pwc-archive/papers-with-abstracts huggingface dataset:
Speed-Up Ramp-Counter ADC Using Locality Principle : A systematic analysis
Authors: HoseinAli Jafari Abeshoori, Seyed Saeed Azadfar
Date: 2020-11-27 00:00:00

This work inspects the parameters that affect the conversion rate of a ramp-counter ADC and propose two small systematical changes to reduce the time needed to complete a single analog to digital conversion by use of the locality principle point of view. System full architecture is modeled and simulated by Matlab script and the results show up to 97% reduction in conversion time for an electro-cardiograph record.
Drone classification from RF fingerprints using deep residual nets
Authors: Sanjoy Basak, Sreeraj Rajendran, Sofie Pollin, Bart Scheers
Date: 2020-11-27 00:00:00
Tasks: Classification, General Classification

Detecting UAVs is becoming more crucial for various industries such as airports and nuclear power plants for improving surveillance and security measures. Exploiting radio frequency (RF) based drone control and communication enables a passive way of drone detection for a wide range of environments and even without favourable line of sight (LOS) conditions. In this paper, we evaluate RF based drone classification performance of various state-of-the-art (SoA) models on a new realistic drone RF dataset. With the help of a newly proposed residual Convolutional Neural Network (CNN) model, we show that the drone RF frequency signatures can be used for effective classification. The robustness of the classifier is evaluated in a multipath environment considering varying Doppler frequencies that may be introduced from a flying drone. We also show that the model achieves better generalization capabilities under different wireless channel and drone speed scenarios. Furthermore, the newly proposed model's classification performance is evaluated on a simultaneous multi-drone scenario. The classifier achieves close to 99 % classification accuracy for signal-to-noise ratio (SNR) 0 dB and at -10 dB SNR it obtains 5 % better classification accuracy compared to the existing framework.
Compact Dual-Polarized Vivaldi Antenna for Ground Penetrating Radar (GPR) Application
Authors: Hai-Han Sun, Yee Hui Lee, Abdulkadir C. Yucel, Genevieve Ow, Mohamed Lokman Mohd Yusof
Date: 2020-11-27 00:00:00
Tasks: GPR

In this paper, a compact dual-polarized Vivaldi antenna is presented. Four Vivaldi elements are used as radiators, and are positioned obliquely and connected in a horn shape. By exciting two sets of elements, two orthogonally polarized radiations can be achieved. The dual-polarized antenna features a low operating frequency band with a wide bandwidth, high port isolation, good directional radiation performance, and a very compact size, making it highly suitable for the ground penetrating radar (GPR) application.
Toe-Heal-Air-Injection Thermal Recovery Production Prediction and Modelling Using Quadratic Poisson Polynomial Regression
Authors: Alan Rezazadeh
Date: 2020-11-27 00:00:00
Tasks: Prediction, regression

This research paper explores application of multivariable regression models using only reservoir temperatures for predicting oil and gas production in a Toe-Heal-Air-Injection (THAI) enhanced oil recovery process. This paper discusses effects of statistical interaction between thermocouples by using second degree quadratic polynomials, which showed significant production forecast accuracy. Interactions among thermocouples statistically include temperature of larger reservoir areas, hence improving the predictive models. The interaction of two thermocouples can be interpreted as temperature gradient of combustion zone as moving forward during THAI operations life cycle. Second degree polynomial regression including interactions showed major prediction improvement for both oil and natural gas productions compare to simple regression models. Application of Poisson regression slightly improved prediction accuracy for oil production and was less effective on improving natural gas production predictions. Quadratic Poisson regression models showed realistic production prediction method for both oil and gas production values, due to the nature of Poisson probability distribution which is non-negative for rates and count values.
Decoding PPP Corrections from BDS B2b Signals Using a Software-defined Receiver: an Initial Performance Evaluation
Authors: Xiangchen Lu, Liang Chen, Nan Shen, Lei Wang, Zhenhang Jiao, Ruizhi Chen
Date: 2020-11-27 00:00:00

