Increased Sampling Rate Research Articles

The effect of multiband sequences on statistical outcome measures in functional magnetic resonance imaging using a gustatory stimulus

Recent technical developments have led to the invention of multiband functional magnetic resonance imaging (fMRI) sequences that allow for faster sampling rates. However, some studies have highlighted problems with these sequences, leading to a decreased temporal signal-to-noise ratio (tSNR). In addition, this temporal noise may interfere with detecting reward-related responses in mesolimbic regions. The blood-oxygen-level-dependent signal utilized in the majority of fMRI measurements is relatively slow. Furthermore, the cerebral response to gustatory stimuli would also be relatively slow. Therefore, given the temporal noise issues with multiband sequences, it is unclear whether multiband sequences are necessary for fMRI studies using gustatory stimuli. We thus conducted an fMRI experiment using a gustatory stimulus to investigate the effects of multiband sequences and increased sampling rates on statistical outcome measures. A single-band sequence with a repetition time (TR) of 2 s of phantom fMRI data and gustatory fMRI data from the gustatory regions exhibited the highest tSNR, although the tSNR of this sequence of gustatory fMRI was not statistically different from tSNR of multiband sequences with a TR of 2 s in any of the selected region of interests. Conventional general linear model analysis of fMRI showed that single-band sequences are more advantageous than multiband sequences for detecting brain responses to gustatory stimuli in the primary gustatory cortex. In addition, a Bayesian data comparison showed that data derived from a single-band sequence with a TR of 2 s was optimal for inferring neuronal connectivity in gustatory processing. Therefore, a conventional single-band sequence with a TR of 2 s is more appropriate for fMRI with gustatory stimuli. Image acquisition sequences should be selected aligned with the study objectives and target brain regions.

Solar Radiation Tracking Design Based on Photoelectric Sensor for Tracing Signal Reconstruction Algorithm

Due to the high precision, low power requirements, and maintenance-free nature, the four-quadrant photoelectric sensor (FQPS) and the laser displacement sensor are employed in the development of the two-axis solar tracker. Among them, when designing the optical path of the FQPS, a cavity-wandering optical path is introduced to control the spot size of the direct sunlight falling on the sensor. The sensor interface has five data transmission lines, of which four positive poles correspond to the four quadrants of the planar two-dimensional coordinate system. AD7793 is used as the signal collector and a differential amplifier with small clutter is integrated. A laser displacement sensor is installed in the solar radiation tracker to realize the self-correction of movement error. The main control circuit adopts DSP chip TMS320F28335 for the whole dual-axis sun tracker. In the experiment, Code Composer Studio was used for development, and SPA algorithm was used to measure the position. A number of error correction variables, such as year, month, day, time, minute, second, ΔT, time zone, longitude, latitude, etc., are added to the algorithm. After debugging the hardware and software, the sunrise and sunset times of a certain place on the solar terms such as spring equinox, summer solstice, autumn equinox and winter solstice in 2022 are tested, and compared with those released by the Meteorological Bureau. The tracking signal is rebuilt using proximal gradient descent technology. The results demonstrate that the signal-to-noise ratio under the reconstruction algorithm is continuously enhanced and tends to stabilize with an increase in sampling rate without noise, and it exhibits good signal reconstruction performance in the presence of noise and growing noise.

Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach.

Deep learning (DL) is emerging as a successful technique for automatic detection and differentiation of spontaneous seizures that may otherwise be missed or misclassified. Herein, we propose a system architecture based on top-performing DL models for binary and multigroup classifications with the non-overlapping window technique, which we tested on the TUSZ dataset. The system accurately detects seizure episodes (87.7% Sn, 91.16% Sp) and carefully distinguishes eight seizure types (95-100% Acc). An increase in EEG sampling rate from 50 to 250 Hz boosted model performance: the precision of seizure detection rose by 5%, and seizure differentiation by 7%. A low sampling rate is a reasonable solution for training reliable models with EEG data. Decreasing the number of EEG electrodes from 21 to 8 did not affect seizure detection but worsened seizure differentiation significantly: 98.24 ± 0.17 vs. 85.14 ± 3.14% recall. In detecting epileptic episodes, all electrodes provided equally informative input, but in seizure differentiation, their informative value varied. We improved model explainability with interpretable ML. Activation maximization highlighted the presence of EEG patterns specific to eight seizure types. Cortical projection of epileptic sources depicted differences between generalized and focal seizures. Interpretable ML techniques confirmed that our system recognizes biologically meaningful features as indicators of epileptic activity in EEG.

