scholarly journals Medical Robotics for Ultrasound Imaging: Current Systems and Future Trends

2021 ◽  
Author(s):  
Felix von Haxthausen ◽  
Sven Böttger ◽  
Daniel Wulff ◽  
Jannis Hagenah ◽  
Verónica García-Vázquez ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Augmented Reality ◽  
Autonomous Systems ◽  
Ultrasound Image ◽  
Image Understanding ◽  
Future Research ◽  
Future Trends ◽  
The Past ◽  
Technical Specifications ◽  
Robotic Ultrasound

Abstract Purpose of Review This review provides an overview of the most recent robotic ultrasound systems that have contemporary emerged over the past five years, highlighting their status and future directions. The systems are categorized based on their level of robot autonomy (LORA). Recent Findings Teleoperating systems show the highest level of technical maturity. Collaborative assisting and autonomous systems are still in the research phase, with a focus on ultrasound image processing and force adaptation strategies. However, missing key factors are clinical studies and appropriate safety strategies. Future research will likely focus on artificial intelligence and virtual/augmented reality to improve image understanding and ergonomics. Summary A review on robotic ultrasound systems is presented in which first technical specifications are outlined. Hereafter, the literature of the past five years is subdivided into teleoperation, collaborative assistance, or autonomous systems based on LORA. Finally, future trends for robotic ultrasound systems are reviewed with a focus on artificial intelligence and virtual/augmented reality.

Augmented Reality Research for Architecture and Design

2012 ◽  
pp. 225-237 ◽  
Author(s):  
Mi Jeong Kim ◽  
Xiangyu Wang ◽  
Xingquan Zhu ◽  
Shih-Chung Kang
Keyword(s):  
Augmented Reality ◽  
Emerging Technology ◽  
Future Research ◽  
Research Directions ◽  
Research Issues ◽  
The Past ◽  
Growing Body ◽  
Future Research Directions ◽  
Architecture And Design

A growing body of research has shown that Augmented Reality (AR) has the potential to contribute to interaction and visualization for architecture and design. While this emerging technology has only been developed for the past decade, numerous journals and conferences in architecture and design have published articles related to AR. This chapter reviews 44 articles on AR especially related to the architecture and design area that were published from 2005 to 2011. Further, this chapter discusses the representative AR research works in terms of four aspects: AR concept, AR implementation, AR evaluation, and AR industry adoption. The chapter draws conclusions about major findings, research issues, and future research directions through the review results. This chapter will be a basis for future research of AR in architecture and design areas.

Epilogue

2018 ◽  
pp. 261-264
Author(s):  
Ingmar Weber
Keyword(s):  
Artificial Intelligence ◽  
Augmented Reality ◽  
Good Reason ◽  
Medical Doctors ◽  
Automated Diagnosis ◽  
Profound Impact ◽  
Customer Support ◽  
The Past ◽  
The Future ◽  
Digital Landscape

Changes in the global digital landscape over the past decade or so have transformed many aspects of society, including how people communicate, socialize, and organize. These transformations have also reconfigured how companies conduct their businesses and altered how states think about security and interact with their citizens. Glancing into the future, there is good reason to believe that nascent technologies such as augmented reality will continue to change how people connect, blurring the lines between our online and offline worlds. Recent breakthroughs in the field of artificial intelligence will also have a profound impact on many aspects of our lives, ranging from the mundane—chat bots as convenient, always available customer support—to the disruptive—replacing medical doctors with automated diagnosis tools....

Strong Artificial Intelligence and Consciousness

2020 ◽  
Vol 07 (01) ◽  
pp. 63-72 ◽  
Author(s):  
Gee Wah Ng ◽  
Wang Chi Leung
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Human Life ◽  
Autonomous Systems ◽  
Holistic Approach ◽  
Future Research ◽  
Strong Ai ◽  
Strong Artificial Intelligence ◽  
Future Research Directions

In the last 10 years, Artificial Intelligence (AI) has seen successes in fields such as natural language processing, computer vision, speech recognition, robotics and autonomous systems. However, these advances are still considered as Narrow AI, i.e. AI built for very specific or constrained applications. These applications have its usefulness in improving the quality of human life; but it is not good enough to do highly general tasks like what the human can do. The holy grail of AI research is to develop Strong AI or Artificial General Intelligence (AGI), which produces human-level intelligence, i.e. the ability to sense, understand, reason, learn and act in dynamic environments. Strong AI is more than just a composition of Narrow AI technologies. We proposed that it has to be a holistic approach towards understanding and reacting to the operating environment and decision-making process. The Strong AI must be able to demonstrate sentience, emotional intelligence, imagination, effective command of other machines or robots, and self-referring and self-reflecting qualities. This paper will give an overview of current Narrow AI capabilities, present the technical gaps, and highlight future research directions for Strong AI. Could Strong AI become conscious? We provide some discussion pointers.

