Biological and materials properties of various cholesterol based systems

Liposomes composed of cationic lipids have become very popular gene delivery vehicles. A great deal of research is being pursued to make efficient vectors by varying their molecular architecture. Cholesterol being ubiquitous component in most of the animal cell membranes is increasingly being used as a hydrophobic segment of synthetic cationic lipids. In this review we describe various cholesterol based cationic lipids and focus on the effect of modifying various structural segments like linker and the head group of the cationic lipids on gene transfection efficiency with a special emphasis on the importance of ether linkage between cholesteryl backbone and the polar head group. Interaction of cationic cholesteryl lipids with dipalmitylphosphatidycholine membranes is also discussed here. Apart from cholesterol being an attractive scaffold in the drug/gene delivery vehicles, certain cholesteryl derivatives have also been shown to be attractive room temperature liquid-crystalline materials.

An (O)over-bar(m(2)n) randomized algorithm to compute a minimum cycle basis of a directed graph

We consider the problem of computing a minimum cycle basis in a directed graph G. The input to this problem is a directed graph whose arcs have positive weights. In this problem a {- 1, 0, 1} incidence vector is associated with each cycle and the vector space over Q generated by these vectors is the cycle space of G. A set of cycles is called a cycle basis of G if it forms a basis for its cycle space. A cycle basis where the sum of weights of the cycles is minimum is called a minimum cycle basis of G. The current fastest algorithm for computing a minimum cycle basis in a directed graph with m arcs and n vertices runs in O(m(w+1)n) time (where w < 2.376 is the exponent of matrix multiplication). If one allows randomization, then an (O) over tilde (m(3)n) algorithm is known for this problem. In this paper we present a simple (O) over tilde (m(2)n) randomized algorithm for this problem. The problem of computing a minimum cycle basis in an undirected graph has been well-studied. In this problem a {0, 1} incidence vector is associated with each cycle and the vector space over F-2 generated by these vectors is the cycle space of the graph. The fastest known algorithm for computing a minimum cycle basis in an undirected graph runs in O(m(2)n + mn(2) logn) time and our randomized algorithm for directed graphs almost matches this running time.

A Long Memory Process Based Parametric Modeling and Recognition of PD Signal

We address the problem of recognition and retrieval of relatively weak industrial signal such as Partial Discharges (PD) buried in excessive noise. The major bottleneck being the recognition and suppression of stochastic pulsive interference (PI) which has similar time-frequency characteristics as PD pulse. Therefore conventional frequency based DSP techniques are not useful in retrieving PD pulses. We employ statistical signal modeling based on combination of long-memory process and probabilistic principal component analysis (PPCA). An parametric analysis of the signal is exercised for extracting the features of desired pules. We incorporate a wavelet based bootstrap method for obtaining the noise training vectors from observed data. The procedure adopted in this work is completely different from the research work reported in the literature, which is generally based on deserved signal frequency and noise frequency.

Dynamical behavior in the nonlinear rheology of surfactant solutions

Several surfactant molecules self-assemble in solution to form long, flexible wormlike micelles which get entangled with each other, leading to viscoelastic gel phases. We discuss our recent work on the rheology of such a gel formed in the dilute aqueous solutions of a surfactant CTAT. In the linear rheology regime, the storage modulus G′(ω) and loss modulus G″(ω) have been measured over a wide frequency range. In the nonlinear regime, the shear stress σ shows a plateau as a function of the shear rate math above a certain cutoff shear rate mathc. Under controlled shear rate conditions in the plateau regime, the shear stress and the first normal stress difference show oscillatory time-dependence. The analysis of the measured time series of shear stress and normal stress has been done using several methods incorporating state space reconstruction by embedding of time delay vectors. The analysis shows the existence of a finite correlation dimension and a positive Lyapunov exponent, unambiguously implying that the dynamics of the observed mechanical instability can be described by that of a dynamical system with a strange attractor of dimension varying from 2.4 to 2.9.

