Media production Assignment Example | Topics and Well Written Essays - 750 words

Media production - Assignment Example The texts are mostly in black and various hues of pink, used to emphasize words in titles. As a whole, the written text, being influenced by the background color now becomes part of the visual. The coordination of colors of the titles, the model’s clothes and other objects in the cover page all come together well to produce an impression of high stylization and a connotation of modern fashion (Machin & Thornborrow, 2003) which Cosmopolitan is known for. Using Miley Cyrus, previously known as a wholesome star of a youth-oriented program, Hannah Montana, as the cover girl who has shed such goody-goody image and embraced a sophisticated-woman-persona may have been a move to stir controversy, and so, attract buyers. Miley’s cover reflects Cosmopolitan’s â€Å"fun, fearless female† philosophy (Streitmatter, 2004). No matter what background Miley Cyrus had, once she becomes a Cosmopolitan woman, she is welcomed into the world of strong, sexually seductive and powerful women (Machin & Thornborrow, 2003). The use of pink background may intentionally indicate Miley’s past as a wholesome teenager. The color Pink usually connotes softness and femininity (Machin, 2007) which were words that could have described her as Hannah Montana. However, Miley’s picture becomes ironic to what Pink represents because she can be perceived as a sexually mature woman, knowledgeable about the ways of the world. Her Cosmopolitan image is far from the innocent Hannah Montana that she characterized just a few years before. The visual elements of the cover page collaborated to successfully depict Miley’s transformation a daring, fun and uninhibited sex symbol and no longer an innocent child. In analyzing the cover page in-depth, the image of Miley Cyrus can be considered symbolic of what she was undergoing at the time of the shoot. While she wears white, a color that connotes purity and wholesomeness, she wears

Organisational Behaviour and the Standard Chartered Bank Scandal Essay

Organisational Behaviour and the Standard Chartered Bank Scandal - Essay Example This was used to fund terrorist groups. The US branch of the bank hid 60,000 transactions with Iran for almost a decade. Speaking in a press conference on August 8th, Standard Chartered Bank CEO, Peter Sands stated that â€Å"there was no systematic attempt to circumvent sanctions in Iran†. He admitted that some deals violated US sanctions of Iran but that was not the whole representative policy of the bank. However, this incident wiped off $17billion from the bank's market value and shares fell by 7% within the 24-hour period (BBC News, 2012a). According to the Telegraph (2012) the US Treasury department showed suspicion that some US banks are collaborating with Iran to fund the nuclear weapons programme of Iran. However, Standard Chartered Bank kept this secret until the lid was opened upon them. A senior business writer for the Guardian in the UK did a thorough critique of the situation at hand (Palmer, 2012). In his critique, he identified important pointers and actions th at are relevant to the case and give a broader view of the concepts involved in the breach. He identifies that widespread illegal activities committed in different parts of the world cannot succeed unless some international banks cooperate with the persons indicted for the activity. In citing a similar case, Palmer identifies that HSBC bank was indicted for helping Mexican drug traffickers to circumvent sanctions by covering up their transactions and presenting them as legal (Palmer, 2012). In the events leading to the Standard Chartered scandal, it is said that the head of Standard Chartered Americas wrote to the director on 5th October, 2005 stating that the UK headquarters' transactions with Iran were â€Å"very serious and even catastrophic enough to cause major reputational damage of the bank† (Palmer, 2012). However, the warning was not heeded and this led to the scandal. However, Standard Chartered went on and altered wire transactions for the Iranian government (see A ppendix 1). Deloitte, the bank's auditors came out and claimed that they had no knowledge of the actions of the bank's employees and this activity was not disclosed to them (Palmer, 2012). However, Palmer insists that the current CEO, Peter Sands served as the finance director between 2002 and 2006 before assuming his current position and he had served long enough to know about this spate of illegal transactions. This is an offence and the management and staff of Standard Chartered Bank are indicted for their role in breaking an international legal convention. This is clearly the case of an unethical behaviour and requires a lot of attention from the major stakeholders. This paper undertakes analysis of the subject and its implications to organisational behaviour. Organisational Structures/Culture and the Scandal â€Å"In banking, there has always been an overlap between corporate governance and banking regulations† (Gup, 2007 p13). This implies that banking has always had a corporate governance culture that requires the people charged with directing the affairs of the bank to follow some important regulations and considerations in decision making and the running of the banks. Gup goes on to state that these rules and imperative requirements were in place since the 1930s where most banks in the developed world had to react to the major economic crises that came up prior to the Second World War (2007).

Riordan Manufacturing Key Stakeholders Essay Example for Free

Riordan Manufacturing Key Stakeholders Essay There are several stakeholders at Riordan Manufacturing to gather requirements from to provide a clear picture of the final project. The first stakeholder to gain input would come from the Chief Operations Officer, he was the requestor of the system upgrade, is responsible for the budget, which includes payroll. The Chief Legal Officer oversees the legal aspects of the human resources programs. The individual human resources managers within the individual facilities are the stakeholders who recruit and handle benefits for employees. Each facility has an accountant that oversees payroll and bonuses as well as the payroll specialist (Apollo, 2011). Information-gathering techniques and systems analysis tools Interviews will be the first form of information gathering that will be used. Interviews and survey forms are a very effective way to gather information. Through the user’s impressions, personal experiences, task related ideas, and attitudes on the current system can give a baseline for needs. The interviews will aid in forming a definition of the scope of the system requirements. Interviews will also allow the system analyst to understand what boundaries by which to take into consideration (Havelka and Lee, April 25, 2002). A checklist will be prepared prior to any interviews. It is important to prepare an interview guide so that interviewer knows what order to ask questions and the amount of time that should be spent on each area of the interview. The interview guide will include open-ended questions that will allow for follow-up discussions. The guide can also double as an outline for a summary of what comes out of the interviews (Havelka and Lee, April 25, 2002). The second information gathering technique will be to observe the stakeholders. Many managers plan their day without taking into account interruptions from phone calls or meetings. During the interview stage, an employee may state he or she is busy with e-mails throughout the day, but by observing the employee, it could be determined that he or she receives minimal e-mails. Observing behavioral measures allow for a more accurate reflection of current system operations and future requirements (Havelka and Lee, April 25, 2002). Gathering and analyzing procedures and other company documents is the third information gathering technique. Close examination of the systems and organizational documentation is another way to determining system requirements. This can lead to discovering defined details relating to current systems and the organizations they support. By analyzing documentation, the analyst can identify problems with existing systems, such as redundant steps or incomplete information. Analyzing allows for determining organizational direction that can influence information systems requirements, such as attempting to link corporate managers to local managers. Analyzing also allows for the identification of titles and names of important individuals who have an interest to the current existing systems (Havelka and Lee, April 25, 2002). Key factors too help ensure the information required for the project is gathered successfully. The first step in the information system development or modification process is to gather requirements. Information system requirements are frequently unreliable and insufficient. The reasons for failure usually are at the fault of management and people involved. Not necessary due to technology but more so due with improper planning (Havelka and Lee, April 25, 2002). Utilizing JAD (Joint Application Design) technique will improve the determining systems requirement process. JAD is a structured process where analysts, managers, and, users work together for several days in a series of intensive meetings to review system requirements. Organizations gather affected individuals that are affected by information systems together to agree on system requirements and design details (Valacich, 2012). A good facilitator should be able to keep the focus of stakeholders and the IT (Information Technology) department participants on the goal of specifying the system requirements. Another option managers have to improve interaction is the care in assignment of members to the project team. The selection of stakeholders and IT personnel with personal interest in the new system improves the chances for success (Havelka and Lee, April 25, 2002). Another factor to ensure success is to train the IT personnel on the system’s applications and processes. How well the IT personnel understand the systems is extremely relevant to the success of system implementation. The involvement of IT personnel, who have knowledge regarding the application requirements, increases the ability of the team to correctly and quickly specify the requirements. Experienced IT personnel on a team help ensure others understand terminology and have more of an understanding of the stakeholders needs (Havelka and Lee, April 25, 2002). Project scope and Why it is Important Project scope defines the goals of the project and the budget of both time and money that has been allocated to achieve the objectives. For the best results to be achieved, both the definition and the allocated budget must be precise and detailed. A good process scope document will define specifically the tasks to be completed or the delivered results. The project scope is important because it will define the specific date for which the deliverables will be due and list the allocated budget for them. The project scope will also list specific items not part of the project scope and are excluded from the work to be delivered (Reh, 2013). Areas of project feasibility that are examined in the analysis phase of the SCLC Project feasibility is examined during the systems analysis and requirements definition phase of the SCLC. This phase involves the setting of specific project goals, which defines the functions and operations, to be included in the project to achieve completion of the project. The project goals should be based on the analysis of the stakeholders information needs. This phase can also be called the feasibility study because it also defines personnel assignments, funds, schedule, goal dates, performances, and interfaces (India, May 18, 2012). References Apollo Group, Inc. (2011). Riordan Manufacturing. Retrieved from BSA/375 course. Havelka, D. and Lee, S (April 25, 2002). Critical Success Factors for Information Requirements Gathering. Retrieved from http://www.techrepublic.com/article/critical-success-factors- for-information-requirements-gathering/1060122 Reh, J. (2013). Project Scope. Retrieved from http://management.about.com/od/PMterms/g/Project-Scope.htm Rose India (May 18, 2012). System Development Life Cycle. Retrieved from www.roseindia.net Valacich, J. S., George, J. F., Hoffer, J. A. (2012). Essentials of systems analysis and design (5th ed.). Upper Saddle River, NJ: Pearson Education

