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Spectrum sensing is one of the key technologies of cognitive radio. Based on noise characteristics estimation and support vector machine (SVM) technology, this paper proposed a frequency domain two-stage spectrum sensing method to improve sensing accuracy under low signal-to-noise ratio scenarios with low system complexity and high generalization ability. In the slow sensing stage, the frequency-domain...
Motivated by the great potential of implicit and seamless user authentication, we attempt to build an efficient middle layer running on mobile devices to support implicit authentication (IA) systems with adaptive sampling. Various activities, such as user location, application usage, user motion, and battery usage have been popular choices to generate behaviors, the soft biometrics, for implicit authentication...
Crowd behaviour analytics focuses on behavioural characteristics of groups of people instead of individuals' activities. This work considers human queuing behaviour which is a specific crowd behavior of groups. We design a plug-and-play system solution to the queue detection problem based on Wi-Fi/Bluetooth Low Energy (BLE) received signal strength indicators (RSSIs) captured by multiple signal sniffers...
The multiple evidence from different information sources of different importance are not equally important when they are combined in fault diagnosis of diesel engine. To calculate and adjust weighting coefficient of multiple evidence, the method of weighted evidence balance based on genetic algorithms is used. First it searches for the optimal weighting coefficients of different evidence using genetic...
In this paper, we present auto-encoder (AE), stacked auto-encoder (SAE) and sparse auto-encoder (SPAE) to classify gaits of horse riding for real riding coaching. The parameters of each auto-encoder are adjusted to compare the performance. The data is collected from 16 inertial sensors attached to a motion capture suit to construct a motion database. We build the motion features as the method of gaits...
The purpose of this work is to develop devices capable of identifying sign language characters and comparing them in order to verify layout with better accuracy and robustness. The recognition is performed using Artificial Neural Networks and all the input data are signals from flex sensors, accelerometers and gyroscopes, positioned differently on each device. After being trained, validated and tested,...
An integral part of modem day health-care is monitoring the physical activities of human beings. In this paper, we deal with automatic recognition of some daily activities based on signals measured using easily-available smart phones. We present a neural-network based methodology to classify these signals. In contrast to typical conventional techniques we use sequential processing of signals and circumvent...
This paper develops a method to use RGB-D cameras to track the motions of a human spinal cord injury patient undergoing spinal stimulation and physical rehabilitation. Because clinicians must remain close to the patient during training sessions, the patient is usually under permanent and transient occlusions due to the training equipment and the movements of the attending clinicians. These occlusions...
Scene understanding is a crucial requirement for robot navigation. Conditional Random Fields (CRF) are commonly used to solve the scene labelling problem since they represent contextual information efficiently and provide efficient inference methods. However, when a robot navigates through an unknown environment, it is often necessary to adjust the parameters of the CRF online to maintain the same...
At present, several studies exist describing the relevance of human factor in air transport with main focus on pilots and flight safety. Within such studies, monitoring of physiological functions is used. There are lot of physiological parameters and methods of their assessment; however, they are mostly based on principles originating from clinical practice. Yet, sensitivity and specificity of these...
This paper considers two approaches for detecting and classifying undesirable events in offshore naturally flowing wells. The operation of an oil well requires many decisions that can avoid production losses and additional costs. Two data-based methods are proposed using k-nearest neighbors, sliding windows and time multiscale. The parameters required by these classifiers are tuned using data from...
Driver behavior affects traffic safety, fuel/energy consumption and gas emissions. The purpose of driver behavior profiling is to understand and have a positive influence on driver behavior. Driver behavior profiling tasks usually involve an automated collection of driving data and the application of computer models to classify what characterizes the aggressiveness of drivers. Different sensors and...
Biometrie systems present some important advantages over the traditional knowledge-or possess-oriented identification systems, such as a guarantee of authenticity and convenience. However, due to their widespread usage in our society and despite the difficulty in attacking them, nowadays criminals are already developing techniques to simulate physical, physiological and behavioral traits of valid...
Pedestrian detection is an important topic in many applications, such as intelligent transportation systems (ITSs) or surveillance. For the purpose of applications used around the clock, the work for detecting pedestrian based on thermal sensors has attracted significant attention. To achieve this, this paper proposes a LBP (local binary pattern) encoded multi-level classifier for detecting pedestrians...
Cardio-Pulmonary Resuscitation (CPR) is a technique that allows a CPR certified person to keep alive someone whose heartbeat and/or breathing has stopped. Through compressions applied to the thorax of the individual in need of help, one can maintain the blood flow and air intake until further help (and equipment) arrives. Training for CPR is performed using manikins that mimic a human torso. In this...
Conventional methods for multicomponent analysis such as partial least squares perform well if the constituents of the chemical mixture are known. However, these methods degrade significantly when unknown interferents are present in the mixture. We describe a sparse active-sensing approach that addresses the interference problem in chemical mixture quantification. The approach assumes that a large...
Wrist-worn devices, such as smartwatches and smart bands, have brought about the unprecedented opportunity to continuously monitor gait during daily routines. However, the use of a single wrist-worn unit for gait analysis is challenging for a variety of reasons. Indeed, the signal collected at the user's wrist is subject to a significant “noise” with respect to other body positions (e.g. waist), mainly...
Gait retraining is an important rehabilitation method for re-establishing health gait patterns resulting from disease or injury. Optical marker-based motion capture systems are effective for sensing but aren't used widely, due to cost and lack of portability. Moreover, to perform gait retraining, feedback is needed in addition to sensing. This paper presents a wearable sensing and haptic feedback...
Support vector machine (SVM) now attracts increasing attention in gas classification due to its high performance towards small samples and nonlinearity problems of the dataset. Previously, the probable mismatch between the dataset and the training parameters determined by trial and error or grid search may hinder the exploration of the best result. In this paper, we propose a novel approach to estimate...
This paper proposes an intelligent scheme based on Bayesian artificial neural network (BNN) for fault detection and isolation (FDI) in variable speed wind turbine. The proposed scheme is based on data-driven fault detection method. The main idea is the use of a certain number of BNN classifiers to deals with different types of faults affecting the wind turbine. Different parts of the process were...
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