Mobile robot controller using novel hybrid system IJECEIAES
Hybrid neuro-fuzzy controller is one of the techniques that is used as a tool to control a mobile robot in unstructured environment. In this paper a novel neuro-fuzzy technique is proposed in order to tackle the problem of mobile robot autonomous navigation in unstructured environment. Obstacle avoidance is an important task in the field of robotics, since the goal of autonomous robot is to reach the destination without collision. The objective is to make the robot move along a collision free trajectory until it reaches its target. The proposed approach uses the artificial neural network instead of the fuzzified engine then the output from it is processed using adaptive inference engine and defuzzification engine. In this approach, the real processing time is reduced that is increase the mobile robot response. The proposed neuro-fuzzy controller is evaluated subjectively and objectively with other approaches and also the processing time is taken in consideration.
A brain-computer interface (BCI), sometimes called a mind-machine interface (MMI), or sometimes called a direct neural interface (DNI), synthetic telepathy interface (STI) or a brain-machine interface (BMI), is a direct communication pathway between the brain and an external device. BCIs are often directed at assisting, augmenting, or repairing human cognitive or sensory-motor functions.Research on BCIs began in the 1970s at the University of California Los Angeles (UCLA) under a grant from the National Science Foundation, followed by a contract from DARPA.[1][2] The papers published after this research also mark the first appearance of the expression brain-computer interface in scientific literature.The field of BCI research and development has since focused primarily on neuroprosthetics applications that aim at restoring damaged hearing, sight and movement. Thanks to the remarkable cortical plasticity of the brain, signals from implanted prostheses can, after adaptation, be handled by the brain like natural sensor or effector channels.[3] Following years of animal experimentation, the first neuroprosthetic devices implanted in humans appeared in the mid-1990s.
Artificial neural networks are fundamental means for providing an attempt at modelling the information
processing capabilities of artificial nervous system which plays an important role in the field of cognitive
science. This paper focuses the features of artificial neural networks studied by reviewing the existing research
works, these features were then assessed and evaluated and comparative analysis. The study and literature
survey metrics such as functional capabilities of neurons, learning capabilities, style of computation, processing
elements, processing speed, connections, strength, information storage, information transmission,
communication media selection, signal transduction and fault tolerance were used as basis for comparison. A
major finding in this paper showed that artificial neural networks served as the platform for neuron computing
technology in the field of cognitive science.
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.
A LOW COST EEG BASED BCI PROSTHETIC USING MOTOR IMAGERY ijitcs
Brain Computer Interfaces (BCI) provide the opportunity to control external devices using the brain
ElectroEncephaloGram (EEG) signals. In this paper we propose two software framework in order to
control a 5 degree of freedom robotic and prosthetic hand. Results are presented where an Emotiv
Cognitive Suite (i.e. the 1st framework) combined with an embedded software system (i.e. an open source
Arduino board) is able to control the hand through character input associated with the taught actions of
the suite. This system provides evidence of the feasibility of brain signals being a viable approach to
controlling the chosen prosthetic. Results are then presented in the second framework. This latter one
allowed for the training and classification of EEG signals for motor imagery tasks. When analysing the
system, clear visual representations of the performance and accuracy are presented in the results using a
confusion matrix, accuracy measurement and a feedback bar signifying signal strength. Experiments with
various acquisition datasets were carried out and with a critical evaluation of the results given. Finally
depending on the classification of the brain signal a Python script outputs the driving command to the
Arduino to control the prosthetic. The proposed architecture performs overall good results for the design
and implementation of economically convenient BCI and prosthesis.
Mobile robot controller using novel hybrid system IJECEIAES
Hybrid neuro-fuzzy controller is one of the techniques that is used as a tool to control a mobile robot in unstructured environment. In this paper a novel neuro-fuzzy technique is proposed in order to tackle the problem of mobile robot autonomous navigation in unstructured environment. Obstacle avoidance is an important task in the field of robotics, since the goal of autonomous robot is to reach the destination without collision. The objective is to make the robot move along a collision free trajectory until it reaches its target. The proposed approach uses the artificial neural network instead of the fuzzified engine then the output from it is processed using adaptive inference engine and defuzzification engine. In this approach, the real processing time is reduced that is increase the mobile robot response. The proposed neuro-fuzzy controller is evaluated subjectively and objectively with other approaches and also the processing time is taken in consideration.
A brain-computer interface (BCI), sometimes called a mind-machine interface (MMI), or sometimes called a direct neural interface (DNI), synthetic telepathy interface (STI) or a brain-machine interface (BMI), is a direct communication pathway between the brain and an external device. BCIs are often directed at assisting, augmenting, or repairing human cognitive or sensory-motor functions.Research on BCIs began in the 1970s at the University of California Los Angeles (UCLA) under a grant from the National Science Foundation, followed by a contract from DARPA.[1][2] The papers published after this research also mark the first appearance of the expression brain-computer interface in scientific literature.The field of BCI research and development has since focused primarily on neuroprosthetics applications that aim at restoring damaged hearing, sight and movement. Thanks to the remarkable cortical plasticity of the brain, signals from implanted prostheses can, after adaptation, be handled by the brain like natural sensor or effector channels.[3] Following years of animal experimentation, the first neuroprosthetic devices implanted in humans appeared in the mid-1990s.
