Journal of Engineering Technology and Sciences

Deep learning models have made great progress in many areas, but they are limited by their fixed structure and single-step optimization process, which makes it hard for them to adjust to changing data over time. This paper presents a Dynamic Nested Optimization Framework (DNOF) based on the idea of nested learning, where learning is organized as a layered system of connected optimization steps that work across different time frames and contexts. Unlike standard methods that use just one type of gradient update, this framework uses multiple layers of optimization. This lets the system learn model settings, optimization techniques, and memory structures at the same time. The layered design helps the system understand both short-term and long-term patterns, making the learning process more stable and the model more accurate. The model was tested on a structured dataset and performed very well, achieving an accuracy of 98.986%. These results show that the model learns efficiently and remains stable. The findings show that nested optimization improves prediction accuracy and makes training more reliable. This research helps move machine learning forward by connecting traditional deep learning methods with adaptable smart systems. It provides a flexible base for ongoing learning and future AI models.

Keywords : Adaptive Systems, Artificial Intelligence, Continual Learning, Convergence Stability, Deep Learning, Dynamic Nested Optimization.

Author : Pagalla Bhavani Shankar1 | Dr M Babu Reddy2 | Surla Kusa Raju3 | Shaik Moulasa4 | Muppalla Sunil5 | Nagula Hema Satya Kumar6 | Dr K Abida Begum 7

Title : Dynamic Nested Optimization with Adaptive Memory Control for Continual Learning

Volume/Issue : 2026;03(04)

Page No : 22-30

This paper presents the design and implementation of an IoT-based real-time patient health monitoring and alert system using an ESP32 microcontroller integrated with a MAX30102 pulse oximeter/heart-rate sensor, DS18B20 digital temperature sensor, and a respiration sensor. Collected physiological data is transmitted wirelessly via Wi-Fi to the ThingSpeak cloud platform for remote visualisation and storage. A Python machine learning module running Random Forest, K-Nearest Neighbours, Support Vector Classifier, and Logistic Regression algorithms classifies health status as normal or abnormal in real time. An automated buzzer issues immediate local alerts when any monitored parameter crosses clinical threshold limits. Experimental results demonstrate measurement accuracy within instrument specifications, stable cloud connectivity, and an approximately 94% classification accuracy, offering a scalable, low-cost solution for continuous patient care in both hospital and home environments.

Keywords : IoT, health monitoring, ESP32, MAX30102, DS18B20, ThingSpeak, machine learning, remote patient care, alert system

Author : Kopalli Venkanna Naidu¹, Naidu Rupasri², Pallothu Durga Asritha³, Upputholla Pavan Kumar?, and Yarabollu Lakshmana Manikanta?

Title : IoT-Based Real-Time Health Monitoring and Alert System for Continuous Patient Care

Volume/Issue : 2026;03(04)

Page No : 15-22

Street lighting has become an essential commodity for human life as it provides lighting in cities, towns and most especially on roads to reduce risk of accidents at night. Though necessary, streetlight systems not properly managed can result in risk of accidents, excessive loss of energy, financial losses and dissatisfaction of customers. This paper proposes a smart system to manage streetlight operation, relying on sensors and Internet of Things technologies. The proposed smart streetlight system consisted of hardware and software design. While the hardware makes use of sensors, Arduino board and GPS devices to build an integrated system capable of reporting the state of the street light through available wireless network, the software aspect consisted of an online application, capable of remote monitoring and control. The smart streetlight was conceived, built and tested. Results showed an effective operation, leading to reduced energy consumption of the streetlight, subsequent reduction of carbon footprint, effective detection of fault, effective location of fault owing to GPS devices and reduced downtime. The system is further expandable to cover a whole town/nation streetlight system.

