Karlheinz Ochs

Privatdozent

Raum: ID 1/148
Tel.: +49 (0)234/32-22855
E-Mail: Karlheinz.Ochs@rub.de

Biography

Karlheinz Ochs (S’99–M’01–SM’17) received the Dipl.-Ing. degree in electrical engineering from the University of Paderborn, Germany, in 1996, and the Dr.-Ing. degree from the same university in 2001. He obtained the Habilitation in Theory of Linear Time-Variant Transmission Systems from the Ruhr University Bochum, Germany, in 2011.

From 1996 to 2001, he was a Doctoral Researcher at the Institute of Communication Theory, University of Paderborn. From 2001 to 2002, he worked as a Development Engineer at Siemens AG, Bruchsal. Between 2002 and 2011, he was a Postdoctoral Researcher with the Chair of Digital Communication Systems, Ruhr University Bochum, where he has been a Senior Researcher since 2012. He also lectured at the University Duisburg-Essen from 2002 to 2005.

He was a Guest and DAAD Visiting Professor at the Universidade Federal do Rio Grande do Norte, Natal, Brazil, in 2005 and 2006. He served as Principal Investigator in the DFG Research Unit 2093 “Memristive Devices for Neural Systems” (2015–2020) and is currently Principal Investigator in the DFG Collaborative Research Centre 1461 “Neurotronics – Bio-Inspired Information Pathways” (since 2020). From 2021 to 2024, he has been a Mentor for the startup Gemesys within the EXIST Transfer of Research Program.

His research interests include wave digital emulation, memristive circuit modeling, and bio-inspired computing. His current work focuses on energy-conserving neuromorphic models, adaptive device dynamics, and port-Hamiltonian frameworks for minimal bio-computing systems.

He received the Best Paper Prize at the Irish Signals and Systems Conference (ISSC) in 2018, the Myril B. Reed Best Paper Award at IEEE MWSCAS in 2017, the Best Paper Award (2nd place) at IEEE MWSCAS in 2009, and the Best Ph.D. Thesis Award from the University of Paderborn in 2001.

Curriculum Vitae

since 2012
Ruhr-Universität Bochum, Senior Researcher, Chair of Digital Communication Systems

2012 – 2002
Ruhr-Universität Bochum, Postdoctoral Researcher, Chair of Digital Communication Systems

2005 / 2006
Universidade Federal do Rio Grande do Norte, Natal (Brazil), Guest Professor and DAAD Visiting Professor

2002 – 2005
University Duisburg-Essen, Contract Lecturer, Electrical Engineering and Information Technology

2002 – 2001
Siemens AG, Bruchsal, Development Engineer for Mobile Communications

2001 – 1996
University of Paderborn, PhD Student, Institute of Communication Theory

1996 – 1993
University of Paderborn, Student of Electrical Engineering (Diplom-Ingenieur)

Research Interests

Memristive Circuits and Devices
This research establishes memristors and related memelements as foundational components for adaptive electronics, moving beyond idealized models toward physically grounded descriptions of real device behavior. The focus lies on modeling and characterization methods that capture complex nonlinear dynamics, including non-volatile state transitions, hysteresis, and short- to long-term plasticity intrinsic to their material mechanisms.
 
A central contribution is the development of energetically consistent models for passive memelements, ensuring compliance with thermodynamic laws and enabling reliable integration into larger circuit systems. This includes detailed, physics-based lumped-element modeling of specific device technologies—such as HfO₂-based RRAM and double-barrier memristive devices—that accurately reproduce internal ion transport, resistive switching, and multi-level conductance behavior.

These models are translated directly into functional circuits through a device–circuit co-design approach, enabling the synthesis of memristive networks that self-organize their topology, emulate synaptic functions, and approximate neuronal dynamics. A key methodological advance is the Wave Digital modeling framework, which provides real-time, structure-preserving digital twins of memristive devices for rapid prototyping and hybrid analog–digital experimentation.

Looking forward, this research expands to address the stochasticity, variability, and multi-physics coupling inherent in emerging memristive technologies. The long-term vision is to realize robust, energy-consistent non–von Neumann architectures built from adaptive electronic elements that seamlessly merge computation, memory, and learning within a unified physical substrate.

Neuromorphic Computing
This research focuses on designing and implementing bio-inspired circuits and systems that embody the computational principles of neural networks, aiming for a new class of energy-efficient, robust, and physically interpretable processors. The approach moves beyond software simulation to realize adaptive analog and mixed-signal hardware in which neuronal dynamics are directly manifested in silicon.
 
A core activity is the circuit synthesis of canonical neuronal models, such as Hindmarsh–Rose and Morris–Lecar, creating electrical equivalents that reproduce characteristic spiking and bursting behaviors. This extends to the emulation of synaptic plasticity—including Spike-Timing-Dependent Plasticity (STDP)—using memristive devices to enable local learning and adaptation within hardware. A major focus lies on Central Pattern Generators (CPGs), where networks of nonlinear oscillators are designed to generate and switch between coordinated rhythmic patterns, such as animal gaits, under sensory or optical control.
 
Beyond single components, this research develops integrated neuromorphic systems exhibiting higher-order cognitive-like functions: decision-making through Kuramoto oscillators with Hebbian learning, anticipation of temporal patterns, and the emulation of perceptual phenomena such as optical illusions. A distinctive contribution is the creation of bio-physical digital twins, where the neuronal activity and energy consumption of organisms like C. elegans and Hydra are modeled and emulated using Wave Digital principles—bridging biology and electronic hardware.
 
Looking ahead, the research is expanding toward closed-loop sensorimotor systems, where neuromorphic processors interact with physical environments in real time. The long-term vision is to achieve on-chip learning and adaptation through heterogeneous integration of CMOS, memristive, and emerging quantum components—laying the groundwork for the next generation of brain-inspired computing architectures.

Unconventional Computing
This research explores computing paradigms that transcend traditional digital logic by harnessing the intrinsic dynamics of physical, biological, and engineered systems. The goal is to formulate and solve computational problems through the self-organizing evolution of physical processes, yielding highly efficient, naturally parallel, and emergent computational substrates.
 
A central direction is the development of physics-based solvers for complex optimization and NP-hard problems. Examples include oscillator-based Ising machines for Max-Cut and Vertex Cover, and memristive networks for graph-theoretic tasks such as Minimum Spanning Trees, Longest Paths, and Subset-Sum. In these systems, the solution arises from the network’s natural energy minimization and dynamical relaxation. The research also investigates reservoir computing, leveraging the transient dynamics of fixed nonlinear networks for temporal pattern recognition and modeling complex systems like predator–prey interactions.
 
