Autonomous Underwater Vehicle

 Artificial Intelligence Methods

Artificial intelligence is a vast, loosely defined area encompassing various aspects of pattern recognition and image processing, natural language and speech processing, automated reasoning and a host of other disciplines. Fuzzy logic and neural network are two of the most widely used approaches in artificial intelligence methods for combining multisensor data. Fuzzy logic involves extension of Boolean set theory and Boolean logic to a continuous-valued logic via the concept of membership functions to quantify imprecise concepts. Neural network is a method designed to mimic a theory of how biological nervous systems work.

About

The oceans cover 70% of the Earth's surface and contain an abundance of living and nonliving resources that remain largely untapped waiting to be discovered. However, a number of complex issues, mainly caused by the nature of underwater environments, make exploration and protection of these resources difficult to perform. In the past few decades, various world-wide research and development activities in underwater robotic systems have increased in order to meet this challenge. Extensive use of ROVs is currently limited to a few applications because of very high operational costs and the need for human presence in conducting a mission. The demand for a more sophisticated underwater robotic technology that minimizes the cost and eliminates the need for human operator and is therefore capable of operating autonomously becomes apparent. These requirements led to the development of Autonomous Underwater Vehicles (AUVs). A key problem with autonomous underwater vehicles is being able to navigate in a generally unknown environment.

Multisensor Data Fusion

It is clear from the previous discussion that information from sensors used in one navigation system need to be combined or fused with information from sensors of other navigation systems to improve the overall accuracy of the system. T o achieve this MSDF techniques, which combine data from multiple sensors and related information from associated databases can be used.

Optical Navigation 

In the context of optical imaging for navigation, the underwater environment is a very special place. The reason for this is that, in addition to visual-sensing issues that must be addressed in land and space-based vehicles, there are also issues specific to underwater imaging. These issues include limited range of visibility, brightness and contrast variation, and non-uniform illumination. Limited range of visibility is caused by the attenuation of light in water by absorption and scattering by suspended matter. Light absorption and scattering cause the amount of reflected light to exponentially decay as a function of distance to scene surfaces.

Dead Reckoning Navigation 

Dead reckoning is a mathematical means to determine position estimates when the vehicle starts from a known point and moves at known velocities, the present position is equal to the time integral of the velocity. Measurement of the vector velocity components of the vehicle is usually accomplished with a compass (to obtain direction) and a water speed sensor.

Radio Navigation 

Radio navigation systems mainly use the Global Positioning System (GPS). The GPS is a satellite-based navigational system that provides the most accurate open ocean navigation available. GPS consists of a constellation of 24 satellites that orbit the earth in 12 hours. The GPS based navigation system is used extensively in surface vessels as these vehicles can directly receive signals radiated by the GPS. Unfortunately, these signals have a limited water-penetrating capability. Therefore to receive the signals, an antenna associated with an AUV employing a GPS system must be clear and free of water. There are three possible antenna configurations to meet this requirement.

Conclusion

It has been suggested in this paper, from the various examples given in AUV navigation, that information coming from a single navigation system is not sufficient to provide a good navigation capability. Therefore MSDF techniques which combine sensory information from other navigation systems to improve the navigation capability is essential.

Disease Detection Using Bio robotics

About

In order to measure quantitatively the neuro-psychomotor conditions of an individual with a view to subsequently detecting his/her state of health, it is necessary to obtain a set of parameters such as reaction time, speed, strength and tremor. By processing these parameters through the use of fuzzy logic it is possible to monitor an individual's state of health, .i.e. whether he/she is healthy or affected by a particular pathology such as Parkinson's disease, dementia, etc. The DDX control system consists of a small board with an internal fuzzy microcontroller that acquires, through the action on a button on the joystick, some important parameters:  reaction time, motion speed, force of the finger on the button, and tremor and analyses them by fuzzy rules in order to detect the patient’s disease class. Moreover this new device also includes a system to detect vocal reaction. The resulting output can be visualized through a display or transmitted by a communication interface.

Background

Reaction time, speed, force, and tremor are parameters that are used to obtain a quantitative instrumental determination of a patient’s neuro-psychophysical health. These parameters have been used in the study of the progression of Parkinson’s disease, a particularly degenerative neural process, but these parameters can also be useful in detecting the wellness of a healthy person. As a matter of fact, these measurements turn out to be an excellent method of finding reactive parameters alteration due not only to a pathology, but also, for example, to the use of drugs, alcohol, drugs used in the treatment of mental conditions, or other substances that could affect a person’s reactive and coordination capabilities.

