scholarly journals Flying Free: A Research Overview of Deep Learning in Drone Navigation Autonomy

Drones ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 52
Author(s):  
Thomas Lee ◽  
Susan Mckeever ◽  
Jane Courtney
Keyword(s):  
Deep Learning ◽  
Research Work ◽  
Learning Approaches ◽  
Clear Definition ◽  
Top Down ◽  
Research Activity ◽  
Comprehensive Overview ◽  
The Past ◽  
Computer Vision Applications ◽  
Definition Of

With the rise of Deep Learning approaches in computer vision applications, significant strides have been made towards vehicular autonomy. Research activity in autonomous drone navigation has increased rapidly in the past five years, and drones are moving fast towards the ultimate goal of near-complete autonomy. However, while much work in the area focuses on specific tasks in drone navigation, the contribution to the overall goal of autonomy is often not assessed, and a comprehensive overview is needed. In this work, a taxonomy of drone navigation autonomy is established by mapping the definitions of vehicular autonomy levels, as defined by the Society of Automotive Engineers, to specific drone tasks in order to create a clear definition of autonomy when applied to drones. A top–down examination of research work in the area is conducted, focusing on drone navigation tasks, in order to understand the extent of research activity in each area. Autonomy levels are cross-checked against the drone navigation tasks addressed in each work to provide a framework for understanding the trajectory of current research. This work serves as a guide to research in drone autonomy with a particular focus on Deep Learning-based solutions, indicating key works and areas of opportunity for development of this area in the future.

scholarly journals A Review on Deep Image Contrast Enhancement

2020 ◽  
Vol 6 (1) ◽  
pp. 4
Author(s):  
Puspad Kumar Sharma ◽  
Nitesh Gupta ◽  
Anurag Shrivastava
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Image Enhancement ◽  
Research Work ◽  
Atmospheric Condition ◽  
Input Image ◽  
Learning Approaches ◽  
Sensing Applications ◽  
Image Contrast Enhancement

In image processing applications, one of the main preprocessing phases is image enhancement that is used to produce high quality image or enhanced image than the original input image. These enhanced images can be used in many applications such as remote sensing applications, geo-satellite images, etc. The quality of an image is affected due to several conditions such as by poor illumination, atmospheric condition, wrong lens aperture setting of the camera, noise, etc [2]. So, such degraded/low exposure images are needed to be enhanced by increasing the brightness as well as its contrast and this can be possible by the method of image enhancement. In this research work different image enhancement techniques are discussed and reviewed with their results. The aim of this study is to determine the application of deep learning approaches that have been used for image enhancement. Deep learning is a machine learning approach which is currently revolutionizing a number of disciplines including image processing and computer vision. This paper will attempt to apply deep learning to image filtering, specifically low-light image enhancement. The review given in this paper is quite efficient for future researchers to overcome problems that helps in designing efficient algorithm which enhances quality of the image.

scholarly journals Smartphone Location Recognition: A Deep Learning-Based Approach

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 214 ◽  
Author(s):  
Itzik Klein
Keyword(s):  
Deep Learning ◽  
Sampling Rate ◽  
Dead Reckoning ◽  
Step Length ◽  
Point Of View ◽  
Learning Approaches ◽  
Location Recognition ◽  
Length Estimation ◽  
Recorded Data ◽  
Definition Of

One of the approaches for indoor positioning using smartphones is pedestrian dead reckoning. There, the user step length is estimated using empirical or biomechanical formulas. Such calculation was shown to be very sensitive to the smartphone location on the user. In addition, knowledge of the smartphone location can also help for direct step-length estimation and heading determination. In a wider point of view, smartphone location recognition is part of human activity recognition employed in many fields and applications, such as health monitoring. In this paper, we propose to use deep learning approaches to classify the smartphone location on the user, while walking, and require robustness in terms of the ability to cope with recordings that differ (in sampling rate, user dynamics, sensor type, and more) from those available in the train dataset. The contributions of the paper are: (1) Definition of the smartphone location recognition framework using accelerometers, gyroscopes, and deep learning; (2) examine the proposed approach on 107 people and 31 h of recorded data obtained from eight different datasets; and (3) enhanced algorithms for using only accelerometers for the classification process. The experimental results show that the smartphone location can be classified with high accuracy using only the smartphone’s accelerometers.

