This PDF file contains the front matter associated with SPIE Proceedings Volume 9652, including the Title Page, Copyright information, Table of Contents, Introduction, Authors, and Conference Committee listing.

Raman spectroscopy is a powerful tool for obtaining molecular structure information of a sample. While Raman spectroscopy is a common laboratory based analytical tool, miniaturization of opto-electronic components has allowed handheld Raman analyzers to become commercially available. These handheld systems are utilized by Military and First Responder operators tasked with rapidly identifying potentially hazardous chemicals in the field. However, one limitation of many handheld Raman detection systems is strong interference caused by fluorescence of the sample or underlying surface which obscures the characteristic Raman signature of the target analyte. Munitions grade chemical warfare agents (CWAs) are produced and stored in large batches and typically have more impurities from the storage container, degradation, or unreacted precursors. In this work, Raman spectra of munitions grade CWAs were collected using a handheld Raman spectrometer with a 1064 nm excitation laser. While Raman scattering generated by a 1064 nm laser is inherently less efficient than excitation at shorter wavelengths, high quality spectra were easily obtained due to significantly reduced fluorescence of the munitions grade CWAs. The spectra of these less pure, but more operationally relevant, munitions grade CWAs were then compared to spectra of CASARM grade CWAs, as well as Raman spectra collected using the more common 785 nm excitation laser.

With the aim of identifying an approach to exploit the differences in the fluorescence signatures of biological agents BAs, we have investigated the response of some BAs simulants to a set of different excitation wavelengths in the UV spectral range (i.e. 266, 273, 280, 300, 340, 355 nm). Our preliminary results on bacterial spores and vegetative forms, dispersed in water, showed that the differences in the fluorescence spectra can be enhanced, and more easily revealed, by using different excitation wavelengths. Specifically, the photo luminescence (PL) spectra coming from different species of Bacillus, in the form of spores (used as simulants of Bacillus anthracis), show significant differences under excitation at all the wavelengths, with slightly larger differences at 300, 340, 355 nm. On the other hand, the vegetative forms of two Bacillus species, did not show any appreciable difference, i.e. the PL spectra are virtually identical, for the excitation wavelengths of 266, 273, 280 nm. Conversely, small yet appreciable difference appear at 300, 340, 355 nm. Finally, large difference appear between the spore and the vegetative form of each species at all the wavelengths, with slightly larger variations at 300, 340, 355 nm. Together, these preliminary results support the hypothesis that a multi-wavelength approach could be used to improve the sensitivity and specificity of UV-LIF based BAs detection systems. The second step of this work concerns the application of a Support Vector Regression (SVR) method, as evaluated in our previous work to define a methodology for the setup of a multispectral database for the stand-off detection of BAs.

Obliterated writing is writing that has been obscured by different-colored materials. There are obliterated writings that cannot be detected by conventional methods. A method for deciphering such obliterated writings was developed in this study. Mid-infrared spectroscopic imaging in the wavelength range of 2.5–14 μm was used for deciphering because the infrared spectrum differs among different brands of colorants. Obliterated writings were made by pressing information protection stamps onto characters written by 4 kinds of colorants. The samples were tested for deciphering by the Fourier-transform infrared imaging system. Two peak areas of two specific wavenumber regions of each reflectance spectrum were calculated and the ratio of the two values is displayed as a unique gray scale in the spectroscopic image. As a result, the absorption peak at various wavenumbers could be used to decipher obliterated writings that could not be detected by the conventional methods. Ten different parameters for deciphering obliterated writing were found in this study.

