pwc-archive/papers-with-abstracts huggingface dataset:
Invariant Representations in Deep Learning for Optoacoustic Imaging
Authors: Matias Vera, Martin G. Gonzalez, Leonardo Rey Vega
Date: 2023-04-29 00:00:00
Tasks: Deep Learning, Image Reconstruction, Out-of-Distribution Generalization

Image reconstruction in optoacoustic tomography (OAT) is a trending learning task highly dependent on measured physical magnitudes present at sensing time. The large number of different settings, and also the presence of uncertainties or partial knowledge of parameters, can lead to reconstructions algorithms that are specifically tailored and designed to a particular configuration which could not be the one that will be ultimately faced in a final practical situation. Being able to learn reconstruction algorithms that are robust to different environments (e.g. the different OAT image reconstruction settings) or invariant to such environments is highly valuable because it allows to focus on what truly matters for the application at hand and discard what are considered spurious features. In this work we explore the use of deep learning algorithms based on learning invariant and robust representations for the OAT inverse problem. In particular, we consider the application of the ANDMask scheme due to its easy adaptation to the OAT problem. Numerical experiments are conducted showing that, when out-of-distribution generalization (against variations in parameters such as the location of the sensors) is imposed, there is no degradation of the performance and, in some cases, it is even possible to achieve improvements with respect to standard deep learning approaches where invariance robustness is not explicitly considered.
Maximum Likelihood based Phase-Retrieval using Fresnel Propagation Forward Models with Optional Constraints
Authors: K. Aditya Mohan, Jean-Baptiste Forien, Venkatesh Sridhar, Jefferson A. Cuadra, Dilworth Parkinson
Date: 2023-04-29 00:00:00
Tasks: 3D Reconstruction, Retrieval

X-ray phase-contrast tomography (XPCT) is widely used for high contrast 3D imaging using either synchrotron or laboratory microfocus X-ray sources. XPCT enables an order of magnitude improvement in image contrast of the reconstructed material interfaces with low X-ray absorption contrast. The dominant approaches to 3D reconstruction using XPCT relies on the use of phase-retrieval algorithms that make one or more limiting approximations for the experimental configuration and material properties. Since many experimental scenarios violate such approximations, the resulting reconstructions contain blur, artifacts, or other quantitative inaccuracies. Our solution to this problem is to formulate new iterative non-linear phase-retrieval (NLPR) algorithms that avoid such limiting approximations. Compared to the widely used state-of-the-art approaches, we show that our proposed algorithms result in sharp and quantitatively accurate reconstruction with reduced artifacts. Unlike existing NLPR algorithms, our approaches avoid the laborious manual tuning of regularization hyper-parameters while still achieving the stated goals. As an alternative to regularization, we propose explicit constraints on the material properties to constrain the solution space and solve the phase-retrieval problem. These constraints are easily user-configurable since they follow directly from the imaged object's dimensions and material properties.
Unified Noise-aware Network for Low-count PET Denoising
Authors: Huidong Xie, Qiong Liu, Bo Zhou, Xiongchao Chen, Xueqi Guo, Chi Liu
Date: 2023-04-28 00:00:00
Tasks: Denoising, Diagnostic

As PET imaging is accompanied by substantial radiation exposure and cancer risk, reducing radiation dose in PET scans is an important topic. However, low-count PET scans often suffer from high image noise, which can negatively impact image quality and diagnostic performance. Recent advances in deep learning have shown great potential for recovering underlying signal from noisy counterparts. However, neural networks trained on a specific noise level cannot be easily generalized to other noise levels due to different noise amplitude and variances. To obtain optimal denoised results, we may need to train multiple networks using data with different noise levels. But this approach may be infeasible in reality due to limited data availability. Denoising dynamic PET images presents additional challenge due to tracer decay and continuously changing noise levels across dynamic frames. To address these issues, we propose a Unified Noise-aware Network (UNN) that combines multiple sub-networks with varying denoising power to generate optimal denoised results regardless of the input noise levels. Evaluated using large-scale data from two medical centers with different vendors, presented results showed that the UNN can consistently produce promising denoised results regardless of input noise levels, and demonstrate superior performance over networks trained on single noise level data, especially for extremely low-count data.
Incomplete hippocampal inversion and hippocampal subfield volumes: Implementation and inter-reliability of automatic segmentation
Authors: Agustina Fragueiro, Giorgia Committeri, Claire Cury
Date: 2023-04-28 00:00:00
Tasks: Hippocampus, Segmentation

