pwc-archive/papers-with-abstracts huggingface dataset:
Enabling 5G on the Ocean: A Hybrid Satellite-UAV-Terrestrial Network Solution
Authors: Xiangling Li, Wei Feng, Jue Wang, Yunfei Chen, Ning Ge, Cheng-Xiang Wang
Date: 2020-06-01 00:00:00
Tasks: Scheduling

Current fifth generation (5G) cellular networks mainly focus on the terrestrial scenario. Due to the difficulty of deploying communications infrastructure on the ocean, the performance of existing maritime communication networks (MCNs) is far behind 5G. This problem can be solved by using unmanned aerial vehicles (UAVs) as agile aerial platforms to enable on-demand maritime coverage, as a supplement to marine satellites and shore-based terrestrial based stations (TBSs). In this paper, we study the integration of UAVs with existing MCNs, and investigate the potential gains of hybrid satellite-UAV-terrestrial networks for maritime coverage. Unlike the terrestrial scenario, vessels on the ocean keep to sea lanes and are sparsely distributed. This provides new opportunities to ease the scheduling of UAVs. Also, new challenges arise due to the more complicated maritime prorogation environment, as well as the mutual interference between UAVs and existing satellites/TBSs. We discuss these issues and show possible solutions considering practical constraints.
Robust Beamforming Design for Intelligent Reflecting Surface Aided MISO Communication Systems
Authors: Gui Zhou, Cunhua Pan, Hong Ren, Kezhi Wang, Marco Di Renzo, Arumugam Nallanathan
Date: 2019-11-14 00:00:00

Perfect channel state information (CSI) is challenging to obtain due to the limited signal processing capability at the intelligent reflection surface (IRS). In this paper, we study the worst-case robust beamforming design for an IRS-aided multiuser multiple-input single-output (MU-MISO) system under the assumption of imperfect CSI. We aim for minimizing the transmit power while ensuring that the achievable rate of each user meets the quality of service (QoS) requirement for all possible channel error realizations. With unit-modulus and rate constraints, this problem is non-convex. The imperfect CSI further increases the difficulty of solving this problem. By using approximation and transformation techniques, we convert this problem into a squence of semidefinite programming (SDP) subproblems that can be efficiently solved. Numerical results show that the proposed robust beamforming design can guarantee the required QoS targets for all the users.
On scenario construction for stochastic shortest path problems in real road networks
Authors: Dongqing Zhang, Stein W. Wallace, Zhaoxia Guo, Yucheng Dong, Michal Kaut
Date: 2020-06-01 00:00:00
Tasks: Computational Efficiency, CPU

Stochastic shortest path computations are often performed under very strict time constraints, so computational efficiency is critical. A major determinant for the CPU time is the number of scenarios used. We demonstrate that by carefully picking the right scenario generation method for finding scenarios, the quality of the computations can be improved substantially over random sampling for a given number of scenarios. We study a real case from a California freeway network with 438 road links and 24 5-minute time periods, implying 10,512 random speed variables, correlated in time and space, leading to a total of 55,245,816 distinct correlations. We find that (1) the scenario generation method generates unbiased scenarios and strongly outperforms random sampling in terms of stability (i.e., relative difference and variance) whichever origin-destination pair and objective function is used; (2) to achieve a certain accuracy, the number of scenarios required for scenario generation is much lower than that for random sampling, typically about 6-10 times lower for a stability level of 1\%; and (3) different origin-destination pairs and different objective functions could require different numbers of scenarios to achieve a specified stability.
Deep Learning Approaches for Open Set Wireless Transmitter Authorization
Authors: Samer Hanna, Samurdhi Karunaratne, Danijela Cabric
Date: 2020-02-18 00:00:00
Tasks: Deep Learning, Open Set Learning

