pwc-archive/papers-with-abstracts huggingface dataset:
Asymptotic Value of Monitoring Structures in Stochastic Games
Authors: Daehyun Kim, Ichiro Obara
Date: 2023-08-17 00:00:00

This paper studies how improved monitoring affects the limit equilibrium payoff set for stochastic games with imperfect public monitoring. We introduce a simple generalization of Blackwell garbling called weighted garbling in order to compare different monitoring structures for this class of games. Our main result is the monotonicity of the limit perfect public equilibrium (PPE) payoff set with respect to this information order. We show that the limit PPE payoff set expands when the monitoring structure gets more informative with respect to the weighted garbling order. We also show that a similar monotonicity holds for strongly symmetric equilibrium for symmetric stochastic games. Finally, we show that our weighted garbling order is useful to compare the limit PPE payoff set for different state transition laws and monitoring structures when the limit feasible payoff set is the same.
INR-LDDMM: Fluid-based Medical Image Registration Integrating Implicit Neural Representation and Large Deformation Diffeomorphic Metric Mapping
Authors: Chulong Zhang, Xiaokun Liang
Date: 2023-08-18 00:00:00
Tasks: Image Registration, Management, Medical Image Registration

We propose a fluid-based registration framework of medical images based on implicit neural representation. By integrating implicit neural representation and Large Deformable Diffeomorphic Metric Mapping (LDDMM), we employ a Multilayer Perceptron (MLP) as a velocity generator while optimizing velocity and image similarity. Moreover, we adopt a coarse-to-fine approach to address the challenge of deformable-based registration methods dropping into local optimal solutions, thus aiding the management of significant deformations in medical image registration. Our algorithm has been validated on a paired CT-CBCT dataset of 50 patients,taking the Dice coefficient of transferred annotations as an evaluation metric. Compared to existing methods, our approach achieves the state-of-the-art performance.
Causal SAR ATR with Limited Data via Dual Invariance
Authors: Chenwei Wang, You Qin, Li Li, Siyi Luo, Yulin Huang, Jifang Pei, Yin Zhang, Jianyu Yang
Date: 2023-08-18 00:00:00

Synthetic aperture radar automatic target recognition (SAR ATR) with limited data has recently been a hot research topic to enhance weak generalization. Despite many excellent methods being proposed, a fundamental theory is lacked to explain what problem the limited SAR data causes, leading to weak generalization of ATR. In this paper, we establish a causal ATR model demonstrating that noise $N$ that could be blocked with ample SAR data, becomes a confounder with limited data for recognition. As a result, it has a detrimental causal effect damaging the efficacy of feature $X$ extracted from SAR images, leading to weak generalization of SAR ATR with limited data. The effect of $N$ on feature can be estimated and eliminated by using backdoor adjustment to pursue the direct causality between $X$ and the predicted class $Y$. However, it is difficult for SAR images to precisely estimate and eliminated the effect of $N$ on $X$. The limited SAR data scarcely powers the majority of existing optimization losses based on empirical risk minimization (ERM), thus making it difficult to effectively eliminate $N$'s effect. To tackle with difficult estimation and elimination of $N$'s effect, we propose a dual invariance comprising the inner-class invariant proxy and the noise-invariance loss. Motivated by tackling change with invariance, the inner-class invariant proxy facilitates precise estimation of $N$'s effect on $X$ by obtaining accurate invariant features for each class with the limited data. The noise-invariance loss transitions the ERM's data quantity necessity into a need for noise environment annotations, effectively eliminating $N$'s effect on $X$ by cleverly applying the previous $N$'s estimation as the noise environment annotations. Experiments on three benchmark datasets indicate that the proposed method achieves superior performance.
Unveiling Causalities in SAR ATR: A Causal Interventional Approach for Limited Data
Authors: Chenwei Wang, Xin Chen, You Qin, Siyi Luo, Yulin Huang, Jifang Pei, Jianyu Yang
Date: 2023-08-18 00:00:00
Tasks: Causal Inference, Data Augmentation

