pwc-archive/papers-with-abstracts huggingface dataset:
WiForce: Wireless Sensing and Localization of Contact Forces on a Space Continuum
Authors: Agrim Gupta, Cedric Girerd, Manideep Dunna, Qiming Zhang, Raghav Subbaraman, Tania Morimoto, Dinesh Bharadia
Date: 2020-12-31 00:00:00
Tasks: TAG

Contact force is a natural way for humans to interact with the physical world around us. However, most of our interactions with the digital world are largely based on a simple binary sense of touch (contact or no contact). Similarly, when interacting with robots to perform complex tasks, such as surgery, richer force information that includes both magnitude and contact location is important for task performance. To address these challenges, we present the design and fabrication of WiForce which is a 'wireless' sensor, sentient to contact force magnitude and location. WiForce achieves this by transducing force magnitude and location, to phase changes of an incident RF signal of a backscattering tag. The phase changes are thus modulated into the backscattered RF signal, which enables measurement of force magnitude and contact location by inferring the phases of the reflected RF signal. WiForce's sensor is designed to support wide-band frequencies all the way up to 3 GHz. We evaluate the force sensing wirelessly in different environments, including through phantom tissue, and achieve force accuracy of 0.3 N and contact location accuracy of 0.6 mm.
Compressible Latent-Space Invertible Networks for Generative Model-Constrained Image Reconstruction
Authors: Varun A. Kelkar, Sayantan Bhadra, Mark A. Anastasio
Date: 2020-07-05 00:00:00
Tasks: Image Reconstruction

There remains an important need for the development of image reconstruction methods that can produce diagnostically useful images from undersampled measurements. In magnetic resonance imaging (MRI), for example, such methods can facilitate reductions in data-acquisition times. Deep learning-based methods hold potential for learning object priors or constraints that can serve to mitigate the effects of data-incompleteness on image reconstruction. One line of emerging research involves formulating an optimization-based reconstruction method in the latent space of a generative deep neural network. However, when generative adversarial networks (GANs) are employed, such methods can result in image reconstruction errors if the sought-after solution does not reside within the range of the GAN. To circumvent this problem, in this work, a framework for reconstructing images from incomplete measurements is proposed that is formulated in the latent space of invertible neural network-based generative models. A novel regularization strategy is introduced that takes advantage of the multiscale architecture of certain invertible neural networks, which can result in improved reconstruction performance over classical methods in terms of traditional metrics. The proposed method is investigated for reconstructing images from undersampled MRI data. The method is shown to achieve comparable performance to a state-of-the-art generative model-based reconstruction method while benefiting from a deterministic reconstruction procedure and easier control over regularization parameters.
Millimeter-wave Multimode Circular Array for Spatially Encoded Beamforming in a Wide Coverage Area
Authors: Stylianos D. Assimonis, M. Ali Babar Abbasi, Vincent F. Fusco
Date: 2020-12-29 00:00:00

This paper summarizes an investigation around millimeter-wave (mmWave) multimode circular antenna array based beamformer capable of specially encoded data transmission in a wide coverage area. The circular antenna array is capable of an entire 360 deg. azimuth sector coverage where broadcast, uni-cast and multi-cast radio transmissions are possible. Orbital angular momentum (OAM) mode transmission along the elevation axis, i.e., perpendicular to the azimuth plane, can also be achieved. These two types of spatially encoded beamforming makes coverage in almost an entire hemisphere possible. The circular array is developed using twelve multilayer patch antennas with unique radiation performance aiding in both azimuth and elevation radiation. The proposed array architecture and its radiation performance makes it a good candidate for spatially encoded beamforming for mmWave 5G.
Resource Allocation for NOMA-based LPWA Networks Powered by Energy Harvesting
Authors: Fatma Benkhelifa, Julie A. McCann
Date: 2020-12-29 00:00:00

