Determination of the SSM Processing Window

Identification of critical temperatures is paramount for semisolid processing. Application of the principles of differential calculus to identify these temperatures on semisolid transformation curves allows the semisolid metal (SSM) processing window to be determined. This paper synthesizes and organizes a methodology that can be used to this end, namely the differentiation method (DM). Examples are given of the application of the method to 356, 355, and 319 aluminum alloys, which are commonly used in SSM processing, and the results are compared with those of numerical simulations performed with Thermo-Calc® (under the Scheil condition). The DM is applied to experimental differential scanning calorimetry (DSC) heat-flow data for cooling and heating cycles under different kinetic conditions (5, 10, 15, 20, and 25 °C/min). The findings indicate that the DM is an efficient tool for identifying critical points such as the solidus, liquidus, and knee as well as tertiary transformations. The results obtained using the method agree well with those obtained using traditional techniques. The method is operator-independent as it uses well-defined mathematical/graphical criteria to identify critical points. Furthermore, the DM identifies an SSM processing window defined in terms of a higher and lower temperature for rheocasting or thixoforming operations (TSSML and TSSMH) between which the sensitivity is less than 0.03 °C-1 and, consequently, the process is highly controllable. This DM has already been published in a partial and dispersed way in different works in the past and the aim here is to present it in a more cohesive and didactic way, synthesizing the presented data and comparing them.

RC-INSANE – an interactive environment for nonlinear analysis of reinforced concrete structures

The development of numerical and computational resources that can present reliable models for the analysis of reinforced concrete structures is mainly driven by its widespread use. Considering that reinforced concrete is a composite material and bond is the load-carrying mechanism, these models must consider that the structural behavior is affected by the interaction between concrete and reinforcement. On this basis, the Finite Element Method (FEM) is a well-established method able to provide consistent results for reinforced concrete modeling through reinforcement and bond models. Nevertheless, to simplify the analysis, the hypothesis of strain compatibility between concrete and reinforcement is usually considered. Under certain loads and specific geometries, this hypothesis is not valid, and the bond-slip phenomenon must be considered to fully characterize the structural behavior. To fulfill this need, this paper presents a graphic interface that enables the modeling of reinforced concrete structures through discrete and embedded reinforcement models, with the possibility to include the bond-slip phenomenon based on several constitutive laws proposed in the literature. The computational implementations were held in the INSANE (INteractive Structural ANalysis Environment), an open-source software based on the Object-Oriented Programming paradigm, which enclosures several constitutive models for nonlinear concrete modeling and different numerical techniques, and a post-processing application able to represent the results by way of a friendly-user graphic interface.

A Hybrid fuzzy based cross neighbor filtering (HF-CNF) for image enhancement of fine and coarse powder scanned electron microscopy (SEM) images

Image enhancement is one of the most critical stages towards any image processing application. The outcome of image enhancement determines the accuracy and precise nature of the overall output from the image processing under interest. This research paper has shown specific interests towards enhancement of Scanned Electron Microscopic (SEM) images which are primarily concerned with projection of fine details exist in internal details of surfaces, metals, fine powders, fibers etc. These fine details play a dominant role in detection of minute cracks, artifacts, progressing faults, texture of powders, their coarseness or fineness, internal details of fibers in forensics. However, due to the image capturing process which is through conventional camera-based models, noise tends to be a major source in degrading or blurring the underlying vital information. A cross neighbor fuzzy filter is a hybrid combination called Hybrid Fuzzy Based Cross Neighbor Filtering (HF-CNF) which is proposed in this research paper in order to minimize impulse and random noise to a great extent also to fine tune the further processing stages. The proposed method has been subjected to extensive analysis by comparison with state of art and recent benchmark methods and superior performance justified in terms of several validation metrics.

Analysis of The Influence of Product Quality, Price, Location, and Promotion on Consumer Purchase Interest in Convection (Case Study on Dira Ashesh)

The high growth of the textile industry in Indonesia and advances in the textile sector have led to the emergence of many small and medium industries in the textile sector and textile production. The large number of garment industries that have sprung up has led to increasingly fierce competition in the garment industry. The independent variables in this study are product quality (X1), Price (X2), Location (X3), and Promotion (X4). The theory used in this research is marketing management theory. The dependent variable in this study is purchase intention (Y1). This study was conducted to determine the effect of four independent variables on convection buying interest. The object of this research is the garment industry between Surabaya and Gresik. The data collection method was carried out by distributing questionnaires to 384 respondents. Research respondents came from a sample that has been determined using the purposive sampling technique. The data in this study were obtained from questionnaires distributed online, to be analyzed using the SPSS data processing application with multiple linear regression analysis equation models. The results showed that all independent variables had an effect on the dependent variable. Garment vendor Dira Ashesh is expected to improve product quality, price, location, and promotion to increase consumer buying interest.