Synthetic aperture radar automatic target recognition (SAR ATR) methods fall short with limited training data. In this letter, we propose a causal interventional ATR method (CIATR) to formulate the problem of limited SAR data which helps us uncover the ever-elusive causalities among the key factors in ATR, and thus pursue the desired causal effect without changing the imaging conditions. A structural causal model (SCM) is comprised using causal inference to help understand how imaging conditions acts as a confounder introducing spurious correlation when SAR data is limited. This spurious correlation among SAR images and the predicted classes can be fundamentally tackled with the conventional backdoor adjustments. An effective implement of backdoor adjustments is proposed by firstly using data augmentation with spatial-frequency domain hybrid transformation to estimate the potential effect of varying imaging conditions on SAR images. Then, a feature discrimination approach with hybrid similarity measurement is introduced to measure and mitigate the structural and vector angle impacts of varying imaging conditions on the extracted features from SAR images. Thus, our CIATR can pursue the true causality between SAR images and the corresponding classes even with limited SAR data. Experiments and comparisons conducted on the moving and stationary target acquisition and recognition (MSTAR) and OpenSARship datasets have shown the effectiveness of our method with limited SAR data.
CMISR: Circular Medical Image Super-Resolution
Authors: Honggui Li, Nahid Md Lokman Hossain, Maria Trocan, Dimitri Galayko, Mohamad Sawan
Date: 2023-08-15 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Classical methods of medical image super-resolution (MISR) utilize open-loop architecture with implicit under-resolution (UR) unit and explicit super-resolution (SR) unit. The UR unit can always be given, assumed, or estimated, while the SR unit is elaborately designed according to various SR algorithms. The closed-loop feedback mechanism is widely employed in current MISR approaches and can efficiently improve their performance. The feedback mechanism may be divided into two categories: local feedback and global feedback. Therefore, this paper proposes a global feedback-based closed-cycle framework, circular MISR (CMISR), with unambiguous UR and advanced SR elements. Mathematical model and closed-loop equation of CMISR are built. Mathematical proof with Taylor-series approximation indicates that CMISR has zero recovery error in steady-state. In addition, CMISR holds plug-and-play characteristic that fuses model-based and learning-based approaches and can be established on any existing MISR algorithms. Five CMISR algorithms are respectively proposed based on the state-of-the-art open-loop MISR algorithms. Experimental results with three scale factors and on three open medical image datasets show that CMISR is superior to MISR in reconstruction performance and is particularly suited to medical images with strong edges or intense contrast.
Optical signal-based improvement of individual nanoparticle tracking analysis
Authors: Minh-Chau Nguyen, Pierre Bon
Date: 2023-08-18 00:00:00

Nanoparticle Tracking Analysis (NTA) provides a simple method to determine individual nanoparticle size. However, because size quantification is based on the slowly converging statistical law of random event, its intrinsic error is large, especially in case of limited event number, e.g. for weak scattering nanoparticles. Here, we introduce an NTA improvement by analyzing each individual NP trajectory while taking into account the other trajectories with a weighting coefficient. This weighting coefficient is directly derived from the optical signature of each particle measured by quantitative phase microscopy. The simulations and experimental results demonstrate the improvement of NTA accuracy, not only for mono-disperse but also for poly-disperse particle solutions.
Target Clustering Based Multi-Bernoulli Filter for Superpositional Sensors
Authors: Wang Sen
Date: 2023-08-18 00:00:00
Tasks: Clustering

The sensor whose output is a function of the sum of contributions from targets present in the surveillance area is called superpositional sensor. In this letter, target clustering based multi-Bernoulli filter for superpositional sensors is proposed.Targets are clustered according to the set of resolution cells illuminated by them. Single target posterior density is strictly derived, and densities of all the targets are combined to a approximate multi-target posterior, which makes the multiBernoulli density is conjugate with respect to the likelihood of superpositional sensors. The Gaussian implementation of the proposed algorithm is also presented, where the multidimensionality and the nonlinearity of update equation are handled by sigma point transformation. The simulation results illustrate that the proposed algorithm is effective confronted with the interaction of multiple targets and long term overlapping of two targets.
SwinJSCC: Taming Swin Transformer for Deep Joint Source-Channel Coding
Authors: Ke Yang, Sixian Wang, Jincheng Dai, Xiaoqi Qin, Kai Niu, Ping Zhang
Date: 2023-08-18 00:00:00

