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arXiv 2017年

作者： Wagy, Mark D. Bongard, Josh C. Bagrow, James P. Hines, Paul D.H. Dept. of Psychological and Brain Sciences Dartmouth College HanoverNH United States Dept. of Computer Science Complex Systems Center University of Vermont BurlingtonVT United States Dept. of Mathematics and Statistics Complex Systems Center University of Vermont BurlingtonVT United States Dept. of Electrical and Biomedical Engineering Complex Systems Center University of Vermont BurlingtonVT United States

Crowdsourcing has been successfully applied in many domains including astronomy, cryptography and biology. In order to test its potential for useful application in a Smart Grid context, this paper investigates the extent to which a crowd can contribute predictive hypotheses to a model of residential electric energy consumption. In this experiment, the crowd generated hypotheses about factors that make one home different from another in terms of monthly energy usage. To implement this concept, we deployed a web-based system within which 627 residential electricity customers posed 632 questions that they thought predictive of energy usage. While this occurred, the same group provided 110,573 answers to these questions as they accumulated. Thus users both suggested the hypotheses that drive a predictive model and provided the data upon which the model is built. We used the resulting question and answer data to build a predictive model of monthly electric energy consumption, using random forest regression. Because of the sparse nature of the answer data, careful statistical work was needed to ensure that these models are valid. The results indicate that the crowd can generate useful hypotheses, despite the sparse nature of the dataset. Copyright © 2017, The Authors. All rights reserved.

关键词： Crowdsourcing

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Traffic engineering for backhaul networks with wireless link scheduling

Traffic engineering for backhaul networks with wireless link...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Nan Zhang Wei-Cheng Liao Mingyi Hong Hamid Farmanbar Zhi-Quan Luo School of Mathematical Sciences Peking University China Dept. of Electrical and Computer Engineering University of Minnesota USA Dept. of Industrial and Manufacturing Systems Engineering Iowa State University USA Huawei Canada Research Center Ottawa Canada

ISBN: (纸本)9781509041183

Traffic engineering (TE) problem is a central component of the next generation cloud-based wireless networks. In this paper, we study a new resource allocation scheme for effective traffic engineering under practical constraints such as the finite buffer size at each node. To reduce the computational effort required in the existing single-slot TE approaches and to deal with practical hardware limitations on link flows and buffers, we propose a two time-scale, low-complexity TE algorithm which incorporates a novel link scheduling component. The algorithm can be distributedly implemented. Simulation results demonstrate the effectiveness of the proposed algorithm.

关键词： Traffic engineering Link Scheduling Two Time-Scale Approach Traffic engineering tellurium Radio links return stroke wireless

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Clustering in block Markov chains

arXiv

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arXiv 2017年

作者： Sanders, Jaron Proutière, Alexandre Yun, Se-Young Kth Royal Institute of Technology School of Electrical Engineering Dept. of Automatic Control Osquldasväg 10 Stockholm10044 Sweden Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Dept. of Quantum and Computer Engineering Mekelweg 4 Delft2628CD Netherlands Korea Advanced Institute of Science and Technology Dept. of Industrial and Systems Engineering 291 Daehak-ro Eoeun-dong Yuseong-gu Korea Republic of

This paper considers cluster detection in Block Markov Chains (BMCs). These Markov chains are characterized by a block structure in their transition matrix. More precisely, the n possible states are divided into a finite number of K groups or clusters, such that states in the same cluster exhibit the same transition rates to other states. One observes a trajectory of the Markov chain, and the objective is to recover, from this observation only, the (initially unknown) clusters. In this paper we devise a clustering procedure that accurately, efficiently, and provably detects the clusters. We first derive a fundamental information-theoretical lower bound on the detection error rate satisfied under any clustering algorithm. This bound identifies the parameters of the BMC, and trajectory lengths, for which it is possible to accurately detect the clusters. We next develop two clustering algorithms that can together accurately recover the cluster structure from the shortest possible trajectories, whenever the parameters allow detection. These algorithms thus reach the fundamental detectability limit, and are optimal in that sense. Copyright © 2017, The Authors. All rights reserved.

