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On multiple-domain cooperative diversity for communications with distributed content in *** networks

INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2017年第1期30卷 1-16页

作者： Gacanin, Haris Ligata, Amir Chahibi, Youssef Alcatel Lucent Bell Mot Div Antwerp Belgium IPSA Inst Sarajevo Bosnia & Herceg Georgia Inst Technol Atlanta GA 30332 USA

Broadband data applications can be delivered to homes over the available home network infrastructure (i.e., mediums). Further enhancement of the network performance is possible by exploiting spatial, time, or frequency diversity. Recently, MIMO systems have been proposed for power line communication (PLC) networks. Because of the extremely diverse physical characteristics of the mediums, an implementation of MIMO systems in *** across different domains represents a non-trivial task. In this paper, we present and theoretically evaluate the performance of multiple-domain diversity mechanism in *** using a cross-layer classification algorithm. In the proposed network architecture, the signal is transmitted over different mediums selected by the classification algorithm in order to meet the quality-of-service demands. At the receiver, joint signal combining and equalization are done to take advantage of the multiple-domain cooperative diversity and increase the signal-to-noise ratio. The merit of the proposed multiple-domain PLC network has been confirmed by analytical performance analysis and computer simulation. Copyright (C) 2017 John Wiley & Sons, Ltd.

关键词： G.hn multiple-domain OFDM classification algorithm

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Balancing noise sensitivity, response latency, and posture accuracy for a computer-assisted canine posture training system

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INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES 2017年 98卷 179-195页

作者： Majikes, John Brugarolas, Rita Winters, Michael Yuschak, Sherrie Mealin, Sean Walker, Katherine Yang, Pu Sherman, Barbara Bozkurt, Alper Roberts, David L. North Carolina State Univ Dept Comp Sci Raleigh NC 27695 USA North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA North Carolina State Univ Dept Clin Sci Raleigh NC 27695 USA

This paper describes a canine posture detection system composed of wearable sensors and instrumented devices that detect the postures sit, stand, and eat. The system consists of a customized harness outfitted with wearable Inertial Measurement Units (IMUs) and a base station for processing IMU data to classify canine postures. Research in operant conditioning, the science of behavior change, indicates that successful animal training requires consistent and accurate feedback on behavior. Properly designed computer systems excel at timeliness and accuracy, which are two characteristics most amateur trainers struggle with and professionals strive for. Therefore, in addition to the system being ergonomically designed to ensure the dog's comfort and well-being, it is engineered to provide posture detection with timing and accuracy on par with a professional trainer. We contend that providing a system with these characteristics will one day aid dogs in learning from humans by overcoming poor or ineffective timing during training. We present the initial steps in the development and validation of a computer-assisted training system designed to work outside of laboratory environments. The main contributions of this work are (a) to explore the trade-off between low-latency responses to changes in time-series IMU data representative of posture changes while maintaining accuracy and timing similar to a professional trainer, and (b) to provide a model for future ACI technologies by documenting the user-centered approach we followed to create a computer-assisted training system that met the criteria identified in (a). Accordingly, in addition to describing our system, we present the results of three experiments to characterize the performance of the system at capturing sit postures of dogs and providing timely reinforcement. These trade-offs are illustrated through the comparison of two algorithms. The first is Random Forest classification and the second is an algorithm which use

关键词： Animal-computer interaction classification algorithm Machine learning Random Forest Wearable technology

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Applicability and effectiveness of classifications models for achieving the twin objectives of growth and outreach of microfinance institutions

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COMPUTATIONAL AND MATHEMATICAL ORGANIZATION THEORY 2017年第4期23卷 451-474页

作者： Chattopadhyay, Manojit Mitra, Subrata Kumar Indian Inst Management Raipur Informat Technol Area GEC Campus Raipur 492015 Madhya Pradesh India Indian Inst Management Raipur Finance Area GEC Campus Raipur 492015 Madhya Pradesh India

Measuring performance of microfinance institutions (MFIs) is challenging as MFIs must achieve the twin objectives of outreach and sustainability. We propose a new measure to capture the performance of MFIs by placing their twin achievements in a 2 x 2 grid of a classification matrix. To make a dichotomous classification, MFIs that meet both their twin objectives are classified as '1' and MFIs who could not meet their dual objectives simultaneously are designated as '0'. Six classifiers are applied to analyze the operating and financial characteristics of MFIs that can offer a predictive modeling solution in achieving their objectives and the results of the classifiers are comprehended using technique for order preference by similarity to ideal solution to identify an appropriate classifier based on ranking of measures of performance. Out of six classifiers applied in the study, kernel lab-support vector machines achieved highest accuracy and lowest classification error rate that discriminates the best achievement of the MFIs' twin objective. MFIs can use both these steps to identify whether they are on the right path to attaining their multiple objectives from their operating characteristics.

