pwc-archive/papers-with-abstracts huggingface dataset:
Robust Model Predictive Longitudinal Position Tracking Control for an Autonomous Vehicle Based on Multiple Models
Date: 2020-10-21 00:00:00
Tasks: Position

The aim of this work is to control the longitudinal position of an autonomous
vehicle with an internal combustion engine. The powertrain has an inherent
dead-time characteristic and constraints on physical states apply since the
vehicle is neither able to accelerate arbitrarily strong, nor to drive
arbitrarily fast. A model predictive controller (MPC) is able to cope with both
of the aforementioned system properties. MPC heavily relies on a model and
therefore a strategy on how to obtain multiple linear state space prediction
models of the nonlinear system via input/output data system identification from
acceleration data is given. The models are identified in different regions of
the vehicle dynamics in order to obtain more accurate predictions. The still
remaining plant-model mismatch can be expressed as an additive disturbance
which can be handled through robust control theory. Therefore modifications to
the models for applying robust MPC tracking control theory are described. Then
a controller which guarantees robust constraint satisfaction and recursive
feasibility is designed. As a next step, modifications to apply the controller
on multiple models are discussed. In this context, a model switching strategy
is provided and theoretical and computational limitations are pointed out.
Lastly, simulation results are presented and discussed, including computational
load when switching between systems.
A Wideband Sliding Correlator based Channel Sounder in 65 nm CMOS: An Evaluation Board Design
Authors: Dipankar Shakya, Ting Wu, Theodore S. Rappaport
Date: 2020-09-28 00:00:00
Tasks: Cloud Computing

Wide swaths of bandwidth at millimeter-wave (mmWave) and Terahertz (THz) frequencies stimulate diverse applications in wireless sensing, imaging, position location, cloud computing, and much more. These emerging applications motivate wireless communications hardware to operate with multi Gigahertz (GHz) bandwidth, at nominal costs, minimal size, and power consumption. Channel sounding system implementations currently used to study and measure wireless channels utilize numerous commercially available components from multiple manufacturers that result in a complex and large assembly with many costly and fragile cable interconnections between the constituents and commonly achieve a system bandwidth under one GHz. This paper presents an evaluation board (EVB) design that features a sliding correlator-based channel sounder with 2 GHz null-to-null RF bandwidth in a single monolithic integrated circuit (IC) fabricated in 65 nm CMOS technology. The EVB landscape provides necessary peripherals for signal interfacing, amplification, buffering, and enables integration into both the transmitter and receiver of a channel sounding system, thereby reducing complexity, size, and cost through integrated design. The channel sounder IC on the EVB is the worlds first to report gigabit-per-second baseband operation using low-cost CMOS technology, allowing the global research community to now have an inexpensive and compact channel sounder system with nanosecond time resolution capability for the detection of multipath signals in a wireless channel.
Multi-center validation study of automated classification of pathological slowing in adult scalp electroencephalograms via frequency features
Authors: Wei Yan Peh, John Thomas, Elham Bagheri, Rima Chaudhari, Sagar Karia, Rahul Rathakrishnan, Vinay Saini, Nilesh Shah, Rohit Srivastava, Yee-Leng Tan, Justin Dauwels
Date: 2020-09-28 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Pathological slowing in the electroencephalogram (EEG) is widely investigated for the diagnosis of neurological disorders. Currently, the gold standard for slowing detection is the visual inspection of the EEG by experts, which is time-consuming and subjective. To address those issues, we propose three automated approaches to detect slowing in EEG: Threshold-based Detecting System (TDS), Shallow Learning-based Detecting System (SLDS), and Deep Learning-based Detecting System (DLDS). These systems are evaluated on channel-, segment- and EEG-level. The TDS, SLDS, and DLDS performs prediction via detecting slowing at individual channels, and those detections are arranged in histograms for detection of slowing at the segment- and EEG-level. We evaluate the systems through Leave-One-Subject-Out (LOSO) cross-validation (CV) and Leave-One-Institution-Out (LOIO) CV on four datasets from the US, Singapore, and India. The DLDS achieved the best overall results: LOIO CV mean balanced accuracy (BAC) of 71.9%, 75.5%, and 82.0% at channel-, segment- and EEG-level, and LOSO CV mean BAC of 73.6%, 77.2%, and 81.8% at channel-, segment-, and EEG-level. The channel- and segment-level performance is comparable to the intra-rater agreement (IRA) of an expert of 72.4% and 82%. The DLDS can process a 30-minutes EEG in 4 seconds and can be deployed to assist clinicians in interpreting EEGs.
Focal to bilateral tonic-clonic seizures are associated with widespread network abnormality in temporal lobe epilepsy
Date: 2020-09-28 00:00:00
Tasks: Diffusion  MRI

