Wi-Fi fingerprinting-based indoor localization has received increased attention due to its proven accuracy and global availability. The common received-signal-strength-based (RSS) fingerprinting presents performance degradation due to well-known signal fluctuations, but more recently, the more stable channel state information (CSI) has gained popularity. In this paper, we present SDR-Fi, the first reported Wi-Fi software-defined radio (SDR) receiver for indoor positioning using CSI measurements as features for deep learning (DL) classification. The CSI measurements are obtained from a fast-prototyping LabVIEWbased 802.11n SDR receiver platform. SDR-Fi measures CSI data passively from pilot beacon frames from a single access point (AP) at almost 10 Hz rate. A feed-forward neural network and a 1D convolutional neural network are examined to estimate location accuracy in representative testing scenarios for an indoor cluttered laboratory area, and an adjacent, covered outdoor area. The proposed DL classification methods leverage CSI-based fingerprinting for low AP scenarios, as opposed to traditional RSS-based systems, which require many APs for reliable positioning. Demonstration results are threefold: (a) A fast-prototyping SDR platform that passively extracts CSI measurements from Wi-Fi beacon frames, providing a genuine possibility for vendor network cards to provide such measurements, (b) two state-of-the-art DL classification methods outperforming traditional RSS-based methods for low AP scenarios, (c) a testing methodology for performance evaluation of the proposed indoor positioning system.INDEX TERMS Channel state information, deep learning, fingerprinting, indoor positioning, neural networks, software-defined radio.
Adjuvant endocrine hormonal therapy (EHT) is highly effective and appropriate for nearly all breast cancer patients with hormone receptor-positive tumors, which represent 75% of all breast cancer diagnoses. Long-term use of EHT reduces recurrence rates and nearly halves the risk of death during the second decade after diagnosis. Despite the proven benefits, about 33% of women receiving EHT do not take their medication as prescribed. This causes an increase in the risk for recurrence and death.To promote adherence to EHT among breast cancer patients, this study will develop and pilot-test an intervention consisting of 1) a bilingual, culturally tailored, personalized, interactive smartphone application (app); and 2) support from a patient navigator. The control group will receive usual care. This 2-group randomized control trial will recruit 120 breast cancer patients receiving EHT at the Mays Cancer Center at UT Health San Antonio. The two-year study will have 3-time assessments (baseline, 3 and 6 months).This theory-based intervention will empower patients' self-monitoring and management. It will facilitate patient education, identification/reporting of side effects, delivery of self-care advice, and simplify communication between the patient and the oncology team. The ultimate goal of this innovative multi-communication intervention is to improve overall survival and life expectancy, enhance quality of life, reduce recurrence, and decrease healthcare cost. The anticipated outcome is a scalable, evidence-based, and easily disseminated intervention with potentially broad use to patients using EHT and other oral anticancer agents.
BioJS is an open source software project that develops visualization tools for different types of biological data. Here we report on the factors that influenced the growth of the BioJS user and developer community, and outline our strategy for building on this growth. The lessons we have learned on BioJS may also be relevant to other open source software projects.DOI:
http://dx.doi.org/10.7554/eLife.07009.001
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