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Particulate matter (PM) is considered the primary contributor to air pollution and has severe implications for general health. PM concentration has high spatial variability and thus needs to be monitored locally. Traditional PM monitoring setups are bulky, expensive and cannot be scaled for dense deployments. This paper argues for a densely deployed network of IoT-enabled PM monitoring devices using low-cost sensors. In this work, 49 devices were deployed in a region of the Indian metropolitan city of Hyderabad out-of this, 43 devices were developed as part of this work and 6 devices were taken off the shelf. The low-cost sensors were calibrated for seasonal variations using a precise reference sensor. A thorough analysis of data collected for seven months has been presented to establish the need for dense deployment of PM monitoring devices. Different analyses such as mean, variance, spatial interpolation and correlation have been employed to generate interesting insights about temporal and seasonal variations of PM. In addition, event-driven spatio-temporal analysis is done for PM values to understand the impact of the bursting of firecrackers on the evening of the Diwali festival. A web-based dashboard is designed for real-time data visualization., Comment: Submitted to IEEE IoT Journal for review

Visible-light-absorbing carbonaceous aerosols within the boundary layer affect the radiance and polarization states of the radiation at the top of the atmosphere. Remote sensing from suborbital and satellite-based platforms utilizes these radiance and polarization signals to retrieve the key properties of these aerosols. Recent retrieval algorithms have shown a progressive trend toward including multi-angular and multi-spectral polarimetric measurements to produce better retrieval accuracy in comparison to those using measurements based on a single viewing angle. Here, we perform a theoretical investigation of the top of atmosphere (TOA) radiance-related reflectance factor (bidirectional reflectance factor (BRF)) and the two types of polarimetry-related factors (polarized bidirectional reflectance factor (pBRF) and the degree of linear polarization (DoLP)) for different types of atmospheric light-absorbing carbonaceous aerosols as a function of particle size distribution. We selected three polarimetric bands corresponding to those utilized by NASA Airborne Multiangle SpectroPolarimetric Imager (AirMSPI) -- near-UV (470 nm), visible (660 nm), and near-infrared (865 nm) -- for our simulations which were performed over ocean surface using the successive order of scattering (SOS) algorithm coupled to a Lorenz-Mie aerosol optics model. The analysis of particle phase matrix elements indicates a close relationship between the angular dependencies of DoLP and associated phase matrix components at the shortest polarimetric band (470 nm). Using Jacobian analysis, we find that the radiance- and polarimetry-related reflectance factors of weakly light-absorbing aerosols, such as brown carbon, are more sensitive to changes in particle size and imaginary refractive index in comparison with those of black carbon, which is strongly light-absorbing., Comment: Includes main text and supplementary material

Terrain, representing features of an earth surface, plays a crucial role in many applications such as simulations, route planning, analysis of surface dynamics, computer graphics-based games, entertainment, films, to name a few. With recent advancements in digital technology, these applications demand the presence of high-resolution details in the terrain. In this paper, we propose a novel fully convolutional neural network-based super-resolution architecture to increase the resolution of low-resolution Digital Elevation Model (LRDEM) with the help of information extracted from the corresponding aerial image as a complementary modality. We perform the super-resolution of LRDEM using an attention-based feedback mechanism named 'Attentional Feedback Network' (AFN), which selectively fuses the information from LRDEM and aerial image to enhance and infuse the high-frequency features and to produce the terrain realistically. We compare the proposed architecture with existing state-of-the-art DEM super-resolution methods and show that the proposed architecture outperforms enhancing the resolution of input LRDEM accurately and in a realistic manner., Comment: Accepted as oral at ACCV 2020

