87 research outputs found
SimHumalator: An Open Source End-to-End Radar Simulator For Human Activity Recognition
Radio-frequency based non-cooperative monitor ing of humans has numerous applications ranging from law enforcement to ubiquitous sensing applications such as ambient assisted living and bio-medical applications for non-intrusively monitoring patients. Large training datasets, almost unlimited memory capacity, and ever- increasing processing speeds of computers could drive forward the data- driven deep-learning focused research in the above applications. However, generating and labeling large volumes of high-quality, diverse radar datasets is an onerous task. Furthermore, unlike the fields of vision and image processing, the radar community has limited access to databases that contain large volumes of experimental data. Therefore, in this article, we present an open-source motion capture data-driven simulation tool, SimHumalator, that can generate large volumes of human micro-Doppler radar data in passive WiFi scenarios. The simulator integrates IEEE 802.11 WiFi standard(IEEE 802.11g, n, and ad) compliant transmissions with the human animation data to generate the micro-Doppler features that incorporate the diversity of human motion characteristics and the sensor parameters. The simulated signatures have been validated with experimental data gathered using an in-house-built hardware prototype. This article describes simulation methodology in detail and provides case studies on the feasibility of using simulated micro-Doppler spectrograms for data augmentation tasks
Augmenting Experimental Data with Simulations to Improve Activity Classification in Healthcare Monitoring
Human micro-Doppler signatures in most passive
WiFi radar (PWR) scenarios are captured through real-world
measurements using various hardware platforms. However,
gathering large volumes of high quality and diverse real radar
datasets has always been an expensive and laborious task. This
work presents an open-source motion capture data-driven simulation tool SimHumalator that is able to generate human microDoppler radar data in PWR scenarios. We qualitatively compare
the micro-Doppler signatures generated through SimHumalator
with the measured real signatures. Here, we present the use of
SimHumalator to simulate a set of human actions. We demonstrate that augmenting a measurement database with simulated
data, using SimHumalator, results in an 8% improvement in
classification accuracy. Our results suggest that simulation data
can be used to augment experimental datasets of limited volume
to address the cold-start problem typically encountered in radar
research
Fisheye Consistency: Keeping Data in Synch in a Georeplicated World
Over the last thirty years, numerous consistency conditions for replicated
data have been proposed and implemented. Popular examples of such conditions
include linearizability (or atomicity), sequential consistency, causal
consistency, and eventual consistency. These consistency conditions are usually
defined independently from the computing entities (nodes) that manipulate the
replicated data; i.e., they do not take into account how computing entities
might be linked to one another, or geographically distributed. To address this
lack, as a first contribution, this paper introduces the notion of proximity
graph between computing nodes. If two nodes are connected in this graph, their
operations must satisfy a strong consistency condition, while the operations
invoked by other nodes are allowed to satisfy a weaker condition. The second
contribution is the use of such a graph to provide a generic approach to the
hybridization of data consistency conditions into the same system. We
illustrate this approach on sequential consistency and causal consistency, and
present a model in which all data operations are causally consistent, while
operations by neighboring processes in the proximity graph are sequentially
consistent. The third contribution of the paper is the design and the proof of
a distributed algorithm based on this proximity graph, which combines
sequential consistency and causal consistency (the resulting condition is
called fisheye consistency). In doing so the paper not only extends the domain
of consistency conditions, but provides a generic provably correct solution of
direct relevance to modern georeplicated systems
A wide-spectrum language for verification of programs on weak memory models
Modern processors deploy a variety of weak memory models, which for
efficiency reasons may (appear to) execute instructions in an order different
to that specified by the program text. The consequences of instruction
reordering can be complex and subtle, and can impact on ensuring correctness.
Previous work on the semantics of weak memory models has focussed on the
behaviour of assembler-level programs. In this paper we utilise that work to
extract some general principles underlying instruction reordering, and apply
those principles to a wide-spectrum language encompassing abstract data types
as well as low-level assembler code. The goal is to support reasoning about
implementations of data structures for modern processors with respect to an
abstract specification.
Specifically, we define an operational semantics, from which we derive some
properties of program refinement, and encode the semantics in the rewriting
engine Maude as a model-checking tool. The tool is used to validate the
semantics against the behaviour of a set of litmus tests (small assembler
programs) run on hardware, and also to model check implementations of data
structures from the literature against their abstract specifications
Understanding the propagation of hard errors to software and implications for resilient system design
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