Wireless signals contain transmitter specific features, which can be used to verify the identity of transmitters and assist in implementing an authentication and authorization system. Most recently, there has been wide interest in using deep learning for transmitter identification. However, the existing deep learning work has posed the problem as closed set classification, where a neural network classifies among a finite set of known transmitters. No matter how large this set is, it will not include all transmitters that exist. Malicious transmitters outside this closed set, once within communications range, can jeopardize the system security. In this paper, we propose a deep learning approach for transmitter authorization based on open set recognition. Our proposed approach identifies a set of authorized transmitters, while rejecting any other unseen transmitters by recognizing their signals as outliers. We propose three approaches for this problem and show their ability to reject signals from unauthorized transmitters on a dataset of WiFi captures. We consider the structure of training data needed, and we show that the accuracy improves by having signals from known unauthorized transmitters in the training set.
Marginal Covariance of Parameters in New Observations
Authors: Jianzhu Huai
Date: 2020-05-09 00:00:00

We have observed a common problem of solving for the marginal covariance of parameters introduced in new observations. This problem arises in several situations, including augmenting parameters to a Kalman filter, and computing weight for relative pose constraints. To handle this problem, we derive a solution in a least squares sense. The solution is applied to the above two instance situations and verified by independently reported results.
sCrop: A Internet-of-Agro-Things (IoAT) Enabled Solar Powered Smart Device for Automatic Plant Disease Prediction
Authors: Venkanna Udutalapally, Saraju P. Mohanty, Vishal Pallagani, Vedant Khandelwal
Date: 2020-05-09 00:00:00
Tasks: Disease Prediction

Internet-of-Things (IoT) is omnipresent, ranging from home solutions to turning wheels for the fourth industrial revolution. This article presents the novel concept of Internet-of-Agro-Things (IoAT) with an example of automated plant disease prediction. It consists of solar enabled sensor nodes which help in continuous sensing and automating agriculture. The existing solutions have implemented a battery powered sensor node. On the contrary, the proposed system has adopted the use of an energy efficient way of powering using solar energy. It is observed that around 80% of the crops are attacked with microbial diseases in traditional agriculture. To prevent this, a health maintenance system is integrated with the sensor node, which captures the image of the crop and performs an analysis with the trained Convolutional Neural Network (CNN) model. The deployment of the proposed system is demonstrated in a real-time environment using a microcontroller, solar sensor nodes with a camera module, and an mobile application for the farmers visualization of the farms. The deployed prototype was deployed for two months and has achieved a robust performance by sustaining in varied weather conditions and continued to remain rust-free. The proposed deep learning framework for plant disease prediction has achieved an accuracy of 99.2% testing accuracy.
Angle-Dependent Phase Shifter Model for Reconfigurable Intelligent Surfaces: Does the Angle-Reciprocity Hold?
Authors: Weicong Chen, Lin Bai, Wankai Tang, Shi Jin, Wei Xiang Jiang, Tie Jun Cui
Date: 2020-05-09 00:00:00

The existing phase shifter models adopted for reconfigurable intelligent surfaces (RISs) have ignored the electromagnetic (EM) waves propagation behavior, thus cannot reveal practical effects of RIS on wireless communication systems. Based on the equivalent circuit, this paper introduces an angle-dependent phase shifter model for varactor-based RISs. To the best of our knowledge, this is the first phase shifter model which reveals that the incident angle of EM waves has influence on the reflection coefficient of RIS. In addition, the angle-reciprocity on RIS is investigated and further proved to be tenable when the reflection phase difference of adjacent RIS unit cells is invariant for an impinging EM wave and its reverse incident one. The angle-dependent characteristic of RIS is verified through full-wave simulation. According to our analysis and the simulation results, we find that the angle-reciprocity of varactor-based RIS only holds under small incident angles of both forward and reverse incident EM waves, thus limits the channel reciprocity in RIS-assisted TDD systems.
Improving Energy Expenditure Estimation in Wearables using a Heat Flux Sensor: First Observations
Authors: Saku Levikari, Antti Immonen, Mikko Kuisma, Heikki Peltonen, Mika Silvennoinen, Heikki Kyrolainen, Pertti Silventoinen
Date: 2020-05-08 00:00:00

Wearable electronics are often used for estimating the energy expenditure of the user based on heart rate measurement. While heart rate is a good predictor of calorie consumption at high intensities, it is less precise at low intensity levels, which translates into inaccurate results when estimating daily net energy expenditure. In this study, heart rate measurement was augmented with heat flux measurement, a form of direct calorimetry. A physical exercise test on a group of 15 people showed that heat flux measurement can significantly improve the accuracy of calorie consumption estimates when used in conjunction with heart rate information and vital background parameters of the user.
Mobile Communications Beyond 52.6 GHz: Waveforms, Numerology, and Phase Noise Challenge
Authors: Toni Levanen, Oskari Tervo, Kari Pajukoski, Markku Renfors, Mikko Valkama
Date: 2019-12-19 00:00:00

