Abstract:Age dependence on the hemispheric asymmetry alteration remains inconclusive partially due to the diversity of methodology. In this study, a multivariate model was applied to evaluate the evolution of the structural hemispheric asymmetry during healthy aging based on magnetic resonance imaging data. The T1-weighted images from 205 subjects were retrieved and categorized into 7 age groups. The morphological features including the surface area, mean curvature index, cortical thickness, and subjacent white matter volume in 34 regions of interest were calculated. The multivariate analysis was then performed on each age group to investigate the composite effect of the four features on asymmetry alterations. The P values were finally mapped onto a FreeSurfer template for visualization. Global reduction of hemispheric laterality was identified with a worse decline in parietal and occipital lobes as age advances. Reservation and left-right shift of laterality were vastly detected in the high-level cognition-related regions, which may imply a compensatory mechanism of healthy brain counteracting the age-associated functional impairment. The age dependence on the hemispheric asymmetry may embed mechanisms that underlie the behavioral and cognitive declination associated with age related neuropsychological diseases.

Abstract:Domain name resolution is a foundation of the Internet, yet there is still no appropriate reputation evaluation mechanism for it. Considering the complexity of domain name service and the pervasiveness of malicious attacks, a customized reputation evaluation mechanism for domain names was presented. The main contributions are: (1) a domain reputation evaluation framework based on multi-index evaluation; (2) a variety of mechanisms that resist malicious attacks and improve the precision and adaptability of the reputation computation model; (3) theoretical support for users to choose their desired service with improved experience, taking into account the users’ personal preferences. Results show that the proposed customized mechanism can accurately reflect the reputation of domain names, fight against malicious evaluation attacks and improve user satisfaction.

Abstract:In traditional orthodontic treatment, archwire preparation is performed by orthodontists manually. This manual pattern not only requires long time trainning of the clinicians, but also can not ensure the clinical requirement of customization and accuracy. It also increases the number of patient’s office visits and long chairside time. Thus, an archwire bending robotic system aiming to perform this task automatically was developed. The springback effect caused by high elasticity of material affects the accuracy of bend forming. In this paper, a compensation method based on an overbend prediction model and an online force detection was presented to eliminate the springback effect of archwire. This method contains two steps. Firstly, an overbend prediction model was constructed to predict the overbend allowance of a target angle, based on calculating the difference of two angles measured by a self-designed instrument and taking the angle after springback as the target angle. Secondly, a force-based bending control algorithm was designed to detect the defined zero-force state using a force sensor to online measure the force at a given target angle. Finally, bending experiments were conducted in the self-developed orthodontic archwire bending system. Results show that the proposed compensation method can eliminate springback effect to minimum and accomplish the preparation of clinical required orthodontic archwire.

Abstract:Surfactants have complex compositions and low molecular weights, which usually leads to the eutrophication of waterbodies. For the treatment of wastewater that contains surfactants, the membrane separation technology is usually adopted. However, the membrane is easily polluted and the cleaning process is usually laborious. In this work, a new surfactants wastewater processing technology was investigated by combining the coagulation and membrane separation techniques. Firstly, the phase inversion technique was used for the preparation of titanium dioxide modified polyvinylidene fluoride ultrafiltration membrane. Structure and properties of the membrane are tested by several instrumentations, like the scanning electron microscopy, fourier transform infrared spectroscopy, and contact angle instrument. Secondly, using sodium dodecyl benzene sulfonate solution and polymerization aluminum chloride as surfactant wastewater and coagulant respectively, the treatment effect is evaluated with respect to ultraviolet light intensity, water flow rate and pH values. By observation, the modified membrane has dense-selective layer on the surface, and its cross-section is composed with finger holes and spongy structures. By the analysis of fourier transform infrared spectroscopy, polyethylene glycol and titanium dioxide in the modified membrane can cover apart of infrared absorption of polyvinylidene fluoride. The modified membrane has relative small contact angle and its performance is also insensitive to the change of ultraviolet light intensity and water flow rate. Moreover, the process can reach best performance while the wastewater with pH value of 4.5. The proposed surfactants wastewater process technique has distinct advantages of high efficiency and low-cost, which makes it with great potentials in the application of large scale industrial and domestic wastewater processing.