Artificial neural networks are fundamental means for providing an attempt at modelling the information
processing capabilities of artificial nervous system which plays an important role in the field of cognitive
science. This paper focuses the features of artificial neural networks studied by reviewing the existing research
works, these features were then assessed and evaluated and comparative analysis. The study and literature
survey metrics such as functional capabilities of neurons, learning capabilities, style of computation, processing
elements, processing speed, connections, strength, information storage, information transmission,
communication media selection, signal transduction and fault tolerance were used as basis for comparison. A
major finding in this paper showed that artificial neural networks served as the platform for neuron computing
technology in the field of cognitive science.
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.
A LOW COST EEG BASED BCI PROSTHETIC USING MOTOR IMAGERY ijitcs
Brain Computer Interfaces (BCI) provide the opportunity to control external devices using the brain
ElectroEncephaloGram (EEG) signals. In this paper we propose two software framework in order to
control a 5 degree of freedom robotic and prosthetic hand. Results are presented where an Emotiv
Cognitive Suite (i.e. the 1st framework) combined with an embedded software system (i.e. an open source
Arduino board) is able to control the hand through character input associated with the taught actions of
the suite. This system provides evidence of the feasibility of brain signals being a viable approach to
controlling the chosen prosthetic. Results are then presented in the second framework. This latter one
allowed for the training and classification of EEG signals for motor imagery tasks. When analysing the
system, clear visual representations of the performance and accuracy are presented in the results using a
confusion matrix, accuracy measurement and a feedback bar signifying signal strength. Experiments with
various acquisition datasets were carried out and with a critical evaluation of the results given. Finally
depending on the classification of the brain signal a Python script outputs the driving command to the
Arduino to control the prosthetic. The proposed architecture performs overall good results for the design
and implementation of economically convenient BCI and prosthesis.
Neuromorphic Chipsets - Industry Adoption AnalysisNetscribes
The concept of emulating neurons on a chip could enhance complex operations to make business decisions secure and cost-effective. Parallel connected neurons can boost AI verticals compared with the conventional processing systems. Non-stop learning and pattern recognition using this human brain architecture can help compute signals and data in the form of visual, speech, olfactory, etc., to perform real-time operations as well as predict outcomes based on detected patterns. Neuromorphic chipsets can also enhance performance owing to their low-power consumption to process AI algorithms.
Based on patent data, this report analyzes the ongoing R&D and investments in neuromorphic chipsets by major institutions across the globe to reveal the top innovators and technology leaders in this space.
For the full report, contact info@netscribes.com
Visit www.netscribes.com
Jeff Hawkins NAISys 2020: How the Brain Uses Reference Frames, Why AI Needs t...Numenta
Jeff Hawkins presents a talk on "How the Brain Uses Reference Frames to Model the World, Why AI Needs to do the Same." In this talk, he gives an overview of The Thousand Brains Theory and discusses how machine intelligence can benefit from working on the same principles as the neocortex.
This talk was first presented at the NAISys conference on November 10, 2020. You can find a re-recording of the talk here: https://youtu.be/mGSG7I9VKDU
Reactive Navigation of Autonomous Mobile Robot Using Neuro-Fuzzy SystemWaqas Tariq
Neuro-fuzzy systems have been used for robot navigation applications because of their ability to exert human like expertise and to utilize acquired knowledge to develop autonomous navigation strategies. In this paper, neuro-fuzzy based system is proposed for reactive navigation of a mobile robot using behavior based control. The proposed algorithm uses discrete sampling based optimal training of neural network. With a view to ascertain the efficacy of proposed system; the proposed neuro-fuzzy system’s performance is compared to that of neural and fuzzy based approaches. Simulation results along with detailed behavior analysis show effectiveness of our algorithm in all kind of obstacle environments.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
EEG Based BCI Applications with Deep LearningRiddhi Jain
Summarised a Survey Paper describing EEG Based BCI Applications and Sensing Technologies and their Computational Intelligence Approach published on Jan 28, 2020
STUDY AND IMPLEMENTATION OF ADVANCED NEUROERGONOMIC TECHNIQUES acijjournal
Research in the area of neuroergonomics has blossomed in recent years with the emergence of noninvasive techniques for monitoring human brain function that can be used to study various aspects of human behavior in relation to technology and work, including mental workload, visual attention, working memory, motor control, human-automation interaction, and adaptive automation. Consequently, this interdisciplinary field is concerned with investigations of the neural bases of human perception, cognition, and performance in relation to systems and technologies in the real world -- for example, in
the use of computers and various other machines at home or in the workplace, and in operating vehicles such as aircraft, cars, trains, and ships. We will look at recent trends in functional magnetic resonance imaging (fMRI), with a special focus on the questions that have been addressed. This focus is
particularly important for functional neuroimaging, whose contributions will be measured by the depth of the questions asked. The ever-increasing understanding of the brain and behavior at work in the real world, the development of theoretical underpinnings, and the relentless spread of facilitative technology in the West and abroad are inexorably broadening the substrates for this interdisciplinary area of
