729,746 research outputs found
Implementation of a Hardware/Software Platform for Real-Timedata-Intensive Applications in Hazardous Environments
Real-Time Technology and Applications Symposium. Brookline, MA, USA, 10-12 Oct. 1996In real-time data-intensive applications, the simultaneous achievement of the required performance and determinism is a difficult issue to address, mainly due to the time needed to perform I/O operations, which is more significant than the CPU processing time. Additional features need to be considered if these applications are intended to perform in hostile environments. In this paper, we address the implementation of a hardware/software platform designed to acquire, transfer, process and store massive amounts of information at sustained rates of several MBytes/sec, capable of supporting real-time applications with stringent throughput requirements under hazardous environmental conditions. A real-world system devoted to the inspection of nuclear power plants is presented as an illustrative examplePublicad
Correlation between the use of derivatives products and the implementation of the monetary policy
In a developing and fluctuant world, derivatives help the investors to avoid risks and moreover to assume them when it is necesary. The trade of the derivatives contributes to the growth of liquidity of the assetsâ market. For the banks, which traditionally avoid risks, this fact combined with low costs for communication and trading, lead to some potential risky investments. The materialization of monetary policy is based on the idea that the needs of the real economy are expressed through the bankimg system, and the changes of the monetary policy influence the evolution of the economy. In this way, the monetary authority has to promote a proper policy, which can lead to the achievement of its main objective â price stability. The use of derivatives made the central bank mission more difficult. The authorities have a difficult task in order to establish new methods, more powerful, for a better implementaton of the monetary policy. In a dynamic environment the equilibrium is not permanent; this is the reason why the central bank and the financial markets participants must prevent first, and then avoid the possible repairs.derivative, monetary policy, risk management, financial markets
Automatic Synthesis of Regular Expressions from Examples
We propose a system for the automatic generation of regular expressions for text-extraction tasks. The user describes the desired task only by means of a set of labeled examples. The generated regexes may be used with common engines such as those that are part of Java, PHP, Perl and so on. Usage of the system does not require any familiarity with regular expressions syntax. We performed an extensive experimental evaluation on 12 different extraction tasks applied to real-world datasets. We obtained very good results in terms of precision and recall, even in comparison to earlier state-of-the-art proposals. Our results are highly promising toward the achievement of a practical surrogate for the specific skills required for generating regular expressions, and significant as a demonstration of what can be achieved with GP-based approaches on modern IT technology
Learning to Navigate in a VUCA Environment: Hierarchical Multi-expert Approach
Despite decades of efforts, robot navigation in a real scenario with
volatility, uncertainty, complexity, and ambiguity (VUCA for short), remains a
challenging topic. Inspired by the central nervous system (CNS), we propose a
hierarchical multi-expert learning framework for autonomous navigation in a
VUCA environment. With a heuristic exploration mechanism considering target
location, path cost, and safety level, the upper layer performs simultaneous
map exploration and route-planning to avoid trapping in a blind alley, similar
to the cerebrum in the CNS. Using a local adaptive model fusing multiple
discrepant strategies, the lower layer pursuits a balance between
collision-avoidance and go-straight strategies, acting as the cerebellum in the
CNS. We conduct simulation and real-world experiments on multiple platforms,
including legged and wheeled robots. Experimental results demonstrate our
algorithm outperforms the existing methods in terms of task achievement, time
efficiency, and security.Comment: 8 pages, 10 figure
ARTEMIS: AI-driven Robotic Triage Labeling and Emergency Medical Information System
Mass casualty incidents (MCIs) pose a formidable challenge to emergency
medical services by overwhelming available resources and personnel. Effective
victim assessment is paramount to minimizing casualties during such a crisis.
In this paper, we introduce ARTEMIS, an AI-driven Robotic Triage Labeling and
Emergency Medical Information System. This system comprises a deep learning
model for acuity labeling that is integrated with a robot, that performs the
preliminary assessment of injury severity in patients and assigns appropriate
triage labels. Additionally, we have developed a frontend (graphical user
interface) that is updated by the robots in real time and is accessible to the
first responders. To validate the reliability of our proposed algorithmic
triage protocol, we employed an off-the-shelf robot kit equipped with sensors
for vital sign acquisition. A controlled laboratory simulation of an MCI was
conducted to assess the system's performance and effectiveness in real-world
scenarios resulting in a triage-level classification accuracy of 92%. This
noteworthy achievement underscores the model's proficiency in discerning
crucial patterns for accurate triage classification, showcasing its promising
potential in healthcare applications
Risk Stratification Before and During Treatment in Newly Diagnosed Multiple Myeloma: From Clinical Trials to the Real-World Setting
Multiple Myeloma (MM) is a hematologic malignancy characterized by a wide clinical and biological heterogeneity leading to different patient outcomes. Various prognostic tools to stratify newly diagnosed (ND)MM patients into different risk groups have been proposed. At baseline, the standard-of-care prognostic score is the Revised International Staging System (R-ISS), which stratifies patients according to widely available serum markers (i.e., albumin, ÎČ 2-microglobulin, lactate dehydrogenase) and high-risk cytogenetic abnormalities detected by fluorescence in situ hybridization. Though this score clearly identifies a low-risk and a high-risk population, the majority of patients are categorized as at âintermediate riskâ. Although new prognostic factors identified through molecular assays (e.g., gene expression profiling, next-generation sequencing) are now available and may improve risk stratification, the majority of them need specialized centers and bioinformatic expertise that may preclude their broad application in the real-world setting. In the last years, new tools to monitor response and measurable residual disease (MRD) with very high sensitivity after the start of treatment have been developed. MRD analyses both inside and outside the bone marrow have a strong prognostic impact, and the achievement of MRD negativity may counterbalance the high-risk behavior identified at baseline. All these techniques have been developed in clinical trials. However, their efficient application in real-world clinical practice and their potential role to guide treatment-decision making are still open issues. This mini review will cover currently known prognostic factors identified before and during first-line treatment, with a particular focus on their potential applications in real-world clinical practice
Implementation and Performance Analysis of Different Hand Gesture Recognition Methods
In recent few years, hand gesture recognition is one of the advanced grooming technologies in the era of human-computer interaction and computer vision due to a wide area of application in the real world. But it is a very complicated task to recognize hand gesture easily due to gesture orientation, light condition, complex background, translation and scaling of gesture images. To remove this limitation, several research works have developed which is successfully decrease this complexity. However, the intention of this paper is proposed and compared four different hand gesture recognition system and apply some optimization technique on it which ridiculously increased the existing model accuracy and model running time. After employed the optimization tricks, the adjusted gesture recognition model accuracy was 93.21% and the run time was 224 seconds which was 2.14% and 248 seconds faster than an existing similar hand gesture recognition model. The overall achievement of this paper could be applied for smart home control, camera control, robot control, medical system, natural talk, and many other fields in computer vision and human-computer interaction
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