Joint Directors Of Laboratories Research Articles

A fusion architecture to deliver multipurpose mobile health services

Mobile Health (mHealth) services typically make use of customized software architectures, leading to development-dependent fragmentation. Nevertheless, irrespective of their specific purpose, most mHealth services share common functionalities, where standard pieces could be reused or adapted to expedite service deployment and even extend the follow-up of appearing conditions under the same service. To harness compatibility and reuse, this article presents a data fusion architecture proposing a common design framework for mHealth services. An exhaustive mapping of mHealth functionalities identified in the literature serves as starting point. The architecture is then conceptualized making use of the Joint Directors of Laboratories (JDL) data fusion model. The aim of the architecture is to exploit the multi-source data acquisition capabilities supported by smartphones and Internet of Things devices, and artificial intelligence-enabled feature fusion. A series of interconnected fusion layers ensure streamlined data management; each layer is composed of microservices which may be implemented or omitted depending on the specific goals of the healthcare service. Moreover, the architecture considers essential features related to authentication mechanisms, data sharing protocols, practitioner-patient communication, context-based notifications and tailored visualization interfaces. The effectiveness of the architecture is underscored by its instantiation for four real cases, encompassing risk assessment for youth with mental health issues, remote monitoring for SARS-CoV-2 patients, liquid intake control for kidney disease patients, and peritoneal dialysis treatment support. This breadth of applications exemplifies how the architecture can effectively serve as a guidance framework to accelerate the design of mHealth services.

Information fusion and information quality assessment for environmental forecasting

Air pollution is a major environmental threat to human health. Therefore, multiple systems have been developed for early prediction of air pollution levels in large cities. However, deterministic models produce uncertainties due to the complexity of the physical and chemical processes of individual systems and transport. In turn, statistical and machine learning techniques require a large amount of historical data to predict the behavior of a variable. In this paper, we propose a data fusion model to spatially and temporally predict air quality and assess its situation and risk for public health. Our model is based on the Joint Directors of Laboratories (JDL) model and focused on Information Quality (IQ), which allows us to fine tune hyper-parameters in different processes and trace information from raw data to knowledge. Expert systems use the information assessment to select and process data, information, and knowledge. The functionality of our model is tested using an environmental database of the Air Quality Monitoring Network of Área Metropolitana del Valle de Aburrá (AMVA in Spanish) in Colombia. Different levels of noise are added to the data to analyze the effects of information quality on the systems' performance throughout the process. Finally, our system is compared with two conventional machine learning-based models: Deep Learning and Support Vector Regression (SVR). The results show that our proposed model exhibits better performance, in terms of air quality forecasting, than conventional models. Furthermore, its capability as a mechanism to support decision making is clearly demonstrated.

Multi-Label Data Fusion to Support Agricultural Vulnerability Assessments

Identifying crop species and varieties adaptable to climate change impacts is one of the main aspects of climate vulnerability assessments. This estimation involves processing, integrating, and analyzing many information sources to provide accurate and timely responses. However, designing this evaluation, examine the information gathered, and reaching agreements among all stakeholders and experts, often requires considerable effort in time, money, and people. In this study, we propose a data fusion strategy to support climate vulnerability assessments by identifying the adaptability of crops in a territory in the short term. This strategy follows the Joint Directors of Laboratories’ data fusion model guidelines. It was evaluated and validated through a case study in Colombia’s upper Cauca river basin. For this purpose, we identified Climate, Soil, Water Quality, Productive Alliances, and Production as the most relevant data sources to be integrated, and using metrics such as Mean IR, SCUMBLE, TCS, among others, we evaluated the combined datasets according to their theoretical complexity. The adaptability of crops in a territory was addressed as a multi-label learning problem, assessing the performance of different multi-label classification and multi-view multi-label classification models with both test and actual data. Comparing the predicted crops with the actual ones, we obtained a 98% similarity without considering crop ranking using the Binary Relevance approach and the Random Forest and XGBoost algorithms. Using a more exhaustive test involving order, we obtained a maximum similarity of 67% applying Binary Relevance and Random Forest.

