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Vehicular edge computing relies on the computational capabilities of interconnected edge devices to manage incoming requests from vehicles. This offloading process enhances the speed and efficiency of data handling, ultimately boosting the safety, performance, and reliability of connected vehicles. While previous studies have concentrated on processor characteristics, they often overlook the significance of the connecting components. Limited memory and storage resources on edge devices pose challenges, particularly in the context of deep learning, where these limitations can significantly affect performance. The impact of memory contention has not been thoroughly explored, especially regarding perception-based tasks. In our analysis, we identified three distinct behaviors of memory contention, each interacting differently with other resources. Additionally, our investigation of Deep Neural Network (DNN) layers revealed that certain convolutional layers experienced computation time increases exceeding 2849%, while activation layers showed a rise of 1173.34%. Through our characterization efforts, we can model workload behavior on edge devices according to their configuration and the demands of the tasks. This allows us to quantify the effects of memory contention. To our knowledge, this study is the first to characterize the influence of memory on vehicular edge computational workloads, with a strong emphasis on memory dynamics and DNN layers. 

We introduce multimodal neural acoustic fields for synthesizing spatial sound and enabling the creation of immersive auditory experiences from novel viewpoints and in completely unseen new environments, both virtual and real. Extending the concept of neural radiance fields to acoustics, we develop a neural network-based model that maps an environment's geometric and visual features to its audio characteristics. Specifically, we introduce a novel hybrid transformer-convolutional neural network to accomplish two core tasks: capturing the reverberation characteristics of a scene from audio-visual data, and generating spatial sound in an unseen new environment from signals recorded at sparse positions and orientations within the original scene. By learning to represent spatial acoustics in a given environment, our approach enables creation of realistic immersive auditory experiences, thereby enhancing the sense of presence in augmented and virtual reality applications. We validate the proposed approach on both synthetic and real-world visual-acoustic data and demonstrate that our method produces nonlinear acoustic effects such as reverberations, and improves spatial audio quality compared to existing methods. Furthermore, we also conduct subjective user studies and demonstrate that the proposed framework significantly improves audio perception in immersive mixed reality applications.