Decompiler For Pseudo Code Generation

Decompiling is an area of interest for researchers in the field of software reverse engineering. When the source code from a high-level programming language is compiled, it loses a great deal of information, including code structure, syntax, and punctuation.The purpose of this research is to develop an algorithm that can efficiently decompile assembly language into pseudo C code. There are tools available that claim to extract high-level code from an executable file, but the results of these tools tend to be inaccurate and unreadable.Our proposed algorithm can decompile assembly code to recover many basic high-level programming structures, including if/else, loops, switches, and math instructions. The approach adopted here is different from that of existing tools. Our algorithm performs three passes through the assembly code, and includes a virtual execution of each assembly instruction. We also construct a dependency graph and incidence list to aid in the decompilation

Finite Element Electromagnetic (EM) Analyses of Induction Heating of Thermoplastic Composites

Thermoplastic composites are in great demand for current and future manufacturing of aircraft and automotive industries. Induction heating and welding of thermoplastic composite laminates is of great significance. The non-contact method of heating and welding is being adopted in aircraft parts, engine parts, and turbine parts manufacturing among many other things. This thesis performs simulations and analyses of Electromagnetic (EM) induction heating of thermoplastic composites materials. The induction heating and welding of thermoplastic composites in the presence of a susceptor alloy (called Monel) consisting of 67% nickel and 27% copper is studied using a Finite Element Analysis (FEA) software. A primary current carrying coil was excited using 500A of current at 292 kHz frequency, which exposed the Monel susceptor underneath it. The EM fields created by the primary coil caused induced current in the Monel mesh, which caused losses. Temperature rise in the material is synonymous to the losses in the material. The I2R losses in the Monel material was used as the basis to calculate the temperature rise in the material. Simulation results clearly show the heating patterns on the Monel mesh, less in the center and high on the edges. Location-based temperature increases due to I2R losses are calculated which show significant heating and welding potentials. The result obtained from the simulation was validated using an experiment. Measured temperature increase showed the reading of 40 and 43.9 degrees respectively (thermocouple and IR camera). These numbers compare favorably with the simulated temperature increase of 38.97 degrees C. Also, the induction heating of Carbon fiber reinforced composites (CFRC) were simulated and studied. The results obtained from the simulation explain that fiber orientation and the presence of resin are two critical parameters that affect the output e.g. the solid loss. Due to the challenges in the high aspect ratio of the models, i.e. very small fiber diameters and many fibers within a very small dimension only smaller sized models were simulated. Furthermore, instead of a circular cross-section a polyhedron cross-section for the fiber model was considered to successfully complete the simulations. It was found that simulation models containing fibers oriented in 0 and 90 orientation yielded higher solid loss than fibers oriented in the same direction. It was observed that for fibers with resin present in between them yielded far greater solid loss compared to no-resin cases, especially for very small separation distances between fibers. Especially, for the 0, 90 orientation of fibers and in the presence of resins solid loss was nearly 200 times that for fibers with 0,0 orientation and that were at short distance from one another

Comparing flexural strength of acrylic processed by three different techniques

Statement of problem: Acrylic resin removable dentures are susceptible to fracture after periods of clinical use. There are many predisposing clinical factors for these denture fractures. Specifically, acrylic resin fractures due to poor flexural strength has historical been a problem with complete denture.;Objective: The purpose of this in vitro study was to measure and compare the flexural strength of denture base acrylic resin processed by three different techniques: Conventional Pressure-Pack (compression molded) method, Injection molded (SR-Ivocap), and Computer Aided Design - Computer Assisted Manufacturing (CAD/CAM) (AvaDent(TM) Digital Dentures).;Methods: A total of 45 specimens (64 mm X 10 mm X varying thicknesses of 2 mm, 3 mm, and 5 mm) were fabricated, 15 for each of the three materials being tested. Specimens were tested according to the American Society for Testing and Materials (ASTM) standard D790-03 for flexural strength of reinforced plastics. The specimens were loaded until fracture or 15 mm of displacement on a three-point bending test machine (InstronRTM Model 5565 Universal Testing Machine).;Results: Data from this flexural strength study indicates that SR- Ivocap Injection Mold technique showed a higher flexural strength than CAD/CAM Avadent and Pressure-pack. When a 3 mm specimen was considered, statistically significant difference was apparent between Injection mold and the other two techniques (CAD/CAM and Pressure-Pack).;Conclusions: The flexural strength test is significantly useful in comparing denture base materials in which stress of this type is applied to the denture during mastication. The results from flexural test indicated that the differences observed can be attributed to the polymer constituents and to the method of polymerization. SR-Ivocap Injection Molding may prove to be more advantageous than CAD/CAM AvaDent(TM) and Pressure-Pack

Experimental investigation of the effect of sonication on the precipitation of grieseofluvin by impinging jets

Almost 80% of drugs on the market are manufactured as solid dosage forms, such as tablets. Drug bioavailability increases as the particle size decreases and the surface area per unit volume of drug increases. Therefore, there is a keen interest by the pharmaceutical industry to develop techniques that can be used to manufacture particles of active pharmaceutical ingredients (API) in the nano/micro particle range. Impinging jets is one of the most promising techniques to do so. In this work, a submerged impinging jet system coupled with an ultrasonic probe (sonicator) was used to precipitate Griseofulvin, a common, poorly water-soluble antifungal drug. The drug was initially dissolved in acetone and then precipitated using water as the antisolvent. Experiments were carried out for different values of the sonication power, impinging jet velocity, and reactor volume. Their effect on the size and morphology of the precipitated crystals was quantified. The crystals were analyzed using a laser diffraction method (for particle size distribution), electron microscopy (for crystal morphology), and X-ray diffraction (for crystallinity). The results obtained here indicate that increasing the sonication power, and, to a much more limited extent, the impinging jet velocity decreases the crystal size, but that eventually an asymptotic value of the mean particle size is achieved. The reactor volume does not appear to play a major role, at least in the system examined here. The results obtained in this work could have important implications for the manufacturing of drug particles for solid dosage form use

A Deep Learning Approach to Structured Signal Recovery

In this paper, we develop a new framework for sensing and recovering structured signals. In contrast to compressive sensing (CS) systems that employ linear measurements, sparse representations, and computationally complex convex/greedy algorithms, we introduce a deep learning framework that supports both linear and mildly nonlinear measurements, that learns a structured representation from training data, and that efficiently computes a signal estimate. In particular, we apply a stacked denoising autoencoder (SDA), as an unsupervised feature learner. SDA enables us to capture statistical dependencies between the different elements of certain signals and improve signal recovery performance as compared to the CS approach