pwc-archive/papers-with-abstracts huggingface dataset:
An Evolutionary Game for Mobile User Access Mode Selection in sub-$6$ GHz/mmWave Cellular Networks
Authors: Christodoulos Skouroumounis, Ioannis Krikidis
Date: 2022-01-10 00:00:00

By utilizing the combination of two powerful tools i.e., stochastic geometry (SG) and evolutionary game theory (EGT), in this paper, we study the problem of mobile user (MU) mode selection in heterogeneous sub-$6$ GHz/millimeter wave (mmWave) cellular networks. Particularly, by using SG tools, we first propose an analytical framework to assess the performance of the considered networks in terms of average signal-to-interference-plus-noise (SINR) ratio, average rate, and mobility-induced time overhead, for scenarios with user mobility{.} According to the SG-based framework, an EGT-based approach is presented to solve the problem of access mode selection. Specifically, two EGT-based models are considered, where for each MU its utility function depends on the average SINR and the average rate, respectively, while the time overhead is considered as a penalty term. A distributed algorithm is proposed to reach the evolutionary equilibrium, where the existence and stability of the equilibrium is theoretically analyzed and proved. Moreover, we extend the formulation by considering information delay exchange and evaluate its impact on the convergence of the proposed algorithm. Our results reveal that the proposed technique can offer better spectral efficiency and connectivity in heterogeneous sub-$6$ GHz/mmWave cellular networks with mobility, compared with the conventional access mode selection techniques.
A High-Level Track Fusion Scheme for Circular Quantities
Authors: Sören Kohnert, Reinhard Stolle
Date: 2022-01-10 00:00:00
Tasks: Vocal Bursts Intensity Prediction

As sensors get more and more integrated, signal processing functions, like tracking, are performed closer to the sensor. Consequently, high level fusion is on the rise. Presented here is a high level fusion scheme incorporating not only linear,but circular quantities as well. Monte Carlo experiments are used to verify our novel fusion operators that work as a weighted average for the Wrapped Normal and the von-Mises distribution. To further verify the new fusion operators, we implemented a full track level fusion scheme and tested it by fusing the measurements of two RADAR sensors.
Linearly-involved Moreau-Enhanced-over-Subspace Model: Debiased Sparse Modeling and Stable Outlier-Robust Regression
Authors: Masahiro Yukawa, Hiroyuki Kaneko, Kyohei Suzuki, Isao Yamada
Date: 2022-01-10 00:00:00
Tasks: regression, Robust classification

We present an efficient mathematical framework based on the linearly-involved Moreau-enhanced-over-subspace (LiMES) model. Two concrete applications are considered: sparse modeling and robust regression. The popular minimax concave (MC) penalty for sparse modeling subtracts, from the $\ell_1$ norm, its Moreau envelope, inducing nearly unbiased estimates and thus yielding remarkable performance enhancements. To extend it to underdetermined linear systems, we propose the projective minimax concave penalty using the projection onto the input subspace, where the Moreau-enhancement effect is restricted to the subspace for preserving the overall convexity. We also present a novel concept of stable outlier-robust regression which distinguishes noise and outlier explicitly. The LiMES model encompasses those two specific examples as well as two other applications: stable principal component pursuit and robust classification. The LiMES function involved in the model is an ``additively nonseparable'' weakly convex function but is defined with the Moreau envelope returning the minimum of a ``separable'' convex function. This mixed nature of separability and nonseparability allows an application of the LiMES model to the underdetermined case with an efficient algorithmic implementation. Two linear/affine operators play key roles in the model: one corresponds to the projection mentioned above and the other takes care of robust regression/classification. A necessary and sufficient condition for convexity of the smooth part of the objective function is studied. Numerical examples show the efficacy of LiMES in applications to sparse modeling and robust regression.
Optimization of Network Throughput of Joint Radar Communication System Using Stochastic Geometry
Authors: Shobha Sundar Ram, Gourab Ghatak
Date: 2022-01-10 00:00:00
Tasks: Activity Recognition, Indoor Localization, Joint Radar-Communication

