Linear Compression of Digital Ink via Point Selection

This work reports on convolutional neural network (CNN) performance on an image texture classification task as a function of linear image processing and number of training images. Detection performance of single and multi-layer CNNs (sCNN/mCNN) are compared to optimal observers. Performance is quantified by the area under the receiver operating characteristic (ROC) curve, also known as the AUC. For perfect detection AUC = 1.0 and AUC = 0.5 for guessing. The Ideal Observer (IO) maximizes AUC but is prohibitive in practice because it depends on high-dimensional image likelihoods. The IO performance is invariant to any fullrank, invertible linear image processing. This work demonstrates the existence of full-rank, invertible linear transforms that can degrade both sCNN and mCNN even in the limit of large quantities of training data. A subsequent invertible linear transform changes the images’ correlation structure again and can improve this AUC. Stationary textures sampled from zero mean and unequal covariance Gaussian distributions allow closed-form analytic expressions for the IO and optimal linear compression. Linear compression is a mitigation technique for high-dimension low sample size (HDLSS) applications. By definition, compression strictly decreases or maintains IO detection performance. For small quantities of training data, linear image compression prior to the sCNN architecture can increase AUC from 0.56 to 0.93. Results indicate an optimal compression ratio for CNN based on task difficulty, compression method, and number of training images.

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Joint Transmission Reception Point Selection and Resource Allocation for Energy-Efficient Millimeter-Wave Communications

IEEE Transactions on Vehicular Technology ◽

10.1109/tvt.2020.3044926 ◽

2020 ◽

pp. 1-1

Author(s):

Yanping Liu ◽

Xuming Fang ◽

Ming Xiao

Keyword(s):

Resource Allocation ◽

Millimeter Wave ◽

Energy Efficient ◽

Point Selection ◽

Reception Point ◽

Joint Transmission ◽

Millimeter Wave Communications

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Joint optimization of network selection and task offloading for vehicular edge computing

Journal of Cloud Computing Advances Systems and Applications ◽

10.1186/s13677-021-00240-y ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Lujie Tang ◽

Bing Tang ◽

Li Zhang ◽

Feiyan Guo ◽

Haiwu He

Keyword(s):

Large Scale ◽

Vehicular Networks ◽

Access Point ◽

Edge Computing ◽

Network Selection ◽

Network Access ◽

Task Execution ◽

Point Selection ◽

Vehicle Movement ◽

Task Offloading

AbstractTaking the mobile edge computing paradigm as an effective supplement to the vehicular networks can enable vehicles to obtain network resources and computing capability nearby, and meet the current large-scale increase in vehicular service requirements. However, the congestion of wireless networks and insufficient computing resources of edge servers caused by the strong mobility of vehicles and the offloading of a large number of tasks make it difficult to provide users with good quality of service. In existing work, the influence of network access point selection on task execution latency was often not considered. In this paper, a pre-allocation algorithm for vehicle tasks is proposed to solve the problem of service interruption caused by vehicle movement and the limited edge coverage. Then, a system model is utilized to comprehensively consider the vehicle movement characteristics, access point resource utilization, and edge server workloads, so as to characterize the overall latency of vehicle task offloading execution. Furthermore, an adaptive task offloading strategy for automatic and efficient network selection, task offloading decisions in vehicular edge computing is implemented. Experimental results show that the proposed method significantly improves the overall task execution performance and reduces the time overhead of task offloading.

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