关键词： Microfinance institution Growth and outreach Performance measure classification algorithm TOPSIS

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High-resolution mass spectra processing for the identification of different pathological tissue types of brain tumors

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EUROPEAN JOURNAL OF MASS SPECTROMETRY 2017年第4期23卷 213-216页

作者： Zhvansky, E. S. Sorokin, A. A. Popov, I. A. Shurkhay, V. A. Potapov, A. A. Nikolaev, E. N. Moscow Inst Phys & Technol Dolgoprudnyi Moscow Region Russia Russian Acad Sci Inst Energy Problems Chem Phys Moscow Russia Minist Healthcare Russian Federat NN Burdenko Natl Sci & Pract Ctr Neurosurg Fed State Autonomous Inst Moscow Russia Skolkovo Inst Sci & Technol Skolkovo Moscow Region Russia

The purpose of the work is to demonstrate the possibilities of identifying the different types of pathological tissue identification directly through tissue mass spectrometry. Glioblastoma parts dissected during neurosurgical operation were investigated. Tumor fragments were investigated by the immunohistochemistry method and were identified as necrotic tissue with necrotized vessels, necrotic tissue with tumor stain, tumor with necrosis (tumor tissue as major), tumor, necrotized tumor (necrotic tissues as major), parts of tumor cells, boundary brain tissue, and brain tissue hyperplasia. The technique of classification of tumor tissues based on mass spectrometric profile data processing is suggested in this paper. Classifiers dividing the researched sample to the corresponding tissue type were created as a result of the processing. Classifiers of necrotic and tumor tissues are shown to yield a combined result when the tissue is heterogeneous and consists of both tumor cells and necrotic tissue.

关键词： Direct mass spectrometry ambient ionization brain tumor tissue identification classification algorithm neurosurgery

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Protein secondary structure prediction: A survey of the state of the art

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JOURNAL OF MOLECULAR GRAPHICS & MODELLING 2017年 76卷 379-402页

作者： Jiang, Qian Jin, Xin Lee, Shin-Jye Yao, Shaowen Yunnan Univ Sch Informat Kunming Yunnan Peoples R China Yunnan Univ Sch Software Kunming Yunnan Peoples R China Univ Cambridge Queens Coll Cambridge CB3 9ET England

Protein secondary structure prediction (PSSP) is a fundamental task in protein science and computational biology, and it can be used to understand protein 3-dimensional (3-D) structures, further, to learn their biological functions. In the past decade, a large number of methods have been proposed for PSSP. In order to learn the latest progress of PSSP, this paper provides a survey on the development of this field. It first introduces the background and related knowledge of PSSP, including basic concepts, data sets, input data features and prediction accuracy assessment. Then, it reviews the recent algorithmic developments of PSSP, which mainly focus on the latest decade. Finally, it summarizes the corresponding tendencies and challenges in this field. This survey concludes that although various PSSP methods have been proposed, there still exist several further improvements or potential research directions. We hope that the presented guidelines will help nonspecialists and specialists to learn the critical progress in PSSP in recent years. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Protein secondary structure prediction Machine learning Neural networks classification algorithm Feature extraction

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Satellite-Based Mapping of Cultivated Area in Gash Delta Spate Irrigation System, Sudan

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REMOTE SENSING 2018年第2期10卷 186-186页

作者： Ghebreamlak, Araya Z. Tanakamaru, Haruya Tada, Akio Adam, Bashir M. Ahmed Elamin, Khalid A. E. Kobe Univ Grad Sch Agr Sci Nada Ku Rokkodai 1-1 Kobe Hyogo 6578501 Japan Agr Res Corp POB 126 Wad Madani Sudan

In this study, a simple methodology for mapping the seasonal cultivated area of the Gash Delta Spate Irrigation System based on satellite images was developed. The methodology combined information from multiple bands to characterize the land surface in terms of spectral indices (e.g., Normalized Difference Vegetation Index (NDVI), and surface temperature (Ts)). Visual interpretations of a conveniently selected image were undertaken to identify and select sample points of interest. The NDVI and Ts values (computed from multi-date images that represented the crop growing period) of the sample points were used to developed typical NDVI and Ts plots. By analyzing these plots and the cropping calendar, an NDVI and Ts threshold-based algorithm was developed to extract the cultivated area of a given season. Analysis of the developed algorithm showed that it was simple, easily modifiable, and had interpretable rules and threshold values. Comparing the extracted cultivated area with the field report area showed a promising application of the methodology to map and estimate the cultivated area from only remote sensing data.

