pwc-archive/papers-with-abstracts huggingface dataset:
Spectral Domain Spline Graph Filter Bank
Authors: Amir Miraki, Hamid Saeedi-Sourck, Nicola Marchetti, Arman Farhang
Date: 2020-11-23 00:00:00
Tasks: Denoising

In this paper, we present a structure for two-channel spline graph filter bank with spectral sampling (SGFBSS) on arbitrary undirected graphs. Our proposed structure has many desirable properties; namely, perfect reconstruction, critical sampling in spectral domain, flexibility in choice of shape and cut-off frequency of the filters, and low complexity implementation of the synthesis section, thanks to our closed-form derivation of the synthesis filter and its sparse structure. These properties play a pivotal role in multi-scale transforms of graph signals. Additionally, this framework can use both normalized and non-normalized Laplacian of any undirected graph. We evaluate the performance of our proposed SGFBSS structure in nonlinear approximation and denoising applications through simulations. We also compare our method with the existing graph filter bank structures and show its superior performance.
RACH in Self-Powered NB-IoT Networks: Energy Availability and Performance Evaluation
Authors: Yan Liu, Yansha Deng, Maged Elkashlan, Arumugam Nallanathan, Jinhong Yuan, Ranjan K. Mallik
Date: 2020-11-23 00:00:00

NarrowBand-Internet of Things (NB-IoT) is a new 3GPP radio access technology designed to provide better coverage for a massive number of low-throughput low-cost devices in delay-tolerant applications with low power consumption. To provide reliable connections with extended coverage, a repetition transmission scheme is introduced to NB-IoT during both Random Access CHannel (RACH) procedure and data transmission procedure. To avoid the difficulty in replacing the battery for IoT devices, the energy harvesting is considered as a promising solution to support energy sustainability in the NB-IoT network. In this work, we analyze RACH success probability in a self-powered NB-IoT network taking into account the repeated preamble transmissions and collisions, where each IoT device with data is active when its battery energy is sufficient to support the transmission. We model the temporal dynamics of the energy level as a birth-death process, derive the energy availability of each IoT device, and examine its dependence on the energy storage capacity and the repetition value. We show that in certain scenarios, the energy availability remains unchanged despite randomness in the energy harvesting. We also derive the exact expression for the RACH success probability of a {randomly chosen} IoT device under the derived energy availability, which is validated under different repetition values via simulations. We show that the repetition scheme can efficiently improve the RACH success probability in a light traffic scenario, but only slightly improves that performance with very inefficient channel resource utilization in a heavy traffic scenario.
Extremely Sparse Co-Prime Sensing: Low Latency Estimation is not a Dream but a Reality
Authors: Usham V. Dias
Date: 2020-11-23 00:00:00

Co-prime sensing is a sub-Nyquist technique for signal acquisition. Several modifications to the prototype co-prime array have been proposed in the literature. Researchers have also demonstrated low latency estimation. This paper describes the functioning of a Family of Adjustable Pivot Co-Prime Arrays. However, the main focus is to introduce the reader to a concept called Extremely Sparse Co-Prime Arrays and Samplers. Adjustable pivot co-prime arrays are a special case of the extremely sparse arrays. The closed-form expressions for the weight function and the correlogram bias window are derived. Low latency estimation is demonstrated using the extremely sparse co-prime scheme. Furthermore, a multidimensional and a hybrid extremely sparse co-prime array is proposed as a straightforward extension of the 1D-theory. Finally, a generalized extremely sparse structure is developed with several design parameters. Most existing structures may be viewed as a special case of the generalized scheme.
Influence of some cardiovascular risk factors on the rela-tionship between age and blood pressure
Authors: Giulia Silveri, Lorenzo Pascazio, Milos Ajcevic, Aleksandar Miladinovic, Agostino Accardo1
Date: 2020-11-23 00:00:00
Tasks: Relation

