# IEEE Signal Processing Letters

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• ### A New Iterative FIR Filter Design Approach Using a Gaussian Approximation

Publication Year: 2018, Page(s):1615 - 1619
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The letter presents a novel iterative methodology for the design of FIR filters based on an approximation of the desired filter frequency response using a Gabor system generated by the Gaussian function. The proposed method exhibits simplicity of implementation, comparable to that of window-based design methods and ensures accuracy in the fulfillment of design requirements, comparable to the one a... View full abstract»

• ### On the Performance of Non-Orthogonal Multiple Access in 5G Systems with Randomly Deployed Users

Publication Year: 2014, Page(s):1501 - 1505
Cited by:  Papers (517)
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In this letter, the performance of non-orthogonal multiple access (NOMA) is investigated in a cellular downlink scenario with randomly deployed users. The developed analytical results show that NOMA can achieve superior performance in terms of ergodic sum rates; however, the outage performance of NOMA depends critically on the choices of the users' targeted data rates and allocated power. In parti... View full abstract»

• ### Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification

Publication Year: 2017, Page(s):279 - 283
Cited by:  Papers (49)
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The ability of deep convolutional neural networks (CNNs) to learn discriminative spectro-temporal patterns makes them well suited to environmental sound classification. However, the relative scarcity of labeled data has impeded the exploitation of this family of high-capacity models. This study has two primary contributions: first, we propose a deep CNN architecture for environmental sound classif... View full abstract»

• ### Making a “Completely Blind” Image Quality Analyzer

Publication Year: 2013, Page(s):209 - 212
Cited by:  Papers (536)
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An important aim of research on the blind image quality assessment (IQA) problem is to devise perceptual models that can predict the quality of distorted images with as little prior knowledge of the images or their distortions as possible. Current state-of-the-art “general purpose” no reference (NR) IQA algorithms require knowledge about anticipated distortions in the form of training examples and... View full abstract»

• ### Analysis of Blockage Sensing by Radars in Random Cellular Networks

Publication Year: 2018, Page(s):1620 - 1624
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We characterize the detection probability of blockage sensing by radars deployed on towers in cellular networks. If the signal-to-interference ratio of the reflected pilot signal is larger than the predefined threshold and there is no other blockage between the radar and the corresponding blockage, the radar successfully detects the blockage. Modeling radar and blockage locations using stochastic ... View full abstract»

• ### A universal image quality index

Publication Year: 2002, Page(s):81 - 84
Cited by:  Papers (2150)  |  Patents (39)
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We propose a new universal objective image quality index, which is easy to calculate and applicable to various image processing applications. Instead of using traditional error summation methods, the proposed index is designed by modeling any image distortion as a combination of three factors: loss of correlation, luminance distortion, and contrast distortion. Although the new index is mathematica... View full abstract»

• ### Frequency Shift Chirp Modulation: The LoRa Modulation

Publication Year: 2017, Page(s):1818 - 1821
Cited by:  Papers (5)
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Low power wide area networks (LPWAN) are emerging as a new paradigm, especially in the field of Internet of Things (IoT) connectivity. LoRa is one of the LPWAN and it is gaining quite a lot of commercial traction. The modulation underlying LoRa is patented and has never been described theoretically. The aim of this letter is to give the first rigorous mathematical signal processing description of ... View full abstract»

• ### Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks

Publication Year: 2016, Page(s):1499 - 1503
Cited by:  Papers (176)
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Face detection and alignment in unconstrained environment are challenging due to various poses, illuminations, and occlusions. Recent studies show that deep learning approaches can achieve impressive performance on these two tasks. In this letter, we propose a deep cascaded multitask framework that exploits the inherent correlation between detection and alignment to boost up their performance. In ... View full abstract»

• ### Effective Guided Image Filtering for Contrast Enhancement

Publication Year: 2018, Page(s):1585 - 1589
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Although the guided image filtering (GIF) has an excellent edge-preserving property, it is prone to suffer from the halo artifacts near the edges. Weighted GIF and gradient-domain GIF try to address the problem by incorporating an edge-aware weighting into GIF. However, they are very sensitive to the regularization parameter and the halo artifacts will become serious as the regularization paramete... View full abstract»

• ### 3-D Convolutional Recurrent Neural Networks With Attention Model for Speech Emotion Recognition

Publication Year: 2018, Page(s):1440 - 1444
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Speech emotion recognition (SER) is a difficult task due to the complexity of emotions. The SER performances are heavily dependent on the effectiveness of emotional features extracted from the speech. However, most emotional features are sensitive to emotionally irrelevant factors, such as the speaker, speaking styles, and environment. In this letter, we assume that calculating the deltas and delt... View full abstract»

• ### Fairness for Non-Orthogonal Multiple Access in 5G Systems

Publication Year: 2015, Page(s):1647 - 1651
Cited by:  Papers (226)
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In non-orthogonal multiple access (NOMA) downlink, multiple data flows are superimposed in the power domain and user decoding is based on successive interference cancellation. NOMA's performance highly depends on the power split among the data flows and the associated power allocation (PA) problem. In this letter, we study NOMA from a fairness standpoint and we investigate PA techniques that ensur... View full abstract»

• ### Automatic Steganographic Distortion Learning Using a Generative Adversarial Network

Publication Year: 2017, Page(s):1547 - 1551
Cited by:  Papers (3)
| | PDF (662 KB) | HTML

Generative adversarial network has shown to effectively generate artificial samples indiscernible from their real counterparts with a united framework of two subnetworks competing against each other. In this letter, we first propose an automatic steganographic distortion learning framework using a generative adversarial network, which is composed of a steganographic generative subnetwork and a ste... View full abstract»

• ### Joint DOA and Frequency Estimation With Sub-Nyquist Sampling in the Sparse Array System

Publication Year: 2018, Page(s):1285 - 1289
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Several array systems along with algorithms based on sub-Nyquist sampling techniques have been extensively studied. This letter is committed to the joint frequency and direction-of-arrival estimation of more sources than sensors in a subband by using sparse arrays with sub-Nyquist sampling. The newly defined block vectorization eliminates the interference from sub-Nyquist sampling. Based on this, ... View full abstract»

• ### Improved Detection Performance for Passive Radars Exploiting Known Communication Signal Form

Publication Year: 2018, Page(s):1625 - 1629
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In this letter, we address the problem of target detection in passive multiple-input multiple-output radar networks. A generalized likelihood ratio test is derived, assuming prior knowledge of the signal format used in the noncooperative transmit stations. The performance of the generalized likelihood ratio test in the known signal format case is often significantly more favorable when compared to... View full abstract»

• ### BM3D-Net: A Convolutional Neural Network for Transform-Domain Collaborative Filtering

Publication Year: 2018, Page(s):55 - 59
Cited by:  Papers (2)
| | PDF (428 KB) | HTML

Denoising is a fundamental task in image processing with wide applications for enhancing image qualities. BM3D is considered as an effective baseline for image denoising. Although learning-based methods have been dominant in this area recently, the traditional methods are still valuable to inspire new ideas by combining with learning-based approaches. In this letter, we propose a new convolutional... View full abstract»

• ### A Progressively Enhanced Network for Video Satellite Imagery Superresolution

Publication Year: 2018, Page(s):1630 - 1634
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Deep convolutional neural networks (CNNs) have been extensively applied to image or video processing and analysis tasks. For single-image superresolution (SR) processing, previous CNN-based methods have led to significant improvements, when compared to the shallow learning-based methods. However, these CNN-based algorithms with simply direct or skip connections are not suitable for satellite image... View full abstract»

• ### Image Fusion With Convolutional Sparse Representation

Publication Year: 2016, Page(s):1882 - 1886
Cited by:  Papers (31)
| | PDF (361 KB) | HTML Media

As a popular signal modeling technique, sparse representation (SR) has achieved great success in image fusion over the last few years with a number of effective algorithms being proposed. However, due to the patch-based manner applied in sparse coding, most existing SR-based fusion methods suffer from two drawbacks, namely, limited ability in detail preservation and high sensitivity to misregistra... View full abstract»