Moreover, for a healthy person, a continuous health monitoring turn out to be an excellent prevention system of some pathology and is an excellent method to acquire consciousness of how lifestyle and behavior have repercussions on one’s psychophysical well-being.

The New Experimental System (Ddx)

DDX is the new experimental bio-robotic system for the acquisition and restitution of human finger movement data. It is a bio-robotic system designed and constructed with medical and clinical data for the analysis of Parkinson’s disease. It was originally used for the analysis of neural disturbances with quantitative evaluation of both the response times and the dynamic action of the subject.

Software

By pressing the button, three beacons are sent, signifying, respectively, beginning pressure, race end, and force. First, the processor sends an impulse (like a warning) to the buzzer, and the timer starts. It begins the sampling and, after a random interval, sends another impulse to the buzzer (in order to obtain the starting signal). The value of the timer is stored in to tj. When the patient has pressed the push button, a beginning pressure beacon is sent, and the value of the timer is assigned to ti  This time is what we call the “Reaction Time”. At the end of the movement stroke, an end-of-race beacon is sent, and the value of the timer is assigned to tf. The speed of patient motion can be calculated from these times. When the stroke ends, the pressure is calculated using a simple circuit based on a strain gauge, a filter, an amplifier and an analog to digital (A/D) converter. Tremor is measured by a routine that reads data from the switching accelerometer on an input/output (I/O) pin.

Abstract

This seminar deals with the design and the development of a bio-robotic system based on fuzzy logic to diagnose and monitor the neuro-psychophysical conditions of an individual. The system, called DDX, is portable without losing efficiency and accuracy in diagnosis and also provides the ability to transfer diagnosis through a remote communication interface, in order to monitor the daily health of a patient.

Conclusions

In this article, an innovative bio-robotic system for neuro-psychophysical health-condition detection is presented. Today, systems of detection are very reliable but not portable and do not generally allow diagnoses to be sent via the internet.

Swarm Intelligence

 About

Ants form and maintain a line to their food source by laying a trail of pheromone, i.e. a chemical to which other members of the same species are very sensitive. They deposit a certain amount of pheromone while walking, and each ant prefers to follow a direction rich in pheromone. This enables the ant colony to quickly find the shortest route. The first ants to return should normally be those on the shortest route, so this will be the first to be doubly marked by pheromone (once in each direction). Thus other ants will be more attracted to this route than to longer ones not yet doubly marked, which means it will become even more strongly marked with pheromone. Thus, the shortest route is doubly marked, and more ants will follow it.  This simple model finds the shortest route between the nest and a food source.  Allowing the pheromone trail to "evaporate" (as in nature) provides the ants a mechanism to explore for alternate food sources when the first is depleting and for alternate routes should the first become blocked.

Self Organisation

Self-organisation is a set of dynamical mechanisms whereby structures appear at the global level of a system from interactions among its lower level components.  The rules specifying the interactions among the system’s constituent units are executed based only on local information, without reference to the global pattern.  The global pattern is said to be an emergent property of the system. 

 Abstract

          The behavior of social insects in general, and of ants living in colonies in particular, has fascinated researchers in ethology but also fascinated computer scientists. Many models have been proposed to explain their capabilities.  Swarm intelligence systems have been offered as a novel computational approach that replaces the traditional emphasis on control, preprogramming, and centralization with designs featuring autonomy, emergence, and distributed functioning.

Traveling Sales Ant

          In the traveling salesman problem,a person must find the shortest  route by which to vcisit a given number of cities,each exactly once.The classic problem is devilishly difficult:for just 15 cities there are billions of route possiblities.  Recently researchers have begun to experiment with antlike agents to derive a solution.The approach relies on the artificial ants laying and following equivalent of pheromone trails. Envision a colony of such ants each independently hopping from city to city, favoring nearby locations but otherwise traveling randomly.After completing a tour of all cities, an ant goes back to links it used and deposit pheromone.The amont of chemical is inversely proportional to the overall length of the tour:the shorter distance ,the more pheromone each of the links receives.Thus,after all ants have completed their tour and spread their pheromone,the links that belong to the highest number of short tours will be richest with the chemical.Because the pheromone evaporates, links in long routes will eventually contain significantally less of the substance than those in short tours will.

Technologies  And Swarm Intelligence

          Swarm Intelligence is a design framework based on social insect behavior. Social insects such as ants, bees, and wasps are unique in the way these simple individuals cooperate to accomplish complex, difficult tasks. This cooperation is distributed among the entire population, without any centralized control. Each individual simply follows a small set of rules influenced by locally available information. This emergent behavior results in great achievements that no single member could complete by themselves. 

