Signal Processing Issues Research Articles

Text-independent speaker identification based on selection of the most similar feature vectors

The speaker recognition has been one of the interesting issues in signal and speech processing over the last few decades. Feature selection is one of the main parts of speaker recognition system which can improve the performance of the system. In this paper, we have proposed two methods to find MFCCs feature vectors with the highest similar that is applied to text independent speaker identification system. These feature vectors show individual properties of each person's vocal tract that are mostly repeated. They are used to build speaker's model and to specify decision boundary. We applied MFCC of each window over main signal as a feature vector and used clustering to obtain feature vectors with the highest similar. The Speaker identification experiments are performed using the ELSDSR database that consists of 22 speakers (12 male and 10 female) and Neural Network is used as a classifier. The effect of three main parameters have been considered in two proposed methods. Experimental results indicate that the performance of speaker identification system has been improved in accuracy and time consumption term.

Acoustic mode travel time variability in the Philippine Sea

During the 2009 NPAL (North Pacific Acoustic Laboratory) PhilSea deep water acoustic propagation experiment, a low frequency source transmitted broadband chirps to a mid-water spanning hydrophone array at a distance of 185 km every three hours for approximately one month. In addition, transmissions took place every five minutes during some time periods. The source and the receiver array contained conductivity and temperature sensors. The motions of the source and receiver moorings were measured using long-baseline acoustic navigation systems. The experiment site was oceanographically dynamic and contained significant internal tide activity. This work builds a model to compare the acoustic and oceanographic variabilities. The acoustic observations are used to estimate the mode travel times, and then related to the internal tide displacements at the source and the receiver arrays. This paper presents the work in three parts. The first part uses matched subspace detectors to estimate the mode travel times. The second uses the environmental observations to build an internal tide model. The third constructs a linear perturbation model to relate the internal tides to the adiabatic mode travel times. The discrepancies between the model and the observations are attributed to factors such as limitations of the perturbation model for the travel times, range variability not accounted for in the adiabatic mode propagation, and signal processing issues.

Superdirective non-linear beamforming with deep neural network

Beamforming has been one of important issues in acoustic signal processing, since it can achieve signal enhancement and sound source localization. In general, traditional beamformers are designed by an analytical approach or an adaptive approach. It is, however, difficult to properly optimize the beamformers under the complicated acoustical scene. An alternative non-linear beamforming can be substituted for the linear beamforming. In this study, a flexible framework for optimizing the beamformer is introduced based on a deep neural network. Capturing acoustic signals using a microphone array is regarded as spatial sampling, so that annoying grating lobes appear in beam-pattern when the relationship between the wavelength and the microphone spacing does not satisfy the sampling theorem. The proposed method achieves sub-band beamforming using the non-uniform microphone array with eight nesting microphones, which are carefully designed not to cause spatial aliasing. Feasibility of the proposed method has been confirmed by computer simulation. The proposed non-linear beamformer could successfully achieve superdirectivity compared with conventional beamformers.

Synchronization Protocols and Implementation Issues in Wireless Sensor Networks: A Review

Time synchronization in wireless sensor networks (WSNs) is a topic that has been attracting the research community in the last decade. Most performance evaluations of the proposed solutions have been limited to theoretical analysis and simulation. They consequently ignored several practical aspects, e.g., packet handling jitters, clock drifting, packet loss, and mote limitations, which affect real implementation on sensor motes. Authors of some pragmatic solutions followed empirical approaches for the evaluation, where the proposed solutions have been implemented on real motes and evaluated in testbed experiments. This paper gives an insight on issues related to the implementation of synchronization protocols in WSN. The challenges related to WSN environment are presented; the importance of real implementation and testbed evaluation are motivated by some experiments we conducted. The most relevant implementations of the literature are then reviewed, discussed, and qualitatively compared. While there are several survey papers that present and compare the protocols from the conception perspectives, as well as others that deal with mathematical and signal processing issues of the estimators, a survey on practical aspects related to the implementation is missing. To our knowledge, this paper is the first one that takes into account the practical aspect of existing solutions.

