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1. Household Water and Food Insecurity Are Positively Associated with Poor Mental and Physical Health among Adults Living with HIV in Western Kenya

   https://doi.org/10.1093/jn/nxab030  

   Miller, Joshua D; Frongillo, Edward A; Weke, Elly; Burger, Rachel; Wekesa, Pauline; Sheira, Lila A; Mocello, A Rain; Bukusi, Elizabeth A; Otieno, Phelgona; Cohen, Craig R; et al (March 2021, The Journal of Nutrition)

   null (Ed.)

   ABSTRACT Background Household food insecurity (FI) and water insecurity (WI) are prevalent public health issues that can co-occur. Few studies have concurrently assessed their associations with health outcomes, particularly among people living with HIV. Objectives We aimed to investigate the associations between FI and WI and how they relate to physical and mental health. Methods Food-insecure adult smallholder farmers living with HIV in western Kenya were recruited to participate in a cluster-randomized controlled trial of a multisectoral agricultural and asset loan intervention. We used baseline data on experiences of FI (using the Household Food Insecurity Access Scale, range: 0–27) and WI (using a modified scale developed for this region, range: 0–51) in the prior month (n = 716). Outcomes included probable depression (using the Hopkins Symptom Checklist), fatigue and diarrhea in the prior month, and overall mental and physical health (using the Medical Outcomes Study HIV Health Survey, range: 0–100). We first assessed Pearson correlations between FI, WI, and sociodemographic characteristics. We then developed 3 regressions for each health outcome (control variables and FI; control variables and WI; control variables, FI, and WI) and compared model fit indexes. Results Correlations between household FI, WI, and wealth were low, meaning they measure distinct constructs. FI and WI were associated with numerous physical and mental health outcomes; accounting for both resource insecurities typically provided the best model fit. For instance, when controlling for FI, each 10-point higher WI score was associated with a 6.42-point lower physical health score (P < 0.001) and 2.92 times greater odds of probable depression (P < 0.001). Conclusions Assessing both FI and WI is important for correctly estimating their relation with health outcomes. Interventions that address food- and water-related issues among persons living with HIV concurrently will likely be more effective at improving health than those addressing a single resource insecurity. This trial was registered at clinicaltrials.gov as NCT02815579. 

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2. Adapting Convolutional Neural Networks for Indoor Localization with Smart Mobile Devices

   Mittal, A.; Tiku, S.; Pasricha, S. (January 2018, ACM Great Lakes Symposium on VLSI (GLSVLSI))

   Indoor localization is emerging as an important application domain for enhanced navigation (or tracking) of people and assets in indoor locales such as buildings, malls, and underground mines. Most indoor localization solutions proposed in prior work do not deliver good accuracy without expensive infrastructure (and even then, the results may lack consistency). Ambient wireless received signal strength indication (RSSI) based fingerprinting using smart mobile devices is a low-cost approach to the problem. However, creating an accurate ‘fingerprinting-only’ solution remains a challenge. This paper presents a novel approach to transform Wi-Fi signatures into images, to create a scalable fingerprinting framework based on Convolutional Neural Networks (CNNs). Our proposed CNN based indoor localization framework (CNN-LOC) is validated across several indoor environments and shows improvements over the best known prior works, with an average localization error of < 2 meters. 

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3. Reservoir Computing Meets Wi-Fi in Software Radios: Neural Network-based Symbol Detection using Training Sequences and Pilots

   https://doi.org/10.1109/WOCC48579.2020.9114937  

   Li, Lianjun; Liu, Lingjia; Zhang, Jianzhong Charlie; Ashdown, Jonathan D.; Yi, Yang (May 2020, 2020 29th Wireless and Optical Communications Conference (WOCC))

   In this paper, we introduce a neural network (NN)-based symbol detection scheme for Wi-Fi systems and its associated hardware implementation in software radios. To be specific, reservoir computing (RC), a special type of recurrent neural network (RNN), is adopted to conduct the task of symbol detection for Wi-Fi receivers. Instead of introducing extra training overhead/set to facilitate the RC-based symbol detection, a new training framework is introduced to take advantage of the signal structure in existing Wi-Fi protocols (e.g., IEEE 802.11 standards), that is, the introduced RC-based symbol detector will utilize the inherent long/short training sequences and structured pilots sent by the Wi-Fi transmitter to conduct online learning of the transmit symbols. In other words, our introduced NN-based symbol detector does not require any additional training sets compared to existing Wi-Fi systems. The introduced RC-based Wi-Fi symbol detector is implemented on the software-defined radio (SDR) platform to further provide realistic and meaningful performance comparisons against the traditional Wi-Fi receiver. Over the air, experiment results show that the introduced RC based Wi-Fi symbol detector outperforms conventional Wi-Fi symbol detection methods in various environments indicating the significance and the relevance of our work. 

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4. Multimodal Information Integration for Indoor Navigation Using a Smartphone

   https://doi.org/10.1109/IRI49571.2020.00017  

   Chang, Y.; Chen, J.; Franklin, T.; Zhang, L.; Ruci, A.; Tang, H.; Zhu, Z. (August 2020, IRI2020 - The 21st IEEE International Conference on Information Reuse and Integration for Data Science)

   We propose an accessible indoor navigation application. The solution integrates information of floor plans, Bluetooth beacons, Wi-Fi/cellular data connectivity, 2D/3D visual models, and user preferences. Hybrid models of interiors are created in a modeling stage with Wi-Fi/ cellular data connectivity, beacon signal strength, and a 3D spatial model. This data is collected, as the modeler walks through the building, and is mapped to the floor plan. Client-server architecture allows scaling to large areas by lazy-loading models according to beacon signals and/or adjacent region proximity. During the navigation stage, a user with the designed mobile app is localized within the floor plan, using visual, connectivity, and user preference data, along an optimal route to their destination. User interfaces for both modeling and navigation use visual, audio, and haptic feedback for targeted users. While the current pandemic event precludes our user study, we describe its design and preliminary results. 

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5. Notification Control and Reminders with UWB Indoor Localization

   https://doi.org/10.1145/3572864.3581593  

   Jaganathan, Vishnu; Sekar, Ajish; Dhekne, Ashutosh (February 2023, HotMobile '23: Proceedings of the 24th International Workshop on Mobile Computing Systems and Applications)

   Localization based context awareness in mobile phones can enable several conveniences for users. This demonstration explores a way to smartly control notification and "Do not disturb" settings for the mobile phones based on the user's indoor location. Furthermore, users can setup location-based reminders which pop-up on the mobile phone when the user visits a specific indoor location. While enabling full-scale indoor localization might be challenging, we use just a few UWB beacons placed strategically, say embedded inside home-assistant devices, Wi-Fi routers, etc. and a UWB enabled phone to provide the required context awareness. Video: https://www.youtube.com/shorts/MbBwPw0BIJU 

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