Updates December 2021

(1) With Jose Matias and Slava Kungurtsev, we have a new paper accepted for the Journal of Process Control:

Jose Matias, Vyacheslav Kungurtsev and Malcolm Egan, “Simultaneous Online Model Identification and Production Optimization Using Modifier Adaptation,” accepted for publication in the Journal of Process Control, 2021.

(2) Three papers have been accepted for WCNC 2022:

Mestrah, Y., Anade, D., Savard, A., Goupil, A., Egan, M., Mary, P., Gorce, J.-M. and Clavier, L., “Unsupervised log-likelihood ratio parameter estimation for short LDPC packets in impulsive noise,” accepted for publication IEEE WCNC, 2022.

Ce Zheng, Malcolm Egan, Laurent Clavier, Anders Kalør and Petar Popovski, “Stochastic Resource Allocation for Outage Minimization in Random Access with Correlated Activation,” accepted for publication IEEE WCNC, 2022.

Homa Nikbakht, Malcolm Egan and Jean-Marie Gorce, “Joint Channel Coding of Consecutive Messages with Heterogeneous Decoding Deadlines in the Finite Blocklength Regime,” accepted for publication IEEE WCNC, 2022.


October 2021 Updates

(1) On Tuesday 19th October, I will give a talk in the Applied Analysis Seminar in the University of Graz.

Title: An Information Theoretic Perspective on Molecular Communication and the Role of Convergence to Equilibrium

Abstract: Chemical signaling is a ubiquitous component of biochemical systems and also used for their study in the form of microfluidics. Often such chemical signaling is considered a form of communication—known as molecular communication—where information is carried by molecules in a fluid rather than, for example, electromagnetic waves. In this talk, I will discuss how techniques from information theory connect with molecular communication in biochemical systems. As a key component of molecular communication is the dynamics of information-carrying molecules, stochastic reaction-diffusion models are critical for understanding the success or failure of the communication. An important aspect is therefore the study of underlying reaction-diffusion PDE models, due to the computational complexity of simulation and difficulty in obtaining tractable solutions of the Fokker-Planck equation for the stochastic model. I will highlight in particular the importance of convergence to equilibrium for molecular communications.

Updates August 2021

(1) With Lélio Chetot and Jean-Marie Gorce, we have a new paper accepted in IEEE Access:

Lélio Chetot, Malcolm Egan and Jean-Marie Gorce, “Joint Identification and Channel Estimation for Fault Detection in Industrial IoT with Correlated Sensors,” accepted for publication in IEEE Access (2021).

Congratulations to Lélio on his first journal paper!

(2) I have a new paper accepted in Frontiers in Communications and Networks:

Malcolm Egan, “Isotropic and non-isotropic signaling in multivariate alpha-stable noise,” accepted for publication in Frontiers in Communications and Networks (2021).

(3) On 30th September, in our ISTA Seminar we will be hosting Mahyar Shirvanimoghaddam from the University of Sydney (Australia):

Speaker: Dr. Mahyar Shirvanimoghaddam (University of Sydney)

Date: 1pm (CET), 30th September 2021

Title: Channel Code Design for Beyond 5G: Primitive Rateless Codes

Updates June 2021

(1) We have a new paper accepted in IEEE Communications Letters:

Ce Zheng, Malcolm Egan, Laurent Clavier, Anders Kalør and Petar Popovski, “Stochastic Resource Optimization of Random Access for Transmitters with Correlated Activation,” IEEE Communications Letters, (2021).

(2) On 10th June, Prof. Dejan Vukobratovic (University of Novi Sad) will present in our seminar:

Speaker: Prof. Dejan Vukobratovic (University of Novi Sad)

Date: 2pm (CET), 10th June 2021

Title: Designing Unequal Error Protection Codes Using Deep Autoencoders

Abstract: In this talk, we will discuss an autoencoder-based approach for designing codes that provide unequal error protection (UEP) capabilities. The proposed design accommodates both message-wise and bit-wise UEP scenarios. For both scenarios, we present the design method for the proposed autoencoder-based UEP codes and compare them with classical UEP code designs available in the literature.