Programmable Optical Vernier‐Sampled Coherent Ranging

AbstractExtended measurement ranges with coherent ranging necessitate an increase in the digitizer sampling rate. Subsampled coherent ranging can reduce the sampling rate, but it has been demonstrated in a short range below around 10 cm. Herein, programmable optical Vernier‐sampled coherent ranging over several tens of meters with a reduced sampling rate using a newly developed stepped‐frequency‐swept laser (stepped‐FSL) is proposed. The measurable range of three‐dimensional subsampled coherent ranging can be extended by increasing the coherence length of the stepped‐FSL. The proposed stepped‐FSL sequentially emits a discrete frequency output by applying a programmable voltage signal to an electro‐optic modulator. The free spectral range of the stepped‐FSL output can be rapidly and akinetically adjusted by changing the number of steps in the programmable voltage signal. The proposed system realizes an extended measurable range (over 20 m) and a high repetition rate (200 kHz). A low sampling rate of 10 MS s−1 is sufficient for operation at a subsampled interference frequency of 1 MHz. This rate is an order of magnitude lower than that of the conventional coherent ranging using an FSL. Experimental results demonstrate that the programmable optical Vernier‐sampled coherent ranging can be improved by leveraging novel optical subsampling with a stepped‐FSL.

Software Methodology for Data Control and Collection for Autonomous Monitoring Systems with a Large Amount of Generated Information on the Example of Software for a Hydrological Radiolocation System

Introduction . The current data monitoring and collection systems produce a growing amount of generated information. Such factors, as the increasing sampling rate of ADCs and the increasing speed of systems for primary processing, receiving, and transmitting information, etc., make the systems operate almost at the bandwidth limit of data transmission interfaces. In some applications, such a flow is redundant and can be optimized through the use of various algorithms for the primary processing of information. However, in some applications, reducing the data flow is impossible, since the received information is processed with a delay. Therefore, the development of a software methodology for controlling and collecting data in the system of automatic monitoring of the sea surface by a hydrological radiolocation system seems a relevant research task. Aim . To synthesize a methodology and to develop software for controlling the information system of radar monitoring of the sea surface. Materials and methods . System approach, software architectural and algorithmic design, software quality management methods, system analysis, Qt framework, C++ programming language. Results . A working methodology for designing software for controlling information and measurement technologies with a large amount of generated data was obtained. The effectiveness of the methodology and software quality were confirmed by control tests. A 3-month autonomous testing of the stability and reliability of the system was carried out. Detection of data loss in the system comprised less than 0.002 %, thus not exceeding the specified critical level of 0.5 %. Conclusion . The developed methodology can be used in designing software for controlling information and measuring systems generating a large amount of data. The approaches used to build a multithread software architecture with asynchronous data flow control has shown their high efficiency.

Analysis of the application methods of film and television media and images in the era of big data cloud

Abstract In order to give full play to the application of big data in film and television media and imaging in the cloud era, this study proposes a communication-efficient distributed deep neural network training method based on the DANE algorithm framework. The DANE algorithm is an approximate Newtonian method that has been widely used in communication-efficient distributed machine learning. It has the advantages of fast convergence and no need to calculate the inverse of the Hessian matrix, which can significantly reduce the communication and computational overhead in high-dimensional situations. In order to further improve the computational efficiency, it is necessary to study how to speed up the local optimization of DANE. It is a feasible method to choose to use the most popular adaptive gradient optimization algorithm Adam to replace the commonly used stochastic gradient descent method to solve the local single-machine suboptimization problem of DANE. Experiments show that Adam-based optimization can converge significantly faster than the original SGD-based implementation with little sacrifice in model generalization performance. With the increase of sampling rate, DANE-Adam significantly outperforms the DANE method in terms of convergence speed, and at the same time, the accuracy can be kept almost unchanged, which are 0.96, 0.88 and 0.75, respectively. This shows that Adam-based optimization can converge significantly faster than the original SGD-based implementation with little sacrifice in model generalization performance, with significant potential value.

