pwc-archive/papers-with-abstracts huggingface dataset:
Information Content in Neuronal Calcium Spike Trains: Entropy Rate Estimation based on Empirical Probabilities
Authors: Sathish Ande, Srinivas Avasarala, Jayanth R Regatti, Neha Pandey, Sarpras Swain, Ajith Karunarathne, Lopamudra Giri, Soumya Jana
Date: 2021-02-01 00:00:00

Quantification of information content and its temporal variation in intracellular calcium spike trains in neurons helps one understand functions such as memory, learning, and cognition. Such quantification could also reveal pathological signaling perturbation that potentially leads to devastating neurodegenerative conditions including Parkinson's, Alzheimer's, and Huntington's diseases. Accordingly, estimation of entropy rate, an information-theoretic measure of information content, assumes primary significance. However, such estimation in the present context is challenging because, while entropy rate is traditionally defined asymptotically for long blocks under the assumption of stationarity, neurons are known to encode information in short intervals and the associated spike trains often exhibit nonstationarity. Against this backdrop, we propose an entropy rate estimator based on empirical probabilities that operates within windows, short enough to ensure approximate stationarity. Specifically, our estimator, parameterized by the length of encoding contexts, attempts to model the underlying memory structures in neuronal spike trains. In an example Markov process, we compared the performance of the proposed method with that of versions of the Lempel-Ziv algorithm as well as with that of a certain stationary distribution method and found the former to exhibit higher accuracy levels and faster convergence. Also, in experimentally recorded calcium responses of four hippocampal neurons, the proposed method showed faster convergence. Significantly, our technique detected structural heterogeneity in the underlying process memory in the responses of the aforementioned neurons. We believe that the proposed method facilitates large-scale studies of such heterogeneity, which could in turn identify signatures of various diseases in terms of entropy rate estimates.
Mode Shape Estimation using Complex Principal Component Analysis and k-Means Clustering
Authors: Hallvar Haugdal, Kjetil Uhlen
Date: 2018-12-07 00:00:00
Tasks: Clustering

We propose an empirical method for identifying low damped modes and corresponding mode shapes using frequency measurements from a Wide Area Monitoring System. The method consists of two main steps: Firstly, Complex Principal Component Analysis is used in combination with the Hilbert Transform and Empirical Mode Decomposition to provide estimates of modes and mode shapes. The estimates are stored as multidimensional points. Secondly, the points are grouped using a clustering algorithm, and new averaged estimates of modes and mode shapes are computed as the centroids of the clusters. Applying the method on data resulting from a non-linear power system simulator yields estimates of dominant modes and corresponding mode shapes that are similar to those resulting from modal analysis of the linearized system model. Encouraged by the results, the method is further tested with real PMU data at transmission grid level. Initial results indicate that the performance of the proposed method is promising.
Modeling Method for the Coupling Relations of Microgrid Cyber-Physical Systems Driven by Hybrid Spatiotemporal Events
Authors: Xiaoyong Bo, Xiaoyu Chen, Huashun Li, Yunchang Dong, Zhaoyang Qu, Lei Wang, Yang Li
Date: 2021-02-01 00:00:00
Tasks: Decision Making

The essence of the microgrid cyber-physical system (CPS) lies in the cyclical conversion of information flow and energy flow. Most of the existing coupling models are modeled with static networks and interface structures, in which the closed-loop data flow characteristic is not fully considered. It is difficult for these models to accurately describe spatiotemporal deduction processes, such as microgrid CPS attack identification, risk propagation, safety assessment, defense control, and cascading failure. To address this problem, a modeling method for the coupling relations of microgrid CPS driven by hybrid spatiotemporal events is proposed in the present work. First, according to the topological correlation and coupling logic of the microgrid CPS, the cyclical conversion mechanism of information flow and energy flow is analyzed, and a microgrid CPS architecture with multi-agents as the core is constructed. Next, the spatiotemporal evolution characteristic of the CPS is described by hybrid automata, and the task coordination mechanism of the multi-agent CPS terminal is designed. On this basis, a discrete-continuous correlation and terminal structure characteristic representation method of the CPS based on heterogeneous multi-groups are then proposed. Finally, four spatiotemporal events, namely state perception, network communication, intelligent decision-making, and action control, are defined. Considering the constraints of the temporal conversion of information flow and energy flow, a microgrid CPS coupling model is established, the effectiveness of which is verified by simulating false data injection attack (FDIA) scenarios.
Teaching Digital Signal Processing by Partial Flipping, Active Learning and Visualization
Authors: Keshab K. Parhi
Date: 2021-01-31 00:00:00
Tasks: Active Learning

