Showing papers by "Aalto University published in 2018"

Planck 2018 results. VI. Cosmological parameters

Abstract: We present cosmological parameter results from the final full-mission Planck measurements of the CMB anisotropies. We find good consistency with the standard spatially-flat 6-parameter $\Lambda$CDM cosmology having a power-law spectrum of adiabatic scalar perturbations (denoted "base $\Lambda$CDM" in this paper), from polarization, temperature, and lensing, separately and in combination. A combined analysis gives dark matter density $\Omega_c h^2 = 0.120\pm 0.001$, baryon density $\Omega_b h^2 = 0.0224\pm 0.0001$, scalar spectral index $n_s = 0.965\pm 0.004$, and optical depth $\tau = 0.054\pm 0.007$ (in this abstract we quote $68\,\%$ confidence regions on measured parameters and $95\,\%$ on upper limits). The angular acoustic scale is measured to $0.03\,\%$ precision, with $100\theta_*=1.0411\pm 0.0003$. These results are only weakly dependent on the cosmological model and remain stable, with somewhat increased errors, in many commonly considered extensions. Assuming the base-$\Lambda$CDM cosmology, the inferred late-Universe parameters are: Hubble constant $H_0 = (67.4\pm 0.5)$km/s/Mpc; matter density parameter $\Omega_m = 0.315\pm 0.007$; and matter fluctuation amplitude $\sigma_8 = 0.811\pm 0.006$. We find no compelling evidence for extensions to the base-$\Lambda$CDM model. Combining with BAO we constrain the effective extra relativistic degrees of freedom to be $N_{\rm eff} = 2.99\pm 0.17$, and the neutrino mass is tightly constrained to $\sum m_ u< 0.12$eV. The CMB spectra continue to prefer higher lensing amplitudes than predicted in base -$\Lambda$CDM at over $2\,\sigma$, which pulls some parameters that affect the lensing amplitude away from the base-$\Lambda$CDM model; however, this is not supported by the lensing reconstruction or (in models that also change the background geometry) BAO data. (Abridged)

Network Slicing and Softwarization: A Survey on Principles, Enabling Technologies, and Solutions

Abstract: Network slicing has been identified as the backbone of the rapidly evolving 5G technology. However, as its consolidation and standardization progress, there are no literatures that comprehensively discuss its key principles, enablers, and research challenges. This paper elaborates network slicing from an end-to-end perspective detailing its historical heritage, principal concepts, enabling technologies and solutions as well as the current standardization efforts. In particular, it overviews the diverse use cases and network requirements of network slicing, the pre-slicing era, considering RAN sharing as well as the end-to-end orchestration and management, encompassing the radio access, transport network and the core network. This paper also provides details of specific slicing solutions for each part of the 5G system. Finally, this paper identifies a number of open research challenges and provides recommendations toward potential solutions.

A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications

Abstract: Bilevel optimization is defined as a mathematical program, where an optimization problem contains another optimization problem as a constraint. These problems have received significant attention from the mathematical programming community. Only limited work exists on bilevel problems using evolutionary computation techniques; however, recently there has been an increasing interest due to the proliferation of practical applications and the potential of evolutionary algorithms in tackling these problems. This paper provides a comprehensive review on bilevel optimization from the basic principles to solution strategies; both classical and evolutionary. A number of potential application problems are also discussed. To offer the readers insights on the prominent developments in the field of bilevel optimization, we have performed an automated text-analysis of an extended list of papers published on bilevel optimization to date. This paper should motivate evolutionary computation researchers to pay more attention to this practical yet challenging area.