• ### Voice Activity Detection Using an Adaptive Context Attention Model

Publication Year: 2018, Page(s):1181 - 1185
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Voice activity detection (VAD) classifies incoming signal segments into speech or background noise; its performance is crucial in various speech-related applications. Although speech-signal context is a relevant VAD asset, its usefulness varies in unpredictable noise environments. Therefore, its usage should be adaptively adjustable to the noise type. This letter improves the use of context inform... View full abstract»

• ### Efficient, Accurate, and Rotation-Invariant Iris Code

Publication Year: 2017, Page(s):1233 - 1237
Cited by:  Papers (3)
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The large scale of the recently demanded biometric systems has put a pressure on creating a more efficient, accurate, and private biometric solutions. Iris biometrics is one of the most distinctive and widely used biometric characteristics. High-performing iris representations suffer from the curse of rotation inconsistency. This is usually solved by assuming a range of rotational errors and perfo... View full abstract»

• ### Robustness of$\ell _1$-Norm Estimation: From Folklore to Fact

Publication Year: 2018, Page(s):1640 - 1644
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The advantages of using ℓ1-norm rather than ℓ1-norm in terms of robustness for signal processing and other data analysis procedures are largely recognized across the scientific literature. However, from the robust statistic viewpoint, at least that based on the concept of breakdown point, ℓ1-norm regression has no better resistance to outliers than least squares, a... View full abstract»

• ### Deep Shrinkage Convolutional Neural Network for Adaptive Noise Reduction

Publication Year: 2018, Page(s):224 - 228
Cited by:  Papers (1)
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The noise level of an image depends on settings of an imaging device. The settings can be used to select appropriate parameters for denoising methods. But denoising methods based on deep convolutional neural networks (deep-CNN) do not have such adjustable parameters. Therefore, a deep-CNN whose training data contain limited levels of noise does not effectively restore images whose noise level is d... View full abstract»

• ### Residual LSTM Attention Network for Object Tracking

Publication Year: 2018, Page(s):1029 - 1033
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In this letter, we propose an attention network for object tracking. To construct the proposed attention network for sequential data, we combine long-short term memory (LSTM) and a residual framework into a residual LSTM (RLSTM). The LSTM, which learns temporal correlation, is used for a temporal learning of object tracking. In the proposed RLSTM method, the residual framework, which achieves the ... View full abstract»

• ### Beyond Frame-level CNN: Saliency-Aware 3-D CNN With LSTM for Video Action Recognition

Publication Year: 2017, Page(s):510 - 514
Cited by:  Papers (18)
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Human activity recognition in videos with convolutional neural network (CNN) features has received increasing attention in multimedia understanding. Taking videos as a sequence of frames, a new record was recently set on several benchmark datasets by feeding frame-level CNN sequence features to long short-term memory (LSTM) model for video activity recognition. This recurrent model-based visual re... View full abstract»

• ### Design of Massive-MIMO-NOMA With Limited Feedback

Publication Year: 2016, Page(s):629 - 633
Cited by:  Papers (44)
| | PDF (339 KB) | HTML

In this letter, a low-feedback nonorthogonal multiple access (NOMA) scheme using massive multiple-input multiple-output (MIMO) transmission is proposed. In particular, the proposed scheme can decompose a massive-MIMO-NOMA system into multiple separated single-input single-output (SISO) NOMA channels, and analytical results are developed to evaluate the performance of the proposed scheme for two sc... View full abstract»

• ### Enhancing Image Quality via Style Transfer for Single Image Super-Resolution

Publication Year: 2018, Page(s):571 - 575
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Recently, by feat of the Generative Adversarial Network (GAN), single image super-resolution (SISR) has achieved great breakthroughs in enhancing the perceptual image quality. However, since the network is trained by minimizing the perceptual loss, the GAN based SISR method (SRGAN) [1] results in images with very low objective quality, i.e., peak signal-to-noise ratio (PSNR). In this letter, we ai... View full abstract»

• ### Joint Cover-Selection and Payload-Allocation by Steganographic Distortion Optimization

Publication Year: 2018, Page(s):1530 - 1534
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This letter proposes a batch steganographic method, which combines cover-selection and payload-allocation by steganographic distortion optimization. We first proved that with the value of payload increasing, the first-order derivative of steganographic distortion of a single cover is monotonically increasing. Then, we deduced that the first-order derivative of steganographic distortion of covers t... View full abstract»