Comparison of desorption effectiveness of Nd³⁺:YAG nanosecond laser sources (λ=266, 354, 532 nm) has been carried out to investigate a possibility of creating a non-contact sampling device for detectors of explosives based on principles of ion mobility spectrometry (IMS) and field asymmetric ion mobility spectrometry (FAIMS). The results of mass spectrometric study of laser desorption of nitroamine, nitrate ester and nitroaromatic compounds from a quartz substrate are presented. It is shown that irradiation of adsorbed layers of studied samples by a single pulse of non-resonant laser radiation (λ=532 nm) leads to efficient desorption at laser intensity 10⁷ W/cm² and above. Excitation of the first singlet state of nitro compounds by resonant radiation (λ=354 nm) provides heating of adsorbed layers and thermal desorption. A strongly non-equilibrium (non-thermal) dissociation process is developed when the second singlet state of nitroaromatic molecules is excited by radiation at λ=266 nm, along with thermal desorption. It is shown that Nd³⁺: YAG laser with wavelength λ=266 nm, pulse duration 5-10 ns, intensity 10⁷-10⁹ W/cm² is the most effective source for creation a non-contact sampling device based on desorption of explosives from surfaces.

A bottle scanner to detect liquid explosive has been developed using technologies of near infrared. Its detection rate of liquid explosive is quite high and its false alarm rate of safe liquids quite low. It uses a light source with wide spectrum such as a halogen lamp. Recently a variety of LEDs have been developed and some of them have near infrared spectrum. Here a near infrared LED is tested as a light source of the liquid explosive detector. Three infrared LEDs that have a main peak of spectrum at 901nm, 936nm, and 1028 nm have been used as a light source to scan liquids. Spectrum widths of these LEDs are quite narrow typically less than 100 nm. Ten typical liquids have been evaluated by these LEDs and the correlation coefficients of a spectrum by an LED and a tungsten lamp were more than 0.98. This experiment shows that the infrared LED can be used as a light source for the liquid scanner. An LED has some merits, such as long life of more than some ten thousand hours and small consumption electric power of less than 0.2 W. When the LED is used as a light source for the liquid scanner, it is also more compact and handy.

Detection of illegal compounds requires a reliable, selective and sensitive detection device. The successful device features automated target acquisition, identification and signal processing. It is portable, fast, user friendly, sensitive, specific, and cost efficient. LEAs are in need of such technology. CRIM-TRACK is developing a sensing device based on these requirements. We engage highly skilled specialists from research institutions, industry, SMEs and LEAs and rely on a team of end users to benefit maximally from our prototypes. Currently we can detect minute quantities of drugs, explosives and precursors thereof in laboratory settings. Using colorimetric technology we have developed prototypes that employ disposable sensing chips. Ease of operation and intuitive sensor response are highly prioritized features that we implement as we gather data to feed into machine learning. With machine learning our ability to detect threat compounds amidst harmless substances improves. Different end users prefer their equipment optimized for their specific field. In an explosives-detecting scenario, the end user may prefer false positives over false negatives, while the opposite may be true in a drug-detecting scenario. Such decisions will be programmed to match user preference. Sensor output can be as detailed as the sensor allows. The user can be informed of the statistics behind the detection, identities of all detected substances, and quantities thereof. The response can also be simplified to “yes” vs. “no”. The technology under development in CRIM-TRACK will provide custom officers, police and other authorities with an effective tool to control trafficking of illegal drugs and drug precursors.

We propose a new approach, based on optical atomic magnetometers and magnetic induction tomography (MIT), for remote and non-invasive detection of conductive targets. Atomic magnetometers overcome the main limitations of conventional MIT instrumentation, in particular their poor low-frequency sensitivity, their large size and their limited scalability. Moreover, atomic magnetometers have been proven to reach extremely high sensitivities, with an improvement of up to 7 orders of magnitude in the 50 MHz to DC band, with respect to a standard pick-up coil of the same size. In the present scheme, an oscillating magnetic field induces eddy currents in a conductive target and laser-pumped atomic magnetometers, either stand-alone or in an array, detect the response of the objects. A phase-sensitive detection scheme rejects the background, allowing remote detection of the secondary field and, thus, mapping of objects, hidden in cargos, underwater or underground. The potential for extreme sensitivity, miniaturization, dynamic range and array operation paves the way to a new generation of non-invasive, active detectors for surveillance, as well as for real-time cargo screening.