Blood Pressure (BP) is a biological signal related to the cardiovascular system that inevitably is affected by ageing. Moreover, it is also influenced by the presence of cardiovascular risk factors. To evaluate how the relationship be-tween BP and age changes with the presence of risk factors in hypertensive and normotensive subjects, we analyzed 880 subjects with and without smoking, obe-sity, diabetes mellitus and dyslipidemia. A regression line fitted each BP/Age relation calculated separately for normotensive and hypertensive subjects with and without risk factors. For each of the four conditions the office and the 24-hour ambulatory BP monitoring (ABPM) were considered. In subjects with and without risk factors, the slopes of the Systolic BP/Age relation were higher in hypertensive than in normotensive subjects in both office and ABPM conditions. Moreover, the presence of risk factors modified the Systolic BP/Age relation in hypertensive subjects by using either office or ABPM measurements. Finally, we confirmed that the difference between the two modalities depends on age too.
Influence of the gender on the relationship between heart rate and blood pressure
Authors: Giulia Silveri, Lorenzo Pascazio, Milos Ajcevic, Aleksandar Miladinovic, Agostino Accardo
Date: 2020-11-23 00:00:00
Tasks: Relation, Rhythm

Blood Pressure (BP) and Heart Rate (HR) provide information on clin-ical condition along 24h. Both signals present circadian changes due to sympa-thetic/parasympathetic control system that influence the relationship between them. Moreover, also the gender could modify this relation, acting on both con-trol systems. Some studies, using office measurements examined the BP/HR re-lation, highlighting a direct association between the two variables, linked to sus-pected coronary heart disease. Nevertheless, till now such relation has not been studied yet using ambulatory technique that is known to lead to additional prog-nostic information about cardiovascular risks. In order to examine in a more ac-curate way this relation, in this work we evaluate the influence of gender on the BP/HR relationship by using hour-to-hour 24h ambulatory measurements. Data coming from 122 female and 50 male normotensive subjects were recorded using a Holter Blood Pressure Monitor and the parameters of the linear regression fit-ting BP/HR were calculated. Results confirmed those obtained in previous stud-ies using punctual office measures in males and underlined a significant relation between Diastolic BP and HR during each hour of the day in females; a different trend in the BP/HR relation between genders was found only during night-time. Moreover, the circadian rhythm of BP/HR is similar in both genders but with different values of HR and BP at different times of the day.
Modeling and Architecture Design of Reconfigurable Intelligent Surfaces Using Scattering Parameter Network Analysis
Authors: Shanpu Shen, Bruno Clerckx, Ross Murch
Date: 2020-11-23 00:00:00

Reconfigurable intelligent surfaces (RISs) are an emerging technology for future wireless communication. The vast majority of recent research on RIS has focused on system level optimizations. However, developing straightforward and tractable electromagnetic models that are suitable for RIS aided communication modeling remains an open issue. In this paper, we address this issue and derive communication models by using rigorous scattering parameter network analysis. We also propose new RIS architectures based on group and fully connected reconfigurable impedance networks that can adjust not only the phases but also the magnitudes of the impinging waves, which are more general and more efficient than conventional single connected reconfigurable impedance network that only adjusts the phases of the impinging waves. In addition, the scaling law of the received signal power of an RIS aided system with reconfigurable impedance networks is also derived. Compared with the single connected reconfigurable impedance network, our group and fully connected reconfigurable impedance network can increase the received signal power by up to 62%, or maintain the same received signal power with a number of RIS elements reduced by up to 21%. We also investigate the proposed architecture in deployments with distance-dependent pathloss and Rician fading channel, and show that the proposed group and fully connected reconfigurable impedance networks outperform the single connected case by up to 34% and 48%, respectively.
Space-based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques
Authors: Giovanni Soldi, Domenico Gaglione, Nicola Forti, Alessio Di Simone, Filippo Cristian Daffinà, Gianfausto Bottini, Dino Quattrociocchi, Leonardo M. Millefiori, Paolo Braca, Sandro Carniel, Peter Willett, Antonio Iodice, Daniele Riccio, Alfonso Farina
Date: 2020-11-23 00:00:00

