Hz Signal Research Articles

Time-series analysis of the characteristic pressure fluctuations in a conical fluidized bed with negative pressure

The negative pressure conical fluidized bed is widely used in the pharmaceutical industry. In this study, experiments based on the negative pressure conical fluidized bed are carried out by changing the material mass and particle size. The pressure fluctuation signals are analyzed by the time and the frequency domain methods. A method for absolutely characterizing the degree of the energy concentration at the main frequency is proposed, where the calculation is to divide the original power spectrum by the average signal power. A phenomenon where the gas velocity curve temporarily stops growing is observed when the material mass is light, and the particle size is small. The standard deviation and kurtosis both rapidly change at the minimum fluidization velocity and thus can be used to determine the flow regime, and the variation rule of the kurtosis is independent of both the material mass and particle size. In the initial fluidization stage, the dominant pressure signal comes from the material movement; with the increase in the gas velocity, the power of a 2.5 Hz signal continues to increase. A method of dividing the main frequency by the average cycle frequency can conveniently determine the fluidized state, and a novel concept called stable fluidized zone proposed in this paper can be obtained. Controlling the gas velocity within the stable fluidized zone ensures that the fluidized bed consistently remains in a stable fluidized state.

Observation of eclipse shadow bands using high altitude balloon and ground-based photodiode arrays

The results of an investigation into whether or not eclipse shadow bands have an atmospheric origin are presented. Using high altitude balloon and ground-based photodiode arrays during the August 21, 2017 total solar eclipse, data revealing the light patterns before and after totality were collected at 600 Hz. These data were then analyzed using spectrograms, which provide information on intensity fluctuations in the frequency space time domain. Both at the altitude of the balloon (~25 km) and on the ground, a sustained ~4.5 Hz signal was detected a few minutes before and after totality. This signal was coherent over a scale greater than 10 cm and detected in four separate balloon photodiodes and 16 separate ground photodiodes. At higher frequencies, up to at least 30 Hz, brief chaotic signals that were disorganized as a function of time were detected on the ground, but not at the altitude of the balloon. These higher frequency signals, which we attribute to atmospheric scintillation, appeared mostly uncorrelated over a length scale of 10 cm. Some of our ground arrays utilized red and blue filters, but neither the sustained 4.5 Hz signal nor the chaotic higher frequency signals showed a strong dependence on filter color. On the ground we made a video of the shadow bands on a scaled white screen. We judged that the bands were roughly parallel to the orientation of the bright thin crescent Sun before and after totality. Thus, if the ν≈4.5 Hz frequency signal is identified with the peak-to-peak shadow band wavelength of λ≈13 cm measured in the video, it can be inferred that their propagation velocity was about v≈59 cm s−1 (≈2.1 km hr−1). Shadow band signals other than the sustained signal at ~4.5 Hz are consistent with atmospheric scintillation theory.These results are surprising. Based on accounts in the literature we expected to confirm the atmospheric scintillation theory of eclipse shadow bands, but instead we detected a sustained ~4.5 Hz signal at both high altitude and on the ground, consistent with the type of shadow band signal visual observers often report before and after totality. This signal cannot be due to atmospheric scintillation and we ran a check to make sure this signal is not an artifact of our electronics. We recommend that additional searches for eclipse shadow bands be made at high altitude in the future.

Deep learning of the binaural masking level difference

The binaural masking level difference (BMLD) is the improvement in the detection of a signal in noise observed for different interaural configurations. Durlach [J. Acoust. Sci. Am. 1206–1218 (1963)] derived equations that accurately predict much of the human BMLD psychophysical data. We trained a deep neural network to predict BMLDs, as calculated with Durlach's equations, based on waveforms of a 500 Hz signal in a white noise each presented from different locations. Crucially, the network was constrained via nodes configured to embody informative representations (Iten etal., 2018; arXiv:1807.10300). The deep neural network accurately predicted BMLDs for stimuli within a simulated azimuth and for stimuli with interaural time differences (ITDs) outside the range of a human head. Further, even though the model was not designed to imitate neural biophysics, we discovered that the dynamics of latent nodes bore similarities with published data on neural ITD tuning and rate-level responses to BMLD stimuli. The work demonstrates how advances in deep learning can be used to consolidate theoretical and experimental approaches to binaural detection.

