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Objective: Macrophages have been shown to demonstrate a high level of plasticity, with the ability to undergo dynamic transition between M1 and M2 polarized phenotypes. We investigate long non-coding RNA (lncRNA) cox-2 in macrophage polarization and the regulatory mechanism functions in hepatocellular carcinoma (HCC). Methods: Lipopolysaccharide (LPS) was used to induce RAW264.7 macrophages into M1 type, and IL-4 was to induce RAW264.7 macrophages into M2 type. We selected mouse hepatic cell line Hepal-6 and hepatoma cell line HepG2 for co-incubation with M1 or M2 macrophages. Quantitative real-time PCR was used to detect the expressions of lncRNA cox-2and mRNAs. ELISA was conducted for testing IL-12 and IL-10 expressions; western blotting for epithelial mesenchymal transition related factors (E-cadherin and Vimentin). An MTT, colony formation assay, flow cytometry, transwell assay and stretch test were conducted to test cell abilities. Results: The M1 macrophages had higher lncRNA cox-2 expression than that in the non-polarized macrophages and M2 macrophages. The lncRNA cox-2 siRNA decreased the expression levels of IL-12, iNOS, and TNF-α in M1 macrophages, increased the expression levels of IL-10, Arg-1, and Fizz-1 in M2 macrophages (all P < 0.05). The lncRNA cox-2 siRNA reduces the ability of M1 macrophages to inhibit HCC cell proliferation, invasion, migration, EMT, angiogenesis and facilitate apoptosis while strengthening the ability of M2 macrophages to promote proliferation HCC cell growth and inhibit apoptosis. Conclusion: These findings indicate that lncRNA cox-2 inhibits HCC immune evasion and tumor growth by inhibiting the polarization of M2 macrophages. This article is protected by copyright. All rights reserved

Recently, correlation filter (CF)-based tracking methods have attracted considerable attention because of their high-speed performance. However, distortion, which refers to the phenomenon that the correlation outputs of CF-based trackers are distorted, remains a major obstacle for these methods. In this paper, we propose a distortion-aware correlation filter framework, which can detect distortions and recover from tracking failures. Our framework employs a simple yet effective feature termed normed correlation response to detect distortions. Meanwhile, we introduce a competition mechanism to handle distortions, in which we build a specialized graph to formulate and handle tracking under distortion as a maximum multi clique problem. Furthermore, a global-local context model is exploited to alleviate underlying distortions during the tracking process. Extensive experiments on the Online Tracking Benchmark show that our tracker can find the optimal target trajectory during the distortion period and retrieve the possibly missing target, consequently outperforms the state-of-the-art methods and improves the performance of CF-based trackers favorably.

Laser devices have received increasing attention in numerous computer-aided applications such as automatic control, 3D modeling and virtual reality. In this paper, aiming at people counting, we propose a novel people detection and tracking method based on the multipoint infrared laser, which can further facilitate intelligent scene modeling and analysis. In our method, a camera with the infrared lens filter is utilized to capture the monitored scene where an array of infrared spots is produced by the multipoint infrared laser. We build a spatial background model based on locations of spots. Pedestrians are detected by clustering of foreground spots. Then, our method tracks and counts the detected pedestrians via inferring the forward–backward motion consistency. Both quantitative and qualitative evaluation and comparison are conducted, and the experimental results demonstrate that the proposed method achieves excellent performance in challenging scenarios.

Aberrant activations of Hedegehog (Hh) signaling were found in hepatocellular carcinoma (HCC) and some other cancer types. However, the details have not been completely understood and the underlying mechanism remains unclear. Here we reported that miR-1249 transcription in HCC cells was regulated through direct binding to the conserved sequences in miR-1249 promoter region by Gli1, which functions as a transcription factor and is a component in the Hh signaling pathway. Interestingly, expression of tumor suppressor PTCH1, which is another component of the Hh signaling pathway, was inhibited by miR-1249 through targeting its 3'-untranslated region. Down-regulation of PTCH1 further enhanced the downstream effects mediated by Gli1. In consistent with these findings, miR-1249 expression level was correlated with degree of prognosis (p=0.005) in HCC patients. Taken together, our results suggested the existence of a positive feedback loop comprised of Gli1, miR-1249 and PTCH1. During the process of HCC progression, this positive feedback loop could be continuously activated to enhance tumor cell growth, migration and invasion.

