Optimizing Delay in Sequential Change Detection on Ad Hoc Wireless Sensor Networks

We consider the classical problem of sequential detection of change in a distribution (from hypothesis 0 to hypothesis 1), where the fusion centre receives vectors of periodic measurements, with the measurements being i.i.d. over time and across the vector components, under each of the two hypotheses. In our problem, the sensor devices ("motes") that generate the measurements constitute an ad hoc wireless network. The motes contend using a random access protocol (such as CSMA/CA) to transmit their measurement packets to the fusion centre. The fusion centre waits for vectors of measurements to accumulate before taking decisions. We formulate the optimal detection problem, taking into account the network delay experienced by the vectors of measurements, and find that, under periodic sampling, the detection delay decouples into network delay and decision delay. We obtain a lower bound on the network delay, and propose a censoring scheme, where lagging sensors drop their delayed observations in order to mitigate network delay. We show that this scheme can achieve the lower bound. This approach is explored via simulation. We also use numerical evaluation and simulation to study issues such as: the optimal sampling rate for a given number of sensors, and the optimal number of sensors for a given measurement rate

Non Identical Duplicate Video Detection using SIFT Method

Non-Identical Duplicate video detection is a challenging research problem. Non-Identical Duplicate video are a pair of videos that are not exactly identical but are almost similar.In this paper, we evaluate two methods - Keyframe -based and Tomography-based methods to determine the Non-Identical Duplicate videos. These two methods make use of the existing scale based shift invariant (SIFT) method to find the match between the key frames in first method, and the cross-sections through the temporal axis of the videos in second method.We provide extensive experimental results and the analysis of accuracy and efficiency of the above two methods on a data set of Non- Identical Duplicate video-pair.

Wavelet based spectral finite element modelling and detection of de-lamination in composite beams

In this paper, a model for composite beam with embedded de-lamination is developed using the wavelet based spectral finite element (WSFE) method particularly for damage detection using wave propagation analysis. The simulated responses are used as surrogate experimental results for the inverse problem of detection of damage using wavelet filtering. The WSFE technique is very similar to the fast fourier transform (FFT) based spectral finite element (FSFE) except that it uses compactly supported Daubechies scaling function approximation in time. Unlike FSFE formulation with periodicity assumption, the wavelet-based method allows imposition of initial values and thus is free from wrap around problems. This helps in analysis of finite length undamped structures, where the FSFE method fails to simulate accurate response. First, numerical experiments are performed to study the effect of de-lamination on the wave propagation characteristics. The responses are simulated for different de-lamination configurations for both broad-band and narrow-band excitations. Next, simulated responses are used for damage detection using wavelet analysis.

Detection and Evaluation of Impact damage in CFRP Laminates Using Ultrasound C-Scan and IR Thermography

Advanced composite structural components made up of Carbon Fibre Reinforced Polymers (CFRP) used in aerospace structures such as in Fuselage, Leading & Trailing edges of wing and tail, Flaps, Elevator, Rudder and entire wing structures encounter most critical type of damage induced by low velocity impact (<10 m/s) loads. Tool dropped during maintenance & service,and hailstone impacts on runways are common and unavoidable low-velocity impacts. These lowvelocity impacts induce defects such as delaminations, matrix cracking and debonding in the layered material, which are sub-surface in nature and are barely visible on the surface known as Barely Visible Impact Damage (BVID). These damages may grow under service load, leading to catastrophic failure of the structure. Hence detection, evaluation and characterization of these types of damage is of major concern in aerospace industries as the life of the component depends on the size and shape of the damage.In this paper, details of experimental investigations carried out and results obtained from a low-velocity impact of 30 Joules corresponding to the hailstone impact on the wing surface,simulated on the 6 mm CFRP laminates using instrumented drop-weight impact testing machine are presented. The Ultrasound C-scan and Infrared thermography imaging techniques were utilized extensively to detect, evaluate and characterize impact damage across the thickness of the laminates.

Direct detection of Salmonella without pre-enrichment in milk, ice-cream and fruit juice by PCR against hilA gene

A novel PCR based assay was devised to specifically detect contamination of any Salmonella serovar in milk, fruit juice and ice-cream without pre-enrichment. This method utilizes primers against hilA gene which is conserved in all Salmonella serovars and absent from the close relatives of Salmonella. An optimized protocol, in terms time and money, is provided for the reduction of PCR contaminants from milk, ice-cream and juice through the use of routine laboratory chemicals. The simplicity, efficiency (time taken 3-4 h) and sensitivity (to about 5-10 CFU/ml) of this technique confers a unique advantage over other previously used time consuming detection techniques. This technique does not involve pre-enrichment of the samples or extensive sample processing, which was a pre-requisite in most of the other reported studies. Hence, this assay can be ideal for adoption, after further fine tuning, by food quality control for timely detection of Salmonella contamination as well as other food-borne pathogens (with species specific primers) in food especially milk, ice-cream and fruit juice. (C) 2011 Elsevier Ltd. All rights reserved.

