Design and Implementation of Robot Assisted Arduino Based Object Recognition and Sorting
Wireless Charging System for Electric Vehicle
Embedded System-Based Enhanced Smart Security Systems for Intelligent Monitoring Applications: A Review
Arduino-Based Vehicle Collision Detection and Warning System
WiFireBot: Smart ESP32 Based Surveillance Car with Temperature and Fire Detection
Verilog Based UART System Design
IoT based Smart Agriculture Monitoring Framework with Automation
Intel ® Processor Architectural and Integrated Development Environment Exploration
An Integrated Model of Digital Fuel Indicator and GPS Tracking System for Vehicles
Designing of an Embedded system for Wireless Sensor Network for Hazardous Gas leakage control for industrial Application
Hardware Implementation of Artificial Neural Networks
Fault Analysis on Grid Connected MPPT BasedPhotovoltaic System
High Efficiency Hybrid Intelligent Street Lighting Using A Zigbee Network And Sensors
Design of Dual-Band Bandpass Filter Using Interdigital Embedded Open Loop Triangular Resonator Loaded with Stubs
License Plate Localization Using Novel Recursive Algorithm And Pixel Count Method
This paper deals with an automatic material handling system that coordinates the movement of a robotic arm to pick up items moving on conveyor belts. The system utilizes advanced sensors and machine learning algorithms to ensure precise and efficient manipulation of objects, enhancing the overall automation and productivity of material handling processes. It aims to organize colored objects approaching on the conveyor by picking and placing them in separate, designated locations. The robotic system employs advanced computer vision algorithms to precisely identify and manipulate the diverse array of colored objects, ensuring efficient and accurate sorting on the conveyor belt. This reduces the tedious work done by humans, ensuring accuracy and rapidity in the process. Additionally, it paves the way for more efficient utilization of human resources, allowing professionals to focus on higher-level tasks that require creativity and critical thinking. The system includes color sensors that detect the items' colors and transmit signals to the controller. Additionally, the controller processes the signals from the color sensors to facilitate accurate identification and sorting of the items. The microcontroller then guides the signal to the motor driving circuit, which operates the different motors of the robotic arm to grasp the object and place it in the correct location. Additionally, the robotic arm's sophisticated sensor feedback system ensures precise positioning and adaptability to varying environmental conditions, enhancing its overall efficiency in object manipulation tasks. Depending on the color sensed, the robotic arm goes to the correct location to release the object and returns to its normal position. Additionally, the robotic arm employs advanced computer vision algorithms to precisely identify and differentiate colors, ensuring accurate execution of tasks in diverse environments.
The majority of electric vehicle systems are built around a number of modules intended to guarantee the high power and stability of the car on the track. Most of these parts are connected to the charging system. Dynamic wireless power transfer can help alleviate range anxiety in electric vehicles and lower the price of on-board batteries. In this regard, pure electric vehicles have long used wireless recharging, enabling charging even while the car is moving. However, due to the complexity of this method's working methodology and the presence of numerous variables and parameters, analysis is challenging. Also, whether the vehicle is moving or not specifies a number of other factors, including the speed of the vehicle, as well as the coil receivers' sizes and characteristics. The dynamic wireless recharging system’s performance can be enhanced using the unique technique presented in this study. By providing a dynamic mathematical model that can describe and measure source-to-vehicle power transfer even while it is in motion, receiver coils have been added to the proposed system to maximize charging power. All the physical parameters of the model were presented and addressed in the suggested mathematical model. The outcomes demonstrated the viability of the suggested model. Additionally, by placing two coil receivers under the car, the simulation results were validated by experimental testing.
The advanced smart security system uses palm vein technology and machine learning to enhance authentication. It combines biometric data with behavioral analysis, continuously adapting to improve security. AI integration allows for anomaly detection, distinguishing normal user interactions from suspicious activities. The user-friendly interface makes it accessible for various applications, ensuring resilient protection against evolving threats. The palm vein technology not only enhances security but also minimizes the risk of false positives and negatives, ensuring a reliable and efficient authentication process. In practical scenarios, the proposed system's versatility extends to securing confidential information in various sectors such as finance, research institutions, and government facilities. Its adaptability and compatibility with existing infrastructure make it a seamless and effective solution for organizations seeking to bolster their security measures. Moreover, the system's integration with mobile devices enables users to receive real-time notifications, allowing for prompt action in the event of a security breach. This feature contributes to the overall responsiveness and effectiveness of the security system, especially in remote locations where immediate intervention may be crucial. In conclusion, the advanced smart security system with palm vein technology not only introduces a novel approach to authentication but also addresses the limitations of existing models. The incorporation of machine learning, behavioral analysis, and real-time notifications significantly enhances its overall security features, making it a costeffective and reliable solution for a wide array of applications.
As the number of vehicles increases day by day, accidents are also rapidly increasing. This surge in accidents poses a significant challenge for road safety authorities, necessitating innovative solutions and stricter enforcement of traffic regulations. Addressing the root causes, such as driver education and infrastructure improvements, is crucial to mitigating the escalating risks on our roads. These accidents can occur due to false estimation of nearby vehicles, disturbances in the mind of the driver, or any other reasons that prevent the driver from maintaining focus on driving. Therefore, not only is the accurate estimation of the distance to other surrounding vehicles necessary, but quick actions are also required to avoid any kind of accidents. In addition, advancements in technology, such as the integration of automated driving assistance systems, aim to enhance the precision of distance estimation and mitigate the risk of accidents caused by human errors. As the automotive industry continues to innovate, the intersection of human vigilance and technological support becomes crucial for ensuring road safety. This paper is specifically focused on preventing accidents. The system is designed to measure the distance between the driving car and the front object. If the driver fails to maintain the minimum safety distance from the front object, the system will warn the driver to slow down the speed of the car and avoid a collision. The driver will receive both visual and audio warnings to ensure that a collision does not occur. Additionally, the paper incorporates advanced sensor technologies and real-time data analysis to enhance the accuracy of distance measurements, providing a robust and reliable solution for accident prevention.
Fire incidents can have severe consequences, including loss of life, property damage, and disability. Industries like nuclear power plants, refineries, and chemical factories are susceptible to major fires. To address this, a fire control robot has been developed, reducing the need for human intervention. Fire accidents are common in daily life, posing challenges for firefighters. In such cases, firefighting robots play a vital role. Simultaneously, surveillance robots have become pivotal in security, industrial monitoring, and home automation. A low-cost, efficient surveillance robot using ESP32-CAM has been created for indoor and outdoor use. These robots offer remote monitoring through the ESP32 CAM module, providing Wi-Fi and Bluetooth connectivity for remote control and data collection. They integrate seamlessly, capturing high-quality imagery and videos, which are wirelessly transmitted for analysis. The ESP32 CAM module offers several advantages, including high-quality data capture, remote control, and wireless data transmission, making it suitable for various applications such as security and monitoring. Combining fire control with surveillance capabilities ensures safety and provides valuable insights. This paper presents a combined design for a fire detection and suppression robot with surveillance capabilities. The robot uses flame and temperature sensors to detect fires and employs various methods to extinguish them. It also features an ESP-32 camera for real-time fire monitoring and can be controlled remotely via a smartphone or tablet.