Abstract:With the development of robot technology and the ever-increasing diversity in shapes of service robots, smart home and intelligent life gradually become a future way of life. The technology and system of service robots for smart home and intelligent life was proposed in this paper. Among many key technologies of the system, the cloud fusion technology for service robots and human-robot interaction technology with high degree of user experience were investigated in this paper. Take the home service robot as an example, the feasibility of the cloud-based architecture for the home service robotic system was verified with human-robot interaction experiments which are respectively based on WeChat and voice cloud. The proposed architecture for service robots is an implementation of smart home and smart living, and provides some valuable ideas for technical solutions of future service robots.

Abstract:The theory of compressed sensing (CS) provides a systematic framework for magnetic resonance (MR) image reconstruction from incoherently under-sampled k-space data. However, severe aliasing artifacts may still occur in cases of high acceleration and noisy measurements. Thereupon, an extensive body of work investigates exploiting additional prior information extracted from a reference image which can be acquired with relative ease in many MR applications. In this work, a CS-based MR image reconstruction method using reference gradient orientation priors was proposed. Specifically, the tangent vector in the target image was regularized to be perpendicular to the corresponding normal vector in the reference image over all spatial locations to make the gradient orientations in the reference and the target image consistent. The proposed method is validated using multi-scan experiment data and is shown to provide high speed and high quality imaging.

Abstract:An adaptive sparse representation regularized reconstruction method for accurate parallel imaging was proposed by exploring the strength of dictionary learning in capturing image fine structures while promoting sparsity. The reconstruction was formulated as a minimization problem, which consisted of a data-fidelity term and a dictionary learning term and was solved by the “divide and conquer” strategy. The comparative results of the proposed method with respect to two popular approaches on an in-vivo dataset demonstrated that the proposed method preserves more image fine details while suppressing noise.

Abstract:The rapid development of Internet and Internet of Things opens the era of big data. Currently, heterogeneous architectures are being widely adopted in large-scale datacenters, for the sake of performance improvement and reduction of energy consumption. This paper presents the design and implementation of Hadoop+, a programming framework that implements MapReduce and enables invocation of parallelized CUDA/OpenCL within a map/reduce task, and helps the user by taking advantage of a heterogeneous task model. Experimental result shows that Hadoop+ attains 1.4× to 16.1× speedups over Hadoop for five commonly used machine learning algorithms. Coupled with a heterogeneous task model that helps allocate computing resouces, Hadoop+ brings a 36.0% improvement in data processing speed for single-application workloads, and for mixed workloads of multiple applications, the execution time is reduced by up to 36.9% with an average 17.6%.

Abstract:It is difficult to accurately locate the interventional devices because the high susceptibility of these devices leads to signal loss and distortion on magnetic resonance (MR) images. A positive contrast magnetic resonance imaging was developed to generate positive contrast of the brachy therapy seeds using a spin echo sequence with shifted 180° pulse and quantitative susceptibility mapping algorithm in 2014. In this paper, we further extended this method and applied it to visualize larger interventional devices (i.e., biopsy needle and vena cava filter) on Siemens MR scanner. The phantom experiment results demonstrate that the proposed method provided positive contrast images with good quality, and therefore improved the visualization and localization of the devices.

Abstract:Magnetic resonance imaging (MRI) pursues high signal-to-noise ratio (SNR) and fast acquisition. Compared with single channel radiofrequency (RF) coil, multi-channel RF coils have the features of high SNR, high resolution and parallel imaging. One software for performance evaluation based on k-space data from multi-channel coils was developed, which serves to help the design and production of RF coils. Specifically, it facilitates to calculate the noise covariance matrix, SNR and geometry factor of multi-channel coils more precisely by using this software.

Abstract:Controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA) is a key technique for fast magnetic resonance imaging because of its high acceleration rate and high signal-to-noise ratio (SNR). Based on the traditional simultaneous multiple-slices excitation (SMS), CAIPIRINHA utilizes phase-modulated radio frequency pulse to simultaneously excite multiple slices to reduce overlaps of different excited slices in the same region, which benefits the separation of individual images with the help of prior information of coil sensitivity. CAIPIRINHA can largely reduce scanning time according to the number of simultaneously excited slices. Additionally, compared with traditional SMS, CAIPIRINHA also increases the SNR of the reconstructed images. In this paper, the principle of CAIPIRINHA was introduced and the sequence of CAIPIRINHA combined with gradient echo was presented as well as the preliminary experiment results on phantom and human brain.