Dense feature correspondence for video-based endoscope three-dimensional motion tracking

This paper presents an improved video-based endoscope tracking approach on the basis of dense feature correspondence. Currently video-based methods often fail to track the endoscope motion due to low-quality endoscopic video images. To address such failure, we use image texture information to boost the tracking performance. A local image descriptor - DAISY is introduced to efficiently detect dense texture or feature information from endoscopic images. After these dense feature correspondence, we compute relative motion parameters between the previous and current endoscopic images in terms of epipolar geometric analysis. By initializing with the relative motion information, we perform 2-D/3-D or video-volume registration and determine the current endoscope pose information with six degrees of freedom (6DoF) position and orientation parameters. We evaluate our method on clinical datasets. Experimental results demonstrate that our proposed method outperforms state-of-the-art approaches. The tracking error was significantly reduced from 7.77 mm to 4.78 mm. © 2014 IEEE

A new class of efficient piecewise nonlinear chaotic maps for secure cryptosystems

In this paper we construct a new class of nonlinear chaotic maps for secure cryptosystems. These maps can overcome the security holes brought by the "piecewise linearity" of the previous Piecewise Linear Chaotic Maps (PWLCM) due to a fact that the chaotic sequences generated by the derived iterative system based on the proposed maps are proved to have perfect dynamic properties, such as uniform invariant distribution, d-like autocorrelation function etc. Furthermore, the relative quantized two-value sequences also have perfect secure statistical characteristics. In terms of computing speed, the proposed maps have faster speed than the recently proposed nonlinear "piecewise-square-root" maps (PSRM), and they actually have equivalently the same computing speed with the linear PWLCM

The effect of comorbid attention-deficit/hyperactivity disorder symptoms on face memory in children with autism spectrum disorder: Insights from transdiagnostic profiles

Face memory impairments are common but heterogeneous in autism spectrum disorder (ASD), which may be influenced by co-occurrence with attention-deficit/hyperactivity disorder (ADHD). Here, we aimed to investigate the phenotype change of face memory in children with ASD comorbid ADHD symptoms, and discuss the potential role of executive function (EF). Ninety-eight children were analyzed in the present study, including ASD- (ASD-only, n = 24), ADHD (n = 23), ASD+ (with ADHD symptoms, n = 23) and neurotypical controls (NTC, n = 28). All participants completed two tests: face encoding and retrieving task and Wisconsin Card Sorting Test (WCST) for measuring face memory and EF, respectively. Results revealed that: compared with the NTC group, children with ASD- exhibited lower accuracy in both face encoding and retrieving, and participants with ASD+ showed lower accuracy only in the retrieving, whereas no differences were found among participants with ADHD. Moreover, in the ASD+ group, face encoding performance was correlated with response perseverative errors (RPE) and failure to maintain sets (FMS) of WCST; significantly, there were no group differences between ASD+ and NTC in these two indices. The transdiagnostic profiles indicated that comorbid ADHD symptoms could modulate the face encoding deficiency of ASD, which may be partially compensated by EF. Shared and distinct intervention strategies to improve social cognition are recommended for children undergoing treatment for each condition

Field-circuit coupled T-S finite element analysis of core losses for induction motor

© 2017 IEEE. A two-dimensional (2-D) field-circuit coupled finite element model of induction motor (IM) is built. The equivalent circuit of three-phase squirrel cage rotor is modeled. Based on time-stepping finite elements analysis (T-S FEA), core loss in the stator of a no-load IM under sinusoidal and Sinusoidal Pulse Width Modulation (SPWM) excitations is studied. The rotating flux density distributions with time variation at different locations of the stator are obtained. Meanwhile, the waveform and trajectory of magnetic flux density are analyzed. The areas where high-order harmonics mainly concentrated are simulated and the core losses in terms of the Bertotti's three-term separation model are calculated. All presented computations and models are verified through experiments

Improving Ant Collaborative Filtering on Sparsity via Dimension Reduction

Recommender systems should be able to handle highly sparse training data that continues to change over time. Among the many solutions, Ant Colony Optimization, as a kind of optimization algorithm modeled on the actions of an ant colony, enjoys the favorable characteristic of being optimal, which has not been easily achieved by other kinds of algorithms. A recent work adopting genetic optimization proposes a collaborative filtering scheme: Ant Collaborative Filtering (ACF), which models the pheromone of ants for a recommender system in two ways: (1) use the pheromone exchange to model the ratings given by users with respect to items; (2) use the evaporation of existing pheromone to model the evolution of users’ preference change over time. This mechanism helps to identify the users and the items most related, even in the case of sparsity, and can capture the drift of user preferences over time. However, it reveals that many users share the same preference over items, which means it is not necessary to initialize each user with a unique type of pheromone, as was done with the ACF. Regarding the sparsity problem, this work takes one step further to improve the Ant Collaborative Filtering’s performance by adding a clustering step in the initialization phase to reduce the dimension of the rate matrix, which leads to the results that K<<#users, where K is the number of clusters, which stands for the maximum number of types of pheromone carried by all users. We call this revised version the Improved Ant Collaborative Filtering (IACF). Experiments are conducted on larger datasets, compared with the previous work, based on three typical recommender systems: (1) movie recommendations, (2) music recommendations, and (3) book recommendations. For movie recommendation, a larger dataset, MoviesLens 10M, was used, instead of MoviesLens 1M. For book recommendation and music recommendation, we used a new dataset that has a much larger size of samples from Douban and NetEase. The results illustrate that our IACF algorithm can better deal with practical recommendation scenarios that handle sparse dataset