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Atrial fibrillation (AF) is the most common arrhythmia, but an estimated 30% of patients with AF are unaware of their conditions. The purpose of this work is to design a model for AF screening from facial videos, with a focus on addressing typical motion disturbances in our real life, such as head movements and expression changes. This model detects a pulse signal from the skin color changes in a facial video by a convolution neural network, incorporating a phase-driven attention mechanism to suppress motion signals in the space domain. It then encodes the pulse signal into discriminative features for AF classification by a coding neural network, using a de-noise coding strategy to improve the robustness of the features to motion signals in the time domain. The proposed model was tested on a dataset containing 1200 samples of 100 AF patients and 100 non-AF subjects. Experimental results demonstrated that VidAF had significant robustness to facial motions, predicting clean pulse signals with the mean absolute error of inter-pulse intervals less than 100 milliseconds. Besides, the model achieved promising performance in AF identification, showing an accuracy of more than 90% in multiple challenging scenarios. VidAF provides a more convenient and cost-effective approach for opportunistic AF screening in the community. [ABSTRACT FROM AUTHOR]

Objective: Currently, BP measurement devices are mainly cuff-based which are not portable or convenient for users. To simplify the measurement of BP, this article proposed a new framework for noninvasive BP estimation using single-channel PPG signals. Methods: Various PPG features that may be related to BP were extracted and a filter-wrapper collaborated feature selection method was used for rejecting irrelevant and redundant features. The features that maximize the correlation with BP were finally selected as the BP-oriented IFS, and a new LASSO-LSTM model was designed to estimate BP from the IFS. Results: Experiments were conducted on a public dataset and a self-collected clinical dataset, respectively. Results demonstrated that the proposed method is superior to previously reported methods in the literature, giving a mean absolute error of 4.95 mmHg for SBP and 3.15 mmHg for DBP which complies with the standard of the AAMI. Conclusion: The proposed filter-wrapper collaborated feature selection method could effectively reject weak correlation and redundant features, and the designed LASSO-LSTM model is capable of learning complicated nonlinear relations between the selected IFS and BP. The proposed method shows improved accuracy of noninvasive BP estimation. [ABSTRACT FROM AUTHOR]

Recent studies have shown that changes in human facial skin color due to varying blood flows can be recorded on videos by consumer-level cameras and, interestingly, can be used to measure pulse rates in a noncontact manner. However, it remains unclear whether the video-based pulse rate detection is feasible when videos are recorded in unstable lighting conditions. To address unstable illumination, a novel algorithm named adaptive spatiotemporal homomorphic filtering (ASTHF) is proposed in this article, with a focus on the separation of pulse signals and illumination-induced noise. This method first employs the homomorphic filtering framework to make illumination-induced noise in a video linearly separable. Subsequently, it incorporates a spatiotemporal filter into the framework, aiming to remove illumination-induced noise from both the time domain and the space domain of the video. To adapt to diverse illumination in the real world, the filter parameters are determined adaptively according to the sparsity in frequency spectrum of pulse signals. ASTHF is tested on two data sets, including 406 video samples. The test results show that ASTHF has stronger robustness to unstable illumination than state-of-the-art methods, improving the Pearson’s correlation coefficient by 5% in a challenging scenario compared with the best contrasting method. The proposed method demonstrates promise to achieve noncontact pulse rate detection from facial videos involving unsteady illumination. [ABSTRACT FROM AUTHOR]

Objective: Modern consumer-level cameras can detect subtle changes in human facial skin color due to varying blood flow; they are beginning to be used as noncontact devices to detect pulse waves. Little, however, do we know about their capacity to perform pulse wave detection when the recorded faces are unstable. Methods: Here, we propose a novel method that can extract pulse waves from videos with drastic facial unsteadiness such as head twists and alternating expressions. The method first uses chrominance characteristics in multiple facial sub-regions to construct a raw pulse matrix. Subsequently, it employs a disturbance-adaptive orthogonal matching pursuit (DAOMP) algorithm to recover the underlying pulse matrix corrupted by facial unsteadiness. Results: To evaluate the efficacy of the method, we perform analyses on two datasets including 268 samples from 67 testing subjects. The results demonstrate that the proposed method outperforms state-of-the-art algorithms, especially in the terrain where drastic facial unsteadiness is present. Conclusion: The proposed framework shows promise to achieve videos-based noncontact pulse wave detection from both steady and unsteady faces recorded by consumer-level cameras. Significance: By employing the proposed method, disturbance robustness in noncontact pulse wave detection can be significantly improved. [ABSTRACT FROM AUTHOR]