Development of Intelligent Sensor System

Development of Intelligent Sensor System Chapter 1 1.1 Introduction What is Automation? Automation in general, can be explained as the use of computers or microcontrollers to control industrial machinery and processes thereby fully replacing human operators. Automation is a kind of transition from mechanization. In mechanization, human operators are provided with machinery to assist their operations, where as automation fully replaces the human operators with computers. The advantages of automation are: Increased productivity and higher production rates. Better product quality and efficient use of resources. Greater control and consistency of products. Improved safety and reduced factory lead times. Home Automation Home automation is the field specializing in the general and specific automation requirements of homes and apartments for their better safety, security and comfort of its residents. It is also called Domotics. Home automation can be as simple as controlling a few lights in the house or as complicated as to monitor and to record the activities of each resident. Automation requirements depend on person to person. Some may be interested in the home security while others will be more into comfort requirements. Basically, home automation is anything that gives automatic control of things in your house. Some of the commonly used features in home automation are: Control of lighting. Climate control of rooms. Security and surveillance systems. Control of home entertainment systems. House plant watering system. Overhead tank water level controllers. Intelligent Sensors Complex large-scale systems consist of a large number of interconnected components. Mastering the dynamic behavior of such systems, calls for distributed control architectures. This can be achieved by implementing control and estimation algorithms in several controllers. Some algorithms manipulate only local variables (which are available in the local interface) but in most cases, algorithms implemented in some given computing device will use variables which are available in this devices local interface, and also variables which are input to the control system via remote interfaces, thus rising the need for communication networks, whose architecture and complexity depend on the amount of data to be exchanged, and on the associated time constraints. Associating computing (and communication) devices with sensing or actuating functions, has given rise to intelligent sensors. These sensors have gained a huge success in the past ten years, especially with the development of neural network s, fuzzy logic, and soft computing algorithms. The modern definition of smart or intelligent sensors can be formulated now as: ‘Smart sensor is an electronic device, including sensing element, interfacing, signal processing and having several intelligence functions as self-testing, self-identification, self-validation or self-adaptation. The keyword in this definition is ‘intelligence. The self-adaptation is a relatively new function of smart sensors and sensor systems. Self-adaptation smart sensors and systems are based on so-called adaptive algorithms and directly connected with precision measurements of frequency-time parameters of electrical signals. The later chapters will give an elaborate view on why we should use intelligent sensors, intelligent sensor structure, characteristics and network standards. Chapter 2 2.1 Conventional Sensors Before talking more on intelligent sensors, first we need to examine regular sensors in order to obtain a solid foundation on which we can develop our understanding on intelligent sensors. Most of the conventional sensors have shortcomings, both technically and economically. For a sensor to work effectively, it must be calibrated. That is, its output must be made to match some predetermined standard so that its reported values correctly reflect the parameter being measured. In the case of a bulb thermometer, the graduations next to the mercury column must be positioned so that they accurately correspond to the level of mercury for a given temperature. If the sensor is not calibrated, the information that it reports wont be accurate, which can be a big problem for the systems that use the reported information. The second concern one has when dealing with sensors is that their properties usually change over time, a phenomenon knows as drift. For instance, suppose we are measuring a DC current in a particular part of a circuit by monitoring the voltage across a resistor in that circuit. In this case, the sensor is the resistor and the physical property that we are measuring the voltage across it. As the resistor ages, its chemical properties will change, thus altering its resistance. As with the issue of calibration, some situations require much stricter drift tolerances than others; the point is that sensor properties will change with time unless we compensate for the drift in some fashion, and these changes are usually undesirable. The third problem is that not only do sensors themselves change with time, but so, too, does the environment in which they operate. An excellent example of that would be the electronic ignition for an internal combustion engine. Immediately after a tune-up, all the belts are tight, the spark plugs are new, the fuel injectors are clean, and the air filter is pristine. From that moment on, things go downhill; the belts loosen, deposits build up on the spark plugs and fuel injectors, and the air filter becomes clogged with ever-increasing amounts of dirt and dust. Unless the electronic ignition can measure how things are changing and make adjustments, the settings and timing sequence that it uses to fire the spark plugs will become progressively mismatched for the engine conditions, resulting in poorer performance and reduced fuel efficiency. The ability to compensate for often extreme changes in the operating environment makes a huge difference in a sensors value to a particular applic ation. Yet a fourth problem is that most sensors require some sort of specialized hardware called signal-conditioning circuitry in order to be of use in monitoring or control applications. The signal-conditioning circuitry is what transforms the physical sensor property that were monitoring (often an analog electrical voltage that varies in some systematic way with the parameter being measured) into a measurement that can be used by the rest of the system. Depending upon the application, the signal conditioning may be as simple as a basic amplifier that boosts the sensor signal to a usable level or it may entail complex circuitry that cleans up the sensor signal and compensates for environmental conditions, too. Frequently, the conditioning circuitry itself has to be tuned for the specific sensor being used, and for analog signals that often means physically adjusting a potentiometer or other such trimming device. In addition, the configuration of the signal-conditioning circuitry tends to be unique to both the specific type of sensor and to the application itself, which means that different types of sensors or different applications frequently need customized circuitry. Finally, standard sensors usually need to be physically close to the control and monitoring systems that receive their measurements. In general, the farther a sensor is from the system using its measurements, the less useful the measurements are. This is due primarily to the fact that sensor signals that are run long distances are susceptible to electronic noise, thus degrading the quality of the readings at the receiving end. In many cases, sensors are connected to the monitoring and control systems using specialized (and expensive) cabling; the longer this cabling is, the more costly the installation, which is never popular with end users. A related problem is that sharing sensor outputs among multiple systems becomes very difficult, particularly if those systems are physically separated. This inability to share outputs may not seem important, but it severely limits the ability to scale systems to large installations, resulting in much higher costs to install and support multiple r edundant sensors. What we really need to do is to develop some technique by which we can solve or at least greatly alleviate these problems of calibration, drift, and signal conditioning. 