ABSTRACT
Automobiles have become an integrated part of our daily life. The development of technology has improved the automobile industry in both cost & efficiency. Still, accidents prove as challenge to technology. Highway accident news are frequently found in the newspapers. The automobile speed has increased with development in technology through years and the complexity of the accidents has also increased. Higher speeds the accidents prove to be more fatal. Man is intelligent with reasoning power and can respond to any critical situation. But under stress and tension he falls as a prey to accidents. The manual control of speed & braking of a car fails during anxiety. Thus automated speed control & braking system is required to prevent accidents. This automation is possible only with the help of Artificial Intelligence (Fuzzy Logic).
In this paper, Fuzzy Logic control system is used to control the speed of the car based on the obstacle sensed. The obstacle sensor unit senses the presence of the obstacle. The sensing distance depends upon the speed of the car. Within this distance, the angle of the obstacle is sensed and the speed is controlled according to the angle subtended by the obstacle. If the obstacle cannot be crossed by the car, then the brakes are applied and the car comes to rest before colliding with the obstacle. Thus, this automated fuzzy control unit can provide an accident free journey.
This document discusses fuzzy logic systems and fuzzy control. It begins by explaining why fuzzy logic is useful for control systems where simplicity and speed of implementation are important. It then provides examples of commercial applications of fuzzy control in various industries. The document goes on to describe the engineering motivation for fuzzy logic, and how to implement a basic fuzzy logic system using fuzzification, fuzzy decision blocks, and defuzzification. It also discusses developing fuzzy logic control rules based on common sense "if-then" statements. Finally, it briefly discusses using fuzzy control in feedback systems to mimic the control procedures of skilled human operators.
This document provides an introduction and overview of fuzzy logic, including:
- Fuzzy sets allow gradual membership rather than crisp membership in sets, addressing limitations of binary logic.
- A case study examines controlling the speed of a room cooler motor based on temperature and humidity using fuzzy logic rules and membership functions.
- Key fuzzy logic concepts covered include fuzzification, fuzzy rules and inference, and defuzzification to obtain a crisp output from fuzzy inputs and rules.
Fuzzy logic is a form of logic that accounts for partial truth and intermediate values between true and false. It is used in control systems to mimic how humans apply fuzzy concepts like "cold" or "hot" temperature. Some key applications of fuzzy logic include temperature controllers, washing machines, air conditioners, and anti-lock braking systems. Fuzzy logic controllers use if-then rules to determine outputs based on fuzzy inputs and degrees of membership rather than binary logic.
This presentation includes what is fuzzy logic, characteristics, membership function with example, fuzzy set theory, De-Morgans Law, Fuzzy logic V/S probability, advantages and disadvantages and application areas of fuzzy logic. This is a presentation is useful for IT students.
Chaotic Secure Communication Using Iterated Filtering Method P. Karthik -Assistant Professor,
D. Gokul Prashanth -UG Scholar,
T. Gokul - UG Scholar,
Department of Electronics and Communication Engineering,
SNS College of Engineering, Coimbatore, India.
How can you deal with Fuzzy Logic. Fuzzy logic is a form of many-valued logic; it deals with reasoning that is approximate rather than fixed and exact. In contrast with traditional logic theory, where binary sets have two-valued logic: true or false, fuzzy logic variables may have a truth value that ranges in degree
between 0 and 1
Fuzzy inference systems use fuzzy logic to map inputs to outputs. There are two main types:
Mamdani systems use fuzzy outputs and are well-suited for problems involving human expert knowledge. Sugeno systems have faster computation using linear or constant outputs.
The fuzzy inference process involves fuzzifying inputs, applying fuzzy logic operators, and using if-then rules. Outputs are determined through implication, aggregation, and defuzzification. Mamdani systems find the centroid of fuzzy outputs while Sugeno uses weighted averages, making it more efficient.
This document summarizes research on optimized fingerprint compression without loss of data. It discusses how fingerprint recognition works by extracting minutiae features from fingerprints. It then describes the proposed fingerprint recognition method using minutiae score matching (FRMSM), which uses block filtering for thinning fingerprints to preserve image quality while extracting minutiae. Experimental results showed the false matching ratio was better than existing algorithms. The document also provides background on biometric systems and fingerprint recognition. It reviews related work on fingerprint enhancement, orientation field estimation, and minutiae extraction. The proposed system describes a line extraction and graph matching approach for fingerprint matching with improved robustness. Modules for the system include authentication, image capturing, fingerprint matching, binarization, and
This document discusses fuzzy logic systems and fuzzy control. It begins by explaining why fuzzy logic is useful for control systems where simplicity and speed of implementation are important. It then provides examples of commercial applications of fuzzy control in various industries. The document goes on to describe the engineering motivation for fuzzy logic, and how to implement a basic fuzzy logic system using fuzzification, fuzzy decision blocks, and defuzzification. It also discusses developing fuzzy logic control rules based on common sense "if-then" statements. Finally, it briefly discusses using fuzzy control in feedback systems to mimic the control procedures of skilled human operators.
This document provides an introduction and overview of fuzzy logic, including:
- Fuzzy sets allow gradual membership rather than crisp membership in sets, addressing limitations of binary logic.
- A case study examines controlling the speed of a room cooler motor based on temperature and humidity using fuzzy logic rules and membership functions.
