Material classification in natural settings is a challenge due to complex interplay of geometry , reflectance properties , and illumination . Previous work on material classification relies strongly on hand-engineered features of visual samples . In this work we use a Convolutional Neural Network ( convnet ) that learns descriptive features for the specific task of material recognition . Traditionally , material surface classification has relied on reflectance spectrum measurement and pixel-wise comparison . For best results , the measurement usually requires measurement of the full spectral reflectance which can be the time-consuming issue and error-prone . This paper describes a novel approach for the segmentation of complex images to determine candidates for accurate material-type classification . The proposed approach identifies classification candidates based on image quality calculated from viewing distance and angle information . The proposed segmentation approach is demonstrated on an autonomous bridge maintenance system and validated using gray level co-occurrence matrix ( GLCM ) features combined with a naive Bayes classifier . Experimental results demonstrate the effects of viewing distance and angle on classification accuracy and the benefits of segmenting images using 3D geometry information to identify candidates for accurate material-type classification . The influence of the fitting algorithm on the classification performances is related to the noise level . The obtained results using neural network demonstrated that the proposed method outperformed in material-based classification and shape-based classification . In the material-based classification , the classifier generally has a weaker ability to distinguish between permeable materials . This paper takes into consideration the task of materials classification int o four main types of organics and metals in images obtained from Dual-Energy X-ray ( DEXA ) security scanner . We a dopt well-known methods of machine learning and conduct experiments to examine the effects of various co mbinations of data and algorithms for generalization of the material classification problem . The methods giv in the best results ( Random Forests and Support Vector Machine ) were used to predict the materials at every p ixel in the testing image . For this reason , robots have been highly successful in manoeuvring deformable objects mainly in the industrial sector , where the types of interactions are predictable and highly restricted , but research in everyday environments remains largely unexplored . The contributions of this thesis are : i ) the application of an elastic/plastic mass-spring method to model and predict the behaviour of deformable objects manipulated by a robot ; ii ) the automatic calibration of the parameters of the model , using images of real objects as ground truth ; iii ) the use of piece-wise regression curves to predict the reaction forces , and iv ) the use of the output of this force prediction model as input for the mass-spring model which in turn predicts object deformations ; v ) the use of the obtained models to solve a material classification problem , where the robot must recognise a material based on interaction with it . The pros and cons of the different spaces and the procedures for converting color data among them are discussed in detail . An experiment based on 12 different image data sets was carried out to comparatively evaluate the performance of each space for material classification purposes . The results showed that CIELAB markedly outperformed the other spaces followed by HSV and CIELUV . There are several differences in technical aspects of the respective models . bell2014material focus on the problem of material classification , while zheng2015crfrnn unroll the CRF mean - field inference steps to convert the whole system into an end - to - end trainable feed - forward network . We have updated our proposed “ DeepLab ” system with much improved methods and results in our latest work chen2016deeplab . In the U.S. , the Fusion Nuclear Science Facility ( FNSF ) is viewed as an essential element of the fusion developmental roadmap . The tritium self-sufficiency , blanket testing , and materials testing are of particular interest since they define a critical element of the FNSF mission . There is a definitive need to breed the majority of , if not all , the tritium required for operation . The selected materials applications discussed illustrate design constraints arising from concerns about plasma-materials interactions , cyclic operation and neutron radiation damage . Materials testing in FED is also discussed with emphasis on a broad view of the role of FED in materials development . A material that can survive the rigorous testing set forth in this flowchart will be a particularly promising candidate for incorporation into an industrially deployable device for PEC hydrogen production . This organized approach to PEC characterization is intended to streamline the process for material screening so that discovery of promising candidates occurs at a faster and more orderly pace . X-ray and /spl gamma/-ray radiography have been indispensable techniques for materials inspection for many years . Internal structure is typically inferred from radiographs by human evaluation . Since each manufacturer has its own unique technology for introducing immersion water , top-coat/resist processes needed not only to meet the end users ' performance criteria but also meet each scanner manufacturer 's requirements . Therefore material screening process and process evaluation became an important factor in immersion technology processes . Defectivity became the primary criterion for the resist process . The desktop-type 3D printing material shaping machine is characterized in that the extruding system , the drive system and the heating/cooling device jointly form the main body part of the desktop-type 3D printing material