Modelling and classification of respiratory volume wavefourms

A technique is proposed for classifying respiratory volume waveforms(RVW) into normal and abnormal categories of respiratory pathways. The proposed method transforms the temporal sequence into frequency domain by using an orthogonal transform, namely discrete cosine transform (DCT) and the transformed signal is pole-zero modelled. A Bayes classifier using model pole angles as the feature vector performed satisfactorily when a limited number of RVWs recorded under deep and rapid (DR) manoeuvre are classified.

Optimal energy management policies for energy harvesting sensor nodes

We study a sensor node with an energy harvesting source. The generated energy can be stored in a buffer. The sensor node periodically senses a random field and generates a packet. These packets are stored in a queue and transmitted using the energy available at that time. We obtain energy management policies that are throughput optimal, i.e., the data queue stays stable for the largest possible data rate. Next we obtain energy management policies which minimize the mean delay in the queue. We also compare performance of several easily implementable sub-optimal energy management policies. A greedy policy is identified which, in low SNR regime, is throughput optimal and also minimizes mean delay.

Classification of road damage due to earthquakes

Earthquakes cause massive road damage which in turn causes adverse effects on the society. Previous studies have quantified the damage caused to residential and commercial buildings; however, not many studies have been conducted to quantify road damage caused by earthquakes. In this study, an attempt has been made to propose a new scale to classify and quantify the road damage due to earthquakes based on the data collected from major earthquakes in the past. The proposed classification for road damage due to earthquake is called as road damage scale (RDS). Earthquake details such as magnitude, distance of road damage from the epicenter, focal depth, and photographs of damaged roads have been collected from various sources with reported modified Mercalli intensity (MMI). The widely used MMI scale is found to be inadequate to clearly define the road damage. The proposed RDS is applied to various reported road damage and reclassified as per RDS. The correlation between RDS and earthquake parameters of magnitude, epicenter distance, hypocenter distance, and combination of magnitude with epicenter and hypocenter distance has been studied using available data. It is observed that the proposed RDS correlates well with the available earthquake data when compared with the MMI scale. Among several correlations, correlation between RDS and combination of magnitude and epicenter distance is appropriate. Summary of these correlations, their limitations, and the applicability of the proposed scale to forecast road damages and to carry out vulnerability analysis in urban areas is presented in the paper.

Materials, Processing, and Testing of Flexible Image Sensor Arrays

Editors' note:Flexible, large-area display and sensor arrays are finding growing applications in multimedia and future smart homes. This article first analyzes and compares current flexible devices, then discusses the implementation, requirements, and testing of flexible sensor arrays.—Jiun-Lang Huang (National Taiwan University) and Kwang-Ting (Tim) Cheng (University of California, Santa Barbara)

Hybrid Bayesian Classifier for Improved Classification Accuracy

The widely used Bayesian classifier is based on the assumption of equal prior probabilities for all the classes. However, inclusion of equal prior probabilities may not guarantee high classification accuracy for the individual classes. Here, we propose a novel technique-Hybrid Bayesian Classifier (HBC)-where the class prior probabilities are determined by unmixing a supplemental low spatial-high spectral resolution multispectral (MS) data that are assigned to every pixel in a high spatial-low spectral resolution MS data in Bayesian classification. This is demonstrated with two separate experiments-first, class abundances are estimated per pixel by unmixing Moderate Resolution Imaging Spectroradiometer data to be used as prior probabilities, while posterior probabilities are determined from the training data obtained from ground. These have been used for classifying the Indian Remote Sensing Satellite LISS-III MS data through Bayesian classifier. In the second experiment, abundances obtained by unmixing Landsat Enhanced Thematic Mapper Plus are used as priors, and posterior probabilities are determined from the ground data to classify IKONOS MS images through Bayesian classifier. The results indicated that HBC systematically exploited the information from two image sources, improving the overall accuracy of LISS-III MS classification by 6% and IKONOS MS classification by 9%. Inclusion of prior probabilities increased the average producer's and user's accuracies by 5.5% and 6.5% in case of LISS-III MS with six classes and 12.5% and 5.4% in IKONOS MS for five classes considered.

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).

