Anna K. Lekova

Redundant fuzzy rules exclusion by genetic algorithms

Abstract A genetic-algorithm-based method for exclusion of the potential redundant if-then fuzzy rules that have been extracted from numerical input-output data is proposed. The main idea is the input-space separation into activation rectangles, corresponding to certain output intervals. The generation of fuzzy rules and the membership functions are based on these activation rectangles and appropriate fuzzy rules inference mechanism is proposed. As the method usually produces too many rules, it is necessary to exclude the potential redundant if-then rules. The concept for varying the family of sensitivity parameters, defining the overlapping of the fuzzy regions is proposed. The genetic algorithms are used to resolve the following combinatorial optimization problem: the generation of families of sensitivity parameters. In this way the potential redundant if-then fuzzy rules are excluded. The method formalizes the synthesis of the fuzzy system and could be used for function approximation, classification and control purposes. An illustrative example for implementation of the method for traffic fuzzy control is given.

Supporting multiple subscription languages by a single event notification overlay in sparse MANETs

The subscription language is an important design decision for distributed event notification services (DENS). In order to minimize resource consumption and enable applications to use rich and complex subscription languages only when they are really needed, we have developed a DENS that separates the concerns of delivering subscriptions and notifications from the subscription specification and event filtering, i.e., the subscription language. To resolve the conflict between subscription language independence in DENS and a strict decoupling of publishers and subscribers through the DENS, we request that for each new subscription language three language specific plug-ins are provided. In this paper, we present the technical details of this solution and describe our proof-of-concept implementation that supports a simple attribute-value based subscription language and a fuzzy concept-based language.

Personality Filter in Mobile Networks with Communication Constraints

A new psychological model for efficient data transmission in mobile networks under communication constrains is proposed. It accounts for user personality characteristics to determine the feasible path for packet transmission from source to target and to predict the availability of the communication resources based on dynamically determined level of node generosity. Two case studies are presented with initial performance better than chance. The model is designed for the developed framework of evolving fuzzy modeling in mobile Ad hoc networks via lightweight online unsupervised learning.

Hand gestures recognition based on lightweight evolving fuzzy clustering method

Robots of the future will socialize with humans. Human-robot interaction (HRI) by a vision-based gesture interface helps to personalize the communication with humans in various contexts - from support of their daily life to social skills training of children with developmental problems. We are especially interested in vision-based hand gesture HRI and propose a hand gesture recognition system based on a novel online extraction and classification scheme, which is lightweight and can be used in a mobile robot. An online Lightweight Evolving Fuzzy Clustering Method is used to categorize the positional and HSV model of pixels for the edges of the gesture image. The result clusters consist of (x, y) coordinates and the averaged grayscale level at these locations. Then these clusters are processed to identify typical for the hand features brighter and darker pixel information. The database consists of averaged grayscale levels in HSV format for neighbor pixels that characterize different features. For feature recognition we use Tanimoto similarity measure for matching the current grayscale patterns to those in the database. Then the feature location is encoded in a binary format. For gesture recognition we use a formalism of Symbol Relation Grammars to describe a gesture, as well as simple and fast bitwise operations to find the position and orientation of the features in the gesture.

A novel gesture driven fuzzy interface system for car racing game

The recently developed Kinect sensor has opened a new horizon to Human-Computer Interface (HCI) and its native connection with Microsofts product line of Xbox 360 and Xbox One video game consoles makes completely hands-free control in next generation of gaming. Games that requires a lot of degree of freedoms, especially the driving control of a car in racing games is best suitable to be driven by gestures, as the use of simple buttons does not scale to the increased number of assistive, comfort, and infotainment functions. In this paper, we propose a Mamdani type-I fuzzy inference system based data processing module which effectively takes into account the dependence of actual steering angle with the distance of two palm positions and angle generated with respect to the sagittal plane. The FIS output variable controls the duration of a virtual “key-pressed” event which mocks the users pressing of actual keys assigned to control car direction in the original game. The acceleration and brake(deceleration) of the vehicle is controlled using the relative displacement of left and right feet. The proposed experimental setup, interfacing Kinect and a desktop based racing game, has shown that the virtual driving environment can be easily applied to any games belonging to this particular genre.

