Heterogeneous Objects Research Articles

A Region-Based Gauss-Newton Approach to Real-Time Monocular Multiple Object Tracking.

We propose an algorithm for real-time 6DOF pose tracking of rigid 3D objects using a monocular RGB camera. The key idea is to derive a region-based cost function using temporally consistent local color histograms. While such region-based cost functions are commonly optimized using first-order gradient descent techniques, we systematically derive a Gauss-Newton optimization scheme which gives rise to drastically faster convergence and highly accurate and robust tracking performance. We furthermore propose a novel complex dataset dedicated for the task of monocular object pose tracking and make it publicly available to the community. To our knowledge, it is the first to address the common and important scenario in which both the camera as well as the objects are moving simultaneously in cluttered scenes. In numerous experiments-including our own proposed dataset-we demonstrate that the proposed Gauss-Newton approach outperforms existing approaches, in particular in the presence of cluttered backgrounds, heterogeneous objects and partial occlusions.

A Synthetic Aperture Radar (SAR)-Based Technique for Microwave Imaging and Material Characterization

This contribution presents a simple and fast Synthetic Aperture Radar (SAR)-based technique for microwave imaging and material characterization from microwave measurements acquired in tomographic systems. SAR backpropagation is one of the simplest and fastest techniques for microwave imaging. However, in the case of heterogeneous objects and media, a priori information about the constitutive parameters (conductivity, permittivity) is needed for an accurate imaging. In some cases, a first guess of the constitutive parameters can be extracted from an uncorrected SAR image, and then the estimated parameters can be introduced in a second step to correct the SAR image. The main advantage of this methodology is that there is little or no need for a priori information about the object to be imaged. Besides, calculation time is not significantly increased with respect to conventional SAR, thus allowing real-time imaging capabilities. The methodology has been validated by means of measurements acquired in a cylindrical setup.

A Use Case Analysis of Heterogeneous Semistructured Objects in Information Security Problems

This paper is devoted to solving the problem of developing a case-based decision support system for information security problems. The source data can be described as heterogeneous semistructured objects and formalized as property vectors. An approach to constructing a knowledge base for such problems using a two-level representation (the level of case-objects and the use case structure level) is given. The authors consider a use case modeling method for preparing a basic data set. Methods for assessing the similarity of heterogeneous semistructured objects and higher-level use cases are proposed. Results of experimental approbation of the described solutions and the architecture of the corresponding decision support system are presented.

Modelling multiply connected heterogeneous objects using mixed-dimensional material reference features

Abstract This paper proposes a general method for ab-initio modelling and representation of heterogeneous objects that are associated with complex material variation over complex geometry. Heterogeneous objects like composites and naturally occurring objects (bones, rocks and meteorites) possess multiple and often conflicting properties (like high hardness and toughness simultaneously), which are associated with random and irregular material distribution. Modelling such objects is desired for numerical analysis and additive manufacturing to develop bio-implants, high-performance tools etc. However, it is difficult to define and map the arbitrary material distribution within the object as the material distribution can be independent of the shape parameters or form features used to construct its solid model. This paper represents the source of random and irregular material distribution by mixed-dimensional entities with a focus on modelling compositional heterogeneity. The domain of effect of each material reference entity is defined automatically by using Medial Axis Transform (MAT), where the material distribution can be intuitively prescribed, starting from the material reference entity and terminating at the medial axis segment bounding the corresponding domain. Within such a domain, the spatial variation of the material is captured by a distance field from the material reference entity, which can be controlled locally and independently. These domains are stored using the neighbourhood relation for efficient operations like altering material distribution across the material reference entity and material evaluation for a given geometric location. Results from an implementation for 2.5D objects are shown and the extension to 3D objects is discussed. Highlights Hybrid representation for complex heterogeneous objects. General method for ab-initio modelling of complex heterogeneous objects capture arbitrary material distribution by automatic domain decomposition. Local control of material composition through MAT around material feature. Specification of material composition without adding new entities to shape model.

