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Full-Text Articles in Engineering
A Deep Learning Approach For Final Grasping State Determination From Motion Trajectory Of A Prosthetic Hand, Cihan Uyanik, Syed F. Hussaini, Erdem Erdemir, Erkan Kaplanoglu, Ali Sekmen
A Deep Learning Approach For Final Grasping State Determination From Motion Trajectory Of A Prosthetic Hand, Cihan Uyanik, Syed F. Hussaini, Erdem Erdemir, Erkan Kaplanoglu, Ali Sekmen
Computer Science Faculty Research
Deep Learning has been gaining popularity due to its numerous implementations and continuous growing capabilities, including the prosthetics industry which has trend of evaluation towards the smart operational decision. The aim of this study is to develop a reliable decision-making system for prosthetic hands which is responsible to grasp or point an object located in the interaction area. In order to achieve this goal, we have exploited the measurements taken from a low-cost inertial measurement unit (IMU) and proposed a convolutional neural network-based decision-making system, which utilizes 9 distinct measurement variables as input, 3 axis accelerometer, 3 axis gyroscope and …
A Deep Learning Approach For Motion Segment Estimation For Pipe Leak Detection Robot, Cihan Uyanik, Erdem Erdemir, Erkan Kaplanoglu, Ali Sekmen
A Deep Learning Approach For Motion Segment Estimation For Pipe Leak Detection Robot, Cihan Uyanik, Erdem Erdemir, Erkan Kaplanoglu, Ali Sekmen
Computer Science Faculty Research
The trajectory motion of a robot can be a valuable information to estimate the localization of an autonomous robotic system, especially in a very dynamic but structurally-known environments like water pipes where the sensor readings are not reliable. The main focus of this research is to estimate the location of meso-scale robots using a deep-learning-based motion trajectory segment detection system from recorded sensory measurements while the robot travels through a pipe system. The idea is based on the classification of the motion measurements, acquired by inertial measurement unit (IMU), by exploiting the deep learning approach. Proposed idea and utilized methodology …
Implementing A Lightweight Schmidt-Samoa Cryptosystem (Ssc) For Sensory Communications, Qasem Abu Al-Haija, Ibrahim Marouf, Mohammad M. Asad, Kamal Al Nasr
Implementing A Lightweight Schmidt-Samoa Cryptosystem (Ssc) For Sensory Communications, Qasem Abu Al-Haija, Ibrahim Marouf, Mohammad M. Asad, Kamal Al Nasr
Computer Science Faculty Research
One of the remarkable issues that face wireless sensor networks (WSNs) nowadays is security. WSNs should provide a way to transfer data securely particularly when employed for mission-critical purposes. In this paper, we propose an enhanced architecture and implementation for 128-bit Schmidt-Samoa cryptosystem (SSC) to secure the data communication for wireless sensor networks (WSN) against external attacks. The proposed SSC cryptosystem has been efficiently implemented and verified using FPGA modules by exploiting the maximum allowable parallelism of the SSC internal operations. To verify the proposed SSC implementation, we have synthesized our VHDL coding using Quartus II CAD tool targeting the …