research and practice. Neuroergonomics blends neuroscience and ergonomics to the mutual benefit of both fields, and extends the study of brain structure and function beyond the contrived laboratory settings often used in neuropsychological, psychophysical, cognitive science, and other neurosciencerelated fields. Neuroergonomics is providing rich observations of the brain and behavior at work, at home, in
transportation, and in other everyday environments in human operators who see, hear, feel, attend, remember, decide, plan, act, move, or manipulate objects among other people and technology in diverse, real-world settings. The neuroergonomics approach is allowing researchers to ask different questions
and develop new explanatory frameworks about humans at work in the real world and in relation to modern automated systems and machines, drawing from principles of neuropsychology, psychophysics, neurophysiology, and anatomy at neuronal and systems levels. The neuroergonomics approach allows researchers to ask different questions and develop new explanatory frameworks about humans at work in
the real world and in relation to modern automated systems and machines. Better understanding of brain function can, for example, provide important guidelines and constraints for theories of information presentation and task design, optimization of alerting and warning signals, development of neural prostheses, and the design of robots. As an interdisciplinary endeavor, neuroergonomics will continue to
benefit from and grow alongside developments in neuroscience, psychology,
In our World of today, the quest to get rich at all cost without working for our money has led some of our youth into crimes such as robbery and kidnapping. As a result of this and by the sheer fact that vehicles are now very expensive to buy these days, there is a need for people to safeguard their vehicles against these hoodlums to avoid loss of their precious Assets to these rampaging criminals. Tracking is technology that is used by many companies and individuals to track a vehicle, an individual or an asset by using many ways like GPS that operates using satellites and ground-based stations or by using our approach which depends on the cellular mobile towers. Vehicle tracking system is a system that can be used in monitoring and locating a vehicle, avoid theft or recover a stolen vehicle, for monitoring of vehicle routes to ensure strict compliance to an already defined vehicle routes, monitor driver’s behavior, predict bus arrival as well as for fleet management. Internet of things has made it very possible to devices to inter communicate amongst themselves and exchange information, helping in acquiring and analyzing information faster that we used to know in the past and this has helped more especially in vehicle monitoring to ensure that vehicle owners feel safe about their investments without fearing about their loss. In this paper, we propose a vehicle monitoring system based on IOT technology, using 4G/LTE to get the get the coordinate, speed, and overall condition of the vehicle, process and send to a remote server to be analyzed and used in locating the vehicle and monitor its other configured parameters. This is realized using Raspberry pi, 4G/LTE, GPS, Accelerometer and other sensors with communicate amongst themselves to get the environmental parameters which is processed and sent to a remote server where it is analyzed and represented on a map to locate the vehicle and monitor the other set parameters. 4G/LTE provides fast internet connectivity with overcomes the usual delay usually experienced in sending the acquired signals to be processed. The True Vehicle position is represented using google geolocation service and the actual position triangulated in real-time.
Location, Location, Location - A Framework for Intelligence and Cortical Comp...Numenta
Jeff Hawkins gave this presentation as part of the Johns Hopkins APL Colloquium Series on Septemer 21, 2018.
View the video of the talk here: https://numenta.com/resources/videos/jeff-hawkins-johns-hopkins-apl-talk/
Presented at International Workshop on
Frontiers of Neuroengineering,
Brain-machine Interfaces
& Neural Prostheses
Zhejiang University, Hangzhou, China
March 29, 2011
This presentation shows the impact of GPU computing on cognitive robotics by showing a series of novel experiments in the area of action and language acquisition in humanoid robots and computer vision. Cognitive robotics is concerned with endowing robots with high-level cognitive capabilities to enable the achievement of complex goals in complex environments. Reaching the ultimate goal of developing cognitive robots will require tremendous amount of computational power, which was until recently provided mostly by standard CPU processors. However, CPU cores are optimised for serial code execution at the expense of parallel execution, which renders them relatively inefficient when it comes to high-performance computing applications. The ever-increasing market demand for high-performance, real-time 3D graphics has evolved the GPU into highly parallel, multithreaded, many-core processor extraordinary computational power and very high memory bandwidth. These vast computational resources of modern GPUs can now be used by the most of the cognitive robotics models as they tend to be inherently parallel. Various interesting and insightful cognitive models were developed and addressed important scientific questions concerning action-language acquisition and computer vision. While they have provided us with important scientific insights, their complexity and application has not improved much over the last years. The experimental tasks as well as the scale of these models are often minimised to avoid excessive training times that grow exponentially with the number of neurons and the training data. However, this impedes further progress and development of complex neurocontrollers that would be able to take the cognitive robotics research a step closer to reaching the ultimate goal of creating intelligent machines. This presentation shows several cases where the application of the GPU computing on cognitive robotics algorithms resulted in the development of large-scale neurocontrollers of previously unseen complexity, which enabled conducting the novel experiments described herein.
Nearest Adjacent Node Discovery Scheme for Routing Protocol in Wireless Senso...IOSR Journals
The broad significance of Wireless Sensor Networks is in most emergency and disaster rescue
domain. The routing process is the main challenges in the wireless sensor network due to lack of physical links.
