pwc-archive/papers-with-abstracts huggingface dataset:
Enabling High-Precision 5G mmWave-Based Positioning for Autonomous Vehicles in Dense Urban Environments
Authors: Qamar Bader, Sharief Saleh, Mohamed Elhabiby, Aboelmagd Noureldin
Date: 2023-05-04 00:00:00
Tasks: Autonomous Vehicles

5G-based mmWave wireless positioning has emerged as a promising solution for autonomous vehicle (AV) positioning in recent years. Previous studies have highlighted the benefits of fusing a line-of-sight (LoS) 5G positioning solution with an Inertial Navigation System (INS) for an improved positioning solution. However, the highly dynamic environment of urban areas, where AVs are expected to operate, poses a challenge, as non-line-of-sight (NLoS) communication can deteriorate the 5G mmWave positioning solution and lead to erroneous corrections to the INS. To address this challenge, we exploit 5G multipath and LoS signals to improve positioning performance in dense urban environments. In addition, we integrate the proposed 5G-based positioning with low-cost onboard motion sensors (OBMS). Moreover, the integration is realized using an unscented Kalman filter (UKF) as an alternative to the widely utilized EKF as a fusion engine to avoid ignoring the higher-order and non-linear terms of the dynamic system model. We also introduce techniques to evaluate the quality of each LoS and multipath measurement prior to incorporation into the filter's correction stage. To validate the proposed methodologies, we performed two test trajectories in the dense urban environment of downtown Toronto, Canada. For each trajectory, quasi-real 5G measurements were collected using a ray-tracing tool incorporating 3D map scans of real-world buildings, allowing for realistic multipath scenarios. For the same trajectories, real OBMS data were collected from two-different low-cost IMUs. Our integrated positioning solution was capable of maintaining a level of accuracy below 30 cm for approximately 97% of the time, which is superior to the accuracy level achieved when multipath signals are not considered, which is only around 91% of the time.
Deep Learning Aided Beamforming for Downlink Non-Orthogonal Multiple Access Systems
Authors: Georgios Konstantopoulos, Yves Louet
Date: 2023-05-04 00:00:00
Tasks: Fairness

In this work, we investigate the optimal beamformer design for the downlink of Multiple-Input Single-Output (MISO) Non-Orthogonal Multiple Access (NOMA), mainly focusing on a two-user scenario. We derive novel closed-form expressions for the Bit Error Rate (BER) experienced by both users when Quadrature Amplitude Modulation (QAM) is employed. Using these expressions, we formulate a fairness-based optimal beamforming problem aiming to minimize the maximum BER encountered by the users. Due to the complexity of this problem and the time-consuming nature of Constraint Optimization (CO) algorithms for real-time telecommunication systems, we propose a deep learning (DL) approach for its solution. The proposed DL architecture possesses specific input and output characteristics that enable the simultaneous training and use of the system by multiple different antenna schemes. By conducting extensive simulations, we demonstrate that our proposed approach outperforms existing beamforming solutions and achieves BER performance close to that given by CO algorithms while significantly reducing the computational time needed. Finally, we conduct simulations to examine the robustness and efficiency of our system in different test scenarios.
A new method using deep learning to predict the response to cardiac resynchronization therapy
Authors: Kristoffer Larsena, Zhuo He, Chen Zhao, Xinwei Zhang, Quiying Sha, Claudio T Mesquitad, Diana Paeze, Ernest V. Garciaf, Jiangang Zou, Amalia Peix, Weihua Zhou
Date: 2023-05-04 00:00:00
Tasks: Sensitivity, Specificity