Purpose The purpose of this paper is to present an efficient algorithm for multi-layer garment fitting simulation based on the geometric method to solve the low time cost problem during penetration detection and processing. This is more practical to design a CAD system to preview the multi-layer garment fitting effect in daily life. Design/methodology/approach The construction of a multi-layer garment based on existing 3D garments is a suitable method because this method is similar to the daily method of multi-layer dressing. The major problem is the penetration phenomenon between different garments because these 3D garment’s geometric shapes are constructed in different situations. In this paper, an efficient algorithm of multi-layer garment simulation is reported. A face-face intersection detection algorithm is designed to detect the penetration region between multi-layer garments fast and a geometric penetration processing algorithm is presented to solve the penetration phenomenon during multi-layer garment simulation. Findings This method can quickly detect the penetration between faces, and then deal with the penetration for multi-layer garment construction. Experimental results show that this method can not only remove the penetration but basically maintain the trend of wrinkles efficiently. At the same time, the garments used in the experiment have almost more than 5,800 faces, but the resolving time is under five seconds. Originality/value The main originalities of the multi-layer garment virtual fitting algorithm based on the geometric method are highly efficient both in terms of time cost and fitting effect. Based on this method, the technology of multi-layer garment virtual fitting can be used to design a novel CAD system to preview the multi-layer garment fitting effect in real time. This is a pressing requirement of virtual garment applications.

© 2019 IEEE. PM2.5 is an important indicator of the severity of air pollution and its level can be predicted through hazy photographs caused by its degradation. Image-based PM2.5 estimation is thus extensively employed in various multimedia applications but is challenging because of its ill-posed property. In this paper, we convert it to the problem of estimating the PM2.5-relevant haze transmission and propose a learning model called the transmission filtering network. Different from most methods that generate a transmission map directly from a hazy image, our model takes the coarse transmission map derived from the dark channel prior as the input. To obtain a transmission map that satisfies the local smoothness constraint without regional boundary degradation, our model performs the edge-preserving smoothing filtering as the refinement on the map. Moreover, we introduce the attention mechanism to the network architecture for more efficient feature extraction and smoothing effects in the transmission estimation. Experimental results prove that our model performs favorably against the state-of-the-art dehazing methods in a variety of hazy scenes.

Given rapid development witnessed by open educational resources (OER) in the past few decades, a considerable number of online educational videos emerge on various MOOC platforms such as Coursera and YouTube. Nevertheless, most educational videos on the internet are lengthy and lack of elaborate annotations, which poses a challenge for learners to explore and locate content of interest efficiently. To address this, we present an automatic note-generating method to establish correspondences between visual entities in the slide-based lecture video and their descriptive speech texts by evaluating the semantic relationship. Firstly, the visual entities are extracted and recognised from the presentation slides. Then, each of visual entities is associated with its corresponding descriptive speech text. Finally, a placement optimisation scheme is put forward to pack the visual entities and speech texts into a note-like layout in a compact fashion, which can help learners to improve their learning efficiency. The experimental results show that the efficient performances about visual entity extraction and correspondence matching are efficient. The user study is also designed to investigate the performance of Lecture2Note in facilitating learning. Compared with peer methods, the auto-generated note created by our method achieves a higher user satisfaction level regarding a properly structured layout as well as efficient content navigation and exploration.

The sketch segmentation problem remains largely unsolved because conventional methods are greatly challenged by the highly abstract appearances of freehand sketches and their numerous shape variations. In this work, we tackle such challenges by exploiting different modes of sketch data in a unified framework. Specifically, we propose a deep neural network SPFusionNet to capture the characteristic of sketch by fusing from its image and point set modes. The image modal component SketchNet learns hierarchically abstract ro-bust features and utilizes multi-level representations to produce pixel-wise feature maps, while the point set-modal component SPointNet captures local and global contexts of the sampled point set to produce point-wise feature maps. Then our framework aggregates these feature maps by a fusion network component to generate the sketch segmentation result. The extensive experimental evaluation and comparison with peer methods on our large SketchSeg dataset verify the effectiveness of the proposed framework.

