A novel algorithm for cooperative distributed sequential spectrum sensing in Cognitive Radio

This paper considers cooperative spectrum sensing in Cognitive Radios. In our previous work we have developed DualSPRT, a distributed algorithm for cooperative spectrum sensing using Sequential Probability Ratio Test (SPRT) at the Cognitive Radios as well as at the fusion center. This algorithm works well, but is not optimal. In this paper we propose an improved algorithm- SPRT-CSPRT, which is motivated from Cumulative Sum Procedures (CUSUM). We analyse it theoretically. We also modify this algorithm to handle uncertainties in SNR's and fading.

SpecNet: Spectrum Sensing Sans Frontieres

While the under-utilization of licensed spectrum based on measurement studies conducted in a few developed countries has spurred lots of interest in opportunistic spectrum access, there exists no infrastructure today for measuring real-time spectrum occupancy across vast geographical regions. In this paper, we present the design and implementation of SpecNet, a first-of-its-kind platform that allows spectrum analyzers around the world to be networked and efficiently used in a coordinated manner for spectrum measurement as well as implementa- tion and evaluation of distributed sensing applications. We demonstrate the value of SpecNet through three applications: 1) remote spectrum measurement, 2) primary transmitter coverage estimation and 3) Spectrum-Cop that quickly identifies and localizes transmitters in a frequency range and geographic region of interest.

Cooperative distributed sequential spectrum sensing

We consider cooperative spectrum sensing for cognitive radios. We develop an energy efficient detector with low detection delay using sequential hypothesis testing. Sequential Probability Ratio Test (SPRT) is used at both the local nodes and the fusion center. We also analyse the performance of this algorithm and compare with the simulations. Modelling uncertainties in the distribution parameters are considered. Slow fading with and without perfect channel state information at the cognitive radios is taken into account.

Frequency detection from multiplexed compressed sensing of noisy signals

Signal acquisition under a compressed sensing scheme offers the possibility of acquisition and reconstruction of signals sparse on some basis incoherent with measurement kernel with sub-Nyquist number of measurements. In particular when the sole objective of the acquisition is the detection of the frequency of a signal rather than exact reconstruction, then an undersampling framework like CS is able to perform the task. In this paper we explore the possibility of acquisition and detection of frequency of multiple analog signals, heavily corrupted with additive white Gaussian noise. We improvise upon the MOSAICS architecture proposed by us in our previous work to include a wider class of signals having non-integral frequency components. This makes it possible to perform multiplexed compressed sensing for general frequency sparse signals.

Random forest algorithm with derived geographical layers for improved classification of remote sensing data

Effective conservation and management of natural resources requires up-to-date information of the land cover (LC) types and their dynamics. The LC dynamics are being captured using multi-resolution remote sensing (RS) data with appropriate classification strategies. RS data with important environmental layers (either remotely acquired or derived from ground measurements) would however be more effective in addressing LC dynamics and associated changes. These ancillary layers provide additional information for delineating LC classes' decision boundaries compared to the conventional classification techniques. This communication ascertains the possibility of improved classification accuracy of RS data with ancillary and derived geographical layers such as vegetation index, temperature, digital elevation model (DEM), aspect, slope and texture. This has been implemented in three terrains of varying topography. The study would help in the selection of appropriate ancillary data depending on the terrain for better classified information.

Discrimination of alpha-Amino Acids Using Green Tea Flavonoid (-)-Epigallocatechin Gallate as a Chiral Solvating Agent

We report a special, hitherto-unexplored property of (-)-epigallocatechin gallate (EGCG) as a chiral solvating agent for enantiodiscrimination of alpha-amino acids in the polar solvent DMSO. This phenomenon has been investigated by H-1 NMR spectroscopy. The mechanism of the interaction property of EGCG with alpha-amino acids has been understood as arising out of hydrogen-bonded noncovalent interactions, where the -OH groups of two phenyl rings of EGCG play dominant roles. The conversion of the enantiomeric mixture into diastereomers yielded well-resolved peaks for D and L amino acids permitting the precise measurement of enantiomeric composition. Often one encounters complex situations when the spectra are severely overlapped or partially resolved hampering the testing of enantiopurity and the precise measurement of enantiomeric excess (ee). Though higher concentration of EGCG yielded better discrimination, the use of lower concentration being economical, we have exploited an appropriate 2D NMR experiment in overcoming such problems. Thus, in the present study we have successfully demonstrated the utility of the bioflavonoid (-)-EGCG, a natural product as a chiral solvating agent for the discrimination of large number of alpha-amino acids in a polar solvent DMSO. Another significant advantage of this new chiral sensing agent is that it is a natural product and does not require tedious multistep synthesis unlike many other chiral auxiliaries.

