Signal-adaptive discrete evolutionary transform as a sparse time–frequency representation

Discrete evolutionary transform (DET) has usually been applied to signals in a blind-way without using any parameters to characterize the signal. For this reason, it is not optimal and needs improvement by using some information about the signal. In this paper, we propose an improvement for the discrete evolutionary transform in order to obtain a sparse representation and redefine the generalized time-bandwidth product optimal short-time Fourier transform as a special case of it. In case of linear FM-type signals, the optimized kernel function of the transform is determined according to signal parameters including the instantaneous frequency. The performance of the adaptive-DET is illustrated on three distinct cases. In case of multi-component LFM signals, when the concentration of the proposed distribution is compared to the ordinary sinusoidal-DET, the improvement is computed as 28% in terms of the ratio of norms. Furthermore we define a new and a general class of distribution functions named as the short-time generalized discrete Fourier transform (ST-GDFT) which is a larger set of signal representations including the adaptive-DET.     

Characterization of Sky‐region Morphological‐temporal Airborne Collision Detection

Automated airborne collision‐detection systems are a key enabling technology for facilitating the integration of unmanned aerial vehicles (UAVs) into the national airspace. These safety‐critical systems must be sensitive enough to provide timely warnings of genuine airborne collision threats, but not so sensitive as to cause excessive false alarms. Hence, an accurate characterization of detection and false‐alarm sensitivity is essential for understanding performance tradeoffs, and system designers can exploit this characterization to help achieve a desired balance in system performance. In this paper, we experimentally evaluate a sky‐region, image‐based, aircraft collision‐detection system that is based on morphological and temporal processing techniques. (Note that the examined detection approaches are not suitable for the detection of potential collision threats against a ground clutter background.) A novel collection methodology for collecting realistic airborne collision‐course target footage in both head‐on and tail‐chase engagement geometries is described. Under (hazy) blue sky conditions, our proposed system achieved detection ranges greater than 1540 m in three flight test cases with no false‐alarm events in 14.14 h of nontarget data (under cloudy conditions, the system achieved detection ranges greater than 1170 m in four flight test cases with no false‐alarm events in 6.63 h of nontarget data). Importantly, this paper is the first documented presentation of detection range versus false‐alarm curves generated from airborne target and nontarget image data. © 2012 Wiley Periodicals, Inc.     

Robust Saturated PI Joint Velocity Control for Robot Manipulators

It is well known that many industrial manipulators use an embedded linear proportional‐integral (PI) joint velocity controller to guarantee motion control through proper velocity commands. However, although this control scheme has been very successful in practice, not much attention has been paid to designing new PI velocity control structures. The problem of analyzing a saturated PI velocity joint velocity controller is addressed in this paper. By using the theory of singularly perturbed systems, the closed‐loop system is studied. The robot dynamics assumed in this paper take into account bounded time–varying disturbances which may include the friction at the joints. An experimental study in a planar two degrees‐of‐freedom direct‐drive robot is also presented, confirming the advantage of the new saturated PI joint velocity controller.     

A document is known by the company it keeps: neighborhood consensus for short text categorization

During the last decades the Web has become the greatest repository of digital information. In order to organize all this information, several text categorization methods have been developed, achieving accurate results in most cases and in very different domains. Due to the recent usage of Internet as communication media, short texts such as news, tweets, blogs, and product reviews are more common every day. In this context, there are two main challenges; on the one hand, the length of these documents is short, and therefore, the word frequencies are not informative enough, making text categorization even more difficult than usual. On the other hand, topics are changing constantly at a fast rate, causing the lack of adequate amounts of training data. In order to deal with these two problems we consider a text classification method that is supported on the idea that similar documents may belong to the same category. Mainly, we propose a neighborhood consensus classification method that classifies documents by considering their own information as well as information about the category assigned to other similar documents from the same target collection. In particular, the short texts we used in our evaluation are news titles with an average of 8 words. Experimental results are encouraging; they indicate that leveraging information from similar documents helped to improve classification accuracy and that the proposed method is especially useful when labeled training resources are limited.     

LEGClust- a clustering algorithm based on layered entropic subgraphs.

Hierarchical clustering is a stepwise clustering method usually based on proximity measures between objects or sets of objects from a given data set. The most common proximity measures are distance measures. The derived proximity matrices can be used to build graphs, which provide the basic structure for some clustering methods. We present here a new proximity matrix based on an entropic measure and also a clustering algorithm (LEGClust) that builds layers of subgraphs based on this matrix, and uses them and a hierarchical agglomerative clustering technique to form the clusters. Our approach capitalizes on both a graph structure and a hierarchical construction. Moreover, by using entropy as a proximity measure we are able, with no assumption about the cluster shapes, to capture the local structure of the data, forcing the clustering method to reflect this structure. We present several experiments on artificial and real data sets that provide evidence on the superior performance of this new algorithm when compared with competing ones.     

