pwc-archive/papers-with-abstracts huggingface dataset:
Dynamic Estimation of Power System Inertia Distribution Using Synchrophasor Measurements
Authors: Mingjian Tuo, Xingpeng Li
Date: 2020-06-20 00:00:00
Tasks: Clustering

Integration of intermittent renewable energy sources in modern power systems is increasing very fast. Replacement of synchronous generators with zero-to-low variable renewables substantially decreases the system inertia. In a large system, inertia distribution may vary significantly, areas of low inertia are more susceptible to frequency deviation, posing risks of load shedding and generation trip. Therefore, it is necessary for operators to evaluate and quantify the system inertia and its distribution in real time. This paper proposes a novel synchronized phasor measurement units (PMUs)-based dynamic system inertia estimation method. The proposed inertia estimation method is derived using electrical distance and clustering algorithm, which considers the impact of location of measurements relative to in-feed load and impact of oscillations. The center of inertia (COI) area and area of low inertia are also determined during the estimation. Numerical simulations are conducted on the IEEE 24-bus system with various load profiles using Transient Security Analysis Tools (TSAT), a core module of the DSATools, which demonstrate the efficacy of the proposed approach.
Speaker conditioned acoustic-to-articulatory inversion using x-vectors
Date: 2020-06-20 00:00:00

Speech production involves the movement of various articulators, including
tongue, jaw, and lips. Estimating the movement of the articulators from the
acoustics of speech is known as acoustic-to-articulatory inversion (AAI).
Recently, it has been shown that instead of training AAI in a speaker specific
manner, pooling the acoustic-articulatory data from multiple speakers is
beneficial. Further, additional conditioning with speaker specific information
by one-hot encoding at the input of AAI along with acoustic features benefits
the AAI performance in a closed-set speaker train and test condition. In this
work, we carry out an experimental study on the benefit of using x-vectors for
providing speaker specific information to condition AAI. Experiments with 30
speakers have shown that the AAI performance benefits from the use of x-vectors
in a closed set seen speaker condition. Further, x-vectors also generalizes
well for unseen speaker evaluation.
Multi-Variant Time Constrained FlexRay Static Segment Scheduling
Date: 2020-06-20 00:00:00
Tasks: Scheduling

The FlexRay bus is a modern standard used in the automotive industry.It
offers deterministic message transmission with zero jitter while using
time-triggered scheduling in the static segment. When several vehicle variants
(i.e. different models and their versions) share the same signal, the car
manufacturers require to schedule such signal at the same time in all vehicle
variants. This requirement simplifies the signal traceability and diagnostics
in different vehicle variants using the same platform and simplifies reuse of
components and tools.
  In this paper, we propose a first fit based heuristic algorithm which creates
the schedules for several vehicle variants at once, while transmitting a given
signal at the same time in all the schedules. The scheduling algorithm also
takes the time constraints as release dates and deadlines into account.
Finally, different algorithm versions are compared on benchmark sets and low
computational time demands are validated on large instances.
A Computer Vision Based Beamforming Scheme for Millimeter Wave Communication in LOS Scenarios
Authors: Tianqi Xiang, Yaxin Wang, Huiwen Li, Boren Guo, Xin Zhang
Date: 2020-06-20 00:00:00
Tasks: Position

A novel location-aware beamforming scheme for millimeter wave communication is proposed for line of sight (LOS) and low mobility scenarios, in which computer vision is introduced to derive the required position or spatial angular information from the image or video captured by camera(s) co-located with mmWave antenna array at base stations. A wireless coverage model is built to investigate the coverage performance and influence of positioning accuracy achieved by convolutional neural network (CNN) for image processing. In addition, videos could be intentionally blurred, or even low-resolution videos could be directly applied, to protect users' privacy with acceptable positioning precision, lower computation complexity and lower camera cost. It is proved by simulations that the beamforming scheme is practicable and the mainstream CNN we employed is sufficient in both aspects of beam directivity accuracy and processing speed in frame per second.
All-Optical Cochlear Implants
Authors: Stylianos E. Trevlakis, Alexandros-Apostolos A. Boulogeorgos, Nestor D. Chatzidiamantis, George K. Karagiannidis
Date: 2020-06-20 00:00:00
Tasks: All

