scholarly journals Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4220 ◽  
Author(s):  
Jamil Fayyad ◽  
Mohammad A. Jaradat ◽  
Dominique Gruyer ◽  
Homayoun Najjaran
Keyword(s):  
Deep Learning ◽  
Sensor Fusion ◽  
Short Range ◽  
Autonomous Vehicle ◽  
Effective Means ◽  
Future Research ◽  
Human Beings ◽  
Ambient Conditions ◽  
Smart Vehicles ◽  
Manufacturing Defects

Autonomous vehicles (AV) are expected to improve, reshape, and revolutionize the future of ground transportation. It is anticipated that ordinary vehicles will one day be replaced with smart vehicles that are able to make decisions and perform driving tasks on their own. In order to achieve this objective, self-driving vehicles are equipped with sensors that are used to sense and perceive both their surroundings and the faraway environment, using further advances in communication technologies, such as 5G. In the meantime, local perception, as with human beings, will continue to be an effective means for controlling the vehicle at short range. In the other hand, extended perception allows for anticipation of distant events and produces smarter behavior to guide the vehicle to its destination while respecting a set of criteria (safety, energy management, traffic optimization, comfort). In spite of the remarkable advancements of sensor technologies in terms of their effectiveness and applicability for AV systems in recent years, sensors can still fail because of noise, ambient conditions, or manufacturing defects, among other factors; hence, it is not advisable to rely on a single sensor for any of the autonomous driving tasks. The practical solution is to incorporate multiple competitive and complementary sensors that work synergistically to overcome their individual shortcomings. This article provides a comprehensive review of the state-of-the-art methods utilized to improve the performance of AV systems in short-range or local vehicle environments. Specifically, it focuses on recent studies that use deep learning sensor fusion algorithms for perception, localization, and mapping. The article concludes by highlighting some of the current trends and possible future research directions.

Vision-based Autonomous Vehicle Recognition

2021 ◽  
Vol 54 (4) ◽  
pp. 1-37
Author(s):  
Azzedine Boukerche ◽  
Xiren Ma
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Comprehensive Evaluation ◽  
Autonomous Vehicle ◽  
Transportation Systems ◽  
Regional Security ◽  
Future Research ◽  
Learning Abilities ◽  
Vehicle Recognition ◽  
Model Recognition

Vision-based Automated Vehicle Recognition (VAVR) has attracted considerable attention recently. Particularly given the reliance on emerging deep learning methods, which have powerful feature extraction and pattern learning abilities, vehicle recognition has made significant progress. VAVR is an essential part of Intelligent Transportation Systems. The VAVR system can fast and accurately locate a target vehicle, which significantly helps improve regional security. A comprehensive VAVR system contains three components: Vehicle Detection (VD), Vehicle Make and Model Recognition (VMMR), and Vehicle Re-identification (VRe-ID). These components perform coarse-to-fine recognition tasks in three steps. In this article, we conduct a thorough review and comparison of the state-of-the-art deep learning--based models proposed for VAVR. We present a detailed introduction to different vehicle recognition datasets used for a comprehensive evaluation of the proposed models. We also critically discuss the major challenges and future research trends involved in each task. Finally, we summarize the characteristics of the methods for each task. Our comprehensive model analysis will help researchers that are interested in VD, VMMR, and VRe-ID and provide them with possible directions to solve current challenges and further improve the performance and robustness of models.

scholarly journals Marine Vision-Based Situational Awareness Using Discriminative Deep Learning: A Survey

2021 ◽  
Vol 9 (4) ◽  
pp. 397
Author(s):  
Dalei Qiao ◽  
Guangzhong Liu ◽  
Taizhi Lv ◽  
Wei Li ◽  
Juan Zhang
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Homeland Security ◽  
Situational Awareness ◽  
Future Research ◽  
Target Vessel ◽  
Human Beings ◽  
Multimodal Data ◽  
Existing Problems ◽  
Multimodal Data Fusion

