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Internet of things (IoT) integrate the technologies such as sensing, communication, networking and cloud computing in wide range monitoring zone. For applications of IoT, the most appropriate monitoring network is wireless sensor networks (WSN). It is most important to develop energy efficient cluster head (CH) selection scheme to increase the network lifetime of WSNs. It is most crucial to save the...
In the real world, sensors are always exposes to noise and error during measurements. These problems will lead to invalid and overshoot readings, as well as blank data. Fortunately, these errors can be treated using filters. For data collection process using laser range finder, the use of filters is necessary as they can help to overcome the laser limitations and improve the scanning input. Here,...
Sleep deprivation distracted most people. The common ways to monitor people sleeping are electroencephalogram and polysomnography. Recently, wearable devices provide function to estimate sleep status. However, in some situations people feel uncomfortable to wear devices, such as elder with dementia. This paper presents a scheme to estimate sleep status based on wearable free device. We utilized SVM...
In this paper we study a wireless passive sensor network. The sensors are deployed to estimate the true values of multiple active target signals. The sensors forward their observation to a fusion center, which processes the observation of each sensor by a set of fusion rules. One achievement of this paper is proposing an unbiased estimator with minimized variance of errors. To do so, we optimize both...
In this paper we present a novel algorithm for estimating system parameters of arbitrary aerial manipulators. We abstract an aerial manipulator as a rigid body with forces and torques applied at arbitrary locations. It is also assumed that there are sensors that measure forces, accelerations and angular velocities at these locations. The problem studied here is to fuse these sensor data in real time...
Compressed sensing theory promises to sample sparse signals using a limited number of samples. It also resolves the problem of under-determined systems of linear equations when the unknown vector is sparse. Those promising applications induced a growing interest for this field in the past decade. In compressed sensing, the sparse signal estimation is performed using the knowledge of the dictionary...
This paper considers a formation control problem for a team of agents that are only able to sense the relative bearings from their local body frame to neighboring agents. It is further assumed that the sensing graph is inherently directed and a common reference frame is not known to all of the agents. Each agent is tasked with maintaining predetermined bearings with their neighbors. Using the recently...
Using probabilistic reasoning to model and perceive driving environments is a challenging problem due to both geometric and dynamic random natures. The occupancy grid system, providing an intermediate representation for complicated environments and having many beneficial implications (such as avoiding direct data association and more freedom in data fusion), has been increasingly becoming a popular...
In this study, we estimate gaze direction using the relationship between the face and eyes. The face and eyes are divided laterally and in lengthwise direction. We set up an object laterally and in lengthwise direction and measured the face angle using a Kinect sensor, which is range image sensor, when gazing at an object. The angle of the eyes is fixed at the angle of the object and grasp of the...
Under-determined mixtures in blind source separation (BSS) are characterized by the case that they have more inputs than outputs. The classical independent component analysis (ICA) methods cannot be applied to the under-determined case. However, sparseness-based approaches can be applied to the under-determined BSS. Two steps method has been widely employed to solve the under-determined BSS problem:...
Existing methods for smart data reduction are typically sensitive to outlier data that do not follow postulated data models. We propose robust censoring as a joint approach unifying the concepts of robust learning and data censoring. We focus on linear inverse problems and formulate robust censoring through a sparse sensing operator, which is a non-convex bilinear problem. We propose two solvers,...
In this paper we consider the problem of distributed spectrum sensing in multiple selforganizing networks sharing the same timefrequency resources. Each of the networks allocates autonomously radio resources so as to minimize mutual interference. Interference sensing is part of this cognitive framework where sensing devices, or secondary users (SUs), exchange local estimates to cooperatively recognize...
The existing works on analyzing/utilizing spectrum whitespace in Cognitive Radio Networks (CRNs) are either empirical studies lacking of theoretical guarantee, or local primary network information based inducing inaccurate analysis and estimation, or overlooking the spectrum whitespace details. Therefore, we propose to systematically analyze the spectrum whitespace in CRNs from a social network perspective...
This paper presents a demonstration of our SECON 2015 paper using Twitter based case studies for social sensing applications. Social sensing has emerged as a new paradigm of data collection, where a group of individuals volunteer (or are recruited) to share certain observations or measurements about the physical world. A key challenge in social sensing applications lies in ascertaining the correctness...
In this contribution, we implement a fully distributed diffusion field estimation algorithm based on the use of average consensus schemes. We show that the field reconstruction problem is equivalent to estimating the sources of the field, and then derive an exact inversion formula for jointly recovering these sources when they are localized and instantaneous. Next we adapt this formula to the sensor...
The availability of high-resolution image radars allows estimating the orientation of vehicles from a single measurement without temporal filtering. This gives the opportunity to react even faster to certain critical traffic scenes. This paper presents an approach for estimating the orientation of a vehicle. The orientated bounding box algorithm known from literature is adapted to this end and a quality...
Crowdsensing has been used quite regularly in recent years to study smartphone usage. However context information associated with smartphone usage is mostly of the type geo-localisation, user mobility, temporal behavior etc. Furthermore most studies are not sufficiently user-centric i.e. don't consider the perception or cognitive aspects of the user. In this paper we collect data about social context...
Lately, pervasive and ubiquitous computing services have been under focus of not only the research community, but developers as well. Different devices generate different types of data with different frequencies. Emergency, healthcare, and latency sensitive services require real-time response. Also, it is necessary to decide what type of data is to be uploaded in the cloud, without burdening the core...
In cooperative spectrum sensing, clustering of the sensor nodes is vastly employed in order to reduce overall energy expenditure due to reporting while preserving high detection performance. Cluster formation is especially difficult for mobile networks since the clusters immediately become invalid. In the mobile scenario, the cluster forming algorithm should take into account the total energy expenditure,...
In this paper, we derive the distributed observable state from first principles. In particular, we extend the estimation setup to a distributed framework where in addition to the state and sensing, we also have communication among the sensors. We consider that each sensor estimates the entire state-vector to recover its unobservability. Combining the estimates at all of the sensors we arrive at the...
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