On Ultra-Fine Leak Detection of Hermetic Wafer Level Packages

Theoretical and practical ranges of leak rates measurable by the helium mass spectrometer are characterized. The effect of noise due to: 1) background helium present in the spectrometer and 2) desorption of helium that attaches itself to the specimen surface during bombing is quantified experimentally. The results guide a framework to extract the true leak rate from the measured leak rate profile. An optical interferometry based hermeticity measurement technique for ultra-fine leaks is proposed. The setup to implement the technique is described and a preliminary experimental result is reported.     

Modeling of tool wear in drilling by statistical analysis and artificial neural network

The useful life of a cutting tool and its operating conditions largely control the economics of the machining operations. Hence, it is imperative that the condition of the cutting tool, particularly some indication as to when it requires changing, to be monitored. The drilling operation is frequently used as a preliminary step for many operations like boring, reaming and tapping, however, the operation itself is complex and demanding.

Back propagation neural networks were used for detection of drill wear. The neural network consisted of three layers input, hidden and output. Drill size, feed, spindle speed, torque, machining time and thrust force are given as inputs to the ANN and the flank wear was estimated. Drilling experiments with 8 mm drill size were performed by changing the cutting speed and feed at two different levels. The number of neurons in the hidden layer were selected from 1, 2, 3, …, 20. The learning rate was selected as 0.01 and no smoothing factor was used. The estimated values of tool wear were obtained by statistical analysis and by various neural network structures. Comparative analysis has been done between statistical analysis, neural network structures and the actual values of tool wear obtained by experimentation.     

Approximate variational inference based on a finite sample of Gaussian latent variables

Variational methods are employed in situations where exact Bayesian inference becomes intractable due to the difficulty in performing certain integrals. Typically, variational methods postulate a tractable posterior and formulate a lower bound on the desired integral to be approximated, e.g. marginal likelihood. The lower bound is then optimised with respect to its free parameters, the so-called variational parameters. However, this is not always possible as for certain integrals it is very challenging (or tedious) to come up with a suitable lower bound. Here, we propose a simple scheme that overcomes some of the awkward cases where the usual variational treatment becomes difficult. The scheme relies on a rewriting of the lower bound on the model log-likelihood. We demonstrate the proposed scheme on a number of synthetic and real examples, as well as on a real geophysical model for which the standard variational approaches are inapplicable.     

Outage Performance of Uplink (UL) NOMA Network

Wireless Personal Communications - This paper proposes and analyses the power allocation coefficient normalization for successive interference cancellation in power-domain non-orthogonal multiple...     

A Comparative Investigation of Optical and Structural Properties of Cu-Doped CdO-Derived Nanostructures

In this report, we studied various structural and optical properties of pure and copper-doped cadmium oxide (CdO) thin films. Nanostructured Cu-doped CdO films were deposited using sol–gel spin-coating technique. The structural and morphological changes have been observed by X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), and atomic force microscopy (AFM) studies. The optical and electrical properties of the pure and Cu-doped CdO thin films were studied by UV–vis spectroscopy and four-point probe method, respectively. The XRD peaks show the formation of nanocrystalline CdO with cubic face-centered crystal structure. The band gaps of the as deposited films were found in the range of 2.32–2.73 eV, while after doping, it decreases due to structural deformation. The electrical resitivity was found to decrease approximately ～10 in Cu-doped CdO thin films.     

Uncountable automatic classes and learning

In this paper we consider uncountable classes recognizable by ω-automata and investigate suitable learning paradigms for them. In particular, the counterparts of explanatory, vacillatory and behaviourally correct learning are introduced for this setting. Here the learner reads in parallel the data of a text for a language L from the class plus an ω-index α and outputs a sequence of ω-automata such that all but finitely many of these ω-automata accept the index α if and only if α is an index for L.It is shown that any class is behaviourally correct learnable if and only if it satisfies Angluin’s tell-tale condition. For explanatory learning, such a result needs that a suitable indexing of the class is chosen. On the one hand, every class satisfying Angluin’s tell-tale condition is vacillatorily learnable in every indexing; on the other hand, there is a fixed class such that the level of the class in the hierarchy of vacillatory learning depends on the indexing of the class chosen.We also consider a notion of blind learning. On the one hand, a class is blind explanatorily (vacillatorily) learnable if and only if it satisfies Angluin’s tell-tale condition and is countable; on the other hand, for behaviourally correct learning, there is no difference between the blind and non-blind version.This work establishes a bridge between the theory of ω-automata and inductive inference (learning theory).     

Identification of Pd3+ ions in zeolite L by temperature programmed reduction and electron paramagnetic resonance spectroscopy

Pd supported on KL zeolite has been studied by temperature programmed reduction (TPR) and electron paramagnetic resonance (EPR) spectroscopy. It has been found that upon calcination in pure oxygen Pd³⁺ and Pd²⁺ ions are formed; the Pd³⁺ ions are identified by their EPR signal. As Pd³⁺ ions in the cancrinite cages are difficult to reduce, the H₂ consumption in conventional TPR is below the stoichiometric amount.     

A robust technique for well-log data inversion

Inverse problems associated with many geophysical measurements are often ill-conditioned, nonunique, and multimodal. Consequently, the gradient-type optimization methods to obtain model parameters become ineffective since the accuracy and convergence of these methods depend highly on initial position and search direction in the parameter space. Evolutionary algorithms which employ direct search global optimization technique are well suited for such problems. In this paper, we apply differential evolution algorithm to invert for the Earth formation properties from measured phase and attenuation by an electromagnetic propagation-type sensor and compare the results with those obtained by conventional method. Results indicate that differential evolution algorithm provides robust results although computationally the method is less efficient.