The structural behavior of buildings is significantly influenced by their geometry and foundation configuration. This study presents a comparative analysis of regular and irregular multi-storey buildings with irregular (stripped) foundations using ETABS software. Irregularities in building geometry and foundation layout often lead to uneven load distribution, increased torsional effects, and differential settlement, which can compromise overall structural stability. In this research, both regular and irregular building models are designed and analyzed under static and dynamic loading conditions as per relevant IS codes. Parameters such as base shear, storey drift, displacement, and bending moments are evaluated to assess the structural performance of each model. The influence of stripped foundations—where continuous footing is partially interrupted—on load transfer and stability is also investigated. Simulation results reveal that irregular structures exhibit higher lateral displacements and stress concentrations compared to regular configurations, particularly under seismic loading. The findings emphasize the importance of foundation uniformity and geometric regularity in achieving safe and efficient structural designs. This study provides valuable insights for engineers in optimizing foundation layouts and improving seismic performance in irregular building systems.

Keywords : ETABS, Structural Analysis, Irregular Buildings, Stripped Foundation, Seismic Performance

Authors : A. Naga Sai

Title : Analysis of Regular and Irregular Buildings in ETABS with Irregular Foundations (Stripped Foundation)

Volume/Issue : 2022;4(3 (May - June))

Page No : 28 - 33

Early and accurate detection of bone fractures is essential for effective medical diagnosis and treatment planning. Traditional radiographic interpretation is often time-consuming and dependent on the expertise of radiologists, which can lead to human error and delayed diagnosis. This study proposes a DenseNet-based deep learning system for automated fracture detection using medical imaging data such as X-rays. DenseNet, or Densely Connected Convolutional Network, enhances feature propagation and mitigates the vanishing gradient problem by connecting each layer to every other layer in a feed-forward manner. The proposed model is trained and validated on a curated dataset of labeled bone images, where preprocessing techniques such as image normalization, augmentation, and noise reduction are applied to improve model robustness. Performance evaluation metrics, including accuracy, precision, recall, and F1-score, are used to assess the system’s diagnostic efficiency. Experimental results demonstrate that the DenseNet-based model outperforms conventional convolutional neural networks (CNNs) in both accuracy and convergence speed. The system provides a reliable and automated solution for assisting clinicians in fracture detection, reducing diagnostic time, and enhancing decision-making in emergency care.

Keywords : Fracture Detection, DenseNet, Deep Learning, Medical Imaging, X-ray Analysis

Authors : Satish Dekka

Title : A Densenet Model based System for Fracture Detection

Volume/Issue : 2022;4(3 (May - June))

Page No : 23 - 27

Voltage Controlled Ring Oscillators (VCROs) are essential components in high-speed communication systems, phase-locked loops (PLLs), and frequency synthesizers. However, one of the key challenges in VCRO design is the unwanted frequency boosting effect, which can degrade stability, increase phase noise, and reduce overall circuit performance. This study focuses on the design and simulation of a frequency boost reduction technique for VCROs using optimized circuit parameters and compensation methods. The proposed approach minimizes frequency variation by controlling the delay elements and tuning voltage sensitivity within the oscillator stages. Simulation is performed using CAD tools such as Cadence or Multisim to evaluate performance metrics including oscillation frequency, power consumption, and phase noise. The results show a significant improvement in frequency stability and linearity over the control voltage range, with a reduction in phase noise and jitter. The proposed VCRO design achieves a balanced trade-off between speed and power efficiency, making it suitable for low-power, high-performance integrated circuit applications. This work demonstrates the potential of adaptive frequency control techniques to enhance the reliability and precision of modern oscillator-based systems.

Keywords : VCRO, Frequency Boost Reduction, Phase Noise, Ring Oscillator Design, Circuit Simulation

Authors : KONA VENKATA GANESH

Title : Design and Simulation of Frequency Boost Reduction for VCRO

Volume/Issue : 2022;4(3 (May - June))

Page No : 16 - 22

Electrocardiogram (ECG) signals are crucial for diagnosing cardiac abnormalities; however, they are often contaminated by various types of noise such as power line interference, baseline wander, and muscle artifacts. These unwanted signals can obscure important cardiac features and lead to inaccurate diagnoses. This study presents a simulation-based approach for noise cancellation in ECG signals using adaptive filtering techniques. The proposed system employs algorithms such as the Least Mean Squares (LMS) and Recursive Least Squares (RLS) filters to effectively reduce noise while preserving essential signal characteristics. MATLAB simulations are conducted to analyze the performance of each adaptive filter in terms of convergence rate, mean square error (MSE), and signal-to-noise ratio (SNR) improvement. Experimental results demonstrate that adaptive filters provide superior performance compared to fixed filters due to their ability to adjust coefficients dynamically based on signal variations. Among the tested methods, the RLS algorithm exhibits faster convergence and higher accuracy, making it suitable for real-time biomedical signal processing. This research highlights the significance of adaptive filtering in improving ECG signal quality, thereby enhancing the reliability of automated cardiac diagnosis systems.

Keywords : ECG Signal Processing, Noise Cancellation, Adaptive Filters, LMS Algorithm, RLS Algorithm

Authors : KONA VENKATA GANESH

Title : Simulation of Noise Cancellation in ECG Signals using Adaptive Fliters

Volume/Issue : 2022;4(3 (May - June))

Page No : 11 - 15

The Virtual Mouse Assistant is an innovative human-computer interaction system designed to provide an intuitive and hands-free alternative to traditional mouse devices. Leveraging advanced computer vision and gesture recognition technologies, the system enables users to control cursor movements, perform clicks, and execute complex commands using simple hand gestures. This approach enhances accessibility, particularly for individuals with physical disabilities, and offers a more ergonomic solution for prolonged computer use. The system integrates real-time image processing with machine learning algorithms to accurately detect and interpret hand movements, ensuring smooth and precise control. Additionally, it supports customizable gestures, allowing users to tailor commands according to their preferences and workflow requirements. Extensive testing demonstrates that the Virtual Mouse Assistant significantly reduces the dependency on physical input devices while maintaining high accuracy and responsiveness. Its applications extend beyond conventional computing, finding relevance in gaming, virtual reality, and remote operation environments. By bridging the gap between human intention and digital execution, the Virtual Mouse Assistant represents a step forward in creating more natural and efficient user interfaces. Future work focuses on enhancing gesture recognition in varying lighting conditions and expanding compatibility across multiple platforms.

Keywords : Virtual Mouse, Gesture Recognition, Human-Computer Interaction, Accessibility, Computer Vision

Authors : Bosubabu Sambana, P J V G PRAKASA RAO, KUPPIREDDY KRISHNA REDDY

Title : Diabetes Detection and Analysis using Machine Learning

Volume/Issue : 2022;4(3 (May - June))

Page No : 1 - 7