2.2 Making Sensors Intelligent Control systems are becoming increasingly complicated and generate increasingly complex control information. Control must nevertheless be exercised, even under such circumstances. Even considering just the detection of abnormal conditions or the problems of giving a suitable warning, devices are required that can substitute for or assist human sensation, by detecting and recognizing multi-dimensional information, and conversion of non visual information into visual form. In systems possessing a high degree of functionality, efficiency must be maximized by division of the information processing function into central processing and processing dispersed to local sites. With increased progress in automation, it has become widely recognized that the bottleneck in such systems lies with the sensors. Such demands are difficult to deal with by simply improvising the sensor devices themselves. Structural reinforcement, such as using array of sensors, or combinations of different types of sensors, and reinforcement from the data processing aspect by a signal processing unit such as a computer, are indispensible. In particular, the data processing and sensing aspects of the various stages involved in multi-dimensional measurement, image construction, characteristic extraction and pattern recognition, which were conventionally performed exclusively by human beings, have been tremendously enhanced by advances in micro-electronics. As a result, in many cases sensor systems have been implemented that substitute for some or all of the intellectual actions of human beings, i.e. intelligent sensor systems. Sensors which are made intelligent in this way are called ‘intelligent sensors or ‘smart sensors. According to Breckenridge and Husson, the smart sensor itself has a data processing function and automatic calibration/automatic compensation function, in which the sensor itself detects and eliminates abnormal values or exceptional values. It incorporates an algorithm, which is capable of being altered, and has a certain degree of memory function. Further desirable characteristics are that the sensor is coupled to other sensors, adapts to changes in environmental conditions, and has a discriminant function. Scientific measuring instruments that are employed for observation and measurement of physical world are indispensible extensions of our senses and perceptions in the scientific examination of nature. In recognizing nature, we mobilize all the resources of information obtained from the five senses of sight, hearing, touch, taste and smell etc. and combine these sensory data in such a way as to avoid contradiction. Thus more reliable, higher order data is obtained by combining data of different types. That is, there is a data processing mechanism that combines and processes a number of sensory data. The concept of combining sensors to implement such a data processing mechanism is called ‘sensor fusion 2.2.1 Digitizing the Sensor Signal The discipline of digital signal processing or DSP, in which signals are manipulated mathematically rather than with electronic circuitry, is well established and widely practiced. Standard transformations, such as filtering to remove unwanted noise or frequency mappings to identify particular signal components, are easily handled using DSP. Furthermore, using DSP principles we can perform operations that would be impossible using even the most advanced electronic circuitry. For that very reason, todays designers also include a stage in the signal-conditioning circuitry in which the analog electrical signal is converted into a digitized numeric value. This step, called analog-to-digital conversion, A/D conversion, or ADC, is vitally important, because as soon as we can transform the sensor signal into a numeric value, we can manipulate it using software running on a microprocessor. Analog-to-digital converters, or ADCs as theyre referred to, are usually single-chip semiconductor devices that can be made to be highly accurate and highly stable under varying environmental conditions. The required signal-conditioning circuitry can often be significantly reduced, since much of the environmental compensation circuitry can be made a part of the ADC and filtering can be performed in software. 2.2.2 Adding Intelligence Once the sensor signal has been digitized, there are two primary options in how we handle those numeric values and the algorithms that manipulate them. We can either choose to implement custom digital hardware that essentially â€Å"hard-wires† our processing algorithm, or we can use a microprocessor to provide the necessary computational power. In general, custom hardware can run faster than microprocessor-driven systems, but usually at the price of increased production costs and limited flexibility. Microprocessors, while not necessarily as fast as a custom hardware solution, offer the great advantage of design flexibility and tend to be lower-priced since they can be applied to a variety of situations rather than a single application. Once we have on-board intelligence, were able to solve several of the problems that we noted earlier. Calibration can be automated, component drift can be virtually eliminated through the use of purely mathematical processing algorithms, and we can compensate for environmental changes by monitoring conditions on a periodic basis and making the appropriate adjustments automatically. Adding a brain makes the designers life much easier. 2.2.3 Communication Interface The sharing of measurements with other components within the system or with other systems adds to the value of these measurements. To do this, we need to equip our intelligent sensor with a standardized means to communicate its information to other elements. By using standardized methods of communication, we ensure that the sensors information can be shared as broadly, as easily, and as reliably as possible, thus maximizing the usefulness of the sensor and the information it produces. Thus these three factors consider being mandatory for an intelligent sensor: A sensing element that measures one or more physical parameters (essentially the traditional sensor weve been discussing), A computational element that analyzes the measurements made by the sensing element, and A communication interface to the outside world that allows the device to exchange information with other components in a larger system. Its the last two elements that really distinguish intelligent sensors from their more common standard sensor relatives because they provide the abilities to turn data directly into information, to use that information locally, and to communicate it to other elements in the system. 2.3 Types of Intelligent Sensors Intelligent sensors are chosen depending on the object, application, precision system, environment of use and cost etc. In such cases consideration must be given as to what is an appropriate evaluation standard. This question involves a multi-dimensional criterion and is usually very difficult. The evaluation standard directly reflects the sense of value itself applied in the design and manufacture of the target system. This must therefore be firmly settled at the system design stage. In sensor selection, the first matter to be considered is determination of the subject of measurement. The second matter to be decided on is the required precision and dynamic range. The third is ease of use, cost, delivery time etc., and ease of maintenance in actual use and compatibility with other sensors in the system. The type of sensor should be matched to such requirements at the design stage. Sensors are usually classified by the subject of measurement and the principle of sensing action. 2.3.1 Classification Based on Type of Input In this, the sensor is classified in accordance with the physical phenomenon that is needed to be detected and the subject of measurement. Some of the examples include voltage, current, displacement and pressure. A list of sensors and their categories are mentioned in the following table. Category Type Dynamic Quantity Flow rate, Pressure, force, tension Speed, acceleration Sound, vibration Distortion, direction proximity Optical Quantities Light (infra red, visible light or radiation) Electromagnetic Quantities Current, voltage, frequency, phase, vibration, magnetism Quantity of Energy or Heat Temperature, humidity, dew point Chemical Quantities Analytic sensors, gas, odour, concentration, pH, ions Sensory Quantities or Biological Quantities Touch, vision, smell Table 2.3.1: Sensed items Classified in accordance with subject of measurement. 2.3.2 Classification Based on Type of Output In an intelligent sensor, it is often necessary to process in an integrated manner the information from several sensors or from a single sensor over a given time range. A computer of appropriate level is employed for such purposes in practically y all cases. For coupling to the computer when constructing an intelligent sensor system, a method with a large degree of freedom is therefore appropriate. It is also necessary to pay careful attention to the type of physical quantity carrying the output information to the sensor, and to the information description format of this physical quantity or dynamic quantity, and for the description format an analog, digital or encoded method etc., might be used. Although any physical quantities could be used as output signal, electrical quantities such as voltage are more convenient for data input to a computer. The format of the output signal can be analog or digital. For convenience in data input to the computer, it is preferable if the output signal of the sensor itself is in the form of a digital electrical signal. In such cases, a suitable means of signal conversion must be provided to input the data from the sensor to the computer 2.3.3 Classification Based on Accuracy When a sensor system is constructed, the accuracy of the sensors employed is a critical factor. Usually sensor accuracy is expressed as the minimum detectable quantity. This is determined by the sensitivity of the sensor and the internally generated noise of the sensor itself. Higher sensitivity and lower internal noise level imply greater accuracy. Generally for commercially available sensors the cost of the sensor is determined by the accuracy which it is required to have. If no commercial sensor can be found with the necessary accuracy, a custom product must be used, which will increase the costs. For ordinary applications an accuracy of about 0.1% is sufficient. Such sensors can easily be selected from commercially available models. Dynamic range (full scale deflection/minimum detectable quantity) has practically the same meaning as accuracy, and is expressed in decibel units. For example a dynamic range of 60dB indicates that the full scale deflection is 103 times the minimum detectable quantity. That is, a dynamic range of 60dB is equivalent to 0.1% accuracy. In conventional sensors, linearity of output was regarded as quite important. However, in intelligent sensor technology the final stage is normally data processing by computer, so output linearity is not a particular problem. Any sensor providing a reproducible relationship of input and output signal can be used in an intelligent sensor system. Chapter 3 3.1 Sensor selection The function of a sensor is to receive some action from a single phenomenon of the subject of measurement and to convert this to another physical phenomenon that can be more easily handled. The phenomenon constituting the subject of measurement is called the input signal, and the phenomenon after conversion is called the output signal. The ratio of the output signal to the input signal is called the transmittance or gain. Since the first function of a sensor is to convert changes in the subject of measurement to a physical phenomenon that can be more easily handled, i.e. its function consists in primary conversion, its conversion efficiency, or the degree of difficulty in delivering the output signal to the transducer constituting the next stage is of secondary importance The first point to which attention must be paid in sensor selection is to preserve as far as possible the information of the input signal. This is equivalent to preventing lowering of the signal-to-noise ratio (SNR). For example, if the SNR of the input signal is 60 dB, a sensor of dynamic range less than 60 dB should not be used. In order