pwc-archive/papers-with-abstracts huggingface dataset:
Modeling of Viral Aerosol Transmission and Detection
Authors: Maryam Khalid, Osama Amin, Sajid Ahmed, Basem Shihada, Mohamed-Slim Alouini
Date: 2020-05-05 00:00:00

In this paper, we propose studying the disease spread mechanism in the atmosphere as an engineering problem. Aerosol transmission is the most significant mode among the viral transmission mechanisms that do not include physical contact, where airflows carry virus-laden droplets over long distances. Throughout this work, we study the transport of these droplets as a molecular communication problem, where one has no control over the transmission source, but a robust receiver can be designed using bio-sensors. To this end, we present a complete system model and derive an end-to-end mathematical model for the transmission channel under certain constraints and boundary conditions. We derive the system response for both continuous sources such as breathing and jet or impulsive sources such as coughing and sneezing. In addition to transmitter and channel, we assumed a receiver architecture composed of air sampler and Silicon Nanowire field-effect transistor. Then, we formulate a detection problem to maximize the likelihood decision rule and minimize the corresponding missed detection probability. Finally, we present several numerical results to observe the impact of parameters that affect the performance and justify the feasibility of the proposed setup in related applications.
See Through Smoke: Robust Indoor Mapping with Low-cost mmWave Radar
Authors: Chris Xiaoxuan Lu, Stefano Rosa, Peijun Zhao, Bing Wang, Changhao Chen, John A. Stankovic, Niki Trigoni, Andrew Markham
Date: 2019-11-01 00:00:00

This paper presents the design, implementation and evaluation of milliMap, a single-chip millimetre wave (mmWave) radar based indoor mapping system targetted towards low-visibility environments to assist in emergency response. A unique feature of milliMap is that it only leverages a low-cost, off-the-shelf mmWave radar, but can reconstruct a dense grid map with accuracy comparable to lidar, as well as providing semantic annotations of objects on the map. milliMap makes two key technical contributions. First, it autonomously overcomes the sparsity and multi-path noise of mmWave signals by combining cross-modal supervision from a co-located lidar during training and the strong geometric priors of indoor spaces. Second, it takes the spectral response of mmWave reflections as features to robustly identify different types of objects e.g. doors, walls etc. Extensive experiments in different indoor environments show that milliMap can achieve a map reconstruction error less than 0.2m and classify key semantics with an accuracy around 90%, whilst operating through dense smoke.
OTHR multitarget tracking with a GMRF model of ionospheric parameters
Authors: Zhen Guo, Zengfu Wang, Hua Lan, Quan Pan, Kun Lu
Date: 2020-05-05 00:00:00
Tasks: State Estimation

The ionosphere is the propagation medium for radio waves transmitted by an over-the-horizon radar (OTHR). Ionospheric parameters, typically, virtual ionospheric heights (VIHs), are required to perform coordinate registration for OTHR multitarget tracking and localization. The inaccuracy of ionospheric parameters has a significant deleterious effect on the target localization of OTHR. Therefore, to improve the localization accuracy of OTHR, it is important to develop accurate models and estimation methods of ionospheric parameters and the corresponding target tracking algorithms. In this paper, we consider the variation of the ionosphere with location and the spatial correlation of the ionosphere in OTHR target tracking. We use a Gaussian Markov random field (GMRF) to model the VIHs, providing a more accurate representation of the VIHs for OTHR target tracking. Based on expectation-conditional maximization and GMRF modeling of the VIHs, we propose a novel joint optimization solution, called ECM-GMRF, to perform target state estimation, multipath data association and VIHs estimation simultaneously. In ECM-GMRF, the measurements from both ionosondes and OTHR are exploited to estimate the VIHs, leading to a better estimation of the VIHs which improves the accuracy of data association and target state estimation, and vice versa. The simulation indicates the effectiveness of the proposed algorithm.
Downlink Coverage and Rate Analysis of Low Earth Orbit Satellite Constellations Using Stochastic Geometry
Authors: Niloofar Okati, Taneli Riihonen, Dani Korpi, Ilari Angervuori, Risto Wichman
Date: 2020-04-28 00:00:00
Tasks: Point Processes

