A tutorial on wavelets from an electrical engineering perspective.I. Discrete wavelet techniques

The objective of this paper is to present the subject of wavelets from a filter-theory perspective, which is quite familiar to electrical engineers. Such a presentation provides both physical and mathematical insights into the problem. It is shown that taking the discrete wavelet transform of a function is equivalent to filtering it by a bank of constant-Q filters, the non-overlapping bandwidths of which differ by an octave. The discrete wavelets are presented, and a recipe is provided for generating such entities. One of the goals of this tutorial is to illustrate how the wavelet decomposition is carried out, starting from the fundamentals, and how the scaling functions and wavelets are generated from the filter-theory perspective. Examples (including image compression) are presented to illustrate the class of problems for which the discrete wavelet techniques are ideally suited. It is interesting to note that it is not necessary to generate the wavelets or the scaling functions in order to implement the discrete wavelet transform. Finally, it is shown how wavelet techniques can be used to solve operator/matrix equations. It is shown that the “orthogonal-transform property” of the discrete wavelet techniques does not hold in numerical computations     

Ultrasonic nondestructive evaluation of fibre-reinforced composite materials — a review

A framework is presented for examining the effectiveness of limiting and modelling arguments used in the analysis of fluid flows. It consists of examining the consequences of the arguments on the flow problem as a whole and breaking down the limiting/modelling process into a sequence of steps and associated sequence of flow problems, termed here as ‘lidels’. The notion of validity of lidels is given. Several examples are given to explain the present approach. Invited general lecture at 3rd ACFM, Tokyo, September 1986     

Dislocations and flux pinning in YBa2Cu3O7

The dislocation structures of bulk textured and epitaxial thin film YBa₂Cu₃O₇ superconductors are examined. Correlations between increases in flux pinning and dislocation densities are noted. A model for flux pinning by individual dislocations is presented. This gives a treatment of strain induced effects and effects of normal state region interactions. It is shown that the values of pinning predicted are in line with experimental observations.     

Liquid phase epitaxial growth of pure and doped GaSb layers: morphological evolution and native defects

Undoped and Te-doped gallium antimonide (GaSb) layers have been grown on GaSb bulk substrates by the liquid phase epitaxial technique from Ga-rich and Sb-rich melts. The nucleation morphology of the grown layers has been studied as a function of growth temperature and substrate orientation. MOS structures have been fabricated on the epilayers to evaluate the native defect content in the grown layers from theC-V characteristics. Layers grown from antimony rich melts always exhibitp-type conductivity. In contrast, a type conversion fromp- ton- was observed in layers grown from gallium rich melts below 400 C. The electron mobility of undopedn-type layers grown from Ga-rich melts and tellurium doped layers grown from Sb- and Ga-rich solutions has been evaluated. Paper presented at the poster session of MRSI AGM VI. Kharagpur. 1995     

High-utility and diverse itemset mining

High-utility Itemset Mining (HUIM) finds patterns from a transaction database with their utility no less than a user-defined threshold. The utility of an itemset is defined as the sum of the utilities of its items. The utility notion enables a data analyst to associate a profit score with each item and thereof to a pattern. We extend the notion of high-utility with diversity to define a new pattern type called High-utility and Diverse pattern (HUD). The notion of diversity of a pattern captures the extent of the different categories covered by the selected items in the pattern. An application of diverse-pattern lies in the recommendation task where a system can recommend to a customer a set of items from a new class based on her previously bought items. Our notion of diversity is easy to compute and also captures the basic essence of a previously proposed diversity notion. The existing algorithm to compute frequent-diverse patterns is 2-phase, i.e., in the first phase, frequent patterns are computed, out of which diverse patterns are filtered out in the second phase. We, in this paper, give an integrated algorithm that efficiently computes high-utility and diverse patterns in a single phase. Our experimental study shows that our proposed algorithm is very efficient as compared to a 2-phase algorithm that extracts high-utility itemsets in the first phase and filters out the diverse itemsets in the second phase.

