Recognition of Human Periodic Movements From Unstructured Information Using A Motion-based Frequency Domain Approach

Feature-based motion cues play an important role in biological visual perception. We present a motion-based frequency-domain scheme for human periodic motion recognition. As a baseline study of feature based recognition we use unstructured feature-point kinematic data obtained directly from a marker-based optical motion capture (MoCap) system, rather than accommodate bootstrapping from the low-level image processing of feature detection. Motion power spectral analysis is applied to a set of unidentified trajectories of feature points representing whole body kinematics. Feature power vectors are extracted from motion power spectra and mapped to a low dimensionality of feature space as motion templates that offer frequency domain signatures to characterise different periodic motions. Recognition of a new instance of periodic motion against pre-stored motion templates is carried out by seeking best motion power spectral similarity. We test this method through nine examples of human periodic motion using MoCap data. The recognition results demonstrate that feature-based spectral analysis allows classification of periodic motions from low-level, un-structured interpretation without recovering underlying kinematics. Contrasting with common structure-based spatio-temporal approaches, this motion-based frequency-domain method avoids a time-consuming recovery of underlying kinematic structures in visual analysis and largely reduces the parameter domain in the presence of human motion irregularities

AGGLOMERATION DURING FLUIDIZED-BED COMBUSTION OF BIOMASS

Wheat stalk is tested to investigate the formation of bed agglomeration. The results show that defluidization time decreases with the combustion temperature increasing. The minimum fluidization velocity of the bed material after the test increases. The K, Ca and Si elements play the most important role in bed defluidization

Design and Operation of Biomass Circulating Fluidized Bed Boiler with High Steam Parameter

Two circulating fluidized bed(CFB) boilers with capacity of 12 MWe and 25 MWe, respectively, with biomass as fuel, adopting the basic technology independently developed by Institute of Engineering Thermophysics (IET), Chinese Academy of Sciences, have been in commercial operation since March 2010 in China. This paper focuses on the design principles, the design specifications and operating results of the two CFB boilers

A no-reference optical flow-based quality evaluator for stereoscopic videos in curvelet domain

Most of the existing 3D video quality assessment (3D-VQA/SVQA) methods only consider spatial information by directly using an image quality evaluation method. In addition, a few take the motion information of adjacent frames into consideration. In practice, one may assume that a single data-view is unlikely to be sufficient for effectively learning the video quality. Therefore, integration of multi-view information is both valuable and necessary. In this paper, we propose an effective multi-view feature learning metric for blind stereoscopic video quality assessment (BSVQA), which jointly focuses on spatial information, temporal information and inter-frame spatio-temporal information. In our study, a set of local binary patterns (LBP) statistical features extracted from a computed frame curvelet representation are used as spatial and spatio-temporal description, and the local flow statistical features based on the estimation of optical flow are used to describe the temporal distortion. Subsequently, a support vector regression (SVR) is utilized to map the feature vectors of each single view to subjective quality scores. Finally, the scores of multiple views are pooled into the final score according to their contribution rate. Experimental results demonstrate that the proposed metric significantly outperforms the existing metrics and can achieve higher consistency with subjective quality assessment

Parameterization of point-cloud freeform surfaces using adaptive sequential learning RBFnetworks

We propose a self-organizing Radial Basis Function (RBF) neural network method for parameterization of freeform surfaces from larger, noisy and unoriented point clouds. In particular, an adaptive sequential learning algorithm is presented for network construction from a single instance of point set. The adaptive learning allows neurons to be dynamically inserted and fully adjusted (e.g. their locations, widths and weights), according to mapping residuals and data point novelty associated to underlying geometry. Pseudo-neurons, exhibiting very limited contributions, can be removed through a pruning procedure. Additionally, a neighborhood extended Kalman filter (NEKF) was developed to significantly accelerate parameterization. Experimental results show that this adaptive learning enables effective capture of global low-frequency variations while preserving sharp local details, ultimately leading to accurate and compact parameterization, as characterized by a small number of neurons. Parameterization using the proposed RBF network provides simple, low cost and low storage solutions to many problems such as surface construction, re-sampling, hole filling, multiple level-of-detail meshing and data compression from unstructured and incomplete range data. Performance results are also presented for comparison

Effectiveness of surface electromyography in pattern classification for upper limb amputees

This study was undertaken to explore 18 time domain (TD) and time-frequency domain (TFD) feature configurations to determine the most discriminative feature sets for classification. Features were extracted from the surface electromyography (sEMG) signal of 17 hand and wrist movements and used to perform a series of classification trials with the random forest classifier. Movement datasets for 11 intact subjects and 9 amputees from the NinaPro online database repository were used. The aim was to identify any optimum configurations that combined features from both domains and whether there was consistency across subject type for any standout features. This work built on our previous research to incorporate the TFD, using a Discrete Wavelet Transform with a Daubechies wavelet. Findings report configurations containing the same features combined from both domains perform best across subject type (TD: root mean square (RMS), waveform length, and slope sign changes; TFD: RMS, standard deviation, and energy). These mixed-domain configurations can yield optimal performance (intact subjects: 90.98%; amputee subjects: 75.16%), but with only limited improvement on single-domain configurations. This suggests there is limited scope in attempting to build a single absolute feature configuration and more focus should be put on enhancing the classification methodology for adaptivity and robustness under actual operating conditions