关键词： Asymptotic analysis

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Lossless image compression algorithm for wireless capsule endoscopy by content-based classification of image blocks

arXiv

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arXiv 2018年

作者： Rajaeefar, Atefe Emami, Ali Soroushmehr, S.M. Reza Karimi, Nader Samavi, Shadrokh Najarian, Kayvan Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Dept. of Information Technology and Elect. Engineering University of Queensland BrisbaneQLD Australia Department of Computational Medicine and Bioinformatics Michigan Center for Integrative Research in Critical Care University of Michigan Ann ArborMI United States Department of Emergency Medicine University of Michigan Ann ArborMI United States Department of Computational Medicine and Bioinformatics Department of Emergency Medicine Michigan Center for Integrative Research in Critical Care University of Michigan Ann ArborMI United States

Recent advances in capsule endoscopy systems have introduced new methods and capabilities. The capsule endoscopy system, by observing the entire digestive tract, has significantly improved diagnosing gastrointestinal disorders and diseases. The system has challenges such as the need to enhance the quality of the transmitted images, low frame rates of transmission, and battery lifetime that need to be addressed. One of the important parts of a capsule endoscopy system is the image compression unit. Better compression of images increases the frame rate and hence improves the diagnosis process. In this paper a high precision compression algorithm with high compression ratio is proposed. In this algorithm we use the similarity between frames to compress the data more efficiently. Copyright © 2018, The Authors. All rights reserved.

关键词： Motion estimation

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Classification of Informative Frames in Colonoscopy Videos Using Convolutional Neural Networks with Binarized Weights

arXiv

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arXiv 2018年

作者： Akbari, Mojtaba Mohrekesh, Majid Rafiei, Shima Soroushmehr, S.M. Reza Karimi, Nader Samavi, Shadrokh Najarian, Kayvan Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Dept. of Compl. Med. and Bioinfo. and Michigan Center for Integrative Research in Critical Care University of Michigan Ann Arbor United States Department of Emergency Medicine University of Michigan Ann Arbor United States Department of Emergency Medicine and Michigan Center for Integrative Research in Critical Care Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor United States

Colorectal cancer is one of the common cancers in the United States. Polyp is one of the main causes of the colonic cancer and early detection of polyps will increase chance of cancer treatments. In this paper, we propose a novel classification of informative frames based on a convolutional neural network with binarized weights. The proposed CNN is trained with colonoscopy frames along with the labels of the frames as input data. We also used binarized weights and kernels to reduce the size of CNN and make it suitable for implementation in medical hardware. We evaluate our proposed method using Asu Mayo Test clinic database, which contains colonoscopy videos of different patients. Our proposed method reaches a dice score of 71.20% and accuracy of more than 90% using the mentioned dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Endoscopy

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Ocean surface current prediction based on HF radar observations using trajectory-oriented association rule mining

Ocean surface current prediction based on HF radar observati...

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IEEE International Conference on Big Data

作者： Kulsawasd Jitkajornwanich Peerapon Vateekul Upa Gupta Teeranai Kormongkolkul Arnon Jirakittayakorn Siam Lawawirojwong Siwapon Srisonphan Dept. of Computer Science King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Dept. of Computer Engineering Chulalongkorn University Bangkok Thailand Chulalongkorn University Big Data Analytics & IoT Center (CUBIC) Dept. of Computer Engineering Faculty of Engineering Chulalongkorn University Bangkok 10330 Thailand GISTDA Academy Geo-Informatics & Space Technology Development Agency (PO) Bangkok Thailand Dept. of Electrical Engineering Kasetsart University Bangkok Thailand

HF (high frequency) coastal radar system is used to capture the surface current behavior - in terms of velocity and direction - in the ocean near the coast. 18 HF coastal radar stations were implemented along the Gulf of Thailand in order to monitor for disasters (e.g., Tsunami) as well as relevant risks. The HF systems are also to serve other life-critical applications, such as water quality control and monitoring, chemical spill backtracking, and marine navigation. However, not all the applications can benefit from this near-real-time HF data; some applications in different domains require forecast values. The examples include search-and-rescue system and hazardous materials spill trajectory prediction. Therefore, in this paper, we propose a predictive model for future current data based on historical HF coastal radar data sets, utilizing association rule mining combined with an object dispersion concept. So, the full potential of HF radar systems can be exploited. The spatial and temporal dimensions are taken into account when designing our predictive system, which consists of two phases: ocean surface current track formulation and spatio-temporal association rule mining. The experiments are performed on a two-year HF radar dataset (2014-2015) using Google Cloud Platform. The resulting forecast current values: velocity and direction are then compared with testing datasets (using 10-fold cross validation) of the actual recorded values and evaluated based on percentage accuracy and RMSE, respectively.

