Road Vehicle Monitoring System Based on Intelligent Visual Internet of Things

In recent years, with the rapid development of video surveillance infrastructure, more and more intelligent surveillance systems have employed computer vision and pattern recognition techniques. In this paper, we present a novel intelligent surveillance system used for the management of road vehicles based on Intelligent Visual Internet of Things (IVIoT). The system has the ability to extract the vehicle visual tags on the urban roads; in other words, it can label any vehicle by means of computer vision and therefore can easily recognize vehicles with visual tags. The nodes designed in the system can be installed not only on the urban roads for providing basic information but also on the mobile sensing vehicles for providing mobility support and improving sensing coverage. Visual tags mentioned in this paper consist of license plate number, vehicle color, and vehicle type and have several additional properties, such as passing spot and passing moment. Moreover, we present a fast and efficient image haze removal method to deal with haze weather condition. The experiment results show that the designed road vehicle monitoring system achieves an average real-time tracking accuracy of 85.80% under different conditions

Off-line Arabic Handwriting Recognition System Using Fast Wavelet Transform

In this research, off-line handwriting recognition system for Arabic alphabet is introduced. The system contains three main stages: preprocessing, segmentation and recognition stage. In the preprocessing stage, Radon transform was used in the design of algorithms for page, line and word skew correction as well as for word slant correction. In the segmentation stage, Hough transform approach was used for line extraction. For line to words and word to characters segmentation, a statistical method using mathematic representation of the lines and words binary image was used. Unlike most of current handwriting recognition system, our system simulates the human mechanism for image recognition, where images are encoded and saved in memory as groups according to their similarity to each other. Characters are decomposed into a coefficient vectors, using fast wavelet transform, then, vectors, that represent a character in different possible shapes, are saved as groups with one representative for each group. The recognition is achieved by comparing a vector of the character to be recognized with group representatives. Experiments showed that the proposed system is able to achieve the recognition task with 90.26% of accuracy. The system needs only 3.41 seconds a most to recognize a single character in a text of 15 lines where each line has 10 words on average

Vehicle surface contamination, unsteady flow and aerodynamic drag

The rear surfaces of blunt-ended vehicles, such as SUVs, are vulnerable to the build-up of contaminants thrown up from wet road surfaces by their tyres. This can compromise drivers’ vision, vehicle visibility, sensor performance and aesthetics. Vision will be reduced if the rear screen and lenses of camera systems become obscured. Similarly, sensing methods such as Light Detection and Ranging [LIDAR], introduced to support higher-level Advanced Driver Assistance Systems [ADAS] and autonomous driving are also vulnerable to contaminant accumulation. In addition, vehicle users may find that dirt is transferred to their hands and clothes as they access the rear load space. Finally, rapid soiling of external surfaces can be perceived as degrading the aesthetics of premium vehicles. Such deposition is a manifestation of unsteady aerodynamics – particularly the interaction between tyre spray, wheel wakes and the vehicle rear wake. These wake structures also strongly influence aerodynamic drag which, in turn affects CO2 emissions for Internal Combustion Engine [ICE] powered cars and the range of Battery Electric Vehicles [BEV]. Hence, automotive manufacturers need a simulation approach that can be used to minimise these characteristics concurrently during vehicle development. This work met that need by developing and deploying an innovative simulation process which predicts both contaminant accumulation and drag at the same time, by numerically representing unsteady aerodynamics, tyre spray and surface water behaviour. It is now integrated into the vehicle development process at Jaguar Land Rover [J/LR] where it is being used to develop new cars. This has been achieved by using a series of novel simplified vehicle geometry and spray systems to incrementally develop and validate the simulation strategy. The work culminated with its application to a production vehicle and subsequent validation against full scale experiments, providing the first quantification of accuracy for simulations of rear surface contamination. This novel simulation approach is combined with original experiments to show that reduced vehicle ride heights can lead to increased rear surface contamination, by reducing underbody flow and moving the vehicle wake closer to the highly contaminated wheel wakes. This provides a challenge for vehicle developers as lower ride heights are used to reduce aerodynamic drag; an increasingly important objective for both ICE and BEV product development, to support lower CO2 emissions and enhanced range, respectively. Finally, the first evidence is presented to suggest that aerodynamically improved underfloors can increase rear surface contamination, or at least redistribute it towards the lower regions of the vehicle rear, such as the bumper. This raises a risk for future BEVs which combine aerodynamically advantageous smooth underfloors with vulnerable ADAS features, such as rear bumper mounted LIDAR