Moment‐Duration Scaling of Low‐Frequency Earthquakes in Guerrero, Mexico

AbstractLow‐frequency earthquakes (LFEs) are detected within tremor, as small, repetitive, impulsive low‐frequency (1–8 Hz) signals. While the mechanism causing this depletion of the high‐frequency content of their signal is still debated, this feature may indicate that the source processes at the origin of LFEs are different from those for regular earthquakes. Key constraints on the LFE‐generating physical mechanisms can be obtained by establishing scaling laws between their seismic moment and source durations. Here we apply a simple spectral analysis method to the S waveforms of LFEs from Guerrero, Mexico, to measure their seismic moments and corner frequencies, a proxy to source duration. We find characteristic values of M0∼3×1012 N.m (Mw∼2.3) and fc∼3.0 Hz with the corner frequency very weakly dependent on the seismic moment. This moment‐duration scaling observed for Mexican LFEs is similar to one previously reported in Cascadia and is very different from the established one for regular earthquakes. This suggests that they could be generated by sources of nearly constant size with strongly varying intensities. LFEs do not exhibit the self‐similarity characteristic of regular earthquakes, suggesting that the physical mechanisms at their origin could be intrinsically different.

Computerized measuring system for study of aluminum alloy defects

The hardware-software complex which allows carrying out defect detection in materials based on various aluminum alloys was developed using a transducer based on eddy current effects. The hardware-software complex included the developed software coded in C++. A digital displacement transducer operated by an Arduino microcontroller has been designed, which can automatically move the transducer over a test object. In order to test the system operability, a series of scans of plates made of duralumin and aluminum-magnesium alloys with knowingly existing internal defects in the form of cuts imitating the presence of cracks in the metal and located at depths from 1 to 5.5 mm was made. This paper provides information illustrating the correlations between the system response to a 700 Hz signal and the presence of internal material defects. The eddy-current transducer exciting winding signal frequency exerts significant influence on this process, and this has been also demonstrated in the study.

Delta-gamma phase-amplitude coupling as a biomarker of postictal generalized EEG suppression.

Postictal generalized EEG suppression is the state of suppression of electrical activity at the end of a seizure. Prolongation of this state has been associated with increased risk of sudden unexpected death in epilepsy, making characterization of underlying electrical rhythmic activity during postictal suppression an important step in improving epilepsy treatment. Phase-amplitude coupling in EEG reflects cognitive coding within brain networks and some of those codes highlight epileptic activity; therefore, we hypothesized that there are distinct phase-amplitude coupling features in the postictal suppression state that can provide an improved estimate of this state in the context of patient risk for sudden unexpected death in epilepsy. We used both intracranial and scalp EEG data from eleven patients (six male, five female; age range 21–41 years) containing 25 seizures, to identify frequency dynamics, both in the ictal and postictal EEG suppression states. Cross-frequency coupling analysis identified that during seizures there was a gradual decrease of phase frequency in the coupling between delta (0.5–4 Hz) and gamma (30+ Hz), which was followed by an increased coupling between the phase of 0.5–1.5 Hz signal and amplitude of 30–50 Hz signal in the postictal state as compared to the pre-seizure baseline. This marker was consistent across patients. Then, using these postictal-specific features, an unsupervised state classifier—a hidden Markov model—was able to reliably classify four distinct states of seizure episodes, including a postictal suppression state. Furthermore, a connectome analysis of the postictal suppression states showed increased information flow within the network during postictal suppression states as compared to the pre-seizure baseline, suggesting enhanced network communication. When the same tools were applied to the EEG of an epilepsy patient who died unexpectedly, ictal coupling dynamics disappeared and postictal phase-amplitude coupling remained constant throughout. Overall, our findings suggest that there are active postictal networks, as defined through coupling dynamics that can be used to objectively classify the postictal suppression state; furthermore, in a case study of sudden unexpected death in epilepsy, the network does not show ictal-like phase-amplitude coupling features despite the presence of convulsive seizures, and instead demonstrates activity similar to postictal. The postictal suppression state is a period of elevated network activity as compared to the baseline activity which can provide key insights into the epileptic pathology.

