pwc-archive/papers-with-abstracts huggingface dataset:
Performance Analysis of RIS-Based nT-FSO Link Over G-G Turbulence With Pointing Errors
Authors: Alain R. Ndjiongue, Telex M. N. Ngatched, Octavia A. Dobre, Ana G. Armada, Harald Haas
Date: 2021-02-06 00:00:00

One of the main problems faced by communication systems is the presence of skip-zones in the targeted areas. With the deployment of the fifth-generation mobile network, solutions are proposed to solve the signal loss due to obstruction by buildings, mountains, and atmospheric or weather conditions. Among these solutions, re-configurable intelligent surfaces (RIS), which are newly proposed modules, may be exploited to reflect the incident signal in the direction of dead zones, increase communication coverage, and make the channel smarter and controllable. In this paper, we tackle the skip-zone problem in near-terrestrial free-space optical (nT-FSO) systems using RIS. We carry out a performance analysis of RIS-aided nT-FSO links affected by turbulence and pointing errors, for both heterodyne detection (HD) and intensity modulation-direct detection (IM/DD) techniques. Turbulence is modeled using the Gamma-Gamma (G-G) distribution. We analyze the model and provide exact closed-form expressions of the probability density function, cumulative distribution function, and moment generating function of the end-to-end signal-to-noise ratio, $\gamma$. Capitalizing on these statistics, we evaluate the system performance through the outage probability, $ P_{out} $, ergodic channel capacity, $\overline{C}$, and average bit-error-rate, $\overline{P}_b$, for selected binary modulation schemes. Numerical results obtained for different turbulence levels and pointing errors confirm that the HD technique outperforms IM/DD even in RIS-aided nT-FSO systems. These results also show that using a blue color offers better channel capacity and communication performance compared to red and green colors.
DOA Estimation with Non-Uniform Linear Arrays: A Phase-Difference Projection Approach
Authors: Hui Chen, Tarig Ballal, Tareq Y. Al-Naffouri
Date: 2021-02-06 00:00:00
Tasks: Diversity

Phase wrapping is a major problem in direction-of-arrival (DOA) estimation using phase-difference observations. For a sensor pair with an inter-sensor spacing greater than half of the wavelength ($\lambda/2$) of the signal, phase wrapping occurs at certain DOA angles leading to phase-difference ambiguities. Existing phase unwrapping methods exploit either frequency or spatial diversity. These techniques work by imposing restrictions on the utilized frequencies or the receiver array geometry. In addition to sensitivity to noise and calibration errors, these methods may also have high computational complexity. We propose a grid-less \emph{phase-difference projection} (PDP) DOA algorithm to overcome these issues. The concept of \emph{wrapped phased-difference pattern} (WPDP) is introduced, which allows the proposed algorithm to compute most of the parameters required for DOA estimation in an offline manner, hence resulting in a superior computational speed in realtime. Simulation results demonstrate the excellent performance of the proposed algorithm, both in terms of accuracy and speed.
Localizing Unsynchronized Sensors with Unknown Sources
Authors: Dalia El Badawy, Viktor Larsson, Marc Pollefeys, Ivan Dokmanić
Date: 2021-02-06 00:00:00

We propose a method for sensor array self-localization using a set of sources at unknown locations. The sources produce signals whose times of arrival are registered at the sensors. We look at the general case where neither the emission times of the sources nor the reference time frames of the receivers are known. Unlike previous work, our method directly recovers the array geometry, instead of first estimating the timing information. The key component is a new loss function which is insensitive to the unknown timings. We cast the problem as a minimization of a non-convex functional of the Euclidean distance matrix of microphones and sources subject to certain non-convex constraints. After convexification, we obtain a semidefinite relaxation which gives an approximate solution; subsequent refinement on the proposed loss via the Levenberg-Marquardt scheme gives the final locations. Our method achieves state-of-the-art performance in terms of reconstruction accuracy, speed, and the ability to work with a small number of sources and receivers. It can also handle missing measurements and exploit prior geometric and temporal knowledge, for example if either the receiver offsets or the emission times are known, or if the array contains compact subarrays with known geometry.
An Efficient Active Set Algorithm for Covariance Based Joint Data and Activity Detection for Massive Random Access with Massive MIMO
Authors: Ziyue Wang, Zhilin Chen, Ya-Feng Liu, Foad Sohrabi, Wei Yu
Date: 2021-02-06 00:00:00
Tasks: Action Detection, Activity Detection, Computational Efficiency, CPU

