arXiv subjects

Manuel Stein

Publications and source records attributed to Manuel Stein.

15 recordsLinked to original sources

arXiv2021

Video-based Analysis of Soccer Matches

With the increasingly detailed investigation of game play and tactics in invasive team sports such as soccer, it becomes ever more important to present causes, actions and findings in a meaningful manner. Visualizations, especially when augmenting relevant information directly inside a video recording of a match, can significantly improve and simplify soccer match preparation and tactic planning. However, while many visualization techniques for soccer have been developed in recent years, few have been directly applied to the video-based analysis of soccer matches. This paper provides a comprehensive overview and categorization of the methods developed for the video-based visual analysis of soccer matches. While identifying the advantages and disadvantages of the individual approaches, we identify and discuss open research questions, soon enabling analysts to develop winning strategies more efficiently, do rapid failure analysis or identify weaknesses in opposing teams.

cs.CV↗

arXiv2019

Traversing Virtual Network Functions from the Edge to the Core: An End-to-End Performance Analysis

Future mobile networks supporting Internet of Things are expected to provide both high throughput and low latency to user-specific services. One way to overcome this challenge is to adopt network function virtualization and Multi-access edge computing (MEC). In this paper, we analyze an end-to-end communication system that consists of both MEC servers and a server at the core network hosting different types of virtual network functions. We develop a queueing model for the performance analysis of the system consisting of both processing and transmission flows. The system is decomposed into subsystems which are independently analyzed in order to approximate the behaviour of the original system. We provide closed-form expressions of the performance metrics such as system drop rate and average number of tasks in the system. Simulation results show that our approximation performs quite well. By evaluating the system under different scenarios, we provide insights for the decision making on traffic flow control and its impact on critical performance metrics.

cs.NI↗

arXiv2019

Adaptive Event Dispatching in Serverless Computing Infrastructures

Serverless computing is an emerging Cloud service model. It is currently gaining momentum as the next step in the evolution of hosted computing from capacitated machine virtualisation and microservices towards utility computing. The term "serverless" has become a synonym for the entirely resource-transparent deployment model of cloud-based event-driven distributed applications. This work investigates how adaptive event dispatching can improve serverless platform resource efficiency and contributes a novel approach that allows for better scaling and fitting of the platform's resource consumption to actual demand.

cs.DC↗

arXiv2019

Interim Report on Adaptive Event Dispatching in Serverless Computing Infrastructures

Serverless computing is an emerging service model in distributed computing systems. The term captures cloud-based event-driven distributed application design and stems from its completely resource-transparent deployment model, i.e. serverless. This work thesisizes that adaptive event dispatching can improve current serverless platform resource efficiency by considering locality and dependencies. These design contemplations have also been formulated by Hendrickson et al., which identifies the requirement that "Serverless load balancers must make low-latency decisions while considering session, code and data locality". This interim report investigates the economical importance of the emerging trend and asserts that existing serverless platforms still do not optimize for data locality, whereas a variety of scheduling methods are available from distributed computing research which have proven to increase resource efficiency.

cs.DC↗

arXiv2018

The Serverless Scheduling Problem and NOAH

The serverless scheduling problem poses a new challenge to Cloud service platform providers because it is rather a job scheduling problem than a traditional resource allocation or request load balancing problem. Traditionally, elastic cloud applications use managed virtual resource allocation and employ request load balancers to orchestrate the deployment. With serverless, the provider needs to solve both the load balancing and the allocation. This work reviews the current Apache OpenWhisk serverless event load balancing and a noncooperative game-theoretic load balancing approach for response time minimization in distributed systems. It is shown by simulation that neither performs well under high system utilization which inspired a noncooperative online allocation heuristic that allows tuning the trade-off between for response time and resource cost of each serverless function.

cs.DC↗

arXiv2018

A Pessimistic Approximation for the Fisher Information Measure

The problem of determining the intrinsic quality of a signal processing system with respect to the inference of an unknown deterministic parameter $θ$ is considered. While the Fisher information measure $F(θ)$ forms a classical tool for such a problem, direct computation of the information measure can become difficult in various situations. For the estimation theoretic performance analysis of nonlinear measurement systems, the form of the likelihood function can make the calculation of the information measure $F(θ)$ challenging. In situations where no closed-form expression of the statistical system model is available, the analytical derivation of $F(θ)$ is not possible at all. Based on the Cauchy-Schwarz inequality, we derive an alternative information measure $S(θ)$. It provides a lower bound on the Fisher information $F(θ)$ and has the property of being evaluated with the mean, the variance, the skewness and the kurtosis of the system model at hand. These entities usually exhibit good mathematical tractability or can be determined at low-complexity by real-world measurements in a calibrated setup. With various examples, we show that $S(θ)$ provides a good conservative approximation for $F(θ)$ and outline different estimation theoretic problems where the presented information bound turns out to be useful.

