Deakin University

About: Deakin University is a education organization based out in Burwood, Victoria, Australia. It is known for research contribution in the topics: Population & Context (language use). The organization has 12118 authors who have published 46470 publications receiving 1188841 citations. The organization is also known as: Deakin.

Dielectrophoresis for manipulation of micro/nano particles in microfluidic systems

Abstract: Dielectrophoretic (DEP) force is exerted when a neutral particle is polarized in a non-uniform electric field, and depends on the dielectric properties of the particle and the suspending medium. The integration of DEP and microfluidic systems offers numerous applications for the separation, trapping, assembling, transportation, and characterization of micro/nano particles. This article reviews the applications of DEP forces in microfluidic systems. It presents the theory of dielectrophoresis, different configurations, and the applications of such systems for particle manipulation and device fabrication.

A Micro-GA Embedded PSO Feature Selection Approach to Intelligent Facial Emotion Recognition

Abstract: This paper proposes a facial expression recognition system using evolutionary particle swarm optimization (PSO)-based feature optimization. The system first employs modified local binary patterns, which conduct horizontal and vertical neighborhood pixel comparison, to generate a discriminative initial facial representation. Then, a PSO variant embedded with the concept of a micro genetic algorithm (mGA), called mGA-embedded PSO, is proposed to perform feature optimization. It incorporates a nonreplaceable memory, a small-population secondary swarm, a new velocity updating strategy, a subdimension-based in-depth local facial feature search, and a cooperation of local exploitation and global exploration search mechanism to mitigate the premature convergence problem of conventional PSO. Multiple classifiers are used for recognizing seven facial expressions. Based on a comprehensive study using within- and cross-domain images from the extended Cohn Kanade and MMI benchmark databases, respectively, the empirical results indicate that our proposed system outperforms other state-of-the-art PSO variants, conventional PSO, classical GA, and other related facial expression recognition models reported in the literature by a significant margin.

Federated Learning With Blockchain for Autonomous Vehicles: Analysis and Design Challenges

Abstract: We propose an autonomous blockchain-based federated learning (BFL) design for privacy-aware and efficient vehicular communication networking, where local on-vehicle machine learning (oVML) model updates are exchanged and verified in a distributed fashion. BFL enables oVML without any centralized training data or coordination by utilizing the consensus mechanism of the blockchain. Relying on a renewal reward approach, we develop a mathematical framework that features the controllable network and BFL parameters (e.g., the retransmission limit, block size, block arrival rate, and the frame sizes) so as to capture their impact on the system-level performance. More importantly, our rigorous analysis of oVML system dynamics quantifies the end-to-end delay with BFL, which provides important insights into deriving optimal block arrival rate by considering communication and consensus delays. We present a variety of numerical and simulation results highlighting various non-trivial findings and insights for adaptive BFL design. In particular, based on analytical results, we minimize the system delay by exploiting the channel dynamics and demonstrate that the proposed idea of tuning the block arrival rate is provably online and capable of driving the system dynamics to the desired operating point. It also identifies the improved dependency on other blockchain parameters for a given set of channel conditions, retransmission limits, and frame sizes. 1 However, a number of challenges (gaps in knowledge) need to be resolved in order to realise these changes. In particular, we identify key bottleneck challenges requiring further investigations, and provide potential future reserach directions. 1 An early version of this work has been accepted for presentation in IEEE WCNC Wksps 2020 [1] .