Distributed Processing Architecture Research Articles

Remote Sensing Thematic Product Generation for Sustainable Development of the Geological Environment

Remote sensing thematic data products are critical for assessing and analyzing geological environments, while efficient generation of thematic products is also highly significant for achieving corresponding sustainable development goals (SDGs). Currently, remote sensing thematic product generation has problems like low levels of automation and efficiency. Addressing these challenges is imperative for advancing sustainable development within the geological environment. This paper aims to address issues related to the generation of geological environment remote sensing thematic products, sorting through the overall process of remote sensing thematic product generation, exploring algorithm encapsulation, combination, and execution under technical methods for container and workflow, and relies on the Spark distributed processing architecture to achieve efficient thematic product generation supported by multiple geological environment data processing models. Finally, taking the three SDGs of SDG6, SDG11, and SDG15 as examples, we achieved the generation of a variety of thematic products such as the interpretation of water body distribution, extraction of urban informal settlements and distribution of water and soil erosion. Meanwhile, we comparatively analyzed the efficiency of thematic product generation on different processing architectures, and the experimental results further verified the feasibility and effectiveness of our proposed solution. This research provides a programme for the automated and intelligent generation of geological environment remote sensing thematic products and effectively assists the construction of sustainable development in the geological environment.

Risk-Based Access Control Mechanism for Internet of Vehicles Using Artificial Intelligence

Internet of Vehicles (IoV) systems are vulnerable to a wide range of attacks because of the lack of security measures. IoV systems can be infiltrated by malicious and unauthorized nodes, which can cause the authenticity, accessibility, and privacy of shared information resources to be compromised. Indeed, the use of an access control system can help; as a result, it is unable to respond to such attacks on time. This paper introduces an artificial intelligence-enabled access control mechanism (AI-ACM) with vehicle nodes and roadside units (RSUs) to overcome these issues. Here, use vehicle nodes as lightweight nodes, while RSUs act as comprehensive and edge nodes to provide access control service. A generative adversarial network (GAN) is used in place of risk prediction (RP) due to the lack of training sets, resulting in a sequence generation rather than an accurate risk prediction. Afterward, the blockchain-based Internet of Vehicles (BIoV) approach is summarized for the security mechanisms of vehicles that are discussed from the aspects of access control and authentication to sustain the distributed processing architecture and solve security issues. The simulation results show that AI-ACM is more accurate than the previous GANs at predicting the future. In addition, the RP model’s access control accuracy can be improved as a result of this technique.

Scalable Decentralized Indexing and Querying of Multi-Streams in the Fog

NOA-AID (Network Overlays for Adaptive information Aggregation, Indexing and Discovery on the fog) is an approach for decentralized indexing, aggregation and discovery of data belonging to streams. It is organized on two network layers. The upper layer is in charge of delivering an information discovery approach by providing a distributed index structure. The lower layer is devoted to resource aggregation based on epidemic protocols designed for highly dynamic environment, well suited to stream-oriented scenarios. It defines a flexible approach to express queries targeting highly heterogeneous data, as well as a self-organizing dynamic system allowing the optimal resolution of queries on the most suitable stream producers. The paper also presents a theoretical study and discusses the costs related to information management operations; it also gives an empirical validation of findings. Finally, it reports an extended experimental evaluation that demonstrated the ability of NOA-AID to be effective and efficient for retrieving information extracted from streams in highly-dynamic and distributed processing architectures.

SPARE: Spiking Neural Network Acceleration Using ROM-Embedded RAMs as In-Memory-Computation Primitives

