Signal Energy Analysis Research Articles

EMG Signal Processing for the Study of Localized Muscle Fatigue-Pilot Study to Explore the Applicability of a Novel Method.

This pilot study aimed to explore a method for characterization of the electromyogram frequency spectrum during a sustained exertion task, performed by the upper limb. Methods: Nine participants underwent an isometric localized muscle fatigue protocol on an isokinetic dynamometer until exhaustion, while monitored with surface electromyography (sEMG) of the shoulder’s external rotators. Firstly, three methods of signal energy analysis based on primer frequency contributors were compared to the energy of the entire spectrum. Secondly, the chosen method of analysis was used to characterize the signal energy at beginning (T1), in the middle (T2) and at the end (T3) of the fatigue protocol and compared to the torque output and the shift in the median frequencies during the trial. Results: There were statistically significant differences between T1 and T3 for signal energy (p < 0.007) and for central frequency of the interval (p = 0.003). Moreover, the isometric peak torque was also different between T1 and T3 (p < 0.001). Overall, there were no differences between the signal energy enclosed in the 40 primer frequency contributors and the analysis of the full spectrum energy; consequently, it was the method of choice. The reported fatigue and the decrease in the produced muscle torque was consistent with fatigue-induced alterations in the electromyogram frequency spectrum. In conclusion, the developed protocol has potential to be considered as an easy-to-use method for EMG-based analysis of isometric muscle exertion until fatigue. Thus, the novelty of the proposed method is to explore, in muscle fatigue, the use of only the main contributors in the frequency domain of the EMG spectrum, avoiding surplus information, that may not represent muscle functioning. However, further studies are needed to investigate the stability of the present findings in a more comprehensive sample.

Validating Automatic Diadochokinesis Analysis Methods Across Dysarthria Severity and Syllable Task in Amyotrophic Lateral Sclerosis.

Oral diadochokinesis (DDK) is a standard dysarthria assessment task. To extract automatic and semi-automatic DDK measurements, numerous DDK analysis algorithms based on acoustic signal processing are available, including amplitude based, spectral based, and hybrid. However, these algorithms have been predominantly validated in individuals with no perceptible to mild dysarthria. The behavior of these algorithms across dysarthria severity is largely unknown. Likewise, these algorithms have not been tested equally for various syllable types. The goal of this study was to evaluate the performance of five common DDK algorithms as a function of dysarthria severity, considering syllable types. We analyzed 282 DDK recordings of /ba/, /pa/, and /ta/ from 145 participants with amyotrophic lateral sclerosis. Recordings were stratified into mild, moderate, or severe dysarthria groups based on individual performance on the Speech Intelligibility Test. Analysis included manual and automatic estimation of the number of syllables, DDK rate, and cycle-to-cycle temporal variability (cTV). Validation metrics included Bland-Altman mixed-effects limits of agreement between manual and automatic syllable counts, recall and precision between manual and automatic syllable boundary detection, and Kendall's tau-b correlations between manual and algorithm-detected DDK rate and cTV. The amplitude-based algorithm (absolute energy) yielded the strongest correlations with manual analysis across all severity groups for DDK rate (τ b = 0.7-0.84) and cTV (τ b = 0.7-0.84) and the narrowest limits of agreement (-5.92 to 7.12 syllable difference). Moreover, this algorithm also provided the highest mean recall and precision across severity groups for /ba/ and /pa/, but with significantly more variation for/ta/. Algorithms based on signal energy analysis appeared to be the most robust for DDK analysis across dysarthria severity and syllable types; however, it remains prone to error against severe dysarthria and alveolar syllable context. Further development is needed to address this important issue.

Analysis of Signal Energy and Noise in Photonic Integrated Interferometric Imaging System

Analysis of Signal Energy and Noise in Photonic Integrated Interferometric Imaging System

Optimal Detection and Identification of DC Series Arc in Power Distribution System on Shipboards

In this paper, a series arc was simulated under resistive load and motor load, which are mainly used in small ships, and the arc signal was analyzed using discrete wavelet transform. After calculating the correlation coefficient between the single arc pulse and the wavelet, Biorthogonal (bior) 3.1 was selected as the optimal mother wavelet, and the signal was analyzed using multiresolution analysis. From the results, arc signals were distributed in the detail components D2, D3, D4 and D5, corresponding to a frequency range of 19.5–312.5 kHz, with the optimal arc signal extracted based on these values. In addition, in order to distinguish between arc and normal conditions, signal energy was analyzed. By applying the magnitude and signal energy analysis method, the DC series arc generated in the power distribution system of a shipboard was identified.