Optimal Stochastic Computing Randomization

Stochastic computing (SC) is a probabilistic-based processing methodology that has emerged as an energy-efficient solution for implementing image processing and deep learning in hardware. The core of these systems relies on the selection of appropriate Random Number Generators (RNGs) to guarantee an acceptable accuracy. In this work, we demonstrate that classical Linear Feedback Shift Registers (LFSR) can be efficiently used for correlation-sensitive circuits if an appropriate seed selection is followed. For this purpose, we implement some basic SC operations along with a real image processing application, an edge detection circuit. Compared with the literature, the results show that the use of a single LFSR architecture with an appropriate seeding has the best accuracy. Compared to the second best method (Sobol) for 8-bit precision, our work performs 7.3 times better for the quadratic function; a 1.5 improvement factor is observed for the scaled addition; a 1.1 improvement for the multiplication; and a 1.3 factor for edge detection. Finally, we supply the polynomials and seeds that must be employed for different use cases, allowing the SC circuit designer to have a solid base for generating reliable bit-streams.

Gate-Level Static Approximate Adders: A Comparative Analysis

Approximate or inaccurate addition is found to be viable for practical applications which have an inherent error tolerance. Approximate addition is realized using an approximate adder, and many approximate adder designs have been put forward in the literature targeting an acceptable trade-off between quality of results and savings in design metrics compared to the accurate adder. Approximate adders can be classified into three categories as: (a) suitable for FPGA implementation, (b) suitable for ASIC type implementation, and (c) suitable for FPGA and ASIC type implementations. Among these, approximate adders, which are suitable for FPGA and ASIC type implementations are particularly interesting given their versatility and they are typically designed at the gate level. Depending on the way approximation is built into an approximate adder, approximate adders can be classified into two kinds as static approximate adders and dynamic approximate adders. This paper compares and analyzes static approximate adders which are suitable for both FPGA and ASIC type implementations. We consider many static approximate adders and evaluate their performance for a digital image processing application using standard figures of merit such as peak signal to noise ratio and structural similarity index metric. We provide the error metrics of approximate adders, and the design metrics of accurate and approximate adders corresponding to FPGA and ASIC type implementations. For the FPGA implementation, we considered a Xilinx Artix-7 FPGA, and for an ASIC type implementation, we considered a 32/28 nm CMOS standard digital cell library. While the inferences from this work could serve as a useful reference to determine an optimum static approximate adder for a practical application, in particular, we found approximate adders HOAANED, HERLOA and M-HERLOA to be preferable.

A Novel Processing Model for P300 Brainwaves Detection

Event-related potential (ERP) is a distinctive pattern of brain activity that is elicited by the brain’s sensitivity and cognition whereas P300 evoked potential changes in cognitive functions. Since P300 wave is a cognitive response across multiple brain channels correlated between the measured electroencephalogram (EEG) and deviant stimulus in a specific period, it requires a suitable signal processing application for interpretation. Moreover, multiple steps of data processing under neuroscience criteria make the P300 reflection difficult to analyze by common methods. Therefore, this study proposes the processing model for brainwave applications based on P300 peak signal detection in multiple brain channels. This study applies 64 channels ERP datasets throughout bandpass filter in fast Fourier transform (FFT) with the specific ranges of signal processing while ERP averaging is applied as a feature extraction method. Furthermore, the experimental metadata is applied with the filtered P300 peak signals in channel classification via a machine learning method, the Decision Tree. The experimental results indicate the accurate mental reflection of P300 evoked potential in different brain channels with high classification accuracy relying on the contrast condition throughout the original data source averaged across the individual electrodes.

Modern Concurrency Techniques: An Exploration

<p>In this thesis, we investigate some of the options programmers have when writing a concurrent program. We explore the use of manually created threads, thread-pools, actors, and Software Transactional Memory. We use these techniques to implement case studies of various kinds: a video game, a physical simulation, an image-processing application, and a concurrent data structure. Through-out these case studies, we notice a common thread: concurrency, applied correctly, can improve the performance of a program—but the correct application may not be readily apparent. Concurrency is an important tool in the toolbox of the modern programmer, especially with the rise of multi-core architectures and the increasing prevalence of distributed systems. And like any tool, it is important to understand how and when to use it.</p>