pwc-archive/papers-with-abstracts huggingface dataset:
Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Survey
Authors: Marcos V. Conde, Florin-Alexandru Vasluianu, Radu Timofte, Jianxing Zhang, Jia Li, Fan Wang, Xiaopeng Li, Zikun Liu, Hyunhee Park, Sejun Song, Changho Kim, Zhijuan Huang, Hongyuan Yu, Cheng Wan, Wending Xiang, Jiamin Lin, Hang Zhong, Qiaosong Zhang, Yue Sun, Xuanwu Yin, Kunlong Zuo, Senyan Xu, Siyuan Jiang, Zhijing Sun, Jiaying Zhu, Liangyan Li, Ke Chen, Yunzhe Li, Yimo Ning, Guanhua Zhao, Jun Chen, Jinyang Yu, Kele Xu, Qisheng Xu, Yong Dou
Date: 2024-04-24 00:00:00
Tasks: Image Super-Resolution, Super-Resolution, Survey

This paper reviews the NTIRE 2024 RAW Image Super-Resolution Challenge, highlighting the proposed solutions and results. New methods for RAW Super-Resolution could be essential in modern Image Signal Processing (ISP) pipelines, however, this problem is not as explored as in the RGB domain. Th goal of this challenge is to upscale RAW Bayer images by 2x, considering unknown degradations such as noise and blur. In the challenge, a total of 230 participants registered, and 45 submitted results during thee challenge period. The performance of the top-5 submissions is reviewed and provided here as a gauge for the current state-of-the-art in RAW Image Super-Resolution.
GAN-HA: A generative adversarial network with a novel heterogeneous dual-discriminator network and a new attention-based fusion strategy for infrared and visible image fusion
Authors: Guosheng Lu, Zile Fang, Jiaju Tian, Haowen Huang, Yuelong Xu, Zhuolin Han, Yaoming Kang, Can Feng, Zhigang Zhao
Date: 2024-04-24 00:00:00
Tasks: Generative Adversarial Network, Infrared And Visible Image Fusion

Infrared and visible image fusion (IVIF) aims to preserve thermal radiation information from infrared images while integrating texture details from visible images. Thermal radiation information is mainly expressed through image intensities, while texture details are typically expressed through image gradients. However, existing dual-discriminator generative adversarial networks (GANs) often rely on two structurally identical discriminators for learning, which do not fully account for the distinct learning needs of infrared and visible image information. To this end, this paper proposes a novel GAN with a heterogeneous dual-discriminator network and an attention-based fusion strategy (GAN-HA). Specifically, recognizing the intrinsic differences between infrared and visible images, we propose, for the first time, a novel heterogeneous dual-discriminator network to simultaneously capture thermal radiation information and texture details. The two discriminators in this network are structurally different, including a salient discriminator for infrared images and a detailed discriminator for visible images. They are able to learn rich image intensity information and image gradient information, respectively. In addition, a new attention-based fusion strategy is designed in the generator to appropriately emphasize the learned information from different source images, thereby improving the information representation ability of the fusion result. In this way, the fused images generated by GAN-HA can more effectively maintain both the salience of thermal targets and the sharpness of textures. Extensive experiments on various public datasets demonstrate the superiority of GAN-HA over other state-of-the-art (SOTA) algorithms while showcasing its higher potential for practical applications.
Perception and Localization of Macular Degeneration Applying Convolutional Neural Network, ResNet and Grad-CAM
Authors: Tahmim Hossain, Sagor Chandro Bakchy
Date: 2024-04-24 00:00:00

A well-known retinal disease that sends blurry visions to the affected patients is Macular Degeneration. This research is based on classifying the healthy and macular degeneration fundus by localizing the affected region of the fundus. A CNN architecture and CNN with ResNet architecture (ResNet50, ResNet50v2, ResNet101, ResNet101v2, ResNet152, ResNet152v2) as the backbone are used to classify the two types of fundus. The data are split into three categories including (a) Training set is 90% and Testing set is 10% (b) Training set is 80% and Testing set is 20%, (c) Training set is 50% and Testing set is 50%. After the training, the best model has been selected from the evaluation metrics. Among the models, CNN with a backbone of ResNet50 performs best which gives the training accuracy of 98.7% for 90% train and 10% test data split. With this model, we have performed the Grad-CAM visualization to get the region of the affected area of the fundus.
Mitigating False Predictions In Unreasonable Body Regions
Authors: Constantin Ulrich, Catherine Knobloch, Julius C. Holzschuh, Tassilo Wald, Maximilian R. Rokuss, Maximilian Zenk, Maximilian Fischer, Michael Baumgartner, Fabian Isensee, Klaus H. Maier-Hein
Date: 2024-04-24 00:00:00
Tasks: Domain Generalization, Image Segmentation, Medical Image Segmentation, Semantic Segmentation