Dielectric properties of mixed mineral and synthetic ester oil

The dielectric properties of 20% synthetic ester oil mixed with mineral oil were studied for its potential applications as an insulator in transformers. The corona inception voltage of mixed oil under AC and DC voltages was measured by the ultra-high frequency (UHF) technique. A statistical study of the breakdown voltage of mixed oil under AC, DC and standard lightning impulse voltages was carried out to obtain a database which would be useful for designing transformer insulation. Further, the mixture was subjected to continuous breakdown studies under AC voltage with lightning impulse voltages, at regular intervals and characterised through measurement of interfacial tension, flash point, ionic mobility as well as UV–vis spectroscopy and frequency-domain spectroscopy studies to understand the liquid's chemical stability. The results of the study have shown that the mixture was stable even after several breakdowns. The ionic mobility and polarisation current have increased, indicating higher losses in the oil. UHF signal energy analysis and phase-resolved partial discharge analysis have revealed a reduction in discharge activity with mixed oils.

Multiple Crack Detection using Wavelet Transforms and Energy Signal Techniques

Wavelet transforms are efficient tools for structural health monitoring (SHM) and damage detection. However, these methods are encountered with some limitations in practice. Thus, signal energy analysis is used as an alternative technique for damage detection. In this paper, discrete wavelet transforms (DWT) and Teager energy operator (TEO) is applied to the curvature of the mode shapes of the beams, and the locations of the damages are identified. The results show that in comparison with the discrete wavelet transform, the signal energy operator has better performance. This superiority in detecting the damages, especially near the supports of the beam, is obvious and has enough sensitivities in low damage intensities. Additionally, the damage detection in the cases that the response data are noisy is investigated. For this purpose, by adding low-intensity noises to the curvature of the mode shapes, the abilities of the mentioned methods are evaluated. The results indicate that each method is not individually efficient in the detection of damages in noisy conditions, but the combination of them under noisy conditions is more reliable

Instantaneous Modal Parameter Identification of Linear Time‐Varying Systems Based on Chirplet Adaptive Decomposition

Instantaneous modal parameter identification of time‐varying dynamic systems is a useful but challenging task, especially in the identification of damping ratio. This paper presents a method for modal parameter identification of linear time‐varying systems by combining adaptive time‐frequency decomposition and signal energy analysis. In this framework, the adaptive linear chirplet transform is applied in time‐frequency analysis of acceleration response for its higher energy concentration, and the response of each mode can be adaptively decomposed via an adaptive Kalman filter. Then, the damping ratio of the time‐varying systems is identified based on energy analysis of component response signal. The proposed method can not only improve the accuracy of instantaneous frequency extraction but also ensure the antinoise ability in identifying the damping ratio. The efficiency of the method is first verified through a numerical simulation of a three‐degree‐of‐freedom time‐varying structure. Then, the method is demonstrated by comparing with the traditional wavelet and time‐domain peak method. The identified results illustrate that the proposed method can obtain more accurate modal parameters in low signal‐to‐noise ratio (SNR) scenarios.

Entropy based detection of molecularly targeted nanoparticle ultrasound contrast agents in tumors

In this study, we demonstrate the use of “joint entropy” of two random variables (X,Y) can be applied to markedly improve tumor conspicuity (where X = f(t) =backscattered waveform and Y = g(t) = a reference waveform; both differentiable functions). Previous studies have shown that a good initial choice of reference is a reflection of the original insonifying pulse taken from a stainless-steel reflector. Using this choice, joint entropy analysis is more sensitive to accumulation of targeted contrast agents than conventional gray-scale or signal energy analysis by roughly a factor of 2[Hughes, M. S., et al., J. Acoust. Soc. Am., 133(1), p 283, 2013]. We now derive an improved reference that is applied to three groups of (MDA-435, breast tumor) flank tumor-implanted athymic nude mice to identify tumor vasculature after binding perfluorocarbon nanoparticles (~250 nm) to neovascular avb3 integrins. Five mice received i.v.avb3-targeted nanoparticles, five received nontargeted nanoparticles, and five received saline at a dose of 1 ml/kg, which was allowed to circulate for up to two hours prior to imaging. Three analogous groups of nonimplanted mice were imaged in the same region following the same imaging protocol. Our results indicate an improvement in contrast by a factor of 2.5 over previously published results. Thus, judicious selection of the reference waveform is critical to improving contrast-to-noise in tumor environments when attempting to detect targeted nanostructures for molecular imaging of sparse features.

South China Sea upper-slope sand dunes acoustics experiment

Very large subaqueous sand dunes were discovered on the upper continental slope of the Northeastern South China Sea. The spatial distribution and scales of these large sand dunes were mapped by two multibeam echo sounding (MBES) surveys, one in 2012 and the other in 2013. These two surveys represented two pilot cruises as part of a multiyear, US-Taiwan collaborative field study designed to characterize these sand dunes, the associated physical processes and the associated acoustic scattering physics. The main experiment will be carried out in 2014. The combination of MBES, coring and acoustic transmission data obtained from the two pilot cruises have provided vital initial knowledge of (1) the spatial and temporal scales of the sand dunes from objective analysis, (2) the geoacoustic properties of the dunes based on forward modeling to matching the measured levels, and (3) the anisotropy and translational variability of the transmission loss based on a signal energy analysis of the repeated 1–2 kHz and 4–6 kHz FM signals transmitted by a calibrated sound source towed along two circular tracks, each surrounding a receiver. The results from the pilot cruises are presented and discussed. [The research is sponsored by the US ONR and the Taiwan NSC.]