Mechanical fault diagnosis and prediction in IoT based on multi-source sensing data fusion

Using multi-source sensing data based on the Internet of Things (IoT) with artificial intelligence and big data processing technology to achieve predictive maintenance of mechanical equipment can remarkably improve the service life of the machine and reduce labor costs when diagnosing mechanical faults, and it has become a highly relevant research topic. In this paper, the multi-source sensing data fusion models and fusion algorithms are studied and discussed. First, the Joint Directors of Laboratories (JDL) fusion model and the Hierarchical fusion model are compared and analyzed. Then, various types of fusion algorithms based on Neural Networks and Deep Learning, including Dempster-Shafer (D-S) evidence theory and their applications in mechanical fault diagnosis and fault prediction, are studied and compared. The findings reveal that exploring and designing a more intelligent fusion model incorporating the beneficial characteristics of different fusion algorithms are challenging and have a certain value for promoting the development of mechanical fault diagnosis and prediction.

Fusing depth and colour information for human action recognition

In recent years, human action recognition systems have been increasingly developed to support a wide range of application areas, such as surveillance, behaviour analysis, security, and many others. In particular, data fusion approaches that use depth and colour information (i.e., RGB-D data) seem to be particularly promising for recognizing large classes of human actions with a high level of accuracy. Anyway, existing data fusion approaches are mainly based on feature fusion strategies, which tend to suffer of some limitations, including the difficult of combining different feature types and the management of missing information. To address the two problems just reported, we propose an RGB-D data based human action recognition system supported by a decision fusion strategy. The system, starting from the well-known Joint Directors of Laboratories (JDL) data fusion model, analyses human actions separately for each channel (i.e., depth and colour). The actions are modelled as a sum of visual words by using the traditional Bag-of-Visual-Words (BoVW) model. Subsequently, on each channel, these actions are classified by using a multi-class Support Vector Machine (SVM) classifier. Finally, the classification results are fused by a Naive Bayes Combination (NBC) method. The effectiveness of the proposed system has been proven on the basis of three public datasets: UTKinect-Action3D, CAD-60, and LIRIS Human Activities. Experimental results, compared with key works of the current state-of-the-art, have shown that what we propose can be considered a concrete contribute to the action recognition field.

Nationwide critical infrastructure monitoring using a common operating picture framework

This paper describes the efforts involved in designing a common operating picture system for monitoring large-scale critical infrastructures. The design leverages the Joint Directors of Laboratories (JDL) data fusion model to enable the integration of different critical infrastructure systems with their dependency relations. The resulting Situational Awareness of Critical Infrastructure and Networks (SACIN) framework offers a platform that provides a common operating picture of a critical infrastructure. A generic data collection component customized to each source system generates events and facilitates JDL level 0 integration. An analysis component collects events and data to produce meaningful information about the current state and future impact estimates in accordance with JDL levels 1 to 3. A brokered architecture supports level 4 control by various components and a JDL level 5 user interface is offered via a web application. Interviews of infrastructure subject matter experts were conducted to obtain the situational awareness requirements. By applying key situational awareness oriented design principles to the situational awareness requirements, a user interface was created for organizing information based on operator situational awareness needs and supporting key cognitive mechanisms that transform data into high levels of situational awareness. Situational awareness measures were used to assess operator performance during critical infrastructure tasks – a “freeze-probe” recall approach (Situational Awareness Global Assessment Technique (SAGAT)), a post-trial subjective rating approach (Situational Awareness Rating Technique (SART)) and the System Usability Scale (SUS). The results indicate that the supply of attentional resources (SART supply) and overall SAGAT score best predict the performance levels of operators.

