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1. An Efficient Pulse Position Modulation Scheme to Improve the Bit Rate of Photoacoustic Communication

   https://doi.org/10.1109/WOCC58016.2023.10139346  

   Islam, Md Shafiqul; Younis, Mohamed; Mahmud, Muntasir; Choa, Fow-Sen (May 2023, IEEE)

   Wireless communication from air-to-underwater is quite challenging because of the lack of proper physical signal that propagates well in both air and water medium. Photoacoustic energy transfer mechanism is the most promising method for such cross-medium communication, where a high energy pulsed light is focused on the water surface, causing the generation of an acoustic signal inside the water. Since acoustic signals can travel a long distance inside the water, this method enables an airborne unit to reach nodes at increased underwater depth. Yet the achievable bit rate for this process is very low. When a pulsed laser light with a higher repetition rate is focused inside the water, a vapor cloud is generated around the focus point, which blocks subsequent generation of acoustic signal and consequently limits the achievable bit rate. This paper opts to overcome such a limitation by proposing a novel pulse position modulation technique which can avoid such generation of vapor cloud and increases the bit rate significantly. 

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2. On Influencing Factors in Human Activity Recognition Using Wireless Networks

   https://doi.org/10.1109/GLOBECOM38437.2019.9014016  

   Hu, Haochen; Sun, Zhi; Su, Lu (December 2019, 2019 IEEE Global Communications Conference (GLOBECOM))

   Driven by the development of machine learning and the development of wireless techniques, lots of research efforts have been spent on the human activity recognition (HAR). Although various deep learning algorithms can achieve high accuracy for recognizing human activities, existing works lack of a theoretical performance upper bound which is the best accuracy that is only limited by the influencing factors in wireless networks such as indoor physical environments and settings of wireless sensing devices regardless of any HAR algorithm. Without the understanding of performance upper bound, mistakenly configuring the influencing factors can reduce the HAR accuracy drastically no matter what deep learning algorithms are utilized. In this paper, we propose the HAR performance upper bound which is the minimum classification error probability that doesn't depend on any HAR algorithms and can be considered as a function of influencing factors in wireless sensing networks for CSI based human activity recognition. Since the performance upper bound can capture the impacts of influencing factors on HAR accuracy, we further analyze the influences of those factors with varying situations such as through the wall HAR and different human activities by MATLAB simulations. 

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3. MuteIt: Jaw Motion Based Unvoiced Command Recognition Using Earable

   https://doi.org/10.1145/3550281  

   Srivastava, Tanmay; Khanna, Prerna; Pan, Shijia; Nguyen, Phuc; Jain, Shubham (September 2022, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   In this paper, we present MuteIt, an ear-worn system for recognizing unvoiced human commands. MuteIt presents an intuitive alternative to voice-based interactions that can be unreliable in noisy environments, disruptive to those around us, and compromise our privacy. We propose a twin-IMU set up to track the user's jaw motion and cancel motion artifacts caused by head and body movements. MuteIt processes jaw motion during word articulation to break each word signal into its constituent syllables, and further each syllable into phonemes (vowels, visemes, and plosives). Recognizing unvoiced commands by only tracking jaw motion is challenging. As a secondary articulator, jaw motion is not distinctive enough for unvoiced speech recognition. MuteIt combines IMU data with the anatomy of jaw movement as well as principles from linguistics, to model the task of word recognition as an estimation problem. Rather than employing machine learning to train a word classifier, we reconstruct each word as a sequence of phonemes using a bi-directional particle filter, enabling the system to be easily scaled to a large set of words. We validate MuteIt for 20 subjects with diverse speech accents to recognize 100 common command words. MuteIt achieves a mean word recognition accuracy of 94.8% in noise-free conditions. When compared with common voice assistants, MuteIt outperforms them in noisy acoustic environments, achieving higher than 90% recognition accuracy. Even in the presence of motion artifacts, such as head movement, walking, and riding in a moving vehicle, MuteIt achieves mean word recognition accuracy of 91% over all scenarios. 

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4. 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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5. Extracting and predicting multipath profiles under high mobility

   https://doi.org/10.1145/3492866.3549710  

   Baig, Ghufran; Ge, Changhan; Qiu, Lili; Li, Yuanjie; Li, Wangyang; Sun, Wei; He, Jian; Zhang, Zhehui; Lu, Songwu (October 2022, Proceedings of ACM MobiHoc'22)

   The wireless signal propagates via multipath arising from different reflections and penetration between a transmitter and receiver. Extracting multipath profiles (e.g., delay and Doppler along each path) from received signals enables many important applications, such as channel prediction and crossband channel estimation (i.e., estimating the channel on a different frequency). The benefit of multipath estimation further increases with mobility since the channel in that case is less stable and more important to track. Yet high-speed mobility poses significant challenges to multipath estimation. In this paper, instead of using time-frequency domain channel representation, we leverage the delay-Doppler domain representation to accurately extract and predict multipath properties. Specifically, we use impulses in the delay-Doppler domain as pilots to estimate the multipath parameters and apply the multipath information to predicting wireless channels as an example application. Our design rationale is that mobility is more predictable than the wireless channel since mobility has inertial while the wireless channel is the outcome of a complicated interaction between mobility, multipath, and noise. We evaluate our approach via both acoustic and RF experiments, including vehicular experiments using USRP. Our results show that the estimated multipath matches the ground truth, and the resulting channel prediction is more accurate than the traditional channel prediction schemes. 

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