A Metaheuristic Algorithm for OCR Baseline Detection of Arabic Languages

Preprocessing is a very important part of cursive languages Optical Character Recognition (OCR) systems. Thus, baseline detection, which is one of the main parts of the preprocessing operation, plays a basic role on OCR systems; improvement on baseline detection could be absolutely useful for decreasing errors in recognition words. In this chapter, a metaheuristic- and mathematical-based algorithm is recommended, which has improved the baseline detection process in relation to the well-known baseline detection algorithms. The most important advantages of the proposed method are simplicity, high speed processing, and reliability. To test this novel solution, IFN/ENIT database, which is a well-known and attending database, is utilized. However, the proposed solution is reliable to any standard database of cursive language's OCR. </jats:p

Canadian Prairie Rangeland and Seeded Forage Classification Using Multiseason Landsat 8 and Summer RADARSAT-2

Rangeland and seeded forage in Canada's Prairie provinces represent productive landscapes that provide multiple ecosystem services. Past efforts to map these resources at regional scales have not achieved consistently high accuracies as they are spatially variable in both ecology and management. In particular, Agriculture and Agri-food Canada needs to distinguish these land use classes from each other and from cropland in its annual national agricultural land cover inventory. Given the potential to distinguish these classes based on seasonal phenological differences, this study used multi-season Landsat 8 top-of-atmosphere reflectance data and derived vegetation and phenological indices, as well as mid-summer RADARSAT-2 data in random forest classification of two ecoregions in Alberta and Manitoba. Classification accuracy was compared for single and multi-date Landsat 8 variables, the vegetation index and phenological variable groups, RADARSAT-2 VV and VH backscatter intensity, and combined datasets. Variable importance analysis showed that spring Landsat 8 reflectance generally contributed most to class discrimination, but accuracy improved with the addition of Landsat 8 data from the other seasons. Vegetation indices and phenological variables produced similar accuracies and were deemed to not warrant the additional processing effort to derive them. RADARSAT-2 VH backscatter was the most important variable for the Manitoba study area, which is wetter with more vegetation structure variability than the Alberta study area. Backscatter intensity significantly increased overall accuracy when it was combined with one or two-season Landsat 8 data. The best overall accuracy was achieved using the three seasons of Landsat 8 and mid-summer RADARSAT-2 data, but it was not significantly better than that for two season Landsat 8 + RADARSAT-2. The methods presented in this paper provide a process for accurate and efficient classification of seeded forage, rangeland and cropland that can be applied over large areas in operational agricultural land cover inventory. © 2018The Rangeland Ecology & Management archives are made available by the Society for Range Management and the University of Arizona Libraries. Contact [email protected] for further information

Prediction of track performance in competitive BMX riders using laboratory measures

Identifying key physiological factors is essential in cycling; however, the unique nature of BMX decreases the validity and transferability of research findings from other cycling disciplines. Therefore, this study highlighted the physical and physiological characteristics of BMX riders that could influence track performance. Fifteen sub-elite BMX riders (male n = 12; age 18.3 ± 3.3 and female n = 3; 17.7 ± 5.7 years) undertook a battery of laboratory tests on three different occasions, including body composition, upper and lower body strength, flexibility, sprint and aerobic capacity measures. On a separate day, participants completed three full lap sprints on an outdoor BMX track. Correlation and multiple linear regression analyses were performed to develop predictive models of performance across the laboratory tests and race time. The final model indicated power to weight ratio, relative back-leg-chest strength and arm span explained ~87% of the variability in finish time (adjusted R2 = 0.87, p &lt; .01). These findings highlighted the importance of a multidimensional approach for developing BMX race performance. Coaches should prioritise these variables in their training programs and selection of future talents. However, further physiological and biomechanical investigation is needed to validate current findings, particularly among elite riders

Power Analysis of Field-Based Bicycle Motor Cross (BMX)

Introduction: Power meter is a useful tool for monitoring cyclists’ training and race performance. However, limited data are available regarding BMX racing power output. The aim of this study was to characterise the power production of BMX riders and investigate its potential role on race performance. Methods: Fourteen male riders (age: 20.3 ± 1.5 years, height: 1.75 ± 0.05 m, mass: 70.2 ± 6.4 kg) participated in this study. The tests consist of performing two races apart from 15-min recovery. SRM power meter was used to record power and cadence. Cyclists’ fastest race was used for the data analysis. Heart rate was recorded at 1-s intervals using a Garmin HR chest strap. Lap time was recorded using four pairs of photocells positioned at the start gate, bottom of the start ramp, end of first corner (time cornering), and on the finish line. Results: There was a large correlation between race time and relative peak power (r = −0.68, p < 0.01) as well as average power with zero value excluded (r = −0.52, p < 0.01). Race time was also significantly associated with time cornering (r = 0.58, p < 0.01). Peak power (1288.7 ± 62.6 W) was reached in the first 2.34 second of the race. With zero values included, the average power was 355.8 ± 25.4 W, which was about 28% of the peak power, compared to 62% when zero values were excluded (795.6 ± 63.5 W). Conclusion: The post-race analysis of the power data might help the cyclists recognizing the need to apply certain strategies on pedalling rates and power production in certain portions of the BMX track, specially, at the start and around the first corner. BMX coaches must consider designing training programs based on the race intensity and power output zones