An autonomous navigational system using GPS and computer vision for futuristic road traffic

Navigational service is one of the most essential dependency towards any transport system and at present, there are various revolutionary approaches that has contributed towards its improvement. This paper has reviewed the global positioning system (GPS) and computer vision based navigational system and found that there is a large gap between the actual demand of navigation and what currently exists. Therefore, the proposed study discusses about a novel framework of an autonomous navigation system that uses GPS as well as computer vision considering the case study of futuristic road traffic system. An analytical model is built up where the geo-referenced data from GPS is integrated with the signals captured from the visual sensors are considered to implement this concept. The simulated outcome of the study shows that proposed study offers enhanced accuracy as well as faster processing in contrast to existing approaches.

Seasonal Accumulated Workloads in Collegiate Women’s Soccer: A Comparison of Starters and Reserves

Research quantifying the unique workload demands of starters and reserves in training and match settings throughout a season in collegiate soccer is limited. Purpose: The purpose of the current study is to compare accumulated workloads between starters and reserves in collegiate soccer. Methods: Twenty-two NCAA Division III female soccer athletes (height: 1.67 ± 0.05 m; body mass: 65.42 ± 6.33 kg; fat-free mass: 48.99 ± 3.81 kg; body fat %: 25.22 ± 4.78%) were equipped with wearable global positioning systems with on-board inertial sensors, which assessed a proprietary training load metric and distance covered for each practice and 22 matches throughout an entire season. Nine players were classified as starters (S), defined as those playing >50% of playing time throughout the entire season. The remaining 17 were reserves (R). Goalkeepers were excluded. A one-way ANOVA was used to determine the extent of differences in accumulated training load throughout the season by player status. Results: Accumulated training load and total distance covered for starters were greater than reserves ((S: 9431 ± 1471 vs. R: 6310 ± 2263 AU; p < 0.001) and (S: 401.7 ± 31.9 vs. R: 272.9 ± 51.4 km; p < 0.001), respectively) throughout the season. Conclusions: Starters covered a much greater distance throughout the season, resulting in almost double the training load compared to reserves. It is unknown if the high workloads experienced by starters or the low workloads of the reserves is more problematic. Managing player workloads in soccer may require attention to address potential imbalances that emerge between starters and reserves throughout a season.

Dynamic Selection Techniques for Detecting GPS Spoofing Attacks on UAVs

Unmanned aerial vehicles are prone to several cyber-attacks, including Global Positioning System spoofing. Several techniques have been proposed for detecting such attacks. However, the recurrence and frequent Global Positioning System spoofing incidents show a need for effective security solutions to protect unmanned aerial vehicles. In this paper, we propose two dynamic selection techniques, Metric Optimized Dynamic selector and Weighted Metric Optimized Dynamic selector, which identify the most effective classifier for the detection of such attacks. We develop a one-stage ensemble feature selection method to identify and discard the correlated and low importance features from the dataset. We implement the proposed techniques using ten machine-learning models and compare their performance in terms of four evaluation metrics: accuracy, probability of detection, probability of false alarm, probability of misdetection, and processing time. The proposed techniques dynamically choose the classifier with the best results for detecting attacks. The results indicate that the proposed dynamic techniques outperform the existing ensemble models with an accuracy of 99.6%, a probability of detection of 98.9%, a probability of false alarm of 1.56%, a probability of misdetection of 1.09%, and a processing time of 1.24 s.

Roles of Geospatial Technologies in Hydrographic Practice

Any seafarer or mariner that uses the sea knows that navigation without correct charts is impossible and hazardous because nautical charts are the most essential and indispensable tools for vessels to sail safely at sea. For vessels to safely sail at sea, the seas and the oceans ought to be charted and this falls within the domain of hydrography. However, the seas cannot be charted effectively in the absence of the deployment of human resources and adequate tools like satellite and aerial imagery, survey boats and other equipment that will facilitate the hydrographic operations. The acquisition of data and information about the sea depths, nature of sea bed, waterways, navigational hazards and navigational objects among others, basically falls within the sphere of hydrography which is primarily known as survey at sea. The paper offers a review of geospatial technologies in hydrographic practice for enhanced safety of navigation at sea. The review is important to both the mariners, shipping industry and the government in order to explore the potentials provided by Geographic Information System, Remote Sensing, cloud GIS, big data GIS and Global Positioning System to enhance the practice of hydrography. The data and materials used for the review were obtained from literature in the internet and other published works. The paper looked at hydrography as a profession, roles of geospatial technologies in hydrographic practice, benefits of hydrography to national development and finally, the weaknesses of geospatial technologies in hydrographic practice were equally examined.

