Space-time coding techniques with bit-interleaved coded modulations for MIMO block-fading channels

The space-time bit-interleaved coded modulation (ST-BICM) is an efficient technique to obtain high diversity and coding gain on a block-fading MIMO channel. Its maximum-likelihood (ML) performance is computed under ideal interleaving conditions, which enables a global optimization taking into account channel coding. Thanks to a diversity upperbound derived from the Singleton bound, an appropriate choice of the time dimension of the space-time coding is possible, which maximizes diversity while minimizing complexity. Based on the analysis, an optimized interleaver and a set of linear precoders, called dispersive nucleo algebraic (DNA) precoders are proposed. The proposed precoders have good performance with respect to the state of the art and exist for any number of transmit antennas and any time dimension. With turbo codes, they exhibit a frame error rate which does not increase with frame length.Comment: Submitted to IEEE Trans. on Information Theory, Submission: January 2006 - First review: June 200

Tehisnärvivõrgud bioloogiliste andmete analüüsimiseks

Väitekirja elektrooniline versioon ei sisalda publikatsiooneTehisnärvivõrgud viimastel aastatel populaarsust kogunud masinõppe algoritm, mis on võimeline näidete põhjal õppima. Erinevad tehisnärvivõrkude alamtüübid on kasutusel mitmetes arvutiteaduse harudes: konvolutsioonilisi võrke rakendatakse objekti- ja näotuvastuses; rekurrentsed võrgud on efektiivsed kõnetuvastuses ja keeletehnoloogias. Need ei ole aga ainsad võimalikud tehisnärvivõrkude rakendamise valdkonnad - selles doktoritöös näitasime me tehisnärvivõrkude kasulikkust kahe bioloogilise probleemi lahendamisel. Esiteks küsisime, kas ainult DNA jupis sisalduva info põhjal on võimalik ennustada, kas see järjestus pärineb viiruse (ja mitte mõnda muud tüüpi organismi) genoomist. Läbi kahe publikatsiooni tõestasime me, et masinõppe algoritmid on selleks tõesti võimelised. Parima täpsuse saavutas konvolutsiooniline närvivõrk. Loodud lahendus võimaldab viroloogidel tuvastada seni tundmatuid viiruseliike, millel võib olla oluline mõju inimese tervisele. Teine käsitletud bioloogiline andmestik pärineb neuroteadusest. Imetajate hipokampuses esineb nn. koharakke, mis aktiveeruvad vaid juhul, kui loom asub teatud ruumipunktis. Näitasime, et rekurrentsete närvivõrkude abil saab vaid mõnekümne koharaku aktiivsuse põhjal ennustada roti asukohta ligi 10 cm täpsusega. Rekurrentsed võrgud osutusid efektiivsemaks kui neuroteaduses enim levinud Bayesi meetodid. Need võrgud suudavad kasutada rakkude eelnevat aktiivsust kontekstina, mis aitab täpsustada asukoha ennustust. Ka teistes neuroandmestikes võib eelnev ajuaktiivsus peegeldada konteksti, mis sisaldab olulist infot hetkel toimuva kohta. Seega võivad rekurrentsed tehisnärvivõrgud osutuda ajusignaalide mõistmisel ülimalt kasulikuks. Samuti on bioinformaatikas veel hulk andmestikke, kus konvolutsioonilised võrgud võivad osutuda efektiivsemaks kui senised meetodid. Loodame, et käesolev töö julgustab teadlasi tehisnärvivõrke proovima ka oma andmestikel.Artificial neural networks (ANNs) are a machine learning algorithm that has gained popularity in recent years. Different subtypes of ANNs are used in various fields of computer science. For example, convolutional networks are useful in object and face recognition systems; whereas recurrent neural networks are effective in speech recognition and natural language processing. However, these examples are not the only possible applications of neural nets - in this thesis we demonstrated the benefits of ANNs in analyzing two biological datasets. First, we investigated if based only on the information contained within a DNA snippet it is possible to predict if the snippet originates from a viral genome or not. Through two publications we demonstrated that machine learning algorithms can make this prediction. Convolutional neural networks (CNNs) proved to be the most accurate. The recommendation system created allows virologists to identify yet unknown viral species, which may have important effects on human health. The second biological dataset analyzed originates from neuroscience. In mammalian hippocampus there are so called place cells which activate only if the animal is in a specific location in space. We showed that recurrent neural networks (RNNs) allow to predict the animal’s location with ~10cm precision based on the activity of only a few dozen place cells. RNNs proved to be more effective than the most commonly used Bayesian methods. These networks use the past neuronal activity as a context that helps fine-tune the location predictions. Also in many other neural datasets the prior brain activity might reflect important information about the current behaviour. Hence, RNNs might turn out to be very useful in making sense of brain signals. Similarly, CNNs are likely to prove more efficient than the currently used methods on many other bioinformatics datasets. We hope this thesis encourages more scientists to try neural networks on their own datasets.https://www.ester.ee/record=b536839

