Abstract

Task: The task is a probabilistic learning task as described in Pasquereau et al., 2007 where four visual cues are associated with different reward probabilities (0.00, 0.33, 0.66 & 1.00). A trial is made of the simultaneous presentation of two random cues with equal salience at two random positions. Some time after the presentation, a switch in the cortex activities is observed, representing the decision taken. After the model has chosen one cue or the other, a reward is given according to the probability associated with the chosen cue. Connections between the cortex and the striatum are then modified using a reinforcement learning rule based on the reward signal. The model is trained over 120 trials such that each combination of cues is presented equal number of times at uniformly sampled positions and the model performance reaches at least 0.9 measuring the ration of optimal choices. The decision switch and the performance are identical to the results when primates are tested with same task. Learning is then disabled and the model is tested using always the same pair of cues A (P (R)= 1) and B (P (R)= 0.33) in the presence of external factors. We study how, despite reward based learning, visual salience of the stimuli and the temporal difference between stimulus presentations affect the model to take a sub-optimal decision.

Abstract

This paper presents a novel approach for algorithmic singularity avoidance for reactionless visual servoing of a satellite mounted space robot. Task priority approach is used to perform visual servoing while reactionless manipulation of the space robot. Algorithmic singularity is prominent in such cases of prioritizing two tasks. The algorithmic singularity is different from the kinematic and dynamic singularities as the former is an artefact of the tasks at hand, and difficult to predict. In this paper, we present a geometric interpretation of its occurrence, and propose a method to avoid it. The method involves path planning in image space, and generates a sequence of images that guides the robot towards goal avoiding algorithmic singularity. The method is illustrated through numerical studies on a 6-DOF planar dual-arm robot mounted on a service satellite.

Abstract

This paper presents a new hierarchical architecture for parallelizing the computation intensive rapidly exploring random tree problem. The architecture resembles a tree like structure that agglutinates minimal inter-module communication of a shared memory with data integrity of a distributed memory. Another novelty of this research has been in quantitatively analysing the performance metrics of the RRT algorithm across numerous embedded hardware solutions and ultimately implementing this algorithm on an FPGA to achieve hardware level optimization that offers real time performance and economical power consumption levels. We then analyse our implementation against hardware implementation of other scalable parallel RRT methods for motion planning.

Abstract

In this paper, we propose a better planning technique of the standard walking gaits for a quadruped robot than the conventional successive gait transition method to realize omnidirectional static walking. The technique involved planning the sequence as well as motion of the swinging and supporting legs. The relationship between the stability margin, the stride length and the duty factor are also formulated mathematically. The proposed modified crawl gait is compared to the conventional method with respect to the above parameters geometrically as well as mathematically and is shown to have positive stability margin at all times. The successive gait transition is demonstrated on the modified crawl and rotation gaits. Computer simulations of a model quadruped robot were performed to validate the theory proposed. Experiments were performed on an actual quadruped robot to realize the omnidirectional static walking with increased stability margin.

Abstract

Overtaking of a vehicle moving on structured roads is one of the most frequent driving behavior. In this work, we have described a Real Time Control System based framework for overtaking maneuver of autonomous vehicles. Proposed framework incorporates Intelligent Planning and Modular control modules. Intelligent Planning module of the framework enables the vehicle to intelligently select the most appropriate behavioral characteristics given the perceived operating environment. Subsequently, Modular control module reduces the search space of overtaking trajectories through an SVM based learning approach. These trajectories are then examined for possible future time collision using Velocity Obstacle. It employs non linear time scaling that provides for continuous trajectories in the space of linear and angular velocities to achieve continuous curvature overtaking maneuvers respecting velocity and acceleration bounds. Further time scaling also can scale velocities to avoid collisions and can compute a time optimal trajectory for the learned behavior. The preliminary results show the appropriateness of our proposed framework in virtual urban environment.

Abstract

Quality of work life is the extent of relationships between individuals and organizational factors that exist in the working environment. Quality of work life is the extent to which workers can satisfy important personal needs through their experiences in the organization. It focuses strongly on providing a work environment conducive to satisfying individual needs. It is assumed that if employees have more positive attitudes about the organization and their productivity increases, everything else being equal, the organization should be more effective. The present study was conducted to examine whether the work related factors and demographic factors have any relationship with the perception of quality of work life and to explore the relationship between quality of work and quality of life in textile industries located at East Godavari district. The work related factors are combined in six categories: working environment, welfare measures, safety measures, supervision, participation in decision making and intercommunication. The results hold that demographic factors and work related factors have significant relationship with perception of quality of work life.

