SLIIT International Conference on Engineering and Technology [SICET]

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Embargo
Steerable Directional Listening for Individuals with Unilateral Hearing Loss
(SLIIT, Faculty of Engineering, 2024-10) Wimalaratne, U.A; Seneviratne, L; Malasinghe, L
A novel hearing aid design which incorporates 3 separate phased arrays with a digital signal processor running an adaptive beamforming algorithm, Generalized Sidelobe Canceller (GSC), providing the user with the capability of effectively focusing their listening to a certain source while suppressing any masking interference sources. The proposed phased array achieved an array directivity of 7.93dB with a half power bandwidth of 35.20º. The GSC and the phased array designed, when simulated, was able to achieve a SINR improvement of 9.72dB under strong noise levels and a 2.55dB SINR improvement under low level noise which were located spatially close to the desired source.
Embargo
Auto-encoder Based Data Clustering for Typical and Atypical Repetitive Child Hand Movement Pattern Identification
(SLIIT, Faculty of Engineering, 2024-10) Wedasingha, N; Samarasinhe, P; Seneviratne, L; Papandrea, M; Puiatti, A
This study is dedicated to the important task of identifying unique repetitive hand movement patterns in children, with the aim of facilitating early anomaly detection. The current body of literature lacks a comprehensive model capable of effectively discerning distinctive patterns in child repetitive hand movements. To address this gap, our innovative approach employs autoencoders to efficiently compress intricate data and extract latent features from a dataset with inherent limitations. By utilizing clustering techniques, we analyze these features to reveal distinct behaviors associated with child hand movements. Despite the challenges posed by binary annotated datasets, our model demonstrates outstanding performance in categorizing movements into four distinct types, thereby providing valuable insights into the intricate landscape of child hand movement patterns. Statistical assessments further underscore the superiority of our autoencoder, achieving a mean Bayesian value of 0.112, outperforming state-of-the-art algorithms in this domain. Subsequent in-depth analysis exposes notable inter-cluster patterns, elucidating transitions from typical to atypical behavior in child hand movements. This research constitutes a significant advancement in the field of child hand movement pattern analysis, offering a powerful and sophisticated tool for healthcare professionals and researchers alike. The automation capabilities embedded in our model empower these professionals to address childhood behavioral disorders more effectively and efficiently. In essence, our research not only contributes to the enhancement of early anomaly detection systems but also serves as a valuable resource for professionals engaged in child healthcare and behavioral research, facilitating a deeper understanding of these nuanced patterns.
Open Access
Support Vector Machine Based an Efficient and Accurate Seasonal Weather Forecasting Approach with Minimal Data Quantities
(SLIIT, 2022-02-11) Chandrasekara, S; Tennekoon, S; Abhayasinghe, N; Seneviratne, L
Climate change makes a big impact in our daily activities. Therefore, forecasting climate changes prior to its actual occurrences is important. Even though highly accurate weather prediction systems throughout the world are available, they require mass amounts of data exceeding thousands of data points to obtain a significant accuracy. This study was aimed at proposing a Support Vector Machine based approach to carryout seasonal weather predictions up to thirty-minute intervals, the results of which would be considerably effective with respect to predictions carried out with models trained with annual datasets. The model was trained utilizing a dataset corresponding to the district of Kandy which consisted of 136 samples, 20 features, and 5 labels. By means of carrying out numerous data preprocessing steps, the model was trained, and the relevant hyperparameters were optimized considering the grid search algorithm to yield a maximum accuracy of 86%, once tested via the k-fold cross validation. The performance of the Support Vector Machine was also then compared for the same dataset with that of the K-Nearest Neighbor algorithm which consumed relatively fewer computing resources. An optimal accuracy of 61% was observed for this model for a K-value of 27. This approach supported the concept of a Support Vector Machine’s ability to perceive time series forecasts to a relatively higher degree and its ability to perform effectively in higher dimensional datasets with smaller number of samples. As per the future work, the Receiver Operating Characteristic analysis is proposed to be carried out to evaluate the performance of the model and the dataset size is proposed to be further enhanced to a maximum of a thousand samples to yield the best performance results.

SLIIT International Conference on Engineering and Technology [SICET]

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