With the rapid development of China's BeiDou Navigation Satellite System(BDS), the application of real-time precise point positioning (RTPPP) based on BDS has become an active research area in the field of Global Navigation Satellite System (GNSS). BDS has provided the service of broadcasting RTPPP information. It indicates that BDS has become the second satellite system that provides RTPPP services, following Galileo among the GNSS, but work based on this direction has yet to be explored. Therefore, this paper evaluates the performance of precise point positioning (PPP) service using a software-defined receiver (SDR). An experiment was carried out tu verify the feasibility of the SDR. The PPP-B2b signal was processed to obtain PPP service information, including orbit corrections, clock corrections, and differential code bias corrections. The time-varying attributes of these corrections of BDS and GPS are evaluated, and the integrity and stability of the PPP service were analyzed. The results show the PPP-B2b signal can stably provide PPP services for staellites in the Asia-Pacific region, including centimeter to decimeter-level orbit corrections and meter-level clock correction for GPS satellites. Finally, detection tip for bitream availability in SDR is proposed. Some content which is not defined in the official document, such as the PPP-B2b frame arrangement, various correction update cycles and the progress of PPP service are discussed.
Weighted Data Spaces for Correlation-Based Array Imaging in Experimental Aeroacoustics
Authors: Hans-Georg Raumer, Carsten Spehr, Thorsten Hohage, Daniel Ernst
Date: 2020-05-27 00:00:00

This article discusses aeroacoustic imaging methods based on correlation measurements in the frequency domain. Standard methods in this field assume that the estimated correlation matrix is superimposed with additive white noise. In this paper we present a mathematical model for the measurement process covering arbitrarily correlated noise. The covariance matrix of correlation data is given in terms of fourth order moments. The aim of this paper is to explore the use of such additional information on the measurement data in imaging methods. For this purpose a class of weighted data spaces is introduced, where each data space naturally defines an associated beamforming method with a corresponding point spread function. This generic class of beamformers contains many well-known methods such as Conventional Beamforming, (Robust) Adaptive Beamforming or beamforming with shading. This article examines in particular weightings that depend on the noise (co)variances. In a theoretical analysis we prove that the beamformer, weighted by the full noise covariance matrix, has minimal variance among all beamformers from the described class. Application of the (co)variance weighted methods on synthetic and experimental data show that the resolution of the results is improved and noise effects are reduced.
Creating a Baseline for Robust Energy Savings Estimation in Households
Authors: Eftychios Protopapadakis, Anastasia Garbi, Anna Malamou, Maria Kaselimi, Zisis Pontikas, Anastasios Doulamis, Nikolaos Doulamis, Kostas Vasilakis, Nassos Michas, Emmanouel Alexakis
Date: 2020-11-26 00:00:00

In this paper we present a methodology appropriate for establishing a user-specific hourly-based benchmark period of energy consumption. Such values can be used as reference for explicitly calculating energy savings. The required equipment is limited to low cost sensors. The so-called baseline allows for an explicit comparison of the household consumption now and then, once appropriate adjustments are made to handle the different conditions (e.g. temperature variation). When involving user motivation strategies, hourly based energy savings can provide multiple advantages. However, there are two major factors affecting the baseline construction: corrupted or missing data values and unordinary patterns. In this paper we provide an analytical methodology to handle such scenarios and create year-long hourly-based consumption baseline.
Fiber as a temperature sensor with portable Correlation-OTDR as interrogator
Authors: Florian Azendorf, Annika Dochhan, Florian Spinty, Mirko Lawin, Bernhard Schmauss, Michael Eiselt
Date: 2020-11-26 00:00:00

In this paper, we report on the integration of a Correlation-OTDR into a portable unit (30 cm x 30 cm), based on a multi-processor system on chip (MPSoC) and a small formfactor pluggable (SFP) 2.5G transceiver. Going beyond telecommunication applications, this system is demonstrated for temperature measurements, based on the change of propagation delay with temperature in a short section of optical fiber. The temperature measurement accuracy is investigated as a function of the fiber length from 4 to 25 meters.
A Reversed and Shift Sparse Array Scheme based on the Difference and Sum Co-array
Authors: Yan Zhou, Jin Li, Nieke Wei
Date: 2020-11-26 00:00:00

