5 resultados para Low Power
em Universidade Complutense de Madrid
Resumo:
This paper presents an experimental study of the sensitivity to 15-MeV neutrons of Advanced Low Power SRAMs (A-LPSRAM) at low bias voltage little above the threshold value that allows the retention of data. This family of memories is characterized by a 3D structure to minimize the area penalty and to cope with latchups, as well as by the presence of integrated capacitors to hinder the occurrence of single event upsets. In low voltage static tests, classical single event upsets were a minor source of errors, but other unexpected phenomena such as clusters of bitflips and hard errors turned out to be the origin of hundreds of bitflips. Besides, errors were not observed in dynamic tests at nominal voltage. This behavior is clearly different than that of standard bulk CMOS SRAMs, where thousands of errors have been reported.
Resumo:
Context. Accretion onto supermassive black holes is believed to occur mostly in obscured active galactic nuclei (AGN). Such objects are proving rather elusive in surveys of distant galaxies, including those at X-ray energies. Aims. Our main goal is to determine whether the revised IRAC criteria of Donley et al. (2012, ApJ, 748, 142; objects with an infrared (IR) power-law spectral shape), are effective at selecting X-ray type-2 AGN (i.e., absorbed N_H > 10^22 cm^-2). Methods. We present the results from the X-ray spectral analysis of 147 AGN selected by cross-correlating the highest spectral quality ultra-deep XMM-Newton and the Spitzer/IRAC catalogues in the Chandra Deep Field South. Consequently it is biased towards sources with high S/N X-ray spectra. In order to measure the amount of intrinsic absorption in these sources, we adopt a simple X-ray spectral model that includes a power-law modified by intrinsic absorption at the redshift of each source and a possible soft X-ray component. Results. We find 21/147 sources to be heavily absorbed but the uncertainties in their obscuring column densities do not allow us to confirm their Compton-Thick nature without resorting to additional criteria. Although IR power-law galaxies are less numerous in our sample than IR non-power-law galaxies (60 versus 87 respectively), we find that the fraction of absorbed (N_H^intr > 10^22 cm^-2) AGN is significantly higher (at about 3 sigma level) for IR-power-law sources (similar to 2/3) than for those sources that do not meet this IR selection criteria (~1/2). This behaviour is particularly notable at low luminosities, but it appears to be present, although with a marginal significance, at all luminosities. Conclusions. We therefore conclude that the IR power-law method is efficient in finding X-ray-absorbed sources. We would then expect that the long-sought dominant population of absorbed AGN is abundant among IR power-law spectral shape sources not detected in X-rays.
Resumo:
Several studies have reported changes in spontaneous brain rhythms that could be used asclinical biomarkers or in the evaluation of neuropsychological and drug treatments in longitudinal studies using magnetoencephalography (MEG). There is an increasing necessity to use these measures in early diagnosis and pathology progression; however, there is a lack of studies addressing how reliable they are. Here, we provide the first test-retest reliability estimate of MEG power in resting-state at sensor and source space. In this study, we recorded 3 sessions of resting-state MEG activity from 24 healthy subjects with an interval of a week between each session. Power values were estimated at sensor and source space with beamforming for classical frequency bands: delta (2–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), low beta (13–20 Hz), high beta (20–30 Hz), and gamma (30–45 Hz). Then, test-retest reliability was evaluated using the intraclass correlation coefficient (ICC). We also evaluated the relation between source power and the within-subject variability. In general, ICC of theta, alpha, and low beta power was fairly high (ICC > 0.6) while in delta and gamma power was lower. In source space, fronto-posterior alpha, frontal beta, and medial temporal theta showed the most reliable profiles. Signal-to-noise ratio could be partially responsible for reliability as low signal intensity resulted inhigh within-subject variability, but also the inherent nature of some brain rhythms in resting-state might be driving these reliability patterns. In conclusion, our results described the reliability of MEG power estimates in each frequency band, which could be considered in disease characterization or clinical trials.
Resumo:
In the study of the spatial characteristics of the visual channels, the power spectrum model of visual masking is one of the most widely used. When the task is to detect a signal masked by visual noise, this classical model assumes that the signal and the noise are previously processed by a bank of linear channels and that the power of the signal at threshold is proportional to the power of the noise passing through the visual channel that mediates detection. The model also assumes that this visual channel will have the highest ratio of signal power to noise power at its output. According to this, there are masking conditions where the highest signal-to-noise ratio (SNR) occurs in a channel centered in a spatial frequency different from the spatial frequency of the signal (off-frequency looking). Under these conditions the channel mediating detection could vary with the type of noise used in the masking experiment and this could affect the estimation of the shape and the bandwidth of the visual channels. It is generally believed that notched noise, white noise and double bandpass noise prevent off-frequency looking, and high-pass, low-pass and bandpass noises can promote it independently of the channel's shape. In this study, by means of a procedure that finds the channel that maximizes the SNR at its output, we performed numerical simulations using the power spectrum model to study the characteristics of masking caused by six types of one-dimensional noise (white, high-pass, low-pass, bandpass, notched, and double bandpass) for two types of channel's shape (symmetric and asymmetric). Our simulations confirm that (1) high-pass, low-pass, and bandpass noises do not prevent the off-frequency looking, (2) white noise satisfactorily prevents the off-frequency looking independently of the shape and bandwidth of the visual channel, and interestingly we proved for the first time that (3) notched and double bandpass noises prevent off-frequency looking only when the noise cutoffs around the spatial frequency of the signal match the shape of the visual channel (symmetric or asymmetric) involved in the detection. In order to test the explanatory power of the model with empirical data, we performed six visual masking experiments. We show that this model, with only two free parameters, fits the empirical masking data with high precision. Finally, we provide equations of the power spectrum model for six masking noises used in the simulations and in the experiments.
Resumo:
Purpose To compare measurements taken using a swept-source optical coherence tomography-based optical biometer (IOLmaster 700) and an optical low-coherence reflectometry biometer (Lenstar 900), and to determine the clinical impacts of differences in their measurements on intraocular lens (IOL) power predictions. Methods Eighty eyes of 80 patients scheduled to undergo cataract surgery were examined with both biometers. The measurements made using each device were axial length (AL), central corneal thickness (CCT), aqueous depth (AQD), lens thickness (LT), mean keratometry (MK), white-to-white distance (WTW), and pupil diameter (PD). Holladay 2 and SRK/T formulas were used to calculate IOL power. Differences in measurement between the two biometers were determined using the paired t-test. Agreement was assessed through intraclass correlation coefficients (ICC) and Bland–Altman plots. Results Mean patient age was 76.3±6.8 years (range 59–89). Using the Lenstar, AL and PD could not be measured in 12.5 and 5.25% of eyes, respectively, while IOLMaster 700 took all measurements in all eyes. The variables CCT, AQD, LT, and MK varied significantly between the two biometers. According to ICCs, correlation between measurements made with both devices was excellent except for WTW and PD. Using the SRK/T formula, IOL power prediction based on the data from the two devices were statistically different, but differences were not clinically significant. Conclusions No clinically relevant differences were detected between the biometers in terms of their measurements and IOL power predictions. Using the IOLMaster 700, it was easier to obtain biometric measurements in eyes with less transparent ocular media or longer AL.