Computational neural learning formalisms for manipulator inverse kinematics

An efficient, adaptive neural learning paradigm for addressing the inverse kinematics of redundant manipulators is presented. The proposed methodology exploits the infinite local stability of terminal attractors - a new class of mathematical constructs which provide unique information processing capabilities to artificial neural systems. For robotic applications, synaptic elements of such networks can rapidly acquire the kinematic invariances embedded within the presented samples. Subsequently, joint-space configurations, required to follow arbitrary end-effector trajectories, can readily be computed. In a significant departure from prior neuromorphic learning algorithms, this methodology provides mechanisms for incorporating an in-training skew to handle kinematics and environmental constraints

Learned navigation in unknown terrains: A retraction method

The problem of learned navigation of a circular robot R, of radius delta (is greater than or equal to 0), through a terrain whose model is not a-priori known is considered. Two-dimensional finite-sized terrains populated by an unknown (but, finite) number of simple polygonal obstacles are also considered. The number and locations of the vertices of each obstacle are unknown to R. R is equipped with a sensor system that detects all vertices and edges that are visible from its present location. In this context two problems are covered. In the visit problem, the robot is required to visit a sequence of destination points, and in the terrain model acquisition problem, the robot is required to acquire the complete model of the terrain. An algorithmic framework is presented for solving these two problems using a retraction of the freespace onto the Voronoi diagram of the terrain. Algorithms are then presented to solve the visit problem and the terrain model acquisition problem

Optimal Distributed Fault-Tolerant Sensor Fusion: Fundamental Limits and Efficient Algorithms

Distributed estimation is a fundamental problem in signal processing which finds applications in a variety of scenarios of interest including distributed sensor networks, robotics, group decision problems, and monitoring and surveillance applications. The problem considers a scenario where distributed agents are given a set of measurements, and are tasked with estimating a target variable. This work considers distributed estimation in the context of sensor networks, where a subset of sensor measurements are faulty and the distributed agents are agnostic to these faulty sensor measurements. The objective is to minimize i) the mean square error in estimating the target variable at each node (accuracy objective), and ii) the mean square distance between the estimates at each pair of nodes (consensus objective). It is shown that there is an inherent tradeoff between satisfying the former and latter objectives. The tradeoff is explicitly characterized and the fundamental performance limits are derived under specific statistical assumptions on the sensor output statistics. Assuming a general stochastic model, the sensor fusion algorithm optimizing this tradeoff is characterized through a computable optimization problem. Finding the optimal sensor fusion algorithm is computationally complex. To address this, a general class of low-complexity Brooks-Iyengar Algorithms are introduced, and their performance, in terms of accuracy and consensus objectives, is compared to that of optimal linear estimators through case study simulations of various scenarios

Energy Efficient Estimation of Gaussian Sources Over Inhomogeneous Gaussian MAC Channels

It has been shown lately the optimality of uncoded transmission in estimating Gaussian sources over homogeneous/symmetric Gaussian multiple access channels (MAC) using multiple sensors. It remains, however, unclear whether it still holds for any arbitrary networks and/or with high channel signal-to-noise ratio (SNR) and high signal-to-measurement-noise ratio (SMNR). In this paper, we first provide a joint source and channel coding approach in estimating Gaussian sources over Gaussian MAC channels, as well as its sufficient and necessary condition in restoring Gaussian sources with a prescribed distortion value. An interesting relationship between our proposed joint approach with a more straightforward separate source and channel coding scheme is then established. We then formulate constrained power minimization problems and transform them to relaxed convex geometric programming problems, whose numerical results exhibit that either separate or uncoded scheme becomes dominant over a linear topology network. In addition, we prove that the optimal decoding order to minimize the total transmission powers for both source and channel coding parts is solely subject to the ranking of MAC channel qualities, and has nothing to do with the ranking of measurement qualities. Finally, asymptotic results for homogeneous networks are obtained which not only confirm the existing optimality of the uncoded approach, but also show that the asymptotic SNR exponents of these three approaches are all the same. Moreover, the proposed joint approach share the same asymptotic ratio with respect to high SNR and high SMNR as the uncoded scheme

Network connectivity under node failure

We examine a non-cooperative model of network formation where players may stop functioning. We identify conditions under which Nash and efficient networks will remain connected after the loss of kk nodes by introducing the notion of kk-Node Super Connectivity

DRSIG: Domain and Range Specific Index Generation for encrypted Cloud data

One of the most fundamental services of cloud computing is Cloud storage service. Huge amount of sensitive data is stored in the cloud for easy remote access and to reduce the cost of storage. The confidential data is encrypt before uploading to the cloud server in order to maintain privacy and security. All conventional searchable symmetric encryption(SSE) schemes enable the users to search on the entire index file. In this paper, we propose the Domain and Range Specific Index Generation(DRSIG) scheme that minimizes the Index Generation time. This scheme adopts collection sort technique to split the index file into D Domains and R Ranges. The Domain is based on the length of the keyword; the Range splits within the domain based on the first letter of the keyword. A mathematical model is used to encrypt the indexed keyword that eliminates the information leakage. The time complexity of the index generation is O(NT × 3) where NT - Number of rows in index document and 3 is Number of columns in index document. Experiments have been conducted on real world dataset to validate proposed DRSIG scheme. It is observed that DRSIG scheme is efficient and provide more secure data than Ranked Searchable Symmetric Encryption(RSSE) Scheme