Ray tracing (RT) is currently the most widely used deterministic channel modeling method. However, its point-by point ray-tracing procedure leads to high computational com plexity, particularly when constructing regional channel models needed for spatially oriented technologies in 6G systems. Recently, the Ray Inversion (RI) method has been proposed to address this challenge by identifying virtual wave sources from a limited number of Channel Impulse Responses (CIRs), enabling highly efficient reconstruction of regional CIRs. Despite its efficiency, the raw RI method suffers from limited reliability and accuracy due to its coarse discrimination strategy, which relies solely on amplitude information. To overcome this limitation, we propose a Spatial-Angular-Constrained Ray Inversion (SAC-RI) method, which leverages the angle of arrival (AoA) for multipath isomorphic grouping, the angle of departure (AoD) for reflection order determination, and introduces a barycentric-coordinate based soft decision mechanism for stable CIR reconstruction under uncertain paths. Experimental results demonstrate that SAC-RI achieves accurate and stable regional CIR reconstruction, with normalized root mean square error reduced to the 10−3 range. This corresponds to more than a 25-fold reduction in NRMSE over RI, while also achieving a maximum speedup of approximately 250 times compared to conventional RT.

Imaging photoplethysmography (iPPG) is an emerging optical technique that allows for the contactless acquisition of arterial Blood Volume Pulse (BVP) signals from video recordings of the human skin. While iPPG offers a non-contact and convenient means for physiological monitoring, the accuracy of the extracted BVP signals remains limited. This limitation hinders its potential for advanced cardiovascular assessments, such as evaluations of arterial stiffness and cardiac function. To address this issue, we propose a novel physiologically informed Gaussian filtering method, based on the prior knowledge that the BVP waveform can be modeled as a mixture of multiple Gaussian components. Specifically, a set of physiological Gaussian kernels is employed to convolve the noisy iPPG signal, generating a Gaussian representation that emphasizes waveform components with physiological relevance. This representation is further refined by a Transformer-based neural network to reconstruct accurate BVP signals. Experimental results demonstrate a notable improvement in BVP accuracy, with the mean absolute error reducing from 0.25 to 0.08. This enhancement in iPPG precision highlights the potential of our approach for advanced medical applications.

The accurate modeling of global channel information in the given area is crucial for the advancement of future wireless systems, which is usually named the channel knowledge map (CKM). Nonetheless, the channel impulse response (CIR) serves as the essential model of mutlipath fading channel, and consequently, the regional CIRs should be the most efficient way to generate CKMs in any domain. However, classical ray tracing (RT) methods can only provide pointwise CIR at discrete locations, which is less efficient for obtaining the regional CIR. To overcome this limitation, this article proposes an efficient area tracing (AT) method to obtain the regional CIR. This method first designs a shadow testing algorithm that partitions the complex visible area of interest into several subareas, within which all locations are affected by the same group of equalized propagation sources. Then, the CIR at any location within the area can simply be computed without repeating the traverse of RT processes. Extensive experiments validate both the feasibility and efficiency of the proposed AT method with a computational cost 46.9 & times; lower than the classical RT method while keeping the same accuracy.

The distributed deep learning architecture between front-deployed sensors and edge-deployed gateways attracts increasing interest. However, the inference performance of distributed deep models is also impacted by the delivery loss of intermediate representation in the wireless link, especially in the harsh industrial fading environments. Traditional communication systems usually focus on transmission errors at bit level, which treat all bits in the packets equally and fail to suit the varying importance in distributed deep models, which urges the essential evolution of the communication method to form a joint co-design paradigm for distributed deep models. This article then proposes to optimize the Mean Time To First Failure (MTTFF) of wireless link instead of traditional bit error rate, which enables a guaranteed transmission window. This paper first derives the analytical model of MTTFF under MIMO systems, then utilizes the kernel mixture distribution to obtain a closed-form solution of MTTFF, which forms a optimization algorithm minimizing the transmitted power while achieving the aiming MTTFF. Extensive reallife experiments show more than 70% satisfaction rate of MTTFF, which leads to more than 10 times higher inference accuracy than the original deep model.

