21 research outputs found
Autonomous Vehicles: Open-Source Technologies, Considerations, and Development
Autonomous vehicles are the culmination of advances in many areas such as
sensor technologies, artificial intelligence (AI), networking, and more. This
paper will introduce the reader to the technologies that build autonomous
vehicles. It will focus on open-source tools and libraries for autonomous
vehicle development, making it cheaper and easier for developers and
researchers to participate in the field. The topics covered are as follows.
First, we will discuss the sensors used in autonomous vehicles and summarize
their performance in different environments, costs, and unique features. Then
we will cover Simultaneous Localization and Mapping (SLAM) and algorithms for
each modality. Third, we will review popular open-source driving simulators, a
cost-effective way to train machine learning models and test vehicle software
performance. We will then highlight embedded operating systems and the security
and development considerations when choosing one. After that, we will discuss
Vehicle-to-Vehicle (V2V) and Internet-of-Vehicle (IoV) communication, which are
areas that fuse networking technologies with autonomous vehicles to extend
their functionality. We will then review the five levels of vehicle automation,
commercial and open-source Advanced Driving Assistance Systems, and their
features. Finally, we will touch on the major manufacturing and software
companies involved in the field, their investments, and their partnerships.
These topics will give the reader an understanding of the industry, its
technologies, active research, and the tools available for developers to build
autonomous vehicles.Comment: 13 pages, 7 figure
SMGRL: Scalable Multi-resolution Graph Representation Learning
Graph convolutional networks (GCNs) allow us to learn topologically-aware
node embeddings, which can be useful for classification or link prediction.
However, they are unable to capture long-range dependencies between nodes
without adding additional layers -- which in turn leads to over-smoothing and
increased time and space complexity. Further, the complex dependencies between
nodes make mini-batching challenging, limiting their applicability to large
graphs. We propose a Scalable Multi-resolution Graph Representation Learning
(SMGRL) framework that enables us to learn multi-resolution node embeddings
efficiently. Our framework is model-agnostic and can be applied to any existing
GCN model. We dramatically reduce training costs by training only on a
reduced-dimension coarsening of the original graph, then exploit
self-similarity to apply the resulting algorithm at multiple resolutions. The
resulting multi-resolution embeddings can be aggregated to yield high-quality
node embeddings that capture both long- and short-range dependencies. Our
experiments show that this leads to improved classification accuracy, without
incurring high computational costs.Comment: 22 page
Extracting Implicit Social Relation for Social Recommendation Techniques in User Rating Prediction
Recommendation plays an increasingly important role in our daily lives.
Recommender systems automatically suggest items to users that might be
interesting for them. Recent studies illustrate that incorporating social trust
in Matrix Factorization methods demonstrably improves accuracy of rating
prediction. Such approaches mainly use the trust scores explicitly expressed by
users. However, it is often challenging to have users provide explicit trust
scores of each other. There exist quite a few works, which propose Trust
Metrics to compute and predict trust scores between users based on their
interactions. In this paper, first we present how social relation can be
extracted from users' ratings to items by describing Hellinger distance between
users in recommender systems. Then, we propose to incorporate the predicted
trust scores into social matrix factorization models. By analyzing social
relation extraction from three well-known real-world datasets, which both:
trust and recommendation data available, we conclude that using the implicit
social relation in social recommendation techniques has almost the same
performance compared to the actual trust scores explicitly expressed by users.