• ### DoA Reliability for Distributed Acoustic Tracking

Publication Year: 2018, Page(s):1320 - 1324
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Distributed acoustic tracking estimates the trajectories of source positions using an acoustic sensor network. As it is often difficult to estimate the source-sensor range from individual nodes, the source positions have to be inferred from the direction-of-arrival (DoA) estimates. Due to reverberation and noise, the sound field becomes increasingly diffuse with increasing source-sensor distance, ... View full abstract»

• ### Semisupervised and Weakly Supervised Road Detection Based on Generative Adversarial Networks

Publication Year: 2018, Page(s):551 - 555
| | PDF (542 KB) | HTML

Road detection is a key component of autonomous driving; however, most fully supervised learning road detection methods suffer from either insufficient training data or high costs of manual annotation. To overcome these problems, we propose a semisupervised learning (SSL) road detection method based on generative adversarial networks (GANs) and a weakly supervised learning (WSL) method based on co... View full abstract»

• ### Dynamic Hand Gesture Recognition With Leap Motion Controller

Publication Year: 2016, Page(s):1188 - 1192
Cited by:  Papers (35)
| | PDF (492 KB) | HTML

Dynamic hand gesture recognition is a crucial but challenging task in the pattern recognition and computer vision communities. In this paper, we propose a novel feature vector which is suitable for representing dynamic hand gestures, and presents a satisfactory solution to recognizing dynamic hand gestures with a Leap Motion controller (LMC) only. These have not been reported in other papers. The ... View full abstract»

• ### Deep Coupled ResNet for Low-Resolution Face Recognition

Publication Year: 2018, Page(s):526 - 530
Cited by:  Papers (1)
| | PDF (239 KB) | HTML

Face images captured by surveillance cameras are often of low resolution (LR), which adversely affects the performance of their matching with high-resolution (HR) gallery images. Existing methods including super resolution, coupled mappings (CMs), multidimensional scaling, and convolutional neural network yield only modest performance. In this letter, we propose the deep coupled ResNet (DCR) model... View full abstract»

• ### An Experimental Study on Speech Enhancement Based on Deep Neural Networks

Publication Year: 2014, Page(s):65 - 68
Cited by:  Papers (203)
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This letter presents a regression-based speech enhancement framework using deep neural networks (DNNs) with a multiple-layer deep architecture. In the DNN learning process, a large training set ensures a powerful modeling capability to estimate the complicated nonlinear mapping from observed noisy speech to desired clean signals. Acoustic context was found to improve the continuity of speech to be... View full abstract»

• ### Distributed Adaptive Filtering of$\alpha$-Stable Signals

Publication Year: 2018, Page(s):1450 - 1454
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A cost-effective framework for distributed adaptive filtering of α-stable signals over sensor networks is proposed. First, the filtering paradigm of α-stable signals through multiple observations made over a network of sensors is revisited and an optimal solution is formulated. Then, an adaptive gradient descent based algorithm for distributed real-time filtering of α-stable signals via multiagent... View full abstract»

• ### Instrumental Variable Based Kalman Filter Algorithm for Three-Dimensional AOA Target Tracking

Publication Year: 2018, Page(s):1605 - 1609
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This letter presents a new three-dimensional (3-D) instrumental variable based Kalman filter (3D-IVKF) algorithm for angle-of-arrival target tracking from azimuth and elevation angle measurements. First, a 3-D pseudolinear Kalman filter (KF) algorithm is derived by applying the classical linear KF to a pseudolinear state-space model. To counter the severe bias problems with this algorithm, bias co... View full abstract»

• ### Fast and Adaptive Empirical Mode Decomposition for Multidimensional, Multivariate Signals

Publication Year: 2018, Page(s):1550 - 1554
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Over the last decade, empirical mode decomposition (EMD) has developed into a versatile tool for adaptive, scale-based modal decomposition. EMD has proven to be capable of decomposing multivariate signals with cross-channel mode alignment. However, the algorithms for envelope identification in multivariate EMD come with a computational burden rendering it unsuitable for the large computational dem... View full abstract»