Off-line Arabic Handwriting Recognition System Using Fast Wavelet Transform

In this research, off-line handwriting recognition system for Arabic alphabet is introduced. The system contains three main stages: preprocessing, segmentation and recognition stage. In the preprocessing stage, Radon transform was used in the design of algorithms for page, line and word skew correction as well as for word slant correction. In the segmentation stage, Hough transform approach was used for line extraction. For line to words and word to characters segmentation, a statistical method using mathematic representation of the lines and words binary image was used. Unlike most of current handwriting recognition system, our system simulates the human mechanism for image recognition, where images are encoded and saved in memory as groups according to their similarity to each other. Characters are decomposed into a coefficient vectors, using fast wavelet transform, then, vectors, that represent a character in different possible shapes, are saved as groups with one representative for each group. The recognition is achieved by comparing a vector of the character to be recognized with group representatives. Experiments showed that the proposed system is able to achieve the recognition task with 90.26% of accuracy. The system needs only 3.41 seconds a most to recognize a single character in a text of 15 lines where each line has 10 words on average

Aerial strategies advance volcanic gas measurements at inaccessible, strongly degassing volcanoes.

Volcanic emissions are a critical pathway in Earth's carbon cycle. Here, we show that aerial measurements of volcanic gases using unoccupied aerial systems (UAS) transform our ability to measure and monitor plumes remotely and to constrain global volatile fluxes from volcanoes. Combining multi-scale measurements from ground-based remote sensing, long-range aerial sampling, and satellites, we present comprehensive gas fluxes-3760 ± [600, 310] tons day-1 CO2 and 5150 ± [730, 340] tons day-1 SO2-for a strong yet previously uncharacterized volcanic emitter: Manam, Papua New Guinea. The CO2/ST ratio of 1.07 ± 0.06 suggests a modest slab sediment contribution to the sub-arc mantle. We find that aerial strategies reduce uncertainties associated with ground-based remote sensing of SO2 flux and enable near-real-time measurements of plume chemistry and carbon isotope composition. Our data emphasize the need to account for time averaging of temporal variability in volcanic gas emissions in global flux estimates

Aerial strategies advance volcanic gas measurements at inaccessible, strongly degassing volcanoes

Volcanic emissions are a critical pathway in Earth’s carbon cycle. Here, we show that aerial measurements of volcanic gases using unoccupied aerial systems (UAS) transform our ability to measure and monitor plumes remotely and to constrain global volatile fluxes from volcanoes. Combining multi-scale measurements from ground-based remote sensing, long-range aerial sampling, and satellites, we present comprehensive gas fluxes—3760 ± [600, 310] tons day−1 CO2 and 5150 ± [730, 340] tons day−1 SO2—for a strong yet previously uncharacterized volcanic emitter: Manam, Papua New Guinea. The CO2/ST ratio of 1.07 ± 0.06 suggests a modest slab sediment contribution to the sub-arc mantle. We find that aerial strategies reduce uncertainties associated with ground-based remote sensing of SO2 flux and enable near–real-time measurements of plume chemistry and carbon isotope composition. Our data emphasize the need to account for time averaging of temporal variability in volcanic gas emissions in global flux estimates

Aerial strategies advance volcanic gas measurements at inaccessible, strongly degassing volcanoes

Volcanic emissions are a critical pathway in Earth's carbon cycle. Here, we show that aerial measurements of volcanic gases using unoccupied aerial systems (UAS) transform our ability to measure and monitor plumes remotely and to constrain global volatile fluxes from volcanoes. Combining multi-scale measurements from ground-based remote sensing, long-range aerial sampling, and satellites, we present comprehensive gas fluxes-3760 ± [600, 310] tons day-1CO2and 5150 ± [730, 340] tons day-1SO2-for a strong yet previously uncharacterized volcanic emitter: Manam, Papua New Guinea. The CO2/ST ratio of 1.07 ± 0.06 suggests a modest slab sediment contribution to the sub-arc mantle. We find that aerial strategies reduce uncertainties associated with ground-based remote sensing of SO2flux and enable near-real-time measurements of plume chemistry and carbon isotope composition. Our data emphasize the need to account for time averaging of temporal variability in volcanic gas emissions in global flux estimates