arXiv subjects

Yi Ren

Publications and source records attributed to Yi Ren.

258 records · Page 15Linked to original sources

arXiv2018

Liquid Metal Enabled Droplet Circuits

Conventional electrical circuits are generally rigid in their components and working styles which are not flexible and stretchable. From an alternative, liquid metal based soft electronics is offering important opportunities for innovating modern bioelectronics and electrical engineering. However, its running in wet environments such as aqueous solution, biological tissues or allied subjects still encounters many technical challenges. Here, we proposed a new conceptual electrical circuit, termed as droplet circuits, to fulfill the special needs as raised in the above mentioned areas. Such unconventional circuits are immersed in solution and composed of liquid metal droplets, conductive ions or wires such as carbon nanotubes. With specifically designed topological or directional structures/patterns, the liquid metal droplets composing the circuit can be discretely existing and disconnected from each other, while achieving the function of electron transport through conductive routes or quantum tunneling effect. The conductive wires serve as the electron transfer stations when the distance between two separate liquid metal droplets is far beyond than that quantum tunneling effects can support. The unique advantage of the current droplet circuit lies in that it allows parallel electron transport, high flexibility, self-healing, regulativity and multi-point connectivity, without needing to worry about circuit break. This would extend the category of classical electrical circuits into the newly emerging areas like realizing room temperature quantum computing, making brain-like intelligence or nerve-machine interface electronics etc. The mechanisms and potential scientific issues of the droplet circuits are interpreted. Future prospects along this direction are outlined.

physics.app-ph↗

arXiv2017

Improving Direct Physical Properties Prediction of Heterogeneous Materials from Imaging Data via Convolutional Neural Network and a Morphology-Aware Generative Model

Direct prediction of material properties from microstructures through statistical models has shown to be a potential approach to accelerating computational material design with large design spaces. However, statistical modeling of highly nonlinear mappings defined on high-dimensional microstructure spaces is known to be data-demanding. Thus, the added value of such predictive models diminishes in common cases where material samples (in forms of 2D or 3D microstructures) become costly to acquire either experimentally or computationally. To this end, we propose a generative machine learning model that creates an arbitrary amount of artificial material samples with negligible computation cost, when trained on only a limited amount of authentic samples. The key contribution of this work is the introduction of a morphology constraint to the training of the generative model, that enforces the resultant artificial material samples to have the same morphology distribution as the authentic ones. We show empirically that the proposed model creates artificial samples that better match with the authentic ones in material property distributions than those generated from a state-of-the-art Markov Random Field model, and thus is more effective at improving the prediction performance of a predictive structure-property model.

physics.comp-ph↗

arXiv2017

Design and Analysis of Dynamic Auto Scaling Algorithm (DASA) for 5G Mobile Networks

Network Function Virtualization (NFV) enables mobile operators to virtualize their network entities as Virtualized Network Functions (VNFs), offering fine-grained on-demand network capabilities. VNFs can be dynamically scale-in/out to meet the performance requirements for future 5G networks. However, designing an auto-scaling algorithm with low operation cost and low latency while considering the capacity of legacy network equipment is a challenge. In this paper, we propose a VNF Dynamic Auto Scaling Algorithm (DASA) considering the tradeoff between performance and operation cost. We also develop an analytical model to quantify the tradeoff and validate the analysis through extensive simulations. The system is modeled as a queueing model while legacy network equipment is considered as a reserved block of servers. The VNF instances are powered on and off according to the number of job requests. The results show that the proposed DASA can significantly reduce operation cost given the latency upper-bound. Moreover, the models provide a quick way to evaluate the cost-performance tradeoff without wide deployment, which can save cost and time.

cs.NI↗

arXiv2016

Design and Analysis of Deadline and Budget Constrained Autoscaling (DBCA) Algorithm for 5G Mobile Networks

In cloud computing paradigm, virtual resource autoscaling approaches have been intensively studied recent years. Those approaches dynamically scale in/out virtual resources to adjust system performance for saving operation cost. However, designing the autoscaling algorithm for desired performance with limited budget, while considering the existing capacity of legacy network equipment, is not a trivial task. In this paper, we propose a Deadline and Budget Constrained Autoscaling (DBCA) algorithm for addressing the budget-performance tradeoff. We develop an analytical model to quantify the tradeoff and cross-validate the model by extensive simulations. The results show that the DBCA can significantly improve system performance given the budget upper-bound. In addition, the model provides a quick way to evaluate the budget-performance tradeoff and system design without wide deployment, saving on cost and time.

cs.NI↗

arXiv2016

Example-Based Image Synthesis via Randomized Patch-Matching

Image and texture synthesis is a challenging task that has long been drawing attention in the fields of image processing, graphics, and machine learning. This problem consists of modelling the desired type of images, either through training examples or via a parametric modeling, and then generating images that belong to the same statistical origin. This work addresses the image synthesis task, focusing on two specific families of images -- handwritten digits and face images. This paper offers two main contributions. First, we suggest a simple and intuitive algorithm capable of generating such images in a unified way. The proposed approach taken is pyramidal, consisting of upscaling and refining the estimated image several times. For each upscaling stage, the algorithm randomly draws small patches from a patch database, and merges these to form a coherent and novel image with high visual quality. The second contribution is a general framework for the evaluation of the generation performance, which combines three aspects: the likelihood, the originality and the spread of the synthesized images. We assess the proposed synthesis scheme and show that the results are similar in nature, and yet different from the ones found in the training set, suggesting that true synthesis effect has been obtained.

cs.CV↗

arXiv2016

Design and Analysis of Optimal Threshold Offloading (OTO) Algorithm for LTE Femtocell/Macrocell Networks

LTE femtocells have been widely deployed to increase network capacity and to offload mobile data traffic from macrocells. While cellular users' mobility behaviors are taken into consideration, a dilemma is formed: Should a User Equipment (UE) either handover into a femtocell or keep the current connection with a macrocell? Indeed, various user mobility behaviors may incur significant signaling overhead and degrade femtocell offloading capability due to frequent handover in/out femtocells. To address this dilemma, in this paper we propose an Optimal Threshold Offloading (OTO) algorithm considering the tradeoff between the signaling overhead and femtocell offloading capability. We develop an analytical model and define two performance metrics to quantify the tradeoff. The proposed model not only models user mobility behaviors but also captures femtocell offloading benefits, and shed light on their fundamental relationship. The correctness of analytical model and simulation model are cross-validated by extensive ns2 simulations. Both analytical and simulation results demonstrate that the OTO algorithm can significantly reduce signaling overhead at the minor cost of femtocell offloading capability. The results enable wide applicability in various scenarios, and therefore, have important theoretical significance. Moreover, the analytical results provide a quick way to evaluate signaling overhead and offloading capability in LTE networks without wide deployment, saving on cost and time.

cs.NI↗