- Key fuzzy logic concepts covered include fuzzification, fuzzy rules and inference, and defuzzification to obtain a crisp output from fuzzy inputs and rules.
Fuzzy logic is a form of logic that accounts for partial truth and intermediate values between true and false. It is used in control systems to mimic how humans apply fuzzy concepts like "cold" or "hot" temperature. Some key applications of fuzzy logic include temperature controllers, washing machines, air conditioners, and anti-lock braking systems. Fuzzy logic controllers use if-then rules to determine outputs based on fuzzy inputs and degrees of membership rather than binary logic.
This presentation includes what is fuzzy logic, characteristics, membership function with example, fuzzy set theory, De-Morgans Law, Fuzzy logic V/S probability, advantages and disadvantages and application areas of fuzzy logic. This is a presentation is useful for IT students.
Chaotic Secure Communication Using Iterated Filtering Method P. Karthik -Assistant Professor,
D. Gokul Prashanth -UG Scholar,
T. Gokul - UG Scholar,
Department of Electronics and Communication Engineering,
SNS College of Engineering, Coimbatore, India.
How can you deal with Fuzzy Logic. Fuzzy logic is a form of many-valued logic; it deals with reasoning that is approximate rather than fixed and exact. In contrast with traditional logic theory, where binary sets have two-valued logic: true or false, fuzzy logic variables may have a truth value that ranges in degree
between 0 and 1
Fuzzy inference systems use fuzzy logic to map inputs to outputs. There are two main types:
Mamdani systems use fuzzy outputs and are well-suited for problems involving human expert knowledge. Sugeno systems have faster computation using linear or constant outputs.
The fuzzy inference process involves fuzzifying inputs, applying fuzzy logic operators, and using if-then rules. Outputs are determined through implication, aggregation, and defuzzification. Mamdani systems find the centroid of fuzzy outputs while Sugeno uses weighted averages, making it more efficient.
This document summarizes research on optimized fingerprint compression without loss of data. It discusses how fingerprint recognition works by extracting minutiae features from fingerprints. It then describes the proposed fingerprint recognition method using minutiae score matching (FRMSM), which uses block filtering for thinning fingerprints to preserve image quality while extracting minutiae. Experimental results showed the false matching ratio was better than existing algorithms. The document also provides background on biometric systems and fingerprint recognition. It reviews related work on fingerprint enhancement, orientation field estimation, and minutiae extraction. The proposed system describes a line extraction and graph matching approach for fingerprint matching with improved robustness. Modules for the system include authentication, image capturing, fingerprint matching, binarization, and
1. Control and budgeting involves establishing standards, measuring performance against those standards, and taking necessary corrective actions. An effective control system is timely, flexible, economical, understandable, and accepted.
2. Budgeting frameworks incorporate corporate strategy, budget periods, responsibility centers, participation from various levels, and incremental or zero-based approaches. Master budgets integrate various functional budgets like production, materials, and expenses.
3. Performance budgeting evaluates organizational performance based on objectives. It identifies specific performance objectives for jobs and links them to overall organizational goals.
This document provides an overview of various welding processes including oxyfuel gas welding, arc welding, electrogas welding, electroslag welding and others. It defines fusion welding as melting materials together through heat from thermal or electrical means. It describes oxyfuel gas welding using acetylene fuel and oxygen to produce a flame to melt metals. Arc welding processes use an electric arc between an electrode and workpiece to generate heat and melt the metals. Electrodes can be consumable or non-consumable. Various arc welding techniques are discussed including shielded metal arc welding and gas metal arc welding.
Biomass — renewable energy from plants and animalsDhananjay Rao
- Biomass is organic material from plants and animals that contains stored energy from the sun. It is a renewable energy source that can be used to produce heat, electricity, methane gas, ethanol, and biodiesel.
- Biomass can be burned directly to produce heat, or processed to release its chemical energy in other usable forms. Common biomass fuels include wood, crops, manure, garbage and other waste materials.
- Biogas is a mixture of methane and carbon dioxide produced from the breakdown of organic matter by bacteria during anaerobic digestion. It can be captured from various waste sources and used as a fuel.
This document discusses various topics related to decision making, including:
1) It describes decision making as encompassing many small decisions made throughout the day, as well as more complex problems that require organized thought and have economic components.
2) It discusses different types of costs like fixed, variable, and semi-variable costs that are considered in economic analysis and how they relate to volume. Capital expenditures and new product design are provided as examples of problems requiring decision making.
3) An example is given showing how total costs change with volume based on the cost-volume relationship, with fixed costs remaining constant and variable costs and average unit costs decreasing as volume increases.
Engineering economy involves applying economic principles to engineering project decisions. It allows engineers to evaluate design alternatives and justify projects based on both technical and financial factors. Some key applications of engineering economy include selecting equipment, determining whether to purchase or lease assets, and evaluating public infrastructure projects based on costs, benefits, and property value impacts. The rational decision-making process in engineering economy helps engineers objectively analyze alternatives and choose optimal solutions for both business and non-profit organizations.
The document provides an overview of biogas production through anaerobic digestion. It discusses how cows naturally produce biogas through digestion and explains the basic process of anaerobic digestion in a biogas plant. It also provides statistics on biogas usage around the world, with China and India having the most biogas units. Key factors that affect the efficiency of anaerobic digestion, such as temperature, retention time, pH levels, and mixing, are also summarized.