• ### Optimized Fourier Bilateral Filtering

Publication Year: 2018, Page(s):1555 - 1559
Cited by:  Papers (1)
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We consider the problem of approximating a truncated Gaussian kernel using Fourier (trigonometric) functions. The computation-intensive bilateral filter can be expressed using fast convolutions by applying such an approximation to its range kernel, where the truncation in question is the dynamic range of the input image. The error from such an approximation depends on the period, the number of sin... View full abstract»

• ### Additive Margin Softmax for Face Verification

Publication Year: 2018, Page(s):926 - 930
Cited by:  Papers (2)
| | PDF (578 KB) | HTML

In this letter, we propose a conceptually simple and intuitive learning objective function, i.e., additive margin softmax, for face verification. In general, face verification tasks can be viewed as metric learning problems, even though lots of face verification models are trained in classification schemes. It is possible when a large-margin strategy is introduced into the classification model to ... View full abstract»

• ### Thermal to Visible Facial Image Translation Using Generative Adversarial Networks

Publication Year: 2018, Page(s):1161 - 1165
| | PDF (518 KB) | HTML

Thermal cameras can capture images invariant to illumination conditions. However, thermal facial images are difficult to be recognized by human examiners. In this letter, an end-to-end framework, which consists of a generative network and a detector network, is proposed to translate thermal facial images into visible ones. The generative network aims at generating visible images given the thermal ... View full abstract»

• ### Local Pairwise Linear Discriminant Analysis for Speaker Verification

Publication Year: 2018, Page(s):1575 - 1579
| | PDF (1755 KB) | HTML

Linear discriminant analysis-probabilistic linear discriminant analysis (LDA-PLDA) is a standard and effective backend in the field of speaker verification. The object of LDA is to perform dimensionality reduction while minimizing within-class covariance and maximizing between-class covariance. For a target class (or speaker), our task is to make a binary decision about whether a test utterance is... View full abstract»

• ### Image Superresolution Using Densely Connected Residual Networks

Publication Year: 2018, Page(s):1565 - 1569
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Recently, convolutional neural networks (CNN) have achieved impressive breakthroughs in single image superresolution. In particular, an efficient nonlinear mapping by increasing the depth and width of the network can be learned between the low-resolution input image and the high-resolution target image. However, this will lead to a substantial increase in network parameters, requiring the massive ... View full abstract»

• ### A Nonquadratic Algorithm Based on the Extended Recursive Least-Squares Algorithm

Publication Year: 2018, Page(s):1535 - 1539
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In adaptiveg filters, several recursive algorithms have been used to track state-space model vectors in nonstationary environments. So far, kernel recursive algorithms showed the best results in this regard. With this letter, we aim to propose an algorithm based on a nonlinear function of the error, motivated by the extended recursive least-squares algorithm. Simulations were performed on the prob... View full abstract»

• ### Nonlocality-Reinforced Convolutional Neural Networks for Image Denoising

Publication Year: 2018, Page(s):1216 - 1220
| | PDF (983 KB) | HTML

We introduce a paradigm for nonlocal sparsity reinforced deep convolutional neural network denoising. It is a combination of a local multiscale denoising by a convolutional neural network (CNN) based denoiser and a nonlocal denoising based on a nonlocal filter (NLF), exploiting the mutual similarities between groups of patches. CNN models are leveraged with noise levels that progressively decrease... View full abstract»

• ### Unsupervised Speech Activity Detection Using Voicing Measures and Perceptual Spectral Flux

Publication Year: 2013, Page(s):197 - 200
Cited by:  Papers (61)
| | PDF (697 KB) | HTML

Effective speech activity detection (SAD) is a necessary first step for robust speech applications. In this letter, we propose a robust and unsupervised SAD solution that leverages four different speech voicing measures combined with a perceptual spectral flux feature, for audio-based surveillance and monitoring applications. Effectiveness of the proposed technique is evaluated and compared agains... View full abstract»

• ### From Local Geometry to Global Structure: Learning Latent Subspace for Low-resolution Face Image Recognition

Publication Year: 2015, Page(s):554 - 558
Cited by:  Papers (16)
| | PDF (948 KB) | HTML