In the present work, we introduce a novel cochlear implant (CI) architecture, namely all-optical CI (AOCI), which directly converts acoustic to optical signals capable of stimulating the cochlear neurons. First, we describe the building-blocks (BBs) of the AOCI, and explain their functionalities as well as their interconnections. Next, we present a comprehensive system model that incorporates the technical characteristics and constraints of each BB, the transdermal-optical-channel particularities, i.e. optical path-loss and external-implanted device stochastic pointing-errors, and the cochlear neurons biological properties. Additionally, in order to prove the feasibility of the AOCI architecture, we conduct a link-budget analysis that outputs novel closed-form expressions for the instantaneous and average photon flux that is emitted on the cochlear neurons. Likewise, we define three new key-performance-indicators (KPIs), namely probability of hearing, probability of false-hearing, and probability of neural damage. The proposed theoretical framework is verified through respective simulations, which not only quantify the efficiency of the proposed architecture, but also reveal an equilibrium between the optical transmission power and the patient's safety, as well as the AOCI BBs specifications. Finally, it is highlighted that the AOCI approach is greener and safer than the conventional CIs.
In-Memory Resistive RAM Implementation of Binarized Neural Networks for Medical Applications
Authors: Bogdan Penkovsky, Marc Bocquet, Tifenn Hirtzlin, Jacques-Olivier Klein, Etienne Nowak, Elisa Vianello, Jean-Michel Portal, Damien Querlioz
Date: 2020-06-20 00:00:00
Tasks: BIG-bench Machine Learning

The advent of deep learning has considerably accelerated machine learning development. The deployment of deep neural networks at the edge is however limited by their high memory and energy consumption requirements. With new memory technology available, emerging Binarized Neural Networks (BNNs) are promising to reduce the energy impact of the forthcoming machine learning hardware generation, enabling machine learning on the edge devices and avoiding data transfer over the network. In this work, after presenting our implementation employing a hybrid CMOS - hafnium oxide resistive memory technology, we suggest strategies to apply BNNs to biomedical signals such as electrocardiography and electroencephalography, keeping accuracy level and reducing memory requirements. We investigate the memory-accuracy trade-off when binarizing whole network and binarizing solely the classifier part. We also discuss how these results translate to the edge-oriented Mobilenet~V1 neural network on the Imagenet task. The final goal of this research is to enable smart autonomous healthcare devices.
A Novel Method for ECG Signal Classification via One-Dimensional Convolutional Neural Network
Authors: Xuan Hua, Jungang Han, Chen Zhao, Haipeng Tang, Zhuo He, Jinshan Tang, Qing-Hui Chen, Shaojie Tang, Weihua Zhou
Date: 2020-06-20 00:00:00
Tasks: Classification, General Classification, Sensitivity

This paper presents an end-to-end ECG signal classification method based on a novel segmentation strategy via 1D Convolutional Neural Networks (CNN) to aid the classification of ECG signals. The ECG segmentation strategy named R-R-R strategy (i.e., retaining ECG data between the R peaks just before and after the current R peak) for segmenting the original ECG data into segments in order to train and test the 1D CNN models. The novel strategy mimics physicians in scanning ECG to a greater extent, and maximizes the inherent information of ECG segments. The performance of the classification models for 5-class and 6-class are verified with ECG signals from 48 records of the MIT-BIH arrhythmia database. As the heartbeat types are divided into 5 classes (i.e., normal beat, left bundle branch block beat, right bundle branch block beat, ventricular ectopic beat, and paced beat) in the MIT-BIH, the best classification accuracy, the area under the curve (AUC), the sensitivity and the F1-score reach 99.24%, 0.9994, 0.99 and 0.99, respectively. As the heartbeat types are divided into 6 classes (i.e., normal beat, left bundle branch block beat, right bundle branch block beat, ventricular ectopic beat, paced beat and other beats) in the MIT-BIH, the beat classification accuracy, the AUC, the sensitivity, and the F1-score reach 97.02%, 0.9966, 0.97, and 0.97, respectively. Meanwhile, according to the recommended practice from the Association for Advancement of Medical Instrumentation (AAMI), the heartbeat types are divided into 5 classes (i.e., normal beat, supraventricular ectopic beats, ventricular ectopic beats, fusion beats, and unclassifiable beats), the beat classification accuracy, the sensitivity, and the F1-score reach 97.45%, 0.97, and 0.97, respectively. The experimental results show that the proposed method achieves better performance than the state-of-the-art methods.
Emulating UAV Motion by Utilizing Robotic Arm for mmWave Wireless Channel Characterization
Authors: Amit Kachroo, Collin A. Thornton, Md Arifur Rahman Sarker, Wooyeol Choi, He Bai, Ickhyun Song, John O'Hara, Sabit Ekin
Date: 2020-06-20 00:00:00