Beyond problem-solving, this work seeks to uncover the principles of emergent computation. It explores how systems operating near criticality can achieve maximal computational capacity and robustness, and how circuits exhibiting self-organization and anticipatory behavior transition from reactive to predictive modes of computation.
 
Looking ahead, the research advances toward hybrid classical–physical architectures, where digital processors offload specialized tasks to adaptive analog substrates. A key challenge lies in establishing systematic benchmarking and scalability frameworks that relate physical computation to digital standards. The long-term vision is to build a foundation for non-Boolean, physically embodied computing, fully exploiting analog, probabilistic, and emergent dynamics as core computational resources.

Energy-Based Learning and Control
This research develops a unified framework for learning and control grounded in energy conservation, dissipation, and flow. The approach is rooted in the Port-Hamiltonian formalism, which provides a geometric structure, defined through Dirac interconnections, to describe how components exchange and regulate energy. This structure ensures that all models remain physically consistent, stable, and interpretable across multiple physical and computational domains.
 
A key contribution is the development of structure- and energy-preserving discretization methods, such as passive Runge–Kutta integration, which maintain the passivity and stability of continuous-time systems during simulation. These methods generalize to distributed and nonlinear systems, enabling circuit-equivalent representations for complex physical processes and the consistent inclusion of adaptive, memory-dependent elements like memristors and memcapacitors.
 
Building upon this deterministic foundation, the framework extends to Stochastic Port-Hamiltonian Systems (SPHS) that integrate Langevin dynamics and Fokker–Planck equations to describe noise, uncertainty, and adaptation within energetic systems. This stochastic extension unifies physical modeling and inference, allowing energy-based formulations to describe both forward dynamics and probabilistic learning in noisy or fluctuating environments.
 
The long-term vision is to develop energy-consistent, self-regulating systems capable of learning and adaptation through SPHS principles—combining the interpretability of physical models with the flexibility of probabilistic inference. By merging dissipative Hamiltonian theory with stochastic thermodynamics, this work establishes the foundations of energy-based learning and control, bridging physics, computation, and neuromorphic intelligence within a single, physically grounded framework.
 

Wave Digital Twins
This research develops structure-preserving, energy-consistent digital twins of physical, electronic, and biological systems using the Wave Digital (WD) paradigm. Unlike conventional numerical simulations, WD models yield real-time, physically faithful emulations that maintain the stability, passivity, and energy balance of the original analog, nonlinear, or neuromorphic systems.
 
The WD framework provides a universal modeling language for constructing digital counterparts of complex dynamical processes. It spans multiple domains—from memristive devices and adaptive electronic circuits to nonlinear oscillator networks and distributed physical systems described by partial differential equations. Through advanced decomposition techniques such as SPQR-tree analysis, large and multi-port systems are efficiently emulated while preserving their energetic structure. Distinctive applications include memristive circuit twins, which replicate hysteresis, plasticity, and learning dynamics of resistive switching devices, as well as bio-physical twins of model organisms like C. elegans and Hydra, where neuronal activity and energy flow are faithfully reproduced.
 
Beyond deterministic replication, this research extends the WD concept toward stochastic and adaptive digital twins. By embedding Stochastic Port-Hamiltonian System (SPHS) formulations and Langevin dynamics, these twins incorporate variability, noise, and adaptation in real time—allowing the digital and physical systems to co-evolve through a shared energetic state space. This establishes a foundation for energy-based inference and control, where the twin not only mirrors but also learns from its physical counterpart within a unified stochastic energy landscape.
 
Looking forward, the research advances toward hardware-accelerated, self-learning, and interactive digital twins implemented on FPGA and neuromorphic platforms. The long-term vision is an ecosystem of adaptive, physically grounded digital twins interlinking memristive electronics, biological networks, and AI models—merging energetic modeling, stochastic learning, and real-time experimentation into a coherent framework for intelligent discovery and design.

Nonlinear and Emergent Dynamical Systems
This research investigates systems where nonlinear interactions give rise to collective behaviors such as synchronization, self-organization, and criticality. The goal is to understand, design, and control these emergent dynamics—ubiquitous in physical, biological, and engineered systems—to harness them as substrates for computation and adaptive system design.
 
A central theme is the analysis and control of synchronization in networks of nonlinear oscillators, from FitzHugh–Nagumo and Chua circuits to Kuramoto ensembles. The research establishes conditions for synchronization under resistive and memristive coupling and introduces quantitative tools such as synchronization metrics based on Poincaré’s sphere. By tuning network parameters toward the critical regime, oscillator ensembles are engineered to exhibit maximal computational capacity, sensitivity, and robustness—properties exploited in oscillator-based Ising machines and other physics-inspired solvers.
 
The approach remains circuit-centric, translating abstract neuronal and dynamical models into physically realizable electrical networks. This allows the exploration of complex dynamics—from spiking and bursting to chaos—within experimentally accessible, energy-conserving systems that serve as models of emergent computation.
 
Looking ahead, this research advances toward a stochastic and energy-based theory of emergence. Using the Stochastic Port-Hamiltonian (SPHS) framework, Langevin dynamics, and the Fokker–Planck formalism, it analyzes how noise, dissipation, and information flow interact to drive adaptation and self-organization. This perspective unifies nonlinear dynamics, thermodynamics, and computation: emergent behavior becomes a form of inference over an evolving energy landscape, where the collective motion of coupled oscillators encodes problem-solving and learning. The long-term vision is a unified framework for emergent computation, in which complex functionality arises naturally from the energetic coupling of simple, stochastic, and interacting elements.

Linear Time-Variant Systems
Most real-world physical and communication processes are time-variant: their parameters, coupling, and energy flow evolve with time. The theory of linear time-variant (LTV) systems provides a general mathematical and physical framework for analyzing, synthesizing, and approximating such systems, extending classical linear time-invariant (LTI) theory to the nonstationary domain.
 
This research develops a generalized system and network theory in which electrical elements, circuits, and dynamical systems can exhibit time-varying parameters such as resistance, inductance, or capacitance. Voltages and currents are described by time-variant complex amplitudes, and interrelations are governed by bilinear operator equations, yielding an extended Kirchhoff framework consistent with energy conservation and causality.
 
A key contribution of this theory is the preservation of analytical tractability. In the stationary limit, it reproduces classical Fourier analysis, while in the general case it allows quasi-analytical solutions in the form of Fourier series or integrals with time-varying coefficients. This representation makes nonstationary processes, such as modulation, adaptive filtering, and parameter variation, analytically transparent and physically interpretable.
 
