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Besides, people have different personalities, and these differences absolutely affect their duties and daily activities. People with different personalities show different emotions in facing events. Also, different personalities play an important role among learners in the learning process. Personality of learners can affect their learning styles [13]. According to their personalities, each person has especial learning style, and therefore the teaching style that must be used for every student varies from student to student.
Abstract—The Personality and emotions are effective parameters in learning process. Thus, virtual learning environments should pay attention to these parameters. In this paper, a new e-learning model is designed and implemented according to these parameters. VTA selects suitable learning style for the learners based on their personality traits. To improve the learning process, the system uses VCA in some of the learning steps.
VCA is an intelligent agent and has its own personality. It is designed so that it can present an attractive and real learning environment in interaction with the learner. Finally, the results of system tested in real environments show that considering the human features in interaction with the learner increases learning quality and satisfies the learner. Personality is an independent parameter in few of them. This paper is organized in the following ways: section 2 is a review of the previous works and literature.
Section 3 explains psychological principles. Section 4 is about the proposed model, and section 5 discuses implementation of the model. Finally, section 6 and 7 explain evaluation of the system, results and future works. In recent years, many organizations have started to use distance learning tools. Although this type of education has some advantages, they don't deal with sufficient dynamism and often the education systems do not have any capability of a real class [29].
Nowadays, an effort is being made to make the virtual learning environments as real-like as possible using intelligent agents with emotions and a personality as well as simulating human behavior. Positive emotions play an important role in creativity and flexibility for solving problems.
On the other There are many efforts in modeling of emotions in the field of virtual learning. The cubic model has three dimensions. According to these three dimensions, eight types of emotions are extracted that are effective in the learning process. In this model, they have used fuzzy science for emotional states [2].
Qiao Xiangjie and his colleague have presented a self-assessment model. They have used a polar model for extracting emotions [46]. Also they used OCC emotional model [9]. Haron have suggested a learning system with a learning module, which can adapt to each learner. This environment uses fuzzy logic and MBTI personality test [1].
Passenger software is designed by Marin and his colleagues to be used for laboratory lessons in distance education. OCC model is used for implementation of the software. The system uses virtual tutor agent [35]. In their environment, the virtual classmate agent has either a competitor or a cooperator [25].
Maldonado and his colleagues have designed a system that provides an environment to provide learning through interaction with software agents. The software agent, who acts as a classmate in the system, tries to answer the learner emotionally. This system is used a cooperated agent to help learner [35]. Some people just have presented personality models, such as Ushida and his colleague who have modeled personality types based on differences in individual emotional states.
Rosis and his colleague have modeled and implemented the personalities according to the change of the agents' priority of goals. Serra and Chittaro have suggested a goal bound method for modeling agents. Ball and Breese have modeled intensity of emotions in the agent with two personality features in a BBN Bayesian Belief Network network. Thalmann and Kishirsagar have used BBN for modeling features. Andre and his colleagues have presented an integrated model of emotions based on OCC model and personality based on FFM model.
At the beginning, they have simulated basic emotions like sadness, joy, fear and anger, and two personality dimensions: extroversion and pleasantness [44] [30]. Jin Due and his colleagues tried to obtain modeling from the learner. Ju and his colleagues have designed a software agent that can cooperate with the learner. They put this agent in a non-synchronic learning environment. The designed system included two subsystems: the teacher subsystem and the learner subsystem.
In the learner subsystem, learners are grouped according to their level of knowledge and willingness to cooperate, and are treated accordingly. The system is implemented in a real class environment. The system is a learner based system [44]. Chaffer and Frasson have suggested ESTEL architecture to determine optimal emotional state of learning and induct it to the leaner.
Chalfoun, Chaffar and Frasson have designed an agent that can predict the emotional state of the learner in the e-learning environment. One of the most famous is OCC model that is used in most researches. This calculating model is established by Ortoney, Clore and Collins in The model determines 22 types of emotions.
Emotions are divided into positive and negative ones, based on positive or negative reactions to events. The OCC model is calculated intensity of emotions based on a set of variables. The variables are divided into two groups: global and local. Global variables affect all the emotions, however; local variables affect just some emotions.
Global variables include senses of reality, proximity, unexpectedness and aroused local variables include desirability, praise worthiness and attraction. The other local variables include desirability for others, deservingness, liking, likelihood, effort, realization, strength of cognitive unit, expectation deviation and familiarity [28].
