scholarly journals Advances in Agriculture Robotics: A State-of-the-Art Review and Challenges Ahead

Robotics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 52
Author(s):  
Luiz F. P. Oliveira ◽  
António P. Moreira ◽  
Manuel F. Silva
Keyword(s):  
Computer Vision ◽  
State Of The Art ◽  
Research Work ◽  
Development Stage ◽  
Robotic Systems ◽  
Future Research ◽  
Yield Estimation ◽  
Commercial Development ◽  
Land Preparation ◽  
Smart Agriculture

The constant advances in agricultural robotics aim to overcome the challenges imposed by population growth, accelerated urbanization, high competitiveness of high-quality products, environmental preservation and a lack of qualified labor. In this sense, this review paper surveys the main existing applications of agricultural robotic systems for the execution of land preparation before planting, sowing, planting, plant treatment, harvesting, yield estimation and phenotyping. In general, all robots were evaluated according to the following criteria: its locomotion system, what is the final application, if it has sensors, robotic arm and/or computer vision algorithm, what is its development stage and which country and continent they belong. After evaluating all similar characteristics, to expose the research trends, common pitfalls and the characteristics that hinder commercial development, and discover which countries are investing into Research and Development (R&D) in these technologies for the future, four major areas that need future research work for enhancing the state of the art in smart agriculture were highlighted: locomotion systems, sensors, computer vision algorithms and communication technologies. The results of this research suggest that the investment in agricultural robotic systems allows to achieve short—harvest monitoring—and long-term objectives—yield estimation.

Learning Non-Taxonomic Relations of Ontologies

2021 ◽  
Vol 17 (1) ◽  
pp. 97-122
Author(s):  
Mohamed Hassan Mohamed Ali ◽  
Said Fathalla ◽  
Mohamed Kholief ◽  
Yasser Fouad Hassan
Keyword(s):  
Systematic Review ◽  
State Of The Art ◽  
Research Work ◽  
Semantic Knowledge ◽  
Future Research ◽  
Automatic Extraction ◽  
Ontology Learning ◽  
Comprehensive Understanding ◽  
Specific Domain ◽  
Taxonomic Relations

Ontologies, as semantic knowledge representation, have a crucial role in various information systems. The main pitfall of manually building ontologies is effort and time-consuming. Ontology learning is a key solution. Learning Non-Taxonomic Relationships of Ontologies (LNTRO) is the process of automatic/semi-automatic extraction of all possible relationships between concepts in a specific domain, except the hierarchal relations. Most of the research works focused on the extraction of concepts and taxonomic relations in the ontology learning process. This article presents the results of a systematic review of the state-of-the-art approaches for LNTRO. Sixteen approaches have been described and qualitatively analyzed. The solutions they provide are discussed along with their respective positive and negative aspects. The goal is to provide researchers in this area a comprehensive understanding of the drawbacks of the existing work, thereby encouraging further improvement of the research work in this area. Furthermore, this article proposes a set of recommendations for future research.

State of the Art and Key Design Challenges of Telesurgical Robotics

2019 ◽  
pp. 1100-1111
Author(s):  
Sajid Nisar ◽  
Osman Hasan
Keyword(s):  
State Of The Art ◽  
Robotic Systems ◽  
Future Research ◽  
Research Directions ◽  
Remote Locations ◽  
Comprehensive Survey ◽  
Future Research Directions ◽  
Surgical System ◽  
Surgical Operations ◽  
Design Challenges

Telesurgical robotic systems allow surgeons to perform surgical operations from remote locations with enhanced comfort and dexterity. Introduction of robotic technology has revolutionized operation theaters but its multidisciplinary nature and high associated costs pose significant challenges. This chapter provides a comprehensive survey of the current progress in the field of surgical robotics with a detailed discussion on various state-of-the-art telesurgical robotic systems. The key design approaches and challenges are identified, and their solutions are recommended. A set of parameters that can be used to assess usefulness of a telesurgical robot are discussed. Finally, guidelines for selection of a suitable surgical system and the future research directions are described.

scholarly journals Deep Learning-Based Monocular Depth Estimation Methods—A State-of-the-Art Review

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2272 ◽  
Author(s):  
Faisal Khan ◽  
Saqib Salahuddin ◽  
Hossein Javidnia
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Research Work ◽  
Depth Estimation ◽  
Autonomous Driving ◽  
Estimation Methods ◽  
Future Research ◽  
Comprehensive Overview ◽  
Ill Posed ◽  
Monocular Depth

