Noncoherent Integration for Signal Detection: Analysis Under Model Uncertainties

The performance of postdetection integration (PDI) techniques for the detection of Global Navigation Satellite Systems (GNSS) signals in the presence of uncertainties in frequency offsets, noise variance, and unknown data-bits is studied. It is shown that the conventional PDI techniques are generally not robust to uncertainty in the data-bits and/or the noise variance. Two new modified PDI techniques are proposed, and they are shown to be robust to these uncertainties. The receiver operating characteristics (ROC) and sample complexity performance of the PDI techniques in the presence of model uncertainties are analytically derived. It is shown that the proposed methods significantly outperform existing methods, and hence they could become increasingly important as the GNSS receivers attempt to push the envelope on the minimum signal-to-noise ratio (SNR) for reliable detection.

Detection of Hepatitis B DNA Sequences on Polyelectrolyte Based Non-Covalently Functionalized Flexible Plastic Substrates

Development of simple functionalization methods to attach biomolecules such as proteins and DNA on inexpensive substrates is important for widespread use of low cost, disposable biosensors. Here, we describe a method based on polyelectrolyte multilayers to attach single stranded DNA molecules to conventional glass slides as well as a completely non-standard substrate, namely flexible plastic transparency sheets. We then use the functionalized transparency sheets to specifically detect single stranded Hepatitis B DNA sequences from samples. We also demonstrate a blocking method for reducing non-specific binding of target DNA sequences using negatively charged polyelectrolyte molecules. The polyelectrolyte based functionalization method, which relies on surface charge as opposed to covalent surface linkages, could be an attractive platform to develop assays on inexpensive substrates for low cost biosensing.

Signal Detection in Generalized Gaussian Noise by Nonlinear Wavelet Denoising

In this paper, a nonlinear suboptimal detector whose performance in heavy-tailed noise is significantly better than that of the matched filter is proposed. The detector consists of a nonlinear wavelet denoising filter to enhance the signal-to-noise ratio, followed by a replica correlator. Performance of the detector is investigated through an asymptotic theoretical analysis as well as Monte Carlo simulations. The proposed detector offers the following advantages over the optimal (in the Neyman-Pearson sense) detector: it is easier to implement, and it is more robust with respect to error in modeling the probability distribution of noise.

Crevasses detection in Himalayan glaciers using ground-penetrating radar

Identification and mapping of crevasses in glaciated regions is important for safe movement. However, the remote and rugged glacial terrain in the Himalaya poses greater challenges for field data collection. In the present study crevasse signatures were collected from Siachen and Samudra Tapu glaciers in the Indian Himalaya using ground-penetrating radar (GPR). The surveys were conducted using the antennas of 250 MHz frequency in ground mode and 350 MHz in airborne mode. The identified signatures of open and hidden crevasses in GPR profiles collected in ground mode were validated by ground truthing. The crevasse zones and buried boulder areas in a glacier were identified using a combination of airborne GPR profiles and SAR data, and the same have been validated with the high-resolution optical satellite imagery (Cartosat-1) and Survey of India mapsheet. Using multi-sensor data, a crevasse map for Samudra Tapu glacier was prepared. The present methodology can also be used for mapping the crevasse zones in other glaciers in the Himalaya.

Data acquisition and processing at ocean bottom for a Tsunami warning system

The design and development of a Bottom Pressure Recorder for a Tsunami Early Warning System is described here. The special requirements that it should satisfy for the specific application of deployment at ocean bed and pressure monitoring of the water column above are dealt with. A high-resolution data digitization and low circuit power consumption are typical ones. The implementation details of the data sensing and acquisition part to meet these are also brought out. The data processing part typically encompasses a Tsunami detection algorithm that should detect an event of significance in the background of a variety of periodic and aperiodic noise signals. Such an algorithm and its simulation are presented. Further, the results of sea trials carried out on the system off the Chennai coast are presented. The high quality and fidelity of the data prove that the system design is robust despite its low cost and with suitable augmentations, is ready for a full-fledged deployment at ocean bed. (C) 2013 Elsevier Ltd. All rights reserved.

AIPE-active green phosphorescent iridium(III) complex impregnated test strips for the vapor-phase detection of 2,4,6-trinitrotoluene (TNT)

Detection of explosives, especially trinitrotoluene (TNT), is of utmost importance due to its highly explosive nature and environmental hazard. Therefore, detection of TNT has been a matter of great concern to the scientific community worldwide. Herein, a new aggregation-induced phosphorescent emission (AIPE)-active iridium(III) bis(2-(2,4-difluorophenyl)pyridinato-NC2') (2-(2-pyridyl)benzimidazolato-N,N') complex FIrPyBiz] has been developed and serves as a molecular probe for the detection of TNT in the vapor phase, solid phase, and aqueous media. In addition, phosphorescent test strips have been constructed by impregnating Whatman filter paper with aggregates of FIrPyBiz for trace detection of TNT in contact mode, with detection limits in nanograms, by taking advantage of the excited state interaction of AIPE-active phosphorescent iridium(III) complex with that of TNT and the associated photophysical properties.