Conditional generative adversarial networks (cGANs) have dominated the research of synthetic aperture radar (SAR)-to-optical (S2O) image translation, attributing to the feature generalization ability of residual convolutional blocks. Nevertheless, the fixed geometric structures of convolutional kernels hinder the feature inference from local to global, resulting in unclear contours and missing details in the generated image. To address this challenge, we proposed a contextual inference transformer block in the generator, dubbed CoIT. It enables the network to capture key features in SAR images through context awareness, providing a more comprehensive feature representation from local details to global structure. The proposed CoIT block contextually encodes input keys to establish the relationship between SAR images and optical images, improving the quality of generated images. Experiments on the public datasets WHU-SEN-City and SEN12MS show that the proposed method not only achieves better visual effects but also makes certain progress in evaluation indicators.

Polar sea ice monitoring is essential for climate change analysis and ship navigation route security. The current urgent problem to be solved is how to build a unified sea ice parameter inversion framework to obtain abundant and robust ice charts. However, most machine learning methods are merely suitable for modeling sea ice classification tasks under a single data source. Thus, a multitask sea ice inversion approach is proposed to address this challenge. First, SAR data and microwave scanning radiometer-2 (AMSR-2) data are jointly employed for multitask inversion, e.g., sea ice concentration (SIC), the stages of sea ice development (SOD), and sea ice floe size (FLOE). Then, the proportional-integral-derivative (PID) controllers are introduced into a three branch convolutional neural network to parse detailed, context and boundary information of the sea ice scene, respectively. Finally, three classifiers assign the multilabels to each of tasks for the parameter inversion. Experiments conducted on the AI4Arctic Sea Ice dataset show that our proposal outperforms typical CNN-based methods.

Most of the existing photoplethysmography (PPG)-based blood pressure (BP) estimation methods aim at training a general BP model applicable to all individuals which neglected the vasculature and anatomical differences among individuals as well as the slow and subtle cardiovascular changes over time, thus, were hard to achieve high accuracy. This study aims at addressing this problem by constructing personalized BP models from PPG signals. First, the PPG features that can well reflect the changes of individual BP with the physiological state were extracted. Afterwards, an error feedback incremental support vector regression (EFISVR) model was designed to achieve high-accuracy BP measurement of a subject, which can quickly be adapted to new samples without retraining the whole model. Results show that the constructed model can accurately predict the BP values of a subject for at least three months. The mean absolute error (MAE) of BP estimation were 3.11 mmHg for systolic BP (SBP) and 2.47 mmHg for diastolic BP (DBP). The proposed EFISVR model is lightweight which can be integrated into wearables and other edge devices, as a part of Internet of Things (IoT) applications. The advantages of lightweight, few-shot learning and high precision make the model suitable for applications in real-life scenarios.

In this letter, we propose the CLN-Fusion, a novel hybrid fusion approach that leverages the merits of CNNs and vision Transformers (ViTs) to couple local-nonlocal feature representations between SAR and MS images. Specifically, we construct a paired token projection (PTP) to match the observation scenario content consistency of the two. Meanwhile, in terms of merging the complementary features between structures in SAR images and textures in MS images, we establish the pyramid CNN and ViT branches that assemble two pure feature volumes with convolutional inductive biases and nonlocal statistical correlation respectively into a mixed one. Furthermore, our CLN-Fusion maintains semantic alignment by maximizing mutual information throughout the PTP. Extensive experiments validate the superiority of the CLN-Fusion in terms of quantitative metrics, achieving SAR/MS image fusion under three scenarios from Sentinel-1 and Landsat8 data. with PSNRs of 33.1565, 30.9815, and 29.9821, showcasing the utmost fusion performance in contrast to other state-of-the-art (SOTA) methods. The codes of this work will be available at https://github.com/Blueseatear/CLN-Fusion.

Arterial stiffness (AS) serves as a crucial indicator of arterial elasticity and function, typically requiring expensive equipment for detection. Given the strong correlation between AS and various photoplethysmography (PPG) features, PPG emerges as a convenient method for assessing AS. However, the limitations of independent PPG features hinder detection accuracy. This study introduces a feature selection method leveraging the interactive relationships between features to enhance the accuracy of predicting AS from a single-channel PPG signal. Initially, an adaptive signal interception method was employed to capture high-quality signal fragments from PPG sequences. 58 PPG features, deemed to have potential contributions to AS estimation, were extracted and analyzed. Subsequently, the interaction factor (IF) was introduced to redefine the interaction and redundancy between features. A feature selection algorithm (IFFS) based on the IF was then proposed, resulting in a combination of interactive features. Finally, the Xgboost model is utilized to estimate AS from the selected features set. The proposed approach is evaluated on datasets of 268 male and 124 female subjects, respectively. The results of AS estimation indicate that IFFS yields interacting features from numerous sources, rejects redundant ones, and enhances the association. The interaction features combined with the Xgboost model resulted in an MAE of 122.42 and 142.12 cm/sec, an SDE of 88.16 and 102.56 cm/sec, and a PCC of 0.88 and 0.85 for the male and female groups, respectively. The findings of this study suggest that the stated method improves the accuracy of predicting AS from single-channel PPG, which can be used as a non-invasive and cost-effective screening tool for atherosclerosis.

