Conferences related to Machine Intelligence

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2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE


ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

The ICASSP meeting is the world's largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class speakers, tutorials, exhibits, and over 50 lecture and poster sessions.


IECON 2020 - 46th Annual Conference of the IEEE Industrial Electronics Society

IECON is focusing on industrial and manufacturing theory and applications of electronics, controls, communications, instrumentation and computational intelligence.


2019 IEEE 17th World Symposium on Applied Machine Intelligence and Informatics (SAMI)

Computational Intelligence and Intelligent Technologies are very important tools in building intelligent systems with various degree of autonomous behaviour. These groups of tools support such features as ability to learn and adaptability of the intelligent systems in various types ofenvironments and situations. The current and future Information Society is expecting to be implemented with the framework of the Ambient Intelligence (AmI) approach into technologies and everyday life.

  • 2018 IEEE 16th World Symposium on Applied Machine Intelligence and Informatics (SAMI)

    Computational Intelligence and Intelligent Technologies are very important tools in building intelligent systems with various degree of autonomous behavior. These groups of tools support such features as ability to learn and adaptability of the intelligent systems in various types ofenvironments and situations. The current and future Information Society is expecting to be implemented with the framework of the Ambient Intelligence (AmI) approach into technologies and everyday life.

  • 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI)

    Computational Intelligence and Intelligent Technologies are very important tools in building intelligent systems with various degree of autonomous behavior. These groups of tools support such features as ability to learn and adaptability of the intelligent systems in various types ofenvironments and situations. The current and future Information Society is expecting to beimplemented with the framework of the Ambient Intelligence (AmI) approach into technologiesand everyday life.

  • 2016 IEEE 14th International Symposium on Applied Machine Intelligence and Informatics (SAMI)

    Computational Intelligence and Intelligent Technologies are very important tools in building intelligent systems with various degree of autonomous behavior. These groups of tools support such features as ability to learn and adaptability of the intelligent systems in various types ofenvironments and situations. The current and future Information Society is expecting to be implemented with the framework of the Ambient Intelligence (AmI) approach into technologies and everyday life.

  • 2015 IEEE 13th International Symposium on Applied Machine Intelligence and Informatics (SAMI)

    Computational Intelligence and Intelligent Technologies are very important tools in buildingintelligent systems with various degree of autonomous behavior. These groups of tools supportsuch features as ability to learn and adaptability of the intelligent systems in various types ofenvironments and situations. The current and future Information Society is expecting to beimplemented with the framework of the Ambient Intelligence (AmI) approach into technologiesand everyday life.

  • 2014 IEEE 12th International Symposium on Applied Machine Intelligence and Informatics (SAMI)

    Computational Intelligence and Intelligent Technologies are very important tools in building intelligent systems with various degree of autonomous behavior. These groups of tools support such features as ability to learn and adaptability of the intelligent systems in various types of environments and situations. The current and future Information Society is expecting to be implemented with the framework of the Ambient Intelligence (AmI) approach into technologies and everyday life.

  • 2013 IEEE 11th International Symposium on Applied Machine Intelligence and Informatics (SAMI)

    Computational Intelligence and Intelligent Technologies are very important tools inbuilding intelligent systems with various degree of autonomous behavior. These groups of tools support suchfeatures as ability to learn and adaptability of the intelligent systems in various types of environments andsituations. The current and future Information Society is expecting to be implemented with the framework ofthe Ambient Intelligence (AmI) approach into technologies and everyday life.

  • 2012 IEEE 10th International Symposium on Applied Machine Intelligence and Informatics (SAMI)

    Computational Intelligence and Intelligent Technologies are very important tools in building intelligent systems with various degree of autonomous behavior. These groups of tools support such features as ability to learn and adaptability of the intelligent systems in various types of environments and situations. The current and future Information Society is expecting to be implemented with the framework of the Ambient Intelligence (AmI) approach into technologies and everyday life.

  • 2011 IEEE 9th International Symposium on Applied Machine Intelligence and Informatics (SAMI)

    Computational Intelligence and Intelligent Technologies are very important tools in building intelligent systems with various degree of autonomous behavior. These groups of tools support such features as ability to learn and adaptability of the intelligent systems in various types of environments and situations. The current and future Information Society is expecting to be implemented with the framework of the Ambient Intelligence (AmI) approach into technologies and everyday life.

