Infrared Photodetectors Based on Reduced Graphene Oxide and Graphene Nanoribbons

The use of reduced graphene oxide (RGO) and graphene nanoribbons (GNRs) as infrared photodetectors is explored, based on recent results dealing with solar cells, light-emitting devices, photodetectors, and ultrafast lasers. IR detection is demonstrated by both RGO and GNRs (see image) in terms of the time-resolved photocurrent and photoresponse. The responsivity of the detectors and their functioning are presented.

Comparison of 10 Multi-Sensor Image Fusion Paradigms for IKONOS Images

Fusion of multi-sensor imaging data enables a synergetic interpretation of complementary information obtained by sensors of different spectral ranges. Multi-sensor data of diverse spectral, spatial and temporal resolutions require advanced numerical techniques for analysis and interpretation. This paper reviews ten advanced pixel based image fusion techniques – Component substitution (COS), Local mean and variance matching, Modified IHS (Intensity Hue Saturation), Fast Fourier Transformed-enhanced IHS, Laplacian Pyramid, Local regression, Smoothing filter (SF), Sparkle, SVHC and Synthetic Variable Ratio. The above techniques were tested on IKONOS data (Panchromatic band at 1 m spatial resolution and Multispectral 4 bands at 4 m spatial resolution). Evaluation of the fused results through various accuracy measures, revealed that SF and COS methods produce images closest to corresponding multi-sensor would observe at the highest resolution level (1 m).

Highly fluorescent GFPm2+-based genome integration-proficient promoter probe vector to study Mycobacterium tuberculosis promoters in infected macrophages

Study of activity of cloned promoters in slow-growing Mycobacterium tuberculosis during long-term growth conditions in vitro or inside macrophages, requires a genome-integration proficient promoter probe vector, which can be stably maintained even without antibiotics, carrying a substrate-independent, easily scorable and highly sensitive reporter gene. In order to meet this requirement, we constructed pAKMN2, which contains mycobacterial codon-optimized gfpm2+ gene, coding for GFPm2+ of highest fluorescence reported till date, mycobacteriophage L5 attP-int sequence for genome integration, and a multiple cloning site. pAKMN2 showed stable integration and expression of GFPm2+ from M. tuberculosis and M. smegmatis genome. Expression of GFPm2+, driven by the cloned minimal promoters of M. tuberculosis cell division gene, ftsZ (MtftsZ), could be detected in the M. tuberculosis/pAKMN2-promoter integrants, growing at exponential phase in defined medium in vitro and inside macrophages. Stable expression from genome-integrated format even without antibiotic, and high sensitivity of detection by flow cytometry and fluorescence imaging, in spite of single copy integration, make pAKMN2 useful for the study of cloned promoters of any mycobacterial species under long-term in vitro growth or stress conditions, or inside macrophages.

Adaptive window zero-crossing-based instantaneous frequency estimation

We address the problem of estimating instantaneous frequency (IF) of a real-valued constant amplitude time-varying sinusoid. Estimation of polynomial IF is formulated using the zero-crossings of the signal. We propose an algorithm to estimate nonpolynomial IF by local approximation using a low-order polynomial, over a short segment of the signal. This involves the choice of window length to minimize the mean square error (MSE). The optimal window length found by directly minimizing the MSE is a function of the higher-order derivatives of the IF which are not available a priori. However, an optimum solution is formulated using an adaptive window technique based on the concept of intersection of confidence intervals. The adaptive algorithm enables minimum MSE-IF (MMSE-IF) estimation without requiring a priori information about the IF. Simulation results show that the adaptive window zero-crossing-based IF estimation method is superior to fixed window methods and is also better than adaptive spectrogram and adaptive Wigner-Ville distribution (WVD)-based IF estimators for different signal-to-noise ratio (SNR).

Polymer based photo detectors

Recent developments in our laboratory related to polymer-based light sensors are reviewed. The inherent processibility of the active polymer medium is utilized in the implementation of different designs for the opto-electronic applications. The utility of these devices as sensitive photodetectors, image sensors and position sensitive detectors is demonstrated. The schottky-type layer formation at interfaces of polymers such as polyalkylthiophenes and aluminum accompanied by the enhanced photo-induced charge separation due to high local electric field is tapped for some of these device structures. The sensitivity of polymer-based field effect transistors to light also provides a convenient lateral geometry for efficient optical-coupling and control of the transistor state. ne range of these polymer-detectors available with the option of operating in the diode and transistor modes should be an attractive feature for many potential applications.