The incomplete hippocampal inversion (IHI) is an atypical anatomical pattern of the hippocampus. However, the hippocampus is not a homogeneous structure, as it consists of segregated subfields with specific characteristics. While IHI is not related to whole hippocampal volume, higher IHI scores have been associated to smaller CA1 in aging. Although the segmentation of hippocampal subfields is challenging due to their small size, there are algorithms allowing their automatic segmentation. By using a Human Connectome Project dataset of healthy young adults, we first tested the inter-reliability of two methods for automatic segmentation of hippocampal subfields, and secondly, we explored the relationship between IHI and subfield volumes. Results evidenced strong correlations between volumes obtained thorough both segmentation methods. Furthermore, higher IHI scores were associated to bigger subiculum and smaller CA1 volumes. Here, we provide new insights regarding IHI subfields volumetry, and we offer support for automatic segmentation inter-method reliability.
An Efficient Hash-based Data Structure for Dynamic Vision Sensors and its Application to Low-energy Low-memory Noise Filtering
Authors: Pradeep Kumar Gopalakrishnan, Chip-Hong Chang, Arindam Basu
Date: 2023-04-28 00:00:00

Events generated by the Dynamic Vision Sensor (DVS) are generally stored and processed in two-dimensional data structures whose memory complexity and energy-per-event scale proportionately with increasing sensor dimensions. In this paper, we propose a new two-dimensional data structure (BF_2) that takes advantage of the sparsity of events and enables compact storage of data using hash functions. It overcomes the saturation issue in the Bloom Filter (BF) and the memory reset issue in other hash-based arrays by using a second dimension to clear 1 out of D rows at regular intervals. A hardware-friendly, low-power, and low-memory-footprint noise filter for DVS is demonstrated using BF_2. For the tested datasets, the performance of the filter matches those of state-of-the-art filters like the BAF/STCF while consuming less than 10% and 15% of their memory and energy-per-event, respectively, for a correlation time constant Tau = 5 ms. The memory and energy advantages of the proposed filter increase with increasing sensor sizes. The proposed filter compares favourably with other hardware-friendly, event-based filters in hardware complexity, memory requirement and energy-per-event - as demonstrated through its implementation on an FPGA. The parameters of the data structure can be adjusted for trade-offs between performance and memory consumption, based on application requirements.
PathRTM: Real-time prediction of KI-67 and tumor-infiltrated lymphocytes
Authors: Steven, Lapp, Eli, David, Nathan S. Netanyahu
Date: 2023-04-23 00:00:00

In this paper, we introduce PathRTM, a novel deep neural network detector based on RTMDet, for automated KI-67 proliferation and tumor-infiltrated lymphocyte estimation. KI-67 proliferation and tumor-infiltrated lymphocyte estimation play a crucial role in cancer diagnosis and treatment. PathRTM is an extension of the PathoNet work, which uses single pixel keypoints for within each cell. We demonstrate that PathRTM, with higher-level supervision in the form of bounding box labels generated automatically from the keypoints using NuClick, can significantly improve KI-67 proliferation and tumorinfiltrated lymphocyte estimation. Experiments on our custom dataset show that PathRTM achieves state-of-the-art performance in KI-67 immunopositive, immunonegative, and lymphocyte detection, with an average precision (AP) of 41.3%. Our results suggest that PathRTM is a promising approach for accurate KI-67 proliferation and tumor-infiltrated lymphocyte estimation, offering annotation efficiency, accurate predictive capabilities, and improved runtime. The method also enables estimation of cell sizes of interest, which was previously unavailable, through the bounding box predictions.
Suspicious Vehicle Detection Using Licence Plate Detection And Facial Feature Recognition
Authors: Vrinda Agarwal, Aaron George Pichappa, Manideep Ramisetty, Bala Murugan MS, Manoj Kumar Rajagopal
Date: 2023-04-18 00:00:00
Tasks: Face Recognition, vehicle detection

