Ignition delay of 3-carene: single pulse shock tube study

Ignition delay experiments of 3-carene, a biofuel, have been carried out in a single-pulse shock tube for three equivalence ratios, 0.5, 1 and 2. The temperature was varied from 1140 to 1606 K. In the above-mentioned conditions, ignition delay was found to vary from 1.180 ms to 144 mu s. The ignition delay values of 3-carene were found to be lower than those of JP-10, a kerosene-based fuel being considered for hypersonic applications.

Pulse transit time differential measurement by fiber Bragg grating pulse recorder

The present study reports a noninvasive technique for the measurement of the pulse transit time differential (PTTD) from the pulse pressure waveforms obtained at the carotid artery and radial artery using fiber Bragg grating pulse recorders (FBGPR). PTTD is defined as the time difference between the arrivals of a pulse pressure waveform at the carotid and radial arterial sites. The PTTD is investigated as an indicator of variation in the systolic blood pressure. The results are validated against blood pressure variation obtained from a Mindray Patient Monitor. Furthermore, the pulse wave velocity computed from the obtained PTTD is compared with the pulse wave velocity obtained from the color Doppler ultrasound system and is found to be in good agreement. The major advantage of the PTTD measurement via FBGPRs is that the data acquisition system employed can simultaneously acquire pulse pressure waveforms from both FBGPRs placed at carotid and radial arterial sites with a single time scale, which eliminates time synchronization complexity. (C) 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)

Inner Bound on the GDOF of the K-User MIMO Gaussian Symmetric Interference Channel

The K-user multiple input multiple output (MIMO) Gaussian symmetric interference channel where each transmitter has M antennas and each receiver has N antennas is studied from a generalized degrees of freedom (GDOF) perspective. An inner bound on the GDOF is derived using a combination of techniques such as treating interference as noise, zero forcing (ZF) at the receivers, interference alignment (IA), and extending the Han-Kobayashi (HK) scheme to K users, as a function of the number of antennas and the log INR/log SNR level. Several interesting conclusions are drawn from the derived bounds. It is shown that when K > N/M + 1, a combination of the HK and IA schemes performs the best among the schemes considered. When N/M < K <= N/M + 1, the HK-scheme outperforms other schemes and is found to be GDOF optimal in many cases. In addition, when the SNR and INR are at the same level, ZF-receiving and the HK-scheme have the same GDOF performance.

Deformability Measurement of Single-Cells at High-Throughput With Imaging Flow Cytometry

Disease conditions like malaria, sickle cell anemia, diabetes mellitus, cancer, etc., are known to significantly alter the deformability of certain types of cells (red blood cells, white blood cells, circulating tumor cells, etc.). To determine the cellular deformability, techniques like micropipette aspiration, atomic force microscopy, optical tweezers, quantitative phase imaging have been developed. Many of these techniques have an advantage of determining the single cell deformability with ultrahigh precision. However, the suitability of these techniques for the realization of a deformability based diagnostic tool is questionable as they are expensive and extremely slow to operate on a huge population of cells. In this paper, we propose a technique for high-throughput (800 cells/s) determination of cellular deformability on a single cell basis. This technique involves capturing the image(s) of cells in flow that have undergone deformation under the influence of shear gradient generated by the fluid flowing through the microfluidic channels. Deformability indices of these cells can be computed by performing morphological operations on these images. We demonstrate the applicability of this technique for examining the deformability index on healthy, diabetic, and sphered red blood cells. We believe that this technique has a strong role to play in the realization of a potential tool that uses deformability as one of the important criteria in disease diagnosis.

Biodiesel Exhaust Treatment with Dielectric Barrier Discharges Coupled with Industrial Waste Byproducts

Biodiesel run engines are gaining popularity since the last few years as a viable alternative to conventional petro-diesel based engines. In biodiesel exhaust the content of volatile organic compounds, oil mist, and mass of particulate matter is considerably lower. However, the concentration of oxides of nitrogen (NOx) is relatively higher. In this paper the biodiesel exhaust from a stationary engine is treated under controlled laboratory conditions for removal of NOx using dielectric barrier discharge plasma in cascade with adsorbents prepared from abundantly available industrial waste byproducts like red mud and copper slag. Results were compared with gamma-alumina, a commercial adsorbent. Two different dielectric barrier discharge (DBD) reactors were tested for their effectiveness under Repetitive pulses /AC energization. NOx removal as high as 80% was achieved with pulse energized reactors when cascaded with red mud as adsorbent.

