with a server system, which is comprised of a database and web server. The server provides the mechanism for web-based monitoring to caregivers. The second system, Reader of Bottle Information (ROBI), is an assistant system which advises the contents of bottles for elders. ROBI is composed of bottles that have connected RFID tags and an advice system, which is composed of a wireless RFID reader, a gateway and a remote database server. The RFID tags are connected to the caps of the bottles are used in conjunction with the advice system These systems have been in use for three years and have proven to be useful for caregivers to provide more ecient and eective care services.

We solve the channel assignment problems (CAPs) with the main objective of minimizing the overall interference level while meeting the channel demand requirements. We use 3 methods, i.e., (1) local search (LS) with an acceptance ratio to re-initialize the search at a predefined threshold; (2) Simulated Annealing (SA); and (3) improve local search (ILS) with two control parameters, namely Restart (RS) and Stop (ST) thresholds. Simulation results on benchmarking CAPs show that these simple methods outperform other more complex heuristics on both the average and minimum cost solutions.

Cells as microorganisms and within multicellular organisms make robust decisions. Knowing how these complex cells make decisions is essential to explain, predict or mimic their behavior. The discovery of multi-layer multiple feedback loops in the signaling pathways of these modular hybrid systems suggests their decision making is sophisticated. Hybrid systems coordinate and integrate signals of various kinds: discrete on/off signals, continuous sensory signals, and stochastic and continuous fluctuations to regulate chemical concentrations. Such signaling networks can form reconfigurable networks of attractors and repellors giving them an extra level of organization that has resilient decision making built in. Work on generic attractor and repellor networks and on the already identified feedback networks and dynamic reconfigurable regulatory topologies in biological cells suggests that biological systems probably exploit such dynamic capabilities. We present a simple behavior of the swimming unicellular alga Chlamydomonas that involves interdependent discrete and continuous signals in feedback loops. We show how to rigorously verify a hybrid dynamical model of a biological system with respect to a declarative description of a cell's behavior. The hybrid dynamical systems we use are based on a unification of discrete structures and continuous topologies developed in prior work on convergence spaces. They involve variables of discrete and continuous types, in the sense of type theory in mathematical logic. A unification such as afforded by convergence spaces is necessary if one wants to take account of the affect of the structural relationships within each type on the dynamics of the system.

Approximate Nearest Neighbors (ANN) in high dimensional vector spaces is a fundamental, yet challenging problem in many areas of computer science, including computer vision, data mining and robotics. In this work, we investigate this problem from the perspective of compressive sensing, especially the dictionary learning aspect. High dimensional feature vectors are seldom seen to be sparse in the feature domain; examples include, but not limited to Scale Invariant Feature Transform (SIFT) descriptors, Histogram Of Gradients, Shape Contexts, etc. Compressive sensing advocates that if a given vector has a dense support in a feature space, then there should exist an alternative high dimensional subspace where the features are sparse. This idea is leveraged by dictionary learning techniques through learning an overcomplete projection from the feature space so that the vectors are sparse in the new space. The learned dictionary aids in refining the search for the nearest neighbors to a query feature vector into the most likely subspace combination indexed by its non-zero active basis elements. Since the size of the dictionary is generally very large, distinct feature vectors are most likely to have distinct non-zero basis. Utilizing this observation, we propose a novel representation of the feature vectors as tuples of non-zero dictionary indices, which then reduces the ANN search problem into hashing the tuples to an index table; thereby dramatically improving the speed of the search. A drawback of this naive approach is that it is very sensitive to feature perturbations. This can be due to two possibilities: (i) the feature vectors are corrupted by noise, (ii) the true data vectors undergo perturbations themselves. Existing dictionary learning methods address the first possibility. In this work we investigate the second possibility and approach it from a robust optimization perspective. This boils down to the problem of learning a dictionary robust to feature perturbations, viz. paving the way for a novel Robust Dictionary Learning (RDL) framework. In addition to the above model, we also propose a novel LASSO based multi-regularization hashing algorithm which utilizes the consistency properties of the non-zero active basis for increasing values of the regularization weights. Even though our algorithm is generic and has wide coverage in different areas of scientific computing, the experiments in the current work are mainly focused towards improving the speed and accuracy of ANN for SIFT descriptors, which are high-dimensional (128D) and are one of the most widely used interest point detectors in computer vision. Preliminary results from SIFT datasets show that our algorithm is far superior to the state-of-the-art techniques in ANN.

A universal implementation for most behavioral Biometric systems is still unknown since some behaviors aren't individual enough for identification. Habitual behaviors which are measurable by sensors are considered 'soft' biometrics (i.e., walking style, typing rhythm), while physical attributes (i.e., iris, fingerprint) are 'hard' biometrics. Thus, biometrics can aid in the identification of a human not only in cyberspace but in the world we live in. Hard biometrics have proven to be a rather successful form of identification, despite a large amount of individual signatures to keep track of. Virtually all soft biometric strategies, however, share a common pitfall. Instead of the classical pass/fail decision based on the measurements used by hard biometrics, a confidence threshold is imposed, increasing False Alarm and False Rejection Rates. This unreliability is a major roadblock for large scale system integration. Common computer security requires users to log-in with a six or more digit PIN (Personal Identification Number) to access files on the disk. Commercially available Keystroke Dynamics (KD) software can separately calculate and keep track of the mean and variance for each time travelled between each key (air time), and the time spent pressing each key (touch time). Despite its apparent utility, KD is not yet a robust, fault-tolerant system. We begin with a simple question: how could a pianist quickly control so many different finger and wrist movements to play music? What information, if any, can be gained from analyzing typing behavior over time? Biology has shown us that the separation of arm and finger motion is due to 3 long nerves in each arm; regulating movement in different parts of the hand. In this paper we wish to capture the underlying behavioral information of a typist through statistical memory and non-linear dynamics. Our method may reveal an inverse Compressive Sensing mapping; a unique individual signature.