Background. Clinical parameters measured from gated single-photon emission computed tomography myocardial perfusion imaging (SPECT MPI) have value in predicting cardiac resynchronization therapy (CRT) patient outcomes, but still show limitations. The purpose of this study is to combine clinical variables, features from electrocardiogram (ECG), and parameters from assessment of cardiac function with polarmaps from gated SPECT MPI through deep learning (DL) to predict CRT response. Methods. 218 patients who underwent rest gated SPECT MPI were enrolled in this study. CRT response was defined as an increase in left ventricular ejection fraction (LVEF) > 5% at a 6-month follow up. A DL model was constructed by combining a pre-trained VGG16 module and a multilayer perceptron. Two modalities of data were input to the model: polarmap images from SPECT MPI and tabular data from clinical features and ECG parameters. Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to the VGG16 module to provide explainability for the polarmaps. For comparison, four machine learning (ML) models were trained using only the tabular features. Results. Modeling was performed on 218 patients who underwent CRT implantation with a response rate of 55.5% (n = 121). The DL model demonstrated average AUC (0.83), accuracy (0.73), sensitivity (0.76), and specificity (0.69) surpassing the ML models and guideline criteria. Guideline recommendations presented accuracy (0.53), sensitivity (0.75), and specificity (0.26). Conclusions. The DL model outperformed the ML models, showcasing the additional predictive benefit of utilizing SPECT MPI polarmaps. Incorporating additional patient data directly in the form of medical imagery can improve CRT response prediction.
Transmissive Reconfigurable Intelligent Surface Transmitter Empowered Cognitive RSMA Networks
Authors: Ziwei Liu, Wen Chen, Zhendong Li, Jinhong Yuan, Qingqing Wu, Kunlun Wang
Date: 2023-05-04 00:00:00

In this paper, we investigated the downlink transmission problem of a cognitive radio network (CRN) equipped with a novel transmissive reconfigurable intelligent surface (TRIS) transmitter. In order to achieve low power consumption and high-rate multi-streams communication, time-modulated arrays (TMA) is implemented and users access the network using rate splitting multiple access (RSMA). With such a network framework, a multi-objective optimization problem with joint design of the precoding matrix and the common stream rate is constructed to achieve higher energy efficiency (EE) and spectral efficiency (SE). Since the objective function is a non-convex fractional function, we proposed a joint optimization algorithm based on difference-of-convex (DC) programming and successive convex approximation (SCA). Numerical results show that under this framework the proposed algorithm can considerably improve and balance the EE and SE.
Integrated 5G mmWave Positioning in Deep Urban Environments: Advantages and Challenges
Authors: Sharief Saleh, Qamar Bader, Malek Karaim, Mohamed Elhabiby, Aboelmagd Noureldin
Date: 2023-05-03 00:00:00
Tasks: Autonomous Vehicles

Achieving the highest levels of autonomy within autonomous vehicles (AVs) requires a precise and dependable positioning solution that is not influenced by the environment. 5G mmWave signals have been extensively studied in the literature to provide such a positioning solution. Yet, it is evident that 5G alone will not be able to provide uninterrupted positioning services, as outages are inevitable to occur. Towards that end, few works have explored the benefits of integrating mmWave positioning with onboard motion sensors (OBMS) like inertial measurement units (IMUs) and odometers. Inspired by INS-GNSS integration literature, all methods defaulted to a tightly-coupled (TC) integration scheme, which hinders the potential of such an integration. Additionally, the proposed methods were validated using simulated 5G and INS data with probability-based line-of-sight (LOS) assumptions. Such an experimental setup fails to highlight the true advantages and challenges of 5G-OBMS integration. Therefore, this study first explores a loosely-coupled (LC) 5G-OBMS integration scheme as a viable alternative to TC schemes. Next, it examines the merits and challenges of such an integration in a deep-urban setting using a novel quasi-real simulation setup. The setup comprises quasi-real 5G measurements from the Siradel simulator and real commercial-grade IMU measurements from a challenging one-hour-long trajectory in downtown Toronto. The trajectory featured multiple natural 5G outages which helped with assessing the integration's performance. The proposed LC method achieved a 14-cm level of accuracy for 95% of the time, while significantly limiting positioning errors during natural 5G outages.
Forged Channel: A Breakthrough Approach for Accurate Parkinson's Disease Classification using Leave-One-Subject-Out Cross-Validation
Authors: A. Hamidi, k. Mohamed-Pour, M. Yousefi
Date: 2023-05-03 00:00:00
Tasks: EEG

