Ground clutter mitigation for slow-time MIMO radar using independent component analysis

The detection of low, slow and small (LSS) targets, such as small drones, is a developing area of research in radar, wherein the presence of ground clutter can be quite challenging. LSS targets, because of their unusual flying mode, can be easily shadowed by ground clutter, leading to poor radar detection performance. In this study, we investigated the feasibility and performance of a ground clutter mitigation method combining slow-time multiple-input multiple-output (st-MIMO) waveforms and independent component analysis (ICA) in a ground-based MIMO radar focusing on LSS target detection. The modeling of ground clutter under the framework of st-MIMO was first defined. Combining the spatial and temporal steering vector of st-MIMO, a universal signal model including the target, ground clutter, and noise was established. The compliance of the signal model for conducting ICA to separate the target was analyzed. Based on this, a st-MIMO-ICA processing scheme was proposed to mitigate ground clutter. The effectiveness of the proposed method was verified with simulation and experimental data collected from an S-band st-MIMO radar system with a desirable target output signal-to-clutter-plus-noise ratio (SCNR). This work can shed light on the use of ground clutter mitigation techniques for MIMO radar to tackle LSS targets

Artificial Intelligence-Enabled ECG Algorithm Based on Improved Residual Network for Wearable ECG

Heart disease is the leading cause of death for men and women globally. The residual network (ResNet) evolution of electrocardiogram (ECG) technology has contributed to our understanding of cardiac physiology. We propose an artificial intelligence-enabled ECG algorithm based on an improved ResNet for a wearable ECG. The system hardware consists of a wearable ECG with conductive fabric electrodes, a wireless ECG acquisition module, a mobile terminal App, and a cloud diagnostic platform. The algorithm adopted in this study is based on an improved ResNet for the rapid classification of different types of arrhythmia. First, we visualize ECG data and convert one-dimensional ECG signals into two-dimensional images using Gramian angular fields. Then, we improve the ResNet-50 network model, add multistage shortcut branches to the network, and optimize the residual block. The ReLu activation function is replaced by a scaled exponential linear units (SELUs) activation function to improve the expression ability of the model. Finally, the images are input into the improved ResNet network for classification. The average recognition rate of this classification algorithm against seven types of arrhythmia signals (atrial fibrillation, atrial premature beat, ventricular premature beat, normal beat, ventricular tachycardia, atrial tachycardia, and sinus bradycardia) is 98.3%

Reconstructing the global stress of marine structures based on Artificial-Intelligence-Generated content

This paper proposes an approach that utilizes Artificial-Intelligence-Generated Content (AIGC) to overcome the constraints of Structural Health Monitoring (SHM) devices in capturing global stress with limited sensors. Feature elements are selected based on correlation analysis among finite elements and used as stress-measured points. An Artificial Neural Network (ANN) is used to establish the relationship between the feature and correlation elements. The proposed method is applied to the connector structure of an offshore platform, and an optimal ANN is established to optimize its performance by considering factors such as the number of sensors, the neural network framework, and the convergence criteria. The generalization performance of the ANN is validated through a real-scale model test, with deviations below 10% and an average deviation of less than 4% in multiple conditions, verifying its accuracy. This technology represents a significant advancement, enhancing the practicality of the SHM technology from “point monitoring” to “field monitoring”

Fully Photonic Integrated Wearable Optical Interrogator

Wearable technology constitutes a pioneering and leading innovation and a market development platform worldwide for technologies worn close to the body. Wearable optical fiber sensors have the most value for advanced multiparameter sensing in digital health monitoring systems. We demonstrated the first example of a fully integrated optical interrogator. By integrating all the optical components on a silicon photonic chip, we realized a stable, miniaturized and low-cost optical interrogator for the continuous, dynamic, and long-term acquisition of human physiological signals. The interrogator was integrated in a wristband, enabling the detection of body temperature and heart sounds. Our study paves the way for the development of watch-sized integrated wearable optical interrogators with potential applications in health monitoring and can be directly exploited for the customized design of ultraminiaturized optical interrogator systems.H.L. acknowledges the support from the Tianjin Talent Special Support Program. J.D.P.G. acknowledges the support from the Serra Hunter Program, the ICREA Academia Program, and the Tianjin Distinguished University Professor Program. This work was supported by the National Natural Science Foundation of China (no. 61675154), the Tianjin Key Research and Development Program (no. 19YFZCSY00180), the Tianjin Major Project for Civil-Military Integration of Science and Technology (no. 18ZXJMTG00260), the Tianjin Science and Technology Program (no. 20YDTPJC01380), and the Tianjin Municipal Special Foundation for Key Cultivation of China (no. XB202007)