The detrimental consequences of two consecutive disasters impacting cytopathology in Ukraine: COVID followed by thewar.

The Covid-19 pandemic and ongoing war in Ukraine caused unprecedented disruption in healthcare, including cytopathology activities. This paper elucidates the effect of two consecutive disasters-the COVID-19 pandemic followed by the war-on cytopathology practice in Ukraine through a single-centre retrospective study. Total testing volumes, geographic distribution, and indicators of laboratory operations were assessed during three periods of 3 months each: the first 3 months of the acute phase of the war (March-May 2022, period 1); summer (June-August 2022, period 2); and the fall (September-November 2022, period 3, associated with massive attacks on the energy infrastructure in Ukraine). These data were compared with the corresponding periods in 2020, during the COVID-19 pandemic, and in 2021, the post-lockdown period. The ongoing war in Ukraine has caused a dramatic disruption in routine health maintenance and cytological practice. A net decline in both PAP testing and non-gynaecological pathology was associated with a geographic redistribution of cytopathological testing, and an increase in the rate of abnormal sample reporting. Despite these challenges, cytopathology practice in Ukraine demonstrates resilience, allowing for maintaining the healthcare system and addressing the needs of the civil population during the war. The ongoing war in Ukraine heavily affected cytological practice. The decline in PAP testing during the early period of the war was associated with an increase in the abnormal sample rate. Further study of the war's impact on the cervical pathology rate and the health of the population in the next decades is needed.

Preparation and application of graphene oxide functionalized melamine-formaldehyde aerogel coated solid-phase microextraction tube

因具有良好的萃取性能,有机气凝胶已被应用于样品前处理领域,为了进一步改善其对多环芳烃类污染物的萃取能力,利用氧化石墨烯对三聚氰胺-甲醛气凝胶进行改性,制备了一种氧化石墨烯功能化三聚氰胺-甲醛气凝胶,将其作为萃取涂层涂覆到不锈钢丝表面,通过扫描电镜和X射线光电子能谱对萃取涂层进行表征,结果表明氧化石墨烯并未破坏气凝胶的三维网络多孔结构。将4根气凝胶涂覆的不锈钢丝装进一根长度30 cm、内径0.75 mm的聚醚醚酮管内,制备了一种新型的纤维填充型固相微萃取管。将萃取管与高效液相色谱联用,构建管内固相微萃取-液相色谱在线富集分析系统。以8种多环芳烃(萘(Nap)、苊烯(Acy)、苊(Ace)、芴(Flu)、菲(Phe)、蒽(Ant)、荧蒽(Fla)和芘(Pyr))作为模型分析物,评价了萃取管的萃取性能,考察了氧化石墨烯对气凝胶萃取性能的改善,结果表明萃取效率被提升至最高2.5倍。详细考察了样品体积、样品流速、样品中有机溶剂浓度以及脱附时间对于萃取效率的影响,并建立了管内固相微萃取-液相色谱在线分析方法。该法对8种多环芳烃分析物的检出限为0.001~0.005 μg/L,萘、苊烯、苊、芴的线性范围为0.017~20.0 μg/L,菲、蒽的线性范围为0.010~20.0 μg/L,荧蒽和芘的线性范围为0.003~15.0 μg/L,精密度良好(日内重复性RSD≤4.8%,日间重复性RSD≤8.6%)。研究所发展的分析方法比已报道的某些分析方法具有更好的灵敏度、更宽的线性范围和更短的分析时间,并具有在线富集和在线分析的独特优点。将该分析方法应用于常见饮用水(包括瓶装矿泉水和饮水机的直饮水)中多环芳烃的分析检测,加标回收率试验结果(76.3%~132.8%)表明该分析方法能够高灵敏、快速、准确地检测饮用水中痕量多环芳烃污染物。经过稳定性考察,发现研究所制备的固相微萃取管在实验过程中表现出良好的使用寿命和化学稳定性。

Fast method for designing broadband achromatic diffractive optical elements.