Objective: To identify if whole-brain structural network alterations in
patients with temporal lobe epilepsy (TLE) and focal to bilateral tonic-clonic
seizures (FBTCS) differ from alterations in patients without FBTCS.
  Methods: We dichotomized a cohort of 83 drug-resistant patients with TLE into
those with and without FBTCS and compared each group to 29 healthy controls.
For each subject, we used diffusion MRI to construct whole-brain structural
networks. First, we measured the extent of alterations by performing
FBTCS-negative (FBTCS-) versus control and FBTCS-positive (FBTCS+) versus
control comparisons, thereby delineating altered sub-networks of the
whole-brain structural network. Second, by standardising networks of each
patient using control networks, we measured the subject-specific abnormality at
every brain region in the network, thereby quantifying the spatial localisation
and the amount of abnormality in every patient.
  Results: Both FBTCS+ and FBTCS- patient groups had altered sub-networks with
reduced fractional anisotropy (FA) and increased mean diffusivity (MD) compared
to controls. The altered subnetwork in FBTCS+ patients was more widespread than
in FBTCS- patients (441 connections altered at t>3, p<0.001 in FBTCS+ compared
to 21 connections altered at t>3, p=0.01 in FBTCS-). Significantly greater
abnormalities-aggregated over the entire brain network as well as assessed at
the resolution of individual brain areas-were present in FBTCS+ patients
(p<0.001, d=0.82). In contrast, the fewer abnormalities present in FBTCS-
patients were mainly localised to the temporal and frontal areas.
  Significance: The whole-brain structural network is altered to a greater and
more widespread extent in patients with TLE and FBTCS. We suggest that these
abnormal networks may serve as an underlying structural basis or consequence of
the greater seizure spread observed in FBTCS.
On the Necessity and Sufficiency of the Zames-Falb Multipliers for Bounded Operators
Authors: Sei Zhen Khong, Lanlan Su
Date: 2020-09-28 00:00:00

This paper analyzes the robust feedback stability of a single-input-single-output stable linear time-invariant (LTI) system against four different classes of nonlinear systems using the Zames-Falb multipliers. The contribution is fourfold. Firstly, we present a generalised S-procedure lossless theorem that involves a countably infinite number of quadratic forms. Secondly, we identify a class of uncertain systems over which the robust feedback stability implies the existence of an appropriate Zames-Falb multiplier based on the generalised S-procedure lossless theorem. Meanwhile, we show that the existence of such a Zames-Falb multiplier is sufficient for the robust feedback stability over a smaller class of uncertain systems. Thirdly, when restricted to be static (a.k.a. memoryless), the second class of systems coincides with the class of sloped-restricted monotone nonlinearities, and the classical result of using the Zames-Falb multipliers to ensure feedback stability is recovered. Lastly, when restricted to be LTI, the second class is demonstrated to be a subset of the third, and the existence of a Zames-Falb multiplier is shown to be sufficient but not necessary for the robust feedback stability.
Robust Monotonic Convergent Iterative Learning Control Design: an LMI-based Method
Date: 2020-09-28 00:00:00