As one of the key techniques to realize semantic communications, end-to-end optimized neural joint source-channel coding (JSCC) has made great progress over the past few years. A general trend in many recent works pushing the model adaptability or the application diversity of neural JSCC is based on the convolutional neural network (CNN) backbone, whose model capacity is yet limited, inherently leading to inferior system coding gain against traditional coded transmission systems. In this paper, we establish a new neural JSCC backbone that can also adapt flexibly to diverse channel conditions and transmission rates within a single model, our open-source project aims to promote the research in this field. Specifically, we show that with elaborate design, neural JSCC codec built on the emerging Swin Transformer backbone achieves superior performance than conventional neural JSCC codecs built upon CNN, while also requiring lower end-to-end processing latency. Paired with two spatial modulation modules that scale latent representations based on the channel state information and target transmission rate, our baseline SwinJSCC can further upgrade to a versatile version, which increases its capability to adapt to diverse channel conditions and rate configurations. Extensive experimental results show that our SwinJSCC achieves better or comparable performance versus the state-of-the-art engineered BPG + 5G LDPC coded transmission system with much faster end-to-end coding speed, especially for high-resolution images, in which case traditional CNN-based JSCC yet falls behind due to its limited model capacity.
Intelligent Reflecting Surface Aided Multi-Tier Hybrid Computing
Authors: Yapeng Zhao, Qingqing Wu, Guangji Chen, Wen Chen, Ruiqi Liu, Ming-Min Zhao, Yuan Wu, Shaodan Ma
Date: 2023-08-18 00:00:00
Tasks: Edge-computing

The digital twin edge network (DITEN) aims to integrate mobile edge computing (MEC) and digital twin (DT) to provide real-time system configuration and flexible resource allocation for the sixth-generation network. This paper investigates an intelligent reflecting surface (IRS)-aided multi-tier hybrid computing system that can achieve mutual benefits for DT and MEC in the DITEN. For the first time, this paper presents the opportunity to realize the network-wide convergence of DT and MEC. In the considered system, specifically, over-the-air computation (AirComp) is employed to monitor the status of the DT system, while MEC is performed with the assistance of DT to provide low-latency computing services. Besides, the IRS is utilized to enhance signal transmission and mitigate interference among heterogeneous nodes. We propose a framework for designing the hybrid computing system, aiming to maximize the sum computation rate under communication and computation resources constraints. To tackle the non-convex optimization problem, alternative optimization and successive convex approximation techniques are leveraged to decouple variables and then transform the problem into a more tractable form. Simulation results verify the effectiveness of the proposed algorithm and demonstrate the IRS can significantly improve the system performance with appropriate phase shift configurations. Moreover, the results indicate that the DT assisted MEC system can precisely achieve the balance between local computing and task offloading since real-time system status can be obtained with the help of DT.
Sampling and Recovery of Signals on a Simplicial Complex using Neighbourhood Aggregation
Authors: Siddartha Reddy, Sundeep Prabhakar Chepuri
Date: 2023-08-18 00:00:00

In this work, we focus on sampling and recovery of signals over simplicial complexes. In particular, we subsample a simplicial signal of a certain order and focus on recovering multi-order bandlimited simplicial signals of one order higher and one order lower. To do so, we assume that the simplicial signal admits the Helmholtz decomposition that relates simplicial signals of these different orders. Next, we propose an aggregation sampling scheme for simplicial signals based on the Hodge Laplacian matrix and a simple least squares estimator for recovery. We also provide theoretical conditions on the number of aggregations and size of the sampling set required for faithful reconstruction as a function of the bandwidth of simplicial signals to be recovered. Numerical experiments are provided to show the effectiveness of the proposed method.
Real-time frequency measurement based on parallel pipeline FFT for time-stretched acquisition system
Authors: Ruiyuan Ming, Peng Ye, Kuojun Yang, Zhixiang Pan, Chenyang Li, Chuang Huang
Date: 2023-08-18 00:00:00