cs.IT↗

arXiv2017

Visual Analysis of Spatio-Temporal Event Predictions: Investigating the Spread Dynamics of Invasive Species

Invasive species are a major cause of ecological damage and commercial losses. A current problem spreading in North America and Europe is the vinegar fly Drosophila suzukii. Unlike other Drosophila, it infests non-rotting and healthy fruits and is therefore of concern to fruit growers, such as vintners. Consequently, large amounts of data about infestations have been collected in recent years. However, there is a lack of interactive methods to investigate this data. We employ ensemble-based classification to predict areas susceptible to infestation by D. suzukii and bring them into a spatio-temporal context using maps and glyph-based visualizations. Following the information-seeking mantra, we provide a visual analysis system Drosophigator for spatio-temporal event prediction, enabling the investigation of the spread dynamics of invasive species. We demonstrate the usefulness of this approach in two use cases.

cs.HC↗

arXiv2016

DOA Parameter Estimation with 1-bit Quantization - Bounds, Methods and the Exponential Replacement

While 1-bit analog-to-digital conversion (ADC) allows to significantly reduce the analog complexity of wireless receive systems, using the exact likelihood function of the hard-limiting system model in order to obtain efficient algorithms in the digital domain can make 1-bit signal processing challenging. If the signal model before the quantizer consists of correlated Gaussian random variables, the tail probability for a multivariate Gaussian distribution with N dimensions (general orthant probability) is required in order to formulate the likelihood function of the quantizer output. As a closed-form expression for the general orthant probability is an open mathematical problem, formulation of efficient processing methods for correlated and quantized data and an analytical performance assessment have, despite their high practical relevance, only found limited attention in the literature on quantized estimation theory. Here we review the approach of replacing the original system model by an equivalent distribution within the exponential family. For 1-bit signal processing, this allows to circumvent calculation of the general orthant probability and gives access to a conservative approximation of the receive likelihood. For the application of blind direction-of-arrival (DOA) parameter estimation with an array of K sensors, each performing 1-bit quantization, we demonstrate how the exponential replacement enables to formulate a pessimistic version of the Cramér-Rao lower bound (CRLB) and to derive an asymptotically achieving conservative maximum-likelihood estimator (CMLE). The 1-bit DOA performance analysis based on the pessimistic CRLB points out that a low-complexity radio front-end design with 1-bit ADC is in particular suitable for blind wireless DOA estimation with a large number of array elements operating in the medium SNR regime.

cs.IT↗

arXiv2016

Measurement-driven Quality Assessment of Nonlinear Systems by Exponential Replacement

We discuss the problem how to determine the quality of a nonlinear system with respect to a measurement task. Due to amplification, filtering, quantization and internal noise sources physical measurement equipment in general exhibits a nonlinear and random input-to-output behaviour. This usually makes it impossible to accurately describe the underlying statistical system model. When the individual operations are all known and deterministic, one can resort to approximations of the input-to-output function. The problem becomes challenging when the processing chain is not exactly known or contains nonlinear random effects. Then one has to approximate the output distribution in an empirical way. Here we show that by measuring the first two sample moments of an arbitrary set of output transformations in a calibrated setup, the output distribution of the actual system can be approximated by an equivalent exponential family distribution. This method has the property that the resulting approximation of the statistical system model is guaranteed to be pessimistic in an estimation theoretic sense. We show this by proving that an equivalent exponential family distribution in general exhibits a lower Fisher information measure than the original system model. With various examples and a model matching step we demonstrate how this estimation theoretic aspect can be exploited in practice in order to obtain a conservative measurement-driven quality assessment method for nonlinear measurement systems.

cs.IT↗

arXiv2016

Performance Analysis for Pilot-based 1-bit Channel Estimation with Unknown Quantization Threshold

Parameter estimation using quantized observations is of importance in many practical applications. Under a symmetric $1$-bit setup, consisting of a zero-threshold hard-limiter, it is well known that the large sample performance loss for low signal-to-noise ratios (SNRs) is moderate ($\frac{2}π$ or $-1.96$dB). This makes low-complexity analog-to-digital converters (ADCs) with $1$-bit resolution a promising solution for future wireless communications and signal processing devices. However, hardware imperfections and external effects introduce the quantizer with an unknown hard-limiting level different from zero. In this paper, the performance loss associated with pilot-based channel estimation, subject to an asymmetric hard limiter with unknown offset, is studied under two setups. The analysis is carried out via the Cramér-Rao lower bound (CRLB) and an expected CRLB for a setup with random parameter. Our findings show that the unknown threshold leads to an additional information loss, which vanishes for low SNR values or when the offset is close to zero.