  • 2010 IEEE 8th International Symposium on Applied Machine Intelligence and Informatics (SAMI)

    Computational Intelligence and Intelligent Technologies are very important tools in building intelligent systems with various degree of autonomous behavior. These groups of tools support such features as ability to learn and adaptability of the intelligent systems in various types of environments and situations. The current and future Information Society is expecting to be implemented with the framework of the Ambient Intelligence (AmI) approach into technologies and everyday life.

  • 2009 7th International Symposium on Applied Machine Intelligence and Informatics (SAMI 2009)

    Computational Intelligence and Intelligent Technologies are very important tools in building intelligent systems with various degree of autonomous behavior. These groups of tools support such features as ability to learn and adaptability of the intelligent systems in various types of environments and situations. The current and future Information Society is expecting to be implemented with the framework of the Ambient Intelligence (AmI) approach into technologies and everyday life. These accomplishments pro

  • 2008 6th International Symposium on Applied Machine Intelligence and Informatics (SAMI 2008)

    Topics include but not limited to * Intelligent Robotics * Intelligent Mechatronics * Computational Intelligence * Artificial Intelligence * CAD/CAM/CAE Systems * Intelligent Manufacturing Systems * Man-Machine Systems * Systems Engineering


2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)

The IEEE ICCI*CC series is a flagship conference of its field. It not only synergizes theories of modern information science, computer science, communication theories, AI, cybernetics, computational intelligence, cognitive science, intelligence science, neuropsychology, brain science, systems science, software science, knowledge science, cognitive robots, cognitive linguistics, and life science, but also promotes novel applications in cognitive computers, cognitive communications, computational intelligence, cognitive robots, cognitive systems, and the AI, IT, and software industries.

  • 2018 IEEE 17th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)

    Informatics models of the brainCognitive processes of the brainThe cognitive foundation of big dataMachine consciousnessNeuroscience foundations of information processingDenotational mathematics (DM)Cognitive knowledge basesAutonomous machine learningNeural models of memoryInternal information processingCognitive sensors and networksCognitive linguisticsAbstract intelligence (aI)Cognitive information theoryCognitive information fusionCognitive computersCognitive systemsCognitive man-machine communicationCognitive InternetWorld-Wide Wisdoms (WWW+)Mathematical engineering for AICognitive vehicle systems Semantic computingDistributed intelligenceMathematical models of AICognitive signal processingCognitive image processing Artificial neural netsGenetic computingMATLAB models of AIBrain-inspired systemsNeuroinformaticsNeurological foundations of the brainSoftware simulations of the brainBrain-system interfacesNeurocomputingeBrain models

  • 2017 IEEE 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)

    Cognitive Informatics is a transdisciplinary field that studies the internal information processing mechanisms of the brain, the underlying abstract intelligence theories and denotational mathematics, and their engineering applications in cognitive computing, computational intelligence, and cognitive systems. Cognitive Computing is a cutting-edge paradigm of intelligent computing methodologies and systems based on CI, which implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. CI and CC not only synergize theories of modern information science, computer science, communication theories, AI, cybernetics, computational intelligence, cognitive science, intelligence science, neuropsychology, brain science, systems science, software science, knowledge science, cognitive robots, cognitive linguistics, and life science, but also reveal exciting applications in cognitive computers, cognitive robots, and computational intelligence.

  • 2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)

    Cognitive Informatics (CI) is a transdisciplinary field that studies the internal information processing mechanisms of the brain, the underlying abstract intelligence (¿I) theories and denotational mathematics, and their engineering applications in cognitive computing, computational intelligence, and cognitive systems. Cognitive Computing (CC) is a cutting-edge paradigm of intelligent computing methodologies and systems based on cognitive informatics, which implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain.

  • 2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)

    The scope of the conference covers cognitive informatics, cognitive computing, cognitive communications, computational intelligence, and computational linguitics.

  • 2014 IEEE 13th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)

    Cognitive informatics, cognitive computing, cognitive science, cognitive robots, artificial intelligence, computational intelligence

  • 2013 12th IEEE International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)

    Cognitive Informatics (CI) is a cutting-edge and multidisciplinary research field that tackles the fundamental problems shared by modern informatics, computing, AI, cybernetics, computational intelligence, cognitive science, intelligence science, neuropsychology, brain science, systems science, software engineering, knowledge engineering, cognitive robots, scientific philosophy, cognitive linguistics, life sciences, and cognitive computing.

  • 2012 11th IEEE International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)

    Cognitive informatics and Cognitive Computing are a transdisciplinary enquiry on the internal information processing mechanisms and processes of the brain and their engineering applications in cognitive computers, computational intelligence, cognitive robots, cognitive systems, and in the AI, IT, and software industries. The 11th IEEE Int l Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC 12) focuses on the theme of e-Brain and Cognitive Computers.