Economics of biogas plants and their role in saving the environmentDhananjay Rao
This document discusses biogas production through anaerobic digestion. It begins by outlining the composition of biogas and common organic materials used for production. These include cattle dung, kitchen waste, and crop residue. The document then describes the 3-phase digestion process and provides breakdowns of gas yield from different feedstocks. Key uses of biogas are also listed, such as cooking, lighting, and power generation. Installation rates of biogas plants in India are then assessed, followed by economics of family-sized plants and their role in environmental protection.
This document summarizes an example of using engineering economics principles to analyze the financial viability of purchasing a hybrid vehicle versus a non-hybrid vehicle. It outlines comparing the Honda Civic Hybrid sedan, which costs more initially but gets better gas mileage, to the gas-only Civic EX sedan to see if the added cost of the hybrid is worthwhile over a 5 year ownership period based on fuel savings.
This document discusses anti-lock braking systems (ABS) for vehicles. It provides information on the basic functions of ABS, how ABS systems work using speed sensors and an onboard computer to pump the brakes rapidly, and what drivers should do when ABS engages, which is to stomp on the brakes and steer without pumping the pedal. It also discusses integrating ABS driver training into defensive driving programs and lists educational resources on ABS systems.
In this ppt, Fuzzy Logic control system is used to control the speed of the car based on the obstacle sensed. The obstacle sensor unit senses the presence of the obstacle. The sensing distance depends upon the speed of the car. Within this distance, the angle of the obstacle is sensed and the speed is controlled according to the angle subtended by the obstacle. If the obstacle cannot be crossed by the car, then the brakes are applied and the car comes to rest before colliding with the obstacle. Thus, this automated fuzzy control unit can provide an accident free journey.
The document discusses advanced manufacturing choices and processes. It introduces different types of energy that can be used for manufacturing including mechanical, electrical, heat, and chemical energy. Students will learn to decide which manufacturing process to use based on a product's specifications and design. They will learn when to use different mechanical and lithography-based machining methods as well as when to employ top-down vs. bottom-up manufacturing. The document provides an overview of topics that will be covered in the course, which include various manufacturing processes, rapid prototyping, and how to match processes to product requirements.
Comparison b/n Two poopular Hero bikes.
This presentation is created during academic submission of CASP subject as per DTE syllabus.
things mentioned in this ppt is based on a feed back of all respective users and document has been mainatained with all rights.
Dj
Vivarthe - Art and Design Studio in BangaloreVivarthe
Vivarthe was founded in 2005 with the intention of providing quality services in the area of Art and Design which appeals to the taste of every one. Our main objective is to make Vivarthe a one-stop-solution for all the art and design requirements, be it in the field of Painting , Murals or Sculpture Designing print and web.
A.Praveen.Raj is seeking a career in a hi-tech environment that allows him to fully utilize his potential. He has over 8 years of experience in customer service and sales roles, including working as a senior telesales executive and team leader. Praveen has strong communication and leadership skills and strives to perform every task to his maximum capability.
This document discusses fuzzy logic control of vehicle speed based on sensed obstacles. It proposes an automated fuzzy control system that can control a vehicle's speed and apply the brakes based on the angle and distance of any obstacles detected. This system aims to provide accident-free driving by taking over manual control of speed and braking during times of stress or tension when human control may falter. The system uses fuzzy logic to smoothly vary the vehicle's speed based on the obstacle parameters in order to stop safely before any potential collisions.
Fuzzy logic control systems can be used to control the speed of automated automobiles based on obstacles sensed. Fuzzy logic is well-suited for control applications like automated driving where there is ambiguity. For more complex driving systems, fuzzy logic provides a powerful and robust method of control compared to mathematical models or neural networks. A fuzzy logic system for automated vehicle speed control uses sensors to detect obstacles and membership functions to characterize fuzzy sets that define the relationship between speed and sensed distance to determine appropriate defuzzified speed values. This fuzzy control system can help prevent accidents by relieving driver tension.
This document proposes using fuzzy logic to develop a collision avoidance system for trains. It describes fuzzy logic and how it can handle imprecise data and model nonlinear functions. The proposed system would use inputs like track vibrations and frequency to determine train distance and speed. It would compare the inputs to predetermined rules and provide outputs to control train speed. Examples show it could determine if a train should maintain speed, stop immediately, or increase speed based on the input conditions and rules. Fuzzy logic allows for a simple, intuitive approach to train collision avoidance.
Reinforcement learning in neuro fuzzy traffic signal controlAbhishek Vishnoi
A neuro-fuzzy traffic signal controller uses a neural network to adjust the parameters of a fuzzy logic controller. The neural network receives feedback about the performance of the fuzzy controller and uses reinforcement learning to fine-tune the membership functions. This allows the fuzzy traffic signal system to adapt over time and reduce vehicle delays at intersections. Experimental results show reductions in average vehicle delay after the neuro-fuzzy system is trained compared to the initial fuzzy controller alone.
This document describes a thesis submitted by Harshdeep Singh to the National Institute of Technology Rourkela for the degree of Bachelor of Technology in Mechanical Engineering. The thesis proposes the design of a water level controller using a fuzzy logic system. It involves developing an electronic water level indicator and a fuzzy logic controller in MATLAB Simulink to control water level in a tank. The fuzzy controller performance will be compared to a PID controller for controlling water level.
