scholarly journals Efficient Management of Road Intersections for Automated Vehicles—The FRFP System Applied to the Various Types of Intersections and Roundabouts

2019 ◽  
Vol 10 (1) ◽  
pp. 316 ◽  
Author(s):  
Basilio Filocamo ◽  
Javier Alonso Ruiz ◽  
Miguel Angel Sotelo
Keyword(s):  
Travel Time ◽  
State Of The Art ◽  
Automatic Driving ◽  
Road Congestion ◽  
Efficient Management ◽  
Different Types ◽  
Road Intersections ◽  
The Right ◽  
New System ◽  
Critical Aspects

In the last decade, automatic driving systems for vehicles circulating on public roads have become increasingly closer to reality. There is always a strong interest in this topic among research centers and car manufacturers. One of the most critical aspects is the management of intersections, i.e., who will have to go first and in what ways? This is the question we want to answer through this research. Clearly, the goal is to manage the intersection safely, making it possible to reduce road congestion, travel time, emissions, and fuel consumption as much as possible. The research is conducted by comparing a new management system with the systems already known in the state of the art for different types of intersections. The new system proposed by us is called FRFP (first to reach the end of the intersection first to pass). In particular, vehicles will increase or decrease their speed in collaboration with each other by making the right decision. The vehicle that can potentially reach the intersection exit first.

scholarly journals Adoption of automatic identification systems by grocery retailersin the Johannesburg area

2011 ◽  
Vol 5 (1) ◽  
Author(s):  
Christopher C. Darlington ◽  
Boris Urban
Keyword(s):  
Supply Chain ◽  
Descriptive Study ◽  
Data Capture ◽  
Decision Makers ◽  
Management Systems ◽  
Automatic Identification ◽  
Chain Management ◽  
Efficient Management ◽  
Different Types ◽  
The Right

Retailers not only need the right data capture technology to meet the requirements of their applications, they must also decide on what the optimum technology is from the different symbologies that have been developed over the years. Automatic identification systems (AIS) are a priority to decision makers as they attempt to obtain the best blend of equipment to ensure greater loss prevention and higher reliability in data capture. However there is a risk of having too simplistic a view of adopting AIS, since no one solution is applicable across an industry or business model. This problem is addressed through an exploratory, descriptive study, where the nature and value of AIS adoption by grocery retailers in the Johannesburg area is interrogated. Mixed empirical results indicate that, as retailers adopt AIS in order to improve their supply chain management systems, different types of applications are associated with various constraints and opportunities. Overall this study is in line with previous research that supports the notion that supply chain decisions are of a strategic nature even though efficient management of information is a day-to-day business operational decision.

scholarly journals The Sliding Scale between Usufruct and Ownership: The Example of Swedish Multi-Family Housing

Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 311
Author(s):  
Anna Granath Hansson ◽  
Peter Ekbäck ◽  
Jenny Paulsson
Keyword(s):  
Property Rights ◽  
General Level ◽  
Multifamily Housing ◽  
Different Types ◽  
Sliding Scale ◽  
Family Housing ◽  
The Right

This paper aims to elucidate the sliding scale between usufruct and ownership by applying a property rights framework to three Swedish forms of tenure in multifamily housing. The framework deconstructs the bundles of rights of rental, tenant-ownership and ownership to highlight commonalities and differences connected to the right to use and exclude, the right to transfer and the right to the value. It is concluded that the three tenure forms have many traits in common but that there are distinct differences in some areas, most notably in connection to the right to the value. The property rights framework applied in the study may be applicable also on a general level as a method to analyze and compare tenures of different types in different countries. Further, ways to improve the framework and cover more facets of outcomes of property rights patterns are suggested.

scholarly journals Multiple objects tracking in the UAV system based on hierarchical deep high-resolution network

2021 ◽  
Author(s):  
Wei Huang ◽  
Xiaoshu Zhou ◽  
Mingchao Dong ◽  
Huaiyu Xu
Keyword(s):  
High Resolution ◽  
Object Tracking ◽  
High Performance ◽  
State Of The Art ◽  
Class Imbalance ◽  
Unified Framework ◽  
Multiple Objects ◽  
Tracking Process ◽  
Objects Tracking ◽  
Different Types

AbstractRobust and high-performance visual multi-object tracking is a big challenge in computer vision, especially in a drone scenario. In this paper, an online Multi-Object Tracking (MOT) approach in the UAV system is proposed to handle small target detections and class imbalance challenges, which integrates the merits of deep high-resolution representation network and data association method in a unified framework. Specifically, while applying tracking-by-detection architecture to our tracking framework, a Hierarchical Deep High-resolution network (HDHNet) is proposed, which encourages the model to handle different types and scales of targets, and extract more effective and comprehensive features during online learning. After that, the extracted features are fed into different prediction networks for interesting targets recognition. Besides, an adjustable fusion loss function is proposed by combining focal loss and GIoU loss to solve the problems of class imbalance and hard samples. During the tracking process, these detection results are applied to an improved DeepSORT MOT algorithm in each frame, which is available to make full use of the target appearance features to match one by one on a practical basis. The experimental results on the VisDrone2019 MOT benchmark show that the proposed UAV MOT system achieves the highest accuracy and the best robustness compared with state-of-the-art methods.