The objective of routing is to find optimum path which is used to transferring packets from source node to
destination node. Routing should generate feasible routes between nodes and send traffic along the selected path
and also achieve high performance. This paper presents a nearest adjacent node scheme based on shortest path
routing algorithm. It is plays an important role in energy conservation. It finds the best location of nearest
adjacent nodes by involving the least number of nodes in transmission of data and set large number of nodes to
sleep in idle mode. Based on simulation result we shows the significant improvement in energy saving and
enhance the life of the network
Neuromorphic Chipsets - Industry Adoption AnalysisNetscribes
The concept of emulating neurons on a chip could enhance complex operations to make business decisions secure and cost-effective. Parallel connected neurons can boost AI verticals compared with the conventional processing systems. Non-stop learning and pattern recognition using this human brain architecture can help compute signals and data in the form of visual, speech, olfactory, etc., to perform real-time operations as well as predict outcomes based on detected patterns. Neuromorphic chipsets can also enhance performance owing to their low-power consumption to process AI algorithms.
Based on patent data, this report analyzes the ongoing R&D and investments in neuromorphic chipsets by major institutions across the globe to reveal the top innovators and technology leaders in this space.
For the full report, contact info@netscribes.com
Visit www.netscribes.com
Jeff Hawkins NAISys 2020: How the Brain Uses Reference Frames, Why AI Needs t...Numenta
Jeff Hawkins presents a talk on "How the Brain Uses Reference Frames to Model the World, Why AI Needs to do the Same." In this talk, he gives an overview of The Thousand Brains Theory and discusses how machine intelligence can benefit from working on the same principles as the neocortex.
This talk was first presented at the NAISys conference on November 10, 2020. You can find a re-recording of the talk here: https://youtu.be/mGSG7I9VKDU
Reactive Navigation of Autonomous Mobile Robot Using Neuro-Fuzzy SystemWaqas Tariq
Neuro-fuzzy systems have been used for robot navigation applications because of their ability to exert human like expertise and to utilize acquired knowledge to develop autonomous navigation strategies. In this paper, neuro-fuzzy based system is proposed for reactive navigation of a mobile robot using behavior based control. The proposed algorithm uses discrete sampling based optimal training of neural network. With a view to ascertain the efficacy of proposed system; the proposed neuro-fuzzy system’s performance is compared to that of neural and fuzzy based approaches. Simulation results along with detailed behavior analysis show effectiveness of our algorithm in all kind of obstacle environments.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
EEG Based BCI Applications with Deep LearningRiddhi Jain
Summarised a Survey Paper describing EEG Based BCI Applications and Sensing Technologies and their Computational Intelligence Approach published on Jan 28, 2020
STUDY AND IMPLEMENTATION OF ADVANCED NEUROERGONOMIC TECHNIQUES acijjournal
Research in the area of neuroergonomics has blossomed in recent years with the emergence of noninvasive techniques for monitoring human brain function that can be used to study various aspects of human behavior in relation to technology and work, including mental workload, visual attention, working memory, motor control, human-automation interaction, and adaptive automation. Consequently, this interdisciplinary field is concerned with investigations of the neural bases of human perception, cognition, and performance in relation to systems and technologies in the real world -- for example, in
the use of computers and various other machines at home or in the workplace, and in operating vehicles such as aircraft, cars, trains, and ships. We will look at recent trends in functional magnetic resonance imaging (fMRI), with a special focus on the questions that have been addressed. This focus is
particularly important for functional neuroimaging, whose contributions will be measured by the depth of the questions asked. The ever-increasing understanding of the brain and behavior at work in the real world, the development of theoretical underpinnings, and the relentless spread of facilitative technology in the West and abroad are inexorably broadening the substrates for this interdisciplinary area of
research and practice. Neuroergonomics blends neuroscience and ergonomics to the mutual benefit of both fields, and extends the study of brain structure and function beyond the contrived laboratory settings often used in neuropsychological, psychophysical, cognitive science, and other neurosciencerelated fields. Neuroergonomics is providing rich observations of the brain and behavior at work, at home, in
transportation, and in other everyday environments in human operators who see, hear, feel, attend, remember, decide, plan, act, move, or manipulate objects among other people and technology in diverse, real-world settings. The neuroergonomics approach is allowing researchers to ask different questions
and develop new explanatory frameworks about humans at work in the real world and in relation to modern automated systems and machines, drawing from principles of neuropsychology, psychophysics, neurophysiology, and anatomy at neuronal and systems levels. The neuroergonomics approach allows researchers to ask different questions and develop new explanatory frameworks about humans at work in
the real world and in relation to modern automated systems and machines. Better understanding of brain function can, for example, provide important guidelines and constraints for theories of information presentation and task design, optimization of alerting and warning signals, development of neural prostheses, and the design of robots. As an interdisciplinary endeavor, neuroergonomics will continue to
benefit from and grow alongside developments in neuroscience, psychology,
In our World of today, the quest to get rich at all cost without working for our money has led some of our youth into crimes such as robbery and kidnapping. As a result of this and by the sheer fact that vehicles are now very expensive to buy these days, there is a need for people to safeguard their vehicles against these hoodlums to avoid loss of their precious Assets to these rampaging criminals. Tracking is technology that is used by many companies and individuals to track a vehicle, an individual or an asset by using many ways like GPS that operates using satellites and ground-based stations or by using our approach which depends on the cellular mobile towers. Vehicle tracking system is a system that can be used in monitoring and locating a vehicle, avoid theft or recover a stolen vehicle, for monitoring of vehicle routes to ensure strict compliance to an already defined vehicle routes, monitor driver’s behavior, predict bus arrival as well as for fleet management. Internet of things has made it very possible to devices to inter communicate amongst themselves and exchange information, helping in acquiring and analyzing information faster that we used to know in the past and this has helped more especially in vehicle monitoring to ensure that vehicle owners feel safe about their investments without fearing about their loss. In this paper, we propose a vehicle monitoring system based on IOT technology, using 4G/LTE to get the get the coordinate, speed, and overall condition of the vehicle, process and send to a remote server to be analyzed and used in locating the vehicle and monitor its other configured parameters. This is realized using Raspberry pi, 4G/LTE, GPS, Accelerometer and other sensors with communicate amongst themselves to get the environmental parameters which is processed and sent to a remote server where it is analyzed and represented on a map to locate the vehicle and monitor the other set parameters. 4G/LTE provides fast internet connectivity with overcomes the usual delay usually experienced in sending the acquired signals to be processed. The True Vehicle position is represented using google geolocation service and the actual position triangulated in real-time.