to detect changes in the quantity being measured as faithfully as possible, a sensor is required to have the following properties. Non-interference. This means that its output should not be changed by factors other than changes in the subject of measurement. Conversion satisfying this condition is called direct measurement. Conversion wherein the measurement quantity is found by calculation from output signals determined under the influence of several input signals is called indirect measurement. High sensitivity. The amount of change of the output signal that is produced by a change of unit amount of the input quantity being measured, i.e. the gain, should be as large as possible. Small measurement pressure. This means that the sensor should not disturb the physical conditions of the subject of measurement. From this point of view, modulation conversion offers more freedom than direct-acting conversion. High speed. The sensor should have sufficiently high speed of reaction to track the maximum anticipated rate of variation of the measured quantity. Low noise. The noise generated by the sensor itself should be as little as possible. Robustness. The output signal must be at least more robust than the quantity being measured, and be easier to handle. Robustness means resistance to environmental changes and/or noise. In general, phenomena of large energy are more resistant to external disturbance such as noise than are phenomena of smaller energy, they are easier to handle, and so have better robustness. If a sensor can be obtained that satisfies all these conditions, there is no problem. However, in practice, one can scarcely expect to obtain a sensor satisfying all these conditions. In such cases, it is necessary to combine the sensor with a suitable compensation mechanism, or to compensate the transducer of the secondary converter. Progress in IC manufacturing technology has made it possible to integrate various sensor functions. With the progressive shift from mainframes to minicomputers and hence to microcomputers, control systems have changed from centralized processing systems to distributed processing systems. Sensor technology has also benefited from such progress in IC manufacturing technology, with the result that systems whereby information from several sensors is combined and processed have changed from centralized systems to dispersed systems. Specifically, attempts are being made to use silicon-integrated sensors in a role combining primary data processing and input in systems that measure and process two-dimensional information such as picture information. This is a natural application of silicon precision working technology and digital circuit technology, which have been greatly advanced by introduction of VLSI manufacturing technology. Three-dimensional integrated circuits for recognizing letter patterns and odour sensors, etc., are examples of this. Such sensor systems can be called perfectly intelligent sensors in that they themselves have a certain data processing capability. It is characteristic of such sensors to combine several sensor inputs and to include a microprocessor that performs data processing. Their output signal is not a simple conversion of the input signal, but rather an abstract quantity obtained by some reorganization and combination of input signals from several sensors. This type of signal conversion is now often performed by a distributed processing mechanism, in which microprocessors are used to carry out the data processing that was previously performed by a centralized computer system having a large number of interfaces to individual sensors. However, the miniaturization obtained by application of integrated circuit techniques brings about an increase in the flexibility of coupling between elements. This has a substantial effect. Sensors of this type constitute a new technology that is at present being researched and developed. Although further progress can be expected, the overall picture cannot be predicted at the present time. Technically, practically free combinations of sensors can be implemented with the object of so-called indirect measurement, in which the signals from several individual sensors that were conventionally present are collected and used as the basis for a new output signal. In many aspects, new ideas are required concerning determination of the object of measurement, i.e. which measured quantities are to be selected, determination of the individual functions to achieve this, and the construction of the framework to organize these as a system. 3.2 Structure of an Intelligent Sensor The rapidity of development in microelectronics has had a profound effect on the whole of instrumentation science, and it has blurred some of the conceptual boundaries which once seemed so firm. In the present context the boundary between sensors and instruments is particularly uncertain. Processes which were once confined to a large electronic instrument are now available within the housing of a compact sensor, and it is some of these processes which we discuss later in this chapter. An instrument in our context is a system which is designed primarily to act as a free standing device for performing a particular set of measurements; the provision of communications facilities is of secondary importance. A sensor is a system which is designed primarily to serve a host system and without its communication channel it cannot serve its purpose. Nevertheless, the structures and processes used within either device, be they hardware or software, are similar. The range of disciplines which arc brought together in intelligent sensor system design is considerable, and the designer of such systems has to become something of a polymath. This was one of the problems in the early days of computer-aided measurement and there was some resistance from the backwoodsmen who practiced the art of measurement. 3.2.1 Elements of Intelligent Sensors The intelligent sensor is an example of a system, and in it we can identify a number of sub-systems whose functions are clearly distinguished from each other. The principal sub-systems within an intelligent sensor are: A primary sensing element Excitation Control Amplification (Possibly variable gain) Analogue filtering Data conversion Compensation Digital Information Processing Digital Communication Processing The figure illustrates the way in which these sub-systems relate to each other. Some of the realizations of intelligent sensors, particularly the earlier ones, may incorporate only some of these elements. The primary sensing element has an obvious fundamental importance. It is more than simply the familiar traditional sensor incorporated into a more up-to-date system. Not only are new materials and mechanisms becoming available for exploitation, but some of those that have been long known yet discarded because of various difficulties of behaviour may now be reconsidered in the light of the presence of intelligence to cope with these difficul ­ties. Excitation control can take a variety of forms depending on the circumstances. Some sensors, such as the thermocouple, convert energy directly from one form to another without the need for additional excitation. Others may require fairly elaborate forms of supply. It may be alternating or pulsed for subsequent coherent or phase-sensitive detection. In some circumstances it may be necessary to provide extremely stable supplies to the sensing element, while in others it may be necessary for those supplies to form part of a control loop to maintain the operating condition of the clement at some desired optimum. While this aspect may not be thought fundamental to intelligent sensors there is a largely unexplored range of possibilities for combining it with digital processing to produce novel instrumentation techniques. Amplification of the electrical output of the primary sensing element is almost invariably a requirement. This can pose design problems where high gain is needed. Noise is a particular hazard, and a circumstance unique to the intelligent form of sensor is the presence of digital buses carrying signals with sharp transitions. For this reason circuit layout is a particularly important part of the design process. Analogue filtering is required at minimum to obviate aliasing effects in the conversion stage, but it is also attractive where digital filtering would lake up too much of the real-time processing power available. Data conversion is the stage of transition between the continuous real world and the discrete internal world of the digital processor. It is important to bear in mind that the process of analogue to digital conversion is a non-linear one and represents a potentially gross distortion of the incoming information. It is important, however, for the intelligent sensor designer always to remember that this corruption is present, and in certain circumstances it can assume dominating importance. Such circumstances would include the case where the conversion process is part of a control loop or where some sort of auto-ranging, overt or covert, is built in to the operational program. Compensation is an inevitable part of the intelligent sensor. The operating point of the sensors may change due to various reasons. One of them is temperature. So an intelligent sensor must have an inbuilt compensation setup to bring the operating point back to its standard set stage. Information processing is, of course, unique to the intelligent form of sensor. There is some overlap between compensation and information processing, but there are also significant areas on independence. An important aspect is the condensation of information, which is necessary to preserve the two most precious resources of the industrial measurement system, the information bus and the central processor. A prime example of data condensa ­tion occurs in the Doppler velocimctcr in which a substantial quantity of informa ­tion is reduced to a single number representing the velocity. Sensor compensation will in general require the processi Development of Intelligent Sensor System Development of Intelligent Sensor System Chapter 1 1.1 Introduction What is Automation? Automation in general, can be explained as the use of computers or microcontrollers to control industrial machinery and processes thereby fully replacing human operators. Automation is a kind of transition from mechanization. In mechanization, human operators are provided with machinery to assist their operations, where as automation fully replaces the human operators with computers. The advantages of automation are: Increased productivity and higher production rates. Better product quality and efficient use of resources. Greater control and consistency of products. Improved safety and reduced factory lead times. Home Automation Home automation is the field specializing in the general and specific automation requirements of homes and apartments for their better safety, security and comfort of its residents. It is also called Domotics. Home automation can be as simple as controlling a few lights in the house or as complicated as to monitor