Diffractive optical elements play a crucial role in the miniaturization of the optical systems, especially in correcting achromatic aberration. Considering the rapidity and validity of the design method, we propose a fast method for designing broadband achromatic diffractive optical elements. Based on the direct binary search algorithm, some improvements have been made including the selection of the initial height map to mitigate the uncertainty, the reduction of the variations to accelerate the optimization and the increase of sampling rate to deal with the large operation bandwidth. The initial height map is calculated instead of random initial value. Due to different regions of the height map contributing to point spread functions differently, the variations are reduced to speed up the optimization. The large operation bandwidth is solved by increasing the sampling rate at unfitted wavelengths instead of setting weighting coefficients. We demonstrate via simulations that our method is effective through several examples. The design of broadband achromatic diffractive optical elements can be quickly achieved by our method.

Feature Matching Optimization of Multimedia Remote Sensing Images Based on Multiscale Edge Extraction.

In order to solve the problem of low efficiency of image feature matching in traditional remote sensing image database, this paper proposes the feature matching optimization of multimedia remote sensing images based on multiscale edge extraction, expounds the basic theory of multiscale edge, and then registers multimedia remote sensing images based on the selection of optimal control points. In this paper, 100 remote sensing images with a size of 3619∗825 with a resolution of 30 m are selected as experimental data. The computer is configured with 2.9 ghz CPU, 16 g memory, and i7 processor. The research mainly includes two parts: image matching efficiency analysis of multiscale model; matching accuracy analysis of multiscale model and formulation of model parameters. The results show that when the amount of image data is large, feature matching takes more time. With the increase of sampling rate, the amount of image data decreases rapidly, and the feature matching time also shortens rapidly, which provides a theoretical basis for the multiscale model to improve the matching efficiency. The data size is the same, 3619 × 1825, which makes the matching time between images have little difference. Therefore, the matching time increases linearly with the increase of the number of images in the database. When the amount of image data in the database is large, a higher number of layers should be used; when the amount of image data in the database is small, the number of layers of the model should be reduced to ensure the accuracy of matching. The availability of the proposed method is proved.

The upgrade of the CMS electromagnetic calorimeter: Future prospects for precision timing and energy measurements at the HL-LHC

The Compact Muon Solenoid (CMS) electromagnetic calorimeter (ECAL) is a high granularity lead tungstate crystal calorimeter operated at the CERN LHC. The future High Luminosity upgrade of the LHC will provide unprecedented instantaneous and integrated luminosity with up to 200 collisions per bunch-crossing [pileup (PU)]. The CMS detector is therefore undergoing an extensive upgrade program to prepare for these demanding conditions. In the ECAL barrel region, the crystals will continue to perform well. The Avalanche Photodiodes (APDs) detecting the scintillation light will also continue to be operational, with some increase in noise that will be mitigated by reducing the operational temperature. Nonetheless, the entire readout and trigger electronics will need to be replaced to cope with the harsh conditions and increased trigger latency requirements. We plan a 25-fold improved readout granularity and a factor 4 sampling rate increase. The timing precision is used in important physics measurements and it is speculated that further improved time information could be exploited for PU mitigation and for the photons assignment to the correct collision vertex. As presented in this paper the endcaps region calorimeter needs to be replaced due to irrecoverable radiation damage of the crystals in the forward region. The upgraded ECAL will preserve the energy resolution, and will improve on time resolution for photons and electrons. In this paper, the ongoing R&D activities for the ECAL barrel readout electronics upgrade are presented.

Linking the sampling frequency with multiscale entropy to classify mitoBK patch-clamp data

We analyze the activity of large-conductance voltage- and Ca2+-activated potassium channels located in the inner mitochondrial membrane (mitoBK) from human dermal fibroblast cells. The ion current activity registered via the patch-clamp technique was taken into consideration. At the preliminary stage, we performed an in-depth analysis of the signal power spectrum to find an optimal sampling frequency and study the impact of different sampling on changes in the information hidden in the signal. We found the optimal 10 kHz sampling frequency for the fibroblast’s mitoBK currents sequences. Interestingly, as the signal sampling rate increases, we can observe a decrease in entropy values. The application of Multiscale Entropy analysis enabled a practical classification of single-channel current traces at various membrane potentials. Using the machine learning techniques such as K-Nearest Neighbors and Support Vector Machine, optimized by the Stochastic Gradient Descent algorithm with Sample Entropy values as inputs, allowed us to assess the more outstanding accuracy scores for the chosen classifiers at membrane the hyperpolarization than at its depolarization.

