Video Superresolution via Parameter-Optimized Particle Swarm Optimization

Video superresolution (VSR) aims to reconstruct a high-resolution video sequence from a low-resolution sequence. We propose a novel particle swarm optimization algorithm named as parameter-optimized multiple swarms PSO (POMS-PSO). We assessed the optimization performance of POMS-PSO by four standard benchmark functions. To reconstruct high-resolution video, we build an imaging degradation model. In view of optimization, VSR is converted to an optimization computation problem. And we take POMS-PSO as an optimization method to solve the VSR problem, which overcomes the poor effect, low accuracy, and large calculation cost in other VSR algorithms. The proposed VSR method does not require exact movement estimation and does not need the computation of movement vectors. In terms of peak signal-to-noise ratio (PSNR), sharpness, and entropy, the proposed VSR method based POMS-PSO showed better objective performance. Besides objective standard, experimental results also proved the proposed method could reconstruct high-resolution video sequence with better subjective quality

Video Superresolution via Parameter-Optimized Particle Swarm Optimization

Video superresolution (VSR) aims to reconstruct a high-resolution video sequence from a low-resolution sequence. We propose a novel particle swarm optimization algorithm named as parameter-optimized multiple swarms PSO (POMS-PSO). We assessed the optimization performance of POMS-PSO by four standard benchmark functions. To reconstruct high-resolution video, we build an imaging degradation model. In view of optimization, VSR is converted to an optimization computation problem. And we take POMS-PSO as an optimization method to solve the VSR problem, which overcomes the poor effect, low accuracy, and large calculation cost in other VSR algorithms. The proposed VSR method does not require exact movement estimation and does not need the computation of movement vectors. In terms of peak signal-to-noise ratio (PSNR), sharpness, and entropy, the proposed VSR method based POMS-PSO showed better objective performance. Besides objective standard, experimental results also proved the proposed method could reconstruct high-resolution video sequence with better subjective quality

Online sensor information and redundancy resolution based obstacle avoidance for high DOF mobile manipulator teleoperation

High degrees of freedom (DOF) mobile manipulators provide more flexibility than conventional manipulators. They also provide manipulation operations with a mobility capacity and have potential in many applications. However, due to high redundancy, planning and control become more complicated and difficult, especially when obstacles occur. Most existing obstacle avoidance methods are based on off-line algorithms and most of them mainly focus on planning a new collision-free path, which is not appropriate for some applications, such as teleoperation and uses many system resources as well. Therefore, this paper presents an online planning and control method for obstacle avoidance in mobile manipulators using online sensor information and redundancy resolution. An obstacle contour reconstruction approach employing a mobile manipulator equipped with an active laser scanner system is also introduced in this paper. This method is implemented using a mobile manipulator with a seven-DOF manipulator and a four-wheel drive mobile base. The experimental results demonstrate the effectiveness of this method. © 2013 Zhang et al.; licensee InTech.Link_to_subscribed_fulltex

Resting-state functional magnetic resonance imaging reveals brain remodeling after Tuina therapy in neuropathic pain model

Tuina, a method of traditional Chinese manual manipulation, is an effective alternative therapy for neuropathic pain (NP), but its analgesic mechanism remains unclear. In this study, we used resting-state functional magnetic resonance imaging (R-fMRI) to explore the analgesic mechanism of Tuina in an NP rat model. After undergoing surgery to induce chronic compression of the dorsal root ganglion (CCD), one group of rats underwent Tuina at the ipsilateral BL40 acupoint once a day for 10 min during the 25 days following surgery while another group did not. Behavioral tests were performed at baseline, on the third day following surgery, and once a week for the next 4 weeks. R-fMRI was performed at baseline and 7 days and 28 days following surgery. Behavioral testing revealed that the Tuina group presented a significant response improvement to mechanical and thermal nociception stimuli compared to the untreated group 2 weeks following CCD surgery. Interestingly, rats submitted to Tuina presented higher measures of spontaneous neuronal activity in basal forebrain region, primary somatosensory cortex barrel field, dentate gyrus, secondary somatosensory cortex, striatum, descending corticofugal pathways, and globus pallidum of the left hemisphere 4 weeks after the CCD surgery compared to rats having undergone CCD only. In addition, on the 28th day, the ALFF signals of the left dentate gyrus, left secondary somatosensory cortex, left striatum, and bilateral primary cingulate cortex were significantly increased while those in the right dentate gyrus and bilateral periaqueductal gray were significantly decreased compared to those on the 7th day. Correlation analysis showed that the ALFF values of the left descending corticofugal pathways and globus pallidum had a positive correlation with mechanical withdrawal threshold and paw withdrawal thermal latency tests. Altogether, these results indicate that NPP induced by CCD surgery affects the plasticity of the cerebral cortex, and that Tuina alleviate pain behavior by promoting cortical remodeling