This paper proposes a computationally efficient algorithm to solve the joint data and activity detection problem for massive random access with massive multiple-input multiple-output (MIMO). The BS acquires the active devices and their data by detecting the transmitted preassigned nonorthogonal signature sequences. This paper employs a covariance based approach that formulates the detection problem as a maximum likelihood estimation (MLE) problem. To efficiently solve the problem, this paper designs a novel iterative algorithm with low complexity in the regime where the device activity pattern is sparse $\unicode{x2013}$ a key feature that existing algorithmic designs have not previously exploited for reducing complexity. Specifically, at each iteration, the proposed algorithm focuses on only a small subset of all potential sequences, namely the active set, which contains a few most likely active sequences (i.e., transmitted sequences by all active devices), and performs the detection for the sequences in the active set. The active set is carefully selected at each iteration based on the current detection result and the first-order optimality condition of the MLE problem. Simulation results show that the proposed active set algorithm enjoys significantly better computational efficiency (in terms of the CPU time) than the state-of-the-art algorithms.
Recent Development in Disease Diagnosis by Information, Communication and Technology
Authors: Shabana Urooj, Astha Sharma, Chitransh Sinha, Fadwa Alrowais
Date: 2021-02-05 00:00:00
Tasks: Management

The usage of Information, Communication and Technology (ICT) in health sector has a great potential in improving the health of individuals and communities, disease detection, prevention and overall strengthening the healthcare systems, vital for development and poverty reduction. Large ICT establishments offer a variety of Artificial Intelligence (AI) based solutions; and their tenacities are inclusive of wearable therapeutic devices, healthcare management arrangements, extrapolative healthcare diagnostics, ailment prevention systems, detection and screening of diseases and automated tactics. In the field of healthcare related instrumentation, AI plays a prevalent role with the amalgamation of several technological progressions. This enables machines to sense, comprehend, act and learn to perform organisational and clinical healthcare functions as well as serves the research and training purposes. Additionally, it enables to accomplish the anticipated directorial and medicinal benefits. The major causes of life threats reported in literature are; heart and brain diseases. In this paper, an extensive review is presented exploring the evolving ICT technologies in machine learning and AI to help ICT enthusiasts to be able to catch up with the emerging trends in healthcare.
Subgraph Signal Processing
Authors: Feng Ji, Wee Peng Tay, Giacomo Kahn
Date: 2020-05-11 00:00:00

Graph signal processing, like the graph Fourier transform, requires the full graph signal at every vertex of the graph. However, in practice, only signals at a subset of vertices may be available. We propose a subgraph signal processing framework that relates a graph shift operator or filter on a subgraph with a filter on the ambient graph through an operator loss. It allows us to define shift operators for the subgraph signal, which has a meaningful interpretation and relation to mixtures of shift invariant filters restricted to different subgraphs of the ambient graph (which we call semi shift invariant). This leads to a notion of frequency domain for the subgraph signal consistent in some sense with that of the full graph signal. We apply the subgraph signal processing machinery to several applications and demonstrate the utility of this framework in cases where only partial graph signals are observed.
ULA Fitting for Sparse Array Design
Authors: Wanlu Shi, Sergiy A. Vorobyov, Yingsong Li
Date: 2021-02-05 00:00:00

