Low SNRs Research Articles

A framework for accurate evaluation of the promise of aquaporin based biomimetic membranes

Aquaporin based membranes (ABMs) are considered a promising biomimetic desalination technology and have been intensively studied over the last few years. The most common strategy to synthesize ABMs is to deposit the aquaporin incorporated lipid or block copolymer (BCP) vesicles onto porous substrates or more recently to integrate them within the active layer of polyamide membranes. However, ABMs with orders of magnitude improvement in permeability and perfect salt rejections proposed in initial work have not been realized. Early results were based on materials and methods that were rudimentary, especially considering the progress that has been made in this field. In particular, low signal to noise ratios (SNRs <50) of stopped flow measurements for vesicle-based assays have led to large inaccuracies in permeability estimation. We show that such low SNRs can result from using vesicle samples with a high concentration of micelles and provide a connection between morphology and data quality. We have conducted a comprehensive evaluation of the true promise of these membranes using improved methods for polymer synthesis, self-assembly, experimental evaluation as well as calculations that more directly compare the outcome of biophysical evaluations to those used in the desalination membrane industry. We propose these as standard methods for use in ABM research. The role of concentration polarization in introducing error into vesicle based permeability measurements is identified. We further describe a simple technique to calculate the expected flux from a membrane synthesized using vesicle immobilization on a permeable substrate that can be used to estimate realistic membrane fluxes from stopped flow data. These calculations show that it is possible to achieve permeabilities one to two orders of magnitude higher than current membranes using ABMs but several innovations will be needed to reach this potential.

보안 시스템을 위한 비명 검출 엔진 설계

Recently, the prevention of crime using CCTV draws special in accordance with the higher crime incidence rate. Therefore security systems like a CCTV with audio capability are developing for giving an instant alarm. This paper proposes a scream detecting engine from various ambient noises in real environment for surveillance systems. The proposed engine detects scream signals among the various ambient noises using the features extracted in time/frequency domain. The experimental result shows the performance of our engine is very promising in comparison with the traditional engines using the model based features like LPC, LPCC and MFCC. The proposed method has a low computational complexity by using FFT and cross correlation coefficients instead of extracting complex features like LPC, LPCC and MFCC. Therefore the proposed engine can be efficient for audio-based surveillance systems with low SNRs in real field.

Detection of Glottal Closure Instants Based on the Microcanonical Multiscale Formalism

This paper presents a novel algorithm for automatic detection of Glottal Closure Instants (GCI) from the speech signal. Our approach is based on a novel multiscale method that relies on precise estimation of a multiscale parameter at each time instant in the signal domain. This parameter quantifies the degree of signal singularity at each sample from a multi-scale point of view and thus its value can be used to classify signal samples accordingly. We use this property to develop a simple algorithm for detection of GCIs and we show that for the case of clean speech, our algorithm performs almost as well as a recent state-of-the-art method. Next, by performing a comprehensive comparison in presence of 14 different types of noises, we show that our method is more accurate (particularly for very low SNRs). Our method has lower computational times compared to others and does not rely on an estimate of pitch period or any critical choice of parameters.

Deficits in audiovisual speech perception in normal aging emerge at the level of whole-word recognition

Over the next 2 decades, a dramatic shift in the demographics of society will take place, with a rapid growth in the population of older adults. One of the most common complaints with healthy aging is a decreased ability to successfully perceive speech, particularly in noisy environments. In such noisy environments, the presence of visual speech cues (i.e., lip movements) provide striking benefits for speech perception and comprehension, but previous research suggests that older adults gain less from such audiovisual integration than their younger peers. To determine at what processing level these behavioral differences arise in healthy-aging populations, we administered a speech-in-noise task to younger and older adults. We compared the perceptual benefits of having speech information available in both the auditory and visual modalities and examined both phoneme and whole-word recognition across varying levels of signal-to-noise ratio. For whole-word recognition, older adults relative to younger adults showed greater multisensory gains at intermediate SNRs but reduced benefit at low SNRs. By contrast, at the phoneme level both younger and older adults showed approximately equivalent increases in multisensory gain as signal-to-noise ratio decreased. Collectively, the results provide important insights into both the similarities and differences in how older and younger adults integrate auditory and visual speech cues in noisy environments and help explain some of the conflicting findings in previous studies of multisensory speech perception in healthy aging. These novel findings suggest that audiovisual processing is intact at more elementary levels of speech perception in healthy-aging populations and that deficits begin to emerge only at the more complex word-recognition level of speech signals.

Simultaneous acquisition of high-rate early, middle, and late auditory evoked potentials.

