Model-Driven Sensing-Node Selection and Power Allocation for Tracking Maneuvering Targets in Perceptive Mobile Networks

Maneuvering target tracking will be an important service of future wireless networks to assist innovative applications such as intelligent transportation. However, tracking maneuvering targets by cellular networks faces many challenges. For example, the dense network and high-speed targets make the selection of the sensing nodes (SNs), e.g., base stations, and the associated power allocation very difficult, given the stringent latency requirement of sensing applications. Existing methods have demonstrated engaging tracking performance, but with very high computational complexity. In this paper, we propose a model-driven deep learning approach for SN selection to meet the latency requirement. To this end, we first propose an iterative SN selection method by jointly exploiting the majorization-minimization (MM) framework and the alternating direction method of multipliers (ADMM). Then, we unfold the iterative algorithm as a deep neural network (DNN) and prove its convergence. The proposed model-driven method has a low computational complexity, because the number of layers is less than the number of iterations required by the original algorithm, and each layer only involves simple matrix-vector additions/multiplications. Finally, we propose an efficient power allocation method based on fixed point (FP) water filling (WF) and solve the joint SN selection and power allocation problem under the alternative optimization framework. Simulation results show that the proposed method achieves better performance than the conventional optimization-based methods with much lower computational complexity

CFD analysis/optimization of thermo-acoustic instabilities in liquid fuelled aero stationary gas turbine combustors

It has been recognized that the evaporation process is one of the pivotal mechanisms driving thermo-acoustic instability in gas turbines and rockets in particular. In this regard, this study is principally focused on studying the evaporation process relevant to thermo-acoustic instability from three complementary viewpoints in an effort to contribute to an overall instability model driven primarily by evaporation in gas turbine combustors. Firstly, a state of the art LES algorithm is employed to validate an evaporation model to be employed in predictive modelling regarding combustion instabilities. Good agreement between the numerical predictions and experimental data is achieved. Additionally, transient sub-critical droplet evaporation is investigated numerically. In particular, a numerical method is proposed to capture the extremely important pressure-velocity-density coupling. Furthermore, the dynamic system nonlinear behaviour encountered in classical thermo-acoustic instability is investigated. The Poincaré map is adopted to analyse the stability of a simple non-autonomous system considering a harmonic oscillation behaviour for the combustion environment. The bifurcation diagram of a one-mode model is obtained where the analysis reveals a variety of chaotic behaviours for some select ranges of the bifurcation parameter. The bifurcation parameter and the corresponding period of a two-mode dynamic model are calculated using both analytical and numerical methods. The results computed by different methods are in good agreement. In addition, the dependence of the bifurcation parameter and the period on all the relevant coefficients in the model is investigated in depth. Moreover, a discrete dynamic model accounting for both combustion and vaporization processes is developed. In terms of different bifurcation parameters relevant to either combustion or evaporation, various bifurcation diagrams are presented. As part of the nonlinear characterization, the governing process Lyapunov exponent is calculated and employed to analyze the stability of the particular dynamic system. The study has shown conclusively that the evaporation process has a significant impact on the intensity and nonlinear behaviour of the system of interest, vis-à-vis a model accounting for only the gaseous combustion process. Furthermore, two particular nonlinear control methodologies are adopted to control the chaotic behaviour displayed by the particular aperiodic motions observed. These algorithms are intended to be implemented for control of combustion instability numerically and experimentally to provide a rational basis for some of the control methodologies employed in the literature. Finally, a state of the art neural network is employed to identify and predict the nonlinear behaviour inherent in combustion instability, and control the ensuing pressure oscillations. Essentially, the NARMAX model is implemented to capture nonlinear dynamics relating the input and output of the system of interest. The simulated results accord with the results reported. Moreover, a control system using the NARMA-L2 algorithm is developed. The simulation conclusively points to the fact that the amplitude of pressure oscillations can be attenuated to an acceptable level and the controller proposed may be implemented in a practical manner.EThOS - Electronic Theses Online ServiceORS/MACE ScholarshipGBUnited Kingdo

CFD analysis/optimization of thermo-acoustic instabilities in liquid fuelled aero stationary gas turbine combustors

It has been recognized that the evaporation process is one of the pivotal mechanisms driving thermo-acoustic instability in gas turbines and rockets in particular. In this regard, this study is principally focused on studying the evaporation process relevant to thermo-acoustic instability from three complementary viewpoints in an effort to contribute to an overall instability model driven primarily by evaporation in gas turbine combustors. Firstly, a state of the art LES algorithm is employed to validate an evaporation model to be employed in predictive modelling regarding combustion instabilities. Good agreement between the numerical predictions and experimental data is achieved. Additionally, transient sub-critical droplet evaporation is investigated numerically. In particular, a numerical method is proposed to capture the extremely important pressure-velocity-density coupling. Furthermore, the dynamic system nonlinear behaviour encountered in classical thermo-acoustic instability is investigated. The Poincaré map is adopted to analyse the stability of a simple non-autonomous system considering a harmonic oscillation behaviour for the combustion environment. The bifurcation diagram of a one-mode model is obtained where the analysis reveals a variety of chaotic behaviours for some select ranges of the bifurcation parameter. The bifurcation parameter and the corresponding period of a two-mode dynamic model are calculated using both analytical and numerical methods. The results computed by different methods are in good agreement. In addition, the dependence of the bifurcation parameter and the period on all the relevant coefficients in the model is investigated in depth. Moreover, a discrete dynamic model accounting for both combustion and vaporization processes is developed. In terms of different bifurcation parameters relevant to either combustion or evaporation, various bifurcation diagrams are presented. As part of the nonlinear characterization, the governing process Lyapunov exponent is calculated and employed to analyze the stability of the particular dynamic system. The study has shown conclusively that the evaporation process has a significant impact on the intensity and nonlinear behaviour of the system of interest, vis-à-vis a model accounting for only the gaseous combustion process. Furthermore, two particular nonlinear control methodologies are adopted to control the chaotic behaviour displayed by the particular aperiodic motions observed. These algorithms are intended to be implemented for control of combustion instability numerically and experimentally to provide a rational basis for some of the control methodologies employed in the literature. Finally, a state of the art neural network is employed to identify and predict the nonlinear behaviour inherent in combustion instability, and control the ensuing pressure oscillations. Essentially, the NARMAX model is implemented to capture nonlinear dynamics relating the input and output of the system of interest. The simulated results accord with the results reported. Moreover, a control system using the NARMA-L2 algorithm is developed. The simulation conclusively points to the fact that the amplitude of pressure oscillations can be attenuated to an acceptable level and the controller proposed may be implemented in a practical manner.EThOS - Electronic Theses Online ServiceORS/MACE ScholarshipGBUnited Kingdo