We demonstrate principal limitations of standard Time Domain Spectroscopy based on a broadband THz pulse for the detection and identification of substance using paper napkins as a sample. To avoid these limitations we propose a new high effective algorithm for this purpose. We demonstrate its applicability in realistic and simulated situation for various substances under consideration. The interaction of a THz pulse with a disordered layered structure was simulated in order to show the influence of the disordered layers on the spectral characteristics of the transmitted and reflected signals. Spectral characteristics of these signals were analyzed in a direct comparison with the spectrum of the incident pulse as well as by means of Spectral Dynamics Analysis method and integral correlation criteria. The efficiency of the detection and identification method, based on integral correlation and likeness criteria, is confirmed on the basis of computer simulation. To demonstrate the possibilities of the integral correlation criteria in real experiment, they were applied for the identification of explosive HMX in the reflection mode.

Public transport security is one of the main priorities of the public authorities when fighting against crime and terrorism. In this context, there is a great demand for autonomous systems able to detect abnormal events such as violent acts aboard passenger cars and intrusions when the train is parked at the depot. To this end, we present an innovative approach which aims at providing efficient automatic event detection by fusing video and audio analytics and reducing the false alarm rate compared to classical stand-alone video detection. The multi-modal system is composed of two microphones and one camera and integrates onboard video and audio analytics and fusion capabilities. On the one hand, for detecting intrusion, the system relies on the fusion of “unusual” audio events detection with intrusion detections from video processing. The audio analysis consists in modeling the normal ambience and detecting deviation from the trained models during testing. This unsupervised approach is based on clustering of automatically extracted segments of acoustic features and statistical Gaussian Mixture Model (GMM) modeling of each cluster. The intrusion detection is based on the three-dimensional (3D) detection and tracking of individuals in the videos. On the other hand, for violent events detection, the system fuses unsupervised and supervised audio algorithms with video event detection. The supervised audio technique detects specific events such as shouts. A GMM is used to catch the formant structure of a shout signal. Video analytics use an original approach for detecting aggressive motion by focusing on erratic motion patterns specific to violent events. As data with violent events is not easily available, a normality model with structured motions from non-violent videos is learned for one-class classification. A fusion algorithm based on Dempster-Shafer’s theory analyses the asynchronous detection outputs and computes the degree of belief of each probable event.

Person tracking across non-overlapping cameras and other types of video analytics benefit from spatial calibration information that allows an estimation of the distance between cameras and a relation between pixel coordinates and world coordinates within a camera. In a large environment with many cameras, or for frequent ad-hoc deployments of cameras, the cost of this calibration is high. This creates a barrier for the use of video analytics. Automating the calibration allows for a short configuration time, and the use of video analytics in a wider range of scenarios, including ad-hoc crisis situations and large scale surveillance systems. We show an autocalibration method entirely based on pedestrian detections in surveillance video in multiple non-overlapping cameras. In this paper, we show the two main components of automatic calibration. The first shows the intra-camera geometry estimation that leads to an estimate of the tilt angle, focal length and camera height, which is important for the conversion from pixels to meters and vice versa. The second component shows the inter-camera topology inference that leads to an estimate of the distance between cameras, which is important for spatio-temporal analysis of multi-camera tracking. This paper describes each of these methods and provides results on realistic video data.

Object recognition and localization are important to automatically interpret video and allow better querying on its content. We propose a method for object localization that learns incrementally and addresses four key aspects. Firstly, we show that for certain applications, recognition is feasible with only a few training samples. Secondly, we show that novel objects can be added incrementally without retraining existing objects, which is important for fast interaction. Thirdly, we show that an unbalanced number of positive training samples leads to biased classifier scores that can be corrected by modifying weights. Fourthly, we show that the detector performance can deteriorate due to hard-negative mining for similar or closely related classes (e.g., for Barbie and dress, because the doll is wearing a dress). This can be solved by our hierarchical classification. We introduce a new dataset, which we call TOSO, and use it to demonstrate the effectiveness of the proposed method for the localization and recognition of multiple objects in images.