We propose a meta-relation network to solve the few shot learning problem, where the classifier must learn to recognize new classes given only few examples from each. Meta-relation networks is based on relation networks and Model-Agnostic Meta-Learning (MAML) training methods, which can be trained end-to-end. After training with MAML algorithm, the meta-relation networks can adapt to learning quickly for a small number of samples from a new task with only a small amount of gradient step. It can also classify new classes of images by calculating the scores between query images and few examples of each new class. In experiments, we will demonstrate that the proposed approach leads to good performance on two few-shot image classification benchmarks.

In the past, RS codes were initially encoded in nonsystematic forms to account for general RS codes that transform all components in a codeword. To avoid information leakage, RS code can also be encoded in nonsystematic forms like the general RS code-based cryptography. This work proposes an algorithm to correct erasures in nonsystematic RS codes for the first time, in which Gaussian elimination is used to calculate the error values. Meanwhile, RS decoding in the frequency domain is reorganized to account for nonsystematic RS codes, in which the first Fourier transform is left for encoding and disappear in the decoder. Then the nonsystematic codes are received and pass only one Fourier transform in RS decoding. The first RS decoding is able to correct erasures in nonsystematic codewords, while the other one utilizes the nonsystematic forms to improve the efficiency of systematic RS decoding. Both scenarios are based on Fourier transforms in the finite field over $GF(2^{m})$. The proposed algorithms and architectures can be implemented by reconfigurable logic, which measures the efficiency and examines the validity of decoding nonsystematic RS codes.

Exploring shape variations on virtual garments is significant but challenging to the aspect of 3D garment modeling. In this paper, we propose a data-driven editing framework for automatic 3D garment modeling, which includes semantic garment segmentation, probabilistic reasoning for component suggestion, and garment component merging. The key idea in this work is to develop a simple but effective garment synthesis that utilizes a continuous style description, which can be characterized by the ratio of area and boundary length on garment components. First, a semi-supervised learning algorithm is proposed to simultaneously segment and label the components in 3D garments. Second, a set of matchable probability measurement is applied to recommend components that can be regarded as a new 3D garment. Third, a variation synthesis is developed to satisfy the garment style criteria while ensuring the realistic-looking plausibility of the results. As demonstrated by the experiments, our method is able to generate various reasonable garments with material effects to enrich existing 3D garments.

Remeshing has been an active research topic in Digital Geometry Processing. In this paper, a novel approach of direct anisotropic quad-dominant remeshing is proposed. We apply the retiling method to the particular problem of quad-dominant remeshing. Compared with other methods, this method can simply partition the surface of an original triangular mesh into connected quads with the mesh edges aligning to the principal directions. The first step in this method is to estimate and smooth the curvature tensor field of the surface at the vertices, and then the quad-dominant mesh is obtained by retiling the quad surface so that quadrilateral edges are parallel to the local principal curvature directions. In addition, to preserve the sharp feature information during remeshing processes, the feature lines can be extracted using mesh segmentation method, and the intersections between the feature lines and the orthogonal planes can be found during the process of retiling. A feature fusion process is presented to join the feature edges and feature points into the quad-dominant mesh. The experiment results show that this new remeshing method is simple and easy to implement. The resolution of the quadrilateral mesh can be controlled during the remeshing. It is applicable to arbitrary genus meshes and can generate high-quality quad-dominant mesh.

Human visual attention system tends to be attracted to perceptual feature points on 3D model surfaces. However, purely geometric-based feature metrics may be insufficient to extract perceptual features, because they tend to detect local structure details. Intuitively, the perceptual importance degree of vertex is associated with the height of its geometry position between original model and a datum plane. So, we propose a novel and straightforward method to extract perceptually important points based on global height field. Firstly, we construct spectral domain using Laplace---Beltrami operator, and we perform spectral synthesis to reconstruct a rough approximation of the original model by adopting low-frequency coefficients, and make it as the 3D datum plane. Then, to build global height field, we calculate the Euclidean distance between vertex geometry position on original surface and the one on 3D datum plane. Finally, we set a threshold to extract perceptual feature vertices. We implement our technique on several 3D mesh models and compare our algorithm to six state-of-the-art interest points detection approaches. Experimental results demonstrate that our algorithm can accurately capture perceptually important points on arbitrary topology 3D model.