Black Box

About

As the technology progressing, the speed of traveling is also increased. The source to destination became so closer to each others. The main advancement in the field of the air traveling system with the help of airplane. This is the major discovery of technology. But as the speed increases , the horror of air crash also introduced. Because at a height of  2000m and above if a plane crashes ,it will be a terror for any body.

Cockpit Voice Recorders

In almost every commercial aircraft, there are several microphones built into the cockpit to track the conversations of the flight crew. These microphones are also designed to track any ambient noise in the cockpit, such as switches being thrown or any knocks or thuds. There may be up to four microphones in the plane's cockpit, each connected to the cockpit voice recorder (CVR).

Solid-State Technology

Solid-state recorders are considered much more reliable than their magnetic-tape counterparts, according to Ron Crotty, a spokesperson for Honeywell, a black-box manufacturer. Solid state uses stacked arrays of memory chips, so they don't have moving parts. With no moving parts, there are fewer maintenance issues and a decreased chance of something breaking during a crash.

                 Data from both the CVR and FDR is stored on stacked memory boards inside the crash-survivable memory unit (CSMU). In recorders made by L-3 Communications, the CSMU is a cylindrical compartment on the recorder. The stacked memory boards are about 1.75 inches (4.45 cm) in diameter and 1 inch (2.54 cm) tall.

Flight Data Recorders

The flight data recorder (FDR) is designed to record the operating data from the plane's systems. There are sensors that are wired from various areas on the plane to the flight-data acquisition unit, which is wired to the FDR. When a switch is turned on or off, that operation is recorded by the FDR.

Here are a few of the parameters recorded by most FDRs:

•        Time

•        Pressure altitude

•        Airspeed

•        Vertical acceleration

•        Magnetic heading

•        Control-column position

•        Rudder-pedal position

•        Control-wheel position

•        Horizontal stabilizer

•        Fuel flow

After A Crash

Although they are called "black boxes," aviation recorders are actually painted bright orange. This distinct color, along with the strips of reflective tape attached to the recorders' exteriors, help investigators locate the black boxes following an accident. These are especially helpful when a plane lands in the water. There are two possible origins of the term "black box": Some believe it is because early recorders were painted black, while others think it refers to the charring that occurs in post-accident fires.

Retrieving Information

After finding the black boxes, investigators take the recorders to a lab where they can download the data from the recorders and attempt to recreate the events of the accident. This process can take weeks or months to complete. In the United States, black-box manufacturers supply the NTSB with the readout systems and software needed to do a full analysis of the recorders' stored data.

Conclusion

Popularly referred to as a "black box" by the media, the data recorded by the FDR is used for accident investigation, as well as for analyzing air safety issues, material degradation and engine performance. Due to their importance in investigating accidents, these ICAO-regulated devices are carefully engineered and stoutly constructed to withstand the force of a high speed impact and the heat of an intense fire. Contrary to the "black box" reference, the exterior of the FDR is coated with heat-resistant bright orange paint for high visibility in wreckage, and the unit is usually mounted in the aircraft's empennage (tail section), where it is more likely to survive a severe crash.

Animatronics

About

The first use of Audio-Animatronics was for Walt Disney's Enchanted Tiki Room in Disneyland, which opened in June, 1963. The Tiki birds were operated using digital controls; that is, something that is either on or off. Tones were recorded onto tape, which on playback would cause a metal reed to vibrate. The vibrating reed would close a circuit and thus operate a relay. The relay sent a pulse of energy (electricity) to the figure's mechanism which would cause a pneumatic valve to operate, which resulted in the action, like the opening of a bird's beak.

          There were two basic ways of programming a figure. The first used two different methods of controlling the voltage regulation. One was a joystick-like device called a transducer, and the other device was a potentiometer (an instrument for measuring an unknown voltage or potential difference by comparison to a standard voltage--like the volume control knob on a radio or television receiver). If this method was used, when a figure was ready to be programmed, each individual action--one at a time-- would be refined, rehearsed, and then recorded. For instance, the programmer, through the use of the potentiometer or transducer, would repeatedly rehearse the gesture of lifting the arm, until it was ready for a "take." This would not include finger movement or any other movements, it was simply the lifting of an arm. The take would then be recorded by laying down audible sound impulses (tones) onto a piece of 35 mm magnetic film stock. The action could then instantly be played back to see if it would work, or if it had to be redone. (The machines used for recording and playback were the 35 mm magnetic units used primarily in the dubbing process for motion pictures. Many additional units that were capable of just playback were also required for this process. Because of their limited function these playback units were called "dummies.")