This paper presents an approach for image restoration in the presence of blur and noise. The image is divided into independent regions modeled with a Gaussian prior. Wavelet-based methods are used for image denoising, while classical Wiener filtering is used for deblurring. The algorithm finds the maximum a posteriori estimate at the intersection of convex sets generated by Wiener filtering. It provides efficient image restoration without sacrificing the simplicity of filtering, and generates a better restored image compared to previous methods.
This paper presents an approach for image restoration in the presence of blur and noise. The image is divided into independent regions modeled with a Gaussian prior. Wavelet based methods are used for image denoising, while classical Wiener filtering is used for deblurring. The algorithm finds the maximum a posteriori estimate at the intersection of convex sets generated by Wiener filtering. It provides efficient image restoration without sacrificing the simplicity of filtering, and generates a better restored image.
1. Control and budgeting involves establishing standards, measuring performance against those standards, and taking necessary corrective actions. An effective control system is timely, flexible, economical, understandable, and accepted.
2. Budgeting frameworks incorporate corporate strategy, budget periods, responsibility centers, participation from various levels, and incremental or zero-based approaches. Master budgets integrate various functional budgets like production, materials, and expenses.
3. Performance budgeting evaluates organizational performance based on objectives. It identifies specific performance objectives for jobs and links them to overall organizational goals.
This document provides an overview of various welding processes including oxyfuel gas welding, arc welding, electrogas welding, electroslag welding and others. It defines fusion welding as melting materials together through heat from thermal or electrical means. It describes oxyfuel gas welding using acetylene fuel and oxygen to produce a flame to melt metals. Arc welding processes use an electric arc between an electrode and workpiece to generate heat and melt the metals. Electrodes can be consumable or non-consumable. Various arc welding techniques are discussed including shielded metal arc welding and gas metal arc welding.
Biomass — renewable energy from plants and animalsDhananjay Rao
- Biomass is organic material from plants and animals that contains stored energy from the sun. It is a renewable energy source that can be used to produce heat, electricity, methane gas, ethanol, and biodiesel.
- Biomass can be burned directly to produce heat, or processed to release its chemical energy in other usable forms. Common biomass fuels include wood, crops, manure, garbage and other waste materials.
- Biogas is a mixture of methane and carbon dioxide produced from the breakdown of organic matter by bacteria during anaerobic digestion. It can be captured from various waste sources and used as a fuel.
This document discusses various topics related to decision making, including:
1) It describes decision making as encompassing many small decisions made throughout the day, as well as more complex problems that require organized thought and have economic components.
2) It discusses different types of costs like fixed, variable, and semi-variable costs that are considered in economic analysis and how they relate to volume. Capital expenditures and new product design are provided as examples of problems requiring decision making.
3) An example is given showing how total costs change with volume based on the cost-volume relationship, with fixed costs remaining constant and variable costs and average unit costs decreasing as volume increases.
Engineering economy involves applying economic principles to engineering project decisions. It allows engineers to evaluate design alternatives and justify projects based on both technical and financial factors. Some key applications of engineering economy include selecting equipment, determining whether to purchase or lease assets, and evaluating public infrastructure projects based on costs, benefits, and property value impacts. The rational decision-making process in engineering economy helps engineers objectively analyze alternatives and choose optimal solutions for both business and non-profit organizations.
The document provides an overview of biogas production through anaerobic digestion. It discusses how cows naturally produce biogas through digestion and explains the basic process of anaerobic digestion in a biogas plant. It also provides statistics on biogas usage around the world, with China and India having the most biogas units. Key factors that affect the efficiency of anaerobic digestion, such as temperature, retention time, pH levels, and mixing, are also summarized.
Economics of biogas plants and their role in saving the environmentDhananjay Rao
This document discusses biogas production through anaerobic digestion. It begins by outlining the composition of biogas and common organic materials used for production. These include cattle dung, kitchen waste, and crop residue. The document then describes the 3-phase digestion process and provides breakdowns of gas yield from different feedstocks. Key uses of biogas are also listed, such as cooking, lighting, and power generation. Installation rates of biogas plants in India are then assessed, followed by economics of family-sized plants and their role in environmental protection.
This document summarizes an example of using engineering economics principles to analyze the financial viability of purchasing a hybrid vehicle versus a non-hybrid vehicle. It outlines comparing the Honda Civic Hybrid sedan, which costs more initially but gets better gas mileage, to the gas-only Civic EX sedan to see if the added cost of the hybrid is worthwhile over a 5 year ownership period based on fuel savings.
This document discusses anti-lock braking systems (ABS) for vehicles. It provides information on the basic functions of ABS, how ABS systems work using speed sensors and an onboard computer to pump the brakes rapidly, and what drivers should do when ABS engages, which is to stomp on the brakes and steer without pumping the pedal. It also discusses integrating ABS driver training into defensive driving programs and lists educational resources on ABS systems.
In this ppt, Fuzzy Logic control system is used to control the speed of the car based on the obstacle sensed. The obstacle sensor unit senses the presence of the obstacle. The sensing distance depends upon the speed of the car. Within this distance, the angle of the obstacle is sensed and the speed is controlled according to the angle subtended by the obstacle. If the obstacle cannot be crossed by the car, then the brakes are applied and the car comes to rest before colliding with the obstacle. Thus, this automated fuzzy control unit can provide an accident free journey.