Novel algorithm for online voltage stability assessment based on feed forward neural network

This paper presents an artificial feed forward neural network (FFNN) approach for the assessment of power system voltage stability. A novel approach based on the input-output relation between real and reactive power, as well as voltage vectors for generators and load buses is used to train the neural net (NN). The input properties of the feed forward network are generated from offline training data with various simulated loading conditions using a conventional voltage stability algorithm based on the L-index. The neural network is trained for the L-index output as the target vector for each of the system loads. Two separate trained NN, corresponding to normal loading and contingency, are investigated on the 367 node practical power system network. The performance of the trained artificial neural network (ANN) is also investigated on the system under various voltage stability assessment conditions. As compared to the computationally intensive benchmark conventional software, near accurate results in the value of L-index and thus the voltage profile were obtained. Proposed algorithm is fast, robust and accurate and can be used online for predicting the L-indices of all the power system buses. The proposed ANN approach is also shown to be effective and computationally feasible in voltage stability assessment as well as potential enhancements within an overall energy management system in order to determining local and global stability indices

Ultrasonic performance of the PVDF thin film sensors under thermal fatigue

In the present work, the ultrasonic strain sensing performance of the large area PVDF thin film subjected to the thermal fatigue is studied. The PVDF thin film is prepared using hot press and the piezoelectric phase (beta-phase) has been achieved by thermo-mechanical treatment and poling under DC field. The sensors used in aircrafts for structural health monitoring applications are likely to be subjected to a wide range of temperature fluctuations which may create thermal fatigue in both aircraft structures and in the sensors. Thus, the sensitivity of the PVDF sensors for thermal fatigue needs to be studied for its effective implementation in the structural health monitoring applications. In present work, the fabricated films have been subjected to certain number of thermal cycles which serve as thermal fatigue and are further tested for ultrasonic strain sensitivity at various different frequencies. The PVDF sensor is bonded on the beam specimen at one end and the ultrasonic guided waves are launched with a piezoelectric wafer bonded on another end of the beam. Sensitivity of PVDF sensor in terms of voltage is obtained for increasing number of thermal cycles. Sensitivity variation is studied at various different extent of thermal fatigue. The variation of the sensor sensitivity with frequency due to thermal fatigue at different temperatures is also investigated. The present investigation shows an appropriate temperature range for the application of the PVDF sensors in structural health monitoring.

Multilevel dodecagonal space-vector generation for induction motor drives by cascading three-level and two-level inverters

This study proposes an inverter circuit topology capable of generating multilevel dodecagonal (12-sided polygon) voltage space vectors by the cascaded connection of two-level and three-level inverters. By the proper selection of DC-link voltages and resultant switching states for the inverters, voltage space vectors whose tips lie on three concentric dodecagons, are obtained. A rectifier circuit for the inverter is also proposed, which significantly improves the power factor. The topology offers advantages such as the complete elimination of the fifth and seventh harmonics in phase voltages and an extension of the linear modulation range. In this study, a simple method for the calculation of pulse width modulation timing was presented along with extensive simulation and experimental results in order to validate the proposed concept.

Fast Likelihood Computation in Speech Recognition using Matrices

Acoustic modeling using mixtures of multivariate Gaussians is the prevalent approach for many speech processing problems. Computing likelihoods against a large set of Gaussians is required as a part of many speech processing systems and it is the computationally dominant phase for Large Vocabulary Continuous Speech Recognition (LVCSR) systems. We express the likelihood computation as a multiplication of matrices representing augmented feature vectors and Gaussian parameters. The computational gain of this approach over traditional methods is by exploiting the structure of these matrices and efficient implementation of their multiplication. In particular, we explore direct low-rank approximation of the Gaussian parameter matrix and indirect derivation of low-rank factors of the Gaussian parameter matrix by optimum approximation of the likelihood matrix. We show that both the methods lead to similar speedups but the latter leads to far lesser impact on the recognition accuracy. Experiments on 1,138 work vocabulary RM1 task and 6,224 word vocabulary TIMIT task using Sphinx 3.7 system show that, for a typical case the matrix multiplication based approach leads to overall speedup of 46 % on RM1 task and 115 % for TIMIT task. Our low-rank approximation methods provide a way for trading off recognition accuracy for a further increase in computational performance extending overall speedups up to 61 % for RM1 and 119 % for TIMIT for an increase of word error rate (WER) from 3.2 to 3.5 % for RM1 and for no increase in WER for TIMIT. We also express pairwise Euclidean distance computation phase in Dynamic Time Warping (DTW) in terms of matrix multiplication leading to saving of approximately of computational operations. In our experiments using efficient implementation of matrix multiplication, this leads to a speedup of 5.6 in computing the pairwise Euclidean distances and overall speedup up to 3.25 for DTW.

