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1. Investigation of Methods to Extract Fetal Electrocardiogram from the Mother’s Abdominal Signal in Practical Scenarios

   https://doi.org/10.3390/technologies8020033  

   Sarafan, Sadaf; Le, Tai; Naderi, Amir Mohammad; Nguyen, Quoc-Dinh; Kuo, Brandon Tiang-Yu; Ghirmai, Tadesse; Han, Huy-Dung; Lau, Michael P.; Cao, Hung (June 2020, Technologies)

   null (Ed.)

   Monitoring of fetal electrocardiogram (fECG) would provide useful information about fetal wellbeing as well as any abnormal development during pregnancy. Recent advances in flexible electronics and wearable technologies have enabled compact devices to acquire personal physiological signals in the home setting, including those of expectant mothers. However, the high noise level in the daily life renders long-entrenched challenges to extract fECG from the combined fetal/maternal ECG signal recorded in the abdominal area of the mother. Thus, an efficient fECG extraction scheme is a dire need. In this work, we intensively explored various extraction algorithms, including template subtraction (TS), independent component analysis (ICA), and extended Kalman filter (EKF) using the data from the PhysioNet 2013 Challenge. Furthermore, the modified data with Gaussian and motion noise added, mimicking a practical scenario, were utilized to examine the performance of algorithms. Finally, we combined different algorithms together, yielding promising results, with the best performance in the F1 score of 92.61% achieved by an algorithm combining ICA and TS. With the data modified by adding different types of noise, the combination of ICA–TS–ICA showed the highest F1 score of 85.4%. It should be noted that these combined approaches required higher computational complexity, including execution time and allocated memory compared with other methods. Owing to comprehensive examination through various evaluation metrics in different extraction algorithms, this study provides insights into the implementation and operation of state-of-the-art fetal and maternal monitoring systems in the era of mobile health. 

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2. A Machine Learning Solution for Beam Tracking in mmWave Systems

   https://doi.org/10.1109/IEEECONF44664.2019.9048770  

   Burghal, Daoud; Abbasi, Naveed A.; Molisch, Andreas F. (November 2019, 2019 53rd Asilomar Conference on Signals, Systems, and Computers)

   Utilizing millimeter-wave (mmWave) frequencies for wireless communication in mobile systems is challenging since it requires continuous tracking of the beam direction. Recently, beam tracking techniques based on channel sparsity and/or Kalman filter-based techniques were proposed where the solutions use assumptions regarding the environment and device mobility that may not hold in practical scenarios. In this paper, we explore a machine learning-based approach to track the angle of arrival (AoA) for specific paths in realistic scenarios. In particular, we use a recurrent neural network (R-NN) structure with a modified cost function to track the AoA. We propose methods to train the network in sequential data, and study the performance of our proposed solution in comparison to an extended Kalman filter based solution in a realistic mmWave scenario based on stochastic channel model from the QuaDRiGa framework. Results show that our proposed solution outperforms an extended Kalman filter-based method by reducing the AoA outage probability, and thus reducing the need for frequent beam search. 

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3. Control of Kalman Filter Based Z-source Inverter in Photovoltaic applications

   Basavaraj, Abhishek Gubbi; Na, Woonki; Wang, Nan; Kim, Jonghoon; Kim, Taehyung (May 2019, 10th International Conference on Power Electronics-ECCE Asia)

   In this research, a Kalman filter-based Z-source inverter is proposed with an enhanced control algorithm for Maximum Power Pointer Tracking(MPPT) and this capacitor voltage stabilization. By implementing Unified Linear Kalman Filter Algorithm with Capacitor Voltage Control (CVC) algorithm for the Z-source inverter, the Kalman Filter can track Maximum Power Point (MPP) faster than traditional algorithm such as Perturb and Observation (P&O) algorithm, that has a minimum impact on rapidly changing atmospheric conditions. Thus, by using the Integrated Kalman Filter and CVC algorithm we can achieve faster, effective and capacitor voltage regulation at the same time. The effectiveness of this proposed Kalman Filter with CVC Algorithm for Z-source inverter is validated in MATLAB/Simulink and a hardware prototype has been built to verify the simulation and theoretical results. 

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4. Node-Asynchronous Implementation of Filter Banks on Graphs

   https://doi.org/10.1109/IEEECONF51394.2020.9443349  

   Teke, Oguzhan; Vaidyanathan, P. P. (November 2020, Proc. Asil. Conf. Sig., Sys., and Comp)

   null (Ed.)

   Filter banks on graphs are shown to be useful for analyzing data defined over networks, as they decompose a graph signal into components with low variation and high variation. Based on recent node-asynchronous implementation of graph filters, this study proposes an asynchronous implementation of filter banks on graphs. In the proposed algorithm nodes follow a randomized collect-compute-broadcast scheme: if a node is in the passive stage it collects the data sent by its incoming neighbors and stores only the most recent data. When a node gets into the active stage at a random time instance, it does the necessary filtering computations locally, and broadcasts a state vector to its outgoing neighbors. When the underlying filters (of the filter bank) are rational functions with the same denominator, the proposed filter bank implementation does not require additional communication between the neighboring nodes. However, computations done by a node increase linearly with the number of filters in the bank. It is also proven that the proposed asynchronous implementation converges to the desired output of the filter bank in the mean-squared sense under mild stability conditions. The convergence is verified also with numerical experiments. 

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5. 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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