The document discusses advanced manufacturing choices and processes. It introduces different types of energy that can be used for manufacturing including mechanical, electrical, heat, and chemical energy. Students will learn to decide which manufacturing process to use based on a product's specifications and design. They will learn when to use different mechanical and lithography-based machining methods as well as when to employ top-down vs. bottom-up manufacturing. The document provides an overview of topics that will be covered in the course, which include various manufacturing processes, rapid prototyping, and how to match processes to product requirements.
Comparison b/n Two poopular Hero bikes.
This presentation is created during academic submission of CASP subject as per DTE syllabus.
things mentioned in this ppt is based on a feed back of all respective users and document has been mainatained with all rights.
Dj
Vivarthe - Art and Design Studio in BangaloreVivarthe
Vivarthe was founded in 2005 with the intention of providing quality services in the area of Art and Design which appeals to the taste of every one. Our main objective is to make Vivarthe a one-stop-solution for all the art and design requirements, be it in the field of Painting , Murals or Sculpture Designing print and web.
A.Praveen.Raj is seeking a career in a hi-tech environment that allows him to fully utilize his potential. He has over 8 years of experience in customer service and sales roles, including working as a senior telesales executive and team leader. Praveen has strong communication and leadership skills and strives to perform every task to his maximum capability.
This document discusses fuzzy logic control of vehicle speed based on sensed obstacles. It proposes an automated fuzzy control system that can control a vehicle's speed and apply the brakes based on the angle and distance of any obstacles detected. This system aims to provide accident-free driving by taking over manual control of speed and braking during times of stress or tension when human control may falter. The system uses fuzzy logic to smoothly vary the vehicle's speed based on the obstacle parameters in order to stop safely before any potential collisions.
Fuzzy logic control systems can be used to control the speed of automated automobiles based on obstacles sensed. Fuzzy logic is well-suited for control applications like automated driving where there is ambiguity. For more complex driving systems, fuzzy logic provides a powerful and robust method of control compared to mathematical models or neural networks. A fuzzy logic system for automated vehicle speed control uses sensors to detect obstacles and membership functions to characterize fuzzy sets that define the relationship between speed and sensed distance to determine appropriate defuzzified speed values. This fuzzy control system can help prevent accidents by relieving driver tension.
This document proposes using fuzzy logic to develop a collision avoidance system for trains. It describes fuzzy logic and how it can handle imprecise data and model nonlinear functions. The proposed system would use inputs like track vibrations and frequency to determine train distance and speed. It would compare the inputs to predetermined rules and provide outputs to control train speed. Examples show it could determine if a train should maintain speed, stop immediately, or increase speed based on the input conditions and rules. Fuzzy logic allows for a simple, intuitive approach to train collision avoidance.
Reinforcement learning in neuro fuzzy traffic signal controlAbhishek Vishnoi
A neuro-fuzzy traffic signal controller uses a neural network to adjust the parameters of a fuzzy logic controller. The neural network receives feedback about the performance of the fuzzy controller and uses reinforcement learning to fine-tune the membership functions. This allows the fuzzy traffic signal system to adapt over time and reduce vehicle delays at intersections. Experimental results show reductions in average vehicle delay after the neuro-fuzzy system is trained compared to the initial fuzzy controller alone.
This document describes a thesis submitted by Harshdeep Singh to the National Institute of Technology Rourkela for the degree of Bachelor of Technology in Mechanical Engineering. The thesis proposes the design of a water level controller using a fuzzy logic system. It involves developing an electronic water level indicator and a fuzzy logic controller in MATLAB Simulink to control water level in a tank. The fuzzy controller performance will be compared to a PID controller for controlling water level.
This paper presents an approach for image restoration in the presence of blur and noise. The image is divided into independent regions modeled with a Gaussian prior. Wavelet-based methods are used for image denoising, while classical Wiener filtering is used for deblurring. The algorithm finds the maximum a posteriori estimate at the intersection of convex sets generated by Wiener filtering. It provides efficient image restoration without sacrificing the simplicity of filtering, and generates a better restored image compared to previous methods.
This paper presents an approach for image restoration in the presence of blur and noise. The image is divided into independent regions modeled with a Gaussian prior. Wavelet based methods are used for image denoising, while classical Wiener filtering is used for deblurring. The algorithm finds the maximum a posteriori estimate at the intersection of convex sets generated by Wiener filtering. It provides efficient image restoration without sacrificing the simplicity of filtering, and generates a better restored image.
This document reviews a fuzzy microscopic traffic model that uses fuzzy logic to simulate traffic streams at signalized intersections. The model represents vehicle parameters like position and velocity as fuzzy numbers. It combines aspects of cellular automata models and fuzzy calculus. Compared to traditional cellular automata models, the fuzzy microscopic model requires fewer simulation runs, stores less data, and estimates output distributions in a single run. Future work could explore a stochastic cellular automata model with fuzzy decision rules to analyze more complex traffic situations.
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.
