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INTRODUCTIONInformation fusion utilizes a collection of data sources for uncertainty reduction, coverage extension, and situation awareness. Future information fusion solutions require systems design [1], coordination with users [2], metrics of performance [3], and methods of multilevel security [4]. A current trend that can enable all of these services is cloud computing. Cloud computing as defined by NIST is:Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. [5] Cloud computing provides capabilities (on-demand self service, broad network access, resource pooling, rapid elasticity, and measured service) over different types of clouds (private, community, public, and hybrid).An area of growing popularity of cloud-based applications is robotic systems [6], [7]. These applications of cloud services have implications for avionics systems in telerobotics [8], space communications [9], multisensor fusion [10], wide-area motion imagery [11], and information management [12]; not to mention numerous other emerging applications. Based on the services provided over a distributed network, cloud computing supports large scale data processing and analytics which is essential for future avionics systems designs.In this paper, we present a comprehensive view on a system level information fusion design using cloud computing technology. A systematic comparison among four different distributed computing paradigms is provided, which illustrates the advantages and constraints of cluster computing, peer-to-peer (P2P) computing, grid computing, and cloud computing. A holistic distributed cloud-enabled robotics framework for information fusion is proposed using robotic systems. The system-level design principles of service-based architectures are highlighted in this framework where we considered the implementation of both the cloud and robot networks with additional security features. In addition, on the cloud side, we include a virtual machine (VM) and a physical machine into our framework as dynamic computing clusters. A preliminary performance evaluation through a case study based on a video tracking application is demonstrated to highlight the advantages of cloud computing. Figure 1 compares the hardware and software stacks of four major distributed computing paradigms in order of development: cluster computing, peer-to-peer (P2P) computing, grid computing, and cloud computing. The solid arrows indicate the layers on which users run their application directly and dashed arrows indicate layers controlled by the middleware layer. It is important to note that cloud computing has emerged as a method for large data management [13] that is useful for distributed avionics systems.The lowest level of the stack is the networking infrastructure, which connects physical computers. Sca...

BackgroundExtravascular lung water (EVLW) is a sensitive prognostic indicator of pulmonary edema. Thus, EVLW may be an advantageous method of fluid management. This study aims to evaluate the outcomes of using EVLW and pulmonary artery wedge pressure (PAWP) as strategies for fluid management in patients with acute respiratory distress syndrome (ARDS).MethodsTwenty-nine patients were randomly divided into the EVLW and PAWP groups. The survival rate, ICU (Intensive Care Unit) length of stay, duration of mechanical ventilation, acute lung injury scores, and oxygenation index of the EVLW and PAWP groups were compared.ResultsNo significant difference in the survival rates at 28 and 60 days (d) after treatment was found between the two groups (p = 0.542). The duration of mechanical ventilation and ICU length of stay were significantly lower (p < 0.05) in the EVLW group than in the PAWP group. The 7 d cumulative fluid balance was -783 ± 391 ml in the EVLW group and -256 ± 514 ml in the PAWP group (p < 0.05). Compared with the PAWP group, the EVLW group showed improved oxygenation index (p = 0.006).ConclusionsEVLW for fluid management improved clinical results in patients with ARDS better than PAWP.

Abstract. Future distributed sensor fusion applications will require efficient methods of information management such as Cloud computing. Using a serverbased cloud-enabled software architecture would increase performance over hardware constraints (e.g., power, memory, and processors). In this paper, we propose a comprehensive framework for information fusion demonstrated for Cloud Robotics, which possesses user favorable features such as good scalability and elasticity. Robots are connected together to form a networked robotic system that is able to accomplish more computationally intensive tasks. Supported by the emerging Cloud computing technology, cloud-enabled robotic systems (CERS) provide even more powerful capabilities to users, yet keeping the simplicity of a set of distributed robots. Through an experimental study, we evaluate the memory, speed, and processors needed for a video tracking application.

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