N. M. Abdul Latiff

About

N. M. Abdul Latiff is a leading researcher in wireless communications and indoor positioning systems, with a focus on ultra-wideband (UWB) technology and deep learning applications. Their most cited work, "Accurate Multiclass NLOS Channels Identification in UWB Indoor Positioning System-Based Deep Neural Network" (2024), addresses the critical challenge of distinguishing between line-of-sight (LOS) and non-line-of-sight (NLOS) propagation channels—a key factor for precise distance measurement in complex indoor environments. This contribution has garnered 4 citations, highlighting its relevance to advancing localization accuracy in dynamic settings. Dr. Abdul Latiff’s research spans signal processing, machine learning, and sensor networks, with notable achievements in developing robust algorithms for real-world deployment. Their work is instrumental for students and researchers exploring autonomous navigation, smart infrastructure, and IoT applications, offering practical solutions to mitigate multipath interference and enhance system reliability. Through innovative integration of deep neural networks, Dr. Abdul Latiff continues to shape the future of indoor positioning technologies.

Research Focus

Key Achievements

Top Papers

1. 1
   Accurate Multiclass NLOS Channels Identification in UWB Indoor Positioning System-Based Deep Neural Network

   4 citations · 2024

Key Collaborators

Contact & Links