Group Iterative Spectrum Thresholding for Super-Resolution Sparse Spectral Selection

Recently, sparsity-based algorithms are proposed for super-resolution spectrum estimation. However, to achieve adequately high resolution in real-world signal analysis, the dictionary atoms have to be close to each other in frequency, thereby resulting in a coherent design. The popular convex compressed sensing methods break down in presence of high coherence and large noise. We propose a new regularization approach to handle model collinearity and obtain parsimonious frequency selection simultaneously. It takes advantage of the pairing structure of sine and cosine atoms in the frequency dictionary. A probabilistic spectrum screening is also developed for fast computation in high dimensions. A data-resampling version of high-dimensional Bayesian Information Criterion is used to determine the regularization parameters. Experiments show the efficacy and efficiency of the proposed algorithms in challenging situations with small sample size, high frequency resolution, and low signal-to-noise ratio

A^2-Net: Molecular Structure Estimation from Cryo-EM Density Volumes

Constructing of molecular structural models from Cryo-Electron Microscopy (Cryo-EM) density volumes is the critical last step of structure determination by Cryo-EM technologies. Methods have evolved from manual construction by structural biologists to perform 6D translation-rotation searching, which is extremely compute-intensive. In this paper, we propose a learning-based method and formulate this problem as a vision-inspired 3D detection and pose estimation task. We develop a deep learning framework for amino acid determination in a 3D Cryo-EM density volume. We also design a sequence-guided Monte Carlo Tree Search (MCTS) to thread over the candidate amino acids to form the molecular structure. This framework achieves 91% coverage on our newly proposed dataset and takes only a few minutes for a typical structure with a thousand amino acids. Our method is hundreds of times faster and several times more accurate than existing automated solutions without any human intervention.Comment: 8 pages, 5 figures, 4 table

A Novice Method for Calibrating the Transient Model of an Automotive HVAC System

A novice method for calibrating the transient model of an automotive HVAC system is presented in this paper. Transient models can be of great importance in the development process of automotive HVAC control algorithms, especially model based ones, as it saves both time and effort. However, the calibration process is usually difficult and relies heavily on experience due to the complexity of the model. A set of customized measurement tools, which consists of several wireless temperature and humidity sensors and an OBD dongle, is used to capture time series data related to the HVAC system during normal driving. Parts of the time series data are then fed into an optimization algorithm to generate a cost function, which can be minimized when the measured data correspond to the simulation data generated by the transient model, while other parts of the data are remained for the validation step. A sensitivity analysis is then performed to find out which parameters in the HVAC transient model need to be optimized to calibrate the model. As the transient model is a physical network model which can be generally considered as a set of differential and algebraic equations, this presented method reduces the calibration process of a complex physical model into solving a common optimization problem. Therefore, various optimization algorithms and tools can be applied. The method is developed and tested during the modeling process of an automotive HVAC system. The efficiency of the modelling process is improved while the calibration results fit better with the measured data.