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1. Communication for Underwater Sensor Networks: A Comprehensive Summary

   https://doi.org/10.1145/3546827  

   Pal, Amitangshu ; Campagnaro, Filippo ; Ashraf, Khadija ; Rahman, Md Rashed ; Ashok, Ashwin ; Guo, Hongzhi ( February 2023 , ACM Transactions on Sensor Networks)

   Sensing and communication technology has been used successfully in various event monitoring applications over the last two decades, especially in places where long-term manual monitoring is infeasible. However, the major applicability of this technology was mostly limited to terrestrial environments. On the other hand, underwater wireless sensor networks (UWSNs) opens a new space for the remote monitoring of underwater species and faunas, along with communicating with underwater vehicles, submarines, and so on. However, as opposed to terrestrial radio communication, underwater environment brings new challenges for reliable communication due to the high conductivity of the aqueous medium which leads to major signal absorption. In this paper, we provide a detailed technical overview of different underwater communication technologies, namely acoustic, magnetic, and visual light, along with their potentials and challenges in submarine environments. Detailed comparison among these technologies have also been laid out along with their pros and cons using real experimental results. 

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2. Deep Learning Based RFI Detection and Mitigation for SMAP Using Convolutional Neural Networks

   Ahmed Manavi Alam*, Ali Cafer ( January 2022 , RFI Workshop 2022)

   Passive Remote Sensing services are indispensable in modern society because of the applications related to climate studies and earth science. Among those, NASA’s Soil Moisture Active and Passive (SMAP) mission provides an essential climate variable such as the moisture content of the soil by using microwave radiation within protected band over 1400-1427 MHz. However, because of the increasing active wireless technologies such as Internet of Things (IoT), unmanned aerial vehicles (UAV), and 5G wireless communication, the SMAP’s passive observations are expected to experience an increasing number of Radio Frequency Interference (RFI). RFI is a well-documented issue and SMAP has a ground processing unit dedicated to tackling this issue. However, advanced techniques are needed to tackle the increasing RFI problem for passive sensing systems and to jointly coexist communication and sensing systems. In this paper, we apply a deep learning approach where a novel Convolutional Neural Network (CNN) architecture for both RFI detection and mitigation is employed. SMAP Level 1A spectrogram of antenna counts and various moments data are used as the inputs to the deep learning architecture. We simulate different types of RFI sources such as pulsed, CW or wideband anthropogenic signals. We then use artificially corrupted SMAP Level 1B antenna measurements in conjunction with RFI labels to train the learning architecture. While the learned detection network classifies input spectrograms as RFI or no-RFI cases, the mitigation network reconstructs the RFI mitigated antenna temperature images. The proposed learning framework both takes advantage of the existing SMAP data and the simulated RFI scenarios. Future remote sensing systems such as radiometers will suffer an increasing RFI problem and spectrum sharing and techniques that will allow coexistance of sensing and communication systems will be utmost importance for both parties. RFI detection and mitigation will remain a prerequisite for these radiometers and the proposed deep learning approach has the potential to provide an additional perspective to existing solutions. We will present detailed analysis on the selected deep learning architecture, obtained RFI detection accuracy levels and RFI mitigation performance. 

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3. Assessment of interatomic parameters for the reproduction of borosilicate glass structures via DFT‐GIPAW calculations

   https://doi.org/10.1111/jace.16655  

   Fortino, Mariagrazia ; Berselli, Alessandro ; Stone‐Weiss, Nicholas ; Deng, Lu ; Goel, Ashutosh ; Du, Jincheng ; Pedone, Alfonso ( June 2019 , Journal of the American Ceramic Society)

