Visual‐quality estimation using objective metrics

Abstract— In the past few years, a big effort in the scientific community has been devoted to the development of better image‐ and video‐quality metrics that correlate well with the human perception of quality. In this paper, an overview of the main ideas used in the design of objective quality metrics is given. More specifically, we briefly describe the different types of objective metrics and present a representative set of the different approaches taken by these algorithms. Finally, the challenges and recent developments in the area of image and video quality are discussed.     

Equivalent Control of Constrained Multibody Systems

This paper investigates the title problem from the perspective ofdetermining which forcesystems, although different, will have the same dynamic effectwhen applied to a mechanical system.A procedure is presented for determining how differences incontrol force/moment input are nullifiedby constraints on the system. This in turn provides a basis foroptimal control. The theoreticaldevelopment is illustrated by a series of examples with a model ofa redundant manipulator – a constrained/controlled triple pendulum.     

Data Communications for Internet Robots

For Internet robots, the most challenging and distinct difficulties are well recognized to be associated with Internet transmission delays, delay jitter and not-guaranteed bandwidth, which might degrade system performance dramatically or even lead to instability. Current approaches to data communication between the remote robot and the human operator employ directly one of the two currently available protocols, i.e. TCP and UPD. In this paper, a teleoperation-oriented data transmission mechanism is implemented. Compared to TCP, the presented scheme provides minimized transmission delays and delay jitter; in the steady state, its transmission rate is much smoother; when available network bandwidth changes, it adapts to the variation quickly without large overshoot. Compared to UDP, it is inter-protocol fairness convergent, intro-protocol convergent, and efficient convergent. The presented mechanism is deployed in a mobile robot teleoperation system developed. In the experiments, users successfully guided a Pioneer-2 mobile robot through a laboratory environment remotely over the Internet via a web browser.     

Filtering Multi‐Layer Shadow Maps for Accurate Soft Shadows

In this paper, we introduce a novel technique for pre‐filtering multi‐layer shadow maps. The occluders in the scene are stored as variable‐length lists of fragments for each texel. We show how this representation can be filtered by progressively merging these lists. In contrast to previous pre‐filtering techniques, our method better captures the distribution of depth values, resulting in a much higher shadow quality for overlapping occluders and occluders with different depths. The pre‐filtered maps are generated and evaluated directly on the GPU, and provide efficient queries for shadow tests with arbitrary filter sizes. Accurate soft shadows are rendered in real‐time even for complex scenes and difficult setups. Our results demonstrate that our pre‐filtered maps are general and particularly scalable.     

Characterization of acoustic signals due to surface discharges on H.V. glass insulators using wavelet radial basis function neural networks

A hybrid model incorporating wavelet and radial basis function neural network is presented which is used to detect, identify and characterize the acoustic signals due to surface discharge activity and hence differentiate abnormal operating conditions from the normal ones. The tests were carried out on cleaned and polluted high voltage glass insulators by using surface tracking and erosion test procedure of international electrotechnical commission 60587. A laboratory experiment was conducted by preparing the prototypes of the discharges. This study suggests a feature extraction and classification algorithm for surface discharge classification, which when combined together reduced the dimensionality of the feature space to a manageable dimension, by “marrying” the wavelet to radial basis function neural network very high levels of classification are achieved. Wavelet signal treatment toolbox is used to recover the surface discharge acoustic signals by eliminating the noisy portion and to reduce the dimension of the feature input vector. A radial basis function neural network classifier was used to classify the surface discharge and assess the suitability of this feature vector in classification. This learning method is proved to be effective by applying the wavelet radial basis function neural network in the classification of surface discharge fault data set. The test results show that the proposed approach is efficient and reliable.     

Enhancing collaborative filtering by user interest expansion via personalized ranking

Recommender systems suggest a few items from many possible choices to the users by understanding their past behaviors. In these systems, the user behaviors are influenced by the hidden interests of the users. Learning to leverage the information about user interests is often critical for making better recommendations. However, existing collaborative-filtering-based recommender systems are usually focused on exploiting the information about the user's interaction with the systems; the information about latent user interests is largely underexplored. To that end, inspired by the topic models, in this paper, we propose a novel collaborative-filtering-based recommender system by user interest expansion via personalized ranking, named iExpand. The goal is to build an item-oriented model-based collaborative-filtering framework. The iExpand method introduces a three-layer, user-interests-item, representation scheme, which leads to more accurate ranking recommendation results with less computation cost and helps the understanding of the interactions among users, items, and user interests. Moreover, iExpand strategically deals with many issues that exist in traditional collaborative-filtering approaches, such as the overspecialization problem and the cold-start problem. Finally, we evaluate iExpand on three benchmark data sets, and experimental results show that iExpand can lead to better ranking performance than state-of-the-art methods with a significant margin.     

