In order to apply indoor localization systems in real environments it is necessary to provide an accurate location without implying a high impact on the user's normal behaviour. To achieve this goal, in this paper, a combination of battery saving techniques with a system based on WiFi fingerprinting is proposed. This is done by transferring the location calculation workload to the server, leaving user's mobile devices the only responsibility of making periodic WiFi network scans at dynamic intervals based on user activity, through an application running on background. There are not many studies analyzing energy consumption of existing localization systems, even though it is an important factor in real applications. In this paper, both energy consumption and accuracy are analyzed, having an energy consumption of only 0.8 Wh (having a 3.7 V battery) during a 24-hour cycle and an average localization error of 4.51 meters. Worth to mention that as computation is done on server side the system can be expanded to multiple buildings and floors. Finally, the dataset used in this paper has been published making possible to test new algorithms in the same environment.
UNSTRUCTURED Only 5% of the molecules tested in oncology phase I trials reach the market after an average of 7.5 years of waiting and at a cost of tens of millions of euros. To reduce the cost and shorten the times of discovery of new treatments, “drug repurposing” (research with molecules already approved for another indication) and the use of secondary data (not collected for the purpose of research) have been proposed. Due to advances in informatics in clinical care, secondary data can, in some cases, be of equal quality to primary data generated through prospective studies. The objective of this study is to identify drugs currently marketed for other indications which may have an effect on the prognosis of cancer patients. For this purpose, a cohort of patients with high-lethality neoplastic diseases, treated in the public health system of Catalonia between 2006 and 2012, will be monitored retrospectively for survival for 5 years after diagnosis or until death. A control cohort, comprising people without neoplasms, will be also retrospectively monitored for 5 years. The following study variables will be extracted from different population databases: type of neoplasm (cancer patient cohort), date and cause of death, pharmacological treatment, sex, age and place of residence. In the first stage of the statistical analysis of the cancer patient cohort, the drugs consumed by the long-term survivors (alive at 5 years) will be compared with those consumed by nonsurvivors. In a second stage, the survival associated with the consumption of each relevant drug will be analysed. For the analyses, groups will be matched for potentially confounding variables, and multivariate analyses will be performed to adjust for residual confounding variables if necessary. The control cohort will be used to verify whether the associations found are exclusive to patients with neoplasms or whether they also occur in patients without cancer. All analyses are considered exploratory; therefore, the results will have to be confirmed in subsequent clinical trials; however, the results of this study may accelerate drug discovery in oncology.
The growth of the Internet has led to the emergence of servers that perform increasingly heavy tasks. Some servers must remain active 24 h a day, but the evolution of network cards has facilitated the use of Data Processing Units (DPUs) to reduce network traffic and alleviate server workloads. This capability makes DPUs good candidates for load alleviation in systems that perform continuous data processing when the data can be pre-filtered. Computer vision systems that use some form of artificial intelligence, such as facial recognition or weapon detection, tend to have high workloads and high power consumption, which is becoming increasingly costly. Reducing the workload is therefore desirable and possible in some scenarios. The main contributions of this study are threefold: (1) to explore the potential benefits of using a DPU to alleviate the workload of a 24-h active server; (2) to present a study that measures the workload reduction of a CCTV weapon detection system and evaluate its performance under different conditions. We observed a 43,123% reduction in workload over the 24 h of video used in the experimentation, reaching more than 98% savings during night hours, which significantly reduces system stress and has a direct impact on electrical energy expenditure; and 3) to provide a framework that can be adapted to other computer vision-based detection systems.
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