     

A Novel High Nitrogen Steel Powder Designed for Minimized Cr2N Precipitations

High nitrogen steels provide excellent mechanical properties and corrosion resistance but are prone to form precipitates which adversely affect the corrosion resistance and toughness. High nitrogen steel powders currently available in the market are not claimed to be precipitate free. It is critical to avoid these precipitates while retaining nitrogen in the dissolved form to realize the value of these powder alloys. However, retaining high level of dissolved nitrogen in steel powder during melt atomization process is very challenging. Instead, solid-state dissolution of nitrogen into the powder alloy followed by rapid cooling may provide a convenient approach to avoid precipitate formation compared to traditional melt processing. This study presents a solution treatment approach to achieve elevated dissolved nitrogen levels (~ 0.4 wt pct) in Fe–Mn–Cr powder alloy with negligible precipitation of nitrides. The influence of starting material, holding time, temperature and cooling rate on the resulting microstructure is presented. A fully austenite matrix with high dissolved nitrogen content resulted in powders with desired mechanical properties.

     

A model-based approximate λ-policy iteration approach to online evasive path planning and the video game Ms. Pac-Man

This paper presents a model-based approximate λ-policy iteration approach using temporal differences for optimizing paths online for a pursuit-evasion problem, where an agent must visit several target positions within a region of interest while simultaneously avoiding one or more actively pursuing adversaries. This method is relevant to applications, such as robotic path planning, mobile-sensor applications, and path exposure. The methodology described utilizes cell decomposition to construct a decision tree and implements a temporal difference-based approximate λ-policy iteration to combine online learning with prior knowledge through modeling to achieve the objectives of minimizing the risk of being caught by an adversary and maximizing a reward associated with visiting target locations. Online learning and frequent decision tree updates allow the algorithm to quickly adapt to unexpected movements by the adversaries or dynamic environments. The approach is illustrated through a modified version of the video game Ms. Pac-Man, which is shown to be a benchmark example of the pursuit-evasion problem. The results show that the approach presented in this paper outperforms several other methods as well as most human players.     

SVM based robust watermarking for enhanced medical image security

Medical images are more typical than any other ordinary images, since it stores patient’s information for diagnosis purpose. Such images need more security and confidentiality as total diagnosis depends on it. In telemedicine applications, transmission of medical image via open channel, demands strong security and copyright protection. In our proposed robust watermarking model, a double layer security is introduced to ensure the robustness of embedded data. The embedded data is scrambled using a unique key and then a transform domain based hybrid watermarking technique is used to embed the scrambled data into the transform coefficients of the host image. The data embedding in medical images involves more attention, so that the diagnosis part must not be affected by any modification. Therefore, Support Vector Machine (SVM) is used as a classifier, which classify a medical image into two regions i.e. Non Region of Interest (NROI) and Region of Interest (ROI) to embed watermark data into the NROI part of the medical image, using the proposed embedding algorithm. The objective of the proposed model is to avoid any quality degradation to the medical image. The simulation is performed to measure the Peak Signal to Noise Ratio (PSNR) for imperceptibility and Structural Similarity Index (SSIM) to test the robustness. The experimented result shows, robustness and imperceptibility with SSIM of more than 0.50 and PSNR of more than 35 dB for proposed watermarking model.

     

POEMS: end-to-end performance design of large parallel adaptive computational systems

The POEMS project is creating an environment for end-to-end performance modeling of complex parallel and distributed systems, spanning the domains of application software, runtime and operating system software, and hardware architecture. Toward this end, the POEMS framework supports composition of component models from these different domains into an end-to-end system model. This composition can be specified using a generalized graph model of a parallel system, together with interface specifications that carry information about component behaviors and evaluation methods. The POEMS Specification Language compiler will generate an end-to-end system model automatically from such a specification. The components of the target system may be modeled using different modeling paradigms and at various levels of detail. Therefore, evaluation of a POEMS end-to-end system model may require a variety of evaluation tools including specialized equation solvers, queuing network solvers, and discrete event simulators. A single application representation based on static and dynamic task graphs serves as a common workload representation for all these modeling approaches. Sophisticated parallelizing compiler techniques allow this representation to be generated automatically for a given parallel program. POEMS includes a library of predefined analytical and simulation component models of the different domains and a knowledge base that describes performance properties of widely used algorithms. The paper provides an overview of the POEMS methodology and illustrates several of its key components. The modeling capabilities are demonstrated by predicting the performance of alternative configurations of Sweep3D, a benchmark for evaluating wavefront application technologies and high-performance, parallel architectures.