In this article, the first considerations for the 5G New Radio (NR) physical layer evolution to support beyond 52.6GHz communications are provided. In addition, the performance of both OFDM based and DFT-s-OFDM based networks are evaluated with special emphasis on the phase noise (PN) induced distortion. It is shown that DFT-s-OFDM is more robust against PN under 5G NR Release 15 assumptions, namely regarding the supported phase tracking reference signal (PTRS) designs, since it enables more effective PN mitigation directly in the time domain. To further improve the PN compensation capabilities, the PTRS design for DFT-s-OFDM is revised, while for the OFDM waveform a novel block PTRS structure is introduced, providing similar link performance as DFT-s-OFDM with enhanced PTRS design. We demonstrate that the existing 5G NR Release 15 solutions can be extended to support efficient mobile communications at 60GHz carrier frequency with the enhanced PTRS structures. In addition, DFT-s-OFDM based downlink for user data could be considered for beyond 52.6GHz communications to further improve system power efficiency and performance with higher order modulation and coding schemes. Finally, network link budget and cell size considerations are provided, showing that at certain bands with specific transmit power regulation, the cell size can eventually be downlink limited.
Prediction of Memory Retrieval Performance Using Ear-EEG Signals
Authors: Jenifer Kalafatovich, Minji Lee, Seong-Whan Lee
Date: 2020-05-04 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG), Retrieval

Many studies have explored brain signals during the performance of a memory task to predict later remembered items. However, prediction methods are still poorly used in real life and are not practical due to the use of electroencephalography (EEG) recorded from the scalp. Ear-EEG has been recently used to measure brain signals due to its flexibility when applying it to real world environments. In this study, we attempt to predict whether a shown stimulus is going to be remembered or forgotten using ear-EEG and compared its performance with scalp-EEG. Our results showed that there was no significant difference between ear-EEG and scalp-EEG. In addition, the higher prediction accuracy was obtained using a convolutional neural network (pre-stimulus: 74.06%, on-going stimulus: 69.53%) and it was compared to other baseline methods. These results showed that it is possible to predict performance of a memory task using ear-EEG signals and it could be used for predicting memory retrieval in a practical brain-computer interface.
Optimizing Temporal Convolutional Network inference on FPGA-based accelerators
Authors: Marco Carreras, Gianfranco Deriu, Luigi Raffo, Luca Benini, Paolo Meloni
Date: 2020-05-07 00:00:00
Tasks: Scheduling, Time Series Analysis, Video Classification

Convolutional Neural Networks are extensively used in a wide range of applications, commonly including computer vision tasks like image and video classification, recognition, and segmentation. Recent research results demonstrate that multilayer(deep) networks involving mono-dimensional convolutions and dilation can be effectively used in time series and sequences classification and segmentation, as well as in tasks involving sequence modelling. These structures, commonly referred to as Temporal Convolutional Networks (TCNs), have been demonstrated to consistently outperform Recurrent Neural Networks in terms of accuracy and training time [1]. While FPGA-based inference accelerators for classic CNNs are widespread, literature is lacking in a quantitative evaluation of their usability on inference for TCN models. In this paper we present such an evaluation, considering a CNN accelerator with specific features supporting TCN kernels as a reference and a set of state-of-the-art TCNs as a benchmark. Experimental results show that, during TCN execution, operational intensity can be critical for the overall performance. We propose a convolution scheduling based on batch processing that can boost efficiency up to 96% of theoretical peak performance. Overall we can achieve up to 111,8 GOPS/s and power efficiency of 33,9 GOPS/s/W on an Ultrascale+ ZU3EG (up to 10x speedup and 3x power efficiency improvement with respect to pure software implementation).
On Phase Offsets of 802.11ac Commodity WiFi
Authors: Anatolij Zubow, Piotr Gawlowicz, Falko Dressler
Date: 2020-05-07 00:00:00