Auditory evoked potentials (AEPs) are typically acquired at rates that facilitate their study as segregated by epochs relative to stimulus onset: early (ABR, 1.5-15 ms), middle (MLR, 15-60 ms), and late (LAEP, ≥60 ms) potentials. In particular, late AEPs are often acquired with stimulus repetition rates between 0.1 Hz and 1 Hz, and are bandpass filtered to contain information only within 1-30 Hz. These low repetition rates, filtering and low SNRs eliminate much of the potential contributions of the early and middle-latency responses in AEP recordings. This study aims to demonstrate a method for acquiring whole-AEP responses at higher stimulus repetition rates of 0.5 Hz to 10 Hz, by utilizing the Continuous Loop Averaging Deconvolution (CLAD) method, increasing the bandwidth of the recordings to 1-300 Hz to include early components, and using short-duration chirps to increase synchronous firing of the cochlear and auditory pathway neurons. Such a method may facilitate diagnostic or functional assessment of single AEP recordings for detection, identification, or evaluation of early, middle and late components of auditory responses.

Spectrum Sensing Based on Time-frequency Dispersion Analysis

A novel spectrum sensing algorithm is proposed based on time-frequency dispersion analysis. Timefrequency ridge line is flrstly calculated of a received data’s mutual correlation to form a series of timefrequency dots. Time-frequency dispersion function is subsequently deflned to describe the dispersion level of the time-frequency dots according to the fact that signal energy gather around the carrier frequency and noise energy disperse evenly among all frequencies. And the dispersion threshold is set to make an existence decision of primary user’s signal. Simulations indicates: the time-frequency dispersion is robust to noise power uncertainty, receiver’s bandwidth and behaves stability with enough data length; the proposed algorithm is efiective both for normal and fading channel and maintains good detecting performance at lower SNRs.

Structural similarity classifier for facial expression recognition

A novel Gabor filter structural similarity algorithm (GFSSIM) is proposed for facial expression recognition (FER) on noisy images. Low-resolution facial images with low SNRs are specifically dealt with FER system. The features are extracted using 40 Gabor filters, and a feature subset is selected for classification. The test image is classified based on proposed GFSSIM algorithm. The experimental results show that the recognition rate for heavily deteriorated images outperforms the conventional classifier method. In addition, the proposed method is more efficient from the computational complexity point of view.

Empirical Mode Decomposition of simulated and real ultrasonic Doppler signals of periodic fetal activity

Simulated signals comprising components (trains of Gaussian packets) resulting from cardiac movements and from pseudorespiratory movements with added white noise were submitted to Empirical Mode Decomposition. The increase of sampling frequency fs (from 0.5kHz to 5kHz) for given signal to noise ratio SNR moves signal components toward higher order intrinsic mode functions (IMFs) and increases their number. The increase of the SNR (from −5dB to 10dB, fixed fs) moves the signal components to lower order IMFs. The separation of components is most efficient for SNR≥5dB and fs not exceeding 1kHz, for lower SNRs fs should be at least 2kHz. SNR=∞ results in erroneous decomposition and therefore limited noise level is beneficial. Recommended number of sifting iterations is 10. Fetal data obtained using 2MHz emission frequency and sampled at 2kHz were decomposed. The cardiac signal always appears in IMF3, frequently also in IMF1 and IMF2. The pseudobreathing signal, appearing mainly in IMF4-6, is easy to separate. Signals resulting from fetal displacements due to maternal respiration appear in IMF7 or IMF8. The EMD performs better than the classic linear filtering as a tool for separation of the pseudorespiration signals and provides inferior results in terms of separation of the cardiac signals.

Manifold learning based speaker dependent dimension reduction for robust text independent speaker verification

Speaker verification has been studied widely from different points of view, including accuracy, robustness and being real-time. Recent studies have turned toward better feature stability and robustness. In this paper we study the effect of nonlinear manifold based dimensionality reduction for feature robustness. Manifold learning is a popular recent approach for nonlinear dimensionality reduction. Algorithms for this task are based on the idea that each data point may be described as a function of only a few parameters. Manifold learning algorithms attempt to uncover these parameters in order to find a low-dimensional representation of the data. From the manifold based dimension reduction approaches, we applied the widely used Isometric mapping (Isomap) algorithm. Since in the problem of speaker verification, the input utterance is compared with the model of the claiming client, a speaker dependent feature transformation would be beneficial for deciding on the identity of the speaker. Therefore, our first contribution is to use Isomap dimension reduction approach in the speaker dependent context and compare its performance with two other widely used approaches, namely principle component analysis and factor analysis. The other contribution of our work is to perform the nonlinear transformation in a speaker-dependent framework. We evaluated this approach in a GMM based speaker verification framework using Tfarsdat Telephone speech dataset for different noises and SNRs and the evaluations have shown reliability and robustness even in low SNRs. The results also show better performance for the proposed Isomap approach compared to the other approaches.

