Project on collision avoidance in static and dynamic environment
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This document proposes a method for real-time path planning of mobile robots to avoid collisions in static and dynamic environments. It involves using an artificial neural network (ANN) for obstacle detection and a vector field histogram method with decision trees for local path planning. When all paths are blocked, fuzzy logic will be used to choose a path. The objective is for the robot to safely navigate around obstacles without communication delays. The proposed method aims to overcome limitations of previous techniques by applying ANN, decision trees, and fuzzy logic for robust, real-time collision avoidance.
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Human Pose Estimation by Deep Learning
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An automated approach for the recognition of bengali license plates presentationMD Abdullah Al Nasim
Automatic Number Plate Recognition (ALPR) is a system for automatically identifying the license plates of any vehicle. This process is important for tracking, ticketing, and any billing system, among other things. With the use of information and communication technology (ICT), all systems are being automated, including the vehicle tracking system. This study proposes a hybrid method for detecting license plates using characters from them. Our captured image information was used for the recognition procedure in Bangladeshi vehicles, which is the topic of this study. Here, for license plate detection, the YOLO model was used where 81\% was correctly predicted. And then, for license plate segmentation, Otsu's Thresholding was used and eventually, for character recognition, the CNN model was applied. This model will allow the vehicle's automated license plate detection system to avoid any misuse.A Comparison of People Counting Techniques via Video Scene Analysis
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Brain Tumor Area Calculation in CT-scan image using Morphological Operations
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
K-Means Clustering in Moving Objects Extraction with Selective Background
K-Means Clustering in Moving Objects Extraction with Selective BackgroundIJCSIS Research Publications
We presents a technique for moving objects extraction. There are several different approaches for moving object extraction, clustering is one of object extraction method with a stronger teorical foundation used in many applications. And need high performance in many extraction process of moving object. We compare K-Means and Self-Organizing Map method for extraction moving objects, for performance measurement of moving object extraction by applying MSE and PSNR. According to experimental result that the MSE value of K-Means is smaller than Self-Organizing Map. It is also that PSNR of K-Means is higher than Self-Organizing Map algorithm. The result proves that K-Means is a promising method to cluster pixels in moving objects extraction.Udirect: accurate and reliable estimation of the facing direction of the mobi...
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Learning of robot navigation tasks by
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studies reported focusing on robot navigation tasks and using same dataset. It was observed the
PNN is the best classification accuracy with 99,635% accuracy using same dataset.
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Obstacle Avoidance ROBOT using ARDUINO
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Neural network based systems have been used in
past years for robot navigation applications because of their
ability to learn human expertise and to utilize this knowledge
to develop autonomous navigation strategies. In this paper,
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navigation. The proposed systems transform sensors’ input to
yield wheel velocities. Novel algorithm is proposed for optimal
training of neural network. With a view to ascertain the efficacy
of proposed system; developed neural system’s performance
is compared to other neural and fuzzy based approaches.
Simulation results show effectiveness of proposed system in
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Wang midterm-defence
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IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
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This document discusses using sampling-based motion planning algorithms like rapidly exploring random trees (RRT) to generate safe trajectories for autonomous vehicles in uncertain, dynamic environments. It describes using RRT to plan trajectories for a vehicle that avoids obstacles while obeying road boundaries and vehicle dynamics constraints. The method generates candidate nodes at each iteration, checks them for collisions, and connects valid nodes to create potential trajectories. While sampling-based planning handles complex constraints, it can produce non-smooth, suboptimal paths. Future work involves optimizing endpoint selection, implementing path smoothing with model predictive control, and testing in realistic scenarios.
LEARNING OF ROBOT NAVIGATION TASKS BY PROBABILISTIC NEURAL NETWORK
This paper reports results of artificial neural network for robot navigation tasks. Machine
learning methods have proven usability in many complex problems concerning mobile robots
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In this study, probabilistic neural network (PNN) structure was used for robot navigation tasks.