  • 2011 10th IEEE International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)

    Cognitive Informatics and Cognitive Computing are a transdisciplinary enquiry on the internal information processing mechanisms and processes of the brain and their engineering applications in cognitive computers, computational intelligence, cognitive robots, cognitive systems, and in the AI, IT, and software industries. The 10th IEEE Int l Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC 11) focuses on the theme of Cognitive Computers and the e-Brain.

  • 2010 9th IEEE International Conference on Cognitive Informatics (ICCI)

    Cognitive Informatics (CI) is a cutting-edge and transdisciplinary research area that tackles the fundamental problems shared by modern informatics, computing, AI, cybernetics, computational intelligence, cognitive science, neuropsychology, medical science, systems science, software engineering, telecommunications, knowledge engineering, philosophy, linguistics, economics, management science, and life sciences.

  • 2009 8th IEEE International Conference on Cognitive Informatics (ICCI)

    The 8th IEEE International Conference on Cognitive Informatics (ICCI 09) focuses on the theme of Cognitive Computing and Semantic Mining. The objectives of ICCI'09 are to draw attention of researchers, practitioners, and graduate students to the investigation of cognitive mechanisms and processes of human information processing, and to stimulate the international effort on cognitive informatics research and engineering applications.

  • 2008 7th IEEE International Conference on Cognitive Informatics (ICCI)

    The 7th IEEE International Conference on Cognitive Informatics (ICCI 08) focuses on the theme of Cognitive Computers and Computational Intelligence. The objectives of ICCI 08 are to draw attention of researchers, practitioners and graduate students to the investigation of cognitive mechanisms and processes of human information processing, and to stimulate the international effort on cognitive informatics research and engineering applications.

  • 2007 6th IEEE International Conference on Cognitive Informatics (ICCI)

  • 2006 5th IEEE International Conference on Cognitive Informatics (ICCI)

  • 2005 4th IEEE International Conference on Cognitive Informatics (ICCI)


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Periodicals related to Machine Intelligence

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Audio, Speech, and Language Processing, IEEE Transactions on

Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...


Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


Biomedical Engineering, IEEE Reviews in

The IEEE Reviews in Biomedical Engineering will review the state-of-the-art and trends in the emerging field of biomedical engineering. This includes scholarly works, ranging from historic and modern development in biomedical engineering to the life sciences and medicine enabled by technologies covered by the various IEEE societies.


Computational Biology and Bioinformatics, IEEE/ACM Transactions on

Specific topics of interest include, but are not limited to, sequence analysis, comparison and alignment methods; motif, gene and signal recognition; molecular evolution; phylogenetics and phylogenomics; determination or prediction of the structure of RNA and Protein in two and three dimensions; DNA twisting and folding; gene expression and gene regulatory networks; deduction of metabolic pathways; micro-array design and analysis; proteomics; ...


Computational Intelligence Magazine, IEEE

The IEEE Computational Intelligence Magazine (CIM) publishes peer-reviewed articles that present emerging novel discoveries, important insights, or tutorial surveys in all areas of computational intelligence design and applications.


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Most published Xplore authors for Machine Intelligence

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Xplore Articles related to Machine Intelligence

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Machine Intelligence in Healthcare and Medical Cyber Physical Systems: A Survey

IEEE Access, 2018

Today, the US healthcare industry alone can save $300 B per year by using machine intelligence to analyze a rich set of existing medical data; results from these analyses can lead to breakthroughs such as more accurate medical diagnoses, discovery of new cures for diseases, and cost savings in the patient admission process at healthcare organizations. Because healthcare applications intrinsically ...


Technology foresight through the collaboration with human expert and machine intelligence

2016 8th International Conference on Knowledge and Smart Technology (KST), 2016

We always make efforts to predict our future from the past and the present, since the prediction can make great changes in our life, especially in the fields of science and technology. Many organizations in the globe have surveys and announces emerging or disruptive technologies every year. Of course, they have developed their own processes to achieve the goal, but ...


Machine Intelligence Techniques for Protein Classification

2018 3rd International Conference for Convergence in Technology (I2CT), 2018

A Lot of large scale biological projects and experiments like Human Genome Project have been carried in recent past which have resulted in generation of mammoth biological data mostly DNA and Protein sequences. It is extremely difficult to manage such a huge amount of data by using traditional methods hence lot of biological databases related to protein families and sequences ...


