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1. A Noise Suppression of LSTM algorithm combined with Kalman filter for Agriculture Automation

   https://doi.org/10.1145/3589883.3589918  

   Barry, Abdoulaye; Kim, Junwhan; Yu, Byunggu; O'Hara, Sabine (March 2023, ACM)

   An immense volume of data is produced by sensor devices in the fields of aquaponics, hydroponics, and soil-based food production, where these devices track various environmental factors. Data stream mining is the method of retrieving data from fast-sampled data sources that are constantly streaming. The accuracy of data obtained through data stream mining is largely determined by the algorithm utilized to filter out noise. For threshold-based automation, an actuator can be activated when the value of sensor data is above a permissible threshold. Noise from sensors may activate the actuator. Several statistical and machine learning-based noise-suppression algorithms have been proposed in the literature. They have been evaluated based on the mean squared error metric (MSE). The Long Short-Term Memory – LSTM filter (MSE: 0.000999943) performs better noise suppression than other traditional filters – Kalman (MSE: 0.0015982). We propose a new noise suppression filter – LSTM combined with Kalman (LSTM-KF). In LSTM-KF, the Kalman filter acts as an encoder and the LSTM becomes the decoder, resulting in a significantly lower MSE – 0.000080789592. The LSTM-KF is installed in our threshold-based aquaponics automation to maximize sustainable food production at minimum cost. 

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2. An LSTM based Kalman Filter for Spatio-temporal Ocean Currents Assimilation

   https://doi.org/10.1145/3366486.3366522  

   Zhang, Ziqiao; Hou, Mengxue; Zhang, Fumin; Edwards, Catherine R. (October 2019, WUWNET'19: Proceedings of the International Conference on Underwater Networks & Systems)

   In this paper, we present a Long Short-Term Memory (LSTM)-based Kalman Filter for data assimilation of a 2D spatio-temporally varying depth-averaged ocean flow field for underwater glider path planning. The data source to the filter combines both the Eulerian flow map with the Lagrangian mobile sensor data stream. The depth-averaged flow is modeled as two components, the tidal and the non-tidal flow component. The tidal flow is modeled with ADCIRC (Advanced Three-Dimensional Circulation Model), while the non-tidal flow field is modeled by a set of spatial basis functions and their time series coefficients. The spatial basis functions are the principal modes derived by performing EOF (Empirical Orthogonal Functions) analysis on the historical surface flow field measured by high frequency radar (HFR), and the temporal coefficients of the spatial basis function are modeled by an LSTM neural network. The Kalman Filter is performed to combine the dynamics derived from the LSTM network, and the observations from the glider flow estimation data. Simulation results demonstrate that the proposed data assimilation method can give flow field prediction of reasonable accuracy. 

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3. LSTM-Enabled Level Curve Tracking in Scalar Fields Using Multiple Mobile Robots

   https://doi.org/10.1115/DETC2021-68554  

   Parikh, Kunj J.; Wu, Wencen (November 2021, ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)

   In this work, we investigate the problem of level curve tracking in unknown scalar fields using a limited number of mobile robots. We design and implement a long short-term memory (LSTM) enabled control strategy for a mobile sensor network to detect and track desired level curves. Based on the existing work of cooperative Kalman filter, we design an LSTM-enhanced Kalman filter that utilizes the sensor measurements and a sequence of past fields and gradients to estimate the current field value and gradient. We also design an LSTM model to estimate the Hessian of the field. The LSTM-enabled strategy has some benefits such as it can be trained offline on a collection of level curves in known fields prior to deployment, where the trained model will enable the mobile sensor network to track level curves in unknown fields for various applications. Another benefit is that we can train using larger resources to get more accurate models while utilizing a limited number of resources when the mobile sensor network is deployed in production. Simulation results show that this LSTM-enabled control strategy successfully tracks the level curve using a mobile multi-robot sensor network. 

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4. Model-based stationarity filtering of long-term memory data applied to resting-state blood-oxygen-level-dependent signal

   https://doi.org/10.1371/journal.pone.0268752  

   Bansal, Ishita Rai; Ashourvan, Arian; Bertolero, Maxwell; Bassett, Danielle S.; Pequito, Sérgio (July 2022, PLOS ONE)

   Giove, Federico (Ed.)

   Resting-state blood-oxygen-level-dependent (BOLD) signal acquired through functional magnetic resonance imaging is a proxy of neural activity and a key mechanism for assessing neurological conditions. Therefore, practical tools to filter out artefacts that can compromise the assessment are required. On the one hand, a variety of tailored methods to preprocess the data to deal with identified sources of noise (e.g., head motion, heart beating, and breathing, just to mention a few) are in place. But, on the other hand, there might be unknown sources of unstructured noise present in the data. Therefore, to mitigate the effects of such unstructured noises, we propose a model-based filter that explores the statistical properties of the underlying signal (i.e., long-term memory). Specifically, we consider autoregressive fractional integrative process filters. Remarkably, we provide evidence that such processes can model the signals at different regions of interest to attain stationarity. Furthermore, we use a principled analysis where a ground-truth signal with statistical properties similar to the BOLD signal under the injection of noise is retrieved using the proposed filters. Next, we considered preprocessed (i.e., the identified sources of noise removed) resting-state BOLD data of 98 subjects from the Human Connectome Project. Our results demonstrate that the proposed filters decrease the power in the higher frequencies. However, unlike the low-pass filters, the proposed filters do not remove all high-frequency information, instead they preserve process-related higher frequency information. Additionally, we considered four different metrics (power spectrum, functional connectivity using the Pearson’s correlation, coherence, and eigenbrains) to infer the impact of such filter. We provided evidence that whereas the first three keep most of the features of interest from a neuroscience perspective unchanged, the latter exhibits some variations that could be due to the sporadic activity filtered out. 

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5. Comparison of Deep Learning and Signal Processing Methods for Removing a Ringing Artifact from Ultrasound Signals

   https://doi.org/10.1109/SAS58821.2023.10254036  

   Sosnovskaya, Yana; Shlizerman, Eli; Hannaford, Blake; Sinanan, Mika N. (July 2023, IEEE)

   Minimally Invasive Surgeries can benefit from having miniaturized sensors on surgical graspers to provide additional information to the surgeons. In this work, a 6 mm ultrasound transducer was added to a surgical grasper, intended to measure acoustic properties of the tissue. However, the ultrasound sensor has a ringing artifact arising from the decaying oscillation of its piezo element, and at short travel distances, the artifact blends with the acoustic echo. Without a method to remove the artifact from the blended signal, this makes it impossible to measure one of the main characteristics of an ultrasound waveform – Time of Flight. In this paper, six filtering methods to clear the artifact from the ultrasound waveform were compared: Bandpass filter, Adaptive Least Mean Squares (LMS) filter, Spectrum Suppression (SPS), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). Following each filtering method, four time of flight extraction methods were compared: Magnitude Threshold, Envelope Peak Detection, Cross-correlation and Short-time Fourier Transform (STFT). The RNN with Cross-correlation method pair was shown to be optimal for this task, performing with the root mean square error of 3.6 %. 

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