Improved genetic algorithm for channel allocation with channel borrowing in mobile computing

This paper exploits the potential of the Genetic Algorithm to solve the cellular resource allocation problem. When a blocked host is to be allocated to a borrowable channel, a crucial decision is which neighboring cell to choose to borrow a channel. It is an optimization problem and the genetic algorithm is efficiently applied to handle this. The Genetic Algorithm, for this particular problem, is improved by introducing a new genetic operator, named pluck, that incorporates a problem-specific knowledge in population generation and leads to a better channel utilization by reducing the average blocked hosts. The pluck operator makes the crucial decision of when and which cell to borrow with the future consideration that the borrowing should not lead the network to chaos. It makes a channel borrowing decision that minimizes the number of blocked hosts and improves the long-term performance of the network. Efficacy of the proposed method has been evaluated by experimentation.     

Security Driven Scheduling Model for Computational Grid Using NSGA-II

Number of software applications demands various levels of security at the time of scheduling in Computational Grid. Grid may offer these securities but may result in the performance degradation due to overhead in offering the desired security. Scheduling performance in a Grid is affected by the heterogeneities of security and computational power of resources. Customized Genetic Algorithms have been effectively used for solving complex optimization problems (NP Hard) and various heuristics have been suggested for solving Multi-objective optimization problems. In this paper a security driven, elitist non-dominated sorting genetic algorithm, Optimal Security with Optimal Overhead Scheduling (OSO₂S), based on NSGA-II, is proposed. The model considers dual objectives of minimizing the security overhead and maximizing the total security achieved. Simulation results exhibit that the proposed algorithm delivers improved makespan and lesser security overhead in comparison to other such algorithms viz. MinMin, MaxMin, SPMinMin, SPMaxMin and SDSG.     

Solving machine loading problems in a flexible manufacturing system using a genetic algorithm based heuristic approach

The machine-loading problem of a flexible manufacturing system (FMS) has been recognized as one of the most important planning problems. In this research, a Genetic Algorithm (GA) based heuristic is proposed to solve the machine loading problem of a random type FMS. The objective of the loading problems is to minimize the system unbalance and maximize the throughput, satisfying the technological constraints such as availability of machining time, and tool slots. The proposed GA-based heuristic determines the part type sequence and the operation-machine allocation that guarantee the optimal solution to the problem, rather than using fixed predetermined part sequencing rules. The efficiency of the proposed heuristic has been tested on ten sample problems and the results obtained have been compared with those of existing methods.     

Studies on the production of B. thuringiensis based biopesticides using wastewater sludge as a raw material

Growth and delta-endotoxin yield of Bacillus thuringiensis (Bt) subsp kurstaki in tryptic soy yeast extract (TSY) medium, soybean meal based commercial medium and wastewater sludge medium were studied. The viable spores (VS) count in sludge medium was comparable to that obtained in laboratory and commercial media. The entomotoxicity of the fermentation liquid (Bt grown sludge) against Choristoneura fumiferana was comparable to the concentrated commercial Bt formulation available in the market (Foray 48B). A higher entomotoxicity was observed in a sludge medium than in the TSY or soybean meal media. The secondary and mixed (mixture of primary and secondary) sludges from various wastewater treatment plants were also evaluated for spore formation and entomotoxicity yield. The VS count was higher in a mixed sludge compared to the secondary sludge at a similar sludge solids concentration. Both VS count and entomotoxicity yield was found to be a function of sludge solids concentration in the medium. The optimum value of solids concentration for Bt production was found to be 25 g (-1) (dry weight basis). Beyond this concentration, a drop in VS count and entomotoxicity yield was observed. A low C:N ratio in the secondary sludge and a high C:N ratio in the mixed sludge resulted in a higher entomotoxicity. The optimum value of C:N ratio in combined sludge for Bt production was found to be 7.9-9.9. Relationships between entomotoxicity and maximum specific growth as well as with specific sporulation rate were developed.     