ONTOLOGY-DRIVEN APPROACH TO MEDICAL DATA FUSION

A large volume of operational and statistical data has been stored within medical information system databases. The stored data represents a loosely-coupled and semi-structured clump. To increase information support of medical procedures it is necessary to implement data linking processes into the medical information systems. The aforementioned processes should be able to collect data from diverse sources and produce a linked view that can be used for medical evaluation, planning, etc. To solve this problem, an approach based on JDL (Joint Directors of Laboratories) data fusion model and ontologies is presented. JDL model defines functional levels of data fusion and their interconnections. Ontologies are used to set links between data and knowledge. To specify and evaluate the proposed approach, a simplified scenario is constructed. The scenario was prepared for the Federal Almazov North-West Medical Research Center. The proposed scenario includes the following actions: extraction of a list of drugs and diagnosis from semi-structured data; annotation of extracted data using author's ontology and third-party ontologies; revealing interacting drugs that are prescribed to a patient; data representation. The scenario shows that ontology-driven data fusion approach has a great potential for linking data and knowledge within medical information systems. It helps to detail each level of data fusion, reveal problems that need further research and describe use cases for Semantic Web instruments.

Adaptation of the JDL Model for Multi-Sensor National Cyber Security Data Fusion

The potential attack surface of a nation is large and no single source of cyber security data provides all the required information to accurately describe the cyber security readiness of a nation. There are a variety of specialised data sources available to assess the state of a nation in key areas such as botnets, spam servers and incorrectly configured hosts. By applying data fusion principles, the potential exists to provide a representative view of all combined data sources. This research will examine a variety of currently available Internet data sources and apply it to an adapted Joint Directors of Laboratories (JDL) data fusion model in order to illustrate the potential gains and current limitations. The JDL model has been adapted to suit national level cyber sensor data fusion with the aim to formally define and reduce data ambiguity and enhance fusion capability in a real world system. A case study highlights the results of applying available open source security information against the model to relate to the current South African cyber landscape.

Data Fusion-based resilient control system under DoS attacks: A game theoretic approach

In this paper, the resilient control under the Denial-of-Service (DoS) attack is rebuilt within the framework of Joint Directors of Laboratories (JDL) data fusion model. The JDL data fusion process is characterized by the so-called Game-in-Game approach, where decisions are made at different layers. The interactions between different JDL levels are considered which take the form of Packet Delivery Rate of the communication channel. Some criterions to judge whether the cyber defense system is able to protect the underlying control system is provided. Finally, a numerical example is proposed to verify the validity of the proposed method.

사이버공간에서 효율적인 정보 활용을 위한 사이버 정보순환 및 융합체계 제안

In this paper, we proposed cyber intelligence cycle and fusion system to efficiently apply collected data in the cyberspace. Until now, situation reaction and operations related to cyber threats are constantly evolving, but intelligence functions for effective application of information don't develop as cyber operational field. So, we proposed cyber intelligence cycle system from information collection to announcement & standardization for efficient information collection. We also derived intelligence fusion system that it can filter only valuable data and respond threat as analyzing the correlation between data. Intelligence fusion system is based on JDL(Joint Directors of Laboratories) model.

The Information Fusion JDL-U model as a reference model for Virtual Manufacturing

This paper presents a description of Modelling and Simulation as used in the Virtual Systems Research Centre at the University of Skövde. It also gives a summarized account of issues discussed in previous work such as phases in a simulation project, Verification, Validation and Accreditation, and the use of simulation as a tool to reduce uncertainty. The role of the human in various phases/activities in simulation projects is highlighted. Examples of both traditional and advanced applications of Virtual Manufacturing are given. Examples of the latter are simulation-based monitoring and diagnostics, and simulation-based optimization. Two models for Information Fusion, the OODA loop and JDL-U model, are discussed, the latter being an extension of the JDL model that describes various levels of information fusion (JDL=“joint directors of laboratories”). Subsequently, the activities and phases in a Modelling and Simulation project are placed in the context of the JDL-U model. This comparison shows that there are very strong similarities between the six (0–5) levels in the JDL-U model and activities/phases in Modelling and Simulation projects. These similarities lead to the conclusion that the JDL-U model with its associated science base can serve as a novel reference model for Modelling and Simulation. In particular, the associated science base on the “user refinement” level could benefit the Virtual Manufacturing community.

