Attribute grammars as a robust technical basis for a human–computer interaction general purpose architecture

Abstract Optimizers in commercial electromagnetic (EM) simulation software packages are the main tools for performing antenna design exploration today. However, these general purpose optimizers are facing challenges in optimization efficiency, supported optimization types and usability for antenna experts without deep knowledge on optimization. Aiming to fill the gaps, a new antenna design exploration tool, called Antenna Design Explorer (ADE), is presented in this paper. The key features are: (1) State-of-the-art antenna design exploration methods are selected and embedded, addressing efficient antenna optimization (critical but unable to be solved by existing tools) and multiobjective antenna optimization (not available in most existing tools); (2) Human-computer interaction for the targeted problem is studied, addressing various usability issues for antenna design engineers, such as automatic algorithmic parameter setting and interactive stopping criteria; (3) Compatibility with existing tools is studied and ADE is able to co-work with existing EM simulators and optimizers, combining advantages. A case study verifies the advantages of ADE. Highlights A new antenna design exploration tool, called Antenna Design Explorer (ADE), is presented in this paper. State-of-the-art antenna design exploration methods are selected and embedded, addressing efficient antenna optimization (critical but difficult to be solved by existing tools) and multiobjective antenna optimization (not available in most existing tools). Human-computer interaction for the targeted problem is studied, addressing various usability issues for antenna design engineers. Compatibility with existing tools is studied and ADE is able to co-work with existing EM simulators and optimizers, combining advantages.

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A low cost SSVEP-EEG based human-computer-interaction system for completely locked-in patients

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v10i4.2923 ◽

2021 ◽

Vol 10 (4) ◽

pp. 2245-2253

Author(s):

Azhar Dilshad ◽

Vali Uddin ◽

Muhammad Rizwan Tanweer ◽

Tariq Javid

Keyword(s):

Human Computer Interaction ◽

Human Life ◽

Low Cost ◽

General Purpose ◽

Control Panel ◽

Motor Disability ◽

Brain Signals ◽

Conventional Manner ◽

Application Specific ◽

Computer Interaction

Human computer interaction (HCI) for completely locked-in patients is a very difficult task. Nowadays, information technology (IT) is becoming an essential part of human life. Patients with completely locked-in state are generally unable to facilitate themselves by these useful technological advancements. Hence, they cannot use modern IT gadgets and applications throughout the lifespan after disability. Advancements in brain computer interface (BCI) enable operating IT devices using brain signals specifically when a person is unable to interact with the devices in conventional manner due to cognitive motor disability. However, existing state-of-the-art application specific BCI devices are comparatively too expensive. This paper presents a research and development work that aims to design and develop a low-cost general purpose HCI system that can be used to operate computers and a general purpose control panel through brain signals. The system is based on steady state visual evoked potentials (SSVEP). In proposed system, these electrical signals are obtained in response of a number of different flickering lights of different frequencies through electroencephalogram (EEG) electrodes and an open source BCI hardware. Successful trails conducted on healthy participants suggest that severely paralyzed subjects can operate a computer or control panel as an alternative to conventional HCI device.

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Recommender Systems: Past, Present, Future

AI Magazine ◽

10.1609/aimag.v42i3.18139 ◽

2022 ◽

Vol 42 (3) ◽

pp. 3-6

Author(s):

Dietmar Jannach ◽

Pearl Pu ◽

Francesco Ricci ◽

Markus Zanker

Keyword(s):

Human Computer Interaction ◽

Recommender Systems ◽

Information Search ◽

Information Filtering ◽

General Purpose ◽

Learning Technology ◽

Computerized System ◽

Interaction Problem ◽

Selection Of ◽

Computer Interaction

The origins of modern recommender systems date back to the early 1990s when they were mainly applied experimentally to personal email and information filtering. Today, 30 years later, personalized recommendations are ubiquitous and research in this highly successful application area of AI is flourishing more than ever. Much of the research in the last decades was fueled by advances in machine learning technology. However, building a successful recommender sys-tem requires more than a clever general-purpose algorithm. It requires an in-depth understanding of the specifics of the application environment and the expected effects of the system on its users. Ultimately, making recommendations is a human-computer interaction problem, where a computerized system supports users in information search or decision-making contexts. This special issue contains a selection of papers reflecting this multi-faceted nature of the problem and puts open research challenges in recommender systems to the fore-front. It features articles on the latest learning technology, reflects on the human-computer interaction aspects, reports on the use of recommender systems in practice, and it finally critically discusses our research methodology.

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