Evaluation of polyvinylidene fluoride nasal sensor to assess deviated nasal septum in comparision with peak nasal inspiratory flow measurements

Background: Deviated nasal septum (DNS) is one of the major causes of nasal obstruction. Polyvinylidene fluoride (PVDF) nasal sensor is the new technique developed to assess the nasal obstruction caused by DNS. This study evaluates the PVDF nasal sensor measurements in comparison with PEAK nasal inspiratory flow (PNIF) measurements and visual analog scale (VAS) of nasal obstruction. Methods: Because of piezoelectric property, two PVDF nasal sensors provide output voltage signals corresponding to the right and left nostril when they are subjected to nasal airflow. The peak-to-peak amplitude of the voltage signal corresponding to nasal airflow was analyzed to assess the nasal obstruction. PVDF nasal sensor and PNIF were performed on 30 healthy subjects and 30 DNS patients. Receiver operating characteristic was used to analyze the DNS of these two methods. Results: Measurements of PVDF nasal sensor strongly correlated with findings of PNIF (r = 0.67; p < 0.01) in DNS patients. A significant difference (p < 0.001) was observed between PVDF nasal sensor measurements and PNIF measurements of the DNS and the control group. A cutoff between normal and pathological of 0.51 Vp-p for PVDF nasal sensor and 120 L/min for PNIF was calculated. No significant difference in terms of sensitivity of PVDF nasal sensor and PNIF (89.7% versus 82.6%) and specificity (80.5% versus 78.8%) was calculated. Conclusion: The result shows that PVDF measurements closely agree with PNIF findings. Developed PVDF nasal sensor is an objective method that is simple, inexpensive, fast, and portable for determining DNS in clinical practice.

Validation of polyvinylidene fluoride nasal sensor to assess nasal obstruction in comparison with subjective technique

Objective: The aim of this study is to validate the applicability of the PolyVinyliDene Fluoride (PVDF) nasal sensor to assess the nasal airflow, in healthy subjects and patients with nasal obstruction and to correlate the results with the score of Visual Analogue Scale (VAS). Methods: PVDF nasal sensor and VAS measurements were carried out in 50 subjects (25-healthy subjects and 25 patients). The VAS score of nasal obstruction and peak-to-peak amplitude (Vp-p) of nasal cycle measured by PVDF nasal sensors were analyzed for right nostril (RN) and left nostril (LN) in both the groups. Spearman's rho correlation was calculated. The relationship between PVDF nasal sensor measurements and severity of nasal obstruction (VAS score) were assessed by ANOVA. Results: In healthy group, the measurement of nasal airflow by PVDF nasal sensor for RN and LN were found to be 51.14 +/- 5.87% and 48.85 +/- 5.87%, respectively. In patient group, PVDF nasal sensor indicated lesser nasal airflow in the blocked nostrils (RN: 23.33 +/- 10.54% and LN: 32.24 +/- 11.54%). Moderate correlation was observed in healthy group (r = 0.710, p < 0.001 for RN and r = 0.651, p < 0.001 for LN), and moderate to strong correlation in patient group (r = 0.751, p < 0.01 for RN and r = 0.885, p < 0.0001 for LN). Conclusion: PVDF nasal sensor method is a newly developed technique for measuring the nasal airflow. Moderate to strong correlation was observed between PVDF nasal sensor data and VAS scores for nasal obstruction. In our present study, PVDF nasal sensor technique successfully differentiated between healthy subjects and patients with nasal obstruction. Additionally, it can also assess severity of nasal obstruction in comparison with VAS. Thus, we propose that the PVDF nasal sensor technique could be used as a new diagnostic method to evaluate nasal obstruction in routine clinical practice. (C) 2015 Elsevier Inc. All rights reserved.