High resolution Digital Elevation Models(DEMs) are an important requirement for many applications like modelling water flow, landslides, avalanches etc. Yet publicly available DEMs have low resolution for most parts of the world. Despite tremendous success in image super resolution task using deep learning solutions, there are very few works that have used these powerful systems on DEMs to generate HRDEMs. Motivated from feedback neural networks, we propose a novel neural network architecture that learns to add high frequency details iteratively to low resolution DEM, turning it into a high resolution DEM without compromising its fidelity. Our experiments confirm that without any additional modality such as aerial images(RGB), our network DSRFB achieves RMSEs of 0.59 to 1.27 across 4 different datasets., Comment: Accepted for publication in IEEE IGARSS 2020 conference

Current air pollution monitoring systems are bulky and expensive resulting in a very sparse deployment. In addition, the data from these monitoring stations may not be easily accessible. This paper focuses on studying the dense deployment based air pollution monitoring using IoT enabled low-cost sensor nodes. For this, total nine low-cost IoT nodes monitoring particulate matter (PM), which is one of the most dominant pollutants, are deployed in a small educational campus in Indian city of Hyderabad. Out of these, eight IoT nodes were developed at IIIT-H while one was bought off the shelf. A web based dashboard website is developed to easily monitor the real-time PM values. The data is collected from these nodes for more than five months. Different analyses such as correlation and spatial interpolation are done on the data to understand efficacy of dense deployment in better understanding the spatial variability and time-dependent changes to the local pollution indicators., Comment: Submitted to PIMRC 2020

Accurate characterization of the asymmetry parameter g is of crucial importance for many radiative transfer and climate models. Here, we present our portable light scattering (PLS) device which is designed for in situ, real-time, and contact-free measurements of the particles phase function with an integration time less than 1 second. With the PLS device, we have characterized carbonaceous aerosols, such as smoldering products from Alaskan peat, soot from a kerosene lamp and non-carbonaceous aerosols such as Arizona Road Dust. We found the g to be 0.664+/-0.002, 0.506+/-0.004, and 0.701+/-0.020 for those three kinds of particles, respectively. Compare to commercial nephelometers, our PLS device has reduced error in g due to angular truncation., Comment: 6 pages, 6 figures

We introduce a generative adversarial network (GAN) model to simulate the 3-dimensional Lagrangian motion of particles trapped in the recirculation zone of a buoyancy-opposed flame. The GAN model comprises a stochastic recurrent neural network, serving as a generator, and a convoluted neural network, serving as a discriminator. Adversarial training was performed to the point where the best-trained discriminator failed to distinguish the ground truth from the trajectory produced by the best-trained generator. The model performance was then benchmarked against a statistical analysis performed on both the simulated trajectories and the ground truth, with regard to the accuracy and generalization criteria., Comment: 19 pages, 9 figures

We introduce a deep learning method to simulate the motion of particles trapped in a chaotic recirculating flame. The Lagrangian trajectories of particles, captured using a high-speed camera and subsequently reconstructed in 3-dimensional space, were used to train a variational autoencoder (VAE) which comprises multiple layers of convolutional neural networks. We show that the trajectories, which are statistically representative of those determined in experiments, can be generated using the VAE network. The performance of our model is evaluated with respect to the accuracy and generalization of the outputs., Comment: 2nd version: typo corrected, corresponding author changed 19 pages, 9 figures

A comprehensive theory encompassing the kinetics of sol-to-gel transition is yet to be formulated due to break-down of the mean-field Smoluchowski Equation. Using high temporal-resolution Monte Carlo simulation of irreversible aggregation systems, we show that this transition has three distinct regimes with kinetic exponent z ranging between 1 and 2, corresponding to aggregation of sol clusters proceeding to the ideal gel point (IGP); z ranging between 2 and 5.7 for gelation of sol clusters beyond IGP; and z ranging between 2 and 3.5 for a hitherto unidentified regime involving aggregation of gels when monomer-dense. We further establish universal power-law scaling relationships that connect the kinetics of these three regimes., Comment: 16 pages, 4 figures, (additional 9 pages, 3 figures supplementary information)