We analyze the phase offsets between RF chains of modern IEEE 802.11ac chips. We investigate both the 2.4 and 5GHz bands on a per OFDM subcarrier level. Results reveal that the phase offset between receive antennas is due to random phase rotations semi-time-invariant with up to four possible values. Moreover, it is frequency-dependent. We propose a simple algorithm, which allows us to correct the phase offset on the fly without any calibration. As proof-of-concept, we implemented Angle of Arrival (AoA) using MUSIC algorithm. To achieve higher accuracy we stitched the thirteen overlapping 20MHz channels available in 2.4GHz band together to effectively have a single 80MHz channel. Results show very good AoA precision although only two receive antennas were used.
Global Synchronization of Pulse-Coupled Oscillator Networks Under Byzantine Attacks
Authors: Zhenqian Wang, Yongqiang Wang
Date: 2020-05-07 00:00:00
Tasks: All

Synchronization of pulse-coupled oscillators (PCOs) has gained significant attention recently due to their increased applications in sensor networks and wireless communications. Given the distributed and unattended nature of wireless sensor networks, it is imperative to enhance the resilience of PCO synchronization against malicious attacks. However, most existing results on attack-resilient pulse-based synchronization are obtained under assumptions of all-to-all coupling topologies or restricted initial phase distributions. In this paper, we propose a new pulse-based synchronization mechanism to improve the attack resilience of PCO synchronization that is applicable to non-all-to-all networks. Under the proposed synchronization mechanism, we prove that perfect synchronization of legitimate oscillators can be guaranteed in the presence of multiple Byzantine attackers who can emit attack pulses arbitrarily without any constraint except that practical bit rate constraint renders the number of pulses from an attacker to be finite. The new mechanism can guarantee synchronization even when the initial phases of all legitimate oscillators are arbitrarily distributed in the entire oscillation period, which is in distinct difference from most existing attack-resilient synchronization approaches (including the seminal paper from Lamport and Melliar-Smith [1]) that require a priori (almost) synchronization among legitimate oscillators. Numerical simulation results are given to confirm the theoretical results.
Multi-fidelity sensor selection: Greedy algorithms to place cheap and expensive sensors with cost constraints
Authors: Emily Clark, Steven L. Brunton, J. Nathan Kutz
Date: 2020-05-07 00:00:00

We develop greedy algorithms to approximate the optimal solution to the multi-fidelity sensor selection problem, which is a cost constrained optimization problem prescribing the placement and number of cheap (low signal-to-noise) and expensive (high signal-to-noise) sensors in an environment or state space. Specifically, we evaluate the composition of cheap and expensive sensors, along with their placement, required to achieve accurate reconstruction of a high-dimensional state. We use the column-pivoted QR decomposition to obtain preliminary sensor positions. How many of each type of sensor to use is highly dependent upon the sensor noise levels, sensor costs, overall cost budget, and the singular value spectrum of the data measured. Such nuances allow us to provide sensor selection recommendations based on computational results for asymptotic regions of parameter space. We also present a systematic exploration of the effects of the number of modes and sensors on reconstruction error when using one type of sensor. Our extensive exploration of multi-fidelity sensor composition as a function of data characteristics is the first of its kind to provide guidelines towards optimal multi-fidelity sensor selection.
Stereo Vision for Unmanned Aerial VehicleDetection, Tracking, and Motion Control
Authors: Maria N. Brunet, Guilherme Aramizo Ribeiro, Nina Mahmoudian, Mo Rastgaar
Date: 2020-05-07 00:00:00
Tasks: Motion Planning, object-detection, Object Detection

An innovative method of detecting Unmanned Aerial Vehicles (UAVs) is presented. The goal of this study is to develop a robust setup for an autonomous multi-rotor hunter UAV, capable of visually detecting and tracking the intruder UAVs for real-time motion planning. The system consists of two parts: object detection using a stereo camera to generate 3D point cloud data and video tracking applying a Kalman filter for UAV motion modeling. After detection, the hunter can aim and shoot a tethered net at the intruder to neutralize it. The computer vision, motion tracking, and planning algorithms can be implemented on a portable computer installed on the hunter UAV.
Design and Analysis of a Sample-and-Hold CMOS Electrochemical Sensor for Aptamer-based Therapeutic Drug Monitoring
Authors: Jun-Chau Chien, Sam W. Baker, H. Tom Soh, Amin Arbabian
Date: 2020-05-07 00:00:00
Tasks: Specificity