shaping machine ; a control system is connected with a controlled system by a control panel and relevant control devices thereof , thereby forming a complete integrated structure of the desktop-type 3D printing material shaping machine . According to the material need of the desktop-type 3D printer provided by the invention , a series of practical problems of few using materials , small material varieties , short storage time , difficulty in novel material screening/synthesizing , and the like in experiment research and in the process of single-piece or small-batch production of an existing 3D printing technology are solved well , and the desktop-type 3D printing material shaping machine has important practical value and practical significance on promoting the practical application of the 3D printing technology , especially designing the 3D experiments of research and development institutions , screening/synthesizing novel materials , accelerating the pace of scientific research , and lowering the research and development cost . The paper presents a conceptual framework which facilitates an interface between material selection and product safety/liability at the three planning-horizon levels of strategic , intermediate , and tactical . The five specific areas of interface include : ( 1 ) strategic material development and usage , ( 2 ) product safety , ( 3 ) product liability , ( 4 ) materials selection and testing and ( 5 ) materials usage and processing . Then , five propositions pertaining to each area of interface at each level of planning are proposed and evaluated for a company as an example . Abstract Changes in attitudes and behavior are just as important as technological innovations and policy decisions when it comes to the transformation of energy systems . However , social science still plays a minor role with respect to energy research . Energy efficiency is key and fundamental in the SC . The transformation of energy systems due to the increased deployment of renewable energy is occurring mostly in the electricity sector , in which recent PV numbers show an undeniable landmark in renewable energies . Being able to detect , to identify and to quantify the severity of defects that appear within modules is essential to constitute a reliable , efficient and safety system , avoiding energy losses , mismatches and safety issues , especially in case of building integrated systems , as overheated anomalies could generate a fire risk or an electrical hazard . These algorithms are useful for local deformations of linear molecules , exact ring closure in cyclic molecules and molecular embedding for short chains . Other possible applications include structure prediction , protein folding , conformation energy analysis and 3D molecular matching and docking . The algorithms are applicable to all serial molecular chains and make no assumptions about their geometry . The costs are assessed with a model that is modified from procedures adopted by International Maritime Organization ( IMO ) . Also an innovative model for deformation energy estimation in ship-ship collisions is presented . The presented study concerns selected area in The Gulf of Finland ( GOF ) , which is one of the most densely trafficked sea areas in the world . The transformation of the energy system requires new methods for its modeling and simulation , as established methods are facing problems in representing the increasing complexity and flexibility . We present the modeling and simulation framework MOCES that is based on the modeling language Modelica . Second , I will discuss the methods for accurate computation of defect properties , which is important as the defectiveness is intrinsic to the low-cost synthesized materials . I will introduce a method for calculation of defect formation energies by minimizing the error due to the “ band-gap problem ” of the density functional theory . I will also discuss approaches to mitigating the effects of defects , e.g. , by passivation . Ltd .. A new fast algorithm isproposed for sub -- band analysis and synthesis method of MPEG for real time . The operation of this algorithm is 1/5 and 1/10 of MPEG respectively . A systematic behavior of the k -space localization parameter σ k correcting the band-gap has been observed in numerical experiments . On that basis some sampling schemes for band-gap correction have been proposed and tested in the prediction of the band-gap behavior in I n x G a ( 1 − x ) N semiconducting alloy , and a very good agreement with independent calculations has been obtained . In the context of the work the issue of electron localization in the r -space has been discussed which , as it has been predicted by Mori-Sanchez et al. [ P. Mori-Sanchez , A.J. Cohen , W. Yang , Phys . In this paper the consistency between the present " exact " theory and three other models will be examined in the context of analyzing a diode , transistor , and thyristor . In addition , the available experimental and theoretical data on band gap narrowing ( BGN ) will be studied including recent data from an electron beam probing experiment ( EBIC ) . The latter allows a model for BGN to be formulated based solely on experimental data ar concentrations up to 1020cm-3 . They are advantageously employed to describe the band structure of the ordered alloys in terms of the random one . Band gap reduction , removal of degeneracy at some symmetry points of the 1BZ and the doubling of the number of bands is concluded for the ordered alloy compared to the ordered one . A quite good agreement is obtained when comparing the ordered band structure obtained by symmetry arguments with an empirical tight binding calculation . Puckering of the rings to achieve a more wurtzite-like structure destabilizes the needles , although this destabilization is reduced by going to infinite needles ( calculated using periodic boundary conditions ) . Calculations of density of states ( DOS ) curves and band gaps