Building on this foundation, the research formulates composition laws for cascaded LTV systems, time-variant Wiener and matched filters for optimal estimation and detection, and approximation methods for nonlinear or periodically driven dynamics. These concepts link directly to the analysis of periodic orbits, entrainment, and synchronization in nonlinear systems, establishing a mathematical connection between classical system theory and emergent dynamical phenomena.
 
Finally, structure-preserving synthesis techniques translate time-varying differential equations into equivalent electrical networks or digitally emulated models, ensuring stability and passivity. Applications include adaptive filtering, radar and mobile communication, parametric circuits, and coupled oscillator networks. They exemplify the framework’s role in linking analytical modeling, physical design, and digital emulation.

Selected DFG Projects

Drosophila Neural Circuit Growth Modeling
This project explores the reciprocal relationship between biological and electrical circuit development, aiming to uncover how neural growth mechanisms can inspire new paradigms for energy-efficient, robust, and information-minimal electronic design. From an engineering perspective, biological growth processes demonstrate remarkable efficiency, compact encoding, and resilience to perturbation. From a biological viewpoint, mathematical and circuit-based realizations offer a tangible framework for analyzing the underlying principles of organization, connectivity, and information flow.

The research uses the development of the Drosophila visual map as a model system. Recent advances in quantitative live imaging and stochastic simulations of its neural circuit formation provide an unprecedented foundation for model validation. Building on preliminary results where the growth process was successfully emulated using Wave Digital (WD) circuit simulation, the project now aims to formalize this process through an ODE/PDE-based modeling framework. This enables direct testing of how local interactions shape robustness and global pattern formation. 
Subsequent work packages will synthesize corresponding electrical circuits that emulate developmental growth—including stochasticity—and implement real-time digital twins for dynamic experimentation. By uniting biological modeling, mathematical analysis, and electronic realization, this project establishes a new interdisciplinary methodology for circuit development. It creates a platform for mutual advancement: biological data validate circuit theories while simultaneously inspiring the design of next-generation, self-organizing, and adaptive hardware architectures.

Funding Agency: Deutsche Forschungsgemeinschaft (DFG)
Program: Individual Project
Title: A bio-inspired electrical engineering approach to circuit development -
        Insights from modeling growth of a Drosophila neural network
Project ID: 564716375
Duration: Since 2025

Hydra Neurodynamics and Sensory Processing
The freshwater polyp Hydra, with its simple yet functional diffuse nerve net, serves as a powerful model system for reverse-engineering the fundamental principles of neural circuit formation and sensory processing. This project investigates how Hydra’s neural network develops during ontogenesis and how it processes environmental cues such as light, with the goal of translating these biological blueprints into novel, bio-inspired neuromorphic architectures.
 
The research is inherently interdisciplinary, combining experimental neuro-biology with computational and circuit-level modeling. A central focus is the development of specialized software emulators and Wave Digital (WD) models that provide hardware-oriented representations of Hydra’s neural activity and connectivity. These digital twins enable the simulation of signal-processing pathways and the testing of hypotheses about network function.
 
The project further aims to translate biological principles—such as guided axon growth and the emergence of light-modulated sensory neuron circuits—into electronic design rules for adaptive and self-organizing systems. By establishing a direct bridge between biological observation and electronic implementation, this project contributes to a new class of neuromorphic systems capable of development-like self-organization and low-power, biologically grounded sensory information processing.
 
Funding Agency: Deutsche Forschungsgemeinschaft (DFG)
Program: Collaborative Research Center CRC 1461 – 
         "Neuroelectronics: Biologically Inspired Information Processing"
Title: Hydra Neural Circuit Modeling
Project ID: 434434223
Duration: Since 2021

Nanoscale Oscillator Network Development
Inspired by the spatiotemporal dynamics and plasticity of biological neural systems, this project seeks to physically realize nanoscale oscillator networks as core building blocks for next-generation neuromorphic hardware. Its central objective is to implement such networks in hardware to perform physics-based computation—solving complex optimization problems through oscillator-based Ising machines and exploiting the computational potential of critical states.
 
The approach follows a hardware-aligned modeling and co-design philosophy. Real-time Wave Digital (WD) emulation and circuit-level simulation are used to map collective dynamics—including synchronization, criticality, and phase transitions—onto implementable electronic structures built from memristively coupled oscillators. This process ensures that theoretical models of collective computation translate directly into physically realizable architectures. The project also develops Application-Specific Integrated Circuits (ASICs), including CMOS ring oscillators and tunable coupling elements, which form the computational core of these systems.
 
By creating a direct pipeline from computational theory to circuit fabrication, the project establishes a central technological pillar within the collaborative research center. It provides the essential link needed to realize robust, adaptive, and energy-efficient non-von Neumann computing systems, where computation emerges naturally from the physical dynamics of the hardware itself.
 
Funding Agency: Deutsche Forschungsgemeinschaft (DFG)
Program: Collaborative Research Center SFB 1461 – 
         "Neuroelectronics: Biologically Inspired Information Processing"
Title: Nanoscale Oscillator Network Development
Project ID: 434434223
Duration: Since 2021

Memristive Oscillator Network Synchronization
This project established a formal theory and a hardware-oriented emulation framework for synchronization in networks of nonlinear oscillators coupled via memristive elements. It treated synchronization not merely as a dynamical phenomenon but as a fundamental computational primitive for adaptive, brain-inspired computing.
 
The methodology was dual-faceted. Control-theoretic concepts were used to derive sufficient conditions and robust design rules for the synchronization of FitzHugh–Nagumo, Chua, and Kuramoto oscillator networks. In parallel, Wave Digital (WD) emulation techniques enabled the synthesis and real-time simulation of corresponding electronic circuits, providing a physically faithful testbed for validating theory and exploring complex, large-scale regimes. This integration successfully translated abstract synchronization principles into implementable circuit-level design.
 
The project produced a suite of theoretical tools, including novel synchronization metrics based on Poincaré’s sphere, and demonstrated their application in emulated systems exhibiting neuroplasticity and self-organizing central pattern generation. These outcomes established a foundational bridge between nonlinear dynamics and neuromorphic electronics, paving the way for architectures in which computation and learning emerge from the collective, synchronized dynamics of adaptive oscillator networks.
Funding Agency: Deutsche Forschungsgemeinschaft (DFG)
Program: Research Unit FOR 2093 – "Memristive Devices for Neural Systems"
Title: Synchronization of Memristively Coupled Oscillator Networks
         – Theory and Emulation
Project ID: 239767484
Duration: 2017 – 2022

Memristive Device and System Modeling
This project established a holistic, multi-scale framework for modeling and simulating memristive devices and systems, creating a bridge from microscopic device physics to circuit-level functionality for bio-inspired computing. The research developed models that capture the nonlinear dynamics and memory effects essential for two key paradigms: neuromorphic networks and unconventional computing architectures inspired by organisms such as Physarum polycephalum.
 