Two-sided allocation problems, decomposability, and the impossibility of efficient trade

Previous literature has shown that private information is a transaction cost that prevents efficient reallocation in two-sided setups with bilateral trade or homogeneous goods. We derive conditions under which the impossibility of efficient trade extends to rich environments in which buyers and sellers have multi-dimensional private types, accommodating many-to-many trades and heterogeneous objects. If agents can be decomposed into unit constituents, the allocation problem can be represented as an assignment game and impossibility obtains through a generalization of Shapley's (1962) result that buyers and sellers are complements. We introduce a general family of payoff functions that ensures decomposability and thus impossibility.

A Novel Framework and Enhanced QoS Big Data Protocol for Smart City Applications.

Various heterogeneous devices or objects will be integrated for transparent and seamless communication under the umbrella of Internet of things (IoT). This would facilitate the open access of data for the growth of various digital services. Building a general framework of IoT is a complex task because of the heterogeneity in devices, technologies, platforms and services operating in the same system. In this paper, we mainly focus on the framework for Big Data analytics in Smart City applications, which being a broad category specifies the different domains for each application. IoT is intended to support the vision of Smart City, where advance technologies will be used for communication to improve the quality of life of citizens. A novel approach is proposed in this paper to enhance energy conservation and reduce the delay in Big Data gathering at tiny sensor nodes used in IoT framework. To implement the Smart City scenario in terms of Big Data in IoT, an efficient (optimized in quality of service) wireless sensor network (WSN) is required where communication of nodes is energy efficient. Thus, a new protocol, QoS-IoT(quality of service enabled IoT), is proposed on the top layer of the proposed architecture (the five-layer architecture consists of technology, data source, data management, application and utility programs) which is validated over the traditional protocols.

Architectural Layers of Internet of Things: Analysis of Security Threats and Their Countermeasures

A pervasive network architecture that interconnect heterogeneous objects, devices, technologies and services called Internet of Things has prompted a drastic change in demand of smart devices which in turn has increased the rate of data exchange. These smart devices are built with numerous sensors which collect information from other interacting devices, process it and send it to remote locations for storage or further processing. Although this mechanism of data processing and sharing has contributed immensely to the information world, it has recently posed high security risk on privacy and data confidentiality. This paper therefore analyses different security threats to data at different architectural layers of Internet of Things, possible countermeasures and other in-depth security measures for Internet of Things. The paper identifies device authentication on IoT network to be of paramount importance in securing IoT systems. This paper also suggests some essential technologies of security such as encryption for securing IoT devices and the data shared over IoT network.

A Robust Data-Driven Approach for Online Learning and Manipulation of Unmodeled 3-D Heterogeneous Compliant Objects

We present a generic data-driven method to address the problem of manipulating a three-dimensional (3-D) compliant object (CO) with heterogeneous physical properties in the presence of unknown disturbances. In this study, we do not assume a prior knowledge about the deformation behavior of the CO and type of the disturbance (e.g., internal or external). We also do not impose any constraints on the CO's physical properties (e.g., shape, mass, and stiffness). The proposed optimal iterative algorithm incorporates the provided visual feedback data to simultaneously learn and estimate the deformation behavior of the CO in order to accomplish the desired control objective. To demonstrate the capabilities and robustness of our algorithm, we fabricated two novel heterogeneous compliant phantoms and performed experiments on the da Vinci Research Kit. Experimental results demonstrated the adaptivity, robustness, and accuracy of the proposed method and, therefore, its suitability for a variety of medical and industrial applications involving CO manipulation.

Agentification of the Internet of Things: A systematic literature review

The Internet of Things is aimed at controlling the physical real world using a global network of heterogeneous smart objects that are interconnected through the Internet. Nonetheless, this emergent paradigm still poses challenges in terms of intelligence, autonomy, security, privacy, and interoperability, all of which must be overcome in order for this approach to be consolidated. The integration of Internet of Things with agent technologies has been proposed as an alternative. In this article, we present a general overview of the main preliminary models, theoretical and experimental studies, and applications currently proposed to carry out the agentification of the Internet of Things through modeling smart objects and networks of Internet of Things objects using software agents. Furthermore, we present an analysis of the main strengths, opportunities, weaknesses, and threats of this approach in terms of computational intelligence. Finally, we discuss the main challenges that must be overcome in order for the agentification process of the Internet of Things to become a consolidated paradigm to build cognitive Internet of Things applications.

Quantitative accuracy of CT numbers: Theoretical analyses and experimental studies.