Black carbon (BC) aerosol, the strongest absorber of visible solar radiation in the atmosphere, contributes to a large uncertainty in direct radiative forcing estimates. A primary reason for this uncertainty is inaccurate parameterizations of BC mass absorption cross-section (MACBC) and its enhancement factor (EMACBC), resulting from internal mixing with non-refractory and non-light absorbing materials, in climate models. Here, applying scaling theory to numerically-exact electromagnetic calculations of simulated BC particles and observational data on BC light absorption, we show that MACBC and EMACBC evolve with increasing internal mixing ratios in simple power-law exponents of 1/3. Remarkably, MACBC remains inversely proportional to wavelength at any mixing ratio. When mixing states are represented using mass-equivalent core-shell spheres, as is done in current climate models, it results in significant under prediction of MACBC. We elucidate the responsible mechanism based on shielding of photons by the skin depth of a sphere and establish a correction factor that scales with a 3/4 power-law exponent., Comment: 16 pages, 4 figures

Constraining the complex refractive indices, optical properties and size of brown carbon (BrC) aerosols is a vital endeavor for improving climate models and satellite retrieval algorithms. Smoldering wildfires are the largest source of primary BrC, and fuel parameters such as moisture content, source depth, geographic origin, and fuel packing density could influence the properties of the emitted aerosol. We measured in situ spectral (375-1047 nm) optical properties of BrC aerosols emitted from smoldering combustion of Boreal and Indonesian peatlands across a range of these fuel parameters. Inverse Lorenz-Mie algorithms used these optical measurements along with simultaneously measured particle size distributions to retrieve the aerosol complex refractive indices (m=n+i{\kappa}). Our results show that the real part n is constrained between 1.5 and 1.7 with no obvious functionality in wavelength ({\lambda}), moisture content, source depth, or geographic origin. With increasing {\lambda} from 375 to 532 nm, {\kappa} decreased from 0.014 to 0.003, with corresponding increase in single scattering albedo from 0.93 to 0.99. For {\lambda} = 532 nm, both {\kappa} and single scattering albedo showed no spectral dependency. We discuss differences between this study and previous work. The imaginary part {\kappa} was sensitive to changes in FPD, and we hypothesize mechanisms that might help explain this observation., Comment: Benjamin J. Sumlin and Yuli W. Heinson contributed equally to this work

The complex refractive index m=n+ik of a particle is an intrinsic property which cannot be directly measured, it must be inferred from its extrinsic properties such as the scattering and absorption cross-sections. Bohren and Huffman called this approach describing the dragon from its tracks, since the inversion of Lorenz-Mie theory equations is intractable without the use of computers. This article describes PyMieScatt, an open-source module for Python that contains functionality for solving the inverse problem for complex m using extensive optical and physical properties as input, and calculating regions where valid solutions may exist within the error bounds of laboratory measurements. Additionally, the module has comprehensive capabilities for studying homogeneous and coated single spheres, as well as ensembles of homogeneous spheres with user-defined size distributions, making it a complete tool for studying the optical behavior of spherical particles., Comment: Includes main text and supplementary material, Journal of Quantitative Spectroscopy and Radiative Transfer, 2017

Ability to understand and model the performance limits of nanowire transistors is the key to design of next generation devices. Here, we report studies on high-mobility junction-less gate-all-around nanowire field effect transistor with carrier mobility reaching 2000 cm2/V.s at room temperature. Temperature-dependent transport measurements reveal activated transport at low temperatures due to surface donors, while at room temperature the transport shows a diffusive behavior. From the conductivity data, the extracted value of sound velocity in InAs nanowires is found to be an order less than the bulk. This low sound velocity is attributed to the extended crystal defects that ubiquitously appear in these nanowires. Analyzing the temperature-dependent mobility data, we identify the key scattering mechanisms limiting the carrier transport in these nanowires. Finally, using these scattering models, we perform drift-diffusion based transport simulations of a nanowire field-effect transistor and compare the device performances with experimental measurements. Our device modeling provides insight into performance limits of InAs nanowire transistors and can be used as a predictive methodology for nanowire-based integrated circuits., Comment: 22 pages, 5 Figures, Nano Letters