关键词： spate irrigation Landsat-7 ETM+ Gash Delta NDVI classification algorithm

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A comparison of data-driven fault detection methods with application to aerospace electro-mechanical actuators

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IFAC-PapersOnLine 2017年第1期50卷 12797-12802页

作者： Mazzoleni, M. Maccarana, Y. Previdi, F. Department of Management Information and Production engineering University of Bergamo via Galvani 2 Dalmine [BG] 24044 Italy

In this paper, a model-free framework is proposed in order to equip electromechanical actuators, deployed in aerospace applications, with health-monitoring capabilities. A large experimental activity has been carried out to perform acquisitions with both healthy and faulty components, taking into consideration the standard regulations for environmental testing of avionics hardware. The injected faults followed a Fault Tree Analysis and Failure Mode and Effect Analysis. Features, belonging to different domains, have been extracted from the measured signals. These indexes are based largely on the motor driving currents, in order to avoid the installation of new sensors. Finally, a Gradient Tree Boosting algorithm has been chosen to detect the system status: the choice has been dictated by a comparison with other known classification algorithms. Furthermore, the most promising features for a classification point of view are reported. © 2017

关键词： Fault detection Aerospace applications Electromechanical actuators Electromechanical devices Environmental regulations Environmental testing Fault tree analysis Learning systems Mechanical actuators Trees (mathematics) classification algorithm Detection methods Different domains Experimental activities Failure mode and effect analysis Gradient tree boosting Health monitoring Measured signals

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Value Analysis and Research in the Field of Mobile Communication

Value Analysis and Research in the Field of Mobile Communica...

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2018 2nd International Conference on Modeling, Simulation and Optimization Technologies and Applications(MSOTA 2018)

作者： Qian ZHU Sui-xiang GAO Jing WANG Yi-xin GENG University of Chinese Academy of Sciences

With the popularization of smart devices, the demand for mobile communication is increasing. The pursuit of stronger signals and faster transmission rates make effective value assessment and rational allocation of resources more important. In this paper, mobile communication value assessment problem is converted into base station classification problem with data, the high-value base station densely distributed area is the high-value mobile communication area. We have given a new value partitioning solution based on machine learning classification model, and on the real test set has achieved far better than the traditional method.

关键词： Multiple data source High value Machine learning classification algorithm

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Automatic Image selection from Images with Similar Contents 25

Automatic Image selection from Images with Similar Contents

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25th Signal Processing and Communications Applications Conference (SIU)

作者： Bardak, Batuhan Demirci, M. Fatih TOBB Ekon & Teknol Univ Bilgisayar Muhendisligi Bolumu Ankara Turkey

ISBN: (纸本)9781509064946

With the increased use of smart devices, digital cameras and abundance of memory in the devices, the pictures of the same scenes have been taken several times, resulting in a number of images consisting of the same or very similar content. in memory. Manually selecting the good ones is time-consuming as well as error prone. In this paper, the features of the images in the data sets were extracted and machine learning algorithms were applied to successfully classify the images as "good" or "bad" with %97.6 precision and %89.3 recall score.

关键词： feature extraction classification algorithm ensemble learning

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Research on Network Security Risk Modeling and Quantitative Analysis Theory 2017

Research on Network Security Risk Modeling and Quantitative ...

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International Conference on Wireless Communications, Networking and Applications (WCNA)

作者： Zhao, Xiao-Lin Yang, Yi-Ning Li, Xu-Hui Jiang, Feng Zhang, Ji Beijing Inst Technol Sch Software Beijing Key Lab Software Secur Engn Technol Beijing 100081 Peoples R China

ISBN: (纸本)9781450353441

(1)This paper realizes the network security assessment model based on hidden Markov. Firstly, the principle and basic problem of Hidden Markov Model are analyzed. Select the Snort software alarm as the input, convert the alarm into the quantification value of the network security risk model, and quantify the network security in real time, then design and implement the whole Hidden Markov Model.

关键词： Network security Hidden Markov Model classification algorithm Learning algorithm

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