Schools are high-risk settings for SARS-CoV-2 transmission, but necessary for children's educational and social-emotional wellbeing. While wastewater monitoring has been implemented to mitigate outbreak risk in universities and residential settings, its effectiveness in community K-12 sites is unknown. We implemented a wastewater and surface monitoring system to detect SARS-CoV-2 in nine elementary schools in San Diego County. Ninety-three percent of identified cases were associated with either a positive wastewater or surface sample; 67% were associated with a positive wastewater sample, and 40% were associated with a positive surface sample. The techniques we utilized allowed for near-complete genomic sequencing of wastewater and surface samples. Passive environmental surveillance can complement approaches that require individual consent, particularly in communities with limited access and/or high rates of testing hesitancy.
This paper proposes a distributed energy-efficient clustering protocol for wireless sensor networks (WSNs). Based on low-energy adaptive clustering hierarchy (LEACH) protocol, the proposed LEACH-eXtended Message-Passing (LEACH-XMP) substantially improves a cluster formation algorithm, which is critical for WSN operations. Unlike the previous approaches, a realistic non-linear energy consumption model is considered, which renders the clustering optimization highly nonlinear and challenging. To this end, a state-of-the-art message-passing approach is introduced to develop an efficient distributed algorithm. The main benefits of the proposed technique are its inherent nature of a distributed algorithm and the saving of computational load imposed for each node. Thus, it proves useful for a practical deployment. In addition, the proposed algorithm rapidly converges to a very accurate solution within a few iterations. Simulation results ensure that the proposed LEACH-XMP maximizes the network lifetime and outperforms existing techniques consistently.
Processing large amounts of data, specially in learning algorithms, poses a challenge for current embedded computing systems. Hyperdimensional computing (HDC) is a brain-inspired computing paradigm that works with high-dimensional vectors,
hypervectors
. HDC replaces several complex learning computations with bitwise and simpler arithmetic operations at the expense of an increased amount of data due to mapping the data into high-dimensional space. These hypervectors, more often than not, can’t be stored in memory, resulting in long data transfers from storage. In this paper, we propose Store-n-Learn, an in-storage computing (ISC) solution that performs HDC classification and clustering by implementing encoding, training, retraining, and inference across the flash hierarchy. To hide the latency of training and enable efficient computation, we introduce the concept of
batching
in HDC. We also present on-chip acceleration for HDC encoding in flash planes. This enables us to exploit the high parallelism provided by the flash hierarchy and encode multiple data points in parallel in both batched and non-batched fashion. Store-n-Learn also implements a single top-level FPGA accelerator with novel implementations for HDC classification training, retraining, inference, and clustering on the encoded data. Our evaluation over ten popular datasets shows that Store-n-Learn is on average 222 × (543 ×) faster than CPU and 10.6 × (7.3 ×) faster than the state-of-the-art ISC solution, INSIDER for HDC classification (clustering).
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