Target tracking complexity within conventional video imagery can be fundamentally attributed to the ambiguity associated with actual 3D scene position of a given tracked object in relation to its observed position in 2D image space. Recent work, within thermal-band infrared imagery, has tackled this challenge head on by returning to classical photogrammetry as a means of recovering the true 3D position of pedestrian targets. A key limitation in such approaches is the assumption of posture – that the observed pedestrian is at full height stance within the scene. Whilst prior work has shown the effects of statistical height variation to be negligible, variations in the posture of the target may still pose a significant source of potential error. Here we present a method that addresses this issue via the use of Support Vector Machine (SVM) regression based pedestrian posture estimation operating on Histogram of Orientated Gradient (HOG) feature descriptors. Within an existing tracking framework, we demonstrate improved target localization that is independent of variations in target posture (i.e. behaviour) and within the statistical error bounds of prior work for pedestrian height posture varying from 0.4-2.4m over a distance to target range of 7-30m.

In recent years, the wide use of video surveillance systems has caused an enormous increase in the amount of data that has to be stored, monitored, and processed. As a consequence, it is crucial to support human operators with automated surveillance applications. Towards this end an intelligent video analysis module for real-time alerting in case of abandoned objects in public spaces is proposed. The overall processing pipeline consists of two major parts. First, person motion is modeled using an Interacting Multiple Model (IMM) filter. The IMM filter estimates the state of a person according to a finite-state, discrete-time Markov chain. Second, the location of persons that stay at a fixed position defines a region of interest, in which a nonparametric background model with dynamic per-pixel state variables identifies abandoned objects. In case of a detected abandoned object, an alarm event is triggered. The effectiveness of the proposed system is evaluated on the PETS 2006 dataset and the i-Lids dataset, both reflecting prototypical surveillance scenarios.

Face detection and alignment are two crucial tasks to face recognition which is a hot topic in the field of defense and security, whatever for the safety of social public, personal property as well as information and communication security. Common approaches toward the treatment of these tasks in recent years are often of three types: template matching-based, knowledge-based and machine learning-based, which are always separate-step, high computation cost or fragile robust. After deep analysis on a great deal of Chinese face images without hats, we propose a novel face detection and coarse alignment method, which is inspired by those three types of methods. It is multi-feature fusion with Simple Multiple Kernel Learning¹ (Simple-MKL) algorithm. The proposed method is contrasted with competitive and related algorithms, and demonstrated to achieve promising results.

In the security domain, cameras are important to assess critical situations. Apart from fixed surveillance cameras we observe an increasing number of sensors on mobile platforms, such as drones, vehicles and persons. Mobile cameras allow rapid and local deployment, enabling many novel applications and effects, such as the reduction of violence between police and citizens. However, the increased use of bodycams also creates potential challenges. For example: how can end-users extract information from the abundance of video, how can the information be presented, and how can an officer retrieve information efficiently? Nevertheless, such video gives the opportunity to stimulate the professionals’ memory, and support complete and accurate reporting. In this paper, we show how video content analysis (VCA) can address these challenges and seize these opportunities. To this end, we focus on methods for creating a complete summary of the video, which allows quick retrieval of relevant fragments. The content analysis for summarization consists of several components, such as stabilization, scene selection, motion estimation, localization, pedestrian tracking and action recognition in the video from a bodycam. The different components and visual representations of summaries are presented for retrospective investigation.

CILAS, subsidiary of Airbus Defense and Space, develops, manufactures and sales laser-based optronics equipment for defense and homeland security applications. Part of its activity is related to active systems for threat detection, recognition and identification. Active surveillance and active imaging systems are often required to achieve identification capacity in case for long range observation in adverse conditions. In order to ease the deployment of active imaging systems often complex and expensive, CILAS suggests a new concept. It consists on the association of two apparatus working together. On one side, a patented versatile laser platform enables high peak power laser illumination for long range observation. On the other side, a small camera add-on works as a fast optical switch to select photons with specific time of flight only. The association of the versatile illumination platform and the fast optical switch presents itself as an independent body, so called “flash module”, giving to virtually any passive observation systems gated active imaging capacity in NIR and SWIR.