Video-based Photoplethysmography (VPPG) offers the capability to measure heart rate (HR) from facial videos. However, the reliability of the HR values extracted through this method remains uncertain, especially when videos are affected by various disturbances. Confronted by this challenge, we introduce an innovative framework for VPPG-based HR measurements, with a focus on capturing diverse sources of uncertainty in the predicted HR values. In this context, a neural network named HRUNet is structured for HR extraction from input facial videos. Departing from the conventional training approach of learning specific weight (and bias) values, we leverage the Bayesian posterior estimation to derive weight distributions within HRUNet. These distributions allow for sampling to encode uncertainty stemming from HRUNet's limited performance. On this basis, we redefine HRUNet's output as a distribution of potential HR values, as opposed to the traditional emphasis on the single most probable HR value. The underlying goal is to discover the uncertainty arising from inherent noise in the input video. HRUNet is evaluated across 1,098 videos from seven datasets, spanning three scenarios: undisturbed, motion-disturbed, and light-disturbed. The ensuing test outcomes demonstrate that uncertainty in the HR measurements increases significantly in the scenarios marked by disturbances, compared to that in the undisturbed scenario. Moreover, HRUNet outperforms state-of-the-art methods in HR accuracy when excluding HR values with 0.4 uncertainty. This underscores that uncertainty emerges as an informative indicator of potentially erroneous HR measurements. With enhanced reliability affirmed, the VPPG technique holds the promise for applications in safety-critical domains.

Remote photoplethysmography (rPPG) can be used to monitor driver’s cardio-respiratory functions in automotive for improving the safety of driving. To understand the challenges of rPPG in this application, we created a benchmark of latest rPPG algorithms based on the MR-NIRP Car dataset, selecting the representative methods from both the physiological model based (PBV and DIS) and deep learning based (Supervised Learning and Contrastive Learning) approaches. The experimental results show that the physiological model based methods are generally more robust in this challenging scenario with vigorous motions and dynamic lighting changes, typically DIS outperforms others, with an average MAE of 6.5 bpm on RGB videos and 15.9 bpm on NIR videos. The benchmark indicates that upgrading the single wavelength NIR setup to multi-wavelength is the essential step towards robust heart-rate monitoring in automotive.

Video-based Photoplethysmography (VPPG) can identify arrhythmic pulses during atrial fibrillation (AF) from facial videos, providing a convenient and cost-effective way to screen for occult AF. However, facial motions in videos always distort VPPG pulse signals and thus lead to the false detection of AF. Photoplethysmography (PPG) pulse signals offer a possible solution to this problem due to the high quality and resemblance to VPPG pulse signals. Given this, a pulse feature disentanglement network (PFDNet) is proposed to discover the common features of VPPG and PPG pulse signals for AF detection. Taking a VPPG pulse signal and a synchronous PPG pulse signal as inputs, PFDNet is pre-trained to extract the motion-robust features that the two signals share. The pre-trained feature extractor of the VPPG pulse signal is then connected to an AF classifier, forming a VPPG-driven AF detector after joint fine-tuning. PFDNet has been tested on 1440 facial videos of 240 subjects (50% AF absence and 50% AF presence). It achieves a Cohen's Kappa value of 0.875 (95% confidence interval: 0.840-0.910, P<0.001) on the video samples with typical facial motions, which is 6.8% higher than that of the state-of-the-art method. PFDNet shows significant robustness to motion interference in the video-based AF detection task, promoting the development of opportunistic screening for AF in the community.

Spatially uneven illuminations are the dominant interference of video-based heart rate (HR) screening for cooperated subjects in a telehealth service. In this letter, a remote photoplethysmography (rPPG) method is introduced to stably extract pulsatile signals against uneven facial illuminations based on the multivariate singular spectrum analysis (MSSA). This method first divides the facial skins into multiple patches, where the hue channels resistant to light intensity variations are prepared from selected optimal patches. Considering the spatial correlations of heartbeats, the hue signals are then decomposed using the MSSA to reconstruct pulses. Finally, the HR is determined as the one with the highest ratio of energy around the dominant frequency from the first group of MSSA reconstructed signals. Experimental results demonstrate the effectiveness of the proposed method on the in-house BSIPL-rPPG database and the public COHFACE database, where the correlation coefficients of the estimated HRs achieve 0.95 and 0.98, respectively, outperforming those of the comparison methods.