A method for tracking blue whales (Balaenoptera musculus) with a widely spaced network of ocean bottom seismometers.

Passive acoustic monitoring is an important tool for studying marine mammals. Ocean bottom seismometer networks provide data sets of opportunity for studying blue whales (Balaenoptera musculus) which vocalize extensively at seismic frequencies. We describe methods to localize calls and obtain tracks using the B call of northeast Pacific blue whale recorded by a large network of widely spaced ocean bottom seismometers off the coast of the Pacific Northwest. The first harmonic of the B call at ~15 Hz is detected using spectrogram cross-correlation. The seasonality of calls, inferred from a dataset of calls identified by an analyst, is used to estimate the probability that detections are true positives as a function of the strength of the detection. Because the spacing of seismometers reaches 70 km, faint detections with a significant probability of being false positives must be considered in multi-station localizations. Calls are located by maximizing a likelihood function which considers each strong detection in turn as the earliest arrival time and seeks to fit the times of detections that follow within a feasible time and distance window. An alternative procedure seeks solutions based on the detections that maximize their sum after weighting by detection strength and proximity. Both approaches lead to many spurious solutions that can mix detections from different B calls and include false detections including misidentified A calls. Tracks that are reliable can be obtained iteratively by assigning detections to localizations that are grouped in space and time, and requiring groups of at least 20 locations. Smooth paths are fit to tracks by including constraints that minimize changes in speed and direction while fitting the locations to their uncertainties or applying the double difference relocation method. The reliability of localizations for future experiments might be improved by increasing sampling rates and detecting harmonics of the B call.

Augmented NETT regularization of inverse problems

We propose aNETT (augmented NETwork Tikhonov) regularization as a novel data-driven reconstruction framework for solving inverse problems. An encoder-decoder type network defines a regularizer consisting of a penalty term that enforces regularity in the encoder domain, augmented by a penalty that penalizes the distance to the signal manifold. We present a rigorous convergence analysis including stability estimates and convergence rates. For that purpose, we prove the coercivity of the regularizer used without requiring explicit coercivity assumptions for the networks involved. We propose a possible realization together with a network architecture and a modular training strategy. Applications to sparse-view and low-dose CT show that aNETT achieves results comparable to state-of-the-art deep-learning-based reconstruction methods. Unlike learned iterative methods, aNETT does not require repeated application of the forward and adjoint models during training, which enables the use of aNETT for inverse problems with numerically expensive forward models. Furthermore, we show that aNETT trained on coarsely sampled data can leverage an increased sampling rate without the need for retraining.

Wind Turbine Multi-Fault Detection based on SCADA Data via an AutoEncoder

Nowadays, wind turbine fault detection strategies are settled as a meaningful pipeline to achieve required levels of efficiency, availability, and reliability, considering there is an increasing installation of this kind of machinery, both in onshore and offshore configuration. In this work, it has been applied a strategy that makes use of SCADA data with an increased sampling rate. The employed wind turbine in this study is based on an advanced benchmark, established by the National Renewable Energy Laboratory (NREL) of USA. Different types of faults on several actuators and sensed by certain installed sensors have been studied. The proposed strategy is based on a normality model by means of an autoencoder. As of this, faulty data are used for testing from which prediction errors were computed to detect if those raise a fault alert according to a defined metric which establishes a threshold on which a wind turbine works securely. The obtained results determine that the proposed strategy is successful since the model detects the considered three types of faults. Finally, even when prediction errors are small, the model is able to detect the faults without problems.