It is becoming increasingly important in both the defence and security domains to conduct persistent wide area surveillance (PWAS) of large populations of targets. Wide Area Motion Imagery (WAMI) is a key technique for achieving this wide area surveillance. The recent development of multi-million pixel sensors has provided sensors with wide field of view replete with sufficient resolution for detection and tracking of objects of interest to be achieved across these extended areas of interest. WAMI sensors simultaneously provide high spatial and temporal resolutions, giving extreme pixel counts over large geographical areas. The high temporal resolution is required to enable effective tracking of targets. The provision of wide area coverage with high frame rates generates data deluge issues; these are especially profound if the sensor is mounted on an airborne platform, with finite data-link bandwidth and processing power that is constrained by size, weight and power (SWAP) limitations. These issues manifest themselves either as bottlenecks in the transmission of the imagery off-board or as latency in the time taken to analyse the data due to limited computational processing power.

An innovative real-time polarimetric method is presented based on the integral polarization-holographic diffraction element developed by us. This element is suggested to be used for real time analysis of the polarization state of light, to help highlight military equipment in a scene. In the process of diffraction, the element decomposes light incoming on them onto orthogonal circular and linear basis. The simultaneous measurement of the intensities of four diffracted beams by means of photodetectors and the appropriate software enable the polarization state of an analyzable light (all the four Stokes parameters) and its change to be obtained in real time. The element with photodetectors and software is a sensor of the polarization state. Such a sensor allows the point-by-point distribution of the polarization state in the images of objects to be determined. The spectral working range of such an element is 530 – 1600 nm. This sensor is compact, lightweight and relatively cheap, and it can be easily installed on any space and airborne platforms. It has no mechanically moving or electronically controlled elements. The speed of its operation is limited only by computer processing. Such a sensor is proposed to be use for the determination of the characteristics of the surface of objects at optical remote sensing by means of the determination of the distribution of the polarization state of light in the image of recognizable object and the dispersion of this distribution, which provides additional information while identifying an object. The possibility of detection of a useful signal of the predetermined polarization on a background of statistically random noise of an underlying surface is also possible. The application of the sensor is also considered for the nondestructive determination of the distribution of stressed state in different constructions based on the determination of the distribution of the polarization state of light reflected from the object under investigation. The prospect of this sensor application in astropolarymetry both for land and space telescopes is also discussed.

In this paper, we investigate an application that integrates holistic appearance based method and feature based method for face recognition. The automatic face recognition system makes use of multiscale Kernel PCA (Principal Component Analysis) characterized approximated face images and reduced the number of invariant SIFT (Scale Invariant Feature Transform) keypoints extracted from face projected feature space. To achieve higher variance in the inter-class face images, we compute principal components in higher-dimensional feature space to project a face image onto some approximated kernel eigenfaces. As long as feature spaces retain their distinctive characteristics, reduced number of SIFT points are detected for a number of principal components and keypoints are then fused using user-dependent weighting scheme and form a feature vector. The proposed method is tested on ORL face database, and the efficacy of the system is proved by the test results computed using the proposed algorithm.

In this paper, we present a hierarchical kernel associative memory (H-KAM) based computational model with Finite Ridgelet Transform (FRIT) representation for multispectral palmprint recognition. To characterize a multispectral palmprint image, the Finite Ridgelet Transform is used to achieve a very compact and distinctive representation of linear singularities while it also captures the singularities along lines and edges. The proposed system makes use of Finite Ridgelet Transform to represent multispectral palmprint image and it is then modeled by Kernel Associative Memories. Finally, the recognition scheme is thoroughly tested with a benchmarking multispectral palmprint database CASIA. For recognition purpose a Bayesian classifier is used. The experimental results exhibit robustness of the proposed system under different wavelengths of palm image.

