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Browsing by Author "Abdullahi Daniyan"

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Now showing 1 - 11 of 11
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    An improved resampling approach for particle filters in tracking
    (IEEE, 2017-11-06) Yu Gong; Sangarapillai Lambotharan; Abdullahi Daniyan
    Resampling is an essential step in particle filtering (PF) methods in order to avoid degeneracy. Systematic resampling is one of a number of resampling techniques commonly used due to some of its desirable properties such as ease of implementation and low computational complexity. However, it has a tendency of resampling very low weight particles especially when a large number of resampled particles are required which may affect state estimation. In this paper, we propose an improved version of the systematic resampling technique which addresses this problem and demonstrate performance improvement.
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    Bayesian data driven modelling of kinetochore dynamics: space-time organisation of the human metaphase plate
    (PLOS Biology, 2025-01-23) Constandina Koki; Alessio V Inchingolo; Abdullahi Daniyan; Enyu Li; Andrew D. McAinsh; Nigel J Burroughs
    Mitosis is a complex self-organising process that achieves high fidelity separation of duplicated chromosomes into two daughter cells through capture and alignment of chromosomes to the spindle mid-plane. Chromosome movements are driven by kinetochores, multi-protein machines that attach chromosomes to microtubules (MTs), both controlling and generating directional forces. Using lattice light sheet microscopy imaging and automated near-complete tracking of kinetochores at fine spatio-temporal resolution, we produce a detailed atlas of kinetochore metaphase-anaphase dynamics in untransformed human cells (RPE1). We fitted 18 biophysical models of kinetochore metaphase-anaphase dynamics to experimental data using Bayesian inference, and determined support for the models with model selection methods, demonstrating substantial sister force asymmetry and time dependence of the mechanical parameters. Our analysis shows that K-fiber pulling and pushing strengths are inversely correlated and that there is substantial spatial organisation of KT dynamic properties both within, and transverse to the metaphase plate. Further, K-fiber forces are tuned over the last 5 mins of metaphase towards a set point, which we refer to as the anaphase ready state.
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    Bayesian Multiple Extended Target Tracking Using Labeled Random Finite Sets and Splines
    (IEEE, 2018-10-04) Abdullahi Daniyan; Sangarapillai Lambotharan; Anastasios Deligiannis; Yu Gong; Wen-Hua Chen
    In this paper, we propose a technique for the joint tracking and labeling of multiple extended targets. To achieve multiple extended target tracking using this technique, models for the target measurement rate, kinematic component, and target extension are defined and jointly propagated in time under the generalized labeled multi-Bernoulli filter framework. In particular, we developed a Poisson mixture variational Bayesian model to simultaneously estimate the measurement rate of multiple extended targets and extended target extension was modeled using B-splines. We evaluated our proposed method with various performance metrics. Results demonstrate the effectiveness of our approach.
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    Data association using game theory for multi-target tracking in passive bistatic radar
    (IEEE, 2017-06-20) Yu Gong; Abdullahi Daniyan; Abdulrazaq Aldowesh; Sangarapillai Lambotharan
    We investigate a game theoretic data association technique for multi-target tracking (MTT) with varying number of targets in a real passive bi-static radar (PBR) environment. The radar measurements were obtained through a PBR developed using National Instrument (NI) Universal Software Radio Peripheral (USRP). We considered the problem of associating target state-estimates-to-tracks for varying number of targets. We use the sequential Monte Carlo probability hypothesis density (SMC-PHD) filter to perform the multi-target tracking in order to obtain the target state estimates and model the interaction between target tracks as a game. Experimental results using this real radar data demonstrate effectiveness of the game theoretic data association for multiple target tracking.
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    Enhanced Kinetochore Detection During Mitotic Human Cell Division using CFAR
    (IEEE, 2024-10-11) Abdullahi Daniyan; Alessio V. Inchingolo; Andrew McAinsh; Nigel Burroughs
    In this paper, we present an innovative application of the Constant False Alarm Rate (CFAR) algorithm, traditionally utilized in radar signal processing, to enhance the accuracy and reliability of kinetochore (KT) tracking in live-cell lattice light-sheet microscopy (LLSM) imaging of human cells during the mitotic phase of cell division. Fluorescently labelled KTs appear as spots in diffraction-limited light microscopy. Traditional KT detection methods, such as adaptive thresholding, often struggle with the dynamic and noisy backgrounds of cells, leading to less efficient KT identification. By adapting the CFAR algorithm to the specific challenges of KT detection in 3D, we present a method that offers improved precision and stability in detecting KTs across different stages of mitosis. The performance of the CFAR-KT method was rigorously compared to the adaptive thresholding approach across a cohort of 31 cells, with results highlighting CFAR-KT’s enhanced detection efficiency. Despite a slightly lower mean detection count compared to the adaptive method, the CFAR-KT method achieved lower false positives and a higher success rate in tracking KTs over the duration of the cell division process, underscoring its effectiveness in capturing the dynamics of KTs.
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    Game theoretic data association for multi-target tracking with varying number of targets
    (IEEE, 2016-06-23) Abdullahi Daniyan; Yu Gong; Sangarapillai Lambotharan
    We investigate a game theoretic data association technique for multi-target tracking (MTT) with varying number of targets. The problem of target state-estimate-to-track data association has been considered. We use the SMC-PHD filter to handle the MTT aspect and obtain target state estimates. We model the interaction between target tracks as a game by considering them as players and the set of target state estimates as strategies. Utility functions for the players are defined and a regret-based learning algorithm with a forgetting factor is used to find the equilibrium of the game. Simulation results are presented to demonstrate the performance of the proposed technique.