With the development and application of fifth-generation mobile communication technologies, cutting-edge techniques, including multiple-input multiple-output and beamforming, have come to the forefront. However, the performance gain of these techniques is in the trade of deep understanding of regional multipath channel. To obtain the channel impulse response (CIR) of a given area will typically require pointwise scanning in both channel measurements or ray tracing (RT)-based modeling method. In this letter, we propose the ray inversion (RI) method. The RI method aims to obtain the region channel response (RCR) with only a limited number of observed CIR and avoid the involvement of surrounding environment information. The method classifies of observed propagation paths first and then inverts the ray transmission process to identify the locations of the secondary wave sources and obtain the local regional response. The method significantly enhances the efficiency of obtaining RCR. An iterative fission method has been further designed to guarantee both the efficiency and accuracy. The utilization of this technology can assist the RT method in achieving rapid acquisition of RCR. The RI method is based on geometric inversion, making it applicable to different frequencies. Compared to the traditional point-by-point scanning RT method, the computation speed in an open scene is increased by 15 times, and in a closed scene by four times when the receiving antenna spacing of RCR is 1 m. The RI method provides an innovative perspective for the research and application of next-generation wireless communication.

Accurate online link quality metrics represented by the Rician parameter are critical to enhancing the reliability of industrial wireless networks subject to temporal fading channels. The Rician parameters can be estimated by fitting the received I/Q symbols with GMM (Gaussian Mixture Model). However, the classical Expectation-Maximization estimations of GMM rely on the preset hyper-parameter of kernel numbers to guarantee the convergence, making it hard to work under adaptive modulation schemes. To address this challenge, we first reveal that the derivative of likelihood is less capable of representing the global optimal, which leads to the well-known local optimal problem and the failure to recognize the false convergence caused by incorrectly configured kernel numbers. A new empirical metric derived from KLD (Kullback-Leibler divergence) has been proposed to identify the local optimal convergence, as well as a new metric tuple to discriminate redundant kernels. A novel estimation algorithm has then been designed to shift the number of kernels from the preset hyper-parameter to the adjustable parameter. This improvement guarantees the global optimal convergence of the GMM with any initial number of kernels. Extensive experiments demonstrate that the proposed method achieves over ten times better accuracy, while requires less than half the iterations.

BACKGROUND AND OBJECTIVE:Dysfunction of the autonomic nervous system (ANS) plays a critical role in the progression and assessment of cardiovascular diseases, neurological disorders, and various other pathologies. Therefore, a quantitative assessment of ANS function is vital for personalized medicine in these diseases. However, direct measurements of ANS activity can be costly and invasive, prompting researchers to adopt indirect methods for quantitative evaluation. These methods typically involve mathematical techniques, such as statistical analysis and mathematical modeling, to interpret cardiovascular fluctuations in response to external stimuli.The purpose of this study is to develop a non-invasive mathematical method that quantitatively assesses ANS function during graded exercise. METHODS:In this study, we present a physiological mathematical model for autonomic regulation of the cardiac system under graded exercise, which recognizes the crucial role of the ANS in controlling heart rate during physical activity. The model utilizes the metabolic equivalent of walking as the input and heart rate as the output, with model parameters serving as quantitative measures of personalized ANS function. Experimental data were collected from groups with different health statuses and genders. Mann-Whitney U non-parametric tests were conducted on the model parameters to assess performance between individuals who frequently engage in aerobic exercise (15 participants, aerobic exercise frequency of more than 4 times/week) and those who barely exercise (15 participants, aerobic exercise frequency of 1 time per week or less), as well as between male and female participants. RESULTS:The experimental results indicate that our model effectively quantitatively assesses ANS function across groups with different health statuses and genders (P < 0.05). Additionally, the model provides precise estimations of heart rate, yielding a Root Mean Square Error of 2.79 beats per minute, a Mean Absolute Error of 2.18 beats per minute, and an R-squared value of 0.93. CONCLUSION:Our findings suggest that the proposed physiological mathematical model offers a non-invasive and user-friendly tool for measuring ANS function and monitoring cardiovascular health. This approach is feasible for home application, thereby reducing the need for professional supervision, and supports the early detection and personalized management of cardiovascular diseases. As a result, it enhances clinical decision-making and improves patient outcomes.