Analysis of integrated optofluidic lab-on-a-chip sensor based on refractive index and absorbance sensing

The analysis of a fully integrated optofluidic lab-on-a-chip sensor is presented in this paper. This device is comprised of collinear input and output waveguides that are separated by a microfluidic channel. When light is passed through the analyte contained in the fluidic gap, optical power loss occurs owing to absorption of light. Apart from absorption, a mode-mismatch between the input and output waveguides occurs when the light propagates through the fluidic gap. The degree of mode-mismatch and quantum of optical power loss due to absorption of light by the fluid form the basis of our analysis. This sensor can detect changes in refractive index and changes in concentration of species contained in the analyte. The sensitivity to detect minute changes depends on many parameters. The parameters that influence the sensitivity of the sensor are mode spot size, refractive index of the fluid, molar concentration of the species contained in the analyte, width of the fluidic gap, and waveguide geometry. By correlating various parameters, an optimal fluidic gap distance corresponding to a particular mode spot size that achieves the best sensitivity is determined both for refractive index and absorbance-based sensing.

Magnetic and transport relaxation property of La0.85Sr0.15CoO3 single crystals

Here, we present a comprehensive investigation of the dc magnetization and magnetotransport studies on La0.85Sr0.15CoO3 single crystals grown by the optical float zone method. The spin freezing temperature in the ac susceptibility study shifts to lower value at higher dc field and this is well described by the de Almeida-Thouless line which is the characteristic of SG behavior. The Magnetotransport study shows that the sample exhibits a huge negative MR of similar to 70% at 10 K which monotonically decreases with the increase in temperature. Besides, the magnetization and the resistivity relaxation give strong indication that the MR scales with sample's magnetization. In essence, all the present experimental findings evidence the SG behavior of La0.85Sr0.15CoO3 single crystals.

Two-axis force sensing and control of a Reorientable scanning probe

This paper presents the design and implementation of a reorientable scanning probe that is capable of two-axis force sensing and control in the 2-D scanning (X-Z) plane. The probe is comprised of three major components, namely a compliant manipulator, laser measurement system, and magnetic actuation system. Control of the position and orientation of the probe tip is realized by means of magnetic actuation combined with a novel structural design. The design of the manipulator's compliance and that of the optical path of the laser measurement system together enable achieving sensitivity to lateral (X) forces that is nearly identical to that of normal (Z) forces. The achieved sensitivity ratio, of about 0.6, is significantly higher than that of conventional scanning probe systems. The theoretical bases for the structural design and the sensitivity of the two-axis force sensing system are presented. Subsequently, fabrication of the manipulator is described and the result of experimental evaluation of the scanning probe's features is discussed. The scanning probe is used to access the vertical and re-entrant features on the two sides of a cylindrical micropipette, which are subsequently scanned by regulating the lateral force of tip-sample interaction.

CO2 sensing at room temperature using carbon nanotubes coated core fiber Bragg grating

The sensing of carbon dioxide (CO2) at room temperature, which has potential applications in environmental monitoring, healthcare, mining, biotechnology, food industry, etc., is a challenge for the scientific community due to the relative inertness of CO2. Here, we propose a novel gas sensor based on clad-etched Fiber Bragg Grating (FBG) with polyallylamine-amino-carbon nanotube coated on the surface of the core for detecting the concentrations of CO2 gas at room temperature, in ppm levels over a wide range (1000 ppm-4000 ppm). The limit of detection observed in polyallylamine-amino-carbon nanotube coated core-FBG has been found to be about 75 ppm. In this approach, when CO2 gas molecules interact with the polyallylamine-amino-carbon nanotube coated FBG, the effective refractive index of the fiber core changes, resulting in a shift in Bragg wavelength. The experimental data show a linear response of Bragg wavelength shift for increase in concentration of CO2 gas. Besides being reproducible and repeatable, the technique is fast, compact, and highly sensitive. (C) 2013 AIP Publishing LLC.