Distributed contextual data fusion with ACIPL

A system for controlling smart sensor networks is described. The system is called the Adaptive Context Information Processing Language (ACLPL) which will allow explicit use of states of context inferred from sensor readings and algorithmic output for distributed control of data fusion in sensor networks. The detailed description of the language including its use for sensor information separation into raw sensor data, feature, objects, and events by the language is described. Furthermore, the concept of context which aids the modeling of sensor data with the data fusion hierarchy is introduced, including its types, properties, and applications. ACIPL loosely follows the Joint Directors of Laboratories' Data Fusion Hierarchy and provides a powerful tool to facilitate the use of smart sensor networks for the purpose of sensor data fusion.

Sensor Resource Management driven by threat projection and priorities

This paper extends previous Sensor Resource Management (SRM) work by addressing information flow from sensor inputs to SRM, through four levels of the US DoD’s Joint Directors of Laboratories (JDL) sensor fusion model. The method flexibly adapts to several domains/problems. Human situation awareness information needs are linked to sensor control in a manner similar to perception management. The key to effective integration of JDL levels is the timely determination of the highest priorities via threat projection accomplished via Probabilistic Accumulative Situation Calculus (PASC), which quantifies threat intent using an appropriate level of automated context-based reasoning. The accuracy of the threat projection is improved over time using self-learning techniques. The multiple sensor system levels are unified primarily using the structure of quantified priorities. Algorithms are presented for a radar sensor resource allocation and adjustment method in which the dwell time per track parameter is the key radar sensor resource to be managed. A developed application of the method to an Integrated Air Defense System (IADS) sensor system problem is detailed, with simulation results shown to demonstrate the effectiveness of the method.

Hall David L, McMullen Sonya AH: Mathematical Techniques in Multisensor Data Fusion

Data fusion has been a trend in the field of imaging and signal/image analysis. Although multisensor data fusion is still not regarded as a formal professional discipline, tremendous progress has been made since the publication of the first edition of this book in 1992. With this second edition, the authors have been successful in updating us with state-of-the-art methods and techniques in multisensor data fusion. The book involves both algorithms and software tools, and also covers contemporary subjects like smart agents, cognitive aides, and so on. This book is written according to the Joint Directors of Laboratories (JDL) data fusion group model. There are five levels in the JDL model. The first level deals with association, correlation, estimation, and identification in the data domain. The second and third levels perform knowledge-based processing and utilize expert systems. The fourth level is focused on process monitoring and optimization. The fifth level is devoted to human computer interaction. Chapter 1 serves as an overview. Then, Chapter 2 introduces the JDL model and associated algorithms. Chapters 3–6 cover processing techniques at level one. Chapter 7 gives methods at levels two and three. Chapter 8 targets the control of sensor and information resources at level four. Chapter 9 is for data fusion at level five. Chapter 10 discusses implementation of fusion systems. Chapters 11 and 12 describe emerging applications and information management. The book contains 100 equations, 75 illustrations and key references. The typesetting quality is generally excellent but it would be better if some labels in the figures have been put in larger size. Note that the additons in this book include materials on fusion system control, DARPA's TRIP model, and applications in data warehousing, medical equipment, and defense systems. Hall (associate dean for research, Pennsylvania State University School of Information Sciences and Technology) and McMullen (captain, US Air Force) are well known experts in the field, and should be congratulated for accomplishing such an excellent job in summarizing the up-to-date essential ideas and results on multisensor fusion. Overall, the book is very informative and not difficult to read for electrical and computer engineers as well as technical managers. These types of practitioners can gain solid advice from the book regarding selection of data fusion methods, balance of trade-offs among commercial off-the-shelf tools, development of multisensor data fusion systems and their applications to solve real-world problems. However, to build a sophisticated data fusion system one may need other references for more comprehensive mathematical theories, more rigorous statistical treatments, and more technical details. The cited literature in the book represents appropriate pointers for that purpose. A good example for further reading is the Handbook [1]. As an engineer in biomedical imaging, I would like to underline the relevance of this book to research on systems biomedicine. Given the completion of the Human Genome Project [2] and the momentum of the Physiome Project [3], in 2003 the NIH defined its Roadmap to guide our efforts towards most important biomedical research, including studies on biological building blocks, pathways, and networks using molecular libraries, imaging, bioinformatics and computational biology. Consequently, multi-modality signal sensing and imaging plays a critical role, which often necessarily generates heterogeous sparse/huge datasets that are multi-dimensional, multi-spectral, dynamic, noisy and/or incomplete. The data fusion techniques discussed in this book are closely related to that for biomedical image registration/fusion. Major challenges and new opportunities are enormous ahead of us. In that context, I hope this book will have a significant impact on the biomedical imaging areas, and recommend it to biomedical imaging researchers in particular and other interested engineers in general.

