Error probability analysis for STBC in Rayleigh fading channels

Master'sMASTER OF ENGINEERIN

An experimental study on the rotational accuracy of variable preload spindle-bearing system

The rotational performance of the spindle-bearing system has critical influence upon the geometric shape and surface roughness of the machined parts. The effects of preload and preload method on the rotational performance of the spindle-bearing system is explored experimentally to reveal the role of preload and preload method in spindle rotational performances under different speeds. A test rig on which both the rigid preload and elastic preload can be realized, equipped with variable preload spindle-bearing system, is developed. Based on the mechanical model, the relationship of the axial preload and negative axial clearance of the spindle-bearing system is provided. Rotating sensitive radial error motion tests are conducted for evaluating synchronous and asynchronous radial errors of the variable preload spindle-bearing system under different rotating speeds and preload methods. The change regularity of synchronous and asynchronous radial errors with preloads under different rotating speeds are given. The results show that the preload plays an important role on the rotational performance of spindle-bearing system. The rigid preload is more efficient in achieving better rotational performance than elastic preload under the same rotating speed. Furthermore, this article significantly guides the preload designing and assembling of the new spindle-bearing system

DVS benchmark datasets for object tracking, action recognition and object recognition

Benchmarks have played a vital role in the advancement of visual object recognition and other fields of computer vision (LeCun et al., 1998; Deng et al., 2009;). The challenges posed by these standard datasets have helped identify and overcome the shortcomings of existing approaches, and have led to great advances of the state of the art. Even the recent massive increase of interest in deep learning methods can be attributed to their success in difficult benchmarks such as ImageNet (Krizhevsky et al., 2012; LeCun et al., 2015). Neuromorphic vision uses silicon retina sensors such as the dynamic vision sensor (DVS; Lichtsteiner et al., 2008). These sensors and their DAVIS (Dynamic and Active-pixel Vision Sensor) and ATIS (Asynchronous Time-based Image Sensor) derivatives (Brandli et al., 2014; Posch et al., 2014) are inspired by biological vision by generating streams of asynchronous events indicating local log-intensity brightness changes. They thereby greatly reduce the amount of data to be processed, and their dynamic nature makes them a good fit for domains such as optical flow, object tracking, action recognition, or dynamic scene understanding. Compared to classical computer vision, neuromorphic vision is a younger and much smaller field of research, and lacks benchmarks, which impedes the progress of the field. To address this we introduce the largest event-based vision benchmark dataset published to date, hoping to satisfy a growing demand and stimulate challenges for the community. In particular, the availability of such benchmarks should help the development of algorithms processing event-based vision input, allowing a direct fair comparison of different approaches. We have explicitly chosen mostly dynamic vision tasks such as action recognition or tracking, which could benefit from the strengths of neuromorphic vision sensors, although algorithms that exploit these features are largely missing

The identification and characterization of nucleic acid chaperone activity of human enterovirus 71 nonstructural protein 3AB

AbstractHuman enterovirus 71 (EV71) belongs to the genus Enterovirus in the family Picornaviridae and has been recognized as one of the most important pathogens that cause emerging infectious disease. Despite of the importance of EV71, the nonstructural protein 3AB from this virus is little understood for its function during EV71 replication. Here we expressed EV71 3AB protein as recombinant protein in a eukaryotic expression system and uncovered that this protein possesses a nucleic acid helix-destabilizing and strand annealing acceleration activity in a dose-dependent manner, indicating that EV71 3AB is a nucleic acid chaperone protein. Moreover, we characterized the RNA chaperone activity of EV71 3AB, and revealed that divalent metal ions, such as Mg2+ and Zn2+, were able to inhibit the RNA helix-destabilizing activity of 3AB to different extents. Moreover, we determined that 3B plus the last 7 amino acids at the C-terminal of 3A (termed 3B+7) possess the RNA chaperone activity, and five amino acids, i.e. Lys-80, Phe-82, Phe-85, Tyr-89, and Arg-103, are critical and probably the active sites of 3AB for its RNA chaperone activity. This report reveals that EV71 3AB displays an RNA chaperone activity, adds a new member to the growing list of virus-encoded RNA chaperones, and provides novel knowledge about the virology of EV71

Iteratively Coupled Multiple Instance Learning from Instance to Bag Classifier for Whole Slide Image Classification

