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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. ...
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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...
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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...
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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 ...
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Authors: Bao, T.; Advisor: -; Participants: Klatt, B. N.; Whitney, S. L.; Sienko, K. H.; Wiens, J. (2019) - Compared to in-clinic balance training, in-home training is not as effective. This is, in part, due to
the lack of feedback from physical therapists (PTs). In this paper, we analyze the feasibility of using trunk sway data and machine learning (ML) techniques to automatically evaluate balance, providing accurate assessments outside of the clinic. We recruited sixteen participants to perform standing balance exercises. For each exercise, we recorded trunk sway data and had a PT rate balance performance on a scale of 1–5. The rating scale was adapted from the Functional Independence Measure. From the trunk sway data, we extracted a 61-dimensional feature vector representing the performance of each exercise. Given these labeled data, we trained a multi-class support vector machine (SV...
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Authors: Khalajzadeh, Hourieh; Advisor: -; Participants: Abdelrazek, Mohamed; Grundy, John; Hosking, John; He, Qiang (2019) - There has been a very large growth in interest in big data analytics to discover patterns and insights. A major challenge in this domain is the need to combine domain knowledge – what the data means (semantics) and what it is used for – with advanced data analytics and visualization techniques to mine and communicate important information from the huge volumes of raw data. Many data analytics tools have been developed for both research and practice to assist in specifying, integrating and deploying data analytics applications. However, delivering such big data analytics applications requires a capable team with different skillsets including data scientists, software engineers and domain experts. Such teams and skillsets usually take a long time to build and have high running costs. ...
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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...
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Authors: Ma, S.; Advisor: -; Participants: Dai, J.; Lu, S.; Li, H.; Zhang, H.; Du, C.; Li, S. (2019) - In this paper, we investigate the design and implementation of machine learning (ML)-based demodulation methods in the physical layer of visible light communication (VLC) systems. We build a exible hardware prototype of an end-to-end VLC system, from which the received signals are collected as the real data. The dataset is available online, which contains eight types of modulated signals. Then, we propose three ML demodulators based on convolutional neural network (CNN), the deep belief network (DBN), and adaptive boosting (AdaBoost), respectively. Speci cally, the CNN-based demodulator converts the modulated signals to images and recognizes the signals by the image classi cation. The proposed DBN-based demodulator contains three restricted Boltzmann machines to extract the modulat...
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Authors: Li, W.; Advisor: -; Participants: Ni, L.; Li, Z.; Duan, S.; Wu, H. (2019) - Land surface temperature (LST) is described as one of the most important environmental parameters of the land
surface biophysical process. Commonly, the remote-sensed LST products yield a tradeoff between high temporal and high spatial resolution. Thus, many downscaling algorithms have been proposed to address this issue. Recently, downscaling with machine learning algorithms, including artificial neural networks (ANNs),
support vector machine (SVM), and random forest (RF), etc., have gained more recognition with fast operation and high computing precision. This paper intends to make a comparison between machine learning algorithms to downscale the LST product of the moderate-resolution imaging spectroradiometer from 990 to 90 m,
and downscaling results would be validated by the r...
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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...
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