In this paper, cysts are detected in the ultrasonic images of ovary. PCOS is an endocrine disorder affecting women of reproductive age. This syndrome is mainly seen in women whose age is in between 25 and 35. We are proposing methods for identifying whether a person is suffering from Polycystic Ovary Syndrome (PCOS) or not. Ultrasound imaging of the follicles gives important information about the size, number and mode of arrangement of follicles, position and response to hormonal stimulation. A thresholding function is applied for denoising the image in the wavelet domain. Before the segmentation process the ultrasonic image is preprocessed using contrast enhancement technique. Morphological approach is used for implementing contrast enhancement. This is performed in order to improve the clarity and quality of the image. Fuzzy c-means clustering algorithm is applied to the resultant image. Finally the cysts are detected with the help of clusters. The efficiency of the algorithm depends upon the value of Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR).
With the widespread of smart mobile devices and the
availability of many applications that provide maps, many programs
have spread to find the closest and fastest routes between
two points on the map. While the exactness and effectiveness of
best path depend on the traffic circumstances, the system needs to
add more parameters such as real traffic density and velocity in
road. In addition, because of the restricted resources of phone devices,
it is not reasonable to be used to calculate the exact optimal
solutions by some familiar deterministic algorithms, which are
usually used to find the shortest path with a map of reasonable
node number. To resolve this issue, this paper put forward to use
the genetic algorithm to reduce the computational time. The proposed
system use the genetic algorithm to find the shortest path
time with miscellaneous situations of real traffic conditions. The
genetic algorithm is clearly demonstrate excellent result when applied
on many types of map, especially when the number of nodes
increased.
Saliency Based Hookworm and Infection Detection for Wireless Capsule Endoscop...IRJET Journal
This document presents a method for detecting hookworm infection and ulcers in wireless capsule endoscopy images using saliency-based segmentation. The proposed method uses multi-level superpixel segmentation followed by feature extraction of color and texture properties. A particle swarm optimization algorithm is then used to classify images as healthy or infected/ulcerous based on the extracted features. Experimental results on capsule endoscopy images demonstrate the effectiveness of the proposed method at automatically detecting abnormalities in an efficient and non-invasive manner.
Information Spread in the Context of Evacuation OptimizationDr. Mirko Kämpf
The document describes a simulation of evacuation from a building using an agent-based model. Agents represent individuals, groups, or people with communication devices. The simulation analyzes how information spreads during evacuation and compares results between open and restricted geometries. Statistical analysis methods are applied to detect phases or transitions in the system. The impacts of different communication technologies and evacuation strategies are also studied. The goal is to define requirements for communication networks and sensors to optimize the evacuation process based on the simulation results.
Design of Mobile Robot Navigation system using SLAM and Adaptive Tracking Con...iosrjce
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.
This document describes a design for mobile robot navigation using simultaneous localization and mapping (SLAM) and an adaptive tracking controller with particle swarm optimization in indoor environments. An adaptive fuzzy tracking controller is designed using 9 fuzzy rules to calculate a reference path for navigation between a starting and goal point. Particle swarm optimization is then used to optimize and reduce the time required for the calculated path. The controller is simulated in two indoor environments containing obstacles, and particle swarm optimization is shown to reduce navigation time compared to without its use. This approach allows for efficient mobile robot navigation in indoor monitoring applications.
The document discusses fuzzy logic and its application in traffic modeling. It provides background on fuzzy set theory and fuzzy logic. It then summarizes research using fuzzy logic models for car-following behavior. One study developed a fuzzy logic car-following model using relative speed and distance as inputs and acceleration as the output. The model was validated using real-world driving data. Other research applied fuzzy rule-based models and inference systems to lane changing decisions under heavy traffic conditions.
Review of fuzzy microscopic traffic flow models by abubakar usman atikuAbubakarUsmanAtiku
The document discusses fuzzy logic and its application in traffic modeling. It provides background on fuzzy logic, describing how it allows for imprecise or ambiguous answers between true and false. It then discusses using a fuzzy logic model for car-following behavior, with inputs like relative speed and distance and output of acceleration. Previous research developed fuzzy models based on real-world driving data to better understand driver behavior. The document also reviews literature on fuzzy rule-based traffic models and their use in transportation engineering.
Parameterized Image Filtering Using fuzzy LogicEditor IJCATR
The principal source of blur in digital images arise during image acquisition (digitization) or transmission. The
performance of imaging sensors is affected by a variety of factors, such as the environmental conditions during image acquisition.
Blurry images are the result of movement of the camera during shooting (not holding it still) or the camera not being capable of
choosing a fast enough shutter speed to freeze the action under the light conditions. For instance, in acquiring images with a camera,
light levels and sensor temperature are major factors affecting the amount of blur in the resulting image.
Blur was implemented by first creating a PSF filter in MatLab that would approximate linear motion blur. This PSF was then
convolved with the original image to produce the blurred image. Convolution is a mathematical process by which a signal, in this case
the image, is acted on by a system, the filter, in order to find the resulting signal. The amount of blur added to the original image
depended on two parameters of the PSF: length of blur (in pixels), and the angle of the blur. This thesis work is going to provide a
new, faster, and more efficient noise reduction method for images corrupted with motion blur. This new filter has two separated steps
or phases: the detection phase and the filtering phase. The detection phase uses fuzzy rules to determine whether a image is blurred or
not. When blurry image is detected, Then we use fuzzy filtering technique focuses only on the on the real blurred pixels.
