27,575 research outputs found
Spatially Guiding Unsupervised Semantic Segmentation Through Depth-Informed Feature Distillation and Sampling
Traditionally, training neural networks to perform semantic segmentation
required expensive human-made annotations. But more recently, advances in the
field of unsupervised learning have made significant progress on this issue and
towards closing the gap to supervised algorithms. To achieve this, semantic
knowledge is distilled by learning to correlate randomly sampled features from
images across an entire dataset. In this work, we build upon these advances by
incorporating information about the structure of the scene into the training
process through the use of depth information. We achieve this by (1) learning
depth-feature correlation by spatially correlate the feature maps with the
depth maps to induce knowledge about the structure of the scene and (2)
implementing farthest-point sampling to more effectively select relevant
features by utilizing 3D sampling techniques on depth information of the scene.
Finally, we demonstrate the effectiveness of our technical contributions
through extensive experimentation and present significant improvements in
performance across multiple benchmark datasets
Joint Optimization Framework for Operational Cost Minimization in Green Coverage-Constrained Wireless Networks
In this work, we investigate the joint optimization of base station (BS)
location, its density, and transmit power allocation to minimize the overall
network operational cost required to meet an underlying coverage constraint at
each user equipment (UE), which is randomly deployed following the binomial
point process (BPP). As this joint optimization problem is nonconvex and
combinatorial in nature, we propose a non-trivial solution methodology that
effectively decouples it into three individual optimization problems. Firstly,
by using the distance distribution of the farthest UE from the BS, we present
novel insights on optimal BS location in an optimal sectoring type for a given
number of BSs. After that we provide a tight approximation for the optimal
transmit power allocation to each BS. Lastly, using the latter two results, the
optimal number of BSs that minimize the operational cost is obtained. Also, we
have investigated both circular and square field deployments. Numerical results
validate the analysis and provide practical insights on optimal BS deployment.
We observe that the proposed joint optimization framework, that solves the
coverage probability versus operational cost tradeoff, can yield a significant
reduction of about in the operational cost as compared to the benchmark
fixed allocation scheme.Comment: 30 pages, 15 figures, submitted to IEEE Transactions on Green
Communications and Networkin
Power adjustment and scheduling in OFDMA femtocell networks
Densely-deployed femtocell networks are used to enhance wireless coverage in public spaces like office buildings, subways, and academic buildings. These networks can increase throughput for users, but edge users can suffer from co-channel interference, leading to service outages. This paper introduces a distributed algorithm for network configuration, called Radius Reduction and Scheduling (RRS), to improve the performance and fairness of the network. RRS determines cell sizes using a Voronoi-Laguerre framework, then schedules users using a scheduling algorithm that includes vacancy requests to increase fairness in dense femtocell networks. We prove that our algorithm always terminate in a finite time, producing a configuration that guarantees user or area coverage. Simulation results show a decrease in outage probability of up to 50%, as well as an increase in Jain's fairness index of almost 200%
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