With the increasing need to strengthen vehicle safety and detection, the availability of pre-existing methods of catching criminals and identifying vehicles manually through the various traffic surveillance cameras is not only time-consuming but also inefficient. With the advancement of technology in every field the use of real-time traffic surveillance models will help facilitate an easy approach. Keeping this in mind, the main focus of our paper is to develop a combined face recognition and number plate recognition model to ensure vehicle safety and real-time tracking of running-away criminals and stolen vehicles.
Human activity recognition using deep learning approaches and single frame cnn and convolutional lstm
Authors: Sheryl Mathew, Annapoorani Subramanian, Pooja, Balamurugan MS, Manoj Kumar Rajagopal
Date: 2023-04-18 00:00:00
Tasks: Action Recognition, Activity Recognition, Human Activity Recognition

Human activity recognition is one of the most important tasks in computer vision and has proved useful in different fields such as healthcare, sports training and security. There are a number of approaches that have been explored to solve this task, some of them involving sensor data, and some involving video data. In this paper, we aim to explore two deep learning-based approaches, namely single frame Convolutional Neural Networks (CNNs) and convolutional Long Short-Term Memory to recognise human actions from videos. Using a convolutional neural networks-based method is advantageous as CNNs can extract features automatically and Long Short-Term Memory networks are great when it comes to working on sequence data such as video. The two models were trained and evaluated on a benchmark action recognition dataset, UCF50, and another dataset that was created for the experimentation. Though both models exhibit good accuracies, the single frame CNN model outperforms the Convolutional LSTM model by having an accuracy of 99.8% with the UCF50 dataset.
HandCT: hands-on computational dataset for X-Ray Computed Tomography and Machine-Learning
Authors: Emilien Valat, Loth Valat
Date: 2023-04-17 00:00:00
Tasks: Denoising

Machine-learning methods rely on sufficiently large dataset to learn data distributions. They are widely used in research in X-Ray Computed Tomography, from low-dose scan denoising to optimisation of the reconstruction process. The lack of datasets prevents the scalability of these methods to realistic 3D problems. We develop a 3D procedural dataset in order to produce samples for data-driven algorithms. It is made of a meshed model of a left hand and a script to randomly change its anatomic properties and pose whilst conserving realistic features. This open-source solution relies on the freeware Blender and its Python core. Blender handles the modelling, the mesh and the generation of the hand's pose, whilst Python processes file format conversion from obj file to matrix and functions to scale and center the volume for further processing. Dataset availability and quality drives research in machine-learning. We design a dataset that weighs few megabytes, provides truthful samples and proposes continuous enhancements using version control. We anticipate this work to be a starting point for anatomically accurate procedural datasets. For instance, by adding more internal features and fine tuning their X-Ray attenuation properties.
Vehicle Safety Management System
Authors: Chanthini Bhaskar, Bharath Manoj Nair, Dev Mehta
Date: 2023-04-16 00:00:00
Tasks: Management, object-detection, Object Detection

Overtaking is a critical maneuver in driving that requires accurate information about the location and distance of other vehicles on the road. This study suggests a real-time overtaking assistance system that uses a combination of the You Only Look Once (YOLO) object detection algorithm and stereo vision techniques to accurately identify and locate vehicles in front of the driver, and estimate their distance. The system then signals the vehicles behind the driver using colored lights to inform them of the safe overtaking distance. The proposed system has been implemented using Stereo vision for distance analysis and You Only Look Once (YOLO) for object identification. The results demonstrate its effectiveness in providing vehicle type and the distance between the camera module and the vehicle accurately with an approximate error of 4.107%. Our system has the potential to reduce the risk of accidents and improve the safety of overtaking maneuvers, especially on busy highways and roads.
Low-Rank Structured MMSE Channel Estimation with Mixtures of Factor Analyzers
Authors: Benedikt Fesl, Nurettin Turan, Wolfgang Utschick
Date: 2023-04-28 00:00:00