GENETIC ALGORITHM FOR FUNCTION APPROXIMATION: AN EXPERIMENTAL INVESTIGATIONijaia
Function Approximation is a popular engineering problems used in system identification or Equation
optimization. Due to the complex search space it requires, AI techniques has been used extensively to spot
the best curves that match the real behavior of the system. Genetic algorithm is known for their fast
convergence and their ability to find an optimal structure of the solution. We propose using a genetic
algorithm as a function approximator. Our attempt will focus on using the polynomial form of the
approximation. After implementing the algorithm, we are going to report our results and compare it with
the real function output.
Implementing ELDs or Electronic Logging Devices is slowly but surely becoming the norm in fleet management. Why? Well, integrating ELDs and associated connected vehicle solutions like fleet tracking devices lets businesses and their in-house fleet managers reap several benefits. Check out the post below to learn more.
Ever been troubled by the blinking sign and didn’t know what to do?
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1. ARTIFICIAL NEURAL NETWORKS
FUZZY LOGIC
(AUTOMATED AUTOMOBILES)
ABSTRACT
Automobiles have become an integrated
part of our daily life. The development of
technology has improved the automobile
industry in both cost & efficiency. Still,
accidents prove as challenge to
technology. Highway accident news are
frequently found in the newspapers. The
automobile speed has increased with
development in technology through years
and the complexity of the accidents has
also increased. Higher speeds the
accidents prove to be more fatal. Man is
intelligent with reasoning power and can
respond to any critical situation. But
under stress and tension he falls as a prey
to accidents. The manual control of speed
& braking of a car fails during anxiety.
Thus automated speed control & braking
system is required to prevent accidents.
This automation is possible only with the
help of Artificial Intelligence (Fuzzy
Logic).
2. In this paper, Fuzzy Logic control system
is used to control the speed of the car
based on the obstacle sensed. The
obstacle sensor unit senses the presence of
the obstacle. The sensing distance
depends upon the speed of the car.
Within this distance, the angle of the
obstacle is sensed and the speed is
controlled according to the angle
subtended by the obstacle. If the obstacle
cannot be crossed by the car, then the
brakes are applied and the car comes to
rest before colliding with the obstacle.
Thus, this automated fuzzy control unit
can provide an accident free journey.
INTRODUCTION
Fuzzy logic is best suited for control
applications, such as temperature control,
traffic control or process control. Fuzzy
logic seems to be most successful in two
kinds of situations:
i) Very complex models where
understanding is strictly limited, in fact,
quite judgmental.
ii) Processes where human reasoning,
human perception, or human decision
making are inextricably involved.
Our understanding of physical processes
is based largely on imprecise human
reasoning. This imprecision (when
compared to the precise quantities
required by computers) is nonetheless a
form of information that can be quite
useful to humans. The ability to embed
such reasoning and complex problem is
the criterion by which the efficacy of
fuzzy logic is judged.
Undoubtedly this ability cannot solve
problems that require precision -
problems such as shooting precision laser
beam over tens of kilometers in space;
milling machine components to
accuracies of parts per billion; or focusing
a microscopic electron beam on a
specimen of size of a nanometer. The
impact of fuzzy logic in these areas might
be years away if ever. But not many
human problems require such precision -
problems such as parking a car,
navigating a car among others on a free
way, washing clothes, controlling traffic
at intersections & so on. Fuzzy logic is
best suited for these problems which do
not require high degree of precision.
Fuzzy Vs. Probability:
Fuzziness describes the ambiguity of an
event, whereas randomness (probability)
describes the uncertainty in the
occurrence of the event.
An example involves a personal choice.
Suppose you are seated at a table on
which rest two glasses of liquid. The
3. liquid in the first glass is described to you
as having a 95 percent change of being
healthful and good. The liquid in the
second glass is described as having a 0.95
membership in the class of “healthful &
good” liquids. Which glass would you
select, keeping in mind first glass has a 5
percent change of being filled with non-
healthful liquids including poisons.
What philosophical distinction can be
made regarding these two forms of
information? Suppose we are allowed to
test the liquids in the glasses. The prior
probability of 0.95 in each case becomes a
posterior probability of 1.0 or 0; i.e., the
liquid is either benign or not. However,
the membership value of 0.95, which
measures the drinkability of the liquid is
“healthful & good”, remains 0.95 after
measuring & testing. These examples
illustrate very clearly the difference in the
information content between change &
ambiguous events.
Complexity of a System vs. Precision in
the model of the System:
For systems with little complexity, hence
little uncertainty, closed-form
mathematical expressions provide precise
descriptions of the systems. For systems
that are a little more complex, but for
which significant data exist, model-free
methods, such as artificial neural
networks, provide a powerful and robust
means to reduce some uncertainty through
learning, based on patterns in the
available data. Finally, for the most
complex systems where few numerical
data exist and where only ambiguous or
imprecise information may be available.
Fuzzy reasoning provides a way to
understand system behavior by allowing
us to interpolate approximately between
observed input and output situations.
Fuzzy Set vs. Crisp Set:
A classical set is defined by crisp
boundaries; i.e., there is no uncertainty in
the prescription or location of the
boundaries of the set. A fuzzy set, on the
other hand, is prescribed by vague or
ambiguous properties; hence its
boundaries are ambiguous.
If complete membership in a set is
represented by the number 1, and no
membership is represented by 0, then
4. point C must have some intermediate
value of membership (partial membership
in fuzzy set
~
A ) on the interval [0, 1].