and to record the activities of each resident. Automation requirements depend on person to person. Some may be interested in the home security while others will be more into comfort requirements. Basically, home automation is anything that gives automatic control of things in your house. Some of the commonly used features in home automation are: Control of lighting. Climate control of rooms. Security and surveillance systems. Control of home entertainment systems. House plant watering system. Overhead tank water level controllers. Intelligent Sensors Complex large-scale systems consist of a large number of interconnected components. Mastering the dynamic behavior of such systems, calls for distributed control architectures. This can be achieved by implementing control and estimation algorithms in several controllers. Some algorithms manipulate only local variables (which are available in the local interface) but in most cases, algorithms implemented in some given computing device will use variables which are available in this devices local interface, and also variables which are input to the control system via remote interfaces, thus rising the need for communication networks, whose architecture and complexity depend on the amount of data to be exchanged, and on the associated time constraints. Associating computing (and communication) devices with sensing or actuating functions, has given rise to intelligent sensors. These sensors have gained a huge success in the past ten years, especially with the development of neural network s, fuzzy logic, and soft computing algorithms. The modern definition of smart or intelligent sensors can be formulated now as: ‘Smart sensor is an electronic device, including sensing element, interfacing, signal processing and having several intelligence functions as self-testing, self-identification, self-validation or self-adaptation. The keyword in this definition is ‘intelligence. The self-adaptation is a relatively new function of smart sensors and sensor systems. Self-adaptation smart sensors and systems are based on so-called adaptive algorithms and directly connected with precision measurements of frequency-time parameters of electrical signals. The later chapters will give an elaborate view on why we should use intelligent sensors, intelligent sensor structure, characteristics and network standards. Chapter 2 2.1 Conventional Sensors Before talking more on intelligent sensors, first we need to examine regular sensors in order to obtain a solid foundation on which we can develop our understanding on intelligent sensors. Most of the conventional sensors have shortcomings, both technically and economically. For a sensor to work effectively, it must be calibrated. That is, its output must be made to match some predetermined standard so that its reported values correctly reflect the parameter being measured. In the case of a bulb thermometer, the graduations next to the mercury column must be positioned so that they accurately correspond to the level of mercury for a given temperature. If the sensor is not calibrated, the information that it reports wont be accurate, which can be a big problem for the systems that use the reported information. The second concern one has when dealing with sensors is that their properties usually change over time, a phenomenon knows as drift. For instance, suppose we are measuring a DC current in a particular part of a circuit by monitoring the voltage across a resistor in that circuit. In this case, the sensor is the resistor and the physical property that we are measuring the voltage across it. As the resistor ages, its chemical properties will change, thus altering its resistance. As with the issue of calibration, some situations require much stricter drift tolerances than others; the point is that sensor properties will change with time unless we compensate for the drift in some fashion, and these changes are usually undesirable. The third problem is that not only do sensors themselves change with time, but so, too, does the environment in which they operate. An excellent example of that would be the electronic ignition for an internal combustion engine. Immediately after a tune-up, all the belts are tight, the spark plugs are new, the fuel injectors are clean, and the air filter is pristine. From that moment on, things go downhill; the belts loosen, deposits build up on the spark plugs and fuel injectors, and the air filter becomes clogged with ever-increasing amounts of dirt and dust. Unless the electronic ignition can measure how things are changing and make adjustments, the settings and timing sequence that it uses to fire the spark plugs will become progressively mismatched for the engine conditions, resulting in poorer performance and reduced fuel efficiency. The ability to compensate for often extreme changes in the operating environment makes a huge difference in a sensors value to a particular applic ation. Yet a fourth problem is that most sensors require some sort of specialized hardware called signal-conditioning circuitry in order to be of use in monitoring or control applications. The signal-conditioning circuitry is what transforms the physical sensor property that were monitoring (often an analog electrical voltage that varies in some systematic way with the parameter being measured) into a measurement that can be used by the rest of the system. Depending upon the application, the signal conditioning may be as simple as a basic amplifier that boosts the sensor signal to a usable level or it may entail complex circuitry that cleans up the sensor signal and compensates for environmental conditions, too. Frequently, the conditioning circuitry itself has to be tuned for the specific sensor being used, and for analog signals that often means physically adjusting a potentiometer or other such trimming device. In addition, the configuration of the signal-conditioning circuitry tends to be unique to both the specific type of sensor and to the application itself, which means that different types of sensors or different applications frequently need customized circuitry. Finally, standard sensors usually need to be physically close to the control and monitoring systems that receive their measurements. In general, the farther a sensor is from the system using its measurements, the less useful the measurements are. This is due primarily to the fact that sensor signals that are run long distances are susceptible to electronic noise, thus degrading the quality of the readings at the receiving end. In many cases, sensors are connected to the monitoring and control systems using specialized (and expensive) cabling; the longer this cabling is, the more costly the installation, which is never popular with end users. A related problem is that sharing sensor outputs among multiple systems becomes very difficult, particularly if those systems are physically separated. This inability to share outputs may not seem important, but it severely limits the ability to scale systems to large installations, resulting in much higher costs to install and support multiple r edundant sensors. What we really need to do is to develop some technique by which we can solve or at least greatly alleviate these problems of calibration, drift, and signal conditioning. 2.2 Making Sensors Intelligent Control systems are becoming increasingly complicated and generate increasingly complex control information. Control must nevertheless be exercised, even under such circumstances. Even considering just the detection of abnormal conditions or the problems of giving a suitable warning, devices are required that can substitute for or assist human sensation, by detecting and recognizing multi-dimensional information, and conversion of non visual information into visual form. In systems possessing a high degree of functionality, efficiency must be maximized by division of the information processing function into central processing and processing dispersed to local sites. With increased progress in automation, it has become widely recognized that the bottleneck in such systems lies with the sensors. Such demands are difficult to deal with by simply improvising the sensor devices themselves. Structural reinforcement, such as using array of sensors, or combinations of different types of sensors, and reinforcement from the data processing aspect by a signal processing unit such as a computer, are indispensible. In particular, the data processing and sensing aspects of the various stages involved in multi-dimensional measurement, image construction, characteristic extraction and pattern recognition, which were conventionally performed exclusively by human beings, have been tremendously enhanced by advances in micro-electronics. As a result, in many cases sensor systems have been implemented that substitute for some or all of the intellectual actions of human beings, i.e. intelligent sensor systems. Sensors which are made intelligent in this way are called ‘intelligent sensors or ‘smart sensors. According to Breckenridge and Husson, the smart sensor itself has a data processing function and automatic calibration/automatic compensation function, in which the sensor itself detects and eliminates abnormal values or exceptional values. It incorporates an algorithm, which is capable of being altered, and has a certain degree of memory function. Further desirable characteristics are that the sensor is coupled to other sensors, adapts to changes in environmental conditions, and has a discriminant function. Scientific measuring instruments that are employed for observation and measurement of physical world are indispensible extensions of our senses and perceptions in the scientific examination of nature. In recognizing nature, we mobilize all the resources of information obtained from the five senses of sight, hearing, touch, taste and smell etc. and combine these sensory data in such a way as to avoid contradiction. Thus more reliable, higher order data is obtained by combining data of different types. That is, there is a data processing mechanism that combines and processes a number of sensory data. The concept of combining sensors to implement such a data processing mechanism is called ‘sensor fusion 2.2.1 Digitizing the Sensor Signal The discipline of digital signal processing or DSP, in which signals are manipulated mathematically rather than with electronic circuitry, is well established and widely practiced. Standard transformations, such as filtering to remove unwanted noise or frequency mappings to identify particular signal components, are easily handled using DSP. Furthermore, using DSP principles we can perform operations that would be impossible using even the most advanced electronic circuitry. For that very reason, todays designers also include a stage in the signal-conditioning circuitry in which the analog electrical signal is converted into a digitized numeric value. This step, called analog-to-digital conversion, A/D conversion, or ADC, is vitally important, because as soon as we can transform the sensor signal into a numeric value, we can manipulate it using software running on a microprocessor. Analog-to-digital converters, or ADCs as theyre referred to, are usually single-chip semiconductor devices that can be made to be highly accurate and highly stable under varying environmental conditions. The required signal-conditioning circuitry can often be significantly reduced, since much of the environmental compensation circuitry can be made a part of the ADC and filtering can be performed in software. 