Effectiveness of teaching digital signal processing can be enhanced by reducing lecture time devoted to theory, and increasing emphasis on applications, programming aspects, visualization and intuitive understanding. An integrated approach to teaching requires instructors to simultaneously teach theory and its applications in storage and processing of audio, speech and biomedical signals. Student engagement can be enhanced by engaging students to work in groups during the class where students can solve short problems and short programming assignments or take quizzes. These approaches will increase student interest in learning the subject and student engagement.
Near-field Image Transmission and EVM Measurements in Rich Scattering Environment
Authors: Mir Lodro, Gabriele Gradoni, Christopher Smartt, Ana Vukovic, David Thomas, Steve Greedy
Date: 2021-01-31 00:00:00

In this work, we present near-field image transmission and error vector magnitude measurement in a rich scattering environment in a metal enclosure. We check the effect of loading metal enclosure on the performance of SDR based near-field communication link. We focus on the key communication receiver parameters to observe the effect of near-field link in presence of rich-scattering and in presence of loading with RF absorber cones. The near-field performance is measured by transmitting wideband OFDM-modulated packets containing image information. Our finding suggests that the performance of OFDM based wideband near-field communication improves when the metal enclosure is loaded with RF absorbers. Near-field EVM improves when the enclosure is loaded with RF absorber cones. Loading of the metal enclosure has the effect of increased coherence bandwidth. Frequency selectivity was observed in an empty enclosure which suggests coherence bandwidth less than the signal bandwidth.
Site Diversity in Downlink Optical Satellite Networks Through Ground Station Selection
Authors: Eylem Erdogan, Ibrahim Altunbas, Gunes Kurt, Michel Bellemare, Guillaume Lamontagne, Halim Yanikomeroglu
Date: 2020-10-05 00:00:00
Tasks: Diversity

Recent advances have shown that satellite communication (SatCom) will be an important enabler for next generation terrestrial networks as it can provide numerous advantages, including global coverage, high speed connectivity, reliability, and instant deployment. An ideal alternative for radio frequency (RF) satellites is its free-space optical (FSO) counterpart. FSO or laser SatCom can mitigate the problems occurring in RF SatCom, while providing important advantages, including reduced mass, lower consumption, better throughput, and lower costs. Furthermore, laser SatCom is inherently resistant to jamming, interception, and interference. Owing to these benefits, this paper focuses on downlink laser SatCom, where the best ground station (GS) is selected among numerous candidates to provide reliable connectivity and maximum site diversity. To quantify the performance of the proposed scheme, we derive closed-form outage probability and ergodic capacity expressions for two different practical GS deployment scenarios. Furthermore, asymptotic analysis is conducted to obtain the overall site diversity gain, and aperture averaging is studied to illustrate the impact of aperture diameter on the overall performance. Finally, important design guidelines that can be useful in the design of practical laser SatComs are outlined.
Estimating Adsorption Isotherm Parameters in Chromatography via a Virtual Injection Promoting Feed-forward Neural Network
Authors: Chen Xu, Ye Zhang
Date: 2021-01-31 00:00:00

