Competition-Congestion-Aware Stable Worker-Task Matching in Mobile Crowd Sensing

Mobile Crowd Sensing is an emerging sensing paradigm that employs massive number of workers’ mobile devices to realize data collection. Unlike most task allocation mechanisms that aim at optimizing the global system performance, stable matching considers workers are selfish and rational individuals, which has become a hotspot in MCS. However, existing stable matching mechanisms lack deep consideration regarding the effects of workers’ competition phenomena and complex behaviors. To address the above issues, this paper investigates the competition-congestion-aware stable matching problem as a multi-objective optimization task allocation problem considering the competition of workers for tasks. First, a worker decision game based on congestion game theory is designed to assist workers in making decisions, which avoids fierce competition and improves worker satisfaction. On this basis, a stable matching algorithm based on extended deferred acceptance algorithm is designed to make workers and tasks mapping stable, and to construct a shortest task execution route for each worker. Simulation results show that the designed model and algorithm are effective in terms of worker satisfaction and platform benefit. IEE

Time-domain Ad-hoc Array Speech Enhancement Using a Triple-path Network

Deep neural networks (DNNs) are very effective for multichannel speech enhancement with fixed array geometries. However, it is not trivial to use DNNs for ad-hoc arrays with unknown order and placement of microphones. We propose a novel triple-path network for ad-hoc array processing in the time domain. The key idea in the network design is to divide the overall processing into spatial processing and temporal processing and use self-attention for spatial processing. Using self-attention for spatial processing makes the network invariant to the order and the number of microphones. The temporal processing is done independently for all channels using a recently proposed dual-path attentive recurrent network. The proposed network is a multiple-input multiple-output architecture that can simultaneously enhance signals at all microphones. Experimental results demonstrate the excellent performance of the proposed approach. Further, we present analysis to demonstrate the effectiveness of the proposed network in utilizing multichannel information even from microphones at far locations.Comment: Accepted for publication in INTERSPEECH 202

Orthogonal surface functionalization through bioactive vapor‐based polymer coatings

Reactive chemical vapor deposition (CVD) polymerization provides a substrate‐independent platform for effective functionalization of virtually any solid substrates, flat, or curved, even with complex geometries. This article reviews bioactive surface functionalization strategies based on CVD polymerization and highlights commonly used surface chemistries. These reactions include alkyne–azide “click” chemistry, reactions of active esters with amine, aldehydes/ketones with hydrazides and alkoxyamines, thiols with alkenes and alkynes and surface‐initiated atom transfer radical polymerization. The resulting biofunctional surface coatings can facilitate orthogonal immobilization of more than one type of ligand on a substrate. CVD polymer coatings with nanoscale thicknesses are widely applicable in biomedical applications and can be easily integrated into micro‐ and nanodevice fabrication. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131 , 40315.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106951/1/app40315.pd