Hybrid Architecture Design for Calculating Variable-Length Fourier Transform

This brief presents a hybrid structure to effectively compute the variable-length Fourier transform by employing the recursive and radix-22 fast algorithm. After applying a hardware-sharing scheme to both fast algorithms, the proposed method not only improves the drawback of higher hardware cost in implementation but also retains the regular and flexible nature of recursive discrete Fourier transform (RDFT). The proposed hardware accelerator only costs four real multipliers and ten real adders with a greater reduction (86.7% and 66.7%, respectively) than Kim et al. 's design. In addition, the number of multiplications and additions for 256-point DFT computations can be reduced by 38.6% and 70%, respectively, compared to Lai et al. 's recent approach. For accuracy analysis, the SNR value of the proposed design, at least, is 4 dB higher than the other RDFT designs. Considering a whole evaluation, a very-large-scale integration chip design was further fabricated using TSMC 0.18- $\mu\mbox{m}$ 1P6M CMOS process. The core size was only 660 × 660 $\mu\mbox{m}^2$ , and the measured power consumption was 8.8 mW @ 25 MHz. The result shows that the proposed chip included data memory is 1.38 times the computational efficiency per unit area of Lai et al. 's work. Therefore, it will be the state-of-the-art RDFT processor in the application of various variable-transform-length digital signal processing issues.

A Fetal Electrocardiogram Signal Extraction Algorithm Based on the Temporal Structure and the Non-Gaussianity.

Fetal electrocardiogram (FECG) extraction is an important issue in biomedical signal processing. In this paper, we develop an objective function for extraction of FECG. The objective function is based on the non-Gaussianity and the temporal structure of source signals. Maximizing the objective function, we can extract the desired FECG. Combining with the solution vector obtained by maximizing the objective function, we further improve the accuracy of the extracted FECG. In addition, the feasibility of the innovative methods is analyzed by mathematical derivation theoretically and the efficiency of the proposed approaches is illustrated with the computer simulations experimentally.

Step-response of a torsional device with multiple discontinuous non-linearities: Formulation of a vibratory experiment

A vehicle clutch damper is intentionally designed to contain multiple discontinuous non-linearities, such as multi-staged springs, clearances, pre-loads, and multi-staged friction elements. The main purpose of this practical torsional device is to transmit a wide range of torque while isolating torsional vibration between an engine and transmission. Improved understanding of the dynamic behavior of the device could be facilitated by laboratory measurement, and thus a refined vibratory experiment is proposed. The experiment is conceptually described as a single degree of freedom non-linear torsional system that is excited by an external step torque. The single torsional inertia (consisting of a shaft and torsion arm) is coupled to ground through parallel production clutch dampers, which are characterized by quasi-static measurements provided by the manufacturer. Other experimental objectives address physical dimensions, system actuation, flexural modes, instrumentation, and signal processing issues. Typical measurements show that the step response of the device is characterized by three distinct non-linear regimes (double-sided impact, single-sided impact, and no-impact). Each regime is directly related to the non-linear features of the device and can be described by peak angular acceleration values. Predictions of a simplified single degree of freedom non-linear model verify that the experiment performs well and as designed. Accordingly, the benchmark measurements could be utilized to validate non-linear models and simulation codes, as well as characterize dynamic parameters of the device including its dissipative properties.

