Synthesis, Processing and Characterization of Polymer Derived Ceramic Nanocomposite Coating Reinforced with Carbon Nanotube Preforms

Ceramics have a number of applications as coating material due to their high hardness, wear and corrosion resistance, and the ability to withstand high temperatures. Critical to the success of these materials is the effective heat transfer through a material to allow for heat diffusion or effective cooling, which is often limited by the low thermal conductivity of many ceramic materials. To meet the challenge of improving the thermal conductivity of ceramics without lowering their performance envelope, carbon nanotubes were selected to improve the mechanical properties and thermal dispersion ability due to its excellent mechanical properties and high thermal conductivity in axial direction. However, the enhancements are far lower than expectation resulting from limited carbon nanotube content in ceramic matrix composites and the lack of alignment. These problems can be overcome if ceramic coatings are reinforced by carbon nanotubes with good dispersion and alignment. In this study, the well-dispersed and aligned carbon nanotubes preforms were achieved in the form of vertically aligned carbon nanotubes (VACNTs) and Buckypaper. Polymer derived ceramic (PDC) was selected as the matrix to fabricate carbon nanotube reinforced ceramic nanocomposites through resin curing and pyrolysis. The SEM images indicates the alignment of carbon nanotubes in the PDC nanocomposites. The mechanical and thermal properties of the PDC nanocomposites were characterized through Vickers hardness measurement and Thermogravimetric Analysis. The ideal anisotropic properties of nanocomposites were confirmed by estimating the electrical conductivity in two orthogonal directions

Advanced NOMA Assisted Semi-Grant-Free Transmission Schemes for Randomly Distributed Users

Non-orthogonal multiple access (NOMA) assisted semi-grant-free (SGF) transmission has recently received significant research attention due to its outstanding ability of serving grant-free (GF) users with grant-based (GB) users' spectrum, {\color{blue}which can greatly improve the spectrum efficiency and effectively relieve the massive access problem of 5G and beyond networks. In this paper, we investigate the performance of SGF schemes under more practical settings.} Firstly, we study the outage performance of the best user scheduling SGF scheme (BU-SGF) by considering the impacts of Rayleigh fading, path loss, and random user locations. Then, a fair SGF scheme is proposed by applying cumulative distribution function (CDF)-based scheduling (CS-SGF), which can also make full use of multi-user diversity. Moreover, by employing the theories of order statistics and stochastic geometry, we analyze the outage performances of both BU-SGF and CS-SGF schemes. Results show that full diversity orders can be achieved only when the served users' data rate is capped, which severely limit the rate performance of SGF schemes. To further address this issue, we propose a distributed power control strategy to relax such data rate constraint, and derive closed-form expressions of the two schemes' outage performances under this strategy. Finally, simulation results validate the fairness performance of the proposed CS-SGF scheme, the effectiveness of the power control strategy, and the accuracy of the theoretical analyses.Comment: 41 pages, 8 figure

Dynamic Resource Management in CDRT Systems through Adaptive NOMA

This paper introduces a novel adaptive transmission scheme to amplify the prowess of coordinated direct and relay transmission (CDRT) systems rooted in non-orthogonal multiple access principles. Leveraging the maximum ratio transmission scheme, we seamlessly meet the prerequisites of CDRT while harnessing the potential of dynamic power allocation and directional antennas to elevate the system's operational efficiency. Through meticulous derivations, we unveil closed-form expressions depicting the exact effective sum throughput. Our simulation results adeptly validate the theoretical analysis and vividly showcase the effectiveness of the proposed scheme.Comment: 11 pages, 7 figures, submitted to IEEE journal for revie

On Secure NOMA-Aided Semi-Grant-Free Systems

Semi-grant-free (SGF) transmission scheme enables grant-free (GF) users to utilize resource blocks allocated for grant-based (GB) users while maintaining the quality of service of GB users. This work investigates the secrecy performance of non-orthogonal multiple access (NOMA)-aided SGF systems. First, analytical expressions for the exact and asymptotic secrecy outage probability (SOP) of NOMA-aided SGF systems with a single GF user are derived. Then, the SGF systems with multiple GF users and a best-user scheduling scheme is considered. By utilizing order statistics theory, closed-form expressions for the exact and asymptotic SOP are derived. Monte Carlo simulation results demonstrate the effects of system parameters on the SOP of the considered system and verify the accuracy of the developed analytical results. The results indicate that both the outage target rate for GB and the secure target rate for GF are the main factors of the secrecy performance of SGF systems