Sensing Mutual Information with Random Signals in Gaussian Channels

Sensing performance is typically evaluated by classical metrics, such as Cramer-Rao bound and signal-to-clutter-plus-noise ratio. The recent development of the integrated sensing and communication (ISAC) framework motivated the efforts to unify the metric for sensing and communication, where researchers have proposed to utilize mutual information (MI) to measure the sensing performance with deterministic signals. However, the need to communicate in ISAC systems necessitates the use of random signals for sensing applications and the closed-form evaluation for the sensing mutual information (SMI) with random signals is not yet available in the literature. This paper investigates the achievable performance and precoder design for sensing applications with random signals. For that purpose, we first derive the closed-form expression for the SMI with random signals by utilizing random matrix theory. The result reveals some interesting physical insights regarding the relation between the SMI with deterministic and random signals. The derived SMI is then utilized to optimize the precoder by leveraging a manifold-based optimization approach. The effectiveness of the proposed methods is validated by simulation results

Potential therapeutic strategy for non-Hodgkin lymphoma by anti-CD20scFvFc/CD28/CD3zeta gene tranfected T cells

<p>Abstract</p> <p>Background</p> <p>Anti-CD20 monoclonal antibody treatment has not only increased survival and cure rates in many non-Hodgkin lymphomas, but also has prompted an explosion in the development of novel antibodies and biologically active substances with specific cellular targets in the field of malignancies treatment. Since the robust immune responses are elicited by the gene-modified T cells, gene based T cell therapy may also provide a powerful tool for cancer immunotherapy.</p> <p>Methods</p> <p>In this study, we developed a vector construction encoding a chimeric T cell receptor that recognizes the CD20 antigen and delivers co-stimulatory signals to achieve T cell activation. One non-Hodgkin lymphoma cell line Raji cells co-cultured with peripheral blood-derived T cells were stably transfected with anti-CD20scFvFc/CD28/CD3zeta gene or anti-CD20scFvFc gene. T cells expressing anti-CD20scFvFc/CD28/CD3zeta or anti-CD20scFvFc gene co-cultured with CD20 positive Raji cells for different times. Cell lysis assay was carried by [³H]TdR release assay. The expressions of Fas, Bcl-2 and Caspase-3 of Raji cells were detected by flow cytometric. The secretion of IFN-gamma and IL-2 in co-culture medium was tested by ELISA assay. Activity of AP-1 was analyzed by EMSA.</p> <p>Results</p> <p>Following efficient transduction of peripheral blood-derived T cells with anti-CD20scFvFc/CD28/CD3zeta gene, an obvious cell lysis of Raji cells was observed in co-culture. T cells transduced anti-CD20scFvFc/CD28/CD3zeta gene had superior secretion of IFN-gamma and IL-2 compared to T cells transduced anti-CD20scFvFc gene. Also it led to a much stronger Fas-induced apoptosis signaling transduction in target cancer cells.</p> <p>Conclusion</p> <p>So adoptively T cells transduced anti-CD20scFvFc/CD28/CD3zeta gene mediates enhanced anti-tumor activities against CD20 positive tumor cells, suggesting a potential of gene-based immunotherapy for non-Hodgkin lymphoma.</p

EVNet: An Explainable Deep Network for Dimension Reduction

Dimension reduction (DR) is commonly utilized to capture the intrinsic structure and transform high-dimensional data into low-dimensional space while retaining meaningful properties of the original data. It is used in various applications, such as image recognition, single-cell sequencing analysis, and biomarker discovery. However, contemporary parametric-free and parametric DR techniques suffer from several significant shortcomings, such as the inability to preserve global and local features and the pool generalization performance. On the other hand, regarding explainability, it is crucial to comprehend the embedding process, especially the contribution of each part to the embedding process, while understanding how each feature affects the embedding results that identify critical components and help diagnose the embedding process. To address these problems, we have developed a deep neural network method called EVNet, which provides not only excellent performance in structural maintainability but also explainability to the DR therein. EVNet starts with data augmentation and a manifold-based loss function to improve embedding performance. The explanation is based on saliency maps and aims to examine the trained EVNet parameters and contributions of components during the embedding process. The proposed techniques are integrated with a visual interface to help the user to adjust EVNet to achieve better DR performance and explainability. The interactive visual interface makes it easier to illustrate the data features, compare different DR techniques, and investigate DR. An in-depth experimental comparison shows that EVNet consistently outperforms the state-of-the-art methods in both performance measures and explainability.Comment: 18 pages, 15 figures, accepted by TVC