Carbon Nanotube based Composite Fibers for Strain Sensing, Signal Processing and Computing

Carbon nanotubes dispersed in polymer matrix have been aligned in the form of fibers and interconnects and cured electrically and by UV light. Conductivity and effective semiconductor tunneling against reverse to forward bias field have been designed to have differentiable current-voltage response of each of the fiber/channel. The current-voltage response is a function of the strain applied to the fibers along axial direction. Biaxial and shear strains are correlated by differentiating signals from the aligned fibers/channels. Using a small doping of magnetic nanoparticles in these composite fibers, magneto-resistance properties are realized which are strong enough to use the resulting magnetostriction as a state variable for signal processing and computing. Various basic analog signal processing tasks such as addition, convolution and filtering etc. can be performed. These preliminary study shows promising application of the concept in combined analog-digital computation in carbon nanotube based fibers. Various dynamic effects such as relaxation, electric field dependent nonlinearities and hysteresis on the output signals are studied using experimental data and analytical model.

Signal-Powered Low-Drop-Diode Equivalent Circuit for Full-Wave Bridge Rectifier

Piezoelectric-device-based vibration energy harvesting requires a rectifier for conversion of input ac to usable dc form. Power loss due to diode drop in rectifier is a significant fraction of the already low levels of harvested power. The proposed circuit is a low-drop-diode equivalent, which mimics a diode using linear region-operated MOSFET. The proposed diode equivalent is powered directly from input signal and requires no additional power supply for its control. Power used by the control circuit is kept at a bare minimum to have an overall output power improvement. Diode equivalent was used to replace the four diodes in a full-wave bridge rectifier, which is the basic full- wave rectifier and is a part of the more advanced rectifiers like switch-only and bias-flip rectifiers. Simulation in 130-nm technology and experiment with discrete components show that a bridge rectifier with the proposed diode provides a 30-169% increase in output power extracted from piezoelectric device, as compared to a bridge rectifier with diode-connected MOSFETs. The bridge rectifier with the proposed diode can extract 90% of the maximum available power from an ideal piezoelectric device-bridge rectifier circuit. Setting aside the constraint of power loss, simulations indicate that diode drop as low as 10 mV at 38 mu A can be achieved.

A Technique to Compute Smooth Amplitude, Phase, and Frequency Modulations From the Analytic Signal

Gabor's analytic signal (AS) is a unique complex signal corresponding to a real signal, but in general, it admits infinitely-many combinations of amplitude and frequency modulations (AM and FM, respectively). The standard approach is to enforce a non-negativity constraint on the AM, but this results in discontinuities in the corresponding phase modulation (PM), and hence, an FM with discontinuities particularly when the underlying AM-FM signal is over-modulated. In this letter, we analyze the phase discontinuities and propose a technique to compute smooth AM and FM from the AS, by relaxing the non-negativity constraint on the AM. The proposed technique is effective at handling over-modulated signals. We present simulation results to support the theoretical calculations.

The origins of the evolutionary signal used to predict protein-protein interactions

Background: The correlation of genetic distances between pairs of protein sequence alignments has been used to infer protein-protein interactions. It has been suggested that these correlations are based on the signal of co-evolution between interacting proteins. However, although mutations in different proteins associated with maintaining an interaction clearly occur (particularly in binding interfaces and neighbourhoods), many other factors contribute to correlated rates of sequence evolution. Proteins in the same genome are usually linked by shared evolutionary history and so it would be expected that there would be topological similarities in their phylogenetic trees, whether they are interacting or not. For this reason the underlying species tree is often corrected for. Moreover processes such as expression level, are known to effect evolutionary rates. However, it has been argued that the correlated rates of evolution used to predict protein interaction explicitly includes shared evolutionary history; here we test this hypothesis. Results: In order to identify the evolutionary mechanisms giving rise to the correlations between interaction proteins, we use phylogenetic methods to distinguish similarities in tree topologies from similarities in genetic distances. We use a range of datasets of interacting and non-interacting proteins from Saccharomyces cerevisiae. We find that the signal of correlated evolution between interacting proteins is predominantly a result of shared evolutionary rates, rather than similarities in tree topology, independent of evolutionary divergence. Conclusions: Since interacting proteins do not have tree topologies that are more similar than the control group of non-interacting proteins, it is likely that coevolution does not contribute much to, if any, of the observed correlations.

