pwc-archive/papers-with-abstracts huggingface dataset:
CNN Classifier for Just-in-Time Woodpeckers Detection and Deterrent
Authors: Alexander Greysukh
Date: 2021-07-21 00:00:00

Woodpeckers can cause significant damage to homes, especially in suburban areas. There are a number of preventing and repelling methods including passive decoys, though these may only provide temporary relief. Subsequently, it may be more efficient to implement a woodpecker deterrent, such as motion, light, sound, or ultrasound that would be triggered by detection of woodpecker signature drumming. To detect the typical 25 Hz drumming frequency, sampling periods under 10 milliseconds with frequent FFTs are required with considerable computational costs. An in-hardware spectrum analyzer may avoid these costs by trading off frequency for time resolutions. The trained model converted to TF Lite Micro, ported to an MCU, and identifies a variety of the prerecorded woodpecker drumming. The plan is to integrate the prototype with a deterrent device making it a completely autonomous solution.
Leveraging PID Gain Selection Towards Adaptive Backstepping Control for a Class of Second-Order Systems
Authors: Ahmad Kourani, Naseem Daher
Date: 2021-07-22 00:00:00

In this work, we establish a convenient similarity between an adaptive backstepping control law and a standard proportional-integral-derivative (PID) controller for a class of second-order systems. The extracted similarity provides a deeper understanding of the adaptive backstepping design from a performance perspective via an intuitive method to select its otherwise abstract controller gains, on top of its traditional stability perspective. Such a similarity analysis opens the door for researchers to use well-established PID tuning methods to predict the performance of Lyapunov stability-based controllers. At the same time, the obtained formulation reveals how the corresponding PID control law can be linked to Lyapunov stability theory.
A Tethered Quadrotor UAV$-$Buoy System for Marine Locomotion
Authors: Ahmad Kourani, Naseem Daher
Date: 2021-07-22 00:00:00

Unmanned aerial vehicles (UAVs) are finding their way into offshore applications. In this work, we postulate an original system that entails a marine locomotive quadrotor UAV that manipulates the velocity of a floating buoy by means of a cable. By leveraging the advantages of UAVs relative to high speed, maneuverability, ease of deployment, and wide field of vision, the proposed UAV$-$buoy system paves the way in front of a variety of novel applications. The dynamic model that couples the buoy, UAV, cable, and water environment is presented using the Euler-Lagrange method. A stable control system design is proposed to manipulate the forward-surge speed of the buoy under two constraints: maintaining the cable in a taut state, and keeping the buoy in contact with the water surface. Polar coordinates are used in the controller design process to attain correlated effects on the tracking performance, whereby each control channel independently affects one control parameter. This results in improved performance over traditional Cartesian-based velocity controllers, as demonstrated via numerical simulations in wave-free and wavy seas.
Application-driven Test and Evaluation Framework for Indoor Localization Systems in Warehouses
Authors: Jakob Schyga, Johannes Hinckeldeyn, Benjamin Bruss, Christoph Bamberger, Jochen Kreutzfeldt
Date: 2021-07-22 00:00:00
Tasks: Indoor Localization

Despite their potential of increasing operational efficiency, transparency, and safety, the use of Localization and Tracking Systems (LTSs) in warehouse environments remains seldom. One reason is the lack of market transparency and stakeholder's trust in the systems' performance as a consequence of poor use of Test and Evaluation (T&E) methods and transferability of the obtained T&E results. The T&E 4Log (Test and Evaluation for Logistics) Framework was developed to examine how the transferability of T&E results to practical scenarios in warehouse environments can be increased. Conventional T&E approaches are integrated and extended under consideration of the warehouse environment, logistics applications, and domain-specific requirements, into an application-driven T&E framework. The application of the proposed framework in standard and application-dependent test cases leads to a set of performance criteria and corresponding application-specific requirements. This enables a well-founded identification of suitable LTSs for given warehouse applications. The T&E 4Log Framework was implemented at the Institute for Technical Logistics (ITL) and validated by T&E of a reflector-based Light Detection and Ranging (LiDAR) LTS, a contour-based LiDAR LTS, and an Ultra-Wideband (UWB) LTS for the exemplary applications Automated Pallet Booking, Goods Tracking, and Autonomous Forklift Navigation.
A Sensor Fusion-based Cutting Device Attitude Control to Improve the Accuracy of Korean Cabbage Harvesting
Authors: Yonghyun Park, Jongpyo Jun, Hyoung Il Son
Date: 2021-07-22 00:00:00
Tasks: Sensor Fusion