Channel Coding in Molecular Communication

This dissertation establishes and analyzes a complete molecular transmission system from a communication engineering perspective. Its focus is on diffusion-based molecular communication in an unbounded three-dimensional fluid medium. As a basis for the investigation of transmission algorithms, an equivalent discrete-time channel model (EDTCM) is developed and the characterization of the channel is described by an analytical derivation, a random walk based simulation, a trained artificial neural network (ANN), and a proof of concept testbed setup. The investigated transmission algorithms cover modulation schemes at the transmitter side, as well as channel equalizers and detectors at the receiver side. In addition to the evaluation of state-of-the-art techniques and the introduction of orthogonal frequency-division multiplexing (OFDM), the novel variable concentration shift keying (VCSK) modulation adapted to the diffusion-based transmission channel, the lowcomplex adaptive threshold detector (ATD) working without explicit channel knowledge, the low-complex soft-output piecewise linear detector (PLD), and the optimal a posteriori probability (APP) detector are of particular importance and treated. To improve the error-prone information transmission, block codes, convolutional codes, line codes, spreading codes and spatial codes are investigated. The analysis is carried out under various approaches of normalization and gains or losses compared to the uncoded transmission are highlighted. In addition to state-of-the-art forward error correction (FEC) codes, novel line codes adapted to the error statistics of the diffusion-based channel are proposed. Moreover, the turbo principle is introduced into the field of molecular communication, where extrinsic information is exchanged iteratively between detector and decoder. By means of an extrinsic information transfer (EXIT) chart analysis, the potential of the iterative processing is shown and the communication channel capacity is computed, which represents the theoretical performance limit for the system under investigation. In addition, the construction of an irregular convolutional code (IRCC) using the EXIT chart is presented and its performance capability is demonstrated. For the evaluation of all considered transmission algorithms the bit error rate (BER) performance is chosen. The BER is determined by means of Monte Carlo simulations and for some algorithms by theoretical derivation

Identification of Biomolecular Conformations from Incomplete Torsion Angle Observations by Hidden Markov Models

We present a novel method for the identification of the most important conformations of a biomolecular system from molecular dynamics or Metropolis Monte Carlo time series by means of Hidden Markov Models (HMMs). We show that identification is possible based on the observation sequences of some essential torsion or backbone angles. In particular, the method still provides good results even if the conformations do have a strong overlap in these angles. To apply HMMs to angular data, we use von Mises output distributions. The performance of the resulting method is illustrated by numerical tests and by application to a hybrid Monte Carlo time series of trialanine and to MD simulation results of a DNA-oligomer

Transmitter and Receiver Architectures for Molecular Communications: A Survey on Physical Design with Modulation, Coding, and Detection Techniques

Inspired by nature, molecular communications (MC), i.e., the use of molecules to encode, transmit, and receive information, stands as the most promising communication paradigm to realize the nanonetworks. Even though there has been extensive theoretical research toward nanoscale MC, there are no examples of implemented nanoscale MC networks. The main reason for this lies in the peculiarities of nanoscale physics, challenges in nanoscale fabrication, and highly stochastic nature of the biochemical domain of envisioned nanonetwork applications. This mandates developing novel device architectures and communication methods compatible with MC constraints. To that end, various transmitter and receiver designs for MC have been proposed in the literature together with numerable modulation, coding, and detection techniques. However, these works fall into domains of a very wide spectrum of disciplines, including, but not limited to, information and communication theory, quantum physics, materials science, nanofabrication, physiology, and synthetic biology. Therefore, we believe it is imperative for the progress of the field that an organized exposition of cumulative knowledge on the subject matter can be compiled. Thus, to fill this gap, in this comprehensive survey, we review the existing literature on transmitter and receiver architectures toward realizing MC among nanomaterial-based nanomachines and/or biological entities and provide a complete overview of modulation, coding, and detection techniques employed for MC. Moreover, we identify the most significant shortcomings and challenges in all these research areas and propose potential solutions to overcome some of them.This work was supported in part by the European Research Council (ERC) Projects MINERVA under Grant ERC-2013-CoG #616922 and MINERGRACE under Grant ERC-2017-PoC #780645