This paper proposes an outliers-robust constant false-alarm rate (OR-CFAR) detector of Gaussian clutter based on the truncated-maximum-likelihood estimator (TMLE) in SAR imagery. The proposed method aims at elevating the detection performance in multiple-target environment, where the sea clutter samples are often contaminated by the interfering target pixels, the azimuth ambiguities, and the breakwater. As a consequence, the parameters used for statistical modeling are over-estimated, resulting in a degradation of the CFAR detection rate. Inspired by the traditional two-parameter CFAR (TP-CFAR) detector of Gaussian clutter, OR-CFAR designs an adaptive threshold-based clutter truncation method to eliminate the high-intensity outliers from the clutter samples in the local reference window, and the probability density function (PDF) of the sea clutter can be accurately modeled through the newly raised TMLE. Furthermore, the optimal truncation depth used for clutter truncation and PDF modeling is evaluated and selected properly to get the best detection results. The OR-CFAR greatly enhances the CFAR detection rate in multiple-target environment, and it is computationally simple and efficient, which has a great application value. The Chinese Gaofen-3 SAR data are used for experiments to show the better detection performance of OR-CFAR. [ABSTRACT FROM AUTHOR]

Traditional constant false alarm rate (CFAR) detectors suffer probability of detection (PD) degradation influenced by the outliers such as interfering ship targets, side lobes, and ghosts, especially in crowded harbors and busy shipping lines. In this paper, a new two-parameter CFAR detector based on adaptively truncated clutter statistics (TS-LNCFAR) is proposed. The new two-parameter CFAR detector uses log-normal as the statistical model; by adaptive-threshold-based clutter truncation in the background window, the outliers are removed from the clutter samples, while the real clutter is preserved to the largest degree. The log-normal model is accurately built using the truncated clutter statistics through the maximum-likelihood estimator. Compared with traditional CFAR detectors, the parameter estimation is more accurate, and TS-LNCFAR has a better false alarm regulation property and a high PD in a multiple-target environment. Furthermore, the parameter estimation and threshold calculation do not need iterative numerical calculation, and TS-LNCFAR has a high computational efficiency. The superiority of the proposed TS-LNCFAR detector is validated on the multilook Envisat-ASAR and TerraSAR-X data. [ABSTRACT FROM PUBLISHER]

Radar backscatter variations that occur because of incidence angle effects constrain the application of Scanning Synthetic Aperture Radar (ScanSAR) data for sea ice monitoring and observations. In this paper, a class-based correction is proposed for normalizing each class in ScanSAR data to a nominal incidence angle. Two tested sea ice synthetic aperture radar (SAR) data sets were acquired: a data set for the Gulf of Saint Lawrence, which was obtained by the RADARSAT-2 satellite, and a data set for the Bohai Sea, which was obtained by the ENVISAT Advanced Synthetic Aperture Radar. An unsupervised classification is performed on each image block prior to normalization, and the incidence angle range of each image block is approximately 5°. Because the distribution of the backscatter coefficients in the azimuth band is discrete and nonlinear, the class-based locally linear mapping (LLM) technique is implemented, based on the assumption that a small quantity of sorted backscatter coefficients is locally linear. This algorithm is a transplantable and easily applied method that requires limited ground data, and it is also a semiautomated technique because nearly all of its parameters can be adaptively determined during the image analysis. The results demonstrate that LLM-corrected ScanSAR images appear to have more detailed textures, and the natural signal variability in the radar data is preserved, which indicates that the LLM produces better results compared with the histogram-based-alike (HIST-alike) technique when correcting the incidence angle in the sea ice SAR data. The results of the data analysis in this paper show that the width of the azimuth band should be selected based on the extent of variation in the incidence angle, and the reference band can be calculated based on the maximum interclass distance principle. The intercomparisons also reveal that the proposed algorithm can improve the accuracy of supervised classifications. [ABSTRACT FROM AUTHOR]

This letter presents a novel method for ship classification that uses synthetic-aperture-radar images to distinguish ships based on superstructure scattering features. The ratio of dimensions, which combines the 2-D and 3-D properties of scattering, is explored as an effective and credible means to describe the scattering features of ships. The proposed method consists of three main stages: 1) ship isolation from the sea; 2) parametric vector $(F)$ estimation; and 3) categorization using a support vector machine (SVM) classifier. To depict ship features more accurately and reduce feature redundancy, we propose employing peak extraction to divide a ship into bow, middle, and stern instead of into three equal parts. The classification method is tested with RadarSat-2 images, and ground-truth information is supplied by an automatic identification system. The experimental results show that the proposed method can achieve satisfactory ship-classification performance compared with existing methods, with an overall accuracy exceeding 80%. [ABSTRACT FROM PUBLISHER]

