Minimum Signal Distortion Research Articles

Rate-Distortion Theory for Clustering in the Perceptual Space

How to extract relevant information from large data sets has become a main challenge in data visualization. Clustering techniques that classify data into groups according to similarity metrics are a suitable strategy to tackle this problem. Generally, these techniques are applied in the data space as an independent step previous to visualization. In this paper, we propose clustering on the perceptual space by maximizing the mutual information between the original data and the final visualization. With this purpose, we present a new information-theoretic framework based on the rate-distortion theory that allows us to achieve a maximally compressed data with a minimal signal distortion. Using this framework, we propose a methodology to design a visualization process that minimizes the information loss during the clustering process. Three application examples of the proposed methodology in different visualization techniques such as scatterplot, parallel coordinates, and summary trees are presented.

Steganography in low bit-rate speech streams based on quantization index modulation controlled by keys

Since low bit-rate speech codecs used for voice over internet protocol (VoIP), such as iLBC (internet low bit-rate codec), G.723.1 and G.729A, have less redundancy due to high compression, it is more challenging to embed information in low bit-rate speech streams of VoIP. In this study, a new method is proposed for steganography in low bit-rate speech streams of VoIP. The core idea of this method is setting up a graph model for the codebook space of the quantizer. Based on the graph model, the method realises a quantization index modulation (QIM)-controlled algorithm for partitioning the codebook space. It can be proved that this method can minimize signal distortion while steganography taking place. Taking into account codeword partition balance and partition diversity, the proposed steganographic algorithm was based on QIM controlled by secret keys, i.e., mapping the ways of codebook division into secret keys, thereby significantly improving the undetectability and robustness of VoIP steganography. Performance measurements and steganalysis experiments showed that the proposed QIM-controlled steganographic algorithm was more secure and robust than the QIM algorithm, the conventional RANDOM algorithm and the original codebook algorithm.

Decision feedback equalization for radiation hard data link at 5 Gbps

The increased particle collision rate following the upgrade of the Large Hadron Collider (LHC) to an increased luminosity requires an increased readout data speed, especially for the ATLAS pixel detector, located closest to the particle interaction point. For this reason, during the Phase-II upgrade of the ATLAS experiment the output data speed of the pixel front-end chips will be increased from 160 Mbps to 5 Gbps. The increased radiation levels will require a radiation hard data transmission link to be designed to carry this data from the pixel front-end to the off-detector system where it will undergo optical conversion. We propose a receiver utilizing the concept of Decision Feedback Equalization (DFE) to be used in this link, where the number of filter taps can be determined from simulations using S-parameter data from measurements of various customized cable prototypes under characterization as candidates to function as transmission medium between the on-chip data driver and the receiver of the link. A dedicated framework has been set up in Matlab to analyze the S-parameter characteristics for the various cable prototypes and investigate the possibilities for signal recovery and maintained signal integrity using DFE, as well as pre-emphasis and different encoding schemes. The simulation results indicate that DFE could be an excellent choice for expanding the system bandwidth to reach required data speeds with minimal signal distortion.

Two-path all-pass based half-band infinite impulse response decimation filters and the effects of their non-linear phase response on ECG signal acquisition

Abstract This paper is based on the novel use of a very high fidelity decimation filter chain for Electrocardiogram (ECG) signal acquisition and data conversion. The multiplier-free and multi-stage structure of the proposed filters lower the power dissipation while minimizing the circuit area which are crucial design constraints to the wireless non-invasive wearable health monitoring products due to the scarce operational resources in their electronic implementation. The decimation ratio of the presented filter is 128, working in tandem with a 1-bit 3 rd order Sigma Delta (ΣD) modulator which achieves 0.04 dB passband ripples and −74 dB stopband attenuation. The work reported here investigates the non-linear phase effects of the proposed decimation filters on the ECG signal by carrying out a comparative study after phase correction. It concludes that the enhanced phase linearity is not crucial for ECG acquisition and data conversion applications since the signal distortion of the acquired signal, due to phase non-linearity, is insignificant for both original and phase compensated filters. To the best of the authors’ knowledge, being free of signal distortion is essential as this might lead to misdiagnosis as stated in the state of the art. This article demonstrates that with their minimal power consumption and minimal signal distortion features, the proposed decimation filters can effectively be employed in biosignal data processing units.

