Negation scope resolution is the process of detecting the negated part of a sentence. Unlike the syntax-based approach employed in previous researches, state-of-the-art methods performed better without the explicit use of syntactic structure. This work revisits the syntax-based approach and re-evaluates the effectiveness of syntactic structure in negation scope resolution. We replace the parser utilized in the prior works with state-of-the-art parsers and modify the syntax-based heuristic rules. The experimental results demonstrate that the simple modifications enhance the performance of the prior syntax-based method to the same level as state-of-the-art end-to-end neural-based methods.

In this paper, a nonlinear quantized precoding scheme for low-resolution digital-analog converters (DACs) in a massive multiple-input multiple-output (MIMO) system is proposed. The nonlinear quantized precoding determines transmit antenna outputs with a transmit symbol and channel state information. In a full-digital massive MIMO system, low-resolution DACs are used to suppress power consumption. Conventional precoding algorithms for low-resolution DACs do not optimize transmit antenna gains individually. Thus, in this paper, a precoding scheme that optimizes individual transmit antenna gains as well as the DAC outputs is proposed. In the proposed scheme, the subarray of massive MIMO antennas is treated virtually as a single antenna element. Numerical results obtained through computer simulation show that the proposed precoding scheme achieves bit error rate performance close to that of the conventional precoding scheme with much smaller antenna gains on a CDL-A channel.

We assess how well the recently created MRI reconstruction technique, Multi-Resolution Convolutional Neural Network (MRCNN), performs in the core medical vision field (classification). The primary goal of MRCNN is to identify the best k-space undersampling patterns to accelerate the MRI. In this study, we use the Figshare brain tumor dataset for MRI classification with 3064 T1-weighted contrast-enhanced MRI (CE-MRI) over three categories: meningioma, glioma, and pituitary tumors. We apply MRCNN to the dataset, which is a method to reconstruct high-quality images from under-sampled k-space signals. Next, we employ the pre-trained VGG16 model, which is a Deep Neural Network (DNN) based image classifier to the MRCNN restored MRIs to classify the brain tumors. Our experiments showed that in the case of MRCNN restored data, the proposed brain tumor classifier achieved 92.79% classification accuracy for a 10% sampling rate, which is slightly higher than that of SRCNN, MoDL, and Zero-filling methods have 91.89%, 91.89%, and 90.98% respectively. Note that our classifier was trained using the dataset consisting of the images with full sampling and their labels, which can be regarded as a model of the usual human diagnostician. Hence our results would suggest MRCNN is useful for human diagnosis. In conclusion, MRCNN significantly enhances the accuracy of the brain tumor classification system based on the tumor location using under-sampled k-space signals.

In this letter, we propose a feature-based knowledge distillation scheme which transfers knowledge between intermediate blocks of teacher and student with flow-based architecture, specifically Normalizing flow in our implementation. In addition to the knowledge transfer scheme, we examine how configuration of the distillation positions impacts on the knowledge transfer performance. To evaluate the proposed ideas, we choose two knowledge distillation baseline models which are based on Normalizing flow on different domains: CS-Flow for anomaly detection and SRFlow-DA for super-resolution. A set of performance comparison to the baseline models with popular benchmark datasets shows promising results along with improved inference speed. The comparison includes performance analysis based on various configurations of the distillation positions in the proposed scheme.

Person images captured by surveillance cameras in real scenes often have low resolution (LR), which suffers from severe degradation in recognition performance when matched with pre-stocked high-resolution (HR) images. There are existing methods which typically employ super-resolution (SR) techniques to address the resolution discrepancy problem in person re-identification (re-ID). However, SR techniques are intended to enhance the human eye visual fidelity of images without caring about the recovery of pedestrian identity information. To cope with this challenge, we propose an orthogonal depth feature decomposition network. And we decompose pedestrian features into resolution-related features and identity-related features who are orthogonal to each other, from which we design the identity-preserving loss and resolution-invariant loss to ensure the recovery of pedestrian identity information. When compared with the SOTA method, experiments on the MLR-CUHK03 and MLR-VIPeR datasets demonstrate the superiority of our method.

