Microwave Breast Imaging Techniques and Measurement Systems

Electromagnetic waves at microwave frequencies allow penetration into many optically non-transparent mediums such as biological tissues. Over the past 30 years, researchers have extensively investigated microwave imaging (MI) approaches including imaging algorithms, measurement systems and applications in biomedical fields, such as breast tumor detection, brain stroke detection, heart imaging and bone imaging. Successful clinical trials of MI for breast imaging brought worldwide excitation, and this achievement further confirmed that the MI has potential to become a low-risk and cost-effective alternative to existing medical imaging tools such as X-ray mammography for early breast cancer detection. This chapter offers comprehensive descriptions of the most important MI approaches for early breast cancer detection, including reconstruction procedures and measurement systems as well as apparatus

Constructing an Urban Population Model for Medical Insurance Scheme Using Microsimulation Techniques

China launched a pilot project of medical insurance reform in 79 cities in 2007 to cover urban nonworking residents. An urban population model was created in this paper for China's medical insurance scheme using microsimulation model techniques. The model made it clear for the policy makers the population distributions of different groups of people, the potential urban residents entering the medical insurance scheme. The income trends of units of individuals and families were also obtained. These factors are essential in making the challenging policy decisions when considering to balance the long-term financial sustainability of the medical insurance scheme

Effects of gap size, temperature and pumping pressure on the fluid dynamics and chemical kinetics of in-line spatial atomic layer deposition of Al2O3

Abstract: Low throughput is a major limitation for industrial level atomic layer deposition (ALD) applications. Spatial ALD is regarded as a promising solution to this issue. With numerical simulations, this paper studies an in-line spatial ALD reactor by investigating the effects of gap size, temperature, and pumping pressure on the flow and surface chemical deposition processes in Al2O3 ALD. The precursor intermixing is a critical issue in spatial ALD system design, and it is highly dependent on the flow and material distributions. By numerical studies, it’s found that bigger gap, e.g., 2 mm, results in less precursor intermixing, but generates slightly lower saturated deposition rate. Wafer temperature is shown as a significant factor in both flow and surface deposition processes. Higher temperature accelerates the diffusive mass transport, which largely contributes to the precursor intermixing. On the other hand, higher temperature increases film deposition rate. Well-maintained pumping pressure is beneficial to decrease the precursor intermixing level, but its effect on the chemical process is shown very weak. It is revealed that the time scale of in-line spatial ALD cycle is only in tens of milliseconds, i.e., ~15 ms. Considering that the in-line spatial ALD is a continuous process without purging step, the ALD cycle time is greatly shortened, and hence the overall throughput is shown as high as ~8 nm/s, compared to several nm/min in traditional ALD

Radar-STDA: A High-Performance Spatial-Temporal Denoising Autoencoder for Interference Mitigation of FMCW Radars

With its small size, low cost and all-weather operation, millimeter-wave radar can accurately measure the distance, azimuth and radial velocity of a target compared to other traffic sensors. However, in practice, millimeter-wave radars are plagued by various interferences, leading to a drop in target detection accuracy or even failure to detect targets. This is undesirable in autonomous vehicles and traffic surveillance, as it is likely to threaten human life and cause property damage. Therefore, interference mitigation is of great significance for millimeter-wave radar-based target detection. Currently, the development of deep learning is rapid, but existing deep learning-based interference mitigation models still have great limitations in terms of model size and inference speed. For these reasons, we propose Radar-STDA, a Radar-Spatial Temporal Denoising Autoencoder. Radar-STDA is an efficient nano-level denoising autoencoder that takes into account both spatial and temporal information of range-Doppler maps. Among other methods, it achieves a maximum SINR of 17.08 dB with only 140,000 parameters. It obtains 207.6 FPS on an RTX A4000 GPU and 56.8 FPS on an NVIDIA Jetson AGXXavier respectively when denoising range-Doppler maps for three consecutive frames. Moreover, we release a synthetic data set called Ra-inf for the task, which involves 384,769 range-Doppler maps with various clutters from objects of no interest and receiver noise in realistic scenarios. To the best of our knowledge, Ra-inf is the first synthetic dataset of radar interference. To support the community, our research is open-source via the link \url{https://github.com/GuanRunwei/rd_map_temporal_spatial_denoising_autoencoder}

