Department of Electrical and Electronics Engineering

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Author: search.filters.author.Yenuganti, SujanHas files: No

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    Semg signal acquisition: of hand movements for feature extraction and classification
    (IEEE, 2025-04) Yenuganti, Sujan
    This paper explores the classification of surface electromyography (sEMG) signals for hand movement recognition using time and frequency domain features used for feature extraction. Three hand movements were recorded from four healthy subjects for signal analysis achieving an SNR range of 14.81dB to 23.14dB. Two machine learning classifiers, medium neural network (MNN) and cubic support vector machine (CSVM), were evaluated to determine their effectiveness performance. MNN achieved the highest accuracy of 93.8%, demonstrating robust feature separation, while CSVM provided a simpler but slightly less precise result at 92.5%. The findings underscore the potential of MNN in classification of hand movements and aids on the development of advanced prosthetic control systems.
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    A study of machine learning algorithms for hand gesture classification of sEMG signals Available to Purchase
    (Emerald, 2025-04) Yenuganti, Sujan
    This paper presents a cost-effective signal acquisition circuitry (SAC) for capturing surface electromyography (sEMG) data to classify different hand movements using advanced machine learning algorithms. The SAC, comprising an instrumentation amplifier, a Sallen–Key band-pass filter and a noninverting amplifier, is designed and tested on a portable printed circuit board. The purpose of this paper is to perform feature extraction and data segmentation for effective analysis and processing of the recorded sEMG signals.
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    An integrated method for identifying liver tumors utilizing convolutional neural networks and residual networks
    (AIP, 2025-07) Yenuganti, Sujan
    The liver is located in the upper right quadrant of the belly. The liver performs essential tasks such as filtering blood, detoxifying chemicals, and metabolizing drugs and alcohol. Tumors develop as a result of an increase in cell proliferation. Metastatic liver cancers are the most common type. Hepatocellular carcinoma accounts for one million out of the total two million liver disorders that result in fatalities annually. The incidence and mortality rates of liver cancer are projected to increase by 55% by the year 2040. This would classify it as the third most prevalent cancer globally and one of the top five most severe cancers in 90 nations. A study conducted by the World Health Organization (WHO) was published in the Journal of Hepatology in October 2022. Medical imaging techniques can be used to identify the presence of these abnormalities, and a liver biopsy is performed to definitively establish the diagnosis. This paper presents a novel crossover technique that utilizes Convolutional Neural Networks (CNN) and Residual Networks (Resnet) to precisely identify liver cancers. This study presents a new approach for predicting lung cancer using a Convolutional Neural Network (CNN) that incorporates modification injection and deep learning-validated features. Picture analysis utilizes modern techniques such as deep learning and image processing. CNN-learned hierarchical features aid in the diagnosis of lung tumors by detecting and analyzing intricate patterns and textures. One of the model’s important features is the utilization of extensive image datasets to facilitate transfer learning on pre-trained models. The technique improves and eliminates noise from photographs of the skin. The skin ailment is classified using the Softmax Classifier after extracting visual features using a convolutional neural network (CNN). The device has the ability to rapidly categorize skin conditions with an accuracy rate over 95%.
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    Design and Implementation of Fuzzy Logic Controller for Boiler Temperature Control using LabView
    (IEEE, 2023-01) Yenuganti, Sujan
    This paperwork aims design, implementation of fuzzy logic controller for controlling of water in the tank using LABVIEW software. This system mainly contains the single tank, Resistance Temperature Detector sensor and myrio. The signal from the temperature sensor is transmitted to LABVIEW software via myrio interface, connected to the system. Fuzzy logic controller (FLC) algorithm is designed using knowledge-based inference engine, which can be used to replace the conventional controllers tuning parameters using analytical equations. The performing of fuzzy logic controller for controlling the temperature has been explored in detail. It provides a improved response, and quickly tracks the set point. FLC functions well with the system involved uncertainties, noise at sensory signal. This developed FLC could also be used to measure several variables like temperature at industrial applications.
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    Implementation of Wired and IoT Based Wireless Transmitters for a Pressure Sensor
    (IEEE, 2023-02) Yenuganti, Sujan
    In this paper, a wired and wireless transmitter for two different designs of hall effect-based pressure sensors are designed and experimentally tested. A wired transmitter is designed by converting the actual sensor output to a 1-5V range using a signal conditioning circuit, followed by a voltage-to-current (V-I) converter to convert the signal conditioning circuit output to a 4-20 mA current signal which can be transmitted to any remote indicator without any data loss. A wireless transmitter is also designed with the same signal conditioning circuit and a Wi-Fi module to transmit the pressure sensor data wirelessly to the IoT cloud server. The Blynk IoT console is used as a server to access the transmitted data through a PC/laptop connected to the Internet. The linearity as % deviation and % error of the V-I converter is calculated for wired transmission and the mismatch between transmitted and received data is also found for the wireless transmission for both sensor designs. The proposed transmitters are of low cost, and simple in design, and the pressure sensor data can be transmitted in real-time using both wired and wireless modes.