Generalized Spherical Array Beamforming for Binaural Speech Reproduction

Microphone arrays are used in speech signal processing applications such as teleconferencing and telepresence, in order to enhance a desired speech signal in the presence of speech signals from other speakers, reverberation and background noise. These arrays usually provide a single-channel output, so that no spatial information is available in the output signal. However, spatial information on the sound sources may increase the intelligibility of a speech signal perceived by a human listener. This work presents a mathematical framework for generalized spherical array beamforming that in addition to suppressing noise and reverberation, is aiming to preserve spatial information on the sources in the recording venue. The generalized beamforming, formulated in the spherical harmonics domain, is based on binaural sound reproduction where the head-related transfer functions are incorporated into a headphones presentation. The performance of the proposed generalized beamformer is compared to that of a single-channel output maximum-directivity beamformer. Listening tests with human subjects show that when the generalized beamformer is used the intelligibility is improved at low input SNRs.

Reduced complexity list sphere decoding for MIMO systems

Depth-first sphere decoding of MIMO systems has near maximum likelihood performance with reasonable computational complexity. In this paper, lower complexity depth-first sphere decoding and list sphere decoding algorithms are proposed. Several criteria for re-ordering the search dimensions are proposed. The proposed sphere decoders are shown to have a significantly reduced decoding complexity at low SNRs. To further reduce the complexity at high SNRs, the point search-space at each ordered dimension is adaptively reduced. Further reductions in the decoding complexity are achieved by inter-layer interference cancellation. It is shown that the proposed sphere decoding algorithms maintain their near-optimal performance, concurrently with a significant complexity reduction, over a wide SNR range.

Asymptotic Performance Analysis of Two-Way Opportunistic Relaying Channels

The work interests in the two-way opportunistic relaying (TWOR) system in which the analog network coding (ANC) employed. For the TWOR-ANC, we first present the traditional max-min best relay selection scheme as well as the corresponding closed-form analytical expressions for the cumulative distribution function (CDF) and the probability density function (PDF) of the end-to-end SNR. Then, to simplify the analysis of system performance, by using the approximation of the modified Bessel function of the second kind, , we obtain the performance bounds of the TWOR-ANC system. The analytical results show that e-z/z determines the lower bound of system performance, and the exact solution of system performance is very close to the lower bound in low SNRs. At the same time, 1/z does the upper bound, and the exact solution is very close to the upper bound in high SNRs. Thus, in high SNRs or lower SNRs, the system performance can be measured approximately by the upper bound or lower bound.

On the Performance Limits of Map-Aware Localization

Establishing bounds on the accuracy achievable by localization techniques represents a fundamental technical issue. Bounds on localization accuracy have been derived for cases in which the position of an agent is estimated on the basis of a set of observations and, possibly, of some a priori information related to them (e.g., information about anchor positions and properties of the communication channel). In this paper, new bounds are derived under the assumption that the localization system is map-aware, i.e., it can benefit not only from the availability of observations, but also from the a priori knowledge provided by the map of the environment where it operates. Our results show that: a) map-aware estimation accuracy can be related to some features of the map (e.g., its shape and area) even though, in general, the relation is complicated; b) maps are really useful in the presence of some combination of low SNRs and specific geometrical features of the map (e.g., the size of obstructions); c) in most cases, there is no need of refined maps since additional details do not improve estimation accuracy.

Blind frame synchronization of Reed–Solomon coded optical transmission systems

It is present in this paper a new blind frame synchronization method for optical packet transmission systems using Reed–Solomon (RS) codes in hard decision situations. The transmitter sends a sequence of RS coded packets without synchronization words to improve the spectral efficiency and the receiver blindly estimates the start position of each packet in the sequence to achieve frame synchronization. The proposed algorithm estimates the synchronization position based on the architecture of RS codes. If the synchronization is not correct, the packets could not form valid RS code words. According to this fact, the proposed algorithm estimates the synchronization position by searching the position that the packets from which contain the lowest error symbols. To estimate the error symbols number for a packet, we calculate the rank of the syndrome matrix which denotes how many errors occurred in a packet. The proposed algorithm is also compared with previous ones and the computer simulations show that our algorithm yields a good result especially on low SNRs.