Whole Slide Image (WSI) classification remains a challenge due to their extremely high resolution and the absence of fine-grained labels. Presently, WSIs are usually classified as a Multiple Instance Learning (MIL) problem when only slide-level labels are available. MIL methods involve a patch embedding process and a bag-level classification process, but they are prohibitively expensive to be trained end-to-end. Therefore, existing methods usually train them separately, or directly skip the training of the embedder. Such schemes hinder the patch embedder's access to slide-level labels, resulting in inconsistencies within the entire MIL pipeline. To overcome this issue, we propose a novel framework called Iteratively Coupled MIL (ICMIL), which bridges the loss back-propagation process from the bag-level classifier to the patch embedder. In ICMIL, we use category information in the bag-level classifier to guide the patch-level fine-tuning of the patch feature extractor. The refined embedder then generates better instance representations for achieving a more accurate bag-level classifier. By coupling the patch embedder and bag classifier at a low cost, our proposed framework enables information exchange between the two processes, benefiting the entire MIL classification model. We tested our framework on two datasets using three different backbones, and our experimental results demonstrate consistent performance improvements over state-of-the-art MIL methods. Code will be made available upon acceptance

Super-Resolution Based Patch-Free 3D Image Segmentation with High-Frequency Guidance

High resolution (HR) 3D images are widely used nowadays, such as medical images like Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). However, segmentation of these 3D images remains a challenge due to their high spatial resolution and dimensionality in contrast to currently limited GPU memory. Therefore, most existing 3D image segmentation methods use patch-based models, which have low inference efficiency and ignore global contextual information. To address these problems, we propose a super-resolution (SR) based patch-free 3D image segmentation framework that can realize HR segmentation from a global-wise low-resolution (LR) input. The framework contains two sub-tasks, of which semantic segmentation is the main task and super resolution is an auxiliary task aiding in rebuilding the high frequency information from the LR input. To furthermore balance the information loss with the LR input, we propose a High-Frequency Guidance Module (HGM), and design an efficient selective cropping algorithm to crop an HR patch from the original image as restoration guidance for it. In addition, we also propose a Task-Fusion Module (TFM) to exploit the inter connections between segmentation and SR task, realizing joint optimization of the two tasks. When predicting, only the main segmentation task is needed, while other modules can be removed for acceleration. The experimental results on two different datasets show that our framework has a four times higher inference speed compared to traditional patch-based methods, while its performance also surpasses other patch-based and patch-free models.Comment: Version #2 uploaded in Jul 10, 202

Evaluation of the performance of a dengue outbreak detection tool for China

An outbreak detection and response system, using time series moving percentile method based on historical data, in China has been used for identifying dengue fever outbreaks since 2008. For dengue fever outbreaks reported from 2009 to 2012, this system achieved a sensitivity of 100%, a specificity of 99.8% and a median time to detection of 3 days, which indicated that the system was a useful decision tool for dengue fever control and risk-management programs in China.This work was supported by the grants from Research and Promotion of Key Technology on Health Emergency Preparation and Dispositions (201202006), the National Key Science and Technology Project on Infectious Disease Surveillance Technique Platform of China (2012ZX10004-201) and Development of Early Warning Systems for Dengue Fever Based on Socio-ecological Factors (NHMRC APP1002608)

Negative thermal expansion in YbMn2Ge2 induced by the dual effect of magnetism and valence transition

AbstractNegative thermal expansion (NTE) is an intriguing property, which is generally triggered by a single NTE mechanism. In this work, an enhanced NTE (αv = −32.9 × 10−6 K−1, ΔT = 175 K) is achieved in YbMn2Ge2 intermetallic compound to be caused by a dual effect of magnetism and valence transition. In YbMn2Ge2, the Mn sublattice that forms the antiferromagnetic structure induces the magnetovolume effect, which contributes to the NTE below the Néel temperature (525 K). Concomitantly, the valence state of Yb increases from 2.40 to 2.82 in the temperature range of 300–700 K, which simultaneously causes the contraction of the unit cell volume due to smaller volume of Yb3+ than that of Yb2+. As a result, such combined effect gives rise to an enhanced NTE. The present study not only sheds light on the peculiar NTE mechanism of YbMn2Ge2, but also indicates the dual effect as a possible promising method to produce enhanced NTE materials