Despite considerable strides in developing deep learning models for 3D medical image segmentation, the challenge of effectively generalizing across diverse image distributions persists. While domain generalization is acknowledged as vital for robust application in clinical settings, the challenges stemming from training with a limited Field of View (FOV) remain unaddressed. This limitation leads to false predictions when applied to body regions beyond the FOV of the training data. In response to this problem, we propose a novel loss function that penalizes predictions in implausible body regions, applicable in both single-dataset and multi-dataset training schemes. It is realized with a Body Part Regression model that generates axial slice positional scores. Through comprehensive evaluation using a test set featuring varying FOVs, our approach demonstrates remarkable improvements in generalization capabilities. It effectively mitigates false positive tumor predictions up to 85% and significantly enhances overall segmentation performance.
A Comprehensive Design Framework for UE-side and BS-Side RIS Deployments
Authors: Mahmoud Raeisi, Aymen Khaleel, Mehmet Cagri Ilter, Majid Gerami, Ertugrul Basar
Date: 2024-04-25 00:00:00

Integrating reconfigurable intelligent surfaces (RISs) in emerging communication systems is a fast-growing research field that has recently earned much attention. While implementing RISs near the base station (BS), i.e., BS-side RIS, or user equipment (UE), i.e., UE-side RIS, exhibits optimum performance, understanding the differences between these two deployments in terms of the system design perspective needs to be clarified. Critical design parameters, such as RIS size, phase shift adjustment, control link, and element type (passive/active), require greater clarity across these scenarios. Overlooking the intricacies of such critical design parameters in light of 6G demands endangers practical implementation, widening the gap between theoretical insights and practical applications. In this regard, our study investigates the impact of each RIS deployment strategy on the anticipated 6G requirements and offers tailored RIS design recommendations to fulfill these forward-looking requirements. Through this, we clarify the practical distinctions and propose a comprehensive framework for differentiating between BS-side and UE-side RIS scenarios in terms of their design parameters. Highlighting the unique needs of each and the potential challenges ahead, we aim to fuse the theoretical underpinnings of RIS with tangible implementation considerations, propelling progress in both the academic sphere and the industry.
Secret Key Generation Rates for Line of Sight Multipath Channels in the Presence of Eavesdroppers
Authors: Amitha Mayya, Arsenia Chorti, Rafael F. Schaefer, Gerhard P. Fettweis
Date: 2024-04-25 00:00:00

In this paper, the feasibility of implementing a lightweight key distribution scheme using physical layer security for secret key generation (SKG) is explored. Specifically, we focus on examining SKG with the received signal strength (RSS) serving as the primary source of shared randomness. Our investigation centers on a frequency-selective line-of-sight (LoS) multipath channel, with a particular emphasis on assessing SKG rates derived from the distributions of RSS. We derive the received signal distributions based on how the multipath components resolve at the receiver. The mutual information (MI) is evaluated based on LoS 3GPP channel models using a numerical estimator. We study how the bandwidth, delay spread, and Rician K-factor impact the estimated MI. This MI then serves as a benchmark setting bounds for the SKG rates in our exploration.
A Novel Channel Coding Scheme for Digital Multiple Access Computing
Authors: Xiaojing Yan, Saeed Razavikia, Carlo Fischione
Date: 2024-04-25 00:00:00