The means to obtain the adsorption isotherms is a fundamental open problem in competitive chromatography. A modern technique of estimating adsorption isotherms is to solve an inverse problem so that the simulated batch separation coincides with actual experimental results. However, this identification process is usually ill-posed in the sense that the small noise in the measured response can lead to a large fluctuation in the estimated quantity of adsorption isotherms. The conventional mathematical method of solving this problem is the variational regularization, which is formulated as a non-convex minimization problem with a regularized objective functional. However, in this method, the choice of regularization parameter and the design of a convergent solution algorithm are quite difficult in practice. Moreover, due to the restricted number of injection profiles in experiments, the types of measured data are extremely limited, which may lead to a biased estimation. In order to overcome these difficulties, in this paper, we develop a new inversion method -- the Virtual Injection Promoting Feed-forward Neural Network (VIP-FNN). In this approach, the training data contain various types of artificial injections and synthetic noisy measurement at outlet, generated by a conventional physics model -- a time-dependent convection-diffusion system. Numerical experiments with both artificial and real data from laboratory experiments show that the proposed VIP-FNN is an efficient and robust algorithm.
Study on Compressed Sensing of Action Potential
Authors: Hyunseok Park, Xilin Liu
Date: 2021-01-30 00:00:00
Tasks: compressed sensing, Compressive Sensing, EEG, Electroencephalogram (EEG)

Compressive sensing (CS) is a signal processing technique that enables sub-Nyquist sampling and near lossless reconstruction of a sparse signal. The technique is particularly appealing for neural signal processing since it avoids the issues relevant to high sampling rate and large data storage. In this project, different CS reconstruction algorithms were tested on raw action potential signals recorded in our lab. Two numerical criteria were set to evaluate the performance of different CS algorithms: Compression Ratio (CR) and Signal-to-Noise Ratio (SNR). In order to do this, individual CS algorithm testing platforms for the EEG data were constructed within MATLAB scheme. The main considerations for the project were the following. 1) Feasibility of the dictionary 2) Tolerance to non-sparsity 3) Applicability of thresholding or interpolation.
Probabilistic RF-Assisted Camera Wake-Up through Self-Supervised Gaussian Process Regression
Authors: Luca Varotto, Angelo Cenedese
Date: 2021-01-30 00:00:00
Tasks: regression

Research on wireless sensors represents a continuously evolving technological domain thanks to their high flexibility and scalability, fast and economical deployment, pervasiveness in industrial, civil and domestic contexts. However, the maintenance costs and the sensors reliability are strongly affected by the battery lifetime, which may limit their use. In this paper we consider a wireless smart camera, equipped with a low-energy radio receiver, and used to visually detect a moving radio-emitting target. To preserve the camera lifetime without sacrificing the detection capabilities, we design a probabilistic energy-aware controller to switch on/off the camera. The radio signal strength is used to predict the target detectability, via self-supervised Gaussian Process Regression combined with Recursive Bayesian Estimation. The automatic training process minimizes the human intervention, while the controller guarantees high detection accuracy and low energy consumption, as numerical and experimental results show.
Statistical guided-waves-based SHM via stochastic non-parametric time series models
Authors: Ahmad Amer, Fotis Kopsaftopoulos
Date: 2021-01-27 00:00:00
Tasks: Decision Making, Sensitivity, Structural Health Monitoring, Time Series, Time Series Analysis

