A new load distribution strategy for linear network with communication delays

In this paper, we propose a new load distribution strategy called ‘send-and-receive’ for scheduling divisible loads, in a linear network of processors with communication delay. This strategy is designed to optimally utilize the network resources and thereby minimizes the processing time of entire processing load. A closed-form expression for optimal size of load fractions and processing time are derived when the processing load originates at processor located in boundary and interior of the network. A condition on processor and link speed is also derived to ensure that the processors are continuously engaged in load distributions. This paper also presents a parallel implementation of ‘digital watermarking problem’ on a personal computer-based Pentium Linear Network (PLN) topology. Experiments are carried out to study the performance of the proposed strategy and results are compared with other strategies found in literature.     

A Direct Link Minimal Resource Allocation Network for Adaptive Noise Cancellation

This paper presents a modification to the Minimal Resource Allocation Network (MRAN) of Yingwei et al. by introducing direct links from inputs to output and investigates its performance for noise cancellation problems. MRAN has the same structure as a Radial Basis Function network but uses a sequential learning algorithm that adds and prunes hidden neurons as input data is received sequentially so as to produce a parsimonious network. Earlier work by Sun Yonghong et al. has demonstrated the capability of MRAN to produce a compact network with excellent noise reduction properties. In this paper the capability of the direct link Minimal Resource Allocation Network (DMRAN) is evaluated by comparing it with MRAN on several nonlinear adaptive noise cancellation problems. The direct link MRAN uses the same learning algorithm as MRAN but with the introduction of direct links we are able to realise even smaller networks than MRAN with better noise reduction properties.     

Parallel implementation of backpropagation neural networks on aheterogeneous array of transputers

This paper analyzes parallel implementation of the backpropagation training algorithm on a heterogeneous transputer network (i.e., transputers of different speed and memory) connected in a pipelined ring topology. Training-set parallelism is employed as the parallelizing paradigm for the backpropagation algorithm. It is shown through analysis that finding the optimal allocation of the training patterns amongst the processors to minimize the time for a training epoch is a mixed integer programming problem. Using mixed integer programming optimal pattern allocations for heterogeneous processor networks having a mixture of T805-20 (20 MHz) and T805-25 (25 MHz) transputers are theoretically found for two benchmark problems. The time for an epoch corresponding to the optimal pattern allocations is then obtained experimentally for the benchmark problems from the T805-20, TS805-25 heterogeneous networks. A Monte Carlo simulation study is carried out to statistically verify the optimality of the epoch time obtained from the mixed integer programming based allocations. In this study pattern allocations are randomly generated and the corresponding time for an epoch is experimentally obtained from the heterogeneous network. The mean and standard deviation for the epoch times from the random allocations are then compared with the optimal epoch time. The results show the optimal epoch time to be always lower than the mean epoch times by more than three standard deviations (3sigma) for all the sample sizes used in the study thus giving validity to the theoretical analysis.     

An alternate and effective approach to Hilbert transform in geophysical applications

The Hilbert transform defined via the Hartley transform in contrast with the well-known Fourier transform is mathematically illustrated with a couple of geophysical applications. Although, the 1-D Fourier and Hartley transforms are identical in amplitude with a phase difference of 45°, the Hilbert transform effectively differs when defined as a function of the Hartley transform in certain geophysical applications. It may be noted here that the Hilbert transform defined through Fourier and Hartley transforms while possessing the same magnitude differs in phase by 270°. It is derived and shown mathematically that the evaluation of depth of subsurface targets is directly equal to the abscissa of the point of intersection of the gravity (magnetic) field and the Hartley–Hilbert transform; however, it is not the case with the Fourier–Hilbert transform. The practical applications are illustrated with the interpretation of gravity anomaly due to an inclined sheet-like structure across the Mobrun ore body, Noranda, Quebec, Canada, and the vertical magnetic anomaly due to a cylindrical structure over a narrow band of quartzite magnetite, Karimnagar, India. The entire process can be automated.     

Nonlinear magnetic storage channel equalization using minimalresource allocation network (MRAN)

This letter presents the application of the recently developed minimal radial basis function neural network called minimal resource allocation network (MRAN) for equalization in highly nonlinear magnetic data storage channels. Using a realistic magnetic channel model, MRAN equalizer's performance is compared with the nonlinear neural equalizer of Nair and Moon (1997), referred to as maximum signal-to-distortion ratio (MSDR) equalizer. MSDR equalizer uses a specially designed neural architecture where all the parameters are determined theoretically. Simulation results indicate that MRAN equalizer has better performance than that of MSDR equalizer in terms of higher signal-to-distortion ratios.     