Rolling Element Bearing Fault Diagnosis Using Integrated Nonlocal Means Denoising with Modified Morphology Filter Operators

The impulses in vibration signals are used to identify faults in the bearings of rotating machinery. However, vibration signals are usually contaminated by noise that makes the process of extracting impulse characteristic of localized defect very challenging. In order to effectively diagnose bearing with noise masking vibration signal, a new methodology is proposed using integrated (i) nonlocal means- (NLM-) based denoising and (ii) improved morphological filter operators. NLM based denoising is first employed to eliminate or reduce the background noise with minimal signal distortion. This denoised signal is then analysed by a proposed modified morphological analysis (MMA). The MMA analysis introduces a new morphological operator which is based on Modified-Different (DIF) filter to include only fault relevant impulsive characteristics of the vibration signal. To improve further performance of the methodology the length of the structure element (SE) used in MMA is optimized using a particle swarm optimization- (PSO-) based kurtosis criterion. The results of simulated and real vibration signal show that the integrated NLM with MMA method as well as the MMA method alone yields superior performance in extracting impulsive characteristics of vibrations signals, especially for signal with high level of noise or presence of other sources masking the fault.

A robust signal preprocessing framework for wrist pulse analysis

Abstract Wrist pulse has been a physical health indicator in Traditional Chinese Medicine (TCM) for a long history. With the development of sensor technology and bioinformatics, quantifying pulse diagnosis by using signal processing technology is attracting increasing attentions in recent years. Since wrist pulse signals collected by the sensors are often corrupted by artifacts in real situations, many approaches on the wrist pulse preprocessing including pulse de-noising and baseline wander removal are introduced for more accurate wrist pulse analysis. However, these scattered methods are incomplete with some limitations when used to preprocess our special pulse data for the clinical applications. This paper presents a robust signal preprocessing framework for wrist pulse analysis. The cascade filter based on frequency-dependent analysis (FDA) is first introduced to remove the high frequency noises and to select the significant pulse intervals. Then the curve fitting method is developed to adjust the direction and the baseline drift with minimum signal distortion. Last, the period segmentation and pulse normalization is applied for the feature extraction. The effectiveness of the proposed pulse preprocessing is validated through experiments on actual pulse records with biochemical markers. In contrast with the traditional methods, the proposed preprocessing framework is effective in extracting more accurate pulse features. And the highest classification rate 91.6% is obtained on diabetes diagnosis. The results demonstrate that our method is superior to the former pulse preprocessing researches and practical for wrist pulse analysis.

Comparison of Some EMD based Technique for Baseline Wander Correction in Fetal ECG Signa

wander (BW) is a low frequency artifact in biomedical electronic recordings. It is usually caused by patient's respiration or movement of equipments. The removal of this artifact is important in ECG recordings for reliable visual interpretation. This paper presents the implementation of Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) and EMD based method to remove this disturbance. The EMD based technique serves as an efficient method to remove baseline wander with minimum signal distortion. The results highlights the main differences among all different methods and also show that the EMD based technique is able to remove best baseline wander. ( 01 01 ) ( t m t x p  

Rolling element bearing fault diagnosis based on non‐local means de‐noising and empirical mode decomposition

