pwc-archive/papers-with-abstracts huggingface dataset:
Sparse2Inverse: Self-supervised inversion of sparse-view CT data
Authors: Nadja Gruber, Johannes Schwab, Elke Gizewski, Markus Haltmeier
Date: 2024-02-26 00:00:00
Tasks: Computed Tomography (CT), Image Reconstruction

Sparse-view computed tomography (CT) enables fast and low-dose CT imaging, an essential feature for patient-save medical imaging and rapid non-destructive testing. In sparse-view CT, only a few projection views are acquired, causing standard reconstructions to suffer from severe artifacts and noise. To address these issues, we propose a self-supervised image reconstruction strategy. Specifically, in contrast to the established Noise2Inverse, our proposed training strategy uses a loss function in the projection domain, thereby bypassing the otherwise prescribed nullspace component. We demonstrate the effectiveness of the proposed method in reducing stripe-artifacts and noise, even from highly sparse data.
Adaptive Online Learning of Separable Path Graph Transforms for Intra-prediction
Authors: Wen-Yang Lu, Eduardo Pavez, Antonio Ortega, Xin Zhao, Shan Liu
Date: 2024-02-26 00:00:00

Current video coding standards, including H.264/AVC, HEVC, and VVC, employ discrete cosine transform (DCT), discrete sine transform (DST), and secondary to Karhunen-Loeve transforms (KLTs) decorrelate the intra-prediction residuals. However, the efficiency of these transforms in decorrelation can be limited when the signal has a non-smooth and non-periodic structure, such as those occurring in textures with intricate patterns. This paper introduces a novel adaptive separable path graph-based transform (GBT) that can provide better decorrelation than the DCT for intra-predicted texture data. The proposed GBT is learned in an online scenario with sequential K-means clustering, which groups similar blocks during encoding and decoding to adaptively learn the GBT for the current block from previously reconstructed areas with similar characteristics. A signaling overhead is added to the bitstream of each coding block to indicate the usage of the proposed graph-based transform. We assess the performance of this method combined with H.264/AVC intra-coding tools and demonstrate that it can significantly outperform H.264/AVC DCT for intra-predicted texture data.
Photon-counting CT using a Conditional Diffusion Model for Super-resolution and Texture-preservation
Authors: Christopher Wiedeman, Chuang Niu, Mengzhou Li, Bruno De Man, Jonathan S Maltz, Ge Wang
Date: 2024-02-25 00:00:00
Tasks: Denoising, Diagnostic, Disentanglement, Super-Resolution

Ultra-high resolution images are desirable in photon counting CT (PCCT), but resolution is physically limited by interactions such as charge sharing. Deep learning is a possible method for super-resolution (SR), but sourcing paired training data that adequately models the target task is difficult. Additionally, SR algorithms can distort noise texture, which is an important in many clinical diagnostic scenarios. Here, we train conditional denoising diffusion probabilistic models (DDPMs) for PCCT super-resolution, with the objective to retain textural characteristics of local noise. PCCT simulation methods are used to synthesize realistic resolution degradation. To preserve noise texture, we explore decoupling the noise and signal image inputs and outputs via deep denoisers, explicitly mapping to each during the SR process. Our experimental results indicate that our DDPM trained on simulated data can improve sharpness in real PCCT images. Additionally, the disentanglement of noise from the original image allows our model more faithfully preserve noise texture.
Technical Note: An Efficient Implementation of the Spherical Radon Transform with Cylindrical Apertures
Authors: Luke Lozenski, Refik Mert Cam, Mark A. Anastasio, Umberto Villa
Date: 2024-02-23 00:00:00
Tasks: Image Reconstruction

