Near-optimal gait generations of a two-legged robot on rough terrains using soft computing

A two-legged robot will have to generate its near-optimal gaits after ensuring maximum dynamic balance margin and minimum power consumption, while moving on the rough terrains containing some staircases and sloping surfaces. Moreover, the changes of joint torques should lie below a pre-specified small value to ensure its smooth walking. The balance of the robot and its power consumption are also dependent on hip trajectory and position of the masses on various limbs. Both neural network- and fuzzy logic-based gait planners have been developed for the same, the training of which are provided using a genetic algorithm off-line. Once optimized, the planners are found to generate optimal gaits of the two-legged robot successfully for the test cases.     

A comparative study on some navigation schemes of a real robot tackling moving obstacles

A comparative study of various robot motion planning schemes has been made in the present study. Two soft computing (SC)-based approaches, namely genetic-fuzzy and genetic-neural systems and a conventional potential field method (PFM) have been developed for this purpose. Training to the SC-based approaches is given off-line and the performance of the optimal motion planner has been tested on a real robot. Results of the SC-based motion planners have been compared between themselves and with those of the conventional PFM. Both the SC-based approaches are found to perform better than the PFM in terms of traveling time taken by the robot. Moreover, the performance of fuzzy logic-based motion planner is seen to be comparable with that of neural network-based motion planner. Comparisons among all these three motion planning schemes have been made in terms of robustness, adaptability, goal reaching capability and repeatability. Both the SC-based approaches are found to be more adaptive and robust compared to the PFM. It may be due to the fact that there is no in-built learning module in the PFM and consequently, it is unable to plan the velocity of the robot properly.     

EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor networks

In recent years, there has been a growing interest in wireless sensor networks. One of the major issues in wireless sensor network is developing an energy-efficient clustering protocol. Hierarchical clustering algorithms are very important in increasing the network’s life time. Each clustering algorithm is composed of two phases, the setup phase and steady state phase. The hot point in these algorithms is the cluster head selection. In this paper, we study the impact of heterogeneity of nodes in terms of their energy in wireless sensor networks that are hierarchically clustered. We assume that a percentage of the population of sensor nodes is equipped with the additional energy resources. We also assume that the sensor nodes are randomly distributed and are not mobile, the coordinates of the sink and the dimensions of the sensor field are known. Homogeneous clustering protocols assume that all the sensor nodes are equipped with the same amount of energy and as a result, they cannot take the advantage of the presence of node heterogeneity. Adapting this approach, we introduce an energy efficient heterogeneous clustered scheme for wireless sensor networks based on weighted election probabilities of each node to become a cluster head according to the residual energy in each node. Finally, the simulation results demonstrate that our proposed heterogeneous clustering approach is more effective in prolonging the network lifetime compared with LEACH.     

Bridging the gap: effect of text query reformulation in multimodal retrieval

Multimedia Tools and Applications - Multimodal Retrieval provides new paradigms and methods aimed at effectively searching through the enormous volume of data. Multimodal retrieval is a well...     

On negative results when using sentiment analysis tools for software engineering research

Recent years have seen an increasing attention to social aspects of software engineering, including studies of emotions and sentiments experienced and expressed by the software developers. Most of these studies reuse existing sentiment analysis tools such as SentiStrength and NLTK. However, these tools have been trained on product reviews and movie reviews and, therefore, their results might not be applicable in the software engineering domain. In this paper we study whether the sentiment analysis tools agree with the sentiment recognized by human evaluators (as reported in an earlier study) as well as with each other. Furthermore, we evaluate the impact of the choice of a sentiment analysis tool on software engineering studies by conducting a simple study of differences in issue resolution times for positive, negative and neutral texts. We repeat the study for seven datasets (issue trackers and Stack Overflow questions) and different sentiment analysis tools and observe that the disagreement between the tools can lead to diverging conclusions. Finally, we perform two replications of previously published studies and observe that the results of those studies cannot be confirmed when a different sentiment analysis tool is used.     

Linearized motion estimation for articulated planes

In this paper, we describe the explicit application of articulation constraints for estimating the motion of a system of articulated planes. We relate articulations to the relative homography between planes and show that these articulations translate into linearized equality constraints on a linear least-squares system, which can be solved efficiently using a Karush-Kuhn-Tucker system. The articulation constraints can be applied for both gradient-based and feature-based motion estimation algorithms and to illustrate this, we describe a gradient-based motion estimation algorithm for an affine camera and a feature-based motion estimation algorithm for a projective camera that explicitly enforces articulation constraints. We show that explicit application of articulation constraints leads to numerically stable estimates of motion. The simultaneous computation of motion estimates for all of the articulated planes in a scene allows us to handle scene areas where there is limited texture information and areas that leave the field of view. Our results demonstrate the wide applicability of the algorithm in a variety of challenging real-world cases such as human body tracking, motion estimation of rigid, piecewise planar scenes, and motion estimation of triangulated meshes.     