A dual U-Net algorithm for automating feature extraction from satellite imagery

2021 ◽  
pp. 154851292098354
Author(s):  
Samuel Humphries ◽  
Trevor Parker ◽  
Bryan Jonas ◽  
Bryan Adams ◽  
Nicholas J Clark
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Satellite Images ◽  
Detection Algorithm ◽  
Military Operations ◽  
Detection Algorithms ◽  
Us Military ◽  
Standard Product ◽  
Different Types ◽  
Road Intersections

Quick identification of building and roads is critical for execution of tactical US military operations in an urban environment. To this end, a gridded, referenced, satellite images of an objective, often referred to as a gridded reference graphic or GRG, has become a standard product developed during intelligence preparation of the environment. At present, operational units identify key infrastructure by hand through the work of individual intelligence officers. Recent advances in Convolutional Neural Networks, however, allows for this process to be streamlined through the use of object detection algorithms. In this paper, we describe an object detection algorithm designed to quickly identify and label both buildings and road intersections present in an image. Our work leverages both the U-Net architecture as well the SpaceNet data corpus to produce an algorithm that accurately identifies a large breadth of buildings and different types of roads. In addition to predicting buildings and roads, our model numerically labels each building by means of a contour finding algorithm. Most importantly, the dual U-Net model is capable of predicting buildings and roads on a diverse set of test images and using these predictions to produce clean GRGs.

scholarly journals Image Restoration by Learning Morphological Opening-Closing Network

2020 ◽  
Vol 4 (1) ◽  
pp. 87-107
Author(s):  
Ranjan Mondal ◽  
Moni Shankar Dey ◽  
Bhabatosh Chanda
Keyword(s):  
Neural Network ◽  
Image Restoration ◽  
State Of The Art ◽  
Source Code ◽  
Back Propagation ◽  
Image Features ◽  
Main Difficulty ◽  
The Right ◽  
Right Order ◽  
Morphological Opening

AbstractMathematical morphology is a powerful tool for image processing tasks. The main difficulty in designing mathematical morphological algorithm is deciding the order of operators/filters and the corresponding structuring elements (SEs). In this work, we develop morphological network composed of alternate sequences of dilation and erosion layers, which depending on learned SEs, may form opening or closing layers. These layers in the right order along with linear combination (of their outputs) are useful in extracting image features and processing them. Structuring elements in the network are learned by back-propagation method guided by minimization of the loss function. Efficacy of the proposed network is established by applying it to two interesting image restoration problems, namely de-raining and de-hazing. Results are comparable to that of many state-of-the-art algorithms for most of the images. It is also worth mentioning that the number of network parameters to handle is much less than that of popular convolutional neural network for similar tasks. The source code can be found here https://github.com/ranjanZ/Mophological-Opening-Closing-Net

scholarly journals Fighting Together against the Pandemic: Learning Multiple Models on Tomography Images for COVID-19 Diagnosis

AI ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 261-273
Author(s):  
Mario Manzo ◽  
Simone Pellino
Keyword(s):  
Network Architecture ◽  
State Of The Art ◽  
Ensemble Classification ◽  
Effective Vaccine ◽  
Rt Pcr ◽  
Neural Network Architecture ◽  
Experimental Phase ◽  
Different Types ◽  
Polymerase Chain ◽  
Better Than

COVID-19 has been a great challenge for humanity since the year 2020. The whole world has made a huge effort to find an effective vaccine in order to save those not yet infected. The alternative solution is early diagnosis, carried out through real-time polymerase chain reaction (RT-PCR) tests or thorax Computer Tomography (CT) scan images. Deep learning algorithms, specifically convolutional neural networks, represent a methodology for image analysis. They optimize the classification design task, which is essential for an automatic approach with different types of images, including medical. In this paper, we adopt a pretrained deep convolutional neural network architecture in order to diagnose COVID-19 disease from CT images. Our idea is inspired by what the whole of humanity is achieving, as the set of multiple contributions is better than any single one for the fight against the pandemic. First, we adapt, and subsequently retrain for our assumption, some neural architectures that have been adopted in other application domains. Secondly, we combine the knowledge extracted from images by the neural architectures in an ensemble classification context. Our experimental phase is performed on a CT image dataset, and the results obtained show the effectiveness of the proposed approach with respect to the state-of-the-art competitors.

