ResearchPad - signal-to-noise-ratio https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[Genetic algorithm-based personalized models of human cardiac action potential]]> https://www.researchpad.co/article/elastic_article_7669 We present a novel modification of genetic algorithm (GA) which determines personalized parameters of cardiomyocyte electrophysiology model based on set of experimental human action potential (AP) recorded at different heart rates. In order to find the steady state solution, the optimized algorithm performs simultaneous search in the parametric and slow variables spaces. We demonstrate that several GA modifications are required for effective convergence. Firstly, we used Cauchy mutation along a random direction in the parametric space. Secondly, relatively large number of elite organisms (6–10% of the population passed on to new generation) was required for effective convergence. Test runs with synthetic AP as input data indicate that algorithm error is low for high amplitude ionic currents (1.6±1.6% for IKr, 3.2±3.5% for IK1, 3.9±3.5% for INa, 8.2±6.3% for ICaL). Experimental signal-to-noise ratio above 28 dB was required for high quality GA performance. GA was validated against optical mapping recordings of human ventricular AP and mRNA expression profile of donor hearts. In particular, GA output parameters were rescaled proportionally to mRNA levels ratio between patients. We have demonstrated that mRNA-based models predict the AP waveform dependence on heart rate with high precision. The latter also provides a novel technique of model personalization that makes it possible to map gene expression profile to cardiac function.

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<![CDATA[A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex]]> https://www.researchpad.co/article/5c58d63bd5eed0c484031922

Birdsong is a complex vocal communication signal, and like humans, birds need to discriminate between similar sequences of sound with different meanings. The caudal mesopallium (CM) is a cortical-level auditory area implicated in song discrimination. CM neurons respond sparsely to conspecific song and are tolerant of production variability. Intracellular recordings in CM have identified a diversity of intrinsic membrane dynamics, which could contribute to the emergence of these higher-order functional properties. We investigated this hypothesis using a novel linear-dynamical cascade model that incorporated detailed biophysical dynamics to simulate auditory responses to birdsong. Neuron models that included a low-threshold potassium current present in a subset of CM neurons showed increased selectivity and coding efficiency relative to models without this current. These results demonstrate the impact of intrinsic dynamics on sensory coding and the importance of including the biophysical characteristics of neural populations in simulation studies.

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<![CDATA[Tensor framelet based iterative image reconstruction algorithm for low-dose multislice helical CT]]> https://www.researchpad.co/article/5c424392d5eed0c4845e0633

In this study, we investigate the feasibility of improving the imaging quality for low-dose multislice helical computed tomography (CT) via iterative reconstruction with tensor framelet (TF) regularization. TF based algorithm is a high-order generalization of isotropic total variation regularization. It is implemented on a GPU platform for a fast parallel algorithm of X-ray forward band backward projections, with the flying focal spot into account. The solution algorithm for image reconstruction is based on the alternating direction method of multipliers or the so-called split Bregman method. The proposed method is validated using the experimental data from a Siemens SOMATOM Definition 64-slice helical CT scanner, in comparison with FDK, the Katsevich and the total variation (TV) algorithm. To test the algorithm performance with low-dose data, ACR and Rando phantoms were scanned with different dosages and the data was equally undersampled with various factors. The proposed method is robust for the low-dose data with 25% undersampling factor. Quantitative metrics have demonstrated that the proposed algorithm achieves superior results over other existing methods.

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<![CDATA[Effect of F0 contour on perception of Mandarin Chinese speech against masking]]> https://www.researchpad.co/article/5c37b7a9d5eed0c48449083a

Intonation has many perceptually significant functions in language that contribute to speech recognition. This study aims to investigate whether intonation cues affect the unmasking of Mandarin Chinese speech in the presence of interfering sounds. Specifically, intelligibility of multi-tone Mandarin Chinese sentences with maskers consisting of either two-talker speech or steady-state noise was measured in three (flattened, typical, and exaggerated) intonation conditions. Different from most of the previous studies, the present study only manipulate and modify the intonation information but preserve tone information. The results showed that recognition of the final keywords in multi-tone Mandarin Chinese sentences was much better under the original F0 contour condition than the decreased F0 contour or exaggerated F0 contour conditions whenever there was a noise or speech masker, and an exaggerated F0 contour reduced the intelligibility of Mandarin Chinese more under the speech masker condition than that under the noise masker condition. These results suggested that speech in a tone language (Mandarin Chinese) is harder to understand when the intonation is unnatural, even if the tone information is preserved, and an unnatural intonation contour decreases releasing Mandarin Chinese speech from masking, especially in a multi-person talking environment.

