arXiv subjects

Pit Hofmann

Publications and source records attributed to Pit Hofmann.

11 recordsLinked to original sources

arXiv2026

VaporISAC: Integrated Sensing and Communication via Molecular Signals

Conventional electromagnetic (EM)-based integrated sensing and communication (ISAC) systems degrade in cluttered, obstructed, and radio-frequency-hostile environments, while macroscopic molecular communication (MC) remains largely unexplored as an ISAC medium. This article introduces VaporISAC, a molecular ISAC framework in which chemical vapor pulses simultaneously convey information and probe the propagation environment, enabling a one signal, two outputs paradigm. The same received waveform is jointly processed to recover transmitted information and infer environmental properties such as airflow, turbulence, smoke, and chemical conditions. Rather than replacing conventional EM-based ISAC, VaporISAC complements existing approaches in chemically dynamic, infrastructure-limited, and EM-challenged environments. The sensing principles, system architecture, proof-of-concept demonstrations, emerging applications, and open research challenges of VaporISAC are presented, positioning it as a promising new paradigm for resilient communication and environmental sensing.

cs.ET↗

arXiv2026

Evaluating Encoding Strategies for Closed-Loop Classification in Biological Neural Networks

Interfacing with Biological Neural Networks (BNNs) requires encoding information into stimulation patterns that can be effectively processed and that enable the underlying system to adapt. Nevertheless, the role of stimulation encoding remains poorly understood. In this work, we compare multiple encoding strategies, including rate-based, phase-based, burst-based, and time-to-first-spike temporal encodings, in a closed-loop neural classification task using cultured BNNs. We encode visual inputs as spatiotemporal stimulation patterns delivered via a Multi-Electrode Array (MEA) and evaluate classification performance for each encoding scheme. We find that burst-based temporal encoding yields the highest observed performance, achieving up to 95.6 % accuracy in a binary classification task, compared to substantially lower performance from rate- and phase-based approaches. We further show that performance is highly sensitive to the spatial distribution of stimulation, with suboptimal electrode selection significantly degrading accuracy. These findings indicate that effective interfacing with biological neural systems requires the joint optimization of temporal and spatial encoding strategies, and highlight temporal encoding as a key design dimension for bio-digital computing.

cs.ET↗

arXiv2026

The Shared Prosperity Internet

The Shared Prosperity Internet (SPI) is a network-computing architecture that makes the benefits of automation and Artificial Intelligence (AI) broadly accessible to the society. To ground its design, this paper maps the physical constraints of Shannon, Landauer, Turing, and Einstein to three design principles: trustworthiness, sustainability, and technological sovereignty, and maps them into three technical pillars: i) post-Shannon, goal-oriented communication that transmits only what the task requires; ii) anticipatory decision-making ("negative latency") with confidence-bounded pre-action and correction; and iii) beyond-digital computing that selects energy-optimal substrates under deadline and computability constraints. The SPI is grounded in three societal use cases: remote teaching for pupils, remote teaching of robots and cyber-physical systems, and elder care. Furthermore, this paper defines measurable outcomes for an SPI, including latency decomposition, bits per event, energy and CO2 per task, safety and privacy indicators, and robustness.

cs.NI↗

arXiv2026

Molecular ISAC via Markov State-Space Modeling: Joint Distance Sensing and Data Detection

This paper develops a molecular integrated sensing and communication (ISAC) framework that exploits the same molecular observations for physical-parameter sensing and data detection. As a representative instantiation, we consider a microfluidic molecular communication (MC) channel and study transmitter--receiver (TX--RX) distance sensing, where the distance affects the propagation delay, transient response, and inter-symbol interference structure. A distance-parameterized Markov state--space model is established to obtain distance-dependent channel impulse responses and a block observation model for on-off keying signaling. Based on this model, we design a pilot-assisted low-complexity receiver that combines distance initialization, decision-feedback equalization (DFE), and iterative joint refinement. Numerical results show accurate distance sensing and improved bit error ratio (BER), demonstrating the mutual benefit between sensing and communication and highlighting microfluidic MC as a representative platform for molecular ISAC.

