arXiv subjects

Pavel Loskot

Publications and source records attributed to Pavel Loskot.

13 recordsLinked to original sources

arXiv2026

Edge-Assisted Multimodal UAV Localization with Resource-Efficient Compression and Robust Fusion

The unmanned aerial vehicles (UAVs) will play an important role in the future urban transportation systems. This requires designing robust localization schemes especially for non-cooperative UAVs that do not share any information about their movements. This paper designs a multimodal UAV localization framework which utilizes camera, LiDAR and radar sensing modalities. The underlying data processing and the subsequent inference of the UAV location are distributed among the sensing nodes and the edge server attached to the base station. The proposed UAV localization framework addresses three key challenges. First, the sensing nodes have limited computing and communication resources, and they contain only single modality sensors. Second, the multimodal data differ greatly in the sampling rates, time alignment and the encodings. Third, the changes in the environment and the hardware failures cause the modal data to degrade, or to be completely missing. The proposed localization framework utilizes several data processing modules including a information-bottleneck (IB)-based compression module that extracts the most relevant features from each modality, a time-encoding alignment module that provides the unified representation in a shared latent space, a multimodal fusion module that accounts for the degraded and missing data, and a Mamba-based regression module that predicts the present UAV location. The experiments involving a real-world dataset demonstrate that the proposed framework accurately and reliably obtains the UAV location while outperforming other existing frameworks.

eess.SP↗

arXiv2023

A Query-Response Causal Analysis of Reaction Events in Biochemical Reaction Networks

The stochastic kinetics of BRN are described by a chemical master equation (CME) and the underlying laws of mass action. The CME must be usually solved numerically by generating enough traces of random reaction events. The resulting event-time series can be evaluated statistically to identify, for example, the reaction clusters, rare reaction events, and the periods of increased or steady-state activity. The aim of this paper is to newly exploit the empirical statistics of the reaction events in order to obtain causally and anti-causally related sub-sequences of reactions. This allows discovering some of the causal dynamics of the reaction networks as well as uncovering their more deterministic behaviors. In particular, it is proposed that the reaction sub-sequences that are conditionally nearly certain or nearly uncertain can be considered as being causally related or unrelated, respectively. Moreover, since time-ordering of reactions is locally irrelevant, the reaction sub-sequences can be transformed into the reaction event sets or multi-sets. The appropriately defined distance metrics can be then used to define equivalences between the reaction sub-sequences. The proposed framework for identifying causally associated reaction sub-sequences has been implemented as a computationally efficient query-response mechanism. The framework was evaluated assuming five selected models of genetic reaction networks in seven defined numerical experiments. The models were simulated in BioNetGen using NFsim, which had to be modified to allow recording of the traces of reaction events. The generated event time-series were analyzed by Python and Matlab scripts. The whole process of data generation, analysis and visualization has been nearly fully automated using shell scripts.

q-bio.MN↗

arXiv2023

A Pulse-Shape Binary Multiplex Modulation

The root raised-cosine pulse commonly used in linear digital modulations yields exactly two intersymbol interference components from the preceding and the subsequent data symbols, provided that the roll-off factor is $100\%$ and the modulation packing factor is set to $50\%$. This can be exploited to symmetrically multiplex two data streams of transmitted symbols. Hence, the proposed scheme is referred to as pulse-shape binary multiplex modulation. The demodulation of the two multiplexed data streams at the receiver can be aided by making the streams mutually orthogonal. It can be achieved by superposition modulation with symbol-by-symbol interference cancellation, proper design of transmission sequences interleaving pilot and data symbols in order to also enable channel estimation, and using orthogonal spreading sequences. The presented numerical results indicate that the proposed modulation scheme can outperform Nyquist signaling in terms of transmission reliability or the time required for transmitting the whole sequence of data symbols. For instance, differentially encoded modulation symbols can be transmitted twice as fast by the proposed modulation scheme with a 3 dB penalty in signal-to-noise ratio over additive white Gaussian noise channels.

cs.IT↗

arXiv2022

Polynomial Distributions and Transformations

Polynomials are common algebraic structures, which are often used to approximate functions including probability distributions. This paper proposes to directly define polynomial distributions in order to describe stochastic properties of systems rather than to assume polynomials for only approximating known or empirically estimated distributions. Polynomial distributions offer a great modeling flexibility, and often, also mathematical tractability. However, unlike canonical distributions, polynomial functions may have non-negative values in the interval of support for some parameter values, the number of their parameters is usually much larger than for canonical distributions, and the interval of support must be finite. In particular, polynomial distributions are defined here assuming three forms of polynomial function. The transformation of polynomial distributions and fitting a histogram to a polynomial distribution are considered. The key properties of polynomial distributions are derived in closed-form. A piecewise polynomial distribution construction is devised to ensure that it is non-negative over the support interval. Finally, the problems of estimating parameters of polynomial distributions and generating polynomially distributed samples are also studied.

