Complex Sparse Signal Recovery with Adaptive Laplace Priors

Because of its self-regularizing nature and uncertainty estimation, the Bayesian approach has achieved excellent recovery performance across a wide range of sparse signal recovery applications. However, most methods are based on the real-value signal model, with the complex-value signal model rarely considered. Typically, the complex signal model is adopted so that phase information can be utilized. Therefore, it is non-trivial to develop Bayesian models for the complex-value signal model. Motivated by the adaptive least absolute shrinkage and selection operator (LASSO) and the sparse Bayesian learning (SBL) framework, a hierarchical model with adaptive Laplace priors is proposed for applications of complex sparse signal recovery in this paper. The proposed hierarchical Bayesian framework is easy to extend for the case of multiple measurement vectors. Moreover, the space alternating principle is integrated into the algorithm to avoid using the matrix inverse operation. In the experimental section of this work, the proposed algorithm is concerned with both complex Gaussian random dictionaries and directions of arrival (DOA) estimations. The experimental results show that the proposed algorithm offers better sparsity recovery performance than the state-of-the-art methods for different types of complex signals

Preterm pigs for preterm birth research: reasonably feasible

Preterm birth will disrupt the pattern and course of organ development, which may result in morbidity and mortality of newborn infants. Large animal models are crucial resources for developing novel, credible, and effective treatments for preterm infants. This review summarizes the classification, definition, and prevalence of preterm birth, and analyzes the relationship between the predicted animal days and one human year in the most widely used animal models (mice, rats, rabbits, sheep, and pigs) for preterm birth studies. After that, the physiological characteristics of preterm pig models at different gestational ages are described in more detail, including birth weight, body temperature, brain development, cardiovascular system development, respiratory, digestive, and immune system development, kidney development, and blood constituents. Studies on postnatal development and adaptation of preterm pig models of different gestational ages will help to determine the physiological basis for survival and development of very preterm, middle preterm, and late preterm newborns, and will also aid in the study and accurate optimization of feeding conditions, diet- or drug-related interventions for preterm neonates. Finally, this review summarizes several accepted pediatric applications of preterm pig models in nutritional fortification, necrotizing enterocolitis, neonatal encephalopathy and hypothermia intervention, mechanical ventilation, and oxygen therapy for preterm infants

Evidence for critical scaling of plasmonic modes at the percolation threshold in metallic nanostructures

In this work we provide the experimental demonstration of critical scaling of plasmonic resonances in a percolation series of periodic structures which evolve from arrays of holes to arrays of quasi-triangles. Our observations are in agreement with the general percolation theory and could lead to sensor and detector applications

Efficient Parallel Implementation of Active Appearance Model Fitting Algorithm on GPU

The active appearance model (AAM) is one of the most powerful model-based object detecting and tracking methods which has been widely used in various situations. However, the high-dimensional texture representation causes very time-consuming computations, which makes the AAM difficult to apply to real-time systems. The emergence of modern graphics processing units (GPUs) that feature a many-core, fine-grained parallel architecture provides new and promising solutions to overcome the computational challenge. In this paper, we propose an efficient parallel implementation of the AAM fitting algorithm on GPUs. Our design idea is fine grain parallelism in which we distribute the texture data of the AAM, in pixels, to thousands of parallel GPU threads for processing, which makes the algorithm fit better into the GPU architecture. We implement our algorithm using the compute unified device architecture (CUDA) on the Nvidia’s GTX 650 GPU, which has the latest Kepler architecture. To compare the performance of our algorithm with different data sizes, we built sixteen face AAM models of different dimensional textures. The experiment results show that our parallel AAM fitting algorithm can achieve real-time performance for videos even on very high-dimensional textures

A Novel Model of Atherosclerosis in Rabbits Using Injury to Arterial Walls Induced by Ferric Chloride as Evaluated by Optical Coherence Tomography as well as Intravascular Ultrasound and Histology

This study aim was to develop a new model of atherosclerosis by FeCl3-induced injury to right common carotid arteries (CCAs) of rabbits. Right CCAs were induced in male New Zealand White rabbits (n = 15) by combination of a cholesterol-rich diet and FeCl3-induced injury to arterial walls. The right and left CCAs were evaluated by histology and in vivo intravascular ultrasound (IVUS) and optical coherence tomography (OCT) examinations of 24 hours (n = 3), 8 weeks (n = 6), and 12 weeks (n = 6) after injury. Each right CCA of the rabbits showed extensive white-yellow plaques. At eight and 12 weeks after injury, IVUS, OCT, and histological findings demonstrated that the right CCAs had evident eccentric plaques. Six plaques (50%) with evident positive remodeling were observed. Marked progression was clearly observed in the same plaque at 12 weeks after injury when it underwent repeat OCT and IVUS. We demonstrated, for the first time, a novel model of atherosclerosis induced by FeCl3. The model is simple, fast, inexpensive, and reproducible and has a high success rate. The eccentric plaques and remodeling of plaques were common in this model. We successfully carried out IVUS and OCT examinations twice in the same lesion within a relatively long period of time

Towards Domain-Independent and Real-Time Gesture Recognition Using mmWave Signal

Human gesture recognition using millimeter wave (mmWave) signals provides attractive applications including smart home and in-car interface. While existing works achieve promising performance under controlled settings, practical applications are still limited due to the need of intensive data collection, extra training efforts when adapting to new domains (i.e. environments, persons and locations) and poor performance for real-time recognition. In this paper, we propose DI-Gesture, a domain-independent and real-time mmWave gesture recognition system. Specifically, we first derive the signal variation corresponding to human gestures with spatial-temporal processing. To enhance the robustness of the system and reduce data collecting efforts, we design a data augmentation framework based on the correlation between signal patterns and gesture variations. Furthermore, we propose a dynamic window mechanism to perform gesture segmentation automatically and accurately, thus enable real-time recognition. Finally, we build a lightweight neural network to extract spatial-temporal information from the data for gesture classification. Extensive experimental results show DI-Gesture achieves an average accuracy of 97.92%, 99.18% and 98.76% for new users, environments and locations, respectively. In real-time scenario, the accuracy of DI-Gesutre reaches over 97% with average inference time of 2.87ms, which demonstrates the superior robustness and effectiveness of our system.Comment: The paper is submitted to the journal of IEEE Transactions on Mobile Computing. And it is still under revie

MIPI 2023 Challenge on RGBW Remosaic: Methods and Results

Developing and integrating advanced image sensors with novel algorithms in camera systems are prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for an in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). With the success of the 1st MIPI Workshop@ECCV 2022, we introduce the second MIPI challenge, including four tracks focusing on novel image sensors and imaging algorithms. This paper summarizes and reviews the RGBW Joint Remosaic and Denoise track on MIPI 2023. In total, 81 participants were successfully registered, and 4 teams submitted results in the final testing phase. The final results are evaluated using objective metrics, including PSNR, SSIM, LPIPS, and KLD. A detailed description of the top three models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found at https://mipi-challenge.org/MIPI2023/.Comment: CVPR 2023 Mobile Intelligent Photography and Imaging (MIPI) Workshop--RGBW Sensor Remosaic Challenge Report. Website: https://mipi-challenge.org/MIPI2023/. arXiv admin note: substantial text overlap with arXiv:2209.08471, arXiv:2209.07060, arXiv:2209.07530, arXiv:2304.1008