The reversed and shift (RAS) sparse array scheme, which is based on the difference and sum co-array (DSCA) and remarkably enhances the capability of identifying sources, is proposed. For the original nested array (NA) or co-prime array (CPA), there exists a large overlap between its difference co-array and sum co-array, which prevents it from obtaining high degrees of freedom (DOFs). Motivated by this fact, the RAS scheme is designed for reducing the overlap while increasing the DOFs for direction of arrival (DOA) estimation. The proposed scheme is effective for both NA and CPA. The closed-form expression for the relationship between the number of physical sensors and the number of consecutive DSCA sensors is derived. Compared with some representative DSCA based sparse arrays, the proposed one can achieve longer consecutive virtual array. Simulation experiments are carried out to exhibit the enhanced DOA estimation performance of RAS scheme.
A Closed-form Localization Method Utilizing Pseudorange Measurements from Two Non-synchronized Positioning Systems
Authors: Sihao Zhao, Xiao-Ping Zhang, Xiaowei Cui, Mingquan Lu
Date: 2020-11-25 00:00:00
Tasks: Form

In a time-of-arrival (TOA) or pseudorange based positioning system, user location is obtained by observing multiple anchor nodes (AN) at known positions. Utilizing more than one positioning systems, e.g., combining Global Positioning System (GPS) and BeiDou Navigation Satellite System (BDS), brings better positioning accuracy. However, ANs from two systems are usually synchronized to two different clock sources. Different from single-system localization, an extra user-to-system clock offset needs to be handled. Existing dual-system methods either have high computational complexity or sub-optimal positioning accuracy. In this paper, we propose a new closed-form dual-system localization (CDL) approach that has low complexity and optimal localization accuracy. We first convert the nonlinear problem into a linear one by squaring the distance equations and employing intermediate variables. Then, a weighted least squares (WLS) method is used to optimize the positioning accuracy. We prove that the positioning error of the new method reaches Cramer-Rao Lower Bound (CRLB) in far field conditions with small measurement noise. Simulations on 2D and 3D positioning scenes are conducted. Results show that, compared with the iterative approach, which has high complexity and requires a good initialization, the new CDL method does not require initialization and has lower computational complexity with comparable positioning accuracy. Numerical results verify the theoretical analysis on positioning accuracy, and show that the new CDL method has superior performance over the state-of-the-art closed-form method. Experiments using real GPS and BDS data verify the applicability of the new CDL method and the superiority of its performance in the real world.
A Lite Distributed Semantic Communication System for Internet of Things
Authors: Huiqiang Xie, Zhijin Qin
Date: 2020-07-21 00:00:00
Tasks: Semantic Communication

The rapid development of deep learning (DL) and widespread applications of Internet-of-Things (IoT) have made the devices smarter than before, and enabled them to perform more intelligent tasks. However, it is challenging for any IoT device to train and run DL models independently due to its limited computing capability. In this paper, we consider an IoT network where the cloud/edge platform performs the DL based semantic communication (DeepSC) model training and updating while IoT devices perform data collection and transmission based on the trained model. To make it affordable for IoT devices, we propose a lite distributed semantic communication system based on DL, named L-DeepSC, for text transmission with low complexity, where the data transmission from the IoT devices to the cloud/edge works at the semantic level to improve transmission efficiency. Particularly, by pruning the model redundancy and lowering the weight resolution, the L-DeepSC becomes affordable for IoT devices and the bandwidth required for model weight transmission between IoT devices and the cloud/edge is reduced significantly. Through analyzing the effects of fading channels in forward-propagation and back-propagation during the training of L-DeepSC, we develop a channel state information (CSI) aided training processing to decrease the effects of fading channels on transmission. Meanwhile, we tailor the semantic constellation to make it implementable on capacity-limited IoT devices. Simulation demonstrates that the proposed L-DeepSC achieves competitive performance compared with traditional methods, especially in the low signal-to-noise (SNR) region. In particular, while it can reach as large as 40x compression ratio without performance degradation.
Latency Measurement of 100 km Fiber Using Correlation-OTDR
Authors: Florian Azendorf, Annika Dochhan, Bernhard Schmauss, Michael Eiselt
Date: 2020-11-25 00:00:00

