647 results found
Li Z, Deng W, Zhu Z, et al., 2021, A layer-wise distribution analysis of the WLMMSE-SIC MIMO receiver for rectilinear or quasi-rectilinear signals, Signal Processing, Vol: 189, ISSN: 0165-1684
In this brief, we investigate the widely linear minimum mean square error (WLMMSE) successive interference cancellation (SIC) detector in multiple-input-multiple-output systems with rectilinear or quasi-rectilinear transmit signals. To this end, layer-wise approximate probability density functions (PDFs) of the post-detection signal-to-interference-plus-noise ratio are first derived. It is next shown that the so-derived PDFs enable explicit and concise performance evaluations in terms of the symbol error rate and the outage probability. For rigor, both the cases with and without inter-layer error propagation are addressed, and closed-form solutions are provided. Validity of theoretical analysis is demonstrated via Monte Carlo simulations.
Xu D, Zhang S, Zhang H, et al., 2021, Convergence of the RMSProp deep learning method with penalty for nonconvex optimization, NEURAL NETWORKS, Vol: 139, Pages: 17-23, ISSN: 0893-6080
Ruiz-Garcia A, Schmidhuber J, Palade V, et al., 2021, Deep neural network representation and Generative Adversarial Learning, NEURAL NETWORKS, Vol: 139, Pages: 199-200, ISSN: 0893-6080
Li S, Yu Z, Mandic D, 2021, A Universal Framework for Learning the Elliptical Mixture Model, IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, Vol: 32, Pages: 3181-3195, ISSN: 2162-237X
Khalifa Y, Mandic D, Sejdic E, 2021, A review of Hidden Markov models and Recurrent Neural Networks for event detection and localization in biomedical signals, INFORMATION FUSION, Vol: 69, Pages: 52-72, ISSN: 1566-2535
Calvi GG, Dees BS, Mandic DP, 2021, A lower bound on the tensor rank based on its maximally square matrix unfolding, SIGNAL PROCESSING, Vol: 180, ISSN: 0165-1684
Scalzo B, Konstantinidis A, Mandic DP, 2021, Analysis of global fixed-income returns using multilinear tensor algebra, Journal of Fixed Income, Vol: 30, Pages: 32-52, ISSN: 1059-8596
Global fixed-income returns exhibit highly structured correlations across maturities and economies (data modes), and their modeling therefore requires analysis tools that are capable of directly capturing the inherent multiway couplings present in such multimodal data. Yet, current analyses typically employ “flat-view” multivariate matrix models and their associated linear algebras; these are agnostic to the global data structure and can only describe local linear pairwise relationships between data entries. To address this issue, the authors first show that global fixed-income returns naturally reside on multi-modal lattice data structures, referred to as tensors. This serves as a basis to introduce a multilinear algebraic approach, inherent in tensors, to the modeling of the global term structure underlying multiple interest rate curves. Owing to the enhanced flexibility of multilinear algebra, statistical descriptors, such as correlations, exist between tensor columns and rows (fibers), as opposed to between individual entries in standard matrix analysis. This allows for the expression of the covariance of global returns as a joint multilinear decomposition of the maturity-domain and country-domain covariances. This not only drastically reduces the number of parameters required to fully capture the global return covariance structure, but also makes it possible to devise rigorous and tractable global portfolio management strategies; the authors tailor these specifically to each of the data domains and thereby fully exploit the lattice structure of global fixed-income returns. The ability of the proposed multilinear tensor approach to compactly describe the macroeconomic environment through economically meaningful factors is validated via empirical analysis that demonstrates the existence of maturity-domain and country-domain covariances underlying the interest rate curves of eight developed economies.
Brajović M, Stanković I, Daković M, et al., 2021, On the Number of Channels in Multicomponent Nonstationary Noisy Signal Decomposition
If acquired using multiple sensors, non-stationary multicomponent signals can be decomposed into individual components by exploiting interdependences of signals from different channels. Earlier, we have proposed a decomposition approach being able to extract individual non-stationary signal components even in the challenging cases when their domains of support overlap in the time, frequency or joint time-frequency (TF) domains. The approach is based upon the eigenvalue analysis of the multichannel autocorrelation matrix and minimizations of concentration measures calculated using TF representations. In this paper, we investigate the influence of the number of sensors (channels) and external noise variance to the outcome of the decomposition process.
