Off-line signature verification using genetically optimized weighted features

This paper is concerned with off-line signature verification. Four different types of pattern representation schemes have been implemented, viz., geometric features, moment-based representations, envelope characteristics and tree-structured Wavelet features. The individual feature components in a representation are weighed by their pattern characterization capability using Genetic Algorithms. The conclusions of the four subsystems teach depending on a representation scheme) are combined to form a final decision on the validity of signature. Threshold-based classifiers (including the traditional confidence-interval classifier), neighbourhood classifiers and their combinations were studied. Benefits of using forged signatures for training purposes have been assessed. Experimental results show that combination of the Feature-based classifiers increases verification accuracy. (C) 1999 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

A game theoretical multiple resource interaction approach to resource allocation in an air campaign

In this paper we propose a multiple resource interaction model in a game-theoretical framework to solve resource allocation problems in theater level military campaigns. An air raid campaign using SEAD aircraft and bombers against an enemy target defended by air defense units is considered as the basic platform. Conditions for the existence of saddle point in pure strategies is proved and explicit feedback strategies are obtained for a simplified model with linear attrition function limited by resource availability. An illustrative example demonstrates the key features.

Receiver-only optimized Vector Quantization for fading channels

This paper considers the design and analysis of a filter at the receiver of a source coding system to mitigate the excess distortion caused due to channel errors. The index output by the source encoder is sent over a fading discrete binary symmetric channel and the possibly incorrect received index is mapped to the corresponding codeword by a Vector Quantization (VQ) decoder at the receiver. The output of the VQ decoder is then processed by a receive filter to obtain an estimate of the source instantiation. The distortion performance is analyzed for weighted mean square error (WMSE) and the optimum receive filter that minimizes the expected distortion is derived for two different cases of fading. It is shown that the performance of the system with the receive filter is strictly better than that of a conventional VQ and the difference becomes more significant as the number of bits transmitted increases. Theoretical expressions for an upper and lower bound on the WMSE performance of the system with the receive filter and a Rayleigh flat fading channel are derived. The design of a receive filter in the presence of channel mismatch is also studied and it is shown that a minimax solution is the one obtained by designing the receive filter for the worst possible channel. Simulation results are presented to validate the theoretical expressions and illustrate the benefits of receive filtering.

An Optimized SDE Model for Slotted Aloha

We consider a stochastic differential equation (SDE) model of slotted Aloha with the retransmission probability as the associated parameter. We formulate the problem in both (a) the finite horizon and (b) the infinite horizon average cost settings. We apply the algorithm of 3] for the first setting, while for the second, we adapt a related algorithm from 2] that was originally developed in the simulation optimization framework. In the first setting, we obtain an optimal parameter trajectory that prescribes the parameter to use at any given instant while in the second setting, we obtain an optimal time-invariant parameter. Our algorithms are seen to exhibit good performance.

Optimal allocation of carbon credits to emitting agents in a carbon economy

Reduction of carbon emissions is of paramount importance in the context of global warming. Countries and global companies are now engaged in understanding systematic ways of achieving well defined emission targets. In fact, carbon credits have become significant and strategic instruments of finance for countries and global companies. In this paper, we formulate and suggest a solution to the carbon allocation problem, which involves determining a cost minimizing allocation of carbon credits among different emitting agents. We address this challenge in the context of a global company which is faced with the challenge of determining an allocation of carbon credit caps among its divisions in a cost effective way. The problem is formulated as a reverse auction problem where the company plays the role of a buyer or carbon planning authority and the different divisions within the company are the emitting agents that specify cost curves for carbon credit reductions. Two natural variants of the problem: (a) with unlimited budget and (b) with limited budget are considered. Suitable assumptions are made on the cost curves and in each of the two cases we show that the resulting problem formulation is a knapsack problem that can be solved optimally using a greedy heuristic. The solution of the allocation problem provides critical decision support to global companies engaged seriously in green programs.

