Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The spatial distribution of sensing nodes plays a crucial role in signal sampling and reconstruction via wireless sensor networks. Although homogeneous Poisson point process (PPP) model is widely adopted for its analytical tractability, it cannot be considered a proper model for all experiencing nodes. The Ginibre point process (GPP) is a class of determinantal point processes that has been recently proposed for wireless networks with repulsiveness beten nodes. A modified GPP can be considered an intermediate class beten the PPP (fully random) and the GPP (relatively regular) that can be derived as limiting cases.   analyze sampling and reconstruction of finite-energy signals in ℝ d  when samples are gathered in space according to a determinantal point process whose second order product density function generalizes to ℝ d  that of a modified GPP in ℝ 2 .  derive closed form expressions for sampled signal energy spectral density (ESD) and for signal reconstruction mean square error (MSE). Results known in the literature are shown to be sub-cases of the proposed framework. The proposed analysis is also able to ansr to the fundamental question: does the higher regularity of GPP also imply an higher signal reconstruction accuracy, according to the intuition? Theoretical results are illustrated through a simple case study.
['Flavio Zabini', 'Andrea Conti']
I am writing a research paper on a idea Ginibre sampling and signal reconstruction
[{"from": "human", "value": "I am writing a research paper on a idea Ginibre sampling and signal reconstruction"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The spatial distribution of sensing nodes plays a crucial role in signal sampling and reconstruction via wireless sensor networks. Although homogeneous Poisson point process (PPP) model is widely adopted for its analytical tractability, it cannot be considered a proper model for all experiencing nodes. The Ginibre point process (GPP) is a class of determinantal point processes that has been recently proposed for wireless networks with repulsiveness beten nodes. A modified GPP can be considered an intermediate class beten the PPP (fully random) and the GPP (relatively regular) that can be derived as limiting cases.   analyze sampling and reconstruction of finite-energy signals in \u211d d  when samples are gathered in space according to a determinantal point process whose second order product density function generalizes to \u211d d  that of a modified GPP in \u211d 2 .  derive closed form expressions for sampled signal energy spectral density (ESD) and for signal reconstruction mean square error (MSE). Results known in the literature are shown to be sub-cases of the proposed framework. The proposed analysis is also able to ansr to the fundamental question: does the higher regularity of GPP also imply an higher signal reconstruction accuracy, according to the intuition? Theoretical results are illustrated through a simple case study."}]