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Precise seizure identification plays a vital role in understanding cortical connectivity and informing treatment decisions. Yet, the manual diagnostic methods for epileptic seizures are both labor-intensive and highly specialized. In this study, we propose a Hyperdimensional Computing (HDC) classifier for accurate and efficient multi-type seizure classification. Despite previous seizure analysis efforts using HDC being limited to binary detection (seizure or no seizure), our work breaks new ground by utilizing HDC to classify seizures into multiple distinct types. HDC offers significant advantages, such as lower memory requirements, a reduced hardware footprint for wearable devices, and decreased computational complexity. Due to these attributes, HDC can be an alternative to traditional machine learning methods, making it a practical and efficient solution, particularly in resource-limited scenarios or applications involving wearable devices. We evaluated the proposed technique on the latest version of TUH EEG Seizure Corpus (TUSZ) dataset and the evaluation result demonstrate noteworthy performance, achieving a weighted F1 score of 94.6%. This outcome is in line with, or even exceeds, the performance achieved by the state-ofthe-art traditional machine learning methods. 

Soil moisture (SM) plays a significant role in determining the probability of flooding in a given area. Currently, SM is most commonly modeled using physically-based numerical hydrologic models. Modeling the natural processes that take place in the soil is difficult and requires assumptions. Besides, hydrologic model runtime is highly impacted by the extent and resolution of the study domain. In this study, we propose a data-driven modeling approach using Deep Learning (DL) models. There are different types of DL algorithms that serve different purposes. For example, the Convolutional Neural Network (CNN) algorithm is well suited for capturing and learning spatial patterns, while the Long Short-Term Memory (LSTM) algorithm is designed to utilize time-series information and to learn from past observations. A DL algorithm that combines the capabilities of CNN and LSTM called ConvLSTM was recently developed. In this study, we investigate the applicability of the ConvLSTM algorithm in predicting SM in a study area located in south Louisiana in the United States. This study reveals that ConvLSTM significantly outperformed CNN in predicting SM. We tested the performance of ConvLSTM based models by using a combination of different sets of predictors and different LSTM sequence lengths. The study results show that ConvLSTM models can predict SM with a mean areal Root Mean Squared Error (RMSE) of 2.5% and mean areal correlation coefficients of 0.9 for our study area. ConvLSTM models can also provide predictions between discrete SM observations, making them potentially useful for applications such as filling observational gaps between satellite overpasses. 

For improved spectrum utilization, the key technique for acquiring spectrum situational awareness (SSA) — spectrum sensing — is greatly improved by cooperation among the active spectrum users, as network size increases. However, the many cooperative spectrum sensing (CSS) schemes that have been proposed are based on the assumptions of accurate noise power estimates, characterizable variation in noise level and absence of false or malicious users. As part of a series of SSA research projects, in this research work, we propose a novel scheme for minimizing the effects of noise power estimation error (NPEE) and received signal power falsification (RSPF) by energy-based reliability evaluation. The scheme adopts the Voting rule for fusing multiple spectrum sensing data. Based on simulation results, the proposed scheme yields significant improvement, 68.2—88.8%, over the conventional CSS schemes, when compared on the basis of the schemes’ stability to uncertainties in noise and signal power.