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Maximilian Schäfer

Publications and source records attributed to Maximilian Schäfer.

At least 37 records · Page 2Linked to original sources
arXiv2023

Closed Loop Molecular Communication Testbed: Setup, Interference Analysis, and Experimental Results

In this paper, we present a fluid-based experimental molecular communication (MC) testbed that, similar to the human cardiovascular system, operates in a closed circuit tube system. The proposed system is designed to be biocompatible, resource-efficient, and controllable from outside the tube. As signaling molecule, the testbed employs the green fluorescent protein variant "Dreiklang" (GFPD). GFPDs can be reversibly switched via light of different wavelengths between a bright fluorescent state and a less fluorescent state. Hence, this property allows for writing and erasing information encoded in the state of the GFPDs already present in the fluid via radiation from outside the tube. The concept of modulating the GFPDs existing in the channel at the transmitter for information transmission, instead of releasing new molecules, is a form of media modulation. In our testbed, due to the closed loop setup and the long experiment durations of up to 250 min, we observe new forms of inter-symbol interferences (ISI), which do not occur in short experiments and open loop systems. In particular, up to four different forms of ISI, namely channel ISI, inter-loop ISI, offset ISI, and permanent ISI, occur in the considered system. To mitigate inter-loop ISI and offset ISI, we propose a light based eraser unit. We experimentally demonstrate reliable information transmission in our testbed achieving error-free transmission of 500 bit at a data rate of 6 bit/min based on a sub-optimal low-complexity detection scheme.

cs.ET↗
arXiv2023

Damping Density of an Absorptive Shoebox Room Derived from the Image-Source Method

The image-source method is widely applied to compute room impulse responses (RIRs) of shoebox rooms with arbitrary absorption. However, with increasing RIR lengths, the number of image sources grows rapidly, leading to slow computation. In this paper, we derive a closed-form expression for the damping density, which characterizes the overall multi-slope energy decay. The omnidirectional energy decay over time is directly derived from the damping density. The resulting energy decay model accurately matches the late reverberation simulated via the image-source method. The proposed model allows the fast stochastic synthesis of late reverberation by shaping noise with the energy envelope. Simulations of various wall damping coefficients demonstrate the model's accuracy. The proposed model consistently outperforms the energy decay prediction accuracy compared to a state-of-the-art approximation method. The paper elaborates on the proposed damping density's applicability to modeling multi-sloped sound energy decay, predicting reverberation time in non-diffuse sound fields, and fast frequency-dependent RIR synthesis.

eess.AS↗
arXiv2023

Area Rate Efficiency in Multi-Link Molecular Communications

We consider a multi-link diffusion-based molecular communication (MC) system where multiple spatially distributed transmitter (TX)-receiver (RX) pairs establish point-to-point communication links employing the same type of signaling molecules. To exploit the full potential of such a system, an in-depth understanding of the interplay between the spatial link density and inter-link interference (ILI) and its impact on system performance is needed. In this paper, we consider a three-dimensional unbounded domain with multiple spatially distributed point-to-point non-cooperative transmission links, where both the TXs and RXs are positioned on a regular fixed grid. For this setup, we first derive an analytical expression for the channel impulse responses (CIRs) between the TXs and RXs in the system. Then, we derive the maximum likelihood (ML) detector for the RXs and show that it reduces to a threshold-based detector. Moreover, we derive an analytical expression for the corresponding detection threshold which depends on the statistics of the desired signal from the dedicated TX, the statistics of the MC channel, and the statistics of the ILI. Finally, we propose two new performance metrics, namely area rate efficiency (ARE) and area and time rate efficiency (ARTE), suitable for holistically evaluating spatially distributed multi-link MC systems. In particular, ARE and ARTE capture the tradeoff between transmission link density and achievable rate per link and the tradeoff between transmission link density, achievable rate per link, and inter-symbol interference (ISI), respectively. Hence, ARE and ARTE can be exploited to determine the optimal transmission link density for maximizing the throughput of the entire system.

cs.IT↗
arXiv2023

Switchable Signaling Molecules for Media Modulation: Fundamentals, Applications, and Research Directions