As low Earth orbit (LEO) satellite communication systems are gaining increasing popularity, new theoretical methodologies are required to investigate such networks' performance at large. This is because deterministic and location-based models that have previously been applied to analyze satellite systems are typically restricted to support simulations only. In this paper, we derive analytical expressions for the downlink coverage probability and average data rate of generic LEO networks, regardless of the actual satellites' locality and their service area geometry. Our solution stems from stochastic geometry, which abstracts the generic networks into uniform binomial point processes. Applying the proposed model, we then study the performance of the networks as a function of key constellation design parameters. Finally, to fit the theoretical modeling more precisely to real deterministic constellations, we introduce the effective number of satellites as a parameter to compensate for the practical uneven distribution of satellites on different latitudes. In addition to deriving exact network performance metrics, the study reveals several guidelines for selecting the design parameters for future massive LEO constellations, e.g., the number of frequency channels and altitude.
A Solution to The Non-linearity of Electro-Optic Modulation Synthetic Aperture Lidar
Authors: Fuping Fang, Heng Hu, Weiming Xu, Rong Shu
Date: 2020-05-05 00:00:00

Synthetic aperture laser radar has higher resolution, so requires higher modulated bandwidth. Because the data volume of chirp or pulse coding schemes is too large, it brings much pressure to data acquisition and data processing. So, we can use dechirp to reduce the amount of data. However, there is a seriously non-linear problem in phase modulation, which strictly prohibited the dechirp usage. Therefore, in order to solve the above problems, we propose a method based on Electro-Optic Modulators to achieve a large-bandwidth chirp signal. Meanwhile, give a method to resolve the seriously non-linear problems. Firstly, by properly setting the amplitude of the input signal of electro-optic modulators, the finite order signal coefficients are guaranteed to have an absolute advantage. Then, an ideal chirp signal can be obtained by filtering through an optical filter. Finally, the feasibility of the scheme is proved by theory and experiments.
Reconfigurable Intelligent Surface (RIS)-Enhanced Two-Way OFDM Communications
Authors: Chandan Pradhan, Ang Li, Lingyang Song, Jun Li, Branka Vucetic, Yonghui Li
Date: 2020-05-05 00:00:00
Tasks: Vocal Bursts Valence Prediction

In this paper, we focus on the reconfigurable intelligent surface (RIS)-enhanced two-way device-to-device (D2D) multi-pair orthogonal-frequency-division-multiplexing (OFDM) communication systems. Specifically, we maximize the minimum bidirectional weighted sum-rate by jointly optimizing the sub-band allocation, the power allocation and the discrete phase shift (PS) design at the RIS. To tackle the main difficulty of the non-convex PS design at the RIS, we firstly formulate a semi-definite relaxation problem and further devise a low-complexity solution for the PS design by leveraging the projected sub-gradient method. We demonstrate the desirable performance gain for the proposed designs through numerical results.
Design and Selection of Additional Residuals to Enhance Fault Isolation of a Turbocharged Spark Ignited Engine System
Authors: K. Y. Ng, E. Frisk, M. Krysander
Date: 2020-02-08 00:00:00

This paper presents a method to enhance fault isolation without adding physical sensors on a turbocharged spark ignited petrol engine system by designing additional residuals from an initial observer-based residuals setup. The best candidates from all potential additional residuals are selected using the concept of sequential residual generation to ensure best fault isolation performance for the least number of additional residuals required. A simulation testbed is used to generate realistic engine data for the design of the additional residuals and the fault isolation performance is verified using structural analysis method.
Impact of Backward Crosstalk in 2x2 MIMO Transmitters on NMSE and Spectral Efficiency
Authors: Peter Händel, Özlem Tuğfe Demir, Emil Björnson, Daniel Rönnow
Date: 2019-12-18 00:00:00

