
显示样式: 排序: IF: - GO 导出
-
Maximum likelihood-based adaptive iteration algorithm design for joint CFO and channel estimation in MIMO-OFDM systems EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2021-01-13 Nan-Hung Cheng, Kai-Chieh Huang, Yung-Fang Chen, Shu-Ming Tseng
In this paper, we present a joint time-variant carrier frequency offset (CFO) and frequency-selective channel response estimation scheme for multiple input-multiple output-orthogonal frequency-division multiplexing (MIMO-OFDM) systems for mobile users. The signal model of the MIMO-OFDM system is introduced, and the joint estimator is derived according to the maximum likelihood criterion. The proposed
-
Noise-robust range alignment method for inverse synthetic aperture radar based on aperture segmentation and average range profile correlation EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2021-01-11 Yue Lu, Jian Yang, Yue Zhang, Shiyou Xu
Range alignment is an essential procedure in the translation motion compensation of inverse synthetic aperture radar imaging. Global optimization or maximum-correlation-based algorithms have been used to realize range alignment. However, it is still challenging to achieve range alignment in low signal-to-noise ratio scenarios, which are common in inverse synthetic aperture radar imaging. In this paper
-
Heart rate tracking in photoplethysmography signals affected by motion artifacts: a review EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2021-01-11 Shahid Ismail, Usman Akram, Imran Siddiqi
Non-invasive photoplethysmography (PPG) technology was developed to track heart rate during motion. Automated analysis of PPG has made it useful in both clinical and non-clinical applications. However, PPG-based heart rate tracking is a challenging problem due to motion artifacts (MAs) which are main contributors towards signal degradation as they mask the location of heart rate peak in the spectra
-
The hybrid Cramér-Rao lower bound for simultaneous self-localization and room geometry estimation EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2021-01-07 Maya Veisman, Yair Noam, Sharon Gannot
This paper addresses the problem of tracking a moving source, e.g., a robot, equipped with both receivers and a source, that is tracking its own location and simultaneously estimating the locations of multiple plane reflectors. We assume a noisy knowledge of the robot’s movement. We formulate this problem, which is also known as simultaneous localization and mapping (SLAM), as a hybrid estimation problem
-
Long-term target tracking combined with re-detection EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2021-01-06 Juanjuan Wang, Haoran Yang, Ning Xu, Chengqin Wu, Zengshun Zhao, Jixiang Zhang, Dapeng Oliver Wu
Long-term visual tracking undergoes more challenges and is closer to realistic applications than short-term tracking. However, the performances of most existing methods have been limited in the long-term tracking tasks. In this work, we present a reliable yet simple long-term tracking method, which extends the state-of-the-art learning adaptive discriminative correlation filters (LADCF) tracking algorithm
-
A covariance matrix-based spectrum sensing technology exploiting stochastic resonance and filters EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2021-01-06 Jin Lu, Ming Huang, Jingjing Yang
Cognitive radio (CR) is designed to implement dynamical spectrum sharing and reduce the negative effect of spectrum scarcity caused by the exponential increase in the number of wireless devices. CR requires that spectrum sensing should detect licenced signals quickly and accurately and enable coexistence between primary and secondary users without interference. However, spectrum sensing with a low
-
Speech enhancement by LSTM-based noise suppression followed by CNN-based speech restoration EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-12-10 Maximilian Strake, Bruno Defraene, Kristoff Fluyt, Wouter Tirry, Tim Fingscheidt
Single-channel speech enhancement in highly non-stationary noise conditions is a very challenging task, especially when interfering speech is included in the noise. Deep learning-based approaches have notably improved the performance of speech enhancement algorithms under such conditions, but still introduce speech distortions if strong noise suppression shall be achieved. We propose to address this
-
A low-area high-efficiency video coding inverse transform core using resource and time sharing architecture EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-11-30 Yuan-Ho Chen, Chieh-Yang Liu
In this paper, a very-large-scale integration (VLSI) design that can support high-efficiency video coding inverse discrete cosine transform (IDCT) for multiple transform sizes is proposed. The proposed two-dimensional (2-D) IDCT is implemented at a low area by using a single one-dimensional (1-D) IDCT core with a transpose memory. The proposed 1-D IDCT core decomposes a 32-point transform into 16-
-
T-S fuzzy systems optimization identification based on FCM and PSO EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-11-24 Yaxue Ren, Fucai Liu, Jinfeng Lv, Aiwen Meng, Yintang Wen
