Search Results for "ndoc-enhanced"

This work is described in: Orzada S, Fielder TM, Quick HH, Ladd ME, "A local SAR compression algorithm with improved compression, speed and flexibility", https://doi.org/10.1002/mrm.28739. This new algorithm is an iterative expansion of the exisisting local SAR compression algorithm for MRI proposed by Lee et al. ( https://doi.org/10.1002/mrm.23140) The number of VOPs is approximately halved with the new algorithm, while at the same time the compression time is reduced with...

**** This work is outdated! For better compression and speed use these: ***** https://sourceforge.net/projects/enhanced-sar-compression/ Virtual Observation Points SAR compression. Contains multiple Algorithms: Compression according to Eichfelder et al. and Lee et al. The Lee algorithm is enhanced by including overestimation control to reduce maximum relative overestimation. All algorithms are implemented in Matlab and were optimized for enhanced speed. ...

S³FD (Single Shot Scale-invariant Face Detector) is a real-time face detection framework designed to handle faces of various sizes with high accuracy using a single deep neural network. Developed by Shifeng Zhang, S³FD introduces a scale-compensation anchor matching strategy and enhanced detection architecture that makes it especially effective for detecting small faces—a long-standing challenge in face detection research. The project builds upon the SSD framework in Caffe, with modifications tailored for face detection tasks. It includes training scripts, evaluation code, and pre-trained models that achieve strong results on popular benchmarks such as AFW, PASCAL Face, FDDB, and WIDER FACE. ...

...The repository includes MATLAB-based training and testing scripts, along with pre-trained models and pre-computed region proposals for reproducibility. Multiple testing configurations are available, including multi-frame input and enhanced versions that refine tracking boxes and integrate detection confidence across frames.

While wireless sensor networks are increasingly equipped to handle more complex functions, in-network processing may require these battery powered sensors to judiciously use their constrained energy to prolong the effective network life time especially in a heterogeneous settings. Clustered techniques have since been employed to optimize energy consumption in this energy constrained wireless sensor networks. We propose an Enhanced-SEP clustering algorithm in a three-tier node scenario to prolong the effective network life-time. Simulation results shows that the Enhanced-SEP protocol achieves better performance in this respect, compared to other existing clustering algorithms such as LEACH and SEP in both heterogeneous and homogenous environments.