Volume 12 | Issue 5

 Abstract

The goal of the project "Self-Navigation Robotics: Mastering Autonomous Path" is to create an intelligent robot car that can drive itself from a starting point to a user-specified destination while dynamically changing its course to avoid obstacles in real time. In order to accomplish flawless autonomous navigation in challenging surroundings, the project blends a powerful combination of sensor technologies, innovative algorithms, and hardware components. This study presents a unique camera module, LiDAR, ROS2, Gazebo, and Raspberry Pi self-navigating vehicle system. LiDAR enables accurate 3D mapping and obstacle recognition, while ROS2 provides seamless system integration and communication. Gazebo provides a reliable simulation environment for verifying system functionality prior to a system being physically deployed. The Raspberry Pi functions as the main processing unit, managing decision-making algorithms and combining sensor data. The camera module improves versatility in a range of circumstances by enhancing perception with visual signals in addition to LiDAR. More advancements are possible when the system architecture prioritizes scalability and real-time responsiveness. Experimental validation the system's autonomous navigation competency highlights its potential for robotics research and industrial automation applications.

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 Keywords

Self-navigating automotive system, Camera module, LiDAR, ROS2, Gazebo, Raspberry Pi, 3D mapping, Obstacle identification, Simulation, Central computing unit, Sensor data fusion, Real-time responsiveness

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In this paper, Sericulture involves raising silkworms to produce silk and is crucial for India's development. Temperature and humidity are vital for healthy silkworm growth. We used a MEGA controller to monitor silkworms in real-time. Image processing helps identify infections of silkworms. The MEGA controller automates tasks like temperature control, reducing manual work for farmers. It also monitors the environment in the silkworm rearing room. Image processing detects color changes in silkworms, indicating the infections.

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SMART SERICULTURE USING IMAGE PROCESSING

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In contemporary times, significant resources are allocated towards implementing antiquated security measures to deter border trespassers. In high-risk areas where conventional military tactics may falter, various military entities employ robotic assistance. These military-grade robots are clandestinely deployed and outfitted with a suite of technologies including cameras, sensors, metal detectors, and camouflage. The primary objective of our system is to seamlessly blend into the surrounding environment, while also boasting additional functionalities such as an infrared sensor for intruder tracking and Wi-Fi connectivity for real-time data processing. Consequently, the proposed Wi-Fi system minimizes errors in defense operations, thereby bolstering national security against potential intruders.

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 Keywords

Blending into environment, Military-grade Robots, IR (Infrared), WI-FI (wireless fidelity)

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This project outlines a cost-effective 3D scanning solution utilizing Arduino Nano technology. It utilizes structured light to capture the geometry of objects. The hardware setup comprises an Arduino Nano microcontroller, a camera module, and a laser module for structured light projection. Software-wise, it employs an open-source computer vision library for image processing and depth map generation. The workflow involves system calibration to ensure optimal performance and accuracy. Once calibrated, the structured light pattern is projected onto the object, and images are captured by the camera. These images are then processed to extract depth information, enabling the reconstruction of the object's 3D model. The resulting point cloud data can be refined further for improved accuracy. To evaluate the scanner's performance, experiments are conducted with objects of various shapes and sizes. Metrics such as accuracy, resolution, and scanning speed are analysed to gauge its practical usability. Overall, the developed 3D scanner demonstrates its ability to generate detailed and accurate 3D models, making it suitable for applications like rapid prototyping, reverse engineering, and educational purposes.

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 Keywords

Low-cost 3D scanner, Arduino Nano microcontroller, Rapid prototyping, Calibration for optimal performance.

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 Abstract

This paper shows a comparison of power requirement and delay consumed between the 4 - bit ALU that is designed using a fin-shaped field-effect transistor [FinFET] and the traditional CMOS to demonstrate a better and efficient 4 - bit ALU in terms of delay and power consumption. The aim of this work is provide a Comparative Analysis of Arithmetic Logic Unit (ALU) Performance using CMOS and FinFET Technologies. ALU is a fundamental component in digital circuits, performing arithmetic and logic operations. The transition from CMOS to FinFET technology has brought significant advancements in terms of power efficiency, speed, and scalability.

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 Keywords

ALU (Arithmetic Logic Unit), CMOS (Complementary Metal Oxide Semiconductor), FinFET (Fin Field Effect Transistor), technology, comparison, performance evaluation

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By combining cutting-edge sensors and machine learning on Raspberry Pi, the "Weather Wizards using Machine Learning and Raspberry Pi" initiative transforms weather forecasting. By implementing Random Forest algorithm we record temperature, humidity, precipitation, wind speed using four sensors namely (wind, temperature, humidity, rain) . We deliver real-time forecasts for sunny, cloudy, windy, and rainy days .This ground-breaking system represents a huge advancement in weather prediction, serving applications and businesses that depend on accurate and fast weather data

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 Keywords

Weather Forecasting, Machine Learning, Raspberry Pi, Real-Time data, Sensors.