Sparse array (SA) geometries, such as coprime and nested arrays, can be regarded as a concatenation of two uniform linear arrays (ULAs). Such arrays lead to a significant increase of the number of degrees of freedom (DOF) when the second-order information is utilized, i.e., they provide long virtual difference coarray (DCA). Thus, the idea of this paper is based on the observation that SAs can be fitted through concatenation of sub-ULAs. A corresponding SA design principle, called ULA fitting, is then proposed. It aims to design SAs from sub-ULAs. Towards this goal, a polynomial model for arrays is used, and based on it, a DCA structure is analyzed if SA is composed of multiple sub-ULAs. SA design with low mutual coupling is considered. ULA fitting enables to transfer the SA design requirements, such as hole free, low mutual coupling and other requirements, into pseudo polynomial equation, and hence, find particular solutions. We mainly focus on designing SAs with low mutual coupling and large uniform DOF. Two examples of SAs with closed-form expressions are then developed based on ULA fitting. Numerical experiments verify the superiority of the proposed SAs in the presence of heavy mutual coupling.
Tracking Performance of Incremental LMS Algorithm over Adaptive Distributed Sensor Networks
Authors: Ehsan Mostafapour, C. Ghobadi, Javad Nourinia, M. Chehel Amirani
Date: 2021-02-04 00:00:00

In this paper we focus on the tracking performance of incremental adaptive LMS algorithm in an adaptive network. For this reason we consider the unknown weight vector to be a time varying sequence. First we analyze the performance of network in tracking a time varying weight vector and then we explain the estimation of Rayleigh fading channel through a random walk model. Closed-form relations are derived for mean square error (MSE), mean square deviation (MSD) and excess mean square error (EMSE)of analyzed network in tracking Rayleigh fading channel and random walk model. Comparison between theoretical and simulation results shows a perfect match and verifies performed calculations.
Autocorrelation-Driven Synthesis of Antenna Arrays -- The Case of DS-Based Planar Isophoric Thinned Arrays
Authors: G. Oliveri, G. Gottardi, M. A. Hannan, N. Anselmi, L. Poli
Date: 2021-02-04 00:00:00
Tasks: valid

A new methodology for the design of isophoric thinned arrays with a priori controlled pattern features is introduced. A fully analytical and general (i.e., valid for any lattice and set of weights) relationship between the autocorrelation of the array excitations and the power pattern samples is first derived. Binary 2-D sequences with known autocorrelation properties, namely the difference sets (DSs), are then chosen as a representative benchmark to prove that it is possible to deduce closed-form synthesis formulas that a priori guarantee to fit requirements on the sidelobe level (SLL), the directivity, the half-power beamwidth, and the power pattern in user-defined directions. The selected results from a wide numerical assessment, which also includes full-wave simulations with realistic radiators, are illustrated to validate the reliability and the accuracy of the proposed design equations and the associated performance bounds.
Histogram Specification by Assignment of Optimal Unique Values
Authors: V. S. Ramos, L. F. d. Q. Silveira, L. G. d. Q. Silveira Júnior
Date: 2021-02-04 00:00:00
Tasks: Image Enhancement

In this paper, we propose two novel algorithms for histogram specification and quantile transformation of data without local information. These are core techniques that can serve as building blocks for applications that require specifying the sample distribution of a given set of data. Histogram specification is best known for its image enhancement applications, whereas quantile transformation is typically employed in data preprocessing for data normalization. In signal processing, methods often require temporal or spatial information; in data preprocessing, methods work by interpolation or by approximation, drawing from results in computational statistics, and have a trade-off between speed and quality. It is nontrivial to accommodate for cases that do not have local information (e.g., tabular data) while also providing a fast, exact solution. For that, we take up a concept in image processing called group mapping law and propose an extension. The proposed extension allows us to formulate a convex functional where we look for the best approximation between the output unique values and the reference histogram. Then, we apply the ordered assignment solution, a result in optimal transport, to reconstruct the output from the optimal unique values. Two sets of results show the effectiveness of the proposed algorithms when compared to traditional and state-of-the-art methods. The proposed algorithms are fast, exact, and least $p$-norm optimal. Further, we define the algorithms as generic data processing methods. Thus, contributions from this paper can be easily incorporated in applications spanning many disciplines, especially in applied data science.
An Irregular Two-Sizes Square Tiling Method for the Design of Isophoric Phased Arrays
Authors: P. Rocca, N. Anselmi, A. Polo, A. Massa
Date: 2021-02-03 00:00:00