Comparative study of two CPU router time management algorithms in cellular IP networks

Providing good quality of service (QoS) in cellular IP networks is an important requirement for performance improvement of the cellular IP network. Resource reservation is one of the methods used in achieving this goal and is proven to be effective. The main resources to be reserved in a cellular IP network are bandwidth, buffer and central processing unit (CPU) cycles. Router CPU cycle is the time taken by the router to process the packet of the flow before forwarding it to the next router (hop). This paper proposes a model for CPU cycle optimization of routers for real‐time flows in a cellular IP network. The model applies both genetic algorithm (GA) and particle swarm optimization (PSO) as soft computing tools to optimize the CPU cycles and reduces the flow processing time at each router in the route taken by a flow. Simulation experiments illustrate a comparative study of the model.     

Large area deposition of Pb(Zr,Ti)O<Subscript>3</Subscript> thin films for piezoelectric MEMS devices

Pb(Zr,Ti)O₃ (PZT) thin films deposited on insulating ZrO₂ buffered silicon wafer are intended to be employed for in-plane polarized piezoelectric MEMS devices. Multi-target reactive sputtering system for large area deposition of in-situ crystallized PZT thin films and the ZrO₂ buffer layer has been employed. The interface analysis of multilayer structures by high resolution transmission microscope, X-ray diffraction, optical refraction, and absorption spectra studies has been presented. At a substrate temperature of 520°C and excess lead deposition condition, the formation of a PZT superstructure has been revealed. The substrate temperature of 580°C leads to the crystallization of PZT directly into a single phase perovskite crystal structure. A pronounced Urbach behavior in our PZT thin films has been observed by optical absorption studies. The surface roughness of PZT films deposited on a ZrO₂ buffer layer is much higher than that on conducting platinized silicon wafer.     

An integrated approach to solving the process plan selection problem in an automated manufacturing system

Selection of a process plan is a crucial decision making problem encountered in manufacturing systems due to the presence of several alternative process plans arising out of availability of several machines, tools, fixtures etc. capable of performing the same operations of the part. Because of its vital impact on the performance of the manufacturing system, several researchers have addressed the plan selection problem in recent years. Although functional integration plays a significant role in the development of current manufacturing systems, many of the functions in manufacturing systems have been developed without a sense of integration. Therefore, it becomes important to emphasize the integration of functions rather than the individual development of the function itself. This paper attempts to address the plan selection problem taking into account the similarity measures among the process plans of the parts. Four algorithms have been developed to integrate the several segments of the process plan selection problem. Application of these algorithms ensures considerable computational simplicity in yielding the feasible process plans of the parts.     

Security‐aware scheduling model for computational grid

Grid applications with stringent security requirements introduce challenging concerns because the schedule devised by nonsecurity‐aware scheduling algorithms may suffer in scheduling security constraints tasks. To make security‐aware scheduling, estimation and quantification of security overhead is necessary. The proposed model quantifies security, in the form of security levels, on the basis of the negotiated cipher suite between task and the grid‐node and incorporates it into existing heuristics MinMin and MaxMin to make it security‐aware MinMin(SA) and MaxMin(SA). It also proposes SPMaxMin (Security Prioritized MinMin) and its comparison with three heuristics MinMin(SA), MaxMin(SA), and SPMinMin on heterogeneous grid/task environment. Extensive computer simulation results reveal that the performance of the various heuristics varies with the variation in computational and security heterogeneity. Its analysis over nine heterogeneous grid/task workload situations indicates that an algorithm that performs better for one workload degrades in another. It is conspicuous that for a particular workload one algorithm gives better makespan while another gives better response time. Finally, a security‐aware scheduling model is proposed, which adapts itself to the dynamic nature of the grid and picks the best suited algorithm among the four analyzed heuristics on the basis of job characteristics, grid characteristics, and desired performance metric. Copyright © 2011 John Wiley & Sons, Ltd.     

Deep assisted dense model based classification of invasive ductal breast histology images

Neural Computing and Applications - Invasive Ductal Carcinoma (IDC) is one of the types of breast cancer which is mostly diagnosed in the female. The diagnosis of IDC becomes a challenging task...