Korean cabbage harvesting lacks mechanization and depends on human power; thus, conducting research on Korean cabbage harvesters is of immense importance. Although these harvesters have been developed in various forms, they have not yet attained commercialization. Most Korean cabbage fields have slopes; thus there are several challenges, that can prevent accurate harvesting. Therefore, to address these challenges at the site, we adopt two cylinders in this study, develop a mechanism that enables attitude control of the cutting device, not driving platform body, to cope with slopes. By maintaining the level, angle, height of cutting, we can reduce loss and improve harvest performance. It is difficult to find examples where these mechanisms have been applied. For basic research, sensor fusion has been carried out based on the Kalman filter, which is commonly utilized for attitude control. The hydraulic cylinder was controlled using the data obtained for maintaining the attitude. Furthermore, field tests were conducted to validate this system, and the root mean square error (RMSE) was obtained and verified to quantitatively assess the presence or absence of attitude control. Therefore, the purpose of this study is to suggest a development direction for Korean cabbage harvesters via the proposed attitude control system.
A Two-Part Controller Synthesis Approach for Nonlinear Stochastic Systems Perturbed by Lévy Noise Using Renewal Theory and HJB-Based Impulse Control
Authors: SooJean Han, Soon-Jo Chung
Date: 2021-07-22 00:00:00

We are motivated by the lack of discussion surrounding methodological control design procedures for nonlinear shot and L\'evy noise stochastic systems to propose a hierarchical controller synthesis method with two parts. The first part is a primitive pattern-learning component which recognizes specific state sequences and stores in memory the corresponding control action that needs to be taken when the sequence has occurred. The second part is a modulation control component which computes the optimal control action for a pattern when it has occurred for the first time. Throughout our presentation of both components, we provide a self-contained discussion of theoretical concepts from Poisson processes theory, renewal theory, and impulse control, all of which are necessary as background. We demonstrate application of this controller to the simplified, concrete case studies of fault-tolerance and vehicle congestion control.
Speed Advisory System Using Real-Time Actuated Traffic Light Phase Length Prediction
Authors: Mikhail Burov, Murat Arcak, Alexander Kurzhanskiy
Date: 2021-07-21 00:00:00

Speed advisory systems for connected vehicles rely on the estimation of green (or red) light duration at signalized intersections. A particular challenge is to predict the signal phases of semi- and fully-actuated traffic lights. In this paper, we introduce an algorithm that processes traffic measurement data collected from advanced detectors on road links and assigns "PASS"/"WAIT" labels to connected vehicles according to their predicted ability to go through the upcoming signalized intersection within the current phase. Additional computations provide an estimate for the duration of the current green phase that can be used by the Speed Advisory System to minimize fuel consumption. Simulation results show 95% prediction accuracy, which yields up to 30% reduction in fuel consumption when used in a driver-assistance system. Traffic progression quality also benefits from our algorithm demonstrating an improvement of 20% at peak for medium traffic demand, reducing delays and idling at intersections.
Dynamic Shimming in the Cervical Spinal Cord for Multi-Echo Gradient-Echo Imaging at 3 T
Authors: E. Alonso-Ortiz, D. Papp, A. D'Astous, J. Cohen-Adad
Date: 2021-07-21 00:00:00

