Millimeter wave (mmWave) radar-based human mesh reconstruction has attracted attention due to its robustness to environments and privacy. However, radar point clouds are susceptible to noise and have limited spatial resolution, thus cannot accurately and completely represent the human body shape. In addition, the lack of robustness and fine-grained local detail features in the existing methods complicates the accurate human mesh reconstruction. Focus on the above problems, a novel Transformer-based human mesh reconstruction method Dilated Point spatio-temporal Mesh Transformer (DPMesh) has been proposed. The DPMesh uses the proposed Multi-Scale Dilated Point spatio-temporal Attention (MSDPA) to learn local spatiotemporal dependencies in radar point clouds. By combining the dilated convolution and attention mechanism, the multiple ranges of local spatio-temporal regions can be sensed, and a wider range and multi-scale local context information can be captured, providing robust and fine-grained perceptual cues for human mesh reconstruction. Moreover, the global spatio-temporal self-attention and the added clipped Gaussian noise during training are used to learn the global spatio-temporal dependencies of radar point clouds and reduce overfitting to the noise in the training set, respectively. In the mmBody dataset, the proposed method can achieve state-of-the-art results.

Various deep-learning-based Low-dose Computed Tomography (LDCT) denoising methods have been proposed to address the issue of CT image noise, often relying on powerful network architecture and meticulously designed loss functions. However, these models are typically lacking efficient noise representations and prior anatomical knowledge, which can limit their overall performance. To address this issue, we introduce UNiversal Anatomy-initialized Noise Distribution Learning Framework (UNAD). The novelties of UNAD are in two aspects: 1) Distribution Regression Layer (DRL) restructures the output layer, enhancing noise prediction capability. 2) A simple yet efficient Predictive Pre-training Framework (PPT), tailored to the unique characteristics of CT images, is used to improve prior anatomical knowledge. We have achieved state-of-the-art results even without a powerful feature extractor like ViT. In addition, UNAD demonstrates remarkable universality, making it easy to incorporate into other LDCT denoising networks. The source code can be found in our official GitHub repository https://github.com/Nioolek/UNAD.

This paper presents VirTeach, the exploitation of using the virtual point cloud (VPC) as an assisted teacher in the learning process for human pose estimation incorporated with the millimeter wave (mmWave) radar point cloud (RPC). Due to the observations that the involvement of different body parts varies in moving ranges and directions while performing postures and mmWave signals possess inherent characteristics (i.e., specular reflection, radical sensitiveness) during their perception, the RPC data suffer from the issues of blind spots and data imbalance for the response points induced by specific joints, leading to insufficient and biased learning and thus large estimation errors for them. To address these issues, we introduce the VPC data driven by real human motions to assist the learning process, which is indispensable in explicitly imposing task-specific constraints for the distorted RPC data in a fashion of learning by teaching. Specifically, we first design a generation module to produce the desired VPC data considering both the global structure and local motions of the human skeleton, serving as the “teacher” to augment the corrupted RPC data. Secondly, we incorporate the global and local guidance from the VPC data within a coarse-to-fine pose estimation framework. The former addresses the blind spots issue by completing the RPC data to facilitate the global skeleton reconstruction, while the latter is targeted for strengthening the contribution of specific joints through constructing the local spatial-temporal neighborhood to further refine their positions. Extensive experiments are conducted to validate the effectiveness of the proposed method.

Abstract Radio Tomographic Imaging (RTI) is a low‐cost computational imaging method realised by the Radio Frequency (RF) signal sensing. The target‐induced shadowing effect in the RF sensing network is reconstructed as a probability image to estimate the target's position. Then, the RTI‐based Device‐free Localization (DFL) is becoming a promising research topic in the Location‐based Services applications by the Internet of Things (IoT). However, the multipath interference in the RF sensing network often induces the imaging degradation and decreases the DFL accuracy. To deal with the multipath‐induced imaging degradation, considering that the target's shadowing occupies a small spatial range in the RF network and expresses some spatial structure, this article explores the spatial correlation in the target's shadowing. Then, a new RTI reconstruction method based on the Structured Sparse Bayesian Learning is proposed to model the spatial correlation implied in the sparse target's shadowing image. Further, the localisation experiments in actual scenes are conducted to validate the utilisation of the spatial correlation in target's shadowing is able to improve the imaging quality of the RTI system by enhancing the robustness towards the multipath‐induced imaging degradation.