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<![CDATA[Differential recordings of local field potential: A genuine tool to quantify functional connectivity]]> https://www.researchpad.co/article/5c2d2ea9d5eed0c484d9af66

Local field potential (LFP) recording is a very useful electrophysiological method to study brain processes. However, this method is criticized for recording low frequency activity in a large area of extracellular space potentially contaminated by distal activity. Here, we theoretically and experimentally compare ground-referenced (RR) with differential recordings (DR). We analyze electrical activity in the rat cortex with these two methods. Compared with RR, DR reveals the importance of local phasic oscillatory activities and their coherence between cortical areas. Finally, we show that DR provides a more faithful assessment of functional connectivity caused by an increase in the signal to noise ratio, and of the delay in the propagation of information between two cortical structures.

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<![CDATA[Unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles using a novel dissimilarity measure]]> https://www.researchpad.co/article/5b4a28c0463d7e4513b89825

Temporally ordered multi-neuron patterns likely encode information in the brain. We introduce an unsupervised method, SPOTDisClust (Spike Pattern Optimal Transport Dissimilarity Clustering), for their detection from high-dimensional neural ensembles. SPOTDisClust measures similarity between two ensemble spike patterns by determining the minimum transport cost of transforming their corresponding normalized cross-correlation matrices into each other (SPOTDis). Then, it performs density-based clustering based on the resulting inter-pattern dissimilarity matrix. SPOTDisClust does not require binning and can detect complex patterns (beyond sequential activation) even when high levels of out-of-pattern “noise” spiking are present. Our method handles efficiently the additional information from increasingly large neuronal ensembles and can detect a number of patterns that far exceeds the number of recorded neurons. In an application to neural ensemble data from macaque monkey V1 cortex, SPOTDisClust can identify different moving stimulus directions on the sole basis of temporal spiking patterns.

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<![CDATA[Effects of Physiological Internal Noise on Model Predictions of Concurrent Vowel Identification for Normal-Hearing Listeners]]> https://www.researchpad.co/article/5989dad7ab0ee8fa60bb84df

Previous studies have shown that concurrent vowel identification improves with increasing temporal onset asynchrony of the vowels, even if the vowels have the same fundamental frequency. The current study investigated the possible underlying neural processing involved in concurrent vowel perception. The individual vowel stimuli from a previously published study were used as inputs for a phenomenological auditory-nerve (AN) model. Spectrotemporal representations of simulated neural excitation patterns were constructed (i.e., neurograms) and then matched quantitatively with the neurograms of the single vowels using the Neurogram Similarity Index Measure (NSIM). A novel computational decision model was used to predict concurrent vowel identification. To facilitate optimum matches between the model predictions and the behavioral human data, internal noise was added at either neurogram generation or neurogram matching using the NSIM procedure. The best fit to the behavioral data was achieved with a signal-to-noise ratio (SNR) of 8 dB for internal noise added at the neurogram but with a much smaller amount of internal noise (SNR of 60 dB) for internal noise added at the level of the NSIM computations. The results suggest that accurate modeling of concurrent vowel data from listeners with normal hearing may partly depend on internal noise and where internal noise is hypothesized to occur during the concurrent vowel identification process.

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<![CDATA[Birdsong Denoising Using Wavelets]]> https://www.researchpad.co/article/5989daccab0ee8fa60bb4afd

Automatic recording of birdsong is becoming the preferred way to monitor and quantify bird populations worldwide. Programmable recorders allow recordings to be obtained at all times of day and year for extended periods of time. Consequently, there is a critical need for robust automated birdsong recognition. One prominent obstacle to achieving this is low signal to noise ratio in unattended recordings. Field recordings are often very noisy: birdsong is only one component in a recording, which also includes noise from the environment (such as wind and rain), other animals (including insects), and human-related activities, as well as noise from the recorder itself. We describe a method of denoising using a combination of the wavelet packet decomposition and band-pass or low-pass filtering, and present experiments that demonstrate an order of magnitude improvement in noise reduction over natural noisy bird recordings.