scholarly journals Lights and Shadows in Immuno-Oncology Drug Development

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 691
Author(s):  
Milana Bergamino Sirvén ◽  
Sonia Pernas ◽  
Maggie C. U. Cheang
Keyword(s):  
Drug Development ◽  
Checkpoint Inhibitors ◽  
Late Phase ◽  
New Drugs ◽  
Successful Outcome ◽  
Different Types ◽  
Cancer Types ◽  
Clinical Trial Designs ◽  
Oncology Drug Development ◽  
Critical Aspects

The rapidly evolving landscape of immuno-oncology (IO) is redefining the treatment of a number of cancer types. IO treatments are becoming increasingly complex, with different types of drugs emerging beyond checkpoint inhibitors. However, many of the new drugs either do not progress from phase I-II clinical trials or even fail in late-phase trials. We have identified at least five areas in the development of promising IO treatments that should be redefined for more efficient designs and accelerated approvals. Here we review those critical aspects of IO drug development that could be optimized for more successful outcome rates in all cancer types. It is important to focus our efforts on the mechanisms of action, types of response and adverse events of these novel agents. The use of appropriate clinical trial designs with robust biomarkers of response and surrogate endpoints will undoubtedly facilitate the development and subsequent approval of these drugs. Further research is also needed to establish biomarker-driven strategies to select which patients may benefit from immunotherapy and identify potential mechanisms of resistance.

scholarly journals Critical Aspects of Person Counting and Density Estimation

2021 ◽  
Vol 7 (2) ◽  
pp. 21
Author(s):  
Roland Perko ◽  
Manfred Klopschitz ◽  
Alexander Almer ◽  
Peter M. Roth
Keyword(s):  
Density Estimation ◽  
Network Architecture ◽  
Reference Data ◽  
State Of The Art ◽  
Limit State ◽  
Ground Truth ◽  
Data Sets ◽  
Ground Truth Generation ◽  
Baseline Approach ◽  
Critical Aspects

Many scientific studies deal with person counting and density estimation from single images. Recently, convolutional neural networks (CNNs) have been applied for these tasks. Even though often better results are reported, it is often not clear where the improvements are resulting from, and if the proposed approaches would generalize. Thus, the main goal of this paper was to identify the critical aspects of these tasks and to show how these limit state-of-the-art approaches. Based on these findings, we show how to mitigate these limitations. To this end, we implemented a CNN-based baseline approach, which we extended to deal with identified problems. These include the discovery of bias in the reference data sets, ambiguity in ground truth generation, and mismatching of evaluation metrics w.r.t. the training loss function. The experimental results show that our modifications allow for significantly outperforming the baseline in terms of the accuracy of person counts and density estimation. In this way, we get a deeper understanding of CNN-based person density estimation beyond the network architecture. Furthermore, our insights would allow to advance the field of person density estimation in general by highlighting current limitations in the evaluation protocols.

Spinoza über Tiere

2014 ◽  
Vol 96 (2) ◽  
Author(s):  
Dominik Perler
Keyword(s):  
Human Beings ◽  
Privileged Status ◽  
Different Types ◽  
The Right

Abstract:According to Spinoza, there is no categorical distinction between human and non-human animals: they all belong to the same nature and all consist of bodies with corresponding ideas. This thesis gives rise to two problems. How is it possible to distinguish different types of animals, in particular nonrational and rational ones, if all of them have the same metaphysical structure? And why does Spinoza nevertheless claim that human beings have a privileged status that gives them the right to use non-rational animals? This paper examines these two problems, arguing that the solution to both of them lies in Spinoza’s all-embracing naturalism.

License Plate Tilt Correction: A Review

2021 ◽  
Vol 39 (1B) ◽  
pp. 101-116
Author(s):  
Nada N. Kamal ◽  
Enas Tariq
Keyword(s):  
State Of The Art ◽  
Recognition System ◽  
License Plate ◽  
License Plate Recognition ◽  
Tilt Correction ◽  
Detection Algorithms ◽  
Essential Step ◽  
Tilting Angle ◽  
Different Types ◽  
Line Fitting

Tilt correction is an essential step in the license plate recognition system (LPR). The main goal of this article is to provide a review of the various methods that are presented in the literature and used to correct different types of tilt that appear in the digital image of the license plates (LP). This theoretical survey will enable the researchers to have an overview of the available implemented tilt detection and correction algorithms. That’s how this review will simplify for the researchers the choice to determine which of the available rotation correction and detection algorithms to implement while designing their LPR system. This review also simplifies the decision for the researchers to choose whether to combine two or more of the existing algorithms or simply create a new efficient one. This review doesn’t recite the described models in the literature in a hard-narrative tale, but instead, it clarifies how the tilt correction stage is divided based on its initial steps. The steps include: locating the plate corners, finding the tilting angle of the plate, then, correcting its horizontal, vertical, and sheared inclination. For the tilt correction stage, this review clarifies how state-of-the-art literature handled each step individually. As a result, it has been noticed that line fitting, Hough transform, and Randon transform are the most used methods to correct the tilt of a LP.

Sign in / Sign up

Export Citation Format

Share Document