LightBTSeg: A lightweight breast tumor segmentation model using ultrasound images via dual-path joint knowledge distillation

The accurate segmentation of breast tumors is an important prerequisite for lesion detection, which has significant clinical value for breast tumor research. The mainstream deep learning-based methods have achieved a breakthrough. However, these high-performance segmentation methods are formidable to implement in clinical scenarios since they always embrace high computation complexity, massive parameters, slow inference speed, and huge memory consumption. To tackle this problem, we propose LightBTSeg, a dual-path joint knowledge distillation framework, for lightweight breast tumor segmentation. Concretely, we design a double-teacher model to represent the fine-grained feature of breast ultrasound according to different semantic feature realignments of benign and malignant breast tumors. Specifically, we leverage the bottleneck architecture to reconstruct the original Attention U-Net. It is regarded as a lightweight student model named Simplified U-Net. Then, the prior knowledge of benign and malignant categories is utilized to design the teacher network combined dual-path joint knowledge distillation, which distills the knowledge from cumbersome benign and malignant teachers to a lightweight student model. Extensive experiments conducted on breast ultrasound images (Dataset BUSI) and Breast Ultrasound Dataset B (Dataset B) datasets demonstrate that LightBTSeg outperforms various counterparts.Comment: 7 pages, 7 figures, conferenc

Trajectory and power design for aerial CRNs with colluding eavesdroppers

Unmanned aerial vehicles (UAVs) can provide wireless access services to terrestrial users without geographical limitations and will become an essential part of the future communication system. However, the openness of wireless channels and the mobility of UAVs make the security of UAV-based communication systems particularly challenging. This work investigates the security of aerial cognitive radio networks (CRNs) with multiple uncertainties colluding eavesdroppers. A cognitive aerial base station transmits messages to cognitive terrestrial users using the spectrum resource of the primary users. All secondary terrestrial users and illegitimate receivers jointly decode the received message. The average secrecy rate of the aerial CRNs is maximized by jointly optimizing the UAV's trajectory and transmission power. An iterative algorithm based on block coordinate descent and successive convex approximation is proposed to solve the non-convex mixed-variable optimization problem. Numerical results verify the effectiveness of our proposed algorithm and show that our scheme improves the secrecy performance of airborne CRNs.Comment: 10 pages, 7 figures.submitted to the IEEE journal for revie

Different MRI-based radiomics models for differentiating misdiagnosed or ambiguous pleomorphic adenoma and Warthin tumor of the parotid gland: a multicenter study

PurposeTo evaluate the effectiveness of MRI-based radiomics models in distinguishing between Warthin tumors (WT) and misdiagnosed or ambiguous pleomorphic adenoma (PA).MethodsData of patients with PA and WT from two centers were collected. MR images were used to extract radiomic features. The optimal radiomics model was found by running nine machine learning algorithms after feature reduction and selection. To create a clinical model, univariate logistic regression (LR) analysis and multivariate LR were used. The independent clinical predictors and radiomics were combined to create a nomogram. Two integrated models were constructed by the ensemble and stacking algorithms respectively based on the clinical model and the optimal radiomics model. The models’ performance was evaluated using the area under the curve (AUC).ResultsThere were 149 patients included in all. Gender, age, and smoking of patients were independent clinical predictors. With the greatest average AUC (0.896) and accuracy (0.839) in validation groups, the LR model was the optimal radiomics model. In the average validation group, the radiomics model based on LR did not have a higher AUC (0.795) than the clinical model (AUC = 0.909). The nomogram (AUC = 0.953) outperformed the radiomics model in terms of discrimination performance. The nomogram in the average validation group had a highest AUC than the stacking model (0.914) or ensemble model (0.798).ConclusionMisdiagnosed or ambiguous PA and WT can be non-invasively distinguished using MRI-based radiomics models. The nomogram exhibited excellent and stable diagnostic performance. In daily work, it is necessary to combine with clinical parameters for distinguishing between PA and WT