Jet Engine Health Signal Denoising Using Optimally Weighted Recursive Median Filters

The removal of noise and outliers from health signals is an important problem in jet engine health monitoring. Typically, health signals are time series of damage indicators, which can be sensor measurements or features derived from such measurements. Sharp or sudden changes in health signals can represent abrupt faults and long term deterioration in the system is typical of gradual faults. Simple linear filters tend to smooth out the sharp trend shifts in jet engine signals and are also not good for outlier removal. We propose new optimally designed nonlinear weighted recursive median filters for noise removal from typical health signals of jet engines. Signals for abrupt and gradual faults and with transient data are considered. Numerical results are obtained for a jet engine and show that preprocessing of health signals using the proposed filter significantly removes Gaussian noise and outliers and could therefore greatly improve the accuracy of diagnostic systems. [DOI: 10.1115/1.3200907].

Three- and Seven-Point Optimally Weighted Recursive Median Filters for Gas Turbine Diagnostics

Measured health signals incorporate significant details about any malfunction in a gas turbine. The attenuation of noise and removal of outliers from these health signals while preserving important features is an important problem in gas turbine diagnostics. The measured health signals are a time series of sensor measurements such as the low rotor speed, high rotor speed, fuel flow, and exhaust gas temperature in a gas turbine. In this article, a comparative study is done by varying the window length of acausal and unsymmetrical weighted recursive median filters and numerical results for error minimization are obtained. It is found that optimal filters exist, which can be used for engines where data are available slowly (three-point filter) and rapidly (seven-point filter). These smoothing filters are proposed as preprocessors of measurement delta signals before subjecting them to fault detection and isolation algorithms.

Shock tunnel measurements of surface pressures in shock induced separated flow field using MEMS sensor array

Characterized not just by high Mach numbers, but also high flow total enthalpies-often accompanied by dissociation and ionization of flowing gas itself-the experimental simulation of hypersonic flows requires impulse facilities like shock tunnels. However, shock tunnel simulation imposes challenges and restrictions on the flow diagnostics, not just because of the possible extreme flow conditions, but also the short run times-typically around 1 ms. The development, calibration and application of fast response MEMS sensors for surface pressure measurements in IISc hypersonic shock tunnel HST-2, with a typical test time of 600 mu s, for the complex flow field of strong (impinging) shock boundary layer interaction with separation close to the leading edge, is delineated in this paper. For Mach numbers 5.96 (total enthalpy 1.3 MJ kg(-1)) and 8.67 (total enthalpy 1.6 MJ kg(-1)), surface pressures ranging from around 200 Pa to 50 000 Pa, in various regions of the flow field, are measured using the MEMS sensors. The measurements are found to compare well with the measurements using commercial sensors. It was possible to resolve important regions of the flow field involving significant spatial gradients of pressure, with a resolution of 5 data points within 12 mm in each MEMS array, which cannot be achieved with the other commercial sensors. In particular, MEMS sensors enabled the measurement of separation pressure (at Mach 8.67) near the leading edge and the sharply varying pressure in the reattachment zone.

Fiber Bragg Grating Sensor Package for Submicron Level Displacement Measurements

In this article, the design and development of a Fiber Bragg Grating (FBG) based displacement sensor package for submicron level displacement measurements are presented. A linear shift of 12.12 nm in Bragg wavelength of the FBG sensor is obtained for a displacement of 6 mm with a calibration factor of 0.495 mu m/pm. Field trials have also been conducted by comparing the FBG displacement sensor package against a conventional dial gauge, on a five block masonry prism specimen loaded using three-point bending technique. The responses from both the sensors are in good agreement, up to the failure of the masonry prism. Furthermore, from the real-time displacement data recorded using FBG, it is possible to detect the time at which early creaks generated inside the body of the specimen which then prorogate to the surface to develop visible surface cracks; the respective load from the load cell can be obtained from the inflection (stress release point) in the displacement curve. Thus the developed FBG displacement sensor package can be used to detect failures in structures much earlier and to provide an adequate time to exercise necessary action, thereby avoiding the possible disaster.