Real-time frequency measurement for non-repetitive and statistically rare signals are challenging problems in the electronic measurement area, which places high demands on the bandwidth, sampling rate, data processing and transmission capabilities of the measurement system. The time-stretching sampling system overcomes the bandwidth limitation and sampling rate limitation of electronic digitizers, allowing continuous ultra-high-speed acquisition at refresh rates of billions of frames per second. However, processing the high sampling rate signals of hundreds of GHz is an extremely challenging task, which becomes the bottleneck of the real-time analysis for non-stationary signals. In this work, a real-time frequency measurement system is designed based on a parallel pipelined FFT structure. Tens of FFT channels are pipelined to process the incoming high sampling rate signals in sequence, and a simplified parabola fitting algorithm is implemented in the FFT channel to improve the frequency precision. The frequency results of these FFT channels are reorganized and finally uploaded to an industrial personal computer for visualization and offline data mining. A real-time transmission datapath is designed to provide a high throughput rate transmission, ensuring the frequency results are uploaded without interruption. Several experiments are performed to evaluate the designed real-time frequency measurement system, the input signal has a bandwidth of 4 GHz, and the repetition rate of frames is 22 MHz. Experimental results show that the frequency of the signal can be measured at a high sampling rate of 20 GSPS, and the frequency precision is better than 1 MHz.
Toeplitz Inverse Eigenvalue Problem (ToIEP) and Random Matrix Theory (RMT) Support for the Toeplitz Covariance Matrix Estimation
Authors: Yuri Abramovich, Tanit Pongsiri
Date: 2023-08-17 00:00:00

"Toeplitzification" or "redundancy (spatial) averaging", the well-known routine for deriving the Toeplitz covariance matrix estimate from the standard sample covariance matrix, recently regained new attention due to the important Random Matrix Theory (RMT) findings. The asymptotic consistency in the spectral norm was proven for the Kolmogorov's asymptotics when the matrix dimension N and independent identically distributed (i.i.d.) sample volume T both tended to infinity (N->inf, T->inf, T/N->c > 0). These novel RMT results encouraged us to reassess the well-known drawback of the redundancy averaging methodology, which is the generation of the negative minimal eigenvalues for covariance matrices with big eigenvalues spread, typical for most covariance matrices of interest. We demonstrate that for this type of Toeplitz covariance matrices, convergence in the spectral norm does not prevent the generation of negative eigenvalues, even for the sample volume T that significantly exceeds the covariance matrix dimension (T >> N). We demonstrate that the ad-hoc attempts to remove the negative eigenvalues by the proper diagonal loading result in solutions with the very low likelihood. We demonstrate that attempts to exploit Newton's type iterative algorithms, designed to produce a Hermitian Toeplitz matrix with the given eigenvalues lead to the very poor likelihood of the very slowly converging solution to the desired eigenvalues. Finally, we demonstrate that the proposed algorithm for restoration of a positive definite (p.d.) Hermitian Toeplitz matrix with the specified Maximum Entropy spectrum, allows for the transformation of the (unstructured) Hermitian maximum likelihood (ML) sample matrix estimate in a p.d. Toeplitz matrix with sufficiently high likelihood.
Data-driven Integrated Sensing and Communication: Recent Advances, Challenges, and Future Prospects
Authors: Hammam Salem, MD Muzakkir Quamar, Adeb Mansoor, Mohammed Elrashidy, Nasir Saeed, Mudassir Masood
Date: 2023-08-17 00:00:00
Tasks: Integrated sensing and communication, ISAC, Reinforcement Learning (RL), Survey

