Coordination self-assembly of tetranuclear Pt(II) macrocycles with an organometallic backbone for sensing of acyclic dicarboxylic acids

Coordination self-assembly of a series of tetranuclear Pt(II) macrocycles containing an organometallic backbone incorporating ethynyl functionality is presented. The 1 : 1 combination of a linear acceptor 1,4-bistrans-Pt(PEt3)(2)(NO3)(ethynyl)]benzene (1) with three different dipyridyl donor `clips' (L-a-L-c) afforded three 2 + 2] self-assembled Pt-4(II) macrocycles (2a-2c) in quantitative yields, respectively L-a = 1,3-bis-(3-pyridyl)isothalamide; L-b = 1,3-bis(3-pyridyl)ethynylbenzene; L-c = 1,8-bis(4-pyridyl)ethynylanthracene]. These macrocycles were characterized by multinuclear NMR (H-1 and P-31); ESI-MS spectroscopy and the molecular structures of 2a and 2b were established by single crystal X-ray diffraction analysis. These macrocycles (2a-2c) are fluorescent in nature. The amide functionalized macrocycle 2a is used as a receptor to check the binding affinity of aliphatic acyclic dicarboxylic acids. Such binding affinity is examined using fluorescence and UV-Vis spectroscopic methods. A solution state fluorescence study showed that macrocycle 2a selectively binds (K-SV = 1.4 x 10(4) M-1) maleic acid by subsequent enhancement in emission intensity. Other aliphatic dicarboxylic acids such as fumaric, succinic, adipic, mesaconic and itaconic acids caused no change in the emission spectra; thereby demonstrating its potential use as a macrocyclic receptor in distinction of maleic acid from other aliphatic dicarboxylic acids.

Error exponent analysis of energy-based bayesian spectrum sensing under fading channels

This paper analyzes the error exponents in Bayesian decentralized spectrum sensing, i.e., the detection of occupancy of the primary spectrum by a cognitive radio, with probability of error as the performance metric. At the individual sensors, the error exponents of a Central Limit Theorem (CLT) based detection scheme are analyzed. At the fusion center, a K-out-of-N rule is employed to arrive at the overall decision. It is shown that, in the presence of fading, for a fixed number of sensors, the error exponents with respect to the number of observations at both the individual sensors as well as at the fusion center are zero. This motivates the development of the error exponent with a certain probability as a novel metric that can be used to compare different detection schemes in the presence of fading. The metric is useful, for example, in answering the question of whether to sense for a pilot tone in a narrow band (and suffer Rayleigh fading) or to sense the entire wide-band signal (and suffer log-normal shadowing), in terms of the error exponent performance. The error exponents with a certain probability at both the individual sensors and at the fusion center are derived, with both Rayleigh as well as log-normal shadow fading. Numerical results are used to illustrate and provide a visual feel for the theoretical expressions obtained.

Compressive sensing for music signals: comparison of transforms with coherent dictionaries

Compressive Sensing (CS) is a new sensing paradigm which permits sampling of a signal at its intrinsic information rate which could be much lower than Nyquist rate, while guaranteeing good quality reconstruction for signals sparse in a linear transform domain. We explore the application of CS formulation to music signals. Since music signals comprise of both tonal and transient nature, we examine several transforms such as discrete cosine transform (DCT), discrete wavelet transform (DWT), Fourier basis and also non-orthogonal warped transforms to explore the effectiveness of CS theory and the reconstruction algorithms. We show that for a given sparsity level, DCT, overcomplete, and warped Fourier dictionaries result in better reconstruction, and warped Fourier dictionary gives perceptually better reconstruction. “MUSHRA” test results show that a moderate quality reconstruction is possible with about half the Nyquist sampling.

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.

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.

Network congestion management by fuzzy inference using virtual flows

Effective network overload alleviation is very much essential in order to maintain security and integrity from the operational viewpoint of deregulated power systems. This paper aims at developing a methodology to reschedule the active power generation from the sources in order to manage the network congestion under normal/contingency conditions. An effective method has been proposed using fuzzy rule based inference system. Using virtual flows concept, which provides partial contributions/counter flows in the network elements is used as a basis in the proposed method to manage network congestions to the possible extent. The proposed method is illustrated on a sample 6 bus test system and on modified IEEE 39 bus system.

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.

Combining product information and process information to build virtual assembly situations for knowledge acquisition

Assembly is an important part of the product development process. To avoid potential issues during assembly in specialized domains such as aircraft assembly, expert knowledge to predict such issues is helpful. Knowledge based systems can act as virtual experts to provide assistance. Knowledge acquisition for such systems however, is a challenge, and this paper describes one part of an ongoing research to acquire knowledge through a dialog between an expert and a knowledge acquisition system. In particular this paper discusses the use of a situation model for assemblies to present experts with a virtual assembly and help them locate the specific context of the knowledge they provide to the system.

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.

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.

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.%.