Pharmacogenomic profiling reveals molecular features of chemotherapy resistance in IDH wild-type primary glioblastoma

Background Although temozolomide (TMZ) has been used as a standard adjuvant chemotherapeutic agent for primary glioblastoma (GBM), treating isocitrate dehydrogenase wild-type (IDH-wt) cases remains challenging due to intrinsic and acquired drug resistance. Therefore, elucidation of the molecular mechanisms of TMZ resistance is critical for its precision application. Methods We stratified 69 primary IDH-wt GBM patients into TMZ-resistant (n = 29) and sensitive (n = 40) groups, using TMZ screening of the corresponding patient-derived glioma stem-like cells (GSCs). Genomic and transcriptomic features were then examined to identify TMZ-associated molecular alterations. Subsequently, we developed a machine learning (ML) model to predict TMZ response from combined signatures. Moreover, TMZ response in multisector samples (52 tumor sectors from 18 cases) was evaluated to validate findings and investigate the impact of intra-tumoral heterogeneity on TMZ efficacy. Results In vitro TMZ sensitivity of patient-derived GSCs classified patients into groups with different survival outcomes (P = 1.12e−4 for progression-free survival (PFS) and 3.63e−4 for overall survival (OS)). Moreover, we found that elevated gene expression of EGR4, PAPPA, LRRC3, and ANXA3 was associated to intrinsic TMZ resistance. In addition, other features such as 5-aminolevulinic acid negative, mesenchymal/proneural expression subtypes, and hypermutation phenomena were prone to promote TMZ resistance. In contrast, concurrent copy-number-alteration in PTEN, EGFR, and CDKN2A/B was more frequent in TMZ-sensitive samples (Fishers exact P = 0.0102), subsequently consolidated by multi-sector sequencing analyses. Integrating all features, we trained a ML tool to segregate TMZ-resistant and sensitive groups. Notably, our method segregated IDH-wt GBM patients from The Cancer Genome Atlas (TCGA) into two groups with divergent survival outcomes (P = 4.58e−4 for PFS and 3.66e−4 for OS). Furthermore, we showed a highly heterogeneous TMZ-response pattern within each GBM patient usingin vitro TMZ screening and genomic characterization of multisector GSCs. Lastly, the prediction model that evaluates the TMZ efficacy for primary IDH-wt GBMs was developed into a webserver for public usage (http://www.wang-lab-hkust.com:3838/TMZEP) Conclusions We identified molecular characteristics associated to TMZ sensitivity, and illustrate the potential clinical value of a ML model trained from pharmacogenomic profiling of patient-derived GSC against IDH-wt GBMs

Pharmacogenomic profiling reveals molecular features of chemotherapy resistance in IDH wild-type primary glioblastoma

Background Although temozolomide (TMZ) has been used as a standard adjuvant chemotherapeutic agent for primary glioblastoma (GBM), treating isocitrate dehydrogenase wild-type (IDH-wt) cases remains challenging due to intrinsic and acquired drug resistance. Therefore, elucidation of the molecular mechanisms of TMZ resistance is critical for its precision application. Methods We stratified 69 primary IDH-wt GBM patients into TMZ-resistant (n = 29) and sensitive (n = 40) groups, using TMZ screening of the corresponding patient-derived glioma stem-like cells (GSCs). Genomic and transcriptomic features were then examined to identify TMZ-associated molecular alterations. Subsequently, we developed a machine learning (ML) model to predict TMZ response from combined signatures. Moreover, TMZ response in multisector samples (52 tumor sectors from 18 cases) was evaluated to validate findings and investigate the impact of intra-tumoral heterogeneity on TMZ efficacy. Results In vitro TMZ sensitivity of patient-derived GSCs classified patients into groups with different survival outcomes (P = 1.12e−4 for progression-free survival (PFS) and 3.63e−4 for overall survival (OS)). Moreover, we found that elevated gene expression of EGR4, PAPPA, LRRC3, and ANXA3 was associated to intrinsic TMZ resistance. In addition, other features such as 5-aminolevulinic acid negative, mesenchymal/proneural expression subtypes, and hypermutation phenomena were prone to promote TMZ resistance. In contrast, concurrent copy-number-alteration in PTEN, EGFR, and CDKN2A/B was more frequent in TMZ-sensitive samples (Fisher’s exact P = 0.0102), subsequently consolidated by multi-sector sequencing analyses. Integrating all features, we trained a ML tool to segregate TMZ-resistant and sensitive groups. Notably, our method segregated IDH-wt GBM patients from The Cancer Genome Atlas (TCGA) into two groups with divergent survival outcomes (P = 4.58e−4 for PFS and 3.66e−4 for OS). Furthermore, we showed a highly heterogeneous TMZ-response pattern within each GBM patient using in vitro TMZ screening and genomic characterization of multisector GSCs. Lastly, the prediction model that evaluates the TMZ efficacy for primary IDH-wt GBMs was developed into a webserver for public usage ( http://www.wang-lab-hkust.com:3838/TMZEP ). Conclusions We identified molecular characteristics associated to TMZ sensitivity, and illustrate the potential clinical value of a ML model trained from pharmacogenomic profiling of patient-derived GSC against IDH-wt GBMs