The human gut microbiome in early-onset type 1 diabetes from the TEDDY study

Abstract: Type 1 diabetes (T1D) is an autoimmune disease that targets pancreatic islet beta cells and incorporates genetic and environmental factors1, including complex genetic elements2, patient exposures3 and the gut microbiome4. Viral infections5 and broader gut dysbioses6 have been identified as potential causes or contributing factors; however, human studies have not yet identified microbial compositional or functional triggers that are predictive of islet autoimmunity or T1D. Here we analyse 10,913 metagenomes in stool samples from 783 mostly white, non-Hispanic children. The samples were collected monthly from three months of age until the clinical end point (islet autoimmunity or T1D) in the The Environmental Determinants of Diabetes in the Young (TEDDY) study, to characterize the natural history of the early gut microbiome in connection to islet autoimmunity, T1D diagnosis, and other common early life events such as antibiotic treatments and probiotics. The microbiomes of control children contained more genes that were related to fermentation and the biosynthesis of short-chain fatty acids, but these were not consistently associated with particular taxa across geographically diverse clinical centres, suggesting that microbial factors associated with T1D are taxonomically diffuse but functionally more coherent. When we investigated the broader establishment and development of the infant microbiome, both taxonomic and functional profiles were dynamic and highly individualized, and dominated in the first year of life by one of three largely exclusive Bifidobacterium species (B. bifidum, B. breve or B. longum) or by the phylum Proteobacteria. In particular, the strain-specific carriage of genes for the utilization of human milk oligosaccharide within a subset of B. longum was present specifically in breast-fed infants. These analyses of TEDDY gut metagenomes provide, to our knowledge, the largest and most detailed longitudinal functional profile of the developing gut microbiome in relation to islet autoimmunity, T1D and other early childhood events. Together with existing evidence from human cohorts7,8 and a T1D mouse model9, these data support the protective effects of short-chain fatty acids in early-onset human T1D.

Nonlinear Optics with 2D Layered Materials.

Abstract: 2D layered materials (2DLMs) are a subject of intense research for a wide variety of applications (e.g., electronics, photonics, and optoelectronics) due to their unique physical properties. Most recently, increasing research efforts on 2DLMs are projected toward the nonlinear optical properties of 2DLMs, which are not only fascinating from the fundamental science point of view but also intriguing for various potential applications. Here, the current state of the art in the field of nonlinear optics based on 2DLMs and their hybrid structures (e.g., mixed-dimensional heterostructures, plasmonic structures, and silicon/fiber integrated structures) is reviewed. Several potential perspectives and possible future research directions of these promising nanomaterials for nonlinear optics are also presented.

Stabilized entanglement of massive mechanical oscillators

Abstract: Quantum entanglement is a phenomenon whereby systems cannot be described independently of each other, even though they may be separated by an arbitrarily large distance 1 . Entanglement has a solid theoretical and experimental foundation and is the key resource behind many emerging quantum technologies, including quantum computation, cryptography and metrology. Entanglement has been demonstrated for microscopic-scale systems, such as those involving photons2–5, ions 6 and electron spins 7 , and more recently in microwave and electromechanical devices8–10. For macroscopic-scale objects8–14, however, it is very vulnerable to environmental disturbances, and the creation and verification of entanglement of the centre-of-mass motion of macroscopic-scale objects remains an outstanding goal. Here we report such an experimental demonstration, with the moving bodies being two massive micromechanical oscillators, each composed of about 10 12 atoms, coupled to a microwave-frequency electromagnetic cavity that is used to create and stabilize the entanglement of their centre-of-mass motion15–17. We infer the existence of entanglement in the steady state by combining measurements of correlated mechanical fluctuations with an analysis of the microwaves emitted from the cavity. Our work qualitatively extends the range of entangled physical systems and has implications for quantum information processing, precision measurements and tests of the limits of quantum mechanics.

Edge Computing for the Internet of Things: A Case Study

Abstract: The amount of data generated by sensors, actuators, and other devices in the Internet of Things (IoT) has substantially increased in the last few years IoT data are currently processed in the cloud, mostly through computing resources located in distant data centers As a consequence, network bandwidth and communication latency become serious bottlenecks This paper advocates edge computing for emerging IoT applications that leverage sensor streams to augment interactive applications First, we classify and survey current edge computing architectures and platforms, then describe key IoT application scenarios that benefit from edge computing Second, we carry out an experimental evaluation of edge computing and its enabling technologies in a selected use case represented by mobile gaming To this end, we consider a resource-intensive 3-D application as a paradigmatic example and evaluate the response delay in different deployment scenarios Our experimental results show that edge computing is necessary to meet the latency requirements of applications involving virtual and augmented reality We conclude by discussing what can be achieved with current edge computing platforms and how emerging technologies will impact on the deployment of future IoT applications