The approach combined distributed-parameter physical models with lumped-parameter circuit equivalents. A key contribution was the development of energetically consistent, physics-based compact models for specific memristive technologies, ensuring thermodynamic compliance and numerical stability. The project also pioneered the use of Wave Digital (WD) emulation to create real-time digital twins of these devices. This foundation enabled the translation of biological principles into circuit implementations, exemplified by oscillator networks with memristive feedback that emulate amoeba-like anticipation and learning.
 
These outcomes provided the theoretical and simulation backbone for the research unit, advancing the design of adaptive neuromorphic circuits and establishing a new paradigm for physics-based computing systems that learn and predict from temporal patterns.
Funding Agency: Deutsche Forschungsgemeinschaft (DFG)
Program: Research Unit FOR 2093 – "Memristive Devices for Neural Systems"
Title: Modeling and Simulation of Memristive Devices and Systems
Project ID: 239767484
Duration: 2014 – 2019

Memreactive Device Modeling
This project investigates the modeling, simulation, and emulation of mem-reactive devices — capacitive or inductive elements with memory — as a foundation for adaptive and energy-consistent circuit design. The emulation framework enables these devices to be incorporated into electrical networks while preserving physical plausibility and stability. To achieve this, memreactive elements are modeled not only by their mathematical behavior but also by their energetic properties, ensuring losslessness or controlled dissipation consistent with real materials.
 
Based on these physically faithful models, equivalent circuits are derived that describe memcapacitive and meminductive behavior within a thermo-dynamically consistent framework. Losses are incorporated via resistive elements, which may themselves possess memory. These hybrid circuits form the basis for studying adaptive resonance phenomena in self-organizing oscillator networks.

As an application example, a memristive oscillator known to anticipate periodic pulse sequences will be extended to handle irregularly appearing pulses. This capability will be demonstrated through a software-based emulation, illustrating how the circuit dynamically anticipates and structurally exploits redundancy in time-varying input patterns. The project thus bridges theoretical device modeling with functional neuromorphic behavior, advancing the understanding of adaptive and predictive electronic systems.
 
Funding Agency: Deutsche Forschungsgemeinschaft (DFG)
Program: Individual Project
Title: Modeling, Simulation, and Emulation of Memreactive Devices for 
         Self-Organizing Oscillator Circuits
Project ID: 404291403
Duration: 2018 – 2024

Publications

Conference-Papers

2

2025
2
An Energetic Analysis of a Sufficient Synchronization Criterion Applied to FitzHugh-Nagumo Oscillator Networks

J. Röhrig | Robin Lautenbacher | K. Ochs | Ralf Köhl

2025 - 21st IEEE Biomedical Circuits and Systems Conference (BioCAS) 2025, Abu Dhabi
1
Synchronization of Chua’s Circuits Coupled via Memristive One-ports

J. Röhrig | Robin Lautenbacher | K. Ochs | Ralf Köhl

2025 - IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), Lansing, Michigan, USA
4

2024
4
Programming an Oscillator-Based Ising Machine with Memristive Coupling

J. Röhrig | B. Al Beattie | K. Ochs

2024 - 2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)
3
Wave Digital Model of a Relaxation Oscillator with Optical Memsensor

S. Jenderny | Rohit Gupta | Roshani Madurawala | Maik-Ivo Terasa | Franz Faupel | Sören Kaps | Rainer Adelung | Alexander Vahl | K. Ochs

2024 - DPG-Tagung 2024, Berlin, Germany
2
A Sensory Driven Adaptive Central Pattern Generator

J. Röhrig | B. Al Beattie | S. Jenderny | K. Ochs

2024 - DPG Tagung 2024, Berlin, Germany
1
A Sufficient Synchronization Criterion for Memristively Coupled FitzHugh-Nagumo Oscillators

J. Röhrig | B. Al Beattie | S. Jenderny | K. Ochs

2024 - DPG-Tagung 2024, Berlin, Germany
12

2023
12
LUT-based RRAM Model for Neural Accelerator Circuit Simulation

Max Uhlmann | Tomasso Rizzi | Jianan Wen | Emilio Queseda | B. Al Beattie | K. Ochs | Eduardo Perez | P. Ostrovskyy | Corrado Carta | Christian Wenger | Gerhard Kahmen

2023 - NANOARCH '23: Proceedings of the 18th ACM International Symposium on Nanoscale Architectures
11
Electrical and Wave Digital Modeling of CMOS-Based Ring Oscillators

B. Al Beattie | Bharath Kumar Singh Muralidhar | Max Uhlmann | Gerhard Kahmen | Robert Rieger | K. Ochs

2023 - 30th IEEE International Conference on Electronics, Circuits and Systems (ICECS)
10
Light-Controlled Switching of Gait Patterns in a Central Pattern Generator: Circuit Design and Emulation

B. Al Beattie | S. Jenderny | J. Röhrig | K. Ochs

2023 - 30th IEEE International Conference on Electronics, Circuits and Systems (ICECS)
9
A Bio-Inspired CMOS Circuit for the Excitation and Inhibition of Neuronal Oscillators

Bharath Kumar Singh Muralidhar | B. Al Beattie | Max Uhlmann | K. Ochs | Gerhard Kahmen | Robert Rieger

2023 - IEEE Asia Pacific Conference on Circuits and Systems (APCCAS)
8
Wave Digital Emulation of a Light-Modulated Central Pattern Generator

S. Jenderny | K. Ochs | Oways Alsoloh

2023 - 21st IEEE NEWCAS, Edinburgh, Scotland
7
A Reservoir Computer-Based Modeling of Hunting Dynamics in Predator-Prey Scenarios

S. Jenderny | K. Ochs | Kamel Naoum Naame

2023 - 21st IEEE NEWCAS, Edinburgh, Scotland
6
A Simplified Hindmarsh-Rose Model Based on Power-Flow Analysis

S. Jenderny | K. Ochs | Matthew Gibson | Philipp Hövel

2023 - 21st IEEE NEWCAS, Edinburgh, Scotland
5
Wave Digital Optimization of a Modified Compact Models of 1T-1R Random Resistive Access Memory Cells

B. Al Beattie | Max Uhlmann | Gerhard Kahmen | K. Ochs

2023 - DPG-Conference 2023, Dresden, Germany
4
On the Correlation of Functionality and Lyapunov Stability in Oscillator-based Ising machines

B. Al Beattie | Maximiliane Noll | Hermann Kohlstedt | K. Ochs

2023 - DPG Conference 2023, Dresden, Germany
3
Mimicking axon-growth by a bio-inspired memristive circuit