The Breakthrough Listen Search for Intelligent Life: A 3.95–8.00 GHz Search for Radio Technosignatures in the Restricted Earth Transit Zone

Abstract We report on a search for artificial narrowband signals of 20 stars within the restricted Earth Transit Zone (rETZ) as a part of the ten-year Breakthrough Listen (BL) search for extraterrestrial intelligence (ETI). The rETZ is the region of the sky from which an observer would see Earth transit the Sun with an impact parameter of less than 0.5. This region of the sky is geometrically unique, providing a potential way for an ETI to discover the solar system. The targets were nearby (7–143 pc) and the search covered an electromagnetic frequency range of 3.95–8.00 GHz. We used the Robert C. Byrd Green Bank Telescope to perform these observations with the standard BL data recorder. We searched these data for artificial narrowband (∼Hz) signals with Doppler drift rates of ±20 Hz s−1. We find no evidence for radio technosignatures from ETI in our observations. The observing campaign would have allowed for detections of emissions that were 10−3 to 0.88 times as powerful as the signaling capability of the Arecibo radar transmitter. We conclude that at least 0.6% of the systems in the rETZ within 150 pc do not possess the type of transmitters searched in this survey. To our knowledge, this is the first targeted search for ETI of the rETZ. All data used in this paper are publicly available via the BL Public Data Archive (http://seti.berkeley.edu/bldr2).

FFBPNN-BASED HIGH DROWSINESS CLASSIFICATION USING EMG AND WPT

The detection of drowsiness level is important because it is the main reason for fatal road accidents. Electromyography of the upper arm and shoulder is an important physiological signal affected by drivers’ drowsiness, in which its amplitude level and frequency band of the sleep-deprived case are different than those of the alert state. Therefore depending on electromyography (EMG), its drowsiness frequency (80–100[Formula: see text]Hz) was detected in order to determine high drowsiness state based on wavelet packet transform (WPT) which decomposes the EMG signal into its approximation and detail coefficients up to level 4 using db2, db7, sym5 and coif5 wavelets. In this research after extraction, the two higher order statistical features, kurtosis and skewness, are computed from 3[Formula: see text]s window of the three EMG channels, and analysis of variance test is used to check whether their mean values are different for the different classes as both [Formula: see text]-values are less than 0.005 under db2 wavelet. Therefore, they were supplied to feed forward back propagation neural network (FFBPNN) as this type of neural network is used for distinguishing and classification purposes for different objects. They obtained an accuracy of 75% for detecting high levels among other levels of normal and low drowsiness with an average sensitivity of 78.63% and specificity of 75.97% because the spectrum of the EMG alert (non-drowsiness) signal of 80–100 Hz is different from that of drowsy 80–90[Formula: see text]Hz and high drowsy 78–95[Formula: see text]Hz signals.

Analyzing high-frequency seismic signals generated during a landslide using source discrepancies between two landslides

High-frequency seismic signals are generated during landslides and can be analyzed to obtain detailed information regarding landslide dynamics. However, a better understanding of the physical process that produces such high-frequency signals is required. The two massive landslides that occurred on April 11, 2013 in the Bingham Canyon Mine pit (Utah, USA) generated low- and high-frequency seismic signals. Using the low-frequency signals, we inverted the force-time functions of the two events and estimated their dynamics. We used the signals generated by the two events and filtered in three frequency bands to obtain the source discrepancies between the two events. By analyzing these source discrepancies, we found that the low- and medium-frequency signals were generated due to the bulk movement of sliding material. Furthermore, the medium-frequency signals contained information regarding small-particle impacts along the sliding path. The high-frequency (1–3 Hz) signals are controlled by small-scale process of sliding materials, and are sum of stochastic phenomena. This clarification of the source of the high-frequency signals is expected to improve the analyses of seismic signals generated during landslides.