Presumably the membership of point C in
~
A approaches a value of 1 as it moves
closer to the control (unshaded) region of
~
A , and membership of point C in
~
A approaches a value of 0 as it moves
closer to leaving the boundary region of
~
A .
Membership Function:
Membership function characterize the
fuzziness in a fuzzy set, whether the
elements in the set are discrete or
continuous - in a graphical form for
eventual use in the mathematical
formalisms of fuzzy set theory. Just as
there is infinite number of ways to
characterize fuzziness, there are an
infinite number of ways to graphically
depict the membership functions that
describe fuzziness.
Features of membership function:
The core of a membership function for
some fuzzy set
~
A is defined as that
region of the universe that is characterized
by complete and full membership in the
set
~
A , i.e., the core comprises those
elements x of the universe such that
~
A (x) = 1.
The support of a membership function for
some fuzzy set
~
A is defined as the region
of the universe that is characterized by
non zero membership in the set A. that is,
the support comprises those elements x of
the universe such that A(x) > 0.
The boundaries of a membership function
for some fuzzy set
~
A are defined as the
region of the universe containing
elements that have non zero membership,
but not complete membership. That is,
the boundaries comprise these elements x
of the universe such that 0 < A (x) < 1.
Fuzzification:
Fuzzification is the process of making a
crisp quantity fuzzy. We do this by
simply recognizing that many of the
quantities that we consider to be crisp &
deterministic are actually not
deterministic at all. They carry
considerable uncertainty. If the form of
uncertainty happens to arise because of
5. imprecision, ambiguity, or fuzzy and can
be represented by a membership function.
In this paper institution method is used for
fozzification of the input variables, as it is
very simple
Defuzzification:
Defuzzification is the conversion of a
fuzzy quantity to a precise quantity, just
as fuzzification is the conversion of
precise quantity to a fuzzy quantity.
Some of the defuzzification techniques
are:
1. Max - Membership Principle:
Also known as the height method, this
scheme is limited to peaked output
junctions. This method is given by the
algebraic expression
c (Z*
)
~
C (Z) for all z Z
2. Centroid Method:
This procedure (also called center of area,
center of gravity) is the most prevalent &
physically appealing of all the
defuzzification methods; it is given by
the algebraic expression:
Z*
=
(Z)dzCμ
(Z).zdzCμ
~
~
3. Weighted Average Method:
This method is only valid for symmetrical
output membership function. It is given
by the algebraic expression:
Z*
=
)z(cμ
z).z(cμ
~
~
, where denotes
an algebraic sum.
4. Means-Max Membership:
This method (also called middle-of-
maxima) is closely related to the first
method, except that the locations of the
maximum membership can be non-unique
(i.e., the maximum membership can be a
plateau rather than a single point. This
method is given by the expression:
Z*
=
2
ba
, where a & b are shown
in the figure.
In this paper, centroid method is used for
defuzzification if the output variables.
FUZZY LOGIC CONTROL SYSTEM:
Obstacle Sensor Unit:
6. The car consists of a sensor in the front
panel to sense the presence of the
obstacle.
Sensing Distance:
The sensing distance depends upon the
speed of the car. As the speed increases
the sensing distance also gets increased,
the obstracle can be sensed at a large
distance for higher speed and the speed
can be controlled by gradual anti skid
braking system.
The speed of the car is taken as the input
and the distance sensed by the sensor is
controlled.
Input Membership Function:
Output Membership Function:
The input & output are properly related
by the If i/p then o/p rules.
The defuzzified values are obtained and
the variation of speed with sensing
distance is plotted as a surface graph
using
matlab
From the graph it is clear that the sensing
distance almost varies linearly with speed.
And the curve is not very smooth because
we deal with fuzzy values.
Speed Control:
The speed of the car is controlled
according to the angle subtended by the
obstacle. The angle subtended by the
obstacle is sensed at every instant.
Obstacles which the car can overcome:
At any instant, if the obstacle subtends an
angle less than 60, then the car can
overcome the obstacles and the speed of
the car reduces according to the angle.
1. Speed breaker
7. 2. Fly over
Obstacles which the car cannot overcome:
At any instant, if the angle subtended by
the obstacle is greater than 60, then the
car comes to rest before colliding with the
obstacle as the car cannot overcome the
obstacle.
E.g. 1. Vehicles
The angle is taken as the i/p & the
o/p speed is controlled.
Input - Membership Function:
Output - Membership Function:
The input & the output functions
are related by the If i/p then o/p rules.
Using matlab the surface graph relating
the speed and angle is obtained.
From the graph it is clear that the
speed becomes zero when the angle of the
obstacle is greater than 60.
8. The rules are applicable not only for
obstacles that have elevation but also
depression like a small pit, subway, etc.
Rear Sensing:
This fuzzy control can be extended to rear
sensing by placing a sensor at the back
side of the car, and can be used to control
the motion of the car when the wheals
rotate in the opposite direction or when
the car is in rear gear.
Simulation Results:
Some of the sample readings
obtained from the matlab simulations are
shown:
Conclusion:
In this world of stress and tension
where the drivers’ concentration is
distracted in many ways, an automated
accident prevention system is necessary to
prevent accidents. The fuzzy logic
control system can relieve the driver from
tension and can prevent accidents. This
fuzzy control unit when fitted in all the
cars can result in an accident free world.