pwc-archive/papers-with-abstracts huggingface dataset:
Self-supervised Learning for Gastrointestinal Pathologies Endoscopy Image Classification with Triplet Loss
Authors: Tai Nguyen-D-P
Date: 2023-03-03 00:00:00
Tasks: image-classification, Image Classification, Self-Supervised Learning, Triplet

Recently, the amount of GI tract datasets is introduced more and more by gathering from contests and challenges. The most common task needs to solve that is to classify images from the GI tract into various classes. However, the contributions of the existing approaches exhibit lots of limitations. In this paper, we aim to develop a computer-aided diagnosis system to classify the pathological findings in endoscopy images, the system can classify some common pathologies including polyps, esophagitis, and ulcerative -- colitis. To evaluate the proposed work, we use the public dataset which is Hyper--Kvasir instead of gathering the data. The key idea of our system is to develop self-supervised learning based on the Barlow Twins framework with a downstream task which is an endoscopy image classification integrated with triplet loss and focal loss functions. The self-supervision framework and focal loss function are used to overcome class-imbalanced data, while the triplet loss function is to tackle the domain-specific properties in endoscopy images which are inter/intra-class problems. An extensive experimental study on the pathological finding images in the Hyper--Kvasir dataset has shown that our proposed system is in general better than the compared methods, whereas using a simple neural network model. This means the proposed system can be used efficiently and capable of accurately for the classification of pathology images in the GI tract.
A stochastic approximate expectation-maximization for structure determination directly from cryo-EM micrographs
Authors: Shay Kreymer, Amit Singer, Tamir Bendory
Date: 2023-02-24 00:00:00

A single-particle cryo-electron microscopy (cryo-EM) measurement, called a micrograph, consists of multiple two-dimensional tomographic projections of a three-dimensional molecular structure at unknown locations, taken under unknown viewing directions. All existing cryo-EM algorithmic pipelines first locate and extract the projection images, and then reconstruct the structure from the extracted images. However, if the molecular structure is small, the signal-to-noise ratio (SNR) of the data is very low, and thus accurate detection of projection images within the micrograph is challenging. Consequently, all standard techniques fail in low-SNR regimes. To recover molecular structures from measurements of low SNR, and in particular small molecular structures, we devise a stochastic approximate expectation-maximization algorithm to estimate the three-dimensional structure directly from the micrograph, bypassing locating the projection images. We corroborate our computational scheme with numerical experiments, and present successful structure recoveries from simulated noisy measurements.
Energy Efficiency Maximization in RIS-Aided Networks with Global Reflection Constraints
Authors: Robert K. Fotock, Alessio Zappone, Marco Di Renzo
Date: 2023-03-06 00:00:00

This work addresses the issue of energy efficiency maximization in a multi-user network aided by reconfigurable intelligent surface (RIS) with global reflection capabilities. Two optimization methods are proposed to optimize the mobile users' powers, the RIS coefficients and the linear receive filters. Both methods are provably convergent and require only the solution of convex optimization problems. The numerical results show that the proposed methods largely outperform heuristic resource allocation schemes.
A Miniaturised Camera-based Multi-Modal Tactile Sensor
Authors: Kaspar Althoefer, Yonggen Ling, Wanlin Li, Xinyuan Qian, Wang Wei Lee, Peng Qi
Date: 2023-03-06 00:00:00