Abstract

Cepstral analysis has been widely used in audio and speech processing applications because of its ability to reveal periodicities in a signal. The presence of cyclic prefix (CP) in orthogonal frequency division multiplexing (OFDM) signals induces periodicities. Motivated by this, the paper focuses on cepstral analysis of OFDM signal. The distributions of cepstral coefficients are de-rived for two scenarios of noise only and OFDM signal in noise. Itis shown that the OFDM cepstrum is significantly different from the additive white Gaussian noise (AWGN) cepstrum and can be used to detect OFDM waveforms. It is also shown that the cepstrum of OFDM is rich in features and can be used to estimate OFDM parameters such as number of sub carriers and length of the CPin an OFDM symbol. These OFDM waveform parameters can be used to automatically recognize or classify different OFDM waveforms, which are important for cognitive radios, coexistence of heterogeneous networks and signal intelligence. Two schemes are proposed to detect OFDM based primary user (PU) signalsin cognitive radio systems. The distributions of the test statistics under the two hypotheses are established.Neyman–Pearson detec-tions trategy is employed.Algorithms for estimating the number of subcarrier and the length of the CP are proposed and their performances studied through simulations. Later the proposed schemes are extended to cooperative sensing scenario with multiple secondary users (SUs) and it is shown that the collaboration between them significantly improve the performance of the proposed cepstrum based detection and estimation schemes.

Abstract

Matroidal networks were introduced by Doughert yet al.and have been well studied in the recent past. It was shown that a network has a scalar linear network coding solution if and only if it is matroidal associated with a representable matroid. A particularly interesting feature of this development is the ability to construct (scalar and vector)linearly solvable networks using certain classes of matroids.Furthermore, it was shown through the connection between network coding and matroid theory that linear network coding is not always sufficient for general network coding scenarios.The current work attempts to establish a connection between matroid theory and network-error correcting and detecting codes. In a similar vein to the theory connecting matroids and network coding, we abstract the essential aspects of linear network-error detecting codes to arrive at the definition of a matroidal error detecting network(and similarly, a matroidal error correcting networkabstracting from network-error correcting codes). An acyclic network (with arbitrary sink demands) is then shown to possess a scalar linear error detecting (correcting)network code if and only if it is a matroidal error detecting(correcting) network associated with a representable matroid.Therefore, constructing such network-error correcting and detecting codes implies the construction of certain representable matroids that satisfy some special conditions, and vice versa.We then present algorithms that enable the construction of matroidal error detecting and correcting networks with a specified capability of network-error correction. Using these construction algorithms, a large class of hitherto unknown scalar linearly solvable networks with multi source, multicast,and multiple- unicast network-error correcting codes is made available for theoretical use and practical implementation,with parameters, such as number of information symbols,number of sinks, number of coding nodes, error correcting capability, and so on, being arbitrary but for computing power(for the execution of the algorithms). The complexity of the construction of these networks is shown to be comparable with the complexity of existing algorithms that design multi cast scalar linear network-error correcting codes. Finally, we also show that linear network coding is not sufficient for the general network-error correction (detection) problem with arbitrary demands. In particular, for the same number of network errors,we show a network for which there is a nonlinear network-error detecting code satisfying the demands at the sinks, where as there are no linear network-error detecting codes that do the same.

Abstract

A code is said to be data-local maximally recoverable if (i) all the information symbols have locality and (ii) any erasure pattern which can be potentially recovered (i.e., the number of equations is equal to the number of unknowns) is recovered by the code. A code is said to be local maximally recoverable if(i) all the symbols of the code have locality and (ii) from the above holds. In this paper, we establish the matroid structures corresponding to data-local and local maximally recoverable codes (MRC). The matroid structures of these codes can be used to determine the associated Tutte polynomial. Greene proved that the weight enumerators of any code can be determined from its associated Tutte polynomial. We will use this result to derive explicit expressions for the weight enumerators of data-local MRC. Also, Britz proved that the higher support weights of any code can be determined from its associated Tutte polynomial.We will use this result to derive expressions for the higher support weights of data-local and local MRC with two local codes.

Abstract

Image based reconstruction of urban environments is a challenging problem that deals with optimization of large number of variables, and has several sources of errors like the presence of dynamic objects. Since most large scale approaches make the assumption of observing static scenes, dynamic objects are relegated to the noise modelling section of such systems. This is an approach of convenience since the RANSAC based framework used to compute most multiview geometric quantities for static scenes naturally confine dynamic objects to the class of outlier measurements. However, reconstructing dynamic objects along with the static environment helps us get a complete picture of an urban environment. Such understanding can then be used for important robotic tasks like path planning for autonomous navigation, obstacle tracking and avoidance, and other areas. In this paper, we propose a system for robust SLAM that works in both static and dynamic environments. To overcome the challenge of dynamic objects in the scene, we propose a new model to incorporate semantic constraints into the reconstruction algorithm. While some of these constraints are based on multi-layered dense CRFs trained over appearance as well as motion cues, other proposed constraints can be expressed as additional terms in the bundle adjustment optimization process that does iterative refinement of 3D structure and camera / object motion trajectories. We show results on the challenging KITTI urban dataset for accuracy of motion segmentation and reconstruction of the trajectory and shape of moving objects relative to ground truth. We are able to show average relative error reduction by 41 % for moving object trajectory reconstruction relative to state-of-the-art methods like TriTrack[16], as well as on standard bundle adjustment algorithms with motion segmentation.