pH Induced switching in hydrogel coated fiber bragg grating sensor

In this paper we report a novel hydrogel functionalized optical Fiber Bragg Grating (FBG) sensor based on chemo-mechanical-optical sensing, and demonstrate its specific application in pH activated process monitoring. The sensing mechanism is based on the stress due to ion diffusion and polymer phase transition which produce strain in the FBG. This results in shift in the Bragg wavelength which is detected by an interrogator system. A simple dip coating method to coat a thin layer of hydrogel on the FBG has been established. The gel consists of sodium alginate and calcium chloride. Gel formation is observed in real-time by continuously monitoring the Bragg wavelength shift. We have demonstrated pH sensing in the range of pH of 2 to 10. Another interesting phenomenon is observed by swelling and deswelling of FBG functionalized with hydrogel by a sequence of alternate dipping between acidic and base solutions. It is observed that the Bragg wavelength undergoes reversible and repeatable pH dependent switching.

Charge Injection through Nanocomposite Electrode in Microfluidic Channel for Electrical Lysis of Biological Cells

Several concepts have been developed in the recent years for nanomaterial based integrated MEMS platform in order to accelerate the process of biological sample preparation followed by selective screening and identification of target molecules. In this context, there exist several challenges which need to be addressed in the process of electrical lysis of biological cells. These are due to (i) low resource settings while achieving maximal lysis (ii) high throughput of target molecules to be detected (iii) automated extraction and purification of relevant molecules such as DNA and protein from extremely small volume of sample (iv) requirement of fast, accurate and yet scalable methods (v) multifunctionality toward process monitoring and (vi) downward compatibility with already existing diagnostic protocols. This paper reports on the optimization of electrical lysis process based on various different nanocomposite coated electrodes placed in a microfluidic channel. The nanocomposites are synthesized using different nanomaterials like Zinc nanorod dispersion in polymer. The efficiency of electrical lysis with various different electrode coatings has been experimentally verified in terms of DNA concentration, amplification and protein yield. The influence of the coating thickness on the injection current densities has been analyzed. We further correlate experimentally the current density vs. voltage relationship with the extent of bacterial cell lysis. A coupled multiphysics based simulation model is used to predict the cell trajectories and lysis efficiencies under various electrode boundary conditions as estimated from experimental results. Detailed in-situ fluorescence imaging and spectroscopy studies are performed to validate various hypotheses.

Enhancing reproducibility of ultrasonic measurements by new users

Perception of operator influences ultrasound image acquisition and processing. Lower costs are attracting new users to medical ultrasound. Anticipating an increase in this trend, we conducted a study to quantify the variability in ultrasonic measurements made by novice users and identify methods to reduce it. We designed a protocol with four presets and trained four new users to scan and manually measure the head circumference of a fetal phantom with an ultrasound scanner. In the first phase, the users followed this protocol in seven distinct sessions. They then received feedback on the quality of the scans from an expert. In the second phase, two of the users repeated the entire protocol aided by visual cues provided to them during scanning. We performed off-line measurements on all the images using a fully automated algorithm capable of measuring the head circumference from fetal phantom images. The ground truth (198.1 +/- 1.6 mm) was based on sixteen scans and measurements made by an expert. Our analysis shows that: (1) the inter-observer variability of manual measurements was 5.5 mm, whereas the inter-observer variability of automated measurements was only 0.6 mm in the first phase (2) consistency of image appearance improved and mean manual measurements was 4-5 mm closer to the ground truth in the second phase (3) automated measurements were more precise, accurate and less sensitive to different presets compared to manual measurements in both phases. Our results show that visual aids and automation can bring more reproducibility to ultrasonic measurements made by new users.

MECHANISM OF CELL LYSIS IN MICROFLUIDIC CHANNEL WITH INTEGRATED NANOCOMPOSITE ELECTRODES

This paper reports on the characterization of an integrated micro-fluidic platform for controlled electrical lysis of biological cells and subsequent extraction of intracellular biomolecules. The proposed methodology is capable of high throughput electrical cell lysis facilitated by nano-composite coated electrodes. The nano-composites are synthesized using Carbon Nanotube and ZnO nanorod dispersion in polymer. Bacterial cells are used to demonstrate the lysis performance of these nanocomposite electrodes. Investigation of electrical lysis in the microchannel is carried out under different parameters, one with continuous DC application and the other under DC biased AC electric field. Lysis in DC field is dependent on optimal field strength and governed by the cell type. By introducing the AC electrical field, the electrokinetics is controlled to prevent cell clogging in the micro-channel and ensure uniform cell dispersion and lysis. Lysis mechanism is analyzed with time-resolved fluorescence imaging which reveal the time scale of electrical lysis and explain the dynamic behavior of GFP-expressing E. coli cells under the electric field induced by nanocomposite electrodes. The DNA and protein samples extracted after lysis are compared with those obtained from a conventional chemical lysis method by using a UV-Visible spectroscopy and fluorimetry. The paper also focuses on the mechanistic understanding of the nano-composite coating material and the film thickness on the leakage charge densities which lead to differential lysis efficiency.

