Design and Implementation an Industrial Application System by using Internet of Things (IOT)

The objective of this thesis is to design and build a real-time system for an automated production line prototype that will consist of CNC machine and robotic arm including many other components like raspberry pi as a controller kit and LDR sensor. It has been programmed in a new approach based on the technology of the Internet Of Things concept, a cloud services from amazon web services (AWS) with the python as a common programming language in the smart system industry to implement this project. Keywords: 4 Industrial Technology, Industrial Internet of things (IIOT), Raspberry Pi

Field programmable gate array implementation of multiwavelet transform based orthogonal frequency division multiplexing system

This article offers an efficient design and implementation of a discrete multiwavelet critical-sampling transform based orthogonal frequency division multiplexing (DMWCST-OFDM) transceiver using field programmable gate array (FPGA) platform. The design uses 16-point discrete multiwavelet critical-sampling transform (DMWCST) and its inverse as main processing modules. All modules were designed using a part of Vivado® Design Suite version (2015.2), which is Xilinx system generator (XSG), and is compatible with MATLAB Simulink version R2013b. The FPGA implementation is carried out on a Zynq (XC7Z020-1CLG484) evaluation board with joint test action group (JTAG) hardware co-simulation. According to the results obtained from the implementation tools, the implemented system is efficient in terms of resource utilization and could support the real-time operations

Convolutional Deep Neural Network and Full Connectivity for Speech Enhancement

The speech signal that is received in real-time has background noise and reverberations, which have an impact on the quality of speech. Therefore, it is crucial to reduce or eliminate the noise and increase the intelligibility and quality of speech signals. In this study, a proposed method that is the most effective and challenging in a low SNR environment for three types of noise are removed, including washing machine, traffic noise, and electric fan noise, and clean speech is recovered. with three samples of noise which are mixed and added to the clean speech signal with a lower level of SNR value fixed at (-5, 0, 5) dBs, that noise source takes equal weights. The enhancement of the corrupted speech signal is done by applying a fully connected and convolutional neural network-based denoising algorithm and comparing their performance. The proposed network shows that a fully connected network (FCN) has less elapsed time than a convolutional network (CNN) while still achieving better performance, demonstrating its applicability for an embedded system. Also, the results obtained show that, overall, the CNN is better than the FCN regarding maximum coloration, PSNR, MES, and STOI