Support Recovery of Sparse Signals

We consider the problem of exact support recovery of sparse signals via noisy measurements. The main focus is the sufficient and necessary conditions on the number of measurements for support recovery to be reliable. By drawing an analogy between the problem of support recovery and the problem of channel coding over the Gaussian multiple access channel, and exploiting mathematical tools developed for the latter problem, we obtain an information theoretic framework for analyzing the performance limits of support recovery. Sharp sufficient and necessary conditions on the number of measurements in terms of the signal sparsity level and the measurement noise level are derived. Specifically, when the number of nonzero entries is held fixed, the exact asymptotics on the number of measurements for support recovery is developed. When the number of nonzero entries increases in certain manners, we obtain sufficient conditions tighter than existing results. In addition, we show that the proposed methodology can deal with a variety of models of sparse signal recovery, hence demonstrating its potential as an effective analytical tool.Comment: 33 page

Realtime Profiling of Fine-Grained Air Quality Index Distribution using UAV Sensing

Given significant air pollution problems, air quality index (AQI) monitoring has recently received increasing attention. In this paper, we design a mobile AQI monitoring system boarded on unmanned-aerial-vehicles (UAVs), called ARMS, to efficiently build fine-grained AQI maps in realtime. Specifically, we first propose the Gaussian plume model on basis of the neural network (GPM-NN), to physically characterize the particle dispersion in the air. Based on GPM-NN, we propose a battery efficient and adaptive monitoring algorithm to monitor AQI at the selected locations and construct an accurate AQI map with the sensed data. The proposed adaptive monitoring algorithm is evaluated in two typical scenarios, a two-dimensional open space like a roadside park, and a three-dimensional space like a courtyard inside a building. Experimental results demonstrate that our system can provide higher prediction accuracy of AQI with GPM-NN than other existing models, while greatly reducing the power consumption with the adaptive monitoring algorithm

Factors Affecting Ion Thruster’s Performance

In this project, we investigated how ion thrusters produce propulsion and how the design of ion thrusters affects the performance of the thruster. In the experiment, we build a high voltage power supply (0- 50 kV) and foil rings to produce ion wind. When considering the design of the thruster, we focus on three variables: the volume of the space, where ions are produced and the electric field intensity. Thus, to investigate the first variable we made foil rings with different radius and change the distance between the ring and positive cathode. To determine the propulsion produced we use a speed sensor to determine the magnitude of the wind produced

DPF-Nutrition: Food Nutrition Estimation via Depth Prediction and Fusion

A reasonable and balanced diet is essential for maintaining good health. With the advancements in deep learning, automated nutrition estimation method based on food images offers a promising solution for monitoring daily nutritional intake and promoting dietary health. While monocular image-based nutrition estimation is convenient, efficient, and economical, the challenge of limited accuracy remains a significant concern. To tackle this issue, we proposed DPF-Nutrition, an end-to-end nutrition estimation method using monocular images. In DPF-Nutrition, we introduced a depth prediction module to generate depth maps, thereby improving the accuracy of food portion estimation. Additionally, we designed an RGB-D fusion module that combined monocular images with the predicted depth information, resulting in better performance for nutrition estimation. To the best of our knowledge, this was the pioneering effort that integrated depth prediction and RGB-D fusion techniques in food nutrition estimation. Comprehensive experiments performed on Nutrition5k evaluated the effectiveness and efficiency of DPF-Nutrition

A theoretical framework of immune cell phenotypic classification and discovery

Immune cells are highly heterogeneous and show diverse phenotypes, but the underlying mechanism remains to be elucidated. In this study, we proposed a theoretical framework for immune cell phenotypic classification based on gene plasticity, which herein refers to expressional change or variability in response to conditions. The system contains two core points. One is that the functional subsets of immune cells can be further divided into subdivisions based on their highly plastic genes, and the other is that loss of phenotype accompanies gain of phenotype during phenotypic conversion. The first point suggests phenotypic stratification or layerability according to gene plasticity, while the second point reveals expressional compatibility and mutual exclusion during the change in gene plasticity states. Abundant transcriptome data analysis in this study from both microarray and RNA sequencing in human CD4 and CD8 single-positive T cells, B cells, natural killer cells and monocytes supports the logical rationality and generality, as well as expansibility, across immune cells. A collection of thousands of known immunophenotypes reported in the literature further supports that highly plastic genes play an important role in maintaining immune cell phenotypes and reveals that the current classification model is compatible with the traditionally defined functional subsets. The system provides a new perspective to understand the characteristics of dynamic, diversified immune cell phenotypes and intrinsic regulation in the immune system. Moreover, the current substantial results based on plasticitomics analysis of bulk and single-cell sequencing data provide a useful resource for big-data–driven experimental studies and knowledge discoveries

Performance tradeoffs for exact support recovery of sparse signals

Abstract—We study the tradeoffs between the number of measurements, the signal sparsity level, and the measurement noise level for exact support recovery of sparse signals via random noisy measurements. By drawing analogy between exact support recovery and communication over the Gaussian multiple access channel, and exploiting mathematical tools developed for the latter problem, we derive sharp asymptotic sufficient and necessary conditions for exact support recovery. Specifically, when the number of nonzero entries is held fixed, the exact asymptotics on the number of measurements for support recovery is developed. When the number of nonzero entries increases in certain manners, we obtain sufficient conditions tighter than existing results. The proposed information theoretic framework for analyzing the performance of support recovery is further demonstrated to be capable of dealing with a variety of sparse signal recovery models. I

Monitoring the therapeutic efficacy of CA4P in the rabbit VX2 liver tumor using dynamic contrast-enhanced MRI

PURPOSE:The present work aims to evaluate whether dynamic contrast-enhanced magnetic resonance Imaging (DCE-MRI) can monitor non-invasively the blocking effect on microvessels of the Combretastatin-A4-phosphate (CA4P) and assess the therapeutic efficacy. METHODS:Forty rabbits were implanted the VX2 tumors specimens. Two weeks later, serial MRI (T1 weighted image, T2 weighted image and DCE) were performed at 0 h, 4 h, 24 h, 3 d and 7 d after CA4P (10 mg/kg) or saline treatment. The parameters of DCE (Ktrans, Kep, Ve and iAUC60) of enhancement tumor portions were measured. Then all the tumor samples were stained to count microvessel density (MVD). At last, two-way repeated measures ANOVA was used to analyze the difference between and within groups. The correlation between the Ktrans, Kep, Ve, iAUC60 and MVD was analyzed by using the Pearson correlation analysis and Spearman's rank correlation.RESULTS:The Ktrans and iAUC60 in the CA4P group were lower than the values of the control group at 4 h after treatment, which have significant differences (D-value: -0.133 min-1, 95%CI: -0.169~-0.097 min-1，F = 59.109, p 0.05). CONCLUSION:The blocking effect of microvessels after CA4P treatment can be evaluated by DCE-MRI, and the parameters of quantitative Ktrans and semi- quantitative iAUC60 can assess the change of the tumor angiogenesis noninvasively