We consider backward crosstalk in 2 x 2 transmitters, which is caused by crosstalk from the outputs of the transmitter to the inputs or by the combination of output crosstalk and impedance mismatch. We analyze its impact via feedback networks together with third-order power amplifier non-linearities. We utilize the Bussgang decomposition to express the distorted output signals of the transmitter as a linear transformation of the input plus uncorrelated distortion. The normalized mean-square errors (NMSEs) between the distorted and desired amplified signals are expressed analytically and the optimal closed-form power back-off that minimizes the worst NMSE of the two branches is derived. In the second part of the paper, an achievable spectral efficiency (SE) is presented for the communication from this ``dirty'' transmitter to another single-antenna receiver. The SE-maximizing precoder is optimally found by exploiting the hardware characteristics. Furthermore, the optimal power back-off is analyzed for two sub-optimal precoders, which either do not exploit any hardware knowledge or only partial knowledge. The simulation results show that the performance of these sub-optimal precoders is close-to-optimal. We also discuss how the analysis in this paper can be extended to transmitters with an arbitrary number of antenna branches.
Impact of user distribution on optical wireless systems
Authors: Khulood D. Alazwary, Osama Zwaid Alsulami, Sarah O. M. Saeed, Sanaa Hamid Mohamed, T. E. H. El-Gorashi, Mohammed T. Alresheedi, Jaafar M. H. Elmirghani
Date: 2020-05-04 00:00:00
Tasks: Diversity

In this paper, we investigate the impact of user distribution on resource allocation in visible light communication (VLC) systems, using a wavelength division multiple access (WDMA) scheme. Two different room layouts are examined in this study. Three 10-user scenarios are considered, while an optical angle diversity receiver (ADR) with four faces is used. A mixed-integer linear programming (MILP) model is utilized to identify the optimum wavelengths and access point (AP) allocation in each scenario. The results show that a change in user distribution can affect the level of channel bandwidth and SINR. However, a uniform distribution of users in the room can provide a higher channel bandwidth as well as high SINR above the threshold (15.6 dB) for all users compared to clustered users, which is a scenario that has the lowest SINR with supported data rate above 3.2 Gbps.
IO-VNBD: Inertial and Odometry Benchmark Dataset for Ground Vehicle Positioning
Authors: Uche Onyekpe, Vasile Palade, Stratis Kanarachos, Alicja Szkolnik
Date: 2020-05-04 00:00:00
Tasks: Autonomous Vehicles, Benchmarking

Low-cost inertial navigation sensors (INS) can be exploited for a reliable tracking solution for autonomous vehicles. However, position errors grow exponentially due to noises in the measurements. Several deep learning techniques have been investigated to mitigate the errors for a better navigation solution [1-10]. However, these studies have involved the use of different datasets not made publicly available. The lack of a robust benchmark dataset has thus hindered the advancement in the research, comparison and adoption of deep learning techniques for vehicle positioning based on inertial navigation. In order to facilitate the benchmarking, fast development and evaluation of positioning algorithms, we therefore present the first of its kind large-scale and information-rich inertial and odometry focused public dataset called IO-VNBD (Inertial Odometry Vehicle Navigation Benchmark Dataset).The vehicle tracking dataset was recorded using a research vehicle equipped with ego-motion sensors on public roads in the United Kingdom, Nigeria, and France. The sensors include a GPS receiver, inertial navigation sensors, wheel-speed sensors amongst other sensors found on the car as well as the inertial navigation sensors and GPS receiver in an android smart phone sampling at 10HZ. A diverse number of scenarios and vehicle dynamics are captured such as traffic, round-abouts, hard-braking etc. on different road types (country roads, motorways etc.) with varying driving patterns. The dataset consists of a total driving time of about 40 hours over 1,300km for the vehicle extracted data and about 58 hours over 4,400 km for the smartphone recorded data. We hope that this dataset will prove valuable in furthering research on the correlation between vehicle dynamics and its displacement as well as other related studies
Validation of the rapid detection approach for enhancing the electronic nose systems performance, using different deep learning models and support vector machines
Authors: Juan C. Rodriguez Gamboa, Adenilton J. da Silva, Ismael C. S. Araujo, Eva Susana Albarracin E., Cristhian M. Duran A
Date: 2020-05-04 00:00:00

