Introducing AI into MEMS can lead us to brain-computer interfaces and super-human intelligence

Sanders, David

doi:10.1108/aa.2009.03329daa.002

Cited by 14 publications

(18 citation statements)

References 48 publications

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“…Multiple sensor networks can be coupled to form device networks. And as they become smart, sensors can pre-process their own data to reduce communications (Sanders 2009a). …”

Section: Miniature System Devices and Their Potentialmentioning

confidence: 99%

“…'Until recently…sensors tended to be simple, unintelligent, connected directly into control systems, and static…, but all that is changing. Wireless networks are becoming increasingly common and some smaller sensors are becoming mobile so that networks of sensors can work in mobile teams (or swarms)… Sensors are becoming "Smart Sensors" that can pre-process their own data to improve quality and reduce communications' (Sanders 2009a). The emphasis will be given to (the integrated large-scale) MEMS, in addition to providing a short account of piezo-materials and VLSI video.…”

Section: Mems Technologymentioning

confidence: 99%

“…Furthermore, constraints on resources such as energy, memory, size, and bandwidth are considered among the difficult issues that face the future research and development in MEMS. In terms of size and energy, Sanders (2009a) points out that the 'potential need for smaller and more energy efficient sensors that can operate autonomously in harsh industrial conditions…will drive research towards more robust and fault tolerant MEMS that can automatically compensate for variables such as temperature'. Considering that electronic sensor systems are sometimes exposed to harsh environmental conditions, MEMS-based sensors or sensors-on-a-chip could be robust in such conditions, as they seem to be able to withstand high humidity, pressure, and temperature (Sanders 2008).…”

Section: Technical and Theoretical Issues And Challengesmentioning

confidence: 99%

“…MEMS will need the ability to cope with technology or communication failures and large-scale deployments and large amounts of data will need new computer science algorithms' (Sanders 2009a).…”

Section: Technical and Theoretical Issues And Challengesmentioning

confidence: 99%

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Context Recognition in AmI Environments: Sensor and MMES Technology, Recognition Approaches, and Pattern Recognition Methods

Bibri

2015

Atlantis Ambient and Pervasive Intelligence

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There exist a vast range of AmI architectures that essentially aim to provide the appropriate infrastructure for AmI systems. Typically, they include many sensors of diverse types, information processing systems or computing devices where modeling and reasoning occur, and actuators through which the system acts, reacts, or pre-acts in the physical world. There are many permutations of enabling technologies and computational processes of AmI, which result in many heterogeneous components (devices and systems and associated software applications) which have to interconnect and communicate seamlessly across disparate networks as part of vast architectures enabling context awareness, machine learning and reasoning, ontological representation and reasoning, and adaptation of services. The sensors are basically utilized to acquire the contextual data needed for the context recognition process-that is, observed information as input for AmI systems to analyze, model, and understand the user's context, so to undertake in a knowledgeable manner actions accordingly. Sensor technology is thus a key enabler of context awareness functionality in AmI systems. Specifically, to acquire, fuse, process, propagate, interpret, and reason about context data in the AmI space to support adaptation of services requires using dedicated sensors and signal and data processing techniques, in addition to sophisticated context recognition algorithms based on a wide variety of methods and techniques for modeling and reasoning. The challenge of incorporating context awareness functionality in the AmI service provision system lies in the complexity associated with sensing, learning, capturing, representing, processing, and managing context information.Context-aware systems are increasingly maturing and rapidly proliferating, spanning a variety of application domains, owing to recent advances in capture technologies, the diversity of recognition approaches, multi-senor fusion techniques, and sensor networks, as well as pattern recognition algorithms and representation and reasoning techniques. Numerous recognition approaches have been developed 129 and studied, and a wide variety of related projects have been carried out within various domains of context awareness. Most of early research work on context awareness focused on user's physical context, which can be inferred using different types of sensing facilities, including stereo-type cameras, RFID, and smart devices. While most attempts to use context awareness within AmI environments were centered on the physical elements of the environment or users, in recent years, research in the area of context recognition has shifted the focus to human elements of context, such as emotional states, cognitive states, physiological states, activities, and behaviors. This has led to the development and employment of different recognition methods, mainly vision-based, multisensory-based, and sensor-based context and activity recognition approaches. Furthermore, investigating methods for context recognition ...

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“…Multiple sensor networks can be coupled to form device networks. And as they become smart, sensors can pre-process their own data to reduce communications (Sanders 2009a). …”

Section: Miniature System Devices and Their Potentialmentioning

confidence: 99%

Section: Mems Technologymentioning

confidence: 99%

Section: Technical and Theoretical Issues And Challengesmentioning

confidence: 99%

Section: Technical and Theoretical Issues And Challengesmentioning

confidence: 99%

See 2 more Smart Citations

Context Recognition in AmI Environments: Sensor and MMES Technology, Recognition Approaches, and Pattern Recognition Methods

Bibri

2015

Atlantis Ambient and Pervasive Intelligence

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“…The many different methods of implementing AI each have their own strengths and weaknesses [9][10][11][12][13][14]. Some effort has been made in combining different methods to produce hybrid techniques with more strengths and fewer weaknesses.…”

Section: Proposed Systemmentioning

confidence: 99%

Improving automatic robotic welding in shipbuilding through the introduction of a corner-finding algorithm to help recognise shipbuilding parts

et al. 2011

Self Cite

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A system that uses a combination of techniques to suggest weld requirements for ships' parts is proposed. These suggestions are evaluated, decisions are made and then weld parameters are sent to a program generator. New image capture methods are being combined with a decision-making system that uses multiple parallel artificial intelligence (AI) techniques. A pattern recognition system recognises shipbuilding parts using shape contour information. Fourier descriptors provide information and neural networks make decisions about shapes. The system has distinguished between various parts, and programs have been generated to validate the approaches used. The system has recently been improved by pre-processing using a simple and accurate corner finder in an edge-detected image.

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Improving Steering of a Powered Wheelchair Using an Expert System to Interpret Hand Tremor

Sanders

Bausch

2015

Intelligent Robotics and Applications

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Simple expert systems are presented that will allow more people to use powered wheelchairs. The systems interpret hand tremor and provide joystick position signals. Signals are mixed with ultrasonic sensor data to identify potentially hazardous situations and assist users to find a safe course. Results are discussed from a series of timed tasks completed by users using a joystick. They suggest that the amount of sensor support should be varied depending on circumstances and skill. Drivers completed progressively more complicated courses both with and without sensors and the most recently published systems are used to compare results. The new expert systems consistently out-performed the most recently published systems.

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Introducing AI into MEMS can lead us to brain-computer interfaces and super-human intelligence

Cited by 14 publications

References 48 publications

Context Recognition in AmI Environments: Sensor and MMES Technology, Recognition Approaches, and Pattern Recognition Methods

Context Recognition in AmI Environments: Sensor and MMES Technology, Recognition Approaches, and Pattern Recognition Methods

Improving automatic robotic welding in shipbuilding through the introduction of a corner-finding algorithm to help recognise shipbuilding parts

Improving Steering of a Powered Wheelchair Using an Expert System to Interpret Hand Tremor

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