Comparison of the performance of a reduced-order dynamic PLS soft sensor with different updating schemes for digester control

In this work, the previously developed reduced-order dynamic PLS (RO-DPLS) soft sensor is extended to its adaptive version to address frequent process changes in a pulp digester. The properties of four model update schemes and the corresponding data scaling methods are investigated through one simulated case study and two industrial case studies of Kamyr digesters. Our findings obtained through extensive experiments are presented, which are expected to provide useful information and some guidance to practitioners. Finally, the effectiveness of the recursive RO-DPLS soft sensor is demonstrated through a digester closed-loop control case study, which shows that the closed-loop control performance can be significantly improved if the soft sensor prediction is fed back to a PID controller.     

Task Planning For A Mobile Robot In An Indoor Environment Using Object-oriented Domain Information

For a mobile robot to be practical, it needs to navigate in dynamically changing environments and manipulate objects in the environment with operating ease. The main challenges to satisfying these requirements in mobile robot research include the collection of robot environment information, storage and organization of this information, and fast task planning based on available information. Conventional approaches to these problems are far from satisfactory due to their requirement of high computation time. In this paper, we specifically address the problems of storage and organization of the environment information and fast task planning in the area of robotic research. We propose an special object-oriented data model (OODM) for information storage and management in order to solve the first problem. This model explicitly represents domain knowledge and abstracts a global perspective about the robot's dynamically changing environment. To solve the second problem, we introduce a fast task planning algorithm that fully uses domain knowledge related to robot applications and to the given environment. Our OODM based task planning method presents a general frame work and representation, into which domain specific information, domain decomposition methods and specific path planners can be tailored for different task planning problems. This method unifies and integrates the salient features from various areas such as database, artificial intelligence, and robot path planning, thus increasing the planning speed significantly     

Editorial

Nowadays control systems can find applications in many areas, like aerospace, motion tracking, chemical engineering, physics, biology, economics. To improve control performance is a perpetual goal. With recent progresses of computing technologies, better control performance can be achieved by more judicious control strategies based on more precise and more complicated, such as time-varying, nonlinear, models. Another trend to improve control performance is built upon the divide-and-conquer philosophy, i.e., a complicated control task is cooperatively accomplished by multiple controllers/agents, instead of a single super-powerful controller. This trend is made possible due to great advances in communication, which enable the information exchange among agents and may unite the less powerful agents. The cost for such control performance improvement is higher spatial complexity of control systems. Although high model complexity and spatial complexity can be physically handled by current powerful controllers/agents, the lack of efficient analysis and synthesis methods prevents the performance improvement of control systems with high model or/and spatial complexity. As we know, conventional control methods are mainly developed under the assumptions of linear, time-invariant and centralized models. In order to resolve the issues due to the aforementioned two types of complexities, new methods are expected, which is exactly the major aim of the present special issue.     

Maximum Likelihood Coordinates

Any point inside a d-dimensional simplex can be expressed in a unique way as a convex combination of the simplex's vertices, and the coefficients of this combination are called the barycentric coordinates of the point. The idea of barycentric coordinates extends to general polytopes with n vertices, but they are no longer unique if n > d+1. Several constructions of such generalized barycentric coordinates have been proposed, in particular for polygons and polyhedra, but most approaches cannot guarantee the non-negativity of the coordinates, which is important for applications like image warping and mesh deformation. We present a novel construction of non-negative and smooth generalized barycentric coordinates for arbitrary simple polygons, which extends to higher dimensions and can include isolated interior points. Our approach is inspired by maximum entropy coordinates, as it also uses a statistical model to define coordinates for convex polygons, but our generalization to non-convex shapes is different and based instead on the project-and-smooth idea of iterative coordinates. We show that our coordinates and their gradients can be evaluated efficiently and provide several examples that illustrate their advantages over previous constructions.