Search results for "artificial"

Recognition of Falls and Daily Living Activities Using Machine Learning

2018

A robust fall detection system is essential to support the independent living of elderlies. In this context, we develop a machine learning framework for fall detection and daily living activity recognition. Using acceleration data from public databases, we test the performance of two algorithms to classify seven different activities including falls and activities of daily living. We extract new features from the acceleration signal and demonstrate their effect on improving the accuracy and the precision of the classifier. Our analysis reveals that the quadratic support vector machine classifier achieves an overall accuracy of 93.2% and outperforms the artificial neural network algorithm. Re…

Is the nonREM–REM sleep cycle reset by forced awakenings from REM sleep?

2002

In selective REM sleep deprivation (SRSD), the occurrence of stage REM is repeatedly interrupted by short awakenings. Typically, the interventions aggregate in clusters resembling the REM episodes in undisturbed sleep. This salient phenomenon can easily be explained if the nonREM–REM sleep process is continued during the periods of forced wakefulness. However, earlier studies have alternatively suggested that awakenings from sleep might rather discontinue and reset the ultradian process. Theoretically, the two explanations predict a different distribution of REM episode duration. We evaluated 117 SRSD treatment nights recorded from 14 depressive inpatients receiving low dosages of Trimipram…

Hydrolysis and chemical speciation of dioxouranium(VI) ion in aqueous media simulating the major ion composition of seawater

2004

Abstract The hydrolysis and chemical speciation of the dioxouranium(VI) ion at 25 °C was studied in a number of binary electrolytes (LiCl, NaCl, MgCl2, CaCl2, Na2SO4) and some mixtures (NaCl–Na2SO4, NaNO3–Na2SO4, CaCl2–MgCl2) as well as artificial seawater (SSWE) as a function of ionic strength. The results in LiCl, CaCl2 and MgCl2 solutions confirmed the formation of (UO2)2(OH)22+, (UO2)3(OH)42+, (UO2)3(OH)5+ and (UO2)3(OH)7− species (at I=0 mol l−1: log Tβ22=−5.76, log Tβ34=−11.82, log Tβ35=−15.89 and log Tβ37=−29.26). For NaNO3, NaCl and artificial seawater the hydrolysis constant for the formation of the UO2(OH)+ species was also determined (at I=0 mol l−1: log Tβ11=−5.19). The results …

ActRec: A Wi-Fi-Based Human Activity Recognition System

2020

In this paper, we develop a Wi-Fi-based activity recognition system called ActRec, which can be used for the remote monitoring of elderly. ActRec comprises two parts: radio-frequency (RF) sensing and machine learning. In the RF sensing part, two laptops act as transmitter and receiver to record the channel transfer function of an indoor environment. This RF data is collected in the presence of seven human participants performing three activities: walking, falling, and sitting. The RF data containing the fingerprints of user activity is then pre-processed with various signal processing algorithms to reduce noise effects and to estimate the mean Doppler shift (MDS) of each data sample. We pro…

Combining Benford's Law and machine learning to detect money laundering. An actual Spanish court case.

2017

Abstract Objectives This paper is based on the analysis of the database of operations from a macro-case on money laundering orchestrated between a core company and a group of its suppliers, 26 of which had already been identified by the police as fraudulent companies. In the face of a well-founded suspicion that more companies have perpetrated criminal acts and in order to make better use of what are very limited police resources, we aim to construct a tool to detect money laundering criminals. Methods We combine Benford’s Law and machine learning algorithms (logistic regression, decision trees, neural networks, and random forests) to find patterns of money laundering criminals in the conte…

A Hierarchical Detection and Response System to Enhance Security Against Lethal Cyber-Attacks in UAV Networks

2018

International audience; Unmanned aerial vehicles (UAVs) networks have not yet received considerable research attention. Specifically, security issues are a major concern because such networks, which carry vital information, are prone to various attacks. In this paper, we design and implement a novel intrusion detection and response scheme, which operates at the UAV and ground station levels, to detect malicious anomalies that threaten the network. In this scheme, a set of detection and response techniques are proposed to monitor the UAV behaviors and categorize them into the appropriate list (normal, abnormal, suspect, and malicious) according to the detected cyber-attack. We focus on the m…

2016

The flexible access to information in working memory is crucial for adaptive behavior. It is assumed that this is realized by switching the focus of attention within working memory. Switching of attention is mirrored in the P3a component of the human event-related brain potential (ERP) and it has been argued that the processes reflected by the P3a are also relevant for selecting information within working memory. The aim of the present study was to further evaluate whether the P3a mirrors genuine switching of attention within working memory by applying an object switching task: Participants updated a memory list of four digits either by replacing one item with another digit or by processing…

Adaptive Bias Field Correction: Application on Abdominal MR Images

2017

Segmentation of medical images is one of the most important phases for disease diagnosis. Accuracy, robustness and stability of the results obtained by image segmentation is a major concern. Many segmentation methods rely on absolute values of intensity level, which are affected by a bias term due to in-homogeneous field in magnetic resonance images. The main objective of this paper is two folded: (1) To show efficiency of an energy minimization based approach, which uses intrinsic component optimization, on abdominal magnetic resonance images. (2) To propose an adaptive method to stop the optimization automatically. The proposed method can control the value of the energy functional and sto…

Using adaptive fuzzy-neural control to minimize response time in cluster-based web systems

2005

We have developed content-aware request distribution algorithm called FARD which is a client-and-server-aware, dynamic and adaptive distribution policy in cluster-based Web systems. It assigns each incoming request to the server with the least expected response time. To estimate the expected response times it uses the fuzzy estimation mechanism. The system is adaptive as it uses a neural network learning ability for its adaptation. Simulations based on traces from the 1998 World Cup show that when we consider the response time, FARD can be more effective than the state-of-the-art content-aware policy LARD.

Distributed Adaptive Control for Asymptotically Consensus Tracking of Uncertain Nonlinear Systems With Intermittent Actuator Faults and Directed Comm…

2019

In this article, we investigate the output consensus tracking problem for a class of high-order nonlinear systems with unknown parameters, uncertain external disturbances, and intermittent actuator faults. Under the directed topology conditions, a novel distributed adaptive controller is proposed. The common time-varying trajectory is allowed to be totally unknown by part of subsystems. Therefore, the assumption on the linearly parameterized trajectory signal in most literature is no longer needed. To achieve the relaxation, extra distributed parameter estimators are introduced in all subsystems. Besides, to handle the actuator faults occurring at possibly infinite times, a new adaptive com…