Emotion Emotions are our reactions to the surrounding world. Damasio have proven that the emotions affect reasoning, memorizing, learning and decision making [11]. Studying has showed that intelligence is effective in learning process as much as emotion, interest rate and individuals do [29]. Other people such as Bower and Cohen believe that emotions affect remembering and decision making [8] [29]. The OCC model has three branches. The first branch is the emotions which show the result of happening events.
According to MBTI grouping, every person has instinctive priorities that are decisive in their behaviors in different conditions [12] [14]. The questionnaire helps to specify the personality features and learning priorities of each person, and to extract the teaching styles are related to the features [40]. MBTI uses four twodimensional functions according to the Jung theory.
The second branch is emotions that are pointed out the result of agent function based on approving or disapproving relative to a set of standards. The second branch has just one class. It includes four emotions Pride, Shame, Reproach, and Admiration.
This branch has just one class that includes two emotions Love and Hate. There is still another class beside these three branches, and includes four compound emotions Anger, Gratitude, Remorse, Gratification [38]. In fact, they have many tendencies for teamwork. They have a lot of friends. They are active and practical. Their emotions are easily expressed [12] [14] [22] [40]. Conversely, introvert people prefer their introverted opinions and internal world and ideas. They are very independent.
They spend a lot of time to think on their tasks; they have a few friends. They try to hold their emotions and express their emotions at certain times to particular people [14] [22] [40] [45]. Personality There are various psychological definitions for personality. They are realists. They usually pay attention to the details, focus on practical subjects [14] [22] [24] [45].
On the other hand, intuitive people, who get information through perception between relationships and results, usually use their conception to get information. They try to make a mental picture of the subject for themselves and then move towards details. Their concentration is more on ideas and their integrity. Their concentration is on the future rather than present [14] [22] [24] [45]. Learning Style The psychological studies show that each person displays several individual features in problem-solving and decisionsmaking.
These features are often considered as learning styles or learning methods [33]. Learning styles are the criteria in perception of information and evaluation of understanding them [1] [14] [32] [47]. There is an important point in the definition, that is, the learning styles reflect preferences and individual priorities in selection of learning conditions [12] [14]. Their decisions are logical and impersonal [33] [22] [24] [45]. Feeling people, on the other hand, have emphasis on harmony and balance.
They enjoy teamwork. Their judgments and decisions are based on personal value. They pay attention to activities which are important to them. Deadlines are important for them [14] [22] [45]. Perceiving people, however, have a flexible life style. They are curious, agreeable and tolerant. They start several projects simultaneously.
They don't pay attention to deadlines. There are many questionnaires that categorize each person according to their learning styles: Kolb questionnaire, Honey and Mumford questionnaire [33], GRSLSS questionnaire [33] [31], etc.
The sixteen groups are shown in Table I. For example, people in ENTP group are all extrovert, intuitive, thinking and perceiving. It is an evaluation instrument related to Jung personality theory, the first time, it used by Kathrin Briggs and Isabel Myers Briggs in [42] [14].
After that it was used in education sciences in [43]. The system sets learner in one of three kinds of independent group, cooperative group with VCA, or competitive group with VCA. Individuals are classified according to personality types that are showed in Table II. This module is displayed in Figure1. According to the fulfilled studies, presence of VCA with an opposed personality is suitable [6] [10] [39].
Studies in [6] [10] [39] and other psychological sources are showed that people are grouped with opposed personality are much better than the people are grouped with similar personality. Former has high efficiency rather than second. According to the fulfilled studies, we have found that only specific emotions are effective in the learning process. The result of the studies in [29] [36] are showed that the first branch of emotions in the OCC model is effective in learning.
Here, we use first and third branch of OCC model. The first branch includes effective emotions in the learning process, and the third branch includes those emotions that the person in the relation with the others e. These variables are used to measure the achievement to goal for an event. V1 V2. The values of each goal are obtained according to several questions in MBTI questionnaire. After normalization, these values are placed in the numerical domain of Eq.
Positive values of aijs show the positive effect of event on the goal and negative value of aijs shows the negative effect of event on the goal. The values of each aijs are calculated for ith event and jth goal. Unexpectedness and Likelihood variables directly are obtained from outside, and calculate based on environmental variables. True or False [26].
Here, in the same approach, calculating the values of these two variables is done, but calculation value of Desirability is different. In this part, we examine the way of calculating them. Effort: Value of the variable is obtained with ask of learner. Value of this variable is between Variables and weights are shown in Table III.