This work proposes a generative modeling-aided channel estimator based on mixtures of factor analyzers (MFA). In an offline step, the parameters of the generative model are inferred via an expectation-maximization (EM) algorithm in order to learn the underlying channel distribution of a whole communication scenario inside a base station (BS) cell. Thereby, the wireless channels are effectively modeled on a piecewise linear subspace which is achieved by the low-rank structure of the learned covariances of the MFA. This suits the low-rank structure of wireless channels at high frequencies and additionally saves parameters and prevents overfitting. Afterwards, the trained MFA model is used online to perform channel estimation with a closed-form solution of the estimator which asymptotically converges to the minimum mean square error (MMSE) estimator. Numerical results based on real-world measurements demonstrate the great potential of the proposed approach for channel estimation.
A Dynamic Model for Frequency Response Optimization in Photovoltaic Visible Light Communication
Authors: Shuyan Chen, Hui Yu, Ni Zhao, Lian-Kuan Chen
Date: 2023-05-01 00:00:00

Photovoltaic (PV) modules are recently employed in photovoltaic visible light communication (PVLC) for simultaneous energy harvesting and visible light communication. A PV-based receiver features large signal output, easy optical alignment, and self-powered operation. However, PV modules usually have a severe bandwidth limitation when used as passive photodetectors. In this paper, we systematically investigate the internal impedance dynamic of PV modules and how that affects their frequency response characteristics under different illuminances. We propose a simplified yet accurate dynamic PV mode AC detection model to capture the frequency response characteristics of a PVLC receiver. The model is validated with the impedance spectroscopy characterization methodologies. Experimental results show that a PV module's internal resistance and capacitance depend on incident illuminance, affecting PV's frequency response. The bandwidth is exacerbated under indoor environments with low illuminance levels due to the increment of internal resistance for PV modules. The RC constant can be reduced for PVLC receivers working near open-circuit voltage conditions by adding a moderate local light to decrease the internal resistance value. For practical implementation, PVLC receivers will employ a load for data recovery. We show that adjusting the forward bias conditions can simultaneously reduce the resistance and capacitance values. With the optimization of equivalent trans-impedance, the data rate of a Cadmium telluride (CdTe) PV module achieves a 3.8 times enhancement under 200 lux. We also demonstrate that the BER of a 5-Mbit/s eight-level pulse amplitude modulation (PAM8) signal can be reduced from 9.8*10-2 to 1.4*10-3 by maximizing the transimpedance gain-bandwidth product.
Pilot Synthesis for Distributed Synchronization
Authors: Vladislav Ryzhov, Anton Laktyushkin
Date: 2023-05-01 00:00:00

Multiuser Massive MIMO is ongoing wireless technology with high prospects in the distributed architecture with various system design and splitting options. Any of such solutions suffer from imperfect synchronization that can be reduced via promising OTA (Over the Air) pilot exchanges. In this paper we introduce requirements and algorithm for flexible non orthogonal pilot design for time synchronization without changes to 3GPP protocol stack. Our solution decreases number of synchronization slots and solves problem of separating guard intervals.
Towards ISAC-Empowered Vehicular Networks: Framework, Advances, and Opportunities
Authors: Zhen Du, Fan Liu, Yunxin Li, Weijie Yuan, Yuanhao Cui, Zenghui Zhang, Christos Masouros, Bo Ai
Date: 2023-05-01 00:00:00
Tasks: ISAC