Experimental identification of the behaviour of and lateral forces from freely-walking pedestrians on laterally oscillating structures in a virtual reality environment

Modelling pedestrian loading on lively structures such as bridges remains a challenge. This is because pedestrians have the capacity to interact with vibrating structures which can lead to amplification of the structural response. Current design guidelines are often inaccurate and limiting as they do not sufficiently acknowledge this effect. This originates in scarcity of data on pedestrian behaviour on vibrating ground and uncertainty as to the accuracy of results from previous experimental campaigns aiming to quantify pedestrian behaviour in this case. To this end, this paper presents a novel experimental setup developed to evaluate pedestrian actions on laterally oscillating ground in the laboratory environment while avoiding the implications of artificiality and allowing for unconstrained gait. A biologically-inspired approach was adopted in its development, relying on appreciation of operational complexities of biological systems, in particular their adaptability and control requirements. In determination of pedestrian forces to the structure consideration was given to signal processing issues which have been neglected in past studies. The results from tests conducted on the setup are related to results from previous experimental investigations and outputs of the inverted pendulum pedestrian model for walking on laterally oscillating ground, which is capable of generating self-excited forces.

Enhancement of Time-Reversal Subwavelength Wireless Transmission Using Pulse Shaping

A novel time-reversal subwavelength transmission technique, based on pulse shaping circuits (PSCs), is proposed. Compared to previously reported approaches, this technique removes the need for complex or electrically large electromagnetic structures by generating channel diversity via pulse shaping instead of angular spectrum transformation. Moreover, the pulse shaping circuits (PSCs) are based on Radio Analog Signal Processing (R-ASP), and therefore do not suffer from the well-known issues of digital signal processing in ultrafast regimes. The proposed PSC time-reversal systems is mathematically shown to offer high channel discrimination under appropriate PSC design conditions, and is experimentally demonstrated for the case of two receivers.

Modeling magnitude statistics of multilook SAR interferograms by generalizing G distributions

Statistical analysis of multilook interferograms is a foundational issue in sensor signal processing of multiple-channel synthetic aperture radar (SAR), such as slow ground moving target indication (GMTI) in along-track interferometric (ATI) SAR. By an approximate derivation of the product of two modified Bessel functions, we propose in this paper a distribution (denoted simply as ΓIn) to model the interferometric magnitude of homogeneous clutter and analyze the capability of approximation using ΓIn according to numerical calculations. Following this, under the frame of the product model and by utilizing ΓIn, we analytically provide two distributions, KIn and GIn0, corresponding to heterogeneous and extremely heterogeneous terrain clutter, respectively. We show that the proposed ΓIn,KIn and GIn0 are the multi-channel generalizations of the well-known Γ,K and G0, respectively, which belong to the special cases of G distribution for single-channel SAR images. Finally, the estimators of the proposed models are obtained by applying the Method of Log Cumulants (MoLC), which can accurately calculate the contained parameters. Experiments performed on the National Aeronautics and Space Administration Jet Propulsion Laboratory’s (NASA/JPL) AirSAR images that used the Kullback–Leibler (KL) divergence as a similarity measurement verified the performance of the proposed models and estimators.

Experimental Research of Vector Hydrophone MVDR Algorithm

The estimation of Direction of Arrival (DOA) is an important issue of underwater acoustic signal processing. The purpose of beamforming is to form the directivity of the target direction and to catch the target efiectively. So, the beamforming technology is widely used in this fleld. Vector Hydrophone simultaneously extract sound pressure and velocity information of sound sources. After that, the phase shift information between the amount of the hydrophone array increases. Beamforming technology based on vector hydrophone performance was signiflcantly better than the sound pressure hydrophone. During this research, flrstly, do conventional beamforming theoretical description and simulation analysis, based on the conventional beamforming where MVDR (Minimum Variance Distortionless Response) beamforming principles were introduced, and then based on vector hydrophone do comparison simulation by various ways. Simulation results show that, MVDR beamforming has a higher resolution and antijamming capability related to conventional beamforming which is suitable for low SNR environment underwater acoustic signal processing. Finally, process the experimental data by the above two methods, the results show MVDR beamforming can be efiectively detected, and has a higher resolution.