Bio: Dejan Vukobratovic received a PhD degree in electrical engineering from the University of Novi Sad, Serbia, in 2008, where he is now a full Professor from 2019. During 2009 and 2010, he was Marie Curie Intra-European Fellow at the University of Strathclyde, Glasgow, UK. He published about 40 journal and 80 conference papers mainly in top-tier IEEE journals and conference venues. He received the best paper award at IEEE MMSP 2010 and his PhD student received the best student paper award at IEEE SmartGridComm 2017. His research interests are in the broad area of information and coding theory, wireless communications, distributed signal and information processing in Smart Grids, and massive machine-type communications in mobile cellular networks.

(3) On 17th June, Dr Xuewen Qian (Centrale Supelec) will present in our seminar:

Speaker: Dr. Xuewen Qian (Centrale Supelec)

Date: 2pm (CET), 17th June 2021

Title: Advanced Detection Schemes for Molecular Communications based on K-Means Clustering Approach

Abstract: We consider non-coherent detection (without channel information) for molecular communication systems in the presence of inter-symbol-interference. In particular, we study non-coherent detectors based on memory-bits-based thresholds in order to achieve low bit-error-ratio (BER) transmission. The main challenge of realizing detectors based on memory-bits-based thresholds is to obtain the channel state information based only on the received signals. We tackle this issue by reformulating the thresholds through intermediate variables, which can be obtained by clustering multi-dimensional data from the received signals, and by using the K-means clustering algorithm. In addition to estimating the thresholds, we show that the transmitted bits can be retrieved from the clustered data. To reduce clustering errors, we propose iterative clustering methods from one-dimensional to multi-dimensional data, which are shown to reduce the BER. Simulation results are presented to verify the effectiveness of the proposed methods.

Bio: Xuewen Qian received the B.Sc. and M.S. degrees with distinction in Electronic Science and Technology from Central South University, Changsha, China in 2014 and 2017, respectively. He obtained the Ph.D. degree from Paris-Saclay University, Paris, France, in 2020. In 2021, he was awarded the NEC Student Research Fellowship Award. Currently, he is a Research Fellow at CentraleSupelec, Paris-Saclay University, Paris, France. His current research interests include wireless communications, molecular communications, machine learning, deep learning, and reconfigurable intelligent surfaces.

(4) On 24th June, Dr. Vyacheslav Kungurtsev (Czech Technical University in Prague) will present in our seminar:

Speaker: Dr. Vyacheslav Kungurtsev (Czech Technical University in Prague)

Date: 2pm (CET), 24th June 2021

Title: Levenberg Marquardt Algorithms for Nonlinear Inverse Least Squares

Abstract: Levenberg Marquardt (LM) algorithms are a class of methods that add a regularization term to a Gauss Newton method to promote better convergence properties. This talk presents three works on this class of methods. The first discusses a new method that simultaneously achieves all types of state of the art convergence guarantees for unconstrained problems. Stochastic LM is discussed next, which is an algorithm to handle noisy data. An example is presented on data assimilation. Finally, a LM method is presented to handle equality constraints, with examples from inverse problems in PDEs.

Bio: Dr. Vyacheslav Kungurtsev is a researcher in the Department of Computer Science, Czech Technical University in Prague, Department of Com- puter Science, Faculty of Electrical Engineering, Prague, Czech Republic.

Updates May 2021

(1) With Bayram Akdeniz and Bao Tang, we have a new paper accepted:

Malcolm Egan, Bayram Akdeniz and Bao Quoc Tang, “Stochastic Reaction and Diffusion Systems in Molecular Communications: Recent Results and Open Problems.” Digital Signal Processing, (2021).

Updates April 2021

(1) On the 1st April, Michael Barros (Univ. Essex, UK) presented in our seminar on information theory and signal processing:

Title: Molecular Communications using Astrocytes for Boolean logic gates implementation in mammalian cells.