Haliassos A, Konstantinidis K, Mandic DP, 2021, Supervised Learning for Nonsequential Data: A Canonical Polyadic Decomposition Approach, IEEE Transactions on Neural Networks and Learning Systems, ISSN: 2162-237X
Efficient modeling of feature interactions underpins supervised learning for nonsequential tasks, characterized by a lack of inherent ordering of features (variables). The brute force approach of learning a parameter for each interaction of every order comes at an exponential computational and memory cost (curse of dimensionality). To alleviate this issue, it has been proposed to implicitly represent the model parameters as a tensor, the order of which is equal to the number of features; for efficiency, it can be further factorized into a compact tensor train (TT) format. However, both TT and other tensor networks (TNs), such as tensor ring and hierarchical Tucker, are sensitive to the ordering of their indices (and hence to the features). To establish the desired invariance to feature ordering, we propose to represent the weight tensor through the canonical polyadic (CP) decomposition (CPD) and introduce the associated inference and learning algorithms, including suitable regularization and initialization schemes. It is demonstrated that the proposed CP-based predictor significantly outperforms other TN-based predictors on sparse data while exhibiting comparable performance on dense nonsequential tasks. Furthermore, for enhanced expressiveness, we generalize the framework to allow feature mapping to arbitrarily high-dimensional feature vectors. In conjunction with feature vector normalization, this is shown to yield dramatic improvements in performance for dense nonsequential tasks, matching models such as fully connected neural networks.
Li Z, Pu R, Xia Y, et al., 2021, A Full Second-Order Analysis of the Widely Linear MVDR Beamformer for Noncircular Signals, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 69, Pages: 4257-4268, ISSN: 1053-587X
Menguc EC, Cnar S, Xiang M, et al., 2021, Online Censoring Based Weighted-Frequency Fourier Linear Combiner for Estimation of Pathological Hand Tremors, IEEE SIGNAL PROCESSING LETTERS, Vol: 28, Pages: 1460-1464, ISSN: 1070-9908
Stankovic L, Brajovic M, Mandic D, et al., 2021, Improved Coherence Index-Based Bound in Compressive Sensing, IEEE SIGNAL PROCESSING LETTERS, Vol: 28, Pages: 1110-1114, ISSN: 1070-9908
Gogineni VC, Talebi SP, Werner S, et al., 2020, Fractional-Order Correntropy Filters for Tracking Dynamic Systems in alpha-Stable Environments, IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, Vol: 67, Pages: 3557-3561, ISSN: 1549-7747
Stankovic L, Mandic D, Dakovic M, et al., 2020, Vertex-frequency graph signal processing: A comprehensive review, DIGITAL SIGNAL PROCESSING, Vol: 107, ISSN: 1051-2004
Phan A-H, Cichocki A, Uschmajew A, et al., 2020, Tensor Networks for Latent Variable Analysis: Novel Algorithms for Tensor Train Approximation, IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, Vol: 31, Pages: 4622-4636, ISSN: 2162-237X
Talebi SP, Werner S, Mandic DP, 2020, Quaternion-Valued Distributed Filtering and Control, IEEE TRANSACTIONS ON AUTOMATIC CONTROL, Vol: 65, Pages: 4246-4257, ISSN: 0018-9286
Zhang X, Xia Y, Li C, et al., 2020, Complex Properness Inspired Blind Adaptive Frequency-Dependent I/Q Imbalance Compensation for Wideband Direct-Conversion Receivers, IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, Vol: 19, Pages: 5982-5992, ISSN: 1536-1276
Davies HJ, Williams I, Peters NS, et al., 2020, In-ear SpO2: a tool for wearable, unobtrusive monitoring of core blood oxygen saturation, Sensors (Basel, Switzerland), Vol: 20, ISSN: 1424-8220
The non-invasive estimation of blood oxygen saturation (SpO2) by pulse oximetry is of vital importance clinically, from the detection of sleep apnea to the recent ambulatory monitoring of hypoxemia in the delayed post-infective phase of COVID-19. In this proof of concept study, we set out to establish the feasibility of SpO2 measurement from the ear canal as a convenient site for long term monitoring, and perform a comprehensive comparison with the right index finger-the conventional clinical measurement site. During resting blood oxygen saturation estimation, we found a root mean square difference of 1.47% between the two measurement sites, with a mean difference of 0.23% higher SpO2 in the right ear canal. Using breath holds, we observe the known phenomena of time delay between central circulation and peripheral circulation with a mean delay between the ear and finger of 12.4 s across all subjects. Furthermore, we document the lower photoplethysmogram amplitude from the ear canal and suggest ways to mitigate this issue. In conjunction with the well-known robustness to temperature induced vasoconstriction, this makes conclusive evidence for in-ear SpO2 monitoring being both convenient and superior to conventional finger measurement for continuous non-intrusive monitoring in both clinical and everyday-life settings.