Understanding the effect of noise on electrical stimulation sequences in cochlear implants and its impact on speech intelligibility

The present study investigates the most important factors that limit the intelligibility of the cochlear implant (CI) processed speech in noisy environments. The electrical stimulation sequences provided in CIs are affected by the noise in the following three manners. First of all, the natural gaps in the speech are filled, which distorts the low-frequency ON/OFF modulations of the speech signal. Secondly, speech envelopes are distorted to include modulations of both speech and noise. Lastly, the N-of-M type of speech coding strategies may select the noise dominated channels instead of the dominant speech channels at low signal-to-noise ratio's (SNRs). Different stimulation sequences are tested with CI subjects to study how these three noise effects individually limit the intelligibility of the CI processed speech. Tests are also conducted with normal hearing (NH) subjects using vocoded speech to identify any significant differences in the noise reduction requirements and speech distortion limitations between the two subject groups. Results indicate that compared to NH subjects CI subjects can tolerate significantly lower levels of steady state speech shaped noise in the speech gaps but at the same time can tolerate comparable levels of distortions in the speech segments. Furthermore, modulations in the stimulus current level have no effect on speech intelligibility as long as the channel selection remains ideal. Finally, wrong maxima selection together with the introduction of noise in the speech gaps significantly degrades the intelligibility. At low SNRs wrong maxima selection introduces interruptions in the speech and makes it difficult to fuse noisy and interrupted speech signals into a coherent speech stream.

Joint Voltage and Phase Unbalance Detector for Three Phase Power Systems

This letter develops a fast detection algorithm for voltage and phase unbalance in three phase power systems. It is suitable for real time applications since the required observation length is one cycle. It is shown to successfully detect small unbalance conditions at low SNRs. Its detection performance is shown to outperform traditional detectors that rely on changes in only a subset of positive, negative and zero sequence voltages. Unbalance detection is formulated as a hypothesis test under a framework of detection theory and solved by applying a generalized likelihood ratio test (GLRT). We first obtain an approximate maximum likelihood estimate (MLE) of the system frequency and then use it to substitute the true unknown frequency in the GLRT. A closed form expression is provided to detect unbalance conditions. Theoretical derivations are supported by simulations.

Sub‐optimal precoding scheme for distributed antenna systems in correlated Rayleigh channel

A sub-optimal precoding scheme for distributed antenna systems in a spatially correlated Rayleigh channel is developed by asymptotical analysis at low and high SNRs. A Newton's method based practical iterative algorithm is presented to find the power allocation coefficients in this scheme. Compared to the existing optimal scheme which requires multidimensional roots finding, the scheme developed in this reported work only needs one-dimensional root finding, and thus it has lower calculation complexity. Moreover, it can converge to the solution quickly, and obtain a performance close to a optimal scheme. Simulation results corroborate the effectiveness of the developed scheme.

基于非均匀周期采样的傅里叶望远镜时域信号采集方法

In order to clearly reconstruct targets in a dim and deep space under the condition of the Lower Signal to Noise(SNR),a new time domain signal acquisition method of Fourier Telescopy(FT) based on Non-uniform Periodic Sampling(NUPS) was proposed.The simulation experiments on the proposed method were performed and the reconstructed images by NUPS method and traditional uniform sample method were compared.With the NUPS method,100 points were respectively collected at the sampling frequency 1 MHz and 5 MHz.Then the signals of two sequences were respectively fast Fourier transformed and the concern frequency information was averaged.With traditional uniform sampling methods,200 points were collected at 1 MHz and 5 MHz sampling frequency and then demodulated average was developed.Compared results show that the Strehl ratio of reconstructed image with diffraction-limited image is enhanced 0.03 compared to the original method when SNR is 50,and the Strehl ratio is 0.531 1 that is improved 0.223 3 compared with uniform sampling when SNR is 20.Obviously,imaging quality is improved by the NUPS method under conditions of low SNRs,thus the laser power is reduced.The NUPS lays the technical foundation for the implementation of the FT engineering system.

Noisy speech enhancement based on an adaptive threshold and a modified hard thresholding function in wavelet packet domain

This paper proposes a speech enhancement approach, which statistically determines an adaptive threshold using the Teager energy operated WP coefficients of noisy speech. The obtained threshold is employed upon the WP coefficients of the noisy speech by employing a modified hard thresholding function. Extensive simulations in the presence of different noises indicate that this new method is very effective for both white noise and color noise reduction from speech, resulting in enhanced speech with better speech quality. Several standard objective measures and subjective observations show that the proposed method outperforms recent state-of-the-art thresholding based approaches from high to low level SNRs.

Analysis on the Anti-Interference Performance of UWB Receivers

UWB technique is widely used in radar detection system benefiting from the large bandwidth and short duration of UWB pulses. In traditional detection system, correlation receivers are usually adopted since lower SNRs are tolerated. This paper analyses the performance of anti-interference with 30dB additive noise for reference signal and 20dB additive noise for received signal as well as the pulse position shift of 100ps in UWB correlation receivers.