The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
State filtering is a key problem in many signal processing applications. From a series of noisy measurement, one would like to estimate the state of some dynamic system. Existing techniques usually adopt a Gaussian noise assumption which may result in a major degradation in performance when the measurements are with the presence of outliers. A robust algorithm immune to the presence of outliers is...
Modeling of a room impulse response (RIR) is required in many audio processing applications; however, this is challenging since room responses are usually long and complex in practice and drastically vary as the source and microphone locations change. In this paper, a subband multichannel modeling method is proposed, which is computationally efficient, precise, and robust against RIR variations. A...
Discriminative least squares regression (DLSR) is a simple yet effective method for multi-class classification. One problem of DLSR is that it is lack of robustness to outliers. In order to tackle this difficulty, in this paper, we propose a novel Robust DLSR (RoDLSR) model. The core idea behind RoDLSR is to find and further ignore the outliers among the support vector set. Specifically, we modify...
With a growing system complexity in the IoT framework, many networked cyber-physical systems work in a hierarchical fashion. Layers of information outputs and command inputs are available. An active area of research is in optimizing the design of policies and control command that influence information flow for such multi-layered systems. Our focus in current research is to first formulate the control...
We present an algorithm that finds planar structures in a Manhattan world from two pictures taken from different viewpoints with unknown baseline. The Manhattan world assumption constrains the homographies induced by the visible planes on the image pair, thus enabling robust reconstruction. We extend the T-linkage algorithm for multistructure discovery to account for constrained homographies, and...
Appearance based person re-identification in real-world video surveillance systems is a challenging problem for many reasons, including ineptness of existing low level features under significant viewpoint, illumination, or camera characteristic changes to robustly describe a person's appearance. One approach to handle appearance variability is to learn similarity metrics or ranking functions to implicitly...
This paper reports the identification of nonlinear models for wireless communications systems. The procedure relies on a novel complex-valued Volterra series (CVS) representation to provide a sparse representation based on statistical hypothesis testing and the Bayesian information criterion (BIC). The approach has been experimentally evaluated with the front-end of a communications transmitter taking...
A nonlinear H-infinity (optimal) control approach is proposed for the problem of control of Synchronous Reluctance Machines (SRMs). Approximate linearization is applied to the dynamic model of the Synchronous Reluctance Machine, round a local operating point. To accomplish this linearization Taylor series expansion and the computation of the associated Jacobian matrices are performed. The robustness...
A nonlinear H-infinity (optimal) control method is proposed for the problem of control of the VSC-HVDC transmission system (Voltage Source Converter - High Voltage DC transmission system). Approximate linearization, round a local operating point, is performed for the dynamic model of the VSC-HVDC transmission system. This local equilibrium consists of the present value of the state vector of the VSC-HVDC...
This work aims at enabling online optimization and control of computationally expensive models by employing Adaptive Neuro Fuzzy Inference System (ANFIS) as surrogates. ANFIS is governed by several parameters whose estimation based on heuristic assumptions degrade its efficiency. A novel surrogate building algorithm is thus proposed, with the aim of designing optimal ANFIS by balancing the aspects...
This paper presents robust stabilizability analysis and stabilizing state-feedback controller design for discrete-time piecewise affine systems in the presence of disturbance inputs. The piecewise affine plant leads to an increasing sequence of robust symbolic models by extending existing symbolic models to the case where bounded disturbance inputs, as well as control inputs, affect the state transfer...
This paper presents a novel Robust Deep Appearance Models (RDAMs) approach to learn the non-linear correlation between shape and texture of face images. In this approach, two crucial components of face images, i.e. shape and texture, are represented by Deep Boltzmann Machines and Robust Deep Boltzmann Machines (RDBM), respectively. The RDBM, an alternative form of Robust Boltzmann Machines, can separate...
Representation-based classifiers (RCs) including sparse RC (SRC) have attracted intensive interest in pattern recognition in recent years. In our previous work, we have proposed a general framework called atomic representation-based classifier (ARC) including many popular RCs as special cases. Despite the empirical success, ARC and conventional RCs utilize the mean square error (MSE) criterion and...
Background estimation can be regarded as a problem to construct the background from a series of video frames including moving objects in the scene. Scene background estimation is the essential prerequisite, or at least can be helpful for many applications such as video surveillance, video segmentation, and privacy protection for videos. To perform this task, in this paper we propose a robust framework...
This paper introduces an effective active contour model for texture segmentation. To improve the robustness against noise and illumination, a novel descriptor named local statistical variation degree (LSVD) is presented to express textural features, which uses corner point deletion and isolated region detection operations to eliminate image patches unrelated with object regions. And then the fused...
This paper presents an improved wave kernel signature (WKS) using the modified particle swarm optimization (MPSO)-based intelligent recognition and matching on 3D shapes. We select the first feature vector from WKS, which represents the 3D shape over the first energy scale. The choice of this vector is to reinforce robustness against non-rigid 3D shapes. Furthermore, an optimized WKS-based method...
In video background modeling, ghosting occurs when an object that belongs to the background is assigned to the foreground. In the context of Principal Component Pursuit, this usually occurs when a moving object occludes a high contrast background object, a moving object suddenly stops, or a stationary object suddenly starts moving. Based on a previously developed incremental PCP method, we propose...
Sparse subspace learning has been demonstrated to be effective in data mining and machine learning. In this paper, we cast the unsupervised feature selection scenario as a matrix factorization problem from the view of sparse subspace learning. By minimizing the reconstruction residual, the learned feature weight matrix with the l2,1-norm and the non-negative constraints not only removes the irrelevant...
In this paper, we address the problem of visual tracking in videos without using a pre-learned model of the object. This type of model-free tracking is a hard problem because of limited information about the object, abrupt object motion, and shape deformation. We propose to integrate an object-agnostic prior, called objectness, which is designed to measure the likelihood of a given location to contain...
In this paper, we introduce an experimental design framework for Karhunen-Loeve compression. This method based on the concept of mean objective of uncertainty determines the best unknown parameter of the covariance matrix to be estimated first in order to improve the quality of the compressed signal. Moreover, we find the closed-form solution to the intrinsically Bayesian robust Karhunen-Loève compression...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.