Despite huge success of artificial intelligence, hardware systems running these algorithms consume orders of magnitude higher energy compared to the human brain, mainly due to heavy data movements between the memory unit and the computation cores. Spiking neural networks (SNNs) built using bio-plausible neuron and synaptic models have emerged as the power-efficient choice for designing cognitive applications. These algorithms involve several lookup-table (LUT) based function evaluations such as high-order polynomials and transcendental functions for solving complex neuro-synaptic models, that typically require additional storage. To that effect, we propose `SPARE' - an in-memory, distributed processing architecture built on ROM-embedded RAM technology, for accelerating SNNs. ROM-embedded RAMs allow storage of LUTs, embedded within a typical memory array, without additional area overhead. Our proposed architecture consists of a 2-D array of Processing Elements (PEs). Since most of the computations are done locally within each PE, unnecessary data transfers are restricted, thereby alleviating the von-Neumann bottleneck. We evaluate SPARE for two different ROM-Embedded RAM structures - CMOS based ROM-Embedded SRAMs (R-SRAMs) and STT-MRAM based ROM-Embedded MRAMs (R-MRAMs). Moreover, we analyze trade-offs in terms of energy, area and performance, for using the two technologies on a range of image classification benchmarks. Furthermore, we leverage the additional storage density to implement complex neuro-synaptic functionalities. This enhances the utility of the proposed architecture by provisioning implementation of any neuron/synaptic behavior as necessitated by the application. Our results show up-to 1.75x, 1.95x and 1.95x improvement in energy, iso-storage area, and iso-area performance, respectively, by using neural network accelerators built on ROM-embedded RAM primitives.

Cellular throughput optimization by game-based power adjustment and outband D2D communication

Device-to-device (D2D) communication is a promising option for the fifth generation (5G) mobile communication network to reduce energy consumption and increase throughput, which makes high throughput applications possible. Also, the improvement for energy and spectrum efficiency is critical in such applications. Without occupation of cellular spectrum resources, outband D2D communication is increasingly applied to high throughput applications to increase spectrum supply. However, energy efficiency is still a key issue that needs to be addressed. Moreover, the overall energy efficiency in cellular networks is severely limited by cell-edge devices. Therefore, in this paper, we apply multi-hop relay-aided outband D2D communication to cellular networks and propose a game-based power adjustment method to address throughput optimization problem. Firstly, we model an interaction relationship of power adjustment for each transmission path as a potential game, where a new utility function is designed for each player (i.e., the receiving end of a transmission path) to evaluate its action gain and to determine whether taking action or not. And then, it is proved that the utility function is an ordinal potential function (OPF) and the game of power adjustment is an ordinal potential game (OPG), which guarantees the convergence of game decision process. Moreover, we propose a new game decision algorithm, which has quicker convergence speed than the existing typical algorithm. In addition, we design a network-assisted distributed processing architecture for solving throughput optimization problem, including receiving mode selection, verification for relay selection, and transmission power adjustment, which can ease the burden of centralized processing. The experimental results show that our scheme is superior to the existing typical work in terms of throughput, delay, energy efficiency, continuous service ability, and convergence performance.

Constrained Multi-Sensor Control Using a Multi-Target MSE Bound and a δ-GLMB Filter.

The existing multi-sensor control algorithms for multi-target tracking (MTT) within the random finite set (RFS) framework are all based on the distributed processing architecture, so the rule of generalized covariance intersection (GCI) has to be used to obtain the multi-sensor posterior density. However, there has still been no reliable basis for setting the normalized fusion weight of each sensor in GCI until now. Therefore, to avoid the GCI rule, the paper proposes a new constrained multi-sensor control algorithm based on the centralized processing architecture. A multi-target mean-square error (MSE) bound defined in our paper is served as cost function and the multi-sensor control commands are just the solutions that minimize the bound. In order to derive the bound by using the generalized information inequality to RFS observation, the error between state set and its estimation is measured by the second-order optimal sub-pattern assignment metric while the multi-target Bayes recursion is performed by using a δ-generalized labeled multi-Bernoulli filter. An additional benefit of our method is that the proposed bound can provide an online indication of the achievable limit for MTT precision after the sensor control. Two suboptimal algorithms, which are mixed penalty function (MPF) method and complex method, are used to reduce the computation cost of solving the constrained optimization problem. Simulation results show that for the constrained multi-sensor control system with different observation performance, our method significantly outperforms the GCI-based Cauchy-Schwarz divergence method in MTT precision. Besides, when the number of sensors is relatively large, the computation time of the MPF and complex methods is much shorter than that of the exhaustive search method at the expense of completely acceptable loss of tracking accuracy.

