Improved estimation of defocus blur and spatial shifts in spatial domain: a homotopy-based approach

This paper presents a homotopy-based algorithm for the recovery of depth cues in the spatial domain. The algorithm specifically deals with defocus blur and spatial shifts, that is 2D motion, stereo disparities and/or zooming disparities. These cues are estimated from two images of the same scene acquired by a camera evolving in time and/or space. We show that they can be simultaneously computed by resolving a system of equations using a homotopy method. The proposed algorithm is tested using synthetic and real images. The results confirm that the use of a homotopy method leads to a dense and accurate estimation of depth cues. This approach has been integrated into an application for relief estimation from remotely sensed images.     

Exploration trees on highly complex scenes: A new approach for 3D segmentation

A new strategy for automatic object extraction in highly complex scenes is presented in this paper. The method proposed gives a solution for 3D segmentation avoiding most restrictions imposed in other techniques. Thus, our technique is applicable on unstructured 3D information (i.e. cloud of points), with a single view of the scene, scenes consisting of several objects where contact, occlusion and shadows are allowed, objects with uniform intensity/texture and without restrictions of shape, pose or location. In order to have a fast segmentation stopping criteria, the number of objects in the scene is taken as input. The method is based on a new distributed segmentation technique that explores the 3D data by establishing a set of suitable observation directions. For each exploration viewpoint, a strategy [3D data]-[2D projected data]-[2D segmentation]-[3D segmented data] is accomplished. It can be said that this strategy is different from current 3D segmentation strategies. This method has been successfully tested in our lab on a set of real complex scenes. The results of these experiments, conclusions and future improvements are also shown in the paper.     

Toward designing intelligent PDEs for computer vision: An optimal control approach

Many computer vision and image processing problems can be posed as solving partial differential equations (PDEs). However, designing a PDE system usually requires high mathematical skills and good insight into the problems. In this paper, we consider designing PDEs for various problems arising in computer vision and image processing in a lazy manner: learning PDEs from training data via an optimal control approach. We first propose a general intelligent PDE system which holds the basic translational and rotational invariance rule for most vision problems. By introducing a PDE-constrained optimal control framework, it is possible to use the training data resulting from multiple ways (ground truth, results from other methods, and manual results from humans) to learn PDEs for different computer vision tasks. The proposed optimal control based training framework aims at learning a PDE-based regressor to approximate the unknown (and usually nonlinear) mapping of different vision tasks. The experimental results show that the learnt PDEs can solve different vision problems reasonably well. In particular, we can obtain PDEs not only for problems that traditional PDEs work well but also for problems that PDE-based methods have never been tried before, due to the difficulty in describing those problems in a mathematical way.     

A general fuzzy-statistical clustering approach for estimating the time of change in variable sampling control charts

Despite their capability in monitoring the variability of the processes, control charts are not effective tools for identifying the real time of such changes. Identifying the real time of the change in a process is recognized as change-point estimation problem. Most of the change-point models in the literature are limited to fixed sampling control charts which are only a special case of more effective charts known as variable sampling charts. In this paper, we develop a general fuzzy-statistical clustering approach for estimating change-points in different types of control charts with either fixed or variable sampling strategy. For this purpose, we devise and evaluate a new similarity measure based on the definition of operation characteristics and power functions. We also develop and examine a new objective function and discuss its relation with maximum-likelihood estimator. Finally, we conduct extensive simulation studies to evaluate the performance of the proposed approach for different types of control charts with different sampling strategies.     

Launch control for sport motorcycles: A clutch-based approach

Launch control is an innovative active system which offers an appealing automatic feature for sport motorcycles. Due to the high power of the engine and to the complexity of the torque transfer dynamics driving a smooth and safe start from a standstill might be difficult for non-expert riders in some working conditions. As such, an active controller capable of safely managing the vehicle acceleration from a standing start might effectively support the rider as far as both safety and performance are concerned. To control such a maneuver, two possible actuators can be employed: the engine throttle and the clutch. This paper illustrates the analysis and the design of a clutch-based launch controller for sport motorcycles, demonstrating that it allows the accurate tracking of an acceleration set-point thanks to a dedicated clutch position controller. Experimental results are presented that demonstrate the effectiveness of the proposed approach.     