In this paper, millimeter wave (mmWave) wireless channel characteristics (Doppler spread and path loss modeling) for Unmanned Aerial Vehicles (UAVs) assisted communication is analyzed and studied by emulating the real UAV motion using a robotic arm. The motion considers the actual turbulence caused by the wind gusts to the UAV in the atmosphere, which is statistically modeled by the widely used Dryden wind model. The frequency under consideration is 28 GHz in an anechoic chamber setting. A total of 11 distance points from 3.5 feet to 23.5 feet in increments of 2 feet were considered in this experiment. At each distance point, 3 samples of data were collected for better inference purposes. In this emulated environment, it was found out that the average Doppler spread at these different distances was around -20 Hz and +20 Hz at the noise floor of -60 dB. On the other hand, the path loss exponent was found to be 1.843. This study presents and lays out a novel framework of emulating UAV motion for mmWave communication systems, which will pave the way out for future design and implementation of next generation UAV-assisted wireless communication systems.
High-Resolution Channel Estimation for Intelligent Reflecting Surface-Assisted MmWave Communications
Authors: C. Jia, J. Cheng, H. Gao, W. Xu
Date: 2020-06-21 00:00:00
Tasks: Vocal Bursts Intensity Prediction

In this paper, we study the high-resolution channel estimation problem for intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) multiple-input-multiple-output (MIMO) communications, which is a prerequisite to guarantee further high-rate data transmission. Considering the typical sparsity of mmWave channels, we formulate the cascaded channel estimation problem from a sparse signal recovery perspective, and then propose a novel two-step cascaded channel estimation protocol to estimate the cascaded user-IRS-base station channel with high-resolution for IRS-assisted mmWave MIMO communications. More specifically, the first step is to estimate the coarse angular domain information (ADI) and further establish the robust uplink by beam training. In the second step, by exploiting the coarse ADI, an adaptive grid matching pursuit (AGMP) algorithm is proposed to estimate the high-resolution cascaded channel state information (CSI) with low complexity. Simulation results verify that the proposed two-step channel estimation protocol significantly outperforms the state-of-the-art scheme, i.e., beam training based channel estimation, and meanwhile can reap near-optimal system performance achieved by perfect CSI.
The necessary and sufficient condition for the uncertain control gain in active disturbance rejection control
Date: 2020-06-21 00:00:00

Considering the control problem for nonlinear uncertain systems, the
tolerable range of uncertain control input gain is a fundamental issue. The
paper presents the necessary and sufficient condition for the well-performed
closed-loop system based on active disturbance rejection control (ADRC) design.
Via the proposed necessary and sufficient condition, the maximum tolerable
range of the uncertain control gain is quantitatively presented, which reveals
the capability of ADRC to handle the uncertainties related with control input.
Moreover, under the proposed necessary and sufficient condition, both the
transient performance and the steady state property of the ADRC based
closed-loop system are rigorously analyzed.
Wavelet Classification for Over-the-Air Non-Orthogonal Waveforms
Authors: Tongyang Xu, Izzat Darwazeh
Date: 2020-06-21 00:00:00
Tasks: Classification, Diversity, General Classification