A Survey and Taxonomy of Electronics Toolkits for Interactive and Ubiquitous Device Prototyping

2021 ◽  
Vol 5 (2) ◽  
pp. 1-24
Author(s):  
Mannu Lambrichts ◽  
Raf Ramakers ◽  
Steve Hodges ◽  
Sven Coppers ◽  
James Devine
Keyword(s):  
Online Survey ◽  
Electronic Devices ◽  
Target Audience ◽  
Future Research ◽  
User Needs ◽  
Design Features ◽  
Intended Application ◽  
The Past ◽  
Comprehensive Survey ◽  
Technical Specifications

Over the past two decades, many toolkits for prototyping interactive and ubiquitous electronic devices have been developed. Although their technical specifications are often easy to look up, they vary greatly in terms of design, features and target audience, resulting in very real strengths and weaknesses depending on the intended application. These less technical characteristics are often reported inconsistently, if at all. In this paper we provide a comprehensive survey of interactive and ubiquitous device prototyping toolkits, systematically analysing their characteristics within the framework of a new taxonomy that we present. In addition to the specific characteristics we cover, we introduce a way to evaluate toolkits more holistically, covering user needs such as 'ease of construction' and 'ease of moving from prototype to product' rather than features. We also present results from an online survey which offers new insights on how the surveyed users prioritize these characteristics during prototyping, and what techniques they use to move beyond prototyping. We hope our analysis will be valuable for others in the community who need to build and potentially scale out prototypes as part of their research. We end by identifying gaps that have not yet been addressed by existing offerings and discuss opportunities for future research into electronics prototyping toolkits.

scholarly journals A Look at the Past, Present and Future Research Trends of Artificial Intelligence in Agriculture

Agronomy ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1839
Author(s):  
José Luis Ruiz-Real ◽  
Juan Uribe-Toril ◽  
José Antonio Torres Arriaza ◽  
Jaime de Pablo Valenciano
Keyword(s):  
Artificial Intelligence ◽  
Web Of Science ◽  
Scientific Literature ◽  
Green Economy ◽  
Future Research ◽  
Agriculture Sector ◽  
The Past ◽  
Use Of Resources ◽  
Research Organizations ◽  
Prolific Authors

Technification in agriculture has resulted in the inclusion of more efficient companies that have evolved into a more complex sector focused on production and quality. Artificial intelligence, one of the relevant areas of technology, is transforming the agriculture sector by reducing the consumption and use of resources. This research uses a bibliometric methodology and a fractional counting method of clustering to analyze the scientific literature on the topic, reviewing 2629 related documents recorded on the Web of Science and Scopus databases. The study found significant results regarding the most relevant and prolific authors (Hoogenboom), supporting research organizations (National Natural Science Foundation of China) and countries (U.S., China, India, or Iran). The identification of leaders in this field gives researchers new possibilities for new lines of research based on previous studies. An in-depth examination of authors’ keywords identified different clusters and trends linking Artificial Intelligence and green economy, sustainable development, climate change, and the environment.

scholarly journals It’s Time to Talk About HPC Storage: Perspectives on the Past and Future

2021 ◽  
Author(s):  
Bradley Settlemyer ◽  
George Amvrosiadis ◽  
Philip Carns ◽  
Robert Ross
Keyword(s):  
Artificial Intelligence ◽  
High Performance ◽  
New Technologies ◽  
Storage Systems ◽  
Storage System ◽  
Status Quo ◽  
Future Research ◽  
The Past ◽  
Experimental Data Analysis ◽  
Performance Computing

High-performance computing (HPC) storage systems are a key component of the success of HPC to date. Recently, we have seen major developments in storage-related technologies, as well as changes to how HPC platforms are used, especially in relation to artificial intelligence and experimental data analysis workloads. These developments merit a revisit of HPC storage system architectural designs. In this paper we discuss the drivers, identify key challenges to status quo posed by these developments, and discuss directions future research might take to unlock the potential of new technologies for the breadth of HPC applications.

scholarly journals Vietnamese Education System and Teacher Training: Focusing on Science Education

2020 ◽  
Vol 6 (1) ◽  
pp. 179-206
Author(s):  
Van Hien Nguyen ◽  
Vu Bich Hien Nguyen ◽  
Thi Mai Huong Vu ◽  
Thi Kim Hue Hoang ◽  
Thi Minh Nguyet Nguyen
Keyword(s):  
Teacher Training ◽  
General Education ◽  
Education System ◽  
Document Analysis ◽  
Future Research ◽  
Special Focus ◽  
Future Trends ◽  
Science Teacher Preparation ◽  
The Past ◽  
Research Questions