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This document presents a modified artificial potential fields algorithm for mobile robot path planning in unknown and dynamic environments. The algorithm uses artificial potential fields to iteratively find optimal points to form a collision-free path from the start to destination. For static obstacles, potential values are used to identify clusters of points around the start and goal, and find a connecting midpoint. This process is repeated iteratively. For dynamic obstacles, Markov models are used to analyze obstacle behavior from sensor data and predict collision points. The robot's path is replanned as needed to avoid collisions based on feedback from sensors and odometry. Simulation results show the algorithm can efficiently plan paths in unknown environments and avoid both static and dynamic obstacles.
A Design of fuzzy controller for Autonomous Navigation of Unmanned Vehicle
The design approach is proposed for fuzzy logic controller for autonomous navigation of a vehicle in an obstacle filled environment. The proposed fuzzy controller is composed obstacle avoidance layer, orientation control layer, passage detection module. Here the fuzzy controller for obstacle avoidance is proposed. It provides a model for multiple sensor input fusion and it is composed of eight individual controllers, each calculating a collision possibility in different directions of movement. By calculating value of collision possibility main controller that performs real-time collision avoidance. The operating frequency & logic cells requirements for different implementation techniques are find out. The designs have been carried out in the digital domain with VHDL using Altera Quartus-II software.
MODEL FOR INTRUSION DETECTION SYSTEM
Advancement in wireless communications lead more and more mobile wireless networks e.g.,
mobile networks [mobile ad hoc networks (MANETs)], wireless sensor networks, etc. Some of
the challenges in MANET include: Dynamic network topology, Speed, Bandwidth, computation
capability, Scalability, Quality of service, Secure and Reliable routing. One of the most
important challenges in mobile wireless networks is the Secure and reliable routing and the
main characteristic of MANET with respect to security is the lack of clear line of defence.
Therefore, the SP routing problem in MANET turns into dynamic optimization problem. In this
paper, a path detection algorithm and a model to detect intruders that is misbehaving nodes in
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LEARNING OF ROBOT NAVIGATION TASKS BY PROBABILISTIC NEURAL NETWORK
This paper reports results of artificial neural network for robot navigation tasks. Machine learning methods have proven usability in many complex problems concerning mobile robots
control. In particular we deal with the well-known strategy of navigating by “wall-following”. In this study, probabilistic neural network (PNN) structure was used for robot navigation tasks.
The PNN result was compared with the results of the Logistic Perceptron, Multilayer Perceptron, Mixture of Experts and Elman neural networks and the results of the previous
studies reported focusing on robot navigation tasks and using same dataset. It was observed the PNN is the best classification accuracy with 99,635% accuracy using same dataset.
Object extraction using edge, motion and saliency information from videos
Abstract Object detection is a process of finding the instances of object of a certain class which is useful in analysis of video or image. There are number of algorithms have been developed so far for object detection. Object detection has got significant role in variety of areas of computer vision like video surveillance, image retrieval`. In this paper presented an efficient algorithm for moving object extraction using edge, motion and saliency information from videos. Out methodology includes 4 stages: Frame generation, Pre-processing, Foreground generation and integration of cues. Foreground generation includes edge detection using sobel edge detection algorithm, motion detection using pixel-based absolute difference algorithm and motion saliency detection. Conditional Random Field (CRF) is applied for integration of cues and thus we get better spatial information of segmented object. Keywords: Object detection, Saliency information, Sobel edge detection, CRF.
Human Re-identification with Global and Local Siamese Convolution Neural Network
This document proposes a global and local structure of Siamese Convolution Neural Network (SCNN) to perform human re-identification in single-shot approaches. The network extracts features from global and local parts of input images. A decision fusion technique then combines the global and local features. Experimental results on the VIPeR dataset show the proposed method achieves a normalized Area Under Curve score of 95.75% without occlusion, outperforming using local or global features alone. With occlusion, the score is 77.5%, still better than alternatives. The method performs well for re-identification including in occlusion cases by leveraging both global and local information.