MODELING AND SIMULATION OF HYDRODYNAMIC INTERACTION OF DNA IN A MICRO-FLUIDIC CHANNEL

The motion of DNA (in the bulk solution) and the non-Newtonian effective fluid behavior are considered separately and self-consistently with the fluid motion satisfying the no-slip boundary condition on the surface of the confining geometry in the presence of channel pressure gradients. A different approach has been developed to model DNA in the micro-channel. In this study the DNA is assumed as an elastic chain with its characteristic Young's modulus, Poisson's ratio and density. The force which results from the fluid dynamic pressure, viscous forces and electromotive forces is applied to the elastic chain in a coupled manner. The velocity fields in the micro-channel are influenced by the transport properties. Simulations are carried out for the DNAs attached to the micro-fluidic wall. Numerical solutions based on a coupled multiphysics finite element scheme are presented. The modeling scheme is derived based on mass conservation including biomolecular mass, momentum balance including stress due to Coulomb force field and DNA-fluid interaction, and charge transport associated to DNA and other ionic complexes in the fluid. Variation in the velocity field for the non-Newtonian flow and the deformation of the DNA strand which results from the fluid-structure interaction are first studied considering a single DNA strand. Motion of the effective center of mass is analyzed considering various straight and coil geometries. Effects of DNA statistical parameters (geometry and spatial distribution of DNAs along the channel) on the effective flow behavior are analyzed. In particular, the dynamics of different DNA physical properties such as radius of gyration, end-to-end length etc. which are obtained from various different models (Kratky-Porod, Gaussian bead-spring etc.) are correlated to the nature of interaction and physical properties under the same background fluid environment.

TCP: thread contention predictor for parallel programs

With proliferation of chip multicores (CMPs) on desktops and embedded platforms, multi-threaded programs have become ubiquitous. Existence of multiple threads may cause resource contention, such as, in on-chip shared cache and interconnects, depending upon how they access resources. Hence, we propose a tool - Thread Contention Predictor (TCP) to help quantify the number of threads sharing data and their sharing pattern. We demonstrate its use to predict a more profitable shared, last level on-chip cache (LLC) access policy on CMPs. Our cache configuration predictor is 2.2 times faster compared to the cycle-accurate simulations. We also demonstrate its use for identifying hot data structures in a program which may cause performance degradation due to false data sharing. We fix layout of such data structures and show up-to 10% and 18% improvement in execution time and energy-delay product (EDP), respectively.

Identifying services from Legacy batch applications

Transaction processing is a key constituent of the IT workload of commercial enterprises (e.g., banks, insurance companies). Even today, in many large enterprises, transaction processing is done by legacy "batch" applications, which run offline and process accumulated transactions. Developers acknowledge the presence of multiple loosely coupled pieces of functionality within individual applications. Identifying such pieces of functionality (which we call "services") is desirable for the maintenance and evolution of these legacy applications. This is a hard problem, which enterprises grapple with, and one without satisfactory automated solutions. In this paper, we propose a novel static-analysis-based solution to the problem of identifying services within transaction-processing programs. We provide a formal characterization of services in terms of control-flow and data-flow properties, which is well-suited to the idioms commonly exhibited by business applications. Our technique combines program slicing with the detection of conditional code regions to identify services in accordance with our characterization. A preliminary evaluation, based on a manual analysis of three real business programs, indicates that our approach can be effective in identifying useful services from batch applications.

Packet flow analysis in IP networks using data-flow analysis

Static analysis (aka offline analysis) of a model of an IP network is useful for understanding, debugging, and verifying packet flow properties of the network. Data-flow analysis is a method that has typically been applied to static analysis of programs. We propose a new, data-flow based approach for static analysis of packet flows in networks. We also investigate an application of our analysis to the problem of inferring a high-level policy from the network, which has been addressed in the past only for a single router.