The presence of faults in the bearings of rotating machinery is usually observed with impulses in the vibration signals. However, the vibration signals are generally non-stationary and usually contaminated by noise because of the compounded background noise present in the measuring device and the effect of interference from other machine elements. Therefore in order to enhance monitoring condition, the vibration signal needs to be properly de-noised before analysis. In this study, a novel fault diagnosis method for rolling element bearings is proposed based on a hybrid technique of non-local means (NLM) de-noising and empirical mode decomposition (EMD). An NLM which removes the noise with minimal signal distortion is first employed to eliminate or at least reduce the background noise present in the measuring device. This de-noised signal is then decomposed into a finite number of stationary intrinsic mode functions (IMF) to extract the impulsive fault features from the effect of interferences from other machine elements. Finally, envelope analyses are performed for IMFs to allow for easier detection of such characteristic fault frequencies. The results of simulated and real bearing vibration signal analyses show that the hybrid feature extraction technique of NLM de-noising, EMD and envelope analyses successfully extract impulsive features from noise signals.

Selection of Parameters for Filtering Distal Arterial Pulse Signal Using Multi-Resolution Wavelet Transforms

A method for multi-resolution wavelet filtration of distal arterial pulse in the presence of motion artifacts is described. The effect of the selected wavelet filtration parameters using multi- resolution transforms on the distortion of the filtered signal in the signal-to-noise ratio range is discussed. Optimal parameters of wavelet filtration of biological signal are defined using the criterion of minimal signal distortion after filtration by comparison with a model signal free of motion artifacts.

Wavelet Notch Filter Design of Spread-Spectrum Communication Systems for High-Precision Wireless Positioning

Spread-spectrum communication systems are now commonly used in the field of cellular telephone positioning. However, wireless positioning systems by low-power spread-spectrum communication are extremely vulnerable to high-power interference, which limits achievable measurement precision. In this paper, a bandwidth variable wavelet notch filter design method is proposed to suppress a large number of jammers in multiple locations with noise interfering with spread-spectrum systems. The filter uses combinations of Gaussian wavelets with optimal time-frequency localization and computational efficiency for real-time operation of denoising. The performance of the adaptive filter has been evaluated by experiments associated with a spread-spectrum communication system model employing a reliable noise detector to locate the filter notch. Experimental results demonstrate that the proposed wavelet notch filter removes the narrow-band interference in accordance with the corrupted frequency contents while minimizing signal distortion and information loss, which leads to high-precision wireless positioning.

Using spectral constancy to encode temporal pitch and improve cochlear-implant melody perception.

Despite good speech performance in quiet, melody perception remains a challenge to cochlear-implant (CI) users. Current CI pitch-encoding schemes significantly alter original signals, producing potentially detrimental effects on speech perception. The present study proposes to take advantage of spectral constancy to encode temporal pitch while minimizing signal distortion. Spectral constancy refers to unaltered timbre perception when the overall level of a signal is roved. For example, the vowel /a/ sounds the same independent of its overall level. The idea is to extract and encode pitch by increasing the gain of all channels proportionally and synchronously according to the pitch period. This scheme alters the temporal envelope of voiced sounds but preserves their spectral envelope in each time frame. It does not alter temporal envelope of unvoiced sounds, making it a good comprise between encoding pitch and preserving spectral and temporal envelopes. The new scheme has been implemented in a real-time speech processor and evaluated in nine nucleus CI users who identified ten melodies with minimal training and in a test protocol that randomized the stimulus presentation order. Compared with the standard continuous interleaved sampling strategy, the new scheme significantly improved CI melody perception by 10–20%.

Optimized Iterative Clipping and Filtering for PAPR Reduction of OFDM Signals

Iterative clipping and filtering (ICF) is a widely used technique to reduce the peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) signals. However, the ICF technique, when implemented with a fixed rectangular window in the frequency-domain, requires many iterations to approach specified PAPR threshold in the complementary cumulative distribution function (CCDF). In this paper, we develop an optimized ICF method which determines an optimal frequency response filter for each ICF iteration using convex optimization techniques. The design of optimal filter is to minimize signal distortion such that the OFDM symbol's PAPR is below a specified value. Simulation results show that our proposed method can achieve a sharp drop of CCDF curve and reduce PAPR to an acceptable level after only 1 or 2 iterations, whereas the classical ICF method would require 8 to 16 iterations to achieve a similar PAPR reduction. Moreover, the clipped OFDM symbols obtained by our optimized ICF method have less distortion and lower out-of-band radiation than the existing method.