The spherical Radon transform (SRT) is an integral transform that maps a function to its integrals over concentric spherical shells centered at specified sensor locations. It has several imaging applications, including synthetic aperture radar and photoacoustic computed tomography. However, computation of the SRT can be expensive. Efficient implementation of SRT on general purpose graphic processing units (GPGPUs) often utilizes non-matched implementation of the adjoint operator, leading to inconsistent gradients in optimization-based image reconstruction methods. This work details an efficient implementation of the SRT and its adjoint for the case of a cylindrical measurement aperture. Exploiting symmetry of the cylindrical geometry, the SRT can then be expressed as the composition of two circular Radon transforms (CRT). Utilizing this formulation then allows for an efficient implementation of the SRT as a discrete-to-discrete operator utilizing sparse matrix representation.
Topological Analysis of Mouse Brain Vasculature via 3D Light-sheet Microscopy Images
Authors: Jiachen Yao, Nina Hagemann, Qiaojie Xiong, Jianxu Chen, Dirk M. Hermann, Chao Chen
Date: 2024-02-23 00:00:00
Tasks: Topological Data Analysis

Vascular networks play a crucial role in understanding brain functionalities. Brain integrity and function, neuronal activity and plasticity, which are crucial for learning, are actively modulated by their local environments, specifically vascular networks. With recent developments in high-resolution 3D light-sheet microscopy imaging together with tissue processing techniques, it becomes feasible to obtain and examine large-scale brain vasculature in mice. To establish a structural foundation for functional study, however, we need advanced image analysis and structural modeling methods. Existing works use geometric features such as thickness, tortuosity, etc. However, geometric features cannot fully capture structural characteristics such as the richness of branches, connectivity, etc. In this paper, we study the morphology of brain vasculature through a topological lens. We extract topological features based on the theory of topological data analysis. Comparing of these robust and multi-scale topological structural features across different brain anatomical structures and between normal and obese populations sheds light on their promising future in studying neurological diseases.
Radar Resource Management for Active Tracking Using Split-Aperture Phased Arrays
Authors: Pepijn B. Cox, Wim L. van Rossum
Date: 2024-02-27 00:00:00
Tasks: Management

Flexible front-end technology will become available in future multifunction radar systems to improve adaptability to the operational theatre. A potential concept to utilize this flexibility is to subdivide radar tasks spatially over the array, the so-called split-aperture phased array (SAPA) concept. As radars are generally designed for their worst-case scenario, e.g., small targets at a large range, the power-aperture budget can be excessive for targets that do not fall within that class. To increase efficiency of the time budget of the radar front-end, the SAPA concept could be applied. In this paper, the SAPA concept is explored to assign radar resources for active tracking tasks of many targets. To do so, we formulate and solve the radar resource management problem for the SAPA concept by employing the quality of service based resource allocation model (Q-RAM) framework. It will be demonstrated by a simulation example that a radar can maintain a larger numbers of active tracking tasks when using the SAPA concept compared to the case that only the full array can be used per task.
AI-enabled STAR-RIS aided MISO ISAC Secure Communications
Authors: Zhengyu Zhu, Mengfei Gong, Gangcan Sun, Peijia Liu, De Mi
Date: 2024-02-26 00:00:00
Tasks: Integrated sensing and communication, ISAC

A simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided integrated sensing and communication (ISAC) dual-secure communication system is studied in this paper. The sensed target and legitimate users (LUs) are situated on the opposite sides of the STAR-RIS, and the energy splitting and time switching protocols are applied in the STAR-RIS, respectively. The long-term average security rate for LUs is maximized by the joint design of the base station (BS) transmit beamforming and receive filter, along with the STAR-RIS transmitting and reflecting coefficients, under guarantying the echo signal-to-noise ratio thresholds and rate constraints for the LUs. Since the channel information changes over time, conventional convex optimization techniques cannot provide the optimal performance for the system, and result in excessively high computational complexity in the exploration of the long-term gains for the system. Taking continuity control decisions into account, the deep deterministic policy gradient and soft actor-critic algorithms based on off-policy are applied to address the complex non-convex problem. Simulation results comprehensively evaluate the performance of the proposed two reinforcement learning algorithms and demonstrate that STAR-RIS is remarkably better than the two benchmarks in the ISAC system.
Conformal Shield: A Novel Adversarial Attack Detection Framework for Automatic Modulation Classification
Authors: Tailai Wen, Da Ke, Xiang Wang, Zhitao Huang
Date: 2024-02-27 00:00:00
Tasks: Adversarial Attack, Adversarial Attack Detection, Classification, Deep Learning, Learning Theory