Abstract: In this talk we will show the use of astrocytes to realize Boolean logic gates, through manipulation of the threshold of Ca2+ ion fows between the cells based on the input signals. Through wet-lab experiments that engineer the astrocytes cells with pcDNA3.1-hGPR17 genes as well as chemical compounds, we show that both AND and OR gates can be implemented by controlling Ca2+ signals that fow through the population. A reinforced learning platform is also presented in the paper to optimize the Ca2+ activated level and time slot of input signals Tb into the gate. This design platform caters for any size and connectivity of the cell population, by taking into consideration the delay and noise produced from the signalling between the cells. To validate the efectiveness of the reinforced learning platform, a Ca2+ signalling simulator was used to simulate the signalling between the astrocyte cells. The results from the simulation show that an optimum value for both the Ca2+ activated level and time slot of input signals Tb is required to achieve up to 90% accuracy for both the AND and OR gates. Our method can be used as the basis for future Neural–Molecular Computing chips, constructed from engineered astrocyte cells, which can form the basis for a new generation of brain implants.

Bio: Dr Barros is an Assistant Professor (Lecturer) since June 2020 in the School of Computer Science and Electronic Engineering at the University of Essex, UK. He is also a MSCA-IF Research Fellow (part-time) at the Tampere University, Finland. He received the PhD in Computer Science at the Waterford Institute of Technology in 2016. He previously held multiple academic positions as a Research Fellow in the Waterford Institute of Technology, Ireland.

(2) On the 9th April, El Houcine Bergou (INRAE, France) presented in our seminar on information theory and signal processing:

Title: Stochastic Three Points Method For Unconstrained Smooth Minimization

Abstract:In this work, we consider the unconstrained minimization problem of a smooth function in a setting where only function evaluations are possible. We design a novel randomized derivative-free algorithm—the stochastic three points (STP) method—and analyze its iteration complexity. At each iteration, STP generates a random search direction according to a certain fixed probability law. Our assumptions on this law are very mild: roughly speaking, all laws which do not concentrate all measure on any halfspace passing through the origin will work. Although, our approach is designed to not explicitly use derivatives, it covers some first order methods. For instance, if the probability law is chosen to be the Dirac distribution concentrated on the sign of the gradient, then STP recovers the Signed Gradient Descent method. If the probability law is the uniform distribution on the coordinates of the gradient, then STP recovers the Randomized Coordinate Descent Method.
The complexity of STP depends on the probability law via a simple characteristic closely related to the cosine measure which is used in the analysis of deterministic direct search (DDS) methods. Unlike in DDS, where $O(n)$ ($n$ is the dimension of the problem) function evaluations must be performed in each iteration in the worst case, our method only requires two new function evaluations per iteration. Consequently, while the complexity of DDS depends quadratically on $n$, our method depends linearly on $n$.

Bio: I am a research scientist in INRAE. My research interests are in all areas that intersect with optimization, including algorithms, machine learning, statistics, and operations research. I am particularly interested in algorithms for large scale optimization including randomised and distributed optimization methods.

(3) I have a new paper to appear in the IEEE International Symposium on Information Theory (ISIT):

Malcolm Egan, “Dependence Testing via Extremes for Regularly Varying Models,” Proc. IEEE International Symposium on Information Theory (ISIT)(2021).

Updates February 2021

(1) With Bayram Akdeniz, we have a new paper accepted:

Bayram Akdeniz and Malcolm Egan, “Molecular Communication for Equilibrium State Estimation in Biochemical Processes on a Lab-on-a-Chip,” accepted for publication in IEEE Transactions on NanoBioscience, (2021).

(2) On 11th of February, Daryus Chandra (Southampton) presented in our seminar on Information Theory and Related Topics.

Speaker: Dr Daryus Chandra (University of Southampton, UK)

Title: Quantum Communications over Noisy Entanglement

Abstract: Within the Quantum Internet framework, multiple quantum devices are interconnected via pre-shared maximally-entangled quantum states for enabling various applications, including the on-demand classical and quantum communication. Hence, the pre-shared entanglement, which is constituted by the EPR pair, can be viewed as the primary consumable resources within the Quantum Internet. However, the generation and the distribution of the EPR pairs are subject to quantum decoherence imposed by the quantum channels, which will manifest as quantum errors. Similar to the classical domain, the quantum errors imposed by the quantum channels can be mitigated using quantum error-correction codes. In this talk, we will explore two approaches for achieving reliable quantum communication over noisy entanglement by incorporating quantum error-correction codes. More specifically, the first approach is constituted by the consecutive steps of quantum entanglement distillation followed by quantum teleportation, while the second approach can be viewed as the direct quantum communication over noisy entanglement. We will also discuss the pros and the cons of each approach while examining their compatibilities for a broader range of applications for the Quantum Internet framework.