Real-time gas classification is an essential issue and challenge in applications such as food and beverage quality control, accident prevention in industrial environments, for instance. In recent years, the Deep Learning (DL) models have shown great potential to classify and forecast data in diverse problems, even in the electronic nose (E-Nose) field. In this work, we used a Support Vector Machines (SVM) algorithm and three different DL models to validate the rapid detection approach (based on processing an early portion of raw signals and a rising window protocol) over different measurement conditions. We performed a set of trials with five different E-Nose databases that include fifteen datasets. Based on the results, we concluded that the proposed approach has a high potential, and it can be suitable to be used for E-nose technologies, reducing the necessary time for making forecasts and accelerating the response time. Because in most cases, it achieved reliable estimates using only the first 30% or fewer of measurement data (counted after the gas injection starts.) The findings suggest that the rapid detection approach generates reliable forecasting models using different classification methods. Still, SVM seems to obtain the best accuracy, right window size, and better training time.
The Bussgang Decomposition of Non-Linear Systems: Basic Theory and MIMO Extensions
Authors: Özlem Tuğfe Demir, Emil Björnson
Date: 2020-05-04 00:00:00
Tasks: Misconceptions, Quantization

Many of the systems that appear in various signal processing applications are non-linear, for example, due to hardware impairments such as non-linear amplifiers and finite-resolution quantization. The Bussgang decomposition is a popular tool for analyzing the performance of systems that involve such non-linear components. In a nutshell, the decomposition provides an exact probabilistic relationship between the output and the input of a non-linearity: the output is equal to a scaled version of the input plus uncorrelated distortion. The decomposition can either be used to compute exact performance results or lower bounds where the uncorrelated distortion is treated as independent noise. This lecture note explains the basic theory, provides key examples, extends the theory to complex-valued vector signals, and clarifies some potential misconceptions.
Characterizing capacity of flexible loads for providing grid support
Authors: Austin R. Coffman, Zhong Guo, Prabir Barooah
Date: 2020-05-04 00:00:00

Flexible loads are a resource for the Balancing Authority (BA) of the future to aid in the balance of supply and demand in the power grid. Consequently, it is of interest for a BA to know how much flexibility a collection of loads has, so to successfully incorporate flexible loads into grid level resource allocation. Loads' flexibility is limited by all their Quality of Service (QoS) requirements. In this work we present a characterization of capacity for a collection of flexible loads. This characterization is in terms of the Power Spectral Density (PSD) of the reference signal. Two advantages of our characterization are: (i) it easily allows for a BA to use the characterization for resource allocation of flexible loads and (ii) it allows for precise definitions of the power and energy capacity for a collection of flexible loads.
Graph Fourier Transform: A Stable Approximation
Authors: João Domingos, José M. F. Moura
Date: 2020-01-14 00:00:00

In Graph Signal Processing (GSP), data dependencies are represented by a graph whose nodes label the data and the edges capture dependencies among nodes. The graph is represented by a weighted adjacency matrix $A$ that, in GSP, generalizes the Discrete Signal Processing (DSP) shift operator $z^{-1}$. The (right) eigenvectors of the shift $A$ (graph spectral components) diagonalize $A$ and lead to a graph Fourier basis $F$ that provides a graph spectral representation of the graph signal. The inverse of the (matrix of the) graph Fourier basis $F$ is the Graph Fourier transform (GFT), $F^{-1}$. Often, including in real world examples, this diagonalization is numerically unstable. This paper develops an approach to compute an accurate approximation to $F$ and $F^{-1}$, while insuring their numerical stability, by means of solving a non convex optimization problem. To address the non-convexity, we propose an algorithm, the stable graph Fourier basis algorithm (SGFA) that we prove to exponentially increase the accuracy of the approximating $F$ per iteration. Likewise, we can apply SGFA to $A^H$ and, hence, approximate the stable left eigenvectors for the graph shift $A$ and directly compute the GFT. We evaluate empirically the quality of SGFA by applying it to graph shifts $A$ drawn from two real world problems, the 2004 US political blogs graph and the Manhattan road map, carrying out a comprehensive study on tradeoffs between different SGFA parameters. We also confirm our conclusions by applying SGFA on very sparse and very dense directed Erd\H os-R\'enyi graphs.
Proof of Concept for Keeping Mobile Communication Channels Static With Partner Antenna With-Movements
Authors: Gerald Artner
Date: 2019-05-27 00:00:00