The division of fuzzy space is very important in the identification of premise parameters, and the Gaussian membership function is applied to the premise fuzzy set. However, the two parameters of Gaussian membership function, center and width, are not easy to be determined. In this paper, based on Fuzzy c-means (FCM) and particle swarm optimization (PSO) algorithm, a novel T-S fuzzy model optimal identification
-
Consistent independent low-rank matrix analysis for determined blind source separation EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-11-16 Daichi Kitamura, Kohei Yatabe
Independent low-rank matrix analysis (ILRMA) is the state-of-the-art algorithm for blind source separation (BSS) in the determined situation (the number of microphones is greater than or equal to that of source signals). ILRMA achieves a great separation performance by modeling the power spectrograms of the source signals via the nonnegative matrix factorization (NMF). Such a highly developed source
-
A robust LU polynomial matrix decomposition for spatial multiplexing EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-11-10 Moustapha Mbaye, Moussa Diallo, Mamadou Mboup
This paper considers time-domain spatial multiplexing in MIMO wideband system, using an LU-based polynomial matrix decomposition. Because the corresponding pre- and post-filters are not paraunitary, the noise output power is amplified and the performance of the system is degraded, compared to QR-based spatial multiplexing approach. Degradations are important as the post-filter polynomial matrix is
-
Gridless DOA estimation with finite rate of innovation reconstruction based on symmetric Toeplitz covariance matrix EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-10-28 Tao Chen, Lin Shi, Yongzhi Yu
Due to the rapid development and wide application of compressed sensing and sparse reconstruction theory, there exists a series of sparsity-based methods for the antenna sensor array direction of arrival (DOA) estimation with excellent performance. However, it is known that this kind of algorithms always suffers from the problem of grid mismatch. To overcome this shortcoming, a gridless DOA estimation
-
A muscle synergies-based movements detection approach for recognition of the wrist movements EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-10-21 Aida Masoumdoost, Reza Saadatyar, Hamid Reza Kobravi
Myoelectric signals are regarded as the control signal for prosthetic limbs. But, the main research challenge is reliable and repeatable movement detection using electromyography. In this study, the analysis of the muscle synergy pattern has been considered as a key idea to cope with this main challenge. The main objective of this research was to provide an analytical tool to recognize six wrist movements
-
The upper bound of multi-source DOA information in sensor array and its application in performance evaluation EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-09-22 Weilin Tu, Dazhuan Xu, Ying Zhou, Chao Shi
Direction of arrival (DOA) estimation has been discussed extensively in the array signal processing field. In this paper, the authors focus on the multi-source DOA information which is defined as the mutual information between the DOA and the received signal contaminated by complex additive white Gaussian noise. A theoretical expression of DOA information with multiple sources is derived for the uniform
-
Tensor recovery from noisy and multi-level quantized measurements EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-09-14 Ren Wang, Meng Wang, Jinjun Xiong
Higher-order tensors can represent scores in a rating system, frames in a video, and images of the same subject. In practice, the measurements are often highly quantized due to the sampling strategies or the quality of devices. Existing works on tensor recovery have focused on data losses and random noises. Only a few works consider tensor recovery from quantized measurements but are restricted to
-
High-dimensional neural feature design for layer-wise reduction of training cost EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-09-10 Alireza M. Javid, Arun Venkitaraman, Mikael Skoglund, Saikat Chatterjee
We design a rectified linear unit-based multilayer neural network by mapping the feature vectors to a higher dimensional space in every layer. We design the weight matrices in every layer to ensure a reduction of the training cost as the number of layers increases. Linear projection to the target in the higher dimensional space leads to a lower training cost if a convex cost is minimized. An ℓ2-norm
-
Orthogonal approach to independent component analysis using quaternionic factorization EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-09-04 Adam Borowicz
Independent component analysis (ICA) is a popular technique for demixing multichannel data. The performance of a typical ICA algorithm strongly depends on the presence of additive noise, the actual distribution of source signals, and the estimated number of non-Gaussian components. Often, a linear mixing model is assumed and source signals are extracted by data whitening followed by a sequence of plane