Association Analysis of Candidate Gene Polymorphisms and Audiometric Measures of Noise-Induced Hearing Loss in Young Musicians.

This study aimed to investigate the association between candidate genetic variants and audiometric measures of noise-induced hearing loss (NIHL) in young musicians. The study analyzed a database by Phillips et al. (Feasibility of a bilateral 4000-6000 Hz notch as a phenotype for genetic association analysis. Int J Audiol 2015;54:645-52.) which included behavioral hearing thresholds, distortion-product otoacoustic emissions (DPOAE), tympanometric, and genetic data of 166 participants meeting the inclusion criteria. Nineteen single nucleotide polymorphisms (SNPs) in 13 cochlear genes previously associated with NIHL in factory workers were included in the present investigation. The average hearing threshold at 3000 and 4000 Hz (AHT) and average DPOAE signal to noise ratio (DPOAE SNR) in both ears were calculated. The regression analyses showed that two SNPs- one in KCNE1 (rs2070358) and the other in CAT (rs12273124) revealed a statistically significant relationship with DPOAE SNR in both ears. Two SNPs in MYH14 and one in GJB4 revealed a significant association with DPOAE SNR in the left ear. Two SNPs in HSP70, one in CDH23 and one in KCNJ10 showed significant association with DPOAE SNR in the right ear. None of the included SNPs showed association with AHT in both ears. A genetic variant in KCNE1 was associated with the strength of the cochlear amplifier as assessed by DPOAE SNR. Musicians carrying causal genetic variants to NIHL might exhibit changes in their auditory functions early in the lifespan even when most subjects had their hearing thresholds within normal limits. These participants are likely to show the clinical manifestation of NIHL in the future if no preventive measures are applied.

Spatiotemporal Relationships between Two Closely‐spaced Strombolian‐style Vents, Yasur, Vanuatu

AbstractThis study details a case of transition of volcanic activity between two closely spaced (20–40 m) vents within the southern crater of Yasur Volcano, Vanuatu, over a period of 12 days. The transition from one vent to another newly emerged one was captured by a multi‐instrument array, with broadband seismic, thermal‐IR camera, and visual recordings presented. The results describe systematic changes in explosive parameters (e.g., style, power, and frequency) at both vents that reflect the transfer of magma and volatile gas flux within a shallow branching conduit system. The decline of activity at the initial vent corresponds with a shift from bomb‐rich to ash‐rich behavior indicating a progressive decrease in magma and volatile gas flux and increasing shallow conduit magma viscosity. The concurrent increase in activity at the emerging vent accompanies more bomb‐rich behavior that indicates increased flux and lower magma viscosity. During the transition period, most explosions were systematically paired between the two vents, occurring within <1 to up to 45 s of each other. Broadband seismic data show that paired explosions produce a common seismic signal that resembles that of a single, nonpaired explosion. The presence of a common low‐frequency (0.06–1 Hz) signal indicates that multiple shallow conduits may transmit the same gas and impulsive energy differently from a single gas rise and burst event. Temporal offsets between paired explosions reflect the different conduit pathways and viscosity states of the magma in each conduit, with the lagging vent always displaying greater ash‐rich activity indicating higher magma viscosity and gas path tortuosity.

Study of Regenerative Braking System and Brake Force using Pulse Width Module

This paper aims to develop regenerative braking using pulse width module (PWM) control. It concerns frequency of regenerative braking period which is used to control regenerative braking. It is usually restricted by the electric power generation of AC synchronous axial motor. It is measured by motor bench tester. It indicates electronic brake torque and regenerative current. The resistance torque and regenerative current characteristics are expressed as second order polynomial equation. The results present the comparison between pulse signal and full period signal. The maximum deceleration is 10 Hz signal at 2.14 m/s2, which is not exceed deceleration of brake comfort. The harvest energy at 10 Hz PWM control is 1.40 Wh. It is closely to use full period regenerative brake, but 10 Hz PWM control has comfortable than other frequencies and efficiency is 54.3 percent.