In this paper, we consider the ChannelComp framework, which facilitates the computation of desired functions by multiple transmitters over a common receiver using digital modulations across a multiple access channel. While ChannelComp currently offers a broad framework for computation by designing digital constellations for over-the-air computation and employing symbol-level encoding, encoding the repeated transmissions of the same symbol and using the corresponding received sequence may significantly improve the computation performance and reduce the encoding complexity. In this paper, we propose an enhancement involving the encoding of the repetitive transmission of the same symbol at each transmitter over multiple time slots and the design of constellation diagrams, with the aim of minimizing computational errors. We frame this enhancement as an optimization problem, which jointly identifies the constellation diagram and the channel code for repetition, which we call ReChCompCode. To manage the computational complexity of the optimization, we divide it into two tractable subproblems. Through numerical experiments, we evaluate the performance of ReChCompCode. The simulation results reveal that ReChCompCode can reduce the computation error by approximately up to 30 dB compared to standard ChannelComp, particularly for product functions.
Mitigating Automotive Radar Interference using Onboard Intelligent Reflective Surface
Authors: Shree Prasad Maruthi, Karrthik G. K., Vijaya Krishna A., Mahbub Hassan, Jinhong Yuan
Date: 2024-04-24 00:00:00

The use of automotive radars is gaining popularity as a means to enhance a vehicle's sensing capabilities. However, these radars can suffer from interference caused by transmissions from other radars mounted on nearby vehicles. To address this issue, we investigate the use of an onboard intelligent reflective surface (IRS) to artificially increase a vehicle's effective radar cross section (RCS), or its "electromagnetic visibility." Our proposed method utilizes the IRS's ability to form a coherent reflection of the incident radar waveform back towards the source radar, thereby improving radar performance under interference. We evaluated both passive and active IRS options. Passive IRS, which does not support reflection amplification, was found to be counter-productive and actually decreased the vehicle's effective RCS instead of enhancing it. In contrast, active IRS, which can amplify the reflection power of individual elements, effectively combats all types of automotive radar interference when the reflective elements are configured with a 15-35 dB reflection gain.
Comparative Analysis of Information Theoretic and Statistical Methods for Line Parameter Estimation
Authors: Anushka Sharma, Antos Cheeramban Varghese, Anamitra Pal
Date: 2024-04-24 00:00:00
Tasks: parameter estimation

Recent studies indicate that the noise characteristics of phasor measurement units (PMUs) can be more accurately described by non-Gaussian distributions. Consequently, estimation techniques based on Gaussian noise assumptions may produce poor results with PMU data. This paper considers the PMU based line parameter estimation (LPE) problem, and investigates the performance of four state-of-the-art techniques in solving this problem in presence of non-Gaussian measurement noise. The rigorous comparative analysis highlights the merits and demerits of each technique w.r.t. the LPE problem, and identifies conditions under which they are expected to give good results.
Accurate Direct Positioning in Distributed MIMO Using Delay-Doppler Channel Measurements
Authors: Benjamin J. B. Deutschmann, Christian Nelson, Mikael Henriksson, Gian Marti, Alva Kosasih, Nuutti Tervo, Erik Leitinger, Fredrik Tufvesson
Date: 2024-04-24 00:00:00
Tasks: Bayesian Inference

Distributed multiple-input multiple-output (D-MIMO) is a promising technology for simultaneous communication and positioning. However, phase synchronization between multiple access points in D-MIMO is challenging and methods that function without the need for phase synchronization are highly desired. Therefore, we present a method for D-MIMO that performs direct positioning of a moving device based on the delay-Doppler characteristics of the channel state information (CSI). Our method relies on particle-filter-based Bayesian inference with a state-space model. We use recent measurements from a sub-6 GHz D-MIMO OFDM system in an industrial environment to demonstrate near-centimeter accuracy under partial line-of-sight (LoS) conditions and decimeter accuracy under fully obstructed LoS.
SNR Maximization and Localization for UAV-IRS-Assisted Near-Field Systems
Authors: Hanfu Zhang, Yidan Mei, Erwu Liu, Rui Wang
Date: 2024-04-24 00:00:00