2.2.2 Adding Intelligence Once the sensor signal has been digitized, there are two primary options in how we handle those numeric values and the algorithms that manipulate them. We can either choose to implement custom digital hardware that essentially â€Å"hard-wires† our processing algorithm, or we can use a microprocessor to provide the necessary computational power. In general, custom hardware can run faster than microprocessor-driven systems, but usually at the price of increased production costs and limited flexibility. Microprocessors, while not necessarily as fast as a custom hardware solution, offer the great advantage of design flexibility and tend to be lower-priced since they can be applied to a variety of situations rather than a single application. Once we have on-board intelligence, were able to solve several of the problems that we noted earlier. Calibration can be automated, component drift can be virtually eliminated through the use of purely mathematical processing algorithms, and we can compensate for environmental changes by monitoring conditions on a periodic basis and making the appropriate adjustments automatically. Adding a brain makes the designers life much easier. 2.2.3 Communication Interface The sharing of measurements with other components within the system or with other systems adds to the value of these measurements. To do this, we need to equip our intelligent sensor with a standardized means to communicate its information to other elements. By using standardized methods of communication, we ensure that the sensors information can be shared as broadly, as easily, and as reliably as possible, thus maximizing the usefulness of the sensor and the information it produces. Thus these three factors consider being mandatory for an intelligent sensor: A sensing element that measures one or more physical parameters (essentially the traditional sensor weve been discussing), A computational element that analyzes the measurements made by the sensing element, and A communication interface to the outside world that allows the device to exchange information with other components in a larger system. Its the last two elements that really distinguish intelligent sensors from their more common standard sensor relatives because they provide the abilities to turn data directly into information, to use that information locally, and to communicate it to other elements in the system. 2.3 Types of Intelligent Sensors Intelligent sensors are chosen depending on the object, application, precision system, environment of use and cost etc. In such cases consideration must be given as to what is an appropriate evaluation standard. This question involves a multi-dimensional criterion and is usually very difficult. The evaluation standard directly reflects the sense of value itself applied in the design and manufacture of the target system. This must therefore be firmly settled at the system design stage. In sensor selection, the first matter to be considered is determination of the subject of measurement. The second matter to be decided on is the required precision and dynamic range. The third is ease of use, cost, delivery time etc., and ease of maintenance in actual use and compatibility with other sensors in the system. The type of sensor should be matched to such requirements at the design stage. Sensors are usually classified by the subject of measurement and the principle of sensing action. 2.3.1 Classification Based on Type of Input In this, the sensor is classified in accordance with the physical phenomenon that is needed to be detected and the subject of measurement. Some of the examples include voltage, current, displacement and pressure. A list of sensors and their categories are mentioned in the following table. Category Type Dynamic Quantity Flow rate, Pressure, force, tension Speed, acceleration Sound, vibration Distortion, direction proximity Optical Quantities Light (infra red, visible light or radiation) Electromagnetic Quantities Current, voltage, frequency, phase, vibration, magnetism Quantity of Energy or Heat Temperature, humidity, dew point Chemical Quantities Analytic sensors, gas, odour, concentration, pH, ions Sensory Quantities or Biological Quantities Touch, vision, smell Table 2.3.1: Sensed items Classified in accordance with subject of measurement. 2.3.2 Classification Based on Type of Output In an intelligent sensor, it is often necessary to process in an integrated manner the information from several sensors or from a single sensor over a given time range. A computer of appropriate level is employed for such purposes in practically y all cases. For coupling to the computer when constructing an intelligent sensor system, a method with a large degree of freedom is therefore appropriate. It is also necessary to pay careful attention to the type of physical quantity carrying the output information to the sensor, and to the information description format of this physical quantity or dynamic quantity, and for the description format an analog, digital or encoded method etc., might be used. Although any physical quantities could be used as output signal, electrical quantities such as voltage are more convenient for data input to a computer. The format of the output signal can be analog or digital. For convenience in data input to the computer, it is preferable if the output signal of the sensor itself is in the form of a digital electrical signal. In such cases, a suitable means of signal conversion must be provided to input the data from the sensor to the computer 2.3.3 Classification Based on Accuracy When a sensor system is constructed, the accuracy of the sensors employed is a critical factor. Usually sensor accuracy is expressed as the minimum detectable quantity. This is determined by the sensitivity of the sensor and the internally generated noise of the sensor itself. Higher sensitivity and lower internal noise level imply greater accuracy. Generally for commercially available sensors the cost of the sensor is determined by the accuracy which it is required to have. If no commercial sensor can be found with the necessary accuracy, a custom product must be used, which will increase the costs. For ordinary applications an accuracy of about 0.1% is sufficient. Such sensors can easily be selected from commercially available models. Dynamic range (full scale deflection/minimum detectable quantity) has practically the same meaning as accuracy, and is expressed in decibel units. For example a dynamic range of 60dB indicates that the full scale deflection is 103 times the minimum detectable quantity. That is, a dynamic range of 60dB is equivalent to 0.1% accuracy. In conventional sensors, linearity of output was regarded as quite important. However, in intelligent sensor technology the final stage is normally data processing by computer, so output linearity is not a particular problem. Any sensor providing a reproducible relationship of input and output signal can be used in an intelligent sensor system. Chapter 3 3.1 Sensor selection The function of a sensor is to receive some action from a single phenomenon of the subject of measurement and to convert this to another physical phenomenon that can be more easily handled. The phenomenon constituting the subject of measurement is called the input signal, and the phenomenon after conversion is called the output signal. The ratio of the output signal to the input signal is called the transmittance or gain. Since the first function of a sensor is to convert changes in the subject of measurement to a physical phenomenon that can be more easily handled, i.e. its function consists in primary conversion, its conversion efficiency, or the degree of difficulty in delivering the output signal to the transducer constituting the next stage is of secondary importance The first point to which attention must be paid in sensor selection is to preserve as far as possible the information of the input signal. This is equivalent to preventing lowering of the signal-to-noise ratio (SNR). For example, if the SNR of the input signal is 60 dB, a sensor of dynamic range less than 60 dB should not be used. In order to detect changes in the quantity being measured as faithfully as possible, a sensor is required to have the following properties. Non-interference. This means that its output should not be changed by factors other than changes in the subject of measurement. Conversion satisfying this condition is called direct measurement. Conversion wherein the measurement quantity is found by calculation from output signals determined under the influence of several input signals is called indirect measurement. High sensitivity. The amount of change of the output signal that is produced by a change of unit amount of the input quantity being measured, i.e. the gain, should be as large as possible. Small measurement pressure. This means that the sensor should not disturb the physical conditions of the subject of measurement. From this point of view, modulation conversion offers more freedom than direct-acting conversion. High speed. The sensor should have sufficiently high speed of reaction to track the maximum anticipated rate of variation of the measured quantity. Low noise. The noise generated by the sensor itself should be as little as possible. Robustness. The output signal must be at least more robust than the quantity being measured, and be easier to handle. Robustness means resistance to environmental changes and/or noise. In general, phenomena of large energy are more resistant to external disturbance such as noise than are phenomena of smaller energy, they are easier to handle, and so have better robustness. If a sensor can be obtained that satisfies all these conditions, there is no problem. However, in practice, one can scarcely expect to obtain a sensor satisfying all these conditions. In such cases, it is necessary to combine the sensor with a suitable compensation mechanism, or to compensate the transducer of the secondary converter. Progress in IC manufacturing technology has made it possible to integrate various sensor functions. With the progressive shift from mainframes to minicomputers and hence to microcomputers, control systems have changed from centralized processing systems to distributed processing systems. Sensor technology has also benefited from such progress in IC manufacturing technology, with the result that systems whereby information from several sensors is combined and processed have changed from centralized systems to dispersed systems. Specifically, attempts are being made to use silicon-integrated sensors in a role combining primary data processing and