Effect of Exogenous Glycine Betaine on the Germination of Tomato Seeds under Cold Stress

Cold stress is known to influence tomato growth, development, and yield. In this study, we analyzed the germination of tomato seeds treated with exogenous glycine betaine (GB) at a low temperature (14 °C). The results showed that cold stress inhibited tomato seed germination, and pretreatment with exogenous GB reduced this inhibition and enhanced the germination rate (GR), germination index (GI), and viability of tomato seeds at low temperatures. Analysis of gene expression and metabolism revealed that GB positively regulated endogenous hormone gibberellin (GA) content and negatively regulated abscisic acid (ABA) content, while GB reduced the starch content in the seeds by up-regulating the amylase gene expression. Gene expression analysis showed that the key genes (SlSOD, SlPOD, and SlchlAPX) involved in reactive oxygen species (ROS) scavenging systems were up-regulated in GB-pretreated tomato seeds compared with the control. At the same time, levels of malondialdehyde and hydrogen peroxide were significantly lower, while the proline content and peroxidase (POD), superoxide dismutase (SOD), and catalase (CAT) levels were elevated compared with those in the control. These results demonstrate that exogenous GB as a positive regulator effectively alleviated the inhibition of tomato seed germination under cold stress by different signal pathways

Ground Clutter Mitigation for Slow-Time MIMO Radar Using Independent Component Analysis

The detection of low, slow and small (LSS) targets, such as small drones, is a developing area of research in radar, wherein the presence of ground clutter can be quite challenging. LSS targets, because of their unusual flying mode, can be easily shadowed by ground clutter, leading to poor radar detection performance. In this study, we investigated the feasibility and performance of a ground clutter mitigation method combining slow-time multiple-input multiple-output (st-MIMO) waveforms and independent component analysis (ICA) in a ground-based MIMO radar focusing on LSS target detection. The modeling of ground clutter under the framework of st-MIMO was first defined. Combining the spatial and temporal steering vector of st-MIMO, a universal signal model including the target, ground clutter, and noise was established. The compliance of the signal model for conducting ICA to separate the target was analyzed. Based on this, a st-MIMO-ICA processing scheme was proposed to mitigate ground clutter. The effectiveness of the proposed method was verified with simulation and experimental data collected from an S-band st-MIMO radar system with a desirable target output signal-to-clutter-plus-noise ratio (SCNR). This work can shed light on the use of ground clutter mitigation techniques for MIMO radar to tackle LSS targets

Research on a Measurement Method for the Ocean Wave Field Based on Stereo Vision

The wave parameter is an important environmental input condition. Traditional contact wave measurement methods are unable to meet the requirements of high precision, non-contact, and ship wave field assessment. Alternatively, stereo vision technology can realize a non-contact and mobile form of measurement. However, this technology suffers from poor timeliness and adaptability. This paper proposes a comprehensive wave measurement method that is based on stereo vision, wherein the gridding of siftGPU is used to achieve the fast matching of large images. The whole algorithm can be run within 6 s and it guarantees more than 20,000 feature-matching logarithms. Furthermore, by utilizing the least squares method and sea surface wave surface theory, the sea surface base level can be calculated without control points, along with the inversion of the sea wave parameters (wave height, period, and wave direction) and error point fitting. The rationality and superiority of the algorithm were verified through multiple comparison experiments. Compared with the Richard Brancker Research (RBR) wave height meter, the measurement error of the wave height is less than 10%, the period error is less than 0.5 s, and the wave direction error is less than 10° with the proposed method

SOI waveguide bragg grating photonic sensor for human body temperature measurement based on photonic integrated interrogator

A waveguide Bragg grating (WBG) provides a flexible way for measurement, and it could even be used to measure body temperature like e-skin. We designed and compared three structures of WBG with the grating period, etching depth, and duty cycle. The two-sided WBG was fabricated. An experimental platform based on photonic integrated interrogator was set up and the experiment on the two-sided WBG was performed. Results show that the two-sided WBG can be used to measure temperature changes over the range of 35–42◦C, with a temperature measurement error of 0.1◦C. This approach has the potential to facilitate application of such a silicon-on-insulator (SOI) WBG photonic sensor to wearable technology and realize the measurement of human temperature