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Distributed, networked communication systems, such as relay beamforming networks are typically designed without considering how the positions of the respective nodes might affect the quality of the communication. That is, network nodes are either assumed to be stationary in space, or, if some of them are moving while communicating, their trajectories are assumed to be independent of the respective...
Compressive sensing (CS) is a viable source of an innovative 5G system, what's more, it's an effective technology to deal with the data redundancy problem of massive machine-to-machine communication (MMC), since it enables the recovery of sparse and approximately sparse signals with significantly fewer samples than demanded by Nyquist-Shannon sampling theory. Interference in signal will lead a series...
Meta learning uses information from base learners (e.g. classifiers or estimators) as well as information about the learning problem to improve upon the performance of a single base learner. For example, the Bayes error rate of a given feature space, if known, can be used to aid in choosing a classifier, as well as in feature selection and model selection for the base classifiers and the meta classifier...
In the analysis of Electroencephalograms (EEG), notably in their graphical modeling, the estimation of the spectral matrix and associated variables is of central importance. Often, when adjusting for the bandwidth of the spectral matrix estimate, singularity issues arise and information derived from the inverse spectral matrix is intractable. This requires the use of regularization methods, which...
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,...
High sampling rate Analog-to-Digital Converters (ADCs) can be obtained by time-interleaving low rate (and thus low cost) ADCs into so-called Time-Interleaved ADCs (TI-ADCs). Nevertheless increasing the sampling frequency involves an increasing sensibility of the system to desynchronization between the different ADCs that leads to time-skew errors, impacting the system with non linear distortions....
In this paper we propose a distributed and adaptive algorithm for collaborative processing of the complex signals. The proposed algorithm, which will be referred to as the incremental augmented affine projection algorithm (IncAAPA), not only utilizes the full second order statistical information in complex domain but also exploits the spatial diversity which is provided by the distribution of the...
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...
Recent technological developments in Micro-ElectroMechanical System (MEMS) and communication system have made the wireless sensor networks (WSNs) with emerging applications. Localization is the essential research problem in WSNs. In this paper, I have projected Received Signal Strength Indicator (RSSI) and improved Distance vector (DV) localization algorithms for accurate position estimation. The...
Localizing brain activity in noisy functional near-infrared spectroscopy (fNIRS) data plays an important role when investigating task-related hemodynamics of the neuronal sites. We present a novel method for capturing drifts in the fNIRS data which increases the effect size of interest of the oxygenated (HbO) and deoxygenated (HbR) hemoglobin responses. Using linear least-squares, a consistent hemo-dynamic...
The desire to improve short-term predictions of wind speed and direction has motivated the development of a spatial covariance-based predictor in a complex valued multichannel structure. Wind speed and direction are modelled as the magnitude and phase of complex time series and measurements from multiple geographic locations are embedded in a complex vector which is then used as input to a multichannel...
This paper considers the problem of source localization under received signal strength in the presence of an intelligent adversary. The adversary's goal is to redirect the location estimate to a specified location away from its true value by slightly perturbing each of the sensors' position measurements. This work takes the viewpoint of the adversary and determines the false positioning information...
In the context of robust covariance matrix estimation, this work generalizes the shrinkage covariance matrix estimator introduced in [1, 2]. The shrinkage method is a way to improve and to regularize the Tyler's estimator [3, 4]. This paper proves that the shrinkage estimator does not require any trace constraint to be well-defined, as it has been previously developed in [1]. The existence and the...
In this work, we present a novel method for accurate affine transformation estimation of image regions. We illustrate the benefits of using such a method in a point matching mechanism that enables locating large amount of point matches with high geometric precision and low rate of false matches. Recent publications have shown that considering the affine transformation model of local regions, is extremely...
In this work we consider the estimation of spatio-temporal covariance matrices in the low sample non-Gaussian regime. We impose covariance structure in the form of a sum of Kronecker products decomposition [1, 2] with diagonal correction [2], which we refer to as DC-KronPCA, in the estimation of multiframe covariance matrices. This paper extends the approaches of [1] in two directions. First, we modify...
The importance of the analysis and understanding of the network traffic has constantly been increasing due to insights that this provides towards determination of user behaviour and resource usage. The data analyses in order to determine the related parameters are performed by selection of a small subset of the complete flow data due to data privacy and heavy computational/memory load issues. That...
When there are more than necessary distance measurements in localization by distance measurements with closed form estimators, forming smaller subgroups of measurements and averaging the location estimates obtained with these subgroups of measurements makes it possible to eliminate outlier measurements if they are present. In order to eliminate these outlier results, the nearest estimate to the geometric...
In crowd surveillance systems, it is important to select the proper analysis algorithm considering the properties of the video content. The inappropriate algorithm selection may result in performance degradation and generation of false alarms. An important feature of crowd videos is the density of the crowd. While object detection and tracking based algorithms are feasible for low density crowds,...
In this study, research activities of IONOLAB group on remote sensing and 2-D imaging of ionosphere in the last 10 years will be summarized. In TUBITAK EEEAG 105E171 and 109E055 projects, a novel Total Electron Content (TEC) estimation method, IONOLAB-TEC, is developed using the dual frequency Global Positioning System (GPS) pseudo range and phase delay recordings. The IONOLAB-TEC computation is provided...
In this paper, robustness of star identification and attitude estimation methods that are operating in Lost-in-Space (LIS) mode, under harsh noise conditions of the near space, are analyzed. Despite star extraction, identification and attitude estimation methods that are proposed as solutions for subproblems, there is a lack of study that investigates the effects of errors in the initial stages on...
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