Learning and control in virtual reality for machine intelligence

2012 Third International Conference on Intelligent Control and Information Processing, 2012

This paper presents a Virtual Reality (VR) interactive platform for learning and control for machine intelligence based on Adaptive Dynamic Programming (ADP). Recent research results have provided strong evidences that ADP could be a key technique for brain-like intelligent systems design, and VR is a powerful human-computer interface which can provide a three-dimensional (3D) active virtual environment. Converge these two ...


Measuring machine intelligence for human-machine cooperative systems using intelligence task graph

Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No.99CH36289), 1999

We present a practical and systematic strategy for measuring machine (robot) intelligence. A lot of research related to intelligent control has been carried out, but the subjects of definition and measurement of machine intelligence are not clearly formulated yet. We propose a human-oriented definition of machine intelligence and an intelligence task graph (ITG) as a modeling and analysis tool. By ...


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Educational Resources on Machine Intelligence

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IEEE-USA E-Books

  • Machine Intelligence in Healthcare and Medical Cyber Physical Systems: A Survey

    Today, the US healthcare industry alone can save $300 B per year by using machine intelligence to analyze a rich set of existing medical data; results from these analyses can lead to breakthroughs such as more accurate medical diagnoses, discovery of new cures for diseases, and cost savings in the patient admission process at healthcare organizations. Because healthcare applications intrinsically imply a vast amount of data, the execution of any algorithm on medical data is computationally intensive. Significant advancements made in computational power in the past decade have provided the opportunity for many researchers to successfully implement various machine intelligence-based healthcare applications, which didn't run efficiently on earlier computational platforms. In this paper, we provide a survey of machine intelligence algorithms within the context of healthcare applications; our survey includes a comprehensive list of the most commonly used computational models and algorithms. We view the application of these algorithms in multiple steps, namely, data acquisition, feature extraction, and aggregation, modeling, algorithm training, and algorithm execution and provide details-as well as representative case studies-for each step. We provide a set of metrics that are used to evaluate modeling and algorithmic performance, which facilitate the comparison of the presented models and algorithms. Medical cyber-physical systems are presented as an emerging application case study of machine intelligence in healthcare. We conclude our paper by providing a list of opportunities and challenges for incorporating machine intelligence in healthcare applications and provide an extensive list of tools and databases to help other researchers.

  • Technology foresight through the collaboration with human expert and machine intelligence

    We always make efforts to predict our future from the past and the present, since the prediction can make great changes in our life, especially in the fields of science and technology. Many organizations in the globe have surveys and announces emerging or disruptive technologies every year. Of course, they have developed their own processes to achieve the goal, but the insights of experts from related domains are usually absolute. In the era of Bigdata, due to the enormous amount of information, domain experts are struggling with timeliness and completeness in developing insights for the future. In KISTI, we introduced a methodology in which human experts are collaborating with machine intelligence to overcome the information flood. Data-intensive analysis methodology is applied to implement the machine intelligence to predict emerging technologies. The intelligent service platform, named InSciTe, includes data gathering, text mining, identity resolution, reasoning, complex event processing, and prescriptive analytics modules. InSciTe generates candidates of emerging technologies with the evidences why they are selected as candidates, and then domain experts make the final decision. In this talk, I will introduce our intelligent service platform based on the data-intensive analysis. Besides, I will show several case studies in the domains of ICT, internet security, and healthcare as joint works with NIPA, KISA, and KRIBB respectively. For the cases with KRIBB, human experts collaborated with machine intelligence interactively to derive the results. We named this approach as Chi(Computer Human Interacting)-Delphi method for technology foresight. As Web goes to connect machine intelligences in the era of Internet of Things, the collaboration between human intelligence and machine intelligence will be eventually the next great wave for predicting the future.

  • Machine Intelligence Techniques for Protein Classification

    A Lot of large scale biological projects and experiments like Human Genome Project have been carried in recent past which have resulted in generation of mammoth biological data mostly DNA and Protein sequences. It is extremely difficult to manage such a huge amount of data by using traditional methods hence lot of biological databases related to protein families and sequences have come into existence. Many databases contain information about proteins and their families. It is important and necessary to know the family of protein (unknown) hence we need to classify proteins. Using laboratory way for doing this is an arduous task, hence in this research we will use the advanced Machine Intelligence computing techniques like Artificial Neural Networks, Naïve Bayes Classifier, Decision Trees etc.

