Continuous advancements in computing technology and artificial intelligence in the past decade have led to improvements in driver monitoring systems. Numerous experimental studies have collected real driver drowsiness data and applied various artificial intelligence algorithms and feature combinations with the goal of significantly enhancing the performance of these systems in real-time. This paper presents an up-to-date review of the driver drowsiness detection systems implemented over the last decade. The paper illustrates and reviews recent systems using different measures to track and detect drowsiness. Each system falls under one of four possible categories, based on the information used. Each system presented in this paper is associated with a detailed description of the features, classification algorithms, and used datasets. In addition, an evaluation of these systems is presented, in terms of the final classification accuracy, sensitivity, and precision. Furthermore, the paper highlights the recent challenges in the area of driver drowsiness detection, discusses the practicality and reliability of each of the four system types, and presents some of the future trends in the field.

In this digital era, organizations and industries are moving towards replacing websites with web applications for many obvious reasons. With this transition towards web-based applications, organizations and industries find themselves surrounded by several threats and vulnerabilities. One of the largest concerns is keeping their infrastructure safe from attacks and misuse. Web security entails applying a set of procedures and practices, by applying several security principles at various layers to protect web servers, web users, and their surrounding environment. In this paper, we will discuss several attacks that may affect web-based applications namely: SQL injection attacks, cookie poisoning, cross-site scripting, and buffer overflow. Additionally, we will discuss detection and prevention methods from such attacks.

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Climate change and global warming are attributed to the increased levels of greenhouse Gases in the atmosphere. Miniature low-cost, lightweight instruments on-board low-cost nanosatellite platforms such as CubeSats could be used to provide precise measurements of greenhouse gases levels. CubeSats, which usually have a narrow field of view, cost a fraction of what more expensive satellites with wide swaths cost. MeznSat is a 3U CubeSat that will carry a shortwave infrared (SWIR) micro-spectrometer as its primary payload, with the aim of deriving greenhouse gas concentrations in the atmosphere by making observations in the 1000–1650 nm wavelength region. The satellite, which is planned for launch in March 2020, is the result of a collaborative project between Khalifa University of Science and Technology (KUST) and the American University of Ras Al-Khaimah (AURAK) with a fund from the United Arab Emirates Space Agency (UAE-SA). The primary payload, Argus 2000, is a miniature, low-cost, space-qualified spectrometer that operates in the shortwave infrared (SWIR) bands. Argus 2000 is a ruggedized unit with a mass of less than 230 g and power consumption of less than 1 W. Also, the Argus 2000 has 0.15 degrees viewing angle and 15 mm fore-optics. The secondary payload will consist of a high definition (HD) camera that will allow post-processing to achieve the high geolocation accuracy required for the SWIR spectrometer data. The RGB combination of visible and SWIR bands setup makes MeznSat a unique CubeSat mission that will generate an interesting dataset to explore atmospheric correction algorithms, which employ SWIR data to process visible channels. This paper describes the mission feasibility, mission analysis, design, and status of MeznSat.