scholarly journals Feasibility Study and Design of a Wearable System-on-a-Chip Pulse Radar for Contactless Cardiopulmonary Monitoring

2008 ◽  
Vol 2008 ◽  
pp. 1-10 ◽  
Author(s):  
Domenico Zito ◽  
Domenico Pepe ◽  
Bruno Neri ◽  
Fabio Zito ◽  
Danilo De Rossi ◽  
...  
Keyword(s):  
Feasibility Study ◽  
System Analysis ◽  
Ieee 802.15.4 ◽  
Building Blocks ◽  
Ultra Wideband ◽  
Radar Sensor ◽  
Pulse Radar ◽  
Wideband Signals ◽  
Cardiopulmonary Monitoring ◽  
System On A Chip

A new system-on-a-chip radar sensor for next-generation wearable wireless interface applied to the human health care and safeguard is presented. The system overview is provided and the feasibility study of the radar sensor is presented. In detail, the overall system consists of a radar sensor for detecting the heart and breath rates and a low-power IEEE 802.15.4 ZigBee radio interface, which provides a wireless data link with remote data acquisition and control units. In particular, the pulse radar exploits 3.1–10.6 GHz ultra-wideband signals which allow a significant reduction of the transceiver complexity and then of its power consumption. The operating principle of the radar for the cardiopulmonary monitoring is highlighted and the results of the system analysis are reported. Moreover, the results obtained from the building-blocks design, the channel measurement, and the ultra-wideband antenna realization are reported.

scholarly journals Improved energy detection receiver for ranging in IEEE 802.15.4a standard

2017 ◽  
Vol 10 (2) ◽  
pp. 141-148
Author(s):  
Abdelmadjid Maali ◽  
Geneviève Baudoin ◽  
Ammar Mesloub
Keyword(s):  
Estimation Algorithm ◽  
Energy Detection ◽  
Ultra Wideband ◽  
Line Of Sight ◽  
Accurate Estimation ◽  
Time Of Arrival ◽  
Power Delay Profile ◽  
Wideband Signals ◽  
Toa Estimation ◽  
Ieee 802.15.4A

In this paper, we propose a novel energy detection (ED) receiver architecture combined with time-of-arrival (TOA) estimation algorithm, compliant to the IEEE 802.15.4a standard. The architecture is based on double overlapping integrators and a sliding correlator. It exploits a series of ternary preamble sequences with perfect autocorrelation property. This property ensures coding gain, which allows an accurate estimation of power delay profile (PDP). To improve TOA estimation, the interpolation of PDP samples is proposed and the architecture is validated by using an ultra-wideband signals measurements platform. These measurements are carried out in line-of-sight and non-line-of-sight multipath environments. The experimental results show that the ranging performances obtained by the proposed architecture are higher than those obtained by the conventional architecture based on a single-integrator in both LOS and NLOS environments.

scholarly journals In-Body Ranging with Ultra-Wideband Signals: Techniques and Modeling of the Ranging Error

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Muzaffer Kanaan ◽  
Memduh Suveren
Keyword(s):  
Neural Networks ◽  
Human Body ◽  
Extreme Value Distribution ◽  
Generalized Extreme Value Distribution ◽  
Ultra Wideband ◽  
Wideband Signals ◽  
Ranging Error ◽  
And Performance ◽  
Ranging Errors ◽  
Wireless Capsule

Results about the problem of accurate ranging within the human body using ultra-wideband signals are shown. The ability to accurately measure the range between a sensor implanted in the human body and an external receiver can make a number of new medical applications such as better wireless capsule endoscopy, next-generation microrobotic surgery systems, and targeted drug delivery systems possible. The contributions of this paper are twofold. First, we propose two novel range estimators: one based on an implementation of the so-called CLEAN algorithm for estimating channel profiles and another based on neural networks. Second, we develop models to describe the statistics of the ranging error for both types of estimators. Such models are important for the design and performance analysis of localization systems. It is shown that the ranging error in both cases follows a heavy-tail distribution known as the Generalized Extreme Value distribution. Our results also indicate that the estimator based on neural networks outperforms the CLEAN-based estimator, providing ranging errors better than or equal to 3.23 mm with 90% probability.

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