Machine-tool selection and operation allocation in FMS: solving a fuzzy goal-programming model using a genetic algorithm

The decision-making process for machine-tool selection and operation allocation in a flexible manufacturing system (FMS) usually involves multiple conflicting objectives. Thus, a fuzzy goal-programming model can be effectively applied to this decision problem. The paper addresses application of a fuzzy goal-programming concept to model the problem of machine-tool selection and operation allocation with explicit considerations given to objectives of minimizing the total cost of machining operation, material handling and set-up. The constraints pertaining to the capacity of machines, tool magazine and tool life are included in the model. A genetic algorithm (GA)-based approach is adopted to optimize this fuzzy goal-programming model. An illustrative example is provided and some results of computational experiments are reported.

A Convex Optimization Framework for Almost Budget Balanced Allocation of a Divisible Good

We address the problem of allocating a single divisible good to a number of agents. The agents have concave valuation functions parameterized by a scalar type. The agents report only the type. The goal is to find allocatively efficient, strategy proof, nearly budget balanced mechanisms within the Groves class. Near budget balance is attained by returning as much of the received payments as rebates to agents. Two performance criteria are of interest: the maximum ratio of budget surplus to efficient surplus, and the expected budget surplus, within the class of linear rebate functions. The goal is to minimize them. Assuming that the valuation functions are known, we show that both problems reduce to convex optimization problems, where the convex constraint sets are characterized by a continuum of half-plane constraints parameterized by the vector of reported types. We then propose a randomized relaxation of these problems by sampling constraints. The relaxed problem is a linear programming problem (LP). We then identify the number of samples needed for ``near-feasibility'' of the relaxed constraint set. Under some conditions on the valuation function, we show that value of the approximate LP is close to the optimal value. Simulation results show significant improvements of our proposed method over the Vickrey-Clarke-Groves (VCG) mechanism without rebates. In the special case of indivisible goods, the mechanisms in this paper fall back to those proposed by Moulin, by Guo and Conitzer, and by Gujar and Narahari, without any need for randomization. Extension of the proposed mechanisms to situations when the valuation functions are not known to the central planner are also discussed. Note to Practitioners-Our results will be useful in all resource allocation problems that involve gathering of information privately held by strategic users, where the utilities are any concave function of the allocations, and where the resource planner is not interested in maximizing revenue, but in efficient sharing of the resource. Such situations arise quite often in fair sharing of internet resources, fair sharing of funds across departments within the same parent organization, auctioning of public goods, etc. We study methods to achieve near budget balance by first collecting payments according to the celebrated VCG mechanism, and then returning as much of the collected money as rebates. Our focus on linear rebate functions allows for easy implementation. The resulting convex optimization problem is solved via relaxation to a randomized linear programming problem, for which several efficient solvers exist. This relaxation is enabled by constraint sampling. Keeping practitioners in mind, we identify the number of samples that assures a desired level of ``near-feasibility'' with the desired confidence level. Our methodology will occasionally require subsidy from outside the system. We however demonstrate via simulation that, if the mechanism is repeated several times over independent instances, then past surplus can support the subsidy requirements. We also extend our results to situations where the strategic users' utility functions are not known to the allocating entity, a common situation in the context of internet users and other problems.

Low complexity Bit allocation algorithms for AAC encoding

We have developed two reduced complexity bit-allocation algorithms for MP3/AAC based audio encoding, which can be useful at low bit-rates. One algorithm derives optimum bit-allocation using constrained optimization of weighted noise-to-mask ratio and the second algorithm uses decoupled iterations for distortion control and rate control, with convergence criteria. MUSHRA based evaluation indicated that the new algorithm would be comparable to AAC but requiring only about 1/10 th the complexity.

An Efficient and Optimized Bluetooth Scheduling Algorithm for Scatternets

Bluetooth is a short-range radio technology operating in the unlicensed industrial-scientific-medical (ISM) band at 2.45 GHz. A scatternet is established by linking several piconets together in ad hoc fashion to yield a global wireless ad hoc network. This paper proposes a polling policy that aims to achieve increased system throughput and reduced packet delays while providing reasonably good fairness among all traffic flows in a Bluetooth Scatternet. Experimental results from our proposed algorithm show performance improvements over a well known existing algorithm.