NESP: Nonlinear enhancement and selection of plane for optimal segmentation and recognition of scene word images

In this paper, we report a breakthrough result on the difficult task of segmentation and recognition of coloured text from the word image dataset of ICDAR robust reading competition challenge 2: reading text in scene images. We split the word image into individual colour, gray and lightness planes and enhance the contrast of each of these planes independently by a power-law transform. The discrimination factor of each plane is computed as the maximum between-class variance used in Otsu thresholding. The plane that has maximum discrimination factor is selected for segmentation. The trial version of Omnipage OCR is then used on the binarized words for recognition. Our recognition results on ICDAR 2011 and ICDAR 2003 word datasets are compared with those reported in the literature. As baseline, the images binarized by simple global and local thresholding techniques were also recognized. The word recognition rate obtained by our non-linear enhancement and selection of plance method is 72.8% and 66.2% for ICDAR 2011 and 2003 word datasets, respectively. We have created ground-truth for each image at the pixel level to benchmark these datasets using a toolkit developed by us. The recognition rate of benchmarked images is 86.7% and 83.9% for ICDAR 2011 and 2003 datasets, respectively.

Evaluation of document binarization using eigen value decomposition

A necessary step for the recognition of scanned documents is binarization, which is essentially the segmentation of the document. In order to binarize a scanned document, we can find several algorithms in the literature. What is the best binarization result for a given document image? To answer this question, a user needs to check different binarization algorithms for suitability, since different algorithms may work better for different type of documents. Manually choosing the best from a set of binarized documents is time consuming. To automate the selection of the best segmented document, either we need to use ground-truth of the document or propose an evaluation metric. If ground-truth is available, then precision and recall can be used to choose the best binarized document. What is the case, when ground-truth is not available? Can we come up with a metric which evaluates these binarized documents? Hence, we propose a metric to evaluate binarized document images using eigen value decomposition. We have evaluated this measure on DIBCO and H-DIBCO datasets. The proposed method chooses the best binarized document that is close to the ground-truth of the document.

Burst error correction using partial fourier matrices and block sparse representation

There is a strong relation between sparse signal recovery and error control coding. It is known that burst errors are block sparse in nature. So, here we attempt to solve burst error correction problem using block sparse signal recovery methods. We construct partial Fourier based encoding and decoding matrices using results on difference sets. These constructions offer guaranteed and efficient error correction when used in conjunction with reconstruction algorithms which exploit block sparsity.

Joint estimation of synchronization impairments in MIMO-OFDM system

A joint Maximum Likelihood (ML) estimation algorithm for the synchronization impairments such as Carrier Frequency Offset (CFO), Sampling Frequency Offset (SFO) and Symbol Timing Error (STE) in single user MIMO-OFDM system is investigated in this work. A received signal model that takes into account the nonlinear effects of CFO, SFO, STE and Channel Impulse Response (CIR) is formulated. Based on the signal model, a joint ML estimation algorithm is proposed. Cramer-Rao Lower Bound (CRLB) for the continuous parameters CFO and SFO is derived for the cases of with and without channel response effects and is used to compare the effect of coupling between different estimated parameters. The performance of the estimation method is studied through numerical simulations.

Adaptive selection of search space in look ahead orthogonal matching pursuit

Compressive Sensing theory combines the signal sampling and compression for sparse signals resulting in reduction in sampling rate and computational complexity of the measurement system. In recent years, many recovery algorithms were proposed to reconstruct the signal efficiently. Look Ahead OMP (LAOMP) is a recently proposed method which uses a look ahead strategy and performs significantly better than other greedy methods. In this paper, we propose a modification to the LAOMP algorithm to choose the look ahead parameter L adaptively, thus reducing the complexity of the algorithm, without compromising on the performance. The performance of the algorithm is evaluated through Monte Carlo simulations.

Projection-based atom selection in orthogonal matching pursuit for compressive sensing

For compressive sensing, we endeavor to improve the atom selection strategy of the existing orthogonal matching pursuit (OMP) algorithm. To achieve a better estimate of the underlying support set progressively through iterations, we use a least squares solution based atom selection method. From a set of promising atoms, the choice of an atom is performed through a new method that uses orthogonal projection along-with a standard matched filter. Through experimental evaluations, the effect of projection based atom selection strategy is shown to provide a significant improvement for the support set recovery performance, in turn, the compressive sensing recovery.

Diversity order vs rate in an AWGN channel

We study the diversity order vs rate of an additive white Gaussian noise (AWGN) channel in the whole capacity region. We show that for discrete input as well as for continuous input, Gallager's upper bounds on error probability have exponential diversity in low and high rate region but only subexponential in the mid-rate region. For the best available lower bounds and for the practical codes one observes exponential diversity throughout the capacity region. However we also show that performance of practical codes is close to Gallager's upper bounds and the mid-rate subexponential diversity has a bearing on the performance of the practical codes. Finally we show that the upper bounds with Gaussian input provide good approximation throughout the capacity region even for finite constellation.