The CT number accuracy, that is, CT number bias, plays an important role in clinical diagnosis. When strategies to reduce radiation dose are discussed, it is important to make sure that the CT number bias is controlled within an acceptable range. The purpose of this paper was to investigate the dependence of CT number bias on radiation dose level and on image contrast (i.e., the difference in CT number between the ROI and the background) in Computed Tomography (CT). A lesion-background model was introduced to theoretically study how the CT number bias changes with radiation exposure level and with CT number contrast when a simple linear reconstruction algorithm such as filtered backprojection (FBP) is used. The theoretical results were validated with experimental studies using a benchtop CT system equipped with a photon-counting detector (XC-HYDRA FX50, XCounter AB, Sweden) and a clinical diagnostic MDCT scanner (Discovery CT750 HD, GE Healthcare, Waukesha, WI, USA) equipped with an energy-integrating detector. The Catphan phantom (Catphan 600, the Phantom Laboratory, Salem, NY, USA) was scanned at different mAs levels and 50 scans were performed for each mAs. The bias of CT number was evaluated for each combination of mAs and ROIs with different contrast levels. An anthropomorphic phantom (ATOM 10-year-old phantom, Model 706, CIRS Inc. Norfolk, VA, USA) with much more heterogeneous object content was used to test the applicability of the theory to the more general image object cases. Both theoretical and experimental studies showed that the CT number bias is inversely proportional to the radiation exposure level yet linearly dependent on the CT number contrast between the lesion and the background, that is, . The quantitative accuracy of CT numbers can be problematic and thus needs some extra attention when radiation dose is reduced. In this work, we showed that the bias of the FBP reconstruction increases as mAs is reduced; both positive and negative bias can be observed depending on the contrast difference between a targeted ROI and its surrounding background tissues.

Dimensions: Building Context for Search and Evaluation

Dimensions is a new database that focussed on a different set of problems to other scholarly search systems. Specifically, by the including not only data about publications and their natural associated citation graph, but by also including awarded grant data, patent data and clinical data and altmetric attention data, Dimensions holds a representation of an n-partite graph associated with the heterogeneous research objects. These objects have been treated as heterogeneous, but need to be brought onto the same footing (and homogenised) in order that they should make sense to a user. The links (or edges) in the expanded network of objects is created through extensive use of text mining and machine learning to extract and to normalise data. This article gives an overview of the techniques used to create the Dimensions dataset.

Coupling of finite element method and integral formulation for vector Helmholtz equation

PurposeConsidering the vector Helmholtz equation in three dimensions, this paper aims to present a novel approach for coupling the finite element method and a boundary integral formulation. It is demonstrated that the method is well-suited for many realistic three-dimensional problems in high-frequency engineering.Design/methodology/approachThe formulation is based on partial solutions fulfilling the global boundary conditions and the iterative interaction between them. In comparison to other coupling formulation, this approach avoids the typical singularity in the integral kernels. The approach applies ideas from domain decomposition techniques and is implemented for a parallel calculation.FindingsUsing confirming elements for the trace space and default techniques to realize the infinite domain, no additional loss in accuracy is introduced compared to a monolithic finite element method approach. Furthermore, the degree of coupling between the finite element method and the integral formulation is reduced. The accuracy and convergence rate are demonstrated on a three-dimensional antenna model.Research limitations/implicationsThis approach introduces additional degrees of freedom compared to the classical coupling approach. The benefit is a noticeable reduction in the number of iterations when the arising linear equation systems are solved separately.Practical implicationsThis paper focuses on multiple heterogeneous objects surrounded by a homogeneous medium. Hence, the method is suited for a wide range of applications.Originality/valueThe novelty of the paper is the proposed formulation for the coupling of both methods.