This letter introduces a novel unmanned aerial vehicle (UAV)-intelligent reflecting surface (IRS) structure into near-field localization systems to enhance the design flexibility of IRS, thereby obtaining additional performance gains. Specifically, a UAV-IRS is utilized to improve the harsh wireless environment and provide localization possibilities. To improve the localization accuracy, a joint optimization problem considering UAV position and UAV-IRS passive beamforming is formulated to maximize the receiving signal-to-noise ratio (SNR). An alternative optimization algorithm is proposed to solve the complex non-convex problem leveraging the projected gradient ascent (PGA) algorithm and the principle of minimizing the phase difference of the receiving signals. Closed-form expressions for UAV-IRS phase shift are derived to reduce the algorithm complexity. In the simulations, the proposed algorithm is compared with three different schemes and outperforms the others in both receiving SNR and localization accuracy.
Rechargeable UAV Trajectory Optimization for Real-Time Persistent Data Collection of Large-Scale Sensor Networks
Authors: Rui Wang, Deshi Li, Qingqing Wu, Kaitao Meng, Boning Feng, Lele Cong
Date: 2024-04-24 00:00:00

Unmanned aerial vehicles (UAVs) have received plenty of attention due to their high flexibility and enhanced communication ability, nonetheless, the limited onboard energy restricts UAVs' application on persistent data collection missions in large areas. In this paper, we propose a rechargeable UAV-assisted periodic data collection scheme, where a UAV is dispatched to periodically collect data from sensor nodes (SNs) in the mission area and charged by a wireless charging platform. Specifically, the periodic data collection completion time is minimized by optimizing the UAV trajectory to reach the optimal balance among the collection time, flight time, and recharging time. The formulated problem is non-convex and difficult to solve directly. To tackle this problem, we divide the main problem into two sub-problems and address them by leveraging successive convex approximation (SCA), bisection search, and heuristic methods. Then, we propose a periodic trajectory optimization algorithm to iteratively solve the two sub-problems to minimize the completion time. Furthermore, to deal with the dynamics of SNs, we propose a low-complexity trajectory adjustment strategy, where the trajectory can be maintained or adjusted locally at the SNs change, which significantly mitigates the computation cost of re-optimization. The simulation results show the superiority and robustness of the proposed scheme and the completion time is on average 39% and 33% lower than the two benchmarks, respectively.
A Reconfigurable Subarray Architecture and Hybrid Beamforming for Millimeter-Wave Dual-Function-Radar-Communication Systems
Authors: Xin Jin, Tiejun Lv, Wei Ni, Zhipeng Lin, Qiuming Zhu, Ekram Hossain, H. Vincent Poor
Date: 2024-04-24 00:00:00

Dual-function-radar-communication (DFRC) is a promising candidate technology for next-generation networks. By integrating hybrid analog-digital (HAD) beamforming into a multi-user millimeter-wave (mmWave) DFRC system, we design a new reconfigurable subarray (RS) architecture and jointly optimize the HAD beamforming to maximize the communication sum-rate and ensure a prescribed signal-to-clutter-plus-noise ratio for radar sensing. Considering the non-convexity of this problem arising from multiplicative coupling of the analog and digital beamforming, we convert the sum-rate maximization into an equivalent weighted mean-square error minimization and apply penalty dual decomposition to decouple the analog and digital beamforming. Specifically, a second-order cone program is first constructed to optimize the fully digital counterpart of the HAD beamforming. Then, the sparsity of the RS architecture is exploited to obtain a low-complexity solution for the HAD beamforming. The convergence and complexity analyses of our algorithm are carried out under the RS architecture. Simulations corroborate that, with the RS architecture, DFRC offers effective communication and sensing and improves energy efficiency by 83.4% and 114.2% with a moderate number of radio frequency chains and phase shifters, compared to the persistently- and fullyconnected architectures, respectively.
Joint Soil and Above-Ground Biomass Characterization Using Radars
Authors: Luke Jacobs, Mohamad Alipour, Adam Watts, Elahe Soltanaghai
Date: 2024-04-23 00:00:00

