This paper studies detection algorithms for diffusion-based molecular communication systems, where molecules freely diffuse as information carrier from a transmitter to a receiver in a fluid medium. The main limitations are strong intersymbol interference due to the random propagation of the molecules, and the low-energy/low-complexity assumption regarding future implementations in so-called nanomachines. In this contribution, a new biologically inspired detection algorithm suitable for binary signaling, named adaptive threshold detection, is proposed, which deals with these limitations. The proposed detector is of low complexity, does not require explicit channel knowledge, and seems to be biologically reasonable. Numerical results demonstrate that the proposed detector can outperform the common low-complexity fixed threshold detector under certain conditions. As a benchmark, maximum-likelihood sequence estimation (MLSE) and reduced-state sequence estimation (RSSE) are also analyzed by means of numerical simulations. In addition, the effect of molecular denaturation on the detection performances is studied. It is shown that denaturation generally improves the detection performances, while RSSE is able to outperform MLSE in the case of no denaturation.

This paper studies spatial diversity techniques applied to multiple-input multiple-output (MIMO) diffusion-based molecular communications (DBMC). Two types of spatial coding techniques, namely Alamouti-type coding and repetition MIMO coding are suggested and analyzed. In addition, we consider receiver-side equal-gain combining, which is equivalent to maximum-ratio combining in symmetrical scenarios. For numerical analysis, the channel impulse responses of a symmetrical 2×2 MIMO-DBMC system are acquired by a trained artificial neural network. It is demonstrated that spatial diversity has the potential to improve the system performance and that repetition MIMO coding outperforms Alamouti-type coding.

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In a paper from 1942 Johannes Iversen described a warm phase prior to the Allerød warming from lake Bølling in western Denmark and thereby extended the common classification of Lateglacial biostratigraphy. Iversen's assumptions concerning the Bølling-Oscillation were based on sedimentological features and high Betula (birch) pollen values in two pollen samples prior to the onset of the Allerød warming. Questions regarding the nature of the Bølling warming later became a discussion due to the expansion of terms into different fields and especially with advances in palynology. To further refine the knowledge on the Bølling-Oscillation, Hartmut Usinger investigated the locus classicus in 1982. The method of pollen-size-frequency distribution was applied on birch pollen in order to distinguish between B. nana-and B. pubescens-type pollen, however, the results were never published and got lost over time. Therefore, these data are presented here and the method as described by Usinger (

This paper introduces the equivalent discrete-time channel model (EDTCM) to the area of diffusion-based molecular communication (DBMC). Emphasis is on an absorbing receiver, which is based on the so-called first passage time concept. In the wireless communications community the EDTCM is well known. Therefore, it is anticipated that the EDTCM improves the accessibility of DBMC and supports the adaptation of classical wireless communication algorithms to the area of DBMC. Furthermore, the EDTCM has the capability to provide a remarkable reduction of computational complexity compared to random walk based DBMC simulators. Besides the exact EDTCM, three approximations thereof based on binomial, Gaussian, and Poisson approximation are proposed and analyzed in order to further reduce computational complexity. In addition, the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm is adapted to all four channel models. Numerical results show the performance of the exact EDTCM, illustrate the performance of the adapted BCJR algorithm, and demonstrate the accuracy of the approximations.