Multilevel Dodecagonal Voltage Space Vector Generation using Flying Capacitor topology for Induction motor drives

In this paper, a multilevel flying capacitor inverter topology suitable for generating multilevel dodecagonal space vectors for an induction motor drive, is proposed. Because of the dodecagonal space vectors, it has increased modulation range with the absence of all 6n +/- 1, (n=odd) harmonics in the phase voltage and currents. The topology, realized by flying capacitor three level inverters feeding an open-end winding induction motor, does not suffer the neutral point voltage imbalance issues seen in NPC inverters and the capacitors have inherent charge-balancing capability with PWM control using switching state redundancies. Furthermore, the proposed technique uses lesser number of power supplies compared to cascaded H-bridge or NPC based dodecagonal schemes and has better ride-through capability. Finally, the voltage control is obtained through a simple carrier-based space vector PWM scheme implemented on a DSP.

Nearly constant switching frequency hysteresis current controller for general n-level inverter fed induction motor drive

A current-error space phasor based hysteresis controller with nearly constant switching frequency is proposed for a general n-level voltage source inverter fed three-phase induction motor drive. Like voltage-controlled space vector PWM (SVPWM), the proposed controller can precisely detect sub-sector changes and for switching it selects only the nearest switching voltage vectors using the information of the estimated fundamental stator voltages along α and β axes. It provides smooth transition between voltage levels, including operation in over modulation region. Due to adjacent switching amongst the nearest switching vectors forming a triangular sub-sector, in which tip of the fundamental stator voltage vector of the machine lies, switching loss is reduced while keeping the current-error space phasor within the varying parabolic boundary. Appropriate dimension and orientation of this parabolic boundary ensures similar switching frequency spectrum like constant switching frequency SVPWM-based induction motor (IM) drive. Inherent advantages of multi-level inverter and space phasor based current hysteresis controller are retained. The proposed controller is simulated as well as implemented on a 5-level inverter fed 7.5 kW open-end winding IM drive.

A Space-Vector-Based Hysteresis Current Controller for a General n-Level Inverter-Fed Drive With Nearly Constant Switching Frequency Control

A current-error space-vector-based hysteresis current controller for a general n-level voltage-source inverter (VSI)-fed three-phase induction motor (IM) drive is proposed here, with control of the switching frequency variation for the full linear modulation range. The proposed current controller monitors the space-vector-based current error of an n-level VSI-fed IM to keep the current error within a parabolic boundary, using the information of the current triangular sector in which the tip of the reference vector lies. Information of the reference voltage vector is estimated using the measured current-error space vectors, along the alpha- and beta-axes. Appropriate dimension and orientation of this parabolic boundary ensure a switching frequency spectrum similar to that of a constant-switching-frequency voltage-controlled space vector pulsewidth modulation (PWM) (SVPWM)-based IM drive. Like SVPWM for multilevel inverters, the proposed controller selects inverter switching vectors, forming a triangular sector in which the tip of the reference vector stays, for the hysteresis PWM control. The sector in the n-level inverter space vector diagram, in which the tip of the fundamental stator voltage stays, is precisely detected, using the sampled reference space vector estimated from the instantaneous current-error space vectors. The proposed controller retains all the advantages of a conventional hysteresis controller such as fast current control, with smooth transition to the overmodulation region. The proposed controller is implemented on a five-level VSI-fed 7.5-kW IM drive.