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.

Imaging photoplethysmography (iPPG) can be used to detect heart rates from facial videos. However, it is sensitive to motion disturbances in realistic environments. To address this problem, a frequency-constrained multilayer sparse coding (FCMSC) algorithm is proposed in this paper. Specifically, FCMSC learns a dictionary about iPPG signals from a large set of clean iPPG signals in the training phase, and then reconstructs distorted iPPG signals with the learned dictionary in the testing phase. Compared to previous methods, FCMSC has stronger learning power due to its multilayer structure and better generalization ability because of its frequency constraint. A total of 3630 clips are cut out from the MAHNOB-HCI (a public video dataset) to train and test FCMSC. Experimental results show that FCMSC outperforms state-of-the-art methods in extracting heart rates from facial videos involving motion disturbances.

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.

With integrated LED lamps and cameras, smartphones are capable of extracting pulse waves from fingertip videos using image photoplethysmography technique. This paper presents a novel method for detecting pulse waves based on smartphone videos, with a focus on preserving vital details in pulse waves (such as limbs and dicrotic waves). Chrominance features are first extracted from videos to obtain a raw pulse wave, whose primary frequency is then used to build its fundamental pulsatile wave. After extracting pulse details from the raw pulse wave, a smooth pulse wave with clear details can be reconstructed by combining the fundamental pulsatile wave with pulse details. Experiments are conducted on a dataset involving 40 videos from 10 subjects under ambient lighting environments. Results have demonstrated the proposed method outperforms state-of-the-art ones in both pulse rate detection and pulse detail preservation especially in cases of motion artifacts.

A priori-knowledge based ship CFAR detection and determination algorithm is proposed in medium and high resolution SAR images. The algorithm first runs CFAR prescreening to get the coarse detection result, then the priori knowledge of the ships such as area, length and width is used for target discrimination. A sliding window with a certain size and a bright pixel number threshold is set, the window slides on the coarse detection image with a certain step, and the bright pixels in the sliding window are determined whether targets or clutter. If the number of bright pixels in the sliding window is larger than the bright pixel number threshold, then all the bright pixels in the sliding window will be determined as targets, otherwise clutter; finally, the Probability of False Alarm (PFA) of the whole algorithm is deduced. Using the algorithm, the false alarm rate (FAR) is greatly reduced while the targets can be insured detected. The simulation results prove the algorithm's effectiveness.

This paper proposes a local CFAR detector based on gray intensity correlation in SAR imagery. The new detector comprehensively uses the local SCR and the strong gray correlation in ship targets. The detector can well adapt to the changing background by using local SCR, further, the detection performance improves greatly using the 2D CFAR detection by modelling the joint gray intensity PDF (JPDF) of neighbouring pixel pairs of the clutter in the local window. By using real clutter extraction procedure in the background cell, the actual 2D joint Log-normal distribution is precisely modelled which fits the JPDF of the clutter well. Using this detector, the false alarm rate (FAR) caused by speckle and local background non-homogeneity can be greatly reduced, and ship targets too close can also be detected. Under the same probability of false alarm (PFA), the probability of detection (PD) improves greatly compared with conventional CFAR detectors.

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%.

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.

This paper proposes a structure preserving bilateral filtering (SPBF) for Polarimetric SAR (PolSAR) image. By adopting the edge structural characteristics and surface scattering features, the performance of filter is improved and the loss of structure is reduced. To begin with, the edge direction is determined by edge templates and an adaptive direction window is selected in span image. Then each scattering mechanism of the pixel is attained by Freeman-Durden decomposition. The surface scattering map is obtained by statistic distribution characteristics of the polarimetric data. Finally, the filtering mask, combining the cluster map with adapted direction window, is introduced for an improved bilateral filtering. Experiments illustrate the effectiveness of the proposed method. The speckle is efficiently reduced and the image edge, strong point target and polarimetric scattering characteristics can be further preserved.

In this paper, a signal broadcasting scheme based on random beamforming is proposed to achieve omnidirectional coverage for public channels in multi-antenna cellular systems. Determining the basic weight vector achieving a pattern amplitude as flat as possible in the angular domain and forming a pair of complementary patterns on each time-frequency resource block, the resulting transmit power become isotropic instantaneously in the whole cell. As a result, it is verified by theoretical analysis and numerical results that the proposed scheme achieves the upper-bound performance in correlated antenna arrays, and is robust to failures and calibration errors of radio frequency chains. Moreover, the proposed scheme is a low-cost and power-efficient solution by making full use of the power amplifiers.