cs.IT↗

arXiv2021

pdfPapers: shell-script utilities for frequency-based multi-word phrase extraction from PDF documents

Biomedical research is intensive in processing information in the previously published papers. This motivated a lot of efforts to provide tools for text mining and information extraction from PDF documents over the past decade. The *nix (Unix/Linux) operating systems offer many tools for working with text files, however, very few such tools are available for processing the contents of PDF files. This paper reports our effort to develop shell script utilities for *nix systems with the core functionality focused on viewing and searching multiple PDF documents combining logical and regular expressions, and enabling more reliable text extraction from PDF documents with subsequent manipulation of the resulting blocks of text. Furthermore, a procedure for extracting the most frequently occurring multi-word phrases was devised and then demonstrated on several scientific papers in life sciences. Our experiments revealed that the procedure is surprisingly robust to deficiencies in text extraction and the actual scoring function used to rank the phrases in terms of their importance or relevance. The keyword relevance is strongly context dependent, the word stemming did not provide any recognizable advantage, and the stop-words should only be removed from the beginning and the end of phrases. In addition, the developed utilities were used to convert the list of acronyms and the index from a PDF e-book into a large list of biochemical terms which can be exploited in other text mining tasks. All shell scripts and data files are available in a public repository named \pp\ on the Github. The key lesson learned in this work is that semi-automated methods combining the power of algorithms with the capabilities of research experience are the most promising for improving the research efficiency.

q-bio.QM↗

arXiv2020

Polynomial Representations of High-Dimensional Observations of Random Processes

The paper investigates the problem of performing correlation analysis when the number of observations is very large. In such a case, it is often necessary to combine the random observations to achieve dimensionality reduction of the problem. A novel class of statistical measures is obtained by approximating the Taylor expansion of a general multivariate scalar function by a univariate polynomial in the variable given as a simple sum of the original random variables. The mean value of the polynomial is then a weighted sum of statistical central sum-moments with the weights being application dependent. Computing the sum-moments is computationally efficient and amenable to mathematical analysis, provided that the distribution of the sum of random variables can be obtained. Among several auxiliary results also obtained, the first order sum-moments corresponding to sample means are used to reduce the numerical complexity of linear regression by partitioning the data into disjoint subsets. Illustrative examples are provided assuming the first and the second order Markov processes.

cs.IT↗

arXiv2019

Variational Bayesian inference of hidden stochastic processes with unknown parameters

Estimating hidden processes from non-linear noisy observations is particularly difficult when the parameters of these processes are not known. This paper adopts a machine learning approach to devise variational Bayesian inference for such scenarios. In particular, a random process generated by the autoregressive moving average (ARMA) linear model is inferred from non-linearity noise observations. The posterior distribution of hidden states are approximated by a set of weighted particles generated by the sequential Monte carlo (SMC) algorithm involving sampling with importance sampling resampling (SISR). Numerical efficiency and estimation accuracy of the proposed inference method are evaluated by computer simulations. Furthermore, the proposed inference method is demonstrated on a practical problem of estimating the missing values in the gene expression time series assuming vector autoregressive (VAR) data model.

cs.LG↗

arXiv2019

Regional Coverage Analysis of LEO Satellites with Kepler Orbits

Low Earth orbit (LEO) satellites are being considered for expanding legacy terrestrial cellular networks. The end users may not be able to optimize satellite orbits and constellations, however, they can optimize locations of ground stations which aggregate terrestrial traffic and inter-connect with over-passing satellites. Such optimization requires a model of satellite visibility to decide when the satellite becomes visible to the ground station in a given geographical location. Our model assumes ideal Kepler orbits parameterized by six orbital elements to describe the satellite movements. The steps of Kepler orbit modeling are presented in detail to enable other studies requiring geometric representation of satellite trajectories in the 3D space or their 2D footprint on the Earth surface. The long-term mean time satellite visibility (MTSV) metric is defined to measure satellite visibility at a given ground station. Numerical results reveal that efficiency of optimizing the ground station locations is dependent on the satellite orbit and other satellite visibility constraints. The ground station location optimization is especially important when MTSV is improved by orthogonal time sharing schemes using multiple satellites on the same or different orbits. Similar conclusions can be drawn assuming other performance metrics such as the capacity of links between the ground station and the satellites.

physics.space-ph↗

arXiv2019

Comprehensive review of models and methods for inferences in bio-chemical reaction networks