Cramer-Rao-Type Bounds for Sparse Bayesian Learning

In this paper, we derive Hybrid, Bayesian and Marginalized Cramer-Rao lower bounds (HCRB, BCRB and MCRB) for the single and multiple measurement vector Sparse Bayesian Learning (SBL) problem of estimating compressible vectors and their prior distribution parameters. We assume the unknown vector to be drawn from a compressible Student-prior distribution. We derive CRBs that encompass the deterministic or random nature of the unknown parameters of the prior distribution and the regression noise variance. We extend the MCRB to the case where the compressible vector is distributed according to a general compressible prior distribution, of which the generalized Pareto distribution is a special case. We use the derived bounds to uncover the relationship between the compressibility and Mean Square Error (MSE) in the estimates. Further, we illustrate the tightness and utility of the bounds through simulations, by comparing them with the MSE performance of two popular SBL-based estimators. We find that the MCRB is generally the tightest among the bounds derived and that the MSE performance of the Expectation-Maximization (EM) algorithm coincides with the MCRB for the compressible vector. We also illustrate the dependence of the MSE performance of SBL based estimators on the compressibility of the vector for several values of the number of observations and at different signal powers.

Fast acoustic likelihood computation using low-rank matrix approximation

Acoustic modeling using mixtures of multivariate Gaussians is the prevalent approach for many speech processing problems. Computing likelihoods against a large set of Gaussians is required as a part of many speech processing systems and it is the computationally dominant phase for LVCSR systems. We express the likelihood computation as a multiplication of matrices representing augmented feature vectors and Gaussian parameters. The computational gain of this approach over traditional methods is by exploiting the structure of these matrices and efficient implementation of their multiplication.In particular, we explore direct low-rank approximation of the Gaussian parameter matrix and indirect derivation of low-rank factors of the Gaussian parameter matrix by optimum approximation of the likelihood matrix. We show that both the methods lead to similar speedups but the latter leads to far lesser impact on the recognition accuracy. Experiments on a 1138 word vocabulary RM1 task using Sphinx 3.7 system show that, for a typical case the matrix multiplication approach leads to overall speedup of 46%. Both the low-rank approximation methods increase the speedup to around 60%, with the former method increasing the word error rate (WER) from 3.2% to 6.6%, while the latter increases the WER from 3.2% to 3.5%.

Near-optimal large-MIMO detection using randomized MCMC and randomized search algorithms

Low-complexity near-optimal detection of signals in MIMO systems with large number (tens) of antennas is getting increased attention. In this paper, first, we propose a variant of Markov chain Monte Carlo (MCMC) algorithm which i) alleviates the stalling problem encountered in conventional MCMC algorithm at high SNRs, and ii) achieves near-optimal performance for large number of antennas (e.g., 16×16, 32×32, 64×64 MIMO) with 4-QAM. We call this proposed algorithm as randomized MCMC (R-MCMC) algorithm. Second, we propose an other algorithm based on a random selection approach to choose candidate vectors to be tested in a local neighborhood search. This algorithm, which we call as randomized search (RS) algorithm, also achieves near-optimal performance for large number of antennas with 4-QAM. The complexities of the proposed R-MCMC and RS algorithms are quadratic/sub-quadratic in number of transmit antennas, which are attractive for detection in large-MIMO systems. We also propose message passing aided R-MCMC and RS algorithms, which are shown to perform well for higher-order QAM.

Coupling of photomechanical and electromechanical actuations in carbon nanotubes

It is known that carbon nanotubes (CNTs) possess multifunctional characteristics, which are applicable for a wide variety of engineering applications. CNT is also recognized as a radiation sensitive material, for example for detecting infrared (IR) radiations. One of the direct implications of exposing CNTs to radiation is the photomechanical actuation and generation of a photovoltage/photocurrent. The present work focuses on coupling electromechanical and photomechanical characteristics to enhance the resulting induced-strain response in CNTs. We have demonstrated that after applying an electric field the induced strain in CNT sheet is enhanced to about similar to 2.18 times for the maximum applied electric field at 2 V as compared to the photo-actuation response alone. This enhancement of the strain at higher bias voltages (> 1 V) can be considered as a sum of individual contributions of the bias voltage and IR stimulus. However, at lower voltage (< 1 V) the enhancement in the resulting strain has been attributed to the associated electrostatic effects when CNTs are stimulated with IR radiation under external bias conditions. This report reveals that voltage bias or IR stimulus alone could not produce the observed strain in the CNT sheet under lower bias conditions.