Abstract: Network slicing in vehicular networks could support emerging Vehicle-to-Vehicle (V2V) applications with diverse QoS requirements. However, time-varying V2V traffic and fast-changing network topology make network slicing in dynamic vehicular communications difficult. This paper proposes…
Abstract: The increased effective mini-batch size in large-scale distributed deep neural network training reduces generalization performance. Variations in learning rate and batch size over epochs and layers or batch normalization have been used to solve…
Abstract: Digital recommendation systems are crucial. They’re used in Spotify and Netflix. Physical exercise recommendation systems receive less research. Sedentary lifestyles cause many diseases and healthcare costs. This paper introduces a recommendation system that recommends…
Abstract: Hypertension and diabetes are diagnosed and monitored using retinal vasculature clues. Such conditions involve the microvascular system, which can only be seen in the retina. Recent advances in retinal imaging and computer vision have…
Abstract: Nearly 57 million people die annually, including over 2.7 million in the US. During the COVID-19 pandemic, institutions and government agencies use death reports to respond to communicable diseases. Even experienced doctors have trouble…
Abstract: Traditional traffic scene prediction methods use current observations to predict the future states of visible objects (not in blind spots). Based on current observations of visible objects, this study predicted the future states of…
Abstract: Physics-informed deep reinforcement learning (RL) is proposed for ATM aircraft conflict resolution. To enable optimal policy searching and human-explainable results for display and decision-making, the learning algorithm integrates prior physics understanding and model. First,…
Abstract: One biometrics modality is gait. Most gait recognition methods use silhouettes or articulated body models. Confounding variables like clothing, carrying, and viewing angle degrade recognition performance in these methods. We propose GaitNet, a new…
Abstract: Model parameters store pretrained deep models’ knowledge. These parameters give trained models “memory” to generalize well on unseen data. In the absence of training data, a trained model is only useful for inference or…
Abstract: Autonomous driving perception should be environment-adaptive. Traffic line number and target system computing power should be considered in essential perception modules for traffic line detection. This paper proposes a traffic line detection method, Point…
Abstract: Social media like Sina Weibo can show traffic information like road jams, illegal behaviors, and emergency measures. Traffic data mining remains difficult. This paper proposes a deep learning-based traffic jam management method using social…
Abstract: Deep learning in histopathology requires large training datasets. Pathologists spend a lot of time labeling virtual slides, even though whole slide imaging scanners speed up data acquisition. Eye gaze annotations may speed slide labeling….
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