Integrated Sensing and Communication (ISAC), combined with data-driven approaches, has emerged as a highly significant field, garnering considerable attention from academia and industry. Its potential to enable wide-scale applications in the future sixth-generation (6G) networks has led to extensive recent research efforts. Machine learning (ML) techniques, including $K$-nearest neighbors (KNN), support vector machines (SVM), deep learning (DL) architectures, and reinforcement learning (RL) algorithms, have been deployed to address various design aspects of ISAC and its diverse applications. Therefore, this paper aims to explore integrating various ML techniques into ISAC systems, covering various applications. These applications span intelligent vehicular networks, encompassing unmanned aerial vehicles (UAVs) and autonomous cars, as well as radar applications, localization and tracking, millimeter wave (mmWave) and Terahertz (THz) communication, and beamforming. The contributions of this paper lie in its comprehensive survey of ML-based works in the ISAC domain and its identification of challenges and future research directions. By synthesizing the existing knowledge and proposing new research avenues, this survey serves as a valuable resource for researchers, practitioners, and stakeholders involved in advancing the capabilities of ISAC systems in the context of 6G networks.
Development of an interface for digital neuromorphic hardware based on an FPGA
Authors: René Harmann, Lukas Sohlbach, Fernando Perez-Peña, Karsten Schmidt
Date: 2023-08-17 00:00:00

Exploring and understanding the functioning of the human brain is one of the greatest challenges for current research. Neuromorphic engineering tries to address this challenge by abstracting biological mechanisms and translating them into technology. Via the abstraction process and experiments with the resulting technical system, an attempt is made to obtain information about the biological counterpart. One subsection of Neuromorphic Engineering (NE) are Spiking Neural Networks (SNN), which describe the structures of the human brain more and more closely than Artificial Neural Networks (ANN). Together with their dedicated hardware, SNNs provide a good platform for developing new algorithms for information processing. In the context of these neuromorphic hardware platforms, this paper aims to develop an interface for a digital hardware platform (SPINN-3 Development Board) to enable the use of industrial or conventional sensors and thus create new approaches for experimental research. The basis for this endeavor is a Field Programmable Gate Array (FPGA), which is placed as a gateway between the sensors and the neuromorphic hardware. Overall, the developed system provides a robust solution for a wide variety of investigations related to neuromorphic hardware and SNNs. Furthermore, the solution also offers suitable possibilities to monitor all processes within the system in order to obtain suitable measurements, which can be examined in search of meaningful results.
FR2 5G Networks for Industrial Scenarios: Experimental Characterization and Beam Management Procedures in Operational Conditions
Authors: Alejandro Ramírez-Arroyo, Melisa López, Ignacio Rodríguez, Troels B. Sørensen, Samantha Caporal del Barrio, Pablo Padilla, Juan F. Valenzuela-Valdés, Preben Mogensen
Date: 2023-08-17 00:00:00
Tasks: Management, Navigate

Industrial environments constitute a challenge in terms of radio propagation due to the presence of machinery and the mobility of the different agents, especially at mmWave bands. This paper presents an experimental evaluation of a FR2 5G network deployed in an operational factory scenario at 26 GHz. The experimental characterization, performed with autonomous mobile robots that self-navigate the industrial lab, leads to the analysis of the received power along the factory and the evaluation of reference path gain models. The proposed assessment deeply analyzes the physical layer of the communication network under operational conditions. Thus, two different network configurations are assessed by measuring the power received in the entire factory, providing a comparison between deployments. Additionally, beam management procedures, such as beam recovery, beam sweeping or beam switching, are analyzed since they are crucial in environments where mobile agents are involved. They aim for a zero interruption approach based on reliable communications. The results analysis shows that beam recovery procedures can perform a beam switching to an alternative serving beam with power losses of less than 1.6 dB on average. Beam sweeping analysis demonstrates the prevalence of the direct component in Line-of-Sight conditions despite the strong scattering component and large-scale fading in the environment.