Objective To investigate whether the Notch–Hif-1α signaling pathway is involved in liver regeneration. Methods Rats were divided into two groups and treated with daily intraperitoneal injections of saline (control) or the gamma-secretase inhibitor, Fli-06, for 2 days. Two-thirds of the rat livers were resected and rats were later euthanized at specific time points post-resection to analyze the remnant livers. Each group's liver/body weight ratio was calculated, and immunostaining and western blotting were used to determine the cell proliferation marker, PCNA and Ki-67 expression. Real-time PCR and western blotting were used to compare the mRNA expression of Notch homolog-1 ( Notch1), hairy and enhancer of split-1 ( Hes1), and vascular endothelial growth factor ( Vegf), and the protein expression of NICD and HIF-1α, respectively. Results The liver/body weight ratios and number of Ki-67- and PCNA-positive cells were significantly lower in the experimental group than the control group, indicating lower levels of liver regeneration following the disruption of Notch signaling by Fli-06. The Hes1 and Vegf mRNA levels and NICD and HIF-1α protein expression levels were all down-regulated by Fli-06 treatment. Conclusion Notch–Hif-α signaling pathway activation plays an important role in liver regeneration, where it may contribute toward liver cell proliferation.

Research on human parsing methods has attracted increasing attention in a wide range of applications. However, dataset imbalance is still a challenging problem in this task, which directly affects the performance of human parsing. There are different types of dataset imbalance problems. For example, the numbers of samples for various labels in a dataset may differ, the scales of objects identified by different labels may vary considerably, the differences between some heterogeneous label types may be much smaller than other cases, and in some extreme situations, images may be labeled incorrectly. In this paper, we propose a rebalanced model for imbalanced human parsing. Two innovative blocks are included in the model, i.e., a pre-bilateral awareness block and a combined-order statistics awareness block. The function of the former is to leverage the multiscale feature extractors to capture the changing scale information in an efficient way from the spatial space. Meanwhile, the function of the latter is to exploit the information of the feature distributions from the channel space. Furthermore, we propose an imbalance data-drop algorithm to simultaneously solve the mislabeling and small sample label weighting problems. Extensive experiments are conducted on three datasets, and the experimental results demonstrate that our method is able to solve the problem of data imbalance efficiently and obtain better human parsing performance.

Research of the clothing recommendation algorithm is important that can be used to provide a more efficient method for consumers to select their expected clothing. Considering the characteristics of clothing product, in this paper, a personalized clothing recommendation algorithm based on fine-grained attributes is reported. In this method, the fine-grained attributes of the clothing are established based on clothing image. And a personalized clothing attributes preference model for each user combining with the fine-grained attributes of the clothing and personal parameters is built. This method is used in an application system based on client/server framework and a mobile phone software based on the Android platform.

Exercise simulation application is important to improve on the health and avoid the irreversible damage of human health. The application of sports health prediction software will be the characteristics of multi-users, and the calculation of human sports simulation will spend lots of time. Therefore, the concurrent optimization scheme of the platform is very important to guarantee the efficiency, usability and stability of multi-user platform. In this paper, a sports health prediction platform based on Client/Server architecture is reported, and a concurrent optimization scheme for this platform is designed and implemented. To solve this problem, a cluster on the server side is developed, a front-end server based on Nginx as a user access entry is built. Using the Spring Boot framework, human sports simulation model on back-end application servers is constructed, and Redis method is used to save information as simulation prediction results and users' preference clothing. The comparison results show that our method has better performances for the multi-user system. It is a suitable framework for the sports health simulation prediction application.

Our method shortens the time of fluid simulation by coupling the two conditions of density-invariant and divergence-free, and achieves the same simulation effect compared with other methods. Further, we regard the displacement of particles as the only basic variable of the continuity equation, which improves the stability of the fluid to a certain extent.

Background: Benefits of yoga practice in patients with knee osteoarthritis and rheumatoid arthritis remains controversial. This study performs a meta-analysis to quantify the efficiency of yoga exercise for patients pain reduction, functional recovery, and general wellbeing. Methods: A computerized search of PubMed and Embase was performed to identify relevant studies. The outcome measures were pain, stiffness, and physical function. Two investigators identified eligible studies and extracted data independently. The quality of citations was measured using Jadad score. Standardized mean differences (SMDs) with 95% confidence intervals (CIs) were calculated for pain, musculoskeletal impairment, quality of life, general wellbeing, and mental wellbeing. Results: A total of 13 clinical trials involving 1557 patients with knee osteoarthritis and rheumatoid arthritis were included in final meta-analysis with the average Jadad score 2.8. The SMD was −0.98 (95% CI −1.18, −0.78, P