Damage detection in active-sensing, guided-waves-based Structural Health Monitoring (SHM) has evolved through multiple eras of development during the past decades. Nevertheless, there still exists a number of challenges facing the current state-of-the-art approaches, both in the industry as well as in research and development, including low damage sensitivity, lack of robustness to uncertainties, need for user-defined thresholds, and non-uniform response across a sensor network. In this work, a novel statistical framework is proposed for active-sensing SHM based on the use of ultrasonic guided waves. This framework is based on stochastic non-parametric time series models and their corresponding statistical properties in order to readily provide healthy confidence bounds and enable accurate and robust damage detection via the use of appropriate statistical decision making tests. Three such methods and corresponding statistical quantities (test statistics) along with decision making schemes are formulated and experimentally assessed via the use of three coupons with different levels of complexity: an Al plate with a growing notch, a Carbon fiber-reinforced plastic (CFRP) plate with added weights to simulate local damages, and the CFRP panel used in the Open Guided Waves project [1], all fitted with piezoelectric transducers and a pitch-catch configuration. The performance of the proposed methods is compared to that of state-of-the-art time-domain damage indices (DIs). The results demonstrate the increased sensitivity and robustness of the proposed methods, with better tracking capability of damage evolution compared to conventional approaches, even for damage-non-intersecting actuator-sensor paths. Overall, the proposed statistical methods exhibit greater damage sensitivity across different components, with enhanced robustness to uncertainty, as well as user-friendly application.
Reconfigurable Intelligent Surface Enabled Vehicular Communication: Joint User Scheduling and Passive Beamforming
Authors: Ahmed Al-Hilo, Moataz Samir, Mohamed Elhattab, Chadi Assi, Sanaa Sharafeddine
Date: 2021-01-28 00:00:00
Tasks: Deep Reinforcement Learning, Scheduling

Given its ability to control and manipulate wireless environments, reconfigurable intelligent surface (RIS), also known as intelligent reflecting surface (IRS), has emerged as a key enabler technology for the six-generation (6G) cellular networks. In the meantime, vehicular environment radio propagation is negatively influenced by a large set of objects that cause transmission distortion such as high buildings. Therefore, this work is devoted to explore the area of RIS technology integration with vehicular communications while considering the dynamic nature of such communication environment. Specifically, we provide a system model where RoadSide Unit (RSU) leverages RIS to provide indirect wireless transmissions to disconnected areas, known as dark zones. Dark zones are spots within RSU coverage where the communication links are blocked due to the existence of blockages. In details, a discrete RIS is utilized to provide communication links between the RSU and the vehicles passing through out-of-service zones. Therefore, the joint problem of RSU resource scheduling and RIS passive beamforming or phase-shift matrix is formulated as an optimization problem with the objective of maximizing the minimum average bit rate. The formulated problem is mixed integer non-convex program which is difficult to be solved and does not account for the uncertain dynamic environment in vehicular networks. Thereby, we resort to alternative methods based on Deep Reinforcement Learning to determine RSU wireless scheduling and Block Coordinate Descent (BCD) to solve for the phase-shift matrix, \textit{i.e.,} passive beamforming, of the RIS. The Markov Decision Process (MDP) is defined and the complexity of the solution approach is discussed. Our numerical results demonstrate the superiority of our proposed approach over baseline techniques.
Windowed total variation denoising and noise variance monitoring
Authors: Zhanhao Liu, Marion Perrodin, Thomas Chambrion, Radu Stoica
Date: 2021-01-28 00:00:00
Tasks: Denoising

We proposed a real time Total-Variation denosing method with an automatic choice of hyper-parameter $\lambda$, and the good performance of this method provides a large application field. In this article, we adapt the developed method to the non stationary signal in using the sliding window, and propose a noise variance monitoring method. The simulated results show that our proposition follows well the variation of noise variance.
Artificial Intelligence Driven UAV-NOMA-MEC in Next Generation Wireless Networks
Authors: Zhong Yang, Mingzhe Chen, Xiao Liu, Yuanwei Liu, Yue Chen, Shuguang Cui, H. Vincent Poor
Date: 2021-01-27 00:00:00
Tasks: Edge-computing, Federated Learning

