We consider a non-Bayesian estimation problem preceded by a detection step. In this framework, the observation model follows one of two hypotheses: an informative hypothesis, where the data contain information about the parameters of interest, and a non-informative hypothesis, where no such information is present. Estimation is only performed if the detection stage declares the informative hypothesis. This setup is common in various signal processing applications, such as communication systems, where decoding occurs only upon message detection, and radar systems, where direction-of-arrival estimation follows target identification. Since the estimation errors are well-defined only when the estimation process occurs, the standard mean-squared error (MSE) criterion is inappropriate for this post-detection estimation problem. To address this, we leverage estimation theory under misspecified models. First, we present the post-selection pseudo-true parameter. Then, we introduce the misspecified post-selection MSE (MPSMSE) criterion, which quantifies the MSE between the estimator and the post-selection pseudo-true parameter conditioning on the event of detecting the informative hypothesis by the considered detector. We developed a new Cramér-Rao-type bound on the MPSMSE that incorporates the knowledge on the detection stage with potential model misspecification. This bound is based on the well-known misspecified Cramér-Rao bound (MCRB), and it provides a robust and accurate benchmark on the MPSMSE for analyzing the performance of practical estimators. Simulations confirm that the proposed bound is a valid lower bound on the MPSMSE of practical estimators and is tighter than existing alternatives.
This paper introduces a framework for spectral analysis of weak signals in the presence of strong interference that can potentially exceed the dynamic range of the analog-to-digital converter (ADC) employed for data acquisition. The framework combines modulo sampling to avoid ADC saturation with interference cancellation (IC) and adaptive spectral estimators such as amplitude and phase estimation of a sinusoid (APES) and Capon for high-resolution spectral analysis. Numerical simulations validate the proposed approach’s ability to accurately recover weak signals’ spectrum with significantly lower mean-squared error (MSE) in amplitude estimation compared to conventional methods in the considered scenarios. Notably, it is shown that APES and Capon are able to effectively reject the out-of-range interference and reveal weak spectral content, without requiring the interference cancellation step.
This paper discusses a method for classification of breast cancer imaging data through the application of an adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) for hyperparameter optimization of the ANFIS system. A robust parameter tuning method is used to select the optimal configuration for the ANFIS and PSO components without expert knowledge of the dataset. Using these methods, high classification accuracies can be achieved for both the original and diagnostic versions of the Wisconsin Breast Cancer Dataset. These results demonstrate the flexibility and potential of a joint ANFIS-PSO system for automated diagnosis while retaining system simplicity and linguistic interpretability to support clinical decision-making.
Use of millimeter waves (mmWave) and other high frequency bands are expected to be a crucial part of 6G networks. Performance analysis of mmWave communication scenarios and modeling of various protocols in those situations have demonstrated the burst-error nature of mmWave channels. With the increasing adoption of such communication standards operating in lossy conditions, advanced communication techniques have started to be explored within the context of bursty channels. Network coding is one such widely utilized technology for achieving high-throughput and reliable communication over lossy channels. This work focuses on modeling the TCP and TCP/NC protocols within a burst-error scenario, mainly focusing on the presence of time-out events as sources of window size reduction. We provide a detailed analysis of throughput and congestion control, along with their formulation and simulated results. We show that, even with the presence of time-out events, TCP/NC outperforms standard TCP by over an order of magnitude.
This article presents a comprehensive review of recent advances in intrinsic Cramer-Rao bounds (ICRBs) for Lie groups (LGs), which play a pivotal role in addressing estimation problems involving parameters and/or observations constrained by geometric structures. The review encompasses both deterministic and Bayesian frameworks, with a detailed examination of their formulation, derivation, and theoretical foundations. Furthermore, we underscore significant theoretical contributions and extend the discussion to practical estimation challenges, offering insights into their applicability. Emphasis is placed on methodologies for validating these bounds, providing a robust framework for performance evaluation across a variety of estimation problems in engineering and applied sciences.
