https://ar.iub.edu.bd/handle/11348/21 2026-09-10T13:52:44Z 2026-09-10T13:52:44Z Khan, Md Iftie Islam Islam, Md Rakibol Sarker, Dipto Ahmed, Tanvir https://ar.iub.edu.bd/handle/11348/1570 2026-09-06T13:05:17Z 2026-08-01T00:00:00Z

Design and development of a low-cost ammonia monitoring system for water quality assessment Khan, Md Iftie Islam; Islam, Md Rakibol; Sarker, Dipto; Ahmed, Tanvir This thesis presents a low-cost ammonia detection and monitoring system for freshwater fish farming. The system uses an MQ-137 gas sensor, Arduino Uno, heater, water pump, thermocouple, and LCD display to measure ammonia concentration in water samples. The sensor was calibrated using known ammonia concentrations and a fourth-order polynomial model to convert sensor readings into ppm values. The system performed effectively within the 0–8 ppm range and was validated against a commercial ammonia test kit. Despite minor errors caused by sensor warm-up time and vapour leakage, the system offers an affordable and practical solution for aquaculture, environmental monitoring, and educational applications, with potential for future IoT-based remote monitoring. This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science (B. Sc.) in Electrical and Electronic Engineering, 2026.

2026-08-01T00:00:00Z Afroz, Rajoana Adityo, Tahmid Ahsan, Sajid Zafry Sultan, Md Parvez https://ar.iub.edu.bd/handle/11348/1546 2026-08-24T09:50:13Z 2026-08-01T00:00:00Z

Design and simulation of a bidirectional underwater open-center micro tidal turbine for harvesting tidal energy Afroz, Rajoana; Adityo, Tahmid; Ahsan, Sajid Zafry; Sultan, Md Parvez Costal and Island regions of Bangladesh require reliable, predictable, and locally adaptable renewable energy solutions. Sandwip and similar coastal locations experience repeated tidal motion, which makes tidal Stream Energy a promising option for decentralized electricity generation. This thesis presents the design, hydrodynamic simulation, and electrical output evaluation of a bidirectional micro tidal stream turbine intended for low-to-moderate tidal current applications. The proposed turbine uses a ducted, open-centered annular geometry with a six-blade rotor. The open-centered rotor allows flow to pass through the central region while the blades extract energy from the surrounding annular flow region. A curved ducted housing is used to guide the incoming tidal current and support the practical turbine assembly. The turbine was first designed in SolidWorks. Two CAD representations were considered: a practical assembly including support base, stator housing, coil slots, rotor-magnet concept, and duct structure; and a simplified no-base geometry used for ANSYS Fluent simulation. The CFD model was analyzed for tidal velocities from 0.4 m/s to 2.4 m/s at a fixed tip-speed ratio of 3. The CFD dataset produced turbine efficiency values mostly between approximately 50% and 56%, The mechanical outputs were then used in a simplified MATLAB/Simulink electrical model. In the third term, the turbine torque and angular speed are applied to a permanent magnet synchronous machine (PMSM) generator, which is directly connected to a balanced 15 ohm three-phase resistive load. Phase RMS voltage, phase RMS current, three-phase load power, load energy, electrical frequency, and mechanical-to-load efficiency were calculated for each operating point. At 2.1 m/s, the system produced about 3362.83W mechanical shaft power and delivered about 528.7W load power. A 21-point power-velocity lookup table was also used with a 24.84-hour tidal velocity profile to estimate energy output over one lunar tidal day. The integrated energy output was approximately 6.675 kWh per lunar tidal day, 193.48 kWh per 30 days, and 2.354 MWh per year This design project is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2026

2026-08-01T00:00:00Z Nirob, Nafish Kabir Islam, Md. Mishurul https://ar.iub.edu.bd/handle/11348/1067 2026-07-08T14:35:12Z 2025-01-01T00:00:00Z

Design and Implementation of a Microstrip Patch Antenna–Based Sensing System for pH Estimation of Aqueous Solutions Using Machine Learning Nirob, Nafish Kabir; Islam, Md. Mishurul This paper presents the design and implementation of a microstrip patch antenna-based sensing system for pH estimation of aqueous solutions using machine learning. Conventional pH sensors are widely used, such as frequent calibration, easy contamination, the measurement range is limited, and the maintenance is expensive. In order to overcome these limitations, the proposed system uses rectangular microstrip patch antenna fabricated using FR4 substrate for the detection of change in the dielectric properties of liquid samples. Variations in resonant frequency, return loss (S11) and bandwidth are then analyzed to provide the predict pH level. Experimental measurements were conducted for solutions with a pH value ranging from 4 to 12 by using a Vector Network Analyzer (VNA) with the results verified by simulation results of CST Studio Suite. Furthermore, Random Forest, Support Vector Regression (SVR) and kNearest Neighbors (kNN) machine learning models were also trained with the collected dataset in order to predict the pH with respect to the antenna response parameters. Out of them, Random Forest showed the highest accuracy (𝑅 2 level of higher than 0.85) with the lowest MAE (Mean Absolute Error) showing also the strong predictive reliability. The obtained results validate the concept that a low-cost, wide-area and contact-less pH monitoring solution is obtained by the combination of microwave sensing and data-driven modeling. The proposed system has potential applications in environmental monitoring, industrial process control, fish farming and agricultural water management especially for resource constrained environments like that of Bangladesh.

2025-01-01T00:00:00Z Sakib, Nazmus Islam, Md Kafiul Faruk, Tasnuva https://ar.iub.edu.bd/handle/11348/1041 2026-06-26T18:01:56Z 2025-06-23T00:00:00Z

A Machine Learning Approach for Multi-Level Anxiety Screening among University-Going Students using Wireless EEG Signals Sakib, Nazmus; Islam, Md Kafiul; Faruk, Tasnuva Anxiety is a widespread mental health condition affecting millions globally, often resulting in significant emotional and physical symptoms. Accurate detection of anxiety levels is essential to provide timely interventions and prevent severe complications. This study explores a machine learning-based approach for multilevel anxiety classification among young adults using EEG signals. The GAD-7 screening tool was used to assess and categorize participants into different anxiety severity groups. EEG data was then recorded, processed, and segmented into 1, 3, and 5 second segments to evaluate the impact of segment duration on classification accuracy. Four channel combinations were tested for comparisons in performance. Feature extraction included eleven time and frequency domain features. The Bagged Trees classifier was applied to classify anxiety levels based on these features. The findings of this work show the potential of EEG-based systems as non-invasive tools for anxiety screening that could support more precise mental health diagnostics. Conference Paper

2025-06-23T00:00:00Z