pwc-archive/papers-with-abstracts huggingface dataset:
Output Based Adaptive Distributed Output Observer for Leader-follower Multiagent Systems
Date: 2020-07-22 00:00:00

The adaptive distributed observer approach has been an effective tool for
synthesizing a distributed control law for solving various control problems of
leader-follower multiagent systems. However, the existing adaptive distributed
observer needs to make use of the full state of the leader system. This
assumption not only precludes many practical applications in which only the
output of the leader system is available, but also leads to a high dimension
observer. In this communique, we propose an adaptive distributed output
observer which only makes use of the output of the leader system, and is thus
more practical than the state based adaptive distributed observer. Moreover,
the dimension and the information exchange among agents of the proposed
adaptive distributed output observer can be significantly smaller than those of
the state based adaptive distributed output observer.
Energy Scheduling for Residential Distributed Energy Resources with Uncertainties Using Model-based Predictive Control
Date: 2020-07-22 00:00:00
Tasks: energy management, Management, Scheduling

This paper proposes a reliable energy scheduling framework for distributed
energy resources (DER) of a residential area to achieve an appropriate daily
electricity consumption with the maximum affordable demand response. Renewable
and non-renewable energy resources are available to respond to customers'
demands using different classes of methods to manage energy during the time.
The optimal operation problem is a mixed-integer-linear-programming (MILP)
investigated using model-based predictive control (MPC) to determine which
dispatchable unit should be operated at what time and at what power level while
satisfying practical constraints. Renewable energy sources (RES), particularly
solar and wind energies recently have expanded their role in electric power
systems. Although they are environment friendly and accessible, there are
challenging issues regarding their performance such as dealing with the
variability and uncertainties concerned with them. This research investigates
the energy management of these systems in three complementary scenarios. The
first and second scenarios are respectively suitable for a market with a
constant and inconstant price. Additionally, the third scenario is proposed to
consider the role of uncertainties in RES and it is designed to recompense the
power shortage using non-renewable resources. The validity of methods is
explored in a residential area for 24 hours and the results thoroughly
demonstrate the competence of the proposed approach for decreasing the
operation cost.
Inferring incubation period distribution of COVID-19 based on SEAIR Model
Date: 2020-07-22 00:00:00
Tasks: Survey

To reduce the biases of traditional survey-based methods, this paper proposes
an epidemic model-based approach to inference the incubation period
distribution of COVID-19 utilizing the publicly reported confirmed case number.
We construct an epidemic model, namely SEAIR, and take advantage of the dynamic
transmission process depicted by SEAIR to estimate the onset probability in
each day of exposed individuals in eight impacted countries. Based on these
estimations, the general incubation probability distribution of COVID-19 has
been revealed. The proposed method can avoid several biases of traditional
survey-based methods. However, due to the mathematical-model-based nature of
this method, the inference results are somewhat sensitive to the setting of
parameters. Therefore, this method should be practiced reasonably on the basis
of a certain understanding of the studied epidemic.
A Novel Mobility Model to Support the Routing of Mobile Energy Resources
Authors: Wei Wang, Xiaofu Xiong, Chao Xiao, Bihui Wei
Date: 2020-07-22 00:00:00
Tasks: Computational Efficiency

Mobile energy resources (MERs) have received increasing attention due to their effectiveness in boosting the power system resilience in a flexible way. In this paper, a novel mobility model for MERs is proposed, which can support the routing of MERs to provide various services for the power system. Two key points, the state transitions and travel time of MERs, are formulated by linear constraints. The feasibility of the proposed model, especially its advantages in model size and computational efficiency for the routing of MERs among many nodes with a small time span, is demonstrated by a series of tests.
A Post-coder Feedback Approach to Overcome Training Asymmetry in MIMO-TDD
Authors: Arun Kumar Miryala, Irina Merin Baby, Kumar Appaiah, Sibi Raj B. Pillai
Date: 2020-07-22 00:00:00
Tasks: Quantization