Design of Optical Fiber Transmission System Based on Absolute Polar Duty Cycle Division Multiplexing (APDCDM)

This paper propose a new multiplexing technique which use the bipolar RZ signal to realize duty cycle division multiplexing (DCDM) ,we call it Absolute Polar Duty Cycle Division Multiplexing (APDCDM).Multiplexing and demultiplexing principle of APDCDM are analyzed. Here we have designed a optical fiber transmission system based on APDCDM of three users by Joint simulation with Optisystem and Matlab. Simulation result shows the feasibility of this system. By the analysis of signal energy, explain APDCDM can carry more users than DCDM.

Speech Segmentation Algorithm Based on an Analysis of the Normalized Power Spectral Density

This article demonstrates a new approach to speaker independent phoneme detection. The core of the algorithm is to measure the distance between normalized power spectral densities in adjacent, short-time segments and verify it based on velocity of changes of values of short-time signal energy analysis. The results of experiment analysis indicate that proposed algorithm allows revealing a phoneme structure of pronounced speech with high probability. The advantages of this algorithm are absence of any prior information on a signal or model of phonemes and speakers that allows the algorithm to be speaker independent and have a low computation complexity.

A unified approach for estimating transient and long-term stability transfer limits

Until now, the estimation of transient stability transfer limits by means of signal energy analysis has been limited to stable time-domain simulations. The paper adapts this analysis to unstable simulations, and extends the method to long-term voltage stability limit estimation as well, thereby proposing a unified approach for both criteria. The method is based on (i) performing transient or long-term time-domain simulations (depending on the type of limit to be found), (ii) extracting the signal energy of the time-varying RMS voltage response at appropriate buses, and (iii) averaging to obtain a system signal energy. In radial power systems, a more localized average is found to provide slightly better limit-estimating performance (i.e. corridor signal energy). However, both system and corridor signal energies are shown to rise asymptotically and predictably to the limit with increasing power for stable cases, and decreasing power for unstable cases. A simple theory explains this behaviour near the limit as being inversely proportional to power transfer, and test cases performed on a validated model of the 1991 Hydro-Quebec demonstrate the value of the approach.

A review of signal energy analysis for the rapid determination of dynamic security limits

This article presents an overview of the signal energy method for the rapid determination of power system dynamic security limits. The key questions concerning this method are addressed. These include: (1) What is the basis of signal energy transient-stability-limit estimation? (2) Which physical quantity(ies) yield(s) the most accurate estimates? (3) How is the method used for accelerating the transient-stability-limit search? and (4) Can it be adapted to multiple-processor environments (i.e., either multiprocessor work stations or local area networks (LANs))? Signal energy limit search strategies, deployed on single-processor hardware, are shown to be extremely rapid and accurate, and to compare favourably to the published performance of the only other known method for accelerating the transient-stability-limit search (i.e., the “second kick” method). Also, tests show that by combining signal energy analysis and multiple processors, transient-stability-transfer-limit determination is achievable, on average, in less than the time required for two simulations and with an error under 1%.

Analysis of digitized waveforms using Shannon entropy

A novel symbol-oriented, information-theoretic approach to signal analysis is described based on application of Shannon entropy. It is shown that this can be a preferred alternate to conventional digital signal energy analysis by making an experimental comparison of scanned ultrasonic images formed from Shannon entropy, signal energy, and log signal energy measurements. In these images, which were made by c scanning a Plexiglas bar with drill-hole ‘‘defects,’’ the Shannon entropy exhibits approximately two to three times the sensitivity of the two energy measures. However, the Shannon entropy analysis of waveforms suffers from a severe fundamental defect. It depends on the properties of the analog-to-digital converter (ADC) used to acquire the waveform as well as the properties of the waveform itself. As digitizer dynamic range is increased the Shannon entropy ultimately saturates at a level governed by the properties of the digitizer alone. This effect arises from the necessary limited bandwidth of a real ADC; if ADC bandwidth were not limited, the Shannon entropy would grow without bound with increasing dynamic range. To overcome this difficulty, a variant of the Shannon entropy is defined that depends only on the properties of the waveform. It is then shown that this variant is sensitive to the same scattering features as is the Shannon entropy for data acquired from the Plexiglas specimen discussed initially. Furthermore, the variant offers the same sensitivity improvements obtained from the Shannon entropy analysis. Finally, computation of this variant for waveforms having noise is described. Derivation of the variant is given in Appendix A of this work. Several of its more interesting features are also discussed there. Derivation of the procedure required to compute the variant is given in Appendix B.