Although visionary applications of molecular communication (MC), such as long-term continuous health monitoring by cooperative in-body nanomachines, have been proposed, MC is still in its infancy when it comes to practical implementation. In particular, long-term experiments and applications face issues such as depletion of signaling molecules (SMs) at the transmitter (TX) and inter-symbol interference (ISI) at the receiver (RX). To overcome these practical challenges, a new class of SMs with switchable states seems to be promising for future MC applications. In this work, we provide an overview of existing switchable SMs, and classify them according to their properties. Furthermore, we highlight how switchable SMs can be utilized as information carriers for media modulation. In addition, we present theoretical and experimental results for an end-to-end MC system employing the green fluorescent protein variant "Dreiklang" (GFPD) as switchable SM. Our experimental results show, for the first time, successful information transmission in a closed-loop pipe system using media modulation. Finally, we discuss media modulation specific challenges and opportunities.

cs.ET↗
arXiv2023

CASPNet++: Joint Multi-Agent Motion Prediction

The prediction of road users' future motion is a critical task in supporting advanced driver-assistance systems (ADAS). It plays an even more crucial role for autonomous driving (AD) in enabling the planning and execution of safe driving maneuvers. Based on our previous work, Context-Aware Scene Prediction Network (CASPNet), an improved system, CASPNet++, is proposed. In this work, we focus on further enhancing the interaction modeling and scene understanding to support the joint prediction of all road users in a scene using spatiotemporal grids to model future occupancy. Moreover, an instance-based output head is introduced to provide multi-modal trajectories for agents of interest. In extensive quantitative and qualitative analysis, we demonstrate the scalability of CASPNet++ in utilizing and fusing diverse environmental input sources such as HD maps, Radar detection, and Lidar segmentation. Tested on the urban-focused prediction dataset nuScenes, CASPNet++ reaches state-of-the-art performance. The model has been deployed in a testing vehicle, running in real-time with moderate computational resources.

cs.CV↗
arXiv2023

Media Modulation based Molecular Communication

In conventional molecular communication (MC) systems, the signaling molecules used for information transmission are stored, released, and then replenished by a transmitter (TX). However, the replenishment of signaling molecules at the TX is challenging in practice. Furthermore, in most envisioned MC applications, e.g., in the medical field, it is not desirable to insert the TX into the MC system, as this might impair natural biological processes. In this paper, we propose the concept of media modulation based MC where the TX is placed outside the channel and utilizes signaling molecules already present inside the system. The signaling molecules can assume different states which can be switched by external stimuli. Hence, in media modulation based MC, the TX modulates information into the state of the signaling molecules. In particular, we exploit the group of photochromic molecules, which undergo light-induced reversible state transitions, for media modulation. We study the usage of these molecules for information transmission in a three-dimensional duct system, which contains an eraser, a TX, and a receiver for erasing, writing, and reading of information via external light, respectively. We develop a statistical model for the received signal which accounts for the distribution of the signaling molecules in the system, the initial states of the signaling molecules, the reliability of the state control mechanism, the randomness of irrepressible, spontaneous state switching, and the randomness of molecule propagation. We adopt a maximum likelihood detector and a threshold based detector. Furthermore, we derive analytical expressions for the optimal threshold value and the resulting bit error rate (BER), respectively. Our results reveal that media modulation enables reliable information transmission, validating it as a promising alternative to MC based on molecule emitting TXs.

q-bio.BM↗
arXiv2023

Microparticle-based Controlled Drug Delivery Systems: From Experiments to Statistical Analysis and Design

Controlled drug delivery (CDD), the controlled release and delivery of therapeutic drugs inside the human body, is a promising approach to increase the efficacy of drug administration and reduce harmful side effects to the body. CDD has been a major research focus in the field of molecular communications (MC) with the goal to aid the design and optimization of CDD systems with communication theoretical analysis. However, the existing studies of CDD under the MC framework are purely theoretical, and the potential of MC for the development of practical CDD applications remains yet to be shown. This paper presents a step towards filling this research gap. Specifically, we present a novel MC-based model for a specific CDD system in which drugs are embedded into microparticles and released gradually towards the target site. It is demonstrated that the proposed model is able to faithfully reproduce experimental data. Furthermore, statistical analysis is conducted to explore the impact of the microparticle size on the drug release. The presented results reveal the sensitivity of the drug release to changes in the microparticle size. In this way, the proposed model can be used for the design of future microparticle-based CDD systems.