-
A novel timing and frequency offset estimation algorithm for filtered OFDM system EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-09-01 Xing-le Feng, Meng-jie Wang, Li Chen, Wen-xia Zhu, Kun Hua
As a critical technology of 5G air interface waveform, filtered orthogonal frequency division multiplexing (F-OFDM) not only inherits the technical advantages of OFDM, but also has outstanding advantages in system flexibility and spectrum efficiency. However, as a multi-carrier technology, it is still extremely sensitive to sample timing offset (STO) and carrier frequency offset (CFO). In this letter
-
A recognition algorithm of VGPO jamming based on discrete chirp-Fourier transform EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-08-17 Chuanzhang Wu, Baixiao Chen
This paper addresses the recognition problem of velocity gate pull-off (VGPO) jamming from the target echo signal for the velocity-based tracking system. The discrete chirp-Fourier transform (DCFT) is studied in this paper to jointly estimate the chirp rates and frequencies of the target and jamming signals. Firstly, the scaling characteristic of the DCFT algorithm is explored. Then, we focus on the
-
A wavelet denoising approach based on unsupervised learning model EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-07-23 Khawla Bnou, Said Raghay, Abdelilah Hakim
Image denoising plays an important role in image processing, which aims to separate clean images from noisy images. A number of methods have been presented to deal with this practical problem over the past several years. The best currently available wavelet-based denoising methods take advantage of the merits of the wavelet transform. Most of these methods, however, still have difficulties in defining
-
Using the complement of the cosine to compute trigonometric functions EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-07-22 David Guerrero Martos, Alejandro Millán Calderón, Jorge Juan Chico, Julian Viejo Cortés, Manuel J. Bellido Díaz, Paulino Ruiz-de-Clavijo Vazquez, Enrique Ostúa Arangüena
The computation of the sine and cosine functions is required in devices ranging from application-specific signal processors to general purpose floating-point units. Even in the latter case, the required functionality can be reduced to computing the sine and/or cosine of multiples of a constant angle. The latency of a sine/cosine generator can be reduced by using look-up tables. However, a direct implementation
-
Elliptic shape prior for object 2D-3D pose estimation using circular feature EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-07-17 Cui Li, Derong Chen, Jiulu Gong, Yangyu Wu
Many objects in real world have circular feature. It is a difficult task to obtain the 2D-3D pose estimation using circular feature in challenging scenarios. This paper proposes a method to incorporate elliptic shape prior for object pose estimation using a level set method. The relationship between the projection of the circular feature of a 3D object and the signed distance function corresponding
-
Salient context-based semantic matching for information retrieval EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-07-11 Yuanyuan Qi, Jiayue Zhang, Weiran Xu, Jun Guo
Neural networks provide new possibilities to uncover semantic relationships between words by involving contextual information, and further a way to learn the matching pattern from document-query word contextual similarity matrix, which has brought promising results in IR. However, most neural IR methods rely on the conventional word-word matching framework for finding a relevant document for a query
-
The ill-posedness of derivative interpolation and regularized derivative interpolation for band-limited functions by sampling EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-07-06 Weidong Chen
In this paper, the ill-posedness of derivative interpolation is discussed, and a regularized derivative interpolation for band-limited signals is presented. The ill-posedness is analyzed by the Shannon sampling theorem. The convergence of the regularized derivative interpolation is studied by the combination of a regularized Fourier transform and the Shannon sampling theorem. The error estimation is
-
Sequence set design for waveform-agile coherent radar systems EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-07-02 Jindong Zhang, Naiqing Xu, Hu Song, Chao Zhang
With increased degrees of freedom of the transmitter, coherent waveform-agile radar system can change its transmission on-the-fly in response to target detection’s requirement. This approach can provide better performance than a single waveform. In this paper, we consider unimodular sequence set design (USSD) problem based on summed range-Doppler ambiguity function (SRDAF) for coherent waveform-agile
-
Ensemble patch transformation: a flexible framework for decomposition and filtering of signal EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-06-26 Donghoh Kim, Guebin Choi, Hee-Seok Oh