In conjunction with huge recent progress in camera and computer vision technology, camera-based sensors have increasingly shown considerable promise in relation to tactile sensing. In comparison to competing technologies (be they resistive, capacitive or magnetic based), they offer super-high-resolution, while suffering from fewer wiring problems. The human tactile system is composed of various types of mechanoreceptors, each able to perceive and process distinct information such as force, pressure, texture, etc. Camera-based tactile sensors such as GelSight mainly focus on high-resolution geometric sensing on a flat surface, and their force measurement capabilities are limited by the hysteresis and non-linearity of the silicone material. In this paper, we present a miniaturised dome-shaped camera-based tactile sensor that allows accurate force and tactile sensing in a single coherent system. The key novelty of the sensor design is as follows. First, we demonstrate how to build a smooth silicone hemispheric sensing medium with uniform markers on its curved surface. Second, we enhance the illumination of the rounded silicone with diffused LEDs. Third, we construct a force-sensitive mechanical structure in a compact form factor with usage of springs to accurately perceive forces. Our multi-modal sensor is able to acquire tactile information from multi-axis forces, local force distribution, and contact geometry, all in real-time. We apply an end-to-end deep learning method to process all the information.
Adaptive Multi-User Channel Estimation Based on Contrastive Feature Learning
Authors: Yihan Xu, Lixiang Lian
Date: 2023-03-06 00:00:00
Tasks: Contrastive Learning

Correlation exploitation is essential for efficient multi-user channel estimation (MUCE) in massive MIMO systems. However, the existing works either rely on presumed strong correlation or learn the correlation through large amount of labeled data, which are difficult to acquire in a real system. In this paper, we propose an adaptive MUCE algorithm based on contrastive feature learning. The contrastive learning (CL) is used to automatically learn the similarity within channels by extracting the channel state information (CSI) features based on location information. The similar features will be fed into the downstream network to explore the strong correlations among CSI features to improve the MUCE performance with a small number of labeled data. Simulation results show that the contrastive feature learning can enhance the overall MUCE performance with high training efficiency.
RIS-aided Wireless Communications: Can RIS Beat Metal Plate?
Authors: Jiangfeng Hu, Haifan Yin, Li Tan, Lin Cao, Xilong Pei
Date: 2023-03-06 00:00:00

Reconfigurable Intelligent Surface (RIS) has recently been regarded as a paradigm-shifting technology beyond 5G, for its flexibility on smartly adjusting the response to the impinging electromagnetic (EM) waves. Usually, RIS can be implemented by properly reconfiguring the adjustable parameters of each RIS unit to align the signal phase on the receiver side. And it is believed that the phase alignment can be also mechanically achieved by a metal plate with the same physical size. However, we found in the prototype experiments that, a well-rotated metal plate can only approximately perform as well as RIS under limited conditions, although its scattering efficiency is relatively higher. When it comes to the case of spherical wave impinging, RIS outperforms the metal plate even beyond the receiving near-field regions. We analyze this phenomenon with wave optics theory and propose explicit scattering models for both the metal plate and RIS in general scenarios. Finally, the models are validated by simulations and field measurements.
Optimal Beamforming for MIMO DFRC Systems with Transmit Covariance Constraints
Authors: Chenhao Yang, Xin Wang, Wei Ni, Yi Jiang
Date: 2023-03-06 00:00:00

This paper optimizes the beamforming design of a downlink multiple-input multiple-output (MIMO) dual-function radar-communication (DFRC) system to maximize the weighted communication sum-rate under a prescribed transmit covariance constraint for radar performance guarantee. In the single-user case, we show that the transmit covariance constraint implies the existence of inherent orthogonality among the transmit beamforming vectors in use. Then, leveraging Cauchy's interlace theorem, we derive the globally optimal transmit and receive beamforming solution in closed form. In the multi-user case, we exploit the connection between the weighted sum-rate and weighted minimum mean squared error (MMSE) to reformulate the intended problem, and develop a block-coordinate-descent (BCD) algorithm to iteratively compute the transmit beamforming and receive beamforming solutions. Under this approach, we reveal that the optimal receive beamforming is given by the classic MMSE one and the optimal transmit beamforming design amounts to solving an orthogonal Procrustes problem, thereby allowing for closed-form solutions to subproblems in each BCD step and fast convergence of the proposed algorithm to a high-quality (near-optimal) overall beamforming design. Numerical results demonstrate the superiority of our approach to the existing methods, with at least 40% higher sum-rate under a multi-user MIMO setting in the high signal-to-noise regime.
Rank and Condition Number Analysis for UAV MIMO Channels Using Ray Tracing
Authors: Donggu Lee, Ismail Guvenc
Date: 2023-03-05 00:00:00