input in systems that measure and process two-dimensional information such as picture information. This is a natural application of silicon precision working technology and digital circuit technology, which have been greatly advanced by introduction of VLSI manufacturing technology. Three-dimensional integrated circuits for recognizing letter patterns and odour sensors, etc., are examples of this. Such sensor systems can be called perfectly intelligent sensors in that they themselves have a certain data processing capability. It is characteristic of such sensors to combine several sensor inputs and to include a microprocessor that performs data processing. Their output signal is not a simple conversion of the input signal, but rather an abstract quantity obtained by some reorganization and combination of input signals from several sensors. This type of signal conversion is now often performed by a distributed processing mechanism, in which microprocessors are used to carry out the data processing that was previously performed by a centralized computer system having a large number of interfaces to individual sensors. However, the miniaturization obtained by application of integrated circuit techniques brings about an increase in the flexibility of coupling between elements. This has a substantial effect. Sensors of this type constitute a new technology that is at present being researched and developed. Although further progress can be expected, the overall picture cannot be predicted at the present time. Technically, practically free combinations of sensors can be implemented with the object of so-called indirect measurement, in which the signals from several individual sensors that were conventionally present are collected and used as the basis for a new output signal. In many aspects, new ideas are required concerning determination of the object of measurement, i.e. which measured quantities are to be selected, determination of the individual functions to achieve this, and the construction of the framework to organize these as a system. 3.2 Structure of an Intelligent Sensor The rapidity of development in microelectronics has had a profound effect on the whole of instrumentation science, and it has blurred some of the conceptual boundaries which once seemed so firm. In the present context the boundary between sensors and instruments is particularly uncertain. Processes which were once confined to a large electronic instrument are now available within the housing of a compact sensor, and it is some of these processes which we discuss later in this chapter. An instrument in our context is a system which is designed primarily to act as a free standing device for performing a particular set of measurements; the provision of communications facilities is of secondary importance. A sensor is a system which is designed primarily to serve a host system and without its communication channel it cannot serve its purpose. Nevertheless, the structures and processes used within either device, be they hardware or software, are similar. The range of disciplines which arc brought together in intelligent sensor system design is considerable, and the designer of such systems has to become something of a polymath. This was one of the problems in the early days of computer-aided measurement and there was some resistance from the backwoodsmen who practiced the art of measurement. 3.2.1 Elements of Intelligent Sensors The intelligent sensor is an example of a system, and in it we can identify a number of sub-systems whose functions are clearly distinguished from each other. The principal sub-systems within an intelligent sensor are: A primary sensing element Excitation Control Amplification (Possibly variable gain) Analogue filtering Data conversion Compensation Digital Information Processing Digital Communication Processing The figure illustrates the way in which these sub-systems relate to each other. Some of the realizations of intelligent sensors, particularly the earlier ones, may incorporate only some of these elements. The primary sensing element has an obvious fundamental importance. It is more than simply the familiar traditional sensor incorporated into a more up-to-date system. Not only are new materials and mechanisms becoming available for exploitation, but some of those that have been long known yet discarded because of various difficulties of behaviour may now be reconsidered in the light of the presence of intelligence to cope with these difficul ­ties. Excitation control can take a variety of forms depending on the circumstances. Some sensors, such as the thermocouple, convert energy directly from one form to another without the need for additional excitation. Others may require fairly elaborate forms of supply. It may be alternating or pulsed for subsequent coherent or phase-sensitive detection. In some circumstances it may be necessary to provide extremely stable supplies to the sensing element, while in others it may be necessary for those supplies to form part of a control loop to maintain the operating condition of the clement at some desired optimum. While this aspect may not be thought fundamental to intelligent sensors there is a largely unexplored range of possibilities for combining it with digital processing to produce novel instrumentation techniques. Amplification of the electrical output of the primary sensing element is almost invariably a requirement. This can pose design problems where high gain is needed. Noise is a particular hazard, and a circumstance unique to the intelligent form of sensor is the presence of digital buses carrying signals with sharp transitions. For this reason circuit layout is a particularly important part of the design process. Analogue filtering is required at minimum to obviate aliasing effects in the conversion stage, but it is also attractive where digital filtering would lake up too much of the real-time processing power available. Data conversion is the stage of transition between the continuous real world and the discrete internal world of the digital processor. It is important to bear in mind that the process of analogue to digital conversion is a non-linear one and represents a potentially gross distortion of the incoming information. It is important, however, for the intelligent sensor designer always to remember that this corruption is present, and in certain circumstances it can assume dominating importance. Such circumstances would include the case where the conversion process is part of a control loop or where some sort of auto-ranging, overt or covert, is built in to the operational program. Compensation is an inevitable part of the intelligent sensor. The operating point of the sensors may change due to various reasons. One of them is temperature. So an intelligent sensor must have an inbuilt compensation setup to bring the operating point back to its standard set stage. Information processing is, of course, unique to the intelligent form of sensor. There is some overlap between compensation and information processing, but there are also significant areas on independence. An important aspect is the condensation of information, which is necessary to preserve the two most precious resources of the industrial measurement system, the information bus and the central processor. A prime example of data condensa ­tion occurs in the Doppler velocimctcr in which a substantial quantity of informa ­tion is reduced to a single number representing the velocity. Sensor compensation will in general require the processi

Actions And Behavior Of The President Essay -- essays research papers

Actions and Behavior of the President The broad language of the second article of the Constitution left many questions about the power and authority of the President and the Executive branch of the Federal Government. Since George Washington, each Chief Executive has come to the position with different beliefs on the responsibility and power of the President. However the performance of the president is often shaped by outside factors which control how he must act as a Chief Executive. The behavior of presidents come from a number of different criteria. A president's personal character, his approach to the position and circumstances during his term all contribute to presidential behavior. Presidents have approached the office from two vague positions. They have believed, to varying degrees, that either the president has a strong leadership position and broad powers to direct the nation in one direction, or that the president has very limited powers dictated by the Constitution and should act like a chief administrator for the Federal Government. These beliefs were reflected in their behavior while in the White House. Franklin Roosevelt believed that the Federal Government had an obligation and interest in bringing the nation out of the depression. In order to do this he initiated a number of agencies and projects to employ people. In the first "Hundred Days" of Roosevelt's first term he initiated a number of programs which increased the size of the Federal Government and the power of the President. He did all that he could to see that his proposals were put into place. This included a failed court packing scheme to have a more friendly Supreme Court to find his programs constitutional (Lowi and Ginsberg 230.) In contrast to this belief in broad presidential authority by Franklin Roosevelt was Howard Taft. Taft believed that Presidential authority was very limited the constitution and had to be specifically granted to the President by Congress or the Constitution (Lowi and Ginsberg 220.) Another example of a passive approach to the presidency to is George Washington. While he is often seen as a very influential president, his position as the first President require that he had to set many standards. In fact President Washington hoped that the presidency would not be dominate. In his inaugural address he argued for a strong legislature which he r... ...t must deal with, it does not necessarily explain how he comes to a position on issues and deals with problems. The behavior of a President can only be explained as a combination of many factors. His personal politics and approach to the power of the Presidency will explain if he will try to lead the whole government and beyond that the whole nation, or if he will act as a clerk, putting into action the orders of Congress. A Presidents character and style of leadership are an important factor in his approach to leadership. The size and duty of the Federal Government also effect a President's behavior and the priorities of his office. Finally a President must react to events at home and abroad which are out of his control. The pressures that these events and the public reaction to them probably have the greatest influence over his behavior and decisions. Actions and behavior of a President are the result of a complex set of circumstances. No one criteria can be used to explain the behavior of the president in any event. Explaining actions on the basis of one criteria is futile and should be reserved to talk radio hosts.

