Ryerson University

About: Ryerson University is a education organization based out in Toronto, Ontario, Canada. It is known for research contribution in the topics: Population & Poison control. The organization has 7671 authors who have published 20164 publications receiving 394976 citations. The organization is also known as: Ryerson Polytechnical Institute & Ryerson Institute of Technology.

Deep Reinforcement Learning-Based Dynamic Resource Management for Mobile Edge Computing in Industrial Internet of Things

Abstract: Nowadays, driven by the rapid development of smart mobile equipments and 5G network technologies, the application scenarios of Internet of Things (IoT) technology are becoming increasingly widespread. The integration of IoT and industrial manufacturing systems forms the industrial IoT (IIoT). Because of the limitation of resources, such as the computation unit and battery capacity in the IIoT equipments (IIEs), computation-intensive tasks need to be executed in the mobile edge computing (MEC) server. However, the dynamics and continuity of task generation lead to a severe challenge to the management of limited resources in IIoT. In this article, we investigate the dynamic resource management problem of joint power control and computing resource allocation for MEC in IIoT. In order to minimize the long-term average delay of the tasks, the original problem is transformed into a Markov decision process (MDP). Considering the dynamics and continuity of task generation, we propose a deep reinforcement learning-based dynamic resource management (DDRM) algorithm to solve the formulated MDP problem. Our DDRM algorithm exploits the deep deterministic policy gradient and can deal with the high-dimensional continuity of the action and state spaces. Extensive simulation results demonstrate that the DDRM can reduce the long-term average delay of the tasks effectively.

Single Cell Photoacoustic Microscopy: A Review

Abstract: Photoacoustic imaging has experienced exponential growth over the past decade, with many applications in biomedicine. One application ideally suited to the analysis of single cells is photoacoustic microscopy (PAM). Using PAM, detailed submicrometer resolution images of single cells can be produced, with contrast dependent primarily on the optical absorption properties of the cell. A multiwavelength approach for targeting specific endogenous or exogenous chromophores can enhance cellular detail and resolve single organelles with contrast not possible with traditional optical microscopy. A quantitative analysis of the photoacoustic signals acquired from single cells can provide insight into their anatomical, biomechanical, and functional properties. This information can be used to identify specific cells, or to enhance the understanding of biological processes at the single cell level. This comprehensive review on PAM covers recent advances in high-resolution PAM, signal processing methods, and potential clinical applications targeting single cells in vitro and in vivo .

Uncorrelated Multilinear Discriminant Analysis With Regularization and Aggregation for Tensor Object Recognition

Abstract: This paper proposes an uncorrelated multilinear discriminant analysis (UMLDA) framework for the recognition of multidimensional objects, known as tensor objects. Uncorrelated features are desirable in recognition tasks since they contain minimum redundancy and ensure independence of features. The UMLDA aims to extract uncorrelated discriminative features directly from tensorial data through solving a tensor-to-vector projection. The solution consists of sequential iterative processes based on the alternating projection method, and an adaptive regularization procedure is incorporated to enhance the performance in the small sample size (SSS) scenario. A simple nearest-neighbor classifier is employed for classification. Furthermore, exploiting the complementary information from differently initialized and regularized UMLDA recognizers, an aggregation scheme is adopted to combine them at the matching score level, resulting in enhanced generalization performance while alleviating the regularization parameter selection problem. The UMLDA-based recognition algorithm is then empirically shown on face and gait recognition tasks to outperform four multilinear subspace solutions (MPCA, DATER, GTDA, TR1DA) and four linear subspace solutions (Bayesian, LDA, ULDA, R-JD-LDA).

Smart metering and functionalities of smart meters in smart grid - a review

Abstract: Reducing the power supply-demand gap and increasing reliability of power supply are the challenges of current energy management. Implementation of smart grid, smart meters and smart metering can be a possible solution for power demand reduction, efficient power supply management, and optimization of management resource usages. Smart meters include sophisticated measurement and calculation hardware, software, calibration and communication capabilities. For interoperability within a smart grid infrastructure, smart meters are designed to perform functions, and store and communicate data according to certain standards. In this work we discuss smart meter and various elements of smart metering, current state of the technologies related to smart grid, smart meter, advanced metering infrastructure (AMI), and meter data flow in smart grid. We also discuss standards related to smart meter, meter data format and data transmission, functions of smart meter, and functionalities of smart meters, currently deployed by utilities around the world.

Space Vector Sequence Investigation and Synchronization Methods for Active Front-End Rectifiers in High-Power Current-Source Drives

Abstract: Space vector pulsewidth modulation (PWM) schemes for the active front end of a high-power drive normally produce low-order and suborder harmonics due to the low switching frequency and the drifting of synchronization between the PWM waveform and the rectifier input frequency. To provide a synchronized PWM and achieve the best harmonic performance, different space vector sequences suitable for a current-source converter are investigated in this paper. Details on how to achieve the waveform symmetries with a minimum switching frequency for each sequence are discussed. A thorough comparison of the harmonic performance of different space vector sequences is carried out. An optimum space vector modulation method by switching between two best sequences is proposed to achieve the best line-current total harmonic distortion with reduced switching losses. In addition, two synchronization methods, namely a PWM frame regulation method and a direct digital phase-locked loop synchronization method, are proposed. Both methods are equally effective in providing tight synchronization of the PWM waveform with the rectifier input frequency. The work has been verified in simulation and experiment.