Harmonic Motion Tracing Patterns in a Rotating Field

Computer vision (CV) and human-computer interaction (HCI) are essential in many technological fields. Researchers in CV are particularly interested in real-time object detection techniques, which have a wide range of applications, including inspection systems. In this study, we design and implement real-time object detection and recognition systems using the single-shoot detector (SSD) algorithm and deep learning techniques with pre-trained models. The system can detect static and moving objects in real-time and recognize the object's class. The primary goals of this research were to investigate and develop a real-time object detection system that employs deep learning and neural systems for real-time object detection and recognition. In addition, we evaluated the free available, pre-trained models with the SSD algorithm on various types of datasets to determine which models have high accuracy and speed when detecting an object. Moreover, the system is required to be operational on reasonable equipment. We tried and evaluated several deep learning structures and techniques during the coding procedure and developed and proposed a highly accurate and efficient object detection system. This system utilizes freely available datasets such as MS Common Objects in Context (COCO), PASCAL VOC, and Kitti. We evaluated our system's accuracy using various metrics such as precision and recall. The proposed system achieved a high accuracy of 97% while detecting and recognizing real-time objects.

Real-time localization and classification of fast-moving objects are crucial in various applications. Traditional imaging approaches face significant challenges, including large data requirements, limited update rates, motion blur, and restrictions in non-visible wavelengths. This paper proposes an image-free method based on complementary single-pixel detection and centralized geometric moments, which effectively integrates target localization and classification into a unified framework. By employing only four specific illumination patterns, the method can simultaneously determine the centroid position and shape of the target at an update rate of up to 5.55 kHz. Theoretical simulations verify the robustness of the proposed method under similarity transformations. Experimental results indicate that the proposed system achieves accurate real-time target localization and classification under diverse conditions, with an RMSE for centroid localization below 0.5 pixels and 93.3% classification accuracy for 30 different objects. The proposed method demonstrates strong adaptability to complicated environments. It holds significant potential for applications in target tracking, character recognition, industrial automation, and the development of optoelectronic neural networks for advanced optical computing tasks.

The real-time detection of objects and persons in their approach remains vital for applications that need safety measures security protocols and efficiency in autonomous systems and surveillance operations and industrial automation. The proposed research creates a durable real-time detection system through sensor combination using ultrasonic along with infrared and LiDAR and radar sensors and computer vision-based sensors to enhance detection accuracy and response times in changing environmental scenarios. The method starts by processing data gathered from arrays of multiple sensors through optimized filtering techniques and extraction methods before machines learn to classify objects while reducing false detection instances. The assessment of performance takes place in different environmental conditions to track both detection speed along with accuracy levels and system reliability. Sensor fusion enables real-time detection systems to perform better because it reduces the impact of environmental changes and sensor distortions. System capabilities increase in different operational areas when researchers demonstrate pre-processing optimization works alongside intelligent data processing systems. The research findings combine autonomous navigation systems with smart surveillance technology together with safety protocols for industrial security which enable real-time scalability and reduced risk elements. Research demonstrates a specific achievement through its analysis that machines learn better when combined with multi-sensor integration over single-sensor models which improves system reliability when used in real-world applications. Multi-sensor arrays and advanced processing techniques generate enhanced system responsiveness alongside increased adaptability and reliability thus improving detection systems within industrial security measures and automation operations.

With the development of computer and embedded technology, ubiquitous computing age is coming. In the ubiquitous age, people could get all kinds of services anywhere and at any time. In this paper, we design an indoor location system based on ultrasonic sensor and radio frequency technology. The ultrasonic positioning technology has many merits of high positioning accuracy, simple circuit structure and low cost. There are some components such as locating system, communication system, location system, security system and so on, emphasized supporting technologies in order to provide researchers with general cognition. The principle and the design of the system are described in detail. The system consists of the single chip circuit, the circuit of ultrasonic, the circuit of radio frequency signal, communication interface circuit and so on. Perceptual system can promote environmental intelligence and human services development, but also can promote public places security monitoring and early warning intelligence and automation.

Aiming at object grasping of indoor mobile robots, this paper develops an object localization system based on the fusion of a monocular camera and a two-axis-controlled laser ranging sensor. The system can locate the specified object in space without obtaining the background depth information of the object, which reduces the consumption of computational resources and has a low cost. According to the coordinate mapping of the object in the image, the measurement point of the laser ranging sensor is controlled to coincide with the target point by two servo motors in perpendicular axes. Thus, the spatial location of the target point can be determined from the rotation angles of the cradle head motors and the laser-measured distance together. To accurately control the rotation angles, a compound strategy combining feedback and feedforward is proposed. On the one hand, the feedback of the laser measurement point is introduced in the image coordinate to realize the close-loop control of the measurement point. On the other hand, a feedforward compensator based on disturbance observation is designed to reduce the influence of robot motion. An experimental setup is developed and substantial experiments are implemented to evaluate the performance of the proposed system. The results show that object localization can be effectively achieved with good accuracy and dynamic response.