Soil moisture sensing through biomass or vegetation canopy has challenged researchers, even those who use SAR sensors with penetration capabilities. This is mainly due to the imposed extra time and phase offsets on Radio Frequency (RF) signals as they travel through the canopy. These offsets depend on the vegetation canopy moisture and height, both of which are typically unknown in agricultural and forest fields. In this paper, we leverage the mobility of an unmanned aerial system (UAS) to collect spatially-diverse radar measurements, enabling the joint estimation of soil moisture, above-ground biomass moisture, and biomass height, all without assuming any calibration steps. We leverage the changes in time-of-flight (ToF) and angle-of-arrival (AoA) measurements of reflected radar signals as the UAS flies above a reflector buried under the soil. We demonstrate the effectiveness of our algorithm by simulating its performance under realistic measurement noises as well as conducting lab experiments with different types of above-ground biomass. Our simulation results conclude that our algorithm is capable of estimating volumetric soil moisture to less than 1% median absolute error (MAE), vegetation height to 11.1cm MAE, and vegetation relative permittivity to 0.32 MAE. Our experimental results demonstrate the effectiveness of the proposed method in practical scenarios for varying biomass moistures and heights.
Digital Twin of Industrial Networked Control System based on Value of Information
Authors: Van-Phuc Bui, Daniel Abode, Pedro M. de Sant Ana, Karthik Muthineni, Shashi Raj Pandey, Petar Popovski
Date: 2024-04-23 00:00:00
Tasks: Graph Neural Network, Scheduling, State Estimation

The paper examines a scenario wherein sensors are deployed within an Industrial Networked Control System, aiming to construct a digital twin (DT) model for a remotely operated Autonomous Guided Vehicle (AGV). The DT model, situated on a cloud platform, estimates and predicts the system's state, subsequently formulating the optimal scheduling strategy for execution in the physical world. However, acquiring data crucial for efficient state estimation and control computation poses a significant challenge, primarily due to constraints such as limited network resources, partial observation, and the necessity to maintain a certain confidence level for DT estimation. We propose an algorithm based on Value of Information (VoI), seamlessly integrated with the Extended Kalman Filter to deliver a polynomial-time solution, selecting the most informative subset of sensing agents for data. Additionally, we put forth an alternative solution leveraging a Graph Neural Network to precisely ascertain the AGV's position with a remarkable accuracy of up to 5 cm. Our experimental validation in an industrial robotic laboratory environment yields promising results, underscoring the potential of high-accuracy DT models in practice.
Multi-Objective Deep Reinforcement Learning for 5G Base Station Placement to Support Localisation for Future Sustainable Traffic
Authors: Ahmed Al-Tahmeesschi, Jukka Talvitie, Miguel López-Benítez, Hamed Ahmadi, Laura Ruotsalainen
Date: 2024-04-23 00:00:00
Tasks: Deep Reinforcement Learning

Millimeter-wave (mmWave) is a key enabler for next-generation transportation systems. However, in an urban city scenario, mmWave is highly susceptible to blockages and shadowing. Therefore, base station (BS) placement is a crucial task in the infrastructure design where coverage requirements need to be met while simultaneously supporting localisation. This work assumes a pre-deployed BS and another BS is required to be added to support both localisation accuracy and coverage rate in an urban city scenario. To solve this complex multi-objective optimisation problem, we utilise deep reinforcement learning (DRL). Concretely, this work proposes: 1) a three-layered grid for state representation as the input of the DRL, which enables it to adapt to the changes in the wireless environment represented by changing the position of the pre-deployed BS, and 2) the design of a suitable reward function for the DRL agent to solve the multi-objective problem. Numerical analysis shows that the proposed deep Q-network (DQN) model can learn/adapt from the complex radio environment represented by the terrain map and provides the same/similar solution to the exhaustive search, which is used as a benchmark. In addition, we show that an exclusive optimisation of coverage rate does not result in improved localisation accuracy, and thus there is a trade-off between the two solutions.
Device-Free 3D Drone Localization in RIS-Assisted mmWave MIMO Networks
Authors: Jiguang He, Charles Vanwynsberghe, Hui Chen, Chongwen Huang, Aymen Fakhreddine
Date: 2024-04-23 00:00:00

In this paper, we investigate the potential of reconfigurable intelligent surfaces (RISs) in facilitating passive/device-free three-dimensional (3D) drone localization within existing cellular infrastructure operating at millimeter-wave (mmWave) frequencies and employing multiple antennas at the transceivers. The developed localization system operates in the bi-static mode without requiring direct communication between the drone and the base station. We analyze the theoretical performance limits via Fisher information analysis and Cram\'er Rao lower bounds (CRLBs). Furthermore, we develop a low-complexity yet effective drone localization algorithm based on coordinate gradient descent and examine the impact of factors such as radar cross section (RCS) of the drone and training overhead on system performance. It is demonstrated that integrating RIS yields significant benefits over its RIS-free counterpart, as evidenced by both theoretical analyses and numerical simulations.