In 3GPP Long Term Evolution (LTE) networks, the frequency reuse schemes such as fractional frequency reuse (FFR) and soft frequency reuse (SFR) are used to improve system capacity. The allocation of transmit power and subcarriers to each cell in these schemes are fixed prior to network deployment. This limits the potential performance of these frequency reuse schemes. In this paper, we propose to improve the capacity of SFR scheme by jointly optimizing subcarrier and power allocation in multi-cell LTE networks. An iterative algorithm that can adaptively vary the number of major subcarriers and adjust the transmit power for each cell according to wireless traffic loads is proposed. Simulation results show that the proposed algorithm outperforms the existing Reuse 1, FFR and static SFR schemes in both system throughput and cell edge user performance.

A random beamforming technique is proposed for broadcast channels in multiple antenna systems. In the proposed scheme, a random weight vector, corresponding to a random pattern, is imposed on each communication resource in time-frequency domain, with a resulting average of the random patterns on all resources to be isotropic. We proved that, the capacity of such a system is upper bounded by that of a single antenna system, on the premise of same power budget. The design criteria and detailed design of random patten sequence are presented. We propose a basic random beamforming and Alamouti enhanced scheme, and a structure of the transmitter and receiver. The performance of the proposed scheme is verified by numerical simulation. It is also observed that it is robust to calibration errors and failures of radio frequency chains.

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.

For a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system operating in high mobility scenarios, channel estimation becomes a challenging issue, due to fast channel variation and severe inter-carrier interference (ICI). In this paper, we propose a novel pilot-aided iterative receiver, based on pilot symbols and iterative soft-estimate of data symbols. The channel is estimated by time-domain interpolation and least-square (LS) methods. Soft-estimate for data symbols are obtained by a maximum-a-posteriori (MAP) decoder and improved subsequently. The simulation results show that the performance of the proposed iterative receiver outperforms the existing schemes. The performance degradation of the proposed receiver structure when users move at speed of up to 324Km/h compared to the performance of a perfect CSI system with a zero Doppler shift is shown to be very marginal.

In this paper, we propose a novel edge-preserving region (EPR)-based representation for synthetic aperture radar (SAR) images, which is incorporated with a region-level Markov random field (MRF) model to offer an efficient approach to the segmentation of SAR sea ice images. The EPR-based representations of SAR images are constructed by applying the speckle reduction anisotropic diffusion (SRAD) algorithm and the watershed transform, which aims at suppressing oversegmentation within objects while accurately locating object edges at region boundaries in the presence of speckle noise. In combination with a region-level MRF, the EPR-based representation largely reduces the search space of optimization process and improves parameter estimation of feature model, leading to considerable computational savings and less probability of false segmentation. Relative to the existing region-level MRF-based methods, testing results have demonstrated that the proposed method achieves more than 50% reduction of computational time and improves the segmentation accuracy especially at high speckle noise.

This paper proposes a structure-preserving speckle reduction (SPSR) algorithm for synthetic aperture radar (SAR) images by exploiting self-similarity of structural patterns based on nonlocal means filter. The SPSR algorithm is featured by discerning pixels of similar structural patterns, which is crucial for a despeckling process to avoid blurring image structure. To alleviate the impact of speckle noise to similarity measure, a two-stage filtering scheme is introduced into the SPSR algorithm. Filtering at the first stage aims at an accurate approximation of true structural similarity, followed by the filtering at the second stage to group pixels with similar neighborhood in a large area. Compared to the traditional Lee filter, enhanced Lee filter and the speckle reducing anisotropic diffusion (SRAD), evaluation results have shown that the SPSR algorithm substantially improves the despeckling performance especially on structure preservation and speckle reduction in homogeneous regions.

For a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system, time-varying multipath fading of channel destroys the orthogonality among subcarriers and leads to serious intercarrier interference (ICI). The system performance degrades more severely as normalized Doppler frequency increases. In order to mitigate the effect of time-varying fading, a low-complexity iterative receiver with joint ICI cancellation and pilot-assisted channel estimation is proposed. The initial channel state information (CSI) is estimated by performing time-domain interpolation and least-square (LS) method on the received pilot symbols. The soft outputs are obtained from the decoders after low-complexity linear minimum mean-square error (LC-LMMSE) detection. In the following stages, the soft outputs are feedback to update the CSI estimation. Furthermore, a ¿linear statistics combining¿ (LSC) technique is used to improve the performance of the proposed equalizer by combining the outputs of LC-LMMSE and parallel interference canceler (PIC) with weighting coefficients estimated by maximizing the signal to interference-plus-noise ratio (SINR) at the output of LSC. The complexity of system is significantly reduced by restricting the interference to neighboring subcarriers and employing the LC-LMMSE by limiting the frequency-domain CSI into diagonal region. The simulation results show that the proposed iterative receiver with estimated CSI approaches the ICI-free bound even at very high mobility scenarios.