Bio: Daryus Chandra is a research fellow at the Next-Generation Wireless Research Group, School of Electronics and Computer Science, University of Southampton, UK. He received the B.Eng. and M.Eng. degree from the Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada, Indonesia, in 2013 and 2014, respectively. He obtained his PhD with the Next-Generation Wireless Research Group, School of Electronics and Computer Science, University of Southampton, UK, in 2020. He returned to Southampton in 2021 after completing a one-year postdoctoral research fellowship at Quantum Internet Research Group, University of Naples Federico II, Italy.

Time: 14h on the 11th February 2021.

(3) On 18th February, Prof. Datta (Univ. Idaho) presented in our seminar on Information Theory and Related Topics:

Speaker: Prof. Somantika Datta

Title: Construction and properties of certain real multi-angle tight frames


Frames are now standard tools in signal processing, and have applications ranging from compressed sensing, to communication systems and quantum sensing. Designing frames with some special structure such as equiangularity and tightness is highly desirable in applications. However, constructing equiangular tight frames (ETFs) with a given size in a specific dimension can be difficult or impossible in some cases. This leads one to consider the construction of frames with few distinct angles among pairs of frame vectors. In the special case of d+1 vectors in a d-dimensional space, it is well known that the vertices of a regular simplex will give an ETF. Using this, we will show a specific construction which, for a given dimension d and integer 1 < k ≤ d, gives a real unit norm tight frame such that the number of distinct angles among the vectors is bounded above by k. We will present several properties of this multi-angle tight frame. We also show how one can strategically choose subsets of such a multi-angle tight frame that will be equiangular or orthogonal. This property is meaningful in the context of erasures. We will also discuss a connection between certain unit norm tight frames with three angles and adjacency matrices of regular graphs.

Bio: Somantika Datta is an associate professor of mathematics at the University of Idaho. She received a Ph.D. in mathematics from the University of Maryland, College Park. This was followed by postdoctoral positions at Arizona State University and Princeton University. Her research interests lie in the area of applied harmonic analysis with focus on frame theory and applications in signal processing.

(4) On the 25th February, Bapi Chatterjee (IST Austria) will present in our seminar on Information Theory and Related Topics.

Speaker: Dr Bapi Chatterjee (IST Austria)

Title: Elastic Consistency: A General Consistency Model for Distributed Stochastic Gradient Descent

Abstract: One key element behind the progress of machine learning in recent years has been the ability to train machine learning models in large-scale distributed shared-memory and message-passing environments. Most of these models are trained employing variants of stochastic gradient descent (SGD) based optimization. In this work, we introduce a general consistency condition covering communication-reduced and asynchronous distributed SGD implementations. Our framework, called elastic consistency, decouples the system-specific aspects of the implementation from the SGD convergence requirements, giving a general way to obtain convergence bounds for a wide variety of distributed SGD methods used in practice. Elastic consistency can be used to re-derive or improve several previous convergence bounds in message-passing and shared-memory settings, but also to analyze new models and distribution schemes. In particular, we propose and analyze a new  synchronization-avoiding scheme for distributed SGD, and show that it can be used to efficiently train deep convolutional models for image classification.

Bio: Bapi Chatterjee is a Postdoc Fellow at IST Austria working in the area of Distributed Machine Learning. He is also a visiting scientist at EmbeDL AB, Gothenburg, Sweden. Prior to joining IST Austria he worked as a Research Staff Member with IBM India Research Lab, where he worked on Blockchains and related technologies. He obtained a PhD in Computer Science and Engineering from Chalmers University of Technology, Sweden. His current research interests includes Distributed Machine Learning, Neural Architecture Search, Concurrent Data Structures and Learned Index Structures.