for finite clusters and infinite needles highlight opportunities for band-gap tuning through kinetic control of nanocrystal growth . The randomness nature of the fabrication process of the photonic crystals is taken into account in this analysis . The technique is applied on the bandgap calculation of two-dimensional photonic crystals to study the effect of manufacturing imperfections on the photonic band gap . This helps to establish a design for manufacturability for the photonic crystals The goal is to explore and extend the design space of lightweight structural systems , by designing multi-functional periodic structural elements , preserving structural stiffness while concurrently enabling sufficiently wideband damping performance over a target frequency range of interest . Following a generalized theoretical modeling framework for bandgap design and analysis in finite structures , the focus is placed on the design , fabrication , and analysis of a load-carrying frame development with internally resonant components . Finite-element modeling is employed to design and analyze the frequency response of the frame and simplified analytical solution is compared with this numerical solution . Fabricated structures have a potential as 2D tunable photonic crystals . Bandgap formation in the near IR band was studied with a FTIR spectrometer . A distinct bandgap located in the vicinity of 1 /spl mu/m was revealed . This paper summarizes improved device physics for numerical simulations of solid-state devices with dopant densities up to about 3 × 1020cm-3and with junction depths as small as 0.1 µm . This improved device physics pertains to bandgap narrowing , effective intrinsic carrier concentrations , carrier mobilities , and lifetimes . When this improved physics is incorporated into device analysis codes such as SEDAN and then used to compute the electrical performance of n-p-n transistors , the predicted values agree very well with the measured values of the current-voltage characteristics and dc common emitter gains for devices with emitter-base junction depths between 10 - 0.16 µm . Feynman predicted that a quantum simulator — a special-purpose ‘ analogue ’ processor built using quantum bits (qubits)—would be inherently suited to solving such problems . In the context of quantum magnetism , a number of experiments have demonstrated the feasibility of this approach , but simulations allowing controlled , tunable interactions between spins localized on two- or three-dimensional lattices of more than a few tens of qubits have yet to be demonstrated , in part because of the technical challenge of realizing large-scale qubit arrays . Here we demonstrate a variable-range Ising-type spin – spin interaction , Ji , j , on a naturally occurring , two-dimensional triangular crystal lattice of hundreds of spin-half particles ( beryllium ions stored in a Penning trap ) . In particular , we present two important results based on simulation which one might consider in their interpretation of magnetic data when selfdemagnetization is present . First , current methods for estimating total magnetization , which are typically applied to the problem of remanent magnetization , do not reliably recover this parameter when the anomalous source bodies have high magnetic susceptibilities . And second , a single value estimation of total magnetization does not provide adequate information to properly resolve subsurface geology through inversion . In three-dimensional space , the only possibilities for identical particles are bosons and fermions . But for physics in two dimensions , there is , as one of the essays describes , a continuous range of intermediate possibilities ( ‘ anyons ’ ) with important applications in solid-state magnetism . I was disappointed by the absence of an account of Wilczek ’s astonishing work with Alfred Shapere , applying modern geometrical ideas to the swimming of small creatures and the reorientation of falling bodies such as cats . Despite considerable advances in quantitative interpretation methods for discrimination , the state of practice is qualitative or empirical . The UXO thrust of the Army Engineer Research and Development Center 's ( ERDC ) Environmental Quality Technology Program seeks to develop enhanced detection and discrimination capability for survey data from total field magnetometry , time-domain electromagnetic induction , and frequency-domain electromagnetic induction methods . Enhanced discrimination capability by formal geophysical inversion is demonstrated at documented test sites and live sites .
Material classification in natural settings is a challenge due to complex interplay of geometry , reflectance properties , and illumination . Previous work on material classification relies strongly on hand-engineered features of visual samples . In this work we use a Convolutional Neural Network ( convnet ) that learns descriptive features for the specific task of material recognition .
Traditionally , material surface classification has relied on reflectance spectrum measurement and pixel-wise comparison . For best results , the measurement usually requires measurement of the full spectral reflectance which can be the time-consuming issue and error-prone .
This paper describes a novel approach for the segmentation of complex images to determine candidates for accurate material-type classification . The proposed approach identifies classification candidates based on image quality calculated from viewing distance and angle information .
The proposed segmentation approach is demonstrated on an autonomous bridge maintenance system and validated using gray level co-occurrence matrix ( GLCM ) features combined with a naive Bayes classifier . Experimental results demonstrate the effects of viewing distance and angle on classification accuracy and the benefits of segmenting images using 3D geometry information to identify candidates for accurate material-type classification .