Recently joint radar communication (JRC) systems have gained considerable interest for several applications such as vehicular communications, indoor localization and activity recognition, covert military communications, and satellite-based remote sensing. In these frameworks, bistatic/passive radar deployments with directional beams explore the angular search space and identify mobile users/radar targets. Subsequently, directional communication links are established with these mobile users. Consequently, JRC parameters such as the time trade-off between the radar exploration and communication service tasks have direct implications on the network throughput. Using tools from stochastic geometry (SG), we derive several system design and planning insights for deploying such networks and demonstrate how efficient radar detection can augment the communication throughput in a JRC system. Specifically, we provide a generalized analytical framework to maximize the network throughput by optimizing JRC parameters such as the exploration/exploitation duty cycle, the radar bandwidth, the transmit power, and the pulse repetition interval. The analysis is further extended to monostatic radar conditions, which is a special case in our framework. The theoretical results are experimentally validated through Monte Carlo simulations. Our analysis highlights that for a larger bistatic range, lower operating bandwidth and a higher duty cycle must be employed to maximize the network throughput. Furthermore, we demonstrate how a reduced success in radar detection due to higher clutter density deteriorates the overall network throughput. Finally, we show peak reliability of 70% of the JRC link metrics for a single bistatic transceiver configuration.
Configurable Independent Component Analysis Preprocessing Accelerator
Authors: Hsi-Hung Lu, Chung-An Shen, Mohammed E. Fouda, Ahmed M. Eltawil
Date: 2022-01-10 00:00:00
Tasks: Anomaly Detection

Independent component analysis (ICA) has been used in many applications, including self-interference cancellation for in-band full-duplex wireless systems and anomaly detection in industrial internet of things. This paper presents a high-throughput and highly efficient configurable preprocessing accelerator for the ICA algorithm. The proposed ICA accelerator has three major blocks that perform data centering, covariance matrix for computation, and eigenvalue decomposition (EVD). Specifically, the proposed accelerator is based on a high-performance matrix multiplication array (MMA). The proposed MMA architecture uses time-multiplexed processing so that the efficiency of hardware utilization is greatly enhanced. Furthermore, the processing flow utilizes parallel processing such that the centering, the calculation of the covariance matrix, and EVD are conducted simultaneously and are individually pipelined to maximize throughput. This paper presents the architecture, circuit design, and performance estimates based on post-layout extraction of the proposed preprocessing ICA accelerator. The proposed design achieves a throughput of 40.7 kMatrices per second at complexity of 73.3 kGE.
Low Earth Orbit Satellite Security and Reliability: Issues, Solutions, and the Road Ahead
Authors: Pingyue Yue, Jianping An, Jiankang Zhang, Jia Ye, Gaofeng Pan, Shuai Wang, Pei Xiao, Lajos Hanzo
Date: 2022-01-09 00:00:00
Tasks: Integrated sensing and communication

Low Earth Orbit (LEO) satellites undergo a period of rapid development driven by ever-increasing user demands, reduced costs, and technological progress. Since there is a paucity of literature on the security and reliability issues of LEO Satellite Communication Systems (SCSs), we aim to fill this knowledge gap. Specifically, we critically appraise the inherent characteristics of LEO SCSs and elaborate on their security and reliability requirements. In light of this, we further discuss their vulnerabilities, including potential security attacks launched against them and reliability risks, followed by outlining the associated lessons learned. Subsequently, we discuss the corresponding security and reliability enhancement solutions, unveil a range of trade-offs, and summarize the lessons gleaned. Furthermore, we shed light on several promising future research directions for enhancing the security and reliability of LEO SCSs, such as integrated sensing and communication, computer vision aided communications, as well as challenges brought about by mega-constellation and commercialization. Finally, we summarize the lessons inferred and crystallize the take-away messages in our design guidelines.
Bandlimited signal reconstruction from leaky integrate-and-fire encoding using POCS
Authors: Nguyen T. Thao, Dominik Rzepka, Marek Miśkowicz
Date: 2022-01-09 00:00:00
Tasks: Quantization, Retrieval