In this paper, we explore perpetual, scalable, Low-powered Wide-area networks (LPWA). Specifically we focus on the uplink transmissions of non-orthogonal multiple access (NOMA)-based LPWA networks consisting of multiple self-powered nodes and a NOMA-based single gateway. The self-powered LPWA nodes use the "harvest-then-transmit" protocol where they harvest energy from ambient sources (solar and radio frequency signals), then transmit their signals. The main features of the studied LPWA network are different transmission times-on-air, multiple uplink transmission attempts, and duty cycle restrictions. The aim of this work is to maximize the time-averaged sum of the uplink transmission rates by optimizing the transmission time-on-air allocation, the energy harvesting time allocation and the power allocation; subject to a maximum transmit power and to the availability of the harvested energy. We propose a low complex solution which decouples the optimization problem into three sub-problems: we assign the LPWA node transmission times (using either the fair or unfair approaches), we optimize the energy harvesting (EH) times using a one-dimensional search method, and optimize the transmit powers using a concave-convex (CCCP) procedure. In the simulation results, we focus on Long Range (LoRa) networks as a practical example LPWA network. We validate our proposed solution and we observe a $15\%$ performance improvement when using NOMA.
Quality-driven Variable Frame-Rate for Green Video Coding in Broadcast Applications
Authors: Glenn Herrou, Charles Bonnineau, Wassim Hamidouche, Patrick Dumenil, Jerome Fournier, Luce Morin
Date: 2020-12-29 00:00:00

The Digital Video Broadcasting (DVB) has proposed to introduce the Ultra-High Definition services in three phases: UHD-1 phase 1, UHD-1 phase 2 and UHD-2. The UHD-1 phase 2 specification includes several new features such as High Dynamic Range (HDR) and High Frame-Rate (HFR). It has been shown in several studies that HFR (+100 fps) enhances the perceptual quality and that this quality enhancement is content-dependent. On the other hand, HFR brings several challenges to the transmission chain including codec complexity increase and bit-rate overhead, which may delay or even prevent its deployment in the broadcast echo-system. In this paper, we propose a Variable Frame Rate (VFR) solution to determine the minimum (critical) frame-rate that preserves the perceived video quality of HFR video. The frame-rate determination is modeled as a 3-class classification problem which consists in dynamically and locally selecting one frame-rate among three: 30, 60 and 120 frames per second. Two random forests classifiers are trained with a ground truth carefully built by experts for this purpose. The subjective results conducted on ten HFR video contents, not included in the training set, clearly show the efficiency of the proposed solution enabling to locally determine the lowest possible frame-rate while preserving the quality of the HFR content. Moreover, our VFR solution enables significant bit-rate savings and complexity reductions at both encoder and decoder sides.
Universal Silicon Microwave Photonic Spectral Shaper
Authors: Xin Guo, Yang Liu, Tangman Yin, Blair Morrison, Mattia Pagani, Okky Daulay, Wim Bogaerts, Benjamin J. Eggleton, Alvaro Casas-Bedoya, David Marpaung
Date: 2020-12-29 00:00:00
Tasks: Blocking

Optical modulation plays arguably the utmost important role in microwave photonic (MWP) systems. Precise synthesis of modulated optical spectra dictates virtually all aspects of MWP system quality including loss, noise figure, linearity, and the types of functionality that can be executed. But for such a critical function, the versatility to generate and transform analog optical modulation is severely lacking, blocking the pathways to truly unique MWP functions including ultra-linear links and low-loss high rejection filters. Here we demonstrate versatile RF photonic spectrum synthesis in an all-integrated silicon photonic circuit, enabling electrically-tailorable universal analog modulation transformation. We show a series of unprecedented RF filtering experiments through monolithic integration of the spectrum-synthesis circuit with a network of reconfigurable ring resonators.
On the Performance of One-Bit DoA Estimation via Sparse Linear Arrays
Authors: Saeid Sedighi, M. R. Bhavani Shankar, Mojtaba Soltanalian, Björn Ottersten
Date: 2020-12-28 00:00:00