In this paper, a non-linear precoding algorithm with low out-of-band (OOB) radiation is proposed for massive multiple-input multiple-output (MIMO) systems. Massive MIMO sets more than one hundred antennas at each base station to achieve higher spectral efficiency and throughput. Full digital massive MIMO may constrain the resolution of digital-to-analog converters (DACs) since each DAC consumes a large amount of power. In massive MIMO systems with low resolution DACs, designing methods of DAC output signals by nonlinear processing are being investigated. The conventional scheme focuses only on a sum rate or errors in the received signals and so triggers large OOB radiation. This paper proposes an optimization criterion that takes OOB radiation power into account. Gibbs sampling is used as an algorithm to find sub-optimal solutions given this criterion. Numerical results obtained through computer simulation show that the proposed criterion reduces mean OOB radiation power by a factor of 10 as compared with the conventional criterion. The proposed criterion also reduces OOB radiation while increasing the average sum rate by optimizing the weight factor for the OOB radiation. As a result, the proposed criterion achieves approximately 1.3 times higher average sum rates than an error-based criterion. On the other hand, as compared with a sum rate based criterion, the throughput on each subcarrier shows less variation which reduces the number of link adaptation options needed although the average sum rate of the proposed criterion is smaller.

This paper proposes a novel interference cancellation technique that prevents radio receivers from degrading due to periodic interference signals caused by electromagnetic waves emitted from high power circuits. The proposed technique cancels periodic interference signals in the frequency domain, even if the periodic interference signals drift in the time domain. We propose a drift estimation based on a super resolution technique such as ESPRIT. Moreover, we propose a sequential drift estimation to enhance the drift estimation performance. The proposed technique employs a linear filter based on the minimum mean square error criterion with assistance of the estimated drifts for the interference cancellation. The performance of the proposed technique is confirmed by computer simulation. The proposed technique achieves a gain of more than 40dB at the higher frequency part in the band. The proposed canceler achieves such superior performance, if the parameter sets are carefully selected. The proposed sequential drift estimation relaxes the parameter constraints, and enables the proposed cancellation to achieve the performance upper bound.

High-quality depth images are required for stable and accurate computer vision. Depth images captured by depth cameras tend to be noisy, incomplete, and of low-resolution. Therefore, increasing the accuracy and resolution of depth images is desirable. We propose a method for reducing the noise and holes from depth images pixel by pixel, and increasing resolution. For each pixel in the target image, the linear space from the focal point of the camera through each pixel to the existing object is divided into equally spaced grids. In each grid, the difference from each grid to the object surface is obtained from multiple tracked depth images, which have noisy depth values of the respective image pixels. Then, the coordinates of the correct object surface are obtainable by reducing the depth random noise. The missing values are completed. The resolution can also be increased by creating new pixels between existing pixels and by then using the same process as that used for noise reduction. Evaluation results have demonstrated that the proposed method can do processing with less GPU memory. Furthermore, the proposed method was able to reduce noise more accurately, especially around edges, and was able to process more details of objects than the conventional method. The super-resolution of the proposed method also produced a high-resolution depth image with smoother and more accurate edges than the conventional methods.

This paper addresses single image super-resolution (SR) based on convolutional neural networks (CNNs). It is known that recovery of high-frequency components in output SR images of CNNs learned by the least square errors or least absolute errors is insufficient. To generate realistic high-frequency components, SR methods using generative adversarial networks (GANs), composed of one generator and one discriminator, are developed. However, when the generator tries to induce the discriminator's misjudgment, not only realistic high-frequency components but also some artifacts are generated, and objective indices such as PSNR decrease. To reduce the artifacts in the GAN-based SR methods, we consider the set of all SR images whose square errors between downscaling results and the input image are within a certain range, and propose to apply the metric projection onto this consistent set in the output layers of the generators. The proposed technique guarantees the consistency between output SR images and input images, and the generators with the proposed projection can generate high-frequency components with few artifacts while keeping low-frequency ones as appropriate for the known noise level. Numerical experiments show that the proposed technique reduces artifacts included in the original SR images of a GAN-based SR method while generating realistic high-frequency components with better PSNR values in both noise-free and noisy situations. Since the proposed technique can be integrated into various generators if the downscaling process is known, we can give the consistency to existing methods with the input images without degrading other SR performance.

In recent years, compared with the traditional face super-resolution (SR) algorithm, the face SR based on deep neural network has shown strong performance. Among these methods, attention mechanism has been widely used in face SR because of its strong feature expression ability. However, the existing attention-based face SR methods can not fully mine the missing pixel information of low-resolution (LR) face images (structural prior). And they only consider a single attention mechanism to take advantage of the structure of the face. The use of multi-attention could help to enhance feature representation. In order to solve this problem, we first propose a new pixel attention mechanism, which can recover the structural details of lost pixels. Then, we design an attention fusion module to better integrate the different characteristics of triple attention. Experimental results on FFHQ data sets show that this method is superior to the existing face SR methods based on deep neural network.