WS1.9. Advances in EEG Analysis – Wide-Band EEG, Dense-Array EEG and Quantitative EEG

1) Wide-band EEG As the faster frequency extreme of wide-band EEG, high frequency oscillation (HFO) was originally described by means of 1) micro-electrode recording in animal model, and ictal HFO occurred before clinical onset. Recently, 2 more types of HFO were well described; 2) invasive electrodes recording in epilepsy surgery (macro-invasive electrodes such as depth-, subdural ones), and 3) scalp electrodes or MEG in epilepsy patients. The multiple mechanisms of HFO generation are considered such as synchronized post-synaptic potentials with sparse pyramidal cell firing, or principal cell action potentials. As the lower frequency of extreme of baseline shifts in the chronic epilepsy focus on epilepsy patients, ictal DC shifts occurred earlier than or as early as ictal HFO significantly (Imamura et al., 2011; Kanazawa et al., 2015). It was newly endorsed by the pathological abnormality, i.e., decreased Kir4.1, selectively seen in the cortices generating ictal DC shifts (Kobayashi et al., 2017). It could suggest more active role of glia before seizure generation, and thus called as active DC shifts. These both findings could represent activity of both epileptic neurons and astrocytes, respectively, mainly as the local field potentials. 2) Quantitative EEG Once digital EEG era has started since 1989, first generation EEG (1989-1996/2000) could provide us with data acquisition of ordinary sampling rate (i.e., 500 Hz) and band pass filter (i.e., 0.5 Hz - 200 Hz). Data display has been much flexible by means of reformat of montage, filter change, sensitivity change, voltage mapping, power spectrum analysis, and 3D source analysis, and so on. It is user friendly, and regarded as the established tool or method. Second generation EEG (1996/2000-present) has been much advanced and complicated tool such as data acquisition such as wide-band EEG, i.e., 0.016 Hz - 600 Hz. It contains DC shifts, infraslow, HFO. Increased sampling rate, i.e., 2000 Hz could widen the extremely faster activitty. Data display such as wide-band display, i.e., 100 ms/page to 5 min/page, time frequency analysis, DSA (Density Modulated Spectral Array), and Ganger causality further led us to the interactive sophisticated circumstances in the clinical and scientific field. 3) Dense-array EEG The more electrodes are placed on the scalp, the better spatial resolution is expected. Once it also could cover the so-called “south hemispheres” of the head, it could delineate the any activity arising from the basal temporal and mesial temporal areas. However, since scalp EEG is attenuated in amplitude and is blurred with wider distribution by means of cortical bone and different soft tissues, the degree of localization map may not be proportionally precise in spite of involvement of many electrodes. In this regard, whole MEG data may overcome of the 2 important factors.

On the development of an efficient, low-complexity and highly reproducible method for systolic peak detection

Background and ObjectivesNumerous methods for systolic peak detection on PPG signals have been reported in the literature. However, such approaches can hardly be replicated and implemented in wearable applications because of their complicated methodologies and high computational requirements. In an attempt to address this issue, an efficient, low-complexity, and highly reproducible method for real-time systolic peak detection is proposed. MethodsThe method calculates the difference between the value of the current PPG sample and the maximum computed over a certain window that includes the current value. If such difference is negative, then the previous PPG value is labeled as a peak provided that the number of times that the difference is equal to zero reaches or exceeds the window size. To overcome some of the disturbances usually accompanying PPG signals, some well-documented, time-domain strategies were included. The performance of the method was assessed off- and online by using free-available and locally-acquired data sets. ResultsThe proposed method can perform faster than several other peak detection methods previously reported in the literature, including some that could not be implemented due to their detection parameters are not available. However, the method performance may be affected by motion artifact corruption, especially when the sampling rate increases. ConclusionGiven its low computational requirements, novel techniques for artifact detection and removal could be added to our method to improve its robustness. More accurate comparisons between results yielded by this and several other studies could be performed as long as detection parameters were properly reported.

Improving the sensitivity of spin-echo fMRI at 3T by highly accelerated acquisitions.