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    Identification of Fake Indian Currency Using Deep Learning Techniques
    (IEEE, 2023) Yenuganti, Sujan
    This article suggests employing deep learning methodologies to automatically identify counterfeit banknotes. Convolutional neural networks (CNNs) are employed to extract distinctive characteristics of Indian currency notes. These attributes are subsequently inputted into another CNN to determine if the money is authentic or counterfeit. Various techniques have been utilized to identify counterfeit objects; however, they often depend on machinery and equipment, which can be less effective and time-consuming. This research presents a hybrid strategy utilizing the Convolutional Neural Network (CNN) and Vgg16 model to accurately detect counterfeit cash. This system uses convolutional neural networks (CNN) and Vgg16 to identify counterfeit cash by analyzing its width, colors, and serial numbers. The proposed methodology is evaluated using a dataset comprising authentic and forged cash notes. This technology surpasses conventional detection methods in terms of accuracy and precision. The result will determine whether the Indian rupee note is genuine or counterfeit. The suggested model effectively identifies counterfeit Indian rupee notes by utilizing Convolutional Neural Network and Vgg16 algorithms, resulting in accuracies of 98.3% and 98.8% respectively. The integration of our proposed technology into current systems will bolster the security of banknotes and effectively safeguard against counterfeiting.
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    Design and Simulation of a Differential Resonant Pressure Sensor
    (IEEE, 2023) Yenuganti, Sujan
    Pressure is one of the critical physical attributes that need to be continuously monitored, accurately measured, and recorded in most of manufacturing industries. This paper proposed a novel depiction of a differential resonant-based pressure sensor with a circular diaphragm and boss structure for gauging pressure in the range of 0–10 bar. The applied pressure is converted into a differential frequency signal at the two beams having one edge rigidly fixed and the other one, connected to a central structure which is coupled to the boss structure of the diaphragm. Both the analytical and numerical modeling were performed on the sensor design. Optimum sensor dimensions were scaled during numerical simulation in such a way that the diaphragm bestows maximum deflection when pressure is applied to it. The differential arrangement of the beams also helps in maintaining their stability for any ambient temperature divergence. Analytical model results performed using MATLAB were also found to be in accordance with the numerical simulation done on COMSOL Multiphysics. Stainless steel was used as the material for the simulations and was also intended to be the material for the fabrication of the sensor. Using stainless steel as the fabricating material and the sensor’s self-packaging design gives it the ability to perform well even at high temperatures and also provides protection from general corrosive environments
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    Design and simulation of a resonance-based MEMS viscosity sensor
    (Springer, 2023-11) Yenuganti, Sujan
    The paper presents the design and simulation of a MEMS-based resonant viscosity sensor using a piezoelectric micro diaphragm. The sensor comprises a vibrating diaphragm as a resonating element with piezoelectric excitation and detection. As the viscosity of the liquid beneath the diaphragm changes, the resonant frequency also changes. A numerical model of a diaphragm is designed in the COMSOL Multiphysics FEM tool, and its resonance characteristics were studied with a fluid of different viscosities beneath it. To support the numerical simulation results, mesoscale experimentation was also performed using a stainless steel thin sheet as a diaphragm and also to verify the proof of concept of the proposed sensor. The major benefit of the proposed sensor is that it uses the resonance measurement principle and can be shown to offer good stable performance, resolution, reliability, and response time. The proposed sensor can also be showcased as a hand-held laboratory product for quick viscosity measurements
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    A Tri-axial Resonating Beam MEMS Accelerometer
    (Springer, 2024-07) Yenuganti, Sujan
    MEMS-based devices have helped in the miniaturization of various transducers, one such being the accelerometer. The current study presents the design and simulation of a MEMS tri-axial resonance-based accelerometer in a differential arrangement to measure acceleration up to 5 g. The final tri-axial accelerometer differential design is derived from five designs which consist of four proof masses, four resonating beams, two vertical and two horizontal hinges. The first three designs are non-differential designs and the next two designs give a differential output only for out-of-plane acceleration. Numerical simulations were carried out in COMSOL Multiphysics for all the designs and the dimensions were optimized to obtain maximum stress on the resonating beam for an applied acceleration. Eigenfrequency analysis was also carried out to estimate the change in resonance frequencies of all the resonating beams in each of the proposed models along with the final differential design. The sensitivities were found to be 33 Hz/g, 33 Hz/g, and 19 Hz/g for the final differential design in X, Y, and Z directions respectively. The differential arrangement will be able to compensate for any temperature variations and the resonance condition can be achieved by piezoelectric excitation and detection
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    Experimental studies on dynamic response of piezoelectric based hemispherical resonator gyroscope
    (Emerald, 2024-08) Rao, Venkatesh K. P.; Yenuganti, Sujan
    This work measures the performance characteristics of a hemispherical resonator gyroscope (HRG) and compares it with a numerical model. This work we explore the optical and piezoelectric measurement methods to determine the resonant frequency of HRG. These experimental results are compared with their numerically obtained values. To explore the performance characteristics, the effect of varying actuation voltages on the sense mode displacement and the piezoelectric sensor output was studied in the absence of input angular rate. The structure was then subjected to range of angular rate signals, at a constant actuation voltage and the corresponding sensor response was analysed.