Hence, we build our method, called Hell-TrustSVD, on top of the
state-of-the-art social recommendation technique to incorporate both the
extracted implicit social relations and ratings given by users on the
prediction of items for an active user. To the best of our knowledge, this is
the first work to extend TrustSVD with extracted social trust information. The
experimental results support the idea of employing implicit trust into matrix
factorization whenever explicit trust is not available, can perform much better
than the state-of-the-art approaches in user rating prediction
Deep Learning Approaches in Pavement Distress Identification: A Review
This paper presents a comprehensive review of recent advancements in image
processing and deep learning techniques for pavement distress detection and
classification, a critical aspect in modern pavement management systems. The
conventional manual inspection process conducted by human experts is gradually
being superseded by automated solutions, leveraging machine learning and deep
learning algorithms to enhance efficiency and accuracy. The ability of these
algorithms to discern patterns and make predictions based on extensive datasets
has revolutionized the domain of pavement distress identification. The paper
investigates the integration of unmanned aerial vehicles (UAVs) for data
collection, offering unique advantages such as aerial perspectives and
efficient coverage of large areas. By capturing high-resolution images, UAVs
provide valuable data that can be processed using deep learning algorithms to
detect and classify various pavement distresses effectively. While the primary
focus is on 2D image processing, the paper also acknowledges the challenges
associated with 3D images, such as sensor limitations and computational
requirements. Understanding these challenges is crucial for further
advancements in the field. The findings of this review significantly contribute
to the evolution of pavement distress detection, fostering the development of
efficient pavement management systems. As automated approaches continue to
mature, the implementation of deep learning techniques holds great promise in
ensuring safer and more durable road infrastructure for the benefit of society
MLGCN: An Ultra Efficient Graph Convolution Neural Model For 3D Point Cloud Analysis
The analysis of 3D point clouds has diverse applications in robotics, vision
and graphics. Processing them presents specific challenges since they are
naturally sparse, can vary in spatial resolution and are typically unordered.
Graph-based networks to abstract features have emerged as a promising
alternative to convolutional neural networks for their analysis, but these can
be computationally heavy as well as memory inefficient. To address these
limitations we introduce a novel Multi-level Graph Convolution Neural (MLGCN)
model, which uses Graph Neural Networks (GNN) blocks to extract features from
3D point clouds at specific locality levels. Our approach employs precomputed
graph KNNs, where each KNN graph is shared between GCN blocks inside a GNN
block, making it both efficient and effective compared to present models. We
demonstrate the efficacy of our approach on point cloud based object
classification and part segmentation tasks on benchmark datasets, showing that
it produces comparable results to those of state-of-the-art models while
requiring up to a thousand times fewer floating-point operations (FLOPs) and
having significantly reduced storage requirements. Thus, our MLGCN model could
be particular relevant to point cloud based 3D shape analysis in industrial
applications when computing resources are scarce
Quantification of Multi-Parametric Magnetic Resonance Imaging Based on Radiomics Analysis for Differentiation of Benign and Malignant Lesions of Prostate
Background: The most common cancer (non-cutaneous) malignancy among men is prostate cancer. Management of prostate cancer, including staging and treatment, playing an important role in decreasing mortality rates. Among all current diagnostic tools, multiparametric MRI (mp-MRI) has shown high potential in localizing and staging prostate cancer. Quantification of mp-MRI helps to decrease the dependency of diagnosis on readers’ opinions.
Objective: The aim of this research is to set a method based on quantification of mp-MRI images for discrimination between benign and malignant prostatic lesions with fusion-guided MR imaging/transrectal ultrasonography biopsy as a pathology validation reference.
Material and Methods: It is an analytical research that 27 patients underwent the mp-MRI examination, including T1- and T2- weighted and diffusion weighted imaging (DWI). Quantification was done by calculating radiomic features from mp-MRI images. Receiver-operating-characteristic curve was done for each feature to evaluate the discriminatory capacity and linear discriminant analysis (LDA) and leave-one-out cross-validation for feature filtering to estimate the sensitivity, specificity and accuracy of the benign and malignant lesion differentiation process is used.
Results: An accuracy, sensitivity and specificity of 92.6%, 95.2% and 83.3%, respectively, were achieved from a subset of radiomics features obtained from T2-weighted images and apparent diffusion coefficient (ADC) maps for distinguishing benign and malignant prostate lesions.Â
Conclusion: Quantification of mp-MRI (T2-weighted images and ADC-maps) based on radiomics feature has potential to distinguish benign with appropriate accuracy from malignant prostate lesions. This technique is helpful in preventing needless biopsies in patients and provides an assisted diagnosis for classifications of prostate lesions