41 research outputs found
Compressed Sensing with General Frames via Optimal-dual-based -analysis
Compressed sensing with sparse frame representations is seen to have much
greater range of practical applications than that with orthonormal bases. In
such settings, one approach to recover the signal is known as
-analysis. We expand in this article the performance analysis of this
approach by providing a weaker recovery condition than existing results in the
literature. Our analysis is also broadly based on general frames and
alternative dual frames (as analysis operators). As one application to such a
general-dual-based approach and performance analysis, an optimal-dual-based
technique is proposed to demonstrate the effectiveness of using alternative
dual frames as analysis operators. An iterative algorithm is outlined for
solving the optimal-dual-based -analysis problem. The effectiveness of
the proposed method and algorithm is demonstrated through several experiments.Comment: 34 pages, 8 figures. To appear in IEEE Transactions on Information
Theor
RIS-aided Real-time Beam Tracking for a Mobile User via Bayesian Optimization
The conventional beam management procedure mandates that the user equipment
(UE) periodically measure the received signal reference power (RSRP) and
transmit these measurements to the base station (BS). The challenge lies in
balancing the number of beams used: it should be large enough to identify
high-RSRP beams but small enough to minimize reporting overhead. This paper
investigates this essential performance-versus-overhead trade-off using
Bayesian optimization. The proposed approach represents the first application
of real-time beam tracking via Bayesian optimization in RIS-assisted
communication systems. Simulation results validate the effectiveness of this
scheme
A Wi-Fi Signal-Based Human Activity Recognition Using High-Dimensional Factor Models
Passive sensing techniques based on Wi-Fi signals have emerged as a promising
technology in advanced wireless communication systems due to their widespread
application and cost-effectiveness. However, the proliferation of low-cost
Internet of Things (IoT) devices has led to dense network deployments,
resulting in increased levels of noise and interference in Wi-Fi environments.
This, in turn, leads to noisy and redundant Channel State Information (CSI)
data. As a consequence, the accuracy of human activity recognition based on
Wi-Fi signals is compromised. To address this issue, we propose a novel CSI
data signal extraction method. We established a human activity recognition
system based on the Intel 5300 network interface cards (NICs) and collected a
dataset containing six categories of human activities. Using our approach,
signals extracted from the CSI data serve as inputs to machine learning (ML)
classification algorithms to evaluate classification performance. In comparison
to ML methods based on Principal Component Analysis (PCA), our proposed
High-Dimensional Factor Model (HDFM) method improves recognition accuracy by
6.8%
The need for genetic variant naming standards in published abstracts of human genetic association studies
We analyzed the use of RefSNP (rs) numbers to identify genetic variants in abstracts of human genetic association studies published from 2001 through 2007. The proportion of abstracts reporting rs numbers increased rapidly but was still only 15% in 2007. We developed a web-based tool called Variant Name Mapper to assist in mapping historical genetic variant names to rs numbers. The consistent use of rs numbers in abstracts that report genetic associations would enhance knowledge synthesis and translation in this field
Design of Reconfigurable Intelligent Surfaces for Wireless Communication: A Review
Existing literature reviews predominantly focus on the theoretical aspects of
reconfigurable intelligent surfaces (RISs), such as algorithms and models,
while neglecting a thorough examination of the associated hardware components.
To bridge this gap, this research paper presents a comprehensive overview of
the hardware structure of RISs. The paper provides a classification of RIS cell
designs and prototype systems, offering insights into the diverse
configurations and functionalities. Moreover, the study explores potential
future directions for RIS development. Notably, a novel RIS prototype design is
introduced, which integrates seamlessly with a communication system for
performance evaluation through signal gain and image formation experiments. The
results demonstrate the significant potential of RISs in enhancing
communication quality within signal blind zones and facilitating effective
radio wave imaging
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Influence of Familial Risk on Diabetes Risk–Reducing Behaviors Among U.S. Adults Without Diabetes
OBJECTIVE: To test the association of family history of diabetes with the adoption of diabetes risk–reducing behaviors and whether this association is strengthened by physician advice or commonly known factors associated with diabetes risk. RESEARCH DESIGN AND METHODS: We used cross-sectional data from the 2005–2008 National Health and Nutrition Examination Survey (NHANES) to examine the effects of family history of diabetes on the adoption of selected risk-reducing behaviors in 8,598 adults (aged ≥20 years) without diabetes. We used multiple logistic regression to model three risk reduction behaviors (controlling or losing weight, increasing physical activity, and reducing the amount of dietary fat or calories) with family history of diabetes. RESULTS: Overall, 36.2% of U.S. adults without diabetes had a family history of diabetes. Among them, ~39.8% reported receiving advice from a physician during the past year regarding any of the three selected behaviors compared with 29.2% of participants with no family history (P < 0.01). In univariate analysis, adults with a family history of diabetes were more likely to perform these risk-reducing behaviors compared with adults without a family history. Physician advice was strongly associated with each of the behavioral changes (P < 0.01), and this did not differ by family history of diabetes. CONCLUSIONS: Familial risk for diabetes and physician advice both independently influence the adoption of diabetes risk–reducing behaviors. However, fewer than half of participants with familial risk reported receiving physician advice for adopting these behaviors