Personalized Recommendation Mechanism Based on Collaborative Filtering in Cloud Computing Environment

With the advent of cloud computing era and the dramatic increase in the amount of data applications, personalized recommendation technology is increasingly important. However, due to large scale and distributed processing architecture and other characteristics of cloud computing, the traditional recommendation techniques which are applied directly to the cloud computing environment will be faced with low recommendation precision, recommended delay, network overhead and other issues, leading to a sharp decline in performance recommendation. To solve these problems, the authors propose a personalized recommendation collaborative filtering mechanism RAC in the cloud computing environment. The first mechanism is to develop distributed score management strategy, by defining the candidate neighbors (CN) concept screening recommended greater impact on the results of the project set. And the authors build two stage index score based on distributed storage system, in order to ensure the recommended mechanism to locate the candidate neighbor. They propose collaborative filtering recommendation algorithm based on the candidate neighbor on this basis (CN-DCF). The target users are searched in candidate neighbors by the nearest neighbor k project score. And the target user's top-N recommendation sets are predicted. The results show that in the cloud computing environment RAC has a good recommendation accuracy and efficiency recommended.

Low-Power Platform and Communications for the Development of Wireless Body Sensor Networks

Although the roles of body sensor networks (BSNs) are similar to those carried out by the generic wireless sensor networks (WSNs), new solutions must be established to optimize communications for true pervasive biomedical monitoring transparent to the user. In this paper, a proposal of a hardware and software platform for biomedical sensors is performed, which is specially designed to minimize energy consumption in BSNs through a modular processing scheme based on the detection of events and information abstraction. The data flow is implemented through a novel communications protocol that enhances the performances of consumption and time delay of the platform. A novel aspect of the protocol is the explicit incorporation of an additional level of communications to support the distributed processing architecture that allows the execution of multiple applications in parallel within the smart sensors. The results obtained with an implementation of a smart sensor for fall detection demonstrate its feasibility as well as the viability of the communication protocol for the development of energy-efficient BSNs.

Analysis of Distributed Computing Architecture Search Principle Based on Hadoop

Distributed search techniques of Hadoop are researched and analyzed. Combined with Lucene indexing objects, a search engine system IS successfully built. Efficiency of the system in both time and space is investigated. Merit of distributed processing architecture for a single architecture in data handling is verified. The access and update of file information in distributed search technology are further explored. The research plays a positive role in promoting study of related fields

Decomposition-Coordination Model and Algorithm for Parallel Calculation of Power System State Estimation Problem

Power system state estimation is the process of computing a reliable estimate of the system state vector composed of bus voltages’ magnitudes and angles from telemetered measurements on the system. This estimate of the state vector provides the description of the system necessary for operation, security monitoring and control. Many methods are described in literature for solving the state estimation problem, the most important of which are the classical weighted least squares and the non-quadratic method. However, both showed drawbacks when it comes to application to large-scale power system networks. In this paper, a new method in the name of decomposition-coordination approach using the weighted least squares is introduced in solving the large-scale power system state estimation problem. The estimation criterion is reformulated; voltage measurement, real and reactive power injections, real and reactive power flows, and real and reactive power flows in tie-line models of a decomposed system are developed. Two level structure of solving the estimation problem is introduced. The first level solves the sub-problem using gradient procedure methods while the second level determines the interconnection variables using predictive method. The positive characteristic of the method is that the coordinator has little work of predicting interconnection variables instead of solving the state estimation problem. The method can be used to solve a multi-area state estimation using parallel or distributed processing architectures.

Remote E-Voting Using the Smart Card Web Server

Voting in elections is the basis of democracy, but voting at polling stations may not be possible for all citizens. Remote (Internet) e-voting uses the voter's own equipment to cast votes, but is potentially vulnerable to many common attacks, which affect the election's integrity. Security can be improved by distributing vote processing over many web servers installed in tamper-resistant, secure environments, using the Smart Card Web Server (SCWS) on a mobile phone Subscriber Identity Module (SIM). A generic voting model is proposed, using a SIM/SCWS voting application with standardised Mobile Network Operator (MNO) management procedures to process the votes cast. E-voting systems Prêt à Voter and Estonian I-voting are used to illustrate the generic model. As the SCWS voting application is used in a distributed processing architecture, e-voting security is enhanced: to compromise an election, an attacker must target many individual mobile devices, rather than a centralised web server.