The present study aimed to determine how the expression and function of HOTTIP modifies, and regulates the metabotropic glutamate receptor 1 (mGluR1) to affect human pancreatic cancer cell viability. HOTTIP expression was higher in human pancreatic cancer tissue compared with in para-carcinoma tissue. However, downregulation of HOTTIP expression was revealed to significantly reduce cell viability, induce apoptosis, promote caspase-3 and caspase-8 activities and increase Bax expression in pancreatic cancer cells. Additionally, downregulation of HOTTIP expression significantly suppressed mGluR1 and mitigated activation of the phosphoinositide 3-kinase (PI3K)/Akt/mechanistic target of rapamycin (mTOR) pathway in pancreatic cancer cells. To the best of our knowledge, the present study is the first to identify that the anticancer effect of HOTTIP against human pancreatic cancer functions the mGluR1 pathway.

Mesh segmentation is one of the important issues in digital geometry processing. Region growing method has been proven to be a efficient method for 3D mesh segmentation. However, in mesh segmentation, feature line extraction algorithm is computationally costly, and the over-segmentation problem still exists during region merging processing. In order to tackle these problems, a fast and efficient mesh segmentation method based on improved region growing is proposed in this paper. Firstly, the dihedral angle of each non-boundary edge is defined and computed simply, then the sharp edges are detected and feature lines are extracted. After region growing process is finished, an improved region merging method will be performed in two steps by considering some geometric criteria. The experiment results show the feature line extraction algorithm can obtain the same geometric information fast with less computational costs and the improved region merging method can solve over-segmentation well.

Thumbnails provide an efficient way to perceive video content and give online viewers instant gratification of making relevance judgements. In this paper, we proposed an automatic approach to generate magazine-cover-like thumbnail using the salient visual and textual metadata extracted from video. Compared with traditional snapshot, the synthesized thumbnail is more informative and attractive, which would be helpful for online video selection.

© 2017 IEEE. Recent progress in sketch-based 3D shape retrieval creates a novel and user-friendly way to explore massive 3D shapes on the Internet. However, current methods on this topic rely on designing invariant features for both sketches and 3D shapes, or complex matching strategies. Therefore, they suffer from problems like arbitrary drawings and inconsistent viewpoints. To tackle this problem, we propose a probabilistic framework based on Multi-View Pairwise Relationship (MVPR) learning. Our framework includes multiple views of 3D shapes as the intermediate layer between sketches and 3D shapes, and transforms the original retrieval problem into the form of inferring pairwise relationship between sketches and views. We accomplish pairwise relationship inference by a novel MVPR net, which can automatically predict and merge the pairwise relationships between a sketch and multiple views, thus freeing us from exhaustively selecting the best view of 3D shapes. We also propose to learn robust features for sketches and views via fine-tuning pre-trained networks. Extensive experiments on a large dataset demonstrate that the proposed method can outperform state-of-the-art methods significantly.

With the development of social economy, increasing importance has been attached to people's health consciousness. Exercise is widely regarded as the first step for health maintenance. For reducing accidents like syncope and dehydration in exercise, researchers and consumers urgently need a user-friendly and reliable tool to assisted analysis the risks in exercise. Aiming at the prediction on the changes of physiological parameters during exercise, a user-friendly exercise simulation prediction platform which stems from the George Fu's transient and three-dimensional thermal theoretical framework is reported. This platform was divided into the preprocessing, numerical simulation and post-processing modules as well as employs client/server architecture. In this platform, the changes of the human body physiological indicators under different environment conditions, at different activity levels and with various clothing can be presented intuitively. This platform has more flexible operability by the means of user-friendly graphical user interface and can be used to simulate a variety of exercise scenes which can make up for the specify scenes cannot be measured experimentally in reality.

Motion recognition is an important application of electromyography (EMG) analysis. While discrete motions such as hand open, hand close and wrist pronation have been extensively investigated, studies on combined motions involving two or more degrees of freedom (DOFs) are relatively few and the classification accuracy of the combined motions reported in previous studies is barely satisfactory. To improve the accuracy of the combined motion recognition, common spatial pattern (CSP) was employed in this study to extract spatial features. 18 forearm motion classes, consisted of 8 discrete motions and 10 combined motions, were classified by the proposed method. Our results showed that the accuracy rate of CSP features was 96.3%, which outperformed the commonly used time-domain (TD) features by 2.4% and TD combined with auto-regression coefficients (TDAR) by 0.6%. Moreover, CSP features cost noticeable much less time than TDAR and quite less time than TD in testing. These results suggest that CSP features could be a better feature set for multi-DOF myoelectric control than conventional features.