A deep understanding of the Channel Impulse Response (CIR) is crucial for the design and optimization of modern communication systems. CIR not only provides a comprehensive description of channel characteristics but also plays a key role in various aspects, including channel estimation, system design, and Multiple-Input Multiple-Output (MIMO) system development. Traditional channel modeling and measurement methods for obtaining regional CIR typically require point-by-point scanning of the region, which significantly increases time and equipment overhead. In this paper, we propose a three-dimensional (3-D) Ray Inversion (RI) method based on the Image Method (IM) in Ray Tracing (RT) method. The RI method does not require knowledge of the geometric information of the surrounding environment and can reconstruct regional CIR based on a limited number of observed CIR. The RI method discretizes the spatial domain into tetrahedral elements, where the CIR at the nodal points is the observed data. The multipath components of the observed CIR are classified first, and then the location of the secondary wave sources are determined from the ray propagation paths, thereby reconstructing the regional CIR within the tetrahedrons. To optimize computational efficiency while maintaining solution accuracy, an adaptive refinement algorithm based on the longest-edge bisection criterion is implemented to ensure the generated subtetrahedra have small and uniform edge lengths. Experimental results show that the RI method maintains high accuracy in both indoor and outdoor scenarios, and the computational efficiency is improved by more than 20 times compared to the IM method, and the efficiency gains are greater as the sampling distance decreases.

Wireless communication technology is evolving rapidly, where multiple-input-multiple-output (MIMO) technology plays a crucial role by effectively leveraging the diversity of spatial multipath channels. Most MIMO algorithms are designed with the simple but effective spatial correlation assumption, which assumes homological multipath characteristics for all elements of the antenna array. However, this ideal assumption may not always hold, which can be broken by the heterogeneity in multipath effects across regions. Thus, identifying and categorizing these heterogeneous regions is essential for both optimization and deployment in next-generation wireless communication systems. In this letter, we treat the heterogeneous as semantic in multipath fading domain, and propose to segment the regional map into different partitions. In detail, this letter introduces a multistacked U-shaped network (U-net) model, designed for effective channel segmentation. The model is trained on datasets generated through ray tracing (RT) methods across diverse scenarios. Extensive experiments demonstrate that the proposed data-driven model achieves a segmentation accuracy of 78.931%, effectively identifying complex multipath regions, while operating several thousand times faster than RT methods.

In industrial environments, the wireless link of IoT systems often experiences complex channel fading effects, making accurate online estimation of link quality crucial for improving system performance. Using Gaussian Mixture Model (GMM) to fit I/Q symbols allows estimation of Rician channel parameters, but traditional GMMs typically rely on prior knowledge of the number of Gaussian components to ensure clustering accuracy, posing challenges for adaptive channel modulation schemes in industrial settings. This paper proposes an adaptive Gaussian mixture model based on Kullback-Leibler divergence (KLD), which autonomously determines the optimal number of clusters through iterative evaluation, achieving optimal clustering performance. Firstly, this study proposes the utilization of forward KLD as an optimization target, leveraging its known optimal prior of zero to avoid local optima. Secondly, the redundancy in the number of clusters is assessed using reverse KLD constructed with the single Gaussian distribution. These improvements ensure that the GMM converges correctly to the global optimum regardless of the initial cluster count settings.