Accelerated RAPID Model Using Heterogeneous Porous Objects

To enhance the capability of three-dimensional (3D) radiative transfer models at the kilometer scale (km-scale), the radiosity applicable to porous individual objects (RAPID) model has been upgraded to RAPID3. The major innovation is that the homogeneous porous object concept (HOMOBJ) used for a tree crown scale is extended to a heterogeneous porous object (HETOBJ) for a forest plot scale. Correspondingly, the radiosity-graphics-combined method has been extended from HOMOBJ to HETOBJ, including the random dynamic projection algorithm, the updated modules of view factors, the single scattering estimation, the multiple scattering solutions, and the bidirectional reflectance factor (BRF) calculations. Five cases of the third radiation transfer model intercomparison (RAMI-3) have been used to verify RAPID3 by the RAMI-3 online checker. Seven scenes with different degrees of topography (valleys and hills) at 500 m size have also been simulated. Using a personal computer (CPU 2.5 GHz, memory 4 GB), the computation time of BRF at 500 m is only approximately 13 min per scene. The mean root mean square error is 0.015. RAPID3 simulated the enhanced contrast of BRF between backward and forward directions due to topography. RAPID3 has been integrated into the free RAPID platform, which should be very useful for the remote sensing community. In addition, the HETOBJ concept may also be useful for the speedup of ray tracing models.

Optimization for Blob-Based Image Reconstruction With Generalized Kaiser–Bessel Basis Functions

Generalized Kaiser–Bessel radial basis functions are frequently used in tomographic reconstructions, and the parameter selection can significantly impact both the qualitative and quantitative image characteristics. Currently, the blob parameters are simply selected based on the first-order approximation of a uniform object. To obtain optimal images, a detailed investigation and optimization of blob parameters is needed. In this paper, we aim at optimizing the parameters of the basis functions for optimal image representation and image reconstruction. We first represent the unknown radiotracer activity distribution as the coefficients of blob basis functions on a Bravais lattice from crystallography. We point out that the optimal sampling lattice is just the reciprocal of the lattice that achieves highest packing of equally sized spheres. Based on multidimensional sampling theorem and the Kepler conjecture (proven by Thomas C. Hales), we obtain that the body-centered cubic (BCC) and hexagonal close-packing lattices are optimal in terms of either reducing computational cost at similar image quality or improving image quality at the same computational cost. To optimize the blob parameters, we formulate two new image quality metrics—total harmonic distortion (THD) and minimum approximation error (MAE) based on the theory of approximation—to analytically characterize the blob representation errors of a nonzero uniform object and an arbitrary object, respectively. The MAE is formulated in terms of the power spectral density of an imaging object, and we use it for object-dependent blob parameter optimization. We validate the THD and MAE through numerical examples using homogeneous and heterogeneous objects, respectively. We also present 3-D blob-based image reconstructions using BCC lattice with different shape parameters to show the usefulness of the proposed optimization.

Methodology for tensile testing historic tapestries

Historic tapestries are very important and complex artworks. Their heterogeneous structure composed with different materials is weaved in warp and weft threads with the objective of render an image to be displayed. Often on open display these objects are exposed to forces caused by their own weight as well as by the strain experience due to moisture adsorption and desorption cycles when temperature and relative humidity of the surrounding environment fluctuate. Since physical damage is usually supported by conservation stitching techniques there is a need to understand how certain decisions would influence future mechanical behaviour of a tapestry. This requires the need of establishing a methodology that helps to understand how different sections of these heterogeneous objects respond when forces are applied to them.Past research considered tensile testing of primarily new woven aged sections which are not representative of the historic tapestries [1]. Digital image correlation (DIC) was also successfully used in order to characterize the strain states of woven tapestries [2]. However, information on how wool and silk behaves as well as the influence of damage and stitching in some areas is still scarce and there is the lack of a methodology for a full strain analysis.In this research, tensile mechanical behaviour is being studied in order to inform on how different fragments of historic tapestries behave when a certain tensile force is applied on them. For this, a new methodology of analysis is proposed. Force strain curves collected from tensile tests are analysed together with complementary DIC data to understand the strain states that were present in different stages of the mechanical tests. Results proved that a holistic methodology, using both techniques required for successful interpretation of data. Furthermore, this experimental work demonstrated that tapestry areas behave differently when a tensile force is applied.

