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The automation and motion control aspects of a developed multi-sensor measuring system are described in this contribution. The measuring system is designed to automatically detect and survey surface defects of microscopic size on macroscopic mechanical components. It is based on the hierarchical combination of optical sensors with different working ranges and resolutions. This measurement approach...
This paper focused on improving the movement accuracy of PMs on the end-effector. Lots of sensors will be used to joints as possible, Some joints with better error performance than original driving joints at each position in the work space will be identified by using error evaluation measures, and their movement information will be transfered to driving joints to realize movements of the end-effector.
With the growth of the Internet community, textual data has proven to be the main tool of communication in human-machine and human-human interaction. This communication is constantly evolving towards the goal of making it as human and real as possible. One way of humanizing such interaction is to provide a framework that can recognize the emotions present in the communication or the emotions of the...
Wireless sensor networks (WSNs) are gaining popularity in practical monitoring and surveillance applications. Because of the limited energy of sensor nodes, many WSNs work in a low duty cycle mode to effectively extend their network lifetime. However, low duty cycling also decreases transmission efficiency and makes data gathering more challenging. By exploiting the redundancy of in real sensing data,...
Access to accurate GSM power spectrograms in large cities can be of great interest to mobile network operators. Acquiring such information in urban settings is very challenging mainly due to the large area and high dynamics of environments. In this paper, we propose a novel scheme to tackle the large-scale mobile sensing problem. We first utilize an open source GSM project to collect RSSI values of...
With the availability of traffic sensors data, various techniques have been proposed to make congestion prediction by utilizing those datasets. One key challenge in predicting traffic congestion is how much to rely on the historical data v.s. The real-time data. To better utilize both the historical and real-time data, in this paper we propose a novel online framework that could learn the current...
The information about the agricultural activities being performed on the farm is useful for providing agriculture advisory to the farmers. In this paper, we present the Neural Network based approach for the classification of agriculture activities like harvesting, bed-making, transplantation, walking and standstill from the acceleration data obtained from mobile phone carried by the farmer. The performance...
We explore the feasibility of measuring learner engagement and classifying the engagement level based on machine learning applied on data from 2D/3D camera sensors and eye trackers in a 1:1 learning setting. Our results are based on nine pilot sessions held in a local high school where we recorded features related to student engagement while consuming educational content. We label the collected data...
In this paper, we propose fractional sequential sensing (FSS) as a novel cooperative sensing scheme for cognitive radio networks. FSS compromises a tradeoff between sensing accuracy and efficiency by formulating an optimization problem whose solution identifies FSS sensing parameters. These parameters include the sensing period and channels allocated for each user. Our simulation results show that...
In wireless localization problems, prior to the implementation of the sensor networks, it is important and valuable to know that, given the localization accuracy constraints, i.e. To ensure the localization error lower than e m at the confidence level of 1-c, then (1) how many location-known sensors (anchors) needed at least and at most? (2) how to select out optimal locations for these anchors from...
Accurate localisation has always been a hot topic for indoor environment. Recently, compressive sensing has been applied to fingerprinting based localisation and achieved good performance. This paper provides an overview of the state-of-the-art compressive sensing based indoor localisation techniques and an introduction to potential solutions to challenges faced by current systems. The main focus...
Higher order statistics based subspace methods are extensively used for Direction of Arrival (DOA) estimation. This paper compares different versions of fourth order cumulant based ESPRIT algorithms for DOA estimation in terms of accuracy, resolution and computational complexity. The popularity of cumulant based methods is because of their better resolution and their ability to perform well even in...
The paper deals with the reduction of the channel outage in a smart grid scenario since it plays a crucial role on the control performance of the Demand/Response Management. A study on a two-way cognitive-based switching procedure is carried out and tools for the optimum sensing-time evaluation are provided. Such evaluation considers a cost function that takes into account both the sensing-accuracy...
Brain-Computer Interfaces (BCIs) provide a way to communicate without movement and can offer significant clinical benefits therefore. Electrical brain activity recorded using electroencephalography (EEG) can be automatically interpreted by supervised learning classifiers according to the descriptive features of the signal. Compressive sensing paradigm commonly used for array antenna design and signal...
Nowadays mobile applications demand higher context awareness. The applications aim to understand the user's context (e.g., home or at work) and provide services tailored to the users. The algorithms responsible for inferring the user's context are the so-called context inference algorithms, the place detection being a particular case. Our hypothesis is that people use mobile phones differently when...
In this work, we present a CMOS image sensor for star centroid measurement in star trackers. The analysis of the star tracker system shows that long integration will cause "tail effect" in the star image. It significantly reduces the signal magnitude, which in turn increases the centroiding errors. In order to capture limited photons generated from dim stars within shortened integration...
Most of people likes living independently at home. Some activity in our daily life is prone to have some accidents, such as falls. Falls can make people in fatal conditions, even death. A prototype of fall detection system using accelerometer and gyroscope based on smartphone is presented in this paper. Accelerometer and gyroscope sensors are embedded in smartphone to get the result of fall detection...
Elderly people with long-term care and some disabilities are more susceptible to falls. Fall can cause accidental or unintentional injury, even deaths. Fall detection monitoring is needed amongst elderly, particularly for elderly people who like living independently. Physical limitations and disabilities of elderly in doing their daily activities, which susceptible to fall, become the reason why fall...
Energy efficiency in cognitive radio networks (CRN) is of paramount importance since secondary users (SU) are often likely to be energy constrained. While spectrum sensing (SS) is a critical CRN function, repetitive SS events can significantly reduce the battery life of sensing devices. However, energy efficiency can be improved by employing spectrum opportunity forecasting (SOF) and optimal scheduling...
Accurate and fine-grained power measurements of computing systems are essential for energy-aware performance optimizations of HPC systems and applications. Although cluster wide instrumentation options are available, fine spatial granularity and temporal resolution are not supported by the system vendors and extra hardware is needed to capture the power consumption information. We introduce the High...
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