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In the world of wireless communication, there is an increasing demand for high-performance MIMO (Multiple Input Multiple Output) antenna systems tailored for smartphone applications. MIMO technology increases data rates, coverage, and reliability by using multiple antennas for transmission and reception. However, integrating MIMO antennas within the constraints of compact smartphone designs is fraught with difficulties, particularly in terms of guaranteeing adequate isolation between antenna parts to minimize interference. This work investigates the design and implementation of a MIMO antenna system specifically designed for smartphone applications in order to establish and maintain excellent isolation between antenna sections. Many design strategies are investigated, including better materials, decoupling techniques, and antenna element placement. In addition, recent advances in the fields of metamaterials, smartphone form factor integration, and micro antenna designs are discussed. Furthermore, the study addresses performance metrics including data throughput, power economy, and dependability, providing an understanding of the practical uses of these advancements. This paper's main objective is to promote MIMO antenna systems for smartphones by offering a comprehensive grasp of the challenges, solutions, and most recent developments in achieving high isolation within small form factors.

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Smartphone applications, High isolation.

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This paper presents and focuses on the design and development of a A Human Following Robo (HFR) is a technology that can follow a human operator in an autonomous manner. Maintaining a constant distance and orientation in relation to the human operator is the major goal of the HFR. while navigating through indoor and outdoor environments. Key components of the system include computer vision for people recognition and tracking, obstacle avoidance mechanisms, and motion control strategies. The effectiveness of the proposed HFR system is evaluated through extensive simulations and real world experiments, demonstrating Its capacity to accurately track and follow a human operator in various scenarios. The results highlight the potential of HFRs as versatile and intelligent robotic assistants capable of enhancing human-machine interaction and productivity in diverse applications. The development of Human Following Robots (HFRs) represents a significant advancement in robotics technology, offering the potential to revolutionize various industries such as surveillance, healthcare, and entertainment. These robots are designed to autonomously track and follow human operators, facilitating seamless human-robot interaction and enhancing productivity in dynamic environments. This paper aims to explore the design, development, and evaluation of an HFR system capable of intelligently tracking and following a human operator. By leveraging a combination of sensors, actuators, and control algorithms, the HFR system enables intuitive and efficient navigation while maintaining a safe and consistent distance from the human operator.

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Human Following Robo (HFR), Obstacle Avoidance, Object detection, Burning source code to processor

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This paper focuses on the development of a versatile robotic arm with advanced functionalities, aiming to enhance automation in diverse industries. Integrating precision engineering and smart control systems, the robotic arm demonstrates agility and adaptability for various tasks, such as assembly, pick-and-place operations, and intricate manipulations. Employing state-of-the-art sensors and machine learning algorithms, the system ensures optimal performance and safety. This project contributes to the evolution of robotic technology, addressing real-world challenges and fostering efficiency in industrial processes. Developing a cutting-edge robotic arm is the primary objective of this project, aimed at revolutionizing automation across industries. Leveraging advanced materials and sophisticated control algorithms, the robotic arm exhibits unparalleled precision and adaptability. Integrating innovative sensing technologies and machine learning, it excels in complex tasks, offering a transformative solution for assembly lines, manufacturing, and research applications. This project underscores the pivotal role of robotics in shaping the future of automation, showcasing a versatile and intelligent robotic arm that pushes the boundaries of technological innovation.

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Robotic arm, Automation, Precision engineering, Machine learning algorithms, Industrial processes

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 Abstract

Wild animals become more aggressive when wounded because they're scared and confused and may think you're trying to capitalize on their vulnerable state. They're more likely to lash out and bite or scratch you than to accept your help. To address this, our project proposes an alerting system using YOLOv3, a real- time object detection algorithm based on deep convolutional neural networks, to classify and monitor animals that are hurt in Zoo or forests. This algorithm enables efficient identification and tracking of animals, aiding safeguarding wildlife observers and ensuring the preservation of wildlife in their natural habitats.By this wildlife monitoring robotic car using IOT, wildlife observers may get up and personal with wild creatures. An ESP32 is used in this system. The system receives these orders using a Wi-Fi module. We can capture the images of the wildlife continuously. And able to detect the wildlife.We are using the MLX9061an infrared thermometer designed for non-contact temperature sensing to measure the temperature of the animals and an ultrasonic non-contact type of sensor used to measure a wildlife's distance.

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 Keywords

Image Processing, YOLO Algorithm, Convolutional Neural Network, COCO, ESP,UNO,IDE.