Monocular depth estimation from Red-Green-Blue (RGB) images is a well-studied ill-posed problem in computer vision which has been investigated intensively over the past decade using Deep Learning (DL) approaches. The recent approaches for monocular depth estimation mostly rely on Convolutional Neural Networks (CNN). Estimating depth from two-dimensional images plays an important role in various applications including scene reconstruction, 3D object-detection, robotics and autonomous driving. This survey provides a comprehensive overview of this research topic including the problem representation and a short description of traditional methods for depth estimation. Relevant datasets and 13 state-of-the-art deep learning-based approaches for monocular depth estimation are reviewed, evaluated and discussed. We conclude this paper with a perspective towards future research work requiring further investigation in monocular depth estimation challenges.

scholarly journals Salient Object Detection Techniques in Computer Vision—A Survey

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1174
Author(s):  
Ashish Kumar Gupta ◽  
Ayan Seal ◽  
Mukesh Prasad ◽  
Pritee Khanna
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Object Detection ◽  
Large Scale ◽  
Research Work ◽  
Salient Object Detection ◽  
Future Research ◽  
Automatic Identification ◽  
Salient Object ◽  
Detection Techniques

Detection and localization of regions of images that attract immediate human visual attention is currently an intensive area of research in computer vision. The capability of automatic identification and segmentation of such salient image regions has immediate consequences for applications in the field of computer vision, computer graphics, and multimedia. A large number of salient object detection (SOD) methods have been devised to effectively mimic the capability of the human visual system to detect the salient regions in images. These methods can be broadly categorized into two categories based on their feature engineering mechanism: conventional or deep learning-based. In this survey, most of the influential advances in image-based SOD from both conventional as well as deep learning-based categories have been reviewed in detail. Relevant saliency modeling trends with key issues, core techniques, and the scope for future research work have been discussed in the context of difficulties often faced in salient object detection. Results are presented for various challenging cases for some large-scale public datasets. Different metrics considered for assessment of the performance of state-of-the-art salient object detection models are also covered. Some future directions for SOD are presented towards end.

scholarly journals A bibliometric approach to quantitatively assess current research trends in 5G security

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
M. Najmul Islam Farooqui ◽  
Junaid Arshad ◽  
Muhammad Mubashir Khan
Keyword(s):  
State Of The Art ◽  
Research Work ◽  
Quantitative Description ◽  
Research Trends ◽  
Security And Privacy ◽  
Future Research ◽  
Content Type ◽  
Research Areas ◽  
Art Research ◽  
5G Security

PurposeAlongside the remarkable evolution of cellular communication to 5G networks, significant security and privacy challenges have risen which can affect the widespread adoption of advanced communication technologies. In this context, the purpose of this paper is to examine research within security and privacy for 5G-based systems highlighting contributions made by the research community and identify research trends within different subdomains of 5G security where open issues still exist.Design/methodology/approachThis paper uses a bibliographic approach to review the state-of-the-art in the field of 5G security and is the pioneering effort to investigate 5G security research using this methodology. Specifically, the paper presents a quantitative description of the existing contributions in terms of authors, organizations, and countries. It then presents detailed keyword and co-citation analysis that shows the quantity and pattern of research work in different subfields. Finally, 5G security areas are identified having open challenges for future research work.FindingsThe study shows that China leads the world in terms of published research in the field of 5G security with USA and India ranked second and third respectively. Xidian University, China is ranked highest for number of publications and h-index followed by University Oulu and AALTO University Finland. IEEE Access, Sensors and IEEE Internet of Things Journal are the top publication venues in the field of 5G security. Using VOSViewer aided analysis with respect to productivity, research areas and keywords, the authors have identified research trends in 5G security among scientific community whilst highlighting specific challenges which require further efforts.Originality/valueExisting studies have focused on surveys covering state-of-the art research in secure 5G network (Zhang et al. 2019), physical layer security (Wu et al., 2018), security and privacy of 5G technologies (Khan et al., 2020) and security and privacy challenges when 5G is used in IoT (Sicari et al. 2020). However, our research has revealed no existing bibliometric studies in this area and therefore, to our best knowledge, this paper represents pioneering such effort for security within 5G.

scholarly journals Engineering Human–Machine Teams for Trusted Collaboration

2020 ◽  
Vol 4 (4) ◽  
pp. 35
Author(s):  
Basel Alhaji ◽  
Janine Beecken ◽  
Rüdiger Ehlers ◽  
Jan Gertheiss ◽  
Felix Merz ◽  
...  
Keyword(s):  
Systems Engineering ◽  
State Of The Art ◽  
Research Work ◽  
Future Research ◽  
Engineering Systems ◽  
Intensive Research ◽  
Intelligent Machines ◽  
Interdisciplinary Approaches ◽  
Industrial Assembly ◽  
Core Requirements

