Intra-Body Communication and Networks:
Galvanic Coupling Intra-Body Communication Testbed
The Galvanic Coupling Intra-Body Communication (GC-IBC) testbed was designed to establish a uni-directional communication link through synthetic human tissue using the GC concept. This testbed has the following objectives: (1) the ability to modify modulation schemes and other parameters for various communication scenarios, (2) real-time transmission of physiological data sets and images, (3) the measurement of BER at the receiver node, and (4) the comparison of the link quality results with other published works that invoke concepts from wireless communication theory and intra-body communication.
The GC-IBC testbed encompasses and controls all aspects of a traditional wireless communication system, including, but not limited to bit generation, automatic gain control, preamble insertion, frequency offset estimation and raised cosine filtering. We presented this framework alongside a MATLAB-based GUI, that allows the user to select various modulation schemes and other transmission parameters, for the link. The GUI at the Rx displays the BER rate and compares the real-time BER measurement to results from previous channel sounding experiments.
Galvanic Coupling Human Body Channel Modeling
This work is focused on channel characterization of the human body tissues considering the propagation of such electrical signals through it that carry data. Experiments were conducted using porcine tissue (in lieu of actual human tissue) with skin, fat and muscle layers in the frequency range of 100 kHz to 1 MHz. By utilizing single-carrier BPSK modulated Pseudorandom Noise Sequences, a correlative channel sounding system was implemented, leading to the following contributions: (1) measurements of the channel impulse and frequency response, (2) a noise analysis and capacity estimation, and (3) the comparison of results with existing models.
Results indicate that the channel response is relatively flat for the frequency range of interest, there exists no presence of multi-path fading, the noise can be approximated as additive white Gaussian, and the achievable capacity lies in the range of Mbps. The comparison of these experimental results with currently existing analytic channel models and experiments show a reasonable amount of accuracy for the chosen empirical modeling method.
Multi-Cast and Multi-Hop Communication Scheme
The work done in this project employs an in-depth signal reflection and refraction analysis of electromagnetic waves through human tissue boundaries (modeled as a lossy dielectric block with four tissue layers) was conducted, and the design of a combined multi-hop and multi-cast communication scheme for communication between implants was developed.
This work proposed in this project is done under the assumption that the use of weak electrical current (known as Galvanic Coupling) is used as a means of intra-body communication for in-body implants. Results reveal that a multi-cast communication scheme can be achieved for IBNs with the appropriate selection of transmission parameters.
Ultrasonic Sensor Networks:
Source Node Localization via Analyzing Multi-path Signals on Aluminum Substrates
This project proposed a novel signal analysis based node localization strategy for sensor networks used in structural health monitoring (SHM) applications. The key idea was to analyze location-dependent multi-path signal patterns in inter-node ultrasonic signals, and use machine-learning mechanisms to detect such patterns for accurate node localization on metal substrates on target structures. The majority of traditional mechanisms rely on radio-based Time Delay of Arrival (TDOA), coupled with multilateration, for node localization in RF sensor networks. However, these techniques rely on: 1) the presence of RF links, and 2) three or more reference beacon nodes for effective multilateration.
The mechanism proposed in this project attempted to solve the localization problem in an ultrasonic sensor network and avoid the use of multiple reference beacon nodes. Instead, methods conducted in this work relied on signal analysis and multi-path signature classification from a single reference node that periodically transmitted ultrasonic localization beacons.
The proposed mechanism relied on the key observation that an ultrasonic signal received at any point on a structure from the reference node is a superposition of the signals received on the direct path and through all possible multi-paths, within a pre-specified delay spread.
It was hypothesized, that if the location of the reference node and the substrate properties are known apriori, it should be possible to train a receiver (i.e., source node) to identify its own location by observing the exact signature of the received signal.
Based on this observation, a machine learning based signal analysis model has been developed and applied for a metal substrate (2024 Aluminum Plate, used for aircraft wing skins) in the form of an offline model where collected signals are post-processed using MATLAB for accuracy evaluation, and a TI MSP-430 based module for implementing a run-time system. It was demonstrated that localization accuracies up to 92% were achieved in the presence of varying spatial resolutions.