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 Abstract

A Camera-Based Surveillance System utilizing Raspberry Pi proves highly advantageous for crime detection, monitoring various environments, and swiftly gathering evidence of illicit activities such as theft. This system is tailored for both residential and commercial spaces, ensuring prompt detection of any unauthorized actions.Operating on Raspberry Pi, equipped with a camera-based circuit, the system continuously scans its surroundings for motion. Upon detecting any motion, it promptly triggers an alert mode. This mode activates an alarm and captures images of the motion for future reference. Consequently, the system functions as an effective security measure, providing real-time monitoring and evidence collection. To further enhance its capabilities, integrating a GSM modem for alert SMS notifications or connecting to IoT for remote alarm activation can be considered. This augmentation ensures seamless communication of alerts to relevant parties, amplifying the system's effectiveness in safeguarding the monitored premises.

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 Keywords

Raspberry Pi, GSM module, Stepper motor, webcam, PIR sensors, GPIO, Ethernet and USB

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 Abstract

This paper introduces the Aquaponics Monitoring and Fish Feeding Automation System (AMFFAS) is an innovative solution leveraging the principles of Internet of Things (IoT) technology. AMFFAS aims to improve the efficiency and sustainability of aquaponics systems by automating fish-feeding processes and providing Inspection and coordination. The system comprises interconnected components, including IoT sensors, actuators, microcontrollers, and a central control unit. FFAAMS integrates an intelligent feeding system that dynamically adjusts feeding schedules and portion sizes based on factors like fish biomass, growth rates, and environmental conditions. This approach optimizes feed utilization, minimizing the risk of overfeeding and promoting healthy fish environments. Furthermore, AMFFAS offers Inspection and coordination through a user-friendly interface accessible via web or mobile applications. Aquaponics practitioners can remotely monitor system parameters, receive alerts for abnormal conditions, and adjust settings as needed. This feature enables proactive management of aquaponics systems, ultimately contributing to heightened productivity and sustainability.

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Wireless dynamic-: Aquaponics, Fish Feeding Automation, IoT, Aquaponics system efficiency, Remote Monitoring

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 Abstract

This paper's primary goal is to use an Arduino Mega microcontroller kit and Internet of Things devices to pinpoint the precise position of a fault in an underground wire. Instead of using overhead electrical lines, the metropolitan area uses an underground electrical cable wire. However, pinpointing the exact position of a break in an underground wire can be challenging, making repairs more challenging. Because of deterioration, subterranean conditions, vermin, etc., underground wires are vulnerable to malfunctions. Since we are unsure of the precise position of the problem, the entire cable needs to be dug out for inspection and fault repair. Our suggestion is to precisely locate the problem so that it can be rectified, making the repair process easier. A fault occurs when two lines are too close together; the network combination of the resistors determines the voltage that is produced at that location. After detecting the voltage change, the microcontroller alerted the user. The user receives information regarding the precise location where that voltage coincides. With the aid of a microcontroller, the fault's information is displayed on the LCD.

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Microcontroller, Internet of things, Electrical cables, Cable fault, Aurdino mega, GSM module.

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 Abstract

Centered on FPGA-based home automation utilizing the Basys 3 board, this project seamlessly integrates a suite of sensors tailored for temperature monitoring, door status detection, and remote-controlled operation of an LCD projector. Leveraging Verilog programming, the system ensures real-time temperature surveillance, with the capability to activate fans for optimal environmental control. Reed sensors are strategically deployed to detect door openings, triggering automated light activation, thus enhancing both convenience and security within the household. Empowered by FPGA control, users can remotely manipulate the lights, fans, and LCD projector, thereby augmenting the flexibility and accessibility of the home entertainment system

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Home automation, FPGA board, Verilog, security

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 Abstract

The project aims to develop an underwater inspection drone that can navigate easily underwater and allow us to vide live video footage underwater. The drone provides following advantages including: Easy to Navigate Underwater, 360 Degree Direction Control, Live Footage Viewing, Dual Motor Propulsion system, Lightweight and anti-rust design for long term usage. The RC drone uses 2 x motors for propulsion and a separate motor for depth/direction control. Both motors are attached with propellers to achieve this task. This mechanism makes use of a unique rudderless mechanism using motor drives to control 360 degree movement of the drone. This mechanism does not make use of ballast tanks to control buoyancy. The drone consists uses the 2 motors to provide front drive as well as for left right direction control. The 3rd motor is used to control the vertical alignment of the drone. This motor in combination with other 2 motors is used to dive in or bring up the drone. All motors and controller unit is enclosed in a water proof chamber. The drone now uses a camera to capture footages underwater. These footages are transmitted to the floating buoy unit from there user can connect via wifi to check the footages. The system makes use of a raspberry pi controller for footage transfer as well as wifi transmission. Also the buoy unit is used to pullout the drone in case it gets stuck or runs out of battery under water

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ballast tanks, RC,under water, buoy unit.