The minimum positional error incurred by any connectivity-based positioning algorithm for mobile wireless systems

A very important problem in many wireless ad-hoc networks, including wireless sensor networks, is positioning or the determination of geographical locations of the wireless nodes. Positioning is used both in infrastructural aspects of sensor networks, like geographic routing and topology maintenance, and in applications like wildlife tracking. Connectivity-based positioning algorithms in mobile wireless systems are studied in this work. These algorithms compute node positions based only on the connectivity, i.e. the neighborhood information of each node. Many algorithms have been proposed for positioning in stationary node systems and bounds on positional error of algorithms have been derived. The design and analysis of positioning algorithms for mobile node systems is a more challenging problem. Node mobility increases the amount of positional information available to a positioning algorithm. The work in this paper establishes a bound on the positional error for connectivity-based algorithms in mobile systems. The formulation from the analysis is used to investigate the benefit of this additional positional information on reducing positional error. There is a limit to the usefulness of positional information from previous node positions due to movement. This captures an important performance tradeoff: historical positional information can yield reduced positional error but requires more connectivity information from the network which requires greater computational resources.     

Automated Cellular Modeling and Prediction on a Large Scale

We describe CHAMP (CHurn Analysis, Modeling, andPrediction), an automated system for modeling cellularsubscriber churn that is predicting which customerswill discontinue cellular phone service. We describevarious issues related to developing and deployingthis system including automating data access from aremote data warehouse, preprocessing, featureselection, model validation, and optimization toreflect business tradeoffs. Using data from GTE'sdata warehouse for cellular phone customers, CHAMP iscapable of developing churn models customized byregion for over one hundred GTE cellular phone marketstotaling over 5 million customers. Every month churnfactors are identified for each geographic region andmodels are updated to generate churn scores predictingwho is likely to churn in the short term. Learningmethods such as decision trees and genetic algorithmsare used for feature selection and a cascade neuralnetwork is used for predicting churn scores. Inaddition to producing churn scores, CHAMP alsoproduces qualitative results in the form of rules andcomparison of market trends that are disseminatedthrough a web based interface.     

Snap-stabilization and PIF in tree networks

The contribution of this paper is threefold. First, we present the paradigm of snap-stabilization. A snap- stabilizing protocol guarantees that, starting from an arbitrary system configuration, the protocol always behaves according to its specification. So, a snap-stabilizing protocol is a time optimal self-stabilizing protocol (because it stabilizes in 0 rounds). Second, we propose a new Propagation of Information with Feedback (PIF) cycle, called Propagation of Information with Feedback and Cleaning (). We show three different implementations of this new PIF. The first one is a basic cycle which is inherently snap-stabilizing. However, the first PIF cycle can be delayed O(h ²) rounds (where h is the height of the tree) due to some undesirable local states. The second algorithm improves the worst delay of the basic algorithm from O(h ²) to 1 round. The state requirement for the above two algorithms is 3 states per processor, except for the root and leaf processors that use only 2 states. Also, they work on oriented trees. We then propose a third snap-stabilizing PIF algorithm on un-oriented tree networks. The state requirement of the third algorithm depends on the degree of the processors, and the delay is at most h rounds. Next, we analyze the maximum waiting time before a PIF cycle can be initiated whether the PIF cycle is infinitely and sequentially repeated or launch as an isolated PIF cycle. The analysis is made for both oriented and un-oriented trees. We show or conjecture that the two best of the above algorithms produce optimal waiting time. Finally, we compute the minimal number of states the processors require to implement a single PIF cycle, and show that both algorithms for oriented trees are also (in addition to being time optimal) optimal in terms of the number of states. WARNING: The concept of snap-stabilization was first introduced in [12]. The concept evolved over the last eight years. We take this evolution in consideration in this paper, which includes the early results published in [10] and [12]. In particular, infinite repetition of computation cycles is a requirement of self-stabilizing systems. This is not required in snap-stabilization because snap-stabilization ensures that the first completed computation cycle is executed according to the specification of the problem. The correctness proofs conform to this basic property.