cs.IT↗

arXiv2015

Asymptotic Performance Analysis for 1-bit Bayesian Smoothing

Energy-efficient signal processing systems require estimation methods operating on data collected with low-complexity devices. Using analog-to-digital converters (ADC) with $1$-bit amplitude resolution has been identified as a possible option in order to obtain low power consumption. The $1$-bit performance loss, in comparison to an ideal receiver with $\infty$-bit ADC, is well-established and moderate for low SNR applications ($2/π$ or $-1.96$ dB). Recently it has been shown that for parameter estimation with state-space models the $1$-bit performance loss with Bayesian filtering can be significantly smaller ($\sqrt{2/π}$ or $-0.98$ dB). Here we extend the analysis to Bayesian smoothing where additional measurements are used to reconstruct the current state of the system parameter. Our results show that a $1$-bit receiver performing smoothing is able to outperform an ideal $\infty$-bit system carrying out filtering by the cost of an additional processing delay $Δ$.

cs.IT↗

arXiv2015

Asymptotic Parameter Tracking Performance with Measurement Data of 1-bit Resolution

The problem of signal parameter estimation and tracking with measurement data of low resolution is considered. In comparison to an ideal receiver with infinite receive resolution, the performance loss of a simplistic receiver with 1-bit resolution is investigated. For the case where the measurement data is preprocessed by a symmetric hard-limiting device with 1-bit output, it is well-understood that the performance for low SNR channel parameter estimation degrades moderately by 2/pi (-1.96 dB). Here we show that the 1-bit quantization loss can be significantly smaller if information about the temporal evolution of the channel parameters is taken into account in the form of a state-space model. By the analysis of a Bayesian bound for the achievable tracking performance, we attain the result that the quantization loss in dB is in general smaller by a factor of two if the channel evolution is slow. For the low SNR regime, this is equivalent to a reduced loss of sqrt(2/pi) (-0.98 dB). By simulating non-linear filtering algorithms for a satellite-based ranging application (GPS) and a UWB channel estimation problem, both with low-complexity 1-bit analog-to-digital converter (ADC) at the receiver, we verify that the analytical characterization of the tracking error is accurate. This shows that the performance loss due to observations with low amplitude resolution can, in practice, be much less pronounced than indicated by classical results. Finally, we discuss the implication of the result for medium SNR applications like channel estimation in the context of mobile wireless communications.

cs.IT↗

arXiv2014

Towards Optimal Schemes for the Half-Duplex Two-Way Relay Channel

A restricted two-way communication problem in a small fully-connected network is investigated. The network consists of three nodes, all having access to a common channel with half-duplex constraint. Two nodes want to establish a dialog while the third node can assist in the bi-directional transmission process. All nodes have agreed on a transmission protocol a priori and the problem is restricted to the dialog encoders not being allowed to establish a cooperation by the use of previous receive signals. The channel is referred to as the restricted half-duplex two-way relay channel. Here the channel is defined and an outer bound on the achievable rates is derived by the application of the cut-set theorem. This shows that the problem consists of six parts. We propose a transmission protocol which takes into account all possible transmit-receive configurations of the network and performs partial decoding of the messages at the relay as well as sequential decoding at the dialog nodes. By the use of random codes and suboptimal decoders, two inner bound on the achievable rates are derived. Restricting to the suggested strategies and fixed input distributions it is argued to be possible to determine optimal transmission schemes with respect to various reasonable objectives at low complexity. In comparison to two-way communication without relay, simulations for an AWGN channel model then show that it is possible to simultaneously increase the communication rates of both dialog messages and to outperform relaying strategies that ignore an available direct path.

cs.IT↗

arXiv2014

Overdemodulation for High-Performance Receivers with Low-Resolution ADC

The design of the analog demodulator for receivers with low-resolution analog-to-digital converters (ADC) is investigated. For infinite ADC resolution, demodulation to baseband with M = 2 orthogonal sinusoidal functions (quadrature demodulation) is an optimum design choice. For receive systems which are restricted to ADC with low amplitude resolution we show here that this classical demodulation approach is suboptimal. To this end we analyze the theoretical channel parameter estimation performance based on a simple pessimistic characterization of the Fisher information measure when forming M > 2 analog demodulation channels prior to an ADC with 1-bit amplitude resolution. In order to emphasize that this inside is also true for communication problems, we provide an additional discussion on the behavior of the Shannon information measure under overdemodulation and 1-bit quantization.

cs.IT↗

arXiv2014

A Lower Bound for the Fisher Information Measure

The problem how to approximately determine the absolute value of the Fisher information measure for a general parametric probabilistic system is considered. Having available the first and second moment of the system output in a parametric form, it is shown that the information measure can be bounded from below through a replacement of the original system by a Gaussian system with equivalent moments. The presented technique is applied to a system of practical importance and the potential quality of the bound is demonstrated.

cs.IT↗