scholarly journals Deep Learning for Historical Document Analysis and Recognition—A Survey

2020 ◽  
Vol 6 (10) ◽  
pp. 110
Author(s):  
Francesco Lombardi ◽  
Simone Marinai
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Point Of View ◽  
Historical Documents ◽  
Learning Approaches ◽  
Historical Document ◽  
Research Directions ◽  
Research Fields ◽  
Definition Of ◽  
Novel Applications

Nowadays, deep learning methods are employed in a broad range of research fields. The analysis and recognition of historical documents, as we survey in this work, is not an exception. Our study analyzes the papers published in the last few years on this topic from different perspectives: we first provide a pragmatic definition of historical documents from the point of view of the research in the area, then we look at the various sub-tasks addressed in this research. Guided by these tasks, we go through the different input-output relations that are expected from the used deep learning approaches and therefore we accordingly describe the most used models. We also discuss research datasets published in the field and their applications. This analysis shows that the latest research is a leap forward since it is not the simple use of recently proposed algorithms to previous problems, but novel tasks and novel applications of state of the art methods are now considered. Rather than just providing a conclusive picture of the current research in the topic we lastly suggest some potential future trends that can represent a stimulus for innovative research directions.

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 The Cheshire explainer. Musings about the training of explainers

2016 ◽  
Vol 15 (04) ◽  
pp. C06
Author(s):  
Antonio Gomes da Costa
Keyword(s):  
Science Communication ◽  
Clear Definition ◽  
Top Down ◽  
Scientific Training ◽  
The Public ◽  
The Core ◽  
Main Factors ◽  
State Of Affairs ◽  
Definition Of

The profession of explainer is still pretty much undefined and underrated and the training of explainers is many times deemed to be a luxury. In the following pages we make the argument that three main factors contribute to this state of affairs and, at the same time, we try to show why the training of explainers should really be at the core of any science communication institution. These factors are: an erroneous perception of what a proper scientific training means for explainers; a lack of clear definition of the aptitudes and role of explainers required by institutions that are evolving and diversifying their missions; and an organizational model based on top-down practices of management and activity development which underappreciates the potential of the personnel working directly with the public.

scholarly journals Comparative Analysis of Deep Learning Techniques to detect Online Public Shaming

2021 ◽  
Vol 40 ◽  
pp. 03030
Author(s):  
Mehdi Surani ◽  
Ramchandra Mangrulkar
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Freedom Of Speech ◽  
Learning Approaches ◽  
Learning Models ◽  
Media Usage ◽  
The Past ◽  
Learning Techniques ◽  
Social Media Platforms ◽  
Machine Learning Models

Over the past years the exponential growth of social media usage has given the power to every individual to share their opinions freely. This has led to numerous threats allowing users to exploit their freedom of speech, thus spreading hateful comments, using abusive language, carrying out personal attacks, and sometimes even to the extent of cyberbullying. However, determining abusive content is not a difficult task and many social media platforms have solutions available already but at the same time, many are searching for more efficient ways and solutions to overcome this issue. Traditional models explore machine learning models to identify negative content posted on social media. Shaming categories are explored, and content is put in place according to the label. Such categorization is easy to detect as the contextual language used is direct. However, the use of irony to mock or convey contempt is also a part of public shaming and must be considered while categorizing the shaming labels. In this research paper, various shaming types, namely toxic, severe toxic, obscene, threat, insult, identity hate, and sarcasm are predicted using deep learning approaches like CNN and LSTM. These models have been studied along with traditional models to determine which model gives the most accurate results.

scholarly journals Ecosystem as Structure

2016 ◽  
Vol 43 (1) ◽  
pp. 39-58 ◽  
Author(s):  
Ron Adner
Keyword(s):  
Supply Chains ◽  
Business Models ◽  
Ecosystem Structure ◽  
Clear Definition ◽  
Value Networks ◽  
The Past ◽  
Structuralist Approach ◽  
Definition Of ◽  
The Relationship

