Hybrid constant factor incremental delta modulators

The paper presents a new adaptive delta modulator, called the hybrid constant factor incremental delta modulator (HCFIDM), which uses instantaneous as well as syllabic adaptation of the step size. Three instantaneous algorithms have been used: two new instantaneous algorithms (CFIDM-3 and CFIDM-2) and the third, Song's voice ADM (SVADM). The quantisers have been simulated on a digital computer and their performances studied. The figure of merit used is the SNR with correlated, /?C-shaped Gaussian signals and real speech as the input. The results indicate that the hybrid technique is superior to the nonhybrid adaptive quantisers. Also, the two new instantaneous algorithms developed have improved SNR and fast response to step inputs as compared to the earlier systems.

Algorithm for incremental compaction of geometrical layouts

In this paper, a new incremental algorithm for layout compaction is proposed. In addition to its linear time performance in terms of the number of rectangles in the layout, we also describe how incremental compaction can form a good feature in the design of a layout editor. The design of such an editor is also described. In the design of the editor, we describe how arrays can be used to implement quadtrees that represent VLSI layouts. Such a representation provides speed of data access and low storage requirements.

Constant factor incremental delta modulator

This paper presents a new algorithm for the step-size change of instantaneous adaptive delta modulator. The present strategy is such that the step-size at any sampling instant can increase or decrease by either of the two constant factors or can remain the same, depending upon the combination of three or four most recent output bits. The quantizer has been simulated on a digital computer, and its performance compared with other quantizers. The figure of merit used is the SNR with gaussian signals as the input. The results indicate that the new design can give an improved SNR over a wider dynamic range and fast response to step inputs, as compared to the earlier systems.

Incremental attribute evaluation through recursive procedures

Incremental semantic analysis in a programming environment based on Attribute Grammars is performed by an Incremental Attribute Evaluator (IAE). Current IAEs are either table-driven or make extensive use of graph structures to schedule reevaluation of attributes. A method of compiling an Ordered Attribute Grammar into mutually recursive procedures is proposed. These procedures form an optimal time Incremental Attribute Evaluator for the attribute grammar, which does not require any graphs or tables.

Optimal advertisement-innovation mix for maximizing the discounted flow of profit

Due to boom in telecommunications market, there is hectic competition among the cellular handset manufacturers. As cellular manufacturing industry operates in an oligopoly framework, often price-rigidity leads to non-price wars. The handset manufacturing firms indulge in product innovation and also advertise their products in order to achieve their objective of maximizing discounted flow of profit. It is of interest to see what would be the optimal advertisement-innovation mix that would maximize the discounted How of profit for the firms. We used differential game theory to solve this problem. We adopted the open-loop solution methodology. We experimented for various scenarios over a 30 period horizon and derived interesting managerial insights.

Influence of subcontracting on innovation and economic performance of SMEs in Indian automobile industry

Trans-national corporations (TNCs) expanding their production bases to developing countries having better conditions of manufacturing and domestic markets provide increasing opportunities for local small and medium enterprises (SMEs) to have subcontracting relationships with these TNCs Even though some theoretical and a few empirical studies throw light on the nature of assistance provided by TNCs to local SMEs through subcontracting relationships none of the studies so far quantitatively analysed the role of this assistance on the innovative performance of SMEs leading to better economic performance This paper probes the extent and diversity of assistance received by SMEs from a TNC through subcontracting and its influence on technological innovations and economic performance of SMEs in the Indian automobile industry Indian SMEs were able to receive mainly product related and purchase process assistance thereby implying that subcontracting is largely confined to purchase-supply relationships However assistance received through subcontracting is beneficial as It promoted technological innovations of SMEs the higher the degree of assistance the higher the level of innovations carried out by these SMEs which in turn facilitated their economic performance Thus this paper substantiates in the Indian context that subcontracting relationship with a TNC can be an important source of technological innovations and enhanced economic performance for SMEs (C) 2010 Elsevier Ltd All rights reserved

