Computational characterization of nanosystems

Thermal radiative emission from a hot surface to a cold surface plays an important role in many applications, including energy conversion, thermal management, lithography, data storage and thermal microscopy. Recent studies on bulk materials have confirmed long-standing theoretical predictions indicating that when the gap between the surfaces is reduced to tens of nanometres, well below the peak wavelength of the blackbody emission spectrum, the radiative heat flux increases by orders of magnitude. However, despite recent attempts, whether such enhancements can be obtained in nanoscale dielectric films thinner than the penetration depth of thermal radiation, as suggested by theory, remains experimentally unknown. Here, using an experimental platform that comprises a heat-flow calorimeter with a resolution of about 100 pW (ref. 7), we experimentally demonstrate a dramatic increase in near-field radiative heat transfer, comparable to that obtained between bulk materials, even for very thin dielectric films (50-100 nm) when the spatial separation between the hot and cold surfaces is comparable to the film thickness. We explain these results by analysing the spectral characteristics and mode shapes of surface phonon polaritons, which dominate near-field radiative heat transport in polar dielectric thin films.

Cycle bases in graphs characterization, algorithms, complexity, and applications

Knowledge graphs play an important role in many applications, such as link prediction and question answering. Translating embedding for knowledge graphs is done with the aim of encoding structured information on entities and their rich relations in a low-dimensional embedding space. TransE is one of the most important methods in translation-based models, and uses translation invariance to implement translating embedding for knowledge graphs. In this line of work, translating embedding models represent the relation as a translation from the head entity to the tail entity and have achieved impressive results. Currently, the TransE model is only developed on single-node machines. Unfortunately, the computing and storage capacities of a single machine can easily reach their limits as knowledge graphs become larger and more complex, which limits the application scope of TransE. In order to solve this problem, we propose a distributed TransE method, known as DTransE, which can utilize distributed computing resources to calculate knowledge graph embeddings. However, building a distributed TransE is complicated and involves challenges of knowledge graph partitioning and computation. To solve these challenges, we provide a high-quality edge partitioning algorithm for the power-law graph by considering the high-degree and low-degree vertices with adaptive weights, which can balance the workload. By using the unactivated Gather-Apply-Scatter model on TransE, the processes periodically exchange messages in a loop. The irregular data distribution among the processes is also optimized to further accelerate communication. As far as we know, this is the first work on a distributed TransE method. We use link prediction to evaluate the DTransE in a distributed environment. Experiments show that, compared to the original TransE method, our proposed DTransE is, on average, 24.5 times faster with a minimum loss of accuracy; compared to the state-of-the-art parallel TransE implementation, DTransE is two times faster on average.

Micrometre- and nanometre-sized particles play important roles in many applications, including catalysis, optics, biosensing and data storage. Organic particles are usually prepared through polymerization of suitable monomers or precipitation methods. In the case of inorganic materials, particle fabrication tends to involve the reduction of a metal salt, or the controlled mixing of salt solutions supplying a metal cation and an elemental anion (for example, S2-, Se2-, O2-), respectively; in some instances, these methods even afford direct control over the shape of the particles produced. Another class of materials are metal-organic coordination polymers, which are based on metal ions coordinated by polydentate organic ligands and explored for potential use in catalysis, gas storage, nonlinear optics and molecular recognition and separations. In a subset of these materials, the use of organometallic complexes as ligands (so-called metalloligands) provides an additional level of tailorability, but these materials have so far not yet been fashioned into nano- or microparticles. Here we show that simple addition of an initiation solvent to a precursor solution of metal ions and metalloligands results in the spontaneous and fully reversible formation of a new class of metal-metalloligand particles. We observe initial formation of particles with diameters of a few hundred nanometres, which then coalesce and anneal into uniform and smooth microparticles. The ease with which these particles can be fabricated, and the ability to tailor their chemical and physical properties through the choice of metal and organic ligand used, should facilitate investigations of their scope for practical applications.

Node localization technique is a fundamental and crucial issue for the wireless sensor networks. Localization algorithms play important role for many applications of wireless sensor networks. In this paper, we discuss wireless senor networks architecture and localization algorithms classification. The DV-Hop algorithm has been concentrated on.

Top-k similarity join on high-dimensional data plays an important role in many applications. The traditional tree-like index based approaches can’t deal with large scale high-dimensional data efficiently because of “curse of dimensionality”. So in this paper, we firstly propose an approach to construct the similarity distribution histogram using stratified sampling method, then to estimate the similarity threshold according to the number of the required returned results, finally we propose a novel Top-k similarity join algorithm based on similarity distribution histogram. We conduct comprehensive experiments and the experimental results show that our proposed approaches has good efficiency and scalability.