Survey on Multi-Access Edge Computing for Internet of Things Realization

Abstract: The Internet of Things (IoT) has recently advanced from an experimental technology to what will become the backbone of future customer value for both product and service sector businesses. This underscores the cardinal role of IoT on the journey toward the fifth generation of wireless communication systems. IoT technologies augmented with intelligent and big data analytics are expected to rapidly change the landscape of myriads of application domains ranging from health care to smart cities and industrial automations. The emergence of multi-access edge computing (MEC) technology aims at extending cloud computing capabilities to the edge of the radio access network, hence providing real-time, high-bandwidth, low-latency access to radio network resources. IoT is identified as a key use case of MEC, given MEC’s ability to provide cloud platform and gateway services at the network edge. MEC will inspire the development of myriads of applications and services with demand for ultralow latency and high quality of service due to its dense geographical distribution and wide support for mobility. MEC is therefore an important enabler of IoT applications and services which require real-time operations. In this survey, we provide a holistic overview on the exploitation of MEC technology for the realization of IoT applications and their synergies. We further discuss the technical aspects of enabling MEC in IoT and provide some insight into various other integration technologies therein.

Advanced Materials through Assembly of Nanocelluloses.

Abstract: There is an emerging quest for lightweight materials with excellent mechanical properties and economic production, while still being sustainable and functionalizable. They could form the basis of the future bioeconomy for energy and material efficiency. Cellulose has long been recognized as an abundant polymer. Modified celluloses were, in fact, among the first polymers used in technical applications; however, they were later replaced by petroleum-based synthetic polymers. Currently, there is a resurgence of interest to utilize renewable resources, where cellulose is foreseen to make again a major impact, this time in the development of advanced materials. This is because of its availability and properties, as well as economic and sustainable production. Among cellulose-based structures, cellulose nanofibrils and nanocrystals display nanoscale lateral dimensions and lengths ranging from nanometers to micrometers. Their excellent mechanical properties are, in part, due to their crystalline assembly via hydrogen bonds. Owing to their abundant surface hydroxyl groups, they can be easily modified with nanoparticles, (bio)polymers, inorganics, or nanocarbons to form functional fibers, films, bulk matter, and porous aerogels and foams. Here, some of the recent progress in the development of advanced materials within this rapidly growing field is reviewed.

Surface-wetting characterization using contact-angle measurements.

Abstract: Wetting, the process of water interacting with a surface, is critical in our everyday lives and in many biological and technological systems. The contact angle is the angle at the interface where water, air and solid meet, and its value is a measure of how likely the surface is to be wetted by the water. Low contact-angle values demonstrate a tendency of the water to spread and adhere to the surface, whereas high contact-angle values show the surface’s tendency to repel water. The most common method for surface-wetting characterization is sessile-drop goniometry, due to its simplicity. The method determines the contact angle from the shape of the droplet and can be applied to a wide variety of materials, from biological surfaces to polymers, metals, ceramics, minerals and so on. The apparent simplicity of the method is misleading, however, and obtaining meaningful results requires minimization of random and systematic errors. This article provides a protocol for performing reliable and reproducible measurements of the advancing contact angle (ACA) and the receding contact angle (RCA) by slowly increasing and reducing the volume of a probe drop, respectively. One pair of ACA and RCA measurements takes ~15–20 min to complete, whereas the whole protocol with repeat measurements may take ~1–2 h. This protocol focuses on using water as a probe liquid, and advice is given on how it can be modified for the use of other probe liquids.

Differentiable Monte Carlo ray tracing through edge sampling

Abstract: Gradient-based methods are becoming increasingly important for computer graphics, machine learning, and computer vision. The ability to compute gradients is crucial to optimization, inverse problems, and deep learning. In rendering, the gradient is required with respect to variables such as camera parameters, light sources, scene geometry, or material appearance. However, computing the gradient of rendering is challenging because the rendering integral includes visibility terms that are not differentiable. Previous work on differentiable rendering has focused on approximate solutions. They often do not handle secondary effects such as shadows or global illumination, or they do not provide the gradient with respect to variables other than pixel coordinates.We introduce a general-purpose differentiable ray tracer, which, to our knowledge, is the first comprehensive solution that is able to compute derivatives of scalar functions over a rendered image with respect to arbitrary scene parameters such as camera pose, scene geometry, materials, and lighting parameters. The key to our method is a novel edge sampling algorithm that directly samples the Dirac delta functions introduced by the derivatives of the discontinuous integrand. We also develop efficient importance sampling methods based on spatial hierarchies. Our method can generate gradients in times running from seconds to minutes depending on scene complexity and desired precision.We interface our differentiable ray tracer with the deep learning library PyTorch and show prototype applications in inverse rendering and the generation of adversarial examples for neural networks.