This paper considers the problem of signal decomposition and filtering by extending its scope to various signals that cannot be effectively dealt with existing methods. For the core of our methodology, we introduce a new approach, termed “ensemble patch transformation” that provides a framework for decomposition and filtering of signals; thus, as a result, it enhances identification of local characteristics
-
Singular spectrum analysis-based image sub-band decomposition filter banks EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-06-17 Julia Evers, Florian Evers, Florian Goppelt, Ronald Schmidt-Vollus
This paper presents a novel type of a filter bank for image decomposition that incorporates “singular spectrum analysis” (SSA). SSA is based on “singular value decomposition” (SVD).The presented filter banks provide good directional selectivity. Sub-band images highlight exposed directional features such as 0∘,90∘,+ 45∘, and − 45∘ of the input image. The proposed method can be seen as a tool to separate
-
Velocity-independent and low-complexity method for 1D DOA estimation using an arbitrary cross-linear array EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-06-16 Gengxin Ning, Guangyu Jing, Xiaopeng Li, Xuejin Zhao
This paper focuses on a low-complexity one-dimensional (1D) direction-of-arrival (DOA) algorithm with an arbitrary cross-linear array. This algorithm is highly accurate without the performance error usually caused by the uncertainty factor of the wave velocity in the underwater environment. The geometric relationship between two crossed linear arrays is employed to derive the expression of DOA estimation
-
Vision-based patient identification recognition based on image content analysis and support vector machine for medical information system EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-05-29 Guo-Shiang Lin, Sin-Kuo Chai, Hsiang-Min Li, Jen-Yung Lin
In this paper, a vision-based patient identification recognition system based on image content analysis and support vector machine is proposed for medical information system, especially in dermatology. This proposed system is composed of three parts: pre-processing, candidate region detection, and digit recognition. To consider the efficiency of the proposed scheme, image normalization is performed
-
DeConFuse: a deep convolutional transform-based unsupervised fusion framework EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-05-29 Pooja Gupta, Jyoti Maggu, Angshul Majumdar, Emilie Chouzenoux, Giovanni Chierchia
This work proposes an unsupervised fusion framework based on deep convolutional transform learning. The great learning ability of convolutional filters for data analysis is well acknowledged. The success of convolutive features owes to the convolutional neural network (CNN). However, CNN cannot perform learning tasks in an unsupervised fashion. In a recent work, we show that such shortcoming can be
-
A survey of Monte Carlo methods for parameter estimation EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-05-29 David Luengo, Luca Martino, Mónica Bugallo, Víctor Elvira, Simo Särkkä
Statistical signal processing applications usually require the estimation of some parameters of interest given a set of observed data. These estimates are typically obtained either by solving a multi-variate optimization problem, as in the maximum likelihood (ML) or maximum a posteriori (MAP) estimators, or by performing a multi-dimensional integration, as in the minimum mean squared error (MMSE) estimators
-
Fundamental limits of single anchor-based cooperative localization in millimeter wave systems EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-05-19 Feng Zhao, Tiancheng Huang, Donglin Wang
Thanks to massive antenna arrays in millimeter wave communications, a much higher resolution can be achieved for the estimation of angle of arrival. By taking full advantage of beamforming capability and high angular resolution in millimeter wave systems, a single anchor is sufficient to obtain a good position estimation for agents by combining angle of arrival and time of arrival. In this paper, a
-
Constrained expectation maximisation algorithm for estimating ARMA models in state space representation EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-05-11 Andreas Galka, Sidratul Moontaha, Michael Siniatchkin
This paper discusses the fitting of linear state space models to given multivariate time series in the presence of constraints imposed on the four main parameter matrices of these models. Constraints arise partly from the assumption that the models have a block-diagonal structure, with each block corresponding to an ARMA process, that allows the reconstruction of independent source components from
-
Preconditioned generalized orthogonal matching pursuit EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-05-07 Zhishen Tong, Feng Wang, Chenyu Hu, Jian Wang, Shensheng Han
Recently, compressed sensing (CS) has aroused much attention for that sparse signals can be retrieved from a small set of linear samples. Algorithms for CS reconstruction can be roughly classified into two categories: (1) optimization-based algorithms and (2) greedy search ones. In this paper, we propose an algorithm called the preconditioned generalized orthogonal matching pursuit (Pre-gOMP) to promote