In modern wireless communication systems, a profound grasp of the channel impulse response (CIR) is pivotal for optimizing the design and functionality of algorithms and systems, especially for multiple-input-multiple-output (MIMO) technology. Conventional methods for gauging and modeling the channels rely on evaluating spatial points sampled discretely, resulting in limitations in acquiring pertinent channel information across a broad region. To overcome these limitations, this study embeds the physical principles of electromagnetic wave propagation into data-driven deep learning models, achieving second-level regional CIR computing efficiency that is hundreds of times faster. The proposed physics-informed deep ray tracing network (PIDRTN) integrates multiple U-shaped network (U-Net) encoder-decoder blocks, capturing radio wave propagation characteristics within a specific region surrounded by buildings, including two equivalent signal propagation directions in a two-dimensional space and a signal intensity correction term. Then, the network employs a parameter-free nonlinear signal transmission module to emulate the physical principles of signal propagation and obtain accurate CIRs from limited anchor locations, which will iteratively generate CIRs for various times within a specified region subjected to enhancement and denoising operations. Furthermore, the PIDRTN-A model, which utilizes anchor data to improve model accuracy, is proposed. A dataset encompassing diverse fading scenarios is constructed using the ray tracing (RT) method. Extensive experiments demonstrate that the proposed models effectively capture directional and reflective properties of signals; using the RT model as a benchmark, normalized root mean squared errors (NRMSEs) of 0.1226 and 0.0969 are obtained for the PIDRTN and PIDRTN-A models, respectively.

Gaussian Mixture Function (GMF) is a widely utilized model for analyzing and elucidating experimental data in science and engineering, where the fitting of GMF with noisy observations is usually rendered a complicated nonlinear regression problem due to the underlying linear superposition of Gaussian components. Classical Newton-type solutions rely on derivatives of the regression objective to facilitate convergence, which are general-purpose and can be inefficient. In this letter, we propose a novel method inspired by Majorization-Minimization (MM) to achieve efficient GMF fitting in a linear manner. The proposed method integrates the contribution of each Gaussian component in GMF to construct a linear surrogate and ensures the consistent convergence of the original nonlinear objective. Extensive experiments demonstrate that the proposed method outperforms classical solutions in convergence speed while maintaining precise fitting accuracy.

The distributed deep learning architecture can support the front-deployment of deep learning systems in resource constrained Internet of Things devices and is attracting increasing interest. However, most ready-to-use deep models are designed for centralized deployment without considering the transmission loss of the intermediate representation inside the distributed architecture. This oversight significantly affects the inference performance of distributed deployed deep models. To alleviate this problem, a state-of-the-art work chooses to retrain the original model to form an intermediate representation with ordered importance and yields better inference accuracy under constrained transmission bandwidth. This article first reveals that this solution is essentially a pruning-like solution, where unimportant information is adaptively pruned to fit within the limited bandwidth. With this understanding, a novel scheme named naturally aggregated intermediate representation (NAIR) has been proposed, which aims to naturally amplify the difference of importance embedded in the intermediate representation from a mature deep model and reassemble the intermediate representation into a hierarchy of importance from high-to-low to accommodate the transmission loss. As a result, this method shows further improved performance in various scenarios, avoids compromising the overall inference performance of the system, and saves astronomical retraining and storage costs. The effectiveness of NAIR has been validated through extensive experiments, achieving a 112% improvement in performance compared to the state-of-the-art work.

Respiratory waveform is one of the most important physiological signals containing essential pathophysiological information. The classical monitoring of respiratory waveform is based on the flow meter with contacted inputs. A non-contact respiratory waveform monitoring method is needed to bring a better patient experience, allow more application scenarios and provide additional measurements to gain an in-depth understanding of the respiration system. In this paper, we proposed a novel infrared image-based non-contact monitoring method which successfully obtains the detailed preserved respiratory waveform for the first time. The obtained infrared image is modelled as temperature distribution over a spatial field instead of a simple grey image, which is decided mainly by the flow speed. And an efficient analytical model guided mapping function from raw high-dimensional observations into temporal flow sequences is developed to replace the simple average over the region of interests. As a result, the manual-involved measurement noises can be significantly suppressed. To further mitigate the residual noises, a deep Kalman filter is designed to make use of the self-evolution model of the respiration system. The experimental results have validated the accuracy of the proposed method.

