Multiclass classification of microarray data samples with a reduced number of genes

<p>Abstract</p> <p>Background</p> <p>Multiclass classification of microarray data samples with a reduced number of genes is a rich and challenging problem in Bioinformatics research. The problem gets harder as the number of classes is increased. In addition, the performance of most classifiers is tightly linked to the effectiveness of mandatory gene selection methods. Critical to gene selection is the availability of estimates about the maximum number of genes that can be handled by any classification algorithm. Lack of such estimates may lead to either computationally demanding explorations of a search space with thousands of dimensions or classification models based on gene sets of unrestricted size. In the former case, unbiased but possibly overfitted classification models may arise. In the latter case, biased classification models unable to support statistically significant findings may be obtained.</p> <p>Results</p> <p>A novel bound on the maximum number of genes that can be handled by binary classifiers in binary mediated multiclass classification algorithms of microarray data samples is presented. The bound suggests that high-dimensional binary output domains might favor the existence of accurate and sparse binary mediated multiclass classifiers for microarray data samples.</p> <p>Conclusions</p> <p>A comprehensive experimental work shows that the bound is indeed useful to induce accurate and sparse multiclass classifiers for microarray data samples.</p

Polarization-ring-switching for nonlinearity-tolerant geometrically-shaped four-dimensional formats maximizing generalized mutual information

In this paper, a new four-dimensional 64-ary polarization ring switching (4D-64PRS) modulation format with a spectral efficiency of 6 bit/4D-sym is introduced. The format is designed by maximizing the generalized mutual information (GMI) and by imposing a constant-modulus on the 4D structure. The proposed format yields an improved performance with respect to state-of-the-art geometrically shaped modulation formats for bit-interleaved coded modulation systems at the same spectral efficiency. Unlike previously published results, the coordinates of the constellation points and the binary labeling of the constellation are jointly optimized. When compared with polarization-multiplexed 8-ary quadrature-amplitude modulation (PM-8QAM), gains of up to 0.7 dB in signal-to-noise ratio are observed in the additive white Gaussian noise (AWGN) channel. For a long-haul nonlinear optical fiber system of 8,000 km, gains of up to 0.27 bit/4D-sym (5.5% data capacity increase) are observed. These gains translate into a reach increase of approximately 16% (1,100 km). The proposed modulation format is also shown to be more tolerant to nonlinearities than PM-8QAM. Results with LDPC codes are also presented, which confirm the gains predicted by the GMI.Comment: 12 pages, 12 figure

Constellation design for future communication systems: a comprehensive survey

[EN] The choice of modulation schemes is a fundamental building block of wireless communication systems. As a key component of physical layer design, they critically impact the expected communication capacity and wireless signal robustness. Their design is also critical for the successful roll-out of wireless standards that require a compromise between performance, efficiency, latency, and hardware requirements. This paper presents a survey of constellation design strategies and associated outcomes for wireless communication systems. The survey discusses their performance and complexity to address the need for some desirable properties, including consistency, channel capacity, system performance, required demapping architecture, flexibility, and independence. Existing approaches for constellation designs are investigated using appropriate metrics and categorized based on their theoretical algorithm design. Next, their application to different communication standards is analyzed in context, aiming at distilling general guidelines applicable to the wireless building block design. Finally, the survey provides a discussion on design directions for future communication system standardization processes.This work was supported in part by the Basque Government under Grant IT1234-19, in part by the PREDOC under Program PRE_2020_2_0105, and in part by the Spanish Government through the Project PHANTOM (MCIU/AEI/FEDER, UE) under Gran

Proceedings of the 35th WIC Symposium on Information Theory in the Benelux and the 4th joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux, Eindhoven, the Netherlands May 12-13, 2014

Compressive sensing (CS) as an approach for data acquisition has recently received much attention. In CS, the signal recovery problem from the observed data requires the solution of a sparse vector from an underdetermined system of equations. The underlying sparse signal recovery problem is quite general with many applications and is the focus of this talk. The main emphasis will be on Bayesian approaches for sparse signal recovery. We will examine sparse priors such as the super-Gaussian and student-t priors and appropriate MAP estimation methods. In particular, re-weighted l2 and re-weighted l1 methods developed to solve the optimization problem will be discussed. The talk will also examine a hierarchical Bayesian framework and then study in detail an empirical Bayesian method, the Sparse Bayesian Learning (SBL) method. If time permits, we will also discuss Bayesian methods for sparse recovery problems with structure; Intra-vector correlation in the context of the block sparse model and inter-vector correlation in the context of the multiple measurement vector problem

Proceedings of the 35th WIC Symposium on Information Theory in the Benelux and the 4th joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux, Eindhoven, the Netherlands May 12-13, 2014

Compressive sensing (CS) as an approach for data acquisition has recently received much attention. In CS, the signal recovery problem from the observed data requires the solution of a sparse vector from an underdetermined system of equations. The underlying sparse signal recovery problem is quite general with many applications and is the focus of this talk. The main emphasis will be on Bayesian approaches for sparse signal recovery. We will examine sparse priors such as the super-Gaussian and student-t priors and appropriate MAP estimation methods. In particular, re-weighted l2 and re-weighted l1 methods developed to solve the optimization problem will be discussed. The talk will also examine a hierarchical Bayesian framework and then study in detail an empirical Bayesian method, the Sparse Bayesian Learning (SBL) method. If time permits, we will also discuss Bayesian methods for sparse recovery problems with structure; Intra-vector correlation in the context of the block sparse model and inter-vector correlation in the context of the multiple measurement vector problem

