3 research outputs found
A HIGHLY-SCALABLE DC-COUPLED DIRECT-ADC NEURAL RECORDING CHANNEL ARCHITECTURE WITH INPUT-ADAPTIVE RESOLUTION
This thesis presents the design, development, and characterization of a novel neural recording channel architecture with (a) quantization resolution that is adaptive to the input signal's level of activity, (b) fully-dynamic power consumption that is linearly proportional to the recording resolution, and (c) immunity to DC offset and drifts at the input. Our results demonstrate the proposed design's capability in conducting neural recording with near lossless input-adaptive data compression, leading to a significant reduction in the energy required for both recording and data transmission, hence allowing for a potential high scaling of the number of recording channels integrated on a single implanted microchip without the need to increase the power budget. The proposed channel with the implemented compression technique is implemented in a standard 130nm CMOS technology with overall power consumption of 7.6uW and active area of 92×92µm for the implemented digital-backend
A HIGHLY-SCALABLE DC-COUPLED DIRECT-ADC NEURAL RECORDING CHANNEL ARCHITECTURE WITH INPUT-ADAPTIVE RESOLUTION
This thesis presents the design, development, and characterization of a novel neural recording channel architecture with (a) quantization resolution that is adaptive to the input signal's level of activity, (b) fully-dynamic power consumption that is linearly proportional to the recording resolution, and (c) immunity to DC offset and drifts at the input. Our results demonstrate the proposed design's capability in conducting neural recording with near lossless input-adaptive data compression, leading to a significant reduction in the energy required for both recording and data transmission, hence allowing for a potential high scaling of the number of recording channels integrated on a single implanted microchip without the need to increase the power budget. The proposed channel with the implemented compression technique is implemented in a standard 130nm CMOS technology with overall power consumption of 7.6uW and active area of 9292m for the implemented digital-backend
In-situ health monitoring for wind turbine blade using acoustic wireless sensor networks at low sampling rates
PhD ThesisThe development of in-situ structural health monitoring (SHM) techniques represents a
challenge for offshore wind turbines (OWTs) in order to reduce the cost of the operation
and maintenance (O&M) of safety-critical components and systems. This thesis propos-
es an in-situ wireless SHM system based on acoustic emission (AE) techniques. The
proposed wireless system of AE sensor networks is not without its own challenges
amongst which are requirements of high sampling rates, limitations in the communication bandwidth, memory space, and power resources. This work is part of the HEMOW-
FP7 Project, ‘The Health Monitoring of Offshore Wind Farms’.
The present study investigates solutions relevant to the abovementioned challenges.
Two related topics have been considered: to implement a novel in-situ wireless SHM
technique for wind turbine blades (WTBs); and to develop an appropriate signal pro-
cessing algorithm to detect, localise, and classify different AE events. The major contri-
butions of this study can be summarised as follows: 1) investigating the possibility of
employing low sampling rates lower than the Nyquist rate in the data acquisition opera-
tion and content-based feature (envelope and time-frequency data analysis) for data
analysis; 2) proposing techniques to overcome drawbacks associated with lowering
sampling rates, such as information loss and low spatial resolution; 3) showing that the
time-frequency domain is an effective domain for analysing the aliased signals, and an
envelope-based wavelet transform cross-correlation algorithm, developed in the course
of this study, can enhance the estimation accuracy of wireless acoustic source localisa-
tion; 4) investigating the implementation of a novel in-situ wireless SHM technique
with field deployment on the WTB structure, and developing a constraint model and
approaches for localisation of AE sources and environmental monitoring respectively.
Finally, the system has been experimentally evaluated with the consideration of the lo-
calisation and classification of different AE events as well as changes of environmental
conditions. The study concludes that the in-situ wireless SHM platform developed in the
course of this research represents a promising technique for reliable SHM for OWTBs
in which solutions for major challenges, e.g., employing low sampling rates lower than
the Nyquist rate in the acquisition operation and resource constraints of WSNs in terms
of communication bandwidth and memory space are presente