S. Jenderny | B. Al Beattie | K. Ochs

2023 - DPG-Conference 2023, Dresden Germany
2
A first approach for mimicking guided axon-growth by electrical circuits

B. Al Beattie | S. Jenderny | K. Ochs | D. Michaelis

2023 - DPG-Conference 2023, Dresden, Germany
1
Power-flow-based circuit synthesis of neuronal dynamics

K. Ochs | S. Jenderny | Philipp Hövel

2023 - DPG-Conference 2023, Dresden Germany
7

2022
7
Wave Digital Emulation of a Bio-Inspired Circuit for Axon Growth

S. Jenderny | K. Ochs

2022 - IEEE BioCAS 2022, Taipei, Taiwan
6
An Ideal Electrical Circuit for Guided Axon-Growth

B. Al Beattie | S. Jenderny | K. Ochs

2022 - 2022 - ColorLine Workshop, Kiel, Germany and Oslo, Norway
5
Synthesis, Analysis and Design of Memristively Coupled Oscillators in ASIC Technology

B. Muralidhar | R. Rieger | B. Al Beattie | K. Ochs | M. Uhlmann | C. Wenger

2022 - 2022 - ColorLine Workshop, Kiel, Germany and Oslo, Norway
4
An Electrical Circuit for Hindmarsh-Rose-like Neuronal Dynamics

S. Jenderny | K. Ochs | P. Hövel

2022 - ColorLine Workshop, Kiel, Germany and Oslo, Norway
3
Neuronal network formation in Hydra and concepts for modelling

C. Noack | S. Jenderny | D. Pavleska | W. Braun | C. Giez | C. Hilgetag | A. Klimovich | K. Ochs | T. Bosch

2022 - ColorLine Workshop, Kiel, Germany and Oslo, Norway
2
Towards A Self-Organzing Neuronal Network Based on Guided Axon-Growth

D. Michaelis | K. Ochs | B. Al Beattie | S. Jenderny

2022 - IEEE 65th MWSCAS 2022, Fukuoka, Japan
1
Towards Wave Digital Modeling of Neural Pathways Using Two-Port Coupling Networks

K. Ochs | B. Al Beattie

2022 - IEEE 55th ISCAS, Austin, Texas, USA
11

2021
11
Wave Digital Emulation of Hydra's Neuronal Activity

S. Jenderny | K. Ochs | Christoph Giez | Alexander Klimovich | Thomas Bosch

2021 - DPG-Tagung, Germany
10
Decision-Making Processes by a Kuramoto Model with Hebbian Learning: Circuit Synthesis and Wave Digital Emulation

S. Jenderny | D. Michaelis | K. Ochs

2021 - DPG-Tagung, Germany
9
A Memristive Circuit for a Delay-Based Supervised Classifier

D. Michaelis | S. Jenderny | K. Ochs

2021 - DPG-Tagung, Germany
8
Mimicking Delay-Based Self-Sustaining Gait Pattern Generators

D. Michaelis | S. Jenderny | K. Ochs

2021 - DPG-Tagung, Germany
7
Towards an Improved Anticipation Circuit with Self-Organized Resonance Frequency Adaption

K. Ochs | S. Jenderny

2021 - DPG-Tagung, Germany
6
Optimal Topology Formation of Memristive Neuronal Networks

D. Michaelis | S. Jenderny | K. Ochs

2021 - DPG-Tagung, Germany
5
Solving the Vertex Cover Problem with a Wave Digital Model of an Ising Machine

B. Al Beattie | K. Ochs

2021 - DPG-Tagung, Germany
4
A Self-Organizing Gait Pattern Generator Exploiting an Electrical Circuit for Axon Growth

D. Michaelis | S. Jenderny | K. Ochs

2021 - IEEE 64th MWSCAS, Michigan, USA
3
A Memristive Circuit for Gait Pattern Classification Based on Self-Organized Axon Growth

D. Michaelis | K. Ochs | S. Jenderny

2021 - IEEE 64th MWSCAS, Michigan, USA
2
Kuramoto Model with Hebbian Learning Mimics Spatial Correlations Causing an Optical Illusion

K. Ochs | D. Michaelis | S. Jenderny | Marc-Kevin Szymendera

2021 - IEEE 64th MWSCAS, Michigan, USA
1
An Ising Machine Solving Max-Cut Problems based on the Circuit Synthesis of the Phase Dynamics of a Modified Kuramoto Model

K. Ochs | B. Al Beattie | S. Jenderny

2021 - IEEE 64th MWSCAS, Michigan, USA
2

2020
2
Mimicking Neuroplasticity by Memristive Circuits

K. Ochs | D. Michaelis | S. Jenderny | Hermann Kohlstedt

2020 - IEEE 63rd MWSCAS, Springfield, USA
1
Anticipation of Irregular Patterns: A Wave Digital Approach

K. Ochs | S. Jenderny | E. Solan

2020 - IEEE 63rd MWSCAS, Springfield, USA
13

2019
13
Synchronization of nonlinearly coupled Networks of Chua Oscillators

Petro Feketa | Alexander Schaum | Thomas Meurer | D. Michaelis | K. Ochs

2019 - IFAC 11th NOLCOS, Vienna, Austria
12
Synchronization in Memristive Circuits and Related Applications

D. Michaelis | K. Ochs

2019 - ColorLine Workshop, Kiel, Germany and Oslo, Norway
11
Wave Digital Emulation of Synapses and Neuronal Oscillators

D. Michaelis | K. Ochs

2019 - ColorLine Workshop, Kiel, Germany and Oslo, Norway
10
Towards a Self-Organizing Deciphering System Based on a Wave Digital Emulator

K. Ochs | E. Solan | D. Michaelis | Leon Schmitz

2019 - IEEE 62nd MWSCAS, Dallas, USA
9
Solving the Longest Path Problem Using a HfO2-Based Wave Digital Memristor Model

K. Ochs | D. Michaelis | E. Solan

2019 - IEEE 62nd MWSCAS, Dallas, USA
8
Wave Digital Emulation of a Memristive Circuit to Find the Minimum Spanning Tree

K. Ochs | D. Michaelis | E. Solan

2019 - IEEE 62nd MWSCAS, Dallas, USA
7
Emulation of a Navigation Processor with Physical Memristors Models

K. Ochs | E. Solan | D. Michaelis | Leonard Hilgers

2019 - 30th ISSC, Maynooth, Ireland
6
Circuit Synthesis and Electrical Interpretation of Synchronization in the Kuramoto Model

K. Ochs | D. Michaelis | Julian Roggendorf

2019 - 30th ISSC, Maynooth, Ireland
5
Towards Wave Digital Memcomputing with Physical Memristor Models