January 2021 Updates

(1) With Bayram Akdeniz (Univ. Oslo) and Bao Tang (Univ. Graz), we have a new paper accepted in IEEE Transactions on Molecular, Biological and Multi-Scale Communications:

Malcolm Egan, Bayram Akdeniz and Bao Quoc Tang, “Equilibrium signaling in spatially inhomogeneous diffusion and external forces,” accepted for publication in IEEE Transactions on Molecular, Biological and Multi-Scale Communications, (2021) [HAL]

(2) I will be presenting a lecture in the International Symposium on Molecular and Biological Communications, held online 1-5th February 2021:

Title: Stochastic Reaction-Diffusion Systems in Molecular Communication: Channel Models, Inference, and Coexistence

Abstract: A fundamental aspect of molecular communication is the motion of colloidal information-carrying molecules. To capture interactions between these molecules and other comprising the fluid, stochastic models play a key role. While the simplest family of these models—namely the Wiener process—-is well known, it is also possible to account for inhomogeneous diffusion, external forces, and general chemical reactions. In this talk, I will begin by overviewing this general family of models (including Langevin diffusions and the reaction-diffusion master equation) and present a recently proposed approach for developing near-optimal detection rules, called equilibrium signaling. I will then turn to other applications of stochastic reaction-diffusion systems in molecular communication related to molecular circuits for inference and modeling interactions with external biological systems to support coexistence, in the process identifying open challenges.

December 2020 Updates

(1) On the 8th of December, Ce Zheng, a PhD student I am co-supervising with Prof. Laurent Clavier (IMT Lille-Douai, France) and Jean-Marie Gorce (INSA-Lyon, France), defended his thesis entitled: “Impulsive and Dependent Interference in IoT Networks”. The jury consisted of:

Prof. Claude Oestges (Ecole Polytechnique de Louvain, Belgium)
Assis. Prof. Lina Mroueh (Institut Supérieur d’Electronique de Paris, France)
Assoc. Prof. Mylene Pischella (Conservatoire National des Arts et Métiers, France)
Prof. Jean-François Hélard (INSA Rennes)
Assoc. Prof. Troels Pedersen (Univ. Aalborg, Denmark)
Prof. Gareth Peters (Heriot-Watt, UK)

Congratulations Chris!

(2) On the 10th of December, Ignacio Rodriguez (Aalborg University) will be presenting in our (currently online) seminar on information theory and related topics. Details are below:

Title: Experimental Research on Wireless Systems for Industrial Automation

Abstract: The fourth industrial revolution – or Industry 4.0 (I4.0), will introduce major shifts in the way that products will be manufactured in the future. By integrating different cyberphysical systems (CPS), Internet-of-Things (IoT) technologies and cloud computing; the factories of the future will be equipped with highly flexible manufacturing equipment offering also a high reliability, thereby increasing the overall production throughput. One of the key enablers for such revolution is wireless communication. By replacing existing wirelines in the current industrial equipment with wireless technologies, the overall cost of deployment will be reduced, while at the same time a faster re-configuration of the smart production facilities will be  enabled. Moreover, the use of wireless technologies will also allow for new industrial use cases requiring full mobility support such as autonomous robots moving items over different workstations in the factory for the sake of manufacturing customized products.
During this talk, the AAU Industrial Automation Applied Research Flow will be introduced and illustrated with application examples detailing the different steps from understanding the needs of a factory and the specific communication requirements of industrial use cases; to the final deployment and optimization of the wireless solutions.

Bio: Ignacio Rodriguez received the B.Sc. and M.Sc. degrees in Telecommunication Engineering from University of Oviedo, Spain, and the M.Sc. degree in Mobile Communications and the Ph.D. degree in Wireless Communications from Aalborg University, Denmark. Since December 2016, he has been a Postdoctoral Researcher at the same institution, where he is currently coordinating the Industry 4.0 experimental research activities at the Wireless Communication Networks Section and the AAU 5G Smart Production Lab in collaboration with the Department of Materials and Production. Ignacio is also an External Research Engineer with Nokia Bell Labs, where he is involved in 3GPP and ITU-R standardization activities. His research interests are mainly related to radio propagation, channel modeling, radio network planning and optimization, machine-to-machine communications, ultra-reliable and low-latency communications, 5G and Industrial IoT. He is a co-recipient of the IEEE VTS 2017 Neal Shepherd Memorial Best Propagation Paper Award, and in 2019, he was awarded with the 5G-prize by the Danish Energy Agency and the Danish Society of Telecommunication Engineers.

