https://joaocarlos.github.io/Hugo-academia/papers/Recent content in Publications on Dr. Jane SmithHugoenSat, 15 Jun 2024 00:00:00 +0000https://joaocarlos.github.io/Hugo-academia/papers/deep-learning-for-urban-traffic-prediction/Sat, 15 Jun 2024 00:00:00 +0000https://joaocarlos.github.io/Hugo-academia/papers/deep-learning-for-urban-traffic-prediction/Abstract We present a novel deep learning architecture for predicting urban traffic patterns using multi-modal sensor data. Our approach combines convolutional neural networks with attention mechanisms to capture both spatial and temporal dependencies in traffic flow. Experiments on real-world datasets from three major cities demonstrate that our method outperforms existing approaches by 15% in prediction accuracy while reducing computational requirements by 40%. Citation @article{smith2024traffic, title = {Deep Learning for Urban Traffic Prediction}, author = {Smith, Jane and Doe, John and Johnson, Alice}, journal = {IEEE Transactions on Intelligent Transportation Systems}, year = {2024}, doi = {10.https://joaocarlos.github.io/Hugo-academia/papers/adaptive-sensing-for-smart-cities-a-reinforcement-learning-approach/Wed, 20 Sep 2023 00:00:00 +0000https://joaocarlos.github.io/Hugo-academia/papers/adaptive-sensing-for-smart-cities-a-reinforcement-learning-approach/Abstract Urban sensing networks face the challenge of balancing data quality with energy consumption. We propose an adaptive sensing framework that uses reinforcement learning to dynamically adjust sampling rates based on environmental conditions and application requirements. Our system reduces energy consumption by 60% while maintaining 95% of the data quality compared to fixed-rate sampling approaches. Citation @inproceedings{smith2023adaptive, title = {Adaptive Sensing for Smart Cities: A Reinforcement Learning Approach}, author = {Smith, Jane and Garcia, Maria}, booktitle = {Proceedings of the ACM Conference on Embedded Networked Sensor Systems (SenSys)}, year = {2023}, doi = {10.https://joaocarlos.github.io/Hugo-academia/papers/edge-computing-for-real-time-environmental-monitoring/Thu, 10 Mar 2022 00:00:00 +0000https://joaocarlos.github.io/Hugo-academia/papers/edge-computing-for-real-time-environmental-monitoring/Abstract Real-time environmental monitoring requires processing large volumes of sensor data with minimal latency. This paper presents an edge computing architecture that performs local data processing and anomaly detection, reducing cloud bandwidth requirements by 80% while enabling sub-second response times for critical events. Citation @article{smith2022edge, title = {Edge Computing for Real-Time Environmental Monitoring}, author = {Smith, Jane and Chen, Robert and Wang, Lisa}, journal = {IEEE Internet of Things Journal}, year = {2022}, doi = {10.