Deep learning algorithms have become an essential component in the field of cognitive radio, especially playing a pivotal role in automatic modulation classification. However, Deep learning also present risks and vulnerabilities. Despite their outstanding classification performance, they exhibit fragility when confronted with meticulously crafted adversarial examples, posing potential risks to the reliability of modulation recognition results. Addressing this issue, this letter pioneers the development of an intelligent modulation classification framework based on conformal theory, named the Conformal Shield, aimed at detecting the presence of adversarial examples in unknown signals and assessing the reliability of recognition results. Utilizing conformal mapping from statistical learning theory, introduces a custom-designed Inconsistency Soft-solution Set, enabling multiple validity assessments of the recognition outcomes. Experimental results demonstrate that the Conformal Shield maintains robust detection performance against a variety of typical adversarial sample attacks in the received signals under different perturbation-to-signal power ratio conditions.
Utilizing U-Net Architectures with Auxiliary Information for Scatter Correction in CBCT Across Different Field-of-View Settings
Authors: Harshit Agrawal, Ari Hietanen, Simo Särkkä
Date: 2024-02-27 00:00:00

Cone-beam computed tomography (CBCT) has become a vital imaging technique in various medical fields but scatter artifacts are a major limitation in CBCT scanning. This challenge is exacerbated by the use of large flat panel 2D detectors. The scatter-to-primary ratio increases significantly with the increase in the size of FOV being scanned. Several deep learning methods, particularly U-Net architectures, have shown promising capabilities in estimating the scatter directly from the CBCT projections. However, the influence of varying FOV sizes on these deep learning models remains unexplored. Having a single neural network for the scatter estimation of varying FOV projections can be of significant importance towards real clinical applications. This study aims to train and evaluate the performance of a U-Net network on a simulated dataset with varying FOV sizes. We further propose a new method (Aux-Net) by providing auxiliary information, such as FOV size, to the U-Net encoder. We validate our method on 30 different FOV sizes and compare it with the U-Net. Our study demonstrates that providing auxiliary information to the network enhances the generalization capability of the U-Net. Our findings suggest that this novel approach outperforms the baseline U-Net, offering a significant step towards practical application in real clinical settings where CBCT systems are employed to scan a wide range of FOVs.
Intelligent Reflecting Surfaces and Next Generation Wireless Systems
Authors: Yashuai Cao, Hetong Wang, Tiejun Lv, Wei Ni
Date: 2024-02-26 00:00:00

Intelligent reflecting surface (IRS) is a potential candidate for massive multiple-input multiple-output (MIMO) 2.0 technology due to its low cost, ease of deployment, energy efficiency and extended coverage. This chapter investigates the slot-by-slot IRS reflection pattern design and two-timescale reflection pattern design schemes, respectively. For the slot-by-slot reflection optimization, we propose exploiting an IRS to improve the propagation channel rank in mmWave massive MIMO systems without need to increase the transmit power budget. Then, we analyze the impact of the distributed IRS on the channel rank. To further reduce the heavy overhead of channel training, channel state information (CSI) estimation, and feedback in time-varying MIMO channels, we present a two-timescale reflection optimization scheme, where the IRS is configured relatively infrequently based on statistical CSI (S-CSI) and the active beamformers and power allocation are updated based on quickly outdated instantaneous CSI (I-CSI) per slot. The achievable average sum-rate (AASR) of the system is maximized without excessive overhead of cascaded channel estimation. A recursive sampling particle swarm optimization (PSO) algorithm is developed to optimize the large-timescale IRS reflection pattern efficiently with reduced samplings of channel samples.
GAN Based Near-Field Channel Estimation for Extremely Large-Scale MIMO Systems
Authors: Ming Ye, Xiao Liang, Cunhua Pan, Yinfei Xu, Ming Jiang, ChunGuo Li
Date: 2024-02-27 00:00:00
Tasks: Generative Adversarial Network