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<![CDATA[Coronary Stent Artifact Reduction with an Edge-Enhancing Reconstruction Kernel – A Prospective Cross-Sectional Study with 256-Slice CT]]> https://www.researchpad.co/article/5989dac5ab0ee8fa60bb2272

Purpose

Metallic artifacts can result in an artificial thickening of the coronary stent wall which can significantly impair computed tomography (CT) imaging in patients with coronary stents. The objective of this study is to assess in vivo visualization of coronary stent wall and lumen with an edge-enhancing CT reconstruction kernel, as compared to a standard kernel.

Methods

This is a prospective cross-sectional study involving the assessment of 71 coronary stents (24 patients), with blinded observers. After 256-slice CT angiography, image reconstruction was done with medium-smooth and edge-enhancing kernels. Stent wall thickness was measured with both orthogonal and circumference methods, averaging thickness from diameter and circumference measurements, respectively. Image quality was assessed quantitatively using objective parameters (noise, signal to noise (SNR) and contrast to noise (CNR) ratios), as well as visually using a 5-point Likert scale.

Results

Stent wall thickness was decreased with the edge-enhancing kernel in comparison to the standard kernel, either with the orthogonal (0.97 ± 0.02 versus 1.09 ± 0.03 mm, respectively; p<0.001) or the circumference method (1.13 ± 0.02 versus 1.21 ± 0.02 mm, respectively; p = 0.001). The edge-enhancing kernel generated less overestimation from nominal thickness compared to the standard kernel, both with the orthogonal (0.89 ± 0.19 versus 1.00 ± 0.26 mm, respectively; p<0.001) and the circumference (1.06 ± 0.26 versus 1.13 ± 0.31 mm, respectively; p = 0.005) methods. The edge-enhancing kernel was associated with lower SNR and CNR, as well as higher background noise (all p < 0.001), in comparison to the medium-smooth kernel. Stent visual scores were higher with the edge-enhancing kernel (p<0.001).

Conclusion

In vivo 256-slice CT assessment of coronary stents shows that the edge-enhancing CT reconstruction kernel generates thinner stent walls, less overestimation from nominal thickness, and better image quality scores than the standard kernel.

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<![CDATA[A high-speed brain-computer interface (BCI) using dry EEG electrodes]]> https://www.researchpad.co/article/5989db4fab0ee8fa60bdbaa5

Recently, brain-computer interfaces (BCIs) based on visual evoked potentials (VEPs) have been shown to achieve remarkable communication speeds. As they use electroencephalography (EEG) as non-invasive method for recording neural signals, the application of gel-based EEG is time-consuming and cumbersome. In order to achieve a more user-friendly system, this work explores the usability of dry EEG electrodes with a VEP-based BCI. While the results show a high variability between subjects, they also show that communication speeds of more than 100 bit/min are possible using dry EEG electrodes. To reduce performance variability and deal with the lower signal-to-noise ratio of the dry EEG electrodes, an averaging method and a dynamic stopping method were introduced to the BCI system. Those changes were shown to improve performance significantly, leading to an average classification accuracy of 76% with an average communication speed of 46 bit/min, which is equivalent to a writing speed of 8.8 error-free letters per minute. Although the BCI system works substantially better with gel-based EEG, dry EEG electrodes are more user-friendly and still allow high-speed BCI communication.

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<![CDATA[Truthful Channel Sharing for Self Coexistence of Overlapping Medical Body Area Networks]]> https://www.researchpad.co/article/5989da23ab0ee8fa60b7fb2b

As defined by IEEE 802.15.6 standard, channel sharing is a potential method to coordinate inter-network interference among Medical Body Area Networks (MBANs) that are close to one another. However, channel sharing opens up new vulnerabilities as selfish MBANs may manipulate their online channel requests to gain unfair advantage over others. In this paper, we address this issue by proposing a truthful online channel sharing algorithm and a companion protocol that allocates channel efficiently and truthfully by punishing MBANs for misreporting their channel request parameters such as time, duration and bid for the channel. We first present an online channel sharing scheme for unit-length channel requests and prove that it is truthful. We then generalize our model to settings with variable-length channel requests, where we propose a critical value based channel pricing and preemption scheme. A bid adjustment procedure prevents unbeneficial preemption by artificially raising the ongoing winner’s bid controlled by a penalty factor λ. Our scheme can efficiently detect selfish behaviors by monitoring a trust parameter α of each MBAN and punish MBANs from cheating by suspending their requests. Our extensive simulation results show our scheme can achieve a total profit that is more than 85% of the offline optimum method in the typical MBAN settings.