Sweep coverage plays an important role in many applications like data gathering, sensing coverage, and devices control, etc. In this paper, we deal with sweep coverage problem with multiple mobile sensors to periodically cover n targets in the surveillance region. We propose three constant-factor approximations, namely CycleSplit, CoCycle, and AugPrim, to minimize the longest trajectory length of mobile sensors under different scenarios respectively. CycleSplit deals with non-cooperative sweep coverage problem (NCSC), in which each mobile sensor works independently along different trajectory cycle. It has an approximation ratio of (5-\frac{2}{n-m+1}), which improves the best known approximation ratio of 5. CoCycle solves the cooperative sweep coverage problem (CSC), in which multiple sensors work together to cover $n$ targets. Its approximation ratio is 4, which outperforms the state-of-art best approximation ratio of 6. AugPrim is also a 6-approximation. It computes the sensor route for multi-sink sweep coverage problem (MSSC), where each sensor has to transmit the gathered data to one of the given sinks in each detection period. It implements the modified Prim algorithm and augment paths in turn according to the sink positions. AugPrim is the first approximation for sweep coverage problem with sinks. Finally, we provide various numerical experiments and comparisons with several previous work to validate the efficiency of our design.

Anonymous channels or similar techniques that achieve sender's anonymity play important roles in many applications, e.g. electronic voting. However, they will be meaningless if cryptographic primitives containing sender's identity are carelessly used during the transmission. In computationally secure settings, this problem may be easily overcome by using public key encryption and group signatures. However, in an unconditionally secure setting, in which no computational difficulty is assumed, this is not an easy case as such. As the increasing computational power approaches the point where security policy can no longer assume the difficulty of solving factoring or discrete logarithm problems, it must shift its focus to assuring the solvency of unconditionally secure schemes that provide long-term security. The main contribution of this paper is to study the security primitives for the above problem. In this paper, we first define the unconditionally secure asymmetric encryption scheme, which is an encryption scheme with unconditional security and where it is impossible for a receiver to deduce the identity of a sender from the encrypted message. We also investigate tight lower bounds on required memory sizes from an information theoretic viewpoint and show an optimal construction based on polynomials. It is remarkable to see that these bounds are considerably different from those in Shannon's model of the conventional unconditionally secure symmetric encryption. Other than the polynomial-based scheme, we also show a construction based on combinatorial theory, a non-malleable scheme and a multi-receiver scheme. Then, we define and formalize the group authentication code (GA-code), which is an unconditionally secure authentication code with anonymity like group signatures. In this scheme, any authenticated user will be able to generate and send an authenticated message while the receiver can verify the legitimacy of the message---that it has been sent from a legitimate user but at the same time retains his anonymity. However, by cooperating with the group authority, such as in the case of disputes, the receiver is able to obtain information of the user's identity. For GA-code, we show two concrete constructions.

High-accuracy distance measurement plays an important role in many applications, such as industry measurement, Aerospace and scientific research. The continual development oflaser ranging technique is always a frontier topic of geometric measurement, therefore this paper develops the application of femtosecond optical frequency comb for length measurement. Time-of-flight principle for distance measurement is widely used by pulse laser, however, achievable resolution reaches only a few millimeters at best due to the limited bandwidth of electronics. In this paper, the temporal coherence of multiple pulse train is analyzed, and an arbitrary and absolute length measurement system is set up based on modified Michelson interferometer by combining multiple pulse train interference and time-of-flight method. The timing difference is separately calculated by the first-order and second-order optical cross-correlation signals, therefore the absolute distance is calculated. An experiment is conducted by measuring a 60 cm length, and the measurement result is compared with a high precision laser displacement sensor. The result shows a good measuring linearity, and the measurement precision of ±0.5 μm is achieved.