In flat panel display (FPD) lithography, a high resolution and large depth of focus (DOF) are required. The demands for high throughput have necessitated the use of large glass plates and exposure areas, thereby increasing focal unevenness and reducing process latitude. Thus, a large DOF is needed, particularly for high-resolution lithography. To manufacture future high-definition displays, 1.0μm line and space (L/S) is predicted to be required, and a technique to achieve this resolution with adequate DOF is necessary. To improve the resolution and DOF, resolution enhancement techniques (RETs) have been introduced. RETs such as off-axis illumination (OAI) and phase-shift masks (PSMs) have been widely used in semiconductor lithography, which utilizes narrowband illumination. To effectively use RETs in FPD lithography, modification for broadband illumination is required because FPD lithography utilizes such illumination as exposure light. However, thus far, RETs for broadband illumination have not been studied. This study aimed to develop techniques to achieve 1.0μm L/S resolution with an acceptable DOF. To this end, this paper proposes a method that combines our previously developed RET, namely, divided spectrum illumination (DSI), with an attenuated PSM (Att. PSM). Theoretical observations and simulations present the design of a PSM for broadband illumination. The transmittance and phase shift, whose degree varies according to the wavelength, are determined in terms of aerial image contrast and resist loss. The design of DSI for an Att. PSM is also discussed considering image contrast, DOF, and illumination intensity. Finally, the exposure results of 1.0μm L/S using DSI and PSM techniques are shown, demonstrating that a PSM greatly improves the resist profile, and DSI enhances the DOF by approximately 30% compared to conventional OAI. Thus, DSI and PSMs can be used in practical applications for achieving 1.0μm L/S with sufficient DOF.

Image resolution under the effect of color speckle was successfully measured for a raster-scan mobile projector, using the modified contrast modulation method. This method was based on the eye-diagram analysis for distinguishing the binary image signals, black-and-white line pairs. The image resolution and the related metrics, illuminance, chromaticity, and speckle contrast were measured at the nine regions on the full-frame area projected on a standard diffusive reflectance screen. The nonuniformity data over the nine regions were discussed and analyzed.

The sum rate performance of nonlinier quantized precoding using Gibbs sampling are evaluated in a massive multiuser multiple-input multiple-output (MU-MIMO) system in this paper. Massive MU-MIMO is a key technology to handle the growth of data traffic. In a full digital massive MU-MIMO system, however, the resolution of digital-to-analogue converters (DACs) in transmit antenna branches have to be low to yield acceptable power consumption. Thus, a combinational optimization problem is solved for the nonlinier quantized precoding to determine transmit signals from finite alphabets output from low resolution DACs. A conventional optimization criterion minimizes errors between desired signals and received signals at user equipments (UEs). However, the system sum rate may decrease as it increases the transmit power. This paper proposes two optimization criteria that take the transmit power into account in order to maximize the sum rate. Mixed Gibbs sampling is applied to obtain the suboptimal solution of the nonlinear optimization problem. Numerical results obtained through computer simulations show that the two proposed criteria achieve higher sum rates than the conventional criterion. On the other hand, the sum rate criterion achieves the largest sum rate while it leads to less throughputs than the MMSE criterion on approximately 60% of subcarriers.

Exploring the structural information as prior to facial images is a key issue of face super-resolution (SR). Although deep convolutional neural networks (CNNs) own powerful representation ability, how to accurately use facial structural information remains challenges. In this paper, we proposed a new residual fusion network to utilize the multi-scale structural information for face SR. Different from the existing methods of increasing network depth, the bottleneck attention module is introduced to extract fine facial structural features by exploring correlation from feature maps. Finally, hierarchical scales of structural information is fused for generating a high-resolution (HR) facial image. Experimental results show the proposed network outperforms some existing state-of-the-art CNNs based face SR algorithms.

In recent years, single face image super-resolution (SR) using deep neural networks have been well developed. However, most of the face images captured by the camera in a real scene are from different views of the same person, and the existing traditional multi-frame image SR requires alignment between images. Due to multi-view face images contain texture information from different views, which can be used as effective prior information, how to use this prior information from multi-views to reconstruct frontal face images is challenging. In order to effectively solve the above problems, we propose a novel face SR network based on multi-view face images, which focus on obtaining more texture information from multi-view face images to help the reconstruction of frontal face images. And in this network, we also propose a texture attention mechanism to transfer high-precision texture compensation information to the frontal face image to obtain better visual effects. We conduct subjective and objective evaluations, and the experimental results show the great potential of using multi-view face images SR. The comparison with other state-of-the-art deep learning SR methods proves that the proposed method has excellent performance.

