1,267 research outputs found
New results on multidimensional Chinese remainder theorem
The Chinese remainder theorem (CRT) [McClellan and Rader 1979] has been well known for applications in fast DFT computations and computer arithmetic. Guessoum and Mersereau [1986] first made headway in extending the CRT to multidimensional (MD) nonseparable systems and showing its usefulness. The present letter generalize the result and present a more general form. This more general MDCRT is an exact counterpart of 1DCRT
Discrete multitone modulation with principal component filter banks
Discrete multitone (DMT) modulation is an attractive method for communication over a nonflat channel with possibly colored noise. The uniform discrete Fourier transform (DFT) filter bank and cosine modulated filter bank have in the past been used in this system because of low complexity. We show in this paper that principal component filter banks (PCFB) which are known to be optimal for data compression and denoising applications, are also optimal for a number of criteria in DMT modulation communication. For example, the PCFB of the effective channel noise power spectrum (noise psd weighted by the inverse of the channel gain) is optimal for DMT modulation in the sense of maximizing bit rate for fixed power and error probabilities. We also establish an optimality property of the PCFB when scalar prefilters and postfilters are used around the channel. The difference between the PCFB and a traditional filter bank such as the brickwall filter bank or DFT filter bank is significant for effective power spectra which depart considerably from monotonicity. The twisted pair channel with its bridged taps, next and fext noises, and AM interference, therefore appears to be a good candidate for the application of a PCFB. This is demonstrated with the help of numerical results for the case of the ADSL channel
Enriched Long-term Recurrent Convolutional Network for Facial Micro-Expression Recognition
Facial micro-expression (ME) recognition has posed a huge challenge to
researchers for its subtlety in motion and limited databases. Recently,
handcrafted techniques have achieved superior performance in micro-expression
recognition but at the cost of domain specificity and cumbersome parametric
tunings. In this paper, we propose an Enriched Long-term Recurrent
Convolutional Network (ELRCN) that first encodes each micro-expression frame
into a feature vector through CNN module(s), then predicts the micro-expression
by passing the feature vector through a Long Short-term Memory (LSTM) module.
The framework contains two different network variants: (1) Channel-wise
stacking of input data for spatial enrichment, (2) Feature-wise stacking of
features for temporal enrichment. We demonstrate that the proposed approach is
able to achieve reasonably good performance, without data augmentation. In
addition, we also present ablation studies conducted on the framework and
visualizations of what CNN "sees" when predicting the micro-expression classes.Comment: Published in Micro-Expression Grand Challenge 2018, Workshop of 13th
IEEE Facial & Gesture 201
Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks
It is desirable to train convolutional networks (CNNs) to run more
efficiently during inference. In many cases however, the computational budget
that the system has for inference cannot be known beforehand during training,
or the inference budget is dependent on the changing real-time resource
availability. Thus, it is inadequate to train just inference-efficient CNNs,
whose inference costs are not adjustable and cannot adapt to varied inference
budgets. We propose a novel approach for cost-adjustable inference in CNNs -
Stochastic Downsampling Point (SDPoint). During training, SDPoint applies
feature map downsampling to a random point in the layer hierarchy, with a
random downsampling ratio. The different stochastic downsampling configurations
known as SDPoint instances (of the same model) have computational costs
different from each other, while being trained to minimize the same prediction
loss. Sharing network parameters across different instances provides
significant regularization boost. During inference, one may handpick a SDPoint
instance that best fits the inference budget. The effectiveness of SDPoint, as
both a cost-adjustable inference approach and a regularizer, is validated
through extensive experiments on image classification
Towards Accurate One-Stage Object Detection with AP-Loss
One-stage object detectors are trained by optimizing classification-loss and
localization-loss simultaneously, with the former suffering much from extreme
foreground-background class imbalance issue due to the large number of anchors.
This paper alleviates this issue by proposing a novel framework to replace the
classification task in one-stage detectors with a ranking task, and adopting
the Average-Precision loss (AP-loss) for the ranking problem. Due to its
non-differentiability and non-convexity, the AP-loss cannot be optimized
directly. For this purpose, we develop a novel optimization algorithm, which
seamlessly combines the error-driven update scheme in perceptron learning and
backpropagation algorithm in deep networks. We verify good convergence property
of the proposed algorithm theoretically and empirically. Experimental results
demonstrate notable performance improvement in state-of-the-art one-stage
detectors based on AP-loss over different kinds of classification-losses on
various benchmarks, without changing the network architectures. Code is
available at https://github.com/cccorn/AP-loss.Comment: 13 pages, 7 figures, 4 tables, main paper + supplementary material,
accepted to CVPR 201
Transient Three-Dimensional Side Load Analysis of Out-of-Round Film Cooled Nozzles
The objective of this study is to investigate the effect of nozzle out-of-roundness on the transient startup side loads. The out-of-roundness could be the result of asymmetric loads induced by hardware attached to the nozzle, asymmetric internal stresses induced by previous tests and/or deformation, such as creep, from previous tests. The rocket engine studied encompasses a regeneratively cooled thrust chamber and a film cooled nozzle extension with film coolant distributed from a turbine exhaust manifold. The computational methodology is based on an unstructured-grid, pressure-based computational fluid dynamics formulation, and a transient inlet history based on an engine system simulation. Transient startup computations were performed with the out-of-roundness achieved by four degrees of ovalization of the nozzle: one perfectly round, one slightly out-of-round, one more out-of-round, and one significantly out-of-round. The computed side load physics caused by the nozzle out-of-roundness and its effect on nozzle side load are reported and discussed
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Correction: Design and characterization of a plasmonic Doppler grating for azimuthal angle-resolved surface plasmon resonances
The authors regret that Fig. 1e of the original paper contained an error in the curves displayed for the silver, aluminium and palladium gratings. Specifically, a different value of the ‘index of the environment’ (1.65) was used in the calculation of these curves compared to that used for calculating the optical response of the gold grating (1.33). The correct Fig. 1 below, displays the curves calculated with the same value of the index of the environment (1.33). No amendments are made to the caption of Fig. 1 or the other sub-figures presented in the figure. This error does not affect any of the results or conclusions reported in the paper; only the display of the figure. (Figure Presented) The Royal Society of Chemistry apologises for these errors and any consequent inconvenience to authors and readers
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