Direction of Arrival (DoA) estimation using Sparse Linear Arrays (SLAs) has recently gained considerable attention in array processing thanks to their capability to provide enhanced degrees of freedom in resolving uncorrelated source signals. Additionally, deployment of one-bit Analog-to-Digital Converters (ADCs) has emerged as an important topic in array processing, as it offers both a low-cost and a low-complexity implementation. In this paper, we study the problem of DoA estimation from one-bit measurements received by an SLA. Specifically, we first investigate the identifiability conditions for the DoA estimation problem from one-bit SLA data and establish an equivalency with the case when DoAs are estimated from infinite-bit unquantized measurements. Towards determining the performance limits of DoA estimation from one-bit quantized data, we derive a pessimistic approximation of the corresponding Cram\'{e}r-Rao Bound (CRB). This pessimistic CRB is then used as a benchmark for assessing the performance of one-bit DoA estimators. We also propose a new algorithm for estimating DoAs from one-bit quantized data. We investigate the analytical performance of the proposed method through deriving a closed-form expression for the covariance matrix of the asymptotic distribution of the DoA estimation errors and show that it outperforms the existing algorithms in the literature. Numerical simulations are provided to validate the analytical derivations and corroborate the resulting performance improvement.
Intelligent Reflecting Surface Aided MISO Uplink Communication Network: Feasibility and Power Minimization for Perfect and Imperfect CSI
Authors: Yang Liu, Jun Zhao, Ming Li, Qingqing Wu
Date: 2020-07-03 00:00:00

In this paper, we consider the weighted sum-power minimization under quality-of-service (QoS) constraints in the multi-user multi-input-single-output (MISO) uplink wireless network assisted by intelligent reflecting surface (IRS). We perform a comprehensive investigation on various aspects of this problem. First, when users have sufficient transmit powers, we present a new sufficient condition guaranteeing arbitrary information rate constraints. This result strengthens the feasibility condition in existing literature. Then, we design novel penalty dual decomposition (PDD) based and nonlinear equality constrained alternative direction method of multipliers (neADMM) based solutions to tackle the IRS-dependent-QoS-constraints, which effectively solve the feasibility check and power minimization problems. Besides, we further extend our proposals to the cases where channel status information (CSI) is imperfect and develop an online stochastic algorithm that satisfy QoS constraints stochastically without requiring prior knowledge of CSI errors. Extensive numerical results are presented to verify the effectiveness of our proposed algorithms.
A Downlink Puncturing Scheme for Simultaneous Transmission of URLLC and eMBB Traffic by Exploiting Data Similarity
Authors: Mohammed AL-Mekhlafi, Mohaned Chraiti, Mohamed Amine Arfaoui, Chadi Assi, Ali Ghrayeb, Amira Alloum
Date: 2020-12-27 00:00:00

Ultra Reliable and Low Latency Communications (URLLC) is deemed to be an essential service in 5G systems and beyond to accommodate a wide range of emerging applications with stringent latency and reliability requirements. Coexistence of URLLC alongside other service categories calls for developing spectrally efficient multiplexing techniques. Specifically, coupling URLLC and conventional enhanced Mobile BroadBand (eMBB) through superposition/puncturing naturally arises as a promising option due to the tolerance of the latter in terms of latency and reliability. The idea here is to transmit URLLC packets over resources occupied by ongoing eMBB transmissions while minimizing the impact on the eMBB transmissions. In this paper, we propose a novel downlink URLLC-eMBB multiplexing technique that exploits possible similarities among URLLC and eMBB symbols, with the objective of reducing the size of the punctured eMBB symbols. We propose that the base station scans the eMBB traffic' symbol sequences and punctures those that have the highest symbol similarity with that of the URLLC users to be served. As the eMBB and URLLC may use different constellation sizes, we introduce the concept of symbol region similarity to accommodate the different constellations. We assess the performance of the proposed scheme analytically, where we derive closed-form expressions for the symbol error rate (SER) of the eMBB and URLLC services. {We also derive an expression for the eMBB loss function due to puncturing in terms of the eMBB SER}. We demonstrate through numerical and simulation results the efficacy of the proposed scheme where we show that 1) the eMBB spectral efficiency is improved by puncturing fewer symbols, 2) the SER and reliability performance of eMBB are improved, and 3) the URLLC data is accommodated within the specified delay constraint while maintaining its reliability.
Machine Learning Enabled Preamble Collision Resolution in Distributed Massive MIMO
Authors: Jie Ding, Daiming Qu, Pei Liu, Jinho Choi
Date: 2020-06-08 00:00:00
Tasks: BIG-bench Machine Learning, Clustering