Carbon Nanotubes and Related Nanomaterials: Critical Advances and Challenges for Synthesis toward Mainstream Commercial Applications

Abstract: Advances in the synthesis and scalable manufacturing of single-walled carbon nanotubes (SWCNTs) remain critical to realizing many important commercial applications. Here we review recent breakthroughs in the synthesis of SWCNTs and highlight key ongoing research areas and challenges. A few key applications that capitalize on the properties of SWCNTs are also reviewed with respect to the recent synthesis breakthroughs and ways in which synthesis science can enable advances in these applications. While the primary focus of this review is on the science framework of SWCNT growth, we draw connections to mechanisms underlying the synthesis of other 1D and 2D materials such as boron nitride nanotubes and graphene.

Electrocatalysis of oxygen reduction on heteroatom-doped nanocarbons and transition metal–nitrogen–carbon catalysts for alkaline membrane fuel cells

Abstract: Over the last decade, great progress has been made in the development of non-precious metal catalysts for the electrochemical oxygen reduction reaction (ORR). Among these, heteroatom-doped carbon nanomaterials and transition metal–nitrogen–carbon (M–N–C) catalysts are especially advantageous in an alkaline environment, showing high electrocatalytic activity for the ORR and good durability. Over the past few years, substantial achievements have also been made in improving the performance of anion exchange membrane fuel cells (AEMFCs) and the commercialisation of these devices has emerged as a viable option. This review article provides an outline to the most relevant studies of the ORR on heteroatom-doped nanocarbons and M–N–C type catalysts in alkaline media. In addition, an overview of the studies employing these materials as cathodes in AEMFCs is presented. A separate section is devoted to the results obtained with alkaline direct methanol and ethanol fuel cells. Further perspectives in the field of AEMFC research and development are also highlighted.

Advanced Biomass-Derived Electrocatalysts for the Oxygen Reduction Reaction

Abstract: Recent progress in advanced nanostructures synthesized from biomass resources for the oxygen reduction reaction (ORR) is reviewed. The ORR plays a significant role in the performance of numerous energy-conversion devices, including low-temperature hydrogen and alcohol fuel cells, microbial fuel cells, as well as metal-air batteries. The viability of such fuel cells is strongly related to the cost of the electrodes, especially the cathodic ORR electrocatalyst. Hence, inexpensive and abundant plant and animal biomass have become attractive options to obtain electrocatalysts upon conversion into active carbon. Bioresource selection and processing criteria are discussed in light of their influence on the physicochemical properties of the ORR nanostructures. The resulting electrocatalytic activity and durability are introduced and compared to those from conventional Pt/C-based electrocatalysts. These ORR catalysts are also active for oxygen or hydrogen evolution reactions.

Hanbury-Brown and Twiss exchange and non-equilibrium-induced correlations in disordered, four-terminal graphene-ribbon conductor.

Abstract: We have investigated current-current correlations in a cross-shaped conductor made of graphene. The mean free path of charge carriers is on the order of the ribbon width which leads to a hybrid conductor where there is diffusive transport in the device arms while the central connection region displays near ballistic transport. Our data on auto and cross correlations deviate from the predictions of Landauer-Buttiker theory, and agreement can be obtained only by taking into account contributions from non-thermal electron distributions at the inlets to the semiballistic center, in which the partition noise becomes strongly modified. The experimental results display distinct Hanbury – Brown and Twiss (HBT) exchange correlations, the strength of which is boosted by the non-equilibrium occupation-number fluctuations internal to this hybrid conductor. Our work demonstrates that variation in electron coherence along atomically-thin, two-dimensional conductors has significant implications on their noise and cross correlation properties.

Gridded global datasets for Gross Domestic Product and Human Development Index over 1990–2015

Abstract: An increasing amount of high-resolution global spatial data are available, and used for various assessments. However, key economic and human development indicators are still mainly provided only at national level, and downscaled by users for gridded spatial analyses. Instead, it would be beneficial to adopt data for sub-national administrative units where available, supplemented by national data where necessary. To this end, we present gap-filled multiannual datasets in gridded form for Gross Domestic Product (GDP) and Human Development Index (HDI). To provide a consistent product over time and space, the sub-national data were only used indirectly, scaling the reported national value and thus, remaining representative of the official statistics. This resulted in annual gridded datasets for GDP per capita (PPP), total GDP (PPP), and HDI, for the whole world at 5 arc-min resolution for the 25-year period of 1990–2015. Additionally, total GDP (PPP) is provided with 30 arc-sec resolution for three time steps (1990, 2000, 2015). Machine-accessible metadata file describing the reported data (ISA-Tab format)