A fuzzy-logic architecture for autonomous multisensor data fusion

Fuzzy logic techniques have become popular to address various processes for multisensor data fusion. Examples include the following: (1) fuzzy membership functions for data association; (2) evaluation of alternative hypotheses in multiple hypothesis trackers; (3) fuzzy-logic-based pattern recognition (e.g., for feature-based object identification); and (4) fuzzy inference schemes for sensor resource allocation. These approaches have been individually successful but are limited to only a single subprocess within a data fusion system. At The Pennsylvania State University, Applied Research Laboratory, a general-purpose fuzzy-logic architecture has been developed that provides for control of sensing resources, fusion of data for tracking, automatic object recognition, control of system resources and elements, and automated situation assessment. This general architecture has been applied to implement an autonomous vehicle capable of self-direction, obstacle avoidance, and mission completion. The fuzzy logic architecture provides interpretation and fusion of multisensor data (i.e., perception) as well as logic for process control (action). This paper provides an overview of the fuzzy-logic architecture and a discussion of its application to data fusion in the context of the Department of Defense (DoD) Joint Directors of Laboratories (JDL) Data Fusion Process Model. A new, robust, fuzzy calculus is introduced. An application example is provided.

BLACKBOARD CONCEPTS FOR DATA FUSION APPLICATIONS

Data fusion has been defined as a process dealing with the association, correlation, and combination of data and information from multiple sources to achieve refined position and identity estimates for entities, and complete and timely assessments of related situations and threats, and their significance. This process (sometimes labeled a “technology”) is pervasive, i.e. capable of broad, multi-domain application. Indeed, data fusion has found extensive application in the commercial/industrial sector as well, in areas such as robotics and process control, and for numerous applications requiring intelligent, autonomous processes and capabilities. One of the purposes of this paper is to describe the evolving standard description of the data fusion process ascribed to by the U.S. Joint Directors of Laboratories (JDL) Data Fusion Subpanel (a Department of Defense organization), as well as components of the attendant lexicon and taxonomy. While the specific definitions of a “situation assessment (SA)” and a “threat assessment (TA)” have proven to be problem-dependent for most defense applications, these notions generally encompass a large quantity of knowledge which reflect the (dynamic) constituency-dependency relationships among objects of various classes as well as events and activities of interest. Formulation of hypotheses about situations and threats is a process having the following properties: • it employs many types of knowledge • it must consider multiple, asynchronous activities • multiple types of dynamic and static data must be processed • numerous sub-networks of interest in the situation/threat picture (numerous constituency-dependency relationships) exist—this leads to feedforward/backward inferencing requirements • information-processing strategies are required to produce estimates of aggregated force structures (given individual unit positions and identities), as well as aggregated behaviors (given individual events or activities) • the situational or threat state is often ephemeral and thus temporal reasoning capabilities must be part of the process The paper expands on the processes and techniques involved in SA and TA analysis, and describes, from various points of view, why the blackboard paradigm is properly applicable to problems of SA and TA analysis. This assessment includes various trade-off factors (features, benefits, and disadvantages or complexities) in applying blackboard concepts to data fusion related reasoning processes. Specific research and development by the authors and synthesis of the results of a survey on data fusion applications (shown within) has led to the formulation of a recommended generic, ideal blackboard architecture for these defense problems described in the paper.