Wide Voltage Input Receiver with Hysteresis Characteristic to Reduce Input Signal Noise Effect

In this paper, an input receiver with a hysteresis characteristic that can work at voltage levels between 0.9 V and 5 V is proposed. The input receiver can be used as a wide voltage range Schmitt trigger also. At the same time, reliable circuit operation is ensured. According to the research findings, this is the first time a wide voltage range Schmitt trigger is being reported. The proposed circuit is compared with previously reported input receivers, and it is shown that the circuit has better noise immunity. The proposed input receiver ends the need for a separate Schmitt trigger and input buffer. The frequency of operation is also higher than that of the previously reported receiver. The circuit is simulated using HSPICE at 035-mu m standard thin oxide technology. Monte Carlo analysis is conducted at different process conditions, showing that the proposed circuit works well for different process conditions at different voltage levels of operation. A noise impulse of (V-CC/2) magnitude is added to the input voltage to show that the receiver receives the correct logic level even in the presence of noise. Here, V-CC is the fixed voltage supply of 3.3 V.

High-resolution restoration of 3D structures from widefield images with extreme low signal-to-noise-ratio

Four-dimensional fluorescence microscopy-which records 3D image information as a function of time-provides an unbiased way of tracking dynamic behavior of subcellular components in living samples and capturing key events in complex macromolecular processes. Unfortunately, the combination of phototoxicity and photobleaching can severely limit the density or duration of sampling, thereby limiting the biological information that can be obtained. Although widefield microscopy provides a very light-efficient way of imaging, obtaining high-quality reconstructions requires deconvolution to remove optical aberrations. Unfortunately, most deconvolution methods perform very poorly at low signal-to-noise ratios, thereby requiring moderate photon doses to obtain acceptable resolution. We present a unique deconvolution method that combines an entropy-based regularization function with kernels that can exploit general spatial characteristics of the fluorescence image to push the required dose to extreme low levels, resulting in an enabling technology for high-resolution in vivo biological imaging.

Combining ``chimie douce'' and green principles for the developing world: Improving industrial viability of photocatalytic water remediation

We highlight the need for a comprehensive, multi-disciplinary approach for the development of cost-effective water remediation methods. Combining ``chimie douce'' and green chemical principles seems essential for making these technologies economically viable and socially relevant (especially in the developing world). A comprehensive approach to water remediation will take into account issues such as nanotoxicity, chemical yield, cost, and ease of deployment in reactors. By considering technological challenges that lie ahead, we will attempt to identify directions that are likely to make photocatalytic water remediation a more global technology than it currently is. (C) 2013 Elsevier Ltd. All rights reserved

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.

Biological sulfate reduction of a sulfate-rich industrial waste liquor using sulfate reducing bacteria

An industrial waste liquor having high sulfate concentrations was subjected to biological treatment using the sulfate-reducing bacteria (SRB) Desulfovibrio desulfuricans. Toxicity levels of different sulfate, cobalt and nickel concentrations toward growth of the SRB with respect to biological sulfate reduction kinetics was initially established. Optimum sulfate concentration to promote SRB growth amounted to 0.8 - 1 g/L. The strain of D. desulfuricans used in this study initially tolerated up to 4 -5 g/L of sulfate or 50 mg/L of cobalt and nickel, while its tolerance could be further enhanced through adaptation by serial subculturing in the presence of increasing concentrations of sulfate, cobalt and nickel. From the waste liquor, more than 70% of sulfate and 95% of cobalt and nickel could be precipitated as sulfides, using a preadapted strain of D. desulfuricans. Probable mechanisms involving biological sulfide precipitation and metal adsorption onto precipitates and bacterial cells are discussed.

Experimental Study on Substrate Noise Effects of a Pulsed Clocking Scheme on PLL Performance

In this brief, the substrate noise effects of a pulsed clocking scheme on the output spur level, the phase noise, and the peak-to-peak (Pk-Pk) deterministic period jitter of an integer-N charge-pump phase-locked loop (PLL) are demonstrated experimentally. The phenomenon of noise coupling to the PLL is also explained through experiments. The PLL output frequency is 500 MHz and it is implemented in the 0.13-mu m CMOS technology. Measurements show a reduction of 12.53 dB in the PLL output spur level at an offset of 5 MHz and a reduction of 107 ps in the Pk-Pk deterministic period jitter upon reducing the duty cycle of the signal injected into the substrate from 50% to 20%. The results of the analyses suggest that using a pulsed clocking scheme for digital systems in mixed-signal integration along with other isolation techniques helps reduce the substrate noise effects on sensitive analog/radio-frequency circuits.