The influence of the fitting algorithm on the classification performances is related to the noise level . The obtained results using neural network demonstrated that the proposed method outperformed in material-based classification and shape-based classification . In the material-based classification , the classifier generally has a weaker ability to distinguish between permeable materials .
This paper takes into consideration the task of materials classification int o four main types of organics and metals in images obtained from Dual-Energy X-ray ( DEXA ) security scanner . We a dopt well-known methods of machine learning and conduct experiments to examine the effects of various co mbinations of data and algorithms for generalization of the material classification problem . The methods giv in the best results ( Random Forests and Support Vector Machine ) were used to predict the materials at every p ixel in the testing image .
For this reason , robots have been highly successful in manoeuvring deformable objects mainly in the industrial sector , where the types of interactions are predictable and highly restricted , but research in everyday environments remains largely unexplored . The contributions of this thesis are : i ) the application of an elastic/plastic mass-spring method to model and predict the behaviour of deformable objects manipulated by a robot ; ii ) the automatic calibration of the parameters of the model , using images of real objects as ground truth ; iii ) the use of piece-wise regression curves to predict the reaction forces , and iv ) the use of the output of this force prediction model as input for the mass-spring model which in turn predicts object deformations ; v ) the use of the obtained models to solve a material classification problem , where the robot must recognise a material based on interaction with it .
The pros and cons of the different spaces and the procedures for converting color data among them are discussed in detail . An experiment based on 12 different image data sets was carried out to comparatively evaluate the performance of each space for material classification purposes . The results showed that CIELAB markedly outperformed the other spaces followed by HSV and CIELUV .
There are several differences in technical aspects of the respective models . bell2014material focus on the problem of material classification , while zheng2015crfrnn unroll the CRF mean - field inference steps to convert the whole system into an end - to - end trainable feed - forward network . We have updated our proposed “ DeepLab ” system with much improved methods and results in our latest work chen2016deeplab .
In the U.S. , the Fusion Nuclear Science Facility ( FNSF ) is viewed as an essential element of the fusion developmental roadmap . The tritium self-sufficiency , blanket testing , and materials testing are of particular interest since they define a critical element of the FNSF mission . There is a definitive need to breed the majority of , if not all , the tritium required for operation .
The selected materials applications discussed illustrate design constraints arising from concerns about plasma-materials interactions , cyclic operation and neutron radiation damage . Materials testing in FED is also discussed with emphasis on a broad view of the role of FED in materials development .
A material that can survive the rigorous testing set forth in this flowchart will be a particularly promising candidate for incorporation into an industrially deployable device for PEC hydrogen production . This organized approach to PEC characterization is intended to streamline the process for material screening so that discovery of promising candidates occurs at a faster and more orderly pace .
X-ray and /spl gamma/-ray radiography have been indispensable techniques for materials inspection for many years . Internal structure is typically inferred from radiographs by human evaluation .
Since each manufacturer has its own unique technology for introducing immersion water , top-coat/resist processes needed not only to meet the end users ' performance criteria but also meet each scanner manufacturer 's requirements . Therefore material screening process and process evaluation became an important factor in immersion technology processes . Defectivity became the primary criterion for the resist process .
The desktop-type 3D printing material shaping machine is characterized in that the extruding system , the drive system and the heating/cooling device jointly form the main body part of the desktop-type 3D printing material shaping machine ; a control system is connected with a controlled system by a control panel and relevant control devices thereof , thereby forming a complete integrated structure of the desktop-type 3D printing material shaping machine . According to the material need of the desktop-type 3D printer provided by the invention , a series of practical problems of few using materials , small material varieties , short storage time , difficulty in novel material screening/synthesizing , and the like in experiment research and in the process of single-piece or small-batch production of an existing 3D printing technology are solved well , and the desktop-type 3D printing material shaping machine has important practical value and practical significance on promoting the practical application of the 3D printing technology , especially designing the 3D experiments of research and development institutions , screening/synthesizing novel materials , accelerating the pace of scientific research , and lowering the research and development cost .
The paper presents a conceptual framework which facilitates an interface between material selection and product safety/liability at the three planning-horizon levels of strategic , intermediate , and tactical . The five specific areas of interface include : ( 1 ) strategic material development and usage , ( 2 ) product safety , ( 3 ) product liability , ( 4 ) materials selection and testing and ( 5 ) materials usage and processing . Then , five propositions pertaining to each area of interface at each level of planning are proposed and evaluated for a company as an example .
Abstract Changes in attitudes and behavior are just as important as technological innovations and policy decisions when it comes to the transformation of energy systems . However , social science still plays a minor role with respect to energy research .