Over the past 20 years, the term “ecosystem” has become pervasive in discussions of strategy, both scholarly and applied. Its rise has mirrored an increasing interest and concern among both researchers and managers with interdependence across organizations and activities. This article presents a structuralist approach to conceptualizing the ecosystem construct. It presents a clear definition of the ecosystem construct, a grammar for characterizing ecosystem structure, and a characterization of the distinctive aspects of ecosystem strategy. This approach offers an explicit examination of the relationship among ecosystems and a host of alternative constructs (business models, platforms, coopetition, multisided markets, networks, technology systems, supply chains, value networks) that helps characterize where the ecosystem construct adds, and does not add, insight for the strategy literature.

scholarly journals A Review on Medical Textual Question Answering Systems Based on Deep Learning Approaches

2021 ◽  
Vol 11 (12) ◽  
pp. 5456
Author(s):  
Emmanuel Mutabazi ◽  
Jianjun Ni ◽  
Guangyi Tang ◽  
Weidong Cao
Keyword(s):  
Deep Learning ◽  
Question Answering ◽  
Medical Information ◽  
Research Work ◽  
Learning Approaches ◽  
Significant Information ◽  
Learning Techniques ◽  
Depth Analysis ◽  
Question Answering Systems ◽  
Medical Domain

The advent of Question Answering Systems (QASs) has been envisaged as a promising solution and an efficient approach for retrieving significant information over the Internet. A considerable amount of research work has focused on open domain QASs based on deep learning techniques due to the availability of data sources. However, the medical domain receives less attention due to the shortage of medical datasets. Although Electronic Health Records (EHRs) are empowering the field of Medical Question-Answering (MQA) by providing medical information to answer user questions, the gap is still large in the medical domain, especially for textual-based sources. Therefore, in this study, the medical textual question-answering systems based on deep learning approaches were reviewed, and recent architectures of MQA systems were thoroughly explored. Furthermore, an in-depth analysis of deep learning approaches used in different MQA system tasks was provided. Finally, the different critical challenges posed by MQA systems were highlighted, and recommendations to effectively address them in forthcoming MQA systems were given out.

scholarly journals Challenges of Deep Learning-based Text Detection in the Wild

2021 ◽  
Vol 6 (1) ◽  
pp. 1-5
Author(s):  
Zobeir Raisi ◽  
Mohamed A. Naiel ◽  
Paul Fieguth ◽  
Steven Wardell ◽  
John Zelek
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Text Detection ◽  
Detection Methods ◽  
Learning Approaches ◽  
Plane Rotation ◽  
The Past ◽  
Perspective Distortion ◽  
Benchmark Datasets ◽  
In The Wild

The reported accuracy of recent state-of-the-art text detection methods, mostly deep learning approaches, is in the order of 80% to 90% on standard benchmark datasets. These methods have relaxed some of the restrictions of structured text and environment (i.e., "in the wild") which are usually required for classical OCR to properly function. Even with this relaxation, there are still circumstances where these state-of-the-art methods fail.  Several remaining challenges in wild images, like in-plane-rotation, illumination reflection, partial occlusion, complex font styles, and perspective distortion, cause exciting methods to perform poorly. In order to evaluate current approaches in a formal way, we standardize the datasets and metrics for comparison which had made comparison between these methods difficult in the past. We use three benchmark datasets for our evaluations: ICDAR13, ICDAR15, and COCO-Text V2.0. The objective of the paper is to quantify the current shortcomings and to identify the challenges for future text detection research.

scholarly journals Microbiota Influences Fitness and Timing of Reproduction in the Fruit Fly Drosophila melanogaster

2021 ◽  
Author(s):  
Melinda K. Matthews ◽  
Jaanna Malcolm ◽  
John M. Chaston
Keyword(s):  
Drosophila Melanogaster ◽  
Life History ◽  
Microbial Communities ◽  
Life History Traits ◽  
Fruit Fly ◽  
Clear Definition ◽  
Animal Life ◽  
Timing Of Reproduction ◽  
The Past ◽  
Definition Of

The ability of associated microorganisms (“microbiota”) to influence animal life history traits has been recognized and investigated, especially in the past 2 decades. For many microbial communities, there is not always a clear definition of whether the microbiota or its members are beneficial, pathogenic, or relatively neutral to their hosts’ fitness.

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