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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)

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   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. Multi-Relay Communications in the Presence of Phase Noise: A Comprehensive Approach

   https://doi.org/10.1109/TCOMM.2016.2622704  

   Salim, Omar H. ; Nasir, Ali A. ; Mehrpouyan, Hani ; Xiang, Wei ( January 2017 , IEEE Transactions on Communications)

   Abstract: Impairments such as time varying phase noise (PHN) and carrier frequency offset (CFO) result in loss of synchronization and poor performance of multi-relay communication systems. Joint estimation of these impairments is necessary in order to correctly decode the received signal at the destination. In this paper, we address spectrally efficient multi-relay transmission scenarios where all the relays simultaneously communicate with the destination. We propose an iterative pilot-aided algorithm based on the expectation conditional maximization for joint estimation of multipath channels, Wiener PHNs, and CFOs in decode-and-forward-based multi-relay orthogonal frequency division multiplexing systems. Next, a new expression of the hybrid Cramér-Rao lower bound (HCRB) for the multi-parameter estimation problem is derived. Finally, an iterative receiver based on an extended Kalman filter for joint data detection and PHN tracking is employed. Numerical results show that the proposed estimator outperforms existing algorithms and its mean square error performance is close to the derived HCRB at different signal-to-noise ratios for different PHN variances. In addition, the combined estimation algorithm and the iterative receiver can significantly improve average bit-error rate (BER) performance compared with existing algorithms. In addition, the BER performance of the proposed system is close to the ideal case of perfect channel impulse responses, PHNs, and CFOs estimation. 

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4. Data-Driven Model Predictive Control for Roll-to-Roll Process Register Error

   https://doi.org/10.1115/IAM2022-96840  

   Shah, Karan ; He, Anqi ; Wang, Zifeng ; Du, Xian ; Jin, Xiaoning ( October 2022 , Proceedings of the 2022 International Additive Manufacturing Conference. 2022 International Additive Manufacturing Conference. Lisbon, Portugal.)

   Roll-to-Roll (R2R) printing techniques are promising for high-volume continuous production of substrate-based products, as opposed to sheet-to-sheet (S2S) approach suited for low-volume work. However, meeting the tight alignment tolerance requirements of additive multi-layer printed electronics specified by device resolution that is usually at micrometer scale has become a major challenge in R2R flexible electronics printing, preventing the fabrication technology from being transferred from conventional S2S to high-speed R2R production. Print registration in a R2R process is to align successive print patterns on the flexible substrate and to ensure quality printed devices through effective control of various process variables. Conventional model-based control methods require an accurate web-handling dynamic model and real-time tension measurements to ensure control laws can be faithfully derived. For complex multistage R2R systems, physics-based state-space models are difficult to derive, and real-time tension measurements are not always acquirable. In this paper, we present a novel data-driven model predictive control (DD-MPC) method to minimize the multistage register errors effectively. We show that the DD-MPC can handle multi-input and multi-output systems and obtain the plant model from sensor data via an Eigensystem Realization Algorithm (ERA) and Observer Kalman filter identification (OKID) system identification method. In addition, the proposed control scheme works for systems with partially measurable system states.

    

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5. A Pedestrian Detection and Tracking Framework for Autonomous Cars: Efficient Fusion of Camera and LiDAR Data

   https://doi.org/10.1109/SMC52423.2021.9658639  

   Islam, Muhammad Mobaidul ; Newaz, Abdullah Al ; Karimoddini, Ali ( October 2021 , 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC))

   This paper presents a novel method for pedestrian detection and tracking by fusing camera and LiDAR sensor data. To deal with the challenges associated with the autonomous driving scenarios, an integrated tracking and detection framework is proposed. The detection phase is performed by converting LiDAR streams to computationally tractable depth images, and then, a deep neural network is developed to identify pedestrian candidates both in RGB and depth images. To provide accurate information, the detection phase is further enhanced by fusing multi-modal sensor information using the Kalman filter. The tracking phase is a combination of the Kalman filter prediction and an optical flow algorithm to track multiple pedestrians in a scene. We evaluate our framework on a real public driving dataset. Experimental results demonstrate that the proposed method achieves significant performance improvement over a baseline method that solely uses image-based pedestrian detection. 

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