Energy efficiency is key and fundamental in the SC . The transformation of energy systems due to the increased deployment of renewable energy is occurring mostly in the electricity sector , in which recent PV numbers show an undeniable landmark in renewable energies . Being able to detect , to identify and to quantify the severity of defects that appear within modules is essential to constitute a reliable , efficient and safety system , avoiding energy losses , mismatches and safety issues , especially in case of building integrated systems , as overheated anomalies could generate a fire risk or an electrical hazard .
These algorithms are useful for local deformations of linear molecules , exact ring closure in cyclic molecules and molecular embedding for short chains . Other possible applications include structure prediction , protein folding , conformation energy analysis and 3D molecular matching and docking . The algorithms are applicable to all serial molecular chains and make no assumptions about their geometry .
The costs are assessed with a model that is modified from procedures adopted by International Maritime Organization ( IMO ) . Also an innovative model for deformation energy estimation in ship-ship collisions is presented . The presented study concerns selected area in The Gulf of Finland ( GOF ) , which is one of the most densely trafficked sea areas in the world .
The transformation of the energy system requires new methods for its modeling and simulation , as established methods are facing problems in representing the increasing complexity and flexibility . We present the modeling and simulation framework MOCES that is based on the modeling language Modelica .
Second , I will discuss the methods for accurate computation of defect properties , which is important as the defectiveness is intrinsic to the low-cost synthesized materials . I will introduce a method for calculation of defect formation energies by minimizing the error due to the “ band-gap problem ” of the density functional theory . I will also discuss approaches to mitigating the effects of defects , e.g. , by passivation .
Ltd .. A new fast algorithm isproposed for sub -- band analysis and synthesis method of MPEG for real time . The operation of this algorithm is 1/5 and 1/10 of MPEG respectively .
A systematic behavior of the k -space localization parameter σ k correcting the band-gap has been observed in numerical experiments . On that basis some sampling schemes for band-gap correction have been proposed and tested in the prediction of the band-gap behavior in I n x G a ( 1 − x ) N semiconducting alloy , and a very good agreement with independent calculations has been obtained . In the context of the work the issue of electron localization in the r -space has been discussed which , as it has been predicted by Mori-Sanchez et al. [ P. Mori-Sanchez , A.J. Cohen , W. Yang , Phys .
In this paper the consistency between the present " exact " theory and three other models will be examined in the context of analyzing a diode , transistor , and thyristor . In addition , the available experimental and theoretical data on band gap narrowing ( BGN ) will be studied including recent data from an electron beam probing experiment ( EBIC ) . The latter allows a model for BGN to be formulated based solely on experimental data ar concentrations up to 1020cm-3 .
They are advantageously employed to describe the band structure of the ordered alloys in terms of the random one . Band gap reduction , removal of degeneracy at some symmetry points of the 1BZ and the doubling of the number of bands is concluded for the ordered alloy compared to the ordered one . A quite good agreement is obtained when comparing the ordered band structure obtained by symmetry arguments with an empirical tight binding calculation .
Puckering of the rings to achieve a more wurtzite-like structure destabilizes the needles , although this destabilization is reduced by going to infinite needles ( calculated using periodic boundary conditions ) . Calculations of density of states ( DOS ) curves and band gaps for finite clusters and infinite needles highlight opportunities for band-gap tuning through kinetic control of nanocrystal growth .
The randomness nature of the fabrication process of the photonic crystals is taken into account in this analysis . The technique is applied on the bandgap calculation of two-dimensional photonic crystals to study the effect of manufacturing imperfections on the photonic band gap . This helps to establish a design for manufacturability for the photonic crystals
The goal is to explore and extend the design space of lightweight structural systems , by designing multi-functional periodic structural elements , preserving structural stiffness while concurrently enabling sufficiently wideband damping performance over a target frequency range of interest . Following a generalized theoretical modeling framework for bandgap design and analysis in finite structures , the focus is placed on the design , fabrication , and analysis of a load-carrying frame development with internally resonant components . Finite-element modeling is employed to design and analyze the frequency response of the frame and simplified analytical solution is compared with this numerical solution .
Fabricated structures have a potential as 2D tunable photonic crystals . Bandgap formation in the near IR band was studied with a FTIR spectrometer . A distinct bandgap located in the vicinity of 1 /spl mu/m was revealed .