Zhang X, Dees BS, Li C, et al., 2020, Analysis of Least Stochastic Entropy Adaptive Filters for Noncircular Gaussian Signals, IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, Vol: 67, Pages: 1364-1368, ISSN: 1549-7747
Phan A-H, Cichocki A, Oseledets I, et al., 2020, Tensor Networks for Latent Variable Analysis: Higher Order Canonical Polyadic Decomposition, IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, Vol: 31, Pages: 2174-2188, ISSN: 2162-237X
Stankovic L, Brajovic M, Dakovic M, et al., 2020, On the decomposition of multichannel nonstationary multicomponent signals, SIGNAL PROCESSING, Vol: 167, ISSN: 0165-1684
Stankovic L, Dakovic M, Mandic D, et al., 2020, A LOW-DIMENSIONALITY METHOD FOR DATA-DRIVEN GRAPH LEARNING, IEEE International Conference on Acoustics, Speech, and Signal Processing, Publisher: IEEE, Pages: 5340-5344, ISSN: 1520-6149
Dees BS, Stankovic L, Constantinides AG, et al., 2020, PORTFOLIO CUTS: A GRAPH-THEORETIC FRAMEWORK TO DIVERSIFICATION, IEEE International Conference on Acoustics, Speech, and Signal Processing, Publisher: IEEE, Pages: 8454-8458, ISSN: 1520-6149
Kisil I, Calvi GG, Scalzo Dees B, et al., 2020, Tensor Decompositions and Practical Applications: A Hands-on Tutorial, Studies in Computational Intelligence, Pages: 69-97
The exponentially increasing availability of big and streaming data comes as a direct consequence of the rapid development and widespread use of multi-sensor technology. The quest to make sense of such large volume and variety of that has both highlighted the limitations of standard flat-view matrix models and the necessity to move toward more versatile data analysis tools. One such model which is naturally suited for data of large volume, variety and veracity are multi-way arrays or tensors. The associated tensor decompositions have been recognised as a viable way to break the “Curse of Dimensionality”, an exponential increase in data volume with the tensor order. Owing to a scalable way in which they deal with multi-way data and their ability to exploit inherent deep data structures when performing feature extraction, tensor decompositions have found application in a wide range of disciplines, from very theoretical ones, such as scientific computing and physics, to the more practical aspects of signal processing and machine learning. It is therefore both timely and important for a wider Data Analytics community to become acquainted with the fundamentals of such techniques. Thus, our aim is not only to provide a necessary theoretical background for multi-linear analysis but also to equip researches and interested readers with an easy to read and understand practical examples in form of a Python code snippets.
Stankovic L, Mandic DP, Dakovic M, et al., 2020, Demystifying the Coherence Index in Compressive Sensing [Lecture Notes], IEEE SIGNAL PROCESSING MAGAZINE, Vol: 37, Pages: 152-162, ISSN: 1053-5888
Xiang M, Xia Y, Mandic DP, 2020, Performance Analysis of Deficient Length Quaternion Least Mean Square Adaptive Filters, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 68, Pages: 65-80, ISSN: 1053-587X
Li Z, Deng W, Pei W, et al., 2020, SINR ANALYSIS OF MIMO SYSTEMS WITH WIDELY LINEAR MMSE RECEIVERS FOR THE RECEPTION OF REAL-VALUED CONSTELLATIONS, 31st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (IEEE PIMRC), Publisher: IEEE, ISSN: 2166-9570
Chen Z, Dees BS, Mandic DP, 2020, A Probabilistic Beat-to-Beat Filtering Model for Continuous and Accurate Blood Pressure Estimation, International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI), Publisher: IEEE, ISSN: 2161-4393
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