This work investigates robust monotonic convergent iterative learning control
(ILC) for uncertain linear systems in both time and frequency domains, and the
ILC algorithm optimizing the convergence speed in terms of $l_{2}$ norm of
error signals is derived. Firstly, it is shown that the robust monotonic
convergence of the ILC system can be established equivalently by the positive
definiteness of a matrix polynomial over some set. Then, a necessary and
sufficient condition in the form of sum of squares (SOS) for the positive
definiteness is proposed, which is amendable to the feasibility of linear
matrix inequalities (LMIs). Based on such a condition, the optimal ILC
algorithm that maximizes the convergence speed is obtained by solving a set of
convex optimization problems. Moreover, the order of the learning function can
be chosen arbitrarily so that the designers have the flexibility to decide the
complexity of the learning algorithm.
Forecasting Short-term load using Econometrics time series model with T-student Distribution
Authors: Kasun Chandrarathna, Arman Edalati, AhmadReza Fourozan tabar
Date: 2020-09-28 00:00:00
Tasks: Econometrics, Load Forecasting, Time Series, Time Series Analysis, Time Series Forecasting

By significant improvements in modern electrical systems, planning for unit commitment and power dispatching of them are two big concerns between the researchers. Short-term load forecasting plays a significant role in planning and dispatching them. In recent years, numerous works have been done on Short-term load forecasting. Having an accurate model for predicting the load can be beneficial for optimizing the electrical sources and protecting energy. Several models such as Artificial Intelligence and Statistics model have been used to improve the accuracy of load forecasting. Among the statistics models, time series models show a great performance. In this paper, an Autoregressive integrated moving average (SARIMA) - generalized autoregressive conditional heteroskedasticity (GARCH) model as a powerful tool for modeling the conditional mean and volatility of time series with the T-student Distribution is used to forecast electric load in short period of time. The attained model is compared with the ARIMA model with Normal Distribution. Finally, the effectiveness of the proposed approach is validated by applying real electric load data from the Electric Reliability Council of Texas (ERCOT). KEYWORDS: Electricity load, Forecasting, Econometrics Time Series Forecasting, SARIMA
Noise Variance Estimation Using Asymptotic Residual in Compressed Sensing
Authors: Ryo Hayakawa
Date: 2020-09-28 00:00:00
Tasks: compressed sensing

In compressed sensing, measurements are typically contaminated by additive noise, and therefore, information about the noise variance is often needed to design algorithms. In this paper, we propose a method for estimating the unknown noise variance in compressed sensing problems. The proposed method, called asymptotic residual matching (ARM), estimates the noise variance from a single measurement vector on the basis of the asymptotic result for the $\ell_{1}$ optimization problem. Specifically, we derive the asymptotic residual corresponding to the $\ell_{1}$ optimization and show that it depends on the noise variance. The proposed ARM approach obtains the estimate by comparing the asymptotic residual with the actual one, which can be obtained by empirical reconstruction without the information on the noise variance. For the proposed ARM, we also propose a method to choose a reasonable parameter based on the asymptotic residual. Simulation results show that the proposed noise variance estimation outperforms several conventional methods, especially when the problem size is small. We also show that, by using the proposed method, we can tune the regularization parameter of the $\ell_{1}$ optimization to achieve good reconstruction performance, even when the noise variance is unknown.
Real-time video streaming in vivo using ultrasound as the communication channel
Authors: Zhengchang Kou, Rita J. Miller, Andrew C. Singer, Michael L. Oelze
Date: 2020-09-28 00:00:00