Maritime surveillance (MS) is of paramount importance for search and rescue operations, fishery monitoring, pollution control, law enforcement, migration monitoring, and national security policies. Since ground-based radars and automatic identification system (AIS) do not always provide a comprehensive and seamless coverage of the entire maritime domain, the use of space-based sensors is crucial to complement them. We reviewed space-based technologies for MS in the first part of this work, titled "Space-based Global Maritime Surveillance. Part I: Satellite Technologies" [1]. However, future MS systems combining multiple terrestrial and space-based sensors with additional information sources will require dedicated artificial intelligence and data fusion techniques for the processing of raw satellite images and fuse heterogeneous information. The second part of our work focuses on the most promising artificial intelligence and data fusion techniques for MS using space-based sensors.
DemodNet: Learning Soft Demodulation from Hard Information Using Convolutional Neural Network
Authors: Shilian Zheng, Xiaoyu Zhou, Shichuan Chen, Peihan Qi, Xiaoniu Yang
Date: 2020-11-23 00:00:00

Soft demodulation is a basic module of traditional communication receivers. It converts received symbols into soft bits, that is, log likelihood ratios (LLRs). However, in the nonideal additive white Gaussian noise (AWGN) channel, it is difficult to accurately calculate the LLR. In this letter, we propose a demodulator, DemodNet, based on a fully convolutional neural network with variable input and output length. We use hard bit information to train the DemodNet, and we propose log probability ratio (LPR) based on the output layer of the trained DemodNet to realize soft demodulation. The simulation results show that under the AWGN channel, the performance of both hard demodulation and soft demodulation of DemodNet is very close to the traditional methods. In three non-ideal channel scenarios, i.e., the presence of frequency deviation, additive generalized Gaussian noise (AGGN) channel, and Rayleigh fading channel, the performance of channel decoding using the soft information LPR obtained by DemodNet is better than the performance of decoding using the exact LLR calculated under the ideal AWGN assumption.
Causality Graph of Vehicular Traffic Flow
Authors: Sina Molavipour, Germán Bassi, Mladen Čičić, Mikael Skoglund, Karl Henrik Johansson
Date: 2020-11-23 00:00:00

In an intelligent transportation system, the effects and relations of traffic flow at different points in a network are valuable features which can be exploited for control system design and traffic forecasting. In this paper, we define the notion of causality based on the directed information, a well-established data-driven measure, to represent the effective connectivity among nodes of a vehicular traffic network. This notion indicates whether the traffic flow at any given point affects another point's flow in the future and, more importantly, reveals the extent of this effect. In contrast with conventional methods to express connections in a network, it is not limited to linear models and normality conditions. In this work, directed information is used to determine the underlying graph structure of a network, denoted directed information graph, which expresses the causal relations among nodes in the network. We devise an algorithm to estimate the extent of the effects in each link and build the graph. The performance of the algorithm is then analyzed with synthetic data and real aggregated data of vehicular traffic.
Space-based Global Maritime Surveillance. Part I: Satellite Technologies
Authors: Giovanni Soldi, Domenico Gaglione, Nicola Forti, Alessio Di Simone, Filippo Cristian Daffinà, Gianfausto Bottini, Dino Quattrociocchi, Leonardo M. Millefiori, Paolo Braca, Sandro Carniel, Peter Willett, Antonio Iodice, Daniele Riccio, Alfonso Farina
Date: 2020-11-23 00:00:00

Maritime surveillance (MS) is crucial for search and rescue operations, fishery monitoring, pollution control, law enforcement, migration monitoring, and national security policies. Since the early days of seafaring, MS has been a critical task for providing security in human coexistence. Several generations of sensors providing detailed maritime information have become available for large offshore areas in real time: maritime radar sensors in the 1950s and the automatic identification system (AIS) in the 1990s among them. However, ground-based maritime radars and AIS data do not always provide a comprehensive and seamless coverage of the entire maritime space. Therefore, the exploitation of space-based sensor technologies installed on satellites orbiting around the Earth, such as satellite AIS data, synthetic aperture radar, optical sensors, and global navigation satellite systems reflectometry, becomes crucial for MS and to complement the existing terrestrial technologies. In the first part of this work, we provide an overview of the main available space-based sensors technologies and present the advantages and limitations of each technology in the scope of MS. The second part, related to artificial intelligence, signal processing and data fusion techniques, is provided in a companion paper, titled: "Space-based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques" [1].
Multi-RIS Aided 3D Secure Precise Wireless Transmission
Authors: Tong Shen, Wenlong Cai, Yan Lin, Shuo Zhang, Jinyong Lin, Feng Shu, Jiangzhou Wang
Date: 2020-11-23 00:00:00