  • Learning and control in virtual reality for machine intelligence

    This paper presents a Virtual Reality (VR) interactive platform for learning and control for machine intelligence based on Adaptive Dynamic Programming (ADP). Recent research results have provided strong evidences that ADP could be a key technique for brain-like intelligent systems design, and VR is a powerful human-computer interface which can provide a three-dimensional (3D) active virtual environment. Converge these two subjects, we design an interactive system to facilitate and demonstrate the learning and control in VR environment with ADP. Our main goal in this paper is two-fold. First, we demonstrate that VR could be a useful platform to demonstrate and visualize machine intelligence research through the simulated 3D environment. Second, the integration of VR into machine intelligence research can provide a powerful platform to simulate, validate and facilitate the real-time interaction between the intelligent system and an unstructured environment. We discuss the detailed design strategy of the VR platform, and also demonstrated the interactive system performance based on the triple link inverted pendulum benchmark.

  • Measuring machine intelligence for human-machine cooperative systems using intelligence task graph

    We present a practical and systematic strategy for measuring machine (robot) intelligence. A lot of research related to intelligent control has been carried out, but the subjects of definition and measurement of machine intelligence are not clearly formulated yet. We propose a human-oriented definition of machine intelligence and an intelligence task graph (ITG) as a modeling and analysis tool. By using an ITG, the machine contribution of human-machine cooperative systems is easily separated from the human contribution and directly described as numerical equations. Therefore we conclude that the ITG is very useful for estimating the machine intelligence quotient. This research will help engineers design intelligent robots which support human-friendly interfaces and perform environment controls with high performance.

  • An egocentric approach to machine intelligence

    Intimate to the functioning and behavior of intelligent systems is the manner in which information is represented internally. The conventional approach to intelligent system design assumes a particular bias in the manner by which this information is represented. Typically, this is characterized by an "abstract" or "objective" design methodology which holds that intelligence is not a function of the physical nature of the system. Such an approach suffers from several shortcomings, most notably problems relating to scaling and complexity. Recent physiological research, however, has demonstrated that physical bodily form is a fundamental building block in the organization of mammalian cortical structures. Consequently, this article explores such a biologically motivated "subjective" or "egocentric" approach to system design, and demonstrates its utility in a simple robot arm control problem.

  • Machine intelligence through 3D waferscale integration

    Bringing computing systems to the stage of Machine Intelligence will require a massive scaling in processing, memory, and interconnectivity, and thus a major change in how electronic systems are designed. Long overlooked because of its unsuitability for the exacting demands of enterprise computing, 3D waferscale integration offers a promising scaling path, due in large part to the fault- tolerant nature of many cognitive algorithms. This work explores this scaling path in greater detail, invoking a simple model of brain connectivity to examine the potential for 3D waferscale integration to meet the demanding interconnectivity requirements of Machine Intelligence.

  • Objective measures of machine intelligence based on a human-interface analogy

    A human-interface analogy is used to determine objective measures of machine intelligence. This measure is calculated for four candidate control systems used in an assistive device study with patients that exhibit neuromotor disability.

  • An Approach of Monitoring the Supply of Products in a Supply Chain Based on Hybrid Machine Intelligence

    The fuzzy logic is applied to resolve the monitoring problem of products quality and products quantity which are increasingly varying as market requirement. A series of fuzzy rules are employed and the fuzzy system may generate suggested supply change rate. At the same time, the operation of supplier is also dynamically changing and the evaluation and selection for supplier are the basis of supply chain cooperation. So whether it is scientific to select a supplier is crucial for sustaining and developing a company. Therefore, in this paper the neural network is introduced to dynamically assess suppliers and recommend to substituting for new ones when necessary, only supplementing fuzzy logic system with its advantages. The methodology is described for the deployment of this proposed hybrid approach to enhance the machine intelligence of a supply chain network with the description of a case study to exemplify its underlying principles.

  • Machine intelligence realised in a FPGA for smart sensor research

    This paper explores the concept of machine intelligence required for the smart sensor environment. The increasing complexity of the hardware requirements for smart sensor applications has led to the inclusion of field programmable gate arrays, FPGA, in the design of these systems. Two main advantages can be capitalized on with this approach, based on the characteristics of the FPGAs. Firstly, the design can be easily modified, reducing the design cycle time considerably. Also, as FPGAs are inherently parallel devices, both local processing and system partitioning are provided. The total system design in this project includes an application specific integrated circuit, ASIC, the FPGA, and a microcomputer. In this fashion the requirements for a smart sensor are realised.



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