Innovative Mini Ultralight Radioprobes to Track Lagrangian Turbulence Fluctuations within Warm Clouds: Electronic Design.

Characterization of dynamics inside clouds remains a challenging task for weather forecasting and climate modeling as cloud properties depend on interdependent natural processes at micro- and macro-scales. Turbulence plays an important role in particle dynamics inside clouds; however, turbulence mechanisms are not yet fully understood partly due to the difficulty of measuring clouds at the smallest scales. To address these knowledge gaps, an experimental method for measuring the influence of small-scale turbulence in cloud formation in situ and producing an in-field cloud Lagrangian dataset is being developed by means of innovative ultralight radioprobes. This paper presents the electronic system design along with the obtained results from laboratory and field experiments regarding these compact (diameter ≈30 cm), lightweight (≈20 g), and expendable devices designed to passively float and track small-scale turbulence fluctuations inside warm clouds. The fully customized mini-radioprobe board (5 cm × 5 cm) embeds sensors to measure local fluctuations and transmit data to the ground in near real time. The tests confirm that the newly developed probes perform well, providing accurate information about atmospheric turbulence as referenced in space. The integration of multiple radioprobes allows for a systematic and accurate monitoring of atmospheric turbulence and its impact on cloud formation.

Experimental Evaluation of a 3D-Printed Fluidic System for a Directional Anemometer.

An evolution of a previously proposed anemometer capable of detecting both the magnitude and the direction of the wind on a plane is proposed. The device is based on a recently formalized principle, consisting of combining the differential pressures measured across distinct diameters of a cylinder to estimate the wind velocity and incidence angle. Differently from previous sensors based on the same principle, the proposed anemometers use 3D printing to fabricate the channel structure that calculates the pressure combination in the fluidic domain. Furthermore, commercial sensors with low power consumption are used to read the two pressures that result from the fluidic processing. The whole fabrication procedure requires inexpensive equipment and can be adopted by small enterprises or research laboratories. Two original channel structures, predicted by previous theoretical work but never experimentally validated, are proposed. The results of detailed experiments performed in a wind tunnel are reported.

Parametrically Amplified Low-Power MEMS Capacitive Humidity Sensor.

We present the design, fabrication, and response of a polymer-based Laterally Amplified Chemo-Mechanical (LACM) humidity sensor based on mechanical leveraging and parametric amplification. The device consists of a sense cantilever asymmetrically patterned with a polymer and flanked by two stationary electrodes on the sides. When exposed to a humidity change, the polymer swells after absorbing the analyte and causes the central cantilever to bend laterally towards one side, causing a change in the measured capacitance. The device features an intrinsic gain due to parametric amplification resulting in an enhanced signal-to-noise ratio (SNR). Eleven-fold magnification in sensor response was observed via voltage biasing of the side electrodes without the use of conventional electronic amplifiers. The sensor showed a repeatable and recoverable capacitance change of 11% when exposed to a change in relative humidity from 25-85%. The dynamic characterization of the device also revealed a response time of ~1 s and demonstrated a competitive response with respect to a commercially available reference chip.

Enhancing Time Synchronization Support in Wireless Sensor Networks.

With the emerging Internet of Things (IoT) technology becoming reality, a number of applications are being proposed. Several of these applications are highly dependent on wireless sensor networks (WSN) to acquire data from the surrounding environment. In order to be really useful for most of applications, the acquired data must be coherent in terms of the time in which they are acquired, which implies that the entire sensor network presents a certain level of time synchronization. Moreover, to efficiently exchange and forward data, many communication protocols used in WSN rely also on time synchronization among the sensor nodes. Observing the importance in complying with this need for time synchronization, this work focuses on the second synchronization problem, proposing, implementing and testing a time synchronization service for low-power WSN using low frequency real-time clocks in each node. To implement this service, three algorithms based on different strategies are proposed: one based on an auto-correction approach, the second based on a prediction mechanism, while the third uses an analytical correction mechanism. Their goal is the same, i.e., to make the clocks of the sensor nodes converge as quickly as possible and then to keep them most similar as possible. This goal comes along with the requirement to keep low energy consumption. Differently from other works in the literature, the proposal here is independent of any specific protocol, i.e., it may be adapted to be used in different protocols. Moreover, it explores the minimum number of synchronization messages by means of a smart clock update strategy, allowing the trade-off between the desired level of synchronization and the associated energy consumption. Experimental results, which includes data acquired from simulations and testbed deployments, provide evidence of the success in meeting this goal, as well as providing means to compare these three approaches considering the best synchronization results and their costs in terms of energy consumption.