Single-Trial Subspace-Based Approach for VEP Extraction

A signal subspace approach for extracting visual evoked potentials (VEPs) from the background electroencephalogram (EEG) colored noise without the need for a prewhitening stage is proposed. Linear estimation of the clean signal is performed by minimizing signal distortion while maintaining the residual noise energy below some given threshold. The generalized eigendecomposition of the covariance matrices of a VEP signal and brain background EEG noise is used to transform them jointly to diagonal matrices. The generalized subspace is then decomposed into signal subspace and noise subspace. Enhancement is performed by nulling the components in the noise subspace and retaining the components in the signal subspace. The performance of the proposed algorithm is tested with simulated and real data, and compared with the recently proposed signal subspace techniques. With the simulated data, the algorithms are used to estimate the latencies of P(100), P(200), and P(300) of VEP signals corrupted by additive colored noise at different values of SNR. With the real data, the VEP signals are collected at Selayang Hospital, Kuala Lumpur, Malaysia, and the capability of the proposed algorithm in detecting the latency of P(100) is obtained and compared with other subspace techniques. The ensemble averaging technique is used as a baseline for this comparison. The results indicated significant improvement by the proposed technique in terms of better accuracy and less failure rate.

A Four-Channel Beamforming Down-Converter in 90-nm CMOS Utilizing Phase-Oversampling

In this paper, a 4-GHz, four-channel, analog-beamforming direct-conversion down-converter in 90-nm CMOS is presented. Down-converting vector modulators (VMs) in each channel multiply the inputs with complex beamforming weights before summation between the different channels. The VMs are based on a phase-oversampling technique that allows the synthesis of inherently linear, high-resolution complex gains without complex variable gain amplifiers. A bank of simple passive mixers driven by a multiphase local oscillator (LO) in each VM performs accurate phase shifting with minimal signal distortion, and a pair of transimpedance amplifiers (TIAs) combines the mixer outputs to perform beamforming weighting and combining. Each individual channel achieves 360° phase shift and gain-setting programmability with 8-bit digital control, a complex gain constellation with a mean error-vector magnitude (EVM) of 24-dB blocker rejection for blockers impinging from different directions and 17-dB signal EVM improvement in the presence of an in-channel blocker.

Numerical removal of water vapour effects from terahertz time-domain spectroscopy measurements

The use of T-rays, or terahertz radiation, to identify substances by their spectroscopic fingerprints is a rapidly moving field. The dominant approach is presently terahertz time-domain spectroscopy. However, a key problem is that ambient water vapour is ubiquitous and the consequent water absorption distorts the T-ray pulses. Water molecules in the gas phase selectively absorb incident T-rays at discrete frequencies corresponding to their molecular rotational transitions. When T-rays propagate through an atmosphere, this results in prominent resonances spread over the T-ray spectrum; furthermore, in the time domain, fluctuations after the main pulse are observed in the T-ray signal. These effects are generally undesired, since they may mask critical spectroscopic data. So, ambient water vapour is commonly removed from the T-ray path by using a closed chamber during the measurement. Yet, in some applications, a closed chamber is not always feasible. This situation, therefore, motivates the need for an optional alternative method for reducing these unwanted artefacts. This paper represents a study on a computational means that is a step towards addressing the problem arising from water vapour absorption over a moderate propagation distance. Initially, the complex frequency response of water vapour is modelled from a spectroscopic catalogue. Using a deconvolution technique, together with fine tuning of the strength of each resonance, parts of the water vapour response are removed from a measured T-ray signal, with minimal signal distortion, thus providing experimental validation of the technique.