This paper introduces a novel technique called "Forged Channel," which aims to comprehensively represent EEG signals in order to achieve accurate classification of Parkinson's disease. The forged channel method prepares EEG signals in a manner that allows a deep learning model to effectively perceive all EEG channels within a single input. By employing this approach alongside a convolutional neural network, an impressive accuracy of 90.32% was achieved using leave-one-subject-out cross-validation. This performance closely reflects real-world conditions, highlighting the superiority of our method compared to similar approaches.
Shap-E: Generating Conditional 3D Implicit Functions
Authors: Heewoo Jun, Alex Nichol
Date: 2023-05-03 00:00:00

We present Shap-E, a conditional generative model for 3D assets. Unlike recent work on 3D generative models which produce a single output representation, Shap-E directly generates the parameters of implicit functions that can be rendered as both textured meshes and neural radiance fields. We train Shap-E in two stages: first, we train an encoder that deterministically maps 3D assets into the parameters of an implicit function; second, we train a conditional diffusion model on outputs of the encoder. When trained on a large dataset of paired 3D and text data, our resulting models are capable of generating complex and diverse 3D assets in a matter of seconds. When compared to Point-E, an explicit generative model over point clouds, Shap-E converges faster and reaches comparable or better sample quality despite modeling a higher-dimensional, multi-representation output space. We release model weights, inference code, and samples at https://github.com/openai/shap-e.
Learning-based Relational Object Matching Across Views
Authors: Cathrin Elich, Iro Armeni, Martin R. Oswald, Marc Pollefeys, Joerg Stueckler
Date: 2023-05-03 00:00:00
Tasks: Graph Neural Network, Image Retrieval, Object, Retrieval, Scene Understanding

Intelligent robots require object-level scene understanding to reason about possible tasks and interactions with the environment. Moreover, many perception tasks such as scene reconstruction, image retrieval, or place recognition can benefit from reasoning on the level of objects. While keypoint-based matching can yield strong results for finding correspondences for images with small to medium view point changes, for large view point changes, matching semantically on the object-level becomes advantageous. In this paper, we propose a learning-based approach which combines local keypoints with novel object-level features for matching object detections between RGB images. We train our object-level matching features based on appearance and inter-frame and cross-frame spatial relations between objects in an associative graph neural network. We demonstrate our approach in a large variety of views on realistically rendered synthetic images. Our approach compares favorably to previous state-of-the-art object-level matching approaches and achieves improved performance over a pure keypoint-based approach for large view-point changes.
Optical identification using physical unclonable functions
Authors: Pantea Nadimi Goki, Stella Civelli, Emanuele Parente, Roberto Caldelli, Thomas Teferi Mulugeta, Nicola Sambo, Marco Secondini, Luca PotÌ
Date: 2023-05-03 00:00:00

In this work, the concept of optical identification (OI) based on physical unclonable functions is introduced for the first time, to our knowledge, in optical communication systems and networks. The OI assigns an optical fingerprint and the corresponding digital representation to each sub-system of the network and estimates its reliability in different measures. We highlight the large potential applications of OI as a physical layer approach for security, identification, authentication, and monitoring purposes. To identify most of the sub-systems of a network, we propose to use the Rayleigh backscattering pattern, which is an optical physical unclonable function and allows to achieve OI with a simple procedure and without additional devices. The applications of OI to fiber and path identification in a network and to the authentication of users in a quantum key distribution system are described.
SimSC: A Simple Framework for Semantic Correspondence with Temperature Learning
Authors: Xinghui Li, Kai Han, Xingchen Wan, Victor Adrian Prisacariu
Date: 2023-05-03 00:00:00
Tasks: Semantic correspondence