(3) With Vyacheslav Kungurtsev, Bapi Chatterjee and Dan Alistarh, we have a new paper accepted to AAAI 2021:

Kungurtsev, V., Egan, M., Chatterjee, B. and Alistarh, D., “Asynchronous Optimization Methods for Efficient Training of Deep Neural Networks with Guarantees”, AAAI Conference on Artificial Intelligence, (2021).

Abstract: Asynchronous distributed algorithms are a popular way to reduce synchronization costs in large-scale optimization, and in particular for neural network training. However, for nonsmooth and nonconvex objectives, few convergence guarantees exist beyond cases where closed-form proximal operator solutions are available. 
As training most popular deep neural networks corresponds to optimizing nonsmooth and nonconvex objectives, there is a pressing need for such convergence guarantees. In this paper, we analyze for the first time the convergence of stochastic \emph{asynchronous} optimization for this general class of objectives. In particular, we focus on stochastic subgradient methods allowing for block variable partitioning, where the shared model is asynchronously updated by concurrent processes. To this end, we use a probabilistic model which captures key features of real asynchronous scheduling between concurrent processes. Under this model, we establish convergence with probability one to an invariant set for stochastic subgradient methods with momentum. 
From a practical perspective, one issue with the family of algorithms that we consider is that they are not efficiently supported by machine learning frameworks, which mostly focus on distributed data-parallel strategies. 
To address this, we propose a new implementation strategy for shared-memory based training of deep neural networks for a partitioned but shared model in single- and multi-GPU settings.  
Based on this implementation, we achieve on average ~1.2x speed-up in comparison to state-of-the-art training methods for popular image classification tasks, without compromising accuracy.

(4) On the 17th of December, Lucien Etienne (IMT Lille-Douai) will be presenting in our (currently online) seminar on information theory and related topics. Details are below:

Speaker: Prof. Lucien Etienne (IMT Lille-Douai, France)

Date: 2pm (CET), 17th December 2020

Title: Self trigger co-design using LASSO regression

Abstract: Networked systems have become more and more pervasive in many modern industrial application.
A good justification for their deployment is that they can be cheaper/faster to set in place as well are scalable while also enabling lower maintenance cost. In the past decade a new paradigm has been developed where the controller is not sampled periodically (i.e. with a time–triggered policy), but rather sampled when some condition has been met (Usually a stability or performance criterion being violated). After recalling some general element on classical control scheme ( Linear Quadratic regulator and model predictive control)
In this talk, the control of a linear time invariant system with self triggered sampling is considered .
In order to address  the controller computation and the future sampling schedule a sparse optimization problem will be considered. A relaxation of the optimal self triggered control can be formulated as a LASSO regression. Using the properties of the solution of the Lasso regression it is shown how to obtain a controller ensuring  practical or asymptotic stability while reducing sampling of the control action.

Bio: Dr. Lucien Etienne received a M.Sc. Degree in applied mathematics at the INSA Rouen in 2012 and a joint Ph.D. in automatic control from the university of L’aquila and the university of Cergy-Pontoise in 2016.  From 2016 to 2017 he was a post doctoral researcher at Inria Lille-Nord Europe. Since 2017 He is  an associate professor at Institut Mines-Télécom Lille Douai. His research interests include switched and hybrid systems, observer synthesis and sampled data systems.

November 2020 Updates

(1) On 17th November, Ido Nevat (TUMCREATE) presented in our seminar on information theory and related topics:

Title: Spatial Field Reconstruction and Sensor Selection in Heterogeneous Sensor Networks with Stochastic Energy Harvesting


We address the two fundamental problems of spatial field reconstruction and sensor selection in heterogeneous sensor networks. 
We consider the case where two types of sensors are deployed: the first consists of expensive, high quality sensors; and the second, of cheap low quality sensors, which are activated only if the intensity of the spatial field exceeds a pre-defined activation threshold (e.g., wind sensors). In addition, these sensors are powered by means of energy harvesting and their time varying energy status impacts on the accuracy of the measurement that may be obtained. 
We then address the following two important problems: (i) how to efficiently perform spatial field reconstruction based on measurements obtained simultaneously from both networks; and (ii) how to perform query based sensor set selection with predictive MSE performance guarantee. 