Soft classification based Sub-pixel allocation model

Land cover (LC) refers to what is actually present on the ground and provide insights into the underlying solution for improving the conditions of many issues, from water pollution to sustainable economic development. One of the greatest challenges of modeling LC changes using remotely sensed (RS) data is of scale-resolution mismatch: that the spatial resolution of detail is less than what is required, and that this sub-pixel level heterogeneity is important but not readily knowable. However, many pixels consist of a mixture of multiple classes. The solution to mixed pixel problem typically centers on soft classification techniques that are used to estimate the proportion of a certain class within each pixel. However, the spatial distribution of these class components within the pixel remains unknown. This study investigates Orthogonal Subspace Projection - an unmixing technique and uses pixel-swapping algorithm for predicting the spatial distribution of LC at sub-pixel resolution. Both the algorithms are applied on many simulated and actual satellite images for validation. The accuracy on the simulated images is ~100%, while IRS LISS-III and MODIS data show accuracy of 76.6% and 73.02% respectively. This demonstrates the relevance of these techniques for applications such as urban-nonurban, forest-nonforest classification studies etc.

High-rate analysis of channel-optimized vector quantization

High-rate analysis of channel-optimized vector quantizationThis paper considers the high-rate performance of channel optimized source coding for noisy discrete symmetric channels with random index assignment. Specifically, with mean squared error (MSE) as the performance metric, an upper bound on the asymptotic (i.e., high-rate) distortion is derived by assuming a general structure on the codebook. This structure enables extension of the analysis of the channel optimized source quantizer to one with a singular point density: for channels with small errors, the point density that minimizes the upper bound is continuous, while as the error rate increases, the point density becomes singular. The extent of the singularity is also characterized. The accuracy of the expressions obtained are verified through Monte Carlo simulations.

Register Allocation and Optimal Spill code Scheduling in Software Pipelined Loops using 0-1 Integer Linear Programming Formulation

In achieving higher instruction level parallelism, software pipelining increases the register pressure in the loop. The usefulness of the generated schedule may be restricted to cases where the register pressure is less than the available number of registers. Spill instructions need to be introduced otherwise. But scheduling these spill instructions in the compact schedule is a difficult task. Several heuristics have been proposed to schedule spill code. These heuristics may generate more spill code than necessary, and scheduling them may necessitate increasing the initiation interval. We model the problem of register allocation with spill code generation and scheduling in software pipelined loops as a 0-1 integer linear program. The formulation minimizes the increase in initiation interval (II) by optimally placing spill code and simultaneously minimizes the amount of spill code produced. To the best of our knowledge, this is the first integrated formulation for register allocation, optimal spill code generation and scheduling for software pipelined loops. The proposed formulation performs better than the existing heuristics by preventing an increase in II in 11.11% of the loops and generating 18.48% less spill code on average among the loops extracted from Perfect Club and SPEC benchmarks with a moderate increase in compilation time.

Incentive compatible mechanisms for group ticket allocation in software maintenance services

A customer reported problem (or Trouble Ticket) in software maintenance is typically solved by one or more maintenance engineers. The decision of allocating the ticket to one or more engineers is generally taken by the lead, based on customer delivery deadlines and a guided complexity assessment from each maintenance engineer. The key challenge in such a scenario is two folds, un-truthful (hiked up) elicitation of ticket complexity by each engineer to the lead and the decision of allocating the ticket to a group of engineers who will solve the ticket with in customer deadline. The decision of allocation should ensure Individual and Coalitional Rationality along with Coalitional Stability. In this paper we use game theory to examine the issue of truthful elicitation of ticket complexities by engineers for solving ticket as a group given a specific customer delivery deadline. We formulate this problem as strategic form game and propose two mechanisms, (1) Division of Labor (DOL) and (2) Extended Second Price (ESP). In the proposed mechanisms we show that truth telling by each engineer constitutes a Dominant Strategy Nash Equilibrium of the underlying game. Also we analyze the existence of Individual Rationality (IR) and Coalitional Rationality (CR) properties to motivate voluntary and group participation. We use Core, solution concept from co-operative game theory to analyze the stability of the proposed group based on the allocation and payments.