The primary task of marine surveillance is to construct a perfect marine situational awareness (MSA) system that serves to safeguard national maritime rights and interests and to maintain blue homeland security. Progress in maritime wireless communication, developments in artificial intelligence, and automation of marine turbines together imply that intelligent shipping is inevitable in future global shipping. Computer vision-based situational awareness provides visual semantic information to human beings that approximates eyesight, which makes it likely to be widely used in the field of intelligent marine transportation. We describe how we combined the visual perception tasks required for marine surveillance with those required for intelligent ship navigation to form a marine computer vision-based situational awareness complex and investigated the key technologies they have in common. Deep learning was a prerequisite activity. We summarize the progress made in four aspects of current research: full scene parsing of an image, target vessel re-identification, target vessel tracking, and multimodal data fusion with data from visual sensors. The paper gives a summary of research to date to provide background for this work and presents brief analyses of existing problems, outlines some state-of-the-art approaches, reviews available mainstream datasets, and indicates the likely direction of future research and development. As far as we know, this paper is the first review of research into the use of deep learning in situational awareness of the ocean surface. It provides a firm foundation for further investigation by researchers in related fields.

scholarly journals The Crosstalk Between Regulatory Non-Coding RNAs and Nuclear Factor Kappa B in Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Yina Zhang ◽  
Jiajia Shao ◽  
Shuangshuang Li ◽  
Yanning Liu ◽  
Min Zheng
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cancer Biology ◽  
Nuclear Factor Kappa B ◽  
Effective Means ◽  
Future Research ◽  
Nuclear Factor ◽  
Human Beings ◽  
Great Loss ◽  
Nuclear Factor Kappa ◽  
Non Coding Rnas

Hepatocellular carcinoma (HCC) is a highly lethal type of malignancies that possesses great loss of life safety to human beings worldwide. However, few effective means of curing HCC exist and its specific molecular basis is still far from being fully elucidated. Activation of nuclear factor kappa B (NF-κB), which is often observed in HCC, is considered to play a significant part in hepatocarcinogenesis and development. The emergence of regulatory non-coding RNAs (ncRNAs), particularly microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), is a defining advance in cancer biology, and related research in this branch has yielded many diagnostic and therapeutic opportunities. Recent studies have suggested that regulatory ncRNAs act as inhibitors or activators in the initiation and progression of HCC by targeting components of NF-κB signaling or regulating NF-κB activity. In this review, we attach importance to the role and function of regulatory ncRNAs in NF-κB signaling of HCC and NF-κB-associated chemoresistance in HCC, then propose future research directions and challenges of regulatory ncRNAs mediated-regulation of NF-κB pathway in HCC.

A Pilot Study of Mobile Telephone Message Interventions with Suicide Attempters in China

Crisis ◽  
2010 ◽  
Vol 31 (2) ◽  
pp. 109-112 ◽  
Author(s):  
Hui Chen ◽  
Brian L. Mishara ◽  
Xiao Xian Liu
Keyword(s):  
Pilot Study ◽  
Effective Means ◽  
Text Message ◽  
Text Messages ◽  
Mobile Telephone ◽  
Future Research ◽  
Quick Method ◽  
Suicide Attempters ◽  
To Receive

Background: In China, where follow-up with hospitalized attempters is generally lacking, there is a great need for inexpensive and effective means of maintaining contact and decreasing recidivism. Aims: Our objective was to test whether mobile telephone message contacts after discharge would be feasible and acceptable to suicide attempters in China. Methods: Fifteen participants were recruited from suicide attempters seen in the Emergency Department in Wuhan, China, to participate in a pilot study to receive mobile telephone messages after discharge. All participants have access to a mobile telephone, and there is no charge for the user to receive text messages. Results: Most participants (12) considered the text message contacts an acceptable and useful form of help and would like to continue to receive them for a longer period of time. Conclusions: This suggests that, as a low-cost and quick method of intervention in areas where more intensive follow-up is not practical or available, telephone messages contacts are accessible, feasible, and acceptable to suicide attempters. We hope that this will inspire future research on regular and long-term message interventions to prevent recidivism in suicide attempters.