The way humans and artificially intelligent machines interact is undergoing a dramatic change. This change becomes particularly apparent in domains where humans and machines collaboratively work on joint tasks or objects in teams, such as in industrial assembly or disassembly processes. While there is intensive research work on human–machine collaboration in different research disciplines, systematic and interdisciplinary approaches towards engineering systems that consist of or comprise human–machine teams are still rare. In this paper, we review and analyze the state of the art, and derive and discuss core requirements and concepts by means of an illustrating scenario. In terms of methods, we focus on how reciprocal trust between humans and intelligent machines is defined, built, measured, and maintained from a systems engineering and planning perspective in literature. Based on our analysis, we propose and outline three important areas of future research on engineering and operating human–machine teams for trusted collaboration. For each area, we describe exemplary research opportunities.

State of the Art and Key Design Challenges of Telesurgical Robotics

2018 ◽  
pp. 6872-6881 ◽  
Author(s):  
Sajid Nisar ◽  
Osman Hasan
Keyword(s):  
State Of The Art ◽  
Robotic Systems ◽  
Future Research ◽  
Research Directions ◽  
Remote Locations ◽  
Comprehensive Survey ◽  
Future Research Directions ◽  
Surgical System ◽  
Surgical Operations ◽  
Design Challenges

Telesurgical robotic systems allow surgeons to perform surgical operations from remote locations with enhanced comfort and dexterity. Introduction of robotic technology has revolutionized operation theaters but it's multidisciplinary nature and high associated costs pose significant challenges. This chapter provides a comprehensive survey of the current progress in the field of surgical robotics with a detailed discussion on various state-of-the-art telesurgical robotic systems. The key design approaches and challenges are identified and their solutions are recommended. A set of parameters that can be used to assess usefulness of a telesurgical robot are discussed. Finally, guidelines for selection of a suitable surgical system and the future research directions are described.

scholarly journals A Review on Computer Vision-Based Methods for Human Action Recognition

2020 ◽  
Vol 6 (6) ◽  
pp. 46
Author(s):  
Mahmoud Al-Faris ◽  
John Chiverton ◽  
David Ndzi ◽  
Ahmed Isam Ahmed
Keyword(s):  
Health Care ◽  
Computer Vision ◽  
Action Recognition ◽  
State Of The Art ◽  
Human Action Recognition ◽  
Human Action ◽  
Generative Models ◽  
Future Research ◽  
Learning Technology ◽  
Recognition Systems

Human action recognition targets recognising different actions from a sequence of observations and different environmental conditions. A wide different applications is applicable to vision based action recognition research. This can include video surveillance, tracking, health care, and human–computer interaction. However, accurate and effective vision based recognition systems continue to be a big challenging area of research in the field of computer vision. This review introduces the most recent human action recognition systems and provides the advances of state-of-the-art methods. To this end, the direction of this research is sorted out from hand-crafted representation based methods including holistic and local representation methods with various sources of data, to a deep learning technology including discriminative and generative models and multi-modality based methods. Next, the most common datasets of human action recognition are presented. This review introduces several analyses, comparisons and recommendations that help to find out the direction of future research.

scholarly journals Review of the State-of-the-art Sewer Monitoring and Maintenance Systems Pune Municipal Corporation - A Case Study

TEM Journal ◽  
2021 ◽  
pp. 1500-1508
Author(s):  
Ravindra R. Patil ◽  
Saniya M. Ansari ◽  
Rajnish Kaur Calay ◽  
Mohamad Y. Mustafa
Keyword(s):  
Computer Vision ◽  
Real Time ◽  
Developing Country ◽  
Real World ◽  
State Of The Art ◽  
Robotic Systems ◽  
Increasing Trend ◽  
Maintenance Systems ◽  
Sewer Inspection

There is an increasing trend of using automated and robotic systems for the tasks that are hazardous or inconvenient and dirty for humans. Sewers maintenance and cleaning is such a task where robots are already being used for inspection of underground pipes for blockages and damage. This paper reviews the existing robotic systems and various platforms and algorithms along with their capabilities and limitations being discussed. A typical mid-size city in a developing country, Pune, India is selected in order to understand the concerns and identify the requirements for developing robotic systems for the same. It is found that major concern of sewers are blockages but there is not enough information on both real-time detection and removal of it with robotic systems. On-board processing with computer vision algorithms has not been efficiently utilized in terms of performance and determinations for real-world implementations of sewer robotic systems. The review highlights the available methodologies that can be utilized in developing sewer inspection and cleaning robotic systems.

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