This article proposes a two-branch learning model, namely, the joint global–local network, for human pose estimation (HPE) using millimeter wave radar. The aim of this work is to remediate the ill-posed problems in HPE arising from using the destructive observations with superimposed reflection signals. In the developed two-branch learning model, the global branch takes use of the superimposed signals from the whole human body to reconstruct the coarse pose estimation from a global perspective, and the local branch is responsible for fining the pose estimations with the decomposed signals from individual body parts in a complementary way. In doing this, two branch learning processes will be coordinated with the followed attention-based fusion module in terms of the local and global consistency. It is remarkable that the learning driven by the decomposed signals is motivated by exploiting the spatial-temporal evolution patterns of individual body parts for inferring the corresponding movements, which plays a crucial yet complementary role in the collaboration with the learning driven by the superimposed signals. With the two-branch learning architecture, the proposed method is advantageous in incorporating the local motion constraints from individual body parts into the coarse global estimation from the whole human body, which contributes to reconstructing plausible yet accurate pose estimations with the local and global kinematic consistency. Extensive experiments are presented to demonstrate the effectiveness of the proposed method.

Objective: Abdominal ECG (AECG) recorded at the maternal abdomen is significantly affected by the maternal ECG (MECG), making the extraction of FECG a challenging task. This paper presents a new MECG elimination method based on Short Time Fourier Transform (STFT) and Convolutional Auto-encoder (CAE). Methods: First, the STFT is used to transform the AECG from one-dimensional (1D) time domain into two-dimensional(2D) time-frequency domain. Next, the CAE model is applied to estimate the 2D-STFT coefficients of MECG. Finally, after the inverse STFT of MECG, we can extract the FECG by subtracting the MECG from the AECG in the time domain. Different from the methods estimated the MECG in the 1D time domain, the novelty of the proposed method relies on estimating the MECG in the 2D time-frequency domain. Specifically, the CAE model learns the end-to-end mappings from the 2D-STFT coefficients of AECG to the MECG. Results: Experimental results show that the proposed method is effective in removing the MECG. Significance: This work enhances the clinical applications of FECG in the early detection of fetal heart diseases.

This study concerns the issue of jointly enhancing noise robustness and promoting signal sparsity in Sparse Bayesian Learning (SBL), which aims at addressing the performance deficiency of sparse signal recovery due to uninformative data with low signal‐to‐noise ratios. In particular, the authors propose a hierarchical prior noise model with a signal‐dependent parametrisation and incorporate it into developing the robust SBL algorithms for sparse signal recovery. The main contribution of the proposed approach is twofold. The first is the new consideration of noise‐robustness enhancement in building SBL algorithms, which devotes to noise awareness in counteracting outliers in measurements. Specifically, the idea of signal‐sparsity enforcing is extended to build a Least Absolute Deviation like loss criterion with the proposed hierarchical prior model of measurement noise. The second is the novelty of using the signal‐dependent parametrisation in the proposed noise model. Indeed, the signal‐dependent mechanism plays an indispensable role in producing the reliable noise parameter estimation jointly with updating signal model parameters under the fast SBL framework. In addition to numerical simulation studies, the real‐life application of radio tomographic imaging is presented to validate the proposed approach.

This paper presents a new approach to identify the stroke parameters in handwriting movement data understanding. A two-step analysis by synthesis paradigm is employed to facilitate the coarse-to-fine parameter identification for all strokes. One is the stroke data extraction, the other is the coarse-to-fine stroke parameter identification. The new consideration of using this two-step paradigm is that the nonnegative primitive factorization technique is incorporated to decouple the overlapped strokes from the measurement data. In comparison to the existing paradigms of using the heuristic stroke data decoupling techniques, our paradigm presented here contributes to alleviating the difficulty of local optimum traps with the well-shaped initializations in the global optimization for jointly identifying stroke parameters. Moreover, our paradigm excludes the iteration between two steps, which contributes to the enhancement of computational efficiency. Experimental results are reported to validate the proposed approach.