The design of isophoric phased arrays composed of two-sized square-shaped tiles that fully cover rectangular apertures is dealt with. The number and the positions of the tiles within the array aperture are optimized to fit desired specifications on the power pattern features. Toward this end, starting from the derivation of theoretical conditions for the complete tileability of the aperture, an ad hoc coding of the admissible arrangements, which implies a drastic reduction of the cardinality of the solution space, and their compact representation with a graph are exploited to profitably apply an effective optimizer based on an integer-coded genetic algorithm. A set of representative numerical examples, concerned with state-of-the-art benchmark problems, is reported and discussed to give some insights on the effectiveness of both the proposed tiled architectures and the synthesis strategy.
Probabilistic Interval Analysis for the Analytic Prediction of the Pattern Tolerance Distribution in Linear Phased Arrays With Random Excitation Errors
Authors: P. Rocca, N. Anselmi, A. Benoni, A. Massa
Date: 2021-02-03 00:00:00

A statistical approach based on the interval analysis (IA) is proposed for the analysis of the effects, on the radiation patterns radiated by phased arrays, of random errors and tolerances in the amplitudes and phases of the array-elements excitations. Starting from the efficient, reliable, and inclusive computation of the bounds of the complex-valued interval array pattern function by means of IA, an analytic method is presented to yield closed-form expressions for the probability of occurrence of a user-chosen value of the power pattern or of its features within the corresponding IA-derived bounds. A set of numerical examples is reported and discussed to assess the reliability of the proposed probabilistic interval analysis (PIA) method with the results from Monte Carlo simulations as well as to point out its effectiveness and potentialities/advantages/efficiency in real applications of great industrial interest.
Comprehensive Study on Denoising of Medical Images Utilizing Neural Network Based Auto-Encoder
Authors: Thoshara Nawarathne, Thanushi Withanage, Samitha Gunarathne, Upekha Delay, Eranda Somathilake, Janith Senanayake, Roshan Godaliyadda, Parakrama Ekanayake, Janaka Wijayakulasooriya, Chathura Rathnayake
Date: 2021-02-03 00:00:00
Tasks: Denoising, Retrieval

Fetal motion discernment utilizing spectral images extracted from accelerometric data incident on pregnant mothers abdomen has gained substantial attention in the state-of-the-art research. It is an essential practice to avoid adverse scenarios such as stillbirths and intrauterine growth restrictions. However, this endeavor of ensuring fetus safety has been arduous due to the existence of random noise in medical images. This novel research is an in depth approach to analyze how the interference of different noise variations affect the retrieval of information in those images. For that, an algorithm employing auto-encoder-based deep learning was modeled and the accuracy of reconstruction of the STFT images mitigating the noise has been measured examining the loss. From the results, it is manifested that even a substantial addition of the Super-Gaussian noises which have a higher correlation of the frequencies possessed by the Fetal movement images can be restored successfully with the slightest error.
Reducing Waiting Times at Charging Stations with Adaptive Electric Vehicle Route Planning
Authors: Sven Schoenberg, Falko Dressler
Date: 2021-02-02 00:00:00