In this equation ei represents the entrance event. At the first learner encounter the system, value of this variable for the cooperative and competitive group is 0 and for the independence group is 1. During learning process, the value of this variable calculated according to Eq. When an event happens in the environment, either positive or negative emotions appear in the leaner based on this event. List of the events as well as their classification to positive and negative groups are shown in Table IV.
Potential of Cooperation: Value of the variable shows learner interesting in cooperative group, the value of this variable calculated according to Eq. VTA also can show emotional states and interact with the learner. According to these states, Love or Hate emotions toward a VCA is obtained and since the numerical value of these two emotions is not important for the rules of our expert system, it is just enough for the it to differentiate them.
The educational domain in this environment is Learning English Language. For better evaluating of the proposed model, this environment is compared with two other environments. For example, one of the rules is as follows: A.
Educational Environment without Emotions This environment is a simple virtual education environment. The learner enters the environment and just tries to solve many exercises. After responding to the questions of a level, the learner is promoted to an upper level. This learning environment is very similar to the educational environment with the emotional VTA. The tactics in the system has been defined for him. Your performance was stupendous! Congratulation Congratulations for your efforts!
Congratulation Congratulations! You reached a good result! The physical behaviors are those emotional behaviors that the VTA expresses for showing his emotional states. These behaviors are executable by the prepared functions of Merlin agent which Microsoft Company has designed. For example, one of the rules is as follows: For each verbal behavior, there is a sentence that VTA presents as text. Continue it.
Considering this fact, the environment is defined so that a VCA is used to communicate with the learner. You obtain an excellent result! Considering this issue, the environment has been designed in such a way that it uses a VCA to interaction with the learner. Congratulation VTA performs some tactics to interact with the learner.
For example, one of the rules is as follows: Rule 1: Uauuuuu! You are very well! Persuade-Student Let think more on this idea, please. There are enough times. Concerning this issue, the environment has been designed in such a way that it uses a VCA for competing with the learner and simulates a competitive environment for the learner.
In this part, like the previous part, for each tactic of the VTA and VCA, a set of physical and verbal behaviors are defined. For a better comparison between the two cooperative and competitive states of the virtual classmate agent, it is given the tactics of persuade-student-to-think in Tables XIII and XIV, respectively. Persuade-Student-to-Think VI. No need to rush. Think them over! After that, they were asked to answer a questionnaire Appendix A.
Evaluating of Learning Rate Figure 8. Evaluating Attractiveness of Educational Environments Figure 9. Figure 5. Also, they believe educational environment 2 is more attractive than educational environment 1. Figure In the future, we will try to complete the system. For presenting this agent more realistically, two other dimensions can be added. We can improve the model by using all of them.
Applying the culture model: Nowadays, lots of researchers on the impacts of the culture on learning styles have been published. In the future, by adding this parameter to personality and emotion models, we can increase the environment credibility for the learners. M and Aldea, A. Al Masum, S. Anderson, T. Berry, M. Botsios, S. ICALT, pp. Capretz L. Chaffar, S. Chalfoun, P. Damasio, A. Du, J. Durling, D. Ellis, S. El-Nasr, M. Fatahi, S. Proceedings of World Congress Engineering , U.
K, London. Kazemifard, and N. Ghasem-Aghaee, N. Harati Zadeh, S. Hartmann, P. Journal of Computers in Human Behavior, Vol. Vienna: Proceeding of Eurographics. Beijing University of Science and Technology, China, pp. Her research activities include 1 simulation of agents with dynamic personality and emotions 2 Computational cognitive modeling 3 Simulation and formalization of cognitive processes 4 Multi Agent Systems 5 Modeling of Human Behavior 6 Fuzzy Expert Systems.
Appendix A. He has been active in simulation since His research interests are modeling and simulation, cognitive simulation including simulation of human behaviour by fuzzy agents, agents with dynamic personality and emotions, artificial intelligence, expert systems, fuzzy logic, object-oriented analysis and design, multi-agent systems and their applications. He published more than documents in Journals and Conferences.
Ismail 1 , Mohammed A. Ramadan 2 , Talaat S. El danaf 3 and Ahmed H. Lecturer , Department of Mathematics, Faculty of Science, Menofia University, Egypt Email : [email protected] Abstract This paper present a novel off-line signature recognition method based on multi scale Fourier Descriptor and wavelet transform. Finally we compare 8 distance measures between feature vectors with respect to the recognition performance.