Measurement of Electron Spin Lifetime and Optical Orientation Efficiency in Germanium Using Electrical Detection of Radio Frequency Modulated Spin Polarization

We propose and demonstrate a technique for electrical detection of polarized spins in semiconductors in zero applied magnetic fields. Spin polarization is generated by optical injection using circularly polarized light which is modulated rapidly using an electro-optic cell. The modulated spin polarization generates a weak time-varying magnetic field which is detected by a sensitive radio-frequency coil. Using a calibrated pickup coil and amplification electronics, clear signals were obtained for bulk GaAs and Ge samples from which an optical spin orientation efficiency of 4.8% could be determined for Ge at 1342 nm excitation wavelength. In the presence of a small external magnetic field, the signal decayed according to the Hanle effect, from which a spin lifetime of 4.6 +/- 1.0 ns for electrons in bulk Ge at 127 K was extracted.

High sensitivity detection of radio-frequency modulated magnetic moment in semiconductors

An experimental setup has been realized to measure weak magnetic moments which can be modulated at radio frequencies (similar to 1-5 MHz). Using an optimized radio-frequency (RF) pickup coil and lock-in amplifier, an experimental sensitivity of 10(-15) Am(2) corresponding to 10(-18) emu has been demonstrated with a 1 s time constant. The detection limit at room temperature is 9.3 x 10(-16) Am(2)/root Hz limited by Johnson noise of the coil. The setup has been used to directly measure the magnetic moment due to a small number (similar to 7 x 10(8)) of spin polarized electrons generated by polarization modulated optical radiation in GaAs and Ge. (C) 2011 American Institute of Physics. [doi: 10.1063/1.3654229]

Detection of a periodic structure embedded in surface roughness, for various correlation functions

This paper deals with surface profilometry, where we try to detect a periodic structure, hidden in randomness using the matched filter method of analysing the intensity of light, scattered from the surface. From the direct problem of light scattering from a composite rough surface of the above type, we find that the detectability of the periodic structure can be hindered by the randomness, being dependent on the correlation function of the random part. In our earlier works, we had concentrated mainly on the Cauchy-type correlation function for the rough part. In the present work, we show that this technique can determine the periodic structure of different kinds of correlation functions of the roughness, including Cauchy, Gaussian etc. We study the detection by the matched filter method as the nature of the correlation function is varied.

Detection and determination of manganese concentration in water using a fiber Bragg grating coupled with nanotechnology

Through this paper we experimentally demonstrate the fabrication of a fiber Bragg grating (FBG) chemical sensor to detect and determine the manganese concentration in water and compare our results with sophisticated spectroscopic methods, such as atomic absorption spectrometry and the inductively coupled plasma method. Here we propose a simple method to develop a thin layer of gold nanoparticles above the etched grating region to enhance the sensitivity of the reflected spectrum of the FBG. By doing so, we achieve a sensitivity of 1.26 nm/parts per million in determining the trace level of Mn in water. Proper reagents are used to detect manganese in water. (C) 2011 Optical Society of America

Robust Observer Design with Application to Fault Detection

A new scheme for robust estimation of the partial state of linear time-invariant multivariable systems is presented, and it is shown how this may be used for the detection of sensor faults in such systems. We consider an observer to be robust if it generates a faithful estimate of the plant state in the face of modelling uncertainty or plant perturbations. Using the Stable Factorization approach we formulate the problem of optimal robust observer design by minimizing an appropriate norm on the estimation error. A logical candidate is the 2-norm, corresponding to an H�¿ optimization problem, for which solutions are readily available. In the special case of a stable plant, the optimal fault diagnosis scheme reduces to an internal model control architecture.

Low-Complexity Detection in Large-Dimension MIMO-ISI Channels Using Graphical Models

In this paper, we deal with low-complexity near-optimal detection/equalization in large-dimension multiple-input multiple-output inter-symbol interference (MIMO-ISI) channels using message passing on graphical models. A key contribution in the paper is the demonstration that near-optimal performance in MIMO-ISI channels with large dimensions can be achieved at low complexities through simple yet effective simplifications/approximations, although the graphical models that represent MIMO-ISI channels are fully/densely connected (loopy graphs). These include 1) use of Markov random field (MRF)-based graphical model with pairwise interaction, in conjunction with message damping, and 2) use of factor graph (FG)-based graphical model with Gaussian approximation of interference (GAI). The per-symbol complexities are O(K(2)n(t)(2)) and O(Kn(t)) for the MRF and the FG with GAI approaches, respectively, where K and n(t) denote the number of channel uses per frame, and number of transmit antennas, respectively. These low-complexities are quite attractive for large dimensions, i.e., for large Kn(t). From a performance perspective, these algorithms are even more interesting in large-dimensions since they achieve increasingly closer to optimum detection performance for increasing Kn(t). Also, we show that these message passing algorithms can be used in an iterative manner with local neighborhood search algorithms to improve the reliability/performance of M-QAM symbol detection.