How Do Avalanche Dogs (and Their Handlers) Cope with Physical Exercise? Heart Rate Changes during Endurance in a Snowy Environment

This study aimed to assess the heart rate (HR) responses of avalanche SAR dogs and handlers under working field conditions. Thirteen SAR units (dogs and handlers) performed an exercise (Endurance) consisting of approximately 5.5 km of rough tracks through deep snow, at an altitude of 1991–2250 m.a.s.l. The exercise was repeated twice for each of the two different tracks. Both handlers and dogs were equipped with a global positioning satellite/heart rate (GPS/HR) system (Polar®). Multivariable models were used to evaluate the effects of environmental (i.e., gradient, altitude, track, and time) and intrinsic (i.e., speed, repetition, and breed) factors on changes from baseline HR (Δ%HR). The dog’s Δ%HR was greater in the flat and uphill compared with downhill, and increased progressively as the speed increased (p < 0.001). Moreover, it rose at altitudes above 2100 m.a.s.l. and peaked after 30 min of the Endurance activity (p < 0.01). These findings indicated that HR monitors could be a valuable tool to contribute to the evaluation of avalanche dogs’ fitness in their real working environment. In contrast, the lack of correlation between the dogs’ and handlers’ HR changes suggests that handlers might not perceive the physical conditions of their dog in real-time. Thus, implementing protocols to monitor avalanche SAR dogs’ fitness using a GPS/HR monitoring system could help handlers to tailor the training and workload and to detect the risk factors for physical distress of working dogs.

PERFORMANCE OF SITE VELOCITY PREDICTION IN SUNDALAND

Global Positioning System (GPS) technique has been extensively implemented in determination of crustal deformation globally. With the ability of providing solution up to milimeter (mm) level, this technique has proven to provide a precise estimate of site velocity that represents the actual motion of tectonic plate over a period. Therefore, this study aims to evaluate the site velocity estimation from GPS-derived daily position of station, respective to the global plate motion model and predicted site velocity via Least-Squares Collocation (LSC) method within the tectonically active region of Sundaland. The findings have indicated that stations with precise velocity estimates were consistent with global plate model and predicted velocity, with velocity residuals of 5 mm – 10 mm. However, stations that were severely impacted by continuous earthquake events such as in Sumatra were believed to be induced by the impact with consistently large velocity residuals up to 37 mm. Following the outcomes, this study has provided an insight on the post-seismic decay period plate motion which are induced by continuous tectonic activities respective to modelled plate motion.

PERFORMANCE OF BURSA-WOLF MODEL FOR DEFORMING REGION: CASE STUDY IN MALAYSIA

Bursa-Wolf model is a common mathematical approach for coordinate transformation practice between two reference frames. For the case of deforming region, the existing reference frame has been experiencing a non-linear shifting over the time due to co-seismic and post seismic occurrences. Imprecise coordinate in the reference frame definition could degrading critical positioning, surveying, and navigation activities. This require a new realization of reference frame and the coordinate transformation linkage is suggested to be developed in relating the new and existing reference frame. This study provides performance of Bursa-Wolf model as coordinate transformation approach for a deforming region that is experiencing non-linear shifting due to the co-seismic and post-seismic events. The Bursa-Wolf were generated from 32 dependent Global Positioning System (GPS) Continuously Operating Reference Stations (CORS) in Malaysia meanwhile another 20 independent neighbouring stations were utilized for assessment purposes. Seven parameters (7p) of Bursa-Wolf were estimated with RMS at ±4.5mm, ±9.2mm and ±2.1mm respectively. The independent stations were classified as internal and external assessment station and the root mean square (RMS) were found at less than 10mm. The internal station has depicted a better RMS in each component which are ±5.1mm, ±6.5mm and ±1.5mm respectively. Meanwhile for external stations RMS in each component are ±6.1mm, ±8.7mm and ±3.5mm respectively. The result shows that Bursa-Wolf model is sufficient to be used as coordinate transformation approach for deforming region.

The content and load of preseason field-based training in a championship-winning professional rugby league team: A case study

This study reports on the content and periodisation of the preseason field-based training for a professional rugby league team. Thirty professional male rugby league players (26 ± 5 years, 180.9 ± 6.5 cm, 94 ± 9 kg) completed an 8-week preseason. Global positioning system devices and heart rate were used to monitor physical and physiological responses of different field-based training components (speed, conditioning, rugby skill and game-based training). Rugby skill training contributed the most to the total distance covered, conditioning was the greatest contributor to high-speed running (>15 km/h) and game-based training provided the greatest high metabolic distance (>20 W/kg) and overall external load. Game-based training provided the greatest time with heart rate ≥80% estimated maximum. The weekly preseason cycle had lower loads on Monday and Thursday whereas Tuesday and Friday produced the highest loads. The preseason described herein adopted a progressive overload comprising a weekly undulating cycle. This study emphasises how skill and games-based training contributes significantly to the overall load of a professional rugby league team's preseason with more traditional conditioning promoting high-speed running load and high metabolic load.

What Kind of Travellers Are Using Carsharing in Beijing? A Study Based on Selective Ensemble Learning

As a new urban travel mode, carsharing is significantly different from private cars, buses and other travel modes. Therefore, clarifying the typical characteristics of carsharing, such as individual users’ attributes, travel environment and travel behaviour, is conducive to accurately grasping the development of carsharing. In this study, a selective ensemble learning model is established to analyse typical travel characteristics of carsharing. Firstly, personal characteristics, environmental characteristics and behavioural characteristics were obtained through integrating order data, global positioning system data and station information. Then, based on a consolidated view of carsharing, different types of carsharing travel characteristics were distinguished using selective ensemble learning. Lastly, all kinds of carsharing travel are described in detail. It was identified through this research that carsharing travel can be divided into four kinds: long distance for leisure and entertainment, medium and short distances for business and commuting, a mixed category of medium and short distances for business and residence, and a mixed category of long distance for business and residence. This study can provide a theoretical reference and practical basis for precise planning and design and the scientific operation of carsharing.