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Register a new userIs Perfect Filtering Enough Leading to Perfect Phase Correction for dMRI data?
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Added by
Feihong Liu
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Being complex-valued and low in signal-to-noise ratios, magnitude-based diffusion MRI is confounded by the noise-floor that falsely elevates signal magnitude and incurs bias to the commonly used diffusion indices, such as fractional anisotropy (FA). To avoid noise-floor, most existing phase correction methods explore improving filters to estimate the noise-free background phase. In this work, after diving into the phase correction procedures, we argue that even a perfect filter is insufficient for phase correction because the correction procedures are incapable of distinguishing sign-symbols of noise, resulting in artifacts (textit{i.e.}, arbitrary signal loss). With this insight, we generalize the definition of noise-floor to a complex polar coordinate system and propose a calibration procedure that could conveniently distinguish noise sign symbols. The calibration procedure is conceptually simple and easy to implement without relying on any external technique while keeping distinctly effective.
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The performance of laser-based active sensing has been severely limited by two types of noise: electrical noise, stemming from elements; optical noise, laser jamming from an eavesdropper and background from environment. Conventional methods to filter optical noise take advantage of the differences between signal and noise in time, wavelength, and polarization. However, they may be limited when the noise and signal share the same information on these degrees of freedoms (DoFs). In order to overcome this drawback, we experimentally demonstrate a groundbreaking noise-filtering method by controlling orbital angular momentum (OAM) to distinguish signal from noise. We provide a proof-of-principle experiment and discuss the dependence of azimuthal index of OAM and detection aperture on signal-to-noise ratio (SNR). Our results suggest that using OAM against noise is an efficient method, offering a new route to optical sensing immersed in high-level noise.
An implicit but pervasive hypothesis of modern computer vision research is that convolutional neural network (CNN) architectures that perform better on ImageNet will also perform better on other vision datasets. We challenge this hypothesis through an extensive empirical study for which we train 500 sampled CNN architectures on ImageNet as well as 8 other image classification datasets from a wide array of application domains. The relationship between architecture and performance varies wildly, depending on the datasets. For some of them, the performance correlation with ImageNet is even negative. Clearly, it is not enough to optimize architectures solely for ImageNet when aiming for progress that is relevant for all applications. Therefore, we identify two dataset-specific performance indicators: the cumulative width across layers as well as the total depth of the network. Lastly, we show that the range of dataset variability covered by ImageNet can be significantly extended by adding ImageNet subsets restricted to few classes.
State-of-the-art temporal action detectors to date are based on two-stream input including RGB frames and optical flow. Although combining RGB frames and optical flow boosts performance significantly, optical flow is a hand-designed representation which not only requires heavy computation, but also makes it methodologically unsatisfactory that two-stream methods are often not learned end-to-end jointly with the flow. In this paper, we argue that optical flow is dispensable in high-accuracy temporal action detection and image level data augmentation (ILDA) is the key solution to avoid performance degradation when optical flow is removed. To evaluate the effectiveness of ILDA, we design a simple yet efficient one-stage temporal action detector based on single RGB stream named DaoTAD. Our results show that when trained with ILDA, DaoTAD has comparable accuracy with all existing state-of-the-art two-stream detectors while surpassing the inference speed of previous methods by a large margin and the inference speed is astounding 6668 fps on GeForce GTX 1080 Ti. Code is available at url{https://github.com/Media-Smart/vedatad}.
We have withdrawn this paper due to a fatal flaw in eqn(38).
Metasurfaces in metal/insulator/metal configuration have recently been widely used in photonics research, with applications ranging from perfect absorption to phase modulation, but why and when such structures can realize what kind of functionalities are not yet fully understood. Here, based on a coupled-mode theory analysis, we establish a complete phase diagram in which the optical properties of such systems are fully controlled by two simple parameters (i.e., the intrinsic and radiation losses), which are in turn dictated by the geometrical/material parameters of the underlying structures. Such a phase diagram can greatly facilitate the design of appropriate metasurfaces with tailored functionalities (e.g., perfect absorption, phase modulator, electric/magnetic reflector, etc.), demonstrated by our experiments and simulations in the Terahertz regime. In particular, our experiments show that, through appropriate structural/material tuning, the device can be switched across the functionality phase boundaries yielding dramatic changes in optical responses. Our discoveries lay a solid basis for realizing functional and tunable photonic devices with such structures.
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