MU-MIMO Communications with MIMO Radar: From Co-existence to Joint Transmission

Beamforming techniques are proposed for a joint multi-input-multi-output (MIMO) radar-communication (RadCom) system, where a single device acts both as a radar and a communication base station (BS) by simultaneously communicating with downlink users and detecting radar targets. Two operational options are considered, where we first split the antennas into two groups, one for radar and the other for communication. Under this deployment, the radar signal is designed to fall into the null-space of the downlink channel. The communication beamformer is optimized such that the beampattern obtained matches the radar's beampattern while satisfying the communication performance requirements. To reduce the optimizations' constraints, we consider a second operational option, where all the antennas transmit a joint waveform that is shared by both radar and communications. In this case, we formulate an appropriate probing beampattern, while guaranteeing the performance of the downlink communications. By incorporating the SINR constraints into objective functions as penalty terms, we further simplify the original beamforming designs to weighted optimizations, and solve them by efficient manifold algorithms. Numerical results show that the shared deployment outperforms the separated case significantly, and the proposed weighted optimizations achieve a similar performance to the original optimizations, despite their significantly lower computational complexity.Comment: 15 pages, 15 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

A prognostic 15-gene model based on differentially expressed genes among metabolic subtypes in diffuse large B-cell lymphoma

The outcomes of patients with diffuse large B-cell lymphoma (DLBCL) vary widely, and about 40% of them could not be cured by the standard first-line treatment, R-CHOP, which could be due to the high heterogeneity of DLBCL. Here, we aim to construct a prognostic model based on the genetic signature of metabolic heterogeneity of DLBCL to explore therapeutic strategies for DLBCL patients. Clinical and transcriptomic data of one training and four validation cohorts of DLBCL were obtained from the GEO database. Metabolic subtypes were identified by PAM clustering of 1,916 metabolic genes in the 7 major metabolic pathways in the training cohort. DEGs among the metabolic clusters were then analyzed. In total, 108 prognosis-related DEGs were identified. Through univariable Cox and LASSO regression analyses, 15 DEGs were used to construct a risk score model. The overall survival (OS) and progression-free survival (PFS) of patients with high risk were significantly worse than those with low risk (OS: HR 2.86, 95%CI 2.04–4.01, p < 0.001; PFS: HR 2.42, 95% CI 1.77–3.31, p < 0.001). This model was also associated with OS in the four independent validation datasets (GSE10846: HR 1.65, p = 0.002; GSE53786: HR 2.05, p = 0.02; GSE87371: HR 1.85, p = 0.027; GSE23051: HR 6.16, p = 0.007) and PFS in the two validation datasets (GSE87371: HR 1.67, p = 0.033; GSE23051: HR 2.74, p = 0.049). Multivariable Cox analysis showed that in all datasets, the risk model could predict OS independent of clinical prognosis factors (p < 0.05). Compared with the high-risk group, patients in the low-risk group predictively respond to R-CHOP (p = 0.0042), PI3K inhibitor (p < 0.05), and proteasome inhibitor (p < 0.05). Therefore, in this study, we developed a signature model of 15 DEGs among 3 metabolic subtypes, which could predict survival and drug sensitivity in DLBCL patients

Involvement of the G-Protein-Coupled Receptor 4 in the Increased Expression of RANK/RANKL/OPG System and Neurotrophins by Nucleus Pulposus Cells under the Degenerated Intervertebral Disc-Like Acidic Microenvironment

Intervertebral disc (IVD) degeneration is associated with local inflammation and increased expression of neurotrophins. Acidic microenvironment is believed to cause the progression of IVD degeneration. However, there is a paucity of information regarding the relationship between acidic microenvironment and the inflammation and expression of neurotrophins in IVD. G-protein-coupled receptor 4 (GPR4) is a pH-sensing receptor, which can activate the inflammation and increase the expression levels of nerve growth factor in acidic microenvironment. In this study, culture media with pH 7.2 (representing the normal IVD-like acidic condition) and pH 6.5 (degenerated IVD-like acidic condition) were prepared. The gene and protein expression levels of GPR4 in SD rat nucleus pulposus cells were determined under the acidic conditions. And cyclic AMP (cAMP), the second messenger of GPR4, was assayed. Furthermore, the expression levels of receptor activator of nuclear factor κ B (RANK), RANKL ligand (RANKL), osteoprotegerin (OPG), nerve growth factor (NGF), brain-derived neurotrophic factor (BDNF), and neurotrophin-3 (NT-3) were also determined. To clarify the involvement of GPR4 in the upregulation of the expression of RANK/RANKL/OPG system and neurotrophins, gene knockdown and forced expression of GPR4 and inhibiting its downstream cAMP accumulation and Ca2+ mobilization were performed. The alternation of the expression levels of matrix metalloproteinase-3 (MMP-3), MMP-13, and aggrecanase-2 (ADAMTS-5) were evaluated by RT-PCR and western blot. The results showed that GPR4 was expressed in rat nucleus pulposus cells, and the expression was upregulated under the degenerated IVD-like acidic microenvironment. cAMP accumulation levels were increased under the degenerated IVD-like acidic culture conditions. The expression levels of RANK, RANKL, OPG, NGF, and BNDF were significantly upregulated under the degenerated IVD-like acidic microenvironment. GPR4 knockdown and reduction of cAMP by the inhibitor SQ22536 abolished the upregulation of the expression of RANK, RANKL, OPG, NGF, and BNDF under the degenerated IVD-like acidic microenvironment. On the opposite, acidosis-induced cAMP accumulation and upregulation of RANK, RANKL, OPG, NGF, and BNDF were further promoted by GPR4 overexpression. The expression levels of MMP-3, MMP-13, and ADAMTS-5 were upregulated under the degenerated IVD-like acidic condition, which can be promoted or attenuated by GPR4 overexpression or knockdown, respectively. We concluded that GPR4-mediated cAMP accumulation was involved in the increased expression of RANK/RANKL/OPG system and neurotrophins by nucleus pulposus cells under the degenerated IVD-like acidic microenvironment