Type 2 fuzzy induced person identification using Kinect sensor

Automatic person recognition problem draws significant popularity in the last decade in the field of human-robot interaction. This paper introduces a novel approach to identify a person automatically whom the robot has already met, based on its walking pattern as gait is a unique characteristic for every individual. Here, the Kinect sensor is used to record the gait pattern of a person by storing 20 3-D joint coordinates in each time stamps. The features like joint angle and joint length are obtained from each complete walk cycle. Among all these features, most significant features are selected using principal component analysis. Later, these features are fuzzified constructing a Gaussian membership function with the mean and standard deviation of each feature at different gait cycle. An Interval Type-2 membership is constructed with all these membership values for a particular feature in different trials. 10 walking data set of 10 subjects are processed here. Now, when any person out of these 10 persons is walking in front of Kinect, features are calculated. But as more than one feature value for a particular feature (each feature corresponds to each gait cycle in a complete walking task) is obtained, mean of all these values for a particular feature is considered as measurement point. Defuzzification is done using t-norm and average operators. The person corresponding to highest defuzzified value is considered as the unknown person. The classification accuracy is 89.667%. The proposed method is also compared with few existing person identification techniques and the results obtained prove the superiority of the proposed algorithm.

Web-Based Application for Telerobotics

Abstract In the present paper the architecture of Web-based telerobotics system is presented. A remote Web interface for telecontrol facilitates natural and easily distributed human-robot interaction and remote coordination in real time. A microrobot with tactile range finders for local map building is used. The user participates in control procedure through finding the optimal trajectory as a virtual hand drawing over the 2D map representation of the environment. Two proxy systems are proposed in order to over come the unreliability of the Internet connection and to ensure smooth running of the interactive process on the client side. In order to achieve these goals a reflexive module and fuzzy prompt system are used.

A novel approach to TSK model based gesture driven robot movement

This paper presents a novel fuzzy based approach to gesture driven human robot interaction. Now a day, gestures are considered to be the most effective communicative medium for remotely controlling a robot. In this work, the gestures are employed to instruct a Khepera II robot to move from a specific starting position to a goal position following a specific path. The main highlight is the determination of exact degree of rotation with proper application of acceleration and brake in order to reach the specified goal position without hitting the obstacles. A Takagi-Sugeno-Kang based fuzzy model with two antecedents (type-2 fuzzy sets) and one consequent (crisp value) has been employed to determine the angle of rotation. The performance of the proposed framework has been tested in terms of a number of parameters like accuracy, precision, error rate etc. And in each case, the formulated strategy has proved its worth.

A general type-2 fuzzy set induced single trial P300 detection

P300 is one of the most widely studied event-related potentials. Unfortunately, most of the existing automatic P300 detection schemes require computations over repetitive trials in both training and recognition phases. Several attempts have recently been endeavored towards the single trial detection of the P300 signals. However, no acceptable solution to the problem is found till date. In the present work, we have attempted to address this problem in the light of latency and (amplitude) deflection of the signal. The intra- and inter-personal variations inherent in these features are managed by the uncertainty management characteristics of General Type-2 Fuzzy Sets. First, these sets are constructed by exploiting the knowledge obtained from different trials of a large number of subjects. The secondary membership functions of the Type-2 Fuzzy Sets are computed based on a novel density dependent measure of the primary membership functions in the footprint of uncertainty. Second, recognition of P300 in an unknown EEG trial is performed based on the agreement of measured feature values with the General Type-2 Fuzzy knowledge-base. Majority voting of the concerned electrodes makes the scheme more robust. The experimental results show that the proposed algorithm is capable of achieving 88.60% accuracy in single trial detection of P300 instances, which is significantly higher than those obtained in state of the art algorithms.

Quantitative evaluation of alternatives for municipal solid waste treatment in Bulgaria

Abstract The present practice for treatment of municipal solid wastes in Bulgaria is to collect them in landfills. This leads to a serious ecological problem related to the emission of landfill gas which is extremely harmful for the environment (causing global warming). In this paper, four alternative policies for municipal waste treatment are defined (landfill without utilization of the landfill gas, landfill with producing synthetical gas from the landfill gas, landfill with producing electricity from the landfill gas and waste incineration with production of electricity). Four criteria to evaluate these alternatives are formulated. Selection of the best alternative is represented as a multi-criteria optimization problem and a strategy to solve this problem is developed.