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Visit Stack ExchangeShannon-Nyquist sampling theorem proposes a sufficient condition for information theoretic bounds on communication. Sampling theory is worked around examples where the incoming signal has a compact/random representation. Recent advances in sampling show that this abstraction does perhaps comes with a price - that the sorts of things we are interested in measuring generally havesparse representations so that these bounds are not tight. Additionally, information can be encoded in a much denser way than originally thought.
Shannon-Nyquist sampling theorem proposes a sufficient condition for information theoretic bounds on communication. Sampling theory is worked around examples where the incoming signal has a compact/random representation. Recent advances in sampling show that this abstraction does perhaps comes with a price - that the sorts of things we are interested in measuring generally havesparse representations so that these bounds are not tight. Additionally, information can be encoded in a much denser way than originally thought.
Shannon-Nyquist sampling theorem proposes a sufficient condition for information theoretic bounds on communication. Sampling theory is worked around examples where the incoming signal has a compact/random representation. Recent advances in sampling show that this abstraction does perhaps comes with a price - that the sorts of things we are interested in measuring generally havesparse representations so that these bounds are not tight. Additionally, information can be encoded in a much denser way than originally thought.
Shannon-Nyquist sampling theorem proposes a sufficient condition for information theoretic bounds on communication. Sampling theory is worked around examples where the incoming signal has a compact/random representation. Recent advances in sampling show that this abstraction does perhaps comes with a price - that the sorts of things we are interested in measuringhave dogenerally havesparse representations so that these bounds are not tight. Additionally, information can be encoded in a much denser way than originally thought.
Shannon-Nyquist sampling theorem proposes a sufficient condition for information theoretic bounds on communication. Sampling theory is worked around examples where the incoming signal has a compact/random representation. Recent advances in sampling show that this abstraction does perhaps comes with a price - that the sorts of things we are interested in measuringhave do havesparse representations. Additionally, information can be encoded in a much denser way than originally thought.
Shannon-Nyquist sampling theorem proposes a sufficient condition for information theoretic bounds on communication. Sampling theory is worked around examples where the incoming signal has a compact/random representation. Recent advances in sampling show that this abstraction does perhaps comes with a price - that the sorts of things we are interested in measuringgenerally havesparse representations so that these bounds are not tight. Additionally, information can be encoded in a much denser way than originally thought.
Shannon-Nyquist sampling theorem proposes a sufficient condition for information theoretic bounds on communication. Sampling theory is worked around examples where the incoming signal has a compact/random representation. Recent advances in sampling show that this abstraction does perhaps comes with a price - that the sorts of things we are interested in measuring have do havesparse representations. Additionally, information can be encoded in a much denser way than originally thought.