Forward Error Correction for High Capacity Transmission Systems

Αυτή η μελέτη διερευνά την αλληλεπίδραση μεταξύ FEC διόρθωσης σφαλμάτων προώθησης και ψηφιακού αντιστάθμιση μη γραμμικότητας DBP σε ένα κανάλι ινών μεγάλων αποστάσεων. Πρώτον, α Η προσέγγιση που βασίζεται στην έρευνα χρησιμοποιείται για τον προσδιορισμό των τεχνολογιών αιχμής στο FEC για το κανάλι ινών και προσαρμόστε τα στο τελικό σχέδιο. Οι σχεδιαστικές επιλογές περιλαμβάνουν το χρήση τετριμμένων bit κωδικοποιημένης διαμόρφωσης αρχιτεκτονικής T-BICM με συνενωμένη σχήμα κώδικα που χρησιμοποιεί έναν επαναληπτικό soft αποκωδικοποιητή. Η απαίτηση για συνενωμένη Η εφαρμογή FEC οδήγησε σε μια άλλη έρευνα για έναν κώδικα καλής απόδοσης συνδυασμός. Το ακανόνιστο LDPC και το οιονεί κυκλικό QC-LDPC, που υιοθετήθηκαν από το DVB-S2 και Τα πρότυπα IEEE 802.11, αντίστοιχα, συνδυάστηκαν με τον κώδικα σκάλας και σύγκριση με βάση τις επιτευχθείσες επιδόσεις. Αποδεικνύουμε ότι αυξάνοντας τις ίνες απόσταση μετάδοσης κατά 1/3, από 300km έως 400km, διατηρώντας παράλληλα την η ίδια απόδοση και η χρήση των ίδιων γενικών εξόδων, δηλαδή 27,5% είναι εφικτό όταν υλοποίηση του DBP με 2 βήματα/περιοχή ή 3 βήματα/περιοχή, ανάλογα με το αν το Οι επαναλήψεις αποκωδικοποίησης είναι 10 ή 5. Αυτή η μελέτη καταλήγει με την εύνοια του LDPC από το DVB-S2 πάνω από το QC-LDPC του IEEE 802.11 για κανάλι ινών μεγάλων αποστάσεων. Το συμπέρασμα βγαίνει με βάση σχετικά με την καλύτερη απόδοση για το LDPC-DVB, λόγω των μεγάλων μηκών κωδικών του και του υποστήριξη για υψηλούς ρυθμούς κωδικοποίησης με αποτέλεσμα χαμηλές γενικές απαιτήσειςThis study investigates the interplay between forward error correction FEC and digital back-propagation DBP nonlinearity compensation on a long-haul fibre channel. First, a research-based approach is used to identify the state-of-the-art technologies in FEC for the fibre channel and adapt them to the final design. The design choices includes the usage of trivial bit interleaved coded modulation T-BICM architecture with a concatenated code scheme that uses an iterative soft decoder. The requirement for a concatenated FEC implementation motivated another investigation of a well-performing code combination. The Irregular LDPC and quasi-cyclic QC-LDPC, adopted from DVB-S2 and IEEE 802.11 standards, respectively, were each concatenated with staircase code and compared based on the attained performance. We prove that increasing the fibre transmission distance by a factor of 1/3, from 300km to 400km, while maintaining the same performance and using the same overhead, i.e. 27.5\% is achievable when implementing DBP with 2 steps/span or 3 steps/span, depending on whether the decoding iterations are 10 or 5. This study concludes with favouring LDPC from DVB-S2 over IEEE 802.11&apos;s QC-LDPC for long haul fibre channel. The conclusion is made based on the better attained performance for LDPC-DVB, due to its long code lengths, and its support for high coding rates resulting low overhead requirement

Spectrally and Energy Efficient Wireless Communications: Signal and System Design, Mathematical Modelling and Optimisation

This thesis explores engineering studies and designs aiming to meeting the requirements of enhancing capacity and energy efficiency for next generation communication networks. Challenges of spectrum scarcity and energy constraints are addressed and new technologies are proposed, analytically investigated and examined. The thesis commences by reviewing studies on spectrally and energy-efficient techniques, with a special focus on non-orthogonal multicarrier modulation, particularly spectrally efficient frequency division multiplexing (SEFDM). Rigorous theoretical and mathematical modelling studies of SEFDM are presented. Moreover, to address the potential application of SEFDM under the 5th generation new radio (5G NR) heterogeneous numerologies, simulation-based studies of SEFDM coexisting with orthogonal frequency division multiplexing (OFDM) are conducted. New signal formats and corresponding transceiver structure are designed, using a Hilbert transform filter pair for shaping pulses. Detailed modelling and numerical investigations show that the proposed signal doubles spectral efficiency without performance degradation, with studies of two signal formats; uncoded narrow-band internet of things (NB-IoT) signals and unframed turbo coded multi-carrier signals. The thesis also considers using constellation shaping techniques and SEFDM for capacity enhancement in 5G system. Probabilistic shaping for SEFDM is proposed and modelled to show both transmission energy reduction and bandwidth saving with advantageous flexibility for data rate adaptation. Expanding on constellation shaping to improve performance further, a comparative study of multidimensional modulation techniques is carried out. A four-dimensional signal, with better noise immunity is investigated, for which metaheuristic optimisation algorithms are studied, developed, and conducted to optimise bit-to-symbol mapping. Finally, a specially designed machine learning technique for signal and system design in physical layer communications is proposed, utilising the application of autoencoder-based end-to-end learning. Multidimensional signal modulation with multidimensional constellation shaping is proposed and optimised by using machine learning techniques, demonstrating significant improvement in spectral and energy efficiencies