We envision that in the near future, humanoid robots would share home space and assist us in our daily and routine activities through object manipulations. One of the fundamental technologies that needs to be developed for the robots is to enable them to detect objects and recognize them for effective manipulations and take real-time decisions involving the same. In this paper, we describe a novel architecture that enables multiple low-compute NAO robots to perform real-time detection, recognition and localization of objects in its camera view and take programmable actions based on the detected objects. The proposed algorithm for object detection and localization is an empirical modification of YOLOv3 along with a distributed architecture to operate multiple robots on a central “inference engine”, based on indoor experiments in multiple scenarios, with a smaller weight size and lesser computational requirements. YOLOv3 was chosen after a comparative study of bounding box algorithms was performed with an objective to choose one that strikes the perfect balance among information retention, low inference time and high accuracy for real-time object detection and localization. Quantization of the weights and re-adjusting filter sizes and layer arrangements for convolutions improved the inference time for low-resolution images from the robot's camera feed. The architecture also comprises of an effective end-to-end pipeline to feed the real-time frames from the camera feed to the neural net and use its results for guiding the robot with customizable actions corresponding to the detected class labels.

The “Floor Cleaning Robot” utilizing the Arduino Uno is an innovative robotic system aimed at automating floor cleaning processes, incorporating both dry and wet cleaning functionalities into a single design. This project focuses on achieving autonomous navigation, obstacle detection, and efficient cleaning using cost-effective and user-friendly technology. The system is built upon the Arduino Uno microcontroller board, integrating essential components such as servo motors, DC motors, ultrasonic sensors, and the Motor Shield L298. The ultrasonic sensor enables obstacle detection, while the Motor Shield L298 drives the DC motors under the control of the Arduino Uno, ensuring precise navigation and cleaning efficiency. As households evolve toward greater automation, this robotic cleaner offers a practical solution for simplifying cleaning tasks, blending functionality with affordability. Future enhancements aim to further optimize its performance and adaptability. Index Terms - Autonomous floor cleaning, Arduino microcontroller, wet and dry cleaning, robotics in home automation, obstacle avoidance, ultrasonic sensors, motor shield integration, cost-effective technology, user-centric design, robotic cleaning systems.

self-governing system that can identify and steer clear of impediments in real time is known as an obstacle-avoiding robot. Actuators are used for movement, a microcontroller is used for data processing, and sensors like ultrasonic or infrared modules are used to sense its environment. These machines are widely employed in a variety of fields, including healthcare, exploration, industrial automation, and service robotics. The main objective is to guarantee effective, collision-free navigation, improving operational dependability and safety. The intelligence and flexibility of obstacle-avoiding robots are being enhanced by developments in artificial intelligence and sensor technology, which makes it possible to employ them in settings that are becoming more dynamic and complicated. Keywords— obstacle, microcontroller, ultrasonic sensor , Arduino, encoder

Real-time object detection and ranging of multiple objects on the road are the essential tasks in the field of autonomous driving. In this paper, we introduce a system for simultaneous detection and ranging of vehicles, people, non-motor vehicles and lanes based on RGB-D images. Among them, the detection of vehicles, people and non-motor vehicles belongs to general detection task and the lane detection belongs to segmentation task. In order to improve the accuracy and speed, we use two networks to complete these two tasks. We propose a real-time synchronization method with multi-GPU, which achieves separate training and simultaneous detection of lane detectior and vehicle, people and non-motor vehicle detector. We also propose a center-selective ranging module based on binocular ranging technology to distance the detected object. The system reaches nearly 15 FPS with four 1080Ti GPUs. We construct datasets about these problems including daytime and night in which the system achieves high accuracy. A real-time test of the system on the streets of Tianjin, China has been conducted by us, it has proved that the system can be applied to actual driving.

Wild blueberry fields are developed in deforested farmland by removing competing vegetation. The presence of significant bare spots and weed patches emphasizes the need for the development of site-specific technologies to increase farm profitability and reduce environmental risks. The innovative low-cost ultrasonic sensor system was developed to detect weeds and bare spots in wild blueberry cropping system. Ultrasonic sensors were mounted besides the rear wheels of the specially designed Farm Motorized Vehicle. Trimble Ag GPS 332 was mounted above the sensors to locate the exact locations of sensor data points for mapping. Custom software interface was developed in LabView 8.5 to collect and store the sensor data along with DGPS co-ordinates in a laptop computer. The ultrasonic system calibrated using the fixed height objects in the laboratory and vegetation in the wild blueberry fields. Linear regression analysis showed significant relationship between actual heights and sensor heights (R2 = 0.98). The survey of the field for weeds and bare spots detection was carried out at a speed of 0.54 m/s. The height maps were generated in ArcView3.2 showing weed patches, bare-spots and wild blueberry plants in selected fields.

