Fitness based weighted flower pollination algorithm with mutation strategies for image enhancement

Multimedia Tools and Applications - Flower Pollination Algorithm (FPA) is a well-known swarm intelligence optimization algorithm, which has shown an effective performance by solving many...     

Supine and prone colon registration using quasi-conformal mapping

In virtual colonoscopy, CT scans are typically acquired with the patient in both supine (facing up) and prone (facing down) positions. The registration of these two scans is desirable so that the user can clarify situations or confirm polyp findings at a location in one scan with the same location in the other, thereby improving polyp detection rates and reducing false positives. However, this supine-prone registration is challenging because of the substantial distortions in the colon shape due to the patient's change in position. We present an efficient algorithm and framework for performing this registration through the use of conformal geometry to guarantee that the registration is a diffeomorphism (a one-to-one and onto mapping). The taeniae coli and colon flexures are automatically extracted for each supine and prone surface, employing the colon geometry. The two colon surfaces are then divided into several segments using the flexures, and each segment is cut along a taenia coli and conformally flattened to the rectangular domain using holomorphic differentials. The mean curvature is color encoded as texture images, from which feature points are automatically detected using graph cut segmentation, mathematic morphological operations, and principal component analysis. Corresponding feature points are found between supine and prone and are used to adjust the conformal flattening to be quasi-conformal, such that the features become aligned. We present multiple methods of visualizing our results, including 2D flattened rendering, corresponding 3D endoluminal views, and rendering of distortion measurements. We demonstrate the efficiency and efficacy of our registration method by illustrating matched views on both the 2D flattened colon images and in the 3D volume rendered colon endoluminal view. We analytically evaluate the correctness of the results by measuring the distance between features on the registered colons.     

A new grouping genetic algorithm approach to the multiple traveling salesperson problem

The multiple traveling salesperson problem (MTSP) is an extension of the well known traveling salesperson problem (TSP). Given m > 1 salespersons and n > m cities to visit, the MTSP seeks a partition of cities into m groups as well as an ordering among cities in each group so that each group of cities is visited by exactly one salesperson in their specified order in such a way that each city is visited exactly once and sum of total distance traveled by all the salespersons is minimized. Apart from the objective of minimizing the total distance traveled by all the salespersons, we have also considered an alternate objective of minimizing the maximum distance traveled by any one salesperson, which is related with balancing the workload among salespersons. In this paper, we have proposed a new grouping genetic algorithm based approach for the MTSP and compared our results with other approaches available in the literature. Our approach outperformed the other approaches on both the objectives.     

Towards load-balanced de-congested multi-robotic agent traffic control by coordinated control at intersections

This paper presents a methodology for the coordination of multiple robotic agents moving from one location to another in an environment embedded with a network of agents, placed at strategic locations such as intersections. These intersection agents, communicate with robotic agents and also with each other to route robots in a way as to minimize the congestion, thus resulting in the continuous flow of robot traffic. A robot’s path to its destination is computed by the network (in this paper, ‘Network’ refers to the collection of ‘Network agents’ operating at the intersections) in terms of the next waypoints to reach. The intersection agents are capable of identifying robots in their proximity based on signal strength. An intersection agent controls the flow of agent traffic around it with the help of the data it collects from the messages received from the robots and other surrounding intersection agents. The congestion of traffic is reduced using a two-layered hierarchical strategy. The primary layer operates at the intersection to reduce the time delay of robots crossing them. The secondary layer maintains coordination between intersection agents and routes traffic such that delay is reduced through effective load balancing. The objective at the primary level, to reduce congestion at the intersection, is achieved through assigning priorities to pathways leading to the intersection based on the robot traffic density. At the secondary level, the load balancing of robots over multiple intersections is achieved through coordination between intersection agents by communication of robot densities in different pathways. Extensive comparisons show the performance gain of the current method over existing ones. Theoretical analysis apart from simulation show the advantages of load-balanced traffic flow over uncoordinated allotment of robotic agents to pathways. Transferring the burden of coordination to the network releases more computational power for the robots to engage in critical assistive activities.     

Exploring optimization and caching for efficient collection operations

Many large programs operate on collection types. Extensive libraries are available in many programming languages, such as the C++ Standard Template Library, which make programming with collections convenient. Extending programming languages to provide collection queries as first class constructs in the language would not only allow programmers to write queries explicitly in their programs but it would also allow compilers to leverage the wealth of experience available from the database domain to optimize such queries. This paper describes an approach to reduce the run time of programs involving explicit collection queries by performing run time query optimization that is effective for single runs of a program. In addition, it also leverages a cache to store previously computed results. The proposed approach relies on histograms built from the data at run time to estimate the selectivity of joins and predicates in order to construct query plans. Information from earlier executions of the same query during run time is leveraged during the construction of the query plans, even when the data has changed between these executions. An effective cache policy is also determined for caching the results of join (sub) queries. The cache is maintained incrementally, when the underlying collections change, and use of the cache space is optimized by a cache replacement policy. Our approach has been implemented within the Java Query Language (JQL) framework using AspectJ. Our approach demonstrated that its run time query optimization in integration with caching sub query result significantly improves the run time of programs with explicit queries over equivalent programs performing collection operations by iterating over those collections. This paper evaluates our approach using synthetic as well as real world Robocode programs by comparing it to JQL as a benchmark. Experimental results show that our approach performs better than the JQL approach with respect to the program run time.     

