arXiv subjects

Juan A. Cabrera

Publications and source records attributed to Juan A. Cabrera.

14 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

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

Fly-PRAC: Packet Recovery for Random Linear Network Coding

Network Coding (NC) is a compelling solution for increasing network efficiency. However, it discards corrupted packets and cannot achieve optimal performance in noisy communications. Since most of the information in corrupted packets is error-free, discarding them is not the best strategy. Several packet recovery techniques such as PRAC and S-PRAC were proposed to exploit corrupted packets. Yet, they are slow and only practical when the packet size is small and communication channels are not very noisy. We propose a packet recovery scheme called Fly-PRAC to address these issues. Fly-PRAC exploits algebraic relations between a group of coded packets to estimate their corrupted parts and recovers them. Unlike previous schemes, Fly-PRAC can recover coded packets at the intermediate node without decoding them. We have compared Fly-PRAC against S-PRAC. Results show when the bit error rate ({\epsilon}) is 10^-4, Fly-PRAC outperforms S-PRAC by two folds for a payload of 900B. In two-hop communication with {\epsilon} = 10^-4 and a payload size of 500B, by enabling the recovery in the intermediate node, Fly-PRAC reduces transmissions by 16%. In a Sparse Network Coding (SNC) scenario, with two non-zero elements in the coefficient vectors and a payload of 800B, there is a reduction by 31% on average for decoding delay.

cs.IT↗

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.

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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↗

arXiv2024

Bidirectional Charging Use Cases: Innovations in E-Mobility and Power-Grid Flexibility

This paper explores the potential of Vehicle-to-Everything (V2X) technology to enhance grid stability and support sustainable mobility in Dresden's Ostra district. By enabling electric vehicles to serve as mobile energy storage units, V2X offers grid stabilization and new business opportunities. We examine pilot projects and business use cases, focusing on Building Integrated Vehicle Energy Solutions (BIVES) and Resilient Energy Storage and Backup (RESB) as stepping stones towards full Vehicle-to-Grid (V2G) implementation. Our analysis highlights the feasibility, advantages, and challenges of implementing V2X in urban settings, underscoring its significant role in transitioning to a resilient, low-carbon urban energy system. The paper concludes with recommendations for addressing technical, regulatory, and business model challenges to accelerate V2X adoption in Dresden and beyond.

eess.SP↗

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↗

arXiv2024

Function Computation and Identification over Locally Homomorphic Multiple-Access Channels

We develop the notion of a locally homomorphic channel and prove an approximate equivalence between those and codes for computing functions. Further, we derive decomposition properties of locally homomorphic channels which we use to analyze and construct codes where two messages must be encoded independently. This leads to new results for identification and K-identification when all messages are sent over multiple-access channels, which yield surprising rate improvements compared to naive code constructions. In particular, we demonstrate that for the example of identification with deterministic encoders, both encoders can be constructed independently.

cs.IT↗

arXiv2022

Network Functional Compression for Control Applications

The trend of future communication systems is to aim for the steering and control of cyber physical systems. These systems can quickly become congested in environments like those presented in Industry 4.0. In these scenarios, a plethora of sensor data is transmitted wirelessly to multiple in network controllers that compute the control functions of the cyber physical systems. In this paper, we show an implementation of network Functional Compression (FC) as a proof of concept to drastically reduce the data traffic in these scenarios. FC is a form of goal-oriented communication scheme in which the objective of the sender receiver pair is to transmit the minimum amount of information to compute a function at the receiver end. In our scenario, the senders transmit an encoded and compressed version of the sensor data to a destination, an in-network controller interested in computing as its target function, a PID controller. We show that it is possible to achieve compression rates of over 50% in some cases by employing FC. We also show that using FC in a distributed cascade fashion can achieve more significant compression rates while reducing computational costs.

cs.NI↗

arXiv2022

Reward-modulated learning using spiking neural networks for vehicle lateral control

This paper presents a vehicle lateral controller based on spiking neural networks capable of replicating the behavior of a model-based controller but with the additional ability to perform online adaptation. By making use of neural plasticity and thanks to reward modulation learning, neural connections are modified to adjust the response according to the committed error. Therefore, the error performs a similar role to dopamine in a biological system, modulating the learning process based on spiking time dependency. The connections are initially set to replicate behavior of the model-based controller. Online adaptation allows tuning connection parameters to improve controller performance. A path controller with a preview is used as a baseline controller to evaluate the performance of the proposed approach. Key performance indicators are obtained from simulation with a step response and a handling track with 20 curves of different radii.

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arXiv2019

S-PRAC: Fast Partial Packet Recovery with Network Coding in Very Noisy Wireless Channels

Well-known error detection and correction solutions in wireless communications are slow or incur high transmission overhead. Recently, notable solutions like PRAC and DAPRAC, implementing partial packet recovery with network coding, could address these problems. However, they perform slowly when there are many errors. We propose S-PRAC, a fast scheme for partial packet recovery, particularly designed for very noisy wireless channels. S-PRAC improves on DAPRAC. It divides each packet into segments consisting of a fixed number of small RLNC encoded symbols and then attaches a CRC code to each segment and one to each coded packet. Extensive simulations show that S-PRAC can detect and correct errors quickly. It also outperforms DAPRAC significantly when the number of errors is high.

cs.NI↗