SPARSE SIGNAL RECONSTRUCTION BASED ON SIGNAL DEPENDENT NON-UNIFORM SAMPLES

The classical approach to A/D conversion has been uniform sampling and we get perfect reconstruction for bandlimited signals by satisfying the Nyquist Sampling Theorem. We propose a non-uniform sampling scheme based on level crossing (LC) time information. We show stable reconstruction of bandpass signals with correct scale factor and hence a unique reconstruction from only the non-uniform time information. For reconstruction from the level crossings we make use of the sparse reconstruction based optimization by constraining the bandpass signal to be sparse in its frequency content. While overdetermined system of equations is resorted to in the literature we use an undetermined approach along with sparse reconstruction formulation. We could get a reconstruction SNR > 20dB and perfect support recovery with probability close to 1, in noise-less case and with lower probability in the noisy case. Random picking of LC from different levels over the same limited signal duration and for the same length of information, is seen to be advantageous for reconstruction.

Robust GNSS signal detection in the presence of navigation data bits

This paper considers the problem of weak signal detection in the presence of navigation data bits for Global Navigation Satellite System (GNSS) receivers. Typically, a set of partial coherent integration outputs are non-coherently accumulated to combat the effects of model uncertainties such as the presence of navigation data-bits and/or frequency uncertainty, resulting in a sub-optimal test statistic. In this work, the test-statistic for weak signal detection is derived in the presence of navigation data-bits from the likelihood ratio. It is highlighted that averaging the likelihood ratio based test-statistic over the prior distributions of the unknown data bits and the carrier phase uncertainty leads to the conventional Post Detection Integration (PDI) technique for detection. To improve the performance in the presence of model uncertainties, a novel cyclostationarity based sub-optimal PDI technique is proposed. The test statistic is analytically characterized, and shown to be robust to the presence of navigation data-bits, frequency, phase and noise uncertainties. Monte Carlo simulation results illustrate the validity of the theoretical results and the superior performance offered by the proposed detector in the presence of model uncertainties.

A finite-rate-of-innovation signal sampling perspective of the source-filter model of speech production

We consider the speech production mechanism and the asso- ciated linear source-filter model. For voiced speech sounds in particular, the source/glottal excitation is modeled as a stream of impulses and the filter as a cascade of second-order resonators. We show that the process of sampling speech signals can be modeled as filtering a stream of Dirac impulses (a model for the excitation) with a kernel function (the vocal tract response),and then sampling uniformly. We show that the problem of esti- mating the excitation is equivalent to the problem of recovering a stream of Dirac impulses from samples of a filtered version. We present associated algorithms based on the annihilating filter and also make a comparison with the classical linear prediction technique, which is well known in speech analysis. Results on synthesized as well as natural speech data are presented.

Some new results on signal reconstruction from Fourier transform magnitude spectrum

We address the problem of signal reconstruction from Fourier transform magnitude spectrum. The problem arises in many real-world scenarios where magnitude-only measurements are possible, but it is required to construct a complex-valued signal starting from those measurements. We present some new general results in this context and show that the previously known results on minimum-phase rational transfer functions, and recoverability of minimum-phase functions from magnitude spectrum, form special cases of the results reported in this paper. Some simulation results are also provided to demonstrate the practical feasibility of the reconstruction methodology.

Channel training signal design for reciprocal multiple antenna systems with beamforming

Fast and efficient channel estimation is key to achieving high data rate performance in mobile and vehicular communication systems, where the channel is fast time-varying. To this end, this work proposes and optimizes channel-dependent training schemes for reciprocal Multiple-Input Multiple-Output (MIMO) channels with beamforming (BF) at the transmitter and receiver. First, assuming that Channel State Information (CSI) is available at the receiver, a channel-dependent Reverse Channel Training (RCT) signal is proposed that enables efficient estimation of the BF vector at the transmitter with a minimum training duration of only one symbol. In contrast, conventional orthogonal training requires a minimum training duration equal to the number of receive antennas. A tight approximation to the capacity lower bound on the system is derived, which is used as a performance metric to optimize the parameters of the RCT. Next, assuming that CSI is available at the transmitter, a channel-dependent forward-link training signal is proposed and its power and duration are optimized with respect to an approximate capacity lower bound. Monte Carlo simulations illustrate the significant performance improvement offered by the proposed channel-dependent training schemes over the existing channel-agnostic orthogonal training schemes.