Multidisciplinary Optimization of Thermal Insulation Layer for Stratospheric Airship with a Solar Array

Stratospheric airships with a solar array have demonstrated overwhelming superiority in many aspects, such as earth observation, meteorological survey, and communication relay. The solar array supplies sufficient power for the airship to be in flight for months, but excessive heat is also transferred to the airship, causing high overpressure of inner gas. However, the optimal arrangement of the insulation layer on the airship has not been investigated. The theoretical method, including the geometry, thermal, and energy models, is developed and validated. The distribution of the temperatures and power of the solar cells, with different installation angles on the curved surface, is investigated. The thickness of insulation layer has a significant effect on the solar output power and internal pressure of the airship. An optimized configuration of the insulation structure is proposed, in order to improve the total output energy of solar array. The optimized configuration of insulations helps to reduce the structural mass by 24.9% and increase the payload mass by 9%. Moreover, the optimized arrangement improves the output energy of solar array in a year, and the maximum improvement is 8.2% on the winter solstice. The work displays the optimization of the thermal insulation layer for the stratospheric airship with a solar array, in order to improve the everyday energy acquirement during flight in a year

Solving TSP via fuzzy dynamic PSO and HNN algorithm

Conference Name:2012 7th International Conference on Computer Science and Education, ICCSE 2012. Conference Address: Melbourne, VIC, Australia. Time:July 14, 2012 - July 17, 2012.University of MelbourneSince the Hopfield network often suffers from slow rate of convergence and low accuracy and being trapped in local extremes when used to solve the traveling salesman problem, this paper combines the fuzzy dynamic particle swarm optimization (PSO) and Hopfield neural networks (HNN) to form a novel algorithm, FDPSO-HNN. Experiments show that the proposed methods outperform the algorithm of EPSO and N-EPSO in terms of both global convergence rate and computation time. 漏 2012 IEEE

Online Learning-Based Surrogate Modeling of Stratospheric Airship Solar Array Output Power

The stratospheric airship is a type of aerostat that uses solar energy as its power source and can fly continuously for months or even years in near space. The rapid and accurate prediction of the output power of its solar array is the key to maintaining energy balance and extending flight time. This paper establishes an online learning model for predicting the output power of the solar array of stratospheric airships. The readings of radiometers arranged on the surface of the airship are used as features for training the model. The parameters of the model can be updated in real-time during the flight process without retraining the entire model. The effect of radiometer placement on the model accuracy was also analyzed. The results show that for the continuous flight of 40 days, the online learning model can achieve an accuracy of 88% after training with 10 days of flight data and the accuracy basically reaches its highest level after 20 days. In addition, placing the radiometers at the four corners of the array can achieve a higher prediction accuracy of 95%. The online model can also accurately identify and reflect the effect of module efficiency attenuation or damage and maintain high accuracy

Application of EMD Technology in Leakage Acoustic Characteristic Extraction of Gas-Liquid, Two-Phase Flow Pipelines

Nowadays, the exploitation and transportation of marine oil and gas are mainly achieved using multiphase flow pipelines. Leakage detection of multiphase flow pipelines has always been the most difficult problem regarding the pipeline safety. Compared to other methods, acoustic detection technology has many advantages and high adaptability. However, multiphase flow pipelines are associated with many noise sources that affect the extraction and recognition of leakage signals. In this study, the mechanism of leakage acoustic source generation in gas-liquid, two-phase pipelines is analyzed. First, an acoustic leakage detection experiment in the multiphase pipelines is conducted. The acoustic signals are divided into two classes in accordance with whether leakage occurs or not. The original signals are processed and analyzed based on empirical mode decomposition (EMD) processing technology. Based on the use of signal processing, this study shows that EMD technology can accurately identify the leakage signal in the gas-liquid, two-phase pipeline. Upon increases in the leakage aperture sizes, the entropy of the EMD information of the acoustic signals gradually increases. Finally, the method of the normalized energies characteristic value of each IMF component is also applied in leakage signal processing. When the liquid flow is maintained constant, the energy values of the IMF components change in a nonlinear manner when the gas flow rate increases. This verifies the feasibility of use of the acoustic wave sensing technology for leak detection in multiphase flow pipelines, which has important theoretical significance for promoting the development of safe and efficient operation in two-phase flow pipelines