In industrial communications, it is imperative that critical information is transmitted without any interruptions or faults throughout the entire transmission process. Traditional communication system, however, typically focus on transmission errors at the bit level and fail to consider the time-varying characteristics of wireless channels in relation to the distribution of failures over time. In this article, we employ a classical reliability measure, i.e., Mean Time To First Failure (MTTFF), into the optimization algorithm of communication system to provide a guaranteed reliable transmission window without any errors. By exploiting the constant envelope properties of Zadoff-Chu (ZC) sequences, we calculate the distribution and cumulative density function of the SNR, and accurately estimate the Level Crossing Rate (LCR), which allows us to estimate the MTTFF under current status. We then implement a optimization strategy that minimizes the transmitted power to achieve the targeted MT-TFF. Extensive experiments validates that the adjusted MTTFF achieves a satisfaction rate of more than 95%.

Objective.The autonomic nervous system (ANS) plays a critical role in regulating not only cardiac functions but also various other physiological processes, such as respiratory rate, digestion, and metabolic activities. The ANS is divided into the sympathetic and parasympathetic nervous systems, each of which has distinct but complementary roles in maintaining homeostasis across multiple organ systems in response to internal and external stimuli. Early detection of ANS dysfunctions, such as imbalances between the sympathetic and parasympathetic branches or impairments in the autonomic regulation of bodily functions, is crucial for preventing or slowing the progression of cardiovascular diseases. These dysfunctions can manifest as irregularities in heart rate, blood pressure regulation, and other autonomic responses essential for maintaining cardiovascular health. Traditional methods for analyzing ANS activity, such as heart rate variability (HRV) analysis and muscle sympathetic nerve activity recording, have been in use for several decades. Despite their long history, these techniques face challenges such as poor temporal resolution, invasiveness, and insufficient sensitivity to individual physiological variations, which limit their effectiveness in personalized health assessments.Approach.This study aims to introduce the open-loop Mathematical Model of Autonomic Regulation of the Cardiac System under Supine-to-stand Maneuver (MMARCS) to overcome the limitations of existing ANS analysis methods. The MMARCS model is designed to offer a balance between physiological fidelity and simplicity, focusing on the ANS cardiac control subsystems' input-output curve. The MMARCS model simplifies the complex internal dynamics of ANS cardiac control by emphasizing input-output relationships and utilizing sensitivity analysis and parameter subset selection to increase model specificity and eliminate redundant parameters. This approach aims to enhance the model's capacity for personalized health assessments.Main results.The application of the MMARCS model revealed significant differences in ANS regulation between healthy (14 females and 19 males, age: 42 ± 18) and diabetic subjects (8 females and 6 males, age: 47 ± 14). Parameters indicated heightened sympathetic activity and diminished parasympathetic response in diabetic subjects compared to healthy subjects (p < 0.05). Additionally, the data suggested a more sensitive and potentially more reactive sympathetic response among diabetic subjects (p < 0.05), characterized by increased responsiveness and intensity of the sympathetic nervous system to stimuli, i.e. fluctuations in blood pressure, leading to more pronounced changes in heart rate, these phenomena can be directly reflected by gain parameters and time response parameters of the model.Significance.The MMARCS model represents an innovative computational approach for quantifying ANS functionality. This model guarantees the accuracy of physiological modeling while reducing mathematical complexity, offering an easy-to-implement and widely applicable tool for clinical measurements of cardiovascular health, disease progression monitoring, and home health monitoring through wearable technology.

