140 research outputs found
A Fast-rate WLAN Measurement Tool for Improved Miss-rate in Indoor Navigation
Recently, location-based services (LBS) have steered attention to indoor
positioning systems (IPS). WLAN-based IPSs relying on received signal strength
(RSS) measurements such as fingerprinting are gaining popularity due to proven
high accuracy of their results. Typically, sets of RSS measurements at selected
locations from several WLAN access points (APs) are used to calibrate the
system. Retrieval of such measurements from WLAN cards are commonly at one-Hz
rate. Such measurement collection is needed for offline radio-map surveying
stage which aligns fingerprints to locations, and for online navigation stage,
when collected measurements are associated with the radio-map for user
navigation. As WLAN network is not originally designed for positioning, an RSS
measurement miss could have a high impact on the fingerprinting system.
Additionally, measurement fluctuations require laborious signal processing, and
surveying process can be very time consuming. This paper proposes a fast-rate
measurement collection method that addresses previously mentioned problems by
achieving a higher probability of RSS measurement collection during a given
one-second window. This translates to more data for statistical processing and
faster surveying. The fast-rate collection approach is analyzed against the
conventional measurement rate in a proposed testing methodology that mimics
real-life scenarios related to IPS surveying and online navigation
Fast prototyping of an SDR WLAN 802.11b receiver for an indoor positioning system
Indoor positioning systems (IPS) are emerging technologies due to an
increasing popularity and demand in location based service (LBS). Because
traditional positioning systems such as GPS are limited to outdoor
applications, many IPS have been proposed in literature. WLAN-based IPS are the
most promising due to its proven accuracy and infrastructure deployment.
Several WLAN-based IPS have been proposed in the past, from which the best
results have been shown by so-called fingerprint-based systems. This paper
proposes an indoor positioning system which extends traditional WLAN
fingerprinting by using received signal strength (RSS) measurements along with
channel estimates as an effort to improve classification accuracy for scenarios
with a low number of Access Points (APs). The channel estimates aim to
characterize complex indoor environments making it a unique signature for
fingerprinting-based IPS and therefore improving pattern recognition in
radio-maps. Since commercial WLAN cards offer limited measurement information,
software-defined radio (SDR) as an emerging trend for fast prototyping and
research integration is chosen as the best cost-effective option to extract
channel estimates. Therefore, this paper first proposes an 802.11b WLAN SDR
beacon receiver capable of measuring RSS and channel estimates. The SDR is
designed using LabVIEW (LV) environment and leverages several inherent platform
acceleration features that achieve real-time capturing. The receiver achieves a
fast-rate measurement capture of 9 packets per second per AP. The
classification of the propose IPS uses a support vector machine (SVM) for
offline training and online navigation. Several tests are conducted in a
cluttered indoor environment with a single AP in 802.11b legacy mode. Finally,
navigation accuracy results are discussed
On Optimal Bit Allocation for Classification-Based Source-Dependent Transform Coding
An optimal bit allocation is presented for
classification-based source-dependent transform coding. A vector
of transform coefficients is considered to have been produced by
a mixture of processes. The available bit resource is distributed
optimally in two stages: (1) bit allocation is performed for each
class of coefficient vectors, and (2) bit allocation is performed for
each vector coefficient. The solution for low bit rates imposing
nonnegative bit resource is also presented. The rate-distortion
bound of the classification-based source coding is
derived
Signal Recognition Particle: An Essential Protein-Targeting Machine
The signal recognition particle (SRP) and its receptor compose a universally
conserved and essential cellular machinery that couples the synthesis
of nascent proteins to their proper membrane localization. The
past decade has witnessed an explosion in in-depth mechanistic investigations
of this targeting machine at increasingly higher resolutions. In
this review, we summarize recent work that elucidates how the SRP and
SRP receptor interact with the cargo protein and the target membrane,
respectively, and how these interactions are coupled to a novel GTPase
cycle in the SRP·SRP receptor complex to provide the driving force
and enhance the fidelity of this fundamental cellular pathway. We also
discuss emerging frontiers in which important questions remain to be
addressed
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