Distributed and Parallel simulation methods for pest control and crop monitoring with IoT assistance

In today's world, Agriculture moves toward technological advancements termed as Modern Agriculture. The usage of multiple pest control and crop management frameworks plays a significant role in crop monitoring. The existing framework faces challenges. It uses a single core Graphical Processing Unit (GPU) to handle the various sensors attached for pest control and crop monitoring system. Therefore, Distributed and parallel simulation Framework (DPSF) with the Internet of Things (IoT) Assistance is proposed for pest control and crop monitoring system. It reduces the stress on a single GPU and shares the process equally and simultaneously to the available GPUs and views data on the dashboard without crashing. The Technique will reduce the execution time. DPSF uses multi-threading concept, in which each core in GPU shares tasks to other cores. This process is done in four layers; each layer is shared to handle the specific task– Crop management, Pest detection and control, Output functions and input functions. The data is processed simultaneously and managed in an optimised and controlled fashion. Simulation analysis shows that the DPSF with IoT Assistance can classify and control pest effectively with IoT assistance

RGBD Video Based Human Hand Trajectory Tracking and Gesture Recognition System

The task of human hand trajectory tracking and gesture trajectory recognition based on synchronized color and depth video is considered. Toward this end, in the facet of hand tracking, a joint observation model with the hand cues of skin saliency, motion and depth is integrated into particle filter in order to move particles to local peak in the likelihood. The proposed hand tracking method, namely, salient skin, motion, and depth based particle filter (SSMD-PF), is capable of improving the tracking accuracy considerably, in the context of the signer performing the gesture toward the camera device and in front of moving, cluttered backgrounds. In the facet of gesture recognition, a shape-order context descriptor on the basis of shape context is introduced, which can describe the gesture in spatiotemporal domain. The efficient shape-order context descriptor can reveal the shape relationship and embed gesture sequence order information into descriptor. Moreover, the shape-order context leads to a robust score for gesture invariant. Our approach is complemented with experimental results on the settings of the challenging hand-signed digits datasets and American sign language dataset, which corroborate the performance of the novel techniques

Dreidimensionale Intervallinterpolation mit algebraischen Polynomen

SIGLECopy held by FIZ Karlsruhe; available from UB/TIB Hannover / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman