Understanding Optical Trapping Phenomena: a Simulation for Undergraduates

Optical trapping is an attractive and multidisciplinary topic that has become the center of attention to a large number of researchers. Moreover, it is a suitable subject for advanced students that requires a knowledge of a wide range of topics. As a result, it has been incorporated into some syllabuses of both undergraduate and graduate programs. In this paper, basic concepts in laser trapping theory are reviewed. To provide a better understanding of the underlying concepts for students, a Java application for simulating the behavior of a dielectric particle trapped in a highly focused beam has been developed. The program illustrates a wide range of theoretical results and features, such as the calculation of the force exerted by a beam in the Mie and Rayleigh regimes or the calibration of the trap stiffness. Some examples that are ready to be used in the classroom or in the computer lab are also supplied.

Optical trapping: a review of essential concepts

Optical tweezers are an innovative technique for the non-contact, all-optical manipulation of small material samples, which has extraordinarily expanded and evolved since its inception in the mid-80s of the last century. Nowadays, the potential of optical tweezers has been clearly proven and a wide range of applications both from the physical and biological sciences have solidly emerged, turning the early ideas and techniques into a powerful paradigm for experimentation in the micro- and nanoworld. This review aims at highlighting the fundamental concepts that are essential for a thorough understanding of optical trapping, making emphasis on both its manipulation and measurement capabilities, as well as on the vast array of important biological applications appeared in the last years.

Microdoping compensation of microcrystalline silicon obtained by Hot-Wire Chemical Vapour Deposition

Undoped hydrogenated microcrystalline silicon was obtained by hot-wire chemical vapour deposition at different silane-to-hydrogen ratios and low temperature (<300 °C). As well as technological aspects of the deposition process, we report structural, optical and electrical characterizations of the samples that were used as the active layer for preliminary p-i-n solar cells. Raman spectroscopy indicates that changing the hydrogen dilution can vary the crystalline fraction. From electrical measurements an unwanted n-type character is deduced for this undoped material. This effect could be due to a contaminant, probably oxygen, which is also observed in capacitance-voltage measurements on Schottky structures. The negative effect of contaminants on the device was dramatic and a compensated p-i-n structure was also deposited to enhance the cell performance.

Synthesis of Janus compounds for the recognition of G-U mismatched nucleobase pairs

The design and synthesis of two Janus-type heterocycles with the capacity to simultaneously recognize guanine and uracyl in G-U mismatched pairs through complementary hydrogen bond pairing is described. Both compounds were conveniently functionalized with a carboxylic function and efficiently attached to a tripeptide sequence by using solid-phase methodologies. Ligands based on the derivatization of such Janus compounds with a small aminoglycoside, neamine, and its guanidinylated analogue have been synthesized, and their interaction with Tau RNA has been investigated by using several biophysical techniques, including UV-monitored melting curves, fluorescence titration experiments, and 1H NMR. The overall results indicated that Janus-neamine/guanidinoneamine showed some preference for the +3 mutated RNA sequence associated with the development of some tauopathies, although preliminary NMR studies have not confirmed binding to G-U pairs. Moreover, a good correlation has been found between the RNA binding affinity of such Janus-containing ligands and their ability to stabilize this secondary structure upon complexation.

Recognition of prior learning in online postgraduate education

The recognition of prior experiential learning (RPEL) involves the assessment ofskills and knowledge acquired by an individual through previous experience, which isnot necessarily related to an academic context. RPEL practices are far from generalisedin higher education, and there is a lack of specific guidelines on how to implement RPLprograms in particular settings, such as management education or online programs. TheRPEL pilot program developed in a Spanish virtual university is used throughout thearticle as the basis for further reflection on the design and implementation of RPEL inonline postgraduate education in the business field. The role of competences as a centraltheoretical foundation for RPEL is explained, and the context and characteristics of theRPEL program described. Special attention is paid to the key elements of the program¿sdesign and to the practical aspects of its implementation. The results of the program areassessed and general conclusions and suggestions for further research are discussed.

Shared feature extraction for nearest neighbor face recognition

In this paper, we propose a new supervised linearfeature extraction technique for multiclass classification problemsthat is specially suited to the nearest neighbor classifier (NN).The problem of finding the optimal linear projection matrix isdefined as a classification problem and the Adaboost algorithmis used to compute it in an iterative way. This strategy allowsthe introduction of a multitask learning (MTL) criterion in themethod and results in a solution that makes no assumptions aboutthe data distribution and that is specially appropriated to solvethe small sample size problem. The performance of the methodis illustrated by an application to the face recognition problem.The experiments show that the representation obtained followingthe multitask approach improves the classic feature extractionalgorithms when using the NN classifier, especially when we havea few examples from each class

Security Authentication using Phase-Encoded Nanoparticle Structures and Polarized Light

Phase encoded nano structures such as Quick Response (QR) codes made of metallic nanoparticles are suggested to be used in security and authentication applications. We present a polarimetric optical method able to authenticate random phase encoded QR codes. The system is illuminated using polarized light and the QR code is encoded using a phase-only random mask. Using classification algorithms it is possible to validate the QR code from the examination of the polarimetric signature of the speckle pattern. We used Kolmogorov-Smirnov statistical test and Support Vector Machine algorithms to authenticate the phase encoded QR codes using polarimetric signatures.

Optical Encryption using Photon-Counting Polarimetric Imaging

We present a polarimetric-based optical encoder for image encryption and verification. A system for generating random polarized vector keys based on a Mach-Zehnder configuration combined with translucent liquid crystal displays in each path of the interferometer is developed. Polarization information of the encrypted signal is retrieved by taking advantage of the information provided by the Stokes parameters. Moreover, photon-counting model is used in the encryption process which provides data sparseness and nonlinear transformation to enhance security. An authorized user with access to the polarization keys and the optical design variables can retrieve and validate the photon-counting plain-text. Optical experimental results demonstrate the feasibility of the encryption method.

Model-based objects recognition in industrial environments for autonomous vehicles control

Behavior-based navigation of autonomous vehicles requires the recognition of the navigable areas and the potential obstacles. In this paper we describe a model-based objects recognition system which is part of an image interpretation system intended to assist the navigation of autonomous vehicles that operate in industrial environments. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using a rule-based cooperative expert system

Model-based objects recognition in man-made environments

We describe a model-based objects recognition system which is part of an image interpretation system intended to assist autonomous vehicles navigation. The system is intended to operate in man-made environments. Behavior-based navigation of autonomous vehicles involves the recognition of navigable areas and the potential obstacles. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using CEES, the C++ embedded expert system shell developed in the Systems Engineering and Automatic Control Laboratory (University of Girona) as a specific rule-based problem solving tool. It has been especially conceived for supporting cooperative expert systems, and uses the object oriented programming paradigm

Surface passivation and optical characterization of Al2O3/a-SiCx stacks on c-Si substrates

Postprint (published version)

All-Optical Unicast/Multicast Routing in WDM Networks

Optical transport networks (OTN) must be prepared in terms of better resource utilization, for accommodating unicast and multicast traffic together. Light-trees have been proposed for supporting multicast connections in OTN. Nevertheless when traffic grooming is applied in light-trees, resources can be underutilized as traffic can be routed to undesirable destinations in order to avoid optical-electrical-optical (OEO) conversions. In this paper, a novel architecture named S/G light- tree for supporting unicast/multicast connections is proposed. The architecture allows traffic dropping and aggregation in different wavelengths without performing OEO conversions. A heuristic that routes traffic demands using less wavelengths by taking advantage of the proposed architecture is designed as well. Simulation results show that the architecture can minimize the number of used wavelengths and OEO conversions when compared to light-trees