Boundary element method (BEM) is one of the numerical methods which is commonly used to solve the forward problem (FP) of electro-magnetic source imaging with realistic head geometries. Application of BEM generates large systems of linear equations with dense matrices. Generation and solution of these matrix equations are time and memory consuming. This study presents a relatively cheap and effective solution for parallel implementation of the BEM to reduce the processing times to clinically acceptable values. This is achieved using a parallel cluster of personal computers on a local area network. We used eight workstations and implemented a parallel version of the accelerated BEM approach that distributes the computation and the BEM matrix efficiently to the processors. The performance of the solver is evaluated in terms of the CPU operations and memory usage for different number of processors. Once the transfer matrix is computed, for a 12,294 node mesh, a single FP solution takes 676 ms on a single processor and 72 ms on eight processors. It was observed that workstation clusters are cost effective tools for solving the complex BEM models in a clinically acceptable time.

Hard example mining methods generally improve the performance of the object detectors, which suffer from imbalanced training sets. In this work, two existing hard example mining approaches (LRM and focal loss, FL) are adapted and combined in a state-of-the-art real-time object detector, YOLOv5. The effectiveness of the proposed approach for improving the performance on hard examples is extensively evaluated. The proposed method increases mAP by 3% compared to using the original loss function and around 1-2% compared to using the hard-mining methods (LRM or FL) individually on 2021 Anti-UAV Challenge Dataset.

Team Cappadocia is participating in the Multi Autonomous Ground-robotic International Challenge (MAGIC 2010), with a set of fully autonomous ground vehicles that can execute an Intelligence, Surveillance and Reconnaissance (ISR) mission in a dynamic urban environment. The design incorporates dynamic mission planning with automatic task assignment and optimized route generation, automated object of interest detection and tracking, decision making, automatic Unmanned Air Vehicle (UAV) image processing, automated local and global map and information integration, and a novel system architecture with modules compliant with the Joint Architecture for Unmanned Systems (JAUS). The cooperators in the team formed under the leadership Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number.Team Cappadocia is participating in the Multi Autonomous Ground-robotic International Challenge (MAGIC 2010), with a set of fully autonomous ground vehicles that can execute an Intelligence, Surveillance and Reconnaissance (ISR) mission in a dynamic urban environment. The design incorporates dynamic mission planning with automatic task assignment and optimized route generation, automated object of interest detection and tracking, decision making, automatic Unmanned Air Vehicle (UAV) image processing, automated local and global map and information integration, and a novel system architecture with modules compliant with the Joint Architecture for Unmanned Systems (JAUS). The cooperators in the team formed under the leadership (Robotics Lab) and the Bogazici University (AI Lab) of Turkey. During the period of the research, Team Cappadocia has put together and validated an UGV system in order to compete in the MAGIC 2010 challenge. A full technical paper, whose abstract has been reproduced here, details the implementation and many of the significant experimental results of the system; the paper has been published for the Land Warfare Conference

Data annotation is a time-consuming, labor-intensive step in supervised learning, mainly for detection and classification. Most of the time, human effort for annotation is required to obtain an accurately labeled dataset, which is time-consuming and sometimes impossible, especially for large datasets. Most of the novel methods use various networks to annotate the data. However, numerous hand-labeled data are still required for those methods. In order to solve this problem, we propose a method to make the process as human-independent as possible while preserving the annotation performance. The proposed method is applicable to datasets, for which the majority of the frames/images contain a single object (or a known number, "n", of objects). The method starts with an initial annotation network that is trained with a small amount of labeled data, %10 of the total training set, and then it continues iteratively. We use the annotation network to select the subset of the training set that is to be hand-labeled for the next iteration. This way, examples that are more likely to improve the annotation network can be selected. The total number of necessary hand-labeled images is dependent on the specific problem. We observed that when the proposed approach was used rather than annotating all the images, manually annotating approximately %25 of the dataset was sufficient. This percentage can vary according to the complexity and the type of the annotation network, as well as the dataset content. Our method can be used with existing (semi) automatic annotation tools.

Hard example mining methods generally improve the performance of the object detectors, which suffer from imbalanced training sets. In this work, two existing hard example mining approaches (LRM and focal loss, FL) are adapted and combined in a state-of-the-art real-time object detector, YOLOv5. The effectiveness of the proposed approach for improving the performance on hard examples is extensively evaluated. The proposed method increases mAP by 3% compared to using the original loss function and around 1-2% compared to using the hard-mining methods (LRM or FL) individually on 2021 Anti-UAV Challenge Dataset.