Practical issues in signal processing for structural flexibility identification

Compared to ambient vibration testing, impact testing has the merit to extract not only structural modal parameters but also structural flexibility. Therefore, structural deflections under any static load can be predicted from the identified results of the impact test data. In this article, a signal processing procedure for structural flexibility identification is first presented. Especially, practical issues in applying the proposed procedure for structural flexibility identification are investigated, which include sensitivity analyses of three pre-defined parameters required in the data pre-processing stage to investigate how they affect the accuracy of the identified structural flexibility. Finally, multiple-reference impact test data of a three-span reinforced concrete T-beam bridge are simulated by the FE analysis, and they are used as a benchmark structure to investigate the practical issues in the proposed signal processing procedure for structural flexibility identification.

The electrophysiology of language production: what could be improved.

OPINION article Front. Psychol., 13 January 2015Sec. Language Sciences Volume 5 - 2014 | https://doi.org/10.3389/fpsyg.2014.01560

Data Driven Sampling of Oscillating Signals

The reduction of the number of samples is a key issue in signal processing for mobile applications. We investigate the link between the smoothness properties of a signal and the number of samples that can be obtained through a level crossing sampling procedure. The algorithm is analyzed and an upper bound of the number of samples is obtained in the worst case. The theoretical results are illustrated with applications to fractional Brownian motions and the Weierstrass function.

Air-Core Sensors Operation Modes for Partial Discharge Detection and On-line Diagnostics in Medium Voltage Networks

Abstract In this paper, sensors for the application of high voltage networks insulation diagnostics are focused on. For detection and localization of the components with insulation deterioration, observing the partial discharge transients is one of the main methods. However, as the partial discharge traces are transients of extremely short duration, monitoring of such transients is also a challenging task. This paper presents the inductive sensors that would be suitable for the on-line partial discharge monitoring and the selection of operating mode and characteristics are discussed. The issues of resonance and signal processing are analyzed for the targets of reaching highest accuracy of the measurements. Operation criteria and performance limits such as sensitivity and upper bandwidth are particularly discussed. It is presented that in order to reach highest accuracy and sensitivity the sensor should be used in damped mode. Applying the accurate air-core inductive sensors would allow the condition monitoring systems to be cheaper and thus mounted for permanent and continuous condition monitoring in various locations in the power distribution networks.

大数据背景下的信号处理

The rapid developments of wideband signal, high-dimensional signal, high resolution image, and multi-sensor network have made the growth rate of data acquisition amount higher than the growth rate of data storage amount and the growth rate of signal processing speed. Signal processing steps into the big data era. This paper points out the key issues of signal processing with big data. Considering the diversity and complexity of the big data produced by multi-sensor networks, it is necessary to conduct information fusion. The main information fusion models are described, and the trends of information fusion technology are analyzed. Intelligent sensor network technology can reduce the capacity of signal processing and communication, and efficiently extract valuable information from big data. The basic architecture of a smart sensor is given, and calculation methods for intelligent sensor networks are described. From viewpoint of high-speed and real-time signal processing requirements of signals with big data, we also introduce developments of high-speed digital signal processing chips and high-performance hardware platforms. The vision of core technology in high-speed signal processing is presented.

Advanced signal processing and HCI issues for interactive multimedia services

With the flood and popularity of various multimedia contents on the Internet, intelligent interactive multimedia services such as personalization and adaptability have become essential in recent multimedia systems. For this reason, they need to be interactive not only through classical modes of interactionwhere a user inputs information through a keyboard ormouse, but also through advanced interaction modes which are more attractive, more user friendly, more human-like and more informative [9]. We have received 18 manuscripts. Each manuscript was blindly reviewed by at least three reviewers consisting of guest editors and external reviewers. After the first and second review processes, eight manuscripts were finally selected to be included in this special issue. In Section 2 and 3, we present a brief description about the signal processing and HCI (Human Computer Interaction) issues for interactive multimedia services, respectively. In the last section, we conclude the paper with some observations and future work.