Spectrum scarcity and intercell interference (ICI) are two fundamental limiting factors in wireless cellular systems. Soft frequency reuse (SFR) has been proposed as an effective way to manage the spectrum and reduce ICI in cellular systems. In a static SFR scheme, the allocations of transmit power or subcarriers in each cell are fixed prior to system deployment. This limits the potential performance of the SFR scheme. In this paper, we propose an intercell resource-allocation algorithm, which is referred to as the adaptive SFR algorithm (ASFR), that dynamically optimizes subcarrier and power allocations for multicell wireless networks to improve system capacity. The ASFR first finds the subcarrier and power allocations in each cell by using exhaustive search and greedy descend methods and then iteratively repeats among cells until a predefined convergence criterion is satisfied. The theoretical analysis proves the convergence of the ASFR algorithm. Simulation results show that the ASFR achieves a higher system throughput and better cell edge user performance than existing frequency reuse schemes. [ABSTRACT FROM PUBLISHER]

A multilevel soft frequency reuse (ML-SFR) scheme and a resource allocation methodology are proposed for wireless communication systems. In the proposed ML-SFR scheme, there are 2\N power density limit levels, achieving a better interference pattern and further improving the cell edge and the overall data rate compared with the traditional two-level SFR scheme. The detailed design of an eight-level SFR scheme is demonstrated. Numerical results show that the cell-edge spectrum efficiency is increased to five times of that of reuse 1, and the overall spectrum efficiency is improved by 31%. The ML-SFR can be utilized in the current 4G system and would be a candidate key technology for future 5G systems. [ABSTRACT FROM PUBLISHER]

A random beamforming (RBF) technique is proposed to achieve omnidirectional coverage for broadcast channels in multiple-antenna systems. In the proposed scheme, the time and frequency resources for communication are divided into many time–frequency blocks (TFBs). A random pattern is employed on each TFB, with the average power in all directions over many TFBs being equal. The design criteria and detailed design of the random pattern sequence are given. We propose a basic RBF scheme and an Alamouti-enhanced scheme, as well as the corresponding structures of the transmitter and the receiver. Simulations are carried out for an eight-element unitary linear array with half-waveform separation in a fast-fading channel, showing that the proposed scheme greatly outperforms the cyclic delay diversity scheme and is robust to calibration errors and failures of radio-frequency chains. [ABSTRACT FROM PUBLISHER]

This paper presents a new approach to sea ice segmentation in synthetic aperture radar (SAR) intensity images by combining an edge-preserving region (EPR)-based representation with region-level MRF models. To construct the EPR-based representation of a SAR image, edge strength is measured using instantaneous coefficient of variation (ICOV) upon which the watershed algorithm is applied to partition the image into primitive regions. In addition, two new metrics for quantitative assessment of region characteristics (region accuracy and region redundancy) are defined and used for parameter estimation in the ICOV extraction process towards desired region characteristics. In combination with a region-level MRF, the EPR-based representation facilitates the segmentation process by largely reducing the search space of optimization process and improving parameter estimation of feature model, leading to considerable computational savings and less probability of false segmentation. The proposed segmentation method has been evaluated using a synthetic sea ice image corrupted with varying levels of speckle noise as well as real SAR sea ice images. Relative to the existing region-level MRF-based methods, testing results have demonstrated that our proposed method substantially improves the segmentation accuracy at high speckle noise and achieves on average 29% reduction of computational time. [ABSTRACT FROM PUBLISHER]

In this paper, a novel frequency domain multi-user detector is proposed for a time division-code division multiple access (TD-CDMA) up-link. Unlike conventional frequency domain detectors, the proposed detector first transforms the system matrix of TD-CDMA systems into a circulant matrix by cyclic truncation. It subsequently uses a new method to convert the circulant matrix into a frequency domain block diagonalized matrix through discrete Fourier transforms and permutations. Therefore, the proposed detector can utilize the channel frequency domain coherence to further decrease its computational complexity with a controlled performance loss. Moreover, a novel approach is proposed to calculate the frequency domain correlation matrix and matched filter. With the help of this novel approach, the proposed detector expresses significant complexity advantage over other frequency domain detectors for a real TD-CDMA system in a short-time-dispersive channel. [ABSTRACT FROM AUTHOR]