The complexity of certain incremental code generation problems

This paper looks at the complexity of four different incremental problems. The following are the problems considered: (1) Interval partitioning of a flow graph (2) Breadth first search (BFS) of a directed graph (3) Lexicographic depth first search (DFS) of a directed graph (4) Constructing the postorder listing of the nodes of a binary tree. The last problem arises out of the need for incrementally computing the Sethi-Ullman (SU) ordering [1] of the subtrees of a tree after it has undergone changes of a given type. These problems are among those that claimed our attention in the process of our designing algorithmic techniques for incremental code generation. BFS and DFS have certainly numerous other applications, but as far as our work is concerned, incremental code generation is the common thread linking these problems. The study of the complexity of these problems is done from two different perspectives. In [2] is given the theory of incremental relative lower bounds (IRLB). We use this theory to derive the IRLBs of the first three problems. Then we use the notion of a bounded incremental algorithm [4] to prove the unboundedness of the fourth problem with respect to the locally persistent model of computation. Possibly, the lower bound result for lexicographic DFS is the most interesting. In [5] the author considers lexicographic DFS to be a problem for which the incremental version may require the recomputation of the entire solution from scratch. In that sense, our IRLB result provides further evidence for this possibility with the proviso that the incremental DFS algorithms considered be ones that do not require too much of preprocessing.

Incremental-angle and angular velocity estimation using a star sensor

A method is described for estimating the incremental angle and angular velocity of a spacecraft using integrated rate parameters with the help of a star sensor alone. The chief advantage of this method is that the measured stars need not be identified, whereas the identification of the stars is necessary in earlier methods. This proposed estimation can be carried out with all of the available measurements by a simple linear Kalman filter, albeit with a time-varying sensitivity matrix. The residuals of estimated angular velocity by the proposed spacecraft incremental-angle and angular velocity estimation method are as accurate as the earlier methods. This method also enables the spacecraft attitude to be reconstructed for mapping the stars into an imaginary unit sphere in the body reference frame, which will preserve the true angular separation of the stars. This will pave the way for identification of the stars using any angular separation or triangle matching techniques applied to even a narrow field of view sensor that is made to sweep the sky. A numerical simulation for inertial as well as Earth pointing spacecraft is carried out to establish the results.

Faster Algorithms for Incremental Topological Ordering.Robert Tarjan

We present two online algorithms for maintaining a topological order of a directed acyclic graph as arcs are added, and detecting a cycle when one is created. Our first algorithm takes O(m 1/2) amortized time per arc and our second algorithm takes O(n 2.5/m) amortized time per arc, where n is the number of vertices and m is the total number of arcs. For sparse graphs, our O(m 1/2) bound improves the best previous bound by a factor of logn and is tight to within a constant factor for a natural class of algorithms that includes all the existing ones. Our main insight is that the two-way search method of previous algorithms does not require an ordered search, but can be more general, allowing us to avoid the use of heaps (priority queues). Instead, the deterministic version of our algorithm uses (approximate) median-finding; the randomized version of our algorithm uses uniform random sampling. For dense graphs, our O(n 2.5/m) bound improves the best previously published bound by a factor of n 1/4 and a recent bound obtained independently of our work by a factor of logn. Our main insight is that graph search is wasteful when the graph is dense and can be avoided by searching the topological order space instead. Our algorithms extend to the maintenance of strong components, in the same asymptotic time bounds.

Incremental natural-gradient actor-critic algorithms

We present four new reinforcement learning algorithms based on actor-critic and natural-gradient ideas, and provide their convergence proofs. Actor-critic rein- forcement learning methods are online approximations to policy iteration in which the value-function parameters are estimated using temporal difference learning and the policy parameters are updated by stochastic gradient descent. Methods based on policy gradients in this way are of special interest because of their com- patibility with function approximation methods, which are needed to handle large or infinite state spaces. The use of temporal difference learning in this way is of interest because in many applications it dramatically reduces the variance of the gradient estimates. The use of the natural gradient is of interest because it can produce better conditioned parameterizations and has been shown to further re- duce variance in some cases. Our results extend prior two-timescale convergence results for actor-critic methods by Konda and Tsitsiklis by using temporal differ- ence learning in the actor and by incorporating natural gradients, and they extend prior empirical studies of natural actor-critic methods by Peters, Vijayakumar and Schaal by providing the first convergence proofs and the first fully incremental algorithms.