Origin of noise in two dimensionally doped Silicon and Germanium

We present the study of low-frequency noise, or 1/f noise, in degenerately doped Si: P and Ge: P delta-layers at low temperatures. For the Si: P d-layers we find that the noise is several orders of magnitude lower than that of bulk Si: P systems in the metallic regime and is one of the lowest values reported for doped semiconductors. Ge: P d-layers as a function of perpendicular magnetic field, shows a factor of two reduction in noise magnitude at the scale of B-phi, where B-phi is phase breaking field. We show that this is a characteristic feature of universal conductance fluctuations.

Nonequilibrium noise in electrophoresis: The microion wind

A colloid supported against gravitational settling by means of an imposed electric field behaves, on average, as if it is at equilibrium in a confining potential T. M. Squires, J. Fluid Mech. 443, 403 (2001)]. We show, however, that the effective Langevin equation for the colloid contains a nonequilibrium noise source, proportional to the field, arising from the thermal motion of dissolved ions. The position fluctuations of the colloid show strong, experimentally testable signatures of nonequilibrium behavior, including a highly anisotropic, frequency-dependent ``effective temperature'' obtained from the fluctuation-dissipation ratio.

A Low-Cost System for Measurement and Spectral Analysis of Motor Acoustic Noise

Workplace noise has become one of the major issues in industry not only because of workers’ health but also due to safety. Electric motors, particularly, inverter fed induction motors emit objectionably high levels of noise. This has led to the emergence of a research area, concerned with measurement and mitigation of the acoustic noise. This paper presents a lowcost option for measurement and spectral analysis of acoustic noise emitted by electric motors. The system consists of an electret microphone, amplifier and filter. It makes use of the windows sound card and associated software for data acquisition and analysis. The measurement system is calibrated using a professional sound level meter. Acoustic noise measurements are made on an induction motor drive using the proposed system as per relevant international standards. These measurements are seen to match closely with those of a professional meter.

Three-dimensional localization of multiple acoustic sources in shallow ocean with non-Gaussian noise

In this paper, a low-complexity algorithm SAGE-USL is presented for 3-dimensional (3-D) localization of multiple acoustic sources in a shallow ocean with non-Gaussian ambient noise, using a vertical and a horizontal linear array of sensors. In the proposed method, noise is modeled as a Gaussian mixture. Initial estimates of the unknown parameters (source coordinates, signal waveforms and noise parameters) are obtained by known/conventional methods, and a generalized expectation maximization algorithm is used to update the initial estimates iteratively. Simulation results indicate that convergence is reached in a small number of (<= 10) iterations. Initialization requires one 2-D search and one 1-D search, and the iterative updates require a sequence of 1-D searches. Therefore the computational complexity of the SAGE-USL algorithm is lower than that of conventional techniques such as 3-D MUSIC by several orders of magnitude. We also derive the Cramer-Rao Bound (CRB) for 3-D localization of multiple sources in a range-independent ocean. Simulation results are presented to show that the root-mean-square localization errors of SAGE-USL are close to the corresponding CRBs and significantly lower than those of 3-D MUSIC. (C) 2014 Elsevier Inc. All rights reserved.

Conductivity noise study of the insulator-metal transition and phase coexistence in epitaxial samarium nickelate thin films

Interaction between the lattice and the orbital degrees of freedom not only makes rare-earth nickelates unusually ``bad metal,'' but also introduces a temperature-driven insulator-metal phase transition. Here we investigate this insulator-metal phase transition in thin films of SmNiO3 using the slow time-dependent fluctuations (noise) in resistivity. The normalized magnitude of noise is found to be extremely large, being nearly eight orders of magnitude higher than thin films of common disordered metallic systems, and indicates electrical conduction via classical percolation in a spatially inhomogeneous medium. The higher-order statistics of the fluctuations indicate a strong non-Gaussian component of noise close to the transition, attributing the inhomogeneity to the coexistence of the metallic and insulating phases. Our experiment offers insight into the impact of lattice-orbital coupling on the microscopic mechanism of electron transport in the rare-earth nickelates.

