Adaptive Signal Processing Techniques Research Articles

Adaptive noise abatement in automatic cab signaling receivers

Under conditions of a priori noise uncertainty, it is effective to use adaptive noise-abatement techniques to improve noise protection of continuous automatic cab signaling (CACS) receivers. The purpose of this article is to study the possibilities of an adaptive signal-processing technique, adaptive noise filtration (AF), providing better noise resistance of the CACS signals. To achieve this goal, the following problems are solved: a mathematical model of the CACS receivers' input signals is developed, a functional circuit and adaptation algorithm of a basic and a modified multichannel CACS receivers' adaptive filters is substantiated, and a computer simulation of adaptive filters is performed, confirming the accuracy of the obtained results and effectiveness of such filters for noise reduction. A distinctive feature of the proposed filters, which significantly expands their functionality, is a priori information on the presence of breaks in the CACS signals used in the adaptive algorithms.

EC-CENTRIC: An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design

The radio transceiver of an Internet of Things (IoT) device is often where most of the energy is consumed. For this reason, most research so far has focused on low-power circuit and energy-efficient physical layer designs, with the goal of reducing the average energy per information bit required for communication. While these efforts are valuable per se, their actual effectiveness can be partially neutralized by ill-designed network, processing, and resource management solutions, which can become a primary factor of performance degradation, in terms of throughput, responsiveness, and energy efficiency. The objective of this paper is to describe an energy-centric and context-aware optimization framework that accounts for the energy impact of the fundamental functionalities of an IoT system and that proceeds along three main technical thrusts: 1) balancing signal-dependent processing techniques (compression and feature extraction) and communication tasks; 2) jointly designing channel access and routing protocols to maximize the network lifetime; and 3) providing self-adaptability to different operating conditions through the adoption of suitable learning architectures and of flexible/reconfigurable algorithms and protocols. After discussing this framework, we present some preliminary results that validate the effectiveness of our proposed line of action, and show how the use of adaptive signal processing and channel access techniques allows an IoT network to dynamically tune lifetime for signal distortion, according to the requirements dictated by the application.

False arrhythmia alarms reduction in the intensive care unit: a multimodal approach

The purpose of this study was to develop algorithms to lower the incidence of false arrhythmia alarms in the ICU using information from independent sources, namely electrocardiogram (ECG), arterial blood pressure (ABP) and photoplethysmogram (PPG). Our approach relies on robust adaptive signal processing techniques in order to extract accurate heart rate (HR) values from the different waveforms. Based on the quality of available signals, heart rate was either estimated from pulsatile waveforms using an adaptive frequency tracking algorithm or computed from ECGs using an adaptive mathematical morphology approach. Furthermore, we developed a supplementary measure based on the spectral purity of the ECGs to determine whether a ventricular tachycardia or flutter/fibrillation arrhythmia has taken place. Finally, alarm veracity was determined based on a set of decision rules on HR and spectral purity values. The proposed method was evaluated on the PhysioNet/CinC Challenge 2015 database, which is composed of 1250 life-threatening alarm recordings, each categorized into either bradycardia, tachycardia, asystole, ventricular tachycardia or ventricular flutter/fibrillation arrhythmia. This resulted in overall true positive rates of 95%/99% and overall true negative rates of 76%/80% on the real-time and retrospective subsets of the test dataset, respectively.

Desired Features of Adaptive Antenna Arrays for GNSS Receivers

Adaptive antenna arrays have become the system of choice for protecting Global Navigation Satellite Systems (GNSS) receivers in strong radio frequency interference environments. GNSS receiver adaptive antenna arrays have small aperture (of the order of a wavelength) and few (e.g., less than 10) elements. In the near future, even smaller vehicles will be equipped with protected GNSS receivers. Therefore, future GNSS receiver antenna arrays will be even smaller in size. At the same time, requirements for availability, accuracy, and integrity monitoring will increase. Novel array signal processing techniques need to be developed and deployed to meet these requirements. This paper presents state-of-the-art GNSS adaptive antenna array signal processing techniques. Some drawbacks of the current GNSS antenna signal processing techniques are also discussed in this paper, and new ideas for further research and development in GNSS adaptive antennas are presented.