Obtaining high quality images of the spinal cord with MRI is difficult, partly due to the fact that the spinal cord is surrounded by a number of structures that have differing magnetic susceptibility. This causes inhomogeneities in the magnetic field, which in turn lead to image artifacts. In order to address this issue, linear compensation gradients can be employed. The latter can be generated using an MRI scanner's first order gradient coils and adjusted on a per-slice basis, in order to correct for through-plane ("z") magnetic field gradients. This approach is referred to as z-shimming. The aim of this study is two-fold. The first aim was to replicate aspects of a previous study wherein z-shimming was found to improve image quality in T2*-weighted echo-planar imaging. Our second aim was to improve upon the z-shimming approach by including in-plane compensation gradients and adjusting the compensation gradients during the image acquisition process so that they take into account respiration-induced magnetic field variations. We refer to this novel approach as realtime dynamic shimming. Measurements performed in a group of 12 healthy volunteers at 3 T show improved signal homogeneity along the spinal cord when using z-shimming. Signal homogeneity may be further improved by including realtime compensation for respiration-induced field gradients and by also doing this for gradients along the in-plane axes.
Wideband photonic blind source separation with optical pulse sampling
Authors: Taichu Shi, Yang Qi, Weipeng Zhang, Paul R. Prucnal, Jie Li, Ben Wu
Date: 2021-07-21 00:00:00
Tasks: blind source separation

We propose and experimentally demonstrate an optical pulse sampling method for photonic blind source separation. The photonic system processes and separates wideband signals based on the statistical information of the mixed signals and thus the sampling frequency can be orders of magnitude lower than the bandwidth of the signals. The ultra-fast optical pulse functions as a tweezer that collects samples of the signals at very low sampling rates, and each sample is short enough to maintain the statistical properties of the signals. The low sampling frequency reduces the workloads of the analog to digital conversion and digital signal processing systems. In the meantime, the short pulse sampling maintains the accuracy of the sampled signals, so the statistical properties of the undersampling signals are the same as the statistical properties of the original signals. With the optical pulses generated from a mode-locked laser, the optical pulse sampling system is able to process and separate mixed signals with bandwidth over 100GHz and achieves a dynamic range of 30dB.
Economic Hysteresis and Its Mathematical Modeling
Authors: Isaak D. Mayergoyz, Can E. Korman
Date: 2021-07-17 00:00:00

Hysteresis is treated as a history dependent branching, and the use of the classical Preisach model for the analysis of macroeconomic hysteresis is first discussed. Then, a new Preisach-type model is introduced as a macroeconomic aggregation of more realistic microeconomic hysteresis than in the case of the classical Preisach model. It is demonstrated that this model is endowed with a more general mechanism of branching and may account for the continuous evolution of the economy and its effect on hysteresis. Furthermore, it is shown that the sluggishness of economic recovery is an intrinsic manifestation of hysteresis branching.
Of Access and Inclusivity Digital Divide in Online Education
Authors: Bheemeshwar Reddy A, Sunny Jose, Vaidehi R
Date: 2021-07-22 00:00:00

Can online education enable all students to participate in and benefit from it equally? Massive online education without addressing the huge access gap and disparities in digital infrastructure would not only exclude a vast majority of students from learning opportunities but also exacerbate the existing socio-economic disparities in educational opportunities.
A Stochastic Control Approach to Public Debt Management
Authors: Matteo Brachetta, Claudia Ceci
Date: 2021-07-22 00:00:00
Tasks: Management

We discuss a class of debt management problems in a stochastic environment model. We propose a model for the debt-to-GDP (Gross Domestic Product) ratio where the government interventions via fiscal policies affect the public debt and the GDP growth rate at the same time. We allow for stochastic interest rate and possible correlation with the GDP growth rate through the dependence of both the processes (interest rate and GDP growth rate) on a stochastic factor which may represent any relevant macroeconomic variable, such as the state of economy. We tackle the problem of a government whose goal is to determine the fiscal policy in order to minimize a general functional cost. We prove that the value function is a viscosity solution to the Hamilton-Jacobi-Bellman equation and provide a Verification Theorem based on classical solutions. We investigate the form of the candidate optimal fiscal policy in many cases of interest, providing interesting policy insights. Finally, we discuss two applications to the debt reduction problem and debt smoothing, providing explicit expressions of the value function and the optimal policy in some special cases.
Time Varying Risk in U.S. Housing Sector and Real Estate Investment Trusts Equity Return
Authors: Masud Alam
Date: 2021-07-22 00:00:00