-
Adaptive dictionary learning based on local configuration pattern for face recognition EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-05-07 Dongmei Wei, Tao Chen, Shuwei Li, Dongmei Jiang, Yuefeng Zhao, Tianping Li
Sparse representation based on classification and collaborative representation based classification with regularized least square has been successfully used in face recognition. The over-completed dictionary is crucial for the approaches based on sparse representation or collaborative representation because it directly determines recognition accuracy and recognition time. In this paper, we proposed
-
Achieve data privacy and clustering accuracy simultaneously through quantized data recovery EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-05-07 Ren Wang, Meng Wang, Jinjun Xiong
This paper develops a data collection and processing framework that achieves individual users’ data privacy and the operator’s information accuracy simultaneously. Data privacy is enhanced by adding noise and applying quantization to the data before transmission, and the privacy of an individual user is measured by information-theoretic analysis. This paper develops a data recovery and clustering method
-
Time-frequency feature extraction for classification of episodic memory EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-05-01 Rachele Anderson, Maria Sandsten
This paper investigates the extraction of time-frequency (TF) features for classification of electroencephalography (EEG) signals and episodic memory. We propose a model based on the definition of locally stationary processes (LSPs), estimate the model parameters, and derive a mean square error (MSE) optimal Wigner-Ville spectrum (WVS) estimator for the signals. The estimator is compared with state-of-the-art
-
A novel approach to extracting useful information from noisy TFDs using 2D local entropy measures EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-04-25 Ana Vranković, Jonatan Lerga, Nicoletta Saulig
The paper proposes a novel approach for extraction of useful information and blind source separation of signal components from noisy data in the time-frequency domain. The method is based on the local Rényi entropy calculated inside adaptive, data-driven 2D regions, the sizes of which are calculated utilizing the improved, relative intersection of confidence intervals (RICI) algorithm. One of the advantages
-
Model set adaptive filtering algorithm using variational Bayesian approximations and Rényi information divergence EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-04-20 Tianli Ma, ChaoBo Chen, Song Gao
The paper presents a model set adaptive filtering algorithm based on variational Bayesian approximation (MSA-VB) for the target tracking system with the model and noise uncertainties. The Rényi information divergence, as a criterion, is to choose the best match model that has the minimum divergence between candidate models and true mode. Subsequently, the model-conditioned estimation based on variational
-
Multi-task learning for abstractive text summarization with key information guide network EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-04-19 Weiran Xu, Chenliang Li, Minghao Lee, Chi Zhang
Neural networks based on the attentional encoder-decoder model have good capability in abstractive text summarization. However, these models are hard to be controlled in the process of generation, which leads to a lack of key information. And some key information, such as time, place, and people, is indispensable for humans to understand the main content. In this paper, we propose a key information
-
Regularized supervised Bayesian approach for image deconvolution with regularization parameter estimation EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-04-07 Bouchra Laaziri, Said Raghay, Abdelilah Hakim
Image deconvolution consists in restoring a blurred and noisy image knowing its point spread function (PSF). This inverse problem is ill-posed and needs prior information to obtain a satisfactory solution. Bayesian inference approach with appropriate prior on the image, in particular with a Gaussian prior, has been used successfully. Supervised Bayesian approach with maximum a posteriori (MAP) estimation
-
DFT-based low-complexity optimal cell ID estimation in NB-IoT EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-04-05 Vincent Savaux
This paper deals with cell identifier (ID) estimation for narrowband-Internet of things (NB-IoT) system. It is suggested to transform the usual maximum likelihood (ML) estimator expression in order to highlight a discrete Fourier transform (DFT), which can be computed with fast algorithms. Therefore, the proposed method is a DFT-based low-complexity cell ID estimator that can be qualified as optimal
-
Radon spectrogram-based approach for automatic IFs separation EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-03-26 Vittoria Bruni, Michela Tartaglione, Domenico Vitulano