The emergence of capsule endoscopy has provided a means of capturing video of the small intestines without having to resort to an invasive procedure involving intubation. However, real-time video streaming to a receiver outside the body remains challenging for capsule endoscopy. Traditional electromagnetic-based solutions are limited in their data rates and available power. Recently, ultrasound was investigated as a communication channel for through-tissue data transmission. To achieve real-time video streaming through tissue, data rates of ultrasound need to exceed 1 Mbps. In a previous study, we demonstrated ultrasound communications with data rates greater than 30 Mbps with two focused ultrasound transducers using a large footprint laboratory system through slabs of lossy tissues [1]. While the form factor of the transmitter is also crucial for capsule endoscopy, it is obvious that a large, focused transducer cannot fit within the size of a capsule. Several other challenges for achieving high-speed ultrasonic communication through tissue include strong reflections leading to multipath effects and attenuation. In this work, we demonstrate ultrasonic video communications using a mm-scale microcrystal transmitter with video streaming supplied by a camera connected to a Field Programmable Gate Array (FPGA). The signals were transmitted through a tissue-mimicking phantom and through the abdomen of a rabbit in vivo. The ultrasound signal was recorded by an array probe connected to a Verasonics Vantage system and decoded back to video. To improve the received signal quality, we combined the signal from multiple channels of the array probe. Orthogonal frequency division multiplexing (OFDM) modulation was used to reduce the receiver complexity under a strong multipath environment.
A Comparison of the Pathway Tools Software with the Reactome Software
Authors: Peter D. Karp
Date: 2020-09-28 00:00:00

This document compares SRI's Pathway Tools (PTools) software with the Reactome software. Both software systems serve the pathway bioinformatics area, including representation and analysis of metabolic pathways and signaling pathways. The comparison covers pathway bioinformatics capabilities, but does not cover other major facets of Pathway Tools that are completely absent from the Reactome software: Pathway Tools genome-informatics capabilities, regulatory informatics capabilities, and table-based analysis tools (SmartTables). Our overall findings are as follows. (1) PTools is significantly ahead of Reactome in its basic information pages. For example, PTools pathway layout algorithms have been developed to an advanced state over several decades, whereas Reactome pathway layouts are illegible, omit important information, and are created manually and therefore cannot scale to thousands of genomes. (2) PTools is far ahead of Reactome in omics analysis. PTools includes all of the omics-analysis methods that Reactome provides, and includes multiple methods that Reactome lacks. (3) PTools contains a metabolic route search tool (searching for paths through the metabolic network), which Reactome lacks. (4) PTools is significantly ahead of Reactome in inference of metabolic pathways from genome information to create new metabolic databases. (5) PTools has an extensive complement of metabolic-modeling tools whereas Reactome has none. (6) PTools is more scalable than Reactome, handling 18,000 genomes versus 90 genomes for Reactome. (7) PTools has a larger user base than Reactome. PTools powers 17 websites versus two for Reactome. PTools has been licensed by 10,800 users (Reactome licensed user count is unknown).
A Nonparametric Unsupervised Learning Approach for Structural Damage Detection
Authors: Kareem Eltouny, Xiao Liang
Date: 2020-09-28 00:00:00
Tasks: Bayesian Optimization, Novelty Detection, Structural Health Monitoring

In a world of aging infrastructure, structural health monitoring (SHM) emerges as a major step towards resilient and sustainable societies. The current advancements in machine learning and sensor technology have made SHM a more promising damage detection method than the traditional non-destructive testing methods. SHM using unsupervised learning methods offers an attractive alternative to the more commonly used supervised learning since it only requires data of the structure in normal conditions for the training process. The density-based novelty detection method provides a statistical element to the damage detection process but it relies heavily on the accuracy of the estimated probability density function (PDF). In this study, a novel unsupervised learning approach for SHM is proposed. It is based on the Kernel Density Maximum Entropy method by leveraging Bayesian optimization for hyperparameter tuning and also by extending the method into the multivariate space by the use of independent components analysis. The proposed approach is evaluated on a numerically simulated three-story reinforced concrete moment frame, where 94% of accuracy is achieved in structural damage detection.
Two-Hop Multi-UAV Relay Network Optimization with Directional Antennas
Authors: Carles Diaz Vilor, Hamid Jafarkhani
Date: 2020-09-29 00:00:00
Tasks: Fairness, Scheduling, Vocal Bursts Valence Prediction

