Performance characteristics of cellular systems with different link adaptation strategies

In this paper, the theoretical performance of cellular systems with different types of link adaptation is analyzed. A general link and system performance analysis framework is developed to enable the system-level performance characteristics of the various link adaptation strategies to be studied and compared. More specifically, this framework is used to compare the downlink performance of fully loaded cellular systems with fixed power and modulation/coding, adaptive modulation/coding (AMC), adaptive power allocation (APA) with system-level AMC, and water-filling (WF). Performance is studied first for idealized methods, and then for cases where some practical constraints are imposed. Finally, a hybrid link adaptation scheme is introduced and studied. The hybrid scheme is shown to overcome most of the performance loss caused by the practical constraints. Moreover, the hybrid scheme, as opposed to WF, enables the system to be tuned to meet the most important performance objective for the system under consideration, such as coverage reliability, capacity, or data rate distribution. The algorithms and the framework presented in this paper can be used to improve the link adaptation performance of modern cellular systems such as HSDPA.     

A reinforcement learning approach based on the fuzzy min-max neural network

The fuzzy min-max neural network constitutes a neural architecture that is based on hyperbox fuzzy sets and can be incrementally trained by appropriately adjusting the number of hyperboxes and their corresponding volumes. Two versions have been proposed: for supervised and unsupervised learning. In this paper a modified approach is presented that is appropriate for reinforcement learning problems with discrete action space and is applied to the difficult task of autonomous vehicle navigation when no a priori knowledge of the enivronment is available. Experimental results indicate that the proposed reinforcement learning network exhibits superior learning behavior compared to conventional reinforcement schemes.     

A motion control framework for autonomous water sampling and swing-free transportation of a multirotor UAV with a cable-suspended mechanism

In this work, we present an end-to-end solution for autonomous water sampling by utilizing an unmanned aerial vehicle (UAV) with a cable-suspended mechanism. Towards this direction, a sampling mechanism is initially designed in such a manner that the water sampling success ratio is maximized. However, the disturbances, acting on the submerged mechanism due to the water flow during the sampling procedure, impede the stabilization of the vehicle above the desired sampling position. Consequently, to achieve the precise hovering of the UAV, the vehicle's sensor suite is further augmented with a load cell, a depth sensor, an ultrasonic sensor, and a camera. The respective measurements are appropriately fused by employing an extended Kalman filter (EKF). Hence, an estimate of the disturbances is available in real-time and is incorporated into a Model Predictive Control scheme which compensates for the aforementioned disturbances and stabilizes the vehicle above the sampling location. Finally, a complete water sampling mission entails the safe and swing-free transportation of the mechanism towards the sampling location and, then, to a position where the collected samples are postprocessed by human operators. Consequently, a model predictive controller is employed which ensures the navigation of the vehicle to the desired waypoints while minimizing the swinging motion of the mechanism. The state of the mechanism is obtained by fusing measurements provided by the load cell and the camera with an EKF. The performance of the proposed framework, which aims to address all the aspects of a water sampling mission, is demonstrated through real experiments with an octorotor.     

Shape-Based Object Detection via Boundary Structure Segmentation

We address the problem of object detection and segmentation using global holistic properties of object shape. Global shape representations are highly susceptible to clutter inevitably present in realistic images, and thus can be applied robustly only using a precise segmentation of the object. To this end, we propose a figure/ground segmentation method for extraction of image regions that resemble the global properties of a model boundary structure and are perceptually salient. Our shape representation, called the chordiogram, is based on geometric relationships of object boundary edges, while the perceptual saliency cues we use favor coherent regions distinct from the background. We formulate the segmentation problem as an integer quadratic program and use a semidefinite programming relaxation to solve it. The obtained solutions provide a segmentation of the object as well as a detection score used for object recognition. Our single-step approach achieves state-of-the-art performance on several object detection and segmentation benchmarks.     