Rapid detection of influenza A viruses using a real-time reverse transcription recombinase-aided amplification assay

IntroductionInfluenza A viruses (IAVs) are important pathogens of respiratory infections, causing not only seasonal influenza but also influenza pandemics and posing a global threat to public health. IAVs infection spreads rapidly, widely, and across species, causing huge losses, especially zoonotic IAVs infections that are more harmful. Fast and sensitive detection of IAVs is critical for controlling the spread of this disease.MethodsHere, a real-time reverse transcription recombinase-aided amplification (real-time RT-RAA) assay targeting conserved positions in the matrix protein gene (M gene) of IAVs, is successfully established to detect IAVs. The assay can be completed within 20 min at 42°C.ResultsThe sensitivity of the real-time RT-RAA assay was 142 copies per reaction at 95% probability, which was comparable to the sensitivity of the RT-qPCR assay. The specificity assay showed that the real-time RT-RAA assay was specific to IAVs, and there was no cross-reactivity with other important viruses. In addition, 100%concordance between the real-time RT-RAA and RT-qPCR assays was achieved after testing 120 clinical specimens.DiscussionThe results suggested that the real-time RT-RAA assay we developed was a specific, sensitive and reliable diagnostic tool for the rapid detection of IAVs

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Parallel Image Completion with Edge and Color Map

Over the last few years, image completion has made significant progress due to the generative adversarial networks (GANs) that are able to synthesize photorealistic contents. However, one of the main obstacles faced by many existing methods is that they often create blurry textures or distorted structures that are inconsistent with surrounding regions. The main reason is the ineffectiveness of disentangling style latent space implicitly from images. To address this problem, we develop a novel image completion framework called PIC-EC: parallel image completion networks with edge and color maps, which explicitly provides image edge and color information as the prior knowledge for image completion. The PIC-EC framework consists of the parallel edge and color generators followed by an image completion network. Specifically, the parallel paths generate edge and color maps for the missing region at the same time, and then the image completion network fills the missing region with fine details using the generated edge and color information as the priors. The proposed method was evaluated over CelebA-HQ and Paris StreetView datasets. Experimental results demonstrate that PIC-EC achieves superior performance on challenging cases with complex compositions and outperforms existing methods on evaluations of realism and accuracy, both quantitatively and qualitatively

On-Board Smartphone-Based Road Hazard Detection with Cloud-Based Fusion

Road hazards are one of the significant sources of fatalities in road accidents. The accurate estimation of road hazards can ensure safety and enhance the driving experience. Existing methods of road condition monitoring are time-consuming, expensive, inefficient, require much human effort, and need to be regularly updated. There is a need for a flexible, cost-effective, and efficient process to detect road conditions, especially road hazards. This work presents a new method to deal with road hazards using smartphones. Since most of the population drives cars with smartphones on board, we aim to leverage this to detect road hazards more flexibly, cost-effectively, and efficiently. This paper proposes a cloud-based deep-learning road hazard detection model based on a long short-term memory (LSTM) network to detect different types of road hazards from the motion data. To address the issue of large data requests for deep learning, this paper proposes to leverage both simulation data and experimental data for the learning process. To address the issue of misdetections from an individual smartphone, we propose a cloud-based fusion approach to further improve detection accuracy. The proposed approaches are validated by experimental tests, and the results demonstrate the effectiveness of road hazard detection