Vapour–liquid–solid growth of monolayer MoS 2 nanoribbons

Abstract: Chemical vapour deposition of two-dimensional materials typically involves the conversion of vapour precursors to solid products in a vapour-solid-solid mode Here, we report the vapour-liquid-solid growth of monolayer MoS2, yielding highly crystalline ribbons with a width of few tens to thousands of nanometres This vapour-liquid-solid growth is triggered by the reaction between MoO3 and NaCl, which results in the formation of molten Na-Mo-O droplets These droplets mediate the growth of MoS2 ribbons in the 'crawling mode' when saturated with sulfur The locally well-defined orientations of the ribbons reveal the regular horizontal motion of the droplets during growth Using atomic-resolution scanning transmission electron microscopy and second harmonic generation microscopy, we show that the ribbons are grown homoepitaxially on monolayer MoS2 with predominantly 2H- or 3R-type stacking Our findings highlight the prospects for the controlled growth of atomically thin nanostructure arrays for nanoelectronic devices and the development of unique mixed-dimensional structures

Political Discourse on Social Media: Echo Chambers, Gatekeepers, and the Price of Bipartisanship

Abstract: Echo chambers, i.e., situations where one is exposed only to opinions that agree with their own, are an increasing concern for the political discourse in many democratic countries. This paper studies the phenomenon of political echo chambers on social media. We identify the two components in the phenomenon: the opinion that is shared, and the »chamber» (i.e., the social network) that allows the opinion to »echo» (i.e., be re-shared in the network) -- and examine closely at how these two components interact. We define a production and consumption measure for social-media users, which captures the political leaning of the content shared and received by them. By comparing the two, we find that Twitter users are, to a large degree, exposed to political opinions that agree with their own. We also find that users who try to bridge the echo chambers, by sharing content with diverse leaning, have to pay a »price of bipartisanship» in terms of their network centrality and content appreciation. In addition, we study the role of »gatekeepers,» users who consume content with diverse leaning but produce partisan content (with a single-sided leaning), in the formation of echo chambers. Finally, we apply these findings to the task of predicting partisans and gatekeepers from social and content features. While partisan users turn out relatively easy to identify, gatekeepers prove to be more challenging.

Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification

Abstract: Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene classification.

Private Information Retrieval From MDS Coded Data in Distributed Storage Systems

Abstract: The problem of providing privacy, in the private information retrieval (PIR) sense, to users requesting data from a distributed storage system (DSS), is considered. The DSS is coded by an $(n,k,d)$ maximum distance separable code to store the data reliably on unreliable storage nodes. Some of these nodes can be spies which report to a third party, such as an oppressive regime, which data is being requested by the user. An information theoretic PIR scheme ensures that a user can satisfy its request while revealing no information on which data is being requested to the nodes. A user can trivially achieve PIR by downloading all the data in the DSS. However, this is not a feasible solution due to its high communication cost. We construct PIR schemes with low download communication cost. When there is $b=1$ spy node in the DSS, in other words, no collusion between the nodes, we construct PIR schemes with download cost $\frac {1}{1-R}$ per unit of requested data ( $R=k/n$ is the code rate), achieving the information theoretic limit for linear schemes. The proposed schemes are universal since they depend on the code rate, but not on the generator matrix of the code. Also, if $b\leq n-\delta k$ nodes collude, with $\delta =\lfloor {\frac {n-b}{k}}\rfloor $ , we construct linear PIR schemes with download cost $\frac {b+\delta k}{\delta }$ .

Multifunctional ferrofluid-infused surfaces with reconfigurable multiscale topography