A common technical difficulty in device-free localization and tracking (DFLT) with a wireless sensor network is that the change of the received signal strength (RSS) of the link often becomes more unpredictable due to the multipath interferences. This challenge can lead to unsatisfactory or even unstable DFLT performance. This work focuses on developing a new RSS model, called Exponential-Rayleigh (ER) model, for addressing this challenge. Based on data from our extensive experiments, we first develop the ER model of the received signal strength. This model consists of two parts: the large-scale exponential attenuation part and the small-scale Rayleigh enhancement part. The new consideration on using the Rayleigh model is to depict the target-induced multipath components. We then explore the use of the ER model with a particle filter in the context of multi-target localization and tracking. Finally, we experimentally demonstrate that our ER model outperforms the existing models. The experimental results highlight the advantages of using the Rayleigh model in mitigating the multipath interferences thus improving the DFLT performance.

This study focuses on the issue of sparse signal recovery with sparse Bayesian learning in the context of a heterogeneous noise model, called by the heterogeneous Bayesian compressive sensing. The main contribution is to exploit the capability of noise variance learning in performance improvement and applicability enhancement. Experimental results on synthetic and real‐world data demonstrate that heterogeneous Bayesian compressive sensing has superior performance in terms of accuracy and sparsity for both homogeneous and heterogeneous noise scenarios.

Device-free motion tracking with radio tomographic networks using received signal strength (RSS) measurements has attracted considerable research efforts. Since the motion scene to be reconstructed can often be assumed sparse, i.e., it consists only of several targets, the Compressed Sensing (CS) framework can be applied. We cast the motion tracking as a CS problem and employ an efficient algorithm, Orthogonal Matching Pursuit (OMP), for sparse recovery. Furthermore, we exploit a feedback structure which leads to a substantial reduction of the amount of measurements. The feedback structure utilizes the prior knowledge (locations of targets) in time sequence to predict next frame support. Compared with the least-square type methods, the proposed motion tracking based on feedback sparse recovery can directly determine where the targets are located in the network area and reduce the amount of measurements required for reliable tracking. Experimental results show its favorable performance.

This paper concerns the issue of motion tracking with Bayesian filtering driven by boolean compressive infrared sampling. In particular, this paper proposes an implementation of boolean compressive infrared sampling modality, and a measurement transformation method which maps the presence state recovered from boolean compressive infrared sampling into functional measurement for Bayesian filtering with a functional vector quantizer. Simulation studies are reported to validate the proposed transformation method in motion tracking.

This paper presents a cluster based routing scheme to perform the distributed Gaussian elimination algorithm for solving the regression algebraic equation towards to data regression. Under this routing framework, the junction tree structure is incorporated to coordinate the clusters so as to decouple the intersections among the neighboring clusters via a new message passing protocol. Simulation-based experimental results are presented. It is shown that the use of synchronous protocol of message passing scalably implemented among clusters is more efficient than the purely asynchronous protocol over entire sensor networks.

This paper presents our research on how support vector regression (SVR) and parametric adaptive learning, which are normally used independently, can be exploited together to benefit adaptive neural control. In the context of friction compensation for servo-motion control systems, we present the notion of support vector networks which play an essential role in combining SVR and adaptive neural network (NN) in cooperation for friction estimation. The analysis shows that the proposed support vector network contributes not only to the performance improvement but also to the practical usefulness in adaptive friction compensation. Experimental results are reported to demonstrate the effectiveness of the proposed approach.

This paper explores the notation of support vector networks, a new paradigm of combining support vector regression (SVR) parametrization with adaptive neural mechanism, in friction compensation for servo-motion systems. The contribution of this work is twofold. The first is to develop an enhanced adaptive friction compensator via SVR parametrization; the second is to present an analysis that shows the evidences of the performance improvement and practical usefulness enhancement due to SVR parametrization. The experimental study was conducted to validate the proposed method

This paper addresses the problem of using different sensors in filter design that can simultaneously satisfy multiple specifications. A novel approach is taken in the design paradigm that integrates the sensing strategy with the filter design, which improves the filtering performance. An application to the estimation of the endpoint vibration rate of a single-link flexible manipulator is presented with experimental verifications.