Search Results (3)

Fire alarm systems are typically equipped with various sensors such as heat, smoke, and gas detectors. These provide fire alerts and notifications of emergency exits when a fire has been detected. However, such systems do not give early warning in order to allow appropriate action to be taken when an alarm is first triggered, as the fire may have already caused severe damage. This paper analyzes a new dataset gathered from controlled realistic fire experiments conducted in an indoor laboratory environment. The experiments were conducted in a controlled manner by triggering the source of fire using electrical devices and charcoal on paperboard, cardboard or clothing. Important data such as humidity, temperature, MQ139, Total Volatile Organic Compounds (TVOC) and eCO₂ were collected using sensor devices. These datasets will be extremely valuable to researchers in the machine learning and data science communities interested in pursuing novel advanced statistical and machine learning techniques and methods for developing early fire detection systems. The analysis of the collected data demonstrates the possibility of using eCO₂ and TVOC reading levels for early detection of smoldering fires. The experimental setup was based on Low-Power Wireless Area Networks (LPWAN), which can be used to reliably deliver fire-related data over long ranges without depending on the status of a cellular or WiFi Network. Full article

Continuous advancements in Information and Communication Technology and the emergence of the Big Data era have altered how traditional power systems function. Such developments have led to increased reliability and efficiency, in turn contributing to operational, economic, and environmental improvements and leading to the development of a new technique known as Demand Side Management or DSM. In essence, DSM is a management activity that encourages users to optimize their electricity consumption by controlling the operation of their electrical appliances to reduce utility bills and their use during peak times. While users may save money on electricity costs by rescheduling their power consumption, they may also experience inconvenience due to the inflexibility of getting power on demand. Hence, several challenges must be considered to achieve a successful DSM. In this work, we analyze the power scheduling techniques in Smart Houses as proposed in most cited papers. We then examine the advantages and drawbacks of such methods and compare their contributions based on operational, economic, and environmental aspects. Full article

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. Full article