Self-organizing map models of language acquisition.

Connectionist models have had a profound impact on theories of language. While most early models were inspired by the classic parallel distributed processing architecture, recent models of language have explored various other types of models, including self-organizing models for language acquisition. In this paper, we aim at providing a review of the latter type of models, and highlight a number of simulation experiments that we have conducted based on these models. We show that self-organizing connectionist models can provide significant insights into long-standing debates in both monolingual and bilingual language development. We suggest future directions in which these models can be extended, to better connect with behavioral and neural data, and to make clear predictions in testing relevant psycholinguistic theories.

Using High Performance Computing to Explore Large Complex Bioacoustic Soundscapes: Case Study for Right Whale Acoustics

This paper describes ongoing work to investigate the development of a complex system designed for extracting information from large acoustic datasets. The system, called DeLMA is based on integrating advanced machine learning with high performance computing (HPC). The goal of this work is to provide the capability to accurately detect and classify whale sounds in large acoustics datasets collected using underwater sensors. The case study for this work is focused on detecting the acoustic communication signals of the North Atlantic Right Whale, Eubalaena glacialis, and uses data collected in the Stellwagen Bank National Marine Sanctuary (SBNMS), USA. A summary of the work done for developing a complex detection-classification system and brief description of several algorithms that are used for classifying whale sounds will be covered. A brief discussion on how standard detection algorithms can be incorporated, with no special modifications, into the HPC system for analysis will be mentioned, and two new right whale detection methods are presented, based on continuous region analysis (CRA) and histogram of oriented gradients (HOG). This paper presents a first-hand look at applying the DeLMA system and these algorithms on a large dataset containing over 60,000 channel-hours of acoustic data from the SBNMS. Results from these new detection methods are compared against Baseline algorithms. With the development of the DeLMA system, sound archives can now be explored using a powerful distributed processing architecture. This advancement will allow for rapid execution and visualization of the data using seasonal graphs called diel plots, which show the distribution of detections on a time-of-day vs. time-of-year plane. Diel plots of Baseline, CRA and HOG algorithm results reveal various large-scale features of the seasonality of whale calling behavior. Results are summarized and the authors discuss future areas for study, especially those relate to handling other big passive acoustic data projects.

Personalization and Adaptation to the Medium and Context in a Fall Detection System

The main objective of this paper is to present a distributed processing architecture that explicitly integrates capabilities for its continuous adaptation to the medium, the context, and the user. This architecture is applied to a falling detection system through: 1) an optimization module that finds the optimal operation parameters for the detection algorithms of the system devices; 2) a distributed processing architecture that provides capabilities for remote firmware update of the smart sensors. The smart sensor also provides an estimation of activities of daily living (ADL), which results very useful in monitoring of the elderly and patients with chronic diseases. The developed experiments have demonstrated the feasibility of the system and specifically, the accuracy of the proposed algorithms and procedures (100% success for impact detection, 100% sensitivity and 95.68% specificity rates for fall detection, and 100% success for ADL level classification). Although the experiments have been developed with a cohort of young volunteers, the personalization and adaption mechanisms of the proposed architecture related to the concepts of "design for all" and "design space" will significantly ease the adaptation of the system for its application to the elderly.