Extremely large-scale multiple-input-multiple-output (XL-MIMO) is a promising technique to achieve ultra-high spectral efficiency for future 6G communications. The mixed line-of-sight (LoS) and non-line-of-sight (NLoS) XL-MIMO near-field channel model is adopted to describe the XL-MIMO near-field channel accurately. In this paper, a generative adversarial network (GAN) variant based channel estimation method is proposed for XL-MIMO systems. Specifically, the GAN variant is developed to simultaneously estimate the LoS and NLoS path components of the XL-MIMO channel. The initially estimated channels instead of the received signals are input into the GAN variant as the conditional input to generate the XL-MIMO channels more efficiently. The GAN variant not only learns the mapping from the initially estimated channels to the XL-MIMO channels but also learns an adversarial loss. Moreover, we combine the adversarial loss with a conventional loss function to ensure the correct direction of training the generator. To further enhance the estimation performance, we investigate the impact of the hyper-parameter of the loss function on the performance of our method. Simulation results show that the proposed method outperforms the existing channel estimation approaches in the adopted channel model. In addition, the proposed method surpasses the Cram$\acute{\mathbf{e}}$r-Rao lower bound (CRLB) under low pilot overhead.
Integrated Interpolation and Block-term Tensor Decomposition for Spectrum Map Construction
Authors: Hao Sun, Junting Chen
Date: 2024-02-27 00:00:00
Tasks: Tensor Decomposition

This paper addresses the challenge of reconstructing a 3D power spectrum map from sparse, scattered, and incomplete spectrum measurements. It proposes an integrated approach combining interpolation and block-term tensor decomposition (BTD). This approach leverages an interpolation model with the BTD structure to exploit the spatial correlation of power spectrum maps. Additionally, nuclear norm regularization is incorporated to effectively capture the low-rank characteristics. To implement this approach, a novel algorithm that combines alternating regression with singular value thresholding is developed. Analytical justification for the enhancement provided by the BTD structure in interpolating power spectrum maps is provided, yielding several important theoretical insights. The analysis explores the impact of the spectrum on the error in the proposed method and compares it to conventional local polynomial interpolation. Extensive numerical results demonstrate that the proposed method outperforms state-of-the-art methods in terms of signal source separation and power spectrum map construction, and remains stable under off-grid measurements and inhomogeneous measurement topologies.
Leveraging power of deep learning for fast and efficient elite pixel selection in time series SAR interferometry
Authors: Ashutosh Tiwari, Nitheshnirmal Sadhashivam, Leonard O. Ohenhen, Jonathan Lucy, Manoochehr Shirzaei
Date: 2024-02-26 00:00:00
Tasks: Time Series

This study proposes a new convolutional long short-term memory (ConvLSTM) based architecture for selection of elite pixels (i.e., less noisy) in time series interferometric synthetic aperture radar (TS-InSAR). The model utilizes the spatial and temporal relation among neighboring pixels to identify both persistent and distributed scatterers. We trained the model on ~20,000 training images (interferograms), each of size 100 by 100 pixels, extracted from InSAR time series interferograms containing both artificial features (buildings and infrastructure) and objects of natural environment (vegetation, forests, barren or agricultural land, water bodies). Based on such categorization, we developed two models, tailormade to detect elite pixels in urban and coastal sites. Training labels were generated from elite pixel selection outputs generated from the wavelet-based InSAR (WabInSAR) software. We used 4 urban and 7 coastal sites for training and validation respectively, and the predicted elite pixel selection maps reveal that the proposed models efficiently learn from WabInSAR-generated labels, reaching a test accuracy of 94%. The models accurately discard pixels affected by geometric and temporal decorrelation while selecting pixels corresponding to urban objects and those with stable phase history unaffected by temporal and geometric decorrelation. The density of pixels in urban areas is comparable to and higher for coastal areas than WabInSAR outputs. With significantly reduced time computation (order of minutes) and improved density of elite pixels, the proposed models can efficiently process long InSAR time series stacks and generate deformation maps quickly, making the time series InSAR technique more suitable for varied (non-urban and urban) terrains and unaddressed land deformation applications.
Separation of biocrude produced from hydrothermal liquefaction of faecal sludge without any solvent
Authors: H M Fairooz Adnan, Md Khalekuzzaman, Md. Atik Fayshal, Md. Mehedi Hasan
Date: 2024-02-26 00:00:00