Leaky integrate-and-fire (LIF) encoding is a model of neuron transfer function in biology that has recently attracted the attention of the signal processing and neuromorphic computing communities as a technique of event-based sampling for data acquisition. While LIF enables the implementation of analog-circuit signal samplers of lower complexity and higher accuracy simultaneously, the core difficulty of this technique is the retrieval of an input from its LIF-encoded output. In this article, we study this problem in the context of bandlimited inputs, by extracting the most abstract features of an LIF encoder as a generalized nonuniform sampler. In this view, the LIF output is seen as the transformation of the input by a known linear operator. We show that the signal reconstruction method of projection onto convex sets (POCS) converges to a weighted pseudo-inverse of this operator. This allows perfect recovery under uniqueness of reconstruction, minimum-norm reconstruction under incomplete sampling, as well as a noise shaping of time quantization that outperforms standard pseudo-inversion. On the practical side, a single iteration of the POCS method can be used to improve any estimate whose LIF samples are not consistent with those of the input, and a rigorous discrete-time implementation of this iteration is proposed that does not require a Nyquist-rate representation of the signals.
Using Wi-Fi Signal as Sensing Medium: Passive Radar, Channel State Information and Followups
Authors: Bo Tan, Bo Sun
Date: 2022-01-09 00:00:00

The idea of exploiting the Wi-Fi bursts as the medium for sensing purposes, particularly for the human targets in the indoor environment, was cultivated in both radar and computer science communities and it has became a noticeable research genre with cross-disciplinary impact in security, healthcare, human-machine interaction etc.This article comparatively introduces passive radar based and channel state information (CSI) based approaches. For each means, the primary design principles, signal processing and representative applications scenarios are shown. At last, some opportunities and challenges of Wi-Fi sensing are pointed out for the sake of stepping closer to the practitioners and end-users.
Secure Transmission for IRS-Assisted MIMO MmWave Systems
Authors: Long Yang, Jiangtao Wang, Xuan Xue, Jia Shi, Yongchao Wang
Date: 2022-01-09 00:00:00

In this paper, we investigate the secure beamforming design in an intelligent reflection surface (IRS) assisted millimeter wave (mmWave) system, where the hybrid beamforming (HB) and the passive beamforming (PB) are employed by the transmitter and the IRS, respectively. To maximize the secrecy capacity, the joint optimization of HB and PB is formulated as a nonconvex problem with constant-modulus constraints. To efficiently solve such a challenging problem, the original problem is decomposed into a PB subproblem and an HB subproblem, then these two subproblems are sequentially solved by proposed algorithms. Simulation results demonstrate the superior performance of proposed approach in comparison with the state-of-the-art works.
Low-Complexity Codebook Design for SCMA based Visible Light Communication
Authors: Saumya Chaturvedi, Dil Nashin Anwar, Vivek Ashok Bohara, Anand Srivastava, Zilong Liu
Date: 2022-01-09 00:00:00

Sparse code multiple access (SCMA), as a code-domain non-orthogonal multiple access (NOMA) scheme, has received considerable research attention for enabling massive connectivity in future wireless communication systems. In this paper, we present a novel codebook (CB) design for SCMA based visible light communication (VLC) system, which suffers from shot noise. In particular, we introduce an iterative algorithm for designing and optimizing CB by considering the impact of shot noise at the VLC receiver. Based on the proposed CB, we derive and analyze the theoretical bit error rate (BER) expression for the resultant SCMA-VLC system. The simulation results show that our proposed CBs outperform CBs in the existing literature for different loading factors with much less complexity. Further, the derived analytical BER expression well aligns with simulated results, especially in high signal power regions.
Integration of Vehicular Clouds and Autonomous Driving: Survey and Future Perspectives
Authors: Yassine Maalej, Elyes Balti
Date: 2022-01-08 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles

For decades, researchers on Vehicular Ad-hoc Networks (VANETs) and autonomous vehicles presented various solutions for vehicular safety and autonomy, respectively. Yet, the developed work in these two areas has been mostly conducted in their own separate worlds, and barely affect one-another despite the obvious relationships. In the coming years, the Internet of Vehicles (IoV), encompassing sensing, communications, connectivity, processing, networking, and computation is expected to bridge many technologies to offer value-added information for the navigation of self-driving vehicles, to reduce vehicle on board computation, and to deliver desired functionalities. Potentials for bridging the gap between these two worlds and creating synergies of these two technologies have recently started to attract significant attention of many companies and government agencies. In this article, we first present a comprehensive survey and an overview of the emerging key challenges related to the two worlds of Vehicular Clouds (VCs) including communications, networking, traffic modelling, medium access, VC Computing (VCC), VC collation strategies, security issues, and autonomous driving (AD) including 3D environment learning approaches and AD enabling deep-learning, computer vision and Artificial Intelligence (AI) techniques. We then discuss the recent related work and potential trends on merging these two worlds in order to enrich vehicle cognition of its surroundings, and enable safer and more informed and coordinated AD systems. Compared to other survey papers, this work offers more detailed summaries of the most relevant VCs and ADs systems in the literature, along with some key challenges and insights on how different technologies fit together to deliver safety, autonomy and infotainment services.
Detecting Anomaly in Chemical Sensors via L1-Kernels based Principal Component Analysis
Authors: Hongyi Pan, Diaa Badawi, Ishaan Bassi, Sule Ozev, Ahmet Enis Cetin
Date: 2022-01-07 00:00:00
Tasks: Anomaly Detection

We propose a kernel-PCA based method to detect anomaly in chemical sensors. We use temporal signals produced by chemical sensors to form vectors to perform the Principal Component Analysis (PCA). We estimate the kernel-covariance matrix of the sensor data and compute the eigenvector corresponding to the largest eigenvalue of the covariance matrix. The anomaly can be detected by comparing the difference between the actual sensor data and the reconstructed data from the dominant eigenvector. In this paper, we introduce a new multiplication-free kernel, which is related to the l1-norm for the anomaly detection task. The l1-kernel PCA is not only computationally efficient but also energy-efficient because it does not require any actual multiplications during the kernel covariance matrix computation. Our experimental results show that our kernel-PCA method achieves a higher area under curvature (AUC) score (0.7483) than the baseline regular PCA method (0.7366).
EEG Based Emotion Sensing using convolutional neural networks
Authors: Shivaditya Shivganesh
Date: 2022-01-06 00:00:00
Tasks: Deep Learning, EEG, Electroencephalogram (EEG), Feature Engineering

Deep Learning has impacted various fields especially in bio-medical applications. Deep learning algorithms work well with both structured and unstructured data. Especially, convolutional neural network work well with signal-based data like EEG data. These types of data may or may not follow a pattern in their data. Algorithms like CNN help feature engineering and simplistic interpretation of the data. These algorithms are also better in comparison to other algorithms when generalised to a data belonging to larger data set.
Discrete-time risk sensitive portfolio optimization with proportional transaction costs
Authors: Marcin Pitera, Łukasz Stettner
Date: 2022-01-08 00:00:00
Tasks: Portfolio Optimization

In this paper we consider a discrete-time risk sensitive portfolio optimization over a long time horizon with proportional transaction costs. We show that within the log-return i.i.d. framework the solution to a suitable Bellman equation exists under minimal assumptions and can be used to characterize the optimal strategies for both risk-averse and risk-seeking cases. Moreover, using numerical examples, we show how a Bellman equation analysis can be used to construct or refine optimal trading strategies in the presence of transaction costs.
Pricing European Options under Stochastic Volatility Models: Case of five-Parameter Variance-Gamma Process
Authors: A. H. Nzokem
Date: 2022-01-06 00:00:00