Location, Location, Location - A Framework for Intelligence and Cortical Comp...Numenta
Jeff Hawkins gave this presentation as part of the Johns Hopkins APL Colloquium Series on Septemer 21, 2018.
View the video of the talk here: https://numenta.com/resources/videos/jeff-hawkins-johns-hopkins-apl-talk/
Presented at International Workshop on
Frontiers of Neuroengineering,
Brain-machine Interfaces
& Neural Prostheses
Zhejiang University, Hangzhou, China
March 29, 2011
This presentation shows the impact of GPU computing on cognitive robotics by showing a series of novel experiments in the area of action and language acquisition in humanoid robots and computer vision. Cognitive robotics is concerned with endowing robots with high-level cognitive capabilities to enable the achievement of complex goals in complex environments. Reaching the ultimate goal of developing cognitive robots will require tremendous amount of computational power, which was until recently provided mostly by standard CPU processors. However, CPU cores are optimised for serial code execution at the expense of parallel execution, which renders them relatively inefficient when it comes to high-performance computing applications. The ever-increasing market demand for high-performance, real-time 3D graphics has evolved the GPU into highly parallel, multithreaded, many-core processor extraordinary computational power and very high memory bandwidth. These vast computational resources of modern GPUs can now be used by the most of the cognitive robotics models as they tend to be inherently parallel. Various interesting and insightful cognitive models were developed and addressed important scientific questions concerning action-language acquisition and computer vision. While they have provided us with important scientific insights, their complexity and application has not improved much over the last years. The experimental tasks as well as the scale of these models are often minimised to avoid excessive training times that grow exponentially with the number of neurons and the training data. However, this impedes further progress and development of complex neurocontrollers that would be able to take the cognitive robotics research a step closer to reaching the ultimate goal of creating intelligent machines. This presentation shows several cases where the application of the GPU computing on cognitive robotics algorithms resulted in the development of large-scale neurocontrollers of previously unseen complexity, which enabled conducting the novel experiments described herein.
Nearest Adjacent Node Discovery Scheme for Routing Protocol in Wireless Senso...IOSR Journals
The broad significance of Wireless Sensor Networks is in most emergency and disaster rescue
domain. The routing process is the main challenges in the wireless sensor network due to lack of physical links.
The objective of routing is to find optimum path which is used to transferring packets from source node to
destination node. Routing should generate feasible routes between nodes and send traffic along the selected path
and also achieve high performance. This paper presents a nearest adjacent node scheme based on shortest path
routing algorithm. It is plays an important role in energy conservation. It finds the best location of nearest
adjacent nodes by involving the least number of nodes in transmission of data and set large number of nodes to
sleep in idle mode. Based on simulation result we shows the significant improvement in energy saving and
enhance the life of the network
ANALYSIS ON MACHINE CELL RECOGNITION AND DETACHING FROM NEURAL SYSTEMSIAEME Publication
One of several major components of a production system is the arrangement, which may considerably affect the cost of internal material handling as well as the flexibility, efficiency, and supervision of the plant. To cut the cost of warehouse management and setup time, cellular manufacturing is a technique that organizes the equipment needed to produce similar products into unit cells. In conjunction with traditional nonlinear relapse or chunk analysis techniques, neural networks are widely used for quantifiable analysis and information modeling. They are typically applied in this way to problems that may be stated in terms of categorizing or measurement. These recommendations update three different ANN algorithms genome Wide. The BP Networking, the KSOM Network, and thus the ART1 Connections are standard techniques. We use such non - linear and non-CF ANN methods for the adjustment of MPIM cell reproduction and proportionate cellular development for both the measurement with considering manufacturing things into consideration.