Time Divison Duplex (TDD) wireless communication systems are inherently bidirectional, which facilitates exploiting channel reciprocity for pilot based channel estimation of both uplink and downlink. However, there exists a gross asymmetry in channel estimation complexity for the uplink and downlink, particularly for Multiple Input Multiple Output (MIMO) TDD systems. Usually, Base Stations (BS) with more antennas need to estimate fewer parameters from each antenna, whereas the estimation requirement is disproportionately higher at the User Equipment (UE). Unlike the UE, the BS has powerful hardware, computational resources and energy to accurately estimate and track channel profiles. To overcome this asymmetry, we propose a solution for MIMO-TDD downlink communication, wherein the post-coder part of the channel matrix is quantized at the BS, and is communicated to the UE via a low-rate channel. Using asymptotically tight lower bounds on the downlink achievable rates, we quantify the performance of the proposed scheme. Simulations reveal that a moderate number of quantization bits are sufficient to achieve rates close to the the link capacity. We further show that, when the BS has many more antennas at than the UE, the channel can be compensated by appropriate transmit domain precoding without post-coder knowledge at the UE.
Dynamic Independent Component/Vector Analysis: Time-Variant Linear Mixtures Separable by Time-Invariant Beamformers
Authors: Zbyněk Koldovský, Václav Kautský, Petr Tichavský
Date: 2020-07-22 00:00:00

A novel extension of Independent Component and Independent Vector Analysis for blind extraction/separation of one or several sources from time-varying mixtures is proposed. The mixtures are assumed to be separable source-by-source in series or in parallel based on a recently proposed mixing model that allows for the movements of the desired source while the separating beamformer is time-invariant. The popular FastICA algorithm is extended for these mixtures in one-unit, symmetric and block-deflation variants. The algorithms are derived within a unified framework so that they are applicable in the real-valued as well as complex-valued domains, and jointly to several mixtures, similar to Independent Vector Analysis. Performance analysis of the one-unit algorithm is provided; it shows its asymptotic efficiency under the given mixing and statistical models. Numerical simulations corroborate the validity of the analysis, confirm the usefulness of the algorithms in separation of moving sources, and show the superior speed of convergence and ability to separate super-Gaussian as well as sub-Gaussian signals.
A Canon of Probabilistic Rationality
Authors: Simone Cerreia-Vioglio, Per Olov Lindberg, Fabio Maccheroni, Massimo Marinacci, Aldo Rustichini
Date: 2020-07-17 00:00:00

We prove that a random choice rule satisfies Luce's Choice Axiom if and only if its support is a choice correspondence that satisfies the Weak Axiom of Revealed Preference, thus it consists of alternatives that are optimal according to some preference, and random choice then occurs according to a tie breaking among such alternatives that satisfies Renyi's Conditioning Axiom. Our result shows that the Choice Axiom is, in a precise formal sense, a probabilistic version of the Weak Axiom. It thus supports Luce's view of his own axiom as a "canon of probabilistic rationality."
The impact of life-saving interventions on fertility
Date: 2020-07-16 00:00:00

Many interventions in global health save lives. One criticism sometimes
lobbed at these interventions invokes the spirit of Malthus. The good done, the
charge goes, is offset by the harm of spreading the earth's limited resources
more thinly: more people, and more misery per person. To the extent this holds,
the net benefit of savings lives is lower than it appears at first. On the
other hand, if lower mortality, especially in childhood, leads families to have
fewer children, life-saving interventions could reduce population. This
document critically reviews the evidence. It finds that the impact of
life-saving interventions on fertility and population growth varies by context,
and is rarely greater than 1:1. In places where lifetime births/woman has been
converging to 2 or lower, saving one child's life should lead parents to avert
a birth they would otherwise have. The impact of mortality drops on fertility
will be nearly 1:1, so population growth will hardly change. In the
increasingly exceptional locales where couples appear not to limit fertility
much, such as Niger and Mali, the impact of saving a life on total births will
be smaller, and may come about mainly through the biological channel of
lactational amenorrhea. Here, mortality-drop-fertility-drop ratios of 1:0.5 and
1:0.33 appear more plausible. But in the long-term, it would be surprising if
these few countries do not join the rest of the world in the transition to
lower and more intentionally controlled fertility.
Optimal Pacing of a Cyclist in a Time Trial Based on Individualized Models of Fatigue and Recovery
Authors: Faraz Ashtiani, Vijay Sarthy M Sreedhara, Ardalan Vahidi, Randolph Hutchison, Gregory Mocko
Date: 2020-07-20 00:00:00