A stochastic dynamical system for image segmentation

Image segmentation is formulated as a stochastic process whose invariant distribution is concentrated at points of the desired region. By choosing multiple seed points, different regions can be segmented. The algorithm is based on the theory of time-homogeneous Markov chains and has been largely motivated by the technique of simulated annealing. The method proposed here has been found to perform well on real-world clean as well as noisy images while being computationally far less expensive than stochastic optimisation techniques

Surface electrode switching of a 16-electrode wireless EIT system using RF-based digital data transmission scheme with 8 channel encoder/decoder ICs

A Radio Frequency (RF) based digital data transmission scheme with 8 channel encoder/decoder ICs is proposed for surface electrode switching of a 16-electrode wireless Electrical Impedance Tomography (EIT) system. A RF based wireless digital data transmission module (WDDTM) is developed and the electrode switching of a EIT system is studied by analyzing the boundary data collected and the resistivity images of practical phantoms. An analog multiplexers based electrode switching module (ESM) is developed with analog multiplexers and switched with parallel digital data transmitted by a wireless transmitter/receiver (T-x/R-x) module working with radio frequency technology. Parallel digital bits are generated using NI USB 6251 card working in LabVIEW platform and sent to transmission module to transmit the digital data to the receiver end. The transmitter/receiver module developed is properly interfaced with the personal computer (PC) and practical phantoms through the ESM and USB based DAQ system respectively. It is observed that the digital bits required for multiplexer operation are sequentially generated by the digital output (D/O) ports of the DAQ card. Parallel to serial and serial to parallel conversion of digital data are suitably done by encoder and decoder ICs. Wireless digital data transmission module successfully transmitted and received the parallel data required for switching the current and voltage electrodes wirelessly. 1 mA, 50 kHz sinusoidal constant current is injected at the phantom boundary using common ground current injection protocol and the boundary potentials developed at the voltage electrodes are measured. Resistivity images of the practical phantoms are reconstructed from boundary data using EIDORS. Boundary data and the resistivity images reconstructed from the surface potentials are studied to assess the wireless digital data transmission system. Boundary data profiles of the practical phantom with different configurations show that the multiplexers are operating in the required sequence for common ground current injection protocol. The voltage peaks obtained at the proper positions in the boundary data profiles proved the sequential operation of multiplexers and successful wireless transmission of digital bits. Reconstructed images and their image parameters proved that the boundary data are successfully acquired by the DAQ system which in turn again indicates a sequential and proper operation of multiplexers as well as the successful wireless transmission of digital bits. Hence the developed RF based wireless digital data transmission module (WDDTM) is found suitable for transmitting digital bits required for electrode switching in wireless EIT data acquisition system. (C) 2011 Elsevier Ltd. All rights reserved.

Linearization and Reconstruction of Non-linear Diffuse Optical Tomographic Image

Diffuse optical tomography (DOT) is one of the ways to probe highly scattering media such as tissue using low-energy near infra-red light (NIR) to reconstruct a map of the optical property distribution. The interaction of the photons in biological tissue is a non-linear process and the phton transport through the tissue is modelled using diffusion theory. The inversion problem is often solved through iterative methods based on nonlinear optimization for the minimization of a data-model misfit function. The solution of the non-linear problem can be improved by modeling and optimizing the cost functional. The cost functional is f(x) = x(T)Ax - b(T)x + c and after minimization, the cost functional reduces to Ax = b. The spatial distribution of optical parameter can be obtained by solving the above equation iteratively for x. As the problem is non-linear, ill-posed and ill-conditioned, there will be an error or correction term for x at each iteration. A linearization strategy is proposed for the solution of the nonlinear ill-posed inverse problem by linear combination of system matrix and error in solution. By propagating the error (e) information (obtained from previous iteration) to the minimization function f(x), we can rewrite the minimization function as f(x; e) = (x + e)(T) A(x + e) - b(T)(x + e) + c. The revised cost functional is f(x; e) = f(x) + e(T)Ae. The self guided spatial weighted prior (e(T)Ae) error (e, error in estimating x) information along the principal nodes facilitates a well resolved dominant solution over the region of interest. The local minimization reduces the spreading of inclusion and removes the side lobes, thereby improving the contrast, localization and resolution of reconstructed image which has not been possible with conventional linear and regularization algorithm.

Mesh Simplification Based on Edge Collapsing Could Improve Computational Efficiency in Near Infrared Optical Tomographic Imaging

The diffusion equation-based modeling of near infrared light propagation in tissue is achieved by using finite-element mesh for imaging real-tissue types, such as breast and brain. The finite-element mesh size (number of nodes) dictates the parameter space in the optical tomographic imaging. Most commonly used finite-element meshing algorithms do not provide the flexibility of distinct nodal spacing in different regions of imaging domain to take the sensitivity of the problem into consideration. This study aims to present a computationally efficient mesh simplification method that can be used as a preprocessing step to iterative image reconstruction, where the finite-element mesh is simplified by using an edge collapsing algorithm to reduce the parameter space at regions where the sensitivity of the problem is relatively low. It is shown, using simulations and experimental phantom data for simple meshes/domains, that a significant reduction in parameter space could be achieved without compromising on the reconstructed image quality. The maximum errors observed by using the simplified meshes were less than 0.27% in the forward problem and 5% for inverse problem.