Key processes in biological and chemical systems are described by networks of chemical reactions. From molecular biology to biotechnology applications, computational models of reaction networks are used extensively to elucidate their non-linear dynamics. Model dynamics are crucially dependent on parameter values which are often estimated from observations. Over past decade, the interest in parameter and state estimation in models of (bio-)chemical reaction networks (BRNs) grew considerably. Statistical inference problems are also encountered in many other tasks including model calibration, discrimination, identifiability and checking as well as optimum experiment design, sensitivity analysis, bifurcation analysis and other. The aim of this review paper is to explore developments of past decade to understand what BRN models are commonly used in literature, and for what inference tasks and inference methods. Initial collection of about 700 publications excluding books in computational biology and chemistry were screened to select over 260 research papers and 20 graduate theses concerning estimation problems in BRNs. The paper selection was performed as text mining using scripts to automate search for relevant keywords and terms. The outcome are tables revealing the level of interest in different inference tasks and methods for given models in literature as well as recent trends. In addition, a brief survey of general estimation strategies is provided to facilitate understanding of estimation methods which are used for BRNs. Our findings indicate that many combinations of models, tasks and methods are still relatively sparse representing new research opportunities to explore those that have not been considered - perhaps for a good reason. The paper concludes by discussing future research directions including research problems which cannot be directly deduced from presented tables.

q-bio.QM↗

arXiv2017

Bitwise Retransmission Schemes for Resources Constrained Uplink Sensor Networks

Novel bitwise retransmission schemes are devised which retransmit only the bits received with small reliability. The retransmissions are used to accumulate the reliabilities of individual bits. Unlike the conventional automatic repeat request (ARQ) schemes, the proposed scheme does not require a checksum for the error detection. The bits to be retransmitted are reported as a combination number, or two synchronized random number generators (RNGs) at the transmitter and receiver are used to greatly compress the feedback message. The bitwise retransmission decisions and/or combining can be performed after the demodulation or after the channel decoding at the receiver. The bit-error rate (BER) expressions are derived for the case of one and two retransmissions, and verified by computer simulations. Assuming three specific retransmission strategies, the scheme parameters are optimized to minimize the overall BER. For the same number of retransmissions and packet length, the proposed schemes always outperform the frequently used stop-and-wait ARQ. The impact of feedback errors is also considered. Finally, practical designs of the bitwise retransmissions for data fusion from sensor nodes in Zigbee, Wifi and Bluetooth networks are presented.

cs.IT↗

arXiv2017

Common Metrics for Analyzing, Developing and Managing Telecommunication Networks

The metrics play increasingly fundamental role in the design, development, deployment and operation of telecommunication systems. Despite their importance, the studies of metrics are usually limited to a narrow area or a well-defined objective. Our study aims to more broadly survey the metrics that are commonly used for analyzing, developing and managing telecommunication networks in order to facilitate understanding of the current metrics landscape. The metrics are simple abstractions of systems, and they directly influence how the systems are perceived by different stakeholders. However, defining and using metrics for telecommunication systems with ever increasing complexity is a complicated matter which has not been so far systematically and comprehensively considered in the literature. The common metrics sources are identified, and how the metrics are used and selected is discussed. The most commonly used metrics for telecommunication systems are categorized and presented as energy and power metrics, quality-of-service metrics, quality-of-experience metrics, security metrics, and reliability and resilience metrics. Finally, the research directions and recommendations how the metrics can evolve, and be defined and used more effectively are outlined.

cs.NI↗

arXiv2017

A Comment on "A New Degree of Freedom For Energy Efficiency of Digital Communication Systems"

This comment recalls a previously proposed encoding scheme involving two synchronized random number generators (RNGs) to compress the transmission message. It is also claimed that the recently proposed random number modulation (RNM) scheme suffers considerably from the severe error propagation, and that, in general, the overall energy consumption is minimized when all information bits are transmitted as fast as possible with the minimum latency.

cs.IT↗

arXiv2011

Order Statistics Based List Decoding Techniques for Linear Binary Block Codes

The order statistics based list decoding techniques for linear binary block codes of small to medium block length are investigated. The construction of the list of the test error patterns is considered. The original order statistics decoding is generalized by assuming segmentation of the most reliable independent positions of the received bits. The segmentation is shown to overcome several drawbacks of the original order statistics decoding. The complexity of the order statistics based decoding is further reduced by assuming a partial ordering of the received bits in order to avoid the complex Gauss elimination. The probability of the test error patterns in the decoding list is derived. The bit error rate performance and the decoding complexity trade-off of the proposed decoding algorithms is studied by computer simulations. Numerical examples show that, in some cases, the proposed decoding schemes are superior to the original order statistics decoding in terms of both the bit error rate performance as well as the decoding complexity.

cs.IT↗