Effect of signal peptide on stability and folding of escherichia coli thioredoxin

The signal peptide plays a key role in targeting and membrane insertion of secretory and membrane proteins in both prokaryotes and eukaryotes. In E. coli, recombinant proteins can be targeted to the periplasmic space by fusing naturally occurring signal sequences to their N-terminus. The model protein thioredoxin was fused at its N-terminus with malE and pelB signal sequences. While WT and the pelB fusion are soluble when expressed, the malE fusion was targeted to inclusion bodies and was refolded in vitro to yield a monomeric product with identical secondary structure to WT thioredoxin. The purified recombinant proteins were studied with respect to their thermodynamic stability, aggregation propensity and activity, and compared with wild type thioredoxin, without a signal sequence. The presence of signal sequences leads to thermodynamic destabilization, reduces the activity and increases the aggregation propensity, with malE having much larger effects than pelB. These studies show that besides acting as address labels, signal sequences can modulate protein stability and aggregation in a sequence dependent manner.

Cognitive radio implementation for a frequency hopping primary signal

Frequency hopping communications, used in the military present significant opportunities for spectrum reuse via the cognitive radio technology. We propose a MAC which incorporates hop instant identification, and supports network discovery and formation, QOS Scheduling and secondary communications. The spectrum sensing algorithm is optimized to deal with the problem of spectral leakage. The algorithms are implemented in a SDR platform based test bed and measurement results are presented.

Single electron transfer-driven multi-dimensional signal read-out function of TCNQ as an ``off-the-shelf'' detector for cyanide

Herein we report the first applications of TCNQ as a rapid and highly sensitive off-the-shelf cyanide detector. As a proof-of-concept, we have applied a kinetically selective single-electron transfer (SET) from cyanide to deep-lying LUMO orbitals of TCNQ to generate a persistently stable radical anion (TCNQ(center dot-)), under ambient condition. In contrast to the known cyanide sensors that operate with limited signal outputs, TCNQ(center dot-) offers a unique multiple signaling platform. The signal readability is facilitated through multichannel absorption in the UV-vis-NIR region and scattering-based spectroscopic methods like Raman spectroscopy and hyper Rayleigh scattering techniques. Particularly notable is the application of the intense 840 nm NIR absorption band to detect cyanide. This can be useful for avoiding background interference in the UV-vis region predominant in biological samples. We also demonstrate the fabrication of a practical electronic device with TCNQ as a detector. The device generates multiorder enhancement in current with cyanide because of the formation of the conductive TCNQ(center dot-).

Narrowband signal detection techniques in shallow ocean by acoustic vector sensor array

This paper presents the formulation and performance analysis of four techniques for detection of a narrowband acoustic source in a shallow range-independent ocean using an acoustic vector sensor (AVS) array. The array signal vector is not known due to the unknown location of the source. Hence all detectors are based on a generalized likelihood ratio test (GLRT) which involves estimation of the array signal vector. One non-parametric and three parametric (model-based) signal estimators are presented. It is shown that there is a strong correlation between the detector performance and the mean-square signal estimation error. Theoretical expressions for probability of false alarm and probability of detection are derived for all the detectors, and the theoretical predictions are compared with simulation results. It is shown that the detection performance of an AVS array with a certain number of sensors is equal to or slightly better than that of a conventional acoustic pressure sensor array with thrice as many sensors.

Looking for an invisible Higgs signal at the LHC

While the recent discovery of a Higgs-like boson at the LHC is an extremely important and encouraging step towards the discovery of the complete Standard Model (SM), the current information on this state does not rule out possibility of beyond standard model (BSM) physics. In fact the current data can still accommodate reasonably large values of the branching fractions of the Higgs into a channel with `invisible' decay products, such a channel being also well motivated theoretically. In this study we revisit the possibility of detecting the Higgs in this invisible channel for both choices of the LHC energies, 8 and 14 TeV, for two production modes; vector boson fusion (VBF) and associated production (ZH). We perform a comprehensive collider analysis for all the above channels and project the reach of LHC to constrain the invisible decay branching fraction for both 8 and 14 TeV energies. For the ZH case we consider decays of the Z boson into a pair of leptons as well as a b (b) over bar pair. For the VBF channel the sensitivity is found to be more than 5 sigma for both the energies up to an invisible branching ratio (Br-inv) similar to 0.80, with luminosities similar to 20/30 fb(-1). The sensitivity is further extended to values of Br-inv similar to 0.25 for 300 fb(-1) at 14 TeV. However the reach is found to be more modest for the ZH mode with leptonic final state; with about 3.5 sigma for the planned luminosity at 8 TeV, reaching 8 sigma only for 14 TeV for 50 fb(-1). In spite of the much larger branching ratio (BR) of the Z into a b (b) over bar channel compared to the dilepton case, the former channel, can provide useful reach up to Br-inv greater than or similar to 0.75, only for the higher luminosity (300 fb(-1)) option using both jet-substructure and jet clustering methods. (C) 2013 Elsevier B.V. All rights reserved.