This paper presents machine learning solutions to a practical problem of Natural Language Generation (NLG), particularly the word formation in agglutinative languages like Tamil, in a supervised manner. The morphological generator is an important component of Natural Language Processing in Artificial Intelligence. It generates word forms given a root and affixes. The morphophonemic changes like addition, deletion, alternation etc., occur when two or more morphemes or words joined together. The Sandhi rules should be explicitly specified in the rule based morphological analyzers and generators. In machine learning framework, these rules can be learned automatically by the system from the training samples and subsequently be applied for new inputs. In this paper we proposed the machine learning models which learn the morphophonemic rules for noun declensions from the given training data. These models are trained to learn sandhi rules using various learning algorithms and the performance of those algorithms are presented. From this we conclude that machine learning of morphological processing such as word form generation can be successfully learned in a supervised manner, without explicit description of rules. The performance of Decision trees and Bayesian machine learning algorithms on noun declensions are discussed., Comment: 13 pages. International Journal of Computer Engineering and Applications, 2013

This article develops a consistent theory of free carrier screening of a two-dimensional electron gas in the silicon inversion layer in the presence of stacked layers of dielectric environment-commonly knows as gate stack in context of field-effect transistors. It is shown that the finite thickness and of dielectric stacks alters the free carrier screening, a crucial quantity, which determines screened coulomb interaction in the inversion layer, and ubiquitously appears in carrier transport theory in semiconductors. Results are analytical and can be used to accurate prediction Coulomb-interaction limited mobility in field-effect transistors., Comment: 4 pages,3 Figures withdrawn...crucial issue/policy

Visible-light-absorbing carbonaceous aerosols within the boundary layer affect the radiance and polarization states of the radiation at the top of the atmosphere. Remote sensing from suborbital and satellite-based platforms utilizes these radiance and polarization signals to retrieve the key properties of these aerosols. Recent retrieval algorithms have shown a progressive trend toward including multi-angular and multi-spectral polarimetric measurements to produce better retrieval accuracy in comparison to those using measurements based on a single viewing angle. Here, we perform a theoretical investigation of the top of atmosphere (TOA) radiance-related reflectance factor (bidirectional reflectance factor (BRF)) and the two types of polarimetry-related factors (polarized bidirectional reflectance factor (pBRF) and the degree of linear polarization (DoLP)) for different types of atmospheric light-absorbing carbonaceous aerosols as a function of particle size distribution. We selected three polarimetric bands corresponding to those utilized by NASA's Airborne Multiangle SpectroPolarimetric Imager (AirMSPI)—near-UV (470 nm), visible (660 nm), and near-infrared (865 nm)—for our simulations which were performed over ocean surface using the successive order of scattering (SOS) algorithm coupled to a Lorenz-Mie aerosol optics model. The analysis of particle phase matrix elements indicates a close relationship between the angular dependencies of DoLP and associated phase matrix components at the shortest polarimetric band (470 nm). Using Jacobian analysis, we find that the radiance- and polarimetry-related reflectance factors of weakly light-absorbing aerosols, such as brown carbon, are more sensitive to changes in particle size and imaginary refractive index in comparison with those of black carbon, which is strongly light-absorbing. Our results suggest that the DoLP data could be used by future retrieval algorithms for reliably estimating microphysical properties of absorbing carbonaceous aerosols with imaginary refractive index less than 0.4.