Study of Wrist Pulse Signals Using a Bi-Modal Gaussian Model

Wrist pulse signals contain important information about the health of a person and hence diagnosis based on pulse signals has assumed great importance. In this paper we demonstrate the efficacy of a two term Gaussian model to extract information from pulse signals. Results have been obtained by conducting experiments on several subjects to record wrist pulse signals for the cases of before exercise and after exercise. Parameters have been extracted from the recorded signals using the model and a paired t-test is performed, which shows that the parameters are significantly different between the two groups. Further, a recursive cluster elimination based support vector machine is used to perform classification between the groups. An average classification accuracy of 99.46% is obtained, along with top classifiers. It is thus shown that the parameters of the Gaussian model show changes across groups and hence the model is effective in distinguishing the changes taking place due to the two different recording conditions. The study has potential applications in healthcare.

Evaluation of Precipitation Retrievals From Orbital Data Products of TRMM Over a Subtropical Basin in India

The spatial error structure of daily precipitation derived from the latest version 7 (v7) tropical rainfall measuring mission (TRMM) level 2 data products are studied through comparison with the Asian precipitation highly resolved observational data integration toward evaluation of the water resources (APHRODITE) data over a subtropical region of the Indian subcontinent for the seasonal rainfall over 6 years from June 2002 to September 2007. The data products examined include v7 data from the TRMM radiometer Microwave Imager (TMI) and radar precipitation radar (PR), namely, 2A12, 2A25, and 2B31 (combined data from PR and TMI). The spatial distribution of uncertainty from these data products were quantified based on performance metrics derived from the contingency table. For the seasonal daily precipitation over a subtropical basin in India, the data product of 2A12 showed greater skill in detecting and quantifying the volume of rainfall when compared with the 2A25 and 2B31 data products. Error characterization using various error models revealed that random errors from multiplicative error models were homoscedastic and that they better represented rainfall estimates from 2A12 algorithm. Error decomposition techniques performed to disentangle systematic and random errors verify that the multiplicative error model representing rainfall from 2A12 algorithm successfully estimated a greater percentage of systematic error than 2A25 or 2B31 algorithms. Results verify that although the radiometer derived 2A12 rainfall data is known to suffer from many sources of uncertainties, spatial analysis over the case study region of India testifies that the 2A12 rainfall estimates are in a very good agreement with the reference estimates for the data period considered.

Joint Channel Estimation and Data Detection in MIMO-OFDM Systems: A Sparse Bayesian Learning Approach

The impulse response of wireless channels between the N-t transmit and N-r receive antennas of a MIMO-OFDM system are group approximately sparse (ga-sparse), i.e., NtNt the channels have a small number of significant paths relative to the channel delay spread and the time-lags of the significant paths between transmit and receive antenna pairs coincide. Often, wireless channels are also group approximately cluster-sparse (gac-sparse), i.e., every ga-sparse channel consists of clusters, where a few clusters have all strong components while most clusters have all weak components. In this paper, we cast the problem of estimating the ga-sparse and gac-sparse block-fading and time-varying channels in the sparse Bayesian learning (SBL) framework and propose a bouquet of novel algorithms for pilot-based channel estimation, and joint channel estimation and data detection, in MIMO-OFDM systems. The proposed algorithms are capable of estimating the sparse wireless channels even when the measurement matrix is only partially known. Further, we employ a first-order autoregressive modeling of the temporal variation of the ga-sparse and gac-sparse channels and propose a recursive Kalman filtering and smoothing (KFS) technique for joint channel estimation, tracking, and data detection. We also propose novel, parallel-implementation based, low-complexity techniques for estimating gac-sparse channels. Monte Carlo simulations illustrate the benefit of exploiting the gac-sparse structure in the wireless channel in terms of the mean square error (MSE) and coded bit error rate (BER) performance.

Generalized Partial Response Equalization and Data-Dependent Noise Predictive Signal Detection Over Media Models for TDMR