VARIATIONAL MONTE-CARLO APPROACH FOR ARTICULATED OBJECT TRACKING
In this paper, we describe a novel variational Monte Carlo approach for modeling and tracking
body parts of articulated objects. An articulated object (human target) is represented as a
dynamic Markov network of the different constituent parts. The proposed approach combines
local information of individual body parts and other spatial constraints influenced by
neighboring parts. The movement of the relative parts of the articulated body is modeled with
local information of displacements from the Markov network and the global information from
other neighboring parts. We explore the effect of certain model parameters (including the
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DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
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Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
UiPath Test Automation using UiPath Test Suite series, part 5
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TrustArc Webinar - 2024 Global Privacy Survey
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
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Project on collision avoidance in static and dynamic environment
- 1. By: Waikhom Pithoijit Singh Gopal Jee Avinash Kumar
- 2. Objective Motivation Literature survey Proposed method Conclusion and future scope References
- 3. objective Collision avoidance in static and dynamic environment technique of real time path planning of mobile robot
- 4. Motivation Robots send on exploration mission If robots finds obstacle, sends a signal to earth station In return earth station respond to that signal Time consuming and ineffective in real time application So this type of inconvenience can be overcome by the application of collision avoiding robots.
- 5. Literature survey Path planning algorithm for motion planning by Zidek, k Rigasa, E. Path planning using edge detection method. In this method, an algorithm tries to determine the position of the vertical edges of the obstacle and then steer the robot around either one of the "visible" edges. The line connecting two visible edges is considered to represent one of the boundaries of the obstacle. A common drawbacks are poor directionality, frequent misreading, specular reflections.
- 6. The Certainty Grid for Obstacle Representation In the certainty grid, the robot's work area is represented by a two-dimensional array of square elements, denoted as cells. Each cell contains a certainty value (CV) that indicates the measure of confidence that an obstacle exists within the cell area. With the CMU method, CVs are updated by a probability function that takes into account the characteristics of a given sensor In CMU's applications of this method, the mobile robot remains stationary while it takes a panoramic scan with its 24 ultrasonic sensors
- 7. Proposed Method Application of ANN for path planning and obstacle detection. Vector field histogram method using DT for local path planning. When all the paths are block then we will used Fuzzy logic.
- 8. Artificial Neural Network (ANN) Compound of a large no. of highly interconnected processing elements (neurons) working in union to solve specific problems. Loosely modelled on biological neural network Neuron: processing unit
- 10. Fuzzy Logic It deals with reasoning that is approximate rather than fixed and exact. Three basic steps involve in fuzzy logic Fuzzification: changing a real scalar value into a fuzzy value. Rule Evaluation: an inference is made based on a set of rules. Defuzzification: the resulting fuzzy output is mapped to a crisp output using the membership functions
- 11. Working Our target is to avoid collision with the obstacle in the path. To choose path we are using ANN-DT tree If all the path are blocked then we will use fuzzy logic to choose the path.
- 12. Future scope Games and Virtual Robot Motion and Navigation Driverless Vehicles Transportation Networks Human Navigation
- 13. References [1] Xiongmin Li, Christine W. Chan , “Application of an enhanced decision tree learning approach for prediction of petroleum production “ , Engineering Applications of Artificial Intelligence , Elsevier, 23 (2010) 102–109. [2] Kweku-Muata Osei-Bryson, “Post-pruning in decision tree induction using multiple performance measures”, Computers & Operations Research, Elsevier, 34 (2007) 3331 – 3345. [3] Hendrik Blockeel *, Luc De Raedt, “Top-down induction of first-order logical decision trees”, Artificial Intelligence, Elsevier, 101 (1998) 285-297. [4] Max Bramer, “Using J-Pruning to reduce overfitting in classification trees”, Knowledge- Based system, Elsevier, 15(2002) 301-308. [5] Hussein Almuallim , “An efficient algorithm for optimal pruning of decision trees”, Artificial Intelligence , Elsevier,83 ( 1996) 347-362 [6] J. Borenstein, Member, IEEE and Y. Koren, Senior Member, IEEE, “THE VECTOR FIELD HISTOGRAM -FAST OBSTACLE AVOIDANCE FOR MOBILE ROBOTS », IEEE Journal of Robotics and Automation Vol 7, No 3, June 1991, pp. 278-288.