1,987 research outputs found
A Hardware Platform for Communication and Localization Performance Evaluation of Devices inside the Human Body
Body area networks (BAN) is a technology gaining widespread attention for application in medical examination, monitoring and emergency therapy. The basic concept of BAN is monitoring a set of sensors on or inside the human body which enable transfer of vital parameters between the patient´s location and the physician in charge. As body area network has certain characteristics, which impose new demands on performance evaluation of systems for wireless access and localization for medical sensors. However, real-time performance evaluation and localization in wireless body area networks is extremely challenging due to the unfeasibility of experimenting with actual devices inside the human body. Thus, we see a need for a real-time hardware platform, and this thesis addressed this need. In this thesis, we introduced a unique hardware platform for performance evaluation of body area wireless access and in-body localization. This hardware platform utilizes a wideband multipath channel simulator, the Elektrobit PROPSimâ„¢ C8, and a typical medical implantable device, the Zarlink ZL70101 Advanced Development Kit. For simulation of BAN channels, we adopt the channel model defined for the Medical Implant Communication Service (MICS) band. Packet Reception Rate (PRR) is analyzed as the criteria to evaluate the performance of wireless access. Several body area propagation scenarios simulated using this hardware platform are validated, compared and analyzed. We show that among three modulations, two forms of 2FSK and 4FSK. The one with lowest raw data rate achieves best PRR, in other word, best wireless access performance. We also show that the channel model inside the human body predicts better wireless access performance than through the human body. For in-body localization, we focus on a Received Signal Strength (RSS) based localization algorithm. An improved maximum likelihood algorithm is introduced and applied. A number of points along the propagation path in the small intestine are studied and compared. Localization error is analyzed for different sensor positions. We also compared our error result with the Cramèr- Rao lower bound (CRLB), shows that our localization algorithm has acceptable performance. We evaluate multiple medical sensors as device under test with our hardware platform, yielding satisfactory localization performance
Semi-Supervised Sound Source Localization Based on Manifold Regularization
Conventional speaker localization algorithms, based merely on the received
microphone signals, are often sensitive to adverse conditions, such as: high
reverberation or low signal to noise ratio (SNR). In some scenarios, e.g. in
meeting rooms or cars, it can be assumed that the source position is confined
to a predefined area, and the acoustic parameters of the environment are
approximately fixed. Such scenarios give rise to the assumption that the
acoustic samples from the region of interest have a distinct geometrical
structure. In this paper, we show that the high dimensional acoustic samples
indeed lie on a low dimensional manifold and can be embedded into a low
dimensional space. Motivated by this result, we propose a semi-supervised
source localization algorithm which recovers the inverse mapping between the
acoustic samples and their corresponding locations. The idea is to use an
optimization framework based on manifold regularization, that involves
smoothness constraints of possible solutions with respect to the manifold. The
proposed algorithm, termed Manifold Regularization for Localization (MRL), is
implemented in an adaptive manner. The initialization is conducted with only
few labelled samples attached with their respective source locations, and then
the system is gradually adapted as new unlabelled samples (with unknown source
locations) are received. Experimental results show superior localization
performance when compared with a recently presented algorithm based on a
manifold learning approach and with the generalized cross-correlation (GCC)
algorithm as a baseline
A Complexity-Efficient High Resolution Propagation Parameter Estimation Algorithm for Ultra-Wideband Large-Scale Uniform Circular Array
Millimeter wave (mm-wave) communication with large-scale antenna array
configuration is seen as the key enabler of the next generation communication
systems. Accurate knowledge of the mm-wave propagation channels is fundamental
and essential. In this contribution, a novel complexity-efficient high
resolution parameter estimation (HRPE) algorithm is proposed for the mm-wave
channel with large-scale uniform circular array (UCA) applied. The proposed
algorithm is able to obtain the high-resolution estimation results of the
spherical channel propagation parameters. The prior channel information in the
delay domain, i.e., the delay trajectories of individual propagation paths
observed across the array elements, is exploited, by combining the
high-resolution estimation principle and the phase mode excitation technique.
Fast initializations, effective interference cancellations and reduced
searching spaces achieved by the proposed schemes significantly decrease the
algorithm complexity. Furthermore, the channel spatial non-stationarity in path
gain across the array elements is considered for the first time in the
literature for propagation parameter estimation, which is beneficial to obtain
more realistic results as well as to decrease the complexity. A mm-wave
measurement campaign at the frequency band of 28-30 GHz using a large-scale UCA
is exploited to demonstrate and validate the proposed HRPE algorithm.Comment: Single column, 28 pages. In review process with IEEE Transactions on
Communication
Localisation of partial discharge sources using radio fingerprinting technique
Partial discharge (PD) is a well-known indicator of the failure of insulators in electrical plant. Operators are pushing toward lower operating cost and higher reliability and this stimulates a demand for a diagnostic system capable of accurately locating PD sources especially in ageing electricity substations. Existing techniques used for PD source localisation can be prohibitively expensive. In this paper, a cost-effective radio fingerprinting technique is proposed. This technique uses the Received Signal Strength (RSS) extracted from PD measurements gathered using RF sensors. The proposed technique models the complex spatial characteristics of the radio environment, and uses this model for accurate PD localisation. Two models were developed and compared: k-nearest neighbour and a feed-forward neural network which uses regression as a form of function approximation. The results demonstrate that the neural network produced superior performance as a result of its robustness against noise
Digital Signal Processing
Contains table of contents for Part III, table of contents for Section 1, an introduction and reports on seventeen research projects.National Science Foundation FellowshipNational Science Foundation (Grant ECS 84-07285)National Science Foundation (Grant MIP 87-14969)U.S. Navy - Office of Naval Research (Contract N00014-81-K-0742)Scholarship from the Federative Republic of BrazilU.S. Air Force - Electronic Systems Division (Contract F19628-85-K-0028)AT&T Bell Laboratories Doctoral Support ProgramCanada, Bell Northern Research ScholarshipCanada, Fonds pour la Formation de Chercheurs et I'Aide a la Recherche Postgraduate FellowshipSanders Associates, Inc.OKI Semiconductor, Inc.Tel Aviv University, Department of Electronic SystemsU.S. Navy - Office of Naval Research (Contract N00014-85-K-0272)Natural Sciences and Engineering Research Council of Canada, Science and Engineering Scholarshi
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