Abstract

          Animatronics is a cross between animation and electronics. Basically, an animatronic is a mechanized puppet. It may be preprogrammed or remotely controlled. An abbreviated term originally coined by Walt Disney as "Audio-Animatronics" (used to describe his mechanized characters), can actually be seen in various forms as far back as Leonardo-Da-Vinci's Automata Lion, (theoretically built to present lillies to the King of France during one of his Visits), and has now developed as a career which may require combined talent in Mechanical Engineering, Sculpting / Casting, Control Technologies, Electrical / Electronic, Airbrushing, Radio-Control.

                  Long before digital effects appeared, animatronics were making cinematic history. The scare generated by the Great White coming out of the water in "Jaws" and  the tender otherworldliness of "E.T." were its outcomes. The Jurassic Park series combined digital effects with animatronics.

Jurassic Park

          Long before digital effects appeared, animatronics were making cinematic history. But it was in Jurassic park that the best possible combination of animatronics and digital effects were used together. Spinosaurus was  a new dinosaur animatronic created for "Jurassic Park III" by Stan Winston Studio (SWS). SWS worked with Universal Studios and the film's production team to develop the Spinosaurus design. Below lies the discussion of the amazing process that creates and controls a huge animatronic like this dinosaur!

Ø Jurassic Machines

Ø Dinosaur Evolution

Ø In the Beginning

Ø Creature Creation

Ø Putting it together

Ø Making it Move

Ø Monster Mash

Armature Fabrication

          Meanwhile, various body armatures are being created and are assembled in the welding metal-fabricating areas. Each of the robot’s movements axis points must have an industrial-rated bearing to provide action and long life. Each individual part requires a custom design and fabrication. These artisans are combining both art and technology to achieve realistic, lifelike moves.

What Is An Animatronics Kit?

          Everything you need (except batteries and imagination) is included in our easy-to-use kit. Connect the cable to your PC's serial port, install the software and you're ready to start. No soldering or programming skills required. If you can use Windows you can use this Animatronics Kit . The software allows you to record the movements of hobby servos (up to two billion moves) and play them back exactly as recorded. Make your creation come to life!

Conclusion

      Animatronics has now developed as a career which may require combined talent in Mechanical Engineering , Sculpting / Casting , Control Technologies , Electrical / Electronic , Airbrushing , Radio-Control etc.But the realistic creatures that it can create are amazing and is rewarding to its creator.

ANN For Misuse Detection

Neural Networks

An artificial neural network consists of a collection of processing elements that are highly interconnected and transform a set of inputs to a set of desired outputs. The result of the transformation is determined by the characteristics of the elements and the weights associated with the interconnections among them. By modifying the connections between the nodes the network is able to adapt to the desired outputs.

                    The neural network gains the experience initially by training the system to correctly identify preselected examples of the problem. The response of the neural network is reviewed and the configuration of the system is refined until the neural network’s analysis of the training data reaches a satisfactory level. In addition to the initial training period, the neural network also gains experience over time as it conducts analyses on data related to the problem.

About

Because of the increasing dependence which companies and government agencies have on their computer networks the importance of protecting these systems from attack is critical. A single intrusion of a computer network can result in the loss or unauthorized utilization or modification of large amounts of data and cause users to question the reliability of all of the information on the network.

               The second general approach to intrusion detection is misuse detection. This technique involves the comparison of a user’s activities with the known behaviors of attackers attempting to penetrate a system.  While anomaly detection typically utilizes threshold monitoring to indicate when a certain established metric has been reached, misuse detection techniques frequently utilize a rule-based approach. When applied to misuse detection, the rules become scenarios for network attacks. The intrusion detection mechanism identifies a potential attack if a user’s activities are found to be consistent with the established rules.

Current Approaches To Intrusion Detection Systems

Most current approaches to the process of detecting intrusions utilize some form of rule-based analysis. Rule-Based analysis relies on sets of predefined rules that are provided by an administrator, automatically created by the system, or both. Expert systems are the most common form of rule-based intrusion detection approaches. The early intrusion detection research efforts realized the inefficiency of any approach that required a manual review of a system audit trail. While the information necessary to identify attacks was believed to be present within the voluminous audit data, an effective review of the material required the use of an automated system.

Mlp Prototype

The first prototype neural network was designed to determine if a neural network was capable of identifying specific events that are indications of misuse. The prototype utilized a MLP architecture that consisted of four fully connected layers with nine input nodes and two output nodes. The number of hidden layers, and the number of nodes in the hidden layers, was determined based on the process of trial and error. Each of the hidden nodes and the output node applied a Sigmoid transfer function (1/ (1 + exp (-x))) to the various connection weights. The neural network was designed to provide an output value of 0.0 and 1.0 in the two output nodes when the analysis indicated no attack and 1.0 and 0.0 in the two output nodes in the event of an attack.