Bioinspired and Low-Power 2D Machine Vision with Adaptive Machine Learning and Forgetting.

Natural intelligence has many dimensions, with some of its most important manifestations being tied to learning about the environment and making behavioral changes. In primates, vision plays a critical role in learning. The underlying biological neural networks contain specialized neurons and synapses which not only sense and process visual stimuli but also learn and adapt with remarkable energy efficiency. Forgetting also plays an active role in learning. Mimicking the adaptive neurobiological mechanisms for seeing, learning, and forgetting can, therefore, accelerate the development of artificial intelligence (AI) and bridge the massive energy gap that exists between AI and biological intelligence. Here, we demonstrate a bioinspired machine vision system based on a 2D phototransistor array fabricated from large-area monolayer molybdenum disulfide (MoS2) and integrated with an analog, nonvolatile, and programmable memory gate-stack; this architecture not only enables dynamic learning and relearning from visual stimuli but also offers learning adaptability under noisy illumination conditions at miniscule energy expenditure. In short, our demonstrated "all-in-one" hardware vision platform combines "sensing", "computing", and "storage" to not only overcome the von Neumann bottleneck of conventional complementary metal-oxide-semiconductor (CMOS) technology but also to eliminate the need for peripheral circuits and sensors.

Demonstration of Stochastic Resonance, Population Coding, and Population Voting Using Artificial MoS2 Based Synapses.

Fast detection of weak signals at low energy expenditure is a challenging but inescapable task for the evolutionary success of animals that survive in resource constrained environments. This task is accomplished by the sensory nervous system by exploiting the synergy between three astounding neural phenomena, namely, stochastic resonance (SR), population coding (PC), and population voting (PV). In SR, the constructive role of synaptic noise is exploited for the detection of otherwise invisible signals. In PC, the redundancy in neural population is exploited to reduce the detection latency. Finally, PV ensures unambiguous signal detection even in the presence of excessive noise. Here we adopt a similar strategies and experimentally demonstrate how a population of stochastic artificial neurons based on monolayer MoS2 field effect transistors (FETs) can use an optimum amount of white Gaussian noise and population voting to detect invisible signals at a frugal energy expenditure (∼10s of nano-Joules). Our findings can aid remote sensing in the emerging era of the Internet of things (IoT) that thrive on energy efficiency.

Novel Self-Heated Gas Sensors Using on-Chip Networked Nanowires with Ultralow Power Consumption.

The length of single crystalline nanowires (NWs) offers a perfect pathway for electron transfer, while the small diameter of the NWs hampers thermal losses to tje environment, substrate, and metal electrodes. Therefore, Joule self-heating effect is nearly ideal for operating NW gas sensors at ultralow power consumption, without additional heaters. The realization of the self-heated NW sensors using the "pick and place" approach is complex, hardly reproducible, low yield, and not applicable for mass production. Here, we present the sensing capability of the self-heated networked SnO2 NWs effectively prepared by on-chip growth. Our developed self-heated sensors exhibit a good response of 25.6 to 2.5 ppm NO2 gas, while the response to 500 ppm H2, 100 ppm NH3, 100 ppm H2S, and 500 ppm C2H5OH is very low, indicating the good selectivity of the sensors to NO2 gas. Furthermore, the detection limit is very low, down to 82 parts-per-trillion. As-obtained sensing performance under self-heating mode is nearly identical to that under external heating mode. While the power consumption under self-heating mode is extremely low, around hundreds of microwatts, as scaled-down the size of the electrode is below 10 µm. The selectivity of the sensors can be controlled simply by tuning the loading power that enables simple detection of NO2 in mixed gases. Remarkable performance together with a significantly facile fabrication process of the present sensors enhances the potential application of NW sensors in next generation technologies such as electronic noses, the Internet of Things, and smartphone sensing.