Abstractions of Random Finite-State Machines

Suppose that M is a large, complex finite-state system (fsm), and we want to construct a smaller model of it. A way of doing so, independent of any special properties that M might have, is to partition its states, inputs, and outputs into classes, collectively referred to as an abstraction. Since abstractions map many fsms into a non-deterministic machine defining an indistinguishability class, given a particular M, an optimal abstraction will minimize the size of this class. Algorithms for constructing such abstractions have been investigated in previous work.In this paper we are interested in large fsms generated by some random procedure, and want to find abstractions that minimize the expected size of an indistinguishability class. We establish various theoretical properties of abstractions optimal in an average sense, and also investigate experimentally how their characteristics change with the parameters governing the structure of the random machines.     

New algorithms for max restricted path consistency

Max Restricted Path Consistency (maxRPC) is a local consistency for binary constraints that enforces a higher order of consistency than arc consistency. Despite the strong pruning that can be achieved, maxRPC is rarely used because existing maxRPC algorithms suffer from overheads and redundancies as they can repeatedly perform many constraint checks without triggering any value deletions. In this paper we propose and evaluate techniques that can boost the performance of maxRPC algorithms by eliminating many of these overheads and redundancies. These include the combined use of two data structures to avoid many redundant constraint checks, and the exploitation of residues to quickly verify the existence of supports. Based on these, we propose a number of closely related maxRPC algorithms. The first one, maxRPC3, has optimal O(end ³) time complexity, displays good performance when used stand-alone, but is expensive to apply during search. The second one, maxRPC3 ^rm , has O(en ² d ⁴) time complexity, but a restricted version with O(end ⁴) complexity can be very efficient when used during search. The other algorithms are simple modifications of maxRPC3 ^rm . All algorithms have O(ed) space complexity when used stand-alone. However, maxRPC3 has O(end) space complexity when used during search, while the others retain the O(ed) complexity. Experimental results demonstrate that the resulting methods constantly outperform previous algorithms for maxRPC, often by large margins, and constitute a viable alternative to arc consistency on some problem classes.     

A model-driven software engineering workflow and tool architecture for servitised manufacturing

Modern manufacturing businesses increasingly engage in servitisation, by offering advanced services along with physical products, and creating “product–service systems”. Information Technology infrastructures, and especially software, are a critical part of modern service provision. However, software development in this context has not been investigated and there are no development methods or tools specifically adapted to the task of creating software for servitised businesses in general, or manufacturing in particular. In this paper, we define the requirements for software engineering in servitised manufacturing. Based on these, we describe a model-driven software engineering workflow for servitised manufacturing, supporting both structural and behavioural modelling of the service system. Furthermore, we elaborate on the architecture of an appropriate model-driven Integrated Development Environment (IDE). The proposed workflow and a prototype implementation of the IDE were evaluated in a set of industrial pilots, demonstrating improved communication and collaboration between participants in the software engineering process.     

Analytical forms for most likely matrices derived from incomplete information

Consider a rectangular matrix describing some type of communication or transportation between a set of origins and a set of destinations, or a classification of objects by two attributes. The problem is to infer the entries of the matrix from limited information in the form of constraints, generally the sums of the elements over various subsets of the matrix, such as rows, columns, etc., or from bounds on these sums, down to individual elements. Such problems are routinely addressed by applying the maximum entropy method to compute the matrix numerically, but in this article we derive analytical, closed-form solutions. For the most complicated cases we consider the solution depends on the root of a non-linear equation, for which we provide an analytical approximation in the form of a power series. Some of our solutions extend to 3-dimensional matrices. Besides being valid for matrices of arbitrary size, the analytical solutions exhibit many of the appealing properties of maximum entropy, such as precise use of the available data, intuitive behaviour with respect to changes in the constraints, and logical consistency.     

Managing contextual preferences

To handle the overwhelming amount of information currently available, personalization systems allow users to specify through preferences which pieces of data interest them. Most often, users have different preferences depending on context. In this paper, we introduce a model for expressing such contextual preferences. Context is modeled using a set of hierarchical attributes, thus allowing context specification at various levels of detail. We formulate the context resolution problem as the problem of selecting appropriate preferences based on context for personalizing a query. We also propose algorithms for context resolution based on data structures that index preferences by exploiting the hierarchical nature of the context attributes. Finally, we evaluate our approach from two perspectives: usability and performance. Usability evaluates the overheads imposed on users for specifying context-dependent preferences, as well as their satisfaction from the quality of the results. Our performance results focus on the context resolution using the proposed indexes.