The separation of overlapping components is a well-known and difficult problem in multicomponent signals analysis and it is shared by applications dealing with radar, biosonar, seismic, and audio signals. In order to estimate the instantaneous frequencies of a multicomponent signal, it is necessary to disentangle signal modes in a proper domain. Unfortunately, if signal modes supports overlap both
-
A variable step-size diffusion LMS algorithm with a quotient form EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-03-24 Muhammad Omer Bin Saeed, Azzedine Zerguine
A new variable step-size strategy for the least mean square (LMS) algorithm is presented for distributed estimation in adaptive networks using the diffusion scheme. This approach utilizes the ratio of filtered and windowed versions of the squared instantaneous error for iteratively updating the step-size. The result is that the dependence of the update on the power of the error is reduced. The performance
-
DFT codebook-based hybrid precoding for multiuser mmWave massive MIMO systems EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-03-05 Yu Huang, Chen Liu, Yunchao Song, Xiaolei Yu
In millimeter wave (mmWave) massive MIMO (multiple-input multiple-output) systems, it is difficult to apply conventional digital precoding techniques due to hardware constraints. Fortunately, the hybrid precoding can be utilized to reduce power consumption and high costs. In this paper, a codebook-based hybrid precoding scheme for downlink multiuser mmWave massive MIMO systems is proposed. Our main
-
A cooperative construction method for the measurement matrix and sensing dictionary used in compression sensing EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-03-04 Zhi Yuan Shen, Xin Miao Cheng, Qian Qian Wang
A measurement matrix and sensing dictionary are the basic tools for signal compression sampling and reconstruction, respectively, which are important aspects in the field of compression sensing. Previous studies which have divided the measurement matrix and sensing dictionary into two separate processes did not make full use of their inherent intercorrelations. In case of which could be fully utilized
-
Object contour tracking via adaptive data-driven kernel EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-02-28 Xin Sun, Wei Wang, Dong Li, Bin Zou, Hongxun Yao
We present a novel approach to non-rigid object tracking in this paper by deriving an adaptive data-driven kernel. In contrast with conventional kernel-based trackers which suffer from the constancy of kernel shape as well as scale and orientation selection problem when the tracking targets are changing in size, the adaptive kernel can robustly achieve the adaptation to target variation and act toward
-
Statistical test for GNSS spoofing attack detection by using multiple receivers on a rigid body EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-02-26 Ashkan Kalantari, Erik G. Larsson
Global navigation satellite systems (GNSS) are being the target of various jamming, spoofing, and meaconing attacks. This paper proposes a new statistical test for the presence of multiple spoofers based on range measurements observed by a plurality of receivers located on a rigid body platform. The relative positions of the receivers are known, but the location and orientation of the platform are
-
Adaptive filtering and analysis of EEG signals in the time-frequency domain based on the local entropy EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-02-24 Guruprasad Madhale Jadav, Jonatan Lerga, Ivan Štajduhar
The brain dynamics in the electroencephalogram (EEG) data are often challenging to interpret, specially when the signal is a combination of desired brain dynamics and noise. Thus, in an EEG signal, anything other than the desired electrical activity, which is produced due to coordinated electrochemical process, can be considered as unwanted or noise. To make brain dynamics more analyzable, it is necessary
-
Root tracking using time-varying autoregressive moving average models and sigma-point Kalman filters EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-02-18 Kyriaki Kostoglou, Michael Lunglmayr
Root tracking is a powerful technique that provides insight into the mechanisms of various time-varying processes. The poles and the zeros of a signal-generating system determine the spectral characteristics of the signal under consideration. In this work, time-frequency analysis is achieved by tracking the roots of time-varying processes using autoregressive moving average (ARMA) models in cascade
-
Random field-aided tracking of autonomous kinetically passive wireless agents EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-02-13 Stephan Schlupkothen, Tim Heidenblut, Gerd Ascheid
Continuous miniaturization of circuitry has open the door for various novel application scenarios of millimeter-sized wireless agents such as for the exploration of difficult-to-access fluid environments. In this context, agents are envisioned to be employed, e.g., for pipeline inspection or groundwater analysis. In either case, the demand for miniature sensors is incompatible with propulsion capabilities
