This paper addresses the challenge of deploying multiple service function chains (SFCs) in 5G networks. We propose a new network model for network function virtualization (NFV) network traffic that captures the dynami...
This paper addresses the challenge of deploying multiple service function chains (SFCs) in 5G networks. We propose a new network model for network function virtualization (NFV) network traffic that captures the dynamically changing nature of service requests and the availability of network resources. The deployment of multiple SFCs is then formulated as a constrained optimal resource allocation problem for dynamical systems, and its approximate solution is obtained by using a constrained least-squares method. Specifically, the proposed approach treats each NFV node as a discrete-time nonlinear dynamical system and seeks the suboptimal deployment of virtual network functions (VNFs) while minimizing the overall cost function for all NFV nodes. The cost function considers both the cost for service providers and quality of service (QoS) for users. Examples are provided to illustrate the proposed new network model. Due to the analytical nature of the obtained constrained least-squares solution, we expect this algorithm to be more efficient in addressing the service requests in large-scale 5G networks.
Collaborative manipulation task often requires negotiation using explicit or implicit communication. An important example is determining where to move when the goal destination is not uniquely specified, and who shoul...
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The Hilbert–space Gaussian Process (hgp) approach offers a hyperparameter-independent basis function approximation for speeding up Gaussian Process (gp) inference by projecting the gp onto M basis functions. These pr...
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ARTTS is a head-worn Augmented Reality (AR) Triage Tool Suite containing an initial sorting tool, virtual assessment tool, and virtual triage tag to assist first responders in mass casualty incidents. The initial sort...
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A polarization-insensitive mode-order converting power splitter using a pixelated region is presented and investigated in this *** TE_(0)and TM_(0)modes are injected into the input port,they are converted into TE_(1)a...
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A polarization-insensitive mode-order converting power splitter using a pixelated region is presented and investigated in this *** TE_(0)and TM_(0)modes are injected into the input port,they are converted into TE_(1)and TM_(1)modes,which evenly come out from the two output *** finite-difference time-domain method and direct-binary-search optimization algorithm are utilized to optimize structural parameters of the pixelated region to attain small insertion loss,low crosstalk,wide bandwidth,excellent power uniformity,polarization-insensitive property,and compact *** results reveal that the insertion loss,crosstalk,and power uniformity of the fabricated device at 1550 nm are 0.57,-19.67,and 0.094 d B in the case of TE polarization,while in the TM polarization,the relevant insertion loss,crosstalk,and power uniformity are 0.57,-19.40,and 0.11 d *** a wavelength range from 1520 to 1600 nm,for the fabricated device working at TE polarization,the insertion loss,crosstalk,and power uniformity are lower than 1.39,-17.64,and 0.14 *** the case of TM polarization,we achieved an insertion loss,crosstalk,and power uniformity less than 1.23,-17.62,and 0.14 dB.
This article presents the performance evaluation of ratio consensus-based distributed power apportioning engine along with centralized net load management (NLM) engine that ensures viable and stable operation of an is...
This article presents the performance evaluation of ratio consensus-based distributed power apportioning engine along with centralized net load management (NLM) engine that ensures viable and stable operation of an islanded microgrid. Managing net load variability in a microgrid with high penetrations of uncertain renewable generation and ever-changing load demands is a crucial need in order to ensure viable and stable operation of the microgrid. Centralized “dispatch-rule”-based and/or multi-agent-based distributed control of distributed energy resources (DERs) in microgrid are well accepted for microgrid by adapting ANSI/ISA-95-based hierarchical control architecture. In the application where microgrid network has large geographical span with multiple DERs dispersed in the network, high penetration of uncertain renewable energy resources, and ever-changing load demands, a judicious selection of techniques/solutions for managing net-load resources for maintaining viability and stability is required. With this motivation, this article proposes a novel solution to mitigate the challenges by incorporating a mixed centralized NLM engine and distributed power apportioning control of DERs and loads. A power-hardware-in-the-loop (PHIL) -based experiment is conducted with the centralized NLM engine and the distributed power apportioning engine along with two commercial inverters. The experimental results validates the efficacy of the proposed method in ensuring viability and stability of a microgrid.
Retrieving speech samples that have specific expressive content has many applications. It is desirable to build a preference learning framework that ranks speech samples according to emotional attribute values that ge...
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Retrieving speech samples that have specific expressive content has many applications. It is desirable to build a preference learning framework that ranks speech samples according to emotional attribute values that generalize well to new domains. A popular architecture for preference learning is the RankNet framework, which uses a function to obtain the preference between pairs of speech sentences. This study explores implementing this function with alternative feature representations that are explicitly selected to reduce the mismatch between source and target domains. In particular, we implement our preference-learning based speech emotion recognition (SER) system using ladder networks and adversarial domain adaptation. The study also proposes a novel combination of these two unsupervised domain adaptation strategies. The experimental results in cross-corpus evaluations using the MSP-Podcast and MSP-IMPROV datasets reveal that the proposed adversarial domain adaptation on a ladder network-based feature representation performs the best across different conditions. The results also show that preference learning leads to better precision for retrieval tasks than comparable SER systems built to directly predict absolute emotional attribute scores.
Existing safety assurance approaches for autonomous vehicles (AVs) perform system-level safety evaluation by placing the AV-under-test in challenging traffic scenarios captured by abstract scenario specifications and ...
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In this paper, we propose a digital twin (DT)-assisted resource demand prediction scheme to enhance prediction accuracy for multicast short video streaming. Particularly, we first construct user DTs (UDTs) for collect...
In this paper, we propose a digital twin (DT)-assisted resource demand prediction scheme to enhance prediction accuracy for multicast short video streaming. Particularly, we first construct user DTs (UDTs) for collecting real-time user status, including channel condition, location, watching duration, and preference. A reinforcement learning-empowered K-means++ algorithm is developed to cluster users based on the collected user status in UDTs. We then analyze users' watching duration and preferences in each multicast group to obtain the swiping probability distribution and recommended videos, respectively. The obtained information is utilized to predict radio and computing resource demand of each multicast group. Initial simulation results demonstrate that the proposed scheme can accurately predict resource demand.
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