Abstract: Developing adaptive materials with geometries that change in response to external stimuli provides fundamental insights into the links between the physical forces involved and the resultant morphologies and creates a foundation for technologically relevant dynamic systems1,2. In particular, reconfigurable surface topography as a means to control interfacial properties3 has recently been explored using responsive gels4, shape-memory polymers5, liquid crystals6-8 and hybrid composites9-14, including magnetically active slippery surfaces12-14. However, these designs exhibit a limited range of topographical changes and thus a restricted scope of function. Here we introduce a hierarchical magneto-responsive composite surface, made by infiltrating a ferrofluid into a microstructured matrix (termed ferrofluid-containing liquid-infused porous surfaces, or FLIPS). We demonstrate various topographical reconfigurations at multiple length scales and a broad range of associated emergent behaviours. An applied magnetic-field gradient induces the movement of magnetic nanoparticles suspended in the ferrofluid, which leads to microscale flow of the ferrofluid first above and then within the microstructured surface. This redistribution changes the initially smooth surface of the ferrofluid (which is immobilized by the porous matrix through capillary forces) into various multiscale hierarchical topographies shaped by the size, arrangement and orientation of the confining microstructures in the magnetic field. We analyse the spatial and temporal dynamics of these reconfigurations theoretically and experimentally as a function of the balance between capillary and magnetic pressures15-19 and of the geometric anisotropy of the FLIPS system. Several interesting functions at three different length scales are demonstrated: self-assembly of colloidal particles at the micrometre scale; regulated flow of liquid droplets at the millimetre scale; and switchable adhesion and friction, liquid pumping and removal of biofilms at the centimetre scale. We envision that FLIPS could be used as part of integrated control systems for the manipulation and transport of matter, thermal management, microfluidics and fouling-release materials.

Adaptive configuration of lora networks for dense IoT deployments

Abstract: Large-scale Internet of Things (IoT) deployments demand long-range wireless communications, especially in urban and metropolitan areas. LoRa is one of the most promising technologies in this context due to its simplicity and flexibility. Indeed, deploying LoRa networks in dense IoT scenarios must achieve two main goals: efficient communications among a large number of devices and resilience against dynamic channel conditions due to demanding environmental settings (e.g., the presence of many buildings). This work investigates adaptive mechanisms to configure the communication parameters of LoRa networks in dense IoT scenarios. To this end, we develop FLoRa, an open-source framework for end-to-end LoRa simulations in OMNeT++. We then implement and evaluate the Adaptive Data Rate (ADR) mechanism built into LoRa to dynamically manage link parameters for scalable and efficient network operations. Extensive simulations show that ADR is effective in increasing the network delivery ratio under stable channel conditions, while keeping the energy consumption low. Our results also show that the performance of ADR is severely affected by a highly-varying wireless channel. We thereby propose an improved version of the original ADR mechanism to cope with variable channel conditions. Our proposed solution significantly increases both the reliability and the energy efficiency of communications over a noisy channel, almost irrespective of the network size. Finally, we show that the delivery ratio of very dense networks can be further improved by using a network-aware approach, wherein the link parameters are configured based on the global knowledge of the network.

Antifouling membranes for oily wastewater treatment: Interplay between wetting and membrane fouling

Abstract: Oily wastewater is an extensive source of pollution to soil and water, and its harmless treatment is of great importance for the protection of our aquatic ecosystems. Membrane filtration is highly desirable for removing oil from oily water because it has the advantages of energy efficiency, easy processing and low maintenance cost. However, membrane fouling during filtration leads to severe flux decline and impedes long-term operation of membranes in practical wastewater treatment. Membrane fouling includes reversible fouling and irreversible fouling. The fouling mechanisms have been explored based on classical fouling models, and on oil droplet behaviors (such as droplet deposition, accumulation, coalescence and wetting) on the membranes. Membrane fouling is dominated by droplet-membrane interaction, which is influenced by the properties of the membrane (e.g., surface chemistry, structure and charge) and the wastewater (e.g., compositions and concentrations) as well as the operation conditions. Typical membrane antifouling strategies, such as surface hydrophilization, zwitterionic polymer coating, photocatalytic decomposition and electrically enhanced antifouling are reviewed, and their cons and pros for practical applications are discussed.

Direction of Arrival Estimation for Multiple Sound Sources Using Convolutional Recurrent Neural Network

Abstract: This paper proposes a deep neural network for estimating the directions of arrival (DOA) of multiple sound sources. The proposed stacked convolutional and recurrent neural network (DOAnet) generates a spatial pseudo-spectrum (SPS) along with the DOA estimates in both azimuth and elevation. We avoid any explicit feature extraction step by using the magnitudes and phases of the spectrograms of all the channels as input to the network. The proposed DOAnet is evaluated by estimating the DOAs of multiple concurrently present sources in anechoic, matched and unmatched reverberant conditions. The results show that the proposed DOAnet is capable of estimating the number of sources and their respective DOAs with good precision and generate SPS with high signal-to-noise ratio.