This study examines how housing sector volatilities affect real estate investment trust (REIT) equity return in the United States. I argue that unexpected changes in housing variables can be a source of aggregate housing risk, and the first principal component extracted from the volatilities of U.S. housing variables can predict the expected REIT equity returns. I propose and construct a factor-based housing risk index as an additional factor in asset price models that uses the time-varying conditional volatility of housing variables within the U.S. housing sector. The findings show that the proposed housing risk index is economically and theoretically consistent with the risk-return relationship of the conditional Intertemporal Capital Asset Pricing Model (ICAPM) of Merton (1973), which predicts an average maximum of 5.6 percent of risk premium in REIT equity return. In subsample analyses, the positive relationship is not affected by sample periods' choice but shows higher housing risk beta values for the 2009-18 sample period. The relationship remains significant after controlling for VIX, Fama-French three factors, and a broad set of macroeconomic and financial variables. Moreover, the proposed housing beta also accurately forecasts U.S. macroeconomic and financial conditions.
Investigating Shift-Variance of Convolutional Neural Networks in Ultrasound Image Segmentation
Authors: Mostafa Sharifzadeh, Habib Benali, Hassan Rivaz
Date: 2021-07-22 00:00:00
Tasks: Data Augmentation, Image Segmentation, Segmentation, Semantic Segmentation

While accuracy is an evident criterion for ultrasound image segmentation, output consistency across different tests is equally crucial for tracking changes in regions of interest in applications such as monitoring the patients' response to treatment, measuring the progression or regression of the disease, reaching a diagnosis, or treatment planning. Convolutional neural networks (CNNs) have attracted rapidly growing interest in automatic ultrasound image segmentation recently. However, CNNs are not shift-equivariant, meaning that if the input translates, e.g., in the lateral direction by one pixel, the output segmentation may drastically change. To the best of our knowledge, this problem has not been studied in ultrasound image segmentation or even more broadly in ultrasound images. Herein, we investigate and quantify the shift-variance problem of CNNs in this application and further evaluate the performance of a recently published technique, called BlurPooling, for addressing the problem. In addition, we propose the Pyramidal BlurPooling method that outperforms BlurPooling in both output consistency and segmentation accuracy. Finally, we demonstrate that data augmentation is not a replacement for the proposed method. Source code is available at https://git.io/pbpunet and http://code.sonography.ai.
A Multimodal Deep Learning Model for Cardiac Resynchronisation Therapy Response Prediction
Authors: Esther Puyol-Antón, Baldeep S. Sidhu, Justin Gould, Bradley Porter, Mark K. Elliott, Vishal Mehta, Christopher A. Rinaldi, Andrew P. King
Date: 2021-07-20 00:00:00
Tasks: Deep Learning, Multimodal Deep Learning, Prediction, Specificity

We present a novel multimodal deep learning framework for cardiac resynchronisation therapy (CRT) response prediction from 2D echocardiography and cardiac magnetic resonance (CMR) data. The proposed method first uses the `nnU-Net' segmentation model to extract segmentations of the heart over the full cardiac cycle from the two modalities. Next, a multimodal deep learning classifier is used for CRT response prediction, which combines the latent spaces of the segmentation models of the two modalities. At inference time, this framework can be used with 2D echocardiography data only, whilst taking advantage of the implicit relationship between CMR and echocardiography features learnt from the model. We evaluate our pipeline on a cohort of 50 CRT patients for whom paired echocardiography/CMR data were available, and results show that the proposed multimodal classifier results in a statistically significant improvement in accuracy compared to the baseline approach that uses only 2D echocardiography data. The combination of multimodal data enables CRT response to be predicted with 77.38% accuracy (83.33% sensitivity and 71.43% specificity), which is comparable with the current state-of-the-art in machine learning-based CRT response prediction. Our work represents the first multimodal deep learning approach for CRT response prediction.
Joint Range and Doppler Adaptive Processing for CBM based DFRC systems
Authors: Jifa Zhang
Date: 2021-07-22 00:00:00