Electric-field-driven jet deposition 3D printing

Multi-scale and multi-material 3D printing technique has been considered as a revolutionary technology and next-generation manufacturing tool which can really fulfill the “creating material” and “creating life”, especially subvert traditional product design and manufacturing scheme. However, very few of the established additive manufacturing processes have now the capability to fully implement the multi-scale and multi-material fabrication. It is still a significant challenging issue for existing additive manufacturing technologies to implement the multi-material and multi-scale 3D printing for fabricating heterogeneous and hierarchical structured object at full scale ranging from nano to macro-scale. This paper presents a novel 3D printing technique, electric-field-driven jet deposition 3D printing, which offers a promsing and feasible approach to really fulfill multi-scale and multi-material additive manufacturing at low cost. Two new printing schemes, which include pulsed cone-jet mode and continuous cone-jet mode, are proposed herein considering both accuracy and efficiency for multi-scale manufacturing. The experimental results have demonstrated and verified the unique and outstanding advantages of the proposed 3D printing process which involve good universality, and are almost unrestricted in terms of arbitrary substrates (conductive, nonconductive, non-planar, curve, etc.), various solutions and melted materials printed, conductive and non-conductive nozzles, stand-off distance between the nozzle and substrate, and the flexibility to macro/micro fabrication. These four typical cases have shown that the electric-field-driven jet deposition 3D printing provides a promising and effective method to implement the multi-material and multi-scale 3D printing at low cost for fabricating heterogeneous and hierarchical structured object at full scale ranging from micro to macro-scale. As a result, this study offers a novel solution for fulfilling multi-scale and multi-material 3D printing at low cost and good universality as well as high resolution.

Intelligent optimal route recommendation among heterogeneous objects with keywords

Textual information is often added to spatial objects, and thus a spatial search query includes both location-based and textual-based information. For example, a user may want to visit a dental hospital with orthodontic appliances and dental lasers, before going to a pharmacy to buy some drugs (ibuprofen and tylenol). Currently, a brute force technique combined with a spatial inverted index is used to find an optimal set of spatial objects that satisfy both location and textual constraints. In this study, we identify some problems with the brute force technique and propose two algorithms that recommend an optimal shortest path among heterogeneous objects. Our method outputs the shortest path from the user point to all the heterogeneous objects. Results obtained using our algorithm demonstrated that it produces the optimal shortest path compared with existing spatial-textual search algorithms.

Automatic Toolpath Generation for Heterogeneous Objects Manufactured by Directed Energy Deposition Additive Manufacturing Process

A heterogeneous object (HO) refers to a solid component consisting of two or more material primitives distributed either continuously or discontinuously within the object. HOs are commonly divided into three categories. The first category has distinct material domains separating the different materials. The second, called functionally graded materials (FGMs), has continuous variation of material composition that produces gradient in material properties. The third category allows for any combinations of the first two categories within the same part. Modeling and manufacturing of HOs has recently generated more interest due to the advent of additive manufacturing (AM) technology that makes it possible to build such parts. Directed energy deposition (DED) processes have the potential for depositing multiple powdered materials in various compositions in the process of creating a single layer of material. To make this possible, tool paths that provide proper positioning of the deposition head and proper control over the material composition are required. This paper presents an approach for automatically generating the toolpath for any type of HO considering the material composition changes that are required on each layer. The toolpath generation takes into account the physical limitations of the machine associated with powder delivery and ability to continually grade the materials. Simulation results using the toolpath generation methodology are demonstrated by several example parts.

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars.

MONTRA: An agile architecture for data publishing and discovery

Background and ObjectiveData catalogues are a common form of capturing and presenting information about a specific kind of entity (e.g. products, services, professionals, datasets, etc.). However, the construction of a web-based catalogue for a particular scenario normally implies the development of a specific and dedicated solution. In this paper, we present MONTRA, a rapid-application development framework designed to facilitate the integration and discovery of heterogeneous objects, which may be characterized by distinct data structures. MethodsMONTRA was developed following a plugin-based architecture to allow dynamic composition of services over represented datasets. The core of MONTRA's functionalities resides in a flexible data skeleton used to characterize data entities, and from which a fully-fledged web data catalogue is automatically generated, ensuring access control and data privacy. ResultsMONTRA is being successfully used by several European projects to collect and manage biomedical databases. In this paper, we describe three of these applications scenarios. ConclusionsThis work was motivated by the plethora of geographically scattered biomedical repositories, and by the role they can play altogether for the understanding of diseases and of the real-world effectiveness of treatments. Using metadata to expose datasets’ characteristics, MONTRA greatly simplifies the task of building data catalogues. The source code is publicly available at https://github.com/bioinformatics-ua/montra.