Synthesis of Distributed Cognitive Systems: Interacting Maps for Sensor Fusion

Event Abstract Back to Event Synthesis of Distributed Cognitive Systems: Interacting Maps for Sensor Fusion Cristian Axenie1* and Jorg Conradt1 1 Technische Universität München, Electrical Engineering and Information Technology, Germany Environmental interaction is an important aspect in the life of every physical entity, which impacts its internal state and allows the acquisition of new behaviors. A prerequisite for such interaction is multimodal sensory fusion with the goal of providing a consistent internal representation of the external world. In technical systems, such a representation is obtained by fusing noisy data from various available sensors, which combined generate a richer representation compared to individual use. State-of-the-art technologies apply probabilistic methods for combining prior and observed information, but these techniques are computationally inefficient for a large number of sensors. In our project we propose extending an existing initial model of interacting maps for sensory interpretation with the focus on multimodal sensory fusion. The core aspect here is to design and implement a neuro-biologically inspired method for real-time distributed interpretation of sensory stimuli in a mobile robotic setting: a distributed graphical computing system with inter-merged information storage and processing that allows efficient parallel reasoning. This network architecture consists of interconnected heterogeneous software units, each encoding and processing a different feature about the environment that is represented in a local map. A map here is defined as a pixel based representation of some perceived or inferred aspect of the current environment, in which each entry contains a value of a certain type (e.g. a scalar of image brightness or a 2D vector of local optic flow). Such extracted pieces of environmental knowledge interact by mutual influence to ensure overall system coherence: the values represented in each map slowly drift in the direction that minimizes disagreement between the two sides of an assumed inter-map relationship through mutual influence. Although by system design each map specializes in representing a certain feature of the environment, the required relations between maps for such influence are established through a learning process: the true relations between maps are a priori unknown, but exist hidden within the observed real-world data. Therefore, our system needs to infer those relations over time from correlated sensory signals. Starting from the proposed distributed processing architecture, our main research investigates self-constructing and adapting internal relations between maps (structural plasticity) that reflect the underlying relations between multimodal sensory information streams. We investigate optimized network distributions on available computing resources without an explicit a priori network topology provided by a systems designer. This new architecture reflects distributed information processing as known to occur in brains, and ensures a fast, robust and scalable computational architecture appropriate for real-time real-world robotic applications. Acknowledgements The foundations of this work have been laid during workshops such as the Telluride and the CapoCaccia Cognitive Neuromorphic Engineering Workshop. Funding and inspiration for application scenarios has come from the DFG Cluster of Excellence “CoTeSys” and the Muinch Bernstein Center for Computational Neuroscience. Keywords: network computation, self-growing systems, sensory fusion Conference: Bernstein Conference 2012, Munich, Germany, 12 Sep - 14 Sep, 2012. Presentation Type: Poster Topic: Sensory processing and perception Citation: Axenie C and Conradt J (2012). Synthesis of Distributed Cognitive Systems: Interacting Maps for Sensor Fusion. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference 2012. doi: 10.3389/conf.fncom.2012.55.00077 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 22 May 2012; Published Online: 12 Sep 2012. * Correspondence: Mr. Cristian Axenie, Technische Universität München, Electrical Engineering and Information Technology, Munich, 80333, Germany, cristian.axenie@tum.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Cristian Axenie Jorg Conradt Google Cristian Axenie Jorg Conradt Google Scholar Cristian Axenie Jorg Conradt PubMed Cristian Axenie Jorg Conradt Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

A Unified Accounting Information Framework To Modeling Bank Accounting Systems

<p class="MsoNormal" style="text-justify: inter-ideograph; text-align: justify; line-height: 12pt; margin: 0in 0.5in 0pt; mso-line-height-rule: exactly;"><span style="font-size: 10pt;"><span style="font-family: Times New Roman;">This paper discusses the use of distributed middlewares as essential tools for facilitating electronic exchange of standard business document between managers, financial institutes, and trading partners in the banking sector. Internally, companies can benefit by creating information architectures that allow systems to easily exchange data. One less expensive and disruptive option that applies to most banks/financial institutes is used traditional mainframe (legacy) system with an array of distributed middlewares to overcome the aforementioned limitations. This paper focuses on developing a new distributed processing architecture based on client-server technology called UAIF – Unified Accounting Information Framework. UAIF is designed to assist managers/financial institutes with a transparent access to information anywhere on the LAN or WAN from any desktop and to meet management specific needs so that some of the accounting and financial works can be widely used for World Wide Web (WWW) applications via Internet or Intranet. </span></span></p><p class="MsoNormal" style="line-height: 15pt; margin: 0in 0in 0pt; mso-line-height-rule: exactly;"><span style="font-size: 10pt;"><span style="font-family: Times New Roman;"> </span></span></p><p class="MsoNormal" style="text-justify: inter-ideograph; text-align: justify; line-height: 12pt; margin: 0in 0.5in 0pt; mso-line-height-rule: exactly;"><span style="font-size: 10pt;"><span style="font-family: Times New Roman;">For concept verification, we utilize UAIF to modeling a bank accounting system, which is based on an industrial standard CORBA architecture, XML and OMG General Ledger Facility. This methodology integrates enterprise accounting information system (AIS) with distributed systems via Internet, Intranet, and Electronic Commerce. </span></span></p>