We propose SimSC, a remarkably simple framework, to address the problem of semantic matching only based on the feature backbone. We discover that when fine-tuning ImageNet pre-trained backbone on the semantic matching task, L2 normalization of the feature map, a standard procedure in feature matching, produces an overly smooth matching distribution and significantly hinders the fine-tuning process. By setting an appropriate temperature to the softmax, this over-smoothness can be alleviated and the quality of features can be substantially improved. We employ a learning module to predict the optimal temperature for fine-tuning feature backbones. This module is trained together with the backbone and the temperature is updated online. We evaluate our method on three public datasets and demonstrate that we can achieve accuracy on par with state-of-the-art methods under the same backbone without using a learned matching head. Our method is versatile and works on various types of backbones. We show that the accuracy of our framework can be easily improved by coupling it with more powerful backbones.
Cross-Stream Contrastive Learning for Self-Supervised Skeleton-Based Action Recognition
Authors: Ding Li, Yongqiang Tang, Zhizhong Zhang, Wensheng Zhang
Date: 2023-05-03 00:00:00
Tasks: Action Recognition, Contrastive Learning, Representation Learning, Self-supervised Skeleton-based Action Recognition, Skeleton Based Action Recognition

Self-supervised skeleton-based action recognition enjoys a rapid growth along with the development of contrastive learning. The existing methods rely on imposing invariance to augmentations of 3D skeleton within a single data stream, which merely leverages the easy positive pairs and limits the ability to explore the complicated movement patterns. In this paper, we advocate that the defect of single-stream contrast and the lack of necessary feature transformation are responsible for easy positives, and therefore propose a Cross-Stream Contrastive Learning framework for skeleton-based action Representation learning (CSCLR). Specifically, the proposed CSCLR not only utilizes intra-stream contrast pairs, but introduces inter-stream contrast pairs as hard samples to formulate a better representation learning. Besides, to further exploit the potential of positive pairs and increase the robustness of self-supervised representation learning, we propose a Positive Feature Transformation (PFT) strategy which adopts feature-level manipulation to increase the variance of positive pairs. To validate the effectiveness of our method, we conduct extensive experiments on three benchmark datasets NTU-RGB+D 60, NTU-RGB+D 120 and PKU-MMD. Experimental results show that our proposed CSCLR exceeds the state-of-the-art methods on a diverse range of evaluation protocols.
Multi-user Goal-oriented Communications with Energy-efficient Edge Resource Management
Authors: Francesco Binucci, Paolo Banelli, Paolo Di Lorenzo, Sergio Barbarossa
Date: 2023-05-03 00:00:00
Tasks: Decoder, Management

Edge Learning (EL) pushes the computational resources toward the edge of 5G/6G network to assist mobile users requesting delay-sensitive and energy-aware intelligent services. A common challenge in running inference tasks from remote is to extract and transmit only the features that are most significant for the inference task. From this perspective, EL can be effectively coupled with goal-oriented communications, whose aim is to transmit only the information {\it relevant} to perform the inference task, under prescribed accuracy, delay, and energy constraints. In this work, we consider a multi-user/single server wireless network, where the users can opportunistically decide whether to perform the inference task by themselves or, alternatively, to offload the data to the edge server for remote processing. The data to be transmitted undergoes a goal-oriented compression stage performed using a convolutional encoder, jointly trained with a convolutional decoder running at the edge-server side. Employing Lyapunov optimization, we propose a method to jointly and dynamically optimize the selection of the most suitable encoding/decoding scheme, together with the allocation of computational and transmission resources, across all the users and the edge server. Extensive simulations confirm the effectiveness of the proposed approaches and highlight the trade-offs between energy, latency, and learning accuracy.
Using Mobile Phones for Participatory Detection and Localization of a GNSS Jammer
Authors: Glädje Karl Olsson, Sara Nilsson, Erik Axell, Erik G. Larsson, Panos Papadimitratos
Date: 2023-05-03 00:00:00
Tasks: Position

