A Black Hole-Aided Deep-Helix Channel Model for DNA

In this article, we present a black-hole-aided deep-helix (bh-dh) channel model to enhance information bound and mitigate a multiple-helix directional issue in Deoxyribonucleic acid (DNA) communications. The recent observations of DNA do not match with Shannon bound due to their multiple-helix directional issue. Hence, we propose a bh-dh channel model in this paper. The proposed bh-dh channel model follows a similar fashion of DNA and enriches the earlier DNA observations as well as achieving a composite like information bound. To do successfully the proposed bh-dh channel model, we first define a black-hole-aided Bernoulli-process and then consider a symmetric bh-dh channel model. After that, the geometric and graphical insight shows the resemblance of the proposed bh-dh channel model in DNA and Galaxy layout. In our exploration, the proposed bh-dh symmetric channel geometrically sketches a deep-pair-ellipse when a deep-pair information bit or digit is distributed in the proposed channel. Furthermore, the proposed channel graphically shapes as a beautiful circulant ring. The ring contains a central-hole, which looks like a central-black-hole of a Galaxy. The coordinates of the inner-ellipses denote a deep-double helix, and the coordinates of the outer-ellipses sketch a deep-parallel strand. Finally, the proposed bh-dh symmetric channel significantly outperforms the traditional binary-symmetric channel and is verified by computer simulations in terms of Shannon entropy and capacity bound.

Enhancement of safety communication model

A fundamental measure of safety communication is the residual error probability, i. e., the probability of undetected errors. For the detection of data errors, typically a Cyclic Redundancy Check (CRC) is applied, and the resulting residual error probability is determined based on the Binary Symmetric Channel (BSC) model. The use of this model had been questioned since several error types cannot be sufficiently described. Especially the increasing introduction of security algorithms into underlying communication layers requires a more adequate channel model. This paper introduces an enhanced model that extends the list of considered data error types by combining the BSC model with a Uniformly Distributed Segments (UDS) model. Although models beyond BSC are applied, the hitherto method of the calculation of the residual error probability can be maintained.

A Black Hole-Aided Deep-Helix Channel Model for DNA

In this article, we present a black-hole-aided deep-helix (bh-dh) channel model to enhance information bound and mitigate a multiple-helix directional issue in Deoxyribonucleic acid (DNA) communications. The recent observations of DNA do not match with Shannon bound due to their multiple-helix directional issue. Hence, we propose a bh-dh channel model in this paper. The proposed bh-dh channel model follows a similar fashion of DNA and enriches the earlier DNA observations as well as achieving a composite like information bound. To do successfully the proposed bh-dh channel model, we first define a black-hole-aided Bernoulli-process and then consider a symmetric bh-dh channel model. After that, the geometric and graphical insight shows the resemblance of the proposed bh-dh channel model in DNA and Galaxy layout. In our exploration, the proposed bh-dh symmetric channel geometrically sketches a deep-pair-ellipse when a deep-pair information bit or digit is distributed in the proposed channel. Furthermore, the proposed channel graphically shapes as a beautiful circulant ring. The ring contains a central-hole, which looks like a central-black-hole of a Galaxy. The coordinates of the inner-ellipses denote a deep-double helix, and the coordinates of the outer-ellipses sketch a deep-parallel strand. Finally, the proposed bh-dh symmetric channel significantly outperforms the traditional binary-symmetric channel and is verified by computer simulations in terms of Shannon entropy and capacity bound.

A Black Hole-Aided Deep-Helix Channel Model for DNA

In this article, we present a black-hole-aided deep-helix (bh-dh) channel model to enhance information bound and mitigate a multiple-helix directional issue in Deoxyribonucleic acid (DNA) communications. The recent observations of DNA do not match with Shannon bound due to their multiple-helix directional issue. Hence, we propose a bh-dh channel model in this paper. The proposed bh-dh channel model follows a similar fashion of DNA and enriches the earlier DNA observations as well as achieving a composite like information bound. To do successfully the proposed bh-dh channel model, we first define a black-hole-aided Bernoulli-process and then consider a symmetric bh-dh channel model. After that, the geometric and graphical insight shows the resemblance of the proposed bh-dh channel model in DNA and Galaxy layout. In our exploration, the proposed bh-dh symmetric channel geometrically sketches a deep-pair-ellipse when a deep-pair information bit or digit is distributed in the proposed channel. Furthermore, the proposed channel graphically shapes as a beautiful circulant ring. The ring contains a central-hole, which looks like a central-black-hole of a Galaxy. The coordinates of the inner-ellipses denote a deep-double helix, and the coordinates of the outer-ellipses sketch a deep-parallel strand. Finally, the proposed bh-dh symmetric channel significantly outperforms the traditional binary-symmetric channel and is verified by computer simulations in terms of Shannon entropy and capacity bound.

Digital Link—A

In a digital link, a transmitter converts bits into signals and a receiver converts the signals it receives into bits. The receiver faces a decision problem that we study in Sect. 7.1. The main tool is Bayes’ Rule. The key notions are maximum a posteriori and maximum likelihood estimates. Transmission systems use codes to reduce the number of bits they need to transmit. Section 7.2 explains the Huffman codes that minimize the expected number of bits needed to transmit symbols; the idea is to use fewer bits for more likely symbols. Section 7.3 explores a commonly used model of a communication channel: the binary symmetric channel. It explains how to calculate the probability of errors. Section 7.4 studies a more complex modulation scheme employed by most smartphones and computers: QAM. Section 7.5 is devoted to a central problem in decision making: how to infer which situation is in force from observations. Does a test reveal the presence of a disease; how to balance the probability of false positive and that of false negative? The main result of that section is the Neyman–Pearson Theorem that the section illustrates with many examples.

Guessing with a Bit of Help

What is the value of just a few bits to a guesser? We study this problem in a setup where Alice wishes to guess an independent and identically distributed (i.i.d.) random vector and can procure a fixed number of k information bits from Bob, who has observed this vector through a memoryless channel. We are interested in the guessing ratio, which we define as the ratio of Alice’s guessing-moments with and without observing Bob’s bits. For the case of a uniform binary vector observed through a binary symmetric channel, we provide two upper bounds on the guessing ratio by analyzing the performance of the dictator (for general k ≥ 1 ) and majority functions (for k = 1 ). We further provide a lower bound via maximum entropy (for general k ≥ 1 ) and a lower bound based on Fourier-analytic/hypercontractivity arguments (for k = 1 ). We then extend our maximum entropy argument to give a lower bound on the guessing ratio for a general channel with a binary uniform input that is expressed using the strong data-processing inequality constant of the reverse channel. We compute this bound for the binary erasure channel and conjecture that greedy dictator functions achieve the optimal guessing ratio.