Noise Detected NMR Spectroscopy

Spin noise phenomenon was predicted way back in 1946. However, experimental investigations regarding spin noise became possible only recently with major technological improvements in NMR hardware. These experiments have several potential novel applications and also demand refinements in the existing theoretical framework to explain the phenomenon. Elegance of noise spectroscopy in gathering information about the properties of a system lies in the fact that it does not require external perturbation, and the system remains in thermal equilibrium. Spin noise is intrinsic magnetic fluctuations, and both longitudinal and transverse components have been detected independently in many systems. Detection of fluctuating longitudinal magnetization leads to field of Magnetic Resonance Force Microscopy (MRFM) that can efficiently probe very few spins even down to the level of single spin utilizing ultrasensitive cantilevers. Transverse component of spin noise, which can simultaneously monitor different resonances over a given frequency range enabling one to distinguish between different chemical environments, has also received considerable attention, and found many novel applications. These experiments demand a detailed understanding of the underlying spin noise phenomenon in order to perform perturbation-free magnetic resonance and widen the highly promising application area. Detailed investigations of noise magnetization have been performed recently using force microscopy on equilibrium ensemble of paramagnetic alkali atoms. It was observed that random fluctuations generate spontaneous spin coherences which has similar characteristics as generated by macroscopic magnetization of polarized ensemble in terms of precession and relaxation properties. Several other intrinsic properties like g-factors, isotope-abundance ratios, hyperfine splitting, spin coherence lifetimes etc. also have been achieved without having to excite the sample. In contrast to MRFM-approaches, detection of transverse spin noise also offers novel applications, attracting considerable attention. This has unique advantage as different resonances over a given frequency range enable one to distinguish between different chemical environments. Since these noise signatures scale inversely with sample size, these approaches lead to the possibility of non-perturbative magnetic resonance of small systems down to nano-scale. In this review, these different approaches will be highlighted with main emphasis on transverse spin noise investigations.

Optimal control of grinding mill circuit using model predictive static programming: A new nonlinear MPC paradigm

The recently developed reference-command tracking version of model predictive static programming (MPSP) is successfully applied to a single-stage closed grinding mill circuit. MPSP is an innovative optimal control technique that combines the philosophies of model predictive control (MPC) and approximate dynamic programming. The performance of the proposed MPSP control technique, which can be viewed as a `new paradigm' under the nonlinear MPC philosophy, is compared to the performance of a standard nonlinear MPC technique applied to the same plant for the same conditions. Results show that the MPSP control technique is more than capable of tracking the desired set-point in the presence of model-plant mismatch, disturbances and measurement noise. The performance of MPSP and nonlinear MPC compare very well, with definite advantages offered by MPSP. The computational speed of MPSP is increased through a sequence of innovations such as the conversion of the dynamic optimization problem to a low-dimensional static optimization problem, the recursive computation of sensitivity matrices and using a closed form expression to update the control. To alleviate the burden on the optimization procedure in standard MPC, the control horizon is normally restricted. However, in the MPSP technique the control horizon is extended to the prediction horizon with a minor increase in the computational time. Furthermore, the MPSP technique generally takes only a couple of iterations to converge, even when input constraints are applied. Therefore, MPSP can be regarded as a potential candidate for online applications of the nonlinear MPC philosophy to real-world industrial process plants. (C) 2014 Elsevier Ltd. All rights reserved.

Effect of measurement noise on Granger causality

Most of the signals recorded in experiments are inevitably contaminated by measurement noise. Hence, it is important to understand the effect of such noise on estimating causal relations between such signals. A primary tool for estimating causality is Granger causality. Granger causality can be computed by modeling the signal using a bivariate autoregressive (AR) process. In this paper, we greatly extend the previous analysis of the effect of noise by considering a bivariate AR process of general order p. From this analysis, we analytically obtain the dependence of Granger causality on various noise-dependent system parameters. In particular, we show that measurement noise can lead to spurious Granger causality and can suppress true Granger causality. These results are verified numerically. Finally, we show how true causality can be recovered numerically using the Kalman expectation maximization algorithm.