Yuanjing Feng

Ant colony pattern search algorithms for unconstrained and bound constrained optimization

Abstract Ant colony optimization is a class of metaheuristics which succeed in NP-hard combinational optimization problems rather than continuous optimization problems. We present and analyze a class of ant colony algorithms for unconstrained and bound constrained optimization on R n : Ant Colony Pattern Search Algorithms (APSAs). APSAs use the ant colony framework guided by objective function heuristic pheromone to perform local searches, whereas global search is handled by pattern search algorithms. The analysis results of APSAs prove that they have a probabilistic, weak stationary point convergence theory. APSAs present interesting emergent properties as it was shown through some analytical test functions.

Distributed performance of white matter properties in chess players: A DWI study using automated fiber quantification

The aim of this study is to verify whether anatomical changes occur in the brains of chess players. Besides, it is a potential attempt to evaluate diffusion properties along the tracts due to the diverse situations at anatomical level in different locations; moreover, conventional voxel-based analysis (VBA) has already been used to calculate the average values within the voxels in the investigated regions as analysis data. In this study, we used automated fiber quantification (AFQ) to automatically identify the major tracts that are related to functional domains of the human brain. AFQ can quantify pointwise white matter (WM) properties to detect specific local differences. We selected chess players with superior logical cognition abilities as the carrier to conduct our AFQ experiments. The diffusion properties of the 20 major tracts of professional chess players (n = 28) and matched controls (n = 29) were calculated using diffusion weighted imaging (DWI) data. We noted significant differences (p < 0.05) in the diffusion properties of some successive locations among 100 equidistant points in several tracts, especially in the left superior longitudinal fasciculus(SLF) and right inferior fronto-occipital fasciculus (IFOF). Professional chess players exhibited increase levels in the studied diffusion metrics with Pearson results paralleled the findings. Afterwards, considering the starting and terminating regions of SLF, IFOF, and thalamic radiation, the connectivity of gray matter (GM) where connections are active in the frontal lobe, temporal lobe, and thalamus was assessed to help with the further experiment. The results confirmed the tendency in which anatomical alterations generated different performances along the tracts; furthermore, long-term cognitive activities, such as chess, may systematically influence the WM properties of early memory, attention, and visual pathways.

A ReliefF-SVM-based method for marking dopamine-based disease characteristics: A study on SWEDD and Parkinson’s disease

&NA; Parkinsons disease (PD) and scans without evidence of dopaminergic deficit (SWEDD) are two distinct neurological disorders that require different therapeutic approaches; therefore its critical to classify the two disorders. The neuroimaging technology based on dMRI provided connectivity information and voxel features that can make it possible for researchers to analyze SWEDD and PD differences. In this work, a novel method of ReliefF‐SVM‐based dMRI analysis was presented to study the potential relations between PD and SWEDD. Some sensorimotor connections were found group‐wise differences, and SVM was suggested to successfully classify PD and SWEDD. These results indicate that our method using connectivity information and voxel features may provide a new strategy for disease analysis with small sample data.

An improved mass spring model based on internal point set domain constraint

Virtual surgery system aims to simulate the real surgery for doctor-training, while the soft tissue deformation is the one of the most significant part. To solve the lacks of object volume feature due to the problem that surface model defects in internal volume force together with the stability and real-time of the mass spring model, this paper proposes a mass spring model based on the internal point set domain constraint. First, this paper performs preprocessing, recording the information between adjoining points so the point search in further algorithm is easy to implement. The internal volume force interaction simulation is attained from establishing internal point set domain constraint, which guarantees the volume feature in deformation. Then, a dynamic deformation method is applied to update the deformed region on the surface based on internal point set domain constraint so the arithmetic speed is enhanced simultaneously. The experimental results show that the proposed method inherits the advantages of the traditional mass spring model while effectively conquers the defect in lacking the interaction of internal volume force and ensures the real-time superiority.

Real-time and authentic blood simulation for surgical training

Blood simulation is an important part in the virtual surgery training system. However, the huge computational complexity and authenticity of blood simulation is of great challenge to the surgical training system. In this paper, a simulation method based on GPU-accelerated is used for blood simulation in surgical training system. The grid method is used to divide the target area, create space grid domain, and search neighboring particles by neighboring grid. We solve the particle control equation and calculate the interaction between blood and solid by parallel computing architecture (CUDA) multi-threaded parallel acceleration technology, which greatly improve the operational efficiency and improve the real-time of training. In addition, an improved marching cube algorithm was used to render the surface of fluid, which improved the authenticity of surgical training. Experimental results show that the authenticity and flexibility of blood meet the simulation requirements during the surgical training when using our method. Furthermore, the speed of blood simulation was significantly improved comparing to the realization of CPU.

A Novel Richardson-Lucy Model with Dictionary Basis and Spatial Regularization for Isolating Isotropic Signals

Diffusion-weighted magnetic resonance imaging is a non-invasive imaging method that has been increasingly used in neuroscience imaging over the last decade. Partial volume effects (PVEs) exist in sampling signal for many physical and actual reasons, which lead to inaccurate fiber imaging. We overcome the influence of PVEs by separating isotropic signal from diffusion-weighted signal, which can provide more accurate estimation of fiber orientations. In this work, we use a novel response function (RF) and the correspondent fiber orientation distribution function (fODF) to construct different signal models, in which case the fODF is represented using dictionary basis function. We then put forward a new index Piso, which is a part of fODF to quantify white and gray matter. The classic Richardson-Lucy (RL) model is usually used in the field of digital image processing to solve the problem of spherical deconvolution caused by highly ill-posed least-squares algorithm. In this case, we propose an innovative model integrating RL model with spatial regularization to settle the suggested double-models, which improve noise resistance and accuracy of imaging. Experimental results of simulated and real data show that the proposal method, which we call iRL, can robustly reconstruct a more accurate fODF and the quantitative index Piso performs better than fractional anisotropy and general fractional anisotropy.