Potential Implementations

There are two general implementations of neural networks in misuse detection systems. The first involves incorporating them into existing or modified expert systems. Unlike the previous attempts to use neural networks in anomaly detection by using them as replacements for existing statistical analysis components, this proposal involves using the neural network to filter the incoming data for suspicious events which may be indicative of misuse and forward these events to the expert system. This configuration should improve the effectiveness of the detection system by reducing the false alarm rate of the expert system. Because the neural network will determine a probability that a particular event is indicative of an attack, a threshold can be established where the event is forwarded to the expert system for additional analysis.

Abstract

          Misuse detection is the process of attempting to identify instances of network attacks by comparing current activity against the expected actions of an intruder. Most current approaches to misuse detection involve the use of rule-based expert systems to identify indications of known attacks.

Conclusion

Research and development of intrusion detection systems has been ongoing since the early 1980’s and the challenges faced by designers increase as the targeted systems because more diverse and complex. Misuse detection is a particularly difficult problem because of the extensive number of vulnerabilities in computer systems and the creativity of the attackers.

Ubisoap

About

               With network connectivity being embedded in most computing devices, any networked device may seamlessly consume but also provide software applications over the network. Service-Oriented Computing (SOC) then introduces natural design abstractions to deal with ubiquitous networking environments. Indeed, networked software applications may conveniently be abstracted as autonomous loosely coupled services, which may be combined to accomplish complex tasks. In addition, the concrete instantiation of SOC paradigms provided by Web Services (WS) technologies by means of Web-based/XML-based open standards (e.g., WSDL, UDDI, HTTP, SOAP) may be exploited for concrete implementation of ubiquitous services. The design rationale for ubiSOAP is discussed in the next section. Sections 3 and 4 then detail the core functionalities of ubiSOAP, namely network-agnostic connectivity and SOAP communication, while Section 5 presents a service-discovery service for ubiquitous environments. The assessment of ubiSOAP is carried out in Section 6, which evaluates ubiSOAP performance, and in Section 7, which shows a set of service oriented applications leveraging ubiSOAP. Finally, Section 8 summarizes our contribution with respect to related work, and Section 9 sketches our perspectives for future work.

Network Agnostic Connectivity

                  The ubiSOAP network-agnostic connectivity layer provides Multiradio Networking (MRN) functionality by means of two entities.  Multiradio Networking Daemon (MRN-Daemon) is the main entity implementing all the provided features, and 2) Multiradio Networking API (MRN-Api) allows for an easy and transparent access to the functionalities offered by MRN-Daemon. On top of this layer, a ubiLET is any entity (e.g., application) that exploits the network-agnostic connectivity layer by accessing the functionalities provided by MRN-Daemon through MRN-Api.

     

Ubisoap Group Transport

                     Specifically, the ubiSOAP group transport is a connectionless transport for one-way communication between multiple peers in multinetwork configurations. The ubiSOAP group transport interacts with the network-agnostic connectivity layer to send group messages based on an MRN@ identifying the group, and with the SOAP engine to deliver the group’s messages to the registered services. Group transport is such that within an IP network, the network’s multicast facility (i.e., IP multicast or higher level group communication like Java Groups) is used for communication among group members.

Ubisoap Related Work

                         Work related to ubiSOAP is manifolds and range different research areas from ubiquitous computing to wireless WS technologies and multiradio networks integration. However, to the best of our knowledge, ubiSOAP is the first attempt to consider all these aspects together to offer an integrated set of middleware facilities for achieving service provision in ubiquitous networking environments. The literature about ubiquitous and pervasive computing proposes plenty of different middleware classes each addressing a specific issue: 1) Context-aware middleware  deals with leveraging context information to provide user-centric computation, 2) Mobile computing middleware aims at providing communication and coordination of distributed mobile-components, and 3) Adaptive middleware enables software to adapt its structure and behavior dynamically in response to changes in its execution environment.

Conclusion

             SOC appears as a promising paradigm for ubiquitous computing systems that shall seamlessly integrate the functionalities offered by networked resources, both mobile and stationary, both resource rich and resource constrained. In particular, the loose coupling of services makes the paradigm much appropriate for wireless, mobile environments that are highly dynamic. However, enabling  SOC in ubiquitous networking environments raises key challenges among which overcoming resource constraints and volatility of wireless, mobile devices. This has in particular led to introduce lightweight service-oriented middleware. However, to the best of our knowledge, none of the existing solutions comprehensively integrate the full capacity of today’s ubiquitous networking environments, which allow wireless devices to interact via multiple network paths.