K. Ochs | E. Solan | D. Michaelis | Maximilian Herbrechter

2019 - IEEE 52nd ISCAS, Sapporo, Japan
4
HfO2-based Memristive Navigation Processor

K. Ochs | E. Solan | D. Michaelis | Leonard Hilgers

2019 - DPG-Frühjahrstagung, Aachen, Germany
3
Universal Self-Organizing Logic Gates: A Wave Digital Emulation

K. Ochs | E. Solan | D. Michaelis | Leon Schmitz

2019 - DPG-Frühjahrstagung, Aachen, Germany
2
Solving the NP-complete Subset Sum Problem with an Electrical Circuit Using Physical Memristor Models

K. Ochs | E. Solan | D. Michaelis | Maximilian Herbrechter

2019 - DPG-Frühjahrstagung, Aachen, Germany
1
Circuit Synthesis of the Kuramoto Model and Electrical Interpretation of its Synchronization Condition

K. Ochs | D. Michaelis | Julian Roggendorf | Petro Feketa | Alexander Schaum | Thomas Meurer

2019 - DPG-Frühjahrstagung, Aachen, Germany
5

2018
5
An Optimized Morris-Lecar Neuron Model Using Wave Digital Principles

K. Ochs | D. Michaelis | S. Jenderny

2018 - IEEE 61st MWSCAS, Windsor, Canada
4
Mimicking Gait Pattern Generators

K. Ochs | D. Michaelis | S. Jenderny

2018 - IEEE 61st MWSCAS, Windsor, Canada
3
Neural Network Topology Formation Using Memristive Jaumann Structures

K. Ochs | D. Michaelis

2018 - IEEE 61st MWSCAS, Windsor, Canada
2
On the Identification of Piecewise Constant LTV Systems Using a Double Periodic System Model

Tim Poguntke | K. Ochs

2018 - 29th Irish Signals and Systems Conference (ISSC)
1
Memristive Devices in a Chua Circuit: Comprizing Memory and deterministic Chaos

Tom Birkoben | Mirko Hansen | Martin Ziegler | K. Ochs | E. Solan | Hermann Kohlstedt

2018 - DPG, Berlin
8

2017
8
A System-Theoretic View on Breathing Detection Using Chirp Sequence Modulated Radar Sensors

Tim Poguntke | Davi Duarte de Carvalho Filho | K. Ochs

2017 - New Generation of CAS
7
A Consistent Modeling of Passive Memcapacitive Systems

K. Ochs | E. Solan

2017 - IEEE 60th MWSCAS, Boston, MA, USA
6
Parameter Identification of a Double Barrier Memristive Device

E. Solan | K. Ochs

2017 - IEEE 60th MWSCAS, Boston, MA, USA
5
Wave Digital Emulation of Spike-Timing Dependent Plasticity

K. Ochs | Eloy Hernandez-Guevara | E. Solan

2017 - IEEE 60th MWSCAS, Boston, MA, USA
4
Wave Digital Information Anticipator

K. Ochs | Eloy Hernandez-Guevara | E. Solan

2017 - IEEE 60th MWSCAS, Boston, MA, USA
3
A Concentrated Model of the Double Barrier Memristive Device for LTSpice Simulations

E. Solan | Sven Dirkmann | Martin Ziegler | Mirko Hansen | Hermann Kohlstedt | Thomas Mussenbrock | K. Ochs

2017 - DPG Dresden
2
An FPGA Implementation of a Memristive System Based on Wave Digital Principles

E. Solan | Benedikt Janssen | K. Ochs | Michael Hübner

2017 - DPG Dresden
1
Ion dynamics in double barrier memristive devices

Sven Dirkmann | Mirko Hansen | Martin Ziegler | E. Solan | K. Ochs | Hermann Kohlstedt | Thomas Mussenbrock

2017 - DPG Dresden
8

2016
8
Wave Digital Emulation of a Double Barrier Memristive Device

K. Ochs | E. Solan | Sven Dirkmann | Thomas Mussenbrock

2016 - IEEE 59th MWSCAS, Abu Dhabi, UAE
7
Sensitivity Analysis of Memristors Based on Emulation Techniques

K. Ochs | E. Solan

2016 - IEEE 59th MWSCAS, Abu Dhabi, UAE
6
Wave Digital Emulation of Charge- or Flux-Controlled Memristors

K. Ochs | E. Solan

2016 - IEEE 59th MWSCAS, Abu Dhabi, UAE
5
Linear Time-Variant System Identification Using FMCW Radar Systems

Tim Poguntke | K. Ochs

2016 - IEEE 59th MWSCAS, Abu Dhabi, UAE
4
Anticipation of Information - Software Demonstrator

E. Solan | K. Ochs

2016 - 1st International Workshop on Memristive devices for neuronal Systems, Kiel-Oslo
3
Wave Digital Emulation of Memristive Devices and Systems

E. Solan | K. Ochs | Sven Dirkmann | Thomas Mussenbrock

2016 - 1st International Workshop on Memristive devices for neuronal Systems, Kiel-Oslo
2
Optimal filter design for signal estimation based on linear time-variant system theory

K. Ochs | Tim Poguntke

2016 - IEEE International Symposium on Circuits and Systems (ISCAS)
1
Ion transport in memristive double barrier devices

Sven Dirkmann | Jan Trieschmann | Tobias Gerg | E. Solan | Mirko Hansen | Martin Ziegler | K. Ochs | Hermann Kohlstedt | Thomas Mussenbrock

2016 - Regensburg, Germany
2

2014
2
A systematic approach for interference alignment in CSIT-less relay-aided X-networks

D. Frank | K. Ochs | A. Sezgin

2014 - 2014 IEEE Wireless Communications and Networking Conference (WCNC)
1
Simultaneous Diagonalization: On the DoF Region of the K-user MIMO Multi-way Relay Channel

A. Chaaban | K. Ochs | A. Sezgin

2014 - European Wireless 2014, Barcelona, Spain
1

2013
1
The Degrees of Freedom of the MIMO Y-channel

A. Chaaban | K. Ochs | A. Sezgin

2013 - Proc. of IEEE International Symposium on Info. Theory (ISIT), Istanbul
1

2011
1
Eine minimale Schaltung für mehrdimensional passive Systeme

K. Ochs

2011 - 17. Steirisches Seminar über Regelungstechnik und Prozessautomatisierung (SSRP), 212-234, Leibnitz, Österreich
1

2010
1
A Modular Approach to Multidimensional Wave Digital Modeling of Passive PDEs