It is well known that GNSS receivers are vulnerable to jamming and spoofing attacks, and numerous such incidents have been reported in the last decade all over the world. The notion of participatory sensing, or crowdsensing, is that a large ensemble of voluntary contributors provides measurements, rather than relying on a dedicated sensing infrastructure. The participatory sensing network under consideration in this work is based on GNSS receivers embedded in, for example, mobile phones. The provided measurements refer to the receiver-reported carrier-to-noise-density ratio ($C/N_0$) estimates or automatic gain control (AGC) values. In this work, we exploit $C/N_0$ measurements to locate a GNSS jammer, using multiple receivers in a crowdsourcing manner. We extend a previous jammer position estimator by only including data that is received during parts of the sensing period where jamming is detected by the sensor. In addition, we perform hardware testing for verification and evaluation of the proposed and compared state-of-the-art algorithms. Evaluations are performed using a Samsung S20+ mobile phone as participatory sensor and a Spirent GSS9000 GNSS simulator to generate GNSS and jamming signals. The proposed algorithm is shown to work well when using $C/N_0$ measurements and outperform the alternative algorithms in the evaluated scenarios, producing a median error of 50 meters when the pathloss exponent is 2. With higher pathloss exponents the error gets higher. The AGC output from the phone was too noisy and needs further processing to be useful for position estimation.
Optimizing Bus Route Selection for University of Pittsburgh Students: A Comparative Analysis of Ridership, On-Time Performance, and Travel Distance
Authors: Logan Warren, Jerek Stegman
Date: 2023-05-03 00:00:00

In this study, we present a comparative analysis of bus routes servicing the University of Pittsburgh to identify the most efficient options for students. We examine factors such as ridership, on-time performance, and travel distance to develop a comprehensive understanding of each route's performance. Our findings suggest that route 18 is the most effective for students, and we propose potential improvements to further optimize the transportation system.
Pseudo channel reciprocity in FDD satellite channels
Authors: Rei Richter, Itsik Bergel, Yair Noam, Ephi Zehavi
Date: 2023-05-02 00:00:00

Channel reciprocity can significantly reduce the overhead of obtaining channel-state information at the transmitter (CSIT). However, true reciprocity only exists in time division duplex (TDD). In this paper, we propose a novel tracking method that exploits implicit reciprocity in FDD line-of-sight (LOS) channels as in low-earth-orbit (LEO) satellite communication (SatCom). This channel reciprocity, dubbed pseudo-reciprocity, is crucial for applying multiple-user multiple-input multiple-output (MU-MIMO) to SatCom, which requires CSIT. We consider an LEO SatCom system where multiple satellites communicate with a multi-antenna land terminal (LT). In this innovative method, the LT can track the downlink channel changes and use them to estimate the uplink channels. The proposed method achieves precoding performance that is comparable to precoding with full CSIT knowledge. Furthermore, the use of pseudo reciprocity typically requires only initial CSIT feedback when the satellite rises. Over very long periods of time, pseudo reciprocity can fail as a result of phase ambiguity, which is referred to as cycle slip. We thus also present a closed-form approximation for the expected time until cycle slip, which indicates that in normal operating conditions, these cycle slips are extremely rare. Our numerical results provide strong support for the derived theory.
Time-Frequency Warped Waveforms for Well-Contained Massive Machine Type Communications
Authors: Mostafa Ibrahim, Huseyin Arslan, Hakan Ali Cirpan, Sabit Ekin
Date: 2023-05-01 00:00:00
Tasks: Vocal Bursts Type Prediction

This paper proposes a novel time-frequency warped waveform for short symbols, massive machine-type communication (mMTC), and internet of things (IoT) applications. The waveform is composed of asymmetric raised cosine (RC) pulses to increase the signal containment in time and frequency domains. The waveform has low power tails in the time domain, hence better performance in the presence of delay spread and time offsets. The time-axis warping unitary transform is applied to control the waveform occupancy in time-frequency space and to compensate for the usage of high roll-off factor pulses at the symbol edges. The paper explains a step-by-step analysis for determining the roll-off factors profile and the warping functions. Gains are presented over the conventional Zero-tail Discrete Fourier Transform-spread-Orthogonal Frequency Division Multiplexing (ZT-DFT-s-OFDM), and Cyclic prefix (CP) DFT-s-OFDM schemes in the simulations section.