Design and characterization of in-plane MEMS yaw rate sensor

In this paper, we present the design and characterization of a vibratory yaw rate MEMS sensor that uses in-plane motion for both actuation and sensing. The design criterion for the rate sensor is based on a high sensitivity and low bandwidth. The required sensitivity of the yawrate sensor is attained by using the inplane motion in which the dominant damping mechanism is the fluid loss due to slide film damping i.e. two-three orders of magnitude less than the squeeze-film damping in other rate sensors with out-of-plane motion. The low bandwidth is achieved by matching the drive and the sense mode frequencies. Based on these factors, the yaw rate sensor is designed and finally realized using surface micromachining. The inplane motion of the sensor is experimentally characterized to determine the sense and the drive mode frequencies, and corresponding damping ratios. It is found that the experimental results match well with the numerical and the analytical models with less than 5% error in frequencies measurements. The measured quality factor of the sensor is approximately 467, which is two orders of magnitude higher than that for a similar rate sensor with out-of-plane sense direction.

Time and Energy Complexity of Distributed Computation of a Class of Functions in Wireless Sensor Networks

We consider a scenario in which a wireless sensor network is formed by randomly deploying n sensors to measure some spatial function over a field, with the objective of computing a function of the measurements and communicating it to an operator station. We restrict ourselves to the class of type-threshold functions (as defined in the work of Giridhar and Kumar, 2005), of which max, min, and indicator functions are important examples: our discussions are couched in terms of the max function. We view the problem as one of message-passing distributed computation over a geometric random graph. The network is assumed to be synchronous, and the sensors synchronously measure values and then collaborate to compute and deliver the function computed with these values to the operator station. Computation algorithms differ in (1) the communication topology assumed and (2) the messages that the nodes need to exchange in order to carry out the computation. The focus of our paper is to establish (in probability) scaling laws for the time and energy complexity of the distributed function computation over random wireless networks, under the assumption of centralized contention-free scheduling of packet transmissions. First, without any constraint on the computation algorithm, we establish scaling laws for the computation time and energy expenditure for one-time maximum computation. We show that for an optimal algorithm, the computation time and energy expenditure scale, respectively, as Theta(radicn/log n) and Theta(n) asymptotically as the number of sensors n rarr infin. Second, we analyze the performance of three specific computation algorithms that may be used in specific practical situations, namely, the tree algorithm, multihop transmission, and the Ripple algorithm (a type of gossip algorithm), and obtain scaling laws for the computation time and energy expenditure as n rarr infin. In particular, we show that the computation time for these algorithms scales as Theta(radicn/lo- g n), Theta(n), and Theta(radicn log n), respectively, whereas the energy expended scales as , Theta(n), Theta(radicn/log n), and Theta(radicn log n), respectively. Finally, simulation results are provided to show that our analysis indeed captures the correct scaling. The simulations also yield estimates of the constant multipliers in the scaling laws. Our analyses throughout assume a centralized optimal scheduler, and hence, our results can be viewed as providing bounds for the performance with practical distributed schedulers.

Sensor network deployment for agronomical data gathering in semi-arid regions

We share our experience in planning, designing and deploying a wireless sensor network of one square kilometre area. Environmental data such as soil moisture, temperature, barometric pressure, and relative humidity are collected in this area situated in the semi-arid region of Karnataka, India. It is a hope that information derived from this data will benefit the marginal farmer towards improving his farming practices. Soon after establishing the need for such a project, we begin by showing the big picture of such a data gathering network, the software architecture we have used, the range measurements needed for determining the sensor density, and the packaging issues that seem to play a crucial role in field deployments. Our field deployment experiences include designing with intermittent grid power, enhancing software tools to aid quicker and effective deployment, and flash memory corruption. The first results on data gathering look encouraging.

