Reconfigurable Architecture for Noise Cancellation in Acoustic Environment Using Single Multiply Accumulate Adaline Filter

The creation of multiple applications with a higher level of complexity has been made possible by the usage of artificial neural networks (ANNs). In this research, an efficient flexible finite impulse response (FIR) filter structure called ADALINE (adaptive linear element) that makes use of a MAC (multiply accumulate) core is proposed. The least mean square (LMS) and recursive least square (RLS) algorithms are the most often used methods for maximizing filter coefficients. Despite outperforming the LMS, the RLS approach has not been favored for real-time applications due to its higher design arithmetic complexity. To achieve less computation, the fundamental filter has utilized an LMS-based tapping delay line filter, which is practically a workable option for an adaptive filtering algorithm. To discover the undiscovered system, the adjustable coefficient filters have been developed in the suggested work utilizing an optimal LMS approach. The 10-tap filter being considered here has been analyzed and synthesized utilizing field programmable gate array (FPGA) devices and programming in hardware description language. In terms of how well the resources were used, the placement and postrouting design performed well. If the implemented filter architecture is compared with the existing filter architecture, it reveals a 25% decrease in resources from the existing one and an increase in clock frequency of roughly 20%

An Efficient EEG Signal Analysis for Emotion Recognition Using FPGA

Electroencephalography (EEG), electromyography (EMG), galvanic skin response (GSR), and electrocardiogram (ECG) are among the techniques developed for collecting psychophysiological data from humans. This study presents a feature extraction technique for identifying emotions in EEG-based data from the human brain. Independent component analysis (ICA) was employed to eliminate artifacts from the raw brain signals before applying signal extraction to a convolutional neural network (CNN) for emotion identification. These features were then learned by the proposed CNN-LSTM (long short-term memory) algorithm, which includes a ResNet-152 classifier. The CNN-LSTM with ResNet-152 algorithm was used for the accurate detection and analysis of human emotional data. The SEED V dataset was employed for data collection in this study, and the implementation was carried out using an Altera DE2 FPGA development board, demonstrating improved performance in terms of FPGA speed and area optimization.</p

High Performance FPGA Implementation of Single MAC Adaptive Filter for Independent Component Analysis

Blind source separation (BSS) is the process of extracting sources from mixed data without or with limited awareness of the sources. This paper uses field programmable gate array (FPGA) to create an effective version of the Blind source separation algorithm (ICA) with a single Multiply Accumulate (MAC) adaptive filter and to optimize it. Recently, space research has paid a lot of attention to this technique. We address this problem in two sections. The first approach is ICA, which seeks a linear revolution that can enhance the mutual independence of the mixture to distinguish the source signals from mixed signals. The second is a powerful flexible finite impulse response (FIR) filter construction that makes use of a MAC core and is adaptable. The adjustable coefficient filters have been used in the proposed study to determine the undiscovered system utilizing an optimal least mean square (LMS) technique. The filter tap under consideration in this paper includes 32 taps, and hardware description language (HDL) and FPGA devices were used to carry out the analysis and synthesis of it. When compared to the described architecture, the executed filter architecture uses 80% fewer resources and increases clock frequency by nearly five times, and speed is increased up to 32%

EEG-based Emotion Recognition Using Hybrid CNN and LSTM Classification

Emotions are a mental state that is accompanied by a distinct physiologic rhythm, as well as physical, behavioral, and mental changes. In the latest days, physiological activity has been used to study emotional reactions. This study describes the EEG signals, the brain wave pattern, and emotion analysis all of these are interrelated and based on the consequences of human behavior and PTSD. Post-Traumatic Stress Disorder effects for long-term illness are associated with considerable suffering, impairment, and social/emotional impairment. PTSD is connected to subcortical responses to injury memories, thoughts, and emotions and alterations in brain circuitry. Predominantly EEG signals are the way of examining the electrical potential of the human feelings cum expression for every changing 22 phenomenon that the individual faces. When going through literature there are some lacunae while analyzing emotions. There exist some reliability issues and also masking of real emotional behavior by the victims. Keeping this research gap and hindrance faced by the previous researchers the present study aims to fulfill the requirements, the efforts can be made to overcome this problem, and the proposed automated CNN-LSTM with ResNet-152 algorithm. Compared with the existing techniques, the proposed techniques achieved a higher level of accuracy of 98% by applying the hybrid deep learning algorith

Finite element simulation and regression modeling of machining attributes on turning AISI 304 stainless steel

To-date, the usage of finite element analysis (FEA) in the area of machining operations has demonstrated to be efficient to investigate the machining processes. The simulated results have been used by tool makers and researchers to optimize the process parameters. As a 3D simulation normally would require more computational time, 2D simulations have been popular choices. In the present article, a Finite Element Model (FEM) using DEFORM 3D is presented, which was used to predict the cutting force, temperature at the insert edge, effective stress during turning of AISI 304 stainless steel. The simulated results were compared with the experimental results. The shear friction factor of 0.6 was found to be best, with strong agreement between the simulated and experimental values. As the cutting speed increased from 125 m/min to 200 m/min, a maximum value of 750 MPa stress as well as a temperature generation of 650 °C at the insert edge have been observed at rather higher feed rate and perhaps a mid level of depth of cut. Furthermore, the Response Surface Methodology (RSM) model is developed to predict the cutting force and temperature at the insert edge

Modelling and Simulation of Machining Attributes in dry Turning of Aircraft Materials Nimonic C263 using CBN

In the current scenario, machinability of the super alloys is of greater importance in an aircraft turbine engine and land-based turbine applications owing to its superior properties. However, the machinability of these alloys is found to be poor owing to its inherent properties. Hence, a predictive model has been developed based on DEFORM 3D to forecast the machining attributes such as cutting force and insert's cutting edge temperature in turning of Nimonic C263 super alloy. The dry turning trials on Nimonic C263 material were carried out based on L27 orthogonal array using CBN insert. Linear regression models were developed to predict the machining attributes. Further, multi response optimization was carried out based on desirability approach for optimizing the machining attributes. The validation test was carried out for optimal parameter values such as cutting speed: 117 m/min, feed rate: 0.055 mm/rev and depth of cut: 0.25 mm. The minimum cutting force of 304N and insert's cutting edge temperature of 468 °C were obtained at optimum level of parameters.The predicted values by FEA and linear regression model were compared with experimental results and found to be closer with minimum percentage error.The minimum percentage error obtained by FEA and linear regression model for the machining attributes (cutting force, temperature) as compared with experimental values were (0.32%, 0.23%) and (2.34%, 1.63%) respectively

Experimental analysis of process parameters in drilling nimonic C263 alloy under nano fluid mixed MQL environment

Nimonic C263 is a super alloy and it is difficult to cut. As this alloy possess high proportion of chromium, cobalt, and molybdenum, which fortify the material by solution hardening, which inhibits the dislocation movement, resulting in higher plastic deformation. In this research, an attempt has been made to model, analysis and investigate the machining characteristics such as thrust force, temperature at drill cutting edge, flank wear and surface finish during drilling of this alloy using silver nano fluid mixed Minimum Quantity Lubrication (MQL) environment. Residual stress at various combinations of process parameters was also observed and discussed. RSM based empirical models of the process parameters and optimization of multi response was developed. Thrust force, Temperature at drill cutting edge, surface roughness and tool wear affected by feed rate (percentage of contribution-60%), spindle speed (percentage of contribution-88.63%), spindle speed (percentage of contribution-71.42%) and feed rate (percentage of contribution-67.76%) respectively followed by other parameters