전이중 통신 방식을 사용하는 무선랜을 위한 간섭 제거 기법

In this paper, we employ the single channel full duplex radio for wireless local area network (WLAN) systems, and design digital interference cancellers using adaptive signal processing. When the full duplex scheme is used for WLAN systems with multiple transmit and receive antennas, some interference is caused through the feedback of transmit signals from multiple antennas. To remove the feedback interference, we derive the least mean square (LMS), normalized LMS (NLMS), and recursive least squares (RLS) algorithms based on adaptive signal processing techniques. In addition, we analyze the theoretical convergence of the proposed LMS and RLS methods. The channel capacity of full duplex radios increases by two times than that of half duplex radios, when the packet error rate (PER) performances for the two systems are identical. Through numerical simulations in WLAN systems, it is shown that the full duplex method with the proposed interference cancellers has a similar PER performance with the conventional half duplex transmission scheme.

Need for Adaptive Signal Processing Technique for Tool Condition Monitoring in Turning Machines

Need for Adaptive Signal Processing Technique for Tool Condition Monitoring in Turning Machines

동해에서의 삼중 선 배열 센서들의 잡음 상관관계 분석

삼중 선 배열은 좌/우 구분된 표적 방위 탐지와 선 배열의 높은 지향지수와 같은 장점을 지닌다. 그러나 이를 구현하기 위해서는 선 배열 대비 높은 연산능력과 센서 간의 좁은 간격으로 인한 높은 센서 간 상관관계를 고려하여야 한다. 연산능력은 급격한 하드웨어의 발달로 다양한 적응빔형성 기법 / 신호처리 기법을 적용하기에 문제가 없으나, 적용 가능한 최적의 신호처리 기법을 파악하고 표적 탐지 능력을 향상시키기 위해서는 수중에서 운용하는 삼중 선 배열로 수신되는 잡음 신호에 대한 센서 간 상관관계에 대한 분석이 필수적으로 수행되어야 한다. 본 논문에서는 동해에서 획득한 실험 데이터를 이용하여 삼중 선 배열을 구성하는 삼중 센서의 각 센서간 잡음 신호의 상관관계, 선형으로 나열된 삼중 센서 잡음 신호간 상관관계, 각 채널에서의 잡음 신호의 시변 특성 분석을 수행하였다. 도출된 결과를 바탕으로 삼중 선 배열의 특성을 고려한 빔형성 및 신호처리 기법에 필요한 정보를 확인하였다. A triplet line array has a capability for solving left/right ambiguity problem and high directivity of line array. For this, high computational powers and correlation between nearby sensors must be considered. Hardware powers are dramatically increased, so various adaptive beamforming techniques and signal processing techniques can be adopted for large triplet line array. To investigate the optimum signal processing method and improve the target detection capability, we need to analyze the noise correlation among the triplet line array sensors in real environment. In this paper we analyze the experimental data in the East Sea, investigate noise correlation between three hydrophones in triplet sensors, among the triplet sensors and time varying properties. Based on the acquired results, it is possible to verify some information for beamforming and signal processing methods with considering the properties of the triplet line array.

A new adaptive algorithm for automated feature extraction in exponentially damped signals for health monitoring of smart structures

In this paper authors introduce a new adaptive signal processing technique for feature extraction and parameter estimation in noisy exponentially damped signals. The iterative 3-stage method is based on the adroit integration of the strengths of parametric and nonparametric methods such as multiple signal categorization, matrix pencil, and empirical mode decomposition algorithms. The first stage is a new adaptive filtration or noise removal scheme. The second stage is a hybrid parametric–nonparametric signal parameter estimation technique based on an output-only system identification technique. The third stage is optimization of estimated parameters using a combination of the primal-dual path-following interior point algorithm and genetic algorithm. The methodology is evaluated using a synthetic signal and a signal obtained experimentally from transverse vibrations of a steel cantilever beam. The method is successful in estimating the frequencies accurately. Further, it estimates the damping exponents. The proposed adaptive filtration method does not include any frequency domain manipulation. Consequently, the time domain signal is not affected as a result of frequency domain and inverse transformations.

