Many signals of interest in the assessment of structural systems lie in the quasi-static range (frequency << 1Hz). This
poses a significant challenge for the development of self-powered sensors that are required not only to monitor these
events but also to harvest the energy for sensing, computation and storage from the signal being monitored. This paper
combines the use of mechanically-equivalent frequency modulators and piezo-powered threshold detection modules
capable of computation and data storage with a total current less than 10nA. The system is able to achieve events
counting for input deformations at frequencies lower than 0.1Hz. The used mechanically-equivalent frequency
modulators allow the transformation of the low-amplitude and low-rate quasi-static deformations into an amplified input
to a piezoelectric transducer. The sudden transitions in unstable mode branch switching, during the elastic postbuckling
response of slender columns and plates, are used to generate high-rate deformations. Experimental results show that an
oscillating semi-crystalline plastic polyvinylidene fluoride (PVDF), attached to the up-converting modules, is able to
generate a harvestable energy at levels between 0.8μJ to 2μJ. In this work, we show that a linear injection response of
our combined frequency up-converter / piezo-floating-gate sensing system can be used for self-powered measurement
and recording of quasi-static deformations levels. The experimental results demonstrate that a sensor fabricated in a 0.5-
μm CMOS technology can count and record the number of quasi-static input events, while operating at a power level
significantly lower than 1μW.
A key challenge in structural health monitoring (SHM) sensors embedded inside civil structures is that elec- tronics need to operate continuously such that mechanical events of interest can be detected and appropriately analyzed. Continuous operation however requires a continuous source of energy which cannot be guaranteed using conventional energy scavenging techniques. The paper describes a hybrid energy scavenging SHM sensor which experiences zero down-time in monitoring mechanical events of interest. At the core of the proposed sensor is an analog floating-gate storage technology that can be precisely programmed at nano-watt and pico- watt power levels. This facilitates self-powered, non-volatile data logging of the mechanical events of interest by scavenging energy directly from the mechanical events itself. Remote retrieval of the stored data is achieved using a commercial off-the-shelf Gen-2 radio-frequency identification (RFID) reader which periodically reads an electronic product code (EPC) that encapsulates the sensor data. The Gen-2 interface also facilitates in simultaneous remote access to multiple sensors and also facilitates in determining the range and orientation of the sensor. The architecture of the sensor is based on a token-ring topology which enables sensor channels to be dynamically added or deleted through software control.
Even though current micro-nano fabrication technology has reached integration levels where ultra-sensitive sensors can
be fabricated, the sensing performance (resolution per joule) of synthetic systems are still orders of magnitude inferior to
those observed in neurobiology. For example, the filiform hairs in crickets operate at fundamental limits of noise; auditory
sensors in a parasitoid fly can overcome fundamental limitations to precisely localize ultra-faint acoustic signatures. Even
though many of these biological marvels have served as inspiration for different types of neuromorphic sensors, the main
focus these designs have been to faithfully replicate the biological functionalities, without considering the constructive role
of "noise". In man-made sensors device and sensor noise are typically considered as a nuisance, where as in neurobiology
"noise" has been shown to be a computational aid that enables biology to sense and operate at fundamental limits of energy
efficiency and performance. In this paper, we describe some of the important noise-exploitation and adaptation principles
observed in neurobiology and how they can be systematically used for designing neuromorphic sensors. Our focus will be
on two types of noise-exploitation principles, namely, (a) stochastic resonance; and (b) noise-shaping, which are unified
within our previously reported framework called Σ▵ learning. As a case-study, we describe the application of Σ▵ learning for the design of a miniature acoustic source localizer whose performance matches that of its biological counterpart(Ormia Ochracea).
The cost and size of the state-of-the-art health and usage monitoring systems (HUMS) are determined by
capacity of on-board energy storage which limits their large scale deployment. In this paper, we present a
miniature low-cost mechanical HUMS integrated circuit (IC) based on the concept of hybrid energy harvesting
where continuous monitoring is achieved by self-powering, where as the programming, localization and
communication with the sensor is achieved using remote RF powering. The self-powered component of the
proposed HUMS is based on our previous result which used a controllable hot electron injection on floatinggate
transistor as an ultra-low power signal processor. We show that the HUMS IC can seamlessly switch
between different energy harvesting modes based on the availability of ambient RF power and that the configuration,
programming and communication functions can be remotely performed without physically accessing
the HUMS device. All the measured results presented in this paper have been obtained from prototypes
fabricated in a 0.5 micron standard CMOS process and the entire system has been successfully integrated on a 1.5cm x 1.5cm package.