By means of C-OTDR (Correlation - Optical Time Domain Reflectometry), we measured the latency of100 km fiber with an accuracy of a few picoseconds. Based on iterating 49 measurements, we calculated a standard deviation of 12 ps between the round-trip latency values. To verify the reflection measurements, we used a single pass setup without reflector, which showed a maximum difference of only 11 ps.
Group Delay Measurements of Multicore Fibers with Correlation Optical Time Domain Reflectometry
Authors: Florian Azendorf, Annika Dochhan, Florian Spinty, Mirko Lawin, Bernhard Schmauss, Michael Eiselt
Date: 2020-11-25 00:00:00
Tasks: Position

Several multi-core fibers (MCF) were characterized using Correlation Optical Time Domain Reflectometry (C-OTDR) in terms of propagation delay and polarization mode dispersion (PMD). The results show that the propagation delay in the cores depends on the position of the core in the fiber and that the differential delay between the cores varies with temperature.
Characterization of Multi-Core Fiber Group Delay with Correlation OTDR and Modulation Phase Shift Methods
Authors: Florian Azendorf, Annika Dochhan, Krzysztof Wilczyński, Łukasz Szostkiewicz, Patryk Urban, Bernhard Schmauss, Francisco Javier Vilchez, Laia Nadal, Michela Svaluto Moreolo, Josep M. Fabrega, Michael Eiselt
Date: 2020-11-25 00:00:00
Tasks: Position

Using a Correlation-OTDR and a modulation phase shift method we characterized four multi-core fibers. The results show that the differential delay depends on the position of the core in the fiber and varies with temperature.
MetaSensing: Intelligent Metasurface Assisted RF 3D Sensing by Deep Reinforcement Learning
Authors: Jingzhi Hu, Hongliang Zhang, Kaigui Bian, Marco Di Renzo, Zhu Han, Lingyang Song
Date: 2020-11-25 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

Using RF signals for wireless sensing has gained increasing attention. However, due to the unwanted multi-path fading in uncontrollable radio environments, the accuracy of RF sensing is limited. Instead of passively adapting to the environment, in this paper, we consider the scenario where an intelligent metasurface is deployed for sensing the existence and locations of 3D objects. By programming its beamformer patterns, the metasurface can provide desirable propagation properties. However, achieving a high sensing accuracy is challenging, since it requires the joint optimization of the beamformer patterns and mapping of the received signals to the sensed outcome. To tackle this challenge, we formulate an optimization problem for minimizing the cross-entropy loss of the sensing outcome, and propose a deep reinforcement learning algorithm to jointly compute the optimal beamformer patterns and the mapping of the received signals. Simulation results verify the effectiveness of the proposed algorithm and show how the sizes of the metasurface and the target space influence the sensing accuracy.
A New Approach of Data Pre-processing for Data Compression in Smart Grids
Authors: Yifei Sun, Hang Zou, Samson Lasaulce, Michel Kieffer, Lucas Saludjian
Date: 2020-11-24 00:00:00
Tasks: Data Compression, Decision Making

The conventional approach to pre-process data for compression is to apply transforms such as the Fourier, the Karhunen-Lo\`{e}ve, or wavelet transforms. One drawback from adopting such an approach is that it is independent of the use of the compressed data, which may induce significant optimality losses when measured in terms of final utility (instead of being measured in terms of distortion). We therefore revisit this paradigm by tayloring the data pre-processing operation to the utility function of the decision-making entity using the compressed (and therefore noisy) data. More specifically, the utility function consists of an Lp-norm, which is very relevant in the area of smart grids. Both a linear and a non-linear use-oriented transforms are designed and compared with conventional data pre-processing techniques, showing that the impact of compression noise can be significantly reduced.
Deep Sequence Learning for Accurate Gestational Age Estimation from a $\$$25 Doppler Device
Authors: Nasim Katebi, Reza Sameni, Gari D. Clifford
Date: 2020-11-24 00:00:00
Tasks: Age Estimation, Time Series, Time Series Analysis

