Inbuilt Tendency of the eIF2 Regulatory System to Counteract Uncertainties

Eukaryotic initiation factor 2 (eIF2) plays a fundamental role in the regulation of protein synthesis. Investigations have revealed that the regulation of eIF2 is robust against intrinsic uncertainties and is able to efficiently counteract them. The robustness properties of the eIF2 pathway against intrinsic disturbances is also well known. However the reasons for this ability to counteract stresses is less well understood. In this article, the robustness conferring properties of the eIF2 dependent regulatory system is explored with the help of a mathematical model. The novelty of the work presented in this article lies in articulating the possible reason behind the inbuilt robustness of the highly engineered eIF2 system against intrinsic perturbations. Our investigations reveal that the robust nature of the eIF2 pathway may originate from the existence of an attractive natural sliding surface within the system satisfying reaching and sliding conditions that are well established in the domain of control engineering

Fuzzy c means based hybrid classifiers for offline recognition of handwritten indian (Arabic) numerals

© Research India Publications. In this study, we present two offline hybrid classification approaches for Indian (Arabic) handwritten numerals in effort to obtain higher reliability and classification rates than those obtained by single classifiers. Both methods work at the pixel level. No feature extraction methods are used as the purpose of this study is to focus on classifiers. The first hybrid classifier introduced is the serial hybrid classifier. It consist of three consecutive single classifiers. The first level is Fuzzy C-Means classifier followed by Support Vector Machine for as second level when more details are required and finally confirmation of classification will be through unique pixels method which forms the third classification level. The second hybrid classifier is the parallel hybrid classifier. It fuses the decisions simultaneously obtained from a Fuzzy C-Means classifier with the decision of a Neural Network to obtain the final decision. Both algorithms are tested on the CENPARNI Indian (Arabic) handwritten numerals dataset. The overall testing accuracy reported is 88%, 89% for the serial hybrid classifier and the parallel hybrid classifier, respectively. The paper also reports the results obtained using different types of single classifiers and compares with the above mentioned hybrid classifiers results. It shows the superiority of the hybrid classifiers over single classifiers

License plate localization based on a probabilistic model

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Probabilistic road maps with obstacle avoidance in cluttered dynamic environment

This paper presents an experimental study of a Probabilistic Road Map (PRM) based obstacle avoiding algorithm, for motion planning of a non-holonomic mobile robot in cluttered dynamic environment. The PRM approach uses a fast and simple local planner to build a network representation of the configuration space. It is trading off the distance to both static objects and moving obstacles in compute the travelled path. Our work has been implemented and tested on Player / Stage, real time robotic software, in extensive simulation runs. The different experiments that runs had demonstrate that our approach is well suited to control the motions of a robot in a cluttered environment and demonstrates its advantages over other techniques. © 2004 IEEE

Unmanned autonomous vehicle control and SLAM problem in 2-D environment

© 2004 IEEE. This paper proposes a method of selecting (autonomously) the artificial landmarks by Laser measurement, to establish the 2D obstacle map. Due to the error in the motion and measurement of the robot, the observed landmarks positions include the uncertainty. In this paper, we discuss the simultaneous laser type localization and map building (SLAM) problems. SLAM problem asks, is it possible for an autonomous vehicle to start in an unknown location in an unknown environment and then incrementally builds a map of this environment, while simultaneously using the map to compute the absolute vehicle location. From the results, we proved that a solution to the SLAM problem is indeed possible for 2D obstacle map. This implementation was made on Real time Player/Stage Robotics Software as well as the Matlab results were obtained, also we demonstrate how key issues such as, map management and data association can be handled in a practical environment