62,276 research outputs found
Body centric antennas for wireless cardiac monitoring
The overwhelming prevalence of cardiac related deaths is the motivation behind this thesis to develop body centric antennas for wireless cardiac monitoring. Cardiac monitoring can diagnose a number of conditions including: arrhythmia, ischemia, premature atrial complexes, abnormal sinus rhythms, heart blocks, atrial fibrillation, and more. A body centric antenna operating within the ISM band (2.4-2.48GHz) has been designed, simulated, and tested. The simulation and testing indicate low mutual coupling between antennas of varying distances has been achieved. In addition, the simulation and testing indicate that a thin layer of skin over the test subject further reduces mutual coupling
Body centric antennas for wireless cardiac monitoring
The overwhelming prevalence of cardiac related deaths is the motivation behind this thesis to develop body centric antennas for wireless cardiac monitoring. Cardiac monitoring can diagnose a number of conditions including: arrhythmia, ischemia, premature atrial complexes, abnormal sinus rhythms, heart blocks, atrial fibrillation, and more. A body centric antenna operating within the ISM band (2.4-2.48GHz) has been designed, simulated, and tested. The simulation and testing indicate low mutual coupling between antennas of varying distances has been achieved. In addition, the simulation and testing indicate that a thin layer of skin over the test subject further reduces mutual coupling
Deep Learning Features at Scale for Visual Place Recognition
The success of deep learning techniques in the computer vision domain has
triggered a range of initial investigations into their utility for visual place
recognition, all using generic features from networks that were trained for
other types of recognition tasks. In this paper, we train, at large scale, two
CNN architectures for the specific place recognition task and employ a
multi-scale feature encoding method to generate condition- and
viewpoint-invariant features. To enable this training to occur, we have
developed a massive Specific PlacEs Dataset (SPED) with hundreds of examples of
place appearance change at thousands of different places, as opposed to the
semantic place type datasets currently available. This new dataset enables us
to set up a training regime that interprets place recognition as a
classification problem. We comprehensively evaluate our trained networks on
several challenging benchmark place recognition datasets and demonstrate that
they achieve an average 10% increase in performance over other place
recognition algorithms and pre-trained CNNs. By analyzing the network responses
and their differences from pre-trained networks, we provide insights into what
a network learns when training for place recognition, and what these results
signify for future research in this area.Comment: 8 pages, 10 figures. Accepted by International Conference on Robotics
and Automation (ICRA) 2017. This is the submitted version. The final
published version may be slightly differen
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