In this paper, multiple reconfigurable intelligent surfaces (RIS) aided secure precise wireless transmission (SPWT) schemes are proposed in the three-dimensional (3D) wireless communication scenario. Unavailable direct path channels from transmitter to receivers are considered when the direct paths are obstructed by obstacles. Then, multiple RISs are utilized to achieve SPWT through the reflection path among transmitter, RISs and receivers in order to enhance the communication performance and energy efficiency simultaneously. First, a maximum-signal-to-interference-and-noise ratio (MSINR) scheme is proposed in a single user scenario. Then, the multi-user scenario is considered where the illegitimate users are regarded as eavesdroppers. A maximum-secrecy-rate (MSR) scheme and a maximum-signal-to-leakage-and-noise ratio (MSLNR) are proposed. The former achieves a better secrecy rate (SR) performance but incurs a higher complexity. The latter has a lower complexity than the MSR scheme with an SR performance loss. Simulation results show that both single-user scheme and multi-user scheme can achieve SPWT which transmits confidential message precisely to location of desired users. Moreover, MSLNR scheme has a lower complexity than the MSR scheme, while the SR performance is close to that of the MSR scheme.
Fast Decentralized Linear Functions Over Edge Fluctuating Graphs
Authors: Siavash Mollaebrahim, Baltasar Beferull-Lozano
Date: 2020-11-23 00:00:00

Implementing linear transformations is a key task in the decentralized signal processing framework, which performs learning tasks on data sets distributed over multi-node networks. That kind of network can be represented by a graph. Recently, some decentralized methods have been proposed to compute linear transformations by leveraging the notion of graph shift operator, which captures the local structure of the graph. However, existing approaches have some drawbacks such as considering some special instances of linear transformations, or reducing the family of transformations by assuming that a shift matrix is given such that a subset of its eigenvectors spans the subspace of interest. In contrast, this paper develops a framework for computing a wide class of linear transformations in a decentralized fashion by relying on the notion of graph shift operator. The main goal of the proposed method is to compute the desired linear transformation in a small number of iterations. To this end, a set of successive graph shift operators is employed, then, a new optimization problem is proposed whose goal is to compute the desired transformation as fast as possible. In addition, usually, the topology of the networks, especially the wireless sensor networks, change randomly because of node failures or random links. In this paper, the effect of edge fluctuations on the performance of the proposed method is studied. To deal with the negative effect of edge fluctuations, an online kernel-based method is proposed which enables nodes to estimate the missed values with their at hand information. The proposed method can also be employed to sparsify the network graph or reduce the number of local exchanges between nodes, which saves sensors power in the wireless sensor networks.
A Novel NOMA Solution with RIS Partitioning
Authors: Aymen Khaleel, Ertugrul Basar
Date: 2020-11-22 00:00:00
Tasks: Fairness

Reconfigurable intelligent surface (RIS) empowered communications with non-orthogonal multiple access (NOMA) has recently become as an appealing research direction for the next-generation wireless communications. In this paper, we propose a novel NOMA solution with RIS partitioning, where we aim to enhance the spectrum efficiency by improving the ergodic rate of all users, and to maximize the user fairness. In the proposed system, we distribute the physical resources among users such that the base station (BS) and RIS are dedicated to serve different clusters of users. Furthermore, we formulate an RIS partitioning optimization problem to slice the RIS elements between the users such that the user fairness is maximized. The formulated problem is a non-convex and non-linear integer programming (NLIP) problem with a combinatorial feasible set, which is very challenging to solve. Therefore, we exploit the structure of the problem to bound its feasible set and obtain a sub-optimal solution by sequentially applying three efficient search algorithms. Furthermore, we derive exact and asymptotic expressions for the outage probability. Simulation results clearly indicate the superiority of the proposed system over the considered benchmark systems in terms of ergodic sum-rate, outage probability, and user fairness performance.
Waveform Optimization with Multiple Performance Metrics for Broadband Joint Communication and Radar Sensing
Authors: Zhitong Ni, J. Andrew Zhang, Kai Yang, Xiaojing Huang, Theodoros A. Tsiftsis
Date: 2020-11-22 00:00:00