Vibration modal shapes and strain measurement of the main shaft assembly of a friction hoist

In order to evaluate the reliability of the main shaft unit of a friction hoisting system, strain measurement is a significant method. In this paper, a test rig of a friction hoisting system was built, which could applied periodically changing load on its main shaft unit; The mechanical analysis under the test load was conducted and the boundary limits were obtained; A three dimensional model of the main shaft unit was built in Pro-E and its finite element analysis was performed in ANSYS; With the analytical result, measuring points for strain rosettes were initially selected; Vibration modal shapes of the main shaft unit were analyzed, based on which Modal Assurance Criterion (MAC) was utilized in the Particle Swarm Optimization (PSO) algorithm to make the final decision of the number and positions of the measuring points; A wireless measurement system was developed to acquire strain signals from the optimized measuring positions; The test result verified the efficiency of the methods employed in this paper and revealed how strain of the main shaft unit changes during running process

MOD-Net: A Machine Learning Approach via Model-Operator-Data Network for Solving PDEs

In this paper, we propose a model-operator-data network (MOD-Net) for solving PDEs. A MOD-Net is driven by a model to solve PDEs based on operator representation with regularization from data. In this work, we use a deep neural network to parameterize the Green's function. The empirical risk consists of the mean square of the governing equation, boundary conditions, and a few labels, which are numerically computed by traditional schemes on coarse grid points with cheap computation cost. With only the labeled dataset or only the model constraints, it is insufficient to accurately train a MOD-Net for complicate problems. Intuitively, the labeled dataset works as a regularization in addition to the model constraints. The MOD-Net is much efficient than original neural operator because the MOD-Net also uses the information of governing equation and the boundary conditions of the PDE rather than purely the expensive labels. Since the MOD-Net learns the Green's function of a PDE, it solves a type of PDEs but not a specific case. We numerically show MOD-Net is very efficient in solving Poisson equation and one-dimensional Boltzmann equation. For non-linear PDEs, where the concept of the Green's function does not apply, the non-linear MOD-Net can be similarly used as an ansatz for solving non-linear PDEs

Drug-induced anaphylaxis in China: a 10 year retrospective analysis of the Beijing Pharmacovigilance Database

Background Few studies on the causes of drug-induced anaphylaxis (DIA) in the hospital setting are available. Objective We aimed to use the Beijing Pharmacovigilance Database (BPD) to identify the causes of DIA in Beijing, China. Setting Anaphylactic case reports from the BPD provided by the Beijing Center for Adverse Drug Reaction Monitoring. Method DIA cases collected by the BPD from January 2004 to December 2014 were adjudicated. Cases were analyzed for demographics, causative drugs and route of administration, and clinical signs and outcomes. Main outcome measure Drugs implicated in DIAs were identified and the signs and symptoms of the DIA cases were analyzed. Results A total of 1189 DIA cases were analyzed. The mean age was 47.6 years, and 732 (61.6%) were aged from 18 to 59 years. A total of 627 patients (52.7%) were females. There was a predominance of cardiovascular (83.8%) followed by respiratory (55.4%), central nervous (50.1%), mucocutaneous (47.4%), and gastrointestinal symptoms (31.3%). A total of 249 different drugs were involved. DIAs were mainly caused by antibiotics (39.3%), traditional Chinese medicines (TCM) (11.9%), radiocontrast agents (11.9%), and antineoplastic agents (10.3%). Cephalosporins accounted for majority (34.5%) of antibiotic-induced anaphylaxis, followed by fluoroquinolones (29.6%), beta-lactam/beta-lactamase inhibitors (15.4%) and penicillins (7.9%). Blood products and biological agents (3.1%), and plasma substitutes (2.1%) were also important contributors to DIAs. Conclusion A variety of drug classes were implicated in DIAs. Patients should be closely monitored for signs and symptoms of anaphylaxis when medications are administered especially with antibiotics, TCM, radiocontrast and antineoplastic agents