In recent years various kinds of biosensors for the detection of pathogens have been developed. A genosensor consists in the immobilization, onto the surface of a chosen transducer, of an oligonucleotide with a specific base sequence called capture probe. The complementary sequence (the analytical target, i.e. a specific sequence of the DNA/RNA of the pathogen) present in the sample is recognized and captured by the probe through the hybridization reaction. The evaluation of the extent of the hybridization allows one to confirm whether the sample contains the complementary sequence of the probe or not. Electrochemical transducers have received considerable attention in connection with the detection of DNA hybridization. Moreover, recently, with the emergence of novel photoelectrochemically active species and new detection schemes, photoelectrochemistry has resulted in substantial progress in its analytical performance for biosensing applications. In this paper, some examples of electrochemical genosensors for multiplexed pathogen detection are shown. Moreover, the preliminary experiments towards the development of a photoelectrochemical genosensor using a TiO2 – nanocrystal-modified ITO electrode are discussed.

Natural macromolecules are very promising row materials to be used in modern technology including security and defense. They are abundant in nature, easy to extract and possess biocompatibility and biodegradability properties. These materials can be modified throughout chemical or physical processes, and can be doped with lithium and rare earth salts, ionic liquids, organic and inorganic acids. In this communication samples of DNA and modified DNA were doped with Prussian Blue (PB), poly(ethylene dioxythiophene) (PEDOT), europium and erbium triflate and organic dyes such as Nile Blue (NB), Disperse Red 1 (DR1) and Disperse Orange 3 (DO3). The colored or colorless membranes were characterized by electrochemical and spectroscopic measurements, and they were applied in electrochromic devices (ECDs) and dye sensitized solar cells (DSSC). ECDs change the color under applied potential, so they can modulate the intensity of transmitted light of 15 to 35%. As the electrochromic materials, WO3 or Prussian blue (PB), are usually blue colored, the color change is from transparent to blue. DNA, and the complexes: DNA-CTMA, DNA-DODA and DNAPEDOT: PSS were also investigated as either hole carrier material (HTM) or polymer electrolyte in dye-sensitized solar cells (DSSC). The DNA-based samples as HTM in small DSSCs revealed a solar energy conversion efficiency of 0.56%. Polymer electrolytes of DNA-CTMA and DNA-DODA, both with 10 wt% of LiI/I₂, applied in small DSSC, exhibited the efficiencies of 0.18 and 0.66%, respectively. The obtained results show that natural macromolecules-based membranes are not only environmentally friendly but are also promising materials to be investigated for several electrochemical devices. However, to obtain better performances more research is still needed.

In order to analyze the spectra of inseparable chemical mixtures, many mathematical methods have been developed to decompose them into the components relevant to species from series of spectral data obtained under different conditions. We formulated a method based on singular value decomposition (SVD) of linear algebra, and applied it to two example systems of organic dyes, being successful in reproducing absorption spectra assignable to cis/trans azocarbazole dyes from the spectral data after photoisomerization and to monomer/dimer of cyanine dyes from those during photodegaradation process. For the example of photoisomerization, polymer films containing the azocarbazole dyes were prepared, which have showed updatable holographic stereogram for real images with high performance. We made continuous monitoring of absorption spectrum after optical excitation and found that their spectral shapes varied slightly after the excitation and during recovery process, of which fact suggested the contribution from a generated photoisomer. Application of the method was successful to identify two spectral components due to trans and cis forms of azocarbazoles. Temporal evolution of their weight factors suggested important roles of long lifetimed cis states in azocarbazole derivatives. We also applied the method to the photodegradation of cyanine dyes doped in DNA-lipid complexes which have shown efficient and durable optical amplification and/or lasing under optical pumping. The same SVD method was successful in the extraction of two spectral components presumably due to monomer and H-type dimer. During the photodegradation process, absorption magnitude gradually decreased due to decomposition of molecules and their decaying rates strongly depended on the spectral components, suggesting that the long persistency of the dyes in DNA-complex related to weak tendency of aggregate formation.