This paper formulates optimal pacing of a cyclist on hilly terrain time-trials as a minimum-time optimal control problem. Maximal power of a cyclist serves as a time-varying constraint and depends on fatigue and recovery which are captured via dynamic models proposed early in the paper. Experimental protocols for identifying the individualized parameters of the proposed fatigue and recovery models are detailed and results for six human subjects are shown. In an analytical treatment via necessary conditions of Pontryagin Minimum Principle, we show that the cyclist's optimal power in a time-trial is limited to only four modes of all-out, coasting, pedaling at a critical power, or constant speed (bang-singular-bang). To determine when to switch between these modes, we resort to numerical solution via dynamic programming. One of the subjects is then simulated on four courses including the 2019 Duathlon National Championship in Greenville, SC. The dynamic programming simulation results show 24% reduction in travel time over experimental results of the self-paced subject who is a competitive amateur cyclist. The paper concludes with description of a pilot lab experiment in which the subject trial time was reduced by 3% when the near-optimal pace was communicated to her in real-time.
Joint Radio Resource Allocation and Cooperative Caching in PD-NOMA-Based HetNets
Authors: Maryam Moghimi, Abolfazl Zakeri, Mohammad Reza Javan, Nader Mokari, Derrick Wing Kwan Ng
Date: 2020-07-22 00:00:00
Tasks: Management

In this paper, we propose a novel joint resource allocation and cooperative caching scheme for power-domain non-orthogonal multiple access (PD-NOMA)-based heterogeneous networks (HetNets). In our scheme, the requested content is fetched directly from the edge if it is cached in the storage of one of the base stations (BSs), and otherwise is fetched via the backhaul. Our scheme consists of two phases: 1. Caching phase where the contents are saved in the storage of the BSs, and 2. Delivery phase where the requested contents are delivered to users. We formulate a novel optimization problem over radio resources and content placement variables. We aim to minimize the network cost subject to quality-of-service (QoS), caching, subcarrier assignment, and power allocation constraints. By exploiting advanced optimization methods, such as alternative search method (ASM), Hungarian algorithm, successive convex approximation (SCA), we obtain an efficient sub-optimal solution of the optimization problem. Numerical results illustrate that our ergodic caching policy via the proposed resource management algorithm can achieve a considerable reduction on the total cost on average compared to the most popular caching and random caching policy. Moreover, our cooperative NOMA scheme outperforms orthogonal multiple access (OMA) in terms of the delivery cost in general with an acceptable complexity increase.
Sparse Signatures with Forward Error Correction Coding for Non-Orthogonal Massive Access
Date: 2020-07-22 00:00:00

In massive connectivity scenarios with short packets, of interest is the regime where users share wireless resources in a non-orthogonal fashion. Small payloads combined with sporadic user activation call for approaches that jointly address the users access to the shared resources and the design of the channel code. In this paper, we propose a transmission scheme that combines sparse signatures with finite-length forward error correction (FEC) coding for non-orthogonal massive access. Our signature design is based on Euler squares, which are special instances of quasi-cyclic partial geometries that yield sparse graphs with favorable decoding properties. Following a graph-theoretic approach, we explicate the benefits of the coding scheme for the receiver processing that involves joint user detection and decoding. The proposed construction is flexible and can be explicitly characterized for a large number of combinations of system parameters, suitable for both grant-based and grant-free massive access. Finally, unlike common existing schemes, our scheme can be applied to unsourced random access (U-RA). We numerically characterize the trade-off between system parameters such as number of users, load and channel coding rate. The performance evaluation against the state of the art illustrates the potential of the scheme to provide an energy-efficient solution for U-RA.
Resilient Secondary Voltage Control of Islanded Microgrids: An ESKBF-Based Distributed Fast Terminal Sliding Mode Control Approach
Date: 2020-07-22 00:00:00