Non-cooperative communications using non-orthogonal multicarrier signals are challenging since self-created inter carrier interference (ICI) exists, which would prevent successful signal classification. Deep learning (DL) can deal with the classification task without domain-knowledge at the cost of training complexity since neural network hyperparameters have to be extensively tuned. Previous work showed that a tremendously trained convolutional neural network (CNN) classifier can efficiently identify feature-diversity dominant signals while it failed when feature-similarity dominates. Therefore, a pre-processing strategy, which can amplify signal feature diversity is of great importance. This work applies single-level wavelet transform to manually extract time-frequency features from non-orthogonal signals. Composite statistical features are investigated and the wavelet enabled two-dimensional time-frequency feature grid is further simplified into a one-dimensional feature vector via proper statistical transform. The dimensionality reduced features are fed to an error-correcting output codes (ECOC) model, consisting of multiple binary support vector machine (SVM) learners, for multiclass signal classification. Low-cost experiments reveal 100% classification accuracy for feature-diversity dominant signals and 90% for feature-similarity dominant signals, which is nearly 28% accuracy improvement when compared with the CNN classification results.
A Temperature Monitoring System Incorporating an Array of Precision Wireless Thermometers
Authors: Amir Javadpour, Hamidreza Memarzadeh Tehran, Fatemeh Saghafi
Date: 2020-06-21 00:00:00

This paper addresses the design and implementation of a real time temperature monitoring system with applications in telemedicine. The system consists of a number of precision wireless thermometers which are conceived and realized to measure the patients body temperature in hospitals and the intensive care units. Each wireless thermometer incorporates an accurate semiconductor temperature sensor, a transceiver operating at 2.4 GHz and a microcontroller that controls the thermometer functionalities. An array of two thermometers are implemented and successfully evaluated in different scenarios, including free space and in vivo tests. Also, an in house developed computer software is used in order to visualize the measurements in addition to detecting rapid increase and alerting high body temperature. The agreement between the experimental data and reference temperature values is significant.
Pruning the Pilots: Deep Learning-Based Pilot Design and Channel Estimation for MIMO-OFDM Systems
Authors: Mahdi Boloursaz Mashhadi, Deniz Gunduz
Date: 2020-06-21 00:00:00

With the large number of antennas and subcarriers the overhead due to pilot transmission for channel estimation can be prohibitive in wideband massive multiple-input multiple-output (MIMO) systems. This can degrade the overall spectral efficiency significantly, and as a result, curtail the potential benefits of massive MIMO. In this paper, we propose a neural network (NN)-based joint pilot design and downlink channel estimation scheme for frequency division duplex (FDD) MIMO orthogonal frequency division multiplex (OFDM) systems. The proposed NN architecture uses fully connected layers for frequency-aware pilot design, and outperforms linear minimum mean square error (LMMSE) estimation by exploiting inherent correlations in MIMO channel matrices utilizing convolutional NN layers. Our proposed NN architecture uses a non-local attention module to learn longer range correlations in the channel matrix to further improve the channel estimation performance. We also propose an effective pilot reduction technique by gradually pruning less significant neurons from the dense NN layers during training. This constitutes a novel application of NN pruning to reduce the pilot transmission overhead. Our pruning-based pilot reduction technique reduces the overhead by allocating pilots across subcarriers non-uniformly and exploiting the inter-frequency and inter-antenna correlations in the channel matrix efficiently through convolutional layers and attention module.
Resource Allocation for Multi-Cell IRS-Aided NOMA Networks
Authors: Wanli Ni, Xiao Liu, Yuanwei Liu, Hui Tian, Yue Chen
Date: 2020-06-21 00:00:00

This paper proposes a novel framework of resource allocation in multi-cell intelligent reflecting surface (IRS) aided non-orthogonal multiple access (NOMA) networks, where an IRS is deployed to enhance the wireless service. The problem of joint user association, subchannel assignment, power allocation, phase shifts design, and decoding order determination is formulated for maximizing the achievable sum rate. The challenging mixed-integer non-linear problem is decomposed into an optimization subproblem (P1) with continuous variables and a matching subproblem (P2) with integer variables. In an effort to tackle the non-convex optimization problem (P1), iterative algorithms are proposed for allocating transmission power, designing reflection matrix, and determining decoding order by invoking relaxation methods such as convex upper bound substitution, successive convex approximation, and semidefinite relaxation. In terms of the combinational problem (P2), swap matching-based algorithms are developed for achieving a two-sided exchange-stable state among users, BSs and subchannels. Numerical results demonstrate that: 1) the sum rate of multi-cell NOMA networks is capable of being increased by 35% with the aid of the IRS; 2) the proposed algorithms for multi-cell IRS-aided NOMA networks can enjoy 22% higher energy efficiency than conventional NOMA counterparts; 3) the trade-off between spectrum efficiency and coverage area can be tuned by judiciously selecting the location of the IRS.
On the Secrecy of UAV Systems With Linear Trajectory
Authors: Gaofeng Pan, Hongjiang Lei, Jianping An, Shuo Zhang, Mohamed-Slim Alouini
Date: 2020-06-21 00:00:00