Localization of acoustic sources using miniature microphone arrays poses a significant challenge due to fundamental
limitations imposed by the physics of sound propagation. With sub-wavelength distances between the
microphones, resolving acute localization cues become difficult due to precision artifacts. In this work, we present
the design of a miniature, microphone array sensor based on a patented Multiple-input Multiple-output (MIMO)
analog-to-feature converter (AFC) chip-sets which overcomes the limitations due to precision artifacts. Measured
results from fabricated prototypes demonstrate a bearing range of 0 degrees to 90 degrees with a resolution less
than 2 degrees. The power dissipation of the MIMO-ADC chip-set for this task was measured to be less than 75
microwatts making it ideal for portable, battery powered sniper and gunshot detection applications.
Many signals of interest in structural engineering, for example seismic activity, lie in the infrasonic range
(frequency less than 20Hz). This poses a significant challenge for developing self-powered structural health
monitoring sensors that are required not only to monitor rare infrasonic events but also to harvest the energy
for sensing, computation and storage from the signal being monitored. In this paper, we show that a linear
injection response of our previously reported piezo-floating-gate sensor is ideal for self-powered sensing and
computation of infrasonic signals. Our experimental results demonstrate that the sensor fabricated in a 0.5-
μm CMOS technology can compute and record level crossing statistics of an input infrasonic event with total
current less than 10nA.
Advances in micro-nano-biosensor fabrication are enabling technology that can integrate a large number of biological recognition elements within a single package. As a result, hundreds to millions of tests can be performed simultaneously and can facilitate rapid detection of multiple pathogens in a given sample. However, it is an open question as to how to exploit the high-dimensional nature of the multi-pathogen testing for improving the detection reliability a typical biosensor system. In this paper, we discuss two complementary high-dimensional encoding/decoding methods for improving the reliability of multi-pathogen detection. The first method uses a support vector machine (SVM) to learn the non-linear detection boundaries in the high-dimensional measurement space. The second method uses a forward error correcting (FEC) technique to synthetically introduce redundant patterns on the biosensor which can then be efficiently decoded. In this paper, experimental and simulation studies are based on a model conductimetric lateral flow immunoassay that uses antigen-antibody interaction in conjunction with a polyaniline transducer to detect presence or absence of pathogen in a given sample. Our results show that both SVM and FEC techniques can improve the detection performance by exploiting cross-reaction amongst multiple recognition sites on the biosensor. This is contrary to many existing methods used in pathogen detection technology where the main emphasis has been reducing the effects of cross-reaction and coupling instead of exploiting them as side information.
Fatigue and overload of mechanical, civil and aerospace structures remains a major problem that can lead to costly repair
and catastrophic failure. Long term monitoring of mechanical loading for these structures could reduce maintenance
cost, improve longevity and enhance safety. However, the powering of these sensors during the lifetime of the
monitored structure remains a major problem. In this paper we describe an implementation of a novel self-powered
fatigue monitoring sensor. The sensor is based on the integration of piezoelectric transduction with floating gate
avalanche injection. The miniaturized sensor enables self-powered continuous battery free monitoring and time-to-failure
predictions of mechanical and civil structures. Measured results from a fabricated prototype in a 0.5&mgr;m CMOS
process indicate that the device can compute cumulative statistics of electrical signals generated by the piezoelectric
transducer, while consuming less that one microwatt of power. Furthermore, the sensor is capable of storing this
information in non-volatile memory which makes it an attractive alternative when the converted electrical energy levels
are low due to small mechanical force inputs. The current microchip is less than 2 square millimeters in area. The non
volatile memory storage is coupled to a radio frequency (RF) identification microchip which allows the sensor to be
interrogated asynchronously through a RF reader. We are currently developing a state vector machine (SVM), neural
network based hardware to be included on the microchip. The SVM hardware will enable low-power processing and
computation of the incoming mechanical loading cycle data.
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