- BB
Authors: Lai, D.; Advisor: -; Participants: Zhang, Y.; Zhang, X.; Su, Y.; Heyat, M. B. Bin (2019) - ABSTRACT Early risk identi cation of an unexpected sudden cardiac death (SCD) in a person who is
suffering malignant ventricular arrhythmias is highly signi cant for timely intervention and increasing
the survival rate. For this purpose, we have presented an automated strategy for prediction of SCD
with a high-level accuracy by using measurable arrhythmic markers in this paper. The set of arrhythmic parameters includes three repolarization interval ratios, such as TpTe/QT, JTp/JTe, and T and two conduction-repolarization markers, such as TpTe/QRS and TpT/(QT QRS). Each of them is calculated directly from the detected QRS complex waves and T-wave of electrocradiogram (ECG) signals. Then, all calculated markers are used for the automatical classi cation of normal and SCD risk group...
|
- BB
Authors: Shao, H.; Advisor: -; Participants: Wang, L.; Ji, Y. (2019) - In Node2Vec, the global structure of the network is neglected and the stochastic gradient descent (SGD) method is easy to fall into local optimum. Based on this algorithm, an improved link prediction algorithm combining machine learning and hierarchical representation learning for network (HARP) is proposed. This method rst uses adaptive learning optimizer Adam instead of SGD to improve Node2Vec, then divides the nodes and edges of the original network graph into a series of smaller layered graphs by merging them according to HARP, and then uses the improved Node2Vec algorithm to extract features continuously, so as to realize network embedding. Finally, a social network link prediction model based on machine learning and HARP is established. A series of social network link predict...
|
- BB
Authors: AlHajri, M. I.; Advisor: -; Participants: Ali, N. T.; Shubair, R. M. (2019) - Evolving Internet-of-things applications often require the use of sensor-based indoor tracking and positioning, for which the performance is significantly improved by identifying the type of the surrounding indoor environment. This identification is of high importance since it leads to higher localization accuracy. This letter presents a novel method based on a cascaded two-stage machine learning approach for highly accurate and robust localization
in indoor environments using adaptive selection and combination of radio frequency (RF) features. In the proposed method, machine learning is first used to identify the type of the surrounding indoor environment. Then, in the second stage, machine learning is employed to identify the most appropriate selection and combination
of RF feature...
|
- BB
Authors: Zhu, C.; Advisor: -; Participants: Zhu, Z.; Xie, Y.; Jiang, W.; Zhang, G. (2019) - Performances of smartphones are profoundly affected by battery life. Maximizing the amount of usage of energy is essential to extend battery life. However, developers might concentrate more on the functionality of applications while ignoring the energy bugs that drain the battery during the development process. There are no quantitative approaches to detect these energy bugs introduced in this fast-paced development process. In this paper, we employ a system-call-based approach to develop a power consumption model for Android devices. Data that measure the energy consumption of mobile devices under different testing scenarios with the number of triggered system calls are utilized in the model training process. A balanced recursive feature elimination with cross-validation approach i...
|
- BB
Authors: Kim, H.; Advisor: -; Participants: Kim, S.; Hwang, J. Y.; Seo, C. (2019) - A blockchain as a trustworthy and secure decentralized and distributed network has been emerged for many applications such as in banking, nance, insurance, healthcare and business. Recently, many communities in blockchain networks want to deploy machine learning models to get meaningful knowledge from geographically distributed large-scale data owned by each participant. To run a learning model without data centralization, distributed machine learning (DML) for blockchain networks has been studied. While several works have been proposed, privacy and security have not been suf ciently addressed, and as we show later, there are vulnerabilities in the architecture and limitations in terms of ef ciency. In this paper, we propose a privacy-preserving DML model for a permissioned blockch...
|
- BB
Authors: Lee, W.; Advisor: -; Participants: Ham, Y.; Ban, T.; Jo, O. (2019) - Estimating the growth performance in pigs is important in order to achieve a high productivity of pig farming. We herein analyze and verify the machine learning based estimations for the growth performance in swine which includes the daily gain of body weight (DG), feed intake (FI), required growth period for growing/ nishing phase (GP), and marketed-pigs per sow per year (MSY), based on the farm speci c data and climate, i.e., temperature, humidity, initial age (IA), initial body weight (IBW), number of pigs (NU) and stocking density (SD). The growth data used in our work is collected from 55 pig farms which are located across South Korea for the period between October 2017 and September 2018. In the estimation of growth performance, four machine learning schemes are applied, which...
|
- BB
Authors: Yang, P.; Advisor: -; Participants: Xiao, Y.; Xiao, M.; Guan, Y.L.; Li, S.; Xiang, W. (2019) - In this paper, we propose a novel framework of low-cost link adaptation for spatial modulation multipleinput
multiple-output (SM-MIMO) systems-based upon the machine learning paradigm. Specifically, we first convert the
problems of transmit antenna selection (TAS) and power allocation (PA) in SM-MIMO to ones-based upon data-driven prediction rather than conventional optimization-driven decisions. Then, supervised-learning classifiers (SLC), such as the K-nearest neighbors (KNN) and support vector machine (SVM) algorithms, are developed to obtain their statistically-consistent solutions. Moreover, for further comparison we integrate deep neural networks (DNN) with these adaptive SM-MIMO schemes, and propose a novel DNN-based multi-label classifier for TAS
and PA parameter evaluation. ...
|
- BB
Authors: Srinivasan, S. M.; Advisor: -; Participants: Truong-Huu, T.; Gurusamy, M. (2019) - With the proliferation of network devices and rapid development in information technology, networks such as
Internet of Things are increasing in size and becoming more complex with heterogeneous wired and wireless links. In such networks, link faults may result in a link disconnection without immediate replacement or a link reconnection, e.g., a wireless node changes its access point. Identifying whether a link disconnection
or a link reconnection has occurred and localizing the failed link become a challenging problem. An active probing approach requires a long time to probe the network by sending signaling messages on different paths, thus incurring significant communication delay and overhead. In this paper, we adopt a passive approach and develop a three-stage machine learningb...
|
- BB
Authors: Amari, A.; Advisor: -; Participants: Lin, X.; Dobre, O. A.; Venkatesan, R.; Alvarado, A. (2019) - We investigate the performance of a machine learning classification technique, called the Parzen window, to mitigate the fiber nonlinearity in the context of dispersion managed and dispersion unmanaged systems. The technique is applied for detection at the receiver side and deals with the non-Gaussian nonlinear effects by designing improved decision boundaries. We also propose a two-stage mitigation technique using digital
back propagation and Parzen window for dispersion unmanaged systems. In this case, digital back propagation compensates for the deterministic nonlinearity and the Parzen window deals with the stochastic nonlinear signal–noise interactions, which are not taken into account by digital back propagation. A performance improvement up to 0.4 dB in terms of Q factor is ob...
|
- BB
Authors: Yang, Z.; Advisor: -; Participants: Bajwa, W. U. (2019) - Distributed machine learning algorithms enable learning of models from datasets that are distributed over a network without gathering the data at a centralized location. While efficient distributed algorithms have been developed under the assumption of faultless networks, failures that can render these algorithms nonfunctional occur frequently in the real world. This paper focuses on the problem of Byzantine failures, which are the hardest
to safeguard against in distributed algorithms. While Byzantine fault tolerance has a rich history, existing work does not translate into efficient and practical algorithms for high-dimensional learning in fully distributed (also known as decentralized) settings. In this paper, an algorithm termed Byzantine-resilient distributed coordinate descent ...
|