Soot aggregates (SAs)-fractal clusters of small, spherical carbonaceous monomers-modulate the incoming visible solar radiation and contribute significantly to climate forcing. Experimentalists and climate modelers typically assume a spherical morphology for SAs when computing their optical properties, causing significant errors. Here, we calculate the optical properties of freshly-generated (fractal dimension Df = 1.8) and aged (Df = 2.6) SAs at 550 nm wavelength using the numericallyexact superposition T-Matrix method. These properties were expressed as functions of equivalent aerosol diameters as measured by contemporary aerosol instruments. This work improves upon previous efforts wherein SA optical properties were computed as a function of monomer number, rendering them unusable in practical applications. Future research will address the sensitivity of variation in refractive index, fractal prefactor, and monomer overlap of SAs on the reported empirical relationships.

Wildfires contribute significantly to global soot emissions, yet their aerosol formation mechanisms and resulting particle properties are poorly understood and parameterized in climate models. The conventional view holds that soot is formed via the cluster-dilute aggregation mechanism in wildfires and emitted as aggregates with fractal dimension D(sub f) approximately equals 1.8 mobility diameter D(sub m) (is) less than or equal to 1 micron, and aerodynamic diameter D(sub a) (is) less than or equal to 300 nm. Here we report the ubiquitous presence of soot superaggregates (SAs) in the outflow from a major wildfire in India. SAs are porous, low-density aggregates of cluster-dilute aggregates with characteristic D(sub f) approximately equals 2.6,D(sub m) (is) greater than 1 micron, and D(sub a) is less than or equal to 300 nm that form via the cluster-dense aggregation mechanism.We present additional observations of soot SAs in wildfire smoke-laden air masses over Northern California, New Mexico, and Mexico City. We estimate that SAs contribute, per unit optical depth, up to 35% less atmospheric warming than freshly-emitted (D(sub f) approximately equals 1.8) aggregates, and approximately equals 90% more warming than the volume-equivalent spherical soot particles simulated in climate models.

Embodiments of a method for selecting particles, such as based on their morphology, is disclosed. In a particular example, the particles are charged and acquire different amounts of charge, or have different charge distributions, based on their morphology. The particles are then sorted based on their flow properties. In a specific example, the particles are sorted using a differential mobility analyzer, which sorts particles, at least in part, based on their electrical mobility. Given a population of particles with similar electrical mobilities, the disclosed process can be used to sort particles based on the net charge carried by the particle, and thus, given the relationship between charge and morphology, separate the particles based on their morphology.

We present radiation data, Total Ionizing Dose and Single Event Effects, on the LSI Logic 0.11 micron commercial process and two modified versions of this process. Modified versions include a buried layer to guarantee Single Event Latchup immunity.

In May 1999 state-of-the-art autonomy technology was allowed to assume command and control of the Deep Space One spacecraft during the Remote Agent Experiment.

In May 1999 state-of-the-art autonomy technology was allowed to assume command and control of the Deep Space One spacecraft during the Remote Agent Experiment.

Deep Space One (DS1) will be the first spaceraft to be controlled by an autonomous closed loop system potientially capable of carrying out a complete mission with minimal commanding from Earth.

The Deep Space One (DS1) mission, scheduled to fly in 1998, will be the first NASA spacecraft to feature an on-board planner. The planner is part of an artificial intelligence based control architecture that comprises the planner/scheduler, a plan execution engine, and a model-based fault diagnosis and reconfiguration engine...This paper describes the on-board planning and scheduling component of the DS1 autonomy architecture.

Attention is given to degradation mechanisms for graphite/polysulfone and graphite/epoxy laminates exposed to ultraviolet and high-energy electron radiation in vacuum up to 960 equivalent sun hours and to 10 to the 9th rads, respectively. The materials showed good electron radiation stability as indicated by the low G values for gas formation and no evidence of mechanical property changes. Quantum yields for gas formation indicate poor stability to ultraviolet radiation. Mechanical property measurements did not show significant changes up to 960 ESH, with the possible exception of P1700/C6000. The main products of irradiation were identified as hydrogen and methane, along with high levels of CO and CO2.

Feasibility of clathrate compounds for carbon dioxide removal from space cabin atmospheres