Driven by the unprecedented high throughput and low latency requirements in next-generation wireless networks, this paper introduces an artificial intelligence (AI) enabled framework in which unmanned aerial vehicles (UAVs) use non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) techniques to service terrestrial mobile users (MUs). The proposed framework enables the terrestrial MUs to offload their computational tasks simultaneously, intelligently, and flexibly, thus enhancing their connectivity as well as reducing their transmission latency and their energy consumption. To this end, the fundamentals of this framework are first introduced. Then, a number of communication and AI techniques are proposed to improve the quality of experiences of terrestrial MUs. To this end, federated learning and reinforcement learning are introduced for intelligent task offloading and computing resource allocation. For each learning technique, motivations, challenges, and representative results are introduced. Finally, several key technical challenges and open research issues of the proposed framework are summarized.
Machine Learning based Soil VWC and Field Capacity Estimation Using Low Cost Sensors
Authors: Idrees Zaman, Nandit Jain, Anna Förster
Date: 2021-01-27 00:00:00
Tasks: BIG-bench Machine Learning, Capacity Estimation

The amount of water present in soil is measured in terms of a parameter commonly referred to as Volumetric Water Content (VWC) and is used for determining the field capacity of any soil. It is an important parameter accounting for ensuring proper irrigation at plantation sites for farming as well as for afforestation activities. The current work is an extension to already going on research in the area of wireless underground sensor networks (WUSNs). Sensor nodes equipped with Decagon 5TM volumetric water content (VWC) and temperature sensor are deployed underground to understand the properties of soil for agricultural activities. The major hindrances in the deployment of such networks over a large field are the cost of VWC sensors and the credibility of the data being collected by these sensors. In this paper, we analyze the use of low-cost moisture and temperature sensors that can either be used to estimate the VWC values and field capacity or cross-validate the data of expensive VWC sensors before the actual deployment. Machine learning algorithms, namely Neural Networks and Random Forests are investigated for predicting the VWC value from low-cost moisture sensors. Several field experiments are carried out to examine the proposed hypothesis. The results showed that low-cost moisture sensors can assist in estimating the VWC and field capacity with a minor trade-off.
Bounds on mutual information of mixture data for classification tasks
Authors: Yijun Ding, Amit Ashok
Date: 2021-01-27 00:00:00
Tasks: Classification, General Classification, speech-recognition, Speech Recognition

The data for many classification problems, such as pattern and speech recognition, follow mixture distributions. To quantify the optimum performance for classification tasks, the Shannon mutual information is a natural information-theoretic metric, as it is directly related to the probability of error. The mutual information between mixture data and the class label does not have an analytical expression, nor any efficient computational algorithms. We introduce a variational upper bound, a lower bound, and three estimators, all employing pair-wise divergences between mixture components. We compare the new bounds and estimators with Monte Carlo stochastic sampling and bounds derived from entropy bounds. To conclude, we evaluate the performance of the bounds and estimators through numerical simulations.
Doppler Estimation for High-Velocity Targets Using Subpulse Processing and the Classic Chinese Remainder Theorem
Authors: Fernando Darío Almeida García, André Saito Guerreiro, Gustavo Rodrigues de Lima Tejerina, José Cândido S. Santos Filho, Gustavo Fraidenraich, Michel Daoud Yacoub
Date: 2021-01-27 00:00:00

In pulsed Doppler radars, the classic Chinese remainder theorem (CCRT) is a common method to resolve Doppler ambiguities caused by fast-moving targets. Another issue concerning high-velocity targets is related to the loss in the signal-to-noise ratio (SNR) after performing range compression. In particular, this loss can be partially mitigated by the use of subpulse processing (SP). Modern radars combine these techniques in order to reliably unfold the target velocity. However, the presence of background noise may compromise the Doppler estimates. Hence, a rigorous statistical analysis is imperative. In this work, we provide a comprehensive analysis on Doppler estimation. In particular, we derive novel closed-form expressions for the probability of detection (PD) and probability of false alarm (PFA). To this end, we consider the newly introduce SP along with the CCRT. A comparison analysis between SP and the classic pulse processing (PP) technique is also carried out. Numerical results and Monte-Carlo simulations corroborate the validity of our expressions and show that the SP-plus-CCRT technique helps to greatly reduce the PFA compared to previous studies, thereby improving radar detection.