Compressive wireless sensing

By Waheed Uz Zaman Bajwa, Jarvis D. Haupt, Akbar M. Sayeed, Robert D. Nowak. 2006

373 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Mechanics, Computer Networks and Communications

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In Biomedical Engineering

In Computational Mechanics

In Computer Networks and Communications

Citations in the corpus

Over-the-air Function Computation in Sensor Networks (2016, background): “Systems like ZigZag [15], Buzz [25], Caraoke [2], and compressive sensing [4, 6], treat wireless collisions as a code and decode the bits despite interference.”
Information fusion for wireless sensor networks: Methods, models, and classifications (2007, background): “Compressive Wireless Sensing (CWS) [Bajwa et al. 2006] is a distributed source-channel communication architecture based on compressive sampling [Haupt and Nowak 2006], for energy ef.cient estimation of sensor data that contain structural regularity.”
Half-Quadratic-Based Iterative Minimization for Robust Sparse Representation (2014, methodology): “By defining different kinds of half-quadratic functions, the proposed HQ framework is applicable to performing both error correction and error detection.”
Enhancing Sparsity by Reweighted ℓ1 Minimization (2007, background): “The implications of these facts are quite far-reaching, with potential applications in data compression [24, 28], digital photography [29], medical imaging [23, 30], error correction [31, 32], analog-todigital conversion [33], sensor networks [34, 35], and so on.”
Ear-phone: an end-to-end participatory urban noise mapping system (2010, methodology): “Compressive sensing has so far been applied in traditional low-power wireless sensor networks [18, 8, 5].”
Compressed Sensing for Networked Data (2008, background): “For these reasons, the advantages of CS have already caught on in the research community, as evidenced by several recent works [6]–[10].”
MIMO Radar Using Compressive Sampling (2009, background): “, image processing [14] and wireless communications [15], [16].”
Compressive sampling for accelerometer signals in structural health monitoring (2011, background): “As in Equation (16), measurement vector y is acquired by a linear projection y 1⁄4 (x , where x is the original data and ( an m n random matrix sampled from the normal distribution with zero mean and variance 1/s The measurement matrix ( can be built into the ADC, which means that the data can be ac”
A survey of visual sensor network platforms (2012, background): “Compressive Wireless Sensing (CWS) [2] reconstructs the sensed data from noisy random projections of the sensor network data and obtains good power-distortion trade-offs given the lack of prior knowledge about the signal.”
Internet of Things (IoT): A vision, architectural elements, and future directions (2012, background): “Compressive wireless sensing (CWS) utilizes synchronous communication to reduce the transmission power of each sensor [52]; transmitting noisy projections of data samples to a central location for aggregation.”
Signal Reconstruction From Noisy Random Projections (2006, background): “Thorough treatments of compressive sampling via random projections in sensor networking applications can be found in our recent work [23], [24].”
Compressive data gathering for large-scale wireless sensor networks (2009, background): “Compressive wireless sensing (CWS) [3] appears to be able to reduce the latency of data gathering in a single-hop network by delivering linear projections of sensor readings through synchronized amplitudemodulated analog transmissions.”
AirShare: Distributed coherent transmission made seamless (2015, methodology): “In the absence of a suitable generic abstraction for distributed coherence, most wireless cooperation protocols have remained theoretical [7], [8], [9], [6].”