On-line recognition refers to a process that the signer uses a special pen called a stylus to create his or her signature, producing the pen locations, speeds and pressures, while off-line recognition just deals with signature images acquired by a scanner or a digital camera. In general, off-line signature recognition is a challenging problem. Off- line handwriting recognition systems are more difficult than online systems as dynamic information like duration, time ordering, number of strokes, and direction of writing are lost.
This chapter deals with an off-line signature recognition and verification system. It is an art of science to use physical and behavioral characteristics to verify or identify a person. Particularly, handwriting is believed to be singular, exclusive, personal for individuals. Handwriting signature is the most popular identification method socially and legally which has been used widely in the bank check and credit card transactions, document certification, etc.
The objective of signature recognition is to recognize the signer for the purpose of recognition or verification. Recognition is finding the identification of the signature owner. Verification is the decision about whether the signature is genuine or forgery as in figure 1. In the last few decades, many approaches have been developed in the pattern recognition area, which approached the offline signature verification problem.
Justino, Bortolozzi and Sabourin proposed an off-line signature verification system using Hidden Markov Model [1]. This noise may cause severe distortions in the digital image and hence ambiguous features and a correspondingly poor recognition and verification rate.
Therefore, a preprocessor is used to remove noise. Preprocessing techniques eliminate much of the variability of signature data. Preprocessor also achieve the scaling and rotation invariant using slant normalization. Arif and Vincent concerned data fusion and its methods for an off-line signature verification problem which are DempsterShafer evidence theory, Possibility theory and Borda count method [4]. Chalechale and Mertins used line segment distribution of sketches for Persian signature recognition [5].
Sansone and Vento increased performance of signature verification system by a serial three stage multi-expert system [6]. The median filter is a sliding-window spatial filter, it replaces the center value in the window with the median of all the pixel values in the window. The kernel is usually square but can be any shape. This method relies on global features that summarize different aspects of signature shape and dynamics of signature production. For designing the algorithm, they have tried to detect the signature without paying any attention to the thickness and size of it [7].
The scale normalization can be made by scaling the image along the x coordinate and y coordinate respectively to the prefixed size. For the slant normalization a moment based algorithm is described in [10]. The basic idea is to compute the major orientation or slant angle of the handwriting strokes according to second moments of foreground pixels and rotate the foreground pixels by the computed angle along the opposite direction such that the major orientation is horizontal.
Tang and TaiPing Zhang presents two models utilizing rotation invariant structure features to tackle the problem. In principle, the elaborately extracted ring-peripheral features are able to describe internal and external structure changes of signatures periodically. In order to evaluate match score quantitatively, discrete fast Fourier transform is employed to eliminate phase shift and verification is conducted based on a distance model. In addition, the ring-hidden Markov model HMM is constructed to directly evaluate similar between test signature and training samples [8].
They are organized into 18 sets, and each set corresponds to one signature enrollment. There are 24 genuine and 24 forgery signatures in a set. Each volunteer was asked to sign his or her own signatures on a white paper 24 times. An examples of the database image are shown in figure 2. In our system, we use implement a new Multi scale Fourier descriptor using wavelet transform as discus in the next section The multi scale representation of the signature image can be achieved using wavelet transform.
Since image generated through rotation, translation and scaling called similarity transform of a image of a same image are similar images , a image representation should be invariant to these operations. In other words, n is invariant to translation, rotation, scaling and change of start point. Also the dimensionality of the feature vector depends on the signature image size. Therefore, the coefficient vectors of different signatures cannot be directly matched in the image retrieval.
The proposed solution for this problem is to apply the Fourier transform to the coefficients obtained from the wavelet transform. In this way, the multi scale signature representation can be transformed to the frequency domain, in which normalization and matching are straightforward operations.
Hence the benefits of multi scale representation and Fourier representation can be combined. In the following experiments, a total of signature images. The experimental platform is the Intel core 2 duo 1. The recognition Distance measures Minkowski distance Correct recognition rate Recognition experiments were performed using the database containing signature image.
Our method tested using different wavelet family and various distance Measures. The best recognition results were achieved using sym8 wavelet family and Manhattan distance. Arif and N. Chalechale and A. Justino, F. Bortolozzi and R. Zhang, M. Fu and H. Digital Image Processing, second ed.