Channel rank and condition number of multi-input multi-output (MIMO) channels can be effective indicators of achievable rates with spatial multiplexing in mobile networks. In this paper, we use extensive ray tracing simulations to investigate channel rank, condition number, and signal coverage distribution for air-to-ground MIMO channels. We consider UAV-based user equipment (UE) at altitudes of 3 m, 30 m, 70 m, and 110 m from the ground. Moreover, we also consider their communication link with a cellular base station in urban and rural areas. In particular, Centennial Campus and Lake Wheeler Road Field Labs of NC State University are considered, and their geographical information extracted from the open street map (OSM) database is incorporated into ray tracing simulations. Our results characterize how the channel rank tends to reduce as a function of UAV altitude, while also providing insights into the effects of geography, building distribution, and threshold parameters on channel rank and condition number.
An RRAM-Based Implementation of a Template Matching Circuit for Low-Power Analogue Classification
Authors: Patrick Foster, Georgios Papandroulidakis, Alex Serb, Spyros Stathopoulos Themis Prodromakis
Date: 2023-03-05 00:00:00
Tasks: Classification, Specificity, Template Matching

Recent advances in machine learning and neuro-inspired systems enabled the increased interest in efficient pattern recognition at the edge. A wide variety of applications, such as near-sensor classification, require fast and low-power approaches for pattern matching through the use of associative memories and their more well-known implementation, Content Addressable Memories (CAMs). Towards addressing the need for low-power classification, this work showcases an RRAM-based analogue CAM (ACAM) intended for template matching applications, providing a low-power reconfigurable classification engine for the extreme edge. The circuit uses a low component count at 6T2R2M, comparable with the most compact existing cells of this type. In this work, we demonstrate a hardware prototype, built with commercial off-the-shelf (COTS) components for the MOSFET-based circuits, that implements rows of 6T2R2M employing TiOx-based RRAM devices developed in-house, showcasing competitive matching window configurability and definition. Furthermore, through simulations, we validate the performance of the proposed circuit by using a commercially available 180nm technology and in-house RRAM data-driven model to assess the energy dissipation, exhibiting 60 pJ per classification event.
Fast Discrete Fourier Transform algorithms requiring less than 0(NlogN) multiplications
Authors: Ryszard Stasinski
Date: 2023-03-05 00:00:00

In the paper it is shown that there exist infinite classes of fast DFT algorithms having multiplicative complexity lower than O(NlogN), i.e. smaller than their arithmetical complexity. The derivation starts with nesting of Discrete Fourier Transform (DFT) of size N = q_1 q_2 ... q_r, where q_i are powers of prime numbers: DFT is mapped into multidimensional one, Rader convolutions of q_i-point DFTs extracted, and combined into multidimensional convolutions processing data in parallel. Crucial to further optimization is the observation that multiplicative complexity of such algorithm is upper bounded by 0(Nlog M_max), where M_max is the size of the greatest structure containing multiplications. Then the size of the structures is diminished: Firstly, computation of a circular convolution can be done as in Rader-Winograd algorithms. Secondly, multidimensional convolutions can be computed using polynomial transforms. It is shown that careful choice of q_i values leads to important reduction of M_max value: Multiplicative complexity of the new DFT algorithms is O(Nlog^c log N) for c\le 1, while for more addition-orietnted ones it is O(Nlog^{1/m} N), m is a natural number denoting class of q_i values. Smaller values of c, 1/m are obtained for algorithms requiring more additions, part of algorithms for c = 1, m=2 have arithmetical complexity smaller than that for the radix-2 FFT for any comparable DFT size, and even lower than that of split-radix FFT for N\le 65520. The approach can be used for finding theoretical lower limit on the DFT multiplicative complexity.
Multi-Target Cooperative Visible Light Positioning: A Compressed Sensing Based Framework
Authors: Xianyao Wang, Sicong Liu
Date: 2023-03-05 00:00:00
Tasks: compressed sensing