High frequency source localization in a shallow ocean sound channel using frequency-difference matched field processing

Matched field processing (MFP) is an established technique for locating remote acoustic sources in known environments. Unfortunately, environment-to-propagation model mismatch prevents successful application of MFP in many circumstances, especially those involving high frequency signals. For beamforming applications, this problem was found to be mitigated through the use of a nonlinear array-signal-processing technique called frequency difference beamforming (Abadi et. al. 2012). Building on that work, this nonlinear technique was extended to Bartlett MFP, where ambiguity surfaces were calculated at frequencies two orders of magnitude lower than the propagated signal, where the detrimental effects of environmental mismatch are much reduced. Previous work determined that this technique has the ability to localize high-frequency broadband sources in a shallow ocean environment with a sparse vertical array, using both simulated and experimental propagation data. Using simulations, the performance of this technique with horizontal arrays and adaptive signal processing techniques was investigated. Results for signals with frequencies from 10 kHz to 30 kHz that propagated in a 100-m-deep shallow ocean sound channel with a downward refracting sound speed profile will be shown for source array ranges of one to several kilometers. [Sponsored by the Office of Naval Research.]

Recognition of Single-stage and Multiple Power Quality Events Using Hilbert–Huang Transform and Probabilistic Neural Network

This article presents the Hilbert–Huang transform based algorithm for recognizing the single-stage and multiple power quality events comprising voltage sag, swell, notches, spikes, harmonics, transients, and flicker. The change of the state during the course of the single-stage power quality event is called a multiple power quality event. Hilbert–Huang transform is an adaptive signal processing technique that combines empirical mode decomposition and Hilbert spectral analysis and makes it an attractive tool for analysis of power quality events. A synthetic database of power quality events is generated in MATLAB (The MathWorks, Natick, Massachusetts, USA) as per Standard IEEE-1995. The significant features are extracted from the instantaneous amplitude, phase, and frequency contours of intrinsic mode functions of each disturbance, and the events are classified using the probabilistic neural network technique.

An Adaptive Signal Processing Framework for PV Power Maximization

This paper discusses the possibility of using adaptive signal processing techniques for maximum power point tracking controllers, in order to extract peak power from individual photovoltaic modules. A new technique grounded on unsupervised Hebbian learning theory (maximum eigenvector of the output power) is presented, which works on-online and is capable of operating without a desired response. Important modifications were made to the generic Hebbian adaptation to accommodate the intrinsic feedback loop between the controller and the plant. Analytic derivation of the new update rule is presented, as well as stability analysis by means of Lyapunov theory. Simulation results showing its effectiveness are presented, as well as experimental results.

Meteor Trail Echo Rejection in Atmospheric Phased Array Radars Using Adaptive Sidelobe Cancellation

AbstractStrong meteor trail echoes are interferences in the wind velocity estimates made from mesosphere radar observations. Contaminated spectra are detected by their discontinuity and are removed at the risk of greater fluctuations of spectra, leading to a severe reduction of the signal-to-noise ratio (SNR) and inaccurate wind estimates for weak atmospheric echoes. This paper presents an adaptive signal processing technique for the suppression of spectral contaminations by meteor trail echoes. The method is based on the norm-constrained and directionally constrained minimization of power (NC-DCMP), which balances the capability of canceling the clutter and the robustness of beam shaping, at the cost of a slight decrease in the SNR, which can be determined in advance. Simulation results show that with a 3-dB decrease of the SNR being allowed, the method improves the signal-to-interference ratio (SIR) by 15 dB, giving wind estimates that are about 8 m s−1 better in terms of root-mean-square error and providing 4 times as wide an observable range when compared with the results of the ordinary nonadaptive beamforming method. The results for an actual observation show that the improvement of both the SIR and the observable range are achieved as in the simulations, which implies that the method should provide the simulated accuracy for the estimation of wind velocity from actual observations.

Assess sleep stage by modern signal processing techniques.

In this paper, two modern adaptive signal processing techniques, empirical intrinsic geometry and synchrosqueezing transform, are applied to quantify different dynamical features of the respiratory and electroencephalographic signals. We show that the proposed features are theoretically rigorously supported, as well as capture the sleep information hidden inside the signals. The features are used as input to multiclass support vector machines with the radial basis function to automatically classify sleep stages. The effectiveness of the classification based on the proposed features is shown to be comparable to human expert classification-the proposed classification of awake, REM, N1, N2, and N3 sleeping stages based on the respiratory signal (resp. respiratory and EEG signals) has the overall accuracy 81.7% (resp. 89.3%) in the relatively normal subject group. In addition, by examining the combination of the respiratory signal with the electroencephalographic signal, we conclude that the respiratory signal consists of ample sleep information, which supplements to the information stored in the electroencephalographic signal.