A Three-Microphone Adaptive Noise Canceller for Minimizing Reverberation and Signal Distortion

This paper introduces an adaptive noise canceller (ANC) to improve the system performance in the presence of signal leakage components. The proposed ANC consists of two adaptive filters and three microphones. The first adaptive filter cancels the signal leakage and the second filter cancels the noise. For best results, a least mean squares adaptive algorithm was also introduced and used in the proposed ANC. In this algorithm the step size was based on both error vector and data normalization. Simulation results, carried out using a real speech, demonstrate significant improvements of the proposed ANC over the conventional one in minimizing signal distortion and reverberation.

Speech Compression using Wavelet Transforms

In this research wavelet technique was used for speech compression. It was translated and scaled to groups which represent mother wavelet function. Applying the adopted algorithm with MATLAB software using Daubechies filters family (with multi-scale for compression level) to compress speech signal for (N=1..9). When the compression level and the distortion level, a best compression level seen at level (5), with different type of wavelet and fixed compression ratio at level (5) with different type of Daubechies filters family. Minimum signal distortion was seen when the adopted algorithm was applied. And with multi type of wavelet it was seen that the scale had direct proportional relation ship with the compression ratio, also speech compression does not need smoothing for the speech signal as compared to other compression methods.

ECG signal denoising and baseline wander correction based on the empirical mode decomposition

The electrocardiogram (ECG) is widely used for diagnosis of heart diseases. Good quality ECG are utilized by physicians for interpretation and identification of physiological and pathological phenomena. However, in real situations, ECG recordings are often corrupted by artifacts. Two dominant artifacts present in ECG recordings are: (1) high-frequency noise caused by electromyogram induced noise, power line interferences, or mechanical forces acting on the electrodes; (2) baseline wander (BW) that may be due to respiration or the motion of the patients or the instruments. These artifacts severely limit the utility of recorded ECGs and thus need to be removed for better clinical evaluation. Several methods have been developed for ECG enhancement. In this paper, we propose a new ECG enhancement method based on the recently developed empirical mode decomposition (EMD). The proposed EMD-based method is able to remove both high-frequency noise and BW with minimum signal distortion. The method is validated through experiments on the MIT–BIH databases. Both quantitative and qualitative results are given. The simulations show that the proposed EMD-based method provides very good results for denoising and BW removal.

Modeling and synthesis of multiport transmission line for multichannel communication

To overcome the limitations of traditional interconnects, multichannel interconnects that transmit signals via high-frequency carriers have recently been proposed and realized for intrachip and interchip communication. To efficiently design such transmission-line-based interconnects, this paper derives a closed-form model for signal-to-noise ratio (SNR) considering multiple ports and branches, and proposes efficient figures of merit (FOMs) to minimize signal distortion. Experiments show that the SNR model is accurate compared to SPICE simulation and the signal distortion FOMs are effective. Using the proposed models, this paper further automatically synthesizes coplanar waveguides (CPWs) for radio-frequency (RF) interconnects with capacitive couplers. The authors minimize the total interconnect area under the constraints of SNR and signal distortion. Compared to the published manual designs, the synthesized solutions can reduce up to 80% area. Furthermore, the optimized solutions vary greatly with respect to number of ports, frequency bands, topologies, and terminations, and therefore automatic synthesis is effective and necessary

Novel wavelet domain Wiener filtering de-noising techniques: Application to bowel sounds captured by means of abdominal surface vibrations

This work focuses on the design and evaluation of efficient and accurate de-noising algorithms that combine robust signal enhancement and minimum signal distortion. The proposed method introduces novel, frequency depended, parametric, Wiener filtering techniques that involve Discrete Wavelet Transform and Wavelet Packets. Implementations of various decomposition schemes, different mother wavelets and various thresholding options were tested, while perceptual criteria were also taken into account. The introduced de-noising approach has been extensively tested on human bowel sounds, captured by means of abdominal surface vibration recordings, in order to be further utilized as a diagnostic tool. Qualitative and quantitative analysis of the method's performance, when applied to various types of recorded and synthetic sounds, revealed that the new approach works excellent with favourable results.