Wireless sensor networks forhuman intruder detection

In this paper we report on the outcomes of a research and demonstration project on human intrusion detection in a large secure space using an ad hoc wireless sensor network. This project has been a unique experience in collaborative research, involving ten investigators (with expertise in areas such as sensors, circuits, computer systems,communication and networking, signal processing and security) to execute a large funded project that spanned three to four years. In this paper we report on the specific engineering solution that was developed: the various architectural choices and the associated specific designs. In addition to developing a demonstrable system, the various problems that arose have given rise to a large amount of basic research in areas such as geographical packet routing, distributed statistical detection, sensors and associated circuits, a low power adaptive micro-radio, and power optimising embedded systems software. We provide an overview of the research results obtained.

Fast Robust Pattern Classification Algorithms for Real Time Neuro-Motor Prosthetic Applications

In this paper, we give a brief review of pattern classification algorithms based on discriminant analysis. We then apply these algorithms to classify movement direction based on multivariate local field potentials recorded from a microelectrode array in the primary motor cortex of a monkey performing a reaching task. We obtain prediction accuracies between 55% and 90% using different methods which are significantly above the chance level of 12.5%.

Minimal unsatisfiable sets: Classification and bounds

Proving the unsatisfiability of propositional Boolean formulas has applications in a wide range of fields. Minimal Unsatisfiable Sets (MUS) are signatures of the property of unsatisfiability in formulas and our understanding of these signatures can be very helpful in answering various algorithmic and structural questions relating to unsatisfiability. In this paper, we explore some combinatorial properties of MUS and use them to devise a classification scheme for MUS. We also derive bounds on the sizes of MUS in Horn, 2-SAT and 3-SAT formulas.

Time Series Classification for Online Tamil Handwritten Character Recognition – A Kernel Based Approach

In this paper, we consider the problem of time series classification. Using piecewise linear interpolation various novel kernels are obtained which can be used with Support vector machines for designing classifiers capable of deciding the class of a given time series. The approach is general and is applicable in many scenarios. We apply the method to the task of Online Tamil handwritten character recognition with promising results.

External-cavity semiconductor laser sensor

The spectral characteristics of a diode laser are significantly affected due to interference caused between the laser diode output and the optical feedback in the external-cavity. This optical feedback effect is of practical use for linewidth reduction, tuning or for sensing applications. A sensor based on this effect is attractive due to its simplicity, low cost and compactness. This optical sensor has been used so far, in different configuration such as for sensing displacement induced by different parameters. In this paper we report a compact optical sensor consisting of a semiconductor laser coupled to an external cavity. Theoretical analysis of the self- mixing interference for optical sensing applications is given for moderate optical feedback case. A comparison is made with our experimental observations. Experimental results are in good agreement with the simulated power modulation based on self-mixing interference theory. Displacements as small as 10-4 nm have been measured using this sensor. The developed sensor showed a fringe sensitivity of one fringe per 400nm displacement for reflector distance of around 10cms. The sensor has also been tested for magnetic field and temperature induced displacement measurements.

A novel interrogation system for fiber Bragg grating sensor array

Interrogation techniques for fiber Bragg grating sensor arrays need particular attention in the case of structural health monitoring applications involving dynamic strain measurement. Typically the performance of the sensing system is dependent on both the sensor type and the interrogation method employed. A novel interrogation system is proposed here that consists of different interrogation units for each sensor in the array, each unit comprising of a circulator, chirped grating and a Mach-Zehnder interferometer. We present an analysis that consists of tracking the spectral changes as the light passes through various elements in the interrogation system. This is expected to help in the optimization of sensor and interrogation elements leading to improved performance of the health monitoring system.

Cd concentration sensor based on fiber grating technology

Fiber Bragg grating (FBG) and Long Period Grating (LPG) chemical sensors are one of the most exciting developments in the field of optical fiber sensors. In this paper we have proposed a simple and effective chemical sensor based on FBG and LPG techniques for detecting the traces of cadmium (Cd) in drinking water at ppm level. The sensitiveness of these two has been compared. Also, these results have been compared with the results obtained by sophisticated spectroscopic atomic absorption and emission spectrometer instruments. For proper designing of FBG to act as a concentration sensor, the cladding region of the grating has been etched using HF solution. We have characterized the FBG concentration sensor sensitivities for different solutions of Cd concentrations varying from 0.01 ppm to 0.04 ppm and observed reflected spectrum in FBG and transmitted spectrum in LPG using Optical Spectrum Analyzer. Proper reagents have been used in the solutions for detection of the Cd species. The overall shift in wavelength is 10 nm in case of LPG and the shift of Bragg wavelength is 0.07 nm in case of FBG for 0.01-0.04 ppm concentrations. (C) 2011 Elsevier B.V. All rights reserved.