Communication Aware UAV Swarm Surveillance Based on Hierarchical Architecture

Multi-agent unmanned aerial vehicle (UAV) teaming becomes an essential part in science mission, modern warfare surveillance, and disaster rescuing. This paper proposes a decentralized UAV swarm persistent monitoring strategy in realizing continuous sensing coverage and network service. A two-layer (high altitude and low altitude) UAV teaming hierarchical structure is adopted in realizing the accurate object tracking in the area of interest (AOI). By introducing the UAV communication channel model in its path planning, both centralized and decentralized control schemes would be evaluated in the waypoint tracking simulation. The UAV swarm network service and object tracking are measured by metrics of communication link quality and waypoints tracking accuracy. UAV swarm network connectivity are evaluated over different aspects, such as stability and volatility. The comparison of proposed algorithms is presented with simulations. The result shows that the decentralized scheme outperforms the centralized scheme in the mission of persistent surveillance, especially on maintaining the stability of inner UAV swarm network while tracking moving objects

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Folate deficiency may increase the risk for elevated TSH in patients with type 2 diabetes mellitus

Abstract Background Type 2 diabetes mellitus (T2DM) and thyroid dysfunction (TD) are two common chronic endocrine disorders that often coexist. Folate deficiency has been reported to be related with the onset and development of T2DM. However, the relationship between folate deficiency and TD remains unclear. This study aims to investigate the association of serum folate with TD in patients with T2DM. Methods The study used data on 268 inpatients with T2DM in the Beijing Chao-yang Hospital, Capital Medical University from October 2020 to February 2021. Thyroid stimulating hormone (TSH), free triiodothyronine (FT3), free thyroxine (FT4), and serum folate were measured with chemiluminescence immunoassay (CLIA), and folate deficiency was defined as a serum folate concentration < 4.4 ng/mL. Ordinary least squares regression models were used to assess the association of serum folate with TSH concentration. Multivariable logistic regression models were performed to explore the correlation of folate deficiency and the risk for elevated TSH. Results 15.3% of T2DM patients had TD. Among those patients with TD, 80.5% had elevated TSH. Compared with the normal-TSH and low-TSH groups, the prevalence of folate deficiency was significantly higher in the elevated-TSH group (P < 0.001). Serum folate level was negatively associated with TSH (β=-0.062, 95%CI: -0.112, -0.012). Folate deficiency was associated with the higher risk for elevated TSH in patients with T2DM (OR = 8.562, 95%CI: 3.108, 23.588). Conclusions A low serum folate concentration was significantly associated with a higher risk for elevated TSH among T2DM patients

Cross-Modality Transfer Learning for Image-Text Information Management

In the past decades, information from all kinds of data has been on a rapid increase. With state-of-the-art performance, machine learning algorithms have been beneficial for information management. However, insufficient supervised training data is still an adversity in many real-world applications. Therefore, transfer learning (TF) was proposed to address this issue. This article studies a not well investigated but important TL problem termed cross-modality transfer learning (CMTL). This topic is closely related to distant domain transfer learning (DDTL) and negative transfer. In general, conventional TL disciplines assume that the source domain and the target domain are in the same modality. DDTL aims to make efficient transfers even when the domains or the tasks are entirely different. As an extension of DDTL, CMTL aims to make efficient transfers between two different data modalities, such as from image to text. As the main focus of this study, we aim to improve the performance of image classification by transferring knowledge from text data. Previously, a few CMTL algorithms were proposed to deal with image classification problems. However, most existing algorithms are very task specific, and they are unstable on convergence. There are four main contributions in this study. First, we propose a novel heterogeneous CMTL algorithm, which requires only a tiny set of unlabeled target data and labeled source data with associate text tags. Second, we introduce a latent semantic information extraction method to connect the information learned from the image data and the text data. Third, the proposed method can effectively handle the information transfer across different modalities (text-image). Fourth, we examined our algorithm on a public dataset, Office-31. It has achieved up to 5% higher classification accuracy than “non-transfer” algorithms and up to 9% higher than existing CMTL algorithms. </jats:p

Communication Aware UAV Swarm Surveillance Based on Hierarchical Architecture

Multi-agent unmanned aerial vehicle (UAV) teaming becomes an essential part in science mission, modern warfare surveillance, and disaster rescuing. This paper proposes a decentralized UAV swarm persistent monitoring strategy in realizing continuous sensing coverage and network service. A two-layer (high altitude and low altitude) UAV teaming hierarchical structure is adopted in realizing the accurate object tracking in the area of interest (AOI). By introducing the UAV communication channel model in its path planning, both centralized and decentralized control schemes would be evaluated in the waypoint tracking simulation. The UAV swarm network service and object tracking are measured by metrics of communication link quality and waypoints tracking accuracy. UAV swarm network connectivity are evaluated over different aspects, such as stability and volatility. The comparison of proposed algorithms is presented with simulations. The result shows that the decentralized scheme outperforms the centralized scheme in the mission of persistent surveillance, especially on maintaining the stability of inner UAV swarm network while tracking moving objects.</jats:p

Communication Aware UAV Swarm Surveillance Based on Hierarchical Architecture

Multi-agent unmanned aerial vehicle (UAV) teaming becomes an essential part in science mission, modern warfare surveillance, and disaster rescuing. This paper proposes a decentralized UAV swarm persistent monitoring strategy in realizing continuous sensing coverage and network service. A two-layer (high altitude and low altitude) UAV teaming hierarchical structure is adopted in realizing the accurate object tracking in the area of interest (AOI). By introducing the UAV communication channel model in its path planning, both centralized and decentralized control schemes would be evaluated in the waypoint tracking simulation. The UAV swarm network service and object tracking are measured by metrics of communication link quality and waypoints tracking accuracy. UAV swarm network connectivity are evaluated over different aspects, such as stability and volatility. The comparison of proposed algorithms is presented with simulations. The result shows that the decentralized scheme outperforms the centralized scheme in the mission of persistent surveillance, especially on maintaining the stability of inner UAV swarm network while tracking moving objects

A Data-Driven Approach for Mitigating Dark Current Noise and Bad Pixels in Complementary Metal Oxide Semiconductor Cameras for Space-based Telescopes

In recent years, there has been a gradual increase in the performance of Complementary Metal Oxide Semiconductor (CMOS) cameras. These cameras have gained popularity as a viable alternative to charge-coupled device (CCD) cameras in a wide range of applications. One particular application is the CMOS camera installed in small space telescopes. However, the limited power and spatial resources available on satellites present challenges in maintaining ideal observation conditions, including temperature and radiation environment. Consequently, images captured by CMOS cameras are susceptible to issues such as dark current noise and defective pixels. In this paper, we introduce a data-driven framework for mitigating dark current noise and bad pixels for CMOS cameras. Our approach involves two key steps: pixel clustering and function fitting. During pixel clustering step, we identify and group pixels exhibiting similar dark current noise properties. Subsequently, in the function fitting step, we formulate functions that capture the relationship between dark current and temperature, as dictated by the Arrhenius law. Our framework leverages ground-based test data to establish distinct temperature-dark current relations for pixels within different clusters. The cluster results could then be utilized to estimate the dark current noise level and detect bad pixels from real observational data. To assess the effectiveness of our approach, we have conducted tests using real observation data obtained from the Yangwang-1 satellite, equipped with a near-ultraviolet telescope and an optical telescope. The results show a considerable improvement in the detection efficiency of space-based telescopes.Comment: Accepted by the AJ, comments are welcome. The complete code could be downloaded from: DOI: 10.12149/10138