In this study faecal sludge is used as raw biomass due to its abundance, low cost, and easy availability. After HTL operation, product separation is getting challenging. Current developed studies observed the separation of aqueous and biocrude oil products occurs during the HTL process more popularly with the use of an organic solvent which is quite expensive. Focusing on this critical issue, this study aims to separate the biocrude and aqueous phase without using any solvent by gravity separation technique. From FTIR analysis data it showed that centrifuged at 6000 rpm partial separation of biocrude and aqueous phase (AP) was noticed. however, at 9000 rpm, FTIR analysis showed that biocrude samples included aliphatic hydrocarbons, phenols, and esters where no signs of any carbon chain were found at AP which indicated the products are successfully separated. The separated Crude portion had the higher A-Factor (0.68) and lower C-Factor (0.58) value which indicates the oil quality was immature grade of lower kerogen type II (i.e., moderate oil-prone). This low-cost technique can be economically advantageous for commercial-scale biocrude production.
Ambiguity Function Shaping in FMCW Automotive Radar
Authors: Zahra Esmaeilbeig, Arindam Bose, Mojtaba Soltanalian
Date: 2024-02-26 00:00:00

Frequency-modulated continuous wave (FMCW) radar with inter-chirp coding produces high side-lobes in the Doppler and range dimensions of the radar's ambiguity function. The high side-lobes may cause miss-detection due to masking between targets that are at similar range and have large received power difference, as is often the case in automotive scenarios. In this paper, we develop a novel code optimization method that attenuates the side-lobes of the radar's ambiguity function. In particular, we introduce a framework for designing radar transmit sequences by shaping the radar Ambiguity Function (AF) to a desired structure. The proposed approach suppresses the average amplitude of the AF of the transmitted signal in regions of interest by efficiently tackling a longstanding optimization problem. The optimization criterion is quartic in nature with respect to the radar transmit code. A cyclic iterative algorithm is introduced that recasts the quartic problem as a unimodular quadratic problem (UQP) which can be tackled using power-method-like iterations (PMLI). Our numerical results demonstrate the effectiveness of the proposed algorithm in designing sequences with desired AF which is of great interest to the future generations of automotive radar sensors.
C-Band Lithium Niobate on Silicon Carbide SAW Resonator With Figure-of-Merit of 124 at 6.5 GHz
Authors: Tzu-Hsuan Hsu, Joshua Campbell, Jack Kramer, Sinwoo Cho, Ming-Huang Li, Ruochen Lu
Date: 2024-02-26 00:00:00

In this work, we demonstrate a C-band shear-horizontal surface acoustic wave (SH-SAW) resonator with high electromechanical coupling (kt2) of 22% and a quality factor (Q) of 565 based on a thin-film lithium niobate (LN) on silicon carbide (SiC) platform, featuring an excellent figure-of-merit (FoM = kt2*Q ) of 124 at 6.5 GHz, the highest FoM reported in this frequency range. The resonator frequency upscaling is achieved through wavelength ($\lambda$) reduction and the use of thin aluminum (Al) electrodes. The LN/SiC waveguide and synchronous resonator design collectively enable effective acoustic energy confinement for a high FoM, even when the normalized thickness of LN approaches a scale of 0.5$\lambda$ to 1$\lambda$. To perform a comprehensive study, we also designed and fabricated five additional resonators, expending the $\lambda$ studied ranging from 480 to 800 nm, in the same 500 nm-thick transferred Y-cut thin-film LN on SiC. The fabricated SH-SAW resonators, operating from 5 to 8 GHz, experimentally demonstrate a kt2 from 20.3% to 22.9% and a Q from 350 to 575, thereby covering the entire C-band with excellent performance.