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Network topology inference from non-stationary graph signals

Network topology inference from non-stationary graph signals

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Rasoul Shafipour Santiago Segarra Antonio G. Marques Gonzalo Mateos Dept. of Electrical and Computer Engineering University of Rochester NY USA Institute for Data Systems and Society Massachusetts Institute of Technology Cambridge USA Dept. of Signal Theory and Communications King Juan Carlos University Madrid Spain

ISBN: (纸本)9781509041183

We address the problem of inferring a graph from nodal observations, which are modeled as non-stationary graph signals generated by local diffusion dynamics that depend on the structure of the sought network. Using the so-called graph-shift operator (GSO) as a matrix representation of the graph, we first identify the eigenvectors of the shift matrix from realizations of the diffused signals, and then we rely on these spectral templates to estimate the eigenvalues by imposing desirable properties on the graph to be recovered. Different from the stationary setting where the GSO and the covariance matrix of the observed signals are simultaneously diagonalizable, here they are not. Hence, estimating the eigenvectors requires first estimating the unknown diffusion (graph) filter - a polynomial in the GSO which does preserve the sought eigenbasis. To carry out this initial system identification step, we leverage different sources of information on the input signal driving the diffusion process on the graph. Numerical tests showcase the effectiveness of the proposed algorithms in recovering social and structural brain graphs.

关键词： Network topology inference graph signal processing (non-)stationary graph processes system identification System identification Line graph Eigenvector Matrix representation Graphics processing input signal signals Diffusion processes variance covariance matrix Information source Network topology diffusion

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Preliminary sUAV component evaluation for inspecting transportation infrastructure systems 10

Preliminary sUAV component evaluation for inspecting transpo...

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10th Annual International systems Conference, SysCon 2016

作者： Daniel Otero, Luis Gagliardo, Nicholas Dalli, Dennis Otero, Carlos E. Dept. of Engineering Systems Florida Institute of Technology MelbourneFL United States Dept. of Electrical and Computer Engineering Florida Institute of Technology MelbourneFL United States

ISBN: (纸本)9781467395182

In the transportation systems engineering field, there is a fast growing interest to use small unmanned aerial systems (UAS) for structural inspections. An application of particular importance is the inspection of bridges and high mast luminaires (HML). This paper describes an ongoing research effort-and results obtained-to evaluate small unmanned aerial vehicles (sUAV) for various environmental conditions and mission objectives. Various propeller types, battery types, and configurations were used in the evaluation of sUAVs to estimate maximum working altitude, motor power capabilities, battery life expectancy under different loads, and maneuverability constraints. The preliminary research results presented in this paper support the use of sUAS to assist in bridge and HML inspections. Altitude, payload, and maneuverability tests were conducted using quad and hexacopters to understand performance and limitation parameters that would directly relate to the use of UAVs for transportation infrastructure inspections. Future research areas are identified to extend the work presented in this paper. © 2016 IEEE.

关键词： Inspection

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Preliminary proof of concept for using sUAS to inspect HML systems 10

Preliminary proof of concept for using sUAS to inspect HML s...

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10th Annual International systems Conference, SysCon 2016

ISBN: (纸本)9781467395182

The inspection of high mast luminaire (HML) structures depends heavily on visual assessments from experienced field inspectors. State agencies rely on these visual inspections to make key decisions about the health of structures-such as allocation of human resources and funds to maintain\repair structures-that significantly affect public safety and costs. This paper describes an ongoing research approach-and results obtained-to evaluate the use of sUAS for HML inspections. Four preliminary field tests were carried out to understand the level of sUAS maneuverability required to capture live data of HML elements in an open field with varying winds speeds, and establish a general testing process for subsequent field tests that included HML inspectors. Three galvanized steel and one weathered steel HMLs were used as test subjects during the field tests. Preliminary results from this research effort provide evidence to support the use of sUAS for HML inspections. Future research areas are identified to extend the work presented in this paper. © 2016 IEEE.

关键词： Antennas

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Impact test for ensuring reliability of gas sensors using large particle size-zeolite

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IEEJ Transactions on Sensors and Micromachines 2018年第9期138卷 430-434页

作者： Yamada, Kentaro Grand, Julien Okamoto, Yuki Reddy, Rangareddygari Ranga Denoual, Matthieu Mintova, Svetlana Tixier-Mita, Agnès Mita, Yoshio Grad. Sch. of Eng. Dept. of Electrical Engineering and Information Systems University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan LCS-CNRS ENSI de Caen University of Normandie Caen Caen14032 France GREYC-CNRS ENSI de Caen University of Normandie Caen Caen14032 France Reseach Center for Advanced Science and Technology University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo153-8904 Japan

To make high-selective and sensitive gas sensors, embedding zeolite particle to MEMS structures is studied. However, if the zeolite is aggregated, the zeolite structures become fragile and fall off from large MEMS structures. In this paper, we propose covering the aggregated zeolite particle embedded in micro-trenches with thin crystalline zeolite films. This integration method can be used for other powder form materials, like MOFs and quantum dots. In addition, we investigate the impact effects to the three kinds of zeolites (FAU, LTL, and MFI) with and without the thin zeolite films. © 2018 The Institute of electrical Engineers of Japan.

关键词： Zeolites

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