eess.SP↗
arXiv2023

Stochastic Chemical Reaction Networks for MAP Detection in Cellular Receivers

In order to fully exploit the potential of molecular communication (MC) for intra-body communication, practically implementable cellular receivers are an important long-term goal. A variety of receiver architectures based on chemical reaction networks (CRNs) and gene-regulatory networks (GRNs) has been introduced in the literature, because cells use these concepts to perform computations in nature. However, practical feasibility is still limited by stochastic fluctuations of chemical reactions and long computation times in GRNs. Therefore, in this paper, we propose two receiver designs based on stochastic CRNs, i.e., CRNs that perform computations by exploiting the intrinsic fluctuations of chemical reactions with very low molecule counts. The first CRN builds on a recent result from chemistry that showed how Boltzmann machines (BMs), a commonly used machine learning model, can be implemented with CRNs. We show that BMs with optimal parameter values and their CRN implementations can act as maximum-a-posteriori (MAP) detectors. Furthermore, we show that BMs can be efficiently trained from simulation data to achieve close-to-MAP performance. While this approach yields a fixed CRN once deployed, our second approach based on a manually designed CRN can be trained with pilot symbols even within the cell and thus adapt to changing channel conditions. We extend the literature by showing that practical robust detectors can achieve close-to-MAP performance even without explicit channel knowledge.

cs.ET↗
arXiv2023

Experimental Research in Synthetic Molecular Communications -- Part I: Overview and Short-Range Systems

Since its emergence from the communication engineering community around one and a half decades ago, the field of Synthetic Molecular Communication (SMC) has experienced continued growth, both in the number of technical contributions from a vibrant community and in terms of research funding. Throughout this process, the vision of SMC as a novel, revolutionary communication paradigm has constantly evolved, driven by feedback from theoretical and experimental studies, respectively. It is believed that especially the latter ones will be crucial for the transition of SMC towards a higher technology readiness level in the near future. In this spirit, we present here a comprehensive survey of experimental research in SMC. In particular, this survey focuses on highlighting the major drivers behind different lines of experimental research in terms of the respective envisioned applications. This approach allows us to categorize existing works and identify current research gaps that still hinder the development of practical SMC-based applications. Our survey consists of two parts; this paper and a companion paper. While the companion paper focuses on SMC with relatively long communication ranges, this paper covers SMC over short distances of typically not more than a few millimeters.

cs.ET↗
arXiv2023

Experimental Research in Synthetic Molecular Communications -- Part II: Long-Range Communication

In this second part of our survey on experimental research in Synthetic Molecular Communication (SMC), we review works on long-range SMC systems, i.e., systems with communication ranges of more than a few millimeters. Despite the importance of experimental research for the evolution of SMC towards a mature communication paradigm that will eventually support revolutionary applications beyond the reach of today's prevalent communication paradigms, the existing body of literature is still comparatively sparse. Long-range SMC systems have been proposed in the literature for information transmission in two types of fluid media, liquid and air. While both types of SMC systems, liquid-based and air-based systems, rely on encoding and transmitting information using molecules, they differ substantially in terms of the physical system designs and in the type of applications they are intended for. In this paper, we present a systematic characterization of experimental works on long-range SMC that reveals the major drivers of these works in terms of the respective target applications. Furthermore, the physical designs for long-range SMC proposed in the literature are comprehensively reviewed. In this way, our survey will contribute to making experimental research in this field more accessible and identifying novel directions for future research.