Addison-Wesley, MA. AnandhaKumar V. Conventional database systems are designed for managing textual and numerical data and retrieving such data is often based on simple comparisons of text or numerical values. However, this method is no longer adequate for images, since the digital presentation of images does not convey the reality of images.
Retrieval of images become difficult when the database is very large. This paper addresses such problems and presents a novel indexing technique, Feature Based Adaptive Tolerance Tree FATT , which is designed to bring an effective solution especially for indexing large databases. The proposed indexing scheme is then used along with a query by image content, in order to achieve the ultimate goal from the user point of view that is retrieval of all relevant images.
Do i need to notify a mod or something, or do they automaticaly see when a file is waiting for approval? EDIT maybe ww will soon also have support for the improved version of ac3enc. So this might be the 11th supported "program" Zyphon 22nd July , EDIT maybe ww will soon also have support for the improved version of ac3enc. So this might be the 11th supported "program" This sounds good, so will it be able to make a nice ac3 file from a wav file then?
Good luck with the updates. Yep : and maybe the 12th app supported will be a commercial ac3 encoder But currently im looking into vst support. Zyphon Yep : and maybe the 12th app supported will be a commercial ac3 encoder But currently im looking into vst support. Cool can't wait to see this version. I test Belight BeSweet, interface bsn. KillaByte 24th July , Dude, your program rocks :thanks: I thought there would be no way to fix the channel order of my ripped DVD-A files with free tools - until I found your program.
Can't wait to see the new version. Keep up the good work : johnman 25th July , Dude, your program rocks :thanks: I thought there would be no way to fix the channel order of my ripped DVD-A files with free tools - until I found your program. But since it is called wavewizard it is high on my priority list again. And when im working on it, ill make sure my program will swallow everything mctools can produce :.
BTW if you think your mapping s might be usefull for others, you might consider posting it in this thread. The mappings are just plain text in the file "mappings. So wavewizard might become a VST host sometime in the near future. KillaByte 25th July , BTW if you think your mapping s might be usefull for others, you might consider posting it in this thread.
But thx anyway. It still cant write them, and i dont see any real reason to do so.
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This produces a Non-inverting Amplifier circuit with very good stability, a very high input impedance, Rin approaching infinity as no current flows into the positive input terminal , and a low output impedance, rout as shown below. Because of this virtual earth node, the resistors Rf 8 and R2 form a simple voltage divider network across the amplifier, and the voltage gain of the circuit is determined by the ratios of R2 and Rf as shown below.
Non-Inverting Amplifier Characteristics Equivalent Voltage Divider Network Then using the formula to calculate the output voltage of a potential divider network, we can calculate the output Voltage Gain of the Non-inverting Amplifier as: We can see that the overall gain of a Non-Inverting Amplifier is greater but never less than 1, is positive, and is determined by the ratio of the values of Rf and R2. If the feedback resistor Rf is zero the gain will be equal to 1, and if resistor R2 is zero the gain will approach infinity, but in practice, it will be limited to the operational amplifiers open-loop differential gain, Ao.
This then makes the Voltage Follower circuit ideal as a Unity Gain Buffer circuit because its isolation properties as impedance or circuit isolation is more important than amplification. Generally R2 is chosen to be greater than the R1.
Non-Inverting Operational Amplifier Circuit Non-Inverting Amplifier Gain As already discussed the constructional view of the non-inverting amplifier it can be considered that the inputs applied at both the terminals are the same. The voltage levels are the same and even the feedback is dependent on both the resistors R1 and R2.
In this way, it makes simple and easy to determine the gain for such types of amplifiers. As the voltage levels applied for both the terminals remain the same indirectly results in the gain levels to be high. The voltage level determined at the inverting terminal is because of the presence of the potential-divider circuit.
Then this results in the equation of the voltage that is: But the gain is the ratio between the ratios of the output values to input values of the applied signals. Therefore, Av represents the overall gain obtained in the circuit. R1 represents the resistance connected to the ground. R2 represents the resistor connected to the feedback. The resistance considered in the above equation is in ohms.
When an different voltage signals in parallel are fed to the non-inverting terminal of the Op-Amp then it becomes a Non-Inverting Summing Amplifier. Non-Inverting Summing Amplifier If the used resistors in the circuit are considered to be equal in terms of resistance. In that case, the equation for the output can be determined as Output Wave forms This amplifier generates the output the same as that of the applied input signal. Both the signals that are applied input and the generated output are in phase.