Keywords : IoT, Arduino Uno, LDR Sensor, IR Sensor, GSM Module, LED Street Light, Smart City, Energy Efficiency, Fault Detection, PWM, Automation _________________________

Author : Kopalli Venkanna Naidu1 | Donkena Venkata Subbalakshmi2 | ketha D V Kameswararao3 | Arepalli Jyothiprakash4 |Karella Archana5

Title : IoT Based Street Light Controller and Monitoring System

Volume/Issue : 2026;03(04)

Page No : 01-12

data forwarding in Network-on-Chip (NoC) environments. The architecture steers each incoming packet toward one of four destination ports via a 2-bit address field in the packet header. Five cooperating modules — router_reg, router_fsm (8-state Moore FSM), router_sync, four 16×9 synchronous router_fifo buffers, and router_top — constitute the complete design. Reliability is ensured through an XOR parity checker and a busy back-pressure signal. Synthesised in Vivado 2025.1 on an Artix-7 FPGA (xc7a35tcpg236-1), a six-scenario self-checking testbench yielded pass_cnt = 6, fail_cnt = 0. Post-implementation resource usage remained below 1% of available Slice LUTs and Registers with zero Block RAM, confirming suitability for embedded NoC fabrics

Keywords : 1×4 Router, Verilog HDL, FSM, FIFO Buffer, Network-on-Chip, Parity Detection, FPGA, Vivado, Artix-7

Author : Ms. M. Usha¹ | D. Reshme² | K. Viswanth³

Title : Design and Simulation of Advanced 1×4 Router Using Verilog HDL

Volume/Issue : 2026;03(04)

Page No : 31-37

This project presents the design and implementation of an Ultrasonic Radar System using the Arduino Uno, buzzer and the HC-SR04 Ultrasonic Sensor. The system is developed to detect objects and determining parameters such as distance, position, direction, speed and range which can be either in fixed or to be in motion. The ultrasonic sensor emits high-frequency sound waves and receives the reflected echo from objects. The Arduino Uno calculates the distance using the echo time-of-flight principle. A servo motor rotates the sensor from 0° to 180° to scan the area, and the measured distance and angle data are displayed on a computer as a radar-like visualization. If the object is detected then the buzzer sound will be activated. This system provides a simple, cost-effective, and efficient method for real-time object detection and distance measurement. It can be applied in obstacle detection, robotic navigation, security systems, and automation projects. The project demonstrates the practical use of embedded systems, sensor integration, and real-time data visualization.

Keywords : Arduino Uno, Ultrasonic Sensor (HC-SR04), Servo Motor (SG90), Jumper Wires, Breadboard, Buzzer , Computer with Processing Software.

Author : V. Durgabai¹ | B. Srinu² | P. Triveni³ | Dr. K. Lakshmi Bhavani?

Title : Design and Implementation of Ultrasonic Radar Using Arduino

Volume/Issue : 2026;03(04)

Page No : 32-38

: Renewable energy sources such as solar photovoltaics and wind are inherently intermittent, and energy storage is essential to match their fluctuating output to the load. No single storage device combines high energy, high power, long life, and low cost, which motivates hybrid energy storage systems that combine complementary devices. This paper evaluates the performance of a hybrid renewable energy storage system supplying a stand-alone microgrid from solar PV and wind, in which a lithium-ion battery is combined with a supercapacitor and coordinated by a frequency-based energy-management strategy. A model of the generation, storage, and control is developed, and the system is assessed through simulation using round-trip efficiency, battery state of charge, dynamic power sharing, loss of power supply probability (LPSP), renewable fraction, and levelised cost of energy (LCOE) as performance metrics. The results show that adding a supercapacitor smooths the battery state of charge, removes high-power transients from the battery, and improves the round-trip efficiency from about 88% to 92% while lowering the LCOE to about 0.29 $/kWh. A storage capacity of 125–150 kWh is found sufficient to meet a 1% reliability target, and an optional hydrogen pathway further improves reliability at the expense of efficiency and cost. The battery–supercapacitor hybrid is identified as the most favourable configuration for typical renewable microgrid operation.

Keywords : Hybrid energy storage, Renewable energy, Battery, Supercapacitor, Microgrid, Round-trip efficiency, LPSP, Levelised cost of energy.

Author : Dr. K. Chandra Sekhar

Title : Performance Evaluation of Hybrid Renewable Energy Storage Systems

Volume/Issue : 2026;03(04)

Page No : 39-50