Detection of OFDM Signals Using Pilot Tones and Applications to Spectrum Sensing for Cognitive Radio Systems

Nowadays there are an increasing number of wireless devices which support wireless networking and the need for higher data rate communication is increasing rabidly. As more and more systems go wireless, approaching technologies will face spectral crowding and existence of wireless devices will be an important issue. Because of the limited bandwidth availability, accepting the request for higher capacity and data rates is a challenging task, demanding advanced technologies that can offers new methods of using the available radio spectrum. Cognitive radio introduces a key solution to the spectral increasing issue by presenting the opportunistic usage of spectrum that is not heavily occupied by licensed users. It is a latest idea in wireless communications systems which objective to have more adaptive and aware communication devices which can make better use of available natural resources. Cognitive radio appears to be an attractive solution to the spectral congestion problem by introducing the notion of opportunistic spectrum use. Cognitive radios can operate as a secondary systems on top of existence system which are called primary (or licensed) systems. In this case, secondary (cognitive) users need to detect the unused spectrum in order to be able to access it. Because of its many advantages, orthogonal frequency division multiplexing (OFDM) has been successfully used in numerous wireless standards and technologies. It\u27s shown that OFDM will play an important role in realizing the cognitive radio concept as well by providing a proven, scalable, and adaptive technology for air interface. Researches show that OFDM technique is considered as a candidate for cognitive radio systems. The objective of this dissertation is to explore detecting of OFDM modulated signals using pilot tones information. Specifically we applying Time-Domain Symbol Cross-Correlation (TDSC) method in the confect of actual 4G wireless standards such as WIMAX and LTE. This detection is only based upon the knowledge of pilot structures without knowledge of received signal so that, it can be performed on every portion of the received signal. The approach induces Cross-Correlation between pilots subcarriers and exploits the deterministic and periodic characteristics of pilot mapping in the time frequency domain

Class Activation Mapping and Uncertainty Estimation in Multi-Organ Segmentation

Deep learning (DL)-based medical imaging and image segmentation algorithms achieve impressive performance on many benchmarks. Yet the efficacy of deep learning methods for future clinical applications may become questionable due to the lack of ability to reason with uncertainty and interpret probable areas of failures in prediction decisions. Therefore, it is desired that such a deep learning model for segmentation classification is able to reliably predict its confidence measure and map back to the original imaging cases to interpret the prediction decisions. In this work, uncertainty estimation for multiorgan segmentation task is evaluated to interpret the predictive modeling in DL solutions. We use the state-of-the-art nnU-Net to perform segmentation of 15 abdominal organs (spleen, right kidney, left kidney, gallbladder, esophagus, liver, stomach, aorta, inferior vena cava, pancreas, right adrenal gland, left adrenal gland, duodenum, bladder, prostate/uterus) using 200 patient cases for the Multimodality Abdominal Multi-Organ Segmentation Challenge 2022. Further, the softmax probabilities from different variants of nnU-Net are used to compute the knowledge uncertainty in the deep learning framework. Knowledge uncertainty from ensemble of DL models is utilized to quantify and visualize class activation map for two example segmented organs. The preliminary result of our model shows that class activation maps may be used to interpret the prediction decision made by the DL model used in this study