A New, Noninvasive Accelerometric Method for Diagnosing Acid andNon-Acid Gastroesophageal Reflux in Premature Infants: Proof of Concept.

Non-acid reflux is common in premature neonates. Current methods of diagnosing gastroesophageal reflux (GER) such as pH probes, multichannel impedance monitoring, X-rays, or endoscopy are either invasive or unable to diagnose non-acid reflux. Passage of a naso-esophageal tube is uncomfortable. Imaging studies are of short duration and may miss reflux entirely. Herein, we present proof of concept of a noninvasive accelerometric device that detects acid and non-acid reflux in premature infants. An accelerometer was taped over the subxiphoid process in patients suspected of having GER who were already scheduled for pH probe or multichannel impedance monitoring. The largest cohort was preterm infants, but term infants and toddlers were also studied. Low-frequency subaudible signals were obtained on a digital recorder (sampling rate 200 Hz) signals. Fast Fourier transforms graphically displayed the frequency and amplitude of signals. Data were then resampled at a rate of 60 Hz to create a spectrogram with a focused range of 0-30 Hz representing reflux-associated events. Proof of concept was attained through successful comparison with results from concurrent pH probes, multichannel impedance recordings, and ultrasound studies. We have thus validated accelerometry as a noninvasive method for assessing both acid and non-acid GER. The noninvasiveness of this diagnostic modality allows for repeated testing to assess the efficacy of anti-reflux medications, even when patients remain on antacids. This technology allows for more rational management of patients with GER and represents a major advance in the diagnosis and treatment of GER.

Single low‐gain amplifier compensated hybrid delta‐sigma modulator

This article presents low-power circuit design techniques to achieve third-order noise shaping by employing multiple stages of passive switched-capacitor gain-boosted integrator and a passive switched-capacitor integrator. First integrator is a passive switched-capacitor for higher linearity. Second integrator is a three-stage passive switched-capacitor gain-boosted integrator to provide passive gain to the loop filter as well as the second DAC feedback. While the third integrator is a two-stage passive switched-capacitor gain-boosted integrator to provide passive gain as well as third DAC feedback for higher stability. Multiple lower stages of passive switched-capacitor gain-boosted integrators employed to enhance the noise shaping order of the modulator and suppress the parasitic effect. The preamplifier is the only active block, while the dynamic comparator used as single-bit quantiser. The complete transistor level with thermal noise simulation can achieve 84 dB of dynamic range (DR), 78.2 dB signal-to-noise-plus-distortion ratio (SNDR), and 78.5 dB signal-to-noise ratio (SNR) for 500 Hz signal bandwidth. It can also achieve signal-to-spurious-free dynamic range (SFDR) of 78.2 dB with estimated power consumption of 220 nW at 1 V supply voltage in SMIC 28 nm CMOS Technology. Finally, it can achieve state-of-the-art Walden Figure-of- Merit (FoM W ) of 33 fJ/Conv-Step and Schreier FoM S of 171.2 dB.