Connected and autonomous vehicle (CAV) networks face several challenges, such as low throughput, high latency, and poor localization accuracy. These challenges severely impede the implementation of CAV networks for immersive metaverse applications and driving safety in future 6G wireless networks. To alleviate these issues, integrated sensing and communications (ISAC) is envisioned as a game-changing technology for future CAV networks. This article presents a comprehensive overview on the application of ISAC techniques in vehicle-to-infrastructure (V2I) networks. We cover the general system framework, representative advances, and a detailed case study on using the 5G New Radio (NR) waveform for sensing-assisted communications in V2I networks. Finally, we highlight open problems and opportunities in the field.
Green Federated Learning Over Cloud-RAN with Limited Fronthual Capacity and Quantized Neural Networks
Authors: Jiali Wang, Yijie Mao, Ting Wang, Yuanming Shi
Date: 2023-04-30 00:00:00
Tasks: Federated Learning

In this paper, we propose an energy-efficient federated learning (FL) framework for the energy-constrained devices over cloud radio access network (Cloud-RAN), where each device adopts quantized neural networks (QNNs) to train a local FL model and transmits the quantized model parameter to the remote radio heads (RRHs). Each RRH receives the signals from devices over the wireless link and forwards the signals to the server via the fronthaul link. We rigorously develop an energy consumption model for the local training at devices through the use of QNNs and communication models over Cloud-RAN. Based on the proposed energy consumption model, we formulate an energy minimization problem that optimizes the fronthaul rate allocation, user transmit power allocation, and QNN precision levels while satisfying the limited fronthaul capacity constraint and ensuring the convergence of the proposed FL model to a target accuracy. To solve this problem, we analyze the convergence rate and propose efficient algorithms based on the alternative optimization technique. Simulation results show that the proposed FL framework can significantly reduce energy consumption compared to other conventional approaches. We draw the conclusion that the proposed framework holds great potential for achieving a sustainable and environmentally-friendly FL in Cloud-RAN.
Dynamic and Robust Sensor Selection Strategies for Wireless Positioning with TOA/RSS Measurement
Authors: Myeung Suk Oh, Seyyedali Hosseinalipour, Taejoon Kim, David J. Love, James V. Krogmeier, Christopher G. Brinton
Date: 2023-04-30 00:00:00

Emerging wireless applications are requiring ever more accurate location-positioning from sensor measurements. In this paper, we develop sensor selection strategies for 3D wireless positioning based on time of arrival (TOA) and received signal strength (RSS) measurements to handle two distinct scenarios: (i) known approximated target location, for which we conduct dynamic sensor selection to minimize the positioning error; and (ii) unknown approximated target location, in which the worst-case positioning error is minimized via robust sensor selection. We derive expressions for the Cram\'er-Rao lower bound (CRLB) as a performance metric to quantify the positioning accuracy resulted from selected sensors. For dynamic sensor selection, two greedy selection strategies are proposed, each of which exploits properties revealed in the derived CRLB expressions. These selection strategies are shown to strike an efficient balance between computational complexity and performance suboptimality. For robust sensor selection, we show that the conventional convex relaxation approach leads to instability, and then develop three algorithms based on (i) iterative convex optimization (ICO), (ii) difference of convex functions programming (DCP), and (iii) discrete monotonic optimization (DMO). Each of these strategies exhibits a different tradeoff between computational complexity and optimality guarantee. Simulation results show that the proposed sensor selection strategies provide significant improvements in terms of accuracy and/or complexity compared to existing sensor selection methods.
Model-Based Monitoring and State Estimation for Digital Twins: The Kalman Filter
Authors: Hao Feng, Cláudio Gomes, Peter Gorm Larsen
Date: 2023-04-29 00:00:00
Tasks: Anomaly Detection, Fault Detection, State Estimation

A digital twin (DT) monitors states of the physical twin (PT) counterpart and provides a number of benefits such as advanced visualizations, fault detection capabilities, and reduced maintenance cost. It is the ability to be able to detect the states inside the DT that enable such benefits. In order to estimate the desired states of a PT, we propose the use of a Kalman Filter (KF). In this tutorial, we provide an introduction and detailed derivation of the KF. We demonstrate the use of KF to monitor and anomaly detection through an incubator system. Our experimental result shows that KF successfully can detect the anomaly during monitoring.