-
Improved through-wall radar imaging using modified Green’s function-based multi-path exploitation method EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-02-12 Shuangshuang Wu, Huilin Zhou, Song Liu, Rongxing Duan
The existence of walls surrounding targets leads to multi-path return at the radar receiver, which provides additional information about the target, and thus can be exploited to strengthen the quality of through-wall radar imaging (TWRI). Based on this, a multi-path exploitation method is proposed to identify the location of the multi-path ghost. An algorithm that combining the modified Green’s function
-
Robust energy disaggregation using appliance-specific temporal contextual information EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-02-11 Pascal Alexander Schirmer, Iosif Mporas, Akbar Sheikh-Akbari
An extension of the baseline non-intrusive load monitoring approach for energy disaggregation using temporal contextual information is presented in this paper. In detail, the proposed approach uses a two-stage disaggregation methodology with appliance-specific temporal contextual information in order to capture time-varying power consumption patterns in low-frequency datasets. The proposed methodology
-
Non-convex block-sparse compressed sensing with coherent tight frames EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-01-23 Xiaohu Luo, Wanzhen Yang, Jincai Ha, Xing Ai, Xishan Tian
In this paper, we present a non-convex ℓ2/ℓq(0
-
Target detection and localization method for distributed monopulse arrays in the presence of mainlobe jamming EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2020-01-16 Qing Sun, Qiliang Zhang, Xueyu Huang, Qian Gao
In this paper, we propose a target detection and localization method on distributed monopulse arrays for tracking radar. An optimized mainlobe jamming (MLJ) cancellation filter was designed by maximizing the power ratio of the received siackgnal to the jamming-plus-noise. By exploiting the different correlation characteristics between the target echo and MLJ on distributed antennas, the designed filter
-
Evaluation of a class of NLFM radar signals EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2019-12-21 Sebastian Alphonse, Geoffrey A. Williamson
Signal design is an important component for good performance of radar systems. Here, the problem of determining a good radar signal with the objective of minimizing autocorrelation sidelobes is addressed, and the first comprehensive comparison of a range of signals proposed in the literature is conducted. The search is restricted to a set of nonlinear, frequency-modulated signals whose frequency function
-
An analog hardware solution for compressive sensing reconstruction using gradient-based method EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2019-12-17 Irena Orović, Nedjeljko Lekić, Marko Beko, Srdjan Stanković
This work proposes an analog implementation of gradient-based algorithm for compressive sensing signal reconstruction. Compressive sensing has appeared as a promising technique for efficient acquisition and reconstruction of sparse signals in many real-world applications. It starts from the assumption that sparse signals can be exactly reconstructed using far less samples than in standard signal processing
-
Community detection in networks: a game-theoretic framework EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2019-12-16 Yan Chen, Xuanyu Cao, K. J. Ray Liu
Real-world networks are often cluttered and hard to organize. Recent studies show that most networks have the community structure, i.e., nodes with similar attributes form a certain community, which enables people to better understand the constitution of the networks and thus gain more insights into the complicated networks. Strategic nodes belonging to different communities interact with each other
-
Feature extraction and classification of heart sound using 1D convolutional neural networks EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2019-12-12 Fen Li; Ming Liu; Yuejin Zhao; Lingqin Kong; Liquan Dong; Xiaohua Liu; Mei Hui
We proposed a one-dimensional convolutional neural network (CNN) model, which divides heart sound signals into normal and abnormal directly independent of ECG. The deep features of heart sounds were extracted by the denoising autoencoder (DAE) algorithm as the input feature of 1D CNN. The experimental results showed that the model using deep features has stronger anti-interference ability than using
-
DOA estimation of spectrally overlapped LFM signals based on STFT and Hough transform EURASIP J. Adv. Signal Process. (IF 1.14) Pub Date : 2019-12-05 Xiaofa Zhang; Weike Zhang; Ye Yuan; Kaibo Cui; Tao Xie; Naichang Yuan
Traditional subspace methods which are based on the spatial time-frequency distribution (STFD) matrix have been investigated for direction-of-arrival (DOA) estimation of linear frequency modulation (LFM) signals. However, the DOA estimation performance may degrade substantially when multiple LFM signals are spectrally overlapped in time-frequency (TF) domain. In order to solve this problem, this paper