Wheelchair user will face various challenges during travelling from one destination to another. Their hands will be required to maneuver the wheelchair around. It is difficult for a wheelchair user to identify the appropriate location to place their luggage within the wheelchair itself. Therefore, luggage carrying following cart is an initiative that could lighten the burden for wheelchair users. The method used is via colour tracking system using Pixy CMUcam5 sensor, Arduino MEGA microcontroller, ultrasonic sensors, servo motor, motor driver and transaxle motor. Pixy CMUcam5 sensor is used to perform colour tracking on target of interest. Arduino MEGA is the main microcontroller that controls all the input and output data based on the program. Servo motor is used to steer the direction of the front wheel while ultrasonic sensors are used to perform obstacles avoidance task. The movement control of transaxle motor is performed through motor driver. Colour tracking technique is based on surface area of the target of interest and brightness from the background. The field of view (FOV) of Pixy CMUcam5 sensor is 73.98° when the distance between the sensor and the target is at 1.50m. In addition, the ultrasonic sensors and servo motor are also discussed to identify the performance and its sensitivity respectively. The maximum error on the ultrasonic sensors is 2.00%. The percentage error for the servo motor without a load is 1.58% while with a load is 15.74%. Lastly, an effective target tracking system is successfully designed for this project.

Robotics technology has developed rapidly and applied to various fields of work that cannot be done by humans. Many forms and types of robots used include the hexapod robot. The purpose of this research is to regulate the direction of the hexapod robot’s motion and be able to avoid obstacles to maintain the stability of its motion using a PID control system. In establishing a good control system, various types of sensors are used, those are ultrasonic and inertial measurement unit sensors. The shape of the hexapod robot is made to be integrated with the robot's legs. Each leg of the robot consists of 3 joints, joined by a servo motor. There are 18 servo motors. Eight ultrasonic sensors are used to detect surrounding objects, run well, and ability to avoid obstacles. The main orientation of the hexapod robot motion is to move forward, but if there are obstacles, it will find a solution to turn using ultrasonic sensors. An inertial measurement unit sensor is used to maintain the robot’s body’s stability.

It has been noticed that a lot of fatalities of lives occur every day due to manually operated rail gates all over Bangladesh. These happen mainly at places where the rail road passes through a city, locality or unmanned gates of the crossing zones. Currently, gatemen mainly operate on the assumption of a train departure schedule from the station to reach a crossing zone. But at times there are departure delays or maybe the train reaches crossing zones earlier, leaving the gateman unprepared to close the gate. Accidents are more likely to happen in such cases, causing severe damage to human lives and properties near the rail crossings. This paper presents the development and implementation of automatic rail gate control system as well as real time monitoring of train and obstacle detection for developing countries like Bangladesh. This research project was carried out using Arduino Nano along with IR Sensor, IR LED, Flame Sensor, Servo Motor, Ultrasonic sensor, DC Gear Motor and USB UART Board. This project is a combination of old technology with recent wireless technology and analytics to provide the best possible service to the nation. This paper also suggests the effectiveness of real time information of train position. The main objective of the proposed humanitarian project is to ensure the efficiency, quality, time management, and most importantly public safety, using wireless based communication network for the development of the railway industry in Bangladesh.

Because of the complexity of the underwater environment, it is difficult to realize the automatic control of underwater welding, and the selection of welding sensor also has been a problem. According to the special situation of underwater welding, this paper designed underwater welding seam automatic tracking system which based on a set of ultrasonic and CCD sensors. This system combines ultrasonic ranging and graphic positioning function, so it would lay the foundation for the further research on automatic seam tracking of underwater welding.

Airport aprons serve as central hubs for various airport operations, including aircraft parking, refueling, maintenance, passenger boarding and deboarding, as well as cargo loading and unloading. Accurate and reliable monitoring of these operations is critical for maintaining operational efficiency and safety. However, airport apron environments are highly dynamic and complex, with aircraft, ground equipment, and personnel in constant motion. The <i>FastGate</i> project aims to develop a digital twin of the apron for monitoring and optimization purposes. The state of the art of the object detection and localization methods are studied, and existing datasets is analyzed. However, the existing solutions either lack full 3D detection and localization capabilities or are unsuitable for the use case at the airport aprons. This paper introduces a novel pipeline for real-time object detection and localization for airport aprons, by combining object detection and segmentation in 2D images, 2D and 3D sensors data fusion, object localization through 3D point cloud, and object orientation detection with temporal data. The apron is surveyed using a RGB camera and LiDAR sensor, to enable the automatic object detection and localization to provide real-time information about vehicles, personnel, and other objects present at the airport apron. The proposed approach integrates camera and LiDAR data for real-time object detection and localization, offering comprehensive scene awareness and enabling precise object detection and localization on airport aprons. The implemented technical solution is evaluated in terms of accuracy, localization precision, and time efficiency.