Structural health monitoring has arisen as an important tool for managing and maintaining civil infrastructure. A critical problem for all structural health monitoring systems is data loss or data corruption due to sensor failure or other malfunctions, which bring into question in subsequent structural health monitoring data analysis and decision-making. Probability density functions play a very important role in many applications for structural health monitoring. This article focuses on data loss compensation for probability density function estimation in structural health monitoring using imputation methods. Different from common data, continuous probability density functions belong to functional data; the conventional distribution-to-distribution regression technique has significant potential in missing probability density function imputation; however, extrapolation and directly borrowing shape information from the covariate probability density function are the main challenges. Inspired by the warping transformation of distributions in the field of functional data analysis, a new distribution regression approach for imputing missing correlated probability density functions is proposed in this article. The warping transformation for distributions is a mapping operation used to transform one probability density function to another by deforming the original probability density function with a warping function. The shape mapping between probability density functions can be characterized well by warping functions. Given a covariate probability density function, the warping function is first estimated by a kernel regression model; then, the estimated warping function is used to transform the covariate probability density function and obtain an imputation for the missing probability density function. To address issues with poor performance when the covariate probability density function is contaminated, a hybrid approach is proposed that fuses the imputations obtained by the warping transformation approach with the conventional distribution-to-distribution regression approach. Experiments based on field monitoring data are conducted to evaluate the performance of the proposed approach. The corresponding results indicate that the proposed approach can outperform the conventional method, especially in extrapolation. The proposed approach shows good potential to provide more reliable estimation of distributions of missing structural health monitoring data.

Torque / load sensors will play an increasingly important role in many applications, provided that functional requirements and cost targets for serial production are met. They enable critical benefits such as efficiency improvements, weight savings, operational safety, enhanced productivity, and cost savings. Magneto-elastic sensors, being truly non-contact based, have inherent advantages over traditional solutions based on strain gauges regarding robustness and cost structures. Recently, they have emerged as a viable, commercially proven alternative in a number of industry sectors with demanding requirements. The technical and commercial readiness level of magneto-elastic sensor technology is presented and discussed in this presentation. Also, an outlook is presented regarding recent developments in making magneto-elastic sensor technology applicable to non-magnetic metals.

As machine vision develops and shows increasing power in solving problems, it is given a more important role in many applications, such as teleoperation. One of the proposed systems is to have wide field of view and high resolution at low cost, by using slaved high resolution insert on top of a low resolution wide field of view background. In such a system, there is a need to register images with rotational/scale/translational (RST) changes introduced by two different cameras. Then two pictures are fused or projected to give a wide field of view with high resolution insert in the area of interest (AOI). For real time implementation of such software, there are a number of factors that have an impact on the performance index such as the estimation accuracy, reliability, and the computational cost. Our analysis show that the relations between these factors and the estimation performance can be quantified and used as guidelines in the implementation. Both a correlation-based approach and a feature-based approach are investigated in this paper.

Localization plays an important role in many applications. One of the positioning systems developed for indoor environments is İÇKON that can calculate the position of an object at cm accuracy by using pure ultrasonic signals. In this study, the ICKON system is switched to wireless sensor network to reduce setup time, and a mobile application is developed to determine transmitter positions. Switching to wireless infrastructure also provides to use the existing wireless communication infrastructure in indoor environments.

The diffusion plays an important role in many applications when the impurities are employed to tune the semiconductor's electrical or optical properties, which make it essential to understand theoretically the microscopic mechanisms governing how dopant defects diffuse. Using first-principles calculations, we compare the diffusion behaviors and migration barriers of interstitial Cu , Ag , and Au atoms in II–VI compounds ZnSe . We consider interstitial diffusion mechanisms and calculate the corresponding activation energies. For noble atoms, we find that the interstitial mediated mechanism is the dominant one. We also find that the relative size of dopant atoms and constituent atoms of II–VI compounds is an important factor affecting the diffusion behaviors. The coupling in ZnSe between Cu d levels and unoccupied host s levels is not as strong as that in CdTe .

ABSTRACTLocal place names are frequently used by residents living in a geographic region. Such place names may not be recorded in existing gazetteers, due to their vernacular nature, relative insignificance to a gazetteer covering a large area (e.g. the entire world), recent establishment (e.g. the name of a newly-opened shopping center) or other reasons. While not always recorded, local place names play important roles in many applications, from supporting public participation in urban planning to locating victims in disaster response. In this paper, we propose a computational framework for harvesting local place names from geotagged housing advertisements. We make use of those advertisements posted on local-oriented websites, such as Craigslist, where local place names are often mentioned. The proposed framework consists of two stages: natural language processing (NLP) and geospatial clustering. The NLP stage examines the textual content of housing advertisements and extracts place name candidates. The geospatial stage focuses on the coordinates associated with the extracted place name candidates and performs multiscale geospatial clustering to filter out the non-place names. We evaluate our framework by comparing its performance with those of six baselines. We also compare our result with four existing gazetteers to demonstrate the not-yet-recorded local place names discovered by our framework.