Application of desktop manufacturing system (SLA) for the manufacturing of a centrifugal pump impeller using CAD (I-DEAS)

Desktop Manufacturing (DTM) systems which combine personal computer, laser and other technologies are being used to sculpt objects from computer generated models created on computer aided design (CAD) workstations. As a member of DTM systems, StereoLithography Apparatus (SLA) transforms 3-dimensional designs into a 3-D output. This can substantially reduce the time required to produce a prototype through the process of photopolymerization. The process involves the transfer of a liquid plastic monomer into a solid polymer by exposing it to ultraviolet light. Although the process looks productive, inefficiencies can occur, if incorrect parameters are selected before its application for a particular prototype fabrication. In understanding the correct requirements of the prototype being built, efficiency can be maximized by the use of desktop manufacturing systems

Key Solution to Vehicle Purchase Process Using Blockchain Technology

Much of everyday shopping such as groceries, clothes, household or vehicle involve human presence in the relevant department. Vehicle sale and purchase depends on dealer's reliability in terms of how precise or accurate the car fax report is. The emission report and vehicle’s history of repair helps client in making the right decision regarding the purchase. The process of vehicle purchase, payment, getting it insured and registered at DMV is a long and tedious work. We present a Blockchain technology based Smart Contractor solution, that will provide all this tedious work on behalf of a person. We present two step approach to the vehicle purchase process, converting the existing business flow of purchase process into Blockchain ledger and design a Smart Contractor

BRanching Artificial Neural Ensemble (BRANE) Algorithm for Supervised Learning

Various models exist to predict a numerical value in supervised learning problems. One of the challenges in predicting an outcome with high degree of precision involves dealing with numerical data points which can be represented using differently. To solve for such challenge and in order to predict the logerror value in Zillow’s competition on Kaggle, we have developed a new model, BRanching Artificial Neural Ensemble (BRANE). This ensemble network uses a number of multilayer perceptrons (MLP) to predict the outcome and combines the results using an additional MLP. This approach not only allowed us to use different datatypes as inputs, but also predicted better and converged faster than traditional MLP models

Multiclass ECG Signal Analysis Using Global Average-Based 2-D Convolutional Neural Network Modeling

Cardiovascular diseases accounted for approximately 836,546 deaths in the United States in 2018. Nearly 2,300 Americans die of cardiovascular disease each day, an average of one death every 38 seconds. To this end, research has been reported in the literature on Electrocardiogram (ECG) signal analysis to determine arrhythmia and other cardiac conditions. This work introduces a classifier that will detect abnormalities of the ECG signal with its analysis as a 2-D image fed to a Convolutional Neural Network (CNN) classifier.The proposed method classifies the ECG signal as normal or ST-change, V-change by transforming the single-lead ECG signal into images and then applying CNN classification. Images are taken from the European ST-T dataset on PhysioNet databank. Our method yields an accuracy of 99.26%