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<![CDATA[Robust and Efficient Frequency Estimator for Undersampled Waveforms Based on Frequency Offset Recognition]]> https://www.researchpad.co/article/5989d9f3ab0ee8fa60b6f11b

This paper proposes an efficient frequency estimator based on Chinese Remainder Theorem for undersampled waveforms. Due to the emphasis on frequency offset recognition (i.e., frequency shift and compensation) of small-point DFT remainders, compared to estimators using large-point DFT remainders, it can achieve higher noise robustness in low signal-to-noise ratio (SNR) cases and higher accuracy in high SNR cases. Numerical results show that, by incorporating a remainder screening method and the Tsui spectrum corrector, the proposed estimator not only lowers the SNR threshold of detection, but also provides a higher accuracy than the large-point DFT estimator when the DFT size decreases to 1/90 of the latter case.

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<![CDATA[A Supplementary System for a Brain-Machine Interface Based on Jaw Artifacts for the Bidimensional Control of a Robotic Arm]]> https://www.researchpad.co/article/5989da75ab0ee8fa60b9653d

Non-invasive Brain-Machine Interfaces (BMIs) are being used more and more these days to design systems focused on helping people with motor disabilities. Spontaneous BMIs translate user's brain signals into commands to control devices. On these systems, by and large, 2 different mental tasks can be detected with enough accuracy. However, a large training time is required and the system needs to be adjusted on each session. This paper presents a supplementary system that employs BMI sensors, allowing the use of 2 systems (the BMI system and the supplementary system) with the same data acquisition device. This supplementary system is designed to control a robotic arm in two dimensions using electromyographical (EMG) signals extracted from the electroencephalographical (EEG) recordings. These signals are voluntarily produced by users clenching their jaws. EEG signals (with EMG contributions) were registered and analyzed to obtain the electrodes and the range of frequencies which provide the best classification results for 5 different clenching tasks. A training stage, based on the 2-dimensional control of a cursor, was designed and used by the volunteers to get used to this control. Afterwards, the control was extrapolated to a robotic arm in a 2-dimensional workspace. Although the training performed by volunteers requires 70 minutes, the final results suggest that in a shorter period of time (45 min), users should be able to control the robotic arm in 2 dimensions with their jaws. The designed system is compared with a similar 2-dimensional system based on spontaneous BMIs, and our system shows faster and more accurate performance. This is due to the nature of the control signals. Brain potentials are much more difficult to control than the electromyographical signals produced by jaw clenches. Additionally, the presented system also shows an improvement in the results compared with an electrooculographic system in a similar environment.

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<![CDATA[Reproducibility and Absolute Quantification of Muscle Glycogen in Patients with Glycogen Storage Disease by 13C NMR Spectroscopy at 7 Tesla]]> https://www.researchpad.co/article/5989dad8ab0ee8fa60bb8edb

Carbon-13 magnetic resonance spectroscopy (13C MRS) offers a noninvasive method to assess glycogen levels in skeletal muscle and to identify excess glycogen accumulation in patients with glycogen storage disease (GSD). Despite the clinical potential of the method, it is currently not widely used for diagnosis or for follow-up of treatment. While it is possible to perform acceptable 13C MRS at lower fields, the low natural abundance of 13C and the inherently low signal-to-noise ratio of 13C MRS makes it desirable to utilize the advantage of increased signal strength offered by ultra-high fields for more accurate measurements. Concomitant with this advantage, however, ultra-high fields present unique technical challenges that need to be addressed when studying glycogen. In particular, the question of measurement reproducibility needs to be answered so as to give investigators insight into meaningful inter-subject glycogen differences. We measured muscle glycogen levels in vivo in the calf muscle in three patients with McArdle disease (MD), one patient with phosphofructokinase deficiency (PFKD) and four healthy controls by performing 13C MRS at 7T. Absolute quantification of the MRS signal was achieved by using a reference phantom with known concentration of metabolites. Muscle glycogen concentration was increased in GSD patients (31.5±2.9 g/kg w. w.) compared with controls (12.4±2.2 g/kg w. w.). In three GSD patients glycogen was also determined biochemically in muscle homogenates from needle biopsies and showed a similar 2.5-fold increase in muscle glycogen concentration in GSD patients compared with controls. Repeated inter-subject glycogen measurements yield a coefficient of variability of 5.18%, while repeated phantom measurements yield a lower 3.2% system variability. We conclude that noninvasive ultra-high field 13C MRS provides a valuable, highly reproducible tool for quantitative assessment of glycogen levels in health and disease.