C. Leuer | K. Ochs

2010 - Proceeding of the 53rd IEEE Intern. Midwest Symposium on Circuits and Systems (MWSCAS), 1081-1084, Seattle, Washington
3

2009
3
Initialization of linear multistep methods in multidimensional wave digital models

G. Hetmanczyk | K. Ochs

2009 - Proc. of the 52nd IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), 786-789, Cancun, Mexiko
2
Systematic derivation of reference circuits for wave digital modeling of passive linear partial differential equations

C. Leuer | K. Ochs

2009 - Proceedings of the 52nd IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), 782-785, Cancun, Mexiko
1
Systematische Wellendigital-Synthese für eine Klasse passiver verteilter Systeme

C. Leuer | K. Ochs

2009 - 16. Steirisches Seminar über Regelungstechnik und Prozessautomatisierung (SSRP), ISBN: 978-3-901439-08-7, 117-149, Leibnitz, Austria
1

2008
1
Wave digital simulation of Burgers' equation using Gear's method

G. Hetmanczyk | K. Ochs

2008 - Proc. of the 51st IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), 161-164, Knoxville, TN
1

2007
1
A Parameterization of Band-Limited Nyquist Pulses

K. Ochs

2007 - 2007 IEEE Workshop on Signal Processing Systems, 452 - 456, Shanghai, China
1

2005
1
Ein systemtheoretischer Ansatz zur Lösung der Neutronendiffusionsgleichungen

K. Ochs

2005 - 14. Steirisches Seminar über Regelungstechnik und Prozessautomatisierung, ISBN: 3-901439-06-4, 182-202, Leibnitz, Österreich
1

2004
1
Electrical Circuit Representations of the Neutron Diffusion Equation

K. Luhmann | K. Ochs

2004 - The 47th IEEE Intern. Midwest Symposium on Circuits and Systems (MWSCAS), 405-408, Hiroshima, Japan
2

2003
2
Wellendigitalsimulation mit passiven Zweischritt-Runge-Kutta-Verfahren

D. Fränken | K. Ochs

2003 - 13. Steirisches Seminar über Regelungstechnik und Prozessautomatisierung, 131-152, Leibnitz, Österreich
1
Generation of wave digital structures for connection networks containing ideal transformers

D. Franken | J. Ochs | K. Ochs

2003 - Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03.
1

2002
1
A passive two-step Runge-Kutta method for the simulation of nonlinear electrical networks

D. Fränken | K. Ochs | M. Schmidt

2002 - Intern. Symp. on Nonlinear Theory and its Applications (NOLTA), 347-350, Xi'an, China
4

2001
4
Passive Runge-Kutta Verfahren für die Wellendigitalmodellierung nichtlinearer elektrischer Netzwerke

D. Fränken | K. Meerkötter | K. Ochs

2001 - 15. Symposium Simulationstechnik (ASIM), 43-48, Paderborn, Deutschland
3
Systematischer Entwurf von Wellendigitalstrukturen

K. Ochs | B. Stein

2001 - 15. Symposium Simulationstechnik (ASIM), 61-66, Paderborn, Deutschland
2
Wave digital simulation of nonlinear electrical networks by means of passive Runge-Kutta methods

D. Fränken | K. Ochs

2001 - IEEE Intern. Symp. on Circuits and Systems (ISCAS), vol. 3, 469-472, Sydney, Australien
1
Numerical stability properties of passive Runge-Kutta methods

D. Fränken | K. Ochs

2001 - IEEE Intern. Symp. on Circuits and Systems (ISCAS), vol. 3, 473-476, Sydney, Australien
1

1998
1
A new digital equalizer based on complex signal processing

K. Meerkötter | K. Ochs

1998 - Communication System & Digital Signal Processing (CSDSP), vol. 1, 113-116, Sheffield, UK
Publications

Journal-Papers

2

2025
2
A sufficient synchronization condition for oscillators coupled diffusively by nonlinear passive systems

Robin Lautenbacher | J. Röhrig | K. Ochs | Ralf Köhl

2025 - Discover Applied Sciences
1
Assembly of a functional neuronal circuit in embryos of an ancestral metazoan is influenced by temperature and the microbiome

Christopher Noack | S. Jenderny | Christoph Giez | Ornina Merza | Lisa-Marie Hofacker | Jörg Wittlieb | Urska Repnik | Marc Bramkamp | K. Ochs | Thomas Bosch

2025 - Proceedings of the National Academy of Sciences of the United States of America
7

2024
7
Stimulus-dependent spiking and bursting behavior in memsensor circuits: experiment and wave digital modeling

S. Jenderny | Rohit Gupta | Roshani Madurawala | Thomas Strunskus | Franz Faupel | Sören Kaps | Rainer Adelung | K. Ochs | Alexander Vahl

2024 - The European Physical Journal B
6
A memristive circuit for self-organized network topology formation based on guided axon growth

S. Jenderny | K. Ochs | Daniel Xue

2024 - Scientific Reports
5
Oscillator networks with N‐shaped nonlinearities: Electrical modeling and wave digital emulation

B. Al Beattie | J. Röhrig | Ahmed Altin | Luis Gödde | K. Ochs

2024 - International Journal of Numerical Modelling Electronic Networks Devices and Fields
4
Power consumption during forward locomotion of C. elegans: an electrical circuit simulation

S. Jenderny | K. Ochs | Philipp Hövel

2024 - The European Physical Journal B
3
Sufficient synchronization conditions for resistively and memristively coupled oscillators of FitzHugh-Nagumo-type

Robin Lautenbacher | B. Al Beattie | K. Ochs | Ralf Köhl

2024 - Discover Applied Sciences
2
Criticality in FitzHugh-Nagumo oscillator ensembles: Design, robustness, and spatial invariance

B. Al Beattie | Petro Feketa | K. Ochs | Hermann Kohlstedt

2024 - Communication Physics, Springer Nature
1
Oscillator-based optimization: design, emulation, and implementation

B. Al Beattie | Maximiliane Noll | Hermann Kohlstedt | K. Ochs

2024 - The European Physical Journal B
6

2023
6
Wave Digital Emulation of an Enhanced Compact Model for RRAM Devices With Multilevel Capability

B. Al Beattie | Emilio Pérez-Bosch Quesada | Max Uhlmann | Eduardo Pérez | Gerhard Kahmen | E. Solan | K. Ochs

2023 - IEEE Transactions on Nanotechnology
5
A better SPQR-tree decomposition of electrical circuits containing multiports and its application to wave digital emulation

B. Al Beattie | K. Ochs

2023 - International Journal of Circuit Theory and Applications
4
A memristor-based circuit approximation of the Hindmarsh–Rose model