This paper proposes a distributed secondary voltage control method based on
extended state Kalman-Bucy filter (ESKBF) and fast terminal sliding mode (FTSM)
control for the resilient operation of an islanded microgrid (MG) with
inverter-based distributed generations (DGs). To tackle the co-existence of
multiple uncertainties, a unified modelling framework is proposed to represent
the set of different types of disturbances, including parameter perturbation,
measurement noise, and immeasurably external variables, by an extended state
method. Kalman-Bucy filter is then applied to accurately estimate the state
information of the extended DG model. In addition, based on the accurate
estimation, a fast terminal sliding mode (FTSM) surface with terminal
attractors is designed to maintain the system stability and accelerate the
convergence of consensus tracking, which significantly improves the performance
of secondary voltage control under both normal and plug-and-play operation.
Finally, case studies are conducted in both MATLAB/Simulink and an experimental
testbed to demonstrate the effectiveness of the proposed method.
Air-to-Ground Channel Characterization for Low-Height UAVs in Realistic Network Deployments
Authors: José Rodríguez-Piñeiro, Tomás Domínguez-Bolaño, Xuesong Cai, Zeyu Huang, Xuefeng Yin
Date: 2020-07-22 00:00:00

Due to the decrease in cost, size and weight, \acp{UAV} are becoming more and more popular for general-purpose civil and commercial applications. Provision of communication services to \acp{UAV} both for user data and control messaging by using off-the-shelf terrestrial cellular deployments introduces several technical challenges. In this paper, an approach to the air-to-ground channel characterization for low-height \acp{UAV} based on an extensive measurement campaign is proposed, giving special attention to the comparison of the results when a typical directional antenna for network deployments is used and when a quasi-omnidirectional one is considered. Channel characteristics like path loss, shadow fading, root mean square delay and Doppler frequency spreads and the K-factor are statistically characterized for different suburban scenarios.
Cell-Free Satellite-UAV Networks for 6G Wide-Area Internet of Things
Authors: Chengxiao Liu, Wei Feng, Yunfei Chen, Cheng-Xiang Wang, Ning Ge
Date: 2020-07-22 00:00:00
Tasks: Fairness

In fifth generation (5G) and beyond Internet of Things (IoT), it becomes increasingly important to serve a massive number of IoT devices outside the coverage of terrestrial cellular networks. Due to their own limitations, unmanned aerial vehicles (UAVs) and satellites need to coordinate with each other in the coverage holes of 5G, leading to a cognitive satellite-UAV network (CSUN). In this paper, we investigate multi-domain resource allocation for CSUNs consisting of a satellite and a swarm of UAVs, so as to improve the efficiency of massive access in wide areas. Particularly, the cell-free on-demand coverage is established to overcome the cost-ineffectiveness of conventional cellular architecture. Opportunistic spectrum sharing is also implemented to cope with the spectrum scarcity problem. To this end, a process-oriented optimization framework is proposed for jointly allocating subchannels, transmit power and hovering times, which considers the whole flight process of UAVs and uses only the slowly-varying large-scale channel state information (CSI). Under the on-board energy constraints of UAVs and interference temperature constraints from UAV swarm to satellite users, we present iterative multi-domain resource allocation algorithms to improve network efficiency with guaranteed user fairness. Simulation results demonstrate the superiority of the proposed algorithms. Moreover, the adaptive cell-free coverage pattern is observed, which implies a promising way to efficiently serve wide-area IoT devices in the upcoming sixth generation (6G) era.
Mean Square Optimal Control by Interconnection for Linear Stochastic Hamiltonian Systems
Date: 2020-07-22 00:00:00