This work presents a proof of concept, that mobile communication channels can be kept static by the partner antenna to which the channel is formed. The partner antenna simultaneously moves along the same trajectory with the first antenna (with-movement). Experimental results are presented for movement along a straight line over a distance of several wavelengths. Measurements were performed with 2.45GHz quarter-wavelength monopole antennas in an anechoic chamber. Statistical analysis and mathematical models of the results are presented.
Supervised Learning Based Online Tracking Filters: An XGBoost Implementation
Authors: Jie Deng, Wei Yi
Date: 2020-04-10 00:00:00
Tasks: parameter estimation

The target state filter is an important module in the traditional target tracking framework. In order to get satisfactory tracking results, traditional Bayesian methods usually need accurate motion models, which require the complicated prior information and parameter estimation. Therefore, the modeling process has a key impact on traditional Bayesian filters for target tracking. However, when encountering unknown prior information or the complicated environment, traditional Bayesian filters have the limitation of greatly reduced accuracy. In this paper, we propose a supervised learning based online tracking filter(SLF). First, a complete tracking filter framework based on supervised learning is established, which is directly based on data-driven and establishes the mapping relationship between data. In other words, the proposed filter does not require the prior information about target dynamics and clutter distribution. Then, an implementation based on eXtreme Gradient Boosting (XGBoost) is provided, which proves the portability and applicability of the SLF framework. Meanwhile, the proposed framework will encourage other researchers to continue to expand the field of combining traditional filters with supervised learning. Finally, numerical simulation experiments prove the effectiveness of the proposed filter.
A Low-Overhead Hierarchical Beam-tracking Algorithm for THz Wireless Systems
Authors: Giorgos Stratidakis, Georgia D. Ntouni, Alexandros-Apostolos A. Boulogeorgos, Dimitrios Kritharidis, Angeliki Alexiou
Date: 2020-05-04 00:00:00

In this paper, a novel hierarchical beamtracking approach, which is suitable for terahertz (THz) wireless systems, is presented. The main idea is to employ a prediction based algorithm with a multi-resolution codebook, in order to decrease the required overhead of tracking and increase its robustness. The efficiency of the algorithm is evaluated in terms of the average number of pilots and mean square error (MSE) and is compared with the corresponding performance of the fast channel tracking (FCT) algorithm. Our results highlight the superiority of the proposed approach in comparison with FCT, in terms of tracking efficiency with low overhead.
End-to-End Automatic Sleep Stage Classification Using Spectral-Temporal Sleep Features
Authors: Hyeong-Jin Kim, Minji Lee, Seong-Whan Lee
Date: 2020-05-04 00:00:00
Tasks: Automatic Sleep Stage Classification, Classification, General Classification, Sleep Staging

Sleep disorder is one of many neurological diseases that can affect greatly the quality of daily life. It is very burdensome to manually classify the sleep stages to detect sleep disorders. Therefore, the automatic sleep stage classification techniques are needed. However, the previous automatic sleep scoring methods using raw signals are still low classification performance. In this study, we proposed an end-to-end automatic sleep staging framework based on optimal spectral-temporal sleep features using a sleep-edf dataset. The input data were modified using a bandpass filter and then applied to a convolutional neural network model. For five sleep stage classification, the classification performance 85.6% and 91.1% using the raw input data and the proposed input, respectively. This result also shows the highest performance compared to conventional studies using the same dataset. The proposed framework has shown high performance by using optimal features associated with each sleep stage, which may help to find new features in the automatic sleep stage method.
Design of Millimeter-Wave Single-Shot Beam Training for True-Time-Delay Array
Authors: Veljko Boljanovic, Han Yan, Erfan Ghaderi, Deukhyoun Heo, Subhanshu Gupta, Danijela Cabric
Date: 2020-02-18 00:00:00

Beam training is one of the most important and challenging tasks in millimeter-wave and sub-terahertz communications. Novel transceiver architectures and signal processing techniques are required to avoid prohibitive training overhead when large antenna arrays with narrow beams are used. In this work, we leverage recent developments in wide range true-time-delay (TTD) analog arrays and frequency dependent probing beams to accelerate beam training. We propose an algorithm that achieves high-accuracy angle of arrival estimation with a single training symbol. Further, the impact of TTD front-end impairments on beam training accuracy is investigated, including the impact of gain, phase, and delay errors. Lastly, the study on impairments and required specifications of resolution and range of analog delay taps are used to provide a design insight of energy efficient TTD array, which employs a novel architecture with discrete-time sampling based TTD elements.