105,614 research outputs found
REAL-TIME MULTI-PATIENT MONITORING SYSTEM USING ARM AND WIRELESS SENSOR NETWORK
Mobile Multi patient monitoring device has become increasingly important in Hospital wards to record real-time data during normal activity for better treatment. However, the current quality and reliability have not been satisfactory due to the size, weight, distance of coverage and also high power consumption. This paper provides a solution for enhancing the reliability, flexibility by improving the performance and power management of the real-time multi-patient monitoring system (MPMS). In the current proposed system the patient health is continuously monitored by the MPMS and the acquired data is transmitted to a centralized ARM server using Wireless Sensor Networks. A ZigBee node is connected to every patient monitor system which will send the patient\u27s vital information .Upon system boot up, the mobile patient monitor system will continuously monitor the patients vital parameters like Heart Beat, body temperature etc and will periodically send those parameters to a centralized server using ZigBee node configured as co-coordinator. If a particular patient’s health parameter falls below the threshold value, a buzzer alert is triggered by the ARM server. Along with a buzzer an automated SMS is posted to the pre-configured Doctors mobile number using a standard GSM module interfaced to the ARM server. The Doctor is continuously connected to the ARM server using GSM Module and he/she can get a record of a particular patient’s information by just posting a SMS message to the centralized ARM server. This will reduce treatment time, cost and power consumption to a greater extent. At the same time, the efficiency of examining ward will be improved by making the system more real-time and robust
The Hierarchic treatment of marine ecological information from spatial networks of benthic platforms
Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.Peer ReviewedPostprint (published version
Statistical Learning in Automated Troubleshooting: Application to LTE Interference Mitigation
This paper presents a method for automated healing as part of off-line
automated troubleshooting. The method combines statistical learning with
constraint optimization. The automated healing aims at locally optimizing radio
resource management (RRM) or system parameters of cells with poor performance
in an iterative manner. The statistical learning processes the data using
Logistic Regression (LR) to extract closed form (functional) relations between
Key Performance Indicators (KPIs) and Radio Resource Management (RRM)
parameters. These functional relations are then processed by an optimization
engine which proposes new parameter values. The advantage of the proposed
formulation is the small number of iterations required by the automated healing
method to converge, making it suitable for off-line implementation. The
proposed method is applied to heal an Inter-Cell Interference Coordination
(ICIC) process in a 3G Long Term Evolution (LTE) network which is based on
soft-frequency reuse scheme. Numerical simulations illustrate the benefits of
the proposed approach.Comment: IEEE Transactions On Vehicular Technology 2010 IEEE transactions on
vehicular technolog
Business Case and Technology Analysis for 5G Low Latency Applications
A large number of new consumer and industrial applications are likely to
change the classic operator's business models and provide a wide range of new
markets to enter. This article analyses the most relevant 5G use cases that
require ultra-low latency, from both technical and business perspectives. Low
latency services pose challenging requirements to the network, and to fulfill
them operators need to invest in costly changes in their network. In this
sense, it is not clear whether such investments are going to be amortized with
these new business models. In light of this, specific applications and
requirements are described and the potential market benefits for operators are
analysed. Conclusions show that operators have clear opportunities to add value
and position themselves strongly with the increasing number of services to be
provided by 5G.Comment: 18 pages, 5 figure
Enabling Communication Technologies for Automated Unmanned Vehicles in Industry 4.0
Within the context of Industry 4.0, mobile robot systems such as automated
guided vehicles (AGVs) and unmanned aerial vehicles (UAVs) are one of the major
areas challenging current communication and localization technologies. Due to
stringent requirements on latency and reliability, several of the existing
solutions are not capable of meeting the performance required by industrial
automation applications. Additionally, the disparity in types and applications
of unmanned vehicle (UV) calls for more flexible communication technologies in
order to address their specific requirements. In this paper, we propose several
use cases for UVs within the context of Industry 4.0 and consider their
respective requirements. We also identify wireless technologies that support
the deployment of UVs as envisioned in Industry 4.0 scenarios.Comment: 7 pages, 1 figure, 1 tabl
- …