A multi-plane approach for text segmentation of complex document images

This study presents a new method, namely the multi-plane segmentation approach, for segmenting and extracting textual objects from various real-life complex document images. The proposed multi-plane segmentation approach first decomposes the document image into distinct object planes to extract and separate homogeneous objects including textual regions of interest, non-text objects such as graphics and pictures, and background textures. This process consists of two stages—localized histogram multilevel thresholding and multi-plane region matching and assembling. Then a text extraction procedure is applied on the resultant planes to detect and extract textual objects with different characteristics in the respective planes. The proposed approach processes document images regionally and adaptively according to their respective local features. Hence detailed characteristics of the extracted textual objects, particularly small characters with thin strokes, as well as gradational illuminations of characters, can be well-preserved. Moreover, this way also allows background objects with uneven, gradational, and sharp variations in contrast, illumination, and texture to be handled easily and well. Experimental results on real-life complex document images demonstrate that the proposed approach is effective in extracting textual objects with various illuminations, sizes, and font styles from various types of complex document images.     

A lattice-based approach for updating access control policies in real-time

Real-time update of access control policies, that is, updating policies while they are in effect and enforcing the changes immediately and automatically, is necessary for many dynamic environments. Examples of such environments include disaster relief and war zone. In such situations, system resources may need re-configuration or operational modes may change, necessitating a change of policies. For the system to continue functioning, the policies must be changed immediately and the modified policies automatically enforced. In this paper, we propose a solution to this problem—we consider real-time update of access control policies in the context of a database system.     

A cooperative approach for topology control in Wireless Sensor Networks

The choice of the transmission power levels adopted in Wireless Sensor Networks (WSNs) is critical to determine the performance of the network itself in terms of energy efficiency, connectivity and spatial reuse, since it has direct impact on the physical network topology.In this paper, a cooperative, lightweight and fully distributed approach is introduced to adaptively tune the transmission power of sensors in order to match local connectivity constraints. To accurately evaluate the topology control solution, a small-scale testbed based on MicaZ sensor nodes is deployed in indoor and outdoor scenarios. Practical measures on local and multi-hop connectivity, convergence time and emitted power are used to compare the proposed approach against previous solutions. Moreover, mathematical programming formulations of the topology (power) control problem are introduced to assess the optimality of the distributed algorithm. Finally, simulation analysis complements the experimental evaluation in large-scale static and mobile WSN scenarios, where a testbed implementation becomes unfeasible.     

A practical approach for robust control of flexible structures

This paper deals with the robust control of linear single-input, single-output (SISO) systems. First, general concepts of the frequency-domain approach are presented. We then focus on the particular problem of flexible structure and control. A new method is proposed which combines the advantages of hyperstability and small gain approaches. Finally, an application of an industrial sight system prototype is detailed.     

A probabilistic approach for predictive congestion control in wireless sensor networks

Any node in a wireless sensor network is a resource constrained device in terms of memory, bandwidth, and energy, which leads to a large number of packet drops, low throughput, and significant waste of energy due to retransmission. This paper presents a new approach for predicting congestion using a probabilistic method and controlling congestion using new rate control methods. The probabilistic approach used for prediction of the occurrence of congestion in a node is developed using data traffic and buffer occupancy. The rate control method uses a back-off selection scheme and also rate allocation schemes, namely rate regulation （RRG） and split protocol （SP）, to improve throughput and reduce packet drop. A back-off interval selection scheme is introduced in combination with rate reduction （RR） and RRG. The back-off interval selection scheme considers channel state and collision-flee transmission to prevent congestion. Simulations were conducted and the results were compared with those of decentralized predictive congestion control （DPCC） and adaptive duty-cycle based congestion control （ADCC）. The results showed that the proposed method reduces congestion and improves performance.     

A neural network-based approach for an assembly cell control

In several robotics applications, the robot must interact with the workspace, and thus its motion is constrained by the task. In this case, pure position control will be ineffective since forces appearing during the contacts must also be controlled. However, simultaneous position and force control called hybrid control is then required. Moreover, the nonlinear plant dynamics, the complexity of the dynamic parameters determination and computation constraints makes more difficult the synthesis of control laws. In order to satisfy all these constraints, an effective hybrid force/position approach based on artificial neural networks for multi-inputs/multi-outputs systems is proposed. This approach realizes, simultaneously, an identification and control of systems, and it is implemented according to two phases: At first, a neural observer is trained off-line on the basis of the data acquired during contact motion, in order to realize a smooth transition from free to contact motion. Then, an online learning of the neural controller is implemented using neural observer parameters so that the closed-loop system maintains a good performance and compensates for uncertain/unknown dynamics of the robot and the environment. A typical example on which we shall focus is an assembly task. Experimental results on a C5 links parallel robot demonstrate that the robot's skill improves effectively and the force control performances are satisfactory, even if the dynamics of the robot and the environment change.     