Android Malware Detection Techniques: A Literature Review

2020 ◽  
Vol 14 ◽  
Author(s):  
Meghna Dhalaria ◽  
Ekta Gandotra
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Malware Detection ◽  
Future Research ◽  
Android Malware ◽  
Detection Techniques ◽  
Android Malware Detection ◽  
Future Research Directions ◽  
To Come ◽  
Tools And Techniques

Purpose: This paper provides the basics of Android malware, its evolution and tools and techniques for malware analysis. Its main aim is to present a review of the literature on Android malware detection using machine learning and deep learning and identify the research gaps. It provides the insights obtained through literature and future research directions which could help researchers to come up with robust and accurate techniques for classification of Android malware. Design/Methodology/Approach: This paper provides a review of the basics of Android malware, its evolution timeline and detection techniques. It includes the tools and techniques for analyzing the Android malware statically and dynamically for extracting features and finally classifying these using machine learning and deep learning algorithms. Findings: The number of Android users is expanding very fast due to the popularity of Android devices. As a result, there are more risks to Android users due to the exponential growth of Android malware. On-going research aims to overcome the constraints of earlier approaches for malware detection. As the evolving malware are complex and sophisticated, earlier approaches like signature based and machine learning based are not able to identify these timely and accurately. The findings from the review shows various limitations of earlier techniques i.e. requires more detection time, high false positive and false negative rate, low accuracy in detecting sophisticated malware and less flexible. Originality/value: This paper provides a systematic and comprehensive review on the tools and techniques being employed for analysis, classification and identification of Android malicious applications. It includes the timeline of Android malware evolution, tools and techniques for analyzing these statically and dynamically for the purpose of extracting features and finally using these features for their detection and classification using machine learning and deep learning algorithms. On the basis of the detailed literature review, various research gaps are listed. The paper also provides future research directions and insights which could help researchers to come up with innovative and robust techniques for detecting and classifying the Android malware.

scholarly journals Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Manan Binth Taj Noor ◽  
Nusrat Zerin Zenia ◽  
M Shamim Kaiser ◽  
Shamim Al Mamun ◽  
Mufti Mahmud
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Neurological Disorders ◽  
Magnetic Resonance Images ◽  
Future Research ◽  
Comparative Performance

Abstract Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided.

scholarly journals Synthesis of short-range ordered aluminosilicates at ambient conditions

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Katharina R. Lenhardt ◽  
Hergen Breitzke ◽  
Gerd Buntkowsky ◽  
Erik Reimhult ◽  
Max Willinger ◽  
...  
Keyword(s):  
Short Range ◽  
Spatial Arrangement ◽  
Aluminium Chloride ◽  
Ambient Conditions ◽  
Soils And Sediments ◽  
Submicron Scale ◽  
Mineral Structure ◽  
Temperature And Pressure ◽  
Physical And Chemical ◽  
Microscopic Techniques

AbstractWe report here on structure-related aggregation effects of short-range ordered aluminosilicates (SROAS) that have to be considered in the development of synthesis protocols and may be relevant for the properties of SROAS in the environment. We synthesized SROAS of variable composition by neutralizing aqueous aluminium chloride with sodium orthosilicate at ambient temperature and pressure. We determined elemental composition, visualized morphology by microscopic techniques, and resolved mineral structure by solid-state 29Si and 27Al nuclear magnetic resonance and Fourier-transform infrared spectroscopy. Nitrogen sorption revealed substantial surface loss of Al-rich SROAS that resembled proto-imogolite formed in soils and sediments due to aggregation upon freezing. The effect was less pronounced in Si-rich SROAS, indicating a structure-dependent effect on spatial arrangement of mass at the submicron scale. Cryomilling efficiently fractured aggregates but did not change the magnitude of specific surface area. Since accessibility of surface functional groups is a prerequisite for sequestration of substances, elucidating physical and chemical processes of aggregation as a function of composition and crystallinity may improve our understanding of the reactivity of SROAS in the environment.

scholarly journals Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease

2021 ◽  
Vol 22 (15) ◽  
pp. 7911
Author(s):  
Eugene Lin ◽  
Chieh-Hsin Lin ◽  
Hsien-Yuan Lane
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Future Research ◽  
Learning Approaches ◽  
Learning Models ◽  
Learning Techniques ◽  
Neuroimaging Data ◽  
Similarities And Differences ◽  
Normal Controls

A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer’s disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.

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