In this letter, we propose a novel approach for learning coupled mappings to improve the performance of low-resolution (LR) face image recognition. The coupled mappings aim to project the LR probe images and high-resolution (HR) gallery images into a unified latent subspace, which is efficient to measure the similarity of face images with different resolutions. In the training phase, we first cons... View full abstract»

• ### Ultrasound Image Enhancement Using Structure Oriented Adversarial Network

Publication Year: 2018, Page(s):1349 - 1353
| | PDF (631 KB) | HTML Media

In this letter, we aim to develop a deep adversarial despeckling approach to enhance the quality of ultrasound images. Most of the existing approaches target a complete removal of speckle, which produces oversmooth outputs and results in loss of structural details. In contrast, the proposed approach reduces the speckle extent without altering the structural and qualitative attributes of the ultras... View full abstract»

• ### Bobrovsky–Zakai-Type Bound for Periodic Stochastic Filtering

Publication Year: 2018, Page(s):1460 - 1464
| | PDF (296 KB) | HTML Media

Mean-squared-error (MSE) lower bounds are commonly used for performance analysis and system design. Recursive algorithms have been derived for computation of Bayesian bounds in stochastic filtering problems. In this letter, we consider stochastic filtering with a mixture of periodic and nonperiodic states. For periodic states, the modulo- T estimation error is of interest and the MSE lower bounds ... View full abstract»

• ### DeepPano: Deep Panoramic Representation for 3-D Shape Recognition

Publication Year: 2015, Page(s):2339 - 2343
Cited by:  Papers (64)
| | PDF (898 KB) | HTML

This letter introduces a robust representation of 3-D shapes, named DeepPano, learned with deep convolutional neural networks (CNN). Firstly, each 3-D shape is converted into a panoramic view, namely a cylinder projection around its principle axis. Then, a variant of CNN is specifically designed for learning the deep representations directly from such views. Different from typical CNN, a row-wise ... View full abstract»

• ### Local AM/FM Parameters Estimation: Application to Sinusoidal Modeling and Blind Audio Source Separation

Publication Year: 2018, Page(s):1600 - 1604
| | PDF (412 KB) | HTML

This letter extends our recently introduced method which was designed to estimate instantaneous frequency and chirp rate of linearly modulated signals. Indeed, we derive several new estimators related to our previous ones which provide in the time-frequency plane all the signal parameters of the investigated model: amplitude, frequency, and their local modulations (AM/FM). Our estimators are first... View full abstract»

• ### Virtual Background Reference Frame Based Satellite Video Coding

Publication Year: 2018, Page(s):1445 - 1449
| | PDF (2460 KB) | HTML

Video transmission from satellites to terrestrial devices usually requires a large amount of channel resources due to the huge amount of satellite video data. Subject to limited transmission bandwidth in space environment, the video encoder for video satellites calls for higher coding efficiency. In this paper, we propose a high efficiency satellite video compression method to eliminate long-term ... View full abstract»

• ### A Novel Observability Gramian-Based Fast Covariance Intersection Rule

Publication Year: 2018, Page(s):1570 - 1574
| | PDF (464 KB) | HTML

In this letter, a new type of fast covariance intersection (CI) rule to deal with unknown correlations is proposed. Different from the existing CI and its variants, our approach can obtain the optimized CI weights offline while preserving a guaranteed filtering accuracy and stability in the online implementation stage. To this end, the connection between the upper bound of the fused error covarian... View full abstract»

• ### Fast Intra Coding of High Dynamic Range Videos in SHVC

Publication Year: 2018, Page(s):1665 - 1669
| | PDF (994 KB) | HTML

Compared with the conventional standard dynamic range (SDR) content, high dynamic range (HDR) content supplies viewers with more immersive experience by offering a much higher range of luminance. Most of current consumer devices cannot afford to this emerging technology, and content providers decide to create both an HDR version and an SDR version of the same video. In this letter, scalable high e... View full abstract»

## Aims & Scope

The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing.

Full Aims & Scope

## Meet Our Editors

Editor-in-Chief
James E. Fowler
Dept Electrical & Computer Engineering
Associate Director
Distributed Analytics and Security Institute
Mississippi State University
Mississippi State, MS 39762 USA
fowler@ece.msstate.edu