Electric vehicles are becoming more popular all over the world. With increasing battery capacities and a growing fast-charging infrastructure, they are becoming suitable for long distance travel. However, queues at charging stations could lead to long waiting times, making efficient route planning even more important. In general, optimal multi-objective route planning is extremely computationally expensive. We propose an adaptive charging and routing strategy, which considers driving, waiting, and charging time. For this, we developed a multi-criterion shortest-path search algorithm using contraction hierarchies. To further reduce the computational effort, we precompute shortest-path trees between the known locations of the charging stations. We propose a central charging station database (CSDB) that helps estimating waiting times at charging stations ahead of time. This enables our adaptive charging and routing strategy to reduce these waiting times. In an extensive set of simulation experiments, we demonstrate the advantages of our concept, which reduces average waiting times at charging stations by up to 97 %. Even if only a subset of the cars uses the CSDB approach, we can substantially reduce waiting times and thereby the total travel time of electric vehicles.
Polyimide-Based Flexible Coupled-Coils Design and Load-Shift Keying Analysis
Authors: Yuan YAO, Wing-Hung Ki, Chi-Ying Tsui
Date: 2021-02-02 00:00:00

Wireless power transfer using inductive coupling is commonly used for medical implantable devices. The design of the secondary coil on the implantable device is important as it will affect the power transfer efficiency, the size of the implant, and also the data transmission between the implant and the in-vitro controller. In this paper, we present a design of the secondary coil on a polyimide-based flexible substrate to achieve high power transfer efficiency. Load shift keying modulation is used for the data communication between the primary and secondary coils. A thorough analysis is done for the ideal and practical scenario and it shows that a mismatched secondary LC tank will affect the communication range and communication correctness. A solution to achieve robust data transmission is proposed and then verified by SPICE simulations.
Recovery of coincident frequency domain multiplexed detector pulses using sequential deconvolution
Authors: M. Mishra, J. Mattingly
Date: 2020-06-08 00:00:00

Multiplexing of radiation detector signals into a single channel significantly reduces the need for a large number of digitizer channels, which reduces the cost and the power consumption of a data acquisition system. We previously demonstrated frequency domain multiplexing by convolution using a prototype system that multiplexed two EJ-309 organic scintillators signals into a single channel. Each detector pulse was converted to a damped sinusoid which was then combined into a single channel. The combined signal was digitized and the original detector signal was recovered from the damped sinusoid by deconvolution. In this paper, we demonstrate the recovery of multiple detector signals that arrive during the same digitized record via a new sequential deconvolution method. When two detectors produce signals in the same digitized record and their pulses do not overlap in time, we found that the charge, arrival time, and particle type can be estimated fairly precisely for the first pulse, but the second pulse exhibits substantial degradation in the precision of the estimated charge and arrival time. When the pulses overlap in time, we demonstrate both theoretically and experimentally that the part of the first pulse that does not overlap with the second can be recovered accurately, so the arrival time and amplitude of the first pulse can be estimated fairly precisely, but not the charge or particle type. None of these quantities can be estimated precisely for the second pulse when the two pulses overlap.
Multiscale Anisotropic Harmonic Filters on non Euclidean domains
Authors: Francesco Conti, Gaetano Scarano, Stefania Colonnese
Date: 2021-02-01 00:00:00

This paper introduces Multiscale Anisotropic Harmonic Filters (MAHFs) aimed at extracting signal variations over non-Euclidean domains, namely 2D-Manifolds and their discrete representations, such as meshes and 3D Point Clouds as well as graphs. The topic of pattern analysis is central in image processing and, considered the growing interest in new domains for information representation, the extension of analogous practices on volumetric data is highly demanded. To accomplish this purpose, we define MAHFs as the product of two components, respectively related to a suitable smoothing function, namely the heat kernel derived from the heat diffusion equations, and to local directional information. We analyse the effectiveness of our approach in multi-scale filtering and variation extraction. Finally, we present an application to the surface normal field and to a luminance signal textured to a mesh, aiming to spot, in a separate fashion, relevant curvature changes (support variations) and signal variations.