The Causes of the Revolutionary War Essay -- essays research papers

There were many events that took place in the 1760’s and 1770’s that led to the Revolutionary war. During these years the British did many things that upset the colonists. These upset colonists would eventually get sick of all the British ways and fight for their freedom.   Ã‚  Ã‚  Ã‚  Ã‚  Many events crucial the Revolutionary War took place in the 1760’s, such as the Sugar Act, Stamp Act, and Declaratory Act. The Sugar Act of 1764 set an import tax on foreign sugar, molasses, and rum entering Britain’s American colonies. Colonial merchants, ship owners, and rum distillers who profited from foreign trade angrily protested the law, but that did little of anything. The Sugar Act was the answer to the British’s question of how to raise revenue for their war debt. Laws had been made before that placed an import tax on sugar, but this was the first time they were seriously enforced. Royal inspectors would search ships, warehouses, and homes for smuggled goods. The Quartering Act of 1765 forced colonists to house and supply British troops. Another important event of the 1760’s was the Stamp Act of 1765. The Stamp Act placed a taxed on all printed matter such as newspapers, advertisements, and playing cards. Many didn’t m ind paying the tax because it was not an extreme amount, it was more about the principle of things. The Stamp Act made people angry because they thought that they should not have to pay taxes on items such as newspapers. The Stamp Act’s purpose was to e...

Horror Film Genre Essay

The two movies I viewed were Texas Chainsaw Massacre and Poltergeist, which were both directed by Tobe Hopper. Both films displayed the general rules horror films generally follow through the use of horror film genre conventions. For instance, both movies follow the good verses evil story lines and in each the end does ultimately win. However, this is not entirely true in Texas Chainsaw Massacre. The friends represented the good and the chainsaw carrying monster and his family portrayed the evil. In the end on of the friends, Sally, ultimately does escape from being killed by the monster in the movie. However, her other four friends were taken down and gruesomely killed by the evil killer in the movie. So although in a bittersweet manner, the good ultimately prevails when Sally escapes in the back of a truck as the chainsaw yielding monster is left behind. In Poltergeist, the â€Å"good† family overcomes the â€Å"evil† ghosts of the house by rescuing their daughter from the other realm she was sucked into and by the whole family escaping the possessed house all together in the very end. The equilibrium outline in each film was questionable. In most, the daily motions of life are disrupted by some â€Å"evil† and in the end regularity is reestablished. The end where normality returns is what is in question in both movies. In Texas Chainsaw Massacre, the friends are enjoying a trip together and then they encounter the chainsaw killer and his cannibal family and their normal life is flipped inside out with each one getting brutally murdered except for Sally. In the end normal life is not restored, all Sally’s friends are gone and although she herself escapes and could live a normal life, what she was put through would not leave her the same as she was before. In Poltergeist, the Freeling family seems to portray the typical suburban family in the 1970’s going about their day as any other family would. That ends however when a host of ghosts possess their house and take their daughter to an unknown world. Normality could be said to be brought back when the family escapes from the house and the house is sucked into some portal. However, we are not left with a definite answer to if everything is restored to what was before for we have no clear understanding of what really happened to the ghosts. Therefore, in both films equilibrium is disrupted and equilibrium is brought back however questionably so. Moreover, one convention apparent in Texas Chainsaw Massacre and Poltergeist was the plot including a search for resolving the situation they are having trouble getting out of. In Texas Chainsaw Massacre, Sally was the main one that got so deeply involved in the situation after her friends were all gone. She went through everything trying to escape such as running from the killer, being at the verge of escape before being tied up and captured, and being tortured by a cannibal family. She never gave up until she hopped in the back of a truck bloody and exhausted but finally at an end to the horrid situation. A similar situation can be found in Poltergeist. The victims, the family, are fighting a number of ghosts in order to rescue their daughter, Carol. As a family would be expected to they do not give up the fight to get their daughter and escape the horrible situation they are in. By hiring supernatural experts and then finally taking matters in their own hands as the mother risks her life to save her daughters’ by entering the ghosts realm, the family finds an end to the situation and finally escapes from the haunted house. In relation to â€Å"The Nightmare World† most movies are thought to revolve around, the convention is relative to Poltergeist more than to Texas Chainsaw Massacre. In Poltergeist, the world of ghosts that jumps at the family is not one considered ordinary or one that would have any sort of rationale behind it. Many would not believe such a thing to be true no matter if they had a house built on top of a graveyard or not. Ghosts and the supernatural do not exist so this showing up in a film brings a sense of irrational thought which would likely keep the audience of the horror film guessing for they do not know what to expect. However, in Texas Chainsaw Massacre, mostly due to the fact the film was based on a true story, there is a rational world where the events taking place could be explained. An audience can believe that such a monster could exist because there are crazy enough people in the world that could be ruthless murderers and cannibals. It is not something that would be a pleasant thought even if it can be explained however rationale can be given behind it which is not typical in horror films. In addition, although being in the dark about something is more frightening Texas Chainsaw Massacre still did a good job of producing a scare in a rational horror film by still keeping the element of surprise and suspense throughout the movie. The timing of the appearance of the evil in each movie was just as similar to other movies, delayed. In Texas Chainsaw Massacre, it takes up to half way through the movie for the chainsaw killer, Leatherface, to appear and attack his first victim. In Poltergeist, the ghosts somewhat appeared throughout the movie but the revelation of the actual monster behind the whole rage finally appeared in the end to most likely build up anticipation and suspense leading up to the sight of the evil in the movie. The technical convention stated kept the audience suspenseful by delaying the appearance of the â€Å"evil† in each film.