By observing the fact that moving in a straight line is a common flying behavior of unmanned aerial vehicles (UAVs) in normal applications, e.g., power line inspections, and air patrols along with highway/streets/borders, in this paper we investigate the secrecy outage performance of a UAV system with linear trajectory, where a UAV ($S$) flies in a straight line and transmits its information over the downlink to a legitimate receiver ($D$) on the ground while an eavesdropping UAV ($E$) trying to overhear the information delivery between $S$ and $D$. Meanwhile, some information is delivered to $S$ over the uplink from $D$, such as commanding messages to control $S$'s detecting operations, which can also be eavesdropped by $E$. The locations of $S$, $D$, and $E$ are randomly distributed. We first characterize the statistical characteristics (including cumulative distribution functions and probability density function) of the received signal-to-noise ratio over both downlink and uplink, and then the closed-form analytical expressions for the lower boundary of the secrecy outage probability of both downlink and uplink have also been derived accordingly. Finally, Monte-Carlo simulations are given to testify our proposed analytical models.
Performance Analysis and Optimization of Cooperative Satellite-Aerial-Terrestrial Systems
Authors: Gaofeng Pan, Jia Ye, Yongqiang Zhang, Mohamed-Slim Alouini
Date: 2020-06-21 00:00:00

Aerial relays have been regarded as an alternative and promising solution to extend and improve satellite-terrestrial communications, as the probability of line-of-sight transmissions increases compared with adopting terrestrial relays. In this paper, a cooperative satellite-aerial-terrestrial system including a satellite transmitter (S), a group of terrestrial receivers (D), and an aerial relay (R) is considered. Specifically, considering the randomness of S and D and employing stochastic geometry, the coverage probability of R-D links in non-interference and interference scenarios is studied, and the outage performance of S-R link is investigated by deriving an approximated expression for the outage probability. Moreover, an optimization problem in terms of the transmit power and the transmission time over S-R and R-D links is formulated and solved to obtain the optimal end-to-end energy efficiency for the considered system. Finally, some numerical results are provided to validate our proposed analysis models, as well as to study the optimal energy efficiency performance of the considered system.
Scalable Resetting Algorithms for Synchronization of Pulse-Coupled Oscillators over Rooted Directed Graphs
Date: 2020-06-21 00:00:00

We study the problem of robust global synchronization of pulse-coupled
oscillators (PCOs) over directed graphs. It is known that when the digraphs are
strongly connected, global synchronization can be achieved by using a class of
deterministic set-valued reset controllers. However, for large-scale networks,
these algorithms are not scalable because some of their tuning parameters have
upper bounds of the order of O(1/N), where N is the number of agents. This
paper resolves this scalability issue by presenting several new results in the
context of global synchronization of PCOs with more general network topologies
using deterministic and stochastic hybrid dynamical systems. First, we
establish that similar deterministic resetting algorithms can achieve robust,
global, and fixed-time synchronization in any rooted acyclic digraph. Moreover,
in this case we show that the synchronization dynamics are now scalable as the
tuning parameters of the algorithm are network independent, i.e., of order
O(1). However, the algorithms cannot be further extended to all rooted
digraphs. We establish this new impossibility result by introducing a
counterexample with a particular rooted digraph for which global
synchronization cannot be achieved, irrespective of the tuning of the reset
rule. Nevertheless, we show that if the resetting algorithms are modified by
accommodating an Erdos-Renyi type random graph model, then the resulting
stochastic resetting dynamics will guarantee global synchronization almost
surely for all rooted digraphs and, moreover, the tunable parameters of the
dynamics are network independent. Stability and robustness properties of the
resetting algorithms are studied using the tools from set-valued hybrid
dynamical systems. Numerical simulations are provided at the end of the paper
for demonstration of the main results.