Analyzing and Comparing the Performance of National Biometric eID Card in Heavy Cryptographic Applications

Today, we are witnessing increased demand for more speed and capacity in the Internet, and more processing power and storage in every end user device. Demand for greater performance is present in every system. Electronic devices and their hosted applications need to be fast, but not to lose their main security features. Authentication and encryption are the main processes in the security aspect, and are required for a secure communication. These processes can be executed in different devices, among them PCs, microprocessors, microcontrollers, biometric cards or mobile devices. Biometric identity cards are becoming increasingly popular, challenging traditional PC devices. This paper compares two processing systems, the efficiency of encryption and signatures on the data executed in national identity biometric card versus PC, known also as the match-on-card versus the match-off-card. It considers how different parameters impact the process and the role they play on the overall process. The results, executed with a predefined set of test vectors, determine which processing system to use in a certain situation. Final conclusions and recommendations are given taking into consideration the efficiency and security of the data

EFFECT OF PROHEXADIONE-CALCIUM (REGALIS) ON DIFFERENTIATION OF FLOWER BUDS AND FRUIT SET IN PEAR VAR. PASSE CRASSANE

Summer pruning and Prohexadione-Ca were the strategies that produced the next shortest shoot length; however, summer pruning registered the lowest return bloom and accumulated yield. Prohexadione-Ca did not have any significant negative effect on either return bloom or yield. Prohexadione-calcium (Regalis) as a shoot growth retardant that inhibits gibberellins biosynthesis has been used to improve the differentiation of flower buds and fruit set in Pear var. Passe Crassane. The aim of this study was to evaluate the efficacy of applying Regalis by foliar applications in Passé Crassane pear orchards to reduce tree vigor or shoot growth and to control the alternate fruit production. Three different dosages were tested: 50ppm, 100ppm 150ppm. Regalis treatments ranging from 50 to 150 ppm were compared with control, without treatments. The first treatment was applied 7days after petal fall and the others every 10 days after the first treatment. Three different dosages were tested: 50ppm, 100ppm 150ppm. Regalis treatments ranging from 50 to 150 ppm were compared with control, without treatments. The first treatment was applied 7days after petal fall and the others every 10 days after the first treatment. The data was collected at the full bloom time and two weeks, at the time of fruit set. The flower number and the fruit number was significantly difference after the treatment of 150 ppm and 100ppm than the application of 50ppm and without treatment. The average fruit weight was greater than other treatments and than non treated-trees. Variability in soluble solids concentration (SSC,0Brix) was not significantly different between different treatment (50ppm, 100ppm and 150ppm)

Maneuver Prediction Using Traffic Scene Graphs via Graph Neural Networks and Recurrent Neural Networks

The driving process involves many layers of planning and navigation, in order to enable tractable solutions for the otherwise highly complex problem of autonomous driving. One such layer involves an inherent discrete layer of decision-making corresponding to tactical maneuvers. Inspired by this, the focus of this work is predicting high-level maneuvers for the ego-vehicle. As maneuver prediction is fundamentally feedback-structured, it requires modeling techniques that take into consideration the interaction awareness of the traffic agents involved. This work addresses this challenge by modeling the traffic scenario as an interaction graph and proposing three deep learning architectures for interaction-aware tactical maneuver prediction of the ego-vehicle. These architectures are based on graph neural networks (GNNs) for extracting spatial features among traffic agents and recurrent neural networks (RNNs) for extracting dynamic motion patterns of surrounding agents. These proposed architectures have been trained and evaluated using BLVD dataset. Moreover, this dataset is expanded using data augmentation, data oversampling and data undersampling approaches, to strengthen model's resilience and enhance the learning process. Lastly, we compare proposed learning architectures for ego-vehicle maneuver prediction in various driving circumstances with various numbers of surrounding traffic agents in order to effectively verify the proposed architectures