In vitro corrosion evaluation of nitrogen ion implanted titanium in simulated body fluid

Surface modification of commercially pure (CP) titanium was attempted by nitrogen ion implantation to investigate corrosion resistance in simulated body fluid. Nitrogen ion was implanted at 70 keV energy for different doses ranging from 5 × 10¹⁵ to 2.5 × 10¹⁷ ions/cm². In Vitro Open Circuit Potential (OCP-time measurements and cyclic polarization studies were carried out to evaluate the corrosion resistance of the implanted specimens with reference to the unimplanted one. Specimens implanted at 4 × 10¹⁶ and 7 × 10¹⁶ ions/cm² showed optimum corrosion resistance, and implantation beyond this dose deteriorated the corrosion resistance. Gracing Incidence X-ray diffraction (GIXD) was employed on implanted specimens to understand the phases formed with increasing doses. The results of the present investigation indicated that nitrogen ion implantation can be used as a viable method for improving corrosion resistance of titanium. Nature of the surface and reason for the variation and improvement in corrosion resistance are discussed in detail.     

Fast computation of the discrete Walsh and Hadamard transforms

The discrete Walsh and Hadamard transforms are often used in image processing tasks such as image coding, pattern recognition, and sequency filtering. A new discrete Walsh transform (DWT) algorithm is derived in which a modified form of the DWT relation is decomposed into smaller-sized transforms using vectorized quantities. A new sequency-ordered discrete Hadamard transform (DHAT) algorithm is also presented. The proposed approach results in more regular algorithms requiring no independent data swapping and fewer array-index updating and bit-reversal operations. An analysis of the computational complexity and the execution time performance are provided. The results are compared with those of the existing algorithms     

Effect of microarc oxidised layer thickness on plain fatigue and fretting fatigue behaviour of Al–Mg–Si alloy

The objective of this work was to investigate the performance of microarc oxide coatings of two different thicknesses (40 and 100 μm) on Al–Mg–Si alloy samples under plain fatigue and fretting fatigue loadings. Tensile residual stress present in the substrate of 40 μm thick coated samples induced early crack initiation in the substrate and so their plain fatigue lives were shorter than those of untreated specimens. Presence of more pores and tensile surface residual stress in 100 μm thick coated samples caused early crack initiation at the surface leading to their inferior plain fatigue lives compared with 40 μm thick coated samples. While the differences between the lives of coated and uncoated specimens were significant under plain fatigue loading, this was not the case under fretting fatigue loading. This may be attributed to relatively higher surface hardness of coated specimens. The performance of 40 μm thick coated samples was better than that of 100 μm thick coated specimens under both plain fatigue and fretting fatigue loadings.     

Risk-sensitive loss functions for sparse multi-category classification problems

In this paper, we propose two risk-sensitive loss functions to solve the multi-category classification problems where the number of training samples is small and/or there is a high imbalance in the number of samples per class. Such problems are common in the bio-informatics/medical diagnosis areas. The most commonly used loss functions in the literature do not perform well in these problems as they minimize only the approximation error and neglect the estimation error due to imbalance in the training set. The proposed risk-sensitive loss functions minimize both the approximation and estimation error. We present an error analysis for the risk-sensitive loss functions along with other well known loss functions. Using a neural architecture, classifiers incorporating these risk-sensitive loss functions have been developed and their performance evaluated for two real world multi-class classification problems, viz., a satellite image classification problem and a micro-array gene expression based cancer classification problem. To study the effectiveness of the proposed loss functions, we have deliberately imbalanced the training samples in the satellite image problem and compared the performance of our neural classifiers with those developed using other well-known loss functions. The results indicate the superior performance of the neural classifier using the proposed loss functions both in terms of the overall and per class classification accuracy. Performance comparisons have also been carried out on a number of benchmark problems where the data is normal i.e., not sparse or imbalanced. Results indicate similar or better performance of the proposed loss functions compared to the well-known loss functions.     

Prediction of an iron oxide concentration in the induction smelting process

Induction smelting process, ISP, is a direct smelting process in which self-fluxed composite pellets (made of iron ore, brown coal and lime) are melted in a single phase induction furnace in conjunction with top blown oxygen. A mass transfer model to predict the concentration of iron oxide, has been developed for this process. Transient mass balance equation with complex boundary conditions has been worked out by adopting a relatively new approach known as the cell model approach. These equations have been solved numerically using finite difference technique to obtain the iron oxide concentration profile in the induction smelting process. Numerical and experimental results obtained for the iron oxide concentration show a reasonable agreement with each other.