eess.SP↗

arXiv2026

Synthetic Biological Intelligence: System-Level Abstractions and Adaptive Bio-Digital Interaction

Concurrent advances across fields such as organoid technology, Microelectrode Arrays (MEAs), neuromorphic computing, and machine learning have given rise to a groundbreaking research paradigm: Synthetic Biological Intelligence (SBI). SBI refers to engineered systems in which living Biological Neural Networks (BNNs) are interfaced with hardware and software to perform task-oriented information processing in a closed loop. This cutting-edge technology, while still in its infancy, has the potential to deliver highly efficient performance across both computing capabilities and energy consumption. The early stage of this field underscores the need for reliable multi-scale and cross-domain interaction interfaces to support applications in robotics, biomedicine, signal processing, and neuroscience research. The hitherto lack of commercially available SBI platforms has slowed the development, as the conditions to produce a testbed are expensive and cumbersome. The introduction of standardized, platform- and cloud-integrated BNNs has been a crucial catalyst for the scientific community, improving the accessibility of SBI and leading the way to further developments. In this survey, we summarize the innovations that contributed to the emergence of SBI and the first testbed interfaces that enabled its embodiment. This work reframes SBI as a bio-digital interaction system and introduces a unified protocol across encoding, decoding, system engineering, and benchmarking.

cs.ET↗

arXiv2026

Markov State--Space Modeling and Channel Characterization for DNA-Based Molecular Communication

In this paper, we study DNA-based molecular communication with microarray-style reception under reversible hybridization, where the bound-state observation exhibits both inter-symbol interference and colored counting noise. To capture these effects in a communication-oriented form, we develop a Markov state-space framework based on a voxelized reaction--diffusion model, in which a block-structured transition matrix describes molecular transport and binding/unbinding dynamics. For the microarray specialization, this representation yields the channel impulse response, the equilibrium gain, and a settling-time-based characterization of the effective channel memory. Building on the resulting symbol-rate observation model for on--off keying, we derive a grouped-binomial counting model and obtain a closed-form expression for the covariance of the counting noise. Based on these statistics, we further develop a differential-threshold detector and a finite-memory decision-feedback equalizer. Numerical results validate the theoretical correlation behavior and show that the relative performance of the proposed receivers depends strongly on the channel-memory regime.

eess.SP↗

arXiv2026

Modulation, ISI, and Detection for Langmuir Adsorption-Based Microfluidic Molecular Communication

This paper studies microfluidic molecular communication receivers with finite-capacity Langmuir adsorption driven by an effective surface concentration. In the reaction-limited regime, we derive a closed-form single-pulse response kernel and a symbol-rate recursion for on-off keying that explicitly exposes channel memory and inter-symbol interference. We further develop short-pulse and long-pulse approximations, revealing an interference asymmetry in the long-pulse regime due to saturation. To account for stochasticity, we adopt a finite-receptor binomial counting model, employ pulse-end sampling, and propose a low-complexity midpoint-threshold detector that reduces to a fixed threshold when interference is negligible. Numerical results corroborate the proposed characterization and quantify detection performance versus pulse and symbol durations.

eess.SP↗

arXiv2025

System Modeling of Microfluidic Molecular Communication: A Markov Approach

This paper presents a Markov-based system model for microfluidic molecular communication (MC) channels. By discretizing the advection-diffusion dynamics, the proposed model establishes a physically consistent state-space formulation. The transition matrix explicitly captures diffusion, advective flow, reversible binding, and flow-out effects. The resulting discrete-time formulation enables analytical characterization of both transient and equilibrium responses through a linear system representation. Numerical results verify that the proposed framework accurately reproduces channel behaviors across a wide range of flow conditions, providing a tractable basis for the design and analysis of MC systems in microfluidic environments.