Active layers of bulk heterojunction are extensively studied because of their great potential for application in low-cost optoelectronic devices like photovoiltaic cells and photodiodes. The performance of such devices is strongly influenced by the formed nanostructures which determine the transport ability of the organic composite. We investigated the charge carrier transport properties of two organic composites: poly(3-hexyothiophene) (P3HT) with (6,6)-phenyl-C60-butyric acid methyl ester (60PCBM)and poly(triarylamine) (PTTA) blended with 60PCBM. The optimised organic blend was used as a matrix material for Cu-In-Se nanocrystals. Adding Cu–In–Se nanocrystals to a P3HT/60PCBM bulk heterojunction leads to a significant improvement of the maximum external quantum efficiency of the investigated system from 48% to 70% (at wavelength 520 nm).

An ultraviolet (UV) photodetector utilizing an inkjet printable , UV photoconducting biopolymer was fabricated and the performance of the photodetector was characterized for varying thickness layers of the biopolymer. The biopolymer was formed of deoxyribonucleic acid (DNA), the Clevios P formulation of poly(3,4-ethylenedioxythiophene)- poly(styrenesulfonate) (PEDOT:PSS), and hexadecyltrimethyl-ammonium chloride (CTMA); this was then combined with phenyl-C61-butyric acid methyl (PCBM) to form the printable, UV photoconducting biopolymer. Using a 260-nm source, the highest measured responsivity of the photodetectors is 1.2 mA/W at 20 V bias.

Circular dichroism (CD) is known to be a very sensitive probe of molecular conformation, and it is in particular widely used in biochemistry. Measuring the CD as a function of time is therefore very appealing to access information on the dynamics of conformational changes in molecules or biomolecules. We have implemented such a time-resolved experiments in two complementary configurations: a sub-picosecond pump-probe one and a microsecond detection of CD coupled to a T-jump experiment. We present two experiments based on these techniques: the ultrafast motion of the carbonyl group in the chromophore of the Photoactive Yellow Protein after photoexcitation and the dynamics of thermal denaturation in model peptides.

The aim of this work was to learn how the influence of various solvents correlates with nonlinear optical properties of metallophthalocyanines (MPcs). The method, which was used to measure nonlinear optical properties, was degenerate four wave mixing (DFWM). We used ethanol, chloroform and dimethyl sulfoxide as solvent of metallophthalocyanines. The absorption spectra of MPcs solutions present a narrow Q-band in the visible region and a relatively wide B-band in the near ultraviolet. In all cases we observed the solvatochromism effect. Nonlinear optical properties of MPcs solutions change with using different polarity of solvent in the following way: n_{2 (ZnPc)} < n_{2 (CoPc)} < n_{2 (CuPc)}.

In this paper we report the synthesis of side chain methacrylic polymers functionalized with styrylquinoline fragments. The polymerization was carried out in dimethylformamide with azobisisobutyronitrile as initiator. The products of polymerization were characterized by ¹H NMR, DSC. A study on the energetics of modelling compounds has been performed by a synergetic use of both electrochemical and optical techniques. The results of photochemical activities of the corresponding polymers are presented.

In this study, we propose an all-optical sensor based on consideration the nonlinear effects on modal propagation and output intensity based on ultra-compact nonlinear multimode interference (NLMMI) coupler. The sensor can be tuned to highest sensitivity in the wavelength and refractive index ranges sufficient to detect water- soluble chemical, air pollutions, and heart operation. The results indicate high output sensitivity to input wavelength. This sensitivity guides us to propose a wave sensor both transverse and longitudinal waves such as acoustic and light wave, when an external wave interacts with input waveguide. For instance, this sensor can be implemented by long input that inserted in the land, then any wave could detected from earth. The visible changes of intensity at output facet in various surrounding layer refractive index show the high sensitivity to the refractive index of surrounding layer that is foundation of introducing a sensor. Also, the results show the high distinguished changes on modal expansion and output throat distribution in various refractive indices of surrounding layer.