Functional MRI and resting state connectivity in white matter - a mini-review

Functional MRI (fMRI) signals are robustly detectable in white matter (WM) but they have been largely ignored in the fMRI literature. Their nature, interpretation, and relevance as potential indicators of brain function remain under explored and even controversial. Blood oxygenation level dependent (BOLD) contrast has for over 25 years been exploited for detecting localized neural activity in the cortex using fMRI. While BOLD signals have been reliably detected in grey matter (GM) in a very large number of studies, such signals have rarely been reported from WM. However, it is clear from our own and other studies that although BOLD effects are weaker in WM, using appropriate detection and analysis methods they are robustly detectable both in response to stimuli and in a resting state. BOLD fluctuations in a resting state exhibit similar temporal and spectral profiles in both GM and WM, and their relative low frequency (0.01–0.1 Hz) signal powers are comparable. They also vary with baseline neural activity e.g. as induced by different levels of anesthesia, and alter in response to a stimulus. In previous work we reported that BOLD signals in WM in a resting state exhibit anisotropic temporal correlations with neighboring voxels. On the basis of these findings, we derived functional correlation tensors that quantify the correlational anisotropy in WM BOLD signals. We found that, along many WM tracts, the directional preferences of these functional correlation tensors in a resting state are grossly consistent with those revealed by diffusion tensors, and that external stimuli tend to enhance visualization of specific and relevant fiber pathways. These findings support the proposition that variations in WM BOLD signals represent tract-specific responses to neural activity. We have more recently shown that sensory stimulations induce explicit BOLD responses along parts of the projection fiber pathways, and that task-related BOLD changes in WM occur synchronously with the temporal pattern of stimuli. WM tracts also show a transient signal response following short stimuli analogous to but different from the hemodynamic response function (HRF) characteristic of GM. Thus there is converging and compelling evidence that WM exhibits both resting state fluctuations and stimulus-evoked BOLD signals very similar (albeit weaker) to those in GM. A number of studies from other laboratories have also reported reliable observations of WM activations. Detection of BOLD signals in WM has been enhanced by using specialized tasks or modified data analysis methods. In this mini-review we report summaries of some of our recent studies that provide evidence that BOLD signals in WM are related to brain functional activity and deserve greater attention by the neuroimaging community.

EEG correlation at a distance: A re-analysis of two studies using a machine learning approach.

Background: In this paper, data from two studies relative to the relationship between the electroencephalogram (EEG) activities of two isolated and physically separated subjects were re-analyzed using machine-learning algorithms. The first dataset comprises the data of 25 pairs of participants where one member of each pair was stimulated with a visual and an auditory 500 Hz signals of 1 second duration. The second dataset consisted of the data of 20 pairs of participants where one member of each pair received visual and auditory stimulation lasting 1 second duration with on-off modulation at 10, 12, and 14 Hz. Methods and Results: Applying a 'linear discriminant classifier' to the first dataset, it was possible to correctly classify 50.74% of the EEG activity of non-stimulated participants, correlated to the remote sensorial stimulation of the distant partner. In the second dataset, the percentage of correctly classified EEG activity in the non-stimulated partners was 51.17%, 50.45% and 51.91%, respectively, for the 10, 12, and 14 Hz stimulations, with respect the condition of no stimulation in the distant partner. Conclusions: The analysis of EEG activity using machine-learning algorithms has produced advances in the study of the connection between the EEG activities of the stimulated partner and the isolated distant partner, opening new insight into the possibility to devise practical application for non-conventional "mental telecommunications" between physically and sensorially separated participants.

EEG correlation at a distance: A re-analysis of two studies using a machine learning approach

Background: In this paper, data from two studies relative to the relationship between the electroencephalogram (EEG) activities of two isolated and physically separated subjects were re-analyzed using machine-learning algorithms. The first dataset comprises the data of 25 pairs of participants where one member of each pair was stimulated with a visual and an auditory 500 Hz signals of 1 second duration. The second dataset consisted of the data of 20 pairs of participants where one member of each pair received visual and auditory stimulation lasting 1 second duration with on-off modulation at 10, 12, and 14 Hz. Methods and Results: Applying a ‘linear discriminant classifier’ to the first dataset, it was possible to correctly classify 50.74% of the EEG activity of non-stimulated participants, correlated to the remote sensorial stimulation of the distant partner. In the second dataset, the percentage of correctly classified EEG activity in the non-stimulated partners was 51.17%, 50.45% and 51.91%, respectively, for the 10, 12, and 14 Hz stimulations, with respect the condition of no stimulation in the distant partner. Conclusions: The analysis of EEG activity using machine-learning algorithms has produced advances in the study of the connection between the EEG activities of the stimulated partner and the isolated distant partner, opening new insight into the possibility to devise practical application for non-conventional “mental telecommunications” between physically and sensorially separated participants.