cs.ET↗

arXiv2025

Communicating Smartly in Molecular Communication Environments: Neural Networks in the Internet of Bio-Nano Things

Recent developments in the Internet of Bio-Nano-Things (IoBNT) are laying the foundation for innovative healthcare applications that envision a network of remotely coordinated nanodevices within the human body to monitor and actuate over potential diseases. However, interconnecting such nanodevices requires communication strategies that can cope with molecular communication (MC) channels, whose complex, stochastic, and dynamic behavior often makes accurate physical modeling infeasible. To explore the limits of nanodevice interconnectivity under these conditions, this survey focuses on data-driven communication strategies for MC systems, with particular emphasis on machine learning (ML) methods and neural network (NN) architectures for a robust and adaptive communication scheme at the nanoscale. Research on NN-enabled MC spans several aspects covered in this survey, including NNs for communication in IoBNT networks, the feasibility of biocompatible NN realization, explainable approaches, and the generation of training datasets. We also include open-source code examples to support reproducible research across key MC scenarios. Finally, we identify emerging challenges, including the need for robust NN architectures, biologically integrated NN modules, and scalable training strategies.

eess.SP↗

arXiv2025

Exhaled Breath Analysis Through the Lens of Molecular Communication: A Survey

Molecular Communication (MC) has long been envisioned to enable an Internet of Bio-Nano Things (IoBNT) with medical applications, where nanomachines within the human body conduct monitoring, diagnosis, and therapy at micro- and nanoscale levels. MC involves information transfer via molecules and is supported by well-established theoretical models. However, practically achieving reliable, energy-efficient, and bio-compatible communication at these scales still remains a challenge. Air-Based Molecular Communication (ABMC) is a type of MC that operates over larger, meter-scale distances and extends even outside the human body. Therefore, devices and techniques to realize ABMC are readily accessible, and associated use cases can be very promising in the near future. Exhaled breath analysis has previously been proposed. It provides a non-invasive approach for health monitoring, leveraging existing commercial sensor technologies and reducing deployment barriers. The breath contains a diverse range of molecules and particles that serve as biomarkers linked to various physiological and pathological conditions. The plethora of proven methods, models, and optimization approaches in MC enable macroscale breath analysis, treating human as the transmitter, the breath as the information carrier, and macroscale sensors as the receiver. Using ABMC to interface with the inherent dynamic networks of cells, tissues, and organs could create a novel Internet of Bio Things (IoBT), a preliminary macroscale stage of the IoBNT. This survey extensively reviews exhaled breath modeling and analysis through the lens of MC, offering insights into theoretical frameworks and practical implementations from ABMC, bringing the IoBT a step closer to real-world use.

cs.ET↗

arXiv2024

Advanced Plaque Modeling for Atherosclerosis Detection Using Molecular Communication

As one of the most prevalent diseases worldwide, plaque formation in human arteries, known as atherosclerosis, is the focus of many research efforts. Previously, molecular communication (MC) models have been proposed to capture and analyze the natural processes inside the human body and to support the development of diagnosis and treatment methods. In the future, synthetic MC networks are envisioned to span the human body as part of the Internet of Bio-Nano Things (IoBNT), turning blood vessels into physical communication channels. By observing and characterizing changes in these channels, MC networks could play an active role in detecting diseases like atherosclerosis. In this paper, building on previous preliminary work for simulating an MC scenario in a plaque-obstructed blood vessel, we evaluate different analytical models for non-Newtonian flow and derive associated channel impulse responses (CIRs). Additionally, we add the crucial factor of flow pulsatility to our simulation model and investigate the effect of the systole-diastole cycle on the received particles across the plaque channel. We observe a significant influence of the plaque on the channel in terms of the flow profile and CIR across different emission times in the cycle. These metrics could act as crucial indicators for early non-invasive plaque detection in advanced future MC methods.

cs.ET↗