3,755 research outputs found

    Survey of smart parking systems

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    The large number of vehicles constantly seeking access to congested areas in cities means that finding a public parking place is often difficult and causes problems for drivers and citizens alike. In this context, strategies that guide vehicles from one point to another, looking for the most optimal path, are needed. Most contributions in the literature are routing strategies that take into account different criteria to select the optimal route required to find a parking space. This paper aims to identify the types of smart parking systems (SPS) that are available today, as well as investigate the kinds of vehicle detection techniques (VDT) they have and the algorithms or other methods they employ, in order to analyze where the development of these systems is at today. To do this, a survey of 274 publications from January 2012 to December 2019 was conducted. The survey considered four principal features: SPS types reported in the literature, the kinds of VDT used in these SPS, the algorithms or methods they implement, and the stage of development at which they are. Based on a search and extraction of results methodology, this work was able to effectively obtain the current state of the research area. In addition, the exhaustive study of the studies analyzed allowed for a discussion to be established concerning the main difficulties, as well as the gaps and open problems detected for the SPS. The results shown in this study may provide a base for future research on the subject.Fil: Diaz Ogás, Mathias Gabriel. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Fabregat Gesa, Ramon. Universidad de Girona; EspañaFil: Aciar, Silvana Vanesa. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentin

    A Review of Hybrid Indoor Positioning Systems Employing WLAN Fingerprinting and Image Processing

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    Location-based services (LBS) are a significant permissive technology. One of the main components in indoor LBS is the indoor positioning system (IPS). IPS utilizes many existing technologies such as radio frequency, images, acoustic signals, as well as magnetic sensors, thermal sensors, optical sensors, and other sensors that are usually installed in a mobile device. The radio frequency technologies used in IPS are WLAN, Bluetooth, Zig Bee, RFID, frequency modulation, and ultra-wideband. This paper explores studies that have combined WLAN fingerprinting and image processing to build an IPS. The studies on combined WLAN fingerprinting and image processing techniques are divided based on the methods used. The first part explains the studies that have used WLAN fingerprinting to support image positioning. The second part examines works that have used image processing to support WLAN fingerprinting positioning. Then, image processing and WLAN fingerprinting are used in combination to build IPS in the third part. A new concept is proposed at the end for the future development of indoor positioning models based on WLAN fingerprinting and supported by image processing to solve the effect of people presence around users and the user orientation problem

    A Suggested Strategic Roadmap for Public Egyptian Universities to Adopt and Adapt to the Requirements of the Fourth Industrial Revolution and Society 5.0 to Prepare Students for the Future Labor Market

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    Currently, the world is facing an unprecedented challenge which is “youth bulge” with a high rate of unemployment. It is argued that the employability challenges will be compounded by the impacts of the Fourth Industrial Revolution (4IR) and Society 5.0, and “Open talent economy” is the new economy replacing traditional permanent employees with talented “free lancers” to perform the required work from anywhere in the world. Currently, institutions around the world prefer to employ those who have skills for specific projects. Gig economy primarily depends on two forms of work: “crowd work” and “work on-demand”. In addition, in the last five years, "artificial intelligence" (AI) has begun to replace people in many of routine jobs, and will continue to replace people in new unimaginable jobs that may arise in the future due to the continuous developments of smart technologies. Accordingly, universities all over the world will face a new problem of preparing students for a new way of life and work with a somewhat uncertain future in the coming era that involves a new industrial revolution whose repercussions are unprecedented.Accordingly, there is increasing trend that calls for the necessity of preparing a flexible or liquid workforce able to constantly adapt itself to the requirements of fast-changing labor market, and establishing a culture of flexibility in moving between businesses according to the needs of the labor market which witnessed the emergence of new types of labor across the world. If universities failed to align employability competences with the requirements of 4IR and Society 5.0, the unemployment gap will increase. Thus, the main objective of this study is to propose a strategic roadmap for public Egyptian universities to adopt and adapt to the requirements of the fourth industrial revolution and society 5.0 to prepare students for the future labor market. To achieve this objective, the author adopted both descriptive and exploratory research design, and used mixed methods research approach. Since the requirements of 4IR and Society 5.0 and Gig economy do not depend on national educational institutions that operate in accordance with national standards, but reliance is on the standards set by transnational "digital institutions" and platform companies, so the national contexts will have minor influence on practices and structure of university education systems, therefore the suggested strategic proposal may be applied by many universities in different educational contexts. Keywords: The Fourth Industrial Revolution; Society 5.0; Strategic Roadmap; Education 4.0; Work 4.0; Egyptian Universities; Gig economy. DOI: 10.7176/JEP/11-29-03 Publication date:October 31st 202

    A review of the internet of floods : near real-time detection of a flood event and its impact

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    Worldwide, flood events frequently have a dramatic impact on urban societies. Time is key during a flood event in order to evacuate vulnerable people at risk, minimize the socio-economic, ecologic and cultural impact of the event and restore a society from this hazard as quickly as possible. Therefore, detecting a flood in near real-time and assessing the risks relating to these flood events on the fly is of great importance. Therefore, there is a need to search for the optimal way to collect data in order to detect floods in real time. Internet of Things (IoT) is the ideal method to bring together data of sensing equipment or identifying tools with networking and processing capabilities, allow them to communicate with one another and with other devices and services over the Internet to accomplish the detection of floods in near real-time. The main objective of this paper is to report on the current state of research on the IoT in the domain of flood detection. Current trends in IoT are identified, and academic literature is examined. The integration of IoT would greatly enhance disaster management and, therefore, will be of greater importance into the future

    Cancer diagnosis using deep learning: A bibliographic review

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    In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements

    Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools

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    Big data has been used widely in many areas including the transportation industry. Using various data sources, traffic states can be well estimated and further predicted for improving the overall operation efficiency. Combined with this trend, this study presents an up-to-date survey of open data and big data tools used for traffic estimation and prediction. Different data types are categorized and the off-the-shelf tools are introduced. To further promote the use of big data for traffic estimation and prediction tasks, challenges and future directions are given for future studies
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