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Human activity recognition (HAR) has a wide range of applications, such as monitoring ambulatory patients' recovery, workers for harmful movement patterns, or elderly populations for falls. These systems often operate in an environment where battery lifespan, power consumption, and hence computational complexity, are of prime concern. This work explores three methods for reducing the dimensionality...
Size and complexity of contemporary High Performance Computing (HPC) systems increases permanently. While the reliability of a single component and compute node is high, the huge amount of components comprising these systems results in the fact that defects happen regularly. This drives the need to manage failure situations. Common issues are component failures or node soft lock-ups that typically...
Monitoring phenology of agricultural plants is a critical understanding in precision agriculture. Vital improvements can be achieved with precise detection of phenological change of plants which would henceforth improve the timing for the harvest, pest control, yield prediction, farm monitoring, disaster warning etc. Many countries across the world have been developing initiatives to build national...
Effective detection and discrimination of surface deformation features in Synthetic Aperture Radar imagery is one of the most important applications of the data. Areas that undergo surface deformation can pose health and safety risks which necessitates an automatic and reliable means of surface deformation discrimination. Due to the similarities between subsidence features and false positives, advanced...
For a safety critical task like driving, it is very important for the driver to be vigilant at all times. In this study, we explore a driver drowsiness monitoring and early warning system, which uses machine learning techniques based on vehicle telemetry data. The proposed system can ensure safe driving by real time monitoring of driving pattern. This proves to be a very cost effective technique over...
This visual paper aims at proposing a framework for detecting depression in cancer patients using prosodic and statistical features extracted by speech, while chatting with a virtual coach.
A method for detection of Alzheimers type dementia though analysis of vocalisation features that can be easily extracted from spontaneous speech is presented. Unlike existing approaches, this method does not rely on transcriptions of the patients speech. Tests of the proposed method on a data set of spontaneous speech recordings of Alzheimers patients (n=214) and elderly controls (n=184) show that...
Advanced Monitoring Systems are fundamental in advanced manufacturing for control, quality and maintenance purposes. Nowadays, with the increasing availability of data in production and equipment, the need for high-dimensional Anomaly Detection techniques is thriving; anomalies are data patterns that have different data characteristics from normal production instances and that may be associated with...
Infrastructure detection and monitoring is a difficult task. Due to the advances in unmanned vehicles and image analytics, it is possible to decrease the human effort and achieve consistent results in infrastructure assessments using aerial image processing. Reliable detection and integrity checking of power infrastructure including conductor lines, pylons and insulators in a diverse background is...
Principal component analysis (PCA) and kernel PCA (KPCA) are the state-of-art machine learning methods widely used in industrial process monitoring and fault detection field. However, these methods build shallow statistical models based on single layer of features and may not achieve the best monitoring performance. In order to sufficiently mine the intrinsic data features, a deep learning based nonlinear...
The usage of remote sensing to observe environments necessitates interdisciplinary approaches to derive effective, impactful research. One remote sensing technique, Synthetic Aperture Radar, has shown significant benefits over traditional remote sensing techniques but comes at the price of additional complexities. To adequately cope with these, researchers have begun to employ advanced machine learning...
Illegal dumping has been a chronicle problem in many cities in the world. The odors and contaminants caused by abandoned household items and dumped garbage, and construction leftovers not only ruin the city view but also threaten citizens health. To reduce the illegal dumping, a few cities have designed community-based voluntary reporting systems and surveillancecamera-based monitoring systems. However,...
Ecologists can assess the health of wetlands by monitoring populations of animals such as Anurans (i.e., frogs and toads), which are sensitive to habitat changes. But, surveying anurans requires trained experts to identify species from the animals' mating calls. This identification task can be streamlined by automation. To this end, we propose an automatic frog-call classification algorithm and a...
Monitoring sleep postures can provide critical information when analyzing an individual's sleep quality and in-bed behavior. Furthermore, tracking sleep posture over time can play an important role in preventing pressure ulcers (bedsores) in bed-bound patients who are unable to move and change their position frequently. Pressure sensing mats consist of gridded and flexible force sensors are now commercially...
Monitoring high performance computing systems has become increasingly difficult as researchers and system analysts face the challenge of synthesizing a wide range of monitoring information in order to detect system problems on ever larger machines. We present a method for anomaly detection on syslog data, one of the most important data streams for determining system health. Syslog messages pose a...
This paper presents a monitoring system (Canoe-Sense) for canoe motion based on wearable Body Sensor Networks (BSNs). An effective motion segmentation method was applied to competitive sport, which can segment human motion phases automatically based on raw time series data that was acquired through wearable Inertial Measurement Units (IMUs). Orientation estimation algorithm was adopted to measure...
Industrial manufacturing plants often suffer from reliability problems during their day-to-day operations which have the potential for causing a great impact on the effectiveness and performance of the overall process and the sub-processes involved. Time-series forecasting of critical industrial signals presents itself as a way to reduce this impact by extracting knowledge regarding the internal dynamics...
The common approaches to academic integrity in the e-learning environment are resource-intensive and require technology and/or dedicated invigilation staff to monitor assessment activities. These approaches are observational and often external to the learning spaces where the majority of the instructional content resides. Authentic assessments such as discussions, projects and portfolios may not always...
In recent years, traditional cybersecurity safeguards have proven ineffective against insider threats. Famous cases of sensitive information leaks caused by insiders, including the WikiLeaks release of diplomatic cables and the Edward Snowden incident, have greatly harmed the U.S. government's relationship with other governments and with its own citizens. Data Leak Prevention (DLP) is a solution for...
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...
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