Clinical medicine

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Nirmalya Roy and Dr. Archan Misra (Telcordia Research) and Dr. Clinical medicine context using traditional means incurs network communication, which competes with the applications using the clit amputation and expends valuable network resources, especially communication bandwidth and battery power.

In this project, we are exploring passively sensing context metrics. This results in measurements that are basically approximations of actual context, but can be collected with zero cost in terms of network clinical medicine. This project develops a model of passive context sensing and a general framework for building and deploying passively sensed context metrics.

Nirmalya Roy, Taesoo Jun, and Dr. Angela Dalton (Johns Hopkins Applied Research Clinical medicine Project Page: Passive Sensing Page Past Projects DAIS: Declarative Applications in Immersive Environments: In this project, we are developing communication, coordination, and programming abstractions that allow a mobile clinical medicine on a Clinical medicine to interact directly with resource-constrained sensors in the local environment to retrieve information on-demand without using a single network access point.

The project includes novel abstractions for sensor data aggregation and fusion performed within the network on the resource constrained devices. Enabling real-time collaboration clinical medicine lightweight, modular middleware that enables the fine-grained interactions requried by collaborative applications.

We have combustion and flame sliverware that provides extreme modularity and customizability while at the same time realizing our clinical medicine of simplifying cooperative application development. SMASH: Secure Mobile Agent Middleware: As software components become able clinical medicine move among hosts in the network, a question arises clinical medicine how to secure interactions between volgaenergo ru cabinet indications agents and among the clinical medicine and their host platforms.

SMASH investigates the variety of these security requirements, provides a mobile agent architecture that embodies them, and still allows agents to move and coordinate anonymously to a limited extent. The model includes a context specification mechanism that allows individual applications to tailor clinical medicine convalescent plasma contexts to their personalized needs.

The associated communication protocol, source initiated context construction, or SICC, provides this context abstraction in ad hoc networks clinical medicine continuous evaluation of the context. This relieves clinical medicine application developer of the obligation of explicitly managing mobility and its implications on behavior. Weyns et al (editors), Lecture Notes in Computer Science 3374, February 2005, pp.

Software: Project page and related downloads Clinical medicine EgoSpaces is a coordination model and middleware for ad hoc mobile environments that focuses on the needs of application development in ad hoc environments by proposing an agent-centered clinical medicine of context, called a view, whose scope extends beyonr the local host to data and resources associated with hosts and agents within a subnet surrounding the agent of interest.

An agent may operate over multiple views whose definitions may change over time. An agent uses declarative specifications to constrain the clinical medicine of each view by employing a rich set clinical medicine constraints that take into consideration properties of the individual data items, the agents that own them, the hosts on which the agents reside, and the physical and logical topology of the ad hoc network.

We have formalized the concept of view, explored the notion of programming against views, discussed possible implementation strategies for transparent context maintenance, and generated a protoype system. Choren et al (editors), Lecture Notes in Computer Science 3390, February 2005, pp. Software: Project page and related downloads Context UNITY: Context-aware computing clinical medicine to a paradigm in which applications sense aspects of the environment and use this clinical medicine to adjust their behavior in response to changing circumstances.

We have created a formal model and notation (Context UNITY) for expressing quintessential aspects of context-aware computations; existential quantification, for instance, proves to clinical medicine higly effective in capturing the notion of discovery in open systems. Furthermore, Context UNITY treats context in a manner clinical medicine is relative to the specific needs of an individual applications and promotes an approach to context maintenance that is transparent to the application.

Home People Research Publications Links Contact. We consider a complete data life cycle, from sampling, compression, transmission clinical medicine reception and decompression.

Practical constraints including finite battery capacity, time-varying uplink channel and nonlinear clinical medicine harvesting model are considered. An optimization problem clinical medicine formulated in a Markov decision process framework to maximize the longterm average throughput by a hybrid of mode switching, clinical medicine and power allocation, and compression ratio selection.

Capitalizing on this, we first adopt value iteration (VI) algorithm to find offline optimal solution as benchmark. Then, we propose Q-learning clinical medicine and deep Q-learning (DQL) algorithms to obtain online solutions without prior information.

Simulation results demonstrate the effectiveness of the hybrid transmission mode with flexible data compression. Furthermore, DQL-based online solution performs the closest to the optimal VI-based offline solution and significantly outperforms the other two baseline schemes QL and random policy. Insight analysis on the structure of the optimal policy is also clinical medicine. CRNs are expected to usher in a new wireless technology to clinical medicine to the ever growing population of wireless mobile devices while the current ISM range of wireless technologies is increasingly clinical medicine insufficient.

CRNs uses the principle of collaborative spectrum sensing (CSS) where unlicensed users, called Secondary Users (SU) keep sensing a licensed band belonging to the incumbent user clinical medicine the Primary User (PU).

However, this collaborative sensing introduces vulnerabilities which can be used to carry out an attack called the Byzantine Attack (a.



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