Computational Efficiency Studies in Computer Vision Tasks

Computer vision has made massive progress in recent years, thanks to hardware and algorithms development. Most methods are performance-driven meanwhile have a lack of consideration for energy efficiency. This dissertation proposes computational efficiency boosting methods for three different vision tasks: ultra-high resolution images segmentation, optical characters recognition for Unmanned Aerial Vehicles (UAV) based videos, and multiple object detection for UAV based videos. The pattern distribution of ultra-high resolution images is usually unbalanced. While part of an image contains complex and fine-grained patterns such as boundaries, most areas are composed of simple and repeated patterns. In the first chapter, we propose to learn a skip map, which can guide a segmentation network to skip simple patterns and hence reduce computational complexity. Specifically, the skip map highlights simple-pattern areas that can be down-sampled for processing at a lower resolution, while the remaining complex part is still segmented at the original resolution. Applied on the state-of-the-art ultra-high resolution image segmentation network GLNet, our proposed skip map saves more than 30% computation while maintaining comparable segmentation performance. In the second chapter, we propose an end-to-end system for UAV videos OCR framework. We first revisit RCNN’s crop & resize training strategy and empirically find that it outperforms aligned RoI sampling on a real-world video text dataset captured by UAV. We further propose a multi-stage image processor that takes videos’ redundancy, continuity, and mixed degradation into account to reduce energy consumption. Lastly, the model is pruned and quantized before deployed on Raspberry Pi. Our proposed energy-efficient video text spotting solution, dubbed as E²VTS, outperforms all previous methods by achieving a competitive tradeoff between energy efficiency and performance. In the last chapter, we propose an energy-efficient video multiple objects detection solution. Besides designing a fast multiple object detector, we propose a data synthesis and a knowledge transfer-based annotation method to overcome class imbalance and domain gap issues. This solution was implemented on LPCVC 2021 UVA challenge and judged to be the first-place winner

Position-Patch Based Face Hallucination Using Super-Pixel Segmentation and Group Lasso

Traditional super-resolution algorithms utilized samples priors to guide image reconstruction by image-patch. All of them use square or rectangle patch for acquiring prior information. However, fixed size patches will diminish structural information obtained by patches. To make patches gain more structural information, we make two adjustments to the face hallucination: superpixel segmentation and Group Lasso. With super-pixel segmentation, we exploit structural features of human faces by segmenting face images into adaptive patches based on their appearances. Group Lasso provides additional structural information through group selection. Our experimental results show that the extra structural information attained by adjustments has a positive impact on the final reconstructed image

E^2TAD: An Energy-Efficient Tracking-based Action Detector

Video action detection (spatio-temporal action localization) is usually the starting point for human-centric intelligent analysis of videos nowadays. It has high practical impacts for many applications across robotics, security, healthcare, etc. The two-stage paradigm of Faster R-CNN inspires a standard paradigm of video action detection in object detection, i.e., firstly generating person proposals and then classifying their actions. However, none of the existing solutions could provide fine-grained action detection to the "who-when-where-what" level. This paper presents a tracking-based solution to accurately and efficiently localize predefined key actions spatially (by predicting the associated target IDs and locations) and temporally (by predicting the time in exact frame indices). This solution won first place in the UAV-Video Track of 2021 Low-Power Computer Vision Challenge (LPCVC)

Corrigendum to: The TianQin project: current progress on science and technology

In the originally published version, this manuscript included an error related to indicating the corresponding author within the author list. This has now been corrected online to reflect the fact that author Jun Luo is the corresponding author of the article

An Objective Holographic Feedback Linearization Based on a Sliding Mode Control for a Buck Converter with a Constant Power Load

As a typical load, the constant power load (CPL) has negative impedance characteristics. The stability of the buck converter system with a mixed load of CPL and resistive load is affected by the size of the CPL. When the resistive load is larger than the CPL, the buck converter with the output voltage as an output function is a non-minimum phase nonlinear system, because its linear approximation has a right-half-plane pole. The non-minimum phase characteristic limits the application of many control techniques, but the objective holographic feedback linearization control (OHFLC) method is a good control strategy that can bypass the non-minimum phase system and make the system stable. However, the traditional OHFLC method, in designing the controller, generally uses a linear optimal quadratic design method to obtain a linear feedback control law. It requires a state quantity component with a one-order relative degree to the system. But it is not easy to find such a suitable state quantity with a one-order relative degree to the system. In this paper, an improved OHFLC method is proposed for Buck converters with a mixed loads of CPL and resistive loads, using the sliding mode control (SMC) theory to design the controller, so that the output state quantity components with different relative degrees to the system can be used in the holographic feedback linearization method. Finally, the simulation and experimental results also demonstrate that this method has the same, or even better, dynamic response performance and robustness than the traditional OHFLC method

Indoor nanoscale particulate matter-induced coagulation abnormality based on a human 3D microvascular model on a microfluidic chip