Investigations in integrative and molecular bioscience

Modern biology is going through a revolution of new methods and insights resulting from the new availability of high-throughput DNA sequencing technology. I here present work contributing mathematical and computational methods for gaining insight from large DNA sequencing data sets at three distinct levels. First, I present a method for improving the accuracy and efficiency of DNA barcodes, short sequences of DNA used to label individual molecules in pooled samples. Many DNA sequencing applications depend on the use of DNA barcodes. However, errors in DNA synthesis and sequencing—substitutions, insertions, and deletions—confound the correct interpretation of these barcodes. I here present Filled/truncated Right End Edit (FREE) barcodes designed for barcode error-correction in the context of a downstream sequence. Second, I present the Chip-Hybridized Affinity Mapping Platform (CHAMP), a novel technology for repurposing used DNA sequencing chips to study the mechanism and sequence preferences of DNA-binding proteins. Since 2012, the CRISPR family of proteins have gained wide application for their efficiency and ease of use in editing genomes in vivo. Using CHAMP, I, in collaboration with experimentalists in Ilya Finkelstein’s lab, investigated the mechanism and sequence preference of the CRISPR Cascade complex, and discovered a novel periodic lack of sequence specificity in DNA binding. I further determined specific nucleotides important for recruitment of and processing by the nuclease domain, Cas3. Third, I present a meta-analysis of the order Chiroptera, the order of bats, using the new wealth of DNA sequence information of eighteen bat species. The transcriptome sequencing data for two of these bats—Hypsignathus monstrosus and Rousettus aegyptiacus, bats associated with studies of the Ebola and Marburg viruses respectively— is novel to this study. Using all this DNA sequence information, I reconstructed a high- confidence Chiropteran phylogeny and found 299 genes with signatures of positive selection, a signature associated with viral antagonism. Further study of these genes may shed light on the mechanism through which several bat viruses relevant to human health hijack the cell, including SARS, Ebola, Hendra, and NipahComputational Science, Engineering, and Mathematic

GABA-modulating bacteria of the human gut microbiota.

The gut microbiota affects many important host functions, including the immune response and the nervous system1. However, while substantial progress has been made in growing diverse microorganisms of the microbiota2, 23-65% of species residing in the human gut remain uncultured3,4, which is an obstacle for understanding their biological roles. A likely reason for this unculturability is the absence in artificial media of key growth factors that are provided by neighbouring bacteria in situ5,6. In the present study, we used co-culture to isolate KLE1738, which required the presence of Bacteroides fragilis to grow. Bioassay-driven purification of B. fragilis supernatant led to the isolation of the growth factor, which, surprisingly, is the major inhibitory neurotransmitter GABA (γ-aminobutyric acid). GABA was the only tested nutrient that supported the growth of KLE1738, and a genome analysis supported a GABA-dependent metabolism mechanism. Using growth of KLE1738 as an indicator, we isolated a variety of GABA-producing bacteria, and found that Bacteroides ssp. produced large quantities of GABA. Genome-based metabolic modelling of the human gut microbiota revealed multiple genera with the predicted capability to produce or consume GABA. A transcriptome analysis of human stool samples from healthy individuals showed that GABA-producing pathways are actively expressed by Bacteroides, Parabacteroides and Escherichia species. By coupling 16S ribosmal RNA sequencing with functional magentic resonance imaging in patients with major depressive disorder, a disease associated with an altered GABA-mediated response, we found that the relative abundance levels of faecal Bacteroides are negatively correlated with brain signatures associated with depression

Channel Estimation in Coded Modulation Systems

With the outstanding performance of coded modulation techniques in fading channels, much research efforts have been carried out on the design of communication systems able to operate at low signal-to-noise ratios (SNRs). From this perspective, the so-called iterative decoding principle has been applied to many signal processing tasks at the receiver: demodulation, detection, decoding, synchronization and channel estimation. Nevertheless, at low SNRs, conventional channel estimators do not perform satisfactorily. This thesis is mainly concerned with channel estimation issues in coded modulation systems where different diversity techniques are exploited to combat fading in single or multiple antenna systems. First, for single antenna systems in fast time-varying fading channels, the thesis focuses on designing a training sequence by exploiting signal space diversity (SSD). Motivated by the power/bandwidth efficiency of the SSD technique, the proposed training sequence inserts pilot bits into the coded bits prior to constellation mapping and signal rotation. This scheme spreads the training sequence during a transmitted codeword and helps the estimator to track fast variation of the channel gains. A comprehensive comparison between the proposed training scheme and the conventional training scheme is then carried out, which reveals several interesting conclusions with respect to both error performance of the system and mean square error of the channel estimator. For multiple antenna systems, different schemes are examined in this thesis for the estimation of block-fading channels. For typical coded modulation systems with multiple antennas, the first scheme makes a distinction between the iteration in the channel estimation and the iteration in the decoding. Then, the estimator begins iteration when the soft output of the decoder at the decoding iteration meets some specified reliability conditions. This scheme guarantees the convergence of the iterative receiver with iterative channel estimator. To accelerate the convergence process and decrease the complexity of successive iterations, in the second scheme, the channel estimator estimates channel state information (CSI) at each iteration with a combination of the training sequence and soft information. For coded modulation systems with precoding technique, in which a precoder is used after the modulator, the training sequence and data symbols are combined using a linear precoder to decrease the required training overhead. The power allocation and the placement of the training sequence to be precoded are obtained based on a lower bound on the mean square error of the channel estimation. It is demonstrated that considerable performance improvement is possible when the training symbols are embedded within data symbols with an equi-spaced pattern. In the last scheme, a joint precoder and training sequence is developed to maximize the achievable coding gain and diversity order under imperfect CSI. In particular, both the asymptotic performance behavior of the system with the precoded training scheme under imperfect CSI and the mean square error of the channel estimation are derived to obtain achievable diversity order and coding gain. Simulation results demonstrate that the joint optimized scheme outperforms the existing training schemes for systems with given precoders in terms of error rate and the amount of training overhead