Data-resolution based optimization of the data-collection strategy for near infrared diffuse optical tomography

Purpose: To optimize the data-collection strategy for diffuse optical tomography and to obtain a set of independent measurements among the total measurements using the model based data-resolution matrix characteristics. Methods: The data-resolution matrix is computed based on the sensitivity matrix and the regularization scheme used in the reconstruction procedure by matching the predicted data with the actual one. The diagonal values of data-resolution matrix show the importance of a particular measurement and the magnitude of off-diagonal entries shows the dependence among measurements. Based on the closeness of diagonal value magnitude to off-diagonal entries, the independent measurements choice is made. The reconstruction results obtained using all measurements were compared to the ones obtained using only independent measurements in both numerical and experimental phantom cases. The traditional singular value analysis was also performed to compare the results obtained using the proposed method. Results: The results indicate that choosing only independent measurements based on data-resolution matrix characteristics for the image reconstruction does not compromise the reconstructed image quality significantly, in turn reduces the data-collection time associated with the procedure. When the same number of measurements (equivalent to independent ones) are chosen at random, the reconstruction results were having poor quality with major boundary artifacts. The number of independent measurements obtained using data-resolution matrix analysis is much higher compared to that obtained using the singular value analysis. Conclusions: The data-resolution matrix analysis is able to provide the high level of optimization needed for effective data-collection in diffuse optical imaging. The analysis itself is independent of noise characteristics in the data, resulting in an universal framework to characterize and optimize a given data-collection strategy. (C) 2012 American Association of Physicists in Medicine. http://dx.doi.org/10.1118/1.4736820]

An approach to multi-temporal MODIS image analysis using image classification and segmentation

This paper discusses an approach for river mapping and flood evaluation based on multi-temporal time series analysis of satellite images utilizing pixel spectral information for image classification and region-based segmentation for extracting water-covered regions. Analysis of MODIS satellite images is applied in three stages: before flood, during flood and after flood. Water regions are extracted from the MODIS images using image classification (based on spectral information) and image segmentation (based on spatial information). Multi-temporal MODIS images from ``normal'' (non-flood) and flood time-periods are processed in two steps. In the first step, image classifiers such as Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs) separate the image pixels into water and non-water groups based on their spectral features. The classified image is then segmented using spatial features of the water pixels to remove the misclassified water. From the results obtained, we evaluate the performance of the method and conclude that the use of image classification (SVM and ANN) and region-based image segmentation is an accurate and reliable approach for the extraction of water-covered regions. (c) 2012 COSPAR. Published by Elsevier Ltd. All rights reserved.

Spatial Filter Based Bessel-Like Beam for Improved Penetration Depth Imaging in Fluorescence Microscopy

Monitoring and visualizing specimens at a large penetration depth is a challenge. At depths of hundreds of microns, several physical effects (such as, scattering, PSF distortion and noise) deteriorate the image quality and prohibit a detailed study of key biological phenomena. In this study, we use a Bessel-like beam in-conjugation with an orthogonal detection system to achieve depth imaging. A Bessel-like penetrating diffractionless beam is generated by engineering the back-aperture of the excitation objective. The proposed excitation scheme allows continuous scanning by simply translating the detection PSF. This type of imaging system is beneficial for obtaining depth information from any desired specimen layer, including nano-particle tracking in thick tissue. As demonstrated by imaging the fluorescent polymer-tagged-CaCO3 particles and yeast cells in a tissue-like gel-matrix, the system offers a penetration depth that extends up to 650 mu m. This achievement will advance the field of fluorescence imaging and deep nano-particle tracking.

Fast image reconstruction for fluorescence microscopy

Real-time image reconstruction is essential for improving the temporal resolution of fluorescence microscopy. A number of unavoidable processes such as, optical aberration, noise and scattering degrade image quality, thereby making image reconstruction an ill-posed problem. Maximum likelihood is an attractive technique for data reconstruction especially when the problem is ill-posed. Iterative nature of the maximum likelihood technique eludes real-time imaging. Here we propose and demonstrate a compute unified device architecture (CUDA) based fast computing engine for real-time 3D fluorescence imaging. A maximum performance boost of 210x is reported. Easy availability of powerful computing engines is a boon and may accelerate to realize real-time 3D fluorescence imaging. Copyright 2012 Author(s). This article is distributed under a Creative Commons Attribution 3.0 Unported License. http://dx.doi.org/10.1063/1.4754604]

A phenanthrene based highly selective fluorogenic and visual sensor for Cu2+ ion with nanomolar detection limit and its application in live cell imaging

A new phenanthrene based chemosensor has been synthesized and investigated to act as highly selective fluorescence and visual sensor for Cu2+ ion with very low detection limit of 1.58 nM: this has also been used to image Cu2+ in human cervical HeLa cancer cells. (C) 2012 Elsevier B.V. All rights reserved.