Preamble collision is a bottleneck that impairs the performance of random access (RA) user equipment (UE) in grant-free RA (GFRA). In this paper, by leveraging distributed massive multiple input multiple output (mMIMO) together with machine learning, a novel machine learning based framework solution is proposed to address the preamble collision problem in GFRA. The key idea is to identify and employ the neighboring access points (APs) of a collided RA UE for its data decoding rather than all the APs, so that the mutual interference among collided RA UEs can be effectively mitigated. To this end, we first design a tailored deep neural network (DNN) to enable the preamble multiplicity estimation in GFRA, where an energy detection (ED) method is also proposed for performance comparison. With the estimated preamble multiplicity, we then propose a K-means AP clustering algorithm to cluster the neighboring APs of collided RA UEs and organize each AP cluster to decode the received data individually. Simulation results show that a decent performance of preamble multiplicity estimation in terms of accuracy and reliability can be achieved by the proposed DNN, and confirm that the proposed schemes are effective in preamble collision resolution in GFRA, which are able to achieve a near-optimal performance in terms of uplink achievable rate per collided RA UE, and offer significant performance improvement over traditional schemes.
Target Detection within Nonhomogeneous Clutter via Total Bregman Divergence-Based Matrix Information Geometry Detectors
Authors: Xiaoqiang Hua, Yusuke Ono, Linyu Peng, Yongqiang Cheng, Hongqiang Wang
Date: 2020-12-27 00:00:00

Information divergences are commonly used to measure the dissimilarity of two elements on a statistical manifold. Differentiable manifolds endowed with different divergences may possess different geometric properties, which can result in totally different performances in many practical applications. In this paper, we propose a total Bregman divergence-based matrix information geometry (TBD-MIG) detector and apply it to detect targets emerged into nonhomogeneous clutter. In particular, each sample data is assumed to be modeled as a Hermitian positive-definite (HPD) matrix and the clutter covariance matrix is estimated by the TBD mean of a set of secondary HPD matrices. We then reformulate the problem of signal detection as discriminating two points on the HPD matrix manifold. Three TBD-MIG detectors, referred to as the total square loss, the total log-determinant and the total von Neumann MIG detectors, are proposed, and they can achieve great performances due to their power of discrimination and robustness to interferences. Simulations show the advantage of the proposed TBD-MIG detectors in comparison with the geometric detector using an affine invariant Riemannian metric as well as the adaptive matched filter in nonhomogeneous clutter.
Illegal Border Cross Detection and Warning System Using IR Sensor and Node MCU
Authors: Afsana Tasnim, Shawan Shurid, AKM Bahalul Haque
Date: 2020-12-24 00:00:00

We often find illegal immigrants moving from one country to another. By means of land these illegal immigrants move over the fence cut the border wires and moves to the other part of the land. What do you think our soldiers are not doing their job? It is not that actually. It is very difficult to watch consistently over the border. Soldiers have a limited vision to check around the whole land mass. So for them we have come up with a solution that we have built a device that will sense the presence of an intruder (illegal immigrant). This device will be installed over the fences. When an intruder passes over the fence this device will transmit the signal to the soldiers Smartphone app (BLYNK app). The soldier will be notified with the signal and after receiving the signal, the soldier can switch on the alarm and the emergency lights via the app. By this the soldiers in the camp will be alerted and can take their respective positions and arrest the intruder which was passing the border illegally. By this device we can alert the soldiers in the border to take more safety precautions to keep our country safe. Index Terms-Node MCU ESP32, infrared sensor, Blynk app.
FPGA prototyping of synchronized chaotic map for UAV secure communication
Authors: Christian Nwachioma, Martins Ezuma, Olusiji . O. Medaiyese
Date: 2020-12-24 00:00:00