EEG correlation at a distance: A re-analysis of two studies using a machine learning approach

Background: In this paper, data from two studies relative to the relationship between the electroencephalogram (EEG) activities of two isolated and physically separated subjects were re-analyzed using machine-learning algorithms. The first dataset comprises the data of 25 pairs of participants where one member of each pair was stimulated with a visual and an auditory 500 Hz signals of 1 second duration. The second dataset consisted of the data of 20 pairs of participants where one member of each pair received visual and auditory stimulation lasting 1 second duration with on-off modulation at 10, 12, and 14 Hz. Methods and Results: Applying a 'linear discriminant classifier' to the first dataset, it was possible to correctly classify 50.74% of the EEG activity of non-stimulated participants, correlated to the remote sensorial stimulation of the distant partner. In the second dataset, the percentage of correctly classified EEG activity in the non-stimulated partners was 51.17%, 50.45% and 51.91%, respectively, for the 10, 12, and 14 Hz stimulations, with respect the condition of no stimulation in the distant partner. Conclusions: The analysis of EEG activity using machine-learning algorithms has produced advances in the study of the connection between the EEG activities of the stimulated partner and the isolated distant partner, opening new insight into the possibility to devise practical application for non-conventional "mental telecommunications" between physically and sensorially separated participants.

A low power passive-active ΔΣ modulator with high-resolution employing an integrator with open-loop unity-gain buffer

This paper presents a new passive-active ΔΣ modulator with high-resolution and low-power applications. An open-loop unity gain buffer is used to improve the performance of a passive switch capacitor integrator. Since this technique compensates phase and gain errors, there is no need for large capacitors, which result in a great reduction in the chip footprint. The first filter is passive, so the output swing of the amplifier is small but large enough to guarantee the linearity and a relaxed slew rate in the modulator structure, which leads to the low power. Using the second-order modulator with an adequate oversampling ratio leads to the desired SNR. The post-layout simulation of the second-order passive-active modulator is performed in Spectre/Cadence electrical simulator using TSMC 0.18 CMOS model in the standard 0.18 μm CMOS process. The dynamic range of 92.4 dB, peak signal to noise ratio of 88.6 dB, peak signal to noise plus distortions of 82.7 dB were achieved while consuming 1.93 μw in a 500 Hz signal bandwidth at 1.5 V supply, giving a FoMWalden of 0.173 pJ/ conv-step and the FoMSchreier is 176.53 dB.

Processing of auditory novelty across the cortical hierarchy: An intracranial electrophysiology study

Under the predictive coding hypothesis, specific spatiotemporal patterns of cortical activation are postulated to occur during sensory processing as expectations generate feedback predictions and prediction errors generate feedforward signals. Establishing experimental evidence for this information flow within cortical hierarchy has been difficult, especially in humans, due to spatial and temporal limitations of non-invasive measures of cortical activity. This study investigated cortical responses to auditory novelty using the local/global deviant paradigm, which engages the hierarchical network underlying auditory predictive coding over short (‘local deviance’; LD) and long (‘global deviance’; GD) time scales. Electrocorticographic responses to auditory stimuli were obtained in neurosurgical patients from regions of interest (ROIs) including auditory, auditory-related and prefrontal cortex. LD and GD effects were assayed in averaged evoked potential (AEP) and high gamma (70–150 Hz) signals, the former likely dominated by local synaptic currents and the latter largely reflecting local spiking activity. AEP LD effects were distributed across all ROIs, with greatest percentage of significant sites in core and non-core auditory cortex. High gamma LD effects were localized primarily to auditory cortex in the superior temporal plane and on the lateral surface of the superior temporal gyrus (STG). LD effects exhibited progressively longer latencies in core, non-core, auditory-related and prefrontal cortices, consistent with feedforward signaling. The spatial distribution of AEP GD effects overlapped that of LD effects, but high gamma GD effects were more restricted to non-core areas. High gamma GD effects had shortest latencies in STG and preceded AEP GD effects in most ROIs. This latency profile, along with the paucity of high gamma GD effects in the superior temporal plane, suggest that the STG plays a prominent role in initiating novelty detection signals over long time scales. Thus, the data demonstrate distinct patterns of information flow in human cortex associated with auditory novelty detection over multiple time scales.