Recently, dual-function radar communication (DFRC) systems have been proposed to integrate radar and communication into one platform for spectrum sharing. Various signalling strategies have been proposed to embed communication information into the radar transmitted waveforms. Among these, complex beampattern modulation (CBM) embeds communication information into the complex transmit beampattens via changing the amplitude and phase of the beampatterns towards the communication receiver. The embedding of random communication information causes the clutter modulation and high range-Doppler sidelobe. What's more, transmitting different waveforms on a pulse to pulse basis degrades the radar target detection capacity when traditional sequential pulse compression (SPC) and moving-target detection (MTD) is utilized. In this paper, a minimum mean square error (MMSE) based filter, denoted as joint range and Doppler adaptive processing (JRDAP) is proposed. The proposed method estimates the targets' impulse response coefficients at each range-Doppler cell adaptively to suppress high range-Doppler sidelobe and clutter modulation. The performance of proposed method is very close to the full-dimension adaptive multiple pulses compression (AMPC) while reducing computational complexity greatly.
Kramers-Kronig Receiver Combined With Digital Resolution Enhancer
Authors: Menno van den Hout, Sjoerd van der Heide, Chigo Okonkwo
Date: 2021-07-22 00:00:00
Tasks: Quantization

A Kramers-Kronig receiver with a continuous wave tone added digitally at the transmitter is combined with a digital resolution enhancer to limit the increase in transmitter quantization noise. Performance increase is demonstrated, as well as the ability to reduce the number of bits in the digital-to-analog converter.
UAV-aided Radio Map Construction Exploiting Environment Semantics
Authors: Wenjie Liu, Junting Chen
Date: 2021-07-22 00:00:00
Tasks: parameter estimation

This paper constructs a full dimensional (6D) radio map to predict the channel gain between any transmitter location and any receiver location based on received signal strength (RSS) measurements between low-altitude aerial nodes and ground nodes. The main challenge is how to describe the signal strength attenuation due to the blockage from the environment. Conventional interpolation-type approaches fail to exploit the close relation between the radio map and the geometry of the environment. This paper proposes to construct radio maps by first estimating and constructing a multi-class 3D virtual obstacle map that describes the geometry of the environment with radio semantics. Mathematically, a least-squares problem is formulated to jointly estimate the virtual obstacle map and the propagation parameters. This problem is found to have a partial quasiconvexity that leads to the development of an efficient parameter estimation and radio map construction algorithm. Numerical experiments confirm that the proposed method substantially reduces the amount of measurement required for achieving the same radio map accuracy. It is also demonstrated that in a unmanned aerial vehicle (UAV)-aided relay communication scenario, a radio map assisted approach for UAV placement can achieve more than 50% capacity gain.
Wideband photonic interference cancellation based on free space optical communication
Authors: Yang Qi, Ben Wu
Date: 2021-07-22 00:00:00
Tasks: Management

We propose and experimentally demonstrate an interference management system that removes wideband wireless interference by using photonic signal processing and free space optical communication. The receiver separates radio frequency interferences by upconverting the mixed signals to optical frequencies and processing the signals with the photonic circuits. Signals with GHz bandwidth are processed and separated in real-time. The reference signals for interference cancellation are transmitted in a free space optical communication link, which provides large bandwidth for multi-band operation and accelerates the mixed signal separation process by reducing the dimensions of the un-known mixing matrix. Experimental results show that the system achieves 30dB real-time cancellation depth with over 6GHz bandwidth. Multiple radio frequency bands can be processed at the same time with a single system. In addition, multiple radio frequency bands can be processed at the same time with a single system.
"Normal" phylogenetic networks may be emerging as the leading class
Authors: Andrew Francis
Date: 2021-07-22 00:00:00

The rich and varied ways that genetic material can be passed between species has motivated extensive research into the theory of phylogenetic networks. Features that align with biological processes, or with desirable mathematical properties, have been used to define classes and prove results, with the goal of developing the theoretical foundations for network reconstruction methods. We may have now reached the point where a collection of recent results can be drawn together to make one class of network, the \emph{normal} networks, a leading contender, sitting in the sweet spot between biological relevance and mathematical tractability.