A switching robust model predictive control approach for nonlinear systems

This work considers enhancing the stability and improving the economic performance of nonlinear model predictive control in the presence of disturbances or model uncertainties. First, a robust control Lyapunov function (RCLF)-based predictive control strategy is proposed. Second, the approximate dynamic programming (ADP) is employed to further improve regulation performance. Finally, the ADP and RCLF-MPC are combined to provide a switching control scheme, which is illustrated on a CSTR example to show its effectiveness.     

A multi-model reference control approach for bandwidth-on-demand protocols in satellite networks

This paper presents a multi-model reference control (MMRC) approach for queue-based bandwidth-on-demand (BoD) procedures in geostationary satellite networks. BoD access protocols address the problem of guaranteeing a high exploitation of the valuable satellite bandwidth while offering acceptable end-to-end delays to the traffic accessing the network. In queue-based protocols, the controller objective is to drive the buffer queue length to a certain target queue length; in the proposed scheme, model references (MRs) are used to address the problem of adapting the target queue length to the statistical characteristics of the traffic. The reference queue length is computed as a weighted sum of the outputs of the different MRs; the weights are computed on-line by evaluating the network utilization achieved by each MR, in response to the actual traffic feeding the system. Moreover, the target network utilization is dynamically varied based on the estimated network load. The BoD performances are evaluated via simulations. The work underlying this paper is based on the results of the GEOCAST,¹ SATIP6² and SATSIX³ projects, financed by European Union.     

Robust fault estimator design for uncertain networked control systems with random time delays: An ILMI approach

This paper investigates the problem of robust fault estimation for a class of uncertain networked control systems (NCSs) with random communication network-induced delays, which are described by Markov processes. Based on the Lyapunov-Razumikhin method, the existence of a delay-dependent fault estimator is given in terms of the solvability of bilinear matrix inequalities (BMIs). An iterative algorithm is proposed to change this non-convex BMI problem into quasi-convex optimization problems, which can be solved effectively by available mathematical tools. The effectiveness of the proposed design methodology is verified by an internet-based DC motor test rig.     

A proportional integral extremum-seeking control approach for discrete-time nonlinear systems

This paper proposes a proportional-integral extremum-seeking control technique for a class of discrete-time nonlinear dynamical systems with unknown dynamics. The technique is a generalisation of existing time-varying extremum-seeking control (ESC) techniques that provides fast transient performance of the closed-loop system to the optimum equilibrium of a measured objective function. The main contribution of the proposed technique is the addition of a proportional action that can be used to minimise the impact of a time-scale separation on the transient performance of the ESC system. The integral action fulfils the role of standard ESC techniques to identify optimal equilibrium conditions. The effectiveness of the proposed approach is demonstrated using a simulation example.     

A new image denoising scheme using support vector machine classification in shiftable complex directional pyramid domain

Edge-preserving image denoising has become a very intensive research topic. In this paper, we propose a new image denoising scheme using support vector machine (SVM) classification in shiftable complex directional pyramid (PDTDFB) domain. Firstly, the noisy image is decomposed into different subbands of frequency and orientation responses using a PDTDFB transform. Secondly, the feature vector for a pixel in a noisy image is formed by the spatial regularity in PDTDFB domain, and the least squares support vector machine (LS-SVM) model is obtained by training. Then the PDTDFB detail coefficients are divided into two classes (edge-related coefficients and noise-related ones) by LS-SVM training model. Finally, the detail subbands of PDTDFB coefficients are denoised by using the different parameters to control the multiscale and multidirectional anisotropic diffusion. Extensive experimental results demonstrate that our method can obtain better performances in terms of both subjective and objective evaluations than those state-of-the-art denoising techniques. Especially, the proposed method can preserve edges very well while removing noise.     

A robust power and rate control method for state-delayed wireless networks

A robust power and rate control algorithm is proposed for distributed wireless networks where the network dynamics is modelled as an uncertain discrete-time state-delayed system.     

A combined modal-joint space control approach for continuum manipulators

Continuum manipulators continue to gain popularity due to their ability to operate within difficult to reach environments, and their inherent safety characteristics. However, their flexible nature makes them prone to both steady-state positioning errors and undesirable vibrations. We propose a combined control system incorporating both a tracking position controller to reduce steady-state error, and a modal-space controller to improve the dynamic properties of the manipulator. To this end, we develop a lumped parameter model for use as a modal-space observer. Simulation results based on a planar two-segment manipulator and experimental results from a single-segment manipulator both show marked improvement using our combined approach. The improvements observed in both simulation and experimental results include reduced settling time, higher bandwidth trajectory control capability, and an improved response to external disturbances.