The paper builds a Variance-Gamma (VG) model with five parameters: location ($\mu$), symmetry ($\delta$), volatility ($\sigma$), shape ($\alpha$), and scale ($\theta$); and studies its application to the pricing of European options. The results of our analysis show that the five-parameter VG model is a stochastic volatility model with a $\Gamma(\alpha, \theta)$ Ornstein-Uhlenbeck type process; the associated L\'evy density of the VG model is a KoBoL family of order $\nu=0$, intensity $\alpha$, and steepness parameters $\frac{\delta}{\sigma^2} - \sqrt{\frac{\delta^2}{\sigma^4}+\frac{2}{\theta \sigma^2}}$ and $\frac{\delta}{\sigma^2}+ \sqrt{\frac{\delta^2}{\sigma^4}+\frac{2}{\theta \sigma^2}}$; and the VG process converges asymptotically in distribution to a L\'evy process driven by a normal distribution with mean $(\mu + \alpha \theta \delta)$ and variance $\alpha (\theta^2\delta^2 + \sigma^2\theta)$. The data used for empirical analysis were obtained by fitting the five-parameter Variance-Gamma (VG) model to the underlying distribution of the daily SPY ETF data. Regarding the application of the five-parameter VG model, the twelve-point rule Composite Newton-Cotes Quadrature and Fractional Fast Fourier (FRFT) algorithms were implemented to compute the European option price. Compared to the Black-Scholes (BS) model, empirical evidence shows that the VG option price is underpriced for out-of-the-money (OTM) options and overpriced for in-the-money (ITM) options. Both models produce almost the same option pricing results for deep out-of-the-money (OTM) and deep-in-the-money (ITM) options
Effect of In-vivo Heat Challenge on Physiological Parameters and Function of Peripheral Blood Mononuclear Cells in Immune Phenotyped Dairy Cattle
Authors: S. L. Cartwright, J Schmied, A Livernois, B. A. Mallard
Date: 2022-01-07 00:00:00

The frequency of heat waves are increasing due to climate change, which leads to an increase in the occurrence of heat stress in dairy cattle. Previous studies have shown that dairy cattle identified as high immune responders have a reduced incidence of disease and improved vaccine response compared to average and low responders. Additionally, it has been observed that when cells from immune phenotyped cattle are exposed to in-vitro heat challenge, high immune responders exhibit increased heat tolerance compared to average and low responders. Therefore, the objective of this study was to evaluate physiological parameters and the function of blood mononuclear cells in immune phenotyped dairy cattle exposed to in-vivo heat challenge. A total of 24 immune phenotyped lactating dairy cattle (8 high, 8 average and 8 low) were housed in the tie-stall area of the barn and exposed to an in-vivo heat challenge for 4 hours on 2 subsequent days. Blood samples were taken both pre- and post-challenge and respiration rates and rectal temperatures were recorded. Temperature and humidity measurements were taken in correspondence with all respiration rate and rectal temperature measurements to calculate the temperature humidity index. Blood mononuclear cells were isolated from blood collected pre and post challenge and the concentration of heat shock protein 70 and cell proliferation were assessed. Results showed that average and low responders had significantly greater respiration rates compared to high responders at a temperature humidity index of 77 and above. High responders had a higher heat shock protein 70 concentration and greater cell proliferation after in-vivo heat challenge compared to average and low responders. These results paralleled those found during in-vitro heat challenge confirming that high responders may be more resilient to heat stress compared average and low responders.
Inference on autoregulation in gene expression
Authors: Yue Wang, Siqi He
Date: 2022-01-10 00:00:00

Some genes can promote or repress their own expressions, which is called autoregulation. Although gene regulation is a central topic in biology, autoregulation is much less studied. In general, it is extremely difficult to determine the existence of autoregulation with direct biochemical approaches. Nevertheless, some papers have observed that certain types of autoregulations are linked to noise levels in gene expression. We generalize these results by two propositions on discrete-state continuous-time Markov chains. These two propositions form a simple but robust method to infer the existence of autoregulation from gene expression data. This method only needs to compare the mean and variance of the gene expression level. Compared to other methods for inferring autoregulation, our method only requires non-interventional one-time data, and does not need to estimate parameters. Besides, our method has few restrictions on the model. We apply this method to four groups of experimental data and find some genes that might have autoregulation. Some inferred autoregulations have been verified by experiments or other theoretical works.
Categorical Hopfield Networks
Authors: Matilde Marcolli
Date: 2022-01-08 00:00:00

This paper discusses a simple and explicit toy-model example of the categorical Hopfield equations introduced in previous work of Manin and the author. These describe dynamical assignments of resources to networks, where resources are objects in unital symmetric monoidal categories and assignments are realized by summing functors. The special case discussed here is based on computational resources (computational models of neurons) as objects in a category of DNNs, with a simple choice of the endofunctors defining the Hopfield equations that reproduce the usual updating of the weights in DNNs by gradient descent.