Spin-echo (SE) functional MRI (fMRI) can be highly advantageous compared to gradient-echo (GE) fMRI with respect to magnetic field-inhomogeneity artifacts. However, at 3T, the majority of blood oxygenation level-dependent (BOLD) fMRI experiments are performed using -weighted GE sequences because of their superior sensitivity compared to SE-fMRI. The presented SE implementation of a highly accelerated GE pulse sequence therefore aims to improve the sensitivity of SE-fMRI while profiting from a reduction of susceptibility-induced signal dropout. Spin-echo MR encephalography (SE-MREG) is compared with the more conventionally used spin-echo echo-planar imaging (SE-EPI) and spin-echo simultaneous multislice (SE-SMS) at 3T in terms of capability to detect neuronal activations and resting-state functional connectivity. For activation analysis, healthy subjects underwent consecutive SE-MREG (pulse repetition time [TR] = 0.25 seconds), SE-SMS (TR = 1.3 seconds), and SE-EPI (TR = 4.4 seconds) scans in pseudorandomized order applied to a visual block design paradigm for generation of t-statistics maps. For the investigation of functional connectivity, additional resting-state data were acquired for 5 minutes and a seed-based correlation analysis using Stanford's FIND (Functional Imaging in Neuropsychiatric Disorders) atlas was performed. The increased sampling rate of SE-MREG relative to SE-SMS and SE-EPI improves the sensitivity to detect BOLD activation by 33% and 54%, respectively, and increases the capability to extract resting-state networks. Compared with a brain region that is not affected by magnetic field inhomogeneities, SE-MREG shows 2.5 times higher relative signal strength than GE-MREG in mesial temporal structures. SE-MREG offers a viable possibility for whole-brain fMRI with consideration of brain regions that are affected by strong susceptibility-induced magnetic field gradients.

Three-Point Synchrophasor Estimator With Better Off-Nominal Frequency Leakage Reduction

Synchronous phasor measurements have long been an important tool in power flow modelling and network state estimation as well as in many other tasks that support power grid observability. Classic phasor estimation algorithms are often preferred over complex alternatives. They are based on a variant of the Discrete Fourier Transform (DFT) while relying on a sampling frequency locked to the system nominal frequency. The spectral leakage caused by the small off-nominal frequency excursions in such settings introduces errors that are handled by the estimation algorithms to a different extent. Well-known three-point averaging filter (3P) is a low-cost spectral leakage reduction technique in one of the simplest estimators based on a single-bin DFT. However, it only partially removes the oscillating components. When an independent standard-based frequency tracking is available, the errors can be further compensated with minimal cost. We propose a frequency-corrected three-point estimator (F3P) to eliminate the induced spurious spectrum progressively with the increasing sampling rate while building on the analytical expression of the remaining error. We show performance benefits of the F3P under finite sampling rates when compared to the baseline 3P and to the classical Interpolated DFT (IPDFT) in static conditions. The F3P removes most of the leakage without the need of data resampling. In less ideal conditions with additive 50-dB noise, up to a 10-fold reduction of the 3P error is achieved. The error ratio of the reference IPDFT over the F3P is approximately 1.4 under the same conditions and largely independent of the frequency excursion. The presented simulation results prove the algorithm's superior performance in most of the steady-state tests defined by the relevant Standard, whereas the dynamic performance can be brought to an acceptable level by the use of a single-cycle observation window, with the response times comparable to those of the IPDFT.

Multiscale Entropy Analysis of Instantaneous Frequency Variation to Overcome the Cross-Over Artifact in Rhythmic EEG

Generally, for healthy adults, the entropy of electroencephalogram (EEG) signals gradually decreases from wake to sleep stages N1, N2, to N3, and increases during REM. However, some researchers found that multiscale entropy curves of sleep and wakefulness intercept, a cross-over phenomenon whose origin remains unexplored. The objective of the present work is to trace the origin of the cross-over phenomenon and to propose a workaround strategy. We simulated EEG by generating 1/f broadband signal and chirp signals with continuously varying frequencies. We then retrieved the rhythmic component from simulated EEG and real-world EEG and conducted MSE analysis of the instantaneous frequency variation (IFV) of the rhythmic component. The simulation revealed that this interception was ubiquitous in the MSE analysis of simulated EEG with rhythmic components of different frequencies. The cross-over point moved toward larger scale factors with the increasing sampling rate. We found that the MSE curve of IFV from real-world EEG for the wakefulness group was higher than that for sleep, showing no interception. These results suggest that (1) for a rhythmic signal like EEG, MSE analysis of the raw signal is highly affected by the rhythmic component, presenting artificial cross-over curves in sleep EEG study, (2) frequency variation of rhythmic components are complex signal which differs between wakefulness and sleep, in accordance with the complexity loss theory.