Joint communication and radar sensing (JCAS) integrates communication and radar/radio sensing into one system, sharing one transmitted signal. In this paper, we investigate JCAS waveform optimization underlying communication signals, where a base station detects radar targets and communicates with mobile users simultaneously. We first develop individual novel waveform optimization problems for communications and sensing, respectively. For communications, we propose a novel lower bound of sum rate by integrating multi-user interference and effective channel gain into one metric that simplifies the optimization of the sum rate. For radar sensing, we consider optimizing one of two metrics, the mutual information or the Cramer-Rao bound. Then, we formulate the JCAS problem by optimizing the communication metric under different constraints of the radar metric, and we obtain both closed-form solutions and iterative solutions to the non-convex JCAS optimization problem. Numerical results are provided and verify the proposed optimization solutions.
A Workbench for Testing and Simulation Faults in Three-phase Electric Motors with Intelligent Electronic Device and Microcontrolled System
Authors: Giovanni Faria, Michel Fernandes Peres, Osmar Moreira da Silva Neto, Jefferson Rodrigo Schuertz, Edson Leonardo dos Santos, Carlos Alexandre Gouvea da Silva
Date: 2020-11-22 00:00:00

Electric motors can be damaged or operate improperly from a possible set of failures. Such failures are related to high or very low voltage and current levels, phase loss or blocked rotor. Therefore, it is important to protect these equipments through appropriate mechanisms. Alternatively, a workbench can simulate detectable failures related to the engines, allowing to change parameters, in which maintenance operators are able to identify the results of these changes. This work presents the development of a workbench as a tool for testing electrical machines and drives. The workbench is based on the Arduino programming platform (microcontroller system), in which it checks the functioning of electric motors under the condition of failures that may occur in this engine. Motor protections are carried out through an Intelligent Electronic Device (IED), which are popularly known as intelligent relays. The results show the development of a workbench that can test and identify several faults in a small three-phase motor.
DFT-spread-OFDM Based Chirp Transmission
Authors: Alphan Sahin, Nozhan Hosseini, Hosseinali Jamal, Safi Shams Muhtasimul Hoque, David W. Matolak
Date: 2020-08-09 00:00:00

In this study, we propose a framework for chirp-based communications by exploiting discrete Fourier transform-spread orthogonal frequency division multiplexing (DFT-s-OFDM). We show that a well-designed frequency-domain spectral shaping (FDSS) filter for DFT-s-OFDM can convert its single-carrier nature to a linear combination of chirps circularly translated in the time domain. Also, by exploiting the properties of the Fourier series and Bessel function of the first kind, we analytically obtain the FDSS filter for an arbitrary chirp. We theoretically show that the chirps with low ripples in the frequency domain result in a lower bit-error ratio (BER) via less noise enhancement. We also address the noise enhancement by exploiting the repetitions in the frequency. The proposed framework offers a new way to efficiently synthesize chirps that can be used in Internet-of-Things (IoT), dual-function radar and communication (DFRC) or wireless sensing applications with existing DFT-s-OFDM transceivers.
Monotonicity of the Trace-Inverse of Covariance Submatrices and Two-Sided Prediction
Authors: Anatoly Khina, Arie Yeredor, Ram Zamir
Date: 2020-11-21 00:00:00

It is common to assess the "memory strength" of a stationary process looking at how fast the normalized log-determinant of its covariance submatrices (i.e., entropy rate) decreases. In this work, we propose an alternative characterization in terms of the normalized trace-inverse of the covariance submatrices. We show that this sequence is monotonically non-decreasing and is constant if and only if the process is white. Furthermore, while the entropy rate is associated with one-sided prediction errors (present from past), the new measure is associated with two-sided prediction errors (present from past and future). This measure can be used as an alternative to Burg's maximum-entropy principle for spectral estimation. We also propose a counterpart for non-stationary processes, by looking at the average trace-inverse of subsets.