Modeling and solving the rooted distance-constrained minimum spanning tree problem

In this paper we discuss models and methods for solving the rooted distance constrained minimum spanning tree problem which is defined as follows: given a graph G=(V,E)$G=(V,E)$ with node set V={0,1,…,n}$V=\{0,1,\dots ,n\}$ and edge set E$E$, two integer weights, a cost _ce$c_e$ and a delay _we$w_e$ associated with each edge e$e$ of E$E$, and a natural (time limit) number H$H$, we wish to find a spanning tree T$T$ of the graph with minimum total cost and such that the unique path from a specified root node, node 0, to any other node has total delay not greater than H$H$. This problem generalizes the well known hop-constrained spanning tree problem and arises in the design of centralized networks with quality of service constraints and also in package shipment with service guarantee constraints. We present three theoretically equivalent modeling approaches, a column generation scheme, a Lagrangian relaxation combined with subgradient optimization procedure, both based on a path formulation of the problem, and a shortest path (compact) reformulation of the problem which views the underlying subproblem as defined in a layered extended graph. We present results for complete graph instances with up to 40 nodes. Our results indicate that the layered graph path reformulation model is still quite good when the arc weights are reasonably small. Lagrangian relaxation combined with subgradient optimization procedure appears to work much better than column generation and seems to be a quite reasonable approach to the problem for large weight, and even small weight, instances.     

Optimal Trajectory Planning for Wheeled Mobile Robots Based on Kinematics Singularity

This research introduces a new optimality criterion for motion planning of wheeled mobile robots based on a cost index that assesses the nearness to singularity of forward and inverse kinematic models. Slip motions, infinite estimation error and impossible control actions are avoided escaping from singularities. In addition, high amplification of wheel velocity errors and high wheel velocity values are also avoided by moving far from the singularity. The proposed cost index can be used directly to complement path-planning and motion-planning techniques (e.g. tree graphs, roadmaps, etc.) in order to select the optimal collision-free path or trajectory among several possible solutions. To illustrate the applications of the proposed approach, an industrial forklift, equivalent to a tricycle-like mobile robot, is considered in a simulated environment. In particular, several results are validated for the proposed optimality criterion, which are extensively compared to those obtained with other classical optimality criteria, such as shortest-path, time-optimal and minimum-energy.     

Self-managed topologies in P2P networks

The problem of efficient resource location is an important open issue in P2P systems. This paper introduces DANTE, a self-adapting P2P system that changes its peer links to form topologies where resources are located in an efficient manner via random walks. Additionally, this same self-adaptation capacity makes DANTE capable of reacting to events like changes in the system load or attacks on well-connected nodes by adjusting the topology to the new scenario. This adaptive behavior emerges as the global result of the individual work of nodes, without the intervention of any central entity or the need for global knowledge. Simulations show that this adaptation process makes the system scalable, resilient to attacks, and tolerant to a high transitivity of peers. Simulations are also used to compare this solution with other well-known self-adapting P2P system. From these results it can be concluded that the topologies achieved by DANTE offer better performance.     

A new control scheme for visual servoing

This research presents a new control scheme for visual servoing that takes into account the delay introduced by image acquisition and image processing. Firstly, previous control schemes for visual servoing are discussed and then a new control scheme is proposed and fully described. Afterwards, the capabilities (steady-state errors, stability margins, step time response, etc.) of the proposed control scheme and of previous ones are analytically analyzed and compared. Next, several simulations and experimental results are provided to validate the analytical results and to illustrate the benefits of the proposed control scheme. In particular, it is shown that this new control scheme clearly improves the performance of the previous ones.     

Highly Conductive MXene/PEDOT:PSS-Integrated Poly(N-Isopropylacrylamide) Hydrogels for Bioinspired Somatosensory Soft Actuators

Sophisticated sensing and actuation capabilities of many living organisms in nature have inspired scientists to develop biomimetic somatosensory soft robots. Herein, the design and fabrication of homogeneous and highly conductive hydrogels for bioinspired somatosensory soft actuators are reported. The conductive hydrogels are synthesized by in situ copolymerization of conductive surface-functionalized MXene/Poly(3,4-ethylenedioxythiophene)/poly(styrenesulfonate) ink with thermoresponsive poly(N-isopropylacrylamide) hydrogels. The resulting hydrogels are found to exhibit high conductivity (11.76 S m⁻¹), strain sensitivity (GF of 9.93), broad working strain range (≈560% strain), and high stability after over 300 loading–unloading cycles at 100% strain. Importantly, shape-programmable somatosensory hydrogel actuators with rapid response, light-driven remote control, and self-sensing capability are developed by chemically integrating the conductive hydrogels with a structurally colored polymer. As the proof-of-concept illustration, structurally colored hydrogel actuators are applied for devising light-driven programmable shape-morphing of an artificial octopus, an artificial fish, and a soft gripper that can simultaneously monitor their own motions via real-time resistance variation. This work is expected to offer new insights into the design of advanced somatosensory materials with self-sensing and actuation capabilities, and pave an avenue for the development of soft-matter-based self-regulatory intelligence via built-in feedback control that is of paramount significance for intelligent soft robotics and automated machines.