This paper summarizes improved device physics for numerical simulations of solid-state devices with dopant densities up to about 3 × 1020cm-3and with junction depths as small as 0.1 µm . This improved device physics pertains to bandgap narrowing , effective intrinsic carrier concentrations , carrier mobilities , and lifetimes . When this improved physics is incorporated into device analysis codes such as SEDAN and then used to compute the electrical performance of n-p-n transistors , the predicted values agree very well with the measured values of the current-voltage characteristics and dc common emitter gains for devices with emitter-base junction depths between 10 - 0.16 µm .
Feynman predicted that a quantum simulator — a special-purpose ‘ analogue ’ processor built using quantum bits (qubits)—would be inherently suited to solving such problems . In the context of quantum magnetism , a number of experiments have demonstrated the feasibility of this approach , but simulations allowing controlled , tunable interactions between spins localized on two- or three-dimensional lattices of more than a few tens of qubits have yet to be demonstrated , in part because of the technical challenge of realizing large-scale qubit arrays . Here we demonstrate a variable-range Ising-type spin – spin interaction , Ji , j , on a naturally occurring , two-dimensional triangular crystal lattice of hundreds of spin-half particles ( beryllium ions stored in a Penning trap ) .
In particular , we present two important results based on simulation which one might consider in their interpretation of magnetic data when selfdemagnetization is present . First , current methods for estimating total magnetization , which are typically applied to the problem of remanent magnetization , do not reliably recover this parameter when the anomalous source bodies have high magnetic susceptibilities . And second , a single value estimation of total magnetization does not provide adequate information to properly resolve subsurface geology through inversion .
In three-dimensional space , the only possibilities for identical particles are bosons and fermions . But for physics in two dimensions , there is , as one of the essays describes , a continuous range of intermediate possibilities ( ‘ anyons ’ ) with important applications in solid-state magnetism . I was disappointed by the absence of an account of Wilczek ’s astonishing work with Alfred Shapere , applying modern geometrical ideas to the swimming of small creatures and the reorientation of falling bodies such as cats .
Despite considerable advances in quantitative interpretation methods for discrimination , the state of practice is qualitative or empirical . The UXO thrust of the Army Engineer Research and Development Center 's ( ERDC ) Environmental Quality Technology Program seeks to develop enhanced detection and discrimination capability for survey data from total field magnetometry , time-domain electromagnetic induction , and frequency-domain electromagnetic induction methods . Enhanced discrimination capability by formal geophysical inversion is demonstrated at documented test sites and live sites .
Transfer Learning for Material Classification using Convolutional Networks A wide dense architecture neural network for material classification based on spectral BTF Image segmentation for surface material-type classification using 3D geometry information Sensitivity Analysis and Classification Algorithms Comparison for Underground Target Detection Learning-based Material Classification in X-ray Security Images Learning to predict the behaviour of deformable objects through and for robotic interaction Experimental comparison of color spaces for material classification Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs Blanket/Materials Testing Strategy for FNSF and Its Breeding Potential Materials issues in the fusion engineering device PEC Characterization Flowchart Computationally efficient 3-D statistical reconstruction from digitized radiographs Practical implementation of immersion resist materials Desktop-type 3D printing material shaping machine Material selection and product safety : theory versus practice Transformation of Energy Systems as Common Projects: An Integration of Different Scientific Approaches to Address Real-World Challenges Analysis and Characterization of Thermographic Defects at the PV Module Level Conformational analysis of molecular chains using nano-kinematics Elements of risk analysis for collision of oil tankers Modeling and simulation of local flexibilities and their effect to the entire power system Computational Design of Solar Energy Harvesting Materials Made of Earth-Abundant Elements A Fast for MPEG-2 Audio Real Time Compression Codec An efficient method of DFT/LDA band-gap correction Achieving accuracy in transistor and thyristor modeling Symmetry properties and electronic band structure of ordered Zn0.5Cd0.5Se alloys Structural Features That Stabilize ZnO Clusters: An Electronic Structure Approach Monte Carlo Simulation of Photonic Band Gap Structures Toward structurally integrated locally resonant metamaterials for vibration attenuation 2D photonic crystals deposited on polymer piezoelectric substrates - new kind of MOEMS Improved physics for simulating submicron bipolar devices Engineered two-dimensional Ising interactions in a trapped-ion quantum simulator with hundreds of spins Influence of Self-demagnetization Effect On Data Interpretation In Strongly Magnetic Environments A walk in a quantum world Enhanced discrimination capability for UXO geophysical surveys
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VISIGRAPP (4: VISAPP) ICLR ICIP (3) ICMENS ICECS
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