News
The emerging mobile edge computing (MEC) technology has been recently applied to improve the Quality of Experience (QoE) of network services, such as live video streaming. In this paper, we study an ...
A multitask 2D CNN model is designed for integrated monitoring stress and damage in concrete specimens utilizing the raw impedance signatures of capsule-like smart aggregated (CSA). The fundamental ...
When a learned model has high accuracy under familiar settings (internal testing) and a big drop in accuracy under slightly different circumstances (external testing) we suspect it is using shortcuts ...
Musical instrument recognition is the act of counting on machine learning or the application of signal processing to isolate and classify various musical instruments in an audio track. Such capability ...
This research presents a comprehensive comparative analysis of various pre-trained backbone models and machine learning techniques for output layers in convolutional neural networks (CNNs) applied to ...
This paper studies the computational offloading of CNN inference in dynamic multi-access edge computing (MEC) networks. To address the uncertainties in communication time and edge servers’ available ...
Cancer, a disease that does not discriminate; impacting people from all walks of life. Numerous types of cancer affect humans, each with distinct characteristics and treatment approaches. One of the ...
Dense 3D shape acquisition of swimming human or live fish is an important research topic for sports, biological science and so on. For this purpose, active stereo sensor is usually used in the air, ...
How to make right decisions in stock trading is a vital and challenging task for investors. Since deep reinforcement learning (DRL) has outperformed human beings in many fields such as playing Atari ...
One of the key problems in spectrum sensing is to design the test statistic. Existing methods generally exploit the model-based features as the test statistic, such as energies and eigenvalues.
Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning ...
Deep Reinforcement Learning on Autonomous Driving Policy With Auxiliary Critic Network - IEEE Xplore
Deep reinforcement learning (DRL) is a machine learning method based on rewards, which can be extended to solve some complex and realistic decision-making problems. Autonomous driving needs to deal ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results