   Borates and borosilicates are potential candidates for the design and development of glass formulations with important industrial and technological applications. A major challenge that retards the pace of development of borate/borosilicate based glasses using predictive modeling is the lack of reliable computational models to predict the structure‐property relationships in these glasses over a wide compositional space. A major hindrance in this pursuit has been the complexity of boron‐oxygen bonding due to which it has been difficult to develop adequate B–O interatomic potentials. In this article, we have evaluated the performance of three B–O interatomic potential models recently developed by Bauchy et al [J.Non‐Cryst. Solids, 2018, 498, 294–304], Du et al [J. Am. Ceram. Soc.https://doi.org/10.1111/jace.16082] and Edèn et al [Phys. Chem. Chem. Phys., 2018, 20, 8192–8209] aiming to reproduce the short‐to‐medium range structures of sodium borosilicate glasses in the system 25 Na₂OxB₂O₃(75 − x) SiO₂(x = 0‐75 mol%). To evaluate the different force fields, we have computed at the density functional theory level the NMR parameters of¹¹B,²³Na, and²⁹Si of the models generated with the three potentials and the simulated MAS NMR spectra compared with the experimental counterparts. It was observed that the rigid ionic models proposed by Bauchy and Du can both reliably reproduce the partitioning between BO₃and BO₄species of the investigated glasses, along with the local environment around sodium in the glass structure. However, they do not accurately reproduce the second coordination sphere of silicon ions and the Si–O–T (T = Si, B) and B‐O‐T distribution angles in the investigated compositional space which strongly affect the NMR parameters and final spectral shape. On the other hand, the core‐shell parameterization model proposed by Edén underestimates the fraction of BO₄species of the glass with composition 25Na₂O 18.4B₂O₃56.6SiO₂but can accurately reproduce the shape of the¹¹B and²⁹Si MAS‐NMR spectra of the glasses investigations due to the narrower B–O–T and Si‐O‐T bond angle distributions. Finally, the effect of the number of boron atoms (also distinguishing the BO₃and BO₄units) in the second coordination sphere of the network former cations on the NMR parameters have been evaluated.

    

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4. Enabling Covert Body Area Network using Electro-Quasistatic Human Body Communication

   https://doi.org/10.1038/s41598-018-38303-x  

   Das, Debayan ; Maity, Shovan ; Chatterjee, Baibhab ; Sen, Shreyas ( March 2019 , Scientific Reports)

   Radiative communication using electro-magnetic (EM) fields amongst the wearable and implantable devices act as the backbone for information exchange around a human body, thereby enabling prime applications in the fields of connected healthcare, electroceuticals, neuroscience, augmented and virtual reality. However, owing to such radiative nature of the traditional wireless communication, EM signals propagate in all directions, inadvertently allowing an eavesdropper to intercept the information. In this context, the human body, primarily due to its high water content, has emerged as a medium for low-loss transmission, termed human body communication (HBC), enabling energy-efficient means for wearable communication. However, conventional HBC implementations suffer from significant radiation which also compromises security. In this article, we present Electro-Quasistatic Human Body Communication (EQS-HBC), a method for localizing signals within the body using low-frequency carrier-less (broadband) transmission, thereby making it extremely difficult for a nearby eavesdropper to intercept critical private data, thus producing a covert communication channel, i.e. the human body. This work, for the first time, demonstrates and analyzes the improvement in private space enabled by EQS-HBC. Detailed experiments, supported by theoretical modeling and analysis, reveal that the quasi-static (QS) leakage due to the on-body EQS-HBC transmitter-human body interface is detectable up to <0.15 m, whereas the human body alone leaks only up to ~0.01 m, compared to >5 mdetection range for on-body EM wireless communication, highlighting the underlying advantage of EQS-HBC to enable covert communication.

    

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5. A Wearable Multi-modal Bio-sensing System Towards Real-world Applications

   Siddharth, S. ; Patel, A. ; Jung, T-P. ; Sejnowski, T. ( April 2019 , IEEE transactions on biomedical engineering)

   Multi-modal bio-sensing has recently been used as effective research tools in affective computing, autism, clinical disorders, and virtual reality among other areas. However, none of the existing bio-sensing systems support multi-modality in a wearable manner outside well-controlled laboratory environments with research-grade measurements. This work attempts to bridge this gap by developing a wearable multi-modal biosensing system capable of collecting, synchronizing, recording and transmitting data from multiple bio-sensors: PPG, EEG, eye-gaze headset, body motion capture, GSR, etc. while also providing task modulation features including visual-stimulus tagging. This study describes the development and integration of the various components of our system. We evaluate the developed sensors by comparing their measurements to those obtained by a standard research-grade bio-sensors. We first evaluate different sensor modalities of our headset, namely earlobe-based PPG module with motion-noise canceling for ECG during heart-beat calculation. We also compare the steady-state visually evoked potentials (SSVEP) measured by our shielded dry EEG sensors with the potentials obtained by commercially available dry EEG sensors. We also investigate the effect of head movements on the accuracy and precision of our wearable eyegaze system. Furthermore, we carry out two practical tasks to demonstrate the applications of using multiple sensor modalities for exploring previously unanswerable questions in bio-sensing. Specifically, utilizing bio-sensing we show which strategy works best for playing Where is Waldo? visual-search game, changes in EEG corresponding to true versus false target fixations in this game, and predicting the loss/draw/win states through biosensing modalities while learning their limitations in a Rock-Paper-Scissors game. 

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