The relationship between artificial intelligence and psychological theoriesEr. rahul abhishek
Psychology is one of the parent elements of artificial
intelligence or we can also say that it is the main source for
artificial intelligence. In this paper we are discussing about the
theories of psychology used in AI. Since psychology is the study
of human brain and its nature and AI is the branch which deals
with the intelligence in machine, so for understanding the
intelligence of a machine we have to compare with human
intelligence because AI means the intelligence shown by a
machine like a human being.
Brain Computer Interface Next Generation of Human Computer InteractionSaurabh Giratkar
In the area of HCI research the main focus is on defining new ways of human interaction with computer system. With the passes of time a number of inventions have been made in this field. In initial days we used only keyboards to access our computer system (e.g. in Unix Terminal). In Second phase, after invention of mouse and other pointing devices, we started using graphical user interface using pointing devices like mouse which make the use of computer more easy and comfortable. Nowadays we are using pressure-driven mechanism, i.e. touch screen, which is common at ATMs, Mobile phones and PDAs etc. Although it is not as common in daily works but the release of tablet PCs and its popularity shows that the day is not much far when we wouldn’t be having keyboards and mouse at all.
All of these inventions have been made for balancing the requirements of society and user. E.g. Games, Multimedia Applications etc are not possible using only-Keyboard so we need mouse driven system for such applications, similarly we cannot have large keyboard on mobile so we need a touch screen system for mobiles. In addition to these traditional HCI models, there are some more advance HCI technology too for adding more flexibility and hence making the product more useful. E.g. swap card system at office doors for attendance and ATM-swap card for shopping. Speech processing systems are also there where we can access our computer system using our speech. Fig 1 shows most popular traditional HCI system.
Artificial Neural Network and its Applicationsshritosh kumar
Abstract
This report is an introduction to Artificial Neural
Networks. The various types of neural networks are
explained and demonstrated, applications of neural
networks like ANNs in medicine are described, and a
detailed historical background is provided. The
connection between the artificial and the real thing is
also investigated and explained. Finally, the
mathematical models involved are presented and
demonstrated.
Aspect oriented a candidate for neural networks and evolvable softwareLinchuan Wang
This is a paper written in 2004 and has been accepted by WASOD workshop 2004
https://people.cs.kuleuven.be/~dirk.craeynest/ada-belgium/events/04/040927-sefm-waosd.html
the meta data can be found in A Bibliography of Aspect-Oriented Software Development, Version 2.0
Similar to Analytical Review on the Correlation between Ai and Neuroscience (20)
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
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Analytical Review on the Correlation between Ai and Neuroscience
1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 14, Issue 3 (Sep. - Oct. 2013), PP 99-103
www.iosrjournals.org
www.iosrjournals.org 99 | Page
Analytical Review on the Correlation between Ai and
Neuroscience
Roohi Sille1
, Gaurav Rajput2
, Aviral Sharma3
1
University of Petroleum and Energy Studies, Dehradun. MTech(AI & ANN), Deptt of CSE
2
University of Petroleum and Energy Studies, Dehradun. MTech(AI & ANN), Deptt of CSE
3
University of Petroleum and Energy Studies, Dehradun.MTech(AI & ANN), Deptt of CSE
Abstract: Neuroscience is the pragmatic study of brain anatomy and physiology. AI and neuroscience are
typical related to the human brain’s behavior. The alliance between artificial intelligence and neuroscience can
produce an understanding of the mechanisms in the brain that generate human cognition. This paper discusses
about the benefits that AI has got from the field of neuroscience. It basically deals with the learning, perception
and reasoning. Neuroscience helps in understanding Natural Intelligence which correlates with the Artificial
Intelligence. A bridge between AI and neuroscience is altercated.
Keywords: Neuroscience, Artificial Intelligence, Artificial Neural Network, Neuroethology, Hybrots.
I. Introduction
AI and neuroscience are the fields that come closest in engineering and biology. Artificial Intelligence
has an important role to play in research, because artificial intelligence focuses on the mechanisms that generate
intelligence and cognition(understanding).Artificial intelligence can also benefit from studying the neural
mechanisms of cognition, because this research can acknowledge important information about the nature of
intelligence and cognition itself.Natural adaptive and intelligent behavior is the result of complex interactions
between nervous system, body and environment. Biological neural systems are embodied(represented) and
embedded (enclosed). Because of this there has been a growing interest in using robots, employing on-board
neural circuitry, to model aspects of animal behavior.
Neuroscientists study the nervous system. They apply a wide range of scientific inculcation: anatomy,
biochemistry, computer science, pharmacology, physiology, psychology, and zoology. It‘s all about
understanding how the brain and nervous system work, and it‘s one of the fastest growing areas of science.
Artificial neural networks became particularly popular in robotics because of a number of key properties, listed
below, that had potential to overcome the weaknesses of traditional AI methods.
• They could generalize and deal with incomplete data.
• They could handle noisy data.
• They couldadapt to emerging circumstances.
• By employing parallel distributed processing they offered a potentially more robust and efficient alternative to
the sequential pipeline model of traditional AI.
The closely related area of computational neuroscience also came out of the shadows.