An adaptive ultrasonic backscattered signal processing technique for instantaneous characteristic frequency detection.

Ultrasonic diagnosis that is convenient and nondestructive to the human body is widely used in medicine. In clinical, ultrasonic backscattered signals characteristics are utilized to acquire information of the human body tissues to perform diagnosis. In this paper, an adaptive ultrasonic backscattered signal processing technique for instantaneous characteristic frequency detection based on the marginal spectrum is presented. In the beginning, the ultrasonic backscattered signal is decomposed into a series of intrinsic mode functions (IMFs) by the Ensemble Empirical Mode Decomposition (EEMD) algorithm. Then the Hilbert spectrum is gained by the Hilbert transform on the IMFs decomposed and screened. Finally, the time-frequency information in the Hilbert spectrum is utilized to extract the instantaneous characteristic frequency based on the marginal spectrum features to detect the objective. With this technique, the spacing between tissues can be estimated for tissue characterization by processing multiple echoes even in the complicated environment. In the simulation study, comparing with the FFT, the technique presented shows its strong noise immunity and indicates its validity in instantaneous characteristic frequency detection.

GPS 항재밍을 위한 적응 배열 안테나의 성능 분석

In anti-jamming GPS receiver, adaptive signal processing techniques in which the radiation pattern of adaptive array antenna of elements may be adaptively changed used to reject interference, clutter, and jamming signals. In this paper, I describes adaptive signal processing technique using the sample matrix inversion(SMI) algorithm. This adaptive signal processing technique can be applied effectively to wideband/narrowband anti-jamming GPS receiver because it does not consider the satellite signal directions and GPS signal power level exists below the thermal noise. I also analyzed the effects of covariance matrix sample size and diagonal loading technique on the system performance of five-element circular array antenna. To attain near optimum performance, more samples required for calculation covariance matrix. Diagonal loading technique reduces the system nulling capability against low-power jamming signals, but this technique improves robustness of adaptive array antenna.Keywords : Adaptive Array Antenna(적응 배열 안테나), Anti-Jamming(항재밍), Adaptive Signal Processing(적응 신호처리), Sample Matrix Inversion(SMI), Diagonal Loading(대각 로드)

Depth Resolution Enhancement Technique for CMOS Time-of-Flight 3-D Image Sensors

Introducing Time-of-Flight 3-D image sensors to actual engineering applications, such as pattern recognition, is constrained not only by their limited depth and lateral resolution, but also by how similar the precision of depth measurement throughout the whole pixel-matrix is. In real operating environment, an observed 3-D-scene hardly exhibits a homogeneous reflectance factor. Moreover, the light-beam (laser source) presents a nonuniform optical power distribution in space. Thus, the amount of the incident light on the sensor surface varies drastically from one pixel to another, and so does the signal-to-noise ratio. To address this problem, this paper investigates the impact of both scene and light-source non-ideal characteristics on the sensor performance. An adaptive on-pixel analog signal processing technique is also presented and applied to the design of a 32 × 32 complementary metal oxide semiconductor (CMOS) range camera, featuring an interesting cost-efficient solution.

Extraction of Breathing Signal and Suppression of Its Effects in Oscillometric Blood Pressure Measurement

Breathing causes fluctuations in blood pressure and contributes to variations in blood pressure estimates. In order to reduce the variability in the blood pressure estimates induced by breathing, either the breathing signal should be removed from the oscillometric blood pressure signal, or its effects should be suppressed. This paper presents a hybrid method that combines homomorphic and adaptive signal processing techniques to extract the breathing signal from the oscillometric signal with or without a simultaneously recorded electrocardiogram (ECG). The quality of the extracted breathing signal and the depth of breathing are assessed using the reference breathing signals. The breathing signals extracted using the accompanying ECG signal were found to be superior in quality compared to the ones extracted from the oscillometric waveform. The blood pressure estimates were evaluated before and after the breathing suppression techniques were implemented. As a result of the breathing suppression, the fluctuation of the systolic and diastolic blood pressure estimates obtained from a database of 85 healthy subjects is reduced.