Motion classification system based on surface Electromyography (sEMG) pattern recognition has achieved good results in experimental condition. But it is still a challenge for clinical implement and practical application. Many factors contribute to the difficulty of clinical use of the EMG based dexterous control. The most obvious and important is the noise in the EMG signal caused by electrode shift, muscle fatigue, motion artifact, inherent instability of signal and biological signals such as Electrocardiogram. In this paper, a novel method based on Canonical Correlation Analysis (CCA) was developed to eliminate the reduction of classification accuracy caused by electrode shift. The average classification accuracy of our method were above 95% for the healthy subjects. In the process, we validated the influence of electrode shift on motion classification accuracy and discovered the strong correlation with correlation coefficient of >0.9 between shift position data and normal position data.

Research of healthy exercise has garnered a keen research for the past few years. It is known that participation in a regular exercise program can help improve various aspects of cardiovascular function and reduce the risk of suffering from illness. But some exercise accidents like dehydration, exertional heatstroke, and even sudden death need to be brought to attention. If these exercise accidents can be analyzed and predicted before they happened, it will be beneficial to alleviate or avoid disease or mortality. To achieve this objective, an exercise health simulation approach is proposed, in which an integrated human thermophysiological model consisting of human thermal regulation model and a nonlinear heart rate regulation model is reported. The human thermoregulatory mechanism as well as the heart rate response mechanism during exercise can be simulated. On the basis of the simulated physiological indicators, a fuzzy finite state machine is constructed to obtain the possible health transition sequence and predict the exercise health status. The experiment results show that our integrated exercise thermophysiological model can numerically simulate the thermal and physiological processes of the human body during exercise and the predicted exercise health transition sequence from finite state machine can be used in healthcare.

This paper proposes a supervised machine learning method for the problem of automatic keyphrase extraction for Social Question Answering (SQA) sites. The method is developed by: 1) Analyzing the structural and activity characteristics of typical SQA sites, 2) Developing and categorizing four types of calculation features that can describe those characteristics, and 3) Developing customized logistic regression model to be trained by the real dataset from six popular SQA sites, in both English and Chinese. Experimental results show the influences from those proposed SQA related features vary, some are helpful to keyphrase extraction for SQA sites of both languages while some are only useful for a specific site. The results also demonstrate a generally better performance comparing to a typical keyphrase extraction algorithms published previously like KEA.

This paper proposes a solution that helps annotate and categorize the content of random web pages, by constructing a four-step method include keyword extraction, conception comparison, semantic statement integration and domain determination. In order to utilize the knowledge support from the multiple domain ontologies, the method develops a specic concept comparing algorithm against the single ontology and a evaluation model to determine proper domain that the web page belongs to. The experiment is conduct upon 572 articles randomly collected online. The results shows that: 1) The proposed method returns satised category task, and 2) Compring to the conventional keyword based information retrieval task, the multiple-domain-ontology based semantic indexing system generated by the proposed method performs signicantly better.

A wearable myoelectric device is essentially a surface electromyography(sEMG) based human machine interaction (HMI) system. The non-stationary property of sEMG could be one of the obstacles that degrade the user experience of wearable myoelectric devices because they need to be put on and taken off frequently, which brings in the time-variation effects specified in this paper. In order to reduce the influence cause by time-variation on wearable myoelectric devices, the common spatial pattern(CSP) was employed to extract relatively robust features from multichannel sEMG signals. As comparation, the conventional time domain(TD) features together with time domain auto regressive(TDAR) features were investigated. To implement the experiment, 16 electrodes were utilized to simulate a wearable myoelectric device, and an absolute value based Teager-Kaiser energy operator(TKEO) strategy was proposed to realize onset detection. The experiment was divided into 2 sessions, corresponding to the situations before and after time-variation happened respectively. The support vector machine (SVM) and linear discrimination analysis(LDA) were used to build the pattern recognition models. In session2, the average classification accuracy of CSP(95.2±2.3%) is superior to TD(90.3±4.4%) and TDAR(92.4±4.2%) under LDA. The result suggests that the CSP feature set is relatively robust against accuracy reduction resulted from time-variation in motion recognition.

