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The consensus error calculation of the multi-agent systems (MASs) with system noises and communication noises is considered in this paper. Each agent is modeled by a continuous-time linear time-invariant dynamics and the communication topology is described by an undirected graph. It is supposed that the agent can obtain its own state accurately and receive the state of the neighbor agent with noisy...
This paper is to study the stabilizability and stabilization issues of linear dynamical systems based on the delayed and noisy feedback control. For the general linear systems, the necessary conditions and sufficient conditions for mean square and almost sure stabilizability are deduced and the corresponding feedback controls are designed according to the generalized algebraic Riccati equation. It...
A scheme to sample bandlimited graph signals in the presence of noise is analyzed. Samples are aggregated at a single node by successive applications of the so-called graph-shift operator that encodes the local structure of the underlying graph. In contrast to the noiseless case, when noise is present the choice of the sampling node and the local sample-selection scheme plays a major role in determining...
A common problem in network analysis is detecting small subgraphs of interest within a large background graph. This includes multi-source fusion scenarios where data from several modalities must be integrated to form the network. This paper presents an application of novel techniques leveraging the signal processing for graphs algorithmic framework, to well-studied collaboration networks in the field...
As a complicated and troublesome research area, the edge detection is a fundamental step in terms of some image processing tasks including segmentation, compression and registration. In this study, we present a new approach for edge linking by applying the concept of the PCA on different types of images to determine the attractive edge segments. To determine the direction by using the angle information,...
The paper is concerned with state predictors that include a disturbance dynamics capable of estimating the disturbance to be rejected. The disturbance dynamics is driven by an unknown input signal, the uncertainty input, which is the output of a dynamic feedback driven by the model error (plant minus model output). As an extension of classical observers, the paper shows the advantage of designing...
Consider a set of agents implementing the discrete time consensus algorithm. At each time step, all agents also transmit their states to a central estimator that wishes to identify the underlying topology and eigenvalues of the network. It does so by using a nonlinear least squares (NLS) algorithm to identify the state evolution matrix used in the consensus algorithm. We present a mechanism to protect...
This paper studies regulated output synchronization for heterogeneous directed networks with non-introspective agents (i.e. agents have no access to their own states or outputs) in the presence of disturbance and measurement noises with known frequencies. A purely decentralized time-invariant protocol based on a low-and-high gain method is designed for each agent to achieve regulated output synchronization,...
In this paper, we investigate a robust spectrum sensing scheme using multiple antennas, which exploits the information of eigenvalues' characteristics of the sample covariance matrix. The difference between the maximum eigenvalue and the average of the remainder eigenvalues of sample covariance matrix is utilized as the test statistic. Besides, by exploiting Gaussian approximation for the test statistic,...
This paper studies the effectiveness of compress-and-forward (CF) relaying scheme for a multiple-input multiple-output (MIMO) Gaussian relay channel with an out-of-band finite-capacity relay-to-destination link in which noises at the relay and destination are correlated due to common sources of interference. This scenario is motivated by the possibility of using device-to-device links for inter-cell...
Event-based sampling strategies allow for reducing the amount of communications between a sensor and the estimation module. This reduction is interesting specially when the sensors are linked by a shared wireless network. This paper proposes new event-based sampling methods based on the Mahalanobis distance concept. Combined with an Event-Based State Estimator, they provide the same level of performance...
In this paper a state estimator for high tech flexible systems with an inherent nonlinearity in the output dynamics is proposed. We consider an application in which sensor measurements of the flexible system become parameter (position) dependent. An LPV setting is proposed for the design of estimators that estimate flexible modes of the system. The possibility of pole placement for the error dynamics...
This paper presents the state-space modeling and control of a coupled-tank liquid level system. Observed-state feedback controller via eigenvalue assignment and LQG control are designed in discrete-time and implemented by an industrial controller PLC. Both control design methods are augmented with integral action for a reference tracking without steady-state error for step inputs. The aim of the study...
Using the nonlinear Fourier transform, one can transform a time-domain fibre channel (characterised by the nonlinear Schrödinger equation) into multiple parallel independent channels. Exploiting this property, nonlinear frequency division multiplexing (NFDM) was proposed [1]. Such an NFT-based transmission approach has prompted the study of the channel noise (on the eigenvalues and spectral amplitudes)...
This paper presents a geometrical method for solving the non-negative Blind Source Separation (BSS) problem. The method is based on a weak sparsity condition: for each source, there should exist at least one observed vector where only this source is non-zero. The method does not require but allows the sum-to-one constraint for the mixing parameters or sources. Considering each observed vector as an...
This paper studies the performance associated with the classification of linear subspaces corrupted by noise with a mismatched classifier. In particular, we consider a problem where the classifier observes a noisy signal, the signal distribution conditioned on the signal class is zero-mean Gaussian with low-rank covariance matrix, and the classifier knows only the mismatched parameters in lieu of...
This paper presents a novel outlier removal method which is capable of fitting ellipse in real-time under high outlier rate, based on the phenomenon that outliers generated by ellipse edge point detector are likely to appear as groups due to real-world nuisances, such as under partial occlusion or illumination change. To confront the grouped outliers while maintaining the fitting efficiency, we introduce...
We consider the traditional compressed sensing problem of recovering a sparse solution from undersampled data. We are in particular interested in the case where the measurements arise from a partial circulant matrix. This is motivated by practical physical setups that are usually implemented through convolutions. We derive a new optimization problem that stems from the traditional ℓ1 minimization...
Reliable surface normal computation is fundamental for a broad range of computer vision application areas, e.g. object segmentation, classification and recognition. Naturally, the surface normal is computed on the acquired depth data, whereby the normal quality is dependent on noise performance and resolution of the underlying image modality. The tendency of combining different imaging sensors into...
Cognitive radio is one way to overcome the limitations of the frequency spectrum. Cognitive radio users can utilize the frequency spectrum that is not being used (white space) by detect the frequency spectrum. Spectrum sensing is done periodically to ensure there is no interference, in that sense spectrum sensing activity in cognitive radio will increase energy consumption. Therefore, energy efficiency...
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