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<![CDATA[Long-range temporal correlations in neural narrowband time-series arise due to critical dynamics]]> https://www.researchpad.co/article/5989db5aab0ee8fa60bdf5df

We show theoretically that the hypothesis of criticality as a theory of long-range fluctuation in the human brain may be distinguished from the theory of passive filtering on the basis of macroscopic neuronal signals such as the electroencephalogram, using novel theory of narrowband amplitude time-series at criticality. Our theory predicts the division of critical activity into meta-universality classes. As a consequence our analysis shows that experimental electroencephalography data favours the hypothesis of criticality in the human brain.

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<![CDATA[Magnetic Particle Imaging (MPI): Experimental Quantification of Vascular Stenosis Using Stationary Stenosis Phantoms]]> https://www.researchpad.co/article/5989d9d4ab0ee8fa60b65327

Magnetic Particle Imaging (MPI) is able to provide high temporal and good spatial resolution, high signal-to-noise ratio and sensitivity. Furthermore, it is a truly quantitative method as its signal strength is proportional to the concentration of its tracer, superparamagnetic iron oxide nanoparticles (SPIOs). Because of that, MPI is proposed to be a promising future method for cardiovascular imaging. Here, an interesting application may be the quantification of vascular pathologies like stenosis by utilizing the proportionality of the SPIO concentration and the MPI signal strength. In this study, the feasibility of MPI based stenosis quantification is evaluated based on this application scenario. Nine different stenosis phantoms with a normal diameter of 10 mm each and different stenoses of 1–9 mm and ten reference phantoms with a straight diameter of 1–10 mm were filled with a 1% Resovist dilution and measured in a preclinical MPI-demonstrator. The MPI signal intensities of the reference phantoms were compared to each other and the change of signal intensity within each stenosis phantom was used to calculate the degree of stenosis. These values were then compared to the known diameters of each phantom. As a second measurement, the 5 mm stenosis phantom was used for a serial dilution measurement down to a Resovist dilution of 1:3200 (0.031%), which is lower than a first pass blood concentration of a Resovist bolus in the peripheral arteries of an average adult human of at least about 1:1000. The correlation of the stenosis values based on MPI signal intensity measurements and based on the known diameters showed a very good agreement, proving the high precision of quantitative MPI in this regard.

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<![CDATA[Spatial Scaling of the Profile of Selective Attention in the Visual Field]]> https://www.researchpad.co/article/5989dabaab0ee8fa60bae4f0

Neural mechanisms of selective attention must be capable of adapting to variation in the absolute size of an attended stimulus in the ever-changing visual environment. To date, little is known regarding how attentional selection interacts with fluctuations in the spatial expanse of an attended object. Here, we use event-related potentials (ERPs) to investigate the scaling of attentional enhancement and suppression across the visual field. We measured ERPs while participants performed a task at fixation that varied in its attentional demands (attentional load) and visual angle (1.0° or 2.5°). Observers were presented with a stream of task-relevant stimuli while foveal, parafoveal, and peripheral visual locations were probed by irrelevant distractor stimuli. We found two important effects in the N1 component of visual ERPs. First, N1 modulations to task-relevant stimuli indexed attentional selection of stimuli during the load task and further correlated with task performance. Second, with increased task size, attentional modulation of the N1 to distractor stimuli showed a differential pattern that was consistent with a scaling of attentional selection. Together, these results demonstrate that the size of an attended stimulus scales the profile of attentional selection across the visual field and provides insights into the attentional mechanisms associated with such spatial scaling.

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<![CDATA[Potency of Full- Length MGF to Induce Maximal Activation of the IGF-I R Is Similar to Recombinant Human IGF-I at High Equimolar Concentrations]]> https://www.researchpad.co/article/5989dadfab0ee8fa60bbb1b4

Aims

To compare full-length mechano growth factor (full-length MGF) with human recombinant insulin-like growth factor-I (IGF-I) and human recombinant insulin (HI) in their ability to activate the human IGF-I receptor (IGF-IR), the human insulin receptor (IR-A) and the human insulin receptor-B (IR-B), respectively. In addition, we tested the stimulatory activity of human MGF and its stabilized analog Goldspink-MGF on the IGF-IR.