In this paper, a compressed sensing (CS) based framework of multi-target cooperative visible light positioning (VLP) is formulated to realize simultaneous highaccuracy localization of multiple targets. The light emitting diodes (LEDs) intended for illumination are utilized to locate multiple target mobile terminals equipped with photodetectors. The indoor area can be divided into a two-dimensional grid of discrete points, and the targets are located in only a few grid points, which has a sparse property. Thus, the multitarget localization problem can be transferred into a sparse recovery problem. Specifically, a CS-based framework is formulated exploiting the superposition of the received visible light signals at the multiple targets to be located via intertarget cooperation. Then it can be efficiently resolved using CS-based algorithms. Moreover, inter-anchor cooperation is introduced to the CS-based framework by the crosscorrelation between the signals corresponding to different LEDs, i.e., anchors, which further improves the localization accuracy. Enabled by the proposed CS-based framework and the devised cooperation mechanism, the proposed scheme can simultaneously locate multiple targets with high precision and low computational complexity. Simulation results show that the proposed schemes can achieve centimeter-level multitarget positioning with sub-meter accuracy, which outperforms existing benchmark schemes.
Millimeter Wave Wireless Communication Assisted Three-Dimensional Simultaneous Localization and Mapping
Authors: Zhiyu Mou, Feifei Gao
Date: 2023-03-05 00:00:00
Tasks: Simultaneous Localization and Mapping

In this paper, we study the three-dimensional (3D) simultaneous localization and mapping (SLAM) problem in complex outdoor and indoor environments based only on millimeter-wave (mmWave) wireless communication signals. Firstly, we propose a deep-learning based mapping (DLM) algorithm that can leverage the reflections point on the first-order none line-of-sight (NLOS) communications links (CLs) to build the 3D point cloud map of the environment. Specifically, we design a classification neural network to identify the first-order NLOS CL and theoretically calculate the geometric coordinates of the reflection points on it. Secondly, we take the advantage of both the inertial measurement unit and the beam-squint assisted localization method to realize real-time and precise localizations. Then, combining the DLM and the adopted localization algorithm, we develop the communication-based SLAM (C-SLAM) framework that can carry out SLAM without any prior knowledge of the environment. Moreover, extensive simulations of both complex outdoor and indoor environments validate the effectiveness of our approach.
GSP = DSP + Boundary Conditions -- The Graph Signal Processing Companion Model
Authors: John Shi, Jose M. F. Moura
Date: 2023-03-04 00:00:00

The paper presents the graph signal processing (GSP) companion model that naturally replicates the basic tenets of classical signal processing (DSP) for GSP. The companion model shows that GSP can be made equivalent to DSP 'plus' appropriate boundary conditions (bc) - this is shown under broad conditions and holds for arbitrary undirected or directed graphs. This equivalence suggests how to broaden GSP - extend naturally a DSP concept to the GSP companion model and then transfer it back to the common graph vertex and graph Fourier domains. The paper shows that GSP unrolls as two distinct models that coincide in DSP, the companion model based on (Hadamard or pointwise) powers of what we will introduce as the spectral frequency vector $\lambda$, and the traditional graph vertex model, based on the adjacency matrix and its eigenvectors. The paper expands GSP in several directions, including showing that convolution in the graph companion model can be achieved with the FFT and that GSP modulation with appropriate choice of carriers exhibits the DSP translation effect that enables multiplexing by modulation of graph signals.
Data Augmentation for Generating Synthetic Electrogastrogram Time Series
Authors: Nadica Miljković, Nikola Milenić, Nenad B. Popović, Jaka Sodnik
Date: 2023-03-04 00:00:00
Tasks: Data Augmentation, Diversity, Rhythm, Time Series, Time Series Analysis