In this paper, we present the design and the analysis of an electrochemical circuit for measuring the concentrations of therapeutic drugs using structure-switching aptamers. Aptamers are single-stranded nucleic acids, whose sequence is selected to exhibit high affinity and specificity toward a molecular target, and change its conformation upon binding. This property, when coupled with a redox reporter and electrochemical detection, enables reagent-free biosensing with a sub-minute temporal resolution for in vivo therapeutic drug monitoring. Specifically, we design a chronoamperometry-based electrochemical circuit that measures the direct changes in the electron transfer (ET) kinetics of a methylene blue reporter conjugated at the distal-end of the aptamer. To overcome the high-frequency noise amplification issue when interfacing with a large-size (> 0.25 mm2) implantable electrode, we present a sample-and-hold (S/H) circuit technique in which the desired electrode potentials are held onto noiseless capacitors during the recording of the redox currents. This allows disconnecting the feedback amplifiers to avoid its noise injection while reducing the total power consumption. A prototype circuit implemented in 65-nm CMOS demonstrates a cell-capacitance-insensitive input-referred noise (IRN) current of 15.2 pArms at a 2.5-kHz filtering bandwidth. Tested in human whole blood samples, changes in the ET kinetics from the redox-labeled aminoglycoside aptamers at different kanamycin concentrations are measured from the recorded current waveforms. By employing principal component analysis (PCA) to compensate for the sampling errors, a detection limit (SNR = 1) of 3.1 uM under 1-sec acquisition is achieved at 0.22-mW power consumption.
Hardware Implementation of Neural Self-Interference Cancellation
Authors: Yann Kurzo, Andreas Toftegaard Kristensen, Andreas Burg, Alexios Balatsoukas-Stimming
Date: 2020-01-13 00:00:00

In-band full-duplex systems can transmit and receive information simultaneously on the same frequency band. However, due to the strong self-interference caused by the transmitter to its own receiver, the use of non-linear digital self-interference cancellation is essential. In this work, we describe a hardware architecture for a neural network-based non-linear self-interference (SI) canceller and we compare it with our own hardware implementation of a conventional polynomial based SI canceller. In particular, we present implementation results for a shallow and a deep neural network SI canceller as well as for a polynomial SI canceller. Our results show that the deep neural network canceller achieves a hardware efficiency of up to $312.8$ Msamples/s/mm$^2$ and an energy efficiency of up to $0.9$ nJ/sample, which is $2.1\times$ and $2\times$ better than the polynomial SI canceller, respectively. These results show that NN-based methods applied to communications are not only useful from a performance perspective, but can also be a very effective means to reduce the implementation complexity.
Frequency Response Analysis and Design Rules for Capacitive Feedback Transimpedance Amplifier
Authors: Jung-hoon Noh
Date: 2020-04-18 00:00:00

Precision instrumentation systems, such as optical receivers and other current-output measurement systems, often contain a transimpedance amplifier (TIA). The commonly used resistive feedback TIA (RF-TIA) has a drawback in its practical implementation; the system bandwidth is limited by the feedback resistor and its parasitic capacitance in high-speed applications. To overcome this drawback, a capacitive feedback TIA (CF-TIA) can be used. The gain and bandwidth performance of the CF-TIA are theoretically equivalent to those of the RF-TIA. Although CF-TIA has been introduced in previous studies for reducing thermal noise and addressing the difficulty in integrating a high resistance with CMOS technology, past analyses have not been in sufficient depth. The ultimate goal of this study is providing designers in this field with helpful design rules and analytical tools. In particular, a transfer function model, the critical design parameters, and a stability analysis are presented. Furthermore, a new CF-TIA configuration involving a simplified integrator DC feedback loop is introduced, which can be deployed as a single stage rather than the conventional two-stage topology. While the applications considered in this study were high-speed sensor measurements and devices with a high input capacitance, such as a laser position detector, the analytical results obtained herein also applicable to a variety of other applications such as emerging biosensors and optical communication system.