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"IEEE Sensors Alert" is a new service of the IEEE Sensors Council. Started as one of its new initiatives, this weekly digest publishes teasers and condensed versions of our journal papers in layperson's language.
Maintaining indoor air quality (IAQ) is crucial for health and wellness. Accurate data analysis and contextual anomaly detection are essential for IAQ monitoring. The paper introduces a hybrid deep-learning model, combining long short-term memory (LSTM) with autoencoder (AE). LSTM learns typical carbon dioxide (CO2) time sequence patterns, while AE computes optimal reconstruction errors and detects anomalies. Achieving 99.50% accuracy in real-world testing, the model shows promise for enhancing IAQ monitoring.
Sea surface target detection is vital for maritime security, surveillance, and rescue operations. The complexity of the marine environment makes it difficult to achieve robust, reliable, and adaptive target detection. Deep learning methods show better feature extraction ability and classification accuracy. The study proposes a new approach to marine target detection in complex background conditions using marine dual-channel convolutional neural networks (MDCCNN) with a false-alarm controllable classifier (FACC).
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