Experimental study on the effects of physical training on the defibrillation threshold

Background: Chest compression artifacts during cardiopulmonary resuscitation (CPR) deteriorate the rhythm diagnosis of automated external defibrillators (AED). Cardiopulmonary resuscitation must therefore be interrupted for a reliable shock/no-shock decision. However, these hands-off intervals adversely affect the defibrillation success, and, in addition, pauses in chest compressions compromise circulation. An accurate diagnosis of the rhythmwhile performing CPR is therefore needed to minimize these hands-off intervals. Methods: The characteristics of the CPR artifact are very variable, and the artifact presents an important spectral overlap with human cardiac arrest rhythms. Consequently, elaborate adaptive signal processing techniques are needed to filter the CPR artifact and reconstruct the underlying artifact-free ECG. Following an additive noise model, several methods were initially tested by artificially mixing human ventricular fibrillation samples and CPR artifacts recorded from pigs in asystole. The performance of the filters was evaluated for different levels of corruption, that is, signal-to-noise ratio. These studies showed satisfactory results after the filters were applied, both in terms of the improved signal-to-noise ratio and the sensitivity of the AED, which exceeded the 90% performance goal set by the American Heart Association (AHA). A posterior study reported similar results using a mixture of human ventricular fibrillation and human CPR artifacts extracted from real out-of-hospital interventions. Results: The first filtering method tested on shockable and nonshockable cardiac arrest rhythms from of out-of-hospital interventions was published in 2004. The artifact was estimated using up to 4 additional reference channels, acquired using a modified version of a commercial AED. The sensitivity after filtering (96.7%) exceeded AHA goals; the method, however, failed to accurately identify nonshockable rhythms because the specificity (79.9%) fell below the 95% AHA performance goal. Later, efforts focused on simplifying the filtering methods by either analyzing the ECG alone or using only 1 reference channel (2-channel methods). The ECG alone is not sufficient to estimate the artifact; however, comparable results were reported for multichannel and 2-channel methods on out-of-hospital rhythms. The possibility of identifying the rhythm by directly analyzing the corrupted ECG has also been proposed, although the reported specificity is also unsatisfactory. Conclusion: Currently, a reliable rhythm analysis during CPR is not possible because the specificity is too low. Two-channel filtering methods combined with rhythm identification applied to the filtered ECG should be further investigated to improve the detection of nonshockable rhythms.

Full-Throttle DSL

Broadband connectivity and new ser vices such as YouTube, IP video, and cloud computing are changing our lives, and we would never want to miss out on them. The associated data rates are as predictable as Moore's law, as they are enabled by the availability of low-cost, highly integrated broad band communication ICs. Advanced modulation and coding incorporating sophisticated adaptive signal processing techniques are pushing the data rate close to the theoretical limit given by Shannon's law. Important advantages such as increased bandwidth, improved energy efficiency, feature enhance ment, and cost reduction are conferred by innovations in integrated circuits and technologies. Finally, standardization with the involvement of semiconductor and telecom system companies and also service and content providers is most important for the success and deployment of new communication technologies and services.

Dynamic spectrum management in wireless networks [Guest Editorial

With the spectrum scarcity and underutilization of certain allocated bands, dynamic spectrum management (DSM) appears as an attractive solution. Novel techniques and technologies are needed to provide the necessary intelligence and adaptation for spectrum sharing and cooperation of multiple wireless networks. Under the so-called cognitive radio approach, environmentaware unlicensed wireless devices can share the spectrum with other licensed networks without degrading their performance and introducing unmanageable interference on the nodes of other networks. There are two ways for coexisting multiple networks to utilize the spectrum. First, the primary network, as the owner of the spectrum, makes a decision to share the spectrum with the secondary network based on a mutual agreement. Alternatively, the secondary network may access the spectrum opportunistically. The recent advances in a multiplicity of technologies involved, ranging from adaptive signal processing and spectral estimation techniques to power control and cooperative communications, have rendered this coexistence feasible. While some initial steps toward the implementation of DSM concepts have already been taken, there are still challenges that need to be tackled before their full potential can be achieved. This Special Issue aims to bring together certain recent work in the area of DSM and cognitive networks.