Abstract This article introduces the reader to past, current, and future trends in science teacher preparation and professional development in Vietnam. The authors rely on document analysis for data collection and focused analysis to describe the general education system and the mechanisms for teacher training in Vietnam from the past to the present. Research questions focused on exploring changes in the organization of the education system over time, identifying advances that have been made, and describing what challenges teacher education faces today. In addition, this paper offers a special focus on how Vietnamese pedagogy institutions are working to prepare new teachers. Finally, the authors describe how Vietnam is preparing to implement a new national general education program that will strongly affect all aspects of education, including training and retraining of teachers. The authors conclude by raising some important questions for future research and development.

scholarly journals A Review on Deep Learning Techniques for 3D Sensed Data Classification

2019 ◽  
Vol 11 (12) ◽  
pp. 1499 ◽  
Author(s):  
David Griffiths ◽  
Jan Boehm
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Point Clouds ◽  
Image Understanding ◽  
Future Research ◽  
National Scale ◽  
The Past ◽  
Current State ◽  
Learning Techniques ◽  
Learning Architectures

Over the past decade deep learning has driven progress in 2D image understanding. Despite these advancements, techniques for automatic 3D sensed data understanding, such as point clouds, is comparatively immature. However, with a range of important applications from indoor robotics navigation to national scale remote sensing there is a high demand for algorithms that can learn to automatically understand and classify 3D sensed data. In this paper we review the current state-of-the-art deep learning architectures for processing unstructured Euclidean data. We begin by addressing the background concepts and traditional methodologies. We review the current main approaches, including RGB-D, multi-view, volumetric and fully end-to-end architecture designs. Datasets for each category are documented and explained. Finally, we give a detailed discussion about the future of deep learning for 3D sensed data, using literature to justify the areas where future research would be most valuable.

scholarly journals A Comparative Analysis of Expert Opinions on Artificial Intelligence: Evolution, Applications, and Its Future

2021 ◽  
Vol 11 (1) ◽  
pp. 10-22
Author(s):  
Falguni Saini ◽  
Tanya Sharma ◽  
Suman Madan
Keyword(s):  
Artificial Intelligence ◽  
Comparative Analysis ◽  
Representation Learning ◽  
Human Intelligence ◽  
Healthcare Education ◽  
Future Trends ◽  
Learning Problem ◽  
The Past ◽  
Expert Opinions ◽  
Clear Idea

Artificial Intelligence (AI) is a field of computer science that primarily focuses on automating tasks that explicitly require human intelligence. The mechanics of AI technology majorly revolves around central affairs including knowledge representation, learning, problem-solving, reasoning, etc. Additionally, each discipline of AI focuses on a particular component to efficiently train the machines. Every branch of AI technology exploits knowledge in machines using diversified practices but with a clear idea of achieving the desired output. AI has evolved drastically over the past two decades and is considered the most in-demand technology at present times in varied fields including healthcare, education, forecasting, security, etc. This paper provides an extensive survey on artificial intelligence and related work going on in this field, how it differs from human intelligence, various subfields of AI and their importance, various issues related to AI and possible solutions along with and future trends related to this technology depicting people’s reliability on it and various possible concerns.

scholarly journals Artificial Intelligence in Neuroanesthesiology and Neurocritical Care

2020 ◽  
Vol 7 (01) ◽  
pp. 11-18
Author(s):  
Vanitha Rajagopalan ◽  
Dilip K. Kulkarni
Keyword(s):  
Artificial Intelligence ◽  
Medical Errors ◽  
Outcome Prediction ◽  
Neurocritical Care ◽  
Healthcare Delivery ◽  
Future Trends ◽  
Surgical Disease ◽  
The Past ◽  
Monitoring Treatment ◽  
Applications Of Ai

AbstractArtificial intelligence (AI) already influences almost every sector of our daily life, including the rapidly evolving technologies and datasets of healthcare delivery. The applications in medicine have significantly evolved over the past few decades and have shown promising results. Despite constant efforts to incorporate AI into the field of anesthesiology since its inception, it is still not commonplace. Neuroanesthesiology and neurocritical care is a discipline of medicine that deals with patients having disorders of the nervous system comprising a complex combination of both medical and surgical disease conditions. AI can be used for better monitoring, treatment, and outcome prediction, thereby reducing healthcare costs, minimizing delays in patient management, and avoiding medical errors. In this review, we have discussed the applications of AI and its potential in aiding the clinician’s judgment in several aspects of neuroanesthesiology and neurocritical care, some of the barriers to its implementation, and the future trends in improving education in this field, all of which will require further work to understand its exact scope.

Sign in / Sign up

Export Citation Format

Share Document