Assessing fetal development is usually carried out by techniques such as ultrasound imaging, which is generally unavailable in rural areas due to the high cost, maintenance, skills and training needed to operate the devices effectively. In this work, we propose a low-cost one-dimensional Doppler-based method for estimating gestational age (GA). Doppler time series were collected from 401 pregnancies between 5 and 9 months GA using a smartphone. The proposed model for GA estimation is based on sequence learning by forming a temporally dependent model using a convolutional long-short-term memory network. Time-frequency features are extracted from Doppler signals and regularized before feeding to the network. The overall mean absolute GA error with respect to the last menstrual period was found to be 0.71 month, which outperforms all previous works.
Towards SAR Tomographic Inversion via Sparse Bayesian Learning
Authors: Kun Qian, Yuanyuan Wang, Xiaoxiang Zhu
Date: 2020-11-24 00:00:00

Existing SAR tomography (TomoSAR) algorithms are mostly based on an inversion of the SAR imaging model, which are often computationally expensive. Previous study showed perspective of using data-driven methods like KPCA to decompose the signal and reduce the computational complexity. This paper gives a preliminary demonstration of a new data-driven method based on sparse Bayesian learning. Experiments on simulated data show that the proposed method significantly outperforms KPCA methods in estimating the steering vectors of the scatterers. This gives a perspective of data-drive approach or combining it with model-driven approach for high precision tomographic inversion of large areas.
Free-Space Optical Communication Using Non-mode-Selective Photonic Lantern Based Coherent Receiver
Authors: Bo Zhang, Renzhi Yuan, Jianfeng Sun, Julian Cheng, Mohamed-Slim Alouini
Date: 2020-07-03 00:00:00

A free-space optical communication system using non-mode-selective photonic lantern (PL) based coherent receiver is studied. Based on the simulation of photon distribution, the power distribution at the single-mode fiber end of the PL is quantitatively described as a truncated Gaussian distribution over a simplex. The signal-to-noise ratios (SNRs) for the communication system using PL based receiver are analyzed using different combining techniques, including selection combining (SC), equal-gain combining (EGC), and maximal-ratio combining (MRC). The integral solution, series lower bound solution and asymptotic solution are presented for bit-error rate (BER) of PL based receiver, single-mode fiber receiver and multimode fiber receiver over the Gamma-Gamma atmosphere turbulence channels. We demonstrate that the power distribution of the PL has no effect on the SNR and BER performance of the PL based receiver when MRC is used; and it only has limited influence when EGC is used. However, the power distribution of the PL can greatly affect the BER performance when SC is used. Besides, the SNR gains of the PL based receiver using EGC over single-mode fiber receiver and multimode fiber receiver are numerically studied under different imperfect device parameters; and the scope of application of the communication system is further provided.
A Data-Fusion-Assisted Telemetry Layer for Autonomous Optical Networks
Authors: Xiaomin Liu, Huazhi Lun, Ruoxuan Gao, Meng Cai, Lilin Yi, Weisheng Hu, Qunbi Zhuge
Date: 2020-11-24 00:00:00

For further improving the capacity and reliability of optical networks, a closed-loop autonomous architecture is preferred. Considering a large number of optical components in an optical network and many digital signal processing modules in each optical transceiver, massive real-time data can be collected. However, for a traditional monitoring structure, collecting, storing and processing a large size of data are challenging tasks. Moreover, strong correlations and similarities between data from different sources and regions are not properly considered, which may limit function extension and accuracy improvement. To address abovementioned issues, a data-fusion-assisted telemetry layer between the physical layer and control layer is proposed in this paper. The data fusion methodologies are elaborated on three different levels: Source Level, Space Level and Model Level. For each level, various data fusion algorithms are introduced and relevant works are reviewed. In addition, proof-of-concept use cases for each level are provided through simulations, where the benefits of the data-fusion-assisted telemetry layer are shown.