This paper presents an image super-resolution technique using a convolutional neural network (CNN) and multi-task learning for multiple image categories. The image categories include natural, manga, and text images. Their features differ from each other. However, several CNNs for super-resolution are trained with a single category. If the input image category is different from that of the training images, the performance of super-resolution is degraded. There are two possible solutions to manage multi-categories with conventional CNNs. The first involves the preparation of the CNNs for every category. This solution, however, requires a category classifier to select an appropriate CNN. The second is to learn all categories with a single CNN. In this solution, the CNN cannot optimize its internal behavior for each category. Therefore, this paper presents a super-resolution CNN architecture for multiple image categories. The proposed CNN has two parallel outputs for a high-resolution image and a category label. The main CNN for the high-resolution image is a normal three convolutional layer-architecture, and the sub neural network for the category label is branched out from its middle layer and consists of two fully-connected layers. This architecture can simultaneously learn the high-resolution image and its category using multi-task learning. The category information is used for optimizing the super-resolution. In an applied setting, the proposed CNN can automatically estimate the input image category and change the internal behavior. Experimental results of 2× image magnification have shown that the average peak signal-to-noise ratio for the proposed method is approximately 0.22 dB higher than that for the conventional super-resolution with no difference in processing time and parameters. We have ensured that the proposed method is useful when the input image category is varying.

In high range resolution radar systems, the detection of range-spread target under correlated non-Gaussian clutter faces many problems. In this paper, a novel detector employing an autoregressive (AR) model is proposed to improve the detection performance. The algorithm is elaborately designed and analyzed considering the clutter characteristics. Numerical simulations and measurement data verify the effectiveness and advantages of the proposed detector for the range-spread target in spatially correlated non-Gaussian clutter.

Low Probability of Intercept (LPI) radar waveform has complex and diverse modulation schemes, which cannot be easily identified by the traditional methods. The research on intrapulse modulation LPI radar waveform recognition has received increasing attention. In this paper, we propose an automatic LPI radar waveform recognition algorithm that uses a multi-resolution fusion convolutional neural network. First, signals embedded within the noise are processed using Choi-William Distribution (CWD) to obtain time-frequency feature images. Then, the images are resized by interpolation and sent to the proposed network for training and identification. The network takes a dual-channel CNN structure to obtain features at different resolutions and makes features fusion by using the concatenation and Inception module. Extensive simulations are carried out on twelve types of LPI radar waveforms, including BPSK, Costas, Frank, LFM, P1~P4, and T1~T4, corrupted with additive white Gaussian noise of SNR from 10dB to -8dB. The results show that the overall recognition rate of the proposed algorithm reaches 95.1% when the SNR is -6dB. We also try various sample selection methods related to the recognition task of the system. The conclusion is that reducing the samples with SNR above 2dB or below -8dB can effectively improve the training speed of the network while maintaining recognition accuracy.

Synthetic aperture interferometric radiometer (SAIR) is a powerful sensors for high-resolution imaging. However, because of the observation errors and small number of visibility sampling points, the accuracy of reconstructed images is usually low. To overcome this deficiency, a novel super-resolution imaging (SrI) method based on super-resolution reconstruction idea is proposed in this paper. In SrI method, sparse visibility functions are first measured at different observation locations. Then the sparse visibility functions are utilized to simultaneously construct the fusion visibility function and the fusion imaging model. Finally, the high-resolution image is reconstructed by solving the sparse optimization of fusion imaging model. The simulation results demonstrate that the proposed SrI method has higher reconstruction accuracy and can improve the imaging quality of SAIR effectively.

Convolutional neural network (CNN)-based image super-resolutions are widely used as a high-quality image-enhancement technique. However, in general, they show little to no luminance isotropy. Thus, we propose two methods, “Luminance Inversion Training (LIT)” and “Luminance Inversion Averaging (LIA),” to improve the luminance isotropy of CNN-based image super-resolutions. Experimental results of 2× image magnification show that the average peak signal-to-noise ratio (PSNR) using Luminance Inversion Averaging is about 0.15-0.20dB higher than that for the conventional super-resolution.