Development and performance evaluation of an improved complex valued radar pulse compressor

Pulse compression is an important and burning issue in radar signal processing. In the recent past, many adaptive and neural network based methods have been proposed to achieve effective pulse compression performance for real coded transmitted waveforms. Even though the radar signal is complex, it is mostly processed as real-valued in-phase and quadrature components. Hence it is desirable that for processing complex radar signal for pulse compression both the structure as well as the learning algorithm associated with it need to be complex in nature. Accordingly in this paper a novel adaptive method is proposed by employing a complex valued fully connected cascaded (CFCC) neural network. For training this network, a new complex Levenberg–Marquardt (CLM) algorithm is derived and used for imparting effective training of its weights. The new CLM based CFCC (CFCC-CLM) model offers superior convergence performance with the least residual mean squared error during training phase compared to those provided by the multilayer perceptron (MLP) trained with complex domain backpropagation (CDBP) and CLM based methods. Further the comparison of peak signal-to-sidelobe ratio (PSR) under noisy and Doppler shift conditions of the proposed method exhibits best performance compared to those offered by the MLP-CDBP, MLP-CLM and the matched filter (MF) based methods.

TERRESTRIAL LASER SCANNER DATA DENOISING BY DICTIONARY LEARNING OF SPARSE CODING

Abstract. Point cloud processing is basically a signal processing issue. The huge amount of data which are collected with Terrestrial Laser Scanners or photogrammetry techniques faces the classical questions linked with signal or image processing. Among others, denoising and compression are questions which have to be addressed in this context. That is why, one has to turn attention to signal theory because it is susceptible to guide one's good practices or to inspire new ideas from the latest developments of this field. The literature have been showing for decades how strong and dynamic, the theoretical field is and how efficient the derived algorithms have become. For about ten years, a new technique has appeared: known as compressive sensing or compressive sampling, it is based first on sparsity which is an interesting characteristic of many natural signals. Based on this concept, many denoising and compression techniques have shown their efficiencies. Sparsity can also be seen as redundancy removal of natural signals. Taken along with incoherent measurements, compressive sensing has appeared and uses the idea that redundancy could be removed at the very early stage of sampling. Hence, instead of sampling the signal at high sampling rate and removing redundancy as a second stage, the acquisition stage itself may be run with redundancy removal. This paper gives some theoretical aspects of these ideas with first simple mathematics. Then, the idea of compressive sensing for a Terrestrial Laser Scanner is examined as a potential research question and finally, a denoising scheme based on a dictionary learning of sparse coding is experienced. Both the theoretical discussion and the obtained results show that it is worth staying close to signal processing theory and its community to take benefit of its latest developments.

Subpicosecond beam length measurement study based on theTM010mode

This article discusses a beam length measurement technique based on the ${\mathrm{TM}}_{010}$ modes of two cavities in frequency domain. Since the ${\mathrm{TM}}_{010}$ pickup monitor has a much better signal-to-noise ratio (SNR) than the other monitors, it can improve the measuring range and resolution limit of the beam length measurement in the frequency domain to achieve the real-time measurement of the pulse duration of the order of 1 ps or less with the resolution of about 10 fs. The root-mean-square length of a beam---which does not necessarily have a Gaussian distribution---can be obtained by choosing the working frequency of the cavity to be much less than the critical frequency of the beam. The modeling and analysis of the above theory were also made simultaneously. The resulting resolution was about 100 fs when the step size was chosen to be 200 fs in this simulation after the higher order mode coupling effect was eliminated and the beam offset was corrected by using the Bessel function. An efficient algorithm was also provided in case the measurement system would achieve the SNR of 112 dB when there could be some issue in signal processing. This algorithm can still provide the length result with the resolution of $3.5\text{ }\text{ }\ensuremath{\mu}\mathrm{m}$ for a beam duration of 0.1 ps when the SNR of the difference signal is as low as 10 dB.