Liquefied Petroleum Gas Sensing of Polyaniline-Titanium Dioxide Nanocomposites

Polyaniline/titaniurn dioxide nanocomposites were prepared using alpha-dextrose as surfactant and ammonium persulphate as an oxidant. The PANI/TiO2 nanocomposite is characterized by FTIR, XRD and TEM. The FTIR spectra revel that the presence of characteristic peaks of benzenoid, qunoide rings and metal-oxygen stretching. The XRD studies show the monoclinic structure of the nanocomposites. The TEM study shows that the size of TiO2 is in the order of 9 nm where as the composite size is of the order of 13 nm and further it was observed that the TiO2 particles are intercalated to form a core shell of PANI. The maximum sensing response for LPG is found to be 90% for 30 wt.% of PANI/TiO2 nanocomposites at 400 ppm whereas for Benzene and Toluene it is negligibly small (<= 20%) and for the cyclohexane sensing response it is around 30% for different wt.%.

Fusion of algorithms for compressed sensing

For compressed sensing (CS), we develop a new scheme inspired by data fusion principles. In the proposed fusion based scheme, several CS reconstruction algorithms participate and they are executed in parallel, independently. The final estimate of the underlying sparse signal is derived by fusing the estimates obtained from the participating algorithms. We theoretically analyze this fusion based scheme and derive sufficient conditions for achieving a better reconstruction performance than any participating algorithm. Through simulations, we show that the proposed scheme has two specific advantages: 1) it provides good performance in a low dimensional measurement regime, and 2) it can deal with different statistical natures of the underlying sparse signals. The experimental results on real ECG signals shows that the proposed scheme demands fewer CS measurements for an approximate sparse signal reconstruction.

Cantilever resonator with integrated actuation and sensing fabricated using a single step lithography

Micro- and nano-mechanical resonators have been proposed for a variety of applications ranging from mass sensing to signal processing. Often their actuation and/or detection involve external subsystems that are much larger than the resonator itself. We have designed a simple microcantilever resonator with integrated sensor and actuator, facilitating the integration of large arrays of resonators. This unique design can be manufactured with a low-cost fabrication process, involving just a single step of lithography. The bilayer cantilever of gold and silicon dioxide is used as piezoresistive sensor as well as thermal bimorph actuator. The ac current used for actuation and the dc current used for piezoresistive detection are separated in the frequency-domain using a bias-tee circuit configuration. The resonant response is measured by detecting the second harmonic of the actuation current using a lock-in amplifier.

Remote sensing applications in water resources

With the introduction of the earth observing satellites, remote sensing has become an important tool in analyzing the Earth's surface characteristics, and hence in supplying valuable information necessary for the hydrologic analysis. Due to their capability to capture the spatial variations in the hydro-meteorological variables and frequent temporal resolution sufficient to represent the dynamics of the hydrologic processes, remote sensing techniques have significantly changed the water resources assessment and management methodologies. Remote sensing techniques have been widely used to delineate the surface water bodies, estimate meteorological variables like temperature and precipitation, estimate hydrological state variables like soil moisture and land surface characteristics, and to estimate fluxes such as evapotranspiration. Today, near-real time monitoring of flood, drought events, and irrigation management are possible with the help of high resolution satellite data. This paper gives a brief overview of the potential applications of remote sensing in water resources.

Improved modeling of groundwater recharge in agricultural watersheds using a combination of crop model and remote sensing

For improved water management and efficiency of use in agriculture, studies dealing with coupled crop-surface water-groundwater models are needed. Such integrated models of crop and hydrology can provide accurate quantification of spatio-temporal variations of water balance parameters such as soil moisture store, evapotranspiration and recharge in a catchment. Performance of a coupled crop-hydrology model would depend on the availability of a calibrated crop model for various irrigated/rainfed crops and also on an accurate knowledge of soil hydraulic parameters in the catchment at relevant scale. Moreover, such a coupled model should be designed so as to enable the use/assimilation of recent satellite remote sensing products (optical and microwave) in order to model the processes at catchment scales. In this study we present a framework to couple a crop model with a groundwater model for applications to irrigated groundwater agricultural systems. We discuss the calibration of the STICS crop model and present a methodology to estimate the soil hydraulic parameters by inversion of crop model using both ground and satellite based data. Using this methodology we demonstrate the feasibility of estimation of potential recharge due to spatially varying soil/crop matrix.