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    Kalman-Gain Aided Particle PHD Filter for Multitarget Tracking
    (IEEE, 2017-04-05) Abdullahi Daniyan; Yu Gong; Sangarapillai Lambotharan; Pengming Feng; Jonathon Chambers
    We propose an efficient sequential Monte Carlo probability hypothesis density (PHD) filter which employs the Kalman-gain approach during weight update to correct predicted particle states by minimizing the mean square error between the estimated measurement and the actual measurement received at a given time in order to arrive at a more accurate posterior. This technique identifies and selects those particles belonging to a particular target from a given PHD for state correction during weight computation. Besides the improved tracking accuracy, fewer particles are required in the proposed approach. Simulation results confirm the improved tracking performance when evaluated with different measures.
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    Performance Analysis of Sequential Monte Carlo MCMC and PHD Filters on Multi-target Tracking in Video
    (IEEE, 2014-10-21) Abdullahi Daniyan
    The Bayesian approach to target tracking has proven to be successful in the tracking of multiple targets in various application contexts. This paper applies sequential Monte Carlo (SMC) filtering techniques such as the Markov Chain Monte Carlo particle filter (MCMC PF) and the SMC probability hypothesis density (PHD) filter as suboptimal Bayesian solutions to multi-target tracking (MTT) in video. The MCMC PF by virtue of its information-centric property, can automatically explore the posterior distribution at each sampling step making it possible to track multiple targets. In doing so, it propagates the full multi-target posterior. The SMC PHD filter however propagates only the first order moment of the multi-target posterior density thereby making it computationally less intensive. A comparison of both filters was carried out in tracking multiple human targets in a video scene demonstrating superior performance by the SMC PHD filter in a realistic scenario. The SMC PHD filter was seen to have higher performance than the MCMC PF in terms of the number of particles, the processing speed, and the tracking performance for multiple targets.
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    Probability hypothesis density filter for parameter estimation of multiple hazardous sources
    (Elsevier, 2024-08-30) Abdullahi Daniyan; Cunjia Liu; Wen-Hua Chen
    This study introduces an advanced methodology for estimating the source term of multiple, variable-number biochemical hazard releases, where the exact count of sources is not predetermined. Focusing on environments monitored via a network of sensors, we tackle this challenge through a multi-source Bayesian filtering paradigm, employing the theory of random finite sets (RFS). Our novel approach leverages a modified particle filter-based probability hypothesis density (PHD) filter within the RFS framework, enabling simultaneous estimation of critical source characteristics (such as location, emission rate, and effective release height) and the quantification of source numbers. This method not only accurately estimates pertinent source parameters but is also adept at identifying the emergence of new sources and the cessation of existing ones within the monitored area. The efficacy of our approach is validated through extensive simulations, which mimic a range of scenarios with varying and unknown source counts, highlighting the proposed method’s robustness and precision.
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    Robust Multi-Target Tracking with a Kalman-Gain CPHD Filter: Simulation and Experimental Validation
    (2025-04-19) Abdullahi Daniyan
    We introduce a novel cardinalized implementation of the Kalman-gain-aided particle probability hypothesis density (KG-SMC-PHD) filter, extending it to form the Kalman-Gain Particle Cardinalized Probability Hypothesis Density (KG- SMC-CPHD) filter. This new approach significantly enhances multi-target tracking by combining the particle-based state correction mechanism with the propagation of both the PHD and target cardinality distribution. Unlike conventional particle filters that require a large number of particles for acceptable performance, our method intelligently corrects selected particles during the weight update stage, resulting in a more accurate posterior with substantially fewer particles. Through comprehensive evaluations on both simulated and experimental datasets, the KG-SMC-CPHD filter demonstrates superior robustness and accuracy, particularly in high-clutter environments and nonlinear target dynamics. Notably, it offers improved cardinality estimation and maintains the computational efficiency and performance advantages of its predecessor, the KG-SMC-PHD filter, making it a powerful tool for advanced multi-target tracking applications.
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    Secrecy Rate Optimizations for MIMO Communication Radar
    (IEEE, 2018-03-28) Anastasios Deligiannis; Abdullahi Daniyan; Sangarapillai Lambotharan; Jonathon A. Chambers
    In this paper, we investigate transmit beampattern optimization techniques for a multiple-input multiple-output radar in the presence of a legitimate communications receiver and an eavesdropping target. The primary objectives of the radar are to satisfy a certain target-detection criterion and to simultaneously communicate safely with a legitimate receiver by maximizing the secrecy rate against the eavesdropping target. Therefore, we consider three optimization problems, namely target return signal-to-interference-plus-noise ratio maximization, secrecy rate maximization, and transmit power minimization. However, these problems are nonconvex due to the nonconcavity of the secrecy rate function, which appears in all three optimizations either as the objective function or as a constraint. To solve this issue, we use Taylor series approximation of the nonconvex elements through an iterative algorithm, which recasts the problem as a convex problem. Two transmit covariance matrices are designed to detect the target and convey the information safely to the communication receiver. Simulation results are presented to validate the efficiency of the aforementioned optimizations.

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