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.

Photoacoustic and thermoacoustic signal characteristics study

Photoacoustic/thermoacoustic imaging is an emerging hybrid imaging modality combining optical/microwave imaging with ultrasound imaging. The photoacoustic/thermoacoustic signal generated are affected by the nature of excitation pulse waveform, pulse width, target object size, transducer size etc. In this study k-wave was used to simulate various configurations of excitation pulse, transducer types, and target object sizes and to see their effect on the photoacoustic/thermoacoustic signals. Numerical blood vessel phantom was also used to see the effect of various pulse waveform and excitation pulse width on the reconstructed images. This study will help in optimizing transducer design and reconstruction methods to obtain the superior reconstructed image.

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.

On Computing Amplitude, Phase, and Frequency Modulations Using a Vector Interpretation of the Analytic Signal

The amplitude-modulation (AM) and phase-modulation (PM) of an amplitude-modulated frequency-modulated (AM-FM) signal are defined as the modulus and phase angle, respectively, of the analytic signal (AS). The FM is defined as the derivative of the PM. However, this standard definition results in a PM with jump discontinuities in cases when the AM index exceeds unity, resulting in an FM that contains impulses. We propose a new approach to define smooth AM, PM, and FM for the AS, where the PM is computed as the solution to an optimization problem based on a vector interpretation of the AS. Our approach is directly linked to the fractional Hilbert transform (FrHT) and leads to an eigenvalue problem. The resulting PM and AM are shown to be smooth, and in particular, the AM turns out to be bipolar. We show an equivalence of the eigenvalue formulation to the square of the AS, and arrive at a simple method to compute the smooth PM. Some examples on synthesized and real signals are provided to validate the theoretical calculations.

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.

Membership models and the design of authentication protocols for MANETs

Authentication protocols are very much essential for secure communication in mobile ad hoc networks (MANETs). A number of authentication protocols for MANETs have been proposed in the literature which provide the basic authentication service while trying to optimize their performance and resource consumption parameters. A problem with most of these protocols is that the underlying networking environment on which they are applicable have been left unspecified. As a result, lack of specifications about the networking environments applicable to an authentication protocol for MANETs can mislead about the performance and the applicability of the protocol. In this paper, we first characterize networking environment for a MANET as its 'Membership Model' which is defined as a set of specifications related to the 'Membership Granting Server' (MGS) and the 'Membership Set Pattern' (MSP) of the MANET. We then identify various types of possible membership models for a MANET. In order to illustrate that while designing an authentication protocol for a MANET, it is very much necessary to consider the underlying membership model of the MANET, we study a set of six representative authentication protocols, and analyze their applicability for the membership models as enumerated in this paper. The analysis shows that the same protocol may not perform equally well in all membership models. In addition, there may be membership models which are important from the point of view of users, but for which no authentication protocol is available.

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.

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.