The prevailing hypothesis in both the neuroscience and AI literatures is that the brain recognizes its environment
using optimized connections. These connections are determined through a gradual update of weights mediated
by learning. The training and test distributions can be constrained to be similar so weights can be optimized for
any arbitrary pattern. Thus both fields fit a mathematical-statistical framework that is well defined and elegant
[1].
II. How Brainreinforcesai?
Brain has natural intelligence; if the same intelligence is developed in a machine then it leads to
artificial intelligence. Brain is not similar to a digital computer because neuron‘s action potential is often
referred to by the AI field as a biological implementation of a binary coding scheme. The brain cannot be
contemplated to the CPU of digital computer because the brain‘s processor is neither central nor a unit. In digital
computer the memory mechanisms are separable from processing mechanism which is not the case in brain.
Brain is asynchronous and continuous [2] (works in discrete and sequential manner). The main attributes of
brain are connectionism and parallelism. These attributes has been tried to implement in artificial neural
networks. In Artificial Neural Network, the neurons in the input layer are connected with the neurons in the
hidden layer and output layer. Artificial Neural Network works quiet similar to the brain. NI has active
perception towards the environment which is absent in AI. Our brain does not crave much training. Crick and
Koch (1990), Llinas and Ribary (1993), Singer (1993), and Singer and Gray (1995) suggest that Consciousness
2. Analytical Review On The Correlation Between Ai And Neuroscience
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might be correlated with particular states of the brain involving coherent oscillations in the 40–70 Hz range,
which would serve to bind together the percepts pertaining to a particular conscious moment [3].The AI
methods do not seem to scale to brain function. Synaptic plasticity is not been implemented yet. Synaptic
plasticity is assumed to occur whenever a long lasting change in communication between two neurons occurs as
a consequence of stimulating them simultaneously. The changes are labeled Long Term Potentiation (LTP) or
Long Term Depression (LTD), if the responses increase or decrease respectively.
Fodor introduces the terms ―horizontal‖ vs. ―vertical‖ to describe two different sorts of decomposition
or disintegration of intelligence. Horizontal decomposition identifies all the cognition processes and vertical
decomposition identifies particular skills or faculties such as mathematics, language or metaphysics [4].
III. Paradigmsof Neuroscience
The terminator is the best example of the bond between neuroscience and AI. The terminator is a life-
life unit that has the capability of having psychological and cognitive functions. On the outside this robot looks
human and because of the advancement of artificial intelligence, this robot is more human than ever. This robot
has human traits on the surface and can interact with people as well as computers [5].
In 1949, Walter, a neurologist and cyberneticist based at the Burden Institute in Bristol, UK, who was
also a world leader in EEG research, completed a pair of revolutionary machines he called ‗tortoises‘. The
devices were three-wheeled and sported a protective ‗shell‘. They had a light sensor, touch sensor, propulsion
motor, steering motor, and an electronic valve (vacuum tube) based analogue ‗nervous system‘.[6] Walter said
that even a simple nervous system could generate complex behavior.
Fig 1: Walter with his ‗tortoises‘
The Lego MindStormss robotics kit consists of an infra-red tower, a programmable Lego brick (called the RCX
- Robotic Control Explorer), a variety of sensors (light, actuator), several motors and normal Lego components
(gears, pulleys, wheels, bricks etc)[7][8]. This kit can be used to build robots which are identified in the manuals
provided, or to create your own custom-made robot. Extra sensors and parts can be purchased to add more
functionality. Initially the programmable brick has no loaded operating system. The user can choose whether to
upload the operating system supplied by Lego or upload an alternative operating system. The operating system
is known as the Firmware. This operating system is called LeJOS (Lego Java Operating System). A fuzzy logic
component was then coded in Java and included with the classes supplied to control the robot, as required by the
end-user.
A mobile robot system nicknamed ‗Shakey‘. The robot had a vision system which gave it the ability to
perceive and model its environment in a limited way. Shakey could perform tasks that required planning, route-
finding, and the rearrangement of simple objects [9]. It became a paradigm case for early AI driven robotics.
The robot was provided with an initial set of axioms and then perceptual routines were used to build up and
modify the world model based on sensory information, particularly from the robot‘s vision system.
A more biologically inspired, and highly influential, example from the mid-1980s is Brook‘s development of the
hexapod robot Ghengis[10][11][12][13]. The body and control system for the robot, were directly inspired by
insect neuroethology (evolutionary and comparative approach to the study of animal behavior and its underlying
mechanistic control by the nervous systems [14][15][16]). A network of 57 augmented finite state machines
3. Analytical Review On The Correlation Between Ai And Neuroscience
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(AFSMs), including six repeated sub-networks (one for each leg), enabled the robot to walk over rough terrain
and follow a person located by its infrared sensors. The networks can be thought of as a variety of hardwired
dynamical neural network. They provided highly distributed efficient control and coordination of multiple
parallel behaviors.
Fig 2: ALVINN
The system, known as ALVINN, using input from a camera, was able to learn in under 5 minutes to
autonomously control the vehicle by watching the reactions of a human driver. ALVINN was successfully
trained to drive in a variety of circumstances including single-lane paved and unpaved roads, and multilane lined
and unlined roads, at speeds of up to 20 miles per hour. Although the system used a standard back-propagation
scheme, results were impressive – in no small part due to the clever on-the-fly training scheme employed, which
involved ‗additional‘ scenarios generated by deliberately shifting the input images to simulate poor driving [17].