As an effective and efficient way to graphically portray ideas and concepts, free-hand sketches are playing an increasingly significant role in today’s image retrieval systems. There are many factors that contribute to a successful sketch-based image retrieval (SBIR) system, but perhaps the most essential ones are the design of powerful image feature descriptors and effective perceptual similarity metrics in the feature space. However, the painting experiences and styles of users vary hugely, which poses a great challenge for consistently accurate feature representation and matching. We introduce an approach for SBIR by characterizing homogeneous sketch drawing styles. Specifically, given a sketch query, the proposed method first infers the user’s painting style from it by analyzing contour features. Then, top K-nearest sketches of historical users with similar drawing characters are identified from the database. Finally, the matched images of the query can be obtained by reranking those of the K-nearest sketches stored in the historical recordings. To evaluate the effectiveness of the proposed method, we conducted quantitative comparisons with several peer SBIR methods. Experimental results indicate that the proposed method is 15% higher than state-of-the-art in terms of mean average precision.

Healthcare is becoming more and more important in modern society. In order to prevent some health symptoms happening in daily life, it is important to develop an efficient model to simulate the human physiological performance for predicting and reducing accidents like dehydration, exertional heatstroke, syncope, even sudden death and so on. In this paper, a novel human physiological computer simulation model is introduced. A nonlinear heart rate regulation model and a two-node thermal regulation model are integrated together to simulate the human physiological indices like core temperature, dehydration amount and heart rate. Experiment results show that the proposed physiological simulation model can well simulate the human physiological mechanisms, and some important numerical computation results predict the same trends as the experimental measurements. These simulation results can be used to analyse human physiological symptoms and assist the health risk assessment in the healthcare.

With the development of digital TV, especially 3D technologies, some applications in the digital home industry have been reported to meet and even exceed customer expectation in the market place for enterprise. In order to evaluate the clothing comfort performance in the digital e-commerce service, the clothing comfort model is very important. Considered the physical, physiological and psychological factors, a theoretical clothing comfort evaluation model is presented based on the adaptive fuzzy neural network in this paper. According to the characters of clothing comfort, Fuzzy Neural Networks (FNN) can provide a very human machine knowledge representation scheme friendly for acquiring, representing and using the knowledge of the domain expert. This is a significant advantage for clothing comfort evaluation where the exact system transfer functions cannot be well modeled and adequate training data sets are not available. The experiment results shown that there has the same prediction trend about the experiment result and simulation result. This clothing comfort evaluation model is used as a component in the smart clothing function user experience system.

According to the anisotropic property in most real-world cloth for virtual fitting, this paper proposes a novel dynamic cloth simulation method via geometric deformation energy model that preserves geometric features well to achieve cloth behaviors with various material effects. We first construct an objective deformation energy with the terms including vertex position, edge length, dihedral angle, and gravitation, then we conduct a numerical solution in the least square sense. In order to establish the dynamic cloth deformation solution, we further analyze the corresponding relationship between different weights in front of geometric energy terms and material properties by comparison with the real photographs of typical real fabrics. Establishing a dynamic weight-regulation measure can model similar cloth anisotropic behaviors for virtual fitting applications in digital home. The experiments show that our approach effectively provide more rich cloth deformation results with distinctive material effects.

Sketching is a simple and natural way of expression and communication for humans. It also gains increasing popularity in human computer interaction, with the emergence of multitouch tablets and styluses. The main characteristics of sketches can be summarized in two aspects: (i) the fuzziness of the sketch information, which can be attributed to that the completeness of the sketch is evolving with the drawing process and there is no fixed mapping between the user expression and the sketch; (ii) the randomness of user input, arising from that users' input is not only associated with their field background, way of thinking, hand-painted habits and preferences, but also affected by types of equipments and environmental factors. These two characteristics make the sketch can express creative thinking, but it is also the two characteristics that make the freehand sketch recognition algorithm must have enough robustness to support user interaction. Neglecting the above two issues in previous studies causes the results to be less satisfactory. In recent years, sketch-based interactive methods are widely used in many retrieval systems. In particular, a variety of sketch-based 3D model retrieval works have been presented. However, almost all these works focus on directly matching sketches with the projection views of 3D models, and they suffer from the large differences between the sketch drawing and the views of 3D models, leading to unsatisfying retrieval results. Therefore, in this article, during the matching procedure in the retrieval, we propose to match the sketch with the ones of each 3D model from historical users instead of projection views. Yet since the sketches between the current user and the historical users can have big difference, we also study appropriate methods to handle users' personalized deviations and differences. To this end, we leverage recommendation algorithms to estimate the drawing style characteristic similarity between the current user and historical users.