Data Acquisition and Management System of LHD

The data acquisition (DAQ) and management system of the Large Helical Device (LHD), named the LABCOM system, has been in development since 1995. The recently acquired data have grown to 7 gigabytes per shot, 10 times bigger than estimated before the experiment. In 2006 during 1-h pulse experiments, 90 gigabytes of data was acquired, a new world record. This data explosion has been enabled by the massively distributed processing architecture and the newly developed capability of real-time streaming acquisition. The former provides linear expandability since increasing the number of parallel DAQs avoids I/O bottlenecks. The latter improves the unit performance from 0.7 megabytes/s in conventional CAMAC digitizers to nonstop 110 megabytes/s in CompactPCI. The technical goal of this system is to be able to handle one hundred 100 megabytes/s concurrent DAQs even for steady-state plasma diagnostics. This is similar to the data production rate of the next-generation experiments, such as ITER. The LABCOM storage has several hundred terabytes of storage in double-tier structure: The first consists of tens of hard drive arrays, and the second some Blu-ray Disc libraries. Multiplex and redundant storage servers are mandatory for higher availability and throughputs. They together serve sharable volumes on Red Hat GFS2 cluster file systems. The LABCOM system is used not only for LHD but also for the QUEST and GAMMA10 experiments, creating a new Fusion Virtual Laboratory remote participation environment that others can access regardless of their location.

A Distributed Processing Architecture Using Driver Behavior and a Physical Model for Large-Scale Traffic Simulation

A Distributed Processing Architecture Using Driver Behavior and a Physical Model for Large-Scale Traffic Simulation

Spectroscopic X-ray imaging with photon counting pixel detectors

Single particle counting hybrid pixel detectors simultaneously provide low noise, high granularity and high readout speed and make it possible to build detector systems offering high spatial resolution paired with good energy resolution. A limiting factor for the spectroscopic performance of such detector systems is charge sharing between neighbouring pixels in the sensor part of the detector. The signal spectrum at the collection electrodes of the readout electronics deviates significantly from the photonic spectrum when planar segmented sensor geometries are used. The Medipix3 implements a novel, distributed signal processing architecture linking neighbouring pixels and aims at eliminating the spectral distortion produced in the sensor by charge sharing and at reducing the impact of fluorescence photons generated in the sensor itself. Preliminary results from the very first Medipix3 readouts bump bonded to 300 μ m Si sensor are presented. Material reconstruction is a possible future application of spectroscopic imaging detectors and an example of material reconstruction in small animal imaging is given.

An event-driven distributed processing architecture for image-guided cardiac ablation therapy

Medical imaging data is becoming increasing valuable in interventional medicine, not only for preoperative planning, but also for real-time guidance during clinical procedures. Three key components necessary for image-guided intervention are real-time tracking of the surgical instrument, aligning the real-world patient space with image-space, and creating a meaningful display that integrates the tracked instrument and patient data. Issues to consider when developing image-guided intervention systems include the communication scheme, the ability to distribute CPU intensive tasks, and flexibility to allow for new technologies. In this work, we have designed a communication architecture for use in image-guided catheter ablation therapy. Communication between the system components is through a database which contains an event queue and auxiliary data tables. The communication scheme is unique in that each system component is responsible for querying and responding to relevant events from the centralized database queue. An advantage of the architecture is the flexibility to add new system components without affecting existing software code. In addition, the architecture is intrinsically distributed, in that components can run on different CPU boxes, and even different operating systems. We refer to this Framework for Image-Guided Navigation using a Distributed Event-Driven Database in Real-Time as the FINDER architecture. This architecture has been implemented for the specific application of image-guided cardiac ablation therapy. We describe our prototype image-guidance system and demonstrate its functionality by emulating a cardiac ablation procedure with a patient-specific phantom. The proposed architecture, designed to be modular, flexible, and intuitive, is a key step towards our goal of developing a complete system for visualization and targeting in image-guided cardiac ablation procedures.