These systems were more robust than the previously designed systems.
Brain-based devices (BBDs) are neurorobotic devices whose development is most closely associated with
Edelman and colleagues at the Neurosciences Institute in San Diego. Edelman‘s ‗Darwin‘ series of BBDs has an
extensive history dating back to 1990 [18] and continuing to the present day [19][20]. BBDs are constructed
according to the methodology of synthetic neural modeling‘, which has four key components [21]. First, a BBD
needs to engage in a behavioral task. Second, its behavior must be controlled by a simulated nervous system
having a design that reflects the brain‘s architecture and dynamics. Third, it needs to be situated in the real
world. And fourth, its behavior and the activity of its simulated nervous system must allow comparison with
empirical data.
Subsumption architecture (Rodney brooks): Subsumption architecture is a reactive robot architecture heavily
associated with behavior-based robotics. The term was introduced by Rodney Brooks and colleagues in 1986
[22][23][24].Subsumption has been widely influential in autonomous robotics and elsewhere in real-time AI.
Subsumption architecture is a way of decomposing complicated intelligent behavior into many "simple"
behavior modules, which are in turn organized into layers. Each layer implements a particular goal of the agent,
and higher layers are increasingly abstract. Each layer's goal subsumes that of the underlying layers. For
example, the decision to move forward by the eat-food layer takes into account the decision of the lowest
obstacle avoidance layer. As opposed to more traditional AI approaches, subsumption architecture uses a
bottom-up design. For example, a robot's lowest layer could be "avoid an object". On top of it would be the
layer "wander around", which in turn lies under "explore the world". Each of these horizontal layers access all of
the sensor data and generate actions for the actuators — the main caveat is that separate tasks can suppress (or
overrule) inputs or inhibit outputs. This way, the lowest layers can work like fast-adapting mechanisms (e.g.
reflexes), while the higher layers work to achieve the overall goal. Feedback is given mainly through the
environment.
The main advantages of the methodology are:
The modularity,
The emphasis on iterative development & testing of real-time systems in their target domain, and
The emphasis on connecting limited, task-specific perception directly to the expressed actions that
require it.
The main disadvantages of this model are:
The inability to have many layers, since the goals begin interfering with each other,
The difficulty of designing action selection through highly distributed system of inhibition and
suppression, and
The consequent rather low flexibility at runtime.
Artificial Intelligence Robot (AIBO): This robot was invented by Sony Digital Creatures Laband was
launched in the year 1999. It is an iconic series of robotic pets designed and manufactured by Sony [25]. The
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[17] Pomerleau, D. (1991).Efficient Training of Artificial Neural Networks for Autonomous Navigation. Neural Computation, 3, 88–97.
doi:10.1162/ neco.1991.3.1.88.
[18] Reeke, G. N., Sporns, O., & Edelman, G. M. (1990). Synthetic neural modeling: The ―Darwin‖ series of recognition automata.
Proceedings of theIEEE, 78(9), 1498–1530. doi:10.1109/5.58327
[19] Fleischer, J. G. (2007). Retrospective and prospective responses arising in a modeled hippocampus during maze navigation by a
brain-based device. Proceedings of the National Academy of Sciencesof the United States of America, 104(9), 3556–3561.
doi:10.1073/pnas.0611571104
[21] McKinstry, J. L. (2008). Embodied models of delayed neural responses: spatiotemporal categorization and predictive motor control
in brain based devices. Neural Networks, 21(4), 553–561. doi:10.1016/j.neunet.2008.01.004
[22] Krichmar, J. L., Seth, A. K., Nitz, D. A., Fleischer, J. G., & Edelman, G. M. (2005b). Spatial navigation and causal analysis in a
brain-based device modeling cortical-hippocampal interactions.Neuroinformatics, 3(3), 197–222. doi:10.1385/ NI:3:3:197.
[23] Brooks, R. (1986). "A robust layered control system for a mobile robot"
(http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1087032).Robotics and Automation, IEEE Journal of [legacy, pre-1988] 2
(1): 14–23. doi:10.1109/JRA.1986.1087032.(http://dx.doi.org/10.1109%2FJRA.1986.1087032). Retrieved 2008-04-14.
[24] Brooks, R. (1986). "Asynchronous distributed control system for a mobile
robot."(http://www.csa.com/partners/viewrecord.php?requester=gs&collection=TRD&recid=1481881CI). SPIE Conference on
Mobile Robots. pp. 77–84.
[25] Brooks, R. A., "A Robust Programming Scheme for a Mobile Robot", Proceedings of NATO Advanced ResearchWorkshop on
Languages for Sensor-Based Control in Robotics, CastelvecchioPascoli, Italy, September 1986.
[26] en.wikipedia.org/wiki/AIBO, archived at web citation.
[27] J. Kevin O‘Regan, A sensorimotor account of vision and visual consciousness, BEHAVIORAL AND BRAIN SCIENCES (2001)
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