Participation in a regular exercise program can improve health status and contribute to an increase in life expectancy. However, exercise accidents like dehydration, exertional heatstroke, syncope, and even sudden death exist. If these accidents can be analyzed or predicted before they happen, it will be beneficial to alleviate or avoid uncomfortable or unacceptable human disease. Therefore, an exercise thermophysiology comfort prediction model is needed. In this paper, coupling the thermal interactions among human body, clothing, and environment (HCE) as well as the human body physiological properties, a human thermophysiology regulatory model is designed to enhance the human thermophysiology simulation in the HCE system. Some important thermal and physiological performances can be simulated. According to the simulation results, a human exercise thermophysiology comfort prediction method based on fuzzy inference system is proposed. The experiment results show that there is the same prediction trend between the experiment result and simulation result about thermophysiology comfort. At last, a mobile application platform for human exercise comfort prediction is designed and implemented.

The designers and manufactures in apparel industry have urgent needs in designing clothing with superior thermal functions with user-friendly and cost-effective design tools. This paper presents a multi-disciplinary strategy for computer-aided clothing thermal engineering design. It provides a systematical approach to integrate multi-disciplinary knowledge and transfer it into engineering-oriented design tools, thus the designers and manufacturers can easily carry out 1D, 2D and even 3D clothing thermal engineering designs according to the practical design requirements with a short design cycle and low design cost. The research work of this strategy begins from the investigation of the role of the thermal functions of clothing in the thermal comfort of human body. Then the framework is proposed to integrate the multi-disciplinary knowledge and illustrate the process to achieve the thermal engineering design of clothing. The important issues in the realization of computational simulation are addressed, including multi-scale model integration, data availability of characteristic parameters and hierarchical computational scheme. To issue easy-to-use design tools, the thermal functional design of clothing is quantified with important influence parameters, and the user-friendly wizard is designed for the CAD system development. Finally, the design applications of this strategy are discussed in terms of 1D, 2D and 3D thermal engineering designs with versatile CAD systems.

Efficient numerical methods for describing a garment's mechanical behavior during wear have been identified as the key technology for garment simulation. This paper presents a finite-element mechanical contact model based on Mindlin-Reissner shell theory for a three-dimensional human body and garment. In this model, the human body and the garment are meshed as basic contact cells, these contact cells between the human body and the garment are defined as the contact pair to describe the contact relationship, and the mathematical formulation of the finite-element model is defined to describe the strain-stress performance of the three-dimensional human body and garment system. By using the solution given by the computer code and the programs specifically developed, the calculations of the mechanics in the basic cells of the human body and the garment have been able to be carried out. The simulation results show that the model of rationality, a good simulation results and simulation efficiency.

To establish the human body model to analyze the heat and moisture transfer on body surface, a new explicit definition of rational L-recursion surface is given and the L-recursion surfaces, in Grassmann spaces, are constructed by using blossom method of the homogeneous normal pyramid form. Based on our human body model, the balance theory of garment simulation, the heat and moisture transfer balance equations, called ICAD-balance equations are obtained. The balance theory of garment simulation integrally studies the complex system of human body–fabric–environment. At the same time, the method of obtaining the heat and moisture transfer balance equations is also based on the mass conservation law, the energy conservation law and the Fish law of capillarity. A finite volume method is employed to solve the ICAD-balance equations.

Engineering design of thermal quality clothing is a promising solution by applying multi-disciplinary knowledge to achieve the design and production of clothing with desirable thermal functions. In this paper, a special simulation-based and lifestyle-oriented CAD system is introduced to help the user in engineering design of thermal quality clothing. The engineering-oriented simulation models endowed with explicit data availability arose from the material parameters that are the key issue for engineering application. To offer an easy-to-use tool, this system is implemented with a lifestyle-oriented design procedure. It can facilitate the designers to quickly implement design and simulate on the wearing scenario, and evaluate and optimize their design. Due to the design of thermal quality clothing can be achieved without making physical prototypes, it is able to speed up the design cycle and reduce the design and development cost.