Methods

The effects of full-length MGF, IGF-I, human mechano growth factor (MGF), Goldspink-MGF and HI were compared using kinase specific receptor activation (KIRA) bioassays specific for IGF-I, IR-A or IR-B, respectively. These assays quantify activity by measuring auto-phosphorylation of the receptor upon ligand binding.

Results

IGF-IR: At high equimolar concentrations maximal IGF-IR stimulating effects generated by full-length MGF were similar to that of IGF-I (89-fold vs. 77-fold, respectively). However, EC50 values of IGF-I and full-length MGF for the IGF-I receptor were 0.86 nmol/L (95% CI 0.69–1.07) and 7.83 nmol/L (95% CI: 4.87–12.58), respectively. No IGF-IR activation was observed by human MGF and Goldspink-MGF, respectively. IR-A/IR-B: At high equimolar concentrations similar maximal IR-A stimulating effects were observed for full -length MGF and HI, but maximal IR-B stimulation achieved by full -length MGF was stronger than that by HI (292-fold vs. 98-fold). EC50 values of HI and full-length MGF for the IR-A were 1.13 nmol/L (95% CI 0.69–1.84) and 73.11 nmol/L (42.87–124.69), respectively; for IR-B these values were 1.28 nmol/L (95% CI 0.64–2.57) and 35.10 nmol/L (95% 17.52–70.33), respectively.

Conclusions

Full-length MGF directly stimulates the IGF-IR. Despite a higher EC50 concentration, at high equimolar concentrations full-length MGF showed a similar maximal potency to activate the IGF-IR as compared to IGF-I. Further research is needed to understand the actions of full-length MGF in vivo and to define the physiological relevance of our in vitro findings.

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<![CDATA[Fast Coding Unit Encoding Mechanism for Low Complexity Video Coding]]> https://www.researchpad.co/article/5989da14ab0ee8fa60b7ab6d

In high efficiency video coding (HEVC), coding tree contributes to excellent compression performance. However, coding tree brings extremely high computational complexity. Innovative works for improving coding tree to further reduce encoding time are stated in this paper. A novel low complexity coding tree mechanism is proposed for HEVC fast coding unit (CU) encoding. Firstly, this paper makes an in-depth study of the relationship among CU distribution, quantization parameter (QP) and content change (CC). Secondly, a CU coding tree probability model is proposed for modeling and predicting CU distribution. Eventually, a CU coding tree probability update is proposed, aiming to address probabilistic model distortion problems caused by CC. Experimental results show that the proposed low complexity CU coding tree mechanism significantly reduces encoding time by 27% for lossy coding and 42% for visually lossless coding and lossless coding. The proposed low complexity CU coding tree mechanism devotes to improving coding performance under various application conditions.

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<![CDATA[Analysis of statistical and standard algorithms for detecting muscle onset with surface electromyography]]> https://www.researchpad.co/article/5989db5aab0ee8fa60bdf69b

The timing of muscle activity is a commonly applied analytic method to understand how the nervous system controls movement. This study systematically evaluates six classes of standard and statistical algorithms to determine muscle onset in both experimental surface electromyography (EMG) and simulated EMG with a known onset time. Eighteen participants had EMG collected from the biceps brachii and vastus lateralis while performing a biceps curl or knee extension, respectively. Three established methods and three statistical methods for EMG onset were evaluated. Linear envelope, Teager-Kaiser energy operator + linear envelope and sample entropy were the established methods evaluated while general time series mean/variance, sequential and batch processing of parametric and nonparametric tools, and Bayesian changepoint analysis were the statistical techniques used. Visual EMG onset (experimental data) and objective EMG onset (simulated data) were compared with algorithmic EMG onset via root mean square error and linear regression models for stepwise elimination of inferior algorithms. The top algorithms for both data types were analyzed for their mean agreement with the gold standard onset and evaluation of 95% confidence intervals. The top algorithms were all Bayesian changepoint analysis iterations where the parameter of the prior (p0) was zero. The best performing Bayesian algorithms were p0 = 0 and a posterior probability for onset determination at 60–90%. While existing algorithms performed reasonably, the Bayesian changepoint analysis methodology provides greater reliability and accuracy when determining the singular onset of EMG activity in a time series. Further research is needed to determine if this class of algorithms perform equally well when the time series has multiple bursts of muscle activity.

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