To address an emerging need for large number of diverse datasets for rigor evaluation of signal processing techniques, we developed and evaluated a new method for generating synthetic electrogastrogram time series. We used electrogastrography (EGG) data from an open database to set model parameters and statistical tests to evaluate synthesized data. Additionally, we illustrated method customization for generating artificial EGG time series alterations caused by the simulator sickness. Proposed data augmentation method generates synthetic EGG data with specified duration, sampling frequency, recording state (postprandial or fasting state), overall noise and breathing artifact injection, and pauses in the gastric rhythm (arrhythmia occurrence) with statistically significant difference between postprandial and fasting states in > 70% cases while not accounting for individual differences. Features obtained from the synthetic EGG signal resembling simulator sickness occurrence displayed expected trends. The code for generation of synthetic EGG time series is not only freely available and can be further customized to assess signal processing algorithms but also may be used to increase data diversity for training artificial intelligence (AI) algorithms. The proposed approach is customized for EGG data synthesis but can be easily utilized for other biosignals with similar nature such as electroencephalogram.
Modeling and Quickest Detection of a Rapidly Approaching Object
Authors: Tim Brucks, Taposh Banerjee, Rahul Mishra
Date: 2023-03-04 00:00:00

The problem of detecting the presence of a signal that can lead to a disaster is studied. A decision-maker collects data sequentially over time. At some point in time, called the change point, the distribution of data changes. This change in distribution could be due to an event or a sudden arrival of an enemy object. If not detected quickly, this change has the potential to cause a major disaster. In space and military applications, the values of the measurements can stochastically grow with time as the enemy object moves closer to the target. A new class of stochastic processes, called exploding processes, is introduced to model stochastically growing data. An algorithm is proposed and shown to be asymptotically optimal as the mean time to a false alarm goes to infinity.
A Physics-based and Data-driven Approach for Localized Statistical Channel Modeling
Authors: Shutao Zhang, Xinzhi Ning, Xi Zheng, Qingjiang Shi, Tsung-Hui Chang, Zhi-Quan Luo
Date: 2023-03-04 00:00:00

Localized channel modeling is crucial for offline performance optimization of 5G cellular networks, but the existing channel models are for general scenarios and do not capture local geographical structures. In this paper, we propose a novel physics-based and data-driven localized statistical channel modeling (LSCM), which is capable of sensing the physical geographical structures of the targeted cellular environment. The proposed channel modeling solely relies on the reference signal receiving power (RSRP) of the user equipment, unlike the traditional methods which use full channel impulse response matrices. The key is to build the relationship between the RSRP and the channel's angular power spectrum. Based on it, we formulate the task of channel modeling as a sparse recovery problem where the non-zero entries of the sparse vector indicate the channel paths' powers and angles of departure. A computationally efficient weighted non-negative orthogonal matching pursuit (WNOMP) algorithm is devised for solving the formulated problem. Finally, experiments based on synthetic and real RSRP measurements are presented to examine the performance of the proposed method.
On competition through growth reduction
Authors: Carles Barril, Àngel Calsina, Odo Diekmann, József Z. Farkas
Date: 2023-03-06 00:00:00

We consider a population organised hierarchically with respect to size in such a way that the growth rate of each individual depends only on the presence of larger individuals. As a concrete example one might think of a forest, in which the incidence of light on a tree (and hence how fast it grows) is affected by shading of taller trees. The model is formulated as a delay equation, more specifically a scalar renewal equation, for the population birth rate. After discussing the well-posedness of the model, we analyse how many stationary birth rates the equation can have in terms of the functional parameters of the model. In particular we show that, under reasonable and rather general assumptions, only one stationary birth rate can exist besides the trivial one (associated to the state in which there are no individuals and the population birth rate is zero). We give conditions for this non-trivial stationary birth rate to exist and we analyse its stability using the principle of linearised stability for delay equations. Finally we relate the results to an alternative formulation of the model taking the form of a quasilinear partial differential equation for the population size-density.