Because of this reason, the potential difference across both the terminals remains the same. Output Wave form of the Non-Inverting Amplifier Advantages and Disadvantages of Non-Inverting Amplifier The advantages of the non-inverting amplifier are as follows: The output signal is obtained without phase inversion. In comparison to the impedance value of the input at the inverting amplifier is high in the non-inverting amplifier. The voltage gain in this amplifier is variable.
Better matching of impedance can be obtained with the non-inverting amplifiers. It has a positive voltage gain. The disadvantages of the non-inverting amplifier are as follows: More stages are utilized based on the requirement of achieving desired gain. Based on the respective amplifiers chosen the input and the output resistance gets varied. The above are some of the advantages and disadvantages of non-inverting amplifiers.
Applications The applications of the non-inverting amplifiers are as follows: The circuits that have the requirement of the high input impedance non-inverting amplifiers are utilized. To isolate the respective cascaded circuits these are used. In the varying gains consideration, these amplifiers are used. Please refer to this link to know more about Non-inverting Amplifier MCQs These non-inverting amplifiers have various applications in terms of the higher values of input impedance.
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Operational Amplifiers - Inverting \u0026 Non Inverting Op-Amps
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Feedback control of the non-inverting amplifier is achieved by applying a small part of the output voltage signal back to the inverting - input terminal via a Rf — R2 voltage divider network, again producing negative feedback. This produces a Non-inverting Amplifier circuit with very good stability, a very high input impedance, Rin approaching infinity as no current flows into the positive input terminal , and a low output impedance, rout as shown below. Because of this virtual earth node, the resistors Rf 8 and R2 form a simple voltage divider network across the amplifier, and the voltage gain of the circuit is determined by the ratios of R2 and Rf as shown below.
Non-Inverting Amplifier Characteristics Equivalent Voltage Divider Network Then using the formula to calculate the output voltage of a potential divider network, we can calculate the output Voltage Gain of the Non-inverting Amplifier as: We can see that the overall gain of a Non-Inverting Amplifier is greater but never less than 1, is positive, and is determined by the ratio of the values of Rf and R2.
If the feedback resistor Rf is zero the gain will be equal to 1, and if resistor R2 is zero the gain will approach infinity, but in practice, it will be limited to the operational amplifiers open-loop differential gain, Ao. It is similar to that of the inverting amplifier. The same parts of the inverting amplifier are utilized in this amplifier.
The only design criteria that must be chosen is that the non-inverting amplifier must possess the high value of the impedance at the input. Circuit Diagram The non-inverting amplifier are designed using an the operational amplifier. In the op-amps there are three basic terminals among those three two will be the input terminals and one is for output consideration.
The applied input to the respective terminal decides whether it is an inverting one or non-inverting one. The circuit designed for a non-inverting amplifier consists of a basic op-amp where the input is connected to a non-inverting terminal. The output obtained from this circuit is a non-inverted one. This is again feedback towards input but to the inverting terminal via a resistor. Further, one more resistor is connected to the inverting terminal in concern to connect it to the ground.
Hence the overall gain of the circuit is dependent on these two resistors that are responsible for the feedback connection. Those two resistors will behave as a voltage divider of the feedback fed to the inverting terminal. Generally R2 is chosen to be greater than the R1. Non-Inverting Operational Amplifier Circuit Non-Inverting Amplifier Gain As already discussed the constructional view of the non-inverting amplifier it can be considered that the inputs applied at both the terminals are the same.
The voltage levels are the same and even the feedback is dependent on both the resistors R1 and R2. In this way, it makes simple and easy to determine the gain for such types of amplifiers. As the voltage levels applied for both the terminals remain the same indirectly results in the gain levels to be high. The voltage level determined at the inverting terminal is because of the presence of the potential-divider circuit.
Then this results in the equation of the voltage that is: But the gain is the ratio between the ratios of the output values to input values of the applied signals. Therefore, Av represents the overall gain obtained in the circuit. R1 represents the resistance connected to the ground. R2 represents the resistor connected to the feedback.
The resistance considered in the above equation is in ohms. When an different voltage signals in parallel are fed to the non-inverting terminal of the Op-Amp then it becomes a Non-Inverting Summing Amplifier. Non-Inverting Summing Amplifier If the used resistors in the circuit are considered to be equal in terms of resistance. In that case, the equation for the output can be determined as Output Wave forms This amplifier generates the output the same as that of the applied input signal.
Both the signals that are applied input and the generated output are in phase. Because of this reason, the potential difference across both the terminals remains the same.
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