Abstract Background A growing body of evidence shows that indoor concentrations of airborne particles are often higher than is typically encountered outdoors. Since exposure to indoor PM2.5 is thought to be associated with cardiovascular disease, the health impacts of indoor air pollution need to be explored. Based on animal models, ambient particulate matter has been proved to promote coagulation which is very likely involved in the pathogenic development of cardiovascular disease. However, animal models are insufficient to predict what will happen with any certainty in humans. For this reason, the precise pathogenic mechanisms behind the development of cardiovascular disease in humans have not yet been determined. Results We generated a 3D functional human microvascular network in a microfluidic device. This model enables human vascular endothelial cells to form tissue-like microvessels that behave very similarly to human blood vessels. The perfusable microvasculature allows the delivery of particles introduced into these generated human-like microvessels to follow the fluid flow. This exposure path effectively simulates the dynamic movement of airborne nanoscale particles (ANPs) within human vessels. In this study, we first identified the existence of ANPs in indoor air pollution. We then showed that ANPs could activate endothelial cells via ROS induced inflammation, and further resulted in abnormal expression of the coagulation factors (TF, TM and t-PA) involved in coagulation cascades. In addition, we found that a protein could cover ANPs, and this biointeraction could interfere with heparan sulfate (HS). Human organotypic 3D microvessel models provide a bridge for how research outcomes can translate to humans. Conclusions The 3D human microvessel model was used to determine the physiological responses of human vessels to ANP stimulation. Based on the obtained data, we concluded that ANPs not only disrupts normal coagulation functions, but also act directly on anticoagulant factors in human vessels. These experimental observations provide a potential biological explanation for the epidemiologically established link between ANPs and coagulation abnormality. This organ-on-chip model may provide a bridge from in vitro results to human responses

Land Subsidence in a Coastal City Based on SBAS-InSAR Monitoring: A Case Study of Zhuhai, China

The superimposed effects of sea level rise caused by global warming and land subsidence seriously threaten the sustainable development of coastal cities. In recent years, an important coastal city in China, Zhuhai, has been suffering from severe and widespread land subsidence; however, the characteristics, triggers, and vulnerability assessment of ground subsidence in Zhuhai are still unclear. Therefore, we used the SBAS-InSAR technique to process 51 Sentinel-1A images to monitor the land subsidence in Zhuhai during the period from August 2016 to June 2019. The results showed that there was extensive land subsidence in the study area, with a maximum rate of −109.75 mm/yr. The surface had sequentially undergone a process of minor uplift and decline fluctuation, sharp settlement, and stable subsidence. The distribution and evolution of land subsidence were controlled by tectonic fractures and triggered by the thickness of soft soil, the intensity of groundwater development, and the seasonal changes of atmospheric precipitation. The comprehensive index method and the analytic hierarchy process were applied to derive extremely high subsidence vulnerability in several village communities and some traffic arteries in Zhuhai. Our research provides a theoretical basis for urban disaster prevention in Zhuhai and the construction planning of coastal cities around the world

Integration of Transcriptome, Proteome and Metabolism Data Reveals the Alkaloids Biosynthesis in <em>Macleaya cordata</em> and <em>Macleaya microcarpa</em>

<div><h3>Background</h3><p>The <em>Macleaya</em> spp., including <em>Macleaya cordata</em> and <em>Macleaya microcarpa</em>, are traditional anti-virus, inflammation eliminating, and insecticide herb medicines for their isoquinoline alkaloids. They are also known as the basis of the popular natural animal food addictive in Europe. However, few studies especially at genomics level were conducted on them. Hence, we performed the <em>Macleaya</em> spp. transcriptome and integrated it with iTRAQ proteome analysis in order to identify potential genes involved in alkaloids biosynthesis.</p> <h3>Methodology and Principal Findings</h3><p>We elaborately designed the transcriptome, proteome and metabolism profiling for 10 samples of both species to explore their alkaloids biosynthesis. From the transcriptome data, we obtained 69367 and 78255 unigenes for <em>M. cordata</em> and <em>M. microcarpa</em>, in which about two thirds of them were similar to sequences in public databases. By metabolism profiling, reverse patterns for alkaloids sanguinarine, chelerythrine, protopine, and allocryptopine were observed in different organs of two species. We characterized the expressions of enzymes in alkaloid biosynthesis pathways. We also identified more than 1000 proteins from iTRAQ proteome data. Our results strongly suggest that the root maybe the organ for major alkaloids biosynthesis of <em>Macleaya</em> spp. Except for biosynthesis, the alkaloids storage and transport were also important for their accumulation. The ultrastructure of laticifers by SEM helps us to prove the alkaloids maybe accumulated in the mature roots.</p> <h3>Conclusions/Significance</h3><p>To our knowledge this is the first study to elucidate the genetic makeup of <em>Macleaya</em> spp. This work provides clues to the identification of the potential modulate genes involved in alkaloids biosynthesis in <em>Macleaya</em> spp., and sheds light on researches for non-model medicinal plants by integrating different high-throughput technologies.</p> </div