28 research outputs found
Signal estimation in cognitive satellite networks for satellite-based industrial internet of things
Satellite industrial Internet of Things (IIoT) plays an important role in industrial manufactures without requiring the support of terrestrial infrastructures. However, due to the scarcity of spectrum resources, existing satellite frequency bands cannot satisfy the demand of IIoT, which have to explore other available spectrum resources. Cognitive satellite networks are promising technologies and have the potential to alleviate the shortage of spectrum resources and enhance spectrum efficiency by sharing both spectral and spatial degrees of freedom. For effective signal estimations, multiple features of wireless signals are needed at receivers, the transmissions of which may cause considerable overhead. To mitigate the overhead, part of parameters, such as modulation order, constellation type, and signal to noise ratio (SNR), could be obtained at receivers through signal estimation rather than transmissions from transmitters to receivers. In this article, a grid method is utilized to process the constellation map to obtain its equivalent probability density function. Then, binary feature matrix of the probability density function is employed to construct a cost function to estimate the modulation order and constellation type for multiple quadrature amplitude modulation (MQAM) signal. Finally, an improved M 2 M ∞ method is adopted to realize the SNR estimation of MQAM. Simulation results show that the proposed method is able to accurately estimate the modulation order, constellation type, and SNR of MQAM signal, and these features are extremely useful in satellite-based IIoT
Learning a Condensed Frame for Memory-Efficient Video Class-Incremental Learning
Recent incremental learning for action recognition usually stores
representative videos to mitigate catastrophic forgetting. However, only a few
bulky videos can be stored due to the limited memory. To address this problem,
we propose FrameMaker, a memory-efficient video class-incremental learning
approach that learns to produce a condensed frame for each selected video.
Specifically, FrameMaker is mainly composed of two crucial components: Frame
Condensing and Instance-Specific Prompt. The former is to reduce the memory
cost by preserving only one condensed frame instead of the whole video, while
the latter aims to compensate the lost spatio-temporal details in the Frame
Condensing stage. By this means, FrameMaker enables a remarkable reduction in
memory but keep enough information that can be applied to following incremental
tasks. Experimental results on multiple challenging benchmarks, i.e., HMDB51,
UCF101 and Something-Something V2, demonstrate that FrameMaker can achieve
better performance to recent advanced methods while consuming only 20% memory.
Additionally, under the same memory consumption conditions, FrameMaker
significantly outperforms existing state-of-the-arts by a convincing margin.Comment: NeurIPS 202
Grow and Merge: A Unified Framework for Continuous Categories Discovery
Although a number of studies are devoted to novel category discovery, most of
them assume a static setting where both labeled and unlabeled data are given at
once for finding new categories. In this work, we focus on the application
scenarios where unlabeled data are continuously fed into the category discovery
system. We refer to it as the {\bf Continuous Category Discovery} ({\bf CCD})
problem, which is significantly more challenging than the static setting. A
common challenge faced by novel category discovery is that different sets of
features are needed for classification and category discovery: class
discriminative features are preferred for classification, while rich and
diverse features are more suitable for new category mining. This challenge
becomes more severe for dynamic setting as the system is asked to deliver good
performance for known classes over time, and at the same time continuously
discover new classes from unlabeled data. To address this challenge, we develop
a framework of {\bf Grow and Merge} ({\bf GM}) that works by alternating
between a growing phase and a merging phase: in the growing phase, it increases
the diversity of features through a continuous self-supervised learning for
effective category mining, and in the merging phase, it merges the grown model
with a static one to ensure satisfying performance for known classes. Our
extensive studies verify that the proposed GM framework is significantly more
effective than the state-of-the-art approaches for continuous category
discovery.Comment: This paper has already been accepted by 36th Conference on Neural
Information Processing Systems (NeurIPS 2022