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In this paper, a noise estimation method is proposed and examined for spectrum sensing purposes. The proposed method is based on the use of the first Intrinsic Mode Function (IMF), which is the output of the Empirical Mode Decomposition (EMD). This technique is blind and a priori knowledge of the received signal characteristics are not required. Two well known noise-dependent spectrum sensing techniques,...
Automated riving is becoming the focus of various research institutions and companies. In this context, road estimation is one of the most important tasks. Many works propose to realize this task by employing one or multiple of the following orthogonal information sources: road markings from optical lane recognition, leading vehicle, digital map. etc. Each of them has its own strength and drawbacks...
Uncertainty is an inherent property of any measure or estimation performed in any physical setting, and therefore it needs to be taken into consideration when modelling systems that manage real data. Although several modelling languages permit the representation of measurement uncertainty for describing certain system attributes, these aspects are not normally incorporated into their type systems...
Estimation of bus travel time is one of crucial issues for passengers to let them know their departure time and arrival time at the destination, and to reduce their waiting time at the bus stop. Furthermore, it can also help to promote the development of city public transportation. To estimate bus travel time, it is worth of knowing whether target routes for passenger's travel is unstable or not,...
Joint detection and estimation is an important yet little-studied problem that arises in many signal processing applications. In this paper, a sequential and robust solution approach is presented. To design the test fulfilling constraints on the error probabilities and the quality of the estimate, the problem is converted into an unconstrained form and subsequently solved using Linear Programming...
As for performance assessment based on MV (minimum variance) benchmark for MIMO systems, the knowledge of Markov parameter matrices is at least required for calculation of MV benchmark. However, in some occasions such as plant uncertainty, it is difficult to obtain the exact Markov parameter matrices. This work is to find the solution for MV-based performance assessment of MIMO systems in the presence...
The paper deals with the problem of simultaneous state and process fault estimation for uncertain dynamic systems. Contrarily to the approaches presented in the literature, the nonlinear estimation problem is reduced to the linear one by introducing a suitable system reparameterization and new estimator structure. Instead of estimating the fault directly, its product with state and the state itself...
Spectrum sensing is one of the key technologies in cognitive radio system. Sensitivity to noise uncertainty is a fundamental limitation of current spectrum sensing strategies in detecting the presence of primary users in cognitive radio. Because of noise uncertainty, the detection performance of traditional detectors such as energy detector, matched filter and even cyclostationary detectors deteriorate...
In this paper we describe a particle filter algorithm that allows incorporation of prior knowledge about future states. Incorporation of such knowledge can significantly reduce the uncertainty in the estimation of future state predictions. Estimation of the state is based on a transition model where the current state is not only conditioned on the previous state but also on an attractive potential...
The multiple model (MM) version of Gaussian mixture probability hypothesis density (GM-PHD) filter is an effective method for multiple maneuvering target tracking. However, the model set used in the MM version of GM-PHD (MM-GM-PHD) filter is the same for each target at each time step. In this paper, we present a variable structure MM-GM-PHD (VSMM-GM-PHD) filter. Different model sets at different time...
Wireless sensor networks (WSN) are core components of the Internet of Things paradigm. By applying techniques such as sensor virtualization, the wireless sensor network infrastructure can be shared by a set of applications. On such scenario, the massive amount of data produced by the widely spread sensors produces a value-added information for the end user. By sharing the same infrastructure with...
This paper introduces an enhanced method for progressive Bayesian estimation based on a set of deterministic samples. The information of a given measurement is gradually introduced in order to avoid particle degeneration, which is usually encountered in standard particle filters. The main contribution of this paper is to derive a new method for exploiting smoothness assumptions about the unknown underlying...
Multi-object density is a fundamental descriptor of a point process and has ability to describe the randomness of number and values of objects, as well as the statistical correlation between objects. Due to its comprehensive nature, it usually has a complicate mathematical structure making the set integral suffer from the curse of dimension and the combinatorial nature of the problem. Hence, efficient...
While online learning is already a part of university education and didactics, not all students have the necessary self-regulation competency to really learn on their own efficiently and effectively. In classroom a teacher can take over a moderating part, set intermediate goals and give feedback to one's progress, but participants of online learning courses (e.g. in blended scenarios or Massive Open...
This paper considers the distributed containment problem for multiple Euler-Lagrange systems with multiple dynamic leaders in the presence of unknown uncertainties and external disturbances under a directed topology graph. First, we propose a finite-time sliding-mode estimator to obtain accurate estimation of the weighted average of the accelerations, velocities, and positions of the leaders, where...
The maneuver control of hypersonic vehicles (HSVs) during re-entry is a challenging work due to the object features of serious nonlinearity, strong uncertainty and fast time variation. In this paper, we design the maneuver control architecture of lateral turning for the HSV first, and then a new self-organizing recurrent functional link network (SORFLN) is proposed to estimate the dynamical uncertainties/disturbances...
We compare ice elevation from TanDEM-X Raw DEMs of summer 2014 and from the SRTM C-band DEM of summer 2000 over the Northern Patagonian Icefield (NPI) in order to obtain a detailed map of ice elevation change rates over the last 14 years. The geodetic method is used to compute the mass balance for this region and for the nearby Southern Patagonian Icefield (SPI). The method is outlined along with...
Tactile sensing for object shape estimation is a key ingredient for real world robots that need to do grasping and manipulation of unknown objects. Since the vision sensors are noisy and suffer from occlusions, the touch to sense approach is beneficial for complementary obtaining local but accurate shape information of objects. Executing exhaustive touches for an object is time consuming and unrealistic,...
A cost/benefit case study was performed to evaluate the results using the Dynamic Interchange Adjustment (DINA) tool to dynamically adjust intertie flows between ISO New England and the New York (NYISO). The DINA tool is designed to provide an online probabilistic estimation of the secure range for possible intra-day and intra-hour net interchange adjustments of a BA within the next dispatch hour...
A robust stability assessment approach is presented to efficiently estimate eigenvalues in microgrids in the presence of bounded uncertainties. Through this method, all possible locations of eigenvalues can be directly obtained, which makes repeatedly eigenvalue calculation unnecessary when dealing with uncertainties. More importantly, a quasi-diagonalization technique is established to reduce the...
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