cs.ET↗
arXiv2023

Signal Reception With Generic Three-State Receptors in Synaptic MC

Synaptic communication is studied by communication engineers for two main reasons. One is to enable novel neuroengineering applications that require interfacing with neurons. The other reason is to draw inspiration for the design of synthetic molecular communication systems. Both of these goals require understanding of how the chemical synaptic signal is sensed and transduced at the synaptic receiver (Rx). While signal reception in synaptic molecular communication (SMC) depends heavily on the kinetics of the receptors employed by the synaptic Rxs, existing channel models for SMC either oversimplify the receptor kinetics or employ complex, high-dimensional kinetic schemes limited to specific types of receptors. Both approaches do not facilitate a comparative analysis of different types of natural synapses. In this paper, we propose a novel deterministic channel model for SMC which employs a generic three-state receptor model that captures the characteristics of the most important receptor types in SMC. The model is based on a transfer function expansion of Fick's diffusion equation and accounts for release, diffusion, and degradation of neurotransmitters as well as their reversible binding to finitely many generic postsynaptic receptors. The proposed SMC model is the first that allows studying the impact of the characteristic dynamics of the main postsynaptic receptor types on synaptic signal transmission. Numerical results indicate that the proposed model indeed exhibits a wide range of biologically plausible dynamics when specialized to specific natural receptor types.

q-bio.NC↗
arXiv2023

Molecular Noise In Synaptic Communication

In synaptic molecular communication (MC), the activation of postsynaptic receptors by neurotransmitters (NTs) is governed by a stochastic reaction-diffusion process. This randomness of synaptic MC contributes to the randomness of the electrochemical downstream signal in the postsynaptic cell, called postsynaptic membrane potential (PSP). Since the randomness of the PSP is relevant for neural computation and learning, characterizing the statistics of the PSP is critical. However, the statistical characterization of the synaptic reaction-diffusion process is difficult because the reversible bi-molecular reaction of NTs with receptors renders the system nonlinear. Consequently, there is currently no model available which characterizes the impact of the statistics of postsynaptic receptor activation on the PSP. In this work, we propose a novel statistical model for the synaptic reaction-diffusion process in terms of the chemical master equation (CME). We further propose a novel numerical method which allows to compute the CME efficiently and we use this method to characterize the statistics of the PSP. Finally, we present results from stochastic particle-based computer simulations which validate the proposed models. We show that the biophysical parameters governing synaptic transmission shape the autocovariance of the receptor activation and, ultimately, the statistics of the PSP. Our results suggest that the processing of the synaptic signal by the postsynaptic cell effectively mitigates synaptic noise while the statistical characteristics of the synaptic signal are preserved. The results presented in this paper contribute to a better understanding of the impact of the randomness of synaptic signal transmission on neuronal information processing.

q-bio.SC↗
arXiv2023

A Chemical Master Equation Model for Synaptic Molecular Communication

In synaptic molecular communication, the activation of postsynaptic receptors by neurotransmitters (NTs) is governed by a stochastic reaction-diffusion process and, hence, inherently random. It is currently not fully understood how this randomness impacts downstream signaling in the target cell and, ultimately, neural computation and learning. The statistical characterization of the reaction-diffusion process is difficult because the reversible bi-molecular reaction of NTs and receptors renders the system nonlinear. Consequently, existing models for the receptor occupancy in the synaptic cleft rely on simplifying assumptions and approximations which limit their practical applicability. In this work, we propose a novel statistical model for the reaction-diffusion process governing synaptic signal transmission in terms of the chemical master equation (CME). We show how to compute the CME efficiently and verify the accuracy of the obtained results with stochastic particle-based computer simulations (PBSs). Furthermore, we compare the proposed model to two benchmark models proposed in the literature and show that it provides more accurate results when compared to PBSs. Finally, the proposed model is used to study the impact of the system parameters on the statistical dependence between binding events of NTs and receptors. In summary, the proposed model provides a step forward towards a complete statistical characterization of synaptic signal transmission.

cs.ET↗
arXiv2023

Statistical Modeling of Airborne Virus Transmission Through Imperfectly Fitted Face Masks