With the application and development of the fifth-generation (5G) communications, it is essential to gain insight understanding of the multi-antenna wireless channel characterizations. In particular, their relevant Channel Impulse Response (CIR) so to ensure the effective design of algorithms and systems. However, both the traditional channel measurement and modelling are essentially based on assessment at discretely sampled spatial points without the capability to obtain the relevant channel information over a given surrounding area. To overcome this limitation, this paper proposes to re-assemble the discretely sampled CIRs into equalized video streams. With this basis, a deep learning based video super-resolution method, namely, the Time Decomposition Video Super-Resolution (TDVSR), has been proposed to restore the area channel information for the first time. Moreover, a time decomposition module based on Bidirectional Long Short-Term Memory (BiLSTM) has been designed to decompose the re-assembled CIRs into video form in the time dimension. A retrained video super-resolution model will then process the composited data and output high-resolution frames, which will be reversed to the CIRs at the dense density target area. A data set with various typical fading scenarios has been constructed by Ray Tracing (RT) method. Extensive experiments demonstrate that the proposed TDVSR model successfully learned the nonlinear propagation laws through the data-driven method, which shows satisfied restoration accuracy with significantly increased computation efficiency.

Imaging Photoplethysmography (IPPG) is an emerging and efficient optical method for non-contact measurement of pulse waves using an image sensor. While the contactless way brings convenience, the inevitable distance between the sensor and the subject results in massive specular reflection interference on the skin surface, which leads to a low Signal to Interference plus Noise Ratio (SINR) of IPPG. To ease this challenge, this work proposes a novel modulation illumination approach to measure the accurate arterial pulse wave via surface reflection interference isolation from IPPG. Based on the proposed skin reflection model, a specific modulation illumination is designed to separate the surface reflections and obtain the subcutaneous diffuse reflections containing the pulse wave information. Compared with the results under ambient illumination and constant supplemental illumination, the SINR of the proposed method is improved by 4.56 and 3.74 dB, respectively.

The electrocardiogram (ECG) is an important non-invasive tool for diagnosing heart and cardiovascular diseases. However, traditional ECG systems often provide images, which makes it challenging to directly utilize the image-based data in various applications that require 1-D signals. Therefore, the digitalization of ECG reports is essential for the establishment of electronic medical archives, advanced diagnostic tools, and intelligent healthcare systems. Previous efforts to digitize paper-based ECGs have been limited by the need to manually select regions of interest, limited accuracy in handling overlapping ECG leads, and lack of batch processing capabilities. We present a fully automated method for digitizing ECG reports that requires only a predefined lead distribution, eliminating the need for manual lead position selection. This method effectively addresses the challenge of overlapping leads and has the capability for automated batch processing. Validation against a subset of the PTB-XL dataset demonstrated high accuracy and reliability (Root Mean Square Error: 0.036 (mV), Pearson Correlation Coefficient: 0.977). To further verify the generalizability of the method, we applied the algorithm to hospital ECG reports. The results, evaluated by expert cardiologists, confirmed that our method produced consistently high-quality digitalization and demonstrated its applicability to different types of ECG reports.

Over-The-Air (OTA) measurement is considered the preferred method for measuring the antenna system and end-to-end performance of Multiple-Input-Multiple-Output (MIMO) devices under test. Spatial correlation has been widely utilized as a key metric for evaluating the accuracy of MIMO OTA measurements. However, there is no guarantee that the standard signal streams convoluted with specified impulse responses will be ideally independent of each other in the implementation of the MIMO OTA testing system. Thus, it is envisaged that the spatial correlation in practical MIMO OTA testing systems may not be exactly equivalent to the expected value of the ideal theoretical model. In this paper, we propose a new evaluation framework for evaluating the spatial correlation performance of MIMO OTA testing system. This evaluation framework provides a novel observation method for spatial correlation, which reflects the non-ideal configuration of the MIMO OTA testing system and can be utilized to predict or cross-validate spatial correlation errors that deviate from the theoretical model. The experimental and simulation results have been verified against the theoretical model, demonstrating good consistency between the theoretical model and the proposed evaluation framework. Furthermore, several test scenarios have been verified with the different varying factors.