To overcome this problem, we solve the first problem by developing a low complexity algorithm based on the spatial best linear unbiased estimator (S-BLUE).  Next, building on the S-BLUE, we address the second problem, and develop an efficient algorithm for query based sensor set selection with performance guarantee. Our algorithm is based on the Cross Entropy method which solves the combinatorial optimization problem in an efficient manner. 
We present a comprehensive study of the performance gain that can be obtained by augmenting the high-quality sensors with low-quality sensors using both synthetic and real insurance storm surge database known as the Extreme Wind Storms Catalogue.

Bio: Ido Nevat received the B.Sc. degree in electrical engineering from the Technion-Israel Institute of Technology, Haifa, Israel, in 1998 and the Ph.D. degree in electrical engineering from the University of New South Wales, Sydney, NSW, Australia, in 2010. 
Between 2010 and 2013, he was a Postdoctoral Research Fellow with the Wireless and Networking Technologies Laboratory at CSIRO, Australia. 
Between 2013 and 2016, he was a Scientist with the Institute for Infocomm Research (I2R), Singapore. 
Since 2017, he has been a team leader and PI of the Cooling Singapore project at TUMCREATE. 
His main research interests include statistical signal processing, machine learning, and Bayesian statistics 

(2) On 16th November, I presented an invited paper with Bayram Akdeniz in the 1st ACM International Workshop on Nanoscale Computing, Communication, and Applications.

(3) With Bayram Akdeniz and Bao Tang, we have a new paper accepted in IEEE Transactions on Communications:

Title: Equilibrium Signaling: Molecular Communication Robust to Geometry Uncertainties

Abstract: A basic property of any diffusion-based molecular communication system is the geometry of the enclosing container. In particular, the geometry influences the system’s behavior near the boundary and in all existing modulation schemes governs receiver design. However, it is not always straightforward to characterize the geometry of the system. This is particularly the case when the molecular communication system operates in vitro, where the geometry may be complex or dynamic. In this paper, we propose a new scheme-called equilibrium signaling-which is robust to uncertainties in the container geometry. In particular, receiver design only depends on the relative volumes of the transmitter or receiver, and the entire container. Our scheme relies on reversible reactions in the transmitter and the receiver, which ensure the existence of an equilibrium state into which information is encoded. In this case, we derive near optimal detection rules and develop a simple and effective estimation method to obtain the container volume. We also show that equilibrium signaling can outperform classical modulation schemes, such as concentration shift keying, under practical sampling constraints imposed by biological oscillators.

(4) With Laurent Clavier, Troels Pedersen, Ignacio Rodriguez, Mads Lauridsen, we have a new paper accepted in IEEE Communications Letters:

Title: Experimental Evidence for Heavy Tailed Interference in the IoT

Abstract: 5G and beyond sees an ever increasing density of connected things. As not all devices are coordinated, there are limited opportunities to mitigate interference. As such, it is crucial to characterize the interference in order to understand its impact on coding, waveform and receiver design. While a number of theoretical models have been developed for the interference statistics in communications for the IoT, there is very little experimental validation. In this paper, we address this key gap in understanding by performing statistical analysis on recent measurements in the unlicensed 863 MHz to 870 MHz band in different regions of Aalborg, Denmark. In particular, we show that the measurement data suggests the distribution of the interference power is heavy tailed, confirming predictions from theoretical models.

(5) On Thursday 25 November, Howard Yang (Zhejiang University) will present in our seminar on information theory and related topics:

Title: Spatiotemporal Modeling of Wireless Networks

Abstract: The rapid growth of wireless applications has brought along new challenges for the next generation network, which is expected to manage a massive number of devices in real-time under a highly dynamic environment. To give an adequate response, it is of necessity to develop an analytical model with which designers can build intuitions, grasp insights, and identify critical issues.

In this talk, I will describe a framework for the analysis of large-scale wireless networks in which the transceivers interact with each other through the interference they caused and hence are correlated in both space and time. The analysis straddles stochastic geometry and queueing theory to cope with the issues of spatially interacting queues, and arrive at handy expressions for the SINR distribution. As a result, a wide variety of systems/architecture can be devised based on this theoretical foundation. Specifically, I will demonstrate how to adopt such a mathematical model to the analysis of two particular network metrics, i.e., the packet delay and age of information, and the subsequent network deployment guidelines based on the analytical results.