The rapid emergence and the disastrous impact of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic on public health, societies, and economies around the world has created an urgent need for understanding the pathways critical for virus transmission. Airborne virus transmission by asymptomatic SARS-CoV-2-infected individuals is considered to be a major contributor to the spread of SARS-CoV-2 and social distancing and wearing of face masks in public have been implemented as countermeasures in many countries. However, a comprehensive risk assessment framework for the airborne transmission of SARS-CoV-2 incorporating realistic assumptions on the filtration of infectious aerosols (IAs) by face masks is not available yet. In particular, in most end-to-end models for airborne virus transmission, it is neglected that the stochastic spread of IAs through imperfectly fitted face masks depends on the dynamics of the breathing of the wearer. In this paper, we consider airborne virus transmission from an infected but asymptomatic person to a healthy person, both wearing imperfectly fitted face masks, in an indoor environment. By framing the end-to-end virus transmission as a Molecular Communications (MC) system, we obtain a statistical description of the number of IAs inhaled by the healthy person subject to the respective configurations of the face masks of both persons. We demonstrate that the exhalation and inhalation air flow dynamics have a significant impact on the stochastic filtering of IAs by the face masks. Furthermore, we show that the fit of the face mask of the infected person can highly impact the infection probability. We conclude that the proposed MC model may contribute a valuable assessment tool to fight the spread of SARS-CoV-2 as it encompasses the randomness of the transmission process and enables comprehensive risk analysis beyond statistical averages.

q-bio.PE↗
arXiv2022

Facebook Shadow Profiles

We quantify Facebook's ability to build shadow profiles by tracking individuals across the web, irrespective of whether they are users of the social network. For a representative sample of US Internet users, we find that Facebook is able to track about 40 percent of the browsing time of both users and non-users of Facebook, including on privacy-sensitive domains and across user demographics. We show that the collected browsing data can produce accurate predictions of personal information that is valuable for advertisers, such as age or gender. Because Facebook users reveal their demographic information to the platform, and because the browsing behavior of users and non-users of Facebook overlaps, users impose a data externality on non-users by allowing Facebook to infer their personal information.

econ.GN↗
arXiv2022

Controlled Signaling and Transmitter Replenishment for MC with Functionalized Nanoparticles

In this paper, we propose novel Transmitter (Tx) models for Molecular Communication (MC) systems based on functionalized Nanoparticles (NPs). Current Tx models often rely on simplifying assumptions for the molecule release and replenishment mechanisms. In contrast, we propose a Tx model where the signaling molecule release is controlled by a switchable membrane driven by an external trigger. Moreover, we propose a reloading mechanism, where signaling molecules are harvested based on an enzymatic reaction. Hence, no repeated injection of signaling molecules is required. For the proposed Tx model, we develop a general mathematical description in terms of a discrete-time transfer function model. Furthermore, we investigate two realizations of the proposed Tx model, i.e., an idealized Tx relying on simplifying assumptions, and a realistic Tx employing practical components for the reloading and release mechanisms. Finally, we numerically evaluate the proposed model and compare our results to stochastic Particle Based Simulations (PBSs).

cs.ET↗
arXiv2022

Physical Modeling using Recurrent Neural Networks with Fast Convolutional Layers

Discrete-time modeling of acoustic, mechanical and electrical systems is a prominent topic in the musical signal processing literature. Such models are mostly derived by discretizing a mathematical model, given in terms of ordinary or partial differential equations, using established techniques. Recent work has applied the techniques of machine-learning to construct such models automatically from data for the case of systems which have lumped states described by scalar values, such as electrical circuits. In this work, we examine how similar techniques are able to construct models of systems which have spatially distributed rather than lumped states. We describe several novel recurrent neural network structures, and show how they can be thought of as an extension of modal techniques. As a proof of concept, we generate synthetic data for three physical systems and show that the proposed network structures can be trained with this data to reproduce the behavior of these systems.

cs.SD↗
arXiv2022

Context-Aware Scene Prediction Network (CASPNet)

Predicting the future motion of surrounding road users is a crucial and challenging task for autonomous driving (AD) and various advanced driver-assistance systems (ADAS). Planning a safe future trajectory heavily depends on understanding the traffic scene and anticipating its dynamics. The challenges do not only lie in understanding the complex driving scenarios but also the numerous possible interactions among road users and environments, which are practically not feasible for explicit modeling. In this work, we tackle the above challenges by jointly learning and predicting the motion of all road users in a scene, using a novel convolutional neural network (CNN) and recurrent neural network (RNN) based architecture. Moreover, by exploiting grid-based input and output data structures, the computational cost is independent of the number of road users and multi-modal predictions become inherent properties of our proposed method. Evaluation on the nuScenes dataset shows that our approach reaches state-of-the-art results in the prediction benchmark.

cs.CV↗