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eess.SP updates on arXiv.org

ECG-biometrics-bench: A Unified Framework for Reproducible Benchmarking of ECG Biometrics Physiology-Aware Masked Cross-Modal Reconstruction for Biosignal Representation Learning Towards Improving Speaker Distance Estimation through Generative Impulse Response Augmentation Federated Learning with Hypergradient-based Online Update of Aggregation Weights Soft Graph Diffusion Transformer for MIMO Detection SPLICE: Latent Diffusion over JEPA Embeddings for Conformal Time-Series Inpainting Sequential Inference for Gaussian Processes: A Signal Processing Perspective Statistical Channel Fingerprint Construction for Massive MIMO: A Unified Tensor Learning Framework Recent Advances in mm-Wave and Sub-THz/THz Oscillators for FutureG Technologies Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods Super-resolution Multi-signal Direction-of-Arrival Estimation by Hankel-structured Sensing and Decomposition Hankel and Toeplitz Rank-1 Decomposition of Arbitrary Matrices with Applications to Signal Direction-of-Arrival Estimation Adaptive Transform Coding for Semantic Compression EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architectures Sparse Graph Learning from Sparse Data via Fiedler Number Maximization A Deep Learning Model for Battery State Prediction towards Intelligent Energy Management Transfer Learning for Tonal Noise Prediction in VRF Units Using Thermodynamic and Vibration Signals EVT-Based Generative AI for Tail-Aware Channel Estimation Monitoring exposure-length variations in submarine power cables using distributed fiber-optic sensing BandRouteNet: An Adaptive Band Routing Neural Network for EEG Artifact Removal Phase-Separated Complex Hilbert PCA on Markerless 3D Pose Estimation Data: A Global Phase Network and Its Extension to a Continuous Field on the Body Surface Selective Correlation Based Knowledge Distillation for Ground Reaction Force Estimation Deep Learning-Enabled Dissolved Oxygen Sensing in Biofouling Environments for Ocean Monitoring Speech Enhancement Based on Drifting Models Robust and Clinically Reliable EEG Biomarkers: A Cross Population Framework for Generalizable Parkinson's Disease Detection An AI-Based Supervisory Measurement Integrity Validation Layer for Cyber-Resilient AC/DC Protection in Inverter-Based Microgrids Explainable AI in Speaker Recognition -- Making Latent Representations Understandable Time-Localized Parametric Decomposition of Respiratory Airflow for Sub-Breath Analysis NAKUL-Med: Spectral-Graph State Space Models with Dynamics Kernels for Medical Signals An Algorithm for On-Sensor Agnostic Detection of Changes in Human Activity for Ultra-Low-Power Applications
Automated Smart Wick System-Based Microfarm Using Interne...
R. Jorda,, C. Alcabasa, A. Buhay, E. C. Dela Cruz, J. P. Mendoza · 2019-10-31 · via eess.SP updates on arXiv.org

This paper presents a study conducted to allow urban farmers to remotely monitor their farm through the design and development of an Internet of Things-based (IoT) microfarm prototype which utilized wick system as planting method. The system involves the detection of three environmental parameters namely, light intensity, soil moisture and temperature through the use of respective sensors which were connected to the Arduino microcontroller, the sensor node of the system. Irregularities in the aforementioned parameters were neutralized through the use of parameter regulators such as LED growlight strips, water pump and air cooler. The data collected by these sensors were gathered by the Arduino microcontroller and were sent to the Web database through the IoT gateway which was the Raspberry Pi computer chip. These data were also sent to an Android unit installed with the Microfarm Companion application which was capable of monitoring and controlling the environmental parameters observed in the microfarm. The application allows the user to view the current value of the parameter involved and to choose whether to control the parameter regulators automatically or manually. The microfarm system runs autonomously which reduces the labor required to produce healthy plants and crops. Mustard greens samples were used in testing the system. After a month of monitoring the height of the samples, it was observed that the average height of the samples is about 0.23 cm taller than the standard height. The proponents has also tested the system functionality by evaluating the sensor data log that provides the values gathered by the sensors and the turn-on times of the parameter regulators. From these data, it can be observed that whenever the values obtained by the sensors fall outside the threshold range, the parameter regulators turns on, indicating that the system is working properly.