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Topic Review
Multimodal Segmentation Techniques in Autonomous Driving
Semantic Segmentation has become one of the key steps toward scene understanding, especially in autonomous driving scenarios. In the standard formulation, Semantic Segmentation uses only data from color cameras, which suffer significantly in dim lighting or adverse weather conditions. A solution to this problem is the use of multiple heterogeneous sensors (e.g., depth and thermal cameras or LiDARs) as the input to machine learning approaches tackling this task, allowing to cover for the shortcomings of color cameras and to extract a more resilient representation of the scene.
  • 826
  • 15 Aug 2022
Topic Review
RSA BSAFE
RSA BSAFE is a FIPS 140-2 validated cryptography library, available in both C and Java, offered by RSA Security. It was one of the most common ones before the RSA patent expired in September 2000. It also contained implementations of the RCx ciphers, with the most common one being RC4. From 2004 to 2013 the default random number generator in the library was a NIST-approved RNG standard, widely known to be insecure from at least 2006, withdrawn in 2014, suspected to contain an alleged kleptographic backdoor from the American National Security Agency (NSA), as part of its secret Bullrun program.
  • 825
  • 25 Nov 2022
Topic Review
Business Email Compromise Defender
In an era of ever-evolving and increasingly sophisticated cyber threats, protecting sensitive information from cyberattacks such as business email compromise (BEC) attacks has become a top priority for individuals and enterprises. According to the available literature, various authentication methods have been explored for validating physical documents using QR codes.
  • 825
  • 18 Mar 2024
Topic Review
Fuel Consumption and CO2 of Light-Duty Vehicles
Due to the alarming rate of climate change, fuel consumption and emission estimates are critical in determining the effects of materials and stringent emission control strategies. In this research, an analytical and predictive study has been conducted using the Government of Canada dataset, containing 4973 light-duty vehicles observed from 2017 to 2021, delivering a comparative view of different brands and vehicle models by their fuel consumption and carbon dioxide emissions. Based on the findings of the statistical data analysis, this study makes evidence-based recommendations to both vehicle users and producers to reduce their environmental impacts. Additionally, Convolutional Neural Networks (CNN) and various regression models have been built to estimate fuel consumption and carbon dioxide emissions for future vehicle designs. This study reveals that the Univariate Polynomial Regression model is the best model for predictions from one vehicle feature input, with up to 98.6% accuracy. Multiple Linear Regression and Multivariate Polynomial Regression are good models for predictions from multiple vehicle feature inputs, with approximately 75% accuracy. Convolutional Neural Network is also a promising method for prediction because of its stable and high accuracy of around 70%. The results contribute to the quantifying process of energy cost and air pollution caused by transportation, followed by proposing relevant recommendations for both vehicle users and producers. Future research should aim towards developing higher performance models and larger datasets for building APIs and applications.
  • 825
  • 20 Jan 2022
Topic Review
Augmented Reality Mobile App to Learn Writing
Augmented reality (AR) has been widely used in education, particularly for child education. This entry presents the design and implementation of a novel mobile app, Learn2Write, using machine learning techniques and augmented reality to teach alphabet writing.
  • 824
  • 10 Jan 2022
Topic Review
Human-Smart Environment Interactions
In the context of the challenges facing human computer interaction (HCI) on the one hand and the future Internet on the other, the purpose of this study is to explore the multi-dimensionality of smart cities, looking at relationships and interdependencies through correlating selected dimensions of smartness. Through a review of the research literature, key dimensions of smartness are identified for exploration in the context of smart cities in this work. Methodologically, this work combines an exploratory case study approach consisting of multiple methods of data collection including survey and in-depth interviews, with an explanatory correlational design. In terms of results, the main findings of this work shed light on relationships between selected dimensions of the multi-dimensionality construct of smartness in data-rich urban environments. This work is significant in that it provides correlational information for smart city dimensionalities while contributing to the research literature in this domain; uses a hybrid case study and correlational design in relation to the study of multi-dimensionality; and opens spaces for the study of innovative urban initiatives, taking the ideas and experiences of people from many sectors into consideration.
  • 823
  • 30 Oct 2020
Topic Review
Robotic Platform for Horticulture
The modern level of development of infocommunication and computer technologies, microprocessor technology and equipment, communication and positioning makes possible the development and practical application of automated and robotic technologies and technical means to improve the efficiency of agricultural production. Currently, intensive horticulture is becoming increasingly widespread due to rapid fruiting and high yield rates. At the same time, the process of harvesting apples in intensive horticulture is the most time-consuming, and harvesting is carried out mainly by a team of pickers. In the production process of cultivating fruit crops, this is an important final stage which requires the development of automated devices and robotic platforms with a control system capable of offline harvesting.
  • 823
  • 02 Dec 2022
Topic Review
Deep Learning Algorithms in Oral Cancer
Oral cancer is a dangerous and extensive cancer with a high death ratio. Oral cancer is the most usual cancer in the world, with more than 300,335 deaths every year. The cancerous tumor appears in the neck, oral glands, face, and mouth. To overcome this dangerous cancer, there are many ways to detect like a biopsy, in which small chunks of tissues are taken from the mouth and tested under a secure and hygienic microscope. However, microscope results of tissues to detect oral cancer are not up to the mark, a microscope cannot easily identify the cancerous cells and normal cells. Detection of cancerous cells using microscopic biopsy images helps in allaying and predicting the issues and gives better results if biologically approaches apply accurately for the prediction of cancerous cells, but during the physical examinations microscopic biopsy images for cancer detection there are major chances for human error and mistake. So, with the development of technology deep learning algorithms plays a major role in medical image diagnosing. Deep learning algorithms are efficiently developed to predict breast cancer, oral cancer, lung cancer, or any other type of medical image.
  • 823
  • 27 Jun 2022
Topic Review
Image Derivatives
Image derivatives can be computed by using small convolution filters of size 2 x 2 or 3 x 3, such as the Laplacian, Sobel, Roberts and Prewitt operators. However, a larger mask will generally give a better approximation of the derivative and examples of such filters are Gaussian derivatives and Gabor filters. Sometimes high frequency noise needs to be removed and this can be incorporated in the filter so that the Gaussian kernel will act as a band pass filter. The use of Gabor filters in image processing has been motivated by some of its similarities to the perception in the human visual system. The pixel value is computed as a convolution where [math]\displaystyle{ \mathbf{d} }[/math] is the derivative kernel and [math]\displaystyle{ G }[/math] is the pixel values in a region of the image and [math]\displaystyle{ \ast }[/math] is the operator that performs the convolution.
  • 822
  • 28 Oct 2022
Topic Review
List of Folding@home Cores
The distributed-computing project Folding@home uses scientific computer programs, referred to as "cores" or "fahcores", to perform calculations. Folding@home's cores are based on modified and optimized versions of molecular simulation programs for calculation, including TINKER, GROMACS, AMBER, CPMD, SHARPEN, ProtoMol and Desmond. These variants are each given an arbitrary identifier (Core xx). While the same core can be used by various versions of the client, separating the core from the client enables the scientific methods to be updated automatically as needed without a client update.
  • 821
  • 29 Nov 2022
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