This paper is concerned with linear stochastic Hamiltonian (LSH) systems
subject to random external forces. Their dynamics are modelled by linear
stochastic differential equations, parameterised by stiffness, mass, damping
and coupling matrices. A class of physical couplings is discussed for such
systems using inerters, springs and dampers. We consider a problem of
minimising a steady-state quadratic cost functional over the coupling
parameters for the interconnection of two LSH systems, one of which plays the
role of an analog controller. For this mean square optimal
control-by-interconnection setting, we outline first-order necessary conditions
of optimality which employ variational methods developed previously for
constrained linear quadratic Gaussian control problems.
Epileptic Seizure Prediction: A Semi-Dilated Convolutional Neural Network Architecture
Authors: Ramy Hussein, Soojin Lee, Rabab Ward, Martin J. McKeown
Date: 2020-07-22 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Seizure prediction, Time Series, Time Series Analysis, Time Series Classification

Accurate prediction of epileptic seizures has remained elusive, despite the many advances in machine learning and time-series classification. In this work, we develop a convolutional network module that exploits Electroencephalogram (EEG) scalograms to distinguish between the pre-seizure and normal brain activities. Since these scalograms have rectangular image shapes with many more temporal bins than spectral bins, the presented module uses "semi-dilated convolutions" to create a proportional non-square receptive field. The proposed semi-dilated convolutions support exponential expansion of the receptive field over the long dimension (image width, i.e. time) while maintaining high resolution over the short dimension (image height, i.e., frequency). The proposed architecture comprises a set of co-operative semi-dilated convolutional blocks, each block has a stack of parallel semi-dilated convolutional modules with different dilation rates. Results show that our proposed solution outperforms the state-of-the-art methods, achieving seizure prediction sensitivity scores of 88.45% and 89.52% for the American Epilepsy Society and Melbourne University EEG datasets, respectively.
Anti-clustering in the national SARS-CoV-2 daily infection counts
Authors: Boudewijn F. Roukema
Date: 2020-07-23 00:00:00
Tasks: Clustering

The noise in daily infection counts of an epidemic should be super-Poissonian due to intrinsic epidemiological and administrative clustering. Here, we use this clustering to classify the official national SARS-CoV-2 daily infection counts and check for infection counts that are unusually anti-clustered. We adopt a one-parameter model of $\phi'_i$ infections per cluster, dividing any daily count $n_i$ into $n_i/\phi'_i$ 'clusters', for 'country' $i$. We assume that $n_i/\phi'_i$ on a given day $j$ is drawn from a Poisson distribution whose mean is robustly estimated from the four neighbouring days, and calculate the inferred Poisson probability $P'_{ij}$ of the observation. The $P'_{ij}$ values should be uniformly distributed. We find the value $\phi_i$ that minimises the Kolmogorov-Smirnov distance from a uniform distribution. We investigate the $(\phi_i, N_i)$ distribution, for total infection count $N_i$. We find that most of the daily infection count sequences are inconsistent with a Poissonian model. Most are found to be consistent with the $\phi_i$ model. The 28-, 14- and 7-day least noisy sequences for several countries are best modelled as sub-Poissonian, suggesting a distinct epidemiological family. The 28-day least noisy sequence of Algeria has a preferred model that is strongly sub-Poissonian, with $\phi_i^{28} < 0.1$. TJ, TR, RU, BY, AL, AE, and NI have preferred models that are also sub-Poissonian, with $\phi_i^{28} < 0.5$. A statistically significant ($P^{\tau} < 0.05$) correlation was found between the lack of media freedom in a country, as represented by a high Reporters sans frontieres Press Freedom Index (PFI$^{2020}$), and the lack of statistical noise in the country's daily counts. The $\phi_i$ model appears to be an effective detector of suspiciously low statistical noise in the national SARS-CoV-2 daily infection counts.