Two-dimensional magnetic recording (2-D TDMR) is an emerging technology that aims to achieve areal densities as high as 10 Tb/in(2) using sophisticated 2-D signal-processing algorithms. High areal densities are achieved by reducing the size of a bit to the order of the size of magnetic grains, resulting in severe 2-D intersymbol interference (ISI). Jitter noise due to irregular grain positions on the magnetic medium is more pronounced at these areal densities. Therefore, a viable read-channel architecture for TDMR requires 2-D signal-detection algorithms that can mitigate 2-D ISI and combat noise comprising jitter and electronic components. Partial response maximum likelihood (PRML) detection scheme allows controlled ISI as seen by the detector. With the controlled and reduced span of 2-D ISI, the PRML scheme overcomes practical difficulties such as Nyquist rate signaling required for full response 2-D equalization. As in the case of 1-D magnetic recording, jitter noise can be handled using a data-dependent noise-prediction (DDNP) filter bank within a 2-D signal-detection engine. The contributions of this paper are threefold: 1) we empirically study the jitter noise characteristics in TDMR as a function of grain density using a Voronoi-based granular media model; 2) we develop a 2-D DDNP algorithm to handle the media noise seen in TDMR; and 3) we also develop techniques to design 2-D separable and nonseparable targets for generalized partial response equalization for TDMR. This can be used along with a 2-D signal-detection algorithm. The DDNP algorithm is observed to give a 2.5 dB gain in SNR over uncoded data compared with the noise predictive maximum likelihood detection for the same choice of channel model parameters to achieve a channel bit density of 1.3 Tb/in(2) with media grain center-to-center distance of 10 nm. The DDNP algorithm is observed to give similar to 10% gain in areal density near 5 grains/bit. The proposed signal-processing framework can broadly scale to various TDMR realizations and areal density points.

An Open-End winding IM drive with Multilevel 12-sided Polygonal Vectors with Symmetric Triangles

Multilevel inverters with hexagonal voltage space vector structures have improved performance of induction motor drives compared to that of the two level inverters. Further reduction in the torque ripple on the motor shaft is possible by using multilevel dodecagonal (12-sided polygon) voltage space vector structures. The advantages of dodecagonal voltage space vector based PWM techniques are the complete elimination of fifth and seventh harmonics in phase voltages for the full modulation range and the extension of linear modulation range. This paper proposes an inverter circuit topology capable of generating multilevel dodecagonal voltage space vectors with symmetric triangles, by cascading two asymmetric three level inverters with isolated H-Bridges. This is made possible by proper selection of DC link voltages and the selection of resultant switching states for the inverters. In this paper, a simple PWM timing calculation method is proposed. Experimental results have also been presented in this paper to validate the proposed concept.

Nanoflowers of PdRu on PEDOT for Electrooxidation of Glycerol and Its Analysis

Poly(3,4-ethylenedioxythiophene) (PEDOT) supported PdRu catalysts with various Pd:Ru atomic ratios are prepared by one step electrodeposition method. The catalysts are characterised by several physico-chemical techniques. The morphology depends on Pd:Ru ratio. The nanoflowers of Pd5Ru catalyst are deposited on PEDOT surface in an alloy form. Cyclic voltammetry experiments indicate that Ru improves the catalytic activity of Pd for glycerol oxidation significantly. However, the oxidation of glycerol is not observed on Ru-PEDOT/C electrode. Amongst all compositions, Pd5Ru nanoflowers on PEDOT exhibit the highest electrocatalytic activity and stability. Cyclic voltammetry and differential pulse voltammetry experiments are performed for the analysis of glycerol. Pd5Ru-PEDOT/C electrode is highly sensitive towards glycerol detection with sensitivity of 99.8 mu A cm(-2) mu M-1 and low detection limit of 0.1 mu M. Thus, electrochemically deposited nanoflowers Pd5Ru on PEDOT are efficient catalysts for direct glycerol oxidation as well as for analysis in alkaline media. (C) 2015 Elsevier Ltd. All rights reserved.

Thermal Modeling of Organic Light-Emitting Diode Display Panels

Power densities required to operate active-matrix organic-light-emitting diode (AMOLED) based displays for high luminance applications, lead to temperature rise due to self heating. Temperature rise leads to significant degradation and consequent reduction in life time. In this work numerical techniques based computational fluid dynamics (CFD) is used to determine the temperature rise and its distribution for an AMOLED based display for a given power density and size. Passive cooling option in form of protruded rectangular fins is implemented to reduce the display temperature.

Ultrasonic Guided Wave Sensor Network Optimization for Monitoring Rivet Line

Guided waves using piezo-electric wafer active sensors (PWAS) is one of the useful techniques of damage detection. Sensor network optimization with minimal network hardware footprint and maximal area of coverage remains a challenging problem. PWAS sensors are placed at discrete locations in order to inspect damages in plates and the idea has the potential to be extended to assembled structures. Various actuator-sensor configurations are possible within the network in order to identify and locate damages. In this paper we present a correlation based approach to monitor cracks emanating from rivet line using a simulated guided wave signal whose sensor is operating in pulse echo mode. Discussions regarding the identification of phase change due to reflections from the crack are also discussed in this paper.