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 Abstract

The literature survey on underwater unmanned vehicles (UUVs) reveals a diverse array of applications stretching across underwater exploration, defense, and various commercial implementations. The technological landscape encompasses sonar-based navigation systems, acoustic communications, and adaptive control algorithms, which propel UUVs to undertake complex missions such as seabed mapping, oceanographic research, mine countermeasures, and offshore infrastructure inspection. Defense applications include mine warfare, autonomous surveillance, and reconnaissance missions. Moreover, commercial applications comprise underwater inspection, maintenance of subsea installations, and environmental monitoring. Advanced technologies are employed in UUVs, such as autonomous underwater vehicle (AUV) designs, sensor fusion, underwater acoustic communication systems, and high-precision navigation algorithms, reflecting the interdisciplinary nature and extensive potential of UUVs in addressing a wide spectrum of underwater challenges and opportunities. This paper compares the evolution of underwater vehicle with technology and advancement for the purpose of underwater communication.

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 Keywords

Underwater unmanned vehicle, Autonomous underwater vehicle, Underwater communication.

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 Abstract

This paper introduces an approach utilizing a fin-shaped field-effect transistor to illustrate the structure of a 6T SRAM cell. The primary goal is to enhance the presentation's competitive edge while concurrently reducing the power usage and read/write delay inherent in conventional 6T SRAM cell designs. Given the increasing demand for rapid mobile computing, traditional CMOS SRAM cell configurations face performance constraints and significant power demands. This research undertakes a thorough examination of power consumption and read/write delay parameters associated with low-power SRAM cell designs. Furthermore, an exhaustive comparative analysis is performed between 45nm nano-scaled technologies and the emerging FinFET-based 6T SRAM cells, with a particular emphasis on their respective power efficiency and operational speed characteristics.

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SRAM Cell Design Using FinFET for Low Power and Delay

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With the miniaturization of electronic devices, interconnects (the pathways for data flow) are not shrinking as fast as the devices themselves. This inefficiency leads to problems with power consumption, area usage, and signal interference (crosstalk). To address this issue, the paper explores the use of serial links, which transmit data one bit at a time, as a replacement for traditional parallel buses that send multiple bits simultaneously. Serial links offer several advantages, including fewer connection points (pins), lower power use, smaller physical connectors, and better resistance to noise. The proposed architecture is simulated using a standard CMOS technology and industry-recognized simulation tools. The simulations are set up to evaluate factors like power consumption and data processing speed (throughput) under realistic operating conditions. The paper then compares the performance of this new design with previously reported architectures, highlighting improvements in power savings, area usage, and overall functionality. By analyzing the simulation results and comparisons, the paper demonstrates the significant benefits of the proposed architecture in terms of reduced power consumption, efficient chip area utilization, and improved data processing. Finally, the paper concludes by summarizing the key findings and advancements achieved with this design.

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SerDes, CDR, Serializer, De-serializer, TSPC Flip Flop.

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 Abstract

This flexibility upgrades its viability in recognizing and neutralizing landmines in different geological areas, making it a profitable device for mine clearance operations around the world. With the use of cutting-edge sensors and technology, the Smart Traffic Density Management and Monitoring System maximizes urban traffic flow. By adjusting traffic lights dynamically in response to real-time density data, it minimizes traffic and travel time. It offers government and commuter interfaces that are easy to use while smoothly integrating with the current infrastructure. Reliability, scalability, and flexibility to shifting traffic conditions are guaranteed by embedded system architecture. The technology also improves safety by pointing out potential dangers and encouraging other forms of transportation during rush hour. All things considered, it's a creative way to successfully control urban traffic and promote sustainable travel.

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Traffic, congestion, traffic light, vehicles

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This study suggests a method for using the Raspberry Pi, an inexpensive, multipurpose microprocessor, to operate anesthetic machines. In order to precisely provide anesthetic gases and vapors to patients during surgical procedures, anesthesia machines are essential medical equipment. Customization and integration with contemporary healthcare systems are frequently restricted by the proprietary hardware and software used by traditional anesthetic machines. Through the utilization of Raspberry Pi's capabilities, we offer an adaptable and economical approach to control anesthetic machines. Our system makes use of sensors to keep an eye on critical factors including oxygen content, pressure, and gas flow rates. It then provides real-time feedback to guarantee precise anesthetic delivery. By analyzing this data and modifying the machine's settings accordingly, the Raspberry Pi maximizes patient comfort and safety .Moreover, our methodology facilitates smooth integration with electronic health records

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Anesthesia, Raspberry Pi, Heartrate, Temperature, SPO2, Body Wetness, infusion.

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