Southeast University

About: Southeast University is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Computer science & MIMO. The organization has 66363 authors who have published 79434 publications receiving 1170576 citations. The organization is also known as: SEU.

A plant tendril mimic soft actuator with phototunable bending and chiral twisting motion modes.

Abstract: In nature, plant tendrils can produce two fundamental motion modes, bending and chiral twisting (helical curling) distortions, under the stimuli of sunlight, humidity, wetting or other atmospheric conditions. To date, many artificial plant-like mechanical machines have been developed. Although some previously reported materials could realize bending or chiral twisting through tailoring the samples into various ribbons along different orientations, each single ribbon could execute only one deformation mode. The challenging task is how to endow one individual plant tendril mimic material with two different, fully tunable and reversible motion modes (bending and chiral twisting). Here we show a dual-layer, dual-composition polysiloxane-based liquid crystal soft actuator strategy to synthesize a plant tendril mimic material capable of performing two different three-dimensional reversible transformations (bending versus chiral twisting) through modulation of the wavelength band of light stimuli (ultraviolet versus near-infrared). This material has broad application prospects in biomimetic control devices.

Energy-Efficient Joint Resource Allocation and Power Control for D2D Communications

Abstract: In this paper, joint resource allocation and power control for energy-efficient device-to-device (D2D) communications underlaying cellular networks are investigated. The resource and power are optimized for maximization of the energy efficiency (EE) of D2D communications. Exploiting the properties of fractional programming, we transform the original nonconvex optimization problem in fractional form into an equivalent optimization problem in subtractive form. Then, an efficient iterative resource allocation and power control scheme is proposed. In each iteration, part of the constraints of the EE optimization problem are removed by exploiting the penalty function approach. We further propose a novel two-layer approach, which allows finding the optimum at each iteration by decoupling the EE optimization problem of joint resource allocation and power control into two separate steps. In the first layer, the optimal power values are obtained by solving a series of maximization problems through root finding, with or without considering the loss of cellular users' rates. In the second layer, the formulated optimization problem belongs to a classical resource-allocation problem with single allocation format, which admits a network flow formulation so that it can be solved to optimality. Simulation results demonstrate the remarkable improvements in terms of EE by using the proposed iterative resource allocation and power control scheme.

Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing.

Abstract: In abdomen computed tomography (CT), repeated radiation exposures are often inevitable for cancer patients who receive surgery or radiotherapy guided by CT images. Low-dose scans should thus be considered in order to avoid the harm of accumulative x-ray radiation. This work is aimed at improving abdomen tumor CT images from low-dose scans by using a fast dictionary learning (DL) based processing. Stemming from sparse representation theory, the proposed patch-based DL approach allows effective suppression of both mottled noise and streak artifacts. The experiments carried out on clinical data show that the proposed method brings encouraging improvements in abdomen low-dose CT images with tumors.