In this paper, we consider the multi-UAV deployment problem for a two-hop relaying system. For a better network performance, UAVs carry directional antennas that are modeled by a realistic radiation pattern. The goal is to maximize the minimum user rates, and therefore achieve fairness in the network. We propose an iterative algorithm to optimize the TDMA scheduling in both hops, UAV trajectories, antenna beamwidths, and transmit power of the base station and relays. Simulation results show the throughput improvement as a result of optimizing the directional antenna radiation patterns. In addition, we derive the optimal power allocation, which combined with the beamwidth optimization yields to a much better performance.
Waveform Design and Accurate Channel Estimation for Frequency-Hopping MIMO Radar-Based Communications
Authors: Kai Wu, J. Andrew Zhang, Xiaojing Huang, Y. Jay Guo, Robert W. Heath Jr
Date: 2020-09-29 00:00:00

Frequency-hopping (FH) MIMO radar-based dual-function radar communication (FH-MIMO DFRC) enables communication symbol rate to exceed radar pulse repetition frequency, which requires accurate estimations of timing offset and channel parameters. The estimations, however, are challenging due to unknown, fast-changing hopping frequencies and the multiplicative coupling between timing offset and channel parameters. In this paper, we develop accurate methods for a single-antenna communication receiver to estimate timing offset and channel for FH-MIMO DFRC. First, we design a novel FH-MIMO radar waveform, which enables a communication receiver to estimate the hopping frequency sequence (HFS) used by radar, instead of acquiring it from radar. Importantly, the novel waveform incurs no degradation to radar ranging performance. Then, via capturing distinct HFS features, we develop two estimators for timing offset and derive mean squared error lower bound of each estimator. Using the bounds, we design an HFS that renders both estimators applicable. Furthermore, we develop an accurate channel estimation method, reusing the single hop for timing offset estimation. Validated by simulations, the accurate channel estimates attained by the proposed methods enable the communication performance of DFRC to approach that achieved based on perfect timing and ideal knowledge of channel.
Integrating Secure and High-Speed Communications into Frequency Hopping MIMO Radar
Authors: Kai Wu, J. Andrew Zhang, Xiaojing Huang, Y. Jay Guo
Date: 2020-09-29 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Dual-function radar-communication (DFRC) based on frequency hopping (FH) MIMO radar (FH-MIMO DFRC) achieves symbol rate much higher than radar pulse repetition frequency. Such DFRC, however, is prone to eavesdropping due to the spatially uniform illumination of FH-MIMO radar. How to enhance the physical layer security of FH-MIMO DFRC is vital yet unsolved. In this paper, we reveal the potential of using permutations of hopping frequencies to achieve secure and high-speed FH-MIMO DFRC. Detecting permutations at a communication user is challenging due to the dependence on spatial angle. We propose a series of baseband waveform processing methods which address the challenge specifically for the legitimate user (Bob) and meanwhile scrambles constellations almost omnidirectionally. We discover a deterministic sign rule from the signals processed by the proposed methods. Based on the rule, we develop accurate algorithms for information decoding at Bob. Confirmed by simulation, our design achieves substantially high physical layer security for FH-MIMO DFRC, improves decoding performance compared with existing designs and reduces mutual interference among radar targets.
Hybrid Heavy-Ball Systems: Reset Methods for Optimization with Uncertainty
Authors: Justin H. Le, Andrew R. Teel
Date: 2020-09-29 00:00:00

Momentum methods for convex optimization often rely on precise choices of algorithmic parameters, based on knowledge of problem parameters, in order to achieve fast convergence, as well as to prevent oscillations that could severely restrict applications of these algorithms to cyber-physical systems. To address these issues, we propose two dynamical systems, named the Hybrid Heavy-Ball System and Hybrid-inspired Heavy-Ball System, which employ a feedback mechanism for driving the momentum state toward zero whenever it points in undesired directions. We describe the relationship between the proposed systems and their discrete-time counterparts, deriving conditions based on linear matrix inequalities for ensuring exponential rates in both continuous time and discrete time. We provide numerical LMI results to illustrate the effects of our reset mechanisms on convergence rates in a setting that simulates uncertainty of problem parameters. Finally, we numerically demonstrate the efficiency and avoidance of oscillations of the proposed systems when solving both strongly convex and non-strongly convex problems.