Simple kinetic sensor to structural transitions

Driven nonequilibrium structural phase transformation has been probed using time-varying resistance fluctuations or noise. We demonstrate that the non-Gaussian component (NGC) of noise obtained by evaluating the higher-order statistics of fluctuations, serves as a simple kinetic detector of these phase transitions. Using the Martensite transformation in free-standing wires of nickel-titanium binary alloys as a prototype, we observe clear deviations from the Gaussian background in the transformation zone, indicative of the long-range correlations in the system as the phase transforms. The viability of non-Gaussian statistics as a robust probe to structural phase transition was also confirmed by comparing the results from differential scanning calorimetry measurements. We further studied the response of the NGC to the modifications in the microstructure on repeated thermal cycling, as well as the variations in the temperature-drive rate, and explained the results using established simplistic models based on the different competing time scales. Our experiments (i) suggest an alternative method to estimate the transformation temperature scales with high accuracy and (ii) establish a connection between the material-specific evolution of microstructure to the statistics of its linear response. Since the method depends on an in-built long-range correlation during transformation, it could be portable to other structural transitions, as well as to materials of different physical origin and size.

Implications of Energy Profile and Storage on Energy Harvesting Sensor Link Performance

Energy harvesting sensors (EHS), which harvest energy from the environment in order to sense and then communicate their measurements over a wireless link, provide the tantalizing possibility of perpetual lifetime operation of a sensor network. The wireless communication link design problem needs to be revisited for these sensors as the energy harvested can be random and small and not available when required. In this paper, we develop a simple model that captures the interactions between important parameters that govern the communication link performance of a EHS node, and analyze its outage probability for both slow fading and fast fading wireless channels. Our analysis brings out the critical importance of the energy profile and the energy storage capability on the EHS link performance. Our results show that properly tuning the transmission parameters of the EHS node and having even a small amount of energy storage capability improves the EHS link performance considerably.

Particle filters for structural system identification using multiple test and sensor data: A combined computational and experimental study

The problem of structural system identification when measurements originate from multiple tests and multiple sensors is considered. An offline solution to this problem using bootstrap particle filtering is proposed. The central idea of the proposed method is the introduction of a dummy independent variable that allows for simultaneous assimilation of multiple measurements in a sequential manner. The method can treat linear/nonlinear structural models and allows for measurements on strains and displacements under static/dynamic loads. Illustrative examples consider measurement data from numerical models and also from laboratory experiments. The results from the proposed method are compared with those from a Kalman filter-based approach and the superior performance of the proposed method is demonstrated. Copyright (C) 2009 John Wiley & Sons, Ltd.

Noise measurements of a phase fluorometric instrument

We present noise measurements of a phase fluorometric oxygen sensor that sets the limits of accuracy for this instrument. We analyze the phase sensitive detection measurement system with the signal ''shot'' noise being the only significant contribution to the system noise. Based on the modulated optical power received by the photomultiplier, the analysis predicts a noise spectral power density that was within 3 dB of the measured power spectral noise density. Our results demonstrate that at a received optical power of 20 fW the noise level was low enough to permit the detection of a change oxygen concentration of 1% at the sensor. We also present noise measurements of a new low-cost version of this instrument that uses a photodiode instead of a photomultiplier. These measurements show that the noise for this instrument was limited by noise generated in the preamplifier following the photodiode. (C) 1996 Society of Photo-Optical Instrumentation Engineers.

Incremental-angle and angular velocity estimation using a star sensor

A method is described for estimating the incremental angle and angular velocity of a spacecraft using integrated rate parameters with the help of a star sensor alone. The chief advantage of this method is that the measured stars need not be identified, whereas the identification of the stars is necessary in earlier methods. This proposed estimation can be carried out with all of the available measurements by a simple linear Kalman filter, albeit with a time-varying sensitivity matrix. The residuals of estimated angular velocity by the proposed spacecraft incremental-angle and angular velocity estimation method are as accurate as the earlier methods. This method also enables the spacecraft attitude to be reconstructed for mapping the stars into an imaginary unit sphere in the body reference frame, which will preserve the true angular separation of the stars. This will pave the way for identification of the stars using any angular separation or triangle matching techniques applied to even a narrow field of view sensor that is made to sweep the sky. A numerical simulation for inertial as well as Earth pointing spacecraft is carried out to establish the results.