Distributed consensus control for multi‐agent systems using terminal sliding mode and Chebyshev neural networks

This paper investigates the problem of consensus tracking control for second‐order multi‐agent systems in the presence of uncertain dynamics and bounded external disturbances. The communication ?ow among neighbor agents is described by an undirected connected graph. A fast terminal sliding manifold based on lumped state errors that include absolute and relative state errors is proposed, and then a distributed finite‐time consensus tracking controller is developed by using terminal sliding mode and Chebyshev neural networks. In the proposed control scheme, Chebyshev neural networks are used as universal approximators to learn unknown nonlinear functions in the agent dynamics online, and a robust control term using the hyperbolic tangent function is applied to counteract neural‐network approximation errors and external disturbances, which makes the proposed controller be continuous and hence chattering‐free. Meanwhile, a smooth projection algorithm is employed to guarantee that estimated parameters remain within some known bounded sets. Furthermore, the proposed control scheme for each agent only employs the information of its neighbor agents and guarantees a group of agents to track a time‐varying reference trajectory even when the reference signals are available to only a subset of the group members. Most importantly, finite‐time stability in both the reaching phase and the sliding phase is guaranteed by a Lyapunov‐based approach. Finally, numerical simulations are presented to demonstrate the performance of the proposed controller and show that the proposed controller exceeds to a linear hyperplane‐based sliding mode controller. Copyright © 2011 John Wiley & Sons, Ltd.     

Harmonics and size reduced microstrip branch‐line baluns using shunt open‐stubs

This article presents the design of harmonic suppressed compact microstrip balun, using shunt open‐stubs. Two approaches using Π‐shaped and T‐shaped open‐stub configurations are investigated. Because of the stopband effect of the open‐stubs, the harmonic passband responses are suppressed effectively. A compact‐size balun is realized because both kinds of structures have slow wave characteristics. To validate theoretical predictions, two prototype baluns operating at f₀ = 1 GHz, showing size reduction up to 75% and no spurious passband up to 4f₀ compared with the conventional design are fabricated in microstrip form. © 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2011.     

Bioremediation of contaminated soil beds and groundwater — A simulation study

Pollution has reached levels which demand immediate attention and scientific and technological solutions are required on an urgent basis. We are concerned in this paper with bioremediation of soil and groundwater, i.e. the use of indigenous micro-organisms to clean up soil beds and groundwater contaminated with organic pollutants. To achieve managedin situ bioremediation in practice, treated water is recycled with added nutrients into the ground so that oxygen and nitrogen are carried with the water to the subsurface regions. Sorption, convective-dispersive flow and chemical and biological transformations are the chief processes involved that have to be modelled. Here we discuss a simulation model developed to aid in designing an efficient system that maximizes the rate of biodegradation. Simulation models are a must in this case since laboratory experiments take time periods of the order of months. An unusual feature of this simulation model is that it is governed by coupled partial and ordinary differential equations. Partial differential equations (PDEs) model the diffusion and biodegradation processes occurring in the micropores of soil aggregates while ordinary differential equations (ODEs) describe the bioremediation in the interstitial spaces between soil aggregates, both partial and ordinary differential equations being nonlinear. The model is applied to the case of high initial contaminant concentrations. This work is part of a joint project with Regional Research Laboratory, Jorhat and has been carried out in close cooperation with N N Dutta. Discussions with K S Yajnik have been very useful.     

Application-Level Fault Tolerance as a Complement to System-Level Fault Tolerance

As multiprocessor systems become more complex, their reliability will need to increase as well. In this paper we propose a novel technique which is applicable to a wide variety of distributed real-time systems, especially those exhibiting data parallelism. System-level fault tolerance involves reliability techniques incorporated within the system hardware and software whereas application-level fault tolerance involves reliability techniques incorporated within the application software. We assert that, for high reliability, a combination of system-level fault tolerance and application-level fault tolerance works best. In many systems, application-level fault tolerance can be used to bridge the gap when system-level fault tolerance alone does not provide the required reliability. We exemplify this with the RTHT target tracking benchmark and the ABF beamforming benchmark.     

Dynamic state restoration using versioning exceptions

We explore the semantics and analysis of a new kind of control structure called a versioning exception that ensures the state of the program, at the point when an exception handler is invoked, reflects the program state at the point when the handler is installed. Versioning exceptions provide a transaction-like versioning semantics to the code protected by a handler: modifications performed within the dynamic context of the corresponding handler are versioned, and committed to the store only if the computation completes normally. Similar to the role of backtracking in logic programming, this facility allows unwanted effects of computations to be discarded when exceptional or undesirable conditions are detected. We define a novel points-to analysis to efficiently track changes to the store within handler-protected scopes. The role of the analysis is to facilitate optimizations that minimize the number of locations which must be restored when a versioning exception is raised. The analysis is defined by a reachability approximation over locations that indicates which objects have been potentially modified within a handler scope. The analysis is defined for programs which support first-class procedures, locations, and exceptions.