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Predicting early signs of illness in older adults by utilizing a continuous, unobtrusive nursing home monitoring system has been shown to increase the quality of life and decrease the cost of care. Illness prediction is based on sensor data such as motion and bed and uses algorithms such as support vector machine (SVM) or k-nearest neighbor (kNN). One of the greatest challenges in developing prediction...
In recent years the use of wireless ad hoc networks has seen an increase of applications. A big part of the research has focused on Mobile Ad Hoc Networks (MAnETs), due to its implementations in vehicular networks, battlefield communications, among others. These peer-to-peer networks usually test novel communications protocols, but leave out the network security part. A wide range of attacks can happen...
Based on the actual traffic detection data of the upstream and downstream video monitor blind area, Support Vector Machines (SVM) algorithm was used to realize the short-term traffic flow forecasting, and VISSIM simulation technology was used build the traffic blind area prediction model. The video blind spot detection algorithm with practical engineering application value and the traffic incident...
The Classroom Attentiveness Classification Tool (ClassACT) is a system designed to monitor student attentiveness in a variety of instructional phases within the learning environment: lectures, group work, assessments, etc. By collecting information about the user, the user's environment, and the device itself via the various sensors built in to the tablet, processing the data, and then passing it...
The quality of software engineering has always been of high importance for many actors. With the complexity of the platforms and its components, this is nowadays becoming crucial at each level in order to detect the eventual defects. Due to that complexity, the current measurement and analysis processes become heavier. Indeed, either for runtime monitoring, QoE, mobile gaming or simply for systems...
Recently, due to the development of automobile industry, the number of vehicles is increasing. The traditional parking lots are unable to meet the growing demands from so many vehicles. Therefore, the current parking lots present many problems. Such as, can't find parking spaces quickly, difficult to manage too many vehicles and manual toll collection costs much time. To solve those problems above,...
Absence seizures are associated with generalized 2.5–5 Hz spike-wave discharges in the electroencephalogram (EEG). Rarely are patients, parents, or physicians aware of the duration or incidence of seizures. Six patients were monitored with a portable EEG-device over four times 24 h to evaluate how easily outpatients are monitored and how well an automatic seizure detection algorithm can identify the...
Failure of a task running on a Hadoop cluster is highly expensive in terms of computational time. A failure occurring even at the end phase of the task may cause the need to redo the entire task. Thus is really important to deploy fault tolerant techniques. Hadoop deploys a technique of checkpointing to prevent data loss. However, computational time-loss still pose a grim threat to critical applications...
Hadoop architecture provides one level of fault tolerance, in a way of rescheduling the job on the faulty nodes to other nodes in the network. But, this approach is inefficient when a fault occurs after most of the job is executed. Thus, it's necessary to predict the fault at the node at quite an early stage so that the rescheduling of the job is not costly in terms of time and efficiency. Prediction...
Load balancers and firewalls are entry points to many key websites in any network infrastructure. Any downtime due to these devices, even for a few minutes, may result in major business impact. There are many proactive event management systems/products which can monitor their health and alert on device failure. However, predicting failure with sufficient lead time still remains a challenging problem...
In this Globalized world, the Call Centers and BPOsare increasing at an exponential rate. There is stiff competitionamong various companies and every company wants to have itsclients happy and satisfied with the resolution of the problems. For this purpose, Agent Quality Monitoring is an importantrequirement. Since in a typical Call Centre, thousands of calls aremade by agents in a single day, it...
One of the global main goal of the safety driving system is protecting the driver, passenger(s), car, and surrounding environment against accident which are caused by external and internal factors. Driver fatigue, one of the major internal factors, is a leading reason of vehicle breakdown according to a survey done by National Highway Traffic Safety Administration (NHTSA). Thus, it is necessary to...
A novel data source selection algorithm is proposed for ambulatory activity tracking of elderly people. The algorithm introduces the concept of dynamic switching between the data collection modules (a smartwatch and a smartphone) to improve accuracy and battery life using contextual information. We show that by making offloading decisions as a function of activity, the proposed algorithm improves...
The purpose of this study is to classify the emotions from human brain signals using electroencephalography (EEG). EEG signals were acquired while subject were watching the emotional stimuli. Subjects were asked to watch four types of emotional stimulus such as, happy, calm, sad and scared. The EEG signals were recorded using 14-channel brain headset. We preprocessed the EEG recorded data with manual...
In this paper we propose an automatic marine life monitoring system. First task in the monitoring process is to detect underwater moving objects as fishes. Second Task is to identify the species of the detected fish. Third task is to track the detected fish to avoid multiple counting and record their activities. Detection is performed using GMM based background subtraction method, classification is...
The goal of this study is to develop an automated algorithm to quantify background electroencephalography (EEG) dynamics in term neonates with hypoxic ischemic encephalopathy. The recorded EEG signal is adaptively segmented and the segments with low amplitudes are detected. Next, depending on the spatial distribution of the low-amplitude segments, the first part of the algorithm detects (dynamic)...
The paper proposes a telemonitoring wearable system to be used by firefighters in dangerous missions. The current work is based on previous research of the authors conducted on heat and mental stress detection and on physiological parameters monitoring using wearable systems. A wearable garment was designed to monitor the wearer's physiological signals and the ambient conditions (temperature and humidity)...
The paper describes the steps involved in designing and implementing a mobile app for real time monitoring of mental stress using voice features and machine learning techniques. The app is easy to use and completely non-invasive. It is called StressID and it is available in the Google Play store. With the use of a server application presenting a web interface, interested parties may remotely monitor...
In this paper, we have investigated the feasibility of detecting drowsiness using hemodynamic brain signals for a passive brain-computer interface (BCI). Functional near-infrared spectroscopy (fNIRS) is used to measure the right dorsolateral-prefrontal brain region in order to investigate the hemodynamic changes corresponding to drowsy and alert states. The data is recorded using five drowsy subjects...
Vigilance analysis associated with safe driving based on EEG has drawn considerable attention of researchers in recent years. Preventing traffic accidents caused by low level vigilance is highly desirable. This paper presents a novel vigilance analysis system by evaluating electroencephalographic (EEG) changes. EEG signals are preprocessed with independent component analysis to eliminate noise from...
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