We propose a design that uses the principle of chaos for UAV secure communication. A UAV identified as an aerial base station communicates with a ground base station over an RF channel. The communication units have dynamics based on the logistic map. The map is chaotic in the appropriate parameter space. Its states are non-periodic, broadband, and noise-like in the frequency domain. They are useful for spreading information during transmission, making it difficult for an eavesdropper to recover the modulated message since state prediction is ultimately impossible. To retrieve it, we propose a variable feedback controller. It asymptotically stabilizes the error dynamics when the information source is off. During transmission, the controller synchronizes the units such that the error contains signatures of the information signal. Therefore, the information signal is retrievable by a suitable detection mechanism. Security depends on the confidentiality of the map, the variable feedback controller, including its scale factor and bounded feedback gain, and the designer`s choice of invertible function for use in the scrambling and descrambling process. Also, the method is less prone to jamming attacks and multipath effects as the broadband spectrum can be used to randomly select RF channels. It uses only a few simple algorithms, including a correlation summation and a detection mechanism. The algorithms collect subsamples of the received signal sequences and averages over each subsample length. The method requires minimal programming efforts and low hardware resource utilization. It is energy-efficient, which is a vital consideration for any UAV security model. Moreover, we realize a prototype of the communication system on field-programmable gate arrays. We presented a digital design of the secure communication system involving the transmission of bitstreams between the ABS and GBS.
Duality between Coronavirus Transmission and Air-based Macroscopic Molecular Communication
Authors: Max Schurwanz, Peter Adam Hoeher, Sunasheer Bhattacharjee, Martin Damrath, Lukas Stratmann, Falko Dressler
Date: 2020-09-10 00:00:00

This contribution exploits the duality between a viral infection process and macroscopic air-based molecular communication. Airborne aerosol and droplet transmission through human respiratory processes is modeled as an instance of a multiuser molecular communication scenario employing respiratory-event-driven molecular variable-concentration shift keying. Modeling is aided by experiments that are motivated by a macroscopic air-based molecular communication testbed. In artificially induced coughs, a saturated aqueous solution containing a fluorescent dye mixed with saliva is released by an adult test person. The emitted particles are made visible by means of optical detection exploiting the fluorescent dye. The number of particles recorded is significantly higher in test series without mouth and nose protection than in those with a wellfitting medical mask. A simulation tool for macroscopic molecular communication processes is extended and used for estimating the transmission of infectious aerosols in different environments. Towards this goal, parameters obtained through self experiments are taken. The work is inspired by the recent outbreak of the coronavirus pandemic.
Parallelized Instantaneous Velocity and Heading Estimation of Objects using Single Imaging Radar
Authors: Nihal Singh, Dibakar Sil, Ankit Sharma
Date: 2020-12-23 00:00:00
Tasks: Autonomous Driving

The development of high-resolution imaging radars introduce a plethora of useful applications, particularly in the automotive sector. With increasing attention on active transport safety and autonomous driving, these imaging radars are set to form the core of an autonomous engine. One of the most important tasks of such high-resolution radars is to estimate the instantaneous velocities and heading angles of the detected objects (vehicles, pedestrians, etc.). Feasible estimation methods should be fast enough in real-time scenarios, bias-free and robust against micro-Dopplers, noise and other systemic variations. This work proposes a parallel-computing scheme that achieves a real-time and accurate implementation of vector velocity determination using frequency modulated continuous wave (FMCW) radars. The proposed scheme is tested against traffic data collected using an FMCW radar at a center frequency of 78.6 GHz and a bandwidth of 4 GHz. Experiments show that the parallel algorithm presented performs much faster than its conventional counterparts without any loss in precision.
Single Stage PFC Flyback AC-DC Converter Design
Authors: Kali Naraharisetti, Janamejaya Channegowda
Date: 2020-12-23 00:00:00

This paper discusses a 100 W single stage Power Factor Correction (PFC) flyback converter operating in boundary mode constant ON time methodology using a synchronous MOS-FET rectifier on the secondary side to achieve higher efficiency. Unlike conventional designs which use two stage approach such as PFC plus a LLC resonant stage or a two stage PFC plus flyback, the proposed design integrates the PFC and constant voltage regulation in a single stage without compromising the efficiency of the converter. The proposed design is advantageous as it has a lower component count. A design of 100 W flyback operating from universal input AC line voltage is demonstrated in this paper. The experimental results show that the power factor (PF) is greater than 0.92 and total harmonic distortion (iTHD) is less than 20% for a load varying from 25 % to 100 %. The experimental results show the advantages of a single stage design.