Fast Mode Decision for 3D-HEVC Depth Intracoding

The emerging international standard of high efficiency video coding based 3D video coding (3D-HEVC) is a successor to multiview video coding (MVC). In 3D-HEVC depth intracoding, depth modeling mode (DMM) and high efficiency video coding (HEVC) intraprediction mode are both employed to select the best coding mode for each coding unit (CU). This technique achieves the highest possible coding efficiency, but it results in extremely large encoding time which obstructs the 3D-HEVC from practical application. In this paper, a fast mode decision algorithm based on the correlation between texture video and depth map is proposed to reduce 3D-HEVC depth intracoding computational complexity. Since the texture video and its associated depth map represent the same scene, there is a high correlation among the prediction mode from texture video and depth map. Therefore, we can skip some specific depth intraprediction modes rarely used in related texture CU. Experimental results show that the proposed algorithm can significantly reduce computational complexity of 3D-HEVC depth intracoding while maintaining coding efficiency

Inflammatory biomarkers and delirium: a Mendelian randomization study

BackgroundThe association between inflammatory biomarkers and individual delirium symptoms remains controversial in observational studies. We investigated the relationship between inflammatory biomarkers and the risk of developing delirium.MethodsA bidirectional two-sample Mendelian randomization (MR) was performed. Genetic instruments associated with peripheral tumor necrosis factor-a (TNF-a) C-reactive protein (CRP), interleukin (IL)-1α, IL-1β, IL-2, IL-8, IL-6, soluble IL-6 receptor alpha (sIL-6Rα), and soluble gp130 were identified in three different large summary genome-wide association studies (GWAS) conducted in the European population. Summary-level statistics for delirium not induced by alcohol and other psychoactive substances were obtained from the FinnGen consortium (2,612 cases and 325,306 controls). The estimated causal effects were performed using instruments' variants at the genome-wide significant level (P < 5e-8 and P < 5e-6), applying a linkage disequilibrium clumping approach with a threshold of r2 < 0.001 for each of the exposures. Reverse causation was also performed. The inverse-variance weighted method (IVW), MR-Egger method, weighted median method, MR-Egger regression, and MR Pleiotropy RESidual Sum were used for MR analyses.ResultsAt the genome-wide significant level (P < 5e-8, r2 < 0.001), genetically predicted sIL-6Rα was significantly associated with a decreased risk of delirium with less than three single-nucleotide polymorphisms (SNPs) in all three GWAS data sources (ORWaldratio = 0.89, 95% CI: 0.79–0.96, PWaldratio = 0.0016; ORIVW = 0.88, 95% CI: 0.79–0.97, PIVW = 0.008; ORIVW = 0.88, 95% CI: 0.80–0.96, PIVW = 0.004). The causal relationship between sIL-6Rα and delirium became non-significant when a more liberal threshold of P of < 5e-6 was applied (all PIVW > 0.05). At the two genome-wide significance levels (P < 5e-8 and P < 5e-6), we found no evidence for the causal effects of peripheral TNF-α, CRP, IL-1α, IL-1β, IL-2, IL-6, IL-8, and soluble gp130 on delirium (all P > 0.05). The MR-Egger intercept and MR-PRESSO results indicated that no SNP had possible pleiotropy (all P > 0.05). Regarding the reverse, no evidence for an effect of delirium on these inflammatory biomarkers could be found (all P > 0.05).ConclusionThe results of this MR analysis did not support that peripheral TNF-α, CRP, IL-1α, IL-1β, IL-2, IL-6, sIL-6Rα, soluble gp130, and IL-8 were causally associated with delirium. More research is needed to explore the role of inflammatory factors in the pathogenesis of delirium

Pattern classification of hand movements using time domain features of electromyography

Myoelectric control of prostheses is a long-established technique, using surface electromyography (sEMG) to detect the electrical signals of muscle activity and perform subsequent mechanical actions. Despite several decades’ research, robust, responsive and intuitive control schemes remain elusive. Current commercial hardware advances offer a variety of movements but the control systems are unnatural, using sequential switching methods triggered by specific sEMG signals. However, recent research with pattern recognition and simultaneous and proportional control shows good promise for natural myoelectric control. This paper investigates several sEMG time domain features using a series of hand movements performed by 11 subjects, taken from a benchmark database, to determine if optimal classification accuracy is dependent on feature set size. The features were extracted from the data using a sliding window process and applied to five machine learning classifiers, of which Random Forest consistently performed best. Results suggest a few simple features such as Root Mean Square and Waveform Length achieve comparable performance to using the entire feature set, when identifying the hand movements, although further work is required for feature optimisation

Automatic segmentation of adipose tissue from thigh magnetic resonance images

Automatic segmentation of adipose tissue in thigh magnetic resonance imaging (MRI) scans is challenging and rarely reported in the literature. To address this problem, we propose a fully automated unsupervised segmentation method involving the use of spatial intensity constraints to guide the segmentation process. The novelty of this method lies in two aspects: firstly, an adaptive distance classifier, incorporating intra-slice spatial continuity, is used for robust region growing and segmentation estimation; secondly, polynomial based intensity inhomogeneity maps are generated to model inter- and intra-slice intensity variation of each pixel class and thus refine the initial classification. Our experimental results have demonstrated the effectiveness of imposing 3D intensity constraints to successfully classify the adipose tissue from muscles in the presence of image noise and considerable amounts of non-uniform MRI intensity. © 2013 Springer-Verlag