S. Jenderny | K. Ochs | Philipp Hövel

2023 - The European Physical Journal B
3
Solving a one-dimensional moving boundary problem based on wave digital principles

B. Al Beattie | K. Ochs

2023 - Springer Nature’s Multidimensional Systems and Signal Processing
2
Synchronization measurement based on Poincaré’s sphere

K. Ochs | B. Al Beattie

2023 - Springer Nature‘s Nonlinear Dynamics
1
A network-theoretical perspective on oscillator-based Ising machines

B. Al Beattie | K. Ochs

2023 - International Journal of Circuit Theory and Applications
1

2022
1
Wave digital model of calcium-imaging-based neuronal activity of mice

S. Jenderny | K. Ochs

2022 - International Journal of Numerical Modelling: Electronic Networks, Devices and Fields
2

2021
2
An equivalent electrical circuit for the Hindmarsh-Rose model

K. Ochs | S. Jenderny

2021 - International Journal of Circuit Theory and Applications
1
Synthesis of an Equivalent Circuit for Spike-Timing-Dependent Axon Growth: What Fires Together Now Really Wires Together

K. Ochs | D. Michaelis | S. Jenderny

2021 - IEEE Transactions on Circuits and Systems I: Regular Papers
1

2020
1
Synthesis, Design, and Synchronization Analysis of Coupled Linear Electrical Networks

K. Ochs | D. Michaelis | E. Solan | Petro Feketa | Alexander Schaum | Thomas Meurer

2020 - IEEE Transactions on Circuits and Systems I: Regular Papers
2

2019
2
Towards Wave Digital Memcomputing with Physical Memristor Models

K. Ochs | D. Michaelis | E. Solan

2019 - IEEE Transactions on Circuits and Systems I: Regular Papers (invited)
1
Wave Digital Model of a TiN/Ti/HfO2/TiN Memristor

E. Solan | Eduardo Perez | D. Michaelis | Christian Wenger | K. Ochs

2019 - International Journal of Numerical Modelling: Electronic Networks, Devices and Fields
2

2018
2
Energetically Consistent Modeling of Passive Memelements

K. Ochs | E. Solan

2018 - AEÜ International Journal of Electronics and Communications
1
Wave Digital Emulation of General Memristors

E. Solan | K. Ochs

2018 - International Journal of Circuit Theory & Applications
3

2017
3
Radar-based living object protection for inductive charging of electric vehicles using two-dimensional signal processing

Tim Poguntke | Philipp Schumann | K. Ochs

2017 - Wireless Power Transfer
2
Anticipation of digital patterns

K. Ochs | Martin Ziegler | Eloy Hernandez-Guevara | E. Solan | Marina Ignatov | Mirko Hansen | Mahal Singh Gill | Hermann Kohlstedt

2017 - International Journal of Circuit Theory & Applications
1
An Enhanced Lumped Element Electrical Model of a Double Barrier Memristive Device

E. Solan | Sven Dirkmann | Mirko Hansen | Dietmar Schröder | Hermann Kohlstedt | Martin Ziegler | Thomas Mussenbrock | K. Ochs

2017 - Journal of Physics D: Applied Physics
1

2013
1
An electronic implementation of amoeba anticipation

M. Ziegler | K. Ochs | Mirko Hansen | Hermann Kohlstedt

2013 - Applied Physics A, Volume 110, Issue 2, Springer-Verlag
2

2012
2
A note on stability of linear time-variant electrical circuits having constant eigenvalues

K. Ochs

2012 - International journal of circuit theory and applications
1
On systematic wave digital modeling of passive hyperbolic partial differential equations

C. Leuer | K. Ochs

2012 - International Journal of Circuit Theory and Applications, DOI: 10.1002/cta.752
2

2011
2
A practical guide to multidimensional wave digital algorithms using an example of fluid dynamics

G. Hetmanczyk | K. Ochs

2011 - International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, vol. 24, no. 2, 154-174, DOI: 10.1002/jnm.768
1
A comprehensive analytical solution of the nonlinear pendulum

K. Ochs

2011 - European Journal of Physics, 32 (2011) 479-490
1

2009
1
Wave digital modeling of passive systems in linear state-space form

K. Ochs

2009 - International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, vol. 23, no. 1, 42-61, DOI: 10.1002/jnm.724
1

2006
1
A novel interpretation of the hyperbolization method used to solve the parabolic neutron diffusion equations by means of the wave digital concept

K. Luhmann | K. Ochs

2006 - International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, vol. 19, no. 4, 345-364
2

2004
2
Automatic Step-size Control in Wave Digital Simulation Using Passive Numerical Integration Methods

D. Fränken | K. Ochs

2004 - Intern. Journal of Electronics and Communications (AEÜ), vol. 58, no. 6, 391-401
1
Klaus Meerkötter on the occasion of his 60th birthday

D. Fränken | K. Ochs

2004 - Intern. Journal of Electronics and Communications (AEÜ), Volume 58, Pages 2-3, Issue 1
1

2003
1
Passive Runge-Kutta methods - properties, parametric representation, and order conditions

D. Fränken | K. Ochs

2003 - IT Numerical Mathematics, vol. 43, no. 2, 339-361
1

2002
1
Improving wave digital simulation by extrapolation techniques

D. Fränken | K. Ochs

2002 - Intern. Journal of Electronics and Communications (AEÜ), vol. 56, no. 5, 327-336
2

2001
2
Synthesis and design of passive Runge-Kutta methods

D. Fränken | K. Ochs

2001 - Intern. Journal of Electronics and Communications (AEÜ), vol. 55, no. 6, 417-425
1
Passive integration methods: Fundamental theory

K. Ochs

2001 - Intern. Journal of Electronics and Communications (AEÜ), vol. 55, no. 3, 153-163
1

2000
1
An extension of Darlington's algorithm for the design of elliptic filters

K. Ochs | M. Vollmer

2000 - IEEE Transactions on Signal Processing, vol. 48, no. 9, 2709-2712
Publications

Monographs

1

2021
1
A Wave Digital Approach Towards Bio-Inspired Computing Using Memristive Networks

D. Michaelis | K. Ochs

2021 - Ziegler, M., Mussenbrock, T., Kohlstedt, H. (eds) Bio-Inspired Information Pathways. Springer Series on Bio- and Neurosystems, vol 16. Springer, Cham.
2

2012
2
Theorie zeitvarianter linearer Übertragungssysteme

K. Ochs

2012 - Reihe: Kommunikationstechnik 978-3-8440-0713-8, Shaker
1
Übertragung digitaler Signale

K. Ochs

2012 - gebundenes Manuskript zur Vorlesung, 253 Seiten, Ruhr-Universität Bochum
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