524 research outputs found
Bandlimited Spatial Field Sampling with Mobile Sensors in the Absence of Location Information
Sampling of physical fields with mobile sensor is an emerging area. In this
context, this work introduces and proposes solutions to a fundamental question:
can a spatial field be estimated from samples taken at unknown sampling
locations?
Unknown sampling location, sample quantization, unknown bandwidth of the
field, and presence of measurement-noise present difficulties in the process of
field estimation. In this work, except for quantization, the other three issues
will be tackled together in a mobile-sampling framework. Spatially bandlimited
fields are considered. It is assumed that measurement-noise affected field
samples are collected on spatial locations obtained from an unknown renewal
process. That is, the samples are obtained on locations obtained from a renewal
process, but the sampling locations and the renewal process distribution are
unknown. In this unknown sampling location setup, it is shown that the
mean-squared error in field estimation decreases as where is the
average number of samples collected by the mobile sensor. The average number of
samples collected is determined by the inter-sample spacing distribution in the
renewal process. An algorithm to ascertain spatial field's bandwidth is
detailed, which works with high probability as the average number of samples
increases. This algorithm works in the same setup, i.e., in the presence of
measurement-noise and unknown sampling locations.Comment: Submitted to IEEE Trans on Signal Processin
Optimal Quantization of TV White Space Regions for a Broadcast Based Geolocation Database
In the current paradigm, TV white space databases communicate the available
channels over a reliable Internet connection to the secondary devices. For
places where an Internet connection is not available, such as in developing
countries, a broadcast based geolocation database can be considered. This
geolocation database will broadcast the TV white space (or the primary services
protection regions) on rate-constrained digital channel.
In this work, the quantization or digital representation of protection
regions is considered for rate-constrained broadcast geolocation database.
Protection regions should not be declared as white space regions due to the
quantization error. In this work, circular and basis based approximations are
presented for quantizing the protection regions. In circular approximation,
quantization design algorithms are presented to protect the primary from
quantization error while minimizing the white space area declared as protected
region. An efficient quantizer design algorithm is presented in this case. For
basis based approximations, an efficient method to represent the protection
regions by an `envelope' is developed. By design this envelope is a sparse
approximation, i.e., it has lesser number of non-zero coefficients in the basis
when compared to the original protection region. The approximation methods
presented in this work are tested using three experimental data-sets.Comment: 8 pages, 12 figures, submitted to IEEE DySPAN (Technology) 201
High-resolution distributed sampling of bandlimited fields with low-precision sensors
The problem of sampling a discrete-time sequence of spatially bandlimited
fields with a bounded dynamic range, in a distributed,
communication-constrained, processing environment is addressed. A central unit,
having access to the data gathered by a dense network of fixed-precision
sensors, operating under stringent inter-node communication constraints, is
required to reconstruct the field snapshots to maximum accuracy. Both
deterministic and stochastic field models are considered. For stochastic
fields, results are established in the almost-sure sense. The feasibility of
having a flexible tradeoff between the oversampling rate (sensor density) and
the analog-to-digital converter (ADC) precision, while achieving an exponential
accuracy in the number of bits per Nyquist-interval per snapshot is
demonstrated. This exposes an underlying ``conservation of bits'' principle:
the bit-budget per Nyquist-interval per snapshot (the rate) can be distributed
along the amplitude axis (sensor-precision) and space (sensor density) in an
almost arbitrary discrete-valued manner, while retaining the same (exponential)
distortion-rate characteristics. Achievable information scaling laws for field
reconstruction over a bounded region are also derived: With N one-bit sensors
per Nyquist-interval, Nyquist-intervals, and total network
bitrate (per-sensor bitrate ), the maximum pointwise distortion goes to zero as
or . This is shown to be possible
with only nearest-neighbor communication, distributed coding, and appropriate
interpolation algorithms. For a fixed, nonzero target distortion, the number of
fixed-precision sensors and the network rate needed is always finite.Comment: 17 pages, 6 figures; paper withdrawn from IEEE Transactions on Signal
Processing and re-submitted to the IEEE Transactions on Information Theor
Lane following using behavioural cloning
With the rise in the research relating to Artificial Intelligence along with the growing concern of everyday road accidents due to human error, the research pertaining to Autonomous Vehicles has been soaring to new highs. However, this technology in its current form has serious limitations such as restricted use during adverse conditions (such as snow), inability to identify manual traffic instructions, abnormal traffic behaviours etc. This is one of the reasons that even the vehicles with most autonomous features, exhibit only a Level 2 or Level 3 of driving automation. Hence, in order to reach further levels of automation, it may be useful to create a symbiotic technology between autonomous vehicles and traffic control models. This thesis work will work as an elementary stepping stone to create such a symbiosis by identifying a Lane Following Model using Convolutional Neural Networks. Specifically, a Behavioural Cloning Model along with a Road Classification Model is developed in order to mimic human driving characteristics which ideally works independent of lane markings and to regulate this driving characteristics by reading road signs with satisfactory levels of accuracy
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